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 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 plot_axis_control(self, axis,
show_mux_cmd=True, show_asctec_cmd=True, show_vicon_meas=True, show_imu_meas=True):
axismap = {'roll': (self.v.control.roll_cmd, 2, +1, self.v.asctec_ctrl_input.roll_cmd, -1),
'pitch': (self.v.control.pitch_cmd, 1, -1, self.v.asctec_ctrl_input.pitch_cmd, -1),
'yaw': (self.v.control.yaw_cmd, 0, -1, self.v.asctec_ctrl_input.yaw_rate_cmd, +1) # not sure about the multiplier here
}
control_mode_cmd, state_axis, imu_mult, asctec_cmd, asctec_cmd_mult = axismap[axis]
newfig("%s Axis Control" % axis.capitalize(), "time [s]", "%s [deg]" % axis.capitalize())
# np.clip() and the [1:] stuff in the following to attempt deal with bogus initial data points in IMU data:
if show_mux_cmd:
plt.plot(self.v.control.t[self.v.control.istart:self.v.control.iend],
control_mode_cmd[self.v.control.istart:self.v.control.iend], label='cmd (from mux)')
if show_vicon_meas:
plt.plot(self.v.state.t[self.v.state.istart:self.v.state.iend],
self.v.state.ori_ypr[self.v.state.istart:self.v.state.iend, state_axis], label='meas (Vicon)')
if show_imu_meas:
plt.plot(np.clip(self.v.imu.t[self.v.imu.istart:self.v.imu.iend], 0, np.inf),
imu_mult*self.v.imu.ori_ypr[self.v.imu.istart:self.v.imu.iend, state_axis], label='meas (IMU)')
if show_asctec_cmd and axis is not 'yaw':
plt.plot(self.v.asctec_ctrl_input.t[self.v.asctec_ctrl_input.istart:self.v.asctec_ctrl_input.iend],
asctec_cmd_mult*asctec_cmd[self.v.asctec_ctrl_input.istart:self.v.asctec_ctrl_input.iend],
label='cmd (AscTec)')
# Plot difference between vicon and imu: (broken, comment it out for now..)
# tout, data_out = uniform_resample((('linear', self.v.imu.t[self.v.asctec_ctrl_input.istart:self.v.asctec_ctrl_input.iend],
# self.v.imu.ori_ypr[self.v.asctec_ctrl_input.istart:self.v.asctec_ctrl_input.iend,state_axis]),
# ('linear', self.v.state.t[self.v.state.istart:self.v.state.iend],
# self.v.state.ori_ypr[self.v.state.istart:self.v.state.iend, state_axis])),
# 0.02)
# plt.plot(tout, imu_mult*data_out[0][0] - data_out[1][0], label='IMU - Vicon')
plt.legend()
self._timeseries_postplot()
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 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 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 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 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 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_precision_recall(precisions, recalls, thresholds):
"""
Plots precision and recall by thresholds.
Requires imports:
from sklearn.metrics import precision_recall_curve, cross_val_predict
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(thresholds, precisions[:-1], 'b--', label='Precision')
plt.plot(thresholds, recalls[:-1], 'g-', label='Recall')
plt.xlabel('Threshold')
plt.legend(loc='center left')
plt.ylim([0, 1])
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 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 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 plot_loss(checkpoint_dir, loss_list, save_pred_every):
x = range(0, len(loss_list) * save_pred_every, save_pred_every)
y = loss_list
plt.switch_backend('agg')
plt.plot(x, y, color='blue', marker='o', label='Train loss')
plt.xticks(range(0, len(loss_list) * save_pred_every + 3, (len(loss_list) * save_pred_every + 10) // 10))
plt.legend()
plt.grid()
plt.savefig(os.path.join(checkpoint_dir, 'loss_fig.pdf'))
plt.close()
def plot_post(cfs,ifshow=False,loc=2,
save_fig_path = None, file_type='png'):
for cf in cfs:
plot_cf(cf, color='blue')
plt.legend(loc=loc)
if ifshow:
plt.show()
if save_fig_path is not None:
plt.savefig(join(save_fig_path, '%s_%s.%s'%(cf.SITE, cf.CMPT, file_type)))
plt.close()
def plot_iou(checkpoint_dir, iou_list):
x = range(0, len(iou_list))
y = iou_list
plt.switch_backend('agg')
plt.plot(x, y, color='red', marker='o', label='IOU')
plt.xticks(range(0, len(iou_list) + 3, (len(iou_list) + 10) // 10))
plt.legend()
plt.grid()
plt.savefig(os.path.join(checkpoint_dir, 'iou_fig.pdf'))
plt.close()
def displayRetireWRate(month, rates, terms):
plt.figure('retireRate')
plt.clf()
for rate in rates:
xvals, yvals = retire(month, rate, terms)
plt.plot(xvals,
yvals,
label='monthly: ' + str(month) + ' rate of: ' +
str(int(rate * 100)))
plt.legend(loc='upper left')
def savefig(self, fname):
# Graph using the parameters
plt.xlim(-1000,
max(self.incomes) *
1.05) # make it a little bigger than needed
plt.ylim(-5, 105)
plt.legend(loc='lower center', fontsize=9)
plt.xticks(rotation=20)
plt.axes().get_xaxis().set_major_formatter(
mp.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
plt.savefig(fname)
def displayRetireWMonthlies(monthlies, rate, terms):
plt.figure('retireMonth')
plt.clf()
for monthly in monthlies:
xvals, yvals = retire(
monthly, rate,
terms) # using base and savings list as x and y values
plt.plot(xvals,
yvals,
label='retire with monthly inst of ' + str(monthly))
plt.legend()
def plot_precisonAndjac(checkpoint_dir, pre_list, jac_list):
x = range(0, len(pre_list))
y = pre_list
y2 = jac_list
plt.switch_backend('agg')
plt.plot(x, y, color='red', marker='o', label='precision')
plt.plot(x, y2, color='blue', marker='o', label='jaccard')
plt.xticks(range(0, len(pre_list) + 3, (len(pre_list) + 10) // 10))
plt.legend()
plt.grid()
plt.savefig(os.path.join(checkpoint_dir, 'precisionAndjac_fig1.pdf'))
plt.close()
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 displayRetireWMonthsandRates(monthlies, rates, terms):
plt.figure('retire both')
plt.clf()
plt.xlim(30 * 12,
40 * 12) # focusing only on the last 10 years of investment
for monthly in monthlies:
for rate in rates:
xvals, yvals = retire(monthly, rate, terms)
plt.plot(xvals,
yvals,
label='retire with ' + str(monthly) + ":" +
str(int(rate * 100)))
plt.legend(loc='upper left')
def draw(x, y, x_text, y_text, title):
plt.figure(figsize=(30, 5))
plt.plot(x, y, color='red', label='data_check_result')
for i in range(1, len(x)):
plt.text(x[i], y[i], str((x[i], round(y[i], 4))))
#plt.text(x,y,(x,y),color='red')
plt.xlabel(x_text)
plt.ylabel(y_text)
plt.title(title)
plt.grid(True)
plt.legend()
pic = time.strftime("%Y-%m-%d_%H_%S_%M", time.localtime()) + ".pdf"
plt.savefig(pic)
plt.show()
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_run(self):
plt.style.use('seaborn')
mpl.rcParams['font.family'] = 'serif'
# 生成市场环境
me_gbm = MarketEnvironment('me_gbm', dt.datetime(2020, 1, 1))
me_gbm.add_constant('initial_value', 36.0)
me_gbm.add_constant('volatility', 0.2)
me_gbm.add_constant('final_date', dt.datetime(2020, 12, 31))
me_gbm.add_constant('currency', 'EUR')
me_gbm.add_constant('frequency', 'M')
me_gbm.add_constant('paths', 1000)
csr = ConstantShortRate('csr', 0.06)
me_gbm.add_curve('discount_curve', csr)
# 生成几何布朗运动模拟类
gbm = GeometricBrownianMotion('gbm', me_gbm)
gbm.generate_time_grid()
print('时间节点：{0};'.format(gbm.time_grid))
paths_1 = gbm.get_instrument_values()
print('paths_1: {0};'.format(paths_1.round(3)))
gbm.update(volatility=0.5)
paths_2 = gbm.get_instrument_values()
# 可视化结果
plt.figure(figsize=(10, 6))
p1 = plt.plot(gbm.time_grid, paths_1[:, :10], 'b')
p2 = plt.plot(gbm.time_grid, paths_2[:, :10], 'r-')
legend1 = plt.legend([p1[0], p2[0]],
['low volatility', 'high volatility'],
loc=2)
plt.gca().add_artist(legend1)
plt.xticks(rotation=30)
plt.show()
def plot_performance(agent):
''' Function to plot the financial gross
performance per training episode.
'''
plt.figure(figsize=(10, 6))
x = range(1, len(agent.performances) + 1)
y = np.polyval(np.polyfit(x, agent.performances, deg=3), x)
plt.plot(x, agent.performances[:], label='training')
plt.plot(x, y, 'r--', label='regression (train)')
if agent.val:
y_ = np.polyval(np.polyfit(x, agent.vperformances, deg=3), x)
plt.plot(x, agent.vperformances[:], label='validation')
plt.plot(x, y_, 'r-.', label='regression (valid)')
plt.xlabel('episodes')
plt.ylabel('gross performance')
plt.legend()
def test_run(self):
# 生成几何布朗运动市场环境
me_gbm = MarketEnvironment('me_gbm', dt.datetime(2020, 1, 1))
me_gbm.add_constant('initial_value', 36.0)
me_gbm.add_constant('volatility', 0.2)
me_gbm.add_constant('final_date', dt.datetime(2020, 12, 31))
me_gbm.add_constant('currency', 'EUR')
me_gbm.add_constant('frequency', 'M')
me_gbm.add_constant('paths', 1000)
csr = ConstantShortRate('csr', 0.06)
me_gbm.add_curve('discount_curve', csr)
# 生成几何布朗运动模拟类
gbm = GeometricBrownianMotion('gbm', me_gbm)
gbm.generate_time_grid()
# 生成跳跃扩散市场环境
me_jd = MarketEnvironment('me_jd', dt.datetime(2020, 1, 1))
me_jd.add_constant('lambda', 0.3)
me_jd.add_constant('mu', -0.75)
me_jd.add_constant('delta', 0.1)
me_jd.add_environment(me_gbm)
# 生成跳跃扩散模拟类
jd = JumpDiffusion('jd', me_jd)
paths_3 = jd.get_instrument_values()
jd.update(lamb=0.9)
paths_4 = jd.get_instrument_values()
# 绘制图形
plt.figure(figsize=(10, 6))
p1 = plt.plot(gbm.time_grid, paths_3[:, :10], 'b')
p2 = plt.plot(gbm.time_grid, paths_4[:, :10], 'r-')
lengend1 = plt.legend([p1[0], p2[0]],
['low intensity', 'high intensity'],
loc=3)
plt.gca().add_artist(lengend1)
plt.xticks(rotation=30)
plt.show()
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 profit_firmA_against_profit_firmB(file_name, folder=None):
if folder is None:
folder = "data/figures"
os.makedirs(folder, exist_ok=True)
bkp = backup.RunBackup.load(file_name=file_name)
parameters = bkp.parameters
profit_max = parameters.n_positions * parameters.n_prices * parameters.unit_value
x = np.arange(parameters.t_max)
y = np.zeros((2, parameters.t_max))
for f in range(2):
for t in range(parameters.t_max):
y[f, t] = np.mean(bkp.profits[:t + 1, f] / profit_max)
# y = np.array([np.mean(for_y[i][t]) for t in range(parameters.t_max)])
# y_err = np.array([np.std(for_y[i][t]) for t in range(parameters.t_max)])
fig = plt.Figure()
plt.plot(x, y[0], label="Firm A")
plt.plot(x, y[1], label="Firm B")
# plt.fill_between(x, y - (y_err / 2), y + (y_err / 2), color="C{}".format(i), alpha=.25)
plt.legend()
plt.xlabel("t")
plt.ylabel("Mean profit")
plt.text(0.005,
0.005,
file_name,
transform=fig.transFigure,
fontsize='x-small',
color='0.5')
plt.title("Evolution of profits over time ($r={}$)".format(
bkp.field_of_view / 2))
plt.tight_layout()
plt.savefig("{}/{}_mean_profit.pdf".format(folder, file_name))
plt.show()
def plot(site):
tp = np.loadtxt('../post_offsets/%s.post'%site)
t = dc.asmjd([ii[0] for ii in tp]) + dc.adjust_mjd_for_plot_date
e = [ii[1] for ii in tp]
n = [ii[2] for ii in tp]
u = [ii[3] for ii in tp]
plt.plot_date(t,e,'x-', label = 'eastings')
plt.plot(t,n,'x-', label = 'northings')
plt.plot(t,u,'x-', label = 'upings')
plt.gcf().autofmt_xdate()
plt.legend(loc=0)
plt.title(site)
plt.savefig('%s.png'%site)
#plt.show()
plt.close()
def create_plot(x, y, styles, labels, axlabels):
'''
Generate plot for multiple series X and Y
Parameters
---------
x = List format. List of Time Series Arrays representing x-axis
y = List format. List of Time Series Arrays representing y-axis
labels = List format. e.g. ['b','b'] if we have two series x and y
axlabels = List format. Define x-axis and y-axis name to be displayed.
'''
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 plot(site):
tp = np.loadtxt('../post_offsets/%s.post' % site)
t = dc.asmjd([ii[0] for ii in tp]) + dc.adjust_mjd_for_plot_date
e = [ii[1] for ii in tp]
n = [ii[2] for ii in tp]
u = [ii[3] for ii in tp]
plt.plot_date(t, e, 'x-', label='eastings')
plt.plot(t, n, 'x-', label='northings')
plt.plot(t, u, 'x-', label='upings')
plt.gcf().autofmt_xdate()
plt.legend(loc=0)
plt.title(site)
plt.savefig('%s.png' % site)
#plt.show()
plt.close()
def draw_predictions(self, data, predictions):
from pylab import plt
true, lstm, bp = [], [], []
for i in range(len(predictions)):
true.append(predictions[i][0])
bp.append(predictions[i][1])
lstm.append(predictions[i][2])
x = [1, 2, 3, 4, 5]
plt.plot(x, true, 'cx--', label='true')
plt.plot(x, bp, 'mo:', label='fusion')
plt.plot(x, lstm, 'kp-.', label='lstm')
plt.legend()
plt.margins(0)
plt.subplots_adjust(bottom=0.15)
plt.xlabel(u"days")
plt.ylabel("price")
plt.title("tendency predictions of different models")
plt.savefig('blog/static/blog/bootstrap/img/presult.jpg')
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 example_gammatone_filter():
from pylab import plt, np
sample_rate = 44100
order = 4
b, a = design_filter(sample_rate=sample_rate,
order=order,
centerfrequency=1000.0,
attenuation_half_bandwidth_db=-3,
band_width_factor=1.0)
x = _create_impulse(1000)
y, states = fosfilter(b, a, order, x)
y = y[:800]
plt.plot(np.real(y), label='Re(z)')
plt.plot(np.imag(y), label='Im(z)')
plt.plot(np.abs(y), label='|z|')
plt.legend()
plt.show()
return y, b, a
def plot_spectral_profile(self,
points,
dd=False,
scale=1,
domain=np.array([1, 2, 3, 4, 5, 7]),
labels=None,
xlab=None,
ylab=None,
ylim=(0, None),
lloc='upper left',
**kwargs):
'''
Assumes the domain is the Landsat TM/ETM+ bands minus the thermal IR
channel (bands 1-5 and 7). If the spectra are in Landsat surface
reflectance units, they should be scaled by 0.01 to get reflectance
as a percentage (by 0.0001 to get it as a proportion).
'''
assert not self.__raveled__, 'Cannot do this when the input array is raveled'
spectra = self.__spectra__(points, dd, scale, domain, self.__nodata__)
xs = range(1, domain.shape[0] + 1)
# Truncate the spectra if necessary
if len(xs) != spectra.shape[1]:
spectra = spectra[:, 0:len(xs)]
# Plot as lines
lines = plot(xs, spectra.transpose(), linewidth=2, **kwargs)
# Set the x-axis tick labels (e.g., skip band 6)
plt.xticks(xs, domain)
plt.ylim(ylim)
if xlab is not None:
plt.xlabel(xlab)
if ylab is not None:
plt.ylabel(ylab)
if labels is not None:
plt.legend(lines, labels, loc=lloc, frameon=False)
plt.show()
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_axis_control(v, axis):
axismap = {'roll': (v.control.roll_cmd, 2, +1, v.asctec_ctrl_input.roll_cmd, -1),
'pitch': (v.control.pitch_cmd, 1, -1, v.asctec_ctrl_input.pitch_cmd, -1),
'yaw': (v.control.yaw_cmd, 0, -1, v.asctec_ctrl_input.yaw_rate_cmd, +1) # not sure about the multiplier here
}
control_mode_cmd, state_axis, imu_mult, asctec_cmd, asctec_cmd_mult = axismap[axis]
newfig("%s Axis Control" % axis.capitalize(), "time [s]", "%s [deg]" % axis.capitalize())
# np.clip() and the [1:] stuff in the following to attempt deal with bogus initial data points in IMU data:
plt.plot(v.control.t, control_mode_cmd, label='cmd (from mux)')
plt.plot(v.state.t[1:], v.state.ori_ypr[1:,state_axis], label='meas (Vicon)')
plt.plot(np.clip(v.imu.t[1:], 0, np.inf), imu_mult*v.imu.ori_ypr[1:,state_axis], label='meas (IMU)')
if axis is not 'yaw':
plt.plot(v.asctec_ctrl_input.t, asctec_cmd_mult*asctec_cmd, label='cmd (AscTec)')
# Plot difference between vicon and imu:
tout, data_out = uniform_resample((('linear', v.imu.t[1:], v.imu.ori_ypr[1:,state_axis]),
('linear', v.state.t[1:], v.state.ori_ypr[1:,state_axis])),
0.02)
plt.plot(tout, imu_mult*data_out[0][0] - data_out[1][0], label='IMU - Vicon')
plt.legend()
def example_gammatone_filter():
from pylab import plt, np
sample_rate = 44100
order = 4
b, a = design_filter(
sample_rate=sample_rate,
order=order,
centerfrequency=1000.0,
attenuation_half_bandwidth_db=-3,
band_width_factor=1.0)
x = _create_impulse(1000)
y, states = fosfilter(b, a, order, x)
y = y[:800]
plt.plot(np.real(y), label='Re(z)')
plt.plot(np.imag(y), label='Im(z)')
plt.plot(np.abs(y), label='|z|')
plt.legend()
plt.show()
return y, b, a
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 show(filename=None, labels=False):
if not labels:
# fix everything if in 3D mode
plt.subplots_adjust(left=0.0, right=1.1, bottom=0.0, top=1.0)
# also do this if in 2d mode
if not is_3d:
frame1 = plt.gca()
frame1.axes.get_xaxis().set_visible(False)
frame1.axes.get_yaxis().set_visible(False)
if legend:
plt.legend(loc="upper left", fontsize=8, prop={'family': "Monaco", 'weight': "roman", 'size': "x-small"})
if filename is not None:
if '.' not in filename:
if not os.path.isdir(filename):
os.makedirs(filename)
filename = os.path.abspath(os.path.join(filename, "%s.png" % util.timestamp()))
figure.savefig(filename, dpi=150, facecolor=figure.get_facecolor(), edgecolor='none')
plt.show()
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()
import glob
import h5py
from pylab import plt
from epochs import epochs
files = sorted(glob.glob('../outs/*'))
for no, file in enumerate(files):
with h5py.File(file, 'r') as fid:
rms = fid['misfit/rms_inland_at_epoch'][...]
#if no==0:
plt.plot(epochs, rms,'x-', label='Nrough=%d'%no)
#plt.plot(epochs, rms,'x-')
plt.grid('on')
plt.xlabel('days after mainshock')
plt.ylabel('RMS(m)')
plt.legend(prop={'size':7}, bbox_to_anchor=(0.18, 1.01))
plt.savefig('RMS_misfits_at_epochs.pdf')
plt.show()
while arr[1] are those of image 2.
Each landmark vector contains 68 x-coordinates followed by 68 y-coordinates.
"""
src = arr[0].reshape(2,-1).T
dst = arr[1].reshape(2,-1).T
data.src=np.require(src,requirements=['C'])
data.dst=np.require(dst,requirements=['C'])
data.dname=os.path.abspath(os.path.dirname(fname))
data.fname=fname
return data
if __name__ == "__main__":
from pylab import plt
import of.plt
name = 'LFW_5_to_6'
data = get_data('LFW_5_to_6')
src = data.src
dst = data.dst
plt.close('all')
plt.figure(1)
plt.plot(src[:,0],src[:,1],'go')
plt.plot(dst[:,0],dst[:,1],'bo')
of.plt.axis_ij()
plt.axis('scaled')
plt.legend(['src','dst'])
tic=time.clock()
tw.calc_T_fwd(tw.x_dense,pts_fwd,level=0,int_quality=1)
pts_fwd.gpu2cpu()
toc=time.clock()
print 'time',toc-tic
# 1/0
pts_recovered = CpuGpuArray.zeros_like(tw.x_dense)
tw.calc_T_inv(pts_fwd,pts_recovered,level=0)
pts_recovered.gpu2cpu()
plt.plot(tw.x_dense.cpu,pts_fwd.cpu)
plt.plot(tw.x_dense.cpu,pts_recovered.cpu)
plt.legend([r'$T(x)$',r'$(T^{-1}\circ T)(x)$',r'$T_{\mathrm{alg}}(x)$'],loc='lower right')
dx = x[1]-x[0]
err = np.abs(pts_fwd_cf-pts_fwd.cpu.ravel())/dx
print 'err1.mean:',err.mean()
pts_fwd_simple = CpuGpuArray.zeros_like(pts_fwd)
tic=time.clock()
cpa_space.calc_T_simple(pts=tw.x_dense,out=pts_fwd_simple,**tw.params_flow_int_fine)
pts_fwd_simple.gpu2cpu()
toc=time.clock()
print 'time',toc-tic
dx = x[1]-x[0]
def runAnalysis( caseDirs , resultsDir , noReweight = False):
# Do a reference for each one
for refDir in caseDirs:
# ID of reference case
refID = refDir.split("/")[-1]
# User info
print "Doing PCA analysis with "+refDir+" as reference"
# Get the PCA limits of component 1-2 plot
limit = 10
with open(refDir+"/analysis/data/pca_limits_1", "r") as fi:
limit = int(float(fi.read()))
limit += 0.01
# Go through the case dirs to plot
for caseDir in caseDirs:
print "Using "+caseDir+" as case"
# ID of case
caseID = caseDir.split("/")[-1]
## PCA PLOTTING ON REF DIR PCA COMPONENTS
#########################################
# Create & run cpptraj for plotting all cases on the axes of the first eigenvector
# Good URLs for PCA in CPPTRAJ:
# http://archive.ambermd.org/201404/0243.html
# PCA plotter
pcaHandler = pcaFuncs.PCA(
resultsDir+"/plots/pcaComparison/PCA_"+caseID+"_on_"+refID+".pdf"
)
# Create new submission file
TEMPLATE = open( caseDir+"/ccptraj_analysis_pca.ptraj", 'r')
TEMP = TEMPLATE.read().replace("[PCAREFERENCE]", refDir )
TEMPLATE.close()
# Write the submission file
FILE = open(caseDir+"/ccptraj_analysis_pca.ptraj","w");
FILE.write( TEMP );
FILE.close();
# Run the cpptraj utility
os.system( "$AMBERHOME/bin/cpptraj -p "+caseDir+"/md-files/peptide_nowat.prmtop -i "+caseDir+"/ccptraj_analysis_pca.ptraj" )
# Do the plots of energy landscapes & distributions
pcaHandler.plotPCA(
"Case: "+caseID+". Ref case: "+refID, # Plot Title
caseDir+"/analysis/data/" , # Data Dir
"global_pca", # Eigenvector file
eigenVectorCount = 2, # Only plot two
plotDistibution = False, # Do not plot the distribution
limits = limit
)
# Save the plot
pcaHandler.savePlot()
## REWEIGHTING OF PCA PLOTS ON RED DIR PCA COMPONENTS
#####################################################
# Check if we should do a reweighted version
if noReweight == False:
if os.path.isfile( caseDir+"/md-logs/weights.dat" ):
# User info
print "aMD weights found. Now attempting 2D reweighting"
# Prepare input file
numLines = 0
with open(caseDir+"/analysis/data/global_pca", "r") as fi:
with open(caseDir+"/analysis/data/global_pca_singleColumn", "w") as fo:
next(fi)
for line in fi:
numLines += 1
fo.write( line.split()[1]+"\t"+line.split()[2]+"\n" )
# Set the discretization
reqBins = 100
discretization = (2*limit) / reqBins
# Get the max value of normal plot
maxValue = math.ceil(pcaHandler.getLatestMax())
# Run the reweighting procedure
command = "python $PLMDHOME/src/PyReweighting/PyReweighting-2D.py \
-input "+caseDir+"/analysis/data/global_pca_singleColumn \
-name "+caseDir+"/analysis/data/global_pca_singleColumn_reweighted \
-Xdim -"+str(limit)+" "+str(limit)+" \
-Ydim -"+str(limit)+" "+str(limit)+" \
-discX "+str(discretization)+" \
-discY "+str(discretization)+" \
-cutoff 10 \
-Emax "+str(maxValue)+" \
-job amdweight_CE \
-weight "+refDir+"/md-logs/weights.dat | tee -a reweight_variable.log"
print "Running command:", command
os.system( command )
# Create long file for PCA module
with open(caseDir+"/analysis/data/global_pca_reweightedDone", "w") as fo:
with open(caseDir+"/analysis/data/global_pca_singleColumn_reweighted-pmf-c2.dat", "r") as fi:
frame = 0
for line in fi:
temp = line.split()
entries = int(float(temp[2])*10)
for i in range(0,entries):
fo.write( str(frame) + "\t" + temp[0] + "\t" + temp[1] +"\n" )
frame += 1
# Print block analysis 'family' : 'Arial',
fig, ax = plt.subplots(figsize=(8, 8), nrows=1, ncols=1 )
font = {'weight' : 'normal','size' : 10}
plt.rc('font', **font)
# Now plot the 2d histogram
hist = np.load(caseDir+"/analysis/data/global_pca_singleColumn_reweighted_c2EnergyHist.npy")
xedges = np.load(caseDir+"/analysis/data/global_pca_singleColumn_reweighted_c2edgesX.npy")
yedges = np.load(caseDir+"/analysis/data/global_pca_singleColumn_reweighted_c2edgesY.npy")
# Remove points above limit
for jy in range(len(hist[0,:])):
for jx in range(len(hist[:,0])):
if hist[jx,jy] >= maxValue:
hist[jx,jy] = float("inf")
# Do plot
img = plt.imshow(hist.transpose(), interpolation='nearest', origin='lower',extent=[yedges[0], yedges[-1],xedges[0], xedges[-1]] , rasterized=True )
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
# Create colorbar
colorbar = plt.colorbar(img, ax=ax, cax = cax)
colorbar.set_label("Kcal / mol")
# Set title, labels etc
plt.legend()
ax.set_xlabel("PC1", fontsize=12)
ax.set_ylabel("PC2", fontsize=12)
ax.set_title( "PCA. Case: "+caseID+" Reweighted. Ref case: "+refID )
plt.rc('font', **font)
# Save figure
fig.savefig(resultsDir+"/plots/pcaComparison/PCA_"+caseID+"_on_"+refID+"_reweighted.pdf")
## CLUSTER PLOTS ON PCA COMPONENTS
##################################
# Do both hier and dbscan
for clusterType in ["dbscan","hier"]:
# Instantiate the class
if os.path.isfile(caseDir+"/analysis/data/cluster_"+clusterType+"_out"):
print "Doing the "+clusterType+" cluster equivalent of the PCA plot"
# Start the cluster handler. Load the file declaring cluster for each frame
clusterHandler = cluster.clusterBase( caseDir+"/analysis/data/cluster_"+clusterType+"_out" )
# Separate the dataset.
# global_pca is the projection file for this case on the ref modes
numPCAdataSets = clusterHandler.separateDataSet(
caseDir+"/analysis/data/global_pca", # Input file
caseDir+"/analysis/data/cluster_"+clusterType+"_pca_", # Output files
xColumn = 1
)
# Create lists of labels and files for plotting
clusterLabels = []
clusterFiles = []
offset = 1 if clusterType == "hier" else 0
for i in range( 0+offset, numPCAdataSets+offset):
clusterLabels.append( "Cluster "+str(i) )
clusterFiles.append( caseDir+"/analysis/data/cluster_"+clusterType+"_pca_d2_c"+str(i) )
# First one is noise
if offset == 0:
clusterLabels[0] = "Noise"
myPlot.plotData(
resultsDir+"/plots/pcaComparison/" ,
clusterType+"_"+caseID+"_on_"+refID,
clusterLabels,
clusterFiles ,
"PC2",
xUnit = "PC1",
scatter = True,
legendLoc = 4,
figWidth = 8,
figHeight = 8,
tightXlimits = False,
legendFrame = 1,
legendAlpha = 1,
xLimits = [-limit,limit],
yLimits = [-limit,limit]
)
def bars(scheme, verbose=None, norm='load'):
"""
Figure to compare link proportional and usage proportional for a single
scheme and put them in ./sensitivity/figures/scheme/
"""
# Load data and results
F = abs(np.load('./results/' + scheme + '-flows.npy'))
quantiles = np.load('./results/quantiles_' + scheme + '_' + str(lapse) + '.npy')
nNodes = 30
names = node_namer(N) # array of node labels
links = range(len(F))
nodes = np.linspace(0.5, 2 * nNodes - 1.5, nNodes)
nodes_shift = nodes + .5
for direction in directions:
N_usages = np.load('./results/Node_contrib_' + scheme + '_' + direction + '_' + str(lapse) + '.npy')
# Compare node transmission to mean load
if verbose:
print('Plotting node comparison - ' + scheme + ' - ' + direction)
# sort node names for x-axis
Total_usage = np.sum(N_usages, 1)
node_ids = np.array(range(len(N))).reshape((len(N), 1))
node_mean_load = [n.mean for n in N]
# Vector for normalisation
if norm == 'cap':
normVec = np.ones(nNodes) * sum(quantiles)
else:
normVec = node_mean_load
# Calculate node proportional
EU_load = np.sum(node_mean_load)
Total_caps = sum(quantiles)
Node_proportional = node_mean_load / EU_load * Total_caps / normVec
Node_proportional = np.reshape(Node_proportional, (len(Node_proportional), 1))
# Calculate link proportional
link_proportional = linkProportional(N, link_dic, quantiles)
link_proportional = [link_proportional[i] / normVec[i] for i in range(nNodes)]
# Calculate old usage proportional
if direction == 'combined':
old_usages = np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_import_all_alpha=same.npy')
old_usages += np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_export_all_alpha=same.npy')
else:
old_usages = np.load('./linkcolouring/old_' + scheme + '_copper_link_mix_' + direction + '_all_alpha=same.npy')
avg_node_usage = np.sum(np.sum(old_usages, axis=2), axis=0) / 70128.
avg_EU_usage = np.sum(np.sum(np.sum(old_usages, axis=2), axis=0)) / 70128.
avg_node_usage /= avg_EU_usage
avg_node_usage /= normVec
avg_node_usage *= 500000
# Calculate usage and sort countries by mean load
normed_usage = Total_usage / normVec
normed_usage = np.reshape(normed_usage, (len(normed_usage), 1))
node_mean_load = np.reshape(node_mean_load, (len(node_mean_load), 1))
data = np.hstack([normed_usage, node_ids, node_mean_load, link_proportional, Node_proportional])
data_sort = data[data[:, 2].argsort()]
names_sort = [names[int(i)] for i in data_sort[:, 1]]
# flip order so largest is first
names_sort = names_sort[::-1]
link_proportional = data_sort[:, 3][::-1]
Node_proportional = data_sort[:, 4][::-1]
data_sort = data_sort[:, 0][::-1]
plt.figure(figsize=(10, 4), facecolor='w', edgecolor='k')
ax = plt.subplot(111)
green = '#009900'
blue = '#000099'
# Plot node proportional
plt.rc('lines', lw=2)
plt.rc('lines', dash_capstyle='round')
plt.plot(np.linspace(0, len(N) * 2 + 2, len(N)), Node_proportional, '--k')
# Plot link proportional
#plt.bar(nodes, link_proportional, width=1, color=green, edgecolor='none')
# Plot old usage proportional
plt.bar(nodes, avg_node_usage[loadOrder], width=1, color=green, edgecolor='none')
# Plot usage proportional
plt.bar(nodes_shift, data_sort, width=1, color=blue, edgecolor='none')
# Magic with ticks and labels
ax.set_xticks(np.linspace(2, len(N) * 2 + 2, len(N) + 1))
ax.set_xticklabels(names_sort, rotation=60, ha="right", va="top", fontsize=10.5)
ax.xaxis.grid(False)
ax.xaxis.set_tick_params(width=0)
if norm == 'cap':
ax.set_ylabel(r'$M_n/ \mathcal{K}^T$')
else:
# ax.set_ylabel(r'Network usage [MW$_T$/MW$_L$]')
ax.set_ylabel(r'$M_n/\left\langle L_n \right\rangle$')
maxes = [max(avg_node_usage), max(data_sort)]
plt.axis([0, nNodes * 2 + .5, 0, 1.15 * max(maxes)])
# Legend
artists = [plt.Line2D([0, 0], [0, 0], ls='dashed', lw=2.0, c='k'), plt.Rectangle((0, 0), 0, 0, ec=green, fc=green), plt.Rectangle((0, 0), 0, 0, ec=blue, fc=blue)]
LABS = ['$M^1$', '$M^{3}_{old}$', '$M^{3}_{new}$']
leg = plt.legend(artists, LABS, loc='upper left', ncol=len(artists), columnspacing=0.6, borderpad=0.4, borderaxespad=0.0, handletextpad=0.2, handleheight=1.2)
leg.get_frame().set_alpha(0)
leg.get_frame().set_edgecolor('white')
ltext = leg.get_texts()
plt.setp(ltext, fontsize=12) # the legend text fontsize
plt.savefig(figPath + scheme + '/network-usage-' + direction + '-' + norm + '.png', bbox_inches='tight')
if verbose:
print('Saved figures to ./figures/compareUsage/' + scheme + '/network-usage-' + direction + '-' + norm + '.png')
import viscojapan as vj
def plot_slip(slip, nx, ny,
label='x-',
legend = None
):
epochs = slip.get_epochs()
s = slip.get_cumu_slip_at_subfault(nx, ny)
plt.plot(epochs, s, label, label=legend)
res_file = '../../outs/nrough_06_naslip_11.h5'
reader = vj.inv.ResultFileReader(res_file)
slip_pred = reader.get_slip()
slip_exp = vj.epoch_3d_array.Slip.load('extra_slip_EXP.h5')
slip_log = vj.epoch_3d_array.Slip.load('extra_slip_LOG.h5')
nx = 1
ny = 1
plot_slip(slip_pred, nx, ny, label='x-', legend='Pred.')
plot_slip(slip_exp, nx, ny, label='^-', legend='EXP')
plot_slip(slip_log, nx, ny, label='o-', legend='LOG')
plt.legend(loc=0)
plt.savefig('extra_%d_%d.png'%(nx, ny))
plt.show()
nPtsDense = 10000
mr = MonotonicRegression(base=[12],nLevels=4)
mr.set_dense(domain_start=-10,domain_end=10)
mr.set_data(x=x,y=y,range_start=range_start,range_end=range_end)
print mr
if 1:
plt.figure(0)
plt.subplot(122)
plt.cla()
mr.plot_dst('.r')
mr.plot_src('.g')
plt.legend(['dst','src'],'lower right')
ax = plt.gca()
ax.tick_params(axis='y', labelsize=50)
ax.tick_params(axis='x', labelsize=30)
mr.set_run_lengths([500,500,500,50000,50000][:mr.nLevels])
theta,inference_record = mr.fit(use_prior=1)
plt.figure(1000)
of.plt.set_figure_size_and_location(50,50,1000,1000)
plt.clf()
mr.plot_inference_summary(inference_record)
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)
def plotPCA(
self,
plotTitleIdentifier,
dataDir,
eigenVectorFile ,
eigenValueFile = False ,
eigenVectorCount = 4,
plotDistibution = True,
limits = False
):
# If this is the first plot, create grid
if self.subPlots == 0:
self.createPdfPage()
# User info
print "Doing principal component analysis"
# Do the four principal vectors
frames = []
# Principal components
pcs = []
for i in range( 0, eigenVectorCount ):
pcs.append([])
# Go through analysis file and get eigenvalues
if eigenValueFile != False:
eigenValues = []
eigenValueTotal = 0
with open(dataDir+eigenValueFile,"r") as f:
for line in f:
temp = line.split()
eigenValueTotal += float(temp[1])
eigenValues.append(float(temp[1]))
eigenValues = (np.array(eigenValues) / eigenValueTotal) * 100
# Get the file with all the projection data
pcaFile = open(dataDir+eigenVectorFile,"r")
n = 0
for aline in pcaFile:
if n > 1 and aline:
values = aline.split()
# Add frames
frames.append( int(values[0]) )
# If requesting more vectors than present
if eigenVectorCount > len(values):
raise Exception("CPPTRAJ has not projected "+str(eigenVectorCount)+" vectors")
# Add to vectors
for i in range( 0, eigenVectorCount ):
pcs[i].append( float(values[i+1]) )
n = n + 1
# Create numpy arrays
frames = np.array( frames )
self.np_arrays = [ np.array( pc ) for pc in pcs ]
# Set the plotting font and default size 'family' : 'Arial',
font = {
'weight' : 'normal',
'size' : 10}
# Do a plot for each PCA
for component in range( 1, len(self.np_arrays) ):
# User Info
print "Plotting component 1 vs. "+str(component)
# Normalize the data DeltaG = -kb T * [ ln( P(v1,v2) ) - ln Pmax ]
boltzman = 0.0019872041
temperature = 300
# Plot both distribution & Energy Landscape
for plotType in [ "energy", "distribution" ] if plotDistibution else [ "energy" ]:
# New subplot
ax = self.getActiveAx()
# Increase subplot counter
self.subPlots += 1
# Do the plotting
if plotType == "energy":
# Create the histogram without plotting, so we can set the units properly
H, xedges, yedges = np.histogram2d(self.np_arrays[component], self.np_arrays[0], bins=100 )
H_normalized = H/len(self.np_arrays[0])
H = -1 * boltzman * temperature * (np.log( H_normalized )-np.log(np.max(H_normalized)))
# Set max energy
for vec in H:
for val in vec:
if not np.isinf(val) and val > self.latestMax:
self.latestMax = val
# Now plot the 2d histogram
img = ax.imshow(H, interpolation='nearest', origin='lower',extent=[yedges[0], yedges[-1],xedges[0], xedges[-1]] , rasterized=True )
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
# Create colorbar
colorbar = plt.colorbar(img, ax=ax, cax = cax)
colorbar.set_label("Kcal / mol")
self.colorBars.append(colorbar)
elif plotType == "distribution":
# Directly do the 2d histogram of matplotlib
_, _, _, img = ax.hist2d(self.np_arrays[0], self.np_arrays[component], bins=100 , rasterized=True, norm=LogNorm() )
colorbar = plt.colorbar(img, ax=ax)
colorbar.set_label("Occurances")
self.colorBars.append(colorbar)
# Set limits if they are not specified
if limits == False:
print "Calculating plot limits based on data"
mini = np.abs(np.min( [np.min(self.np_arrays[0]), np.min(self.np_arrays[component])] ))
maxi = np.abs(np.max( [np.max(self.np_arrays[0]), np.max(self.np_arrays[component])] ))
limits = int(math.ceil(np.max( [mini,maxi] )))
print "Setting plot limits to: ",limits
ax.set_ylim([-limits,limits])
ax.set_xlim([-limits,limits])
# Save the limits for the component
with open(dataDir+"pca_limits_"+str(component), "w") as fo:
fo.write( str(limits) )
# Set title, labels etc
plt.legend()
if eigenValueFile != False:
ax.set_xlabel("PC1 ({0:.2f}%)".format(eigenValues[0]), fontsize=12)
ax.set_ylabel("PC"+str(component+1)+" ({0:.2f}%)".format(eigenValues[component]), fontsize=12)
else:
ax.set_xlabel("PC1", fontsize=12)
ax.set_ylabel("PC"+str(component+1), fontsize=12)
ax.set_title( "PCA. "+plotTitleIdentifier )
plt.rc('font', **font)
# Save pdf page if it's filled
if self.subPlots >= (self.rows*self.columns):
print "Now saving to PDF. Number of plots: ",self.subPlots
self.savePdfPage()
self.subPlots = 0
def disp(self,sampler,interp_type_during_visualization):
level=sampler.level
theta=sampler.theta_current
tw=self.tw
# interval=self.interval
# interval_dense=self.interval_dense
markersize = 5
fontsize=30
cpa_space = tw.ms.L_cpa_space[level]
plt.subplot(231)
sampler.plot_ll()
plt.title('ll',fontsize=fontsize)
sampler.plot_wlp()
sampler.plot_wlp_plus_ll()
if sampler.lp_func:
plt.legend(['ll','wlp','ll+wlp'])
plt.subplot(232)
sampler.plot_ar()
plt.title('accept ratio',fontsize=fontsize)
# print theta
cpa_space.theta2As(theta=theta)
tw.update_pat_from_Avees(level=level)
tw.calc_v(level=level)
tw.v_dense.gpu2cpu()
src = self.src
# dst = self.dst
transformed = self.transformed
# src_dense=self.src_dense
# transformed_dense=self.transformed_dense
# tw.calc_T(src_dense, transformed_dense, mysign=1, level=level,
#
# transformed_dense.gpu2cpu()
tw.calc_T_inv(src, transformed, level=level,
int_quality=+1)
transformed.gpu2cpu()
if interp_type_during_visualization=='gpu_linear':
my_dtype = np.float64
else:
my_dtype = np.float32 # For opencv
img_src = self.signal.src.cpu.reshape(tw.nRows,tw.nCols)
img_src = CpuGpuArray(img_src.astype(my_dtype))
img_wrapped = CpuGpuArray.zeros_like(img_src)
img_dst = self.signal.dst.cpu.reshape(tw.nRows,tw.nCols)
img_dst = CpuGpuArray(img_dst)
if interp_type_during_visualization=='gpu_linear':
tw.remap_fwd(transformed,img_src,img_wrapped)
else:
tw.remap_fwd_opencv(transformed,img_src,img_wrapped,interp_type_during_visualization)
img_wrapped.gpu2cpu()
plt.subplot(233)
plt.imshow(img_src.cpu,interpolation="None")
plt.gray()
cpa_space.plot_cells('r')
tw.config_plt(axis_on_or_off='on')
plt.title(r'$I_{\mathrm{src}}$')
plt.subplot(234)
plt.imshow(img_wrapped.cpu,interpolation="None")
plt.gray()
# cpa_space.plot_cells('w')
tw.config_plt(axis_on_or_off='on')
plt.title(r'$I_{\mathrm{src}}\circ T^{\theta}$')
plt.subplot(235)
plt.imshow(img_dst.cpu,interpolation="None")
plt.gray()
plt.title(r'$I_{\mathrm{dst}}$')
# cpa_space.plot_cells('w')
tw.config_plt(axis_on_or_off='on')
plt.subplot(2,6,11)
self.tw.imshow_vx()
pylab.jet()
tw.config_plt(axis_on_or_off='on')
plt.title(r'$v_x$')
plt.subplot(2,6,12)
self.tw.imshow_vy()
pylab.jet()
tw.config_plt(axis_on_or_off='on')
plt.title(r'$v_y$')
def disp(self,sampler,ds_quiver=None):
level=sampler.level
theta=sampler.theta_current
tw=self.tw
scale_quiver=self.scale_quiver
if ds_quiver is None:
ds_quiver=min([tw.nCols,tw.nRows])/32
markersize = 4
fontsize=30
cpa_space = tw.ms.L_cpa_space[level]
plt.subplot(231)
sampler.plot_ll()
plt.title('ll',fontsize=fontsize)
sampler.plot_wlp()
sampler.plot_wlp_plus_ll()
plt.subplot(232)
sampler.plot_ar()
plt.title('accept ratio',fontsize=fontsize)
cpa_space.theta2As(theta)
tw.update_pat_from_Avees(level=level)
tw.calc_v(level=level)
tw.v_dense.gpu2cpu()
src = self.src
dst = self.dst
transformed = self.transformed
src_dense=self.src_dense
transformed_dense=self.transformed_dense
tw.calc_T_fwd(src_dense, transformed_dense,level=level,int_quality=0)
transformed_dense.gpu2cpu()
cpa_space.theta2As(theta)
tw.update_pat_from_Avees(level=level)
tw.calc_v(level=level)
tw.v_dense.gpu2cpu()
transformed.gpu2cpu()
plt.subplot(233)
# class TF:
# use_hand_data =False
if self.kind == 'landmarks' and self.landmarks_are_lin_ordered:
lin_order=1
else:
lin_order=0
if lin_order==False:
# plt.plot(src.cpu[:,0],src.cpu[:,1],'go',ms=markersize)
plt.plot(transformed.cpu[:,0],transformed.cpu[:,1],'ro',ms=markersize)
plt.plot(dst.cpu[:,0],dst.cpu[:,1],'bo',ms=markersize)
tw.config_plt(axis_on_or_off='on')
else:
# plt.plot(src.cpu[:,0],src.cpu[:,1],'g-o',ms=markersize)
plt.plot(transformed.cpu[:,0],transformed.cpu[:,1],'r-o',ms=markersize)
plt.plot(dst.cpu[:,0],dst.cpu[:,1],'b-o',ms=markersize)
tw.config_plt(axis_on_or_off='on')
plt.subplot(234)
tw.quiver(scale=scale_quiver,ds=ds_quiver)
# 1/0
# cpa_space.plot_cells()
# if TF.use_hand_data == False:
# cpa_space_gt.theta2As(theta_gt)
# tw.update_pat(level=level_gt)
# tw.calc_v(level=level_gt)
# tw.v_dense.gpu2cpu()
if lin_order:
plt.plot(src.cpu[:,0],src.cpu[:,1],'g-o',ms=markersize)
plt.plot(dst.cpu[:,0],dst.cpu[:,1],'b-o',ms=markersize)
# plt.plot(transformed.cpu[:,0],transformed.cpu[:,1],'r-o',ms=markersize)
tw.config_plt(axis_on_or_off='on')
if lin_order== False:
plt.subplot(234)
tw.quiver(scale=scale_quiver)
cpa_space.plot_cells()
tw.config_plt(axis_on_or_off='on')
plt.title(r'$v^\theta$',
fontsize=20)
else:
plt.subplot(235)
tw.imshow_vx()
plt.title(r'$v^\theta_{\mathrm{horizontal}}$',
fontsize=20)
cpa_space.plot_cells()
tw.config_plt(axis_on_or_off='on')
plt.subplot(236)
tw.imshow_vy()
plt.title(r'$v^\theta_{\mathrm{vertical}}$',
fontsize=20)
cpa_space.plot_cells()
tw.config_plt(axis_on_or_off='on')
if self.kind == 'landmarks' and self.landmarks_are_lin_ordered:
plt.subplot(233)
plt.legend([r'$T^\theta(\mathrm{src})$',r'$\mathrm{dst}$'],loc='lower right',
fontsize=20)
plt.subplot(234)
plt.legend([r'$\mathrm{src}$',r'$\mathrm{dst}$'],loc='lower right',
fontsize=20)
def plot_alt_control(v):
newfig("Altitude Control", "time [s]", "Alt [m]")
plt.plot(v.control.t, v.control.alt_cmd, label="cmd")
plt.plot(v.state.t[1:], v.state.up[1:], label="meas (Vicon)")
plt.legend()
import glob
from pylab import plt
import viscojapan as vj
def plot(fn, marker, label):
result_file = fn
fid = vj.inv.ResultFileReader(result_file)
epochs = fid.epochs
rms = fid.rms_inland_at_epoch
plt.plot(epochs, rms, 'o-', label=label)
# coupled model
result_file = '../../outs/nrough_06_naslip_11.h5'
plot(result_file, 'o-', 'SDco = 0.2')
result_file = '../../outs/nrough_06_naslip_10.h5'
plot(result_file, 'o-', 'SDco = 0.5')
plt.xlabel('days after the mainshock')
plt.ylabel('RMS misfit (m)')
plt.legend(loc=0,prop={'size':10})
plt.savefig('mis_fit_comparison.png')
plt.show()
