def plot_daq_main():
"""
main function for plot_daq command-line program
"""
import matplotlib.pylab as pylab
# Setup input option parser
usage = """%prog [OPTION]...
%prog simple script for plotting data captured by daq_acquire
commmand-line program. """
# Set up command line option parser
parser = optparse.OptionParser(usage=usage)
options, args = parser.parse_args()
datafile = sys.argv[1]
data = pylab.load(datafile)
t = data[:, 0]
samples = data[:, 1:]
for i in range(samples.shape[1]):
pylab.figure(i)
pylab.plot(t, samples[:, i])
pylab.xlabel('t (sec)')
pylab.ylabel('(V)')
pylab.title('channel %d' % (i, ))
pylab.show()
def findbruteforcefit(kit=None, *args, **kwargs):
if kit != 1:
pylab.save('bftfile', 'kit')
else:
pylab.load('bftfile', 'kit')
kit['tightnesssettings'] = copy(
settingsfromtightness(kit['tightnesssettings']['scalartightness']))
# findbruteforcefit.m:6
random(1)
ABCexact = array([8572.0553, 3640.1063, 2790.9666])
# findbruteforcefit.m:9
ABCguess = array([8572.0, 3640.0, 2790.0])
# findbruteforcefit.m:10
ABCguess = multiply(ABCguess, array([0.99, 1.01, 0.99]))
# findbruteforcefit.m:11
kit = trimkit(kit,
kit['tightnesssettings']['bruteforce']['numexperimentlines'])
# findbruteforcefit.m:12
flimits = array([min(kit['onedpeakfs']), max(kit['onedpeakfs'])])
# findbruteforcefit.m:14
theoryset = linesforbruteforce2(
ABCguess, flimits,
kit['tightnesssettings']['bruteforce']['numtheorylines'],
max(kit['onedpeakhsunassigned']))
# findbruteforcefit.m:15
linestouse['lines'] = copy(theoryset)
# findbruteforcefit.m:17
linestouse['heighttouse'] = copy('sixKweakpulsestrength')
# findbruteforcefit.m:18
linestouse['fitdescriptor'] = copy('made up brute force fit')
# findbruteforcefit.m:19
linestouse['ABCxxxxx'] = copy(array([ABCguess, 0, 0, 0, 0, 0]))
# findbruteforcefit.m:20
#addC13swithlinelist
ABClist = [ABCguess]
# findbruteforcefit.m:23
dAdBdC = array([0.01, 0.01, 0.01])
# findbruteforcefit.m:24
linestouse['ABClist'] = copy(ABClist)
# findbruteforcefit.m:26
linestouse['dAdBdC'] = copy(dAdBdC)
# findbruteforcefit.m:27
kit['findfitsettings'] = copy(kit['tightnesssettings']['bruteforce'])
# findbruteforcefit.m:28
allfits = findfits(linestouse, kit)
# findbruteforcefit.m:30
fit = pullbest(allfits, kit)
# findbruteforcefit.m:32
fit['patternType'] = copy('bruteforce')
# findbruteforcefit.m:33
kit = addfittokit(kit, fit)
# findbruteforcefit.m:34
displaykitwithfits(kit)
1
return fit
from datetime import datetime
import json
from matplotlib import pylab as plt
import re
import pandas
key_words = [
"Corporate Social Responsibility", "Corporate Responsibility",
"Sustainability", "Sustainable development", "Corporate Accountability",
"Crating Shared Value", "Citizenship", "Social Responsibility",
"Environmental, Social and Governance", "shared value", "social",
"responsibility", "CSR ", "CR ", "ESG"
]
data = plt.load('company-tweets.npy', allow_pickle='TRUE').tolist()
# data = plt.load('competitor-tweets.npy', allow_pickle='TRUE').tolist()
found = {}
for key in data:
print(key, len(data[key]))
found[key] = []
for tweet in data[key]:
#print(tweet)
for kw in key_words:
kw = kw.lower()
if hasattr(tweet, 'retweeted_status'):
if kw in tweet.retweeted_status.full_text.lower():
text = tweet.retweeted_status.full_text.replace("\n", " ")
# print(f"found{kw} in retweet {tweet.retweeted_status.full_text} of {key}")
found[key].append({
"tweet": text,
# ySpec = cspline1d_eval(sCoef,x)
noise = N.zeros_like(spec)
for i in xrange(len(xSpec[:-1])):
noise[xSpec[i]:xSpec[i + 1]] = ySpec[i]
return noise, minNoise
#return xSpec, ySpec
if __name__ == '__main__':
# ms = P.load('J15.csv', delimiter = ',')
# x, ms = interpolate_spectrum(ms)
# ms = normalize(topHat(ms, 0.01))
# ms = roundLen(ms)
# ms = P.load('N3_Norm.txt')
ms = P.load('exampleMS.txt')
print ms.max(), len(ms)
# x = N.arange(len(ms))
# print ms.shape, x.shape
# x = N.r_[0:10]
# dx = x[1]-x[0]
# newx = N.r_[-3:13:0.1] # notice outside the original domain
#
# y = N.sin(x)
# cj = cspline1d(y)
# newy = cspline1d_eval(cj, newx, dx=dx,x0=x[0])
# print x.shape, newx.shape, cj.shape
numSegs = int(len(ms) * 0.0015)
print "Numsegs: ", numSegs
# x, msSm = SplitNSmooth(ms, numSegs, 5)
"Corporate Responsibility",
"Sustainability",
"Sustainable development",
"Corporate Accountability",
"Crating Shared Value",
"Citizenship",
"Social Responsibility",
"Environmental, Social and Governance",
"shared value",
"social",
"responsibility",
"CSR ",
"CR ",
"ESG"]
data = plt.load('women-ceo-tweets.npy', allow_pickle='TRUE').tolist()
found = []
for dictionary in data:
for key in dictionary:
for tweet in dictionary[key]:
for kw in key_words:
kw = kw.lower()
if hasattr(tweet, 'retweeted_status'):
if kw in tweet.retweeted_status.full_text.lower():
text = tweet.retweeted_status.full_text.replace("\n", " ")
# print(f"found{kw} in retweet {tweet.retweeted_status.full_text} of {key}")
found.append({"tweet":text, "user":key, "date":tweet.created_at})
else:
if kw in tweet.full_text.lower():
noise[xSpec[i]:xSpec[i+1]] = ySpec[i]
return noise, minNoise
#return xSpec, ySpec
if __name__ == '__main__':
# ms = P.load('J15.csv', delimiter = ',')
# x, ms = interpolate_spectrum(ms)
# ms = normalize(topHat(ms, 0.01))
# ms = roundLen(ms)
# ms = P.load('N3_Norm.txt')
ms = P.load('exampleMS.txt')
print ms.max(), len(ms)
# x = N.arange(len(ms))
# print ms.shape, x.shape
# x = N.r_[0:10]
# dx = x[1]-x[0]
# newx = N.r_[-3:13:0.1] # notice outside the original domain
#
# y = N.sin(x)
# cj = cspline1d(y)
# newy = cspline1d_eval(cj, newx, dx=dx,x0=x[0])
# print x.shape, newx.shape, cj.shape
numSegs = int(len(ms)*0.0015)
print "Numsegs: ", numSegs
# x, msSm = SplitNSmooth(ms, numSegs, 5)