From 55f441ef2322d9d811d62904cd9a884271fcce4b Mon Sep 17 00:00:00 2001
From: Aron Mirwald <aron.mirwald@sed.ethz.ch>
Date: Tue, 27 Aug 2024 19:09:51 +0200
Subject: [PATCH] modified tests

---
 seismostats/analysis/estimate_beta.py         |    74 +-
 .../tests/data/simulated_magnitudes.csv       | 10001 ++++++++++++++++
 .../analysis/tests/test_estimate_beta.py      |   145 +-
 .../analysis/tests/test_estimate_mc.py        |    32 +-
 4 files changed, 10109 insertions(+), 143 deletions(-)
 create mode 100644 seismostats/analysis/tests/data/simulated_magnitudes.csv

diff --git a/seismostats/analysis/estimate_beta.py b/seismostats/analysis/estimate_beta.py
index 462b799..bf38a4b 100644
--- a/seismostats/analysis/estimate_beta.py
+++ b/seismostats/analysis/estimate_beta.py
@@ -41,7 +41,7 @@ def estimate_b(
     weights: list | None = None,
     b_parameter: str = "b_value",
     return_std: bool = False,
-    method="classic",
+    method="tinti",
     return_n: bool = False,
 ) -> float | tuple[float, float] | tuple[float, float, float]:
     """Return the maximum likelihood estimator for the Gutenberg-Richter
@@ -63,9 +63,9 @@ def estimate_b(
         method:     method to use for estimation of beta/b-value. Options
                 are:
 
-                - 'classic',default, this is the is the classic estimator, see
-                  :func:`seismostats.analysis.estimate_b_classic`
-                - 'positive' (this is b-positive, which applies the 'classic'
+                - 'tinti',default, this is the is the classic estimator, see
+                  :func:`seismostats.analysis.estimate_b_tinti`
+                - 'positive' (this is b-positive, which applies the 'tinti'
                   method to the positive differences, see
                   :func:`seismostats.analysis.estimate_b_positive`. To
                   achieve the effect of reduced STAI, the magnitudes must
@@ -98,8 +98,8 @@ def estimate_b(
                 "check if mc is chosen correctly"
             )
 
-    if method == "classic":
-        return estimate_b_classic(
+    if method == "tinti":
+        return estimate_b_tinti(
             magnitudes,
             mc=mc,
             delta_m=delta_m,
@@ -118,10 +118,10 @@ def estimate_b(
         )
 
     else:
-        raise ValueError("method must be either 'classic' or 'positive'")
+        raise ValueError("method must be either 'tinti' or 'positive'")
 
 
-def estimate_b_classic(
+def estimate_b_tinti(
     magnitudes: np.ndarray,
     mc: float,
     delta_m: float = 0,
@@ -301,7 +301,7 @@ def estimate_b_positive(
     # previous one. delta_m is added to avoid numerical errors
     mag_diffs = abs(mag_diffs[mag_diffs > dmc - delta_m / 2])
 
-    out = estimate_b_classic(
+    out = estimate_b_tinti(
         mag_diffs,
         mc=dmc,
         delta_m=delta_m,
@@ -375,7 +375,7 @@ def estimate_b_more_positive(
     # only take the values where the next earthquake is larger
     mag_diffs = abs(mag_diffs[mag_diffs > - delta_m / 2])
 
-    out = estimate_b_classic(
+    out = estimate_b_tinti(
         mag_diffs,
         mc=dmc,
         delta_m=delta_m,
@@ -447,60 +447,6 @@ def make_more_incomplete(
     return magnitudes, times
 
 
-def estimate_b_laplace(
-    magnitudes: np.ndarray,
-    delta_m: float = 0,
-    b_parameter: str = "b_value",
-    return_std: bool = False,
-    return_n: bool = False,
-) -> float | tuple[float, float]:
-    """Return the b-value estimate calculated using
-    all the  differences between magnitudes.
-    (This has a little less variance than the
-    :func:`seismostats.analysis.estimate_b_positive`
-    method.)
-
-    Source:
-        Van der Elst 2021 (J Geophysical Research: Solid Earth, Vol 126, Issue
-        2)
-
-    Args:
-        magnitudes: vector of magnitudes differences, sorted in time (first
-                    entry is the earliest earthquake)
-        delta_m:    discretization of magnitudes. default is no discretization
-        b_parameter:either 'b-value', then the corresponding value  of the
-                Gutenberg-Richter law is returned, otherwise 'beta' from the
-                exponential distribution [p(M) = exp(-beta*(M-mc))]
-        return_std: if True the standard deviation of beta/b-value (see above)
-                is returned
-
-    Returns:
-        b:      maximum likelihood beta or b-value, depending on value of
-                input variable 'b_parameter'. Note that the difference is just a
-                factor [b_value = beta * log10(e)]
-        std:    Shi and Bolt estimate of the beta/b-value estimate
-    """
-    mag_diffs = differences(magnitudes)
-    mag_diffs = abs(mag_diffs)
-    mag_diffs = mag_diffs[mag_diffs > 0]
-
-    out = estimate_b_classic(
-        mag_diffs,
-        mc=delta_m,
-        delta_m=delta_m,
-        b_parameter=b_parameter,
-        return_std=return_std,
-    )
-
-    if return_n:
-        if type(out) is tuple:
-            return out + tuple([len(mag_diffs)])
-        else:
-            return out, len(mag_diffs)
-    else:
-        return out
-
-
 def estimate_b_weichert(
     magnitudes: np.ndarray,
     dates: list[np.datetime64],
diff --git a/seismostats/analysis/tests/data/simulated_magnitudes.csv b/seismostats/analysis/tests/data/simulated_magnitudes.csv
new file mode 100644
index 0000000..aabe031
--- /dev/null
+++ b/seismostats/analysis/tests/data/simulated_magnitudes.csv
@@ -0,0 +1,10001 @@
+b_value = 1,b_value = 1.5,b_value = 0.5
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diff --git a/seismostats/analysis/tests/test_estimate_beta.py b/seismostats/analysis/tests/test_estimate_beta.py
index 1934b99..849bd16 100644
--- a/seismostats/analysis/tests/test_estimate_beta.py
+++ b/seismostats/analysis/tests/test_estimate_beta.py
@@ -4,6 +4,7 @@
 import numpy as np
 import pytest
 import datetime as dt
+import pandas as pd
 
 # import functions to be tested
 from seismostats.analysis.estimate_beta import (
@@ -17,11 +18,14 @@
     shi_bolt_confidence,
     estimate_b_more_positive,
     make_more_incomplete,
+    b_value_to_beta,
 )
-from seismostats.utils.simulate_distributions import simulate_magnitudes_binned
+from seismostats.utils.simulate_distributions import (
+    simulate_magnitudes_binned,
+    bin_to_precision)
 
 
-@pytest.mark.parametrize(
+@ pytest.mark.parametrize(
     "method, return_std, return_n, b_parameter",
     [
         ("classic", True, True, "beta"),
@@ -94,53 +98,70 @@ def test_estimate_b(
         assert w[-1].category == UserWarning
 
 
-@pytest.mark.parametrize(
-    "n,b,mc,delta_m,b_parameter,precision",
-    [
-        (1000000, 1.2, 3, 0, "b_value", 0.005),
-        (1000000, np.log(10), 3, 0.1, "beta", 0.01),
-    ],
-)
-def test_estimate_b_classic(
-    n: int,
-    b: float,
-    mc: float,
-    delta_m: float,
-    b_parameter: str,
-    precision: float,
-):
-    mags = simulate_magnitudes_binned(
-        n, b, mc, delta_m, b_parameter=b_parameter
-    )
-    b_estimate = estimate_b_classic(mags, mc, delta_m, b_parameter=b_parameter)
+def magnitudes(b: float):
+    # print the current directory
+    import os
+    print(os.getcwd())
 
-    assert abs(b - b_estimate) / b <= precision
+    df_mags = pd.read_csv(
+        'seismostats/analysis/tests/data/simulated_magnitudes.csv')
+    if b == 0.5:
+        mags = df_mags["b_value = 0.5"].values
+    elif b == 1:
+        mags = df_mags["b_value = 1"].values
+    elif b == 1.5:
+        mags = df_mags["b_value = 1.5"].values
+    return mags
 
 
-@pytest.mark.parametrize(
-    "n,b,mc,delta_m,b_parameter,precision",
+@ pytest.mark.parametrize(
+    "b, mags, mc, delta_m, precision, b_parameter",
     [
-        (1000000, 1.2 * np.log(10), 3, 0, "beta", 0.005),
-        (1000000, 0.8, 3, 0.1, "b_value", 0.01),
+        (1, magnitudes(1), 0, 0.1, 0.002, "b_value"),
+        (1.5, magnitudes(1.5), 0.5, 0.01, 0.01, "b_value"),
+        (b_value_to_beta(0.5), magnitudes(0.5), 2, 0.2, 0.003, "beta"),
     ],
 )
-def test_estimate_b_utsu(
-    n: int,
+def test_estimate_b_classic(
     b: float,
+    mags: np.ndarray,
     mc: float,
     delta_m: float,
-    b_parameter: str,
     precision: float,
+    b_parameter: str,
 ):
-    mags = simulate_magnitudes_binned(
-        n, b, mc, delta_m, b_parameter=b_parameter
-    )
+    print('test_estimate_b_classic')
+    mags = bin_to_precision(mags, delta_m)
+    mags = mags[mags >= mc - delta_m / 2]
+    b_estimate = estimate_b_classic(mags, mc, delta_m, b_parameter=b_parameter)
 
-    b_estimate = estimate_b_utsu(mags, mc, delta_m, b_parameter=b_parameter)
     assert abs(b - b_estimate) / b <= precision
 
 
-@pytest.mark.parametrize(
+# @ pytest.mark.parametrize(
+#     "b, mags, mc, delta_m, precision, b_parameter",
+#     [
+#         (1, magnitudes(1), 0, 0.01, 0.002, "b_value"),
+#         (1.5, magnitudes(1.5), 0.5, 0.01, 0.01, "b_value"),
+#         (b_value_to_beta(0.5), magnitudes(0.5), 2, 0.2, 0.003, "beta"),
+#     ],
+# )
+# def test_estimate_b_utsu(
+#     b: float,
+#     mags: np.ndarray,
+#     mc: float,
+#     delta_m: float,
+#     precision: float,
+#     b_parameter: str,
+# ):
+#     print('test_estimate_b_utsu')
+#     mags = bin_to_precision(mags, delta_m)
+#     mags = mags[mags >= mc - delta_m / 2]
+#     b_estimate = estimate_b_utsu(mags, mc, delta_m, b_parameter=b_parameter)
+#     assert abs(b - b_estimate) / b <= precision
+
+
+@ pytest.mark.parametrize(
     "magnitudes,mag_diffs",
     [(np.array([1, -2, 3]), np.array([-3, 5, 2]))],
 )
@@ -149,74 +170,72 @@ def test_differences(magnitudes: np.ndarray, mag_diffs: np.ndarray):
     assert (y == mag_diffs).all()
 
 
-@pytest.mark.parametrize(
-    "n,b,mc,delta_m,b_parameter,dmc,precision",
+@ pytest.mark.parametrize(
+    "b, mags, mc, delta_m, dmc, precision, b_parameter",
     [
-        (1000000, 1.2, 3, 0, "b_value", None, 0.005),
-        (1000000, np.log(10), 3, 0.1, "beta", 1, 0.01),
+        (1, magnitudes(1), 0, 0.1, 0.3, 0.008, "b_value"),
+        (1.5, magnitudes(1.5), 0.5, 0.01, None, 0.02, "b_value"),
+        (b_value_to_beta(0.5), magnitudes(0.5), 2, 0.2, None, 0.02, "beta"),
     ],
 )
 def test_estimate_b_positive(
-    n: int,
     b: float,
+    mags: np.ndarray,
     mc: float,
     delta_m: float,
-    b_parameter: str,
     dmc: float,
     precision: float,
+    b_parameter: str,
 ):
-    mags = simulate_magnitudes_binned(
-        n, b, mc, delta_m, b_parameter=b_parameter
-    )
+    mags = bin_to_precision(mags, delta_m)
+    mags = mags[mags >= mc - delta_m / 2]
     b_estimate = estimate_b_positive(
         mags, delta_m=delta_m, dmc=dmc, b_parameter=b_parameter
     )
     assert abs(b - b_estimate) / b <= precision
 
 
-@pytest.mark.parametrize(
-    "n,b,mc,delta_m,b_parameter,precision",
+@ pytest.mark.parametrize(
+    "b, mags, mc, delta_m, precision, b_parameter",
     [
-        (1000, 1.2 * np.log(10), 3, 0, "beta", 0.6),
-        (1000, 1, 3, 0.1, "b_value", 0.2),
+        (1, magnitudes(1)[:1500], 0, 0.01, 0.06, "b_value"),
     ],
 )
 def test_estimate_b_laplace(
-    n: int,
     b: float,
+    mags: np.ndarray,
     mc: float,
     delta_m: float,
-    b_parameter: str,
     precision: float,
+    b_parameter: str,
 ):
-    mags = simulate_magnitudes_binned(
-        n, b, mc, delta_m, b_parameter=b_parameter
-    )
+    mags = bin_to_precision(mags, delta_m)
+    mags = mags[mags >= mc - delta_m / 2]
     b_estimate = estimate_b_laplace(
         mags, delta_m=delta_m, b_parameter=b_parameter
     )
     assert abs(b - b_estimate) / b <= precision
 
 
-@pytest.mark.parametrize(
-    "n,b,mc,delta_m,b_parameter,dmc,precision",
+@ pytest.mark.parametrize(
+    "b, mags, mc, delta_m, dmc, precision, b_parameter",
     [
-        (100000, 1.2 * np.log(10), 3, 0, "beta", None, 0.01),
-        (100000, 1, 3, 0.1, "b_value", 1, 0.04),
+        (1, magnitudes(1), 0, 0.1, 0.3, 0.04, "b_value"),
+        (1.5, magnitudes(1.5), 0.5, 0.01, None, 0.02, "b_value"),
+        (b_value_to_beta(0.5), magnitudes(0.5), 2, 0.2, None, 0.04, "beta"),
     ],
 )
 def test_estimate_b_more_positive(
-    n: int,
     b: float,
+    mags: np.ndarray,
     mc: float,
     delta_m: float,
-    b_parameter: str,
     dmc: float,
     precision: float,
+    b_parameter: str,
 ):
-    mags = simulate_magnitudes_binned(
-        n, b, mc, delta_m, b_parameter=b_parameter
-    )
+    mags = bin_to_precision(mags, delta_m)
+    mags = mags[mags >= mc - delta_m / 2]
     b_estimate = estimate_b_more_positive(
         mags, delta_m=delta_m, dmc=dmc, b_parameter=b_parameter
     )
@@ -328,7 +347,7 @@ def _create_test_catalog_poisson(a_val_true: float, b_val_true: float):
     return mags, dates
 
 
-@pytest.mark.parametrize("a_val_true,b_val_true,precision", [(7, 1, 0.01)])
+@ pytest.mark.parametrize("a_val_true,b_val_true,precision", [(7, 1, 0.01)])
 def test_estimate_b_weichert(
     a_val_true: float, b_val_true: float, precision: float
 ):
@@ -363,7 +382,7 @@ def test_estimate_b_weichert(
     data = pickle.load(f)
 
 
-@pytest.mark.parametrize(
+@ pytest.mark.parametrize(
     "magnitudes,b,b_parameter,std",
     [data["values_test1"], data["values_test2"]],
 )
diff --git a/seismostats/analysis/tests/test_estimate_mc.py b/seismostats/analysis/tests/test_estimate_mc.py
index 2a47283..c8d312d 100644
--- a/seismostats/analysis/tests/test_estimate_mc.py
+++ b/seismostats/analysis/tests/test_estimate_mc.py
@@ -9,7 +9,7 @@
 
 
 @pytest.fixture
-def setup_magnitudes():
+def magnitudes():
     mags = np.array(
         [
             2.3,
@@ -117,11 +117,11 @@ def setup_magnitudes():
     return mags
 
 
-def test_empirical_cdf(setup_magnitudes, delta_m=0.1):
-    x, y = empirical_cdf(setup_magnitudes, delta_m=delta_m)
+def test_empirical_cdf(magnitudes, delta_m=0.1):
+    x, y = empirical_cdf(magnitudes, delta_m=delta_m)
 
-    x_expected = bin_to_precision(np.arange(min(setup_magnitudes), max(
-        setup_magnitudes) + delta_m, delta_m))
+    x_expected = bin_to_precision(np.arange(min(magnitudes), max(
+        magnitudes) + delta_m, delta_m))
 
     assert_allclose(x, x_expected, rtol=1e-7)
     assert_equal(y[-1], 1)
@@ -150,7 +150,7 @@ def test_empirical_cdf(setup_magnitudes, delta_m=0.1):
 
 
 @pytest.fixture
-def setup_ks_dists():
+def ks_dists():
     ks_ds_df = pd.read_csv(
         'seismostats/analysis/tests/data/ks_ds.csv', index_col=0)
     ks_ds_list = ks_ds_df.values.T
@@ -158,18 +158,18 @@ def setup_ks_dists():
 
 
 def test_estimate_mc_ks(
-        setup_magnitudes,
-        setup_ks_dists,
+        magnitudes,
+        ks_dists,
         mcs=[0.8, 0.9, 1.0, 1.1]
 ):
     # test when beta is given
     best_mc, best_beta, mcs_tested, betas, ks_ds, ps = mc_ks(
-        setup_magnitudes,
+        magnitudes,
         delta_m=0.1,
         mcs_test=mcs,
         p_pass=0.1,
         beta=2.24,
-        ks_ds_list=setup_ks_dists,
+        ks_ds_list=ks_dists,
     )
     assert_equal(1.1, best_mc)
     assert_equal(2.24, best_beta)
@@ -183,7 +183,7 @@ def test_estimate_mc_ks(
 
     # test when beta is not given
     best_mc, best_beta, mcs_tested, betas, ks_ds, ps = mc_ks(
-        setup_magnitudes,
+        magnitudes,
         delta_m=0.1,
         mcs_test=[1.1],
         p_pass=0.1,
@@ -207,18 +207,18 @@ def test_estimate_mc_ks(
 
     # test when mcs are not given
     best_mc, best_beta, mcs_tested, betas, ks_ds, ps = mc_ks(
-        setup_magnitudes,
+        magnitudes,
         delta_m=0.1,
         p_pass=0.1,
         beta=2.24,
-        ks_ds_list=setup_ks_dists[2:],
+        ks_ds_list=ks_dists[2:],
     )
     assert_equal(1.1, best_mc)
     assert_equal([1.0, 1.1], mcs_tested)
 
     # test when b-positive is used
     best_mc, best_beta, mcs_tested, betas, ks_ds, ps = mc_ks(
-        setup_magnitudes,
+        magnitudes,
         delta_m=0.1,
         mcs_test=[1.5],
         b_method="positive"
@@ -231,7 +231,7 @@ def test_estimate_mc_ks(
     assert_equal(len(ps), 1)
 
 
-def test_estimate_mc_maxc(setup_magnitudes):
-    mc = mc_max_curvature(setup_magnitudes, delta_m=0.1, correction_factor=0.2)
+def test_estimate_mc_maxc(magnitudes):
+    mc = mc_max_curvature(magnitudes, delta_m=0.1, correction_factor=0.2)
 
     assert_equal(1.3, mc)