def plot_spectrograms(bsl,rec,rate,title):
plt.close()
fig, ax = plt.subplots(nrows=9, ncols=2, sharex='col', sharey='row')
plt.subplots_adjust(wspace = .05,hspace = 0.4 )
ny_nfft=1024
i=0
while i<9:
Pxx, freq, bins, im = ax[i,0].specgram(bsl[i],NFFT=ny_nfft,Fs=rate)
ax[i,0].set_ylim([0, 40])
if(i==8):
ax[i,0].set_xlabel("Time, seconds")
ax[i,0].set_ylabel("Freq, Hz")
ax[i,0].set_title(title+' baseline sleep, REM stage, Channel:'+str(i+1))
i=i+1
i=0
while i<9:
Pxx, freq, bins, im = ax[i,1].specgram(rec[i],NFFT=ny_nfft,Fs=rate)
#ax[i,1].ylim(0,40)
ax[i,1].set_ylim([0, 40])
#ax[i,1].set_xlim([0, 10000]) #13000])
if(i==8):
ax[i,1].set_xlabel("Time, seconds")
#ax[i,1].set_ylabel("Freq, Hz")
ax[i,1].set_title(title+' recovery sleep, REM stage, Channel:'+str(i+1))
i=i+1
plt.show()
return
def plot_setup_post(figure_number = None, show = True, save_file = None, legend = True, legend_location = 0): """ Handles post-figure setup, including legends, file saving (save_file is desired filename), showing the figure, and clearing it. """ if figure_number: pyp.figure(figure_number) pyp.subplots_adjust(bottom=.5) # adjustment to give more xlabel space # change limits # pyp.xlim( xmin = 0, xmax = 10000 ) # pyp.ylim( ymin = 0, ymax = 10000 ) # pyp.ylim( (0,10000) ) # equivalent to the line above if legend: pyp.legend(loc = legend_location) if save_file: pyp.savefig(save_file) if show: pyp.show() else: pyp.clf() # clears figure if not plotted
def paired_boxplot_o(boxes):
"""
Wrapper around plt.boxplot to draw paired boxplots
for a set of boxes.
Input is the same as plt.boxplot:
Array or a sequence of vectors.
"""
fig = plt.figure(figsize=(len(boxes) / 2.5, 4))
ax1 = fig.add_subplot(111)
plt.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
bp = ax1.boxplot(boxes, notch=0, positions=np.arange(len(boxes)) +
1.5 * (np.arange(len(boxes)) / 2), patch_artist=True)
[p.set_color(colors[0]) for p in bp['boxes'][::2]]
[p.set_color('black') for p in bp['whiskers']]
[p.set_color('black') for p in bp['fliers']]
[p.set_alpha(.4) for p in bp['fliers']]
[p.set_alpha(.6) for p in bp['boxes']]
[p.set_edgecolor('black') for p in bp['boxes']]
ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',
alpha=0.5)
# Hide these grid behind plot objects
ax1.set_axisbelow(True)
ax1.set_ylabel('$Log_{2}$ RNA Expression')
ax1.set_xticks(3.5 * np.arange(len(boxes) / 2) + .5)
return ax1, bp
def display_collision3D(collision):
jets,muons,electrons,photons,met = collision
lines = draw_beams()
pmom = np.array(jets).transpose()[1:4].transpose()
origin = np.zeros((len(jets),3))
lines += draw_jet3D(origin=origin,pmom=pmom)
pmom = np.array(muons).transpose()[1:4].transpose()
origin = np.zeros((len(muons),3))
lines += draw_muon3D(origin=origin,pmom=pmom)
pmom = np.array(electrons).transpose()[1:4].transpose()
origin = np.zeros((len(electrons),3))
lines += draw_electron3D(origin=origin,pmom=pmom)
pmom = np.array(photons).transpose()[1:4].transpose()
origin = np.zeros((len(photons),3))
lines += draw_photon3D(origin=origin,pmom=pmom)
fig = plt.figure(figsize=(6,4),dpi=100)
ax = fig.add_subplot(1,1,1)
ax = fig.gca(projection='3d')
plt.subplots_adjust(top=0.98,bottom=0.02,right=0.98,left=0.02)
for l in lines:
ax.add_line(l)
ax.set_xlim(-200,200)
ax.set_ylim(-200,200)
ax.set_zlim(-200,200)
def plot_scatter(self, iclus):
observed = rfn.read_data()
fig = plt.figure(2)
plt.clf()
trueM = observed[iclus*self.nlos, 1]
mass = observed[iclus*self.nlos:(iclus+1)*self.nlos, 2:]
mass, masstype = self.get_valid_array_altogether(mass, trueM)
ii = 0
for iobs in xrange(self.nobs):
for jobs in xrange(iobs+1, self.nobs, 1):
ax = fig.add_subplot(4, 3, ii+1)
ax.plot(mass[:, iobs], mass[:, jobs], 'bo', ms=2.5)
plt.subplots_adjust(**self.adjustparam)
ax.xaxis.set_major_locator(plt_ticker.MaxNLocator(3))
ax.yaxis.set_major_locator(plt_ticker.MaxNLocator(3))
#ax.set_xlabel("%s/<%s>" % (self.obsname[iobs], self.obsname[iobs]))
#ax.set_ylabel("%s/<%s>" % (self.obsname[jobs], self.obsname[jobs]))
fontsize=9
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
ii += 1
print self.obsname[iobs], self.obsname[jobs]
plt.savefig(os.path.join("paper", "figure", "scatter_%s_clus%d.eps" % (masstype, iclus)), orientation='portrait', transparent=True)
def run(self):
lines = open(self.inFilename).readlines()
data = []
for line in lines:
data.append(float(line.strip()))
x = np.asarray(data)
fig = plt.figure(figsize=(7, 3))
ax = fig.add_subplot(111)
plt.subplots_adjust(left = 0.15, bottom = 0.15, wspace = 0)
plt.xlabel(self.options.xlab)
plt.ylabel(self.options.ylab)
if self.options.logy == True:
ax.set_yscale('log')
plt.title(self.options.title)
if self.options.plotType == 'hist':
plt.xlim(0,x.max())
n,bins,patches = plt.hist(x, self.options.bins, histtype='bar',
color=['crimson'],normed=False, alpha=0.85)
else:
plt.xlim(0,x.size)
line, = plt.plot(range(x.size), x, 'r-', label = self.options.label)
if self.options.label:
ax.legend()
plt.savefig(self.outFilename)
def draw(self, description=None, ofile="test.png"):
plt.clf()
for f in self.funcs:
f()
plt.axis("off")
ax = plt.gca()
ax.set_aspect("equal", "datalim")
f = plt.gcf()
f.set_size_inches(12.8, 7.2)
if description is not None:
plt.text(0.025, 0.05, description, transform=f.transFigure)
if self.xlim is not None:
plt.xlim(*self.xlim)
if self.ylim is not None:
plt.ylim(*self.ylim)
plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
# dpi = 100 for 720p, 150 for 1080p
plt.savefig(ofile, dpi=150)
def plot_spectrograms(data, rate, subject, condition):
"""
Creates spectrogram subplots for all 9 channels
"""
fig = plt.figure()
# common title
fname = 'Spectrogram - '+'Subject #'+subject+' '+condition+' Dataset'
fig.suptitle(fname, fontsize=14, fontweight='bold')
# common ylabel
fig.text(0.06, 0.5, 'ylabel',
ha='center', va='center', rotation='vertical',
fontsize=14, fontweight='bold')
# use this to stack EEG, EOG, EMG on top of each other
sub_order = [1,4,7,10,2,5,3,6,9]
for ch in range(0, len(data)):
plt.subplot(4, 3, sub_order[ch])
plt.subplots_adjust(hspace=.6) # adds space between subplots
plt.title(channel_name[ch])
Pxx, freqs, bins, im = plt.specgram(data[ch],NFFT=512,Fs=rate)
plt.ylim(0,70)
plt.xlabel('Time (Seconds)')
plt.ylabel('Frequency (Hz)')
#fig.savefig(fname+'.pdf', format='pdf') buggy resolution problem
return
def plot_data(tag):
data_array = tag.references[0]
voltage = np.zeros(data_array.data.shape)
data_array.data.read_direct(voltage)
x_axis = data_array.dimensions[0]
time = x_axis.axis(data_array.data_extent[0])
spike_times = tag.positions[:]
feature_data_array = tag.features[0].data
snippets = tag.features[0].data[:]
single_snippet = tag.retrieve_feature_data(3, 0)[:]
snippet_time_dim = feature_data_array.dimensions[1]
snippet_time = snippet_time_dim.axis(feature_data_array.data_extent[1])
response_axis = plt.subplot2grid((2, 2), (0, 0), rowspan=1, colspan=2)
single_snippet_axis = plt.subplot2grid((2, 2), (1, 0), rowspan=1, colspan=1)
average_snippet_axis = plt.subplot2grid((2, 2), (1, 1), rowspan=1, colspan=1)
response_axis.plot(time, voltage, color="dodgerblue", label=data_array.name)
response_axis.scatter(spike_times, np.ones(spike_times.shape) * np.max(voltage), color="red", label=tag.name)
response_axis.set_xlabel(x_axis.label + ((" [" + x_axis.unit + "]") if x_axis.unit else ""))
response_axis.set_ylabel(data_array.label + ((" [" + data_array.unit + "]") if data_array.unit else ""))
response_axis.set_title(data_array.name)
response_axis.set_xlim(0, np.max(time))
response_axis.set_ylim((1.2 * np.min(voltage), 1.2 * np.max(voltage)))
response_axis.legend()
single_snippet_axis.plot(snippet_time, single_snippet.T, color="red", label=("snippet No 4"))
single_snippet_axis.set_xlabel(
snippet_time_dim.label + ((" [" + snippet_time_dim.unit + "]") if snippet_time_dim.unit else "")
)
single_snippet_axis.set_ylabel(
feature_data_array.label + ((" [" + feature_data_array.unit + "]") if feature_data_array.unit else "")
)
single_snippet_axis.set_title("single stimulus snippet")
single_snippet_axis.set_xlim(np.min(snippet_time), np.max(snippet_time))
single_snippet_axis.set_ylim((1.2 * np.min(snippets[3, :]), 1.2 * np.max(snippets[3, :])))
single_snippet_axis.legend()
mean_snippet = np.mean(snippets, axis=0)
std_snippet = np.std(snippets, axis=0)
average_snippet_axis.fill_between(
snippet_time, mean_snippet + std_snippet, mean_snippet - std_snippet, color="red", alpha=0.5
)
average_snippet_axis.plot(snippet_time, mean_snippet, color="red", label=(feature_data_array.name + str(4)))
average_snippet_axis.set_xlabel(
snippet_time_dim.label + ((" [" + snippet_time_dim.unit + "]") if snippet_time_dim.unit else "")
)
average_snippet_axis.set_ylabel(
feature_data_array.label + ((" [" + feature_data_array.unit + "]") if feature_data_array.unit else "")
)
average_snippet_axis.set_title("spike-triggered average")
average_snippet_axis.set_xlim(np.min(snippet_time), np.max(snippet_time))
average_snippet_axis.set_ylim((1.2 * np.min(mean_snippet - std_snippet), 1.2 * np.max(mean_snippet + std_snippet)))
plt.subplots_adjust(left=0.15, top=0.875, bottom=0.1, right=0.98, hspace=0.35, wspace=0.25)
plt.show()
def plot_transition(df1, df2):
"""
plot stage transitions
df1: normal sleep (df1 = analyse(base))
df2: sleep depravation (df2 = analyse(depr))
"""
N = 5
ind = np.arange(N) # the x locations for the groups
width = 0.2 # the width of the bars
plt.close()
fig, ax = plt.subplots(nrows=6, ncols=6, sharex='col', sharey='row')
plt.subplots_adjust(wspace = 0.2,hspace = 0.4 )
for i in range(0,6): # do not care about stage transitions > 5
for j in range(0,6):
clef = 't' + str(i) + '-' + str(j)
normal = df1[clef].tolist()
mean = sum(normal) / len(normal)
normal.extend([mean])
rects1 = ax[i,j].bar(ind, normal, width, color='r')
depravation = df2[clef].tolist()
mean = sum(depravation) / len(depravation)
depravation.extend([mean])
rects2 = ax[i,j].bar(ind+width, depravation, width, color='y')
ax[i,j].set_title('t' + str(i) + '-' + str(j))
ax[i,j].set_xticks(ind+width)
ax[i,j].set_xticklabels( ('1', '2', '3', '4', 'Avg') )
def segmentation(self, threshold):
img = self.spectrogram["data"]
mask = (img > threshold).astype(np.float)
hist, bin_edges = np.histogram(img, bins=60)
bin_centers = 0.5*(bin_edges[:-1] + bin_edges[1:])
binary_img = mask > 0.5
plt.figure(figsize=(11,8))
plt.subplot(131)
plt.imshow(img)
plt.axis('off')
plt.subplot(132)
plt.plot(bin_centers, hist, lw=2)
print(threshold)
plt.axvline(threshold, color='r', ls='--', lw=2)
plt.text(0.57, 0.8, 'histogram', fontsize=20, transform = plt.gca().transAxes)
plt.text(0.45, 0.75, 'threshold = '+ str(threshold)[0:5], fontsize=15, transform = plt.gca().transAxes)
plt.yticks([])
plt.subplot(133)
plt.imshow(binary_img)
plt.axis('off')
plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)
plt.show()
print(img.max())
print(binary_img.max())
return mask
def SVD_plot(SVStreams, SValues, stachans, title=False):
r"""Function to plot the singular vectors from the clustering routines, one\
plot for each stachan
:type SVStreams: list of :class:Obspy.Stream
:param SVStreams: See clustering.SVD_2_Stream - will assume these are\
ordered by power, e.g. first singular vector in the first stream
:type SValues: list of float
:param SValues: List of the singular values corresponding to the SVStreams
:type stachans: list
:param stachans: List of station.channel
"""
for stachan in stachans:
print(stachan)
plot_traces = [SVStream.select(station=stachan.split('.')[0],
channel=stachan.split('.')[1])[0]
for SVStream in SVStreams]
fig, axes = plt.subplots(len(plot_traces), 1, sharex=True)
axes = axes.ravel()
for i, tr in enumerate(plot_traces):
y = tr.data
x = np.linspace(0, len(y) * tr.stats.delta, len(y))
axes[i].plot(x, y, 'k', linewidth=1.1)
ylab = 'SV '+str(i+1)+'='+str(round(SValues[i] / len(SValues), 2))
axes[i].set_ylabel(ylab, rotation=0)
axes[i].yaxis.set_ticks([])
print(i)
axes[-1].set_xlabel('Time (s)')
plt.subplots_adjust(hspace=0)
if title:
axes[0].set_title(title)
else:
axes[0].set_title(stachan)
plt.show()
return
def plot_transition_ratio(df1, df2):
"""
plot stage transitions
df1: normal sleep (df1 = analyse(base))
df2: sleep depravation (df2 = analyse(depr))
"""
N = 5
ind = np.arange(N) # the x locations for the groups
width = 0.2 # he width of the bars
plt.close()
plt.rc('font', family='Arial')
fig, ax = plt.subplots(nrows=6, ncols=6, sharex='col', sharey='row')
fig.suptitle("Comparison of the number of stage transitions (% of total transitions) (origin stage " + u'\u2192' + " dest. stage)", fontsize=20)
plt.subplots_adjust(wspace = 0.2,hspace = 0.4 )
for i in range(0,6): # do not care about stage transitions > 5
for j in range(0,6):
clef = '%t' + str(i) + '-' + str(j)
normal = df1[clef].tolist()
mean = sum(normal) / len(normal)
normal.extend([mean])
rects1 = ax[i,j].bar(ind, normal, width, color='b')
depravation = df2[clef].tolist()
mean = sum(depravation) / len(depravation)
depravation.extend([mean])
rects2 = ax[i,j].bar(ind+width, depravation, width, color='r')
for label in (ax[i,j].get_xticklabels() + ax[i,j].get_yticklabels()):
label.set_fontname('Arial')
label.set_fontsize(8)
ax[i,j].set_title(str(i) + ' ' + u'\u2192' + ' ' + str(j))
ax[i,j].set_xticks(ind+width)
ax[i,j].set_xticklabels( ('1', '2', '3', '4', 'Avg') )
ax[i,j].set_yticks(np.arange(0, 6, 2))
ax[i,j].set_ylim([0,6])
fig.legend( (rects1[0], rects2[0]), ('Baseline', 'Recovery'), loc = 'lower right', fontsize=10)
def doit():
# test it out
L = 1
R = 10
theta = np.radians(np.arange(0,361))
# draw a circle
plt.plot(R*np.cos(theta), R*np.sin(theta), c="b")
# draw some people
angles = [30, 60, 90, 120, 180, 270, 300]
for l in angles:
center = ( (R + 0.5*L)*np.cos(np.radians(l)),
(R + 0.5*L)*np.sin(np.radians(l)) )
draw_person(center, L, np.radians(l - 90), color="r")
L = 1.1*L
plt.axis("off")
ax = plt.gca()
ax.set_aspect("equal", "datalim")
plt.subplots_adjust(left=0.05, right=0.98, bottom=0.05, top=0.98)
plt.axis([-1.2*R, 1.2*R, -1.2*R, 1.2*R])
f = plt.gcf()
f.set_size_inches(6.0, 6.0)
plt.savefig("test.png")
def plot_time_domain_waveform(fig, waveform, imag=False, mag=False,
xlim=None, xlabel=r'$tc^3/GM$',
ylabel_pol=r'$h_+ + i h_\times$',
ylabel_amp=r'$A$', ylabel_phase=r'$\Phi$',
pol_legend=True, wave_legend=False):
"""Plot the amplitude, phase, and polarizations of a waveform.
"""
# Polarization plot
axes = fig.add_subplot(311)
t = waveform.time
hcomp = waveform.get_complex()
label = r'$h_+$' if pol_legend else ''
line_list = axes.plot(t, hcomp.real, ls='-', label=label)
color = line_list[0].get_color()
if imag:
label = r'$h_\times$' if pol_legend else ''
axes.plot(t, hcomp.imag, ls='--', c=color, label=label)
if mag:
label = r'$|h_+ + ih_\times|$' if pol_legend else ''
axes.plot(waveform.time, waveform.amp, ls=':', c=color, label=label)
if xlim is not None: axes.set_xlim(xlim)
axes.set_ylabel(ylabel_pol, fontsize=16)
axes.set_xticklabels(axes.get_xticks(), fontsize=14)
axes.set_yticklabels(axes.get_yticks(), fontsize=14)
axes.minorticks_on()
axes.tick_params(which='major', width=2, length=8)
axes.tick_params(which='minor', width=2, length=4)
axes.xaxis.set_major_formatter(NullFormatter()) # get rid of x-axis numbers
axes.legend(fontsize=14, loc='best', ncol=3)
# Amplitude plot
axes = fig.add_subplot(312)
axes.plot(waveform.time, waveform.amp, c=color)
if xlim is not None: axes.set_xlim(xlim)
axes.set_ylabel(ylabel_amp, fontsize=16)
axes.set_xticklabels(axes.get_xticks(), fontsize=14)
axes.set_yticklabels(axes.get_yticks(), fontsize=14)
axes.minorticks_on()
axes.tick_params(which='major', width=2, length=8)
axes.tick_params(which='minor', width=2, length=4)
axes.xaxis.set_major_formatter(NullFormatter()) # get rid of x-axis numbers
# Phase plot
axes = fig.add_subplot(313)
label = wave_legend if wave_legend is not False else ''
axes.plot(waveform.time, waveform.phase, c=color, label=label)
if xlim is not None: axes.set_xlim(xlim)
axes.set_xlabel(xlabel, fontsize=16)
axes.set_ylabel(ylabel_phase, fontsize=16)
axes.set_xticklabels(axes.get_xticks(), fontsize=14)
axes.set_yticklabels(axes.get_yticks(), fontsize=14)
axes.minorticks_on()
axes.tick_params(which='major', width=2, length=8)
axes.tick_params(which='minor', width=2, length=4)
axes.legend(fontsize=14, loc='best', ncol=2)
subplots_adjust(hspace=0.07)
def plot_fits(direction_rates,fit_curve,title):
"""
This function takes the x-values and the y-values in units of spikes/s
(found in the two columns of direction_rates and fit_curve) and plots the
actual values with circles, and the curves as lines in both linear and
polar plots.
"""
#print direction_rates
# print fit_curve
plt.subplots_adjust(hspace = 0.6)
y_max = np.max(direction_rates[:,1]) + 5
plt.subplot(2,2,3)
plt.axis([0,360,0,y_max])
plt.plot(direction_rates[:,0], direction_rates[:,1],'o')
plt.plot(fit_curve[:,0],fit_curve[:,1], '-')
plt.xlabel("Direction of Motion (degrees)")
plt.ylabel("Firing Rate (spikes/s)")
plt.title(title)
plt.subplot(2,2,4, polar = True)
spikecounts = direction_rates[:,1]
spikecounts2 = np.append(spikecounts, direction_rates[0,1])
r = np.arange(0, 361, 45)*np.pi/180
plt.polar(r, spikecounts2,'o')
plt.polar(fit_curve[:,0]*np.pi/180,fit_curve[:,1],'-', label="Firing Rate (spikes/s)")
plt.title(title)
plt.legend(loc=8)
def plot_ratio_cormats():
nprops = 19
cormat_yn = N.loadtxt("cormat_to_do_pca.dat")
cormat_jc = N.loadtxt("corrcoeff_jc.dat", usecols=[2])
cormat_jc = cormat_jc.reshape(nprops, nprops)
rat = cormat_yn/cormat_jc
fig = plt.figure(1)
plt.clf()
plt.subplots_adjust(hspace=0.3, wspace=0.3)
fontsize=8
for ip in xrange(nprops):
ax = fig.add_subplot(4, 5, ip+1)
ax.plot(N.arange(nprops), rat[ip, :], 'ro', ms=2.5)
ax.xaxis.set_major_locator(plt_ticker.MaxNLocator(4))
ax.yaxis.set_major_locator(plt_ticker.MaxNLocator(4))
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(fontsize)
ii = N.where(N.abs(rat[ip, :]-1.) > 0.01)[0]
print "-"*10
print "{:d}: {:}".format(ip, ii)
print cormat_yn[ip, ii]
print cormat_jc[ip, ii]
def trace_plot(data, ylim):
clf()
plt.subplots_adjust(left=0.15);
plt.subplot(411)
plt.plot(np.arange(0,30000*180)/30000., data[0][:30000*3*60])
plt.xticks(alpha = 0)
plt.yticks(fontsize = 'large')
plt.ylabel('Voltage (mV)', size = 'x-large')
plt.ylim(ylim)
plt.subplot(412)
plt.plot(np.arange(0,30000*180)/30000.,data[1][:30000*3*60])
plt.yticks(alpha = 0)
plt.xticks(alpha = 0)
plt.ylim(ylim)
plt.subplot(413)
plt.plot(np.arange(0,30000*180)/30000.,data[2][:30000*3*60])
plt.xticks(alpha = 0)
plt.yticks(alpha = 0)
plt.ylim(ylim)
plt.subplot(414)
plt.plot(np.arange(0,30000*180)/30000., data[3][:30000*3*60])
plt.xlabel('Time (s)', size = 'x-large')
plt.xticks(fontsize = 'large')
plt.yticks(alpha = 0)
plt.ylim(ylim)
def plot_proj_scatter(pccoefs, eval, figname):
"""
plot scatters using the values projected on pc
"""
fig = plt.figure(4)
plt.clf()
eval /= N.sum(eval)
param = dict(fontsize='small')
range = N.max(N.abs(pccoefs))
labelx = -0.15
nplt = 4
ncol = 3
nrow = 2
iplt = 1
for iprops in xrange(nplt):
for jprops in xrange(iprops+1, nplt):
ax = fig.add_subplot(nrow, ncol, iplt)
plt.subplots_adjust(hspace=0.2, wspace=0.25, left=0.08, right=0.95, bottom=0.08, top=0.9)
ax.plot(pccoefs[:, iprops], pccoefs[:, jprops], 'ro', ms=3.)
plt.axis('equal')
#ax.set_xlim(-range, range)
#ax.set_ylim(-range, range)
ax.set_xlabel('PC%d(%.2f)' % (iprops, eval[iprops]), **param)
ax.set_ylabel('PC%d(%.2f)' % (jprops, eval[jprops]), **param)
ax.yaxis.set_label_coords(labelx, 0.5)
iplt += 1
plt.savefig(figname)
def animate_plot(w_dir=None, **kwargs):
if w_dir == None: w_dir=os.getcwd()
cdir = os.getcwd()
files = []
os.chdir(w_dir)
os.system('mkdir j_movie')
D0 = plp.pload(0,w_dir=w_dir)
fig = plt.figure(num=1,figsize=[7,7])
ax = fig.add_subplot(111)
plt.subplots_adjust(left=0.1, bottom=0.15)
frnum = kwargs.get('frames',plp.time_info(w_dir=w_dir)['time'])
for i in [0,10,25,50,150,250,319,325,340,450,638]: # 50 frames
D = plp.pload(i,w_dir=w_dir)
Ra = da.Rad_Average()
xitem = D.x1
yitem = D.x1*D.v3[:,32]
# yitem = Ra.Sigma(D,ul=1.0,urho=1.0e-9,Mstar=10.0,Gammae=5.0/3.0)
ax.cla()
if kwargs.get('pltype','normal') == 'normal':
ax.plot(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'logx':
ax.semilogx(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'logy':
ax.semilogy(xitem,yitem,'k-')
if kwargs.get('pltype','normal') == 'loglog':
ax.loglog(xitem,yitem,'k-')
ax.minorticks_on()
ax.set_xlabel(kwargs.get('xlabel',r'XLabel'))
ax.set_ylabel(kwargs.get('ylabel',r'YLabel'))
ax.set_title(kwargs.get('title',r'Title'))
#ax.text(90.0,1.3,r'$N_{\rm rot} = %04d$'%i)
axcolor = 'white'
axtime = plt.axes([0.1, 0.01, 0.8, 0.02], axisbg=axcolor)
axtime.cla()
stime = Slider(axtime, 'Nsteps', 0, frnum , valinit=D.time)
#plt.draw()
fname = w_dir+'j_movie/_tmp%03d.png'%i
print 'Saving frame', fname
fig.savefig(fname)
files.append(fname)
print 'Making movie animation.mpg - this make take a while'
os.system("mencoder 'mf://j_movie/_tmp*.png' -mf type=png:fps=10 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o j_movie/animation.mpg")
os.chdir(cdir)
def plot_synth_real(real_template, synthetic, channels=False):
r"""Plot multiple channels of data for real data and synthetic.
:type real_template: obspy.Stream
:param real_template: Stream of the real template
:type synthetic: obspy.Stream
:param synthetic: Stream of synthetic template
:type channels: list of str
:param channels: List of tuples of (station, channel) to plot, default is\
False, which plots all.
"""
from obspy.signal.cross_correlation import xcorr
from obspy import Stream
colours = ['k', 'r']
labels = ['Real', 'Synthetic']
if channels:
real = []
synth = []
for stachan in channels:
real.append(real_template.select(station=stachan[0],
channel=stachan[1]))
synth.append(synthetic.select(station=stachan[0],
channel=stachan[1]))
real_template = Stream(real)
synthetic = Stream(synth)
# Extract the station and channels
stachans = list(set([(tr.stats.station, tr.stats.channel)
for tr in real_template]))
fig, axes = plt.subplots(len(stachans), 1, sharex=True, figsize=(5, 10))
axes = axes.ravel()
for i, stachan in enumerate(stachans):
real_tr = real_template.select(station=stachan[0],
channel=stachan[1])[0]
synth_tr = synthetic.select(station=stachan[0],
channel=stachan[1])[0]
shift, corr = xcorr(real_tr, synth_tr, 2)
print('Shifting by: '+str(shift)+' samples')
if corr < 0:
synth_tr.data = synth_tr.data * -1
corr = corr * -1
if shift < 0:
synth_tr.data = synth_tr.data[abs(shift):]
real_tr.data = real_tr.data[0:len(synth_tr.data)]
elif shift > 0:
real_tr.data = real_tr.data[abs(shift):]
synth_tr.data = synth_tr.data[0:len(real_tr.data)]
for j, tr in enumerate([real_tr, synth_tr]):
y = tr.data
y = y / float(max(abs(y)))
x = np.linspace(0, len(y) * tr.stats.delta, len(y))
axes[i].plot(x, y, colours[j], linewidth=2.0, label=labels[j])
axes[i].get_yaxis().set_ticks([])
ylab = stachan[0]+'.'+stachan[1]+' cc='+str(round(corr, 2))
axes[i].set_ylabel(ylab, rotation=0)
plt.subplots_adjust(hspace=0)
# axes[0].legend()
axes[-1].set_xlabel('Time (s)')
plt.show()
def mso_orbit(orbit_number, res=60):
o = mex.orbits[orbit_number]
start = o.start
finish = o.finish
et = np.arange(start, finish, res)
i = np.sum(et < o.periapsis)
inbound = np.arange(0, i, 60)
outbound = np.arange(i + 60, et.shape[0], 60)
pos = mex.mso_position(et) / mex.mars_mean_radius_km
lims = (-3, 3)
off = 0.2
plt.rcParams["font.size"] = 8
phi = np.linspace(-np.pi, np.pi, 100)
x = 0.78 + 0.96 * np.cos(phi) / (1 + 0.9 * np.cos(phi))
y = 0.96 * np.sin(phi) / (1 + 0.9 * np.cos(phi))
f = plt.figure(figsize=(8, 4))
def panel(no, xn, yn, bs=True):
plt.subplot(no, aspect="equal")
if bs:
plt.plot(x, y, "k--")
plt.xlabel(xn + r"$ / R_M$")
celsius.ylabel(yn + r"$ / R_M$", offset=off)
plt.xlim(*lims)
plt.ylim(*lims)
plt.gca().add_patch(plt.Circle((0.0, 0.0), 1.0, fill=False))
panel(141, r"$X$", r"$\rho$")
plt.title("MEX ORBIT %d" % o.number)
rho = np.sqrt(pos[1] ** 2.0 + pos[2] ** 2.0)
plt.plot(pos[0], rho, "k-")
plt.gca().add_patch(plt.Circle((0.0, 0.0), 1.0 + 1200.0 / mex.mars_mean_radius_km, fill=False, linestyle="dotted"))
plt.gca().add_patch(plt.Circle((0.0, 0.0), 1.0 + 1500.0 / mex.mars_mean_radius_km, fill=False, linestyle="dotted"))
plt.gca().add_patch(plt.Circle((0.0, 0.0), 1.0 + 2000.0 / mex.mars_mean_radius_km, fill=False, linestyle="dotted"))
plt.plot(pos[0, inbound], rho[inbound], "ko")
plt.plot(pos[0, outbound], rho[outbound], "ko", mfc="white")
panel(142, r"$X$", r"$Z$")
plt.plot(pos[0], pos[2], "k-")
plt.plot(pos[0, inbound], pos[2, inbound], "ko")
plt.plot(pos[0, outbound], pos[2, outbound], "ko", mfc="white")
panel(143, r"$X$", r"$Y$")
plt.plot(pos[0], pos[1], "k-")
plt.plot(pos[0, inbound], pos[1, inbound], "ko")
plt.plot(pos[0, outbound], pos[1, outbound], "ko", mfc="white")
panel(144, r"$Y$", r"$Z$", bs=False)
plt.plot(pos[1], pos[2], "k-")
plt.plot(pos[1, inbound], pos[2, inbound], "ko")
plt.plot(pos[1, outbound], pos[2, outbound], "ko", mfc="white")
plt.subplots_adjust(wspace=0.3, right=0.96, top=0.94, bottom=0.07)
def fourcumplot(x1,x2,x3,x4,xmin,xmax,x1leg='$x_1$',x2leg='$x_2$',x3leg='$x_3$',x4leg='$x_3$',xlabel='',ylabel='$N(x>x\')$',fig=1,sharey=False,fontsize=12,bins1=50,bins2=50,bins3=50,bins4=50):
"""
Script that plots the cumulative histograms of four variables x1, x2, x3 and x4
sharing the same X-axis. For each bin, Y is the fraction of the sample
with values above X.
Arguments:
- x1,x2,x3,x4: arrays with data to be plotted
- xmin,xmax: lower and upper range of plotted values, will be used to set a consistent x-range
for both histograms.
- x1leg, x2leg, x3leg, x4leg: legends for each histogram
- xlabel: self-explanatory.
- sharey: sharing the Y-axis among the histograms?
- bins1,bins2,...: number of bins in each histogram
- fig: which plot window should I use?
Inspired by `Scipy <http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label>`_.
v1 Jun. 2012: inherited from fourhists.
"""
pylab.rcParams.update({'font.size': fontsize})
fig=pylab.figure(fig)
pylab.clf()
a=fig.add_subplot(4,1,1)
if sharey==True:
b=fig.add_subplot(4,1,2, sharex=a, sharey=a)
c=fig.add_subplot(4,1,3, sharex=a, sharey=a)
d=fig.add_subplot(4,1,4, sharex=a, sharey=a)
else:
b=fig.add_subplot(4,1,2, sharex=a)
c=fig.add_subplot(4,1,3, sharex=a)
d=fig.add_subplot(4,1,4, sharex=a)
a.hist(x1,bins1,label=x1leg,color='b',cumulative=-True,normed=True,histtype='stepfilled')
a.legend(loc='best',frameon=False)
a.set_xlim(xmin,xmax)
b.hist(x2,bins2,label=x2leg,color='r',cumulative=-True,normed=True,histtype='stepfilled')
b.legend(loc='best',frameon=False)
c.hist(x3,bins3,label=x3leg,color='y',cumulative=-True,normed=True,histtype='stepfilled')
c.legend(loc='best',frameon=False)
d.hist(x4,bins4,label=x4leg,color='g',cumulative=-True,normed=True,histtype='stepfilled')
d.legend(loc='best',frameon=False)
pylab.setp(a.get_xticklabels(), visible=False)
pylab.setp(b.get_xticklabels(), visible=False)
pylab.setp(c.get_xticklabels(), visible=False)
d.set_xlabel(xlabel)
c.set_ylabel(ylabel)
pylab.minorticks_on()
pylab.subplots_adjust(hspace=0.15)
pylab.draw()
pylab.show()
def riemann():
# grid info
xmin = 0.0
xmax = 1.0
nzones = 2
ng = 0
gr = gp.FVGrid(nzones, xmin=xmin, xmax=xmax)
#------------------------------------------------------------------------
# plot a domain without ghostcells
gr.draw_grid()
gr.label_center(0, r"$i$", fontsize="medium")
gr.label_center(1, r"$i+1$", fontsize="medium")
gr.label_edge(1, r"$i+1/2$", fontsize="medium")
plt.arrow(gr.xc[0]+0.05*gr.dx, 0.5, 0.12*gr.dx, 0,
shape='full', head_width=0.05, head_length=0.025,
lw=1, width=0.02,
edgecolor="none", facecolor="r",
length_includes_head=True, zorder=100)
plt.arrow(gr.xc[1]-0.1*gr.dx, 0.5, -0.12*gr.dx, 0,
shape='full', head_width=0.05, head_length=0.025,
lw=1, width=0.02,
edgecolor="none", facecolor="r",
length_includes_head=True, zorder=100)
gr.mark_cell_left_state(1, r"$a_{i+1/2,L}^{n+1/2}$", fontsize="large",
color="b")
gr.mark_cell_right_state(0, r"$a_{i+1/2,R}^{n+1/2}$", fontsize="large",
color="b")
gr.label_cell_center(0, r"$a_i$")
gr.label_cell_center(1, r"$a_{i+1}$")
plt.xlim(gr.xl[0]-0.125*gr.dx,gr.xr[2*ng+nzones-1]+0.125*gr.dx)
plt.ylim(-0.25, 1.0)
plt.axis("off")
plt.subplots_adjust(left=0.05,right=0.95,bottom=0.05,top=0.95)
f = plt.gcf()
f.set_size_inches(7.0,2.0)
plt.tight_layout()
plt.savefig("riemann-adv.pdf")
def display_icecube_detectors(collision):
fig = plt.figure(figsize=(7,5),dpi=100)
ax = fig.add_subplot(1,1,1)
ax = fig.gca(projection='3d')
plt.subplots_adjust(top=0.98,bottom=0.02,right=0.98,left=0.02)
q,t,x,y,z = get_detector_info(collision)
ax.scatter(x,y,z,s=20*q)
def images(data, zero_to_one=True, show=True, subplots=None, caption=None):
"""
Display images that range a grid. Especially designed for probability images, ranging from 0 to 1.
Parameters
----------
data : ndarray
An array of images or a single image of shape. The values should range between 0 and 1 (at least, that is how they will be colorized).
zero_to_one : bool
If True, then 0.0 and below will be pitch black, and 1.0 and above will be chalk white.
show : bool
Call `pylab.show()` inside the function.
subplots : tuple or None
Specify the shape of the subplots manually.
caption : lambda(index, element)
A function that takes the index and the element (``data[index]``) and should return a string.
"""
import matplotlib.pylab as plt
settings = {
'interpolation': 'nearest',
'cmap': plt.cm.gray,
}
if zero_to_one:
settings['vmin'] = 0.0
settings['vmax'] = 1.0
if isinstance(data, np.ndarray) and data.ndim == 2:
fig = plt.figure()
plt.subplot(111).set_axis_off()
plt.imshow(data, **settings)
else:
# TODO: Better find out pleasing aspect ratios
N = len(data)
if subplots is not None:
sh = subplots
assert len(data) <= np.prod(subplots)
elif N <= 3:
sh = (1, len(data))
elif N == 5 or N == 6:
sh = (2, 3)
elif N == 12:
sh = (3, 4)
else:
perside = int(math.ceil(math.sqrt(len(data))))
sh = (perside,)*2
fig = plt.figure()
for i, im in enumerate(data):
plt.subplot(sh[0], sh[1], 1+i).set_axis_off()
plt.subplots_adjust(left=0.01, right=0.99, top=0.99, bottom=0.01)
plt.imshow(im, **settings)
if caption is not None:
plt.title(caption(i, im))
if show:
plt.show()
def TwoD_Hexbin(x, panel): # plt.xlim(np.min(x), np.max(x)) # plt.ylim(np.min(y), np.max(y)) # plt.grid(True) # plt.hexbin(x, y, bins='log', cmap=plt.cm.YlOrRd_r) plt.hist(x) plt.subplots_adjust(left=0.03, right=0.98, top=0.98, bottom=0.07) panel.canvas.draw() return
def ao_plot(o):
a = AISReview(o)
# fig = plt.figure()
fig, ax = plt.subplots(2, 1, squeeze=True, figsize=(4,4), dpi=70, num=plt.gcf().number + 1)
plt.subplots_adjust(hspace=0.3,wspace=0.0, right=0.85)
a.density_along_orbit(ax[0], vmax=4.)
a.modb_along_orbit(ax[1], vmax=100.)
def plot_autocorrs(self, axis=0, n_rows=4, n_cols=8):
""" Plot autocorrelations for all antennas
"""
self.current_plot = 'multi'
self.ax_zoomed = False
bls = self.uv.d_uv_data['BASELINE']
# Extract the relevant baselines using a truth array
# bls = bls.tolist()
bl_ids = set([256*i + i for i in range(1, n_rows * n_cols + 1)])
bl_truths = np.array([(b in bl_ids) for b in bls])
#print self.uv.d_uv_data['DATA'].shape
#x_data = self.d_uv_data['DATA'][bl_truths,0,0,:,0,axis] # Baselines, freq and stokes
#x_cplx = x_data[:,:,0] + 1j * x_data[:,:,1]
x_cplx = self.stokes[axis][bl_truths]
# Plot the figure
#print self.uv.n_ant
fig = self.sp_fig
figtitle = '%s %s: %s -- %s'%(self.uv.telescope, self.uv.instrument, self.uv.source, self.uv.date_obs)
for i in range(n_rows):
for j in range(n_cols):
ax = fig.add_subplot(n_rows, n_cols, i*n_cols + j +1)
ax.set_title(self.uv.d_array_geometry['ANNAME'][i*n_cols + j], fontsize=10)
#ax.set_title("%s %s"%(i, j))
x = x_cplx[i*n_cols+j::self.uv.n_ant]
if self.scale_select.currentIndex() == 0 or self.scale_select.currentIndex() == 1:
if x.shape[0] == self.uv.n_ant:
self.plot_spectrum(ax, x, label_axes=False)
else:
self.plot_spectrum(ax, x, stat='max', label_axes=False)
self.plot_spectrum(ax, x, stat='med', label_axes=False)
self.plot_spectrum(ax, x, stat='min', label_axes=False)
else:
self.plot_spectrum(ax, x, label_axes=False)
self.updateFreqAxis(ax)
if i == n_rows-1:
ax.set_xlabel('Freq')
if j == 0:
ax.set_ylabel('Amplitude')
plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.tick_params(axis='both', which='major', labelsize=10)
plt.tick_params(axis='both', which='minor', labelsize=8)
plt.xticks(rotation=30)
plt.subplots_adjust(left=0.05, right=0.98, top=0.95, bottom=0.1, wspace=0.3, hspace=0.45)
return fig, ax
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
"""Helper function to plot a gallery of portraits"""
pl.figure(figsize=(1.8 * n_col, 2.4 * n_row))
pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
for i in range(n_row * n_col):
pl.subplot(n_row, n_col, i + 1)
pl.imshow(images[i].reshape((h, w)), cmap=pl.cm.gray)
pl.title(titles[i], size=12)
pl.xticks(())
pl.yticks(())
def plot_relative_error(self, show=True, save=False):
ue_scaled, ve_scaled, er = self.compute_error(
errors=["ue_scaled", "ve_scaled", "er"])
plot_extent = [self.x.min(), self.x.max(), self.y.min(), self.y.max()]
fig3 = pl.figure(figsize=(self.figW, self.figH))
plot = fig3.add_subplot(1, 3, 1, aspect='equal')
im3 = plot.imshow(ue_scaled,
vmin=0,
vmax=10,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Rel. Error U",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig3.colorbar(im3, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
plot = fig3.add_subplot(1, 3, 2, aspect='equal')
im3 = plot.imshow(ve_scaled,
vmin=0,
vmax=10,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Rel. Error U",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig3.colorbar(im3, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
plot = fig3.add_subplot(1, 3, 3, aspect='equal')
im3 = plot.imshow(er,
vmin=0,
vmax=10,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Rel. Error U",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig3.colorbar(im3, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
pl.subplots_adjust(left=0.06,
bottom=0.47,
right=0.94,
top=0.88,
wspace=0.48,
hspace=0.0)
if show:
pl.show()
if save:
fig3.savefig("rel_error.png", dpi=300)
ls=so.const.symbols[2],label='Narrow')
plt.plot(d_bands,2*(3.0/(s1[0]/n1[0]))**2,c=so.const.colors[3], \
ls=so.const.symbols[0])
plt.plot(d_bands,2*(3.0/(s1[1]/n1[1]))**2,c=so.const.colors[3], \
ls=so.const.symbols[2])
plt.axhline(2 * (3.0 / (s189 / n189))**2, c=so.const.colors[0])
plt.axhline(2 * (3.0 / (s209 / n209))**2, c=so.const.colors[3])
#plt.xlim(4,7.5)
#plt.ylim(200,4000)
plt.xlabel('Band Spacing (nm)')
plt.ylabel('N$_{exp}$')
plt.legend(loc='best')
plt.subplots_adjust(bottom=0.17, left=0.19)
plt.savefig('./plots/best_filter.png')
exposures = np.arange(100)
def plot_potassium_setup():
plt.style.use('dark_background')
fig, ax = plt.subplots(1, 1, sharex=True, figsize=(10, 6))
ax.plot(so.exo.v, so.exo.s)
#ax2 = ax.twinx() # use this for stellar contamination later
#ax2.set_zorder(ax.get_zorder()+1) # put ax in front of ax2
#ax2.fill_between(so.exo.v,y1=so.filt.s_alluxa_on,y2=0*so.exo.v,facecolor='m',alpha=0.5)
darkpink, lightpink =normrgb([255,20,147]), normrgb([255,182,193])
darkblue, lightblue = normrgb([0,0,128]),normrgb([135,206, 250])
for gender in genders:
for pclass in classes:
if gender=='male':
colorscheme = [lightblue, darkblue]
row=0
else:
colorscheme = [lightpink, darkpink]
row=1
group = traindf[(traindf.Sex==gender)&(traindf.Pclass==pclass)]
group = group.groupby(['Embarked', 'Survived']).size().unstack()
group = group.div(group.sum(1), axis=0)
group.plot(kind='barh', ax=axes2[row, (int(pclass)-1)], color=colorscheme, stacked=True, legend=False).set_title('Class '+str(pclass)).axes.get_xaxis().set_ticks([])
plt.subplots_adjust(wspace=0.4, hspace=1.3)
fhandles, flabels = axes2[1,2].get_legend_handles_labels()
mhandles, mlabels = axes2[0,2].get_legend_handles_labels()
plt.figlegend(fhandles, ('die', 'live'), title='Female', loc='center', bbox_to_anchor=(0.06, 0.45, 1.1, .102))
plt.figlegend(mhandles, ('die', 'live'), 'center', title='Male',bbox_to_anchor=(-0.15, 0.45, 1.1, .102))
fig2.show()
def removeBadStringFromString(string, badStringList):
for badString in badStringList:
string = string.replace(badString, '')
return string
def removeBadStringFromLabels(ax, badStringList):
labels = [item.get_text() for item in ax.get_yticklabels()]
#plt.ylim(-4E+05,4E+05)
vel_obs.legend()
#------------------------------------save figure---------------------------
plt.savefig('TNG Filtered 0.3Hz-250s', format='png', dpi=1200)
#####################################figure 4######################################
plt.figure(4)
fig4 = plt.figure(4)
fig4.suptitle("JCJI", fontsize=14)
plt.rc('xtick', labelsize=6)
plt.rc('ytick', labelsize=6)
plt.rc('legend', fontsize=8)
plt.subplots_adjust(top=0.8)
#------------------------------graph 1---------------------------------
vel_obs = fig4.add_subplot(111, label="1")
vel_ = fig4.add_subplot(111, label="2", frame_on=False)
tr_obs = obs[0]
obs_filt = tr_obs.copy()
obs_filt.filter('lowpass', freq=0.3, corners=2, zerophase=True)
t_obs = np.arange(0, obs[0].stats.npts / obs[0].stats.sampling_rate,
obs[0].stats.delta)
vel_obs.plot(t_obs, obs_filt.data, label='Seismogram observasi', linewidth=0.5)
vel_obs.set_ylabel('Kecepatan (counts)', fontsize=7)
vel_obs.set_xlabel('Waktu (s)', fontsize=8)
vel_obs.tick_params(axis='x', colors='b')
def pair_plot(df,
var_list,
var_lims,
var_labels,
num_inputs=4,
vmax=4,
bins=30,
alpha=0.7,
colors=None,
marker='.',
ms=20,
clabels=True,
opt='hexbin',
fig=None,
ax=None,
figsize=[16, 12]):
if fig == None:
Nx = len(var_list)
Ny = len(var_list)
fig, ax = plt.subplots(
Nx,
Ny,
gridspec_kw={
'width_ratios':
[2 * figsize[0] / figsize[1] for i in range(Ny - 1)] + [1],
'height_ratios': [1] + [2 for i in range(Nx - 1)],
},
figsize=figsize)
for i, var_name in enumerate(var_list):
for j, var_name2 in enumerate(var_list):
if (j < i):
y = df[var_name].values
x = df[var_name2].values
if i >= num_inputs:
if opt == 'hexbin':
hexbin = ax[i, j].hexbin(x,
y,
gridsize=bins,
cmap=Reds,
mincnt=0,
extent=var_lims[var_name2] +
var_lims[var_name])
ax[i, j].hexbin(x,
y,
gridsize=bins,
bins='log',
cmap=Reds,
vmin=0,
vmax=vmax,
alpha=alpha,
edgecolors='none',
mincnt=0,
extent=var_lims[var_name2] +
var_lims[var_name])
density_contour(hexbin,
ax=ax[i, j],
alpha=1.,
colors='grey',
c='k',
clabels=clabels)
elif opt == 'scatter':
if colors == None:
ax[i,
j].scatter(x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=sns.color_palette('Reds_d')[1],
zorder=10000)
else:
ax[i, j].scatter(x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=colors,
zorder=10000)
else:
if opt == 'hexbin':
hexbin = ax[i, j].hexbin(x,
y,
gridsize=bins,
cmap=Blues,
mincnt=0,
extent=var_lims[var_name2] +
var_lims[var_name])
ax[i, j].hexbin(x,
y,
gridsize=bins,
bins='log',
cmap=Blues,
vmin=0,
vmax=vmax,
alpha=alpha,
edgecolors='none',
mincnt=0,
extent=var_lims[var_name2] +
var_lims[var_name])
density_contour(hexbin,
ax=ax[i, j],
alpha=1,
colors='grey',
c='k',
clabels=clabels)
elif opt == 'scatter':
if colors == None:
ax[i, j].scatter(
x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=sns.color_palette("Blues_d")[1],
zorder=10000)
else:
ax[i, j].scatter(x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=colors,
zorder=10000)
ax[i, j].set_ylim(var_lims[var_name])
ax[i, j].set_xlim(var_lims[var_name2])
elif (i == 0) & (j < len(var_list) - 1):
y = df[var_name2].values
if colors == None:
if i >= num_inputs:
ax[i, j].hist(y,
bins=bins,
normed=True,
range=var_lims[var_name2],
lw=0,
color=sns.color_palette('Reds_d')[1])
else:
ax[i, j].hist(y,
bins=bins,
normed=True,
range=var_lims[var_name2],
lw=0,
color=sns.color_palette('Blues_d')[1])
else:
ax[i, j].hist(y,
bins=bins,
normed=True,
range=var_lims[var_name2],
lw=1,
color='k',
histtype=u'step')
ax[i, j].set_xlim(var_lims[var_name2])
ax[i, j].set_yticklabels([])
elif (j == len(var_list) - 1) & (i > 0):
x = df[var_name].values
if colors == None:
if i >= num_inputs:
ax[i, j].hist(x,
bins=bins,
normed=True,
range=var_lims[var_name],
lw=0,
color=sns.color_palette('Reds_d')[1],
orientation=u'horizontal')
else:
ax[i, j].hist(x,
bins=bins,
normed=True,
range=var_lims[var_name],
lw=0,
color=sns.color_palette('Blues_d')[1],
orientation=u'horizontal')
else:
ax[i, j].hist(x,
bins=bins,
normed=True,
range=var_lims[var_name],
lw=1,
color='k',
histtype=u'step',
orientation=u'horizontal')
ax[i, j].set_ylim(var_lims[var_name])
ax[i, j].set_xticklabels([])
else:
ax[i, j].set_visible(False)
if i != 0:
ax[j, i].set_yticklabels([])
if i == 0:
if j > 0:
ax[j, i].set_ylabel(var_labels[var_name2])
else:
ax[j, i].set_ylabel('PDF')
if i != len(var_list) - 1:
ax[i, j].set_xticklabels([])
if i == len(var_list) - 1:
if j < len(var_list) - 1:
ax[i, j].set_xlabel(var_labels[var_name2])
else:
ax[i, j].set_xlabel('PDF')
#xticks = ax[i,j].xaxis.get_major_ticks()
#xticks[-1].label1.set_visible(False)
#if len(xticks)>8:
# for itick in range(1,len(xticks)-1,2):
# xticks[itick].label1.set_visible(False)
plt.subplots_adjust(
left=.1, # the left side of the subplots of the figure
right=.99, # the right side of the subplots of the figure
bottom=.10, # the bottom of the subplots of the figure
top=.99, # the top of the subplots of the figure
wspace=
0.1, # the amount of width reserved for blank space between subplots
hspace=0.1
) # the amount of height reserved for white space between subplots
return fig, ax
def NR_plot(stream, NR_stream, detections, false_detections=False,\
size=(18.5,10), save=False, title=False):
"""
Function to plot the Network response alongside the streams used - highlights
detection times in the network response
:type stream: :class: obspy.Stream
:param stream: Stream to plot
:type NR_stream: :class: obspy.Stream
:param NR_stream: Stream for the network response
:type detections: List of datetime objects
:param detections: List of the detections
:type false_detections: List of datetime
:param false_detections: Either False (default) or list of false detection\
times
:type size: tuple
:param size: Size of figure, default is (18.5,10)
:type save: bool
:param save: Save figure or plot to screen, if not False, must be string of\
save path
:type title: str
:param title: String for the title of the plot, set to False
"""
import datetime as dt
import matplotlib.dates as mdates
fig, axes = plt.subplots(len(stream) + 1, 1, sharex=True, figsize=size)
if len(stream) > 1:
axes = axes.ravel()
else:
return
mintime = stream.sort(['starttime'])[0].stats.starttime
stream.sort(['network', 'station', 'starttime'])
for i, tr in enumerate(stream):
delay = tr.stats.starttime - mintime
delay *= tr.stats.sampling_rate
y = tr.data
x=[tr.stats.starttime + dt.timedelta(seconds=s/tr.stats.sampling_rate)\
for s in xrange(len(y))]
x = mdates.date2num(x)
axes[i].plot(x, y, 'k', linewidth=1.1)
axes[i].set_ylabel(tr.stats.station + '.' + tr.stats.channel,
rotation=0)
axes[i].yaxis.set_ticks([])
axes[i].set_xlim(x[0], x[-1])
# Plot the network response
tr = NR_stream[0]
delay = tr.stats.starttime - mintime
delay *= tr.stats.sampling_rate
y = tr.data
x=[tr.stats.starttime + dt.timedelta(seconds=s/tr.stats.sampling_rate)\
for s in range(len(y))]
x = mdates.date2num(x)
axes[i].plot(x, y, 'k', linewidth=1.1)
axes[i].set_ylabel(tr.stats.station + '.' + tr.stats.channel, rotation=0)
axes[i].yaxis.set_ticks([])
axes[-1].set_xlabel('Time')
axes[-1].set_xlim(x[0], x[-1])
# Plot the detections!
ymin, ymax = axes[-1].get_ylim()
if false_detections:
for detection in false_detections:
xd = mdates.date2num(detection)
axes[-1].plot((xd, xd), (ymin, ymax),
'k--',
linewidth=0.5,
alpha=0.5)
for detection in detections:
xd = mdates.date2num(detection)
axes[-1].plot((xd, xd), (ymin, ymax), 'r--', linewidth=0.75)
# Set formatters for x-labels
mins = mdates.MinuteLocator()
if (tr.stats.endtime.datetime-tr.stats.starttime.datetime).total_seconds() >= 10800\
and (tr.stats.endtime.datetime-tr.stats.starttime.datetime).total_seconds() <= 25200:
hours = mdates.MinuteLocator(byminute=[0, 15, 30, 45])
elif (tr.stats.endtime.datetime -
tr.stats.starttime.datetime).total_seconds() <= 1200:
hours = mdates.MinuteLocator(byminute=range(0, 60, 2))
elif (tr.stats.endtime.datetime-tr.stats.starttime.datetime).total_seconds() > 25200\
and (tr.stats.endtime.datetime-tr.stats.starttime.datetime).total_seconds() <= 172800:
hours = mdates.HourLocator(byhour=range(0, 24, 3))
elif (tr.stats.endtime.datetime -
tr.stats.starttime.datetime).total_seconds() > 172800:
hours = mdates.DayLocator()
else:
hours = mdates.MinuteLocator(byminute=range(0, 60, 5))
hrFMT = mdates.DateFormatter('%Y/%m/%d %H:%M:%S')
axes[-1].xaxis.set_major_locator(hours)
axes[-1].xaxis.set_major_formatter(hrFMT)
axes[-1].xaxis.set_minor_locator(mins)
plt.gcf().autofmt_xdate()
axes[-1].fmt_xdata = mdates.DateFormatter('%Y/%m/%d %H:%M:%S')
plt.subplots_adjust(hspace=0)
if title:
axes[0].set_title(title)
if not save:
plt.show()
plt.close()
else:
plt.savefig(save)
return
def plotreport(self, lc_data, ext_image=None, save=False):
time = lc_data[:, 0]
flux = lc_data[:, 1]
xpos = lc_data[:, 2]
ypos = lc_data[:, 3]
pixa = lc_data[:, 4]
msky = lc_data[:, 5]
#searching NaN value in flux
nani = np.where(np.isnan(lc_data[:, 1]) == 1)
# transferring list to array
nan_array = np.asarray(nani[0])
#remove NaN elements from data
vali = np.where(np.isnan(lc_data[:, 1]) == 0)
flux_sans_NaN = lc_data[vali, 1]
m_flux = np.median(flux_sans_NaN)
std_flux = np.std(flux_sans_NaN)
# check total number of pixels in aperture less than 3-sigam of median value
tnp_index = np.where(pixa < np.median(pixa) - np.std(pixa) * 3.0)
plt.clf()
plt.figure(1, figsize=(8, 11))
plt.subplots_adjust(hspace=0.2)
win2 = plt.subplot(411)
win2.plot(time, flux, 'k-', drawstyle='steps-mid')
win2.plot(time[tnp_index],
time[tnp_index] * 0.0 + (m_flux + 3.0 * std_flux), 'rv')
if nan_array.size > 0:
for index in range(nan_array.size):
win2.axvline(x=time[nan_array[index]], color='r', ls='--')
win2.axhline(y=(m_flux - 3.0 * std_flux), color='r', ls='-.')
win2.axhline(y=(m_flux + 3.0 * std_flux), color='r', ls='-.')
win2.set_xlim(0, time[-1])
win2.set_ylim(0, m_flux + 5.0 * std_flux)
win2.set_ylabel('Intensity (count)')
win2.set_title('Light Curves: ' + op.basename(self.filename) + ' [f' +
str(self.fibre) + ']')
win1 = plt.subplot(412, sharex=win2)
win1.plot(time, xpos, 'g.', label='x')
win1.plot(time, ypos, 'b.', label='y')
#an = np.linspace(0,2*np.pi,359)
#win1.imshow(ext_image[0], cmap=plt.cm.gray, vmin=10, vmax=1000)
#win1.plot(5*np.sin(an)+xpos[0], 5*np.cos(an)+ypos[0], 'r-')
#win1.plot(10*np.sin(an)+xpos[0], 10*np.cos(an)+ypos[0], 'r--')
win1.set_xlim(0, time[-1])
win1.set_ylim(0, 40)
win1.set_ylabel('Position')
win1.legend()
win3 = plt.subplot(413, sharex=win2)
win3.plot(time, pixa, 'k-', drawstyle='steps-mid')
espected_pixa = np.pi * self.aperture**2
win3.axhline(y=espected_pixa, color='r', ls='--')
if nan_array.size > 0:
for index in range(nan_array.size):
win3.axvline(x=time[nan_array[index]], color='r', ls='--')
win3.set_xlim(0, time[-1])
win3.set_ylim(
np.mean(pixa) - 6.0 * np.std(pixa),
np.mean(pixa) + 6.0 * np.std(pixa))
win3.set_ylabel('Pixel')
win3.set_title('Total pixels in aperture (%2d)' % (self.aperture))
win4 = plt.subplot(414, sharex=win2)
if nan_array.size > 0:
for index in range(nan_array.size):
win4.axvline(x=time[nan_array[index]], color='r', ls='--')
win4.plot(time, msky, 'k-', drawstyle='steps-mid')
win4.set_xlim(0, time[-1])
win4.set_xlabel('Time (sec)')
win4.set_ylabel('Sky value (count/pixel)')
win4.set_title('Median sky value')
xticklabels = win1.get_xticklabels() + win2.get_xticklabels(
) + win3.get_xticklabels()
plt.setp(xticklabels, visible=False)
plt.show()
if save == 1:
save_filename = 'f' + str(self.fibre) + '_' + op.basename(
self.filename)[:-4] + 'png'
plt.savefig(save_filename, format='png')
plt.subplot(2, 3, 2)
plt.errorbar(data2[7:14, 0], data2[7:14, 1], marker=".")
plt.title("l1 = 2, l2 = 3")
plt.xlabel("N")
plt.ylabel("<r2>")
plt.grid()
plt.subplot(2, 3, 3)
plt.errorbar(data2[14:21, 0], data2[14:21, 1], marker=".")
plt.title("l1 = 3, l2 = 4")
plt.xlabel("N")
plt.ylabel("<r2>")
plt.grid()
plt.subplot(2, 3, 4)
plt.errorbar(data2[21:28, 0], data2[21:28, 1], marker=".")
plt.title("l1 = 4, l2 = 5")
plt.xlabel("N")
plt.ylabel("<r2>")
plt.grid()
plt.subplot(2, 3, 5)
plt.errorbar(data2[28:, 0], data2[28:, 1], marker=".")
plt.title("l1 = 5, l2 = 6")
plt.xlabel("N")
plt.ylabel("<r2>")
plt.grid()
plt.subplots_adjust(hspace=.3)
plt.savefig("plots2.png")
def plot_by_subs(data,
output_name,
colors,
shapes,
sub_ids,
loc_ids,
roi_name,
figsize=(15, 5)):
"""
Plot_by_subs takes a data rec_array and loops through three measures:
fa, md, and vol_vox and plots each on separate plots with the
x-axis representing the individual subjects
Required: data rec_array (eg: data)
output_name (eg: results_dir/'plot_by_subs.png')
colors (eg: colors)
shapes (eg: shapes)
sub_ids (eg: sub_ids)
loc_ids (eg: loc_ids)
Optional: figsize (default 15 x 5)
Example usage:
plot_by_subs(data=data, output_name=results_dir/'plot_by_subs.png',
colors=colors, shapes=shapes, sub_ids=sub_ids,
loc_ids=loc_ids, figsize=(15,5))
"""
#==========================================================================
import numpy as np
import matplotlib.pylab as plt
from matplotlib.ticker import MaxNLocator
#==========================================================================
fig = plt.figure(figsize=figsize)
ax1 = plt.subplot(131)
ax2 = plt.subplot(132)
ax3 = plt.subplot(133)
ax = [ax1, ax2, ax3]
# Loop through the three measures (FA, MD, VOL_VOX)
for count, measure in enumerate(['fa', 'md', 'vol_vox']):
# Now loop through the subjects and the locations
for i, sub in enumerate(sub_ids):
for j, loc in enumerate(loc_ids):
# Assign the correct color and shape for the marker
c = colors[j, i]
m = shapes[j]
# Mask the data so you only have this sub at this loc's numbers
mask = (data['sub'] == sub) & (data['loc_id'] == loc)
# Find the number of data points you have for this sub at this location
n = np.sum(mask)
if n > 1:
# If you have more than one data point then we're going to plot
# the individual points (kinda small and a little transparent)
ax[count].scatter(np.ones(n) * sub,
data[measure][mask],
c=c,
edgecolor=c,
marker=m,
s=20,
alpha=0.5)
# ... connect them with a line ...
ax[count].plot(np.ones(n) * sub, data[measure][mask], c=c)
# And for everyone we'll plot the average
# (which is just the data if you only have one point)
mean = np.average(data[measure][mask])
ax[count].scatter(sub, mean, c=c, edgecolor=c, marker=m, s=50)
# Set the y limits
# This is to deal with very small numbers (the MaxNLocator gets all turned around!)
buffer = (np.max(data[measure]) - np.min(data[measure])) / 10
upper = np.max(data[measure]) + buffer
lower = np.min(data[measure]) - buffer
ax[count].set_ybound(upper, lower)
# Set the axis labels
ax[count].set_ylabel('{}'.format(measure.upper()))
ax[count].set_xlabel('Subject ID')
# Adjust the power limits so that you use scientific notation on the y axis
plt.ticklabel_format(style='sci', axis='y')
[a.yaxis.major.formatter.set_powerlimits((-3, 3)) for a in ax]
# Adjust the y axis ticks so that there are 6 at sensible places
[a.yaxis.set_major_locator(MaxNLocator(6)) for a in ax]
# Set the xaxis ticks to be sensible
[a.xaxis.set_major_locator(MaxNLocator(4)) for a in ax]
# Set the x axis ticks to be at the sub_ids
[a.set_xticks(sub_ids) for a in ax]
# Set the overall title
fig.suptitle('Region of interest: {}'.format(roi_name), fontsize=20)
plt.subplots_adjust(top=0.85)
# And now save it
plt.savefig(output_name,
bbox_inches=0,
facecolor='w',
edgecolor='w',
transparent=True)
def pair_plot_last_row(df,
var_list,
var_lims,
var_labels,
num_inputs=4,
vmax=4,
bins=30,
alpha=0.7,
colors=None,
marker='.',
ms=20,
clabels=True,
opt='hexbin',
fig=None,
ax=None,
figsize=[12, 3],
xlabels=True):
if fig == None:
fig, ax = plt.subplots(1,
len(var_list),
gridspec_kw={
'width_ratios':
[2 for i in range(len(var_list) - 1)] + [1]
},
figsize=figsize)
i = 0
var_name = var_list[-1]
for j, var_name2 in enumerate(var_list):
if (j < len(var_list) - 1):
y = df[var_name].values
x = df[var_name2].values
if opt == 'hexbin':
hexbin = ax[j].hexbin(x,
y,
gridsize=bins,
cmap=Reds,
mincnt=0,
extent=var_lims[var_name2] +
var_lims[var_name])
ax[j].hexbin(x,
y,
gridsize=bins,
bins='log',
cmap=Reds,
vmin=0,
vmax=vmax,
alpha=alpha,
edgecolors='none',
mincnt=0,
extent=var_lims[var_name2] + var_lims[var_name])
density_contour(hexbin,
ax=ax[j],
alpha=1.,
colors='grey',
c='k',
clabels=clabels)
elif opt == 'scatter':
if colors == None:
ax[j].scatter(x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=sns.color_palette('Reds_d')[1],
zorder=10000)
else:
ax[j].scatter(x,
y,
alpha=alpha,
marker=marker,
s=ms,
color=colors,
zorder=10000)
ax[j].set_ylim(var_lims[var_name])
ax[j].set_xlim(var_lims[var_name2])
else:
x = df[var_name].values
if colors == None:
ax[j].hist(x,
bins=bins,
normed=True,
range=var_lims[var_name],
lw=0,
color=sns.color_palette('Reds_d')[1],
orientation=u'horizontal')
else:
ax[j].hist(x,
bins=bins,
normed=True,
range=var_lims[var_name],
lw=1,
color='k',
histtype=u'step',
orientation=u'horizontal')
ax[j].set_ylim(var_lims[var_name])
ax[j].set_yticklabels([])
if j != 0:
ax[j].set_yticklabels([])
else:
ax[j].set_ylabel(var_labels[var_name])
if xlabels:
if j < len(var_list) - 1:
ax[j].set_xlabel(var_labels[var_name2])
xticks = ax[0].xaxis.get_major_ticks()
xticks[0].label1.set_visible(False)
else:
ax[j].set_xticklabels([])
ax[j].set_xlabel('PDF')
else:
ax[j].set_xticklabels([])
plt.subplots_adjust(
left=.07, # the left side of the subplots of the figure
right=.99, # the right side of the subplots of the figure
bottom=.25, # the bottom of the subplots of the figure
top=.95, # the top of the subplots of the figure
wspace=
0.1, # the amount of width reserved for blank space between subplots
hspace=0.1
) # the amount of height reserved for white space between subplots
return fig, ax
print(filename)
image_name = "D:/IDC_regular_ps50_idx5/9135/1/9135_idx5_x1701_y1851_class1.png" #Image to be used as query
def plotImage(image_location):
image = cv2.imread(image_name)
image = cv2.resize(image, (50,50))
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)); plt.axis('off')
return
plotImage(image_name)
# Plot Multiple Images
bunchOfImages = imagePatches
i_ = 0
plt.rcParams['figure.figsize'] = (10.0, 10.0)
plt.subplots_adjust(wspace=0, hspace=0)
for l in bunchOfImages[:25]:
im = cv2.imread(l)
im = cv2.resize(im, (50, 50))
plt.subplot(5, 5, i_+1) #.set_title(l)
plt.imshow(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)); plt.axis('off')
i_ += 1
def randomImages(a):
r = random.sample(a, 4)
plt.figure(figsize=(16,16))
plt.subplot(131)
plt.imshow(cv2.imread(r[0]))
plt.subplot(132)
plt.imshow(cv2.imread(r[1]))
plt.subplot(133)
# # Apply the newly found best focus value:
# if float(optimum_focus) > 1.5 and float(optimum_focus) < 3.5:
# try:
# print set_tsi.set_position_instrumental_hexapod_z_offset(param=float(optimum_focus))
# except Exception,e:
# print e
# print "Could not preset focus value of hexapod"
# else:
# print "The focus found was not within accepted values..."
# except Exception,e:
# print e
plt.figure(figsize=(10.0, 8.0), dpi=40, facecolor='w', edgecolor='k')
plt.subplots_adjust(left=0.15,
bottom=0.20,
right=0.95,
top=0.90,
wspace=0.4,
hspace=0.3)
plt.plot(numpy.array(focus_arr), numpy.array(fwhm_arr), 'r*')
plt.plot(numpy.array(new_focus_arr), numpy.array(new_fwhm_arr), 'b*')
plt.xlim([min(focus_arr) - 0.1, max(focus_arr) + 0.1])
plt.ylim(
[min(numpy.array(fwhm_arr)) - 1.0,
max(numpy.array(fwhm_arr)) + 1.0])
plt.ylabel("FWHM [arcseconds]")
plt.xlabel("Focus [mm]")
import matplotlib.pylab as plt
plt.rcParams['ytick.major.pad'] = '12'
from numpy import linspace
plt.ion()
bias = 6.
layers = 24
layer_list = linspace(-(layers - 1.) / 2., (layers - 1.) / 2., layers)
v = linspace(-bias / 2, bias / 2, layers)
fig = plt.figure()
plt.plot(layer_list, v, 'b-o', label="V")
leg = plt.legend(loc="best", prop={'size': 30})
leg.draw_frame(False)
plt.xlim(-12, 12)
plt.xlabel("n", fontsize=30)
plt.ylabel("V(n)", fontsize=30)
plt.xticks(fontsize=30)
plt.yticks(fontsize=30)
plt.subplots_adjust(left=0.2, right=0.975, top=0.95, bottom=0.2)
fig.set_size_inches(10, 7)
plt.savefig("test.pdf")
raw_input()
def plot_kukv(self, show=True, save=False):
plot_extent = [self.x.min(), self.x.max(), self.y.min(), self.y.max()]
raw_ku = self.ku
raw_kv = self.kv
ku = np.sqrt(self.ku)
kv = np.sqrt(self.kv)
fig3 = pl.figure(figsize=(self.figW, self.figH))
plot = fig3.add_subplot(1, 3, 1, aspect='equal')
im5 = plot.imshow(ku,
vmin=0,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Ku (Standard Deviation)", size=self.text_size)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
fig3.colorbar(im5, fraction=0.046, pad=0.04)
plot = fig3.add_subplot(1, 3, 2, aspect='equal')
im6 = plot.imshow(kv,
vmin=0,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Ku (Standard Deviation)", size=self.text_size)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
fig3.colorbar(im6, fraction=0.046, pad=0.04)
plot = fig3.add_subplot(1, 3, 3, aspect='equal')
im7 = plot.imshow(np.sqrt(
np.divide(np.add(np.power(raw_ku, 2), np.power(raw_kv, 2)), 2)),
vmin=0,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.plot(self.Xo[:, self.dim - 1],
self.Xo[:, self.dim - 2],
'or',
markersize=self.marker_size)
plot.set_title("GPR Ku (Standard Deviation)", size=self.text_size)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
fig3.colorbar(im7, fraction=0.046, pad=0.04)
pl.subplots_adjust(left=0.05,
bottom=0.47,
right=0.95,
top=0.88,
wspace=0.35,
hspace=0.0)
if show:
pl.show()
if save:
fig3.savefig("kukv.png", dpi=300)
def multi_event_singlechan(streams, picks, clip=10.0, pre_pick=2.0,\
freqmin=False, freqmax=False, realign=False, \
cut=(-3.0,5.0), PWS=False, title=False):
"""
Function to plot data from a single channel at a single station for multiple
events - data will be alligned by their pick-time given in the picks
:type streams: List of :class:obspy.stream
:param streams: List of the streams to use, can contain more traces than\
you plan on plotting
:type picks: List of :class:PICK
:param picks: List of picks, one for each stream
:type clip: float
:param clip: Length in seconds to plot, defaults to 10.0
:type pre_pick: Float
:param pre_pick: Length in seconds to extract and plot before the pick,\
defaults to 2.0
:type freqmin: float
:param freqmin: Low cut for bandpass in Hz
:type freqmax: float
:param freqmax: High cut for bandpass in Hz
:type realign: Bool
:param realign: To compute best alignement based on correlation or not.
:type cut: tuple:
:param cut: tuple of start and end times for cut in seconds from the pick
:type PWS: bool
:param PWS: compute Phase Weighted Stack, if False, will compute linear stack
:type title: str
:param title: Plot title.
:returns: Alligned and cut traces, and new picks
"""
from eqcorrscan.utils import stacking
import copy
from eqcorrscan.core.match_filter import normxcorr2
from obspy import Stream
fig, axes = plt.subplots(len(picks) + 1, 1, sharex=True, figsize=(7, 12))
axes = axes.ravel()
traces = []
al_traces = []
# Keep input safe
plist = copy.deepcopy(picks)
st_list = copy.deepcopy(streams)
for i, pick in enumerate(plist):
if st_list[i].select(station=pick.station, \
channel='*'+pick.channel[-1]):
tr=st_list[i].select(station=pick.station, \
channel='*'+pick.channel[-1])[0]
else:
print 'No data for ' + pick.station + '.' + pick.channel
continue
tr.detrend('linear')
if freqmin:
tr.filter('bandpass', freqmin=freqmin, freqmax=freqmax)
if realign:
tr_cut = tr.copy()
tr_cut.trim(pick.time+cut[0], pick.time+cut[1],\
nearest_sample=False)
if len(tr_cut.data) <= 0.5 * (cut[1] -
cut[0]) * tr_cut.stats.sampling_rate:
print 'Not enough in the trace for ' + pick.station + '.' + pick.channel
print 'Suggest removing pick from sfile at time ' + str(
pick.time)
else:
al_traces.append(tr_cut)
else:
tr.trim(pick.time-pre_pick, pick.time+clip-pre_pick,\
nearest_sample=False)
if len(tr.data) == 0:
print 'No data in the trace for ' + pick.station + '.' + pick.channel
print 'Suggest removing pick from sfile at time ' + str(pick.time)
continue
traces.append(tr)
if realign:
shift_len = int(0.25 * (cut[1] - cut[0]) *
al_traces[0].stats.sampling_rate)
shifts = stacking.align_traces(al_traces, shift_len)
for i in xrange(len(shifts)):
print 'Shifting by ' + str(shifts[i]) + ' seconds'
pick.time -= shifts[i]
traces[i].trim(pick.time - pre_pick, pick.time + clip-pre_pick,\
nearest_sample=False)
# We now have a list of traces
traces = [(trace, trace.stats.starttime.datetime) for trace in traces]
traces.sort(key=lambda tup: tup[1])
traces = [trace[0] for trace in traces]
# Plot the traces
for i, tr in enumerate(traces):
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate # convert to seconds
axes[i + 1].plot(x, y, 'k', linewidth=1.1)
# axes[i+1].set_ylabel(tr.stats.starttime.datetime.strftime('%Y/%m/%d %H:%M'),\
# rotation=0)
axes[i + 1].yaxis.set_ticks([])
traces = [Stream(trace) for trace in traces]
if PWS:
linstack = stacking.PWS_stack(traces)
else:
linstack = stacking.linstack(traces)
tr=linstack.select(station=picks[0].station, \
channel='*'+picks[0].channel[-1])[0]
y = tr.data
x = np.arange(len(y))
x = x / tr.stats.sampling_rate
axes[0].plot(x, y, 'r', linewidth=2.0)
axes[0].set_ylabel('Stack', rotation=0)
axes[0].yaxis.set_ticks([])
for i, slave in enumerate(traces):
cc = normxcorr2(tr.data, slave[0].data)
axes[i + 1].set_ylabel('cc=' + str(round(np.max(cc), 2)), rotation=0)
axes[i+1].text(0.9, 0.15, str(round(np.max(slave[0].data))), \
bbox=dict(facecolor='white', alpha=0.95),\
transform=axes[i+1].transAxes)
axes[i+1].text(0.7, 0.85, slave[0].stats.starttime.datetime.strftime('%Y/%m/%d %H:%M:%S'), \
bbox=dict(facecolor='white', alpha=0.95),\
transform=axes[i+1].transAxes)
axes[-1].set_xlabel('Time (s)')
if title:
axes[0].set_title(title)
plt.subplots_adjust(hspace=0)
plt.show()
return traces, plist
def visualize_clusters(seqs, true_systems, true_cluster_ids, transform_fns,
plot_filepath):
"""Visualizes learned params with colors of ground truth clusters.
Only plots the first two dimensions of learned params.
Args:
seqs: A list of numpy arrays.
true_systems: A list of LinearDynamicalSystem objects.
true_cluster_ids: A numpy array of ground truth cluster ids.
transform_fns: The transfrom fns for clustering.
filepath: Path to save the plot.
"""
num_subplots = len(transform_fns)
subplot_id = 1
subplot_cols = (num_subplots + 1) / 2
pylab.figure(figsize=(4 * 2, 4 * subplot_cols))
if true_systems is not None:
num_subplots += 1
subplot_cols = (num_subplots + 1) / 2
pylab.figure(figsize=(4 * 2, 4 * subplot_cols))
pylab.subplot(subplot_cols, 2, subplot_id)
subplot_id += 1
ax = sns.scatterplot(x=[a.get_spectrum()[0] for a in true_systems],
y=[a.get_spectrum()[1] for a in true_systems],
palette=sns.color_palette(
'Set2',
np.max(true_cluster_ids) + 1),
hue=true_cluster_ids)
ax.set(xlabel='true eig value 1', ylabel='true eig value 2')
ax.set_title('Ground Truth')
plot_data_collection = None
for i, k in enumerate(transform_fns.keys()):
pylab.subplot(subplot_cols, 2, subplot_id)
subplot_id += 1
learned_params = [transform_fns[k](s) for s in seqs]
# pylint: disable=g-complex-comprehension
plot_data = {str(i): [p[i] for p in learned_params] for i \
in xrange(len(learned_params[0]))}
plot_data['method'] = [k] * len(learned_params)
plot_data['true_cluster_id'] = list(true_cluster_ids)
if plot_data_collection is None:
plot_data_collection = plot_data
else:
for data_key in plot_data:
plot_data_collection[data_key].extend(plot_data[data_key])
ax = sns.scatterplot(x=[p[0] for p in learned_params],
y=[p[1] for p in learned_params],
palette=sns.color_palette(
'Set2', len(np.unique(true_cluster_ids))),
hue=true_cluster_ids)
ax.set(xlabel='Learned param 1', ylabel='Learned param 2')
ax.set_title(k)
pd.DataFrame(plot_data_collection).to_csv(plot_filepath + '_data.csv')
pylab.subplots_adjust(hspace=0.3, wspace=0.4)
output = six.StringIO()
pylab.savefig(output, format='png')
image = output.getvalue()
with open(plot_filepath, 'w+') as f:
f.write(image)
def pretty_template_plot(template, size=(18.5, 10.5), save=False, title=False,\
background=False):
"""
Function to make a pretty plot of a single template, designed to work better
than the default obspy plotting routine for short data lengths.
:type template: :class: obspy.Stream
:param template: Template stream to plot
:type size: tuple
:param size: tuple of plot size
:type save: Boolean
:param save: if False will plot to screen, if True will save
:type title: Boolean
:param title: String if set will be the plot title
:type backrgound: :class: obspy.stream
:param background: Stream to plot the template within.
"""
fig, axes = plt.subplots(len(template), 1, sharex=True, figsize=size)
if len(template) > 1:
axes = axes.ravel()
else:
return
if not background:
mintime = template.sort(['starttime'])[0].stats.starttime
else:
mintime = background.sort(['starttime'])[0].stats.starttime
template.sort(['network', 'station', 'starttime'])
for i, tr in enumerate(template):
delay = tr.stats.starttime - mintime
delay *= tr.stats.sampling_rate
y = tr.data
x = np.arange(len(y))
x += delay
x = x / tr.stats.sampling_rate
# x=np.arange(delay, (len(y)*tr.stats.sampling_rate)+delay,\
# tr.stats.sampling_rate)
if background:
btr=background.select(station=tr.stats.station, \
channel=tr.stats.channel)[0]
bdelay = btr.stats.starttime - mintime
bdelay *= btr.stats.sampling_rate
by = btr.data
bx = np.arange(len(by))
bx += bdelay
bx = bx / btr.stats.sampling_rate
axes[i].plot(bx, by, 'k', linewidth=1)
axes[i].plot(x, y, 'r', linewidth=1.1)
else:
axes[i].plot(x, y, 'k', linewidth=1.1)
print tr.stats.station + ' ' + str(len(x)) + ' ' + str(len(y))
axes[i].set_ylabel(tr.stats.station + '.' + tr.stats.channel,
rotation=0)
axes[i].yaxis.set_ticks([])
axes[i - 1].set_xlabel('Time (s) from start of template')
plt.subplots_adjust(hspace=0)
if title:
axes[0].set_title(title)
if not save:
plt.show()
plt.close()
else:
plt.savefig(save)
#Give a sample plot
pl.subplot(337)
bl = bls[0]
i,j = np.argwhere(AntNos==bl[0]).squeeze(),np.argwhere(AntNos==bl[1]).squeeze()
pl.vlines(D_ij[pol][bl]/bl_len[bl],0,1.1,color='k')
pl.vlines((Tau[pol][i]-Tau[pol][j])/bl_len[bl],0,1.1,color='r')
pl.vlines((-1,1),0,1.1,linestyles='dotted',color='k')
pl.plot(delays/bl_len[bl],DD[pol][bl],'b')
pl.title('Sample baseline, %d_%d'%bl)
pl.xlim([-1.5,1.5])
pl.ylim([0,1.1])
pl.xlabel('Delay, baseline lenghth.')
pl.ylabel('Amplitude')
pl.subplots_adjust(wspace=0,hspace=0,top=0.95,bottom=0.05)
pl.draw()
figcnt += 1
#Plot a matrix of all delays
for pol in pols:
pl.figure(figcnt)
DDD = np.zeros((Nant,Nant))
for i,ant1 in enumerate(AntNos):
for j,ant2 in enumerate(AntNos):
if not (ant1,ant2) in D_ij[pol].keys(): continue
DDD[i,j] = D_ij[pol][(ant1,ant2)]
DDD = np.ma.array(DDD,mask=np.where(DDD==0,1,0))
pl.matshow(DDD,aspect='auto')
pl.colorbar()
pl.draw()
def plot_synth_real(real_template, synthetic, channels=False):
"""
Plot multiple channels of data for real data and synthetic
:type real_template: obspy.Stream
:param real_template: Stream of the real template
:type synthetic: obspy.Stream
:param synthetic: Stream of synthetic template
:type channels: List of str
:param channels: List of tuples of (station, channel) to plot, default is\
False, which plots all.
"""
from obspy.signal.cross_correlation import xcorr
from obspy import Stream
colours = ['k', 'r']
labels = ['Real', 'Synthetic']
if channels:
real = []
synth = []
for stachan in channels:
real.append(real_template.select(station=stachan[0],\
channel=stachan[1]))
synth.append(synthetic.select(station=stachan[0],\
channel=stachan[1]))
real_template = Stream(real)
synthetic = Stream(synth)
# Extract the station and channels
stachans = list(set([(tr.stats.station, tr.stats.channel)\
for tr in real_stream]))
fig, axes = plt.subplots(len(stachans), 1, sharex=True, figsize=(5, 10))
axes = axes.ravel()
for i, stachan in enumerate(stachans):
real_tr=real_template.select(station=stachan[0],\
channel=stachan[1])[0]
synth_tr=synthetic.select(station=stachan[0],\
channel=stachan[1])[0]
shift, corr = xcorr(real_tr, synth_tr, 2)
print 'Shifting by: ' + str(shift) + ' samples'
if corr < 0:
synth_tr.data = synth_tr.data * -1
corr = corr * -1
if shift < 0:
synth_tr.data = synth_tr.data[abs(shift):]
real_tr.data = real_tr.data[0:len(synth_tr.data)]
elif shift > 0:
real_tr.data = real_tr.data[abs(shift):]
synth_tr.data = synth_tr.data[0:len(real_tr.data)]
for j, tr in enumerate([real_tr, synth_tr]):
y = tr.data
y = y / float(max(abs(y)))
x = np.arange(0, len(y))
x = x / tr.stats.sampling_rate
axes[i].plot(x, y, colours[j], linewidth=2.0, label=labels[j])
axes[i].get_yaxis().set_ticks([])
axes[i].set_ylabel(stachan[0]+'.'+stachan[1]+' cc='+str(round(corr,2)),\
rotation=0)
plt.subplots_adjust(hspace=0)
# axes[0].legend()
axes[-1].set_xlabel('Time (s)')
plt.show()
for i in range(len(CELLS)):
Rm_data[i] = 1e-6 / CELLS[i]['Gl']
soma1, stick1, params1 = adjust_model_prop(Rm_data[i], soma, stick)
Rtf_model[i] = get_the_mean_transfer_resistance_to_soma(
soma1, stick1, params1)
print('---------------------------------------------------')
print('Comparison between model and data for Tm')
print('MODEL, mean = ', 1e-6 * Rtf_model.mean(), 'ms +/-',
1e-6 * Rtf_model.std())
print('---------------------------------------------------')
fig, [ax, ax2] = plt.subplots(1, 2, figsize=(8, 3))
plt.subplots_adjust(bottom=.3, wspace=.4)
# histogram
# ax.hist(1e3*Rtf_data, color='r', label='data')
ax.hist(1e-6 * Rtf_model, color='b', label='model')
ax.legend(frameon=False, prop={'size': 'x-small'}, loc='best')
set_plot(ax,
xlabel='transfer resistance \n to soma $(M \Omega)$',
ylabel='cell #')
# correl Rm-Tm
ax2.plot(Rm_data, 1e-6 * Rtf_model, 'ob', label='model')
ax2.legend(prop={'size': 'xx-small'}, loc='best')
set_plot(ax2,
xlabel='somatic input \n resistance $(M \Omega)$',
ylabel='transfer resistance \n to soma $(M \Omega)$')
plt.show()
def select_windows(data_trace,
synthetic_trace,
event_latitude,
event_longitude,
event_depth_in_km,
station_latitude,
station_longitude,
minimum_period,
maximum_period,
min_cc=0.10,
max_noise=0.10,
max_noise_window=0.4,
min_velocity=2.4,
threshold_shift=0.30,
threshold_correlation=0.75,
min_length_period=1.5,
min_peaks_troughs=2,
max_energy_ratio=10.0,
min_envelope_similarity=0.2,
verbose=False,
plot=False):
"""
Window selection algorithm for picking windows suitable for misfit
calculation based on phase differences.
Returns a list of windows which might be empty due to various reasons.
This function is really long and a lot of things. For a more detailed
description, please see the LASIF paper.
:param data_trace: The data trace.
:type data_trace: :class:`~obspy.core.trace.Trace`
:param synthetic_trace: The synthetic trace.
:type synthetic_trace: :class:`~obspy.core.trace.Trace`
:param event_latitude: The event latitude.
:type event_latitude: float
:param event_longitude: The event longitude.
:type event_longitude: float
:param event_depth_in_km: The event depth in km.
:type event_depth_in_km: float
:param station_latitude: The station latitude.
:type station_latitude: float
:param station_longitude: The station longitude.
:type station_longitude: float
:param minimum_period: The minimum period of the data in seconds.
:type minimum_period: float
:param maximum_period: The maximum period of the data in seconds.
:type maximum_period: float
:param min_cc: Minimum normalised correlation coefficient of the
complete traces.
:type min_cc: float
:param max_noise: Maximum relative noise level for the whole trace.
Measured from maximum amplitudes before and after the first arrival.
:type max_noise: float
:param max_noise_window: Maximum relative noise level for individual
windows.
:type max_noise_window: float
:param min_velocity: All arrivals later than those corresponding to the
threshold velocity [km/s] will be excluded.
:type min_velocity: float
:param threshold_shift: Maximum allowable time shift within a window,
as a fraction of the minimum period.
:type threshold_shift: float
:param threshold_correlation: Minimum normalised correlation coeeficient
within a window.
:type threshold_correlation: float
:param min_length_period: Minimum length of the time windows relative to
the minimum period.
:type min_length_period: float
:param min_peaks_troughs: Minimum number of extrema in an individual
time window (excluding the edges).
:type min_peaks_troughs: float
:param max_energy_ratio: Maximum energy ratio between data and
synthetics within a time window. Don't make this too small!
:type max_energy_ratio: float
:param min_envelope_similarity: The minimum similarity of the envelopes of
both data and synthetics. This essentially assures that the
amplitudes of data and synthetics can not diverge too much within a
window. It is a bit like the inverse of the ratio of both envelopes
so a value of 0.2 makes sure neither amplitude can be more then 5
times larger than the other.
:type min_envelope_similarity: float
:param verbose: No output by default.
:type verbose: bool
:param plot: Create a plot of the algortihm while it does its work.
:type plot: bool
"""
# Shortcuts to frequently accessed variables.
data_starttime = data_trace.stats.starttime
data_delta = data_trace.stats.delta
dt = data_trace.stats.delta
npts = data_trace.stats.npts
synth = synthetic_trace.data
data = data_trace.data
times = data_trace.times()
# Fill cache if necessary.
if not TAUPY_MODEL_CACHE:
from obspy.taup import TauPyModel # NOQA
TAUPY_MODEL_CACHE["model"] = TauPyModel("AK135")
model = TAUPY_MODEL_CACHE["model"]
# -------------------------------------------------------------------------
# Geographical calculations and the time of the first arrival.
# -------------------------------------------------------------------------
dist_in_deg = geodetics.locations2degrees(station_latitude,
station_longitude,
event_latitude, event_longitude)
dist_in_km = geodetics.calcVincentyInverse(
station_latitude, station_longitude, event_latitude,
event_longitude)[0] / 1000.0
# Get only a couple of P phases which should be the first arrival
# for every epicentral distance. Its quite a bit faster than calculating
# the arrival times for every phase.
# Assumes the first sample is the centroid time of the event.
tts = model.get_travel_times(source_depth_in_km=event_depth_in_km,
distance_in_degree=dist_in_deg,
phase_list=["ttp"])
# Sort just as a safety measure.
tts = sorted(tts, key=lambda x: x.time)
first_tt_arrival = tts[0].time
# -------------------------------------------------------------------------
# Window settings
# -------------------------------------------------------------------------
# Number of samples in the sliding window. Currently, the length of the
# window is set to a multiple of the dominant period of the synthetics.
# Make sure it is an uneven number; just to have a trivial midpoint
# definition and one sample does not matter much in any case.
window_length = int(round(float(2 * minimum_period) / dt))
if not window_length % 2:
window_length += 1
# Use a Hanning window. No particular reason for it but its a well-behaved
# window and has nice spectral properties.
taper = np.hanning(window_length)
# =========================================================================
# check if whole seismograms are sufficiently correlated and estimate
# noise level
# =========================================================================
# Overall Correlation coefficient.
norm = np.sqrt(np.sum(data**2)) * np.sqrt(np.sum(synth**2))
cc = np.sum(data * synth) / norm
if verbose:
_log_window_selection(data_trace.id,
"Correlation Coefficient: %.4f" % cc)
# Estimate noise level from waveforms prior to the first arrival.
idx_end = int(np.ceil((first_tt_arrival - 0.5 * minimum_period) / dt))
idx_end = max(10, idx_end)
idx_start = int(np.ceil((first_tt_arrival - 2.5 * minimum_period) / dt))
idx_start = max(10, idx_start)
if idx_start >= idx_end:
idx_start = max(0, idx_end - 10)
abs_data = np.abs(data)
noise_absolute = abs_data[idx_start:idx_end].max()
noise_relative = noise_absolute / abs_data.max()
if verbose:
_log_window_selection(data_trace.id,
"Absolute Noise Level: %e" % noise_absolute)
_log_window_selection(data_trace.id,
"Relative Noise Level: %e" % noise_relative)
# Basic global rejection criteria.
accept_traces = True
if (cc < min_cc) and (noise_relative > max_noise / 3.0):
msg = "Correlation %.4f is below threshold of %.4f" % (cc, min_cc)
if verbose:
_log_window_selection(data_trace.id, msg)
accept_traces = msg
if noise_relative > max_noise:
msg = "Noise level %.3f is above threshold of %.3f" % (noise_relative,
max_noise)
if verbose:
_log_window_selection(data_trace.id, msg)
accept_traces = msg
# Calculate the envelope of both data and synthetics. This is to make sure
# that the amplitude of both is not too different over time and is
# used as another selector. Only calculated if the trace is generally
# accepted as it is fairly slow.
if accept_traces is True:
data_env = obspy.signal.filter.envelope(data)
synth_env = obspy.signal.filter.envelope(synth)
# -------------------------------------------------------------------------
# Initial Plot setup.
# -------------------------------------------------------------------------
# All the plot calls are interleaved. I realize this is really ugly but
# the alternative would be to either have two functions (one with plots,
# one without) or split the plotting function in various subfunctions,
# neither of which are acceptable in my opinion. The impact on
# performance is minimal if plotting is turned off: all imports are lazy
# and a couple of conditionals are cheap.
if plot:
import matplotlib.pylab as plt # NOQA
import matplotlib.patheffects as PathEffects # NOQA
if accept_traces is True:
plt.figure(figsize=(18, 12))
plt.subplots_adjust(left=0.05,
bottom=0.05,
right=0.98,
top=0.95,
wspace=None,
hspace=0.0)
grid = (31, 1)
# Axes showing the data.
data_plot = plt.subplot2grid(grid, (0, 0), rowspan=8)
else:
# Only show one axes it the traces are not accepted.
plt.figure(figsize=(18, 3))
# Plot envelopes if needed.
if accept_traces is True:
plt.plot(times,
data_env,
color="black",
alpha=0.5,
lw=0.4,
label="data envelope")
plt.plot(synthetic_trace.times(),
synth_env,
color="#e41a1c",
alpha=0.4,
lw=0.5,
label="synthetics envelope")
plt.plot(times, data, color="black", label="data", lw=1.5)
plt.plot(synthetic_trace.times(),
synth,
color="#e41a1c",
label="synthetics",
lw=1.5)
# Symmetric around y axis.
middle = data.mean()
d_max, d_min = data.max(), data.min()
r = max(d_max - middle, middle - d_min) * 1.1
ylim = (middle - r, middle + r)
xlim = (times[0], times[-1])
plt.ylim(*ylim)
plt.xlim(*xlim)
offset = (xlim[1] - xlim[0]) * 0.005
plt.vlines(first_tt_arrival, ylim[0], ylim[1], colors="#ff7f00", lw=2)
plt.text(first_tt_arrival + offset,
ylim[1] - (ylim[1] - ylim[0]) * 0.02,
"first arrival",
verticalalignment="top",
horizontalalignment="left",
color="#ee6e00",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.vlines(first_tt_arrival - minimum_period / 2.0,
ylim[0],
ylim[1],
colors="#ff7f00",
lw=2)
plt.text(first_tt_arrival - minimum_period / 2.0 - offset,
ylim[0] + (ylim[1] - ylim[0]) * 0.02,
"first arrival - min period / 2",
verticalalignment="bottom",
horizontalalignment="right",
color="#ee6e00",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
for velocity in [6, 5, 4, 3, min_velocity]:
tt = dist_in_km / velocity
plt.vlines(tt, ylim[0], ylim[1], colors="gray", lw=2)
if velocity == min_velocity:
hal = "right"
o_s = -1.0 * offset
else:
hal = "left"
o_s = offset
plt.text(tt + o_s,
ylim[0] + (ylim[1] - ylim[0]) * 0.02,
str(velocity) + " km/s",
verticalalignment="bottom",
horizontalalignment=hal,
color="0.15")
plt.vlines(dist_in_km / min_velocity + minimum_period / 2.0,
ylim[0],
ylim[1],
colors="gray",
lw=2)
plt.text(dist_in_km / min_velocity + minimum_period / 2.0 - offset,
ylim[1] - (ylim[1] - ylim[0]) * 0.02,
"min surface velocity + min period / 2",
verticalalignment="top",
horizontalalignment="right",
color="0.15",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.hlines(noise_absolute,
xlim[0],
xlim[1],
linestyle="--",
color="gray")
plt.hlines(-noise_absolute,
xlim[0],
xlim[1],
linestyle="--",
color="gray")
plt.text(offset,
noise_absolute + (ylim[1] - ylim[0]) * 0.01,
"noise level",
verticalalignment="bottom",
horizontalalignment="left",
color="0.15",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.legend(loc="lower right",
fancybox=True,
framealpha=0.5,
fontsize="small")
plt.gca().xaxis.set_ticklabels([])
# Plot the basic global information.
ax = plt.gca()
txt = (
"Total CC Coeff: %.4f\nAbsolute Noise: %e\nRelative Noise: %.3f" %
(cc, noise_absolute, noise_relative))
ax.text(0.01,
0.95,
txt,
transform=ax.transAxes,
fontdict=dict(fontsize="small", ha='left', va='top'),
bbox=dict(boxstyle="round", fc="w", alpha=0.8))
plt.suptitle("Channel %s" % data_trace.id, fontsize="larger")
# Show plot and return if not accepted.
if accept_traces is not True:
txt = "Rejected: %s" % (accept_traces)
ax.text(0.99,
0.95,
txt,
transform=ax.transAxes,
fontdict=dict(fontsize="small", ha='right', va='top'),
bbox=dict(boxstyle="round", fc="red", alpha=1.0))
plt.show()
if accept_traces is not True:
return []
# Initialise masked arrays. The mask will be set to True where no
# windows are chosen.
time_windows = np.ma.ones(npts)
time_windows.mask = False
if plot:
old_time_windows = time_windows.copy()
# Elimination Stage 1: Eliminate everything half a period before or
# after the minimum and maximum travel times, respectively.
# theoretical arrival as positive.
min_idx = int((first_tt_arrival - (minimum_period / 2.0)) / dt)
max_idx = int(
math.ceil((dist_in_km / min_velocity + minimum_period / 2.0) / dt))
time_windows.mask[:min_idx + 1] = True
time_windows.mask[max_idx:] = True
if plot:
plt.subplot2grid(grid, (8, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="TRAVELTIME ELIMINATION")
old_time_windows = time_windows.copy()
# -------------------------------------------------------------------------
# Compute sliding time shifts and correlation coefficients for time
# frames that passed the traveltime elimination stage.
# -------------------------------------------------------------------------
# Allocate arrays to collect the time dependent values.
sliding_time_shift = np.ma.zeros(npts, dtype="float32")
sliding_time_shift.mask = True
max_cc_coeff = np.ma.zeros(npts, dtype="float32")
max_cc_coeff.mask = True
for start_idx, end_idx, midpoint_idx in _window_generator(
npts, window_length):
if not min_idx < midpoint_idx < max_idx:
continue
# Slice windows. Create a copy to be able to taper without affecting
# the original time series.
data_window = data[start_idx:end_idx].copy() * taper
synthetic_window = \
synth[start_idx: end_idx].copy() * taper
# Elimination Stage 2: Skip windows that have essentially no energy
# to avoid instabilities. No windows can be picked in these.
if synthetic_window.ptp() < synth.ptp() * 0.001:
time_windows.mask[midpoint_idx] = True
continue
# Calculate the time shift. Here this is defined as the shift of the
# synthetics relative to the data. So a value of 2, for instance, means
# that the synthetics are 2 timesteps later then the data.
cc = np.correlate(data_window, synthetic_window, mode="full")
time_shift = cc.argmax() - window_length + 1
# Express the time shift in fraction of the minimum period.
sliding_time_shift[midpoint_idx] = (time_shift * dt) / minimum_period
# Normalized cross correlation.
max_cc_value = cc.max() / np.sqrt(
(synthetic_window**2).sum() * (data_window**2).sum())
max_cc_coeff[midpoint_idx] = max_cc_value
if plot:
plt.subplot2grid(grid, (9, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="NO ENERGY IN CC WINDOW")
# Axes with the CC coeffs
plt.subplot2grid(grid, (15, 0), rowspan=4)
plt.hlines(0, xlim[0], xlim[1], color="lightgray")
plt.hlines(-threshold_shift,
xlim[0],
xlim[1],
color="gray",
linestyle="--")
plt.hlines(threshold_shift,
xlim[0],
xlim[1],
color="gray",
linestyle="--")
plt.text(5,
-threshold_shift - (2) * 0.03,
"threshold",
verticalalignment="top",
horizontalalignment="left",
color="0.15",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.plot(times,
sliding_time_shift,
color="#377eb8",
label="Time shift in fraction of minimum period",
lw=1.5)
ylim = plt.ylim()
plt.yticks([-0.75, 0, 0.75])
plt.xticks([300, 600, 900, 1200, 1500, 1800])
plt.ylim(ylim[0], ylim[1] + ylim[1] - ylim[0])
plt.ylim(-1.0, 1.0)
plt.xlim(xlim)
plt.gca().xaxis.set_ticklabels([])
plt.legend(loc="lower right",
fancybox=True,
framealpha=0.5,
fontsize="small")
plt.subplot2grid(grid, (10, 0), rowspan=4)
plt.hlines(threshold_correlation,
xlim[0],
xlim[1],
color="0.15",
linestyle="--")
plt.hlines(1, xlim[0], xlim[1], color="lightgray")
plt.hlines(0, xlim[0], xlim[1], color="lightgray")
plt.text(5,
threshold_correlation + (1.4) * 0.01,
"threshold",
verticalalignment="bottom",
horizontalalignment="left",
color="0.15",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.plot(times,
max_cc_coeff,
color="#4daf4a",
label="Maximum CC coefficient",
lw=1.5)
plt.ylim(-0.2, 1.2)
plt.yticks([0, 0.5, 1])
plt.xticks([300, 600, 900, 1200, 1500, 1800])
plt.xlim(xlim)
plt.gca().xaxis.set_ticklabels([])
plt.legend(loc="lower right",
fancybox=True,
framealpha=0.5,
fontsize="small")
# Elimination Stage 3: Mark all areas where the normalized cross
# correlation coefficient is under threshold_correlation as negative
if plot:
old_time_windows = time_windows.copy()
time_windows.mask[max_cc_coeff < threshold_correlation] = True
if plot:
plt.subplot2grid(grid, (14, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="CORRELATION COEFF THRESHOLD ELIMINATION")
# Elimination Stage 4: Mark everything with an absolute travel time
# shift of more than # threshold_shift times the dominant period as
# negative
if plot:
old_time_windows = time_windows.copy()
time_windows.mask[np.ma.abs(sliding_time_shift) > threshold_shift] = True
if plot:
plt.subplot2grid(grid, (19, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="TIME SHIFT THRESHOLD ELIMINATION")
# Elimination Stage 5: Mark the area around every "travel time shift
# jump" (based on the traveltime time difference) negative. The width of
# the area is currently chosen to be a tenth of a dominant period to
# each side.
if plot:
old_time_windows = time_windows.copy()
sample_buffer = int(np.ceil(minimum_period / dt * 0.1))
indices = np.ma.where(np.ma.abs(np.ma.diff(sliding_time_shift)) > 0.1)[0]
for index in indices:
time_windows.mask[index - sample_buffer:index + sample_buffer] = True
if plot:
plt.subplot2grid(grid, (20, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="TIME SHIFT JUMPS ELIMINATION")
# Clip both to avoid large numbers by division.
stacked = np.vstack([
np.ma.clip(synth_env,
synth_env.max() * min_envelope_similarity * 0.5,
synth_env.max()),
np.ma.clip(data_env,
data_env.max() * min_envelope_similarity * 0.5,
data_env.max())
])
# Ratio.
ratio = stacked.min(axis=0) / stacked.max(axis=0)
# Elimination Stage 6: Make sure the amplitudes of both don't vary too
# much.
if plot:
old_time_windows = time_windows.copy()
time_windows.mask[ratio < min_envelope_similarity] = True
if plot:
plt.subplot2grid(grid, (25, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="ENVELOPE AMPLITUDE SIMILARITY ELIMINATION")
if plot:
plt.subplot2grid(grid, (21, 0), rowspan=4)
plt.hlines(min_envelope_similarity,
xlim[0],
xlim[1],
color="gray",
linestyle="--")
plt.text(5,
min_envelope_similarity + (2) * 0.03,
"threshold",
verticalalignment="bottom",
horizontalalignment="left",
color="0.15",
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="white")
])
plt.plot(times,
ratio,
color="#9B59B6",
label="Envelope amplitude similarity",
lw=1.5)
plt.yticks([0, 0.2, 0.4, 0.6, 0.8, 1.0])
plt.ylim(0.05, 1.05)
plt.xticks([300, 600, 900, 1200, 1500, 1800])
plt.xlim(xlim)
plt.gca().xaxis.set_ticklabels([])
plt.legend(loc="lower right",
fancybox=True,
framealpha=0.5,
fontsize="small")
# First minimum window length elimination stage. This is cheap and if
# not done it can easily destabilize the peak-and-trough marching stage
# which would then have to deal with way more edge cases.
if plot:
old_time_windows = time_windows.copy()
min_length = \
min(minimum_period / dt * min_length_period, maximum_period / dt)
for i in flatnotmasked_contiguous(time_windows):
# Step 7: Throw away all windows with a length of less then
# min_length_period the dominant periodele
if (i.stop - i.start) < min_length:
time_windows.mask[i.start:i.stop] = True
if plot:
plt.subplot2grid(grid, (26, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="MINIMUM WINDOW LENGTH ELIMINATION 1")
# -------------------------------------------------------------------------
# Peak and trough marching algorithm
# -------------------------------------------------------------------------
final_windows = []
for i in flatnotmasked_contiguous(time_windows):
# Cut respective windows.
window_npts = i.stop - i.start
synthetic_window = synth[i.start:i.stop]
data_window = data[i.start:i.stop]
# Find extrema in the data and the synthetics.
data_p, data_t = find_local_extrema(data_window)
synth_p, synth_t = find_local_extrema(synthetic_window)
window_mask = np.ones(window_npts, dtype="bool")
closest_peaks = find_closest(data_p, synth_p)
diffs = np.diff(closest_peaks)
for idx in np.where(diffs == 1)[0]:
if idx > 0:
start = synth_p[idx - 1]
else:
start = 0
if idx < (len(synth_p) - 1):
end = synth_p[idx + 1]
else:
end = -1
window_mask[start:end] = False
closest_troughs = find_closest(data_t, synth_t)
diffs = np.diff(closest_troughs)
for idx in np.where(diffs == 1)[0]:
if idx > 0:
start = synth_t[idx - 1]
else:
start = 0
if idx < (len(synth_t) - 1):
end = synth_t[idx + 1]
else:
end = -1
window_mask[start:end] = False
window_mask = np.ma.masked_array(window_mask, mask=window_mask)
if window_mask.mask.all():
continue
for j in flatnotmasked_contiguous(window_mask):
final_windows.append((i.start + j.start, i.start + j.stop))
if plot:
old_time_windows = time_windows.copy()
time_windows.mask[:] = True
for start, stop in final_windows:
time_windows.mask[start:stop] = False
if plot:
plt.subplot2grid(grid, (27, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="PEAK AND TROUGH MARCHING ELIMINATION")
# Loop through all the time windows, remove windows not satisfying the
# minimum number of peaks and troughs per window. Acts mainly as a
# safety guard.
old_time_windows = time_windows.copy()
for i in flatnotmasked_contiguous(old_time_windows):
synthetic_window = synth[i.start:i.stop]
data_window = data[i.start:i.stop]
data_p, data_t = find_local_extrema(data_window)
synth_p, synth_t = find_local_extrema(synthetic_window)
if np.min([len(synth_p), len(synth_t), len(data_p), len(data_t)]) < \
min_peaks_troughs:
time_windows.mask[i.start:i.stop] = True
if plot:
plt.subplot2grid(grid, (28, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="PEAK/TROUGH COUNT ELIMINATION")
# Second minimum window length elimination stage.
if plot:
old_time_windows = time_windows.copy()
min_length = \
min(minimum_period / dt * min_length_period, maximum_period / dt)
for i in flatnotmasked_contiguous(time_windows):
# Step 7: Throw away all windows with a length of less then
# min_length_period the dominant period.
if (i.stop - i.start) < min_length:
time_windows.mask[i.start:i.stop] = True
if plot:
plt.subplot2grid(grid, (29, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="MINIMUM WINDOW LENGTH ELIMINATION 2")
# Final step, eliminating windows with little energy.
final_windows = []
for j in flatnotmasked_contiguous(time_windows):
# Again assert a certain minimal length.
if (j.stop - j.start) < min_length:
continue
# Compare the energy in the data window and the synthetic window.
data_energy = (data[j.start:j.stop]**2).sum()
synth_energy = (synth[j.start:j.stop]**2).sum()
energies = sorted([data_energy, synth_energy])
if energies[1] > max_energy_ratio * energies[0]:
if verbose:
_log_window_selection(
data_trace.id,
"Deselecting window due to energy ratio between "
"data and synthetics.")
continue
# Check that amplitudes in the data are above the noise
if noise_absolute / data[j.start: j.stop].ptp() > \
max_noise_window:
if verbose:
_log_window_selection(
data_trace.id,
"Deselecting window due having no amplitude above the "
"signal to noise ratio.")
final_windows.append((j.start, j.stop))
if plot:
old_time_windows = time_windows.copy()
time_windows.mask[:] = True
for start, stop in final_windows:
time_windows.mask[start:stop] = False
if plot:
plt.subplot2grid(grid, (30, 0), rowspan=1)
_plot_mask(time_windows,
old_time_windows,
name="LITTLE ENERGY ELIMINATION")
if verbose:
_log_window_selection(
data_trace.id, "Done, Selected %i window(s)" % len(final_windows))
# Final step is to convert the index value windows to actual times.
windows = []
for start, stop in final_windows:
start = data_starttime + start * data_delta
stop = data_starttime + stop * data_delta
windows.append((start, stop))
if plot:
# Plot the final windows to the data axes.
import matplotlib.transforms as mtransforms # NOQA
ax = data_plot
trans = mtransforms.blended_transform_factory(ax.transData,
ax.transAxes)
for start, stop in final_windows:
ax.fill_between([start * data_delta, stop * data_delta],
0,
1,
facecolor="#CDDC39",
alpha=0.5,
transform=trans)
plt.show()
return windows
y = np.r_[0:1:0.1, 9:10:0.1] #fig,(ax,ax2) = plt.subplots(1, 2, sharey=True) fig,(ax2,ax) = plt.subplots(2, 1, sharex=True) # plot the same data on both axes ax.plot(x, y, 'bo') ax2.plot(x, y, 'bo') # zoom-in / limit the view to different portions of the data #ax.set_xlim(0,1) # most of the data ax.set_ylim(0,1) # most of the data #ax2.set_xlim(9,10) # outliers only ax2.set_ylim(9,10) # outliers only # hide the spines between ax and ax2 #ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) #ax2.spines['left'].set_visible(False) ax2.spines['bottom'].set_visible(False) ax.yaxis.tick_left() ax.tick_params(labeltop='off') # don't put tick labels at the top #ax2.yaxis.tick_right() ax2.yaxis.tick_left() # Make the spacing between the two axes a bit smaller plt.subplots_adjust(wspace=0.15) plt.show()
noise_scale)
theta[t, :] = theta_next.reshape(1, Nosc)
phase_dynamics[t, :] = func_oscillator_approx_fourier_series(
theta[t, :], K1, K2, omega)
for i in range(Nosc):
theta_unwrap = np.unwrap(deepcopy(theta[t - 1:t + 1, i]))
dtheta[t, i] = (theta_unwrap[1] - theta_unwrap[0]) / h
#%% plot phase
axis = np.arange(theta.shape[0])
fig = plt.figure(figsize=(20, 8))
gs = fig.add_gridspec(Nosc, 1)
plt.subplots_adjust(wspace=0.4, hspace=0.0)
for n in range(Nosc):
#####################
ax11 = fig.add_subplot(gs[n, 0])
plt.plot(axis, theta[:, n])
plt.ylabel('$\\phi_{%d}$' % (n + 1))
plt.xticks(np.arange(0, Nt + 1, int(
Nt / 2))) # plt.xticks(np.arange(0, Nt+1, int(Nt/3)))
plt.yticks([0, np.pi, 2 * np.pi], labels=['0', '$\\pi$', '$2 \\pi$'])
plt.grid()
plt.xlabel('# sample')
plt.show()
plt.ylabel("Correlation", fontsize=12)
plt.ylim([-0.65, 1.])
plt.yticks([-0.6, -0.4, -0.2, 0., 0.2, 0.4, 0.6, 0.8, 1.], fontsize=12)
plt.axhline(y=0., color='k', linestyle='-', linewidth=2.)
plt.xlim([-3., 3.])
plt.xlabel("Lag [years]", fontsize=12)
return 0
ti = np.arange(-float(t / 12) + 1. / 12, float(t / 12) - 1. / 12., 1. / 12.)
fig = plt.figure(figsize=(10, 10))
ax = plt.subplots_adjust(left=0.1,
right=0.98,
bottom=0.06,
top=0.95,
hspace=0.35,
wspace=0.3)
titles = [
"Tropical mean - Low clouds", "Nino3.4 region - Low clouds",
"Nino3.4 region - Tropical mean", "Low clouds - Tropical cloud cover"
]
for i in range(4):
ax = plt.subplot(2, 2, i + 1)
plt.title(titles[i])
for j in range(m):
plt.plot(t, corr[j, i], 'k', alpha=0.2)
plt.plot(t, np.median(corr[:, i], axis=0), 'k', linewidth=2.)
#plt.scatter(z_samples[i,:,0],z_samples[i,:,1])
plt.axis('square');
plt.title('q(z|x={})'.format(y[i]))
plt.xlim([xmin,xmax])
plt.ylim([xmin,xmax])
plt.xticks([])
plt.yticks([]);
plt.subplot(2,5,5+i+1)
plt.contour(xrange, xrange, np.exp(logprior+llh[i]).reshape(300,300).T, cmap='Greens')
plt.axis('square');
plt.title('p(z|x={})'.format(y[i]))
plt.xlim([xmin,xmax])
plt.ylim([xmin,xmax])
plt.xticks([])
plt.yticks([]);
plt.text(-60,20,'KLR Loss: %f, NELBO: %f, KL0: %f, KL1: %f, KL2: %f, KL3: %f, KL4: %f' % (dl, NELBO, KL0, KL1, KL2, KL3, KL4))
plt.text(-28,-7,'KLAVG: %f' %(KLAVG))
plt.savefig('FiguresJCKL\Fig %i'%(j))
plt.close()
plt.subplot(2,1,1)
plt.plot(KLAVGBRUH)
plt.xlabel('Iterations (x100)')
plt.ylabel('Avg KL Div')
plt.subplot(2,1,2)
plt.plot(ISTHISLOSS)
plt.xlabel('Iterations (x100)')
plt.ylabel('KLR Loss')
plt.subplots_adjust(top=0.95,bottom=0.15,right=0.95,hspace=0.4)
plt.savefig('FiguresJCKL\Diag Plot', bbox_inches='tight')
plt.close()
plt.rc('ytick.minor', width=2.0)
plt.rc('lines', markersize=8, markeredgewidth=0.0, linewidth=2.0)
#plt.rcParams['ps.fonttype'] = 42
#plt.rcParams['pdf.fonttype'] = 42
fig = plt.figure(num=1, figsize=(9, 7), dpi=600, facecolor='w', edgecolor='k')
left = 0.10 # the left side of the subplots of the figure
right = 0.94 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.96 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.0 # the amount of height reserved for white space between subplots
plt.subplots_adjust(left=left,
bottom=bottom,
right=right,
top=top,
wspace=wspace,
hspace=hspace)
ax1 = plt.subplot(1, 1, 1)
hist = plt.hist(
peak_sep_arr_norm,
bins=binarr,
weights=np.ones(len(peak_sep_arr_norm)) / len(peak_sep_arr_norm),
density=False
) #note weights and density arguement make total of y values 1, regardless of bin values
plt.xticks(binarr, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(0, 0.25)
plt.xlabel(r'Period/$\tau_{dyn}^{' + galaxyname + '}$', fontsize=15)
plt.ylabel('Fraction of Orbits', fontsize=15)
import numpy as np
from scipy.linalg import expm
import matplotlib.pylab as plt
if __name__ == '__main__':
A = np.array([[-1, 1], [0, -1]])
B = np.array([[-1, 5], [0, -2]])
t = np.linspace(0.1, 5, 50)
norm_a, norm_b = [], []
for i in t:
norm_a.append(np.linalg.norm(expm(A * i)))
norm_b.append(np.linalg.norm(expm(B * i)))
plt.xkcd()
fig = plt.figure()
ax = fig.add_subplot(111)
plt.subplots_adjust(bottom=0.15, left=0.15)
ax.plot(norm_a, 'b')
ax.plot(norm_b, 'k')
ticks = ax.get_xticks().astype('i')
ax.set_xticklabels(ticks, fontsize=16, weight='bold')
ticks = ax.get_yticks()
ax.set_yticklabels(ticks, fontsize=16, weight='bold')
ax.set_xlabel('Time', fontsize=18, weight='bold')
ax.set_ylabel(r'$||exp(At)||$', fontsize=18, weight='bold')
plt.savefig('spectra.pdf', axis='tight')
plt.show()
def display_collision3D(collision, fig=None, ax=None, color_blind=False):
if fig is None:
fig = plt.figure(figsize=(6, 4), dpi=100)
if ax is None:
ax = fig.add_subplot(1, 1, 1)
ax = fig.gca(projection='3d')
plt.subplots_adjust(top=0.98, bottom=0.02, right=0.98, left=0.02)
jets, muons, electrons, photons, met = collision
lines = draw_beams()
if (color_blind == False):
pmom = np.array(jets).transpose()[1:4].transpose()
origin = np.zeros((len(jets), 3))
lines += draw_jet3D(origin=origin, pmom=pmom)
pmom = np.array(muons).transpose()[1:4].transpose()
origin = np.zeros((len(muons), 3))
lines += draw_muon3D(origin=origin, pmom=pmom)
pmom = np.array(electrons).transpose()[1:4].transpose()
origin = np.zeros((len(electrons), 3))
lines += draw_electron3D(origin=origin, pmom=pmom)
pmom = np.array(photons).transpose()[1:4].transpose()
origin = np.zeros((len(photons), 3))
lines += draw_photon3D(origin=origin, pmom=pmom)
if (color_blind == True):
pmom = np.array(jets).transpose()[1:4].transpose()
origin = np.zeros((len(jets), 3))
lines += draw_jet3D(origin=origin, pmom=pmom, ls='solid', color='gray')
pmom = np.array(muons).transpose()[1:4].transpose()
origin = np.zeros((len(muons), 3))
lines += draw_muon3D(origin=origin,
pmom=pmom,
ls='dashed',
color='black')
pmom = np.array(electrons).transpose()[1:4].transpose()
origin = np.zeros((len(electrons), 3))
lines += draw_electron3D(origin=origin,
pmom=pmom,
ls='dashed',
color='gray')
pmom = np.array(photons).transpose()[1:4].transpose()
origin = np.zeros((len(photons), 3))
lines += draw_photon3D(origin=origin,
pmom=pmom,
ls='solid',
color='black')
for l in lines:
ax.add_line(l)
ax.set_xlim(-200, 200)
ax.set_ylim(-200, 200)
ax.set_zlim(-200, 200)
def plot_div(self, show=True, save=False):
init_div = svf.SpiralVectorField.get_divergence(self.u, self.v)
reg_div = svf.SpiralVectorField.get_divergence(self.ur, self.vr)
diff = init_div - reg_div
plot_extent = [self.x.min(), self.x.max(), self.y.min(), self.y.max()]
fig8 = pl.figure(figsize=(self.figW, self.figH))
plot = fig8.add_subplot(1, 3, 1, aspect='equal')
im1 = plot.imshow(init_div,
vmin=-0.5,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.set_title("Initial Divergence",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig8.colorbar(im1, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
plot = fig8.add_subplot(1, 3, 2, aspect='equal')
im2 = plot.imshow(reg_div,
vmin=-0.5,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.set_title("GPR Model Divergence",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig8.colorbar(im2, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
plot = fig8.add_subplot(1, 3, 3, aspect='equal')
im3 = plot.imshow(diff,
vmin=-0.5,
vmax=0.5,
origin='center',
extent=plot_extent,
cmap='jet')
plot.set_title("Divergence Error",
size=self.text_size,
pad=self.title_pad)
plot.set_xlim(-5, 5)
plot.set_ylim(-5, 5)
plot.tick_params(labelsize=self.tick_label_size)
cbar = fig8.colorbar(im3, fraction=0.046, pad=0.04)
cbar.ax.tick_params(labelsize=self.cbar_label_size)
pl.subplots_adjust(left=0.06,
bottom=0.47,
right=0.94,
top=0.88,
wspace=0.48,
hspace=0.0)
if show:
pl.show()
if save:
fig8.savefig("divergence.png", dpi=300)
del ode, J, x, ts
try:
from matplotlib import pylab
except ImportError:
print("matplotlib not available")
raise SystemExit
import numpy as np
ii = np.asarray([v[0] for v in history])
tt = np.asarray([v[1] for v in history])
xx = np.asarray([v[2] for v in history])
pylab.suptitle('Rober')
pylab.subplot(2, 2, 1)
pylab.subplots_adjust(wspace=0.3)
pylab.semilogy(
ii[:-1],
np.diff(tt),
)
pylab.xlabel('step number')
pylab.ylabel('timestep')
for i in range(0, 3):
pylab.subplot(2, 2, i + 2)
pylab.semilogx(tt, xx[:, i], "rgb"[i])
pylab.xlabel('t')
pylab.ylabel('x%d' % i)
pylab.show()