run pre-commit hooks in all files

docs/examples/new_sample.py

@@ -32,7 +32,8 @@ class DetectLinesAnalysis(
     AnalysisMixin
 ):  # Subclass Analysis for each analysis you want to implement for a given sample
     """Write a good documentation:
-    This analysis detects lines in a 2D image through a progressive probabilistic hough transform."""
+    This analysis detects lines in a 2D image through a progressive probabilistic hough transform.
+    """
 
     # Define the __init__
     def __init__(self):

src/microscopemetrics/analysis/__init__.py

@@ -46,9 +46,7 @@ def get_image_data(image, device):
     if raw_img.shape[2] == 1:
         raw_img = np.squeeze(raw_img, 2)  # TODO: Fix this time dimension.
     else:
-        raise Exception(
-            "Image has a time dimension. Time is not yet implemented for this analysis"
-        )
+        raise Exception("Image has a time dimension. Time is not yet implemented for this analysis")
     pixel_size = interface.get_pixel_size(image)
     pixel_size_units = interface.get_pixel_size_units(image)
 
@@ -69,7 +67,6 @@ def get_image_data(image, device):
 def save_data_table(
     conn, table_name, col_names, col_descriptions, col_data, interface_obj, namespace
 ):
-
     table_ann = interface.create_annotation_table(
         connection=conn,
         table_name=table_name,
@@ -118,7 +115,6 @@ def create_image(
     source_image_id=None,
     metrics_generated_tag_id=None,
 ):
-
     zct_list = list(
         product(
             range(image_intensities.shape[0]),
@@ -175,9 +171,7 @@ def analyze_dataset(connection, script_params, dataset, analysis_config, device_
         "sources": list(),
     }
 
-    for section, analyses, handler in zip(
-        SAMPLE_SECTIONS, SAMPLE_ANALYSES, SAMPLE_HANDLERS
-    ):
+    for section, analyses, handler in zip(SAMPLE_SECTIONS, SAMPLE_ANALYSES, SAMPLE_HANDLERS):
         if analysis_config.has_section(section):
             module_logger.info(f"Running analysis on {section.capitalize()} sample(s)")
             section_conf = analysis_config[section]

src/microscopemetrics/devices/devices.py

@@ -207,9 +207,7 @@ def update_settings(self, incoming, init=False):
             my_keys = set(self._settings.keys())
             their_keys = set(incoming.keys())
             update_keys = set(
-                key
-                for key in my_keys & their_keys
-                if self.get_setting(key) != incoming[key]
+                key for key in my_keys & their_keys if self.get_setting(key) != incoming[key]
             )
         results = {}
         # Update values.
@@ -246,9 +244,7 @@ def _get_conf_objective_nr(self, image):
                 "More than one reference to an objective lens was found in the image name. Only the first one will be considered."
             )
         elif len(obj_nrs) == 0:
-            module_logger.info(
-                "No references to any objective lens were found in the image name"
-            )
+            module_logger.info("No references to any objective lens were found in the image name")
             return None
         return obj_nrs[0]
 
@@ -290,9 +286,7 @@ def _get_conf_channel_nrs(self, image):
         # Sort channels according to their position in the name
         ch_nrs = tuple(ch for _, ch in sorted(zip(token_positions, ch_nrs)))
         if len(ch_nrs) == 0:
-            module_logger.info(
-                "No references to any channel were found in the image name"
-            )
+            module_logger.info("No references to any channel were found in the image name")
             return None
         return ch_nrs
 
@@ -405,9 +399,7 @@ def get_all_settings(self, **kwargs):
         return settings
 
     def _get_conf_objective_lens_refr_index(self, image):
-        return self._get_conf_objective_setting(
-            "objective_lens_refractive_index", image
-        )
+        return self._get_conf_objective_setting("objective_lens_refractive_index", image)
 
     def _get_name_objective_lens_refr_index(self, image):
         return self._get_metadata_from_name("_RI=", "_", float, image)
@@ -419,9 +411,7 @@ def _get_name_objective_lens_na(self, image):
         return self._get_metadata_from_name("_NA=", "_", float, image)
 
     def _get_conf_objective_lens_nominal_magnification(self, image):
-        return self._get_conf_objective_setting(
-            "objective_lens_nominal_magnification", image
-        )
+        return self._get_conf_objective_setting("objective_lens_nominal_magnification", image)
 
     def _get_name_objective_lens_nominal_magnification(self, image):
         return self._get_metadata_from_name("_MAG=", "_", float, image)
@@ -500,9 +490,7 @@ def get_theoretical_res_fwhm(self, **kwargs):
         na = self.get_setting("objective_lens_na", **kwargs)
         for em in self.get_setting("emission_wavelengths", **kwargs):
             try:
-                theoretical_res["resolution_theoretical_fwhm_lateral"].append(
-                    0.353 * em / na
-                )
+                theoretical_res["resolution_theoretical_fwhm_lateral"].append(0.353 * em / na)
             except TypeError as e:
                 module_logger.warning(
                     "FWHM theoretical resolution could not be calculated. Verify configuration files."
@@ -538,9 +526,7 @@ def get_theoretical_res_rayleigh(self, **kwargs):
         ri = self.get_setting("objective_lens_refractive_index", **kwargs)
         for em in self.get_setting("emission_wavelengths", **kwargs):
             try:
-                theoretical_res["resolution_theoretical_rayleigh_lateral"].append(
-                    0.61 * em / na
-                )
+                theoretical_res["resolution_theoretical_rayleigh_lateral"].append(0.61 * em / na)
             except TypeError as e:
                 module_logger.warning(
                     "Rayleigh theoretical lateral resolution could not be calculated. Verify configuration files."
@@ -585,9 +571,7 @@ def get_nyquist(self, **kwargs):
                 )
                 nyquist_delta["nyquist_lateral"].append(None)
             try:
-                nyquist_delta["nyquist_axial"].append(
-                    em / (2 * ri * (1 - cos(asin(na / ri))))
-                )
+                nyquist_delta["nyquist_axial"].append(em / (2 * ri * (1 - cos(asin(na / ri)))))
             except TypeError as e:
                 module_logger.warning(
                     "Axial Nyquist criterion could not be calculated. Verify configuration files."

src/microscopemetrics/plots/__init__.py

@@ -71,9 +71,7 @@ def plot_distances_maps(distances, x_dim, y_dim):
         distances_map = np.asarray(p["dist_3d"])
 
         grid_x, grid_y = np.mgrid[0:x_dim:1, 0:y_dim:1]
-        interpolated = griddata(
-            positions_map, distances_map, (grid_x, grid_y), method="cubic"
-        )
+        interpolated = griddata(positions_map, distances_map, (grid_x, grid_y), method="cubic")
 
         ax = axes.ravel()
         ax[(p["channels"][0] * 4) + p["channels"][1]].imshow(
@@ -89,19 +87,14 @@ def plot_distances_maps(distances, x_dim, y_dim):
 
 
 def plot_homogeneity_map(raw_stack, spots_properties, spots_positions, labels_stack):
-
     nb_of_channels = raw_stack.shape[1]
     x_dim = raw_stack.shape[-2]
     y_dim = raw_stack.shape[-1]
 
-    fig, axes = plt.subplots(
-        ncols=nb_of_channels, nrows=3, squeeze=False, figsize=(12, 6)
-    )
+    fig, axes = plt.subplots(ncols=nb_of_channels, nrows=3, squeeze=False, figsize=(12, 6))
 
     for c in range(nb_of_channels):
-        weighted_centroid = np.array(
-            [x["weighted_centroid"][0] for x in spots_properties[c]]
-        )
+        weighted_centroid = np.array([x["weighted_centroid"][0] for x in spots_properties[c]])
         areas = np.array([x["area"] for x in spots_properties[c]])
         max_intensity = np.array([x["max_intensity"] for x in spots_properties[c]])
         grid_x, grid_y = np.mgrid[0:x_dim, 0:y_dim]
@@ -143,12 +136,9 @@ def plot_homogeneity_map(raw_stack, spots_properties, spots_positions, labels_st
 
 
 def plot_peaks(profiles, peaks, properties, resolutions, res_indexes):
-    fig, axes = plt.subplots(
-        ncols=1, nrows=len(profiles), squeeze=False, figsize=(48, 24)
-    )
+    fig, axes = plt.subplots(ncols=1, nrows=len(profiles), squeeze=False, figsize=(48, 24))
 
     for c, profile in enumerate(profiles):
-
         ax = axes.ravel()
 
         ax[c].plot(profile)

src/microscopemetrics/plots/argolight.py

@@ -33,9 +33,7 @@ def plot_homogeneity_map(self, image):
         x_dim = image.getSizeX()
         y_dim = image.getSizeY()
 
-        tables = self.get_tables(
-            image, namespace_start="metrics", name_filter="properties"
-        )
+        tables = self.get_tables(image, namespace_start="metrics", name_filter="properties")
         if len(tables) != 1:
             raise Exception(
                 "There are none or more than one properties tables. Verify data integrity."
@@ -69,12 +67,7 @@ def plot_homogeneity_map(self, image):
                 ),
             )
             max_intensity = np.array(
-                [
-                    val
-                    for col in data.columns
-                    for val in col.values
-                    if col.name == "max_intensity"
-                ]
+                [val for col in data.columns for val in col.values if col.name == "max_intensity"]
             )
             integrated_intensity = np.array(
                 [
@@ -165,9 +158,7 @@ def plot_distances_map(self, image):
         col_names = [c.name for c in table.getHeaders()]
 
         # We need the positions too
-        pos_tables = get_tables(
-            image, namespace_start="metrics", name_filter="properties"
-        )
+        pos_tables = get_tables(image, namespace_start="metrics", name_filter="properties")
         if len(tables) != 1:
             raise Exception(
                 "There are none or more than one positions tables. Verify data integrity."
@@ -205,12 +196,7 @@ def plot_distances_map(self, image):
             )
 
             mask_labels = np.array(
-                [
-                    val
-                    for col in pos_data.columns
-                    for val in col.values
-                    if col.name == "mask_labels"
-                ]
+                [val for col in pos_data.columns for val in col.values if col.name == "mask_labels"]
             )
             x_positions = np.array(
                 [
@@ -251,36 +237,16 @@ def plot_distances_map(self, image):
                 )
                 labels_map += 1  # Mask labels are augmented by one as 0 is background
                 distances_map_3d = np.array(
-                    [
-                        val
-                        for col in data.columns
-                        for val in col.values
-                        if col.name == "distance_3d"
-                    ]
+                    [val for col in data.columns for val in col.values if col.name == "distance_3d"]
                 )
                 distances_map_x = np.array(
-                    [
-                        val
-                        for col in data.columns
-                        for val in col.values
-                        if col.name == "distance_x"
-                    ]
+                    [val for col in data.columns for val in col.values if col.name == "distance_x"]
                 )
                 distances_map_y = np.array(
-                    [
-                        val
-                        for col in data.columns
-                        for val in col.values
-                        if col.name == "distance_y"
-                    ]
+                    [val for col in data.columns for val in col.values if col.name == "distance_y"]
                 )
                 distances_map_z = np.array(
-                    [
-                        val
-                        for col in data.columns
-                        for val in col.values
-                        if col.name == "distance_z"
-                    ]
+                    [val for col in data.columns for val in col.values if col.name == "distance_z"]
                 )
 
                 filtered_positions = positions_map[

src/microscopemetrics/plots/field_illum.py

@@ -99,6 +99,7 @@ def get_norm_intensity_profile(img, save_path=""):
 
     return fig
 
+
 def get_intensity_plot(img, save_path=""):
     """
     get the distribution of pixel intensities of the mid

src/microscopemetrics/samples/__init__.py

@@ -2,11 +2,10 @@
 
 import logging
 from abc import ABC, abstractmethod
-from typing import List, Union, Dict
-
-import numpy as np
+from typing import Dict, List, Union
 
 import microscopemetrics_schema.datamodel as mm_schema
+import numpy as np
 
 # We are defining some global dictionaries to register the different analysis types
 IMAGE_ANALYSIS_REGISTRY = {}

src/microscopemetrics/samples/argolight.py

@@ -2,6 +2,7 @@
 from itertools import product
 from typing import Any, Dict, List, Tuple, Union
 
+import microscopemetrics_schema.datamodel as mm_schema
 import numpy as np
 import pandas as pd
 from numpy import float64, int64, ndarray
@@ -11,7 +12,6 @@
 from scipy.signal import find_peaks
 from skimage.transform import hough_line  # hough_line_peaks, probabilistic_hough_line
 
-import microscopemetrics_schema.datamodel as mm_schema
 from microscopemetrics.analysis.tools import (
     compute_distances_matrix,
     compute_spots_properties,
@@ -108,9 +108,13 @@ def run(self) -> bool:
             ch_properties_kv["channel"] = ch
             ch_properties_kv["nr_of_spots"] = len(ch_df)
             ch_properties_kv["intensity_max_spot"] = ch_df["integrated_intensity"].max().item()
-            ch_properties_kv["intensity_max_spot_roi"] = ch_df["integrated_intensity"].argmax().item()
+            ch_properties_kv["intensity_max_spot_roi"] = (
+                ch_df["integrated_intensity"].argmax().item()
+            )
             ch_properties_kv["intensity_min_spot"] = ch_df["integrated_intensity"].min().item()
-            ch_properties_kv["intensity_min_spot_roi"] = ch_df["integrated_intensity"].argmin().item()
+            ch_properties_kv["intensity_min_spot_roi"] = (
+                ch_df["integrated_intensity"].argmin().item()
+            )
             ch_properties_kv["mean_intensity"] = ch_df["integrated_intensity"].mean().item()
             ch_properties_kv["median_intensity"] = ch_df["integrated_intensity"].median().item()
             ch_properties_kv["std_mean_intensity"] = ch_df["integrated_intensity"].std().item()

src/microscopemetrics/samples/dataset.py

@@ -29,9 +29,7 @@ def __init__(self, config=None):
             "laser_power_measurement": self.analyze_laser_power_measurement,
         }
         self.configurator = DatasetConfigurator(config)
-        super().__init__(
-            config=config, dataset_analysis_to_func=dataset_analysis_to_func
-        )
+        super().__init__(config=config, dataset_analysis_to_func=dataset_analysis_to_func)
 
     @staticmethod
     def analyze_laser_power_measurement(dataset, config):

src/microscopemetrics/samples/field_illumination.py

@@ -1,12 +1,12 @@
 from datetime import datetime
 from typing import Dict, Tuple
 
+import microscopemetrics_schema.datamodel as mm_schema
 import numpy as np
 import scipy
 from skimage.filters import gaussian
 from skimage.measure import regionprops
 
-import microscopemetrics_schema.datamodel as mm_schema
 from microscopemetrics.samples import AnalysisMixin, logger, numpy_to_inlined_image
 from microscopemetrics.utilities.utilities import is_saturated
 

src/microscopemetrics/samples/psf_beads.py

@@ -125,7 +125,6 @@ def _analyze_bead(image):
 
     @staticmethod
     def _find_beads(image, min_distance, sigma=None):
-
         image = np.squeeze(image)
         image_mip = np.max(image, axis=0)
 

src/microscopemetrics/utilities/utilities.py

@@ -57,7 +57,6 @@ def multi_airy_fun(x: ndarray, *params) -> ndarray:
 
 
 def wavelength_to_rgb(wavelength, gamma=0.8):
-
     """
     Copied from https://www.noah.org/wiki/Wavelength_to_RGB_in_Python
     This converts a given wavelength of light to an

tests/test_utilities.py

@@ -1,7 +1,9 @@
-from tests.constants import TEST_DATA_DIR
+from os import path
 from urllib.parse import urlparse
+
 import requests
-from os import path
+
+from tests.constants import TEST_DATA_DIR
 
 
 def get_file(file_url):

tests/unit/schema/test_core_schema.py

@@ -1,11 +1,11 @@
 from typing import List
 
+import microscopemetrics_schema.datamodel as mm_schema
 import numpy as np
 import pytest
 from linkml_runtime.dumpers import YAMLDumper
 from linkml_runtime.loaders import YAMLLoader
 
-import microscopemetrics_schema.datamodel as mm_schema
 from microscopemetrics.samples import numpy_to_inlined_image, numpy_to_inlined_mask