def square_root_diffusion_exact(initial_val=0.05,
kappa=3.0,
theta=0.02,
sigma=0.1,
time_year=2,
num_samples=10000,
num_time_interval_discretization=50):
x = np.zeros((num_time_interval_discretization + 1, num_samples))
x[0] = initial_val
dt = time_year / num_time_interval_discretization
for t in range(1, num_time_interval_discretization + 1):
df = 4 * theta * kappa / sigma**2
c = (sigma**2 * (1 - np.exp(-kappa * dt))) / (4 * kappa)
nc = np.exp(-kappa * dt) / c * x[t - 1]
x[t] = c * npr.noncentral_chisquare(df, nc, size=num_samples)
plt.figure(figsize=(10, 6))
plt.hist(x[-1], bins=50)
plt.title("Square root diffusion Exact")
plt.xlabel('value')
plt.ylabel('frequency')
plt.show()
plt.figure(figsize=(10, 6))
plt.plot(x[:, :10], lw=1.5)
plt.xlabel('time')
plt.ylabel('index level')
plt.title('Sample Path SRD Exact')
plt.show()
return x
def geometric_brownian_motion_option_pricing(
initial_val=100,
num_samples=10000,
riskless_rate=0.05,
volatility_sigma=0.25,
time_year=2.0,
num_time_interval_discretization=50):
dt = time_year / num_time_interval_discretization
samples = np.zeros((num_time_interval_discretization + 1, num_samples))
samples[0] = initial_val
for t in range(1, num_time_interval_discretization + 1):
samples[t] = samples[t - 1] * np.exp(
(riskless_rate - 0.5 * (volatility_sigma**2)) * dt +
volatility_sigma * np.sqrt(dt) * npr.standard_normal(num_samples))
print(45 * "=")
print(samples[1])
plt.figure(figsize=(10, 6))
plt.hist(samples[50], bins=50)
plt.title("Geometric Brownian Motion")
plt.xlabel('index level')
plt.ylabel('frequency')
plt.show()
plt.figure(figsize=(10, 6))
plt.plot(samples[:, :10], lw=1.5)
plt.xlabel('time')
plt.ylabel('index level')
plt.title('Sample Path')
plt.show()
return samples
def square_root_diffusion_euler(initial_val=0.05,
kappa=3.0,
theta=0.02,
sigma=0.1,
time_year=2,
num_samples=10000,
num_time_interval_discretization=50):
dt = time_year / num_time_interval_discretization
xh = np.zeros((num_time_interval_discretization + 1, num_samples))
x = np.zeros_like(xh)
xh[0] = initial_val
x[0] = initial_val
for t in range(1, num_time_interval_discretization + 1):
xh[t] = (xh[t - 1] + kappa * (theta - np.maximum(xh[t - 1], 0)) * dt +
sigma * np.sqrt(np.maximum(xh[t - 1], 0)) * math.sqrt(dt) *
npr.standard_normal(num_samples))
x = np.maximum(xh, 0)
plt.figure(figsize=(10, 6))
plt.hist(x[-1], bins=50)
plt.xlabel('value')
plt.ylabel('frequency')
plt.title('Square root diffusion Approx Euler')
plt.show()
plt.figure(figsize=(10, 6))
plt.plot(x[:, :10], lw=1.5)
plt.xlabel('time')
plt.ylabel('index level')
plt.title('Sample Path SRD approx')
plt.show()
return x
def pure_data_plot(self,connect=False,suffix='',cmap=cm.jet,bg=cm.bone(0.3)):
#fig=plt.figure()
ax=plt.axes()
plt.axhline(y=0,color='grey', zorder=-1)
plt.axvline(x=0,color='grey', zorder=-2)
if cmap is None:
if connect: ax.plot(self.x,self.y, 'b-',lw=2,alpha=0.5)
ax.scatter(self.x,self.y, marker='o', c='b', s=40)
else:
if connect:
if cmap in [cm.jet,cm.brg]:
ax.plot(self.x,self.y, 'c-',lw=2,alpha=0.5,zorder=-1)
else:
ax.plot(self.x,self.y, 'b-',lw=2,alpha=0.5)
c=[cmap((f-self.f[0])/(self.f[-1]-self.f[0])) for f in self.f]
#c=self.f
ax.scatter(self.x, self.y, marker='o', c=c, edgecolors=c, zorder=True, s=40) #, cmap=cmap)
#plt.axis('equal')
ax.set_xlim(xmin=-0.2*amax(self.x), xmax=1.2*amax(self.x))
ax.set_aspect('equal') #, 'datalim')
if cmap in [cm.jet,cm.brg]:
ax.set_axis_bgcolor(bg)
if self.ZorY == 'Z':
plt.xlabel(r'resistance $R$ in Ohm'); plt.ylabel(r'reactance $X$ in Ohm')
if self.ZorY == 'Y':
plt.xlabel(r'conductance $G$ in Siemens'); plt.ylabel(r'susceptance $B$ in Siemens')
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'c{}_{}_circle_data'.format(self.sdc.case,self.ZorY)+self.sdc.suffix+self.sdc.outsuffix+suffix+'.png'), dpi=240)
plt.close()
def plot_weightings():
"""Plots all weighting functions defined in :module: splweighting."""
from scipy.signal import freqz
from pylab import plt, np
sample_rate = 48000
num_samples = 2 * 4096
fig, ax = plt.subplots()
for name, weight_design in sorted(_weighting_coeff_design_funsd.items()):
b, a = weight_design(sample_rate)
w, H = freqz(b, a, worN=num_samples)
freq = w * sample_rate / (2 * np.pi)
ax.semilogx(freq,
20 * np.log10(np.abs(H) + 1e-20),
label='{}-Weighting'.format(name))
plt.legend(loc='lower right')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping / dB')
plt.grid(True)
plt.axis([10, 20000, -80, 5])
return fig, ax
def plot2(self,
figNum,
time1,
data1,
time2,
data2,
title='',
units='',
options=''):
plt.figure(figNum)
# plt.hold(True);
plt.grid(True)
if title:
self.title = title
if not units:
self.units = units
# plt.cla()
if self.preTitle:
fig = plt.gcf()
fig.canvas.set_window_title("Figure %d - %s" %
(figNum, self.preTitle))
plt.title("%s" % (self.title))
plt.plot(time1, data1, options)
plt.plot(time2, data2, options)
plt.ylabel('(%s)' % (self.units))
plt.xlabel('Time (s)')
plt.margins(0.04)
def plot_inv_conv(fvals, name, direc):
plt.figure()
plt.semilogy(fvals, 'ko-')
plt.xlabel('Iterations')
plt.ylabel('Cost Function')
plt.savefig(os.path.join(direc, name + '.png'), dpi=300)
plt.close()
def subplotSingle2x(self,
figNum,
plotNum,
numRows,
numCols,
time,
data,
title='',
units='',
options=''):
print("subplotSingle2x")
plt.figure(figNum)
if title:
self.title = title
if not units:
self.units = units
if self.preTitle:
fig = plt.gcf()
fig.canvas.set_window_title("%s" % (figNum, self.preTitle))
if not figNum in self.sharex.keys():
self.sharex[figNum] = plt.subplot(numRows, numCols, plotNum)
plt.plot(time, data, options)
plt.subplot(numRows, numCols, plotNum, sharex=self.sharex[figNum])
# plt.hold(True);
plt.grid(True)
plt.title("%s" % (self.title))
plt.plot(time, data, options)
plt.ylabel('(%s)' % (self.units))
plt.margins(0.04)
def CHART_Running_Annual_Vol_with_Hourly_Samples(frequency,window,trading_hours_per_day):
Trading_Hours_in_Trading_Year=252*trading_hours_per_day #Calculates Trading Hours in a year
Sample=data['Price'][frequency-1::frequency] #Creates New Sampling list based on frequency input
Returns=np.log(Sample) - np.log(Sample.shift(1)) #Calculates Returns on New Sample
Running_Variance=Returns.rolling(window).var() #Calculates hourly running variance based on 'window size' input
Running_Annual_Vol=np.sqrt(Running_Variance)*np.sqrt(Trading_Hours_in_Trading_Year/frequency)
#Place Running Vols and Time Series in DataFrame
DF=pd.DataFrame(Sample)
DF['Running_Vol']=Running_Annual_Vol
#Create Plot
DF.Price.plot()
plt.legend()
plt.ylabel('Yield (%)')
DF.Running_Vol.plot(secondary_y=True, style='g',rot=90)
plt.xlabel('Date')
plt.ylabel('Running Vol')
plt.title('10 Year Bund Yield vs Annualized Running Vol (Window Size=200)')
plt.legend(bbox_to_anchor=(0.8, 1))
plt.text(0.8, 5.4, "Frequency={}. Window Size={}. Trading Hours per Day={}".format(frequency, window,trading_hours_per_day))
return plt
def CHART_Running_Annual_Vol_with_Daily_Samples_on_Specific_Time_of_Day(frequency,sampling_time,window,trading_hours_per_day):
Original_DAILY_Sample=data['Price'][sampling_time-1::trading_hours_per_day] #Grabs the Hourly Data and Converts it into Daily Data based on Sampling Time of Day AND Trading Hours per Day
NEW_Sample=Original_DAILY_Sample[frequency-1::frequency] #Creates New Sampling list based on sampling frequency input
Returns=np.log(NEW_Sample) - np.log(NEW_Sample.shift(1)) #Calculates Returns on New Sample
Running_Variance=Returns.rolling(window).var() #Calculates daily running variance based on 'window size' input
Running_Annual_Vol=np.sqrt(Running_Variance)*np.sqrt(252/frequency)
#Place NEW Sampled data (prices) and Running Vols in DataFrame
DF=pd.DataFrame(NEW_Sample)
DF['Running_Vol']=Running_Annual_Vol
#Create Plot
DF.Price.plot()
plt.legend()
#data.Price.plot()
plt.ylabel('Yield (%)')
DF.Running_Vol.plot(secondary_y=True, style='g',rot=90)
plt.xlabel('Date')
plt.ylabel('Running Vol')
plt.title('10 Year Bund Yield vs Annualized Running Vol ')
plt.legend(bbox_to_anchor=(0.8, 1))
plt.text(0.8, 3.5, "Sampling Time={}. Window Size={}. Trading Hours per Day={}".format(sampling_time, window,trading_hours_per_day))
return plt
def plot_cumu_disp_decomposition(self,
site,
cmpt,
loc=2,
leg_fs=7,
if_ylim=False):
self.plot_cumu_obs_linres(site, cmpt)
y = self.plot_cumu_disp_pred_added(site, cmpt, label='pred.')
y += self.plot_R_co(site,
cmpt,
style='-^',
label='Rco',
color='orange')
y += self.plot_E_cumu_slip(site, cmpt, color='green')
y += self.plot_R_aslip(site, cmpt, color='black')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
plt.ylabel(r'meter')
plt.legend(loc=loc, prop={'size': leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site=get_site_true_name(site_id=site), cmpt=cmpt))
def plot_post_disp_decomposition(
self,
site,
cmpt,
loc=2,
leg_fs=7,
marker_for_obs='x',
):
y = self.plot_post_obs_linres(site,
cmpt,
label='obs.',
marker=marker_for_obs)
y += self.plot_post_disp_pred_added(site, cmpt, label='pred.')
y += self.plot_R_co(site,
cmpt,
style='-^',
label='Rco',
color='orange')
y += self.plot_E_aslip(site, cmpt, color='green')
y += self.plot_R_aslip(site, cmpt, color='black')
plt.grid('on')
plt.legend(loc=loc, prop={'size': leg_fs})
plt.ylabel(r'meter')
plt.gcf().autofmt_xdate()
plt.title('Postseismic Disp. : {site} - {cmpt}'.format(
site=get_site_true_name(site_id=site), cmpt=cmpt))
def print_statistics(a1, a2, a1_type, a2_type):
''' Prints selected statistics.
Parameters
==========
a1, a2: ndarray objects
results objects from simulation
'''
sta1 = scs.describe(a1)
sta2 = scs.describe(a2)
print('%14s %14s %14s' % ('statistic', 'data set 1', 'data set 2'))
print(45 * "-")
print('%14s %14.0f %14.0f' % ('size', sta1[0], sta2[0]))
print('%14s %14.3f %14.3f' % ('min', sta1[1][0], sta2[1][0]))
print('%14s %14.3f %14.3f' % ('max', sta1[1][1], sta2[1][1]))
print('%14s %14.3f %14.3f' % ('mean', sta1[2], sta2[2]))
print('%14s %14.3f %14.3f' % ('std', np.sqrt(sta1[3]), np.sqrt(sta2[3])))
print('%14s %14.3f %14.3f' % ('skew', sta1[4], sta2[4]))
print('%14s %14.3f %14.3f' % ('kurtosis', sta1[5], sta2[5]))
a1_sort = np.sort(a1)
a2_sort = np.sort(a2)
plt.scatter(x=a1_sort, y=a2_sort, marker='.', color='darkred')
plt.plot(a1_sort, a1_sort, linestyle='dashed', color='darkblue', alpha=0.4)
plt.xlabel(a1_type)
plt.ylabel(a2_type)
def plot_roc_curve(fpr, tpr, label=None):
"""
Plots Rceiver Operating Characteristic (ROC) curve from false_positive_rate(fpr), true_positive_rate(tpr)
Requires imports:
from sklearn.metrics import roc_curve
Returns:
Nothing
"""
from pylab import mpl, plt
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'arial'
np.random.seed(1000)
np.set_printoptions(suppress=True, precision=4)
plt.plot(fpr, tpr, linewidth=2, label=label)
plt.plot([0, 1], [0, 1], 'k--')
plt.axis([0, 1, 0, 1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Negatove Rate')
plot_roc_curve(fpr, tpr)
def lagger():
global cols, scores
lag_counts = range(1, lags + 1)
cols = []
scores = []
for lag in range(1, lags + 1):
col = 'lag_{}'.format(lag)
data[col] = np.sign(data['returns'].shift(lag))
cols.append(col)
data.dropna(inplace=True)
print('Iteration number: {}'.format(lag))
%time model.fit(data[cols], np.sign(data['returns']))
model.predict(data[cols])
data['prediction'] = model.predict(data[cols])
data['prediction'].value_counts()
score = accuracy_score(data['prediction'], np.sign(data['returns']))
scores.append(score)
plt.figure()
plt.plot(lag_counts, scores, lw=2)
plt.xlabel('# of Lags')
plt.ylabel('Test Score')
return scores, cols
def serve_css(name, length, keys, values):
from pylab import plt, mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
from matplotlib.font_manager import FontProperties
# font = FontProperties(fname="d:\Users\ll.tong\Desktop\msyh.ttf", size=12)
font = FontProperties(fname="/usr/share/fonts/msyh.ttf", size=11)
plt.xlabel(u'')
plt.ylabel(u'出现次数',fontproperties=font)
plt.title(u'词频统计',fontproperties=font)
plt.grid()
keys = keys.decode("utf-8").split(' ')
values = values.split(' ')
valuesInt = []
for value in values:
valuesInt.append(int(value))
plt.xticks(range(int(length)), keys)
plt.plot(range(int(length)), valuesInt)
plt.xticks(rotation=defaultrotation, fontsize=9,fontproperties=font)
plt.yticks(fontsize=10,fontproperties=font)
name = name + str(datetime.now().date()).replace(':', '') + '.png'
imgUrl = 'static/temp/' + name
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(12.2, 2)
plt.savefig(imgUrl, bbox_inches='tight', figsize=(20,4), dpi=100)
plt.close()
tempfile = static_file(name, root='./static/temp/')
#os.remove(imgUrl)
return tempfile
def MakePlot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
for i in range(len(x)):
plt.plot(x[i], y[i], styles[i], label=labels[i])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
def draw(cls, t_max, agents_proportions, eco_idx, parameters):
color_set = ["green", "blue", "red"]
for agent_type in range(3):
plt.plot(np.arange(t_max),
agents_proportions[:, agent_type],
color=color_set[agent_type],
linewidth=2.0,
label="Type-{} agents".format(agent_type))
plt.ylim([-0.1, 1.1])
plt.xlabel("$t$")
plt.ylabel("Proportion of indirect exchanges")
# plt.suptitle('Direct choices proportion per type of agents', fontsize=14, fontweight='bold')
plt.legend(loc='upper right', fontsize=12)
print(parameters)
plt.title(
"Workforce: {}, {}, {}; displacement area: {}; vision area: {}; alpha: {}; tau: {}\n"
.format(parameters["x0"], parameters["x1"], parameters["x2"],
parameters["movement_area"], parameters["vision_area"],
parameters["alpha"], parameters["tau"]),
fontsize=12)
if not path.exists("../../figures"):
mkdir("../../figures")
plt.savefig("../../figures/figure_{}.pdf".format(eco_idx))
plt.show()
def test_screenstate_1(self):
from gdesk import gui
from pylab import plt
from pathlib import Path
gui.load_layout('console')
samplePath = Path(r'./samples')
gui.img.select(1)
gui.img.open(samplePath / 'kodim05.png')
gui.img.zoom_fit()
plt.plot(gui.vs.mean(2).mean(1))
plt.title('Column means of image 1')
plt.xlabel('Column Number')
plt.ylabel('Mean')
plt.grid()
plt.show()
gui.img.select(2)
gui.img.open(samplePath / 'kodim23.png')
gui.img.zoom_full()
plt.figure()
plt.plot(gui.vs.mean(2).mean(0))
plt.title('Row means of image 2')
plt.xlabel('Row Number')
plt.ylabel('Mean')
plt.grid()
plt.show()
def link_level_bars(levels, usages, quantiles, scheme, direction, color, nnames, lnames, admat=None):
"""
Bar plots of nodes' link usage of links at different levels.
"""
if not admat:
admat = np.genfromtxt('./settings/eadmat.txt')
if color == 'solar':
cmap = Oranges_cmap
elif color == 'wind':
cmap = Blues_cmap
elif color == 'backup':
cmap = 'Greys'
nodes, links = usages.shape
usageLevels = np.zeros((nodes, levels))
usageLevelsNorm = np.zeros((nodes, levels))
for node in range(nodes):
nl = neighbor_levels(node, levels, admat)
for lvl in range(levels):
ll = link_level(nl, lvl, nnames, lnames)
ll = np.array(ll, dtype='int')
usageSum = sum(usages[node, ll])
linkSum = sum(quantiles[ll])
usageLevels[node, lvl] = usageSum / linkSum
if lvl == 0:
usageLevelsNorm[node, lvl] = usageSum
else:
usageLevelsNorm[node, lvl] = usageSum / usageLevelsNorm[node, 0]
usageLevelsNorm[:, 0] = 1
# plot all nodes
usages = usageLevels.transpose()
plt.figure(figsize=(11, 3))
ax = plt.subplot()
plt.pcolormesh(usages[:, loadOrder], cmap=cmap)
plt.colorbar().set_label(label=r'$U_n^{(l)}$', size=11)
ax.set_yticks(np.linspace(.5, levels - .5, levels))
ax.set_yticklabels(range(1, levels + 1))
ax.yaxis.set_tick_params(width=0)
ax.xaxis.set_tick_params(width=0)
ax.set_xticks(np.linspace(1, nodes, nodes))
ax.set_xticklabels(loadNames, rotation=60, ha="right", va="top", fontsize=10)
plt.ylabel('Link level')
plt.savefig(figPath + '/levels/' + str(scheme) + '/' + 'total' + '_' + str(direction) + '_' + color + '.pdf', bbox_inches='tight')
plt.close()
# plot all nodes normalised to usage of first level
usages = usageLevelsNorm.transpose()
plt.figure(figsize=(11, 3))
ax = plt.subplot()
plt.pcolormesh(usages[:, loadOrder], cmap=cmap)
plt.colorbar().set_label(label=r'$U_n^{(l)}$', size=11)
ax.set_yticks(np.linspace(.5, levels - .5, levels))
ax.set_yticklabels(range(1, levels + 1))
ax.yaxis.set_tick_params(width=0)
ax.xaxis.set_tick_params(width=0)
ax.set_xticks(np.linspace(1, nodes, nodes))
ax.set_xticklabels(loadNames, rotation=60, ha="right", va="top", fontsize=10)
plt.ylabel('Link level')
plt.savefig(figPath + '/levels/' + str(scheme) + '/' + 'total_norm_cont_' + str(direction) + '_' + color + '.pdf', bbox_inches='tight')
plt.close()
def hexbin_plot(self, var1, var2, force=False):
fig_name = "{}/hexbin_{}_{}.pdf".format(self.fig_folder, var1, var2)
if path.exists(fig_name) and not force:
return
if var1 == "customer_extra_view_choices" and var2 == "delta_position":
print("Doing hexbin plot '{}' against '{}'.".format(var2, var1))
x = np.asarray(self.stats.data[var1])
y = np.asarray(self.stats.data[var2])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.xlim(self.range_var[var1])
plt.ylim(self.range_var[var2])
plt.xlabel(self.format_label(var1))
plt.ylabel(self.format_label(var2))
hb = ax.hexbin(x=x, y=y, gridsize=20, cmap='inferno')
ax.set_facecolor('black')
cb = fig.colorbar(hb, ax=ax)
cb.set_label('counts')
plt.savefig(fig_name)
if self.display:
plt.show()
plt.close()
def plot_comfort(fingers_org=range(1, 6, 1),
fingers_dst=range(1, 6, 1),
jumps=range(-12, 13, 1)):
import seaborn
from mpl_toolkits.mplot3d import Axes3D
from pylab import plt
xs, ys, zs, cs = calculate_comforts(fingers_org, fingers_dst, jumps)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs, ys, zs, c=cs)
ax.set_zlabel("Interval (half steps)", fontsize=15)
ax.set_zlim(jumps[0], jumps[-1])
# ax.set_zticks(jumps)
plt.xticks(fingers_org)
plt.xlim(fingers_org[0], fingers_org[-1])
plt.xlabel("From finger", fontsize=15)
plt.yticks(fingers_dst)
plt.ylim(fingers_dst[0], fingers_dst[-1])
plt.ylabel("To finger", fontsize=15)
plt.title("Difficulty of finger passages", fontsize=25)
plt.savefig('./figures/image.png', figsize=(16, 12), dpi=300)
plt.show()
def plot_stat(rows, cache):
"Use matplotlib to plot DAS statistics"
if not PLOT_ALLOWED:
raise Exception('Matplotlib is not available on the system')
if cache in ['cache', 'merge']: # cachein, cacheout, mergein, mergeout
name_in = '%sin' % cache
name_out = '%sout' % cache
else: # webip, webq, cliip, cliq
name_in = '%sip' % cache
name_out = '%sq' % cache
def format_date(date):
"Format given date"
val = str(date)
return '%s-%s-%s' % (val[:4], val[4:6], val[6:8])
date_range = [r['date'] for r in rows]
formated_dates = [format_date(str(r['date'])) for r in rows]
req_in = [r[name_in] for r in rows]
req_out = [r[name_out] for r in rows]
plt.plot(date_range, req_in , 'ro-',\
date_range, req_out, 'gv-',)
plt.grid(True)
plt.axis([min(date_range), max(date_range), \
0, max([max(req_in), max(req_out)])])
plt.xticks(date_range, tuple(formated_dates), rotation=17)
# plt.xlabel('dates [%s, %s]' % (date_range[0], date_range[-1]))
plt.ylabel('DAS %s behavior' % cache)
plt.savefig('das_%s.pdf' % cache, format='pdf', transparent=True)
plt.close()
def example_filterbank():
from pylab import plt
import numpy as np
x = _create_impulse(2000)
gfb = GammatoneFilterbank(density=1)
analyse = gfb.analyze(x)
imax, slopes = gfb.estimate_max_indices_and_slopes()
fig, axs = plt.subplots(len(gfb.centerfrequencies), 1)
for (band, state), imx, ax in zip(analyse, imax, axs):
ax.plot(np.real(band))
ax.plot(np.imag(band))
ax.plot(np.abs(band))
ax.plot(imx, 0, 'o')
ax.set_yticklabels([])
[ax.set_xticklabels([]) for ax in axs[:-1]]
axs[0].set_title('Impulse responses of gammatone bands')
fig, ax = plt.subplots()
def plotfun(x, y):
ax.semilogx(x, 20 * np.log10(np.abs(y)**2))
gfb.freqz(nfft=2 * 4096, plotfun=plotfun)
plt.grid(True)
plt.title('Absolute spectra of gammatone bands.')
plt.xlabel('Normalized Frequency (log)')
plt.ylabel('Attenuation /dB(FS)')
plt.axis('Tight')
plt.ylim([-90, 1])
plt.show()
return gfb
def plot_stat(rows, cache):
"Use matplotlib to plot DAS statistics"
if not PLOT_ALLOWED:
raise Exception('Matplotlib is not available on the system')
if cache in ['cache', 'merge']: # cachein, cacheout, mergein, mergeout
name_in = '%sin' % cache
name_out = '%sout' % cache
else: # webip, webq, cliip, cliq
name_in = '%sip' % cache
name_out = '%sq' % cache
def format_date(date):
"Format given date"
val = str(date)
return '%s-%s-%s' % (val[:4], val[4:6], val[6:8])
date_range = [r['date'] for r in rows]
formated_dates = [format_date(str(r['date'])) for r in rows]
req_in = [r[name_in] for r in rows]
req_out = [r[name_out] for r in rows]
plt.plot(date_range, req_in , 'ro-',
date_range, req_out, 'gv-',
)
plt.grid(True)
plt.axis([min(date_range), max(date_range), \
0, max([max(req_in), max(req_out)])])
plt.xticks(date_range, tuple(formated_dates), rotation=17)
# plt.xlabel('dates [%s, %s]' % (date_range[0], date_range[-1]))
plt.ylabel('DAS %s behavior' % cache)
plt.savefig('das_%s.pdf' % cache, format='pdf', transparent=True)
plt.close()
def square_root_diffusion_euler():
x0 = 0.25
kappa = 3.0
theta = 0.15
sigma = 0.1
I = 10000
M = 50
dt = T / M
xh = np.zeros((M + 1, I))
x = np.zeros_like(xh)
xh[0] = x0
x[0] = x0
for t in range(1, M + 1):
xh[t] = (xh[t - 1] + kappa * (theta - np.maximum(xh[t - 1], 0)) * dt +
sigma * np.sqrt(np.maximum(xh[t - 1], 0)) * math.sqrt(dt) *
npr.standard_normal(I))
x = np.maximum(xh, 0)
plt.figure(figsize=(10, 6))
plt.hist(x[-1], bins=50)
plt.xlabel('value(SRT(T)')
plt.ylabel('frequency')
plt.show()
plt.figure(figsize=(10, 6))
plt.plot(x[:, :100], lw=1.5)
plt.xlabel('time')
plt.ylabel('index level')
plt.show()
return x
def plot_zipf(*freq):
'''
basic plotting using matplotlib and pylab
'''
ranks, frequencies = [], []
langs, colors = [], []
langs = ["English", "German", "Finnish"]
colors = ['#FF0000', '#00FF00', '#0000FF']
if bonus_part:
colors.extend(['#00FFFF', '#FF00FF', '#FFFF00'])
langs.extend(["English (Stemmed)", "German (Stemmed)", "Finnish (Stemmed)"])
plt.subplot(111) # 1, 1, 1
num = 6 if bonus_part else 3
for i in xrange(num):
ranks.append(range(1, len(freq[i]) + 1))
frequencies.append([e[1] for e in freq[i]])
# log x and y axi, both with base 10
plt.loglog(ranks[i], frequencies[i], marker='', basex=10, color=colors[i], label=langs[i])
plt.legend()
plt.grid(True)
plt.title("Zipf's law!")
plt.xlabel('Rank')
plt.ylabel('Frequency')
plt.show()
def jump_diffusion():
S0 = 100.0
r = 0.05
sigma = 0.2
lamb = 0.05
mu = -0.6
delta = 0.25
rj = lamb * (math.exp(mu + 0.5 * delta**2) - 1)
T = 1.0
M = 50
I = 10000
dt = T / M
S = np.zeros((M + 1, I))
S[0] = S0
sn1 = npr.standard_normal((M + 1, I))
sn2 = npr.standard_normal((M + 1, I))
poi = npr.poisson(lamb * dt, (M + 1, I))
for t in range(1, M + 1, 1):
S[t] = S[t - 1] * (np.exp(
(r - rj - 0.5 * sigma**2) * dt + sigma * math.sqrt(dt) * sn1[t]) +
(np.exp(mu + delta * sn2[t]) - 1) * poi[t])
S[t] = np.maximum(S[t], 0)
plt.figure(figsize=(10, 6))
plt.hist(S[-1], bins=50)
plt.xlabel('value')
plt.ylabel('frequency')
plt.show()
plt.figure(figsize=(10, 6))
plt.plot(S[:, :100], lw=1.)
plt.xlabel('time')
plt.ylabel('index level')
plt.show()
def curve_plot(self, variable, t_max):
print("Doing curve plot for variable '{}'.".format(variable))
var = Variable(name=variable)
if var.data is None:
self.extract_single_dimension(var, t_max=t_max)
x = np.arange(t_max)
mean = var.data["mean"]
std = var.data["std"]
plt.plot(x, mean, c='black', lw=2)
plt.plot(x, mean + std, c='black', lw=.1)
plt.plot(x, mean - std, c='black', lw=.1)
plt.fill_between(x, mean + std, mean - std, color='black', alpha=.1)
plt.xlabel("t")
plt.ylabel(self.format_label(variable))
plt.savefig("{}/curve_plot_{}.pdf".format(self.fig_folder, variable))
if self.display:
plt.show()
plt.close()
def draw(cls, t_max, agents_proportions, eco_idx, parameters):
color_set = ["green", "blue", "red"]
for agent_type in range(3):
plt.plot(np.arange(t_max), agents_proportions[:, agent_type],
color=color_set[agent_type], linewidth=2.0, label="Type-{} agents".format(agent_type))
plt.ylim([-0.1, 1.1])
plt.xlabel("$t$")
plt.ylabel("Proportion of indirect exchanges")
# plt.suptitle('Direct choices proportion per type of agents', fontsize=14, fontweight='bold')
plt.legend(loc='upper right', fontsize=12)
print(parameters)
plt.title(
"Workforce: {}, {}, {}; displacement area: {}; vision area: {}; alpha: {}; tau: {}\n"
.format(
parameters["x0"],
parameters["x1"],
parameters["x2"],
parameters["movement_area"],
parameters["vision_area"],
parameters["alpha"],
parameters["tau"]
), fontsize=12)
if not path.exists("../../figures"):
mkdir("../../figures")
plt.savefig("../../figures/figure_{}.pdf".format(eco_idx))
plt.show()
def hexbin_plot(self, var1, var2):
print("Doing hexbin plot '{}' against '{}'.".format(var2, var1))
x = np.asarray(self.stats.data[var1])
y = np.asarray(self.stats.data[var2])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.xlim(self.range_var[var1])
plt.ylim(self.range_var[var2])
plt.xlabel(self.format_label(var1))
plt.ylabel(self.format_label(var2))
hb = ax.hexbin(x=x, y=y, gridsize=20, cmap='inferno')
ax.set_facecolor('black')
cb = fig.colorbar(hb, ax=ax)
cb.set_label('counts')
plt.savefig("{}/hexbin_{}_{}.pdf".format(self.fig_folder, var1, var2))
if self.display:
plt.show()
plt.close()
def example_filterbank():
from pylab import plt
import numpy as np
x = _create_impulse(2000)
gfb = GammatoneFilterbank(density=1)
analyse = gfb.analyze(x)
imax, slopes = gfb.estimate_max_indices_and_slopes()
fig, axs = plt.subplots(len(gfb.centerfrequencies), 1)
for (band, state), imx, ax in zip(analyse, imax, axs):
ax.plot(np.real(band))
ax.plot(np.imag(band))
ax.plot(np.abs(band))
ax.plot(imx, 0, 'o')
ax.set_yticklabels([])
[ax.set_xticklabels([]) for ax in axs[:-1]]
axs[0].set_title('Impulse responses of gammatone bands')
fig, ax = plt.subplots()
def plotfun(x, y):
ax.semilogx(x, 20*np.log10(np.abs(y)**2))
gfb.freqz(nfft=2*4096, plotfun=plotfun)
plt.grid(True)
plt.title('Absolute spectra of gammatone bands.')
plt.xlabel('Normalized Frequency (log)')
plt.ylabel('Attenuation /dB(FS)')
plt.axis('Tight')
plt.ylim([-90, 1])
plt.show()
return gfb
def plot_weightings():
"""Plots all weighting functions defined in :module: splweighting."""
from scipy.signal import freqz
from pylab import plt, np
sample_rate = 48000
num_samples = 2*4096
fig, ax = plt.subplots()
for name, weight_design in sorted(
_weighting_coeff_design_funsd.items()):
b, a = weight_design(sample_rate)
w, H = freqz(b, a, worN=num_samples)
freq = w*sample_rate / (2*np.pi)
ax.semilogx(freq, 20*np.log10(np.abs(H)+1e-20),
label='{}-Weighting'.format(name))
plt.legend(loc='lower right')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping / dB')
plt.grid(True)
plt.axis([10, 20000, -80, 5])
return fig, ax
def generate_start_time_figures(self):
recording_time_grouped_by_patient = self.pain_data[["PatientID", "NRSTimeFromEndSurgery_mins"]].groupby("PatientID")
recording_start_minutes = recording_time_grouped_by_patient.min()
fig1 = "fig1.pdf"
fig2 = "fig2.pdf"
plt.figure(figsize=[8,4])
plt.title("Pain score recording start times", fontsize=14).set_y(1.05)
plt.ylabel("Occurrences", fontsize=14)
plt.xlabel("Recording Start Time (minutes)", fontsize=14)
plt.hist(recording_start_minutes.values, bins=20, color="0.5")
plt.savefig(os.path.join(self.tmp_directory, fig1), bbox_inches="tight")
plt.figure(figsize=[8,4])
plt.title("Pain score recording start times, log scale", fontsize=14).set_y(1.05)
plt.ylabel("Occurrences", fontsize=14)
plt.xlabel("Recording Start Time (minutes)", fontsize=14)
plt.hist(recording_start_minutes.values, bins=20, log=True, color="0.5")
plt.savefig(os.path.join(self.tmp_directory, fig2), bbox_inches="tight")
#save the figures in panel format
f = open(os.path.join(self.tmp_directory, "tmp.tex"), 'w')
f.write(r"""
\documentclass[%
,float=false % this is the new default and can be left away.
,preview=true
,class=scrartcl
,fontsize=20pt
]{standalone}
\usepackage[active,tightpage]{preview}
\usepackage{varwidth}
\usepackage{graphicx}
\usepackage[justification=centering]{caption}
\usepackage{subcaption}
\usepackage[caption=false,font=footnotesize]{subfig}
\renewcommand{\thesubfigure}{\Alph{subfigure}}
\begin{document}
\begin{preview}
\begin{figure}[h]
\begin{subfigure}{0.5\textwidth}
\includegraphics[width=\textwidth]{""" + fig1 + r"""}
\caption{Normal scale}
\end{subfigure}\begin{subfigure}{0.5\textwidth}
\includegraphics[width=\textwidth]{""" + fig2 + r"""}
\caption{Log scale}
\end{subfigure}
\end{figure}
\end{preview}
\end{document}
""")
f.close()
subprocess.call(["pdflatex",
"-halt-on-error",
"-output-directory",
self.tmp_directory,
os.path.join(self.tmp_directory, "tmp.tex")])
shutil.move(os.path.join(self.tmp_directory, "tmp.pdf"),
os.path.join(self.output_directory, "pain_score_start_times.pdf"))
def plot_charts(self):
print(self.final_portfolio_valuation)
plt.figure(figsize=(10, 6))
plt.hist( self.final_portfolio_valuation, bins=100)
plt.title("Final Exit Valuation complete Portfolio after {} year as Geometric Brownian Motion".format(self.max_year))
plt.xlabel('Exit Valuation')
plt.ylabel('frequency');
plt.show()
def plot(self, new_plot=False, xlim=None, ylim=None, title=None, figsize=None,
xlabel=None, ylabel=None, fontsize=None, show_legend=True, grid=True):
"""
Plot data using matplotlib library. Use show() method for matplotlib to see result or ::
%pylab inline
in IPython to see plot as cell output.
:param bool new_plot: create or not new figure
:param xlim: x-axis range
:param ylim: y-axis range
:type xlim: None or tuple(x_min, x_max)
:type ylim: None or tuple(y_min, y_max)
:param title: title
:type title: None or str
:param figsize: figure size
:type figsize: None or tuple(weight, height)
:param xlabel: x-axis name
:type xlabel: None or str
:param ylabel: y-axis name
:type ylabel: None or str
:param fontsize: font size
:type fontsize: None or int
:param bool show_legend: show or not labels for plots
:param bool grid: show grid or not
"""
xlabel = self.xlabel if xlabel is None else xlabel
ylabel = self.ylabel if ylabel is None else ylabel
figsize = self.figsize if figsize is None else figsize
fontsize = self.fontsize if fontsize is None else fontsize
self.fontsize_ = fontsize
self.show_legend_ = show_legend
title = self.title if title is None else title
xlim = self.xlim if xlim is None else xlim
ylim = self.ylim if ylim is None else ylim
new_plot = self.new_plot or new_plot
if new_plot:
plt.figure(figsize=figsize)
plt.xlabel(xlabel, fontsize=fontsize)
plt.ylabel(ylabel, fontsize=fontsize)
plt.title(title, fontsize=fontsize)
plt.tick_params(axis='both', labelsize=fontsize)
plt.grid(grid)
if xlim is not None:
plt.xlim(xlim)
if ylim is not None:
plt.ylim(ylim)
self._plot()
if show_legend:
plt.legend(loc='best', scatterpoints=1)
def create_plot(x, y, styles, labels, axlabels):
plt.figure(figsize=(10, 6))
plt.scatter(x[0], y[0])
plt.scatter(x[1], y[1])
plt.xlabel(axlabels[0])
plt.ylabel(axlabels[1])
plt.legend(loc=0)
plt.show()
def convert_all_to_png(vis_path, out_dir="maps_png", size=None):
units = {
'gas_density': 'Gas Density [g/cm$^3$]',
'Tm': 'Temperature [K]',
'Tew': 'Temperature [K]',
'S': 'Entropy []',
'dm': 'DM Density [g/cm$^3$]',
'v': 'Velocity [km/s]'
}
log_list = ['gas_density']
for vis_file in os.listdir(vis_path):
if ".dat" not in vis_file:
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size,
axis_values) = read_visualization_data(vis_path + "/" + vis_file,
size)
print "image width in Mpc/h: ", axis_values[-1] * 2.0
x, y = np.meshgrid(axis_values, axis_values)
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5, 4))
if map_type in log_list:
plt.pcolor(x, y, image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else:
plt.pcolor(x, y, image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys():
cbar.ax.set_ylabel(units[map_type])
plt.axis(
[axis_values[0], axis_values[-1], axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir + "/" + out_file, dpi=150)
plt.close()
plt.clf()
def plot_treward(agent):
''' Function to plot the total reward
per training eposiode.
'''
plt.figure(figsize=(10, 6))
x = range(1, len(agent.averages) + 1)
y = np.polyval(np.polyfit(x, agent.averages, deg=3), x)
plt.plot(x, agent.averages, label='moving average')
plt.plot(x, y, 'r--', label='regression')
plt.xlabel('episodes')
plt.ylabel('total reward')
plt.legend()
def plot_smoothed_alpha_comparison(self,rmsval,suffix=''):
plt.plot(self.f,self.alpha,'ko',label='data set')
plt.plot(self.f,self.salpha,'c-',lw=2,label='smoothed angle $\phi$')
plt.xlabel('frequency in Hz')
plt.ylabel('angle $\phi$ in coordinates of circle')
plt.legend()
ylims=plt.axes().get_ylim()
plt.yticks((arange(9)-4)*0.5*pi, ['$-2\pi$','$-3\pi/2$','$-\pi$','$-\pi/2$','$0$','$\pi/2$','$\pi$','$3\pi/2$','$2\pi$'])
plt.ylim(ylims)
plt.title('RMS offset from smooth curve: {:.4f}'.format(rmsval))
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'salpha','c{}_salpha_on_{}_circle'.format(self.sdc.case,self.ZorY)+self.sdc.suffix+self.sdc.outsuffix+suffix+'.png'), dpi=240)
plt.close()
def convert_all_to_png(vis_path, out_dir = "maps_png", size = None) :
units = { 'gas_density' : 'Gas Density [g/cm$^3$]',
'Tm' : 'Temperature [K]',
'Tew' : 'Temperature [K]',
'S' : 'Entropy []',
'dm' : 'DM Density [g/cm$^3$]',
'v' : 'Velocity [km/s]' }
log_list = ['gas_density']
for vis_file in os.listdir(vis_path) :
if ".dat" not in vis_file :
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size, axis_values) = read_visualization_data(vis_path+"/"+vis_file, size)
print "image width in Mpc/h: ", axis_values[-1]*2.0
x, y = np.meshgrid( axis_values, axis_values )
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5,4))
if map_type in log_list:
plt.pcolor(x,y,image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else :
plt.pcolor(x,y,image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys() :
cbar.ax.set_ylabel(units[map_type])
plt.axis([axis_values[0], axis_values[-1],axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir+"/"+out_file, dpi=150 )
plt.close()
plt.clf()
def plot_fault_framework(fault_framework):
fm = fault_framework
plt.plot(fm.Y_PC, fm.DEP, '-o')
plt.axis('equal')
plt.axhline(0, color='black')
plt.gca().set_yticks(fm.DEP)
plt.gca().set_xticks(fm.Y_PC)
plt.grid('on')
plt.xlabel('From trench to continent(km)')
plt.ylabel('depth (km)')
for xi, yi, dip in zip(fm.Y_PC, fm.DEP, fm.DIP_D):
plt.text(xi, yi, 'dip = %.1f'%dip)
plt.gca().invert_yaxis()
def plot_overview(self,suffix=''):
x=self.x; y=self.y; r=self.radius; cx,cy=self.center.real,self.center.imag
ax=plt.axes()
plt.scatter(x,y, marker='o', c='b', s=40)
plt.axhline(y=0,color='grey', zorder=-1)
plt.axvline(x=0,color='grey', zorder=-2)
t=linspace(0,2*pi,201)
circx=r*cos(t) + cx
circy=r*sin(t) + cy
plt.plot(circx,circy,'g-')
plt.plot([cx],[cy],'gx',ms=12)
if self.ZorY == 'Z':
philist,flist=[self.phi_a,self.phi_p,self.phi_n],[self.fa,self.fp,self.fn]
elif self.ZorY == 'Y':
philist,flist=[self.phi_m,self.phi_s,self.phi_r],[self.fm,self.fs,self.fr]
for p,f in zip(philist,flist):
if f is not None:
xpos=cx+r*cos(p); ypos=cy+r*sin(p); xos=0.2*(xpos-cx); yos=0.2*(ypos-cy)
plt.plot([0,xpos],[0,ypos],'co-')
ax.annotate('{:.3f} Hz'.format(f), xy=(xpos,ypos), xycoords='data',
xytext=(xpos+xos,ypos+yos), textcoords='data', #textcoords='offset points',
arrowprops=dict(arrowstyle="->", shrinkA=0, shrinkB=10)
)
#plt.xlim(0,0.16)
#plt.ylim(-0.1,0.1)
plt.axis('equal')
if self.ZorY == 'Z':
plt.xlabel(r'resistance $R$ in Ohm'); plt.ylabel(r'reactance $X$ in Ohm')
if self.ZorY == 'Y':
plt.xlabel(r'conductance $G$ in Siemens'); plt.ylabel(r'susceptance $B$ in Siemens')
plt.title("fitting the admittance circle with Powell's method")
tx1='best fit (fmin_powell):\n'
tx1+='center at G+iB = {:.5f} + i*{:.8f}\n'.format(cx,cy)
tx1+='radius = {:.5f}; '.format(r)
tx1+='residue: {:.2e}'.format(self.resid)
txt1=plt.text(-r,cy-1.1*r,tx1,fontsize=8,ha='left',va='top')
txt1.set_bbox(dict(facecolor='gray', alpha=0.25))
idxlist=self.to_be_annotated('triple')
ofs=self.annotation_offsets(idxlist,factor=0.1,xshift=0.15)
for i,j in enumerate(idxlist):
xpos,ypos = x[j],y[j]; xos,yos = ofs[i].real,ofs[i].imag
ax.annotate('{:.1f} Hz'.format(self.f[j]), xy=(xpos,ypos), xycoords='data',
xytext=(xpos+xos,ypos+yos), textcoords='data', #textcoords='offset points',
arrowprops=dict(arrowstyle="->", shrinkA=0, shrinkB=10)
)
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'c{}_fitted_{}_circle'.format(self.sdc.case,self.ZorY)+suffix+'.png'), dpi=240)
plt.close()
def update_img((expected, output)):
plt.cla()
plt.ylim((vmin, vmin+vmax))
plt.xlim((vmin, vmin+vmax))
ax = fig.add_subplot(111)
plt.plot([vmin, vmin+vmax], [vmin, vmin+vmax])
ax.grid(True)
plt.xlabel("expected output")
plt.ylabel("network output")
plt.legend()
expected = expected*vmax + vmin
output = output*vmax + vmin
#scat.set_offsets((expected, output))
scat = ax.scatter(expected, output)
return scat
def test_dep(self):
xf = arange(0, 425)
deps = self.fm.get_dep(xf)
plt.plot(xf,deps)
plt.gca().set_yticks(self.fm.DEP)
plt.gca().set_xticks(self.fm.Y_PC)
plt.grid('on')
plt.title('Ground x versus depth')
plt.xlabel('Ground X (km)')
plt.ylabel('depth (km)')
plt.axis('equal')
plt.gca().invert_yaxis()
plt.savefig(join(self.outs_dir, '~Y_PC_vs_deps.png'))
plt.close()
def start_plot(self,w=1.3,connect=False):
self.fig=plt.figure()
self.ax=plt.axes()
plt.axhline(y=0,color='grey', zorder=-1)
plt.axvline(x=0,color='grey', zorder=-2)
self.plot_data(connect=connect)
#plt.axis('equal')
self.ax.set_aspect('equal', 'datalim')
if self.center is not None:
cx,cy=self.center.real,self.center.imag; r=self.radius
self.ax.axis([cx-w*r,cx+w*r,cy-w*r,cy+w*r])
else:
xmx=amax(self.x); ymn,ymx=amin(self.y),amax(self.y)
cx=0.5*xmx; cy=0.5*(ymn+ymx); r=0.5*(ymx-ymn)
self.ax.axis([cx-w*r,cx+w*r,cy-w*r,cy+w*r])
if self.ZorY == 'Z':
plt.xlabel(r'resistance $R$ in Ohm'); plt.ylabel(r'reactance $X$ in Ohm')
if self.ZorY == 'Y':
plt.xlabel(r'conductance $G$ in Siemens'); plt.ylabel(r'susceptance $B$ in Siemens')
def _plot_base(dep, val, deplim_small, xlim_small, xlabel):
plt.subplot(1,2,1)
plt.plot(val, dep)
plt.gca().invert_yaxis()
plt.grid('on')
plt.ylabel('depth/km')
plt.xlabel(xlabel)
locs, labels = plt.xticks()
plt.setp(labels, rotation=-45)
plt.subplot(1,2,2)
plt.plot(val, dep)
plt.gca().invert_yaxis()
plt.grid('on')
plt.ylim(deplim_small)
plt.xlim(xlim_small)
plt.xlabel(xlabel)
locs, labels = plt.xticks()
plt.setp(labels, rotation=-45)
def plotter(mode,Bc,Tc,Q):
col = ['#000080','#0000FF','#4169E1','#6495ED','#00BFFF','#B0E0E6']
plt.figure()
ax = plt.subplot(111)
for p in range(Bc.shape[1]):
plt.plot(Tc[:,p],Bc[:,p],'-',color=str(col[p]))
plt.xlabel('Tc [TW]')
plt.ylabel('Bc normalised to total EU load')
plt.title(str(mode)+' flow')
# Shrink current axis by 25% to make room for legend
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
plt.legend(\
([str(Q[i]*100) for i in range(len(Q))]),\
loc='center left', bbox_to_anchor=(1, 0.5),title='Quantiles')
plt.savefig('figures/bctc_'+str(mode)+'.eps')
def freqz(sosmat, nsamples=44100, sample_rate=44100, plot=True):
"""Plots Frequency response of sosmat."""
from pylab import np, plt, fft, fftfreq
x = np.zeros(nsamples)
x[nsamples/2] = 0.999
y, states = sosfilter_double_c(x, sosmat)
Y = fft(y)
f = fftfreq(len(x), 1.0/sample_rate)
if plot:
plt.grid(True)
plt.axis([0, sample_rate / 2, -100, 5])
L = 20*np.log10(np.abs(Y[:len(x)/2]) + 1e-17)
plt.semilogx(f[:len(x)/2], L, lw=0.5)
plt.hold(True)
plt.title('freqz sos filter')
plt.xlabel('Frequency / Hz')
plt.ylabel('Damping /dB(FS)')
plt.xlim((10, sample_rate/2))
plt.hold(False)
return x, y, f, Y
def plot_vel_decomposition(self, site, cmpt, loc=0, leg_fs=7,
if_ylim=False
):
y = self.plot_pred_vel_added(site, cmpt, label='total')
y += self.plot_vel_R_co(site, cmpt,
style='-^', label='Rco', color='orange')
y += self.plot_vel_E_cumu_slip(site, cmpt, color='green')
y += self.plot_vel_R_aslip(site, cmpt, color='black')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
plt.ylabel(r'mm/yr')
plt.legend(loc=loc, prop={'size':leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site = get_site_true_name(site_id=site),
cmpt = cmpt
))
def plot_post_disp_decomposition(self, site, cmpt, loc=2, leg_fs=7,
added_label = None,
marker_for_obs = 'x',
):
y = self.plot_post_obs_linres(site,cmpt, label='obs.', marker=marker_for_obs)
y += self.plot_post_disp_pred_from_result_file(site,cmpt, label='pred.')
y += self.plot_R_co(site, cmpt,
style = '-^', label='Rco', color='orange')
y += self.plot_E_aslip(site, cmpt, color='green')
plt.grid('on')
self.plot_post_disp_pred_added(site, cmpt, label=added_label)
plt.legend(loc=loc, prop={'size':leg_fs})
plt.ylabel(r'm')
plt.gcf().autofmt_xdate()
plt.title('Postseismic Disp. : {site} - {cmpt}'.format(
site = get_site_true_name(site_id = site),
cmpt = cmpt
))
def plot_cumu_disp_decomposition(self, site, cmpt, loc=2, leg_fs=7,
if_ylim=False,
added_label = None,
):
self.plot_cumu_obs_linres(site, cmpt)
y = self.plot_cumu_disp_pred_from_result_file(site, cmpt, label='pred.')
y += self.plot_R_co(site, cmpt,
style='-^', label='Rco', color='orange')
y += self.plot_E_cumu_slip(site, cmpt, color='green')
plt.grid('on')
if if_ylim:
plt.ylim(calculate_lim(y))
self.plot_cumu_disp_pred_added(site, cmpt, label=added_label)
plt.ylabel(r'm')
plt.legend(loc=loc, prop={'size':leg_fs})
plt.gcf().autofmt_xdate()
plt.title('Cumulative Disp.: {site} - {cmpt}'.format(
site = get_site_true_name(site_id=site),
cmpt = cmpt
))
def plotLive(combine_Type, combine_Name, lat_Name, long_Name, massFlow_Name, filename):
data = pd.read_csv(filename)
if combine_Type != 0:
comb_df = data[data[combine_Name] == combine_Type]
lat_df = comb_df[lat_Name]
lon_df = comb_df[long_Name]
y = comb_df[massFlow_Name]
else:
lat_df = data[lat_Name]
lon_df = data[long_Name]
y = data[massFlow_Name]
e,n = convertToUTM(lat_df, lon_df)
def makeFig():
plt.plot(x,y)
plt.ylabel('Easting')
plt.xlabel('Northing')
plt.ion() # enable interactivity
plt.grid()
fig = plt.figure() # make a figure
x=list()
y=list()
for i in arange(len(n)):
x.append(n[i])
y.append(e[i])
i+=1
drawnow(makeFig)
def plot_L_curve(files,
nlin_pars = ['log10_He_','log10_visM_','rake'],
nlin_pars_ylabels = [r'$log_{10}(He)$',
r'$log_{10}(visM)$',
'rake'],
):
nreses = collect_from_result_files(files, 'residual_norm_weighted')
nroughs = collect_from_result_files(files, 'roughening_norm')
num_subplots = 1 + len(nlin_pars)
x1 = amin(nreses)
x2 = amax(nreses)
dx = x2 - x1
xlim = (x1-dx*0.02, x2+dx*0.2)
xticks = range(int(x1), int(x2),5)
plt.subplot(num_subplots,1,1)
plt.loglog(nreses, nroughs,'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.ylabel('roughening')
plt.xlabel('Residual Norm')
plt.grid('on')
nth = 2
for par, par_label in zip(nlin_pars, nlin_pars_ylabels):
y = collect_from_result_files(files, par)
plt.subplot(num_subplots,1,nth)
plt.semilogx(nreses, y,'o-')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.gca().get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.ylabel(par_label)
plt.xlabel('Residual Norm')
plt.grid('on')
nth += 1
import h5py
from pylab import plt
def collect_results(outs_files, key):
outs = []
for file in outs_files:
with h5py.File(file, 'r') as fid:
out = fid[key][...]
outs.append(out)
return outs
files = sorted(glob.glob('../outs/ano_??.h5'))
nrough1 = collect_results(files, 'regularization/roughening/norm')
nres1 = collect_results(files, 'misfit/norm_weighted')
files = sorted(glob.glob('../../run0/outs/ano_??.h5'))
nrough0 = collect_results(files, 'regularization/roughening/norm')
nres0 = collect_results(files, 'misfit/norm_weighted')
plt.loglog(nres0, nrough0, '.', label='Result0')
plt.loglog(nres1, nrough1, '.', label='Result1')
plt.grid('on')
plt.xlabel('norm of weighted residual')
plt.ylabel('norm of solution roughness')
plt.xlim([.7,5])
plt.legend()
plt.savefig('compare_misfit.png')
plt.show()
Fv = []
# For each frequency bin, estimate the stats
t_init = time()
for t in time_bins:
covmats = covest.fit_transform(epochs_data[:, ::1, t:(t+window)])
p_test = PermutationDistance(1000, metric='riemann', mode='pairwise')
p, F = p_test.test(covmats, labels, verbose=False)
pv.append(p)
Fv.append(F[0])
duration = time() - t_init
# plot result
fig, axes = plt.subplots(1, 1, figsize=[6, 3], sharey=True)
sig = 0.05
times = np.array(time_bins)/float(Fs) + tmin
axes.plot(times, Fv, lw=2, c='k')
plt.xlabel('Time (sec)')
plt.ylabel('Score')
a = np.where(np.diff(np.array(pv) < sig))[0]
a = a.reshape(int(len(a)/2), 2)
st = (times[1] - times[0])/2.0
for p in a:
axes.axvspan(times[p[0]]-st, times[p[1]]+st, facecolor='g', alpha=0.5)
axes.legend(['Score', 'p<%.2f' % sig])
axes.set_title('Pairwise distance - %.1f sec.' % duration)
sns.despine()
plt.tight_layout()
plt.show()
import numpy as np
from pylab import plt
import h5py
from epochs import epochs
def read_inland_rms_from_files(files):
rms = []
for file in files:
with h5py.File(file) as fid:
rms.append(fid['misfit/rms_inland'][...])
return np.asarray(rms)
nrough = 10
files = sorted(glob.glob('../outs/epoch_????_rough_%02d.h5'%nrough))
rms = read_inland_rms_from_files(files)
plt.plot(epochs, rms*100., 'o-', label='RMS (afterslip only model)')
with open('rms_deconv.pkl','rb') as fid:
t,y = pickle.load(fid)
plt.plot(t, np.asarray(y)*100., 'o-', label='RMS (afterslip + viscoelastic relax.)')
plt.xlabel('days after the mainshock')
plt.ylabel('RMS misfit (cm)')
plt.grid('on')
plt.title('Slip only RMS misfit')
plt.legend(loc=0)
plt.savefig('rms_static_vs_deconv.png')
plt.show()
Hes.append(m[-2])
rakes.append(m[-1])
nrough = fid['regularization/roughening/norm'][...]
nroughs.append(nrough)
xlim = (7, 22)
xlim = None
xticks = range(7,22)
plt.subplot(411)
plt.semilogx(nreses, visMs,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('log10(visM/(Pa.s))')
plt.subplot(412)
plt.semilogx(nreses, Hes,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.grid('on')
plt.ylabel('He/km')
plt.subplot(413)
plt.semilogx(nreses, rakes,'o')
plt.xlim(xlim)
plt.gca().set_xticks(xticks)
plt.ylabel('rake')
plt.grid('on')
def get_linear_model_histogramDouble(code, ptype='f', dtype='d', start=None, end=None, vtype='close', filter='n',
df=None):
# 399001','cyb':'zs399006','zxb':'zs399005
# code = '999999'
# code = '601608'
# code = '000002'
# asset = ts.get_hist_data(code)['close'].sort_index(ascending=True)
# df = tdd.get_tdx_Exp_day_to_df(code, 'f').sort_index(ascending=True)
# vtype='close'
# if vtype == 'close' or vtype==''
# ptype=
if start is not None and filter == 'y':
if code not in ['999999', '399006', '399001']:
index_d, dl = tdd.get_duration_Index_date(dt=start)
log.debug("index_d:%s dl:%s" % (str(index_d), dl))
else:
index_d = cct.day8_to_day10(start)
log.debug("index_d:%s" % (index_d))
start = tdd.get_duration_price_date(code, ptype='low', dt=index_d)
log.debug("start:%s" % (start))
if df is None:
# df = tdd.get_tdx_append_now_df(code, ptype, start, end).sort_index(ascending=True)
df = tdd.get_tdx_append_now_df_api(code, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df = tdd.get_tdx_stock_period_to_type(df, dtype).sort_index(ascending=True)
asset = df[vtype]
log.info("df:%s" % asset[:1])
asset = asset.dropna()
dates = asset.index
if not code.startswith('999') or not code.startswith('399'):
if code[:1] in ['5', '6', '9']:
code2 = '999999'
elif code[:1] in ['3']:
code2 = '399006'
else:
code2 = '399001'
df1 = tdd.get_tdx_append_now_df_api(code2, ptype, start, end).sort_index(ascending=True)
# df1 = tdd.get_tdx_append_now_df(code2, ptype, start, end).sort_index(ascending=True)
if not dtype == 'd':
df1 = tdd.get_tdx_stock_period_to_type(df1, dtype).sort_index(ascending=True)
asset1 = df1.loc[asset.index, vtype]
startv = asset1[:1]
# asset_v=asset[:1]
# print startv,asset_v
asset1 = asset1.apply(lambda x: round(x / asset1[:1], 2))
# print asset1[:4]
# 画出价格随时间变化的图像
# _, ax = plt.subplots()
# fig = plt.figure()
fig = plt.figure(figsize=(16, 10))
# fig = plt.figure(figsize=(16, 10), dpi=72)
# fig.autofmt_xdate() #(no fact)
# plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
plt.subplots_adjust(left=0.05, bottom=0.08, right=0.95, top=0.95, wspace=0.15, hspace=0.25)
# set (gca,'Position',[0,0,512,512])
# fig.set_size_inches(18.5, 10.5)
# fig=plt.fig(figsize=(14,8))
ax1 = fig.add_subplot(321)
# asset=asset.apply(lambda x:round( x/asset[:1],2))
ax1.plot(asset)
# ax1.plot(asset1,'-r', linewidth=2)
ticks = ax1.get_xticks()
# start, end = ax1.get_xlim()
# print start, end, len(asset)
# print ticks, ticks[:-1]
# (ticks[:-1] if len(asset) > end else np.append(ticks[:-1], len(asset) - 1))
ax1.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))],
rotation=15) # Label x-axis with dates
# 拟合
X = np.arange(len(asset))
x = sm.add_constant(X)
model = regression.linear_model.OLS(asset, x).fit()
a = model.params[0]
b = model.params[1]
# log.info("a:%s b:%s" % (a, b))
log.info("X:%s a:%s b:%s" % (len(asset), a, b))
Y_hat = X * b + a
# 真实值-拟合值，差值最大最小作为价值波动区间
# 向下平移
i = (asset.values.T - Y_hat).argmin()
c_low = X[i] * b + a - asset.values[i]
Y_hatlow = X * b + a - c_low
# 向上平移
i = (asset.values.T - Y_hat).argmax()
c_high = X[i] * b + a - asset.values[i]
Y_hathigh = X * b + a - c_high
plt.plot(X, Y_hat, 'k', alpha=0.9);
plt.plot(X, Y_hatlow, 'r', alpha=0.9);
plt.plot(X, Y_hathigh, 'r', alpha=0.9);
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.title(code + " | " + str(dates[-1])[:11], fontsize=14)
plt.legend([asset.iat[-1]], fontsize=12, loc=4)
plt.grid(True)
# plt.legend([code]);
# plt.legend([code, 'Value center line', 'Value interval line']);
# fig=plt.fig()
# fig.figsize = [14,8]
scale = 1.1
zp = zoompan.ZoomPan()
figZoom = zp.zoom_factory(ax1, base_scale=scale)
figPan = zp.pan_factory(ax1)
ax2 = fig.add_subplot(323)
# ax2.plot(asset)
# ticks = ax2.get_xticks()
ax2.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15)
# plt.plot(X, Y_hat, 'k', alpha=0.9)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y, 'r', alpha=0.9);
c = c + d
# asset=asset.apply(lambda x:round(x/asset[:1],2))
ax2.plot(asset)
# ax2.plot(asset1,'-r', linewidth=2)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price', fontsize=12)
plt.grid(True)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 将Y-Y_hat股价偏离中枢线的距离单画出一张图显示，对其边界线之间的区域进行均分，大于0的区间为高估，小于0的区间为低估，0为价值中枢线。
ax3 = fig.add_subplot(322)
# distance = (asset.values.T - Y_hat)
distance = (asset.values.T - Y_hat)[0]
if code.startswith('999') or code.startswith('399'):
ax3.plot(asset)
plt.plot(distance)
ticks = ax3.get_xticks()
ax3.set_xticklabels([dates[i] for i in (np.append(ticks[:-1], len(asset) - 1))], rotation=15)
n = 5
d = (-c_high + c_low) / n
c = c_high
while c <= c_low:
Y = X * b + a - c
plt.plot(X, Y - Y_hat, 'r', alpha=0.9);
c = c + d
ax3.plot(asset)
# plt.xlabel('Date', fontsize=12)
plt.ylabel('Price-center price', fontsize=14)
plt.grid(True)
else:
as3 = asset.apply(lambda x: round(x / asset[:1], 2))
ax3.plot(as3)
ax3.plot(asset1, '-r', linewidth=2)
plt.grid(True)
zp3 = zoompan.ZoomPan()
figZoom = zp3.zoom_factory(ax3, base_scale=scale)
figPan = zp3.pan_factory(ax3)
# plt.title(code, fontsize=14)
# plt.legend([code])
# 统计出每个区域内各股价的频数，得到直方图，为了更精细的显示各个区域的频数，这里将整个边界区间分成100份。
ax4 = fig.add_subplot(325)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
# now_distanse=int((asset.iat[-1]-Y_hat[-1])/Y_hat[-1]*100)
# log.debug("dis:%s now:%s"%(distance[:2],now_distanse))
# log.debug("now_distanse:%s"%now_distanse)
distance = (asset.values.T - Y_hat)
now_distanse = asset.iat[-1] - Y_hat[-1]
# distance = (asset.values.T-Y_hat)[0]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel('Undervalue ------------------------------------------> Overvalue', fontsize=12)
plt.ylabel('Frequency', fontsize=14)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1], str(dates[-1])[5:11]], fontsize=12)
plt.grid(True)
# plt.show()
# import os
# print(os.path.abspath(os.path.curdir))
ax5 = fig.add_subplot(326)
# fig.figsize=(5, 10)
log.info("assert:len:%s %s" % (len(asset.values.T - Y_hat), (asset.values.T - Y_hat)[0]))
# distance = map(lambda x:int(x),(asset.values.T - Y_hat)/Y_hat*100)
distance = (asset.values.T - Y_hat) / Y_hat * 100
now_distanse = ((asset.iat[-1] - Y_hat[-1]) / Y_hat[-1] * 100)
log.debug("dis:%s now:%s" % (distance[:2], now_distanse))
log.debug("now_distanse:%s" % now_distanse)
# n, bins = np.histogram(distance, 50)
# print n, bins[:2]
pd.Series(distance).plot(kind='hist', stacked=True, bins=100)
# plt.plot((asset.iat[-1].T-Y_hat),'b',alpha=0.9)
plt.axvline(now_distanse, hold=None, label="1", color='red')
# plt.axhline(now_distanse,hold=None,label="1",color='red')
# plt.axvline(asset.iat[0],hold=None,label="1",color='red',linestyle="--")
plt.xlabel('Undervalue ------------------------------------------> Overvalue', fontsize=14)
plt.ylabel('Frequency', fontsize=12)
# plt.title('Undervalue & Overvalue Statistical Chart', fontsize=14)
plt.legend([code, asset.iat[-1]], fontsize=12)
plt.grid(True)
ax6 = fig.add_subplot(324)
h = df.loc[:, ['open', 'close', 'high', 'low']]
highp = h['high'].values
lowp = h['low'].values
openp = h['open'].values
closep = h['close'].values
lr = LinearRegression()
x = np.atleast_2d(np.linspace(0, len(closep), len(closep))).T
lr.fit(x, closep)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
xt = np.atleast_2d(np.linspace(0, len(closep) + 200, len(closep) + 200)).T
yt = lr.predict(xt)
bV = []
bP = []
for i in range(1, len(highp) - 1):
if highp[i] <= highp[i - 1] and highp[i] < highp[i + 1] and lowp[i] <= lowp[i - 1] and lowp[i] < lowp[i + 1]:
bV.append(lowp[i])
bP.append(i)
d, p = LIS(bV)
idx = []
for i in range(len(p)):
idx.append(bP[p[i]])
lr = LinearRegression()
X = np.atleast_2d(np.array(idx)).T
Y = np.array(d)
lr.fit(X, Y)
estV = lr.predict(xt)
ax6.plot(closep, linewidth=2)
ax6.plot(idx, d, 'ko')
ax6.plot(xt, estV, '-r', linewidth=3)
ax6.plot(xt, yt, '-g', linewidth=3)
plt.grid(True)
# plt.tight_layout()
zp2 = zoompan.ZoomPan()
figZoom = zp2.zoom_factory(ax6, base_scale=scale)
figPan = zp2.pan_factory(ax6)
# plt.ion()
plt.show(block=False)
