def update_design(self):
ax = self.ax
ax.cla()
ax2 = self.ax2
ax2.cla()
wp = self.wp
ws = self.ws
gpass = self.gpass
gstop = self.gstop
b, a = ss.iirdesign(wp, ws, gpass, gstop, ftype=self.ftype, output='ba')
self.a = a
self.b = b
#b = [1,2]; a = [1,2]
#Print this on command line so we can use it in our programs
print 'b = ', pylab.array_repr(b)
print 'a = ', pylab.array_repr(a)
my_w = pylab.logspace(pylab.log10(.1*self.ws[0]), 0.0, num=512)
#import pdb;pdb.set_trace()
w, h = freqz(b, a, worN=my_w*pylab.pi)
gp = 10**(-gpass/20.)#Go from db to regular
gs = 10**(-gstop/20.)
self.design_line, = ax.plot([.1*self.ws[0], self.ws[0], wp[0], wp[1], ws[1], 1.0], [gs, gs, gp, gp, gs, gs], 'ko:', lw=2, picker=5)
ax.semilogx(w/pylab.pi, pylab.absolute(h),lw=2)
ax.text(.5,1.0, '{:d}/{:d}'.format(len(b), len(a)))
pylab.setp(ax, 'xlim', [.1*self.ws[0], 1.2], 'ylim', [-.1, max(1.1,1.1*pylab.absolute(h).max())], 'xticklabels', [])
ax2.semilogx(w/pylab.pi, pylab.unwrap(pylab.angle(h)),lw=2)
pylab.setp(ax2, 'xlim', [.1*self.ws[0], 1.2])
ax2.set_xlabel('Normalized frequency')
pylab.draw()
def tune_alpha(self, drug_name, alphas=None, N=80, l1_ratio=0.5,
n_folds=10, show=True, shuffle=False, alpha_range=[-2.8,0.1]):
"""Interactive tuning of the model (alpha).
This is much faster than :meth:`plot_cindex` but much slower than
ElasticNetCV
.. plot::
:include-source:
from gdsctools import *
ic = IC50(gdsctools_data("IC50_v5.csv.gz"))
gf = GenomicFeatures(gdsctools_data("genomic_features_v5.csv.gz"))
en = GDSCElasticNet(ic, gf)
en.tune_alpha(1047, N=40, l1_ratio=0.1)
"""
if alphas is None:
# logspace returns a vector in natural space that guarantees a
# uniform spacing in a log space (log10 or ln)
# -2.8 to 0.5 means alpha from 1.58e-3 to 3.16
# This is equivalent to log(1.58e-3)=-6.45 to log(3.16)=1.15 in ln
# scale
a1, a2 = alpha_range
alphas = pylab.logspace(a1, a2, N)
# Let us now do a CV across difference alphas
all_scores = []
for alpha in alphas:
scores = self.fit(drug_name, alpha, l1_ratio=l1_ratio,
n_folds=n_folds, shuffle=shuffle)
all_scores.append(scores)
# We can now plot the results that is the mean scores + error enveloppe
df = pd.DataFrame(all_scores)
# we also identify the max correlation and corresponding alpha
maximum = df.mean(axis=1).max()
alpha_best = alphas[df.mean(axis=1).argmax()]
if show is True:
mu = df.mean(axis=1)
sigma = df.var(axis=1)
pylab.clf()
pylab.errorbar(pylab.log(alphas), mu, yerr=sigma, color="gray")
pylab.plot(pylab.log(alphas), mu, 'or')
pylab.axvline(pylab.log(alpha_best), lw=4, alpha=0.5, color='g')
pylab.title("Mean scores (pearson) across alphas for Kfold=%s" % n_folds)
pylab.xlabel("ln(alpha)")
pylab.ylabel("mean score (pearson)")
pylab.grid()
results = {"alpha_best":alpha_best, "ln_alpha":pylab.log(alpha_best),
"maximum_Rp":maximum}
return results
def _do_plot_ldf(self, coincidence, x0, y0, shower_size):
x = plt.logspace(-1, 3, 100)
y = self.solver.ldf_given_size(x, shower_size)
plt.loglog(x, y, label='LDF')
r, dens = self.solver.get_ldf_measurements_for_core_position((x0, y0))
plt.loglog(r, dens, 'o', label="signals")
plt.legend()
plt.xlabel("Core distance [m]")
plt.ylabel("Particle density [$m^{-2}$]")
def _do_plot_ldf(self, coincidence, x0, y0, shower_size):
x = plt.logspace(-1, 3, 100)
y = self.solver.ldf_given_size(x, shower_size)
plt.loglog(x, y, label='LDF')
r, dens = self.solver.get_ldf_measurements_for_core_position((x0, y0))
plt.loglog(r, dens, 'o', label="signals")
plt.legend()
plt.xlabel("Core distance [m]")
plt.ylabel("Particle density [$m^{-2}$]")
def DFA(data, npoints=None, degree=1, use_median=False):
"""
computes the detrended fluctuation analysis
returns the fluctuation F and the corresponding window length L
:args:
data (n-by-1 array): the data from which to compute the DFA
npoints (int): the number of points to evaluate; if omitted the log(n)
will be used
degree (int): degree of the polynomial to use for detrending
use_median (bool): use median instead of mean fluctuation
:returns:
F, L: the fluctuation F as function of the window length L
"""
# max window length: n/4
#0th: compute integral
integral = cumsum(data - mean(data))
#1st: compute different window lengths
n_samples = npoints if npoints is not None else int(log(len(data)))
lengths = sort(array(list(set(
logspace(2,log(len(data)/4.),n_samples,base=exp(1)).astype(int)
))))
#print lengths
all_flucs = []
used_lengths = []
for wlen in lengths:
# compute the fluctuation of residuals from a linear fit
# according to Kantz&Schreiber, ddof must be the degree of polynomial,
# i.e. 1 (or 2, if mean also counts? -> see in book)
curr_fluc = []
# rrt = 0
for startIdx in arange(0,len(integral),wlen):
pt = integral[startIdx:startIdx+wlen]
if len(pt) > 3*(degree+1):
resids = pt - polyval(polyfit(arange(len(pt)),pt,degree),
arange(len(pt)))
# if abs(wlen - lengths[0]) < -1:
# print resids[:20]
# elif rrt == 0:
# print "wlen", wlen, "l0", lengths[0]
# rrt += 1
curr_fluc.append(std(resids, ddof=degree+1))
if len(curr_fluc) > 0:
if use_median:
all_flucs.append(median(curr_fluc))
else:
all_flucs.append(mean(curr_fluc))
used_lengths.append(wlen)
return array(all_flucs), array(used_lengths)
def ConvergenceMesh():
pl.figure()
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[ell, 0.1, 1e-3])
te_list.append(te)
tf_list.append(tf)
pl.loglog(Roell_list,
tf_list,
"bo-",
alpha=0.5,
label=r"$h=1\times10^{-3}$")
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[ell, 0.1, 4e-3])
te_list.append(te)
tf_list.append(tf)
pl.loglog(Roell_list,
tf_list,
"g*-",
alpha=0.5,
label=r"$h=2\times10^{-3}$")
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[ell, 0.1, 1e-2])
te_list.append(te)
tf_list.append(tf)
pl.loglog(Roell_list,
tf_list,
"r>-",
alpha=0.5,
label=r"$h=1\times10^{-2}$")
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("Remote stress at fracture $\sigma_\infty/\sigma_0$")
pl.legend(loc="best")
pl.title(r"$\rho=0.1$").set_y(1.04)
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("conv_h.pdf", bbox_inches='tight')
def tune_alpha(self,
drug_name,
alphas=None,
N=100,
l1_ratio=0.5,
n_folds=10,
plot=True):
"""
.. plot::
an.tune_alpha("1047", N=100, l1_ratio=0.1)
an.tune_alpha("1047", N=100, l1_ratio=0.01)
an.tune_alpha("1047", N=100, l1_ratio=0.001)
an.tune_alpha("1047", N=100, l1_ratio=0.0001)
29, 34, 52, 1014, 1015, 1024, 1036, 1047, 1061
"""
# alphas = 10**-linspace(6,1,100)
if alphas is None:
alphas = pylab.logspace(-5, 0, N)
all_scores = []
median_scores = []
for alpha in alphas:
scores = self.elastic_net(drug_name,
alpha,
l1_ratio=l1_ratio,
n_folds=n_folds)
median_scores.append(np.mean(scores))
all_scores.append(scores)
#pylab.plot(pylab.log(alphas), median_scores, '-o')
df = pd.DataFrame(all_scores)
maximum = df.mean(axis=1).max()
alpha_best = alphas[df.mean(axis=1).argmax()]
if plot is True:
mu = df.mean(axis=1)
sigma = df.std(axis=1)
pylab.clf()
pylab.errorbar(pylab.log(alphas), mu, yerr=sigma)
pylab.plot(pylab.log(alphas), mu, 'or')
pylab.axvline(pylab.log(alpha_best), lw=4, alpha=0.5, color='g')
pylab.title("Mean scores across alphas")
pylab.xlabel("alpha")
pylab.ylabel("mean score")
return alphas, all_scores, maximum, alpha_best
def plot(df, *args, **kw):
N = df["count"].sum()
deltas_ = df["count"].values[:-1] - df["count"].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df["count"] / N) ** 0.5
N_a = df["count"][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100, *args, **kw)
def plot(df, *args, **kw):
N = df["count"].sum()
deltas_ = df['count'].values[:-1] - df['count'].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df['count'] / N) ** 0.5
N_a = df['count'][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100, *args, **kw)
def tune_alpha(self, drug_name, alphas=None, N=100, l1_ratio=0.5,
n_folds=10, plot=True):
"""
.. plot::
an.tune_alpha("1047", N=100, l1_ratio=0.1)
an.tune_alpha("1047", N=100, l1_ratio=0.01)
an.tune_alpha("1047", N=100, l1_ratio=0.001)
an.tune_alpha("1047", N=100, l1_ratio=0.0001)
29, 34, 52, 1014, 1015, 1024, 1036, 1047, 1061
"""
# alphas = 10**-linspace(6,1,100)
if alphas is None:
alphas = pylab.logspace(-5,0,N)
all_scores = []
median_scores = []
for alpha in alphas:
scores = self.elastic_net(drug_name, alpha, l1_ratio=l1_ratio,
n_folds=n_folds)
median_scores.append(np.mean(scores))
all_scores.append(scores)
#pylab.plot(pylab.log(alphas), median_scores, '-o')
df = pd.DataFrame(all_scores)
maximum = df.mean(axis=1).max()
alpha_best = alphas[df.mean(axis=1).argmax()]
if plot is True:
mu = df.mean(axis=1)
sigma = df.std(axis=1)
pylab.clf()
pylab.errorbar(pylab.log(alphas), mu, yerr=sigma)
pylab.plot(pylab.log(alphas), mu, 'or')
pylab.axvline(pylab.log(alpha_best), lw=4, alpha=0.5, color='g')
pylab.title("Mean scores across alphas")
pylab.xlabel("alpha")
pylab.ylabel("mean score")
return alphas, all_scores, maximum, alpha_best
def bodeplot():
# denominator and numerator
denominator = polymul([1, 0], [1, 16, 100])
numerator = [7.5]
numerator = polymul(numerator, [0.2, 1])
numerator = polymul(numerator, [1, 1])
system = signal.lti(numerator, denominator)
domain = logspace(-3, 3)
omega, magnitude, phase = signal.bode(system, 2*pi*domain)
# break frequencies
break_frequencies = [1, 5, 10]
_, plot = plt.subplots(2, sharex=True)
plot[0].semilogx(omega, magnitude)
plot[0].vlines(break_frequencies, ymin=min(magnitude), ymax=max(magnitude), colors='C1')
plot[0].set_title('Bode Plot')
plot[1].semilogx(omega, phase)
plot[1].vlines(break_frequencies, ymin=min(magnitude), ymax=max(magnitude), colors='C1')
def advectConv(initial, Func_name, width, order):
#parameters for run
#---------------------------------------------------------------------------
a = -1. # min x
b = 1. # max x
steps = [step_lax_fried, step_lax_wendroff]
names = ["Lax_friedrich","Lax_Wendroff"]
L1 = { }
Nx = { }
for step,name in zip(steps,names):
L1[name] = [ ]
Nx[name] = [ ]
print "Running method:", name
for N_points in pylab.logspace(1,order,20):
h = (b-a)/N_points # the step size in x
x, dx = pylab.linspace(a, b, N_points+1, retstep = True)
t_f = 2/A
t_i = 0.
k = abs(.5*h/A)
#actual integration
#----------------------------------------------------------------------------
t = t_i
U = pylab.array([initial(xi,width) for xi in x])
while t<= t_f:
U = step(U,k/dx)
t+= k
U_anal = [initial(xi,width) for xi in x]
L1[name].append(L1_norm(U, [initial(xi,width) for xi in x], dx))
Nx[name].append(int(N_points))
for name in L1:
pylab.loglog(Nx[name], L1[name], '-o', label= name)
pylab.title("Convergence rate for %s" % Func_name)
pylab.xlabel("Number of points")
pylab.ylabel("L1_norm")
pylab.legend()
def calcSpatScaling(data, idx, nRadii=None):
"""
calculates the spatial scaling behavior
computes the number of points in a neighbourhood of radius r for each point
whose index is given in "indices".
data must be an array of NxD - format, N: number of observations,
D: system dimension
returns: [(radius, N), ... ]
to estimate dimension from that:
D ~ lim(radius -> 0){ d log(N) / d log(radius) }
"""
nRadii = data.shape[0] / 100 if nRadii is None else nRadii
#for idx in indices:
sq_dists = sqrt(sum((data - data[idx, :])**2, axis=1))
radii = logspace(-5, log(max(sq_dists)) + .1, nRadii, base=exp(1))
sq_dists.sort()
# this is rather slow; a "manual walk-through would be faster -> implement
# if required
return vstack([(r, len(find(sq_dists < r))) for r in radii])
def zipf_law(df):
# Plot of absolute frequency
from pylab import arange, argsort, loglog, logspace, log10, text
counts = df.total
tokens = df.index
ranks = arange(1, len(counts)+1)
indices = argsort(-counts)
frequencies = counts[indices]
fig, ax = plt.subplots(figsize=(8,6))
ax.set_ylim(1,10**6)
ax.set_xlim(1,10**6)
loglog(ranks, frequencies, marker=".")
ax.plot([1,frequencies[0]],[frequencies[0],1],color='r')
#ax.set_title("Zipf plot for phrases tokens")
ax.set_xlabel("Frequency rank of token")
ax.set_ylabel("Absolute frequency of token")
ax.grid(True)
for n in list(logspace(-0.5, log10(len(counts)-2), 15).astype(int)):
dummy = text(ranks[n], frequencies[n], " " + tokens[indices[n]],
verticalalignment="bottom",
horizontalalignment="left")
ax.figure.savefig(figOutputPath / '2_zipf_law.png', format='png')
print('Exported 2_zipf_law.png')
def calcSpatScaling(data,idx,nRadii = None):
"""
calculates the spatial scaling behavior
computes the number of points in a neighbourhood of radius r for each point
whose index is given in "indices".
data must be an array of NxD - format, N: number of observations,
D: system dimension
returns: [(radius, N), ... ]
to estimate dimension from that:
D ~ lim(radius -> 0){ d log(N) / d log(radius) }
"""
nRadii = data.shape[0]/100 if nRadii is None else nRadii
#for idx in indices:
sq_dists = sqrt(sum((data - data[idx,:])**2,axis=1))
radii = logspace(-5,log(max(sq_dists))+.1,
nRadii,base=exp(1))
sq_dists.sort()
# this is rather slow; a "manual walk-through would be faster -> implement
# if required
return vstack([(r,len(find(sq_dists<r))) for r in radii])
def EllipticSlope():
rho_list = [0.5]
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
markers = ["bo-", "gv-", "r>-"]
for i, rho in enumerate(rho_list):
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(
["material.ell", "problem.rho", "problem.hsize"],
[ell, rho, 1e-3])
te_list.append(te)
tf_list.append(tf)
pl.figure(i)
pl.loglog(Roell_list, tf_list, markers[i], label=r"$\rho=%g$" % (rho))
ind1 = 8
ind2 = 5
coeff = pl.polyfit(pl.log(Roell_list[ind1:-ind2]),
pl.log(tf_list[ind1:-ind2]), 1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind1:-ind2])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind1:-ind2], fit, "k-", linewidth=3.0, alpha=0.2)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_rho_" + str(rho).replace(".", "x") + ".pdf",
bbox_inches='tight')
import pylab
import mpmath as mp
import scipy
from PyPPL import *
from Dispersion_ConstTheta import *
# Plasma Dispersion function
#for theta in [ 0.033, 0.050, 0.133, 0.15, 0.2 ]:
for theta in [ 0.10]:
Setup = { 'eta' : 4., 'kx' : 0.0, 'v_te' : mp.sqrt(2.), 'rho_te2' : 1., 'tau' : 1., 'theta' : 0. , 'm_ie' : 400., 'lambda_D2' : 0.}
ky_list = pylab.logspace(pylab.log10(0.2), pylab.log10(40.), 101)
#ky_list = pylab.linspace(20., 60.,101)
Setup['theta'] = theta
#ky, gamma, err = getGrowth(ky_list, Setup, disp="Gyro", init= 0.02 + 0.045j)
#pylab.semilogx(ky, pylab.imag(gamma), 'b-', label='Gyro')
#pylab.plot(ky, pylab.imag(gamma), 'b-', label='Gyro')
#ky, gamma, err = getGrowth(ky_list, Setup, disp="Gyro1st", init=0.02+0.045j)
#pylab.semilogx(ky[:350], pylab.imag(gamma)[:350], 'g-', label='Gyro $\\mathcal{O}{(1)}$')
#ky, gamma, err = getGrowth(ky_list, Setup, disp="Fluid", init = 0.02 + 0.045j)
#pylab.semilogx(ky, pylab.imag(gamma), 'r-', label='Fluid ')
ky, gamma, err = getGrowth(ky_list, Setup, disp="GyroKin", init=-0.01 + 0.01j)
#ky, gamma, err = getGrowth(ky_list, Setup, disp="GyroKin", init= 0.02 + 0.045j)
pylab.semilogx(ky, pylab.imag(gamma), 'm-', label='Kinetic')
#pylab.plot(ky, pylab.imag(gamma), 'm-', label='Kinetic')
pylab.legend(ncol=3, loc='best').draw_frame(0)
def plot(self,
bins=100,
cmap="hot_r",
fontsize=10,
Nlevels=4,
xlabel=None,
ylabel=None,
norm=None,
range=[],
contour=True,
**kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram
:param fontsize: fontsize for the labels
:param contour: show some contours
:param int Nlevels: must be more than 2
.. plot::
:include-source:
:width: 50%
>>> from msdas import *
>>> r = replicates.ReplicatesYeast(get_yeast_raw_data())
>>> r.drop_na_count(54) # to speed up the plot creation
>>> r.hist2d_mu_versus_cv()
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X,
Y,
bins=bins,
cmap=cmap,
norm=colors.LogNorm())
else:
res = pylab.hist2d(X, Y, bins=bins, cmap=cmap, range=range)
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
levels = [
round(x) for x in pylab.logspace(
0, pylab.log10(res[0].max().max()), Nlevels)
]
pylab.contour(X, Y, res[0].transpose(), levels[2:], color="g")
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel == None:
ylabel = self.df.columns[1]
if xlabel == None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
pylab.grid(True)
return res
def plot_color_vs_mass_hist():
galaxies = mk_galaxy_struc()
# Definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left+width+0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# Add the figures
# Mass vs color plot I-H
f1 = pyl.figure(1,figsize=(8,8))
f1s1 = f1.add_axes(rect_scatter)
f1s2 = f1.add_axes(rect_histx)
f1s3 = f1.add_axes(rect_histy)
# Mass vs color plot J-H
f2 = pyl.figure(2,figsize=(8,8))
f2s1 = f2.add_axes(rect_scatter)
f2s2 = f2.add_axes(rect_histx)
f2s3 = f2.add_axes(rect_histy)
#f2s1 = f2.add_subplot(111)
# Mass vs color plot Z-H
f3 = pyl.figure(3,figsize=(8,8))
f3s1 = f3.add_axes(rect_scatter)
f3s2 = f3.add_axes(rect_histx)
f3s3 = f3.add_axes(rect_histy)
#f3s1 = f3.add_subplot(111)
mass1 = []
color1 = []
mass2 = []
color2 = []
mass3 = []
color3 = []
for i in range(len(galaxies)):
# Color vs Mass Plots
if galaxies[i].ston_I >30.0:
if galaxies[i].Mips >=10.0:
f1s1.plot(galaxies[i].Mass,galaxies[i].Imag-galaxies[i].Hmag,
c='#FFAB19', marker='o', markersize=9)
mass1.append(galaxies[i].Mass) # Get the right mass
color1.append(galaxies[i].Imag-galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_IH > 0.1:
f1s1.plot(galaxies[i].Mass,galaxies[i].Imag-galaxies[i].Hmag,
c='None', marker='s', markersize=10)
else:
f1s1.plot(galaxies[i].Mass,galaxies[i].Imag-galaxies[i].Hmag,
c='#196DFF', marker='*')
elif 20.0 < galaxies[i].ston_I and galaxies[i].ston_I < 30.0 :
f1s1.plot(galaxies[i].Mass,galaxies[i].Imag-galaxies[i].Hmag,
c='0.8', marker='s', alpha=0.4)
else:
f1s1.plot(galaxies[i].Mass,galaxies[i].Imag-galaxies[i].Hmag,
c='0.8', marker='.', alpha=0.4)
if galaxies[i].ston_Z >30.0:
if galaxies[i].Mips >=10.0:
f2s1.plot(galaxies[i].Mass,galaxies[i].Zmag-galaxies[i].Hmag,
c='#FFAB19', marker='o', markersize=9)
mass2.append(galaxies[i].Mass) # Get the right mass
color2.append(galaxies[i].Zmag-galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_ZH > 0.05:
f2s1.plot(galaxies[i].Mass,galaxies[i].Zmag-galaxies[i].Hmag,
c='None', marker='s', markersize=10)
else:
f2s1.plot(galaxies[i].Mass,galaxies[i].Zmag-galaxies[i].Hmag,
c='#196DFF', marker='*')
elif 20.0 < galaxies[i].ston_Z and galaxies[i].ston_Z < 30.0 :
f2s1.plot(galaxies[i].Mass,galaxies[i].Zmag-galaxies[i].Hmag,
c='0.8', marker='s', alpha=0.4)
else:
f2s1.plot(galaxies[i].Mass,galaxies[i].Zmag-galaxies[i].Hmag,
c='0.8', marker='.', alpha=0.4)
if galaxies[i].ston_J >30.0:
if galaxies[i].Mips >=10.0:
f3s1.plot(galaxies[i].Mass,galaxies[i].Jmag-galaxies[i].Hmag,
c='#FFAB19', marker='o', markersize=9)
mass3.append(galaxies[i].Mass) # Get the right mass
color3.append(galaxies[i].Jmag-galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_JH > 0.03:
f3s1.plot(galaxies[i].Mass,galaxies[i].Jmag-galaxies[i].Hmag,
c='None', marker='s', markersize=10)
else:
f3s1.plot(galaxies[i].Mass,galaxies[i].Jmag-galaxies[i].Hmag,
c='#196DFF', marker='*')
elif 20.0 < galaxies[i].ston_J and galaxies[i].ston_J < 30.0 :
f3s1.plot(galaxies[i].Mass,galaxies[i].Jmag-galaxies[i].Hmag,
c='0.8', marker='s', alpha=0.4)
else:
f3s1.plot(galaxies[i].Mass,galaxies[i].Jmag-galaxies[i].Hmag,
c='0.8', marker='.', alpha=0.4)
############
# FIGURE 1 #
############
pyl.figure(1)
f1s1.set_xscale('log')
f1s1.set_xlim(3e7,1e12)
f1s1.set_ylim(0,4.5)
f1s1.set_xlabel(r"$Log_{10}(M_{\odot})$",fontsize=20)
f1s1.set_ylabel("$(I-H)_{Observed}$",fontsize=20)
f1s1.tick_params(axis='both',pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f1s1.get_ylim()[0], f1s1.get_ylim()[1] + 0.25)
f1s2.hist(mass1, bins=binsx)
f1s2.set_xlim( f1s1.get_xlim() )
f1s2.tick_params(labelbottom='off')
f1s2.set_xscale('log')
f1s3.hist(color1, bins=binsy, orientation='horizontal')
f1s3.set_ylim( f1s1.get_ylim() )
f1s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_IH.eps')
############
# FIGURE 2 #
############
pyl.figure(2)
f2s1.set_xscale('log')
f2s1.set_xlim(3e7,1e12)
f2s1.set_xlabel(r"$Log_{10}(M_{\odot})$",fontsize=20)
f2s1.set_ylabel("$(Z-H)_{Observed}$",fontsize=20)
f2s1.tick_params(axis='both',pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f2s1.get_ylim()[0], f2s1.get_ylim()[1] + 0.25)
f2s2.hist(mass2, bins=binsx)
f2s2.set_xlim( f2s1.get_xlim() )
f2s2.tick_params(labelbottom='off')
f2s2.set_xscale('log')
f2s3.hist(color2, bins=binsy, orientation='horizontal')
f2s3.set_ylim( f2s1.get_ylim() )
f2s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_ZH.eps')
############
# FIGURE 3 #
############
pyl.figure(3)
f3s1.set_xscale('log')
f3s1.set_xlim(3e7,1e12)
f3s1.set_ylim(-0.5,2)
f3s1.set_xlabel(r"$Log_{10}(M_{\odot})$",fontsize=20)
f3s1.set_ylabel("$(J-H)_{Observed}$",fontsize=20)
f3s1.tick_params(axis='both',pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f3s1.get_ylim()[0], f3s1.get_ylim()[1] + 0.25)
f3s2.hist(mass3, bins=binsx)
f3s2.set_xlim( f3s1.get_xlim() )
f3s2.tick_params(labelbottom='off')
f3s2.set_xscale('log')
f3s3.hist(color3, bins=binsy, orientation='horizontal')
f3s3.set_ylim( f3s1.get_ylim() )
f3s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_JH.eps')
pyl.show()
#!/usr/bin/env python
import pandas as pd
import pylab as pl
df = pd.DataFrame.from_csv("surnames.csv", header=1)
N = df["count"].sum()
deltas_ = df["count"].values[:-1] - df["count"].values[1:]
deltas = pl.array([deltas_[0]] + list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) + [deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df["count"] / N) ** 0.5
N_a = df["count"][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100)
pl.xlabel("Number of queries")
pl.ylabel("% of surnames revealed")
pl.yticks(range(0, 110, 10))
pl.grid(True)
pl.show()
from pylab import logspace, loglog, log10
from pyhalo import Cosmology
from math import pi,sin
from scipy.integrate import quad
# Parameters
Delta_c = 180
M = 1e13
c = 1
C = Cosmology()
r_h = ( 3*M / (4*pi*2.78e11*C.om_0) / Delta_c ) ** (1./3.)
ks = logspace(-2,2,100)
y = 0 * ks
for i in range(len(ks)):
k = ks[i]
core = lambda r: 4*pi*r**2 / ( r*c/r_h * (1+r*c/r_h)**2 ) * sin(k*r) / (k*r)
x = quad(core,0,10*r_h)
y[i] = x[0] / M
y = y / y[0]
loglog(ks,y)
def plot_invtransformed_tail(f, vt):
from pylab import loglog, show, logspace
X = logspace(1, 50, 1000)
Y = f(vt.var_change(X))
loglog(X, Y)
#File Handling Errors
except:
print(f'Unable to Open/Process fitting.dat')
sys.exit()
def g(t,A = 1.05,B = -0.105):
return A*(sp.jn(2,t)) + B*t
t = data[:,0]
Y = data[:,1:]
true_y = g(t)
####################################################
#Part3 - Plotting the data obtained
sigma = pl.logspace(-1,-3,9) #The Standard Deviations of noise
fig = pl.figure(0)
pl.title('Plot of Data')
pl.plot(t,pl.c_[Y,true_y])
pl.xlabel(r'$t$')
pl.legend(list(sigma) + ['true value'])
pl.grid(True)
#pl.savefig('dataplot.png')
pl.show()
pl.close(fig)
####################################################
#Part4 - Plotting the true value
fig = pl.figure(0)
pl.title('Plot of True value')
import pylab
import mpmath as mp
import scipy
from Dispersion_ConstTheta import *
for m_ie in [ 0. ]:
Setup = { 'eta' : 6., 'kx' : 0.0, 'v_te' : 1., 'rho_te2' : 1., 'tau' : 1., 'theta' : 0.01 , 'm_ie' : m_ie, 'lambda_D2' : 0.}
ky_list = pylab.logspace(-1, 1.3, 101)
disp="Gyro1stKin"
if m_ie == 0. : disp ="Gyro1st"
ky, gamma, res = getGrowth(ky_list, Setup, disp=disp)
pylab.semilogx(ky, pylab.imag(gamma), label='$m_ie=%.3f$' % m_ie)
pylab.legend(ncol=1, loc='best').draw_frame(0)
pylab.ylim((-0.01,0.1))
#pylab.xlim((0.,2.0))
pylab.xlabel("Poloidal Wavenumber $k_y/\\rho_{te}$")
pylab.ylabel("Growthrate $\\gamma_L [\\nu_{te}/L_T]$")
pylab.savefig("Dispersion_Nakata.pdf", bbox_inches='tight')
pylab.savefig("Dispersion_Nakata.png", bbox_inches='tight')
# In[167]:
def perlin_covariance_corr(delta,N=1000000,bound=1):
ts = bound*pl.rand(N)
tds = ts+delta
ps = [p(t) for t in ts]
pds = [p(td) for td in tds]
#cov = pl.mean([pp*pd for pp,pd in zip(ps,pds)])
cov = pl.mean([(pp-pd)**2 for pp,pd in zip(ps,pds)])
corr = pl.mean([pp*pd for pp,pd in zip(ps,pds)])
return cov, corr
# In[157]:
deltas = pl.logspace(-8,1,46)
# In[168]:
cv_stats = [perlin_covariance_corr(d) for d in deltas]
# In[169]:
zip(deltas,cv_stats)
# In[170]:
[(d,cov/d) for (d,(cov,corr)) in zip(deltas,cv_stats)]
def smith(smithR=1,
chart_type='z',
draw_labels=False,
border=False,
ax=None,
ref_imm=1.0,
draw_vswr=None):
'''
plots the smith chart of a given radius
Parameters
-----------
smithR : number
radius of smith chart
chart_type : ['z','y','zy', 'yz']
Contour type. Possible values are
* *'z'* : lines of constant impedance
* *'y'* : lines of constant admittance
* *'zy'* : lines of constant impedance stronger than admittance
* *'yz'* : lines of constant admittance stronger than impedance
draw_labels : Boolean
annotate real and imaginary parts of impedance on the
chart (only if smithR=1)
border : Boolean
draw a rectangular border with axis ticks, around the perimeter
of the figure. Not used if draw_labels = True
ax : matplotlib.axes object
existing axes to draw smith chart on
ref_imm : number
Reference immittance for center of Smith chart. Only changes
labels, if printed.
draw_vswr : list of numbers, Boolean or None
draw VSWR circles. If True, default values are used.
'''
##TODO: fix this function so it doesnt suck
if ax == None:
ax1 = plb.gca()
else:
ax1 = ax
# contour holds matplotlib instances of: pathes.Circle, and lines.Line2D, which
# are the contours on the smith chart
contour = []
# these are hard-coded on purpose,as they should always be present
rHeavyList = [0, 1]
xHeavyList = [1, -1]
#TODO: fix this
# these could be dynamically coded in the future, but work good'nuff for now
if not draw_labels:
rLightList = plb.logspace(3, -5, 9, base=.5)
xLightList = plb.hstack([
plb.logspace(2, -5, 8, base=.5),
-1 * plb.logspace(2, -5, 8, base=.5)
])
else:
rLightList = plb.array([0.2, 0.5, 1.0, 2.0, 5.0])
xLightList = plb.array(
[0.2, 0.5, 1.0, 2.0, 5.0, -0.2, -0.5, -1.0, -2.0, -5.0])
# vswr lines
if isinstance(draw_vswr, (tuple, list)):
vswrVeryLightList = draw_vswr
elif draw_vswr is True:
# use the default I like
vswrVeryLightList = [1.5, 2.0, 3.0, 5.0]
else:
vswrVeryLightList = []
# cheap way to make a ok-looking smith chart at larger than 1 radii
if smithR > 1:
rMax = (1. + smithR) / (1. - smithR)
rLightList = plb.hstack([plb.linspace(0, rMax, 11), rLightList])
if chart_type.startswith('y'):
y_flip_sign = -1
else:
y_flip_sign = 1
# draw impedance and/or admittance
both_charts = chart_type in ('zy', 'yz')
# loops through Verylight, Light and Heavy lists and draws circles using patches
# for analysis of this see R.M. Weikles Microwave II notes (from uva)
superLightColor = dict(ec='whitesmoke', fc='none')
veryLightColor = dict(ec='lightgrey', fc='none')
lightColor = dict(ec='grey', fc='none')
heavyColor = dict(ec='black', fc='none')
# vswr circules verylight
for vswr in vswrVeryLightList:
radius = (vswr - 1.0) / (vswr + 1.0)
contour.append(Circle((0, 0), radius, **veryLightColor))
# impedance/admittance circles
for r in rLightList:
center = (r / (1. + r) * y_flip_sign, 0)
radius = 1. / (1 + r)
if both_charts:
contour.insert(
0, Circle((-center[0], center[1]), radius, **superLightColor))
contour.append(Circle(center, radius, **lightColor))
for x in xLightList:
center = (1 * y_flip_sign, 1. / x)
radius = 1. / x
if both_charts:
contour.insert(
0, Circle((-center[0], center[1]), radius, **superLightColor))
contour.append(Circle(center, radius, **lightColor))
for r in rHeavyList:
center = (r / (1. + r) * y_flip_sign, 0)
radius = 1. / (1 + r)
contour.append(Circle(center, radius, **heavyColor))
for x in xHeavyList:
center = (1 * y_flip_sign, 1. / x)
radius = 1. / x
contour.append(Circle(center, radius, **heavyColor))
# clipping circle
clipc = Circle([0, 0], smithR, ec='k', fc='None', visible=True)
ax1.add_patch(clipc)
#draw x and y axis
ax1.axhline(0, color='k', lw=.1, clip_path=clipc)
ax1.axvline(1 * y_flip_sign, color='k', clip_path=clipc)
ax1.grid(0)
# Set axis limits by plotting white points so zooming works properly
ax1.plot(smithR * npy.array([-1.1, 1.1]),
smithR * npy.array([-1.1, 1.1]),
'w.',
markersize=0)
ax1.axis('image') # Combination of 'equal' and 'tight'
if not border:
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.items():
spine.set_color('none')
if draw_labels:
#Clear axis
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.items():
spine.set_color('none')
# Make annotations only if the radius is 1
if smithR is 1:
#Make room for annotation
ax1.plot(npy.array([-1.25, 1.25]),
npy.array([-1.1, 1.1]),
'w.',
markersize=0)
ax1.axis('image')
#Annotate real part
for value in rLightList:
# Set radius of real part's label; offset slightly left (Z
# chart, y_flip_sign == 1) or right (Y chart, y_flip_sign == -1)
# so label doesn't overlap chart's circles
rho = (value - 1) / (value + 1) - y_flip_sign * 0.01
if y_flip_sign is 1:
halignstyle = "right"
else:
halignstyle = "left"
ax1.annotate(str(value * ref_imm),
xy=(rho * smithR, 0.01),
xytext=(rho * smithR, 0.01),
ha=halignstyle,
va="baseline")
#Annotate imaginary part
radialScaleFactor = 1.01 # Scale radius of label position by this
# factor. Making it >1 places the label
# outside the Smith chart's circle
for value in xLightList:
#Transforms from complex to cartesian
S = (1j * value - 1) / (1j * value + 1)
S *= smithR * radialScaleFactor
rhox = S.real
rhoy = S.imag * y_flip_sign
# Choose alignment anchor point based on label's value
if ((value == 1.0) or (value == -1.0)):
halignstyle = "center"
elif (rhox < 0.0):
halignstyle = "right"
else:
halignstyle = "left"
if (rhoy < 0):
valignstyle = "top"
else:
valignstyle = "bottom"
#Annotate value
ax1.annotate(str(value * ref_imm) + 'j',
xy=(rhox, rhoy),
xytext=(rhox, rhoy),
ha=halignstyle,
va=valignstyle)
#Annotate 0 and inf
ax1.annotate('0.0',
xy=(-1.02, 0),
xytext=(-1.02, 0),
ha="right",
va="center")
ax1.annotate('$\infty$',
xy=(radialScaleFactor, 0),
xytext=(radialScaleFactor, 0),
ha="left",
va="center")
# annotate vswr circles
for vswr in vswrVeryLightList:
rhoy = (vswr - 1.0) / (vswr + 1.0)
ax1.annotate(str(vswr),
xy=(0, rhoy * smithR),
xytext=(0, rhoy * smithR),
ha="center",
va="bottom",
color='grey',
size='smaller')
# loop though contours and draw them on the given axes
for currentContour in contour:
cc = ax1.add_patch(currentContour)
cc.set_clip_path(clipc)
def plot(self, bins=100, cmap="hot_r", fontsize=10, Nlevels=4,
xlabel=None, ylabel=None, norm=None, range=None, normed=False,
colorbar=True, contour=True, grid=True, **kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram (either a float or list
of 2 binning values).
:param cmap: a valid colormap (defaults to hot_r)
:param fontsize: fontsize for the labels
:param int Nlevels: must be more than 2
:param str xlabel: set the xlabel (overwrites content of the dataframe)
:param str ylabel: set the ylabel (overwrites content of the dataframe)
:param norm: set to 'log' to show the log10 of the values.
:param normed: normalise the data
:param range: as in pylab.Hist2D : a 2x2 shape [[-3,3],[-4,4]]
:param contour: show some contours (default to True)
:param bool grid: Show unerlying grid (defaults to True)
If the input is a dataframe, the xlabel and ylabel will be populated
with the column names of the dataframe.
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X, Y, bins=bins, normed=normed,
cmap=cmap, norm=colors.LogNorm())
else:
res = pylab.hist2d(X, Y, bins=bins, cmap=cmap,
normed=normed, range=range)
if colorbar is True:
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
if res[0].max().max() < 10 and norm == 'log':
pylab.contour(X, Y, res[0].transpose(), color="g")
else:
levels = [round(x) for x in
pylab.logspace(0, pylab.log10(res[0].max().max()), Nlevels)]
pylab.contour(X, Y, res[0].transpose(), levels[2:], color="g")
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel is None:
ylabel = self.df.columns[1]
if xlabel is None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
if grid is True:
pylab.grid(True)
return res
def tf_idf(self):
""" Normalize the frequency of stock name in the articles"""
print "Initiating the word frequency test..."
list_of_prepositions = [u'above', u'about', u'across', u'against',u'The', u'is',u'an',
u'along', u'among', u'around', u'at', u'before',
u'behind', u'below', u'beneath', u'beside', u'between',
u'beyond', u'by', u'during', u'except',
u'for', u'from',u'in', u'inside', u'into',u'like',u'near',u'of',
u'off', u'on',u'since',u'to', u'toward', u'through',u'under', u'until', u'up', u'upon',
u'with', u'within', u'the', u'a', u'and', u'for']
try:
#Zipf Distribution to assess threshold
paragraphs = " ".join(self.targeted_paragraphs)
words = paragraphs.split()
frequency = Counter(x for x in words if x not in list_of_prepositions)
counts = array(frequency.values())
tokens = frequency.keys()
ranks = arange(1, len(counts)+1)
indices = argsort(-counts)
frequencies = counts[indices]
loglog(ranks, frequencies, marker=".")
title("Zipf plot for Combined Article Paragraphs")
xlabel("Frequency Rank of Token")
ylabel("Absolute Frequency of Token")
grid(True)
for n in list(logspace(-0.5, log10(len(counts)-1), 20).astype(int)):
dummy = text(ranks[n], frequencies[n], " " + tokens[indices[n]],
verticalalignment="bottom",
horizontalalignment="left")
#plt.plot(np.unique(ranks), np.poly1d(np.polyfit(ranks, frequencies, 10))(np.unique(ranks)))
slope=np.polyfit(ranks, frequencies, 0)
for value in slope:
print value
zipf_threshold = value
## NEED TO ADD- FITTED LINE. Due to an error in the value of threshold its currently not in-use while paragraph extraction.##
except Exception as e:
print "Error occurred during Zipf Distribution: " + str(e)
#Inverse-document Frequency
document_size = self.result_scan_web["article_count"]
document_success = len(self.article_url)
try:
idf = math.log(float(document_size)/float(document_success))
except Exception as e:
print "No Successful Articles in this webpage: " + str(e)
#Term Frequency
list_total_count = []
for paragraph in self.targeted_paragraphs:
words = paragraph.split()
word_frequency = Counter(x for x in words if x not in list_of_prepositions)
stock_frequency = word_frequency[self.stock_name]
list_total_count.append(stock_frequency)
total_count = sum(list_total_count)
print "TOTAL WORD COUNT:" , total_count
print "COUNT BEFORE:"
print "urls = " , len(self.article_url)
list_fail_values = []
for paragraph in self.targeted_paragraphs:
words = paragraph.split()
word_frequency = Counter(x for x in words if x not in list_of_prepositions)
stock_frequency = word_frequency[self.stock_name]
ticker_symbol_frequency = word_frequency[self.stock_id]
print "TERM FREQUENCY: ", stock_frequency
print "DIVISION: " , (float(stock_frequency)/float(total_count))
try:
tf_idf_weight = (float(stock_frequency)/float(total_count)) * float(idf)
except Exception as e:
print "Error ocurred during division. total_count is zero, meaning no successful articles: " + str(e)
print "TF-IDF-WEIGHT = " , tf_idf_weight
if tf_idf_weight <= 0.30: # Gives the articles that lie within the 30th percentile of success.
index = self.targeted_paragraphs.index(paragraph)
self.targeted_paragraphs.remove(paragraph)
url_value = self.article_url[index]
title_value = self.article_title[index]
fail_dict = {'url': url_value , 'title' : title_value}
list_fail_values.append(fail_dict)
print "IRRELEVANT ARTICLE DETECTED..."
print "DELETING " , url_value
del self.article_url[index]
del self.article_title[index]
else:
pass
print "COUNT AFTER: "
print "urls = " , len(self.article_url)
print "FAILED ARTICLES: "
print "# " , len(list_fail_values)
print "articles:" , list_fail_values
#titles_in_fail = {d['title'] for d in list_fail_values if 'title' in d}
#self.json_results[:] = [d for d in self.json_results if d.get('title') not in titles_in_fail]
self.json_results[:] = [i for i in self.json_results if i not in list_fail_values]
self.results_tone_analyzer["successful_articles"] = self.json_results
print " ----------------------------"
print "Number of Successful Articles = " , len(self.article_url)
print "SUCCESSFUL URLs: " ,
for article in self.article_url:
print article
print " ----------------------------"
data = loadtxt('w_spectra.dat')
w = transpose(data[:,1:])
zLevels = range(1,51,1)
dz = 8.0
del data
if __name__=="__main__":
import matplotlib.pyplot as p
with open('wSpectrumPeakWavenumber','w') as wPeakFile:
wPeakFile.write('iz wSpectrumPeakWavenumber'+'\n')
for z in range(size(zLevels)):
wPeakFile.write(str(z+1)+' '+str(w[z,:].argmax())+'\n')
print logspace(-7,3,11)
#Make spectra plots at each level
for z in range(size(zLevels)):
p.figure()
p.loglog(wavenumber,u[z,:],label='U')
p.loglog(wavenumber,v[z,:],label='V')
p.loglog(wavenumber,w[z,:],label='W')
km5by3 = 200.0*wavenumber**(-5.0/3.0)
p.loglog(wavenumber, km5by3,'k--',label='km5by3')
p.loglog(w[z,:].argmax() * ones(11), logspace(-7,3,11),'k')
# p.xlim(0,0.01)
p.title('z = '+str((z+0.5)*dz)+'m')
p.xlabel('k (in 1/m')
p.ylabel('Amplitude')
p.savefig('z'+str(z)+'_Spectra.png')
def _parseNewInput(self,config):
r'''Function reading the input file and setting the
:self.Parameters: acordingly.
'''
self.Parameters['oldInputFormat'] = False
self.Parameters['saveDir'] = config.get('Main','save dir')
self.Parameters['dataDir'] = config.get('Main','data dir')
self.Parameters['flatFieldFile'] = config.get('Main','flat field')
try:
self.Parameters['maskFile'] = config.get('Main','mask')
except:
print 'Mask not set in the config file'
self.Parameters['defaultMaskFile'] = config.get('Main','default mask')
self.Parameters['q1'] = config.getfloat('Main','q1')
self.Parameters['q2'] = config.getfloat('Main','q2')
self.Parameters['qs'] = config.getfloat('Main','qs')
self.Parameters['dq'] = config.getfloat('Main','dq')
self.Parameters['outPrefix'] = config.get('Main','sample name')
self.Parameters['wavelength'] = config.getfloat('Main','wavelength')
self.Parameters['cenx'] = config.getfloat('Main','cenx')
self.Parameters['ceny'] = config.getfloat('Main','ceny')
self.Parameters['pixSize'] = config.getfloat('Main','pix')
self.Parameters['sdDist'] = config.getfloat('Main','sddist')
if config.has_option('Main','mode'):
self.Parameters['mode'] = config.get('Main','mode')
else:
self.Parameters['mode'] = 'XSVS' # Assuming default XSVS data mode
if self.Parameters['mode'] == 'XSVS':
secList = config.sections()
secList.remove('Main')
exposureList = numpy.sort(secList)
self.Parameters['exposureList'] = exposureList
for i in xrange(len(exposureList)):
exposure = exposureList[i]
currExpParams = {}
currExpParams['dataSuf'] = config.get(exposure,'data suffix')
currExpParams['dataPref'] = config.get(exposure,'data prefix')
currExpParams['n1'] = config.getint(exposure,'first data file')
currExpParams['n2'] = config.getint(exposure,'last data file')
currExpParams['expTime'] = config.getfloat(exposure,'exp time')
self.Parameters['exposureParams'][exposure] = currExpParams
elif self.Parameters['mode'] == 'XPCS':
expParams = {}
self.Parameters['exposureList'] = ['Exp_bins']
expParams['dataSuf'] = config.get('Exp_bins','data suffix')
expParams['dataPref'] = config.get('Exp_bins','data prefix')
expParams['n1'] = config.getint('Exp_bins','first data file')
expParams['n2'] = config.getint('Exp_bins','last data file')
expParams['expTime'] = config.getfloat('Exp_bins','exp time')
expParams['binStart'] = config.getfloat('Exp_bins','bin start')
expParams['binStop'] = config.getfloat('Exp_bins','bin stop')
expParams['binStep'] = config.getint('Exp_bins','bin step')
self.Parameters['exposureParams']['Exp_bins'] = expParams
# Generate different exposures by binning frames
#fileBins = range(expParams['binStart'],
# expParams['binStop'],expParams['binStep'])
fileBins = [int(x) for x in pylab.logspace(expParams['binStart'],
expParams['binStop'],
expParams['binStep'])]
exposureList = range(len(fileBins))
for ii in xrange(len(fileBins)):
exposureLabel = 'Exp_%d' % ii
exposureList[ii] = exposureLabel
currExpParams = {}
currExpParams['dataSuf'] = expParams['dataSuf']
currExpParams['dataPref'] = expParams['dataPref']
currExpParams['n1'] = expParams['n1']
currExpParams['n2'] = expParams['n2']
currExpParams['expTime'] = expParams['expTime'] * fileBins[ii]
currExpParams['img_to_bin'] = fileBins[ii]
self.Parameters['exposureParams'][exposureLabel] = currExpParams
self.Parameters['exposureList'] = exposureList
def smith(smithR=1, chart_type = 'z',ax=None):
'''
plots the smith chart of a given radius
Parameters
-----------
smithR : number
radius of smith chart
chart_type : ['z','y']
Contour type. Possible values are
* *'z'* : lines of constant impedance
* *'y'* : lines of constant admittance
ax : matplotlib.axes object
existing axes to draw smith chart on
'''
##TODO: fix this function so it doesnt suck
if ax == None:
ax1 = plb.gca()
else:
ax1 = ax
# contour holds matplotlib instances of: pathes.Circle, and lines.Line2D, which
# are the contours on the smith chart
contour = []
# these are hard-coded on purpose,as they should always be present
rHeavyList = [0,1]
xHeavyList = [1,-1]
#TODO: fix this
# these could be dynamically coded in the future, but work good'nuff for now
rLightList = plb.logspace(3,-5,9,base=.5)
xLightList = plb.hstack([plb.logspace(2,-5,8,base=.5), -1*plb.logspace(2,-5,8,base=.5)])
# cheap way to make a ok-looking smith chart at larger than 1 radii
if smithR > 1:
rMax = (1.+smithR)/(1.-smithR)
rLightList = plb.hstack([ plb.linspace(0,rMax,11) , rLightList ])
if chart_type is 'y':
y_flip_sign = -1
else:
y_flip_sign = 1
# loops through Light and Heavy lists and draws circles using patches
# for analysis of this see R.M. Weikles Microwave II notes (from uva)
for r in rLightList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
contour.append( Circle( center, radius, ec='grey',fc = 'none'))
for x in xLightList:
center = (1*y_flip_sign,1./x)
radius = 1./x
contour.append( Circle( center, radius, ec='grey',fc = 'none'))
for r in rHeavyList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
contour.append( Circle( center, radius, ec= 'black', fc = 'none'))
for x in xHeavyList:
center = (1*y_flip_sign,1./x)
radius = 1./x
contour.append( Circle( center, radius, ec='black',fc = 'none'))
# clipping circle
clipc = Circle( [0,0], smithR, ec='k',fc='None',visible=True)
ax1.add_patch( clipc)
#draw x and y axis
ax1.axhline(0, color='k', lw=.1, clip_path=clipc)
ax1.axvline(1*y_flip_sign, color='k', clip_path=clipc)
ax1.grid(0)
#set axis limits
ax1.axis('equal')
ax1.axis(smithR*npy.array([-1., 1., -1., 1.]))
# loop though contours and draw them on the given axes
for currentContour in contour:
cc=ax1.add_patch(currentContour)
cc.set_clip_path(clipc)
# script to generate data files for the least squares assignment
from pylab import linspace, meshgrid, logspace, dot, plot, xlabel, ylabel, ones, randn, diag, title, grid, savetxt, show, c_
import scipy.special as sp
N = 101 # no of data points
k = 9
# no of sets of data with varying noise
# generate the data points and add noise
t = linspace(0, 10, N)
# t vector
y = 1.05 * sp.jv(2, t) - 0.105 * t # f(t) vector
Y = meshgrid(y, ones(k), indexing="ij")[0] # make k copies
scl = logspace(-1, -3, k) # noise stdev
n = dot(randn(N, k), diag(scl)) # generate k vectors
yy = Y + n
# add noise to signal
# shadow plot
plot(t, yy)
xlabel("t", size=20)
ylabel("f(t)+n", size=20)
title("Plot of the data to be fitted")
grid(True)
savetxt("fitting.dat", c_[t, yy]) # write out matrix to file
show()
pylab.plot(Vo[0],Vi,label=r'$V_{in}$')
display(1,Vo[0],Vo[1],r't$\rightarrow$',r'$V\rightarrow$')
def highpass(R1,R3,C1,C2,G,Vi):
''' High pass filter '''
s=symbols('s')
A=Matrix([[0,0,1,-1/G],[-1/(1+1/(s*R3*C2)),1,0,0],[0,-G,G,1],[-s*C1-s*C2-1/R1,s*C2,0,1/R1]])
b=Matrix([0,0,0,-Vi*s*C1])
V = A.inv()*b
return (A,b,V)
# Magnitude response of the high pass filter
A,b,V = highpass(10000,10000,1e-9,1e-9,1.586,1)
Vo = V[3]
H = sympy_to_lti(Vo)
w=pylab.logspace(0,8,801)
ss=1j*w
hf=lambdify(s,Vo,'numpy')
v=hf(ss)
pylab.loglog(w,abs(v),lw=2)
pylab.xlabel(r'$w\rightarrow$')
pylab.ylabel(r'$|H(jw)|\rightarrow$')
pylab.grid(True)
pylab.show()
# response for damped sinusoids
t = np.linspace(0,10,1000)
Vi = np.multiply(np.multiply(np.exp(-0.5*t),np.sin(2*np.pi*t)),np.heaviside(t,0.5))
Vo = sp.lsim(H,Vi,T=t)
pylab.figure(2)
pylab.plot(Vo[0],Vi,label=r'$V_{in}$')
return sp.lti(nm, dm)
#--------------- impulse response
s = symbols("s")
A, b, V = lowpass(1e4, 1e4, 1e-9, 1e-9, 1.586, 1000, 1)
Vo = V[3].evalf()
H1 = get_lti(Vo)
x, t = sp.impulse(H1, None, npm.linspace(0, 10**-3, 10000))
p.plot(x, t)
p.title("time domain impulse response")
p.xlabel("time")
p.ylabel("h(t)")
p.grid(True)
#p.show()
w = p.logspace(0, 8, 801)
ss = 1j * w
hf = lambdify(s, Vo, 'numpy')
v = hf(ss)
p.loglog(w, abs(v), lw=2)
p.xlabel("frequency")
p.ylabel("H(jw)")
p.grid(True)
p.title("impulse response in frequency domain")
#p.show()
#------------------step response
A, b, V = lowpass(1e4, 1e4, 1e-9, 1e-9, 1.586, 1000, 1 / s)
Vo = V[3]
pprint(Vo)
X1 = get_lti(Vo)
t, x = sp.impulse(X1, None, npm.linspace(0, 10**-3, 10000))
def Circular():
fig = pl.figure()
ax = fig.add_subplot(111)
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_xlim([9e-2, 1e2])
# Elastic limit
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
ax.plot([Roell_list[0], Roell_list[-1]], [1 / 3, 1 / 3],
"k",
linewidth=3.0,
alpha=0.2)
# Numerical results
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[ell, 1, 1e-3])
te_list.append(te)
tf_list.append(tf)
ax.plot(Roell_list, tf_list, "bo-") # label="Num."
ind = int(len(Roell_list) / 2.5)
coeff = pl.polyfit(pl.log(Roell_list[ind:-ind]), pl.log(tf_list[ind:-ind]),
1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind:-ind])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind:-ind], fit, "k-", linewidth=3.0, alpha=0.2)
# Experimental results
E = 3e3
sigc = 72
Gc = 290e-3
ell = 3 / 8 * Gc * E / sigc**2
print ell
ell = 26e-3
data = pl.loadtxt("literature/exp.csv", delimiter=",")
Roell_exp = data[:, 0] / (2 * ell)
Roell_exp[0] = Roell_list[0]
sig_center = (pl.amin(data[:, 1:], 1) + pl.amax(data[:, 1:], 1)) / 2
err1 = -(pl.amin(data[:, 1:], 1) - sig_center) / sigc
err2 = (pl.amax(data[:, 1:], 1) - sig_center) / sigc
pl.errorbar(Roell_exp,
sig_center / sigc,
yerr=[err1, err2],
label="Exp.",
fmt="g.")
# Numerical results from C. Kuhn
# data = loadtxt("literature/Kuhn.csv", delimiter=",")
# # ell = 0.00885
# Roell_Kuhn = data[:, 0]/ell
# ax.plot(Roell_Kuhn, data[:, 1], "r>-", label="Kuhn")
pl.ylim([1.0 / 4.0, 1.1])
pl.xlabel("Relative hole size $R/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.savefig("circular_tf.pdf", bbox_inches='tight')
import pylab as pyl
import os
import sys
curdir = os.getcwd()
data_dict = {} #dictionary of files
n_range_LU = pyl.logspace(1, 3, num=3)
n_range_tridiag = pyl.logspace(1, 4, num=4)
for n in n_range_tridiag:
#loop through different n's
with open(curdir + "/data/dderiv_u_c++_n%d_tridiag.dat" % (int(n)),
'r') as infile:
full_file = infile.read() #read entire file into text
lines = full_file.split('\n') #separate by EOL-characters
lines = lines[:-1] #remove last line (empty line)
keys = lines.pop(0).split(', ') #use top line as keys for dictionary
dict_of_content = {}
for i, key in zip(range(len(keys)),
keys): #loop over keys, create approp. arrays
dict_of_content[key] = [] #empty list
for j in range(len(
lines)): #loop though the remaining lines of the data-set
line = lines[j].split(', ') #split the line into string-lists
word = line[
i] # append the correct value to the correct list with the correct key
try:
word = float(word) #check if value can be float
except ValueError: #word cannot be turned to number
print word
sys.exit(
def plot(self, bins=100, cmap="hot_r", fontsize=10, Nlevels=4,
xlabel=None, ylabel=None, norm=None, range=None,
contour=True, **kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram
:param fontsize: fontsize for the labels
:param contour: show some contours
:param int Nlevels: must be more than 2
:param range: as in pylab.hist2d : a 2x2 shape [[-3,3],[-4,4]]
.. plot::
:include-source:
:width: 50%
>>> from msdas import *
>>> r = replicates.ReplicatesYeast(get_yeast_raw_data())
>>> r.drop_na_count(54) # to speed up the plot creation
>>> r.hist2d_mu_versus_cv()
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X, Y, bins=bins,
cmap=cmap, norm=colors.LogNorm())
else:
res = pylab.hist2d(X, Y, bins=bins, cmap=cmap, range=range)
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
levels = [round(x) for x in pylab.logspace(0, pylab.log10(res[0].max().max()),Nlevels)]
pylab.contour(X, Y, res[0].transpose(), levels[2:], color="g")
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel == None:
ylabel = self.df.columns[1]
if xlabel == None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
pylab.grid(True)
return res
def plot_invtransformed_tail(f, vt):
from pylab import loglog, show, logspace
X = logspace(1, 50, 1000)
Y = f(vt.var_change(X))
loglog(X, Y)
def lowpass(R1, R2, C1, C2, G, Vi):
A = Matrix([[0, 0, 1, -1 / G], [(-1) / (1 + s * R2 * C2), 1, 0, 0],
[0, -G, G, 1],
[((-1) / (R1)) - (1 / R2) - s * C1, 1 / R2, 0, s * C1]])
b = Matrix([0, 0, 0, Vi / R1])
V = A.inv() * b
return (A, b, V)
#Obtaining output voltage, H(jw) graph for the low pass filter and plotting
A, b, V = lowpass(10000, 10000, 1e-9, 1e-9, 1.586, 1)
Vo = V[3]
w = py.logspace(0, 8, 801)
ss = 1j * w
hf = lambdify(s, Vo, 'numpy')
v = hf(ss)
plt.figure(0)
py.loglog(w, abs(v), lw=2)
py.title(
'Bode plot of the step response of the output voltage \n for the low pass filter using matrices'
)
py.xlabel('frequency (omega)')
py.ylabel('H')
py.grid(True)
py.show()
#Getting impulse response of the low pass filter
V = A.inv() * b
return (A, b, V)
def highpass(R1, R3, C1, C2, G, Vi):
A = Matrix([[1, -G, 0, 0], [-1, -G, G, 0],
[0, 0, s * C2 + (1 / R3), -C2 * s],
[-1 / R1, 0, -C2 * s, s * (C1 + C2) + 1 / R1]])
b = Matrix([0, 0, 0, s * C1 * Vi])
V = A.inv() * b
return (A, b, V)
A, b, V = lowpass(10000, 10000, 1e-11, 1e-11, 1.586, 1)
Vo = V[3]
ww = p.logspace(0, 8, 801)
ss = 1j * ww
hf = lambdify(s, Vo, 'numpy')
v = hf(ss)
# Transfer Function Of Lowpass Filter
p.figure(0)
p.title("Transfer Function Of Lowpass Filter")
p.loglog(ww, abs(v), lw=2)
# Step Response of Lowpass Filter
p.figure(1)
p.title("Step Response of Lowpass Filter")
Y = sympy_to_lti(Vo)
t1 = p.linspace(0, 0.05, 1000)
t, x = sp.step(Y, None, t1)
p.plot(x, t)
# Output of Lowpass Filter
# Script To Generate Data Files For The Least Squares Assignment
from pylab import c_, diag, dot, grid, linspace, logspace, meshgrid, ones, plot, randn, savetxt, show, title, xlabel, ylabel
import scipy.special as sp
N = 101 # Number Of Data Points
k = 9 # Number Of Sets Of Data With Varying Noise
# Generating The Data Points And Adding Noise
t = linspace(0, 10, N) # Vector t
f = 1.05 * sp.jv(2, t) - 0.105 * t # Vector f(t)
func = meshgrid(f, ones(k), indexing='ij')[0] # Generating Signal
scl = logspace(-1, -3, k) # Noise
noise = dot(randn(N, k), diag(scl)) # Generating k Vectors
y = func + noise # Adding Noise To The Signal
# Shadow Plot
plot(t, y) # Ploting Time And Signal With Noise
xlabel(r'$t$', size=20) # X-axis -> Time
ylabel(r'$f(t)+n$', size=20) # Y-axis -> Signal With Noise
title(r'Plot Of The Data To Be Fitted') # Title
grid(True) # Show Grid
# Write Out The Matrix Into A File (fitting.dat)
savetxt("fitting.dat", c_[t, y])
show()
#!/usr/bin/env python
import pandas as pd
import pylab as pl
df = pd.read_csv("surnames.csv", header=1)
N = df["count"].sum()
deltas_ = df['count'].values[:-1] - df['count'].values[1:]
deltas = pl.array([deltas_[0]] +
list(pl.amin([deltas_[1:], deltas_[:-1]], axis=0)) +
[deltas_[-1]])
dfrac = (deltas + 1e-20) / N
def frac_leaked(q):
attacked = dfrac * q > (2 * q * df['count'] / N)**0.5
N_a = df['count'][attacked].sum()
return float(N_a) / N
Ns = pl.logspace(0, 15, 100)
fracs = pl.array(map(frac_leaked, Ns))
pl.semilogx(Ns, fracs * 100)
pl.xlabel("Number of queries")
pl.ylabel("% of surnames revealed")
pl.yticks(range(0, 110, 10))
pl.grid(True)
pl.show()
def plot_fit(directory, file_, title_, units, f, par, out, fig, residuals,
xlimp, XYfun, Xscale, Yscale, Xlab, Ylab, X, Y, dX, dY):
"""
Parameters
----------
Returns
-------
"""
gs = gridspec.GridSpec(4, 1)
gne = figure(fig + "_1")
if (fig == file_):
clf()
if residuals == True:
ax1 = gne.add_subplot(gs[:-1, :])
setp(ax1.get_xticklabels(), visible=False)
#subplot(211)
title(title_)
if Xscale == "log":
xscale("log")
if Yscale == "log":
yscale("log")
errorbar(X, Y, dY, dX, fmt=",", ecolor="black", capsize=0.5)
if residuals == False:
xlabel(Xlab)
ylabel(Ylab)
xlima = array(xlimp) / 100
if out == True:
X_ol, Y_ol, dX_ol, dY_ol, smin, smax = _outlier_(
directory, file_, units, X, XYfun)
else:
smin = min(X)
smax = max(X)
if Xscale == "log":
l = logspace(log10(smin) * xlima[0], log10(smax * xlima[1]), 1000)
else:
l = linspace(smin * xlima[0], smax * xlima[1], 1000)
grid(b=True)
plot(l, f(l, *par), "red")
if out == True:
outlier = errorbar(X_ol,
Y_ol,
dY_ol,
dX_ol,
fmt="g^",
ecolor="black",
capsize=0.5)
legend([outlier], ['outlier'], loc="best")
if residuals == True:
# if out==True: # these commented lines are useless
# cause the flag "out" is already passed to "_residuals" as an argument
_residuals(fig, gne, gs, ax1, f, par, out, X, dX, Xlab, Xscale, Y, dY,
X_ol, Y_ol, dY_ol)
# _residuals(fig, gne, gs, ax1, f, par, out, X, dX, Xlab, Xscale, Y, dY)
savefig(directory + "grafici/fit_" + fig + ".pdf")
savefig(directory + "grafici/fit_" + fig + ".png")
def Elliptic():
rho_list = pl.logspace(-1, 0, 2)
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
markers = ["bo-", "gv-", "r>-"]
# Elastic limit
pl.plot([Roell_list[0], Roell_list[-1]], [1 / 3, 1 / 3],
"k",
linewidth=3.0,
alpha=0.2)
for i, rho in enumerate(rho_list):
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(
["material.ell", "problem.rho", "problem.hsize"],
[ell, rho, 1e-3])
te_list.append(te)
tf_list.append(tf)
pl.savetxt("Rolist%s.txt" % i, Roell_list)
pl.savetxt("tf_list%s.txt" % i, tf_list)
pl.savetxt("te_list%s.txt" % i, te_list)
pl.figure(1)
pl.loglog(Roell_list, tf_list, markers[i], label=r"$\rho=%g$" % (rho))
pl.savefig("elliptic_tf_%s.pdf" % i)
pl.figure(2)
pl.loglog(Roell_list, te_list, markers[i], label=r"$\rho=%g$" % (rho))
pl.savefig("elliptic_te_%s.pdf" % i)
if near(rho, 0.1):
pl.figure(1)
ind = 12
coeff = pl.polyfit(pl.log(Roell_list[ind:]), pl.log(tf_list[ind:]),
1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind:])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind:], fit, "k-", linewidth=3.0, alpha=0.2)
pl.figure(1)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_tf.pdf")
pl.figure(2)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at loss of elasticity")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_te.pdf")
pl.figure(3)
rho_list = pl.linspace(0.1, 1, 10)
te_list = []
for i, rho in enumerate(rho_list):
te, tf = find_data(["material.ell", "problem.rho", "problem.hsize"],
[1.0, rho, 1e-3])
te_list.append(te)
pl.plot(rho_list, te_list, "o", label="Num.")
pl.savetxt("rho_list.txt", rho_list)
pl.savetxt("rho_te_list.txt", te_list)
rho_list = pl.linspace(0.1, 1, 1000)
pl.plot(rho_list,
rho_list / (rho_list + 2),
"k",
label="Theo.",
linewidth=3.0,
alpha=0.2)
pl.xlabel(r"Ellipticity $\rho$")
pl.ylabel("$\sigma/\sigma_0$ at loss of elasticity")
pl.legend(loc="best")
pl.grid(True)
pl.savefig("elliptic_te_rho.pdf", bbox_inches='tight')
print("b = {0} +/- {1}".format(
pyl.mean(samples[:, 1]), pyl.std(samples[:, 1])))
print("s = {0} +/- {1}".format(
pyl.mean(samples[:, 2]), pyl.std(samples[:, 2])))
xl = pyl.linspace(0.8, 2.4)
# plot crediable region
m, b = samples.T[:2]
yfit = m[:, None] * xl + b[:, None] # This creates individual points
mu = yfit.mean(0)
sig = yfit.std(0) # 2sigma confidence
pyl.fill_between(xl, mu - sig, mu + sig, color='lightgray', zorder=0)
pyl.plot(xl, mu, '-k', label='Fit', zorder=0)
# add the Rykoff2012 relation
x = pyl.logspace(0.8, 2.4)
lny = 1.48 + 1.06 * pyl.log(x / 60.)
y = pyl.exp(lny) * 1e14 / 0.7
ax.plot(
pyl.log10(x),
pyl.log10(y),
ls=':',
c='k',
zorder=0,
label='Rykoff+2012')
# add the Farahi2016 relation
lny = 0.44 + 1.31 * pyl.log(x / 30.)
y = pyl.exp(lny) * 1e14
ax.plot(
pyl.log10(x),
def tune_alpha(self,
drug_name,
alphas=None,
N=80,
l1_ratio=0.5,
kfolds=10,
show=True,
shuffle=False,
alpha_range=[-2.8, 0.1],
randomize_Y=False):
"""Interactive tuning of the model (alpha).
This is much faster than :meth:`plot_cindex` but much slower than
:meth:`runCV`.
.. plot::
:include-source:
from gdsctools import *
ic = IC50(gdsctools_data("IC50_v5.csv.gz"))
gf = GenomicFeatures(gdsctools_data("genomic_features_v5.csv.gz"))
en = GDSCElasticNet(ic, gf)
en.tune_alpha(1047, N=40, l1_ratio=0.1)
"""
if alphas is None:
# logspace returns a vector in natural space that guarantees a
# uniform spacing in a log space (log10 or ln)
# -2.8 to 0.5 means alpha from 1.58e-3 to 3.16
# This is equivalent to log(1.58e-3)=-6.45 to log(3.16)=1.15 in ln
# scale
a1, a2 = alpha_range
alphas = pylab.logspace(a1, a2, N)
# Let us now do a CV across difference alphas
all_scores = []
for alpha in alphas:
scores = self.fit(drug_name,
alpha,
l1_ratio=l1_ratio,
kfolds=kfolds,
shuffle=shuffle,
randomize_Y=randomize_Y)
all_scores.append(scores)
# We can now plot the results that is the mean scores + error enveloppe
df = pd.DataFrame(all_scores)
# we also identify the max correlation and corresponding alpha
maximum = df.mean(axis=1).max()
alpha_best = alphas[df.mean(axis=1).argmax()]
if show is True:
mu = df.mean(axis=1)
sigma = df.var(axis=1)
pylab.clf()
pylab.errorbar(pylab.log(alphas), mu, yerr=sigma, color="gray")
pylab.plot(pylab.log(alphas), mu, 'or')
pylab.axvline(pylab.log(alpha_best), lw=4, alpha=0.5, color='g')
pylab.title("Mean scores (pearson) across alphas for Kfold=%s" %
kfolds)
pylab.xlabel("ln(alpha)")
pylab.ylabel("mean score (pearson)")
pylab.grid()
results = {
"alpha_best": alpha_best,
"ln_alpha": pylab.log(alpha_best),
"maximum_Rp": maximum
}
return results
def smith(smithR=1, chart_type = 'z', draw_labels = False, border=False,
ax=None, ref_imm = 1.0, draw_vswr=None):
'''
plots the smith chart of a given radius
Parameters
-----------
smithR : number
radius of smith chart
chart_type : ['z','y','zy', 'yz']
Contour type. Possible values are
* *'z'* : lines of constant impedance
* *'y'* : lines of constant admittance
* *'zy'* : lines of constant impedance stronger than admittance
* *'yz'* : lines of constant admittance stronger than impedance
draw_labels : Boolean
annotate real and imaginary parts of impedance on the
chart (only if smithR=1)
border : Boolean
draw a rectangular border with axis ticks, around the perimeter
of the figure. Not used if draw_labels = True
ax : matplotlib.axes object
existing axes to draw smith chart on
ref_imm : number
Reference immittance for center of Smith chart. Only changes
labels, if printed.
draw_vswr : list of numbers, Boolean or None
draw VSWR circles. If True, default values are used.
'''
##TODO: fix this function so it doesnt suck
if ax == None:
ax1 = plb.gca()
else:
ax1 = ax
# contour holds matplotlib instances of: pathes.Circle, and lines.Line2D, which
# are the contours on the smith chart
contour = []
# these are hard-coded on purpose,as they should always be present
rHeavyList = [0,1]
xHeavyList = [1,-1]
#TODO: fix this
# these could be dynamically coded in the future, but work good'nuff for now
if not draw_labels:
rLightList = plb.logspace(3,-5,9,base=.5)
xLightList = plb.hstack([plb.logspace(2,-5,8,base=.5), -1*plb.logspace(2,-5,8,base=.5)])
else:
rLightList = plb.array( [ 0.2, 0.5, 1.0, 2.0, 5.0 ] )
xLightList = plb.array( [ 0.2, 0.5, 1.0, 2.0 , 5.0, -0.2, -0.5, -1.0, -2.0, -5.0 ] )
# vswr lines
if isinstance(draw_vswr, (tuple,list)):
vswrVeryLightList = draw_vswr
elif draw_vswr is True:
# use the default I like
vswrVeryLightList = [1.5, 2.0, 3.0, 5.0]
else:
vswrVeryLightList = []
# cheap way to make a ok-looking smith chart at larger than 1 radii
if smithR > 1:
rMax = (1.+smithR)/(1.-smithR)
rLightList = plb.hstack([ plb.linspace(0,rMax,11) , rLightList ])
if chart_type.startswith('y'):
y_flip_sign = -1
else:
y_flip_sign = 1
# draw impedance and/or admittance
both_charts = chart_type in ('zy', 'yz')
# loops through Verylight, Light and Heavy lists and draws circles using patches
# for analysis of this see R.M. Weikles Microwave II notes (from uva)
superLightColor = dict(ec='whitesmoke', fc='none')
veryLightColor = dict(ec='lightgrey', fc='none')
lightColor = dict(ec='grey', fc='none')
heavyColor = dict(ec='black', fc='none')
# vswr circules verylight
for vswr in vswrVeryLightList:
radius = (vswr-1.0) / (vswr+1.0)
contour.append( Circle((0, 0), radius, **veryLightColor))
# impedance/admittance circles
for r in rLightList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
if both_charts:
contour.insert(0, Circle((-center[0], center[1]), radius, **superLightColor))
contour.append(Circle(center, radius, **lightColor))
for x in xLightList:
center = (1*y_flip_sign,1./x)
radius = 1./x
if both_charts:
contour.insert(0, Circle( (-center[0], center[1]), radius, **superLightColor))
contour.append(Circle(center, radius, **lightColor))
for r in rHeavyList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
contour.append(Circle(center, radius, **heavyColor))
for x in xHeavyList:
center = (1*y_flip_sign,1./x)
radius = 1./x
contour.append(Circle(center, radius, **heavyColor))
# clipping circle
clipc = Circle( [0,0], smithR, ec='k',fc='None',visible=True)
ax1.add_patch( clipc)
#draw x and y axis
ax1.axhline(0, color='k', lw=.1, clip_path=clipc)
ax1.axvline(1*y_flip_sign, color='k', clip_path=clipc)
ax1.grid(0)
# Set axis limits by plotting white points so zooming works properly
ax1.plot(smithR*npy.array([-1.1, 1.1]), smithR*npy.array([-1.1, 1.1]), 'w.', markersize = 0)
ax1.axis('image') # Combination of 'equal' and 'tight'
if not border:
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.items():
spine.set_color('none')
if draw_labels:
#Clear axis
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.items():
spine.set_color('none')
# Make annotations only if the radius is 1
if smithR is 1:
#Make room for annotation
ax1.plot(npy.array([-1.25, 1.25]), npy.array([-1.1, 1.1]), 'w.', markersize = 0)
ax1.axis('image')
#Annotate real part
for value in rLightList:
# Set radius of real part's label; offset slightly left (Z
# chart, y_flip_sign == 1) or right (Y chart, y_flip_sign == -1)
# so label doesn't overlap chart's circles
rho = (value - 1)/(value + 1) - y_flip_sign*0.01
if y_flip_sign is 1:
halignstyle = "right"
else:
halignstyle = "left"
ax1.annotate(str(value*ref_imm), xy=(rho*smithR, 0.01),
xytext=(rho*smithR, 0.01), ha = halignstyle, va = "baseline")
#Annotate imaginary part
radialScaleFactor = 1.01 # Scale radius of label position by this
# factor. Making it >1 places the label
# outside the Smith chart's circle
for value in xLightList:
#Transforms from complex to cartesian
S = (1j*value - 1) / (1j*value + 1)
S *= smithR * radialScaleFactor
rhox = S.real
rhoy = S.imag * y_flip_sign
# Choose alignment anchor point based on label's value
if ((value == 1.0) or (value == -1.0)):
halignstyle = "center"
elif (rhox < 0.0):
halignstyle = "right"
else:
halignstyle = "left"
if (rhoy < 0):
valignstyle = "top"
else:
valignstyle = "bottom"
#Annotate value
ax1.annotate(str(value*ref_imm) + 'j', xy=(rhox, rhoy),
xytext=(rhox, rhoy), ha = halignstyle, va = valignstyle)
#Annotate 0 and inf
ax1.annotate('0.0', xy=(-1.02, 0), xytext=(-1.02, 0),
ha = "right", va = "center")
ax1.annotate('$\infty$', xy=(radialScaleFactor, 0), xytext=(radialScaleFactor, 0),
ha = "left", va = "center")
# annotate vswr circles
for vswr in vswrVeryLightList:
rhoy = (vswr-1.0) / (vswr+1.0)
ax1.annotate(str(vswr), xy=(0, rhoy*smithR),
xytext=(0, rhoy*smithR), ha="center", va="bottom",
color='grey', size='smaller')
# loop though contours and draw them on the given axes
for currentContour in contour:
cc=ax1.add_patch(currentContour)
cc.set_clip_path(clipc)
def plot(self,
bins=100,
cmap="hot_r",
fontsize=10,
Nlevels=4,
xlabel=None,
ylabel=None,
norm=None,
range=None,
normed=False,
colorbar=True,
contour=True,
grid=True,
**kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram (either a float or list
of 2 binning values).
:param cmap: a valid colormap (defaults to hot_r)
:param fontsize: fontsize for the labels
:param int Nlevels: must be more than 2
:param str xlabel: set the xlabel (overwrites content of the dataframe)
:param str ylabel: set the ylabel (overwrites content of the dataframe)
:param norm: set to 'log' to show the log10 of the values.
:param normed: normalise the data
:param range: as in pylab.Hist2D : a 2x2 shape [[-3,3],[-4,4]]
:param contour: show some contours (default to True)
:param bool grid: Show unerlying grid (defaults to True)
If the input is a dataframe, the xlabel and ylabel will be populated
with the column names of the dataframe.
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X,
Y,
bins=bins,
density=normed,
cmap=cmap,
norm=colors.LogNorm())
else:
res = pylab.hist2d(X,
Y,
bins=bins,
cmap=cmap,
density=normed,
range=range)
if colorbar is True:
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
if res[0].max().max() < 10 and norm == 'log':
pylab.contour(X, Y, res[0].transpose())
else:
levels = [
round(x) for x in pylab.logspace(
0, pylab.log10(res[0].max().max()), Nlevels)
]
pylab.contour(X, Y, res[0].transpose(), levels[2:])
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel is None:
ylabel = self.df.columns[1]
if xlabel is None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
if grid is True:
pylab.grid(True)
return res
def plot_color_vs_mass_hist():
galaxies = mk_galaxy_struc()
# Definitions for the axes
left, width = 0.1, 0.65
bottom, height = 0.1, 0.65
bottom_h = left_h = left + width + 0.02
rect_scatter = [left, bottom, width, height]
rect_histx = [left, bottom_h, width, 0.2]
rect_histy = [left_h, bottom, 0.2, height]
# Add the figures
# Mass vs color plot I-H
f1 = pyl.figure(1, figsize=(8, 8))
f1s1 = f1.add_axes(rect_scatter)
f1s2 = f1.add_axes(rect_histx)
f1s3 = f1.add_axes(rect_histy)
# Mass vs color plot J-H
f2 = pyl.figure(2, figsize=(8, 8))
f2s1 = f2.add_axes(rect_scatter)
f2s2 = f2.add_axes(rect_histx)
f2s3 = f2.add_axes(rect_histy)
#f2s1 = f2.add_subplot(111)
# Mass vs color plot Z-H
f3 = pyl.figure(3, figsize=(8, 8))
f3s1 = f3.add_axes(rect_scatter)
f3s2 = f3.add_axes(rect_histx)
f3s3 = f3.add_axes(rect_histy)
#f3s1 = f3.add_subplot(111)
mass1 = []
color1 = []
mass2 = []
color2 = []
mass3 = []
color3 = []
for i in range(len(galaxies)):
# Color vs Mass Plots
if galaxies[i].ston_I > 30.0:
if galaxies[i].Mips >= 10.0:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass1.append(galaxies[i].Mass) # Get the right mass
color1.append(galaxies[i].Imag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_IH > 0.1:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_I and galaxies[i].ston_I < 30.0:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f1s1.plot(galaxies[i].Mass,
galaxies[i].Imag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
if galaxies[i].ston_Z > 30.0:
if galaxies[i].Mips >= 10.0:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass2.append(galaxies[i].Mass) # Get the right mass
color2.append(galaxies[i].Zmag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_ZH > 0.05:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_Z and galaxies[i].ston_Z < 30.0:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f2s1.plot(galaxies[i].Mass,
galaxies[i].Zmag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
if galaxies[i].ston_J > 30.0:
if galaxies[i].Mips >= 10.0:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='#FFAB19',
marker='o',
markersize=9)
mass3.append(galaxies[i].Mass) # Get the right mass
color3.append(galaxies[i].Jmag -
galaxies[i].Hmag) # Get the color
if galaxies[i].ICD_JH > 0.03:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='None',
marker='s',
markersize=10)
else:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='#196DFF',
marker='*')
elif 20.0 < galaxies[i].ston_J and galaxies[i].ston_J < 30.0:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='0.8',
marker='s',
alpha=0.4)
else:
f3s1.plot(galaxies[i].Mass,
galaxies[i].Jmag - galaxies[i].Hmag,
c='0.8',
marker='.',
alpha=0.4)
############
# FIGURE 1 #
############
pyl.figure(1)
f1s1.set_xscale('log')
f1s1.set_xlim(3e7, 1e12)
f1s1.set_ylim(0, 4.5)
f1s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f1s1.set_ylabel("$(I-H)_{Observed}$", fontsize=20)
f1s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f1s1.get_ylim()[0], f1s1.get_ylim()[1] + 0.25)
f1s2.hist(mass1, bins=binsx)
f1s2.set_xlim(f1s1.get_xlim())
f1s2.tick_params(labelbottom='off')
f1s2.set_xscale('log')
f1s3.hist(color1, bins=binsy, orientation='horizontal')
f1s3.set_ylim(f1s1.get_ylim())
f1s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_IH.eps')
############
# FIGURE 2 #
############
pyl.figure(2)
f2s1.set_xscale('log')
f2s1.set_xlim(3e7, 1e12)
f2s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f2s1.set_ylabel("$(Z-H)_{Observed}$", fontsize=20)
f2s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f2s1.get_ylim()[0], f2s1.get_ylim()[1] + 0.25)
f2s2.hist(mass2, bins=binsx)
f2s2.set_xlim(f2s1.get_xlim())
f2s2.tick_params(labelbottom='off')
f2s2.set_xscale('log')
f2s3.hist(color2, bins=binsy, orientation='horizontal')
f2s3.set_ylim(f2s1.get_ylim())
f2s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_ZH.eps')
############
# FIGURE 3 #
############
pyl.figure(3)
f3s1.set_xscale('log')
f3s1.set_xlim(3e7, 1e12)
f3s1.set_ylim(-0.5, 2)
f3s1.set_xlabel(r"$Log_{10}(M_{\odot})$", fontsize=20)
f3s1.set_ylabel("$(J-H)_{Observed}$", fontsize=20)
f3s1.tick_params(axis='both', pad=7)
binsx = pyl.logspace(7, 12)
binsy = pyl.linspace(f3s1.get_ylim()[0], f3s1.get_ylim()[1] + 0.25)
f3s2.hist(mass3, bins=binsx)
f3s2.set_xlim(f3s1.get_xlim())
f3s2.tick_params(labelbottom='off')
f3s2.set_xscale('log')
f3s3.hist(color3, bins=binsy, orientation='horizontal')
f3s3.set_ylim(f3s1.get_ylim())
f3s3.tick_params(labelleft='off')
pyl.savefig('color_vs_mass_hist_JH.eps')
pyl.show()
def smith(smithR=1, chart_type = 'z', draw_labels = False, border=False,
ax=None):
'''
plots the smith chart of a given radius
Parameters
-----------
smithR : number
radius of smith chart
chart_type : ['z','y']
Contour type. Possible values are
* *'z'* : lines of constant impedance
* *'y'* : lines of constant admittance
draw_labels : Boolean
annotate real and imaginary parts of impedance on the
chart (only if smithR=1)
border : Boolean
draw a rectangular border with axis ticks, around the perimeter
of the figure. Not used if draw_labels = True
ax : matplotlib.axes object
existing axes to draw smith chart on
'''
##TODO: fix this function so it doesnt suck
if ax == None:
ax1 = plb.gca()
else:
ax1 = ax
# contour holds matplotlib instances of: pathes.Circle, and lines.Line2D, which
# are the contours on the smith chart
contour = []
# these are hard-coded on purpose,as they should always be present
rHeavyList = [0,1]
xHeavyList = [1,-1]
#TODO: fix this
# these could be dynamically coded in the future, but work good'nuff for now
if not draw_labels:
rLightList = plb.logspace(3,-5,9,base=.5)
xLightList = plb.hstack([plb.logspace(2,-5,8,base=.5), -1*plb.logspace(2,-5,8,base=.5)])
else:
rLightList = plb.array( [ 0.2, 0.5, 1.0, 2.0, 5.0 ] )
xLightList = plb.array( [ 0.2, 0.5, 1.0, 2.0 , 5.0, -0.2, -0.5, -1.0, -2.0, -5.0 ] )
# cheap way to make a ok-looking smith chart at larger than 1 radii
if smithR > 1:
rMax = (1.+smithR)/(1.-smithR)
rLightList = plb.hstack([ plb.linspace(0,rMax,11) , rLightList ])
if chart_type is 'y':
y_flip_sign = -1
else:
y_flip_sign = 1
# loops through Light and Heavy lists and draws circles using patches
# for analysis of this see R.M. Weikles Microwave II notes (from uva)
for r in rLightList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
contour.append( Circle( center, radius, ec='grey',fc = 'none'))
for x in xLightList:
center = (1*y_flip_sign,1./x)
radius = 1./x
contour.append( Circle( center, radius, ec='grey',fc = 'none'))
for r in rHeavyList:
center = (r/(1.+r)*y_flip_sign,0 )
radius = 1./(1+r)
contour.append( Circle( center, radius, ec= 'black', fc = 'none'))
for x in xHeavyList:
center = (1*y_flip_sign,1./x)
radius = 1./x
contour.append( Circle( center, radius, ec='black',fc = 'none'))
# clipping circle
clipc = Circle( [0,0], smithR, ec='k',fc='None',visible=True)
ax1.add_patch( clipc)
#draw x and y axis
ax1.axhline(0, color='k', lw=.1, clip_path=clipc)
ax1.axvline(1*y_flip_sign, color='k', clip_path=clipc)
ax1.grid(0)
#set axis limits
ax1.axis('equal')
ax1.axis(smithR*npy.array([-1.1, 1.1, -1.1, 1.1]))
if not border:
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.iteritems():
spine.set_color('none')
if draw_labels:
#Clear axis
ax1.yaxis.set_ticks([])
ax1.xaxis.set_ticks([])
for loc, spine in ax1.spines.iteritems():
spine.set_color('none')
#Will make annotations only if the radius is 1 and it is the impedance smith chart
if smithR is 1 and y_flip_sign is 1:
#Make room for annotation
ax1.axis(smithR*npy.array([-1., 1., -1.2, 1.2]))
#Annotate real part
for value in rLightList:
rho = (value - 1)/(value + 1)
ax1.annotate(str(value), xy=((rho-0.12)*smithR, 0.01*smithR), \
xytext=((rho-0.12)*smithR, 0.01*smithR))
#Annotate imaginary part
deltax = plb.array([-0.17, -0.14, -0.06, 0., 0.02, -0.2, -0.2, -0.08, 0., 0.03])
deltay = plb.array([0., 0.03, 0.01, 0.02, 0., -0.02, -0.06, -0.09, -0.08, -0.05])
for value, dx, dy in zip(xLightList, deltax, deltay):
#Transforms from complex to cartesian and adds a delta to x and y values
rhox = (-value**2 + 1)/(-value**2 - 1) * smithR * y_flip_sign + dx
rhoy = (-2*value)/(-value**2 - 1) * smithR + dy
#Annotate value
ax1.annotate(str(value) + 'j', xy=(rhox, rhoy), xytext=(rhox, rhoy))
#Annotate 0 and inf
ax1.annotate('0.0', xy=(-1.15, -0.02), xytext=(-1.15, -0.02))
ax1.annotate('$\infty$', xy=(1.02, -0.02), xytext=(1.02, -0.02))
# loop though contours and draw them on the given axes
for currentContour in contour:
cc=ax1.add_patch(currentContour)
cc.set_clip_path(clipc)
print " proton ky : " , ky , " " , complex(Disp(omega,kp,b,eta, 1.) )
print " electron ky : " , ky , " " , complex(mp.sqrt(m_ie)*Disp(omega,kp,b,eta,m_ie) )
"""
#print " electron ky : " , ky , " " , complex(Disp(omega,kp * mp.sqrt(m_ie), b/m_ie, eta/mp.sqrt(m_ie) ) + 1. - Gamma0(b/m_ie))
return (pylab.array(ky_list), pylab.array(results), pylab.array(residuum))
#for theta in [ 0.033, 0.050, 0.133, 0.15, 0.2 ]:
for theta in [ 0.10]:
Setup = { 'eta' : 4., 'kx' : 0.0, 'v_te' : mp.sqrt(2.), 'rho_te2' : 1., 'tau' : 1., 'theta' : 0. , 'm_ie' : 100., 'lambda_D2' : 0.}
ky_list = pylab.logspace(pylab.log10(0.2), pylab.log10(10.), 51)
#ky_list = pylab.linspace(20., 60.,101)
Setup['theta'] = theta
ky, gamma, err = getGrowth(ky_list, Setup, disp="Gyro", init= 0.02 + 0.045j)
pylab.semilogx(ky, pylab.imag(gamma), 'b-', label='Gyro')
#pylab.plot(ky, pylab.imag(gamma), 'b-', label='Gyro')
#ky, gamma, err = getGrowth(ky_list, Setup, disp="Gyro1st", init=0.02+0.045j)
#pylab.semilogx(ky[:350], pylab.imag(gamma)[:350], 'g-', label='Gyro $\\mathcal{O}{(1)}$')
#ky, gamma, err = getGrowth(ky_list, Setup, disp="Fluid", init = 0.02 + 0.045j)
#pylab.semilogx(ky, pylab.imag(gamma), 'r-', label='Fluid ')
#ky, gamma, err = getGrowth(ky_list, Setup, disp="GyroKin", init=-0.01 + 0.01j)
#ky, gamma, err = getGrowth(ky_list, Setup, disp="GyroKin", init= 0.02 + 0.045j)
