def plot_reaction_rate_burning_rate(df_rate):
fig, ax = pplot.subplots(aspect=(4, 3), axwidth=4)
c1 = pplot.scale_luminance('cerulean', 0.5)
c2 = pplot.scale_luminance('red', 0.5)
df_rate.sort_values(by="time", inplace=True)
lns1 = ax.plot(df_rate["time"],
df_rate["vol_averaged_reaction_rate"],
color=c1,
label="Reaction rate")
max_rate = df_rate["vol_averaged_reaction_rate"].max()
ax.format(xlabel="Time (s)",
ylabel="Volume-Averaged coke reaction rate (kg/m$^3$/s)",
ycolor=c1,
ylim=(-1, max_rate * 1.1))
ax2 = ax.twinx()
lns2 = ax2.plot(df_rate["time"],
df_rate["burning_fraction"] * 100,
color=c2,
linestyle="--",
label="Conversion")
ax2.format(xlabel="Time (s)",
ylabel="Conversion (%)",
ycolor=c2,
ylim=(-1, 100))
lns = lns1 + lns2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc="upper right", fancybox=True)
return ax, ax2, fig
def plot_transverse_averages(transverse_data_folder, time):
df_transverse = pd.read_csv(
os.path.join(transverse_data_folder, f"{time}.csv"))
fig, ax = plt.subplots()
c1 = pplot.scale_luminance('cerulean', 0.5)
c2 = pplot.scale_luminance('red', 0.5)
ax.plot(df_transverse["x"], df_transverse["O2Conc"], color=c1)
ax.set_xlabel("X (m)")
ax.set_ylabel("Tranversely Averaged O$_2$ mole concentration (mol/m$^3$)",
color=c1)
ax.tick_params(axis='y', colors=c1)
ax2 = ax.twinx()
ax2.plot(df_transverse["x"], df_transverse["T"], color=c2)
ax2.set_ylabel("Tranversely Averaged Temperature (K)", color=c2)
ax2.tick_params(axis='y', colors=c2)
fig.tight_layout()
return ax, ax2, fig
# like `~matplotlib.axes.Axes.set_xticks` and
# `~matplotlib.axes.Axes.set_yticks`). See
# `~proplot.axes.CartesianAxes.format` and `~proplot.constructor.Locator` for
# details.
#
# To generate lists of tick locations, we recommend using ProPlot's
# `~proplot.utils.arange` function -- it’s basically an *endpoint-inclusive*
# version of `numpy.arange`, which is usually what you'll want in this
# context.
# %%
import proplot as plot
import numpy as np
state = np.random.RandomState(51423)
plot.rc.update(
facecolor=plot.scale_luminance('powderblue', 1.15),
linewidth=1, fontsize=10,
color='dark blue', suptitlecolor='dark blue',
titleloc='upper center', titlecolor='dark blue', titleborder=False,
)
fig, axs = plot.subplots(nrows=8, axwidth=5, aspect=(8, 1), share=0)
axs.format(suptitle='Tick locators demo')
# Step size for tick locations
axs[0].format(
xlim=(0, 200), xminorlocator=10, xlocator=30,
title='MultipleLocator'
)
# Specific list of locations
axs[1].format(
def plot_O2_flux_reaction_rate(df_O2_flux_at_inlet,
df_rate,
pixelResolution,
sampling_rate=5,
ylim=(1e-9, 1e-4),
ls=13,
ts=11):
MCoke = 12
MO2 = 32
df_O2_flux_at_inlet["diffusive_flux"] = np.array(
df_O2_flux_at_inlet["O2_diffusive_Fluxs"])
df_O2_flux_at_inlet["advective_flux"] = np.array(
df_O2_flux_at_inlet["O2_adv_flux_by_deltaX"]) * pixelResolution
df_O2_flux_at_inlet["total_flux"] = df_O2_flux_at_inlet[
"diffusive_flux"] + df_O2_flux_at_inlet["advective_flux"]
df_O2_flux_at_inlet["diffusion flux"] = df_O2_flux_at_inlet[
"diffusive_flux"] / df_O2_flux_at_inlet["total_flux"]
df_O2_flux_at_inlet["advection flux"] = df_O2_flux_at_inlet[
"advective_flux"] / df_O2_flux_at_inlet["total_flux"]
c1 = pplot.scale_luminance('cerulean', 0.5)
c2 = pplot.scale_luminance('red', 0.5)
fig, axs = pplot.subplots(ncols=1, nrows=2, aspect=(4, 3), \
axwidth=4,hspace=(0),sharey=0,sharex=3)
ax1 = axs[0]
ax1.plot(df_O2_flux_at_inlet["time"],
df_O2_flux_at_inlet["diffusive_flux"],
color=c1,
label="Diffusion Flux",
linestyle="-.")
ax1.plot(df_O2_flux_at_inlet["time"],
df_O2_flux_at_inlet["advective_flux"],
color=c1,
label="Advection Flux",
linestyle="--")
ax1.plot(df_O2_flux_at_inlet["time"],
df_O2_flux_at_inlet["total_flux"],
color=c1,
label="Total Flux",
linestyle="-")
ax1.format(xlabel="Time (s)",
ylim=ylim,
yformatter='sci',
yscale='log',
ylabel="O$_2$ flux at inlet (kg/s)",
ycolor=c1)
ax1.legend(loc="best", ncols=1, fancybox=True)
ax2 = ax1.twinx()
df_sampling = df_rate[df_rate.index % sampling_rate == 0]
ax2.scatter(df_sampling["time"],
df_sampling["total_reaction_rate"] / MCoke * MO2 *
pixelResolution * pixelResolution,
label="Total O$_2$ Reaction Rate",
color=c2)
ax2.format(xlabel="Time (s)",
ylim=ylim,
yformatter='sci',
yscale='log',
ylabel='O$_2$ reaction rate within domain (kg/s)',
ycolor=c2)
df_sampling = df_O2_flux_at_inlet[df_O2_flux_at_inlet.index %
sampling_rate == 0]
df_sampling.reset_index(drop=True, inplace=True)
df_ratios = df_sampling[["diffusion flux", "advection flux"]]
ax3 = axs[1]
ax3.format(ylim=(0, 1.1))
ax3.set_ylabel(ylabel="Percentage of different O$_2$ fluxes", labelpad=20)
num = df_sampling.shape[0]
maxTime = df_sampling["time"].iloc[-1]
ax3.bar(df_sampling["time"],
df_ratios,
stacked=True,
cycle='Blues',
legend='ur',
edgecolor='blue9',
width=maxTime / (num * 1.2))
axs = [ax1, ax2, ax3]
for ax in axs:
ax.tick_params(labelsize=ts)
ax.yaxis.label.set_size(ls)
ax3.xaxis.label.set_size(ls)
# ax3.yaxis.label.set_labelpad(10)
return ax1, ax2, fig
import proplot as pplot # there are some nice colormaps in the proplot package
import concurrent.futures
import argparse
import json
import math
import traceback
import tracemalloc
# mpl.rcParams['font.family'] = 'Helvetica', #default font family
mpl.rcParams['mathtext.fontset'] = 'cm' #font for math
C_GREEN = fg('green')
C_RED = fg('red')
C_BLUE = fg('blue')
C_DEFAULT = attr('reset')
c1 = pplot.scale_luminance('cerulean', 0.5)
c2 = pplot.scale_luminance('red', 0.5)
def use_paper_style(mpl, fontsize=9):
mpl.rcParams['axes.titlesize'] = fontsize
mpl.rcParams['axes.labelsize'] = fontsize
mpl.rcParams['xtick.labelsize'] = fontsize
mpl.rcParams['ytick.labelsize'] = fontsize
mpl.rcParams['legend.fontsize'] = fontsize
mpl.rcParams['axes.titlesize'] = fontsize
mpl.rcParams['axes.titlesize'] = fontsize
def read_postProcess_csv_data(postProcessDir, timeName):
fileName = f"{timeName}.csv"
x = state.normal(size=(N, ))
y = state.normal(size=(N, ))
bins = pplt.arange(-3, 3, 0.25)
# Histogram with marginal distributions
fig, axs = pplt.subplots(ncols=2, refwidth=2.3)
axs.format(abc='A.',
abcloc='l',
titleabove=True,
ylabel='y axis',
suptitle='Histograms with marginal distributions')
colors = ('indigo9', 'red9')
titles = ('Group 1', 'Group 2')
for ax, which, color, title in zip(axs, 'lr', colors, titles):
ax.hist2d(x,
y,
bins,
vmin=0,
vmax=10,
levels=50,
cmap=color,
colorbar='b',
colorbar_kw={'label': 'count'})
color = pplt.scale_luminance(color, 1.5) # histogram colors
px = ax.panel(which, space=0)
px.histh(y, bins, color=color, fill=True, ec='k')
px.format(grid=False, xlocator=[], xreverse=(which == 'l'))
px = ax.panel('t', space=0)
px.hist(x, bins, color=color, fill=True, ec='k')
px.format(grid=False, ylocator=[], title=title, titleloc='l')
yformatter='none',
)
# Shifted hue
N = 50
fmt = pplt.SimpleFormatter()
marker = 'o'
for shift in (0, -60, 60):
x, y = state.rand(2, N)
color = pplt.shift_hue('grass', shift)
axs[0].scatter(x, y, marker=marker, c=color, legend='b', label=fmt(shift))
# Scaled luminance
for scale in (0.2, 1, 2):
x, y = state.rand(2, N)
color = pplt.scale_luminance('bright red', scale)
axs[1].scatter(x, y, marker=marker, c=color, legend='b', label=fmt(scale))
# Scaled saturation
for scale in (0, 1, 3):
x, y = state.rand(2, N)
color = pplt.scale_saturation('ocean blue', scale)
axs[2].scatter(x, y, marker=marker, c=color, legend='b', label=fmt(scale))
# %% [raw] raw_mimetype="text/restructuredtext"
# .. _ug_colors_cmaps:
#
# Colors from colormaps
# ---------------------
#
# If you want to draw an individual color from a colormap or a color cycle,