teng_ml/data_preprocess.py

@@ -1,127 +0,0 @@
-import pandas as pd
-import numpy as np
-import scipy.signal as signal
-import matplotlib.pyplot as plt
-from time import sleep
-from random import choice as r_choice
-from sys import exit
-
-
-if __name__ == "__main__":
-    if __package__ is None:
-        # make relative imports work as described here: https://peps.python.org/pep-0366/#proposed-change
-        __package__ = "teng_ml"
-        import sys
-        from os import path
-        filepath = path.realpath(path.abspath(__file__))
-        sys.path.insert(0, path.dirname(path.dirname(filepath)))
-
-from .util.transform import Normalize
-from .util.data_loader import get_datafiles
-
-file = "/home/matth/Uni/TENG/teng_2/data/2023-06-28_foam_black_1_188mm_06V001.csv"
-
-class InteractiveDataSelector:
-    """
-    Helper class for "iterating" through selected peaks.
-    """
-    def __init__(self, out_name, out_dir, fig, ax):
-        self._out_dir = out_dir
-        self._out_name = out_name
-        self._fig = fig
-        self._ax = ax
-
-        self._fig.canvas.mpl_connect("button_press_event", lambda ev: self._fig_on_button_press(ev))
-        self._fig.canvas.mpl_connect("key_press_event", lambda ev: self._fig_on_key_press(ev))
-
-        self._splits_lines = None  # vlines
-        self._excludes_lines = None
-        self._excludes_areas = [] # list of areas
-
-        self._splits: list[int] = []
-        self._excludes: list[int] = []
-        self._mode = None  # split or exclude
-        self._set_mode("split")
-
-    def run(self):
-        while plt.fignum_exists(self._fig.number):
-            plt.pause(0.01)
-
-    def _fig_on_button_press(self, event):
-        if event.xdata in self._excludes or event.xdata in self._splits: return
-        if event.button == 1:  # left click, add position
-            if self._mode == "split":
-                self._splits.append(event.xdata)
-            else:
-                self._excludes.append(event.xdata)
-        elif event.button == 3:  # right click, undo
-            if self._mode == "split":
-                if len(self._splits) > 0:
-                    self._splits.pop()
-            else:
-                if len(self._excludes) > 0:
-                    self._excludes.pop()
-        self._update_lines()
-
-    def _fig_on_key_press(self, event):
-        if event.key == 'S':
-            self._set_mode("split")
-        elif event.key == 'e':
-            self._set_mode("exclude")
-
-    def _set_mode(self, mode):
-        help_str = "[(e)xclude - (S)plit]"
-        if mode == "split":
-            self._mode = "split"
-            fig.suptitle(f"-> split mode {help_str}")
-        else:
-            self._mode = "exclude"
-            fig.suptitle(f"-> exclude mode {help_str}")
-
-    def _update_lines(self):
-        print(self._splits, self._excludes)
-        ymin, ymax = self._ax.get_ylim()
-
-        if self._splits_lines is not None: self._splits_lines.remove()
-        self._splits_lines = self._ax.vlines(self._splits, ymin, ymax, color="b")
-
-        if self._excludes_lines is not None: self._excludes_lines.remove()
-        self._excludes_lines = self._ax.vlines(self._excludes, ymin, ymax, color="r")
-
-        for area in self._excludes_areas:
-            area.remove()
-        self._excludes_areas.clear()
-        excludes = self._excludes.copy()
-        if len(excludes) % 2 == 1: excludes.pop()  # only draw pairs
-        excludes.sort()
-        for i in range(1, len(excludes), 2):
-            self._excludes_areas.append(self._ax.axvspan(excludes[i-1], excludes[i], facecolor='r', alpha=0.3))
-
-        self._ax.set_ylim(ymin, ymax)  # reset, since margins are added to lines
-        self._fig.canvas.draw()
-
-    def _save_as_new_files(self):
-
-
-
-
-if __name__ == "__main__":
-    """
-    Peak identification:
-    plot, let user choose first, second, last and lowest peak for identification
-    """
-    df = pd.read_csv(file)
-    a = df.to_numpy()
-
-    # a2 = interpolate_to_linear_time()
-    # print(a2)
-    # exit()
-
-    vdata = Normalize(0, 1)(a[:,2])
-    plt.ion()
-    fig, ax = plt.subplots()
-    ax.plot(vdata)
-    ax.grid(True)
-    selector = InteractiveDataSelector("bla", "test", fig, ax)
-    selector.run()
-

teng_ml/peaks.py

@@ -10,7 +10,7 @@ from sys import exit
 if __name__ == "__main__":
     if __package__ is None:
         # make relative imports work as described here: https://peps.python.org/pep-0366/#proposed-change
-        __package__ = "teng_ml"
+        __package__ = "teng-ml"
         import sys
         from os import path
         filepath = path.realpath(path.abspath(__file__))

teng_ml/util/transform.py

@@ -9,37 +9,20 @@ class Normalize:
         assert(low < high)
         self.low = low
         self.high = high
-    def __call__(self, data):
-        min_ = np.min(data)
-        data = data - min_   # smallest point is 0 now
-        max_ = np.max(data)
+    def __call__(self, a):
+        min_ = np.min(a)
+        a = a - min_
+        max_ = np.max(a)
         if max_ != 0:
-            data = (data / max_)
+            a = (a / max_)
         # now normalized between 0 and 1
-        data *= (self.high - self.low)
-        data += self.low
-        return data
+        a *= (self.high - self.low)
+        a -= self.low
+        return a
 
     def __repr__(self):
         return f"Normalize(low={self.low}, high={self.high})"
 
-class NormalizeAmplitude:
-    """
-    scale data so that all values are between -high and high
-    """
-    def __init__(self, high=1):
-        self.high = high
-
-    def __call__(self, data):
-        min_ = np.min(data)
-        max_ = np.max(data)
-        scale = np.max([np.abs(min_), np.abs(max_)])
-        if scale != 0:
-            data = data / scale * self.high
-        return data
-    def __repr__(self):
-        return f"NormalizeAmplitude(high={self.high})"
-
 
 class ConstantInterval:
     """
@@ -49,15 +32,15 @@ class ConstantInterval:
     def __init__(self, interval):
         self.interval = interval
 
-    def __call__(self, data):
+    def __call__(self, a):
         """
         array: [timestamps, data1, data2...]
         """
-        timestamps = data[:,0]
+        timestamps = a[:,0]
         new_stamps = np.arange(timestamps[0], timestamps[-1], self.interval)
         ret = new_stamps
-        for i in range(1, data.shape[1]):  # 
-            interp = interp1d(timestamps, data[:,i])
+        for i in range(1, a.shape[1]):  # 
+            interp = interp1d(timestamps, a[:,i])
             new_vals = interp(new_stamps)
             ret = np.vstack((ret, new_vals))
         return ret.T