[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

fluorescence_assay/analysis.py

@@ -1,18 +1,20 @@
 """Module to analyze data, including curve fitting."""
 
 import logging
-from typing import Dict, List
+from typing import Dict, List, Union
 
 import numpy as np
 import pandas as pd
-
-from typing import Dict, List, Union
-
 from scipy.optimize import curve_fit
 
 logger = logging.getLogger(__name__)
 
-def calculate_dose_response(dosed: Union[List[float],List[np.array]], ref: Union[List[float],List[np.array]], conc: Union[List[float],List[np.array]]) -> pd.Series:
+
+def calculate_dose_response(
+    dosed: Union[list[float], list[np.array]],
+    ref: Union[list[float], list[np.array]],
+    conc: Union[list[float], list[np.array]],
+) -> pd.Series:
     """"""
 
     dosed = np.array(dosed)
@@ -23,6 +25,7 @@ def calculate_dose_response(dosed: Union[List[float],List[np.array]], ref: Union
 
     return pd.Series(diff, index=conc)
 
+
 def calculate_fraction_bound(dose_response: pd.Series) -> pd.Series:
     """"""
 
@@ -32,6 +35,6 @@ def calculate_fraction_bound(dose_response: pd.Series) -> pd.Series:
     F_max = np.max(arr)
     F_min = np.min(arr)
 
-    F_bound = (arr - F_min)/(F_max - F_min)
+    F_bound = (arr - F_min) / (F_max - F_min)
 
-    return pd.Series(F_bound, index=conc)
+    return pd.Series(F_bound, index=conc)

fluorescence_assay/plate_reader.py

@@ -106,17 +106,26 @@ def get_well(self, well: str) -> pd.Series:
         """"""
 
         return self.df[well]
-    
-    def get_row(self, row: Union[str, int]) -> List[pd.Series]:
+
+    def get_row(self, row: Union[str, int]) -> list[pd.Series]:
         """"""
 
-        num2alpha = {"0": "A", "1": "B", "2": "C", "3": "D", "4": "E", "5": "F", "6": "G", "7": "H"}
+        num2alpha = {
+            "0": "A",
+            "1": "B",
+            "2": "C",
+            "3": "D",
+            "4": "E",
+            "5": "F",
+            "6": "G",
+            "7": "H",
+        }
 
         if type(row) == int:
             row = num2alpha[str(row)]
 
         return [self.get_well(pos) for pos in [f"{row}{i}" for i in range(1, 13)]]
-    
+
     @property
     def pd(self):
         """"""
@@ -145,10 +154,10 @@ def plate(self):
             plate[row, col] = self.series.loc[series_index]
 
         return plate
-    
+
     def get_row(self, row: Union[str, int]):
 
         if type(row) == str:
             row = self.alpha2num[row]
 
-        return self.plate[row]
+        return self.plate[row]

fluorescence_assay/plotting.py

@@ -1,18 +1,21 @@
 """Module to plot parsed plate reader ouptputs."""
 
 from dataclasses import dataclass
-from typing import List, Dict, Tuple, Callable, Union, Optional
-
-from matplotlib.axes import Axes
-from matplotlib.figure import Figure
+from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 from matplotlib.axes import Axes
+from matplotlib.figure import Figure
 
 
-def plot_fluorescence_spectra(spectra: List[pd.Series], concentrations: List[float], axes: Optional[Axes] = None, cmap: Optional[str] = None) -> None:
+def plot_fluorescence_spectra(
+    spectra: list[pd.Series],
+    concentrations: list[float],
+    axes: Optional[Axes] = None,
+    cmap: Optional[str] = None,
+) -> None:
     """"""
 
     if axes is None:
@@ -34,29 +37,45 @@ def plot_fluorescence_spectra(spectra: List[pd.Series], concentrations: List[flo
         xx_i = [int(x) for x in spectrum.index.to_list()]
         yy_i = spectrum.to_numpy()
 
-        c = cmap(1-norm(concentrations[i]))
+        c = cmap(1 - norm(concentrations[i]))
 
         axes.plot(xx_i, yy_i, color=c)
 
         # TODO: Colorbar
 
-def plot_absorbance_spectrum(concentrations: List[float], spectrum: List[float], axes: Optional[Axes] = None) -> None:
+
+def plot_absorbance_spectrum(
+    concentrations: list[float], spectrum: list[float], axes: Optional[Axes] = None
+) -> None:
     """"""
 
     if axes is None:
         fig, axes = plt.subplots()
 
     axes.plot(concentrations, spectrum)
 
-def plot_dose_response(concentrations: List[float], dose_response: List[float], axes: Optional[Axes] = None) -> None:
+
+def plot_dose_response(
+    concentrations: list[float], dose_response: list[float], axes: Optional[Axes] = None
+) -> None:
     """"""
 
     if axes is None:
         fig, axes = plt.subplots()
 
     axes.plot(concentrations, dose_response)
 
-def create_grid_of_plots(rows: int, cols: int, hspace: Optional[float], wspace: Optional[float], xlabel: Optional[str] = None, ylabel: Optional[str] = None, titles: Optional[List[str]] = None, fig: Optional[Figure] = None) -> List[Axes]:
+
+def create_grid_of_plots(
+    rows: int,
+    cols: int,
+    hspace: Optional[float],
+    wspace: Optional[float],
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    titles: Optional[list[str]] = None,
+    fig: Optional[Figure] = None,
+) -> list[Axes]:
     """"""
 
     if fig is None:
@@ -75,12 +94,12 @@ def create_grid_of_plots(rows: int, cols: int, hspace: Optional[float], wspace:
         ax.label_outer()
 
     if xlabel is not None:
-        xplots = np.arange(rows*cols - cols, rows*cols)
+        xplots = np.arange(rows * cols - cols, rows * cols)
         for i in xplots:
             axes[i].set_xlabel(xlabel)
 
     if ylabel is not None:
-        yplots = cols*np.arange(0, rows)
+        yplots = cols * np.arange(0, rows)
         for i in yplots:
             axes[i].set_ylabel(ylabel)
 
@@ -89,4 +108,4 @@ def create_grid_of_plots(rows: int, cols: int, hspace: Optional[float], wspace:
         for i in titleplots:
             axes[i].set_title(titles[i])
 
-    return axes
+    return axes