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calc_life.py
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calc_life.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import optimize
import sys, os, json, time
from processing import Processing
class LifeTime():
def __init__(self, file_str, window_length, n_average):
self.file_folder = "/data/"
self.file_str = file_str
self.window_length = window_length
self.n_average = n_average
self.bud = Processing(self.file_folder + self.file_str, verbose=True)
self.n_buffer = 26214400
def diagnosis(self, n_point=None, offset=0):
if n_point is None or n_point < self.window_length * self.n_average:
frame = self.n_buffer // (self.window_length * self.n_average)
elif n_point < -1:
frame = -1
else:
frame = n_point // (self.window_length * self.n_average)
if offset > self.bud.n_sample:
offset = 0
frequencies, times, spectrogram, n_dof = self.bud.time_average_2d(window_length = self.window_length, n_frame = frame, n_offset = offset, padding_ratio = 2, n_average = self.n_average, estimator = 'p')
plt.close("all")
fig, ax = plt.subplots()
color_style = "viridis"
pcm = ax.pcolormesh(frequencies, times, spectrogram, cmap=color_style)
cax = fig.colorbar(pcm, ax=ax)
cax.set_label("power spectral density [arb. unit]")
ax.set_title(self.file_str)
ax.set_xlabel("frequency - {:g} MHz [kHz]".format(self.bud.center_frequency*1e-6))
ax.set_ylabel("times [s]")
ax.set_ylim([times[0],times[-1]])
plt.show()
def load_data(self, freq_min="", freq_max=""):
sample_num = self.bud.n_sample
frame = self.n_buffer // (self.window_length * self.n_average)
self.freq_min = freq_min if freq_min != "" else - self.bud.span / 2 * 1e-3
self.freq_max = freq_max if freq_max != "" else self.bud.span / 2 * 1e-3
header_meta = {
'window length': self.window_length,
'average': self.n_average,
'center frequency': self.bud.center_frequency,
'frequency min': self.freq_min,
'frequency max': self.freq_max
}
def processing_func(setting):
n_frame = setting[0]
n_offset = setting[1]
frequencies, times, spectrogram, n_dof = self.bud.time_average_2d(window_length = self.window_length, n_frame = setting[0], n_offset = setting[1], padding_ratio = 2, n_average = self.n_average, estimator = 'p')
arg_min = np.searchsorted(frequencies, self.freq_min, side="left")
arg_max = np.searchsorted(frequencies, self.freq_max, side="right")
time = (times[:-1] + times[1:]) / 2
peakArea = np.sum(spectrogram[:,arg_min:arg_max], axis=1) * self.bud.sampling_rate * 1e-3
life_data = pd.DataFrame(np.transpose([time,peakArea]), columns=['time', 'peakArea'])
life_data.to_csv("life_" + self.file_str.split(".")[0] + ".csv", mode='a', header=False, columns=['time', 'peakArea'], index=False)
if (os.path.exists("life_" + self.file_str.split(".")[0] + ".csv")):
os.remove("life_" + self.file_str.split(".")[0] + ".csv")
with open("life_" + self.file_str.split(".")[0] + ".csv", 'w') as header:
json.dump(header_meta, header, indent=4, sort_keys=True)
header.write("\n")
life_data = pd.DataFrame({'time': [], 'peakArea': []})
life_data.to_csv("life_" + self.file_str.split(".")[0] + ".csv", mode='a', header=True, columns=['time', 'peakArea'], index=0)
init_time = time.time()
k = 0
while True:
offset = self.window_length * self.n_average * frame * k
sample_num -= self.window_length * self.n_average * frame
if sample_num >= 0:
processing_func([frame, offset])
else:
processing_func([-1, offset])
break
k += 1
print("information of data load:\n--------------------")
print("data load time\t\t\t{:} s".format(time.time()-init_time))
print("window length\t\t\t{:d}".format(self.window_length))
print("average\t\t\t\t{:d}".format(self.n_average))
print("center frequency\t\t{:g} MHz".format(self.bud.center_frequency * 1e-6))
print("frequency start\t\t\t{:} kHz".format(self.freq_min))
print("frequency end\t\t\t{:} kHz".format(self.freq_max))
print("--------------------")
def analyze_data(self, time_min="", time_max="", fit=False, Method="exp"):
if (os.path.exists("life_" + self.file_str.split(".")[0] + ".csv") == False):
print("No availble .csv file. Please load the file first!")
return
with open("life_" + self.file_str.split(".")[0] + ".csv", "r") as header:
lines = ''
for i in range(7):
line = header.readline()
lines += line
header_meta = json.loads(lines)
life_data = pd.read_csv(header)
window_length = header_meta['window length']
n_average = header_meta['average']
center_frequency = header_meta['center frequency']
freq_min = header_meta['frequency min']
freq_max = header_meta['frequency max']
print("information of data:\n--------------------")
print("window length\t\t\t{:d}".format(window_length))
print("average\t\t\t\t{:d}".format(n_average))
print("center frequency\t\t{:g} MHz".format(center_frequency * 1e-6))
print("frequency start\t\t\t{:} kHz".format(freq_min))
print("frequency end\t\t\t{:} kHz".format(freq_max))
print("--------------------")
time = life_data['time'].values
peakArea = life_data['peakArea'].values
index_min = np.searchsorted(time, time_min, side="left") if time_min != "" else 0
index_max = np.searchsorted(time, time_max, side="right") if time_max != "" else len(time)
time = time[index_min:index_max]
peakArea = peakArea[index_min:index_max]
plt.close("all")
plt.figure()
plt.plot(time, peakArea, 'bo')
if fit:
if Method == "lin":
p0 = [-1/time[int(len(time)/2)] * np.log(2), np.log(np.max(time) - np.min(time))]
half_life, curve_fit = self.fitting(p0, time, np.log(peakArea), "lin")
plt.plot(time, np.exp(curve_fit), 'r', lw=2)
else:
p0 = [np.max(time) - np.min(time), -1/time[int(len(time)/2)] * np.log(2), np.min(time)]
half_life, curve_fit = self.fitting(p0, time, peakArea, "exp")
plt.plot(time, curve_fit, 'r', lw=2)
print("half-life: {:} s".format(half_life))
plt.xlabel("times [s]")
plt.ylabel("area [arb. unit]")
plt.title("decay curve")
plt.show()
def fitting(self, p0, x, y, Method="exp"):
def test_func_exp(x, a, b, c):
return a * np.exp(b * x) + c
def test_func_lin(x, b, d):
return b * x + d
if Method == "exp":
params, params_convariance = optimize.curve_fit(test_func_exp, x, y, p0=p0)
half_life = -np.log(2) / params[1]
return half_life, test_func_exp(x, *params)
else:
params, params_convariance = optimize.curve_fit(test_func_lin, x, y, p0=p0)
half_life = -np.log(2) / params[0]
return half_life, test_func_lin(x, *params)
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: {} path/to/file".format(__file__))
sys.exit()
lifetime = LifeTime(sys.argv[-1], window_length=1000, n_average=100)
lifetime.diagnosis()
#lifetime.diagnosis(n_point=None,offset=0)
#lifetime.load_data(freq_min="", freq_max="")
#lifetime.load_data(freq_min=-10, freq_max=10)
#lifetime.analyze_data(time_min="", time_max="", fit=False, Method="exp")
#lifetime.analyze_data(time_min=25, time_max=600, fit=True, Method="exp")