def polyFitToCorr(zs, corrs, pnum):
pfit = scipy.polyfit(zs, corrs, pnum)
evalpfit = scipy.polyval(pfit, zs)
newzs = scipy.linspace(zs[0], zs[-1], len(zs) * 10)
neweval = scipy.polyval(pfit, newzs)
w = scipy.argmax(neweval)
return evalpfit, newzs[w]
def scat_flat(d,c_ob,c_co):
Centers_co = np.zeros((len(c_co),d.shape[1]))
Centers_ob = np.zeros((len(c_ob),d.shape[1]))
ejx = np.arange(d.shape[1])
ejy = np.arange(d.shape[0])
for i in range(len(c_co)):
Centers_co[i,:]=np.around(scipy.polyval(c_co[i],ejx)).astype('int')
Centers_ob[i,:]=np.around(scipy.polyval(c_ob[i],ejx)).astype('int')
i = 0
while i < d.shape[1]:
line = d[:,i]
refmins = []
valmins = []
for j in range(len(Centers_co)):
p1 = Centers_ob[j,i]
p2 = Centers_co[j,i]
minv = line[p1-10:p2+10]
refv = ejy[p1-10:p2+10]
im = np.argmin(minv)
refmins.append(refv[im])
valmins.append(minv[im])
refmins,valmis = np.array(refmins),np.array(valmins)
plot(line)
plot(refmins,valmins,'ro')
show()
print gfd
def plot_separation(self, i, snp, sep, data):
'''Plot separation of IBD clique at sample i, SNP snp.'''
parents = [
self.ped.sample_id[self.ped.graph.predecessors(i)[a]]
for a in im.constants.ALLELES
]
print data
print 'data[0]', data[0]
print 'data[1]', data[1]
(k00, k10, k01, k11), line = data[0:2]
P.figure(1)
P.clf()
P.hold(True)
# P.scatter([p], [q], color='k')
P.scatter(k00, k10, color='r')
P.scatter(k01, k11, color='b')
if line is not None:
a0, b0, a1, b1 = line
t = linspace(0, max(k00))
P.plot(t, polyval([a0, b0], t), 'r')
t = linspace(0, max(k01))
P.plot(t, polyval([a1, b1], t), 'b')
P.title('Sample %d, SNP %d, inbreeding=%.4f, separation=%.2f' %
(i, snp, self.params.kinship(parents[0], parents[1]), sep))
P.show()
P.savefig(os.environ['OBER'] + '/doc/poo/sep-chr%d-%d-%d.png' %
(self.chrom, i, snp))
def x_do_contact(self, args):
"""
DEBUG COMMAND to be activated in the future
"""
xext, ysub, contact = self.find_contact_point2(debug=True)
contact_plot = self.plots[0]
contact_plot.add_set(xext, ysub)
contact_plot.add_set([xext[contact]], [self.plots[0].vectors[0][1][contact]])
# contact_plot.add_set([first_point[0]],[first_point[1]])
# contact_plot.add_set([last_point[0]],[last_point[1]])
contact_plot.styles = [None, None, None, "scatter"]
self._send_plot([contact_plot])
return
index, regr, regr_contact = self.find_contact_point2(debug=True)
print regr
print regr_contact
raw_plot = self.current.curve.default_plots()[0]
xret = raw_plot.vectors[0][0]
# nc_line=[(item*regr[0])+regr[1] for item in x_nc]
nc_line = scipy.polyval(regr, xret)
c_line = scipy.polyval(regr_contact, xret)
contact_plot = self.current.curve.default_plots()[0]
contact_plot.add_set(xret, nc_line)
contact_plot.add_set(xret, c_line)
contact_plot.styles = [None, None, None, None]
# contact_plot.styles.append(None)
contact_plot.destination = 1
self._send_plot([contact_plot])
def _qfunc_pure(psi, alpha_vec):
"""
Calculate the Q-function for a pure state.
"""
n = np.prod(psi.shape)
# Gengyan: maximun number to use factorial()
nmax = 170
if isinstance(psi, Qobj):
psi = psi.full().flatten()
else:
psi = psi.T
if n < nmax:
qvec = abs(
polyval(
fliplr([psi / sqrt(factorial(arange(n)))])[0],
conjugate(alpha_vec)))**2 * exp(-abs(alpha_vec)**2)
else:
# Gengyan: for m < nmax, use factorial()
qvec = polyval(
fliplr([psi[0:nmax] / sqrt(factorial(arange(nmax)))])[0],
conjugate(alpha_vec)) * exp(-abs(alpha_vec)**2 / 2)
# Gengyan: for m >= nmax, use Stirling's approximation
for m in range(nmax, n):
qvec += (conjugate(alpha_vec)/sqrt(m))**m*psi[m] * \
exp((m-abs(alpha_vec)**2)/2)*(2*pi*m)**(-0.25)
qvec = abs(qvec)**2
return np.real(qvec) / pi
def time_fitting(x_fit,y_fit):
"""Fit a linear relation to the x_fit and y_fit parameters
Returns the actual fit and the parameters of the fit,
"""
import numpy as np
x_fit = np.array( x_fit )
y_fit = np.array( y_fit )
###First fit iteration and remove outliers
POLY_FIT_ORDER = 1
slope,intercept = scipy.polyfit(x_fit,y_fit,POLY_FIT_ORDER)
fit = scipy.polyval((slope,intercept),x_fit)
fit_sigma = fit.std()
include_index = np.where(np.abs(fit-y_fit) < 1.5*fit_sigma)[0]
if len(include_index) < 4:
return None,None,False
###Final Fit
x_fit_clipped = x_fit[include_index]
y_fit_clipped = y_fit[include_index]
parameters = scipy.polyfit(x_fit_clipped,y_fit_clipped,POLY_FIT_ORDER)
fit = scipy.polyval(parameters,x_fit)
return fit,parameters,True
def LinearAdjust():
from scipy import polyfit
from scipy import polyval
from sklearn import metrics
global PredictedMsdRange1
global ScoreR2Range1
global TotalMsdAdjustRange1
global PredictedMsdRange2
global ScoreR2Range2
global TotalMsdAdjustRange2
global PredictedMsdRange3
global ScoreR2Range3
global TotalMsdAdjustRange3
TotalMsdAdjustRange1 = polyfit(TimeRange1, TotalMsdRange1, 1)
PredictedMsdRange1 = polyval(TotalMsdAdjustRange1, TimeRange1)
ScoreR2Range1 = metrics.r2_score(TotalMsdRange1, PredictedMsdRange1)
print("The total msd linear regression was done in range 1, r2 = " +
str(ScoreR2Range1))
TotalMsdAdjustRange2 = polyfit(TimeRange2, TotalMsdRange2, 1)
PredictedMsdRange2 = polyval(TotalMsdAdjustRange2, TimeRange2)
ScoreR2Range2 = metrics.r2_score(TotalMsdRange2, PredictedMsdRange2)
print("The total msd linear regression was done in range 2, r2 = " +
str(ScoreR2Range2))
TotalMsdAdjustRange3 = polyfit(TimeRange3, TotalMsdRange3, 1)
PredictedMsdRange3 = polyval(TotalMsdAdjustRange3, TimeRange3)
ScoreR2Range3 = metrics.r2_score(TotalMsdRange3, PredictedMsdRange3)
print("The total msd linear regression was done in range 3, r2 = " +
str(ScoreR2Range3))
def test_scipy():
#Sample data creation
#number of points
n=50
t=linspace(-5,5,n)
#parameters
a=0.8; b=-4
x=polyval([a,b],t)
#add some noise
xn=x+randn(n)
#Linear regressison -polyfit - polyfit can be used other orders polys
(ar,br)=polyfit(t,xn,1)
xr=polyval([ar,br],t)
#compute the mean square error
err=sqrt(sum((xr-xn)**2)/n)
print('Linear regression using polyfit')
print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, ms error= %.3f' % (a,b,ar,br,err))
#matplotlib ploting
title('Linear Regression Example')
plot(t,x,'g.--')
plot(t,xn,'k.')
plot(t,xr,'r.-')
legend(['original','plus noise', 'regression'])
show()
#Linear regression using stats.linregress
(a_s,b_s,r,tt,stderr)=stats.linregress(t,xn)
print('Linear regression using stats.linregress')
print('parameters: a=%.2f b=%.2f \nregression: a=%.2f b=%.2f, std error= %.3f' % (a,b,a_s,b_s,stderr))
def main(FileName):
for trial in kep.iofiles.passFileToTrialList(FileName):
tr = kep.pipelinepars.keptrial(trial)
kw = kep.keplc.kw(**tr.kw)
X = kep.keplc.keplc(tr.kid)
X.runPipeline(kw)
lc = X.lcData.lcData
Y = kep.qats.qatslc(X.lcFinal,X.KID)
idx = num.where((X.lcFinal['eMask'] == False))[0]
Y.padLC(flagids=idx)
Y.addNoise()
Y.runQATS(f=0.01)
P,ignored = calcPeriodogram(Y.periods, lc['x'], lc['ydt'], lc['yerr']) ### ADDED ###
coeff = num.polyfit(num.log10(Y.periods),num.log10(Y.snrLC),1)
outy = scipy.polyval(coeff,num.log10(Y.periods))
normalizedPower = 10**(outy)
coeff2 = num.polyfit(num.log10(Y.periods),num.log10(Y.snrFLAT),1) ### ADDED ###
outy2 = scipy.polyval(coeff2,num.log10(Y.periods)) ### ADDED ###
normalizedPower2 = 10**(outy2) ### ADDED ###
fittedr=Y.snrFLAT/normalizedPower2 ### ADDED ###
fittedb=Y.snrLC/normalizedPower2 ### ADDED ###
normalizedPower3 = normalizedPower2/normalizedPower2 ### ADDED ###
plt.subplot(211) ### ADDED ###
plt.title('unflipped,'+' KID = '+X.KID)
plt.plot(Y.periods,Y.snrFLAT/normalizedPower,'r-')
plt.plot(Y.periods,Y.snrLC/normalizedPower,'b-')
plt.plot(Y.periods,normalizedPower/normalizedPower,'k-')
#plt.plot(Y.periods,normalizedPower2, 'k-') ### ADDED ###
plt.setp(plt.gca().set_xscale('log'))
plt.ylabel('Signal Power')
plt.subplot(212)
plt.plot(Y.periods,P,'g-') ### ADDED ###
plt.setp(plt.gca().set_xscale('log')) ### ADDED ###
plt.xlabel('Period (days)') ### MOVED ###
plt.ylabel('F Transform')
plt.savefig('sn.SG0XX.unflipped.'+X.KID+'.png') ### MOVED ###
#coeff = num.polyfit(num.log10(Y.periods),num.log10(Y.snrLC),1)
#outy = scipy.polyval(coeff,num.log10(Y.periods))
#normalizedPower = 10**(outy)
#plt.plot(Y.periods,Y.snrFLAT,'r-')
#plt.plot(Y.periods,Y.snrLC,'b-')
#plt.plot(Y.periods,normalizedPower,'k-')
#plt.setp(plt.gca().set_xscale('log'))
#plt.savefig('sn.'+X.KID+'.png')
dfile = open('signal.'+X.KID+'.data','w')
print >> dfile,'#',X.KID,'|', Y.periods[num.argmax(Y.snrLC/normalizedPower)],\
Y.periods[num.argmax(Y.snrLC)], max(Y.snrLC/normalizedPower), max(Y.snrLC)
for i in range(len(Y.SignalPower)):
print >> dfile, Y.periods[i],'|',Y.snrLC[i],'|',\
Y.snrFLAT[i],'|',normalizedPower[i]
dfile.close()
def spl3(x, y, x2, s=3):
polycoeffs = array(scipy.polyfit(x, y, s)).flatten()
y2 = scipy.polyval(polycoeffs, x2)
dercoeffs = scipy.polyder(polycoeffs, 1)
dy2 = scipy.polyval(dercoeffs, x2)
return y2, dy2
def go(
mags=(15, 30.1, 0.5), redshifts=(0.01, 12, 0.01), coeff_file="prior_K_zmax7_coeff.dat", outfile="prior_K_extend.dat"
):
fp = open(coeff_file)
lines = fp.readlines()
fp.close()
mag_list = np.cast[float](lines[0].split()[2:])
z0 = np.cast[float](lines[1].split()[1:])
gamma = np.cast[float](lines[2].split()[1:])
z_grid = np.arange(redshifts[0], redshifts[1], redshifts[2])
NZ = z_grid.shape[0]
mag_grid = np.arange(mags[0], mags[1], mags[2])
NM = mag_grid.shape[0]
#### Polynomial extrapolation not reliable
# p_z0 = scipy.polyfit(mag_list, z0, order)
# z0_grid = np.maximum(scipy.polyval(p_z0, mag_grid), 0.05)
# p_gamma = scipy.polyfit(mag_list, gamma, order)
# gamma_grid = np.maximum(scipy.polyval(p_gamma, mag_grid), 0.05)
#### Interpolations on the defined grid
z0_grid = np.interp(mag_grid, mag_list, z0)
gamma_grid = np.interp(mag_grid, mag_list, gamma)
#### Linear extrapolations of fit coefficients
p_z0 = scipy.polyfit(mag_list[:3], z0[:3], 1)
z0_grid[mag_grid < mag_list[0]] = np.maximum(scipy.polyval(p_z0, mag_grid[mag_grid < mag_list[0]]), 0.05)
p_z0 = scipy.polyfit(mag_list[-3:], z0[-3:], 1)
z0_grid[mag_grid > mag_list[-1]] = np.maximum(scipy.polyval(p_z0, mag_grid[mag_grid > mag_list[-1]]), 0.05)
p_gamma = scipy.polyfit(mag_list[:3], gamma[:3], 1)
gamma_grid[mag_grid < mag_list[0]] = np.maximum(scipy.polyval(p_gamma, mag_grid[mag_grid < mag_list[0]]), 0.05)
p_gamma = scipy.polyfit(mag_list[-3:], gamma[-3:], 1)
gamma_grid[mag_grid > mag_list[-1]] = np.maximum(scipy.polyval(p_gamma, mag_grid[mag_grid > mag_list[-1]]), 0.05)
out_matrix = np.zeros((NZ, NM + 1))
out_matrix[:, 0] = z_grid
for i in range(NM):
pz = z_grid * np.exp(-(z_grid / z0_grid[i]) ** gamma_grid[i])
pz /= np.trapz(pz, z_grid)
plt.plot(z_grid, pz, label=mag_grid[i])
out_matrix[:, i + 1] = pz
plt.legend(ncol=3, loc="upper right")
header = "# z "
for m in mag_grid:
header += "%6.1f" % (m)
fp = open(outfile, "w")
fp.write(header + "\n")
np.savetxt(fp, out_matrix, fmt="%6.3e")
fp.close()
def concat_extrap_ends(x, npts, polyorder=1, lowside=True, highside=True):
i=numpy.arange(npts, dtype='float64')
if lowside:
ans=scipy.polyfit(-1*(i+1.), x[:npts], polyorder)
x=numpy.concatenate([scipy.polyval(list(ans), i[::-1]), x])
if highside:
ans=scipy.polyfit(-1*(i[::-1]-1.), x[-1*npts:], polyorder)
x=numpy.concatenate([x, scipy.polyval(list(ans), i)])
return x
def anaAuxFunc(self, ep, points, n=-1):
r"""
Evalaute the analytic auxiliary functions reconstruction, based on g and h
Taylor expansion at `points`.
From the definitions of \(g\) and \(h\) the two eigenvalues can be written as
$$
\begin{align}
\lambda_{+} &= \frac{g+\sqrt{h}}{2}, \\
\lambda_{-} &= \frac{g-\sqrt{h}}{2}.
\end{align}
$$
These expressions can be approximated through the truncated Taylor series \( T_h\) and \(T_g\)
$$
\begin{align}
{A}_{\lambda_{+}} &= \frac{T_g+\sqrt{T_h}}{2}, \\
{A}_{\lambda_{-}} &= \frac{T_g-\sqrt{T_h}}{2}.
\end{align}
$$
Parameters
----------
ep: EP instance
The EP instance that store h and g successive derivatives
points: array-like
the value where the serie is evaluated. Give the absolute value, not the relative % nu0.
n: integer [optional]
The number of terms considered in the expansion
if no value is given or if n=-1, the size of the array dlda is considered.
Returns
-------
ldap,ldam : array_like
the reconstructed eigenvalue pair
"""
# compute the analytic auxiliary functions g and h from lda dérivatives
ep._dh()
ep._dg()
if n > -1:
# truncate
dgTay = ep._dgTay[:n]
dhTay = ep._dhTay[:n]
else:
dgTay = ep._dgTay
dhTay = ep._dhTay
# evaluate Taylor series
mapg = sp.polyval(dgTay[::-1], points - self.nu0)
maph = sp.polyval(dhTay[::-1], points - self.nu0)
ldap = 0.5 * (mapg + np.sqrt(maph))
ldam = 0.5 * (mapg - np.sqrt(maph))
return ldap, ldam
def mNm_2_raw(self, calib, tq):
#ticks: calibrated sensor value
#calib: yaml config calibration map
if self.ctype == 'sea_vertx_14bit':
val = (tq - calib['cb_bias']) / calib['cb_scale']
val = float(polyval(calib['cb_inv_torque'], val))
return max(0, min(val, VERTX_14BIT_MAX))
if self.ctype == 'adc_poly':
val = (tq - calib['cb_bias']) / calib['cb_scale']
val = float(polyval(calib['cb_inv_torque'], val))
return max(0, min(val, M3EC_ADC_TICKS_MAX))
def main():
# Load JSON
filename = raw_input('Name of Data file?')
with open(filename) as f:
data = json.load(f)
# Extract (ID, time) points from data
points = extract_id(data)
# Fit line onto points
coeff = polyfit(points[0], points[1], 1)
# Calculate endpoints of line for drawing the line
x1 = min(points[0])
x2 = max(points[0])
y1 = polyval(coeff, x1)
y2 = polyval(coeff, x2)
# Calculate throughput points (ID/time)
throughput = ([], [])
for i in range(len(points[0])):
id1 = points[0][i]
t = points[1][i]
through = id1 * 1000 / t
throughput[1].append(through)
throughput[0].append(id1)
# Calculate the ID/MT points. Sort by x-axis. Plot.
coeff = polyfit(throughput[0], throughput[1], 1)
# Calculate endpoints of line for drawing the line
x11 = min(throughput[0])
x21 = max(throughput[0])
y11 = polyval(coeff, x11)
y21 = polyval(coeff, x21)
# Draw everything
plt.figure(num="Samples")
plt.xlabel("Index of Difficulty")
plt.ylabel("Movement Time (ms)")
plt.xlim(0, max(points[0]) * 1.2)
plt.ylim(0, max(points[1]) * 1.2)
plt.plot(points[0], points[1], "bo", label="Linear Regression")
plt.plot([x1,x2], [y1, y2], "r-")
plt.figure(num="Throughput")
plt.xlabel("Index of Difficulty")
plt.ylabel("Throughput (bits/s)")
plt.xlim(0, max(throughput[0]) * 1.2)
plt.ylim(0, max(throughput[1]) * 1.2)
plt.plot(throughput[0], throughput[1], "yo", label="Throughput")
plt.plot([x11,x21], [y11, y21], "r")
print("Regression coefficients: A={}, B={}".format(coeff[0] / 1000, coeff[1] / 1000))
plt.show()
def calculo(self):
self.entrada = self.kwargs["entrada"]
self.rendimientoCalculado = Dimensionless(self.kwargs["rendimiento"])
if self.kwargs["Pout"]:
DeltaP = Pressure(self.kwargs["Pout"] - self.entrada.P)
elif self.kwargs["deltaP"]:
DeltaP = Pressure(self.kwargs["deltaP"])
elif self.kwargs["Carga"]:
DeltaP = Pressure(self.kwargs["Carga"] * self.entrada.Liquido.rho *
g)
else:
DeltaP = Pressure(0)
if self.kwargs["usarCurva"]:
if self.kwargs["diametro"] != self.kwargs["curvaCaracteristica"][
0] or self.kwargs["velocidad"] != self.kwargs[
"curvaCaracteristica"][1]:
self.curvaActual = self.calcularCurvaActual()
else:
self.curvaActual = self.kwargs["curvaCaracteristica"]
self.Ajustar_Curvas_Caracteristicas()
if not self.kwargs["usarCurva"]:
head = Length(DeltaP / g / self.entrada.Liquido.rho)
power = Power(head * g * self.entrada.Liquido.rho *
self.entrada.Q / self.rendimientoCalculado)
P_freno = Power(power * self.rendimientoCalculado)
elif not self.kwargs["incognita"]:
head = Length(polyval(self.CurvaHQ, self.entrada.Q))
self.DeltaP = Pressure(head * g * self.entrada.Liquido.rho)
power = Power(self.entrada.Q * DeltaP)
P_freno = Power(polyval(self.CurvaPotQ, self.entrada.Q))
self.rendimientoCalculado = Dimensionless(power / P_freno)
else:
head = Length(self.DeltaP / g / self.entrada.Liquido.rho)
caudalvolumetrico = roots(
[self.CurvaHQ[0], self.CurvaHQ[1], self.CurvaHQ[2] - head])[0]
power = Power(caudalvolumetrico * self.DeltaP)
self.entrada = Corriente(
self.entrada.T, self.entrada.P.atm,
caudalvolumetrico * self.entrada.Liquido.rho * 3600,
self.entrada.mezcla, self.entrada.solido)
P_freno = Power(polyval(self.CurvaPotQ, caudalvolumetrico))
self.rendimientoCalculado = Dimensionless(power / P_freno)
self.headCalculada = head
self.power = power
self.P_freno = P_freno
self.salida = [self.entrada.clone(P=self.entrada.P + DeltaP)]
self.Pin = self.entrada.P
self.PoutCalculada = self.salida[0].P
self.Q = self.entrada.Q.galUSmin
self.volflow = self.entrada.Q
def mNm_2_raw(self,calib,tq): #ticks: calibrated sensor value #calib: yaml config calibration map if self.ctype=='sea_vertx_14bit': val=(tq-calib['cb_bias'])/calib['cb_scale'] val=float(polyval(calib['cb_inv_torque'],val)) return max(0,min(val,VERTX_14BIT_MAX)) if self.ctype=='adc_poly': val=(tq-calib['cb_bias'])/calib['cb_scale'] val=float(polyval(calib['cb_inv_torque'],val)) return max(0,min(val,M3EC_ADC_TICKS_MAX))
def calculo(self):
entrada = self.kwargs["entrada"]
self.rendimientoCalculado = Dimensionless(self.kwargs["rendimiento"])
if self.kwargs["Pout"]:
DeltaP = Pressure(self.kwargs["Pout"] - entrada.P)
elif self.kwargs["deltaP"]:
DeltaP = Pressure(self.kwargs["deltaP"])
elif self.kwargs["Carga"]:
DeltaP = Pressure(self.kwargs["Carga"] * entrada.Liquido.rho * g)
else:
DeltaP = Pressure(0)
if self.kwargs["usarCurva"]:
b1 = self.kwargs["diametro"] != self.kwargs["curvaCaracteristica"][
0] # noqa
b2 = self.kwargs["velocidad"] != self.kwargs[
"curvaCaracteristica"][1] # noqa
if b1 or b2:
self.curvaActual = self.calcularCurvaActual()
else:
self.curvaActual = self.kwargs["curvaCaracteristica"]
self.Ajustar_Curvas_Caracteristicas()
if not self.kwargs["usarCurva"]:
head = Length(DeltaP / g / entrada.Liquido.rho)
power = Power(head * g * entrada.Liquido.rho * entrada.Q /
self.rendimientoCalculado)
P_freno = Power(power * self.rendimientoCalculado)
elif not self.kwargs["incognita"]:
head = Length(polyval(self.CurvaHQ, entrada.Q))
DeltaP = Pressure(head * g * entrada.Liquido.rho)
power = Power(entrada.Q * DeltaP)
P_freno = Power(polyval(self.CurvaPotQ, entrada.Q))
self.rendimientoCalculado = Dimensionless(power / P_freno)
else:
head = Length(self.DeltaP / g / entrada.Liquido.rho)
poli = [self.CurvaHQ[0], self.CurvaHQ[1], self.CurvaHQ[2] - head]
Q = roots(poli)[0]
power = Power(Q * self.DeltaP)
entrada = entrada.clone(split=Q / entrada.Q)
P_freno = Power(polyval(self.CurvaPotQ, Q))
self.rendimientoCalculado = Dimensionless(power / P_freno)
self.deltaP = DeltaP
self.headCalculada = head
self.power = power
self.P_freno = P_freno
self.salida = [entrada.clone(P=entrada.P + DeltaP)]
self.Pin = entrada.P
self.PoutCalculada = self.salida[0].P
self.volflow = entrada.Q
self.cp_cv = entrada.Liquido.cp_cv
def estimate_rate_func(t, T, N, plot_flag=False, method='central diff'):
t_est_pts = scipy.linspace(t.min(), t.max(), N + 2)
interp_func = scipy.interpolate.interp1d(t, T, 'linear')
T_est_pts = interp_func(t_est_pts)
if plot_flag == True:
pylab.figure()
pylab.subplot(211)
pylab.plot(t_est_pts, T_est_pts, 'or')
# Estimate slopes
slope_pts = scipy.zeros((N, ))
T_slope_pts = scipy.zeros((N, ))
if method == 'local fit':
for i in range(1, (N + 1)):
mask0 = t > 0.5 * (t_est_pts[i - 1] + t_est_pts[i])
mask1 = t < 0.5 * (t_est_pts[i + 1] + t_est_pts[i])
mask = scipy.logical_and(mask0, mask1)
t_slope_est = t[mask]
T_slope_est = T[mask]
local_fit = scipy.polyfit(t_slope_est, T_slope_est, 2)
dlocal_fit = scipy.polyder(local_fit)
slope_pts[i - 1] = scipy.polyval(dlocal_fit, t_est_pts[i])
T_slope_pts[i - 1] = scipy.polyval(local_fit, t_est_pts[i])
if plot_flag == True:
t_slope_fit = scipy.linspace(t_slope_est[0], t_slope_est[-1],
100)
T_slope_fit = scipy.polyval(local_fit, t_slope_fit)
pylab.plot(t_slope_fit, T_slope_fit, 'g')
elif method == 'central diff':
dt = t_est_pts[1] - t_est_pts[0]
slope_pts = (T_est_pts[2:] - T_est_pts[:-2]) / (2.0 * dt)
T_slope_pts = T_est_pts[1:-1]
else:
raise ValueError, 'unkown method %s' % (method, )
# Fit line to slope estimates
fit = scipy.polyfit(T_slope_pts, slope_pts, 1)
if plot_flag == True:
T_slope_fit = scipy.linspace(T_slope_pts.min(), T_slope_pts.max(), 100)
slope_fit = scipy.polyval(fit, T_slope_fit)
pylab.subplot(212)
pylab.plot(T_slope_fit, slope_fit, 'r')
pylab.plot(T_slope_pts, slope_pts, 'o')
pylab.show()
rate_slope = fit[0]
rate_offset = fit[1]
return rate_slope, rate_offset
def rms_ms(coeffs_pix2wav, pixel_centers, wavelengths, npix, Cheby=False):
" Returns rms deviation of best fit in m/s"
if (Cheby):
residuals = Cheby_eval(coeffs_pix2wav,pixel_centers,npix) - wavelengths
central_wav = 0.5 * (Cheby_eval(coeffs_pix2wav,50.,npix) + Cheby_eval(coeffs_pix2wav,npix-50,npix))
else:
residuals = scipy.polyval(coeffs_pix2wav,pixel_centers) - wavelengths
central_wav = 0.5 * (scipy.polyval(coeffs_pix2wav,50.) + scipy.polyval(coeffs_pix2wav,npix-50))
rms_ms = np.sqrt( np.var( residuals ) ) * 299792458.0 / central_wav
return rms_ms, residuals
def estimate_rate_func(t, T, N, plot_flag=False, method='central diff'):
t_est_pts = scipy.linspace(t.min(), t.max(), N+2)
interp_func = scipy.interpolate.interp1d(t,T,'linear')
T_est_pts = interp_func(t_est_pts)
if plot_flag == True:
pylab.figure()
pylab.subplot(211)
pylab.plot(t_est_pts, T_est_pts,'or')
# Estimate slopes
slope_pts = scipy.zeros((N,))
T_slope_pts = scipy.zeros((N,))
if method == 'local fit':
for i in range(1,(N+1)):
mask0 = t > 0.5*(t_est_pts[i-1] + t_est_pts[i])
mask1 = t < 0.5*(t_est_pts[i+1] + t_est_pts[i])
mask = scipy.logical_and(mask0, mask1)
t_slope_est = t[mask]
T_slope_est = T[mask]
local_fit = scipy.polyfit(t_slope_est,T_slope_est,2)
dlocal_fit = scipy.polyder(local_fit)
slope_pts[i-1] = scipy.polyval(dlocal_fit,t_est_pts[i])
T_slope_pts[i-1] = scipy.polyval(local_fit,t_est_pts[i])
if plot_flag == True:
t_slope_fit = scipy.linspace(t_slope_est[0], t_slope_est[-1], 100)
T_slope_fit = scipy.polyval(local_fit,t_slope_fit)
pylab.plot(t_slope_fit, T_slope_fit,'g')
elif method == 'central diff':
dt = t_est_pts[1] - t_est_pts[0]
slope_pts = (T_est_pts[2:] - T_est_pts[:-2])/(2.0*dt)
T_slope_pts = T_est_pts[1:-1]
else:
raise ValueError, 'unkown method %s'%(method,)
# Fit line to slope estimates
fit = scipy.polyfit(T_slope_pts, slope_pts,1)
if plot_flag == True:
T_slope_fit = scipy.linspace(T_slope_pts.min(), T_slope_pts.max(),100)
slope_fit = scipy.polyval(fit,T_slope_fit)
pylab.subplot(212)
pylab.plot(T_slope_fit, slope_fit,'r')
pylab.plot(T_slope_pts, slope_pts,'o')
pylab.show()
rate_slope = fit[0]
rate_offset = fit[1]
return rate_slope, rate_offset
def calculo(self):
entrada = self.kwargs["entrada"]
self.rendimientoCalculado = Dimensionless(self.kwargs["rendimiento"])
if self.kwargs["Pout"]:
DeltaP = Pressure(self.kwargs["Pout"]-entrada.P)
elif self.kwargs["deltaP"]:
DeltaP = Pressure(self.kwargs["deltaP"])
elif self.kwargs["Carga"]:
DeltaP = Pressure(self.kwargs["Carga"]*entrada.Liquido.rho*g)
else:
DeltaP = Pressure(0)
if self.kwargs["usarCurva"]:
b1 = self.kwargs["diametro"] != self.kwargs["curvaCaracteristica"][0] # noqa
b2 = self.kwargs["velocidad"] != self.kwargs["curvaCaracteristica"][1] # noqa
if b1 or b2:
self.curvaActual = self.calcularCurvaActual()
else:
self.curvaActual = self.kwargs["curvaCaracteristica"]
self.Ajustar_Curvas_Caracteristicas()
if not self.kwargs["usarCurva"]:
head = Length(DeltaP/g/entrada.Liquido.rho)
power = Power(head*g*entrada.Liquido.rho*entrada.Q /
self.rendimientoCalculado)
P_freno = Power(power*self.rendimientoCalculado)
elif not self.kwargs["incognita"]:
head = Length(polyval(self.CurvaHQ, entrada.Q))
DeltaP = Pressure(head*g*entrada.Liquido.rho)
power = Power(entrada.Q*DeltaP)
P_freno = Power(polyval(self.CurvaPotQ, entrada.Q))
self.rendimientoCalculado = Dimensionless(power/P_freno)
else:
head = Length(self.DeltaP/g/entrada.Liquido.rho)
poli = [self.CurvaHQ[0], self.CurvaHQ[1], self.CurvaHQ[2]-head]
Q = roots(poli)[0]
power = Power(Q*self.DeltaP)
entrada = entrada.clone(split=Q/entrada.Q)
P_freno = Power(polyval(self.CurvaPotQ, Q))
self.rendimientoCalculado = Dimensionless(power/P_freno)
self.deltaP = DeltaP
self.headCalculada = head
self.power = power
self.P_freno = P_freno
self.salida = [entrada.clone(P=entrada.P+DeltaP)]
self.Pin = entrada.P
self.PoutCalculada = self.salida[0].P
self.volflow = entrada.Q
self.cp_cv = entrada.Liquido.cp_cv
def LinearRegressInverse(data, year, kind):
'''
Use the linear regression of the ratio of the winner's votes to the loser's votes.
'''
Dcontested = []
Rcontested = []
for D, R, I in data[year][kind]:
if D is None or R is None:
continue
if D > R:
Dcontested.append((D, R, I))
else:
Rcontested.append((D, R, I))
Dinverse = [(D, float(D) / R) for D, R, _ in Dcontested]
Rinverse = [(R, float(R) / D) for D, R, _ in Rcontested]
Da_s, Db_s, Dr, Dtt, Derr = scipy.stats.linregress(
[V for V, R in Dinverse], [R for V, R in Dinverse])
Deval = scipy.polyval([Da_s, Db_s], [V for V, R in Dinverse])
Ra_s, Rb_s, Rr, Rtt, Rerr = scipy.stats.linregress(
[V for V, R in Rinverse], [R for V, R in Rinverse])
Reval = scipy.polyval([Ra_s, Rb_s], [V for V, R in Rinverse])
if hasPyLab:
pylab.title('Inverse regression')
pylab.plot([V for V, R in Dinverse], [R for V, R in Dinverse], 'b.')
pylab.plot([V for V, R in Dinverse], Deval, 'b-')
pylab.plot([V for V, R in Rinverse], [R for V, R in Rinverse], 'r.')
pylab.plot([V for V, R in Rinverse], Reval, 'r-')
pylab.legend(['D ratio', 'D l-r', 'R ratio', 'R l-r'])
pylab.show()
results = []
for D, R, I in data[year][kind]:
Ik = I if I is not None else 0
if D is None:
Dk = .75 * R / scipy.polyval([Ra_s, Rb_s], R)
else:
Dk = D
if R is None:
Rk = .75 * D / scipy.polyval([Da_s, Db_s], D)
else:
Rk = R
results.append((Dk, Rk, Ik))
return results
def fit_curve(): global data, running if running == True or len(data[0])==0: return aa = [] bb = [] for k in range(len(data[0])): if data[1][k] > 1.0: aa.append(data[0][k]) bb.append(data[1][k]) x = array(bb) y = array(aa) from scipy import polyfit, polyval (ar,br)=polyfit(x,y,1) print polyval([ar,br],[0])
def fit_curve():
global data, running
if running == True or len(data[0]) == 0:
return
aa = []
bb = []
for k in range(len(data[0])):
if data[1][k] > 1.0:
aa.append(data[0][k])
bb.append(data[1][k])
x = array(bb)
y = array(aa)
from scipy import polyfit, polyval
(ar, br) = polyfit(x, y, 1)
print polyval([ar, br], [0])
def ctrl_voltage(ND):
coefs = array([
3.85013993e-18, -6.61616152e-15, 4.62228606e-12, -1.68733555e-09,
3.43138077e-07, -3.82875899e-05, 2.20822016e-03, -8.38473034e-02,
1.52678586e+00
])
return scipy.polyval(coefs, ND)
def interpMaskedAreas(x, dtfunc):
idx = num.where(dtfunc == -1)[0]
# find consecutive points of dtfunc set at -1
diffs = idx - num.roll(idx, 1)
setIdx1 = num.where(diffs > 1)[0]
setIdx2 = num.where(diffs > 1)[0] - 1
setIdx1 = num.hstack( (num.array([0]), setIdx1) )
setIdx2 = num.hstack( (setIdx2, num.array([-1])) )
# each int in setIdx1 is beginning of a set of points
# each int in setIdx2 is end of a set of points
for i in range(len(setIdx1)):
i1 = setIdx1[i]
i2 = setIdx2[i]
vals_to_interp = dtfunc[ idx[i1]:idx[i2]+1]
# construct interpolation from points on either side of vals
# first ydata
nVals = len(vals_to_interp)
left = num.array( \
[ dtfunc[idx[i1]-1-nVals], dtfunc[idx[i1]-1] ])
right = num.array( \
[ dtfunc[idx[i2]+1], dtfunc[idx[i2]+1+nVals] ])
vals_to_construct_interp = num.hstack( (left, right) )
vtci = vals_to_construct_interp
# now xdata
leftx = num.array([ x[idx[i1]-1-nVals], x[idx[i1]-1] ])
rightx = num.array([ x[idx[i2]+1], x[idx[i2]+1+nVals] ])
x_vtci = num.hstack( (leftx, rightx) )
# conduct interpolation
intpCoeffs = scipy.polyfit(x_vtci, vtci, 3)
dtfunc[ idx[i1]:idx[i2]+1 ] = \
scipy.polyval(intpCoeffs, x[ idx[i1]:idx[i2]+1 ])
return dtfunc
def scatter_fits(sys, scores, values, R, pval, aucname, show=False):
auclabel=get_label(aucname)
if sys=='apo':
format='ko'
label=
if sys=='bi':
format='ro'
if sys=='car':
format='bo'
if pval< 0.0001:
pval=0.0001
pylab.figure()
pylab.plot(scores, values, format)
(ar,br)=polyfit(scores, values, 1)
xr=polyval([ar,br], scores)
pylab.plot(scores,xr,'%s-' % format[0], label='R=%s, pval=%s' %
(round(R,2), round(pval,4)))
if aucname=='types':
pylab.plot(range(0, 15), [0.5]*len(range(0,15)), 'k--', label='Random Disc.')
pylab.ylim(0.3, 0.9)
else:
pylab.ylim(0.5, 1.0)
pylab.xlim(0, 15)
lg=pylab.legend()
lg.draw_frame(False)
pylab.title('%s States' % label)
pylab.xticks(range(0, 15), [' ']*2+ ['inactive']+[' ']*(len(range(0,15))-6)+['active']+[' ']*2)
pylab.xlabel('Pathway Progress')
pylab.ylabel('%s Aucs' % (auclabel))
pylab.savefig('%s_%saucs.png' % (sys, aucname), dpi=300)
if show==True:
pylab.show()
def PCoeff(filename,b,Aperture,RON,Gain,NSigma,S,N,marsh_alg,min_col,max_col):
hdulist = pyfits.open(filename) # Here we obtain the image...
data=hdulist[0].data # ... and we obtain the image matrix.
Result=Marsh.ObtainP((data.flatten()).astype(double),scipy.polyval(b,numpy.arange(data.shape[1])).astype(double),data.shape[0],data.shape[1],data.shape[1],Aperture,RON,Gain,NSigma,S,N,marsh_alg,min_col,max_col)
FinalMatrix=asarray(Result) # After the function, we convert our list to a Numpy array.
FinalMatrix.resize(data.shape[0],data.shape[1]) # And return the array in matrix-form.
return FinalMatrix
def main(FileName):
for trial in kep.iofiles.passFileToTrialList(FileName):
tr = kep.pipelinepars.keptrial(trial)
kw = kep.keplc.kw(**tr.kw)
X = kep.keplc.keplc(tr.kid)
X.runPipeline(kw)
Y = kep.qats.qatslc(X.lcFinal,X.KID,maske=kw.maske)
Y.padLC()
Y.addNoise()
Y.runQATS(f=0.01)
coeff = num.polyfit(num.log10(Y.periods),num.log10(Y.snrLC),1)
outy = scipy.polyval(coeff,num.log10(Y.periods))
normalizedPower = 10**(outy)
plt.plot(Y.periods,Y.snrFLAT,'r-')
plt.plot(Y.periods,Y.snrLC,'b-')
plt.plot(Y.periods,normalizedPower,'k-')
plt.setp(plt.gca().set_xscale('log'))
plt.savefig('sn.'+X.KID+'.png')
dfile = open('signal.'+X.KID+'.data','w')
print >> dfile,'#',X.KID,'|', Y.periods[num.argmax(Y.snrLC/normalizedPower)],\
Y.periods[num.argmax(Y.snrLC)], max(Y.snrLC/normalizedPower), max(Y.snrLC)
for i in range(len(Y.SignalPower)):
print >> dfile, Y.periods[i],'|',Y.snrLC[i],'|',\
Y.snrFLAT[i],'|',normalizedPower[i],'|',\
Y.tmin[i],'|',Y.tmax[i],'|',Y.q[i]
dfile.close()
def transform(self, rmout):
irt = {}
for rawspectrum in self.rt:
for peptide in self.rt[rawspectrum]:
self.irt[rawspectrum][peptide] = scipy.polyval(
[self.a[rawspectrum], self.b[rawspectrum]],
self.rt[rawspectrum][peptide])
if peptide not in irt:
irt[peptide] = []
irt[peptide].append(self.irt[rawspectrum][peptide])
for peptide in irt:
if len(irt) == 1:
self.irt_merged[peptide] = round(irt[peptide][0], 5)
else:
if rmout and len(irt) > 2:
self.irt_merged[peptide] = round(
lmedian(
array(irt[peptide])[invert(
chauvenet(array(irt[peptide]),
array(irt[peptide])))]), 5)
else:
self.irt_merged[peptide] = round(lmedian(irt[peptide]), 5)
for i in range(0, len(self.blocks)):
self.blocks[i].replace(self.irt_merged[self.blocks[i].peptide])
def _getACDataAndCharPulse(ps):
"""Create a data array and characteristic pulse array by summing the AC data
and characteristic pulse for each APD. Background fluctuations are removed
from the data by fitting using a 2nd order polynomial. The two arrays are
then scaled so they are approximately the same size. This is all done in
order to facilitate finding the t0 point.
Parameters:
ps -- A PolySegData object.
Returns: A tuple (dataArray, charPulseArray)
"""
# First we sum the data from the APDs to make the pulse easier to find.
yData = zeros_like(ps.acq_voltData[0])
for data in ps.acq_voltData:
yData += data
# Fit the averaged data with a polynomial to (hopefully) fit the
# background. Currently using a 2nd degree polynomial.
xData = arange(ps.acq_voltData.shape[1])
ps.t0ACBgCoeffs = polyfit(xData, yData, 2) # The coefficients
yFit = polyval(ps.t0ACBgCoeffs, xData) # The polynomial as an array
yData -= yFit
# Create a composite characteristic pulse by summing.
cPulse = zeros_like(ps.calib.charPulseAC[0])
for data in ps.calib.charPulseAC:
cPulse += data
# Normalize yData to the size of cPulse.
# I don't know if this is really necessary.
yData *= min(cPulse) / min(yData)
return (yData, cPulse)
def fit_data(xdata, ydata):
""" Fit a regression line to shift data points
Parameters
----------
xdata : astropy.table.column.Column
A list of x values (time)
ydata : astropy.table.column.Column
A list of y values (shifts)
Returns
-------
fit : ndarray
The fit line
xdata : astropy.table.column.Column
List of x values for fit
parameters : tuple
fitting parameters
err : int
Value returned on whether the fit was a sucess.
"""
stats = linregress(xdata, ydata)
parameters = (stats[0], stats[1])
err = 0
fit = scipy.polyval(parameters, xdata)
return fit, xdata, parameters, err
def draw(_case, specie, foo, color, label):
stats = gen_stats(foo, _case, specie)
x = range(len(stats))
y = polyfit(x, stats, 4)
y_pred = polyval(y, x)
plt.plot(stats, '%s--' % color, label=label)
plt.plot(y_pred, '%s-' % color, label='_nolegend_')
def linearRegression(self,Data,Flag):
savePath = '/home/yotoo/Project/comparison/'
# plot original data
title = 'test data'
if (Flag == '19 + Ranked'):
title = 'Linear Regression Result(Border size: 19; Game Type: Ranked)'
elif (Flag == '19 + Free'):
title = 'Linear Regression Result(Border size: 19; Game Type: Free)'
elif (Flag == '9 + Free'):
title = 'Linear Regression Result(Border size: 9; Game Type: Free)'
degreeArray = []
playerArray = []
degreeList = []
playerList = []
for record in Data:
record = record.split()
degree = int(record[0])
player = int(record[1])
degreeList.append(degree)
playerList.append(player)
degree = float(record[0])
player = float(record[1])
degreeArray.append(math.log10(degree))
playerArray.append(math.log10(player))
degreeArray = np.array(degreeArray)
playerArray = np.array(playerArray)
# linear regression
n = len(degreeList)
slope,intercept = polyfit(degreeArray,playerArray,1)
playerRegression=polyval([degreeArray,playerArray],degreeArray)
ms_error=sqrt(sum((playerRegression-playerArray)**2)/n)
# plot the original data points
plt.grid(True)
plt.xlabel('Log Degree')
plt.ylabel('Log Players')
plt.title(title)
plt.plot(degreeArray,playerArray,'bo')
# error
# plot regression line
x = [x for x in range(0,5)]
y = [i*slope+intercept for i in x]
regressLineText = 'Slope = %.3f \nIntercept = %.3f \nMean Square Error = %.3f' %(slope,intercept,ms_error)
plt.plot(x,y,'r.-',label = regressLineText)
plt.legend()
plt.savefig(savePath + title + ".png")
plt.show()
print regressLineText
def raw_2_deg(self,calib,qei_on,qei_period,qei_rollover, qs_to_qj = 1.0): #qei: raw sensor value #calib: yaml config calibration map if self.ctype=="vertx_14bit": val = qs_to_qj*360.0*(qei_on)/VERTX_14BIT_MAX return val*calib['cb_scale']+calib['cb_bias'] if self.ctype=="ma3_12bit": if qei_period==0.0: return 0.0 val = ((qei_on*MA3_12BIT_PERIOD_US)/qei_period)-1 val= qs_to_qj*360.0*(val/MA3_12BIT_MAX_PULSE_US) return val*calib['cb_scale']+calib['cb_bias'] if self.ctype=="ma3_10bit": if qei_period==0.0: return 0.0 val = ((qei_on*MA3_10BIT_PERIOD_US)/qei_period)-1 val= qs_to_qj*360.0*(val/MA3_10BIT_MAX_PULSE_US) return val*calib['cb_scale']+calib['cb_bias'] if self.ctype=="ma3_12bit_poly": if qei_period==0.0: return 0.0 val = qs_to_qj*(((qei_on*MA3_12BIT_PERIOD_US)/qei_period)-1) val=float(polyval(calib['cb_theta'],val)) return val*calib['cb_scale']+calib['cb_bias']
def getDiameterQuantilesAlongSinglePath(self,path,G,counter=None):
G=self.filterPathDiameters(path, G,self.__scale)
x=[]
y=[]
length=0
vprop=G.vertex_properties["vp"]
for i in path:
length+=1
if vprop[i]['diameter'] > 0:
x.append(length)
y.append(vprop[i]['diameter'])
coeffs=polyfit(x,y,1)
besty = polyval ( coeffs , x)
self.__io.saveArray(x,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterX')
self.__io.saveArray(y,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterY')
l=len(y)-1
l25=int(l*0.25)
l50=int(l*0.5)
l75=int(l*0.75)
l90=int(l*0.90)
d25=np.average(y[:l25])
d50=np.average(y[l25:l50])
d75=np.average(y[l50:l75])
d90=np.average(y[l90:])
self.__io.saveArray(y,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterHistoTP')
return d25,d50,d75,d90
def plotLinearRegression(item, indicatorList):
"""Saves to file a linear regression plot of any indicator in item
that contains at least 30 values"""
country = loadPickle(item)
print len(item[1])*"-" + "\n" + item[1] + "\n" + len(item[1]) * "-"
country.indicators.sort()
for indicator in country.indicators:
indicator.values.sort()
if indicator.id in indicatorList:
#if len(indicator.values) >= 40:
#if indicator.id == "NY.GDP.MKTP.KD":
print " ", indicator.name
xvals = []
yvals = []
for entry in indicator.values:
xvals.append(entry.year)
yvals.append(entry.value)
(a, b, r, tt, err) = stats.linregress(xvals, yvals)
regVals = scipy.polyval([a, b], xvals)
plt.title(indicator.name + "\n" + item[1])
plt.plot(xvals, yvals, 'k.')
plt.plot(xvals, regVals, 'r.-')
plt.legend(['data', 'regression'])
dir = os.path.join(os.pardir, 'plots', 'linear_regressions',
indicator.id)
if not os.path.exists(dir):
os.makedirs(dir)
plt.savefig(os.path.join(dir,item[0] + ".pdf"))
plt.clf()
def linearRegression(histogram):
x=[]
y=[]
for i in range(256):
for k in range(256):
if histogram.GetBinContent(i,k)!=0:
x.append(i)
y.append(k)
(ar,br)=polyfit(x,y,1)
xr=polyval([ar,br],x)
err=sqrt(sum((xr-y)**2)/len(y))
if abs(ar)>1.0:
(ar,br)=polyfit(y,x,1)
xr=polyval([ar,br],y)
err=sqrt(sum((xr-x)**2)/len(y))
return err
def construct_linear_regression(x_learn, y_dev_learn, x_test, y_dev_test):
regression = sp.polyfit(x_learn, y_dev_learn, 1)
y_exp_learn = sp.polyval(regression, x_learn)
y_exp_test = sp.polyval(regression, x_test)
print("For train dataset:\n\ty = a*x + b\n\ta = %f\n\tb = %f" % (regression[0], regression[1]))
mse_learn = np.sqrt(np.mean((y_exp_learn - y_dev_learn) ** 2))
mse_test = np.sqrt(np.mean((y_exp_test - y_dev_test) ** 2))
mse_total = np.sqrt((((mse_learn**2) * learn_set_size)
+ ((mse_test**2) * (dataset_size - learn_set_size)))
/ dataset_size)
print("Train MSE = %f\nTest MSE = %f\nTotal MSE = %f" % (mse_learn, mse_test,mse_total ))
return regression, mse_learn, mse_test, mse_total, y_exp_learn, y_exp_test
def raw_2_mA(self,calib,ticks_a,ticks_b): #ticks: adc sensor value #calib: yaml config calibration map if self.ctype=='adc_linear_5V': mV_a = 5000.0*(ticks_a-calib['cb_ticks_at_zero_a'])/M3EC_ADC_TICKS_MAX mV_b = 5000.0*(ticks_b-calib['cb_ticks_at_zero_b'])/M3EC_ADC_TICKS_MAX i_a = 1000.0*mV_a/calib['cb_mV_per_A'] i_b = 1000.0*mV_b/calib['cb_mV_per_A'] val=max(abs(i_a), abs(i_b)) return max(0,val*calib['cb_scale']+calib['cb_bias']) if self.ctype=='adc_poly': i_a= float(polyval(calib['cb_current_a'],ticks_a)) i_b= float(polyval(calib['cb_current_b'],ticks_b)) val=max(abs(i_a), abs(i_b)) return val*calib['cb_scale']+calib['cb_bias'] return 0.0
def graph_stock_regression(data, filename):
"""
This function should take a list containing two lists of the form
returned by get_yahoo_data (list of date, adj. close tuples) and
save the graph of the series of daily return pairs as well as
the regression line. The graph should be saved to the given
filename.
"""
one = []
two = []
for i in xrange(1, len(data[0])):
one.append((data[0][i][1]-data[0][i-1][1])/data[0][i-1][1])
two.append((data[1][i][1]-data[1][i-1][1])/data[1][i-1][1])
(a_s, b_s, r, tt, stderr) = linregress(one, two)
line = scipy.polyval([a_s, b_s], one)
title = filename.split('.')[0]
axies = title.split('vs')
pylab.title(title)
pylab.plot(one, two, 'r.', one, line, 'k')
pylab.xlabel(axies[0])
pylab.ylabel(axies[1])
pylab.legend(['data', 'regression'])
pylab.savefig(filename)
def calculate_polyfit(x, y):
""" Calculate a linear regression using polyfit instead """
# FIXME(pica) This doesn't work for large ranges (tens of orders of
# magnitude). No idea why as the fixRange() function above should make
# all values greater than one. The trouble mainly seems to happen when
# log10(min(x)) negative.
# Check the smallest value in x or y isn't below 2x10-7 otherwise
# we hit numerical instabilities.
xFactorFix, xFactor = False, 0
yFactorFix, yFactor = False, 0
minX = min(x)
minY = min(y)
if minX < 2e-7:
x, xFactor, xFactorFix = fixRange(x)
if minY < 2e-7:
y, yFactor, yFactorFix = fixRange(y)
(ar, br) = polyfit(x, y, 1)
yf = polyval([ar, br], x)
xf = x
if xFactorFix:
xf = xf / 10**xFactor
if yFactorFix:
yf = yf / 10**yFactor
return xf, yf
def plot_rate_vs_mass(array_vs,ks):
#array_v=array_vs
#k_growth = ks
colors = ['#800000','#c87942']#,'#008080','#82bcd3']
#col='#82bcd3'
f1 = p.figure()
for array_v, k_growth,col in zip(array_vs,ks,colors):
zahl = max([len(i) for i in array_v['R'].flat if i]) # longest list of X values
nr_cells=array_v.size
tmp2=zeros((nr_cells,zahl),dtype=float)
tmp3=zeros((nr_cells,zahl),dtype=float)
for k1, vector in enumerate(array_v.flat):
if vector['R']:
for k2, r, am, ad, v in zip(range(zahl-len(vector['R']),zahl),vector['R'],vector['B_Am'],vector['B_Ad'],vector['V']):
if vector['S_entry'] != 0:
tmp2[k1,k2] = log2(r + am + ad) # cell mass
tmp3[k1,k2] = log2(r * (am + ad) * k_growth / v) # metabolic rate
tmp2 = array([i for i in tmp2 if i[-1]!=0])
tmp3 = array([i for i in tmp3 if i[-1]!=0])
p.plot(tmp2[:,-1],tmp3[:,-1],'o',color=col)
(a_s,b_s,r,tt,stderr)=stats.linregress(tmp2[:,-1],tmp3[:,-1])
p.text(min(tmp2[:,-1])+1,min(tmp3[:,-1]),'slope = %s'%(round(a_s,2)))
p.plot(linspace(min(tmp2[:,-1]),max(tmp2[:,-1]),2),polyval([a_s,b_s],linspace(min(tmp2[:,-1]),max(tmp2[:,-1]),2)),'-',color='#25597c',lw=3)
p.xlabel('Cell mass')
p.ylabel('Metabolic rate')
f1.savefig('rate_vs_mass_0.02_and_0.03_SG2_68_G2_cells.pdf')
def raw_2_mNm(self,calib,ticks): #ticks: raw sensor value #calib: yaml config calibration map if self.ctype=='sea_vertx_14bit' or self.ctype=='adc_poly': val=float(polyval(calib['cb_torque'],ticks)) return val*float(calib['cb_scale'])+float(calib['cb_bias']) return 0.0
def session_fits(session_id, fit_length=50,
output_filename='results/check_fit.dat',
write=True):
dbs = database.DBSession()
session = dbs.query(database.Session).get(session_id)
runs = session.experiments[0].runs
data = []
for run in runs:
parameter = run.parameters['release_rate']
fitness = run.get_objective('pi_fit')
if fitness is not None:
data.append((parameter, fitness))
fit_sorted_data = sorted(data, key=operator.itemgetter(1))
px, py = zip(*fit_sorted_data[:fit_length])
coeffs, R, n, svs, rcond = scipy.polyfit(px, py, 2, full=True)
x, y = zip(*sorted(data))
peak = float(-coeffs[1] / (2 * coeffs[0]))
fit = float(R/fit_length)
if write:
p = scipy.polyval(coeffs, x)
header = '# Parabola peak at %s\n# Parabola R^2/n = %s\n'
rows = zip(x, y, p)
_small_writer(output_filename, rows, ['release_rate', 'pi_fit', 'parabola'],
header=header % (peak, fit))
return session.parameters.get('release_cooperativity'), peak, fit
def raw_2_C(self,calib,ticks): print "type1:", self.ctype #ticks: adc sensor value #calib: yaml config calibration map if self.ctype=='adc_linear_3V3': mV=ticks/(M3EC_ADC_TICKS_MAX/3300.0) bias = 25.0-(calib['cb_mV_at_25C']/calib['cb_mV_per_C']) val = mV/calib['cb_mV_per_C'] + bias if self.ctype=='adc_linear_5V': mV=ticks/(M3EC_ADC_TICKS_MAX/5000.0) bias = 25.0-(calib['cb_mV_at_25C']/calib['cb_mV_per_C']) val = mV/calib['cb_mV_per_C'] + bias if self.ctype=='adc_linear_5V_ns': mV=ticks/(M3EC_ADC_TICKS_MAX/3300.0) bias = 25.0-(calib['cb_mV_at_25C']/calib['cb_mV_per_C']) val = mV/calib['cb_mV_per_C'] + bias if self.ctype=='adc_poly': val = float(polyval(calib['cb_temp'],ticks)) if self.ctype=='temp_25C': return 25.0 if self.ctype=='none': return 0.0 if self.ctype == 'dsp_calib': val = ticks print "type2:", self.ctype return val*calib['cb_scale']+calib['cb_bias']
def session_fits(session_id,
fit_length=50,
output_filename='results/check_fit.dat',
write=True):
dbs = database.DBSession()
session = dbs.query(database.Session).get(session_id)
runs = session.experiments[0].runs
data = []
for run in runs:
parameter = run.parameters['release_rate']
fitness = run.get_objective('pi_fit')
if fitness is not None:
data.append((parameter, fitness))
fit_sorted_data = sorted(data, key=operator.itemgetter(1))
px, py = zip(*fit_sorted_data[:fit_length])
coeffs, R, n, svs, rcond = scipy.polyfit(px, py, 2, full=True)
x, y = zip(*sorted(data))
peak = float(-coeffs[1] / (2 * coeffs[0]))
fit = float(R / fit_length)
if write:
p = scipy.polyval(coeffs, x)
header = '# Parabola peak at %s\n# Parabola R^2/n = %s\n'
rows = zip(x, y, p)
_small_writer(output_filename,
rows, ['release_rate', 'pi_fit', 'parabola'],
header=header % (peak, fit))
return session.parameters.get('release_cooperativity'), peak, fit
def setBestFit(self):
imin = self.chiSqrs.argmin()
self.polynom = scipy.polyval(self.coeffs[imin][:-1], \
self.periods)
self.bestFit = self.polynom + self.coeffs[imin][-1] * \
firstFareyVals(self.periods, self.periods[imin])
self.minSqr = self.chiSqrs[imin]
def getDiameterQuantilesAlongSinglePath(self,path,G,counter=None):
G=self.filterPathDiameters(path, G,self.__scale)
x=[]
y=[]
length=0
vprop=G.vertex_properties["vp"]
for i in path:
length+=1
if vprop[i]['diameter'] > 0:
x.append(length)
y.append(vprop[i]['diameter'])
coeffs=polyfit(x,y,1)
besty = polyval ( coeffs , x)
self.__io.saveArray(x,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterX')
self.__io.saveArray(y,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterY')
l=len(y)-1
l25=int(l*0.25)
l50=int(l*0.5)
l75=int(l*0.75)
l90=int(l*0.90)
d25=np.average(y[:l25])
d50=np.average(y[l25:l50])
d75=np.average(y[l50:l75])
d90=np.average(y[l90:])
self.__io.saveArray(y,self.__io.getHomePath()+'Plots/'+self.__io.getFileName()+'_DiameterHistoTP')
return d25,d50,d75,d90
def scatter_fits(sys, scores, values, R, pval, aucname, show=False):
auclabel=get_label(aucname)
if sys=='apo':
format='ko'
label=get_label(sys)
if sys=='bi':
format='ro'
label=get_label(sys)
if sys=='car':
format='bo'
label=get_label(sys)
if pval< 0.0001:
pval=0.0001
pylab.figure()
pylab.plot(scores, values, format)
(ar,br)=polyfit(scores, values, 1)
xr=polyval([ar,br], scores)
pylab.plot(scores,xr,'%s-' % format[0], label='R=%s, pval=%s' %
(round(R,2), round(pval,4)))
if aucname=='types':
pylab.plot(range(0, 15), [0.5]*len(range(0,15)), 'k--', label='Random Disc.')
pylab.ylim(0.3, 0.9)
else:
pylab.ylim(0.5, 1.0)
pylab.xlim(0, 15)
lg=pylab.legend()
lg.draw_frame(False)
pylab.title('%s States' % label)
pylab.xticks(range(0, 15), [' ']*2+ ['inactive']+[' ']*(len(range(0,15))-6)+['active']+[' ']*2)
pylab.xlabel('Pathway Progress')
pylab.ylabel('%s Aucs' % (auclabel))
pylab.savefig('%s_%saucs.png' % (sys, aucname), dpi=300)
if show==True:
pylab.show()
def show_dialog_len():
import numpy as np
from scipy import polyfit,polyval
import matplotlib.pyplot as plt
import LetsgoCorpus as lc
import operator
corpus = lc.Corpus('D:/Data/training',prep=True)
l = [];avg_cs = []
for dialog in corpus.dialogs():
if len(dialog.turns) > 40:
continue
l.append(len(dialog.turns))
avg_cs.append(reduce(operator.add,map(lambda x:x['CS'],dialog.turns))/l[-1])
(ar,br) = polyfit(l,avg_cs,1)
csr = polyval([ar,br],l)
plt.plot(l,avg_cs,'g.',alpha=0.75)
plt.plot(l,csr,'r-',alpha=0.75,linewidth=2)
plt.axis([0,50,0,1.0])
plt.xlabel('Dialog length (turn)')
plt.ylabel('Confidence score')
plt.title('Dialog length vs. Confidence score')
plt.grid(True)
plt.savefig('img/'+'Dialog length vs Confidence score'+'.png')
# plt.show()
plt.clf()
def plot_ci_bootstrap(xs, ys, resid, nboot=500, ax=None):
"""Return an axes of confidence bands using a bootstrap approach.
Notes
-----
The bootstrap approach iteratively resampling residuals.
It plots `nboot` number of straight lines and outlines the shape of a band.
The density of overlapping lines indicates improved confidence.
Returns
-------
ax : axes
- Cluster of lines
- Upper and Lower bounds (high and low) (optional) Note: sensitive to outliers
References
----------
.. [1] J. Stults. "Visualizing Confidence Intervals", Various Consequences.
http://www.variousconsequences.com/2010/02/visualizing-confidence-intervals.html
"""
if ax is None:
ax = plt.gca()
bootindex = scipy.random.randint
for _ in range(nboot):
resamp_resid = resid[bootindex(0, len(resid) - 1, len(resid))]
# Make coeffs of for polys
pc = scipy.polyfit(xs, ys + resamp_resid, 1)
# Plot bootstrap cluster
ax.plot(xs, scipy.polyval(pc, xs), "b-", linewidth=2, alpha=3.0 / float(nboot))
return ax
def calculatePolyfit(x, y):
""" Calculate a linear regression using polyfit instead """
# FIXME(pica) This doesn't work for large ranges (tens of orders of
# magnitude). No idea why as the fixRange() function above should make
# all values greater than one. The trouble mainly seems to happen when
# log10(min(x)) negative.
# Check the smallest value in x or y isn't below 2x10-7 otherwise
# we hit numerical instabilities.
xFactorFix, xFactor = False, 0
yFactorFix, yFactor = False, 0
minX = min(x)
minY = min(y)
if minX < 2e-7:
x, xFactor, xFactorFix = fixRange(x)
if minY < 2e-7:
y, yFactor, yFactorFix = fixRange(y)
(ar, br) = polyfit(x, y, 1)
yf = polyval([ar, br], x)
xf = x
if xFactorFix:
xf = xf / 10**xFactor
if yFactorFix:
yf = yf / 10**yFactor
return xf, yf
def objective_scale(p, Ax, data, spec_wave, fit_mask, sivarf, Nphot, Next, return_coeffs):
"""
Objective function for fitting for a scale term between photometry and
spectra
"""
import scipy.optimize
from scipy import polyval
scale = np.ones(Ax.shape[1])
scale[:-Nphot] = polyval(p[::-1]/10., (spec_wave-1.e4)/1000.)
AxT = Ax*scale
# Remove scaling from background component
for i in range(Next):
AxT[i, :] /= scale
#AxT = AxT[:,fit_mask].T
# (Ax*scale)[:,fit_mask].T
#AxT[:,:Next] = 1.
coeffs, rnorm = scipy.optimize.nnls(AxT[:, fit_mask].T, data[fit_mask])
full = np.dot(coeffs, AxT/sivarf)
resid = data/sivarf - full # - background
chi2 = np.sum(resid[fit_mask]**2*sivarf[fit_mask]**2)
#print('{0} {1}'.format(p, chi2))
if return_coeffs:
return coeffs, full, resid, chi2, AxT
else:
return chi2
def acceptable_fit(self):
ordered_y, ordered_x = zip(
*sorted(self.pairs, key=operator.itemgetter(1)))
parabola_y = scipy.polyval(self.coeffs, ordered_x)
if self.plot:
import matplotlib.pyplot
pyplot = matplotlib.pyplot
pyplot.ion()
pyplot.draw()
a = pyplot.subplot(1, 1, 1)
a.clear()
# a.set_xscale('log')
# a.set_yscale('log')
pyplot.plot(ordered_x, ordered_y, 'ro')
pyplot.plot(ordered_x, parabola_y, 'b-')
pyplot.axvline(self.parabola_peak, 0, 1, linestyle=':', color='g')
pyplot.draw()
return ((self.last_parabola_peak is not None)
and ((abs(self.parabola_peak - self.last_parabola_peak) /
self.parabola_peak) < self.parameter_tolerance))
def linearRegression(histogram):
x = []
y = []
for i in range(256):
for k in range(256):
if histogram.GetBinContent(i, k) != 0:
x.append(i)
y.append(k)
(ar, br) = polyfit(x, y, 1)
xr = polyval([ar, br], x)
err = sqrt(sum((xr - y)**2) / len(y))
if abs(ar) > 1.0:
(ar, br) = polyfit(y, x, 1)
xr = polyval([ar, br], y)
err = sqrt(sum((xr - x)**2) / len(y))
return err
def show_correlation_num_comments_and_socre():
f = open(os.getcwd()+"\\data\\all_data.json","r")
data = json.loads(f.read())
f.close()
#for data,sr in data:
x = []
y = []
for item in data['children']:
x.append(item['num_comment'])
y.append(item['ups']-item['downs'])
slope, intercept, r, p, std = stats.linregress(x,y)
ry = polyval([slope, intercept], x)
print stats.pearsonr(x,y)
print(slope, intercept, r, p, std)
#print(ry)
plot(x,y, 'k.')
plot(x,ry, 'r.-')
title("all data")
pylab.xlabel('num_comments')
pylab.ylabel('score')
#legend(['original', 'regression'])
show()
def ar1fit(ts):
'''
Fits an AR(1) model to the time series data ts. AR(1) is a
linear model of the form
x_t = beta * x_{t-1} + c + e_{t-1}
where beta is the coefficient of term x_{t-1}, c is a constant
and x_{t-1} is an i.i.d. noise term. Here we assume that e_{t-1}
is normally distributed.
Returns the tuple (beta, c, sigma).
'''
# Fitting AR(1) entails finding beta, c, and the noise term.
# Beta is well approximated by the coefficient of OLS regression
# on the lag of the data with itself. Since the noise term is
# assumed to be i.i.d. and normal, we must only estimate sigma,
# the standard deviation.
# Estimate beta
x = ts[0:-1]
y = ts[1:]
p = sp.polyfit(x, y, 1)
beta = p[0]
# Estimate c
c = sp.mean(ts) * (1 - beta)
# Estimate the variance from the residuals of the OLS regression.
yhat = sp.polyval(p, x)
variance = sp.var(y - yhat)
sigma = sp.sqrt(variance)
return beta, c, sigma
def setBestFit(self):
imin = self.chiSqrs.argmin()
self.polynom = scipy.polyval(self.coeffs[imin][:-1], \
self.periods[:-1])
self.bestFit = self.polynom + self.coeffs[imin][-1] * \
1. / sqrtMN(self.periods[imin], self.periods)
self.minSqr = self.chiSqrs[imin]