def plot_latent_sweep(latent, n_pairs):
for d in range(latent.shape[1]):
dim = latent[:, d]
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
data_df = pd.DataFrame()
data_df["Label"] = n_pairs * ["Left"] + n_pairs * ["Right"]
data_df["Latent"] = dim[:2 * n_pairs]
data_df["Index"] = list(range(n_pairs)) + list(range(n_pairs))
ax = sns.scatterplot(data=data_df,
x="Index",
y="Latent",
hue="Label",
ax=ax,
s=15)
add_connections(
range(n_pairs),
range(n_pairs),
dim[:n_pairs],
dim[n_pairs:2 * n_pairs],
ax=ax,
color="grey",
)
remove_spines(ax)
ax.xaxis.set_major_locator(plt.FixedLocator([0]))
ax.yaxis.set_major_locator(plt.FixedLocator([0]))
ax.set_title(f"Dimension {d}")
def plotMatrix(matrix, plot_path, labels, title, ticks, vmin=-1., vmax=1.):
"""Plot matrix.
param matrix: two dimensional array. The matrix to plot
param plot_path : string. Full path and name of the plotting picture
param labels: list. The labels
param title: string. The title of the plot
param ticks: list. The ticks of the plot
vmin: float. Minimum value
vmax: float. Maximum value
return: None
"""
ticks = list(map(lambda x: x - 0.5, ticks))
ticks_middle = [(((ticks[i + 1] - ticks[i]) / 2) + ticks[i])
for i in range(0,
len(ticks) - 1)]
fig, ax = plt.subplots()
fig.set_size_inches(16.5, 9.5)
ax.xaxis.set_minor_locator(plt.FixedLocator(ticks[1:]))
ax.yaxis.set_minor_locator(plt.FixedLocator(ticks[1:]))
ax.grid(color='black', linestyle='-', linewidth=1.2, which='minor')
plt.title(label=title, fontsize=20)
plotting.plot_matrix(matrix, colorbar=True, axes=ax, vmin=vmin, vmax=vmax)
plt.yticks(ticks_middle, list(labels))
plt.xticks(ticks_middle,
list(labels),
rotation=55,
horizontalalignment='right')
for item in (ax.get_xticklabels() + ax.get_yticklabels()):
item.set_color(net_dic.labelToColorDic[item.get_text()])
item.set_fontsize(14)
fig.savefig(plot_path)
plt.close()
def visualize_samples(samples, discretized_samples, grid, low=None, high=None):
"""Visualize original and discretized samples on a given 2-dimensional grid."""
fig, ax = plt.subplots(figsize=(10, 10))
# Show grid
ax.xaxis.set_major_locator(plt.FixedLocator(grid[0]))
ax.yaxis.set_major_locator(plt.FixedLocator(grid[1]))
ax.grid(True)
# If bounds (low, high) are specified, use them to set axis limits
if low is not None and high is not None:
ax.set_xlim(low[0], high[0])
ax.set_ylim(low[1], high[1])
else:
# Otherwise use first, last grid locations as low, high (for further mapping discretized samples)
low = [splits[0] for splits in grid]
high = [splits[-1] for splits in grid]
# Map each discretized sample (which is really an index) to the center of corresponding grid cell
grid_extended = np.hstack((np.array([low]).T, grid, np.array([high]).T)) # add low and high ends
grid_centers = (grid_extended[:, 1:] + grid_extended[:, :-1]) / 2 # compute center of each grid cell
locs = np.stack(grid_centers[i, discretized_samples[:, i]] for i in range(len(grid))).T # map discretized samples
ax.plot(samples[:, 0], samples[:, 1], 'o') # plot original samples
ax.plot(locs[:, 0], locs[:, 1], 's') # plot discretized samples in mapped locations
ax.add_collection(mc.LineCollection(list(zip(samples, locs)), colors='orange')) # add a line connecting each original-discretized sample
ax.legend(['original', 'discretized'])
def psshow(f, a, title):
# Take the fourier transform of the image.
f1 = fftpack.fft2(f)
print(f.shape)
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
f2 = fftpack.fftshift(f1)
# Calculate a 2D power spectrum
psd2D = np.abs(f2)**2
# Display at log 10
psd2D_log = np.log10(psd2D)
min5 = np.percentile(psd2D_log, 5)
print(min5)
max95 = np.max(psd2D_log)
print(max95)
a.imshow(psd2D_log, vmin=min5, vmax=max95, cmap='gray')
# Look and feel
a.xaxis.set_major_locator(plt.FixedLocator([len(psd2D[0]) - 1]))
a.yaxis.set_major_locator(plt.FixedLocator([len(psd2D[0]) - 1]))
a.set_title(title)
def imshow(frame, a, title):
# Calculate events at super resolution
min5 = np.percentile(frame, 1)
max95 = np.percentile(frame, 99)
a.imshow(frame, vmin=min5, vmax=max95, cmap='gray')
# Look and feel
a.xaxis.set_major_locator(plt.FixedLocator([len(frame[0]) - 1]))
a.yaxis.set_major_locator(plt.FixedLocator([len(frame[0]) - 1]))
a.set_title(title)
return frame
def obspred_plot(paramsplot, fityrs, startage, endage,
trans = 'none', trans_time_coords = False, trans_y_coords = False):
obsframe = paramsplot['test'].rx2('obs')
ptype = paramsplot['ptype']
obsage = obsframe.rx2('age')
predage = base.seq(startage, endage, by = 5)
xlinlab = r'$\mathrm{log}(t)$'
ratelinlab = r'$\mathrm{log}(r(t))$'
survlinlab = r'$\mathrm{log}[-\mathrm{log}[S(t)]]$'
plotlabs = {'none': {'x': '$t$', 'rate': '$r(t)$', 'surv': '$S(t)$'},
'gomp_lin': {'x': '$t$', 'rate': ratelinlab, 'surv': survlinlab},
'weib_lin': {'x': xlinlab,'rate': ratelinlab, 'surv': survlinlab}}
plt.close()
fig = plt.figure()
ax = fig.add_subplot(111)
def trans_y_ticks(x, p):
ticks_round = np.round(predcols['none'][ptype], 6)
return str(ticks_round[p]).replace('.', ',')
for yr in fityrs:
obs = obsframe.rx2(str(yr))
pred = stats.predict(paramsplot['test'].rx2('sourcefit').rx2(str(yr)),
newdata = ro.r['list'](age = predage))
obscols = transdict(np.array(obsage), np.array(obs))
predcols = transdict(np.array(predage), np.array(pred))
obsplot = ax.plot(obscols[trans]['x'], obscols[trans][ptype],
'o', label = str(yr))
curcolor = obsplot[0].get_color()
predplot = ax.plot(predcols[trans]['x'], predcols[trans][ptype],
color = curcolor)
plt.legend(loc = 2, framealpha = 0.5)
ax.set_title('Observerad vs förutsedd ' + paramsplot['plottitle'])
if (trans_time_coords):
ax.xaxis.set_major_locator(plt.FixedLocator(predcols[trans]['x']))
ax.xaxis.set_major_formatter(plt.FixedFormatter(predcols['none']['x']))
ax.set_xlabel(plotlabs['none']['x'])
plt.xticks(rotation = 90)
else:
ax.set_xlabel(plotlabs[trans]['x'])
if (trans_y_coords):
ax.yaxis.set_major_locator(plt.FixedLocator(predcols[trans][ptype]))
ax.yaxis.set_major_formatter(plt.FuncFormatter(trans_y_ticks))
ax.set_ylabel(plotlabs['none'][ptype])
else:
ax.set_ylabel(plotlabs[trans][ptype])
def plotMatrix(matrix, plot_path, labels, title, ticks, vmin, vmax):
"""Plot matrix.
param matrix: two dimensional array. The matrix to plot
param plot_path : string. Full path and name of the plotting picture
param labels: list. The labels
param title: string. The title of the plot
param ticks: list. The ticks of the plot
vmin: float. Minimum value
vmax: float. Maximum value
return: None
"""
ticks = list(map(lambda x: x - 0.5, ticks))
labelToColorDic = {
"Uncertain": "olive",
"SSH": "cyan",
"SSM": "orange",
"CO": "purple",
"Auditory": "m",
"DMN": "red",
"Memory": "grey",
"Visual": "blue",
"FP": "gold",
"Salience": "black",
"Subcortical": "brown",
"VAN": "teal",
"DAN": "green",
"Cerebellum": "purple"
}
ticks_middle = [(((ticks[i + 1] - ticks[i]) / 2) + ticks[i])
for i in range(0,
len(ticks) - 1)]
fig, ax = plt.subplots()
fig.set_size_inches(16.5, 9.5)
plt.yticks(ticks_middle, labels)
plt.xticks(ticks_middle, labels, rotation=55, horizontalalignment='right')
ax.xaxis.set_minor_locator(plt.FixedLocator(ticks[1:]))
ax.yaxis.set_minor_locator(plt.FixedLocator(ticks[1:]))
ax.grid(color='black', linestyle='-', linewidth=1.2, which='minor')
plt.title(label=title, fontsize=20)
for item in (ax.get_xticklabels() + ax.get_yticklabels()):
item.set_color(labelToColorDic[item.get_text()])
item.set_fontsize(14)
plotting.plot_matrix(matrix,
colorbar=True,
figure=fig,
vmin=vmin,
vmax=vmax)
fig.savefig(plot_path)
plt.close()
def plotBar(pos, cases, dt, color, title):
"""
This plots based base on the time series data
:param pos:
:param cases:
:param dt:
:param color:
:param title:
:return:
"""
labels = [val[1] for val in cases]
labels2 = [
"Test"
] + labels # this is to overcome a bug in the bar function
x = np.arange(len(labels)) # the label locations
axs[pos].bar(x, [val[0] for val in cases],
color=color,
edgecolor='k')
axs[pos].set_title("{} on {}".format(title, str(dt)), fontsize=15)
axs[pos].xaxis.set_major_locator(plt.FixedLocator(x))
axs[pos].xaxis.set_major_formatter(plt.FixedFormatter(labels))
#axs[pos].set_xticklabels(labels2)
axs[pos].grid(axis="y")
plt.setp(axs[pos].xaxis.get_majorticklabels(),
rotation=35,
fontsize=12)
def generate_histrogram_strings(buckets, title, x_label, y_label, output):
fig = plt.figure()
ax = fig.add_subplot(111)
sorted_keys = sorted(buckets.keys(),
key=lambda item: int(''.join(x for x in item
if x.isdigit())))
indexes = np.arange(0, len(sorted_keys)) # the x locations for the groups
width = 0.1 # the width of the bars
ax.set_yscale('log')
ax.grid(zorder=0, axis="y")
bars1 = ax.bar(indexes, [buckets.get(sorted_keys[i], 0) for i in indexes],
align='center',
color='blue')
# axes and labels
ax.set_ylim(0.5, max(buckets.values()) + 1)
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.set_title(title)
ax.xaxis.set_major_locator(plt.FixedLocator(indexes))
ax.xaxis.set_major_formatter(plt.FixedFormatter(sorted_keys))
labels = ax.get_xticklabels()
for tick in labels:
tick.set_rotation(90)
plt.tight_layout()
fig.savefig(output)
def plot_bar_from_counter(counter, title, xlabel, ylabel, ax=None):
""""
This function creates a bar plot from a counter.
:param counter: This is a counter object, a dictionary with the item as the key
and the frequency as the value
:param ax: an axis of matplotlib
:return: the axis wit the object in it
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
frequencies = counter.values()
names = list(counter.keys())
x_coordinates = np.arange(len(counter))
ax.bar(x_coordinates, frequencies, align='center')
ax.xaxis.set_major_locator(plt.FixedLocator(x_coordinates))
ax.xaxis.set_major_formatter(plt.FixedFormatter(names))
ax.xaxis.set_label_text(xlabel)
ax.yaxis.set_label_text(ylabel)
ax.set_title(title)
plt.autoscale(enable=True, axis='x')
DefaultSize = fig.get_size_inches()
fig.set_size_inches((DefaultSize[0] * 5, DefaultSize[1] * 2.5))
mkdir_p(IMG_URL)
plt.savefig(IMG_URL + 'GIST Data Histogram.png')
plt.close()
im = crop_image(IMG_URL + 'GIST Data Histogram.png')
return ax
def plot_bar_from_counter(counter, ax=None):
""""
This function creates a bar plot from a counter.
:param counter: This is a counter object, a dictionary with the item as the key
and the frequency as the value
:param ax: an axis of matplotlib
:return: the axis wit the object in it
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
frequencies = counter.values()
names = counter.keys()
x_coordinates = np.arange(len(counter))
ax.bar(x_coordinates, frequencies, align='center')
for idx in xrange(len(frequencies)):
x = x_coordinates[idx]
y = frequencies[idx]
ax.text(x + 3, y + 3, str(v), color='red', fontweight='bold')
ax.xaxis.set_major_locator(plt.FixedLocator(x_coordinates))
ax.xaxis.set_major_formatter(plt.FixedFormatter(names))
ax.set_ylim(0, np.max(frequencies) * 1.1)
return ax
def ohlc_plot(ax, df, open_='open', high_='high', low_='low', close_='close', t_='trading_day', width_=0.7, n_=10):
assert isinstance(df, pd.DataFrame)
assert all([x in df.columns for x in (open_, high_, low_, close_)])
n = len(df)
width = width_
offset = width / 2.0
for i in range(n):
op = df[open_].values[i]
hi = df[high_].values[i]
lo = df[low_].values[i]
cl = df[close_].values[i]
x = i
y = min(op, cl)
height = abs(cl - op)
is_raise = op < cl
clr = 'red' if is_raise else 'green'
fill = True # True if is_raise else False
rect = Rectangle((x - offset, y), width, height, facecolor=clr, edgecolor=clr, linewidth=1, fill=fill)
vline = Line2D(xdata=(x, x), ydata=(lo, hi), linewidth=1, color=clr)
ax.add_patch(rect)
ax.add_line(vline)
ax.autoscale_view()
x_label_idx = n_ * np.array(range(len(df) // n_ + 1))
x_labels = df[t_].iloc[x_label_idx].values
ax.xaxis.set_major_locator(plt.FixedLocator(x_label_idx))
ax.xaxis.set_major_formatter(plt.FixedFormatter(x_labels))
def plot_summary(self):
fig_size = (30, 20)
multi_plot = plt.figure(figsize=fig_size, dpi=300)
plot_order = ['wind_speed', 'wind_dir', 'T', 'rh_air', 'H']
for sub_name in plot_order:
sub_plot = multi_plot.add_subplot(
self._get_pos(sub_name, plot_order),
xlim=[self.data.index[0].date(), self.data.index[-1].date()],
ylim=self.config.plot_settings[sub_name]['ylim'])
plt.plot(self.data[sub_name],
self.config.plot_settings[sub_name]['style'],
**self.config.plot_settings[sub_name]['params'])
sub_plot.tick_params(axis='both',
length=10,
which='major',
direction='in',
labelsize=14)
sub_plot.xaxis.set_major_formatter(dates.DateFormatter('%m/%d'))
sub_plot.xaxis.set_major_locator(dates.DayLocator())
sub_plot.set_xlabel('Date', fontsize=18)
sub_plot.set_ylabel(self.config.plot_settings[sub_name]['label'],
fontsize=18)
sub_plot.yaxis.set_major_locator(
plt.FixedLocator(
self.config.plot_settings[sub_name]['y_ticks']))
# multi_plot.tight_layout()
os.makedirs(self.config.plot_path, exist_ok=True)
plt.savefig(self.config.plot_path + '\\all_ADV.png')
plt.close(multi_plot)
def setup_axis():
ax = plt.gca()
# ax.xaxis.set_major_locator(plt.MultipleLocator(50.0)) # 设置x主坐标间隔 1
# ax.xaxis.set_minor_locator(plt.MultipleLocator(10.0)) # 设置x从坐标间隔 0.1
ax.yaxis.set_major_locator(
plt.FixedLocator([
70,
77,
78,
84,
85,
92,
97,
100,
])) # 设置y主坐标间隔 1
ax.yaxis.set_minor_locator(plt.MultipleLocator(1)) # 设置y从坐标间隔 0.1
ax.spines['top'].set_linewidth(0.2)
ax.spines['bottom'].set_linewidth(0.2)
ax.spines['left'].set_linewidth(0.2)
ax.spines['right'].set_linewidth(0.2)
#
ax.grid(which='major', linewidth=0.2, linestyle='-')
ax.grid(which='minor', linewidth=0.1, linestyle='-')
def display_all_etas(etas, event_names, groups=None):
for i, event in enumerate(etas):
for j, neuron in enumerate(event):
if neuron > 1000:
etas[i][j] = 1000
elif neuron < -1000:
etas[i][j] = -1000
etas = [[etas[i][n] for i in range(len(etas))]
for n in range(len(etas[0]))]
etas = normalise_vrvs(etas)
fig, ax = plt.subplots()
fig.set_size_inches(1.85 * len(event_names), 20)
if groups:
ordered_atas = []
indexes = [
j for sub in [groups[i] for i in groups.keys()] for j in sub
]
for i in indexes:
ordered_atas.append(etas[i])
ax.pcolor(ordered_atas, cmap='coolwarm')
transition_points = [
len(groups[key]) for i, key in enumerate(groups.keys())
]
cumulative_tps = []
for i, t in enumerate(transition_points):
if i == 0:
continue
cumulative_tps.append(sum(transition_points[:i]))
transition_points = cumulative_tps
# cluster_labels = [i for i in range(len(transition_points))]
def format_func_cluster(value, tick):
for i, tp in enumerate(transition_points):
if value < tp:
return i
return len(transition_points)
for t in transition_points:
ax.axhline(t, color="black", linewidth=1)
ax.set_yticks(transition_points, minor=True)
ax2 = ax.secondary_yaxis("right")
# ax2.yaxis.grid(True, which='minor', linewidth=20, linestyle='-', color="b")
ax2.yaxis.set_major_locator(plt.FixedLocator(transition_points))
ax2.yaxis.set_major_formatter(plt.FuncFormatter(format_func_cluster))
ax2.set_ylabel("Cluster ID", fontsize=40)
ax2.tick_params(axis='y', labelsize=20)
else:
# atas, t, cat = order_vectors_by_kmeans(atas)
ax.pcolor(etas, cmap='coolwarm')
# ax.grid(True, which='minor', axis='both', linestyle='-', color='k')
plt.xticks(range(len(event_names)), [i for i in event_names], fontsize=25)
# ax.set_yticks(lat_grid, minor=True)
ax.tick_params(axis="x", labelrotation=45)
ax.set_ylabel("Neuron", fontsize=20)
ax.set_xlabel("Event", fontsize=40)
fig.tight_layout()
plt.show()
def plot_bar_from_counter(counter, ax=None):
""""
This function creates a bar plot from a counter.
:param counter: This is a counter object, a dictionary with the item as the key
and the frequency as the value
:param ax: an axis of matplotlib
:return: the axis wit the object in it
"""
if ax is None:
fig = plt.figure(figsize=(7, 5))
ax = fig.add_subplot(111)
frequencies = [f[1] for f in counter]
names = [f[0] for f in counter]
x_coordinates = np.arange(len(counter))
ax.barh(x_coordinates, frequencies, align='center')
ax.yaxis.set_major_locator(plt.FixedLocator(x_coordinates))
ax.yaxis.set_major_formatter(plt.FixedFormatter(names))
ax.yaxis.set_tick_params(labelsize=10)
ax.set_ylim(-1, len(counter) + 1)
ax.set_xlim(0, max(frequencies) + 300)
for i, v in enumerate(frequencies):
ax.text(v + 20,
i - 0.25,
str(v),
color='black',
fontweight='normal',
fontsize=10)
fig.subplots_adjust(left=0.4)
return ax
def graph_flow_generator(total):
ticket_dict = {}
ticket_dict["Information"] = total[0]
ticket_dict["Mineure"] = total[1]
ticket_dict["Majeure"] = total[2]
ticket_dict["Bloquante"] = total[3]
ticket_dict["Autre"] = total[4]
fig = plt.figure()
ax = fig.add_subplot(111)
frequencies = ticket_dict.values()
names = ticket_dict.keys()
x_coordinates = np.arange(len(ticket_dict))
ax.bar(x_coordinates, frequencies, align='center')
ax.xaxis.set_major_locator(plt.FixedLocator(x_coordinates))
ax.xaxis.set_major_formatter(plt.FixedFormatter(names))
plt.savefig("flow" + contract_id + ".svg")
# plt.show()
img_insert("Repartition des demandes", "flow" + contract_id + ".svg",
"Repartition des demandes")
def plot_bar_graph_within(names, observered_data, result_data, title, fig,
sub_plot_index):
"""
:param names: labels of x-axis
:param observered_data: as variable name
:param result_data: as variable name
:param title: the title of the subplot
:param fig: the fig object
:param sub_plot_index: the index number is in order,
such 221 means the generate a graph has 2 by 2 figure, which could have 4 subplots in it
the 1 means the first subplot in the figure.
:return: the fig object
"""
# define the subplot
ax = fig.add_subplot(sub_plot_index)
x = np.arange(names.__len__()) * 2
# set the value
ax.bar(x, observered_data, align='center', color='red')
ax.bar(x + 1, result_data, align='center', color='blue')
# create the patch
red_patch = mpatches.Patch(color='red', label='Observed Value')
blue_patch = mpatches.Patch(color='blue', label='Simulated Value')
# Set the label x-axis
ax.xaxis.set_major_locator(plt.FixedLocator(x + 0.5))
ax.xaxis.set_major_formatter(plt.FixedFormatter(names))
ax.set_title(title)
plt.legend(handles=[red_patch, blue_patch])
return fig
def plot_distr(binned_data_list,binning_increment):
norm_concs = []
bin_mids = []
for row in binned_data_list:
bin_mid = row[0]
bin_conc = row[1]
bin_conc_norm = bin_conc/(math.log(bin_mid+binning_increment/2.,10)-math.log(bin_mid-binning_increment/2.,10))
norm_concs.append(bin_conc_norm)
bin_mids.append(bin_mid)
popt,perr = SP2_utilities.fitFunction(SP2_utilities.lognorm,bin_mids,norm_concs,p0=(100,180,0.5))
fit_bin_mids = np.arange(10,1000,5)
fit_concs = []
for fit_bin_mid in fit_bin_mids:
fit_conc_norm = SP2_utilities.lognorm(fit_bin_mid, popt[0], popt[1], popt[2])
fit_concs.append(fit_conc_norm)
ticks = [10,20,30,40,50,60,80,120,200,300,400,600,1000]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.plot(fit_bin_mids,fit_concs)
ax1.scatter(bin_mids,norm_concs)
ax1.xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax1.xaxis.set_major_locator(plt.FixedLocator(ticks))
ax1.set_xscale('log')
ax1.set_xlim(10,1000)
ax1.set_ylabel('dM/dlogD (ng/m3)')
ax1.set_xlabel('rBC VED (nm)')
plt.show()
def make_graph(humidities, dates_):
firstdate = dates_[0]
lastdate = dates_[-1]
graph_title = "{} - {}".format(firstdate.strftime(DAY_FORMAT),
lastdate.strftime(DAY_FORMAT))
debug_print("Making graph for {}".format(graph_title))
# create new plot
fig, ax = plt.subplots()
fig.set_size_inches(40.5, 20.5)
fig.subplots_adjust(bottom=0.2)
# configure axes
ax.xaxis_date()
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_minor_locator(HourLocator())
ax.xaxis.set_major_formatter(DateFormatter(DATE_FORMAT))
ax.yaxis.set_major_locator(
plt.FixedLocator([
10, 20, 30, 110, 120, 130, 140, 150, 160, 300, 350, 365, 380, 400,
430, 480
]))
ax.set_xlim(firstdate, lastdate)
ax.set_ylim(10, 500)
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.xticks(rotation=15)
# add data
plt.plot(dates_, humidities, ".")
plt.grid()
plt.title(graph_title)
return fig
def matrix_plot(fig, ax, matrix, position='right'):
''' Create a matrix plot of pop. rates for the stabilization manuscript
'''
ax.yaxis.set_ticks_position('none')
cm = plt.get_cmap('rainbow')
cm = cm.from_list('mycmap', [myblue, myblue2,
'white', myred2, myred], N=256)
masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
cm.set_under('0.3')
cm.set_bad('k')
im = ax.pcolormesh(masked_matrix, norm=LogNorm(
vmin=0.001, vmax=500.), cmap=cm)
ax.set_xlim(0, 32)
ax.set_xticks([0.5, 3.5, 14.5, 24.5, 28.5])
ax.xaxis.set_major_locator(plt.FixedLocator([0, 1, 4, 9, 24, 31]))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_locator(plt.FixedLocator([0.5, 2.5, 6.5, 16.5,
27.5, 31.5]))
ax.set_xticklabels([8, 7, 6, 5, 4, 2], minor=True, size=8)
ax.tick_params(axis='x', which='minor', length=0.)
ax.set_xlabel('Architectural type', labelpad=-0.2)
y_index = list(range(8))
y_index = [a + 0.5 for a in y_index]
t = plt.FixedLocator([0.01, 0.1, 1., 10., 100., 500.])
cb = plt.colorbar(im, ticks=t, ax=ax)
if position == 'left':
ax.set_yticklabels(population_labels, size=8)
ax.set_yticks(y_index[::-1])
ax.set_ylabel('Population', labelpad=-0.1)
cb.remove()
elif position == 'center':
ax.set_yticks([])
cb.remove()
elif position == 'right':
ax.set_yticks([])
ax.text(45., 6, r'$\nu (\mathrm{spikes/s})$', rotation=90)
if position == 'single':
ax.set_yticklabels(population_labels)
ax.set_yticks(y_index[::-1])
ax.set_ylabel('Population', labelpad=-0.1)
ax.text(45., 6, r'$\nu (\mathrm{spikes/s})$', rotation=90)
def rate_matrix_plot(fig, ax, matrix, position):
''' Create a matrix plot of pop. rates for the stabilization manuscript
'''
cm = plt.get_cmap('rainbow')
cm = cm.from_list('mycmap', [myblue, myblue2, 'white', myred2, myred],
N=256)
masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
cm.set_under('0.3')
cm.set_bad('k')
im = ax.pcolormesh(masked_matrix,
norm=LogNorm(vmin=0.01, vmax=500.),
cmap=cm)
ax.set_xlim(0, 32)
R = plt.Rectangle((area_list.index('TH') + 1, 4),
1,
2,
edgecolor='none',
facecolor='k')
ax.add_patch(R)
ax.set_xticks([0.5, 3.5, 14.5, 24.5, 28.5])
ax.xaxis.set_major_locator(plt.FixedLocator([0, 1, 4, 9, 24, 31]))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_locator(
plt.FixedLocator([0.5, 2.5, 6.5, 16.5, 27.5, 31.5]))
ax.set_xticklabels([8, 7, 6, 5, 4, 2], minor=True)
ax.tick_params(axis='x', which='minor', length=0.)
ax.set_xlabel('Arch. type', labelpad=-0.2)
y_index = list(range(8))
y_index = [a + 0.5 for a in y_index]
t = plt.FixedLocator([0.01, 0.1, 1., 10., 100.])
cb = plt.colorbar(im, ticks=t, ax=ax)
if position == 'left':
ax.set_yticklabels(population_labels)
ax.set_yticks(y_index[::-1])
ax.set_ylabel('Population')
ax.yaxis.set_label_coords(-0.2, 0.5)
# cb.remove()
elif position == 'right':
ax.set_yticks([])
ax.text(42, 5, r'$\nu (1/\mathrm{s})$', rotation=90)
def get_midi_to_performance_difference(midi_hz,
measured_hz,
performance_key,
plot=False,
plot_dir="./"):
"""
Computes frame-wise differences in cents between the midi score and measured f0 when both are non-silent.
:param midi_hz: numpy array of frame-wise MIDI frequencies in Hz
:param measured_hz: numpy array of frame-wise performance pitch track frequencies in Hz
:param performance_key: Performance identifier
:return: array of differences in cents when both arrays are non-zero (not silent)
"""
# find non-silent frames
singing_region = np.where((measured_hz > 1.0) & (midi_hz > 1.0))[0]
# compute the differences in cents
cent_differences = np.log2((midi_hz[singing_region] + 1e-10) /
(measured_hz[singing_region] + 1e-10)) * 1200
if plot:
fig, ax = plt.subplots()
ax.set_xlabel("Frames")
ax.set_ylabel("Cents")
ax.set_title("Difference between MIDI and pYIN in non-silent frames")
plt.plot(cent_differences)
plt.tight_layout()
plt.savefig(os.path.join(
plot_dir,
"midi_performance_diff_cents_" + performance_key + ".eps"),
format="eps")
plt.close()
fig, ax = plt.subplots(figsize=(6, 4))
ax.set_xlabel("Frames")
ax.set_ylabel("Scientific pitch")
note_names = [
'Gb4', 'G4', 'Ab4', 'A4', 'Bb4', 'B4', 'C5', 'Db5', 'D5', 'Eb5',
'E5', 'F5', 'Gb5', 'G5', 'Ab5', 'A5', 'Bb5', 'B5', 'C6'
]
ax.set_ylim(np.log2(370), np.log2(940))
ax.yaxis.set_major_locator(
plt.FixedLocator(np.log2(440 * np.power(2,
np.arange(-3, 14) / 12))))
ax.set_yticklabels(note_names, fontsize='small')
for label in (ax.get_xticklabels()):
label.set_fontsize('small')
plt.plot(np.log2(midi_hz[singing_region][500:1500]),
color="green",
label="MIDI")
plt.plot(np.log2(measured_hz[singing_region][500:1500]),
color="purple",
label="pYIN")
plt.legend(loc=4, fontsize='small')
plt.tight_layout()
plt.savefig(os.path.join(
plot_dir,
"midi_and_pyin_aligned_active_" + performance_key + ".eps"),
format="eps")
plt.close()
return cent_differences
def palplot(k, cmap="viridis"):
pal = sns.color_palette(palette=cmap, n_colors=k)
fig, ax = plt.subplots(1, 1)
pal = np.array(pal)
pal = pal.reshape((k, 1, 3))
ax.imshow(pal)
ax.xaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_formatter(plt.FixedFormatter(np.arange(k, dtype=int)))
ax.yaxis.set_major_locator(plt.FixedLocator(np.arange(k)))
return ax
def plot_bar_from_counter(counter):
ax = fig.add_subplot(1, 1, 1)
x_coordinates = np.arange(len(counter))
ax.bar(x_coordinates, counter.values(), align='center')
ax.xaxis.set_tick_params(rotation=90)
ax.xaxis.set_major_locator(plt.FixedLocator(x_coordinates))
ax.xaxis.set_major_formatter(plt.FixedFormatter(list(counter.keys())))
return ax
def plot2d(self, **kwargs):
"""2D Colormap plot of the Rainflow matrix
Args:
**kwargs: **kwargs are passed to plt.imshow()
Returns:
figure: matplotlib figure object
axes: matplotlib axes object
"""
# create fig, ax
fig = plt.figure()
ax = fig.add_subplot((111))
# imshow plot
cax = ax.imshow(self.values,
cmap=plt.get_cmap("Blues"),
extent=(self.bins[0][0], self.bins[0][-1],
self.bins[1][-1], self.bins[1][0]),
**kwargs)
# create colorbar
fig.colorbar(cax)
# create grid and ticks
ax.grid(which='minor', alpha=0.8, linewidth=0.3)
ax.xaxis.tick_top()
ax.xaxis.set_label_position('top')
ax.xaxis.set_minor_locator(plt.FixedLocator(self.bins[0]))
ax.yaxis.set_minor_locator(plt.FixedLocator(self.bins[0]))
ax.tick_params(which="minor", direction="in")
if self.matrixtype == 'FromTo':
ylabel = 'From'
xlabel = 'To'
elif self.matrixtype == 'RangeMean':
ylabel = 'Range'
xlabel = 'Mean'
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
fig.tight_layout()
return fig, ax
def draw_together(n, Ts, c_num, s, pmin):
fig, ax = plt.subplots(figsize=(11, 3))
sim_num = 6
colors = ['b', 'g', 'r', 'c', 'm']
c_show_max = 15
major_ticks = []
minor_ticks = []
ymax = 0
for i, T in enumerate(Ts):
df = read_csv("results/simu_n{}t{}cnum{}pmin{}.csv".format(
n, T, c_num, pmin),
index_col=0)
for c in df.columns:
if int(c) > c_show_max:
continue
tick = (c_show_max + 1) * i + int(c)
ax.plot([tick] * (sim_num - 1), df[c].values, 'o', color=colors[i])
ymax = max(ymax, np.max(df[c].values))
if int(c) % 2 == 0:
major_ticks.append(tick)
minor_ticks.append(tick)
save_path = "results/simu_n{}cnum{}pmin{}".format(n, c_num, pmin)
ax.xaxis.set_major_locator(plt.FixedLocator(major_ticks))
ax.xaxis.set_minor_locator(plt.FixedLocator(minor_ticks))
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
ax.yaxis.set_major_locator(
plt.FixedLocator(get_yticks_my_way(0, int(ymax))))
ax.yaxis.set_minor_locator(plt.FixedLocator(list(range(0, int(ymax) + 1))))
# save png and low quality jpg
fig.savefig(save_path + ".png", dpi=500)
fig.savefig(save_path + ".jpg", dpi=100, optimize=True, quality=30)
# clean before doing next graph
plt.clf()
def imshow(e, a):
# Calculate events at super resolution
x = e['x'] * super_resolution
y = e['y'] * super_resolution
data = np.ones(len(x))
d = scipy.sparse.coo_matrix((data, (y, x)), shape=(shape, shape), dtype=np.uint32)
frame = d.todense()
min5 = np.percentile(frame, 5)
max95 = np.percentile(frame, 95)
im = a.imshow(frame, vmin=min5, vmax=max95, cmap='gray')
# Look and feel
a.xaxis.set_major_locator(plt.FixedLocator([shape - 1]))
a.yaxis.set_major_locator(plt.FixedLocator([shape - 1]))
# Color bar
# colorbar(im)
return im
def plot_inertia_silhouette(inertia_vect, silhouette_vect, strategy):
"""Plot inertia and silhouette vs number of clusters."""
fig, axs = plt.subplots(1, 2)
k_vect = np.arange(2, 2 + len(inertia_vect))
for ax, y, label in zip(axs, (inertia_vect, silhouette_vect), LABELS):
ax.plot(k_vect, y)
ax.xaxis.set_major_locator(plt.FixedLocator(k_vect))
ax.set(xlabel="k", ylabel=label)
fig.suptitle(f"Strategy: {strategy}", fontsize=16)
plt.show()
def plot_cdf_raw(values, ax, **kwargs):
"""Plot the cdf of values.
:param values: values to plot.
:param ax: axis to plot.
:param kwargs: any other kwargs that are passed to plot function.
"""
probabilities = np.linspace(0, 1, len(values))
ax.plot(np.sort(values), probabilities, **kwargs)
ax.yaxis.set_major_locator(plt.FixedLocator(np.linspace(0, 1, 5)))
ax.set_ylabel('cdf')
ax.grid(True)