more accurate timestep

.gitignore

@@ -139,4 +139,5 @@ cython_debug/
 
 # Local stuff to exclude
 .vscode/
-mainwin.py
+mainwin.py
+*.sqlite

app.py

@@ -6,6 +6,10 @@ from db import mem
 from mainwin import Ui_MainWindow
 from widgets import BaseGraph
 import pathlib
+from re import search as re_search
+from varname.core import nameof
+from hashlib import md5
+from sqlitedict import SqliteDict
 
 class AppWindow(QtWidgets.QMainWindow, Ui_MainWindow):
 
@@ -13,6 +17,16 @@ class AppWindow(QtWidgets.QMainWindow, Ui_MainWindow):
         super(AppWindow, self).__init__()
         self.setupUi(self)
 
+        # Define syncables
+        synced_value_widgets = []
+        synced_check_widgets = []
+        for wname in vars(self):
+            w = vars(self).get(wname)
+            if re_search(r"^spin\_(.*)$", wname) != None:
+                synced_value_widgets.append(w)
+            elif re_search(r"^check\_(.*)$", wname) != None:
+                synced_check_widgets.append(w)
+
         # Helpers
 
         def display_warning(message: str):
@@ -40,9 +54,11 @@ class AppWindow(QtWidgets.QMainWindow, Ui_MainWindow):
                 return
             mem['x_data'] = data.transpose()[0]
             mem['y_data'] = data.transpose()[1]
-            timestep = mem['x_data'][1] - mem['x_data'][0]
+            # timestep = mem['x_data'][1] - mem['x_data'][0]
+            timestep = (mem['x_data'][-1] - mem['x_data'][0]) / len(mem['x_data'])
             mem['timestep'] = timestep
             self.spin_timestep.setValue(timestep)
+            print(timestep)
             self.raw_data_graph.plot() # Graph new stuff
 
             # self.groups_graph.clear() # Clear old stuff
@@ -61,6 +77,37 @@ class AppWindow(QtWidgets.QMainWindow, Ui_MainWindow):
                 self.voltage.setVisible(False)
                 self.graph_tabs.setCurrentIndex(0)
 
+            # Load from persistent storage & bind write actions
+
+            # path_hash = md5(filename.encode('utf-8')).hexdigest()
+
+            # def set_value(name, val):
+            #     with SqliteDict(f"./db/{path_hash}.sqlite", autocommit=True) as storage:
+            #         print(f"Change {name} to {val}.")
+            #         storage[name] = val
+            #         print(f"Check: {storage[name]}")
+
+            # with SqliteDict(f"./db/{path_hash}.sqlite", autocommit=True) as storage:
+
+            #     for w in synced_value_widgets:
+            #         name = w.objectName()
+            #         try:
+            #             w.setValue(bool(storage[name]))
+            #             print(f"Loaded {name}.")
+            #         except KeyError:
+            #             print(f"Failed to load object {name}.")
+            #             pass
+            #         w.valueChanged.connect(lambda x: set_value(name, x))
+
+            #     for w in synced_check_widgets:
+            #         name = w.objectName()
+            #         try:
+            #             w.setChecked(storage[name])
+            #             print(f"Loaded {name}.")
+            #         except KeyError:
+            #             print(f"Failed to load object {name}.")
+            #             pass
+            #         w.stateChanged.connect(lambda: set_value(name, w.isChecked()))
 
         # Universal Actions stuff
 

crds_calc.py

@@ -1,7 +1,7 @@
 import numpy as np
 from scipy.signal import find_peaks, correlate
 from scipy.optimize import curve_fit
-from memdb import mem
+from db import mem
 
 def minmax(data):
     return np.min(data), np.max(data)
@@ -281,6 +281,7 @@ def fit_peaks(
         overlayed_peak_row = []
         for peak_data in peaks_cut:
             x_data = np.arange(len(peak_data)) # just placeholder indices
+            # x_data = np.arange(0, len(peak_data)*mem['timestep'], mem['timestep'])
             if not use_advanced:
                 peak_index = np.argmax(peak_data, axis=0)
             else:
@@ -289,7 +290,6 @@ def fit_peaks(
             params_guess = (peak_index+shift_over, a, y0, tau)
             x_data_target = x_data[peak_index+shift_over:]
             peak_data_target = peak_data[peak_index+shift_over:]
-            # popt, pcov = curve_fit(exp_func, x_data_target, peak_data_target, bounds=([-np.inf, 0.0, -np.inf, 0.0], np.inf))
             popt, pcov = curve_fit(exp_func, x_data_target, peak_data_target, bounds=([-np.inf, 0.0, -np.inf, 0.0], np.inf), p0=params_guess, maxfev=10000000)
             equation_row.append({'popt': popt, 'pcov': pcov})
             overlayed_peak_row.append(peak_index)
@@ -300,9 +300,9 @@ def fit_peaks(
     return equations # list linked with isolated_peaks
 
 
-def get_tau_data(equation_data):
+def get_time_constants(equation_data):
     """
-    Extracts time constant from all equations (2d array)
+    Extracts time constant from all fit-output equations (2d array)
     
     Returns
     -------
@@ -316,6 +316,8 @@ def get_tau_data(equation_data):
         for e in r:
             tau = e['popt'][3]
             row.append(tau)
-        equation_data.append(row)
+        tau_data.append(row)
+
+    print(tau_data)
 
     return tau_data

requirements.txt

@@ -1,5 +1,6 @@
 PyQt5
 pandas
+matplotlib
 numpy
 pyinstaller==4.4
 sqlitedict

ui/mainwin.ui

@@ -78,7 +78,7 @@
                 <number>6</number>
                </property>
                <property name="minimum">
-                <double>-99.989999999999995</double>
+                <double>0.000000000000000</double>
                </property>
                <property name="singleStep">
                 <double>0.010000000000000</double>