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name: readthedocs/actions | ||
on: | ||
pull_request_target: | ||
types: | ||
- opened | ||
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permissions: | ||
pull-requests: write | ||
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jobs: | ||
pull-request-links: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: readthedocs/actions/preview@v1 | ||
with: | ||
project-slug: "readthedocs-preview" |
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# This workflow will install Python dependencies, run tests and lint with a variety of Python versions | ||
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python | ||
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name: Python package | ||
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on: | ||
push: | ||
branches: [ "master" ] | ||
pull_request: | ||
branches: [ "master" ] | ||
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jobs: | ||
build: | ||
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runs-on: ubuntu-latest | ||
strategy: | ||
fail-fast: false | ||
matrix: | ||
python-version: ["3.9", "3.10", "3.11"] | ||
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steps: | ||
- uses: actions/checkout@v3 | ||
- name: Set up Python ${{ matrix.python-version }} | ||
uses: actions/setup-python@v3 | ||
with: | ||
python-version: ${{ matrix.python-version }} | ||
- name: Install dependencies | ||
run: | | ||
python -m pip install --upgrade pip | ||
python -m pip install flake8 pytest | ||
- name: Install package | ||
run: | | ||
python -m pip install -e . | ||
- name: Test with pytest | ||
run: | | ||
pytest |
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from __future__ import annotations | ||
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import logging | ||
from datetime import datetime | ||
from os import PathLike | ||
from pathlib import Path | ||
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import numpy as np | ||
import pandas as pd | ||
import xarray as xr | ||
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from emiproc import PROCESS | ||
from emiproc.exports.netcdf import NetcdfAttributes | ||
from emiproc.grids import RegularGrid | ||
from emiproc.inventories import Inventory | ||
from emiproc.profiles.temporal_profiles import create_scaling_factors_time_serie | ||
from emiproc.profiles.utils import get_desired_profile_index | ||
from emiproc.regrid import remap_inventory | ||
from emiproc.utilities import HOUR_PER_YR, PER_M2_UNITS, SEC_PER_YR, Units | ||
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logger = logging.getLogger(__name__) | ||
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def export_hourly_emissions( | ||
inv: Inventory, | ||
path: PathLike, | ||
start_time: datetime, | ||
end_time: datetime, | ||
netcdf_attributes: NetcdfAttributes, | ||
var_name_format: str = "{substance}_{category}", | ||
filename_format: str = "%Y%m%dT%H%M%SZ.nc", | ||
unit: Units = Units.KG_PER_HOUR, | ||
) -> Path: | ||
"""Export the inventory to hourly netcdf files. | ||
Supports structured and unstructured grids. Supports gridded emissions | ||
and point sources. | ||
A file given at a specific hour is valid for the whole hour. | ||
(ex file for 14h00 is valid from 14h00 to 14h59m59s) | ||
:param inv: the inventory to export | ||
:param path: the path to the output directory | ||
:param netcdf_attributes: NetCDF attributes to add to the file. | ||
These can be generated using | ||
:py:func:`emiproc.exports.netcdf.nc_cf_attributes` . | ||
:param weights_path: Optionally, | ||
The path to the weights file to use for regridding. | ||
If not given, the weights will be calculated on the fly. | ||
:param var_name_format: The format string to use for the variable names. | ||
The format string should contain two named fields: ``substance`` and ``category``. | ||
:param filename_format: The format string to use for the file names. | ||
The format string should contain fields for date and time. | ||
:param unit: The unit of the emissions. | ||
""" | ||
# Check if the inventory is gridded | ||
if inv.gdfs: | ||
raise NotImplementedError("Shapped sources are not implemented yet") | ||
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if inv.t_profiles_indexes is None or inv.t_profiles_groups is None: | ||
raise ValueError( | ||
"The inventory does not contain temporal profiles required for hourly" | ||
" exports." | ||
) | ||
for invalid_dim in ["type", "country", "cell"]: | ||
if invalid_dim in inv.t_profiles_indexes: | ||
raise ValueError(f"Temporal profiles with {invalid_dim=} are not supported") | ||
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grid = inv.grid | ||
is_regular_grid = isinstance(grid, RegularGrid) | ||
crs = grid.crs | ||
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# add the history | ||
netcdf_attributes["emiproc_history"] = str(inv.history) | ||
netcdf_attributes["projection"] = f"{crs}" | ||
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if unit == Units.KG_PER_YEAR: | ||
conversion_factor = 1.0 | ||
elif unit == Units.KG_PER_HOUR: | ||
conversion_factor = 1.0 / HOUR_PER_YR | ||
elif unit == Units.KG_PER_M2_PER_S: | ||
conversion_factor = 1.0 / SEC_PER_YR / np.array(grid.cell_areas) | ||
else: | ||
raise NotImplementedError(f"Unknown {unit=}") | ||
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# Create the scaling factors for all the time profiles | ||
reqired_profiles_indexes = np.unique(inv.t_profiles_indexes) | ||
df_scaling_factors = pd.DataFrame( | ||
{ | ||
index: create_scaling_factors_time_serie( | ||
start_time=start_time, | ||
end_time=end_time, | ||
profiles=inv.t_profiles_groups[index], | ||
) | ||
for index in reqired_profiles_indexes | ||
if index >= -1 | ||
} | ||
) | ||
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coords = { | ||
"substance": inv.substances, | ||
"category": inv.categories, | ||
"cell": np.arange(len(grid)), | ||
} | ||
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if is_regular_grid: | ||
coords["lat"] = ( | ||
"lat", | ||
grid.lat_range, | ||
{ | ||
"standard_name": "latitude", | ||
"long_name": "latitude", | ||
"units": "degrees_north", | ||
"comment": "center_of_cell", | ||
"bounds": "lat_bnds", | ||
"projection": f"{grid.crs}", | ||
"axis": "Y", | ||
}, | ||
) | ||
coords["lon"] = ( | ||
"lon", | ||
grid.lon_range, | ||
{ | ||
"long_name": "longitude", | ||
"units": "degrees_east", | ||
"standard_name": "longitude", | ||
"comment": "center_of_cell", | ||
"bounds": "lon_bnds", | ||
"projection": f"{grid.crs}", | ||
"axis": "X", | ||
}, | ||
) | ||
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data_dim = ["lat", "lon"] if is_regular_grid else ["cell"] | ||
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base_ds = xr.Dataset( | ||
coords=coords, | ||
attrs=netcdf_attributes, | ||
) | ||
if unit in PER_M2_UNITS: | ||
# add the cell area | ||
areas = np.array(grid.cell_areas).reshape(grid.shape).T | ||
if not is_regular_grid: | ||
areas = areas.reshape(-1) | ||
base_ds["cell_area"] = ( | ||
data_dim, | ||
areas, | ||
{ | ||
"standard_name": "cell_area", | ||
"long_name": "cell_area", | ||
"units": "m2", | ||
"comment": "area of the cell", | ||
"projection": f"{crs}", | ||
}, | ||
) | ||
path = Path(path) | ||
logger.log(PROCESS, f"Exporting hourly emissions to {path}") | ||
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# Iterrate over time | ||
for dt, row in df_scaling_factors.iterrows(): | ||
ds = base_ds.copy() | ||
ds["time"] = dt | ||
vars = {} | ||
for cat in inv.categories: | ||
for sub in inv.substances: | ||
# Get the scaling factor | ||
try: | ||
index = get_desired_profile_index( | ||
inv.t_profiles_indexes, cat=cat, sub=sub | ||
) | ||
except ValueError as ve: | ||
logger.warning( | ||
f"Could not find profile for {cat=} {sub=}: {ve} \n Assuming" | ||
" constant profile" | ||
) | ||
index = -1 | ||
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if index == -1: | ||
scaling_factor = 1.0 | ||
else: | ||
scaling_factor = row[index] | ||
if (cat, sub) not in inv.gdf.columns: | ||
# Ignore non present cat-sub | ||
continue | ||
# Get the emissions | ||
emissions = inv.gdf[(cat, sub)].to_numpy().astype(float) | ||
# Multiply by the scaling factor | ||
emissions *= scaling_factor * conversion_factor | ||
name = var_name_format.format(substance=sub, category=cat) | ||
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if is_regular_grid: | ||
emissions = emissions.reshape(grid.shape).T | ||
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vars[name] = xr.DataArray( | ||
emissions, | ||
dims=data_dim, | ||
attrs={ | ||
"standard_name": f"{sub}_{cat}", | ||
"long_name": f"{sub}_{cat}", | ||
"units": str(unit.value), | ||
"comment": f"emissions of {sub} in {cat}", | ||
}, | ||
name=name, | ||
) | ||
# Add to the dataset | ||
ds.update(vars) | ||
dt: pd.Timestamp | ||
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ds.to_netcdf(path / f"{dt.strftime(filename_format)}") |
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