def plot_decision_function(clf, data_range=None,
features=None, labels=None, feature_columns=[0, 1],
n_gridpoints=201):
"""Plot the decision function of a classifier in 2D.
Parameters
----------
clf : scikit-learn classifier
The classifier to be evaluated.
data_range : tuple of int, optional
The range of values to be evaluated.
features : 2D array of float, optional
The features of the training data.
labels : 1D array of int, optional
The labels of the training data.
feature_columns : tuple of int, optional
Which feature columns to plot, if there are more than two.
n_gridpoints : int, optional
The number of points to place on each dimension of the 2D grid.
"""
if features is not None:
features = features[:, feature_columns]
minfeat, maxfeat = np.min(features), np.max(features)
featrange = maxfeat - minfeat
if data_range is None:
if features is None:
data_range = (0, 1)
else:
data_range = (minfeat - 0.05 * featrange,
maxfeat + 0.05 * featrange)
data_range = np.array(data_range)
grid = np.linspace(*data_range, num=n_gridpoints, endpoint=True)
rr, cc = np.meshgrid(grid, grid, sparse=False)
feature_space = np.hstack((np.reshape(rr, (-1, 1)),
np.reshape(cc, (-1, 1))))
prediction = clf.predict_proba(feature_space)[:, 1] # Pr(class(X)=1)
prediction = np.reshape(prediction, (n_gridpoints, n_gridpoints))
fig, ax = plt.subplots()
ax.imshow(prediction, cmap='RdBu')
ax.set_xticks([])
ax.set_yticks([])
features = (features - data_range[0]) / (data_range[1] - data_range[0])
if features is not None:
if labels is not None:
label_colors = cm.viridis(labels.astype(float) / np.max(labels))
else:
label_colors = cm.viridis(np.zeros(features.shape[0]))
ax.scatter(*(features.T * n_gridpoints), c=label_colors)
plt.show()
def bokeh_plot(df):
tooltip = """
<div>
<div>
<img
src="@image_files" height="60" alt="image"
style="float: left; margin: 0px 15px 15px 0px; image-rendering: pixelated;"
border="2"
></img>
</div>
<div>
<span style="font-size: 17px;">@source_filenames</span>
</div>
</div>
"""
filenames = b64_image_files(df['images'])
df['image_files'] = filenames
colors_raw = cm.viridis((df['time'] - df['time'].min()) /
(df['time'].max() - df['time'].min()), bytes=True)
colors_str = ['#%02x%02x%02x' % tuple(c[:3]) for c in colors_raw]
df['color'] = colors_str
source = ColumnDataSource(df)
bplot.output_file('plot.html')
hover0 = HoverTool(tooltips=tooltip)
hover1 = HoverTool(tooltips=tooltip)
tools0 = [t() for t in TOOLS] + [hover0]
tools1 = [t() for t in TOOLS] + [hover1]
pca = bplot.figure(tools=tools0)
pca.circle('PC1', 'PC2', color='color', source=source)
tsne = bplot.figure(tools=tools1)
tsne.circle('tSNE-0', 'tSNE-1', color='color', source=source)
p = bplot.gridplot([[pca, tsne]])
bplot.show(p)
def result_length_of_sub_dist(path):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
data = np.load(result_data_path)
beta = data['beta']
num_of_strings = data['num_of_strings']
L = data['L']
frames = data['frames']
Ls = data['Ls'].astype(np.float)
size_dist = data['size_dist']
N = np.sum(size_dist, axis=0)
N_freq = N / np.sum(N)
X = np.arange(1, len(N)+1)
ax.semilogy(X, N_freq, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
# ax.semilogy(X[::2], N_freq[::2], '.', label=r'$\beta = %2.2f$' % beta,
# color=cm.viridis(float(i) / len(path)))
# ax.semilogy(X[1::2], N_freq[1::2], '.', label=r'$\beta = %2.2f$' % beta,
# color=cm.viridis(float(i) / len(path)))
ax.legend(loc='best')
ax.set_title("Appearance frequency of the subcluster of size $N$")
ax.set_xlabel("$N$")
ax.set_ylabel("Freq")
plt.show()
def silhouette_plot(self, X, X_predicted, title, filename):
plt.clf()
plt.cla()
cluster_labels = np.unique(X_predicted)
n_clusters = cluster_labels.shape[0]
silhouette_vals = silhouette_samples(X, X_predicted, metric='euclidean')
y_ax_lower, y_ax_upper = 0, 0
color=iter(cm.viridis(np.linspace(0,1,cluster_labels.shape[0])))
yticks = []
for i, c in enumerate(cluster_labels):
c_silhouette_vals = silhouette_vals[X_predicted == c]
c_silhouette_vals.sort()
y_ax_upper += len(c_silhouette_vals)
plt.barh(range(y_ax_lower, y_ax_upper), c_silhouette_vals, height=1.0, edgecolor='none', color=next(color))
yticks.append((y_ax_lower + y_ax_upper) / 2.)
y_ax_lower += len(c_silhouette_vals)
silhouette_avg = np.mean(silhouette_vals)
plt.axvline(silhouette_avg, color="red", linestyle="--")
plt.yticks(yticks, cluster_labels + 1)
plt.ylabel('Cluster')
plt.xlabel('Silhouette Coefficient')
plt.title(title)
plt.tight_layout()
plt.savefig(filename)
plt.close('all')
def fermi(path, fixed_a, fixed_loc, save_image=False):
matplotlib.rcParams['savefig.dpi'] = 300
def modified_gamma_2(x, scale):
a = fixed_a
loc = fixed_loc
return gamma.pdf(x, a=a, loc=loc, scale=scale)
betas = []
scale = []
L = []
S = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
L.append(Ls)
S.append(M_ave)
popt = curve_fit(modified_gamma_2, xdata=Ls, ydata=M_ave, p0=[10.])[0]
# print beta, popt
betas.append(beta)
theta = popt[0]
scale.append(theta)
ax.plot(Ls / theta, M_ave * theta, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
ax.set_title(r'Collapsed data')
ax.set_xlabel(r'$L / \theta$')
ax.set_ylabel(r'$\theta * f(L)$')
plt.show()
def plot_sample_trajectory(data, subj_id, trial_no):
x_lim = [-1.2, 1.2]
y_lim = [-0.2, 1.2]
tickLabelFontSize = 20
axisLabelFontSize = 24
sns.set_style('white')
fig = plt.figure(tight_layout=True)
ax = fig.add_subplot(111)
ax.set_xlabel(r'x coordinate', fontsize=axisLabelFontSize)
ax.set_ylabel(r'y coordinate', fontsize=axisLabelFontSize)
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
ax.tick_params(axis='both', which='major', labelsize=tickLabelFontSize)
traj_color = cm.viridis(0.1)
trajectory = data.loc[subj_id, trial_no]
ax.plot(trajectory.x, trajectory.y, color=traj_color, ls='none', marker='o', ms=15,
markerfacecolor='none', markeredgewidth=2, markeredgecolor=traj_color,
label='Mouse trajectory')
# draw screen above the surface and choice options on it
patches = get_choice_patches()
for patch in patches:
ax.add_patch(patch)
ax.set_axis_off()
plt.savefig('figures/sample_traj.pdf')
def plot_variance_coefficients(df, coeff='a', yAxis='center', xAxis='n', zAxis='tDiff', i1='mdi', i2='mdi'):
"""Takes a dataframe containing fit coefficients for variance grids and
plots them as a function of yaxis.
coeff: what coefficient that is being plotted
yAxis: which coefficient (center, middle, outer disk) to plot
xAxis: the independent variable to plot_fits
cAxis: secondary variable to separate data sets
i1: reference instrument
i2: secondary instrument
"""
x = df[xAxis][(df['i1'] == i1) & (df['i2'] == i2)].as_matrix()
y = df[yAxis][(df['i1'] == i1) & (df['i2'] == i2)].as_matrix()
z = df[zAxis][(df['i1'] == i1) & (df['i2'] == i2)].as_matrix().astype(np.float32)
z[(z < 1)] = .25
norm1 = matplotlib.colors.LogNorm(vmin=np.min(z), vmax=np.max(z))
norm2 = matplotlib.colors.LogNorm(vmin=np.min(x), vmax=np.max(x))
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
for segment in sorted(set(z)):
ind = (z == segment)
ax1.scatter(x[ind], y[ind], c=cm.viridis(norm1(z[ind][0])),
edgecolor='face', cmap='viridis')
ax1.plot(x[ind], y[ind],
c=cm.viridis(norm1(z[ind][0])),
label='{0} hr'.format(segment))
for segment in sorted(set(x)):
ind = (x == segment)
ax2.scatter(z[ind], y[ind], c=cm.viridis(norm2(x[ind][0])),
edgecolor='face', cmap='viridis')
ax2.plot(z[ind][np.argsort(z[ind])], y[ind][np.argsort(z[ind])],
c=cm.viridis(norm2(x[ind][0])),
label='n = {0}'.format(segment))
f = plt.gcf()
fig_title = "{0}/{1}".format(i1.upper(), i2.upper())
f.suptitle(fig_title, y=.95, fontsize=30, fontweight='bold')
ax1.set_ylabel('{0} Disk {1} Coefficient'.format(yAxis.title(), coeff))
ax1.set_xlabel('{0}'.format(xAxis))
ax2.set_xlabel('{0}'.format(zAxis))
plt.legend(loc=4)
def fit_a_x0_scale(path):
betas = []
a = []
loc = []
scale = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls, M_ave, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
popt = curve_fit(gamma.pdf, xdata=Ls, ydata=M_ave, p0=[2.5, -5., 10.])[0]
print beta, popt
betas.append(beta)
a.append(popt[0])
loc.append(popt[1])
scale.append(popt[2])
x = np.linspace(0, max(Ls), num=5*max(Ls))
ax.plot(x, gamma.pdf(x, a=popt[0], loc=popt[1], scale=popt[2]),
'-', label=r'fitted $\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
plt.show()
betas = np.array(betas)
a = np.array(a)
loc = np.array(loc)
scale = np.array(scale)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1)
ax1.plot(betas, a, 'o')
[ax.set_xlabel(r'$\beta$') for ax in [ax1, ax2, ax3]]
[ax.set_xlim((0, max(betas))) for ax in [ax1, ax2, ax3]]
ax1.set_ylabel(r'Shape parameter: $a$')
ax2.plot(betas, loc, 'o')
ax2.set_ylabel(r'Translation parameter: $x_{0}$')
# ax3.plot(-betas, -scale) # お試し
ax3.plot(betas, scale, 'o')
ax3.set_ylabel(r'Scale parameter: $\theta$')
plt.show()
def plot_init_means(x, mus, algs, fname):
import matplotlib.cm as cm
fig = plt.figure()
plt.scatter(x[:,0], x[:,1], c='gray', cmap='viridis', s=20, alpha= 0.4, label='data')
for mu, alg, clr in zip(mus, algs, cm.viridis(np.linspace(0, 1, len(mus)))):
plt.scatter(mu[:,0], mu[:, 1], c=clr, s=50, label=alg)
plt.scatter(mu[:, 0], mu[:, 1], c='black', s=10, alpha=1)
legend = plt.legend(loc='upper right', fontsize='small')
plt.title('Initial guesses for centroids')
fig.savefig(fname)
def arr2png(arr, path, size=1080):
# min zero
arr = arr - np.min(arr)
# max one
arr = arr / np.max(arr)
im = Image.fromarray(np.uint8(cm.viridis(arr) * 255))
im = im.resize((size, size))
im.save(path)
return im
def fit_a_scale(path, fixed_loc):
def modified_gamma(x, a, scale):
# loc = c * a + d
loc = fixed_loc
return gamma.pdf(x, a=a, loc=loc, scale=scale)
betas = []
a = []
scale = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls, M_ave, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
popt = curve_fit(modified_gamma, xdata=Ls, ydata=M_ave, p0=[2.5, 10.])[0]
print beta, popt
betas.append(beta)
a.append(popt[0])
scale.append(popt[1])
x = np.linspace(0, max(Ls), num=5*max(Ls))
ax.plot(x, modified_gamma(x, a=popt[0], scale=popt[1]),
'-', label=r'fitted $\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
plt.show()
betas = np.array(betas)
a = np.array(a)
scale = np.array(scale)
fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.set_title(r'Fitting parameter (fixed: $x_{0} = 0$)')
ax1.plot(betas, a, 'o')
[ax.set_xlabel(r'$\beta$') for ax in [ax1, ax2]]
[ax.set_xlim((0, max(betas))) for ax in [ax1, ax2]]
ax1.set_ylabel(r'Shape parameter: $a$')
ax2.plot(betas, scale, 'o')
ax2.set_ylabel(r'Scale parameter: $\theta$')
plt.show()
def sradcmap():
# This function returns the colormap and bins for the PM spatial plots
# this is designed to have a vmin =0 and vmax = 140
# return cmap,bins
colors1 = cm.viridis(linspace(0, 1, 128))
colors2 = cm.plasma(linspace(.2, 1, 128))
colors = vstack((colors1, colors2))
return mcolors.LinearSegmentedColormap.from_list('sradcmap',
colors), arange(
0, 1410., 10)
def pm10cmap():
# This function returns the colormap and bins for the NO2/NO/NOx spatial plots
# this is designed to have a vmin =0 and vmax = 140
# return cmap,bins
colors1 = cm.viridis(linspace(0, 1, 128))
colors2 = cm.plasma_r(linspace(.042, .75, 128))
colors = vstack((colors1, colors2))
return mcolors.LinearSegmentedColormap.from_list('noxcmap',
colors), arange(
0, 150.5, .5)
def _biplot(xidx, yidx, data, pc_columns, columns, singular_values, components,
explained_variance_ratio, alpha=1, ax=None, hue=None, key_col=None):
if ax is None:
ax = plt.gca()
xs = data[pc_columns[xidx]] * singular_values[xidx] ** alpha
ys = data[pc_columns[yidx]] * singular_values[yidx] ** alpha
if key_col is not None and hue is not None:
groups = data[hue].unique()
k = len(data[hue].unique())
colors = cm.viridis(np.arange(k).astype(float) / k)
for j, color in zip(range(k), colors):
group_data = data[data[hue] == groups[j]]
for idx in group_data.index:
ax.text(xs[idx], ys[idx], data[key_col][idx], color=color, va='center', ha='center')
ax.legend([Patch(color=colors[i]) for i, _ in enumerate(groups)], groups.tolist())
elif key_col is not None and hue is None:
for i in range(data.shape[0]):
ax.text(xs[i], ys[i], data[key_col][i], color='black', va='center', ha='center')
elif hue is not None:
sns.scatterplot(xs, ys, hue=data[hue], data=data, ax=ax)
else:
sns.scatterplot(xs, ys, data=data, ax=ax)
ax.set_xlabel('%s (%0.4f)' % (pc_columns[xidx], explained_variance_ratio[xidx]))
ax.set_ylabel('%s (%0.4f)' % (pc_columns[yidx], explained_variance_ratio[yidx]))
axs = components[xidx] * singular_values[xidx] ** (1 - alpha)
ays = components[yidx] * singular_values[yidx] ** (1 - alpha)
xmax = np.amax(np.concatenate((xs, axs * 1.5)))
xmin = np.amin(np.concatenate((xs, axs * 1.5)))
ymax = np.amax(np.concatenate((ys, ays * 1.5)))
ymin = np.amin(np.concatenate((ys, ays * 1.5)))
for i, col in enumerate(columns):
x, y = axs[i], ays[i]
ax.arrow(0, 0, x, y, color='r', width=0.001, head_width=0.05)
ax.text(x * 1.3, y * 1.3, col, color='r', ha='center', va='center')
ys, ye = ax.get_ylim()
xs, xe = ax.get_xlim()
m = 1.2
ax.set_xlim(xmin * m, xmax * m)
ax.set_ylim(ymin * m, ymax * m)
# plt.title('PCA result with two components')
# plt.show()
plt_two = plt2MD(plt)
plt.clf()
return plt_two
def segments(mesh, plot):
vs, _, edges = mesh
if edges_values_map == "sort":
for i, (_, edge_idx) in enumerate(sorted(edges, key=lambda x: x[0])):
edge = vs[edge_idx]
line = a3.art3d.Line3DCollection([edge], linewidths=.8)
line.set_color(cm.viridis((i + 1) / len(edges)))
plot[0].add_collection3d(line)
else:
for (edge_c, edge_idx) in edges:
edge = vs[edge_idx]
line = a3.art3d.Line3DCollection([edge], linewidths=.8)
if edges_values_map == "sqrt":
line.set_color(cm.viridis(np.sqrt(edge_c)))
elif edges_values_map == "log2":
line.set_color(cm.viridis(np.log2(1 + edge_c)))
else:
line.set_color(cm.viridis(edge_c))
plot[0].add_collection3d(line)
return plot
def pass_rose(df_passes, palette=None):
"""Based from https://gist.github.com/phobson/41b41bdd157a2bcf6e14"""
if "pass_angle_deg" in df_passes.columns:
pass
else:
print("Adding Pass Angle Degrees")
df_passes['pass_angle_deg'] = (
df_passes['pass_angle'].apply(pass_angle_deg))
total_count = df_passes.shape[0]
print('{} total observations'.format(total_count))
dir_bins = np.arange(-7.5, 370, 15)
dir_labels = (dir_bins[:-1] + dir_bins[1:]) / 2
rosedata = (df_passes.assign(PassAngle_bins=lambda df: (pd.cut(
df['pass_angle_deg'], bins=dir_bins, labels=dir_labels, right=False))))
rosedata.loc[rosedata["PassAngle_bins"] == 360., "PassAngle_bins"] = 0.
rosedata["PassAngle_bins"].cat.remove_categories([360], inplace=True)
rosedata = (
rosedata.groupby(by=['PassAngle_bins']).agg({"pass_length": "size"})
# .unstack(level='PassAngle_bins')
.fillna(0).sort_index(axis=0)
#.applymap(lambda x: x / total_count * 100)
)
pass_dirs = np.arange(0, 360, 15)
if palette is None:
palette = sns.color_palette('inferno', n_colors=rosedata.shape[1])
bar_dir, bar_width = _convert_dir(pass_dirs)
fig, ax = plt.subplots(figsize=(5, 5), subplot_kw=dict(polar=True))
ax.set_theta_direction('clockwise')
ax.set_theta_zero_location('N')
c1 = "pass_length"
colors = cm.viridis(rosedata[c1].values / float(max(rosedata[c1].values)))
print(len(bar_dir))
print(len(rosedata[c1].values))
# first column only
ax.bar(bar_dir,
rosedata[c1].values,
width=bar_width,
color=colors,
edgecolor='none',
label=c1,
linewidth=0)
leg = ax.legend(loc=(0.75, 0.95), ncol=2)
def combined_line_plot(dictionary, timestep,
xlabel, ylabel, title,
outputname, init_year):
"""Creates a combined line plot of timestep vs dictionary
Parameters
----------
dictionary: dictionary
dictionary with "key=description of timestep, and
value=list of timestep progressions"
timestep: numpy linspace
timestep of simulation
xlabel: str
xlabel of plot
ylabel: str
ylabel of plot
title: str
title of plot
init_year: int
initial year of simulation
Returns
-------
"""
# set different colors for each bar
color_index = 0
plt.figure()
# for every country, create bar chart with different color
for key in dictionary:
# label is the name of the nuclide (converted from ZZAAA0000 format)
if isinstance(key, str) is True:
label = key.replace('_government', '')
else:
label = str(key)
plt.plot(timestep_to_years(init_year, timestep),
dictionary[key],
label=label,
color=cm.viridis(float(color_index) / len(dictionary)))
color_index += 1
if sum(sum(dictionary[k]) for k in dictionary) > 1000:
ax = plt.gca()
ax.get_yaxis().set_major_formatter(
plt.FuncFormatter(lambda x, loc: "{:,}".format(int(x))))
plt.ylabel(ylabel)
plt.title(title)
plt.xlabel(xlabel)
plt.legend(loc=(1.0, 0), prop={'size': 10})
plt.grid(True)
plt.savefig(label + '_' + outputname + '.png',
format='png',
bbox_inches='tight')
plt.close()
def strategy_3d_map(model):
# target_angle_list = np.arange(-3,3.3,0.3)
target_angle_list = np.arange(-2, 2.1, 0.1)
v_list = np.arange(10 / 3.6, 55 / 3.6, 1)
output = np.zeros((len(v_list), len(target_angle_list)))
# print(output)
# print(len(v_list),len(rc_list))
target_angle_mesh, v_mesh = np.meshgrid(target_angle_list, v_list)
# print(target_angle_mesh)
# print(v_mesh[3,8],rc_mesh[3,8])
# print(v_mesh)
for i in range(0, (len(v_list))):
for j in range(0, (len(target_angle_list))):
# print(i,j)
output[i][j] = model.choose_action(
np.array([target_angle_mesh[i, j], v_mesh[i, j]]))
# print(i,j,output[i][j])
# print(output)
from matplotlib import cm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
# plt.ion()
norm = plt.Normalize(output.min(), output.max())
colors = cm.viridis(norm(output))
rcount, ccount, _ = colors.shape
fig3 = plt.figure(3)
fig3.canvas.manager.window.wm_geometry('+3000+500')
plt.cla()
ax = fig3.gca(projection='3d')
# surf = ax.plot_surface(v_mesh, target_angle_mesh*180/np.pi, output*180/np.pi, rcount=rcount, ccount=ccount, facecolors=colors, shade=False)
surf = ax.plot_surface(target_angle_mesh * 180 / np.pi,
v_mesh * 3.6,
output * 180 / np.pi,
rcount=rcount,
ccount=ccount,
facecolors=colors,
shade=False)
surf.set_facecolor((0, 0, 0, 0))
# ax.view_init(30, -90)
# ax.view_init(30, -60)
# ax.view_init(30, -70)
ax.view_init(30, -110)
zplate = ax.plot_surface(target_angle_mesh * 180 / np.pi,
v_mesh * 3.6,
np.zeros((len(v_list), len(target_angle_list))),
alpha=0.5)
ax.plot(np.zeros_like(v_list), v_list * 3.6, 0, color='k', linewidth=2)
ax.set_xlabel("target_angle(degree)")
ax.set_ylabel("vehicle_speed(km/h)")
ax.set_zlabel("steering_angle_compensation(degree)")
def plot_rw_rccar_var001_var016():
label_params = [
# ['exp', ('exp_name',)],
['policy', ('policy', 'GCGPolicy', 'outputs', 0, 'name')],
['H', ('policy', 'H')],
['target', ('policy', 'use_target')],
['obs_shape', ('alg', 'env', 'params', 'obs_shape')]
]
experiment_groups = [
ExperimentGroup(os.path.join(DATA_DIR, 'rw_rccar/var{0:03d}'.format(num)),
label_params=label_params,
plot={
})
for num in [1, 5, 9, 12, 13]]
mec = MultiExperimentComparison(experiment_groups)
lengths_list = []
for exp in mec.list:
eval_folder = os.path.join(exp.folder, 'eval_itr_0039')
eval_pkl_fname = os.path.join(eval_folder, 'itr_0039_eval_rollouts.pkl')
rollouts = mypickle.load(eval_pkl_fname)['rollouts']
assert (len(rollouts) == 24)
lengths = [len(r['dones']) for r in rollouts]
lengths_list.append(lengths)
f, ax = plt.subplots(1, 1)
xs = np.vstack((np.r_[0:8.] + 0.,
np.r_[0:8.] + 0.1,
np.r_[0:8.] + 0.2,)).T.ravel()
legend_patches = []
for i, (exp, lengths) in enumerate(zip(mec.list, lengths_list)):
lengths = np.reshape(lengths, (8, 3))
width = 0.6 / float(len(lengths_list))
color = cm.viridis(i / float(len(lengths_list)))
label = 'median: {0}, {1}'.format(np.median(lengths), exp.plot['label'])
bp = ax.boxplot(lengths.T, positions=np.arange(len(lengths)) + 1.2 * i * width, widths=width, patch_artist=True)
for patch in bp['boxes']:
patch.set_facecolor(color)
legend_patches.append(mpatches.Patch(color=color, label=label))
# ax.plot(xs, lengths, label=exp.plot['label'], linestyle='None', marker='o')
ax.legend(handles=legend_patches)
ax.xaxis.set_ticks(np.arange(8))
ax.xaxis.set_ticklabels(np.arange(8))
ax.set_xlim((-0.5, 8.5))
ax.set_xlabel('Start Position Number')
ax.set_ylabel('Timesteps survived')
plt.show()
import IPython; IPython.embed()
def no_fit(path, fixed_a, fixed_loc, _a, _b, save_image=False):
matplotlib.rcParams['savefig.dpi'] = 300
def modified_gamma_3(x, beta):
a = fixed_a
loc = fixed_loc
# scale = _a * beta + _b
scale = _a * np.log(beta) + _b
return gamma.pdf(x, a=a, loc=loc, scale=scale)
betas = []
scale = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls, M_ave, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
betas.append(beta)
x = np.linspace(0, max(Ls), num=5*max(Ls))
ax.plot(x, modified_gamma_3(x, beta),
'-',
# label=r'fitted $\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
if save_image:
result_image_path = "../results/img/diecutting/fitted_gamma_fixed_a_x0"
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
pdf = PdfPages(result_image_path + ".pdf")
plt.savefig(result_image_path + ".png")
pdf.savefig()
pdf.close()
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
plt.close()
def plotRegression(simple, turner, slope, intercept, r_value):
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import matplotlib.cm as cm
# make histogram plot
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (7, 5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
pylab.rcParams.update(params)
#fig, axes = plt.subplots(3, 1, sharex=False, sharey=False)
fig = plt.figure()
plt.xlabel("Energy of Structure in Simple Model [kcal/mol]")
plt.ylabel("Energy of Structure in Turner Model [kcal/mol]")
fig.legend().set_visible(False)
color_i = 0.0
for t in slope.keys():
plt.plot(simple[:, t],
turner[:, t],
'.',
color=cm.viridis(color_i),
label=str(t),
alpha=0.5)
plt.plot(simple[:, t],
slope[t] * simple[:, t] + intercept[t],
'-',
color=cm.viridis(color_i))
fig.text(0.16,
0.81 - color_i / 7,
"$R^{2}$ = " + "{0:.3f}".format(r_value[t]),
color=cm.viridis(color_i))
# increment color
color_i += 0.3
#plt.legend(loc='upper left')
fig.savefig('regression.svg', dpi=300)
def prepare_spec_for_render(spec, score, scale_factor=5):
spec_excerpt = cv2.resize(
np.flipud(spec),
(spec.shape[1] * scale_factor, spec.shape[0] * scale_factor))
perf_img = np.pad(cm.viridis(spec_excerpt)[:, :, :3],
((score.shape[0] // 2 - spec_excerpt.shape[0] // 2 + 1,
score.shape[0] // 2 - spec_excerpt.shape[0] // 2),
(20, 20), (0, 0)),
mode="constant")
return perf_img
def husimi_3d(state,
xrange,
yrange,
N=100,
fname='fig_husimi_3d.eps',
cmap='viridis',
alpha=1.0):
"""
to visualize a 3d Husimi function
Parameters:
------------
state: quantum object
A given quantum state needed to visualize
xrange, yrange: array-like(2)
The minimum and maximum values of the coordinates
N: integer
number of steps for xrange, yrange
fname: string
File name
cmap: str or Colormap
A colormap instance or colormap name (default: 'viridis')
Returns:
A file with fname
"""
xarray = np.linspace(xrange[0], xrange[1], N)
yarray = np.linspace(yrange[0], yrange[1], N)
zarray = husimi(state, xarray, yarray)
zarray /= amax(zarray)
xx, yy = meshgrid(xarray, yarray)
norm = plt.Normalize(zarray.min(), zarray.max())
colors = cm.viridis(norm(zarray))
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(xx,
yy,
zarray,
cmap=cmap,
rstride=1,
cstride=1,
linewidth=0,
facecolors=colors)
plt.xlabel("x")
plt.ylabel("y")
_printout(fname)
plt.savefig(fname, dpi=25)
def plot_test_different_smoothing(args):
OUTPUT = dict(np.load(args.datafile_input))
fig_optimum, [[ax, ax1]] = figure(figsize=(.5, .16),
right=0.85,
top=0.9,
bottom=1.2,
left=.6,
wspace=1.8,
axes=(1, 2))
i0 = np.argmax(np.mean(OUTPUT['CROSS_CORRELS'], axis=-1))
mean_Output = np.mean(OUTPUT['CROSS_CORRELS'], axis=-1)
Tsmooth = 1e3 * OUTPUT['T_SMOOTH']
ax.plot(Tsmooth, mean_Output, color='k', lw=2)
ax.scatter([1e3 * OUTPUT['T_SMOOTH'][i0]],
[np.mean(OUTPUT['CROSS_CORRELS'], axis=-1)[i0]],
marker='o',
color=Brown,
facecolor='None')
ax.annotate('$T_{opt}$', (Tsmooth[i0] + 4, ax.get_ylim()[0]),
color=Brown,
fontsize=FONTSIZE)
ax.plot(np.array([Tsmooth[i0], Tsmooth[i0]]),
[mean_Output[i0], ax.get_ylim()[0]],
'--',
color=Brown,
lw=1)
order = np.argsort(np.mean(OUTPUT['CROSS_CORRELS'], axis=0))
for i in range(len(order)):
ax1.plot(Tsmooth,
OUTPUT['CROSS_CORRELS'][:, order[i]],
color=viridis(i / (len(order) - 1)))
ax1.plot(Tsmooth, mean_Output, '-', color='k', lw=0.5)
ax1.fill_between(Tsmooth,\
mean_Output+np.std(OUTPUT['CROSS_CORRELS'], axis=-1),
mean_Output-np.std(OUTPUT['CROSS_CORRELS'], axis=-1),
lw=0, color='k', alpha=.2)
set_plot(ax, xlabel=' $T_{smoothing}$ (ms)', ylabel='cc $V_m$-pLFP')
set_plot(ax1, xlabel=' $T_{smoothing}$ (ms)', ylabel='cc $V_m$-pLFP')
acb = plt.axes([.86, .4, .02, .4])
cb = build_bar_legend(np.arange(len(order)),
acb,
viridis,
no_ticks=True,
label='cell index \n (n=' + str(len(order)) +
'cells)')
return [fig_optimum]
def gaussian_plot(gmm, X, labels, true_labels=True, fig_num=0, title=''):
plt.figure(fig_num)
plt.ylabel('p(X)')
plt.xlabel('VOT')
plt.title(title)
means = gmm.means_.flatten()
stdevs = [np.sqrt(x) for x in gmm.covariances_.flatten()]
weights = gmm.weights_.flatten()
x = np.arange(min(X), max(X), 5) #range between data min and max by 5
pdfs = [
p * ss.norm.pdf(x, mu, sd)
for mu, sd, p in zip(means, stdevs, weights)
]
density = np.sum(np.array(pdfs), axis=0)
#get colors
start = 0.0
stop = 1.0
num_lines = len(gmm.means_) #num_classes.
#note: for dpgmm only predicted gaussians will be plot, but gmm.means_ etc is len(max num allowed gaussians)
print(np.unique(
labels)) #(debug) prints idxs of final gaussians relative to init
print('num classes: %d' % len(np.unique(labels)))
cm_subsection = np.linspace(start, stop, num_lines)
colors = [cm.viridis(x) for x in cm_subsection]
#if true_labels map gaussian order to label order
if true_labels:
mean_order = np.argsort(means)
sorted_colors = [colors[i] for i in mean_order]
color_labels = [sorted_colors[i] for i in labels]
else:
color_labels = [colors[i] for i in labels]
#plot gmm
plt.plot(x, density, 'k--')
plt.scatter(X,
len(X) * [0],
c=color_labels,
s=40,
cmap='viridis',
alpha=0.1)
#plot individual gaussians
for i, (mu, sd, p) in enumerate(zip(means, stdevs, weights)):
if not any(x == i for x in labels):
continue
plt.plot(x, ss.norm.pdf(x, mu, sd), color=colors[i])
return
def eval_model(global_step, writer, device, model, checkpoint_dir, ismultispeaker):
# harded coded
texts = [
"Scientists at the CERN laboratory say they have discovered a new particle.",
"There's a way to measure the acute emotional intelligence that has never gone out of style.",
"President Trump met with other leaders at the Group of 20 conference.",
"Generative adversarial network or variational auto-encoder.",
"Please call Stella.",
"Some have accepted this as a miracle without any physical explanation.",
]
import synthesis
synthesis._frontend = _frontend
eval_output_dir = join(checkpoint_dir, "eval")
os.makedirs(eval_output_dir, exist_ok=True)
# Prepare model for evaluation
model_eval = build_model().to(device)
model_eval.load_state_dict(model.state_dict())
# hard coded
speaker_ids = [0, 1, 10] if ismultispeaker else [None]
for speaker_id in speaker_ids:
speaker_str = "multispeaker{}".format(speaker_id) if speaker_id is not None else "single"
for idx, text in enumerate(texts):
signal, alignment, _, mel = synthesis.tts(
model_eval, text, p=0, speaker_id=speaker_id, fast=True)
signal /= np.max(np.abs(signal))
# Alignment
path = join(eval_output_dir, "step{:09d}_text{}_{}_alignment.png".format(
global_step, idx, speaker_str))
save_alignment(path, alignment)
tag = "eval_averaged_alignment_{}_{}".format(idx, speaker_str)
writer.add_image(tag, np.uint8(cm.viridis(np.flip(alignment, 1).T) * 255), global_step)
# Mel
writer.add_image("(Eval) Predicted mel spectrogram text{}_{}".format(idx, speaker_str),
prepare_spec_image(mel), global_step)
# Audio
path = join(eval_output_dir, "step{:09d}_text{}_{}_predicted.wav".format(
global_step, idx, speaker_str))
audio.save_wav(signal, path)
try:
writer.add_audio("(Eval) Predicted audio signal {}_{}".format(idx, speaker_str),
signal, global_step, sample_rate=fs)
except Exception as e:
warn(str(e))
pass
def lda_analysis(self, X_train, X_test, y_train, y_test, data_set_name):
scl = RobustScaler()
X_train_scl = scl.fit_transform(X_train)
X_test_scl = scl.transform(X_test)
##
## Plots
##
ph = plot_helper()
scores = []
train_scores = []
rng = range(1, X_train_scl.shape[1]+1)
for i in rng:
lda = LinearDiscriminantAnalysis(n_components=i)
cv = KFold(X_train_scl.shape[0], 3, shuffle=True)
# cross validation
cv_scores = []
for (train, test) in cv:
lda.fit(X_train_scl[train], y_train[train])
score = lda.score(X_train_scl[test], y_train[test])
cv_scores.append(score)
mean_score = np.mean(cv_scores)
scores.append(mean_score)
# train score
lda = LinearDiscriminantAnalysis(n_components=i)
lda.fit(X_train_scl, y_train)
train_score = lda.score(X_train_scl, y_train)
train_scores.append(train_score)
print(i, mean_score)
##
## Score Plot
##
title = 'Score Summary Plot (LDA) for ' + data_set_name
name = data_set_name.lower() + '_lda_score'
filename = './' + self.out_dir + '/' + name + '.png'
ph.plot_series(rng,
[scores, train_scores],
[None, None],
['cross validation score', 'training score'],
cm.viridis(np.linspace(0, 1, 2)),
['o', '*'],
title,
'n_components',
'Score',
filename)
def viz_duo(x_sample, y_sample, name="test.png", show=True, alpha=0.2):
x_sample = resize(x_sample,
(12, 12, 12)) # resize, otherwise it's super slow
y_sample = resize(y_sample,
(12, 12, 12)) # resize, otherwise it's super slow
fig = plt.figure()
ax = fig.add_subplot(121, projection="3d")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
ax.set_title("Real")
colours = cm.viridis(x_sample)
colours = explode(colours)
filled = colours[:, :, :, -1] != 0
x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))
ax.voxels(x, y, z, filled, facecolors=colours, alpha=alpha)
ax = fig.add_subplot(122, projection="3d")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
ax.set_title("Predicted")
colours = cm.viridis(y_sample)
colours = explode(colours)
filled = colours[:, :, :, -1] != 0
x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))
ax.voxels(x, y, z, filled, facecolors=colours, alpha=alpha)
if show:
plt.show()
plt.close()
def result_n2(path):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls[1:], n2, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
ax.legend(loc='best')
ax.set_title('Averaged number of the sites on the cutting edges which \
is connected to two neighbors.' +
' (sample: {})'.format(num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$n_{2}$')
plt.show()
def result_N_minus_rate(self):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(self.data_path_list):
self.load_data(result_data_path)
ax.plot(self.Ls[1:], self.N_minus_rate[1:], '.',
label=r'$\beta = %2.2f$' % self.beta,
color=cm.viridis(float(i) / len(self.data_path_list)))
ax.legend(loc='best')
ax.set_title('The rate of not occupied site in all N' +
' (sample: {})'.format(self.num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$N_{-1} / N_{\mathrm{all}}$')
plt.show()
def task_c():
n = 5
Betas = 10.**(-1 * np.linspace(1, 4, n))
for i, b in enumerate(Betas):
Ngs, Ns = loop(b, d * log(2 / b))
plt.plot(Ns * (1 / sqrt(b))**3,
Ngs / Ns,
color=cm.viridis(i / n),
label="$\\beta={:.3f}$".format(b))
plt.legend()
plt.show()
def save_gradcam(gcam, original_image, axarr, i):
cmap = cm.viridis(np.squeeze(gcam.numpy()))[..., :3] * 255.0
raw_image = (
(
(original_image - original_image.min())
/ (original_image.max() - original_image.min())
)
* 255
).astype("uint8")
gcam = (cmap.astype(float) + raw_image.astype(float)) / 2
axarr[1].imshow(np.uint8(gcam))
axarr[1].axis("off")
plt.savefig("CNN_viz1_{}.png".format(i), dpi=200, bbox_inches="tight")
def export_response(self):
# resp = np.mean(self.norm_stack[..., self.n_baseline:(self.n_baseline+30)], 2)
if 'resp_map' not in self.file['df']:
self.resp_mapping()
resp = self.file['df']['resp_map'][()]
# resp = np.clip(resp, 0, 2/100)
# resp = exposure.equalize_hist(resp)
resp = viridis(gauss_filt(resp, 3))[..., :3]
resp[self.max_project <= 0, :] = 0
# resp = 255 * (resp - resp.min()) / (resp.max() - resp.min())
resp = img_to_uint8(resp)
# resp = np.uint8(resp)
imsave(self.path.parent / f'response_{self.path.name}.png', resp)
def f():
color = cm.viridis(0.5)
f, ax = plt.subplots(1, 1)
ax.plot(iter, totalreward, color=color)
ax.legend()
ax.set_xlabel('Iteration')
ax.set_ylabel('Return')
exp_dir = 'Plot/'
if not os.path.exists(exp_dir):
os.makedirs(exp_dir, exist_ok=True)
else:
os.makedirs(exp_dir, exist_ok=True)
f.savefig(os.path.join('Plot', 'reward' + '.png'), dpi=1000)
def plot_cube(cube, name='test.png'):
cube = normalize_plot(cube)
facecolors = cm.viridis(cube)
facecolors[:, :, :, -1] = 0.1 * cube
facecolors = explode(facecolors)
filled = facecolors[:, :, :, -1] != 0
x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.voxels(x, y, z, filled, facecolors=facecolors, alpha=0.5)
plt.savefig(name)
def scatter(x1, x2, Y):
if Y is None:
Y = np.ones(x1.shape[0])
uY = np.unique(Y)
colors = cm.viridis(np.linspace(0, 1, len(uY)))
for i, y in enumerate(uY):
inds = Y == y
plt.scatter(x1[inds], x2[inds], s=10, color=colors[i], label=y)
def make_frame(time):
i = int(time*fps)
autoim =autocorrImages[i]
norm = mplcol.Normalize(vmin=minA,vmax=maxA)
#ts = img.metadata['t_s']
ts = i/24.
frame = i
autoim= Image.fromarray(img_as_ubyte(cm.viridis(norm(autoim)))) #get rid of alpha channel, it confuses moviepy, and multiply by 255 as that is how moviepy likes its colors for some reason
draw= ImageDraw.Draw(autoim)
draw.text((0,0),"time: "+str(datetime.timedelta(seconds=float(ts))),font=font,fill=(255,255,255,255))
draw.text((0,400),"frame: "+str(frame),font=font,fill=(255,255,255,255))
return np.asarray(autoim)[:,:,:3]
def result_N(self):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(self.data_path_list):
self.load_data(result_data_path)
ax.plot(self.Ls[1:], self.N[1:], '.',
label=r'$\beta = %2.2f$' % self.beta,
color=cm.viridis(float(i) / len(self.data_path_list)))
ax.legend(loc='best')
ax.set_title('Occupied points in the cutting region' +
' (sample: {})'.format(self.num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$N$')
plt.show()
def plot_kde():
# Load files
print('Loading DIALS files')
expt_file = "dials_temp_files/mega_ultra_refined.expt"
refl_file = "dials_temp_files/mega_ultra_refined.refl"
elist = ExperimentListFactory.from_json_file(expt_file, check_format=False)
refls = reflection_table.from_file(refl_file)
# Remove outliers
print('Removing outliers')
idx = refls.get_flags(refls.flags.used_in_refinement).as_numpy_array()
idy = np.arange(len(elist))[idx].tolist()
elist = ExperimentList([elist[i] for i in idy])
refls = refls.select(flex.bool(idx))
# Generate KDE
normalized_resolution, lams, kde = gen_kde(elist, refls)
# Make a mesh
print('Making a meshgrid')
N = 50
x = np.linspace(min(normalized_resolution), max(normalized_resolution), N)
y = np.linspace(min(lams), max(lams), N)
z = np.zeros(shape=(len(x), len(y)))
zeros = np.zeros(len(x))
# Evaluate PDF on mesh
for x0, y0 in it.product(x, y):
i = np.where(x == x0)[0][0]
j = np.where(y == y0)[0][0]
z[i, j] = kde.pdf([x0, y0])
# Plot a wireframe
print('Plotting')
norm = plt.Normalize(z.min(), z.max())
colors = cm.viridis(norm(z))
rcount, ccount, _ = colors.shape
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
surface = ax.plot_surface(x[:, None] + zeros,
y[None, :] + zeros,
z,
rcount=rcount,
ccount=ccount,
facecolors=colors,
shade=False)
surface.set_facecolor((0, 0, 0, 0))
plt.xlabel('$1/d^2$ (A$^{-2}$)')
plt.ylabel('$\lambda$ (A)')
plt.title('PDF of Reflections')
plt.show()
def show_gen_results(self,title='Generation evolution'):
import matplotlib.cm as cm
final_arc = self.ea_archive
gens=[]
div = 100
lengen = len(self.storegens)
num = int(lengen / div)
# print num
# print lengen
i = 0
# print self.storegens
while i < len(self.storegens):
gens.append(self.storegens[i])
i = i + num
x = []
y = []
for f in final_arc:
x.append(f.fitness[0])
y.append(f.fitness[1])
x = -np.array(x)
y = np.array(y)
# fig = plt.figure()
# ax = plt.axes()
# ax = fig.add_subplot(111)
fig,ax = plt.subplots()
ax.grid()
ax.set_axisbelow(True)
ax.yaxis.grid(color='gray', linestyle='dashed')
ax.xaxis.grid(color='gray', linestyle='dashed')
cols = cm.viridis(np.linspace(0,1,div+1))
alpha = .2
for i in range(0,len(gens)):
# print i
xi = -np.array(gens[i])[:,0]
yi = np.array(gens[i])[:,1]
sc=ax.scatter(xi,yi,marker='o',c=cols[i],s=7,alpha=0.5)
# ax.scatter(xi,yi,marker='*')
alpha = alpha + 0.8/(lengen/num)
# print alpha
ax.scatter(x, y, c=cols[len(gens)-1],marker='o',s=8)
cax, _ = matplotlib.colorbar.make_axes(ax)
cmap = matplotlib.cm.get_cmap('viridis')
normalize = matplotlib.colors.Normalize(vmin=0,vmax=self.generations)
cbar = matplotlib.colorbar.ColorbarBase(cax,cmap=cmap,norm=normalize)
cbar.set_label('Generation',rotation=270,fontsize=12)
ax.set_title(title,fontsize=12)
ax.set_xlabel('L/D',fontsize=12)
ax.set_ylabel('Mass (kg)',fontsize=12)
plt.show()
def result_S(self):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(self.data_path_list):
self.load_data(result_data_path)
ax.plot(self.Ls[1:], self.S[1:] / np.sum(self.S[1:]), '.',
label=r'$\beta = %2.2f$' % self.beta,
color=cm.viridis(float(i) / len(self.data_path_list)))
ax.legend(loc='best')
ax.set_ylim([0, ax.get_ylim()[1]])
ax.set_title('Averaged number of the subclusters in the cutted region.'
+ ' (sample: {})'.format(self.num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$S$')
plt.show()
def result_n_minus(self):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(self.data_path_list):
self.load_data(result_data_path)
ax.plot(self.Ls[1:], self.n_minus, '.',
label=r'$\beta = %2.2f$' % self.beta,
color=cm.viridis(float(i) / len(self.data_path_list)))
ax.legend(loc='best')
ax.set_title('Averaged number of the sites which is not occupied on \
the cutting edges.' +
' (sample: {})'.format(self.num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$n_{-1}$')
plt.show()
def task_b():
n = 3
Betas = 10.**(-1 * np.linspace(1, 5, n))
for i, b in enumerate(Betas):
Ngs, Ns = loop(b, d)
plt.plot(Ns,
Ngs / Ns,
color=cm.viridis(i / n),
label="$\\beta={:.5f}$".format(b))
plt.legend()
plt.show()
def power_density_3ds(srs, surface, ax,
normal=1, rotations=[0, 0, 0], translation=[0, 0, 0], nparticles=0, gpu=0, nthreads=0,
alpha=0.4, transparent=True, max_level=-2):
"""calculate power density for and plot a parametric surface in 3d"""
points = []
for u in np.linspace(surface.ustart, surface.ustop, surface.nu):
for v in np.linspace(surface.vstart, surface.vstop, surface.nv):
points.append([surface.position(u, v), surface.normal(u, v)])
power_density = srs.calculate_power_density(points=points, normal=normal, rotations=rotations, translation=translation, nparticles=nparticles, gpu=gpu, nthreads=nthreads, max_level=max_level)
P = [item[1] for item in power_density]
X2 = []
Y2 = []
Z2 = []
for i in range(surface.nu):
tX = []
tY = []
tZ = []
for j in range(surface.nv):
tX.append(power_density[i * surface.nv + j][0][0])
tY.append(power_density[i * surface.nv + j][0][1])
tZ.append(power_density[i * surface.nv + j][0][2])
X2.append(tX)
Y2.append(tY)
Z2.append(tZ)
colors =[]
MAXP = max(P)
PP = []
for i in range(surface.nu):
tmpP = []
tmpPP = []
for j in range(surface.nv):
tmpP.append(P[i * surface.nv + j] / MAXP)
tmpPP.append(P[i * surface.nv + j])
colors.append(tmpP)
PP.append(tmpPP)
#ax.invert_xaxis()
return ax.plot_surface(X2, Z2, Y2, facecolors=cm.viridis(colors), rstride=1, cstride=1, alpha=alpha)
def plot_tortuosity():
result_data_paths = get_paths()
fig = plt.figure()
ax = fig.add_subplot(111)
betas = [0, 2, 4, 6, 8, 10]
for beta, result_data_path in zip(betas, result_data_paths):
x, y = calc_tortuosity_for_each_beta(result_data_path)
ax.plot(x, y, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(beta) / (2 * (len(betas) - 1))))
ax.set_xlabel(r'Path length $L$')
ax.set_ylabel(r'Tortuosity $T$')
ax.set_ylim(0, ax.get_ylim()[1])
ax.set_title(r'Tortuosity $T = L / \lambda_{avg}$')
ax.legend(loc='best')
plt.show()
def plot_simple_bar(self, x, values, labels, xlab, ylab, title, filename):
plt.clf()
plt.cla()
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(ticker.MaxNLocator(integer=True))
ax = plt.subplot()
ax.bar(np.arange(1, len(values)+1), values, align='center', color=cm.viridis(np.linspace(0, 1, len(values))))
ax.set_xticks(x)
ax.set_xticklabels(labels)
plt.grid()
plt.title(title)
plt.xlabel(xlab)
plt.ylabel(ylab)
plt.savefig(filename)
def result_n0(path):
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
# ax.plot(Ls, S, '.', label=r'$\beta = %2.2f$' % beta,
# color=cm.viridis(float(i) / len(path)))
# ax.plot(Ls, N0, '.', label=r'$\beta = %2.2f$' % beta,
# color=cm.viridis(float(i) / len(path)))
ax.plot(Ls, N1 / (6. * Ls) , '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
ax.legend(loc='best')
ax.set_ylim((0., 20.))
ax.set_title('Strings in hexagonal region' +
' (sample: {})'.format(num_of_strings))
ax.set_xlabel(r'Cutting size $L$')
ax.set_ylabel(r'$n_{0}$')
plt.show()
def regression_sample(true_func=np.sin, x_scale=3.):
"""
regression problem for continuous targets
:param float x_scale: データのスケール. [-x_scale, x_scale] の範囲のデータを生成する.
:return:
"""
x, t = generate_continuous_data(true_function=true_func, x_scale=x_scale)
trained_models = []
iteration_dist = [5, 10, 20, 40, 100]
for n_iter in iteration_dist:
# GradientBoostedDTの定義
# 連続変数に対しての回帰問題なので
# 目的関数：二乗ロス（LeastSquare)
# 活性化関数：恒等写像（f(x)=x)
# 今の当てはまりがどの程度なのか評価するロス関数に二乗ロス関数を与える
rmse_objective = gb.LeastSquare()
loss_function = gb.functions.least_square
clf = gb.GradientBoostedDT(
objective=rmse_objective, loss=loss_function,
max_depth=4, num_iter=n_iter, gamma=.01, lam=.1, eta=.1)
clf.fit(x=x, t=t)
trained_models.append(clf)
x_test = np.linspace(-x_scale, x_scale, 100).reshape(100, 1)
fig = plt.figure(figsize=(6, 6))
ax_i = fig.add_subplot(1, 1, 1)
ax_i.plot(x_test, true_func(x_test), "--", label='True Function', color="C0")
ax_i.scatter(x, t, s=50, label='Training Data', linewidth=1., edgecolors="C0", color="white")
ax_i.set_xlabel("Input")
ax_i.set_ylabel("Target")
for i, (n_iter, model) in enumerate(zip(iteration_dist, trained_models)):
y = model.predict(x_test)
ax_i.plot(x_test, y, "-", label='n_iter: {}'.format(n_iter), color=cm.viridis(i / len(iteration_dist), 1))
ax_i.legend(loc=4)
ax_i.set_title("Transition by Number of Iterations")
fig.savefig('experiment_figures/regression.png')
# ]
Ds = np.array(Ds)
return Ds
if __name__ == '__main__':
frames_list = [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]
## 0 1 2 3 4 5 6 7 8 9
beta_list = [0, 2, 4, 6, 8, 10]
## 0 1 2 3 4 5
Ds = read_from_csv('./results/img/mass_in_r/data_170122.csv').T
# Ds = manual_data()
markers = ['o', 'v', '^', 's', 'D', 'h']
fig, ax = plt.subplots()
for i, beta in enumerate(beta_list):
color = cm.viridis(float(i) / (len(beta_list) - 1))
ax.plot(frames_list, Ds[i], marker=markers[i % len(markers)],
ls='', color=color, label=r'$\beta = %2.2f$' % beta)
# ax.legend(loc='best')
ax.legend(bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=0, numpoints=1)
fig.subplots_adjust(right=0.8)
ax.set_title(r'Fractal dimension $D$')
ax.set_xlabel(r'$T$')
ax.set_ylabel(r'$D$')
ax.set_xlim(0, 2200)
ax.set_ylim(1., 2.)
plt.show()
def plotTransitPerCapita(station, trip, lmstats):
lm_stations = station.groupby(['landmark', 'station_id'])['station_id'].count()
lm_stations = { lm: list(lm_stations[lm].index.values) for lm in station['landmark'].values}
# Prep all of the fields we want
term_st_counts = trip.groupby(['Start Terminal'])['Start Terminal'].count()
term_st_counts.name = 'Start Terminal Count'
term_sub_perc_counts = trip.groupby(['Start Terminal', 'Subscription Type'])['Subscription Type'].count()
term_sub_perc_counts = term_sub_perc_counts.unstack('Subscription Type')
term_sub_perc_counts = term_sub_perc_counts['Subscriber']
term_sub_perc_counts.name = 'Subscriber Count'
term_dur_cum = trip.groupby(['Start Terminal'])['Duration'].sum()
term_dur_cum.name = 'Cum Duration'
term_lm = pd.Series([ station[station['station_id'] == i]['landmark'].values[0] for i in term_st_counts.index.values ], index=term_st_counts.index.values)
term_lm.name = 'landmark'
# Group by landmark and clean up data
term_stats = pd.concat([term_lm, term_st_counts, term_sub_perc_counts, term_dur_cum], axis=1)
term_stats = term_stats.groupby('landmark')['Start Terminal Count', 'Subscriber Count', 'Cum Duration'].sum()
term_stats['Subscriber Fraction'] = term_stats['Subscriber Count'] / term_stats['Start Terminal Count']
term_stats['Avg Duration'] = term_stats['Cum Duration'] / term_stats['Start Terminal Count']
# get some of that altitude data in here!
lmelev = station.groupby(['landmark'])['elev'].agg({'Elevation Mean': np.mean, 'Elevation Std': np.std})
# Lump in our population and income stats from wikipedia
term_stats = pd.concat([term_stats, lmstats, lmelev], axis=1);
term_stats['household_median_income'] = term_stats['household_median_income'] / 1000
term_stats['per_cap_income'] = term_stats['per_cap_income'] / 1000
term_stats['Avg Duration'] = term_stats['Avg Duration'] / 60
print(term_stats)
fig = plt.figure()
fig.suptitle('Trends Across BABS Locations')
lm_colors = [cm.viridis(x) for x in np.arange(0,5)/4]
ax = fig.add_subplot(221)
ind = np.arange(0,5)
bar_width = 0.6
bars = ax.bar(ind+bar_width*0.5, term_stats['Start Terminal Count']/term_stats['population'], width=bar_width, color=lm_colors)
ax.set_xlim(0, 5)
ax.set_ylabel('Trips Per Capita')
ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useOffset=False))
ax.tick_params(axis='x', which='both', bottom='off', top='off', right='off', left='on', labelbottom='off', labelleft='on', labelright='off')
ax = fig.add_subplot(223)
ax.yaxis.grid(b=True, which='major', color='lightgray', linewidth=1.0, linestyle='-')
ax.scatter(term_stats['per_cap_income'], term_stats['Avg Duration'], c=lm_colors, s=200)
ax.set_xlabel('Per Capita Income ($1000s)')
ax.set_ylabel('Avg Trip Duration (min)')
ax.set_axisbelow(True)
ax = fig.add_subplot(222)
ax.yaxis.grid(b=True, which='major', color='lightgray', linewidth=1.0, linestyle='-')
ax.scatter(term_stats['household_median_income'], term_stats['Subscriber Fraction'], c=lm_colors, s=200)
ax.set_xlabel('Household Median Income ($1000s)')
ax.set_ylabel('Subscription Fraction')
ax.set_axisbelow(True)
ax = fig.add_subplot(224)
ind = np.arange(0,5)
bar_width = 0.6
bars = ax.bar(ind+bar_width*0.5, term_stats['Elevation Std'], width=bar_width, color=lm_colors)
ax.set_xlim(0, 5)
ax.set_ylabel('Station Elevation Std. (m)')
ax.xaxis.set_major_formatter(ticker.ScalarFormatter(useOffset=False))
ax.tick_params(axis='x', which='both', bottom='off', top='off', right='off', left='on', labelbottom='off', labelleft='on', labelright='off')
fig.legend([mpatches.Patch(color=i) for i in lm_colors],term_stats.index.values, 'upper right')
fig.set_size_inches(12, 12)
fig.savefig(os.path.join(os.path.dirname(__file__),'plots','BikeShareTrends.png'), transparent=True)
plt.show()
def plot_wing(self):
n_names = len(self.names)
self.ax.cla()
az = self.ax.azim
el = self.ax.elev
dist = self.ax.dist
for j, name in enumerate(self.names):
mesh0 = self.mesh[self.curr_pos*n_names+j].copy()
self.ax.set_axis_off()
if self.show_wing:
def_mesh0 = self.def_mesh[self.curr_pos*n_names+j]
x = mesh0[:, :, 0]
y = mesh0[:, :, 1]
z = mesh0[:, :, 2]
try: # show deformed mesh option may not be available
if self.show_def_mesh.get():
x_def = def_mesh0[:, :, 0]
y_def = def_mesh0[:, :, 1]
z_def = def_mesh0[:, :, 2]
self.c2.grid(row=0, column=3, padx=5, sticky=Tk.W)
if self.ex_def.get():
z_def = (z_def - z) * 10 + z_def
def_mesh0 = (def_mesh0 - mesh0) * 30 + def_mesh0
else:
def_mesh0 = (def_mesh0 - mesh0) * 2 + def_mesh0
self.ax.plot_wireframe(x_def, y_def, z_def, rstride=1, cstride=1, color='k')
self.ax.plot_wireframe(x, y, z, rstride=1, cstride=1, color='k', alpha=.3)
else:
self.ax.plot_wireframe(x, y, z, rstride=1, cstride=1, color='k')
self.c2.grid_forget()
except:
self.ax.plot_wireframe(x, y, z, rstride=1, cstride=1, color='k')
cg = self.cg[self.curr_pos]
# self.ax.scatter(cg[0], cg[1], cg[2], s=100, color='r')
if self.show_tube:
# Get the array of radii and thickness values for the FEM system
r0 = self.radius[self.curr_pos*n_names+j]
t0 = self.thickness[self.curr_pos*n_names+j]
# Create a normalized array of values for the colormap
colors = t0
colors = colors / np.max(colors)
# Set the number of rectangular patches on the cylinder
num_circ = 12
fem_origin = self.fem_origin_dict[name.split('.')[-1] + '_fem_origin']
# Get the number of spanwise nodal points
n = mesh0.shape[1]
# Create an array of angles around a circle
p = np.linspace(0, 2*np.pi, num_circ)
# This is just to show the deformed mesh if selected
if self.show_wing:
if self.show_def_mesh.get():
mesh0[:, :, 2] = def_mesh0[:, :, 2]
# Loop through each element in the FEM system
for i, thick in enumerate(t0):
# Get the radii describing the circles at each nodal point
r = np.array((r0[i], r0[i]))
R, P = np.meshgrid(r, p)
# Get the X and Z coordinates for all points around the circle
X, Z = R*np.cos(P), R*np.sin(P)
# Get the chord and center location for the FEM system
chords = mesh0[-1, :, 0] - mesh0[0, :, 0]
comp = fem_origin * chords + mesh0[0, :, 0]
# Add the location of the element centers to the circle coordinates
X[:, 0] += comp[i]
X[:, 1] += comp[i+1]
Z[:, 0] += fem_origin * (mesh0[-1, i, 2] - mesh0[0, i, 2]) + mesh0[0, i, 2]
Z[:, 1] += fem_origin * (mesh0[-1, i+1, 2] - mesh0[0, i+1, 2]) + mesh0[0, i+1, 2]
# Get the spanwise locations of the spar points
Y = np.empty(X.shape)
Y[:] = np.linspace(mesh0[0, i, 1], mesh0[0, i+1, 1], 2)
# Set the colors of the rectangular surfaces
col = np.zeros(X.shape)
col[:] = colors[i]
# Plot the rectangular surfaces for each individual FEM element
try:
self.ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
facecolors=cm.viridis(col), linewidth=0)
except:
self.ax.plot_surface(X, Y, Z, rstride=1, cstride=1,
facecolors=cm.coolwarm(col), linewidth=0)
lim = 0.
for j in range(n_names):
ma = np.max(self.mesh[self.curr_pos*n_names+j], axis=(0,1,2))
if ma > lim:
lim = ma
lim /= float(self.zoom_scale)
self.ax.auto_scale_xyz([-lim, lim], [-lim, lim], [-lim, lim])
self.ax.set_title("Iteration: {}".format(self.curr_pos))
# round_to_n = lambda x, n: round(x, -int(np.floor(np.log10(abs(x)))) + (n - 1))
if self.opt:
obj_val = self.obj[self.curr_pos]
self.ax.text2D(.15, .05, self.obj_key + ': {}'.format(obj_val),
transform=self.ax.transAxes, color='k')
self.ax.view_init(elev=el, azim=az) # Reproduce view
self.ax.dist = dist
def plotAttribute(cur, planners, attribute, typename):
"""Create a plot for a particular attribute. It will include data for
all planners that have data for this attribute."""
labels = []
measurements = []
nanCounts = []
if typename == 'ENUM':
cur.execute('SELECT description FROM enums where name IS "%s"' % attribute)
descriptions = [t[0] for t in cur.fetchall()]
numValues = len(descriptions)
for planner in planners:
cur.execute('SELECT %s FROM runs WHERE plannerid = %s AND %s IS NOT NULL' \
% (attribute, planner[0], attribute))
measurement = [t[0] for t in cur.fetchall() if t[0] is not None]
if measurement:
cur.execute('SELECT count(*) FROM runs WHERE plannerid = %s AND %s IS NULL' \
% (planner[0], attribute))
nanCounts.append(cur.fetchone()[0])
labels.append(planner[1])
if typename == 'ENUM':
scale = 100. / len(measurement)
measurements.append([measurement.count(i)*scale for i in range(numValues)])
else:
measurements.append(measurement)
if not measurements:
print('Skipping "%s": no available measurements' % attribute)
return
plt.clf()
ax = plt.gca()
if typename == 'ENUM':
width = .5
measurements = np.transpose(np.vstack(measurements))
colsum = np.sum(measurements, axis=1)
rows = np.where(colsum != 0)[0]
heights = np.zeros((1, measurements.shape[1]))
ind = range(measurements.shape[1])
for i in rows:
plt.bar(ind, measurements[i], width, bottom=heights[0], \
color=viridis(int(floor(i * 256 / numValues))), \
label=descriptions[i])
heights = heights + measurements[i]
xtickNames = plt.xticks([x+width/2. for x in ind], labels, rotation=30)
ax.set_ylabel(attribute.replace('_', ' ') + ' (%)')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
props = matplotlib.font_manager.FontProperties()
props.set_size('small')
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop=props)
elif typename == 'BOOLEAN':
width = .5
measurementsPercentage = [sum(m) * 100. / len(m) for m in measurements]
ind = range(len(measurements))
plt.bar(ind, measurementsPercentage, width)
xtickNames = plt.xticks([x + width / 2. for x in ind], labels, rotation=30)
ax.set_ylabel(attribute.replace('_', ' ') + ' (%)')
else:
plt.boxplot(measurements, notch=0, sym='k+', vert=1, whis=1.5, bootstrap=1000)
ax.set_ylabel(attribute.replace('_', ' '))
xtickNames = plt.setp(ax, xticklabels=labels)
plt.setp(xtickNames, rotation=25)
ax.set_xlabel('Motion planning algorithm')
ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)
if max(nanCounts) > 0:
maxy = max([max(y) for y in measurements])
for i in range(len(labels)):
x = i + width / 2 if typename == 'BOOLEAN' else i + 1
ax.text(x, .95*maxy, str(nanCounts[i]), horizontalalignment='center', size='small')
plt.show()
def run(fi):
ret = []
print('Doing file: ' + fi)
dic = xr.open_dataset(fi)
outt = dic['tc_lag0'].values
outp = dic['p'].values
outpc = dic['pconv'].values
outplot = outp.copy()
outt[np.isnan(outt)] = 150
outt[outt >= -40] = 150
grad = np.gradient(outt)
outt[outt == 150] = np.nan
outp[np.isnan(outt)] = np.nan
outpc[np.isnan(outt)] = np.nan
area = np.sum(outt <= -40)
try:
bulk_pmax = np.max(outp[(np.isfinite(outp)) & (np.isfinite(outt))])
except ValueError:
return ret
try:
bulk_pmin = np.min(outp[(np.isfinite(outp)) & (np.isfinite(outt))])
except ValueError:
return ret
if (area * 25 < 15000) or (area * 25 > 800000) or (bulk_pmax > 200) or (bulk_pmin < 0):
print(area*25)
print('throw out')
return
perc = np.percentile(outt[np.isfinite(outt)], 60) # 60
print('perc:',perc)
perc=-60
clat = np.min(dic.lat.values) + ((np.max(dic.lat.values) - np.min(dic.lat.values)) * 0.5)
clon = np.min(dic.lon.values) + ((np.max(dic.lon.values) - np.min(dic.lon.values)) * 0.5)
if (clon > 28) or (clon < -17.2) or (clat < 4.1):
return
figure = np.zeros_like(outt)
o2 = outt.copy()
o2[np.isnan(o2)] = perc
nok = np.where(abs(grad[0]) > 80)
d = 2
i = nok[0]
j = nok[1]
for ii, jj in zip(i, j):
kern = o2[ii - d:ii + d + 1, jj - d:jj + d + 1]
o2[ii - d:ii + d + 1, jj - d:jj + d + 1] = ndimage.gaussian_filter(kern, 3, mode='nearest')
wav = util.waveletT(o2, 5)
arr = np.array(wav['scales'], dtype=str)
arrf = np.array(wav['scales'], dtype=float)
scale_ind = range(arr.size)
yp, xp = np.where(outp > 30)
figure = np.zeros_like(outt)
wll = wav['t']
maxs = np.zeros_like(wll)
yyy=[]
xxx=[]
scal=[]
for nb in scale_ind[::-1]:
orig = float(arr[nb])
print(np.round(orig))
wl = wll[nb, :, :]
maxout = (
wl == ndimage.maximum_filter(wl, (5,5), mode='constant', cval=np.amax(wl) + 1)) # (np.round(orig / 5))
try:
yy, xx = np.where((maxout == 1) & (outt <= -40) & (wl >= np.percentile(wl[wl >= 0.5], 90)) & (wl > orig**.5))# & (wl > orig**.5) )) #(wl >= np.percentile(wl[wl >= 0.5], 90)))# & (wl > orig**.5))#& (wl >= np.percentile(wl[wl >= 0.5], 90))) #)& (wl > orig**.5) (wl >= np.percentile(wl[wl >= 0.1], 90)) )#(wl > orig**.5))# & (wlperc > orig**.5))# & (wlperc > np.percentile(wlperc[wlperc>=0.1], 80)))# & (wlperc > np.percentile(wlperc[wlperc>=0.1], 80) )) # & (wl100 > 5)
except IndexError:
continue
for y, x in zip(yy, xx):
ss = orig
iscale = (np.ceil(ss / 2. / 5.)).astype(int)
if ss <= 20:
iscale = iscale+1
ycirc, xcirc = ua.draw_cut_circle(x, y, iscale, outp)
figure[ycirc, xcirc] = np.round(orig)
xxx.append(x)
yyy.append(y)
scal.append(orig)
figure[np.isnan(outt)]=0
##file 130!!! nR
spos = np.where(np.array(scal, dtype=int) == 15)
figure[figure == 0] = np.nan
f = plt.figure(figsize = (7,6), dpi=300)
ax2 = f.add_subplot(111)
# ax2.autoscale = False
ttest = outplot.copy()
ttest = ttest + 1
ttest[np.isnan(ttest)] = 0
ax2.contourf(np.arange(wll.shape[2]) , np.arange(wll.shape[1]) , outplot, cmap='Blues', zorder=1)
ax2.imshow(outt, cmap='Greys', vmax=-40, zorder=2)
mt = ax2.imshow(figure, cmap='OrRd_r', vmax=180, zorder=3)
ax2.contour(np.arange(wll.shape[2]), np.arange(wll.shape[1]), ttest, cmap='Blues', levels=[-0.5, 0.5], zorder=4)
plt.plot(np.array(xxx)[spos], np.array(yyy)[spos], 'wo', markersize=3, label='Wavelet power maximum', zorder=5)
ax2.invert_yaxis()
ax2.set_xlim(20, 140)
ax2.set_ylim(20, 140)
ax2.set_xticklabels(np.arange(100, 800, 100)-100)
ax2.set_yticklabels(np.arange(100, 800, 100)-100)
ax2.plot(xp , yp , 'o', markersize=3, label='Rain > 30mm h$^{-1}$', zorder=10)
ax2.set_xlabel('Location (km)')
ax2.set_ylabel('Location (km)')
plt.colorbar(mt, label = 'Scale (km)')
plt.tight_layout()
plt.show()
spath = '/users/global/cornkle/C_paper/wavelet/figs/paper/'
plt.savefig(spath + '/method_circles2.png', dpi=300)
f = plt.figure(figsize = (7,6), dpi=300)
ax2 = f.add_subplot(111)
# ax2.autoscale = False
ttest = outplot.copy()
ttest = ttest + 1
ttest[np.isnan(ttest)] = 0
ax2.imshow(outt, cmap='viridis', vmax=-40, zorder=2)
ax2.invert_yaxis()
ax2.set_xlim(20, 140)
ax2.set_ylim(20, 140)
ax2.set_xticklabels(np.arange(100, 800, 100)-100)
ax2.set_yticklabels(np.arange(100, 800, 100)-100)
ax2.plot(xp , yp , 'o', markersize=3, label='Rain > 30mm h$^{-1}$', zorder=10)
ax2.set_xlabel('Location (km)')
ax2.set_ylabel('Location (km)')
plt.tight_layout()
plt.show()
spath = '/users/global/cornkle/C_paper/wavelet/figs/paper/'
plt.savefig(spath + '/method_temp.png', dpi=300)
#bla = wcno.file_loop(files[130])
f = plt.figure(figsize = (6.5,11), dpi=300)
gridspec.GridSpec(4,1)
posi = 116
ax1 = plt.subplot2grid((4,1),(0,0),rowspan=2)
ax2 = plt.subplot2grid((4,1),(2,0))
ax3 = plt.subplot2grid((4,1),(3,0))
lev = np.arange(-90,-39,4)
ax1.contourf(np.arange(wll.shape[2]) * 5, np.arange(wll.shape[1]) * 5, outplot, cmap='Blues')
mt = ax1.contourf(np.arange(wll.shape[2])*5,np.arange(wll.shape[1])*5,outt, cmap='Greys', vmax=-40, levels = lev)
ax1.plot(np.arange(wll.shape[2])*5, [posi*5]*len(np.arange(wll.shape[2])*5), linestyle = '--', linewidth=2, color = 'black')
ttest = outplot.copy()
ttest = ttest+1
ttest[np.isnan(ttest)] = 0
ax1.contour(np.arange(wll.shape[2]) * 5, np.arange(wll.shape[1]) * 5, ttest, cmap='Blues' , levels=[-0.5,0.5])
ax1.invert_yaxis()
ax1.set_xlim(100,700)
ax1.set_ylim(100, 700)
ax1.set_xticklabels(np.arange(100, 800, 100) - 100)
ax1.set_yticklabels(np.arange(100, 800, 100) - 100)
ax1.plot(xp*5, yp*5, 'o', markersize=3, label='Rain > 30mm h$^{-1}$')
ax1.legend(loc=4)
ax1.set_ylabel('Location (km)')
ax1.set_title(str(dic['time.year'].values)+'-'+str(dic['time.month'].values)+'-'+str(dic['time.day'].values)+' '+str(dic['time.hour'].values)+':'+str(dic['time.minute'].values)+'UTC')
colors = cm.viridis(np.linspace(0, 1, len([0,1, 2,5,10,20,40,60,80])))
ax2.plot(np.arange(wll.shape[2])*5, outt[posi,:], color='r') #118
ax2.set_xlim(100, 700)
ax2.set_xticklabels(np.arange(100, 800, 100) - 100)
ax2.set_ylabel('Cloud-top temperature ($^{\circ}$C)')
ax22 = ax2.twinx()
ax22.set_xlim(100, 700)
ax22.plot(np.arange(wll.shape[2])*5, outp[posi,:])
ax22.set_ylabel('Rain (mm h$^{-1}$)')
print(np.nanmax(wll[:,posi,:]))
mp = ax3.contourf(np.arange(wll.shape[2])*5, arr,wll[:,posi,:], levels=[0,1, 2,5,10,20,40,80,100], colors=colors)
maxs = np.mean(maxs[:,posi-1:posi+2, :], 1) # -1, +2
ppos = np.where(maxs)
for p1, p2 in zip(ppos[1], ppos[0]):
ax3.errorbar((np.arange(wll.shape[2])*5)[p1], arrf[p2], xerr=arrf[p2]/2, fmt='o', ecolor='white', color='white', capthick=3, ms=3, elinewidth=0.7)
ax3.set_xlim(100,700)
ax3.set_xticklabels(np.arange(100, 800, 100) - 100)
ax3.set_ylim(15, 180)
ax3.set_xlabel('Location (km)')
ax3.set_ylabel('Length scale (km)')
plt.tight_layout()
f.subplots_adjust(right=0.86)
cax = f.add_axes([0.87, 0.545, 0.025, 0.415])
cb = plt.colorbar(mt, cax=cax, label='Cloud-top temperature ($^{\circ}$C)')
cb.ax.tick_params(labelsize=12)
cax = f.add_axes([0.87, 0.065, 0.025, 0.175])
cb = plt.colorbar(mp, cax=cax, label='Wavelet power')
cb.ax.tick_params(labelsize=12)
fsiz = 14
x = 0.02
plt.annotate('a)', xy=(x, 0.96), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('b)', xy=(x, 0.51), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('c)', xy=(x, 0.245), xytext=(0, 4), size=fsiz, xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.show()
spath = '/users/global/cornkle/C_paper/wavelet/figs/paper/'
plt.savefig(spath+'/method2.png', dpi=300)
dic.close()
plt.close('all')
#fig_size = (16, 9)
fig_size = (4,3)
fig = plt.figure(num=None, figsize=fig_size, dpi=200, facecolor='w', edgecolor='k')
with open('acc.json') as data_file:
data = json.load(data_file)
df_acc = pd.DataFrame(data["Accelerator"])
df_lum = pd.DataFrame(data["Luminosity"])
df_acc['logE'] = df_acc["Energy_MeV"].apply(np.log)
df_lum['logLum'] = df_lum["Lum_per_cm2s"].apply(np.log)
types_acc = np.hstack(np.array(df_acc['Type']))
types_lum = np.hstack(np.array(df_lum['Type']))
uniq_acc = np.unique(types_acc)
values = cm.viridis(np.linspace(0,1,len(uniq_acc)))
col = dict(zip(uniq_acc, values))
for i,acc in zip(xrange(len(uniq_acc)),uniq_acc):
temp = df_acc[df_acc['Type'] == acc]
year = np.hstack(np.array(temp['Year']))
logE = np.hstack(np.array(temp['logE']))
plt.scatter(year, logE, alpha=1,color=col[acc],label=r'%s' %(acc))
plt.xlabel('Year')
plt.ylabel(r'Energy $\mathrm{log(MeV)}$')
plt.title('Livingston Plot for Energy')
plt.legend(loc=4)
plt.savefig("acc_logE.png")
fig = plt.figure(num=None, figsize=fig_size, dpi=200, facecolor='w', edgecolor='k')
import pandas as pd
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.cm as cm
import numpy as np
#data = pd.DataFrame.from_csv('events/evnt_1.csv')
data = pd.DataFrame.from_csv('truth.csv')
data = data.apply(pd.to_numeric, errors='coerce')
fig = plt.figure(figsize=(16,9))
ax = fig.add_subplot(111, projection='3d')
mothers = np.hstack(np.array(data['mother']))
uniq_mom = np.unique(mothers)
values = cm.viridis(np.linspace(0,1,len(uniq_mom)))
#values = cm.Paired(np.linspace(0,1,len(uniq_mom)))
col = dict(zip(uniq_mom, values))
for mom in uniq_mom:
temp = data[data['mother'] == mom]
xs = np.hstack(np.array(temp['X']))
ys = np.hstack(np.array(temp['Y']))
zs = np.hstack(np.array(temp['Z']))
ax.scatter(xs, ys, zs,alpha=1,color=col[mom])
plt.show()
'''
program: discrete_colorbar.py
author: tc
created: 2016-06-13 -- 18 CEST
'''
import numpy
import matplotlib.pyplot as plt
import matplotlib.cm as cm
N = 5
colors = iter([cm.viridis(x) for x in numpy.linspace(0.0, 0.9, N)])
x = numpy.linspace(0, 10)
for i in range(N):
plt.plot(x, x / 2.0 + i, lw=3, c=colors.next())
plt.show()
def fit_scale(path, fixed_a, fixed_loc, save_image=False):
matplotlib.rcParams['savefig.dpi'] = 300
def modified_gamma_2(x, scale):
a = fixed_a
loc = fixed_loc
return gamma.pdf(x, a=a, loc=loc, scale=scale)
betas = []
scale = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls, M_ave, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
popt = curve_fit(modified_gamma_2, xdata=Ls, ydata=M_ave, p0=[10.])[0]
# print beta, popt
betas.append(beta)
scale.append(popt[0])
x = np.linspace(0, max(Ls), num=5*max(Ls))
ax.plot(x, modified_gamma_2(x, scale=popt[0]),
'-',
# label=r'fitted $\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
## critical point
# critcal_point = 2. * popt[0] # x = (a - 1) * scale
# ax.plot([critcal_point] * 2, [0., 0.05], '-',
# color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
if save_image:
result_image_path = "../results/img/diecutting/fitted_gamma_fixed_a_x0"
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
pdf = PdfPages(result_image_path + ".pdf")
plt.savefig(result_image_path + ".png")
pdf.savefig()
pdf.close()
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
plt.close()
betas = np.array(betas)
scale = np.array(scale)
# beta_theta = lambda x, a, b: a*x + b
beta_theta = lambda x, a, b: a*np.log(x) + b
fig, ax = plt.subplots()
ax.set_title(r'Fitting parameter')
ax.plot(betas, scale, 'o')
popt = curve_fit(beta_theta, xdata=betas, ydata=scale, p0=[15., 0.])[0]
x = np.linspace(min(betas), max(betas))
# ax.plot(x, beta_theta(x, popt[0], popt[1]), '-', color='k',
# label=r'$\theta = {} \beta + {}$'.format(*popt),
# )
ax.plot(x, beta_theta(x, popt[0], popt[1]), '-', color='k',
label=r'$\theta = {} \log \beta + {}$'.format(*popt),
)
ax.legend(loc='best')
ax.set_xlim((0, max(betas)))
ax.set_ylim((0, ax.get_ylim()[1]))
ax.set_xlabel(r'$\beta$')
ax.set_ylabel(r'Scale parameter: $\theta$')
if save_image:
result_image_path = "../results/img/diecutting/fitted_parameters_fixed_a_x0"
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
pdf = PdfPages(result_image_path + ".pdf")
plt.savefig(result_image_path + ".png")
pdf.savefig()
pdf.close()
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
plt.close()
plt.show()
def fit_fermi(path, save_image=False):
matplotlib.rcParams['savefig.dpi'] = 300
def fitting_func(x, theta):
return 0.5 * ((x ** 2.) / ((theta ** 3.) * (np.exp(x / theta) - 1.)))
betas = []
scale = []
fig, ax = plt.subplots()
for i, result_data_path in enumerate(path):
globals().update(load_data(result_data_path))
ax.plot(Ls, M_ave, '.', label=r'$\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
popt = curve_fit(fitting_func, xdata=Ls, ydata=M_ave, p0=[10.,])[0]
# print beta, popt
betas.append(beta)
scale.append(popt[0])
x = np.linspace(0, max(Ls), num=5*max(Ls))
ax.plot(x, fitting_func(x, theta=popt[0]),
'-',
# label=r'fitted $\beta = %2.2f$' % beta,
color=cm.viridis(float(i) / len(path)))
## critical point
# critcal_point = 2. * popt[0] # x = (a - 1) * scale
# ax.plot([critcal_point] * 2, [0., 0.05], '-',
# color=cm.viridis(float(i) / len(path)))
show_plot1(ax, num_of_strings)
if save_image:
result_image_path = "../results/img/diecutting/fitted_gamma_fixed_a_x0"
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
pdf = PdfPages(result_image_path + ".pdf")
plt.savefig(result_image_path + ".png")
pdf.savefig()
pdf.close()
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
plt.close()
betas = np.array(betas)
scale = np.array(scale)
fig, ax = plt.subplots()
ax.set_title(r'Fitting parameter')
ax.plot(betas, scale, 'o')
ax.set_xlabel(r'$\beta$')
ax.set_xlim((0, max(betas)))
ax.set_ylabel(r'$\theta$')
if save_image:
result_image_path = "../results/img/diecutting/fitted_parameters_fixed_a_x0"
result_image_path += "_" + time.strftime("%y%m%d_%H%M%S")
pdf = PdfPages(result_image_path + ".pdf")
plt.savefig(result_image_path + ".png")
pdf.savefig()
pdf.close()
plt.close()
print "[saved] " + result_image_path
else:
plt.show()
plt.close()
plt.show()