def graphical_abstract_figures(N=60, q=1, beta=0.1):
"""
Three dimensional process examples.
"""
a = 0
b = 1
m = [[a, b, b], [b, a, b], [b, b, a]]
mu = (3. / 2) * 1. / N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta, q=q)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=3,
mu=mu)
d = stationary_distribution(edges, iterations=None)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_stationary.eps", dpi=600)
d = expected_divergence(edges, q_d=0)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_d_0.eps", dpi=600)
d = expected_divergence(edges, q_d=1)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_d_1.eps", dpi=600)
def bomze_figures(N=60, beta=1, process="incentive", directory=None):
"""
Makes plots of the stationary distribution and expected divergence for each
of the plots in Bomze's classification.
"""
if not directory:
directory = "bomze_paper_figures_%s" % process
ensure_directory(directory)
for i, m in enumerate(bomze_matrices()):
mu = 3./2 * 1./N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta)
edges = incentive_process.multivariate_transitions(N, incentive,
num_types=3, mu=mu)
d1 = stationary_distribution(edges)
filename = os.path.join(directory, "%s_%s_stationary.eps" % (i, N))
figure, tax = ternary.figure(scale = N)
tax.heatmap(d1)
tax.savefig(filename=filename)
pyplot.close(figure)
for q_d in [0., 1.]:
d2 = expected_divergence(edges, q_d=q_d)
filename = os.path.join(directory, "%s_%s_%s.eps" % (i, N, q_d))
figure, tax = ternary.figure(scale = N)
tax.heatmap(d2)
tax.savefig(filename=filename)
pyplot.close(figure)
def bomze_figures(N=60, beta=1, process="incentive", directory=None):
"""
Makes plots of the stationary distribution and expected divergence for each
of the plots in Bomze's classification.
"""
if not directory:
directory = "bomze_paper_figures_%s" % process
ensure_directory(directory)
for i, m in enumerate(bomze_matrices()):
mu = 3. / 2 * 1. / N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=3,
mu=mu)
d1 = stationary_distribution(edges)
filename = os.path.join(directory, "%s_%s_stationary.eps" % (i, N))
figure, tax = ternary.figure(scale=N)
tax.heatmap(d1)
tax.savefig(filename=filename)
pyplot.close(figure)
for q_d in [0., 1.]:
d2 = expected_divergence(edges, q_d=q_d)
filename = os.path.join(directory, "%s_%s_%s.eps" % (i, N, q_d))
figure, tax = ternary.figure(scale=N)
tax.heatmap(d2)
tax.savefig(filename=filename)
pyplot.close(figure)
def graphical_abstract_figures(N=60, q=1, beta=0.1):
"""
Three dimensional process examples.
"""
a = 0
b = 1
m = [[a, b, b], [b, a, b], [b, b, a]]
mu = (3. / 2 ) * 1. / N
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=beta, q=q)
edges = incentive_process.multivariate_transitions(N, incentive, num_types=3, mu=mu)
d = stationary_distribution(edges, iterations=None)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_stationary.eps", dpi=600)
d = expected_divergence(edges, q_d=0)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_d_0.eps", dpi=600)
d = expected_divergence(edges, q_d=1)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scale=N)
tax.savefig(filename="ga_d_1.eps", dpi=600)
def random_heatmap(scale=4):
d = generate_random_heatmap_data(scale)
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="t")
tax.boundary(color='black')
tax.set_title("Heatmap Test: Triangular")
ax = pyplot.subplot(gs[0, 1])
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="d")
tax.boundary(color='black')
tax.set_title("Heatmap Test Dual")
pyplot.show()
def random_heatmap(scale=4):
d = generate_random_heatmap_data(scale)
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="t")
tax.boundary(color='black')
tax.set_title("Heatmap Test: Triangular")
ax = pyplot.subplot(gs[0,1])
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="d")
tax.boundary(color='black')
tax.set_title("Heatmap Test Dual")
pyplot.show()
def plot_dictionary(s, ax=None):
"""
Plot two or three dimensional dictionary on a simplex partition, such as a
stationary distribution.
"""
num_types = len(s.keys()[0])
N = sum(s.keys()[0])
if not ax:
fig, ax = pyplot.subplots()
if num_types == 2:
domain = list(range(0, N+1))
values = []
for i in domain:
state = (i, N-i)
values.append(s[state])
if not ax:
fig, ax = pyplot.subplots()
pyplot.plot(domain, values)
if num_types == 3:
fig, tax = ternary.figure(scale=N, ax=ax)
tax.heatmap(s, style='d')
def figure_factory(background=False):
figure, tax = ternary.figure(scale=1.0)
figure.set_size_inches(6, 6)
if background:
tax.set_title("", fontsize=20)
tax.bottom_axis_label("Case", fontsize=14, offset=-0.05)
tax.left_axis_label("Control", fontsize=14)
tax.right_axis_label("Shared", fontsize=14)
tax.ticks(axis='lbr', linewidth=1, multiple=0.1, tick_formats="%.1f")
tax.gridlines(multiple=0.05, color="grey")
else:
tax.get_axes().axis('off')
tax.clear_matplotlib_ticks()
tax.line((0, 1, 0), (0.5, 0, 0.5),
linewidth=.5,
color='black',
linestyle="-")
figure.subplots_adjust(left=0,
bottom=0,
right=1,
top=1,
wspace=None,
hspace=None)
tax._redraw_labels()
return figure, tax
def conc_err_plot(fname):
"""Plots the error in the CE data.
This plots the error in the CE predictions within a ternary concentration diagram.
Parameters
----------
fname : string containing the input file name.
"""
energies = _read_data(fname)
enthalpy = _energy_to_enthalpy(energies)
this_errors = _find_error(enthalpy)
points = []
colors = []
for er in this_errors:
concs = er[0]
points.append((concs[0] * 100, concs[1] * 100, concs[2] * 100))
colors.append(er[1])
scale = 100
figure, tax = ternary.figure(scale=scale)
tax.boundary(linewidth=1.0)
tax.set_title("Errors in Convex Hull Predictions.", fontsize=20)
tax.gridlines(multiple=10, color="blue")
tax.scatter(points,
vmax=max(colors),
colormap=plt.cm.viridis,
colorbar=True,
c=colors,
cmap=plt.cm.viridis)
tax.show()
def plot_dictionary(s, ax=None):
"""
Plot two or three dimensional dictionary on a simplex partition, such as a
stationary distribution.
"""
num_types = len(s.keys()[0])
N = sum(s.keys()[0])
if not ax:
fig, ax = pyplot.subplots()
if num_types == 2:
domain = list(range(0, N + 1))
values = []
for i in domain:
state = (i, N - i)
values.append(s[state])
if not ax:
fig, ax = pyplot.subplots()
pyplot.plot(domain, values)
if num_types == 3:
fig, tax = ternary.figure(scale=N, ax=ax)
tax.heatmap(s, style='d')
def plot_simplex(self, data, title, filename, tax=None, cb_kwargs={}):
if tax is None:
fig, tax = ternary.figure(scale=self.simplex_range)
fig.set_size_inches(10, 8)
else:
fig = None
tax.heatmap(data, style="triangular", **cb_kwargs)
tax.boundary(linewidth=2.0)
tax.set_title(title)
tax.bottom_axis_label("Delay weight $\\alpha$",
fontsize=12,
offset=0.14)
tax.right_axis_label("Throughput weight $\\beta$",
fontsize=12,
offset=0.14)
tax.left_axis_label("Flow maximization $\\gamma$",
fontsize=12,
offset=0.14)
tax.ticks(axis='lbr', linewidth=1, multiple=5)
tax.clear_matplotlib_ticks()
tax._redraw_labels()
if fig is not None:
if self.headless:
fig.savefig(
os.path.join(self.image_path, self.folder, filename))
plt.close(fig)
else:
return fig
def plot(self, filename, panel_list, ancestries_df):
dataset_label, _ = self._unique_dataset_and_K_check(ancestries_df)
dataset = Dataset(dataset_label)
population_order = Dataset.used_populations()
rows, cols = 1, len(panel_list)
width, height = self.PLOT_SIZE
fig = plt.figure(figsize=(cols * width, rows * height), dpi=30)
fig.set_size_inches((cols*width), (rows*height))
ax_ids = (np.arange(rows * cols) + 1).tolist()[::-1]
# One subplot per panel
for panel in panel_list:
df_lite = ancestries_df.xs(panel.label, level="panel")
df_lite = df_lite.reset_index(drop=True).set_index("population")
plot_title = "Dataset: {}\n{}".format(dataset.name, panel.name)
ax = fig.add_subplot(rows, cols, ax_ids.pop())
fig, tax = ternary.figure(scale=1, ax=ax)
df_lite = df_lite.loc[population_order]
df_lite = df_lite[["EUR", "AFR", "AMR"]].dropna()
df_grouped = df_lite.groupby(level="population", sort=False)
for population, df_pop_group in df_grouped:
tax.scatter(
df_pop_group.values, label=population, s=45,
alpha=0.75, color=population_colors(population),
marker=population_markers(population)
)
self._ternary_plot_aesthetics(tax, plot_title, df_lite)
makedirs(self.PLOTS_DIR, exist_ok=True)
plt.savefig(join(self.PLOTS_DIR, filename), bbox_inches="tight")
def four_dim_figures(N=30, beta=1., q=1.):
"""
Four dimensional example. Three dimensional slices are plotted
for illustation.
"""
m = [[0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 0, 1], [0, 0, 0, 1]]
num_types = len(m[0])
fitness_landscape = linear_fitness_landscape(m)
mu = 4. / 3 * 1. / N
incentive = fermi(fitness_landscape, beta=beta, q=q)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=num_types,
mu=mu)
d1 = expected_divergence(edges, q_d=0, boundary=True)
d2 = stationary_distribution(edges)
# We need to slice the 4dim dictionary into three-dim slices for plotting.
for slice_index in range(4):
for d in [d1, d2]:
slice_dict = slice_dictionary(d, N, slice_index=3)
figure, tax = ternary.figure(scale=N)
tax.heatmap(slice_dict, style="d")
pyplot.show()
def basic_example():
# Projection dynamic.
initial_state = normalize(numpy.array([1, 1, 4]))
m = rock_scissors_paper(a=1., b=-2.)
fitness = linear_fitness(m)
incentive = replicator_incentive_power(fitness, 0)
mu = uniform_mutation_matrix(3, ep=0.2)
t = compute_trajectory(initial_state,
incentive,
escort=power_escort(0),
iterations=10000,
verbose=True,
mu=mu)
figure, tax = ternary.figure()
tax.plot(t, linewidth=2, color="black")
tax.boundary()
## Lyapunov Quantities
pyplot.figure()
# Replicator Lyapunov
e = normalize(numpy.array([1, 1, 1]))
v = [kl_divergence(e, x) for x in t]
pyplot.plot(range(len(t)), v, color='b')
d = q_divergence(0)
v = [d(e, x) for x in t]
pyplot.plot(range(len(t)), v, color='r')
pyplot.show()
def conc_err_plot(fname):
"""Plots the error in the CE data.
This plots the error in the CE predictions within a ternary concentration diagram.
Parameters
----------
fname : string containing the input file name.
"""
energies = _read_data(fname)
enthalpy = _energy_to_enthalpy(energies)
this_errors = _find_error(enthalpy)
points = []
colors = []
for er in this_errors:
concs = er[0]
points.append((concs[0]*100,concs[1]*100,concs[2]*100))
colors.append(er[1])
scale = 100
figure, tax = ternary.figure(scale=scale)
tax.boundary(linewidth = 1.0)
tax.set_title("Errors in Convex Hull Predictions.",fontsize=20)
tax.gridlines(multiple=10,color="blue")
tax.scatter(points,vmax=max(colors),colormap=plt.cm.viridis,colorbar=True,c=colors,cmap=plt.cm.viridis)
tax.show()
def plot_3way(scale, title, data, color, fname, vmax=100, vmin=-100):
# afig, ax = plt.subplots(figsize=(8,6))
# figure, tax = ternary.figure(ax, scale=scale)
figure, tax = ternary.figure(scale=scale)
figure.set_dpi(300)
tax.boundary(linewidth=2.0)
tax.gridlines(color="black", multiple=5)
# tax.gridlines(color="grey", multiple=1, linewidth=0.5)
fontsize = 17
tax.set_title(title, fontsize=fontsize + 2, y=1.03)
tax.clear_matplotlib_ticks()
plt.axis('off')
tax.left_axis_label("% Trans. Raising Input",
fontsize=fontsize,
offset=0.14)
tax.right_axis_label("% Canon. Raising Input",
fontsize=fontsize,
offset=0.14)
tax.bottom_axis_label("% Non-Productive Raising Input",
fontsize=fontsize,
offset=0.10)
tax.ticks(axis='lbr', linewidth=1, multiple=10, offset=0.025, fontsize=14)
hax = tax.heatmap(data, scale=scale, cmap=color, vmax=vmax, vmin=vmin)
# hax.cbar.ax.tick_params(labelsize=20)
tax._redraw_labels()
ternary.plt.savefig(fname)
ternary.plt.show()
def ternary_heatmapping(
x_values: np.ndarray,
y_values: np.ndarray,
i_values: np.ndarray,
number_of_bins: int,
scale_divider: float = 1.0,
ax: Optional[Axes] = None,
) -> Tuple[Figure, ternary.TernaryAxesSubplot]:
"""
Plot ternary heatmap.
Modified from: https://github.com/marcharper/python-ternary/issues/81
"""
scale = number_of_bins / scale_divider
histogram, _ = np.histogramdd(
(x_values, y_values, i_values),
bins=(number_of_bins, number_of_bins, number_of_bins),
range=((0, 1), (0, 1), (0, 1)),
)
histogram_normed = histogram / np.sum(histogram)
# 3D smoothing and interpolation
kde = gaussian_filter(histogram_normed, sigma=2)
interp_dict = dict()
binx = np.linspace(0, 1, number_of_bins)
for i in range(len(binx)):
for j in range(len(binx)):
for k in range(len(binx)):
interp_dict[(i, j, k)] = kde[i, j, k]
fig, tax = ternary.figure(ax=ax, scale=scale)
tax.heatmap(interp_dict)
return fig, tax
def buildPettijohnSST():
scale = 100
figure, tax = ternary.figure(scale=scale)
figure.set_size_inches(10, 10)
tax.set_title("Pettijohn", fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(multiple=5, color="black")
tax.left_axis_label("Feldespar", fontsize=15)
tax.right_axis_label("Quartz", fontsize=15)
tax.bottom_axis_label("Lithics", fontsize=15)
p1 = (5, 90, 5)
p1_1 = (5, 95, 0)
p1_2 = (0, 95, 5)
p2 = (75, 0, 25)
p3 = (50, 0, 50)
p4 = (25, 0, 75)
tax.line(p1_2, p1, linewidth=3., color='k', linestyle="-")
tax.line(p1, p3, linewidth=3., color='k', linestyle="-")
tax.line(p1_1, p1, linewidth=3., color='k', linestyle="-")
tax.line((0, 75), (25, 50), linewidth=3., color='k', linestyle="-")
tax.line((25, 50), (25, 75), linewidth=3., color='k', linestyle="-")
figure.gca().text(24, 75, 'Subarkose', fontsize=15, rotation=0)
figure.gca().text(61, 75, 'Sublitharenite', fontsize=15, rotation=0)
figure.gca().text(55, 85, 'Quartzarenite', fontsize=15, rotation=0)
figure.gca().text(25, 20, 'Arkosic\narenite', fontsize=15, rotation=0)
figure.gca().text(65, 20, 'Lithic\narenite', fontsize=15, rotation=0)
tax.ticks(axis='lbr', linewidth=1, multiple=5)
tax.clear_matplotlib_ticks()
figure.gca().axis('off')
return figure, tax
def ternary_from_data(data, elems=["Left", "Center", "Right"], scale=32):
_, ax = plt.subplots()
# Make the plot
fig, tax = ternary.figure(scale=scale, ax=ax)
tax.gridlines(color="black", multiple=10)
tax.boundary(linewidth=1)
# Generate the heatmap
sc = heatmap(data,
scale,
cmap=make_cmap(),
ax=ax,
vmin=0.5,
vmax=1,
colorbar=True)
# Make it prettier
plt.axis('off')
ax.text(1.05 * scale, -0.05 * scale, elems[0], ha='right',
fontsize=12) # 1st elem
ax.text(.50 * scale, .90 * scale, elems[1], ha='center',
fontsize=12) # 2nd elem
ax.text(-.05 * scale, -.05 * scale, elems[2], ha='left',
fontsize=12) # 3rd elem
def buildCong():
scale = 100
figure, tax = ternary.figure(scale=scale)
figure.set_size_inches(10, 10)
tax.set_title("Gravel and Conglomerates", fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(multiple=5, color="black")
tax.left_axis_label("Mud", fontsize=15)
tax.right_axis_label("Gravel", fontsize=15)
tax.bottom_axis_label("Sand", fontsize=15)
tax.horizontal_line(80, c='k', lw=3)
tax.horizontal_line(30, c='k', lw=3)
tax.horizontal_line(5, c='k', lw=3)
tax.line((50, 0), (10, 80), linewidth=3., color='k', linestyle="-")
tax.line((90, 0), (18, 80), linewidth=3., color='k', linestyle="-")
tax.line((5, 5), (5, 0), linewidth=3., color='k', linestyle="-")
figure.gca().text(-1, -5, 'Mud-', fontsize=15, rotation=0)
figure.gca().text(20, 1, 'Sandy mud-', fontsize=15, rotation=0)
figure.gca().text(60, 1, 'Muddy sand-', fontsize=15, rotation=0)
figure.gca().text(91, -5, 'Sand-', fontsize=15, rotation=0)
figure.gca().text(30, 10, 'Gravelly\nmud-', fontsize=15, rotation=0)
figure.gca().text(60, 10, 'Gravelly\nmuddy sand-', fontsize=15, rotation=0)
figure.gca().text(30, 42, 'Muddy\ngravel/cong', fontsize=15, rotation=0)
figure.gca().text(51,
40,
'Muddy\nsandy\ngravel/cong.',
fontsize=15,
rotation=0)
figure.gca().text(61, 75, 'Gravel/Cong', fontsize=15, rotation=0)
figure.gca().text(73, 55, 'Sandy\ngravel/cong', fontsize=15, rotation=0)
figure.gca().text(94, 15, 'Gravelly\nsand-', fontsize=15, rotation=0)
tax.ticks(axis='lbr', linewidth=1, multiple=5)
tax.clear_matplotlib_ticks()
figure.gca().axis('off')
return figure, tax
def _plotTernaryMixture_(self,cmap = 'viridis'):
''' When there are three parents, plot the value of the comparison function
in a ternary plot '''
#Create a ternary figure
figure, tax = ternary.figure(scale = 100.0)
## Boundary and Gridlines
# Draw Boundary and Gridlines
tax.boundary(linewidth=2.0)
# Set Axis labels and Title
tax.left_axis_label('% '+self.parentNames[2], fontsize=13)
tax.right_axis_label('% '+self.parentNames[1], fontsize=13)
tax.bottom_axis_label('% '+self.parentNames[0], fontsize=13)
# Set ticks
tax.ticks(axis='lbr', linewidth=1, multiple=10)
# Remove default Matplotlib Axes
tax.clear_matplotlib_ticks()
d = dict()
for i,mixCoeff in enumerate(self.mixingCoeffs):
d[(round(mixCoeff[0],3)*100.0,round(mixCoeff[1],3)*100.0,round(mixCoeff[2],3)*100.0)] = self.mixtureFunctionValue[i]
tax.heatmap(d, style='hexagonal',cmap = cmap)
bestFit = self.mixingCoeffs[0]
tax.scatter([(round(bestFit[0],3)*100.0,round(bestFit[1],3)*100.0,round(bestFit[2],3)*100.0)], marker='o', lw=3, color='black')
# Save the current figure to a temporary PDF file
tax.savefig(os.path.join(RESULT_FOLDER, 'temp.pdf'))
tax.close()
return tax
def ternary_plot(points, title):
import ternary
#tax is the axes object
#For more information please vist https://github.com/marcharper/python-ternary
figure, tax = ternary.figure(scale=100)
# Remove default Matplotlib Axes
tax.clear_matplotlib_ticks()
#formatting the ternary diagram
left_kwargs = {'color': 'blue'}
right_kwargs = {'color': 'red'}
tax.set_title(title, fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(color="black",
multiple=5,
left_kwargs=left_kwargs,
right_kwargs=right_kwargs)
#should be three stack of three different lines
tax.scatter(points, linewidth=1)
#axis settings
fontsize = 10
tax.left_axis_label("%Clay", fontsize=fontsize)
tax.right_axis_label("%Silt", fontsize=fontsize)
tax.bottom_axis_label("%Sand", fontsize=fontsize)
tax.ticks(axis='lbr', linewidth=1, multiple=10, clockwise=True)
tax.get_axes().axis('off')
#present
ternary.plt.show()
def plot_ternary(av_df, labels_ordered):
scale = 1
fig, ax = plt.subplots()
figure, tax = ternary.figure(ax=ax, scale=scale)
# figure.set_size_inches(10, 10)
# Draw Boundary and Gridlines
tax.boundary(linewidth=2.0)
tax.gridlines(color="blue", multiple=0.1)
# Set Axis labels and Title
fontsize = 20
# tax.set_title("Various Lines", fontsize=20)
tax.left_axis_label(labels_ordered[1], fontsize=fontsize)
tax.right_axis_label(labels_ordered[2], fontsize=fontsize)
tax.bottom_axis_label(labels_ordered[0], fontsize=fontsize)
# Draw lines parallel to the axes
# tax.horizontal_line(16)
# tax.left_parallel_line(10, linewidth=2., color='red', linestyle="--")
# tax.right_parallel_line(20, linewidth=3., color='blue')
# Draw an arbitrary line, ternary will project the points for you
p1 = (0.2, 0.35, 0.45)
p2 = (0, 0, 1)
tax.line(p1, p2, linewidth=3., marker='s', color='green', linestyle=":")
ticks = list(np.arange(0, 1.1, 0.1))
tax.ticks(ticks=ticks, axis='brl', multiple=0.1, tick_formats="%0.1f")
tax.clear_matplotlib_ticks()
plt.tight_layout()
plt.show()
def buildProvenancePlot():
#(Lithics, Qtz, Fs)
scale = 100
figure, tax = ternary.figure(scale=scale)
figure.set_size_inches(10, 10)
tax.set_title("Tectonic Provenance", fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(multiple=5, color="black")
tax.left_axis_label("Feldespar", fontsize=18)
tax.right_axis_label("Quartz", fontsize=18)
tax.bottom_axis_label("Lithics", fontsize=18)
p1 = (75, 25, 0)
p2 = (0, 100 - 45, 45)
tax.line(p1, p2, linewidth=3., color='k', linestyle="-")
q1 = (3, 97, 0)
q2 = (15, 0, 100 - 15)
tax.line(q1, q2, linewidth=3., color='k', linestyle="-")
tax.line((0, 100 - 18), (5, 80), linewidth=3., color='k', linestyle="-")
tax.line((75, 25, 0), (50, 0), linewidth=3., color='k', linestyle="-")
tax.line((13, 18), (54, 33.5), linewidth=3., color='k', linestyle="-")
tax.ticks(axis='lbr', linewidth=1, multiple=5)
tax.clear_matplotlib_ticks()
figure.gca().text(21, 80, 'Craton Interior', fontsize=15, color='k')
figure.gca().text(15,
60,
'Transitional\nContinental',
fontsize=15,
color='k')
figure.gca().text(-5, 20, 'Basement\nUplift', fontsize=15, color='k')
figure.gca().text(50, 50, 'Recycled\nOrogenic', fontsize=15, color='k')
figure.gca().text(35, 25, 'Dissected\nArc', fontsize=15, color='k')
figure.gca().text(40, 10, 'Transitional\nArc', fontsize=15, color='k')
figure.gca().text(70, 2, 'Undissected\nArc', fontsize=15, color='k')
figure.gca().axis('off')
return figure, tax
def plot(data, figure=None, **kwargs):
if figure is None:
fig, tax = ternary.figure()
else:
fig, tax = figure
gammaData = np.ones(len(data[:, 0])) - data[:, 0] - data[:, 1]
# Plot curve
tax.plot(np.stack((data[:, 0], gammaData, data[:, 1]), axis=1), **kwargs)
def streck76_ol_2px(*puntos):
fig, tax = tr.figure(scale=100)
fig.set_size_inches(12, 12)
tax.set_title(
"Diagrama de clasificación de rocas plutónicas (Streckeisen 1976)",
pad=50,
Fontsize=20)
tax.gridlines(multiple=10, color="k")
tax.gridlines(5)
tax.get_axes().axis('off')
tax.horizontal_line(40)
tax.left_parallel_line(90)
tax.horizontal_line(90)
tax.right_parallel_line(90)
tax.ticks(multiple=10)
tax.line([5, 90], [90, 5])
tax.line([5, 5], [5, 90])
tax.line([90, 5], [5, 5, 90])
tax.line([65, 40], [160, 40], ls='dashed', color='k')
tax.left_corner_label("Opx", Fontsize=18, position=(-0.02, 0.05, 0))
tax.right_corner_label("Cpx", Fontsize=18, position=(0.97, 0.05, 0))
tax.top_corner_label("Ol", Fontsize=18, offset=0.18)
coordenadas = [[2, 95], [2.5, 60], [20, 60], [37, 60], [2.5, 20], [40, 20],
[77, 20], [3, 2.5], [47, 2.5], [93, 2.5]]
for indice, coordenada in enumerate(coordenadas):
tax.annotate(str(indice + 1), (coordenada[0], coordenada[1]),
fontsize=9)
indices = '''1- Dunita
2- Hazburguita
3- Lherzolita
4- Wehrlita
5- Ortopiroxenita olivínica
6- Websterita olivínica
7- Clinoperoxenita olivínica
8- Ortopiroxenita
9- Websterita
10- Clinopiroxenita'''
tax.annotate(indices,
position=(40, 100, 0),
size=10,
ha='left',
va='top',
bbox=dict(boxstyle='round', fc='w'))
for punto in puntos:
tax.scatter([[float(punto[0]),
float(punto[1]),
float(punto[2])]],
label=punto[3])
fig.legend(fontsize=10,
bbox_to_anchor=(0.18, 0.8),
bbox_transform=fig.transFigure).get_frame().set_edgecolor('k')
tax.show()
def streck76_plut_maf_hbl(*puntos):
fig, tax = tr.figure(scale=100)
fig.set_size_inches(12, 12)
tax.set_title(
"Diagrama de clasificación de rocas gabróideas con hornblenda (Streckeisen 1976)",
pad=50,
Fontsize=20)
tax.gridlines(multiple=10, color="k")
tax.gridlines(5)
tax.get_axes().axis('off')
tax.horizontal_line(10)
tax.horizontal_line(90)
tax.line([5, 65], [70, 65], color='r')
tax.line([5, 35], [100, 35], color='r')
tax.ticks(multiple=10)
tax.line([5, 10], [5, 90])
tax.line([50, 0], [45, 10])
tax.line([85, 10], [5, 90])
tax.left_parallel_line(90)
tax.right_parallel_line(90)
tax.left_corner_label("Px", Fontsize=18, position=(-0.02, 0.05, 0))
tax.right_corner_label("Hbl", Fontsize=18, position=(0.97, 0.05, 0))
tax.top_corner_label("Pl", Fontsize=18, offset=0.18)
coordenadas = [[3, 93], [2, 50], [25, 50], [46.5, 50], [3, 4], [25, 4],
[70, 4], [93, 4]]
for indice, coordenada in enumerate(coordenadas):
tax.annotate(str(indice + 1), (coordenada[0], coordenada[1]),
fontsize=9)
indices = '''1- Anortosita
2- Gabro, Gabronorita, norita
3- Gabro/Gabronorita/norita de hornblenda y piroxeno
4- Gabro hornblendico
5- Piroxenita rica en plagioclasa
6- Piroxenita de hornblenda rica en plagioclasa
7- Hornblendita de piroxeno rica en plagioclasa
8- Hornblendita rica en plagioclasa
'''
tax.annotate(indices,
position=(40, 100, 0),
size=10,
ha='left',
va='top',
bbox=dict(boxstyle='round', fc='w'))
for punto in puntos:
tax.scatter([[float(punto[0]),
float(punto[1]),
float(punto[2])]],
label=punto[3])
fig.legend(fontsize=10,
bbox_to_anchor=(0.18, 0.8),
bbox_transform=fig.transFigure).get_frame().set_edgecolor('k')
tax.show()
def plot_tern(data_point):
# Scatter Plot
scale = 100
figure, tax = ternary.figure(scale=scale)
figure.set_size_inches(10, 10)
tax.set_title("Neon Isotopes", fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(multiple=10, color="black")
tax.left_axis_label("Neon-22", offset=0.1)
tax.right_axis_label("Neon-21", offset=0.1)
tax.bottom_axis_label("Neon-20", offset=-0.05)
# Air
points = []
points.append([90.48, 00.27, 09.25])
tax.scatter(points, marker='s', color='red', label="Air")
# Cosmogenic (Quartz; Schaefer et al. 1999)
points = []
points.append([35.59, 28.47, 35.94])
tax.scatter(points, marker='s', color='green', label="Cosmogenic (Quartz)")
# Cosmogenic (Pyroxene; Schaefer et al. 1999)
points = []
points.append([29.67, 32.64, 37.69])
tax.scatter(points,
marker='s',
color='blue',
label="Cosmogenic (Pyroxene)")
# Mantle
points = []
points.append([92.80, 00.47, 06.73])
tax.scatter(points, marker='s', color='purple', label="Mantle")
# Nucleogenic (Bulk Crustal)
points = []
points.append([08.39, 89.73, 01.88])
tax.scatter(points,
marker='s',
color='grey',
label="Nucleogenic (Bulk Crust)")
points = []
points.append(data_point)
tax.scatter(points, marker='s', color='black', label="Data")
tax.legend()
tax.ticks(axis='lbr', multiple=10, linewidth=1, offset=0.012)
tax.clear_matplotlib_ticks()
tax.show()
def QFOth_ternary_plot(self,
selected_phi_classes=None,
save_filename=None):
"""Ternary diagram plot of pcg's modal mineralogy with trajectory
along the model's steps. A=pcg_Q, B=pcg_PK, C=pcg_BOA.
Data has been grouped by whole phi grain size classes.
Parameters:
-----------
selected_phi_classes : None or list (optional)
List of phi classes to plot; defaults to None so that all
phi classes are plotted.
save_filename : str (optional)
Name to use for saving figure; defaults to None so that no
figure is saved.
"""
# Calculate data
T_norm = self.calculate_QFOth_data()
# Filter data
if selected_phi_classes is None:
selected_phi_classes = self.unit_phi_classes
# Plot data
fig, ax = plt.subplots(figsize=(12, 10.8))
figure, tax = ternary.figure(ax=ax, scale=1.0)
tax.boundary()
tax.gridlines(multiple=0.2, color="black")
# tax.set_title("QFR plot", fontsize=20)
fontsize = 12
tax.right_corner_label("Others", fontsize=fontsize)
tax.top_corner_label("Quartz", fontsize=fontsize)
tax.left_corner_label("Feldspar", fontsize=fontsize)
points = []
# Get data
for p, phi in enumerate(T_norm):
if self.unit_phi_classes[p] in selected_phi_classes:
points = phi
# Plot the data
tax.plot(points,
linewidth=2.0,
label=f"{self.unit_phi_classes[p]}_phi",
marker='o',
color=plt.cm.tab20(p))
tax.ticks(axis='lbr', multiple=0.2, linewidth=1, tick_formats="%.1f")
tax.legend(loc=1)
plt.axis('off')
# plt.tight_layout()
if save_filename:
tax.savefig(
f"_FIGURES/ternary_diagrams/QFO_ternary_plot_{save_filename}.pdf"
)
tax.show()
def folk_arena_arcilla(*puntos):
fig, tax = tr.figure(scale=100)
fig.set_size_inches(12, 12)
tax.set_title(
"Diagrama de clasificación textural de rocas siliciclásticas (Folk 1954)",
pad=50,
Fontsize=20)
tax.gridlines(multiple=10, color="k")
tax.gridlines(5)
tax.get_axes().axis('off')
tax.horizontal_line(10)
tax.horizontal_line(50)
tax.horizontal_line(90)
tax.ticks(multiple=10)
tax.line([(100 / 3), 0], intersec([(100 / 3), 0], [0, 100], 90))
tax.line([(100 / 3 * 2), 0], intersec([(100 / 3 * 2), 0], [0, 100], 90))
tax.left_corner_label("Arcilla", Fontsize=18, position=(-0.02, 0.05, 0))
tax.right_corner_label("Limo", Fontsize=18, position=(0.97, 0.05, 0))
tax.top_corner_label("Arena", Fontsize=18, offset=0.18)
coordenadas = [[2, 95], [5, 70], [15, 70], [25, 70], [12, 30], [35, 30],
[60, 30], [17, 5], [47, 5], [80, 5]]
for indice, coordenada in enumerate(coordenadas):
tax.annotate(str(indice + 1), (coordenada[0], coordenada[1]),
fontsize=9)
indices = '''1- Arenisca
2- Arenisca arcillosa
3- Arenisca lodosa
4- Arenisca limosa
5- Arcillolita arenosa
6- Lodolita arenosa
7- Limolita arenosa
8- Arcillolita
9- Lodolita
10- Limolita'''
tax.annotate(indices,
position=(40, 100, 0),
size=10,
ha='left',
va='top',
bbox=dict(boxstyle='round', fc='w'))
for punto in puntos:
tax.scatter([[float(punto[0]),
float(punto[1]),
float(punto[2])]],
label=punto[3])
fig.legend(fontsize=10,
bbox_to_anchor=(0.18, 0.8),
bbox_transform=fig.transFigure).get_frame().set_edgecolor('k')
tax.show()
def create_graphs(Ncomps, dpi):
scale = 100
figure, tax = ternary.figure(scale=scale)
figure, pax = ternary.figure(scale=scale)
# Draw Boundary and Gridlines
tax.boundary(linewidth=0.25)
tax.gridlines(color="blue", multiple=10)
pax.boundary(linewidth=0.55)
pax.gridlines(color="blue", multiple=10)
# Set Axis labels and Title
fontsize = 10
tax.set_title("BaxCuyArz Composition diagram", fontsize=20)
tax.left_axis_label("Ba", fontsize=fontsize)
tax.right_axis_label("Cu", fontsize=fontsize)
tax.bottom_axis_label("Ar", fontsize=fontsize)
fontsize = 10
pax.set_title("BaxCuyArz Composition diagram", fontsize=20)
pax.left_axis_label("Ba", fontsize=fontsize)
pax.right_axis_label("Cu", fontsize=fontsize)
pax.bottom_axis_label("Ar", fontsize=fontsize)
p1 = []
with open("composition.dat") as handle:
for line in handle:
p1.append(list(map(float, line.split(' '))))
tax.scatter(p1)
pax.scatter(p1)
tax.legend()
pax.ticks(axis='lbr', multiple=10, linewidth=1)
for i in range(Ncomps):
tax.annotate(str(p1[i]), (p1[i]), fontsize=7, rotation=10)
tax.savefig('Ternary_with_labels.png', dpi)
pax.savefig('Ternary_no_labels.png', dpi)
def plot_prediction_ternary(result, scale=100, multiple=20, plot_name=None):
correct = result['correct']
wrong = result['wrong']
unknown = result['unknown']
fontsize = 12
axis_fontsize = 8
figure, tax = ternary.figure(scale=scale)
#tax.set_title("Training Data", fontsize=20)
tax.boundary(linewidth=1.0)
tax.gridlines(multiple=multiple, color="blue")
tax.left_axis_label("$C_{10}$", fontsize=fontsize)
tax.right_axis_label("$C_{6}$", fontsize=fontsize)
tax.bottom_axis_label("$C_{1}$", fontsize=fontsize)
correct_points = correct[0] * scale
wrong_points = wrong[0] * scale
unknown_points = unknown[0] * scale
correct_points = np.rint(correct_points)
correct_points = correct_points.astype(int)
wrong_points = np.rint(wrong_points)
wrong_points = wrong_points.astype(int)
unknown_points = np.rint(unknown_points)
unknown_points = unknown_points.astype(int)
if (correct_points.shape[0] > 0):
tax.scatter(correct_points, s=1, color='red', label="Correct")
if (wrong_points.shape[0] > 0):
tax.scatter(wrong_points, s=3, color='blue', label="Wrong")
if (unknown_points.shape[0] > 0):
tax.scatter(unknown_points, s=2, color='green', label="Uncertain")
tax.legend()
tax.clear_matplotlib_ticks()
tax.ticks(axis='lbr', linewidth=1, multiple=multiple, fontsize=8)
tax._redraw_labels()
for spine in plt.gca().spines.values():
spine.set_visible(False)
if plot_name is None:
plot_name = 'stab-prediction-result'
file_name = plot_name + '.eps'
tax.savefig(file_name)
file_name = plot_name + '.pdf'
tax.savefig(file_name)
def render_stationary(s):
"""
Renders a stationary distribution.
"""
# Put the stationary distribution into a dictionary
d = dict()
for state, value in s:
d[state] = value
N = sum(state)
# Plot it
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scientific=True, style='d')
def draw_triangle_ax(self, ax):
ancestries = self.admixture.result[3]
fig, tax = ternary.figure(scale=1, ax=ax)
ancestries = ancestries[["EUR", "AFR", "AMR"]].dropna()
by_population = ancestries.groupby(level="population", sort=False)
for population, df in by_population:
tax.scatter(df.values, label=population, s=45, marker='o',
color=self.colors[population])
plot_title = self._make_title(3).replace(' - ', '\n')
self._ternary_plot_aesthetics(tax, plot_title, ancestries)
return tax
def simplexPath(episodes, title=""):
# Sample trajectory plot
figure, tax = ternary.figure(scale=1.0)
figure.set_size_inches(15, 15, forward=True)
tax.boundary()
tax.gridlines(multiple=0.2, color="black")
tax.set_title(title, fontsize=20)
points = []
# Plot the data
tax.plot(episodes, linewidth=2.0, label="Curve")
tax.ticks(axis='lbr', multiple=0.2, linewidth=1, tick_formats="%.1f")
tax.legend()
tax.show()
def render_stationary(s):
"""
Renders a stationary distribution.
"""
# Put the stationary distribution into a dictionary
d = dict()
for state, value in s:
d[state] = value
N = sum(state)
# Plot it
figure, tax = ternary.figure(scale=N)
tax.heatmap(d, scientific=True, style='d')
def plot_ternary_scatter(data_matrix):
### Scatter Plot
scale = 1
figure, tax = ternary.figure(scale=scale)
tax.set_title("Scatter Plot", fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(multiple=0.1, color="blue")
# Plot a few different styles with a legend
# points = [data_matrix]
# tax.heatmap()
tax.scatter(data_matrix, marker='s', color='red', label="Red Squares")
tax.legend()
tax.ticks(axis='lbr', linewidth=1, multiple=0.1)
def folk_comp(*puntos):
fig, tax = tr.figure(scale=100)
fig.set_size_inches(12, 12)
tax.set_title(
"Diagrama de clasificación composicional de rocas siliciclásticas (Folk 1974)",
pad=50,
Fontsize=20)
tax.gridlines(multiple=10, color="k")
tax.gridlines(5)
tax.get_axes().axis('off')
tax.horizontal_line(95)
tax.horizontal_line(75)
tax.ticks(multiple=10)
tax.line([(100 / 4), 0], intersec([(100 / 4), 0], [0, 100], 75))
tax.line([(100 / 4 * 3), 0], intersec([(100 / 4 * 3), 0], [0, 100], 75))
tax.line([50, 0], intersec([50, 0], [0, 100], 95))
tax.left_corner_label("F", Fontsize=18, position=(-0.02, 0.05, 0))
tax.right_corner_label("L", Fontsize=18, position=(0.97, 0.05, 0))
tax.top_corner_label("Q", Fontsize=18, offset=0.18)
coordenadas = [[1.1, 97], [3, 85], [11, 85], [7, 30], [25, 30], [45, 30],
[60, 30]]
for indice, coordenada in enumerate(coordenadas):
tax.annotate(str(indice + 1), (coordenada[0], coordenada[1]),
fontsize=9)
indices = '''1- Cuarzoarenita
2- Sublitoarenita
3- Subarcosa
4- Litoarenita
5- Litoarenita Feldespática
6- Arcosa Lítica
7- Arcosa'''
tax.annotate(indices,
position=(40, 100, 0),
size=10,
ha='left',
va='top',
bbox=dict(boxstyle='round', fc='w'))
for punto in puntos:
tax.scatter([[float(punto[0]),
float(punto[1]),
float(punto[2])]],
label=punto[3])
fig.legend(fontsize=10,
bbox_to_anchor=(0.18, 0.8),
bbox_transform=fig.transFigure).get_frame().set_edgecolor('k')
tax.show()
def make_ternary_legend(elements):
"""Makes the legend for concentraition plots."""
scale = 100
data = _generate_heatmap_data(scale)
figure, tax = ternary.figure(scale=100, permutation="210")
tax.heatmap(data, colormap=False)
tax.boundary(linewidth = 0.0)
tax.right_corner_label(elements[0], position=(0.95,0.1), fontsize=16)
tax.top_corner_label(elements[1], position=(-0.075,1.15), fontsize=16)
tax.left_corner_label(elements[2], position=(-0.05,0.1), fontsize=16)
plt.axis("off")
plt.show()
def render_stationary(s):
"""
Renders a stationary distribution.
"""
# Put the stationary distribution into a dictionary
d = dict()
for state, value in s:
d[state] = value
N = sum(list(d.keys())[0])
# Plot it
figure, tax = ternary.figure(scale=N)
tax.heatmap(remove_boundary(d), scientific=True, style='triangular',
cmap="jet")
return tax
def plot_graph(points):
### Scatter Plot
scale = 1
figure, tax = ternary.figure(scale=scale)
tax.set_title("Scatter Plot", fontsize=20)
tax.boundary(color="black", linewidth=0.5)
#tax.gridlines(multiple=5, color="blue")
# Plot a few different styles with a legend
tax.scatter(points, marker='s', color='red', label="Red Squares")
#points = random_points(30, scale=scale)
#tax.scatter(points, marker='D', color='green', label="Green Diamonds")
tax.legend()
tax.ticks(axis='lbr', color="black", linewidth=1, multiple=0.1)
tax.show()
def rps_figures(N=60, q=1, beta=1.):
"""
Three rock-paper-scissors examples.
"""
m = [[0, -1, 1], [1, 0, -1], [-1, 1, 0]]
num_types = len(m[0])
fitness_landscape = linear_fitness_landscape(m)
for i, mu in enumerate([1./math.sqrt(N), 1./N, 1./N**(3./2)]):
# Approximate calculation
mu = 3/2. * mu
incentive = fermi(fitness_landscape, beta=beta, q=q)
edges = incentive_process.multivariate_transitions(N, incentive, num_types=num_types, mu=mu)
d = stationary_distribution(edges, lim=1e-10)
figure, tax = ternary.figure()
tax.heatmap(d, scale=N)
tax.savefig(filename="rsp_mu_" + str(i) + ".eps", dpi=600)
def draw(self, ax=None, setup=True, marker='o', color='k'): # pragma: no cover
"""
Plot the entropy triangle.
Parameters
----------
ax : Axis or None
The matplotlib axis to plot on. If none is provided, one will be
constructed.
setup : bool
If true, labels, tick marks, gridlines, and a boundary will be added
to the plot. Defaults to True.
marker : str
The matplotlib marker shape to use.
color : str
The color of marker to use.
"""
import ternary
if ax is None:
fig, ax = ternary.figure()
fig.set_size_inches(10, 8)
else:
ax = ternary.TernaryAxesSubplot(ax=ax)
if setup:
ax.boundary()
ax.gridlines(multiple=0.1)
fontsize = 20
ax.set_title("Entropy Triangle", fontsize=fontsize)
ax.left_axis_label(self.left_label, fontsize=fontsize)
ax.right_axis_label(self.right_label, fontsize=fontsize)
ax.bottom_axis_label(self.bottom_label, fontsize=fontsize)
ax.ticks(axis='lbr', multiple=0.1, linewidth=1)
ax.clear_matplotlib_ticks()
ax.scatter(self.points, marker=marker, color=color)
ax._redraw_labels()
return ax
def basic_example():
# Projection dynamic.
initial_state = normalize(numpy.array([1,1,4]))
m = rock_scissors_paper(a=1., b=-2.)
fitness = linear_fitness(m)
incentive = replicator_incentive_power(fitness, 0)
mu = uniform_mutation_matrix(3, ep=0.2)
t = compute_trajectory(initial_state, incentive, escort=power_escort(0), iterations=10000, verbose=True, mu=mu)
figure, tax = ternary.figure()
tax.plot(t, linewidth=2, color="black")
tax.boundary()
## Lyapunov Quantities
pyplot.figure()
# Replicator Lyapunov
e = normalize(numpy.array([1,1,1]))
v = [kl_divergence(e, x) for x in t]
pyplot.plot(range(len(t)), v, color='b')
d = q_divergence(0)
v = [d(e, x) for x in t]
pyplot.plot(range(len(t)), v, color='r')
pyplot.show()
def ER_figure_beta3(N, m, mu, betas, iss_states, labels, stationary_beta=0.35,
pickle_filename="figure_beta3.pickle"):
"""Varying Beta, three dimensional example"""
ss = []
plot_data = [[] for _ in range(len(iss_states))]
if os.path.exists(pickle_filename):
with open(pickle_filename, 'rb') as f:
plot_data = pickle.load(f)
else:
for beta in betas:
print(beta)
e, s = compute_entropy_rate(
N=N, m=m, n=3, beta=beta, exact=False, mu=mu, lim=1e-10)
ss.append(s)
for i, iss_state in enumerate(iss_states):
s_max = s[iss_state]
plot_data[i].append((e, s_max, e / s_max))
with open(pickle_filename, 'wb') as f:
pickle.dump(plot_data, f)
gs = gridspec.GridSpec(3, 2)
ax1, ax2, ax3 = plot_data_sub(betas, plot_data, gs, labels=labels,
use_log=True, sci=False)
ax3.set_xlabel("Strength of selection $\\beta$")
ax2.legend(loc="upper right")
# Plot example stationary
ax4 = plt.subplot(gs[:, 1])
_, s = compute_entropy_rate(
N=N, m=m, n=3, beta=stationary_beta, exact=False, mu=mu, lim=1e-15)
_, tax = ternary.figure(ax=ax4, scale=N,)
tax.heatmap(s, cmap="jet", style="triangular")
tax.ticks(axis='lbr', linewidth=1, multiple=10, offset=0.015)
tax.clear_matplotlib_ticks()
ax4.set_xlabel("Population States $a_1 + a_2 + a_3 = N$")
def PrintTernary(AtPct, IshiiAtPct, IshiiAtPctSD):
if ternary == None:
print "For ternary plot please pip install python-ternary"
return
### Now print a ternary plot
if plt.fignum_exists(2):
plt.figure(2)
plt.close()
fig, tax = ternary.figure(scale=100)
tax.boundary(linewidth=4)
tax.gridlines(color='black', multiple=10)
tax.left_axis_label("Fe-S", fontsize=FontSizeBasis)
tax.bottom_axis_label("Si", fontsize=FontSizeBasis)
tax.right_axis_label("Mg", fontsize=FontSizeBasis)
# Remove default matplotlib ticks.
tax.ticks(axis='lbr', linewidth=1, multiple=10)
tax.clear_matplotlib_ticks()
# Compute and plot this point on the ternary for this composition
FeminusS = AtPct[pb.Fe - 1] - AtPct[pb.S - 1]
ThisPoint = array([FeminusS, AtPct[pb.Si - 1], AtPct[pb.Mg - 1]]).astype('float')
ThisPoint = ThisPoint / sum(ThisPoint) * 100
ThisPoint = vstack((ThisPoint, ThisPoint)) # Seems to be a bug -- you need a minimum of two points...
tax.scatter(ThisPoint, marker='.', s=300, alpha=0.5, color='blue', label="This Spectrum")
# Compute and plot the point for the average of GEMS.
FeminusS = IshiiAtPct[pb.Fe - 1] - IshiiAtPct[pb.S - 1]
IshiiPoint = array([FeminusS, IshiiAtPct[pb.Si - 1], IshiiAtPct[pb.Mg - 1]]).astype('float')
IshiiPoint = IshiiPoint / sum(IshiiPoint) * 100
IshiiPoint = vstack((IshiiPoint, IshiiPoint)) # Seems to be a bug -- you need a minimum of two points...
tax.scatter(IshiiPoint, marker='.', s=300, alpha=0.5, color='red', label="Ishii et al., 2008")
tax.legend()
plt.title('At %')
def four_dim_figures(N=30, beta=1., q=1.):
"""
Four dimensional example. Three dimensional slices are plotted
for illustation.
"""
m = [[0, 1, 1, 1], [1, 0, 1, 1], [1, 1, 0, 1], [0, 0, 0, 1]]
num_types = len(m[0])
fitness_landscape = linear_fitness_landscape(m)
mu = 4. / 3 * 1. / N
incentive = fermi(fitness_landscape, beta=beta, q=q)
edges = incentive_process.multivariate_transitions(N, incentive, num_types=num_types, mu=mu)
d1 = expected_divergence(edges, q_d=0, boundary=True)
d2 = stationary_distribution(edges)
# We need to slice the 4dim dictionary into three-dim slices for plotting.
for slice_index in range(4):
for d in [d1, d2]:
slice_dict = slice_dictionary(d, N, slice_index=3)
figure, tax = ternary.figure(scale=N)
tax.heatmap(slice_dict, style="d")
pyplot.show()
def ternary_plot(points,title):
import ternary
#tax is the axes object
#For more information please vist https://github.com/marcharper/python-ternary
figure, tax = ternary.figure(scale =100)
# Remove default Matplotlib Axes
tax.clear_matplotlib_ticks()
#formatting the ternary diagram
left_kwargs = {'color': 'blue'}
right_kwargs = {'color': 'red'}
tax.set_title(title, fontsize=20)
tax.boundary(linewidth=2.0)
tax.gridlines(color="black", multiple=5, left_kwargs=left_kwargs,right_kwargs=right_kwargs)
#should be three stack of three different lines
tax.scatter(points,linewidth=1)
#axis settings
fontsize = 10
tax.left_axis_label("%Clay", fontsize=fontsize)
tax.right_axis_label("%Silt", fontsize=fontsize)
tax.bottom_axis_label("%Sand", fontsize=fontsize)
tax.ticks(axis='lbr', linewidth=1, multiple= 10,clockwise= True)
tax.get_axes().axis('off')
#present
ternary.plt.show()
def draw_ternary_plot(controls, rcontrols, filename):
m = np.shape(controls)[0]
n = 20
ten_per = int(floor(m/5.0))
swap_cols(controls, 2, 0)
swap_cols(rcontrols, 2, 0)
swap_cols(controls, 0, 1)
swap_cols(rcontrols, 0, 1)
first_ten_per = controls[range(0, ten_per),:]
rfirst_ten_per = rcontrols[range(0, ten_per),:]
rest = controls[range(ten_per, m),:]
rrest = rcontrols[range(ten_per, m),:]
figure, tax = ternary.figure(scale=1.0)
tax.boundary(color='black')
tax.plot(first_ten_per, linewidth=3, label="Weight cuts", color="#00d3e4")
tax.plot(rfirst_ten_per, linewidth=3, label="Random cuts", color="#75b765")
tax.plot(rest, linewidth=1, label="Weight cuts", color="#007e88")
tax.plot(rrest, linewidth=1, label="Random cuts", color="#466d3c")
tax.left_axis_label("No external dilation control")
tax.right_axis_label("No source control")
tax.bottom_axis_label("No internal dilation control")
tax.legend()
#tax.show()
figure.savefig('graphs/ternary/'+filename+'-ternary.png')
# E210.append(float("%.3f" % float(row['x'])))
# M210.append(float("%.3f" % float(row['y'])))
# T210.append(float("%.3f" % float(row['z'])))
# w210.append(float("%.3f" % float(row['w'])))
point1=[(scale*0.3980599331366308,scale*0.3112164005027349,scale*0.2907236663606344)]
point2=[(scale*0.34669820676138435,scale*0.3336302826666826,scale*0.31967151057193305)]
point3=[(scale*0.55214511226237,scale*0.24397475401089158,scale*0.20388013372673847)]
point4=[(scale*0.24397475401089158,scale*0.3784580469945782,scale*0.3775671989945304)]
democratic=[(scale*1/3,scale*1/3,scale*1/3)]
point5=[(scale*0.4494216595118771,scale*0.2888025183387871,scale*0.26177582214933576)]
ice1=[(scale*0,scale*0.2,scale*0.8)]
ice2=[(scale*0.18,scale*0.41,scale*0.41)]
"""ternary settings"""
figure, d=t.figure(scale=scale)
d.boundary(linewidth=2)
d.gridlines(multiple=scale/10,color="blue",linewidth=0.2)
d.set_title(r"source flavour composition ",fontsize=20)
d.left_axis_label(r"$\nu_\tau$",offset=0.12,fontsize=20)
d.right_axis_label(r"$\nu_\mu$",offset=0.12,fontsize=20)
d.bottom_axis_label(r"$\nu_e$",offset=0,fontsize=20)
d._redraw_labels()
ticks = [round(i / float(10), 1) for i in range(10+1)]
d.ticks(ticks=ticks)
p.axis('off')
"""scatter version"""
#max110=max(w110)
#max100=max(w100)
#max120=max(w120)
import ternary
import matplotlib.pyplot as plt
# Function to visualize for heat map
def f(x):
return 1.0 * x[0] / (1.0 * x[0] + 0.2 * x[1] + 0.05 * x[2])
# dictionary of axes colors for bottom (b), left (l), right (r)
axes_colors = {'b': 'g', 'l': 'r', 'r':'b'}
scale = 10
fig, ax = plt.subplots()
ax.axis("off")
figure, tax = ternary.figure(ax=ax, scale=scale)
tax.heatmapf(f, boundary=False,
style="hexagonal", cmap=plt.cm.get_cmap('Blues'),
cbarlabel='Component 0 uptake',
vmax=1.0, vmin=0.0)
tax.boundary(linewidth=2.0, axes_colors=axes_colors)
tax.left_axis_label("$x_1$", offset=0.16, color=axes_colors['l'])
tax.right_axis_label("$x_0$", offset=0.16, color=axes_colors['r'])
tax.bottom_axis_label("$x_2$", offset=-0.06, color=axes_colors['b'])
tax.gridlines(multiple=1, linewidth=2,
horizontal_kwargs={'color':axes_colors['b']},
left_kwargs={'color':axes_colors['l']},
right_kwargs={'color':axes_colors['r']},
return points
def random_points(num_points=25, scale=40):
points = []
for i in range(num_points):
x = random.randint(1, scale)
y = random.randint(0, scale - x)
z = scale - x - y
points.append((x, y, z))
return points
if __name__ == '__main__':
# Show Coordinates
scale = 3
fig, tax = ternary.figure(scale=scale, permutation="120")
points_lists = [[(0, 0, 3), (1, 0, 2), (2, 0, 1)],
[(3, 0, 0), (2, 1, 0), (1, 2, 0)],
[(0, 3, 0), (0, 2, 1), (0, 1, 2)],
[(1, 1, 1)]]
colors = ['b', 'r', 'g', 'black']
markers = ['o', 'v', '*', 'd']
for i, points in enumerate(points_lists):
for point in points:
tax.scatter([tuple(point)], color=colors[i], marker=markers[i])
tax.annotate("".join(map(str, point)), tuple(point), color=colors[i])
tax.gridlines(multiple=1.)
## Boundary and Gridlines
scale = 40
fig, tax = ternary.figure(scale=scale)
def plot_ternary(self, T, scale=10): # pragma: no cover
if not has_matplotlib:
raise Exception('Optional dependency matplotlib is required for plotting')
try:
import ternary
except:
raise Exception('Optional dependency ternary is required for ternary plotting')
if self.N != 3:
raise Exception('Ternary plotting requires a mixture of exactly three components')
P_values = []
def P_dew_at_T_zs(zs):
zs = self.un_zero_zs(zs)
self.flash(T=T, zs=zs, VF=0)
P_values.append(self.P)
return self.P
def P_bubble_at_T_zs(zs):
zs = self.un_zero_zs(zs)
self.flash(T=T, zs=zs, VF=1)
return self.P
axes_colors = {'b': 'g', 'l': 'r', 'r':'b'}
ticks = [round(i / float(10), 1) for i in range(10+1)]
fig, ax = plt.subplots(1, 3, gridspec_kw = {'width_ratios':[4, 4, 1]})
ax[0].axis("off") ; ax[1].axis("off") ; ax[2].axis("off")
for axis, f, i in zip(ax[0:2], [P_dew_at_T_zs, P_bubble_at_T_zs], [0, 1]):
figure, tax = ternary.figure(ax=axis, scale=scale)
figure.set_size_inches(12, 4)
if not i:
tax.heatmapf(f, boundary=True, colorbar=False, vmin=0)
else:
tax.heatmapf(f, boundary=True, colorbar=False, vmin=0, vmax=max(P_values))
tax.boundary(linewidth=2.0)
tax.left_axis_label("mole fraction $x_2$", offset=0.16, color=axes_colors['l'])
tax.right_axis_label("mole fraction $x_1$", offset=0.16, color=axes_colors['r'])
tax.bottom_axis_label("mole fraction $x_3$", offset=-0.06, color=axes_colors['b'])
tax.ticks(ticks=ticks, axis='rlb', linewidth=1, clockwise=True,
axes_colors=axes_colors, offset=0.03)
tax.gridlines(multiple=scale/10., linewidth=2,
horizontal_kwargs={'color':axes_colors['b']},
left_kwargs={'color':axes_colors['l']},
right_kwargs={'color':axes_colors['r']},
alpha=0.5)
norm = plt.Normalize(vmin=0, vmax=max(P_values))
sm = plt.cm.ScalarMappable(cmap=plt.get_cmap('viridis'), norm=norm)
sm._A = []
cb = plt.colorbar(sm, ax=ax[2])
cb.locator = matplotlib.ticker.LinearLocator(numticks=7)
cb.formatter = matplotlib.ticker.ScalarFormatter()
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
plt.tight_layout()
fig.suptitle("Bubble pressure vs composition (left) and dew pressure vs composition (right) at %s K, in Pa" %T, fontsize=14);
fig.subplots_adjust(top=0.85)
plt.show()
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="t")
tax.boundary(color='black')
tax.set_title("Heatmap Test: Triangular")
ax = pyplot.subplot(gs[0,1])
figure, tax = ternary.figure(scale=scale, ax=ax)
tax.heatmap(d, style="d")
tax.boundary(color='black')
tax.set_title("Heatmap Test Dual")
pyplot.show()
if __name__ == '__main__':
# Show Coordinates
scale = 3
figure, tax = ternary.figure(scale=scale, permutation="120")
points_lists = [[(0,0,3), (1,0,2), (2,0,1)],
[(3,0,0), (2,1,0), (1,2,0)],
[(0,3,0), (0,2,1), (0,1,2)],
[(1,1,1)]]
colors = ['b', 'r', 'g', 'black']
markers = ['o', 'v', '*', 'd']
for i, points in enumerate(points_lists):
for point in points:
tax.scatter([tuple(point)], color=colors[i], marker=markers[i])
tax.annotate("".join(map(str, point)), tuple(point), color=colors[i])
tax.gridlines(multiple=1.)
## Boundary and Gridlines
scale = 40
figure, ternary_ax = ternary.figure(scale=scale)
nutau=[]
for i in range(1000):
nue.append(NUE[i]/N)
numu.append(NUMU[i]/N)
nutau.append(NUTAU[i]/N)
#p.title(r"$(\nu_e,\nu_\mu,\nu_\tau)=(\frac{1}{2},\frac{1}{2},0)$",fontsize=15)
#p.xlabel("probability")
#p.ylabel(r"$\nu_e,\nu_\mu,\nu_\tau$")
#p.step(P1,nue,label=r"$\nu_e$")
#p.step(P1,numu,label=r"$\nu_\mu$")
#p.step(P1,nutau,label=r"$\nu_\tau$")
#p.legend()
"""ternary plot/triangular plot configuration/settings"""
figure, d=t.figure(scale=1)
d.boundary(linewidth=2)
d.gridlines(multiple=0.1,color="blue",linewidth=0.8)
#d.gridlines(multiple=0.02,color="green",linewidth=0.2)
d.set_title(r"source flavour composition $\nu_e,\nu_\mu,\nu_\tau$",fontsize=20)
d.left_axis_label(r"$\nu_\tau$",fontsize=20,offset=0.12)
d.right_axis_label(r"$\nu_\mu$",fontsize=20,offset=-0.01)
d.bottom_axis_label(r"$\nu_e$",fontsize=20,offset=0.01)
d._redraw_labels()
"""data points"""
point1=[(0.3980599331366308,0.3112164005027349,0.2907236663606344)]
point2=[(0.55214511226237,0.24397475401089158,0.20388013372673847)]
point3=[(0.34669820676138435,0.3336302826666826,0.31967151057193305)]
# full_filename = os.path.join(filename)
# data = dict()
# handle = open(full_filename)
# for line in handle:
# line = line.strip()
# i, j, k, v = line.split(' ')
# data[(float(i), float(j), float(k))] = float(v)
# return data
#data1 = load_sample_heatmap_data1()
#data2 = load_sample_heatmap_data2()
#data3 = load_sample_heatmap_data3()
#data4 = load_sample_heatmap_data4()
"""heatmap"""
figure, d=t.figure(scale=grid)
d.boundary(linewidth=2)
d.gridlines(multiple=0.1,color="blue",linewidth=0.8)
d.set_title(r"source flavour composition $(\nu_e,\nu_\mu,\nu_\tau)=(\frac{1}{2},\frac{1}{2},0)$",fontsize=20)
d.left_axis_label(r"$\nu_\tau$",offset=0.12)
d.right_axis_label(r"$\nu_\mu$",offset=0.1)
d.bottom_axis_label(r"$\nu_e$",offset=0)
#d._redraw_labels()
d.ticks(axis='brl',multiple=0.1)
p.axis('off')
#d.heatmap(data1,style="triangular")
#d.boundary()
#d.heatmap(data2,style="triangular")
#d.heatmap(data3,style="hexagonal",cmap='Blues')
#d.heatmap(data4,style="triangular",cmap='viridis')
#d.resize_drawing_canvas(scale=1.15)
def dist(x,L):
constt=(psat_bar/P)-1
dxdl= np.zeros_like(x)
dxdl= np.dot(np.diag(constt),x/L)
return dxdl
#specifying initial conditions
x0= np.array(x0)
x0= np.reshape(x0,(len(x0),))
L_initial= conditions.iloc[0,2] #inital qty
L_final= conditions.iloc[0,3] # final qty
L= np.linspace(L_initial,L_final, 10)
#integrate differnetial equations
x= sci.odeint(dist,x0,L)
print x
#plotting ternary diagram
figure,tax=ternary.figure(scale=1.0)
tax.boundary()
tax.gridlines(multiple=10, color="black")
tax.left_axis_label("A component", fontsize=10)
tax.right_axis_label("C component", fontsize=10)
tax.bottom_axis_label("B component", fontsize=20)
tax.set_title("Plotting of residue curve", fontsize=20)
tax.plot(x)
tax.show()
import ternary
## Boundary and Gridlines
scale = 9
figure, tax = ternary.figure(scale=scale)
tax.ax.axis("off")
figure.set_facecolor('w')
# Draw Boundary and Gridlines
tax.boundary(linewidth=1.0)
tax.gridlines(color="black", multiple=1, linewidth=0.5,ls='-')
# Set Axis labels and Title
fontsize = 15
tax.left_axis_label("Barleygrow", fontsize=fontsize, offset=0.12)
tax.right_axis_label("Beans", fontsize=fontsize, offset=0.12)
tax.bottom_axis_label("Oats", fontsize=fontsize, offset=0.025)
# Set ticks
tax.ticks(axis='blr', linewidth=1,multiple=1)
# Scatter some points
points = [(2,3,5),(3,6,1),(5,4,1),(3,4,3),(2,2,6)]
c = [90,20,30,10,64]
cb_kwargs = {"shrink" : 0.6,
"orientation" : "horizontal",
"fraction" : 0.1,
"pad" : 0.05,
if __name__ == '__main__':
N = 40
mu = 3./2. * 1./N
m = [[1, 2], [2, 1]]
fitness_landscape = linear_fitness_landscape(m, normalize=False)
incentive = replicator(fitness_landscape)
death_probabilities = even_death(N)
edges = variable_population_transitions(
N, fitness_landscape, death_probabilities, incentive=incentive, mu=mu)
s = stationary_distribution(edges, iterations=10000)
# Print out the states with the highest stationary probabilities
vs = [(v, k) for (k, v) in s.items()]
vs.sort(reverse=True)
print(vs[:10])
# Plot the stationary distribution and expected divergence
figure, tax = ternary.figure(scale=N)
tax.heatmap(s)
d2 = expected_divergence(edges, q_d=0)
d = dict()
for k, v in d2.items():
d[k] = math.sqrt(v)
figure, tax = ternary.figure(scale=N)
tax.heatmap(d)
pyplot.show()
continue
return -1.*s
def random_points(num_points=25, scale=40):
points = []
for i in range(num_points):
x = random.randint(1, scale)
y = random.randint(0, scale - x)
z = scale - x - y
points.append((x,y,z))
return points
if __name__ == '__main__':
## Boundary and Gridlines
scale = 40
figure, ternary_ax = ternary.figure(scale=scale)
left_kwargs = {'color': 'blue'}
right_kwargs = {'color': 'red'}
# Draw Boundary and Gridlines
ternary_ax.boundary(color="black", linewidth=2.0)
ternary_ax.gridlines(color="blue", multiple=5, left_kwargs=left_kwargs,
right_kwargs=right_kwargs)
# Draw Boundary and Gridlines
ternary_ax.boundary(color="black", linewidth=2.0)
ternary_ax.gridlines(color="blue", multiple=5)
# Set Axis labels and Title
fontsize = 20
