Added rnn
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Matthias@Dell
parent
98a07fd64f
commit
13d436acef
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import pandas as pd
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import numpy as np
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import scipy.signal as signal
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import matplotlib.pyplot as plt
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from time import sleep
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from random import choice as r_choice
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from sys import exit
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from .util.transform import Normalize
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if __name__ == "__main__":
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if __package__ is None:
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# make relative imports work as described here: https://peps.python.org/pep-0366/#proposed-change
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__package__ = "teng-ml"
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import sys
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from os import path
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filepath = path.realpath(path.abspath(__file__))
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sys.path.insert(0, path.dirname(path.dirname(filepath)))
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from .utility.data import load_dataframe
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file = "/home/matth/data/2023-04-25_kapton_8.2V_179mm002.csv"
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class PeakInfo:
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"""
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Helper class for "iterating" through selected peaks.
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"""
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def __init__(self):
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self.reset()
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def reset(self):
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self._peak_names = [ "first", "second", "last", "lowest" ]
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self._peaks = { p: None for p in self._peak_names }
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self._iter = 0
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def current(self):
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# return (self._peak_names[self._iter]), self._peaks[self._peak_names[self._iter]]
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return self._peaks[self._peak_names[self._iter]]
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def name(self):
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return self._peak_names[self._iter]
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def next(self):
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if self._iter < len(self._peak_names) - 1: self._iter += 1
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return self.current()
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def prev(self):
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if self._iter > 0: self._iter -= 1
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return self.current()
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def set(self, value):
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"""Assign a value to the current peak"""
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self._peaks[self._peak_names[self._iter]] = value
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def is_done(self):
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for peak in self._peaks.values():
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if peak is None: return False
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return True
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def __getitem__(self, key):
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return self._peaks[key]
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def __setitem__(self, key, value):
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self._peaks[key] = value
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def __repr__(self):
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return f"{self._peak_names[self._iter]} peak"
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def find_peaks(a):
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peaks = signal.find_peaks(a)
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def on_click(fig, ax, peaks, event):
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"""
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Let the user select first, second and last peak by clicking on them in this order.
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Right click undos the last selection
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"""
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select = None
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if event.button == 1: # left click
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peaks.set((event.xdata, event.ydata))
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print(f"{peaks}: {event.xdata} - {event.ydata}")
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ax.set_title(f"{peaks}: {event.xdata} - {event.ydata}")
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peaks.next()
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elif event.button == 3: # right click
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ax.set_title(f"Undo {peaks.name()}")
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if not peaks.is_done():
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peaks.prev()
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peaks.set(None)
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if peaks.is_done(): message = "Close window when done"
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else: message = f"Click on {peaks}"
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fig.suptitle(message)
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fig.canvas.draw()
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# fig1.canvas.flush_events()
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def calc_peaks(peaks):
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# get the peak points from the information of a Peaks object
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# 90% distance between first and second
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min_distance = max(1, (peaks["second"][0] - peaks["first"][0]) * 0.9)
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min_height = peaks["lowest"][1] * 0.99
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vpeaks = signal.find_peaks(vdata, height=min_height, distance=min_distance)
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return vpeaks
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if __name__ == "__main__":
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"""
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Peak identification:
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plot, let user choose first, second, last and lowest peak for identification
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"""
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df = load_dataframe(file)
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a = df.to_numpy()
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# a2 = interpolate_to_linear_time()
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# print(a2)
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# exit()
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vdata = Normalize(0, 1)(a[:,2])
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plt.ion()
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# vpeaks[0] is the list of the peaks
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vpeaks = signal.find_peaks(vdata)[0]
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fig, ax = plt.subplots()
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ax.plot(vdata)
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peak_lines = ax.vlines(vpeaks, 0, 1, colors="r")
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ax.grid(True)
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fig.suptitle("Click on first peak")
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peak_info = PeakInfo()
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# handle clicks
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fig.canvas.mpl_connect("button_press_event", lambda ev: on_click(fig, ax, peak_info, ev))
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# run until user closes, events are handled with on_click function
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print(vdata.size)
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while plt.fignum_exists(fig.number):
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plt.pause(0.01)
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if (peak_info.is_done()):
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vpeaks = calc_peaks(peak_info)[0]
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x_margin = (a[-1,0] - a[0,0]) * 0.05 # allow some margin if user clicked not close enough on peak
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vpeaks = vpeaks[(vpeaks >= peak_info["first"][0] - x_margin) & (vpeaks <= peak_info["last"][0] + x_margin)] # remove peaks before first and after last
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peak_lines.remove()
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peak_lines = ax.vlines(vpeaks, 0, 1, colors="r")
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peak_info.reset()
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print(a[:,0], vpeaks)
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# separate peaks
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indices = np.arange(0, a[:,0].size)
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peak_datas = []
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for i in range(len(vpeaks) - 1):
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# TODO: user <= or <
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peak_datas.append(vdata[(indices >= vpeaks[i]) & (indices < vpeaks[i+1])])
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plt.plot(peak_datas[i])
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print(peak_datas)
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plt.pause(20)
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39
teng-ml/rnn.py
Normal file
39
teng-ml/rnn.py
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@ -0,0 +1,39 @@
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import torch
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import torch.nn as nn
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# BiLSTM Model
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class RNN(nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, num_classes, if_bidirectional):
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super(RNN, self).__init__()
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self.num_layers = num_layers
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self.hidden_size = hidden_size
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self.if_bidirectional = if_bidirectional
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=if_bidirectional)
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if if_bidirectional == True:
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self.fc = nn.Linear(hidden_size * 2, num_classes)
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else:
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self.fc = nn.Linear(hidden_size, num_classes)
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def forward(self, x):
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D = 2 if self.if_bidirectional == True else 1
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Batch = x.batch_sizes[0]
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h0 = torch.zeros(D * self.num_layers, Batch, self.hidden_size).to(device)
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c0 = torch.zeros(D * self.num_layers, Batch, self.hidden_size).to(device)
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x.to(device)
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_, (h_n, _) = self.lstm(x, (h0, c0))
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final_state = h_n.view(self.num_layers, D, Batch, self.hidden_size)[-1] # num_layers, num_directions, batch, hidden_size
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if D == 1:
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X = final_state.squeeze()
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elif D == 2:
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h_1, h_2 = final_state[0], final_state[1] # forward & backward pass
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#X = h_1 + h_2 # Add both states
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X = torch.cat((h_1, h_2), 1) # Concatenate both states, X-size: (Batch, hidden_size * 2）
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output = self.fc(X) # fully-connected layer
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return output
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