def test_line_markers(self):
"""test high-level usage for simple example.
Test is successfull if generated tikz code saves correct amount of lines
"""
x = np.linspace(1, 100, 20)
y = np.linspace(1, 100, 20)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.plot(x, y, linestyle="-", marker="*")
ax.set_ylim([20, 80])
ax.set_xlim([20, 80])
raw = get_tikz_code()
clean_figure(fig)
clean = get_tikz_code()
# Use number of lines to test if it worked.
# the baseline (raw) should have 20 points
# the clean version (clean) should have 2 points
# the difference in line numbers should therefore be 2
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 6
plt.close("all")
def test_trisurface3D(self):
import matplotlib.pyplot as plt
import numpy as np
n_radii = 8
n_angles = 36
# Make radii and angles spaces (radius r=0 omitted to eliminate duplication).
radii = np.linspace(0.125, 1.0, n_radii)
angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)
# Repeat all angles for each radius.
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
# Convert polar (radii, angles) coords to cartesian (x, y) coords.
# (0, 0) is manually added at this stage, so there will be no duplicate
# points in the (x, y) plane.
x = np.append(0, (radii * np.cos(angles)).flatten())
y = np.append(0, (radii * np.sin(angles)).flatten())
# Compute z to make the pringle surface.
z = np.sin(-x * y)
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.gca(projection="3d")
ax.plot_trisurf(x, y, z, linewidth=0.2, antialiased=True)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def plot_neat_training():
with open(os.path.join(base_path, 'resources/neat-results.csv'), 'r') as f:
neat_data = load_metrics(f)
with open(os.path.join(base_path, 'resources/neat-recurrent-results.csv'),
'r') as f:
neat_rec_data = load_metrics(f)
neat_data, neat_rec_data = crop_data(neat_data, neat_rec_data)
fig = plt.figure()
plt.plot(*neat_data['reward'], c='b', linewidth=1.0, label='feed-forward')
plt.plot(*neat_rec_data['reward'], c='r', linewidth=1.0, label='recurrent')
plt.xlabel('generation')
plt.ylabel('return')
plt.legend()
plt.grid(linestyle='--')
#plt.title('PAAC training cummulative reward')
#plt.xlim(-, 1.2e7)
ax = plt.gca()
#plt.xlim(-, 1.2e7)
plt.tight_layout()
format_axes(ax)
plt.savefig(os.path.join(output_path, "neat-reward.pdf"))
plt.savefig(os.path.join(output_path, "neat-reward.eps"))
tikzplotlib.clean_figure()
tikzplotlib.save(os.path.join(output_path, "neat-reward.tex"))
def test_surface3D(self):
from matplotlib import cm
from matplotlib.ticker import FormatStrFormatter, LinearLocator
# Make data.
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.gca(projection="3d")
# Plot the surface.
surf = ax.plot_surface(X,
Y,
Z,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter("%.02f"))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def test_quiver3D(self):
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.gca(projection="3d")
# Make the grid
x, y, z = np.meshgrid(
np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.2),
np.arange(-0.8, 1, 0.8),
)
# Make the direction data for the arrows
u = np.sin(np.pi * x) * np.cos(np.pi * y) * np.cos(np.pi * z)
v = -np.cos(np.pi * x) * np.sin(np.pi * y) * np.cos(np.pi * z)
w = (
np.sqrt(2.0 / 3.0)
* np.cos(np.pi * x)
* np.cos(np.pi * y)
* np.sin(np.pi * z)
)
ax.quiver(x, y, z, u, v, w, length=0.1, normalize=True)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def test_plot3d(self):
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
ax.plot(x, y, z)
ax.set_xlim([-2, 2])
ax.set_ylim([-2, 2])
ax.set_zlim([-2, 2])
ax.view_init(30, 30)
raw = get_tikz_code(fig)
clean_figure(fig)
clean = get_tikz_code()
# Use number of lines to test if it worked.
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 14
plt.close("all")
def save_rateplot(filename, source=None, plot_title=None,
width=size.w(1.25), height=size.h(1.25)):
tpl.clean_figure()
filepath = imgpath + filename + '.tex'
tpl.save(filepath, wrap=False, axis_height=height,
axis_width=width)
add_begin_content(filepath)
add_end_content(filepath, source=source, plot_title=plot_title)
def to_tikz(fig):
tikzplotlib.clean_figure(fig)
output = tikzplotlib.get_tikz_code(figure=fig,
filepath=None,
axis_width=None,
axis_height=None,
textsize=10.0,
table_row_sep="\n")
return output
def save_figure_as_tikz_tex_file(fig: plt.Figure,
target_path: Union[str, os.fspath]):
try:
tikzplotlib.clean_figure(fig=fig)
tikzplotlib.save(figure=fig, filepath=target_path, strict=True)
except Exception as e:
logging.error(
f"Exception ({e.__class__.__name__}) occurred in attempt to export plot in tikz raw text format!\n"
f"The following tikz tex file was not produced.\n\t{target_path}\n"
f"The following lines show additional information on the {e.__class__.__name__}",
exc_info=e)
def test_hist(self):
x = np.linspace(1, 100, 20)
y = np.linspace(1, 100, 20)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.hist(x, y)
ax.set_ylim([20, 80])
ax.set_xlim([20, 80])
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def test_contour3D(self):
from matplotlib import cm
from mpl_toolkits.mplot3d import axes3d
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y, Z = axes3d.get_test_data(0.05)
cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)
ax.clabel(cset, fontsize=9, inline=1)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def xplotsplit(parameter):
fig, axes = plt.subplots(1)
if parameter == 'all':
fig.set_size_inches(width_inches, height_inches)
plot_all(axes, data)
else:
fig.set_size_inches(width_inches, height_inches)
split_and_plot(axes, data, parameter)
# fig.savefig('hyper_'+parameter+'_unfiltered.pgf')
tikzplotlib.clean_figure()
tikzplotlib.save(filepath='hyperv2_' + parameter + '_sigma_all.tex', strict=True, axis_height='4cm',
axis_width='5cm')
fig.show()
def plottruss():
'''
plot the truss
'''
if model.plot_truss == "yes":
if model.ndof == 1:
for i in range(model.nel):
XX = np.array([model.x[model.IEN[i, 0]-1],
model.x[model.IEN[i, 1]-1]])
YY = np.array([0.0, 0.0])
plt.plot(XX, YY, "blue")
if model.plot_node == "yes":
plt.text(XX[0], YY[0], str(model.IEN[i, 0]))
plt.text(XX[1], YY[1], str(model.IEN[i, 1]))
elif model.ndof == 2:
for i in range(model.nel):
XX = np.array([model.x[model.IEN[i, 0]-1],
model.x[model.IEN[i, 1]-1]])
YY = np.array([model.y[model.IEN[i, 0]-1],
model.y[model.IEN[i, 1]-1]])
plt.plot(XX, YY, "blue")
if model.plot_node == "yes":
plt.text(XX[0], YY[0], str(model.IEN[i, 0]))
plt.text(XX[1], YY[1], str(model.IEN[i, 1]))
elif model.ndof == 3:
# insert your code here for 3D
# ...
pass # delete or comment this line after your implementation for 3D
else:
raise ValueError("The dimension (ndof = {0}) given for the \
plottruss is invalid".format(model.ndof))
plt.title("Truss Plot")
plt.xlabel(r"$x$")
plt.ylabel(r"$y$")
plt.savefig("truss.pdf")
# Convert matplotlib figures into PGFPlots figures stored in a Tikz file,
# which can be added into your LaTex source code by "\input{fe_plot.tex}"
if model.plot_tex == "yes":
import tikzplotlib
tikzplotlib.clean_figure()
tikzplotlib.save("fe_plot.tex")
print("\t2D Truss Params \n")
print(model.Title + "\n")
print("No. of Elements {0}".format(model.nel))
print("No. of Nodes {0}".format(model.nnp))
print("No. of Equations {0}".format(model.neq))
def _plot_training_plt(self,
outputpath,
metrics=None,
splits=None,
epochs=None):
# outputpath: filepath where the plot should be saved
# metrics can be a list of metrics names, or None if all metrics should be plotted
color_dict = {}
query = self.means_per_epoch(metrics, splits, epochs, squeeze=False)
os.makedirs(outputpath, exist_ok=True)
# Compute traces
for idx, (m, s_dict) in enumerate(query.items()):
f, ax = plt.subplots()
ax.set_title(m)
ax.set_xlabel("Epoch")
axis_scale = 1.0
if m in self.metrics.keys():
meta = self.metrics[m]
if meta is not None:
if "axis_label" in meta.keys():
ax.set_ylabel(meta["axis_label"])
if "axis_limits" in meta.keys():
ax.set_ylim(*meta["axis_limits"])
if "axis_scale" in meta.keys():
axis_scale = float(meta["axis_scale"])
for s, e_dict in s_dict.items():
if s not in color_dict.keys():
trace_color = COLORS[len(color_dict.keys()) % len(COLORS)]
color_dict[s] = trace_color
showInLegend = True
else:
trace_color = color_dict[s]
showInLegend = False
y_values = list(e_dict.values())
y_values = [y * axis_scale for y in y_values]
ax.plot(
x=list(e_dict.keys()),
y=y_values,
c=trace_color,
label=s,
)
tikzplotlib.clean_figure()
tikzplotlib.save(os.path.join(outputpath, "{}.tex".format(m)))
plt.close()
def test_subplot(self):
"""octave code
```octave
addpath ("../matlab2tikz/src")
x = linspace(1, 100, 20);
y1 = linspace(1, 100, 20);
figure
subplot(2, 2, 1)
plot(x, y1, "-")
subplot(2, 2, 2)
plot(x, y1, "-")
subplot(2, 2, 3)
plot(x, y1, "-")
subplot(2, 2, 4)
plot(x, y1, "-")
xlim([20, 80])
ylim([20, 80])
set(gcf,'Units','Inches');
set(gcf,'Position',[2.5 2.5 5 5])
cleanfigure;
```
"""
x = np.linspace(1, 100, 20)
y = np.linspace(1, 100, 20)
with plt.rc_context(rc=RC_PARAMS):
fig, axes = plt.subplots(2, 2, figsize=(5, 5))
plotstyles = [("-", "o"), ("-", "None"), ("None", "o"),
("--", "x")]
for ax, style in zip(axes.ravel(), plotstyles):
ax.plot(x, y, linestyle=style[0], marker=style[1])
ax.set_ylim([20, 80])
ax.set_xlim([20, 80])
raw = get_tikz_code()
clean_figure(fig)
clean = get_tikz_code()
# Use number of lines to test if it worked.
# the baseline (raw) should have 20 points
# the clean version (clean) should have 2 points
# the difference in line numbers should therefore be 2
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 36
plt.close("all")
def main():
# Create plot for male - female beta distribution example
beta_example_gender()
# Save figure
tikzplotlib.clean_figure()
tikzplotlib.save(imgpath + 'beta_example_gender.tex',
axis_width=size.w(1.25),
axis_height=size.h(1.25))
# Create beta distribution examples that show how they change
# This is for the slide deck and wil be smaller
beta_example_change(ab0=(1, 1), history=[1, 0, 1])
# Create plot for paper
beta_example_change_paper()
def test_wireframe3D(self):
from mpl_toolkits.mplot3d import axes3d
# Grab some test data.
X, Y, Z = axes3d.get_test_data(0.05)
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
# Plot a basic wireframe.
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def save_areaplot(filename, title):
tpl.clean_figure()
filepath = imgpath + filename + '.tex'
tpl.save(filepath, wrap=False,
extra_axis_parameters={"height=180pt, width=150pt",
"reverse legend",
"legend style={"
+ "at={(2.02, 0.5)},"
+ "anchor=west,"
+ "}"},
extra_groupstyle_parameters={"horizontal sep=0.8cm",
"group name=my plots"},
)
add_begin_content(filepath)
title_str = title + " - number Bachelor's degrees awarded (thousands)"
add_end_content(filepath, title_str)
def test_xlog_2(self):
x = np.arange(1, 100)
y = np.arange(1, 100)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1)
ax.plot(x, y)
ax.set_xscale("log")
raw = get_tikz_code()
clean_figure()
clean = get_tikz_code()
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 51
assert numLinesClean == 71
plt.close("all")
def test_xlog(self):
y = np.linspace(0, 3, 100)
x = np.exp(y)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1)
ax.plot(x, y)
ax.set_xscale("log")
raw = get_tikz_code()
clean_figure()
clean = get_tikz_code()
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 98
assert numLinesClean == 25
plt.close("all")
def test_xlog_3(self):
x = np.logspace(-3, 3, 20)
y = np.linspace(1, 100, 20)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.plot(x, y)
ax.set_xscale("log")
ax.set_xlim([10**(-2), 10**(2)])
ax.set_ylim([20, 80])
raw = get_tikz_code()
clean_figure(fig)
clean = get_tikz_code()
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 18
plt.close("all")
def save(filepath: Path,
comment: Union[str, dict],
figure="gcf",
axis_width=None,
axis_height=None,
textsize=10.0,
table_row_sep="\n"):
tikzplotlib.clean_figure(fig=figure)
output = tikzplotlib.get_tikz_code(figure=figure,
filepath=filepath,
axis_width=axis_width,
axis_height=axis_height,
textsize=textsize,
table_row_sep=table_row_sep)
output = _add_comment(output, comment)
with open(filepath, "w") as tikz_file:
tikz_file.write(output)
def main():
args = parser.parse_args()
fping_df = None
if args.fping:
fping_df = load_df(args.fping)
iperf_df = None
if args.iperf:
iperf_df = load_df(args.iperf)
# delay histograms
distribution_type = 'ccdf'
fit_distribution = False
plotf = plt.loglog
plt.figure()
if fping_df is not None:
plot_distribution(
fping_df, label='Ping', plotf=plotf,
type=distribution_type, fit_distribution=fit_distribution,
)
if iperf_df is not None:
bs = [1, 2, 3, 10]
for b in bs:
g = iperf_df.loc[iperf_df['bin_mb'] == b]
print(f'{b} MB: {len(g)} samples')
plot_distribution(
g, label=f'{b} MB', plotf=plotf,
type=distribution_type, fit_distribution=fit_distribution,
)
plt.xlim(1e-5, 1)
plt.ylim(1e-4, 1)
plt.title('Network delay')
plt.xlabel('Delay t [s]')
plt.ylabel('Pr(delay > t)')
plt.grid()
plt.legend()
tikzplotlib.clean_figure()
tikzplotlib.save('ccdf.tex')
plt.show()
return
def plot_paac_training():
with open(os.path.join(base_path, 'resources/paac-training'), 'r') as f:
paac_data = load_metrics(f)
fig = plt.figure()
plt.plot(*paac_data['reward'], c='r', linewidth=0.7, label='reward')
plt.xlabel('frames')
plt.ylabel('return')
plt.grid(linestyle='--')
#plt.title('PAAC training cummulative reward')
#plt.xlim(-, 1.2e7)
ax = plt.gca()
#plt.xlim(-, 1.2e7)
plt.tight_layout()
format_axes(ax)
plt.savefig(os.path.join(output_path, "paac-reward.pdf"))
plt.savefig(os.path.join(output_path, "paac-reward.eps"))
tikzplotlib.clean_figure()
tikzplotlib.save(os.path.join(output_path, "paac-reward.tex"))
def test_bar3D(self):
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
for c, z in zip(["r", "g", "b", "y"], [30, 20, 10, 0]):
xs = np.arange(20)
ys = np.random.rand(20)
# You can provide either a single color or an array. To demonstrate this,
# the first bar of each set will be colored cyan.
cs = [c] * len(xs)
cs[0] = "c"
ax.bar(xs, ys, zs=z, zdir="y", color=cs, alpha=0.8)
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def save_comboplot(cip_cls, filename):
filepath = imgpath + filename + '.tex'
file_handle = codecs.open(filepath, 'w')
# Rate graph
cip_cls.plot_rate()
# To do: figure out why computer science ('11') raises error here
try:
tpl.clean_figure()
except ValueError:
pass
code = tpl.get_tikz_code(axis_height='140pt',
axis_width='300pt',
# axis_width='150pt',
# extra_axis_parameters={'x post scale=2',
# 'y post scale=1'}
)
file_handle.write(code)
file_handle.write('\n\\vspace{0.1cm}\n\\begin{tikzpicture}')
file_handle.close()
# area graph
cip_cls.plot_area()
tpl.clean_figure()
code = tpl.get_tikz_code(
wrap=False,
extra_axis_parameters={"height=90pt, width=160pt",
"reverse legend",
"legend style={"
+ "at={(2.02, 0.5)},"
+ "anchor=west,"
+ "}"},
extra_groupstyle_parameters={"horizontal sep=0.8cm",
"group name=my plots"},
)
with open(filepath, 'a+') as file_handle:
content = file_handle.read()
file_handle.seek(0, 0)
file_handle.write('\n' + code + '\n' + content)
group_title = 'Number Bachelor\'s degrees awarded (thousands)'
add_end_content(filepath, group_title, title_space="0.25cm")
def test_scatter(self):
x = np.linspace(1, 100, 20)
y = np.linspace(1, 100, 20)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.scatter(x, y)
ax.set_ylim([20, 80])
ax.set_xlim([20, 80])
raw = get_tikz_code()
clean_figure()
clean = get_tikz_code()
# Use number of lines to test if it worked.
# the baseline (raw) should have 20 points
# the clean version (clean) should have 2 points
# the difference in line numbers should therefore be 2
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 6
plt.close("all")
def test_sine(self):
x = np.linspace(1, 2 * np.pi, 100)
y = np.sin(8 * x)
with plt.rc_context(rc=RC_PARAMS):
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
ax.plot(x, y, linestyle="-", marker="*")
ax.set_xlim([0.5 * np.pi, 1.5 * np.pi])
ax.set_ylim([-1, 1])
raw = get_tikz_code()
clean_figure(fig)
clean = get_tikz_code()
# Use number of lines to test if it worked.
# the baseline (raw) should have 20 points
# the clean version (clean) should have 2 points
# the difference in line numbers should therefore be 2
numLinesRaw = raw.count("\n")
numLinesClean = clean.count("\n")
assert numLinesRaw - numLinesClean == 39
plt.close("all")
def test_polygon3D(self):
from matplotlib import colors as mcolors
from matplotlib.collections import PolyCollection
with plt.rc_context(rc=RC_PARAMS):
fig = plt.figure()
ax = fig.gca(projection="3d")
def cc(arg):
"""
:param arg:
"""
return mcolors.to_rgba(arg, alpha=0.6)
xs = np.arange(0, 10, 0.4)
verts = []
zs = [0.0, 1.0, 2.0, 3.0]
for z in zs:
ys = np.random.rand(len(xs))
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
poly = PolyCollection(
verts, facecolors=[cc("r"), cc("g"),
cc("b"), cc("y")])
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir="y")
ax.set_xlabel("X")
ax.set_xlim3d(0, 10)
ax.set_ylabel("Y")
ax.set_ylim3d(-1, 4)
ax.set_zlabel("Z")
ax.set_zlim3d(0, 1)
with pytest.warns(Warning):
clean_figure(fig)
plt.close("all")
def plot_flow_linear(sol, X, T, h, k, c, U_0, number=5):
if number > 12:
number = 12
colors = ['midnightblue', 'royalblue', 'teal', 'mediumturquoise', 'mediumseagreen', 'forestgreen', 'yellowgreen',
'goldenrod', 'darkorange', 'orangered', 'red', 'firebrick']
counter = 0
fig = plt.figure(figsize=(15, 5))
ax = plt.subplot()
for datapoint_raw in np.linspace(0, T - 1, number):
datapoint = int(np.floor(datapoint_raw / k))
data = sol[datapoint, :]
n = len(U_0[0, :])
shift = int(np.floor(c * datapoint_raw/h))
left_bound = min(max(0 + shift, 0), n)
right_bound = max(min(n + shift, n), 0)
velo = np.copy(U_0)
velo[0, :left_bound] = U_0[0, 0]
velo[0, right_bound:] = U_0[0, n-1]
velo[0, left_bound:right_bound] = U_0[0, left_bound - shift:right_bound - shift]
x_mesh = np.arange(0, X, h)
ax.plot(x_mesh, data, color=colors[counter])
ax.plot(x_mesh, velo[0], ':', color=colors[counter])
counter += 1
ax.plot(np.array([]), np.array([]), '-', color='black', label='aproximation')
ax.plot(np.array([]), np.array([]), ':', color='black', label='true solution')
plt.legend()
plt.tight_layout()
tikzplotlib.clean_figure()
tikzplotlib.save("test.tex")
plt.show()