def create_scurve(scurves, scurveFile):
parse_functions.removing_existing_file(scurveFile)
print("Creating scurve file: " + scurveFile)
import matplotlib.pylab as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.set_ylim([0, 9])
for i in range(1,10,1):
plt.axhline(y = i, color ="black", linestyle ="--", linewidth=0.1, dashes=(5, 10))
for strategy in scurves:
length = len(scurves[strategy])
middle = int(length/2-1)
markers_on = [0, middle, length-1]
#plt.plot(scurves[strategy], config_generator.symbols[strategy], color=config_generator.colors[strategy], markevery=marks, label=config_generator.abbreviations[strategy])
plt.plot(scurves[strategy], config_generator.symbols[strategy], color=config_generator.colors[strategy], markevery=markers_on, label=config_generator.abbreviations[strategy], dashes=(5, 5))
plt.ylabel('Normalized Times')
plt.locator_params(nbins=3)
plt.xticks([]) # hide axis x
plt.legend() # show line names
plt.savefig(scurveFile, format='eps')
def set_axes_and_legend(ax):
ax.spines['left'].set_position('zero')
plt.locator_params(axis='x', nbins=4)
ax.axes.yaxis.set_ticks([])
if has_seaborn:
sns.despine(ax=ax)
plt.legend(loc='upper right', fontsize='x-small')
def multiPlot(myData):
ax=plt.subplot2grid((ncol,nrow),(yPanel,xPanel))
ax.hist(df[quantVar[myData]],alpha=0.5)#alpha??
ax.set_xlabel(quantVar[myData],fontsize=14,fontweight='bold')
#ax.set_ylabel('Y',fontsize=14,fontweight='bold',rotation='Vertical')
plt.locator_params(axis = 'x', nbins = 2)
ax.tick_params(labelsize=14)
def set_axes_and_legend(ax):
ax.spines["left"].set_position("zero")
plt.locator_params(axis="x", nbins=4)
ax.axes.yaxis.set_ticks([])
if has_seaborn:
sns.despine(ax=ax)
plt.legend(loc="upper right", fontsize="x-small")
def plot_pr_curve(precision, recall, title):
plt.figure()
plt.rcParams['figure.figsize'] = 7, 5
plt.locator_params(axis = 'x', nbins = 5)
plt.plot(precision, recall, 'b-', linewidth=4.0, color = '#B0017F')
plt.title(title)
plt.xlabel('Precision')
plt.ylabel('Recall')
plt.rcParams.update({'font.size': 16})
def plot_convolved_rates_in_time(spike_data, prm):
print('plotting spike rastas...')
save_path = prm.get_output_path() + '/Figures/ConvolvedRates_InTime'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
for cluster_index in range(len(spike_data)):
cluster_index = spike_data.cluster_id.values[cluster_index] - 1
spikes_on_track = plt.figure(figsize=(4, 5))
ax = spikes_on_track.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
firing_rate = spike_data.loc[cluster_index].spike_rate_in_time
speed = spike_data.loc[cluster_index].speed_rate_in_time
x_max = np.max(firing_rate)
ax.plot(firing_rate, speed, '|', color='Black', markersize=4)
plt.ylabel('Firing rate (Hz)', fontsize=12, labelpad=10)
plt.xlabel('Speed (cm/sec)', fontsize=12, labelpad=10)
plt.xlim(0, 200)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plot_utility.style_track_plot(ax, 200)
plot_utility.style_vr_plot(ax, x_max)
plt.locator_params(axis='y', nbins=4)
try:
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
except ValueError:
continue
plt.savefig(save_path + '/' + spike_data.session_id[cluster_index] +
'_rate_versus_SPEED_' + str(cluster_index + 1) + '.png',
dpi=200)
plt.close()
spikes_on_track = plt.figure(figsize=(4, 5))
ax = spikes_on_track.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
position = spike_data.loc[cluster_index].location_rate_in_time
ax.plot(firing_rate, position, '|', color='Black', markersize=4)
plt.ylabel('Firing rate (Hz)', fontsize=12, labelpad=10)
plt.xlabel('Location (cm)', fontsize=12, labelpad=10)
plt.xlim(0, 200)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plot_utility.style_track_plot(ax, 200)
plot_utility.style_vr_plot(ax, x_max)
plt.locator_params(axis='y', nbins=4)
try:
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
except ValueError:
continue
plt.savefig(save_path + '/' + spike_data.session_id[cluster_index] +
'_rate_versus_POSITION_' + str(cluster_index + 1) + '.png',
dpi=200)
plt.close()
def wordshift_plot(topvals, xpadding=0.0, textopts={}):
c1 = plt.rcParams['axes.color_cycle'][0]
c2 = plt.rcParams['axes.color_cycle'][1]
all_ixs = np.arange(len(topvals))
scolor = 0
is_pos = np.array((topvals.ws >= 0).tolist())
kw = {
'color': topvals.cols.values.tolist()
} if 'cols' in topvals.columns else {}
plt.barh(range(len(topvals)),
topvals.ws.values,
edgecolor='None',
height=.4,
**kw)
plt.vlines(0, -20, 20, color='k', lw=LINEWIDTH)
xmin, xmax = None, None
invTrans = plt.gca().transData.inverted()
for y, x in enumerate(topvals.ws):
cword = topvals.index.values[y]
ckw = textopts.copy()
if 'cols' in topvals.columns:
ckw['color'] = topvals.iloc[y].cols
textobj = plt.text(x,
y - 0.1,
' ' + cword + ' ',
ha='left' if x > 0 else 'right',
va='center',
**ckw)
plt.draw()
we = textobj.get_window_extent()
cxmax, _ = invTrans.transform((we.xmax, we.ymax))
if xmax is None or xmax < cxmax:
xmax = cxmax
cxmin, _ = invTrans.transform((we.xmin, we.ymin))
if xmin is None or xmin > cxmin:
xmin = cxmin
plt.xlim([xmin - xpadding, xmax + xpadding])
#plt.xlim([xmin*1.1-xpadding, xmax*1.1+xpadding])
plt.ylim([-0.5, len(topvals)])
plt.yticks([])
#plt.gca().spines['top'].setp('color', 'k')
plt.gca().spines['left'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
plt.locator_params(axis='x', nbins=5)
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# wt = c_nsat_reader.read_c_nsat_weights_evo(0)[:, 1, 1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(post=[0])
in_spikelist = SL
out_spikelist = nsat.importAER(nsat.read_from_file(nsat.fnames.events+'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
i = 1
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
i = 4
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(wt[0][:, 1, 1], 'r', lw=3)
ax.set_ylabel('$w$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
plt.savefig('/tmp/%s.png' % (os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
# wt = c_nsat_reader.read_c_nsat_weights_evo(0)[:, 1, 1]
wt = c_nsat_reader.read_c_nsat_weights_evo(0)
in_spikelist = SL
out_spikelist = nsat.importAER(
nsat.read_from_file(c_nsat_writer.fname.events + '_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
i = 1
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
i = 4
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
return fig_dim
plt.figure(5, figsize=set_size(width / 2))
for i in range(filters.shape[1]):
plt.plot(total_params,
filters[:, i],
label="Layer {}".format(i + 1),
color=cmap(float(i) / filters.shape[1]))
plt.plot(total_params,
filt[i],
label="Layer {} (fit)".format(i + 1),
linestyle="dashed",
color=cmap(float(i) / filters.shape[1]))
plt.locator_params(axis='x', nbins=5)
# plt.title("Layer's Filter vs Total Parameter")
plt.xlabel("Total Parameters")
plt.ylabel("Filters")
plt.legend()
plt.grid()
plt.tight_layout()
plt.savefig("savefigs/growth_{}_{}.pdf".format(args.model, args.dataset))
# ######
total_params = np.arange(1000, 100000000, 5000)
filt = np.array([total_params**b for b in beta])
filt = np.multiply(filt.transpose(), alpha).transpose()
print(filt[0])
for i in range(filters.shape[1]):
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[core][0], states[core][1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(
post=[130, 150, 120])
wt, pids = wt[0], pids[0]
in_spikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=[0])
plt.matplotlib.rcParams['figure.subplot.bottom'] = .1
plt.matplotlib.rcParams['figure.subplot.left'] = .2
plt.matplotlib.rcParams['figure.subplot.right'] = .98
plt.matplotlib.rcParams['figure.subplot.hspace'] = .1
plt.matplotlib.rcParams['figure.subplot.top'] = 1.0
# Plot the results
fig = plt.figure(figsize=(14, 10))
i = 4
ax1 = fig.add_subplot(5, 1, i)
for t in SL[100].spike_times:
plt.axvline(t, color='g', alpha=.4)
ax1.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax1.set_ylabel('$x_m$')
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 5
ax = fig.add_subplot(5, 1, i, sharex=ax1)
for t in SL[0].spike_times:
plt.axvline(t, color='k', alpha=.4)
ax.plot(wt[:, 19, 1], 'r', lw=3)
# ax.imshow(wt[:, :, 1], aspect='auto', interpolation='nearest')
ax.set_ylabel('$w$')
ax.set_xlabel('Time Step')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
for t in np.ceil(SL[0].spike_times):
plt.axvline(t, color='k', alpha=.4)
ax1 = fig.add_subplot(5, 1, 1, sharex=ax1)
out_spikelist.id_slice([0]).raster_plot(display=ax1, kwargs={'color': 'b'})
out_spikelist.id_slice(list(range(1,
30))).raster_plot(display=ax1,
kwargs={'color': 'k'})
ax1.set_xlabel('')
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.tight_layout()
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def plot_spikes_on_track(spike_data, raw_position_data,
processed_position_data, prm, prefix):
print('plotting spike rastas...')
save_path = prm.get_output_path() + '/Figures/spike_trajectories'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
rewarded_locations = np.array(
processed_position_data['rewarded_stop_locations'].dropna(axis=0)) #
rewarded_trials = np.array(
processed_position_data['rewarded_trials'].dropna(axis=0))
for cluster_index in range(len(spike_data)):
cluster_index = spike_data.cluster_id.values[cluster_index] - 1
x_max = max(
np.array(spike_data.at[cluster_index,
'beaconed_trial_number'])) + 1
spikes_on_track = plt.figure(figsize=(4, (x_max / 32)))
ax = spikes_on_track.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
#uncomment if you want to plot stops
#ax.plot(beaconed[:,0], beaconed[:,1], 'o', color='LimeGreen', markersize=2, alpha=0.5)
#ax.plot(nonbeaconed[:,0], nonbeaconed[:,1], 'o', color='LimeGreen', markersize=2, alpha=0.5)
#ax.plot(probe[:,0], probe[:,1], 'o', color='LimeGreen', markersize=2, alpha=0.5)
ax.plot(spike_data.loc[cluster_index].beaconed_position_cm,
spike_data.loc[cluster_index].beaconed_trial_number,
'|',
color='Black',
markersize=4)
ax.plot(spike_data.loc[cluster_index].nonbeaconed_position_cm,
spike_data.loc[cluster_index].nonbeaconed_trial_number,
'|',
color='Red',
markersize=4)
ax.plot(spike_data.loc[cluster_index].probe_position_cm,
spike_data.loc[cluster_index].probe_trial_number,
'|',
color='Blue',
markersize=4)
ax.plot(rewarded_locations,
rewarded_trials,
'>',
color='Red',
markersize=3)
plt.ylabel('Spikes on trials', fontsize=12, labelpad=10)
plt.xlabel('Location (cm)', fontsize=12, labelpad=10)
plt.xlim(0, 200)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
plot_utility.style_track_plot(ax, 200)
plot_utility.style_vr_plot(ax, x_max)
plt.locator_params(axis='y', nbins=4)
try:
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
except ValueError:
continue
plt.savefig(save_path + '/' + spike_data.session_id[cluster_index] +
'_track_firing_Cluster_' + str(cluster_index + 1) + '.png',
dpi=200)
plt.close()
def freq_stack():
global nboot, RSqStore, stname, RFstack, RFstack_mean, RFstack_std
from matplotlib import pylab as plt
from stack_synthetics import generate_synthetics
DeconMethod, highT, path2rf, outfile, baz1, baz2, SNR_min, \
nboot, Taup_Misfit_Min, Taup_Misfit_Max, average_type, allSyntheticParams = parse_inline_input()
tmp = path2rf[0].split('/')[-1]
#Manually change if you want to calc. goodness of fit
CalculateGoodnessOfFit=False
RSqStore={}
#Dump information for the GRL publication
if True:
FileForPub = open('ForPub.txt', 'a')
FileForPub.write('# \n')
FileForPub.write('# %s \n' % tmp)
FileForPub.write('# \n')
#Prepare synthetics - set to true if a new database of synthetics needs to be generated (may take days!)
if False:
#for SyntheticParams in allSyntheticParams:
# for RayParam in [0.09, 0.092, 0.094, 0.096, 0.098, 0.1, 0.102, 0.104, 0.106, 0.108, 0.11, 0.112, 0.114, 0.116, 0.118]:
# generate_synthetics(SyntheticParams, RayParam)
#Set up figture
fig = plt.figure(1, figsize=(3.5, 1.75))
params = {'legend.fontsize': 4,
'figure.figsize': (3, 1.5),
'axes.labelsize': 5,
'axes.titlesize': 7,
'figure.titlesize': 7,
'figure.dpi': 500,
'xtick.labelsize': 5,
'ytick.labelsize': 5}
plt.rcParams.update(params)
#For the GRL paper, the goodness-of-fit calculation was carried out only at 16s
#
if CalculateGoodnessOfFit:
ListOfPeriods = [16]
else:
ListOfPeriods = [20, 16, 12, 8, 4, 2]
#Loop over periods and plot each RF
#
for isub, lowT in enumerate(ListOfPeriods):
rect1 = [0.1, 0.15, 0.7, 0.8]
rect2 = [0.82, 0.15, 0.1, 0.8]
ax = fig.add_axes(rect1)
ax4 = fig.add_axes(rect2)
offset = isub
if True:
lenRFs, RPs, BAZIs, depths, NRFs = multi_station_stack(ax, ax4, path2rf, lowT, highT,
offset=offset,
label='%.0f-%.0f s' % (lowT, highT),
baz1=baz1,
baz2=baz2, SNR_min=SNR_min,
Taup_Misfit_Min=Taup_Misfit_Min,
Taup_Misfit_Max=Taup_Misfit_Max,
average_type=average_type,
DeconMethod=DeconMethod)
frp=open('RP_BAZI/file.txt','w')
for itmp,tmp in enumerate(RPs):
frp.write('%f %f\n' % (RPs[itmp], BAZIs[itmp]))
frp.close()
if True:
from numpy import histogram, arange, sum
bin_edges = arange(0.09, 0.122, 0.002)
Weights, bin_edges = histogram(RPs,bins=bin_edges,density=True)
Weights = Weights/sum(Weights)
rps = bin_edges[:-1]
lines = []
labels = []
syns = []
for isyn in range(len(allSyntheticParams)):
SyntheticParams = allSyntheticParams[isyn]
SyntheticParams["DeconMethod"] = DeconMethod
SyntheticParams["RayParams"] = rps
SyntheticParams["Weights"] = Weights
SyntheticParams["offset"] = offset
SyntheticParams["nIter"] = 100 # for ITDD only
SyntheticParams["lowT"] = lowT
SyntheticParams["highT"] = highT
#l0, RFsyn = add_synthetics(ax, SyntheticParams, Rescale=True)
#lines.append(l0)
#labels.append(SyntheticParams["label"])
#syns.append(RFsyn)
if CalculateGoodnessOfFit:
if lowT == 16.:
for ii in range(len(depths)):
from numpy import shape
#print shape(depths), shape(RFstack), shape(RFstack_std), shape(syns)
#FileForPub.write('%6.1f %e %e %e %e %e %e\n' %
# (depths[ii], RFstack_mean[ii], RFstack_std[ii], syns[0][ii], syns[1][ii], syns[2][ii], syns[3][ii]))
FileForPub.write('%6.1f %e %e ' % (depths[ii], RFstack_mean[ii], RFstack_std[ii]))
for isyn in range(len(syns)):
FileForPub.write('%e ' % (syns[isyn][ii]))
FileForPub.write('\n')
if False:
if len(allSyntheticParams)>0:
ax.legend(lines,labels)
ax4.fill_betweenx(depths, NRFs, x2=0.0, facecolor='lightblue')
ylims=[0., 310.]
ax.set_ylim(ylims)
ax4.set_ylim(ylims)
ax.set_xlim([-1, 8])
ax.set_xticks([])
ax.set_ylabel('Depth (km)')
ax.invert_yaxis()
ax4.invert_yaxis()
ax4.set_yticklabels([])
ax4.set_xlim(0, lenRFs*1.25)
ax4.set_xticks([0, lenRFs])
ax4.set_xlabel('RFs used\nin stack')
if False:
plt.suptitle('%s\n%d Receiver Functions -- SNR > %.1f -- nboot = %d -- Average: %s -- %s' % (
path2rf, lenRFs, SNR_min, nboot, average_type, DeconMethod))
if True:
left, bottom, width, height = [0.62, 0.50, 0.15, 0.15]
ax2 = fig.add_axes([left, bottom, width, height])
(n, bins, patches) = ax2.hist(RPs)
plt.annotate('Frequency', xy=(-0.25,0.5), xycoords='axes fraction', fontsize=3, rotation=90,va='center')
plt.annotate('Ray Parameter (s/km)', xy=(0.5,-0.47), xycoords='axes fraction', fontsize=3,ha='center')
plt.locator_params(axis='y', nbins=4)
plt.locator_params(axis='x', nbins=5)
plt.xlim([0.09, 0.120])
left, bottom, width, height = [0.62, 0.25, 0.15, 0.15]
ax3 = fig.add_axes([left, bottom, width, height])
ax3.hist(BAZIs)
plt.annotate('Frequency', xy=(-0.25,0.5), xycoords='axes fraction', fontsize=3, rotation=90, va='center')
plt.annotate('Backazimuth (degrees)', xy=(0.5,-0.47), xycoords='axes fraction', fontsize=3, ha='center')
plt.locator_params(axis='y', nbins=4)
plt.locator_params(axis='x', nbins=5)
plt.xlim([-180, 180])
plt.xticks([-180,-60,60,180])
for ax in [ax2, ax3]:
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(3)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=0)
plt.setp(ax.yaxis.get_majorticklabels(), rotation=0)
for tick in ax.yaxis.get_majorticklabels():
tick.set_x(+0.09)
for tick in ax.xaxis.get_majorticklabels():
tick.set_y(+0.18)
tick.set_rotation(45)
plt.savefig(outfile)
#
#
#
if CalculateGoodnessOfFit:
nsyn=len(allSyntheticParams)
#
fout=open('RSq_%s.out' % stname,'w')
#
fout.write('%d %d\n' % (nsyn,nboot))
for iboot in range(nboot):
tmpa=iboot
fout.write('%5d ' % tmpa)
for isyn in range(1,nsyn+1):
tmpb=RSqStore[(isyn, 16, iboot)]
fout.write('%7e ' % tmpb)
fout.write('\n')
#tmpstr = '%5d %7e %7e %7e %7e\n' % (tmpa, tmpb, tmpc, tmpd, tmpe)
#fout.write(tmpstr)
fout.close()
fout=open('Model_List.out','w')
for isyn in range(nsyn):
tmp=allSyntheticParams[isyn]
tmpa,tmpb,tmpc=tmp["LABDepth"], tmp["LABdlnv"], tmp["LABTransitionThickness"]
fout.write('%f %f %f\n' % (tmpa,tmpb,tmpc) )
fout.close()
print 'Finished with %s ' % (outfile)
return
def add_synthetics(ax, syntheticParams, Rescale=False):
from matplotlib import pylab as plt
global RFstack, RFstack_mean, nboot, RSqStore, RFstack_std
from TOOLS import stack_synthetics
from scipy.stats import linregress
depths, RF = stack_synthetics(syntheticParams)
scale = 6.5
if syntheticParams["lowT"] > 10:
scale = scale * 1.0
if syntheticParams["DeconMethod"] == 'ITDD':
scale = scale * 2.0
refline = depths * 0. + syntheticParams["offset"] # plot everything wrt this line
# Enhance deep
from numpy import array
RF = RF*scale
RFstack_meanScl = RFstack_mean * scale
RFstack_stdScl = RFstack_std * scale
RFstackScl = RFstack * scale
if Rescale:
phi, _, _, _, _ = linregress(RF, RFstack_meanScl)
print 'rescaling by factor %f' % (phi)
RF = RF * phi
tmp = RF
xs = tmp+refline
l0 = ax.plot(xs, depths, ls=syntheticParams["LineStyle"], color=syntheticParams["Color"], linewidth=0.5)
for iboot in range(nboot):
mask = (depths > 150.0) * (depths < 250.0)
RSq = sum((RF - RFstackScl[iboot,:])**2 / (RFstack_stdScl)**2 * mask)
RSq = RSq / sum(mask)
#plt.figure(13)
#plt.plot(RFstackScl[iboot,:] - RFstack_stdScl)
#plt.plot(RFstackScl[iboot,:] + RFstack_stdScl)
#plt.plot(RF,'--')
#plt.show()
key=(syntheticParams["isyn"],syntheticParams["lowT"],iboot)
RSqStore[key]=RSq
#print key, RSq
return l0, RF / scale
def multi_station_stack(ax, ax4, path2rf, lowT, highT,
offset=0.0, label='', scale_bar=False, baz1=-999.0, baz2=999.0,
SNR_min=0.0, Taup_Misfit_Min=-9999., Taup_Misfit_Max=9999.,
average_type='mean', DeconMethod='none'):
"""
This code performs the single-station stacking by reading in individual RFs from the .mat files.
The name of the subroutine is accurate because multiple stations and or channels can be stacked together
by making the list path2rf longer than one element.
"""
global RFstack_mean, RFstack, RFstack_std, nboot, stname
from TOOLS import loadmat
from numpy import zeros, isnan, std, mean, nanstd
RFs_all = []
RPs_all = []
BAZIs_all = []
SNRs_all = []
for each_path2rf in path2rf:
file_name = '%s/RF_Depth_%ds_%ds_%s_UsePostfilter_0.mat' % (each_path2rf, lowT, highT, DeconMethod)
print '...loading %s' % (file_name)
###Open SNR file
SNR = []
Taup_Misfit = []
snrfile = '%s/SNR_%ds_%ds.txt' % (each_path2rf, lowT, highT)
file = open(snrfile)
print "...%s snrfile loaded" % (snrfile)
for line in file.readlines():
nfo = line.strip('\n').split()
SNR.append(float(nfo[1]))
Taup_Misfit.append(float(nfo[2]))
file.close()
###Get matfile
matfile = loadmat(file_name)
print "...%s matfile loaded" % (file_name)
RFs = matfile["rfs"][:, :]
stdRF0 = nanstd(RFs, axis=0)
BAZIs = matfile["BAZIsave"][:]
RPs = matfile["RPsave"][:]
depths = matfile["RF_Depth"][:, 0]
meanRF0 = matfile["RF_Depth"][:, 1]
# check for bad snrfile
if len(SNR) != len(RFs):
print '***Warning: len(SNR) != len(RFs) , %d , %d ' % (len(SNR), len(RFs))
dum = raw_input('Press enter to continue')
stname = path2rf[0].split('/')[-1]
#This is for printing data for bazi RFs
if False:
fout = open('%s_%02ds.txt' % (stname, lowT), 'w')
for ii in range(len(RFs)):
fout.write('%7f %7f \n' % (BAZIs[ii], SNR[ii]))
tmp = RFs[ii]
for jj in range(len(tmp)):
fout.write(' %10e\n' % (tmp[jj]))
fout.close()
# cull out undesirables
for ii in range(len(RFs)):
if BAZIs[ii] >= baz1 and BAZIs[ii] <= baz2 and \
SNR[ii] >= SNR_min and \
Taup_Misfit[ii] >= Taup_Misfit_Min and Taup_Misfit[ii] <= Taup_Misfit_Max:
#
# check if RF deviates far from mean RF - Emily's method
ndev = 0
for idep in range(len(depths)):
if RFs[ii, idep] > meanRF0[idep] + 1.0 * stdRF0[idep] or \
RFs[ii, idep] < meanRF0[idep] - 1.0 * stdRF0[idep]:
ndev = ndev + 1
determ = float(ndev) / float(len(depths))
if determ < 0.75:
RFs_all.append(RFs[ii])
RPs_all.append(RPs[ii])
BAZIs_all.append(BAZIs[ii])
SNRs_all.append(SNR[ii])
### Estimate spatial coherence
#from TOOLS import estimate_spatial_coherence
#station=path2rf[0].split('/')[-1]
#Dmin, Dmax = 150.0, 250.0
#estimate_spatial_coherence(RFs_all, RPs_all, BAZIs_all, depths, Dmin, Dmax,
# outfile='SPATCOH/spatcoh_%s_%ds_%dkm_%dkm.txt' % (station, lowT, Dmin, Dmax))
#fdump=open('out.datadump','w')
#
#fdump.write('%d\n' % len(RFs_all))
#for ii,tmp in enumerate(RFs_all):
# header = '%10f %10f %10f\n' % (RPs_all[ii], BAZIs_all[ii], SNRs_all[ii])
# fdump.write(header)
# for eachval in tmp:
# fdump.write('%-10.3e ' % (eachval))
#
# fdump.write('\n')
#
#fdump.close()
#Dmin, Dmax = 80.0, 150.0
#estimate_spatial_coherence(RFs_all, RPs_all, BAZIs_all, depths, Dmin, Dmax,
# outfile='SPATCOH/spatcoh_%s_%ds_%dkm_%dkm.txt' % (station, lowT, Dmin, Dmax))
#Dmin, Dmax = 150.0, 250.0
#estimate_spatial_coherence(RFs_all, RPs_all, BAZIs_all, depths, Dmin, Dmax,
# outfile='SPATCOH/spatcoh_%s_%ds_%dkm_%dkm.txt' % (station, lowT, Dmin, Dmax))
### Begin stacking and bootstrapping
RFstack = zeros(nboot * len(RFs_all[0])).reshape(nboot, len(RFs_all[0]))
NRFs = []
## New section
from numpy import nanmedian, shape, nanmean
NRF, NDEP = shape(RFs_all)
tmp=zeros(NRF*NDEP).reshape(NRF,NDEP)
for ii in range(NRF):
for jj in range(NDEP):
tmp[ii,jj] = RFs_all[ii][jj]
from numpy.random import randint
for iboot in range(nboot):
resamp=zeros(NRF*NDEP).reshape(NRF,NDEP)
for ii in range(NRF):
index=randint(0, NRF-1)
resamp[ii,:]=tmp[index,:]
if average_type == 'median':
RFstack[iboot, :] = nanmedian(resamp,axis=0)
elif average_type == 'mean':
RFstack[iboot, :] = nanmean(resamp, axis=0)
else:
from sys import exit
exit('Error: bad average type')
for idep, depth in enumerate(depths):
amp_at_dep = [] # list containing RF amps at depth
for iRF in range(len(RFs_all)):
if not isnan(RFs_all[iRF][idep]):
amp_at_dep.append(RFs_all[iRF][idep])
#
NRF = len(amp_at_dep)
NRFs.append(NRF)
#
# for iboot in range(nboot):
# if average_type == 'mean':
# RFstack[iboot, idep] = mean(choice(amp_at_dep, NRF))
# elif average_type == 'median':
# RFstack[iboot, idep] = median(choice(amp_at_dep, NRF))
# elif average_type == 'tmean':
# tmp_m = mean(choice(amp_at_dep, NRF))
# tmp_std = std(choice(amp_at_dep, NRF))
# lwr = tmp_m - 2.0 * tmp_std
# upr = tmp_m + 2.0 * tmp_std
# tmp_tm = tmean(choice(amp_at_dep, NRF), limits=(lwr, upr))
# RFstack[iboot, idep] = tmp_tm
RFstack_mean = mean(RFstack, axis=0)
RFstack_std = std(RFstack, axis=0)
refline = depths * 0. + offset # plot everything wrt this line
scale = 6.0
if lowT > 10:
scale = scale * 1.0
if DeconMethod == 'ITDD':
scale = scale * 2.0
ax.plot(refline, depths, '--', color='black', linewidth=0.2)
nsigma = 2.0
fmin = (RFstack_mean - nsigma * RFstack_std) * scale + refline
fmax = (RFstack_mean + nsigma * RFstack_std) * scale + refline
ax.fill_betweenx(depths, fmin, fmax, facecolor='gray', edgecolor='None')
fmin = RFstack_mean * 0.0 + refline
fmax = (RFstack_mean - nsigma * RFstack_std) * scale + refline
ax.fill_betweenx(depths, fmin, fmax, where=fmax > fmin, facecolor='red', edgecolor='None')
fmax = RFstack_mean * 0.0 + refline
fmin = (RFstack_mean + nsigma * RFstack_std) * scale + refline
ax.fill_betweenx(depths, fmin, fmax, where=fmax > fmin, facecolor='blue', edgecolor='None')
ax.text(refline[0], 330, label, fontsize=5,
horizontalalignment='center',rotation=30.0)
#Scale Bar
if True:
ls=6.7
rs=ls+ scale * 0.1
ys=45.
ax.plot([ls, rs], [ys, ys],linewidth=1, color='black')
ax.text((ls+rs)/2.,ys-5.,'10% parent\namplitude', ha='center', weight='light', style='italic', fontsize=3.5)
ax.annotate(stname, xy=(0.01,0.05) ,xycoords='axes fraction', weight='bold',fontsize=5)
return len(RFs_all), RPs_all, BAZIs_all, depths, NRFs
def parse_inline_input():
# These are the stacking params
DeconMethod = raw_input('Deconvolution Method (ETMTM): \n').split()[0]
highT = float(raw_input('High T in seconds (100): \n').split()[0])
numsta = int(raw_input('Number of channels (1): \n').split()[0])
path2rf=[]
tmp = raw_input('Path for channels (separated by spaces): \n').split()
for ista in range(numsta):
path2rf.append(tmp[ista])
outfile = raw_input('Outfile: \n').split()[0]
baz1, baz2 = raw_input('Baz1, Baz2: \n').split()[0:2]
baz1,baz2 = float(baz1), float(baz2)
SNR_min = float(raw_input('SNR Min: \n').split()[0])
nboot = int(raw_input('Nboot: \n').split()[0])
Taup_Misfit_Min, Taup_Misfit_Max = raw_input('Taup Misfit Min, Max: \n').split()[0:2]
Taup_Misfit_Min, Taup_Misfit_Max = float(Taup_Misfit_Min), float(Taup_Misfit_Max)
average_type = raw_input('Average Type (median): \n').split()[0]
#Put synthetic params in a list of dictionary
numsyn = int(raw_input('Number of synthetics (0): \n').split()[0])
allSyntheticParams=[]
for isyn in range(numsyn):
tmp={}
a,b = raw_input('Moho: Depth (km), Fractional Velocity Jump \n').split()[0:2]
tmp["MohoDepth"], tmp["Mohodlnv"] = float(a), float(b)
a,b = raw_input('MLD: Depth (km), Fractional Velocity Jump \n').split()[0:2]
tmp["MLDDepth"], tmp["MLDdlnv"] = float(a), float(b)
a,b,c = raw_input('LAB: Depth (km), Fractional Velocity Jump \n').split()[0:3]
print a,b,c
tmp["LABDepth"], tmp["LABdlnv"], tmp["LABTransitionThickness"] = float(a), float(b), float(c)
tmp["PulseWidth"] = float(raw_input('Pulse Width (s) \n').split()[0])
a,b = raw_input('Line Style and Color (- black): \n').split()[0:2]
tmp['LineStyle']=a
tmp['Color']=b
tmp['label'] = raw_input('Line label: \n')
tmp['propMat'] = True
tmp['isyn'] = isyn + 1 #python counts from 0
allSyntheticParams.append(tmp)
return DeconMethod, highT, path2rf, outfile, baz1, baz2,\
SNR_min, nboot, Taup_Misfit_Min, Taup_Misfit_Max, average_type, \
allSyntheticParams
def write_depth_integrated_variance(RFstack_std, SNR_min):
fout = open('depth_integrated_variance.txt', 'a')
text = 'Depth-integrated variance, SNR_min = %f, %f\n' % (sum(RFstack_std), SNR_min)
fout.write(text)
fout.close()
return
def parse_user_input_old():
from sys import argv
inputs = argv
dummy = inputs.pop(0)
DeconMethod = inputs.pop(0)
# use popleft to take one arg at a time
highT = float(inputs.pop(0))
# print inputs
numsta = int(inputs.pop(0))
path2rf = []
for ista in range(numsta):
path2rf.append(inputs.pop(0))
outfile = inputs.pop(0)
baz1 = float(inputs.pop(0))
baz2 = float(inputs.pop(0))
SNR_min = float(inputs.pop(0))
nboot = int(inputs.pop(0))
Taup_Misfit_Min = float(inputs.pop(0))
Taup_Misfit_Max = float(inputs.pop(0))
average_type = inputs.pop(0)
# print 'Saving to %s ' % (outfile)
return DeconMethod, highT, path2rf, outfile, baz1, baz2, SNR_min, nboot, Taup_Misfit_Min, Taup_Misfit_Max, average_type
def draw_barchart(Time):
df_frame = (df[df["date"].eq(Time)].sort_values(
by="counts", ascending=True).tail(num_of_elements))
ax.clear()
normal_colors = dict(
zip(df["country"].unique(), rgb_colors_opacity))
dark_colors = dict(zip(df["country"].unique(),
rgb_colors_dark))
ax.barh(
df_frame["country"],
df_frame["counts"],
color=[normal_colors[x] for x in df_frame["country"]],
height=0.8,
edgecolor=([dark_colors[x] for x in df_frame["country"]]),
linewidth="6",
)
dx = float(df_frame["counts"].max()) / 200
for i, (value, name) in enumerate(
zip(df_frame["counts"], df_frame["country"])):
ax.text(
value + dx,
i + (num_of_elements / 50),
" " + name,
size=14,
weight="bold",
ha="left",
va="center",
fontdict={"fontname": "Trebuchet MS"},
)
ax.text(
value + dx * 10,
i - (num_of_elements / 50),
f" {value:,.0f}",
size=14,
ha="left",
va="center",
)
time_unit_displayed = re.sub(r"\^(.*)", r"", str(Time))
ax.text(
1.0,
1.14,
time_unit_displayed,
transform=ax.transAxes,
color="#666666",
size=14,
ha="right",
weight="bold",
fontdict={"fontname": "Trebuchet MS"},
)
# ax.text(-0.005, 1.06, 'Number of confirmed cases', transform=ax.transAxes, size=14, color='#666666')
ax.text(
-0.005,
1.14,
"Number of {} cases ".format(field),
transform=ax.transAxes,
size=14,
weight="bold",
ha="left",
fontdict={"fontname": "Trebuchet MS"},
)
ax.xaxis.set_major_formatter(
ticker.StrMethodFormatter("{x:,.0f}"))
ax.xaxis.set_ticks_position("top")
ax.tick_params(axis="x", colors="#666666", labelsize=12)
ax.set_yticks([])
ax.set_axisbelow(True)
ax.margins(0, 0.01)
ax.grid(which="major", axis="x", linestyle="-")
plt.locator_params(axis="x", nbins=4)
plt.box(False)
plt.subplots_adjust(
left=0.075,
right=0.75,
top=0.825,
bottom=0.05,
wspace=0.2,
hspace=0.2,
)
p = mets(met)
p_met[nm] = p
nm += 1
###########################################################################
tag = sys.argv[1].split('/')[-1].split('.')[0]
print "Making the plots....."
# Histograms of momentum
plt.figure()
plt.subplot(321)
lch.hist_err(p_jets[p_jets>-999],bins=50,range=(0,400),fmt='o',markersize=5,color='black',ecolor='black')
plt.title("%s: Jet momentum" % (tag))
plt.locator_params(nbins=6)
#plt.xlabel("Momentum")
plt.subplot(322)
lch.hist_err(p_muons[p_muons>-999],bins=50,range=(0,300),fmt='o',markersize=5,color='red',ecolor='red')
plt.title("%s: Muon momentum" % (tag))
#plt.xlabel("Momentum")
plt.subplot(323)
lch.hist_err(p_electrons[p_electrons>-999],bins=50,range=(0,200),fmt='o',markersize=5,color='green',ecolor='green')
plt.title("%s: Electron momentum" % (tag))
#plt.xlabel("Momentum")
plt.subplot(324)
lch.hist_err(p_photons[p_photons>-999],bins=50,range=(0,200),fmt='o',markersize=5,color='orange',ecolor='orange')
plt.title("%s: Photon momentum" % (tag))
c=colors.red,
ls='dotted')
plt.plot(xfit,
pars[0][2] * polya(xfit, pars[0][0], pars[0][1], 1) / 10000.0,
c=colors.red,
ls='dashed')
plt.plot(xfit,
pars[0][3] * polya(xfit, pars[0][0], pars[0][1], 2) / 10000.0,
c=colors.red,
ls='-.')
plt.ylabel(r'Counts$\times10^4$/ 2$\times10^{-4}$ a.u.', labelpad=15)
plt.xlim(0, xmax)
plt.ylim(0, 4)
plt.tick_params(axis='x', which='both', labelbottom='off')
plt.legend(loc='upper right', framealpha=0, fontsize=legendFS)
plt.locator_params(axis='y', nticks=3)
plt.yticks([0, 1, 2, 3, 4])
plt.sca(ax[1])
plt.errorbar(x[c], (n[c] / pFit3(x[c], pars[0])),
yerr=(n[c] / pFit3(x[c], pars[0])) *
np.sqrt((nerr[c] / n[c])**2 + (1.0 / pFit3(x[c], pars[0]))**2),
marker='+',
color=colors.red,
linestyle='None')
plt.axhline(1, c=colors.black, linestyle='dashed')
plt.xlabel('Current [a.u.]')
plt.ylabel(r'Ratio')
plt.xlim(0, xmax)
plt.ylim(0.85, 1.15)
plt.yticks([0.9, 1, 1.1])