def plot_coherence_fft(self, s1, s2, chan_a, chan_b):
plt.figure()
plt.ylabel("Coherence")
plt.xlabel("Frequency (Hz)")
plt.title(self.plot_title("Coherence between channels "+chan_a+" and " +chan_b +" in the "+str(config['band'][0])+"-"+str(config['band'][1])+" Hz band"))
plt.grid(True)
plt.xlim(config['band'][0],config['band'][1])
cxy, f = plt.cohere(s1, s2, NFFT, fs_Hz)
self.plotit(plt)
def plot_coherence_fft(self, s1, s2, chan_a, chan_b):
plt.figure()
plt.ylabel("Coherence")
plt.xlabel("Frequency (Hz)")
plt.title(self.plot_title("Coherence between channels "+chan_a+" and " +chan_b +" in the "+str(config['band'][0])+"-"+str(config['band'][1])+" Hz band"))
plt.grid(True)
plt.xlim(config['band'][0],config['band'][1])
cxy, f = plt.cohere(s1, s2, NFFT, fs_Hz)
self.plotit(plt)
def coherence_over_time(west, north, name):
west = np.array(west)
north = np.array(north)
to_plot = zip(west, north) #let's have transform
cxy, f = plt.cohere(west, north) #for freqs first
with open("./coherence_stats/" + name, "w") as stats_file:
stats_file.write(",".join(map(str, f)))
stats_file.write("\n")
stats_file.write(",".join(map(str, cxy)))
def plot_coherence(self, sensor_1: str, sensor_2: str):
"""
Plot Coherence between two sensors.
:param sensor_1: The value of the column name from the loaded files where the desired information is. [SENSOR 1 NAME]
:param sensor_2: The value of the column name from the loaded files where the desired information is. [SENSOR 2 NAME]
:param sampling_freq : The signal sampling frequency.
:return Return Instance so that it can be linearly written in code.
"""
# Get Columns as Series.
s1 = self.data_read[sensor_1] # each row is a list
s2 = self.data_read[sensor_2] # each row is a list
plt.cohere(s1, s2, self.sampling_rate)
# Setup Plot Parameters.
plt.title('Coherence Function between ' + sensor_1 + ' and ' +
sensor_2)
plt.show()
return self # Return Instance so that it can be linearly written in code.
def coherences_over_time(wests, norths):
plt.close()
coherences = []
for west, north in zip(wests, norths):
curr_coherences = []
for x in xrange(0, 100): ######## use np.roll
curr_coherences.append(plt.cohere(west, north)[0])
#get freqs and cxy, must plot according to those
plt.plot(curr_coherences[-1], color="blue", alpha=0.1)
coherences.append(curr_coherences)
plt.ylabel("coherence synchrony score")
plt.xlabel("offset")
plt.title("simultaneous coherence plot")
plt.savefig("./wholes/coherence_mc")
plt.close()
"""
def coherences_over_time(wests, norths):
plt.close()
coherences = []
for west, north in zip(wests, norths):
curr_coherences = []
for x in xrange(0, 100): ######## use np.roll
curr_coherences.append(plt.cohere(west, north)[0])
#get freqs and cxy, must plot according to those
plt.plot(curr_coherences[-1], color="blue", alpha=0.1)
coherences.append(curr_coherences)
plt.ylabel("coherence synchrony score")
plt.xlabel("offset")
plt.title("simultaneous coherence plot")
plt.savefig("./wholes/coherence_mc")
plt.close()
"""
def acrossCoherence():
# https://pythontic.com/visualization/signals/coherence
data1 = request.json.get('data1', [])
data2 = request.json.get('data2', [])
# print(request.json.get('name', ""))
# Create sine wave1
# time = np.arange(0, 100, 0.1)
# sinewave1 = np.sin(time)
# Create sine wave2 as replica of sine wave1
# time1 = np.arange(0, 100, 0.1)
# sinewave2 = np.sin(time1)
# Plot the sine waves - subplot 1
# plot.title('Two sine waves with coherence as 1')
# plot.subplot(211)
# plot.grid(True, which='both')
# plot.xlabel('time')
# plot.ylabel('amplitude')
# plot.plot(time, sinewave1, time1, sinewave2)
# Plot the coherence - subplot 2
# plot.subplot(212)
# coh, f = plot.cohere(sinewave1, sinewave2, 256, 1./.01)
# print("Coherence between two signals:")
# print(coh)
# plot.ylabel('coherence')
# plot.show()
# print(sinewave1)
# print(sinewave2)
print(len(data1))
coh, f = plot.cohere(data1, data2, 64, 256)
output = {}
output['code'] = 20000
output['data'] = {}
output['data']['coherence'] = coh.tolist()
output['data']['frequency'] = f.tolist()
return Response(json.dumps(output), mimetype='application/json')
plt.subplots_adjust(wspace=0.5)
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t)) # white noise 1
nse2 = np.random.randn(len(t)) # white noise 2
r = np.exp(-t/0.05)
cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
# two signals with a coherent part and a random part
s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
plt.subplot(211)
plt.plot(t, s1, 'b-', t, s2, 'g-')
plt.xlim(0, 5)
plt.xlabel('time')
plt.ylabel('s1 and s2')
plt.grid(True)
plt.subplot(212)
cxy, f = plt.cohere(s1, s2, 256, 1./dt)
plt.ylabel('coherence')
file = 'fig2_py.pdf' # Nombre del fichero
plt.savefig(file) # Salva la imagen en PDF
os.system('pdfcrop ' + file + ' ' + file) # Recorta el fichero PDF para ajustar el BB
# plt.savefig('tex_demo') # Salva la imagen en png
plt.show()
import numpy as np
import matplotlib.pyplot as plt
# make a little extra space between the subplots
plt.subplots_adjust(wspace=0.5)
dt = 0.01
t = np.arange(0, 30, dt)
nse1 = np.random.randn(len(t)) # white noise 1
nse2 = np.random.randn(len(t)) # white noise 2
r = np.exp(-t/0.05)
cnse1 = np.convolve(nse1, r, mode='same')*dt # colored noise 1
cnse2 = np.convolve(nse2, r, mode='same')*dt # colored noise 2
# two signals with a coherent part and a random part
s1 = 0.01*np.sin(2*np.pi*10*t) + cnse1
s2 = 0.01*np.sin(2*np.pi*10*t) + cnse2
plt.subplot(211)
plt.plot(t, s1, 'b-', t, s2, 'g-')
plt.xlim(0, 5)
plt.xlabel('time')
plt.ylabel('s1 and s2')
plt.grid(True)
plt.subplot(212)
cxy, f = plt.cohere(s1, s2, 256, 1./dt)
plt.ylabel('coherence')
plt.show()
c1 = np.convolve(n1, r, mode='same') * dt
c2 = np.convolve(n2, r, mode='same') * dt
s1 = 0.01 * np.sin(2 * np.pi * 10 * t) + c1
s2 = 0.01 * np.sin(2 * np.pi * 10 * t) + c2
plt.subplot(211)
plt.plot(t, s1, t, s2)
plt.xlim(0, 5)
plt.xlabel('time')
plt.ylabel('s1&s2')
plt.grid(True)
plt.subplot(212)
plt.cohere(s1, s2, 256, 1. / dt)
plt.ylabel('coherernece')
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
# In[ ]:
plt.title(titleText)
else: #对特定通道进行分析
channelN = pressuresData.getColumnData(CHANNEL_NUM)[0:dataSize]
plt.subplots_adjust(wspace=0.5)
x = czySignal.getXByFs(dataSize, fs)
print('进行%d通道相关分析' % (CHANNEL_NUM))
plt.subplot(211)
plt.plot(x, channelN, 'r')
plt.plot(x, channelEnd, 'b')
if SHOW_AXIS_TEXT:
plt.xlabel('times(s)')
plt.ylabel('amplitude')
if SHOW_TITLE:
plt.title(titleText)
plt.subplot(212)
cxy, f = plt.cohere(channelN,
channelEnd,
NFFT=cohereNFFTSize,
Fs=fs,
detrend='mean')
if MARK_MAX:
index = np.argmax(cxy)
plt.plot(f[index], cxy[index], 'o')
if SHOW_AXIS_TEXT:
plt.xlabel('frequency(Hz)')
plt.ylabel('coherence')
plt.gcf().set_facecolor('w')
plt.show()
y = np.random.randn(100)
colors = np.random.rand(100)
sizes = 1000 * np.random.rand(100)
plt.scatter(x, y, c = colors, s = sizes, alpha = 0.3, cmap = 'magma')
plt.colorbar()
plt.show()
print()
# x와 y의 일관성 차트
# 2개의 일관성 출력
print('2개의 일관성 출력')
dt = 0.01
t = np.arange(0, 30, dt)
n1 = np.random.randn(len(t))
n2 = np.random.randn(len(t))
s1 = 1.5 * np.sin(2 * np.pi * 10 * t) + n1
s2 = np.cos(np.pi * t) + n2
plt.cohere(s1, s2 ** 2, 128, 1./dt)
plt.xlabel('time')
plt.ylabel('coherence')
plt.show()
print()
# =============================================================================
ax.set_zlabel('coherence')
plt.subplots_adjust(hspace=0.31)#水平间距
plt.title(titleText)
else:#对特定通道进行分析
channelN = pressuresData.getColumnData(CHANNEL_NUM)[0:dataSize]
plt.subplots_adjust(wspace=0.5)
x = czySignal.getXByFs(dataSize,fs)
print('进行%d通道相关分析'%(CHANNEL_NUM))
plt.subplot(211)
plt.plot(x,channelN,'r')
plt.plot(x,channelEnd,'b')
if SHOW_AXIS_TEXT:
plt.xlabel('times(s)')
plt.ylabel('amplitude')
if SHOW_TITLE:
plt.title(titleText)
plt.subplot(212)
cxy,f = plt.cohere(channelN,channelEnd,NFFT = cohereNFFTSize,Fs = fs,detrend='mean')
if MARK_MAX:
index = np.argmax(cxy)
plt.plot(f[index],cxy[index],'o')
if SHOW_AXIS_TEXT:
plt.xlabel('frequency(Hz)')
plt.ylabel('coherence')
plt.gcf().set_facecolor('w')
plt.show()
from sys import exit
x = np.genfromtxt("x.csv",delimiter=';')
y = np.genfromtxt("y.csv",delimiter=';')
coh = np.genfromtxt("coh.csv",delimiter=';')
fr = np.genfromtxt("f.csv",delimiter=";")
SN = 256
w = [1.]*SN
hann = [0.0]*SN
for i in range(SN):
# hann[i] = 0.5*(1-np.cos(2*np.pi*i/SN))
hann[i] = 1.0
c,f = plt.cohere(x,y,NFFT=SN,Fs=1,window=hann)
plt.subplot(2,2,1)
plt.plot(x)
plt.title("x")
plt.subplot(2,2,2)
plt.plot(y)
plt.title("y")
plt.subplot(2,2,3)
plt.plot(fr,coh)
plt.title("coh")
plt.subplot(2,2,4)
def cohere(*args, **kwargs):
r"""starkplot wrapper for cohere"""
return _pyplot.cohere(*args, **kwargs)
plot.title('Two sine waves with coherence as 1')
plot.subplot(211)
plot.grid(True, which='both')
plot.xlabel('time')
plot.ylabel('amplitude')
plot.plot(time, sinewave1, time1, sinewave2)
# Plot the coherence - subplot 2
plot.subplot(212)
coh, f = plot.cohere(sinewave1, sinewave2, 256, 1. / .01)
print("Coherence between two signals:")
print(coh)
print("Frequncies of the coherence vector:")
print(f)
plot.ylabel('coherence')
plot.show()
plt.show()
##更复杂的子绘图区域： gridspec子类
import matplotlib.gridspec as gridspec
gs = gridspec.GridSpec(3, 3) # 设定3x3网格
ax1 = plt.subplot(gs[0, :]) # 选中网格，确定选中的行列网格数
ax2 = plt.subplot(gs[1, :-1]
# 常用绘图函数
plt.boxplot(data, notch, position) # 在图形中增加带箭头的注解
plt.bar(left, height, width, bottom) # 横向条形图
plt.barh(width, bottom, left, height) #条形图
plt.hist(x,bins,normed) # 直方图
plt.scatter(x,y) # 散点图
plt.cohere(x,y,NFFT=256, Fs) # X-Y 的相关性函数
plt.vlines() # 垂线图
plt.plot_date() # 数据日期
plt.polar(theta, r)
plt.pie(data, explode) # 饼图
# 例1 饼图：
labels = 'Frogs', 'Hogs', 'Dogs','Logs'
sizes = [15, 30, 45, 10] # 占比
explode = (0, 0.1, 0, 0)
plt.pie(sizes, explode=explode, labels=labels,
autopct='%1.1f%%',shadow=False, startangle=90) # 饼图
plt.axis('equal')
# 例2 直方图：
np.random.seed(0)
