def sfine2(mom,leps,thk=254,emax=3.4,minor=0,dofit=4,inorm=[1,1,1],doplot=False):
'''
dofit==1: fit only thickness, renorm parameters fixed
minor: minim. value of refer. spectrum to include point in chi2
'''
import profit
inipars=[thk]+inorm
enxz=enx[enx<emax]
def zres(pars):
model1=profit.plate(enxz,[leps,epssi[enx<emax]],pars[:1])
dif=0
for i in range(len(mom)):
dsel=(mom[i]>0)*(enx<emax)
if minor>0: dsel*=nor[i]>minor
dif+=((mom[i][dsel]*pars[i+1]-model1[dsel[enx<emax]])**2*nor[i][dsel]).sum()*wnor[i]
return dif
from scipy import optimize as op
if dofit==1:
fitpars=op.fmin(lambda p:zres(list(p)+inipars[1:]),inipars[:1],disp=0)
qpars=inipars[1:]
else:
fitpars=op.fmin(zres,inipars,disp=0)
qpars=fitpars[1:]
if doplot:
from matplotlib import plot as pl
model1=profit.plate(enxz,[leps,epssi[enx<emax]],fitpars[:1])
pl.plot(enxz,model1)
for i in range(len(mom)):
m=mom[i]
pl.plot(enx[(m>0)*(enx<emax)],m[(m>0)*(enx<emax)]*qpars[i],'rgm'[i])
if dofit==1:
return fitpars,zres(list(fitpars)+inipars[1:])
else:
return fitpars,zres(fitpars)
def plotData(x, *y):
colors = ["b","g","r","c","m","y","k"]
plt.xlabel("timestamp")
plt.ylabel("values")
for i in range(len(y)):
plt.plot(x, y[i], colors[i%7])
plt.show()
def plot_results(a_plot_list, b_plot_list, k_plot_list, n):
# plotting the results of the two algorithms against k value and runtime
plt.plot(k_plot_list, a_plot_list, "r-")
plt.plot(k_plot_list, b_plot_list, "k-")
plt.xlabel("Searching Set Size (k value)")
plt.ylabel("Time (seconds)")
plt.title(str("Time complexity with data set of size n=" + str(n)))
red = mpatches.Patch(color="red", label="Linear Search")
black = mpatches.Patch(color="black", label="Binary Search")
plt.legend(handles=[red, black])
plt.grid(True)
plt.show()
def renorm(preps=[]):
qcorr=np.array([ 1.40927795, 1.35854274, 1.53249378])
if (len(preps)==0): #reload
xpos=[np.loadtxt(indir+a)[:,0] for a in ls1 if a.find('dark')==0]
drk=[np.loadtxt(indir+a)[:,1] for a in ls1 if a.find('dark')==0]
ref=[np.loadtxt(indir+a)[:,1] for a in ls1 if a.find('ref')==0]
nref=np.array(ref[:3])-np.array(drk)
nref[nref<5]=5
preps=[l[:8] for l in ls1 if l.find('_00')>0 and l[:2]=='ti']
ratall=[renor([np.loadtxt(indir+a)[:,1] for a in ls1 if a.find(pf)==0]) for pf in preps]
band2,bsel2=cb.gettrans(enx,nref,xpos,smot=0.01,skiplowest=7,rep=1,scale=1/qcorr,spow=1)
mom2all=[np.sum([rt[i][bsel2[i]].dot(band2[i])*sior for i in range(3)],axis=0) for rt in ratall]
esel*=enx>1.35
ok=[pl.plot(enx[esel],zoo[esel]) for zoo in mom2all[2:-1]]
nor=[nref[i][bsel2[i]].dot(band2[i]) for i in range(len(nref))]
wnor=[nref[i].sum()/1e4/band2[i].sum() for i in range(len(nref))]
locfile="tio2_tl-eps.mat"
en2,er,ei=np.genfromtxt("http://physics.muni.cz/~munz/manip/CVD/"+locfile,skip_header=3)[::-1].T
epstio=lambda e:ip.interp1d(en2,er)(e)+1j*ip.interp1d(en2,ei)(e)
conv1=[ 2.56081863e+02, 8.21570652e-02]
allfit=[spectra.fitting(enx[esel],zoo[esel]) for zoo in mom2all[2:-1]]
results=np.zeros((len(momall),4))
results[:,0]=[conv1[0]/(f[0][3]+conv1[1]) for f in allfit]
results[:,1:]=qcorr
restemp=np.zeros_like(results)
### Analysis settings
FN = r'D:\measuring\data\20130830\002947_Teleportation_testing_lt1-4_optical_rabi\002947_Teleportation_testing_lt2-9_optical_rabi.hdf5'
r = 1 # after which rep to look
f = h5py.File(FN, 'r')
#channel = f['/HH_channel-1'].value
sync_time = f['/HH_sync_time-1'].value
sync_nr = f['/HH_sync_nr-1'].value
f.close()
hist = np.zeros(max(sync_time))
for i in sync_time[where(sync_nr%1000==r)]:
hist[i] += 1
fig, ax = subplots(1,1, figsize=((4,4)))
plt.plot(hist)
ax.set_xlabel('sync time')
ax.set_ylabel('counts')
import matplotlib.plot as p import math x = [i/500. for i in range(1000)] f = [math.exp(-x_i) for x_i in x] r = [x_i * f_i for x_i, f_i in zip(x, f)] p.plot(x, r) p.grid() p.show()
from matplotlib import plot as plt x = range(2, 26, 2) y = [1, 2, 3, 4, 5, 6, 7, 8, 20, 12, 12, 11] plt.plot(x, y) plt.show()
import matplotlib.plot as p import math x = [i / 500. for i in range(1000)] f = [math.exp(-x_i) for x_i in x] r = [x_i * f_i for x_i, f_i in zip(x, f)] p.plot(x, r) p.grid() p.show()
#distributution by factors
g = sns.FacetGrid(df, row= "factor1",col="factor2", margin_titles=True,size=5)
bins = np.linspace(0, 60, 13)
g.map(sns.distplot, "x", color="steelblue", bins=bins)
#group b plots
def fraction_plot(grpvar,ax):
grp=df[[grpvar,'colsum','x']].groupby(grpvar).sum()
return grp.div(grp['colsum'],0)['x'].plot(kind='bar',ax=ax)
fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(30, 15))
#plot iwth confidence interval
plt.gca().invert_xaxis()
plt.plot(nbrs, fmeanstr, color=c0, label="training");
plt.fill_between(nbrs, fmeanstr - fstdsstr, fmeanstr+fstdsstr, color=c0, alpha=0.3)
plt.plot(nbrs, fmeanste, color=c1, label="testing");
plt.fill_between(nbrs, fmeanste - fstdsste, fmeanste+fstdsste, color=c1, alpha=0.5)
plt.legend();
############################### Numpy/Sparse #############################
#add indicator variables based on a categorical column
#determine the unique genres
genres = set()
for genre in largedf.genres:
genres.update(genre)
import numpy as np
import h5py
from matplotlib import plot as plt
### Analysis settings
FN = r'D:\measuring\data\20130830\002947_Teleportation_testing_lt1-4_optical_rabi\002947_Teleportation_testing_lt2-9_optical_rabi.hdf5'
r = 1 # after which rep to look
f = h5py.File(FN, 'r')
#channel = f['/HH_channel-1'].value
sync_time = f['/HH_sync_time-1'].value
sync_nr = f['/HH_sync_nr-1'].value
f.close()
hist = np.zeros(max(sync_time))
for i in sync_time[where(sync_nr % 1000 == r)]:
hist[i] += 1
fig, ax = subplots(1, 1, figsize=((4, 4)))
plt.plot(hist)
ax.set_xlabel('sync time')
ax.set_ylabel('counts')
return data
def bbands(self):
c = TechIndicators(key=self.api_key, output_format='pandas')
data, meta_data = c.get_bbands(symbol=self.stock_name)
return data
def sma(self):
d = TechIndicators(key=self.api_key, output_format='pandas')
data, meta_data = d.get_sma(symbol=self.stock_name, time_period=30)
return data
def close(self):
d = TimeSeries(key=self.api_key, output_format='pandas')
data, meta_data = d.get_daily(symbol=self.stock_name,
outputsize='full')
return data
if __name__ == "__main__":
TI = TechnicalIndicators("AAPL")
rsi_data = TI.rsi()
plt.plot(rsi_data)
plt.show()
ts = TimeSeries(key=API_KEY, output_format='pandas')
data, meta_data = ts.get_intraday(symbol="MSFT",
interval='1min',
outputsize='full')
data.columns = ["open", "high", "low", "close", "volume"]