def plot_stat(rows, cache):
"Use matplotlib to plot DAS statistics"
if not PLOT_ALLOWED:
raise Exception('Matplotlib is not available on the system')
if cache in ['cache', 'merge']: # cachein, cacheout, mergein, mergeout
name_in = '%sin' % cache
name_out = '%sout' % cache
else: # webip, webq, cliip, cliq
name_in = '%sip' % cache
name_out = '%sq' % cache
def format_date(date):
"Format given date"
val = str(date)
return '%s-%s-%s' % (val[:4], val[4:6], val[6:8])
date_range = [r['date'] for r in rows]
formated_dates = [format_date(str(r['date'])) for r in rows]
req_in = [r[name_in] for r in rows]
req_out = [r[name_out] for r in rows]
plt.plot(date_range, req_in , 'ro-',\
date_range, req_out, 'gv-',)
plt.grid(True)
plt.axis([min(date_range), max(date_range), \
0, max([max(req_in), max(req_out)])])
plt.xticks(date_range, tuple(formated_dates), rotation=17)
# plt.xlabel('dates [%s, %s]' % (date_range[0], date_range[-1]))
plt.ylabel('DAS %s behavior' % cache)
plt.savefig('das_%s.pdf' % cache, format='pdf', transparent=True)
plt.close()
def plot_comfort(fingers_org=range(1, 6, 1),
fingers_dst=range(1, 6, 1),
jumps=range(-12, 13, 1)):
import seaborn
from mpl_toolkits.mplot3d import Axes3D
from pylab import plt
xs, ys, zs, cs = calculate_comforts(fingers_org, fingers_dst, jumps)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(xs, ys, zs, c=cs)
ax.set_zlabel("Interval (half steps)", fontsize=15)
ax.set_zlim(jumps[0], jumps[-1])
# ax.set_zticks(jumps)
plt.xticks(fingers_org)
plt.xlim(fingers_org[0], fingers_org[-1])
plt.xlabel("From finger", fontsize=15)
plt.yticks(fingers_dst)
plt.ylim(fingers_dst[0], fingers_dst[-1])
plt.ylabel("To finger", fontsize=15)
plt.title("Difficulty of finger passages", fontsize=25)
plt.savefig('./figures/image.png', figsize=(16, 12), dpi=300)
plt.show()
def hexbin_plot(self, var1, var2, force=False):
fig_name = "{}/hexbin_{}_{}.pdf".format(self.fig_folder, var1, var2)
if path.exists(fig_name) and not force:
return
if var1 == "customer_extra_view_choices" and var2 == "delta_position":
print("Doing hexbin plot '{}' against '{}'.".format(var2, var1))
x = np.asarray(self.stats.data[var1])
y = np.asarray(self.stats.data[var2])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.xlim(self.range_var[var1])
plt.ylim(self.range_var[var2])
plt.xlabel(self.format_label(var1))
plt.ylabel(self.format_label(var2))
hb = ax.hexbin(x=x, y=y, gridsize=20, cmap='inferno')
ax.set_facecolor('black')
cb = fig.colorbar(hb, ax=ax)
cb.set_label('counts')
plt.savefig(fig_name)
if self.display:
plt.show()
plt.close()
def test_plot_dep_contour(self):
plot = MapPlotSlab()
plot.plot_dep_contour(-20, color='red')
plot.plot_dep_contour(-40, color='blue')
plot.plot_dep_contour(-60, color='black')
plt.savefig(join(this_test_path, '~outs/plot_dep_contour.png'))
plt.close()
def serve_css(name, length, keys, values):
from pylab import plt, mpl
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
from matplotlib.font_manager import FontProperties
# font = FontProperties(fname="d:\Users\ll.tong\Desktop\msyh.ttf", size=12)
font = FontProperties(fname="/usr/share/fonts/msyh.ttf", size=11)
plt.xlabel(u'')
plt.ylabel(u'出现次数',fontproperties=font)
plt.title(u'词频统计',fontproperties=font)
plt.grid()
keys = keys.decode("utf-8").split(' ')
values = values.split(' ')
valuesInt = []
for value in values:
valuesInt.append(int(value))
plt.xticks(range(int(length)), keys)
plt.plot(range(int(length)), valuesInt)
plt.xticks(rotation=defaultrotation, fontsize=9,fontproperties=font)
plt.yticks(fontsize=10,fontproperties=font)
name = name + str(datetime.now().date()).replace(':', '') + '.png'
imgUrl = 'static/temp/' + name
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(12.2, 2)
plt.savefig(imgUrl, bbox_inches='tight', figsize=(20,4), dpi=100)
plt.close()
tempfile = static_file(name, root='./static/temp/')
#os.remove(imgUrl)
return tempfile
def hexbin_plot(self, var1, var2):
print("Doing hexbin plot '{}' against '{}'.".format(var2, var1))
x = np.asarray(self.stats.data[var1])
y = np.asarray(self.stats.data[var2])
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.xlim(self.range_var[var1])
plt.ylim(self.range_var[var2])
plt.xlabel(self.format_label(var1))
plt.ylabel(self.format_label(var2))
hb = ax.hexbin(x=x, y=y, gridsize=20, cmap='inferno')
ax.set_facecolor('black')
cb = fig.colorbar(hb, ax=ax)
cb.set_label('counts')
plt.savefig("{}/hexbin_{}_{}.pdf".format(self.fig_folder, var1, var2))
if self.display:
plt.show()
plt.close()
def main(data_folder="../data", figure_folder="../figures"):
deviation_from_equilibrium = dict()
for i in [(1, 0), (2, 0), (2, 1)]:
deviation_from_equilibrium[i] = \
json.load(open("{}/deviation_from_equilibrium_{}.json".format(data_folder, i), mode="r"))
x = np.arange(len(list(deviation_from_equilibrium.values())[0]))
fig, ax = plt.subplots()
for i in deviation_from_equilibrium.keys():
ax.plot(x,
deviation_from_equilibrium[i],
label='{} against {}'.format(i[0], i[1]))
ax.legend(fontsize=12) # loc='upper center
ax.set_xlabel("generation")
ax.set_ylabel("actual price / equilibrium price")
ax.set_title(
"Price Dynamics in Scarf Three-good Economy \n(relative deviation from equilibrium prices)"
)
if not path.exists(figure_folder):
mkdir(figure_folder)
plt.savefig("{}/figure.pdf".format(figure_folder))
plt.show()
def plot(cls, data, figure_folder, msg="", suffix=""):
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.2)
x = np.arange(len(data[:]))
ax.plot(x, data[:, 0], c="red", linewidth=2, label="agent 01")
ax.plot(x, data[:, 1], c="blue", linewidth=2, label="agent 12")
ax.plot(x, data[:, 2], c="green", linewidth=2, label="agent 20")
plt.ylim([-0.01, 1.01])
plt.text(0, -0.23, "PARAMETERS. {}".format(msg))
ax.legend(fontsize=12, bbox_to_anchor=(1.1, 1.05)) # loc='upper center'
ax.set_xlabel("$t$")
ax.set_ylabel("Proportion of agents proceeding to indirect exchange")
ax.set_title("Money emergence with a basal ganglia model")
# Save fig
if not exists(figure_folder):
mkdir(figure_folder)
fig_name = "{}/figure_{}.pdf".format(figure_folder, suffix.split(".p")[0])
plt.savefig(fig_name)
plt.close()
def pure_data_plot(self,connect=False,suffix='',cmap=cm.jet,bg=cm.bone(0.3)):
#fig=plt.figure()
ax=plt.axes()
plt.axhline(y=0,color='grey', zorder=-1)
plt.axvline(x=0,color='grey', zorder=-2)
if cmap is None:
if connect: ax.plot(self.x,self.y, 'b-',lw=2,alpha=0.5)
ax.scatter(self.x,self.y, marker='o', c='b', s=40)
else:
if connect:
if cmap in [cm.jet,cm.brg]:
ax.plot(self.x,self.y, 'c-',lw=2,alpha=0.5,zorder=-1)
else:
ax.plot(self.x,self.y, 'b-',lw=2,alpha=0.5)
c=[cmap((f-self.f[0])/(self.f[-1]-self.f[0])) for f in self.f]
#c=self.f
ax.scatter(self.x, self.y, marker='o', c=c, edgecolors=c, zorder=True, s=40) #, cmap=cmap)
#plt.axis('equal')
ax.set_xlim(xmin=-0.2*amax(self.x), xmax=1.2*amax(self.x))
ax.set_aspect('equal') #, 'datalim')
if cmap in [cm.jet,cm.brg]:
ax.set_axis_bgcolor(bg)
if self.ZorY == 'Z':
plt.xlabel(r'resistance $R$ in Ohm'); plt.ylabel(r'reactance $X$ in Ohm')
if self.ZorY == 'Y':
plt.xlabel(r'conductance $G$ in Siemens'); plt.ylabel(r'susceptance $B$ in Siemens')
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'c{}_{}_circle_data'.format(self.sdc.case,self.ZorY)+self.sdc.suffix+self.sdc.outsuffix+suffix+'.png'), dpi=240)
plt.close()
def plot_stat(rows, cache):
"Use matplotlib to plot DAS statistics"
if not PLOT_ALLOWED:
raise Exception('Matplotlib is not available on the system')
if cache in ['cache', 'merge']: # cachein, cacheout, mergein, mergeout
name_in = '%sin' % cache
name_out = '%sout' % cache
else: # webip, webq, cliip, cliq
name_in = '%sip' % cache
name_out = '%sq' % cache
def format_date(date):
"Format given date"
val = str(date)
return '%s-%s-%s' % (val[:4], val[4:6], val[6:8])
date_range = [r['date'] for r in rows]
formated_dates = [format_date(str(r['date'])) for r in rows]
req_in = [r[name_in] for r in rows]
req_out = [r[name_out] for r in rows]
plt.plot(date_range, req_in , 'ro-',
date_range, req_out, 'gv-',
)
plt.grid(True)
plt.axis([min(date_range), max(date_range), \
0, max([max(req_in), max(req_out)])])
plt.xticks(date_range, tuple(formated_dates), rotation=17)
# plt.xlabel('dates [%s, %s]' % (date_range[0], date_range[-1]))
plt.ylabel('DAS %s behavior' % cache)
plt.savefig('das_%s.pdf' % cache, format='pdf', transparent=True)
plt.close()
def link_level_bars(levels, usages, quantiles, scheme, direction, color, nnames, lnames, admat=None):
"""
Bar plots of nodes' link usage of links at different levels.
"""
if not admat:
admat = np.genfromtxt('./settings/eadmat.txt')
if color == 'solar':
cmap = Oranges_cmap
elif color == 'wind':
cmap = Blues_cmap
elif color == 'backup':
cmap = 'Greys'
nodes, links = usages.shape
usageLevels = np.zeros((nodes, levels))
usageLevelsNorm = np.zeros((nodes, levels))
for node in range(nodes):
nl = neighbor_levels(node, levels, admat)
for lvl in range(levels):
ll = link_level(nl, lvl, nnames, lnames)
ll = np.array(ll, dtype='int')
usageSum = sum(usages[node, ll])
linkSum = sum(quantiles[ll])
usageLevels[node, lvl] = usageSum / linkSum
if lvl == 0:
usageLevelsNorm[node, lvl] = usageSum
else:
usageLevelsNorm[node, lvl] = usageSum / usageLevelsNorm[node, 0]
usageLevelsNorm[:, 0] = 1
# plot all nodes
usages = usageLevels.transpose()
plt.figure(figsize=(11, 3))
ax = plt.subplot()
plt.pcolormesh(usages[:, loadOrder], cmap=cmap)
plt.colorbar().set_label(label=r'$U_n^{(l)}$', size=11)
ax.set_yticks(np.linspace(.5, levels - .5, levels))
ax.set_yticklabels(range(1, levels + 1))
ax.yaxis.set_tick_params(width=0)
ax.xaxis.set_tick_params(width=0)
ax.set_xticks(np.linspace(1, nodes, nodes))
ax.set_xticklabels(loadNames, rotation=60, ha="right", va="top", fontsize=10)
plt.ylabel('Link level')
plt.savefig(figPath + '/levels/' + str(scheme) + '/' + 'total' + '_' + str(direction) + '_' + color + '.pdf', bbox_inches='tight')
plt.close()
# plot all nodes normalised to usage of first level
usages = usageLevelsNorm.transpose()
plt.figure(figsize=(11, 3))
ax = plt.subplot()
plt.pcolormesh(usages[:, loadOrder], cmap=cmap)
plt.colorbar().set_label(label=r'$U_n^{(l)}$', size=11)
ax.set_yticks(np.linspace(.5, levels - .5, levels))
ax.set_yticklabels(range(1, levels + 1))
ax.yaxis.set_tick_params(width=0)
ax.xaxis.set_tick_params(width=0)
ax.set_xticks(np.linspace(1, nodes, nodes))
ax.set_xticklabels(loadNames, rotation=60, ha="right", va="top", fontsize=10)
plt.ylabel('Link level')
plt.savefig(figPath + '/levels/' + str(scheme) + '/' + 'total_norm_cont_' + str(direction) + '_' + color + '.pdf', bbox_inches='tight')
plt.close()
def plot_inv_conv(fvals, name, direc):
plt.figure()
plt.semilogy(fvals, 'ko-')
plt.xlabel('Iterations')
plt.ylabel('Cost Function')
plt.savefig(os.path.join(direc, name + '.png'), dpi=300)
plt.close()
def curve_plot(self, variable, t_max):
print("Doing curve plot for variable '{}'.".format(variable))
var = Variable(name=variable)
if var.data is None:
self.extract_single_dimension(var, t_max=t_max)
x = np.arange(t_max)
mean = var.data["mean"]
std = var.data["std"]
plt.plot(x, mean, c='black', lw=2)
plt.plot(x, mean + std, c='black', lw=.1)
plt.plot(x, mean - std, c='black', lw=.1)
plt.fill_between(x, mean + std, mean - std, color='black', alpha=.1)
plt.xlabel("t")
plt.ylabel(self.format_label(variable))
plt.savefig("{}/curve_plot_{}.pdf".format(self.fig_folder, variable))
if self.display:
plt.show()
plt.close()
def plot(cls, data, figure_folder, msg="", suffix=""):
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.2)
x = np.arange(len(data[:]))
ax.plot(x, data[:, 0], c="red", linewidth=2, label="agent 01")
ax.plot(x, data[:, 1], c="blue", linewidth=2, label="agent 12")
ax.plot(x, data[:, 2], c="green", linewidth=2, label="agent 20")
plt.ylim([-0.01, 1.01])
plt.text(0, -0.23, "PARAMETERS. {}".format(msg))
ax.legend(fontsize=12,
bbox_to_anchor=(1.1, 1.05)) # loc='upper center'
ax.set_xlabel("$t$")
ax.set_ylabel("Proportion of agents proceeding to indirect exchange")
ax.set_title("Money emergence with a basal ganglia model")
# Save fig
if not exists(figure_folder):
mkdir(figure_folder)
fig_name = "{}/figure_{}.pdf".format(figure_folder,
suffix.split(".p")[0])
plt.savefig(fig_name)
plt.close()
def plot_profits(self, player, period):
profits = self.results["profits"][-period:]
plt.title("Profits")
time_window = 100
x = np.arange(len(profits[:, player]))
y = []
for i in x:
if i < time_window:
y_value = np.mean(profits[:i + 1, player])
else:
y_value = np.mean(profits[i - time_window:i + 1, player])
y.append(y_value)
plt.plot(x, y, color="black")
maximum_profit = \
self.parameters["n_positions"] * \
self.parameters["n_prices"]
plt.ylim(0, maximum_profit)
plt.annotate("Time window: {}".format(time_window),
xy=(0.8, 0.1),
xycoords='axes fraction',
fontsize=6)
# plt.annotate(self.string_parameters, xy=(-0.05, -0.1), xycoords='axes fraction', fontsize=6)
plt.savefig(self.format_fig_name("profits_player{}".format(player)))
plt.close()
def plot_response(data, plate_name, save_folder = 'Figures/'):
"""
"""
if not os.path.isdir(save_folder):
os.makedirs(save_folder)
for block in data:
#
group = group_similar(data[block].keys())
names = data[block].keys()
names.sort()
#
plt.figure(figsize=(16, 4 + len(names)/8), dpi=300)
#
for i, name in enumerate(names):
a, b, c = get_index(group, name)
color, pattern = color_shade_pattern(a, b, c, group)
mean = data[block][name]['mean'][0]
std = data[block][name]['std'][0]
plt.barh([i], [mean], height=1.0, color=color, hatch=pattern)
plt.errorbar([mean], [i+0.5], xerr=[std], ecolor = [0,0,0], linestyle = '')
plt.yticks([i+0.5 for i in xrange(len(names))], names, size = 8)
plt.title(plate_name)
plt.ylim(0, len(names))
plt.xlabel('change')
plt.tight_layout()
plt.savefig(save_folder + 'response_' + str(block + 1))
#
return None
def prices_and_profits(pool_backup, fig_name=None, ax_price=None, ax_profit=None):
# Create figure and axes if not given in args
if ax_price is None or ax_profit is None:
fig = plt.figure(figsize=(4, 5), dpi=200)
n_rows = 2
n_cols = 1
ax_price = fig.add_subplot(n_rows, n_cols, 1)
ax_profit = fig.add_subplot(n_rows, n_cols, 2)
prices_over_fov(pool_backup, ax_price)
profits_over_fov(pool_backup, ax_profit)
if fig_name is not None:
# Cut margins
plt.tight_layout()
# Create directories if not already existing
os.makedirs(os.path.dirname(fig_name), exist_ok=True)
# Save fig
plt.savefig(fig_name)
plt.close()
def draw(cls, t_max, agents_proportions, eco_idx, parameters):
color_set = ["green", "blue", "red"]
for agent_type in range(3):
plt.plot(np.arange(t_max), agents_proportions[:, agent_type],
color=color_set[agent_type], linewidth=2.0, label="Type-{} agents".format(agent_type))
plt.ylim([-0.1, 1.1])
plt.xlabel("$t$")
plt.ylabel("Proportion of indirect exchanges")
# plt.suptitle('Direct choices proportion per type of agents', fontsize=14, fontweight='bold')
plt.legend(loc='upper right', fontsize=12)
print(parameters)
plt.title(
"Workforce: {}, {}, {}; displacement area: {}; vision area: {}; alpha: {}; tau: {}\n"
.format(
parameters["x0"],
parameters["x1"],
parameters["x2"],
parameters["movement_area"],
parameters["vision_area"],
parameters["alpha"],
parameters["tau"]
), fontsize=12)
if not path.exists("../../figures"):
mkdir("../../figures")
plt.savefig("../../figures/figure_{}.pdf".format(eco_idx))
plt.show()
def draw_plot(company_name, hyper_parameter, plot_data):
point_list = ['C0o-', 'C1o-', 'C2o-', 'C3o-', 'C4o-', 'C5o-']
x = np.arange(len(plot_data[0][1]))
x_label = np.zeros(len(plot_data[0][1]))
for i, val in enumerate(plot_data[0][1]):
x_label[i] = val[0]
fig, ax = plt.subplots(1, 1)
for j, model in enumerate(plot_data):
model_name = model[0]
y = np.zeros(len(model[1]))
for i, val in enumerate(model[1]):
y[i] = val[1]
ax.plot(x, y, point_list[j], label=model_name)
ax.set_title(company_name + "'s MSE results w/ " + hyper_parameter,
fontdict={
'fontsize': 18,
'weight': 'bold'
})
ax.set_ylabel('MSE', fontdict={'fontsize': 15, 'weight': 'bold'})
ax.set_xlabel(hyper_parameter, fontdict={'fontsize': 15, 'weight': 'bold'})
ax.set_xticks(x)
ax.set_xticklabels(x_label)
ax.legend()
dir_path = 'MSE_figures\\' + company_name
os.system('mkdir ' + dir_path)
plt.savefig(dir_path + '\\' + company_name + '_' + hyper_parameter +
'.png')
def create_image(csv_path):
csv_data, _, _ = read_csv_data(csv_path)
plt.figure()
plt.plot(csv_data)
plt.axis('off')
plt.savefig('wind_data.png', bbox_inches='tight', dpi=500)
def draw(cls, t_max, agents_proportions, eco_idx, parameters):
color_set = ["green", "blue", "red"]
for agent_type in range(3):
plt.plot(np.arange(t_max),
agents_proportions[:, agent_type],
color=color_set[agent_type],
linewidth=2.0,
label="Type-{} agents".format(agent_type))
plt.ylim([-0.1, 1.1])
plt.xlabel("$t$")
plt.ylabel("Proportion of indirect exchanges")
# plt.suptitle('Direct choices proportion per type of agents', fontsize=14, fontweight='bold')
plt.legend(loc='upper right', fontsize=12)
print(parameters)
plt.title(
"Workforce: {}, {}, {}; displacement area: {}; vision area: {}; alpha: {}; tau: {}\n"
.format(parameters["x0"], parameters["x1"], parameters["x2"],
parameters["movement_area"], parameters["vision_area"],
parameters["alpha"], parameters["tau"]),
fontsize=12)
if not path.exists("../../figures"):
mkdir("../../figures")
plt.savefig("../../figures/figure_{}.pdf".format(eco_idx))
plt.show()
def phase_diagram(data,
labels,
n_good,
title=None,
ax=None,
letter=None,
n_ticks=3,
fig_name=None):
if ax is None:
print('No ax given, I will create a fig.')
fig, ax = plt.subplots()
im = ax.imshow(data, cmap="binary", origin="lower", vmin=0.2,
vmax=0.8) # , vmin=0.5)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax)
step = int(len(labels) / n_ticks)
lab_to_display = labels[::step]
ax.set_xticklabels(lab_to_display)
ax.set_yticklabels(lab_to_display)
ticks = list(range(len(labels)))[::step]
ax.set_xticks(ticks)
ax.set_yticks(ticks)
ax.tick_params(labelsize=8)
ax.set_xlabel(f'$x_{n_good-2}$')
ax.set_ylabel(f'$x_{n_good-1}$')
ax.set_aspect(1)
if title is not None:
ax.set_title(title)
if letter:
ax.text(s=letter,
x=-0.1,
y=-0.2,
horizontalalignment='center',
verticalalignment='center',
transform=ax.transAxes,
fontsize=20)
if 'fig' in locals():
print('Saving fig.')
# noinspection PyUnboundLocalVariable
fig.tight_layout()
if fig_name is None:
fig_name = f'fig/phase_{n_good}.pdf'
os.makedirs(os.path.dirname(fig_name), exist_ok=True)
plt.savefig(fig_name)
def generate_start_time_figures(self):
recording_time_grouped_by_patient = self.pain_data[["PatientID", "NRSTimeFromEndSurgery_mins"]].groupby("PatientID")
recording_start_minutes = recording_time_grouped_by_patient.min()
fig1 = "fig1.pdf"
fig2 = "fig2.pdf"
plt.figure(figsize=[8,4])
plt.title("Pain score recording start times", fontsize=14).set_y(1.05)
plt.ylabel("Occurrences", fontsize=14)
plt.xlabel("Recording Start Time (minutes)", fontsize=14)
plt.hist(recording_start_minutes.values, bins=20, color="0.5")
plt.savefig(os.path.join(self.tmp_directory, fig1), bbox_inches="tight")
plt.figure(figsize=[8,4])
plt.title("Pain score recording start times, log scale", fontsize=14).set_y(1.05)
plt.ylabel("Occurrences", fontsize=14)
plt.xlabel("Recording Start Time (minutes)", fontsize=14)
plt.hist(recording_start_minutes.values, bins=20, log=True, color="0.5")
plt.savefig(os.path.join(self.tmp_directory, fig2), bbox_inches="tight")
#save the figures in panel format
f = open(os.path.join(self.tmp_directory, "tmp.tex"), 'w')
f.write(r"""
\documentclass[%
,float=false % this is the new default and can be left away.
,preview=true
,class=scrartcl
,fontsize=20pt
]{standalone}
\usepackage[active,tightpage]{preview}
\usepackage{varwidth}
\usepackage{graphicx}
\usepackage[justification=centering]{caption}
\usepackage{subcaption}
\usepackage[caption=false,font=footnotesize]{subfig}
\renewcommand{\thesubfigure}{\Alph{subfigure}}
\begin{document}
\begin{preview}
\begin{figure}[h]
\begin{subfigure}{0.5\textwidth}
\includegraphics[width=\textwidth]{""" + fig1 + r"""}
\caption{Normal scale}
\end{subfigure}\begin{subfigure}{0.5\textwidth}
\includegraphics[width=\textwidth]{""" + fig2 + r"""}
\caption{Log scale}
\end{subfigure}
\end{figure}
\end{preview}
\end{document}
""")
f.close()
subprocess.call(["pdflatex",
"-halt-on-error",
"-output-directory",
self.tmp_directory,
os.path.join(self.tmp_directory, "tmp.tex")])
shutil.move(os.path.join(self.tmp_directory, "tmp.pdf"),
os.path.join(self.output_directory, "pain_score_start_times.pdf"))
def save_images(images, path):
fig = plt.figure()
for i, image in enumerate(images):
fig.add_subplot(1, len(images), i + 1)
plt.imshow(image)
plt.axis('off')
plt.savefig(path, bbox_inches='tight')
plt.close()
def test_plot_slip(self):
ep = EpochalIncrSlip(self.file_incr_slip)
plot = MapPlotFault(self.file_fault)
plot.plot_slip(ep(0))
plt.savefig(join(self.outs_dir, 'plot_slip.png'))
plt.close()
def test_plot(self):
res_file = '/home/zy/workspace/viscojapan/tests/share/nrough_05_naslip_11.h5'
reader = vj.inv.ResultFileReader(res_file)
slip = reader.get_slip()
plotter = vj.slip.plot.plot_slip_and_rate_at_subflt(slip, 8, 8)
plt.savefig(join(self.outs_dir, 'slip_and_rate.pdf'))
def test_plot_slip(self):
ep = EpochalIncrSlip(self.file_incr_slip)
plot = MapPlotFault(self.file_fault)
plot.plot_slip(ep(0))
plt.savefig(join(self.outs_dir, 'plot_slip.png'))
plt.close()
def plot_data(self):
for i,dat in enumerate(self.data):
plt.imshow(dat, cmap=cm.jet, interpolation=None, extent=[11,22,-3,2])
txt='plot '.format(self.n[i])
txt+='\nmin {0:.2f} und max {1:.2f}'.format(self.min[i],self.max[i])
txt+='\navg. min {0:.2f} und avg. max {1:.2f}'.format(mean(self.min),mean(self.max))
plt.suptitle(txt,x=0.5,y=0.98,ha='center',va='top',fontsize=10)
plt.savefig(join(self.plotpath,'pic_oo_'+str(self.n[i])+'.png'))
plt.close() # wichtig, sonst wird in den selben Plot immer mehr reingepackt
def test_plot(self):
res_file = '/home/zy/workspace/viscojapan/tests/share/nrough_05_naslip_11.h5'
reader = vj.inv.ResultFileReader(res_file)
slip = reader.get_slip()
plotter = vj.slip.plot.plot_slip_and_rate_at_subflt(
slip, 8, 8)
plt.savefig(join(self.outs_dir, 'slip_and_rate.pdf'))
def plot_bw(ax, x, y, file_name, fig_folder):
ax.scatter(x, y, facecolor="black", edgecolor='none', s=25, alpha=0.15)
plt.tight_layout()
if file_name:
plt.savefig("{}/{}_gray.pdf".format(fig_folder, file_name))
plt.show()
def test_dip(self):
xf = arange(0, 425)
dips = self.fm.get_dip(xf)
plt.plot(xf,dips)
plt.grid('on')
plt.gca().set_xticks(self.fm.Y_PC)
plt.ylim([0, 30])
plt.gca().invert_yaxis()
plt.savefig(join(self.outs_dir, '~y_fc_dips.png'))
plt.close()
def plot_mat(self, mat, fn):
plt.matshow(asarray(mat.todense()))
plt.axis('equal')
sh = mat.shape
plt.gca().set_yticks(range(0,sh[0]))
plt.gca().set_xticks(range(0,sh[1]))
plt.grid('on')
plt.colorbar()
plt.savefig(join(self.outs_dir, fn))
plt.close()
def plot_mat(self, mat, fn):
plt.matshow(asarray(mat.todense()))
plt.axis('equal')
sh = mat.shape
plt.gca().set_yticks(range(0, sh[0]))
plt.gca().set_xticks(range(0, sh[1]))
plt.grid('on')
plt.colorbar()
plt.savefig(join(self.outs_dir, fn))
plt.close()
def plot_post(cfs,ifshow=False,loc=2,
save_fig_path = None, file_type='png'):
for cf in cfs:
plot_cf(cf, color='blue')
plt.legend(loc=loc)
if ifshow:
plt.show()
if save_fig_path is not None:
plt.savefig(join(save_fig_path, '%s_%s.%s'%(cf.SITE, cf.CMPT, file_type)))
plt.close()
def test_dip(self):
xf = arange(0, 425)
dips = self.fm.get_dip(xf)
plt.plot(xf, dips)
plt.grid('on')
plt.gca().set_xticks(self.fm.Y_PC)
plt.ylim([0, 30])
plt.gca().invert_yaxis()
plt.savefig(join(self.outs_dir, '~y_fc_dips.png'))
plt.close()
def plot_iou(checkpoint_dir, iou_list):
x = range(0, len(iou_list))
y = iou_list
plt.switch_backend('agg')
plt.plot(x, y, color='red', marker='o', label='IOU')
plt.xticks(range(0, len(iou_list) + 3, (len(iou_list) + 10) // 10))
plt.legend()
plt.grid()
plt.savefig(os.path.join(checkpoint_dir, 'iou_fig.pdf'))
plt.close()
def plot_loss(checkpoint_dir, loss_list, save_pred_every):
x = range(0, len(loss_list) * save_pred_every, save_pred_every)
y = loss_list
plt.switch_backend('agg')
plt.plot(x, y, color='blue', marker='o', label='Train loss')
plt.xticks(range(0, len(loss_list) * save_pred_every + 3, (len(loss_list) * save_pred_every + 10) // 10))
plt.legend()
plt.grid()
plt.savefig(os.path.join(checkpoint_dir, 'loss_fig.pdf'))
plt.close()
def plotSigmoidTanh(fname=None):
fig, ax = plt.subplots()
xs = np.linspace(-10.0, 10.0, num = 50, endpoint=True)
ys = [sigmoidTanh(x, 0.9) for x in xs]
ax.plot(xs, ys, 'black')
plt.title("y=sigmoid(s)")
plt.grid(True)
if fname:
plt.savefig(fname)
plt.show()
def test_pcolor_on_fault(self):
ep = EpochalIncrSlip(self.file_incr_slip)
fio = FaultFileIO(self.file_fault)
slip = ep(0).reshape(
[fio.num_subflt_along_dip, fio.num_subflt_along_strike])
plot = MapPlotFault(self.file_fault)
plot.pcolor_on_fault(slip)
plt.savefig(join(self.outs_dir, 'pcolor_on_fault.png'))
plt.close()
def drawHeatMap(dataDF, groupByVariableNames, subplotIndex, subplotIndexName, valueIndexName):
figure = plt.figure(figsize=(12, 10))
order = [7,8,9,4,5,6,1,2,3]
numOfplot = 0
for key, subDF in dataDF.groupby(groupByVariableNames):
subplot = figure.add_subplot(subplotIndex[0], subplotIndex[1], order[numOfplot])
plotDF = subDF.reset_index()
plotDF.plot.scatter(x=subplotIndexName[0], y=subplotIndexName[1], c=valueIndexName, colormap="jet", ax=subplot, vmin=0, vmax=9)
numOfplot = numOfplot + 1
plt.subplots_adjust(wspace=0.8, hspace=0.4)
plt.savefig("./Graphs/Factor3_fixed_50sample_actionDistribution_divergence_HeatMap.png")
def plot_color(fig, ax, x, y, z, file_name, fig_folder):
abc = ax.scatter(x, y, c=z, zorder=10, alpha=0.25)
fig.colorbar(abc, label="Profits")
plt.tight_layout()
if file_name:
plt.savefig("{}/{}.pdf".format(fig_folder, file_name))
plt.show()
def convert_all_to_png(vis_path, out_dir="maps_png", size=None):
units = {
'gas_density': 'Gas Density [g/cm$^3$]',
'Tm': 'Temperature [K]',
'Tew': 'Temperature [K]',
'S': 'Entropy []',
'dm': 'DM Density [g/cm$^3$]',
'v': 'Velocity [km/s]'
}
log_list = ['gas_density']
for vis_file in os.listdir(vis_path):
if ".dat" not in vis_file:
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size,
axis_values) = read_visualization_data(vis_path + "/" + vis_file,
size)
print "image width in Mpc/h: ", axis_values[-1] * 2.0
x, y = np.meshgrid(axis_values, axis_values)
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5, 4))
if map_type in log_list:
plt.pcolor(x, y, image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else:
plt.pcolor(x, y, image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys():
cbar.ax.set_ylabel(units[map_type])
plt.axis(
[axis_values[0], axis_values[-1], axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir + "/" + out_file, dpi=150)
plt.close()
plt.clf()
def savefig(self, fname):
# Graph using the parameters
plt.xlim(-1000,
max(self.incomes) *
1.05) # make it a little bigger than needed
plt.ylim(-5, 105)
plt.legend(loc='lower center', fontsize=9)
plt.xticks(rotation=20)
plt.axes().get_xaxis().set_major_formatter(
mp.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
plt.savefig(fname)
def test_pcolor_on_fault(self):
ep = EpochalIncrSlip(self.file_incr_slip)
fio = FaultFileIO(self.file_fault)
slip = ep(0).reshape([fio.num_subflt_along_dip,
fio.num_subflt_along_strike])
plot = MapPlotFault(self.file_fault)
plot.pcolor_on_fault(slip)
plt.savefig(join(self.outs_dir, 'pcolor_on_fault.png'))
plt.close()
def test_share_basemap(self):
bm = MyBasemap(x_interval = 1)
p1 = MapPlotSlab(basemap = bm)
p1.plot_top()
p2 = MapPlotFault(fault_file=join(this_script_dir, 'share/fault.h5'),
basemap = bm)
p2.plot_fault()
plt.savefig(join(this_script_dir, '~outs/share_basemap.png'))
plt.close()
def test_fault2geo(self):
xf = linspace(0., 700, 25)
yf = linspace(0, 425, 30)
xxf, yyf = meshgrid(xf, yf)
LLons, LLats = self.fm.fault2geo(xxf, yyf)
self.plt_map.basemap.plot(LLons,LLats,color='gray',latlon=True)
self.plt_map.basemap.plot(ascontiguousarray(LLons.T),
ascontiguousarray(LLats.T),
color='gray',latlon=True)
plt.savefig('~test_fault2geo.png')
plt.close()
def plot_smoothed_alpha_comparison(self,rmsval,suffix=''):
plt.plot(self.f,self.alpha,'ko',label='data set')
plt.plot(self.f,self.salpha,'c-',lw=2,label='smoothed angle $\phi$')
plt.xlabel('frequency in Hz')
plt.ylabel('angle $\phi$ in coordinates of circle')
plt.legend()
ylims=plt.axes().get_ylim()
plt.yticks((arange(9)-4)*0.5*pi, ['$-2\pi$','$-3\pi/2$','$-\pi$','$-\pi/2$','$0$','$\pi/2$','$\pi$','$3\pi/2$','$2\pi$'])
plt.ylim(ylims)
plt.title('RMS offset from smooth curve: {:.4f}'.format(rmsval))
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'salpha','c{}_salpha_on_{}_circle'.format(self.sdc.case,self.ZorY)+self.sdc.suffix+self.sdc.outsuffix+suffix+'.png'), dpi=240)
plt.close()
def test_ground2geo(self):
xp = linspace(0, 700, 25)
yp = linspace(0, 425, 30)
xxp, yyp = meshgrid(xp, yp)
LLons, LLats = self.fm.ground2geo(xxp, yyp)
self.plt_map.basemap.plot(LLons,LLats,color='gray',latlon=True)
self.plt_map.basemap.plot(ascontiguousarray(LLons.T),
ascontiguousarray(LLats.T),
color='gray',latlon=True)
plt.savefig('~test_ground2geo.png')
plt.close()
def convert_all_to_png(vis_path, out_dir = "maps_png", size = None) :
units = { 'gas_density' : 'Gas Density [g/cm$^3$]',
'Tm' : 'Temperature [K]',
'Tew' : 'Temperature [K]',
'S' : 'Entropy []',
'dm' : 'DM Density [g/cm$^3$]',
'v' : 'Velocity [km/s]' }
log_list = ['gas_density']
for vis_file in os.listdir(vis_path) :
if ".dat" not in vis_file :
continue
print "converting %s" % vis_file
map_type = re.search('sigma_(.*)_[xyz]', vis_file).group(1)
(image, pixel_size, axis_values) = read_visualization_data(vis_path+"/"+vis_file, size)
print "image width in Mpc/h: ", axis_values[-1]*2.0
x, y = np.meshgrid( axis_values, axis_values )
cmap_max = image.max()
cmap_min = image.min()
''' plotting '''
plt.figure(figsize=(5,4))
if map_type in log_list:
plt.pcolor(x,y,image, norm=LogNorm(vmax=cmap_max, vmin=cmap_min))
else :
plt.pcolor(x,y,image, vmax=cmap_max, vmin=cmap_min)
cbar = plt.colorbar()
if map_type in units.keys() :
cbar.ax.set_ylabel(units[map_type])
plt.axis([axis_values[0], axis_values[-1],axis_values[0], axis_values[-1]])
del image
plt.xlabel(r"$Mpc/h$", fontsize=18)
plt.ylabel(r"$Mpc/h$", fontsize=18)
out_file = vis_file.replace("dat", "png")
plt.savefig(out_dir+"/"+out_file, dpi=150 )
plt.close()
plt.clf()
def plot_overview_B(self,suffix='',ansize=8,anspread=0.15,anmode='quarters',datbg=True,datbgsource=None,checkring=False):
self.start_plot()
if datbg: # data background desired
self.plot_bg_data(datbgsource=datbgsource)
#self.plot_data()
self.plot_fitcircle()
if checkring:
self.plot_checkring()
idxlist=self.to_be_annotated(anmode)
self.annotate_data_points(idxlist,ansize,anspread)
self.plot_characteristic_freqs(annotate=True,size=ansize,spread=anspread)
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'c{}_fitted_{}_circle'.format(self.sdc.case,self.ZorY)+self.sdc.suffix+self.sdc.outsuffix+suffix+'.png'), dpi=240)
plt.close()
def main(args=sys.argv[1:]):
# there are some cases when this script is run on systems without DISPLAY variable being set
# in such case matplotlib backend has to be explicitly specified
# we do it here and not in the top of the file, as inteleaving imports with code lines is discouraged
import matplotlib
matplotlib.use('Agg')
from pylab import plt, ylabel, grid, xlabel, array
parser = argparse.ArgumentParser()
parser.add_argument("rst_file", help="location of rst file in TRiP98 format", type=str)
parser.add_argument("output_file", help="location of PNG file to save", type=str)
parser.add_argument("-s", "--submachine", help="Select submachine to plot.", type=int, default=1)
parser.add_argument("-f", "--factor", help="Factor for scaling the blobs. Default is 1000.", type=int, default=1000)
parser.add_argument("-v", "--verbosity", action='count', help="increase output verbosity", default=0)
parser.add_argument('-V', '--version', action='version', version=pt.__version__)
args = parser.parse_args(args)
file = args.rst_file
sm = args.submachine
fac = args.factor
a = pt.Rst()
a.read(file)
# convert data in submachine to a nice array
b = a.machines[sm]
x = []
y = []
z = []
for _x, _y, _z in b.raster_points:
x.append(_x)
y.append(_y)
z.append(_z)
title = "Submachine: {:d} / {:d} - Energy: {:.3f} MeV/u".format(sm, len(a.machines), b.energy)
print(title)
cc = array(z)
cc = cc / cc.max() * fac
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(x, y, c=cc, s=cc, alpha=0.75)
ylabel("mm")
xlabel("mm")
grid(True)
plt.title(title)
plt.savefig(args.output_file)
plt.close()
def draw(cls, t_max, agents_proportions, suffix):
color_set = ["green", "blue", "red"]
for agent_type in range(3):
plt.plot(np.arange(t_max), agents_proportions[:, agent_type],
color=color_set[agent_type], linewidth=1.0)
plt.ylim([0, 1])
# plt.suptitle('Direct choices proportion per type of agents', fontsize=14, fontweight='bold')
# plt.legend(loc='lower left', frameon=False)
plt.savefig("figure_{}.pdf".format(suffix))
def drawAdoptionNetworkMPL(G, fnum=1, show=False, writeFile=None):
"""Draws the network to matplotlib, coloring the nodes based on adoption.
Looks for the node attribute 'adopted'. If the attribute is True, colors
the node a different color, showing adoption visually. This function assumes
that the node attributes have been pre-populated.
:param networkx.Graph G: Any NetworkX Graph object.
:param int fnum: The matplotlib figure number. Defaults to 1.
:param bool show:
:param str writeFile: A filename/path to save the figure image. If not
specified, no output file is written.
"""
Gclean = G.subgraph([n for n in G.nodes() if n not in nx.isolates(G)])
plt.figure(num=fnum, figsize=(6,6))
# clear figure
plt.clf()
# Blue ('b') node color for adopters, red ('r') for non-adopters.
nodecolors = ['b' if Gclean.node[n]['adopted'] else 'r' \
for n in Gclean.nodes()]
layout = nx.spring_layout(Gclean)
nx.draw_networkx_nodes(Gclean, layout, node_size=80,
nodelist=Gclean.nodes(),
node_color=nodecolors)
nx.draw_networkx_edges(Gclean, layout, alpha=0.5) # width=4
# TODO: Draw labels of Ii values. Maybe vary size of node.
# TODO: Color edges blue based on influences from neighbors
influenceEdges = []
for a in Gclean.nodes():
for n in Gclean.node[a]['influence']:
influenceEdges.append((a,n))
if len(influenceEdges)>0:
nx.draw_networkx_edges(Gclean, layout, alpha=0.5, width=5,
edgelist=influenceEdges,
edge_color=['b']*len(influenceEdges))
#some extra space around figure
plt.xlim(-0.05,1.05)
plt.ylim(-0.05,1.05)
plt.axis('off')
if writeFile != None:
plt.savefig(writeFile)
if show:
plt.show()
def plot_overview(self,suffix=''):
x=self.x; y=self.y; r=self.radius; cx,cy=self.center.real,self.center.imag
ax=plt.axes()
plt.scatter(x,y, marker='o', c='b', s=40)
plt.axhline(y=0,color='grey', zorder=-1)
plt.axvline(x=0,color='grey', zorder=-2)
t=linspace(0,2*pi,201)
circx=r*cos(t) + cx
circy=r*sin(t) + cy
plt.plot(circx,circy,'g-')
plt.plot([cx],[cy],'gx',ms=12)
if self.ZorY == 'Z':
philist,flist=[self.phi_a,self.phi_p,self.phi_n],[self.fa,self.fp,self.fn]
elif self.ZorY == 'Y':
philist,flist=[self.phi_m,self.phi_s,self.phi_r],[self.fm,self.fs,self.fr]
for p,f in zip(philist,flist):
if f is not None:
xpos=cx+r*cos(p); ypos=cy+r*sin(p); xos=0.2*(xpos-cx); yos=0.2*(ypos-cy)
plt.plot([0,xpos],[0,ypos],'co-')
ax.annotate('{:.3f} Hz'.format(f), xy=(xpos,ypos), xycoords='data',
xytext=(xpos+xos,ypos+yos), textcoords='data', #textcoords='offset points',
arrowprops=dict(arrowstyle="->", shrinkA=0, shrinkB=10)
)
#plt.xlim(0,0.16)
#plt.ylim(-0.1,0.1)
plt.axis('equal')
if self.ZorY == 'Z':
plt.xlabel(r'resistance $R$ in Ohm'); plt.ylabel(r'reactance $X$ in Ohm')
if self.ZorY == 'Y':
plt.xlabel(r'conductance $G$ in Siemens'); plt.ylabel(r'susceptance $B$ in Siemens')
plt.title("fitting the admittance circle with Powell's method")
tx1='best fit (fmin_powell):\n'
tx1+='center at G+iB = {:.5f} + i*{:.8f}\n'.format(cx,cy)
tx1+='radius = {:.5f}; '.format(r)
tx1+='residue: {:.2e}'.format(self.resid)
txt1=plt.text(-r,cy-1.1*r,tx1,fontsize=8,ha='left',va='top')
txt1.set_bbox(dict(facecolor='gray', alpha=0.25))
idxlist=self.to_be_annotated('triple')
ofs=self.annotation_offsets(idxlist,factor=0.1,xshift=0.15)
for i,j in enumerate(idxlist):
xpos,ypos = x[j],y[j]; xos,yos = ofs[i].real,ofs[i].imag
ax.annotate('{:.1f} Hz'.format(self.f[j]), xy=(xpos,ypos), xycoords='data',
xytext=(xpos+xos,ypos+yos), textcoords='data', #textcoords='offset points',
arrowprops=dict(arrowstyle="->", shrinkA=0, shrinkB=10)
)
if self.show: plt.show()
else: plt.savefig(join(self.sdc.plotpath,'c{}_fitted_{}_circle'.format(self.sdc.case,self.ZorY)+suffix+'.png'), dpi=240)
plt.close()
def test_dep(self):
xf = arange(0, 425)
deps = self.fm.get_dep(xf)
plt.plot(xf,deps)
plt.gca().set_yticks(self.fm.DEP)
plt.gca().set_xticks(self.fm.Y_PC)
plt.grid('on')
plt.title('Ground x versus depth')
plt.xlabel('Ground X (km)')
plt.ylabel('depth (km)')
plt.axis('equal')
plt.gca().invert_yaxis()
plt.savefig(join(self.outs_dir, '~Y_PC_vs_deps.png'))
plt.close()
def plot(site):
tp = np.loadtxt('../post_offsets/%s.post'%site)
t = dc.asmjd([ii[0] for ii in tp]) + dc.adjust_mjd_for_plot_date
e = [ii[1] for ii in tp]
n = [ii[2] for ii in tp]
u = [ii[3] for ii in tp]
plt.plot_date(t,e,'x-', label = 'eastings')
plt.plot(t,n,'x-', label = 'northings')
plt.plot(t,u,'x-', label = 'upings')
plt.gcf().autofmt_xdate()
plt.legend(loc=0)
plt.title(site)
plt.savefig('%s.png'%site)
#plt.show()
plt.close()
def plot_baseline(data, plate_name, save_folder = r'Figures/'):
"""
"""
colors = ((0.2, 0.2, 0.2),
(0.5, 0.5, 0.5),
(0.7, 0.7, 0.7),
(0.3, 0.3, 0.3))
names = data.keys()
names.sort()
fig, axs = plt.subplots(figsize=(8,3))
for index, name in enumerate(names):
for value in data[name]['original_data']:
plot_color = colors[index % len(colors)]
if abs(value - data[name]['mean'][0]) > data[name]['std'][0] * 2.0:
axs.plot([value], [index], 'ko', markerfacecolor = [1,1,1])
else:
axs.plot([value], [index], 'ko', color = plot_color)
axs.plot([data[name]['mean'][0] for _ in xrange(2)],
[index-0.25, index+0.25],
'k-')
axs.plot([data[name]['mean'][0] - data[name]['std'][0] for _ in xrange(2)],
[index-0.25, index+0.25],
'k--')
axs.plot([data[name]['mean'][0] + data[name]['std'][0] for _ in xrange(2)],
[index-0.25, index+0.25],
'k--')
plt.yticks([i for i in xrange(len(names))], names, size = 10)
plt.title(plate_name)
plt.ylim(-0.5,len(names)-0.5)
plt.xlabel('Fluorescent intensity')
plt.tight_layout()
save_filename = save_folder + 'baseline_average'
pdf = PdfPages(save_filename.split('.')[0] + '.pdf')
pdf.savefig(fig)
pdf.close()
plt.savefig(save_filename)
#
return None
def test_make_grids(self):
north = 80
south = 12
east = 200
west = 80
dx = 200e3
dy = 200e3
grids = make_grids(north, south, west, east, dx, dy)
lons = [ii[0] for ii in grids]
lats = [ii[1] for ii in grids]
bm = vj.plots.MyBasemap(
region_box = (west+20, south-40, east+70, north),
x_interval = 20,
y_interval = 20)
bm.plot(lons, lats, 'x', latlon=True)
plt.savefig(join(self.outs_dir, 'map_gridding_point.png'))
