#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig', tight_layout=True)
# Define what to plot
t = np.arange(0.01, 10.0, 0.01)
s1 = np.exp(t)
s2 = np.sin(2 * np.pi * t)
# Define graphs
fig.addgraph('graph1', x_label='time (s)', y_label='exp')
fig.addtwingraph('graph2', 'graph1', y_label='sin', axis='x')
# Insert object in plots
exp = lt.ltPlotFct(t, s1, color='b')
sin = lt.ltPlotPts(t, s2, color='r', marker='.')
# Add fit line
sin.plot(fig, 'graph2')
exp.plot(fig, 'graph1')
ax1 = fig.graphs['graph1'].graph
ax1.set_ylabel(fig.graphs['graph1'].y_label, color='b')
ax1.tick_params('y', colors='b', which='major')
ax1.tick_params('y', colors='b', which='minor')
np.pi / steps)
print(' Creating orbitals: scatter plots...')
ntot = len(data.orbitals)
ncur = 0
size = 2000
for key in data.orbitals:
cmap = 'coolwarm'
if key in ['1s', 's', '00']:
cmap += '_r'
ncur += 1
fig = lt.ltFigure(name='orbitale-' + key, height_width_ratio=1)
max_range = data.r_on_a0_max["3x"] * data.a0
fig.addgraph('graph1',
projection='3d',
x_ticks=False,
y_ticks=False,
z_ticks=False,
x_min=-max_range,
x_max=max_range,
y_min=-max_range,
y_max=max_range,
z_min=-max_range,
z_max=max_range)
n = int(key[0])
l = data.orbital_to_L[key[1]]
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Example taken from
# https://www.physique-experimentale.com/python/ajustement_de_courbe.py
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig')
# Define what to plot
# data
x = np.array([1.0, 2.1, 3.1, 3.9])
y = np.array([2.1, 3.9, 6.1, 7.8])
# uncertainties (1 sigma)
ux = np.array([0.2, 0.3, 0.2, 0.2])
uy = np.array([0.3, 0.3, 0.2, 0.3])
reg = lt.ltPlotRegLin(x,
y,
ux,
uy,
info_placement='lower right',
label='Data',
label_reg='Regression')
# Définir le graphique
fig.addgraph('graph1',
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig', height_width_ratio=.725, width_frac=120.59/112.05)
# Define what to plot
price_data = np.load('goog.npz')['price_data'].view(np.recarray)
price_data = price_data[-250:] # get the most recent 250 trading days
delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]
# Marker size in units of points^2
volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2
close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]
# Define graphs
fig.addgraph('graph1', x_label='$\Delta_i$', y_label='$\Delta_{i+1}$', title='Volume and percent change', show_grid=True)
# Insert object in plots
fig.addplot(lt.ltPlotPts(delta1[:-1], delta1[1:], surface=volume, color=close, marker='o', alpha=0.5, cmap=None), 'graph1')
# Save figure
fig.save(format='pdf')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig', tight_layout=True, height_width_ratio=.725)
# Define what to plot
# make these smaller to increase the resolution
dx = 0.05
dy = dx
# generate 2 2d grids for the x & y bounds
x = np.arange(1, 5, dx)
y = np.arange(1, 5, dy)
def z_fct(x, y):
return np.sin(x)**10 + np.cos(10 + y * x) * np.cos(x)
# Define graphs
fig.addgraph('graph1',
show_cmap_legend=True,
position=211,
title='Scalar field plot')
fig.addgraph('graph2',
show_cmap_legend=True,
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig', height_width_ratio=1)
# Define what to plot
def get_demo_image():
import numpy as np
z = np.load('bivariate_normal.npy')
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
# make data
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30 + ny, 30:30 + nx] = Z
xs = np.linspace(extent[0], extent[1], Z2.shape[0])
ys = np.linspace(extent[2], extent[3], Z2.shape[1])
zs = Z2
field = lt.ltPlotScalField(xs, ys, z_fct=zs, cmap='viridis')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='EpH-fig')
# Define what to plot
C_tr = 0.01
EpHplt = lt.ltPlotEpH('Fe', C_tr) # uses the EpH data of this package
# Define graphs
fig.addgraph('graph1',
x_label='pH',
y_label='$E$ ($\\SI{}{V}$)',
show_legend=True,
legend_on_side=False)
# Insert objects in graphs
fig.addplot(EpHplt, 'graph1')
# Save figure
fig.save(format='pdf')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='fig', page_width_cm=26, width_frac=.6, height_width_ratio=1)
# Define what to plot
X = np.arange(-5, 5, 0.25)
Y = np.arange(-5, 5, 0.25)
def z(x, y):
return np.sin(np.sqrt(x**2+y**2))
surf = lt.ltPlotSurf(X, Y, z_fct=z, cmap='coolwarm', alpha=1, use_cmap=True, norm_xyz=False)
# Define graphs
fig.addgraph('graph1', show_cmap_legend=True, projection='3d')
# Insert objects in graphs
fig.addplot(surf, 'graph1')
# Save figure
fig.save(format='pdf')
################################################################################
# Create figure
fig2 = lt.ltFigure(name='fig2', height_width_ratio=.5, tight_layout=True)
theta = np.arange(0, np.pi * (1 + 1. / steps), np.pi / steps)
phi = np.arange(-np.pi * (1 + 1. / steps), np.pi * (1 + 1. / steps),
np.pi / steps)
print(' Creating orbitals: Ylm real representations...')
ntot = 1 # len(data.Y_fcts_R)
ncur = 0
for key, Ylm in data.Y_fcts_R.items():
if key != 'dz2':
continue
cmap = 'coolwarm'
if key in ['s', '00']:
cmap += '_r'
ncur += 1
fig = lt.ltFigure(name='orbitale-' + key, height_width_ratio=1)
def R_fct(theta, phi):
return (np.absolute(Ylm(theta, phi)))**2
def C_fct(theta, phi):
return (2 * np.sign(np.real(Ylm(theta, phi))) +
np.sign(np.imag(Ylm(theta, phi)))) + 0 * theta
max_range = 3 / 8
fig.addgraph('graph1',
projection='3d',
x_ticks=False,
y_ticks=False,
z_ticks=False,
x_min=-max_range,
def create(self, element, C_tr):
fig = lt.ltFigure(name='test')
fig.addgraph('graph1', x_label='pH', y_label='$E$ ($\\SI{}{V}$)')
EpHplt = lt.ltPlotEpH(element, C_tr, pH_min=5, pH_max=6)
fig.addplot(EpHplt, 'graph1')
return EpHplt, fig.graphs['graph1']
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
import ltLaTeXpyplot.data.orbitals.orbitals_data as data # uses the orbital data of this package
r_on_a0 = np.arange(0, data.r_on_a0_MAX, 0.1)
radius = r_on_a0 * data.a0
Rnl = data.Rnl
for n, l in [(3, 0), (2, 1)]:
figs = {
'R': lt.ltFigure(name='fig-R{}{}'.format(n, l), width_frac=.25),
'R2': lt.ltFigure(name='fig-R{}{}_squared'.format(n, l),
width_frac=.25),
'p': lt.ltFigure(name='fig-R{}{}_proba'.format(n, l), width_frac=.25),
}
x = r_on_a0
ys = {
'R': Rnl(radius, n, l),
'R2': Rnl(radius, n, l)**2,
'p': 4 * np.pi * (radius)**2 * Rnl(radius, n, l)**2,
}
for subkey, fig in figs.items():
fig.addgraph('graph1',
x_min=0,
x_max=data.r_on_a0_max['{}x'.format(n)],
y_ticks=False,
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig1 = lt.ltFigure(name='fig1')
fig2 = lt.ltFigure(name='fig2')
# Define what to plot
time = np.linspace(0, 100, 100)
x = np.sin(time / 10)
y = np.cos(time / 10)
z = time / time.max()
fct1 = lt.ltPlotFct3d(x, y, z, label='F1', color='C3', norm_xyz=True)
fct2 = lt.ltPlotFct3d(x, y, z, label='F1', color='C3', norm_xyz=False)
# Define graphs
fig1.addgraph('graph1', projection='3d')
fig2.addgraph('graph1', projection='3d')
# Insert object in plots
fct1.plot(fig1, 'graph1')
fct2.plot(fig2, 'graph1')
# Save figure
fig1.save(format='pdf')
fig2.save(format='pdf')
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import ltLaTeXpyplot as lt
# Create figure
fig = lt.ltFigure(name='RMN-fig')
# Define what to plot
# signals = [signal1, signal2, ...]
# signalX = [deltaX, integral, multiplet, Js(Hz)]
signals = [
lt.ltNMRsignal(4.999, 3, [4], [6.40]),
lt.ltNMRsignal(1.374, 9, [2], [5.85])
]
spectrum = lt.ltPlotNMR(delta_min=.5,
delta_max=6,
Freq_MHz=90,
show_integral=True)
for signal in signals:
spectrum.addsignal(signal)
# Define graphs
fig.addgraph('graph1')
# Insert objects in graphs
fig.addplot(spectrum, 'graph1')
