diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..fab7161 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,42 @@ +name: Docs + +on: + push: + branches: + - main + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.12' + + - name: Install dependencies + run: | + pip install sphinx sphinx_rtd_theme sphinx-autodoc-typehints nbsphinx + + - name: Install hydrocnhs + run: | + pip install . + + - name: Generate API documentation + run: | + sphinx-apidoc -o docs/source/api src/hydrocnhs + + - name: Build documentation + run: | + cd docs + make html + + - name: Deploy to GitHub Pages + uses: peaceiris/actions-gh-pages@v3 + with: + github_token: ${{ secrets.GH_PAT }} + publish_dir: ./docs/_build/html diff --git a/.gitignore b/.gitignore index 527e6ca..9d16b42 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,178 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# VS Code .vscode/ -!to_do_list.txt \ No newline at end of file + +# outputs +#outputs/ + +# Archive folders +**/Archive/* + +# R +*.Rhistory +scenarios/*.RData + +# Ignore any .py file with prefix "debug_" +debug_*.py + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..3907783 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,28 @@ +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.4.0 # Use the latest version + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml +- repo: https://github.com/psf/black + rev: 23.9.1 + hooks: + - id: black + # By default, targets Python files only +#- repo: https://github.com/lorenzwalthert/precommit +# rev: v0.4.2 # Use the latest release from the repository +# hooks: +# - id: style-files + # R package styler to format R files according to the tidyverse style guide. + +# Instrunctions to install pre-commit +# To auto run pre-commit on every commit, do the following: +# 1. Install pre-commit: `pip install pre-commit` +# 2. 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"num_ellite": 1, - "prob_cross": 0.5, - "prob_mut": 0.15, - "stochastic": False, - "max_gen": 100, - "sampling_method": "LHC", - "drop_record": False, - "paral_cores": -1, - "paral_verbose": 1, - "auto_save": True, - "print_level": 1, - "plot": True - } - return config - -def get_inputs_template(): - inputs = { - "par_name": ["a","b","c"], - "par_bound": [[1,2],[1,2],[1,2]], - "wd": "working directory" - } - print("\nNote:\n Converter() can assist users", - "to generate calibration inputs.") - return inputs \ No newline at end of file diff --git a/HydroCNHS/calibration/__pycache__/__init__.cpython-310.pyc b/HydroCNHS/calibration/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index acbfdea..0000000 Binary files a/HydroCNHS/calibration/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/HydroCNHS/calibration/__pycache__/__init__.cpython-38.pyc b/HydroCNHS/calibration/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 7db367d..0000000 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/dev/null differ diff --git a/HydroCNHS/indicators.py b/HydroCNHS/indicators.py deleted file mode 100644 index 9206764..0000000 --- a/HydroCNHS/indicators.py +++ /dev/null @@ -1,311 +0,0 @@ -# Indicator module -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) -# Last update at 2021/12/23. - -import numpy as np -import pandas as pd - -class Indicator(object): - def __init__(self) -> None: - """A class containing following indicator functions. - - r : Correlation of correlation - r2 : Coefficient of determination - rmse: Root mean square error - NSE : Nash–Sutcliffe efficiency - iNSE: NSE with inverse transformed Q. - CP : Correlation of persistence - RSR : RMSE-observations standard deviation ratio - KGE : Kling–Gupta efficiency - iKGE: KGE with inverse transformed Q. - """ - pass - - @staticmethod - def remove_na(x_obv, y_sim): - """Remove nan in x_obv and y_sim. - - This function makes sure there is no nan involves in the indicator - calculation. If nan is detected, data points will be remove from x_obv - and y_sim simultaneously. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - - Returns - ------- - tuple - Updated (x_obv, y_sim) - """ - x_obv = np.array(x_obv) - y_sim = np.array(y_sim) - index = [True if np.isnan(x) == False and np.isnan(y) == False \ - else False for x, y in zip(x_obv, y_sim)] - x_obv = x_obv[index] - y_sim = y_sim[index] - print("Usable data ratio = {}/{}.".format(len(index), len(x_obv))) - return x_obv, y_sim - - @staticmethod - def cal_indicator_df(x_obv, y_sim, index_name="value", - indicators_list=None, r_na=True): - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - dict = {"r" : Indicator.r(x_obv, y_sim, False), - "r2" : Indicator.r2(x_obv, y_sim, False), - "rmse": Indicator.rmse(x_obv, y_sim, False), - "NSE" : Indicator.NSE(x_obv, y_sim, False), - "iNSE": Indicator.iNSE(x_obv, y_sim, False), - "KGE" : Indicator.KGE(x_obv, y_sim, False), - "iKGE": Indicator.iKGE(x_obv, y_sim, False), - "CP" : Indicator.CP(x_obv, y_sim, False), - "RSR" : Indicator.RSR(x_obv, y_sim, False)} - df = pd.DataFrame(dict, index=[index_name]) - if indicators_list is None: - return df - else: - return df.loc[:, indicators_list] - - @staticmethod - def r(x_obv, y_sim, r_na=True): - """Correlation. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - r coefficient. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - r = np.corrcoef(x_obv, y_sim)[0,1] - if np.isnan(r): - # We don't consider 2 identical horizontal line as r = 1! - r = 0 - return r - - @staticmethod - def r2(x_obv, y_sim, r_na=True): - """Coefficient of determination. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - r2 coefficient. - """ - r = Indicator.r(x_obv, y_sim, r_na) - return r**2 - - @staticmethod - def rmse(x_obv, y_sim, r_na=False): - """Root mean square error. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Root mean square error. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - return np.nanmean((x_obv - y_sim)**2)**0.5 - - @staticmethod - def NSE(x_obv, y_sim, r_na=False): - """Nash–Sutcliffe efficiency. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Nash–Sutcliffe efficiency. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - mu_xObv = np.nanmean(x_obv) - return 1 - np.nansum((y_sim-x_obv)**2) / np.nansum((x_obv-mu_xObv)**2) - - @staticmethod - def iNSE(x_obv, y_sim, r_na=False): - """Inverse Nash–Sutcliffe efficiency. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Inverse Nash–Sutcliffe efficiency. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - # Prevent dividing zero. - if np.nanmean(x_obv) == 0: - x_obv = 1 / (x_obv + 0.0000001) - else: - x_obv = 1 / (x_obv + 0.01*np.nanmean(x_obv)) - - if np.nanmean(y_sim) == 0: - y_sim = 1 / (y_sim + 0.0000001) - else: - y_sim = 1 / (y_sim + 0.01*np.nanmean(y_sim)) - mu_xObv = np.nanmean(x_obv) - return 1 - np.nansum((y_sim-x_obv)**2) / np.nansum((x_obv-mu_xObv)**2) - - @staticmethod - def CP(x_obv, y_sim, r_na=False): - """Correlation of persistence. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Correlation of persistence. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - a = np.nansum((x_obv[1:] - x_obv[:-1])**2) - if a == 0: - a = 0.0000001 - return 1 - np.nansum((x_obv[1:] - y_sim[1:])**2) / a - - @staticmethod - def RSR(x_obv, y_sim, r_na=False): - """RMSE-observations standard deviation ratio. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - RMSE-observations standard deviation ratio. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - sig_xObv = np.nanstd(x_obv) - return Indicator.rmse(x_obv, y_sim) / sig_xObv - - @staticmethod - def KGE(x_obv, y_sim, r_na=True): - """Kling–Gupta efficiency. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Kling–Gupta efficiency. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - - mu_ySim = np.nanmean(y_sim); mu_xObv = np.nanmean(x_obv) - sig_ySim = np.nanstd(y_sim); sig_xObv = np.nanstd(x_obv) - kge = 1 - ((Indicator.r(x_obv, y_sim, False) - 1)**2 - + (sig_ySim/sig_xObv - 1)**2 - + (mu_ySim/mu_xObv - 1)**2)**0.5 - return kge - - @staticmethod - def iKGE(x_obv, y_sim, r_na=True): - """Inverse Kling–Gupta efficiency. - - Parameters - ---------- - x_obv : array - Observation data. - y_sim : array - Simulation data. - r_na : bool, optional - Remove nan, by default True - - Returns - ------- - float - Inverse Kling–Gupta efficiency. - """ - if r_na: - x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) - - # Prevent dividing zero. - if np.nanmean(x_obv) == 0: - x_obv = 1/(x_obv + 0.0000001) - else: - x_obv = 1/(x_obv + 0.01*np.nanmean(x_obv)) - - if np.nanmean(y_sim) == 0: - y_sim = 1/(y_sim + 0.0000001) - else: - y_sim = 1/(y_sim + 0.01*np.nanmean(y_sim)) - - mu_ySim = np.nanmean(y_sim); mu_xObv = np.nanmean(x_obv) - sig_ySim = np.nanstd(y_sim); sig_xObv = np.nanstd(x_obv) - ikge = 1 - ((Indicator.r(x_obv, y_sim, False) - 1)**2 - + (sig_ySim/sig_xObv - 1)**2 - + (mu_ySim/mu_xObv - 1)**2)**0.5 - return ikge \ No newline at end of file diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-310.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 39a2a0b..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-38.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-38.pyc deleted file mode 100644 index 4dfe61e..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-38.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-39.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index ecea4c4..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-310.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-310.pyc deleted file mode 100644 index 66e45d8..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-310.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-38.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-38.pyc deleted file mode 100644 index 6e9d8c7..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-38.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-39.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-39.pyc deleted file mode 100644 index 7365a5c..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/abcd.cpython-39.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-310.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-310.pyc deleted file mode 100644 index b56b487..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-310.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-38.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-38.pyc deleted file mode 100644 index eb730b9..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-38.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-39.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-39.pyc deleted file mode 100644 index 0601389..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/gwlf.cpython-39.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-310.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-310.pyc deleted file mode 100644 index 19764e9..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-310.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-38.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-38.pyc deleted file mode 100644 index acc7d04..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-38.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-39.pyc b/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-39.pyc deleted file mode 100644 index 49b7381..0000000 Binary files a/HydroCNHS/rainfall_runoff_model/__pycache__/pet_hamon.cpython-39.pyc and /dev/null differ diff --git a/HydroCNHS/rainfall_runoff_model/abcd.py b/HydroCNHS/rainfall_runoff_model/abcd.py deleted file mode 100644 index 5681fbc..0000000 --- a/HydroCNHS/rainfall_runoff_model/abcd.py +++ /dev/null @@ -1,115 +0,0 @@ -# ABCD module. -# The ABCD model is mainly follow (Guillermo et al., 2010); however, with -# different snow module. -# https://doi.org/10.1029/2009WR008294 -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) -# Last update at 2021/12/22. - -import numpy as np -import logging - -def run_ABCD(pars, inputs, temp, prec, pet, data_length, **kwargs): - """ABCD rainfall-runoff model. - - Inputs: - Area: [ha] Subbasin area. - XL: [cm] Initial saturated soil water content. - Latitude: [deg] - SnowS: [cm] Snow storage. - Pars: - a: Controls the amount of runoff and recharge during unsaturated soil. - b: Controls Saturation level of the soils. - c: Ratio of groundwater recharge to runoff. - d: Controls groundwater discharge rate. - Df: [cm/degC] Degree-day coefficient - - Parameters - ---------- - pars : dict - Parameter dictionary containing 5 parameters: a, b, c, d, Df. - inputs : dict - Input dictionary containing 4 inputs: Area, Latitude, XL, SnowS. - temp : array - [degC] Daily mean temperature. - prec : array - [cm] Daily precipitation. - pet : array - [cm] Daily potential evaportranspiration. - data_length : int - Total data length (i.e., simulation period). - - Returns - ------- - array - [cms] Discharge - """ - - # Data - temp = np.array(temp) # [degC] Daily mean temperature. - prec = np.array(prec) # [cm] Daily precipitation. - pet = np.array(pet) # [cm] Daily potential evapotranspiration. - - # Variable - SnowSt = inputs["SnowS"] # [cm] Initial snow storage. - QU = 0 # [cm] Runoff. - QL = 0 # [cm] Baseflow. - XL = inputs["XL"] # [cm] Initial saturated soil water content. - ## [0, 400] - XU = 0 # [cm] Soil water storage (Antecedent Moisture). - - # Pars - a = pars["a"] # [0, 1] - b = pars["b"] # [cm] [0, 400] - c = pars["c"] # [0, 1] - d = pars["d"] # [0, 1] - Df = pars["Df"] # [0, 1] - - #----- Loop through all days (Python for loop ending needs +1) ------------ - CMS = np.zeros(data_length) # Create a 1D array to store results - for i in range(data_length): - - # Determine rainfall, snowfall and snow accumulation------------------- - if temp[i] > 0: # If temperature is above 0 degC, - # precipitation is rainfall (cm) and no snow accumulation - Rt = prec[i] - else: - # Else, precipitation is snowfall (cm) so rainfall = 0 - Rt = 0 - # Snowfall will accumulated and become snow storage(cm) - SnowSt = SnowSt + prec[i] - #---------------------------------------------------------------------- - # Determine snowmelt (Degree-day method)------------------------------- - if temp[i] > 0: # Temperature above 0 degC - # Snowmelt (cm) capped by snow storage - Mt = min(SnowSt, Df * temp[i]) - SnowSt = SnowSt - Mt # Update snow storage - else: - Mt = 0 - - #---------------------------------------------------------------------- - # Determin available water (P) - P = Rt + Mt + XU - - #---------------------------------------------------------------------- - # ET opportunity (Guillermo et al., 2010) - Pb = P+b - a2 = 2*a - In = (Pb/a2)**2 - P*b/a # In should >= 0 - EO = Pb/a2 - In**0.5 - - # EO = EO.real - #---------------------------------------------------------------------- - # Actual evapotranspiration (E) - E = EO * ( 1-np.exp(-pet[i]/b) ) - E = min( pet[i], max(0 , E) ) - XU = EO - E - AW = P - EO - XL = (XL + c*AW) / (1+d) - QL = d * XL - QU = (1-c) * AW - Q = QL + QU - #---------------------------------------------------------------------- - # Change unit to cms (m^3/sec)----------------------------------------- - # Area [ha] - CMS[i] = (Q * 0.01 * inputs["Area"] * 10000) / 86400 - return CMS \ No newline at end of file diff --git a/HydroCNHS/rainfall_runoff_model/gwlf.py b/HydroCNHS/rainfall_runoff_model/gwlf.py deleted file mode 100644 index 9b8dcf5..0000000 --- a/HydroCNHS/rainfall_runoff_model/gwlf.py +++ /dev/null @@ -1,209 +0,0 @@ -# GWLF module. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) -# Last update at 2021/12/22. - -import numpy as np -from pandas import date_range, to_datetime, DataFrame - -def run_GWLF(pars, inputs, temp, prec, pet, start_date, data_length, **kwargs): - """GWLF rainfall-runoff model. - - Inputs: - Area: [ha] Subbasin area. - Latitude: [deg] - S0: [cm] Shallow saturated soil water content. - U0: [cm] Unsaturated soil water content. - SnowS: [cm] Snow storage. - - Pars: - CN2: Curve number. - IS: Interception coefficient. - Res: Recession coefficient. - Sep: Deep seepage coefficient. - Alpha: Baseflow coefficient. - Beta: Percolation coefficient. - Ur: [cm] Available/Soil water capacity. - Kc: Land cover coefficient. - Df: [cm/degC] Degree-day coefficient. - Note that the simulation period has to be longer than a month. - - Parameters - ---------- - pars : dict - Parameter dictionary containing 9 parameters: CN2, IS, Res, Sep, Alpha, - Beta, Ur, Kc, Df. - inputs : dict - Input dictionary containing 5 inputs: Area, Latitude, S0, U0, SnowS. - temp : array - [degC] Daily mean temperature. - prec : array - [cm] Daily precipitation. - pet : array - [cm] Daily potential evaportranspiration. - start_date: str - Start date "yyyy/mm/dd". - - data_length : int - Total data length (i.e., simulation period). - - Returns - ------- - array - [cms] Discharge - """ - #----- Setup ------------------------------------------------------------- - - r""" - Inputs: - Area: # [ha] Sub-basin area. - S0: 10 # [cm] Shallow saturated soil water content. - U0: 10 # [cm] Unsaturated soil water content - SnowS: 5 # [cm] Snow storage. - """ - - # Data - temp = np.array(temp) # [degC] Daily mean temperature. - prec = np.array(prec) # [cm] Daily precipitation. - pet = np.array(pet) # [cm] Daily potential evapotranspiration. - - # Pars - CN2 = pars["CN2"] # Curve number - IS = pars["IS"] # Interception coefficient 0.05 ~ 0.2 - Res = pars["Res"] # Recession coefficient - Sep = pars["Sep"] # Deep seepage coefficient - Alpha = pars["Alpha"] # Baseflow coefficient - ##(Eq 4 in Luo et al. (2012)) - Beta = pars["Beta"] # Deep seepage coefficient - ##(Eq 2 in Luo et al. (2012)) - Ur = pars["Ur"] # [cm] Avaliable/Soil water capacity - ##(Root zone) - Df = pars["Df"] # [cm/degC] Degree-day coefficient for snowmelt. - Kc = pars["Kc"] # Land cover coefficient. - - # Variables - Gt = Res*inputs["S0"] # [cm] Initialize saturated zone discharge to - ##the stream. - BFt = Gt # [cm] Initialize baseflow to stream. - St = inputs["S0"] # [cm] Initialize shallow saturated soil water - ##content. - Ut = inputs["U0"] # [cm] Initialize unsaturated soil water - ##content. - SnowSt = inputs["SnowS"] # [cm] Initial snow storage. - AnteMois = [0, 0, 0, 0, 0] # [cm] Define the initial Antecedent Moisture - ##(5 days) as 0. - - # Calculate monthly mean temperature for distinguishing growing season - # purpose. - start_date = to_datetime(start_date, format="%Y/%m/%d") - pdDatedateIndex = date_range(start=start_date, periods=data_length, - freq="D") - MonthlyTavg = DataFrame(index=pdDatedateIndex) - MonthlyTavg["T"] = temp - MonthlyTavg = MonthlyTavg.resample("MS").mean() - # Broadcast back to daily sequence. - # Note: Data has to longer than a month or it will show error. - try: - MonthlyTavg.index = [pdDatedateIndex[0]] + list(MonthlyTavg.index[1:-1]) \ - + [pdDatedateIndex[-1]] - MonthlyTavg = MonthlyTavg.resample('D').ffill()["T"].to_numpy() - except Exception as e: - print(e) - print("The simulation period has to be longer than a month.") - #-------------------------------------------------------------------------- - - #----- Loop through all days (Python for loop ending needs +1) ------------ - CMS = np.zeros(data_length) # Create a 1D array to store results - for i in range(data_length): - # Determine the am1 and am2 values based on growing season for CN - # calculation. - # Growing season (mean monthly temperature > 10 deg C). - if MonthlyTavg[i] > 10: # MonthlyTavg - am1 = 3.6 - am2 = 5.3 - else: - am1 = 1.3 - am2 = 2.8 - - # Determine rainfall, snowfall and snow accumulation------------------- - if temp[i] > 0: # If temperature is above 0 degC, - Rt = prec[i] # precipitation is rainfall (cm) and no snow - ##accumulation - else: - Rt = 0 # Else, precipitation is snowfall (cm) so rainfall = 0 - # Snowfall will accumulated and become snow storage(cm) - SnowSt = SnowSt + prec[i] - #---------------------------------------------------------------------- - # Determine snowmelt (Degree-day method)------------------------------- - if temp[i] > 0: # Temperature above 0 degC - # Snowmelt (cm) capped by snow storage - Mt = min(SnowSt, Df * temp[i]) - SnowSt = SnowSt - Mt # Update snow storage - else: - Mt = 0 - #---------------------------------------------------------------------- - # Calculate the Antecedent Moisture (at)------------------------------- - AnteMois = [Rt + Mt] + AnteMois[0:4] # Add new data and drop last data. - at = np.sum(AnteMois) # Five days antecedent moisture - #---------------------------------------------------------------------- - # CN calculation (Following GWLF2 setting)----------------------------- - CN1 = (4.2 * CN2) / (10 - 0.058 * CN2) - CN3 = (23 * CN2) / (10 + 0.13 * CN2) - CN = None - if at < am1: - CN = CN1 + ((CN2 - CN1) / am1) * at - - if (am1 <= at and at <= am2): - CN = CN2 + ((CN3 - CN2) / (am2 - am1)) * (at - am1) - - if am2 < at: - CN = CN3 - #---------------------------------------------------------------------- - # Calculate runoff (Qt)------------------------------------------------ - DSkt = (2540 / CN) - 25.4 # Detention parameter (cm) - if (Rt + Mt) > IS * DSkt: - Qt = (((Rt + Mt) - (IS * DSkt))**2) / ((Rt + Mt) + ((1-IS) * DSkt)) - else: - Qt = 0 - #---------------------------------------------------------------------- - # Calculate Evapotranspiration (Et)------------------------------------ - # Consider water stress (Ks) and land cover (Kc). - if (Ut >= Ur*0.5): - Ks = 1 - else: - Ks = Ut/(Ur*0.5) - Et = min((Ut+ Rt + Mt - Qt), Ks*Kc*pet[i]) - #---------------------------------------------------------------------- - # Calculate Percolation (Pct) from unsaturated zone (Ut) to shallow - # saturated zone (St)-------------------------------------------------- - PCt = max((Ut+ Rt + Mt - Qt - Et - Ur), 0) - #---------------------------------------------------------------------- - # Update unsaturated zone soil moistures (Ut)-------------------------- - Ut = Ut + Rt + Mt - Qt - Et - PCt # Rt+Mt-Qt is infiltration - #---------------------------------------------------------------------- - # Calculate groundwater discharge (Gt) and deep seepage (Dt)----------- - Gt = Res * St - Dt = Sep * St - #---------------------------------------------------------------------- - # Update shallow saturated zone soil moistures (St)-------------------- - St = St + PCt - Gt - Dt - #---------------------------------------------------------------------- - # Groundwater: Deep seepage loss (Dset)-------------------------------- - Dset = Beta * Dt #Eq 2 in Luo et al. (2012) - #---------------------------------------------------------------------- - # Groundwater: Recharge (Ret)------------------------------------------ - Ret = Dt - Dset #Eq 3 in Luo et al. (2012) - #---------------------------------------------------------------------- - # Groundwater:Baseflow (BFt)------------------------------------------- - # Eq 4 in Luo et al. (2012) - BFt = BFt * np.exp(-Alpha) + Ret * (1-np.exp(-Alpha)) - #---------------------------------------------------------------------- - # Calculate streamflow (SF) and Monthly Qt, prec, Et, Gt and SF-------- - # Streamflow = surface quick flow + subsurface flow + baseflow - SF = Qt + Gt + BFt - #---------------------------------------------------------------------- - # Change unit to cms (m^3/sec)----------------------------------------- - # Area [ha] - CMS[i] = (SF * 0.01 * inputs["Area"] * 10000) / 86400 - #---------------------------------------------------------------------- - # return the result array - return CMS \ No newline at end of file diff --git a/HydroCNHS/routing.py b/HydroCNHS/routing.py deleted file mode 100644 index 3f4871a..0000000 --- a/HydroCNHS/routing.py +++ /dev/null @@ -1,210 +0,0 @@ -# Lohmann routing module -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) -# 2021/02/05 -# Last update at 2021/12/22. - -import numpy as np -from scipy.stats import gamma - -# Inputs -# FlowLength = [m] Travel distence of flow between two outlets. -# InstreamControl = [bool] Instream control (e.g., dams). -# Parameters -# GShape = Subbasin's UH shape parameter (gamma function). -# GScale = Subbasin's UH scale parameter (gamma function). -# Velo = [m/s] Diffusion wave celerity in the linearized Saint-Venant equation. -# Diff = [m2/s] Diffusive coefficient in the linearized Saint-Venant equation. - -# Note: -# We fix the base time setting here. For future development, it is better to -# turn entire Lohmann routing into a class with base time setting as attributes -# which allow to be changed accordingly. - -def form_UH_Lohmann(inputs, routing_pars, force_ingrid_off=False): - """Derive HRU's UH at the (watershed) outlet. - - Parameters - ---------- - inputs : dict - Inputs dictionary containing FlowLength [m] Travel distence of flow - between two outlets [float] and InstreamControl [bool]. - routing_pars : dict - Four parameters for routing: GShape, GScale, Velo, Diff [float] - force_ingrid_off : bool, optional - If True, then within subbasin routing will be forced to turn off, by default - False. - """ - flow_len = inputs["FlowLength"] - instream_control = inputs["InstreamControl"] - - #----- Base Time for within subbasin UH and river/channel - # routing UH -------------------------------------------------------------- - # Within-subbasin routing - T_IG = 12 # [day] Base time for within subbasin UH - # River routing - T_RR = 96 # [day] Base time for river routing UH - dT_sec = 3600 # [sec] Time step in second for solving - ##Saint-Venant equation. This will affect Tmax_hr. - Tmax_hr = T_RR * 24 # [hr] Base time of river routing UH in hour - ##because dT_sec is for an hour - Tgr_hr = 48 * 50 # [hr] Base time for Green function values - #-------------------------------------------------------------------------- - - #----- Within-subbasin routing UH (daily) represented by Gamma distribution - UH_IG = np.zeros(T_IG) - if instream_control or force_ingrid_off: - # No time delay for within subbasin routing when the water is released - # through instream control objects (e.g. dam). - UH_IG[0] = 1 - elif (routing_pars.get("GShape") is None - and routing_pars.get("GScale") is None): - # No time delay for within subbasin routing since Q is given by user - # and we assume Q is observed streamflow, which no need to consider - # time delay for within subbasin routing. This is trigger automatically - # in HydroCNHS module. - UH_IG[0] = 1 - else: - shape = routing_pars["GShape"] - scale = routing_pars["GScale"] - if scale <= 0.0001: - scale = 0.0001 # Since we cannot divide zero. - for i in range(T_IG): - # x-axis is in hr unit. We calculate in daily time step. - UH_IG[i] = gamma.cdf(24 * (i + 1), a=shape, loc=0, scale=scale) \ - - gamma.cdf(24 * i, a=shape, loc=0, scale=scale) - #-------------------------------------------------------------------------- - - #----- Derive Daily River Impulse Response Function (Green's function) ---- - UH_RR = np.zeros(T_RR) - if flow_len == 0: - # No time delay for river routing when the outlet is gauged outlet. - UH_RR[0] = 1 - else: - Velo = routing_pars["Velo"] - Diff = routing_pars["Diff"] - - # Calculate h(x, t) - t = 0 - UH_RRm = np.zeros(Tgr_hr) # h() in hour - for k in range(Tgr_hr): - # Since Velo is m/s, we use the t in sec with 1 hr as time step. - t = t + dT_sec - pot = ((Velo * t - flow_len) ** 2) / (4 * Diff * t) - if pot <= 69: # e^(-69) = E-30 a cut-off threshold - H = flow_len /(2 * t * (np.pi * t * Diff)**0.5) * np.exp(-pot) - else: - H = 0 - UH_RRm[k] = H - - if sum(UH_RRm) == 0: - UH_RRm[0] = 1.0 - else: - UH_RRm = UH_RRm / sum(UH_RRm) # Force UH_RRm to sum to 1 - - # Much quicker!! Think about S-hydrograph process. - # And remember we should have [0] in UH_RRm[0]. - # Therefore, we use i+1 and 23 to form S-hydrolograph. - FR = np.zeros((Tmax_hr+23, Tmax_hr-1)) - for i in range(Tmax_hr-1): - FR[:,i] = np.pad(UH_RRm, (i+1, 23), 'constant', - constant_values=(0, 0))[:Tmax_hr+23] - FR = np.sum(FR, axis = 1)/24 - # Lag 24 hrs - FR = FR[:Tmax_hr] - np.pad(FR, (24, 0), 'constant', - constant_values=(0, 0))[:Tmax_hr] - - # Aggregate to daily UH - for t in range(T_RR): - UH_RR[t] = sum(FR[(24*(t+1)-24):(24*(t+1)-1)]) - - #----- Combined UH_IG and UH_RR for HRU's response at the (watershed) - # outlet ------------------------------------------------------------------ - UH_direct = np.zeros(T_IG + T_RR - 1) # Convolute total time step [day] - for k in range (0, T_IG): - for u in range (0, T_RR): - UH_direct[k+u] = UH_direct[k+u] + UH_IG[k] * UH_RR[u] - UH_direct = UH_direct/sum(UH_direct) - # Trim zero from back. So when we run routing, we don't need to run whole - # array. - UH_direct = np.trim_zeros(UH_direct, 'b') - return UH_direct - -def run_step_Lohmann(routing_outlet, routing, UH_Lohmann, Q, Q_runoff, t): - """Calculate a single time step routing for a given routing_outlet at time t. - - Parameters - ---------- - routing_outlet : str - routing outlet. - routing : dict - Routing setting dictionary from model.yaml file. - UH_Lohmann : dict - A dictionary containing pre-formed UHs. - Q : dict - A dictionary containing newest routed flows. - Q_runoff : dict - A dictionary containing newest unrouted flows without. - t : int - Index of current time step [day]. - - Returns - ------- - float - Routed flow of routing_outlet at time t. - """ - Qt = None - ro = routing_outlet - Qresult = 0 - Subbasin = list(routing[ro].keys()) - for sb in Subbasin: - l = len(UH_Lohmann[(sb, ro)]) - 1 - # t+1 is length, t is index. - UH = UH_Lohmann[(sb, ro)][0 : min(t + 1, l) ] - if ro == sb: - # Q[ro] is routed Q. We need to use unrouted Q (Q_runoff) to run the - # routing. - Q_reverse = np.flip(Q_runoff[sb][ max(t-(l-1), 0) : t+1]) - else: - Q_reverse = np.flip(Q[sb][ max(t-(l-1), 0) : t+1]) - Qresult += np.sum(UH * Q_reverse) - Qt = Qresult # Store the result for time t - return Qt - -def run_step_Lohmann_convey(routing_outlet, routing, UH_Lohmann_convey, - Q_convey, t): - """Calculate a single time step conveying water routing for a given - routing_outlet at time t. - - Parameters - ---------- - routing_outlet : str - routing outlet. - routing : dict - Routing setting dictionary from model.yaml file. - UH_Lohmann_convey : dict - A dictionary containing pre-formed conveying UHs (i.e., no within - subbasin routing). - Q_convey : dict - A dictionary containing conveying water. - t : int - Index of current time step [day]. - - Returns - ------- - float - Routed conveying flow of routing_outlet at time t. - """ - Qt = None - ro = routing_outlet - Qresult = 0 - Subbasin = list(routing[ro].keys()) - for sb in Subbasin: - uh_convey = UH_Lohmann_convey.get((sb, ro)) - if uh_convey is not None: - l = len(uh_convey) - 1 - # t+1 is length, t is index. - UH = uh_convey[0 : min(t + 1, l)] - Q_reverse = np.flip(Q_convey[sb][ max(t-(l-1), 0) : t+1]) - Qresult += np.sum(UH * Q_reverse) - Qt = Qresult # Store the result for time t - return Qt diff --git a/LICENSE b/LICENSE index f288702..d49d1f1 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,28 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. +BSD 3-Clause License + +Copyright (c) 2024, Chung-Yi Lin + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 6e84955..0000000 --- a/MANIFEST.in +++ /dev/null @@ -1,5 +0,0 @@ -include HydroCNHS/*.yaml -recursive-include docs * -recursive-include tutorials * -include HydroCNHS/calibration/*.py -include HydroCNHS/rainfall_runoff_model/*.py \ No newline at end of file diff --git a/README.md b/README.md index b2a546e..f939cc5 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ A Python Package of Hydrological Model for Coupled Natural–Human Systems Complex Adaptive Water System -Modeling Coupled Natural–Human Systems (CNHS) to inform comprehensive water resources management policies or describe hydrological cycles in the Anthropocene has become popular in recent years. To fulfill this need, we developed a semi-distributed Hydrological model for Coupled Natural–Human Systems, HydroCNHS. The HydroCNHS is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into the HydroCNHS. Specifically, we design Dam API, RiverDiv API, Conveying API, and InSitu API to integrate, respectively, customized man-made infrastructures such as reservoirs, off-stream diversions, trans-basin aqueducts, and drainage systems that abstract human behaviors (e.g., operator and farmers’ water use decisions). Each of the HydroCNHS APIs has a unique plug-in structure that respects within-subbasin and inter-subbasin (i.e., river) routing logic for maintaining the water balance. In addition, the HydroCNHS uses a single model configuration file to organize input features for the hydrological model and case-specific human systems models. Also, HydroCNHS enables the model calibration using parallel computing power. We demonstrate the functionalities of the HydroCNHS package through a case study in the Northwest United States. Given the integrity of the modeling framework, HydroCNHS can benefit water resources planning and management in various aspects, including the uncertainty analysis in CNHS modeling and more complex agent design. +Modeling Coupled Natural–Human Systems (CNHS) to inform comprehensive water resources management policies or describe hydrological cycles in the Anthropocene has become popular in recent years. To fulfill this need, we developed a semi-distributed Hydrological model for Coupled Natural–Human Systems, HydroCNHS. The HydroCNHS is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into the HydroCNHS. Specifically, we design Dam API, RiverDiv API, Conveying API, and InSitu API to integrate, respectively, customized man-made infrastructures such as reservoirs, off-stream diversions, trans-basin aqueducts, and drainage systems that abstract human behaviors (e.g., operator and farmers’ water use decisions). Each of the HydroCNHS APIs has a unique plug-in structure that respects within-subbasin and inter-subbasin (i.e., river) routing logic for maintaining the water balance. In addition, the HydroCNHS uses a single model configuration file to organize input features for the hydrological model and case-specific human systems models. Also, HydroCNHS enables the model calibration using parallel computing power. We demonstrate the functionalities of the HydroCNHS package through a case study in the Northwest United States. Given the integrity of the modeling framework, HydroCNHS can benefit water resources planning and management in various aspects, including the uncertainty analysis in CNHS modeling and more complex agent design. ## Install @@ -14,7 +14,7 @@ Install HydroCNHS by *pip*. ``` pip install hydrocnhs ``` -To install the latest version (beta version) of HydroCNHS, users can (1) install HydroCNHS by *git*. +To install the latest version (recommend) of HydroCNHS, users can (1) install HydroCNHS by *git*. ``` pip install git+https://github.com/philip928lin/HydroCNHS.git ``` @@ -35,8 +35,8 @@ Click [![Documentation Status](https://readthedocs.org/projects/hydrocnhs/badge/ 1. Want to build a hydrological model with auto-parameter-tuning (calibration) features. 2. Want to add human components into a hydrological model under a unified framework without worrying about routing logic. 3. Want to calibrate the entire integrated model (hydrological + ABM modules) with customized agents' parameters. -4. Want to design human behaviors with a high degree of freedom, including coupling with external software. -5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). +4. Want to design human behaviors with a high degree of freedom, including coupling with external software. +5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). ## Feature highlights - Built-in genetic algorithm calibration module that can utilize parallel computing power. diff --git a/docs/Makefile b/docs/Makefile index d4bb2cb..d0c3cbf 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -5,8 +5,8 @@ # from the environment for the first two. 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When it is not found, a full rebuild will be done. -config: 69604ae30759eee0fcc9236894b360c7 -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_images/CAWS.png b/docs/_build/html/_images/CAWS.png deleted file mode 100644 index 47c6e2a..0000000 Binary files a/docs/_build/html/_images/CAWS.png and /dev/null differ diff --git a/docs/_build/html/_images/logo.png b/docs/_build/html/_images/logo.png deleted file mode 100644 index 00857cb..0000000 Binary files a/docs/_build/html/_images/logo.png and /dev/null differ diff --git a/docs/_build/html/_sources/calibration.rst.txt b/docs/_build/html/_sources/calibration.rst.txt deleted file mode 100644 index a8e9f0c..0000000 --- a/docs/_build/html/_sources/calibration.rst.txt +++ /dev/null @@ -1,340 +0,0 @@ -Calibration -================ -HydroCNHS equips with a genetic algorithm powered by Distributed Evolutionary -Algorithms in Python (`DEAP `_), which -can be used for calibration in parallel. The unique feature about HydroCNHS is -that both the parameters of the hydrological model and user-defined ABM can be -calibrated simutaneously as long as those parameters are defined the model file -(.yaml). Furthermore, users are allowed to assign initial guesses to the -algorithm. Note that this GA module are not limited to HydroCNHS model. - -Genetic algorithm calibration code outline -------------------------------------------- -To use this genetic algorithm (GA) module, three things are required. - -1. The evaluation function - - The evaluation function has to follow a certain protocal. Please see the - example code below. - -2. GA configuration dictionary - - GA configuration dictionary template can be obtained by - cali.get_config_template(). - -3. Calibration inputs dictionary - - The calibration inputs dictionary template can be obtained by - cali.get_inputs_template(). - -.. note:: - Currently, GA module only support calibrating real number parameters. - - -.. code-block:: python - - import HydroCNHS - import HydroCNHS.calibration as cali - - # Assisting functions - cali.helper() # Get instructions. - cali.get_config_template() - cali.get_inputs_template() - - # Must have individual and info arguments for evaluation function. - def evaluation(individual, info): - # individual: A 1D array of generated parameter set. - # info: The info tuple contains five items such as the calibration - # working directory (cali_wd), current generation number - # (current_generation), the index of the current individual of the - # generation (ith_individual), formtter for the Converter, and a random - # number generator (random_generator). - # Note the random_generator should be used to generate random number to - # ensure the reproducibility. (Do not use np.random directly.) - (cali_wd, current_generation, ith_individual, - formatter, random_generator) = info - - # Run model. - # Calculate fitness. - # E.g., - fitness = sum(individual) - return (fitness,) # Has to be a tuple format. - - # GA configuration - config = {'min_or_max': 'max', - 'pop_size': 200, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.1, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} - - # GA inputs (Can be generated by the Converter shown in the next section.) - cali_inputs = {"par_name": ["a","b","c"], - "par_bound": [[1,2],[1,2],[1,2]], - "wd": "working directory"} - - seed = 5 - # Create a random number generator for GA. - rn_gen = HydroCNHS.create_rn_gen(seed) # Optional - # Initialize GA object. - ga = cali.GA_DEAP(evaluation, rn_gen) - # Set up GA object (or load the previously auto-saved pickle file) - ga.set(cali_inputs, config, name="Cali_example") - # Run - ga.run() - # Run the evaluation with the given individual (e.g., the solution). - ga.run_individual(ga.solution) - # Show the result summary. - summary = ga.summary - print(summary) - - - -Converter ---------- -Convertor helps user to convert a list of parameter dataframes (can -contain nan values) into an 1D array (parameters for calibration, -automatically exclude nan values) that can be used for GA calibration. -And the formatter created by Convertor can be used to convert 1D array -back to a list of original dataframes. The inputs dictionary for GA can also be -generated by Convertor. Besides, we provide option for defining fixed -parameters, which those parameters will not enter the -calibration process (exclude from the 1D array). -Note that the dataframe index is parameter names. - -.. code-block:: python - - import numpy as np - import pandas as pd - import HydroCNHS - import HydroCNHS.calibration as cali - - ### Prepare testing data. - par_df1 = pd.DataFrame({"Subbasin1": [1000,1000,3], "Subbasin2": [4,5,6]}, - index=["a", "b", "c"]) - par_df2 = pd.DataFrame({"Agent1": [9,8,7], "Agent2": [6,5,None]}, - index=["Par1", "Par2", "Par3"]) - - bound_df1 = pd.DataFrame({"Subbasin1": [[0,1000],[0,1000],[0,10]], "Subbasin2": [[0,10],[0,10],[0,10]]}, - index=["a", "b", "c"]) - bound_df2 = pd.DataFrame({"Agent1": [[0,10],[0,10],[0,10]], "Agent2": [[0,10],[0,10],None]}, - index=["Par1", "Par2", "Par3"]) - - df_list = [par_df1, par_df2] - par_bound_df_list = [bound_df1, bound_df2] - - - ### Create a object called Converter. - converter = cali.Convertor() - - ### Generate GA inputs with fixed a & b parameters for Subbasin1. - fixed_par_list = [[(["a","b"], ["Subbasin1"])],[]] - cali_inputs = converter.gen_cali_inputs( - "working directory", df_list, par_bound_df_list, fixed_par_list) - ### Get formatter - formatter = converter.formatter - - ### Show cali_inputs - print(cali_inputs) - r""" - print(cali_inputs) - {'wd': 'working directory', - 'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2', - 'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2', - 'Par3|Agent1'], - 'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], - [0, 10], [0, 10]]} - """ - - ### to 1D array - converter.to_1D_array(df_list, formatter) - r""" - # Out[31]: array([4., 5., 3., 6., 9., 6., 8., 5., 7.]) - # Note the order of the array corresponds to "par_name" in the cali_inputs. - """ - - ### to df_list - var_array = np.array([5]*9) - converter.to_df_list(var_array, formatter) - r""" - Out[46]: - [ Subbasin1 Subbasin2 - a 1000.0 5.0 - b 1000.0 5.0 - c 5.0 5.0, - Agent1 Agent2 - Par1 5.0 5.0 - Par2 5.0 5.0 - Par3 5.0 NaN] - """ - - -TRB calibration example ------------------------ - -.. code-block:: python - - import os - import pandas as pd - from copy import deepcopy - import pickle - import HydroCNHS - import HydroCNHS.calibration as cali - - ##### Path and Load Model Test - # Get this file directory. - prj_path, this_filename = os.path.split(__file__) - model_path = os.path.join(prj_path, "Template_for_calibration", "TRB_dm_gwlf.yaml") - bound_path = os.path.join(prj_path, "ParBound") - wd = prj_path - - # Update model paths. - model_dict = HydroCNHS.load_model(model_path) - model_dict["Path"]["WD"] = wd - model_dict["Path"]["Modules"] = os.path.join(prj_path, "ABM_modules") - - ##### Gen cali information - # Convert parameter sections in the model file (i.e., model_dict) to a list of - # dataframes (df_list). - df_list, df_name = HydroCNHS.write_model_to_df(model_dict, key_option=["Pars"]) - # Load the parameter bounds. The order of the list is corresponding to df_list. - par_bound_df_list = [ - pd.read_csv(os.path.join(bound_path, "gwlf_par_bound.csv"), index_col=[0]), - pd.read_csv(os.path.join(bound_path, "routing_par_bound.csv"), index_col=[0]), - pd.read_csv(os.path.join(bound_path, "abm_par_bound_dm.csv"), index_col=[0])] - # Initialize Convertor. - converter = cali.Convertor() - cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) - formatter = converter.formatter - - # Load inputs from pickle file. - with open(os.path.join(prj_path, "Inputs", "TRB_inputs.pickle"), "rb") as file: - (temp, prec, pet, obv_D, obv_M, obv_Y) = pickle.load(file) - - #%% - # ============================================================================= - # Calibration - # ============================================================================= - def cal_batch_indicator(period, target, df_obv, df_sim): - """Compute mean indicator over targets""" - df_obv = df_obv[period[0]:period[1]] - df_sim = df_sim[period[0]:period[1]] - Indicator = HydroCNHS.Indicator() - df = pd.DataFrame() - for item in target: - df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item], - index_name=item) - df = pd.concat([df, df_i], axis=0) - df_mean = pd.DataFrame(df.mean(axis=0), columns=["Mean"]).T - df = pd.concat([df, df_mean], axis=0) - return df - - def evaluation(individual, info): - cali_wd, current_generation, ith_individual, formatter, _ = info - name = "{}-{}".format(current_generation, ith_individual) - - ##### individual -> model - # Convert 1D array to a list of dataframes. - df_list = cali.Convertor.to_df_list(individual, formatter) - # Feed dataframes in df_list to model dictionary. - model = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") - - ##### Run simuluation - model = HydroCNHS.Model(model, name) - Q = model.run(temp, prec, pet) - - ##### Get simulation data - # Streamflow of routing outlets. - cali_target = ["DLLO", "WSLO"] - cali_period = ("1981-1-1", "2005-12-31") - vali_period = ("2006-1-1", "2013-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[cali_target] - # Agents' outputs stored in the data_collector. - sim_Q_D["DivAgt"] = model.data_collector.DivAgt["Diversion"] - sim_Q_D["ResAgt"] = model.data_collector.ResAgt["Release"] - cali_target += ["DivAgt", "ResAgt"] - # Resample the daily simulation output to monthly and annually outputs. - sim_Q_M = sim_Q_D[cali_target].resample("MS").mean() - sim_Q_Y = sim_Q_D[cali_target].resample("YS").mean() - - df_cali_Q_D = cal_batch_indicator(cali_period, cali_target, obv_D, sim_Q_D) - df_cali_Q_M = cal_batch_indicator(cali_period, cali_target, obv_M, sim_Q_M) - df_cali_Q_Y = cal_batch_indicator(cali_period, cali_target, obv_Y, sim_Q_Y) - - df_vali_Q_D = cal_batch_indicator(vali_period, cali_target, obv_D, sim_Q_D) - df_vali_Q_M = cal_batch_indicator(vali_period, cali_target, obv_M, sim_Q_M) - df_vali_Q_Y = cal_batch_indicator(vali_period, cali_target, obv_Y, sim_Q_Y) - - ##### Save output.txt - # Only exercute when ga.run_individual(ga.solution) - if current_generation == "best": - with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), 'w') as f: - f.write("Annual cali/vali result\n") - f.write(df_cali_Q_Y.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n") - f.write(df_vali_Q_Y.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n\nMonthly cali/vali result\n") - f.write(df_cali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n") - f.write(df_vali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n\nDaily cali/vali result\n") - f.write(df_cali_Q_D.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n") - f.write(df_vali_Q_D.round(3).to_csv(sep='\t').replace("\n", "")) - f.write("\n=========================================================\n") - f.write("Sol:\n" ) - df = pd.DataFrame(individual, index=cali_inputs["par_name"]).round(4) - f.write(df.to_string(header=False, index=True)) - - fitness = df_cali_Q_M.loc["Mean", "KGE"] - return (fitness,) - - # cali.helper() - # cali.get_config_template() - # cali.get_inputs_template() - - config = {'min_or_max': 'max', - 'pop_size': 200, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.1, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} - - # Calibrate with 3 seeds. - seeds = [5,10,13] - for seed in seeds: - rn_gen = HydroCNHS.create_rn_gen(seed) - ga = cali.GA_DEAP(evaluation, rn_gen) - ga.set(cali_inputs, config, formatter, name="Cali_gwlf_abm_KGE_{}".format(seed)) - ga.run() - ga.run_individual(ga.solution) # Output performance (.txt) of solution. - - ##### Output the calibrated model. - individual = ga.solution - df_list = cali.Convertor.to_df_list(individual, formatter) - model_best = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_gwlf_abm_KGE.yaml")) - - summary = ga.summary \ No newline at end of file diff --git a/docs/_build/html/_sources/files/1_introduction.rst.txt b/docs/_build/html/_sources/files/1_introduction.rst.txt deleted file mode 100644 index 65ebd35..0000000 --- a/docs/_build/html/_sources/files/1_introduction.rst.txt +++ /dev/null @@ -1,47 +0,0 @@ -Introduction -============ - -The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. - -When should you use HydroCNHS? ------------------------------- -1. Want to build a hydrological model with auto-parameter-tuning (calibration) features. -2. Want to add human components into a hydrological model under a unified framework without worrying about routing logic. -3. Want to calibrate the entire integrated model (hydrological + ABM modules) with customized agents' parameters. -4. Want to design human behaviors with a high degree of freedom, including coupling with external software. -5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). - -Feature highlights ------------------- -- Built-in genetic algorithm calibration module that can utilize parallel computing power. -- A unified framework allowing calibration of the entire integrated model (hydrological + ABM models), where parameters in the ABM module (i.e., human model) are customizable. -- Automatic integration and simulation through four APIs. -- Built-in data collector module that can collect data from the hydrological and user-defined ABM modules. -- Built-in model builder module to assist users in creating the HydroCNHS model and generating ABM module (*.py*) templates. -- Built-in indicator calculation and visualization tools for simple result analysis. - -Supporting APIs for incorporating human/agent components --------------------------------------------------------- - -.. figure:: ../figs/fig3_hydrocnhs.png - :align: center - :width: 500 - :alt: Logo - -Dam API -^^^^^^^ -Link to human actions that completely redefine downstream flow, e.g., instream objects like reservoirs. The downstream flow is governed by a reservoir's operational rules and operators' decisions. - -RiverDiv API -^^^^^^^^^^^^ -Link to human actions that divert water from the river (with return water to the downstream subbasins), e.g., off-stream irrigation water diversions governed by farmers or regional district managers' diversion behaviors. - -Conveying API -^^^^^^^^^^^^^ -Link to human actions that convey water beyond basin boundaries and gravitational limitations, e.g., trans-basin aqueducts that convey water from one basin to another and pump stations that convey water from downstream to upstream. - -InSitu API -^^^^^^^^^^ -Link to human actions that locally alter the runoff in a subbasin, e.g., drainage systems, urbanization, and water diversion from wells or local streams (within-subbasin). - -Those human/agent actions can be modeled with various complexities according to users' ABM design. Actions can simply be an inputted daily time series or governed by complex endogenous behavior rules (i.e., decision-making rules). Institutional decisions (a group of agents making decisions together with institutional rules) are allowed in HydroCNHS. Users require to have some level of object-oriented programing concept to design an ABM module (*.py*). \ No newline at end of file diff --git a/docs/_build/html/_sources/files/2_installation.rst.txt b/docs/_build/html/_sources/files/2_installation.rst.txt deleted file mode 100644 index 37ddc9c..0000000 --- a/docs/_build/html/_sources/files/2_installation.rst.txt +++ /dev/null @@ -1,27 +0,0 @@ -Installation -============ -Install HydroCNHS by *pip*. - -.. code-block:: python - - pip install hydrocnhs - -To install the latest version (beta version) of `HydroCNHS `_, users can (1) install HydroCNHS by *git*. - -.. code-block:: python - - pip install git+https://github.com/philip928lin/HydroCNHS.git - -Or, (2) download the HydroCNHS package directly from the HydroCNHS GitHub repository. Then, install HydroCNHS from the *setup.py*. - -.. code-block:: python - - # Need to move to the folder containing setup.py first. - python setup.py install - - -If you fail to install HydroCNHS due to the DEAP package, first downgrade setuptools to 57 and try to install HydroCNHS again. - -.. code-block:: python - - pip install setuptools==57 \ No newline at end of file diff --git a/docs/_build/html/_sources/files/3_101_classroom.rst.txt b/docs/_build/html/_sources/files/3_101_classroom.rst.txt deleted file mode 100644 index 0d6de48..0000000 --- a/docs/_build/html/_sources/files/3_101_classroom.rst.txt +++ /dev/null @@ -1,110 +0,0 @@ -101 classroom -============= - -Watershed/Basin ---------------- - -A land area that drains water to a particular water body like lakes, rivers, and oceans. Ridges, mountains, and hills usually define the boundary of a watershed/basin. - -Subbasin --------- - -Subbasin is a sub-drainage land unit inside a watershed. HydroCNHS uses subbasins as the base unit for hydrological modeling. Each subbasin has a corresponding outlet. It is similar to the concept of the hydrologic response unit used in the SWAT model. However, users can freely determine the subbasins' resolution to balance the computational cost and required resolution of the output information. - -Semi-distributed hydrological model ------------------------------------ - -HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in :numref:`fig1`. A lumped model describes the water balance of the entire watershed as a water bucket (:numref:`fig1`\a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (:numref:`fig1`\c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins' (not grid) runoffs simulated by lumped models (:numref:`fig1`\b). - -.. _fig1: -.. figure:: ../figs/fig1_modeling_schema.png - :align: center - :width: 700 - :alt: Comparison of three hydrological modeling schema. - - Comparison of three hydrological modeling schema: a) Lumped model, b) Semi-distributed model, and c) Distributed model. - - -Rainfall-runoff process modeling --------------------------------- - -The rainfall-runoff process is a series of mechanisms describing how a raindrop or snowfall becomes a runoff. This process often includes evapotranspiration, interception, infiltration, snow melting, and groundwater recession. HydroCNHS supports two process-based modeling options: (1) the General Water Loading Function (GWLF; Haith et al., 1987) with nine parameters and (2) the ABCD model (Thomas, 1981) with five parameters. Detailed calculations can be found in the supplementary material in Lin et al. (2022). Users can also pre-calculate subbasins' runoffs using their preferred rainfall-runoff models and input them into HydroCNHS. - -Routing & Routing outlets -------------------------- -Routing captures the delay in water movement from upstream to downstream. HydroCNHS adopts the Lohmann routing model (Lohmann et al., 1998) and the unit hydrograph parameterization described in Wi et al. (2015), with the former tracing the runoff from subbasins through the river channel (i.e., inter-subbasin routing) and the latter accounting for the within-subbasin routing process (:numref:`fig2`\a). A gamma distribution is adopted to represent the unit hydrograph of within-subbasin routing. - -In HydroCNHS, we define routing outlets as those subbasin outlets with routed streamflow information. In other words, for subbasin outlets that are not defined as routing outlets, HydroCNHS will not route the streamflow, and only the runoff information is stored. However, we do not encourage users to define too many routing outlets if the streamflow information is not necessary at those outlets. Minimizing the number of routing outlets will reduce the model complexity and usually lead to a better model performance. - -A water system in node-link structure -------------------------------------- -HydroCNHS represents a water system in a node-link structure. Therefore, users must learn how to abstract a water system with a node-link representation and understand the routing logic behind a given node-link structure used by HydroCNHS. We introduce three typical cases, shown in :numref:`fig2`. - -.. _fig2: -.. figure:: ../figs/fig2_routing_schema.png - :align: center - :width: 700 - :alt: Routing schema using in HydroCNHS. - - Routing schema using in HydroCNHS. a) single routing outlet, b) multiple routing outlets, and c) with a reservoir agent. - -Single outlet with no human components (:numref:`fig2`\a) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In :numref:`fig2`\a, we are only interested in the streamflow at the basin outlet, assigned as the only routing outlet. In this case, HydroCNHS will route each runoff generated in each subbasin to the basin outlet (white dot). Namely, each subbasin contains both within-subbasin (represented by a unit hydrologic response curve) and inter-subbasin routing. - -Multiple routing outlets (:numref:`fig2`\b) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In :numref:`fig2`\b, we are interested in the streamflow at multiple (in here two) outlets. In this case, calibration is possible if streamflow data is available for the two outlets. In Figure 2b, the three most upstream outlets (black dots) are routed to the upstream routing outlet and will NOT be routed again for the downstream routing outlet. Instead, the routed streamflow at the upstream routing outlet will be further routed to the downstream outlet with only river routing (i.e., only inter-subbasin and no within-subbasin routing). - -Routing with human components using Dam API (:numref:`fig2`\c) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:numref:`fig2`\c introduces human components (e.g., a reservoir; ResAgt) integrated with Dam API to the node-link structure. A reservoir is considered an in-stream object that completely redefines the downstream flow according to its water releasing rules. Therefore, such an in-stream object (e.g., a reservoir) is defined as a pseudo routing outlet used for the downstream routing. Note that the upstream node of a reservoir has to be a routing outlet to simulate the inflow. Or, we can view that ResAgt takes water from its upstream routing outlet and release water based on the reservoir's operational rules. More details about other APIs are presented in the following section. - -Design a water system with the supporting APIs ----------------------------------------------- - -.. _fig3: -.. figure:: ../figs/fig3_hydrocnhs.png - :align: center - :width: 500 - :alt: A generic example of HydroCNHS coupling APIs and water system description. - - A generic example of HydroCNHS coupling APIs and water system description. - -The four APIs in the HydroCNHS (:numref:`fig3`) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API. - -**Dam API** is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in :numref:`fig3`) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents' water releases decision. - -**RiverDiv API** is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in :numref:`fig3`. This API ensures the diverted outlet is routed before agents' diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location. - -**Conveying API** is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin's runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step). - -**InSitu API** is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in :numref:`fig3`). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step. - -We summarize the supported connections of each API in :numref:`table1`\. - -.. _table1: -.. table:: The supported connections of each API. - :align: center - :width: 100% - - +-----------------------+-------------------------+-------------------------+ - |APIs |Minus (divert/take) from |Plus (return/release) to | - | +----------------+--------+----------------+--------+ - | |Routing outlets |Outlets |Routing outlets |Outlets | - +=======================+================+========+================+========+ - |Dam API\ :sup:`*`\ |**V** |**X** |-- |-- | - +-----------------------+----------------+--------+----------------+--------+ - |RiverDiv API |**V** |**X** |**V** |**V** | - +-----------------------+----------------+--------+----------------+--------+ - |Conveying API |**V** |**X** |**V** |**V** | - +-----------------------+----------------+--------+----------------+--------+ - |InSitu API\ :sup:`**`\ |**V** |**V** |**V** |**V** | - +-----------------------+----------------+--------+----------------+--------+ - | | \ :sup:`*`\ Human components using Dam API will serve as a pseudo | - | routing outlet for the downstream routing. | - | | \ :sup:`**`\ InSitu API only works on a single outlet (i.e., subbasin). | - +---------------------------------------------------------------------------+ \ No newline at end of file diff --git a/docs/_build/html/_sources/files/4_input_output.rst.txt b/docs/_build/html/_sources/files/4_input_output.rst.txt deleted file mode 100644 index 3efac38..0000000 --- a/docs/_build/html/_sources/files/4_input_output.rst.txt +++ /dev/null @@ -1,174 +0,0 @@ -Input/Output -============ - -Inputs ------- - -HydroCNHS has the following three main inputs: - -(1) Daily climate data including precipitation, temperature, and (optional) potential evapotranspiration (python *dictionary object*), - -(2) A model configuration file (*.yaml*; settings for the HydroCNHS and ABM models), and - -(3) ABM modules (*.py*; customized human models). - -Outputs -------- - -The outputs of HydroCNHS are stored in a data collector object, an attribute of HydroCNHS (e.g., *model.dc*). The main output is the daily streamflow at routing outlets. However, this data collector object will also contain other user-specified agent outputs as long as users use this data collector object in their ABM modules. See :ref:`Integrate an ABM` and :ref:`Data collector` for more information. - -Daily climate data ------------------- - -Temperature (temp; degC) and precipitation (prec; cm) are two required weather inputs for a simulation. Potential evapotranspiration (pet; cm) can be automatically calculated by HydroCNHS using the Hamon method. However, we encourage users to pre-calculate pet and provide it to HydroCNHS as an input to avoid repeated calculation, especially when running calibration. The daily climate inputs are in a python dictionary format, in which the subbasin outlet name is the key, and a daily time series (*list*) is the value, as shown below. - -.. code-block:: python - - temp = {'subbasin1': [7.7, 7.0, 6.6, 6.3, .......], - 'subbasin2': [8.5, 4.0, 5.3, 6.2, .......]} - -Model configuration file ------------------------- - -A model configuration file is a YAML file that contains settings for the entire water system. The main sections in a model configuration file include Path, WaterSystem, LSM, Routing, and ABM. Please see the following description for each setting. Parameter and inputs definitions for rainfall-runoff and routing models are summarized in :numref:`table2` and :numref:`table3`, respectively. HydroCNHS has an embedded model builder. Therefore, users do not need to manually create the model configuration file (see the example in the following section). - -.. code-block:: yaml - - Path: - WD: - Modules: - - WaterSystem: - StartDate: - EndDate: - DataLength: - NumSubbasins: - Outlets: - NodeGroups: - LSM: - Routing: - ABM: - Modules: - InstitDMClasses: - - DMClasses: - DamAPI: - RiverDivAPI: - InsituAPI: - ConveyingAPI: - Institutions: - - - RainfallRunoff: - : - Inputs: - Pars: - : - Inputs: - Pars: - - Routing: - : - : - Inputs: - Pars: - : - Inputs: - Pars: - - ABM: - : - : - Attributes: "agent's attributes dict, e.g., {}" - Inputs: - Priority: - Links: - DMClass: - Pars: - - -.. _table2: -.. table:: Hydrological model parameters and suggested bounds. - :align: center - :width: 100% - - +----------+------------------------------------------+--------------+-------------------+---------------------+ - |Module |Parameter name |Unit |Parameter |Bound | - +==========+==========================================+==============+===================+=====================+ - |GWLF |Curve number |-- |:math:`CN2` |[25, 100] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Interception coefficient |-- |:math:`IS` |[0, 0.5] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Recession coefficient |-- |:math:`Res` |[10\ :sup:`-3`\, 0.5]| - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Deep seepage coefficient |-- |:math:`Sep` |[0, 0.5] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Baseflow coefficient |-- |:math:`\alpha` |[0, 1] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Percolation coefficient |-- |:math:`\beta` |[0, 1] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Available/soil water capacity |cm |:math:`U_r` |[1, 15] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Degree-day coefficient for snowmelt |cm/°C |:math:`D_f` |[0, 1] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Land cover coefficient |-- |:math:`K_c` |[0.5, 1.5] | - +----------+------------------------------------------+--------------+-------------------+---------------------+ - |ABCD |Controls the amount of runoff and recharge|-- |:math:`a` |[0, 1] | - | |during unsaturated soil | | | | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Control of the saturation level of the |-- |:math:`b` |[0, 400] | - | |soils | | | | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Ratio of groundwater recharge to runoff |-- |:math:`c` |[0, 1] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Control of groundwater discharge rate |-- |:math:`d` |[0, 1] | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Degree-day coefficient for snowmelt |cm/°C |:math:`D_f` |[0, 1] | - +----------+------------------------------------------+--------------+-------------------+---------------------+ - | | Lohmann|Subbasin unit hydrograph shape parameter |-- |:math:`G_{shape}` |[1, 100] | - | | routing+------------------------------------------+--------------+-------------------+---------------------+ - | |Subbasin unit hydrograph rate parameter |-- |:math:`G_{scale}` |[10\ :sup:`-2`\, 150]| - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Wave velocity in the linearized Saint- |m/s |:math:`Velo` |[0.5, 100] | - | |Venant equation | | | | - | +------------------------------------------+--------------+-------------------+---------------------+ - | |Diffusivity in the linearized Saint- |m\ :sup:`2`\/s|:math:`Diff` |[200, 5000] | - | |Venant equation | | | | - +----------+------------------------------------------+--------------+-------------------+---------------------+ - - -.. _table3: -.. table:: Hydrological model inputs and default values. - :align: center - :width: 100% - - +----------+---------------------------------------------+--------------+------------------------+---------------------+ - |Module |Parameter name |Unit |Parameter |Default | - +==========+=============================================+==============+========================+=====================+ - |GWLF |Subbasin's drainage area |ha |:math:`Area` |-- | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Latitude |deg |:math:`Latitude` |-- | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Initial shallow saturated soil water content |cm |:math:`S0` |2 | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Initial unsaturated soil water content |cm |:math:`U0` |10 | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Initial snow storage |cm |:math:`SnowS` |5 | - +----------+---------------------------------------------+--------------+------------------------+---------------------+ - |ABCD |Subbasin's drainage area |-- |:math:`Area` |-- | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Latitude |deg |:math:`Latitude` |-- | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Initial saturated soil water content |cm |:math:`XL` |2 | - | +---------------------------------------------+--------------+------------------------+---------------------+ - | |Initial snow storage |cm |:math:`SnowS` |5 | - +----------+---------------------------------------------+--------------+------------------------+---------------------+ - | | Lohmann|Flow length between two outlets |m |:math:`FlowLength` |-- | - | | routing+---------------------------------------------+--------------+------------------------+---------------------+ - | |An instream control object, e.g., a reservoir|-- |:math:`InstreamControl` |False | - +----------+---------------------------------------------+--------------+------------------------+---------------------+ - -ABM modules ------------ - -ABM modules are customized python scripts in which human components are designed through programming agent type classes and decision-making classes. HydroCNHS will load those user-specified classes and use them to initialize agents. More details are provided in the :ref:`Integrate an ABM` section. diff --git a/docs/_build/html/_sources/files/5_build_a_hydrological_model.rst.txt b/docs/_build/html/_sources/files/5_build_a_hydrological_model.rst.txt deleted file mode 100644 index 15068f0..0000000 --- a/docs/_build/html/_sources/files/5_build_a_hydrological_model.rst.txt +++ /dev/null @@ -1,242 +0,0 @@ -Build a hydrological model -========================== - -To get familiar with the HydroCNHS model construction process, let's start with a hydrological model without human components. We will go through the following steps: - -1. Create a model configuration file (*.yaml*) using a model builder. - -2. Complete a model configuration file (*.yaml*) - -3. Run a calibration - -4. Run a simulation - -We will adopt the following delineated watershed (:numref:`fig4`) as a demonstration, in which we want to build a semi-distributed hydrological model to simulate the streamflow at the basin outlet, WSLO. The corresponding subbasins' information is provided in :numref:`table4`. - - -.. _fig4: -.. figure:: ../figs/fig4_example_watershed.png - :align: center - :width: 300 - :alt: Example watershed. - - Example watershed. - - -.. _table4: -.. table:: Subbasins' information. - :align: center - :width: 100% - - +----------------+-------------------+---------------+-----------------------+ - |Subbasin/outlet |Drainage area [ha] |Latitude [deg] |Flow length to WSLO [m]| - +================+===================+===============+=======================+ - |Hagg |10034.2408 |45.469 |101469.139 | - +----------------+-------------------+---------------+-----------------------+ - |DLLO |22568.2404 |45.475 |91813.075 | - +----------------+-------------------+---------------+-----------------------+ - |TRGC |24044.6363 |45.502 |80064.864 | - +----------------+-------------------+---------------+-----------------------+ - |DAIRY |59822.7546 |45.520 |70988.164 | - +----------------+-------------------+---------------+-----------------------+ - |RCTV |19682.6046 |45.502 |60398.680 | - +----------------+-------------------+---------------+-----------------------+ - |WSLO |47646.8477 |45.350 |0 | - +----------------+-------------------+---------------+-----------------------+ - -Step 1: Create a model configuration file ------------------------------------------ - -HydroCNHS comes with a model builder to help users create an initial model configuration file. - -First, initiate a model builder object with a working directory (your working folder directory). - -.. code-block:: python - - import os - import HydroCNHS - prj_path, this_filename = os.path.split(__file__) - -Second, set up the water system with the simulation period (i.e., start date and end date). - -.. code-block:: python - - wd = prj_path - mb = HydroCNHS.ModelBuilder(wd) - mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") - -Third, we can add subbasins (i.e., outlet_list) into the model. We adopt the GWLF model as the rainfall-runoff model (i.e., runoff_model). We also assign the corresponding subbasins' areas (i.e., area_list) and latitude (i.e., lat_list) using the information from :numref:`table4`. Note that area_list and lat_list are optional arguments. Users can manually enter that information to the model configuration file (*.yaml*). - -.. code-block:: python - - outlet_list = ['Hagg', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] - area_list = [10034.2408, 22568.2404, 24044.6363, 59822.7546, 19682.6046, - 47646.8477] - lat_list = [45.469, 45.475, 45.502, 45.520, 45.502, 45.350] - mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="GWLF") - -Fourth, we want to add the routing setting. Here, we only have one routing outlet, WSLO, hence, we only need to run the following code once, and all the outlets are considered upstream outlets of WSLO. - -.. code-block:: python - - flow_length_list = [101469.139, 91813.075, 80064.864, 70988.164, 60398.680, 0] - mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=outlet_list, - flow_length_list=flow_length_list) - -Finally, we can print out the model for quick view and then write it to *.yaml* file with the given name. - -.. code-block:: python - - ### Print the model in the console - mb.print_model() - - ### Output initial model configuration file (.yaml) - mb.write_model_to_yaml(filename="HydroModel.yaml") - -Now, we should have the initial model configuration file (i.e., *HydroModel.yaml*) in the corresponding folder. - -Step 2: Complete a model configuration file -------------------------------------------- - -Before using the created *HydroModel.yaml* for the later experiment, we need to open it and ensure the information is correct. The “Inputs” in LSM and Routing sections should be all populated. Some default values may be used (e.g., :math:`S0`, :math:`U0`, and :math:`SnowS`). They should be updated according to users' needs. For this example, we don't need to modify anything. You might notice that all the parameters have **a value of -99**. This is the default value in HydroCNHS, meaning that those values need to be calibrated if not provided by users. - -Step 3: Run a calibration -------------------------- - -First, we load the climate data, observed flow data at WSLO, and the model configuration file. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, we load it as the inputs as well. - -.. code-block:: python - - import matplotlib.pyplot as plt - import pandas as pd - import HydroCNHS.calibration as cali - from copy import deepcopy - - # Load climate data - temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") - prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") - pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") - - # Load flow gauge monthly data at WSLO - obv_flow_WSLO = pd.read_csv(os.path.join(wd,"Data","WSLO_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) - - # Load model - model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroModel.yaml")) - -Second, we generate default parameter bounds and create a convertor for calibration. Details about the converter are provided in the Calibration section. - -.. code-block:: python - - # Generate default parameter bounds - df_list, df_name = HydroCNHS.write_model_to_df(model_dict) - par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) - - # Create convertor for calibration - converter = cali.Convertor() - cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) - formatter = converter.formatter - -Third, we program the evaluation function for a genetic algorithm (GA). Kling-Gupta efficiency (KGE; Gupta et al., 2009) is adopted to represent the model performance of simulated monthly streamflow at the WSLO routing outlet. - -.. code-block:: python - - # Code evaluation function for GA algorthm - def evaluation(individual, info): - cali_wd, current_generation, ith_individual, formatter, _ = info - name = "{}-{}".format(current_generation, ith_individual) - - ##### individual -> model - # Convert 1D array to a list of dataframes. - df_list = cali.Convertor.to_df_list(individual, formatter) - # Feed dataframes in df_list to model dictionary. - model = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") - - ##### Run simuluation - model = HydroCNHS.Model(model, name) - Q = model.run(temp, prec, pet) - - ##### Get simulation data - # Streamflow of routing outlets. - cali_target = ["WSLO"] - cali_period = ("1981-1-1", "2005-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[cali_target] - # Resample the daily simulation output to monthly outputs. - sim_Q_M = sim_Q_D[cali_target].resample("MS").mean() - - KGE = HydroCNHS.Indicator().KGE( - x_obv=obv_flow_WSLO[cali_period[0]:cali_period[1]][cali_target], - y_sim=sim_Q_M[cali_period[0]:cali_period[1]][cali_target]) - - fitness = KGE - return (fitness,) - -Forth, we set up a GA for calibration. Again, we will explain calibration in more detail in the Calibration section. Here, the code is demonstrated. Note that calibration might take some time to run, depending on your system specifications. Users can lower down 'pop_size ' and 'max_gen' if they just want to experience the process instead of seeking convergence. Note that to debug your code, please set 'paral_cores' to 1 to show the error messages. - -.. code-block:: python - - config = {'min_or_max': 'max', - 'pop_size': 100, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.15, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} - - seed = 5 - rn_gen = HydroCNHS.create_rn_gen(seed) - ga = cali.GA_DEAP(evaluation, rn_gen) - ga.set(cali_inputs, config, formatter, name="Cali_HydroModel_gwlf_KGE") - ga.run() - summary = ga.summary - individual = ga.solution - -Finally, we export the calibrated model (i.e., *Best_HydroModel_gwlf_KGE.yaml*). - -.. code-block:: python - - ##### Output the calibrated model. - df_list = cali.Convertor.to_df_list(individual, formatter) - model_best = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml")) - -Step 4: Run a simulation ------------------------- - -After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (e.g., *Best_HydroModel_gwlf_KGE.yaml*) can be directly loaded into HydroCNHS to run a simulation. - -.. code-block:: python - - ### Run a simulation. - model = HydroCNHS.Model(os.path.join(wd, "Cali_HydroModel_gwlf_KGE", - "Best_HydroModel_gwlf_KGE.yaml")) - Q = model.run(temp, prec, pet) - result = pd.DataFrame(Q, index=model.pd_date_index).resample("MS").mean() - - ### Plot - fig, ax = plt.subplots() - ax.plot(obv_flow_WSLO.index, obv_flow_WSLO.loc[:, "WSLO"], label="Obv") - ax.plot(obv_flow_WSLO.index, result["WSLO"], ls="--", label="Sim") - ax.legend() - -The complete script can be found at *./tutorials/Hydro_example/Main_HydroModel.py*. - -The following section will further introduce how to incorporate user-defined ABM modules into the model. \ No newline at end of file diff --git a/docs/_build/html/_sources/files/6_Integrate_an_ABM.rst.txt b/docs/_build/html/_sources/files/6_Integrate_an_ABM.rst.txt deleted file mode 100644 index 5cdae25..0000000 --- a/docs/_build/html/_sources/files/6_Integrate_an_ABM.rst.txt +++ /dev/null @@ -1,452 +0,0 @@ -Integrate an ABM -================= - -After getting familiar with HydroCNHS from the hydrological model example in the previous section, we want to build a water system with human components (i.e., ABM). We will go through a similar process in the last example and focus on adding the human components. - -1. Create a model configuration file (*.yaml*) using a model builder. - -2. Complete a model configuration file (*.yaml*) - -3. Program an ABM module (*.py*) - -4. Run a calibration - -5. Run a simulation - -We adopt the Tualatin River Basin (TRB; :numref:`fig5`\; :numref:`table5`) as the tutorial example. The corresponding subbasins' information is shown in :numref:`table5`. In this example, we consider three human components (:numref:`table6`), including (1) a reservoir (*ResAgt*), (2) an irrigation diversion (*DivAgt*), and (3) a trans-basin water transfer (*PipeAgt*), to demonstrate the HydroCNHS's functionalities. Also, we model each human agent with different levels of behavioral complexities to provide users with a sense of how an agent can be modeled (:numref:`table6`). We will calibrate the model using the streamflow at DLLO and WSLO, reservoir releases from ResAgt, and water diversions from DivAgt on a monthly scale. More details about TRB can be found in Lin et al. (2022). Here, we focus on the coding part. - -.. _fig5: -.. figure:: ../figs/fig5_TRB_node_link.png - :align: center - :width: 600 - :alt: The node-link structure of the Tualatin River Basin with human components. - - The node-link structure of the Tualatin River Basin with human components. - - -.. _table5: -.. table:: Subbasins' information for the Tualatin River Basin. - :align: center - :width: 100% - - +-----------------+--------------------+----------------+-----------------------+ - |Subbasin/outlet |Drainage area [ha] |Latitude [deg] |Flow length [m] | - +=================+====================+================+=======================+ - |HaggIn |10034.2408 |45.469 |0 (to HaggIn) | - +-----------------+--------------------+----------------+-----------------------+ - |TRTR |329.8013 |45.458 |30899.4048 (to DLLO) | - +-----------------+--------------------+----------------+-----------------------+ - |ResAgt\ :sup:`*`\|-- |-- |9656.064 (to DLLO) | - +-----------------+--------------------+----------------+-----------------------+ - |DLLO |22238.4391 |45.475 | | 0 (to DLLO) | - | | | | | 11748.211 (to TRGC) | - +-----------------+--------------------+----------------+-----------------------+ - |TRGC |24044.6363 |45.502 | | 0 (to TRGC) | - | | | | | 80064.864 (to WSLO) | - +-----------------+--------------------+----------------+-----------------------+ - |DAIRY |59822.7546 |45.520 |70988.164 (to WSLO) | - +-----------------+--------------------+----------------+-----------------------+ - |RCTV |19682.6046 |45.502 |60398.680 (to WSLO) | - +-----------------+--------------------+----------------+-----------------------+ - |WSLO |47646.8477 |45.350 |0 (to WSLO) | - +-----------------+--------------------+----------------+-----------------------+ - |\ :sup:`*`\ResAgt is a reservoir agent integrated with Dam API. It is | - |considered a pseudo routing outlet. | - +-------------------------------------------------------------------------------+ - -.. _table6: -.. table:: Summary of human components' setting. - :align: center - :width: 100% - - +--------------------+----------------+-----------------+--------------------------+--------+-----+ - |Item |Agent name |API |Behavioral design |From |To | - +====================+================+=================+==========================+========+=====+ - |Reservoir |ResAgt |Dam API |Fixed operational rules |HaggIn |-- | - +--------------------+----------------+-----------------+--------------------------+--------+-----+ - | | Irrigation |DivAgt |RiverDiv API |Calibrated adaptive |TRGC |WSLO | - | | diversion | | |behavioral rules | | | - +--------------------+----------------+-----------------+--------------------------+--------+-----+ - | | Trans-basin |PipeAgt |Conveying API |External input data |-- |TRTR | - | | water transfer | | | | | | - +--------------------+----------------+-----------------+--------------------------+--------+-----+ - -Step 1: Create a model configuration file ------------------------------------------ - -Creating a node-link structure of a modeled water system is a vital step before using HydroCNHS. Subbasin outlets are determined based on the major tributaries in the TRB. However, the subbasin outlet, TRTR, is given because we have an inlet there for trans-basin water transfer. For the routing outlet assignment, DLLO and WSLO are selected because the streamflow at these two is part of the calibration targets, TRGC is also chosen since an agent integrated using RiverDiv API can only divert water from a routing outlet, and HaggIn is picked because it is the inflow of ResAgt (i.e., ResAgt takes water from HaggIn). - -With a node-link structure of the TRB water system, we can follow the same process shown in the "Build a hydrological model" to initialize a model builder, set up the water system with the simulation period, add subbasins, and add four routing outlets. Note that ResAgt is considered a pseudo routing outlet that needs to be assigned to one of the upstream outlets of a routing outlet. - -.. code-block:: python - - import os - import HydroCNHS - prj_path, this_filename = os.path.split(__file__) - - ### Initialize a model builder object. - wd = prj_path - mb = HydroCNHS.ModelBuilder(wd) - - ### Setup a water system simulation information - mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") - - ### Setup land surface model (rainfall-runoff model) - # Here we have seven subbasins and we select GWLF as the rainfall-runoff model. - outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] - area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546, - 19682.6046, 47646.8477] - lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350] - mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="GWLF") - - ### Setup routing outlets - # Add WSLO - mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], - flow_length_list=[80064.864, 70988.164, 60398.680, 0]) - # Add TRGC - mb.set_routing_outlet(routing_outlet="TRGC", - upstream_outlet_list=["DLLO", "TRGC"], - flow_length_list=[11748.211, 0]) - # Add DLLO - # Specify that ResAgt is an instream object. - mb.set_routing_outlet(routing_outlet="DLLO", - upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], - flow_length_list=[9656.064, 30899.4048, 0], - instream_objects=["ResAgt"]) - # Add HaggIn - mb.set_routing_outlet(routing_outlet="HaggIn", - upstream_outlet_list=["HaggIn"], - flow_length_list=[0]) - -Initialize ABM setting -^^^^^^^^^^^^^^^^^^^^^^ -To add human components, we need to first initialize the ABM setting block by assigning an ABM module folder's directory and planned ABM module filename. If they are not given, default values will be applied, namely, working directory and "ABM_module.py, "respectively. abm_module_name will be used as the filename for the ABM module template if users choose to generate one using the model builder. - -.. code-block:: python - - mb.set_ABM(abm_module_folder_path=wd, abm_module_name="TRB_ABM.py") - -Add agents -^^^^^^^^^^ -Next, we add human components (i.e., agents) to the model builder. -We first **add a reservoir agent (ResAgt)**, in which its corresponding agent type class, agent name, api, link dictionary, and decision-making class can be assigned at this stage. Although not all information has to be provided now (i.e., it can be manually added to the model configuration file later), we encourage users to provide complete details here. - -.. code-block:: python - - mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt", - api=mb.api.Dam, - link_dict={"HaggIn": -1, "ResAgt": 1}, - dm_class="ReleaseDM") - -The setting shown above means that ResAgt (an agent object) will be created from Reservoir_AgtType (an agent type class) and integrated into HydroCNHS using the Dam API. A decision-making object will be created from ReleaseDM (a decision-making class) and assigned to ResAgt as its attribute. This agent, ResAgt, will take water (factor = -1) from HaggIn routing outlet and release (factor = 1) water to ResAgt. Remember that ResAgt itself is a pseudo routing outlet. - -Following a similar procedure, we **add a water diversion agent (DivAgt)**. However, we have parameters, including ReturnFactor, a, and b, involved in this agent. Hence, a dictionary is provided to the par_dict argument. The format of the parameter dictionary is that keys are parameter names, and values are parameter values (-99 means waiting to be calibrated). - -However, if the parameter is the factor used in the link_dict, users need to follow the format shown here. For example, we want to calibrate a return factor (ReturnFactor) to determine the portion of diverted water returned to the WSLO subbasin. To do that, a list, ["ReturnFactor", 0, "Plus"], is given to link_dict at WSLO. HydroCNHS will interpret it as taking the factor value from parameter ReturnFactor with a list index of 0. "Plus" tells HydroCNHS we add water to WSLO. If water is taken from WSLO, then "Minus" should be assigned. - -.. code-block:: python - - mb.add_agent(agt_type_class="Diversion_AgType", agt_name="DivAgt", - api=mb.api.RiverDiv, - link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, - dm_class="DivertDM", - par_dict={"ReturnFactor": [-99], "a": -99, "b":-99}) - -Finally, we **add a trans-basin water transfer agent (PipeAgt)**. - -.. code-block:: python - - mb.add_agent(agt_type_class="Pipe_AgType", agt_name="PipeAgt", - api=mb.api.Conveying, - link_dict={"TRTR": 1}, - dm_class="TransferDM") - -Add institution -^^^^^^^^^^^^^^^ - -We did not include an institution in this TRB example; however if users want to assign an institution (e.g., "ResDivInstitution") to ResAgt and DivAgt, they should do so by assuming that there is a cooperation between water release decisions and water diversion decisions. Namely, release decisions from ResAgt and diversion decisions from DivAgt are made simultaneously using a single decision-making object (:numref:`fig6`). Users can do the following. - -.. code-block:: python - - mb.add_institution(institution="ResDivInstitution", - instit_dm_class=" ResDivDMClass", - agent_list=[" ResAgt ", "DivAgt"]) - - -Note that ResDivInstitution will overwrite the originally assigned DM classes (if any) of ResAgt and DivAgt. The above command means a single ResDivInstitution decision-making object initialized from ResDivDMClass will be assigned to ResAgt and DivAgt's attributes (e.g., *self.dm*). Users can utilize this property to design their agents. - -Generate ABM module template & output model configuration file -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In addition to outputting a model configuration file (*.yaml*), the model builder can generate an ABM module template (*.py*) for users, in which the model builder will create the outline of agent type classes and decision-making classes, and users can concentrate on programming the calculation for each class given in the template. - -.. code-block:: python - - ### Output initial model configuration file (.yaml) and ABM module template. - mb.write_model_to_yaml(filename="HydroABMModel.yaml") - mb.gen_ABM_module_template() - - -Step 2: Complete a model configuration file -------------------------------------------- - -After the model configuration file (*.yaml*) is created, users should open the file to complete and correct any missing or misinterpreted values. For this example, again, we will keep the default values. - -Step 3: Program ABM module (*.py*) ------------------------------------- - -In the generated ABM module (*.py*), users can find mainly two types of classes, including agent type classes (AgtType) and decision-making classes (DMClass/Institutional DMClass). Agent type classes are used to define agents' actions and store up-to-date information (e.g., current date and current time step) in agents' attributes. Decision-making classes are used to program a specific decision-making process. Decision-making classes can be further separated into DMClass and Institutional DMClass. - -The ABM design logic is illustrated in :numref:`fig6`. A "class" is a template for objects that can be initiated with object-specific attributes and settings. For example, Agent1 and Agent2 are initiated from the same AgtType1 class. Agent 2, Agent 4, and Agent 5 are initiated from the AgtType2 class. Each agent will be assigned a DM object or Institution object as one of its attributes. DM objects initiated from DMClass are NOT shared with other agents; Namely, agents with DM objects will only have one unique DM object (e.g., Agent 1 and Agent 2 in :numref:`fig6`). In contrast, an Institution object can be shared with multiple agents, in which those agents can make decisions together. For example, multiple irrigation districts make diversion decisions together to share the water shortage during a drought period. We will not implement the Institutional DMClass in this TRB example; however, we will show how to add an institution through a model builder. - -.. _fig6: -.. figure:: ../figs/fig6_ABM_design_logic.png - :align: center - :width: 600 - :alt: ABM design logic. - - ABM design logic. An agent is a combination of an AgtType class and an (Institutional) DM class. An Institution object can be shared among a group of agent objects (i.e., make decisions together), while a DM object can only be assigned to one agent object. - -Agent type class (AgtType): -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- **self.name** = agent's name -- **self.config** = agent's configuration dictionary, {'Attributes': ..., 'Inputs': ..., 'Pars': ...}. -- **self.start_date** = start date (datetime object). -- **self.current_date** = current date (datetime object). -- **self.data_length** = data/simulation length. -- **self.t** = current timestep. -- **self.dc** = data collector object containing data. Routed streamflow (Q_routed) is also collected here. -- **self.rn_gen** = NumPy random number generator. -- **self.agents** = a dictionary of all initialized agents, {agt_name: agt object}. -- **self.dm** = (institutional) decision-making object if DMClass or institution is assigned to the agent, else None. - -.. code-block:: python - - # AgtType - class XXX_AgtType(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # The AgtType inherited attributes are applied. - # See the note at top. - - def act(self, outlet): - # Read corresponding factor of the given outlet - factor = read_factor(self.config, outlet) - - # Common usage: - # Get streamflow of outlet at timestep t - Q = self.dc.Q_routed[outlet][self.t] - - # Make decision from (Institutional) decision-making - # object if self.dm is not None. - #decision = self.dm.make_dm(your_arguments) - - if factor <= 0: # Divert from the outlet - action = 0 - elif factor > 0: # Add to the outlet - action = 0 - - return action - - -(Institutional) decision-making classes (DMClass): -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -- **self.name** = name of the agent or institute. -- **self.dc** = data collector object containing data. Routed streamflow (Q_routed) is also collected here. -- **self.rn_gen** = NumPy random number generator. - -.. code-block:: python - - # DMClass - class XXX_DM(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. - # See the note at top. - - def make_dm(self, your_arguments): - # Decision-making calculation. - decision = None - return decision - -To keep the manual concise, we provide a complete ABM module for the TRB example at *./tutorials/HydroABM_example/TRB_ABM_complete.py*. Theoretical details can be found in Lin et al. (2022), and more coding tips are available at :ref:`Advanced ABM coding tips`. - -Step 4: Run a calibration -------------------------- - -First, we load the model configuration file, the climate data, and the observed monthly flow data for DLLO and WSLO, reservoir releases of ResAgt, and water diversions of DivAgt. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, PET data is input along with other data. Note that we manually change the ABM module from "TRB_ABM.py" to "TRB_ABM_complete.py." - -.. code-block:: python - - import matplotlib.pyplot as plt - import pandas as pd - import HydroCNHS.calibration as cali - from copy import deepcopy - - # Load climate data - temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") - prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") - pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") - - # Load flow gauge monthly data at WSLO - obv_flow_data = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) - - # Load model - model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroABMModel.yaml")) - # Change the ABM module to the complete one. - model_dict["WaterSystem"]["ABM"]["Modules"] = ["TRB_ABM_complete.py"] - -Second, we generate default parameter bounds and create a convertor for calibration. Note that we manually change the default ABM parameter bounds as shown in the code. Details about the Converter are provided in the Calibration section. - -.. code-block:: python - - # Generate default parameter bounds - df_list, df_name = HydroCNHS.write_model_to_df(model_dict) - par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) - - # Modify the default bounds of ABM - df_abm_bound = par_bound_df_list[2] - df_abm_bound.loc["ReturnFactor.0", [('DivAgt', 'Diversion_AgType')]] = "[0, 0.5]" - df_abm_bound.loc["a", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]" - df_abm_bound.loc["b", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]" - - # Create convertor for calibration - converter = cali.Convertor() - cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) - formatter = converter.formatter - -Third, we program the evaluation function for a genetic algorithm (GA). The four calibration targets' mean Kling-Gupta efficiency (KGE; Gupta et al., 2009) is adopted to represent the model performance. - -.. code-block:: python - - # Code evaluation function for GA algorthm - def evaluation(individual, info): - cali_wd, current_generation, ith_individual, formatter, _ = info - name = "{}-{}".format(current_generation, ith_individual) - - ##### individual -> model - # Convert 1D array to a list of dataframes. - df_list = cali.Convertor.to_df_list(individual, formatter) - # Feed dataframes in df_list to model dictionary. - model = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") - - ##### Run simuluation - model = HydroCNHS.Model(model, name) - Q = model.run(temp, prec, pet) - - ##### Get simulation data - # Streamflow of routing outlets. - cali_target = ["WSLO","DLLO","ResAgt","DivAgt"] - cali_period = ("1981-1-1", "2005-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] - sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] - sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] - # Resample the daily simulation output to monthly outputs. - sim_Q_M = sim_Q_D[cali_target].resample("MS").mean() - - KGEs = [] - for target in cali_target: - KGEs.append(HydroCNHS.Indicator().KGE( - x_obv=obv_flow_data[cali_period[0]:cali_period[1]][[target]], - y_sim=sim_Q_M[cali_period[0]:cali_period[1]][[target]])) - - fitness = sum(KGEs)/4 - return (fitness,) - -Fourth, we set up a GA for calibration. Again, we will explain calibration in more detail in the Calibration section. Here, only the code is demonstrated. Note that calibration might take some time to run, depending on your system specifications. Users can lower down 'pop_size' and 'max_gen' if they want to experience the process instead of seeking convergence. In order to debug your code, set 'paral_cores' to 1 to show the error message. - -.. code-block:: python - - config = {'min_or_max': 'max', - 'pop_size': 100, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.15, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} - - seed = 5 - rn_gen = HydroCNHS.create_rn_gen(seed) - ga = cali.GA_DEAP(evaluation, rn_gen) - ga.set(cali_inputs, config, formatter, name="Cali_HydroABMModel_gwlf_KGE") - ga.run() - summary = ga.summary - individual = ga.solution - -Finally, we export the calibrated model (i.e., *Best_HydroABMModel_gwlf_KGE.yaml*). - -.. code-block:: python - - ##### Output the calibrated model. - df_list = cali.Convertor.to_df_list(individual, formatter) - model_best = deepcopy(model_dict) - for i, df in enumerate(df_list): - s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) - -Step 5: Run a simulation ------------------------- - -After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (i.e., *Best_HydroABMModel_gwlf_KGE.yaml*) can be directly loaded into HydroCNHS to run a simulation. - -.. code-block:: python - - ### Run a simulation. - model = HydroCNHS.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) - Q = model.run(temp, prec, pet) - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] - sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] - sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] - sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() - ### Plot - fig, axes = plt.subplots(nrows=4, sharex=True) - axes = axes.flatten() - x = sim_Q_M.index - axes[0].plot(x, sim_Q_M["DLLO"], label="$M_{gwlf}$") - axes[1].plot(x, sim_Q_M["WSLO"], label="$M_{gwlf}$") - axes[2].plot(x, sim_Q_M["ResAgt"], label="$M_{gwlf}$") - axes[3].plot(x, sim_Q_M["DivAgt"], label="$M_{gwlf}$") - - axes[0].plot(x, obv_flow_data["DLLO"], ls="--", lw=1, color="black", label="Obv") - axes[1].plot(x, obv_flow_data["WSLO"], ls="--", lw=1, color="black", label="Obv") - axes[2].plot(x, obv_flow_data["ResAgt"], ls="--", lw=1, color="black", label="Obv") - axes[3].plot(x, obv_flow_data["DivAgt"], ls="--", lw=1, color="black", label="Obv") - - axes[0].set_ylim([0,75]) - axes[1].set_ylim([0,230]) - axes[2].set_ylim([0,23]) - axes[3].set_ylim([0,2]) - - axes[0].set_ylabel("DLLO\n($m^3/s$)") - axes[1].set_ylabel("WSLO\n($m^3/s$)") - axes[2].set_ylabel("Release\n($m^3/s$)") - axes[3].set_ylabel("Diversion\n($m^3/s$)") - - axes[0].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1) - axes[1].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1) - axes[2].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1) - axes[3].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1) - - axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) - - fig.align_ylabels(axes) - diff --git a/docs/_build/html/_sources/files/7_data_collector.rst.txt b/docs/_build/html/_sources/files/7_data_collector.rst.txt deleted file mode 100644 index 364ed70..0000000 --- a/docs/_build/html/_sources/files/7_data_collector.rst.txt +++ /dev/null @@ -1,91 +0,0 @@ -Data collector -============== - -A data collector is a container object created by HydroCNHS in each simulation that can be passed around HydroCNHS and user-defined ABM modules. A data collector object can store dictionaries and lists. Each of the items is associated with a unique field name. Each field has properties including data, data type (e.g., a dictionary or a list), description, and unit. We provide some usage examples below. - -First, we manually create a data collector object for demonstration. - -.. code-block:: python - - import HydroCNHS - - ### Create a data collector object - dc = HydroCNHS.Data_collector() - -Then, we add two fields, "field_1" and "field 2", with corresponding field information to the collector. Spaces are not allowed here, and the code will convert "field 2" to "field_2". - -.. code-block:: python - ### Add fields - dc.add_field("field_1", data_type={}, desc="Demo dc ex1.", unit="no unit") - dc.add_field("field 2", data_type=[1,2,3], unit="cm") - ### Print out existed fields - dc.list_fields() - # field_1 - # type - # - # desc - # Demo dc ex1. - # unit - # no unit - # field_2 - # type - # - # desc - # None - # unit - # cm - -To read the data in a data collector, e.g., reading field_2, we may do the following: - -.. code-block:: python - - ### Read data - dc.field_2 - # Out[0]: [1, 2, 3] - -We can also create a shortcut for accessing a field by the following command, in which any modifications on the shortcut will be passed into the data collector object. - -.. code-block:: python - - ### Get a field shortcut - shortcut = dc.get_field("field_1", copy=False) - shortcut["new_key"] = "new value" - dc.field_1 - # Out[0]: {'new_key': 'new value'} - -If we want to get a copy of a field (not a shortcut), we must assign "True" to the "copy" argument. - -.. code-block:: python - - ### Get a copy of a field - copied = dc.get_field("field_1", copy=True) - copied["new_key2"] = "new value2" - dc.field_1 - # Out[0]: {'new_key': 'new value'} - print(copied) - # {'new_key': 'new value', 'new_key2': 'new value2'} - -We can also delete a field using the following commands. - -.. code-block:: python - - ### Delete a field - dc.del_field("field_1") - dc.list_fields() - # field_2 - # type - # - # desc - # None - # unit - # cm - -Finally, users can export the entire data collector to a dictionary. - -.. code-block:: python - - ### Export the entire data collector to a dictionary - dictionary = dc.get_dict(copy=True) - print(dictionary) - # {'field_info': {'field_2': {'type': , 'desc': None, 'unit': 'cm'}}, - # 'field_2': [1, 2, 3]} diff --git a/docs/_build/html/_sources/files/8_calibration.rst.txt b/docs/_build/html/_sources/files/8_calibration.rst.txt deleted file mode 100644 index 9d2600f..0000000 --- a/docs/_build/html/_sources/files/8_calibration.rst.txt +++ /dev/null @@ -1,215 +0,0 @@ -Calibration -=========== - -HydroCNHS is equipped with a genetic algorithm package powered by Distributed Evolutionary Algorithms in Python (DEAP), which can be used for calibration in parallel. HydroCNHS is unique in that aspect that both the parameters of the hydrological model and user-defined ABM can be calibrated simultaneously as long as they are defined in the model configuration file (*.yaml*). Furthermore, users are allowed to assign initial guesses to the algorithm. - -Calibration with the genetic algorithm (GA) -------------------------------------------- - -An evaluation function, a calibration input dictionary, and a GA configuration dictionary are required to use the GA calibration module supported by the HydroCNHS. - -Evaluation function -^^^^^^^^^^^^^^^^^^^ - -Assuming we want to maximize :math:`y=-x_1^2+5x_1-x_2` with :math:`x_1,x_2 \in [-5,5]`, we can design the evaluation function as: - -.. code-block:: python - - import os - import HydroCNHS - import HydroCNHS.calibration as cali - prj_path, this_filename = os.path.split(__file__) - - def evaluation(individual, info): - x = individual - fitness = -x[0]**2 + 5*x[0] - x[1] - return (fitness,) - - cali_wd, current_generation, ith_individual, formatter, _ = info - name = "{}-{}".format(current_generation, ith_individual) - - ##### individual -> model - # Convert 1D array to a list of dataframes. - df_list = cali.Convertor.to_df_list(individual, formatter) - # Feed dataframes in df_list to model dictionary. - -The evaluation must have "individual" and "info" arguments. Also, the return fitness value has to be of tuple format, e.g., (fitness,). The "info" contains additional information for users to design a more complex evaluation, as shown in "Build a hydrological model" and "Integrate an ABM" sections. This "info" variable contains information, including -1. working directory to the folder of this calibration experiment (cali_wd), -2. current generation of the GA run (current_generation), -3. index of the individual in the current generation (ith_individual), -4. formatter (formatter) for a converter, and -5. NumPy random number generator (rn_gen). - -.. code-block:: python - - cali_wd, current_generation, ith_individual, formatter, rn_gen = info - -cali_wd, current_generation, and ith_individual can be used to create sub-folders or output files for each evaluation. This is especially useful for calibrating a large model that requires a long simulation time. The formatter contains information to convert 1-D array individuals back to the original parameter format (e.g., a list of DataFrames) through a converter object. We will introduce the Converter in the next section. rn_gen is recommended to be used to generate random numbers (e.g., input to a HydroCNHS object like model = HydroCNHS.Model(model, name, rn_gen)). By applying rn_gen, HydroCNHS can guarantee the reproducibility of the numerical experiment with a given random seed. - -Calibration input dictionary -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A calibration input dictionary contains three keys, including par_name, par_bound, and wd. par_name is a list of the parameter names, par_bound is a list of parameter bounds, and wd is the working directory for the calibration experiment. The calibration input dictionary for this example is shown below. - -.. code-block:: python - - # Calibration inputs - cali.get_inputs_template() # print an input template. - - inputs = {'par_name': ['x1', 'x2'], - 'par_bound': [[-5, 5], [-5, 5]], - 'wd': 'working directory'} - inputs["wd"] = prj_path - -Note that users can use a converter to generate the calibration input dictionary automatically. - -GA configuration dictionary -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -A GA configuration dictionary contains many control options. Please see the following code for the explanation of each option. - -.. code-block:: python - - # GA configuration - cali.get_config_template() - - config = {'min_or_max': 'max', # maximize or minimize the evaluation function. - 'pop_size': 100, # Size of the population. - 'num_ellite': 1, # Number of ellites. - 'prob_cross': 0.5, # Crossover probability for uniform crossover operator. - 'prob_mut': 0.15, # Mutation probability of each parameter. - 'stochastic': False, # Is the evaluation stochastic? - 'max_gen': 100, # Maximum generation number. - 'sampling_method': 'LHC', # Sampling method for the initial population. - 'drop_record': False, # Whether to drop historical records to save space. - 'paral_cores': -1, # Number of parallel cores. -1 means all available cores. - 'paral_verbose': 1, # Higher value will output more console messages. - 'auto_save': True, # If true, users may continue the run later on by loading the auto-save file. - 'print_level': 1, # Control the number of generations before the printing summary of GA run. - 'plot': True} # Plot to time series of the best fitnesses over a generation. - -Finally, we can run the GA calibration. - -.. code-block:: python - - # Run GA - rn_gen = HydroCNHS.create_rn_gen(seed=3) - ga = cali.GA_DEAP(evaluation, rn_gen) - ga.set(inputs, config, formatter=None, name="Cali_example") - ga.run() - ga.solution - # Out[0]: array([ 2.47745344, -4.96991833]) - -After the GA terminated, we got the solution :math:`x_1=2.4775` and :math:`x_2=-4.9699`\, in which the theoretical values are 2.5 and -5 for :math:`x_1` and :math:`x_2`\, respectively. The fitness plot (:numref:`fig7`) and auto-saved file (*GA_auto_save.pickle*) will be stored in the GA working directory. This GA_auto_save.pickle can be loaded into the GA object and continue the calibration when algorithms encounter a sudden breakdown or continue the run with a larger "max_gen," as shown below. - -.. code-block:: python - - # Continue the run with larger "max_gen" - ga = cali.GA_DEAP(evaluation, rn_gen) - ga.load(os.path.join(prj_path, "Cali_example", "GA_auto_save.pickle"), - max_gen=120) - ga.run() - # =====Generation 120===== - # Elapsed time 00:00:05 - # Min -6.69464 - # Max 11.21948 - # Avg 10.99626 - # Std 1.77931 - - # GA done! - -.. _fig7: -.. figure:: ../figs/fig7_ga_fitness.png - :align: center - :width: 500 - :alt: The fitness and within-population standard deviation plot. - - The fitness and within-population standard deviation plot. - - -Converter ---------- - -The Converter is designed to convert a list of parameter DataFrames into a 1-D array for GA calibration and return a 1-D array to the original list of DataFrames. It can automatically exclude NaN values in DataFrames and fix parameters not intended to be calibrated. Also, a converter can generate the calibration input dictionary. - -We use the following synthetic list of DataFrames as an example. - -.. code-block:: python - - import numpy as np - import pandas as pd - import HydroCNHS - import HydroCNHS.calibration as cali - - ### Prepare testing data. - par_df1 = pd.DataFrame({"Subbasin1": [1000,1000,3], "Subbasin2": [4,5,6]}, - index=["a", "b", "c"]) - par_df2 = pd.DataFrame({"Agent1": [9,8,7], "Agent2": [6,5,None]}, - index=["Par1", "Par2", "Par3"]) - - bound_df1 = pd.DataFrame({"Subbasin1": [[0,1000],[0,1000],[0,10]], "Subbasin2": [[0,10],[0,10],[0,10]]}, - index=["a", "b", "c"]) - bound_df2 = pd.DataFrame({"Agent1": [[0,10],[0,10],[0,10]], "Agent2": [[0,10],[0,10],None]}, - index=["Par1", "Par2", "Par3"]) - - df_list = [par_df1, par_df2] - par_bound_df_list = [bound_df1, bound_df2] - -Now, we want to generate the calibration input dictionary with fixed "a" and "b" parameters for "Subbasin1." We can do the following: - -.. code-block:: python - - ### Create an object called Converter. - converter = cali.Convertor() - - ### Generate GA inputs with fixed a & b parameters for Subbasin1. - fixed_par_list = [[(["a","b"], ["Subbasin1"])], []] - cali_inputs = converter.gen_cali_inputs( - "working directory", df_list, par_bound_df_list, fixed_par_list) - ### Get formatter - formatter = converter.formatter - - ### Show cali_inputs - print(cali_inputs) - r""" - {'wd': 'working directory', - 'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2', - 'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2', - 'Par3|Agent1'], - 'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], - [0, 10], [0, 10]]} - """ - -We can see the 'par_name' in cali_inputs does not contain 'a|Subbasin1' and 'b|Subbasin1.' The outputted a formatter contains the relationship between a 1-D array and a list of DataFrames. - -Convert from a list of DataFrames to a 1D array -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code-block:: python - - ### to 1D array - converter.to_1D_array(df_list, formatter) - r""" - # Out[31]: array([4., 5., 3., 6., 9., 6., 8., 5., 7.]) - # Note the order of the array corresponds to "par_name" in the cali_inputs. - """ - -Convert from a 1D array to the original list of DataFrames -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -.. code-block:: python - - ### to df_list - var_array = np.array([5]*9) - converter.to_df_list(var_array, formatter) - r""" - Out[46]: - [ Subbasin1 Subbasin2 - a 1000.0 5.0 - b 1000.0 5.0 - c 5.0 5.0, - Agent1 Agent2 - Par1 5.0 5.0 - Par2 5.0 5.0 - Par3 5.0 NaN] - """ \ No newline at end of file diff --git a/docs/_build/html/_sources/files/9_advanced ABM coding tips.rst.txt b/docs/_build/html/_sources/files/9_advanced ABM coding tips.rst.txt deleted file mode 100644 index e1d66bd..0000000 --- a/docs/_build/html/_sources/files/9_advanced ABM coding tips.rst.txt +++ /dev/null @@ -1,35 +0,0 @@ -Advanced ABM coding tips -======================== - -In this section, we provide some coding tips for ABM module designs. - -Enhancing computational speed ------------------------------ - -Avoid extensive calls to DataFrame -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Reading data to a DataFrame (e.g., *df.loc[ , ]*) tends to be slow. We suggest users use NumPy array, list, or dictionary for calculations or data storage. - -Avoid repeated loading of external data -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -If there is common data among multiple agent-type classes, we suggest loading the data once to a global variable at the top of the ABM module and using the variable across classes. This might save some time from repeated loading of external data inside each class (e.g., at *def __init__(self)*). - -Avoid extensive deepcopy -^^^^^^^^^^^^^^^^^^^^^^^^ -deepcopy is a function to create a copy with a different storage address (not just copy a pointer that points to the same storage address). Therefore, it will take a longer time to complete the task. We suggest using deepcopy only when it is necessary. - -Avoid storing redundant data in a data collector -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -A data collector is a data container object to store model outputs. We encourage users to utilize it to store agents' results; however, users will need to consider the storage capacity of their computing devices, especially with a considerable number of agents. Overusing the computer storage might also slow down the computational speed. - -Logging -------- - -Logging is a python package to organize model output messages. We encourage users to adopt it in their ABM module design. This will help you to integrate your agent output messages into HydroCNHS. - -.. code-block:: python - - import logging - logger = logging.getLogger("ABM") - logger.info() - logger.error() diff --git a/docs/_build/html/_sources/files/t.rst.txt b/docs/_build/html/_sources/files/t.rst.txt deleted file mode 100644 index f7461c5..0000000 --- a/docs/_build/html/_sources/files/t.rst.txt +++ /dev/null @@ -1,106 +0,0 @@ -101 classroom -============= - -Watershed/Basin ---------------- - -A land area that drains water to a particular water body like lakes, rivers, and oceans. Ridges, mountains, and hills usually define the boundary of a watershed/basin. - -Subbasin --------- - -Subbasin is a sub-drainage land unit inside a watershed. HydroCNHS uses subbasins as the base unit for hydrological modeling. Each subbasin has a corresponding outlet. It is similar to the concept of the hydrologic response unit used in the SWAT model. However, users can freely determine the subbasins’ resolution to balance the computational cost and required resolution of the output information. - -Semi-distributed hydrological model ------------------------------------ - -HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in :numref:`fig1`. A lumped model describes the water balance of the entire watershed as a water bucket (:numref:`fig1`a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (:numref:`fig1`c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins’ (not grid) runoffs simulated by lumped models (:numref:`fig1`b). - -.. _fig1: -.. figure:: ./figs/fig1_modeling_schema.png - :align: center - :width: 500 - :alt: Comparison of three hydrological modeling schema. - - Comparison of three hydrological modeling schema: a) Lumped model, b) Semi-distributed model, and c) Distributed model. - -Rainfall-runoff process modeling --------------------------------- - -The rainfall-runoff process is a series of mechanisms describing how a raindrop or snowfall becomes a runoff. This process often includes evapotranspiration, interception, infiltration, snow melting, and groundwater recession. HydroCNHS supports two process-based modeling options: (1) the General Water Loading Function (GWLF; Haith et al., 1987) with nine parameters and (2) the ABCD model (Thomas, 1981) with five parameters. Detailed calculations can be found in the supplementary material in Lin et al. (2022). Users can also pre-calculate subbasins’ runoffs using their preferred rainfall-runoff models and input them into HydroCNHS. - -Routing & Routing outlets -------------------------- -Routing captures the delay in water movement from upstream to downstream. HydroCNHS adopts the Lohmann routing model (Lohmann et al., 1998) and the unit hydrograph parameterization described in Wi et al. (2015), with the former tracing the runoff from subbasins through the river channel (i.e., inter-subbasin routing) and the latter accounting for the within-subbasin routing process (:numref:`fig2`a). A gamma distribution is adopted to represent the unit hydrograph of within-subbasin routing. - -In HydroCNHS, we define routing outlets as those subbasin outlets with routed streamflow information. In other words, for subbasin outlets that are not defined as routing outlets, HydroCNHS will not route the streamflow, and only the runoff information is stored. However, we do not encourage users to define too many routing outlets if the streamflow information is not necessary at those outlets. Minimizing the number of routing outlets will reduce the model complexity and usually lead to a better model performance. - -A water system in node-link structure -------------------------------------- -HydroCNHS represents a water system in a node-link structure. Therefore, users must learn how to abstract a water system with a node-link representation and understand the routing logic behind a given node-link structure used by HydroCNHS. We introduce three typical cases, shown in :numref:`fig2`. - -.. _fig2: -.. figure:: ./figs/fig2_routing_schema.png - :align: center - :width: 500 - :alt: Routing schema using in HydroCNHS. - - Routing schema using in HydroCNHS. a) single routing outlet, b) multiple routing outlets, and c) with a reservoir agent. - -Single outlet with no human components (:numref:`fig2`a) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In :numref:`fig2`a, we are only interested in the streamflow at the basin outlet, assigned as the only routing outlet. In this case, HydroCNHS will route each runoff generated in each subbasin to the basin outlet (white dot). Namely, each subbasin contains both within-subbasin (represented by a unit hydrologic response curve) and inter-subbasin routing. - -Multiple routing outlets (:numref:`fig2`b) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In :numref:`fig2`b, we are interested in the streamflow at multiple (in here two) outlets. In this case, calibration is possible if streamflow data is available for the two outlets. In Figure 2b, the three most upstream outlets (black dots) are routed to the upstream routing outlet and will NOT be routed again for the downstream routing outlet. Instead, the routed streamflow at the upstream routing outlet will be further routed to the downstream outlet with only river routing (i.e., only inter-subbasin and no within-subbasin routing). - -Routing with human components using Dam API (:numref:`fig2`c) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Figure 2c introduces human components (e.g., a reservoir; ResAgt) integrated with Dam API to the node-link structure. A reservoir is considered an in-stream object that completely redefines the downstream flow according to its water releasing rules. Therefore, such an in-stream object (e.g., a reservoir) is defined as a pseudo routing outlet used for the downstream routing. Note that the upstream node of a reservoir has to be a routing outlet to simulate the inflow. Or, we can view that ResAgt takes water from its upstream routing outlet and release water based on the reservoir’s operational rules. More details about other APIs are presented in the following section. - -Design a water system with the supporting APIs ----------------------------------------------- - -.. _fig3: -.. figure:: ./figs/fig3_hydrocnhs.png - :align: center - :width: 500 - :alt: A generic example of HydroCNHS coupling APIs and water system description. - - A generic example of HydroCNHS coupling APIs and water system description. - -The four APIs in the HydroCNHS (:numref:`fig3`) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API. - -**Dam API** is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in :numref:`fig3`) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents’ water releases decision. - -**RiverDiv API** is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in :numref:`fig3`. This API ensures the diverted outlet is routed before agents’ diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location. - -**Conveying API** is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin’s runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step). - -**InSitu API** is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in :numref:`fig3`). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step. - -We summarize the supported connections of each API in :numref:`table1`. - -.. _table1: -.. table:: The supported connections of each API. - - +-------------+-------------------------+-------------------------+ - |APIs |Minus (divert/take) from |Plus (return/release) to | - | +----------------+--------+----------------+--------+ - | |Routing outlets |Outlets |Routing outlets |Outlets | - +=============+================+========+================+========+ - |Dam API\ :sup:`*`\ |**V** |**X** |-- |-- | - +-------------+----------------+--------+----------------+--------+ - |RiverDiv API |**V** |**X** |**V** |**V** | - +-------------+----------------+--------+----------------+--------+ - |Conveying API|**V** |**X** |**V** |**V** | - +-------------+----------------+--------+----------------+--------+ - |InSitu API\ :sup:`**`\ |**V** |**V** |**V** |**V** | - +-----------------------------------------------------------------+ - |\ :sup:`*`\ Human components using Dam API will serve as a pseudo routing outlet for the downstream routing.| - |\ :sup:`**`\ InSitu API only works on a single outlet (i.e., subbasin).| - +-----------------------------------------------------------------+ \ No newline at end of file diff --git a/docs/_build/html/_sources/how_to_build_abm.rst.txt b/docs/_build/html/_sources/how_to_build_abm.rst.txt deleted file mode 100644 index f5fac82..0000000 --- a/docs/_build/html/_sources/how_to_build_abm.rst.txt +++ /dev/null @@ -1,425 +0,0 @@ -How to build a ABM module? -=========================== - -HydroCNHS is designed to integrate multiple highly customized ABM modules, where users are able to create various agent type classes and decision-making classes. However, few design protocals have to be followed. - -Please see the following ABM module design example for the Tualatin River Basin. -The script contains primary three parts: - -1. Import packages and set up logger. -2. ABM global setting. -3. Create agent type classes and decision-making classes. - - -.. code-block:: python - - import os - import pickle - import logging - import numpy as np - from HydroCNHS.abm import Base, read_factor # import some helpers. - logger = logging.getLogger("ABM") # Set logger to log msg. - - # ============================================================================= - # ABM Global Setting - # Users can load common data that can be read by every agent. - # ============================================================================= - # E.g., - this_dir, this_filename = os.path.split(__file__) # Get this file directory. - with open(os.path.join( - this_dir, "TRB_ABM_database.pickle"), "rb") as file: - database = pickle.load(file) - # Items included in database (the pickle file) for the TRB example: - # ['Storage_q95', 'Storage_mean', 'Storage_q05', 'Storage_max', - # 'Release_q50', 'Release_max', - # 'Div_M_median_mean', 'Div_M_median_max', 'Div_M_median_min', 'Prec_M_mean', - # 'Pipe_M_median'] - - #%% =========================================================================== - # Customized ABM Module Design Protocal - # ============================================================================= - - ##### Base class - # This Base() class should be inherited by users when designing their agent - # type and decision-making classes. This class will load all arguments sent - # from HydroCNHS. We show the items that will be assigned when agent - # type and decision-making instances created by HydroCNHS below. Namely, users - # can use those items by calling "self." when designing agent type - # and decision-making classes. - - ##### Agent_type class design outline - r""" - class Agent_type(Base): # Inherit base class. - #### Initialization - def __init__(self, **kwargs): # Copy this. - super().__init__(**kwargs) # Copy this. - ### Agent_type class's available items: - # name: agent's name. - # config: agent's configuration dictionary the model file (.yaml). - # start_date: datetime object. - # data_length: length of the simulation. - # data_collector: a container to store simulated data. - # rn_gen: random number generator to ensure reproducibility (e.g., - # self.rn_gen.random()). Note that do NOT set a global random seed in - # this module! All type of random number should be created by "rn_gen." - # dm: decision making object if assigned in the model file (.yaml). - - ### Create fields in data_collector for storing agent's outputs if - # needed. - # e.g., - # add_field(name of the field, field type => {} or []) - self.data_collector.add_field("field name", {}) - # get_field(name of the field) for further operation. - field = self.data_collector.get_field("field name") - - # Other initialization that you need for this agent_type. - # e.g., - self.threshold = 0.5 - - #### Must have this act method with exact same arguments show below. - def act(self, Q, outlet, agent_dict, current_date, t): # Copy this - - # Read corresponding factor (defined in .yaml model file) of the given - # outlet. - factor = read_factor(self.config, outlet) - - # Do some calculation - if factor > 0: # Calculation for adding water to the outlet. - amount = ..... - - elif factor < 0: # Calculation for diverting water from the outlet. - amount = ..... - - # Calculate agent's action at time t. - action = factor * amount - - # This returned action will change the streamflow at the outlet by - # new_flow = old_flow + action - return action - """ - ##### Decision-making class design outline - r""" - class Agent_DM(Base): # Inherit base class. - #### Initialization - def __init__(self, **kwargs): # Copy this. - super().__init__(**kwargs) # Copy this. - ### Decision-making class's available items: - # start_date: datetime object. - # data_length: length of the simulation. - # abm: the ABM configuration dictionary from the model file (.yaml). - # data_collector: a container to store simulated data. - # rn_gen: random number generator to ensure reproducibility (e.g., - # self.rn_gen.random()). Note that do NOT set a global random seed in - # this module! All type of random number should be created by "rn_gen." - - ### Create fields in data_collector for storing agent's outputs if - # needed. - # e.g., - # add_field(name of the field, field type => {} or []) - self.data_collector.add_field("field name", {}) - # get_field(name of the field) for further operation. - field = self.data_collector.get_field("field name") - - # Other initialization that you need for this agent_type. - # e.g., - self.threshold = 0.5 - - # Your customized decision-making function. You can add any arguments. - def make_dm(self, **kwargs): - pass - return some_decisions - """ - - ##### Other design tips - # 1. Use numpy or list do the operation instead of using pandas dataframe. - # df.loc tend to slow down the calculation speed. - # 2. Store only the necessary data to the data collector to control the storage - # requirement. - # 3. We highly encourage users to follow the above design outlines. - - #%% =========================================================================== - # The Tualatin River Basin Example - # ============================================================================= - - ##### Reservoir Agent Type Class ###### - class ResDam_AgType(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # self.config is auto loaded into the class. See above explanation. - self.inputs = self.config["Inputs"] - - def act(self, Q, outlet, agent_dict, current_date, t): - # Read corresponding factor - factor = read_factor(self.config, outlet) - - # Release (factor should be 1) - if factor <= 0: - print("Something is not right in ResDam agent.") - elif factor > 0: - # Make the water release decision by self.dm.make_dm, which - # is an instance of the ResDM class defined below that is sent - # by HydroCNHS during the simulation. - # Q["HaggIn"][t] is the resevoir inflow at time t. - res_t = self.dm.make_dm(Q["HaggIn"][t], current_date) - action = res_t - return action - - ##### Reservoir Decision-making Class ###### - r""" - Reservoir agents determine reservoir releases by a generic operational - rule modified from Neitsch et al. (2011), where target storages and - target releases are adopted for flood control (October - May) and storage - control (June - September) period. See the supplementary material of Lin et al. - (2022) for more details. - """ - class ResDM(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.database = database # [m^3] - self.flood_control = [True, True, True, True, True, False, - False, False, False, True, True, True] - # [cms] Use quantile 0.01 of the historical data. - self.min_release = 0.263335 - self.min_release_vol = self.min_release * 86400 # [m^3] - self.data_collector.add_field("ResAgt", {}) - records = self.data_collector.get_field("ResAgt") - records["Storage"] = [] - records["Release"] = [] - - def make_dm(self, inflow, current_date): - db = self.database - flood_control = self.flood_control - records = self.data_collector.ResAgt - min_release = self.min_release - min_res_vol = self.min_release_vol - day_of_year = current_date.dayofyear - - inflow_vol = inflow * 86400 # cms to m^3 - if records["Storage"] == []: # Initial value [m^3] - storage = 42944903.6561 + inflow_vol - else: - storage = records["Storage"][-1] + inflow_vol - release = 0 - - if flood_control[current_date.month-1]: - storage_max = db["Storage_q95"][day_of_year-1] - if storage > storage_max: - release = (storage - storage_max) / 86400 # m^3 to cms - storage = storage_max - else: - if storage - min_res_vol < 0: - release = 0 - else: - release = min_release - storage = storage - min_res_vol - else: # Target storage control - release_target = db["Release_q50"][day_of_year-1] - storage_temp = storage - release_target * 86400 - if storage_temp > db["Storage_max"][day_of_year-1]: - release = (storage - db["Storage_max"][day_of_year-1]) / 86400 - storage = db["Storage_max"][day_of_year-1] - elif storage_temp < db["Storage_q05"][day_of_year-1]: - release = (storage - db["Storage_q05"][day_of_year-1]) / 86400 - storage = db["Storage_q05"][day_of_year-1] - if release < 0: - release = 0 - storage = records["Storage"][-1] + inflow_vol - else: - release = release_target - storage = storage_temp - records["Storage"].append(storage) - records["Release"].append(release) - return release - - - ##### Diversion Agent Type Class ###### - class IrrDiv_AgType(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - self.pars = self.config["Pars"] - self.data_collector.add_field(self.name, {}) - records = self.data_collector.get_field(self.name) - records["DivReq"] = [] - records["Diversion"] = [] - records["Shortage"] = [] - logger.info("Initialize irrigation diversion agent: {}".format( - self.name)) - - def act(self, Q, outlet, agent_dict, current_date, t): - self.current_date = current_date - self.t = t - records = self.data_collector.get_field(self.name) - - # Get factor - factor = read_factor(self.config, outlet) - - # For parameterized (for calibration) factor. - if isinstance(factor, list): - factor = self.pars[factor[0]][factor[1]] - - # Compute actual diversion (factor < 0) or return flow (factor >= 0) - if factor < 0: # Diversion - # Make diversion request at 1st of each month - if current_date.day == 1: - # Get the parameters. - a = self.pars["a"] - b = self.pars["b"] - # Make the diversion request decision by self.dm.make_dm, which - # is an instance of the DivDM class defined below that is sent - # by HydroCNHS during the simulation. - div_req = self.dm.make_dm(a, b, current_date) - records["DivReq"] = records["DivReq"] + div_req - - # Apply the physical constraints for the available water at time t. - div_req_t = records["DivReq"][t] - available_water_t = Q[outlet][t] - if div_req_t > available_water_t: - shortage_t = div_req_t - available_water_t - div_t = available_water_t - else: - div_t = div_req_t - shortage_t = 0 - records["Diversion"].append(div_t) - records["Shortage"].append(shortage_t) - action = factor * div_t - elif factor >= 0: # factor > 0; Return flow - div_t = records["Diversion"][t] - action = factor * div_t - - return action - - ##### Diversion Request Decision-making Class ###### - r""" - Diversion agents make monthly-diversion-request decisions at the beginning of - each month and has return flow back to the river. For this example, we design - the diversion-request decisions in June to September are governed by linear - functions (i.e., y = ax + b), where predictors are the perfect forecast of - monthly precipitation (Prec_M_mean). Minor diversions in other months are - filled with monthly mean values (Div_M_median_mean). Note that we bound the - monthly-diversion-request decision by the historical maximum (Div_M_median_max) - and minimum (Div_M_median_min) monthly diversion values to prevent unrealistic - decisions. See the supplementary material of Lin et al. (2022) for more details. - """ - class DivDM(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # Global input data loaded at the top of the file. - self.database = database - - def make_dm(self, a, b, current_date): - db = self.database - prec_M_mean = db["Prec_M_mean"][current_date.year-1981, - (current_date.month-1)] - div_M_mean = db["Div_M_median_mean"][(current_date.month-1)] - div_M_max = db["Div_M_median_max"][(current_date.month-1)] - div_M_min = db["Div_M_median_min"][(current_date.month-1)] - if current_date.month in [6,7,8,9]: - div_M_req = div_M_mean + a * prec_M_mean + b - # Bound by historical max and min - div_M_req = min( max(div_M_req, div_M_min), div_M_max) - else: - div_M_req = div_M_mean - div_D_req = [div_M_req] * (current_date.days_in_month) - return div_D_req - - - ##### Pipe Agent Type Class ###### - class Pipe_AgType(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # Historical inputs - self.assigned_behavior = database["Pipe_M_median"] - - def act(self, Q, outlet, agent_dict, current_date, t): - - # Get factor - factor = read_factor(self.config, outlet) - - # Release (factor should be 1) - if factor <= 0: - print("Something is not right in TRTR agent.") - elif factor > 0: - # Assuming that diversion has beed done, get the actual release at - # time t. - y = current_date.year - m = current_date.month - - if y < 1991: - Res_t = 0 - else: - # Historical inputs. - Res_t = self.assigned_behavior[y-1991, m-1] - action = factor * Res_t - return action - - ##### Conveying Water Decision-making Class ###### - r""" - This is an empty decision-making class. We simply assigned the amount of - conveying water to the pipe agent with a historical inputs. Namely, PipeDM() - can be deleted and removed from the model file (.yaml). - """ - class PipeDM(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - pass - - ##### Drainage System Agent Type Class ###### - class Drain_AgType(Base): - def __init__(self, **kwargs): - super().__init__(**kwargs) - # We assume a linear urbanization rate. Namely, urbanized areas are - # linearly increase from 5% to 50% of the subbasin area in 33 years. - # We assume the urbanization will increase 50% of the orignal runoff - # contributed by the unbanized region. - # Therefore, the subbasin's runoff change due to the unbanization is - # equal to unbanized_area% * 75% * original_runoff - ini = 0.05 - end = 0.5 - interval = (end-ini)/32 - self.urbanized_ratio = np.arange(0.05, 0.5 + interval, interval) - self.rn = self.data_collector.add_field(self.name, []) - - def act(self, Q, outlet, agent_dict, current_date, t): - # Get factor - factor = read_factor(self.config, outlet) - Qt_change = self.urbanized_ratio[current_date.year-1981] * 0.75 \ - * Q[outlet][t] - action = factor * Qt_change - - #### Test rn_gen - rn = self.data_collector.get_field(self.name) - rn.append(self.rn_gen.random()) - return action - r""" - No runoff changing decision-making class is defined here. As you may have - observed, we code the simple runoff changing calculation in the Drain_AgType() - directly. - """ - - -Decision-making class V.S. Agent group ---------------------------------------- -* Decision-making class: - - Decision-making class should be used when multiple agent type class share a - similar decision-making process. By seperating those calculations into a - decision-making class, we could make the code concise and easier to maintain. - -* Agent group: - - The agent group should be used when multiple agents make decision - simuteneously (not piority-based). For example, diversion agt_a, agt_b, and - agt_c make the diversion requests together, where they share the water - shortage together. To program this example, instead of define a **diversion - agent class** to create three instances for agt_a, agt_b, and agt_c, - repectively, users are required to define a **diversion agent group class**, - which HydroCNHS will only create one instance from this agent group class to - represent agt_a, agt_b, and agt_c. Namely, diversion agent group class will - return different actions depending on outlet infomation, which is required - users to code such statement explicitly. Note that the **config** sent to the - agent group class is a dictionary of all members' configurations. E.g., - config = {"agt_a": agt_a's config, "agt_b": agt_b's config, - "agt_c": agt_c's config}. This config also apply to the - **group decision-making class** if there is any. - - diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index d91e3d3..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,40 +0,0 @@ -.. HydroCNHS documentation master file, created by - sphinx-quickstart on Thu Dec 23 20:33:17 2021. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -HydroCNHS User Manual -===================================== - -**The Hydrological model for Coupled Natural-Human Systems (HydroCNHS)** is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. - -Manual contributors: Chung-Yi Lin & Ali Ghaffari - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - ./files/2_installation - ./files/3_101_classroom - ./files/4_input_output - ./files/5_build_a_hydrological_model - ./files/6_Integrate_an_ABM - ./files/7_data_collector - ./files/8_calibration - ./files/9_advanced ABM coding tips - ./source/HydroCNHS - -How to cite? -================== -Lin, C. Y., Yang, Y. C. E., & Wi S. (2022). `HydroCNHS: A Python Package of Hydrological Model for Coupled Natural Human Systems `_, Journal of Water Resources Planning and Management, 148(12), 6022005. - -Related studies -================== -Lin, C. Y., & Yang, Y. E. (2022). `The effects of model complexity on model output uncertainty in co-evolved coupled natural-human systems `_. Earth's Future, e2021EF002403. - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_build/html/_sources/installation.rst.txt b/docs/_build/html/_sources/installation.rst.txt deleted file mode 100644 index 8e72430..0000000 --- a/docs/_build/html/_sources/installation.rst.txt +++ /dev/null @@ -1,22 +0,0 @@ -Installation -================= -Install HydroCNHS by pip. - -.. code-block:: python - - pip install hydrocnhs - -If you fail to install hydrocnhs due to DEAP package, please downgrade setuptools to 57 by - -.. code-block:: python - - pip install setuptools==57 - -Now install hydrochns again. - -if users would like to obtain the latest version (beta version), you can install the latest version from `HydroCNHS GitHub respository `_ by: - -.. code-block:: python - - pip install git+https://github.com/philip928lin/HydroCNHS.git - diff --git a/docs/_build/html/_sources/model_builder.rst.txt b/docs/_build/html/_sources/model_builder.rst.txt deleted file mode 100644 index 09fc6b9..0000000 --- a/docs/_build/html/_sources/model_builder.rst.txt +++ /dev/null @@ -1,297 +0,0 @@ -Model builder -=================== - -The ModelBuilder() of model builder module can quickly help users to establish -the outline of a model (.yaml). Namely, ModelBuilder can create a template -model for :ref:`calibration`. First, we initialize a ModelBuilder -object as shown in the following. - -.. code-block:: python - - import HydroCNHS - - wd = "working directory" - mb = HydroCNHS.ModelBuilder(wd) - - r''' - # Print out message. - Follow the following steps to create model template: - Step 1: set_water_system() - Step 2: set_lsm() - Step 3: set_routing_outlet(), one at a time. - Step 4: set_ABM() if you want to build a coupled model. - Step 5: write_model_to_yaml() - After creating model.yaml template, open it and further edit it. - Use .help to re-print the above instruction. - ''' - - -Then, we follow the print out instructions to create a model template. - -Here, we demonstrate how to create a coupled model template for the Tualatin -River Basin (TRB). - -.. figure:: ./figs/TRB.png - :align: center - :width: 500 - :alt: The Tualatin River Basin system diagram. - - The Tualatin River Basin system diagram (Lin et al., 2022). TRTR, - Hagg\ :sub:`In`\, DLLO, TRGC, DAIRY, RCTV, and WSLO are seven subbasins. - PipeAgt, ResAgt, and DivAgt are trans-basin aqueduct, Hagg reservoir, and - irrigation diversion agents, respectively. DrainAgt1 and DrainAgt2 are two - drainage-system agents for the runoff-changing scenario. - - -.. code-block:: python - - ### Setup a water system simulation information - mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") - - ### Setup land surface model (rainfall-runoff model) - # Here we have seven subbasins and we select GWLF as the rainfall-runoff model. - outlet_list = ["TRTR", "HaggIn", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"] - mb.set_lsm(outlet_list=outlet_list, lsm_model="GWLF") # or lsm_model="ABCD" - - ### Setup routing - # We have four routing outlets, which we will add them into the model one by - # one. - mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"]) - mb.set_routing_outlet(routing_outlet="TRGC", - upstream_outlet_list=["DLLO", "TRGC"]) - mb.set_routing_outlet(routing_outlet="DLLO", - upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], - instream_outlets=["ResAgt"]) - # Note: ResAgt (Hagg Lake) is the reservoir agent, whcih is considerred as - # an instream control object. - - mb.set_routing_outlet(routing_outlet="HaggIn", - upstream_outlet_list=["HaggIn"]) - - ### Setup ABM - abm_module_path="abm_module path" - mb.set_ABM(abm_module_path=abm_module_path) - - ### Save to a .yaml model file for further editting. - filename = "output directory/model.yaml" - mb.write_model_to_yaml(filename) - - -After "write_model_to_yaml()", open the create model.yaml (see below) to -further edit it. Users need to manually add necessary "inputs" information -for all sections. Namely, users need to fill all **null** *except those nested -under "Pars" subsections*. For the ABM section, users are asked to manually add -"agents" to the model. - -Note that the **-99** values under "Pars" subsections are the parameters -required :ref:`calibration`. - -.. code-block:: yaml - - Path: {WD: working directory, Modules: abm_module path} - WaterSystem: - StartDate: 1981/1/1 - EndDate: 2013/12/31 - NumSubbasins: 7 - NumGauges: null - NumAgents: null - Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO] - GaugedOutlets: [] - DataLength: 12053 - LSM: - Model: GWLF - TRTR: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - HaggIn: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - DLLO: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - TRGC: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - DAIRY: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - RCTV: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - WSLO: - Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - Routing: - Model: Lohmann - WSLO: - TRGC: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - DAIRY: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - RCTV: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - WSLO: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - TRGC: - DLLO: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - TRGC: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - DLLO: - ResAgt: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - TRTR: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - DLLO: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - HaggIn: - HaggIn: - Inputs: {FlowLength: null, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - ABM: - Inputs: - DamAgentTypes: [] - RiverDivAgentTypes: [] - InsituAgentTypes: [] - ConveyAgentTypes: [] - DMClasses: [] - Modules: [] - AgGroup: null - -After filling in the necessary information (e.g., Inputs and ABM settings, we -will obtain a model template (see below) ready to be calibrated (i.e., those --99 values). - -.. code-block:: yaml - - Path: {WD: working directory, Modules: abm_module path} - WaterSystem: - StartDate: 1981/1/1 - EndDate: 2013/12/31 - NumSubbasins: 7 - NumGauges: 2 - NumAgents: 3 - Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO] - GaugedOutlets: [DLLO, WSLO] - DataLength: 12053 - LSM: - Model: GWLF - TRTR: - Inputs: {Area: 329.8013, Latitude: 45.458136, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - HaggIn: - Inputs: {Area: 10034.2408, Latitude: 45.469444, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - DLLO: - Inputs: {Area: 22238.4391, Latitude: 45.475, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - TRGC: - Inputs: {Area: 24044.6363, Latitude: 45.502222, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - DAIRY: - Inputs: {Area: 59822.7546, Latitude: 45.52, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - RCTV: - Inputs: {Area: 19682.6046, Latitude: 45.502222, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - WSLO: - Inputs: {Area: 47646.8477, Latitude: 45.350833, XL: 2.0, SnowS: 5.0} - Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, - Df: -99, Kc: -99} - Routing: - Model: Lohmann - WSLO: - TRGC: - Inputs: {FlowLength: 80064.864, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - DAIRY: - Inputs: {FlowLength: 70988.16384, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - RCTV: - Inputs: {FlowLength: 60398.68032, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - WSLO: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - TRGC: - DLLO: - Inputs: {FlowLength: 11748.2112, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - TRGC: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - DLLO: - ResAgt: - Inputs: {FlowLength: 9656.064, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99} - TRTR: - Inputs: {FlowLength: 30899.4048, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} - DLLO: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - HaggIn: - HaggIn: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} - ABM: - Inputs: - DamAgentTypes: [ResDam_AgType] - RiverDivAgentTypes: [IrrDiv_AgType] - InsituAgentTypes: [] - ConveyAgentTypes: [Pipe_AgType] - DMClasses: [ResDM, DivDM, PipeDM] - Modules: [TRB_ABM_dm.py] # user-provided ABM module. - AgGroup: null - Pipe_AgType: - PipeAgt: - Attributes: {} - Inputs: - Piority: 0 - Links: {TRTR: 1} - DMClass: PipeDM - Pars: {} # No parameter - ResDam_AgType: - ResAgt: - Attributes: {} - Inputs: - Piority: 0 - Links: {SCOO: -1, R1: 1} - DMClass: ResDM - Pars: {} # No parameter - IrrDiv_AgType: - DivAgt: - Attributes: {} - Inputs: - Piority: 1 - Links: - TRGC: -1 - WSLO: [ReturnFactor, 0, Plus] - DMClass: DivDM - Pars: - ReturnFactor: [-99] - a: -99 - b: -99 diff --git a/docs/_build/html/_sources/quickstart.rst.txt b/docs/_build/html/_sources/quickstart.rst.txt deleted file mode 100644 index 80e7989..0000000 --- a/docs/_build/html/_sources/quickstart.rst.txt +++ /dev/null @@ -1,452 +0,0 @@ -Quick start! -============ -Let's start with running a simulation using the Tualatin River Basin (TRB) example! - -The TRB tutorial codes are located at **./HydroCNHS/tutorials/Tualatin River Basin/** - -.. _TRB: -.. figure:: ./figs/TRB.png - :align: center - :width: 500 - :alt: The Tualatin River Basin system diagram. - - The Tualatin River Basin system diagram (Lin et al., 2022). TRTR, - Hagg\ :sub:`In`\, DLLO, TRGC, DAIRY, RCTV, and WSLO are seven subbasins. - PipeAgt, ResAgt, and DivAgt are trans-basin aqueduct, Hagg reservoir, and - irrigation diversion agents, respectively. DrainAgt1 and DrainAgt2 are two - drainage system agents for the runoff-changing scenario. - - -The TRB, consisting of 1844.07 km2 in northwest Oregon, US, is covered by -densely populated area (20%), agricultural area (30%), and forest (50%) -(Tualatin River Watershed Council, 2021). Its agriculture heavily relies on -irrigation because seasonal rainfall concentrates in winter (November - -February). The Spring Hill Pumping Plant is the largest diversion facility in -the TRB for irrigating Tualatin Valley Irrigation District (TVID; DivAgt), -where the Hagg reservoir (ResAgt) is the primary water source. During the -summer period, water is transferred from the Barney reservoir (outside of the -TRB) through a trans-basin aqueduct (PipeAgt) to augment the low flow for -ecological purposes. - -The full background of the TRB can be found in Lin et al., (2022). - -Run a simulation ------------------ -Assuming we have already calibrated the TRB model, let's take one minute to -browse through the following simulation code. Then, you should be able run the -script directly. - - -.. code-block:: python - - import os - import pickle - import HydroCNHS - - ##### Setup Working Directory - # Get this .py file's directory. - prj_path, this_filename = os.path.split(__file__) - wd = prj_path - - ##### Load Daily Weather Time Series. - # We store all required time series data in the pickle format. - with open(os.path.join(prj_path, "Inputs", "TRB_inputs.pickle"), "rb") as file: - (temp, prec, pet, obv_D, obv_M, obv_Y) = pickle.load(file) - - ##### Load Model.yaml. - best_gwlf_abm_path = os.path.join(prj_path, "Calibrated_model", "Best_gwlf_abm_KGE.yaml") - model_dict_gwlf = HydroCNHS.load_model(best_gwlf_abm_path) - # Change the path according to this .py file's directory. - model_dict_gwlf["Path"]["WD"] = wd - model_dict_gwlf["Path"]["Modules"] = os.path.join(prj_path, "ABM_modules") - - ##### Create HydroCNHS Model Object for Simulation. - model_gwlf = HydroCNHS.Model(model_dict_gwlf) - - ##### Run a simulation - Q_gwlf = model_gwlf.run(temp, prec, pet) # pet is optional. - - -To run a coupled natural human model simulation, three items are required. - -1. Daily weather time series including temperture (temp) and precipitation (prec). Potential evapotranspiration (pet) is optional. -2. Model file (e.g., Best_gwlf_abm_KGE.yaml). -3. User-provided ABM module(s) (e.g., TRB_ABM_dm.py). This is not required for pure hydrological model, non-coupled model. - -Now, we are going to introduce these three items in a more detail. - - -Daily weather time series -------------------------- -Temperture and precipitation are two required weather inputs for the -simulation. If potential evapotranspiration is not provided, HydroCNHS will -automatically calculate it by Hamon method. The weather inputs are in a -dictionary format shown below. - -.. code-block:: python - - # 'DAIRY', 'DLLO', 'RCTV', 'HaggIn', 'TRGC', 'TRTR', and 'WSLO' are subbasins' names. - temp = {'DAIRY': [7.7, 7.0, 6.6, 6.3, .......], - 'DLLO': [7.9, 7.5, 7.1, 6.1, .......], - 'RCTV': [8.0, 7.8, 7.8, 7.5, .......], - 'HaggIn': [8.1, 7.4, 7.0, 6.2, .......], - 'TRGC': [5.7, 5.5, 5.1, 4.0, .......], - 'TRTR': [7.9, 6.9, 6.5, 6.1, .......], - 'WSLO': [7.8, 7.4, 7.3, 7.3, .......]} - # Similar to prep and pet. - -The dictionary will contain weather time series (i.e., a list) for each -subbasin. The length of each time series has to be identical. - - -Model.yaml -------------------------- -The model file (.yaml) contains settings for hydrological model (e.g., -rainfall-runoff and routing) and ABM model (e.g., how to coupled). -The model file has six sections: - -1. Path -^^^^^^^^^^^^^^^^^^^ -.. code-block:: yaml - - # Path for working directory (outputing log file) and user-provided ABM - # modules (optional). - Path: { - WD: 'wd path', - Modules: 'ABM module path'} - - -2. WaterSystem -^^^^^^^^^^^^^^^^^^^ -.. code-block:: yaml - - WaterSystem: - StartDate: 1981/1/1 - EndDate: 2013/12/31 - NumSubbasins: 7 - NumGauges: 2 - NumAgents: 3 - Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO] - GaugedOutlets: [DLLO, WSLO] # Optional - DataLength: 12053 - -DataLength can be automatically calculated if EndDate is provided, vice versa. - -3. LSM -^^^^^^^^^^^^^^^^^^^ -HydroCNHS provides user two rainfall-runoff simulation options, General -Water Loading Function (GWLF; 9 parameters) and ABCD (5 parameters). Their -settings are shown below. - -The detailed documentation for GWLF and ABCD can be found at the supplementary -material of (Lin et al., 2022). - -**GWLF:** - -.. code-block:: yaml - - LSM: - Model: GWLF - TRTR: - Inputs: {Area: 329.80, Latitude: 45.45, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 97.148, IS: 0.404, Res: 0.178, Sep: 0.482, Alpha: 0.868, Beta: 0.708, - Ur: 14.341, Df: 0.124, Kc: 0.988} - HaggIn: - Inputs: {Area: 10034.24, Latitude: 45.46, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 36.984, IS: 0.178, Res: 0.122, Sep: 0.178, Alpha: 0.235, Beta: 0.112, - Ur: 13.932, Df: 0.588, Kc: 0.765} - DLLO: - Inputs: {Area: 22238.43, Latitude: 45.47, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 98.249, IS: 0.369, Res: 0.281, Sep: 0.117, Alpha: 0.909, Beta: 0.381, - Ur: 9.0, Df: 0.947, Kc: 1.057} - TRGC: - Inputs: {Area: 24044.63, Latitude: 45.50, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 89.963, IS: 0.098, Res: 0.047, Sep: 0.418, Alpha: 0.535, Beta: 0.115, - Ur: 9.107, Df: 0.727, Kc: 1.013} - DAIRY: - Inputs: {Area: 59822.75, Latitude: 45.52, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 95.962, IS: 0.026, Res: 0.241, Sep: 0.199, Alpha: 0.432, Beta: 0.299, - Ur: 4.049, Df: 0.034, Kc: 1.012} - RCTV: - Inputs: {Area: 19682.60, Latitude: 45.50, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 93.049, IS: 0.341, Res: 0.319, Sep: 0.05, Alpha: 0.316, Beta: 0.7, - Ur: 11.288, Df: 0.741, Kc: 0.746} - WSLO: - Inputs: {Area: 47646.84, Latitude: 45.35, S0: 2.0, U0: 10.0, SnowS: 5.0} - Pars: {CN2: 63.818, IS: 0.039, Res: 0.305, Sep: 0.045, Alpha: 0.207, Beta: 0.496, - Ur: 13.919, Df: 0.175, Kc: 1.451} - -**ABCD:** - -.. code-block:: yaml - - LSM: - Model: ABCD - TRTR: - Inputs: {Area: 329.80, Latitude: 45.45, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.998, b: 53.403, c: 0.345, d: 0.559, Df: 0.636} - HaggIn: - Inputs: {Area: 10034.24, Latitude: 45.46, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.196, b: 2.316, c: 0.78, d: 0.248, Df: 0.697} - DLLO: - Inputs: {Area: 22238.43, Latitude: 45.47, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.237, b: 16.746, c: 0.693, d: 0.834, Df: 0.16} - TRGC: - Inputs: {Area: 24044.63, Latitude: 45.50, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.306, b: 91.475, c: 0.695, d: 0.263, Df: 0.727} - DAIRY: - Inputs: {Area: 59822.75, Latitude: 45.52, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.836, b: 5.536, c: 0.795, d: 0.491, Df: 0.515} - RCTV: - Inputs: {Area: 19682.60, Latitude: 45.50, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.516, b: 6.632, c: 0.971, d: 0.148, Df: 0.681} - WSLO: - Inputs: {Area: 47646.84, Latitude: 45.35, XL: 2.0, SnowS: 5.0} - Pars: {a: 0.777, b: 307.23, c: 0.453, d: 0.638, Df: 0.159} - -4. Routing -^^^^^^^^^^^^^^^^^^^ -HydroCNHS adopts Lohmann routing model to simulate within-subbasin routing and -inter-subbasin routing process. We adpot a nested struture to setup the routing -setting for each routing outlets (:numref:`TRB`), as shown below. - -.. code-block:: yaml - - Routing: - Model: Lohmann - # WSLO, TRGC, DLLO, and HaggIn are four routing outlets. - WSLO: - # TRGC is a routing outlet. No within-subbasin routing at here (at - # its own routing setting below). Namely, TRGC will be routed - # first. Therefore, GShape and GScale are null. - TRGC: - Inputs: {FlowLength: 80064.86, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: 29.84, Diff: 2840.985} - DAIRY: - Inputs: {FlowLength: 70988.16, InstreamControl: false} - Pars: {GShape: 38.652, GScale: 393.641, Velo: 15.281, Diff: 1753.171} - RCTV: - Inputs: {FlowLength: 60398.68, InstreamControl: false} - Pars: {GShape: 55.996, GScale: 751.942, Velo: 2.973, Diff: 2573.258} - # WSLO is the routing outlet itself. No river routing is needed - # since the FlowLength is 0. Therefore, Velo and Diff are null. - WSLO: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 1.026, GScale: 119.961, Velo: null, Diff: null} - TRGC: - # DLLO is a routing outlet. No within-subbasin routing at here (at - # its own routing setting below). Namely, DLLO will be routed - # first. Therefore, GShape and GScale are null. - DLLO: - Inputs: {FlowLength: 11748.21, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: 15.178, Diff: 2382.476} - # TRGC is the routing outlet itself. No river routing is needed - # since the FlowLength is 0. Therefore, Velo and Diff are null. - TRGC: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 3.823, GScale: 694.564, Velo: null, Diff: null} - DLLO: - # ResAgt is a reservoir agent. There is no within-subbasin routing. - # Its release flow is the streamflow at this spot. Therefore, - # GShape and GScale are null. - ResAgt: - Inputs: {FlowLength: 9656.06, InstreamControl: true} - Pars: {GShape: null, GScale: null, Velo: 40.546, Diff: 2250.142} - TRTR: - Inputs: {FlowLength: 30899.40, InstreamControl: false} - Pars: {GShape: 50.454, GScale: 444.699, Velo: 27.168, Diff: 3625.861} - # DLLO is the routing outlet itself. No river routing is needed - # since the FlowLength is 0. Therefore, Velo and Diff are null. - DLLO: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 2.505, GScale: 85.004, Velo: null, Diff: null} - HaggIn: - # HaggIn is the routing outlet itself. No river routing is needed - # since the FlowLength is 0. Therefore, Velo and Diff are null - HaggIn: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 49.412, GScale: 1.774, Velo: null, Diff: null} - -5. ABM (optional) -^^^^^^^^^^^^^^^^^^^ -ABM section is a highly customized setting section. Users will assign each -active agent type class to corresponding APIs. After that, users will define -agents created by each agent type class in a nested structure. - -.. code-block:: yaml - - ABM: - Inputs: - # Assign user-defined agent classes to corresponding APIs. Here, we - # defined three agent classes in TRB_ABM_dm.py: ResDam_AgType - # (reservoir), IrrDiv_AgType (diversion), and Pipe_AgType - # (trans-basin conveying water). - DamAgentTypes: [ResDam_AgType] # Dam API - RiverDivAgentTypes: [IrrDiv_AgType] # RiverDiv API - InsituAgentTypes: [Drain_AgType] # InSitu API - ConveyAgentTypes: [Pipe_AgType] # Conveying API - # Activate user-defined decision-making classes in TRB_ABM_dm.py. - DMClasses: [ResDM, DivDM, PipeDM] - # User-defined ABM module, TRB_ABM_dm.py. - Modules: [TRB_ABM_dm.py] - # Agent group is for agents make decisions and act together. - AgGroup: null - Pipe_AgType: # Agent type class's name. - PipeAgt: # Agent created by the above agent type class. - Attributes: {} - Inputs: - Piority: 0 - Links: {TRTR: 1} - DMClass: PipeDM - Pars: null - ResDam_AgType: - ResAgt: - Attributes: {} - Inputs: - Piority: 0 - Links: {HaggIn: -1, ResAgt: 1} - DMClass: ResDM - Pars: null - IrrDiv_AgType: - DivAgt: - Attributes: {} - Inputs: - Piority: 1 - Links: - TRGC: -1 - WSLO: [ReturnFactor, 0, Plus] - DMClass: DivDM - Pars: - ReturnFactor: [0.12] - a: 0.52 - b: -0.04 - Drain_AgType: - DrainAgt1: - Attributes: {} - Inputs: - Piority: 1 - Links: - RCTV: 1 - DMClass: null - Pars: null - DrainAgt2: - Attributes: {} - Inputs: - Piority: 1 - Links: - WSLO: 1 - DMClass: null - Pars: null - - -For the "Inputs" setting of ABM section, first, we assign the user-defined -agent classes (defined in ABM modules) to corresponding coupling APIs. Then, -we ativate the decision-making classes (defined in ABM modules) if any. Next, -we assign ABM module(s), where agent classes and decision-making classes are -defined. Finally, agent group is for agents make decisions and act together. -For example, two diversion agents make diversion requests together and share -the water deficiency together based on their water rights. Namely, their -diversion behaviors are not piority-based. The agent group will be defined as -a single function in a ABM module, which users can define a more detailed -interactions among agents in an agent group. See -:ref:`How to build a ABM module?` for more details. - -Following the "Inputs" setting, we will define agent objects created by certain -agent classes (defined in ABM modules). For example, we create PipeAgt agent -using Pipe_AgType class. Under each agent object (e.g., PipeAgt), it has three -sub-sections: "Attributes", "Inputs", and "Pars." - -**a) Inputs** are required information including "Piority", "Links", and "DMClass." - -* Piority: - -The lower value has higher piority when conflicts happen. For example, two -diverion agents divert at the same routing outlet. If users want a -non-priority-based behaviors. "AgGroup" should be applied. See -:ref:`How to build a ABM module?` for more details. - -Note that agents coupling with Dam API has to have Piority = 0. - -* Links: - -"Links" is a dictionary containing information of which outlets for agent to -take/add water from/to (e.g., {: factor}). The **positive** factor -means add the water to that outlet. The **negative** number factor take water -from that outlet. The "factor" is a number in a range of [-1,1] defining the -portion of the agent's decision to be implemented at this specific outlet. For -example, an irrigation diversion agent divert from *a* outlet but reture to *b* -and *c* outlets (i.e., subbasins) with the ratios, 0.3 and 0.7. Then, we have - -.. code-block:: yaml - - Links: {a: -1, b: 0.3, c: 0.7} - -Assuming the diversion request is 10, the actual diversion is also 10 (i.e., no -deficiency), and return flow coefficent is 0.5, we have - -.. math:: - - Flow_{a,new} = Flow_{a,org} + factor_a \times 10 = Flow_{a,org} -1 \times 10 - -.. math:: - - Flow_{return} = 0.5 \times 10 = 5 - -.. math:: - - Flow_{b,new} = Flow_{b,org} + factor_b \times 5 = Flow_{b,org} + 0.3 \times 5 - -.. math:: - - Flow_{c,new} = Flow_{c,org} + factor_c \times 5 = Flow_{c,org} + 0.7 \times 5 - - -If the "factor" is a calibrated parameter (i.e., an unknown). We can link it to -a parameter by *[parameter name, its index, Plus/Minus]*. The parameter has to -be a list format. For example, if *Links = {WSLO: [ReturnFactor, 0, Plus]}*, -the factor will be extracted from ReturnFactor parameter (a list) at index 0. -"Plus" will tell the program that we add water to WSLO ("Minus" means divert -from the given outlet). - -* DMClass: - -This is optional. If there is no specific decision-making class to be assigned, -put "null" instead. Namely, users can code everything in "agent type class." See -:ref:`How to build a ABM module?` for more details. - -**b) Pars** collects agents' parameters for calibration. We -offer two types of parameter formats: - -(1) A constant (e.g., 9), or - -(2) A list of constants (e.g., [9, 4.5]). - -**c) Attributes** is a space for users to store any other information for their -agents' calculation that is not belong to "Pars" or "Inputs." - -6. SystemParsedData (auto-generated) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -This section will be automatically generated by HydroCNHS. The model file don't -need to include this section. - -.. note:: - :ref:`ModelBuilder` can help you to create an initial model - template! Check it out! - - -ABM module(s) -------------------------- -Agent-based model (ABM) is an user-provided human model. HydroCNHS support -multiple ABM modules to be used at a single simulation. In the ABM module, -users have 100% of freedom to design agent class (e.g., irrigation diversion -agent class, reservoir agent class, etc.); however, some input and output -protocals have to be followed. Please visit -:ref:`How to build a ABM module?` for more detailed -instructions. - -.. note:: - If you only need a hydrological model and do not require any human - components, then you can skip this ABM part! - - diff --git a/docs/_build/html/_sources/source/HydroCNHS.calibration.rst.txt b/docs/_build/html/_sources/source/HydroCNHS.calibration.rst.txt deleted file mode 100644 index 71c8517..0000000 --- a/docs/_build/html/_sources/source/HydroCNHS.calibration.rst.txt +++ /dev/null @@ -1,29 +0,0 @@ -HydroCNHS.calibration package -============================= - -Submodules ----------- - -HydroCNHS.calibration.convertor module --------------------------------------- - -.. automodule:: HydroCNHS.calibration.convertor - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.calibration.ga\_deap module -------------------------------------- - -.. automodule:: HydroCNHS.calibration.ga_deap - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: HydroCNHS.calibration - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/source/HydroCNHS.rainfall_runoff_model.rst.txt b/docs/_build/html/_sources/source/HydroCNHS.rainfall_runoff_model.rst.txt deleted file mode 100644 index c40b8eb..0000000 --- a/docs/_build/html/_sources/source/HydroCNHS.rainfall_runoff_model.rst.txt +++ /dev/null @@ -1,37 +0,0 @@ -HydroCNHS.rainfall\_runoff\_model package -========================================= - -Submodules ----------- - -HydroCNHS.rainfall\_runoff\_model.abcd module ---------------------------------------------- - -.. automodule:: HydroCNHS.rainfall_runoff_model.abcd - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.rainfall\_runoff\_model.gwlf module ---------------------------------------------- - -.. automodule:: HydroCNHS.rainfall_runoff_model.gwlf - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.rainfall\_runoff\_model.pet\_hamon module ---------------------------------------------------- - -.. automodule:: HydroCNHS.rainfall_runoff_model.pet_hamon - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: HydroCNHS.rainfall_runoff_model - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/source/HydroCNHS.rst.txt b/docs/_build/html/_sources/source/HydroCNHS.rst.txt deleted file mode 100644 index 8cb2fac..0000000 --- a/docs/_build/html/_sources/source/HydroCNHS.rst.txt +++ /dev/null @@ -1,92 +0,0 @@ -HydroCNHS package -================= - -HydroCNHS.abm module --------------------- - -.. automodule:: HydroCNHS.abm - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.abm\_script module ----------------------------- - -.. automodule:: HydroCNHS.abm_script - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.data\_collector module --------------------------------- - -.. automodule:: HydroCNHS.data_collector - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.hydrocnhs module --------------------------- - -.. automodule:: HydroCNHS.hydrocnhs - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.indicators module ---------------------------- - -.. automodule:: HydroCNHS.indicators - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.model\_builder module -------------------------------- - -.. automodule:: HydroCNHS.model_builder - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.routing module ------------------------- - -.. automodule:: HydroCNHS.routing - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.util module ---------------------- - -.. automodule:: HydroCNHS.util - :members: - :undoc-members: - :show-inheritance: - -HydroCNHS.visual module ------------------------ - -.. automodule:: HydroCNHS.visual - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: HydroCNHS - :members: - :undoc-members: - :show-inheritance: - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - HydroCNHS.calibration - HydroCNHS.rainfall_runoff_model - diff --git a/docs/_build/html/_sources/source/modules.rst.txt b/docs/_build/html/_sources/source/modules.rst.txt deleted file mode 100644 index 454da46..0000000 --- a/docs/_build/html/_sources/source/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -HydroCNHS -========= - -.. toctree:: - :maxdepth: 4 - - HydroCNHS diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css deleted file mode 100644 index be19270..0000000 --- a/docs/_build/html/_static/basic.css +++ /dev/null @@ -1,856 +0,0 @@ -/* - * basic.css - * ~~~~~~~~~ - * - * Sphinx stylesheet -- basic theme. - * - * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/* -- main layout ----------------------------------------------------------- */ - -div.clearer { - clear: both; -} - 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Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keydown(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box, textarea, dropdown or button - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' - && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey - && !event.shiftKey) { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js deleted file mode 100644 index 2fa8c97..0000000 --- a/docs/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,12 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - BUILDER: 'html', - FILE_SUFFIX: '.html', - LINK_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false -}; \ No newline at end of file diff --git a/docs/_build/html/_static/file.png b/docs/_build/html/_static/file.png deleted file mode 100644 index a858a41..0000000 Binary files a/docs/_build/html/_static/file.png and /dev/null differ diff --git a/docs/_build/html/_static/jquery-3.5.1.js b/docs/_build/html/_static/jquery-3.5.1.js deleted file mode 100644 index 5093733..0000000 --- a/docs/_build/html/_static/jquery-3.5.1.js +++ /dev/null @@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; 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- }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " - - - - - - - - - - - -
- - -
- -
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- -
-

Calibration

-

HydroCNHS equips with a genetic algorithm powered by Distributed Evolutionary -Algorithms in Python (DEAP), which -can be used for calibration in parallel. The unique feature about HydroCNHS is -that both the parameters of the hydrological model and user-defined ABM can be -calibrated simutaneously as long as those parameters are defined the model file -(.yaml). Furthermore, users are allowed to assign initial guesses to the -algorithm. Note that this GA module are not limited to HydroCNHS model.

-
-

Genetic algorithm calibration code outline

-

To use this genetic algorithm (GA) module, three things are required.

-
    -
  1. The evaluation function

    -

    The evaluation function has to follow a certain protocal. Please see the -example code below.

    -
  2. -
  3. GA configuration dictionary

    -

    GA configuration dictionary template can be obtained by -cali.get_config_template().

    -
  4. -
  5. Calibration inputs dictionary

    -

    The calibration inputs dictionary template can be obtained by -cali.get_inputs_template().

    -
  6. -
-
-

Note

-

Currently, GA module only support calibrating real number parameters.

-
-
import HydroCNHS
-import HydroCNHS.calibration as cali
-
-# Assisting functions
-cali.helper()               # Get instructions.
-cali.get_config_template()
-cali.get_inputs_template()
-
-# Must have individual and info arguments for evaluation function.
-def evaluation(individual, info):
-    # individual: A 1D array of generated parameter set.
-    # info: The info tuple contains five items such as the calibration
-    # working directory (cali_wd), current generation number
-    # (current_generation), the index of the current individual of the
-    # generation (ith_individual), formtter for the Converter, and a random
-    # number generator (random_generator).
-    # Note the random_generator should be used to generate random number to
-    # ensure the reproducibility. (Do not use np.random directly.)
-    (cali_wd, current_generation, ith_individual,
-    formatter, random_generator) = info
-
-    # Run model.
-    # Calculate fitness.
-    # E.g.,
-    fitness = sum(individual)
-    return (fitness,)   # Has to be a tuple format.
-
-# GA configuration
-config = {'min_or_max': 'max',
-        'pop_size': 200,
-        'num_ellite': 1,
-        'prob_cross': 0.5,
-        'prob_mut': 0.1,
-        'stochastic': False,
-        'max_gen': 100,
-        'sampling_method': 'LHC',
-        'drop_record': False,
-        'paral_cores': -1,
-        'paral_verbose': 1,
-        'auto_save': True,
-        'print_level': 1,
-        'plot': True}
-
-# GA inputs (Can be generated by the Converter shown in the next section.)
-cali_inputs = {"par_name":    ["a","b","c"],
-            "par_bound":   [[1,2],[1,2],[1,2]],
-            "wd":          "working directory"}
-
-seed = 5
-# Create a random number generator for GA.
-rn_gen = HydroCNHS.create_rn_gen(seed)  # Optional
-# Initialize GA object.
-ga = cali.GA_DEAP(evaluation, rn_gen)
-# Set up GA object (or load the previously auto-saved pickle file)
-ga.set(cali_inputs, config, name="Cali_example")
-# Run
-ga.run()
-# Run the evaluation with the given individual (e.g., the solution).
-ga.run_individual(ga.solution)
-# Show the result summary.
-summary = ga.summary
-print(summary)
-
-
-
-
-

Converter

-

Convertor helps user to convert a list of parameter dataframes (can -contain nan values) into an 1D array (parameters for calibration, -automatically exclude nan values) that can be used for GA calibration. -And the formatter created by Convertor can be used to convert 1D array -back to a list of original dataframes. The inputs dictionary for GA can also be -generated by Convertor. Besides, we provide option for defining fixed -parameters, which those parameters will not enter the -calibration process (exclude from the 1D array). -Note that the dataframe index is parameter names.

-
import numpy as np
-import pandas as pd
-import HydroCNHS
-import HydroCNHS.calibration as cali
-
-### Prepare testing data.
-par_df1 = pd.DataFrame({"Subbasin1": [1000,1000,3], "Subbasin2": [4,5,6]},
-                    index=["a", "b", "c"])
-par_df2 = pd.DataFrame({"Agent1": [9,8,7], "Agent2": [6,5,None]},
-                    index=["Par1", "Par2", "Par3"])
-
-bound_df1 = pd.DataFrame({"Subbasin1": [[0,1000],[0,1000],[0,10]], "Subbasin2": [[0,10],[0,10],[0,10]]},
-                    index=["a", "b", "c"])
-bound_df2 = pd.DataFrame({"Agent1": [[0,10],[0,10],[0,10]], "Agent2": [[0,10],[0,10],None]},
-                    index=["Par1", "Par2", "Par3"])
-
-df_list = [par_df1, par_df2]
-par_bound_df_list = [bound_df1, bound_df2]
-
-
-### Create a object called Converter.
-converter = cali.Convertor()
-
-### Generate GA inputs with fixed a & b parameters for Subbasin1.
-fixed_par_list = [[(["a","b"], ["Subbasin1"])],[]]
-cali_inputs = converter.gen_cali_inputs(
-    "working directory", df_list, par_bound_df_list, fixed_par_list)
-### Get formatter
-formatter = converter.formatter
-
-### Show cali_inputs
-print(cali_inputs)
-r"""
-print(cali_inputs)
-{'wd': 'working directory',
-'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2',
-            'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2',
-            'Par3|Agent1'],
-'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10],
-            [0, 10], [0, 10]]}
-"""
-
-### to 1D array
-converter.to_1D_array(df_list, formatter)
-r"""
-# Out[31]: array([4., 5., 3., 6., 9., 6., 8., 5., 7.])
-# Note the order of the array corresponds to "par_name" in the cali_inputs.
-"""
-
-### to df_list
-var_array = np.array([5]*9)
-converter.to_df_list(var_array, formatter)
-r"""
-Out[46]:
-[   Subbasin1  Subbasin2
-a     1000.0        5.0
-b     1000.0        5.0
-c        5.0        5.0,
-    Agent1  Agent2
-Par1     5.0     5.0
-Par2     5.0     5.0
-Par3     5.0     NaN]
-"""
-
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-
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-

TRB calibration example

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import os
-import pandas as pd
-from copy import deepcopy
-import pickle
-import HydroCNHS
-import HydroCNHS.calibration as cali
-
-##### Path and Load Model Test
-# Get this file directory.
-prj_path, this_filename = os.path.split(__file__)
-model_path = os.path.join(prj_path, "Template_for_calibration", "TRB_dm_gwlf.yaml")
-bound_path = os.path.join(prj_path, "ParBound")
-wd = prj_path
-
-# Update model paths.
-model_dict = HydroCNHS.load_model(model_path)
-model_dict["Path"]["WD"] = wd
-model_dict["Path"]["Modules"] = os.path.join(prj_path, "ABM_modules")
-
-##### Gen cali information
-# Convert parameter sections in the model file (i.e., model_dict) to a list of
-# dataframes (df_list).
-df_list, df_name = HydroCNHS.write_model_to_df(model_dict, key_option=["Pars"])
-# Load the parameter bounds. The order of the list is corresponding to df_list.
-par_bound_df_list = [
-    pd.read_csv(os.path.join(bound_path, "gwlf_par_bound.csv"), index_col=[0]),
-    pd.read_csv(os.path.join(bound_path, "routing_par_bound.csv"), index_col=[0]),
-    pd.read_csv(os.path.join(bound_path, "abm_par_bound_dm.csv"), index_col=[0])]
-# Initialize Convertor.
-converter = cali.Convertor()
-cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list)
-formatter = converter.formatter
-
-# Load inputs from pickle file.
-with open(os.path.join(prj_path, "Inputs", "TRB_inputs.pickle"), "rb") as file:
-    (temp, prec, pet, obv_D, obv_M, obv_Y) = pickle.load(file)
-
-#%%
-# =============================================================================
-# Calibration
-# =============================================================================
-def cal_batch_indicator(period, target, df_obv, df_sim):
-    """Compute mean indicator over targets"""
-    df_obv = df_obv[period[0]:period[1]]
-    df_sim = df_sim[period[0]:period[1]]
-    Indicator = HydroCNHS.Indicator()
-    df = pd.DataFrame()
-    for item in target:
-        df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item],
-                                            index_name=item)
-        df = pd.concat([df, df_i], axis=0)
-    df_mean = pd.DataFrame(df.mean(axis=0), columns=["Mean"]).T
-    df = pd.concat([df, df_mean], axis=0)
-    return df
-
-def evaluation(individual, info):
-    cali_wd, current_generation, ith_individual, formatter, _ = info
-    name = "{}-{}".format(current_generation, ith_individual)
-
-    ##### individual -> model
-    # Convert 1D array to a list of dataframes.
-    df_list = cali.Convertor.to_df_list(individual, formatter)
-    # Feed dataframes in df_list to model dictionary.
-    model = deepcopy(model_dict)
-    for i, df in enumerate(df_list):
-        s = df_name[i].split("_")[0]
-        model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars")
-
-    ##### Run simuluation
-    model = HydroCNHS.Model(model, name)
-    Q = model.run(temp, prec, pet)
-
-    ##### Get simulation data
-    # Streamflow of routing outlets.
-    cali_target = ["DLLO", "WSLO"]
-    cali_period = ("1981-1-1", "2005-12-31")
-    vali_period = ("2006-1-1", "2013-12-31")
-    sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[cali_target]
-    # Agents' outputs stored in the data_collector.
-    sim_Q_D["DivAgt"] = model.data_collector.DivAgt["Diversion"]
-    sim_Q_D["ResAgt"] = model.data_collector.ResAgt["Release"]
-    cali_target += ["DivAgt", "ResAgt"]
-    # Resample the daily simulation output to monthly and annually outputs.
-    sim_Q_M = sim_Q_D[cali_target].resample("MS").mean()
-    sim_Q_Y = sim_Q_D[cali_target].resample("YS").mean()
-
-    df_cali_Q_D = cal_batch_indicator(cali_period, cali_target, obv_D, sim_Q_D)
-    df_cali_Q_M = cal_batch_indicator(cali_period, cali_target, obv_M, sim_Q_M)
-    df_cali_Q_Y = cal_batch_indicator(cali_period, cali_target, obv_Y, sim_Q_Y)
-
-    df_vali_Q_D = cal_batch_indicator(vali_period, cali_target, obv_D, sim_Q_D)
-    df_vali_Q_M = cal_batch_indicator(vali_period, cali_target, obv_M, sim_Q_M)
-    df_vali_Q_Y = cal_batch_indicator(vali_period, cali_target, obv_Y, sim_Q_Y)
-
-    ##### Save output.txt
-    # Only exercute when ga.run_individual(ga.solution)
-    if current_generation == "best":
-        with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), 'w') as f:
-            f.write("Annual cali/vali result\n")
-            f.write(df_cali_Q_Y.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n")
-            f.write(df_vali_Q_Y.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n\nMonthly cali/vali result\n")
-            f.write(df_cali_Q_M.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n")
-            f.write(df_vali_Q_M.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n\nDaily cali/vali result\n")
-            f.write(df_cali_Q_D.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n")
-            f.write(df_vali_Q_D.round(3).to_csv(sep='\t').replace("\n", ""))
-            f.write("\n=========================================================\n")
-            f.write("Sol:\n" )
-            df = pd.DataFrame(individual, index=cali_inputs["par_name"]).round(4)
-            f.write(df.to_string(header=False, index=True))
-
-    fitness = df_cali_Q_M.loc["Mean", "KGE"]
-    return (fitness,)
-
-# cali.helper()
-# cali.get_config_template()
-# cali.get_inputs_template()
-
-config = {'min_or_max': 'max',
-        'pop_size': 200,
-        'num_ellite': 1,
-        'prob_cross': 0.5,
-        'prob_mut': 0.1,
-        'stochastic': False,
-        'max_gen': 100,
-        'sampling_method': 'LHC',
-        'drop_record': False,
-        'paral_cores': -1,
-        'paral_verbose': 1,
-        'auto_save': True,
-        'print_level': 1,
-        'plot': True}
-
-# Calibrate with 3 seeds.
-seeds = [5,10,13]
-for seed in seeds:
-    rn_gen = HydroCNHS.create_rn_gen(seed)
-    ga = cali.GA_DEAP(evaluation, rn_gen)
-    ga.set(cali_inputs, config, formatter, name="Cali_gwlf_abm_KGE_{}".format(seed))
-    ga.run()
-    ga.run_individual(ga.solution)  # Output performance (.txt) of solution.
-
-    ##### Output the calibrated model.
-    individual = ga.solution
-    df_list = cali.Convertor.to_df_list(individual, formatter)
-    model_best = deepcopy(model_dict)
-    for i, df in enumerate(df_list):
-        s = df_name[i].split("_")[0]
-        model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars")
-    HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_gwlf_abm_KGE.yaml"))
-
-    summary = ga.summary
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Introduction

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The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS.

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When should you use HydroCNHS?

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  1. Want to build a hydrological model with auto-parameter-tuning (calibration) features.

  2. -
  3. Want to add human components into a hydrological model under a unified framework without worrying about routing logic.

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  5. Want to calibrate the entire integrated model (hydrological + ABM modules) with customized agents’ parameters.

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  7. Want to design human behaviors with a high degree of freedom, including coupling with external software.

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  9. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules).

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Feature highlights

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  • Built-in genetic algorithm calibration module that can utilize parallel computing power.

  • -
  • A unified framework allowing calibration of the entire integrated model (hydrological + ABM models), where parameters in the ABM module (i.e., human model) are customizable.

  • -
  • Automatic integration and simulation through four APIs.

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  • Built-in data collector module that can collect data from the hydrological and user-defined ABM modules.

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  • Built-in model builder module to assist users in creating the HydroCNHS model and generating ABM module (.py) templates.

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  • Built-in indicator calculation and visualization tools for simple result analysis.

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Supporting APIs for incorporating human/agent components

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Dam API

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Link to human actions that completely redefine downstream flow, e.g., instream objects like reservoirs. The downstream flow is governed by a reservoir’s operational rules and operators’ decisions.

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RiverDiv API

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Link to human actions that divert water from the river (with return water to the downstream subbasins), e.g., off-stream irrigation water diversions governed by farmers or regional district managers’ diversion behaviors.

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Conveying API

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Link to human actions that convey water beyond basin boundaries and gravitational limitations, e.g., trans-basin aqueducts that convey water from one basin to another and pump stations that convey water from downstream to upstream.

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InSitu API

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Link to human actions that locally alter the runoff in a subbasin, e.g., drainage systems, urbanization, and water diversion from wells or local streams (within-subbasin).

-

Those human/agent actions can be modeled with various complexities according to users’ ABM design. Actions can simply be an inputted daily time series or governed by complex endogenous behavior rules (i.e., decision-making rules). Institutional decisions (a group of agents making decisions together with institutional rules) are allowed in HydroCNHS. Users require to have some level of object-oriented programing concept to design an ABM module (.py).

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Installation

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Install HydroCNHS by pip.

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pip install hydrocnhs
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To install the latest version (beta version) of HydroCNHS, users can (1) install HydroCNHS by git.

-
pip install git+https://github.com/philip928lin/HydroCNHS.git
-
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Or, (2) download the HydroCNHS package directly from the HydroCNHS GitHub repository. Then, install HydroCNHS from the setup.py.

-
# Need to move to the folder containing setup.py first.
-python setup.py install
-
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If you fail to install HydroCNHS due to the DEAP package, first downgrade setuptools to 57 and try to install HydroCNHS again.

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pip install setuptools==57
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101 classroom

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Watershed/Basin

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A land area that drains water to a particular water body like lakes, rivers, and oceans. Ridges, mountains, and hills usually define the boundary of a watershed/basin.

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Subbasin

-

Subbasin is a sub-drainage land unit inside a watershed. HydroCNHS uses subbasins as the base unit for hydrological modeling. Each subbasin has a corresponding outlet. It is similar to the concept of the hydrologic response unit used in the SWAT model. However, users can freely determine the subbasins’ resolution to balance the computational cost and required resolution of the output information.

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Semi-distributed hydrological model

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HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in Fig. 1. A lumped model describes the water balance of the entire watershed as a water bucket (Fig. 1a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (Fig. 1c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins’ (not grid) runoffs simulated by lumped models (Fig. 1b).

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-Comparison of three hydrological modeling schema. -

Fig. 1 Comparison of three hydrological modeling schema: a) Lumped model, b) Semi-distributed model, and c) Distributed model.

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Rainfall-runoff process modeling

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The rainfall-runoff process is a series of mechanisms describing how a raindrop or snowfall becomes a runoff. This process often includes evapotranspiration, interception, infiltration, snow melting, and groundwater recession. HydroCNHS supports two process-based modeling options: (1) the General Water Loading Function (GWLF; Haith et al., 1987) with nine parameters and (2) the ABCD model (Thomas, 1981) with five parameters. Detailed calculations can be found in the supplementary material in Lin et al. (2022). Users can also pre-calculate subbasins’ runoffs using their preferred rainfall-runoff models and input them into HydroCNHS.

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Routing & Routing outlets

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Routing captures the delay in water movement from upstream to downstream. HydroCNHS adopts the Lohmann routing model (Lohmann et al., 1998) and the unit hydrograph parameterization described in Wi et al. (2015), with the former tracing the runoff from subbasins through the river channel (i.e., inter-subbasin routing) and the latter accounting for the within-subbasin routing process (Fig. 2a). A gamma distribution is adopted to represent the unit hydrograph of within-subbasin routing.

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In HydroCNHS, we define routing outlets as those subbasin outlets with routed streamflow information. In other words, for subbasin outlets that are not defined as routing outlets, HydroCNHS will not route the streamflow, and only the runoff information is stored. However, we do not encourage users to define too many routing outlets if the streamflow information is not necessary at those outlets. Minimizing the number of routing outlets will reduce the model complexity and usually lead to a better model performance.

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Design a water system with the supporting APIs

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-A generic example of HydroCNHS coupling APIs and water system description. -

Fig. 3 A generic example of HydroCNHS coupling APIs and water system description.

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The four APIs in the HydroCNHS (Fig. 3) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API.

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Dam API is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in Fig. 3) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents’ water releases decision.

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RiverDiv API is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in Fig. 3. This API ensures the diverted outlet is routed before agents’ diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location.

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Conveying API is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin’s runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step).

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InSitu API is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in Fig. 3). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step.

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We summarize the supported connections of each API in Table 1.

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Table 1 The supported connections of each API.

APIs

Minus (divert/take) from

Plus (return/release) to

Routing outlets

Outlets

Routing outlets

Outlets

Dam API*

V

X

RiverDiv API

V

X

V

V

Conveying API

V

X

V

V

InSitu API**

V

V

V

V

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*Human components using Dam API will serve as a pseudo -routing outlet for the downstream routing.
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**InSitu API only works on a single outlet (i.e., subbasin).
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Input/Output

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Inputs

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HydroCNHS has the following three main inputs:

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  3. A model configuration file (.yaml; settings for the HydroCNHS and ABM models), and

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  5. ABM modules (.py; customized human models).

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Outputs

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The outputs of HydroCNHS are stored in a data collector object, an attribute of HydroCNHS (e.g., model.dc). The main output is the daily streamflow at routing outlets. However, this data collector object will also contain other user-specified agent outputs as long as users use this data collector object in their ABM modules. See Integrate an ABM and Data collector for more information.

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Daily climate data

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Temperature (temp; degC) and precipitation (prec; cm) are two required weather inputs for a simulation. Potential evapotranspiration (pet; cm) can be automatically calculated by HydroCNHS using the Hamon method. However, we encourage users to pre-calculate pet and provide it to HydroCNHS as an input to avoid repeated calculation, especially when running calibration. The daily climate inputs are in a python dictionary format, in which the subbasin outlet name is the key, and a daily time series (list) is the value, as shown below.

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temp = {'subbasin1': [7.7, 7.0, 6.6, 6.3, .......],
-        'subbasin2': [8.5, 4.0, 5.3, 6.2, .......]}
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Model configuration file

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A model configuration file is a YAML file that contains settings for the entire water system. The main sections in a model configuration file include Path, WaterSystem, LSM, Routing, and ABM. Please see the following description for each setting. Parameter and inputs definitions for rainfall-runoff and routing models are summarized in Table 2 and Table 3, respectively. HydroCNHS has an embedded model builder. Therefore, users do not need to manually create the model configuration file (see the example in the following section).

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Path:
-  WD:       <working directory>
-  Modules:  <directory to the folder containing ABM modules>
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-WaterSystem:
-  StartDate:    <simulation start date, e.g., 1981/1/1>
-  EndDate:      <simulation end date, e.g., 2013/12/31>
-  DataLength:   <total simulation length/days, e.g., 12053>
-  NumSubbasins: <number of subbasins>
-  Outlets:      <a list of subbasins'/outlets' names, e.g., ["outlet1", "outlet2"]>
-  NodeGroups:   <a list of node group lists, e.g., [["outlet1", "outlet2"], []]>
-  LSM:          <selected rainfall-runoff models, e.g., 'GWLF' or 'ABCD' or 'Other'>
-  Routing:      <selected routing model, e.g., 'Lohmann'>
-  ABM:
-    Modules:    <a list of ABM modules, e.g., ['TRB_ABM_Instit.py']>
-    InstitDMClasses:
-        <a dict of {InstitDMClass: a list of institutional decision-making objects}>
-    DMClasses:      <a list of decision-making classes, e.g., ['ReleaseDM', 'TransferDM']>
-    DamAPI:         <a list of agent type classes using DamAPI, e.g., ['Reservoir_AgtType']>
-    RiverDivAPI:    <a list of agent type classes using RiverDivAPI, e.g., ['Diversion_AgtType']>
-    InsituAPI:      <a list of agent type classes using InsituAPI, e.g., ['Drain_AgtType']>
-    ConveyingAPI:   <a list of agent type classes using ConveyingAPI, e.g., ['Pipe_AgtType']>
-    Institutions:
-        <a dict of {an institutional decision-making agent: a list of agent members}>
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-RainfallRunoff:
-  <an outlet name, e.g., 'outlet1'>:
-    Inputs: <input dict for selected rainfall-runoff model at outlet1>
-    Pars:   <parameter dict for selected rainfall-runoff model at outlet1>
-  <an outlet name, e.g., 'outlet2'>:
-    Inputs: <input dict for selected rainfall-runoff model at outlet2>
-    Pars:   <parameter dict for selected rainfall-runoff model at outlet2>
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-Routing:
-  <a routing outlet name, e.g., 'outlet1'>:
-    <an upstream outlet name, e.g., 'outlet1'>:
-      Inputs:   <input dict of Lohmann routing model for link between outlet1 and the routing outlet>
-      Pars:     <parameter dict of Lohmann routing model for link between outlet1 and the routing outlet>
-    <an upstream outlet name, e.g., 'outlet2'>:
-      Inputs:   <input dict of Lohmann routing model for link between outlet2 and the routing outlet>
-      Pars:     <parameter dict of Lohmann routing model for link between outlet2 and the routing outlet>
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-ABM:
-  <an agent type class name, e.g., 'Reservoir_AgtType'>:
-    <an agent name belongs to this class, e.g., 'ResAgt'>:
-      Attributes: "agent's attributes dict, e.g., {}"
-      Inputs:
-        Priority:   <exercution piority is conflict occurs, e.g., 0>
-        Links:      <linkage dict, e.g., divert from 'outlet1' and return to 'outlet2,' {'outlet1': -1, 'outlet2': 1}>
-        DMClass:    <assigned decision-making class or institution or none, e.g., 'ReleaseDM'>
-      Pars:     <parameter dict of the agent, e.g., {}>
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- - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table 2 Hydrological model parameters and suggested bounds.

Module

Parameter name

Unit

Parameter

Bound

GWLF

Curve number

\(CN2\)

[25, 100]

Interception coefficient

\(IS\)

[0, 0.5]

Recession coefficient

\(Res\)

[10-3, 0.5]

Deep seepage coefficient

\(Sep\)

[0, 0.5]

Baseflow coefficient

\(\alpha\)

[0, 1]

Percolation coefficient

\(\beta\)

[0, 1]

Available/soil water capacity

cm

\(U_r\)

[1, 15]

Degree-day coefficient for snowmelt

cm/°C

\(D_f\)

[0, 1]

Land cover coefficient

\(K_c\)

[0.5, 1.5]

ABCD

Controls the amount of runoff and recharge -during unsaturated soil

\(a\)

[0, 1]

Control of the saturation level of the -soils

\(b\)

[0, 400]

Ratio of groundwater recharge to runoff

\(c\)

[0, 1]

Control of groundwater discharge rate

\(d\)

[0, 1]

Degree-day coefficient for snowmelt

cm/°C

\(D_f\)

[0, 1]

-
Lohmann
-
routing
-
-

Subbasin unit hydrograph shape parameter

\(G_{shape}\)

[1, 100]

Subbasin unit hydrograph rate parameter

\(G_{scale}\)

[10-2, 150]

Wave velocity in the linearized Saint- -Venant equation

m/s

\(Velo\)

[0.5, 100]

Diffusivity in the linearized Saint- -Venant equation

m2/s

\(Diff\)

[200, 5000]

- - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table 3 Hydrological model inputs and default values.

Module

Parameter name

Unit

Parameter

Default

GWLF

Subbasin’s drainage area

ha

\(Area\)

Latitude

deg

\(Latitude\)

Initial shallow saturated soil water content

cm

\(S0\)

2

Initial unsaturated soil water content

cm

\(U0\)

10

Initial snow storage

cm

\(SnowS\)

5

ABCD

Subbasin’s drainage area

\(Area\)

Latitude

deg

\(Latitude\)

Initial saturated soil water content

cm

\(XL\)

2

Initial snow storage

cm

\(SnowS\)

5

-
Lohmann
-
routing
-
-

Flow length between two outlets

m

\(FlowLength\)

An instream control object, e.g., a reservoir

\(InstreamControl\)

False

-
-
-

ABM modules

-

ABM modules are customized python scripts in which human components are designed through programming agent type classes and decision-making classes. HydroCNHS will load those user-specified classes and use them to initialize agents. More details are provided in the Integrate an ABM section.

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Build a hydrological model

-

To get familiar with the HydroCNHS model construction process, let’s start with a hydrological model without human components. We will go through the following steps:

-
    -
  1. Create a model configuration file (.yaml) using a model builder.

  2. -
  3. Complete a model configuration file (.yaml)

  4. -
  5. Run a calibration

  6. -
  7. Run a simulation

  8. -
-

We will adopt the following delineated watershed (Fig. 4) as a demonstration, in which we want to build a semi-distributed hydrological model to simulate the streamflow at the basin outlet, WSLO. The corresponding subbasins’ information is provided in Table 4.

-
-Example watershed. -

Fig. 4 Example watershed.

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Table 4 Subbasins’ information.

Subbasin/outlet

Drainage area [ha]

Latitude [deg]

Flow length to WSLO [m]

Hagg

10034.2408

45.469

101469.139

DLLO

22568.2404

45.475

91813.075

TRGC

24044.6363

45.502

80064.864

DAIRY

59822.7546

45.520

70988.164

RCTV

19682.6046

45.502

60398.680

WSLO

47646.8477

45.350

0

-
-

Step 1: Create a model configuration file

-

HydroCNHS comes with a model builder to help users create an initial model configuration file.

-

First, initiate a model builder object with a working directory (your working folder directory).

-
import os
-import HydroCNHS
-prj_path, this_filename = os.path.split(__file__)
-
-
-

Second, set up the water system with the simulation period (i.e., start date and end date).

-
wd = prj_path
-mb = HydroCNHS.ModelBuilder(wd)
-mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31")
-
-
-

Third, we can add subbasins (i.e., outlet_list) into the model. We adopt the GWLF model as the rainfall-runoff model (i.e., runoff_model). We also assign the corresponding subbasins’ areas (i.e., area_list) and latitude (i.e., lat_list) using the information from Table 4. Note that area_list and lat_list are optional arguments. Users can manually enter that information to the model configuration file (.yaml).

-
outlet_list = ['Hagg', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO']
-area_list = [10034.2408, 22568.2404, 24044.6363, 59822.7546, 19682.6046,
-             47646.8477]
-lat_list = [45.469, 45.475, 45.502, 45.520, 45.502, 45.350]
-mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list,
-                       lat_list=lat_list, runoff_model="GWLF")
-
-
-

Fourth, we want to add the routing setting. Here, we only have one routing outlet, WSLO, hence, we only need to run the following code once, and all the outlets are considered upstream outlets of WSLO.

-
flow_length_list = [101469.139, 91813.075, 80064.864, 70988.164, 60398.680, 0]
-mb.set_routing_outlet(routing_outlet="WSLO",
-                      upstream_outlet_list=outlet_list,
-                      flow_length_list=flow_length_list)
-
-
-

Finally, we can print out the model for quick view and then write it to .yaml file with the given name.

-
### Print the model in the console
-mb.print_model()
-
-### Output initial model configuration file (.yaml)
-mb.write_model_to_yaml(filename="HydroModel.yaml")
-
-
-

Now, we should have the initial model configuration file (i.e., HydroModel.yaml) in the corresponding folder.

-
-
-

Step 2: Complete a model configuration file

-

Before using the created HydroModel.yaml for the later experiment, we need to open it and ensure the information is correct. The “Inputs” in LSM and Routing sections should be all populated. Some default values may be used (e.g., \(S0\), \(U0\), and \(SnowS\)). They should be updated according to users’ needs. For this example, we don’t need to modify anything. You might notice that all the parameters have a value of -99. This is the default value in HydroCNHS, meaning that those values need to be calibrated if not provided by users.

-
-
-

Step 3: Run a calibration

-

First, we load the climate data, observed flow data at WSLO, and the model configuration file. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, we load it as the inputs as well.

-
import matplotlib.pyplot as plt
-import pandas as pd
-import HydroCNHS.calibration as cali
-from copy import deepcopy
-
-# Load climate data
-temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-
-# Load flow gauge monthly data at WSLO
-obv_flow_WSLO = pd.read_csv(os.path.join(wd,"Data","WSLO_M_cms.csv"),
-                            index_col=["Date"], parse_dates=["Date"])
-
-# Load model
-model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroModel.yaml"))
-
-
-

Second, we generate default parameter bounds and create a convertor for calibration. Details about the converter are provided in the Calibration section.

-
# Generate default parameter bounds
-df_list, df_name = HydroCNHS.write_model_to_df(model_dict)
-par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict)
-
-# Create convertor for calibration
-converter = cali.Convertor()
-cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list)
-formatter = converter.formatter
-
-
-

Third, we program the evaluation function for a genetic algorithm (GA). Kling-Gupta efficiency (KGE; Gupta et al., 2009) is adopted to represent the model performance of simulated monthly streamflow at the WSLO routing outlet.

-
# Code evaluation function for GA algorthm
-def evaluation(individual, info):
-    cali_wd, current_generation, ith_individual, formatter, _ = info
-    name = "{}-{}".format(current_generation, ith_individual)
-
-    ##### individual -> model
-    # Convert 1D array to a list of dataframes.
-    df_list = cali.Convertor.to_df_list(individual, formatter)
-    # Feed dataframes in df_list to model dictionary.
-    model = deepcopy(model_dict)
-    for i, df in enumerate(df_list):
-        s = df_name[i].split("_")[0]
-        model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars")
-
-    ##### Run simuluation
-    model = HydroCNHS.Model(model, name)
-    Q = model.run(temp, prec, pet)
-
-    ##### Get simulation data
-    # Streamflow of routing outlets.
-    cali_target = ["WSLO"]
-    cali_period = ("1981-1-1", "2005-12-31")
-    sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[cali_target]
-    # Resample the daily simulation output to monthly outputs.
-    sim_Q_M = sim_Q_D[cali_target].resample("MS").mean()
-
-    KGE = HydroCNHS.Indicator().KGE(
-        x_obv=obv_flow_WSLO[cali_period[0]:cali_period[1]][cali_target],
-        y_sim=sim_Q_M[cali_period[0]:cali_period[1]][cali_target])
-
-    fitness = KGE
-    return (fitness,)
-
-
-

Forth, we set up a GA for calibration. Again, we will explain calibration in more detail in the Calibration section. Here, the code is demonstrated. Note that calibration might take some time to run, depending on your system specifications. Users can lower down ‘pop_size ‘ and ‘max_gen’ if they just want to experience the process instead of seeking convergence. Note that to debug your code, please set ‘paral_cores’ to 1 to show the error messages.

-
config = {'min_or_max': 'max',
-     'pop_size': 100,
-     'num_ellite': 1,
-     'prob_cross': 0.5,
-     'prob_mut': 0.15,
-     'stochastic': False,
-     'max_gen': 100,
-     'sampling_method': 'LHC',
-     'drop_record': False,
-     'paral_cores': -1,
-     'paral_verbose': 1,
-     'auto_save': True,
-     'print_level': 1,
-     'plot': True}
-
-seed = 5
-rn_gen = HydroCNHS.create_rn_gen(seed)
-ga = cali.GA_DEAP(evaluation, rn_gen)
-ga.set(cali_inputs, config, formatter, name="Cali_HydroModel_gwlf_KGE")
-ga.run()
-summary = ga.summary
-individual = ga.solution
-
-
-

Finally, we export the calibrated model (i.e., Best_HydroModel_gwlf_KGE.yaml).

-
##### Output the calibrated model.
-df_list = cali.Convertor.to_df_list(individual, formatter)
-model_best = deepcopy(model_dict)
-for i, df in enumerate(df_list):
-    s = df_name[i].split("_")[0]
-    model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars")
-HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml"))
-
-
-
-
-

Step 4: Run a simulation

-

After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (e.g., Best_HydroModel_gwlf_KGE.yaml) can be directly loaded into HydroCNHS to run a simulation.

-
### Run a simulation.
-model = HydroCNHS.Model(os.path.join(wd, "Cali_HydroModel_gwlf_KGE",
-                                    "Best_HydroModel_gwlf_KGE.yaml"))
-Q = model.run(temp, prec, pet)
-result = pd.DataFrame(Q, index=model.pd_date_index).resample("MS").mean()
-
-### Plot
-fig, ax = plt.subplots()
-ax.plot(obv_flow_WSLO.index, obv_flow_WSLO.loc[:, "WSLO"], label="Obv")
-ax.plot(obv_flow_WSLO.index, result["WSLO"], ls="--", label="Sim")
-ax.legend()
-
-
-

The complete script can be found at ./tutorials/Hydro_example/Main_HydroModel.py.

-

The following section will further introduce how to incorporate user-defined ABM modules into the model.

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Integrate an ABM

-

After getting familiar with HydroCNHS from the hydrological model example in the previous section, we want to build a water system with human components (i.e., ABM). We will go through a similar process in the last example and focus on adding the human components.

-
    -
  1. Create a model configuration file (.yaml) using a model builder.

  2. -
  3. Complete a model configuration file (.yaml)

  4. -
  5. Program an ABM module (.py)

  6. -
  7. Run a calibration

  8. -
  9. Run a simulation

  10. -
-

We adopt the Tualatin River Basin (TRB; Fig. 5; Table 5) as the tutorial example. The corresponding subbasins’ information is shown in Table 5. In this example, we consider three human components (Table 6), including (1) a reservoir (ResAgt), (2) an irrigation diversion (DivAgt), and (3) a trans-basin water transfer (PipeAgt), to demonstrate the HydroCNHS’s functionalities. Also, we model each human agent with different levels of behavioral complexities to provide users with a sense of how an agent can be modeled (Table 6). We will calibrate the model using the streamflow at DLLO and WSLO, reservoir releases from ResAgt, and water diversions from DivAgt on a monthly scale. More details about TRB can be found in Lin et al. (2022). Here, we focus on the coding part.

-
-The node-link structure of the Tualatin River Basin with human components. -

Fig. 5 The node-link structure of the Tualatin River Basin with human components.

-
- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table 5 Subbasins’ information for the Tualatin River Basin.

Subbasin/outlet

Drainage area [ha]

Latitude [deg]

Flow length [m]

HaggIn

10034.2408

45.469

0 (to HaggIn)

TRTR

329.8013

45.458

30899.4048 (to DLLO)

ResAgt*

9656.064 (to DLLO)

DLLO

22238.4391

45.475

-
0 (to DLLO)
-
11748.211 (to TRGC)
-
-

TRGC

24044.6363

45.502

-
0 (to TRGC)
-
80064.864 (to WSLO)
-
-

DAIRY

59822.7546

45.520

70988.164 (to WSLO)

RCTV

19682.6046

45.502

60398.680 (to WSLO)

WSLO

47646.8477

45.350

0 (to WSLO)

*ResAgt is a reservoir agent integrated with Dam API. It is -considered a pseudo routing outlet.

- - -------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table 6 Summary of human components’ setting.

Item

Agent name

API

Behavioral design

From

To

Reservoir

ResAgt

Dam API

Fixed operational rules

HaggIn

-
Irrigation
-
diversion
-
-

DivAgt

RiverDiv API

Calibrated adaptive -behavioral rules

TRGC

WSLO

-
Trans-basin
-
water transfer
-
-

PipeAgt

Conveying API

External input data

TRTR

-
-

Step 1: Create a model configuration file

-

Creating a node-link structure of a modeled water system is a vital step before using HydroCNHS. Subbasin outlets are determined based on the major tributaries in the TRB. However, the subbasin outlet, TRTR, is given because we have an inlet there for trans-basin water transfer. For the routing outlet assignment, DLLO and WSLO are selected because the streamflow at these two is part of the calibration targets, TRGC is also chosen since an agent integrated using RiverDiv API can only divert water from a routing outlet, and HaggIn is picked because it is the inflow of ResAgt (i.e., ResAgt takes water from HaggIn).

-

With a node-link structure of the TRB water system, we can follow the same process shown in the “Build a hydrological model” to initialize a model builder, set up the water system with the simulation period, add subbasins, and add four routing outlets. Note that ResAgt is considered a pseudo routing outlet that needs to be assigned to one of the upstream outlets of a routing outlet.

-
import os
-import HydroCNHS
-prj_path, this_filename = os.path.split(__file__)
-
-### Initialize a model builder object.
-wd = prj_path
-mb = HydroCNHS.ModelBuilder(wd)
-
-### Setup a water system simulation information
-mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31")
-
-### Setup land surface model (rainfall-runoff model)
-# Here we have seven subbasins and we select GWLF as the rainfall-runoff model.
-outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO']
-area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546,
-            19682.6046, 47646.8477]
-lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350]
-mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list,
-                    lat_list=lat_list, runoff_model="GWLF")
-
-### Setup routing outlets
-# Add WSLO
-mb.set_routing_outlet(routing_outlet="WSLO",
-                    upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"],
-                    flow_length_list=[80064.864, 70988.164, 60398.680, 0])
-# Add TRGC
-mb.set_routing_outlet(routing_outlet="TRGC",
-                    upstream_outlet_list=["DLLO", "TRGC"],
-                    flow_length_list=[11748.211, 0])
-# Add DLLO
-# Specify that ResAgt is an instream object.
-mb.set_routing_outlet(routing_outlet="DLLO",
-                    upstream_outlet_list=["ResAgt", "TRTR", "DLLO"],
-                    flow_length_list=[9656.064, 30899.4048, 0],
-                    instream_objects=["ResAgt"])
-# Add HaggIn
-mb.set_routing_outlet(routing_outlet="HaggIn",
-                    upstream_outlet_list=["HaggIn"],
-                    flow_length_list=[0])
-
-
-
-

Initialize ABM setting

-

To add human components, we need to first initialize the ABM setting block by assigning an ABM module folder’s directory and planned ABM module filename. If they are not given, default values will be applied, namely, working directory and “ABM_module.py, “respectively. abm_module_name will be used as the filename for the ABM module template if users choose to generate one using the model builder.

-
mb.set_ABM(abm_module_folder_path=wd, abm_module_name="TRB_ABM.py")
-
-
-
-
-

Add agents

-

Next, we add human components (i.e., agents) to the model builder. -We first add a reservoir agent (ResAgt), in which its corresponding agent type class, agent name, api, link dictionary, and decision-making class can be assigned at this stage. Although not all information has to be provided now (i.e., it can be manually added to the model configuration file later), we encourage users to provide complete details here.

-
mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt",
-             api=mb.api.Dam,
-             link_dict={"HaggIn": -1, "ResAgt": 1},
-             dm_class="ReleaseDM")
-
-
-

The setting shown above means that ResAgt (an agent object) will be created from Reservoir_AgtType (an agent type class) and integrated into HydroCNHS using the Dam API. A decision-making object will be created from ReleaseDM (a decision-making class) and assigned to ResAgt as its attribute. This agent, ResAgt, will take water (factor = -1) from HaggIn routing outlet and release (factor = 1) water to ResAgt. Remember that ResAgt itself is a pseudo routing outlet.

-

Following a similar procedure, we add a water diversion agent (DivAgt). However, we have parameters, including ReturnFactor, a, and b, involved in this agent. Hence, a dictionary is provided to the par_dict argument. The format of the parameter dictionary is that keys are parameter names, and values are parameter values (-99 means waiting to be calibrated).

-

However, if the parameter is the factor used in the link_dict, users need to follow the format shown here. For example, we want to calibrate a return factor (ReturnFactor) to determine the portion of diverted water returned to the WSLO subbasin. To do that, a list, [“ReturnFactor”, 0, “Plus”], is given to link_dict at WSLO. HydroCNHS will interpret it as taking the factor value from parameter ReturnFactor with a list index of 0. “Plus” tells HydroCNHS we add water to WSLO. If water is taken from WSLO, then “Minus” should be assigned.

-
mb.add_agent(agt_type_class="Diversion_AgType", agt_name="DivAgt",
-             api=mb.api.RiverDiv,
-             link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]},
-             dm_class="DivertDM",
-             par_dict={"ReturnFactor": [-99], "a": -99, "b":-99})
-
-
-

Finally, we add a trans-basin water transfer agent (PipeAgt).

-
mb.add_agent(agt_type_class="Pipe_AgType", agt_name="PipeAgt",
-             api=mb.api.Conveying,
-             link_dict={"TRTR": 1},
-             dm_class="TransferDM")
-
-
-
-
-

Add institution

-

We did not include an institution in this TRB example; however if users want to assign an institution (e.g., “ResDivInstitution”) to ResAgt and DivAgt, they should do so by assuming that there is a cooperation between water release decisions and water diversion decisions. Namely, release decisions from ResAgt and diversion decisions from DivAgt are made simultaneously using a single decision-making object (Fig. 6). Users can do the following.

-
mb.add_institution(institution="ResDivInstitution",
-                   instit_dm_class=" ResDivDMClass",
-                   agent_list=[" ResAgt ", "DivAgt"])
-
-
-

Note that ResDivInstitution will overwrite the originally assigned DM classes (if any) of ResAgt and DivAgt. The above command means a single ResDivInstitution decision-making object initialized from ResDivDMClass will be assigned to ResAgt and DivAgt’s attributes (e.g., self.dm). Users can utilize this property to design their agents.

-
-
-

Generate ABM module template & output model configuration file

-

In addition to outputting a model configuration file (.yaml), the model builder can generate an ABM module template (.py) for users, in which the model builder will create the outline of agent type classes and decision-making classes, and users can concentrate on programming the calculation for each class given in the template.

-
### Output initial model configuration file (.yaml) and ABM module template.
-mb.write_model_to_yaml(filename="HydroABMModel.yaml")
-mb.gen_ABM_module_template()
-
-
-
-
-
-

Step 2: Complete a model configuration file

-

After the model configuration file (.yaml) is created, users should open the file to complete and correct any missing or misinterpreted values. For this example, again, we will keep the default values.

-
-
-

Step 3: Program ABM module (.py)

-

In the generated ABM module (.py), users can find mainly two types of classes, including agent type classes (AgtType) and decision-making classes (DMClass/Institutional DMClass). Agent type classes are used to define agents’ actions and store up-to-date information (e.g., current date and current time step) in agents’ attributes. Decision-making classes are used to program a specific decision-making process. Decision-making classes can be further separated into DMClass and Institutional DMClass.

-

The ABM design logic is illustrated in Fig. 6. A “class” is a template for objects that can be initiated with object-specific attributes and settings. For example, Agent1 and Agent2 are initiated from the same AgtType1 class. Agent 2, Agent 4, and Agent 5 are initiated from the AgtType2 class. Each agent will be assigned a DM object or Institution object as one of its attributes. DM objects initiated from DMClass are NOT shared with other agents; Namely, agents with DM objects will only have one unique DM object (e.g., Agent 1 and Agent 2 in Fig. 6). In contrast, an Institution object can be shared with multiple agents, in which those agents can make decisions together. For example, multiple irrigation districts make diversion decisions together to share the water shortage during a drought period. We will not implement the Institutional DMClass in this TRB example; however, we will show how to add an institution through a model builder.

-
-ABM design logic. -

Fig. 6 ABM design logic. An agent is a combination of an AgtType class and an (Institutional) DM class. An Institution object can be shared among a group of agent objects (i.e., make decisions together), while a DM object can only be assigned to one agent object.

-
-
-

Agent type class (AgtType):

-
    -
  • self.name = agent’s name

  • -
  • self.config = agent’s configuration dictionary, {‘Attributes’: …, ‘Inputs’: …, ‘Pars’: …}.

  • -
  • self.start_date = start date (datetime object).

  • -
  • self.current_date = current date (datetime object).

  • -
  • self.data_length = data/simulation length.

  • -
  • self.t = current timestep.

  • -
  • self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected here.

  • -
  • self.rn_gen = NumPy random number generator.

  • -
  • self.agents = a dictionary of all initialized agents, {agt_name: agt object}.

  • -
  • self.dm = (institutional) decision-making object if DMClass or institution is assigned to the agent, else None.

  • -
-
# AgtType
-class XXX_AgtType(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # The AgtType inherited attributes are applied.
-        # See the note at top.
-
-    def act(self, outlet):
-        # Read corresponding factor of the given outlet
-        factor = read_factor(self.config, outlet)
-
-        # Common usage:
-        # Get streamflow of outlet at timestep t
-        Q = self.dc.Q_routed[outlet][self.t]
-
-        # Make decision from (Institutional) decision-making
-        # object if self.dm is not None.
-        #decision = self.dm.make_dm(your_arguments)
-
-        if factor <= 0:     # Divert from the outlet
-            action = 0
-        elif factor > 0:    # Add to the outlet
-            action = 0
-
-        return action
-
-
-
-
-

(Institutional) decision-making classes (DMClass):

-
    -
  • self.name = name of the agent or institute.

  • -
  • self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected here.

  • -
  • self.rn_gen = NumPy random number generator.

  • -
-
# DMClass
-class XXX_DM(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # The (Institutional) DMClass inherited attributes are applied.
-        # See the note at top.
-
-    def make_dm(self, your_arguments):
-        # Decision-making calculation.
-        decision = None
-        return decision
-
-
-

To keep the manual concise, we provide a complete ABM module for the TRB example at ./tutorials/HydroABM_example/TRB_ABM_complete.py. Theoretical details can be found in Lin et al. (2022), and more coding tips are available at Advanced ABM coding tips.

-
-
-
-

Step 4: Run a calibration

-

First, we load the model configuration file, the climate data, and the observed monthly flow data for DLLO and WSLO, reservoir releases of ResAgt, and water diversions of DivAgt. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, PET data is input along with other data. Note that we manually change the ABM module from “TRB_ABM.py” to “TRB_ABM_complete.py.”

-
import matplotlib.pyplot as plt
-import pandas as pd
-import HydroCNHS.calibration as cali
-from copy import deepcopy
-
-# Load climate data
-temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"),
-                index_col=["Date"]).to_dict(orient="list")
-
-# Load flow gauge monthly data at WSLO
-obv_flow_data = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"),
-                            index_col=["Date"], parse_dates=["Date"])
-
-# Load model
-model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroABMModel.yaml"))
-# Change the ABM module to the complete one.
-model_dict["WaterSystem"]["ABM"]["Modules"] = ["TRB_ABM_complete.py"]
-
-
-

Second, we generate default parameter bounds and create a convertor for calibration. Note that we manually change the default ABM parameter bounds as shown in the code. Details about the Converter are provided in the Calibration section.

-
# Generate default parameter bounds
-df_list, df_name = HydroCNHS.write_model_to_df(model_dict)
-par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict)
-
-# Modify the default bounds of ABM
-df_abm_bound = par_bound_df_list[2]
-df_abm_bound.loc["ReturnFactor.0", [('DivAgt', 'Diversion_AgType')]] = "[0, 0.5]"
-df_abm_bound.loc["a", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]"
-df_abm_bound.loc["b", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]"
-
-# Create convertor for calibration
-converter = cali.Convertor()
-cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list)
-formatter = converter.formatter
-
-
-

Third, we program the evaluation function for a genetic algorithm (GA). The four calibration targets’ mean Kling-Gupta efficiency (KGE; Gupta et al., 2009) is adopted to represent the model performance.

-
# Code evaluation function for GA algorthm
-def evaluation(individual, info):
-    cali_wd, current_generation, ith_individual, formatter, _ = info
-    name = "{}-{}".format(current_generation, ith_individual)
-
-    ##### individual -> model
-    # Convert 1D array to a list of dataframes.
-    df_list = cali.Convertor.to_df_list(individual, formatter)
-    # Feed dataframes in df_list to model dictionary.
-    model = deepcopy(model_dict)
-    for i, df in enumerate(df_list):
-        s = df_name[i].split("_")[0]
-        model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars")
-
-    ##### Run simuluation
-    model = HydroCNHS.Model(model, name)
-    Q = model.run(temp, prec, pet)
-
-    ##### Get simulation data
-    # Streamflow of routing outlets.
-    cali_target = ["WSLO","DLLO","ResAgt","DivAgt"]
-    cali_period = ("1981-1-1", "2005-12-31")
-    sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]]
-    sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"]
-    sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"]
-    # Resample the daily simulation output to monthly outputs.
-    sim_Q_M = sim_Q_D[cali_target].resample("MS").mean()
-
-    KGEs = []
-    for target in cali_target:
-        KGEs.append(HydroCNHS.Indicator().KGE(
-            x_obv=obv_flow_data[cali_period[0]:cali_period[1]][[target]],
-            y_sim=sim_Q_M[cali_period[0]:cali_period[1]][[target]]))
-
-    fitness = sum(KGEs)/4
-    return (fitness,)
-
-
-

Fourth, we set up a GA for calibration. Again, we will explain calibration in more detail in the Calibration section. Here, only the code is demonstrated. Note that calibration might take some time to run, depending on your system specifications. Users can lower down ‘pop_size’ and ‘max_gen’ if they want to experience the process instead of seeking convergence. In order to debug your code, set ‘paral_cores’ to 1 to show the error message.

-
config = {'min_or_max': 'max',
-        'pop_size': 100,
-        'num_ellite': 1,
-        'prob_cross': 0.5,
-        'prob_mut': 0.15,
-        'stochastic': False,
-        'max_gen': 100,
-        'sampling_method': 'LHC',
-        'drop_record': False,
-        'paral_cores': -1,
-        'paral_verbose': 1,
-        'auto_save': True,
-        'print_level': 1,
-        'plot': True}
-
-seed = 5
-rn_gen = HydroCNHS.create_rn_gen(seed)
-ga = cali.GA_DEAP(evaluation, rn_gen)
-ga.set(cali_inputs, config, formatter, name="Cali_HydroABMModel_gwlf_KGE")
-ga.run()
-summary = ga.summary
-individual = ga.solution
-
-
-

Finally, we export the calibrated model (i.e., Best_HydroABMModel_gwlf_KGE.yaml).

-
##### Output the calibrated model.
-df_list = cali.Convertor.to_df_list(individual, formatter)
-model_best = deepcopy(model_dict)
-for i, df in enumerate(df_list):
-    s = df_name[i].split("_")[0]
-    model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars")
-HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml"))
-
-
-
-
-

Step 5: Run a simulation

-

After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (i.e., Best_HydroABMModel_gwlf_KGE.yaml) can be directly loaded into HydroCNHS to run a simulation.

-
### Run a simulation.
-model = HydroCNHS.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml"))
-Q = model.run(temp, prec, pet)
-sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]]
-sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"]
-sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"]
-sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean()
-### Plot
-fig, axes = plt.subplots(nrows=4, sharex=True)
-axes = axes.flatten()
-x = sim_Q_M.index
-axes[0].plot(x, sim_Q_M["DLLO"], label="$M_{gwlf}$")
-axes[1].plot(x, sim_Q_M["WSLO"], label="$M_{gwlf}$")
-axes[2].plot(x, sim_Q_M["ResAgt"], label="$M_{gwlf}$")
-axes[3].plot(x, sim_Q_M["DivAgt"], label="$M_{gwlf}$")
-
-axes[0].plot(x, obv_flow_data["DLLO"], ls="--", lw=1, color="black", label="Obv")
-axes[1].plot(x, obv_flow_data["WSLO"], ls="--", lw=1, color="black", label="Obv")
-axes[2].plot(x, obv_flow_data["ResAgt"], ls="--", lw=1, color="black", label="Obv")
-axes[3].plot(x, obv_flow_data["DivAgt"], ls="--", lw=1, color="black", label="Obv")
-
-axes[0].set_ylim([0,75])
-axes[1].set_ylim([0,230])
-axes[2].set_ylim([0,23])
-axes[3].set_ylim([0,2])
-
-axes[0].set_ylabel("DLLO\n($m^3/s$)")
-axes[1].set_ylabel("WSLO\n($m^3/s$)")
-axes[2].set_ylabel("Release\n($m^3/s$)")
-axes[3].set_ylabel("Diversion\n($m^3/s$)")
-
-axes[0].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1)
-axes[1].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1)
-axes[2].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1)
-axes[3].axvline(pd.to_datetime("2006-1-1"), color="grey", ls="-", lw=1)
-
-axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9)
-
-fig.align_ylabels(axes)
-
-
-
-
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- - - - \ No newline at end of file diff --git a/docs/_build/html/files/7_data_collector.html b/docs/_build/html/files/7_data_collector.html deleted file mode 100644 index 8dba62f..0000000 --- a/docs/_build/html/files/7_data_collector.html +++ /dev/null @@ -1,163 +0,0 @@ - - - - - - Data collector — HydroCNHS documentation - - - - - - - - - - - - - - -
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Data collector

-

A data collector is a container object created by HydroCNHS in each simulation that can be passed around HydroCNHS and user-defined ABM modules. A data collector object can store dictionaries and lists. Each of the items is associated with a unique field name. Each field has properties including data, data type (e.g., a dictionary or a list), description, and unit. We provide some usage examples below.

-

First, we manually create a data collector object for demonstration.

-
import HydroCNHS
-
-### Create a data collector object
-dc = HydroCNHS.Data_collector()
-
-
-

Then, we add two fields, “field_1” and “field 2”, with corresponding field information to the collector. Spaces are not allowed here, and the code will convert “field 2” to “field_2”.

-

To read the data in a data collector, e.g., reading field_2, we may do the following:

-
### Read data
-dc.field_2
-# Out[0]: [1, 2, 3]
-
-
-

We can also create a shortcut for accessing a field by the following command, in which any modifications on the shortcut will be passed into the data collector object.

-
### Get a field shortcut
-shortcut = dc.get_field("field_1", copy=False)
-shortcut["new_key"] = "new value"
-dc.field_1
-# Out[0]: {'new_key': 'new value'}
-
-
-

If we want to get a copy of a field (not a shortcut), we must assign “True” to the “copy” argument.

-
### Get a copy of a field
-copied = dc.get_field("field_1", copy=True)
-copied["new_key2"] = "new value2"
-dc.field_1
-# Out[0]: {'new_key': 'new value'}
-print(copied)
-# {'new_key': 'new value', 'new_key2': 'new value2'}
-
-
-

We can also delete a field using the following commands.

-
### Delete a field
-dc.del_field("field_1")
-dc.list_fields()
-# field_2
-#   type
-#     <class 'list'>
-#   desc
-#     None
-#   unit
-#     cm
-
-
-

Finally, users can export the entire data collector to a dictionary.

-
### Export the entire data collector to a dictionary
-dictionary = dc.get_dict(copy=True)
-print(dictionary)
-# {'field_info': {'field_2': {'type': <class 'list'>, 'desc': None, 'unit': 'cm'}},
-#  'field_2': [1, 2, 3]}
-
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- - - - \ No newline at end of file diff --git a/docs/_build/html/files/8_calibration.html b/docs/_build/html/files/8_calibration.html deleted file mode 100644 index ab0f7a4..0000000 --- a/docs/_build/html/files/8_calibration.html +++ /dev/null @@ -1,307 +0,0 @@ - - - - - - Calibration — HydroCNHS documentation - - - - - - - - - - - - - - - -
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Calibration

-

HydroCNHS is equipped with a genetic algorithm package powered by Distributed Evolutionary Algorithms in Python (DEAP), which can be used for calibration in parallel. HydroCNHS is unique in that aspect that both the parameters of the hydrological model and user-defined ABM can be calibrated simultaneously as long as they are defined in the model configuration file (.yaml). Furthermore, users are allowed to assign initial guesses to the algorithm.

-
-

Calibration with the genetic algorithm (GA)

-

An evaluation function, a calibration input dictionary, and a GA configuration dictionary are required to use the GA calibration module supported by the HydroCNHS.

-
-

Evaluation function

-

Assuming we want to maximize \(y=-x_1^2+5x_1-x_2\) with \(x_1,x_2 \in [-5,5]\), we can design the evaluation function as:

-
import os
-import HydroCNHS
-import HydroCNHS.calibration as cali
-prj_path, this_filename = os.path.split(__file__)
-
-def evaluation(individual, info):
-    x = individual
-    fitness = -x[0]**2 + 5*x[0] - x[1]
-    return (fitness,)
-
-    cali_wd, current_generation, ith_individual, formatter, _ = info
-    name = "{}-{}".format(current_generation, ith_individual)
-
-    ##### individual -> model
-    # Convert 1D array to a list of dataframes.
-    df_list = cali.Convertor.to_df_list(individual, formatter)
-    # Feed dataframes in df_list to model dictionary.
-
-
-

The evaluation must have “individual” and “info” arguments. Also, the return fitness value has to be of tuple format, e.g., (fitness,). The “info” contains additional information for users to design a more complex evaluation, as shown in “Build a hydrological model” and “Integrate an ABM” sections. This “info” variable contains information, including -1. working directory to the folder of this calibration experiment (cali_wd), -2. current generation of the GA run (current_generation), -3. index of the individual in the current generation (ith_individual), -4. formatter (formatter) for a converter, and -5. NumPy random number generator (rn_gen).

-
cali_wd, current_generation, ith_individual, formatter, rn_gen = info
-
-
-

cali_wd, current_generation, and ith_individual can be used to create sub-folders or output files for each evaluation. This is especially useful for calibrating a large model that requires a long simulation time. The formatter contains information to convert 1-D array individuals back to the original parameter format (e.g., a list of DataFrames) through a converter object. We will introduce the Converter in the next section. rn_gen is recommended to be used to generate random numbers (e.g., input to a HydroCNHS object like model = HydroCNHS.Model(model, name, rn_gen)). By applying rn_gen, HydroCNHS can guarantee the reproducibility of the numerical experiment with a given random seed.

-
-
-

Calibration input dictionary

-

A calibration input dictionary contains three keys, including par_name, par_bound, and wd. par_name is a list of the parameter names, par_bound is a list of parameter bounds, and wd is the working directory for the calibration experiment. The calibration input dictionary for this example is shown below.

-
# Calibration inputs
-cali.get_inputs_template() # print an input template.
-
-inputs = {'par_name': ['x1', 'x2'],
-          'par_bound': [[-5, 5], [-5, 5]],
-          'wd': 'working directory'}
-inputs["wd"] = prj_path
-
-
-

Note that users can use a converter to generate the calibration input dictionary automatically.

-
-
-

GA configuration dictionary

-

A GA configuration dictionary contains many control options. Please see the following code for the explanation of each option.

-
# GA configuration
-cali.get_config_template()
-
-config = {'min_or_max': 'max',      # maximize or minimize the evaluation function.
-        'pop_size': 100,            # Size of the population.
-        'num_ellite': 1,            # Number of ellites.
-        'prob_cross': 0.5,          # Crossover probability for uniform crossover operator.
-        'prob_mut': 0.15,           # Mutation probability of each parameter.
-        'stochastic': False,        # Is the evaluation stochastic?
-        'max_gen': 100,             # Maximum generation number.
-        'sampling_method': 'LHC',   # Sampling method for the initial population.
-        'drop_record': False,       # Whether to drop historical records to save space.
-        'paral_cores': -1,          # Number of parallel cores. -1 means all available cores.
-        'paral_verbose': 1,         # Higher value will output more console messages.
-        'auto_save': True,          # If true, users may continue the run later on by loading the auto-save file.
-        'print_level': 1,           # Control the number of generations before the printing summary of GA run.
-        'plot': True}               # Plot to time series of the best fitnesses over a generation.
-
-
-

Finally, we can run the GA calibration.

-
# Run GA
-rn_gen = HydroCNHS.create_rn_gen(seed=3)
-ga = cali.GA_DEAP(evaluation, rn_gen)
-ga.set(inputs, config, formatter=None, name="Cali_example")
-ga.run()
-ga.solution
-# Out[0]: array([ 2.47745344, -4.96991833])
-
-
-

After the GA terminated, we got the solution \(x_1=2.4775\) and \(x_2=-4.9699\), in which the theoretical values are 2.5 and -5 for \(x_1\) and \(x_2\), respectively. The fitness plot (Fig. 7) and auto-saved file (GA_auto_save.pickle) will be stored in the GA working directory. This GA_auto_save.pickle can be loaded into the GA object and continue the calibration when algorithms encounter a sudden breakdown or continue the run with a larger “max_gen,” as shown below.

-
# Continue the run with larger "max_gen"
-ga = cali.GA_DEAP(evaluation, rn_gen)
-ga.load(os.path.join(prj_path, "Cali_example", "GA_auto_save.pickle"),
-        max_gen=120)
-ga.run()
-# =====Generation 120=====
-#   Elapsed time 00:00:05
-#   Min -6.69464
-#   Max 11.21948
-#   Avg 10.99626
-#   Std 1.77931
-
-# GA done!
-
-
-
-The fitness and within-population standard deviation plot. -

Fig. 7 The fitness and within-population standard deviation plot.

-
-
-
-
-

Converter

-

The Converter is designed to convert a list of parameter DataFrames into a 1-D array for GA calibration and return a 1-D array to the original list of DataFrames. It can automatically exclude NaN values in DataFrames and fix parameters not intended to be calibrated. Also, a converter can generate the calibration input dictionary.

-

We use the following synthetic list of DataFrames as an example.

-
import numpy as np
-import pandas as pd
-import HydroCNHS
-import HydroCNHS.calibration as cali
-
-### Prepare testing data.
-par_df1 = pd.DataFrame({"Subbasin1": [1000,1000,3], "Subbasin2": [4,5,6]},
-                       index=["a", "b", "c"])
-par_df2 = pd.DataFrame({"Agent1": [9,8,7], "Agent2": [6,5,None]},
-                       index=["Par1", "Par2", "Par3"])
-
-bound_df1 = pd.DataFrame({"Subbasin1": [[0,1000],[0,1000],[0,10]], "Subbasin2": [[0,10],[0,10],[0,10]]},
-                         index=["a", "b", "c"])
-bound_df2 = pd.DataFrame({"Agent1": [[0,10],[0,10],[0,10]], "Agent2": [[0,10],[0,10],None]},
-                         index=["Par1", "Par2", "Par3"])
-
-df_list = [par_df1, par_df2]
-par_bound_df_list = [bound_df1, bound_df2]
-
-
-

Now, we want to generate the calibration input dictionary with fixed “a” and “b” parameters for “Subbasin1.” We can do the following:

-
### Create an object called Converter.
-converter = cali.Convertor()
-
-### Generate GA inputs with fixed a & b parameters for Subbasin1.
-fixed_par_list = [[(["a","b"], ["Subbasin1"])], []]
-cali_inputs = converter.gen_cali_inputs(
-    "working directory", df_list, par_bound_df_list, fixed_par_list)
-### Get formatter
-formatter = converter.formatter
-
-### Show cali_inputs
-print(cali_inputs)
-r"""
-{'wd': 'working directory',
-'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2',
-            'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2',
-            'Par3|Agent1'],
-'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10],
-            [0, 10], [0, 10]]}
-"""
-
-
-

We can see the ‘par_name’ in cali_inputs does not contain ‘a|Subbasin1’ and ‘b|Subbasin1.’ The outputted a formatter contains the relationship between a 1-D array and a list of DataFrames.

-
-

Convert from a list of DataFrames to a 1D array

-
### to 1D array
-converter.to_1D_array(df_list, formatter)
-r"""
-# Out[31]: array([4., 5., 3., 6., 9., 6., 8., 5., 7.])
-# Note the order of the array corresponds to "par_name" in the cali_inputs.
-"""
-
-
-
-
-

Convert from a 1D array to the original list of DataFrames

-
### to df_list
-var_array = np.array([5]*9)
-converter.to_df_list(var_array, formatter)
-r"""
-Out[46]:
-[   Subbasin1  Subbasin2
-a     1000.0        5.0
-b     1000.0        5.0
-c        5.0        5.0,
-    Agent1  Agent2
-Par1     5.0     5.0
-Par2     5.0     5.0
-Par3     5.0     NaN]
-"""
-
-
-
-
-
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-
- -
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- - - - \ No newline at end of file diff --git a/docs/_build/html/files/9_advanced ABM coding tips.html b/docs/_build/html/files/9_advanced ABM coding tips.html deleted file mode 100644 index 210cbf4..0000000 --- a/docs/_build/html/files/9_advanced ABM coding tips.html +++ /dev/null @@ -1,149 +0,0 @@ - - - - - - Advanced ABM coding tips — HydroCNHS documentation - - - - - - - - - - - - - - -
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Advanced ABM coding tips

-

In this section, we provide some coding tips for ABM module designs.

-
-

Enhancing computational speed

-
-

Avoid extensive calls to DataFrame

-

Reading data to a DataFrame (e.g., df.loc[ , ]) tends to be slow. We suggest users use NumPy array, list, or dictionary for calculations or data storage.

-
-
-

Avoid repeated loading of external data

-

If there is common data among multiple agent-type classes, we suggest loading the data once to a global variable at the top of the ABM module and using the variable across classes. This might save some time from repeated loading of external data inside each class (e.g., at def __init__(self)).

-
-
-

Avoid extensive deepcopy

-

deepcopy is a function to create a copy with a different storage address (not just copy a pointer that points to the same storage address). Therefore, it will take a longer time to complete the task. We suggest using deepcopy only when it is necessary.

-
-
-

Avoid storing redundant data in a data collector

-

A data collector is a data container object to store model outputs. We encourage users to utilize it to store agents’ results; however, users will need to consider the storage capacity of their computing devices, especially with a considerable number of agents. Overusing the computer storage might also slow down the computational speed.

-
-
-
-

Logging

-

Logging is a python package to organize model output messages. We encourage users to adopt it in their ABM module design. This will help you to integrate your agent output messages into HydroCNHS.

-
import logging
-logger = logging.getLogger("ABM")
-logger.info(<general information message>)
-logger.error(<error message>)
-
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- - - - \ No newline at end of file diff --git a/docs/_build/html/files/t.html b/docs/_build/html/files/t.html deleted file mode 100644 index 24ae6c8..0000000 --- a/docs/_build/html/files/t.html +++ /dev/null @@ -1,160 +0,0 @@ - - - - - - 101 classroom — HydroCNHS documentation - - - - - - - - - - - - -
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101 classroom

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-

Watershed/Basin

-

A land area that drains water to a particular water body like lakes, rivers, and oceans. Ridges, mountains, and hills usually define the boundary of a watershed/basin.

-
-
-

Subbasin

-

Subbasin is a sub-drainage land unit inside a watershed. HydroCNHS uses subbasins as the base unit for hydrological modeling. Each subbasin has a corresponding outlet. It is similar to the concept of the hydrologic response unit used in the SWAT model. However, users can freely determine the subbasins’ resolution to balance the computational cost and required resolution of the output information.

-
-
-

Semi-distributed hydrological model

-

HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in fig1. A lumped model describes the water balance of the entire watershed as a water bucket (:numref:`fig1`a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (:numref:`fig1`c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins’ (not grid) runoffs simulated by lumped models (:numref:`fig1`b).

-
-Comparison of three hydrological modeling schema. -

Comparison of three hydrological modeling schema: a) Lumped model, b) Semi-distributed model, and c) Distributed model.

-
-
-
-

Rainfall-runoff process modeling

-

The rainfall-runoff process is a series of mechanisms describing how a raindrop or snowfall becomes a runoff. This process often includes evapotranspiration, interception, infiltration, snow melting, and groundwater recession. HydroCNHS supports two process-based modeling options: (1) the General Water Loading Function (GWLF; Haith et al., 1987) with nine parameters and (2) the ABCD model (Thomas, 1981) with five parameters. Detailed calculations can be found in the supplementary material in Lin et al. (2022). Users can also pre-calculate subbasins’ runoffs using their preferred rainfall-runoff models and input them into HydroCNHS.

-
-
-

Routing & Routing outlets

-

Routing captures the delay in water movement from upstream to downstream. HydroCNHS adopts the Lohmann routing model (Lohmann et al., 1998) and the unit hydrograph parameterization described in Wi et al. (2015), with the former tracing the runoff from subbasins through the river channel (i.e., inter-subbasin routing) and the latter accounting for the within-subbasin routing process (:numref:`fig2`a). A gamma distribution is adopted to represent the unit hydrograph of within-subbasin routing.

-

In HydroCNHS, we define routing outlets as those subbasin outlets with routed streamflow information. In other words, for subbasin outlets that are not defined as routing outlets, HydroCNHS will not route the streamflow, and only the runoff information is stored. However, we do not encourage users to define too many routing outlets if the streamflow information is not necessary at those outlets. Minimizing the number of routing outlets will reduce the model complexity and usually lead to a better model performance.

-
- -
-

Design a water system with the supporting APIs

-
-A generic example of HydroCNHS coupling APIs and water system description. -

A generic example of HydroCNHS coupling APIs and water system description.

-
-

The four APIs in the HydroCNHS (fig3) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API.

-

Dam API is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in fig3) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents’ water releases decision.

-

RiverDiv API is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in fig3. This API ensures the diverted outlet is routed before agents’ diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location.

-

Conveying API is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin’s runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step).

-

InSitu API is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in fig3). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step.

-

We summarize the supported connections of each API in table1.

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- - - - \ No newline at end of file diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html deleted file mode 100644 index cb67b20..0000000 --- a/docs/_build/html/genindex.html +++ /dev/null @@ -1,621 +0,0 @@ - - - - - - Index — HydroCNHS documentation - - - - - - - - - - - - -
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  • Index
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Index

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- A - | B - | C - | D - | F - | G - | H - | I - | K - | L - | M - | N - | P - | R - | S - | T - | U - | V - | W - -
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B

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C

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D

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F

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G

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H

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  • - HydroCNHS.indicators - -
  • -
  • - HydroCNHS.model_builder - -
  • -
  • - HydroCNHS.rainfall_runoff_model - -
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  • - HydroCNHS.rainfall_runoff_model.abcd - -
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  • - HydroCNHS.rainfall_runoff_model.gwlf - -
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  • - HydroCNHS.rainfall_runoff_model.pet_hamon - -
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  • - HydroCNHS.routing - -
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  • - HydroCNHS.util - -
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  • - HydroCNHS.visual - -
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How to build a ABM module?

-

HydroCNHS is designed to integrate multiple highly customized ABM modules, where users are able to create various agent type classes and decision-making classes. However, few design protocals have to be followed.

-

Please see the following ABM module design example for the Tualatin River Basin. -The script contains primary three parts:

-
    -
  1. Import packages and set up logger.

  2. -
  3. ABM global setting.

  4. -
  5. Create agent type classes and decision-making classes.

  6. -
-
import os
-import pickle
-import logging
-import numpy as np
-from HydroCNHS.abm import Base, read_factor     # import some helpers.
-logger = logging.getLogger("ABM")               # Set logger to log msg.
-
-# =============================================================================
-# ABM Global Setting
-# Users can load common data that can be read by every agent.
-# =============================================================================
-# E.g.,
-this_dir, this_filename = os.path.split(__file__)   # Get this file directory.
-with open(os.path.join(
-        this_dir, "TRB_ABM_database.pickle"), "rb") as file:
-    database = pickle.load(file)
-# Items included in database (the pickle file) for the TRB example:
-# ['Storage_q95', 'Storage_mean', 'Storage_q05', 'Storage_max',
-# 'Release_q50', 'Release_max',
-# 'Div_M_median_mean', 'Div_M_median_max', 'Div_M_median_min', 'Prec_M_mean',
-# 'Pipe_M_median']
-
-#%% ===========================================================================
-# Customized ABM Module Design Protocal
-# =============================================================================
-
-##### Base class
-# This Base() class should be inherited by users when designing their agent
-# type and decision-making classes. This class will load all arguments sent
-# from HydroCNHS. We show the items that will be assigned when agent
-# type and decision-making instances created by HydroCNHS below. Namely, users
-# can use those items by calling "self.<item name>" when designing agent type
-# and decision-making classes.
-
-##### Agent_type class design outline
-r"""
-class Agent_type(Base):                 # Inherit base class.
-    #### Initialization
-    def __init__(self, **kwargs):       # Copy this.
-        super().__init__(**kwargs)      # Copy this.
-        ### Agent_type class's available items:
-        # name: agent's name.
-        # config: agent's configuration dictionary the model file (.yaml).
-        # start_date: datetime object.
-        # data_length: length of the simulation.
-        # data_collector: a container to store simulated data.
-        # rn_gen: random number generator to ensure reproducibility (e.g.,
-        # self.rn_gen.random()). Note that do NOT set a global random seed in
-        # this module! All type of random number should be created by "rn_gen."
-        # dm: decision making object if assigned in the model file (.yaml).
-
-        ### Create fields in data_collector for storing agent's outputs if
-        # needed.
-        # e.g.,
-        # add_field(name of the field, field type => {} or [])
-        self.data_collector.add_field("field name", {})
-        # get_field(name of the field) for further operation.
-        field = self.data_collector.get_field("field name")
-
-        # Other initialization that you need for this agent_type.
-        # e.g.,
-        self.threshold = 0.5
-
-    #### Must have this act method with exact same arguments show below.
-    def act(self, Q, outlet, agent_dict, current_date, t):   # Copy this
-
-        # Read corresponding factor (defined in .yaml model file) of the given
-        # outlet.
-        factor = read_factor(self.config, outlet)
-
-        # Do some calculation
-        if factor > 0:      # Calculation for adding water to the outlet.
-            amount = .....
-
-        elif factor < 0:    # Calculation for diverting water from the outlet.
-            amount = .....
-
-        # Calculate agent's action at time t.
-        action = factor * amount
-
-        # This returned action will change the streamflow at the outlet by
-        # new_flow = old_flow + action
-        return action
-"""
-##### Decision-making class design outline
-r"""
-class Agent_DM(Base):                   # Inherit base class.
-    #### Initialization
-    def __init__(self, **kwargs):       # Copy this.
-        super().__init__(**kwargs)      # Copy this.
-        ### Decision-making class's available items:
-        # start_date: datetime object.
-        # data_length: length of the simulation.
-        # abm: the ABM configuration dictionary from the model file (.yaml).
-        # data_collector: a container to store simulated data.
-        # rn_gen: random number generator to ensure reproducibility (e.g.,
-        # self.rn_gen.random()). Note that do NOT set a global random seed in
-        # this module! All type of random number should be created by "rn_gen."
-
-        ### Create fields in data_collector for storing agent's outputs if
-        # needed.
-        # e.g.,
-        # add_field(name of the field, field type => {} or [])
-        self.data_collector.add_field("field name", {})
-        # get_field(name of the field) for further operation.
-        field = self.data_collector.get_field("field name")
-
-        # Other initialization that you need for this agent_type.
-        # e.g.,
-        self.threshold = 0.5
-
-    # Your customized decision-making function. You can add any arguments.
-    def make_dm(self, **kwargs):
-        pass
-        return some_decisions
-"""
-
-##### Other design tips
-# 1. Use numpy or list do the operation instead of using pandas dataframe.
-#    df.loc tend to slow down the calculation speed.
-# 2. Store only the necessary data to the data collector to control the storage
-#    requirement.
-# 3. We highly encourage users to follow the above design outlines.
-
-#%% ===========================================================================
-# The Tualatin River Basin Example
-# =============================================================================
-
-##### Reservoir Agent Type Class ######
-class ResDam_AgType(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # self.config is auto loaded into the class. See above explanation.
-        self.inputs = self.config["Inputs"]
-
-    def act(self, Q, outlet, agent_dict, current_date, t):
-        # Read corresponding factor
-        factor = read_factor(self.config, outlet)
-
-        # Release (factor should be 1)
-        if factor <= 0:
-            print("Something is not right in ResDam agent.")
-        elif factor > 0:
-            # Make the water release decision by self.dm.make_dm, which
-            # is an instance of the ResDM class defined below that is sent
-            # by HydroCNHS during the simulation.
-            # Q["HaggIn"][t] is the resevoir inflow at time t.
-            res_t = self.dm.make_dm(Q["HaggIn"][t], current_date)
-            action = res_t
-            return action
-
-##### Reservoir Decision-making Class ######
-r"""
-Reservoir agents determine reservoir releases by a generic operational
-rule modified from Neitsch et al. (2011), where target storages and
-target releases  are adopted for flood control (October - May) and storage
-control (June - September) period. See the supplementary material of Lin et al.
-(2022) for more details.
-"""
-class ResDM(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.database = database        # [m^3]
-        self.flood_control = [True, True, True, True, True, False,
-                            False, False, False, True, True, True]
-        # [cms] Use quantile 0.01 of the historical data.
-        self.min_release = 0.263335
-        self.min_release_vol = self.min_release * 86400     # [m^3]
-        self.data_collector.add_field("ResAgt", {})
-        records = self.data_collector.get_field("ResAgt")
-        records["Storage"] = []
-        records["Release"] = []
-
-    def make_dm(self, inflow, current_date):
-        db = self.database
-        flood_control = self.flood_control
-        records = self.data_collector.ResAgt
-        min_release = self.min_release
-        min_res_vol = self.min_release_vol
-        day_of_year = current_date.dayofyear
-
-        inflow_vol = inflow * 86400     # cms to m^3
-        if records["Storage"] == []:    # Initial value [m^3]
-            storage = 42944903.6561 + inflow_vol
-        else:
-            storage = records["Storage"][-1] + inflow_vol
-        release = 0
-
-        if flood_control[current_date.month-1]:
-            storage_max = db["Storage_q95"][day_of_year-1]
-            if storage > storage_max:
-                release = (storage - storage_max) / 86400 # m^3 to cms
-                storage = storage_max
-            else:
-                if storage - min_res_vol < 0:
-                    release = 0
-                else:
-                    release = min_release
-                    storage = storage - min_res_vol
-        else:   # Target storage control
-            release_target = db["Release_q50"][day_of_year-1]
-            storage_temp = storage - release_target * 86400
-            if storage_temp > db["Storage_max"][day_of_year-1]:
-                release = (storage - db["Storage_max"][day_of_year-1]) / 86400
-                storage = db["Storage_max"][day_of_year-1]
-            elif storage_temp < db["Storage_q05"][day_of_year-1]:
-                release = (storage - db["Storage_q05"][day_of_year-1]) / 86400
-                storage = db["Storage_q05"][day_of_year-1]
-                if release < 0:
-                    release = 0
-                    storage = records["Storage"][-1] + inflow_vol
-            else:
-                release = release_target
-                storage = storage_temp
-        records["Storage"].append(storage)
-        records["Release"].append(release)
-        return release
-
-
-##### Diversion Agent Type Class ######
-class IrrDiv_AgType(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.pars = self.config["Pars"]
-        self.data_collector.add_field(self.name, {})
-        records = self.data_collector.get_field(self.name)
-        records["DivReq"] = []
-        records["Diversion"] = []
-        records["Shortage"] = []
-        logger.info("Initialize irrigation diversion agent: {}".format(
-            self.name))
-
-    def act(self, Q, outlet, agent_dict, current_date, t):
-        self.current_date = current_date
-        self.t = t
-        records = self.data_collector.get_field(self.name)
-
-        # Get factor
-        factor = read_factor(self.config, outlet)
-
-        # For parameterized (for calibration) factor.
-        if isinstance(factor, list):
-            factor = self.pars[factor[0]][factor[1]]
-
-        # Compute actual diversion (factor < 0) or return flow (factor >= 0)
-        if factor < 0:  # Diversion
-            # Make diversion request at 1st of each month
-            if current_date.day == 1:
-                # Get the parameters.
-                a = self.pars["a"]
-                b = self.pars["b"]
-                # Make the diversion request decision by self.dm.make_dm, which
-                # is an instance of the DivDM class defined below that is sent
-                # by HydroCNHS during the simulation.
-                div_req = self.dm.make_dm(a, b, current_date)
-                records["DivReq"] = records["DivReq"] + div_req
-
-            # Apply the physical constraints for the available water at time t.
-            div_req_t = records["DivReq"][t]
-            available_water_t = Q[outlet][t]
-            if div_req_t > available_water_t:
-                shortage_t = div_req_t - available_water_t
-                div_t = available_water_t
-            else:
-                div_t = div_req_t
-                shortage_t = 0
-            records["Diversion"].append(div_t)
-            records["Shortage"].append(shortage_t)
-            action = factor * div_t
-        elif factor >= 0:   # factor > 0; Return flow
-            div_t = records["Diversion"][t]
-            action = factor * div_t
-
-        return action
-
-##### Diversion Request Decision-making Class ######
-r"""
-Diversion agents make monthly-diversion-request decisions at the beginning of
-each month and has return flow back to the river. For this example, we design
-the diversion-request decisions in June to September are governed by linear
-functions (i.e., y = ax + b), where predictors are the perfect forecast of
-monthly precipitation (Prec_M_mean). Minor diversions in other months are
-filled with monthly mean values (Div_M_median_mean). Note that we bound the
-monthly-diversion-request decision by the historical maximum (Div_M_median_max)
-and minimum (Div_M_median_min) monthly diversion values to prevent unrealistic
-decisions. See the supplementary material of Lin et al. (2022) for more details.
-"""
-class DivDM(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # Global input data loaded at the top of the file.
-        self.database = database
-
-    def make_dm(self, a, b, current_date):
-        db = self.database
-        prec_M_mean = db["Prec_M_mean"][current_date.year-1981,
-                                        (current_date.month-1)]
-        div_M_mean = db["Div_M_median_mean"][(current_date.month-1)]
-        div_M_max = db["Div_M_median_max"][(current_date.month-1)]
-        div_M_min = db["Div_M_median_min"][(current_date.month-1)]
-        if current_date.month in [6,7,8,9]:
-            div_M_req = div_M_mean + a * prec_M_mean + b
-            # Bound by historical max and min
-            div_M_req = min( max(div_M_req, div_M_min), div_M_max)
-        else:
-            div_M_req = div_M_mean
-        div_D_req = [div_M_req] * (current_date.days_in_month)
-        return div_D_req
-
-
-##### Pipe Agent Type Class ######
-class Pipe_AgType(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # Historical inputs
-        self.assigned_behavior = database["Pipe_M_median"]
-
-    def act(self, Q, outlet, agent_dict, current_date, t):
-
-        # Get factor
-        factor = read_factor(self.config, outlet)
-
-        # Release (factor should be 1)
-        if factor <= 0:
-            print("Something is not right in TRTR agent.")
-        elif factor > 0:
-            # Assuming that diversion has beed done, get the actual release at
-            # time t.
-            y = current_date.year
-            m = current_date.month
-
-            if y < 1991:
-                Res_t = 0
-            else:
-                # Historical inputs.
-                Res_t = self.assigned_behavior[y-1991, m-1]
-            action = factor * Res_t
-            return action
-
-##### Conveying Water Decision-making Class ######
-r"""
-This is an empty decision-making class. We simply assigned the amount of
-conveying water to the pipe agent with a historical inputs. Namely, PipeDM()
-can be deleted and removed from the model file (.yaml).
-"""
-class PipeDM(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        pass
-
-##### Drainage System Agent Type Class ######
-class Drain_AgType(Base):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        # We assume a linear urbanization rate. Namely, urbanized areas are
-        # linearly increase from 5% to 50% of the subbasin area in 33 years.
-        # We assume the urbanization will increase 50% of the orignal runoff
-        # contributed by the unbanized region.
-        # Therefore, the subbasin's runoff change due to the unbanization is
-        # equal to unbanized_area% * 75% * original_runoff
-        ini = 0.05
-        end = 0.5
-        interval = (end-ini)/32
-        self.urbanized_ratio = np.arange(0.05, 0.5 + interval, interval)
-        self.rn = self.data_collector.add_field(self.name, [])
-
-    def act(self, Q, outlet, agent_dict, current_date, t):
-        # Get factor
-        factor = read_factor(self.config, outlet)
-        Qt_change = self.urbanized_ratio[current_date.year-1981] * 0.75 \
-                    * Q[outlet][t]
-        action = factor * Qt_change
-
-        #### Test rn_gen
-        rn = self.data_collector.get_field(self.name)
-        rn.append(self.rn_gen.random())
-        return action
-r"""
-No runoff changing decision-making class is defined here. As you may have
-observed, we code the simple runoff changing calculation in the Drain_AgType()
-directly.
-"""
-
-
-
-

Decision-making class V.S. Agent group

-
    -
  • Decision-making class:

    -

    Decision-making class should be used when multiple agent type class share a -similar decision-making process. By seperating those calculations into a -decision-making class, we could make the code concise and easier to maintain.

    -
  • -
  • Agent group:

    -

    The agent group should be used when multiple agents make decision -simuteneously (not piority-based). For example, diversion agt_a, agt_b, and -agt_c make the diversion requests together, where they share the water -shortage together. To program this example, instead of define a diversion -agent class to create three instances for agt_a, agt_b, and agt_c, -repectively, users are required to define a diversion agent group class, -which HydroCNHS will only create one instance from this agent group class to -represent agt_a, agt_b, and agt_c. Namely, diversion agent group class will -return different actions depending on outlet infomation, which is required -users to code such statement explicitly. Note that the config sent to the -agent group class is a dictionary of all members’ configurations. E.g., -config = {“agt_a”: agt_a’s config, “agt_b”: agt_b’s config, -“agt_c”: agt_c’s config}. This config also apply to the -group decision-making class if there is any.

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- - - - \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html deleted file mode 100644 index 6ca0bdf..0000000 --- a/docs/_build/html/index.html +++ /dev/null @@ -1,189 +0,0 @@ - - - - - - HydroCNHS User Manual — HydroCNHS documentation - - - - - - - - - - - - - -
- - -
- -
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HydroCNHS User Manual

-

The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS.

-

Manual contributors: Chung-Yi Lin & Ali Ghaffari

- -
-
-

How to cite?

-

Lin, C. Y., Yang, Y. C. E., & Wi S. (2022). HydroCNHS: A Python Package of Hydrological Model for Coupled Natural Human Systems, Journal of Water Resources Planning and Management, 148(12), 6022005.

-
- -
-

Indices and tables

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- - - - \ No newline at end of file diff --git a/docs/_build/html/installation.html b/docs/_build/html/installation.html deleted file mode 100644 index 89cf356..0000000 --- a/docs/_build/html/installation.html +++ /dev/null @@ -1,119 +0,0 @@ - - - - - - Installation — HydroCNHS documentation - - - - - - - - - - - - - - -
- - -
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Installation

-

Install HydroCNHS by pip.

-
pip install hydrocnhs
-
-
-

If you fail to install hydrocnhs due to DEAP package, please downgrade setuptools to 57 by

-
pip install setuptools==57
-
-
-

Now install hydrochns again.

-

if users would like to obtain the latest version (beta version), you can install the latest version from HydroCNHS GitHub respository by:

-
pip install git+https://github.com/philip928lin/HydroCNHS.git
-
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- - - - \ No newline at end of file diff --git a/docs/_build/html/model_builder.html b/docs/_build/html/model_builder.html deleted file mode 100644 index 1da32ae..0000000 --- a/docs/_build/html/model_builder.html +++ /dev/null @@ -1,387 +0,0 @@ - - - - - - Model builder — HydroCNHS documentation - - - - - - - - - - - - - - -
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-

Model builder

-

The ModelBuilder() of model builder module can quickly help users to establish -the outline of a model (.yaml). Namely, ModelBuilder can create a template -model for calibration. First, we initialize a ModelBuilder -object as shown in the following.

-
import HydroCNHS
-
-wd = "working directory"
-mb = HydroCNHS.ModelBuilder(wd)
-
-r'''
-# Print out message.
-Follow the following steps to create model template:
-    Step 1: set_water_system()
-    Step 2: set_lsm()
-    Step 3: set_routing_outlet(), one at a time.
-    Step 4: set_ABM() if you want to build a coupled model.
-    Step 5: write_model_to_yaml()
-After creating model.yaml template, open it and further edit it.
-Use .help to re-print the above instruction.
-'''
-
-
-

Then, we follow the print out instructions to create a model template.

-

Here, we demonstrate how to create a coupled model template for the Tualatin -River Basin (TRB).

-
-The Tualatin River Basin system diagram. -

Fig. 6 The Tualatin River Basin system diagram (Lin et al., 2022). TRTR, -HaggIn, DLLO, TRGC, DAIRY, RCTV, and WSLO are seven subbasins. -PipeAgt, ResAgt, and DivAgt are trans-basin aqueduct, Hagg reservoir, and -irrigation diversion agents, respectively. DrainAgt1 and DrainAgt2 are two -drainage-system agents for the runoff-changing scenario.

-
-
### Setup a water system simulation information
-mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31")
-
-### Setup land surface model (rainfall-runoff model)
-# Here we have seven subbasins and we select GWLF as the rainfall-runoff model.
-outlet_list = ["TRTR", "HaggIn", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"]
-mb.set_lsm(outlet_list=outlet_list, lsm_model="GWLF")   # or lsm_model="ABCD"
-
-### Setup routing
-# We have four routing outlets, which we will add them into the model one by
-# one.
-mb.set_routing_outlet(routing_outlet="WSLO",
-                    upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"])
-mb.set_routing_outlet(routing_outlet="TRGC",
-                    upstream_outlet_list=["DLLO", "TRGC"])
-mb.set_routing_outlet(routing_outlet="DLLO",
-                    upstream_outlet_list=["ResAgt", "TRTR", "DLLO"],
-                    instream_outlets=["ResAgt"])
-# Note: ResAgt (Hagg Lake) is the reservoir agent, whcih is considerred as
-# an instream control object.
-
-mb.set_routing_outlet(routing_outlet="HaggIn",
-                    upstream_outlet_list=["HaggIn"])
-
-### Setup ABM
-abm_module_path="abm_module path"
-mb.set_ABM(abm_module_path=abm_module_path)
-
-### Save to a .yaml model file for further editting.
-filename = "output directory/model.yaml"
-mb.write_model_to_yaml(filename)
-
-
-

After “write_model_to_yaml()”, open the create model.yaml (see below) to -further edit it. Users need to manually add necessary “inputs” information -for all sections. Namely, users need to fill all null except those nested -under “Pars” subsections. For the ABM section, users are asked to manually add -“agents” to the model.

-

Note that the -99 values under “Pars” subsections are the parameters -required calibration.

-
Path: {WD: working directory, Modules: abm_module path}
-WaterSystem:
-StartDate: 1981/1/1
-EndDate: 2013/12/31
-NumSubbasins: 7
-NumGauges: null
-NumAgents: null
-Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO]
-GaugedOutlets: []
-DataLength: 12053
-LSM:
-Model: GWLF
-TRTR:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-HaggIn:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-DLLO:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-TRGC:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-DAIRY:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-RCTV:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-WSLO:
-    Inputs: {Area: null, Latitude: null, S0: null, U0: null, SnowS: null}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-Routing:
-Model: Lohmann
-WSLO:
-    TRGC:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    DAIRY:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    RCTV:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    WSLO:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-TRGC:
-    DLLO:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    TRGC:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-DLLO:
-    ResAgt:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    TRTR:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    DLLO:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-HaggIn:
-    HaggIn:
-        Inputs: {FlowLength: null, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-ABM:
-Inputs:
-    DamAgentTypes: []
-    RiverDivAgentTypes: []
-    InsituAgentTypes: []
-    ConveyAgentTypes: []
-    DMClasses: []
-    Modules: []
-    AgGroup: null
-
-
-

After filling in the necessary information (e.g., Inputs and ABM settings, we -will obtain a model template (see below) ready to be calibrated (i.e., those --99 values).

-
Path: {WD: working directory, Modules: abm_module path}
-WaterSystem:
-StartDate: 1981/1/1
-EndDate: 2013/12/31
-NumSubbasins: 7
-NumGauges: 2
-NumAgents: 3
-Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO]
-GaugedOutlets: [DLLO, WSLO]
-DataLength: 12053
-LSM:
-Model: GWLF
-TRTR:
-    Inputs: {Area: 329.8013, Latitude: 45.458136, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-HaggIn:
-    Inputs: {Area: 10034.2408, Latitude: 45.469444, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-DLLO:
-    Inputs: {Area: 22238.4391, Latitude: 45.475, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-TRGC:
-    Inputs: {Area: 24044.6363, Latitude: 45.502222, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-DAIRY:
-    Inputs: {Area: 59822.7546, Latitude: 45.52, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-RCTV:
-    Inputs: {Area: 19682.6046, Latitude: 45.502222, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-WSLO:
-    Inputs: {Area: 47646.8477, Latitude: 45.350833, XL: 2.0, SnowS: 5.0}
-    Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99,
-    Df: -99, Kc: -99}
-Routing:
-Model: Lohmann
-WSLO:
-    TRGC:
-        Inputs: {FlowLength: 80064.864, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    DAIRY:
-        Inputs: {FlowLength: 70988.16384, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    RCTV:
-        Inputs: {FlowLength: 60398.68032, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    WSLO:
-        Inputs: {FlowLength: 0, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-TRGC:
-    DLLO:
-        Inputs: {FlowLength: 11748.2112, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    TRGC:
-        Inputs: {FlowLength: 0, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-DLLO:
-    ResAgt:
-        Inputs: {FlowLength: 9656.064, InstreamControl: false}
-        Pars: {GShape: null, GScale: null, Velo: -99, Diff: -99}
-    TRTR:
-        Inputs: {FlowLength: 30899.4048, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99}
-    DLLO:
-        Inputs: {FlowLength: 0, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-HaggIn:
-    HaggIn:
-        Inputs: {FlowLength: 0, InstreamControl: false}
-        Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null}
-ABM:
-Inputs:
-    DamAgentTypes: [ResDam_AgType]
-    RiverDivAgentTypes: [IrrDiv_AgType]
-    InsituAgentTypes: []
-    ConveyAgentTypes: [Pipe_AgType]
-    DMClasses: [ResDM, DivDM, PipeDM]
-    Modules: [TRB_ABM_dm.py]        # user-provided ABM module.
-    AgGroup: null
-Pipe_AgType:
-    PipeAgt:
-    Attributes: {}
-    Inputs:
-        Piority: 0
-        Links: {TRTR: 1}
-        DMClass: PipeDM
-    Pars: {}    # No parameter
-ResDam_AgType:
-    ResAgt:
-    Attributes: {}
-    Inputs:
-        Piority: 0
-        Links: {SCOO: -1, R1: 1}
-        DMClass: ResDM
-    Pars: {}    # No parameter
-IrrDiv_AgType:
-    DivAgt:
-    Attributes: {}
-    Inputs:
-        Piority: 1
-        Links:
-        TRGC: -1
-        WSLO: [ReturnFactor, 0, Plus]
-        DMClass: DivDM
-    Pars:
-        ReturnFactor: [-99]
-        a: -99
-        b: -99
-
-
-
- - -
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- - - - \ No newline at end of file diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv deleted file mode 100644 index 8ea32db..0000000 Binary files a/docs/_build/html/objects.inv and /dev/null differ diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html deleted file mode 100644 index acfca78..0000000 --- a/docs/_build/html/py-modindex.html +++ /dev/null @@ -1,201 +0,0 @@ - - - - - - Python Module Index — HydroCNHS documentation - - - - - - - - - - - - - - - -
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Python Module Index

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- h -
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- h
- HydroCNHS -
    - HydroCNHS.abm -
    - HydroCNHS.abm_script -
    - HydroCNHS.calibration -
    - HydroCNHS.calibration.convertor -
    - HydroCNHS.calibration.ga_deap -
    - HydroCNHS.data_collector -
    - HydroCNHS.hydrocnhs -
    - HydroCNHS.indicators -
    - HydroCNHS.model_builder -
    - HydroCNHS.rainfall_runoff_model -
    - HydroCNHS.rainfall_runoff_model.abcd -
    - HydroCNHS.rainfall_runoff_model.gwlf -
    - HydroCNHS.rainfall_runoff_model.pet_hamon -
    - HydroCNHS.routing -
    - HydroCNHS.util -
    - HydroCNHS.visual -
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- - - - \ No newline at end of file diff --git a/docs/_build/html/quickstart.html b/docs/_build/html/quickstart.html deleted file mode 100644 index 9b9eef2..0000000 --- a/docs/_build/html/quickstart.html +++ /dev/null @@ -1,527 +0,0 @@ - - - - - - Quick start! — HydroCNHS documentation - - - - - - - - - - - - - - - -
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Quick start!

-

Let’s start with running a simulation using the Tualatin River Basin (TRB) example!

-

The TRB tutorial codes are located at ./HydroCNHS/tutorials/Tualatin River Basin/

-
-The Tualatin River Basin system diagram. -

Fig. 5 The Tualatin River Basin system diagram (Lin et al., 2022). TRTR, -HaggIn, DLLO, TRGC, DAIRY, RCTV, and WSLO are seven subbasins. -PipeAgt, ResAgt, and DivAgt are trans-basin aqueduct, Hagg reservoir, and -irrigation diversion agents, respectively. DrainAgt1 and DrainAgt2 are two -drainage system agents for the runoff-changing scenario.

-
-

The TRB, consisting of 1844.07 km2 in northwest Oregon, US, is covered by -densely populated area (20%), agricultural area (30%), and forest (50%) -(Tualatin River Watershed Council, 2021). Its agriculture heavily relies on -irrigation because seasonal rainfall concentrates in winter (November - -February). The Spring Hill Pumping Plant is the largest diversion facility in -the TRB for irrigating Tualatin Valley Irrigation District (TVID; DivAgt), -where the Hagg reservoir (ResAgt) is the primary water source. During the -summer period, water is transferred from the Barney reservoir (outside of the -TRB) through a trans-basin aqueduct (PipeAgt) to augment the low flow for -ecological purposes.

-

The full background of the TRB can be found in Lin et al., (2022).

-
-

Run a simulation

-

Assuming we have already calibrated the TRB model, let’s take one minute to -browse through the following simulation code. Then, you should be able run the -script directly.

-
import os
-import pickle
-import HydroCNHS
-
-##### Setup Working Directory
-# Get this .py file's directory.
-prj_path, this_filename = os.path.split(__file__)
-wd = prj_path
-
-##### Load Daily Weather Time Series.
-# We store all required time series data in the pickle format.
-with open(os.path.join(prj_path, "Inputs", "TRB_inputs.pickle"), "rb") as file:
-        (temp, prec, pet, obv_D, obv_M, obv_Y) = pickle.load(file)
-
-##### Load Model.yaml.
-best_gwlf_abm_path = os.path.join(prj_path, "Calibrated_model", "Best_gwlf_abm_KGE.yaml")
-model_dict_gwlf = HydroCNHS.load_model(best_gwlf_abm_path)
-# Change the path according to this .py file's directory.
-model_dict_gwlf["Path"]["WD"] = wd
-model_dict_gwlf["Path"]["Modules"] = os.path.join(prj_path, "ABM_modules")
-
-##### Create HydroCNHS Model Object for Simulation.
-model_gwlf = HydroCNHS.Model(model_dict_gwlf)
-
-##### Run a simulation
-Q_gwlf = model_gwlf.run(temp, prec, pet) # pet is optional.
-
-
-

To run a coupled natural human model simulation, three items are required.

-
    -
  1. Daily weather time series including temperture (temp) and precipitation (prec). Potential evapotranspiration (pet) is optional.

  2. -
  3. Model file (e.g., Best_gwlf_abm_KGE.yaml).

  4. -
  5. User-provided ABM module(s) (e.g., TRB_ABM_dm.py). This is not required for pure hydrological model, non-coupled model.

  6. -
-

Now, we are going to introduce these three items in a more detail.

-
-
-

Daily weather time series

-

Temperture and precipitation are two required weather inputs for the -simulation. If potential evapotranspiration is not provided, HydroCNHS will -automatically calculate it by Hamon method. The weather inputs are in a -dictionary format shown below.

-
# 'DAIRY', 'DLLO', 'RCTV', 'HaggIn', 'TRGC', 'TRTR', and 'WSLO' are subbasins' names.
-temp = {'DAIRY': [7.7, 7.0, 6.6, 6.3, .......],
-        'DLLO': [7.9, 7.5, 7.1, 6.1, .......],
-        'RCTV': [8.0, 7.8, 7.8, 7.5, .......],
-        'HaggIn': [8.1, 7.4, 7.0, 6.2, .......],
-        'TRGC': [5.7, 5.5, 5.1, 4.0, .......],
-        'TRTR': [7.9, 6.9, 6.5, 6.1, .......],
-        'WSLO': [7.8, 7.4, 7.3, 7.3, .......]}
-# Similar to prep and pet.
-
-
-

The dictionary will contain weather time series (i.e., a list) for each -subbasin. The length of each time series has to be identical.

-
-
-

Model.yaml

-

The model file (.yaml) contains settings for hydrological model (e.g., -rainfall-runoff and routing) and ABM model (e.g., how to coupled). -The model file has six sections:

-
-

1. Path

-
# Path for working directory (outputing log file) and user-provided ABM
-# modules (optional).
-Path: {
-  WD: 'wd path',
-  Modules: 'ABM module path'}
-
-
-
-
-

2. WaterSystem

-
WaterSystem:
-        StartDate: 1981/1/1
-        EndDate: 2013/12/31
-        NumSubbasins: 7
-        NumGauges: 2
-        NumAgents: 3
-        Outlets: [TRTR, HaggIn, DLLO, TRGC, DAIRY, RCTV, WSLO]
-        GaugedOutlets: [DLLO, WSLO]             # Optional
-        DataLength: 12053
-
-
-

DataLength can be automatically calculated if EndDate is provided, vice versa.

-
-
-

3. LSM

-

HydroCNHS provides user two rainfall-runoff simulation options, General -Water Loading Function (GWLF; 9 parameters) and ABCD (5 parameters). Their -settings are shown below.

-

The detailed documentation for GWLF and ABCD can be found at the supplementary -material of (Lin et al., 2022).

-

GWLF:

-
LSM:
-        Model: GWLF
-        TRTR:
-                Inputs: {Area: 329.80, Latitude: 45.45, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 97.148, IS: 0.404, Res: 0.178, Sep: 0.482, Alpha: 0.868, Beta: 0.708,
-                Ur: 14.341, Df: 0.124, Kc: 0.988}
-        HaggIn:
-                Inputs: {Area: 10034.24, Latitude: 45.46, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 36.984, IS: 0.178, Res: 0.122, Sep: 0.178, Alpha: 0.235, Beta: 0.112,
-                Ur: 13.932, Df: 0.588, Kc: 0.765}
-        DLLO:
-                Inputs: {Area: 22238.43, Latitude: 45.47, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 98.249, IS: 0.369, Res: 0.281, Sep: 0.117, Alpha: 0.909, Beta: 0.381,
-                Ur: 9.0, Df: 0.947, Kc: 1.057}
-        TRGC:
-                Inputs: {Area: 24044.63, Latitude: 45.50, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 89.963, IS: 0.098, Res: 0.047, Sep: 0.418, Alpha: 0.535, Beta: 0.115,
-                Ur: 9.107, Df: 0.727, Kc: 1.013}
-        DAIRY:
-                Inputs: {Area: 59822.75, Latitude: 45.52, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 95.962, IS: 0.026, Res: 0.241, Sep: 0.199, Alpha: 0.432, Beta: 0.299,
-                Ur: 4.049, Df: 0.034, Kc: 1.012}
-        RCTV:
-                Inputs: {Area: 19682.60, Latitude: 45.50, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 93.049, IS: 0.341, Res: 0.319, Sep: 0.05, Alpha: 0.316, Beta: 0.7,
-                Ur: 11.288, Df: 0.741, Kc: 0.746}
-        WSLO:
-                Inputs: {Area: 47646.84, Latitude: 45.35, S0: 2.0, U0: 10.0, SnowS: 5.0}
-                Pars: {CN2: 63.818, IS: 0.039, Res: 0.305, Sep: 0.045, Alpha: 0.207, Beta: 0.496,
-                Ur: 13.919, Df: 0.175, Kc: 1.451}
-
-
-

ABCD:

-
LSM:
-        Model: ABCD
-        TRTR:
-                Inputs: {Area: 329.80, Latitude: 45.45, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.998, b: 53.403, c: 0.345, d: 0.559, Df: 0.636}
-        HaggIn:
-                Inputs: {Area: 10034.24, Latitude: 45.46, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.196, b: 2.316, c: 0.78, d: 0.248, Df: 0.697}
-        DLLO:
-                Inputs: {Area: 22238.43, Latitude: 45.47, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.237, b: 16.746, c: 0.693, d: 0.834, Df: 0.16}
-        TRGC:
-                Inputs: {Area: 24044.63, Latitude: 45.50, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.306, b: 91.475, c: 0.695, d: 0.263, Df: 0.727}
-        DAIRY:
-                Inputs: {Area: 59822.75, Latitude: 45.52, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.836, b: 5.536, c: 0.795, d: 0.491, Df: 0.515}
-        RCTV:
-                Inputs: {Area: 19682.60, Latitude: 45.50, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.516, b: 6.632, c: 0.971, d: 0.148, Df: 0.681}
-        WSLO:
-                Inputs: {Area: 47646.84, Latitude: 45.35, XL: 2.0, SnowS: 5.0}
-                Pars: {a: 0.777, b: 307.23, c: 0.453, d: 0.638, Df: 0.159}
-
-
-
-
-

4. Routing

-

HydroCNHS adopts Lohmann routing model to simulate within-subbasin routing and -inter-subbasin routing process. We adpot a nested struture to setup the routing -setting for each routing outlets (Fig. 5), as shown below.

-
Routing:
-        Model: Lohmann
-        # WSLO, TRGC, DLLO, and HaggIn are four routing outlets.
-        WSLO:
-                # TRGC is a routing outlet. No within-subbasin routing at here (at
-                # its own routing setting below). Namely, TRGC will be routed
-                # first. Therefore, GShape and GScale are null.
-                TRGC:
-                        Inputs: {FlowLength: 80064.86, InstreamControl: false}
-                        Pars: {GShape: null, GScale: null, Velo: 29.84, Diff: 2840.985}
-                DAIRY:
-                        Inputs: {FlowLength: 70988.16, InstreamControl: false}
-                        Pars: {GShape: 38.652, GScale: 393.641, Velo: 15.281, Diff: 1753.171}
-                RCTV:
-                        Inputs: {FlowLength: 60398.68, InstreamControl: false}
-                        Pars: {GShape: 55.996, GScale: 751.942, Velo: 2.973, Diff: 2573.258}
-                # WSLO is the routing outlet itself. No river routing is needed
-                # since the FlowLength is 0. Therefore, Velo and Diff are null.
-                WSLO:
-                        Inputs: {FlowLength: 0, InstreamControl: false}
-                Pars: {GShape: 1.026, GScale: 119.961, Velo: null, Diff: null}
-        TRGC:
-                # DLLO is a routing outlet. No within-subbasin routing at here (at
-                # its own routing setting below). Namely, DLLO will be routed
-                # first. Therefore, GShape and GScale are null.
-                DLLO:
-                        Inputs: {FlowLength: 11748.21, InstreamControl: false}
-                        Pars: {GShape: null, GScale: null, Velo: 15.178, Diff: 2382.476}
-                # TRGC is the routing outlet itself. No river routing is needed
-                # since the FlowLength is 0. Therefore, Velo and Diff are null.
-                TRGC:
-                        Inputs: {FlowLength: 0, InstreamControl: false}
-                        Pars: {GShape: 3.823, GScale: 694.564, Velo: null, Diff: null}
-        DLLO:
-                # ResAgt is a reservoir agent. There is no within-subbasin routing.
-                # Its release flow is the streamflow at this spot. Therefore,
-                # GShape and GScale are null.
-                ResAgt:
-                        Inputs: {FlowLength: 9656.06, InstreamControl: true}
-                        Pars: {GShape: null, GScale: null, Velo: 40.546, Diff: 2250.142}
-                TRTR:
-                        Inputs: {FlowLength: 30899.40, InstreamControl: false}
-                        Pars: {GShape: 50.454, GScale: 444.699, Velo: 27.168, Diff: 3625.861}
-                # DLLO is the routing outlet itself. No river routing is needed
-                # since the FlowLength is 0. Therefore, Velo and Diff are null.
-                DLLO:
-                        Inputs: {FlowLength: 0, InstreamControl: false}
-                        Pars: {GShape: 2.505, GScale: 85.004, Velo: null, Diff: null}
-        HaggIn:
-                # HaggIn is the routing outlet itself. No river routing is needed
-                # since the FlowLength is 0. Therefore, Velo and Diff are null
-                HaggIn:
-                        Inputs: {FlowLength: 0, InstreamControl: false}
-                        Pars: {GShape: 49.412, GScale: 1.774, Velo: null, Diff: null}
-
-
-
-
-

5. ABM (optional)

-

ABM section is a highly customized setting section. Users will assign each -active agent type class to corresponding APIs. After that, users will define -agents created by each agent type class in a nested structure.

-
ABM:
-        Inputs:
-                # Assign user-defined agent classes to corresponding APIs. Here, we
-                # defined three agent classes in TRB_ABM_dm.py: ResDam_AgType
-                # (reservoir), IrrDiv_AgType (diversion), and Pipe_AgType
-                # (trans-basin conveying water).
-                DamAgentTypes: [ResDam_AgType]          # Dam API
-                RiverDivAgentTypes: [IrrDiv_AgType]     # RiverDiv API
-                InsituAgentTypes: [Drain_AgType]        # InSitu API
-                ConveyAgentTypes: [Pipe_AgType]         # Conveying API
-                # Activate user-defined decision-making classes in TRB_ABM_dm.py.
-                DMClasses: [ResDM, DivDM, PipeDM]
-                # User-defined ABM module, TRB_ABM_dm.py.
-                Modules: [TRB_ABM_dm.py]
-                # Agent group is for agents make decisions and act together.
-                AgGroup: null
-        Pipe_AgType:    # Agent type class's name.
-                PipeAgt:        # Agent created by the above agent type class.
-                Attributes: {}
-                Inputs:
-                        Piority: 0
-                        Links: {TRTR: 1}
-                        DMClass: PipeDM
-                Pars: null
-        ResDam_AgType:
-                ResAgt:
-                Attributes: {}
-                Inputs:
-                        Piority: 0
-                        Links: {HaggIn: -1, ResAgt: 1}
-                        DMClass: ResDM
-                Pars: null
-        IrrDiv_AgType:
-                DivAgt:
-                Attributes: {}
-                Inputs:
-                        Piority: 1
-                        Links:
-                        TRGC: -1
-                        WSLO: [ReturnFactor, 0, Plus]
-                        DMClass: DivDM
-                Pars:
-                        ReturnFactor: [0.12]
-                        a: 0.52
-                        b: -0.04
-        Drain_AgType:
-                DrainAgt1:
-                Attributes: {}
-                Inputs:
-                        Piority: 1
-                        Links:
-                        RCTV: 1
-                        DMClass: null
-                Pars: null
-                DrainAgt2:
-                Attributes: {}
-                Inputs:
-                        Piority: 1
-                        Links:
-                        WSLO: 1
-                        DMClass: null
-                Pars: null
-
-
-

For the “Inputs” setting of ABM section, first, we assign the user-defined -agent classes (defined in ABM modules) to corresponding coupling APIs. Then, -we ativate the decision-making classes (defined in ABM modules) if any. Next, -we assign ABM module(s), where agent classes and decision-making classes are -defined. Finally, agent group is for agents make decisions and act together. -For example, two diversion agents make diversion requests together and share -the water deficiency together based on their water rights. Namely, their -diversion behaviors are not piority-based. The agent group will be defined as -a single function in a ABM module, which users can define a more detailed -interactions among agents in an agent group. See -How to build a ABM module? for more details.

-

Following the “Inputs” setting, we will define agent objects created by certain -agent classes (defined in ABM modules). For example, we create PipeAgt agent -using Pipe_AgType class. Under each agent object (e.g., PipeAgt), it has three -sub-sections: “Attributes”, “Inputs”, and “Pars.”

-

a) Inputs are required information including “Piority”, “Links”, and “DMClass.”

-
    -
  • Piority:

  • -
-

The lower value has higher piority when conflicts happen. For example, two -diverion agents divert at the same routing outlet. If users want a -non-priority-based behaviors. “AgGroup” should be applied. See -How to build a ABM module? for more details.

-

Note that agents coupling with Dam API has to have Piority = 0.

-
    -
  • Links:

  • -
-

“Links” is a dictionary containing information of which outlets for agent to -take/add water from/to (e.g., {<outlet>: factor}). The positive factor -means add the water to that outlet. The negative number factor take water -from that outlet. The “factor” is a number in a range of [-1,1] defining the -portion of the agent’s decision to be implemented at this specific outlet. For -example, an irrigation diversion agent divert from a outlet but reture to b -and c outlets (i.e., subbasins) with the ratios, 0.3 and 0.7. Then, we have

-
Links: {a: -1, b: 0.3, c: 0.7}
-
-
-

Assuming the diversion request is 10, the actual diversion is also 10 (i.e., no -deficiency), and return flow coefficent is 0.5, we have

-
-\[Flow_{a,new} = Flow_{a,org} + factor_a \times 10 = Flow_{a,org} -1 \times 10\]
-
-\[Flow_{return} = 0.5 \times 10 = 5\]
-
-\[Flow_{b,new} = Flow_{b,org} + factor_b \times 5 = Flow_{b,org} + 0.3 \times 5\]
-
-\[Flow_{c,new} = Flow_{c,org} + factor_c \times 5 = Flow_{c,org} + 0.7 \times 5\]
-

If the “factor” is a calibrated parameter (i.e., an unknown). We can link it to -a parameter by [parameter name, its index, Plus/Minus]. The parameter has to -be a list format. For example, if Links = {WSLO: [ReturnFactor, 0, Plus]}, -the factor will be extracted from ReturnFactor parameter (a list) at index 0. -“Plus” will tell the program that we add water to WSLO (“Minus” means divert -from the given outlet).

-
    -
  • DMClass:

  • -
-

This is optional. If there is no specific decision-making class to be assigned, -put “null” instead. Namely, users can code everything in “agent type class.” See -How to build a ABM module? for more details.

-

b) Pars collects agents’ parameters for calibration. We -offer two types of parameter formats:

-
    -
  1. A constant (e.g., 9), or

  2. -
  3. A list of constants (e.g., [9, 4.5]).

  4. -
-

c) Attributes is a space for users to store any other information for their -agents’ calculation that is not belong to “Pars” or “Inputs.”

-
-
-

6. SystemParsedData (auto-generated)

-

This section will be automatically generated by HydroCNHS. The model file don’t -need to include this section.

-
-

Note

-

ModelBuilder can help you to create an initial model -template! Check it out!

-
-
-
-
-

ABM module(s)

-

Agent-based model (ABM) is an user-provided human model. HydroCNHS support -multiple ABM modules to be used at a single simulation. In the ABM module, -users have 100% of freedom to design agent class (e.g., irrigation diversion -agent class, reservoir agent class, etc.); however, some input and output -protocals have to be followed. Please visit -How to build a ABM module? for more detailed -instructions.

-
-

Note

-

If you only need a hydrological model and do not require any human -components, then you can skip this ABM part!

-
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\ No newline at end of file diff --git a/docs/_build/html/source/HydroCNHS.calibration.html b/docs/_build/html/source/HydroCNHS.calibration.html deleted file mode 100644 index bf4f070..0000000 --- a/docs/_build/html/source/HydroCNHS.calibration.html +++ /dev/null @@ -1,396 +0,0 @@ - - - - - - HydroCNHS.calibration package — HydroCNHS documentation - - - - - - - - - - - - - - -
- - -
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HydroCNHS.calibration package

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Submodules

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HydroCNHS.calibration.convertor module

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-
-class HydroCNHS.calibration.convertor.Convertor
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Bases: object

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-gen_cali_inputs(wd, df_list, par_bound_df_list, fixed_par_list=None)
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Generate inputs dictionary required for calibration.

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wdstr

Working directory.

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-
df_listlist

A list of parameter dataframes. Dataframe index is parameter names.

-
-
par_bound_df_listlist

A list of parameter bound dataframes.

-
-
fixed_par_listlist, optional

A list contains a list of tuples of fixed parameter location like -[([“CN2”], [“outlet1”, “outlet2”]), ([“b”], [“outlet1”])], by -default None. The number of tuples in the list is corresponding to -the number of dataframes.

-
-
-
-
dict

Input for calibration.

-
-
-
- -
-
-gen_formatter(df_list, fixed_par_list=None)
-

Generate formatter for given list of dataframe objects. This is -already included in gen_cali_inputs().

-
-
df_listlist

A list of parameter dataframes. Dataframe index is parameter names.

-
-
fixed_par_listlist, optional

A list contains a list of tuples of fixed parameter location. For -example, we want to fix par1 of outlet1 & 2 and all par2 in -dataframe 1 and not to fix df2. We can input the following list, -[[([“par1”], [“outlet1”, “outlet2”]), ([“par2”], [“:”])], []]. -fixed_par_list is by default None. The number of lists in the list -is corresponding to the number of dataframes.

-
-
-
-
dict

formatter.

-
-
-
- -
-
-static to_1D_array(df_list, formatter)
-

Convert a list of dataframes to a 1D array.

-
-
df_listlist

A list of parameter dataframes. Dataframe index is parameter names.

-
-
formatterdict

Formatter, generated by gen_formatter or gen_cali_inputs. It is -stored in attributes of the Convertor object.

-
-
-
-
array

1D array.

-
-
-
- -
-
-static to_df_list(var_array, formatter)
-

Convert 1D array back to a list of dataframes.

-
-
var_arrayarray

1D array.

-
-
formatterdict

Formatter, generated by gen_formatter or gen_cali_inputs. It is -stored in attributes of the Convertor object.

-
-
-
-
list

A list of dataframes.

-
-
-
- -
- -
-
-

HydroCNHS.calibration.ga_deap module

-
-
-class HydroCNHS.calibration.ga_deap.GA_DEAP(evaluation_func=<function GA_DEAP.<lambda>>, rn_gen=None)
-

Bases: object

-
-
-auto_save()
-
- -
-
-find_best_and_record(pop)
-
- -
-
-gen_rn_gens(seed, size)
-
- -
-
-load(GA_auto_save_file, max_gen=None)
-

Load save pickle file (i.e., continue previous run).

-
-
GA_auto_save_filestr

Filename.

-
-
max_genint, optional

This allow user to increase max_gen for continuing calibration for -a longer searching, by default None.

-
-
-
- -
-
-run(guess_pop=None)
-

Run calibration.

-
-
guess_pop2darray, optional

Assigned initial guesses, by default None. guess_pop has the size = -[number of guesses, number of parameters]

-
-
-
- -
-
-run_individual(individual='best', name='best')
-

Run the evaluation for a given individual.

-

Warning! run_individual() does not generantee the same rn_gen will be -assign to the evaluation, but the same one will be used for -run_individual()

-
-
individual1darray, optional

Individual or solution, by default “best”.

-
-
namestr, optional

This will be sent to the evaluation function through info = -(cali_wd, name, name, formatter, rn_gen), by default “best”.

-
-
-
- -
-
-set(inputs, config, formatter=None, name='Calibration')
-

Setup the GA calibration.

-
-
inputsdict

Calibration input dictionary generated by Convertor. Or, get the -template by calling get_inputs_template().

-
-
configdict

Calibration configuration dictionary. Get the template by calling -get_config_template().

-
-
formatterdict, optional

Formatter generated by Convertor, by default None.

-
-
namestr, optional

Name of the calibration, by default “Calibration”.

-
-
-
- -
- -
-
-HydroCNHS.calibration.ga_deap.cxUniform(ind1, ind2, indpb, rn_gen_gen)
-
- -
-
-HydroCNHS.calibration.ga_deap.descale(individual, bound_scale, lower_bound)
-

individual is 1d ndarray.

-
- -
-
-HydroCNHS.calibration.ga_deap.gen_init_pop(creator, size, method='LHC', guess_pop=None, rn_gen_gen=None)
-
- -
-
-HydroCNHS.calibration.ga_deap.logger = <Logger HydroCNHS.GA (INFO)>
-
-
inputs = {“par_name”: [“a”,”b”,”c”],

“par_bound”: [[0,1],[0,1],[0,1]], -“wd”: r””}

-
-
config = {“min_or_max”: “min”,

“pop_size”: 100, -“num_ellite”: 1, -“prob_cross”: 0.5, -“prob_mut”: 0.1, -“stochastic”: False, -“max_gen”: 100, -“sampling_method”: “LHC”, -“drop_record”: False, -“paral_cores”: -1, -“paral_verbose”: 1, -“auto_save”: True, -“print_level”: 10, -“plot”: True -}

-
-
-
- -
-
-HydroCNHS.calibration.ga_deap.mut_middle(individual, p1, p2, prob_mut, rn_gen_gen)
-
- -
-
-HydroCNHS.calibration.ga_deap.mut_uniform(individual, prob_mut, rn_gen_gen)
-
- -
-
-HydroCNHS.calibration.ga_deap.sample_by_LHC(size, rn_gen_gen)
-
- -
-
-HydroCNHS.calibration.ga_deap.sample_by_MC(size, rn_gen_gen)
-
- -
-
-HydroCNHS.calibration.ga_deap.scale(individual, bound_scale, lower_bound)
-

individual is 1d ndarray.

-
- -
-
-HydroCNHS.calibration.ga_deap.selRoulette(individuals, k, rn_gen_gen, fit_attr='fitness')
-
- -
-
-

Module contents

-
-
-HydroCNHS.calibration.get_config_template()
-
- -
-
-HydroCNHS.calibration.get_inputs_template()
-
- -
-
-HydroCNHS.calibration.helper()
-
- -
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/source/HydroCNHS.html b/docs/_build/html/source/HydroCNHS.html deleted file mode 100644 index e9cc202..0000000 --- a/docs/_build/html/source/HydroCNHS.html +++ /dev/null @@ -1,1161 +0,0 @@ - - - - - - HydroCNHS package — HydroCNHS documentation - - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

HydroCNHS package

-
-

HydroCNHS.abm module

-
-
-class HydroCNHS.abm.Base(**kwargs)
-

Bases: object

-

Agent_type class’s available items: -* name: agent’s name. -* config: agent’s configuration dictionary the model file (.yaml). -* start_date: datetime object. -* data_length: length of the simulation. -* data_collector: a container to store simulated data. -* rn_gen: random number generator to ensure reproducibility (e.g., -* self.rn_gen.random()). Note that do NOT set a global random seed in -* this module! All type of random number should be created by “rn_gen.” -* dm: decision making object if assigned in the model file (.yaml).

-
-

Decision-making class’s available items:

-
-
    -
  • start_date: datetime object.

  • -
  • data_length: length of the simulation.

  • -
  • abm: the ABM configuration dictionary from the model file (.yaml).

  • -
  • data_collector: a container to store simulated data.

  • -
  • rn_gen: random number generator to ensure reproducibility (e.g.,

  • -
  • self.rn_gen.random()). Note that do NOT set a global random seed in

  • -
  • this module! All type of random number should be created by “rn_gen.

  • -
-
- -
-
-class HydroCNHS.abm.agent
-

Bases: object

-
- -
-
-HydroCNHS.abm.read_factor(ag_config, outlet)
-

Read factor from agent’s ag_config at a given outlet.

-

The function will automatically parse the list-like value assignment. e.g., -[ReturnFactor, 0, Plus].

-
-
ag_configdict

Agent’s configuration.

-
-
outletstr

Outlet name.

-
-
-
- -
-
-

HydroCNHS.abm_script module

-
-
-HydroCNHS.abm_script.add_agt_class(agt_type)
-
- -
-
-HydroCNHS.abm_script.add_dm_class(dm_type, is_institution=False)
-
- -
-
-

HydroCNHS.data_collector module

-
-
-class HydroCNHS.data_collector.Data_collector
-

Bases: object

-
-
-add_field(field, data_type={}, desc=None, unit=None, check_exist=True)
-

Add a field to the data collector.

-

A field can be a dictionary or a list. A Data_collector object -cannot have duplicated field name.

-
-
fieldstr

Field name. Cannot have space in a field name.

-
-
data_typedict, optional

Data type of the field (e.g., {} and []), by default {}. User can -also populate the field by directly assigning data here.

-
-
descstr

Field description.

-
-
unit

Unit of the field.

-
-
check_existbool, optional

If Ture, check the given field name is not existed before adding, -by default True.

-
-
-
- -
-
-del_field(field)
-

Delete a field from the data collector.

-
-
fieldstr

Field name.

-
-
-
- -
-
-get_dict(copy=False)
-

Get data collector in dictionary format.

-

Note that if copy=False, any modification on a variable assigned with -the returned dictionary will also affect the data stored in the data -collector object.

-
-
copystr

If true, a copy of dictionary will be returned, else a pointer will -be returned. Default False.

-
-
-
-
dict

A dictionary contains all fields.

-
-
-
- -
-
-get_field(field, copy=False)
-

Get a field.

-

This function create a shortcut to access a field. Namely, changes of -a local variable of get_field() will be accumulated back to the -original data_collector. copy=Ture if a copy of a field is needed.

-
-
fieldstr

Field name.

-
-
copybool

If true, create a copy of a field, which has seperate storage

-
-
-

pointer than the original data_collector. Otherwise, return a shortcut -of a field to the original data_collector. -Returns -——- -Assigned field type.

-
- -
-
-list_fields()
-

Print available fields in the data collector.

-
- -
- -
-
-

HydroCNHS.hydrocnhs module

-
-
-class HydroCNHS.hydrocnhs.Model(model, name=None, rn_gen=None, checked=False, parsed=False, log_filename=None, paral_setting={'cores_formUH': - 1, 'cores_pet': - 1, 'cores_runoff': - 1, 'verbose': 0})
-

Bases: object

-
-
-get_model_object()
-

Get the model object in a dictionary form.

-
-
dict

model object dictionary.

-
-
-
- -
-
-load_weather_data(temp, prec, pet=None, outlets=[])
-

Load temperature, precipitation, and otential evapotranpiration data.

-
-
tempdict

[degC] Daily mean temperature time series data (value) for each -subbasin named by its outlet. E.g., {“subbasin1”:[…], -“subbasin2”:[…]}

-
-
precdict

[cm] Daily precipitation time series data (value) for each -subbasin named by its outlet. E.g., {“subbasin1”:[…], -“subbasin2”:[…]}

-
-
petdict, optional

[cm] Daily potential evapotranpiration time series data (value) for -each subbasin named by its outlet, by default None. E.g., -{“subbasin1”:[…], “subbasin2”:[…]}

-
-
outletslist, optional

Outlets that need weather data for rainfall-runoffs simulation.

-
-
-
- -
-
-run(temp, prec, pet=None, assigned_Q={}, assigned_UH={}, disable=False)
-

Run HydroCNHS simulation.

-
-
tempdict

[degC] Daily mean temperature.

-
-
precdict

[cm] Daily precipitation.

-
-
petdict, optional

[cm] Potential evapotranspiration, by -default None. If none, pet is calculted by Hamon’s method.

-
-
assigned_Qdict, optional

[cms] If user want to manually assign Q for certain outlets -{“outlet”: array}, by default {}.

-
-
assigned_UHdict, optional

If user want to manually assign UH (Lohmann) for certain outlet -{“outlet”: array}, by default {}.

-
-
disablebool, optional

Disable display progress bar, by default False.

-
-
-
-
dict

A dictionary of flow time series.

-
-
-
- -
- -
-
-

HydroCNHS.indicators module

-
-
-class HydroCNHS.indicators.Indicator
-

Bases: object

-
-
-static CP(x_obv, y_sim, r_na=False)
-

Correlation of persistence.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Correlation of persistence.

-
-
-
- -
-
-static KGE(x_obv, y_sim, r_na=True)
-

Kling–Gupta efficiency.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Kling–Gupta efficiency.

-
-
-
- -
-
-static NSE(x_obv, y_sim, r_na=False)
-

Nash–Sutcliffe efficiency.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Nash–Sutcliffe efficiency.

-
-
-
- -
-
-static RSR(x_obv, y_sim, r_na=False)
-

RMSE-observations standard deviation ratio.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

RMSE-observations standard deviation ratio.

-
-
-
- -
-
-static cal_indicator_df(x_obv, y_sim, index_name='value', indicators_list=None, r_na=True)
-
- -
-
-static iKGE(x_obv, y_sim, r_na=True)
-

Inverse Kling–Gupta efficiency.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Inverse Kling–Gupta efficiency.

-
-
-
- -
-
-static iNSE(x_obv, y_sim, r_na=False)
-

Inverse Nash–Sutcliffe efficiency.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Inverse Nash–Sutcliffe efficiency.

-
-
-
- -
-
-static r(x_obv, y_sim, r_na=True)
-

Correlation.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

r coefficient.

-
-
-
- -
-
-static r2(x_obv, y_sim, r_na=True)
-

Coefficient of determination.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

r2 coefficient.

-
-
-
- -
-
-static remove_na(x_obv, y_sim)
-

Remove nan in x_obv and y_sim.

-

This function makes sure there is no nan involves in the indicator -calculation. If nan is detected, data points will be remove from x_obv -and y_sim simultaneously.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
-
-
tuple

Updated (x_obv, y_sim)

-
-
-
- -
-
-static rmse(x_obv, y_sim, r_na=False)
-

Root mean square error.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
r_nabool, optional

Remove nan, by default True

-
-
-
-
float

Root mean square error.

-
-
-
- -
- -
-
-

HydroCNHS.model_builder module

-
-
-class HydroCNHS.model_builder.ModelBuilder(wd, model=None)
-

Bases: object

-
-
-add_agent(agt_type_class, agt_name, api, priority=1, link_dict={}, dm_class=None, par_dict={}, attr_dict={})
-

Add agent.

-
-
agt_type_classstr

Assigned agent type class.

-
-
agt_namestr

Agent name.

-
-
apistr

The API to integrate the agent to the HydroCNHS. -e.g., mb.Dam.

-
-
priorityint, optional

Priority of the agent if conflicts occur, by default 1.

-
-
link_dictdict, optional

Linkage dictionary, by default {}.

-
-
dm_classstr, optional

Assigned decision-making class, by default None

-
-
par_dictdict, optional

Parameter dictionary, by default {}

-
-
attr_dictdict, optional

Attribution dictionary, by default {}

-
-
-
- -
-
-add_institution(institution, instit_dm_class, agent_list)
-

Add a institution.

-
-
institutionstr

Institution name.

-
-
instit_dm_classstr

Assigned institutional decision-making class.

-
-
agent_listlist

Agent member list of the institute.

-
-
-
- -
-
-add_sediment(subbasin, area_list, cool_months, K_list, Ac, Aw, LS_list, DR, id_list=None, CP=- 99, Sa=- 99, Sb=- 99, Sq=- 99)
-
- -
-
-gen_ABM_module_template()
-

Generate ABM module template based on the ABM setting.

-
- -
-
-help()
-
- -
-
-print_model(indentor='  ', level=1)
-

Print model to the console

-
-
indentorstr, optional

Indentor, by default ” “.

-
-
levelint, optional

Print out level of a nested dictionary, by default 1.

-
-
-
- -
-
-set_ABM(abm_module_folder_path=None, abm_module_name='ABM_module.py')
-

Set up ABM

-
-
abm_module_folder_pathstr, optional

Folder directory of ABM modules. It it is not given, working -directory will be assigned, by default None.

-
-
abm_module_namestr, optional

The ABM module name, by default “ABM_module.py”

-
-
-
- -
-
-set_rainfall_runoff(outlet_list, area_list=None, lat_list=None, runoff_model='GWLF')
-

Set up RainfallRunoff.

-
-
outlet_listlist

A list of subbasin outlet names.

-
-
area_listlist, optional

Area [ha] list corresponding to outlet_list, by default None.

-
-
lat_liststr, optional

Latitude [deg] list corresponding to outlet_list, by default None.

-
-
runoff_modelstr, optional

“GWLF” or “ABCD” or “Other”, by default None.

-
-
Note

If “Other” is selected for runoff_model, users must provide -precalculated runoffs for each subbasin as an input to HydroCNHS.

-
-
-
- -
-
-set_routing_outlet(routing_outlet, upstream_outlet_list, instream_objects=[], flow_length_list=None, routing_model='Lohmann')
-

Set up a routing outlet.

-
-
routing_outletstr

Name of routing outlet. routing_outlet should be one of outlets in -RainfallRunoff.

-
-
upstream_outlet_listlist

A list of outlets or dam agents that contribute to the streamflow -at routing_outlet.

-
-
instream_objectslist, optional

A list of instream objects’ names (i.e., dam agents), by default [].

-
-
flow_length_listlist, optional

A list of flow lengths. The order has to consist to the -upstream_outlet_list.

-
-
routing_modellist, optional

Routing model, by default “Lohmann”.

-
-
-
- -
-
-set_sediment(start_month)
-
- -
-
-set_water_system(start_date, end_date)
-

Set up WaterSystem.

-
-
start_datestr

“yyyy/mm/dd”

-
-
end_datestr

“yyyy/mm/dd”

-
-
-
- -
-
-write_model_to_yaml(filename)
-

Output model configuration file (.yaml)

-
-
filenamestr

Filename

-
-
-
- -
- -
-
-

HydroCNHS.routing module

-
-
-HydroCNHS.routing.form_UH_Lohmann(inputs, routing_pars, force_ingrid_off=False)
-

Derive HRU’s UH at the (watershed) outlet.

-
-
inputsdict

Inputs dictionary containing FlowLength [m] Travel distence of flow -between two outlets [float] and InstreamControl [bool].

-
-
routing_parsdict

Four parameters for routing: GShape, GScale, Velo, Diff [float]

-
-
force_ingrid_offbool, optional

If True, then within subbasin routing will be forced to turn off, by default -False.

-
-
-
- -
-
-HydroCNHS.routing.run_step_Lohmann(routing_outlet, routing, UH_Lohmann, Q, Q_runoff, t, *args)
-

Calculate a single time step routing for a given routing_outlet at time t.

-
-
routing_outletstr

routing outlet.

-
-
routingdict

Routing setting dictionary from model.yaml file.

-
-
UH_Lohmanndict

A dictionary containing pre-formed UHs.

-
-
Qdict

A dictionary containing newest routed flows.

-
-
Q_runoffdict

A dictionary containing newest unrouted flows without.

-
-
tint

Index of current time step [day].

-
-
-
-
float

Routed flow of routing_outlet at time t.

-
-
-
- -
-
-HydroCNHS.routing.run_step_Lohmann_convey(routing_outlet, routing, UH_Lohmann_convey, Q_convey, t, *args)
-

Calculate a single time step conveying water routing for a given -routing_outlet at time t.

-
-
routing_outletstr

routing outlet.

-
-
routingdict

Routing setting dictionary from model.yaml file.

-
-
UH_Lohmann_conveydict

A dictionary containing pre-formed conveying UHs (i.e., no within -subbasin routing).

-
-
Q_conveydict

A dictionary containing conveying water.

-
-
tint

Index of current time step [day].

-
-
-
-
float

Routed conveying flow of routing_outlet at time t.

-
-
-
- -
-
-HydroCNHS.routing.run_step_Lohmann_convey_sed(routing_outlet, routing, UH_Lohmann_convey, Q_convey, t, Q_frac)
-

Calculate a single time step conveying water routing for a given -routing_outlet at time t.

-
-
routing_outletstr

routing outlet.

-
-
routingdict

Routing setting dictionary from model.yaml file.

-
-
UH_Lohmann_conveydict

A dictionary containing pre-formed conveying UHs (i.e., no within -subbasin routing).

-
-
Q_conveydict

A dictionary containing conveying water.

-
-
tint

Index of current time step [day].

-
-
-
-
float

Routed conveying flow of routing_outlet at time t.

-
-
-
- -
-
-HydroCNHS.routing.run_step_Lohmann_sed(routing_outlet, routing, UH_Lohmann, Q, Q_runoff, t, Q_frac)
-

Calculate a single time step routing for a given routing_outlet at time t.

-
-
routing_outletstr

routing outlet.

-
-
routingdict

Routing setting dictionary from model.yaml file.

-
-
UH_Lohmanndict

A dictionary containing pre-formed UHs.

-
-
Qdict

A dictionary containing newest routed flows.

-
-
Q_runoffdict

A dictionary containing newest unrouted flows without.

-
-
tint

Index of current time step [day].

-
-
-
-
float

Routed flow of routing_outlet at time t.

-
-
-
- -
-
-

HydroCNHS.util module

-
-
-HydroCNHS.util.check_RainfallRunoff(model_dict)
-
- -
-
-HydroCNHS.util.check_WS(model_dict)
-
- -
-
-HydroCNHS.util.check_agent_in_routing(model_dict)
-
- -
-
-HydroCNHS.util.check_model(model_dict)
-

Check the model dictionary

-
-
model_dictdict

Model dictionary

-
-
-
-
bool

True if pass the check.

-
-
-
- -
-
-HydroCNHS.util.create_rn_gen(seed)
-

Create random number generator based on numpy module.

-
-
seedint

Random seed.

-
-
-
-
object

Random number generator.

-
-
-
- -
-
-HydroCNHS.util.dict_to_string(dictionary, indentor='  ', level=1)
-

Ture a dictionary into a printable string.

-
-
dictionarydict

A dictionary.

-
-
indentorstr, optional

Indentor, by default ” “

-
-
-
-
str

String.

-
-
-
- -
-
-HydroCNHS.util.form_sim_seq(node_list, back_tracking_dict)
-
- -
-
-HydroCNHS.util.gen_default_bounds(model_dict, key_option=['Pars'])
-

Generate default parameter bounds in the format of a list of -DataFrames.

-
-
model_dictdict

Model dictionary.

-
-
key_optionlist, optional

Output sections (e.g., Pars, Inputs, Attributes), by default [“Pars”].

-
-
-
-
tuple

A list of parameter bound DataFrames, A list of DataFrame names.

-
-
-
- -
-
-HydroCNHS.util.list_callable_public_object(obj)
-
- -
-
-HydroCNHS.util.load_customized_module_to_class(Class, module_name, path)
-

Load classes and functions in a user defined module (.py) into a given -Class.

-
-
Classclass

A class to collect classes and functions in a given module.

-
-
module_namestr

filename.py or filename.

-
-
pathstr

Directory of filename.py.

-
-
-
- -
-
-HydroCNHS.util.load_df_to_model_dict(model_dict, df, section, key)
-

Load dataframe to model dictionary.

-

Note that the column and index names have to be identical as outputted from -write_model_to_df() function.

-
-
model_dictdict

Model dictionary.

-
-
dfDataFrame

Dataframe.

-
-
sectionstr

RainfallRunoff or Routing or ABM.

-
-
keystr

nputs or Pars or Attributes.

-
-
-
-
dict

Updated model_dict.

-
-
-
- -
-
-HydroCNHS.util.load_model(model, checked=False, parsed=False, print_summary=True)
-

Load model.yaml or model dictionary.

-
-
modelstr/dict

Filename (e.g., model.yaml) or model dictionary.

-
-
checkedbool, optional

If True, no pre-check will be implemented, by default False.

-
-
parsedbool, optional

If True, model will not be re-parses, by default False.

-
-
print_summarybool, optional

Print the summary of loaded model, by default True.

-
-
-
-
dict

Model dictionary.

-
-
-
- -
-
-HydroCNHS.util.parse_model(model_dict, print_summary=True)
-

Parse model dictionary. Populate SystemParsedData.

-
-
Args:

model_dict (dict): Load from model.yaml.

-
-
Returns:

dict: model_dict

-
-
-
- -
-
-HydroCNHS.util.parse_sim_seq(model_dict, print_summary=True)
-
- -
-
-HydroCNHS.util.set_logging_config(log_filename=None)
-

Set up logging config.

-
-
log_filenamestr, optional

Log filename, by default None

-
-
-
- -
-
-HydroCNHS.util.update_sim_seq_with_group(sim_seq, group, back_tracking_dict)
-
- -
-
-HydroCNHS.util.write_model(model_dict, filename, org_model=None)
-

Write model dictionary to .yaml file

-

If org_model is given, comments in the original file will be kept in the -output model file.

-
-
model_dictdict

Model dictionary.

-
-
filenamestr

Model configuration filename (e.g. model.yaml).

-
-
org_modelstr, optional

Original model name (e.g. org_model.yaml), by default None.

-
-
-
-
Common error :
    -
  1. value type is numpy.float64 => convert it to float.

  2. -
-
-
-
- -
-
-HydroCNHS.util.write_model_to_csv(folder_path, model_dict, key_option=['Pars'], prefix='')
-

Write model (dictionary) to csv files.

-
-
folder_pathstr

Output folder directory.

-
-
model_dictdict

Model dictionary.

-
-
key_optionlist, optional

Output sections (e.g., Pars, Inputs, Attributes), by default [“Pars”].

-
-
prefixstr, optional

Prefix for the filenames, by default “”.

-
-
-
-
list

A list of filenames.

-
-
-
- -
-
-HydroCNHS.util.write_model_to_df(model_dict, key_option=['Pars'], prefix='')
-

Write model dictionary to dataframes given key options.

-

This function will convert model dictionary to dataframes according to -user’s specified sections (key_option).

-
-
model_dictdict

Model dictionary.

-
-
key_optionlist, optional

Output sections (e.g., Pars, Inputs, Attributes), by default [“Pars”].

-
-
prefixstr, optional

Prefix for the filenames, by default “”.

-
-
-
- -
-
-

HydroCNHS.visual module

-
-
-class HydroCNHS.visual.Visual
-

Bases: object

-

Collection of some plotting functions.

-
-
-static plot_reg(x_obv, y_sim, title=None, xy_labal=None, same_xy_limit=True, return_reg_par=False, save_fig_path=None, show=True)
-

Plot regression.

-
-
x_obvarray

Observation data.

-
-
y_simarray

Simulation data.

-
-
titlestr, optional

Title, by default None.

-
-
xy_laballist, optional

List of x and y labels, by default None.

-
-
same_xy_limitbool, optional

If True same limit will be applied to x and y axis, by default True.

-
-
return_reg_parbool, optional

If True, slope and interception will be return, by default False.

-
-
save_fig_pathstr, optional

If given, plot will be save as .png, by default None.

-
-
showbool, optional

If True, the plot will be shown in the console, by default True.

-
-
-
-
ax or list

axis object or [slope, intercept].

-
-
-
- -
-
-static plot_simple_ts(df, title=None, xy_labal=None, data_dots=True, save_fig_path=None, **kwargs)
-

Plot timeseries.

-
-
dfDataFrame

Dataframe.

-
-
titlestr, optional

Title, by default None.

-
-
xy_laballist, optional

List of x and y labels, by default None.

-
-
data_dotsbool, optional

If Ture, show data marker, by default True.

-
-
save_fig_pathstr, optional

If given, plot will be save as .png, by default None.

-
-
-
-
object

axis object.

-
-
-
- -
-
-static plot_timeseries(x_obv, y_sim, xticks=None, title=None, xy_labal=None, save_fig_path=None, legend=True, show=True, **kwargs)
-

Plot timeseries.

-

This function can plot two DataFrames with same column names.

-
-
x_obvarray/DataFrame

Observation data.

-
-
y_simarray/DataFrame

Simulation data.

-
-
xtickslist, optional

Ticks for x-axis, by default None.

-
-
titlestr, optional

Title, by default None.

-
-
xy_laballist, optional

List of x and y labels, by default None.

-
-
save_fig_pathstr, optional

If given, plot will be save as .png, by default None.

-
-
legendbool, optional

If True, plot legend, by default None.

-
-
showbool, optional

If True, the plot will be shown in the console, by default True.

-
-
kwargsoptional

Other keywords for matplotlib.

-
-
-
-
object

axis object.

-
-
-
- -
- -
-
-

Module contents

-
- -
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/source/HydroCNHS.rainfall_runoff_model.html b/docs/_build/html/source/HydroCNHS.rainfall_runoff_model.html deleted file mode 100644 index f75f260..0000000 --- a/docs/_build/html/source/HydroCNHS.rainfall_runoff_model.html +++ /dev/null @@ -1,250 +0,0 @@ - - - - - - HydroCNHS.rainfall_runoff_model package — HydroCNHS documentation - - - - - - - - - - - - - -
- - -
- -
-
-
- -
-
-
-
- -
-

HydroCNHS.rainfall_runoff_model package

-
-

Submodules

-
-
-

HydroCNHS.rainfall_runoff_model.abcd module

-
-
-HydroCNHS.rainfall_runoff_model.abcd.run_ABCD(pars, inputs, temp, prec, pet, vars=None, **kwargs)
-

ABCD rainfall-runoff model.

-
-
Inputs:

Area: [ha] Subbasin area. -XL: [cm] Initial saturated soil water content. -Latitude: [deg] -SnowS: [cm] Snow storage.

-
-
Pars:

a: Controls the amount of runoff and recharge during unsaturated soil. -b: Controls Saturation level of the soils. -c: Ratio of groundwater recharge to runoff. -d: Controls groundwater discharge rate. -Df: [cm/degC] Degree-day coefficient

-
-
-
-
parsdict

Parameter dictionary containing 5 parameters: a, b, c, d, Df.

-
-
inputsdict

Input dictionary containing 4 inputs: Area, Latitude, XL, SnowS.

-
-
temparray

[degC] Daily mean temperature.

-
-
precarray

[cm] Daily precipitation.

-
-
petarray

[cm] Daily potential evaportranspiration.

-
-
varsint, optional

Initial values.

-
-
-
-
array

[cms] Discharge

-
-
-
- -
-
-

HydroCNHS.rainfall_runoff_model.gwlf module

-
-
-HydroCNHS.rainfall_runoff_model.gwlf.run_GWLF(pars, inputs, temp, prec, pet, monthly_Tavg, vars=None, **kwargs)
-

GWLF rainfall-runoff model.

-
-
Inputs:

Area: [ha] Subbasin area. -Latitude: [deg] -S0: [cm] Shallow saturated soil water content. E.g., 10 -U0: [cm] Unsaturated soil water content. E.g., 10 -SnowS: [cm] Snow storage. E.g., 5

-
-
Pars:

CN2: Curve number. -IS: Interception coefficient. -Res: Recession coefficient. -Sep: Deep seepage coefficient. -Alpha: Baseflow coefficient. -Beta: Percolation coefficient. -Ur: [cm] Available/Soil water capacity. -Kc: Land cover coefficient. -Df: [cm/degC] Degree-day coefficient.

-
-
-

Note that the simulation period has to be longer than a month.

-
-
parsdict

Parameter dictionary containing 9 parameters: CN2, IS, Res, Sep, Alpha, -Beta, Ur, Kc, Df.

-
-
inputsdict

Input dictionary containing 5 inputs: Area, Latitude, S0, U0, SnowS.

-
-
temparray

[degC] Daily mean temperature.

-
-
precarray

[cm] Daily precipitation.

-
-
petarray

[cm] Daily potential evaportranspiration.

-
-
monthly_Tavg: str

Monthly average temperature in a daily format.

-
-
varsint, optional

Initial values.

-
-
-
-
array

[cms] Discharge

-
-
-
- -
-
-

HydroCNHS.rainfall_runoff_model.pet_hamon module

-
-
-HydroCNHS.rainfall_runoff_model.pet_hamon.cal_pet_Hamon(temp, Lat, start_date, dz=None)
-

Calculate potential evapotranspiration (pet) with Hamon (1961) equation.

-
-
temparray

[degC] Daily mean temperature.

-
-
Latfloat

[deg] Latitude.

-
-
start_datestr

Start date “yyyy/mm/dd”.

-
-
dzfloat, optional

[m] Altitude temperature adjustment, by default None.

-
-
-
-
array

[cm/day] Potential evapotranspiration

-
-
-
- -
-
-

Module contents

-
-
- - -
-
- -
-
-
-
- - - - \ No newline at end of file diff --git a/docs/_build/html/source/modules.html b/docs/_build/html/source/modules.html deleted file mode 100644 index a4005a0..0000000 --- a/docs/_build/html/source/modules.html +++ /dev/null @@ -1,139 +0,0 @@ - - - - - - HydroCNHS — HydroCNHS documentation - - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 40119fa..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,55 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# -import os -import sys -sys.path.insert(0, os.path.abspath('..')) # To include sub-modules - - -# -- Project information ----------------------------------------------------- - -project = 'HydroCNHS' -copyright = '2021, Chung-Yi Lin' -author = 'Chung-Yi Lin' - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = ["sphinx.ext.autodoc", - #"nbsphinx", - 'sphinx.ext.autosectionlabel'] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path. -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -numfig = True -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -import sphinx_rtd_theme -html_theme = 'sphinx_rtd_theme' -# html_theme = 'alabaster' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] \ No newline at end of file diff --git a/docs/figs/TRB.png b/docs/figs/TRB.png deleted file mode 100644 index 97976a6..0000000 Binary files a/docs/figs/TRB.png and /dev/null differ diff --git a/docs/figs/fig1_modeling_schema.png b/docs/figs/fig1_modeling_schema.png deleted file mode 100644 index 50715f3..0000000 Binary files a/docs/figs/fig1_modeling_schema.png and /dev/null differ diff --git a/docs/figs/fig2_routing_schema.png b/docs/figs/fig2_routing_schema.png deleted file mode 100644 index 1ff193a..0000000 Binary files a/docs/figs/fig2_routing_schema.png and /dev/null differ diff --git a/docs/figs/fig3_hydrocnhs.png b/docs/figs/fig3_hydrocnhs.png deleted file mode 100644 index d4ce53e..0000000 Binary files a/docs/figs/fig3_hydrocnhs.png and /dev/null differ diff --git a/docs/figs/fig7_ga_fitness.png b/docs/figs/fig7_ga_fitness.png deleted file mode 100644 index e9414f5..0000000 Binary files a/docs/figs/fig7_ga_fitness.png and /dev/null differ diff --git a/docs/make.bat b/docs/make.bat index 8084272..dc1312a 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -7,10 +7,8 @@ REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) -set SOURCEDIR=. -set BUILDDIR=_build - -if "%1" == "" goto help +set SOURCEDIR=source +set BUILDDIR=build %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( @@ -25,6 +23,8 @@ if errorlevel 9009 ( exit /b 1 ) +if "%1" == "" goto help + %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end diff --git a/docs/source/HydroCNHS.calibration.rst b/docs/source/api/hydrocnhs.calibration.rst similarity index 57% rename from docs/source/HydroCNHS.calibration.rst rename to docs/source/api/hydrocnhs.calibration.rst index 71c8517..b300ef5 100644 --- a/docs/source/HydroCNHS.calibration.rst +++ b/docs/source/api/hydrocnhs.calibration.rst @@ -1,21 +1,21 @@ -HydroCNHS.calibration package +hydrocnhs.calibration package ============================= Submodules ---------- -HydroCNHS.calibration.convertor module +hydrocnhs.calibration.convertor module -------------------------------------- -.. automodule:: HydroCNHS.calibration.convertor +.. automodule:: hydrocnhs.calibration.convertor :members: :undoc-members: :show-inheritance: -HydroCNHS.calibration.ga\_deap module +hydrocnhs.calibration.ga\_deap module ------------------------------------- -.. automodule:: HydroCNHS.calibration.ga_deap +.. automodule:: hydrocnhs.calibration.ga_deap :members: :undoc-members: :show-inheritance: @@ -23,7 +23,7 @@ HydroCNHS.calibration.ga\_deap module Module contents --------------- -.. automodule:: HydroCNHS.calibration +.. automodule:: hydrocnhs.calibration :members: :undoc-members: :show-inheritance: diff --git a/docs/source/HydroCNHS.rainfall_runoff_model.rst b/docs/source/api/hydrocnhs.rainfall_runoff_model.rst similarity index 53% rename from docs/source/HydroCNHS.rainfall_runoff_model.rst rename to docs/source/api/hydrocnhs.rainfall_runoff_model.rst index c40b8eb..c5f6dec 100644 --- a/docs/source/HydroCNHS.rainfall_runoff_model.rst +++ b/docs/source/api/hydrocnhs.rainfall_runoff_model.rst @@ -1,29 +1,29 @@ -HydroCNHS.rainfall\_runoff\_model package +hydrocnhs.rainfall\_runoff\_model package ========================================= Submodules ---------- -HydroCNHS.rainfall\_runoff\_model.abcd module +hydrocnhs.rainfall\_runoff\_model.abcd module --------------------------------------------- -.. automodule:: HydroCNHS.rainfall_runoff_model.abcd +.. automodule:: hydrocnhs.rainfall_runoff_model.abcd :members: :undoc-members: :show-inheritance: -HydroCNHS.rainfall\_runoff\_model.gwlf module +hydrocnhs.rainfall\_runoff\_model.gwlf module --------------------------------------------- -.. automodule:: HydroCNHS.rainfall_runoff_model.gwlf +.. automodule:: hydrocnhs.rainfall_runoff_model.gwlf :members: :undoc-members: :show-inheritance: -HydroCNHS.rainfall\_runoff\_model.pet\_hamon module +hydrocnhs.rainfall\_runoff\_model.pet\_hamon module --------------------------------------------------- -.. automodule:: HydroCNHS.rainfall_runoff_model.pet_hamon +.. automodule:: hydrocnhs.rainfall_runoff_model.pet_hamon :members: :undoc-members: :show-inheritance: @@ -31,7 +31,7 @@ HydroCNHS.rainfall\_runoff\_model.pet\_hamon module Module contents --------------- -.. automodule:: HydroCNHS.rainfall_runoff_model +.. automodule:: hydrocnhs.rainfall_runoff_model :members: :undoc-members: :show-inheritance: diff --git a/docs/source/HydroCNHS.rst b/docs/source/api/hydrocnhs.rst similarity index 52% rename from docs/source/HydroCNHS.rst rename to docs/source/api/hydrocnhs.rst index 8cb2fac..e66e247 100644 --- a/docs/source/HydroCNHS.rst +++ b/docs/source/api/hydrocnhs.rst @@ -1,74 +1,94 @@ -HydroCNHS package +hydrocnhs package ================= -HydroCNHS.abm module +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + hydrocnhs.calibration + hydrocnhs.rainfall_runoff_model + +Submodules +---------- + +hydrocnhs.abm module -------------------- -.. automodule:: HydroCNHS.abm +.. automodule:: hydrocnhs.abm :members: :undoc-members: :show-inheritance: -HydroCNHS.abm\_script module +hydrocnhs.abm\_script module ---------------------------- -.. automodule:: HydroCNHS.abm_script +.. automodule:: hydrocnhs.abm_script :members: :undoc-members: :show-inheritance: -HydroCNHS.data\_collector module +hydrocnhs.data\_collector module -------------------------------- -.. automodule:: HydroCNHS.data_collector +.. automodule:: hydrocnhs.data_collector :members: :undoc-members: :show-inheritance: -HydroCNHS.hydrocnhs module +hydrocnhs.hydrocnhs module -------------------------- -.. automodule:: HydroCNHS.hydrocnhs +.. automodule:: hydrocnhs.hydrocnhs :members: :undoc-members: :show-inheritance: -HydroCNHS.indicators module +hydrocnhs.indicators module --------------------------- -.. automodule:: HydroCNHS.indicators +.. automodule:: hydrocnhs.indicators :members: :undoc-members: :show-inheritance: -HydroCNHS.model\_builder module +hydrocnhs.model\_builder module ------------------------------- -.. automodule:: HydroCNHS.model_builder +.. automodule:: hydrocnhs.model_builder :members: :undoc-members: :show-inheritance: -HydroCNHS.routing module +hydrocnhs.routing module ------------------------ -.. automodule:: HydroCNHS.routing +.. automodule:: hydrocnhs.routing :members: :undoc-members: :show-inheritance: -HydroCNHS.util module +hydrocnhs.sediment module +------------------------- + +.. automodule:: hydrocnhs.sediment + :members: + :undoc-members: + :show-inheritance: + +hydrocnhs.util module --------------------- -.. automodule:: HydroCNHS.util +.. automodule:: hydrocnhs.util :members: :undoc-members: :show-inheritance: -HydroCNHS.visual module +hydrocnhs.visual module ----------------------- -.. automodule:: HydroCNHS.visual +.. automodule:: hydrocnhs.visual :members: :undoc-members: :show-inheritance: @@ -76,17 +96,7 @@ HydroCNHS.visual module Module contents --------------- -.. automodule:: HydroCNHS +.. automodule:: hydrocnhs :members: :undoc-members: :show-inheritance: - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - HydroCNHS.calibration - HydroCNHS.rainfall_runoff_model - diff --git a/docs/source/modules.rst b/docs/source/api/modules.rst similarity index 64% rename from docs/source/modules.rst rename to docs/source/api/modules.rst index 454da46..cf6e220 100644 --- a/docs/source/modules.rst +++ b/docs/source/api/modules.rst @@ -1,7 +1,7 @@ -HydroCNHS +hydrocnhs ========= .. toctree:: :maxdepth: 4 - HydroCNHS + hydrocnhs diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..d490405 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,36 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'hydrocnhs' +copyright = '2024, Chung-Yi Lin' +author = 'Chung-Yi Lin' +release = 'v1.1.2' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration +import os +import sys +sys.path.insert(0, os.path.abspath('../..')) +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.napoleon', + 'sphinx_autodoc_typehints', + 'sphinx.ext.autosectionlabel', + 'nbsphinx', +] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +numfig = True + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'sphinx_rtd_theme' +html_static_path = ['_static'] diff --git a/docs/figs/Logo.png b/docs/source/figs/Logo.png similarity index 100% rename from docs/figs/Logo.png rename to docs/source/figs/Logo.png diff --git a/docs/_build/html/_images/TRB.png b/docs/source/figs/TRB.png similarity index 100% rename from docs/_build/html/_images/TRB.png rename to docs/source/figs/TRB.png diff --git a/docs/_build/html/_images/fig1_modeling_schema.png b/docs/source/figs/fig1_modeling_schema.png similarity index 100% rename from docs/_build/html/_images/fig1_modeling_schema.png rename to docs/source/figs/fig1_modeling_schema.png diff --git a/docs/_build/html/_images/fig2_routing_schema.png b/docs/source/figs/fig2_routing_schema.png similarity index 100% rename from docs/_build/html/_images/fig2_routing_schema.png rename to docs/source/figs/fig2_routing_schema.png diff --git a/docs/_build/html/_images/fig3_hydrocnhs.png b/docs/source/figs/fig3_hydrocnhs.png similarity index 100% rename from docs/_build/html/_images/fig3_hydrocnhs.png rename to docs/source/figs/fig3_hydrocnhs.png diff --git a/docs/_build/html/_images/fig4_example_watershed.png b/docs/source/figs/fig4_example_watershed.png similarity index 100% rename from docs/_build/html/_images/fig4_example_watershed.png rename to docs/source/figs/fig4_example_watershed.png diff --git a/docs/_build/html/_images/fig5_TRB_node_link.png b/docs/source/figs/fig5_TRB_node_link.png similarity index 100% rename from docs/_build/html/_images/fig5_TRB_node_link.png rename to docs/source/figs/fig5_TRB_node_link.png diff --git a/docs/_build/html/_images/fig6_ABM_design_logic.png b/docs/source/figs/fig6_ABM_design_logic.png similarity index 100% rename from docs/_build/html/_images/fig6_ABM_design_logic.png rename to docs/source/figs/fig6_ABM_design_logic.png diff --git a/docs/_build/html/_images/fig7_ga_fitness.png b/docs/source/figs/fig7_ga_fitness.png similarity index 100% rename from docs/_build/html/_images/fig7_ga_fitness.png rename to docs/source/figs/fig7_ga_fitness.png diff --git a/docs/files/1_introduction.rst b/docs/source/files/1_introduction.rst similarity index 95% rename from docs/files/1_introduction.rst rename to docs/source/files/1_introduction.rst index 65ebd35..141fb82 100644 --- a/docs/files/1_introduction.rst +++ b/docs/source/files/1_introduction.rst @@ -1,15 +1,15 @@ Introduction ============ -The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. +The Hydrological model for Coupled Natural-Human Systems (HydroCNHS) is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. When should you use HydroCNHS? ------------------------------ 1. Want to build a hydrological model with auto-parameter-tuning (calibration) features. 2. Want to add human components into a hydrological model under a unified framework without worrying about routing logic. 3. Want to calibrate the entire integrated model (hydrological + ABM modules) with customized agents' parameters. -4. Want to design human behaviors with a high degree of freedom, including coupling with external software. -5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). +4. Want to design human behaviors with a high degree of freedom, including coupling with external software. +5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). Feature highlights ------------------ @@ -26,7 +26,7 @@ Supporting APIs for incorporating human/agent components .. figure:: ../figs/fig3_hydrocnhs.png :align: center :width: 500 - :alt: Logo + :alt: Logo Dam API ^^^^^^^ @@ -44,4 +44,4 @@ InSitu API ^^^^^^^^^^ Link to human actions that locally alter the runoff in a subbasin, e.g., drainage systems, urbanization, and water diversion from wells or local streams (within-subbasin). -Those human/agent actions can be modeled with various complexities according to users' ABM design. Actions can simply be an inputted daily time series or governed by complex endogenous behavior rules (i.e., decision-making rules). Institutional decisions (a group of agents making decisions together with institutional rules) are allowed in HydroCNHS. Users require to have some level of object-oriented programing concept to design an ABM module (*.py*). \ No newline at end of file +Those human/agent actions can be modeled with various complexities according to users' ABM design. Actions can simply be an inputted daily time series or governed by complex endogenous behavior rules (i.e., decision-making rules). Institutional decisions (a group of agents making decisions together with institutional rules) are allowed in HydroCNHS. Users require to have some level of object-oriented programing concept to design an ABM module (*.py*). diff --git a/docs/files/2_installation.rst b/docs/source/files/2_installation.rst similarity index 72% rename from docs/files/2_installation.rst rename to docs/source/files/2_installation.rst index 37ddc9c..8122047 100644 --- a/docs/files/2_installation.rst +++ b/docs/source/files/2_installation.rst @@ -17,11 +17,4 @@ Or, (2) download the HydroCNHS package directly from the HydroCNHS GitHub reposi .. code-block:: python # Need to move to the folder containing setup.py first. - python setup.py install - - -If you fail to install HydroCNHS due to the DEAP package, first downgrade setuptools to 57 and try to install HydroCNHS again. - -.. code-block:: python - - pip install setuptools==57 \ No newline at end of file + pip install -e . diff --git a/docs/files/3_101_classroom.rst b/docs/source/files/3_101_classroom.rst similarity index 96% rename from docs/files/3_101_classroom.rst rename to docs/source/files/3_101_classroom.rst index 0d6de48..b7df333 100644 --- a/docs/files/3_101_classroom.rst +++ b/docs/source/files/3_101_classroom.rst @@ -14,13 +14,13 @@ Subbasin is a sub-drainage land unit inside a watershed. HydroCNHS uses subbasin Semi-distributed hydrological model ----------------------------------- -HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in :numref:`fig1`. A lumped model describes the water balance of the entire watershed as a water bucket (:numref:`fig1`\a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (:numref:`fig1`\c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins' (not grid) runoffs simulated by lumped models (:numref:`fig1`\b). +HydroCNHS is a semi-distributed hydrological model with resolution/complexity between a lumped hydrological model and a distributed hydrological model, as shown in :numref:`fig1`. A lumped model describes the water balance of the entire watershed as a water bucket (:numref:`fig1`\a). A distributed model simulates water responses in each grid and routes them to the downstream outlets (:numref:`fig1`\c). A semi-distributed hydrological model is a hybrid approach for the two mentioned methods. The streamflow at a downstream outlet is calculated by routing the subbasins' (not grid) runoffs simulated by lumped models (:numref:`fig1`\b). .. _fig1: .. figure:: ../figs/fig1_modeling_schema.png :align: center :width: 700 - :alt: Comparison of three hydrological modeling schema. + :alt: Comparison of three hydrological modeling schema. Comparison of three hydrological modeling schema: a) Lumped model, b) Semi-distributed model, and c) Distributed model. @@ -44,7 +44,7 @@ HydroCNHS represents a water system in a node-link structure. Therefore, users m .. figure:: ../figs/fig2_routing_schema.png :align: center :width: 700 - :alt: Routing schema using in HydroCNHS. + :alt: Routing schema using in HydroCNHS. Routing schema using in HydroCNHS. a) single routing outlet, b) multiple routing outlets, and c) with a reservoir agent. @@ -74,15 +74,15 @@ Design a water system with the supporting APIs A generic example of HydroCNHS coupling APIs and water system description. -The four APIs in the HydroCNHS (:numref:`fig3`) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API. +The four APIs in the HydroCNHS (:numref:`fig3`) are (1) Dam API, (2) RiverDiv API, (3) Conveying API, and (4) InSitu API. -**Dam API** is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in :numref:`fig3`) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents' water releases decision. +**Dam API** is designed for integrating in-stream agents like reservoirs (e.g., R1 and R2 in :numref:`fig3`) that could significantly alter the streamflow regime. Agents with Dam API will be considered pseudo routing outlets (no routing is needed) involved in the routing scheme. Namely, streamflow is directly defined by agents' water releases decision. -**RiverDiv API** is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in :numref:`fig3`. This API ensures the diverted outlet is routed before agents' diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location. +**RiverDiv API** is created for agents that divert water from rivers and may have return flows to other outlets, e.g., diversion agent D1 diverts water from N3 and returns water to N1 in :numref:`fig3`. This API ensures the diverted outlet is routed before agents' diversions. At the outlet receiving return flow, the subbasin runoff and returned flow join and enter the within-subbasin routing process since return flows often have no explicit return location. -**Conveying API** is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin's runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step). +**Conveying API** is designed for transferring water to another outlet from a routing outlet where the routing process has already been executed. The transferred water has no within-subbasin routing (no within-subbasin delay like runoff). Therefore, they will be routed separately from the subbasin's runoffs. If an agent wants to convey water from the downstream outlet to the upstream outlet (e.g., pump stations), the water will be delivered with delays (e.g., C2 diverts water from N3 first and delivers it to S2 at a later time step). -**InSitu API** is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in :numref:`fig3`). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step. +**InSitu API** is developed for agents that directly affect runoffs via “within subbasin activities” (e.g., I1 in :numref:`fig3`). For example, those runoff changes may come from land-use changes due to urbanization or exploiting groundwater through wells. Such adjustments will be made before any routing process at each time step. We summarize the supported connections of each API in :numref:`table1`\. @@ -107,4 +107,4 @@ We summarize the supported connections of each API in :numref:`table1`\. | | \ :sup:`*`\ Human components using Dam API will serve as a pseudo | | routing outlet for the downstream routing. | | | \ :sup:`**`\ InSitu API only works on a single outlet (i.e., subbasin). | - +---------------------------------------------------------------------------+ \ No newline at end of file + +---------------------------------------------------------------------------+ diff --git a/docs/files/4_input_output.rst b/docs/source/files/4_input_output.rst similarity index 99% rename from docs/files/4_input_output.rst rename to docs/source/files/4_input_output.rst index 3efac38..5c246c7 100644 --- a/docs/files/4_input_output.rst +++ b/docs/source/files/4_input_output.rst @@ -6,9 +6,9 @@ Inputs HydroCNHS has the following three main inputs: -(1) Daily climate data including precipitation, temperature, and (optional) potential evapotranspiration (python *dictionary object*), +(1) Daily climate data including precipitation, temperature, and (optional) potential evapotranspiration (python *dictionary object*), -(2) A model configuration file (*.yaml*; settings for the HydroCNHS and ABM models), and +(2) A model configuration file (*.yaml*; settings for the HydroCNHS and ABM models), and (3) ABM modules (*.py*; customized human models). @@ -40,7 +40,7 @@ A model configuration file is a YAML file that contains settings for the entire WaterSystem: StartDate: - EndDate: + EndDate: DataLength: NumSubbasins: Outlets: @@ -49,7 +49,7 @@ A model configuration file is a YAML file that contains settings for the entire Routing: ABM: Modules: - InstitDMClasses: + InstitDMClasses: DMClasses: DamAPI: diff --git a/docs/files/5_build_a_hydrological_model.rst b/docs/source/files/5_build_a_hydrological_model.rst similarity index 89% rename from docs/files/5_build_a_hydrological_model.rst rename to docs/source/files/5_build_a_hydrological_model.rst index 15068f0..41de505 100644 --- a/docs/files/5_build_a_hydrological_model.rst +++ b/docs/source/files/5_build_a_hydrological_model.rst @@ -5,9 +5,9 @@ To get familiar with the HydroCNHS model construction process, let's start with 1. Create a model configuration file (*.yaml*) using a model builder. -2. Complete a model configuration file (*.yaml*) +2. Complete a model configuration file (*.yaml*) -3. Run a calibration +3. Run a calibration 4. Run a simulation @@ -18,7 +18,7 @@ We will adopt the following delineated watershed (:numref:`fig4`) as a demonstra .. figure:: ../figs/fig4_example_watershed.png :align: center :width: 300 - :alt: Example watershed. + :alt: Example watershed. Example watershed. @@ -27,7 +27,7 @@ We will adopt the following delineated watershed (:numref:`fig4`) as a demonstra .. table:: Subbasins' information. :align: center :width: 100% - + +----------------+-------------------+---------------+-----------------------+ |Subbasin/outlet |Drainage area [ha] |Latitude [deg] |Flow length to WSLO [m]| +================+===================+===============+=======================+ @@ -47,25 +47,25 @@ We will adopt the following delineated watershed (:numref:`fig4`) as a demonstra Step 1: Create a model configuration file ----------------------------------------- -HydroCNHS comes with a model builder to help users create an initial model configuration file. +HydroCNHS comes with a model builder to help users create an initial model configuration file. First, initiate a model builder object with a working directory (your working folder directory). .. code-block:: python import os - import HydroCNHS + import hydrocnhs prj_path, this_filename = os.path.split(__file__) Second, set up the water system with the simulation period (i.e., start date and end date). .. code-block:: python - + wd = prj_path - mb = HydroCNHS.ModelBuilder(wd) + mb = hydrocnhs.ModelBuilder(wd) mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") -Third, we can add subbasins (i.e., outlet_list) into the model. We adopt the GWLF model as the rainfall-runoff model (i.e., runoff_model). We also assign the corresponding subbasins' areas (i.e., area_list) and latitude (i.e., lat_list) using the information from :numref:`table4`. Note that area_list and lat_list are optional arguments. Users can manually enter that information to the model configuration file (*.yaml*). +Third, we can add subbasins (i.e., outlet_list) into the model. We adopt the GWLF model as the rainfall-runoff model (i.e., runoff_model). We also assign the corresponding subbasins' areas (i.e., area_list) and latitude (i.e., lat_list) using the information from :numref:`table4`. Note that area_list and lat_list are optional arguments. Users can manually enter that information to the model configuration file (*.yaml*). .. code-block:: python @@ -92,7 +92,7 @@ Finally, we can print out the model for quick view and then write it to *.yaml* ### Print the model in the console mb.print_model() - ### Output initial model configuration file (.yaml) + ### Output initial model configuration file (.yaml) mb.write_model_to_yaml(filename="HydroModel.yaml") Now, we should have the initial model configuration file (i.e., *HydroModel.yaml*) in the corresponding folder. @@ -100,18 +100,18 @@ Now, we should have the initial model configuration file (i.e., *HydroModel.yaml Step 2: Complete a model configuration file ------------------------------------------- -Before using the created *HydroModel.yaml* for the later experiment, we need to open it and ensure the information is correct. The “Inputs” in LSM and Routing sections should be all populated. Some default values may be used (e.g., :math:`S0`, :math:`U0`, and :math:`SnowS`). They should be updated according to users' needs. For this example, we don't need to modify anything. You might notice that all the parameters have **a value of -99**. This is the default value in HydroCNHS, meaning that those values need to be calibrated if not provided by users. +Before using the created *HydroModel.yaml* for the later experiment, we need to open it and ensure the information is correct. The “Inputs” in LSM and Routing sections should be all populated. Some default values may be used (e.g., :math:`S0`, :math:`U0`, and :math:`SnowS`). They should be updated according to users' needs. For this example, we don't need to modify anything. You might notice that all the parameters have **a value of -99**. This is the default value in hydrocnhs, meaning that those values need to be calibrated if not provided by users. -Step 3: Run a calibration +Step 3: Run a calibration ------------------------- First, we load the climate data, observed flow data at WSLO, and the model configuration file. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, we load it as the inputs as well. .. code-block:: python - import matplotlib.pyplot as plt - import pandas as pd - import HydroCNHS.calibration as cali + import matplotlib.pyplot as plt + import pandas as pd + import hydrocnhs.calibration as cali from copy import deepcopy # Load climate data @@ -127,15 +127,15 @@ First, we load the climate data, observed flow data at WSLO, and the model confi index_col=["Date"], parse_dates=["Date"]) # Load model - model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroModel.yaml")) + model_dict = hydrocnhs.load_model(os.path.join(wd, "HydroModel.yaml")) Second, we generate default parameter bounds and create a convertor for calibration. Details about the converter are provided in the Calibration section. .. code-block:: python # Generate default parameter bounds - df_list, df_name = HydroCNHS.write_model_to_df(model_dict) - par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) + df_list, df_name = hydrocnhs.write_model_to_df(model_dict) + par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict) # Create convertor for calibration converter = cali.Convertor() @@ -158,10 +158,10 @@ Third, we program the evaluation function for a genetic algorithm (GA). Kling-Gu model = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") + model = hydrocnhs.load_df_to_model_dict(model, df, s, "Pars") ##### Run simuluation - model = HydroCNHS.Model(model, name) + model = hydrocnhs.Model(model, name) Q = model.run(temp, prec, pet) ##### Get simulation data @@ -172,10 +172,10 @@ Third, we program the evaluation function for a genetic algorithm (GA). Kling-Gu # Resample the daily simulation output to monthly outputs. sim_Q_M = sim_Q_D[cali_target].resample("MS").mean() - KGE = HydroCNHS.Indicator().KGE( + KGE = hydrocnhs.Indicator().KGE( x_obv=obv_flow_WSLO[cali_period[0]:cali_period[1]][cali_target], y_sim=sim_Q_M[cali_period[0]:cali_period[1]][cali_target]) - + fitness = KGE return (fitness,) @@ -199,7 +199,7 @@ Forth, we set up a GA for calibration. Again, we will explain calibration in mor 'plot': True} seed = 5 - rn_gen = HydroCNHS.create_rn_gen(seed) + rn_gen = hydrocnhs.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) ga.set(cali_inputs, config, formatter, name="Cali_HydroModel_gwlf_KGE") ga.run() @@ -215,18 +215,18 @@ Finally, we export the calibrated model (i.e., *Best_HydroModel_gwlf_KGE.yaml*). model_best = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml")) + model = hydrocnhs.load_df_to_model_dict(model_best, df, s, "Pars") + hydrocnhs.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml")) Step 4: Run a simulation ------------------------ -After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (e.g., *Best_HydroModel_gwlf_KGE.yaml*) can be directly loaded into HydroCNHS to run a simulation. +After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (e.g., *Best_HydroModel_gwlf_KGE.yaml*) can be directly loaded into hydrocnhs to run a simulation. .. code-block:: python ### Run a simulation. - model = HydroCNHS.Model(os.path.join(wd, "Cali_HydroModel_gwlf_KGE", + model = hydrocnhs.Model(os.path.join(wd, "Cali_HydroModel_gwlf_KGE", "Best_HydroModel_gwlf_KGE.yaml")) Q = model.run(temp, prec, pet) result = pd.DataFrame(Q, index=model.pd_date_index).resample("MS").mean() @@ -239,4 +239,4 @@ After obtaining a calibrated model, users can now use it for any simulation-base The complete script can be found at *./tutorials/Hydro_example/Main_HydroModel.py*. -The following section will further introduce how to incorporate user-defined ABM modules into the model. \ No newline at end of file +The following section will further introduce how to incorporate user-defined ABM modules into the model. diff --git a/docs/files/6_Integrate_an_ABM.rst b/docs/source/files/6_Integrate_an_ABM.rst similarity index 94% rename from docs/files/6_Integrate_an_ABM.rst rename to docs/source/files/6_Integrate_an_ABM.rst index 5cdae25..ac028af 100644 --- a/docs/files/6_Integrate_an_ABM.rst +++ b/docs/source/files/6_Integrate_an_ABM.rst @@ -5,11 +5,11 @@ After getting familiar with HydroCNHS from the hydrological model example in the 1. Create a model configuration file (*.yaml*) using a model builder. -2. Complete a model configuration file (*.yaml*) +2. Complete a model configuration file (*.yaml*) 3. Program an ABM module (*.py*) -4. Run a calibration +4. Run a calibration 5. Run a simulation @@ -81,12 +81,12 @@ With a node-link structure of the TRB water system, we can follow the same proce .. code-block:: python import os - import HydroCNHS + import hydrocnhs prj_path, this_filename = os.path.split(__file__) ### Initialize a model builder object. wd = prj_path - mb = HydroCNHS.ModelBuilder(wd) + mb = hydrocnhs.ModelBuilder(wd) ### Setup a water system simulation information mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") @@ -101,21 +101,21 @@ With a node-link structure of the TRB water system, we can follow the same proce lat_list=lat_list, runoff_model="GWLF") ### Setup routing outlets - # Add WSLO + # Add WSLO mb.set_routing_outlet(routing_outlet="WSLO", upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], flow_length_list=[80064.864, 70988.164, 60398.680, 0]) - # Add TRGC + # Add TRGC mb.set_routing_outlet(routing_outlet="TRGC", upstream_outlet_list=["DLLO", "TRGC"], flow_length_list=[11748.211, 0]) - # Add DLLO + # Add DLLO # Specify that ResAgt is an instream object. mb.set_routing_outlet(routing_outlet="DLLO", upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], flow_length_list=[9656.064, 30899.4048, 0], - instream_objects=["ResAgt"]) - # Add HaggIn + instream_objects=["ResAgt"]) + # Add HaggIn mb.set_routing_outlet(routing_outlet="HaggIn", upstream_outlet_list=["HaggIn"], flow_length_list=[0]) @@ -131,36 +131,36 @@ To add human components, we need to first initialize the ABM setting block by as Add agents ^^^^^^^^^^ Next, we add human components (i.e., agents) to the model builder. -We first **add a reservoir agent (ResAgt)**, in which its corresponding agent type class, agent name, api, link dictionary, and decision-making class can be assigned at this stage. Although not all information has to be provided now (i.e., it can be manually added to the model configuration file later), we encourage users to provide complete details here. +We first **add a reservoir agent (ResAgt)**, in which its corresponding agent type class, agent name, api, link dictionary, and decision-making class can be assigned at this stage. Although not all information has to be provided now (i.e., it can be manually added to the model configuration file later), we encourage users to provide complete details here. .. code-block:: python mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt", api=mb.api.Dam, - link_dict={"HaggIn": -1, "ResAgt": 1}, + link_dict={"HaggIn": -1, "ResAgt": 1}, dm_class="ReleaseDM") The setting shown above means that ResAgt (an agent object) will be created from Reservoir_AgtType (an agent type class) and integrated into HydroCNHS using the Dam API. A decision-making object will be created from ReleaseDM (a decision-making class) and assigned to ResAgt as its attribute. This agent, ResAgt, will take water (factor = -1) from HaggIn routing outlet and release (factor = 1) water to ResAgt. Remember that ResAgt itself is a pseudo routing outlet. -Following a similar procedure, we **add a water diversion agent (DivAgt)**. However, we have parameters, including ReturnFactor, a, and b, involved in this agent. Hence, a dictionary is provided to the par_dict argument. The format of the parameter dictionary is that keys are parameter names, and values are parameter values (-99 means waiting to be calibrated). +Following a similar procedure, we **add a water diversion agent (DivAgt)**. However, we have parameters, including ReturnFactor, a, and b, involved in this agent. Hence, a dictionary is provided to the par_dict argument. The format of the parameter dictionary is that keys are parameter names, and values are parameter values (-99 means waiting to be calibrated). However, if the parameter is the factor used in the link_dict, users need to follow the format shown here. For example, we want to calibrate a return factor (ReturnFactor) to determine the portion of diverted water returned to the WSLO subbasin. To do that, a list, ["ReturnFactor", 0, "Plus"], is given to link_dict at WSLO. HydroCNHS will interpret it as taking the factor value from parameter ReturnFactor with a list index of 0. "Plus" tells HydroCNHS we add water to WSLO. If water is taken from WSLO, then "Minus" should be assigned. .. code-block:: python - mb.add_agent(agt_type_class="Diversion_AgType", agt_name="DivAgt", + mb.add_agent(agt_type_class="Diversion_AgType", agt_name="DivAgt", api=mb.api.RiverDiv, link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, dm_class="DivertDM", par_dict={"ReturnFactor": [-99], "a": -99, "b":-99}) -Finally, we **add a trans-basin water transfer agent (PipeAgt)**. +Finally, we **add a trans-basin water transfer agent (PipeAgt)**. .. code-block:: python - mb.add_agent(agt_type_class="Pipe_AgType", agt_name="PipeAgt", + mb.add_agent(agt_type_class="Pipe_AgType", agt_name="PipeAgt", api=mb.api.Conveying, - link_dict={"TRTR": 1}, + link_dict={"TRTR": 1}, dm_class="TransferDM") Add institution @@ -197,15 +197,15 @@ After the model configuration file (*.yaml*) is created, users should open the f Step 3: Program ABM module (*.py*) ------------------------------------ -In the generated ABM module (*.py*), users can find mainly two types of classes, including agent type classes (AgtType) and decision-making classes (DMClass/Institutional DMClass). Agent type classes are used to define agents' actions and store up-to-date information (e.g., current date and current time step) in agents' attributes. Decision-making classes are used to program a specific decision-making process. Decision-making classes can be further separated into DMClass and Institutional DMClass. +In the generated ABM module (*.py*), users can find mainly two types of classes, including agent type classes (AgtType) and decision-making classes (DMClass/Institutional DMClass). Agent type classes are used to define agents' actions and store up-to-date information (e.g., current date and current time step) in agents' attributes. Decision-making classes are used to program a specific decision-making process. Decision-making classes can be further separated into DMClass and Institutional DMClass. -The ABM design logic is illustrated in :numref:`fig6`. A "class" is a template for objects that can be initiated with object-specific attributes and settings. For example, Agent1 and Agent2 are initiated from the same AgtType1 class. Agent 2, Agent 4, and Agent 5 are initiated from the AgtType2 class. Each agent will be assigned a DM object or Institution object as one of its attributes. DM objects initiated from DMClass are NOT shared with other agents; Namely, agents with DM objects will only have one unique DM object (e.g., Agent 1 and Agent 2 in :numref:`fig6`). In contrast, an Institution object can be shared with multiple agents, in which those agents can make decisions together. For example, multiple irrigation districts make diversion decisions together to share the water shortage during a drought period. We will not implement the Institutional DMClass in this TRB example; however, we will show how to add an institution through a model builder. +The ABM design logic is illustrated in :numref:`fig6`. A "class" is a template for objects that can be initiated with object-specific attributes and settings. For example, Agent1 and Agent2 are initiated from the same AgtType1 class. Agent 2, Agent 4, and Agent 5 are initiated from the AgtType2 class. Each agent will be assigned a DM object or Institution object as one of its attributes. DM objects initiated from DMClass are NOT shared with other agents; Namely, agents with DM objects will only have one unique DM object (e.g., Agent 1 and Agent 2 in :numref:`fig6`). In contrast, an Institution object can be shared with multiple agents, in which those agents can make decisions together. For example, multiple irrigation districts make diversion decisions together to share the water shortage during a drought period. We will not implement the Institutional DMClass in this TRB example; however, we will show how to add an institution through a model builder. .. _fig6: .. figure:: ../figs/fig6_ABM_design_logic.png :align: center :width: 600 - :alt: ABM design logic. + :alt: ABM design logic. ABM design logic. An agent is a combination of an AgtType class and an (Institutional) DM class. An Institution object can be shared among a group of agent objects (i.e., make decisions together), while a DM object can only be assigned to one agent object. @@ -229,26 +229,26 @@ Agent type class (AgtType): class XXX_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. #decision = self.dm.make_dm(your_arguments) - + if factor <= 0: # Divert from the outlet action = 0 elif factor > 0: # Add to the outlet action = 0 - + return action @@ -265,7 +265,7 @@ Agent type class (AgtType): class XXX_DM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): @@ -275,16 +275,16 @@ Agent type class (AgtType): To keep the manual concise, we provide a complete ABM module for the TRB example at *./tutorials/HydroABM_example/TRB_ABM_complete.py*. Theoretical details can be found in Lin et al. (2022), and more coding tips are available at :ref:`Advanced ABM coding tips`. -Step 4: Run a calibration +Step 4: Run a calibration ------------------------- First, we load the model configuration file, the climate data, and the observed monthly flow data for DLLO and WSLO, reservoir releases of ResAgt, and water diversions of DivAgt. Here, we have calculated the evapotranspiration using the Hamon method. Therefore, PET data is input along with other data. Note that we manually change the ABM module from "TRB_ABM.py" to "TRB_ABM_complete.py." .. code-block:: python - import matplotlib.pyplot as plt - import pandas as pd - import HydroCNHS.calibration as cali + import matplotlib.pyplot as plt + import pandas as pd + import hydrocnhs.calibration as cali from copy import deepcopy # Load climate data @@ -300,7 +300,7 @@ First, we load the model configuration file, the climate data, and the observed index_col=["Date"], parse_dates=["Date"]) # Load model - model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroABMModel.yaml")) + model_dict = hydrocnhs.load_model(os.path.join(wd, "HydroABMModel.yaml")) # Change the ABM module to the complete one. model_dict["WaterSystem"]["ABM"]["Modules"] = ["TRB_ABM_complete.py"] @@ -309,8 +309,8 @@ Second, we generate default parameter bounds and create a convertor for calibrat .. code-block:: python # Generate default parameter bounds - df_list, df_name = HydroCNHS.write_model_to_df(model_dict) - par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) + df_list, df_name = hydrocnhs.write_model_to_df(model_dict) + par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict) # Modify the default bounds of ABM df_abm_bound = par_bound_df_list[2] @@ -339,10 +339,10 @@ Third, we program the evaluation function for a genetic algorithm (GA). The four model = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") + model = hydrocnhs.load_df_to_model_dict(model, df, s, "Pars") ##### Run simuluation - model = HydroCNHS.Model(model, name) + model = hydrocnhs.Model(model, name) Q = model.run(temp, prec, pet) ##### Get simulation data @@ -357,10 +357,10 @@ Third, we program the evaluation function for a genetic algorithm (GA). The four KGEs = [] for target in cali_target: - KGEs.append(HydroCNHS.Indicator().KGE( + KGEs.append(hydrocnhs.Indicator().KGE( x_obv=obv_flow_data[cali_period[0]:cali_period[1]][[target]], y_sim=sim_Q_M[cali_period[0]:cali_period[1]][[target]])) - + fitness = sum(KGEs)/4 return (fitness,) @@ -384,7 +384,7 @@ Fourth, we set up a GA for calibration. Again, we will explain calibration in mo 'plot': True} seed = 5 - rn_gen = HydroCNHS.create_rn_gen(seed) + rn_gen = hydrocnhs.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) ga.set(cali_inputs, config, formatter, name="Cali_HydroABMModel_gwlf_KGE") ga.run() @@ -400,8 +400,8 @@ Finally, we export the calibrated model (i.e., *Best_HydroABMModel_gwlf_KGE.yaml model_best = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) + model = hydrocnhs.load_df_to_model_dict(model_best, df, s, "Pars") + hydrocnhs.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) Step 5: Run a simulation ------------------------ @@ -411,7 +411,7 @@ After obtaining a calibrated model, users can now use it for any simulation-base .. code-block:: python ### Run a simulation. - model = HydroCNHS.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) + model = hydrocnhs.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) Q = model.run(temp, prec, pet) sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] @@ -449,4 +449,3 @@ After obtaining a calibrated model, users can now use it for any simulation-base axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) fig.align_ylabels(axes) - diff --git a/docs/files/7_data_collector.rst b/docs/source/files/7_data_collector.rst similarity index 92% rename from docs/files/7_data_collector.rst rename to docs/source/files/7_data_collector.rst index 364ed70..2627598 100644 --- a/docs/files/7_data_collector.rst +++ b/docs/source/files/7_data_collector.rst @@ -7,12 +7,12 @@ First, we manually create a data collector object for demonstration. .. code-block:: python - import HydroCNHS + import hydrocnhs ### Create a data collector object - dc = HydroCNHS.Data_collector() + dc = hydrocnhs.Data_collector() -Then, we add two fields, "field_1" and "field 2", with corresponding field information to the collector. Spaces are not allowed here, and the code will convert "field 2" to "field_2". +Then, we add two fields, "field_1" and "field 2", with corresponding field information to the collector. Spaces are not allowed here, and the code will convert "field 2" to "field_2". .. code-block:: python ### Add fields @@ -48,7 +48,7 @@ We can also create a shortcut for accessing a field by the following command, in .. code-block:: python ### Get a field shortcut - shortcut = dc.get_field("field_1", copy=False) + shortcut = dc.get_field("field_1", copy=False) shortcut["new_key"] = "new value" dc.field_1 # Out[0]: {'new_key': 'new value'} @@ -58,7 +58,7 @@ If we want to get a copy of a field (not a shortcut), we must assign "True" to t .. code-block:: python ### Get a copy of a field - copied = dc.get_field("field_1", copy=True) + copied = dc.get_field("field_1", copy=True) copied["new_key2"] = "new value2" dc.field_1 # Out[0]: {'new_key': 'new value'} diff --git a/docs/files/8_calibration.rst b/docs/source/files/8_calibration.rst similarity index 95% rename from docs/files/8_calibration.rst rename to docs/source/files/8_calibration.rst index 9d2600f..a7d5bcf 100644 --- a/docs/files/8_calibration.rst +++ b/docs/source/files/8_calibration.rst @@ -16,8 +16,8 @@ Assuming we want to maximize :math:`y=-x_1^2+5x_1-x_2` with :math:`x_1,x_2 \in [ .. code-block:: python import os - import HydroCNHS - import HydroCNHS.calibration as cali + import hydrocnhs + import hydrocnhs.calibration as cali prj_path, this_filename = os.path.split(__file__) def evaluation(individual, info): @@ -44,7 +44,7 @@ The evaluation must have "individual" and "info" arguments. Also, the return fit cali_wd, current_generation, ith_individual, formatter, rn_gen = info -cali_wd, current_generation, and ith_individual can be used to create sub-folders or output files for each evaluation. This is especially useful for calibrating a large model that requires a long simulation time. The formatter contains information to convert 1-D array individuals back to the original parameter format (e.g., a list of DataFrames) through a converter object. We will introduce the Converter in the next section. rn_gen is recommended to be used to generate random numbers (e.g., input to a HydroCNHS object like model = HydroCNHS.Model(model, name, rn_gen)). By applying rn_gen, HydroCNHS can guarantee the reproducibility of the numerical experiment with a given random seed. +cali_wd, current_generation, and ith_individual can be used to create sub-folders or output files for each evaluation. This is especially useful for calibrating a large model that requires a long simulation time. The formatter contains information to convert 1-D array individuals back to the original parameter format (e.g., a list of DataFrames) through a converter object. We will introduce the Converter in the next section. rn_gen is recommended to be used to generate random numbers (e.g., input to a HydroCNHS object like model = hydrocnhs.Model(model, name, rn_gen)). By applying rn_gen, HydroCNHS can guarantee the reproducibility of the numerical experiment with a given random seed. Calibration input dictionary ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -61,12 +61,12 @@ A calibration input dictionary contains three keys, including par_name, par_boun 'wd': 'working directory'} inputs["wd"] = prj_path -Note that users can use a converter to generate the calibration input dictionary automatically. +Note that users can use a converter to generate the calibration input dictionary automatically. GA configuration dictionary ^^^^^^^^^^^^^^^^^^^^^^^^^^^ -A GA configuration dictionary contains many control options. Please see the following code for the explanation of each option. +A GA configuration dictionary contains many control options. Please see the following code for the explanation of each option. .. code-block:: python @@ -80,7 +80,7 @@ A GA configuration dictionary contains many control options. Please see the foll 'prob_mut': 0.15, # Mutation probability of each parameter. 'stochastic': False, # Is the evaluation stochastic? 'max_gen': 100, # Maximum generation number. - 'sampling_method': 'LHC', # Sampling method for the initial population. + 'sampling_method': 'LHC', # Sampling method for the initial population. 'drop_record': False, # Whether to drop historical records to save space. 'paral_cores': -1, # Number of parallel cores. -1 means all available cores. 'paral_verbose': 1, # Higher value will output more console messages. @@ -93,7 +93,7 @@ Finally, we can run the GA calibration. .. code-block:: python # Run GA - rn_gen = HydroCNHS.create_rn_gen(seed=3) + rn_gen = hydrocnhs.create_rn_gen(seed=3) ga = cali.GA_DEAP(evaluation, rn_gen) ga.set(inputs, config, formatter=None, name="Cali_example") ga.run() @@ -138,8 +138,8 @@ We use the following synthetic list of DataFrames as an example. import numpy as np import pandas as pd - import HydroCNHS - import HydroCNHS.calibration as cali + import hydrocnhs + import hydrocnhs.calibration as cali ### Prepare testing data. par_df1 = pd.DataFrame({"Subbasin1": [1000,1000,3], "Subbasin2": [4,5,6]}, @@ -180,7 +180,7 @@ Now, we want to generate the calibration input dictionary with fixed "a" and "b" [0, 10], [0, 10]]} """ -We can see the 'par_name' in cali_inputs does not contain 'a|Subbasin1' and 'b|Subbasin1.' The outputted a formatter contains the relationship between a 1-D array and a list of DataFrames. +We can see the 'par_name' in cali_inputs does not contain 'a|Subbasin1' and 'b|Subbasin1.' The outputted a formatter contains the relationship between a 1-D array and a list of DataFrames. Convert from a list of DataFrames to a 1D array ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -212,4 +212,4 @@ Convert from a 1D array to the original list of DataFrames Par1 5.0 5.0 Par2 5.0 5.0 Par3 5.0 NaN] - """ \ No newline at end of file + """ diff --git a/docs/files/9_advanced ABM coding tips.rst b/docs/source/files/9_advanced ABM coding tips.rst similarity index 99% rename from docs/files/9_advanced ABM coding tips.rst rename to docs/source/files/9_advanced ABM coding tips.rst index e1d66bd..b29771c 100644 --- a/docs/files/9_advanced ABM coding tips.rst +++ b/docs/source/files/9_advanced ABM coding tips.rst @@ -25,7 +25,7 @@ A data collector is a data container object to store model outputs. We encourage Logging ------- -Logging is a python package to organize model output messages. We encourage users to adopt it in their ABM module design. This will help you to integrate your agent output messages into HydroCNHS. +Logging is a python package to organize model output messages. We encourage users to adopt it in their ABM module design. This will help you to integrate your agent output messages into HydroCNHS. .. code-block:: python diff --git a/docs/index.rst b/docs/source/index.rst similarity index 75% rename from docs/index.rst rename to docs/source/index.rst index d91e3d3..71c86bd 100644 --- a/docs/index.rst +++ b/docs/source/index.rst @@ -1,12 +1,13 @@ -.. HydroCNHS documentation master file, created by - sphinx-quickstart on Thu Dec 23 20:33:17 2021. +.. hydrocnhs documentation master file, created by + sphinx-quickstart on Sun Nov 3 00:06:40 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. + HydroCNHS User Manual ===================================== -**The Hydrological model for Coupled Natural-Human Systems (HydroCNHS)** is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. +**The Hydrological model for Coupled Natural-Human Systems (HydroCNHS)** is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling (ABM) concept, into the HydroCNHS. Manual contributors: Chung-Yi Lin & Ali Ghaffari @@ -22,7 +23,7 @@ Manual contributors: Chung-Yi Lin & Ali Ghaffari ./files/7_data_collector ./files/8_calibration ./files/9_advanced ABM coding tips - ./source/HydroCNHS + ./api/hydrocnhs How to cite? ================== @@ -31,6 +32,8 @@ Lin, C. Y., Yang, Y. C. E., & Wi S. (2022). `HydroCNHS: A Python Package of Hydr Related studies ================== Lin, C. Y., & Yang, Y. E. (2022). `The effects of model complexity on model output uncertainty in co-evolved coupled natural-human systems `_. Earth's Future, e2021EF002403. +Lin, C.-Y., Yang, Y. E., & Chaudhary, A. K. (2023). `Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies `_. Journal of Hydrology, 624, 129959. + Indices and tables ================== @@ -38,3 +41,4 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` + diff --git a/hydrocnhs.egg-info/PKG-INFO b/hydrocnhs.egg-info/PKG-INFO deleted file mode 100644 index f54a349..0000000 --- a/hydrocnhs.egg-info/PKG-INFO +++ /dev/null @@ -1,104 +0,0 @@ -Metadata-Version: 2.1 -Name: hydrocnhs -Version: 0.0.3 -Summary: A Python Package of Hydrological Model for Coupled Natural–Human Systems. -Home-page: https://github.com/philip928lin/HydroCNHS -Author: Chung-Yi Lin -Author-email: philip928lin@gmail.com -License: GPL-3.0 License -Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3) -Classifier: Framework :: IPython -Classifier: Intended Audience :: Developers -Classifier: Intended Audience :: Education -Classifier: Intended Audience :: Science/Research -Classifier: Programming Language :: Python :: 3 :: Only -Classifier: Programming Language :: Python :: 3.8 -Classifier: Operating System :: MacOS :: MacOS X -Classifier: Operating System :: Microsoft :: Windows -Classifier: Operating System :: Unix -Classifier: Topic :: Education -Classifier: Topic :: Scientific/Engineering -Classifier: Topic :: Scientific/Engineering :: Hydrology -Requires-Python: >=3.8 -Description-Content-Type: text/markdown -License-File: LICENSE -Requires-Dist: ruamel.yaml -Requires-Dist: tqdm -Requires-Dist: numpy -Requires-Dist: pandas -Requires-Dist: joblib -Requires-Dist: scipy -Requires-Dist: matplotlib -Requires-Dist: scikit-learn -Requires-Dist: pyyaml -Requires-Dist: deap - -[![Documentation Status](https://readthedocs.org/projects/hydrocnhs/badge/?version=latest)](https://hydrocnhs.readthedocs.io) - - -# HydroCNHS -A Python Package of Hydrological Model for Coupled Natural–Human Systems - -Complex Adaptive Water System - -Modeling Coupled Natural–Human Systems (CNHS) to inform comprehensive water resources management policies or describe hydrological cycles in the Anthropocene has become popular in recent years. To fulfill this need, we developed a semi-distributed Hydrological model for Coupled Natural–Human Systems, HydroCNHS. The HydroCNHS is an open-source Python package supporting four Application Programming Interfaces (APIs) that enable users to integrate their human decision models, which can be programmed with the agent-based modeling concept, into the HydroCNHS. Specifically, we design Dam API, RiverDiv API, Conveying API, and InSitu API to integrate, respectively, customized man-made infrastructures such as reservoirs, off-stream diversions, trans-basin aqueducts, and drainage systems that abstract human behaviors (e.g., operator and farmers’ water use decisions). Each of the HydroCNHS APIs has a unique plug-in structure that respects within-subbasin and inter-subbasin (i.e., river) routing logic for maintaining the water balance. In addition, the HydroCNHS uses a single model configuration file to organize input features for the hydrological model and case-specific human systems models. Also, HydroCNHS enables the model calibration using parallel computing power. We demonstrate the functionalities of the HydroCNHS package through a case study in the Northwest United States. Given the integrity of the modeling framework, HydroCNHS can benefit water resources planning and management in various aspects, including the uncertainty analysis in CNHS modeling and more complex agent design. - - -## Install -Install HydroCNHS by *pip*. -``` -pip install hydrocnhs -``` -To install the latest version (beta version) of HydroCNHS, users can (1) install HydroCNHS by *git*. -``` -pip install git+https://github.com/philip928lin/HydroCNHS.git -``` -Or, (2) download the HydroCNHS package directly from the HydroCNHS GitHub repository. Then, install HydroCNHS from the *setup.py*. -``` -# Need to move to the folder containing setup.py first. -python setup.py install -``` -If you fail to install HydroCNHS due to the DEAP package, first downgrade setuptools to 57 and try to install HydroCNHS again. -``` -pip install setuptools==57 -``` - -## User Manual & Example -Click [![Documentation Status](https://readthedocs.org/projects/hydrocnhs/badge/?version=latest)](https://hydrocnhs.readthedocs.io) - -## When should you use HydroCNHS? -1. Want to build a hydrological model with auto-parameter-tuning (calibration) features. -2. Want to add human components into a hydrological model under a unified framework without worrying about routing logic. -3. Want to calibrate the entire integrated model (hydrological + ABM modules) with customized agents' parameters. -4. Want to design human behaviors with a high degree of freedom, including coupling with external software. -5. Want to conduct extensive numerical experiments to test various human behavioral designs (i.e., integrating and testing many ABM modules). - -## Feature highlights -- Built-in genetic algorithm calibration module that can utilize parallel computing power. -- A unified framework allowing calibration of the entire integrated model (hydrological + ABM models), where parameters in the ABM module (i.e., human model) are customizable. -- Automatic integration and simulation through four APIs. -- Built-in data collector module that can collect data from the hydrological and user-defined ABM modules. -- Built-in model builder module to assist users in creating the HydroCNHS model and generating ABM module (*.py*) templates. -- Built-in indicator calculation and visualization tools for simple result analysis. - -## Supporting APIs for incorporating human/agent components -### Dam API -Link to human actions that completely redefine downstream flow, e.g., instream objects like reservoirs. The downstream flow is governed by a reservoir's operational rules and operators' decisions. - -### RiverDiv API -Link to human actions that divert water from the river (with return water to the downstream subbasins), e.g., off-stream irrigation water diversions governed by farmers or regional district managers' diversion behaviors. - -### Conveying API -Link to human actions that convey water beyond basin boundaries and gravitational limitations, e.g., trans-basin aqueducts that convey water from one basin to another and pump stations that convey water from downstream to upstream. - -### InSitu API -Link to human actions that locally alter the runoff in a subbasin, e.g., drainage systems, urbanization, and water diversion from wells or local streams (within-subbasin). - - -Those human/agent actions can be modeled with various complexities according to users' ABM design. Actions can simply be an inputted daily time series or governed by complex endogenous behavior rules (i.e., decision-making rules). Institutional decisions (a group of agents making decisions together with institutional rules) are allowed in HydroCNHS. Users require to have some level of object-oriented programing concept to design an ABM module (*.py*). - -## How to cite? -Lin, C. Y., Yang, Y. C. E., & Wi S. (2022). [HydroCNHS: A Python Package of Hydrological Model for Coupled Natural Human Systems](https://doi.org/10.1061/(ASCE)WR.1943-5452.0001630), Journal of Water Resources Planning and Management, 148(12), 6022005. - -## Related studies -Lin, C. Y., & Yang, Y. E. (2022). [The effects of model complexity on model output uncertainty in co‐evolved coupled natural–human systems](https://doi.org/10.1029/2021EF002403). 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(2022)/__pycache__/TRB_ABM_complete.cpython-310.pyc \ No newline at end of file diff --git a/hydrocnhs.egg-info/dependency_links.txt b/hydrocnhs.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/hydrocnhs.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/hydrocnhs.egg-info/not-zip-safe b/hydrocnhs.egg-info/not-zip-safe deleted file mode 100644 index 8b13789..0000000 --- a/hydrocnhs.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/hydrocnhs.egg-info/requires.txt b/hydrocnhs.egg-info/requires.txt deleted file mode 100644 index 0e639fb..0000000 --- a/hydrocnhs.egg-info/requires.txt +++ /dev/null @@ -1,10 +0,0 @@ -ruamel.yaml -tqdm -numpy -pandas -joblib -scipy -matplotlib -scikit-learn -pyyaml -deap diff --git a/hydrocnhs.egg-info/top_level.txt b/hydrocnhs.egg-info/top_level.txt deleted file mode 100644 index 63a8117..0000000 --- a/hydrocnhs.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -HydroCNHS diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..dcc0559 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,41 @@ +[project] +name = "hydrocnhs" +version = "1.1.2" +authors = [{name = "Chung-Yi Lin", email = "philip928lin@gmail.com"}] +license = {file = "LICENSE"} +description = "A Python Package of Hydrological Model for Coupled Natural–Human Systems" +dependencies = [ + "joblib", + "matplotlib", + "numpy", + "pandas", + "ruamel.yaml", + "scipy", + "scikit-learn", + "tqdm", + "pyyaml", + "deap" +] + + +[project.optional-dependencies] +dev = [ + "ipython", +] +lint = [ + "pre-commit>=2.20.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +# tools +[tool.black] +line-length = 88 +target-version = ["py310", "py311", "py312"] + +[tool.hatch.envs.default] +dependencies = [ + "pytest", +] \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 8f8ad6c..0000000 --- a/requirements.txt +++ /dev/null @@ -1,10 +0,0 @@ -deap -joblib -matplotlib -numpy -pandas -ruamel.yaml -scipy -sklearn -tqdm -pyyaml \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index d00f392..0000000 --- a/setup.py +++ /dev/null @@ -1,47 +0,0 @@ -import pathlib -from setuptools import setup - -# The directory containing this file -HERE = pathlib.Path(__file__).parent - -# The text of the README file -#README = (HERE / "README.md").read_text() -with open("README.md", "r", encoding='utf8') as fh: - README = fh.read() - - -setup(name='hydrocnhs', - version='0.0.3', - description='A Python Package of Hydrological Model for Coupled Natural–Human Systems.', - long_description=README, - long_description_content_type="text/markdown", - url='https://github.com/philip928lin/HydroCNHS', - author='Chung-Yi Lin', - author_email='philip928lin@gmail.com', - license='GPL-3.0 License', - packages=['HydroCNHS'], - install_requires = ["ruamel.yaml", "tqdm", "numpy", "pandas", "joblib", - "scipy", "matplotlib", "scikit-learn","pyyaml", "deap"], - classifiers=[ - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", - "Framework :: IPython", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Intended Audience :: Science/Research", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.8", - "Operating System :: MacOS :: MacOS X", - "Operating System :: Microsoft :: Windows", - "Operating System :: Unix", - "Topic :: Education", - "Topic :: Scientific/Engineering", - "Topic :: Scientific/Engineering :: Hydrology", - ], - zip_safe=False, - # Enable MANIFEST.in - # https://python-packaging.readthedocs.io/en/latest/non-code-files.html - include_package_data = True, - python_requires='>=3.8') - - - diff --git a/HydroCNHS/Config_logging.yaml b/src/hydrocnhs/Config_logging.yaml similarity index 94% rename from HydroCNHS/Config_logging.yaml rename to src/hydrocnhs/Config_logging.yaml index 0beecaa..ca54108 100644 --- a/HydroCNHS/Config_logging.yaml +++ b/src/hydrocnhs/Config_logging.yaml @@ -1,4 +1,4 @@ -# Logging configuration. +# Logging configuration. # Do not change anything here unless you fully understand logging and HydroCNHS # structure. version: 1 @@ -26,12 +26,12 @@ handlers: # Set console and log file handlers. backupCount: 5 # If the log file is filled, then XXX.log.1 loggers: - HydroCNHS: # Logger name. Do not not modify this! + HydroCNHS: # Logger name. Do not not modify this! level: INFO # Msg level ['DEBUG', 'INFO', 'WARNING', 'ERROR'] handlers: [console] # Will be overwrote in set_logging_config()! propagate: no # Root logger. If there is not specific logger is assign, then system will # default root logger. -root: +root: level: DEBUG - handlers: [console] \ No newline at end of file + handlers: [console] diff --git a/HydroCNHS/__init__.py b/src/hydrocnhs/__init__.py similarity index 70% rename from HydroCNHS/__init__.py rename to src/hydrocnhs/__init__.py index ba2e4de..dbc90bb 100644 --- a/HydroCNHS/__init__.py +++ b/src/hydrocnhs/__init__.py @@ -1,17 +1,23 @@ # Load functions that directly available for user when the package is loaded. -from .util import (load_model, write_model, write_model_to_df, - write_model_to_csv, load_df_to_model_dict, create_rn_gen, - set_logging_config, gen_default_bounds) +from .util import ( + load_model, + write_model, + write_model_to_df, + write_model_to_csv, + load_df_to_model_dict, + create_rn_gen, + set_logging_config, + gen_default_bounds, +) from .hydrocnhs import Model from .model_builder import ModelBuilder from .data_collector import Data_collector from .indicators import Indicator from .visual import Visual + # from .rainfall_runoff_model.pet_hamon import cal_pet_Hamon # Setup logging when HydroCNHS is imported. # Default only show console log. Log file is created in the HydroCNHS class # according to user's setting. set_logging_config() - - diff --git a/HydroCNHS/abm.py b/src/hydrocnhs/abm.py similarity index 89% rename from HydroCNHS/abm.py rename to src/hydrocnhs/abm.py index 0d085ab..bc411ca 100644 --- a/HydroCNHS/abm.py +++ b/src/hydrocnhs/abm.py @@ -1,8 +1,9 @@ # ABM helper function and class. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2022/01/14. -class Base(): + +class Base: """ Agent_type class's available items: * name: agent's name. @@ -10,28 +11,30 @@ class Base(): * start_date: datetime object. * data_length: length of the simulation. * data_collector: a container to store simulated data. - * rn_gen: random number generator to ensure reproducibility (e.g., - * self.rn_gen.random()). Note that do NOT set a global random seed in + * rn_gen: random number generator to ensure reproducibility (e.g., + * self.rn_gen.random()). Note that do NOT set a global random seed in * this module! All type of random number should be created by "rn_gen." * dm: decision making object if assigned in the model file (.yaml). - + Decision-making class's available items: * start_date: datetime object. * data_length: length of the simulation. * abm: the ABM configuration dictionary from the model file (.yaml). * data_collector: a container to store simulated data. - * rn_gen: random number generator to ensure reproducibility (e.g., - * self.rn_gen.random()). Note that do NOT set a global random seed in + * rn_gen: random number generator to ensure reproducibility (e.g., + * self.rn_gen.random()). Note that do NOT set a global random seed in * this module! All type of random number should be created by "rn_gen. """ + def __init__(self, **kwargs): for key in kwargs: # Load back all the previous class attributions. setattr(self, key, kwargs[key]) + def read_factor(ag_config, outlet): """Read factor from agent's ag_config at a given outlet. - - The function will automatically parse the list-like value assignment. e.g., + + The function will automatically parse the list-like value assignment. e.g., [ReturnFactor, 0, Plus]. Parameters @@ -44,12 +47,12 @@ def read_factor(ag_config, outlet): factor = ag_config["Inputs"]["Links"][outlet] # For parameterized factor. if isinstance(factor, list): - factor = ag_config["Pars"][factor[0]][factor[1]] + factor = ag_config["Pars"][factor[0]][int(factor[1])] return factor -#%% -class agent(): +# %% +class agent: def __inti__(self): # Property self.dc = None @@ -57,4 +60,3 @@ def __inti__(self): self.current_outlet = None self.current_date = None self.t = None - \ No newline at end of file diff --git a/HydroCNHS/abm_script.py b/src/hydrocnhs/abm_script.py similarity index 83% rename from HydroCNHS/abm_script.py rename to src/hydrocnhs/abm_script.py index 03a2a15..3b13012 100644 --- a/HydroCNHS/abm_script.py +++ b/src/hydrocnhs/abm_script.py @@ -1,11 +1,11 @@ import_str = """import logging -from HydroCNHS.abm import Base, read_factor +from hydrocnhs.abm import Base, read_factor logger = logging.getLogger("ABM") # logger can be used to log message (e.g., logger.info(msg)). ### Global variable and data -# Users can load data or define variables that are commonly available to all -# agent type classes and decision-making classes here. +# Users can load data or define variables that are commonly available to all +# agent type classes and decision-making classes here. """ @@ -13,13 +13,13 @@ \"\"\" This is the auto-generated script template for a ABM module. Make sure to add ABM module (this filename) to the model file -(.yaml). +(.yaml). Note: -Below is the list of inherited attributes for agent type -classes (AgtType) and (institutional) decision-making classes +Below is the list of inherited attributes for agent type +classes (AgtType) and (institutional) decision-making classes (DMClass). These attributes will be assigned to each -initialized object in a HydroCNHS simulation. Namely, they +initialized object in a HydroCNHS simulation. Namely, they can be used for model design. Agent type class (AgtType): @@ -30,10 +30,10 @@ self.current_date = current date (datetime object). self.data_length = data/simulation length. self.t = current timestep. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. -self.agents = a dictionary of all initialized agents, +self.agents = a dictionary of all initialized agents, {agt_name: agt object}. self.dm = (institutional) decision-making object if DMClass or institution is assigned to the @@ -41,7 +41,7 @@ (Institutional) decision-making classes (DMClass): self.name = name of the agent or institute. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. @@ -52,34 +52,37 @@ \"\"\" """ + def add_agt_class(agt_type): string = """\n # AgtType class {}(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. #decision = self.dm.make_dm(your_arguments) - + if factor <= 0: # Divert from the outlet action = 0 elif factor > 0: # Add to the outlet action = 0 - + return action -""".format(agt_type) +""".format( + agt_type + ) return string @@ -92,12 +95,14 @@ def add_dm_class(dm_type, is_institution=False): class {}(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): # Decision-making calculation. decision = None return decision -""".format(t, dm_type) - return string \ No newline at end of file +""".format( + t, dm_type + ) + return string diff --git a/src/hydrocnhs/calibration/__init__.py b/src/hydrocnhs/calibration/__init__.py new file mode 100644 index 0000000..96bdaee --- /dev/null +++ b/src/hydrocnhs/calibration/__init__.py @@ -0,0 +1,43 @@ +from .convertor import Convertor +from .ga_deap import GA_DEAP + + +def helper(): + print( + "(1) Code your customize evaluation function for GA algorithm with", + "certain arguments.\nEx:\ndef evaluation(individual, info):\n\t# ", + "info = (cali_wd, current_generation, ith_individual, formatter, ", + "random_generator)\n\tRun model\n\tCalculate fitness\n\n\treturn " + "(fitness,)\n\n(2) Create calibration object from GA_DEAP() class", + "given inputs and config.\n\n(3) Run GA.", + ) + + +def get_config_template(): + config = { + "min_or_max": "max", + "pop_size": 100, + "num_ellite": 1, + "prob_cross": 0.5, + "prob_mut": 0.15, + "stochastic": False, + "max_gen": 100, + "sampling_method": "LHC", + "drop_record": False, + "paral_cores": -1, + "paral_verbose": 1, + "auto_save": True, + "print_level": 1, + "plot": True, + } + return config + + +def get_inputs_template(): + inputs = { + "par_name": ["a", "b", "c"], + "par_bound": [[1, 2], [1, 2], [1, 2]], + "wd": "working directory", + } + print("\nNote:\n Converter() can assist users", "to generate calibration inputs.") + return inputs diff --git a/HydroCNHS/calibration/convertor.py b/src/hydrocnhs/calibration/convertor.py similarity index 85% rename from HydroCNHS/calibration/convertor.py rename to src/hydrocnhs/calibration/convertor.py index 1dccc42..786657d 100644 --- a/HydroCNHS/calibration/convertor.py +++ b/src/hydrocnhs/calibration/convertor.py @@ -1,10 +1,11 @@ # Convertor module. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2021/12/22. import ast import pandas as pd import numpy as np + class Convertor(object): def __init__(self): """Convertor helps user to convert multiple parameter dataframes (can @@ -18,9 +19,9 @@ def __init__(self): Note: Dataframe index is parameter names. """ pass - + def gen_formatter(self, df_list, fixed_par_list=None): - """Generate formatter for given list of dataframe objects. This is + """Generate formatter for given list of dataframe objects. This is already included in gen_cali_inputs(). Parameters @@ -29,18 +30,18 @@ def gen_formatter(self, df_list, fixed_par_list=None): A list of parameter dataframes. Dataframe index is parameter names. fixed_par_list : list, optional A list contains a list of tuples of fixed parameter location. For - example, we want to fix par1 of outlet1 & 2 and all par2 in + example, we want to fix par1 of outlet1 & 2 and all par2 in dataframe 1 and not to fix df2. We can input the following list, - [[(["par1"], ["outlet1", "outlet2"]), (["par2"], [":"])], []]. + [[(["par1"], ["outlet1", "outlet2"]), (["par2"], [":"])], []]. fixed_par_list is by default None. The number of lists in the list is corresponding to the number of dataframes. - + Returns ------- dict formatter. """ - + for i in range(len(df_list)): # Convert index and column into String, since tuple is not directly # callable. @@ -48,32 +49,36 @@ def gen_formatter(self, df_list, fixed_par_list=None): parsed_col = [str(item) for item in df_list[i].columns] df_list[i].index = parsed_index df_list[i].columns = parsed_col - - formatter = {"num_of_df": None, - "shape_list": [], - "col_name_list": [], - "index_name_list": [], - "index": [0], - "none_index": None, - "fixed_par_list": None, - "fixed_par_val_list": []} + + formatter = { + "num_of_df": None, + "shape_list": [], + "col_name_list": [], + "index_name_list": [], + "index": [0], + "none_index": None, + "fixed_par_list": None, + "fixed_par_val_list": [], + } if fixed_par_list is not None: - formatter["fixed_par_list"] = fixed_par_list - + formatter["fixed_par_list"] = fixed_par_list + formatter["num_of_df"] = len(df_list) - var_array = [] + var_array = [] for i, df in enumerate(df_list): formatter["shape_list"].append(df.shape) formatter["col_name_list"].append(df.columns.values) formatter["index_name_list"].append(df.index.values) # Store fixed par and replace their values with None in df. - + if fixed_par_list is not None: fixed_par_val_list = [] for tup in fixed_par_list[i]: - if tup[1] == ":": # For all columns. + if "." in tup[0]: + tup = ([j for j in list(df.index) if tup[0] in j], tup[1]) + if tup[1] == ":": # For all columns. tup = (tup[0], list(df)) - if tup[0] == ":": # For all index. + if tup[0] == ":": # For all index. tup = (list(df.index), tup[1]) value = df.loc[tup[0], tup[1]].to_numpy() fixed_par_val_list.append(value) @@ -86,17 +91,15 @@ def gen_formatter(self, df_list, fixed_par_list=None): var_array = var_array + list(df.to_numpy().flatten("C")) # Add index (where it ends in the 1D array) formatter["index"].append(len(var_array)) - - var_array = np.array(var_array) # list to array + + var_array = np.array(var_array) # list to array formatter["none_index"] = list( np.argwhere(np.isnan(var_array)).flatten() - ) # Find index for np.nan values. + ) # Find index for np.nan values. self.formatter = formatter return formatter - - - def gen_cali_inputs(self, wd, df_list, par_bound_df_list, - fixed_par_list=None): + + def gen_cali_inputs(self, wd, df_list, par_bound_df_list, fixed_par_list=None): """Generate inputs dictionary required for calibration. Parameters @@ -112,20 +115,21 @@ def gen_cali_inputs(self, wd, df_list, par_bound_df_list, [(["CN2"], ["outlet1", "outlet2"]), (["b"], ["outlet1"])], by default None. The number of tuples in the list is corresponding to the number of dataframes. - + Returns ------- dict Input for calibration. """ + # Parse value from str to list or others. def parse(i): try: val = ast.literal_eval(i) return val except: - return i - + return i + # Compute formatter # We use par_bound_df_list to determine None. self.gen_formatter(df_list, fixed_par_list) @@ -140,25 +144,27 @@ def parse(i): # Make sure index and column is callable and identical to df_list. par_bound_df_list[i].index = index_name_list_d par_bound_df_list[i].columns = col_name_list_d - - # Assignment starts here. + + # Assignment starts here. for par in index_name_list_d: for c in col_name_list_d: - par_name.append(str(par)+"|"+str(c)) - par_bound.append(parse(par_bound_df_list[i].loc[par,c])) + par_name.append(str(par) + "|" + str(c)) + par_bound.append(parse(par_bound_df_list[i].loc[par, c])) + # Remove elements in None index from formatter. This includes fixed - # pars and pars with None values. + # pars and pars with None values. def delete_multiple_element(list_object, indices): indices = sorted(indices, reverse=True) for idx in indices: if idx < len(list_object): list_object.pop(idx) + delete_multiple_element(par_name, none_index) delete_multiple_element(par_bound, none_index) inputs = {"wd": wd, "par_name": par_name, "par_bound": par_bound} self.inputs = inputs return inputs - + @staticmethod def to_1D_array(df_list, formatter): """Convert a list of dataframes to a 1D array. @@ -176,20 +182,21 @@ def to_1D_array(df_list, formatter): array 1D array. """ - var_array = [] + var_array = [] for df in df_list: # Convert to 1d array - var_array = var_array + list(df.to_numpy().flatten("C")) - + var_array = var_array + list(df.to_numpy().flatten("C")) + def delete_multiple_element(list_object, indices): indices = sorted(indices, reverse=True) for idx in indices: if idx < len(list_object): list_object.pop(idx) + delete_multiple_element(var_array, formatter["none_index"]) - + return np.array(var_array) - + @staticmethod def to_df_list(var_array, formatter): """Convert 1D array back to a list of dataframes. @@ -212,31 +219,35 @@ def to_df_list(var_array, formatter): # Insert np.nan to var_array following none_index var_array = var_array.astype("float") for i in none_index: - var_array = np.insert(var_array,i,np.nan) - + var_array = np.insert(var_array, i, np.nan) + # Form df_list df_list = [] for i in range(formatter["num_of_df"]): - # 1d array to dataframe - df = np.reshape(var_array[index[i]: index[i+1]], - formatter["shape_list"][i], "C") + # 1d array to dataframe + df = np.reshape( + var_array[index[i] : index[i + 1]], formatter["shape_list"][i], "C" + ) df = pd.DataFrame(df) df.index = formatter["index_name_list"][i] df.columns = formatter["col_name_list"][i] # Add fixed values back if formatter["fixed_par_list"] is not None: for ii, tup in enumerate(formatter["fixed_par_list"][i]): - if tup[1] == ":": # For all columns. + if "." in tup[0]: + tup = ([j for j in list(df.index) if tup[0] in j], tup[1]) + if tup[1] == ":": # For all columns. tup = (tup[0], list(df)) - if tup[0] == ":": # For all index. + if tup[0] == ":": # For all index. tup = (list(df.index), tup[1]) aa = formatter["fixed_par_val_list"][i][ii] df.loc[tup[0], tup[1]] = aa df_list.append(df) - + return df_list -#%% + +# %% ### Example # import numpy as np # import pandas as pd @@ -272,11 +283,11 @@ def to_df_list(var_array, formatter): # print(cali_inputs) # r""" # print(cali_inputs) -# {'wd': 'working directory', -# 'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2', -# 'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2', -# 'Par3|Agent1'], -# 'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], +# {'wd': 'working directory', +# 'par_name': ['a|Subbasin2', 'b|Subbasin2', 'c|Subbasin1', 'c|Subbasin2', +# 'Par1|Agent1', 'Par1|Agent2', 'Par2|Agent1', 'Par2|Agent2', +# 'Par3|Agent1'], +# 'par_bound': [[0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], [0, 10], # [0, 10], [0, 10]]} # """ @@ -291,7 +302,7 @@ def to_df_list(var_array, formatter): # var_array = np.array([5]*9) # converter.to_df_list(var_array, formatter) # r""" -# Out[46]: +# Out[46]: # [ Subbasin1 Subbasin2 # a 1000.0 5.0 # b 1000.0 5.0 @@ -300,4 +311,4 @@ def to_df_list(var_array, formatter): # Par1 5.0 5.0 # Par2 5.0 5.0 # Par3 5.0 NaN] -# """ \ No newline at end of file +# """ diff --git a/HydroCNHS/calibration/ga_deap.py b/src/hydrocnhs/calibration/ga_deap.py similarity index 77% rename from HydroCNHS/calibration/ga_deap.py rename to src/hydrocnhs/calibration/ga_deap.py index 4e18540..f089dd9 100644 --- a/HydroCNHS/calibration/ga_deap.py +++ b/src/hydrocnhs/calibration/ga_deap.py @@ -7,6 +7,7 @@ from joblib import Parallel, delayed from deap import base, creator, tools import logging + logger = logging.getLogger("HydroCNHS.GA") r""" @@ -30,6 +31,8 @@ "plot": True } """ + + def scale(individual, bound_scale, lower_bound): """individual is 1d ndarray.""" individual = individual.reshape(bound_scale.shape) @@ -37,6 +40,7 @@ def scale(individual, bound_scale, lower_bound): scaled_individual = np.add(scaled_individual, lower_bound) return scaled_individual.flatten() + def descale(individual, bound_scale, lower_bound): """individual is 1d ndarray.""" individual = individual.reshape(bound_scale.shape) @@ -44,9 +48,11 @@ def descale(individual, bound_scale, lower_bound): descaled_individual = np.divide(descaled_individual, bound_scale) return descaled_individual.flatten() + def sample_by_MC(size, rn_gen_gen): return rn_gen_gen.uniform(0, 1, size) + def sample_by_LHC(size, rn_gen_gen): # size = [pop_size, num_par] pop_size = size[0] @@ -57,36 +63,38 @@ def sample_by_LHC(size, rn_gen_gen): temp = np.empty([pop_size]) # Uniformly sample in each interval. for j in range(pop_size): - temp[j] = rn_gen_gen.uniform(low=j*d, high=(j + 1)*d) + temp[j] = rn_gen_gen.uniform(low=j * d, high=(j + 1) * d) # Shuffle to random order. rn_gen_gen.shuffle(temp) # Scale [0,1] to its bound. - pop[:,i] = temp + pop[:, i] = temp return pop + def gen_init_pop(creator, size, method="LHC", guess_pop=None, rn_gen_gen=None): # size = [pop_size, num_par] pop_size = size[0] pop = np.empty(size) if guess_pop is not None: ass_size = guess_pop.shape[0] - pop[:ass_size,:] = guess_pop + pop[:ass_size, :] = guess_pop # Randomly initialize the rest of population size[0] = pop_size - ass_size else: ass_size = 0 if method == "MC": - pop[ass_size:,:] = sample_by_MC(size, rn_gen_gen) + pop[ass_size:, :] = sample_by_MC(size, rn_gen_gen) elif method == "LHC": - pop[ass_size:,:] = sample_by_LHC(size, rn_gen_gen) + pop[ass_size:, :] = sample_by_LHC(size, rn_gen_gen) # Convert to DEAP individual objects. individuals = [] for i in range(pop_size): - individual = pop[i,:] + individual = pop[i, :] individuals.append(creator(individual)) return individuals + def mut_uniform(individual, prob_mut, rn_gen_gen): num_par = len(individual) mut = rn_gen_gen.binomial(n=1, p=prob_mut, size=num_par) == 1 @@ -94,6 +102,7 @@ def mut_uniform(individual, prob_mut, rn_gen_gen): individual[mut] = new_sample.flatten()[mut] return individual + def mut_middle(individual, p1, p2, prob_mut, rn_gen_gen): num_par = len(individual) new_sample = rn_gen_gen.uniform(0, 1, num_par) @@ -101,13 +110,14 @@ def mut_middle(individual, p1, p2, prob_mut, rn_gen_gen): rnd = rn_gen_gen.random() if rnd < prob_mut: if p1[i] < p2[i]: - individual[i] = p1[i] + rn_gen_gen.random() * (p2[i]-p1[i]) + individual[i] = p1[i] + rn_gen_gen.random() * (p2[i] - p1[i]) elif p1[i] > p2[i]: - individual[i] = p2[i] + rn_gen_gen.random() * (p1[i]-p2[i]) + individual[i] = p2[i] + rn_gen_gen.random() * (p1[i] - p2[i]) else: individual[i] = new_sample[i] return individual + def selRoulette(individuals, k, rn_gen_gen, fit_attr="fitness"): # From DEAP s_inds = sorted(individuals, key=attrgetter(fit_attr), reverse=True) @@ -122,6 +132,8 @@ def selRoulette(individuals, k, rn_gen_gen, fit_attr="fitness"): chosen.append(ind) break return chosen + + def cxUniform(ind1, ind2, indpb, rn_gen_gen): # From DEAP size = min(len(ind1), len(ind2)) @@ -143,19 +155,19 @@ def cxUniform(ind1, ind2, indpb, rn_gen_gen): tb = base.Toolbox() -tb.register("crossover", cxUniform) # apply customized rn_gen +tb.register("crossover", cxUniform) # apply customized rn_gen tb.register("mutate_uniform", mut_uniform) tb.register("mutate_middle", mut_middle) -tb.register("select", selRoulette) # apply customized rn_gen +tb.register("select", selRoulette) # apply customized rn_gen tb.register("ellite", tools.selBest) class GA_DEAP(object): - def __init__(self, evaluation_func, rn_gen=None): + def __init__(self, evaluation_func=lambda individual, info: None, rn_gen=None): """Initialize the GA calibration object. Note that this GA algorithm only allows to calibrate real numbers. - + Parameters ---------- evaluation_func : function @@ -168,10 +180,12 @@ def evaluation(individual, info): Random number generator created by create_rn_gen(), by default None. If given, randomness of the designed model is controled by rn_gen. We encourage user to assign it to maintain the reproducibility of - the stochastic simulation. + the stochastic simulation. """ - print("GA Calibration Guide\n" - +"Step 1: set or load (GA_auto_save.pickle).\nStep 2: run.") + print( + "GA Calibration Guide\n" + + "Step 1: set or load (GA_auto_save.pickle).\nStep 2: run." + ) if rn_gen is None: # Assign a random seed. seed = np.random.randint(0, 100000) @@ -203,38 +217,46 @@ def load(self, GA_auto_save_file, max_gen=None): config = self.config max_gen_org = config["max_gen"] if max_gen is None: - print("Enter the new max_gen (original max_gen = {})".format(max_gen) - +" or Press Enter to continue.") + print( + "Enter the new max_gen (original max_gen = {})".format(max_gen) + + " or Press Enter to continue." + ) ans1 = input() else: ans1 = max_gen if ans1 != "": ans2 = int(ans1) if ans2 <= max_gen_org: - print("Fail to update MaxGen. Note that new max_gen must be" - +" larger than original max_gen. Please reload.") + print( + "Fail to update MaxGen. Note that new max_gen must be" + + " larger than original max_gen. Please reload." + ) else: self.config["max_gen"] = ans2 # Add random seed if increased max gen. - self.rng_seeds += self.ss.spawn(ans2-max_gen_org) + self.rng_seeds += self.ss.spawn(ans2 - max_gen_org) # Add toolbox if config["min_or_max"] == "min": tb.register("population", gen_init_pop, creator.Individual_min) else: tb.register("population", gen_init_pop, creator.Individual_max) - tb.register("scale", scale, bound_scale=self.bound_scale, - lower_bound=self.lower_bound) - tb.register("descale", descale, bound_scale=self.bound_scale, - lower_bound=self.lower_bound) - - def set(self, inputs, config, formatter=None, - name="Calibration"): + tb.register( + "scale", scale, bound_scale=self.bound_scale, lower_bound=self.lower_bound + ) + tb.register( + "descale", + descale, + bound_scale=self.bound_scale, + lower_bound=self.lower_bound, + ) + + def set(self, inputs, config, formatter=None, name="Calibration"): """Setup the GA calibration. Parameters ---------- inputs : dict - Calibration input dictionary generated by Convertor. Or, get the + Calibration input dictionary generated by Convertor. Or, get the template by calling get_inputs_template(). config : dict Calibration configuration dictionary. Get the template by calling @@ -248,7 +270,7 @@ def set(self, inputs, config, formatter=None, self.config = config self.inputs = inputs self.formatter = formatter - #self.system_config = loadConfig() + # self.system_config = loadConfig() self.size = (config["pop_size"], len(inputs["par_name"])) # Continue run setup @@ -265,14 +287,14 @@ def set(self, inputs, config, formatter=None, self.summary["std"] = [] # Random number generators' seed for each generation (from 0 to max_gen) - self.rng_seeds = self.ss.spawn(config["max_gen"]+1) + self.rng_seeds = self.ss.spawn(config["max_gen"] + 1) # Scale setting bound_scale = [] lower_bound = [] par_bound = inputs["par_bound"] for i in range(self.size[1]): - # for i, ty in enumerate(inputs["par_type"]): + # for i, ty in enumerate(inputs["par_type"]): # if ty == "real": Only allow real number for now. bound_scale.append(par_bound[i][1] - par_bound[i][0]) lower_bound.append(par_bound[i][0]) @@ -289,10 +311,15 @@ def set(self, inputs, config, formatter=None, tb.register("population", gen_init_pop, creator.Individual_min) else: tb.register("population", gen_init_pop, creator.Individual_max) - tb.register("scale", scale, bound_scale=self.bound_scale, - lower_bound=self.lower_bound) - tb.register("descale", descale, bound_scale=self.bound_scale, - lower_bound=self.lower_bound) + tb.register( + "scale", scale, bound_scale=self.bound_scale, lower_bound=self.lower_bound + ) + tb.register( + "descale", + descale, + bound_scale=self.bound_scale, + lower_bound=self.lower_bound, + ) # Create calibration folder under WD self.cali_wd = os.path.join(inputs["wd"], name) @@ -300,10 +327,12 @@ def set(self, inputs, config, formatter=None, if os.path.isdir(self.cali_wd) is not True: os.mkdir(self.cali_wd) else: - logger.warning("Current calibration folder exists." - +" Default to overwrite the folder!" - +"\n{}".format(self.cali_wd)) - #--------------------------------------- + logger.warning( + "Current calibration folder exists." + + " Default to overwrite the folder!" + + "\n{}".format(self.cali_wd) + ) + # --------------------------------------- def run_individual(self, individual="best", name="best"): """Run the evaluation for a given individual. @@ -311,16 +340,16 @@ def run_individual(self, individual="best", name="best"): Warning! run_individual() does not generantee the same rn_gen will be assign to the evaluation, but the same one will be used for run_individual() - + Parameters ---------- individual : 1darray, optional Individual or solution, by default "best". name : str, optional - This will be sent to the evaluation function through info = + This will be sent to the evaluation function through info = (cali_wd, name, name, formatter, rn_gen), by default "best". """ - + if individual == "best": sol = self.solution else: @@ -329,18 +358,18 @@ def run_individual(self, individual="best", name="best"): cali_wd = self.cali_wd # Warning! Does not generantee the same rn_gen will be assign to the # evaluation, but the same one will be used for run_individual() - rn_gen_pop = self.gen_rn_gens(self.rng_seeds[self.current_gen-1], - self.size[0]) - fitness = tb.evaluate( - sol, (cali_wd, name, name, formatter, rn_gen_pop[0])) - + rn_gen_pop = self.gen_rn_gens( + self.rng_seeds[self.current_gen - 1], self.size[0] + ) + fitness = tb.evaluate(sol, (cali_wd, name, name, formatter, rn_gen_pop[0])) + print("Fitness: {}".format(fitness)) def gen_rn_gens(self, seed, size): # Create rn_gen for each individual in the pop with predefined # generation specific seed. rn_gen_gen = np.random.default_rng(seed) - ind_seeds = rn_gen_gen.bit_generator._seed_seq.spawn(size+1) + ind_seeds = rn_gen_gen.bit_generator._seed_seq.spawn(size + 1) rn_gen_pop = [np.random.default_rng(s) for s in ind_seeds] # rn_gen for selection, crossover, mutation for each generation self.rn_gen_gen = rn_gen_gen @@ -372,49 +401,50 @@ def run(self, guess_pop=None): if self.done_ini is False: # self.rng_seeds[0] will be used for initialize population as well # as form pop for the first generation. - rn_gen_pop = self.gen_rn_gens(self.rng_seeds[self.current_gen], - size[0]) + rn_gen_pop = self.gen_rn_gens(self.rng_seeds[self.current_gen], size[0]) - pop = tb.population(self.size, config["sampling_method"], - guess_pop, self.rn_gen_gen) + pop = tb.population( + self.size, config["sampling_method"], guess_pop, self.rn_gen_gen + ) self.done_ini = True scaled_pop = list(map(tb.scale, pop)) # Note np.array(scaled_pop[k]) is necessary for serialization. # Use joblib instead of DEAP document of Scoop or muliprocessing, # so we don't have to run in external terminal. - fitnesses = Parallel(n_jobs=paral_cores, verbose=paral_verbose) \ - ( delayed(tb.evaluate)\ - (np.array(scaled_pop[k]), - (cali_wd, self.current_gen, k, formatter, - rn_gen_pop[k])) \ - for k in range(len(scaled_pop)) ) + fitnesses = Parallel(n_jobs=paral_cores, verbose=paral_verbose)( + delayed(tb.evaluate)( + np.array(scaled_pop[k]), + (cali_wd, self.current_gen, k, formatter, rn_gen_pop[k]), + ) + for k in range(len(scaled_pop)) + ) # Note that we assign fitness to original pop not the scaled_pop. for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit self.find_best_and_record(pop) - else: # Load previous run - pop = self.records[self.current_gen-1] + else: # Load previous run + pop = self.records[self.current_gen - 1] # To continue the random sequence from previous run # (do ga things using seed from last generation) self.rn_gen_gen = np.random.default_rng( - self.rng_seeds[self.current_gen-1]) + self.rng_seeds[self.current_gen - 1] + ) # Iteration prob_cross = config["prob_cross"] prob_mut = config["prob_mut"] while self.current_gen <= max_gen: - # Select the next generation individuals parents = tb.select(pop, size[0], self.rn_gen_gen) # Clone the selected individuals offspring = list(map(tb.clone, parents)) # Apply crossover and mutation on the offspring - for p1, p2, child1, child2 in zip(parents[::2], parents[1::2], - offspring[::2], offspring[1::2]): - + for p1, p2, child1, child2 in zip( + parents[::2], parents[1::2], offspring[::2], offspring[1::2] + ): # Keep the parent with some probability prob_cross # if np.random.uniform() < prob_cross: # tb.crossover(child1, child2, prob_cross) @@ -437,18 +467,17 @@ def run(self, guess_pop=None): if stochastic: invalid_ind = offspring else: - invalid_ind = [ind for ind in offspring \ - if not ind.fitness.valid] + invalid_ind = [ind for ind in offspring if not ind.fitness.valid] scaled_pop = list(map(tb.scale, invalid_ind)) - rn_gen_pop = self.gen_rn_gens(self.rng_seeds[self.current_gen], - size[0]) - fitnesses = Parallel(n_jobs=paral_cores, verbose=paral_verbose) \ - ( delayed(tb.evaluate)\ - (np.array(scaled_pop[k]), - (cali_wd, self.current_gen, k, formatter, - rn_gen_pop[k])) \ - for k in range(len(invalid_ind)) ) + rn_gen_pop = self.gen_rn_gens(self.rng_seeds[self.current_gen], size[0]) + fitnesses = Parallel(n_jobs=paral_cores, verbose=paral_verbose)( + delayed(tb.evaluate)( + np.array(scaled_pop[k]), + (cali_wd, self.current_gen, k, formatter, rn_gen_pop[k]), + ) + for k in range(len(invalid_ind)) + ) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit @@ -468,7 +497,7 @@ def find_best_and_record(self, pop): self.records[self.current_gen] = list(map(tb.clone, pop)) if config["drop_record"]: # Delete previous generation's record - self.records.pop(self.current_gen-1,"") + self.records.pop(self.current_gen - 1, "") self.current_gen += 1 @@ -477,12 +506,13 @@ def find_best_and_record(self, pop): fits = [ind.fitness.values[0] for ind in pop] length = len(pop) mean = sum(fits) / length - sum2 = sum(x*x for x in fits) - std = abs(sum2 / length - mean**2)**0.5 + sum2 = sum(x * x for x in fits) + std = abs(sum2 / length - mean**2) ** 0.5 elapsed_time = time.monotonic() - self.start_time self.summary["elapsed_time"] = time.strftime( - "%H:%M:%S", time.gmtime(elapsed_time)) + "%H:%M:%S", time.gmtime(elapsed_time) + ) self.summary["max_fitness"].append(max(fits)) self.summary["min_fitness"].append(min(fits)) self.summary["avg"].append(mean) @@ -492,14 +522,15 @@ def find_best_and_record(self, pop): if config["auto_save"]: self.auto_save() - if ((self.current_gen-1) % config["print_level"] == 0 - or self.current_gen > config["max_gen"]): - print("\n=====Generation {}=====".format(self.current_gen-1)) + if (self.current_gen - 1) % config[ + "print_level" + ] == 0 or self.current_gen > config["max_gen"]: + print("\n=====Generation {}=====".format(self.current_gen - 1)) print(" Elapsed time %s" % self.summary["elapsed_time"]) - print(" Min %s" % round(min(fits),5)) - print(" Max %s" % round(max(fits),5)) - print(" Avg %s" % round(mean,5)) - print(" Std %s" % round(std,5)) + print(" Min %s" % round(min(fits), 5)) + print(" Max %s" % round(max(fits), 5)) + print(" Avg %s" % round(mean, 5)) + print(" Std %s" % round(std, 5)) if config["plot"]: # Plot progress @@ -512,24 +543,36 @@ def find_best_and_record(self, pop): fitness = self.summary["min_fitness"] ax1.set_ylabel("Fitness (Min)") x = np.arange(len(fitness)) - lns1 = ax1.plot(x, fitness, label="Fitness", linewidth=2, - color="black", marker=".", zorder=2) - lns2 = ax2.plot(x, self.summary["std"], label="Fitness std", - linewidth=2, color="grey", linestyle="--", - marker="x", zorder=1) + lns1 = ax1.plot( + x, + fitness, + label="Fitness", + linewidth=2, + color="black", + marker=".", + zorder=2, + ) + lns2 = ax2.plot( + x, + self.summary["std"], + label="Fitness std", + linewidth=2, + color="grey", + linestyle="--", + marker="x", + zorder=1, + ) ax2.set_ylabel("Fitness standard deviation") - ax1.set_title( - self.name + " [{}]".format(self.summary["elapsed_time"])) + ax1.set_title(self.name + " [{}]".format(self.summary["elapsed_time"])) ax1.set_xlim([0, config["max_gen"]]) ax1.set_xlabel("Generation") - lns = lns1+lns2 + lns = lns1 + lns2 labs = [l.get_label() for l in lns] ax1.legend(lns, labs) plt.tight_layout() - filename = os.path.join(self.cali_wd, - "Fitness_" + self.name + ".png") + filename = os.path.join(self.cali_wd, "Fitness_" + self.name + ".png") try: fig.savefig(filename, dpi=300) except Exception as e: @@ -543,9 +586,8 @@ def find_best_and_record(self, pop): def auto_save(self): cali_wd = self.cali_wd snap_shot = self.__dict__ - with open(os.path.join(cali_wd, "GA_auto_save.pickle"), - 'wb') as outfile: + with open(os.path.join(cali_wd, "GA_auto_save.pickle"), "wb") as outfile: # protocol=pickle.HIGHEST_PROTOCOL # print() pickle.dump(snap_shot, outfile) - return None \ No newline at end of file + return None diff --git a/HydroCNHS/data_collector.py b/src/hydrocnhs/data_collector.py similarity index 80% rename from HydroCNHS/data_collector.py rename to src/hydrocnhs/data_collector.py index f4b6a74..a8b344c 100644 --- a/HydroCNHS/data_collector.py +++ b/src/hydrocnhs/data_collector.py @@ -1,42 +1,44 @@ # Data collector module # Here we create a basic Data_collector aiming to collect agents' outputs. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2021/12/23. # Future plan: # Turn field into a class, which can contain unit, attributes, etc. import logging + logger = logging.getLogger("HydroCNHS.dc") from copy import deepcopy from .util import dict_to_string - + class Data_collector(object): def __init__(self): """A general data container, where each added field is a sub-container. - + A field can be a dictionary or a list. It can be called as an attribute of a Data_collector object. """ self.field_info = {} pass + def add_field(self, field, data_type={}, desc=None, unit=None, check_exist=True): - """Add a field to the data collector. - + """Add a field to the data collector. + A field can be a dictionary or a list. A Data_collector object - cannot have duplicated field name. + cannot have duplicated field name. Parameters ---------- field : str Field name. Cannot have space in a field name. data_type : dict, optional - Data type of the field (e.g., {} and []), by default {}. User can + Data type of the field (e.g., {} and []), by default {}. User can also populate the field by directly assigning data here. desc : str Field description. - unit : + unit : Unit of the field. check_exist : bool, optional If Ture, check the given field name is not existed before adding, @@ -45,20 +47,27 @@ def add_field(self, field, data_type={}, desc=None, unit=None, check_exist=True) field = field.replace(" ", "_") if check_exist: if field in self.field_info.keys(): - logger.error("Field {} already exist in the ".format(field)+ - "data collector. {} is not added.".format(field)) + logger.error( + "Field {} already exist in the ".format(field) + + "data collector. {} is not added.".format(field) + ) else: setattr(self, field, data_type) - self.field_info[field] = {"type": type(data_type), - "desc": desc, - "unit": unit} + self.field_info[field] = { + "type": type(data_type), + "desc": desc, + "unit": unit, + } logger.info("Add field {} ({}).".format(field, type(data_type))) else: setattr(self, field, data_type) - self.field_info[field] = {"type": type(data_type), - "desc": desc, - "unit": unit} + self.field_info[field] = { + "type": type(data_type), + "desc": desc, + "unit": unit, + } logger.info("Add field {} ({}).".format(field, type(data_type))) + def del_field(self, field): """Delete a field from the data collector. @@ -73,11 +82,12 @@ def del_field(self, field): logger.info("Delete field {}.".format(field)) else: logger.info("Field {} is not exist.".format(field)) + def get_field(self, field, copy=False): """Get a field. - + This function create a shortcut to access a field. Namely, changes of - a local variable of get_field() will be accumulated back to the + a local variable of get_field() will be accumulated back to the original data_collector. copy=Ture if a copy of a field is needed. Parameters @@ -85,8 +95,8 @@ def get_field(self, field, copy=False): field : str Field name. copy : bool - If true, create a copy of a field, which has seperate storage - pointer than the original data_collector. Otherwise, return a shortcut + If true, create a copy of a field, which has seperate storage + pointer than the original data_collector. Otherwise, return a shortcut of a field to the original data_collector. Returns ------- @@ -100,25 +110,24 @@ def get_field(self, field, copy=False): except Exception as e: print(e) logger.error(e) - + def list_fields(self): - """Print available fields in the data collector. - """ + """Print available fields in the data collector.""" print(dict_to_string(self.field_info)) - + def get_dict(self, copy=False): """Get data collector in dictionary format. - - Note that if copy=False, any modification on a variable assigned with + + Note that if copy=False, any modification on a variable assigned with the returned dictionary will also affect the data stored in the data collector object. - + Parameters ---------- copy : str If true, a copy of dictionary will be returned, else a pointer will be returned. Default False. - + Returns ------- dict @@ -128,4 +137,3 @@ def get_dict(self, copy=False): return deepcopy(self.__dict__) else: return self.__dict__ - \ No newline at end of file diff --git a/HydroCNHS/hydrocnhs.py b/src/hydrocnhs/hydrocnhs.py similarity index 53% rename from HydroCNHS/hydrocnhs.py rename to src/hydrocnhs/hydrocnhs.py index 7075df1..97a90e7 100644 --- a/HydroCNHS/hydrocnhs.py +++ b/src/hydrocnhs/hydrocnhs.py @@ -7,23 +7,48 @@ import time import traceback from copy import Error, deepcopy +import datetime import numpy as np -from pandas import date_range, to_datetime +from pandas import date_range, to_datetime, DataFrame from joblib import Parallel, delayed from tqdm import tqdm import logging from .rainfall_runoff_model.abcd import run_ABCD from .rainfall_runoff_model.gwlf import run_GWLF from .rainfall_runoff_model.pet_hamon import cal_pet_Hamon -from .routing import form_UH_Lohmann, run_step_Lohmann, run_step_Lohmann_convey -from .util import (set_logging_config, load_model, - load_customized_module_to_class, - list_callable_public_object) +from .routing import ( + form_UH_Lohmann, + run_step_Lohmann, + run_step_Lohmann_convey, + run_step_Lohmann_sed, + run_step_Lohmann_convey_sed, +) +from .sediment import run_TSS +from .util import ( + set_logging_config, + load_model, + load_customized_module_to_class, + list_callable_public_object, +) from .data_collector import Data_collector + class Model(object): - def __init__(self, model, name=None, rn_gen=None, checked=False, - parsed=False, log_filename=None): + def __init__( + self, + model, + name=None, + rn_gen=None, + checked=False, + parsed=False, + log_filename=None, + paral_setting={ + "verbose": 0, + "cores_pet": -1, + "cores_formUH": -1, + "cores_runoff": -1, + }, + ): """Create a HydroCNHS model. Parameters @@ -42,9 +67,15 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, If true, no checking process will be conducted, by default False. parsed : bool, optional If true, the model will not be re-parsed, by default False. - log_filename : If log filename is given, a log file will be created, by + log_filename : str, optional + If log filename is given, a log file will be created, by default None. Note: Do not create the log file when calbrating the model in parallel. Unexpected I/O errors may occur. + paral_setting : dict, optional + Parellel computation setting. Default {"verbose": 0, "cores_pet": + -1, "cores_formUH": -1, "cores_runoff": -1}. -1 means all cores + and treads, -2 means all threads - 1. See joblib package for more + details. """ # Assign model name and get logger. self.name = name @@ -55,13 +86,11 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, if name is None: self.logger = logging.getLogger("HydroCNHS") else: - self.logger = logging.getLogger("HydroCNHS."+name) + self.logger = logging.getLogger("HydroCNHS." + name) logger = self.logger # Parallelization setting - self.paral_setting = {"verbose": 0, - "cores_formUH": -1, - "cores_runoff": -1} + self.paral_setting = paral_setting # Load model.yaml model = load_model(model, checked=checked, parsed=parsed) @@ -74,16 +103,17 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, # User-provided rn generator self.rn_gen = rn_gen self.ss = rn_gen.bit_generator._seed_seq - logger.info( - "User-provided random number generator is assigned.") + logger.info("User-provided random number generator is assigned.") + rn_gen = self.rn_gen # Verify model contain all following keys. try: self.path = model["Path"] self.ws = model["WaterSystem"] # ws: Water system - self.runoff = model["RainfallRunoff"] # runoff: rainfall-runoff model - self.routing = model["Routing"] # routing: Routing - self.abm = model.get("ABM") # abm can be none (decoupled model) + self.runoff = model["RainfallRunoff"] # runoff: rainfall-runoff model + self.routing = model["Routing"] # routing: Routing + self.sediment = model.get("Sediment") + self.abm = model.get("ABM") # abm can be none (decoupled model) self.sys_parsed_data = model["SystemParsedData"] except: logger.error("Model file is incomplete or error.") @@ -95,35 +125,84 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, self.data_length = ws["DataLength"] start_date = self.start_date data_length = self.data_length - - # Assign rainfall-runoff function - ## Note the routing function is fixed to Lohmann. - if ws["RainfallRunoff"] == "GWLF": - self.runoff_func = run_GWLF - logger.info("Set rainfall-runoff to GWLF.") - elif ws["RainfallRunoff"] == "ABCD": - self.runoff_func = run_ABCD - logger.info("Set rainfall-runoff to ABCD.") - else: - logger.info("No assigned rainfall-runoff model.") + self.pd_date_index = date_range(start=start_date, periods=data_length, freq="D") + pd_date_index = self.pd_date_index + self.sim_sediment = False + self.run_sed = [False] * data_length # Initialize data_collector self.dc = Data_collector() # For collecting ABM's data. dc = self.dc - dc.add_field("Q_routed", {}, - desc="Routed streamflow of routing outlets.", - unit="cms") - dc.add_field("Q_runoff", {}, - desc="Subbasin runoffs of outlets.", - unit="cms") + dc.add_field( + "Q_runoff", + {sb: np.zeros(data_length) for sb in ws["Outlets"]}, + desc="Subbasin runoffs of outlets.", + unit="cms", + ) + dc.add_field( + "Q_routed", + {sb: np.zeros(data_length) for sb in ws["Outlets"]}, + desc="Routed streamflow of routing outlets.", + unit="cms", + ) + instream_agents = self.sys_parsed_data["DamAgents"] + # Add instream agent to Q_routed -------------------------------------- + if instream_agents is None: + instream_agents = [] + dc.Q_routed.update({isag: np.zeros(data_length) for isag in instream_agents}) + + if ws.get("Sediment") is not None: + self.sim_sediment = True + # Create index for monthly sediment simulation. + start_month = ws["Sediment"]["StartMonth"] + end_month = (start_month + 10) % 12 + 1 + sed_from_index = [ + i + for i, d in enumerate(pd_date_index) + if d.month == start_month and d.is_month_start + ] + sed_to_index = [ + i + for i, d in enumerate(pd_date_index) + if d.month == end_month and d.is_month_end + ] + if sed_to_index[0] < sed_from_index[0]: + sed_to_index = sed_to_index[1:] + if sed_from_index[-1] > sed_to_index[-1]: + sed_from_index = sed_from_index[:-1] + self.sed_from_index = sed_from_index + self.sed_to_index = sed_to_index + n_sed_month = len(sed_to_index) * 12 + + dc.add_field( + "TSS", + {sb: np.zeros(n_sed_month) for sb in ws["Outlets"]}, + desc="Total suspended sediment.", + unit="Mg", + ) + dc.TSS.update({isag: np.zeros(n_sed_month) for isag in instream_agents}) + + routing = self.routing + Q_frac = { + ro: {sb: np.zeros(data_length) for sb in list(sbs.keys())} + for ro, sbs in routing.items() + } + dc.add_field( + "Q_frac", + Q_frac, + desc="Each subbasin's streamflow contribution to routing outlets.", + unit="cms", + ) + dc.add_field("prec", {}, desc="Precipitation.", unit="cm") dc.add_field("temp", {}, desc="Temperature.", unit="degC") - dc.add_field("pet", {}, desc="Potential evapotranspiration.", - unit="cm") - dc.add_field("UH_Lohmann", {}, - desc="Unit hydrograph of Lohmann routing.") - dc.add_field("UH_Lohmann_convey", {}, - desc="Unit hydrograph of Lohmann routing for convey agent.") + dc.add_field("pet", {}, desc="Potential evapotranspiration.", unit="cm") + dc.add_field("UH_Lohmann", {}, desc="Unit hydrograph of Lohmann routing.") + dc.add_field( + "UH_Lohmann_convey", + {}, + desc="Unit hydrograph of Lohmann routing for convey agent.", + ) # ----- Load external modules ----------------------------------------- # We will automatically detect whether the ABM section is available. @@ -138,11 +217,10 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, # designing proper modules for HydroCNHS,. Certain protocals have to be # followed. - - self.agents = {} # Store all agent objects {agt_id: agt object}. - self.dms = {} # Store all dm objects {agt_id: dm object}. - self.instit_dms = {} # Store all institutional dm objects - # {institution: dm object}. + self.agents = {} # Store all agent objects {agt_id: agt object}. + self.dms = {} # Store all dm objects {agt_id: dm object}. + self.instit_dms = {} # Store all institutional dm objects + # {institution: dm object}. agents = self.agents dms = self.dms instit_dms = self.instit_dms @@ -155,9 +233,11 @@ def __init__(self, model, name=None, rn_gen=None, checked=False, # functions. class UserModules: pass + for module_name in ws["ABM"]["Modules"]: - load_customized_module_to_class(UserModules, module_name, - module_path) + load_customized_module_to_class( + UserModules, module_name, module_path + ) user_object_name_list = list_callable_public_object(UserModules) # Initialize agents and decision-making objects --------------- @@ -167,92 +247,147 @@ class UserModules: if ag_type in user_object_name_list: # Load from user-defined module. try: - agents[agt_id] = eval("UserModules."+ag_type)( - name=agt_id, config=ag_config, start_date=start_date, - current_date=start_date, data_length=data_length, - t=0, dc=dc, rn_gen=rn_gen) + agents[agt_id] = eval("UserModules." + ag_type)( + name=agt_id, + config=ag_config, + start_date=start_date, + current_date=start_date, + data_length=data_length, + t=0, + dc=dc, + rn_gen=rn_gen, + ) logger.info( - "Create {} from {} class".format(agt_id, ag_type)) + "Create {} from {} class".format(agt_id, ag_type) + ) except Exception as e: logger.error(traceback.format_exc()) raise Error( - "Fail to create {} from {} class.".format(agt_id, ag_type) - +"\nMake sure the class is well-defined in " - +"given modules.") from e + "Fail to create {} from {} class.".format( + agt_id, ag_type + ) + + "\nMake sure the class is well-defined in " + + "given modules." + ) from e else: # Try to load from built-in classes. try: agents[agt_id] = eval(ag_type)( - name=agt_id, config=ag_config, start_date=start_date, - current_date=start_date, data_length=data_length, - t=0, dc=dc, rn_gen=rn_gen) + name=agt_id, + config=ag_config, + start_date=start_date, + current_date=start_date, + data_length=data_length, + t=0, + dc=dc, + rn_gen=rn_gen, + ) logger.info( "Create {} from {} ".format(agt_id, ag_type) - +"from the built-in classes.") + + "from the built-in classes." + ) except Exception as e: logger.error(traceback.format_exc()) raise Error( - "Fail to create {} from {} class.".format(agt_id, ag_type) - +"\n{} is not a built-in class.".format(ag_type) - ) from e + "Fail to create {} from {} class.".format( + agt_id, ag_type + ) + + "\n{} is not a built-in class.".format(ag_type) + ) from e # Initialize dm or instit_dm is given in an agent object. dm_name = abm[ag_type][agt_id]["Inputs"].get("DMClass") ## instit_dm if dm_name is not None: instit_list = list(ws["ABM"]["Institutions"].keys()) - if (dm_name in instit_list - and dm_name in list(instit_dms.keys())): + if dm_name in instit_list and dm_name in list( + instit_dms.keys() + ): # Add institutional decision-making object to the agent object. # Agents belong to a institute will share one dm object. agents[agt_id].dm = instit_dms[dm_name] - elif (dm_name in instit_list - and dm_name not in list(instit_dms.keys())): - d = ws["ABM"]["InstitutionalDM"] - instit_dm_class = list(d.keys())[[dm_name in v \ - for v in list(d.values())].index(True)] + elif dm_name in instit_list and dm_name not in list( + instit_dms.keys() + ): + d = ws["ABM"]["InstitDMClasses"] + instit_dm_class = list(d.keys())[ + [dm_name in v for v in list(d.values())].index(True) + ] try: instit_dms[dm_name] = eval( - "UserModules."+instit_dm_class)( - name=dm_name, dc=dc, rn_gen=rn_gen) + "UserModules." + instit_dm_class + )( + name=dm_name, + dc=dc, + rn_gen=rn_gen, + abm_config=ws["ABM"], + start_date=start_date, + data_length=data_length, + ) logger.info( "Create institute {} from {} class.".format( - dm_name, instit_dm_class)) + dm_name, instit_dm_class + ) + ) except Exception as e: logger.error(traceback.format_exc()) raise Error( "Fail to create institute {} from {}".format( - dm_name, instit_dm_class)+" class.") from e + dm_name, instit_dm_class + ) + + " class." + ) from e agents[agt_id].dm = instit_dms[dm_name] # No built-in for institution - if (dm_name in user_object_name_list - and dm_name not in instit_list): + if ( + dm_name in user_object_name_list + and dm_name not in instit_list + ): try: - dms[agt_id] = eval( - "UserModules."+dm_name)( - name=agt_id, dc=dc, rn_gen=rn_gen) + dms[agt_id] = eval("UserModules." + dm_name)( + name=agt_id, + dc=dc, + rn_gen=rn_gen, + abm_config=ws["ABM"], + start_date=start_date, + data_length=data_length, + ) logger.info( - "Create {} from {} class.".format( - dm_name, dm_name)) + "Create {} from {} class.".format(dm_name, dm_name) + ) except Exception as e: logger.error(traceback.format_exc()) raise Error( "Fail to create {} from {} class.".format( - dm_name, dm_name)) from e + dm_name, dm_name + ) + ) from e agents[agt_id].dm = dms[agt_id] - elif (dm_name not in user_object_name_list - and dm_name not in instit_list): + elif ( + dm_name not in user_object_name_list + and dm_name not in instit_list + ): try: dms[agt_id] = eval(dm_name)( - name=agt_id, dc=dc, rn_gen=rn_gen) + name=agt_id, + dc=dc, + rn_gen=rn_gen, + abm_config=ws["ABM"], + start_date=start_date, + data_length=data_length, + ) logger.info( "Create {} from built-in class.".format( - dm_name, dm_name)) + dm_name + ) + ) except Exception as e: logger.error(traceback.format_exc()) raise Error( "Fail to create {} from built-in class.".format( - dm_name, dm_name)) from e + dm_name + ) + ) from e agents[agt_id].dm = dms[agt_id] # Make all agents accessible to all agents @@ -276,28 +411,41 @@ def load_weather_data(self, temp, prec, pet=None, outlets=[]): [cm] Daily potential evapotranpiration time series data (value) for each subbasin named by its outlet, by default None. E.g., {"subbasin1":[...], "subbasin2":[...]} + outlets : list, optional + Outlets that need weather data for rainfall-runoffs simulation. """ - ws = self.ws + pd_date_index = self.pd_date_index runoff = self.runoff logger = self.logger if pet is None: pet = {} # Default: calculate pet with Hamon's method and no dz adjustment. - for sb in outlets: - pet[sb] = cal_pet_Hamon(temp[sb], - runoff[sb]["Inputs"]["Latitude"], - ws["StartDate"], dz=None) - logger.info("Compute pet by Hamon method. Users can improve " - +"the efficiency by assigning pre-calculated pet.") + paral_setting = self.paral_setting + PetParel = Parallel( + n_jobs=paral_setting["cores_pet"], verbose=paral_setting["verbose"] + )( + delayed(cal_pet_Hamon)( + temp[sb], runoff[sb]["Inputs"]["Latitude"], pd_date_index, dz=None + ) + for sb in outlets + ) + pet = {sb: PetParel[i] for i, sb in enumerate(outlets)} + # for sb in outlets: + # pet[sb] = cal_pet_Hamon(temp[sb], + # runoff[sb]["Inputs"]["Latitude"], + # ws["StartDate"], dz=None) + logger.info( + "Compute pet by Hamon method. Users can improve " + + "the efficiency by assigning pre-calculated pet." + ) self.dc.temp = temp self.dc.prec = prec self.dc.pet = pet - #self.weather = {"temp":temp, "prec":prec, "pet":pet} - logger.info("Load temp & prec & pet with total length " - +"{}.".format(ws["DataLength"])) + logger.info( + "Load temp & prec & pet with total length " + "{}.".format(self.data_length) + ) - def run(self, temp, prec, pet=None, assigned_Q={}, assigned_UH={}, - disable=False): + def run(self, temp, prec, pet=None, assigned_Q={}, assigned_UH={}, disable=False): """Run HydroCNHS simulation. Parameters @@ -324,16 +472,21 @@ def run(self, temp, prec, pet=None, assigned_Q={}, assigned_UH={}, A dictionary of flow time series. """ # Variables + logger = self.logger + name = self.name + data_length = self.data_length + pd_date_index = self.pd_date_index paral_setting = self.paral_setting - sys_parsed_data = self.sys_parsed_data - routing_outlets = sys_parsed_data["RoutingOutlets"] + sim_sediment = self.sim_sediment ws = self.ws - start_date = self.start_date - data_length = self.data_length + # Setting runoff = self.runoff routing = self.routing - name = self.name - logger = self.logger + sediment = self.sediment + + sys_parsed_data = self.sys_parsed_data + routing_outlets = sys_parsed_data["RoutingOutlets"] + # Data collector/container. # This dc will be passed around HydroCNHS and ABM. dc = self.dc @@ -341,14 +494,15 @@ def run(self, temp, prec, pet=None, assigned_Q={}, assigned_UH={}, # ----- Start a timer ------------------------------------------------- start_time = time.monotonic() self.elapsed_time = 0 + def get_elapsed_time(): elapsed_time = time.monotonic() - start_time - self.elapsed_time = time.strftime("%H:%M:%S", - time.gmtime(elapsed_time)) + self.elapsed_time = time.strftime("%H:%M:%S", time.gmtime(elapsed_time)) return self.elapsed_time # ----- Rainfall-runoff simulation ------------------------------------ Q_runoff = dc.Q_runoff + Q_routed = dc.Q_routed outlets = ws["Outlets"] # Remove sub-basin that don't need to be simulated. outlets = list(set(outlets) - set(assigned_Q.keys())) @@ -365,61 +519,123 @@ def get_elapsed_time(): routing[ro][sb]["Pars"]["GScale"] = None logger.info( "Turn {}'s GShape and GScale to ".format((sb, ro)) - +"None in the routing setting. There is no " - +"in-grid time lag with given observed Q.") - - # Start runoff simulation in parallel. - logger.info("Pre-calculate rainfall-runoffs for {} sub-basins. [{}]".format( - len(outlets), get_elapsed_time())) - QParel = Parallel(n_jobs=paral_setting["cores_runoff"], - verbose=paral_setting["verbose"]) \ - ( delayed(self.runoff_func)\ - (pars=runoff[sb]["Pars"], inputs=runoff[sb]["Inputs"], - temp=dc.temp[sb], prec=dc.prec[sb], - pet=dc.pet[sb], start_date=ws["StartDate"], - data_length=data_length) \ - for sb in outlets ) - # ----- Add user assigned Q first. ------------------------------------ - # Collect QParel results - Q_runoff = deepcopy(assigned_Q) # Necessary deepcopy! - for i, sb in enumerate(outlets): - Q_runoff[sb] = QParel[i] + + "None in the routing setting. There is no " + + "in-grid time lag with given observed Q." + ) + # Setup runoff simulation. + # Assign rainfall-runoff function + runoff_vars = { + sb: None for sb in outlets + } # Record vars for continuous run of rr. + if ws["RainfallRunoff"] == "GWLF": + runoff_func = run_GWLF + logger.info("Set rainfall-runoff to GWLF.") + # Pre-calculate MonthlyTavg. + MonthlyTavg = DataFrame(temp, index=pd_date_index) + MonthlyTavg = MonthlyTavg.resample("MS").mean() + # Broadcast back to daily sequence. + # Note: Data has to longer than a month or it will show error. + try: + MonthlyTavg.index = ( + [pd_date_index[0]] + + list(MonthlyTavg.index[1:-1]) + + [pd_date_index[-1]] + ) + MonthlyTavg = MonthlyTavg.resample("D").ffill().to_dict(orient="list") + except Exception as e: + print(e) + print("The simulation period has to be longer than a month.") + elif ws["RainfallRunoff"] == "ABCD": + runoff_func = run_ABCD + logger.info("Set rainfall-runoff to ABCD.") + else: + logger.info("No assigned rainfall-runoff model.") + # ----- Add user assigned Q first. ------------------------------------ + Q_runoff.update(deepcopy(assigned_Q)) # Necessary deepcopy! # Q_routed will be continuously updated for routing. # Necessary deepcopy to isolate self.Q_runoff and self.Q_routed storage # pointer! - dc.Q_routed = deepcopy(Q_runoff) - logger.info("Complete rainfall-runoff simulation. [{}]".format( - get_elapsed_time())) + Q_routed.update(deepcopy(Q_runoff)) + + def run_rainfall_runoff(s=0, l=data_length): + """s: start index, l: lenght""" + if ws["RainfallRunoff"] == "GWLF": + QParel = Parallel( + n_jobs=paral_setting["cores_runoff"], + verbose=paral_setting["verbose"], + )( + delayed(runoff_func)( + pars=runoff[sb]["Pars"], + inputs=runoff[sb]["Inputs"], + temp=dc.temp[sb][s:l], + prec=dc.prec[sb][s:l], + pet=dc.pet[sb][s:l], + monthly_Tavg=MonthlyTavg[sb][s:l], + vars=runoff_vars[sb], + ) + for sb in outlets + ) + elif ws["RainfallRunoff"] == "ABCD": + QParel = Parallel( + n_jobs=paral_setting["cores_runoff"], + verbose=paral_setting["verbose"], + )( + delayed(runoff_func)( + pars=runoff[sb]["Pars"], + inputs=runoff[sb]["Inputs"], + temp=dc.temp[sb][s:l], + prec=dc.prec[sb][s:l], + pet=dc.pet[sb][s:l], + monthly_Tavg=None, + vars=runoff_vars[sb], + ) + for sb in outlets + ) + + # Collect QParel results (not right) + for i, sb in enumerate(outlets): + Q_runoff[sb][s:l] = QParel[i][0] + Q_routed[sb][s:l] = QParel[i][0] + runoff_vars[sb] = QParel[i][1] + logger.info("\nCompute rainfall-runoffs for {} time steps.".format(l)) + return None + + logger.info( + "Complete rainfall-runoff simulation setup. [{}]".format(get_elapsed_time()) + ) # ----- Form UH for Lohmann routing method ---------------------------- # if ws_abm["Routing"] == "Lohmann": # No other choice # Form combination - UH_List = [(sb, ro) for ro in routing_outlets \ - for sb in routing[ro]] + UH_List = [(sb, ro) for ro in routing_outlets for sb in routing[ro]] # Remove assigned UH from the list. UH_List_Lohmann = list(set(UH_List) - set(assigned_UH.keys())) # Start forming UH_Lohmann in parallel. logger.info( "Start forming {} UHs for Lohmann routing. [{}]".format( - len(UH_List_Lohmann), get_elapsed_time())) + len(UH_List_Lohmann), get_elapsed_time() + ) + ) # pair = (outlet, routing outlet) - UHParel = Parallel(n_jobs=paral_setting["cores_formUH"], - verbose=paral_setting["verbose"]) \ - ( delayed(form_UH_Lohmann)\ - (routing[pair[1]][pair[0]]["Inputs"], - routing[pair[1]][pair[0]]["Pars"]) \ - for pair in UH_List_Lohmann ) + UHParel = Parallel( + n_jobs=paral_setting["cores_formUH"], verbose=paral_setting["verbose"] + )( + delayed(form_UH_Lohmann)( + routing[pair[1]][pair[0]]["Inputs"], routing[pair[1]][pair[0]]["Pars"] + ) + for pair in UH_List_Lohmann + ) # Form UH --------------------------------------------------------- # Add user assigned UH first. UH_Lohmann = dc.UH_Lohmann - UH_Lohmann = deepcopy(assigned_UH) # Necessary deepcopy! + UH_Lohmann.update(deepcopy(assigned_UH)) # Necessary deepcopy! for i, pair in enumerate(UH_List_Lohmann): UH_Lohmann[pair] = UHParel[i] logger.info( - "Complete forming UHs for Lohmann routing. [{}]".format( - get_elapsed_time())) + "Complete forming UHs for Lohmann routing. [{}]".format(get_elapsed_time()) + ) # Form UH for conveyed nodes -------------------------------------- # No in-grid routing. @@ -432,59 +648,87 @@ def get_elapsed_time(): if uh in list(assigned_UH.keys()): logger.error( "Cannot process routing of conveying agents " - +"since {} unit hydrograph is assigned. We will " - +"use the assigned UH for simulation; however, " - +"the results might not be accurate.".format(uh)) + + "since {} unit hydrograph is assigned. We will " + + "use the assigned UH for simulation; however, " + + "the results might not be accurate.".format(uh) + ) UH_Lohmann_convey = UH_Lohmann[uh] else: UH_convey_List.append(uh) - UHParel = Parallel(n_jobs=paral_setting["cores_formUH"], - verbose=paral_setting["verbose"]) \ - ( delayed(form_UH_Lohmann)\ - (routing[pair[1]][pair[0]]["Inputs"], - routing[pair[1]][pair[0]]["Pars"], - force_ingrid_off=True) \ - for pair in UH_convey_List ) + UHParel = Parallel( + n_jobs=paral_setting["cores_formUH"], verbose=paral_setting["verbose"] + )( + delayed(form_UH_Lohmann)( + routing[pair[1]][pair[0]]["Inputs"], + routing[pair[1]][pair[0]]["Pars"], + force_ingrid_off=True, + ) + for pair in UH_convey_List + ) for i, pair in enumerate(UH_convey_List): UH_Lohmann_convey[pair] = UHParel[i] logger.info( "Complete forming UHs for conveyed nodes. [{}]".format( - get_elapsed_time())) + get_elapsed_time() + ) + ) + + # Sediment + if sim_sediment: + sed_from_index = self.sed_from_index + sed_to_index = self.sed_to_index + Q_frac = dc.Q_frac + routing_func = run_step_Lohmann_sed + routing_convey_func = run_step_Lohmann_convey_sed + instream_agents = sys_parsed_data["DamAgents"] + if instream_agents is None: + instream_agents = [] + # RE_dict = {} + # for sb, v in sediment.items(): + # cm = v["Inputs"]["CoolMonths"] + # ac = v["Pars"]["Ac"] + # aw = v["Pars"]["Aw"] + # RE_dict[sb] = 64.6 * np.array(prec[sb])**1.81 \ + # * np.array([ac if d.month in cm else aw for d in pd_date_index]) + run_sed = [True if i in sed_to_index else False for i in range(data_length)] + else: + Q_frac = None + routing_func = run_step_Lohmann + routing_convey_func = run_step_Lohmann_convey + run_sed = [False] * data_length # ----- Time step simulation (Coupling hydrological model and ABM) ---- - Q_routed = dc.Q_routed - # Obtain datetime index ----------------------------------------------- - pd_date_index = date_range(start=start_date, periods=data_length, - freq="D") - self.pd_date_index = pd_date_index # So users can use it directly. - # Load system-parsed data --------------------------------------------- sim_seq = sys_parsed_data["SimSeq"] ag_sim_seq = sys_parsed_data["AgSimSeq"] - instream_agents = sys_parsed_data["DamAgents"] - if instream_agents is None: - instream_agents = [] - - # Add instream agent to Q_routed -------------------------------------- - # instream_agents include DamAgents - for isag in instream_agents: - Q_routed[isag] = np.zeros(data_length) ##### Only a semi-distributed hydrological model ###################### - ##### (Only RainfallRunoff and Routing) + yi = 0 # for sediment simulation + if ws["ABM"] is None: logger.info("Start a pure hydrological simulation (no human component).") + run_rainfall_runoff(s=0, l=data_length) # Run step-wise routing to update Q_routed ------------------------ - for t in tqdm(range(data_length), desc=name, disable=disable): + for t, sed_TF in tqdm( + zip(range(data_length), run_sed), desc=name, disable=disable + ): current_date = pd_date_index[t] for node in sim_seq: if node in routing_outlets: - #----- Run Lohmann routing model for one routing outlet + # ----- Run Lohmann routing model for one routing outlet # (node) for 1 timestep (day). - Qt = run_step_Lohmann(node, routing, UH_Lohmann, - Q_routed, Q_runoff, t) - #----- Store Qt to final output. + Qt = routing_func( + node, routing, UH_Lohmann, Q_routed, Q_runoff, t, Q_frac + ) + # ----- Store Qt to final output. Q_routed[node][t] = Qt + if sed_TF and sim_sediment: + fi = sed_from_index[yi] + ti = sed_to_index[yi] + # run_TSS(RE_dict, sediment, routing, instream_agents, + # pd_date_index, Q_frac, dc.TSS, + # yi, fi, ti) + yi += 1 ##### HydroCNHS model (Coupled model) ################################# # We create four interfaces for "two-way coupling" between natural @@ -498,8 +742,15 @@ def get_elapsed_time(): for c_node in conveyed_nodes: Q_convey[c_node] = np.zeros(data_length) - for t in tqdm(range(data_length), desc=name, disable=disable): + for t, sed_TF in tqdm( + zip(range(data_length), run_sed), desc=name, disable=disable + ): current_date = pd_date_index[t] + # Simulate rainfall runoffs + # can be modified to update rr pars. e.g., land use changes. + if t == 0: + run_rainfall_runoff(s=0, l=data_length) + # Update agents' attributes to new timestep. for name, agt in agents.items(): agt.current_date = current_date @@ -508,25 +759,30 @@ def get_elapsed_time(): for node in sim_seq: # Load active agent for current node at time t ------------ river_div_ags_plus = ag_sim_seq["AgSimPlus"][node].get( - "RiverDivAgents") + "RiverDivAgents" + ) river_div_ags_minus = ag_sim_seq["AgSimMinus"][node].get( - "RiverDivAgents") + "RiverDivAgents" + ) insitu_ags_minus = ag_sim_seq["AgSimMinus"][node].get( - "InsituAgents") - insitu_ags_plus = ag_sim_seq["AgSimPlus"][node].get( - "InsituAgents") - dam_ags_plus = ag_sim_seq["AgSimPlus"][node].get( - "DamAgents") + "InsituAgents" + ) + insitu_ags_plus = ag_sim_seq["AgSimPlus"][node].get("InsituAgents") + dam_ags_plus = ag_sim_seq["AgSimPlus"][node].get("DamAgents") convey_ags_plus = ag_sim_seq["AgSimPlus"][node].get( - "ConveyingAgents") + "ConveyingAgents" + ) convey_ags_minus = ag_sim_seq["AgSimMinus"][node].get( - "ConveyingAgents") + "ConveyingAgents" + ) # Note for the first three if, we should only enter one of # them at each node. - if (insitu_ags_minus is not None + if ( + insitu_ags_minus is not None or insitu_ags_plus is not None - or river_div_ags_plus is not None): + or river_div_ags_plus is not None + ): r""" For InsituAPI, they change water directly from the runoff in each sub-basin or grid. @@ -584,13 +840,15 @@ def get_elapsed_time(): Q_convey[o][t] += delta if node in routing_outlets: - #----- Run Lohmann routing model for one routing outlet + # ----- Run Lohmann routing model for one routing outlet # (node) for 1 time step (day). - Qt = run_step_Lohmann( - node, routing, UH_Lohmann, Q_routed, Q_runoff, t) - Qt_convey = run_step_Lohmann_convey( - node, routing, UH_Lohmann_convey, Q_convey, t) - #----- Store Qt to final output. + Qt = routing_func( + node, routing, UH_Lohmann, Q_routed, Q_runoff, t, Q_frac + ) + Qt_convey = routing_convey_func( + node, routing, UH_Lohmann_convey, Q_convey, t, Q_frac + ) + # ----- Store Qt to final output. Q_routed[node][t] = Qt + Qt_convey if convey_ags_minus is not None: @@ -606,10 +864,17 @@ def get_elapsed_time(): for ag, o in river_div_ags_minus: delta = agents[ag].act(outlet=o) Q_routed[o][t] += delta + + if sed_TF and sim_sediment: + fi = sed_from_index[yi] + ti = sed_to_index[yi] + # run_TSS(RE_dict, sediment, routing, instream_agents, + # pd_date_index, Q_frac, dc.TSS, + # yi, fi, ti) + yi += 1 # --------------------------------------------------------------------- - print("") # Force the logger to start a new line after tqdm. - logger.info( - "Complete HydroCNHS simulation! [{}]\n".format(get_elapsed_time())) + print("") # Force the logger to start a new line after tqdm. + logger.info("Complete HydroCNHS simulation! [{}]\n".format(get_elapsed_time())) # [cms] Streamflow for routing outlets (Gauged outlets and inflow # outlets of instream agents). For other variables users need to # extract them manually from this class. @@ -623,4 +888,4 @@ def get_model_object(self): dict model object dictionary. """ - return self.__dict__ \ No newline at end of file + return self.__dict__ diff --git a/src/hydrocnhs/indicators.py b/src/hydrocnhs/indicators.py new file mode 100644 index 0000000..407a42f --- /dev/null +++ b/src/hydrocnhs/indicators.py @@ -0,0 +1,407 @@ +# Indicator module +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# Last update at 2021/12/23. + +import numpy as np +import pandas as pd +import warnings + +class Indicator: + """A class for calculating indicators.""" + + def __init__(self) -> None: + """Initialize an Indicator class to manage various hydrological calculations. + + r : Correlation of correlation + r2 : Coefficient of determination + rmse: Root mean square error + nse : Nash-Sutcliffe efficiency + inse: nse with inverse transformed Q. + cp : Correlation of persistence + rsr : RMSE-observations standard deviation ratio + kge : Kling-Gupta efficiency + ikge: kge with inverse transformed Q. + + Note + ---- + The code is adopted from HydroCNHS (Lin et al., 2022). + Lin, C. Y., Yang, Y. C. E., & Wi, S. (2022). HydroCNHS: A Python Package of + Hydrological Model for Coupled Natural-Human Systems. Journal of Water + Resources Planning and Management, 148(12), 06022005. + """ + pass + + @staticmethod + def remove_na(x_obv, y_sim): + """Remove nan in x_obv and y_sim. + + This function makes sure there is no nan involves in the indicator + calculation. If nan is detected, data points will be remove from x_obv + and y_sim simultaneously. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + + Returns + ------- + tuple + Updated (x_obv, y_sim) + """ + x_obv = np.array(x_obv) + y_sim = np.array(y_sim) + + # Create a mask where neither x_obv nor y_sim has NaN + mask = ~np.isnan(x_obv) & ~np.isnan(y_sim) + + # Apply the mask to both arrays + x_obv = x_obv[mask] + y_sim = y_sim[mask] + + return x_obv, y_sim + + @staticmethod + def cal_indicator_df( + x_obv, y_sim, index_name="value", indicators_list=None, r_na=True + ): + """Calculate indicators and return as a DataFrame. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + index_name : str, optional + Index name, by default "value". + indicators_list : list, optional + List of indicators, by default None. + r_na : bool, optional + Remove nan, by default True. + + Returns + ------- + DataFrame + A DataFrame of indicators. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + indicators_dict = { + "r": Indicator.get_r(x_obv, y_sim, False), + "r2": Indicator.get_r2(x_obv, y_sim, False), + "rmse": Indicator.get_rmse(x_obv, y_sim, False), + "nse": Indicator.get_nse(x_obv, y_sim, False), + "inse": Indicator.get_inse(x_obv, y_sim, False), + "kge": Indicator.get_kge(x_obv, y_sim, False), + "ikge": Indicator.get_ikge(x_obv, y_sim, False), + "cp": Indicator.get_cp(x_obv, y_sim, False), + "rsr": Indicator.get_rsr(x_obv, y_sim, False), + } + df = pd.DataFrame(indicators_dict, index=[index_name]) + if indicators_list is None: + return df + else: + return df.loc[:, indicators_list] + + @staticmethod + def get_df_indicator(x_obv_df, y_sim_df, indicators_list=None, r_na=True): + """Calculate indicators and return as a DataFrame. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + indicators_list : list, optional + List of indicators, by default None. + r_na : bool, optional + Remove nan, by default True. + + Returns + ------- + DataFrame + A DataFrame of indicators. + """ + # Get common columns between two dataframes + common_columns = x_obv_df.columns.intersection(y_sim_df.columns) + + results = pd.DataFrame(index=indicators_list) + for col in common_columns: + x_obv = x_obv_df[col] + y_sim = y_sim_df[col] + + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=RuntimeWarning) + + inds = [eval(f"Indicator.get_{ind}(x_obv, y_sim, False)") for ind in indicators_list] + results[col] = inds + + return results + + @staticmethod + def get_r(x_obv, y_sim, r_na=True): + """Correlation. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + r coefficient. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + r = np.corrcoef(x_obv, y_sim)[0, 1] + if np.isnan(r): + # We don't consider 2 identical horizontal line as r = 1! + r = 0 + return r + + @staticmethod + def get_r2(x_obv, y_sim, r_na=True): + """Coefficient of determination. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + r2 coefficient. + """ + r = Indicator.get_r(x_obv, y_sim, r_na) + return r**2 + + @staticmethod + def get_rmse(x_obv, y_sim, r_na=False): + """Root mean square error. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Root mean square error. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + return np.nanmean((x_obv - y_sim) ** 2) ** 0.5 + + @staticmethod + def get_nse(x_obv, y_sim, r_na=False): + """Nash-Sutcliffe efficiency. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Nash-Sutcliffe efficiency. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + + mu_xObv = np.nanmean(x_obv) + return 1 - np.nansum((y_sim - x_obv) ** 2) / np.nansum((x_obv - mu_xObv) ** 2) + + @staticmethod + def get_inse(x_obv, y_sim, r_na=False): + """Inverse Nash-Sutcliffe efficiency. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Inverse Nash-Sutcliffe efficiency. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + # Prevent dividing zero. + if np.nanmean(x_obv) == 0: + x_obv = 1 / (x_obv + 0.0000001) + else: + x_obv = 1 / (x_obv + 0.01 * np.nanmean(x_obv)) + + if np.nanmean(y_sim) == 0: + y_sim = 1 / (y_sim + 0.0000001) + else: + y_sim = 1 / (y_sim + 0.01 * np.nanmean(y_sim)) + + mu_xObv = np.nanmean(x_obv) + return 1 - np.nansum((y_sim - x_obv) ** 2) / np.nansum((x_obv - mu_xObv) ** 2) + + @staticmethod + def get_cp(x_obv, y_sim, r_na=False): + """Correlation of persistence. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Correlation of persistence. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + a = np.nansum((x_obv[1:] - x_obv[:-1]) ** 2) + if a == 0: + a = 0.0000001 + return 1 - np.nansum((x_obv[1:] - y_sim[1:]) ** 2) / a + + @staticmethod + def get_rsr(x_obv, y_sim, r_na=False): + """RMSE-observations standard deviation ratio. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + RMSE-observations standard deviation ratio. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + sig_xObv = np.nanstd(x_obv) + return Indicator.get_rmse(x_obv, y_sim) / sig_xObv + + @staticmethod + def get_kge(x_obv, y_sim, r_na=True): + """Kling-Gupta efficiency. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Kling-Gupta efficiency. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + + mu_ySim = np.nanmean(y_sim) + mu_xObv = np.nanmean(x_obv) + sig_ySim = np.nanstd(y_sim) + sig_xObv = np.nanstd(x_obv) + kge = ( + 1 + - ( + (Indicator.get_r(x_obv, y_sim, False) - 1) ** 2 + + (sig_ySim / sig_xObv - 1) ** 2 + + (mu_ySim / mu_xObv - 1) ** 2 + ) + ** 0.5 + ) + return kge + + @staticmethod + def get_ikge(x_obv, y_sim, r_na=True): + """Inverse Kling-Gupta efficiency. + + Parameters + ---------- + x_obv : array + Observation data. + y_sim : array + Simulation data. + r_na : bool, optional + Remove nan, by default True + + Returns + ------- + float + Inverse Kling-Gupta efficiency. + """ + if r_na: + x_obv, y_sim = Indicator.remove_na(x_obv, y_sim) + + # Prevent dividing zero. + if np.nanmean(x_obv) == 0: + x_obv = 1 / (x_obv + 0.0000001) + else: + x_obv = 1 / (x_obv + 0.01 * np.nanmean(x_obv)) + + if np.nanmean(y_sim) == 0: + y_sim = 1 / (y_sim + 0.0000001) + else: + y_sim = 1 / (y_sim + 0.01 * np.nanmean(y_sim)) + + mu_ySim = np.nanmean(y_sim) + mu_xObv = np.nanmean(x_obv) + sig_ySim = np.nanstd(y_sim) + sig_xObv = np.nanstd(x_obv) + ikge = ( + 1 + - ( + (Indicator.get_r(x_obv, y_sim, False) - 1) ** 2 + + (sig_ySim / sig_xObv - 1) ** 2 + + (mu_ySim / mu_xObv - 1) ** 2 + ) + ** 0.5 + ) + return ikge diff --git a/HydroCNHS/model_builder.py b/src/hydrocnhs/model_builder.py similarity index 62% rename from HydroCNHS/model_builder.py rename to src/hydrocnhs/model_builder.py index 373e80c..95e7bd4 100644 --- a/HydroCNHS/model_builder.py +++ b/src/hydrocnhs/model_builder.py @@ -1,5 +1,5 @@ # Model builder module. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2022/5/19. import os from copy import deepcopy @@ -8,8 +8,7 @@ from .abm_script import * model_template = { - "Path": {"WD": "", - "Modules": ""}, + "Path": {"WD": "", "Modules": ""}, "WaterSystem": { "StartDate": "yyyy/mm/dd", "EndDate": "yyyy/mm/dd", @@ -19,105 +18,110 @@ "NodeGroups": [], "RainfallRunoff": None, "Routing": "Lohmann", - "ABM": None + "ABM": None, }, "RainfallRunoff": {}, - "Routing": {} + "Routing": {}, } GWLF_template = { - "Inputs": {"Area": None, - "Latitude": None, - "S0": 2, - "U0": 10, - "SnowS": 5}, - "Pars": {"CN2": -99, - "IS": -99, - "Res": -99, - "Sep": -99, - "Alpha": -99, - "Beta": -99, - "Ur": -99, - "Df": -99, - "Kc": -99} + "Inputs": {"Area": None, "Latitude": None, "S0": 2, "U0": 10, "SnowS": 5}, + "Pars": { + "CN2": -99, + "IS": -99, + "Res": -99, + "Sep": -99, + "Alpha": -99, + "Beta": -99, + "Ur": -99, + "Df": -99, + "Kc": -99, + }, } ABCD_template = { - "Inputs": {"Area": None, - "Latitude": None, - "XL": 2, - "SnowS": 5}, - "Pars": {"a": -99, - "b": -99, - "c": -99, - "d": -99, - "Df": -99} + "Inputs": {"Area": None, "Latitude": None, "XL": 2, "SnowS": 5}, + "Pars": {"a": -99, "b": -99, "c": -99, "d": -99, "Df": -99}, } -Other_template = { - "Inputs": {}, - "Pars": {} -} +Other_template = {"Inputs": {}, "Pars": {}} Lohmann_template = { - "Inputs": {"FlowLength": None, - "InstreamControl": False}, - "Pars": {"GShape": -99, - "GScale": -99, - "Velo": -99, - "Diff": -99} + "Inputs": {"FlowLength": None, "InstreamControl": False}, + "Pars": {"GShape": -99, "GScale": -99, "Velo": -99, "Diff": -99}, } ABM_template = { "Modules": [], - "InstitDMClasses": {}, # {InstitDMClasses: Institution} + "InstitDMClasses": {}, # {InstitDMClasses: Institution} "DMClasses": [], "DamAPI": [], "RiverDivAPI": [], "InsituAPI": [], - "ConveyingAPI": [], - "Institutions": {} # {Institution: [agent list]} + "ConveyingAPI": [], + "Institutions": {}, # {Institution: [agent list]} } agent_template = { "Attributes": {}, - "Inputs":{ - "Priority": 0, - "Links": {}, - "DMClass": None + "Inputs": {"Priority": 0, "Links": {}, "DMClass": None}, + "Pars": {}, +} + +sediment_template = { + "Inputs": {"IDs": [], "Areas": [], "CoolMonths": [], "WarmMonths": []}, + "Pars": { + "K": [], + "Ac": -99, + "Aw": -99, + "LS": [], + "DR": -99, + "CP": -99, + "Sa": -99, + "Sb": -99, + "Sq": -99, }, - "Pars": {} } +# "CP" can be a value or a list + class ModelBuilder(object): - def __init__(self, wd): - self.model = deepcopy(model_template) + def __init__(self, wd, model=None): + if model is None: + self.model = deepcopy(model_template) + else: + self.model = deepcopy(model) self.model["Path"]["WD"] = wd self.wd = wd self.help() print("Use .help to re-print the above instructions.") - + class APIs(object): def __init__(self): self.Dam = "DamAPI" self.InSitu = "InSituAPI" self.RiverDiv = "RiverDivAPI" self.Conveying = "ConveyingAPI" + self.api = APIs() - + def help(self): - print("Follow the following steps to create model & ABM script templates:\n" - +"\tStep 1: set_water_system()\n" - +"\tStep 2: set_rainfall_runoff()\n" - +"\tStep 3: set_routing_outlet(), one at a time.\n" - +"\tStep 4: (optional) set_ABM().\n" - +"\tStep 5: (optional) add_agent().\n" - +"\tStep 6: (optional) add_institution().\n" - +"\tStep 7: write_model_to_yaml()\n" - +"\tStep 8: gen_ABM_script_template()\n" - +"Open generated model.yaml & ABM module template " - +"and further edit them.") - + print( + "Follow the following steps to create model & ABM script templates:\n" + + "\tStep 1: set_water_system()\n" + + "\tStep 2: set_rainfall_runoff()\n" + + "\tStep 3: set_routing_outlet(), one at a time.\n" + + "\tStep 4: (optional) set_sediment().\n" + + "\tStep 5: (optional) add_sediment(), one at a time.\n" + + "\tStep 6: (optional) set_ABM().\n" + + "\tStep 7: (optional) add_agent().\n" + + "\tStep 8: (optional) add_institution().\n" + + "\tStep 9: write_model_to_yaml()\n" + + "\tStep 10: (optional) gen_ABM_script_template()\n" + + "Open the generated draft 'model.yaml' (& ABM module template) " + + "and further edit them." + ) + def set_water_system(self, start_date, end_date): """Set up WaterSystem. @@ -130,12 +134,17 @@ def set_water_system(self, start_date, end_date): """ self.model["WaterSystem"]["StartDate"] = start_date self.model["WaterSystem"]["EndDate"] = end_date - end_date = pd.to_datetime(end_date, format='%Y/%m/%d') - start_date = pd.to_datetime(start_date, format='%Y/%m/%d') - self.model["WaterSystem"]["DataLength"] = (end_date-start_date).days+1 - - def set_rainfall_runoff(self, outlet_list, area_list=None, lat_list=None, - runoff_model="GWLF"): + end_date = pd.to_datetime(end_date, format="%Y/%m/%d") + start_date = pd.to_datetime(start_date, format="%Y/%m/%d") + self.model["WaterSystem"]["DataLength"] = (end_date - start_date).days + 1 + + self.model["WaterSystem"]["Outlets"] = [] + self.model["WaterSystem"]["NumSubbasins"] = 0 + self.model["WaterSystem"]["RainfallRunoff"] = None + + def set_rainfall_runoff( + self, outlet_list, area_list=None, lat_list=None, runoff_model="GWLF", activate=True + ): """Set up RainfallRunoff. Parameters @@ -148,15 +157,15 @@ def set_rainfall_runoff(self, outlet_list, area_list=None, lat_list=None, Latitude [deg] list corresponding to outlet_list, by default None. runoff_model : str, optional "GWLF" or "ABCD" or "Other", by default None. - - Note : - If "Other" is selected for runoff_model, users must provide + + Note : + If "Other" is selected for runoff_model, users must provide precalculated runoffs for each subbasin as an input to HydroCNHS. """ - self.model["WaterSystem"]["Outlets"] = outlet_list - self.model["WaterSystem"]["NumSubbasins"] = len(outlet_list) + self.model["WaterSystem"]["Outlets"] += outlet_list + self.model["WaterSystem"]["NumSubbasins"] += len(outlet_list) self.model["WaterSystem"]["RainfallRunoff"] = runoff_model - + # Select RainfallRunoff templete. if runoff_model == "GWLF": RainfallRunoff_templete = GWLF_template @@ -166,21 +175,33 @@ def set_rainfall_runoff(self, outlet_list, area_list=None, lat_list=None, RainfallRunoff_templete = Other_template else: raise ValueError( - "Given rainfall-runoff model, {}, is not eligible.".format(runoff_model)) - + "Given rainfall-runoff model, {}, is not eligible.".format(runoff_model) + ) + for sub in outlet_list: - self.model["RainfallRunoff"][sub] = deepcopy(RainfallRunoff_templete) - + self.model["RainfallRunoff"][sub] = deepcopy(RainfallRunoff_templete) + if activate is False: + self.model["RainfallRunoff"][sub]["Pars"] = { + k: None for k in self.model["RainfallRunoff"][sub]["Pars"] + } if area_list is not None: for i, sub in enumerate(outlet_list): self.model["RainfallRunoff"][sub]["Inputs"]["Area"] = area_list[i] if lat_list is not None: for i, sub in enumerate(outlet_list): self.model["RainfallRunoff"][sub]["Inputs"]["Latitude"] = lat_list[i] - - def set_routing_outlet(self, routing_outlet, upstream_outlet_list, - instream_objects=[], flow_length_list=None, - routing_model="Lohmann"): + + + + def set_routing_outlet( + self, + routing_outlet, + upstream_outlet_list, + instream_objects=[], + flow_length_list=None, + routing_model="Lohmann", + activate=True, + ): """Set up a routing outlet. Parameters @@ -194,7 +215,7 @@ def set_routing_outlet(self, routing_outlet, upstream_outlet_list, instream_objects : list, optional A list of instream objects' names (i.e., dam agents), by default []. flow_length_list : list, optional - A list of flow lengths. The order has to consist to the + A list of flow lengths. The order has to consist to the upstream_outlet_list. routing_model : list, optional Routing model, by default "Lohmann". @@ -202,25 +223,29 @@ def set_routing_outlet(self, routing_outlet, upstream_outlet_list, """ outlet_list = self.model["WaterSystem"]["Outlets"] self.model["WaterSystem"]["Routing"] = routing_model - + if routing_outlet not in outlet_list: raise ValueError( "Given routing_outlet, {}, ".format(routing_outlet) - +"is not in the outlet list {}.".format(str(outlet_list)) - +"Please run set_runoff() first.") - + + "is not in the outlet list {}.".format(str(outlet_list)) + + "Please run set_runoff() first." + ) + if flow_length_list is None: - flow_length_list = [None]*len(upstream_outlet_list) - + flow_length_list = [None] * len(upstream_outlet_list) + if routing_outlet not in upstream_outlet_list: upstream_outlet_list.append(routing_outlet) flow_length_list.append(0) - + self.model["Routing"][routing_outlet] = {} route_ro = self.model["Routing"][routing_outlet] - + for i, o in enumerate(upstream_outlet_list): - if o in instream_objects: + if activate is False: + route_ro[o] = deepcopy(Lohmann_template) + route_ro[o]["Pars"] = {k: None for k in route_ro[o]["Pars"]} + elif o in instream_objects: # No within-subbasin routing. route_ro[o] = deepcopy(Lohmann_template) route_ro[o]["Pars"]["GShape"] = None @@ -229,8 +254,9 @@ def set_routing_outlet(self, routing_outlet, upstream_outlet_list, elif o not in outlet_list: raise ValueError( "Given upstream outlet, {}, ".format(o) - +"is not in the outlet list {}.".format(str(outlet_list)) - +"Please run set_runoff() first.") + + "is not in the outlet list {}.".format(str(outlet_list)) + + "Please run set_runoff() first." + ) elif o == routing_outlet: # No inter-subbasin routing. route_ro[o] = deepcopy(Lohmann_template) @@ -238,11 +264,10 @@ def set_routing_outlet(self, routing_outlet, upstream_outlet_list, route_ro[o]["Pars"]["Diff"] = None else: route_ro[o] = deepcopy(Lohmann_template) - + route_ro[o]["Inputs"]["FlowLength"] = flow_length_list[i] - - - # Turn of within-subbasin routing if upstream outlets of other routing + + # Turn off within-subbasin routing if upstream outlets of other routing # are routing_outlet (this newly added one). routing_outlets = list(self.model["Routing"].keys()) for ro in routing_outlets: @@ -250,9 +275,8 @@ def set_routing_outlet(self, routing_outlet, upstream_outlet_list, if o in routing_outlets and o != ro: self.model["Routing"][ro][o]["Pars"]["GShape"] = None self.model["Routing"][ro][o]["Pars"]["GScale"] = None - - def set_ABM(self, abm_module_folder_path=None, - abm_module_name="ABM_module.py"): + + def set_ABM(self, abm_module_folder_path=None, abm_module_name="ABM_module.py"): """Set up ABM Parameters @@ -262,21 +286,30 @@ def set_ABM(self, abm_module_folder_path=None, directory will be assigned, by default None. abm_module_name : str, optional The ABM module name, by default "ABM_module.py" - """ + """ if abm_module_folder_path is None: abm_module_folder_path = self.wd if abm_module_name[-3:] != ".py": abm_module_name = abm_module_name + ".py" - + self.abm_module_folder_path = abm_module_folder_path self.abm_module_name = abm_module_name self.model["WaterSystem"]["ABM"] = deepcopy(ABM_template) self.model["WaterSystem"]["ABM"]["Modules"].append(abm_module_name) self.model["Path"]["Modules"] = abm_module_folder_path self.model["ABM"] = {} - - def add_agent(self, agt_type_class, agt_name, api, priority=1, link_dict={}, - dm_class=None, par_dict={}, attr_dict={}): + + def add_agent( + self, + agt_type_class, + agt_name, + api, + priority=1, + link_dict={}, + dm_class=None, + par_dict={}, + attr_dict={}, + ): """Add agent. Parameters @@ -286,7 +319,7 @@ def add_agent(self, agt_type_class, agt_name, api, priority=1, link_dict={}, agt_name : str Agent name. api : str - The API to integrate the agent to the HydroCNHS. + The API to integrate the agent to the HydroCNHS. e.g., mb.Dam. priority : int, optional Priority of the agent if conflicts occur, by default 1. @@ -301,11 +334,11 @@ def add_agent(self, agt_type_class, agt_name, api, priority=1, link_dict={}, """ if self.model.get("ABM") is None: raise KeyError("ABM has not been set. Please run 'set_ABM' first.") - + abm = self.model["ABM"] if abm.get(agt_type_class) is None: abm[agt_type_class] = {} - + ws_abm = self.model["WaterSystem"]["ABM"] if api == self.api.Dam: ws_abm["DamAPI"].append(agt_type_class) @@ -321,22 +354,24 @@ def add_agent(self, agt_type_class, agt_name, api, priority=1, link_dict={}, ws_abm["InsituAPI"].append(agt_type_class) ws_abm["InsituAPI"] = list(set(ws_abm["InsituAPI"])) else: - raise KeyError("Assigned api is not eligible. Eligible apis " - +"including {}, {}, {}, and {}".format( - self.api.Dam, self.api.RiverDiv, - self.api.Conveying, self.api.InSitu)) - - ws_abm["DMClasses"].append(dm_class) - ws_abm["DMClasses"] = list(set(ws_abm["DMClasses"])) - + raise KeyError( + "Assigned api is not eligible. Eligible apis " + + "including {}, {}, {}, and {}".format( + self.api.Dam, self.api.RiverDiv, self.api.Conveying, self.api.InSitu + ) + ) + + if dm_class is not None: + ws_abm["DMClasses"].append(dm_class) + ws_abm["DMClasses"] = list(set(ws_abm["DMClasses"])) + abm[agt_type_class][agt_name] = deepcopy(agent_template) - abm[agt_type_class][agt_name]["Attributes"] = deepcopy(attr_dict) + abm[agt_type_class][agt_name]["Attributes"] = deepcopy(attr_dict) abm[agt_type_class][agt_name]["Inputs"]["Priority"] = priority abm[agt_type_class][agt_name]["Inputs"]["Links"] = deepcopy(link_dict) abm[agt_type_class][agt_name]["Inputs"]["DMClass"] = dm_class - abm[agt_type_class][agt_name]["Pars"] = deepcopy(par_dict) - - + abm[agt_type_class][agt_name]["Pars"] = deepcopy(par_dict) + def add_institution(self, institution, instit_dm_class, agent_list): """Add a institution. @@ -351,25 +386,66 @@ def add_institution(self, institution, instit_dm_class, agent_list): """ if self.model.get("ABM") is None: raise KeyError("ABM has not been set. Please run 'set_ABM' first.") - + abm = self.model["ABM"] for _, agts_dict in abm.items(): for agt, agt_config in agts_dict.items(): if agt in agent_list: agt_config["Inputs"]["DMClass"] = institution - + instit_dm = self.model["WaterSystem"]["ABM"]["InstitDMClasses"] if instit_dm.get(instit_dm_class) is None: instit_dm[instit_dm_class] = [] instit_dm[instit_dm_class].append(institution) - + instits = self.model["WaterSystem"]["ABM"]["Institutions"] instits[institution] = agent_list - + # Remove from DMClass if exist if institution in self.model["WaterSystem"]["ABM"]["DMClasses"]: self.model["WaterSystem"]["ABM"]["DMClasses"].remove(institution) - + + def set_sediment(self, start_month): + self.model["WaterSystem"]["Sediment"] = {"StartMonth": start_month} + + self.model["Sediment"] = {} + outlet_list = self.model["WaterSystem"]["Outlets"] + for sub in outlet_list: + self.model["Sediment"][sub] = deepcopy(sediment_template) + + def add_sediment( + self, + subbasin, + area_list, + cool_months, + K_list, + Ac, + Aw, + LS_list, + DR, + id_list=None, + CP=-99, + Sa=-99, + Sb=-99, + Sq=-99, + ): + sed_sub = self.model["Sediment"][subbasin] + sed_sub["Inputs"]["IDs"] = id_list + sed_sub["Inputs"]["Areas"] = area_list + sed_sub["Inputs"]["CoolMonths"] = cool_months + sed_sub["Inputs"]["WarmMonths"] = [ + i + 1 for i in range(12) if i + 1 not in cool_months + ] + sed_sub["Pars"]["K"] = K_list + sed_sub["Pars"]["Ac"] = Ac + sed_sub["Pars"]["Aw"] = Aw + sed_sub["Pars"]["LS"] = LS_list + sed_sub["Pars"]["DR"] = DR + sed_sub["Pars"]["CP"] = CP + sed_sub["Pars"]["Sa"] = Sa + sed_sub["Pars"]["Sb"] = Sb + sed_sub["Pars"]["Sq"] = Sq + def write_model_to_yaml(self, filename): """Output model configuration file (.yaml) @@ -382,17 +458,17 @@ def write_model_to_yaml(self, filename): filename = filename + ".yaml" filename = os.path.join(self.wd, filename) write_model(self.model, filename) - print("Model configuration file (.yaml) have been save at {}.".format(filename), - "Please open the file and further edit it.") - + print( + "Model configuration file (.yaml) have been save at {}.".format(filename), + "Please open the file and further edit it.", + ) + def gen_ABM_module_template(self): - """Generate ABM module template based on the ABM setting. - """ - filename = os.path.join(self.abm_module_folder_path, - self.abm_module_name) + """Generate ABM module template based on the ABM setting.""" + filename = os.path.join(self.abm_module_folder_path, self.abm_module_name) ws_abm = self.model["WaterSystem"]["ABM"] abm = self.model["ABM"] - with open(filename, 'w') as f: + with open(filename, "w") as f: f.write(import_str + design_str) for agt_type in list(abm.keys()): f.write(add_agt_class(agt_type)) @@ -400,12 +476,14 @@ def gen_ABM_module_template(self): f.write(add_dm_class(dm_class, is_institution=False)) for intit_dm_class in list(ws_abm["InstitDMClasses"].keys()): f.write(add_dm_class(intit_dm_class, is_institution=True)) - print("ABM module template (.py) have been save at {}.".format(filename), - "Please open the file and further edit it.") - + print( + "ABM module template (.py) have been save at {}.".format(filename), + "Please open the file and further edit it.", + ) + def print_model(self, indentor=" ", level=1): """Print model to the console - + Parameters ---------- indentor : str, optional @@ -413,4 +491,4 @@ def print_model(self, indentor=" ", level=1): level : int, optional Print out level of a nested dictionary, by default 1. """ - print(dict_to_string(self.model, indentor, level)) \ No newline at end of file + print(dict_to_string(self.model, indentor, level)) diff --git a/HydroCNHS/rainfall_runoff_model/__init__.py b/src/hydrocnhs/rainfall_runoff_model/__init__.py similarity index 100% rename from HydroCNHS/rainfall_runoff_model/__init__.py rename to src/hydrocnhs/rainfall_runoff_model/__init__.py diff --git a/src/hydrocnhs/rainfall_runoff_model/abcd.py b/src/hydrocnhs/rainfall_runoff_model/abcd.py new file mode 100644 index 0000000..32e323f --- /dev/null +++ b/src/hydrocnhs/rainfall_runoff_model/abcd.py @@ -0,0 +1,223 @@ +# ABCD module. +# The ABCD model is mainly follow (Guillermo et al., 2010); however, with +# different snow module. +# https://doi.org/10.1029/2009WR008294 +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# Last update at 2021/12/22. + +import numpy as np + + +def run_ABCD(pars, inputs, temp, prec, pet, vars=None, **kwargs): + """ABCD rainfall-runoff model. + + Inputs: + Area: [ha] Subbasin area. + XL: [cm] Initial saturated soil water content. + Latitude: [deg] + SnowS: [cm] Snow storage. + Pars: + a: Controls the amount of runoff and recharge during unsaturated soil. + b: Controls Saturation level of the soils. + c: Ratio of groundwater recharge to runoff. + d: Controls groundwater discharge rate. + Df: [cm/degC] Degree-day coefficient + + Parameters + ---------- + pars : dict + Parameter dictionary containing 5 parameters: a, b, c, d, Df. + inputs : dict + Input dictionary containing 4 inputs: Area, Latitude, XL, SnowS. + temp : array + [degC] Daily mean temperature. + prec : array + [cm] Daily precipitation. + pet : array + [cm] Daily potential evaportranspiration. + vars : int, optional + Initial values. + + Returns + ------- + array + [cms] Discharge + """ + sim_length = len(prec) + + # Pars + a = pars["a"] # [0, 1] + b = pars["b"] # [cm] [0, 400] + c = pars["c"] # [0, 1] + d = pars["d"] # [0, 1] + Df = pars["Df"] # [0, 1] + + # Variable + if vars is None: + SnowSt = inputs["SnowS"] # [cm] Initial snow storage. + # QU = 0 # [cm] Runoff. + # QL = 0 # [cm] Baseflow. + XL = inputs["XL"] # [cm] Initial saturated soil water content. + ## [0, 400] + XU = 0 # [cm] Soil water storage (Antecedent Moisture). + else: + XU = vars["XU"] + XL = vars["XL"] + SnowSt = vars["SnowSt"] + + # ----- Loop through all days (Python for loop ending needs +1) ------------ + CMS = np.zeros(sim_length) # Create a 1D array to store results + for i in range(sim_length): + # Determine rainfall, snowfall and snow accumulation------------------- + if temp[i] > 0: # If temperature is above 0 degC, + # precipitation is rainfall (cm) and no snow accumulation + Rt = prec[i] + else: + # Else, precipitation is snowfall (cm) so rainfall = 0 + Rt = 0 + # Snowfall will accumulated and become snow storage(cm) + SnowSt = SnowSt + prec[i] + # ---------------------------------------------------------------------- + # Determine snowmelt (Degree-day method)------------------------------- + if temp[i] > 0: # Temperature above 0 degC + # Snowmelt (cm) capped by snow storage + Mt = min(SnowSt, Df * temp[i]) + SnowSt = SnowSt - Mt # Update snow storage + else: + Mt = 0 + + # ---------------------------------------------------------------------- + # Determin available water (P) + P = Rt + Mt + XU + + # ---------------------------------------------------------------------- + # ET opportunity (Guillermo et al., 2010) + Pb = P + b + a2 = 2 * a + In = (Pb / a2) ** 2 - P * b / a # In should >= 0 + EO = Pb / a2 - In**0.5 + + # EO = EO.real + # ---------------------------------------------------------------------- + # Actual evapotranspiration (E) + E = EO * (1 - np.exp(-pet[i] / b)) + E = min(pet[i], max(0, E)) + XU = EO - E + AW = P - EO + XL = (XL + c * AW) / (1 + d) + QL = d * XL + QU = (1 - c) * AW + Q = QL + QU + # ---------------------------------------------------------------------- + # Change unit to cms (m^3/sec)----------------------------------------- + # Area [ha] + CMS[i] = (Q * 0.01 * inputs["Area"] * 10000) / 86400 + vars = {"XU": XU, "XL": XL, "SnowSt": SnowSt} + return CMS, vars + + +# def run_ABCD(pars, inputs, temp, prec, pet, data_length, **kwargs): +# """ABCD rainfall-runoff model. + +# Inputs: +# Area: [ha] Subbasin area. +# XL: [cm] Initial saturated soil water content. +# Latitude: [deg] +# SnowS: [cm] Snow storage. +# Pars: +# a: Controls the amount of runoff and recharge during unsaturated soil. +# b: Controls Saturation level of the soils. +# c: Ratio of groundwater recharge to runoff. +# d: Controls groundwater discharge rate. +# Df: [cm/degC] Degree-day coefficient + +# Parameters +# ---------- +# pars : dict +# Parameter dictionary containing 5 parameters: a, b, c, d, Df. +# inputs : dict +# Input dictionary containing 4 inputs: Area, Latitude, XL, SnowS. +# temp : array +# [degC] Daily mean temperature. +# prec : array +# [cm] Daily precipitation. +# pet : array +# [cm] Daily potential evaportranspiration. +# data_length : int +# Total data length (i.e., simulation period). + +# Returns +# ------- +# array +# [cms] Discharge +# """ + +# # Data +# temp = np.array(temp) # [degC] Daily mean temperature. +# prec = np.array(prec) # [cm] Daily precipitation. +# pet = np.array(pet) # [cm] Daily potential evapotranspiration. + +# # Variable +# SnowSt = inputs["SnowS"] # [cm] Initial snow storage. +# QU = 0 # [cm] Runoff. +# QL = 0 # [cm] Baseflow. +# XL = inputs["XL"] # [cm] Initial saturated soil water content. +# ## [0, 400] +# XU = 0 # [cm] Soil water storage (Antecedent Moisture). + +# # Pars +# a = pars["a"] # [0, 1] +# b = pars["b"] # [cm] [0, 400] +# c = pars["c"] # [0, 1] +# d = pars["d"] # [0, 1] +# Df = pars["Df"] # [0, 1] + +# #----- Loop through all days (Python for loop ending needs +1) ------------ +# CMS = np.zeros(data_length) # Create a 1D array to store results +# for i in range(data_length): + +# # Determine rainfall, snowfall and snow accumulation------------------- +# if temp[i] > 0: # If temperature is above 0 degC, +# # precipitation is rainfall (cm) and no snow accumulation +# Rt = prec[i] +# else: +# # Else, precipitation is snowfall (cm) so rainfall = 0 +# Rt = 0 +# # Snowfall will accumulated and become snow storage(cm) +# SnowSt = SnowSt + prec[i] +# #---------------------------------------------------------------------- +# # Determine snowmelt (Degree-day method)------------------------------- +# if temp[i] > 0: # Temperature above 0 degC +# # Snowmelt (cm) capped by snow storage +# Mt = min(SnowSt, Df * temp[i]) +# SnowSt = SnowSt - Mt # Update snow storage +# else: +# Mt = 0 + +# #---------------------------------------------------------------------- +# # Determin available water (P) +# P = Rt + Mt + XU + +# #---------------------------------------------------------------------- +# # ET opportunity (Guillermo et al., 2010) +# Pb = P+b +# a2 = 2*a +# In = (Pb/a2)**2 - P*b/a # In should >= 0 +# EO = Pb/a2 - In**0.5 + +# # EO = EO.real +# #---------------------------------------------------------------------- +# # Actual evapotranspiration (E) +# E = EO * ( 1-np.exp(-pet[i]/b) ) +# E = min( pet[i], max(0 , E) ) +# XU = EO - E +# AW = P - EO +# XL = (XL + c*AW) / (1+d) +# QL = d * XL +# QU = (1-c) * AW +# Q = QL + QU +# #---------------------------------------------------------------------- +# # Change unit to cms (m^3/sec)----------------------------------------- +# # Area [ha] +# CMS[i] = (Q * 0.01 * inputs["Area"] * 10000) / 86400 +# return CMS diff --git a/src/hydrocnhs/rainfall_runoff_model/gwlf.py b/src/hydrocnhs/rainfall_runoff_model/gwlf.py new file mode 100644 index 0000000..ab9c66c --- /dev/null +++ b/src/hydrocnhs/rainfall_runoff_model/gwlf.py @@ -0,0 +1,388 @@ +# GWLF module. +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# Last update at 2021/12/22. + +import numpy as np +from pandas import date_range, to_datetime, DataFrame + + +def run_GWLF(pars, inputs, temp, prec, pet, monthly_Tavg, vars=None, **kwargs): + """GWLF rainfall-runoff model. + + Inputs: + Area: [ha] Subbasin area. + Latitude: [deg] + S0: [cm] Shallow saturated soil water content. E.g., 10 + U0: [cm] Unsaturated soil water content. E.g., 10 + SnowS: [cm] Snow storage. E.g., 5 + + Pars: + CN2: Curve number. + IS: Interception coefficient. + Res: Recession coefficient. + Sep: Deep seepage coefficient. + Alpha: Baseflow coefficient. + Beta: Percolation coefficient. + Ur: [cm] Available/Soil water capacity. + Kc: Land cover coefficient. + Df: [cm/degC] Degree-day coefficient. + Note that the simulation period has to be longer than a month. + + Parameters + ---------- + pars : dict + Parameter dictionary containing 9 parameters: CN2, IS, Res, Sep, Alpha, + Beta, Ur, Kc, Df. + inputs : dict + Input dictionary containing 5 inputs: Area, Latitude, S0, U0, SnowS. + temp : array + [degC] Daily mean temperature. + prec : array + [cm] Daily precipitation. + pet : array + [cm] Daily potential evaportranspiration. + monthly_Tavg: str + Monthly average temperature in a daily format. + vars : int, optional + Initial values. + + Returns + ------- + array + [cms] Discharge + """ + # ----- Setup ------------------------------------------------------------- + sim_length = len(prec) + # Pars + CN2 = pars["CN2"] # Curve number + IS = pars["IS"] # Interception coefficient 0.05 ~ 0.2 + Res = pars["Res"] # Recession coefficient + Sep = pars["Sep"] # Deep seepage coefficient + Alpha = pars["Alpha"] # Baseflow coefficient + ##(Eq 4 in Luo et al. (2012)) + Beta = pars["Beta"] # Deep seepage coefficient + ##(Eq 2 in Luo et al. (2012)) + Ur = pars["Ur"] # [cm] Avaliable/Soil water capacity + ##(Root zone) + Df = pars["Df"] # [cm/degC] Degree-day coefficient for snowmelt. + Kc = pars["Kc"] # Land cover coefficient. + + # Variables + if vars is None: + BFt = Res * inputs["S0"] # [cm] Initialize baseflow to stream. + ## = Gt = Res*inputs["S0"] + St = inputs["S0"] # [cm] Initialize shallow saturated soil water + ##content. + Ut = inputs["U0"] # [cm] Initialize unsaturated soil water + ##content. + SnowSt = inputs["SnowS"] # [cm] Initial snow storage. + AnteMois = [0, 0, 0, 0, 0] # [cm] Define the initial Antecedent Moisture + ##(5 days) as 0. + else: + BFt = vars["BFt"] + St = vars["St"] + Ut = vars["Ut"] + SnowSt = vars["SnowSt"] + AnteMois = vars["AnteMois"] + # -------------------------------------------------------------------------- + + # ----- Loop through all days (Python for loop ending needs +1) ------------ + CMS = np.empty(sim_length) # Create a 1D array to store results + for i in range(sim_length): + # Determine the am1 and am2 values based on growing season for CN + # calculation. Growing season (mean monthly temperature > 10 deg C). + if monthly_Tavg[i] > 10: # monthly_Tavg in a daily format + am1 = 3.6 + am2 = 5.3 + else: + am1 = 1.3 + am2 = 2.8 + + # Determine rainfall, snowfall and snow accumulation------------------- + if temp[i] > 0: # If temperature is above 0 degC, + Rt = prec[i] # precipitation is rainfall (cm) and no snow + ##accumulation + else: + Rt = 0 # Else, precipitation is snowfall (cm) so rainfall = 0 + # Snowfall will accumulated and become snow storage(cm) + SnowSt = SnowSt + prec[i] + # ---------------------------------------------------------------------- + # Determine snowmelt (Degree-day method)------------------------------- + if temp[i] > 0: # Temperature above 0 degC + # Snowmelt (cm) capped by snow storage + Mt = min(SnowSt, Df * temp[i]) + SnowSt = SnowSt - Mt # Update snow storage + else: + Mt = 0 + # ---------------------------------------------------------------------- + # Calculate the Antecedent Moisture (at)------------------------------- + AnteMois = [Rt + Mt] + AnteMois[0:4] # Add new data and drop last data. + at = np.sum(AnteMois) # Five days antecedent moisture + # ---------------------------------------------------------------------- + # CN calculation (Following GWLF2 setting)----------------------------- + CN1 = (4.2 * CN2) / (10 - 0.058 * CN2) + CN3 = (23 * CN2) / (10 + 0.13 * CN2) + CN = None + if at < am1: + CN = CN1 + ((CN2 - CN1) / am1) * at + + if am1 <= at and at <= am2: + CN = CN2 + ((CN3 - CN2) / (am2 - am1)) * (at - am1) + + if am2 < at: + CN = CN3 + # ---------------------------------------------------------------------- + # Calculate runoff (Qt)------------------------------------------------ + DSkt = (2540 / CN) - 25.4 # Detention parameter (cm) + if (Rt + Mt) > IS * DSkt: + Qt = (((Rt + Mt) - (IS * DSkt)) ** 2) / ((Rt + Mt) + ((1 - IS) * DSkt)) + else: + Qt = 0 + # ---------------------------------------------------------------------- + # Calculate Evapotranspiration (Et)------------------------------------ + # Consider water stress (Ks) and land cover (Kc). + if Ut >= Ur * 0.5: + Ks = 1 + else: + Ks = Ut / (Ur * 0.5) + Et = min((Ut + Rt + Mt - Qt), Ks * Kc * pet[i]) + # ---------------------------------------------------------------------- + # Calculate Percolation (Pct) from unsaturated zone (Ut) to shallow + # saturated zone (St)-------------------------------------------------- + PCt = max((Ut + Rt + Mt - Qt - Et - Ur), 0) + # ---------------------------------------------------------------------- + # Update unsaturated zone soil moistures (Ut)-------------------------- + Ut = Ut + Rt + Mt - Qt - Et - PCt # Rt+Mt-Qt is infiltration + # ---------------------------------------------------------------------- + # Calculate groundwater discharge (Gt) and deep seepage (Dt)----------- + Gt = Res * St + Dt = Sep * St + # ---------------------------------------------------------------------- + # Update shallow saturated zone soil moistures (St)-------------------- + St = St + PCt - Gt - Dt + # ---------------------------------------------------------------------- + # Groundwater: Deep seepage loss (Dset)-------------------------------- + Dset = Beta * Dt # Eq 2 in Luo et al. (2012) + # ---------------------------------------------------------------------- + # Groundwater: Recharge (Ret)------------------------------------------ + Ret = Dt - Dset # Eq 3 in Luo et al. (2012) + # ---------------------------------------------------------------------- + # Groundwater:Baseflow (BFt)------------------------------------------- + # Eq 4 in Luo et al. (2012) + BFt = BFt * np.exp(-Alpha) + Ret * (1 - np.exp(-Alpha)) + # ---------------------------------------------------------------------- + # Calculate streamflow (SF) and Monthly Qt, prec, Et, Gt and SF-------- + # Streamflow = surface quick flow + subsurface flow + baseflow + SF = Qt + Gt + BFt + # ---------------------------------------------------------------------- + # Change unit to cms (m^3/sec)----------------------------------------- + # Area [ha] + CMS[i] = (SF * 0.01 * inputs["Area"] * 10000) / 86400 + # ---------------------------------------------------------------------- + # return the result array + vars = {"BFt": BFt, "St": St, "Ut": Ut, "SnowSt": SnowSt, "AnteMois": AnteMois} + return CMS, vars + + +# def run_GWLF(pars, inputs, temp, prec, pet, start_date, data_length, **kwargs): +# """GWLF rainfall-runoff model. + +# Inputs: +# Area: [ha] Subbasin area. +# Latitude: [deg] +# S0: [cm] Shallow saturated soil water content. +# U0: [cm] Unsaturated soil water content. +# SnowS: [cm] Snow storage. + +# Pars: +# CN2: Curve number. +# IS: Interception coefficient. +# Res: Recession coefficient. +# Sep: Deep seepage coefficient. +# Alpha: Baseflow coefficient. +# Beta: Percolation coefficient. +# Ur: [cm] Available/Soil water capacity. +# Kc: Land cover coefficient. +# Df: [cm/degC] Degree-day coefficient. +# Note that the simulation period has to be longer than a month. + +# Parameters +# ---------- +# pars : dict +# Parameter dictionary containing 9 parameters: CN2, IS, Res, Sep, Alpha, +# Beta, Ur, Kc, Df. +# inputs : dict +# Input dictionary containing 5 inputs: Area, Latitude, S0, U0, SnowS. +# temp : array +# [degC] Daily mean temperature. +# prec : array +# [cm] Daily precipitation. +# pet : array +# [cm] Daily potential evaportranspiration. +# start_date: str +# Start date "yyyy/mm/dd". + +# data_length : int +# Total data length (i.e., simulation period). + +# Returns +# ------- +# array +# [cms] Discharge +# """ +# #----- Setup ------------------------------------------------------------- + +# r""" +# Inputs: +# Area: # [ha] Sub-basin area. +# S0: 10 # [cm] Shallow saturated soil water content. +# U0: 10 # [cm] Unsaturated soil water content +# SnowS: 5 # [cm] Snow storage. +# """ + +# # Data +# temp = np.array(temp) # [degC] Daily mean temperature. +# prec = np.array(prec) # [cm] Daily precipitation. +# pet = np.array(pet) # [cm] Daily potential evapotranspiration. + +# # Pars +# CN2 = pars["CN2"] # Curve number +# IS = pars["IS"] # Interception coefficient 0.05 ~ 0.2 +# Res = pars["Res"] # Recession coefficient +# Sep = pars["Sep"] # Deep seepage coefficient +# Alpha = pars["Alpha"] # Baseflow coefficient +# ##(Eq 4 in Luo et al. (2012)) +# Beta = pars["Beta"] # Deep seepage coefficient +# ##(Eq 2 in Luo et al. (2012)) +# Ur = pars["Ur"] # [cm] Avaliable/Soil water capacity +# ##(Root zone) +# Df = pars["Df"] # [cm/degC] Degree-day coefficient for snowmelt. +# Kc = pars["Kc"] # Land cover coefficient. + +# # Variables +# Gt = Res*inputs["S0"] # [cm] Initialize saturated zone discharge to +# ##the stream. +# BFt = Gt # [cm] Initialize baseflow to stream. +# St = inputs["S0"] # [cm] Initialize shallow saturated soil water +# ##content. +# Ut = inputs["U0"] # [cm] Initialize unsaturated soil water +# ##content. +# SnowSt = inputs["SnowS"] # [cm] Initial snow storage. +# AnteMois = [0, 0, 0, 0, 0] # [cm] Define the initial Antecedent Moisture +# ##(5 days) as 0. + +# # Calculate monthly mean temperature for distinguishing growing season +# # purpose. +# start_date = to_datetime(start_date, format="%Y/%m/%d") +# pdDatedateIndex = date_range(start=start_date, periods=data_length, +# freq="D") +# MonthlyTavg = DataFrame(index=pdDatedateIndex) +# MonthlyTavg["T"] = temp +# MonthlyTavg = MonthlyTavg.resample("MS").mean() +# # Broadcast back to daily sequence. +# # Note: Data has to longer than a month or it will show error. +# try: +# MonthlyTavg.index = [pdDatedateIndex[0]] + list(MonthlyTavg.index[1:-1]) \ +# + [pdDatedateIndex[-1]] +# MonthlyTavg = MonthlyTavg.resample('D').ffill()["T"].to_numpy() +# except Exception as e: +# print(e) +# print("The simulation period has to be longer than a month.") +# #-------------------------------------------------------------------------- + +# #----- Loop through all days (Python for loop ending needs +1) ------------ +# CMS = np.zeros(data_length) # Create a 1D array to store results +# for i in range(data_length): +# # Determine the am1 and am2 values based on growing season for CN +# # calculation. +# # Growing season (mean monthly temperature > 10 deg C). +# if MonthlyTavg[i] > 10: # MonthlyTavg +# am1 = 3.6 +# am2 = 5.3 +# else: +# am1 = 1.3 +# am2 = 2.8 + +# # Determine rainfall, snowfall and snow accumulation------------------- +# if temp[i] > 0: # If temperature is above 0 degC, +# Rt = prec[i] # precipitation is rainfall (cm) and no snow +# ##accumulation +# else: +# Rt = 0 # Else, precipitation is snowfall (cm) so rainfall = 0 +# # Snowfall will accumulated and become snow storage(cm) +# SnowSt = SnowSt + prec[i] +# #---------------------------------------------------------------------- +# # Determine snowmelt (Degree-day method)------------------------------- +# if temp[i] > 0: # Temperature above 0 degC +# # Snowmelt (cm) capped by snow storage +# Mt = min(SnowSt, Df * temp[i]) +# SnowSt = SnowSt - Mt # Update snow storage +# else: +# Mt = 0 +# #---------------------------------------------------------------------- +# # Calculate the Antecedent Moisture (at)------------------------------- +# AnteMois = [Rt + Mt] + AnteMois[0:4] # Add new data and drop last data. +# at = np.sum(AnteMois) # Five days antecedent moisture +# #---------------------------------------------------------------------- +# # CN calculation (Following GWLF2 setting)----------------------------- +# CN1 = (4.2 * CN2) / (10 - 0.058 * CN2) +# CN3 = (23 * CN2) / (10 + 0.13 * CN2) +# CN = None +# if at < am1: +# CN = CN1 + ((CN2 - CN1) / am1) * at + +# if (am1 <= at and at <= am2): +# CN = CN2 + ((CN3 - CN2) / (am2 - am1)) * (at - am1) + +# if am2 < at: +# CN = CN3 +# #---------------------------------------------------------------------- +# # Calculate runoff (Qt)------------------------------------------------ +# DSkt = (2540 / CN) - 25.4 # Detention parameter (cm) +# if (Rt + Mt) > IS * DSkt: +# Qt = (((Rt + Mt) - (IS * DSkt))**2) / ((Rt + Mt) + ((1-IS) * DSkt)) +# else: +# Qt = 0 +# #---------------------------------------------------------------------- +# # Calculate Evapotranspiration (Et)------------------------------------ +# # Consider water stress (Ks) and land cover (Kc). +# if (Ut >= Ur*0.5): +# Ks = 1 +# else: +# Ks = Ut/(Ur*0.5) +# Et = min((Ut+ Rt + Mt - Qt), Ks*Kc*pet[i]) +# #---------------------------------------------------------------------- +# # Calculate Percolation (Pct) from unsaturated zone (Ut) to shallow +# # saturated zone (St)-------------------------------------------------- +# PCt = max((Ut+ Rt + Mt - Qt - Et - Ur), 0) +# #---------------------------------------------------------------------- +# # Update unsaturated zone soil moistures (Ut)-------------------------- +# Ut = Ut + Rt + Mt - Qt - Et - PCt # Rt+Mt-Qt is infiltration +# #---------------------------------------------------------------------- +# # Calculate groundwater discharge (Gt) and deep seepage (Dt)----------- +# Gt = Res * St +# Dt = Sep * St +# #---------------------------------------------------------------------- +# # Update shallow saturated zone soil moistures (St)-------------------- +# St = St + PCt - Gt - Dt +# #---------------------------------------------------------------------- +# # Groundwater: Deep seepage loss (Dset)-------------------------------- +# Dset = Beta * Dt #Eq 2 in Luo et al. (2012) +# #---------------------------------------------------------------------- +# # Groundwater: Recharge (Ret)------------------------------------------ +# Ret = Dt - Dset #Eq 3 in Luo et al. (2012) +# #---------------------------------------------------------------------- +# # Groundwater:Baseflow (BFt)------------------------------------------- +# # Eq 4 in Luo et al. (2012) +# BFt = BFt * np.exp(-Alpha) + Ret * (1-np.exp(-Alpha)) +# #---------------------------------------------------------------------- +# # Calculate streamflow (SF) and Monthly Qt, prec, Et, Gt and SF-------- +# # Streamflow = surface quick flow + subsurface flow + baseflow +# SF = Qt + Gt + BFt +# #---------------------------------------------------------------------- +# # Change unit to cms (m^3/sec)----------------------------------------- +# # Area [ha] +# CMS[i] = (SF * 0.01 * inputs["Area"] * 10000) / 86400 +# #---------------------------------------------------------------------- +# # return the result array +# return CMS diff --git a/HydroCNHS/rainfall_runoff_model/pet_hamon.py b/src/hydrocnhs/rainfall_runoff_model/pet_hamon.py similarity index 63% rename from HydroCNHS/rainfall_runoff_model/pet_hamon.py rename to src/hydrocnhs/rainfall_runoff_model/pet_hamon.py index c1a9ce4..304adc9 100644 --- a/HydroCNHS/rainfall_runoff_model/pet_hamon.py +++ b/src/hydrocnhs/rainfall_runoff_model/pet_hamon.py @@ -1,10 +1,11 @@ # Hamon pet module. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2021/12/22. import numpy as np from pandas import date_range, to_datetime, to_numeric + def cal_pet_Hamon(temp, Lat, start_date, dz=None): """Calculate potential evapotranspiration (pet) with Hamon (1961) equation. @@ -29,27 +30,31 @@ def cal_pet_Hamon(temp, Lat, start_date, dz=None): if dz is not None: # Assume temperature decrease 0.6 degC for every 100 m elevation. tlaps = 0.6 - temp = temp - tlaps*dz/100 + temp = temp - tlaps * dz / 100 # Calculate Julian days - data_length = len(temp) - start_date = to_datetime(start_date, format="%Y/%m/%d") - pdDatedateIndex = date_range(start=start_date, periods=data_length, - freq="D") - JDay = to_numeric(pdDatedateIndex.strftime('%j')) # convert to Julian days + if isinstance(start_date, str): + data_length = len(temp) + start_date = to_datetime(start_date, format="%Y/%m/%d") + pdDatedateIndex = date_range(start=start_date, periods=data_length, freq="D") + else: + pdDatedateIndex = start_date # for internal use. + JDay = to_numeric(pdDatedateIndex.strftime("%j")) # convert to Julian days # Calculate solar declination [rad] from day of year (JDay) based on # equations 24 in ALLen et al (1998). - sol_dec = 0.4093 * np.sin(2. * 3.141592654 / 365. * JDay - 1.39) - Lat_rad = Lat*np.pi/180 + sol_dec = 0.4093 * np.sin(2.0 * 3.141592654 / 365.0 * JDay - 1.39) + Lat_rad = Lat * np.pi / 180 # Calculate sunset hour angle from latitude and solar declination [rad] # based on equations 25 in ALLen et al (1998). omega = np.arccos(-np.tan(sol_dec) * np.tan(Lat_rad)) # Calculate maximum possible daylight length [hr] - dl = 24 / np.pi * omega + dl = 24 / np.pi * omega # From Prudhomme(hess, 2013) # https://hess.copernicus.org/articles/17/1365/2013/hess-17-1365-2013-supplement.pdf # Slightly different from what we used to. - pet = (dl / 12) ** 2 * np.exp(temp / 16) - pet = np.array(pet/10) # Convert from mm to cm - pet[np.where(temp <= 0)] = 0 # Force pet = 0 when temperature is below 0. - return pet # [cm/day] -# More ET method code can be found at https://github.com/phydrus/PyEt \ No newline at end of file + pet = (dl / 12) ** 2 * np.exp(temp / 16) + pet = np.array(pet / 10) # Convert from mm to cm + pet[np.where(temp <= 0)] = 0 # Force pet = 0 when temperature is below 0. + return pet # [cm/day] + + +# More ET method code can be found at https://github.com/phydrus/PyEt diff --git a/src/hydrocnhs/routing.py b/src/hydrocnhs/routing.py new file mode 100644 index 0000000..16bc3a9 --- /dev/null +++ b/src/hydrocnhs/routing.py @@ -0,0 +1,305 @@ +# Lohmann routing module +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# 2021/02/05 +# Last update at 2021/12/22. + +import numpy as np +from scipy.stats import gamma + +# Inputs +# FlowLength = [m] Travel distence of flow between two outlets. +# InstreamControl = [bool] Instream control (e.g., dams). +# Parameters +# GShape = Subbasin's UH shape parameter (gamma function). +# GScale = Subbasin's UH scale parameter (gamma function). +# Velo = [m/s] Diffusion wave celerity in the linearized Saint-Venant equation. +# Diff = [m2/s] Diffusive coefficient in the linearized Saint-Venant equation. + +# Note: +# We fix the base time setting here. For future development, it is better to +# turn entire Lohmann routing into a class with base time setting as attributes +# which allow to be changed accordingly. + + +def form_UH_Lohmann(inputs, routing_pars, force_ingrid_off=False): + """Derive HRU's UH at the (watershed) outlet. + + Parameters + ---------- + inputs : dict + Inputs dictionary containing FlowLength [m] Travel distence of flow + between two outlets [float] and InstreamControl [bool]. + routing_pars : dict + Four parameters for routing: GShape, GScale, Velo, Diff [float] + force_ingrid_off : bool, optional + If True, then within subbasin routing will be forced to turn off, by default + False. + """ + flow_len = inputs["FlowLength"] + instream_control = inputs["InstreamControl"] + + # ----- Base Time for within subbasin UH and river/channel + # routing UH -------------------------------------------------------------- + # Within-subbasin routing + T_IG = 12 # [day] Base time for within subbasin UH + # River routing + T_RR = 96 # [day] Base time for river routing UH + dT_sec = 3600 # [sec] Time step in second for solving + ##Saint-Venant equation. This will affect Tmax_hr. + Tmax_hr = T_RR * 24 # [hr] Base time of river routing UH in hour + ##because dT_sec is for an hour + Tgr_hr = 48 * 50 # [hr] Base time for Green function values + # -------------------------------------------------------------------------- + + # ----- Within-subbasin routing UH (daily) represented by Gamma distribution + UH_IG = np.zeros(T_IG) + if instream_control or force_ingrid_off: + # No time delay for within subbasin routing when the water is released + # through instream control objects (e.g. dam). + UH_IG[0] = 1 + elif routing_pars.get("GShape") is None and routing_pars.get("GScale") is None: + # No time delay for within subbasin routing since Q is given by user + # and we assume Q is observed streamflow, which no need to consider + # time delay for within subbasin routing. This is trigger automatically + # in HydroCNHS module. + UH_IG[0] = 1 + else: + shape = routing_pars["GShape"] + scale = routing_pars["GScale"] + if scale <= 0.0001: + scale = 0.0001 # Since we cannot divide zero. + for i in range(T_IG): + # x-axis is in hr unit. We calculate in daily time step. + UH_IG[i] = gamma.cdf(24 * (i + 1), a=shape, loc=0, scale=scale) - gamma.cdf( + 24 * i, a=shape, loc=0, scale=scale + ) + # -------------------------------------------------------------------------- + + # ----- Derive Daily River Impulse Response Function (Green's function) ---- + UH_RR = np.zeros(T_RR) + if flow_len == 0: + # No time delay for river routing when the outlet is gauged outlet. + UH_RR[0] = 1 + else: + Velo = routing_pars["Velo"] + Diff = routing_pars["Diff"] + + # Calculate h(x, t) + t = 0 + UH_RRm = np.zeros(Tgr_hr) # h() in hour + for k in range(Tgr_hr): + # Since Velo is m/s, we use the t in sec with 1 hr as time step. + t = t + dT_sec + pot = ((Velo * t - flow_len) ** 2) / (4 * Diff * t) + if pot <= 69: # e^(-69) = E-30 a cut-off threshold + H = flow_len / (2 * t * (np.pi * t * Diff) ** 0.5) * np.exp(-pot) + else: + H = 0 + UH_RRm[k] = H + + if sum(UH_RRm) == 0: + UH_RRm[0] = 1.0 + else: + UH_RRm = UH_RRm / sum(UH_RRm) # Force UH_RRm to sum to 1 + + # Much quicker!! Think about S-hydrograph process. + # And remember we should have [0] in UH_RRm[0]. + # Therefore, we use i+1 and 23 to form S-hydrolograph. + FR = np.zeros((Tmax_hr + 23, Tmax_hr - 1)) + for i in range(Tmax_hr - 1): + FR[:, i] = np.pad(UH_RRm, (i + 1, 23), "constant", constant_values=(0, 0))[ + : Tmax_hr + 23 + ] + FR = np.sum(FR, axis=1) / 24 + # Lag 24 hrs + FR = ( + FR[:Tmax_hr] + - np.pad(FR, (24, 0), "constant", constant_values=(0, 0))[:Tmax_hr] + ) + + # Aggregate to daily UH + for t in range(T_RR): + UH_RR[t] = sum(FR[(24 * (t + 1) - 24) : (24 * (t + 1) - 1)]) + + # ----- Combined UH_IG and UH_RR for HRU's response at the (watershed) + # outlet ------------------------------------------------------------------ + UH_direct = np.zeros(T_IG + T_RR - 1) # Convolute total time step [day] + for k in range(0, T_IG): + for u in range(0, T_RR): + UH_direct[k + u] = UH_direct[k + u] + UH_IG[k] * UH_RR[u] + UH_direct = UH_direct / sum(UH_direct) + # Trim zero from back. So when we run routing, we don't need to run whole + # array. + UH_direct = np.trim_zeros(UH_direct, "b") + return UH_direct + + +def run_step_Lohmann_sed(routing_outlet, routing, UH_Lohmann, Q, Q_runoff, t, Q_frac): + """Calculate a single time step routing for a given routing_outlet at time t. + + Parameters + ---------- + routing_outlet : str + routing outlet. + routing : dict + Routing setting dictionary from model.yaml file. + UH_Lohmann : dict + A dictionary containing pre-formed UHs. + Q : dict + A dictionary containing newest routed flows. + Q_runoff : dict + A dictionary containing newest unrouted flows without. + t : int + Index of current time step [day]. + + Returns + ------- + float + Routed flow of routing_outlet at time t. + """ + Qt = None + ro = routing_outlet + Qresult = 0 + Subbasin = list(routing[ro].keys()) + for sb in Subbasin: + l = len(UH_Lohmann[(sb, ro)]) - 1 + # t+1 is length, t is index. + UH = UH_Lohmann[(sb, ro)][0 : min(t + 1, l)] + if ro == sb: + # Q[ro] is routed Q. We need to use unrouted Q (Q_runoff) to run the + # routing. + Q_reverse = np.flip(Q_runoff[sb][max(t - (l - 1), 0) : t + 1]) + else: + Q_reverse = np.flip(Q[sb][max(t - (l - 1), 0) : t + 1]) + q_frac = np.sum(UH * Q_reverse) + Q_frac[ro][sb][t] = q_frac + Qresult += q_frac + Qt = Qresult # Store the result for time t + return Qt + + +def run_step_Lohmann_convey_sed( + routing_outlet, routing, UH_Lohmann_convey, Q_convey, t, Q_frac +): + """Calculate a single time step conveying water routing for a given + routing_outlet at time t. + + Parameters + ---------- + routing_outlet : str + routing outlet. + routing : dict + Routing setting dictionary from model.yaml file. + UH_Lohmann_convey : dict + A dictionary containing pre-formed conveying UHs (i.e., no within + subbasin routing). + Q_convey : dict + A dictionary containing conveying water. + t : int + Index of current time step [day]. + + Returns + ------- + float + Routed conveying flow of routing_outlet at time t. + """ + Qt = None + ro = routing_outlet + Qresult = 0 + Subbasin = list(routing[ro].keys()) + for sb in Subbasin: + uh_convey = UH_Lohmann_convey.get((sb, ro)) + if uh_convey is not None: + l = len(uh_convey) - 1 + # t+1 is length, t is index. + UH = uh_convey[0 : min(t + 1, l)] + Q_reverse = np.flip(Q_convey[sb][max(t - (l - 1), 0) : t + 1]) + # Q_frac[sb][t] should already have the value from the normal routing. + q_frac = np.sum(UH * Q_reverse) + Q_frac[ro][sb][t] += q_frac + Qresult += q_frac + Qt = Qresult # Store the result for time t + return Qt + + +def run_step_Lohmann(routing_outlet, routing, UH_Lohmann, Q, Q_runoff, t, *args): + """Calculate a single time step routing for a given routing_outlet at time t. + + Parameters + ---------- + routing_outlet : str + routing outlet. + routing : dict + Routing setting dictionary from model.yaml file. + UH_Lohmann : dict + A dictionary containing pre-formed UHs. + Q : dict + A dictionary containing newest routed flows. + Q_runoff : dict + A dictionary containing newest unrouted flows without. + t : int + Index of current time step [day]. + + Returns + ------- + float + Routed flow of routing_outlet at time t. + """ + Qt = None + ro = routing_outlet + Qresult = 0 + Subbasin = list(routing[ro].keys()) + for sb in Subbasin: + l = len(UH_Lohmann[(sb, ro)]) - 1 + # t+1 is length, t is index. + UH = UH_Lohmann[(sb, ro)][0 : min(t + 1, l)] + if ro == sb: + # Q[ro] is routed Q. We need to use unrouted Q (Q_runoff) to run the + # routing. + Q_reverse = np.flip(Q_runoff[sb][max(t - (l - 1), 0) : t + 1]) + else: + Q_reverse = np.flip(Q[sb][max(t - (l - 1), 0) : t + 1]) + Qresult += np.sum(UH * Q_reverse) + Qt = Qresult # Store the result for time t + return Qt + + +def run_step_Lohmann_convey( + routing_outlet, routing, UH_Lohmann_convey, Q_convey, t, *args +): + """Calculate a single time step conveying water routing for a given + routing_outlet at time t. + + Parameters + ---------- + routing_outlet : str + routing outlet. + routing : dict + Routing setting dictionary from model.yaml file. + UH_Lohmann_convey : dict + A dictionary containing pre-formed conveying UHs (i.e., no within + subbasin routing). + Q_convey : dict + A dictionary containing conveying water. + t : int + Index of current time step [day]. + + Returns + ------- + float + Routed conveying flow of routing_outlet at time t. + """ + Qt = None + ro = routing_outlet + Qresult = 0 + Subbasin = list(routing[ro].keys()) + for sb in Subbasin: + uh_convey = UH_Lohmann_convey.get((sb, ro)) + if uh_convey is not None: + l = len(uh_convey) - 1 + # t+1 is length, t is index. + UH = uh_convey[0 : min(t + 1, l)] + Q_reverse = np.flip(Q_convey[sb][max(t - (l - 1), 0) : t + 1]) + Qresult += np.sum(UH * Q_reverse) + Qt = Qresult # Store the result for time t + return Qt diff --git a/src/hydrocnhs/sediment.py b/src/hydrocnhs/sediment.py new file mode 100644 index 0000000..ce40b58 --- /dev/null +++ b/src/hydrocnhs/sediment.py @@ -0,0 +1,160 @@ +import numpy as np +from pandas import DataFrame +def cal_usle(RE, K, CP, LS, Area): + """Calculate the erosion using USLE. + + Parameters + ---------- + RE : 1darray + Rainfall erosivity. + K : 1darray + Soil erodibility factor. + CP : 1darray + Vegetation management factor = cover and management factor * support + practice factor. + LS : 1darray + Topographic factor. + Area : 1darray + [ha] Area + + Returns + ------- + 2darray + [Mg] Sediment supply + """ + RE = np.array(RE).reshape((-1,1)) + K = np.array(K).reshape((-1,1)) + CP = np.array(CP).reshape((-1,1)) + LS = np.array(LS).reshape((-1,1)) + Area = np.array(Area).reshape((-1,1)) + X = 0.132 * np.dot(K*CP*LS*Area, RE.T) + return X # k x t + +def cal_LS(SL, PS): + """Calculate topographic factor, LS. + + Parameters + ---------- + SL : 1darray + Slope length. + PS : 1darray + Percent slope. + + Returns + ------- + 1darray + LS + """ + SL = np.array(SL) + PS = np.array(PS) + if PS >= 5: + b = 0.5 + elif PS < 5 and PS > 3: + b = 0.4 + elif PS <= 3 and PS >= 1: + b = 0.3 + elif PS <1: + b = 0.2 + theta = np.arctan(PS/100) + LS = (0.045*SL)**b * (65.41*np.sin(theta)**2 + 4.56*np.sin(theta) + 0.065) + return LS # k x 1 + +def cal_SX(DR, X, pd_date_index): + """Calculate total sediment supply, SX, of subbasins. + + Parameters + ---------- + DR : 1darray + Delivery ratio. + X : 2darray + Sediment supply. + pd_date_index : pd.Datetime + Pandas datetime index. + + Returns + ------- + 1darray + SX + """ + DR = np.array(DR) + SX = DR * DataFrame(X.T, index=pd_date_index).sum(axis=1).resample("MS").sum() + return SX.to_numpy(), SX.index # 1 x t + +# calculate entire set for a routing node +def cal_TR_B(Q_frac, pd_date_index, fi, ti): + """Calculate monthly allocation propotions based on transportation capacity. + + Parameters + ---------- + Q_frac : dict + A dictionary of streamflow portion of each subbasion that contributes + their corresponding routing outlets. + pd_date_index : pd.Datetime + Pandas datetime index. + + Returns + ------- + dict + A dictionary of monthly allocation ratios. + """ + + # Note that Q_frac.keys() is the outlets involve in the routing + DamAPI + # agents. + + # Transport factor, + TR = DataFrame(Q_frac, index=pd_date_index) + TR = TR.iloc[fi:ti+1,:]**(5/3) + TR = TR.resample("MS").sum() + # The total transport capacity, sb x t. A reversed cumulative sum. + B = TR.loc[::-1, :].cumsum()[::-1] + + ratio_dict = {m: TR.iloc[:m+1,:]/B.iloc[m,:] for m in range(12)} + + return ratio_dict # {t x sb} + +def run_TSS(RE_dict, sediment_setting, routing_setting, + dam_agts, pd_date_index, Q_frac, dc_TSS, yi, fi, ti): + # instream object they need to manually assign value of Y + outlets = list(sediment_setting.keys()) + SX = [] + for sb in outlets: + inputs = sediment_setting[sb]["Inputs"] + pars = sediment_setting[sb]["Pars"] + RE = RE_dict[sb][fi:ti+1] + #LS = cal_LS(pars["SL"], pars["PS"]) # k x 1 + LS = pars["LS"] + CP = pars["CP"] + if isinstance(CP, (np.ndarray, np.generic, list)) is False: + CP = [CP]*len(LS) + X = cal_usle(RE, pars["K"], CP, LS, + inputs["Area"]) # k x t + SX_sb, pd_date_index_m = cal_SX(pars["DR"], X, pd_date_index[fi:ti+1]) + SX.append(SX_sb) + + # Add DamAgts' month sediment yield (they have to provide this without knowing + # upstream sediment information) + dc_TSS # monthly + for agt in dam_agts: + SX.append(dc_TSS[agt][yi:yi+12]) + outlets.append(agt) + SX = DataFrame(np.array(SX).T, index=pd_date_index_m, columns=outlets) + #SX = np.array(SX) # t x sb + + # Transportation capacity + ratio_dict = cal_TR_B(Q_frac, pd_date_index, fi, ti) # t x sb + + # Route sediment yield. + for m in range(12): + ratio = ratio_dict[m] + for ro in list(routing_setting.keys()): + o_list = list(routing_setting[ro].keys()) + sx = SX[o_list].to_numpy() + ra = ratio[o_list].to_numpy() + ro_y = (sx[:m+1,:] * ra[:m+1,:]).sum() + + # add cap here if required + + dc_TSS[ro][yi*12+m] = ro_y + # Update SX for downstream outlet routing + SX.iloc[m, outlets.index(ro)] + return None \ No newline at end of file diff --git a/HydroCNHS/util.py b/src/hydrocnhs/util.py similarity index 71% rename from HydroCNHS/util.py rename to src/hydrocnhs/util.py index 9dcf34b..c3d71b7 100644 --- a/HydroCNHS/util.py +++ b/src/hydrocnhs/util.py @@ -1,8 +1,8 @@ # Utility functions' module -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2022/1/16. -import os +import os import ast import itertools import logging @@ -11,16 +11,19 @@ import numpy as np import pandas as pd import yaml -import ruamel.yaml # For round trip modification (keep comments) +import ruamel.yaml # For round trip modification (keep comments) from copy import deepcopy -import importlib.util # For importing customized module. -logger = logging.getLogger("HydroCNHS.sc") +import importlib.util # For importing customized module. +logger = logging.getLogger("HydroCNHS.sc") -#----------------------------------------------- -#---------- Read and Write Functions ---------- -# Acquire "this" file path. + +# ----------------------------------------------- +# ---------- Read and Write Functions ---------- +# Acquire "this" file path. this_dir, this_filename = os.path.split(__file__) + + def set_logging_config(log_filename=None): """Set up logging config. @@ -29,7 +32,7 @@ def set_logging_config(log_filename=None): log_filename : str, optional Log filename, by default None """ - with open(os.path.join(this_dir, 'Config_logging.yaml'), 'rt') as file: + with open(os.path.join(this_dir, "Config_logging.yaml"), "rt") as file: logging_config = yaml.safe_load(file.read()) if log_filename is not None: logging_config["loggers"]["HydroCNHS"]["handlers"] = ["console", "file"] @@ -44,6 +47,7 @@ def set_logging_config(log_filename=None): logging_config["handlers"].pop("file") config.dictConfig(logging_config) + def load_model(model, checked=False, parsed=False, print_summary=True): """Load model.yaml or model dictionary. @@ -64,31 +68,32 @@ def load_model(model, checked=False, parsed=False, print_summary=True): Model dictionary. """ logger = logging.getLogger("HydroCNHS") - if isinstance(model, dict): + if isinstance(model, dict): model = model else: - with open(model, 'rt') as file: + with open(model, "rt") as file: try: model = yaml.safe_load(file.read()) except Exception as e: # Logs the error appropriately. - logger.error(traceback.format_exc()) - return None - + logger.error(traceback.format_exc()) + return None + # Check model is consist and correct. - if checked is not True: + if checked is not True: if check_model(model) is not True: return None - + # Parse model if parsed is not True: model = parse_model(model, print_summary) - + return model + def write_model(model_dict, filename, org_model=None): """Write model dictionary to .yaml file - + If org_model is given, comments in the original file will be kept in the output model file. @@ -100,31 +105,32 @@ def write_model(model_dict, filename, org_model=None): Model configuration filename (e.g. model.yaml). org_model : str, optional Original model name (e.g. org_model.yaml), by default None. - + Note ---------- Common error : 1. value type is numpy.float64 => convert it to float. """ - if org_model is not None: # Contain comments in the original model file. + if org_model is not None: # Contain comments in the original model file. # Defaults to round-trip if no parameters given - yaml_round = ruamel.yaml.YAML() - with open(os.path.join(org_model), 'rt') as file: + yaml_round = ruamel.yaml.YAML() + with open(os.path.join(org_model), "rt") as file: model = yaml_round.load(file.read()) model = model_dict - with open(os.path.join(filename), 'w') as file: + with open(os.path.join(filename), "w") as file: yaml_round.dump(model, file) - else: # Dump without comments in the original model file. - with open(filename, 'w') as file: + else: # Dump without comments in the original model file. + with open(filename, "w") as file: saved_model = yaml.safe_dump( model_dict, file, sort_keys=False, default_flow_style=None - ) + ) logger.info("Model is saved at {}.".format(filename)) + def write_model_to_df(model_dict, key_option=["Pars"], prefix=""): """Write model dictionary to dataframes given key options. - - This function will convert model dictionary to dataframes according to + + This function will convert model dictionary to dataframes according to user's specified sections (key_option). Parameters @@ -136,6 +142,7 @@ def write_model_to_df(model_dict, key_option=["Pars"], prefix=""): prefix : str, optional Prefix for the filenames, by default "". """ + def convert_dict_to_df(dict, colname): df = {} for k in dict: @@ -143,67 +150,78 @@ def convert_dict_to_df(dict, colname): df[k] = dict[k] elif isinstance(dict[k], list): for i, v in enumerate(dict[k]): - df[k+".{}".format(i)] = v + df[k + ".{}".format(i)] = v else: df[k] = str(dict[k]) df = pd.DataFrame.from_dict(df, orient="index", columns=[colname]) return df - + def merge_dicts(dict_list): - dict_list = list(filter(None, dict_list)) # Remove None + dict_list = list(filter(None, dict_list)) # Remove None Len = len(dict_list) if Len == 0: return {} - res_dict = deepcopy(dict_list[0]) # Avoid to modify the original dict. + res_dict = deepcopy(dict_list[0]) # Avoid to modify the original dict. if Len > 1: - for d in range(1,Len): + for d in range(1, Len): res_dict.update(dict_list[d]) return res_dict - - allowed_output_sections = ["RainfallRunoff", "Routing", "ABM"] + + allowed_output_sections = ["RainfallRunoff", "Routing", "ABM", "Sediment"] section_list = [i for i in allowed_output_sections if i in model_dict] - df_name = []; output_df_list = [] + df_name = [] + output_df_list = [] for s in section_list: if s == "RainfallRunoff" and key_option != ["Attributes"]: - df_name.append(prefix + "Runoff_" - + model_dict["WaterSystem"]["RainfallRunoff"]) + df_name.append( + prefix + "Runoff_" + model_dict["WaterSystem"]["RainfallRunoff"] + ) df = pd.DataFrame() for sub in model_dict[s]: dict_list = [model_dict[s][sub].get(i) for i in key_option] merged_dict = merge_dicts(dict_list) converted_dict = convert_dict_to_df(merged_dict, sub) df = pd.concat([df, converted_dict], axis=1) - output_df_list.append(df) + output_df_list.append(df) elif s == "Routing" and key_option != ["Attributes"]: - df_name.append(prefix + "Routing_" - + model_dict["WaterSystem"]["Routing"]) + df_name.append(prefix + "Routing_" + model_dict["WaterSystem"]["Routing"]) df = pd.DataFrame() for ro in model_dict[s]: for o in model_dict[s][ro]: - dict_list = [model_dict[s][ro][o].get(i) \ - for i in key_option] + dict_list = [model_dict[s][ro][o].get(i) for i in key_option] merged_dict = merge_dicts(dict_list) converted_dict = convert_dict_to_df(merged_dict, (o, ro)) df = pd.concat([df, converted_dict], axis=1) - output_df_list.append(df) + output_df_list.append(df) elif s == "ABM": df_name.append(prefix + "ABM") df = pd.DataFrame() abm_setting = model_dict["WaterSystem"]["ABM"] - AgTypes = abm_setting["DamAPI"]+ \ - abm_setting["RiverDivAPI"]+ \ - abm_setting["InsituAPI"]+ \ - abm_setting["ConveyingAPI"] + AgTypes = ( + abm_setting["DamAPI"] + + abm_setting["RiverDivAPI"] + + abm_setting["InsituAPI"] + + abm_setting["ConveyingAPI"] + ) for agtype in AgTypes: for ag in model_dict[s][agtype]: - dict_list = [model_dict[s][agtype][ag].get(i) \ - for i in key_option] + dict_list = [model_dict[s][agtype][ag].get(i) for i in key_option] merged_dict = merge_dicts(dict_list) converted_dict = convert_dict_to_df(merged_dict, (ag, agtype)) df = pd.concat([df, converted_dict], axis=1) - output_df_list.append(df) + output_df_list.append(df) + elif s == "Sediment": + df_name.append(prefix + "Sediment") + df = pd.DataFrame() + for sub in model_dict[s]: + dict_list = [model_dict[s][sub].get(i) for i in key_option] + merged_dict = merge_dicts(dict_list) + converted_dict = convert_dict_to_df(merged_dict, sub) + df = pd.concat([df, converted_dict], axis=1) + output_df_list.append(df) return output_df_list, df_name + def gen_default_bounds(model_dict, key_option=["Pars"]): """Generate default parameter bounds in the format of a list of DataFrames. @@ -221,33 +239,48 @@ def gen_default_bounds(model_dict, key_option=["Pars"]): A list of parameter bound DataFrames, A list of DataFrame names. """ df_list, df_name = write_model_to_df(model_dict, key_option, prefix="") - + # Default parameter bounds. - gwlf_bounds = {"CN2": [25, 100], "IS": [0, 0.5], "Res": [0.001, 0.5], - "Sep": [0, 0.5], "Alpha": [0,1], "Beta": [0,1], - "Ur": [1, 15], "Df": [0, 1], "Kc": [0.5, 1.5]} - abcd_bounds = {"a": [0, 1], "b": [0, 400], "c": [0, 1], "d": [0, 1], - "Df": [0, 1]} - routing = {"GShape": [1, 100], "GScale": [0.01, 150], - "Velo": [0.5, 55], "Diff": [200, 4000]} + gwlf_bounds = { + "CN2": [25, 100], + "IS": [0, 0.5], + "Res": [0.001, 0.5], + "Sep": [0, 0.5], + "Alpha": [0, 1], + "Beta": [0, 1], + "Ur": [1, 15], + "Df": [0, 1], + "Kc": [0.5, 1.5], + } + abcd_bounds = {"a": [0, 1], "b": [0, 400], "c": [0, 1], "d": [0, 1], "Df": [0, 1]} + routing_bounds = { + "GShape": [1, 100], + "GScale": [0.01, 150], + "Velo": [0.5, 55], + "Diff": [200, 4000], + } + sediment_bounds = {"CP": [1e-7, 1e-1], "Sa": [0, 4], "Sb": [1, 5], "Sq": [1, 5]} for df, name in zip(df_list, df_name): if "Runoff_GWLF" in name: for k, v in gwlf_bounds.items(): - df.loc[k, :] = str(v) + df.loc[k, :] = str(v) elif "Runoff_ABCD" in name: for k, v in abcd_bounds.items(): df.loc[k, :] = str(v) elif "Routing_Lohmann" in name: - for k, v in routing.items(): + for k, v in routing_bounds.items(): df.loc[k, df.loc[k, :].isna() == False] = str(v) + elif "Sediment" in name: + df.loc[:, :] = np.nan + for k, v in sediment_bounds.items(): + df.loc[k, :] = str(v) else: for k in list(df.index): - df.loc[k, df.loc[k, :].isna() == False] = str([0,1]) + df.loc[k, df.loc[k, :].isna() == False] = str([0, 1]) return df_list, df_name - - -def write_model_to_csv(folder_path, model_dict, key_option=["Pars"], - prefix=""): + + +def write_model_to_csv(folder_path, model_dict, key_option=["Pars"], prefix=""): """Write model (dictionary) to csv files. Parameters @@ -267,15 +300,16 @@ def write_model_to_csv(folder_path, model_dict, key_option=["Pars"], A list of filenames. """ output_df_list, df_name = write_model_to_df(model_dict, key_option, prefix) - df_name = [i+".csv" for i in df_name] + df_name = [i + ".csv" for i in df_name] for i, df in enumerate(output_df_list): df.to_csv(os.path.join(folder_path, df_name[i])) logger.info("Output files {} at {}.".format(df_name, folder_path)) - return df_name # CSV filenames + return df_name # CSV filenames + def load_df_to_model_dict(model_dict, df, section, key): """Load dataframe to model dictionary. - + Note that the column and index names have to be identical as outputted from write_model_to_df() function. @@ -289,7 +323,7 @@ def load_df_to_model_dict(model_dict, df, section, key): RainfallRunoff or Routing or ABM. key : str nputs or Pars or Attributes. - + Returns ------- dict @@ -302,59 +336,63 @@ def parse(i): val = ast.literal_eval(i) return val except: - return i - + return i + def to_native_py_type(val): # Make sure the value is Native Python Type, which can be safely # dump to yaml. if "numpy" in str(type(val)): - # Convert df's null value, which is np.nan into None. So yaml + # Convert df's null value, which is np.nan into None. So yaml # will displat null instead of .nan - if np.isnan(val): + if np.isnan(val): return None else: val = val.item() - if val == -99: val = int(val) # HydroCNHS special setting. + if val == -99: + val = int(val) # HydroCNHS special setting. return val - else: # Sometime np.nan is not consider as numpy.float type. - # Convert df's null value, which is np.nan into None. So yaml + else: # Sometime np.nan is not consider as numpy.float type. + # Convert df's null value, which is np.nan into None. So yaml # will displat null instead of .nan if np.isnan(val): return None else: - return val # return other type - + return val # return other type + # Form a corresponding dictionary for Pars. - Col = [parse(i) for i in df.columns] # Since we might have tuples. + Col = [parse(i) for i in df.columns] # Since we might have tuples. df.columns = Col # Identify list items by ".". - Ind = [i.split(".")[0] for i in df.index] - Ind_dup = {v: (Ind.count(v) if "." in df.index[i] else 0) \ - for i, v in enumerate(Ind)} + Ind = [i.split(".")[0] for i in df.index] + Ind_dup = { + v: (Ind.count(v) if "." in df.index[i] else 0) for i, v in enumerate(Ind) + } # Parse entire df df = df.applymap(parse) # Form structured dictionary with only native python type object. Dict = {} # Add each sub model setting to the dict. The value is either list or # str or float or int. - for i in Col: + for i in Col: temp = {} for par in Ind_dup: if Ind_dup[par] > 0: temp[par] = list( - df.loc[[par+"."+str(k) for k in range(Ind_dup[par])], - [i]].to_numpy().flatten() - ) + df.loc[[par + "." + str(k) for k in range(Ind_dup[par])], [i]] + .to_numpy() + .flatten() + ) temp[par] = [to_native_py_type(val) for val in temp[par]] # Ensure W, Theta, LR ... lists won't contain None. - temp[par] = [val for val in temp[par] if val is not None] + temp[par] = [val for val in temp[par] if val is not None] else: temp[par] = to_native_py_type(df.loc[par, [i]].values[0]) Dict[i] = temp return Dict - df_dict = parse_df_to_dict(df) # df to Dict - - #Replace the original modelDict accordingly. + + df_dict = parse_df_to_dict(df) # df to Dict + + # Replace the original modelDict accordingly. if section == "Runoff": runoff = model_dict["RainfallRunoff"] for sub in df_dict: @@ -367,9 +405,14 @@ def to_native_py_type(val): ABM = model_dict["ABM"] for agagType in df_dict: ABM[agagType[1]][agagType[0]][key] = df_dict[agagType] - + if section == "Sediment": + sed = model_dict["Sediment"] + for sub in df_dict: + sed[sub][key] = df_dict[sub] + return model_dict + def load_customized_module_to_class(Class, module_name, path): """Load classes and functions in a user defined module (.py) into a given Class. @@ -383,11 +426,12 @@ def load_customized_module_to_class(Class, module_name, path): path : str Directory of filename.py. """ - spec = importlib.util.spec_from_file_location(module_name, - os.path.join(path, module_name)) - module = importlib.util.module_from_spec(spec) + spec = importlib.util.spec_from_file_location( + module_name, os.path.join(path, module_name) + ) + module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) - + # Add classes and functions to a class. namespace = vars(module) public = (name for name in namespace if name[:1] != "_") @@ -395,16 +439,19 @@ def load_customized_module_to_class(Class, module_name, path): setattr(Class, name, namespace[name]) # globals().update(module.__dict__) # Load all classes to globel. + def list_callable_public_object(obj): - name_list = [name for name in dir(obj) - if (callable(getattr(obj, name)) and name[:1] != "_")] + name_list = [ + name for name in dir(obj) if (callable(getattr(obj, name)) and name[:1] != "_") + ] return name_list - -#----------------------------------------------- -#----------------------------------------- -#---------- Auxiliary Functions ---------- +# ----------------------------------------------- + +# ----------------------------------------- +# ---------- Auxiliary Functions ---------- + def dict_to_string(dictionary, indentor=" ", level=1): """Ture a dictionary into a printable string. @@ -415,22 +462,25 @@ def dict_to_string(dictionary, indentor=" ", level=1): A dictionary. indentor : str, optional Indentor, by default " " - + Returns ------- str String. """ + def dict_to_string_list(dictionary, indentor=" ", count=0, string=[]): - for key, value in dictionary.items(): + for key, value in dictionary.items(): string.append(indentor * count + str(key)) if isinstance(value, dict) and count <= level: - string = dict_to_string_list(value, indentor, count+1, string) + string = dict_to_string_list(value, indentor, count + 1, string) else: - string.append(indentor * (count+1) + str(value)) + string.append(indentor * (count + 1) + str(value)) return string + return "\n".join(dict_to_string_list(dictionary, indentor)) + def create_rn_gen(seed): """Create random number generator based on numpy module. @@ -446,10 +496,13 @@ def create_rn_gen(seed): """ rn_gen = np.random.default_rng(seed) return rn_gen -#----------------------------------------- -#----------------------------------------------- -#---------- Check and Parse Functions ---------- + +# ----------------------------------------- + + +# ----------------------------------------------- +# ---------- Check and Parse Functions ---------- def check_model(model_dict): """Check the model dictionary @@ -467,16 +520,16 @@ def check_model(model_dict): # Need to make sure simulation period is longer than a month (GWLF part) # Name of keys (Agent and subbasin name) cannot be dulicated. - # Name of keys (Agent and subbasin name) cannot have "." - + # Name of keys (Agent and subbasin name) cannot have "." + Pass = check_WS(model_dict) Pass = check_RainfallRunoff(model_dict) - - if (model_dict.get("Routing") is not None - and model_dict.get("ABM") is not None): + + if model_dict.get("Routing") is not None and model_dict.get("ABM") is not None: Pass = check_agent_in_routing(model_dict) return Pass + def parse_model(model_dict, print_summary=True): """Parse model dictionary. Populate SystemParsedData. @@ -487,87 +540,97 @@ def parse_model(model_dict, print_summary=True): dict: model_dict """ model_dict["SystemParsedData"] = {} - - if model_dict.get("Routing") is not None : + + if model_dict.get("Routing") is not None: model_dict = parse_sim_seq(model_dict, print_summary) - return model_dict + return model_dict + def check_WS(model_dict): Pass = True ws = model_dict["WaterSystem"] - - #--- Check keys + + # --- Check keys # "StartDate", "EndDate", "DataLength" are checked below ideal_keys = ["NumSubbasins", "Outlets", "RainfallRunoff", "Routing", "ABM"] ws_items = list(ws.keys()) if all(item in ws_items for item in ideal_keys) is False: logger.error( "Missing items in WaterSystem setting. {}".format( - set(ideal_keys)-set(ws_items)) + set(ideal_keys) - set(ws_items) ) + ) Pass = False - + data_length = ws.get("DataLength") end_date = ws.get("EndDate") if data_length is not None: - end_date = (pd.to_datetime(ws["StartDate"], format='%Y/%m/%d')\ - + pd.DateOffset(data_length-1)) - ws["EndDate"] = end_date.strftime('%Y/%m/%d') + end_date = pd.to_datetime(ws["StartDate"], format="%Y/%m/%d") + pd.DateOffset( + data_length - 1 + ) + ws["EndDate"] = end_date.strftime("%Y/%m/%d") elif end_date is not None: - end_date = pd.to_datetime(ws["EndDate"], format='%Y/%m/%d') - start_date = pd.to_datetime(ws["StartDate"], format='%Y/%m/%d') + end_date = pd.to_datetime(ws["EndDate"], format="%Y/%m/%d") + start_date = pd.to_datetime(ws["StartDate"], format="%Y/%m/%d") ws["DataLength"] = (end_date - start_date).days + 1 else: - logger.error("Either DataLength or EndDate has to be provided in " - +"WaterSystem setting.") + logger.error( + "Either DataLength or EndDate has to be provided in " + + "WaterSystem setting." + ) Pass = False - + outlets = ws["Outlets"] if len(outlets) != ws["NumSubbasins"]: logger.error("Outlets is inconsist to NumSubbasins.") Pass = False - + if len(outlets) != len(set(outlets)): logger.error("Duplicates exist in Outlets.") Pass = False - + if any("." in o for o in outlets): - logger.error("\".\" is not allowed in the outlet name.") + logger.error('"." is not allowed in the outlet name.') Pass = False - - #--- Check selected RainfallRunoff model. + + # --- Check selected RainfallRunoff model. runoff_model = ws["RainfallRunoff"] runoff_options = ["GWLF", "ABCD", "Other"] if runoff_model not in runoff_options: logger.error( "Invalid rainfall-runoff model {}. Acceptable options: {}".format( - runoff_model, runoff_options) + runoff_model, runoff_options ) + ) Pass = False return Pass + def check_RainfallRunoff(model_dict): runoff = model_dict["RainfallRunoff"] Pass = True - #--- Check keys + # --- Check keys sb_list = model_dict["WaterSystem"]["Outlets"] ideal_keys = set(sb_list) runoff_keys = set(runoff) if runoff_keys != ideal_keys: - logger.error("Inconsist RainfallRunoff keys {}\nto {}".format( - runoff_keys, ideal_keys)) + logger.error( + "Inconsist RainfallRunoff keys {}\nto {}".format(runoff_keys, ideal_keys) + ) Pass = False - #--- Check subbasins + # --- Check subbasins ideal_keys2 = set(["Inputs", "Pars"]) for sb in sb_list: sb_keys = set(runoff[sb]) if sb_keys != ideal_keys2: - logger.error("Inconsist RainfallRunoff keys {}\nto {}".format( - sb_keys, ideal_keys)) + logger.error( + "Inconsist RainfallRunoff keys {}\nto {}".format(sb_keys, ideal_keys) + ) Pass = False return Pass + def check_agent_in_routing(model_dict): # To make sure InStreamAgentInflows outlets are assigned in the routing # section. @@ -576,89 +639,102 @@ def check_agent_in_routing(model_dict): routing = model_dict["Routing"] abm = model_dict["ABM"] ws = model_dict["WaterSystem"] - #--- Check In-stream agents' Links and collect instream_Ag_inflows: - # DamAPI is eligble in the routing setting. + # --- Check In-stream agents' Links and collect instream_Ag_inflows: + # DamAPI is eligble in the routing setting. # DamAPI' agents will completely split the water system into half. - instream_ag_inflows = [] + instream_ag_inflows = [] instream_ag_types = ws["ABM"]["DamAPI"] for ISagType in instream_ag_types: for ag in abm[ISagType]: links = abm[ISagType][ag]["Inputs"]["Links"] - inflow_outlets = [outlet for outlet in links \ - if links[outlet] == -1 and outlet != ag] + inflow_outlets = [ + outlet for outlet in links if links[outlet] == -1 and outlet != ag + ] if inflow_outlets == []: - logger.error("[Check model failed] No inflow outlets " - +"(e.g., Links:{InflowOutlet: -1}) are found " - +"for {}.".format(ag)) + logger.error( + "[Check model failed] No inflow outlets " + + "(e.g., Links:{InflowOutlet: -1}) are found " + + "for {}.".format(ag) + ) Pass = False - if links.get(ag) is None: + if links.get(ag) is None: logger.info("Auto-fill outflow link for {}.".format(ag)) abm[ISagType][ag]["Inputs"]["Links"][ag] = 1 instream_ag_inflows += inflow_outlets # Eliminate duplicates. - instream_ag_inflows = list(set(instream_ag_inflows)) + instream_ag_inflows = list(set(instream_ag_inflows)) - #--- Check InStreamAgents' inflow outlets are in RoutingOutlets. + # --- Check InStreamAgents' inflow outlets are in RoutingOutlets. routing_outlets = list(routing.keys()) for vro in instream_ag_inflows: if vro not in routing_outlets: - logger.error("[Check model failed] Cannot find in-stream agent's " - +"inflow outlets in the Routing section. Routing " - +"outlets should include {}.".format(vro)) + logger.error( + "[Check model failed] Cannot find in-stream agent's " + + "inflow outlets in the Routing section. Routing " + + "outlets should include {}.".format(vro) + ) Pass = False - else: # Check if start belong to others routing_outlets' starts. + else: # Check if start belong to others routing_outlets' starts. for start in routing[vro]: for ro in routing_outlets: if ro != vro: - if any( start in others for others in routing[ro] ): + if any(start in others for others in routing[ro]): # If it is in the upstream of VirROutlet, it's # fine. - if ro in routing[vro]: + if ro in routing[vro]: pass else: logging.error( "[Check model failed] " - +"{} sub-basin outlet ".format(start) - +"shouldn't be in {}'s ".format(ro) - +"(routing outlet) catchment outlets due " - +"to the seperation of in-stream control " - +"agent {}.".format(vro) - ) + + "{} sub-basin outlet ".format(start) + + "shouldn't be in {}'s ".format(ro) + + "(routing outlet) catchment outlets due " + + "to the seperation of in-stream control " + + "agent {}.".format(vro) + ) Pass = False - - #--- RiverDivAPI will not add a new routing outlet (agent itself) + + # --- RiverDivAPI will not add a new routing outlet (agent itself) # like DamAPI do to split the system into half but their diverting # outlet must be in routing outlets. river_div_ag_inflows = [] for river_div_type in ws["ABM"]["RiverDivAPI"]: for ag in abm[river_div_type]: links = abm[river_div_type][ag]["Inputs"]["Links"] + def get_div(x): try: return x <= 0 except: return x[-1] == "Minus" - divert_outlet = [outlet for outlet in links \ - if get_div(links[outlet]) and outlet != ag] + + divert_outlet = [ + outlet for outlet in links if get_div(links[outlet]) and outlet != ag + ] if divert_outlet == []: - logger.error("[Check model failed] No diverted outlets " - +"(e.g., Links:{DivertOutlet: -1}) are found " - +"for {}.".format(ag)) + logger.error( + "[Check model failed] No diverted outlets " + + "(e.g., Links:{DivertOutlet: -1}) are found " + + "for {}.".format(ag) + ) Pass = False river_div_ag_inflows += divert_outlet # Eliminate duplicates. river_div_ag_inflows = list(set(river_div_ag_inflows)) - - #--- Check RiverDivAgents' diverting outlets are in routing_outlets. + + # --- Check RiverDivAgents' diverting outlets are in routing_outlets. for vro in river_div_ag_inflows: if vro not in routing_outlets: - logger.error("[Check model failed] Cannot find RiverDivAgent " - +"diverting outlets in the Routing section. Routing " - +"outlets should include {}.".format(vro)) - Pass = False - + logger.error( + "[Check model failed] Cannot find RiverDivAgent " + + "diverting outlets in the Routing section. Routing " + + "outlets should include {}.".format(vro) + ) + Pass = False + return Pass + def form_sim_seq(node_list, back_tracking_dict): key_set = set(back_tracking_dict) if isinstance(node_list, str): @@ -670,38 +746,44 @@ def form_sim_seq(node_list, back_tracking_dict): if node not in key_set: back_tracking_dict[node] = [] sim_seq = node_list + def find_upstream_node(node): upstream_node = set(back_tracking_dict[node]) not_top_node = upstream_node.intersection(key_set) top_node = upstream_node - not_top_node return list(not_top_node), list(top_node) + while node_list != []: layer_nodes = [] for node in node_list: not_top_node, top_node = find_upstream_node(node) sim_seq = top_node + sim_seq layer_nodes = layer_nodes + not_top_node - # Back filling + # Back filling sim_seq = layer_nodes + sim_seq node_list = layer_nodes return sim_seq + def update_sim_seq_with_group(sim_seq, group, back_tracking_dict): branch_dict = {} for node in group: branch_dict[node] = form_sim_seq(node, back_tracking_dict)[:-1] - update_seq = list( - itertools.chain.from_iterable(branch_dict.values()) - ) + update_seq = list(itertools.chain.from_iterable(branch_dict.values())) update_seq = update_seq + group if len(update_seq) != len(set(update_seq)): - print("Given group {} is not eligible. Update simulation sequence fail.".format(group)) + print( + "Given group {} is not eligible. Update simulation sequence fail.".format( + group + ) + ) return sim_seq # Not update else: remain_node = [n for n in sim_seq if n not in update_seq] update_seq = update_seq + remain_node return update_seq + def parse_sim_seq(model_dict, print_summary=True): model_dict["SystemParsedData"]["SimSeq"] = None model_dict["SystemParsedData"]["AgSimSeq"] = None @@ -714,55 +796,58 @@ def parse_sim_seq(model_dict, print_summary=True): model_dict["SystemParsedData"]["Edges"] = None # Store info for constructing routing UH for conveyed water of those node. model_dict["SystemParsedData"]["ConveyToNodes"] = [] - - #----- Collect in-stream agents + + # ----- Collect in-stream agents ## In-stream agents here means those agents will re-define the streamflow # completely. Namely, only DamAgents. ## RiverDivAPI, in our definition, only modify the streamflow. - - if (model_dict.get("ABM") is not None - and model_dict["WaterSystem"]["ABM"] is not None): + + if ( + model_dict.get("ABM") is not None + and model_dict["WaterSystem"]["ABM"] is not None + ): ws_abm = model_dict["WaterSystem"]["ABM"] abm = model_dict["ABM"] - for ag_types in ["DamAPI", "RiverDivAPI", - "InsituAPI", "ConveyingAPI"]: + for ag_types in ["DamAPI", "RiverDivAPI", "InsituAPI", "ConveyingAPI"]: agents = [] for ag_type in ws_abm[ag_types]: for end in abm[ag_type]: # "RiverDivAPI" since we can multiple ag divert from # the same gauge. We modify streamflow directly. agents.append(end) - model_dict["SystemParsedData"][ag_types[:-3] + "Agents"] = agents + model_dict["SystemParsedData"][ag_types[:-3] + "Agents"] = agents instream_agents = model_dict["SystemParsedData"]["DamAgents"] if instream_agents is None: instream_agents = [] - - #----- Step1: Form the simulation sequence of routing outlets and in-stream + + # ----- Step1: Form the simulation sequence of routing outlets and in-stream # control agents ----- - # Collected edges and track-back dictionary. + # Collected edges and track-back dictionary. # Edges can be further used for plotting routing simulation schema using # networkx. - edges = [] + edges = [] back_tracking_dict = {} routing = model_dict["Routing"] routing_outlets = list(routing.keys()) for end in routing_outlets: for start in routing[end]: if start == end: - pass # We don't add self edge. + pass # We don't add self edge. else: # Eliminate only-sub-basin outlets. If need full stream node # sequence, remove this. - if start in routing_outlets + instream_agents: + if start in routing_outlets + instream_agents: edges.append((start, end)) if back_tracking_dict.get(end) is None: back_tracking_dict[end] = [start] else: back_tracking_dict[end].append(start) - - # Add in-stream agents connectinons if ABM sections exists. - if (model_dict.get("ABM") is not None - and model_dict["WaterSystem"]["ABM"] is not None): + + # Add in-stream agents connectinons if ABM sections exists. + if ( + model_dict.get("ABM") is not None + and model_dict["WaterSystem"]["ABM"] is not None + ): ws_abm = model_dict["WaterSystem"]["ABM"] instream_ag_types = ws_abm["DamAPI"] for ag_type in instream_ag_types: @@ -777,8 +862,9 @@ def parse_sim_seq(model_dict, print_summary=True): # well. # We don't want those edges (e.g. (end, end)) to ruin the # simulation sequence formation. - inflow_nodes = [node for node in links \ - if links[node] == -1 and end != node] + inflow_nodes = [ + node for node in links if links[node] == -1 and end != node + ] for start in inflow_nodes: edges.append((start, end)) if back_tracking_dict.get(end) is None: @@ -787,43 +873,44 @@ def parse_sim_seq(model_dict, print_summary=True): back_tracking_dict[end].append(start) model_dict["SystemParsedData"]["BackTrackingDict"] = back_tracking_dict model_dict["SystemParsedData"]["Edges"] = edges - + # Construct sim_seq - if back_tracking_dict == {}: # If there is only a single outlet! + if back_tracking_dict == {}: # If there is only a single outlet! sim_seq = routing_outlets else: - # end nodes - start nodes = watershed outlets node_list. - node_list = list( - set(back_tracking_dict.keys()) - set([i[0] for i in edges]) - ) + # end nodes - start nodes = watershed outlets node_list. + node_list = list(set(back_tracking_dict.keys()) - set([i[0] for i in edges])) sim_seq = form_sim_seq(node_list, back_tracking_dict) group_nodes = model_dict["WaterSystem"].get("NodeGroups") if group_nodes is not None: for group in group_nodes: - sim_seq = update_sim_seq_with_group(sim_seq, group, - back_tracking_dict) + sim_seq = update_sim_seq_with_group(sim_seq, group, back_tracking_dict) model_dict["SystemParsedData"]["SimSeq"] = sim_seq # Sort RoutingOutlets based on SimSeq - model_dict["SystemParsedData"]["RoutingOutlets"] = [ro for ro in sim_seq \ - if ro in routing_outlets] - #-------------------------------------------------------------------------- - - #----- Step2: Form AgSim dictionary ----- - # Aggregate to only SimSeq's node. - if (model_dict.get("ABM") is not None - and model_dict["WaterSystem"]["ABM"] is not None): + model_dict["SystemParsedData"]["RoutingOutlets"] = [ + ro for ro in sim_seq if ro in routing_outlets + ] + # -------------------------------------------------------------------------- + + # ----- Step2: Form AgSim dictionary ----- + # Aggregate to only SimSeq's node. + if ( + model_dict.get("ABM") is not None + and model_dict["WaterSystem"]["ABM"] is not None + ): ws_abm = model_dict["WaterSystem"]["ABM"] dam_ag_types = ws_abm["DamAPI"] river_ag_types = ws_abm["RiverDivAPI"] insitu_ag_types = ws_abm["InsituAPI"] convey_ag_types = ws_abm["ConveyingAPI"] # Ordered routing_outlets - routing_outlets = model_dict["SystemParsedData"]["RoutingOutlets"] - + routing_outlets = model_dict["SystemParsedData"]["RoutingOutlets"] + # Institutions: Institutions = {"AgType":{"Name": []}} institutions = ws_abm.get("Institutions") - if institutions is None: institutions = [] - + if institutions is None: + institutions = [] + def search_routing_outlet(agQ): """Find in which routing outlet first need agQ to adjust original Q. (from upstream). @@ -837,21 +924,26 @@ def search_routing_outlet(agQ): for ro in routing_outlets: if agQ in routing[ro]: return ro - + def create_empty_list(dict, key): if dict.get(key) is None: dict[key] = [] - - ag_sim_seq = {} ; priority = {} - ag_sim_seq["AgSimMinus"] = {}; priority["AgSimMinus"] = {} - ag_sim_seq["AgSimPlus"] = {} ; priority["AgSimPlus"] = {} + + ag_sim_seq = {} + priority = {} + ag_sim_seq["AgSimMinus"] = {} + priority["AgSimMinus"] = {} + ag_sim_seq["AgSimPlus"] = {} + priority["AgSimPlus"] = {} for ss in sim_seq: - ag_sim_seq["AgSimMinus"][ss] = {}; priority["AgSimMinus"][ss] = {} - ag_sim_seq["AgSimPlus"][ss] = {} ; priority["AgSimPlus"][ss] = {} - + ag_sim_seq["AgSimMinus"][ss] = {} + priority["AgSimMinus"][ss] = {} + ag_sim_seq["AgSimPlus"][ss] = {} + priority["AgSimPlus"][ss] = {} + for ag_type in dam_ag_types: if ag_type in institutions: - ag_list = institutions[ag_type].keys() # Use the group name + ag_list = institutions[ag_type].keys() # Use the group name else: ag_list = abm[ag_type].keys() # Note that in-stream agent (ag) will be in SimSeq @@ -860,13 +952,13 @@ def create_empty_list(dict, key): create_empty_list(priority["AgSimPlus"][ag], "DamAgents") # No AgSimMinus is needed since in-stream agents replace # original streamflow. - ag_sim_seq["AgSimPlus"][ag]["DamAgents"].append((ag,ag)) + ag_sim_seq["AgSimPlus"][ag]["DamAgents"].append((ag, ag)) # In-stream agent always has priority 0. priority["AgSimPlus"][ag]["DamAgents"].append(0) - + for ag_type in river_ag_types: if ag_type in institutions: - ag_list = institutions[ag_type].keys() # Use the group name + ag_list = institutions[ag_type].keys() # Use the group name group = True else: ag_list = abm[ag_type].keys() @@ -880,7 +972,8 @@ def create_empty_list(dict, key): links = abm[ag_type][member]["Inputs"]["Links"] # "list" is our special offer to calibrate return flow factor # (Inputs). - plus = []; minus = [] + plus = [] + minus = [] for p in links: if isinstance(links[p], list): if links[p][-1] == "Minus": @@ -892,10 +985,10 @@ def create_empty_list(dict, key): plus.append(p) else: minus.append(p) - - # plus = [p if isinstance(links[p], list) else p - # if links[p] >= 0 else None for p in links] - # minus = [None if isinstance(links[p], list) else p + + # plus = [p if isinstance(links[p], list) else p + # if links[p] >= 0 else None for p in links] + # minus = [None if isinstance(links[p], list) else p # if links[p] <= 0 else None for p in links] # plus = list(filter(None, plus)) # Drop None in a list. # minus = list(filter(None, minus)) # Drop None in a list. @@ -903,31 +996,27 @@ def create_empty_list(dict, key): # Return flow can be added to non-routing outlets. So we # need to find the associate routing outlet in the SimSeq. ro = search_routing_outlet(p) - create_empty_list(ag_sim_seq["AgSimPlus"][ro], - "RiverDivAgents") - create_empty_list(priority["AgSimPlus"][ro], - "RiverDivAgents") - ag_sim_seq["AgSimPlus"][ro]["RiverDivAgents"].append((ag,p)) + create_empty_list(ag_sim_seq["AgSimPlus"][ro], "RiverDivAgents") + create_empty_list(priority["AgSimPlus"][ro], "RiverDivAgents") + ag_sim_seq["AgSimPlus"][ro]["RiverDivAgents"].append((ag, p)) priority["AgSimPlus"][ro]["RiverDivAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) + ) for m in minus: # RiverDivAgents diverted outlets should belong to one # routing outlet. - # ro = search_routing_outlet(m) + # ro = search_routing_outlet(m) ro = m - create_empty_list(ag_sim_seq["AgSimMinus"][ro], - "RiverDivAgents") - create_empty_list(priority["AgSimMinus"][ro], - "RiverDivAgents") - ag_sim_seq["AgSimMinus"][ro]["RiverDivAgents"].append((ag,m)) + create_empty_list(ag_sim_seq["AgSimMinus"][ro], "RiverDivAgents") + create_empty_list(priority["AgSimMinus"][ro], "RiverDivAgents") + ag_sim_seq["AgSimMinus"][ro]["RiverDivAgents"].append((ag, m)) priority["AgSimMinus"][ro]["RiverDivAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) - + ) + for ag_type in convey_ag_types: if ag_type in institutions: - ag_list = institutions[ag_type].keys() # Use the group name + ag_list = institutions[ag_type].keys() # Use the group name group = True else: ag_list = abm[ag_type].keys() @@ -939,39 +1028,36 @@ def create_empty_list(dict, key): else: member = ag links = abm[ag_type][member]["Inputs"]["Links"] - plus = []; minus = [] + plus = [] + minus = [] for p in links: if links[p] >= 0: plus.append(p) else: minus.append(p) - + model_dict["SystemParsedData"]["ConveyToNodes"] = plus for p in plus: # Flow can be added to non-routing outlets. So we # need to find the associate routing outlet in the SimSeq. ro = search_routing_outlet(p) - create_empty_list(ag_sim_seq["AgSimPlus"][ro], - "ConveyingAgents") - create_empty_list(priority["AgSimPlus"][ro], - "ConveyingAgents") - ag_sim_seq["AgSimPlus"][ro]["ConveyingAgents"].append((ag,p)) + create_empty_list(ag_sim_seq["AgSimPlus"][ro], "ConveyingAgents") + create_empty_list(priority["AgSimPlus"][ro], "ConveyingAgents") + ag_sim_seq["AgSimPlus"][ro]["ConveyingAgents"].append((ag, p)) priority["AgSimPlus"][ro]["ConveyingAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) + ) for m in minus: # ConveyingAgents convey from a routing outlet. - # ro = search_routing_outlet(m) + # ro = search_routing_outlet(m) ro = m - create_empty_list(ag_sim_seq["AgSimMinus"][ro], - "ConveyingAgents") - create_empty_list(priority["AgSimMinus"][ro], - "ConveyingAgents") - ag_sim_seq["AgSimMinus"][ro]["ConveyingAgents"].append((ag,m)) + create_empty_list(ag_sim_seq["AgSimMinus"][ro], "ConveyingAgents") + create_empty_list(priority["AgSimMinus"][ro], "ConveyingAgents") + ag_sim_seq["AgSimMinus"][ro]["ConveyingAgents"].append((ag, m)) priority["AgSimMinus"][ro]["ConveyingAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) - + ) + for ag_type in insitu_ag_types: # insitu_ag_types is a simple diversion agent type, which only # divert/add water from a single sub-basin. @@ -979,7 +1065,7 @@ def create_empty_list(dict, key): # Note that it divert from runoff of a single sub-basin not river # and no return flow option. if ag_type in institutions: - ag_list = institutions[ag_type].keys() # Use the group name + ag_list = institutions[ag_type].keys() # Use the group name group = True else: ag_list = abm[ag_type].keys() @@ -993,8 +1079,9 @@ def create_empty_list(dict, key): links = abm[ag_type][member]["Inputs"]["Links"] # No special "list" offer to calibrate return flow factor # (Inputs). - # No return flow option. - plus = []; minus = [] + # No return flow option. + plus = [] + minus = [] for p in links: if links[p] >= 0: plus.append(p) @@ -1004,44 +1091,48 @@ def create_empty_list(dict, key): # Return flow can be added to non-routing outlets. So we # need to find the associate routing outlet in the SimSeq. ro = search_routing_outlet(p) - create_empty_list(ag_sim_seq["AgSimPlus"][ro], - "InsituAgents") - create_empty_list(priority["AgSimPlus"][ro], - "InsituAgents") - ag_sim_seq["AgSimPlus"][ro]["InsituAgents"].append((ag,p)) + create_empty_list(ag_sim_seq["AgSimPlus"][ro], "InsituAgents") + create_empty_list(priority["AgSimPlus"][ro], "InsituAgents") + ag_sim_seq["AgSimPlus"][ro]["InsituAgents"].append((ag, p)) priority["AgSimPlus"][ro]["InsituAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) + ) for m in minus: - ro = search_routing_outlet(m) - create_empty_list(ag_sim_seq["AgSimMinus"][ro], - "InsituAgents") - create_empty_list(priority["AgSimMinus"][ro], - "InsituAgents") - ag_sim_seq["AgSimMinus"][ro]["InsituAgents"].append( - (ag,m)) + ro = search_routing_outlet(m) + create_empty_list(ag_sim_seq["AgSimMinus"][ro], "InsituAgents") + create_empty_list(priority["AgSimMinus"][ro], "InsituAgents") + ag_sim_seq["AgSimMinus"][ro]["InsituAgents"].append((ag, m)) priority["AgSimMinus"][ro]["InsituAgents"].append( abm[ag_type][member]["Inputs"]["Priority"] - ) - - # Sort agents based on their piorities + ) + + # Sort agents based on their piorities for pm in ag_sim_seq: for ag_types in ag_sim_seq[pm]: for ro in ag_sim_seq[pm][ag_types]: agents = ag_sim_seq[pm][ag_types][ro] piorities = priority[pm][ag_types][ro] - agents = [ag for _,ag in sorted(zip(piorities, agents))] + agents = [ag for _, ag in sorted(zip(piorities, agents))] # Remove duplicated ags. - ag_sim_seq[pm][ag_types][ro] = list(set(agents)) + ag_sim_seq[pm][ag_types][ro] = list(set(agents)) - model_dict["SystemParsedData"]["AgSimSeq"] = ag_sim_seq - #---------------------------------------- + model_dict["SystemParsedData"]["AgSimSeq"] = ag_sim_seq + # ---------------------------------------- if print_summary: summary_dict = {} - for i in ["SimSeq","RoutingOutlets","DamAgents", "ConveyingAgents", - "RiverDivAgents","InsituAgents","AgSimSeq"]: + for i in [ + "SimSeq", + "RoutingOutlets", + "DamAgents", + "ConveyingAgents", + "RiverDivAgents", + "InsituAgents", + "AgSimSeq", + ]: summary_dict[i] = model_dict["SystemParsedData"][i] parsed_model_summary = dict_to_string(summary_dict, indentor=" ") logger.info("Parsed model data summary:\n" + parsed_model_summary) return model_dict -#------------------------------------- + + +# ------------------------------------- diff --git a/HydroCNHS/visual.py b/src/hydrocnhs/visual.py similarity index 59% rename from HydroCNHS/visual.py rename to src/hydrocnhs/visual.py index 4cc69b3..ab84d40 100644 --- a/HydroCNHS/visual.py +++ b/src/hydrocnhs/visual.py @@ -1,7 +1,7 @@ # Visualization module -# This module is not the core module, which is unlikely to be maintained. +# This module is not the core module, which is unlikely to be maintained. # Some simple functions for users to quickly visualize the simulated results. -# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) +# by Chung-Yi Lin @ Lehigh University (philip928lin@gmail.com) # Last update at 2021/12/23. import numpy as np import pandas as pd @@ -9,12 +9,21 @@ import matplotlib.pyplot as plt from .indicators import Indicator -class Visual(): - """Collection of some plotting functions. - """ + +class Visual: + """Collection of some plotting functions.""" + @staticmethod - def plot_reg(x_obv, y_sim, title=None, xy_labal=None, same_xy_limit=True, - return_reg_par=False, save_fig_path=None, show=True): + def plot_reg( + x_obv, + y_sim, + title=None, + xy_labal=None, + same_xy_limit=True, + return_reg_par=False, + save_fig_path=None, + show=True, + ): """Plot regression. Parameters @@ -42,34 +51,36 @@ def plot_reg(x_obv, y_sim, title=None, xy_labal=None, same_xy_limit=True, axis object or [slope, intercept]. """ if title is None: - title = "Regression" + title = "Regression" else: title = title - + if xy_labal is None: - x_label = "Obv"; y_label = "Sim" + x_label = "Obv" + y_label = "Sim" else: - x_label = xy_labal[0]; y_label = xy_labal[1] - + x_label = xy_labal[0] + y_label = xy_labal[1] + # Create figure fig, ax = plt.subplots() ax.set_title(title) ax.set_xlabel(x_label) ax.set_ylabel(y_label) - + # Regression calculation and plot x_obv = np.array(x_obv) y_sim = np.array(y_sim) mask = ~np.isnan(x_obv) & ~np.isnan(y_sim) # Mask to ignore nan slope, intercept, r_value, p_value, std_err = stats.linregress( - x_obv[mask], y_sim[mask]) # Calculate the regression line - line = slope*x_obv+intercept # For plotting regression line - ax.plot(x_obv, line, 'r', label='y={:.2f}x+{:.2f}'.format(slope, - intercept)) + x_obv[mask], y_sim[mask] + ) # Calculate the regression line + line = slope * x_obv + intercept # For plotting regression line + ax.plot(x_obv, line, "r", label="y={:.2f}x+{:.2f}".format(slope, intercept)) # Plot data point ax.scatter(x_obv, y_sim, color="k", s=3.5) - ax.legend(fontsize=9, loc='upper right') + ax.legend(fontsize=9, loc="upper right") if same_xy_limit: Max = max([np.nanmax(x_obv), np.nanmax(y_sim)]) Min = min([np.nanmin(x_obv), np.nanmin(y_sim)]) @@ -77,50 +88,71 @@ def plot_reg(x_obv, y_sim, title=None, xy_labal=None, same_xy_limit=True, ax.set_ylim(Min, Max) # Add 45 degree line interval = (Max - Min) / 10 - diagonal = np.arange(Min, Max+interval, interval) - ax.plot(diagonal, diagonal, "b", linestyle='dashed', lw=1) - - + diagonal = np.arange(Min, Max + interval, interval) + ax.plot(diagonal, diagonal, "b", linestyle="dashed", lw=1) + # PLot indicators - name = {"r": "$r$", - "r2":"$r^2$", - "rmse":"RMSE", - "NSE": "NSE", - "CP": "CP", - "RSR": "RSR", - "KGE": "KGE"} + name = { + "r": "$r$", + "r2": "$r^2$", + "rmse": "RMSE", + "NSE": "NSE", + "CP": "CP", + "RSR": "RSR", + "KGE": "KGE", + } indicators = {} indicators["r"] = Indicator.r(x_obv, y_sim) indicators["r2"] = Indicator.r2(x_obv, y_sim) indicators["rmse"] = Indicator.rmse(x_obv, y_sim) indicators["NSE"] = Indicator.NSE(x_obv, y_sim) indicators["KGE"] = Indicator.KGE(x_obv, y_sim) - - string = "\n".join(['{:^4}: {}'.format(name[keys], round(values,5)) - for keys,values in indicators.items()]) - + + string = "\n".join( + [ + "{:^4}: {}".format(name[keys], round(values, 5)) + for keys, values in indicators.items() + ] + ) + # these are matplotlib.patch.Patch properties - props = dict(boxstyle='round', facecolor='wheat', alpha=0.85) - ax.annotate(string, xy=(0.05, 0.95), xycoords='axes fraction', - verticalalignment='top', horizontalalignment='left', - transform=ax.transAxes, fontsize=9, bbox=props) + props = dict(boxstyle="round", facecolor="wheat", alpha=0.85) + ax.annotate( + string, + xy=(0.05, 0.95), + xycoords="axes fraction", + verticalalignment="top", + horizontalalignment="left", + transform=ax.transAxes, + fontsize=9, + bbox=props, + ) if show: plt.show() - + if save_fig_path is not None: fig.savefig(save_fig_path) plt.close() - + if return_reg_par: return [slope, intercept] else: return ax @staticmethod - def plot_timeseries(x_obv, y_sim, xticks=None, title=None, xy_labal=None, - save_fig_path=None, legend=True, show=True, **kwargs): + def plot_timeseries( + x_obv, + y_sim, + xticks=None, + title=None, + xy_labal=None, + save_fig_path=None, + legend=True, + show=True, + **kwargs, + ): """Plot timeseries. - + This function can plot two DataFrames with same column names. Parameters @@ -142,49 +174,59 @@ def plot_timeseries(x_obv, y_sim, xticks=None, title=None, xy_labal=None, show : bool, optional If True, the plot will be shown in the console, by default True. kwargs : optional - Other keywords for matplotlib. + Other keywords for matplotlib. Returns ------- object axis object. - """ + """ if title is None: - title = "Timeseries" + title = "Timeseries" else: title = title - + if xy_labal is None: - x_label = "Obv"; y_label = "Sim" + x_label = "Obv" + y_label = "Sim" else: - x_label = xy_labal[0]; y_label = xy_labal[1] - + x_label = xy_labal[0] + y_label = xy_labal[1] + if xticks is None: if isinstance(x_obv, pd.DataFrame): xticks = x_obv.index else: - xticks = np.arange(0,len(x_obv)) + xticks = np.arange(0, len(x_obv)) else: assert len(xticks) == len(x_obv), print( "Input length of x is not corresponding to the length of data." - ) + ) fig, ax = plt.subplots() if isinstance(x_obv, pd.DataFrame): for i, c in enumerate(list(x_obv)): - ax.plot(xticks, x_obv[c], - label = x_label +"_"+ str(c), - color = "C{}".format(i%10), - **kwargs) + ax.plot( + xticks, + x_obv[c], + label=x_label + "_" + str(c), + color="C{}".format(i % 10), + **kwargs, + ) else: - ax.plot(xticks, x_obv, label = x_label, **kwargs) - + ax.plot(xticks, x_obv, label=x_label, **kwargs) + if isinstance(y_sim, pd.DataFrame): for i, c in enumerate(list(y_sim)): - ax.plot(xticks, y_sim[c], linestyle='dashed', - label = y_label + "_"+ str(c), - color = "C{}".format(i%10), alpha = 0.5, - **kwargs) + ax.plot( + xticks, + y_sim[c], + linestyle="dashed", + label=y_label + "_" + str(c), + color="C{}".format(i % 10), + alpha=0.5, + **kwargs, + ) else: - ax.plot(xticks, y_sim, linestyle='dashed', label = y_label, **kwargs) + ax.plot(xticks, y_sim, linestyle="dashed", label=y_label, **kwargs) if legend: ax.legend(fontsize=9) ax.set_title(title) @@ -192,16 +234,17 @@ def plot_timeseries(x_obv, y_sim, xticks=None, title=None, xy_labal=None, ax.set_ylabel("Value") if show: plt.show() - + if save_fig_path is not None: fig.savefig(save_fig_path) plt.close() - + return ax - + @staticmethod - def plot_simple_ts(df, title=None, xy_labal=None, data_dots=True, - save_fig_path=None, **kwargs): + def plot_simple_ts( + df, title=None, xy_labal=None, data_dots=True, save_fig_path=None, **kwargs + ): """Plot timeseries. Parameters @@ -223,42 +266,55 @@ def plot_simple_ts(df, title=None, xy_labal=None, data_dots=True, axis object. """ if title is None: - title = "" + title = "" else: title = title - + if xy_labal is None: - x_label = "Time"; y_label = "Value" + x_label = "Time" + y_label = "Value" else: - x_label = xy_labal[0]; y_label = xy_labal[1] - + x_label = xy_labal[0] + y_label = xy_labal[1] + # Create figure fig, ax = plt.subplots() ax.set_title(title) ax.set_xlabel(x_label) ax.set_ylabel(y_label) - + # Regression calculation and plot - x = np.arange(1, len(df)+1) + x = np.arange(1, len(df) + 1) for i, v in enumerate(df): - mask = ~np.isnan(df[v]) # Mask to ignore nan + mask = ~np.isnan(df[v]) # Mask to ignore nan slope, intercept, r_value, p_value, std_err = stats.linregress( - x[mask], df[v][mask]) # Calculate the regression line - line = slope*x+intercept # For plotting regression line - ax.plot(df.index, line, color="C{}".format(i%10), - label='y={:.2f}x+{:.2f}'.format(slope, intercept), - linestyle="dashed", **kwargs) + x[mask], df[v][mask] + ) # Calculate the regression line + line = slope * x + intercept # For plotting regression line + ax.plot( + df.index, + line, + color="C{}".format(i % 10), + label="y={:.2f}x+{:.2f}".format(slope, intercept), + linestyle="dashed", + **kwargs, + ) if data_dots: - df[[v]].plot(ax=ax, marker='o', ls='', - color="C{}".format(i%10), ms=2, alpha=0.6) + df[[v]].plot( + ax=ax, + marker="o", + ls="", + color="C{}".format(i % 10), + ms=2, + alpha=0.6, + ) else: - df[[v]].plot(ax=ax, color="C{}".format(i%10), alpha=0.5) - ax.legend() + df[[v]].plot(ax=ax, color="C{}".format(i % 10), alpha=0.5) + ax.legend() plt.show() - + if save_fig_path is not None: fig.savefig(save_fig_path) plt.close() - + return ax - \ No newline at end of file diff --git a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png b/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png deleted file mode 100644 index ef07754..0000000 Binary files a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png and /dev/null differ diff --git a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle b/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle deleted file mode 100644 index c0eef32..0000000 Binary files a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle and /dev/null differ diff --git a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml b/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml deleted file mode 100644 index c4d74a9..0000000 --- a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml +++ /dev/null @@ -1,78 +0,0 @@ -Path: {WD: 'C:\...\Hydro_example', Modules: ''} -WaterSystem: - StartDate: 1981/1/1 - EndDate: 2013/12/31 - DataLength: 12053 - NumSubbasins: 6 - Outlets: [Hagg, DLLO, TRGC, DAIRY, RCTV, WSLO] - NodeGroups: [] - RainfallRunoff: GWLF - Routing: Lohmann - ABM: null -RainfallRunoff: - Hagg: - Inputs: {Area: 10034.2408, Latitude: 45.469, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 63.09695606034307, IS: 0.3128685792282655, Res: 0.03639665432501598, - Sep: 0.4770264981998682, Alpha: 0.6762603057001111, Beta: 0.42425653762375987, - Ur: 12.67110175142286, Df: 0.9932841079939798, Kc: 0.556847752899636} - DLLO: - Inputs: {Area: 22568.2404, Latitude: 45.475, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 27.493282164148393, IS: 0.3754209122706855, Res: 0.0798032441363718, - Sep: 0.3439527035156522, Alpha: 0.6158533427928817, Beta: 0.8853751993383168, - Ur: 5.615863093039824, Df: 0.2781622827765176, Kc: 1.0570144104883532} - TRGC: - Inputs: {Area: 24044.6363, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 83.17200566189449, IS: 0.3310123375394237, Res: 0.28964437774977775, - Sep: 0.3376288007753774, Alpha: 0.47976558828009064, Beta: 0.22294514503212542, - Ur: 6.697318575842338, Df: 0.11655372801068908, Kc: 1.3530261612520254} - DAIRY: - Inputs: {Area: 59822.7546, Latitude: 45.52, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 88.86414473923892, IS: 0.11293313216194407, Res: 0.44166520828663897, - Sep: 0.09367865724046644, Alpha: 0.8750703368551056, Beta: 0.23901808135616115, - Ur: 10.46352262943156, Df: 0.09418995887277179, Kc: 0.9125597734950432} - RCTV: - Inputs: {Area: 19682.6046, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 51.93553675633895, IS: 0.04544525392905914, Res: 0.41630987338687586, - Sep: 0.07612768409480669, Alpha: 0.3201069937808326, Beta: 0.6165427782963586, - Ur: 12.664690022486988, Df: 0.018412723893973637, Kc: 0.9820122835135058} - WSLO: - Inputs: {Area: 47646.8477, Latitude: 45.35, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 93.79210560424185, IS: 0.3888491040226842, Res: 0.31693790608030337, - Sep: 0.21415663885558625, Alpha: 0.3599470064557994, Beta: 0.48174576091678445, - Ur: 2.3132602960709763, Df: 0.013234578500869204, Kc: 0.9327761992492039} -Routing: - WSLO: - Hagg: - Inputs: {FlowLength: 101469.139, InstreamControl: false} - Pars: {GShape: 20.177413055043488, GScale: 38.80934833396521, Velo: 41.281902314304624, - Diff: 2644.3966968787668} - DLLO: - Inputs: {FlowLength: 91813.075, InstreamControl: false} - Pars: {GShape: 97.40794840377617, GScale: 30.16630653097553, Velo: 3.7267786351942274, - Diff: 3222.342605985671} - TRGC: - Inputs: {FlowLength: 80064.864, InstreamControl: false} - Pars: {GShape: 97.18521451798718, GScale: 66.23351686521556, Velo: 13.856558796786434, - Diff: 811.5880722153371} - DAIRY: - Inputs: {FlowLength: 70988.164, InstreamControl: false} - Pars: {GShape: 70.34417060709683, GScale: 84.36857151539756, Velo: 39.54697093453211, - Diff: 1753.9895815416344} - RCTV: - Inputs: {FlowLength: 60398.68, InstreamControl: false} - Pars: {GShape: 72.67526968771351, GScale: 135.6229957711671, Velo: 50.547721236734645, - Diff: 1711.2491697016608} - WSLO: - Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 1.690103253847504, GScale: 23.023843488589463, Velo: null, Diff: null} -SystemParsedData: - SimSeq: [WSLO] - AgSimSeq: null - RoutingOutlets: [WSLO] - DamAgents: null - RiverDivAgents: null - InsituAgents: null - ConveyAgents: null - BackTrackingDict: {} - Edges: [] - ConveyToNodes: [] diff --git a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png b/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png deleted file mode 100644 index 26ded9f..0000000 Binary files a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png and /dev/null differ diff --git a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/GA_auto_save.pickle b/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/GA_auto_save.pickle deleted file mode 100644 index 2560d5b..0000000 Binary files a/tutorials/Hydro_example/Cali_HydroModel_gwlf_KGE/GA_auto_save.pickle and /dev/null differ diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_10/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_10/cali_indiv_best-best.txt index 1f49158..5ea3510 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_10/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_10/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.932 0.868 19.308 0.847 0.415 0.777 0.376 0.743 0.392 DLLO 0.934 0.872 3.807 0.867 -2.998 0.905 -0.403 0.799 0.364 ResAgt 0.799 0.638 1.897 0.58 -1.607 0.776 0.035 0.628 0.648 DivAgt 0.932 0.869 0.195 0.868 0.164 0.905 0.101 0.776 0.363 Mean 0.899 0.812 6.302 0.791 -1.006 0.841 0.027 0.736 0.442 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.953 0.908 15.633 0.892 0.711 0.836 0.674 0.87 0.329 DLLO 0.938 0.88 3.803 0.861 0.505 0.889 0.704 0.861 0.372 ResAgt 0.899 0.808 1.343 0.79 -2.666 0.893 -0.232 0.845 0.458 DivAgt 0.934 0.873 0.198 0.871 0.194 0.885 0.523 0.796 0.359 Mean 0.931 0.867 5.244 0.854 -0.314 0.876 0.417 0.843 0.38 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.932 0.868 19.308 0.847 0.415 0.777 0.376 0.743 0.392 DLLO 0.934 0.872 3.807 0.867 -2.998 0.905 -0.403 0.799 0.364 ResAgt 0.799 0.638 1.897 0.58 -1.607 0.776 0.035 0.628 0.648 DivAgt 0.932 0.869 0.195 0.868 0.164 0.905 0.101 0.776 0.363 Mean 0.899 0.812 6.302 0.791 -1.006 0.841 0.027 0.736 0.442 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.953 0.908 15.633 0.892 0.711 0.836 0.674 0.87 0.329 DLLO 0.938 0.88 3.803 0.861 0.505 0.889 0.704 0.861 0.372 ResAgt 0.899 0.808 1.343 0.79 -2.666 0.893 -0.232 0.845 0.458 DivAgt 0.934 0.873 0.198 0.871 0.194 0.885 0.523 0.796 0.359 Mean 0.931 0.867 5.244 0.854 -0.314 0.876 0.417 0.843 0.38 ========================================================= Sol: a|HaggIn 0.9912 @@ -68,4 +68,4 @@ Diff|('ResAgt', 'DLLO') 3652.2594 Diff|('TRTR', 'DLLO') 3639.8695 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.2379 a|('DivAgt', 'Diversion_AgtType') 1.2969 -b|('DivAgt', 'Diversion_AgtType') -0.1128 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.1128 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_13/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_13/cali_indiv_best-best.txt index 29c7758..e0d92d7 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_13/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_13/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.936 0.877 18.913 0.853 0.553 0.773 0.587 0.754 0.383 DLLO 0.95 0.903 3.451 0.891 -1.651 0.887 -0.076 0.836 0.33 ResAgt 0.781 0.61 1.829 0.609 -1.155 0.7 0.176 0.654 0.625 DivAgt 0.948 0.899 0.172 0.897 0.158 0.921 0.096 0.825 0.321 Mean 0.904 0.822 6.091 0.813 -0.523 0.82 0.196 0.767 0.415 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.958 0.917 14.481 0.907 0.797 0.852 0.863 0.895 0.305 DLLO 0.955 0.912 3.461 0.885 0.159 0.86 0.503 0.889 0.339 ResAgt 0.861 0.741 1.52 0.732 -2.501 0.761 -0.184 0.821 0.518 DivAgt 0.954 0.91 0.166 0.909 0.206 0.936 0.529 0.856 0.302 Mean 0.932 0.87 4.907 0.858 -0.335 0.852 0.428 0.865 0.366 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.936 0.877 18.913 0.853 0.553 0.773 0.587 0.754 0.383 DLLO 0.95 0.903 3.451 0.891 -1.651 0.887 -0.076 0.836 0.33 ResAgt 0.781 0.61 1.829 0.609 -1.155 0.7 0.176 0.654 0.625 DivAgt 0.948 0.899 0.172 0.897 0.158 0.921 0.096 0.825 0.321 Mean 0.904 0.822 6.091 0.813 -0.523 0.82 0.196 0.767 0.415 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.958 0.917 14.481 0.907 0.797 0.852 0.863 0.895 0.305 DLLO 0.955 0.912 3.461 0.885 0.159 0.86 0.503 0.889 0.339 ResAgt 0.861 0.741 1.52 0.732 -2.501 0.761 -0.184 0.821 0.518 DivAgt 0.954 0.91 0.166 0.909 0.206 0.936 0.529 0.856 0.302 Mean 0.932 0.87 4.907 0.858 -0.335 0.852 0.428 0.865 0.366 ========================================================= Sol: a|HaggIn 0.9200 @@ -68,4 +68,4 @@ Diff|('ResAgt', 'DLLO') 3914.8122 Diff|('TRTR', 'DLLO') 3594.3769 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.0677 a|('DivAgt', 'Diversion_AgtType') 0.3432 -b|('DivAgt', 'Diversion_AgtType') -0.0010 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.0010 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_5/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_5/cali_indiv_best-best.txt index e972f72..6a535fb 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_5/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_abcd_abm_KGE_5/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.924 0.853 20.245 0.832 0.436 0.766 0.409 0.717 0.41 DLLO 0.911 0.829 4.404 0.823 -1.825 0.851 -0.082 0.731 0.421 ResAgt 0.763 0.582 1.937 0.562 -0.77 0.679 0.193 0.612 0.662 DivAgt 0.929 0.863 0.199 0.863 0.165 0.901 0.101 0.766 0.371 Mean 0.882 0.782 6.696 0.77 -0.498 0.799 0.155 0.707 0.466 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.946 0.894 16.41 0.881 0.717 0.832 0.708 0.861 0.345 DLLO 0.933 0.87 3.896 0.854 0.325 0.876 0.646 0.859 0.382 ResAgt 0.83 0.689 1.64 0.688 -1.476 0.781 0.093 0.783 0.559 DivAgt 0.931 0.867 0.203 0.865 0.193 0.879 0.523 0.786 0.368 Mean 0.91 0.83 5.537 0.822 -0.06 0.842 0.493 0.822 0.413 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.924 0.853 20.245 0.832 0.436 0.766 0.409 0.717 0.41 DLLO 0.911 0.829 4.404 0.823 -1.825 0.851 -0.082 0.731 0.421 ResAgt 0.763 0.582 1.937 0.562 -0.77 0.679 0.193 0.612 0.662 DivAgt 0.929 0.863 0.199 0.863 0.165 0.901 0.101 0.766 0.371 Mean 0.882 0.782 6.696 0.77 -0.498 0.799 0.155 0.707 0.466 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.946 0.894 16.41 0.881 0.717 0.832 0.708 0.861 0.345 DLLO 0.933 0.87 3.896 0.854 0.325 0.876 0.646 0.859 0.382 ResAgt 0.83 0.689 1.64 0.688 -1.476 0.781 0.093 0.783 0.559 DivAgt 0.931 0.867 0.203 0.865 0.193 0.879 0.523 0.786 0.368 Mean 0.91 0.83 5.537 0.822 -0.06 0.842 0.493 0.822 0.413 ========================================================= Sol: a|HaggIn 0.5987 @@ -68,4 +68,4 @@ Diff|('ResAgt', 'DLLO') 664.6523 Diff|('TRTR', 'DLLO') 2805.0002 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.3281 a|('DivAgt', 'Diversion_AgtType') 1.4472 -b|('DivAgt', 'Diversion_AgtType') -0.1271 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.1271 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_10/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_10/cali_indiv_best-best.txt index 490b09f..680c990 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_10/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_10/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.928 0.861 19.215 0.848 -12.749 0.917 -2.16 0.74 0.39 DLLO 0.934 0.873 3.828 0.866 -24.286 0.927 -3.364 0.797 0.366 ResAgt 0.815 0.665 1.749 0.643 -2.089 0.804 -0.221 0.684 0.597 DivAgt 0.898 0.807 0.241 0.799 0.154 0.873 0.092 0.657 0.449 Mean 0.894 0.801 6.258 0.789 -9.742 0.88 -1.413 0.72 0.45 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.942 0.888 18.897 0.842 -6.91 0.833 -1.346 0.795 0.398 DLLO 0.926 0.857 4.084 0.84 -9.351 0.906 -1.683 0.842 0.4 ResAgt 0.91 0.829 1.314 0.8 -4.28 0.851 -0.746 0.856 0.448 DivAgt 0.894 0.8 0.249 0.796 0.208 0.882 0.529 0.677 0.452 Mean 0.918 0.843 6.136 0.819 -5.083 0.868 -0.811 0.793 0.424 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.928 0.861 19.215 0.848 -12.749 0.917 -2.16 0.74 0.39 DLLO 0.934 0.873 3.828 0.866 -24.286 0.927 -3.364 0.797 0.366 ResAgt 0.815 0.665 1.749 0.643 -2.089 0.804 -0.221 0.684 0.597 DivAgt 0.898 0.807 0.241 0.799 0.154 0.873 0.092 0.657 0.449 Mean 0.894 0.801 6.258 0.789 -9.742 0.88 -1.413 0.72 0.45 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.942 0.888 18.897 0.842 -6.91 0.833 -1.346 0.795 0.398 DLLO 0.926 0.857 4.084 0.84 -9.351 0.906 -1.683 0.842 0.4 ResAgt 0.91 0.829 1.314 0.8 -4.28 0.851 -0.746 0.856 0.448 DivAgt 0.894 0.8 0.249 0.796 0.208 0.882 0.529 0.677 0.452 Mean 0.918 0.843 6.136 0.819 -5.083 0.868 -0.811 0.793 0.424 ========================================================= Sol: CN2|HaggIn 67.5171 @@ -96,4 +96,4 @@ Diff|('ResAgt', 'DLLO') 3700.2597 Diff|('TRTR', 'DLLO') 3598.9538 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.3925 a|('DivAgt', 'Diversion_AgtType') 2.9996 -b|('DivAgt', 'Diversion_AgtType') -0.1921 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.1921 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_13/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_13/cali_indiv_best-best.txt index 3831c89..dc0f2dd 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_13/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_13/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.926 0.858 18.685 0.856 -3.728 0.912 -0.642 0.755 0.379 DLLO 0.926 0.858 4.022 0.852 -19.58 0.919 -2.837 0.775 0.385 ResAgt 0.814 0.662 1.797 0.623 -3.729 0.806 -0.602 0.666 0.614 DivAgt 0.913 0.833 0.22 0.832 0.165 0.891 0.102 0.715 0.409 Mean 0.895 0.803 6.181 0.791 -6.718 0.882 -0.994 0.727 0.447 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.931 0.867 18.664 0.846 -1.7 0.896 -0.29 0.8 0.393 DLLO 0.92 0.846 4.328 0.82 -3.703 0.886 -0.6 0.814 0.424 ResAgt 0.92 0.847 1.277 0.81 -4.866 0.851 -0.888 0.861 0.435 DivAgt 0.919 0.845 0.218 0.843 0.194 0.875 0.523 0.752 0.396 Mean 0.923 0.851 6.122 0.83 -2.519 0.877 -0.314 0.807 0.412 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.926 0.858 18.685 0.856 -3.728 0.912 -0.642 0.755 0.379 DLLO 0.926 0.858 4.022 0.852 -19.58 0.919 -2.837 0.775 0.385 ResAgt 0.814 0.662 1.797 0.623 -3.729 0.806 -0.602 0.666 0.614 DivAgt 0.913 0.833 0.22 0.832 0.165 0.891 0.102 0.715 0.409 Mean 0.895 0.803 6.181 0.791 -6.718 0.882 -0.994 0.727 0.447 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.931 0.867 18.664 0.846 -1.7 0.896 -0.29 0.8 0.393 DLLO 0.92 0.846 4.328 0.82 -3.703 0.886 -0.6 0.814 0.424 ResAgt 0.92 0.847 1.277 0.81 -4.866 0.851 -0.888 0.861 0.435 DivAgt 0.919 0.845 0.218 0.843 0.194 0.875 0.523 0.752 0.396 Mean 0.923 0.851 6.122 0.83 -2.519 0.877 -0.314 0.807 0.412 ========================================================= Sol: CN2|HaggIn 62.9987 @@ -96,4 +96,4 @@ Diff|('ResAgt', 'DLLO') 1051.7036 Diff|('TRTR', 'DLLO') 1641.9822 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.2873 a|('DivAgt', 'Diversion_AgtType') 1.9828 -b|('DivAgt', 'Diversion_AgtType') -0.1604 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.1604 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_5/cali_indiv_best-best.txt b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_5/cali_indiv_best-best.txt index 3eb27ac..9e724cd 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_5/cali_indiv_best-best.txt +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Cali_gwlf_abm_KGE_5/cali_indiv_best-best.txt @@ -1,8 +1,8 @@ Monthly cali/vali result - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.962 0.926 13.466 0.925 -3.777 0.958 -0.723 0.872 0.273 DLLO 0.92 0.846 4.243 0.835 -18.641 0.916 -2.755 0.755 0.406 ResAgt 0.8 0.64 1.851 0.6 -8.939 0.783 -1.452 0.644 0.633 DivAgt 0.947 0.896 0.173 0.896 0.17 0.917 0.106 0.823 0.323 Mean 0.907 0.827 4.933 0.814 -7.797 0.894 -1.206 0.774 0.409 - r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.974 0.948 12.341 0.933 -1.594 0.894 -0.318 0.913 0.26 DLLO 0.897 0.805 4.913 0.768 -7.492 0.865 -1.305 0.761 0.481 ResAgt 0.897 0.804 1.474 0.748 -5.293 0.811 -1.026 0.816 0.502 DivAgt 0.955 0.913 0.166 0.91 0.191 0.898 0.522 0.857 0.3 Mean 0.931 0.867 4.723 0.84 -3.547 0.867 -0.532 0.837 0.386 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.962 0.926 13.466 0.925 -3.777 0.958 -0.723 0.872 0.273 DLLO 0.92 0.846 4.243 0.835 -18.641 0.916 -2.755 0.755 0.406 ResAgt 0.8 0.64 1.851 0.6 -8.939 0.783 -1.452 0.644 0.633 DivAgt 0.947 0.896 0.173 0.896 0.17 0.917 0.106 0.823 0.323 Mean 0.907 0.827 4.933 0.814 -7.797 0.894 -1.206 0.774 0.409 + r r2 rmse NSE iNSE KGE iKGE CP RSR WSLO 0.974 0.948 12.341 0.933 -1.594 0.894 -0.318 0.913 0.26 DLLO 0.897 0.805 4.913 0.768 -7.492 0.865 -1.305 0.761 0.481 ResAgt 0.897 0.804 1.474 0.748 -5.293 0.811 -1.026 0.816 0.502 DivAgt 0.955 0.913 0.166 0.91 0.191 0.898 0.522 0.857 0.3 Mean 0.931 0.867 4.723 0.84 -3.547 0.867 -0.532 0.837 0.386 ========================================================= Sol: CN2|HaggIn 60.5200 @@ -96,4 +96,4 @@ Diff|('ResAgt', 'DLLO') 294.9069 Diff|('TRTR', 'DLLO') 3056.8375 ReturnFactor.0|('DivAgt', 'Diversion_AgtType') 0.0104 a|('DivAgt', 'Diversion_AgtType') 0.2130 -b|('DivAgt', 'Diversion_AgtType') -0.0325 \ No newline at end of file +b|('DivAgt', 'Diversion_AgtType') -0.0325 diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_ABCD.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_ABCD.py index af72969..21f63ac 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_ABCD.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_ABCD.py @@ -1,7 +1,8 @@ import os import HydroCNHS + prj_path, this_filename = os.path.split(__file__) -#%% =========================================================================== +# %% =========================================================================== # Step 1: Create a model configuration file # ============================================================================= ### Initialize a model builder object. @@ -13,48 +14,71 @@ ### Setup land surface model (rainfall-runoff model) # Here we have seven subbasins and we select ABCD as the rainfall-runoff model. -outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] -area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546, - 19682.6046, 47646.8477] +outlet_list = ["HaggIn", "TRTR", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"] +area_list = [ + 10034.2408, + 329.8013, + 22238.4391, + 24044.6363, + 59822.7546, + 19682.6046, + 47646.8477, +] lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350] -mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="ABCD") +mb.set_rainfall_runoff( + outlet_list=outlet_list, area_list=area_list, lat_list=lat_list, runoff_model="ABCD" +) ### Setup routing outlets # Add WSLO -mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], - flow_length_list=[80064.864, 70988.164, 60398.680, 0]) +mb.set_routing_outlet( + routing_outlet="WSLO", + upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], + flow_length_list=[80064.864, 70988.164, 60398.680, 0], +) # Add TRGC -mb.set_routing_outlet(routing_outlet="TRGC", - upstream_outlet_list=["DLLO", "TRGC"], - flow_length_list=[11748.211, 0]) +mb.set_routing_outlet( + routing_outlet="TRGC", + upstream_outlet_list=["DLLO", "TRGC"], + flow_length_list=[11748.211, 0], +) # Add DLLO # Specify that ResAgt is an instream object. -mb.set_routing_outlet(routing_outlet="DLLO", - upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], - flow_length_list=[9656.064, 30899.4048, 0], - instream_objects=["ResAgt"]) +mb.set_routing_outlet( + routing_outlet="DLLO", + upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], + flow_length_list=[9656.064, 30899.4048, 0], + instream_objects=["ResAgt"], +) # Add HaggIn -mb.set_routing_outlet(routing_outlet="HaggIn", - upstream_outlet_list=["HaggIn"], - flow_length_list=[0]) +mb.set_routing_outlet( + routing_outlet="HaggIn", upstream_outlet_list=["HaggIn"], flow_length_list=[0] +) ### Setup ABM mb.set_ABM(abm_module_folder_path=wd, abm_module_name="TRB_ABM.py") -mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt", - api=mb.api.Dam, - link_dict={"HaggIn": -1, "ResAgt": 1}, - dm_class="ReleaseDM") -mb.add_agent(agt_type_class="Diversion_AgtType", agt_name="DivAgt", - api=mb.api.RiverDiv, - link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, - dm_class="DivertDM", - par_dict={"ReturnFactor": [-99], "a": -99, "b":-99}) -mb.add_agent(agt_type_class="Pipe_AgtType", agt_name="PipeAgt", - api=mb.api.Conveying, - link_dict={"TRTR": 1}, - dm_class="TransferDM") +mb.add_agent( + agt_type_class="Reservoir_AgtType", + agt_name="ResAgt", + api=mb.api.Dam, + link_dict={"HaggIn": -1, "ResAgt": 1}, + dm_class="ReleaseDM", +) +mb.add_agent( + agt_type_class="Diversion_AgtType", + agt_name="DivAgt", + api=mb.api.RiverDiv, + link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, + dm_class="DivertDM", + par_dict={"ReturnFactor": [-99], "a": -99, "b": -99}, +) +mb.add_agent( + agt_type_class="Pipe_AgtType", + agt_name="PipeAgt", + api=mb.api.Conveying, + link_dict={"TRTR": 1}, + dm_class="TransferDM", +) ### Print the model in the console mb.print_model() @@ -63,20 +87,20 @@ mb.write_model_to_yaml(filename="TRB_ABCD.yaml") mb.gen_ABM_module_template() -#%% =========================================================================== +# %% =========================================================================== # Step 2: Populate a model configuration file # ============================================================================= # Nothing need to be manually modified in this example. -#%% =========================================================================== +# %% =========================================================================== # Step 3: Program ABM module (.py) # ============================================================================= # We provide a complete ABM module for the TRB example (i.e., # TRB_ABM_complete.py). Theorical details can be found in Lin et al. (2022). -#%% =========================================================================== +# %% =========================================================================== # Step 4: Run a calibration # ============================================================================= import matplotlib.pyplot as plt @@ -84,19 +108,24 @@ import HydroCNHS.calibration as cali from copy import deepcopy import time -time.sleep(10*3600) + +time.sleep(10 * 3600) # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data -obv_M = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) +obv_M = pd.read_csv( + os.path.join(wd, "Data", "Cali_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) # Load model model_dict = HydroCNHS.load_model(os.path.join(wd, "TRB_ABCD.yaml")) @@ -109,30 +138,31 @@ # Modify the default bounds of ABM df_abm_bound = par_bound_df_list[2] -df_abm_bound.loc["ReturnFactor.0", [('DivAgt', 'Diversion_AgtType')]] = "[0, 0.5]" -df_abm_bound.loc["a", [('DivAgt', 'Diversion_AgtType')]] = "[0, 3]" -df_abm_bound.loc["b", [('DivAgt', 'Diversion_AgtType')]] = "[-2, 2]" +df_abm_bound.loc["ReturnFactor.0", [("DivAgt", "Diversion_AgtType")]] = "[0, 0.5]" +df_abm_bound.loc["a", [("DivAgt", "Diversion_AgtType")]] = "[0, 3]" +df_abm_bound.loc["b", [("DivAgt", "Diversion_AgtType")]] = "[-2, 2]" # Create convertor for calibration converter = cali.Convertor() cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) formatter = converter.formatter + # Code evaluation function for GA algorthm def cal_batch_indicator(period, target, df_obv, df_sim): """Compute mean indicator over targets""" - df_obv = df_obv[period[0]:period[1]] - df_sim = df_sim[period[0]:period[1]] + df_obv = df_obv[period[0] : period[1]] + df_sim = df_sim[period[0] : period[1]] Indicator = HydroCNHS.Indicator() df = pd.DataFrame() for item in target: - df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item], - index_name=item) + df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item], index_name=item) df = pd.concat([df, df_i], axis=0) df_mean = pd.DataFrame(df.mean(axis=0), columns=["Mean"]).T df = pd.concat([df, df_mean], axis=0) return df + def evaluation(individual, info): cali_wd, current_generation, ith_individual, formatter, _ = info name = "{}-{}".format(current_generation, ith_individual) @@ -151,10 +181,10 @@ def evaluation(individual, info): ##### Get simulation data # Streamflow of routing outlets. - cali_target = ["WSLO","DLLO","ResAgt","DivAgt"] + cali_target = ["WSLO", "DLLO", "ResAgt", "DivAgt"] cali_period = ("1981-1-1", "2005-12-31") vali_period = ("2006-1-1", "2013-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] + sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] # Resample the daily simulation output to monthly outputs. @@ -167,36 +197,39 @@ def evaluation(individual, info): ##### Save output.txt # Only exercute when ga.run_individual(ga.solution) if current_generation == "best": - with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), 'w') as f: + with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), "w") as f: f.write("\n\nMonthly cali/vali result\n") - f.write(df_cali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) + f.write(df_cali_Q_M.round(3).to_csv(sep="\t").replace("\n", "")) f.write("\n") - f.write(df_vali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) + f.write(df_vali_Q_M.round(3).to_csv(sep="\t").replace("\n", "")) f.write("\n=========================================================\n") - f.write("Sol:\n" ) + f.write("Sol:\n") df = pd.DataFrame(individual, index=cali_inputs["par_name"]).round(4) f.write(df.to_string(header=False, index=True)) fitness = df_cali_Q_M.loc["Mean", "KGE"] return (fitness,) -config = {'min_or_max': 'max', - 'pop_size': 200, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.1, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} + +config = { + "min_or_max": "max", + "pop_size": 200, + "num_ellite": 1, + "prob_cross": 0.5, + "prob_mut": 0.1, + "stochastic": False, + "max_gen": 100, + "sampling_method": "LHC", + "drop_record": False, + "paral_cores": -1, + "paral_verbose": 1, + "auto_save": True, + "print_level": 1, + "plot": True, +} # Calibrate with 3 seeds. -seeds = [5,10,13] +seeds = [5, 10, 13] for seed in seeds: rn_gen = HydroCNHS.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) @@ -211,10 +244,11 @@ def evaluation(individual, info): for i, df in enumerate(df_list): s = df_name[i].split("_")[0] model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, - os.path.join(ga.cali_wd, "Calibrated_TRB_ABCD.yaml")) + HydroCNHS.write_model( + model_best, os.path.join(ga.cali_wd, "Calibrated_TRB_ABCD.yaml") + ) summary = ga.summary -#%% =========================================================================== +# %% =========================================================================== # Step 5: Run a simulation # ============================================================================= ### Run a simulation. @@ -222,10 +256,10 @@ def evaluation(individual, info): filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF.yaml") model = HydroCNHS.Model(filename) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() +sim_Q_M = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() ### Plot fig, axes = plt.subplots(nrows=4, sharex=True) axes = axes.flatten() @@ -240,10 +274,10 @@ def evaluation(individual, info): axes[2].plot(x, obv_M["ResAgt"], ls="--", lw=1, color="black", label="Obv") axes[3].plot(x, obv_M["DivAgt"], ls="--", lw=1, color="black", label="Obv") -axes[0].set_ylim([0,75]) -axes[1].set_ylim([0,230]) -axes[2].set_ylim([0,23]) -axes[3].set_ylim([0,2]) +axes[0].set_ylim([0, 75]) +axes[1].set_ylim([0, 230]) +axes[2].set_ylim([0, 23]) +axes[3].set_ylim([0, 2]) axes[0].set_ylabel("DLLO\n($m^3/s$)") axes[1].set_ylabel("WSLO\n($m^3/s$)") @@ -258,19 +292,3 @@ def evaluation(individual, info): axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) fig.align_ylabels(axes) - - - - - - - - - - - - - - - - diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_GWLF.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_GWLF.py index a5cf28c..7254d40 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_GWLF.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/Main_TRB_GWLF.py @@ -1,7 +1,8 @@ import os import HydroCNHS + prj_path, this_filename = os.path.split(__file__) -#%% =========================================================================== +# %% =========================================================================== # Step 1: Create a model configuration file # ============================================================================= ### Initialize a model builder object. @@ -13,48 +14,71 @@ ### Setup land surface model (rainfall-runoff model) # Here we have seven subbasins and we select GWLF as the rainfall-runoff model. -outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] -area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546, - 19682.6046, 47646.8477] +outlet_list = ["HaggIn", "TRTR", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"] +area_list = [ + 10034.2408, + 329.8013, + 22238.4391, + 24044.6363, + 59822.7546, + 19682.6046, + 47646.8477, +] lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350] -mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="GWLF") +mb.set_rainfall_runoff( + outlet_list=outlet_list, area_list=area_list, lat_list=lat_list, runoff_model="GWLF" +) ### Setup routing outlets # Add WSLO -mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], - flow_length_list=[80064.864, 70988.164, 60398.680, 0]) +mb.set_routing_outlet( + routing_outlet="WSLO", + upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], + flow_length_list=[80064.864, 70988.164, 60398.680, 0], +) # Add TRGC -mb.set_routing_outlet(routing_outlet="TRGC", - upstream_outlet_list=["DLLO", "TRGC"], - flow_length_list=[11748.211, 0]) +mb.set_routing_outlet( + routing_outlet="TRGC", + upstream_outlet_list=["DLLO", "TRGC"], + flow_length_list=[11748.211, 0], +) # Add DLLO # Specify that ResAgt is an instream object. -mb.set_routing_outlet(routing_outlet="DLLO", - upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], - flow_length_list=[9656.064, 30899.4048, 0], - instream_objects=["ResAgt"]) +mb.set_routing_outlet( + routing_outlet="DLLO", + upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], + flow_length_list=[9656.064, 30899.4048, 0], + instream_objects=["ResAgt"], +) # Add HaggIn -mb.set_routing_outlet(routing_outlet="HaggIn", - upstream_outlet_list=["HaggIn"], - flow_length_list=[0]) +mb.set_routing_outlet( + routing_outlet="HaggIn", upstream_outlet_list=["HaggIn"], flow_length_list=[0] +) ### Setup ABM mb.set_ABM(abm_module_folder_path=wd, abm_module_name="TRB_ABM.py") -mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt", - api=mb.api.Dam, - link_dict={"HaggIn": -1, "ResAgt": 1}, - dm_class="ReleaseDM") -mb.add_agent(agt_type_class="Diversion_AgtType", agt_name="DivAgt", - api=mb.api.RiverDiv, - link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, - dm_class="DivertDM", - par_dict={"ReturnFactor": [-99], "a": -99, "b":-99}) -mb.add_agent(agt_type_class="Pipe_AgtType", agt_name="PipeAgt", - api=mb.api.Conveying, - link_dict={"TRTR": 1}, - dm_class="TransferDM") +mb.add_agent( + agt_type_class="Reservoir_AgtType", + agt_name="ResAgt", + api=mb.api.Dam, + link_dict={"HaggIn": -1, "ResAgt": 1}, + dm_class="ReleaseDM", +) +mb.add_agent( + agt_type_class="Diversion_AgtType", + agt_name="DivAgt", + api=mb.api.RiverDiv, + link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, + dm_class="DivertDM", + par_dict={"ReturnFactor": [-99], "a": -99, "b": -99}, +) +mb.add_agent( + agt_type_class="Pipe_AgtType", + agt_name="PipeAgt", + api=mb.api.Conveying, + link_dict={"TRTR": 1}, + dm_class="TransferDM", +) ### Print the model in the console mb.print_model() @@ -63,20 +87,20 @@ mb.write_model_to_yaml(filename="TRB_GWLF.yaml") mb.gen_ABM_module_template() -#%% =========================================================================== +# %% =========================================================================== # Step 2: Populate a model configuration file # ============================================================================= # Nothing need to be manually modified in this example. -#%% =========================================================================== +# %% =========================================================================== # Step 3: Program ABM module (.py) # ============================================================================= # We provide a complete ABM module for the TRB example (i.e., # TRB_ABM_complete.py). Theorical details can be found in Lin et al. (2022). -#%% =========================================================================== +# %% =========================================================================== # Step 4: Run a calibration # ============================================================================= import matplotlib.pyplot as plt @@ -85,16 +109,20 @@ from copy import deepcopy # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data -obv_M = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) +obv_M = pd.read_csv( + os.path.join(wd, "Data", "Cali_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) # Load model model_dict = HydroCNHS.load_model(os.path.join(wd, "TRB_GWLF.yaml")) @@ -107,30 +135,31 @@ # Modify the default bounds of ABM df_abm_bound = par_bound_df_list[2] -df_abm_bound.loc["ReturnFactor.0", [('DivAgt', 'Diversion_AgtType')]] = "[0, 0.5]" -df_abm_bound.loc["a", [('DivAgt', 'Diversion_AgtType')]] = "[0, 3]" -df_abm_bound.loc["b", [('DivAgt', 'Diversion_AgtType')]] = "[-2, 2]" +df_abm_bound.loc["ReturnFactor.0", [("DivAgt", "Diversion_AgtType")]] = "[0, 0.5]" +df_abm_bound.loc["a", [("DivAgt", "Diversion_AgtType")]] = "[0, 3]" +df_abm_bound.loc["b", [("DivAgt", "Diversion_AgtType")]] = "[-2, 2]" # Create convertor for calibration converter = cali.Convertor() cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) formatter = converter.formatter + # Code evaluation function for GA algorthm def cal_batch_indicator(period, target, df_obv, df_sim): """Compute mean indicator over targets""" - df_obv = df_obv[period[0]:period[1]] - df_sim = df_sim[period[0]:period[1]] + df_obv = df_obv[period[0] : period[1]] + df_sim = df_sim[period[0] : period[1]] Indicator = HydroCNHS.Indicator() df = pd.DataFrame() for item in target: - df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item], - index_name=item) + df_i = Indicator.cal_indicator_df(df_obv[item], df_sim[item], index_name=item) df = pd.concat([df, df_i], axis=0) df_mean = pd.DataFrame(df.mean(axis=0), columns=["Mean"]).T df = pd.concat([df, df_mean], axis=0) return df + def evaluation(individual, info): cali_wd, current_generation, ith_individual, formatter, _ = info name = "{}-{}".format(current_generation, ith_individual) @@ -150,10 +179,10 @@ def evaluation(individual, info): ##### Get simulation data # Streamflow of routing outlets. - cali_target = ["WSLO","DLLO","ResAgt","DivAgt"] + cali_target = ["WSLO", "DLLO", "ResAgt", "DivAgt"] cali_period = ("1981-1-1", "2005-12-31") vali_period = ("2006-1-1", "2013-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] + sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] # Resample the daily simulation output to monthly outputs. @@ -166,36 +195,39 @@ def evaluation(individual, info): ##### Save output.txt # Only exercute when ga.run_individual(ga.solution) if current_generation == "best": - with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), 'w') as f: + with open(os.path.join(cali_wd, "cali_indiv_" + name + ".txt"), "w") as f: f.write("\n\nMonthly cali/vali result\n") - f.write(df_cali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) + f.write(df_cali_Q_M.round(3).to_csv(sep="\t").replace("\n", "")) f.write("\n") - f.write(df_vali_Q_M.round(3).to_csv(sep='\t').replace("\n", "")) + f.write(df_vali_Q_M.round(3).to_csv(sep="\t").replace("\n", "")) f.write("\n=========================================================\n") - f.write("Sol:\n" ) + f.write("Sol:\n") df = pd.DataFrame(individual, index=cali_inputs["par_name"]).round(4) f.write(df.to_string(header=False, index=True)) fitness = df_cali_Q_M.loc["Mean", "KGE"] return (fitness,) -config = {'min_or_max': 'max', - 'pop_size': 200, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.1, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} + +config = { + "min_or_max": "max", + "pop_size": 200, + "num_ellite": 1, + "prob_cross": 0.5, + "prob_mut": 0.1, + "stochastic": False, + "max_gen": 100, + "sampling_method": "LHC", + "drop_record": False, + "paral_cores": -1, + "paral_verbose": 1, + "auto_save": True, + "print_level": 1, + "plot": True, +} # Calibrate with 3 seeds. -seeds = [5,10,13] +seeds = [5, 10, 13] for seed in seeds: rn_gen = HydroCNHS.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) @@ -210,10 +242,11 @@ def evaluation(individual, info): for i, df in enumerate(df_list): s = df_name[i].split("_")[0] model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") - HydroCNHS.write_model(model_best, - os.path.join(ga.cali_wd, "Calibrated_TRB_GWLF.yaml")) + HydroCNHS.write_model( + model_best, os.path.join(ga.cali_wd, "Calibrated_TRB_GWLF.yaml") + ) summary = ga.summary -#%% =========================================================================== +# %% =========================================================================== # Step 5: Run a simulation # ============================================================================= ### Run a simulation. @@ -221,10 +254,10 @@ def evaluation(individual, info): filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF.yaml") model = HydroCNHS.Model(os.path.join(filename)) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() +sim_Q_M = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() ### Plot fig, axes = plt.subplots(nrows=4, sharex=True) axes = axes.flatten() @@ -239,10 +272,10 @@ def evaluation(individual, info): axes[2].plot(x, obv_M["ResAgt"], ls="--", lw=1, color="black", label="Obv") axes[3].plot(x, obv_M["DivAgt"], ls="--", lw=1, color="black", label="Obv") -axes[0].set_ylim([0,75]) -axes[1].set_ylim([0,230]) -axes[2].set_ylim([0,23]) -axes[3].set_ylim([0,2]) +axes[0].set_ylim([0, 75]) +axes[1].set_ylim([0, 230]) +axes[2].set_ylim([0, 23]) +axes[3].set_ylim([0, 2]) axes[0].set_ylabel("DLLO\n($m^3/s$)") axes[1].set_ylabel("WSLO\n($m^3/s$)") @@ -257,19 +290,3 @@ def evaluation(individual, info): axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) fig.align_ylabels(axes) - - - - - - - - - - - - - - - - diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM.py index 6e17fa8..64dcfc5 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM.py @@ -1,24 +1,24 @@ import logging from HydroCNHS.abm import Base, read_factor + logger = logging.getLogger("ABM") # logger can be used to log message (e.g., logger.info(msg)). ### Global variable and data -# Users can load data or define variables that are commonly available to all -# agent type classes and decision-making classes here. - +# Users can load data or define variables that are commonly available to all +# agent type classes and decision-making classes here. """ This is the auto-generated script template for a ABM module. Make sure to add ABM module (this filename) to the model file -(.yaml). +(.yaml). Note: -Below is the list of inherited attributes for agent type -classes (AgtType) and (institutional) decision-making classes +Below is the list of inherited attributes for agent type +classes (AgtType) and (institutional) decision-making classes (DMClass). These attributes will be assigned to each -initialized object in a HydroCNHS simulation. Namely, they +initialized object in a HydroCNHS simulation. Namely, they can be used for model design. Agent type class (AgtType): @@ -29,10 +29,10 @@ self.current_date = current date (datetime object). self.data_length = data/simulation length. self.t = current timestep. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. -self.agents = a dictionary of all initialized agents, +self.agents = a dictionary of all initialized agents, {agt_name: agt object}. self.dm = (institutional) decision-making object if DMClass or institution is assigned to the @@ -40,7 +40,7 @@ (Institutional) decision-making classes (DMClass): self.name = name of the agent or institute. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. @@ -55,26 +55,26 @@ class Reservoir_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. - #decision = self.dm.make_dm(your_arguments) - - if factor <= 0: # Divert from the outlet + # decision = self.dm.make_dm(your_arguments) + + if factor <= 0: # Divert from the outlet action = 0 - elif factor > 0: # Add to the outlet + elif factor > 0: # Add to the outlet action = 0 - + return action @@ -82,26 +82,26 @@ def act(self, outlet): class Diversion_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. - #decision = self.dm.make_dm(your_arguments) - - if factor <= 0: # Divert from the outlet + # decision = self.dm.make_dm(your_arguments) + + if factor <= 0: # Divert from the outlet action = 0 - elif factor > 0: # Add to the outlet + elif factor > 0: # Add to the outlet action = 0 - + return action @@ -109,26 +109,26 @@ def act(self, outlet): class Pipe_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. - #decision = self.dm.make_dm(your_arguments) - - if factor <= 0: # Divert from the outlet + # decision = self.dm.make_dm(your_arguments) + + if factor <= 0: # Divert from the outlet action = 0 - elif factor > 0: # Add to the outlet + elif factor > 0: # Add to the outlet action = 0 - + return action @@ -136,7 +136,7 @@ def act(self, outlet): class ReleaseDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): @@ -149,7 +149,7 @@ def make_dm(self, your_arguments): class DivertDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): @@ -162,7 +162,7 @@ def make_dm(self, your_arguments): class TransferDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM_complete.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM_complete.py index 4091979..e8b78ef 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM_complete.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_ABM_complete.py @@ -3,15 +3,15 @@ import logging import numpy as np from HydroCNHS.abm import Base, read_factor + logger = logging.getLogger("ABM") # logger can be used to log message (e.g., logger.info(msg)). ### Global variable and data # Users can load data or define variables that are commonly available to all # agent type classes and decision-making classes here. -this_dir, this_filename = os.path.split(__file__) # Get this file directory. -with open(os.path.join( - this_dir, "TRB_ABM_database.pickle"), "rb") as file: +this_dir, this_filename = os.path.split(__file__) # Get this file directory. +with open(os.path.join(this_dir, "TRB_ABM_database.pickle"), "rb") as file: database = pickle.load(file) @@ -70,8 +70,9 @@ def act(self, outlet): logger.error("Something is not right in ResDam agent.") elif factor > 0: # Q_routed["HaggIn"][t] is the resevoir inflow at time t. - res_t = self.dm.make_dm(self.dc.Q_routed["HaggIn"][self.t], - self.current_date) + res_t = self.dm.make_dm( + self.dc.Q_routed["HaggIn"][self.t], self.current_date + ) action = res_t return action @@ -81,8 +82,7 @@ class Diversion_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) self.pars = self.config["Pars"] - self.dc.add_field(self.name, - {"DivReq": [], "Diversion": [], "Shortage": []}) + self.dc.add_field(self.name, {"DivReq": [], "Diversion": [], "Shortage": []}) def act(self, outlet): records = self.dc.get_field(self.name) @@ -93,8 +93,9 @@ def act(self, outlet): if factor < 0: # Diversion # Make diversion request at the first day of each month if self.current_date.day == 1: - div_req = self.dm.make_dm(self.pars["a"], self.pars["b"], - self.current_date) + div_req = self.dm.make_dm( + self.pars["a"], self.pars["b"], self.current_date + ) # Accumulate diversion request records["DivReq"] = records["DivReq"] + div_req @@ -110,19 +111,19 @@ def act(self, outlet): records["Diversion"].append(div_t) records["Shortage"].append(shortage_t) action = factor * div_t - elif factor >= 0: # Return flow + elif factor >= 0: # Return flow div_t = records["Diversion"][self.t] action = factor * div_t return action + # AgtType class FixedDiversion_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) self.pars = self.config["Pars"] - self.dc.add_field(self.name, - {"DivReq": [], "Diversion": [], "Shortage": []}) + self.dc.add_field(self.name, {"DivReq": [], "Diversion": [], "Shortage": []}) def act(self, outlet): records = self.dc.get_field(self.name) @@ -149,12 +150,13 @@ def act(self, outlet): records["Diversion"].append(div_t) records["Shortage"].append(shortage_t) action = factor * div_t - elif factor >= 0: # Return flow + elif factor >= 0: # Return flow div_t = records["Diversion"][self.t] action = factor * div_t return action + # AgtType class Pipe_AgtType(Base): def __init__(self, **kwargs): @@ -172,11 +174,13 @@ def act(self, outlet): Res_t = 0 else: # Historical inputs. - Res_t = self.obv_data[self.current_date.year-1991, - self.current_date.month-1] + Res_t = self.obv_data[ + self.current_date.year - 1991, self.current_date.month - 1 + ] action = factor * Res_t return action + # AgtType class Drain_AgtType(Base): def __init__(self, **kwargs): @@ -189,27 +193,43 @@ def __init__(self, **kwargs): # equal to unbanized_area% * 75% * original_runoff ini = 0.05 end = 0.5 - interval = (end-ini)/32 + interval = (end - ini) / 32 self.urbanized_ratio = np.arange(0.05, 0.5 + interval, interval) def act(self, outlet): # Get factor factor = read_factor(self.config, outlet) - Qt_change = self.urbanized_ratio[self.current_date.year-1981] * 0.75 \ - * self.dc.Q_routed[outlet][self.t] + Qt_change = ( + self.urbanized_ratio[self.current_date.year - 1981] + * 0.75 + * self.dc.Q_routed[outlet][self.t] + ) action = factor * Qt_change return action + # DMClass class ReleaseDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - self.db = database # [m^3] - self.flood_control = [True, True, True, True, True, False, - False, False, False, True, True, True] + self.db = database # [m^3] + self.flood_control = [ + True, + True, + True, + True, + True, + False, + False, + False, + False, + True, + True, + True, + ] # [cms] Use quantile 0.01 of the historical data. - self.min_release = 0.263335 # [cms] - self.min_release_vol = self.min_release * 86400 # [m^3] + self.min_release = 0.263335 # [cms] + self.min_release_vol = self.min_release * 86400 # [m^3] # Add a new field to the data collector (dc) to record storage and # release self.dc.add_field("ResAgt", {"Storage": [], "Release": []}) @@ -222,17 +242,17 @@ def make_dm(self, inflow, current_date): min_res_vol = self.min_release_vol day_of_year = current_date.dayofyear - inflow_vol = inflow * 86400 # cms to m^3 - if records["Storage"] == []: # Initial value [m^3] + inflow_vol = inflow * 86400 # cms to m^3 + if records["Storage"] == []: # Initial value [m^3] storage = 42944903.6561 + inflow_vol else: storage = records["Storage"][-1] + inflow_vol release = 0 - if flood_control[current_date.month-1]: # Flood control - storage_max = db["Storage_q95"][day_of_year-1] + if flood_control[current_date.month - 1]: # Flood control + storage_max = db["Storage_q95"][day_of_year - 1] if storage > storage_max: - release = (storage - storage_max) / 86400 # m^3 to cms + release = (storage - storage_max) / 86400 # m^3 to cms storage = storage_max else: if storage - min_res_vol < 0: @@ -240,15 +260,15 @@ def make_dm(self, inflow, current_date): else: release = min_release storage = storage - min_res_vol - else: # Target storage control - release_target = db["Release_q50"][day_of_year-1] + else: # Target storage control + release_target = db["Release_q50"][day_of_year - 1] storage_temp = storage - release_target * 86400 - if storage_temp > db["Storage_max"][day_of_year-1]: - release = (storage - db["Storage_max"][day_of_year-1]) / 86400 - storage = db["Storage_max"][day_of_year-1] - elif storage_temp < db["Storage_q05"][day_of_year-1]: - release = (storage - db["Storage_q05"][day_of_year-1]) / 86400 - storage = db["Storage_q05"][day_of_year-1] + if storage_temp > db["Storage_max"][day_of_year - 1]: + release = (storage - db["Storage_max"][day_of_year - 1]) / 86400 + storage = db["Storage_max"][day_of_year - 1] + elif storage_temp < db["Storage_q05"][day_of_year - 1]: + release = (storage - db["Storage_q05"][day_of_year - 1]) / 86400 + storage = db["Storage_q05"][day_of_year - 1] if release < 0: release = 0 storage = records["Storage"][-1] + inflow_vol @@ -267,21 +287,23 @@ def __init__(self, **kwargs): self.db = database def make_dm(self, a, b, current_date): - prec_M_mean = self.db["Prec_M_mean"][current_date.year-1981, - (current_date.month-1)] - div_M_mean = self.db["Div_M_median_mean"][(current_date.month-1)] - div_M_max = self.db["Div_M_median_max"][(current_date.month-1)] - div_M_min = self.db["Div_M_median_min"][(current_date.month-1)] - if current_date.month in [6,7,8,9]: # Major irrigation diverion months. + prec_M_mean = self.db["Prec_M_mean"][ + current_date.year - 1981, (current_date.month - 1) + ] + div_M_mean = self.db["Div_M_median_mean"][(current_date.month - 1)] + div_M_max = self.db["Div_M_median_max"][(current_date.month - 1)] + div_M_min = self.db["Div_M_median_min"][(current_date.month - 1)] + if current_date.month in [6, 7, 8, 9]: # Major irrigation diverion months. div_M_req = div_M_mean + a * prec_M_mean + b # Bound by historical max and min - div_M_req = min( max(div_M_req, div_M_min), div_M_max) - else: # Minor irrigation diversion months. + div_M_req = min(max(div_M_req, div_M_min), div_M_max) + else: # Minor irrigation diversion months. div_M_req = div_M_mean # Uniformly allocate monthly diversion to daily. div_D_req = [div_M_req] * (current_date.days_in_month) return div_D_req + # DMClass class FixedDivertDM(Base): def __init__(self, **kwargs): @@ -289,7 +311,7 @@ def __init__(self, **kwargs): self.db = database def make_dm(self, current_date): - div_M_mean = self.db["Div_M_median_mean"][(current_date.month-1)] + div_M_mean = self.db["Div_M_median_mean"][(current_date.month - 1)] # Based on the historical mean value. div_M_req = div_M_mean # Uniformly allocate monthly diversion to daily. diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_ABCD_plot.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_ABCD_plot.py index 19fdee8..1e977ed 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_ABCD_plot.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_ABCD_plot.py @@ -2,21 +2,26 @@ import pandas as pd import matplotlib.pyplot as plt import HydroCNHS + prj_path, this_filename = os.path.split(__file__) wd = prj_path # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data -obv_M = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) +obv_M = pd.read_csv( + os.path.join(wd, "Data", "Cali_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) filename = os.path.join(wd, "Cali_gwlf_abm_KGE_5", "Calibrated_TRB_GWLF.yaml") model = HydroCNHS.load_model(filename) @@ -24,10 +29,10 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M_gwlf = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() +sim_Q_M_gwlf = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() filename = os.path.join(wd, "Cali_abcd_abm_KGE_10", "Calibrated_TRB_ABCD.yaml") model = HydroCNHS.load_model(filename) @@ -35,12 +40,12 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M_abcd = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() +sim_Q_M_abcd = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() -#%% +# %% ##### Plot Time Series Streamflow. fig, axes = plt.subplots(nrows=4, sharex=True) axes = axes.flatten() @@ -60,10 +65,10 @@ axes[2].plot(x, obv_M["ResAgt"], ls="--", lw=1, color="black", label="Obv") axes[3].plot(x, obv_M["DivAgt"], ls="--", lw=1, color="black", label="Obv") -axes[0].set_ylim([0,75]) -axes[1].set_ylim([0,290]) -axes[2].set_ylim([0,23]) -axes[3].set_ylim([0,2]) +axes[0].set_ylim([0, 75]) +axes[1].set_ylim([0, 290]) +axes[2].set_ylim([0, 23]) +axes[3].set_ylim([0, 2]) axes[0].set_ylabel("DLLO\n($m^3/s$)") axes[1].set_ylabel("WSLO\n($m^3/s$)") @@ -77,4 +82,4 @@ axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) -fig.align_ylabels(axes) \ No newline at end of file +fig.align_ylabels(axes) diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_scenarios_plot.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_scenarios_plot.py index a894c01..69fb051 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_scenarios_plot.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_GWLF_scenarios_plot.py @@ -4,23 +4,33 @@ import matplotlib.pyplot as plt from copy import deepcopy import HydroCNHS + prj_path, this_filename = os.path.split(__file__) wd = prj_path # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data -obv_M = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) -prec_M = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), index_col=["Date"], - parse_dates=["Date"]).resample("MS").mean() +obv_M = pd.read_csv( + os.path.join(wd, "Data", "Cali_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) +prec_M = ( + pd.read_csv( + os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"], parse_dates=["Date"] + ) + .resample("MS") + .mean() +) folder = "Cali_gwlf_abm_KGE_5" filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF.yaml") @@ -32,35 +42,48 @@ model_urban = deepcopy(model) model_urban["WaterSystem"]["ABM"]["InsituAPI"].append("Drain_AgtType") model_urban["ABM"]["Drain_AgtType"] = { - "DrainAgt1": {"Attributes": {}, - "Inputs": {"Priority": 1, - "Links": {"RCTV": 1}, - "DMClass": None}, - "Pars": {}}, - "DrainAgt2": {"Attributes": {}, - "Inputs": {"Priority": 1, - "Links": {"WSLO": 1}, - "DMClass": None}, - "Pars": {}}} - -HydroCNHS.write_model(model_urban, os.path.join(wd, folder, "Calibrated_TRB_GWLF_urban.yaml")) + "DrainAgt1": { + "Attributes": {}, + "Inputs": {"Priority": 1, "Links": {"RCTV": 1}, "DMClass": None}, + "Pars": {}, + }, + "DrainAgt2": { + "Attributes": {}, + "Inputs": {"Priority": 1, "Links": {"WSLO": 1}, "DMClass": None}, + "Pars": {}, + }, +} + +HydroCNHS.write_model( + model_urban, os.path.join(wd, folder, "Calibrated_TRB_GWLF_urban.yaml") +) # Fixed diversion scenario model_fixed_div = deepcopy(model) -model_fixed_div["WaterSystem"]["ABM"]["DMClasses"] = ["ReleaseDM", "TransferDM", "FixedDivertDM"] +model_fixed_div["WaterSystem"]["ABM"]["DMClasses"] = [ + "ReleaseDM", + "TransferDM", + "FixedDivertDM", +] model_fixed_div["WaterSystem"]["ABM"]["RiverDivAPI"] = ["FixedDiversion_AgtType"] rf = model_fixed_div["ABM"]["Diversion_AgtType"]["DivAgt"]["Pars"]["ReturnFactor"] model_fixed_div["ABM"]["FixedDiversion_AgtType"] = { - "DivAgt": {"Attributes": {}, - "Inputs": {"Priority": 1, - "Links": {"TRGC": -1, - "WSLO": ["ReturnFactor", 0, "Plus"]}, - "DMClass": "FixedDivertDM"}, - "Pars": {"ReturnFactor": rf}}} + "DivAgt": { + "Attributes": {}, + "Inputs": { + "Priority": 1, + "Links": {"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, + "DMClass": "FixedDivertDM", + }, + "Pars": {"ReturnFactor": rf}, + } +} model_fixed_div["ABM"].pop("Diversion_AgtType") -HydroCNHS.write_model(model_fixed_div, os.path.join(wd, folder, "Calibrated_TRB_GWLF_fixedDiv.yaml")) -#%% +HydroCNHS.write_model( + model_fixed_div, os.path.join(wd, folder, "Calibrated_TRB_GWLF_fixedDiv.yaml") +) +# %% ##### Urbanization scenario filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF.yaml") model = HydroCNHS.load_model(filename) @@ -68,10 +91,10 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_Y = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("YS").mean() +sim_Q_Y = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("YS").mean() filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF_urban.yaml") model = HydroCNHS.load_model(filename) @@ -79,23 +102,23 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_Y_urban = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("YS").mean() +sim_Q_Y_urban = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("YS").mean() ##### Plot Time Series Streamflow. fig, ax = plt.subplots() -x = np.arange(1981,2014) -ax.plot(x,sim_Q_Y_urban["WSLO"], label="with urbanization") -ax.plot(x,sim_Q_Y["WSLO"], label="without urbanization", ls="dashed") +x = np.arange(1981, 2014) +ax.plot(x, sim_Q_Y_urban["WSLO"], label="with urbanization") +ax.plot(x, sim_Q_Y["WSLO"], label="without urbanization", ls="dashed") ax.set_xlim([1981, 2013]) ax.legend() ax.set_ylabel("Streamflow ($m^3$/sec)") ax.set_xlabel("Year") -#%% +# %% ##### Fixed diversion scenario filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF.yaml") model = HydroCNHS.load_model(filename) @@ -103,13 +126,13 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() -sim_Q_Y = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("YS").mean() -mask = [i.month in [6,7,8,9] for i in sim_Q_M.index] -sim_Y = sim_Q_M.loc[mask,:].resample("YS").mean() +sim_Q_M = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() +sim_Q_Y = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("YS").mean() +mask = [i.month in [6, 7, 8, 9] for i in sim_Q_M.index] +sim_Y = sim_Q_M.loc[mask, :].resample("YS").mean() filename = os.path.join(wd, folder, "Calibrated_TRB_GWLF_fixedDiv.yaml") model = HydroCNHS.load_model(filename) @@ -117,47 +140,38 @@ model["Path"]["Modules"] = wd model = HydroCNHS.Model(model) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M_FixedDiv = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() -sim_Q_Y_FixedDiv = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("YS").mean() -mask = [i.month in [6,7,8,9] for i in sim_Q_M_FixedDiv.index] -sim_Y_FixedDiv = sim_Q_M_FixedDiv.loc[mask,:].resample("YS").mean() +sim_Q_M_FixedDiv = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() +sim_Q_Y_FixedDiv = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("YS").mean() +mask = [i.month in [6, 7, 8, 9] for i in sim_Q_M_FixedDiv.index] +sim_Y_FixedDiv = sim_Q_M_FixedDiv.loc[mask, :].resample("YS").mean() -df = (sim_Y.std()-sim_Y_FixedDiv.std())[["ResAgt","DLLO","DivAgt","WSLO"]] +df = (sim_Y.std() - sim_Y_FixedDiv.std())[["ResAgt", "DLLO", "DivAgt", "WSLO"]] ##### Plot barplot -ax = df.plot(kind='bar', color=(df > 0).map({True: 'g', False: 'r'})) +ax = df.plot(kind="bar", color=(df > 0).map({True: "g", False: "r"})) ax.axhline(0, color="k", lw=0.5) -ax.tick_params(axis='x', labelrotation=0) +ax.tick_params(axis="x", labelrotation=0) ax.set_ylabel("($M_{gwlf,endog} - M_{gwlf,fixed} (m^3/sec$)") -#%% Plot two scenarios together +# %% Plot two scenarios together fig, axes = plt.subplots(ncols=2) plt.tight_layout() axes = axes.flatten() ax = axes[0] -df.plot(kind='bar', color=(df > 0).map({True: 'g', False: 'r'}), ax=ax) +df.plot(kind="bar", color=(df > 0).map({True: "g", False: "r"}), ax=ax) ax.axhline(0, color="k", lw=0.5) -ax.tick_params(axis='x', labelrotation=0) +ax.tick_params(axis="x", labelrotation=0) ax.set_ylabel("($M_{gwlf,endog} - M_{gwlf,fixed} (m^3/sec$)") ax = axes[1] -x = np.arange(1981,2014) -ax.plot(x,sim_Q_Y_urban["WSLO"], label="with urbanization") -ax.plot(x,sim_Q_Y["WSLO"], label="without urbanization", ls="dashed") +x = np.arange(1981, 2014) +ax.plot(x, sim_Q_Y_urban["WSLO"], label="with urbanization") +ax.plot(x, sim_Q_Y["WSLO"], label="without urbanization", ls="dashed") ax.set_xlim([1981, 2013]) ax.legend() ax.set_ylabel("Streamflow ($m^3$/sec)") ax.set_xlabel("Year") - - - - - - - - - diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_std_boxplot.py b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_std_boxplot.py index cf1f427..fde3631 100644 --- a/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_std_boxplot.py +++ b/tutorials/Tualatin River Basin - Lin et al. (2022)/TRB_std_boxplot.py @@ -7,15 +7,17 @@ # Get this file directory. prj_path, this_filename = os.path.split(__file__) + def get_stds(model): def evaluation(): pass + # Collect GA evaluation from three random seeds. df_ga_all = pd.DataFrame() - for seed in [5,10,13]: + for seed in [5, 10, 13]: save_ga_file_path = os.path.join( - prj_path, - "Cali_{}_abm_KGE_{}/GA_auto_save.pickle".format(model, seed)) + prj_path, "Cali_{}_abm_KGE_{}/GA_auto_save.pickle".format(model, seed) + ) ga = cali.GA_DEAP(evaluation) ga.load(save_ga_file_path, "") all_indiv = [] @@ -30,14 +32,15 @@ def evaluation(): df_ga_q99 = df_ga_all[df_ga_all["fitness"] > df_ga_all["fitness"].quantile(0.95)] stds = df_ga_q99.std() return list(stds)[:-1] -#%% + + +# %% # Get stds of calibrated parameters (top 1% fitness) -data = [get_stds("gwlf"), - get_stds("abcd")] +data = [get_stds("gwlf"), get_stds("abcd")] # Plot boxplot fig, ax = plt.subplots() ax.set_ylabel("Standard deviation", fontsize=13) -ax.set_xticklabels(["M$_{gwlf}$","M$_{abcd}$"]) +ax.set_xticklabels(["M$_{gwlf}$", "M$_{abcd}$"]) ax.boxplot(data) -plt.show() \ No newline at end of file +plt.show() diff --git a/tutorials/Tualatin River Basin - Lin et al. (2022)/__pycache__/TRB_ABM_complete.cpython-310.pyc b/tutorials/Tualatin River Basin - Lin et al. (2022)/__pycache__/TRB_ABM_complete.cpython-310.pyc deleted file mode 100644 index ab7b832..0000000 Binary files a/tutorials/Tualatin River Basin - Lin et al. (2022)/__pycache__/TRB_ABM_complete.cpython-310.pyc and /dev/null differ diff --git a/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml new file mode 100644 index 0000000..7f69d82 --- /dev/null +++ b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Best_HydroModel_gwlf_KGE.yaml @@ -0,0 +1,79 @@ +Path: {WD: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model', + Modules: ''} +WaterSystem: + StartDate: 1981/1/1 + EndDate: 2013/12/31 + DataLength: 12053 + NumSubbasins: 6 + Outlets: [Hagg, DLLO, TRGC, DAIRY, RCTV, WSLO] + NodeGroups: [] + RainfallRunoff: GWLF + Routing: Lohmann + ABM: null +RainfallRunoff: + Hagg: + Inputs: {Area: 10034.2408, Latitude: 45.469, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 74.6444144670715, IS: 0.16508897968293956, Res: 0.14544285234209645, + Sep: 0.024750159879674716, Alpha: 0.1822230123225981, Beta: 0.24097803620210456, + Ur: 1.7752800632939325, Df: 0.07351291395969362, Kc: 0.9330722212403914} + DLLO: + Inputs: {Area: 22568.2404, Latitude: 45.475, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 80.02537824897247, IS: 0.23112503633467738, Res: 0.3314100030542884, + Sep: 0.004198649472475182, Alpha: 0.118052026036783, Beta: 0.16583123077457707, + Ur: 4.629604085070294, Df: 0.13130728111074574, Kc: 1.3785389470613643} + TRGC: + Inputs: {Area: 24044.6363, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 26.80954509779678, IS: 0.32753984021324645, Res: 0.21573877866051988, + Sep: 0.07804985096793338, Alpha: 0.1298856713271982, Beta: 0.26131079425084575, + Ur: 8.955499591227511, Df: 0.6044699024705011, Kc: 1.0239210423315073} + DAIRY: + Inputs: {Area: 59822.7546, Latitude: 45.52, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 80.87754436675743, IS: 0.17912580592641858, Res: 0.4409075068929814, + Sep: 0.31983833021558306, Alpha: 0.13936221068615484, Beta: 0.7835696426615357, + Ur: 8.338884841102871, Df: 0.4683366381699043, Kc: 0.695585419450911} + RCTV: + Inputs: {Area: 19682.6046, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 47.473589728305015, IS: 0.2930432935228726, Res: 0.24108442162795046, + Sep: 0.18348053059487712, Alpha: 0.010270460917531365, Beta: 0.8717458687978454, + Ur: 4.486341133367363, Df: 0.18885718937470744, Kc: 1.41058171295884} + WSLO: + Inputs: {Area: 47646.8477, Latitude: 45.35, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 25.49265494222201, IS: 0.30601089053199665, Res: 0.3685813055644937, + Sep: 0.24978894557134262, Alpha: 0.0014093768185595502, Beta: 0.7926293881948485, + Ur: 13.6914149020542, Df: 0.21425173893736993, Kc: 0.586114300781308} +Routing: + WSLO: + Hagg: + Inputs: {FlowLength: 101469.139, InstreamControl: false} + Pars: {GShape: 40.87086864041019, GScale: 8.04131495396319, Velo: 3.7640981580315285, + Diff: 1409.6536139260222} + DLLO: + Inputs: {FlowLength: 91813.075, InstreamControl: false} + Pars: {GShape: 84.29969695629671, GScale: 31.59435539309738, Velo: 26.106546490649073, + Diff: 1535.672411453341} + TRGC: + Inputs: {FlowLength: 80064.864, InstreamControl: false} + Pars: {GShape: 50.462006331265634, GScale: 97.98939293793097, Velo: 47.86600865097231, + Diff: 2043.197987180312} + DAIRY: + Inputs: {FlowLength: 70988.164, InstreamControl: false} + Pars: {GShape: 37.378143259505045, GScale: 104.63181253689957, Velo: 29.582360057837338, + Diff: 422.3544641056891} + RCTV: + Inputs: {FlowLength: 60398.68, InstreamControl: false} + Pars: {GShape: 57.3125304304215, GScale: 29.922284527088117, Velo: 34.095717506027675, + Diff: 3754.5159313571185} + WSLO: + Inputs: {FlowLength: 0, InstreamControl: false} + Pars: {GShape: 18.384030154384195, GScale: 3.5087484412691397, Velo: null, Diff: null} +SystemParsedData: + SimSeq: [WSLO] + AgSimSeq: null + RoutingOutlets: [WSLO] + DamAgents: null + RiverDivAgents: null + InsituAgents: null + ConveyingAgents: null + BackTrackingDict: {} + Edges: [] + ConveyToNodes: [] diff --git a/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png new file mode 100644 index 0000000..125b7e2 Binary files /dev/null and b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Fitness_Cali_HydroModel_gwlf_KGE.png differ diff --git a/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/GA_auto_save.pickle b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/GA_auto_save.pickle new file mode 100644 index 0000000..435ba09 Binary files /dev/null and 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tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Data/Temp_degC.csv diff --git a/tutorials/Hydro_example/Data/WSLO_M_cms.csv b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Data/WSLO_M_cms.csv similarity index 100% rename from tutorials/Hydro_example/Data/WSLO_M_cms.csv rename to tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Data/WSLO_M_cms.csv diff --git a/docs/figs/fig4_example_watershed.png b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/NB_Figs/example_watershed.png similarity index 100% rename from docs/figs/fig4_example_watershed.png rename to tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/NB_Figs/example_watershed.png diff --git a/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model.ipynb b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model.ipynb new file mode 100644 index 0000000..002e605 --- /dev/null +++ b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model.ipynb @@ -0,0 +1,2950 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 1: Build a Semi-distributed Hydrological Model\n", + "To get familiar with the HydroCNHS model construction process, let’s start with a hydrological model without human components. \n", + "\n", + "We adopted the following delineated watershed as a demonstration (Figure 1), in which we want to build a semi-distributed hydrological model to simulate the streamflow at the basin outlet, WSLO. The corresponding subbasins’ information is provided in Table 1.\n", + "\n", + "\"Example\n", + "\n", + "*Figure 1. Example watershed*\n", + "\n", + "*Table 1. Subbasins’ information*\n", + "| Subbasin/outlet | Drainage area [ha] | Latitude [deg] | Flow length to WSLO [m] |\n", + "|-----------------|-------------------:|---------------:|------------------------:|\n", + "| Hagg | 10034.2408 | 45.469 | 101469.139 |\n", + "| DLLO | 22568.2404 | 45.475 | 91813.075 |\n", + "| TRGC | 24044.6363 | 45.502 | 80064.864 |\n", + "| DAIRY | 59822.7546 | 45.520 | 70988.164 |\n", + "| RCTV | 19682.6046 | 45.502 | 60398.680 |\n", + "| WSLO | 47646.8477 | 45.350 | 0 |\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "import hydrocnhs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run a simulation\n", + "\n", + "First, let's set up the working directory to run this tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\n" + ] + } + ], + "source": [ + "# Set the working directory\n", + "wd = os.path.abspath(\n", + " os.path.join(\n", + " os.path.dirname(hydrocnhs.__file__),\n", + " '..', 'tutorials',\n", + " 'Tutorial_1-Build_a_Semi-distributed_Hydrological_Model'\n", + " )\n", + " )\n", + "\n", + "print(wd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we load the climate inputs, including temperature, precipitation, and potential evapotranspiration, from `.csv` and convert them into `dictionaries`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load climate inputs\n", + "data_path = os.path.join(wd, \"Data\")\n", + "\n", + "temp = pd.read_csv(\n", + " os.path.join(data_path, \"Temp_degC.csv\"), index_col=[\"Date\"]\n", + ").to_dict(orient=\"list\")\n", + "\n", + "prec = pd.read_csv(os.path.join(data_path, \"Prec_cm.csv\"), index_col=[\"Date\"]).to_dict(\n", + " orient=\"list\"\n", + ")\n", + "\n", + "pet = pd.read_csv(os.path.join(data_path, \"Pet_cm.csv\"), index_col=[\"Date\"]).to_dict(\n", + " orient=\"list\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load a calibrated model to give a taste of how a simulation looks like before jumping into details about how to construct and calibrated model.\n", + "\n", + "The `Model` in HydroCNHS is defined in a single `.yaml` file. The file will be loaded into Python and displayed as a `dictionary`. We highly recommend that you open `Best_HydroModel_gwlf_KGE.yaml`, located in `Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/Cali_HydroModel_gwlf_KGE/Cali_HydroModel_gwlf_KGE`, to get a quick look at how a model is constructed.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\\Cali_HydroModel_gwlf_KGE\\Best_HydroModel_gwlf_KGE.yaml\n", + "[09/07 12:03:50] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['WSLO']\n", + "RoutingOutlets\n", + " ['WSLO']\n", + "DamAgents\n", + " None\n", + "ConveyingAgents\n", + " None\n", + "RiverDivAgents\n", + " None\n", + "InsituAgents\n", + " None\n", + "AgSimSeq\n", + " None\n" + ] + } + ], + "source": [ + "# Add model path\n", + "model_path = os.path.join(\n", + " wd, \"Cali_HydroModel_gwlf_KGE\", \"Best_HydroModel_gwlf_KGE.yaml\"\n", + " )\n", + "print(model_path)\n", + "\n", + "# Load the model\n", + "model_dict = hydrocnhs.load_model(model_path)\n", + "# Here we manually change the working directory in the model directory\n", + "model_dict[\"Path\"][\"WD\"] = wd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After `Best_HydroModel_gwlf_KGE.yaml` is loaded, a summary printout shows a single routing outlet, 'WSLO,' with no other agents listed.\n", + "\n", + "Now, we can apply `model_dict` to create a model object for the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 12:03:50] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['WSLO']\n", + "RoutingOutlets\n", + " ['WSLO']\n", + "DamAgents\n", + " None\n", + "ConveyingAgents\n", + " None\n", + "RiverDivAgents\n", + " None\n", + "InsituAgents\n", + " None\n", + "AgSimSeq\n", + " None\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field Q_runoff ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field Q_routed ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field prec ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field temp ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field pet ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field UH_Lohmann ().\n", + "[09/07 12:03:50] HydroCNHS.dc [INFO] Add field UH_Lohmann_convey ().\n" + ] + } + ], + "source": [ + "# Create the model object\n", + "model = hydrocnhs.Model(model_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that, we can start the simulation by calling `run` method with climate inputs as arguments." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 12:03:50] HydroCNHS [INFO] Load temp & prec & pet with total length 12053.\n", + "[09/07 12:03:50] HydroCNHS [INFO] Set rainfall-runoff to GWLF.\n", + "[09/07 12:03:51] HydroCNHS [INFO] Complete rainfall-runoff simulation setup. [00:00:00]\n", + "[09/07 12:03:51] HydroCNHS [INFO] Start forming 6 UHs for Lohmann routing. [00:00:00]\n", + "[09/07 12:03:53] HydroCNHS [INFO] Complete forming UHs for Lohmann routing. [00:00:02]\n", + "[09/07 12:03:53] HydroCNHS [INFO] Start a pure hydrological simulation (no human component).\n", + "[09/07 12:03:55] HydroCNHS [INFO] \n", + "Compute rainfall-runoffs for 12053 time steps.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "12053it [00:00, 39096.06it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[09/07 12:03:55] HydroCNHS [INFO] Complete HydroCNHS simulation! [00:00:04]\n", + "\n", + " Hagg DLLO TRGC DAIRY RCTV WSLO\n", + "1981-01-01 2.526067 4.067260 12.838741 26.357862 5.005605 51.985477\n", + "1981-02-01 4.960201 4.975534 15.321548 34.900794 8.212731 73.016122\n", + "1981-03-01 1.547300 1.163473 2.473601 9.360561 1.253275 44.330615\n", + "1981-04-01 1.720808 1.688222 4.057178 11.438015 1.174595 29.586883\n", + "1981-05-01 0.335844 0.000063 0.000188 0.194160 0.000208 0.397095\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Run a simulation.\n", + "Q = model.run(temp, prec, pet)\n", + "\n", + "# Ture the output to a pandas DataFrame. Here, we resample the daily output to monthly.\n", + "results = pd.DataFrame(Q, index=model.pd_date_index).resample(\"MS\").mean()\n", + "print(results.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, we compare the simulated discharge at WSLO (routing outlet) with the observation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Load the observed flow data\n", + "obv_flow_WSLO = pd.read_csv(\n", + " os.path.join(wd, \"Data\", \"WSLO_M_cms.csv\"), index_col=[\"Date\"], parse_dates=[\"Date\"]\n", + ")\n", + "\n", + "# Plot\n", + "fig, ax = plt.subplots()\n", + "ax.plot(obv_flow_WSLO.index, obv_flow_WSLO.loc[:, \"WSLO\"], label=\"Obv\")\n", + "ax.plot(obv_flow_WSLO.index, results[\"WSLO\"], ls=\"--\", label=\"Sim\")\n", + "ax.set_ylabel(\"Discharge (cubic meters per second)\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Construct a model\n", + "\n", + "Now, we have experience how to run a simulation using `HydroCNHS`. Next, we want to dive a little bit further to construct a model ourselves. \n", + "\n", + "To that, HydroCNHS includes a model builder designed to assist users in creating a draft `model.yaml` file.\n", + "\n", + "First, initialize a model builder object with a working directory. The instructions will then pop out to help you create a model file. You can recall the instructions by calling `mb.help()`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Follow the following steps to create model & ABM script templates:\n", + "\tStep 1: set_water_system()\n", + "\tStep 2: set_rainfall_runoff()\n", + "\tStep 3: set_routing_outlet(), one at a time.\n", + "\tStep 4: (optional) set_sediment().\n", + "\tStep 5: (optional) add_sediment(), one at a time.\n", + "\tStep 6: (optional) set_ABM().\n", + "\tStep 7: (optional) add_agent().\n", + "\tStep 8: (optional) add_institution().\n", + "\tStep 9: write_model_to_yaml()\n", + "\tStep 10: (optional) gen_ABM_script_template()\n", + "Open the generated draft 'model.yaml' (& ABM module template) and further edit them.\n", + "Use .help to re-print the above instructions.\n" + ] + } + ], + "source": [ + "# We reuse the working directory used above.\n", + "mb = hydrocnhs.ModelBuilder(wd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following the instructions, we first configure a water system by setting the simulation period, which includes the start and end dates." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "mb.set_water_system(start_date=\"1981/1/1\", end_date=\"2013/12/31\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we can add subbasins (i.e., `outlet_list`) to the model. We adopt the GWLF model as the rainfall-runoff model (i.e., `runoff_model`). We also assign the corresponding subbasins' areas (i.e., `area_list`) and latitudes (i.e., `lat_list`) using the information from Table 4. Note that `area_list` and `lat_list` are optional arguments. Users can manually enter this information into the `model.yaml` file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# outlet list includes all the outlets in the model, routing outlets and non-routing outlets.\n", + "outlet_list = ['Hagg', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO']\n", + "area_list = [10034.2408, 22568.2404, 24044.6363, 59822.7546, 19682.6046, 47646.8477]\n", + "lat_list = [45.469, 45.475, 45.502, 45.520, 45.502, 45.350]\n", + "mb.set_rainfall_runoff(\n", + " outlet_list=outlet_list,area_list=area_list,\n", + " lat_list=lat_list, runoff_model=\"GWLF\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we specify the routing outlet setting. Here, we only have one routing outlet, WSLO, hence, we only need to run the following code once, and all the outlets are considered upstream outlets of WSLO." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "flow_length_list = [101469.139, 91813.075, 80064.864, 70988.164, 60398.680, 0] # km\n", + "mb.set_routing_outlet(\n", + " routing_outlet=\"WSLO\",\n", + " upstream_outlet_list=outlet_list,\n", + " flow_length_list=flow_length_list\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we don't have any agents in this tutorial, we have done all the setup. We can print out the model for quick view and then write it to `model.yaml` file." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Path\n", + " WD\n", + " C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\n", + " Modules\n", + " \n", + "WaterSystem\n", + " StartDate\n", + " 1981/1/1\n", + " EndDate\n", + " 2013/12/31\n", + " DataLength\n", + " 12053\n", + " NumSubbasins\n", + " 6\n", + " Outlets\n", + " ['Hagg', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO']\n", + " NodeGroups\n", + " []\n", + " RainfallRunoff\n", + " GWLF\n", + " Routing\n", + " Lohmann\n", + " ABM\n", + " None\n", + "RainfallRunoff\n", + " Hagg\n", + " Inputs\n", + " {'Area': 10034.2408, 'Latitude': 45.469, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + " DLLO\n", + " Inputs\n", + " {'Area': 22568.2404, 'Latitude': 45.475, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + " TRGC\n", + " Inputs\n", + " {'Area': 24044.6363, 'Latitude': 45.502, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + " DAIRY\n", + " Inputs\n", + " {'Area': 59822.7546, 'Latitude': 45.52, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + " RCTV\n", + " Inputs\n", + " {'Area': 19682.6046, 'Latitude': 45.502, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + " WSLO\n", + " Inputs\n", + " {'Area': 47646.8477, 'Latitude': 45.35, 'S0': 2, 'U0': 10, 'SnowS': 5}\n", + " Pars\n", + " {'CN2': -99, 'IS': -99, 'Res': -99, 'Sep': -99, 'Alpha': -99, 'Beta': -99, 'Ur': -99, 'Df': -99, 'Kc': -99}\n", + "Routing\n", + " WSLO\n", + " Hagg\n", + " {'Inputs': {'FlowLength': 101469.139, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': -99, 'Diff': -99}}\n", + " DLLO\n", + " {'Inputs': {'FlowLength': 91813.075, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': -99, 'Diff': -99}}\n", + " TRGC\n", + " {'Inputs': {'FlowLength': 80064.864, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': -99, 'Diff': -99}}\n", + " DAIRY\n", + " {'Inputs': {'FlowLength': 70988.164, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': -99, 'Diff': -99}}\n", + " RCTV\n", + " {'Inputs': {'FlowLength': 60398.68, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': -99, 'Diff': -99}}\n", + " WSLO\n", + " {'Inputs': {'FlowLength': 0, 'InstreamControl': False}, 'Pars': {'GShape': -99, 'GScale': -99, 'Velo': None, 'Diff': None}}\n", + "[09/07 12:27:18] HydroCNHS.sc [INFO] Model is saved at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\\model.yaml.\n", + "Model configuration file (.yaml) have been save at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\\model.yaml. Please open the file and further edit it.\n" + ] + } + ], + "source": [ + "# Print the model in the console\n", + "mb.print_model()\n", + "\n", + "# Output draft model configuration file (model.yaml)\n", + "mb.write_model_to_yaml(filename=\"model.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model calibraion\n", + "\n", + "Before applying the draft `model.yaml` for calibration, we need to ensure the information is correct. \n", + "\n", + "The \"Inputs\" in the `LSM` and `Routing` sections should all be populated. Some default values may be used (e.g., `S0`, `U0`, and `SnowS`). They should be updated according to the user's needs. For this tutorial, no modifications are needed. You might notice that all the parameters have a value of `-99`. This is the default value in HydroCNHS, indicating that those values are subject to calibration if not provided by the user.\n", + "\n", + "Below, we provide a sample calibration code using genetic algorithm (GA) and Kling-Gupta efficiency (KGE) at WSLO as the objective. The calibration will take around 2 to 3 hours depending on your hardware.\n", + "\n", + "Note: By running the following code, the entire `Cali_HydroModel_gwlf_KGE` folder will be overwritten. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 12:47:25] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['WSLO']\n", + "RoutingOutlets\n", + " ['WSLO']\n", + "DamAgents\n", + " None\n", + "ConveyingAgents\n", + " None\n", + "RiverDivAgents\n", + " None\n", + "InsituAgents\n", + " None\n", + "AgSimSeq\n", + " None\n", + "GA Calibration Guide\n", + "Step 1: set or load (GA_auto_save.pickle).\n", + "Step 2: run.\n", + "[09/07 12:47:25] HydroCNHS.GA [INFO] User-provided random number generator is assigned.\n", + "[09/07 12:47:25] HydroCNHS.GA [WARNING] Current calibration folder exists. Default to overwrite the folder!\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\\Cali_HydroModel_gwlf_KGE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. 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finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=====Generation 99=====\n", + " Elapsed time 01:25:27\n", + " Min -0.0348\n", + " Max -0.0\n", + " Avg -0.00103\n", + " Std 0.00477\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 16.0s\n", + "[Parallel(n_jobs=-1)]: Done 99 out of 99 | elapsed: 46.0s finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=====Generation 100=====\n", + " Elapsed time 01:26:15\n", + " Min -0.08777\n", + " Max -0.0\n", + " Avg -0.00221\n", + " Std 0.01022\n", + "\n", + "GA done!\n", + "\n", + "[09/07 02:13:42] HydroCNHS.sc [INFO] Model is saved at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model\\Cali_HydroModel_gwlf_KGE\\Best_HydroModel_gwlf_KGE.yaml.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:371: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(parse)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:371: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(parse)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import hydrocnhs.calibration as cali\n", + "from copy import deepcopy\n", + "\n", + "# Load climate inputs\n", + "temp = pd.read_csv(\n", + " os.path.join(wd, \"Data\", \"Temp_degC.csv\"), index_col=[\"Date\"]\n", + ").to_dict(orient=\"list\")\n", + "prec = pd.read_csv(os.path.join(wd, \"Data\", \"Prec_cm.csv\"), index_col=[\"Date\"]).to_dict(\n", + " orient=\"list\"\n", + ")\n", + "pet = pd.read_csv(os.path.join(wd, \"Data\", \"Pet_cm.csv\"), index_col=[\"Date\"]).to_dict(\n", + " orient=\"list\"\n", + ")\n", + "\n", + "# Load flow gauge monthly data at WSLO\n", + "obv_flow_WSLO = pd.read_csv(\n", + " os.path.join(wd, \"Data\", \"WSLO_M_cms.csv\"), index_col=[\"Date\"], parse_dates=[\"Date\"]\n", + ")\n", + "\n", + "# Load model (the model can be loaded by giving the path of the model configuration file)\n", + "model_dict = hydrocnhs.load_model(os.path.join(wd, \"model.yaml\"))\n", + "\n", + "# Generate default parameter bounds\n", + "df_list, df_name = hydrocnhs.write_model_to_df(model_dict)\n", + "par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict)\n", + "\n", + "# Create convertor for calibration. Check the manual for converter details.\n", + "converter = cali.Convertor()\n", + "cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list)\n", + "formatter = converter.formatter\n", + "\n", + "# Code evaluation function for GA algorthm\n", + "def evaluation(individual, info):\n", + " cali_wd, current_generation, ith_individual, formatter, _ = info\n", + " name = \"{}-{}\".format(current_generation, ith_individual)\n", + "\n", + " ##### individual -> model\n", + " # Convert 1D array to a list of dataframes.\n", + " df_list = cali.Convertor.to_df_list(individual, formatter)\n", + " # Feed dataframes in df_list to model dictionary.\n", + " model = deepcopy(model_dict)\n", + " for i, df in enumerate(df_list):\n", + " s = df_name[i].split(\"_\")[0]\n", + " model = hydrocnhs.load_df_to_model_dict(model, df, s, \"Pars\")\n", + "\n", + " ##### Run simuluation\n", + " model = hydrocnhs.Model(model, name)\n", + " Q = model.run(temp, prec, pet)\n", + "\n", + " ##### Get simulation data\n", + " # Streamflow of routing outlets.\n", + " cali_target = [\"WSLO\"]\n", + " cali_period = (\"1981-1-1\", \"2005-12-31\")\n", + " sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[cali_target]\n", + " # Resample the daily simulation output to monthly outputs.\n", + " sim_Q_M = sim_Q_D[cali_target].resample(\"MS\").mean()\n", + "\n", + " KGE = hydrocnhs.Indicator().KGE(\n", + " x_obv=obv_flow_WSLO[cali_period[0] : cali_period[1]][cali_target],\n", + " y_sim=sim_Q_M[cali_period[0] : cali_period[1]][cali_target],\n", + " )\n", + "\n", + " fitness = KGE\n", + " return (fitness,)\n", + "\n", + "\n", + "config = {\n", + " \"min_or_max\": \"max\",\n", + " \"pop_size\": 100,\n", + " \"num_ellite\": 1,\n", + " \"prob_cross\": 0.5,\n", + " \"prob_mut\": 0.15,\n", + " \"stochastic\": False,\n", + " \"max_gen\": 100,\n", + " \"sampling_method\": \"LHC\",\n", + " \"drop_record\": False,\n", + " \"paral_cores\": -1,\n", + " \"paral_verbose\": 1,\n", + " \"auto_save\": True,\n", + " \"print_level\": 1,\n", + " \"plot\": True,\n", + "}\n", + "\n", + "seed = 5\n", + "rn_gen = hydrocnhs.create_rn_gen(seed)\n", + "ga = cali.GA_DEAP(evaluation, rn_gen)\n", + "ga.set(cali_inputs, config, formatter, name=\"Cali_HydroModel_gwlf_KGE\")\n", + "ga.run()\n", + "summary = ga.summary\n", + "individual = ga.solution\n", + "\n", + "##### Output the calibrated model.\n", + "df_list = cali.Convertor.to_df_list(individual, formatter)\n", + "model_best = deepcopy(model_dict)\n", + "for i, df in enumerate(df_list):\n", + " s = df_name[i].split(\"_\")[0]\n", + " model = hydrocnhs.load_df_to_model_dict(model_best, df, s, \"Pars\")\n", + "hydrocnhs.write_model(\n", + " model_best, os.path.join(ga.cali_wd, \"Best_HydroModel_gwlf_KGE.yaml\")\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cnhs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/Hydro_example/Main_HydroModel.py b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/archive_main_HydroModel.py similarity index 52% rename from tutorials/Hydro_example/Main_HydroModel.py rename to tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/archive_main_HydroModel.py index a89e004..052474c 100644 --- a/tutorials/Hydro_example/Main_HydroModel.py +++ b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/archive_main_HydroModel.py @@ -1,78 +1,86 @@ import os -import HydroCNHS +import hydrocnhs + prj_path, this_filename = os.path.split(__file__) -#%% =========================================================================== +# %% =========================================================================== # Step 1: Create a model configuration file # ============================================================================= ### Initialize a model builder object. wd = prj_path -mb = HydroCNHS.ModelBuilder(wd) +mb = hydrocnhs.ModelBuilder(wd) ### Setup a water system simulation information mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") ### Setup land surface model (rainfall-runoff model) # Here we have seven subbasins and we select GWLF as the rainfall-runoff model. -outlet_list = ['Hagg', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] -area_list = [10034.2408, 22568.2404, 24044.6363, 59822.7546, 19682.6046, - 47646.8477] +outlet_list = ["Hagg", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"] +area_list = [10034.2408, 22568.2404, 24044.6363, 59822.7546, 19682.6046, 47646.8477] lat_list = [45.469, 45.475, 45.502, 45.520, 45.502, 45.350] -mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="GWLF") +mb.set_rainfall_runoff( + outlet_list=outlet_list, area_list=area_list, lat_list=lat_list, runoff_model="GWLF" +) -### Setup routing +### Setup routing flow_length_list = [101469.139, 91813.075, 80064.864, 70988.164, 60398.680, 0] -mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=outlet_list, - flow_length_list=flow_length_list) +mb.set_routing_outlet( + routing_outlet="WSLO", + upstream_outlet_list=outlet_list, + flow_length_list=flow_length_list, +) ### Print the model in the console mb.print_model() -### Output initial model configuration file (.yaml) -mb.write_model_to_yaml(filename="HydroModel.yaml") +### Output initial model configuration file (.yaml) +mb.write_model_to_yaml(filename="model.yaml") -#%% =========================================================================== +# %% =========================================================================== # Step 2: Populate a model configuration file # ============================================================================= # Nothing need to be manually modified in this example. -#%% =========================================================================== -# Step 3: Run a calibration +# %% =========================================================================== +# Step 3: Run a calibration # ============================================================================= -import matplotlib.pyplot as plt -import pandas as pd -import HydroCNHS.calibration as cali +import matplotlib.pyplot as plt +import pandas as pd +import hydrocnhs.calibration as cali from copy import deepcopy # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data at WSLO -obv_flow_WSLO = pd.read_csv(os.path.join(wd,"Data","WSLO_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) +obv_flow_WSLO = pd.read_csv( + os.path.join(wd, "Data", "WSLO_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) # Load model -model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroModel.yaml")) +model_dict = hydrocnhs.load_model(os.path.join(wd, "model.yaml")) # Generate default parameter bounds -df_list, df_name = HydroCNHS.write_model_to_df(model_dict) -par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) +df_list, df_name = hydrocnhs.write_model_to_df(model_dict) +par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict) # Create convertor for calibration converter = cali.Convertor() cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) formatter = converter.formatter + # Code evaluation function for GA algorthm def evaluation(individual, info): cali_wd, current_generation, ith_individual, formatter, _ = info @@ -85,10 +93,10 @@ def evaluation(individual, info): model = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") + model = hydrocnhs.load_df_to_model_dict(model, df, s, "Pars") ##### Run simuluation - model = HydroCNHS.Model(model, name) + model = hydrocnhs.Model(model, name) Q = model.run(temp, prec, pet) ##### Get simulation data @@ -99,30 +107,34 @@ def evaluation(individual, info): # Resample the daily simulation output to monthly outputs. sim_Q_M = sim_Q_D[cali_target].resample("MS").mean() - KGE = HydroCNHS.Indicator().KGE( - x_obv=obv_flow_WSLO[cali_period[0]:cali_period[1]][cali_target], - y_sim=sim_Q_M[cali_period[0]:cali_period[1]][cali_target]) - + KGE = hydrocnhs.Indicator().KGE( + x_obv=obv_flow_WSLO[cali_period[0] : cali_period[1]][cali_target], + y_sim=sim_Q_M[cali_period[0] : cali_period[1]][cali_target], + ) + fitness = KGE return (fitness,) -config = {'min_or_max': 'max', - 'pop_size': 100, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.15, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} + +config = { + "min_or_max": "max", + "pop_size": 100, + "num_ellite": 1, + "prob_cross": 0.5, + "prob_mut": 0.15, + "stochastic": False, + "max_gen": 100, + "sampling_method": "LHC", + "drop_record": False, + "paral_cores": -1, + "paral_verbose": 1, + "auto_save": True, + "print_level": 1, + "plot": True, +} seed = 5 -rn_gen = HydroCNHS.create_rn_gen(seed) +rn_gen = hydrocnhs.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) ga.set(cali_inputs, config, formatter, name="Cali_HydroModel_gwlf_KGE") ga.run() @@ -134,15 +146,18 @@ def evaluation(individual, info): model_best = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") -HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml")) + model = hydrocnhs.load_df_to_model_dict(model_best, df, s, "Pars") +hydrocnhs.write_model( + model_best, os.path.join(ga.cali_wd, "Best_HydroModel_gwlf_KGE.yaml") +) -#%% =========================================================================== -# Step 3: Run a simulation +# %% =========================================================================== +# Step 3: Run a simulation # ============================================================================= ### Run a simulation. -model = HydroCNHS.Model(os.path.join(wd, "Cali_HydroModel_gwlf_KGE", - "Best_HydroModel_gwlf_KGE.yaml")) +model = hydrocnhs.Model( + os.path.join(wd, "Cali_HydroModel_gwlf_KGE", "Best_HydroModel_gwlf_KGE.yaml") +) Q = model.run(temp, prec, pet) result = pd.DataFrame(Q, index=model.pd_date_index).resample("MS").mean() @@ -151,19 +166,3 @@ def evaluation(individual, info): ax.plot(obv_flow_WSLO.index, obv_flow_WSLO.loc[:, "WSLO"], label="Obv") ax.plot(obv_flow_WSLO.index, result["WSLO"], ls="--", label="Sim") ax.legend() - - - - - - - - - - - - - - - - diff --git a/tutorials/Hydro_example/HydroModel.yaml b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/model.yaml similarity index 94% rename from tutorials/Hydro_example/HydroModel.yaml rename to tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/model.yaml index 17c380e..23853bc 100644 --- a/tutorials/Hydro_example/HydroModel.yaml +++ b/tutorials/Tutorial_1-Build_a_Semi-distributed_Hydrological_Model/model.yaml @@ -1,4 +1,5 @@ -Path: {WD: 'C:\...\Hydro_example', Modules: ''} +Path: {WD: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_1-Build_a_Semi-distributed_Hydrological_Model', + Modules: ''} WaterSystem: StartDate: 1981/1/1 EndDate: 2013/12/31 diff --git a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml similarity index 50% rename from tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml index 0232d9e..ac28ff2 100644 --- a/tutorials/HydroABM_example/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Best_HydroABMModel_gwlf_KGE.yaml @@ -1,4 +1,5 @@ -Path: {WD: 'C:\...\HydroABM_example', Modules: 'C:\...\HydroABM_example'} +Path: {WD: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_2-Build_a_Coupled_Natural_Human_System', + Modules: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_2-Build_a_Coupled_Natural_Human_System'} WaterSystem: StartDate: 1981/1/1 EndDate: 2013/12/31 @@ -11,7 +12,7 @@ WaterSystem: ABM: Modules: [TRB_ABM_complete.py] InstitDMClasses: {} - DMClasses: [DivertDM, TransferDM, ReleaseDM] + DMClasses: [TransferDM, DivertDM, ReleaseDM] DamAPI: [Reservoir_AgtType] RiverDivAPI: [Diversion_AgType] InsituAPI: [] @@ -20,77 +21,77 @@ WaterSystem: RainfallRunoff: HaggIn: Inputs: {Area: 10034.2408, Latitude: 45.469, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 32.55847141795829, IS: 0.2509482026086072, Res: 0.10332839437744035, - Sep: 0.4121426034106978, Alpha: 0.6636086196220089, Beta: 0.7011169266858093, - Ur: 11.268230971393667, Df: 0.42612956309009764, Kc: 0.8601015901649349} + Pars: {CN2: 68.93643710592278, IS: 0.2086596161011156, Res: 0.3507946778212136, + Sep: 0.15650906150520755, Alpha: 0.7910839940290268, Beta: 0.36306682612531194, + Ur: 7.784761202099398, Df: 0.3283353319909811, Kc: 0.5507033725096631} TRTR: Inputs: {Area: 329.8013, Latitude: 45.458, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 83.61434239774799, IS: 0.15221715009050993, Res: 0.02225389460711096, - Sep: 0.40102363071506575, Alpha: 0.5473636294086688, Beta: 0.5095253404860495, - Ur: 8.327090566155288, Df: 0.07233546913361824, Kc: 1.2861231730209866} + Pars: {CN2: 98.834122997826, IS: 0.10796748267440348, Res: 0.0997783287391581, + Sep: 0.43973657308513253, Alpha: 0.7764313575752806, Beta: 0.40654715098728444, + Ur: 5.787866134414838, Df: 0.41401229833972275, Kc: 0.8809606772315259} DLLO: Inputs: {Area: 22238.4391, Latitude: 45.475, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 64.5885398459751, IS: 0.43223332055262953, Res: 0.14780797108800064, - Sep: 0.06359192793232918, Alpha: 0.6505423293535648, Beta: 0.21147742978851614, - Ur: 13.545863878879102, Df: 0.7017887503543895, Kc: 1.2762639517864547} + Pars: {CN2: 97.73443203784741, IS: 0.3737185345472489, Res: 0.43303957047942504, + Sep: 0.417327218779993, Alpha: 0.9531505052808162, Beta: 0.660426885064227, + Ur: 1.641160509256432, Df: 0.9045299474669743, Kc: 1.1072207129026865} TRGC: Inputs: {Area: 24044.6363, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 39.78986702805449, IS: 0.2321062884540274, Res: 0.04736900368282452, - Sep: 0.13351498694796424, Alpha: 0.707720542695872, Beta: 0.6039783742365933, - Ur: 6.214805479974864, Df: 0.8002927140919674, Kc: 0.7590346902565737} + Pars: {CN2: 42.983959531476195, IS: 0.020338192354784346, Res: 0.4185176997624667, + Sep: 0.36737524760145657, Alpha: 0.7479352532047789, Beta: 0.4800293861059478, + Ur: 7.270332348482153, Df: 0.005631886067896108, Kc: 0.6701252006603959} DAIRY: Inputs: {Area: 59822.7546, Latitude: 45.52, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 71.72758269406737, IS: 0.32898723193349994, Res: 0.2277348985638952, - Sep: 0.005926874229079879, Alpha: 0.6701504056109664, Beta: 0.8060144318645857, - Ur: 13.290046074481403, Df: 0.8283757137431986, Kc: 1.165020841499873} + Pars: {CN2: 74.98158772444654, IS: 0.11461729273980237, Res: 0.31270598415447626, + Sep: 0.005054660836260938, Alpha: 0.9683766007926107, Beta: 0.18419623597268364, + Ur: 2.9404683268702456, Df: 0.6206908306785274, Kc: 0.5359676062763483} RCTV: Inputs: {Area: 19682.6046, Latitude: 45.502, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 58.04858069974539, IS: 0.42659674803777065, Res: 0.15619255096899315, - Sep: 0.03805071800915036, Alpha: 0.7645399182920019, Beta: 0.6608458201123291, - Ur: 9.486486453520461, Df: 0.7210345599551773, Kc: 1.1020434624797228} + Pars: {CN2: 63.902171347982986, IS: 0.37552351063281264, Res: 0.0053387071322855304, + Sep: 0.1994372771259345, Alpha: 0.36584899093612544, Beta: 0.5622514280976224, + Ur: 4.561139256512691, Df: 0.07363311548316465, Kc: 1.480135965960375} WSLO: Inputs: {Area: 47646.8477, Latitude: 45.35, S0: 2, U0: 10, SnowS: 5} - Pars: {CN2: 33.01349699988276, IS: 0.38699867271537425, Res: 0.08727819335352137, - Sep: 0.2123324366561249, Alpha: 0.3260932621661091, Beta: 0.3908324150778604, - Ur: 14.352975388298812, Df: 0.639379916587526, Kc: 1.404516033835841} + Pars: {CN2: 51.14629988747825, IS: 0.10994195720045369, Res: 0.22425358554983632, + Sep: 0.06331786390744337, Alpha: 0.5471456470086891, Beta: 0.78420650809213, + Ur: 11.000111492298052, Df: 0.3792755899847703, Kc: 1.388761379936499} Routing: WSLO: TRGC: Inputs: {FlowLength: 80064.864, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: 43.76342034565672, Diff: 204.27093071623588} + Pars: {GShape: null, GScale: null, Velo: 21.801892339850298, Diff: 1434.073524377015} DAIRY: Inputs: {FlowLength: 70988.164, InstreamControl: false} - Pars: {GShape: 26.586632483927648, GScale: 78.35266450172936, Velo: 22.351204674749777, - Diff: 673.2443188782019} + Pars: {GShape: 62.81148195594129, GScale: 28.32955746561255, Velo: 32.543820097299, + Diff: 3349.962370131385} RCTV: Inputs: {FlowLength: 60398.68, InstreamControl: false} - Pars: {GShape: 34.03364438629384, GScale: 124.30551168754486, Velo: 36.46899692041166, - Diff: 1976.873060463899} + Pars: {GShape: 86.30186957973923, GScale: 110.33798749504054, Velo: 22.788479158416074, + Diff: 557.9725551995641} WSLO: Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 52.20273017282001, GScale: 73.18182980228916, Velo: null, Diff: null} + Pars: {GShape: 48.263808386071986, GScale: 2.3115037425180613, Velo: null, Diff: null} TRGC: DLLO: Inputs: {FlowLength: 11748.211, InstreamControl: false} - Pars: {GShape: null, GScale: null, Velo: 3.733330051534749, Diff: 2887.8857835122744} + Pars: {GShape: null, GScale: null, Velo: 49.88396319301491, Diff: 1012.7106391722431} TRGC: Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 86.40584271828081, GScale: 121.75999362218042, Velo: null, Diff: null} + Pars: {GShape: 23.21065084595202, GScale: 5.731929078842648, Velo: null, Diff: null} DLLO: ResAgt: Inputs: {FlowLength: 9656.064, InstreamControl: true} - Pars: {GShape: null, GScale: null, Velo: 33.122065911563936, Diff: 1767.7265446908345} + Pars: {GShape: null, GScale: null, Velo: 49.46471354565588, Diff: 1427.2074512982667} TRTR: Inputs: {FlowLength: 30899.4048, InstreamControl: false} - Pars: {GShape: 63.040070065167754, GScale: 144.60739298726318, Velo: 3.329830245554578, - Diff: 307.97743894377055} + Pars: {GShape: 75.95063439339201, GScale: 0.8946837214791474, Velo: 31.099908170939205, + Diff: 2270.162070883352} DLLO: Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 78.76743743736597, GScale: 145.34756288707763, Velo: null, Diff: null} + Pars: {GShape: 9.820020110330582, GScale: 23.234272558215466, Velo: null, Diff: null} HaggIn: HaggIn: Inputs: {FlowLength: 0, InstreamControl: false} - Pars: {GShape: 19.27523227821823, GScale: 81.74156199096869, Velo: null, Diff: null} + Pars: {GShape: 8.996944397494927, GScale: 11.191971388385262, Velo: null, Diff: null} ABM: Reservoir_AgtType: ResAgt: @@ -113,9 +114,9 @@ ABM: WSLO: [ReturnFactor, 0, Plus] DMClass: DivertDM Pars: - ReturnFactor: [0.4055973282947464] - a: -0.7544486202188252 - b: 0.7577809269154834 + ReturnFactor: [0.2951033369654512] + a: 0.00770490734043916 + b: -0.01361494672857222 Pipe_AgType: PipeAgt: Attributes: {} diff --git a/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png new file mode 100644 index 0000000..1387886 Binary files /dev/null and b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/Fitness_Cali_HydroABMModel_gwlf_KGE.png differ diff --git a/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle new file mode 100644 index 0000000..ec2a815 Binary files /dev/null and b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Cali_HydroABMModel_gwlf_KGE/GA_auto_save.pickle differ diff --git a/tutorials/HydroABM_example/Data/Cali_M_cms.csv b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Cali_M_cms.csv similarity index 100% rename from tutorials/HydroABM_example/Data/Cali_M_cms.csv rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Cali_M_cms.csv diff --git a/tutorials/HydroABM_example/Data/Pet_cm.csv b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Pet_cm.csv similarity index 100% rename from tutorials/HydroABM_example/Data/Pet_cm.csv rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Pet_cm.csv diff --git a/tutorials/HydroABM_example/Data/Prec_cm.csv b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Prec_cm.csv similarity index 100% rename from tutorials/HydroABM_example/Data/Prec_cm.csv rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Prec_cm.csv diff --git a/tutorials/HydroABM_example/Data/Temp_degC.csv b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Temp_degC.csv similarity index 100% rename from tutorials/HydroABM_example/Data/Temp_degC.csv rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Data/Temp_degC.csv diff --git a/docs/figs/fig6_ABM_design_logic.png b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/NB_Figs/ABM_design_logic.png similarity index 100% rename from docs/figs/fig6_ABM_design_logic.png rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/NB_Figs/ABM_design_logic.png diff --git a/docs/figs/fig5_TRB_node_link.png b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/NB_Figs/TRB_node_link.png similarity index 100% rename from docs/figs/fig5_TRB_node_link.png rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/NB_Figs/TRB_node_link.png diff --git a/tutorials/HydroABM_example/TRB_ABM.py b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM.py similarity index 86% rename from tutorials/HydroABM_example/TRB_ABM.py rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM.py index e4a9390..34ecf5b 100644 --- a/tutorials/HydroABM_example/TRB_ABM.py +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM.py @@ -1,24 +1,24 @@ import logging -from HydroCNHS.abm import Base, read_factor +from hydrocnhs.abm import Base, read_factor logger = logging.getLogger("ABM") # logger can be used to log message (e.g., logger.info(msg)). ### Global variable and data -# Users can load data or define variables that are commonly available to all -# agent type classes and decision-making classes here. +# Users can load data or define variables that are commonly available to all +# agent type classes and decision-making classes here. """ This is the auto-generated script template for a ABM module. Make sure to add ABM module (this filename) to the model file -(.yaml). +(.yaml). Note: -Below is the list of inherited attributes for agent type -classes (AgtType) and (institutional) decision-making classes +Below is the list of inherited attributes for agent type +classes (AgtType) and (institutional) decision-making classes (DMClass). These attributes will be assigned to each -initialized object in a HydroCNHS simulation. Namely, they +initialized object in a HydroCNHS simulation. Namely, they can be used for model design. Agent type class (AgtType): @@ -29,10 +29,10 @@ self.current_date = current date (datetime object). self.data_length = data/simulation length. self.t = current timestep. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. -self.agents = a dictionary of all initialized agents, +self.agents = a dictionary of all initialized agents, {agt_name: agt object}. self.dm = (institutional) decision-making object if DMClass or institution is assigned to the @@ -40,7 +40,7 @@ (Institutional) decision-making classes (DMClass): self.name = name of the agent or institute. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = NumPy random number generator. @@ -55,26 +55,26 @@ class Reservoir_AgtType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. #decision = self.dm.make_dm(your_arguments) - + if factor <= 0: # Divert from the outlet action = 0 elif factor > 0: # Add to the outlet action = 0 - + return action @@ -82,26 +82,26 @@ def act(self, outlet): class Diversion_AgType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. #decision = self.dm.make_dm(your_arguments) - + if factor <= 0: # Divert from the outlet action = 0 elif factor > 0: # Add to the outlet action = 0 - + return action @@ -109,34 +109,34 @@ def act(self, outlet): class Pipe_AgType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The AgtType inherited attributes are applied. + # The AgtType inherited attributes are applied. # See the note at top. def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + # Common usage: # Get streamflow of outlet at timestep t Q = self.dc.Q_routed[outlet][self.t] - + # Make decision from (Institutional) decision-making # object if self.dm is not None. #decision = self.dm.make_dm(your_arguments) - + if factor <= 0: # Divert from the outlet action = 0 elif factor > 0: # Add to the outlet action = 0 - + return action # DMClass -class DivertDM(Base): +class TransferDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): @@ -146,10 +146,10 @@ def make_dm(self, your_arguments): # DMClass -class TransferDM(Base): +class DivertDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): @@ -162,7 +162,7 @@ def make_dm(self, your_arguments): class ReleaseDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - # The (Institutional) DMClass inherited attributes are applied. + # The (Institutional) DMClass inherited attributes are applied. # See the note at top. def make_dm(self, your_arguments): diff --git a/tutorials/HydroABM_example/TRB_ABM_complete.py b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_complete.py similarity index 70% rename from tutorials/HydroABM_example/TRB_ABM_complete.py rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_complete.py index e429fd3..8048117 100644 --- a/tutorials/HydroABM_example/TRB_ABM_complete.py +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_complete.py @@ -1,29 +1,29 @@ import os import pickle import logging -from HydroCNHS.abm import Base, read_factor +from hydrocnhs.abm import Base, read_factor + logger = logging.getLogger("ABM") # logger can be used to log message (e.g., logger.info(msg)). ### Global variable and data -# Users can load data or define variables that are commonly available to all -# agent type classes and decision-making classes here. -this_dir, this_filename = os.path.split(__file__) # Get this file directory. -with open(os.path.join( - this_dir, "TRB_ABM_database.pickle"), "rb") as file: +# Users can load data or define variables that are commonly available to all +# agent type classes and decision-making classes here. +this_dir, this_filename = os.path.split(__file__) # Get this file directory. +with open(os.path.join(this_dir, "TRB_ABM_database.pickle"), "rb") as file: database = pickle.load(file) """ This is the auto-generated script template for a ABM module. Make sure to add ABM module (this filename) to the model file -(.yaml). +(.yaml). Note: -Below is the list of inherited attributes for agent type -classes (AgtType) and (institutional) decision-making classes +Below is the list of inherited attributes for agent type +classes (AgtType) and (institutional) decision-making classes (DMClass). These attributes will be assigned to each -initialized object in a HydroCNHS simulation. Namely, they +initialized object in a HydroCNHS simulation. Namely, they can be used for model design. Agent type class (AgtType): @@ -34,10 +34,10 @@ self.current_date = current date (datetime object). self.data_length = data/simulation length. self.t = current timestep. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = numpy random number generator. -self.agents = a dictionary of all initialized agents, +self.agents = a dictionary of all initialized agents, {agt_name: agt object}. self.dm = (institutional) decision-making object if DMClass or institution is assigned to the @@ -45,7 +45,7 @@ (Institutional) decision-making classes (DMClass): self.name = name of the agent or institute. -self.dc = data collector object containing data. Routed streamflow +self.dc = data collector object containing data. Routed streamflow (Q_routed) is also collected at here. self.rn_gen = numpy random number generator. @@ -64,13 +64,14 @@ def __init__(self, **kwargs): def act(self, outlet): # Read corresponding factor of the given outlet factor = read_factor(self.config, outlet) - + if factor <= 0: logger.error("Something is not right in ResDam agent.") elif factor > 0: # Q_routed["HaggIn"][t] is the resevoir inflow at time t. - res_t = self.dm.make_dm(self.dc.Q_routed["HaggIn"][self.t], - self.current_date) + res_t = self.dm.make_dm( + self.dc.Q_routed["HaggIn"][self.t], self.current_date + ) action = res_t return action @@ -80,8 +81,7 @@ class Diversion_AgType(Base): def __init__(self, **kwargs): super().__init__(**kwargs) self.pars = self.config["Pars"] - self.dc.add_field(self.name, - {"DivReq": [], "Diversion": [], "Shortage": []}) + self.dc.add_field(self.name, {"DivReq": [], "Diversion": [], "Shortage": []}) def act(self, outlet): records = self.dc.get_field(self.name) @@ -92,8 +92,9 @@ def act(self, outlet): if factor < 0: # Diversion # Make diversion request at the first day of each month if self.current_date.day == 1: - div_req = self.dm.make_dm(self.pars["a"], self.pars["b"], - self.current_date) + div_req = self.dm.make_dm( + self.pars["a"], self.pars["b"], self.current_date + ) # Accumulate diversion request records["DivReq"] = records["DivReq"] + div_req @@ -109,10 +110,10 @@ def act(self, outlet): records["Diversion"].append(div_t) records["Shortage"].append(shortage_t) action = factor * div_t - elif factor >= 0: # Return flow + elif factor >= 0: # Return flow div_t = records["Diversion"][self.t] action = factor * div_t - + return action @@ -128,13 +129,14 @@ def act(self, outlet): if factor <= 0: logger.error("Something is wrong about TRTR agent.") - elif factor > 0: + elif factor > 0: if self.current_date.year < 1991: Res_t = 0 else: # Historical inputs. - Res_t = self.obv_data[self.current_date.year-1991, - self.current_date.month-1] + Res_t = self.obv_data[ + self.current_date.year - 1991, self.current_date.month - 1 + ] action = factor * Res_t return action @@ -143,13 +145,25 @@ def act(self, outlet): class ReleaseDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - self.db = database # [m^3] - self.flood_control = [True, True, True, True, True, False, - False, False, False, True, True, True] + self.db = database # [m^3] + self.flood_control = [ + True, + True, + True, + True, + True, + False, + False, + False, + False, + True, + True, + True, + ] # [cms] Use quantile 0.01 of the historical data. - self.min_release = 0.263335 # [cms] - self.min_release_vol = self.min_release * 86400 # [m^3] - # Add a new field to the data collector (dc) to record storage and + self.min_release = 0.263335 # [cms] + self.min_release_vol = self.min_release * 86400 # [m^3] + # Add a new field to the data collector (dc) to record storage and # release self.dc.add_field("ResAgt", {"Storage": [], "Release": []}) @@ -161,17 +175,17 @@ def make_dm(self, inflow, current_date): min_res_vol = self.min_release_vol day_of_year = current_date.dayofyear - inflow_vol = inflow * 86400 # cms to m^3 - if records["Storage"] == []: # Initial value [m^3] + inflow_vol = inflow * 86400 # cms to m^3 + if records["Storage"] == []: # Initial value [m^3] storage = 42944903.6561 + inflow_vol else: storage = records["Storage"][-1] + inflow_vol release = 0 - if flood_control[current_date.month-1]: # Flood control - storage_max = db["Storage_q95"][day_of_year-1] + if flood_control[current_date.month - 1]: # Flood control + storage_max = db["Storage_q95"][day_of_year - 1] if storage > storage_max: - release = (storage - storage_max) / 86400 # m^3 to cms + release = (storage - storage_max) / 86400 # m^3 to cms storage = storage_max else: if storage - min_res_vol < 0: @@ -179,15 +193,15 @@ def make_dm(self, inflow, current_date): else: release = min_release storage = storage - min_res_vol - else: # Target storage control - release_target = db["Release_q50"][day_of_year-1] + else: # Target storage control + release_target = db["Release_q50"][day_of_year - 1] storage_temp = storage - release_target * 86400 - if storage_temp > db["Storage_max"][day_of_year-1]: - release = (storage - db["Storage_max"][day_of_year-1]) / 86400 - storage = db["Storage_max"][day_of_year-1] - elif storage_temp < db["Storage_q05"][day_of_year-1]: - release = (storage - db["Storage_q05"][day_of_year-1]) / 86400 - storage = db["Storage_q05"][day_of_year-1] + if storage_temp > db["Storage_max"][day_of_year - 1]: + release = (storage - db["Storage_max"][day_of_year - 1]) / 86400 + storage = db["Storage_max"][day_of_year - 1] + elif storage_temp < db["Storage_q05"][day_of_year - 1]: + release = (storage - db["Storage_q05"][day_of_year - 1]) / 86400 + storage = db["Storage_q05"][day_of_year - 1] if release < 0: release = 0 storage = records["Storage"][-1] + inflow_vol @@ -203,19 +217,20 @@ def make_dm(self, inflow, current_date): class DivertDM(Base): def __init__(self, **kwargs): super().__init__(**kwargs) - self.db = database + self.db = database def make_dm(self, a, b, current_date): - prec_M_mean = self.db["Prec_M_mean"][current_date.year-1981, - (current_date.month-1)] - div_M_mean = self.db["Div_M_median_mean"][(current_date.month-1)] - div_M_max = self.db["Div_M_median_max"][(current_date.month-1)] - div_M_min = self.db["Div_M_median_min"][(current_date.month-1)] - if current_date.month in [6,7,8,9]: # Major irrigation diverion months. + prec_M_mean = self.db["Prec_M_mean"][ + current_date.year - 1981, (current_date.month - 1) + ] + div_M_mean = self.db["Div_M_median_mean"][(current_date.month - 1)] + div_M_max = self.db["Div_M_median_max"][(current_date.month - 1)] + div_M_min = self.db["Div_M_median_min"][(current_date.month - 1)] + if current_date.month in [6, 7, 8, 9]: # Major irrigation diverion months. div_M_req = div_M_mean + a * prec_M_mean + b # Bound by historical max and min - div_M_req = min( max(div_M_req, div_M_min), div_M_max) - else: # Minor irrigation diversion months. + div_M_req = min(max(div_M_req, div_M_min), div_M_max) + else: # Minor irrigation diversion months. div_M_req = div_M_mean # Uniformly allocate monthly diversion to daily. div_D_req = [div_M_req] * (current_date.days_in_month) diff --git a/tutorials/HydroABM_example/TRB_ABM_database.pickle b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_database.pickle similarity index 100% rename from tutorials/HydroABM_example/TRB_ABM_database.pickle rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_database.pickle diff --git a/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Tutorial 2 - Build a Coupled Natural Human System.ipynb b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Tutorial 2 - Build a Coupled Natural Human System.ipynb new file mode 100644 index 0000000..711ac49 --- /dev/null +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/Tutorial 2 - Build a Coupled Natural Human System.ipynb @@ -0,0 +1,3109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial 2 - Build a Coupled Natural Human System\n", + "\n", + "Assuming that you have gone through Tutorial 1, Tutorial 2 will focus on how to add human layer into the system. Namely, we will couple an agent-based model (ABM) with a semi-distributed model.\n", + "\n", + "We adopt the Tualatin River Basin (TRB; Figure 1; Table 1) as the tutorial example. The corresponding subbasins' information is shown in Table 1. In this example, we consider three human components (Table 2), including (1) a reservoir (`ResAgt`), (2) an irrigation diversion (`DivAgt`), and (3) a trans-basin water transfer (`PipeAgt`), to demonstrate HydroCNHS's functionalities. Additionally, we model each human agent with different levels of behavioral complexity to provide users with a sense of how an agent can be modeled (Table 2). We will calibrate the model using the streamflow at DLLO and WSLO, reservoir releases from `ResAgt`, and water diversions from `DivAgt` on a monthly scale. More details about TRB can be found in *Lin et al. (2022)*. Here, we focus on the coding part.\n", + "\n", + "\"TRB_node_link\"\n", + "\n", + "*Figure 1. The node-link structure of the Tualatin River Basin with human components.*\n", + "\n", + "\n", + "*Table 1. Subbasins’ information for the Tualatin River Basin.*\n", + "\n", + "| Subbasin/outlet | Drainage area [ha] | Latitude [deg] | Flow length [m] |\n", + "|-----------------|-------------------:|---------------:|----------------:|\n", + "| HaggIn | 10034.2408 | 45.469 | 0 (to HaggIn) |\n", + "| TRTR | 329.8013 | 45.458 | 30899.4048 (to DLLO) |\n", + "| ResAgt\\* | – | – | 9656.064 (to DLLO) |\n", + "| DLLO | 22238.4391 | 45.475 | 11748.211 (to TRGC) |\n", + "| TRGC | 24044.6363 | 45.502 | 0 (to TRGC) |\n", + "| DAIRY | 59822.7546 | 45.520 | 70988.164 (to WSLO) |\n", + "| RCTV | 19682.6046 | 45.502 | 60398.680 (to WSLO) |\n", + "| WSLO | 47646.8477 | 45.350 | 0 (to WSLO) |\n", + "\n", + "\\*ResAgt is a reservoir agent integrated with Dam API. It is considered a pseudo routing outlet.\n", + "\n", + "\n", + "*Table 2. Summary of human components’ setting.*\n", + "\n", + "| Item | Agent name | API | Behavioral design | From |\n", + "|-----------------------------|------------|-----------------|-----------------------------------------|--------|\n", + "| Reservoir | ResAgt | Dam API | Fixed operational rules | HaggIn |\n", + "| Irrigation diversion | DivAgt | RiverDiv API | Calibrated adaptive behavioral rules | TRGC |\n", + "| Trans-basin water transfer | PipeAgt | Conveying API | External input data | – |\n", + "\n", + "\n", + "\n", + "## Step 1: Create a model configuration file (`model.yaml`)\n", + "\n", + "Creating a node-link structure of a modeled water system is a vital step before using HydroCNHS. Subbasin outlets are determined based on the major tributaries in the TRB. However, the subbasin outlet, TRTR, is predefined because there is an inlet for trans-basin water transfer. For the routing outlet assignment, DLLO and WSLO are selected because the streamflow at these locations is part of the calibration targets. TRGC is also chosen since an agent integrated using the RiverDiv API can only divert water from a routing outlet. HaggIn is picked because it is the inflow for ResAgt (i.e., ResAgt takes water from HaggIn).\n", + "\n", + "With a node-link structure of the TRB water system, we can follow the same process shown in Tutorial 1 to initialize a model builder, set up the water system with the simulation period, add subbasins, and add four routing outlets. Note that ResAgt is considered a pseudo-routing outlet that needs to be assigned to one of the upstream outlets of a routing outlet.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Follow the following steps to create model & ABM script templates:\n", + "\tStep 1: set_water_system()\n", + "\tStep 2: set_rainfall_runoff()\n", + "\tStep 3: set_routing_outlet(), one at a time.\n", + "\tStep 4: (optional) set_sediment().\n", + "\tStep 5: (optional) add_sediment(), one at a time.\n", + "\tStep 6: (optional) set_ABM().\n", + "\tStep 7: (optional) add_agent().\n", + "\tStep 8: (optional) add_institution().\n", + "\tStep 9: write_model_to_yaml()\n", + "\tStep 10: (optional) gen_ABM_script_template()\n", + "Open the generated draft 'model.yaml' (& ABM module template) and further edit them.\n", + "Use .help to re-print the above instructions.\n" + ] + } + ], + "source": [ + "import os\n", + "import hydrocnhs\n", + "\n", + "# Set the working directory\n", + "wd = os.path.abspath(\n", + " os.path.join(\n", + " os.path.dirname(hydrocnhs.__file__),\n", + " '..', 'tutorials',\n", + " 'Tutorial_2-Build_a_Coupled_Natural_Human_System'\n", + " )\n", + " )\n", + "\n", + "# Initialize a model builder object.\n", + "mb = hydrocnhs.ModelBuilder(wd)\n", + "\n", + "# Setup a water system simulation information\n", + "mb.set_water_system(start_date=\"1981/1/1\", end_date=\"2013/12/31\")\n", + "\n", + "# Setup land surface model (i.e., rainfall-runoff model)\n", + "# Here we have seven subbasins and we select GWLF as the rainfall-runoff model.\n", + "outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO']\n", + "area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546,\n", + " 19682.6046, 47646.8477]\n", + "lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350]\n", + "mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list,\n", + " lat_list=lat_list, runoff_model=\"GWLF\")\n", + "\n", + "# Setup routing outlets\n", + "# Add WSLO\n", + "mb.set_routing_outlet(routing_outlet=\"WSLO\",\n", + " upstream_outlet_list=[\"TRGC\", \"DAIRY\", \"RCTV\", \"WSLO\"],\n", + " flow_length_list=[80064.864, 70988.164, 60398.680, 0])\n", + "# Add TRGC\n", + "mb.set_routing_outlet(routing_outlet=\"TRGC\",\n", + " upstream_outlet_list=[\"DLLO\", \"TRGC\"],\n", + " flow_length_list=[11748.211, 0])\n", + "# Add DLLO\n", + "# Specify that ResAgt is an instream object.\n", + "mb.set_routing_outlet(routing_outlet=\"DLLO\",\n", + " upstream_outlet_list=[\"ResAgt\", \"TRTR\", \"DLLO\"],\n", + " flow_length_list=[9656.064, 30899.4048, 0],\n", + " instream_objects=[\"ResAgt\"])\n", + "# Add HaggIn\n", + "mb.set_routing_outlet(routing_outlet=\"HaggIn\",\n", + " upstream_outlet_list=[\"HaggIn\"],\n", + " flow_length_list=[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initialize ABM setting\n", + "\n", + "To add human components, we first need to initialize the ABM setting block by assigning the directory of an ABM module folder and the planned ABM module filename. If these are not provided, default values will be applied, namely the working directory and \"ABM_module.py,\" respectively. The `abm_module_name` will be used as the filename for the ABM module template if users choose to generate one using the model builder.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "mb.set_ABM(abm_module_folder_path=wd, abm_module_name=\"TRB_ABM.py\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we add human components (i.e., agents) to the model builder. We first add a reservoir agent (`ResAgt`), where its corresponding agent type class, agent name, API, link dictionary, and decision-making class can be assigned at this stage. Although not all information has to be provided now (i.e., it can be manually added to the `model.yaml` file later), we encourage users to provide complete details here.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "mb.add_agent(\n", + " agt_type_class=\"Reservoir_AgtType\", agt_name=\"ResAgt\",\n", + " api=mb.api.Dam,\n", + " link_dict={\"HaggIn\": -1, \"ResAgt\": 1},\n", + " dm_class=\"ReleaseDM\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The setting shown above means that `ResAgt` (an agent object) will be created from `Reservoir_AgtType` (an agent type class) and integrated into HydroCNHS using the Dam API. A decision-making object will be created from `ReleaseDM` (a decision-making class) and assigned to `ResAgt` as its attribute. This agent, `ResAgt`, will take water (factor = -1) from the `HaggIn` routing outlet and release (factor = 1) water to `ResAgt`. Remember that `ResAgt` itself is a pseudo-routing outlet.\n", + "\n", + "Following a similar procedure, we add a water diversion agent (`DivAgt`). However, we have parameters, including `ReturnFactor`, `a`, and `b`, involved in this agent. Hence, a dictionary is provided to the `par_dict` argument. The format of the parameter dictionary is that keys are parameter names, and values are parameter values (-99 means waiting to be calibrated).\n", + "\n", + "However, if the parameter is the factor used in the `link_dict`, users need to follow the format shown here. For example, if we want to calibrate a return factor (`ReturnFactor`) to determine the portion of diverted water returned to the `WSLO` subbasin, we provide a list, `[\"ReturnFactor\", 0, \"Plus\"]`, to the `link_dict` at `WSLO`. HydroCNHS will interpret it as taking the factor value from the parameter `ReturnFactor` with a list index of 0. \"Plus\" tells HydroCNHS to add water to `WSLO`. If water is taken from `WSLO`, then \"Minus\" should be assigned.\n", + "\n", + "Note: `-99` is a default value indicating that this parameter is subject to calibration." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "mb.add_agent(\n", + " agt_type_class=\"Diversion_AgType\", agt_name=\"DivAgt\",\n", + " api=mb.api.RiverDiv,\n", + " link_dict={\"TRGC\": -1, \"WSLO\": [\"ReturnFactor\", 0, \"Plus\"]},\n", + " dm_class=\"DivertDM\",\n", + " par_dict={\"ReturnFactor\": [-99], \"a\": -99, \"b\":-99}\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we add a trans-basin water transfer agent (`PipeAgt`)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mb.add_agent(\n", + " agt_type_class=\"Pipe_AgType\", agt_name=\"PipeAgt\",\n", + " api=mb.api.Conveying,\n", + " link_dict={\"TRTR\": 1},\n", + " dm_class=\"TransferDM\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add institution\n", + "\n", + "We did not include an institution in this TRB example; however, if users want to assign an institution (e.g., “ResDivInstitution”) to `ResAgt` and `DivAgt`, they should do so by assuming that there is cooperation between water release decisions and water diversion decisions. Namely, release decisions from `ResAgt` and diversion decisions from `DivAgt` are made simultaneously using a single decision-making object (Figure 2). Users can do the following.\n", + "\n", + "Note: `ResDivInstitution` will overwrite the originally assigned decision-making (DM) classes (if any) of `ResAgt` and `DivAgt`. The above command means that a single `ResDivInstitution` decision-making object, initialized from `ResDivDMClass`, will be assigned to the attributes (e.g., `self.dm`) of `ResAgt` and `DivAgt`. Users can utilize this property to design their agents.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nmb.add_institution(\\n institution=\"ResDivInstitution\",\\n instit_dm_class=\" ResDivDMClass\",\\n agent_list=[\" ResAgt \", \"DivAgt\"]\\n )\\n'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Do not run this cell for this tutorial\n", + "\"\"\"\n", + "mb.add_institution(\n", + " institution=\"ResDivInstitution\",\n", + " instit_dm_class=\" ResDivDMClass\",\n", + " agent_list=[\" ResAgt \", \"DivAgt\"]\n", + " )\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate `ABM module template` & output `model.yaml`\n", + "\n", + "In addition to outputting a model configuration file (`.yaml`), the model builder can generate an ABM module template (`.py`) for users. In this template, the model builder will create the outline of agent type classes and decision-making classes, allowing users to focus on programming the calculations for each class provided in the template.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 03:12:01] HydroCNHS.sc [INFO] Model is saved at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_2-Build_a_Coupled_Natural_Human_System\\model.yaml.\n", + "Model configuration file (.yaml) have been save at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_2-Build_a_Coupled_Natural_Human_System\\model.yaml. Please open the file and further edit it.\n", + "ABM module template (.py) have been save at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_2-Build_a_Coupled_Natural_Human_System\\TRB_ABM.py. Please open the file and further edit it.\n" + ] + } + ], + "source": [ + "# Output draft model configuration file (i.e., model.yaml) and ABM module template (i.e., TRB_ABM.py).\n", + "mb.write_model_to_yaml(filename=\"model.yaml\")\n", + "mb.gen_ABM_module_template()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Complete a `model.yaml`\n", + "\n", + "After the model configuration file (`.yaml`) is created, users should open the file to complete and correct any missing or misinterpreted values. For this example, we will again keep the default values.\n", + "\n", + "## Step 3: Program ABM module (.py)**\n", + "\n", + "In the generated ABM module (`.py`), users will find two main types of classes: agent type classes (`AgtType`) and decision-making classes (`DMClass`/`Institutional DMClass`). Agent type classes are used to define agents' actions and store up-to-date information (e.g., current date and current time step) in agents' attributes. Decision-making classes are used to program specific decision-making processes. These decision-making classes can be further divided into `DMClass` and `Institutional DMClass`.\n", + "\n", + "The ABM design logic is illustrated in Figure 2. A \"class\" is a template for objects that can be instantiated with object-specific attributes and settings. For example, `Agent1` and `Agent2` are instantiated from the same `AgtType1` class. `Agent2`, `Agent4`, and `Agent5` are instantiated from the `AgtType2` class. Each agent will be assigned a DM object or Institution object as one of its attributes. DM objects instantiated from `DMClass` are not shared with other agents; namely, agents with DM objects will each have a unique DM object (e.g., `Agent1` and `Agent2` in Fig. 6). In contrast, an Institution object can be shared with multiple agents, allowing those agents to make decisions together. For example, multiple irrigation districts might make diversion decisions together to share the water shortage during a drought period. We will not implement the `Institutional DMClass` in this TRB example; however, we will show how to add an institution through the model builder.\n", + "\n", + "\"ABM_design_logic.\"\n", + "\n", + "*Figure 2. ABM Design Logic. An agent is a combination of an `AgtType` class and a `(Institutional) DM` class. An Institution object can be shared among a group of agent objects (i.e., making decisions together), while a DM object can only be assigned to one agent object.*\n", + "\n", + "\n", + "\n", + "### Agent Type Class (`AgtType`):\n", + "\n", + "- `self.name`: agent’s name\n", + "- `self.config`: agent’s configuration dictionary, `{‘Attributes’: …, ‘Inputs’: …, ‘Pars’: …}`\n", + "- `self.start_date`: start date (`datetime` object)\n", + "- `self.current_date`: current date (`datetime` object)\n", + "- `self.data_length`: data/simulation length\n", + "- `self.t`: current timestep\n", + "- `self.dc`: data collector object containing data. Routed streamflow (`Q_routed`) is also collected here.\n", + "- `self.rn_gen`: NumPy random number generator\n", + "- `self.agents`: a dictionary of all initialized agents, `{agt_name: agt object}`\n", + "- `self.dm`: (Institutional) decision-making object if `DMClass` or institution is assigned to the agent, otherwise `None`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from hydrocnhs.abm import Base, read_factor\n", + "\n", + "# AgtType\n", + "class XXX_AgtType(Base):\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + " # The AgtType inherited attributes are applied.\n", + " # See the note at top.\n", + "\n", + " def act(self, outlet):\n", + " # Read corresponding factor of the given outlet\n", + " factor = read_factor(self.config, outlet)\n", + "\n", + " # Common usage:\n", + " # Get streamflow of outlet at timestep t\n", + " Q = self.dc.Q_routed[outlet][self.t]\n", + "\n", + " # Make decision from (Institutional) decision-making\n", + " # object if self.dm is not None.\n", + " #decision = self.dm.make_dm(your_arguments)\n", + "\n", + " if factor <= 0: # Divert from the outlet\n", + " action = 0\n", + " elif factor > 0: # Add to the outlet\n", + " action = 0\n", + "\n", + " return action" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (Institutional) decision-making classes (`DMClass`):\n", + "\n", + "- `self.name`: name of the agent or institution\n", + "- `self.dc`: data collector object containing data. Routed streamflow (`Q_routed`) is also collected here.\n", + "- `self.rn_gen`: NumPy random number generator\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# DMClass\n", + "class XXX_DM(Base):\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + " # The (Institutional) DMClass inherited attributes are applied.\n", + " # See the note at top.\n", + "\n", + " def make_dm(self, your_arguments):\n", + " # Decision-making calculation.\n", + " decision = None\n", + " return decision" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To keep the manual concise, we provide a complete ABM module for the TRB example at `./tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/TRB_ABM_complete.py`. Formulation details can be found in *Lin et al. (2022)*, and more coding tips are available in the \"Advanced ABM Coding Tips\" section in the manual.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Run a calibration\n", + "\n", + "First, we load `model.yaml`, the climate data, and the observed monthly flow data for DLLO and WSLO, reservoir releases from `ResAgt`, and water diversions from `DivAgt`. Here, we have calculated the evapotranspiration using the Hamon method, so PET data is input along with other data. \n", + "\n", + "Note: We manually change the ABM module from `TRB_ABM.py` to `TRB_ABM_complete.py` in `model.yaml` before continuing this tutorial. See the following code.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 03:32:23] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['HaggIn', 'ResAgt', 'DLLO', 'TRGC', 'WSLO']\n", + "RoutingOutlets\n", + " ['HaggIn', 'DLLO', 'TRGC', 'WSLO']\n", + "DamAgents\n", + " ['ResAgt']\n", + "ConveyingAgents\n", + " ['PipeAgt']\n", + "RiverDivAgents\n", + " ['DivAgt']\n", + "InsituAgents\n", + " []\n", + "AgSimSeq\n", + " AgSimMinus\n", + " HaggIn\n", + " {}\n", + " ResAgt\n", + " {}\n", + " DLLO\n", + " {}\n", + " TRGC\n", + " {'RiverDivAgents': [('DivAgt', 'TRGC')]}\n", + " WSLO\n", + " {}\n", + " AgSimPlus\n", + " HaggIn\n", + " {}\n", + " ResAgt\n", + " {'DamAgents': [('ResAgt', 'ResAgt')]}\n", + " DLLO\n", + " {'ConveyingAgents': [('PipeAgt', 'TRTR')]}\n", + " TRGC\n", + " {}\n", + " WSLO\n", + " {'RiverDivAgents': [('DivAgt', 'WSLO')]}\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import hydrocnhs.calibration as cali\n", + "from copy import deepcopy\n", + "\n", + "# Load climate data\n", + "temp = pd.read_csv(\n", + " os.path.join(wd,\"Data\",\"Temp_degC.csv\"), index_col=[\"Date\"]\n", + " ).to_dict(orient=\"list\")\n", + "prec = pd.read_csv(\n", + " os.path.join(wd,\"Data\",\"Prec_cm.csv\"), index_col=[\"Date\"]\n", + " ).to_dict(orient=\"list\")\n", + "pet = pd.read_csv(\n", + " os.path.join(wd,\"Data\",\"Pet_cm.csv\"), index_col=[\"Date\"]\n", + " ).to_dict(orient=\"list\")\n", + "\n", + "# Load flow gauge monthly data at WSLO\n", + "obv_flow_data = pd.read_csv(os.path.join(wd,\"Data\",\"Cali_M_cms.csv\"),\n", + " index_col=[\"Date\"], parse_dates=[\"Date\"])\n", + "\n", + "# Load model\n", + "model_dict = hydrocnhs.load_model(os.path.join(wd, \"model.yaml\"))\n", + "\n", + "# Change the ABM module to the complete one.\n", + "model_dict[\"WaterSystem\"][\"ABM\"][\"Modules\"] = [\"TRB_ABM_complete.py\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Second, we generate default parameter bounds and create a converter for calibration. Note that we manually change the default ABM parameter bounds as shown in the code. Details about the converter are provided in the `Calibration` section in the manual.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:266: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[25, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, :] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[1, 100]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0.5, 55]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0.5, 55]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:272: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0.5, 55]' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str(v)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:279: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '[0, 1]' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n", + " df.loc[k, df.loc[k, :].isna() == False] = str([0, 1])\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\calibration\\convertor.py:89: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " df = df.replace({None: np.nan})\n" + ] + } + ], + "source": [ + "# Generate default parameter bounds\n", + "df_list, df_name = hydrocnhs.write_model_to_df(model_dict)\n", + "par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict)\n", + "\n", + "# Modify the default bounds of ABM\n", + "df_abm_bound = par_bound_df_list[2]\n", + "df_abm_bound.loc[\"ReturnFactor.0\", [('DivAgt', 'Diversion_AgType')]] = \"[0, 0.5]\"\n", + "df_abm_bound.loc[\"a\", [('DivAgt', 'Diversion_AgType')]] = \"[-1, 1]\"\n", + "df_abm_bound.loc[\"b\", [('DivAgt', 'Diversion_AgType')]] = \"[-1, 1]\"\n", + "\n", + "# Create convertor for calibration\n", + "converter = cali.Convertor()\n", + "cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list)\n", + "formatter = converter.formatter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Third, we program the evaluation function for a genetic algorithm (GA). The mean Kling-Gupta efficiency (KGE) of the four calibration targets is adopted to represent the model performance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Code evaluation function for GA algorthm\n", + "def evaluation(individual, info):\n", + " cali_wd, current_generation, ith_individual, formatter, _ = info\n", + " name = \"{}-{}\".format(current_generation, ith_individual)\n", + "\n", + " ##### individual -> model\n", + " # Convert 1D array to a list of dataframes.\n", + " df_list = cali.Convertor.to_df_list(individual, formatter)\n", + " # Feed dataframes in df_list to model dictionary.\n", + " model = deepcopy(model_dict)\n", + " for i, df in enumerate(df_list):\n", + " s = df_name[i].split(\"_\")[0]\n", + " model = hydrocnhs.load_df_to_model_dict(model, df, s, \"Pars\")\n", + "\n", + " ##### Run simuluation\n", + " model = hydrocnhs.Model(model, name)\n", + " Q = model.run(temp, prec, pet)\n", + "\n", + " ##### Get simulation data\n", + " # Streamflow of routing outlets.\n", + " cali_target = [\"WSLO\",\"DLLO\",\"ResAgt\",\"DivAgt\"]\n", + " cali_period = (\"1981-1-1\", \"2005-12-31\")\n", + " sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[[\"WSLO\",\"DLLO\"]]\n", + " sim_Q_D[\"ResAgt\"] = model.dc.ResAgt[\"Release\"]\n", + " sim_Q_D[\"DivAgt\"] = model.dc.DivAgt[\"Diversion\"]\n", + " # Resample the daily simulation output to monthly outputs.\n", + " sim_Q_M = sim_Q_D[cali_target].resample(\"MS\").mean()\n", + "\n", + " KGEs = []\n", + " for target in cali_target:\n", + " KGEs.append(hydrocnhs.Indicator().KGE(\n", + " x_obv=obv_flow_data[cali_period[0]:cali_period[1]][[target]],\n", + " y_sim=sim_Q_M[cali_period[0]:cali_period[1]][[target]]))\n", + "\n", + " fitness = sum(KGEs)/4\n", + " return (fitness,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fourth, we set up a GA for calibration. Again, we will explain calibration in more detail in the Calibration section. Here, only the code is demonstrated. Note that calibration might take some time to run, depending on your system specifications. Users can lower the `pop_size` and `max_gen` values if they want to experience the process rather than seeking convergence. To debug your code, set `paral_cores` to 1 to display the error message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GA Calibration Guide\n", + "Step 1: set or load (GA_auto_save.pickle).\n", + "Step 2: run.\n", + "[09/07 03:51:04] HydroCNHS.GA [INFO] User-provided random number generator is assigned.\n", + "[09/07 03:51:04] HydroCNHS.GA [WARNING] Current calibration folder exists. Default to overwrite the folder!\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_2-Build_a_Coupled_Natural_Human_System\\Cali_HydroABMModel_gwlf_KGE\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 30.4s\n", + "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed: 1.8min finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=====Generation 0=====\n", + " Elapsed time 00:01:46\n", + " Min -0.52451\n", + " Max -0.14027\n", + " Avg -0.37339\n", + " Std 0.10851\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 26 tasks | elapsed: 35.1s\n", + "[Parallel(n_jobs=-1)]: Done 99 out of 99 | elapsed: 1.7min finished\n" + ] + }, + { + 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finished\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "=====Generation 100=====\n", + " Elapsed time 02:33:53\n", + " Min -0.21378\n", + " Max -0.00047\n", + " Avg -0.00616\n", + " Std 0.02949\n", + "\n", + "GA done!\n", + "\n" + ] + } + ], + "source": [ + "config = {\n", + " 'min_or_max': 'max',\n", + " 'pop_size': 100,\n", + " 'num_ellite': 1,\n", + " 'prob_cross': 0.5,\n", + " 'prob_mut': 0.15,\n", + " 'stochastic': False,\n", + " 'max_gen': 100,\n", + " 'sampling_method': 'LHC',\n", + " 'drop_record': False,\n", + " 'paral_cores': -1,\n", + " 'paral_verbose': 1,\n", + " 'auto_save': True,\n", + " 'print_level': 1,\n", + " 'plot': True\n", + " }\n", + "\n", + "seed = 5\n", + "rn_gen = hydrocnhs.create_rn_gen(seed)\n", + "ga = cali.GA_DEAP(evaluation, rn_gen)\n", + "ga.set(cali_inputs, config, formatter, name=\"Cali_HydroABMModel_gwlf_KGE\")\n", + "ga.run()\n", + "summary = ga.summary\n", + "individual = ga.solution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we export the calibrated model (i.e., Best_HydroABMModel_gwlf_KGE.yaml)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 10:08:38] HydroCNHS.sc [INFO] Model is saved at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_2-Build_a_Coupled_Natural_Human_System\\Cali_HydroABMModel_gwlf_KGE\\Best_HydroABMModel_gwlf_KGE.yaml.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:371: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(parse)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:371: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(parse)\n", + "C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\hydrocnhs\\util.py:371: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n", + " df = df.applymap(parse)\n" + ] + } + ], + "source": [ + "# Output the calibrated model.\n", + "df_list = cali.Convertor.to_df_list(individual, formatter)\n", + "model_best = deepcopy(model_dict)\n", + "for i, df in enumerate(df_list):\n", + " s = df_name[i].split(\"_\")[0]\n", + " model = hydrocnhs.load_df_to_model_dict(model_best, df, s, \"Pars\")\n", + "hydrocnhs.write_model(model_best, os.path.join(ga.cali_wd, \"Best_HydroABMModel_gwlf_KGE.yaml\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 5: Run a simulation\n", + "\n", + "After obtaining a calibrated model, users can now use it for any simulation-based experiment (e.g., streamflow uncertainty under climate change). The calibrated model configuration file (i.e., `Best_HydroABMModel_gwlf_KGE.yaml`) can be directly loaded into HydroCNHS to run a simulation.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 10:08:45] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['HaggIn', 'ResAgt', 'DLLO', 'TRGC', 'WSLO']\n", + "RoutingOutlets\n", + " ['HaggIn', 'DLLO', 'TRGC', 'WSLO']\n", + "DamAgents\n", + " ['ResAgt']\n", + "ConveyingAgents\n", + " ['PipeAgt']\n", + "RiverDivAgents\n", + " ['DivAgt']\n", + "InsituAgents\n", + " []\n", + "AgSimSeq\n", + " AgSimMinus\n", + " HaggIn\n", + " {}\n", + " ResAgt\n", + " {}\n", + " DLLO\n", + " {}\n", + " TRGC\n", + " {'RiverDivAgents': [('DivAgt', 'TRGC')]}\n", + " WSLO\n", + " {}\n", + " AgSimPlus\n", + " HaggIn\n", + " {}\n", + " ResAgt\n", + " {'DamAgents': [('ResAgt', 'ResAgt')]}\n", + " DLLO\n", + " {'ConveyingAgents': [('PipeAgt', 'TRTR')]}\n", + " TRGC\n", + " {}\n", + " WSLO\n", + " {'RiverDivAgents': [('DivAgt', 'WSLO')]}\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field Q_runoff ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field Q_routed ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field prec ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field temp ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field pet ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field UH_Lohmann ().\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field UH_Lohmann_convey ().\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create ResAgt from Reservoir_AgtType class\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field ResAgt ().\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create ReleaseDM from ReleaseDM class.\n", + "[09/07 10:08:45] HydroCNHS.dc [INFO] Add field DivAgt ().\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create DivAgt from Diversion_AgType class\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create DivertDM from DivertDM class.\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create PipeAgt from Pipe_AgType class\n", + "[09/07 10:08:45] HydroCNHS [INFO] Create TransferDM from TransferDM class.\n", + "[09/07 10:08:45] HydroCNHS [INFO] Load temp & prec & pet with total length 12053.\n", + "[09/07 10:08:45] HydroCNHS [INFO] Set rainfall-runoff to GWLF.\n", + "[09/07 10:08:45] HydroCNHS [INFO] Complete rainfall-runoff simulation setup. [00:00:00]\n", + "[09/07 10:08:45] HydroCNHS [INFO] Start forming 10 UHs for Lohmann routing. [00:00:00]\n", + "[09/07 10:08:51] HydroCNHS [INFO] Complete forming UHs for Lohmann routing. [00:00:06]\n", + "[09/07 10:08:53] HydroCNHS [INFO] Complete forming UHs for conveyed nodes. [00:00:07]\n", + "[09/07 10:08:53] HydroCNHS [INFO] Start a HydroCNHS simulation.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "0it [00:00, ?it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 10:08:55] HydroCNHS [INFO] \n", + "Compute rainfall-runoffs for 12053 time steps.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "12053it [00:02, 4530.33it/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[09/07 10:08:56] HydroCNHS [INFO] Complete HydroCNHS simulation! [00:00:10]\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "### Run a simulation.\n", + "model = hydrocnhs.Model(os.path.join(wd, \"Cali_HydroABMModel_gwlf_KGE\", \"Best_HydroABMModel_gwlf_KGE.yaml\"))\n", + "Q = model.run(temp, prec, pet)\n", + "sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[[\"WSLO\",\"DLLO\"]]\n", + "sim_Q_D[\"ResAgt\"] = model.dc.ResAgt[\"Release\"]\n", + "sim_Q_D[\"DivAgt\"] = model.dc.DivAgt[\"Diversion\"]\n", + "sim_Q_M = sim_Q_D[[\"WSLO\",\"DLLO\",\"ResAgt\",\"DivAgt\"]].resample(\"MS\").mean()\n", + "### Plot\n", + "fig, axes = plt.subplots(nrows=4, sharex=True)\n", + "axes = axes.flatten()\n", + "x = sim_Q_M.index\n", + "axes[0].plot(x, sim_Q_M[\"DLLO\"], label=\"$M_{gwlf}$\")\n", + "axes[1].plot(x, sim_Q_M[\"WSLO\"], label=\"$M_{gwlf}$\")\n", + "axes[2].plot(x, sim_Q_M[\"ResAgt\"], label=\"$M_{gwlf}$\")\n", + "axes[3].plot(x, sim_Q_M[\"DivAgt\"], label=\"$M_{gwlf}$\")\n", + "\n", + "axes[0].plot(x, obv_flow_data[\"DLLO\"], ls=\"--\", lw=1, color=\"black\", label=\"Obv\")\n", + "axes[1].plot(x, obv_flow_data[\"WSLO\"], ls=\"--\", lw=1, color=\"black\", label=\"Obv\")\n", + "axes[2].plot(x, obv_flow_data[\"ResAgt\"], ls=\"--\", lw=1, color=\"black\", label=\"Obv\")\n", + "axes[3].plot(x, obv_flow_data[\"DivAgt\"], ls=\"--\", lw=1, color=\"black\", label=\"Obv\")\n", + "\n", + "axes[0].set_ylim([0,75])\n", + "axes[1].set_ylim([0,230])\n", + "axes[2].set_ylim([0,23])\n", + "axes[3].set_ylim([0,2])\n", + "\n", + "axes[0].set_ylabel(\"DLLO\\n($m^3/s$)\")\n", + "axes[1].set_ylabel(\"WSLO\\n($m^3/s$)\")\n", + "axes[2].set_ylabel(\"Release\\n($m^3/s$)\")\n", + "axes[3].set_ylabel(\"Diversion\\n($m^3/s$)\")\n", + "\n", + "axes[0].axvline(pd.to_datetime(\"2006-1-1\"), color=\"grey\", ls=\"-\", lw=1)\n", + "axes[1].axvline(pd.to_datetime(\"2006-1-1\"), color=\"grey\", ls=\"-\", lw=1)\n", + "axes[2].axvline(pd.to_datetime(\"2006-1-1\"), color=\"grey\", ls=\"-\", lw=1)\n", + "axes[3].axvline(pd.to_datetime(\"2006-1-1\"), color=\"grey\", ls=\"-\", lw=1)\n", + "\n", + "axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9)\n", + "\n", + "fig.align_ylabels(axes)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cnhs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/HydroABM_example/Main_HydroABM.py b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/archive_main_HydroABM.py similarity index 51% rename from tutorials/HydroABM_example/Main_HydroABM.py rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/archive_main_HydroABM.py index f5daf39..9f62a70 100644 --- a/tutorials/HydroABM_example/Main_HydroABM.py +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/archive_main_HydroABM.py @@ -1,60 +1,84 @@ import os -import HydroCNHS +import hydrocnhs + prj_path, this_filename = os.path.split(__file__) -#%% =========================================================================== +# %% =========================================================================== # Step 1: Create a model configuration file # ============================================================================= ### Initialize a model builder object. wd = prj_path -mb = HydroCNHS.ModelBuilder(wd) +mb = hydrocnhs.ModelBuilder(wd) ### Setup a water system simulation information mb.set_water_system(start_date="1981/1/1", end_date="2013/12/31") ### Setup land surface model (rainfall-runoff model) # Here we have seven subbasins and we select GWLF as the rainfall-runoff model. -outlet_list = ['HaggIn', 'TRTR', 'DLLO', 'TRGC', 'DAIRY', 'RCTV', 'WSLO'] -area_list = [10034.2408, 329.8013, 22238.4391, 24044.6363, 59822.7546, - 19682.6046, 47646.8477] +outlet_list = ["HaggIn", "TRTR", "DLLO", "TRGC", "DAIRY", "RCTV", "WSLO"] +area_list = [ + 10034.2408, + 329.8013, + 22238.4391, + 24044.6363, + 59822.7546, + 19682.6046, + 47646.8477, +] lat_list = [45.469, 45.458, 45.475, 45.502, 45.520, 45.502, 45.350] -mb.set_rainfall_runoff(outlet_list=outlet_list,area_list=area_list, - lat_list=lat_list, runoff_model="GWLF") +mb.set_rainfall_runoff( + outlet_list=outlet_list, area_list=area_list, lat_list=lat_list, runoff_model="GWLF" +) ### Setup routing outlets -# Add WSLO -mb.set_routing_outlet(routing_outlet="WSLO", - upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], - flow_length_list=[80064.864, 70988.164, 60398.680, 0]) -# Add TRGC -mb.set_routing_outlet(routing_outlet="TRGC", - upstream_outlet_list=["DLLO", "TRGC"], - flow_length_list=[11748.211, 0]) -# Add DLLO +# Add WSLO +mb.set_routing_outlet( + routing_outlet="WSLO", + upstream_outlet_list=["TRGC", "DAIRY", "RCTV", "WSLO"], + flow_length_list=[80064.864, 70988.164, 60398.680, 0], +) +# Add TRGC +mb.set_routing_outlet( + routing_outlet="TRGC", + upstream_outlet_list=["DLLO", "TRGC"], + flow_length_list=[11748.211, 0], +) +# Add DLLO # Specify that ResAgt is an instream object. -mb.set_routing_outlet(routing_outlet="DLLO", - upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], - flow_length_list=[9656.064, 30899.4048, 0], - instream_objects=["ResAgt"]) -# Add HaggIn -mb.set_routing_outlet(routing_outlet="HaggIn", - upstream_outlet_list=["HaggIn"], - flow_length_list=[0]) +mb.set_routing_outlet( + routing_outlet="DLLO", + upstream_outlet_list=["ResAgt", "TRTR", "DLLO"], + flow_length_list=[9656.064, 30899.4048, 0], + instream_objects=["ResAgt"], +) +# Add HaggIn +mb.set_routing_outlet( + routing_outlet="HaggIn", upstream_outlet_list=["HaggIn"], flow_length_list=[0] +) ### Setup ABM mb.set_ABM(abm_module_folder_path=wd, abm_module_name="TRB_ABM.py") -mb.add_agent(agt_type_class="Reservoir_AgtType", agt_name="ResAgt", - api=mb.api.Dam, - link_dict={"HaggIn": -1, "ResAgt": 1}, - dm_class="ReleaseDM") -mb.add_agent(agt_type_class="Diversion_AgType", agt_name="DivAgt", - api=mb.api.RiverDiv, - link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, - dm_class="DivertDM", - par_dict={"ReturnFactor": [-99], "a": -99, "b":-99}) -mb.add_agent(agt_type_class="Pipe_AgType", agt_name="PipeAgt", - api=mb.api.Conveying, - link_dict={"TRTR": 1}, - dm_class="TransferDM") +mb.add_agent( + agt_type_class="Reservoir_AgtType", + agt_name="ResAgt", + api=mb.api.Dam, + link_dict={"HaggIn": -1, "ResAgt": 1}, + dm_class="ReleaseDM", +) +mb.add_agent( + agt_type_class="Diversion_AgType", + agt_name="DivAgt", + api=mb.api.RiverDiv, + link_dict={"TRGC": -1, "WSLO": ["ReturnFactor", 0, "Plus"]}, + dm_class="DivertDM", + par_dict={"ReturnFactor": [-99], "a": -99, "b": -99}, +) +mb.add_agent( + agt_type_class="Pipe_AgType", + agt_name="PipeAgt", + api=mb.api.Conveying, + link_dict={"TRTR": 1}, + dm_class="TransferDM", +) ### Print the model in the console mb.print_model() @@ -63,59 +87,64 @@ mb.write_model_to_yaml(filename="HydroABMModel.yaml") mb.gen_ABM_module_template() -#%% =========================================================================== +# %% =========================================================================== # Step 2: Populate a model configuration file # ============================================================================= # Nothing need to be manually modified in this example. -#%% =========================================================================== +# %% =========================================================================== # Step 3: Program ABM module (.py) # ============================================================================= -# We provide a complete ABM module for the TRB example (i.e., +# We provide a complete ABM module for the TRB example (i.e., # TRB_ABM_complete.py). Theorical details can be found in Lin et al. (2022). -#%% =========================================================================== -# Step 4: Run a calibration +# %% =========================================================================== +# Step 4: Run a calibration # ============================================================================= -import matplotlib.pyplot as plt -import pandas as pd -import HydroCNHS.calibration as cali +import matplotlib.pyplot as plt +import pandas as pd +import hydrocnhs.calibration as cali from copy import deepcopy # Load climate data -temp = pd.read_csv(os.path.join(wd,"Data","Temp_degC.csv"), - index_col=["Date"]).to_dict(orient="list") -prec = pd.read_csv(os.path.join(wd,"Data","Prec_cm.csv"), - index_col=["Date"]).to_dict(orient="list") -pet = pd.read_csv(os.path.join(wd,"Data","Pet_cm.csv"), - index_col=["Date"]).to_dict(orient="list") +temp = pd.read_csv( + os.path.join(wd, "Data", "Temp_degC.csv"), index_col=["Date"] +).to_dict(orient="list") +prec = pd.read_csv(os.path.join(wd, "Data", "Prec_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) +pet = pd.read_csv(os.path.join(wd, "Data", "Pet_cm.csv"), index_col=["Date"]).to_dict( + orient="list" +) # Load flow gauge monthly data at WSLO -obv_flow_data = pd.read_csv(os.path.join(wd,"Data","Cali_M_cms.csv"), - index_col=["Date"], parse_dates=["Date"]) +obv_flow_data = pd.read_csv( + os.path.join(wd, "Data", "Cali_M_cms.csv"), index_col=["Date"], parse_dates=["Date"] +) # Load model -model_dict = HydroCNHS.load_model(os.path.join(wd, "HydroABMModel.yaml")) +model_dict = hydrocnhs.load_model(os.path.join(wd, "HydroABMModel.yaml")) # Change the ABM module to the complete one. model_dict["WaterSystem"]["ABM"]["Modules"] = ["TRB_ABM_complete.py"] # Generate default parameter bounds -df_list, df_name = HydroCNHS.write_model_to_df(model_dict) -par_bound_df_list, df_name = HydroCNHS.gen_default_bounds(model_dict) +df_list, df_name = hydrocnhs.write_model_to_df(model_dict) +par_bound_df_list, df_name = hydrocnhs.gen_default_bounds(model_dict) # Modify the default bounds of ABM df_abm_bound = par_bound_df_list[2] -df_abm_bound.loc["ReturnFactor.0", [('DivAgt', 'Diversion_AgType')]] = "[0, 0.5]" -df_abm_bound.loc["a", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]" -df_abm_bound.loc["b", [('DivAgt', 'Diversion_AgType')]] = "[-1, 1]" +df_abm_bound.loc["ReturnFactor.0", [("DivAgt", "Diversion_AgType")]] = "[0, 0.5]" +df_abm_bound.loc["a", [("DivAgt", "Diversion_AgType")]] = "[-1, 1]" +df_abm_bound.loc["b", [("DivAgt", "Diversion_AgType")]] = "[-1, 1]" # Create convertor for calibration converter = cali.Convertor() cali_inputs = converter.gen_cali_inputs(wd, df_list, par_bound_df_list) formatter = converter.formatter + # Code evaluation function for GA algorthm def evaluation(individual, info): cali_wd, current_generation, ith_individual, formatter, _ = info @@ -128,17 +157,17 @@ def evaluation(individual, info): model = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model, df, s, "Pars") + model = hydrocnhs.load_df_to_model_dict(model, df, s, "Pars") ##### Run simuluation - model = HydroCNHS.Model(model, name) + model = hydrocnhs.Model(model, name) Q = model.run(temp, prec, pet) ##### Get simulation data # Streamflow of routing outlets. - cali_target = ["WSLO","DLLO","ResAgt","DivAgt"] + cali_target = ["WSLO", "DLLO", "ResAgt", "DivAgt"] cali_period = ("1981-1-1", "2005-12-31") - sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] + sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] # Resample the daily simulation output to monthly outputs. @@ -146,30 +175,36 @@ def evaluation(individual, info): KGEs = [] for target in cali_target: - KGEs.append(HydroCNHS.Indicator().KGE( - x_obv=obv_flow_data[cali_period[0]:cali_period[1]][[target]], - y_sim=sim_Q_M[cali_period[0]:cali_period[1]][[target]])) - - fitness = sum(KGEs)/4 + KGEs.append( + hydrocnhs.Indicator().KGE( + x_obv=obv_flow_data[cali_period[0] : cali_period[1]][[target]], + y_sim=sim_Q_M[cali_period[0] : cali_period[1]][[target]], + ) + ) + + fitness = sum(KGEs) / 4 return (fitness,) -config = {'min_or_max': 'max', - 'pop_size': 100, - 'num_ellite': 1, - 'prob_cross': 0.5, - 'prob_mut': 0.15, - 'stochastic': False, - 'max_gen': 100, - 'sampling_method': 'LHC', - 'drop_record': False, - 'paral_cores': -1, - 'paral_verbose': 1, - 'auto_save': True, - 'print_level': 1, - 'plot': True} + +config = { + "min_or_max": "max", + "pop_size": 100, + "num_ellite": 1, + "prob_cross": 0.5, + "prob_mut": 0.15, + "stochastic": False, + "max_gen": 100, + "sampling_method": "LHC", + "drop_record": False, + "paral_cores": -1, + "paral_verbose": 1, + "auto_save": True, + "print_level": 1, + "plot": True, +} seed = 5 -rn_gen = HydroCNHS.create_rn_gen(seed) +rn_gen = hydrocnhs.create_rn_gen(seed) ga = cali.GA_DEAP(evaluation, rn_gen) ga.set(cali_inputs, config, formatter, name="Cali_HydroABMModel_gwlf_KGE") ga.run() @@ -181,19 +216,21 @@ def evaluation(individual, info): model_best = deepcopy(model_dict) for i, df in enumerate(df_list): s = df_name[i].split("_")[0] - model = HydroCNHS.load_df_to_model_dict(model_best, df, s, "Pars") -HydroCNHS.write_model(model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) + model = hydrocnhs.load_df_to_model_dict(model_best, df, s, "Pars") +hydrocnhs.write_model( + model_best, os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml") +) -#%% =========================================================================== -# Step 5: Run a simulation +# %% =========================================================================== +# Step 5: Run a simulation # ============================================================================= ### Run a simulation. -model = HydroCNHS.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) +model = hydrocnhs.Model(os.path.join(ga.cali_wd, "Best_HydroABMModel_gwlf_KGE.yaml")) Q = model.run(temp, prec, pet) -sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO","DLLO"]] +sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[["WSLO", "DLLO"]] sim_Q_D["ResAgt"] = model.dc.ResAgt["Release"] sim_Q_D["DivAgt"] = model.dc.DivAgt["Diversion"] -sim_Q_M = sim_Q_D[["WSLO","DLLO","ResAgt","DivAgt"]].resample("MS").mean() +sim_Q_M = sim_Q_D[["WSLO", "DLLO", "ResAgt", "DivAgt"]].resample("MS").mean() ### Plot fig, axes = plt.subplots(nrows=4, sharex=True) axes = axes.flatten() @@ -208,10 +245,10 @@ def evaluation(individual, info): axes[2].plot(x, obv_flow_data["ResAgt"], ls="--", lw=1, color="black", label="Obv") axes[3].plot(x, obv_flow_data["DivAgt"], ls="--", lw=1, color="black", label="Obv") -axes[0].set_ylim([0,75]) -axes[1].set_ylim([0,230]) -axes[2].set_ylim([0,23]) -axes[3].set_ylim([0,2]) +axes[0].set_ylim([0, 75]) +axes[1].set_ylim([0, 230]) +axes[2].set_ylim([0, 23]) +axes[3].set_ylim([0, 2]) axes[0].set_ylabel("DLLO\n($m^3/s$)") axes[1].set_ylabel("WSLO\n($m^3/s$)") @@ -226,19 +263,3 @@ def evaluation(individual, info): axes[0].legend(ncol=3, bbox_to_anchor=(1, 1.5), fontsize=9) fig.align_ylabels(axes) - - - - - - - - - - - - - - - - diff --git a/tutorials/HydroABM_example/HydroABMModel.yaml b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/model.yaml similarity index 92% rename from tutorials/HydroABM_example/HydroABMModel.yaml rename to tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/model.yaml index 796e711..d2bbe34 100644 --- a/tutorials/HydroABM_example/HydroABMModel.yaml +++ b/tutorials/Tutorial_2-Build_a_Coupled_Natural_Human_System/model.yaml @@ -1,4 +1,5 @@ -Path: {WD: 'C:\...\HydroABM_example', Modules: 'C:\...\HydroABM_example'} +Path: {WD: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_2-Build_a_Coupled_Natural_Human_System', + Modules: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_2-Build_a_Coupled_Natural_Human_System'} WaterSystem: StartDate: 1981/1/1 EndDate: 2013/12/31 @@ -9,9 +10,9 @@ WaterSystem: RainfallRunoff: GWLF Routing: Lohmann ABM: - Modules: [TRB_ABM.py] + Modules: [TRB_ABM_complete.py] InstitDMClasses: {} - DMClasses: [DivertDM, TransferDM, ReleaseDM] + DMClasses: [TransferDM, DivertDM, ReleaseDM] DamAPI: [Reservoir_AgtType] RiverDivAPI: [Diversion_AgType] InsituAPI: [] diff --git a/tutorials/Tutorial_3-Sediment_Routing/Calibrated_model/Best_hydro_sed_Sb5_seed3_iter100.yaml b/tutorials/Tutorial_3-Sediment_Routing/Calibrated_model/Best_hydro_sed_Sb5_seed3_iter100.yaml new file mode 100644 index 0000000..6a5dfe0 --- /dev/null +++ b/tutorials/Tutorial_3-Sediment_Routing/Calibrated_model/Best_hydro_sed_Sb5_seed3_iter100.yaml @@ -0,0 +1,115 @@ +Path: {WD: '', + Modules: ''} +WaterSystem: + StartDate: 1984/01/01 + EndDate: 2020/12/31 + DataLength: 13515 + NumSubbasins: 3 + Outlets: [Sb5_1, Sb5_2, Sb5_3] + NodeGroups: [] + RainfallRunoff: GWLF + Routing: Lohmann + ABM: null + Sediment: {StartMonth: 4} +RainfallRunoff: + Sb5_1: + Inputs: {Area: 249499.9087, Latitude: 40.12180448, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 65.23175269037966, IS: 0.47749908898073096, Res: 0.3659895631593886, + Sep: 0.3633159394195378, Alpha: 0.7419226229765157, Beta: 0.1959818063023802, + Ur: 11.505259834168495, Df: 0.7421410045668849, Kc: 1.3539117069510187} + Sb5_2: + Inputs: {Area: 256889.9875, Latitude: 40.54906266, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 93.85453198122858, IS: 0.17243437452381424, Res: 0.46944613814366437, + Sep: 0.07316910705660121, Alpha: 0.24983744725113463, Beta: 0.123830090833294, + Ur: 10.126577775096258, Df: 0.3661399859040953, Kc: 0.8920513342125365} + Sb5_3: + Inputs: {Area: 375813.597, Latitude: 40.44323525, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: 78.51925904218974, IS: 0.4523435002429107, Res: 0.3022744464715537, + Sep: 0.12552649653264622, Alpha: 0.7995202708115519, Beta: 0.6215453116474725, + Ur: 10.809675532472824, Df: 0.8148548884618968, Kc: 1.4928042546415246} +Routing: + Sb5_3: + Sb5_1: + Inputs: {FlowLength: 113610, InstreamControl: false} + Pars: {GShape: 84.77644603524541, GScale: 7.4310412670361226, Velo: 30.179647349450626, + Diff: 3237.726083905347} + Sb5_2: + Inputs: {FlowLength: 113610, InstreamControl: false} + Pars: {GShape: 93.94506480924876, GScale: 7.672320454129773, Velo: 46.62356211015884, + Diff: 963.9297653203345} + Sb5_3: + Inputs: {FlowLength: 0, InstreamControl: false} + Pars: {GShape: 5.0448472105360604, GScale: 97.36426519326962, Velo: null, Diff: null} +Sediment: + Sb5_1: + Inputs: + IDs: [239, 240, 241, 242, 243, 244, 245, 246, 247, 248] + Areas: [24007.88, 41977.72, 6652.29, 5092.57, 60572.95, 9214.4, 34662.85, 7131.67, + 48171.69, 6438.16] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + CP: 1.0e-07 + Sa: 1.808757770249474 + Sb: 2.7927642237781205 + Sq: 1.6764064182245135 + K: [0.279878, 0.252931, 0.257783, 0.278456, 0.257783, 0.276558, 0.283873, 0.280144, + 0.278456, 0.286623] + Ac: 0.12 + Aw: 0.3 + LS: [12.1108, 12.0538, 28.3945, 23.1777, 22.5051, 11.1695, 13.1847, 17.2242, + 16.5769, 16.4281] + DR: 0.17679259 + Sb5_2: + Inputs: + IDs: [249, 250, 251, 252, 253, 254, 255, 256, 257, 258] + Areas: [20326.24, 16197.82, 43399.29, 42483.32, 6107.14, 18852.39, 7985.62, + 58730.64, 10727.11, 15446.86] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + CP: 0.001517237249800152 + Sa: 1.8054791114847888 + Sb: 3.9104314945346528 + Sq: 1.963288662380977 + K: [0.301795, 0.292281, 0.278817, 0.277941, 0.255192, 0.27601, 0.324109, 0.278304, + 0.249827, 0.289758] + Ac: 0.12 + Aw: 0.3 + LS: [18.9785, 14.7366, 14.6366, 14.1249, 11.8873, 16.0428, 18.5651, 13.6198, + 16.6243, 4.50807] + DR: 0.176148707 + Sb5_3: + Inputs: + IDs: [259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, + 273, 274, 275, 276, 277] + Areas: [27033.29, 47636.37, 25739.15, 27064.9, 8795.92, 15053.58, 14495.28, + 5491.19, 33454.16, 10075.8, 9693.86, 19016.68, 17613.84, 15808.73, 13911.57, + 32852.56, 13578.69, 9694.49, 6204.17] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + CP: 0.05570174865133581 + Sa: 1.6590489249396967 + Sb: 1.0 + Sq: 2.289293468589767 + K: [0.284577, 0.251023, 0.267515, 0.270688, 0.250267, 0.261403, 0.25683, 0.26481, + 0.271265, 0.296323, 0.293839, 0.272687, 0.28643, 0.252859, 0.284577, 0.261502, + 0.273619, 0.252859, 0.286623] + Ac: 0.12 + Aw: 0.3 + LS: [23.6745, 14.1201, 17.3279, 13.684, 21.1632, 16.2043, 28.0488, 15.0742, + 18.1346, 11.9816, 35.2188, 14.2318, 34.1431, 24.2254, 24.5473, 37.8374, 34.6398, + 15.2428, 11.9012] + DR: 0.167967902 +SystemParsedData: + SimSeq: [Sb5_3] + AgSimSeq: null + RoutingOutlets: [Sb5_3] + DamAgents: null + RiverDivAgents: null + InsituAgents: null + ConveyingAgents: null + BackTrackingDict: {} + Edges: [] + ConveyToNodes: [] diff --git a/tutorials/Tutorial_3-Sediment_Routing/Data/SRB_HU8.csv b/tutorials/Tutorial_3-Sediment_Routing/Data/SRB_HU8.csv new file mode 100644 index 0000000..3d0ecf6 --- /dev/null +++ b/tutorials/Tutorial_3-Sediment_Routing/Data/SRB_HU8.csv @@ -0,0 +1,19 @@ +ID,name,huc8,Shape_Leng,Shape_Area,area_ha,central_la,central_lo,Note,states,DR +Sb1_1,Tioga,2050104,4.663067812,0.389188486,358078.7439,42.02266661,-77.36800825, ,"NY,PA",0.168985928 +Sb1_2,Chemung,2050105,5.117947789,0.341821827,313421.6133,42.22811557,-77.05589502, ,"NY,PA",0.171823175 +Sb2_1,Chenango,2050102,4.95808096,0.456177185,415731.8658,42.57801563,-75.80664796, ,NY,0.165861731 +Sb2_2,Upper Susquehanna,2050101,6.487120381,0.648157514,591892.2299,42.44314663,-75.24567256, ,"NY,PA",0.158696566 +Sb2_3,Owego-Wappasening,2050103,3.882701278,0.294530052,270195.2366,42.16795205,-76.28542546, ,"NY,PA",0.175040339 +Sb3_1,Upper Susquehanna-Tunkhannock,2050106,5.292372621,0.561490618,518988.1886,41.67885826,-76.16999297, ,PA,0.161325557 +Sb3_2,Upper Susquehanna-Lackawanna,2050107,6.414272044,0.490664349,456990.7098,41.17484895,-76.09862054, ,PA,0.163911504 +Sb4_1,Upper West Branch Susquehanna,2050201,4.286974329,0.441849125,413914.1238,40.91003613,-78.41911165, ,PA,0.165952606 +Sb4_2,Sinnemahoning,2050202,3.655510406,0.288491157,268048.2395,41.43913889,-78.24623586, ,PA,0.175214982 +Sb4_3,Bald Eagle,2050204,3.400543187,0.214284484,200370.2384,40.98238725,-77.72441121, ,PA,0.181705765 +Sb4_4,Middle West Branch Susquehanna,2050203,3.190208417,0.218595691,203212.7785,41.36651184,-77.68389871, ,PA,0.181386091 +Sb4_5,Pine,2050205,3.942665084,0.274344192,254051.5738,41.60742124,-77.45007638, ,PA,0.176393518 +Sb4_6,Lower West Branch Susquehanna,2050206,5.043790373,0.503610611,468474.2792,41.28098696,-76.85105374, ,PA,0.163403795 +Sb5_1,Raystown,2050303,3.992386375,0.263269588,249499.9087,40.12180448,-78.3931208, ,PA,0.17679259 +Sb5_2,Upper Juniata,2050302,3.429897294,0.272833544,256889.9875,40.54906266,-78.19063799, ,PA,0.176148707 +Sb5_3,Lower Juniata,2050304,4.719614233,0.398749627,375813.597,40.44323525,-77.61804467, ,PA,0.167967902 +Sb6_1,Lower Susquehanna-Penns,2050301,5.493886874,0.399845471,375011.1554,40.73733407,-76.92880222, ,PA,0.168012786 +Sb6_2,Lower Susquehanna-Swatara,2050305,6.402928415,0.746659833,706083.9217,40.1768873,-76.96361285,We extend the area to fit 1576000 outlet.,PA,0.155235419 diff --git a/tutorials/Tutorial_3-Sediment_Routing/Data/TSS_M_Mg.csv b/tutorials/Tutorial_3-Sediment_Routing/Data/TSS_M_Mg.csv new file mode 100644 index 0000000..901cf37 --- /dev/null +++ b/tutorials/Tutorial_3-Sediment_Routing/Data/TSS_M_Mg.csv @@ -0,0 +1,433 @@ +,1531000,1515000,1540500,1553500,1567000,1576000 +10/1/1984,,,1109.440673,1252.866463,244.6675248, +11/1/1984,,,3388.33224,5184.55656,4953.22464, +12/1/1984,,,50058.41312,29388.22568,13358.2844, +1/1/1985,,,34731.53944,13569.20468,928.049232, +2/1/1985,,,56009.54016,34672.57248,25528.15776, +3/1/1985,,,73962.71152,38949.94504,8900.835816, +4/1/1985,,,38646.0384,49396.1688,23541.4248, +5/1/1985,,,13850.43172,4288.71236,9800.762344, +6/1/1985,,,9539.03976,5021.26344,3497.19432, +7/1/1985,,,4907.411848,1799.853056,480.8982384, +8/1/1985,,,5301.129704,618.699488,638.3853808, +9/1/1985,,,44905.608,1905.0864,235.414248, +10/1/1985,,,24607.366,1212.088542,535.7375112, +11/1/1985,,,70079.964,79197.1632,47763.2376, +12/1/1985,,,34028.47184,28825.7716,2516.982008, +1/1/1986,,,66088.3544,16873.6224,1203.651731, +2/1/1986,,,54993.49408,29592.34208,31878.44576, +3/1/1986,,,884459.0408,192640.5224,147644.196, +4/1/1986,,,53886.7296,32114.3136,9089.98368, +5/1/1986,,,33887.85832,4879.289144,4105.914784, +6/1/1986,,,51029.1,15512.8464,5742.47472, +7/1/1986,,,6622.896792,4330.896416,3163.8042, +8/1/1986,,,38246.87744,1996.711984,1434.257904, +9/1/1986,,,2463.00456,1632.9312,304.813824, +10/1/1986,,,12598.97139,6243.240288,285.4454456,7340.025744 +11/1/1986,,,153767.688,39734.6592,8409.59568,292566.84 +12/1/1986,,,38949.94504,27279.02288,7874.35712,161705.548 +1/1/1987,,,5694.84756,5329.252408,3121.620144,25029.20656 +2/1/1987,,,1968.58928,1409.763936,2502.013472,15240.6912 +3/1/1987,,,110240.9997,28544.54456,5230.822944,179985.3056 +4/1/1987,,,398707.368,57969.0576,84912.4224,711685.848 +5/1/1987,,,12528.66463,9139.8788,15467.4872,53854.97816 +6/1/1987,,,5796.90576,3129.7848,2136.41832,33883.3224 +7/1/1987,,,22216.93616,7086.921408,2123.264152,25591.66064 +8/1/1987,,,2938.822568,662.2896792,883.0529056,11572.4927 +9/1/1987,,,86817.5088,13879.9152,3959.85816,145603.032 +10/1/1987,,,9505.473952,8914.897168,520.270024,19264.05224 +11/1/1987,,,16873.6224,2476.61232,1159.381152,21228.1056 +12/1/1987,,,29247.61216,8985.203928,5385.497816,77196.82248 +1/1/1988,,,3627.828816,3135.681496,8661.792832,22076.32264 +2/1/1988,,,52485.13032,104312.5522,12970.00965,310438.3648 +3/1/1988,,,45840.00752,25029.20656,5540.172688,124021.1246 +4/1/1988,,,10382.72088,5892.16008,8355.16464,67766.6448 +5/1/1988,,,154674.872,60041.97304,53573.75112,561047.9448 +6/1/1988,,,8355.16464,1456.03032,1973.1252,20955.9504 +7/1/1988,,,6693.203552,735.4087096,511.8332128,16029.94128 +8/1/1988,,,4274.651008,28263.31752,431.6835064,13611.38874 +9/1/1988,,,14424.2256,2748.76752,1015.138896,25718.6664 +10/1/1988,,,4682.430216,1130.532701,165.9239536,7340.025744 +11/1/1988,,,28984.5288,5688.04368,1918.69416,50484.7896 +12/1/1988,,,3698.135576,1546.74872,1701.423592,7058.798704 +1/1/1989,,,1954.527928,6088.565416,3515.338,18982.8252 +2/1/1989,,,11608.32646,17272.78336,3416.454944,48008.17728 +3/1/1989,,,51605.16184,65385.2868,18982.8252,112631.4295 +4/1/1989,,,174179.328,62595.696,3687.70296,368770.296 +5/1/1989,,,291069.9864,123739.8976,46402.4616,784623.4416 +6/1/1989,,,231331.92,114441.2616,81918.7152,453138.408 +7/1/1989,,,25029.20656,9899.191808,120646.4002,224981.632 +8/1/1989,,,3360.663128,1006.792803,4879.289144,17717.30352 +9/1/1989,,,5851.3368,710.325072,989.284152,14968.536 +10/1/1989,,,44996.3264,15748.71424,5933.890544,119802.719 +11/1/1989,,,25718.6664,9620.68632,2449.3968,74570.5248 +12/1/1989,,,4555.878048,1019.44802,133.7234575,8746.160944 +1/1/1990,,,53995.59168,24747.97952,5385.497816,143425.7904 +2/1/1990,,,123449.5987,27560.24992,17399.78912,227340.3104 +3/1/1990,,,32341.1096,11909.96514,2826.331752,63979.1516 +4/1/1990,,,41231.5128,32658.624,1823.43984,172818.552 +5/1/1990,,,144831.9256,20670.18744,16873.6224,267165.688 +6/1/1990,,,18234.3984,4027.89696,14015.9928,62323.5408 +7/1/1990,,,14905.03312,12430.23517,6636.958144,10250.72561 +8/1/1990,,,12430.23517,7761.866304,4626.184808,38809.33152 +9/1/1990,,,6300.39288,5810.51352,2000.34072,25854.744 +10/1/1990,,,406373.0728,89852.03928,64963.44624,892895.852 +11/1/1990,,,75931.3008,9171.63024,4027.89696,69943.8864 +12/1/1990,,,188422.1168,71291.05464,47246.14272,293882.2568 +1/1/1991,,,150456.4664,65244.67328,31919.26904,367001.2872 +2/1/1991,,,29973.35936,25020.13472,7112.32256,96524.3776 +3/1/1991,,,161705.548,76774.98192,20951.41448,310755.8792 +4/1/1991,,,74842.68,16329.312,12478.31592,125463.5472 +5/1/1991,,,31778.65552,5891.706488,4766.798328,50058.41312 +6/1/1991,,,3510.80208,601.462992,729.375936,8219.08704 +7/1/1991,,,815.558416,542.7681872,593.3890544,4134.037488 +8/1/1991,,,1068.662752,264.3534176,527.3007,3740.319632 +9/1/1991,,,1243.749264,239.496576,274.876752,2898.45288 +10/1/1991,,,968.8271528,357.1583408,171.5484944,2573.227416 +11/1/1991,,,6382.03944,1019.221224,383.738832,6803.88 +12/1/1991,,,8183.706864,2629.472824,608.8565416,29669.45272 +1/1/1992,,,17998.53056,5441.743224,416.2160192,23060.61728 +2/1/1992,,,9628.850976,4630.267136,1068.118442,19336.62696 +3/1/1992,,,264353.4176,47386.75624,12697.40086,471055.292 +4/1/1992,,,110631.0888,30753.5376,12260.59176,197312.52 +5/1/1992,,,45418.16696,3937.17856,5722.970264,69603.6924 +6/1/1992,,,36468.7968,3157.00032,4272.83664,68038.8 +7/1/1992,,,14342.57904,39653.01264,1003.980533,51042.70776 +8/1/1992,,,25872.88768,12570.84869,355.7522056,55682.95392 +9/1/1992,,,16873.6224,7810.85424,586.494456,29528.8392 +10/1/1992,,,17998.53056,2784.147696,271.3840936,20670.18744 +11/1/1992,,,55247.5056,8464.02672,997.448808,156489.24 +12/1/1992,,,31356.81496,10475.70724,6271.362992,86617.92832 +1/1/1993,,,92945.53672,24326.13896,7747.804952,178579.1704 +2/1/1993,,,3911.777408,1727.278336,869.989456,3860.975104 +3/1/1993,,,617293.3528,187015.9816,76493.75488,793060.2528 +4/1/1993,,,1442422.56,289845.288,78380.6976,2150026.08 +5/1/1993,,,28403.93104,12205.25354,6088.565416,130770.5736 +6/1/1993,,,3565.23312,650.450928,1918.69416,15240.6912 +7/1/1993,,,1476.44196,681.975572,656.6651384,5497.988632 +8/1/1993,,,2474.797952,1490.503312,423.2466952,8127.461456 +9/1/1993,,,3225.03912,3306.68568,548.392728,9144.41472 +10/1/1993,,,2966.945272,2446.675248,327.6295016,8914.897168 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+1,Sb1_1_1,Sb1_1,-77.147093574,42.048807128,11.2048,0.30729,14724.2,N36101SU4_0690_0650,163594,5384,37332,42716,0.032910742,0.228199078,0.261109821,-31948,1,0.12,0.3 +2,Sb1_1_2,Sb1_1,-77.5001444753,42.0362204787,8.118,0.303681,19402.78,N36101SU2_0740_0741,215532,5661,87498,93159,0.026265241,0.40596292,0.432228161,-81837,2,0.12,0.3 +3,Sb1_1_3,Sb1_1,-77.4052175336,41.9489610114,16.9352,0.304004,30349.62,N42117SU2_0740_0741,337145,12146,94271,106417,0.036026042,0.279615596,0.315641638,-82125,3,0.12,0.3 +4,Sb1_1_4,Sb1_1,-77.6642300739,41.9444373295,10.5401,0.307567,11793.26,N42105SU1_0820_0740,130892,1742,40880,42622,0.013308682,0.312318553,0.325627235,-39138,4,0.12,0.3 +5,Sb1_1_5,Sb1_1,-77.5633398916,41.9029348838,13.9228,0.305088,9663.88,N42117SU1_0820_0740,107356,1564,35754,37318,0.014568352,0.33304147,0.347609822,-34190,5,0.12,0.3 +6,Sb1_1_6,Sb1_1,-77.0526683577,41.8037541691,17.0335,0.297904,11324.72,N42117SU2_0830_0831,125836,1452,42324,43776,0.011538828,0.336342541,0.347881369,-40872,6,0.12,0.3 +7,Sb1_1_7,Sb1_1,-77.0235845769,41.8574194289,21.915,0.300611,22679.56,N42117SU3_0831_0790,252035,1379,74164,75543,0.005471462,0.294260718,0.29973218,-72785,7,0.12,0.3 +8,Sb1_1_8,Sb1_1,-77.3088495223,41.8490783793,12.9438,0.29471,27831.66,N42117SU1_0840_0831,309211,3906,97874,101780,0.012632151,0.316528196,0.329160347,-93968,8,0.12,0.3 +9,Sb1_1_9,Sb1_1,-77.1245389838,41.9257464113,16.0936,0.300611,10961.07,N42117SU3_0790_0770,121794,3266,28870,32136,0.026815771,0.237039591,0.263855362,-25604,9,0.12,0.3 +10,Sb1_1_10,Sb1_1,-77.2237931408,42.1401707678,11.664,0.30507,6941.22,N36101SU4_0630_0650,77131,8120,17295,25415,0.10527544,0.224228909,0.32950435,-9175,10,0.12,0.3 +11,Sb1_1_11,Sb1_1,-77.4124087596,42.1013230361,8.06523,0.301281,33237.61,N36101SU2_0640_0630,369244,13664,140064,153728,0.037005341,0.379326407,0.416331748,-126400,11,0.12,0.3 +12,Sb1_1_12,Sb1_1,-77.0511155566,41.7096123006,10.158,0.29882,36502.37,N42117SU2_0920_0830,405619,2172,74481,76653,0.005354779,0.183623055,0.188977834,-72309,12,0.12,0.3 +13,Sb1_1_13,Sb1_1,-77.6503778584,42.380932493,11.0181,0.306725,19301.09,N36101SU2_0510_0570,214199,45145,32902,78047,0.210761955,0.153604825,0.36436678,12243,13,0.12,0.3 +14,Sb1_1_14,Sb1_1,-77.7957215271,42.3302970558,7.59699,0.290976,21723.1,N36003SU2_0510_0570,240988,8154,32465,40619,0.03383571,0.134716251,0.168551961,-24311,14,0.12,0.3 +15,Sb1_1_15,Sb1_1,-77.4421825055,42.2045437097,17.3524,0.29592,29589.98,N36101SU4_0580_0630,328732,14463,58884,73347,0.043996325,0.179124636,0.223120962,-44421,15,0.12,0.3 +16,Sb1_1_16,Sb1_1,-77.6401356658,42.2986938043,14.5723,0.291068,7502.38,N36101SU2_0570_0580,83350,4404,17570,21974,0.052837433,0.21079784,0.263635273,-13166,16,0.12,0.3 +17,Sb1_1_17,Sb1_1,-77.6564348754,42.1760876149,12.2621,0.293713,24675.47,N36101SU2_0620_0580,274005,7481,60106,67587,0.027302421,0.219360961,0.246663382,-52625,17,0.12,0.3 +18,Sb1_2_18,Sb1_2,-77.3692926567,42.4101747912,9.23608,0.287707,103514.38,N36101SU3_0370_0490,1149236,164494,242357,406851,0.143133351,0.210885319,0.35401867,-77863,18,0.12,0.3 +19,Sb1_2_19,Sb1_2,-77.0564215701,42.4183428574,5.3733,0.302275,10417.93,N36097SU3_0370_0490,115518,3831,33176,37007,0.033163663,0.287193338,0.320357001,-29345,19,0.12,0.3 +20,Sb1_2_20,Sb1_2,-77.0392488387,42.3219383326,8.80568,0.312524,9335.46,N36097SU3_0490_0530,103616,1574,7593,9167,0.015190704,0.073280188,0.088470893,-6019,20,0.12,0.3 +21,Sb1_2_21,Sb1_2,-77.1627920597,42.23053543,11.1862,0.268529,26050.88,N36101SU3_0490_0530,289471,13966,49769,63735,0.048246629,0.171930867,0.220177496,-35803,21,0.12,0.3 +22,Sb1_2_22,Sb1_2,-76.7158225834,41.9361888217,11.4938,0.302276,13569.79,N42015SU5_0610_0600,150822,1304,51613,52917,0.008645954,0.342211348,0.350857302,-50309,22,0.12,0.3 +23,Sb1_2_23,Sb1_2,-76.613034748,42.0805330205,14.4039,0.300871,12164.45,N36015SU5_0600_0750,135211,4542,26971,31513,0.033591941,0.199473416,0.233065357,-22429,23,0.12,0.3 +24,Sb1_2_24,Sb1_2,-76.9715414995,41.9571674613,9.24185,0.305088,8712.29,N42117SU2_0680_0610,96832,1634,44616,46250,0.016874587,0.460756775,0.477631362,-42982,24,0.12,0.3 +25,Sb1_2_25,Sb1_2,-76.8448073114,41.9524626625,11.4259,0.305079,14001.44,N42015SU2_0680_0610,155607,792,54717,55509,0.005089745,0.351635852,0.356725597,-53925,25,0.12,0.3 +26,Sb1_2_26,Sb1_2,-76.8687539456,42.0331434148,13.7903,0.307517,11486.91,N36015SU2_0680_0610,127656,3528,28690,32218,0.027636774,0.224744626,0.2523814,-25162,26,0.12,0.3 +27,Sb1_2_27,Sb1_2,-76.5910180122,41.9579690258,33.8648,0.30507,10268.51,N42015SU5_0600_0750,114113,5594,24918,30512,0.049021584,0.2183625,0.267384084,-19324,27,0.12,0.3 +28,Sb1_2_28,Sb1_2,-76.7081088475,42.0787522371,28.7397,0.305088,16206.11,N36015SU5_0610_0600,180141,8532,35282,43814,0.047362899,0.195857689,0.243220588,-26750,28,0.12,0.3 +29,Sb1_2_29,Sb1_2,-76.8305551987,42.1682198265,10.6211,0.307008,43497.55,N36015SU5_0530_0610,483099,12068,94068,106136,0.024980387,0.194717853,0.21969824,-82000,29,0.12,0.3 +30,Sb1_2_30,Sb1_2,-77.022183904,42.1511456469,13.5818,0.314761,21088.57,N36101SU5_0530_0610,234343,3975,30106,34081,0.016962316,0.128469807,0.145432123,-26131,30,0.12,0.3 +31,Sb2_1_31,Sb2_1,-75.9840540842,42.8310832857,5.89822,0.316622,10794.71,N36067SU2_0120_0260,119647,25695,17755,43450,0.214756743,0.148394862,0.363151604,7940,31,0.12,0.3 +32,Sb2_1_32,Sb2_1,-76.0748286408,42.5343345978,10.5683,0.298597,30985.09,N36023SU4_0260_0350,344489,13785,99019,112804,0.040015792,0.287437335,0.327453126,-85234,32,0.12,0.3 +33,Sb2_1_33,Sb2_1,-75.9050652724,42.6250949695,12.0532,0.297745,7062.53,N36023SU2_0110_0240,78519,3874,18849,22723,0.049338377,0.240056547,0.289394924,-14975,33,0.12,0.3 +34,Sb2_1_34,Sb2_1,-75.9481285632,42.4942229213,8.78899,0.300733,19117.34,N36023SU3_0240_0350,212557,6291,65044,71335,0.029596767,0.30600733,0.335604097,-58753,34,0.12,0.3 +35,Sb2_1_35,Sb2_1,-75.816759583,42.56278593,6.81403,0.290683,5211.96,N36017SU3_0240_0350,57951,996,12961,13957,0.017186934,0.223654467,0.2408414,-11965,35,0.12,0.3 +36,Sb2_1_36,Sb2_1,-75.4603890444,42.8211880732,8.23398,0.311916,11119.28,N36053SU1_0050_0090,123600,12766,31326,44092,0.10328479,0.253446602,0.356731392,-18560,36,0.12,0.3 +37,Sb2_1_37,Sb2_1,-75.6408354309,42.6216625518,7.22031,0.293717,15970.9,N36017SU1_0200_0270,177608,1711,34773,36484,0.009633575,0.1957851,0.205418675,-33062,37,0.12,0.3 +38,Sb2_1_38,Sb2_1,-75.4994155071,42.6247520961,11.0343,0.293292,18834.77,N36017SU3_0170_0270,209445,16289,49222,65511,0.077772208,0.235011578,0.312783786,-32933,38,0.12,0.3 +39,Sb2_1_39,Sb2_1,-76.1901116339,42.6726249402,7.14899,0.297727,23061.37,N36023SU2_0160_0260,256201,46043,54269,100312,0.179714365,0.211821968,0.391536333,-8226,39,0.12,0.3 +40,Sb2_1_40,Sb2_1,-75.6165332058,42.8466384672,6.88323,0.295228,29093.2,N36053SU2_0070_0090,323048,40621,82401,123022,0.125742924,0.255073549,0.380816473,-41780,40,0.12,0.3 +41,Sb2_1_41,Sb2_1,-75.5502473715,42.7124295984,8.84273,0.2586,11650.06,N36017SU3_0090_0170,129525,14033,37284,51317,0.108342019,0.287851766,0.396193785,-23251,41,0.12,0.3 +42,Sb2_1_42,Sb2_1,-75.5171719758,42.7661141741,8.23398,0.295229,9330.71,N36053SU3_0090_0170,103774,3052,21044,24096,0.029410064,0.202786825,0.232196889,-17992,42,0.12,0.3 +43,Sb2_1_43,Sb2_1,-75.8332458525,42.7872150432,7.91715,0.305038,8697.78,N36053SU2_0120_0260,96579,11494,15841,27335,0.119011379,0.164021164,0.283032543,-4347,43,0.12,0.3 +44,Sb2_1_44,Sb2_1,-76.0150900476,42.7006362928,11.9698,0.298414,30590.14,N36023SU2_0120_0260,340086,31567,59031,90598,0.092820639,0.173576684,0.266397323,-27464,44,0.12,0.3 +45,Sb2_1_45,Sb2_1,-75.7519473845,42.7811340673,8.53928,0.305038,9980.27,N36053SU2_0110_0240,110912,3204,20020,23224,0.028887767,0.180503462,0.209391229,-16816,45,0.12,0.3 +46,Sb2_1_46,Sb2_1,-75.6374855597,42.4384994925,9.14291,0.299354,53923.74,N36017SU4_0270_0430,599562,13226,109844,123070,0.022059437,0.183207074,0.205266511,-96618,46,0.12,0.3 +47,Sb2_1_47,Sb2_1,-76.0520972111,42.3732388213,10.8767,0.296998,8958.64,N36007SU4_0260_0350,99595,6505,37145,43650,0.065314524,0.37296049,0.438275014,-30640,47,0.12,0.3 +48,Sb2_1_48,Sb2_1,-75.9021380687,42.3250315876,12.0351,0.305152,11941.32,N36007SU4_0350_0420,132765,2648,31210,33858,0.019945016,0.235077016,0.255022031,-28562,48,0.12,0.3 +49,Sb2_1_49,Sb2_1,-75.7915869832,42.4641738912,6.4815,0.291526,25926.49,N36017SU2_0280_0430,288280,1460,42308,43768,0.005064521,0.146760094,0.151824615,-40848,49,0.12,0.3 +50,Sb2_1_50,Sb2_1,-75.7292070152,42.2779562352,5.05551,0.30507,5270.76,N36017SU5_0420_0500,58619,802,13735,14537,0.013681571,0.234309695,0.247991266,-12933,50,0.12,0.3 +51,Sb2_1_51,Sb2_1,-75.8493244092,42.2016768232,14.1367,0.304872,25972.23,N36007SU5_0420_0500,288789,1918,52648,54566,0.006641527,0.182306113,0.18894764,-50730,51,0.12,0.3 +52,Sb2_1_52,Sb2_1,-75.8024737647,42.6642735892,13.0557,0.297745,21002.77,N36017SU2_0110_0240,233514,11998,41971,53969,0.051380217,0.179736547,0.231116764,-29973,52,0.12,0.3 +53,Sb2_2_53,Sb2_2,-74.7951300618,42.7195580702,12.7081,0.246418,23701.11,N36077SU2_0040_0180,263273,6845,41476,48321,0.025999628,0.157539892,0.18353952,-34631,53,0.12,0.3 +54,Sb2_2_54,Sb2_2,-75.4403335295,41.899514474,9.49946,0.302599,8402.07,N42127SU6_0480_0520,93114,18,13263,13281,0.000193311,0.142438301,0.142631613,-13245,54,0.12,0.3 +55,Sb2_2_55,Sb2_2,-75.7196081081,41.9275996324,18.0894,0.299582,61609.64,N42115SU6_0480_0520,685073,656,96530,97186,0.000957562,0.140904692,0.141862254,-95874,55,0.12,0.3 +56,Sb2_2_56,Sb2_2,-75.692406895,42.0820226041,19.5191,0.30041,46361.3,N36007SU6_0480_0520,515204,10556,78994,89550,0.020488971,0.153325673,0.173814644,-68438,56,0.12,0.3 +57,Sb2_2_57,Sb2_2,-75.1724978681,42.6416489792,8.03314,0.303164,15480.75,N36077SU1_0080_0210,172152,3185,45360,48545,0.018501092,0.26348808,0.281989172,-42175,57,0.12,0.3 +58,Sb2_2_58,Sb2_2,-74.9634530087,42.8975502059,3.85051,0.140828,6555.95,N36043SU2_0020_0140,72705,8611,30462,39073,0.118437521,0.418980813,0.537418334,-21851,58,0.12,0.3 +59,Sb2_2_59,Sb2_2,-75.2241129465,42.7981284064,13.6067,0.302103,9774.64,N36077SU3_0010_0190,108687,10604,27386,37990,0.097564566,0.251971257,0.349535823,-16782,59,0.12,0.3 +60,Sb2_2_60,Sb2_2,-75.2681849667,42.9064135398,6.36575,0.303834,8081.03,N36065SU3_0010_0190,89735,18417,11467,29884,0.205237644,0.127787374,0.333025018,6950,60,0.12,0.3 +61,Sb2_2_61,Sb2_2,-75.0168743305,42.7910829177,8.13616,0.289992,19969.23,N36077SU2_0020_0140,222058,8189,63824,72013,0.036877753,0.287420404,0.324298156,-55635,61,0.12,0.3 +62,Sb2_2_62,Sb2_2,-75.2016501009,42.7352492273,9.48508,0.300439,24039.42,N36077SU1_0060_0190,267319,10560,86373,96933,0.039503365,0.323108346,0.362611711,-75813,62,0.12,0.3 +63,Sb2_2_63,Sb2_2,-75.3166753372,42.8062139993,9.86522,0.303818,13920.09,N36053SU3_0010_0190,154780,6375,32070,38445,0.041187492,0.207197312,0.248384804,-25695,63,0.12,0.3 +64,Sb2_2_64,Sb2_2,-75.1321716869,42.9162781089,4.82827,0.248271,11663.84,N36043SU3_0010_0190,129334,28251,34750,63001,0.218434441,0.268684182,0.487118623,-6499,64,0.12,0.3 +65,Sb2_2_65,Sb2_2,-75.3585248819,42.6884277975,10.3431,0.296442,9503.69,N36017SU3_0010_0190,105687,5473,33259,38732,0.051784988,0.314693387,0.366478375,-27786,65,0.12,0.3 +66,Sb2_2_66,Sb2_2,-75.6400230379,42.2594830452,6.779,0.29527,5279.96,N36017SU1_0410_0480,58694,287,11867,12154,0.004889767,0.20218421,0.207073977,-11580,66,0.12,0.3 +67,Sb2_2_67,Sb2_2,-74.8788839215,42.7857342929,9.91459,0.21701,20482.85,N36077SU2_0030_0140,227642,21351,53811,75162,0.093792007,0.236384323,0.330176329,-32460,67,0.12,0.3 +68,Sb2_2_68,Sb2_2,-75.3580354505,42.2500699466,7.78578,0.290705,7923.38,N36025SU1_0380_0400,88043,82,18998,19080,0.000931363,0.215780925,0.216712288,-18916,68,0.12,0.3 +69,Sb2_2_69,Sb2_2,-75.5653274222,42.2975105843,6.34577,0.30507,10833.86,N36017SU2_0360_0460,120476,1162,30093,31255,0.009645075,0.249784189,0.259429264,-28931,69,0.12,0.3 +70,Sb2_2_70,Sb2_2,-75.2674551213,42.2933232768,11.7732,0.305079,9859.05,N36025SU5_0340_0310,109634,1311,23594,24905,0.011957969,0.215206961,0.227164931,-22283,70,0.12,0.3 +71,Sb2_2_71,Sb2_2,-74.7947805317,42.6003153713,13.88,0.310299,30798.07,N36077SU2_0100_0230,342325,7579,60035,67614,0.022139779,0.175374279,0.197514058,-52456,71,0.12,0.3 +72,Sb2_2_72,Sb2_2,-75.4799589472,42.3297054096,8.98931,0.299669,9170.96,N36017SU5_0310_0400,101967,3296,23043,26339,0.032324183,0.225984877,0.258309061,-19747,72,0.12,0.3 +73,Sb2_2_73,Sb2_2,-75.3585945366,42.3765614628,16.3463,0.305088,5307.95,N36077SU4_0300_0310,59040,2482,11262,13744,0.042039295,0.190752033,0.232791328,-8780,73,0.12,0.3 +74,Sb2_2_74,Sb2_2,-75.4453672722,42.3974712675,9.79767,0.305088,9276.33,N36017SU4_0300_0310,103153,2781,25036,27817,0.026959953,0.242707435,0.269667387,-22255,74,0.12,0.3 +75,Sb2_2_75,Sb2_2,-75.0608096696,42.3563663568,9.79233,0.292246,25661.07,N36025SU2_0290_0291,285213,2151,66867,69018,0.007541732,0.234445835,0.241987567,-64716,75,0.12,0.3 +76,Sb2_2_76,Sb2_2,-75.829703068,42.0916158154,21.3317,0.309226,14153.3,N36007SU6_0520_0500,157353,322,25995,26317,0.002046354,0.165201807,0.167248162,-25673,76,0.12,0.3 +77,Sb2_2_77,Sb2_2,-75.2143550938,42.425586982,16.2712,0.297475,19010.41,N36077SU4_0250_0340,211384,3702,41324,45026,0.017513151,0.195492563,0.213005715,-37622,77,0.12,0.3 +78,Sb2_2_78,Sb2_2,-74.6422746971,42.5282385541,8.66302,0.320431,10598.19,N36095SU2_0150_0220,117568,53,16629,16682,0.000450803,0.141441549,0.141892352,-16576,78,0.12,0.3 +79,Sb2_2_79,Sb2_2,-74.9697509288,42.6432793833,11.3983,0.324109,8172.19,N36077SU3_0140_0180,90871,2610,19424,22034,0.028722035,0.213753563,0.242475597,-16814,79,0.12,0.3 +80,Sb2_2_80,Sb2_2,-75.0931443061,42.5931994581,10.2343,0.301276,28267.99,N36077SU2_0130_0250,314332,5528,73466,78994,0.017586501,0.233721034,0.251307535,-67938,80,0.12,0.3 +81,Sb2_2_81,Sb2_2,-74.8133804508,42.4530795436,12.0344,0.310827,26644.9,N36025SU2_0150_0220,296241,3025,51340,54365,0.010211281,0.173304843,0.183516124,-48315,81,0.12,0.3 +82,Sb2_2_82,Sb2_2,-74.9791632169,42.5511260789,15.3257,0.291051,9081.24,N36077SU3_0180_0230,101002,5011,17424,22435,0.049612879,0.172511435,0.222124314,-12413,82,0.12,0.3 +83,Sb2_2_83,Sb2_2,-74.7543085797,42.5421647452,9.45247,0.310299,8199.6,N36077SU2_0150_0220,91166,513,15624,16137,0.005627098,0.171379681,0.177006779,-15111,83,0.12,0.3 +84,Sb2_2_84,Sb2_2,-75.2990313311,42.5047893761,11.1268,0.303672,18250.62,N36077SU2_0210_0300,202966,3727,45269,48996,0.018362681,0.223037356,0.241400037,-41542,84,0.12,0.3 +85,Sb2_2_85,Sb2_2,-75.0586175931,42.468771527,31.0103,0.292246,5484.24,N36077SU4_0220_0250,60976,317,5788,6105,0.005198767,0.094922592,0.100121359,-5471,85,0.12,0.3 +86,Sb2_2_86,Sb2_2,-75.4069491466,42.5472457995,9.157,0.302265,15896.74,N36017SU3_0190_0300,176743,5820,43240,49060,0.032929168,0.24464901,0.277578178,-37420,86,0.12,0.3 +87,Sb2_3_87,Sb2_3,-76.2837213984,42.35967227,7.76475,0.306876,6461.82,N36109SU1_0390_0560,71769,1452,11759,13211,0.020231576,0.163845114,0.18407669,-10307,87,0.12,0.3 +88,Sb2_3_88,Sb2_3,-76.4290698195,42.3158858398,8.29112,0.293553,10263.7,N36109SU2_0470_0590,113770,852,9680,10532,0.007488793,0.085083941,0.092572734,-8828,88,0.12,0.3 +89,Sb2_3_89,Sb2_3,-76.4118472925,42.2183211508,10.568,0.302732,28416.87,N36107SU2_0470_0590,315836,14013,68709,82722,0.044367963,0.217546448,0.261914411,-54696,89,0.12,0.3 +90,Sb2_3_90,Sb2_3,-76.2613823502,42.2813766485,10.0175,0.306876,13576.88,N36107SU1_0390_0560,150935,3545,34176,37721,0.023486931,0.226428595,0.249915527,-30631,90,0.12,0.3 +91,Sb2_3_91,Sb2_3,-76.5879662304,42.198406561,11.8831,0.30086,13256.56,N36015SU2_0450_0720,147322,822,22209,23031,0.005579615,0.150751415,0.15633103,-21387,91,0.12,0.3 +92,Sb2_3_92,Sb2_3,-76.6491369676,42.3276236539,8.93589,0.290705,7129.51,N36109SU2_0450_0720,78928,1408,6890,8298,0.017839043,0.08729475,0.105133793,-5482,92,0.12,0.3 +93,Sb2_3_93,Sb2_3,-76.5283604046,42.0935330721,18.9116,0.305079,6279.06,N36107SU2_0450_0720,69803,786,11426,12212,0.011260261,0.16368924,0.174949501,-10640,93,0.12,0.3 +94,Sb2_3_94,Sb2_3,-76.6970774684,42.3208626433,13.2394,0.306161,9672.33,N36097SU2_0450_0720,107400,3075,14168,17243,0.028631285,0.131918063,0.160549348,-11093,94,0.12,0.3 +95,Sb2_3_95,Sb2_3,-76.1203525111,42.2166266267,5.15124,0.310147,7728.88,N36107SU2_0440_0550,85936,432,27473,27905,0.005026997,0.319691398,0.324718395,-27041,95,0.12,0.3 +96,Sb2_3_96,Sb2_3,-76.0492704641,42.2305098345,9.3835,0.30777,21779.21,N36007SU2_0440_0550,242135,3025,64239,67264,0.012493031,0.265302414,0.277795445,-61214,96,0.12,0.3 +97,Sb2_3_97,Sb2_3,-76.1810407087,42.2910975664,10.4478,0.303654,18186.58,N36107SU1_0330_0560,202207,5529,52013,57542,0.027343267,0.257226506,0.284569773,-46484,97,0.12,0.3 +98,Sb2_3_98,Sb2_3,-76.1817874237,42.4463868752,8.11756,0.30507,7577.09,N36023SU1_0330_0560,84146,3077,16550,19627,0.036567395,0.196681958,0.233249352,-13473,98,0.12,0.3 +99,Sb2_3_99,Sb2_3,-76.007352135,41.9577842474,9.84291,0.307552,7896.38,N42115SU6_0500_0550,87797,105,14573,14678,0.001195941,0.16598517,0.167181111,-14468,99,0.12,0.3 +100,Sb2_3_100,Sb2_3,-75.995243637,42.0976588268,12.7713,0.30041,21173.01,N36007SU6_0500_0550,235407,81,27657,27738,0.000344085,0.117485886,0.117829971,-27576,100,0.12,0.3 +101,Sb2_3_101,Sb2_3,-76.3820516537,42.0761716911,15.0952,0.309472,35743.31,N36107SU7_0540_0720,397329,21960,111054,133014,0.055269059,0.27950137,0.33477043,-89094,101,0.12,0.3 +102,Sb2_3_102,Sb2_3,-76.2765109324,41.9600209031,14.673,0.302242,18064.89,N42015SU7_0540_0720,200824,2952,52569,55521,0.014699438,0.261766522,0.27646596,-49617,102,0.12,0.3 +103,Sb2_3_103,Sb2_3,-76.1805502319,42.0802275087,10.7814,0.30234,19752.19,N36107SU7_0550_0540,219593,3738,52733,56471,0.017022401,0.240139713,0.257162114,-48995,103,0.12,0.3 +104,Sb3_1_104,Sb3_1,-76.6254329731,41.6328267703,14.9642,0.293321,20159.46,N42015SU1_0940_0900,224078,79,959,1038,0.000352556,0.00427976,0.004632316,-880,104,0.12,0.3 +105,Sb3_1_105,Sb3_1,-75.9399243486,41.7150386723,8.52925,0.288079,25045.03,N42115SU2_0760_0960,278453,1894,103113,105007,0.006801866,0.370306659,0.377108525,-101219,105,0.12,0.3 +106,Sb3_1_106,Sb3_1,-76.5096916929,41.8736420828,61.1538,0.300253,13581.96,N42015SU7_0750_0850,150996,13772,41517,55289,0.091207714,0.274954303,0.366162018,-27745,106,0.12,0.3 +107,Sb3_1_107,Sb3_1,-76.699365029,41.8118959637,13.9457,0.296742,49146.39,N42015SU2_0800_0850,546226,15956,230989,246945,0.029211352,0.422881738,0.45209309,-215033,107,0.12,0.3 +108,Sb3_1_108,Sb3_1,-76.008192712,41.565068909,47.3897,0.293193,6157.27,N42131SU7_0950_1010,68468,2910,16964,19874,0.042501607,0.247765379,0.290266986,-14054,108,0.12,0.3 +109,Sb3_1_109,Sb3_1,-75.7766181207,41.4438934651,30.2603,0.28702,9139.0,N42069SU7_0980_1120,101605,2403,30010,32413,0.023650411,0.29535948,0.319009891,-27607,109,0.12,0.3 +110,Sb3_1_110,Sb3_1,-76.341513743,41.8180149714,42.3874,0.298781,48849.3,N42015SU7_0730_0860,543029,17576,181894,199470,0.032366596,0.334961853,0.367328448,-164318,110,0.12,0.3 +111,Sb3_1_111,Sb3_1,-75.5949446464,41.6790259356,9.9415,0.293407,12156.47,N42115SU2_0780_0710,135176,650,35670,36320,0.004808546,0.263878203,0.268686749,-35020,111,0.12,0.3 +112,Sb3_1_112,Sb3_1,-75.9263032689,41.5742979512,21.0944,0.301949,5504.44,N42131SU3_0910_1010,61195,1278,12822,14100,0.020884059,0.209526922,0.230410981,-11544,112,0.12,0.3 +113,Sb3_1_113,Sb3_1,-75.8269916457,41.5949881456,20.592,0.301936,13985.93,N42131SU3_0710_0910,155512,3410,38618,42028,0.021927568,0.248328103,0.270255672,-35208,113,0.12,0.3 +114,Sb3_1_114,Sb3_1,-76.7292196102,41.6859335721,12.5634,0.29059,30519.42,N42015SU2_0890_0900,339210,13640,136208,149848,0.040211079,0.401544766,0.441755844,-122568,114,0.12,0.3 +115,Sb3_1_115,Sb3_1,-76.4414976861,41.6472529925,17.206,0.298315,15727.81,N42015SU3_0870_0730,174848,3613,32656,36269,0.020663662,0.186767936,0.207431598,-29043,115,0.12,0.3 +116,Sb3_1_116,Sb3_1,-76.1363550378,41.6169614858,55.0912,0.296323,7441.98,N42131SU7_0860_0960,82735,4828,17492,22320,0.058354989,0.21142201,0.269776999,-12664,116,0.12,0.3 +117,Sb3_1_117,Sb3_1,-76.0938960261,41.7040345516,7.04939,0.296308,5010.18,N42115SU7_0860_0960,55698,436,22713,23149,0.007827929,0.40778843,0.41561636,-22277,117,0.12,0.3 +118,Sb3_1_118,Sb3_1,-75.7903617096,41.7392705416,10.7774,0.300176,20730.17,N42115SU3_0710_0910,230512,1346,69921,71267,0.005839175,0.303329111,0.309168286,-68575,118,0.12,0.3 +119,Sb3_1_119,Sb3_1,-76.018432409,41.8339699622,12.1675,0.304845,36923.58,N42115SU2_0700_0860,410499,2859,105951,108810,0.006964694,0.258102943,0.265067637,-103092,119,0.12,0.3 +120,Sb3_1_120,Sb3_1,-76.1007159317,41.4360758194,18.47,0.301939,6837.16,H42131SU2_1020_0980,76002,0,482,482,0.0,0.006341938,0.006341938,-482,120,0.12,0.3 +121,Sb3_1_121,Sb3_1,-76.0082278751,41.454083847,18.4161,0.297942,16135.83,N42131SU2_1020_0980,179386,3387,31080,34467,0.018881072,0.173257668,0.19213874,-27693,121,0.12,0.3 +122,Sb3_1_122,Sb3_1,-76.195773847,41.3563680679,10.3356,0.296323,5050.89,H42079SU2_1020_0980,56140,0,6,6,0.0,0.000106876,0.000106876,-6,122,0.12,0.3 +123,Sb3_1_123,Sb3_1,-75.6798881396,41.5668884323,10.9653,0.30193,20492.46,N42069SU3_0710_0910,227874,1065,58278,59343,0.004673635,0.255746597,0.260420232,-57213,123,0.12,0.3 +124,Sb3_1_124,Sb3_1,-76.1910558668,41.4210617596,10.5217,0.296323,8064.83,H42131SU2_0990_0950,89660,0,69,69,0.0,0.000769574,0.000769574,-69,124,0.12,0.3 +125,Sb3_1_125,Sb3_1,-75.6502460248,41.7770419625,11.0688,0.294781,26544.99,N42115SU1_0660_0710,295131,1945,64875,66820,0.006590294,0.21981764,0.226407934,-62930,125,0.12,0.3 +126,Sb3_1_126,Sb3_1,-76.2549543087,41.6429768227,26.4271,0.296308,24506.06,N42015SU7_0860_0960,272449,6126,72377,78503,0.022484942,0.265653388,0.28813833,-66251,126,0.12,0.3 +127,Sb3_1_127,Sb3_1,-75.8749928591,41.4771045978,43.7983,0.30193,15422.46,N42131SU7_0980_1120,171460,4668,36284,40952,0.027225009,0.21161787,0.238842879,-31616,127,0.12,0.3 +128,Sb3_1_128,Sb3_1,-76.1996097222,41.7858200621,14.9522,0.299289,20048.54,N42015SU2_0700_0860,222899,4797,70163,74960,0.021520958,0.314774853,0.336295811,-65366,128,0.12,0.3 +129,Sb3_1_129,Sb3_1,-76.1546060706,41.5119721219,23.9264,0.301939,15414.55,N42131SU2_0990_0950,171365,655,19068,19723,0.003822251,0.111271263,0.115093514,-18413,129,0.12,0.3 +130,Sb3_2_130,Sb3_2,-76.0477788495,41.1845756039,37.4071,0.264549,76538.13,N42079SU7_1140_1420,850905,16372,79966,96338,0.019240691,0.093977589,0.113218279,-63594,130,0.12,0.3 +131,Sb3_2_131,Sb3_2,-75.8243265819,41.2972263321,138.768,0.220509,7550.7,L42079SU7_1120_1140,83943,1250,1126,2376,0.014891057,0.013413864,0.028304921,124,131,0.12,0.3 +132,Sb3_2_132,Sb3_2,-75.8756237972,41.3007407379,8.28207,0.272895,19614.47,N42079SU7_1120_1140,218052,116,19876,19992,0.000531983,0.091152569,0.091684552,-19760,132,0.12,0.3 +133,Sb3_2_133,Sb3_2,-75.6687123933,41.4109122703,26.4415,0.25727,10738.17,L42069SU3_0970_1120,119414,169,1977,2146,0.001415244,0.016555848,0.017971092,-1808,133,0.12,0.3 +134,Sb3_2_134,Sb3_2,-75.587839097,41.3824988111,10.7914,0.290899,45746.07,N42069SU3_0970_1120,508311,34,24627,24661,6.68882e-05,0.048448686,0.048515574,-24593,134,0.12,0.3 +135,Sb3_2_135,Sb3_2,-76.2384344196,41.2399661197,8.49568,0.287202,20908.95,N42079SU2_1200_1310,232447,9651,42208,51859,0.041519142,0.181581178,0.22310032,-32557,135,0.12,0.3 +136,Sb3_2_136,Sb3_2,-76.3722323668,41.2253092005,16.9349,0.293193,16630.84,N42037SU2_1210_1310,184855,13625,36342,49967,0.073706419,0.196597333,0.270303752,-22717,136,0.12,0.3 +137,Sb3_2_137,Sb3_2,-75.4959077924,41.7673541777,9.38909,0.303887,6680.47,H42115SU2_0670_0810,74272,46,12676,12722,0.000619345,0.17066997,0.171289315,-12630,137,0.12,0.3 +138,Sb3_2_138,Sb3_2,-76.6259538564,41.0033630701,11.2467,0.283627,8089.1,N42093SU8_1530_1760,89906,14449,19208,33657,0.1607123,0.213645363,0.374357662,-4759,138,0.12,0.3 +139,Sb3_2_139,Sb3_2,-76.7113891641,40.9229852643,39.7497,0.296432,12087.24,N42097SU8_1530_1760,134342,32916,11143,44059,0.245016451,0.082945021,0.327961471,21773,139,0.12,0.3 +140,Sb3_2_140,Sb3_2,-76.3294289973,40.9494108813,20.5643,0.269889,16424.96,N42037SU2_1550_1620,182588,17029,12967,29996,0.093264618,0.071017811,0.164282428,4062,140,0.12,0.3 +141,Sb3_2_141,Sb3_2,-76.1668921419,40.883357946,14.7794,0.301943,19620.87,N42107SU2_1550_1620,218084,17170,11373,28543,0.078731131,0.05214963,0.130880762,5797,141,0.12,0.3 +142,Sb3_2_142,Sb3_2,-76.4087020331,40.8802380542,12.3134,0.286193,20166.7,N42037SU1_1630_1610,224195,57640,24339,81979,0.257097616,0.108561743,0.365659359,33301,142,0.12,0.3 +143,Sb3_2_143,Sb3_2,-76.039339257,40.9719515694,11.745,0.295957,14858.26,N42079SU1_1480_1500,165016,3770,3925,7695,0.022846269,0.023785572,0.046631842,-155,143,0.12,0.3 +144,Sb3_2_144,Sb3_2,-76.4248841258,41.3306192079,17.8589,0.294479,11321.11,H42113SU2_1210_1310,125836,0,176,176,0.0,0.001398646,0.001398646,-176,144,0.12,0.3 +145,Sb3_2_145,Sb3_2,-75.5181021589,41.5734343085,14.489,0.296093,13048.16,N42069SU3_0810_0970,145060,0,14069,14069,0.0,0.096987453,0.096987453,-14069,145,0.12,0.3 +146,Sb3_2_146,Sb3_2,-76.497919238,41.0993466445,16.4872,0.298944,26665.2,N42037SU3_1280_1590,296414,41491,75776,117267,0.139976519,0.255642446,0.395618965,-34285,146,0.12,0.3 +147,Sb3_2_147,Sb3_2,-75.9562597856,41.0533188528,8.1276,0.279279,24544.94,N42079SU2_1290_1500,272657,14872,24931,39803,0.054544721,0.091437227,0.145981948,-10059,147,0.12,0.3 +148,Sb3_2_148,Sb3_2,-76.4371480259,41.141418781,13.5231,0.301949,11247.67,N42037SU3_1310_1280,125048,19599,38511,58110,0.156731815,0.30796974,0.464701555,-18912,148,0.12,0.3 +149,Sb3_2_149,Sb3_2,-76.3218470742,41.0447633836,42.2298,0.301949,17374.61,N42037SU7_1420_1590,193144,35242,35864,71106,0.182464897,0.185685292,0.368150188,-622,149,0.12,0.3 +150,Sb4_1_150,Sb4_1,-78.8476761555,40.7906886456,6.86786,0.294986,16321.79,N42063SW2_1950_1940,180874,9609,30535,40144,0.05312538,0.168819178,0.221944558,-20926,150,0.12,0.3 +151,Sb4_1_151,Sb4_1,-78.6752512439,40.6154391074,9.6918,0.297555,19098.61,N42021SW2_2020_1940,211839,26594,37846,64440,0.125538735,0.178654544,0.304193279,-11252,151,0.12,0.3 +152,Sb4_1_152,Sb4_1,-78.5575954463,41.0406905983,12.1172,0.267376,34482.46,N42033SW4_1720_1650,382511,1019,42360,43379,0.002663976,0.110741913,0.113405889,-41341,152,0.12,0.3 +153,Sb4_1_153,Sb4_1,-78.0930363898,40.924195077,11.0928,0.238007,36133.93,N42027SW3_1750_1540,401140,195,4743,4938,0.000486115,0.011823802,0.012309917,-4548,153,0.12,0.3 +154,Sb4_1_154,Sb4_1,-78.2023660778,40.9494987161,14.0243,0.264117,17093.76,N42033SW3_1750_1540,189739,987,15304,16291,0.005201883,0.080658167,0.08586005,-14317,154,0.12,0.3 +155,Sb4_1_155,Sb4_1,-78.672088439,40.8002606547,11.6771,0.294648,14418.23,N42033SW2_2020_1940,159943,3057,15392,18449,0.019113059,0.096234283,0.115347342,-12335,155,0.12,0.3 +156,Sb4_1_156,Sb4_1,-78.3616401142,40.9918766262,18.115,0.258183,5896.77,N42033SW3_1800_1650,65431,1193,6137,7330,0.018232948,0.093793462,0.11202641,-4944,156,0.12,0.3 +157,Sb4_1_157,Sb4_1,-77.9881990825,41.1413120323,34.7671,0.238295,14967.42,N42027SW5_1540_1400,166171,241,2356,2597,0.001450313,0.014178166,0.015628479,-2115,157,0.12,0.3 +158,Sb4_1_158,Sb4_1,-78.6455142303,40.9494338052,47.6306,0.267376,13678.76,N42033SW4_1860_1720,151683,568,16647,17215,0.003744652,0.10974862,0.113493272,-16079,158,0.12,0.3 +159,Sb4_1_159,Sb4_1,-78.5389437029,40.6171517749,8.01411,0.267666,42090.1,N42021SW3_1870_1800,466996,26641,43925,70566,0.057047598,0.094058621,0.151106219,-17284,159,0.12,0.3 +160,Sb4_1_160,Sb4_1,-78.4849125466,40.8444612185,12.071,0.262992,53530.8,N42033SW3_1870_1800,594012,7405,81655,89060,0.012466078,0.137463553,0.149929631,-74250,160,0.12,0.3 +161,Sb4_1_161,Sb4_1,-77.9826246172,41.2120046769,20.2562,0.231477,13400.99,N42035SW5_1540_1400,148796,0,1387,1387,0.0,0.009321487,0.009321487,-1387,161,0.12,0.3 +162,Sb4_1_162,Sb4_1,-78.183485357,41.1765116805,14.3234,0.24684,22153.91,N42033SW5_1540_1400,245916,46,3166,3212,0.000187056,0.012874315,0.013061371,-3120,162,0.12,0.3 +163,Sb4_1_163,Sb4_1,-78.7653769379,40.822417541,12.9882,0.285849,15235.33,N42033SW2_1950_1940,168945,5785,20358,26143,0.034241913,0.120500755,0.154742668,-14573,163,0.12,0.3 +164,Sb4_1_164,Sb4_1,-78.7671593956,40.6554134364,10.4098,0.307487,12098.46,N42021SW2_1950_1940,134198,5484,18622,24106,0.040864991,0.138765108,0.179630099,-13138,164,0.12,0.3 +165,Sb4_1_165,Sb4_1,-78.3432609923,40.829327069,5.73307,0.277941,8705.64,N42033SW3_1920_1750,96615,389,10363,10752,0.00402629,0.107260777,0.111287067,-9974,165,0.12,0.3 +166,Sb4_1_166,Sb4_1,-78.2947411248,40.7875101738,8.31784,0.250267,8882.95,N42027SW3_1920_1750,98602,0,1277,1277,0.0,0.012951056,0.012951056,-1277,166,0.12,0.3 +167,Sb4_1_167,Sb4_1,-78.3009675625,41.0809652796,14.6126,0.264117,56889.66,N42033SW5_1650_1540,631440,6528,41505,48033,0.010338274,0.065730711,0.076068985,-34977,167,0.12,0.3 +168,Sb4_2_168,Sb4_2,-78.1897109866,41.4667441947,18.661,0.272919,18338.53,N42023SW3_1270_1370,203582,117,3088,3205,0.000574707,0.015168335,0.015743042,-2971,168,0.12,0.3 +169,Sb4_2_169,Sb4_2,-78.5279696634,41.1989624712,10.3973,0.275917,24808.38,N42033SW2_1360_1430,275247,173,7050,7223,0.000628526,0.025613358,0.026241885,-6877,169,0.12,0.3 +170,Sb4_2_170,Sb4_2,-78.3366041958,41.5392743633,20.235,0.269629,23106.93,N42023SW2_1240_1270,256398,101,3455,3556,0.000393919,0.013475144,0.013869063,-3354,170,0.12,0.3 +171,Sb4_2_171,Sb4_2,-78.2696051523,41.37248247,15.6053,0.283381,16558.84,N42023SW2_1360_1430,183790,78,2187,2265,0.000424397,0.01189945,0.012323848,-2109,171,0.12,0.3 +172,Sb4_2_172,Sb4_2,-77.989517404,41.2795079025,43.8229,0.283604,7379.9,N42035SW4_1490_1400,81928,0,588,588,0.0,0.007177033,0.007177033,-588,172,0.12,0.3 +173,Sb4_2_173,Sb4_2,-78.1182975177,41.2760092488,25.5177,0.273054,11047.88,N42023SW4_1490_1400,122647,30,522,552,0.000244604,0.004256117,0.004500722,-492,173,0.12,0.3 +174,Sb4_2_174,Sb4_2,-78.4260165635,41.3252039352,15.6053,0.283381,53554.19,N42047SW2_1360_1430,594137,324,18268,18592,0.000545329,0.030747117,0.031292446,-17944,174,0.12,0.3 +175,Sb4_2_175,Sb4_2,-78.1627347436,41.6266963403,17.4344,0.280121,6036.57,N42105SW1_1170_1270,66984,0,1987,1987,0.0,0.0296638,0.0296638,-1987,175,0.12,0.3 +176,Sb4_2_176,Sb4_2,-78.1541196578,41.3837877673,34.5359,0.273054,5548.87,N42023SW3_1370_1430,61599,493,662,1155,0.008003377,0.010746928,0.018750304,-169,176,0.12,0.3 +177,Sb4_2_177,Sb4_2,-78.4751437752,41.5032405617,11.7894,0.288484,14153.29,N42047SW2_1240_1270,156942,32,8987,9019,0.000203897,0.057263193,0.05746709,-8955,177,0.12,0.3 +178,Sb4_2_178,Sb4_2,-78.0561573071,41.3721559536,30.059,0.265894,6087.69,N42023SW3_1380_1490,67582,7,508,515,0.000103578,0.007516794,0.007620372,-501,178,0.12,0.3 +179,Sb4_2_179,Sb4_2,-77.9994100275,41.6225093057,20.8211,0.279892,52757.16,N42105SW3_1090_1091,585364,763,24651,25414,0.001303462,0.042112258,0.043415721,-23888,179,0.12,0.3 +180,Sb4_2_180,Sb4_2,-78.0396819433,41.4313865614,45.3733,0.265894,5438.67,N42023SW3_1091_1380,60386,12,512,524,0.000198722,0.008478786,0.008677508,-500,180,0.12,0.3 +181,Sb4_2_181,Sb4_2,-78.2116646149,41.5672843336,23.2982,0.287026,8195.74,N42023SW1_1170_1270,90980,19,2091,2110,0.000208837,0.022983073,0.02319191,-2072,181,0.12,0.3 +182,Sb4_3_182,Sb4_3,-77.874142589,41.0847886672,12.6732,0.232362,30092.42,N42027SW0_1520_1600,334150,207,5437,5644,0.000619482,0.016271136,0.016890618,-5230,182,0.12,0.3 +183,Sb4_3_183,Sb4_3,-77.7008905716,40.9815603385,15.7753,0.242096,18883.73,N42027SW3_1690_1660,209715,24621,20827,45448,0.117402189,0.09931097,0.216713158,3794,183,0.12,0.3 +184,Sb4_3_184,Sb4_3,-77.7856571977,40.7862843284,7.05534,0.278054,15025.62,N42027SW1_1910_1890,166878,38491,23089,61580,0.230653531,0.138358561,0.369012093,15402,184,0.12,0.3 +185,Sb4_3_185,Sb4_3,-77.5860232544,40.9561507814,10.9246,0.242096,13258.63,N42027SW3_1580_1440,147261,20457,9512,29969,0.138916617,0.064592798,0.203509415,10945,185,0.12,0.3 +186,Sb4_3_186,Sb4_3,-77.4166671368,41.0536927171,18.0007,0.274024,41737.49,N42035SW3_1580_1440,463642,68977,47047,116024,0.148772113,0.101472688,0.250244801,21930,186,0.12,0.3 +187,Sb4_3_187,Sb4_3,-77.7349616436,41.0455506693,15.1714,0.233926,12298.44,N42027SW3_1660_1580,136600,5445,15261,20706,0.039860908,0.111720351,0.151581259,-9816,187,0.12,0.3 +188,Sb4_3_188,Sb4_3,-77.7681232849,41.1756440616,13.7279,0.233912,9456.56,N42035SW0_1520_1600,105017,0,1281,1281,0.0,0.012198025,0.012198025,-1281,188,0.12,0.3 +189,Sb4_3_189,Sb4_3,-77.8583415596,40.8257462437,8.86001,0.283214,6809.3,N42027SW1_1890_1830,75603,11711,10182,21893,0.154901261,0.134677195,0.289578456,1529,189,0.12,0.3 +190,Sb4_3_190,Sb4_3,-77.8155331353,40.8693531142,9.96354,0.271662,15997.71,N42027SW1_1830_1690,177626,33026,23785,56811,0.185929988,0.133904946,0.319834934,9241,190,0.12,0.3 +191,Sb4_3_191,Sb4_3,-77.9744401468,40.8799532227,14.2001,0.245305,30822.99,N42027SW2_1770_1690,342235,5081,29039,34120,0.014846524,0.084851053,0.099697576,-23958,191,0.12,0.3 +192,Sb4_4_192,Sb4_4,-77.7332046045,41.5846042208,20.2095,0.29066,31984.31,N42105SW2_1100_1130,355234,1959,16669,18628,0.005514675,0.046923999,0.052438674,-14710,192,0.12,0.3 +193,Sb4_4_193,Sb4_4,-77.8027335586,41.3328989045,25.3438,0.253923,13305.18,N42035SW6_1330_1230,147748,0,1412,1412,0.0,0.009556813,0.009556813,-1412,193,0.12,0.3 +194,Sb4_4_194,Sb4_4,-77.8938840219,41.5154109002,19.5999,0.278698,9015.11,N42105SW3_1130_1390,100096,12,1198,1210,0.000119885,0.01196851,0.012088395,-1186,194,0.12,0.3 +195,Sb4_4_195,Sb4_4,-77.9135390302,41.4169744069,45.4422,0.278482,15394.5,N42035SW3_1130_1390,170956,0,3883,3883,0.0,0.022713447,0.022713447,-3883,195,0.12,0.3 +196,Sb4_4_196,Sb4_4,-77.9015617412,41.2936556959,31.2742,0.265868,10873.54,N42035SW6_1400_1330,120735,111,823,934,0.000919369,0.006816582,0.007735951,-712,196,0.12,0.3 +197,Sb4_4_197,Sb4_4,-77.6685130801,41.5036503637,13.8358,0.262355,7005.75,N42105SW6_1230_1440,77806,0,362,362,0.0,0.004652597,0.004652597,-362,197,0.12,0.3 +198,Sb4_4_198,Sb4_4,-77.3539453956,41.1591294965,18.3725,0.282402,16705.21,N42035SW6_1440_1470,185602,14196,9964,24160,0.076486245,0.053684766,0.130171011,4232,198,0.12,0.3 +199,Sb4_4_199,Sb4_4,-77.6187720838,41.2938932246,24.564,0.262355,88156.44,N42035SW6_1230_1440,979115,2796,15082,17878,0.00285564,0.015403706,0.018259346,-12286,199,0.12,0.3 +200,Sb4_5_200,Sb4_5,-77.7775751469,41.7096916624,15.7383,0.2823,18567.83,N42105SW2_1050_0930,206171,122,4072,4194,0.000591742,0.019750595,0.020342337,-3950,200,0.12,0.3 +201,Sb4_5_201,Sb4_5,-77.2826813616,41.64538642,14.3485,0.280274,33237.74,N42117SW2_1000_1110,369288,7810,54820,62630,0.021148805,0.148447824,0.169596629,-47010,201,0.12,0.3 +202,Sb4_5_202,Sb4_5,-77.4627869292,41.5808977624,24.1991,0.278363,6559.07,H42117SW4_1110_1150,72830,17,145,162,0.00023342,0.001990938,0.002224358,-128,202,0.12,0.3 +203,Sb4_5_203,Sb4_5,-77.3820366947,41.5175717608,38.334,0.297977,6409.03,N42081SW4_1110_1150,71211,238,3559,3797,0.00334218,0.049978234,0.053320414,-3321,203,0.12,0.3 +204,Sb4_5_204,Sb4_5,-77.325572487,41.2579721406,43.0758,0.298994,9279.52,N42081SW5_1350_1470,103081,3150,5738,8888,0.030558493,0.055664963,0.086223455,-2588,204,0.12,0.3 +205,Sb4_5_205,Sb4_5,-77.6342673672,41.7704209165,16.687,0.297407,10641.56,N42105SW3_0930_1110,118184,161,14824,14985,0.001362283,0.12543153,0.126793813,-14663,205,0.12,0.3 +206,Sb4_5_206,Sb4_5,-77.7488411932,41.8181442103,15.2111,0.311728,24258.84,N42105SW2_0880_0930,269261,3004,20194,23198,0.011156462,0.074997865,0.086154326,-17190,206,0.12,0.3 +207,Sb4_5_207,Sb4_5,-77.4760063934,41.4235074033,30.7863,0.300045,33580.98,N42081SW4_1150_1350,373042,158,3897,4055,0.000423545,0.010446545,0.01087009,-3739,207,0.12,0.3 +208,Sb4_5_208,Sb4_5,-77.267561453,41.4571046483,23.1381,0.297977,36263.29,N42081SW3_1080_1350,402890,3003,25806,28809,0.007453647,0.064052223,0.07150587,-22803,208,0.12,0.3 +209,Sb4_5_209,Sb4_5,-77.1583741307,41.5796295878,6.44377,0.304756,10471.79,N42117SW3_1080_1350,116376,1466,28504,29970,0.012597099,0.244930226,0.257527325,-27038,209,0.12,0.3 +210,Sb4_5_210,Sb4_5,-77.4433646542,41.7439349556,22.8267,0.29471,49654.83,N42117SW3_0930_1110,551574,5511,56864,62375,0.009991406,0.103094054,0.113085461,-51353,210,0.12,0.3 +211,Sb4_6_211,Sb4_6,-76.9019621872,41.421717287,13.1257,0.300054,9498.34,N42081SW1_1160_1260,105570,676,10462,11138,0.006403334,0.099100123,0.105503457,-9786,211,0.12,0.3 +212,Sb4_6_212,Sb4_6,-77.2266487545,41.3444130964,15.8431,0.29133,22965.93,N42081SW3_1250_1340,255177,4932,33950,38882,0.019327761,0.133044906,0.152372667,-29018,212,0.12,0.3 +213,Sb4_6_213,Sb4_6,-76.6880101676,41.5334885048,16.2598,0.2868,9518.76,L42113SW3_1060_1260,105793,563,16380,16943,0.005321713,0.15483066,0.160152373,-15817,213,0.12,0.3 +214,Sb4_6_214,Sb4_6,-76.4011565161,41.4028356423,4.25096,0.283627,6435.16,H42113SW2_1070_1060,71532,0,179,179,0.0,0.002502377,0.002502377,-179,214,0.12,0.3 +215,Sb4_6_215,Sb4_6,-76.4148159781,41.4602022472,11.8486,0.301274,26738.19,N42113SW2_1070_1060,297231,43,7169,7212,0.000144669,0.024119288,0.024263956,-7126,215,0.12,0.3 +216,Sb4_6_216,Sb4_6,-76.4845044895,41.5276396676,13.9921,0.302044,16672.99,N42113SW2_1030_1060,185324,2438,51309,53747,0.013155339,0.276861065,0.290016404,-48871,216,0.12,0.3 +217,Sb4_6_217,Sb4_6,-76.943702898,41.5070779928,19.4855,0.297828,31928.67,N42081SW3_1040_1220,354812,1469,11240,12709,0.004140221,0.031678748,0.035818969,-9771,217,0.12,0.3 +218,Sb4_6_218,Sb4_6,-76.9890471448,41.5927977607,13.3581,0.297828,12113.5,N42117SW3_1040_1220,134612,1657,44523,46180,0.012309452,0.330750602,0.343060054,-42866,218,0.12,0.3 +219,Sb4_6_219,Sb4_6,-76.6883260104,41.4591091414,19.8346,0.2868,19474.0,N42113SW3_1060_1260,216472,2898,9057,11955,0.013387413,0.041839129,0.055226542,-6159,219,0.12,0.3 +220,Sb4_6_220,Sb4_6,-77.1251194783,41.2191279794,75.4473,0.297391,18203.61,N42081SW7_1340_1320,202263,17654,37115,54769,0.0872824,0.183498712,0.270781112,-19461,220,0.12,0.3 +221,Sb4_6_221,Sb4_6,-76.9719235397,41.1497393472,15.8939,0.299014,23585.64,N42081SW7_1410_1460,262164,34134,29417,63551,0.130200943,0.112208389,0.242409332,4717,221,0.12,0.3 +222,Sb4_6_222,Sb4_6,-76.8409659288,41.1297946806,23.6117,0.30193,6502.91,N42097SW7_1410_1460,72256,24750,13307,38057,0.342532108,0.184164637,0.526696745,11443,222,0.12,0.3 +223,Sb4_6_223,Sb4_6,-76.6483777397,41.2510447705,15.9597,0.301949,34397.26,N42081SW2_1190_1410,382294,39507,88958,128465,0.103341931,0.232695255,0.336037186,-49451,223,0.12,0.3 +224,Sb4_6_224,Sb4_6,-76.9939465088,41.070240841,19.5399,0.275911,5702.34,N42119SW1_1560_1460,63357,1595,1379,2974,0.025174803,0.021765551,0.046940354,216,224,0.12,0.3 +225,Sb4_6_225,Sb4_6,-76.9888992559,41.2474241339,36.6127,0.294479,8589.56,N42081SW7_1320_1300,95435,3215,10540,13755,0.03368785,0.110441662,0.144129512,-7325,225,0.12,0.3 +226,Sb4_6_226,Sb4_6,-76.7042417055,41.0149097212,12.1527,0.294758,10073.73,N42093SW1_1510_1680,111963,49030,14677,63707,0.437912525,0.131087949,0.569000473,34353,226,0.12,0.3 +227,Sb4_6_227,Sb4_6,-76.6857730875,41.1100280561,7.0594,0.299116,8884.44,N42093SW1_1450_1510,98750,39352,20837,60189,0.398501266,0.211007595,0.609508861,18515,227,0.12,0.3 +228,Sb4_6_228,Sb4_6,-76.8062732997,41.0696892196,14.2477,0.299188,12140.99,N42097SW7_1460_1640,134951,60320,18345,78665,0.446977051,0.135938229,0.58291528,41975,228,0.12,0.3 +229,Sb4_6_229,Sb4_6,-77.2470554154,41.1017449207,11.5694,0.274024,5986.82,N42035SW7_1470_1340,66524,2630,4796,7426,0.039534604,0.072094282,0.111628886,-2166,229,0.12,0.3 +230,Sb4_6_230,Sb4_6,-76.8269082745,41.2537245949,39.2938,0.297561,11318.63,N42081SW7_1300_1410,125798,18583,22946,41529,0.147720949,0.182403536,0.330124485,-4363,230,0.12,0.3 +231,Sb4_6_231,Sb4_6,-76.9065770773,41.322549515,21.5205,0.301936,16297.8,N42081SW4_1260_1300,181129,13775,39142,52917,0.07605077,0.216100128,0.292150898,-25367,231,0.12,0.3 +232,Sb4_6_232,Sb4_6,-77.1913737743,41.1582678148,25.6786,0.275787,11143.82,N42081SW7_1470_1340,123809,18365,21366,39731,0.14833332,0.172572269,0.320905588,-3001,232,0.12,0.3 +233,Sb4_6_233,Sb4_6,-76.923969169,40.9337991337,6.96991,0.303401,5772.76,N42119SW7_1640_1680,64144,25983,3981,29964,0.405072961,0.062063482,0.467136443,22002,233,0.12,0.3 +234,Sb4_6_234,Sb4_6,-77.0299447617,40.9782886914,8.55437,0.275911,27238.87,N42119SW1_1570_1640,302678,104512,19009,123521,0.345291035,0.062802714,0.40809375,85503,234,0.12,0.3 +235,Sb4_6_235,Sb4_6,-76.5866846436,41.3610565278,16.4756,0.301949,11176.77,N42113SW2_1190_1410,124221,750,6628,7378,0.006037626,0.053356518,0.059394144,-5878,235,0.12,0.3 +236,Sb4_6_236,Sb4_6,-77.0821968445,41.3541429389,20.8,0.295289,25619.78,N42081SW3_1220_1320,284686,6399,44259,50658,0.022477396,0.155466022,0.177943418,-37860,236,0.12,0.3 +237,Sb4_6_237,Sb4_6,-76.8019050108,41.3936334177,22.2332,0.300054,16866.66,N42081SW3_1060_1260,187434,162,3548,3710,0.000864304,0.01892933,0.019793634,-3386,237,0.12,0.3 +238,Sb4_6_238,Sb4_6,-77.16824613,40.9800919769,13.3592,0.275488,6113.35,H42119SW1_1570_1640,67925,14,37,51,0.00020611,0.000544718,0.000750828,-23,238,0.12,0.3 +239,Sb5_1_239,Sb5_1,-78.3798377175,40.184689554,12.1108,0.279878,24007.88,N42009SJ2_2570_2660,266417,83676,28932,112608,0.314079057,0.108596674,0.42267573,54744,239,0.12,0.3 +240,Sb5_1_240,Sb5_1,-78.5839128841,40.1717442521,12.0538,0.252931,41977.72,N42009SJ2_2530_2820,465663,41057,86598,127655,0.088168912,0.185967105,0.274136017,-45541,240,0.12,0.3 +241,Sb5_1_241,Sb5_1,-78.2338273797,40.1738610713,28.3945,0.257783,6652.29,N42009SJ4_2660_2360,73827,1350,3891,5241,0.018285993,0.052704295,0.070990288,-2541,241,0.12,0.3 +242,Sb5_1_242,Sb5_1,-78.2407479596,40.2156213938,23.1777,0.278456,5092.57,N42009SJ4_2360_2340,56522,2590,3508,6098,0.045822865,0.062064329,0.107887194,-918,242,0.12,0.3 +243,Sb5_1_243,Sb5_1,-78.3584112435,39.9956360734,22.5051,0.257783,60572.95,N42009SJ4_2740_2660,672113,70197,117905,188102,0.104442259,0.175424371,0.27986663,-47708,243,0.12,0.3 +244,Sb5_1_244,Sb5_1,-78.2101245931,39.9494148555,11.1695,0.276558,9214.4,N42057SJ4_2740_2660,102155,3677,11643,15320,0.035994322,0.113973863,0.149968186,-7966,244,0.12,0.3 +245,Sb5_1_245,Sb5_1,-78.6306158767,40.0027354279,13.1847,0.283873,34662.85,N42009SJ2_2900_2740,384498,15615,58489,74104,0.040611395,0.152117826,0.192729221,-42874,245,0.12,0.3 +246,Sb5_1_246,Sb5_1,-78.803510516,39.9754528939,17.2242,0.280144,7131.67,N42111SJ2_2900_2740,78966,245,5345,5590,0.003102601,0.067687359,0.07078996,-5100,246,0.12,0.3 +247,Sb5_1_247,Sb5_1,-78.116211089,40.313093492,16.5769,0.278456,48171.69,N42061SJ4_2360_2340,534731,17238,43428,60666,0.03223677,0.081214667,0.113451436,-26190,247,0.12,0.3 +248,Sb5_1_248,Sb5_1,-78.4560177791,40.076082322,16.4281,0.286623,6438.16,N42009SJ2_2820_2740,71446,4544,17175,21719,0.063600481,0.240391345,0.303991826,-12631,248,0.12,0.3 +249,Sb5_2_249,Sb5_2,-77.9884816931,40.6159600499,18.9785,0.301795,20326.24,N42061SJ4_2060_2010,225647,20397,23785,44182,0.090393402,0.105408005,0.195801407,-3388,249,0.12,0.3 +250,Sb5_2_250,Sb5_2,-78.0871704978,40.6657187689,14.7366,0.292281,16197.82,N42061SJ3_1980_2060,179860,47357,14343,61700,0.263299233,0.079745358,0.34304459,33014,250,0.12,0.3 +251,Sb5_2_251,Sb5_2,-77.9048720309,40.585898205,14.6366,0.278817,43399.29,N42061SJ4_2010_2210,481871,26438,40030,66468,0.054865306,0.083072026,0.137937332,-13592,251,0.12,0.3 +252,Sb5_2_252,Sb5_2,-78.302662737,40.6247120566,14.1249,0.277941,42483.32,N42013SJ3_2040_1980,471492,31400,36472,67872,0.0665971,0.077354441,0.143951541,-5072,252,0.12,0.3 +253,Sb5_2_253,Sb5_2,-78.2115579236,40.7636279818,11.8873,0.255192,6107.14,N42027SJ3_2040_1980,67784,175,2138,2313,0.00258173,0.031541367,0.034123097,-1963,253,0.12,0.3 +254,Sb5_2_254,Sb5_2,-78.2227250437,40.4117782296,16.0428,0.27601,18852.39,N42013SJ3_2230_2060,209252,65129,14802,79931,0.311246726,0.070737675,0.381984402,50327,254,0.12,0.3 +255,Sb5_2_255,Sb5_2,-78.1245776088,40.5463217345,18.5651,0.324109,7985.62,N42061SJ3_2230_2060,88636,12379,13979,26358,0.139661086,0.157712442,0.297373528,-1600,255,0.12,0.3 +256,Sb5_2_256,Sb5_2,-78.3780684015,40.4094498955,13.6198,0.278304,58730.64,N42013SJ3_2250_2230,651779,71753,70510,142263,0.110087929,0.10818084,0.218268769,1243,256,0.12,0.3 +257,Sb5_2_257,Sb5_2,-78.5225049337,40.4362002835,16.6243,0.249827,10727.11,H42013SJ3_2250_2230,119036,435,2017,2452,0.003654357,0.016944454,0.02059881,-1582,257,0.12,0.3 +258,Sb5_2_258,Sb5_2,-77.9867615047,40.7521044468,4.50807,0.289758,15446.86,N42027SJ3_1980_2060,171498,46530,16650,63180,0.271315117,0.09708568,0.368400798,29880,258,0.12,0.3 +259,Sb5_3_259,Sb5_3,-77.2305497097,40.5961808056,23.6745,0.284577,27033.29,N42067SJ6_1970_2130,300331,65948,44224,110172,0.219584392,0.147250867,0.366835258,21724,259,0.12,0.3 +260,Sb5_3_260,Sb5_3,-77.5977674225,40.6790409691,14.1201,0.251023,47636.37,N42087SJ2_1900_1930,529136,105610,56466,162076,0.19958952,0.106713586,0.306303105,49144,260,0.12,0.3 +261,Sb5_3_261,Sb5_3,-77.9718609039,40.166124107,17.3279,0.267515,25739.15,N42061SJ2_2580_2500,285752,11580,38953,50533,0.040524651,0.136317506,0.176842157,-27373,261,0.12,0.3 +262,Sb5_3_262,Sb5_3,-78.0479085557,40.0694989889,13.684,0.270688,27064.9,N42057SJ2_2580_2500,300358,11968,39843,51811,0.039845784,0.132651702,0.172497486,-27875,262,0.12,0.3 +263,Sb5_3_263,Sb5_3,-77.8822208578,40.305040506,21.1632,0.250267,8795.92,N42061SJ3_2450_2120,97686,8838,17566,26404,0.090473558,0.179821059,0.270294617,-8728,263,0.12,0.3 +264,Sb5_3_264,Sb5_3,-77.8425549612,40.2297799259,16.2043,0.261403,15053.58,N42061SJ1_2280_2450,167132,5157,16062,21219,0.03085585,0.096103679,0.126959529,-10905,264,0.12,0.3 +265,Sb5_3_265,Sb5_3,-77.9140271465,40.4559249578,28.0488,0.25683,14495.28,N42061SJ5_2210_2320,160931,9140,10486,19626,0.056794527,0.06515836,0.121952887,-1346,265,0.12,0.3 +266,Sb5_3_266,Sb5_3,-77.575129333,40.3474786452,15.0742,0.26481,5491.19,N42099SJ3_2160_2170,60987,618,1555,2173,0.010133307,0.025497237,0.035630544,-937,266,0.12,0.3 +267,Sb5_3_267,Sb5_3,-77.5792092565,40.4189193931,18.1346,0.271265,33454.16,N42067SJ3_2160_2170,371536,42432,45084,87516,0.114206968,0.121344903,0.235551871,-2652,267,0.12,0.3 +268,Sb5_3_268,Sb5_3,-77.7683451758,40.259651344,11.9816,0.296323,10075.8,N42061SJ3_2160_2170,111881,7253,9924,17177,0.064827808,0.088701388,0.153529196,-2671,268,0.12,0.3 +269,Sb5_3_269,Sb5_3,-77.9350499752,40.3489171841,35.2188,0.293839,9693.86,N42061SJ5_2320_2120,107647,1567,7061,8628,0.014556839,0.065594025,0.080150863,-5494,269,0.12,0.3 +270,Sb5_3_270,Sb5_3,-77.3140246948,40.4497488358,14.2318,0.272687,19016.68,N42099SJ2_2150_2130,211260,32221,29626,61847,0.152518224,0.140234782,0.292753006,2595,270,0.12,0.3 +271,Sb5_3_271,Sb5_3,-77.3618020452,40.6230156458,34.1431,0.28643,17613.84,N42067SJ6_1930_1970,195693,31059,22426,53485,0.158712882,0.114597865,0.273310747,8633,271,0.12,0.3 +272,Sb5_3_272,Sb5_3,-77.4514540005,40.6642465647,24.2254,0.252859,15808.73,N42087SJ6_1930_1970,175589,16271,19682,35953,0.092665258,0.112091304,0.204756562,-3411,272,0.12,0.3 +273,Sb5_3_273,Sb5_3,-77.1788230559,40.5342641263,24.5473,0.284577,13911.57,N42099SJ6_1970_2130,154572,30723,14938,45661,0.198761742,0.096641048,0.29540279,15785,273,0.12,0.3 +274,Sb5_3_274,Sb5_3,-77.7216387553,40.4897384513,37.8374,0.261502,32852.56,N42087SJ5_2120_1930,364827,51977,37659,89636,0.142470267,0.103224268,0.245694535,14318,274,0.12,0.3 +275,Sb5_3_275,Sb5_3,-77.1146224484,40.4591730761,34.6398,0.273619,13578.69,N42099SJ6_2130_2050,150888,16391,19766,36157,0.108630242,0.130997826,0.239628069,-3375,275,0.12,0.3 +276,Sb5_3_276,Sb5_3,-77.5106496036,40.537177924,15.2428,0.252859,9694.49,N42067SJ2_2110_2170,107682,8836,10096,18932,0.082056425,0.093757545,0.175813971,-1260,276,0.12,0.3 +277,Sb5_3_277,Sb5_3,-77.7001156995,40.2277654299,11.9012,0.286623,6204.17,N42055SJ3_2160_2170,68882,7622,13752,21374,0.110653001,0.199645771,0.310298772,-6130,277,0.12,0.3 +278,Sb6_1_278,Sb6_1,-77.0473454561,40.6446940217,9.6428,0.274585,7262.15,N42067SL8_1880_2030,80690,14994,8835,23829,0.185822283,0.109493122,0.295315405,6159,278,0.12,0.3 +279,Sb6_1_279,Sb6_1,-76.7309844876,40.6712402403,16.033,0.283627,9543.42,N42097SL2_1810_2030,106067,47227,12484,59711,0.4452563,0.11769919,0.56295549,34743,279,0.12,0.3 +280,Sb6_1_280,Sb6_1,-76.8629248308,40.6761746216,53.6222,0.282189,6630.26,N42097SL8_1880_2030,73681,14793,6868,21661,0.200770891,0.093212633,0.293983524,7925,280,0.12,0.3 +281,Sb6_1_281,Sb6_1,-76.7468094593,40.6253912276,19.9051,0.283627,7371.93,N42043SL2_1810_2030,81953,27173,8309,35482,0.331568094,0.101387381,0.432955474,18864,281,0.12,0.3 +282,Sb6_1_282,Sb6_1,-76.5220823322,40.6689088583,13.7413,0.266666,25706.95,N42107SL2_1810_2030,285721,72879,20408,93287,0.255070506,0.071426321,0.326496827,52471,282,0.12,0.3 +283,Sb6_1_283,Sb6_1,-77.0174614539,40.5762371809,9.43282,0.276606,6226.28,N42099SL8_2030_2080,69185,17941,8468,26409,0.259319217,0.122396473,0.38171569,9473,283,0.12,0.3 +284,Sb6_1_284,Sb6_1,-76.7825433469,40.5733467049,11.4989,0.225206,21926.13,N42043SL3_1960_2080,243661,72548,23062,95610,0.297741534,0.094647892,0.392389426,49486,284,0.12,0.3 +285,Sb6_1_285,Sb6_1,-76.8754850706,40.4764303555,31.5738,0.285217,22713.54,N42043SL8_2080_2050,252412,51940,33773,85713,0.205774686,0.133801087,0.339575773,18167,285,0.12,0.3 +286,Sb6_1_286,Sb6_1,-77.0024620684,40.4976468626,10.4296,0.273619,5457.6,N42099SL8_2080_2050,60638,9644,7614,17258,0.159042185,0.125564827,0.284607012,2030,286,0.12,0.3 +287,Sb6_1_287,Sb6_1,-77.135214912,40.7748912773,11.6761,0.28375,44198.26,N42109SL1_1790_1840,491110,88525,61289,149814,0.180254933,0.124796889,0.305051821,27236,287,0.12,0.3 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+2017-01-01,84.48707753424657,277.4114569863014,530.814418630137,339.23504986301367,114.66639178082193,1146.3799939726027 +2018-01-01,131.4039791780822,326.07352958904113,799.6025794520548,610.9922134246575,256.64217479452054,2052.5516671232876 +2019-01-01,85.88654602739726,269.6188167123288,564.256224109589,405.00383123287673,149.05437287671234,1378.5540863013698 +2020-01-01,67.88282786885246,198.59958469945357,406.99818934426236,278.957843715847,112.89408442622951,977.7575027322404 diff --git a/tutorials/Tutorial_3-Sediment_Routing/Inputs/cli_sed_sim_1999_2020.pkl b/tutorials/Tutorial_3-Sediment_Routing/Inputs/cli_sed_sim_1999_2020.pkl new file mode 100644 index 0000000..693df10 Binary files /dev/null and b/tutorials/Tutorial_3-Sediment_Routing/Inputs/cli_sed_sim_1999_2020.pkl differ diff --git a/tutorials/Tutorial_3-Sediment_Routing/Inputs/hydro_cali_1984_2020.pkl b/tutorials/Tutorial_3-Sediment_Routing/Inputs/hydro_cali_1984_2020.pkl new file mode 100644 index 0000000..593950d 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Here, we demostrate how to construct a model that has sediment routing feature activated. We adopted a subbasin, Sb5, in Susquehena River Basin. For more details, please refer to (Lin et al., 2023).\n", + "\n", + "Lin, C.-Y., Yang, Y. E., & Chaudhary, A. K. (2023). Pay-for-practice or Pay-for-performance? A coupled agent-based evaluation tool for assessing sediment management incentive policies. Journal of Hydrology, 624, 129959.\n", + "\n", + "\"SRB_map\"\n", + "\n", + "## Create a draft `model.yaml` using model builder" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Follow the following steps to create model & ABM script templates:\n", + "\tStep 1: set_water_system()\n", + "\tStep 2: set_rainfall_runoff()\n", + "\tStep 3: set_routing_outlet(), one at a time.\n", + "\tStep 4: (optional) set_sediment().\n", + "\tStep 5: (optional) add_sediment(), one at a time.\n", + "\tStep 6: (optional) set_ABM().\n", + "\tStep 7: (optional) add_agent().\n", + "\tStep 8: (optional) add_institution().\n", + "\tStep 9: write_model_to_yaml()\n", + "\tStep 10: (optional) gen_ABM_script_template()\n", + "Open the generated draft 'model.yaml' (& ABM module template) and further edit them.\n", + "Use .help to re-print the above instructions.\n", + "[09/07 11:04:04] HydroCNHS.sc [INFO] Model is saved at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_3-Sediment_Routing\\model.yaml.\n", + "Model configuration file (.yaml) have been save at C:\\Users\\CL\\Documents\\GitHub\\HydroCNHS\\tutorials\\Tutorial_3-Sediment_Routing\\model.yaml. Please open the file and further edit it.\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "import hydrocnhs\n", + "\n", + "# Set the working directory\n", + "wd = os.path.abspath(\n", + " os.path.join(\n", + " os.path.dirname(hydrocnhs.__file__),\n", + " '..', 'tutorials',\n", + " 'Tutorial_3-Sediment_Routing'\n", + " )\n", + " )\n", + "data_path = os.path.join(wd, \"Data\")\n", + "\n", + "# We only model a subbasin (Sb5) in this tutorial\n", + "sb = \"Sb5\"\n", + "\n", + "# Load data\n", + "df_hu8 = pd.read_csv(os.path.join(data_path, \"SRB_HU8.csv\"))\n", + "df_hu8.index = list(df_hu8.ID)\n", + "df_hu8_sb = df_hu8.loc[[x for x in df_hu8[\"ID\"] if sb in x], :]\n", + "\n", + "df_agt = pd.read_csv(os.path.join(data_path, \"agt_shp_info.csv\"))\n", + "\n", + "# Create a model using model builder\n", + "mb = hydrocnhs.ModelBuilder(wd)\n", + "mb.set_water_system(\"1984/01/01\", \"2020/12/31\")\n", + "mb.set_rainfall_runoff(\n", + " list(df_hu8_sb[\"ID\"]),\n", + " list(df_hu8_sb[\"area_ha\"]),\n", + " list(df_hu8_sb[\"central_la\"]),\n", + " runoff_model=\"GWLF\"\n", + " )\n", + "\n", + "mb.set_routing_outlet(\n", + " routing_outlet=\"Sb5_3\",\n", + " upstream_outlet_list=[\"Sb5_1\", \"Sb5_2\", \"Sb5_3\"],\n", + " flow_length_list=[113610, 113610, 0] # km\n", + " )\n", + "\n", + "# Activate sediment routing section in the model\n", + "mb.set_sediment(start_month=4)\n", + "\n", + "outlets = mb.model[\"WaterSystem\"][\"Outlets\"]\n", + "for o in outlets:\n", + " mb.add_sediment(\n", + " subbasin=o,\n", + " area_list=list(df_agt.loc[df_agt[\"ID\"]==o, \"area_ha\"]),\n", + " cool_months=[10,11,12,1,2,3],\n", + " K_list=list(df_agt.loc[df_agt[\"ID\"]==o, \"Value_K\"]),\n", + " Ac=0.12, Aw=0.3,\n", + " LS_list=list(df_agt.loc[df_agt[\"ID\"]==o, \"Value_LS\"]),\n", + " DR=float(df_hu8.loc[o, \"DR\"]),\n", + " id_list=list(df_agt.loc[df_agt[\"ID\"]==o, \"Agt_ID\"])\n", + " )\n", + "\n", + "mb.write_model_to_yaml(filename=\"model.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We encourage you to open `model.yaml` to give a sense of how it looks like. Users can follow the calibration steps shown in the previous tutorials to calibrate the model. Here we will skip the calibration step and adopted the calibrated parameters used in (Lin et al., 2023).\n", + "\n", + "The calibrated model is located in `./Calibrated_model/Best_hydro_sed_Sb5_seed3_iter100.yaml`.\n", + "\n", + "Now, let's used the calibrated model to run a simulation.\n", + "\n", + "## [Pending for completion] Run sediment routing simulation " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Load data\n", + "inputs_path = os.path.join(wd, \"Inputs\")\n", + "\n", + "with open(os.path.join(inputs_path, \"hydro_cali_1984_2020.pkl\"), \"rb\") as file:\n", + " (prec, temp, pet, Q_M, Q_Y, sed_M, sed_Y, _) = pickle.load(file)\n", + "\n", + "with open(os.path.join(inputs_path, \"cli_sed_sim_1999_2020.pkl\"), \"rb\") as file:\n", + " prec_sed = pickle.load(file)[0]\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 11:36:27] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['Sb5_3']\n", + "RoutingOutlets\n", + " ['Sb5_3']\n", + "DamAgents\n", + " None\n", + "ConveyingAgents\n", + " None\n", + "RiverDivAgents\n", + " None\n", + "InsituAgents\n", + " None\n", + "AgSimSeq\n", + " None\n", + "[09/07 11:36:27] HydroCNHS.sc [INFO] Parsed model data summary:\n", + "SimSeq\n", + " ['Sb5_3']\n", + "RoutingOutlets\n", + " ['Sb5_3']\n", + "DamAgents\n", + " None\n", + "ConveyingAgents\n", + " None\n", + "RiverDivAgents\n", + " None\n", + "InsituAgents\n", + " None\n", + "AgSimSeq\n", + " None\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field Q_runoff ().\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field Q_routed ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field TSS ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field Q_frac ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field prec ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field temp ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field pet ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field UH_Lohmann ().\n", + "[09/07 11:36:27] HydroCNHS.dc [INFO] Add field UH_Lohmann_convey ().\n", + "[09/07 11:36:27] HydroCNHS [INFO] Load temp & prec & pet with total length 13515.\n", + "[09/07 11:36:27] HydroCNHS [INFO] Set rainfall-runoff to GWLF.\n", + "[09/07 11:36:27] HydroCNHS [INFO] Complete rainfall-runoff simulation setup. [00:00:00]\n", + "[09/07 11:36:28] HydroCNHS [INFO] Start forming 3 UHs for Lohmann routing. [00:00:00]\n", + "[09/07 11:36:31] HydroCNHS [INFO] Complete forming UHs for Lohmann routing. [00:00:03]\n", + "[09/07 11:36:31] HydroCNHS [INFO] Start a pure hydrological simulation (no human component).\n", + "[09/07 11:36:33] HydroCNHS [INFO] \n", + "Compute rainfall-runoffs for 13515 time steps.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "13515it [00:00, 55263.75it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[09/07 11:36:34] HydroCNHS [INFO] Complete HydroCNHS simulation! [00:00:06]\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Load model.yaml\n", + "model_dict = hydrocnhs.load_model(\n", + " os.path.join(wd, \"Calibrated_model\", \"Best_hydro_sed_Sb5_seed3_iter100.yaml\")\n", + " )\n", + "# Change the working directory\n", + "model_dict[\"Path\"][\"WD\"] = wd\n", + "\n", + "# Run the model\n", + "model = hydrocnhs.Model(model_dict)\n", + "Q = model.run(temp, prec, pet)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Run sediment simulation\n", + "sed_sim_period = (\"1999-1-1\", \"2019-12-31\") # -1 and +1 year\n", + "sed_perf_period = (\"2000-1-1\", \"2019-12-31\")\n", + "dc = model.dc\n", + "Q_frac = dc.get_field(\"Q_frac\", copy=True)\n", + "Q_frac = {ro: pd.DataFrame(sbs, index=model.pd_date_index)[sed_sim_period[0]:\"2020-12-31\"] \\\n", + " for ro, sbs in Q_frac.items()}\n", + "sim_seq = model.sys_parsed_data[\"SimSeq\"]\n", + "\n", + "_, _, sim_sed_M, sim_sed_Y = sim_sed(\n", + " model_dict, prec_sed, Q_frac, sed_sim_period[0], sed_sim_period[1], sim_seq)\n", + "\n", + "df_perf_sed_M = cal_batch_indicator(sed_perf_period, cali_target, sed_M, sim_sed_M)\n", + "df_perf_sed_Y = cal_batch_indicator(sed_perf_period, cali_target, sed_Y, sim_sed_Y)\n", + "fitness = (df_cali_Q_M.loc[\"Mean\", \"KGE\"] + df_perf_sed_M.loc[\"Mean\", \"KGE\"])/2\n", + "##### Record performance\n", + "performance[f] = [df_cali_Q_M.loc[\"Mean\", \"KGE\"], df_perf_sed_M.loc[\"Mean\", \"KGE\"],\n", + " df_cali_Q_Y.loc[\"Mean\", \"KGE\"], df_perf_sed_Y.loc[\"Mean\", \"KGE\"],\n", + " fitness]\n", + "sim_sed_M_dict[f] = sim_sed_M[sed_perf_period[0]: sed_perf_period[1]]\n", + "sim_sed_Y_dict[f] = sim_sed_Y[sed_perf_period[0]: sed_perf_period[1]]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Sb5_1': array([400.40242073, 171.15351551, 76.21969939, ..., 61.01088311,\n", + " 28.18771103, 13.18862614]),\n", + " 'Sb5_2': array([505.02861438, 307.47435197, 200.20797688, ..., 66.26275449,\n", + " 60.26508671, 30.73452566]),\n", + " 'Sb5_3': array([1.26208804e-02, 3.34247654e-01, 1.99366605e+00, ...,\n", + " 1.05574546e+02, 1.52564172e+02, 2.25909361e+02])}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Q" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Sb1_2 Sb2_3 Sb3_2 Sb4_6 Sb5_3 \\\n", + "2000-01-01 NaN NaN 22638.77672 4288.71236 1387.855442 \n", + "2000-02-01 NaN NaN 199943.35360 64586.96488 6708.625680 \n", + "2000-03-01 NaN NaN 285445.44560 58917.06488 21373.255040 \n", + "2000-04-01 NaN NaN 190508.64000 60146.29920 14968.536000 \n", + "2000-05-01 NaN NaN 99554.37216 15467.48720 9308.615024 \n", + "\n", + " Sb6_2 \n", + "2000-01-01 53292.52408 \n", + "2000-02-01 230197.94000 \n", + "2000-03-01 492147.32000 \n", + "2000-04-01 372852.62400 \n", + "2000-05-01 244667.52480 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "##### Get simulation data\n", + "sim_Q_D = pd.DataFrame(Q, index=model.pd_date_index)[['Sb5_3']]\n", + "# Resample the daily simulation output to monthly and annually outputs.\n", + "sim_Q_M = sim_Q_D.resample(\"MS\").mean()\n", + "sim_Q_Y = sim_Q_D.resample(\"YS\").mean()\n", + "\n", + "sim_Q_M.head()\n", + "sed_M.head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cnhs", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/Tutorial_3-Sediment_Routing/model.yaml b/tutorials/Tutorial_3-Sediment_Routing/model.yaml new file mode 100644 index 0000000..0437130 --- /dev/null +++ b/tutorials/Tutorial_3-Sediment_Routing/model.yaml @@ -0,0 +1,99 @@ +Path: {WD: 'C:\Users\CL\Documents\GitHub\HydroCNHS\tutorials\Tutorial_3-Sediment_Routing', + Modules: ''} +WaterSystem: + StartDate: 1984/01/01 + EndDate: 2020/12/31 + DataLength: 13515 + NumSubbasins: 3 + Outlets: [Sb5_1, Sb5_2, Sb5_3] + NodeGroups: [] + RainfallRunoff: GWLF + Routing: Lohmann + ABM: null + Sediment: {StartMonth: 4} +RainfallRunoff: + Sb5_1: + Inputs: {Area: 249499.9087, Latitude: 40.12180448, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, + Df: -99, Kc: -99} + Sb5_2: + Inputs: {Area: 256889.9875, Latitude: 40.54906266, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, + Df: -99, Kc: -99} + Sb5_3: + Inputs: {Area: 375813.597, Latitude: 40.44323525, S0: 2, U0: 10, SnowS: 5} + Pars: {CN2: -99, IS: -99, Res: -99, Sep: -99, Alpha: -99, Beta: -99, Ur: -99, + Df: -99, Kc: -99} +Routing: + Sb5_3: + Sb5_1: + Inputs: {FlowLength: 113610, InstreamControl: false} + Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} + Sb5_2: + Inputs: {FlowLength: 113610, InstreamControl: false} + Pars: {GShape: -99, GScale: -99, Velo: -99, Diff: -99} + Sb5_3: + Inputs: {FlowLength: 0, InstreamControl: false} + Pars: {GShape: -99, GScale: -99, Velo: null, Diff: null} +Sediment: + Sb5_1: + Inputs: + IDs: [239, 240, 241, 242, 243, 244, 245, 246, 247, 248] + Areas: [24007.88, 41977.72, 6652.29, 5092.57, 60572.95, 9214.4, 34662.85, 7131.67, + 48171.69, 6438.16] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + K: [0.279878, 0.252931, 0.257783, 0.278456, 0.257783, 0.276558, 0.283873, 0.280144, + 0.278456, 0.286623] + Ac: 0.12 + Aw: 0.3 + LS: [12.1108, 12.0538, 28.3945, 23.1777, 22.5051, 11.1695, 13.1847, 17.2242, + 16.5769, 16.4281] + DR: 0.17679259 + CP: -99 + Sa: -99 + Sb: -99 + Sq: -99 + Sb5_2: + Inputs: + IDs: [249, 250, 251, 252, 253, 254, 255, 256, 257, 258] + Areas: [20326.24, 16197.82, 43399.29, 42483.32, 6107.14, 18852.39, 7985.62, + 58730.64, 10727.11, 15446.86] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + K: [0.301795, 0.292281, 0.278817, 0.277941, 0.255192, 0.27601, 0.324109, 0.278304, + 0.249827, 0.289758] + Ac: 0.12 + Aw: 0.3 + LS: [18.9785, 14.7366, 14.6366, 14.1249, 11.8873, 16.0428, 18.5651, 13.6198, + 16.6243, 4.50807] + DR: 0.176148707 + CP: -99 + Sa: -99 + Sb: -99 + Sq: -99 + Sb5_3: + Inputs: + IDs: [259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, + 273, 274, 275, 276, 277] + Areas: [27033.29, 47636.37, 25739.15, 27064.9, 8795.92, 15053.58, 14495.28, + 5491.19, 33454.16, 10075.8, 9693.86, 19016.68, 17613.84, 15808.73, 13911.57, + 32852.56, 13578.69, 9694.49, 6204.17] + CoolMonths: [10, 11, 12, 1, 2, 3] + WarmMonths: [4, 5, 6, 7, 8, 9] + Pars: + K: [0.284577, 0.251023, 0.267515, 0.270688, 0.250267, 0.261403, 0.25683, 0.26481, + 0.271265, 0.296323, 0.293839, 0.272687, 0.28643, 0.252859, 0.284577, 0.261502, + 0.273619, 0.252859, 0.286623] + Ac: 0.12 + Aw: 0.3 + LS: [23.6745, 14.1201, 17.3279, 13.684, 21.1632, 16.2043, 28.0488, 15.0742, + 18.1346, 11.9816, 35.2188, 14.2318, 34.1431, 24.2254, 24.5473, 37.8374, 34.6398, + 15.2428, 11.9012] + DR: 0.167967902 + CP: -99 + Sa: -99 + Sb: -99 + Sq: -99 diff --git a/tutorials/calibration_example.py b/tutorials/calibration_example.py index 69ad03e..692d76e 100644 --- a/tutorials/calibration_example.py +++ b/tutorials/calibration_example.py @@ -1,11 +1,13 @@ import os import HydroCNHS import HydroCNHS.calibration as cali + prj_path, this_filename = os.path.split(__file__) + def evaluation(individual, info): x = individual - fitness = -x[0]**2 + 5*x[0] - x[1] + fitness = -x[0] ** 2 + 5 * x[0] - x[1] return (fitness,) cali_wd, current_generation, ith_individual, formatter, _ = info @@ -15,32 +17,37 @@ def evaluation(individual, info): # Convert 1D array to a list of dataframes. df_list = cali.Convertor.to_df_list(individual, formatter) # Feed dataframes in df_list to model dictionary. - + + # Calibration inputs -cali.get_inputs_template() # print an input template. +cali.get_inputs_template() # print an input template. -inputs = {'par_name': ['x1', 'x2'], - 'par_bound': [[-5, 5], [-5, 5]], - 'wd': 'working directory'} +inputs = { + "par_name": ["x1", "x2"], + "par_bound": [[-5, 5], [-5, 5]], + "wd": "working directory", +} inputs["wd"] = prj_path # GA configuration cali.get_config_template() -config = {'min_or_max': 'max', # maximize or minimize the evaluation function. - 'pop_size': 100, # Size of the population. - 'num_ellite': 1, # Number of ellites. - 'prob_cross': 0.5, # Crossover probability for uniform crossover operator. - 'prob_mut': 0.15, # Mutation probability of each parameter. - 'stochastic': False, # Is the evaluation stochastic? - 'max_gen': 100, # Maximum generation number. - 'sampling_method': 'LHC', # Sampling method for the initial population. - 'drop_record': False, # Whether to drop historical records to save space. - 'paral_cores': -1, # Number of parallel cores. -1 means all available cores. - 'paral_verbose': 1, # Higher value will output more console messages. - 'auto_save': True, # If true, users may continue the run later on by loading the auto-save file. - 'print_level': 1, # Control the number of generations before the printing summary of GA run. - 'plot': True} # Plot to time series of the best fitnesses over a generation. +config = { + "min_or_max": "max", # maximize or minimize the evaluation function. + "pop_size": 100, # Size of the population. + "num_ellite": 1, # Number of ellites. + "prob_cross": 0.5, # Crossover probability for uniform crossover operator. + "prob_mut": 0.15, # Mutation probability of each parameter. + "stochastic": False, # Is the evaluation stochastic? + "max_gen": 100, # Maximum generation number. + "sampling_method": "LHC", # Sampling method for the initial population. + "drop_record": False, # Whether to drop historical records to save space. + "paral_cores": -1, # Number of parallel cores. -1 means all available cores. + "paral_verbose": 1, # Higher value will output more console messages. + "auto_save": True, # If true, users may continue the run later on by loading the auto-save file. + "print_level": 1, # Control the number of generations before the printing summary of GA run. + "plot": True, +} # Plot to time series of the best fitnesses over a generation. # Run GA rn_gen = HydroCNHS.create_rn_gen(seed=3) @@ -50,11 +57,10 @@ def evaluation(individual, info): ga.solution # Out[0]: array([ 2.47745344, -4.96991833]) -#%% +# %% # Continue the run with larger "max_gen" ga = cali.GA_DEAP(evaluation, rn_gen) -ga.load(os.path.join(prj_path, "Cali_example", "GA_auto_save.pickle"), - max_gen=120) +ga.load(os.path.join(prj_path, "Cali_example", "GA_auto_save.pickle"), max_gen=120) ga.run() # =====Generation 120===== # Elapsed time 00:00:05 @@ -64,4 +70,3 @@ def evaluation(individual, info): # Std 1.77931 # GA done! - diff --git a/tutorials/data_collector_example.py b/tutorials/data_collector_example.py index e04142f..61e0f72 100644 --- a/tutorials/data_collector_example.py +++ b/tutorials/data_collector_example.py @@ -5,7 +5,7 @@ ### Add fields dc.add_field("field_1", data_type={}, desc="Demo dc ex1.", unit="no unit") -dc.add_field("field 2", data_type=[1,2,3], unit="cm") +dc.add_field("field 2", data_type=[1, 2, 3], unit="cm") ### Print out existed fields dc.list_fields() @@ -29,13 +29,13 @@ # Out[0]: [1, 2, 3] ### Get a field shortcut -shortcut = dc.get_field("field_1", copy=False) +shortcut = dc.get_field("field_1", copy=False) shortcut["new_key"] = "new value" dc.field_1 # Out[0]: {'new_key': 'new value'} ### Get a copy of a field -copied = dc.get_field("field_1", copy=True) +copied = dc.get_field("field_1", copy=True) copied["new_key2"] = "new value2" dc.field_1 # Out[0]: {'new_key': 'new value'} @@ -58,4 +58,3 @@ print(dictionary) # {'field_info': {'field_2': {'type': , 'desc': None, 'unit': 'cm'}}, # 'field_2': [1, 2, 3]} - diff --git a/tutorials/model configuration file.yaml b/tutorials/model configuration file.yaml index def8a60..3dc3c65 100644 --- a/tutorials/model configuration file.yaml +++ b/tutorials/model configuration file.yaml @@ -1,10 +1,10 @@ -Path: +Path: WD: Modules: WaterSystem: StartDate: - EndDate: + EndDate: DataLength: NumSubbasins: Outlets:
@@ -13,7 +13,7 @@ WaterSystem: Routing: ABM: Modules: - InstitDMClasses: + InstitDMClasses: DMClasses: DamAPI: @@ -46,6 +46,6 @@ ABM: Attributes: "agent's attributes dict, e.g., {}" Inputs: Priority: - Links: + Links: "" DMClass: Pars: