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Analysis_Positive_Delta.py
171 lines (120 loc) · 4.72 KB
/
Analysis_Positive_Delta.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
#import statistics
import pdb
def CalDelta(vf, vi):
delta = vf - vi
return delta
# if delta <= 0:
# return ('nv', Deltas)
def Stads(Data_list): # funcion de calculo de delta
mean_data = 0
for data in Data_list:
data += data
mean_data = data
mean_data = mean_data * len(Data_list)
#std_data = statistics.stdev(Data_list)
return mean_data
def Analisys1(data_list, window, mean_factor):
temp_delta_list = [] # lista temporal para calculo de los Deltas
final_data = [] # Datos finales de los deltas frecuencia
time = []
count = 0 # Lleva un conteo para cada delta
# salto entre valores para tomar el dental
for counter in range(len(data_list)):
# Recorremos la lista data list, por posicion
if counter < window: # No empezamos si no hasta recorrer tantos valores como el window
pass # Si la condicion no se cumple, pasamos
if counter >= window and counter <= len(data_list):
# recorremos data list por posicion y realizamos la accion segun hasta terminar la lista
# Calculamos el delta
delta = CalDelta(data_list[counter], data_list[counter - window])
# Agregamos el valor de delta a tdelta_list
temp_delta_list.append(delta)
# hacemos la estadistica para obtener la DS y el Mean
mean_delta = Stads(temp_delta_list)
factor = mean_delta*mean_factor
# Recorremos nuevamente la lista de deltas y seleccionamos segun el
# criterio de la DS y el mean
for delta in temp_delta_list:
if delta > (mean_delta + factor) or delta < -(mean_delta - factor):
pass
if delta <= (mean_delta + factor) or delta >= -(mean_delta - factor):
time.append(count)
final_data.append(delta)
count += 1
return [time, final_data, mean_delta, factor]
def Analisys2(data_list, mean_delta, window, factor):
final_data = []
time = []
count = 0
for counter in range(len(data_list)):
if counter < window:
pass
if counter >= window and counter <= len(data_list):
delta = CalDelta(data_list[counter], data_list[counter - window])
if delta > (mean_delta + factor) or delta < -(mean_delta - factor):
pass
if delta <= (mean_delta + factor) or delta >= -(mean_delta - factor):
time.append(count)
final_data.append(data_list[counter])
count += 1
return [time, final_data]
file_name = str(sys.argv[1])
window = 2
mean_factor = 0.00005
reader = pd.ExcelFile(file_name)
sheetlist = reader.sheet_names
df1 = pd.read_excel(file_name, sheet_name=sheetlist[2], header=None, usecols="A, B")
df1_headers = df1.head(0)
df2 = pd.read_excel(file_name, sheet_name=sheetlist[3], header=None, usecols="A, B")
df2_headers = df2.head(0)
df3 = pd.read_excel(file_name, sheet_name=sheetlist[4], header=None, usecols="A, B")
df3_headers = df2.head(0)
probe = [df1, df2, df3]
t_Z_OFF_I = []
t_Z_ON = []
t_Z_OFF_F = []
f_Z_OFF_I = []
f_Z_ON = []
f_Z_OFF_F = []
#for df in range(len(probe)):
for data in range(len(df1[0])):
t_Z_OFF_I.append(df1[0][data])
f_Z_OFF_I.append(df1[1][data])
for data in range(len(df2[0])):
t_Z_ON.append(df2[0][data])
f_Z_ON.append(df2[1][data])
for data in range(len(df3[0])):
t_Z_OFF_F.append(df3[0][data])
f_Z_OFF_F.append(df3[1][data])
#Realizamos el analisis de deltas
[time1, delta_data, mean_delta, factor] = Analisys1(f_Z_ON, window, mean_factor)
#Realizamos la seleccion
[time2, frequency_data] = Analisys2(f_Z_ON, mean_delta, window, factor)
#pdb.set_trace()
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.set_ylim(min(delta_data), max(delta_data) + 0.2)
# ax1.set_xlim(min(time), max(time))
plt.xticks(np.arange(min(time1), max(time1), step=60), fontsize=8)
plt.yticks(np.arange(min(delta_data), max(delta_data) + 0.2, step=0.1), fontsize=8)
ax1.set_xlabel("time(s)", fontsize=6)
ax1.set_ylabel("Delta Frequency (Hz)", fontsize=6)
ax1.grid()
# plt.xticks(np.arange(min(time), max(time), step=60), fontsize=8)
# plt.yticks(np.arange(min(f_data), min(f_data), step=0.15), fontsize=8)
ax1.plot(time1, delta_data)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
ax2.set_ylim(min(frequency_data), max(frequency_data) + 0.2)
plt.xticks(np.arange(min(time2), max(time2), step=60), fontsize=8)
plt.yticks(np.arange(min(frequency_data), max(frequency_data) + 0.2, step=0.1), fontsize=8)
ax2.set_xlabel("time(s)", fontsize=6)
ax2.set_ylabel("Frequency (Hz)", fontsize=6)
ax2.grid()
ax2.plot(time2, frequency_data)
plt.show()
#pdb.set_trace()