def run():
import seg1d
import numpy as np
import matplotlib.pylab as plt
# create an array of data
x = np.linspace(-np.pi * 2, np.pi * 2, 2000)
# get an array of data from a sin function
targ = np.sin(x)
# define a segment within the sine wave to use as reference
t_s, t_e = 200, 400
# cut a segment out to use as a reference data
refData = [{'0': targ[t_s:t_e]}]
targData = {'0': targ}
refWeights = {'0': 1}
### define some test parameters
minWin = 98 # minimum percent to scale down reference data
maxWin = 105 # maximum percent to scale up reference data
sizeStep = 1 # step to use for correlating reference to target data
# call the segmentation algorithm
segments = seg1d.segment_data(refData, targData, refWeights, minWin,
maxWin, sizeStep)
print(segments)
plt.figure(figsize=(10, 3))
# plot the full sine wave
plt.plot(x, targ, linewidth=8, alpha=0.2, label='Target')
# plot the original reference segment
plt.plot(x[t_s:t_e],
targ[t_s:t_e],
linewidth=6,
alpha=0.7,
label='Reference')
# plot all segments found
seg_num = 1
for s, e, c in segments:
plt.plot(x[s:e],
targ[s:e],
dashes=[1, 1],
linewidth=4,
alpha=0.8,
label='Segment {}'.format(seg_num))
plt.xlabel('Angle [rad]')
plt.ylabel('sin(x)')
plt.legend()
plt.tight_layout()
plt.show()
def run():
import seg1d
import numpy as np
import matplotlib.pylab as plt
import scipy.signal as signal
# create an array of data
x = np.linspace(-1, 1, 2000)
# get an array of data from a Gaussian pulse
targ = signal.gausspulse(x, fc=5)
# define a segment within the pulse to use as reference
t_s, t_e = 950, 1050
# cut a segment out to use as a reference data
refData = [{'gauss': targ[t_s:t_e]}]
targData = {'gauss': targ}
refWeights = {'gauss': 1}
### define some test parameters
minWin = 98 # minimum percent to scale down reference data
maxWin = 105 # maximum percent to scale up reference data
sizeStep = 1 # step to use for correlating reference to target data
# call the segmentation algorithm
segments = seg1d.segment_data(refData, targData, refWeights, minWin,
maxWin, sizeStep)
print(np.around(segments, decimals=7))
plt.figure(figsize=(15, 4))
# plot the full pulse
plt.plot(x, targ, linewidth=6, alpha=0.2, label='Target')
# plot the original reference segment
plt.plot(x[t_s:t_e],
targ[t_s:t_e],
linewidth=8,
alpha=0.5,
label='Reference')
# plot all segments found
seg_num = 1
for s, e, c in segments:
plt.plot(x[s:e],
targ[s:e],
dashes=[0.5, 0.5],
linewidth=4,
alpha=0.8,
label='Segment {}'.format(seg_num))
plt.legend()
plt.show()
# To use the basic method interface, the data must be labeled
# define a segment within the sine wave to use as reference
t_s, t_e = 200, 400
# cut a segment out to use as a reference data
refData = [{'0': targ[t_s:t_e]}]
targData = {'0': targ}
refWeights = {'0': 1}
### define some test parameters
minWin = 98 #minimum percent to scale down reference data
maxWin = 105 #maximum percent to scale up reference data
sizeStep = 1 #step to use for correlating reference to target data
#call the segmentation algorithm
segments = seg1d.segment_data(refData, targData, refWeights, minWin, maxWin,
sizeStep)
np.around(segments, decimals=7)
# array([[2.000000e+02, 4.000000e+02, 1.000000e+00],
# [1.200000e+03, 1.398000e+03, 9.999999e-01]])
# Using matplotlib we can visualize the results
plt.figure(figsize=(10, 3)) #doctest: +SKIP
# plot the full sine wave
plt.plot(x, targ, linewidth=6, alpha=0.2, label='Target') #doctest: +SKIP
# plot the original reference segment
plt.plot(x[t_s:t_e], targ[t_s:t_e], linewidth=8, alpha=0.7,
label='Reference') #doctest: +SKIP
# >>>
# plot all segments found
seg_num = 1
'''
>>> import numpy as np
>>> import seg1d
>>> #retrieve the sample reference, target, and weight data
>>> r,t,w = seg1d.sampleData()
>>> # define some test parameters
>>> minW = 70 #minimum percent to scale down reference data
>>> maxW = 150 #maximum percent to scale up reference data
>>> step = 1 #step to use for correlating reference to target data
>>> #call the segmentation algorithm
>>> np.around(seg1d.segment_data(r,t,w,minW,maxW,step), 5)
array([[207. , 240. , 0.91242],
[342. , 381. , 0.88019],
[ 72. , 112. , 0.87768]])
'''
import seg1d
#retrieve the sample reference, target, and weight data
r, t, w = seg1d.sampleData()
### define some test parameters
minW = 70 #minimum percent to scale down reference data
maxW = 150 #maximum percent to scale up reference data
step = 1 #step to use for correlating reference to target data
#call the segmentation algorithm
segments = seg1d.segment_data(r, t, w, minW, maxW, step)
print(segments)