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utils.py
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utils.py
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import matplotlib.pyplot as plt
import asammdf
import numpy as np
import pandas as pd
#for checking skipselect
import slice_file_john as SFJ
def load_file(file_name):
file = asammdf.MDF(file_name)
return file
def print_channels(file):
channels = file.search("*", mode = 'wildcard')
with open(r'channels.txt', 'w') as fp:
for item in channels:
print(item)
fp.write("%s\n" % item)
print('Done: All available channels saved to "channels.txt"')
print("There are " + str(len(channels)) + " channels in the datafile")
return
def get_start_stop(var):
start = var.timestamps[0]
stop = var.timestamps[-1]
if SFJ.skipselect == False:
print("The raw data will now be displayed. Please review the figure and pick a start and stop window.\n")
print("Then close the figure and follow the input prompt.\n")
gen_lineplot(var)
start = 'a'
stop = 'a'
while (not start.isdigit()) or (not stop.isdigit()):
print("\n## Please enter digits only. ##")
start = input("Please enter the starting timestamp: \n")
stop = input("Please enter the stopping timestamp: \n")
return float(start), float(stop)
def gen_lineplot(var):
plt.figure()
varsamples = smooth([var])[0]
plt.plot(var.timestamps, varsamples)
plt.xlabel('Time (s)')
plt.ylabel(var.name + ' (' + var.unit + ')')
plt.suptitle(var.name + ' vs Time')
plt.show()
return
def len_match(var1, var2):
var1_corrected = var1.samples
if len(var1.samples) != len (var2.samples):
array1 = np.array(range(0,len(var1.samples)))
array2 = np.array(range(0,len(var2.samples)))
var1_corrected = np.interp(array2, array1, np.array(var1.samples))
return var1_corrected
def smooth(varlist):
#var.samples is the array of sensor data
window_size = 0
while window_size == 0:
smoothfactor = 'a'
while not smoothfactor.isdigit():
print("\n## Please enter digits only. ##")
smoothfactor = input("Please enter the nonzero smoothing factor:\n(If unsure put 10 and adjust based on noise level. Enter 1 for no smoothing.)\n")
window_size = int(smoothfactor)
smoothed_varlist = []
for var in varlist:
series = pd.Series(var.samples)
smoothed_series = series.rolling(window=window_size, min_periods=1).mean()
smoothed_varlist.append(smoothed_series.tolist())
return smoothed_varlist
def get_peak(file, var=[], start=None, stop=None):
if start != None:
file_cut = file.cut(start, stop)
min_val_ls = []
max_val_ls = []
min_time_ls = []
max_time_ls = []
for v in var:
signal = file_cut.get(v) #get the signal data for the current variable
min_val = min(signal.samples)
max_val = max(signal.samples)
min_time = signal.timestamps[signal.samples == min_val]
max_time = signal.timestamps[signal.samples == max_val]
min_val_ls.append(min_val)
max_val_ls.append(max_val)
min_time_ls.append(min_time)
max_time_ls.append(max_time)
peak_dict = {'Variable':var, 'min val':min_val_ls, 'min time':min_time_ls,
'max val':max_val_ls, 'max time':max_time_ls}
df = pd.DataFrame(peak_dict)
for row in range(len(df)):
print(df.iloc[row])
print('\n')
return peak_dict