Reformatting fitting logic with ruff

qudi_hira_analysis/_fitmethods/antibunchingmethods.py

@@ -5,7 +5,8 @@
 
 
 def make_antibunching_model(self, prefix=None):
-    def antibunching(x: np.ndarray, n: float, a: float, b: float, tau0: float, tau1: float, tau2: float) \
+    def antibunching(x: np.ndarray, n: float, a: float, b: float, tau0: float,
+                     tau1: float, tau2: float) \
             -> np.ndarray:
         """
         Fit to function
@@ -34,7 +35,8 @@ def estimate_antibunching_dip(self, x_axis, data, params):
     return error, params
 
 
-def make_antibunching_fit(self, x_axis, data, estimator, units=None, add_params=None, **kwargs):
+def make_antibunching_fit(self, x_axis, data, estimator, units=None, add_params=None,
+                          **kwargs):
     model, params = self.make_antibunching_model()
 
     error, params = estimator(x_axis, data, params)
@@ -47,7 +49,7 @@ def make_antibunching_fit(self, x_axis, data, estimator, units=None, add_params=
     except:
         result = model.fit(data, x=x_axis, params=params, **kwargs)
         self.log.warning('The 1D antibunching fit did not work. Error '
-                         'message: {0}\n'.format(result.message))
+                         f'message: {result.message}\n')
 
     # Write the parameters to allow human-readable output to be generated
     result_str_dict = OrderedDict()

qudi_hira_analysis/_fitmethods/hyperbolicsaturationmethods.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 This file contains methods for hyperbolic saturation fitting, these methods
 are imported by class FitLogic.
@@ -20,11 +19,9 @@
 top-level directory of this distribution and at <https://github.com/Ulm-IQO/qudi/>
 """
 
-
 import numpy as np
 from lmfit.models import Model
 
-
 ################################################################################
 #                                                                              #
 #                Hyperbolic saturation models                                  #
@@ -63,9 +60,9 @@ def hyperbolicsaturation_function(x, I_sat, P_sat):
         return I_sat * (x / (x + P_sat))
 
     if not isinstance(prefix, str) and prefix is not None:
-        self.log.error('The passed prefix <{0}> of type {1} is not a string and'
-                     'cannot be used as a prefix and will be ignored for now.'
-                     'Correct that!'.format(prefix, type(prefix)))
+        self.log.error('The passed prefix <{}> of type {} is not a string and'
+                       'cannot be used as a prefix and will be ignored for now.'
+                       'Correct that!'.format(prefix, type(prefix)))
 
         mod_sat = Model(hyperbolicsaturation_function, independent_vars=['x'])
     else:
@@ -80,7 +77,8 @@ def hyperbolicsaturation_function(x, I_sat, P_sat):
     return complete_model, params
 
 
-def make_hyperbolicsaturation_fit(self, x_axis, data, estimator, units=None, add_params=None, **kwargs):
+def make_hyperbolicsaturation_fit(self, x_axis, data, estimator, units=None,
+                                  add_params=None, **kwargs):
     """ Perform a fit on the provided data with a fluorescence depending function.
 
     @param numpy.array x_axis: 1D axis values
@@ -128,23 +126,22 @@ def estimate_hyperbolicsaturation(self, x_axis, data, params):
 
     error = self._check_1D_input(x_axis=x_axis, data=data, params=params)
 
-    x_axis_half = x_axis[len(x_axis)//2:]
-    data_half = data[len(x_axis)//2:]
+    x_axis_half = x_axis[len(x_axis) // 2:]
+    data_half = data[len(x_axis) // 2:]
 
     results_lin = self.make_linear_fit(x_axis=x_axis_half, data=data_half,
-                                           estimator=self.estimate_linear)
+                                       estimator=self.estimate_linear)
 
     est_slope = results_lin.params['slope'].value
     est_offset = data.min()
 
-    data_red = data - est_slope*x_axis - est_offset
-    est_I_sat = np.mean(data_red[len(data_red)//2:])
-    est_P_sat = est_I_sat/2
+    data_red = data - est_slope * x_axis - est_offset
+    est_I_sat = np.mean(data_red[len(data_red) // 2:])
+    est_P_sat = est_I_sat / 2
 
     params['I_sat'].value = est_I_sat
     params['slope'].value = est_slope
     params['offset'].value = est_offset
     params['P_sat'].value = est_P_sat
 
-
     return error, params

qudi_hira_analysis/_fitmethods/linearmethods.py

@@ -1,4 +1,3 @@
-# -*- coding: utf-8 -*-
 """
 This file contains methods for linear fitting, these methods
 are imported by class FitLogic. The functions can be used for amy
@@ -55,6 +54,7 @@ def make_constant_model(self, prefix=None):
     For further information have a look in:
     http://cars9.uchicago.edu/software/python/lmfit/builtin_models.html#models.GaussianModel
     """
+
     def constant_function(x, offset):
         """ Function of a constant value.
 
@@ -67,9 +67,10 @@ def constant_function(x, offset):
         return offset
 
     if not isinstance(prefix, str) and prefix is not None:
-        self.log.error('The passed prefix <{0}> of type {1} is not a string and cannot be used as '
-                       'a prefix and will be ignored for now. Correct that!'.format(prefix,
-                                                                                    type(prefix)))
+        self.log.error(
+            'The passed prefix <{}> of type {} is not a string and cannot be used as '
+            'a prefix and will be ignored for now. Correct that!'.format(prefix,
+                                                                         type(prefix)))
         model = Model(constant_function, independent_vars=['x'])
     else:
         model = Model(constant_function, independent_vars=['x'], prefix=prefix)
@@ -103,9 +104,10 @@ def amplitude_function(x, amplitude):
         return amplitude
 
     if not isinstance(prefix, str) and prefix is not None:
-        self.log.error('The passed prefix <{0}> of type {1} is not a string and cannot be used as '
-                       'a prefix and will be ignored for now. Correct that!'.format(prefix,
-                                                                                    type(prefix)))
+        self.log.error(
+            'The passed prefix <{}> of type {} is not a string and cannot be used as '
+            'a prefix and will be ignored for now. Correct that!'.format(prefix,
+                                                                         type(prefix)))
         model = Model(amplitude_function, independent_vars=['x'])
     else:
         model = Model(amplitude_function, independent_vars=['x'], prefix=prefix)
@@ -139,9 +141,10 @@ def slope_function(x, slope):
         return slope
 
     if not isinstance(prefix, str) and prefix is not None:
-        self.log.error('The passed prefix <{0}> of type {1} is not a string and cannot be used as '
-                       'a prefix and will be ignored for now. Correct that!'.format(prefix,
-                                                                                    type(prefix)))
+        self.log.error(
+            'The passed prefix <{}> of type {} is not a string and cannot be used as '
+            'a prefix and will be ignored for now. Correct that!'.format(prefix,
+                                                                         type(prefix)))
         model = Model(slope_function, independent_vars=['x'])
     else:
         model = Model(slope_function, independent_vars=['x'], prefix=prefix)
@@ -174,9 +177,10 @@ def linear_function(x):
         return x
 
     if not isinstance(prefix, str) and prefix is not None:
-        self.log.error('The passed prefix <{0}> of type {1} is not a string and cannot be used as '
-                       'a prefix and will be ignored for now. Correct that!'.format(prefix,
-                                                                                    type(prefix)))
+        self.log.error(
+            'The passed prefix <{}> of type {} is not a string and cannot be used as '
+            'a prefix and will be ignored for now. Correct that!'.format(prefix,
+                                                                         type(prefix)))
         linear_mod = Model(linear_function, independent_vars=['x'])
     else:
         linear_mod = Model(linear_function, independent_vars=['x'], prefix=prefix)
@@ -190,7 +194,8 @@ def linear_function(x):
     return model, params
 
 
-def make_linear_fit(self, x_axis, data, estimator, units=None, add_params=None, **kwargs):
+def make_linear_fit(self, x_axis, data, estimator, units=None, add_params=None,
+                    **kwargs):
     """ Performe a linear fit on the provided data.
 
     @param numpy.array x_axis: 1D axis values
@@ -218,11 +223,11 @@ def make_linear_fit(self, x_axis, data, estimator, units=None, add_params=None,
     if units is None:
         units = ['arb. unit', 'arb. unit']
 
-    result_str_dict = dict()
+    result_str_dict = {}
 
     result_str_dict['Slope'] = {'value': result.params['slope'].value,
                                 'error': result.params['slope'].stderr,
-                                'unit': '{0}/{1}'.format(units[1], units[0])}
+                                'unit': f'{units[1]}/{units[0]}'}
     result_str_dict['Offset'] = {'value': result.params['offset'].value,
                                  'error': result.params['offset'].stderr,
                                  'unit': units[1]}
@@ -256,10 +261,10 @@ def estimate_linear(self, x_axis, data, params):
         x_mean = x_axis.mean()
         data_mean = data.mean()
 
-        for i in range(0, len(x_axis)):
-            a_1 += (x_axis[i]-x_mean)*(data[i]-data_mean)
-            a_2 += np.power(x_axis[i]-x_mean, 2)
-        slope = a_1/a_2
+        for i in range(len(x_axis)):
+            a_1 += (x_axis[i] - x_mean) * (data[i] - data_mean)
+            a_2 += np.power(x_axis[i] - x_mean, 2)
+        slope = a_1 / a_2
         intercept = data_mean - slope * x_mean
         params['offset'].value = intercept
         params['slope'].value = slope
@@ -269,4 +274,3 @@ def estimate_linear(self, x_axis, data, params):
         params['offset'].value = 0
 
     return error, params
-