From 13d436acef0e2168ea833c1d6d532b85ebdd38cf Mon Sep 17 00:00:00 2001 From: "Matthias@Dell" Date: Fri, 28 Apr 2023 16:04:11 +0200 Subject: [PATCH] Added rnn --- teng-ml/prepare.py | 145 --------------------------------------------- teng-ml/rnn.py | 39 ++++++++++++ 2 files changed, 39 insertions(+), 145 deletions(-) delete mode 100644 teng-ml/prepare.py create mode 100644 teng-ml/rnn.py diff --git a/teng-ml/prepare.py b/teng-ml/prepare.py deleted file mode 100644 index 18f3456..0000000 --- a/teng-ml/prepare.py +++ /dev/null @@ -1,145 +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 - -from .util.transform import Normalize - -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 .utility.data import load_dataframe - -file = "/home/matth/data/2023-04-25_kapton_8.2V_179mm002.csv" - -class PeakInfo: - """ - Helper class for "iterating" through selected peaks. - """ - def __init__(self): - self.reset() - - def reset(self): - self._peak_names = [ "first", "second", "last", "lowest" ] - self._peaks = { p: None for p in self._peak_names } - self._iter = 0 - - def current(self): - # return (self._peak_names[self._iter]), self._peaks[self._peak_names[self._iter]] - return self._peaks[self._peak_names[self._iter]] - def name(self): - return self._peak_names[self._iter] - - def next(self): - if self._iter < len(self._peak_names) - 1: self._iter += 1 - return self.current() - def prev(self): - if self._iter > 0: self._iter -= 1 - return self.current() - - def set(self, value): - """Assign a value to the current peak""" - self._peaks[self._peak_names[self._iter]] = value - def is_done(self): - for peak in self._peaks.values(): - if peak is None: return False - return True - - def __getitem__(self, key): - return self._peaks[key] - def __setitem__(self, key, value): - self._peaks[key] = value - def __repr__(self): - return f"{self._peak_names[self._iter]} peak" - - -def find_peaks(a): - peaks = signal.find_peaks(a) - - -def on_click(fig, ax, peaks, event): - """ - Let the user select first, second and last peak by clicking on them in this order. - Right click undos the last selection - """ - select = None - if event.button == 1: # left click - peaks.set((event.xdata, event.ydata)) - print(f"{peaks}: {event.xdata} - {event.ydata}") - ax.set_title(f"{peaks}: {event.xdata} - {event.ydata}") - peaks.next() - elif event.button == 3: # right click - ax.set_title(f"Undo {peaks.name()}") - if not peaks.is_done(): - peaks.prev() - peaks.set(None) - if peaks.is_done(): message = "Close window when done" - else: message = f"Click on {peaks}" - fig.suptitle(message) - fig.canvas.draw() - # fig1.canvas.flush_events() - -def calc_peaks(peaks): - # get the peak points from the information of a Peaks object - # 90% distance between first and second - min_distance = max(1, (peaks["second"][0] - peaks["first"][0]) * 0.9) - min_height = peaks["lowest"][1] * 0.99 - vpeaks = signal.find_peaks(vdata, height=min_height, distance=min_distance) - return vpeaks - - -if __name__ == "__main__": - """ - Peak identification: - plot, let user choose first, second, last and lowest peak for identification - """ - df = load_dataframe(file) - a = df.to_numpy() - - # a2 = interpolate_to_linear_time() - # print(a2) - # exit() - - vdata = Normalize(0, 1)(a[:,2]) - plt.ion() - # vpeaks[0] is the list of the peaks - vpeaks = signal.find_peaks(vdata)[0] - fig, ax = plt.subplots() - ax.plot(vdata) - peak_lines = ax.vlines(vpeaks, 0, 1, colors="r") - ax.grid(True) - fig.suptitle("Click on first peak") - peak_info = PeakInfo() - # handle clicks - fig.canvas.mpl_connect("button_press_event", lambda ev: on_click(fig, ax, peak_info, ev)) - # run until user closes, events are handled with on_click function - print(vdata.size) - while plt.fignum_exists(fig.number): - plt.pause(0.01) - if (peak_info.is_done()): - vpeaks = calc_peaks(peak_info)[0] - x_margin = (a[-1,0] - a[0,0]) * 0.05 # allow some margin if user clicked not close enough on peak - vpeaks = vpeaks[(vpeaks >= peak_info["first"][0] - x_margin) & (vpeaks <= peak_info["last"][0] + x_margin)] # remove peaks before first and after last - peak_lines.remove() - peak_lines = ax.vlines(vpeaks, 0, 1, colors="r") - peak_info.reset() - print(a[:,0], vpeaks) - - # separate peaks - indices = np.arange(0, a[:,0].size) - peak_datas = [] - for i in range(len(vpeaks) - 1): - # TODO: user <= or < - peak_datas.append(vdata[(indices >= vpeaks[i]) & (indices < vpeaks[i+1])]) - plt.plot(peak_datas[i]) - print(peak_datas) - plt.pause(20) - diff --git a/teng-ml/rnn.py b/teng-ml/rnn.py new file mode 100644 index 0000000..2733d4b --- /dev/null +++ b/teng-ml/rnn.py @@ -0,0 +1,39 @@ +import torch +import torch.nn as nn + +# BiLSTM Model + +class RNN(nn.Module): + def __init__(self, input_size, hidden_size, num_layers, num_classes, if_bidirectional): + super(RNN, self).__init__() + self.num_layers = num_layers + self.hidden_size = hidden_size + self.if_bidirectional = if_bidirectional + self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=if_bidirectional) + + if if_bidirectional == True: + self.fc = nn.Linear(hidden_size * 2, num_classes) + else: + self.fc = nn.Linear(hidden_size, num_classes) + + + def forward(self, x): + D = 2 if self.if_bidirectional == True else 1 + Batch = x.batch_sizes[0] + + h0 = torch.zeros(D * self.num_layers, Batch, self.hidden_size).to(device) + c0 = torch.zeros(D * self.num_layers, Batch, self.hidden_size).to(device) + x.to(device) + _, (h_n, _) = self.lstm(x, (h0, c0)) + final_state = h_n.view(self.num_layers, D, Batch, self.hidden_size)[-1] # num_layers, num_directions, batch, hidden_size + + if D == 1: + X = final_state.squeeze() + elif D == 2: + h_1, h_2 = final_state[0], final_state[1] # forward & backward pass + #X = h_1 + h_2 # Add both states + X = torch.cat((h_1, h_2), 1) # Concatenate both states, X-size: (Batch, hidden_size * 2） + + output = self.fc(X) # fully-connected layer + + return output