edited docstrings, pre-commit

1. checked docstrings of `IsotopicAveragineDistribution`
    + Done
2. Tests of `IsotopicAveragineDistribution` failed, due to
   wrong scope of fixtures inside class.
    + fixed
    + Done
3. run pre-commit

.gitignore

@@ -30,4 +30,4 @@ docs_src/
 .pytest_cache/
 
 # output of dvc plots
-dvc_plots/
+dvc_plots/

pyproject.toml

@@ -1,4 +1,4 @@
 [build-system]
 # Minimum requirements for the build system to execute.
 requires = ["setuptools", "wheel"]  # PEP 508 specifications.
-# from https://peps.python.org/pep-0518/
+# from https://peps.python.org/pep-0518/

src/pystoms/models_3d/abstract_3d.py

@@ -18,6 +18,7 @@
 import os
 from numpy.typing import ArrayLike
 from time import time
+
 # typing
 NDArrayFloat = npt.NDArray[np.float64]
 NDArrayInt = npt.NDArray[np.int64]
@@ -452,7 +453,15 @@ def arviz_plots(
             else:
                 plt.show()
 
-            az.plot_trace(idata_sliced, var_names, legend=True, chain_prop={"linestyle": ("solid", "dotted", "dashed", "dashdot"),"color":("black","blue","green","red")})
+            az.plot_trace(
+                idata_sliced,
+                var_names,
+                legend=True,
+                chain_prop={
+                    "linestyle": ("solid", "dotted", "dashed", "dashdot"),
+                    "color": ("black", "blue", "green", "red"),
+                },
+            )
             if save_fig:
                 plt.tight_layout()
                 plt.savefig(feature_path + "trace.png")
@@ -484,41 +493,43 @@ def arviz_plots(
 
             # posterior predictive lm
             idata_feature_sliced = self.idata.isel(feature=idx)
-            az.plot_lm(y=idata_feature_sliced.observed_data.obs,
-                       y_hat=idata_feature_sliced.posterior_predictive.obs,
-                       num_samples=500)
+            az.plot_lm(
+                y=idata_feature_sliced.observed_data.obs,
+                y_hat=idata_feature_sliced.posterior_predictive.obs,
+                num_samples=500,
+            )
             if save_fig:
                 plt.savefig(feature_path + "posterior_predictive_lm.png")
                 plt.close()
             else:
                 plt.show()
 
             # prior predictive lm
-            fig,ax = plt.subplots(1,1)
-            az.plot_lm(y=idata_feature_sliced.observed_data.obs,
-                       y_hat=idata_feature_sliced.prior_predictive.obs,
-                       num_samples=500,
-                       legend=False,
-                       axes=ax)
-            h,l = ax.get_legend_handles_labels()
-            for idx,lab in enumerate(l):
+            fig, ax = plt.subplots(1, 1)
+            az.plot_lm(
+                y=idata_feature_sliced.observed_data.obs,
+                y_hat=idata_feature_sliced.prior_predictive.obs,
+                num_samples=500,
+                legend=False,
+                axes=ax,
+            )
+            h, l = ax.get_legend_handles_labels()
+            for idx, lab in enumerate(l):
                 if lab == "Posterior predictive samples":
                     l[idx] = "Prior predictive samples"
-            ax.legend(h,l)
+            ax.legend(h, l)
             if save_fig:
                 fig.savefig(feature_path + "prior_predictive_lm.png")
                 plt.close()
             else:
                 plt.show()
 
-            az.plot_parallel(idata_sliced,norm_method="minmax")
+            az.plot_parallel(idata_sliced, norm_method="minmax")
             if save_fig:
                 plt.savefig(feature_path + "plot_parallel.png")
                 plt.close()
             else:
                 plt.show()
-
-
 
     def evaluation(
         self,
@@ -531,7 +542,7 @@ def evaluation(
         progressbar: bool = True,
         pred_name_list: Optional[List[str]] = None,
         **plot_kwargs,
-    ) -> Tuple[az.InferenceData,float]:
+    ) -> Tuple[az.InferenceData, float]:
         """Evaluate precursor feature model.
 
         This function is wrapping several steps
@@ -571,7 +582,7 @@ def evaluation(
         end_sampling = time()
 
         if pred_name_list is None:
-            pred_name_list = ["obs","mu"]
+            pred_name_list = ["obs", "mu"]
 
         if plots is None:
             # make 'in' operator available for plots
@@ -588,7 +599,9 @@ def evaluation(
 
         if prior_pred_out:
             # prior predictive analysis out-of-sample
-            self._sample_predictive(is_prior=True, is_grid_predictive=True, var_names=pred_name_list)
+            self._sample_predictive(
+                is_prior=True, is_grid_predictive=True, var_names=pred_name_list
+            )
             if "prior_pred_out" in plots:
                 for pred_name in pred_name_list:
                     plot_kwargs["pred_name"] = pred_name
@@ -606,10 +619,14 @@ def evaluation(
 
         if posterior_pred_out:
             # posterior predictive analysis out-of-sample
-            self._sample_predictive(is_grid_predictive=True, progressbar=progressbar, var_names=pred_name_list)
+            self._sample_predictive(
+                is_grid_predictive=True,
+                progressbar=progressbar,
+                var_names=pred_name_list,
+            )
             if "posterior_pred_out" in plots:
                 for pred_name in pred_name_list:
                     plot_kwargs["pred_name"] = pred_name
                     self.visualize_predictions_scatter(in_sample=False, **plot_kwargs)
 
-        return (self.idata.copy(),end_sampling-start_sampling)
+        return (self.idata.copy(), end_sampling - start_sampling)

src/pystoms/models_3d/model_3d_m1.py

@@ -33,7 +33,7 @@ class ModelM1(AbstractModel):
     Args:
         features (AlignedFeatureData): Feature's data wrapped in
             pystoms.AlignedFeatureData container.
-        model_parameters (Dictionary, optional): parameters for 
+        model_parameters (Dictionary, optional): parameters for
             priors and hyperpriors. Defaults to None.
         likelihood (str, optional): Likelihood distribution. Currently
             supported: 'Normal', 'StudentT'. Defaults to 'Normal'.
@@ -62,7 +62,9 @@ def __init__(
         if model_parameters is None:
             model_parameters = {}
         self.model_parameters = model_parameters.copy()
-        super().__init__(feature_ids, batch_size, random_number_generator, name, coords=coords)
+        super().__init__(
+            feature_ids, batch_size, random_number_generator, name, coords=coords
+        )
         self.setup_mutable_data(features)
         # TODO is there a nicer way to share this between init and setup_mutable_data()
         dims_2d = ["data_point", "feature"]
@@ -158,12 +160,21 @@ def setup_mutable_data(
                 axis=0,
                 weights=intensity,
             ).reshape((1, num_features, 1))
-            self.model_parameters.setdefault("mz_sigma",10)
-            mz_sigma = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["mz_sigma"]
+            self.model_parameters.setdefault("mz_sigma", 10)
+            mz_sigma = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["mz_sigma"]
+            )
             self.model_parameters.setdefault("alpha_lam", 1 / intensity.max(axis=0))
-            alpha_lam = np.ones((1, num_features), dtype="float") * self.model_parameters["alpha_lam"]
-            self.model_parameters.setdefault("me_sigma",10)
-            me_sigma = np.ones((1, num_features), dtype="float") * self.model_parameters["me_sigma"]
+            alpha_lam = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["alpha_lam"]
+            )
+            self.model_parameters.setdefault("me_sigma", 10)
+            me_sigma = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["me_sigma"]
+            )
             z = np.array(charge).reshape((1, num_features, 1))
             mz_tile = np.tile(mz, (1, 1, num_isotopic_peaks))
             peaks = np.arange(num_isotopic_peaks)

src/pystoms/models_3d/model_3d_m2.py

@@ -2,7 +2,7 @@
 model M2
 """
 
-from typing import Optional,Dict
+from typing import Optional, Dict
 import pymc as pm
 import pymc.math as pmath
 import numpy as np
@@ -33,7 +33,7 @@ class ModelM2(AbstractModel):
     Args:
         features (AlignedFeatureData): Feature's data wrapped in
             pystoms.AlignedFeatureData container.
-        model_parameters (Dictionary, optional): parameters for 
+        model_parameters (Dictionary, optional): parameters for
             priors and hyperpriors. Defaults to None.
         likelihood (str, optional): Likelihood distribution. Currently
             supported: 'Normal', 'StudentT'. Defaults to 'Normal'.
@@ -62,7 +62,9 @@ def __init__(
         if model_parameters is None:
             model_parameters = {}
         self.model_parameters = model_parameters.copy()
-        super().__init__(feature_ids, batch_size, random_number_generator, name, coords=coords)
+        super().__init__(
+            feature_ids, batch_size, random_number_generator, name, coords=coords
+        )
         self.setup_mutable_data(features)
         # TODO is there a nicer way to share this between init and setup_mutable_data()
         dims_2d = ["data_point", "feature"]
@@ -164,18 +166,36 @@ def setup_mutable_data(
                 axis=0,
                 weights=intensity,
             ).reshape((1, num_features, 1))
-            self.model_parameters.setdefault("mz_sigma",10)
-            mz_sigma = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["mz_sigma"]
-            self.model_parameters.setdefault("mz_s_scale",0.05)
-            ms_s_scale = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["mz_s_scale"]
-            self.model_parameters.setdefault("ms_s_alpha",2)
-            ms_s_alpha = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["ms_s_alpha"]
-            self.model_parameters.setdefault("alpha_scale",intensity.max(axis=0))
-            alpha_scale = np.ones((1, num_features), dtype="float") * self.model_parameters["alpha_scale"]
-            self.model_parameters.setdefault("alpha_alpha",2)
-            alpha_alpha = np.ones((1, num_features), dtype="float") * self.model_parameters["alpha_alpha"]
-            self.model_parameters.setdefault("me_sigma",10)
-            me_sigma = np.ones((1, num_features), dtype="float") * self.model_parameters["me_sigma"]
+            self.model_parameters.setdefault("mz_sigma", 10)
+            mz_sigma = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["mz_sigma"]
+            )
+            self.model_parameters.setdefault("mz_s_scale", 0.05)
+            ms_s_scale = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["mz_s_scale"]
+            )
+            self.model_parameters.setdefault("ms_s_alpha", 2)
+            ms_s_alpha = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["ms_s_alpha"]
+            )
+            self.model_parameters.setdefault("alpha_scale", intensity.max(axis=0))
+            alpha_scale = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["alpha_scale"]
+            )
+            self.model_parameters.setdefault("alpha_alpha", 2)
+            alpha_alpha = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["alpha_alpha"]
+            )
+            self.model_parameters.setdefault("me_sigma", 10)
+            me_sigma = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["me_sigma"]
+            )
             z = np.array(charge).reshape((1, num_features, 1))
             mz_tile = np.tile(mz, (1, 1, num_isotopic_peaks))
             peaks = np.arange(num_isotopic_peaks)

src/pystoms/models_3d/model_3d_m3.py

@@ -2,7 +2,7 @@
 model M3
 """
 
-from typing import Optional,Dict
+from typing import Optional, Dict
 import pymc as pm
 import pymc.math as pmath
 import numpy as np
@@ -33,7 +33,7 @@ class ModelM3(AbstractModel):
     Args:
         features (AlignedFeatureData): Feature's data wrapped in
             pystoms.AlignedFeatureData container.
-        model_parameters (Dictionary, optional): parameters for 
+        model_parameters (Dictionary, optional): parameters for
             priors and hyperpriors. Defaults to None.
         likelihood (str, optional): Likelihood distribution. Currently
             supported: 'Normal', 'StudentT'. Defaults to 'Normal'.
@@ -62,7 +62,9 @@ def __init__(
         if model_parameters is None:
             model_parameters = {}
         self.model_parameters = model_parameters.copy()
-        super().__init__(feature_ids, batch_size, random_number_generator, name, coords=coords)
+        super().__init__(
+            feature_ids, batch_size, random_number_generator, name, coords=coords
+        )
         self.setup_mutable_data(features)
         # TODO is there a nicer way to share this between init and setup_mutable_data()
         dims_2d = ["data_point", "feature"]
@@ -164,18 +166,36 @@ def setup_mutable_data(
                 axis=0,
                 weights=intensity,
             ).reshape((1, num_features, 1))
-            self.model_parameters.setdefault("mz_sigma",1)
-            mz_sigma = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["mz_sigma"]
-            self.model_parameters.setdefault("mz_s_scale",0.05)
-            ms_s_scale = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["mz_s_scale"]
-            self.model_parameters.setdefault("ms_s_alpha",2)
-            ms_s_alpha = np.ones((1, num_features, 1), dtype="float") * self.model_parameters["ms_s_alpha"]
-            self.model_parameters.setdefault("alpha_scale",intensity.max(axis=0))
-            alpha_scale = np.ones((1, num_features), dtype="float") * self.model_parameters["alpha_scale"]
-            self.model_parameters.setdefault("alpha_alpha",2)
-            alpha_alpha = np.ones((1, num_features), dtype="float") * self.model_parameters["alpha_alpha"]
-            self.model_parameters.setdefault("me_lambda",1)
-            me_lambda = np.ones((1, num_features), dtype="float") * self.model_parameters["me_lambda"]
+            self.model_parameters.setdefault("mz_sigma", 1)
+            mz_sigma = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["mz_sigma"]
+            )
+            self.model_parameters.setdefault("mz_s_scale", 0.05)
+            ms_s_scale = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["mz_s_scale"]
+            )
+            self.model_parameters.setdefault("ms_s_alpha", 2)
+            ms_s_alpha = (
+                np.ones((1, num_features, 1), dtype="float")
+                * self.model_parameters["ms_s_alpha"]
+            )
+            self.model_parameters.setdefault("alpha_scale", intensity.max(axis=0))
+            alpha_scale = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["alpha_scale"]
+            )
+            self.model_parameters.setdefault("alpha_alpha", 2)
+            alpha_alpha = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["alpha_alpha"]
+            )
+            self.model_parameters.setdefault("me_lambda", 1)
+            me_lambda = (
+                np.ones((1, num_features), dtype="float")
+                * self.model_parameters["me_lambda"]
+            )
             z = np.array(charge).reshape((1, num_features, 1))
             mz_tile = np.tile(mz, (1, 1, num_isotopic_peaks))
             peaks = np.arange(num_isotopic_peaks)

src/pystoms/plotting.py

@@ -7,7 +7,9 @@
 from pystoms.aligned_feature_data import AlignedFeatureData
 
 
-def plot_marginals(feature: AlignedFeatureData, plot_path:Optional[str] = None) -> None:
+def plot_marginals(
+    feature: AlignedFeatureData, plot_path: Optional[str] = None
+) -> None:
     """plots marginal mz, scan and intensities
 
     Args:
@@ -18,19 +20,27 @@ def plot_marginals(feature: AlignedFeatureData, plot_path:Optional[str] = None)
     feature_dataset = feature.feature_data
     feature_ids = feature.accepted_feature_ids
     for feature_id in feature_ids:
-        feature_data = feature_dataset.sel({"feature":feature_id}).to_dataframe()
+        feature_data = feature_dataset.sel({"feature": feature_id}).to_dataframe()
         charge = feature_data["Charge"].values[0]
-        Fig,axs = plt.subplot_mosaic("AB;CC",figsize=(5,5),sharey=True)
+        Fig, axs = plt.subplot_mosaic("AB;CC", figsize=(5, 5), sharey=True)
         Fig.set_dpi(300)
         axs["A"].hist(x=feature_data.Intensity.values)
         axs["A"].set_xlabel("Intensity")
         axs["A"].set_ylabel("Count")
-        axs["A"].annotate(f"Max: {np.max(feature_data.Intensity.values)}", xy=(0.5, 0.9), xycoords='axes fraction')
+        axs["A"].annotate(
+            f"Max: {np.max(feature_data.Intensity.values)}",
+            xy=(0.5, 0.9),
+            xycoords="axes fraction",
+        )
         axs["B"].hist(x=feature_data.Scan.values)
         axs["B"].set_xlabel("Scan")
-        axs["B"].annotate(f"n: {len(feature_data.Scan.values)}", xy=(0.7, 0.9), xycoords='axes fraction')
-        axs["C"].hist(x=feature_data.Mz.values,bins=100)
-        axs["C"].annotate(f"Charge: {charge}", xy=(0.8, 0.9), xycoords='axes fraction')
+        axs["B"].annotate(
+            f"n: {len(feature_data.Scan.values)}",
+            xy=(0.7, 0.9),
+            xycoords="axes fraction",
+        )
+        axs["C"].hist(x=feature_data.Mz.values, bins=100)
+        axs["C"].annotate(f"Charge: {charge}", xy=(0.8, 0.9), xycoords="axes fraction")
         axs["C"].set_xlabel("Mz")
         axs["C"].set_ylabel("Count")
         plt.tight_layout()
@@ -39,4 +49,4 @@ def plot_marginals(feature: AlignedFeatureData, plot_path:Optional[str] = None)
         else:
             if not os.path.exists(f"{plot_path}/feature{feature_id}/"):
                 os.mkdir(f"{plot_path}/feature{feature_id}/")
-            plt.savefig(f"{plot_path}/feature{feature_id}/marginals.png")
+            plt.savefig(f"{plot_path}/feature{feature_id}/marginals.png")