def plot_fft_psbs(f=F_FIG_FFT_PSBS):
plt.cla()
plt.clf()
accuracies = np.load(F_FFT_ACC_PSBS)
plot_psbs_acc(accuracies)
plt.plot([0, 0.02], [0, 0.02], 'r--', linewidth=2)
plt.savefig(f)
def plot_grid_psbs(f=F_FIG_GRID_PSBS):
plt.cla()
plt.clf()
accuracies = np.load(F_GRID_ACC_PSBS)
plot_psbs_acc(accuracies)
plt.plot([0, 0.02], [0, 0.02], 'b--', linewidth=2)
plt.savefig(f)
def plot_rotated_histograms_for_cell_type(field_data, cell_type='grid', animal='mouse'):
print('analyze ' + cell_type + ' cells')
list_of_cells = field_data.unique_cell_id.unique()
histograms = []
for cell in list_of_cells:
cell_type_filter = field_data['cell type'] == cell_type # filter for cell type
accepted = field_data['accepted_field'] == True
fields_of_cell = field_data.unique_cell_id == cell # filter for cell
if not field_data[fields_of_cell & cell_type_filter & accepted].empty:
histogram = field_data[fields_of_cell & cell_type_filter & accepted].hd_hist_from_all_fields_rotated.iloc[0]
histograms.append(histogram)
plt.cla()
hd_polar_fig = plt.figure()
ax = hd_polar_fig.add_subplot(1, 1, 1)
print('Number of ' + cell_type + ' cells: ' + str(len(histograms)))
histograms_to_plot = []
for histogram in histograms:
if not np.isnan(histogram).any():
theta = np.linspace(0, 2 * np.pi, 361)
ax = plt.subplot(1, 1, 1, polar=True)
ax = plot_utility.style_polar_plot(ax)
ax.plot(theta[:-1], histogram, color='gray', linewidth=2, alpha=70)
histograms_to_plot.append(histogram)
# combine them to make one polar plot
average_histogram = np.average(histograms_to_plot, axis=0)
theta = np.linspace(0, 2 * np.pi, 361)
ax.plot(theta[:-1], average_histogram, color='red', linewidth=10)
plt.savefig(analysis_path + animal + '_rotated_hd_histograms_' + cell_type + '.png', dpi=300, bbox_inches="tight")
plt.close()
def plot_grid_vs_fft():
plt.cla()
plt.clf()
acc_grid = np.load(F_GRID_ACC_DIAG)
acc_fft = np.load(F_FFT_ACC_DIAG)
compare_plot([acc_grid, acc_fft], ['GridNet', 'FFTNet'])
plt.savefig(F_FIG_COMPARE_GRID_FFT)
def plot_peristimulus_raster(peristimulus_spikes, prm):
assert len(peristimulus_spikes.groupby('session_id')['session_id'].nunique()) == 1
save_path = prm.get_output_path() + '/Figures/opto_stimulation'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
cluster_ids = np.unique(peristimulus_spikes.cluster_id)
for cluster in cluster_ids:
cluster_rows_boolean = peristimulus_spikes.cluster_id.astype(int) == int(cluster)
cluster_rows_annotated = peristimulus_spikes[cluster_rows_boolean]
cluster_rows = cluster_rows_annotated.iloc[:, 2:]
print(cluster_rows.head())
plt.cla()
peristimulus_figure = plt.figure()
peristimulus_figure.set_size_inches(5, 5, forward=True)
ax = peristimulus_figure.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
sample_times = np.argwhere(cluster_rows.to_numpy().astype(int) == 1)[:, 1]
trial_numbers = np.argwhere(cluster_rows.to_numpy().astype(int) == 1)[:, 0]
stimulation_start = cluster_rows.shape[1] / 2 - 45 # todo remove magic number
stimulation_end = cluster_rows.shape[1] / 2 + 45
ax.axvspan(stimulation_start, stimulation_end, 0, cluster_rows.shape[0], alpha=0.5, color='lightblue')
ax.vlines(x=sample_times, ymin=trial_numbers, ymax=(trial_numbers + 1), color='black', zorder=2, linewidth=3)
plt.xlabel('Time (sampling points)')
plt.ylabel('Trial (sampling points)')
plt.ylim(0, cluster_rows.shape[0])
plt.savefig(save_path + '/' + cluster + '_peristimulus_raster.png', dpi=300)
plt.close()
def draw_heatmap(allstats, outfigure, order=None):
conds, seqname, mean = allstats.Get(_.cond, _.seqname, _.mean)();
df = pd.DataFrame({ 'conds': conds, 'seqname': seqname, 'mean':mean})
df = df.pivot('conds', 'seqname', 'mean')
plt.cla(); plt.clf();
ax = sns.heatmap(df, center=1.0)
plt.savefig('%s/condlibratios.svg' % output_dir)
def plot_real_vs_estimated(field,
norm_hist_real,
estimate,
animal,
ratio,
color='red'):
plt.cla()
fig, ax = plt.subplots()
ax.plot(norm_hist_real, color=color, alpha=0.4, linewidth=5)
ax.plot(estimate, color='black', alpha=0.4, linewidth=5)
plt.title('hd ' + str(round(field.hd_score, 1)) + ' grid ' +
str(round(field.grid_score, 1)) + ' ratio ' +
str(round(ratio, 4)))
# legend = plt.legend()
# legend.get_frame().set_facecolor('none')
plt.savefig(local_path + animal + field.session_id +
str(field.cluster_id) + str(field.field_id) +
'estimated_hd_rate_vs_real.png')
plt.close()
save_path = local_path + animal + field.session_id + str(
field.cluster_id) + str(field.field_id) + 'estimated_hd_rate_vs_real'
plot_polar_hd_hist(norm_hist_real,
estimate,
field.cluster_id,
save_path,
color1=color,
color2='black',
title='')
def plotEngine(input1,input2):
### Draw columns in tables as functions of time ###
if "," in input1:
tableNames = input1.split(",")
if tableNames[1] == "":
tableNames = [tableNames[0]]
if "," not in input1:
tableNames = [input1]
for i in range(0, len(tableNames)):
table = np.array(sc.db.tableToArrayTR(tableNames[i]))
if table != []:
for j in range(2,len(sc.db.columnList(tableNames[i]))):
if "NOPLT" not in sc.db.columnList(tableNames[i])[j]:
pl.plot(table[:,1],table[:,j], label='%s ((elem %s))'%(sc.db.columnList(tableNames[i])[j],tableNames[i]))
### Format Plot ###
pl.legend(loc='best', fancybox=True, framealpha=.5)
pl.xlabel('Time (Seconds)')
if len(tableNames) > 1:
note1 = " (multiple data sets)"
if len(tableNames) == 1:
note1 = ""
pl.ylabel('%s%s'%(input2,note1))
pl.savefig('%s/DXE_OUTPUT/DXE_OUT_MONITOR/DXE_OUT_MONITOR_%s.png' %(path(),input2))
pl.clf()
pl.cla()
def draw_lineplot(x, y, title="title", xlab="x", ylab="y", odir="", xlim=None, ylim=None, outfmt='eps'):
if len(x) == 0 or len(y) == 0:
return;
#fi
plt.cla();
plt.plot(x, y, marker='x');
plt.xlabel(xlab);
plt.ylabel(ylab);
plt.title(title);
if xlim == None:
xmin = min(x);
xmax = max(x);
xlim = [xmin, xmax];
#fi
if ylim == None:
ymin = min(y);
ymax = max(y);
ylim = [ymin, ymax];
#fi
plt.xlim(xlim);
plt.ylim(ylim);
plt.savefig('%s%s.%s' % (odir + ('/' if odir else ""), '_'.join(title.split(None)), outfmt), format=outfmt);
return '%s.%s' % ('_'.join(title.split(None)), outfmt), title;
def locate_objects(image_file):
"""
Detects objects in an image using AlexNet output along with an SVM trained
on the top 1000 principle components from HOG analysis.
"""
pil_image = Image.open(image_file).convert('RGB')
np_image = np.array(pil_image)
windows = get_windows(np_image, 4)
windows += get_windows(np_image, 8)
windows += get_windows(np_image, 16)
detections = []
for w in windows:
plot_window(np_image, w)
pil_sub = pil_image.crop((w['xmin'], w['ymin'], w['xmax'], w['ymax']))
np_sub = np_image[w['ymin']:w['ymax'], w['xmin']:w['xmax']]
nn_out = alex_nn(Variable(
preprocessFn(pil_sub).unsqueeze(0))).data.numpy()
hog = get_hog_vector(np_sub)
features = np.hstack((nn_out, pca.transform(hog.reshape(1, -1))))
probs = svm_model.predict_proba(features)[0]
label = svm_model.predict(features)
max_idx = int(np.where(classes == label)[0])
detections.append({
'window': w,
'p': probs[max_idx],
'label': classes[max_idx]
})
plt.cla()
best_detections = get_top_windows(detections)
plot_detections(np_image, best_detections)
def judge_stable_analyze(data, col, address):
num_counts = pd.crosstab(data['applymonth'], data[col], margins=True)
num_counts_percents = num_counts.div(num_counts['All'], axis=0)
num_counts_percents = num_counts_percents.drop('All', axis=1)
num_counts_percents = num_counts_percents.drop('All', axis=0)
bad_percents = pd.crosstab(index=data['applymonth'],
columns=data[col],
values=data['fraud_y'],
aggfunc='mean',
margins=True)
bad_percents = bad_percents.drop('All', axis=1)
bad_percents = bad_percents.drop('All', axis=0)
plt.cla()
plt.figure(2)
ax1 = plt.subplot(121)
ax2 = plt.subplot(122)
for cn in num_counts_percents.columns:
plt.sca(ax1)
plt.plot(num_counts_percents[cn])
plt.xticks(rotation=90)
plt.legend()
plt.title(col)
plt.sca(ax2)
plt.plot(bad_percents[cn])
plt.xticks(rotation=90)
plt.legend()
plt.title(col)
plt.savefig(address + col + '.jpg')
plt.show()
count_data = pd.concat([num_counts_percents, bad_percents], axis=0)
count_data.to_csv(address + col + '.csv')
def adjust(self):
try:
if self.isOpen:
if not self.adjusted:
if not self.predicted:
self.predict()
self.adjust_img, predicted = self.auto.adjust(self.img)
if not predicted:
self.predict()
self.adjust_img, predicted = self.auto.adjust(self.img)
self.img = self.adjust_img
plt.cla()
self.fig, self.ax = plt.subplots()
self.ax.axis('off')
self.ax.imshow(self.showRoate(self.img)[:, :,
self.imgIndex],
interpolation='nearest',
aspect='auto',
cmap='gray')
cavan = FigureCanvas(self.fig)
for i in reversed(range(self.verticalLayout.count())):
self.verticalLayout.itemAt(i).widget().setParent(None)
self.verticalLayout.addWidget(cavan)
self.adjusted = True
else:
pass
else:
QMessageBox.information(self, "Tip",
"Please open file first:)")
except:
pass
def plot_distance_distribution_maps(distance_map, label, save_dir):
plt.cla()
n, h, w = distance_map.shape
cm_rz = np.squeeze(np.reshape(tonumpy(distance_map), (n, h * w)), axis=0)
gt = np.array(tonumpy(label), np.int32)
gt_rz = np.squeeze(np.reshape(gt, (n, h * w)), axis=0)
x_t, y_t = [], []
colors = ['#0000ff', '#009900']
markers = ['*', 'o']
for i in range(h * w):
t = np.random.random() * 2 * np.pi - np.pi
dist, lbl = cm_rz[i], gt_rz[i]
x, y = dist * np.cos(t), dist * np.sin(t)
x_t.append(x), y_t.append(y)
x_t, y_t = np.array(x_t), np.array(y_t)
for i in range(2):
plt.scatter(x_t[gt_rz == i], y_t[gt_rz == i], c=colors[i], s=40, marker=markers[i])
plt.legend(['unchanged', 'changed'], loc='upper right')
plt.xlim(min(x_t) * 1.2, max(x_t) * 1.2)
plt.ylim(min(y_t) * 1.2, max(y_t) * 1.2)
plt.xlabel('x')
plt.ylabel('y')
plt.title('distance distribution')
plt.grid(True)
plt.savefig(save_dir)
def plot_curve(name_list,
recall_list,
precision_list,
plot_path,
sample_num=100):
colorList = ['r', 'g', 'b', 'y', 'c', 'm', 'k', 'g', 'b']
lineList = ['*', '^', 's', 'p', '*', 'v', 'd', 's', '^']
plt.ioff()
fig = plt.figure(figsize=(12, 10), dpi=180)
for idx, name in enumerate(name_list):
color_index = idx % (len(colorList))
line_index = idx // (len(colorList)) % len(lineList)
style = '{}{}-'.format(colorList[color_index], lineList[line_index])
recall = recall_list[idx]
precision = precision_list[idx]
recall, precision = sample_index(recall,
precision,
sample_num=sample_num)
plt.plot(recall, precision, style, label='{}'.format(name))
plt.xlabel('time')
plt.ylabel('price')
plt.xticks(np.arange(0, max(recall), max(recall) / 10))
plt.yticks(np.arange(0, max(precision), max(precision) / 10))
plt.grid()
plt.legend(loc=4)
plt.savefig(plot_path)
plt.clf()
plt.cla()
plt.close()
def draw_light_plot():
if "userid" not in session:
return redirect(url_for("index"))
uid = session["userid"]
if uid not in users:
return redirect(url_for("index"))
users[uid].record_as_active()
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
# create light summary plot
fig = plt.figure()
ax = fig.add_subplot(111)
users[uid].summary.plot_light(ax)
fig.patch.set_facecolor("#fff9f0")
fig.set_size_inches(8, 8)
# post-process for html
canvas = FigureCanvas(fig)
png_output = StringIO.StringIO()
canvas.print_png(png_output)
response = make_response(png_output.getvalue())
response.headers["Content-Type"] = "image/png"
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
return response
def show_curve(method, step, text):
pylab.cla()
xlist = numpy.arange(a, b + step, step)
ylist = []
x = a
while x <= b:
r = method(x, step)
ylist.append(r)
x += step
pylab.plot(xlist, ylist, label=text)
print(f"Методом Рунге-Кутта: {ylist}")
pylab.grid(True)
pylab.legend()
pylab.show()
ylist2 = ylist
ylist = []
x = a
while x <= b:
r = accurate_solution(x)
ylist.append(r)
x += step
pylab.cla()
pylab.plot(xlist, ylist, label="Точное решение")
print(f"Точное решение: {ylist}")
pylab.grid(True)
pylab.legend()
pylab.show()
rez = []
for item in range(len(ylist)):
rez.append(abs(ylist[item] - ylist2[item]))
print(f"Погрешность между зачениями: {rez}")
def plot_timeseries(self, ax=None, vmin=None, vmax=None,
colorbar=False, label=True):
if vmin is None:
vmin = self.vmin
if vmax is None:
vmax = self.vmax
if ax is None:
ax = plt.gca()
plt.sca(ax)
plt.cla()
plt.imshow(self.tser_arr[::-1,:], vmin=vmin, vmax=vmax,
interpolation='Nearest', extent=self.extent, origin='upper',aspect='auto')
plt.xlim(self.extent[0], self.extent[1])
plt.ylim(self.extent[2], self.extent[3])
# plt.vlines(self.ionogram_list[0].time, self.extent[2], self.extent[3], 'r')
if label:
celsius.ylabel('f / MHz')
if colorbar:
old_ax = plt.gca()
plt.colorbar(
cax = celsius.make_colorbar_cax(), ticks=self.cbar_ticks
).set_label(r"$Log_{10} V^2 m^{-2} Hz^{-1}$")
plt.sca(old_ax)
def plot_shuffled_number_of_bins_vs_observed(cells):
for index, cell in cells.iterrows():
shuffled_distribution = cell.number_of_different_bins_shuffled_corrected_p
plt.cla()
fig = plt.figure(figsize=(6, 3))
plt.yticks([0, 1000])
ax = fig.add_subplot(1, 1, 1)
ax.hist(shuffled_distribution, bins=range(20), color='gray')
ax.axvline(x=cell.number_of_different_bins_bh,
color='navy',
linewidth=3)
ax.xaxis.set_tick_params(labelsize=20)
ax.yaxis.set_tick_params(labelsize=20)
max_x_value = max(cell.number_of_different_bins_bh,
shuffled_distribution.max())
plt.xlim(0, max_x_value + 1)
# plt.xscale('log')
plt.ylabel('N', fontsize=24)
plt.xlabel('Number of significant bins', fontsize=24)
plt.tight_layout()
plt.savefig(analysis_path + 'percentile/' + cell.session_id +
str(cell.cluster_id) + str(cell.field_id) +
'number_of_significant_bars_shuffled_vs_real_' +
str(cell.directional_percentile) + '.png')
plt.close()
def save_image(dataRaw, dataFilter, threshold_u_l, dir_data, csv_name,
del_samples_xyz): #引数が大きすぎる気がする。del_samplesはセットで考えた方が綺麗に見えないかな?
upper_x, lower_x, upper_y, lower_y, upper_z, lower_z = threshold_u_l
del_samples_x, del_samples_y, del_samples_z = del_samples_xyz
fig = plt.figure(figsize=(20, 10))
fig.suptitle("Diff Filtered file: %s \n Delete times X:%d Y:%d Z:%d" %
(csv_name, del_samples_x, del_samples_y,
del_samples_z)) #ここから87ページまでは辞書とfor文を使って解決できないか?
ax_raw_x = fig.add_subplot(231)
ax_raw_y = fig.add_subplot(232)
ax_raw_z = fig.add_subplot(233)
ax_fil_x = fig.add_subplot(234, sharey=ax_raw_x)
ax_fil_y = fig.add_subplot(235, sharey=ax_raw_y)
ax_fil_z = fig.add_subplot(236, sharey=ax_raw_z)
ax_raw_x.plot(range(len(dataRaw)), dataRaw[:, 0], "b-", lw=1)
ax_raw_y.plot(range(len(dataRaw)), dataRaw[:, 1], "r-", lw=1)
ax_raw_z.plot(range(len(dataRaw)), dataRaw[:, 2], "g-", lw=1)
ax_raw_x.hlines([upper_x, lower_x],
xmin=0,
xmax=len(dataRaw),
linestyle="dashed",
lw=1)
ax_raw_y.hlines([upper_y, lower_y],
xmin=0,
xmax=len(dataRaw),
linestyle="dashed",
lw=1)
ax_raw_z.hlines([upper_z, lower_z],
xmin=0,
xmax=len(dataRaw),
linestyle="dashed",
lw=1)
ax_fil_x.plot(range(len(dataFilter)), dataFilter[:, 0], "b-", lw=1)
ax_fil_y.plot(range(len(dataFilter)), dataFilter[:, 1], "r-", lw=1)
ax_fil_z.plot(range(len(dataFilter)), dataFilter[:, 2], "g-", lw=1)
ax_fil_x.hlines([upper_x, lower_x],
xmin=0,
xmax=len(dataFilter),
linestyle="dashed",
lw=1)
ax_fil_y.hlines([upper_y, lower_y],
xmin=0,
xmax=len(dataFilter),
linestyle="dashed",
lw=1)
ax_fil_z.hlines([upper_z, lower_z],
xmin=0,
xmax=len(dataFilter),
linestyle="dashed",
lw=1)
root, ext = os.path.splitext(csv_name)
output_filename = os.path.join(dir_data + "filter/", root + ".png")
fig.savefig(output_filename)
plt.close(fig)
plt.cla()
plt.clf()
del fig
def plot_flow_contentions(exp_num, exp_nums):
global markerslist
exp_num=str(exp_num)
read_directory = log_dir+"exp"+exp_num+"/analysis/averages"
conf = minidom.parse(log_dir+'exp'+exp_num+'/config.xml')
configurations = conf.getElementsByTagName('config')
copies=configurations[0].getElementsByTagName('copies')[0].childNodes[0].nodeValue
priQ=configurations[0].getElementsByTagName('use_different_priorities')[0].childNodes[0].nodeValue
purging=configurations[0].getElementsByTagName('purging')[0].childNodes[0].nodeValue
directory=plot_dir+"exp"+str(exp_nums)
if not os.path.exists(directory):
os.mkdir( directory );
files=[]
for file in os.listdir(read_directory):
if file.startswith("flowContentions"):
files.append(file)
while files:
current_files = []
current_files.append(files.pop())
# print current_files[0][:-5]
for k in xrange(1,int(copies)+1):
try:
i=files.index(current_files[0][:-5]+str(k)+".csv")
current_files.append(files.pop(i))
# print i
except:
pass
#plot figure
# print current_files
fig=pl.figure()
local_marker_list = markerslist[:]
for f in current_files:
flowIds=[]
contentions=[]
with open(read_directory+"/"+f, 'r') as csvfile:
for line in csvfile:
lineList = line.split(",")
flowIds.append(lineList[0])
contentions.append(lineList[1].split("\n")[0])
lab=f[-5:-4]
pl.plot(flowIds, contentions, label=lab,marker=local_marker_list.pop(0))
lg = pl.legend(bbox_to_anchor=legend_pos)#(loc='center left', bbox_to_anchor=(1, 0.5))
lg.draw_frame(True)
t =f[-8:-6]
if int(priQ):
t=t+" - with queues"
if int(purging):
t=t+" - with purging"
pl.title(t)
pl.ylim([0,25])
pl.xlim([2000,2500])
fig.savefig(directory+"/exp"+exp_num+"_"+f[-8:-6]+".png", bbox_inches='tight', transparent=False)
pl.cla() # Clear axis
pl.clf() # Clear figure
pl.close() # Close a figure window
def plot_and_save_polar_histogram(histogram, name, color='gray', line_width=10):
plt.cla()
theta = np.linspace(0, 2 * np.pi, 361)
ax = plt.subplot(1, 1, 1, polar=True)
ax = plot_utility.style_polar_plot(ax)
ax.plot(theta[:-1], histogram, color=color, linewidth=line_width)
plt.savefig(analysis_path + 'rotated_hd_histograms_' + name + '.png', dpi=300, bbox_inches="tight")
plt.close()
def initAlgoritmo(self):
self.area.setPlainText("")
self.area.repaint()
self.tabs.hide()
plt.cla()
if os.path.exists("grafico.jpg"):
os.remove("grafico.jpg")
def bad_pix_qual(startx_fn, endx_fn, starty_fn, endy_fn, chosenPixels3d_fn):
means_array_fn = np.zeros([endy_fn - starty_fn, endx_fn - startx_fn],
dtype='float')
stds_array_fn = np.zeros([endy_fn - starty_fn, endx_fn - startx_fn],
dtype='float')
heights_array_fn = np.zeros([endy_fn - starty_fn, endx_fn - startx_fn],
dtype='float')
bad_fits_counter = 0
means_fn = np.array([])
stds_fn = np.array([])
heights_fn = np.array([])
x_vals_fn = np.array([])
y_vals_fn = np.array([])
for pix_y in range(0, (endy_fn - starty_fn)): #(endy-starty)*(endx-startx)
for pix_x in range(
0, (endx_fn - startx_fn)): #(endy-starty)*(endx-startx)
badfit = False
if (pix_x % 30 == 0 and pix_y % 30 == 0):
print(pix_x, pix_y)
histValues = plt.hist(chosenPixels3d_fn[:, pix_y, pix_x], bins=45)
mphist = midpoints(histValues[1])
# meanh=chosenPixels3d_fn[:,pix_y,pix_x].mean()
meanh = mphist[histValues[0].argmax()]
stdh = chosenPixels3d_fn[:, pix_y, pix_x].std()
try:
popt, pcov = curve_fit(gaus,
mphist,
histValues[0],
p0=[15, meanh, stdh])
except:
bad_fits_counter += 1
popt = [-1, -1, -1]
badfit = True
heights_array_fn[pix_y, pix_x] = popt[0]
means_array_fn[pix_y, pix_x] = popt[1]
stds_array_fn[pix_y, pix_x] = popt[2]
# if((pix_x==47) and (pix_y==32)):
# plt.plot(histValues[1][:-1],gaus(histValues[1][:-1],*popt),'ro:',label='fit')
# plt.show()
plt.cla()
# plt.clf()
# print(pix_x,pix_y,popt)
# if badfit=False:
x_vals_fn = np.append(x_vals_fn, pix_x)
y_vals_fn = np.append(y_vals_fn, pix_y)
heights_fn = np.append(heights_fn, popt[0])
means_fn = np.append(means_fn, popt[1])
stds_fn = np.append(stds_fn, popt[2])
print("badfits", bad_fits_counter)
return heights_fn, means_fn, stds_fn, x_vals_fn, y_vals_fn, means_array_fn, stds_array_fn, heights_array_fn
def ICP_matching(ppoints, cpoints):
"""
icpマッチングを行うことでマスクアンカー、アノテーションとの残差を計算
Iterative Closest Point matching
- input
ppoints: 2D points in the previous frame
cpoints: 2D points in the current frame
- output
R: Rotation matrix
T: Translation vector
"""
H = None # homogeneous transformation matrix
dError = 1000.0
preError = 1000.0
count = 0
ppoints = np.array([ppoints[0],ppoints[1]])
cpoints = np.array([cpoints[0],cpoints[1]])
while dError >= EPS:
count += 1
if show_animation: # pragma: no cover
plt.cla()
plt.plot(ppoints[0, :], ppoints[1, :], ".r")
plt.plot(cpoints[0, :], cpoints[1, :], ".b")
plt.plot(0.0, 0.0, "xr")
plt.axis("equal")
plt.pause(1.0)
plt.savefig("../../GoogleDrive/icp_test_{0}.png".format(count))
inds, error = nearest_neighbor_assosiation(ppoints, cpoints)
Rt, Tt = SVD_motion_estimation(ppoints[:, inds], cpoints)
# update current points
cpoints = (Rt @ cpoints) + Tt[:, np.newaxis]
H = update_homogeneous_matrix(H, Rt, Tt)
dError = abs(preError - error)
preError = error
if dError <= EPS:
break
elif MAXITER <= count:
print("Not Converge...", error, dError, count)
break
R = np.array(H[0:2, 0:2])
T = np.array(H[0:2, 2])
try:
R = math.degrees(math.acos(R[0][0]))
except:
R = 0
return R,T
def showSkyline(self, l):
pylab.cla()
for tpl in l:
st = tpl[0]
end = tpl[1]
height = tpl[2]
pylab.bar(st, height, (end - st))
pylab.show()
print "Done"
def update_iq_power_plot_single(self, iq_power):
# plt.figure (1)
plt.cla()
plt.plot(iq_power, 'd-', color='b')
plt.grid(True)
plt.ylim([-100, -30])
plt.tight_layout()
plt.show()
plt.pause(0.00001)
def showSkyline(l):
pylab.cla()
for tuple in l:
st = tuple[0]
end = tuple[1]
height = tuple[2]
pylab.bar(st,height,(end-st))
pylab.show()
print "Done"
def render(self, mode='human'):
x = np.linspace(-self.length, self.length, 100)
y = [self.func(i) for i in x]
plt.cla()
plt.plot(x, y, 'r', linewidth=2)
plt.scatter(self.state, self.func(self.state), linewidth=10)
plt.title(
"Find the solution corresponding to the minimum objective value")
plt.pause(0.1)
def draw_building_zoom():
if "userid" not in session:
return redirect(url_for("index"))
uid = session["userid"]
if uid not in users:
return redirect(url_for("index"))
users[uid].record_as_active()
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
radius = ZOOM_SIZE / 2 * 1.5
for key in users[uid].buildings:
if users[uid].obs.distance_from_building(users[uid].buildings[key]) < radius:
if key not in users[uid].building_keys_at_address:
users[uid].buildings[key].plot_footprint(ax, color="k")
else:
users[uid].buildings[key].plot_footprint(ax, color=COLOR_LIGHTBROWN)
users[uid].obs.plot_observers_location(ax, color="r")
L = ZOOM_SIZE / 2 # half size of the plotted area in meters
ax.set_xlim([users[uid].obs.x - L, users[uid].obs.x + L])
ax.set_ylim([users[uid].obs.y - L, users[uid].obs.y + L])
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
ax.axis("off")
ax.set_aspect("equal")
number_of_floors = users[uid].get_number_of_floors()
ax.text(
users[uid].obs.x,
users[uid].obs.y + L / 5,
"floors: " + str(number_of_floors),
verticalalignment=u"center",
horizontalalignment=u"center",
size="large",
color="r",
)
fig.patch.set_facecolor("#d4c3a8")
fig.set_size_inches(5, 5)
# post-process for html
canvas = FigureCanvas(fig)
png_output = StringIO.StringIO()
canvas.print_png(png_output)
response = make_response(png_output.getvalue())
response.headers["Content-Type"] = "image/png"
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
return response
def fit_rotcur(galaxy, vmode):
try:
data = fits.getdata("./fits/%s.%s_model.fits" % (galaxy, vmode))
R = data[0]
V = data[1]
mask = V > 5
R = R[mask]
V = V[mask]
min_vel, max_vel = int(np.nanmin(V)), int(np.nanmax(V))
temp = []
v_max_temp = []
R_turn_temp = []
for i in model:
best_params = fit_RC(R, V, i)
data = best_params[:-1]
Vmax, Rturn = data[0], data[1]
if Vmax > 340: Vmax = 0
if Vmax == 0: Vmax = np.nan
temp.append(Vmax)
temp.append(Rturn)
v_max_temp.append(Vmax)
R_turn_temp.append(Rturn)
ax.plot(R, V, "k-")
ax.plot(R, V, "ks")
ax.plot(R, best_params[-1], label=i)
mean_V, median_V, std_V = np.nanmedian(v_max_temp), np.nanmean(
v_max_temp), np.nanstd(v_max_temp)
mean_R, median_R, std_R = np.nanmedian(R_turn_temp), np.nanmean(
R_turn_temp), np.nanstd(R_turn_temp)
table = [
galaxy, temp[0], temp[1], temp[2], temp[3], temp[4], temp[5],
temp[6], temp[7], temp[8], temp[9], temp[10], temp[11], mean_V,
median_V, std_V, mean_R, median_R, std_R
]
write(table, "manga_vmax_velfit_1.5.%s.csv" % vmode, column=False)
plt.legend(fontsize="xx-small")
ax.set_xlabel("r (arcsec)", fontsize=12, labelpad=0)
ax.set_ylabel('V$_\mathrm{ROT}$ (km/s)', fontsize=12, labelpad=0)
ax.set_ylim(-50, max_vel + 50)
AXIS(ax)
plt.savefig("./vmax_rturn/%s.fit_rotcur.%s.png" % (galaxy, vmode),
dpi=300)
#plt.show()
plt.cla()
return median_V, median_R
except (IOError, OSError):
table = [galaxy, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
write(table, "manga_vmax_velfit_1.5.%s.csv" % vmode, column=False)
return 1, 1
pass
def __del__(self):
"""
Figures created through the pyplot interface
(`matplotlib.pyplot.figure`) are retained until explicitly
closed and may co nsume too much memory.
"""
plt.cla()
plt.clf()
plt.close()
def plot_once(self, data):
u"""
データを描画する
Args:
data: データ(list of value)
"""
plt.cla()
plt.plot(data)
plt.pause(0.01)
def write_tn(self, msi, ts):
time_stamp = f'{ts:.2f}'.replace('.', '_')
plt.figure()
show_rgb_comp(msi, n_leds=self.n_leds)
plt.savefig(os.path.join(self.tn_path, f'{time_stamp}_tn.png'))
cv2.imwrite(os.path.join(self.tn_path, f'{time_stamp}_white.png'),
msi.mean(axis=2))
plt.cla()
plt.clf()
plt.close()
def __del__(self):
"""
Figures created through the pyplot interface
(`matplotlib.pyplot.figure`) are retained until explicitly
closed and may co nsume too much memory.
"""
plt.cla()
plt.clf()
plt.close()
def plot_maps(self, df, file_name):
cmap = plt.cm.coolwarm
if self.scenario == 1:
fig, axes = plt.subplots(nrows=2, ncols=1)
# plotting class labels
class_numbers = np.array(df['Class Number'])
width = int(np.sqrt(len(class_numbers)))
class_numbers = np.concatenate((class_numbers, 75 * np.ones(width - len(class_numbers) % width))).reshape(
(-1, width))
label_dict = dict([(label, i) for i, label in enumerate(self.labels)])
label_dict['empty'] = len(label_dict)
rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, len(label_dict))))
axes[0].imshow(class_numbers, cmap=cmap)
# axes[0].legend(handles=patches, loc=2, bbox_to_anchor=(1.01, 1))
axes[0].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
axes[0].tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
# plotting tags
tags = df['Tag']
tags_enum = []
tags_dict = dict([(tag, i) for i, tag in enumerate(np.unique(self.tags))])
tags_dict['empty'] = len(tags_dict)
for tag in tags:
tags_enum.append(tags_dict[tag])
tags_enum = np.array(tags_enum)
tags_enum = np.concatenate((
tags_enum, (len(tags_dict) - 1) * np.ones(width - len(tags_enum) % width))).reshape((-1, width))
rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, len(tags_dict))))
axes[1].imshow(tags_enum, cmap=cmap)
# axes[1].legend(handles=patches, loc=2, bbox_to_anchor=(1.01, 1))
axes[1].tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
axes[1].tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
else:
fig, axes = plt.subplots(1, 2, figsize=(6, 12))
class_numbers = np.array(df['Class Number'])
label_dict = dict([(label, i) for i, label in enumerate(self.labels)])
label_dict['empty'] = len(label_dict)
width = int(np.sqrt(len(class_numbers)))
class_numbers = np.concatenate(
(class_numbers, (len(label_dict) - 1) * np.ones(width - len(class_numbers) % width))).reshape((-1, width))
rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, len(label_dict))))
axes[0].imshow(class_numbers, cmap=cmap)
cmaplist = [cmap(int(i * 3.1)) for i in range(len(label_dict))]
cmaplist[len(label_dict) - 1] = [1.0, 1.0, 1.0, 1.0]
cmap = mpl.colors.LinearSegmentedColormap.from_list('Custom cmap', cmaplist, len(label_dict))
axes[0].imshow(class_numbers, cmap=cmap)
patches = [mpatches.Patch(color=cmap(v), label=k.upper()) for k, v in label_dict.items() if
v in class_numbers]
axes[1].legend(handles=patches, ncol=len(patches) // 20 + 1)
plt.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
plt.tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
plt.savefig(str(file_name))
plt.cla()
return
def __init__(self, cells, value_function, cmap=default_colormap,
hold=True, vmin=None, vmax=None, **kwargs):
"""
Creates a new map from cells
:param cells:
:param value_function:
:param cmap:
:param hold:
:param vmin:
:param vmax:
:param kwargs:
"""
if not hasattr(cmf.Cell, 'geometry'):
raise NotImplementedError('The geometry of the cells can not be used, shapely is not installed')
self.cmap = cmap
self.cells = [c for c in cells if c.geometry]
self.__f = value_function
was_interactive = plt.isinteractive()
if was_interactive:
plt.ioff()
self.polygons = {}
def flatten_multipolygons(shape):
if hasattr(shape, "geoms"):
return shape.geoms
else:
return [shape]
geos = [flatten_multipolygons(c.geometry) for c in self.cells]
values = [self.f(c) for c in self.cells]
self.maxvalue = vmax or max(values)
self.minvalue = vmin or min(values)
if self.minvalue >= self.maxvalue:
self.minvalue = self.maxvalue - 1
if not hold:
plt.cla()
for cell, shapes, v in zip(self.cells, geos, values):
c = self.cmap(float(v - self.minvalue) / float(self.maxvalue - self.minvalue))
self.polygons[cell] = [self.draw_shapes(s, c, **kwargs) for s in shapes]
plt.axis('equal')
norm = Normalize(self.minvalue, self.maxvalue)
plt.matplotlib.cm.ScalarMappable.__init__(self, norm, cmap)
if was_interactive:
plt.draw()
plt.ion()
def plots(u,xvals,t):
plt.cla()
plt.plot(xvals,u)
plt.ylim(-0.1, 1.1)
plt.xlim( 0.0, 1.0)
plt.xlabel(r'$x$',fontsize = 15)
plt.ylabel(r'$u$',fontsize = 15)
plt.title(r'Time: $t$ = ' + str(t).ljust(4, str(0)))
plt.grid("on")
fig.canvas.draw()
return
def plot_frequency_altitude(self, f=2.0, ax=None, median_filter=False,
vmin=None, vmax=None, altitude_range=(-99.9, 399.9), colorbar=False, return_image=False, annotate=True):
if vmin is None:
vmin = self.vmin
if vmax is None:
vmax = self.vmax
if ax is None:
ax = plt.gca()
plt.sca(ax)
plt.cla()
freq_extent = (self.extent[0], self.extent[1],
altitude_range[1], altitude_range[0])
i = self.ionogram_list[0]
inx = 1.0E6* (i.frequencies.shape[0] * f) / (i.frequencies[-1] - i.frequencies[0])
img = self.tser_arr_all[:,int(inx),:]
new_altitudes = np.arange(altitude_range[0], altitude_range[1], 14.)
new_img = np.zeros((new_altitudes.shape[0], img.shape[1])) + np.nan
for i in self.ionogram_list:
e = int( round((i.time - self.extent[0]) / ais_code.ais_spacing_seconds ))
pos = mex.iau_r_lat_lon_position(float(i.time))
altitudes = pos[0] - ais_code.speed_of_light_kms * ais_code.ais_delays * 0.5 - mex.mars_mean_radius_km
s = np.argsort(altitudes)
new_img[:, e] = np.interp(new_altitudes, altitudes[s], img[s,e], left=np.nan, right=np.nan)
plt.imshow(new_img, vmin=vmin, vmax=vmax,
interpolation='Nearest', extent=freq_extent, origin='upper', aspect='auto')
plt.xlim(freq_extent[0], freq_extent[1])
plt.ylim(*altitude_range)
ax.set_xlim(self.extent[0], self.extent[1])
ax.xaxis.set_major_locator(celsius.SpiceetLocator())
celsius.ylabel(r'Alt./km')
if annotate:
plt.annotate('f = %.1f MHz' % f, (0.02, 0.9),
xycoords='axes fraction', color='cyan', verticalalignment='top', fontsize='small')
if colorbar:
old_ax = plt.gca()
plt.colorbar(cax = celsius.make_colorbar_cax(), ticks=self.cbar_ticks).set_label(r"$Log_{10} V^2 m^{-2} Hz^{-1}$")
plt.sca(old_ax)
if return_image:
return new_img, freq_extent, new_altitudes
def draw_graph(plot_data, rankingSystem, numberOfUv, hue):
plot_data['world_rank'] = plot_data['world_rank'].astype(int)
ax = sns.pointplot(x='year', y='world_rank', hue=hue, data=plot_data);
pylab.title("Top " + str(numberOfUv) + " university by " + rankingSystem, fontsize=26)
pylab.xticks(fontsize=20)
pylab.yticks(fontsize=20)
pylab.ylabel("World Rank", fontsize=26)
pylab.xlabel("Year", fontsize=26)
pylab.savefig('resources/images/topuv.png')
pylab.cla()
pylab.clf()
pylab.close()
def mkHistogram(data,sndx):
print type(data[2])
histDat = pl.hist(data,360,normed=1,color='black',histtype='step',label='Angle %s' %(sndx))
pl.legend()
pl.xlim(-180,180)
pl.xlabel('Angle [deg.]',fontsize=16)
pl.ylabel('Probability density',fontsize=16)
pl.xticks(fontsize=12)
pl.yticks(fontsize=12)
pl.savefig('%s_hist.pdf' %(sndx))
pl.cla()
return histDat # sp.histogram(data,360)
def draw_gene_isoforms(D, gene_id, outfile, outfmt):
import matplotlib.patches as mpatches;
from matplotlib.collections import PatchCollection;
ISO = D[_.orig_gene == gene_id].GroupBy(_.alt_gene).Without(_.orig_gene, _.orig_exon_start, _.orig_exon_end).Sort(_.alt_gene);
plt.cla();
y_loc = 0;
y_step = 30;
n_iso = ISO.alt_gene.Shape()();
origins = np.array([ [0, y] for y in xrange((y_step * (n_iso+1)),n_iso,-y_step) ]);
patch_h = 10;
xlim = [ ISO.exon_start.Min().Min()(), ISO.exon_end.Max().Max()()];
ylim = [ y_step, (y_step * (n_iso+1)) + 2*patch_h];
patches = [];
for (origin, alt_id, starts, ends, exons, retention, alt5, alt3, skipped, new, ident) in zip(origins, *ISO()):
plt.gca().text( min(starts), origin[1] + patch_h, alt_id, fontsize=10);
for (exon_start, exon_end, exon_coverage, exon_retention, exon_alt5, exon_alt3, exon_skipped, exon_new, exon_ident) in zip(starts, ends, exons, retention, alt5, alt3, skipped, new, ident):
if not(exon_skipped):
patch = mpatches.FancyBboxPatch(origin + [ exon_start, 0], exon_end - exon_start, patch_h, boxstyle=mpatches.BoxStyle("Round", pad=0), color=draw_gene_isoforms_color(exon_retention, exon_alt5, exon_alt3, exon_skipped, exon_new, exon_ident));
text_x, text_y = origin + [ exon_start, +patch_h/2];
annots = zip(['Retention', "Alt 5'", "Alt 3'", "Skipped", 'New'], [exon_retention, exon_alt5, exon_alt3, exon_skipped, exon_new]);
text = '%s: %s' %( ','.join([str(exid) for exid in exon_coverage]), '\n'.join([ s for (s,b) in annots if b]));
plt.gca().text(text_x, text_y, text, fontsize=10, rotation=-45);
plt.gca().add_patch(patch);
if all(ident):
plt.gca().plot([exon_start, exon_start], [origin[1], 0], '--k', alpha=0.3);
plt.gca().plot([exon_end, exon_end], [origin[1], 0], '--k', alpha=0.3);
#fi
#fi
#efor
#efor
plt.xlim(xlim);
plt.ylim(ylim);
plt.title('Isoforms for gene %s' % gene_id);
plt.xlabel('Location on chromosome');
plt.gca().get_yaxis().set_visible(False);
plt.gca().spines['top'].set_color('none');
plt.gca().spines['left'].set_color('none');
plt.gca().spines['right'].set_color('none');
plt.tick_params(axis='x', which='both', top='off', bottom='on');
plt.savefig(outfile, format=outfmt);
return ISO;
def drawBarAXBarChart(bar1_series, bar2_series, title, xlabel, y_bar1_label, y_bar2_label, xticklabels, bar1_label, bar2_label):
n_groups = xticklabels.size
fig, ax = plt.subplots(dpi=100, figsize=(16,8))
index = np.arange(n_groups)
bar_width = 0.25
opacity = 0.4
error_config = {'ecolor': '0.3'}
def autolabel(ax, rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.005*height,
'%d' % int(height),
ha='center', va='bottom', fontproperties=myFont, size=tipSize-1)
rects = plt.bar(index, bar1_series, bar_width,
alpha=opacity,
color='b',
error_kw=error_config,
label=bar1_label)
autolabel(ax,rects)
plt.xlabel(xlabel, fontproperties=myFont, size=titleSize)
plt.ylabel(y_bar1_label, fontproperties=myFont, size=titleSize, color='b')
for label in ax.get_yticklabels():
label.set_color('b')
plt.title(title, fontproperties=myFont, size=titleSize)
plt.xticks(index + (1/2.)*bar_width, xticklabels, fontproperties=myFont, size=tipSize)
plt.legend(prop=font_manager.FontProperties(fname='/Library/Fonts/Songti.ttc', size=tipSize))
print 'drawNBarChart',title,'1 over'
ax1 = ax.twinx()
rects1 = plt.bar(index+1*bar_width, bar2_series, bar_width,
alpha=opacity,
color='r',
error_kw=error_config,
label=bar2_label, axes=ax1)
autolabel(ax1,rects1)
for label in ax1.get_yticklabels():
label.set_color('r')
plt.ylabel(y_bar2_label, fontproperties=myFont, size=titleSize, color='r')
plt.xticks(index + (2/2.)*bar_width, xticklabels, fontproperties=myFont, size=tipSize)
plt.legend((rects, rects1), (bar1_label, bar2_label),prop=font_manager.FontProperties(fname='/Library/Fonts/Songti.ttc', size=tipSize))
plt.tight_layout()
plt.savefig(sv_file_dir+'/'+title+'.png', format='png')
plt.cla()
plt.clf()
plt.close()
print 'drawNBarChart',title,'2 over'
def drawPieChart(data, labels, title):
fig = plt.figure(dpi=100, figsize=(8,8))
# first axes
ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
patches, texts, autotexts = ax1.pie(data, labels=labels, autopct='%1.1f%%', colors=['yellowgreen', 'gold', 'lightskyblue', 'lightcoral'])
plt.setp(autotexts, fontproperties=myFont, size=tipSize)
plt.setp(texts, fontproperties=myFont, size=tipSize)
ax1.set_title(title,fontproperties=myFont, size=titleSize)
ax1.set_aspect(1)
#plt.show()
plt.savefig(sv_file_dir+'/'+title+'.png', format='png')
plt.cla()
plt.clf()
plt.close()
print 'drawPieChart',title,'over'
def draw_inverted_polar_plot():
if "userid" not in session:
return redirect(url_for("index"))
uid = session["userid"]
if uid not in users:
return redirect(url_for("index"))
users[uid].record_as_active()
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
fig = plt.figure()
ax = fig.add_axes([0, 0, 1, 1])
users[uid].sil.draw_inverted_polar(ax, color="k")
this_year = dt.datetime.today().year
dates_to_plot = [dt.datetime(this_year, 6, 21), dt.datetime.today(), dt.datetime(this_year, 12, 22)]
morning_colors = ["#ffc469", "#fd8181", "#ffc469"]
afternoon_colors = ["#f98536", "r", "#f98536"]
labels = ["Jun 21", "today", "Dec 22"]
text_colors = ["#f98536", "r", "#f98536"]
for i in range(0, len(dates_to_plot)):
d = dates_to_plot[i]
cm = morning_colors[i]
ca = afternoon_colors[i]
ct = text_colors[i]
l = labels[i]
sun = SunPath(stepsize=SUN_STEPSIZE, lat=users[uid].obs.lat, lon=users[uid].obs.lon, date=d)
sun.calculate_path()
sun.calculate_visibility(users[uid].sil)
sun.draw_inverted_polar(ax, morning_color=cm, afternoon_color=ca, text_color=ct, label=l)
ax.axis("off")
ax.set_aspect("equal")
fig.patch.set_facecolor("#fff9f0")
fig.set_size_inches(8, 8)
# post-process for html
canvas = FigureCanvas(fig)
png_output = StringIO.StringIO()
canvas.print_png(png_output)
response = make_response(png_output.getvalue())
response.headers["Content-Type"] = "image/png"
plt.cla() # Clear axis
plt.clf() # Clear figure
plt.close() # Close a figure window
return response
def plot_update(fig, axarr, data1, data2, round):
plt.cla()
dota1 = data1 / STARTING_PCT
dota2 = data2 / STARTING_PCT
center_of_mass1 = ndimage.measurements.center_of_mass(dota1)
center_of_mass2 = ndimage.measurements.center_of_mass(dota2)
axarr = [plt.subplot(fig[0, 0]), plt.subplot2grid((2, 2), (1, 0), colspan=2), plt.subplot(fig[0, 1])]
img1 = axarr[0].imshow(
dota1,
interpolation="nearest",
cmap=plt.cm.ocean,
extent=(0.5, np.shape(dota1)[0] + 0.5, 0.5, np.shape(dota1)[1] + 0.5),
)
axarr[0].plot([center_of_mass1[1]], [center_of_mass1[0]], "or") # adds dot at center of mass
axarr[0].plot([center_of_mass2[1]], [center_of_mass2[0]], "oy") # adds dot for other teams c o m
axarr[0].axis((1, 101, 1, 101))
axarr[1].plot(avg_deal_data1, color="green")
axarr[1].set_xlim(0, rounds)
axarr[1].set_title("Current Round:" + str(round))
axarr[0].set_title("Player1")
axarr[2].set_title("Player2")
axarr[0].set_ylabel("Give")
axarr[0].set_xlabel("Accept")
axarr[1].set_ylabel("Average Cash per Deal")
axarr[1].set_xlabel("Round Number")
plt.colorbar(img1, ax=axarr[0], label="Prevalence vs. Uniform")
img2 = axarr[2].imshow(
dota2,
interpolation="nearest",
cmap=plt.cm.ocean,
extent=(0.5, np.shape(dota2)[0] + 0.5, 0.5, np.shape(dota2)[1] + 0.5),
)
axarr[2].plot([center_of_mass2[1]], [center_of_mass2[0]], "or") # adds dot at center of mass
axarr[2].plot([center_of_mass1[1]], [center_of_mass1[0]], "oy") # adds dot for other teams c o m
axarr[2].axis((1, 101, 1, 101))
axarr[1].plot(avg_deal_data2, color="purple")
plt.title("Current Round:" + str(round))
axarr[2].set_ylabel("Give")
axarr[2].set_xlabel("Accept")
plt.colorbar(img2, ax=axarr[2], label="Prevalence vs. Uniform")
plt.draw()
def plot_frequency_range(self, f_min=0., f_max=0.2, ax=None, median=False,
vmin=None, vmax=None, colorbar=False, max_value=False):
if vmin is None:
vmin = self.vmin
if vmax is None:
vmax = self.vmax
if ax is None:
ax = plt.gca()
plt.sca(ax)
plt.cla()
freq_extent = (self.extent[0], self.extent[1],
ais_code.ais_max_delay*1E3, ais_code.ais_min_delay*1E3)
i = self.ionogram_list[0]
inx, = np.where((i.frequencies > f_min*1E6) & (i.frequencies < f_max*1E6))
if inx.shape[0] < 2:
raise ValueError("Only %d frequency bins selected." % inx.shape[0])
print("Averaging over %d frequency bins" % inx.shape[0])
if median:
if inx.shape[0] < 3:
raise ValueError("Median here only really makes sense for 3 or more bins")
img = np.median(self.tser_arr_all[:,inx,:],1)
elif max_value:
img = np.max(self.tser_arr_all[:,inx,:],1)
else:
img = np.mean(self.tser_arr_all[:,inx,:],1)
plt.imshow(img, vmin=vmin, vmax=vmax,
interpolation='Nearest', extent=freq_extent, origin='upper',aspect='auto')
plt.xlim(freq_extent[0], freq_extent[1])
plt.ylim(freq_extent[2], freq_extent[3])
# plt.vlines(i.time,freq_extent[2],freq_extent[3], 'r')
celsius.ylabel(r'$\tau_D / ms$' '\n' '%.1f-%.1f MHz' % (f_min, f_max))
# plt.annotate('f = %.1f - %.1f MHz' % (f_min, f_max), (0.02, 0.9), xycoords='axes fraction',
# color='grey', verticalalignment='top', fontsize='small')
if colorbar:
old_ax = plt.gca()
plt.colorbar(cax = celsius.make_colorbar_cax(), ticks=self.cbar_ticks).set_label(r"$Log_{10} V^2 m^{-2} Hz^{-1}$")
plt.sca(old_ax)
def mkProfile(hvals,sndx):
xval = hvals[1][:-1]
yval = hvals[0]
yval = [-0.0019*float(300)*np.log(i) for i in yval]
minVal = float(min(yval))
yval = [i-minVal for i in yval]
yvalS = smoothData(yval,10)
pl.plot(xval,yval,'.',color='black',label='Calculated %s' %(sndx))
pl.plot(xval,yvalS,'-',color='black',label='Smooth %s' %(sndx))
pl.legend()
pl.xlim(-180,180)
pl.ylim(0,6)
pl.xlabel('Angle [deg.]',fontsize=16)
pl.ylabel('Energy [kcal/mol/deg2',fontsize=16)
pl.xticks(fontsize=12)
pl.yticks(fontsize=12)
pl.savefig('%s_prof.pdf' %(sndx))
pl.cla()
def density_of_accessing(picture_name):
coordinats = []
with open("matrix_processing_result.log") as file:
for line in file:
coordinats.append(list(map(int, line.split() )))
xlist, ylist = zip(*coordinats)
xlist = np.array(xlist)
ylist = np.array(ylist)
heatmap, xedges, yedges = np.histogram2d(xlist, ylist, bins=150)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
plt.clf()
plt.cla()
plt.imshow(heatmap, extent=extent)
plt.savefig(picture_name)
def draw_hist(x, title="title", xlab="x", ylab="y", normed=True, odir="", nbins=None, outfmt='eps'):
if len(x) == 0:
return;
#fi
plt.cla();
if nbins:
plt.hist(x, nbins, normed=normed);
else:
plt.hist(x, normed=normed);
#fi
plt.xlabel(xlab);
plt.ylabel(ylab);
plt.title(title);
plt.savefig('%s%s.%s' % (odir + ('/' if odir else ""), '_'.join(title.split(None)), outfmt), format=outfmt);
return '%s.%s' % ('_'.join(title.split(None)), outfmt), title;
def plot_update(fig, axarr, data, round):
"""Update the plot.
Args:
fig:
axarr:
data:
round:
"""
# Clear plots
plt.cla()
# Compare distribution to uniform
dota = data / STARTING_PCT
# Create subplots
axarr = [plt.subplot(fig[0,0]), plt.subplot(fig[1, 0])]
# Plot data (or dota, rather...)
img = axarr[0].imshow(dota, interpolation='nearest', cmap = plt.cm.ocean,
extent = (0.5, np.shape(dota)[0] + 0.5, 0.5,
np.shape(dota)[1] + 0.5))
# Plot average deal data
axarr[1].plot(avg_deal_data)
axarr[1].set_xlim(0, rounds)
# Show round
plt.title("Current Round:" + str(round))
# Set labels
axarr[0].set_ylabel("Give")
axarr[0].set_xlabel("Accept")
axarr[1].set_ylabel("Average Cash per Deal")
axarr[1].set_xlabel("Round Number")
# Set color bar
plt.colorbar(img,ax=axarr[0],label= "Prevalence vs. Uniform")
# Draw plots
plt.draw()
def splineFunction(xnodi,fnodi, xx, fType, sType):
if(sType==0):
s1 = interpolate.interp1d(xnodi, fnodi, 'linear')
sname='lineare'
else:
s1 = interpolate.interp1d(xnodi, fnodi, 'cubic')
sname='cubica'
if(fType==0):
fname='f=cos(x)'
else:
fname='g=1/(1+25*(x**2))'
ys = s1(xx)
error.append(np.max(np.abs(ys-yy)))
plt.figure(i)
plt.cla()
plt.title('Spline %s per la funzione %s' %(sname,fname))
plt.plot(xx,ys,xnodi,fnodi,'o',xx,yy,'--')
plt.show()
def animate(nframe):
print "Animate %d" % nframe
#if nframe > 0:
plt.cla()
xlim, ylim = plot_gt()
sl = 0.002*numpy.random.random()-0.001
bi = 35*numpy.random.random()+50
print sl
print bi
g = lambda t: sl*t + bi
#g = lambda t: 0.00013*t + 70+nframe
xx = g(T)
plt.plot(T, xx, lw=5.0, color="r", alpha=0.5, label="current guess")
me = np.sum( [ np.abs(g(_t)-f(_t)) for _t in t ] )
global best_x, best_me
if best_x is not None:
plt.plot(T, best_x, lw=2.5, color="g", alpha=0.5,label="best guess")
if best_x is None or me < best_me:
best_me = me
best_x = xx
print me
plt.text(5e5, 30, "Current error: %.2f" % me, fontsize=15, color='red',)
plt.text(5e5, 50, "Best error: %.2f" % best_me, fontsize=15, color='green',)
plt.text(5e4, 450, "Attempt: %d" % (nframe+1))
plt.xlim([0, 1e6])
plt.ylim([0, 500])
plt.legend()
if nframe == 5:
plt.savefig("random_guess_animation.png")
def old_stuff(self):
print res2['chi2'], res2['chi0']
if self.verbose:
print "New Period is %.8f day" % period
plt.figure(2)
plt.cla()
tt=(self.x0/period) % 1.; s=tt.argsort()
plt.errorbar (tt[s],new_y[s],self.dy_orig[s],fmt='o',c="b")
plt.plot(tt[s],res2['model'][s],c="r")
f = open("lc.dat","w")
z = zip(tt[s] - 0.5,new_y[s],self.dy_orig[s])
for l in z:
f.write("%f %f %f\n" % l)
f.close()
f = open("lc0.dat","w")
z = zip(self.x0,new_y,self.dy_orig)
for l in z:
f.write("%f %f %f\n" % l)
f.close()
psdr,res2 = lombr(self.x0,new_y,self.dy0,f0/2.,df,numf)
period1=1./res2['freq']
if self.verbose:
print "New Period is %.8f day" % period1
plt.figure(4)
plt.cla()
tt=(self.x0/period1) % 1.; s=tt.argsort()
plt.errorbar (tt[s],new_y[s],self.dy_orig[s],fmt='o',c="b")
plt.plot(tt[s],res2['model'][s],c="r")
print res2['chi2'], res2['chi0']
f = open("lc2.dat","w")
z = zip(tt[s] - 0.5,new_y[s],self.dy_orig[s])
for l in z:
f.write("%f %f %f\n" % l)
f.close()
def drawLineChart(pd_series, title, xlabel, ylabel, xticks, xticklabels):
ax = pd_series.plot(figsize=(14,8))
#for label in ax.get_xticklabels(): #xtick
# label.set_fontproperties(myFont)
for label in ax.get_yticklabels(): #ytick
label.set_fontproperties(myFont)
for label in ax.get_label(): # legend
label.set_fontproperties(myFont)
ax.set_title(title, fontproperties=myFont, size=titleSize)
ax.set_xlabel(xlabel, fontproperties=myFont, size=tipSize)
ax.set_ylabel(ylabel, fontproperties=myFont, size=tipSize)
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, fontproperties=myFont, size=tipSize)
ax.xaxis.grid(True, which='major') #x坐标轴的网格使用主刻度
ax.yaxis.grid(True, which='major') #y坐标轴的网格使用主刻度
#ax.yaxis.grid(True, which='minor') #y坐标轴的网格使用次刻度
plt.savefig(sv_file_dir+'/'+title+'.png', format='png')
plt.cla()
plt.clf()
plt.close()
print 'drawLineChart',title,'over'
def main():
"""
Main routine: Print User stats
"""
if len(sys.argv) < 2:
sys.exit('Usage: ' + sys.argv[0] + ' [Accounting files]')
else:
joblist = sys.argv[1:]
corehours = np.zeros(1000)
jobs = jobstats.alljobs(joblist)
for job in jobs:
if job.cores < 1000:
corehours[job.cores] += job.cores*job.walltime/3600.0
plt.cla()
plt.bar(range(1,1001), list(corehours))
plt.xlabel('Cores')
plt.ylabel('Core hours')
plt.savefig('corehoursvprocs.png')
return
def drawNBarChart(data_label_colors, xindex, xlabel, ylabel, title):
n_groups = xindex.size
fig, ax = plt.subplots(dpi=100, figsize=(14,8))
index = np.arange(n_groups)
bar_width = 0.35
opacity = 0.4
error_config = {'ecolor': '0.3'}
def autolabel(rects):
# attach some text labels
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width()/2., 1.005*height,
'%d' % int(height),
ha='center', va='bottom', fontproperties=myFont, size=tipSize-1)
i = 0
for data, label, color in data_label_colors:
rects = plt.bar(index+i*bar_width, data, bar_width,
alpha=opacity,
color=color,
error_kw=error_config,
label=label)
autolabel(rects)
i += 1
ax.yaxis.grid(True, which='major') #y坐标轴的网格使用主刻度
plt.xlabel(xlabel, fontproperties=myFont, size=titleSize)
plt.ylabel(ylabel, fontproperties=myFont, size=titleSize)
plt.title(title, fontproperties=myFont, size=titleSize)
plt.xticks(index + (len(data_label_colors)/2.)*bar_width, xindex, fontproperties=myFont, size=tipSize)
plt.legend(prop=font_manager.FontProperties(fname='/Library/Fonts/Songti.ttc', size=tipSize))
plt.tight_layout()
plt.savefig(sv_file_dir+'/'+title+'.png', format='png')
plt.cla()
plt.clf()
plt.close()
print 'drawNBarChart',title,'over'
def bargraph_from_dict(h_dict, group_labels, log=False, colors=None, save=None,
title=None, xlabel=None, ylabel=None, legend=False, loc='best'):
bars = len(h_dict) + 2 # Add one for a space between groups
groups = len(group_labels)
print h_dict
for v in h_dict.values():
if len(v) != groups:
raise ValueError("h_dict must contain sequences with the same number as labels")
plt.cla()
plts = []
if log:
plt.yscale("log")
if colors == "greyscale":
colors = [str(i*(.75/len(h_dict))) for i in xrange(len(h_dict))]
elif colors is None or colors == "spectrum":
colors = "rygbcmk"
keys = sorted(h_dict.keys())
for n, k in enumerate(keys):
plts.append(plt.bar(range(n+1, bars*groups+1, bars), h_dict[k],
color=colors[n % len(colors)]))
plt.xticks([1 + i*bars + bars/2 for i in xrange(groups)], group_labels)
if log:
ymin, ymax = plt.ylim()
ymin = min(reduce(lambda acc, x: acc+x, h_dict.values(), []))/2.0
plt.ylim((ymin, ymax))
if legend:
plt.legend( [p[0] for p in plts], [k.replace('_', '\_') for k in keys], loc=loc)
if title:
plt.title(title)
if ylabel:
plt.ylabel(ylabel)
if xlabel:
plt.xlabel(xlabel)
if save is not None:
plt.savefig(save)
def _get_pulsational_period(self,min_freq=10.0,doplot=False,max_pulse_period=400.0):
self.x0 = self.t
self.y = self.m
self.dy = self.merr
self.dy0 = np.sqrt(self.dy**2+self.sys_err**2)
self.x0 -= self.x0.min()
self.nepochs = len(self.x0)
# define the frequency grid
Xmax = self.x0.max()
if not self.fix_initial_period:
f0 = 1.0/max_pulse_period; df = 0.1/Xmax; fe = min_freq
numf = int((fe-f0)/df)
else:
f0 = 1./self.initial_period
df = 1e-7
numf = 1
psdr,res2 = lombr(self.x0,self.y,self.dy0,f0,df,numf,detrend_order=1)
period=1./res2['freq']
self.rrlp = period
if self.verbose:
print "Initial pulstional Period is %.8f day" % self.rrlp
self.features.update({"p_pulse_initial": self.rrlp})
if self.allow_plotting and doplot:
try:
plt.figure(3)
plt.cla()
tt=(self.x0/period) % 1.; s=tt.argsort()
plt.errorbar (tt,self.y,self.dy,fmt='o'); plt.plot(tt[s],res2['model'][s])
plt.ylim(self.y.max()+0.05,self.y.min()-0.05)
plt.title("P=%f" % (self.rrlp))
plt.draw()
except:
pass
return res2
def plot_frequency(self, f=2.0, ax=None, median_filter=False,
vmin=None, vmax=None, colorbar=False):
if vmin is None:
vmin = self.vmin
if vmax is None:
vmax = self.vmax
if ax is None:
ax = plt.gca()
plt.sca(ax)
plt.cla()
freq_extent = (self.extent[0], self.extent[1],
ais_code.ais_max_delay*1E3, ais_code.ais_min_delay*1E3)
i = self.ionogram_list[0]
inx = 1.0E6* (i.frequencies.shape[0] * f) / (i.frequencies[-1] - i.frequencies[0])
img = self.tser_arr_all[:,int(inx),:]
# img -= np.mean(img, 0)
plt.imshow(img, vmin=vmin, vmax=vmax,
interpolation='Nearest', extent=freq_extent, origin='upper',aspect='auto')
# plt.imshow(img, interpolation='Nearest', extent=freq_extent, origin='upper',aspect='auto')
plt.xlim(freq_extent[0], freq_extent[1])
plt.ylim(freq_extent[2], freq_extent[3])
# plt.vlines(i.time,freq_extent[2],freq_extent[3], 'r')
celsius.ylabel(r'$\tau_D / ms$' '\n' '%.1f MHz' % f)
# plt.annotate('f = %.1f MHz' % f, (0.02, 0.9), xycoords='axes fraction',
# color='grey', verticalalignment='top', fontsize='small')
if colorbar:
old_ax = plt.gca()
plt.colorbar(cax = celsius.make_colorbar_cax(),
ticks=self.cbar_ticks).set_label(
r"$Log_{10} V^2 m^{-2} Hz^{-1}$")
plt.sca(old_ax)
