def inset_momentum_axes(cd):
# TODO: This plot does not refresh correctly, skip the inset
fig = mpl.figure(cd.plotfigure.figno)
axes = fig.add_subplot(111)
# Plot main figure
axes.plot(cd.x, hu_1(cd), 'b-')
axes.plot(cd.x, hu_2(cd), 'k--')
axes.set_xlim(xlimits)
axes.set_ylim(ylimits_momentum)
momentum_axes(cd)
# Create inset plot
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
inset_axes = zoomed_inset_axes(axes, 0.5, loc=3)
inset_axes.plot(cd.x, hu_1(cd), 'b-')
inset_axes.plot(cd.x, hu_2(cd), 'k--')
inset_axes.set_xticklabels([])
inset_axes.set_yticklabels([])
x_zoom = [-120e3,-30e3]
y_zoom = [-10,10]
inset_axes.set_xlim(x_zoom)
inset_axes.set_ylim(y_zoom)
mark_inset(axes, inset_axes, loc1=2, loc2=4, fc='none', ec="0.5")
# mpl.ion()
mpl.draw()
def chickling_pd_zoom(shotno, date=time.strftime("%Y%m%d")):
fname, data = file_finder(shotno,date)
data_1550 = data[0]['phasediff_co2'][100:]
plot_time = np.linspace(0,1,data_1550.size)
fig, ax = plt.subplots()
ax.plot(plot_time, data_1550)
ax.set_ybound(max(data_1550)+0.6, min(data_1550)-0.01)
plt.title("Phase Difference for shot " + str(shotno) + " Date " + str(date))
plt.xlabel("Time, s")
plt.ylabel("Phase Difference, Radians")
x_zoom_bot = int(data_1550.size*(10/100))
x_zoom_top = int(data_1550.size*(15/100))
x1, x2, y1, y2 = 0.1, 0.15, max(data_1550[x_zoom_bot:x_zoom_top])+0.01, min(data_1550[x_zoom_bot:x_zoom_top])-0.01
axins = inset_axes(ax, 4.3,1, loc=9)
axins.plot(plot_time[x_zoom_bot:x_zoom_top], data_1550[x_zoom_bot:x_zoom_top])
axins.set_xlim(x1, x2)
if y1 < y2:
axins.set_ylim(y1, y2)
else:
axins.set_ylim(y2, y1)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5",lw=2)
plt.show()
def show_insets(image, insets_coords, size=(6, 3)):
fig = pyplot.figure(figsize=size)
line_colors = colors.LinearSegmentedColormap.from_list("line_colors", ["red", "blue"], len(insets_coords))
gs = gridspec.GridSpec(len(insets_coords), 2)
main_ax = fig.add_subplot(gs[:, 0])
main_extent = (0, image.shape[1], image.shape[0], 0)
main_ax_img = main_ax.imshow(image, interpolation="nearest", extent=main_extent, origin="upper")
main_ax_img.set_cmap("binary_r")
pyplot.setp(main_ax.get_yticklabels(), visible=False)
pyplot.setp(main_ax.get_xticklabels(), visible=False)
for inset_index, inset_coords in enumerate(insets_coords):
inset_ax = fig.add_subplot(gs[inset_index, 1])
inset_ax_img = inset_ax.imshow(image, interpolation="nearest", extent=main_extent, origin="upper")
inset_ax_img.set_cmap("binary_r")
inset_ax.set_ylim(inset_coords[0].stop, inset_coords[0].start)
inset_ax.set_xlim(inset_coords[1].start, inset_coords[1].stop)
pyplot.setp(inset_ax.get_yticklabels(), visible=False)
pyplot.setp(inset_ax.get_xticklabels(), visible=False)
mark_inset(main_ax, inset_ax, loc1=1, loc2=3, fc="none", ec=line_colors(inset_index))
gs.tight_layout(fig)
return fig
def render_input_spectrum():
folder = "/cosma/home/durham/rhgk18/ls_structure/pks"
bao = folder+"/wig.txt"
nbao = folder+"/nowig.txt"
pkbao = np.loadtxt(bao)
pknbao = np.loadtxt(nbao)
fig, ax = plt.subplots()
ax.loglog(pkbao[:,0] ,pkbao[:,1] , color='r', label='With BAO')
ax.loglog(pknbao[:,0] ,pknbao[:,1] , color='b', label='Without BAO')
ax.set_xlabel("Wavenumber, $k$ [$h/Mpc$]", size=28)
ax.set_ylabel("Power, $P(k)$ [$(Mpc/h)^3$]", size=28)
ax.legend(prop={'size':28})
axins = zoomed_inset_axes(ax, 5, loc=3)
axins.loglog(pkbao[:,0] ,pkbao[:,1] , color='r', label='iWith BAO')
axins.loglog(pknbao[:,0] ,pknbao[:,1] , color='b', label='iWithout BAO')
x1, x2, y1, y2 = 0.02, 0.1, 5000, 30000
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax, axins, loc1=2, loc2=1, fc="none", ec="0.5")
plt.show()
def QQplot(obs, Q, pos, color=None, ax=None, axins=True):
if not color:
color='blue'
if ax is None:
ax = plt.gca()
mean = np.mean(obs, axis=0)
# obs = np.random.multivariate_normal(mean, S, 3000)
md2 = np.diag(np.dot(np.dot(obs - mean, Q), (obs -mean).T))
sorted_md2 = np.sort(md2)
v = (np.arange(1, obs.shape[0] + 1) - 0.375) / (obs.shape[0] + 0.25)
quantiles = scipy.stats.chi2.ppf(v, df=obs.shape[1])
# axins = inset_axes(ax, width="60%", height=1., loc=2)
if axins:
axins = inset_axes(ax, width="60%", height=1., loc=2)
axins.axis(pos)
axins.get_xaxis().tick_bottom()
axins.get_yaxis().tick_left()
# Remove the tick marks; they are unnecessary with the tick lines we just plotted.
axins.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="off", left="off", right="off", labelleft="off")
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
axins.xaxis.set_major_locator(MaxNLocator(nbins=1, prune='lower'))
axins.scatter(quantiles, sorted_md2, color=colorscheme[color], alpha=0.3)
axins.plot(quantiles, quantiles, color=colorscheme['green'], lw=2.5)
ax.scatter(quantiles, sorted_md2, color=colorscheme[color], alpha=0.3)
ax.plot(quantiles, quantiles, color=colorscheme['green'], lw=2.5)
_clean_axes(ax)
def plot_linear_kernel_pairs(pairs):
xdata, ydata = zip(*pairs)
maxval = max(max(xdata), max(ydata))
fig, ax = plt.subplots()
ax.plot(xdata, ydata, '.')
ax.plot([0, maxval], [0, maxval], '--')
plt.xlabel("Linear Ridge Mean Absolute Error (kcal/mol)")
plt.ylabel("Kernel Ridge Mean Absolute Error (kcal/mol)")
# 15 is the zoom, loc is nuts
axins = zoomed_inset_axes(ax, 15, loc=5)
axins.plot(xdata, ydata, '.')
axins.plot([0, maxval], [0, maxval], '--')
# sub region of the original image
axins.set_xlim(1, 6)
axins.set_ylim(1, 6)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.draw()
plt.show()
def plot_target_pred(train_xdata, test_xdata, train_ydata, test_ydata, loc=5):
maxval = max(train_xdata.max(), test_xdata.max(), train_ydata.max(), test_ydata.max())
minval = min(train_xdata.min(), test_xdata.min(), train_ydata.min(), test_ydata.min())
fig, ax = plt.subplots()
ax.plot(train_xdata, train_ydata, '.', label="Train")
ax.plot(test_xdata, test_ydata, '.', label="Test")
plt.legend(loc="best")
ax.plot([minval, maxval], [minval, maxval], '--')
plt.xlabel("Target Value (kcal/mol)")
plt.ylabel("Predicted Value (kcal/mol)")
axins = zoomed_inset_axes(ax, 30, loc=loc) # 30 is zoom, loc is .... nuts
axins.plot(train_xdata, train_ydata, '.', label="Train")
axins.plot(test_xdata, test_ydata, '.', label="Test")
axins.plot([minval, maxval], [minval, maxval], '--')
# sub region of the original image
middle = test_xdata.mean() - 170
x1, x2, y1, y2 = -15+middle, 15+middle, -15+middle, 15+middle
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax, axins, loc1=2, loc2=3, fc="none", ec="0.5")
plt.draw()
plt.show()
def plot_us(lats, lons, save_name=None):
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
big_map = Basemap(resolution='h',
lat_0=36, lon_0=-107.5,
llcrnrlat=32, llcrnrlon=-125,
urcrnrlat=43, urcrnrlon=-110)
big_map.drawcoastlines()
big_map.drawstates()
big_map.drawcountries()
big_map.drawmapboundary(fill_color='#7777ff')
big_map.fillcontinents(color='#ddaa66', lake_color='#7777ff', zorder=0)
x, y = big_map(lons, lats)
big_map.plot(x[0], y[0], 'ro', markersize=2)
axins = zoomed_inset_axes(ax, 20, loc=1)
ll_lat, ll_lon = 37.8, -122.78
ur_lat, ur_lon = 38.08, -122.43
axins.set_xlim(ll_lon, ur_lon)
axins.set_ylim(ur_lon, ur_lat)
small_map = Basemap(resolution='h',
llcrnrlat=ll_lat, llcrnrlon=ll_lon,
urcrnrlat=ur_lat, urcrnrlon=ur_lon,
ax=axins)
small_map.drawcoastlines()
small_map.drawmapboundary(fill_color='#7777ff')
small_map.fillcontinents(color='#ddaa66', lake_color='#7777ff', zorder=0)
x, y = small_map(lons, lats)
small_map.plot(x, y, 'ro', markersize=3)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
if save_name:
fig.savefig(save_name)
def plotBinned(cls_in,dcls_in,l_out,bins,output_prefix,title=None,theory=None,dtheory=None,delta=None,cosmic=None):
fig=plt.figure(1)
plt.clf()
ax=fig.add_subplot(111)
good_l=np.logical_and( l_out > 25 , l_out <= 250)
if not (theory is None) :
ax.plot(l_out,theory,'r-')
if not (cosmic is None) :
ax.fill_between(l_out,(theory-cosmic),(theory+cosmic),alpha=.5,facecolor='red')
#plt.fill_between(l_out,(theory-dtheory),(theory+dtheory),alpha=.5,facecolor='red')
if not (dtheory is None) :
ax.errorbar(l_out,theory,yerr=dtheory,color='red')
ax.errorbar(l_out,cls_in,yerr=dcls_in,color='black',fmt='k.',linestyle='None')
if not (delta is None) :
ax.fill_between(l_out,cls_in-delta,cls_in+delta,color='gray',alpha=0.5)
#plt.xlim([0,np.max(l_out+bins)])
#plt.ylim([np.min(b_cl['llcl']-b_dcl['llcl']),np.max(b_cl['llcl']+b_dcl['llcl'])])
ax.set_xlabel('$\ell$')
ax.set_ylabel('$\\frac{\ell(\ell+1)}{2\pi}C_{\ell}\ \\frac{\mu K^{2}}{m^{4}}$')
#ax.set_xlim([0,np.max(l_out+bins)])
# ax.autoscale(axis='y',tight=True)
if title:
ax.set_title(title)
else:
ax.set_title('Binned Cls {:02d}'.format(bins))
axins = inset_axes(ax,width="50%",height="30%",loc=9)
if not (theory is None) :
axins.plot(l_out[good_l],theory[good_l],'r-')
if not (cosmic is None) :
axins.fill_between(l_out[good_l],(theory-cosmic)[good_l],(theory+cosmic)[good_l],alpha=.5,facecolor='red')
#plt.fill_between(l_out,(theory-dtheory),(theory+dtheory),alpha=.5,facecolor='red')
if not (dtheory is None) :
axins.errorbar(l_out[good_l],theory[good_l],yerr=dtheory[good_l],color='red')
axins.errorbar(l_out[good_l],cls_in[good_l],yerr=dcls_in[good_l],color='black',fmt='k.',linestyle='None')
if not (delta is None) :
axins.fill_between(l_out[good_l],(cls_in-delta)[good_l],(cls_in+delta)[good_l],color='gray',alpha=0.5)
axins.set_xlim(25,255)
axins.set_yscale('log')
#axins.set_ylim(ymax=1e5)
#axins.set_ylim(-1e5,1e5)
mark_inset(ax,axins,loc1=2,loc2=4,fc='none',ec='0.1')
#axins.set_xlim([25,250])
axins.autoscale('y',tight=True)
plt.draw()
#plt.show(block=True)
fig.savefig(output_prefix+'_linear_{:02d}.eps'.format(bins),format='eps')
fig.savefig(output_prefix+'_linear_{:02d}.png'.format(bins),format='png')
ax.set_yscale('log')
fig.savefig(output_prefix+'_log_{:02d}.eps'.format(bins),format='eps')
fig.savefig(output_prefix+'_log_{:02d}.png'.format(bins),format='png')
def plot_default_topo(bmap,x, y, data):
assert isinstance(bmap, Basemap)
fig = plt.figure(figsize=(10, 6))
gs = GridSpec(1, 2, width_ratios=[4, 1], height_ratios=[3, 1])
ax = fig.add_subplot(gs[0, 1])
bmap.etopo()
img = bmap.contourf(x, y, data, norm=LogNorm(), alpha=0.8, ax=ax)
cb = bmap.colorbar(img)
cb.ax.set_title(r"${\rm km^2}$")
bmap.drawcoastlines(linewidth=0.3)
bmap.drawmeridians(np.arange(-180, 180, 20))
bmap.drawparallels(np.arange(-90, 110, 20))
bmap.readshapefile("data/shp/wri_basins2/wribasin", "basin", color="k", linewidth=2, ax=ax)
lower_left_lat_lon = (-50, 45)
axins = fig.add_subplot(gs[0, 0]) # plt.axes([-0.15, 0.1, 0.6, 0.8]) # zoomed_inset_axes(ax, 2, loc=1) # zoom = 6
#bmap.etopo(ax=axins)
bm_zoom = plot_changes_in_seasonal_means.get_arctic_basemap_nps(round=False, resolution="l")
lower_left_xy = bmap(*lower_left_lat_lon)
upper_right_xy = bmap(-160, 55)
bm_zoom.etopo(ax=axins)
#axins.set_xlim(lower_left_xy[0], lower_left_xy[0] + 400000)
#axins.set_ylim(lower_left_xy[1], lower_left_xy[1] + 5000000)
print(lower_left_xy)
print(upper_right_xy)
#bm_zoom.etopo(ax=axins)
assert isinstance(axins, Axes)
img = bm_zoom.contourf(x, y, data, norm=LogNorm(), alpha=0.8, ax=axins)
bm_zoom.drawcoastlines(linewidth=0.3)
bm_zoom.readshapefile("data/shp/wri_basins2/wribasin", "basin", color="k", linewidth=2, ax=axins)
axins.set_xlim(lower_left_xy[0], upper_right_xy[0])
axins.set_ylim(lower_left_xy[1], upper_right_xy[1])
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=1, loc2=4, fc="none", ec="0.5")
fig.tight_layout()
fig.savefig("simple2.png", bbox_inches="tight")
plt.show()
def test_gettightbbox():
fig, ax = plt.subplots(figsize=(8, 6))
l, = ax.plot([1, 2, 3], [0, 1, 0])
ax_zoom = zoomed_inset_axes(ax, 4)
ax_zoom.plot([1, 2, 3], [0, 1, 0])
mark_inset(ax, ax_zoom, loc1=1, loc2=3, fc="none", ec='0.3')
remove_ticks_and_titles(fig)
bbox = fig.get_tightbbox(fig.canvas.get_renderer())
np.testing.assert_array_almost_equal(bbox.extents,
[-17.7, -13.9, 7.2, 5.4])
def makeplot(refl, winds, w, stride, map, gs, title, file_name, box=None):
pylab.figure()
axmain = pylab.axes((0, 0.025, 1, 0.9))
gs_x, gs_y = gs
nx, ny = refl.shape
xs, ys = np.meshgrid(gs_x * np.arange(nx), gs_y * np.arange(ny))
pylab.contourf(xs, ys, refl, levels=np.arange(10, 80, 10))
pylab.colorbar()
pylab.contour(xs, ys, w, levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs, ys, w, levels=np.arange(2, 12, 2), colors='#666666', style='-')
u, v = winds
wind_slice = tuple([ slice(None, None, stride) ] * 2)
pylab.quiver(xs[wind_slice], ys[wind_slice], u[wind_slice], v[wind_slice])
if box:
lb_y, lb_x = [ b.start for b in box ]
ub_y, ub_x = [ b.stop for b in box ]
box_xs = gs_x * np.array([ lb_x, lb_x, ub_x, ub_x, lb_x])
box_ys = gs_y * np.array([ lb_y, ub_y, ub_y, lb_y, lb_y])
map.plot(box_xs, box_ys, '#660099')
axins = zoomed_inset_axes(pylab.gca(), 4, loc=4)
pylab.sca(axins)
pylab.contourf(xs[box], ys[box], refl[box], levels=np.arange(10, 80, 10))
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(-10, 0, 2), colors='#666666', style='--')
pylab.contour(xs[box], ys[box], w[box], levels=np.arange(2, 12, 2), colors='#666666', style='-')
pylab.quiver(xs[box], ys[box], u[box], v[box])
drawPolitical(map)
pylab.xlim([lb_x * gs_x, ub_x * gs_x - 1])
pylab.ylim([lb_y * gs_y, ub_y * gs_y - 1])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
drawPolitical(map)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def test_inset_axes():
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3, 4, -4, 3)
fig, ax = plt.subplots(figsize=[5, 4])
# prepare the demo image
Z, extent = get_demo_image()
Z2 = np.zeros([150, 150], dtype="d")
ny, nx = Z.shape
Z2[30:30 + ny, 30:30 + nx] = Z
# extent = [-3, 4, -4, 3]
ax.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
# creating our inset axes with a bbox_transform parameter
axins = inset_axes(ax, width=1., height=1., bbox_to_anchor=(1, 1),
bbox_transform=ax.transAxes)
axins.imshow(Z2, extent=extent, interpolation="nearest",
origin="lower")
axins.yaxis.get_major_locator().set_params(nbins=7)
axins.xaxis.get_major_locator().set_params(nbins=7)
# sub region of the original image
x1, x2, y1, y2 = -1.5, -0.9, -2.5, -1.9
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
asb = AnchoredSizeBar(ax.transData,
0.5,
'0.5',
loc='lower center',
pad=0.1, borderpad=0.5, sep=5,
frameon=False)
ax.add_artist(asb)
def plotConfusion(confusion, grid, title, file_name, inset=None, fudge=16):
pylab.figure()
axmain = pylab.axes()
tick_labels = [ "Missing", "Correct\nNegative", "False\nAlarm", "Miss", "Hit" ]
min_label = -1
xs, ys = grid.getXY()
pylab.pcolormesh(xs, ys, confusion, cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
tick_locs = np.linspace(-1, min_label + len(tick_labels) - 2, len(tick_labels))
tick_locs += (tick_locs[1] - tick_locs[0]) / 2
bar = pylab.colorbar()
bar.locator = FixedLocator(tick_locs)
bar.formatter = FixedFormatter(tick_labels)
pylab.setp(pylab.getp(bar.ax, 'ymajorticklabels'), fontsize='large')
bar.update_ticks()
grid.drawPolitical()
if inset:
lb_y, lb_x = [ b.start for b in inset ]
ub_y, ub_x = [ b.stop + fudge for b in inset ]
inset_exp = (slice(lb_y, ub_y), slice(lb_x, ub_x))
axins = zoomed_inset_axes(pylab.gca(), 2, loc=4)
pylab.sca(axins)
pylab.pcolormesh(xs[inset_exp], ys[inset_exp], confusion[inset_exp], cmap=confusion_cmap, vmin=min_label, vmax=(min_label + len(tick_labels) - 1))
grid.drawPolitical()
gs_x, gs_y = grid.getGridSpacing()
pylab.xlim([lb_x * gs_x, (ub_x - 1) * gs_x])
pylab.ylim([lb_y * gs_y, (ub_y - 1) * gs_y])
mark_inset(axmain, axins, loc1=1, loc2=3, fc='none', ec='k')
pylab.sca(axmain)
pylab.suptitle(title)
pylab.savefig(file_name)
pylab.close()
return
def draw_inset(plt, m, pos, lat, lon):
ax = plt.subplot(111)
zoom = 50
axins = zoomed_inset_axes(ax, zoom, loc=1)
m.plot(lon, lat, '.b--', zorder=10, latlon=True)
m.scatter(lon, lat, # longitude first!
latlon=True, # lat and long in degrees
zorder=11) # on top of all
x1, y1 = m(lon[1] - 0.005, lat[0] - 0.0025)
x2, y2 = m(lon[1] + 0.005, lat[0] + 0.0025)
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
def plot_calibration(c, insert = True):
fig = plt.figure()
ax = plt.gca()
for baseline, correlation in c.time_domain_correlations_values.items():
correlation_max_val = correlation[np.argmax(correlation)]
correlation_max_time = c.time_domain_correlations_times[baseline][np.argmax(correlation)]
lines = ax.plot(
c.time_domain_correlations_times[baseline] * 1e9,
correlation / correlation_max_val,
label = baseline)
#ax.plot([correlation_max_time, correlation_max_time], [0, correlation_max_val], color = lines[0].get_color())
ax.set_ylim(top=1.2)
ax.xaxis.set_ticks(np.arange(
-200,
200,
2))
if insert == True:
#axins = zoomed_inset_axes(ax, 5, loc=1)
axins = zoomed_inset_axes(ax, 9, loc=1)
for baseline, correlation in c.time_domain_correlations_values.items():
correlation_max_val = correlation[np.argmax(correlation)]
correlation_max_time = c.time_domain_correlations_times[baseline][np.argmax(correlation)]
lines = axins.plot(
c.time_domain_correlations_times[baseline] * 1e9,
correlation / correlation_max_val,
label = baseline,
linewidth=2)
#axins.plot([correlation_max_time, correlation_max_time], [0, correlation_max_val], color = lines[0].get_color())
#axins.set_xlim(-0.4, 2.9)
axins.set_xlim(-0.4, 0.4)
#axins.set_ylim(0.90, 1.04)
axins.set_ylim(0.96, 1.03)
#axins.xaxis.set_ticks(np.arange(-0.4, 2.9, 0.4))
axins.xaxis.set_ticks(np.arange(-0.4, 0.4, 0.2))
mark_inset(ax, axins, loc1=2, loc2=3, fc='none', ec='0.5')
plt.xticks(visible=True)
plt.yticks(visible=False)
ax.set_title("Time domain cross correlations with broad band noise\n arriving through full RF chain AFTER calibration")
ax.set_xlabel("Time delay (ns)")
ax.set_ylabel("Cross correlation value (normalised)")
ax.legend(loc=2)
#ax.legend()
plt.show()
def plot_site(options):
options['prefix'] = 'site'
fig = MyFig(options, figsize=(10, 8), legend=True, grid=False, xlabel=r'Probability $p_s$', ylabel=r'Reachability~$\reachability$', aspect='auto')
fig_vs = MyFig(options, figsize=(10, 8), legend=True, grid=False, xlabel=r'Fraction of Forwarded Packets~$\forwarded$', ylabel=r'Reachability~$\reachability$', aspect='auto')
if options['grayscale']:
colors = options['graycm'](pylab.linspace(0, 1.0, 3))
else:
colors = fu_colormap()(pylab.linspace(0, 1.0, 3))
nodes = 105
axins = None
if options['inset_loc'] >= 0:
axins = zoomed_inset_axes(fig_vs.ax, options['inset_zoom'], loc=options['inset_loc'])
axins.set_xlim(options['inset_xlim'])
axins.set_ylim(options['inset_ylim'])
axins.set_xticklabels([])
axins.set_yticklabels([])
mark_inset(fig_vs.ax, axins, loc1=2, loc2=3, fc="none", ec="0.5")
for i, (name, label) in enumerate(names):
data = parse_site_only(options['datapath'][0], name)
rs = numpy.array([r for p, r, std, conf, n, fw, fw_std, fw_conf in data if r <= options['limit']])
fws = numpy.array([fw for p, r, std, conf, n, fw, fw_std, fw_conf in data if r <= options['limit']])/(nodes-1)
ps = numpy.array([p for p, r, std, conf, n, fw, fw_std, fw_conf in data]) #[0:len(rs)])
yerr = numpy.array([conf for p,r,std,conf, n, fw, fw_std, fw_conf in data]) #[0:len(rs)])
patch_collection = PatchCollection([conf2poly(ps, list(rs+yerr), list(rs-yerr), color=colors[i])], match_original=True)
patch_collection.set_alpha(0.3)
patch_collection.set_linestyle('dashed')
fig.ax.add_collection(patch_collection)
fig.ax.plot(ps, rs, label=label, color=colors[i])
fig_vs.ax.plot(fws, rs, label=label, color=colors[i])
if axins:
axins.plot(fws, rs, color=colors[i])
fig.ax.set_ylim(0, options['limit'])
fig.legend_title = 'Graph'
fig.save('graphs')
fig_vs.legend_title = 'Graph'
fig_vs.ax.set_xlim(0,1)
fig_vs.ax.set_ylim(0,1)
fig_vs.save('vs_pb')
def plot_figure(self, ax, xaxis, yaxis, title='', legend='',
drawline=True, legend_outside=False, marker=None,
linestyle=None, xlabel='', ylabel='', xlog=False, ylog=False,
zoom=False, zoom_ax=None, zoom_xaxis=[], zoom_yaxis=[],
legend_pos='best', xlabel_format=0, xlimit=0, ylimit=0, legend_markscale=1.0):
if drawline:
ax.plot(xaxis, yaxis, marker=marker if marker else '+', ls=linestyle if linestyle else '-', label=legend)
else: #draw dots
ax.plot(xaxis, yaxis, marker=marker if marker else 'o', markerfacecolor='r', ms=4, ls='None', label=legend)
#ax.vlines(xaxis, [0], yaxis)
if xlog:
ax.set_xscale('log')
if ylog:
ax.set_yscale('log')
if xlimit > 0:
ax.set_xlim(0, ax.get_xlim()[1] if ax.get_xlim()[1]<xlimit else xlimit)
if ylimit > 0:
ax.set_ylim(0, ax.get_ylim()[1] if ax.get_ylim()[1]<ylimit else ylimit)
ax.set_title(title)
ax.legend(loc=legend_pos, markerscale=legend_markscale)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
# zoom
if zoom:
new_zoom_ax = False
if zoom_ax is None:
new_zoom_ax = True
zoom_ax = inset_axes(ax,
width="70%", # width = 50% of parent_bbox
height="70%", # height : 1 inch
loc=7) # center right
if drawline:
zoom_ax.plot(zoom_xaxis, zoom_yaxis, marker=marker if marker else '+', ls=linestyle if linestyle else '-')
else: #draw dots
zoom_ax.plot(zoom_xaxis, zoom_yaxis, marker=marker if marker else 'o', markerfacecolor='r', ms=4, ls='None')
#zoom_ax.vlines(zoom_xaxis, [0], zoom_yaxis)
if new_zoom_ax:
zoom_ax.ticklabel_format(axis='y', style='sci', scilimits=(0,0))
mark_inset(ax, zoom_ax, loc1=2, loc2=4, fc="none", ec="0.5")
return ax, zoom_ax
def graph(data_, j_size, title=None):
fig, ax = plt.subplots()
data = pd.read_csv(data_, index_col = 0)
jitter_ = jitter(-j_size, j_size, len(list(data.iterrows())[0][1]))
max_1 = 0
max_2 = 0
for i, d in enumerate(data.iterrows()):
if max(list(d[1])) > max_1 and i > 2:
max_1 = max(list(d[1]))
elif max(list(d[1])) > max_2 and i < 3:
max_2 = max(list(d[1]))
plt.scatter(x = np.array([i]*len(jitter_)+jitter_), y = list(d[1]))
plt.xlim(-0.1, 5.1) # apply the x-limits
c1 = 1
while max_1/(c1*10) > 1:
c1 *= 10
c2 = 1
while max_2/(c2*10) > 1:
c2 *= 10
cb = max(c1,c2)
cs = min(c1,c2)
max1 = max(max_1, max_2)
max2 = min(max_1, max_2)
plt.ylim(-cb, (max1/cb+2)*cb)
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
if c2 > c1:
axins = inset_axes(ax, 4,3, loc=5)
else:
axins = inset_axes(ax, 4,3, loc=6)
axins.yaxis.tick_right()
for i, d in enumerate(data.iterrows()):
plt.scatter(x = np.array([i]*len(jitter_)+jitter_), y = list(d[1]))
if c2 > c1:
axins.set_xlim(2.9, 5.1) # apply the x-limits
else:
axins.set_xlim(-0.1, 2.1)
plt.ylim(-cs, (max2/cs+2)*cs)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.show()
def nice_plot_e_of_T():
Ts1 = np.arange(273.15-10, 273.15-5, 1.)
Ts2 = np.arange(273.15-3, 273.15-1, .1)
Ts3 = np.arange(273.15-1, 273.15+1, .0001)
Ts4 = np.arange(273.15+1, 273.15+5, .1)
Ts5 = np.arange(273.15+5, 273.15+50, 1.)
Ts = np.concatenate((Ts1, Ts2, Ts3, Ts4, Ts5))
# water content at T=273.65, p=85000.
wc = water_content(273.65, 85000., saturations_A)
ie = []
for T in Ts:
# for each T, determine the p required to maintain same mass
(sl,si,sg),p = saturation_with_wc(T,wc, saturations_A)
ie.append(internal_energy_per_mol(T,p, saturations_A))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(Ts - 273.15,np.array(ie), 'b')
ax.set_xlim(-10,20)
ax.set_ylim(-8000,2000)
plt.xlabel("temperature [C]")
plt.ylabel("energy density (water) [J/mol]")
axins = inset_axes(ax, 3, 3, loc=4)
axins.plot(Ts - 273.15,np.array(ie), 'b')
axins.set_xlim(-.01,.01)
axins.set_ylim(-7000,500)
axins.set_xticks([-0.01, 0, 0.01])
axins.tick_params(labeltop=1, labelbottom=0)
axins.set_yticks([-6000,-4000,-2000,0])
mark_inset(ax, axins, loc1=1, loc2=3, fc="none", ec="0.5")
plt.show()
def plot_network(net, inset={}, path=None, with_secnet=False):
"""
"""
fig = plt.figure(figsize=(40, 40), dpi=72)
ax = fig.add_subplot(111)
# Draw nodes
DrawNodes(net, ax, label='S', color='dodgerblue', size=2000)
DrawNodes(net, ax, label='T', color='green', size=25)
DrawNodes(net, ax, label='R', color='black', size=2.0)
if with_secnet: DrawNodes(net, ax, label='H', color='crimson', size=2.0)
# Draw edges
DrawEdges(net, ax, label='P', color='black', width=2.0)
DrawEdges(net, ax, label='E', color='dodgerblue', width=2.0)
if with_secnet: DrawEdges(net, ax, label='S', color='crimson', width=1.0)
ax.tick_params(left=False,
bottom=False,
labelleft=False,
labelbottom=False)
# Inset figures
for sub in inset:
axins = zoomed_inset_axes(ax,
inset[sub]['zoom'],
loc=inset[sub]['loc'])
axins.set_aspect(1.3)
# Draw nodes
DrawNodes(inset[sub]['graph'],
axins,
label='S',
color='dodgerblue',
size=2000)
DrawNodes(inset[sub]['graph'],
axins,
label='T',
color='green',
size=25)
DrawNodes(inset[sub]['graph'],
axins,
label='R',
color='black',
size=2.0)
if with_secnet:
DrawNodes(inset[sub]['graph'],
axins,
label='H',
color='crimson',
size=2.0)
# Draw edges
DrawEdges(inset[sub]['graph'],
axins,
label='P',
color='black',
width=2.0)
DrawEdges(inset[sub]['graph'],
axins,
label='E',
color='dodgerblue',
width=2.0)
if with_secnet:
DrawEdges(inset[sub]['graph'],
axins,
label='S',
color='crimson',
width=1.0)
axins.tick_params(bottom=False,
left=False,
labelleft=False,
labelbottom=False)
mark_inset(ax,
axins,
loc1=inset[sub]['loc1'],
loc2=inset[sub]['loc2'],
fc="none",
ec="0.5")
# Legend for the plot
leghands = [
Line2D([0], [0],
color='black',
markerfacecolor='black',
marker='o',
markersize=0,
label='primary network'),
Line2D([0], [0],
color='dodgerblue',
markerfacecolor='dodgerblue',
marker='o',
markersize=0,
label='high voltage feeder'),
Line2D([0], [0],
color='white',
markerfacecolor='green',
marker='o',
markersize=20,
label='transformer'),
Line2D([0], [0],
color='white',
markerfacecolor='dodgerblue',
marker='o',
markersize=20,
label='substation')
]
if with_secnet:
leghands.insert(
1,
Line2D([0], [0],
color='crimson',
markerfacecolor='crimson',
marker='o',
markersize=0,
label='secondary network'))
leghands.insert(
-1,
Line2D([0], [0],
color='white',
markerfacecolor='red',
marker='o',
markersize=20,
label='residence'))
ax.legend(handles=leghands, loc='best', ncol=1, prop={'size': 25})
if path != None:
fig.savefig("{}{}.png".format(path, '-51121-dist'),
bbox_inches='tight')
return
1e-3,
color='lavender',
label='chemical accuracy')
axins.grid(b=True,
which='major',
color='black',
linestyle='--',
linewidth=0.5)
axins.grid(b=True,
which='minor',
color='black',
linestyle='-.',
linewidth=0.2)
axins.set_xlim(x1, x2)
axins.xaxis.tick_top()
axins.set_ylim(y1, y2)
axins.yaxis.tick_right()
axins.set_yscale('log')
# axins.xaxis.set_minor_locator(MultipleLocator(125))
# axins.xaxis.set_major_locator(MultipleLocator(250))
mark_inset(ax, axins, loc1=2, loc2=3, fc="none", ec="0", linewidth=.75)
# ax.legend(loc=3)#, bbox_to_anchor=(1,0.4))
ax.legend(loc=9, fontsize=10.5) #, bbox_to_anchor=(1,0.4))
plt.show()
print('macaroni')
def sigmoid(x):
def plot_binned_stat(allIouVsCls, val_to_plot, bins=10, pltAll = 0, linestyle = ':', plttype = 'stat',applysigx = True , applysigy = False, plt_unitline=False):
color=cm.rainbow(np.linspace(0,1, len(allIouVsCls.keys())))
legendK = []
if pltAll:
legendK = allIouVsCls.keys()
for i, cls in enumerate(allIouVsCls):
xval = FN.sigmoid(torch.FloatTensor(allIouVsCls[cls][val_to_plot[0]])).numpy() if applysigx else allIouVsCls[cls][val_to_plot[0]]
yval = FN.sigmoid(torch.FloatTensor(allIouVsCls[cls][val_to_plot[1]])).numpy() if applysigy else allIouVsCls[cls][val_to_plot[1]]
if plttype == 'stat':
aClsVsRec = binned_statistic(xval, yval,statistic='mean', bins=bins)
aClsVsRec_std = binned_statistic(xval, yval,statistic=np.std, bins=bins)
#plt.plot((aClsVsRec[1][:-1]+aClsVsRec[1][1:])/2, aClsVsRec[0],color=color[i],marker='o',linestyle=linestyle);
plt.errorbar((aClsVsRec[1][:-1]+aClsVsRec[1][1:])/2, aClsVsRec[0], yerr = aClsVsRec_std[0], color=color[i],marker='o',linestyle=linestyle);
else:
plt.scatter(xval, yval,alpha=0.5,color=color[i],s=20)
if pltAll < 2:
legendK = legendK + ['all']
allX = np.concatenate([allIouVsCls[cls][val_to_plot[0]] for cls in allIouVsCls])
allY = np.concatenate([allIouVsCls[cls][val_to_plot[1]] for cls in allIouVsCls])
xval = FN.sigmoid(torch.FloatTensor(allX)).numpy() if applysigx else allX
yval = FN.sigmoid(torch.FloatTensor(allY)).numpy() if applysigy else allY
if plttype == 'stat':
aClsVsRec = binned_statistic(xval, yval,statistic='mean', bins=bins)
aClsVsRec_std = binned_statistic(xval, yval,statistic=np.std, bins=bins)
#plt.plot((aClsVsRec[1][:-1]+aClsVsRec[1][1:])/2, aClsVsRec[0],color=color[-1],marker='o',linestyle='-', linewidth=2);
plt.errorbar((aClsVsRec[1][:-1]+aClsVsRec[1][1:])/2, aClsVsRec[0], yerr = aClsVsRec_std[0], color=color[-1],marker='o',linestyle='-', linewidth=2);
else:
plt.scatter(xval,yval,alpha=0.4,color=color[-1],s=20)
plt.xlabel(val_to_plot[0])
plt.ylabel(val_to_plot[1])
plt.legend(legendK)
if plt_unitline:
plt.plot(xval,xval, 'k-');
plt.show()
fname = 'removeEvalResults/fullres/train_checkpoint_stargan_coco_fulleditor_LowResMask_pascal_RandDiscrWdecay_wgan_30pcUnion_noGT_reg_biasM_randRot_fixedD_randDisc_smM_fixInp_imnet_IN_maxPool_V2_180_1227'
tr_res = json.load(open(fname,'r'))
selected_attrs = ['person', 'bird', 'cat', 'cow', 'dog', 'horse', 'sheep', 'airplane', 'bicycle', 'boat', 'bus', 'car', 'motorcycle', 'train', 'bottle', 'couch', "dining table", "potted plant", 'chair','tv']
attToIdx = {att:i for i,att in enumerate(selected_attrs)}
res = tr_res
allIouVsCls = {}
for key,img in res['images'].items():
for cls in img['perclass']:
if cls not in allIouVsCls:
allIouVsCls[cls] = {'iou':[], 'recall':[], 'precision':[], 'ocls':[],'acls':[],'gtsize':[], 'predsize':[], 'false_damage':[], 'n_obj':[], 'diff':[]}
allIouVsCls[cls]['iou'].append(img['perclass'][cls]['iou'])
allIouVsCls[cls]['recall'].append(img['perclass'][cls]['rec'])
allIouVsCls[cls]['precision'].append(img['perclass'][cls]['prec'])
allIouVsCls[cls]['ocls'].append(img['real_scores'][attToIdx[cls]])
allIouVsCls[cls]['acls'].append(img['perclass'][cls]['remove_scores'][attToIdx[cls]])
allIouVsCls[cls]['rSucc'].append(float(img['perclass'][cls]['remove_scores'][attToIdx[cls]]<0.))
allIouVsCls[cls]['diff'].append(img['real_scores'][attToIdx[cls]] - img['perclass'][cls]['remove_scores'][attToIdx[cls]])
allIouVsCls[cls]['gtsize'].append(img['perclass'][cls]['gtSize'])
allIouVsCls[cls]['predsize'].append(img['perclass'][cls]['predSize'])
#allIouVsCls[cls]['false_damage'].append(np.max([img['real_scores'][oclsId] - img['perclass'][cls]['remove_scores'][oclsId] for oclsId in img['real_label'] if selected_attrs[oclsId]!=cls])/(len(img['real_label'])-1+1e-6) )
allIouVsCls[cls]['false_damage'].append(np.max([img['real_scores'][oclsId] - img['perclass'][cls]['remove_scores'][oclsId] for oclsId in img['real_label'] if selected_attrs[oclsId]!=cls]) if len(img['real_label'])>1 else np.nan)
allIouVsCls[cls]['n_obj'].append(len(img['real_label']))
val_to_plot = ['ocls','recall']
'person' ,'bird' , 'cat' , 'cow' , 'dog' , 'horse' , 'sheep' , 'airplane' , 'bicycle' ,'boat' , 'bus' , 'car' , 'motorcycle' , 'train' , 'bottle' , 'couch' , 'dining table' , 'potted plant', 'chair' , 'tv'
cat2id= {}
data = {} ; data['images'] = {}
for ann in train_ann:
annSp = ann.split()
imgid = int(annSp[0].split('.')[0])
cls = annSp[1].lower()
if imgid not in data['images']:
finfo = subprocess.check_output(['file', 'flickr_logos_27_dataset_images/'+annSp[0]])
data['images'][imgid] = {'bboxAnn': [], 'id': imgid, 'filename':annSp[0], 'split':'train','imgSize': map(int, finfo.split(',')[-2].split('x'))}
if cls not in cat2id:
cat2id[cls] = len(cat2id)
bbox = map(int,annSp[-4:])
img_w,img_h = data['images'][imgid]['imgSize']
bbox = [float(bbox[0])/float(img_w), float(bbox[1])/float(img_h), float(bbox[2]-bbox[0])/float(img_w), float(bbox[3] - bbox[1])/float(img_h)]
data['images'][imgid]['bboxAnn'].append({'bbox': bbox, 'cid': cat2id[cls]})
data['categories'] = [{'id':cat2id[cat], 'name':cat} for cat in cat2id]
for ann in val_ann:
annSp = ann.split()
imgid = int(annSp[0].split('.')[0])
cls = annSp[1].lower()
if imgid not in data['images']:
finfo = subprocess.check_output(['file', 'flickr_logos_27_dataset_images/'+annSp[0]])
data['images'][imgid] = {'bboxAnn': [], 'id': imgid, 'filename':annSp[0], 'split':'train','imgSize': map(int, finfo.split(',')[-2].split('x'))}
if cls not in cat2id:
cat2id[cls] = len(cat2id)
bbox = [0., 0., 1., 1.]
data['images'][imgid]['bboxAnn'].append({'bbox': bbox, 'cid': cat2id[cls]})
for ann in val_ann:
annSp = ann.split()
imgid = int(annSp[0].split('.')[0])
cls = annSp[1].lower()
data['images'][imid2index[imgid]]['split'] = 'val'
cat2id= {}
data = {} ; data['images'] = {}
for ann in tqdm(train_ann):
annSp = ann.split()
if annSp[4]:
imgid = int(annSp[2].split('.')[0])
cls = annSp[1].lower()
if imgid not in data['images']:
finfo = subprocess.check_output(['file', 'images/'+annSp[2]])
data['images'][imgid] = {'bboxAnn': [], 'id': imgid, 'filename':annSp[2], 'split':'train','imgSize': map(int, finfo.split(',')[-2].split('x'))}
if cls not in cat2id:
cat2id[cls] = len(cat2id)
bbox = map(int,annSp[-4:])
img_w,img_h = data['images'][imgid]['imgSize']
bbox = [float(bbox[0])/float(img_w), float(bbox[1])/float(img_h), float(bbox[2]-bbox[0])/float(img_w), float(bbox[3] - bbox[1])/float(img_h)]
data['images'][imgid]['bboxAnn'].append({'bbox': bbox, 'cid': cat2id[cls]})
data['categories'] = [{'id':cat2id[cat], 'name':cat} for cat in cat2id]
for fname in tqdm(notPresentImgs):
finfo = subprocess.check_output(['file', 'images/'+fname])
imgid = int(fname.split('.')[0])
data['images'].append({'bboxAnn': [], 'id': imgid, 'filename':fname, 'split':'train','imgSize': map(int, finfo.split(',')[-2].split('x'))})
import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import seaborn
from PIL import Image
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
fig, ax = plt.subplots();
ax.imshow(img, origin='upper', extent=[0,128, 128,0]);
axins = zoomed_inset_axes(ax, zoom=3, loc=7)
extent = [50, 60, 70, 60]
axins.imshow(img, interpolation="nearest", origin='upper', extent=[0,128, 0,128])
axins.set_xlim(*extent[:2])
axins.set_ylim(*extent[2:])
axins.yaxis.get_major_locator().set_params(nbins=7)
axins.xaxis.get_major_locator().set_params(nbins=7)
plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax, axins, loc1=1, loc2=3, fc="none", ec="0.5")
ax.set_axis_off()
plt.draw(); plt.show()
fig, ax = plt.subplots(frameon=False);
ax.imshow(img, origin='lower');
axins = zoomed_inset_axes(ax, zoom=3, loc=7)
extent = [55, 65, 44, 54]
axins.imshow(img, interpolation="nearest", origin='lower')
axins.set_xlim(*extent[:2])
axins.set_ylim(*extent[2:])
axins.yaxis.get_major_locator().set_params(nbins=7)
axins.xaxis.get_major_locator().set_params(nbins=7)
#axins.set_axis_off()
plt.xticks(visible=False)
plt.yticks(visible=False)
mark_inset(ax, axins, loc1=2, loc2=3, fc="none", ec="r")
ax.set_axis_off()
plt.draw(); plt.show()
import numpy as np
import json
from scipy.special import expit
import matplotlib.pyplot as plt
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=90)
plt.yticks(tick_marks, classes)
fmt = '.1f'
thresh = cm.max() / 2.
#for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
# plt.text(j, i, format(cm[i, j], fmt),
# horizontalalignment="center",
# color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('Removed Object')
plt.xlabel('Change in Classifier Scores after removal')
def plot_2d_scan_unbinned(self,
to_file=True,
nll_func=None,
iminuit_object=None):
"""
Plot the result of the one-dimensional profiled likelihood scan.
:param to_file: bool
"""
try:
mt_, nll_func = self.iminuit_objects[
f"2D_unbinned__{self.sfu.__name__}_{self.bfu.__name__}"]
except KeyError:
try:
self.calc_1d_unbinned_iminuit()
mt_, nll_func = self.iminuit_objects[
f"2D_unbinned__{self.sfu.__name__}_{self.bfu.__name__}"]
except KeyError:
mt_, nll_func = iminuit_object, nll_func
if mt_ is None or nll_func is None:
raise NotImplementedError
@np.vectorize
def nll_vec(alpha, mass):
return nll_func(alpha, mass)
p_alpha_ = PlotHelper.get_error_points_from_iminuit_obj(mt_, "alpha")
p_mass_ = PlotHelper.get_error_points_from_iminuit_obj(mt_, "mass")
if self.verbose:
print("Start plotting: Initialize", flush=True, end="\r")
alpha_ = np.sort(
np.unique(np.append(np.array(p_alpha_), np.linspace(0.0, 0.8,
50))))
nll_alpha = nll_vec(alpha_, mt_.np_values()[1])
nll_alpha = nll_alpha - np.amin(nll_alpha)
if self.verbose:
print("Plotting: Got alpha main window", flush=True, end="\r")
mass_ = np.sort(
np.unique(
np.append(
np.array(p_mass_),
np.linspace(p_mass_[0] - 0.25, p_mass_[-1] + 0.25, 50))))
nll_mass = nll_vec(mt_.np_values()[0], mass_)
nll_mass = nll_mass - np.amin(nll_mass)
if self.verbose:
print("Plotting: Got mass main window", flush=True, end="\r")
z_ = np.sqrt(
nll_func(0.0,
mt_.np_values()[1]) - nll_func(mt_.np_values()[0],
mt_.np_values()[1]))
f = plt.figure(figsize=(14, 8))
f.subplots_adjust(left=0.15,
bottom=0.07,
right=0.98,
top=0.95,
wspace=0.23,
hspace=1.0)
gs = gridspec.GridSpec(1, 1, figure=f)
gs0 = gridspec.GridSpecFromSubplotSpec(7, 2, subplot_spec=gs[0])
ax1 = f.add_subplot(gs0[:-3, :])
PlotHelper.plot_nll_scan_main_window(ax_obj_=ax1,
x_=alpha_,
y_=nll_alpha,
z_=z_)
ax_in = inset_axes(ax1, width='50%', height='40%', loc="upper right")
PlotHelper.plot_nll_scan_inside_window(ax_obj_=ax_in,
x_=alpha_,
y_=nll_alpha,
x_ticks_=p_alpha_)
mark_inset(ax1,
ax_in,
loc1=2,
loc2=4,
fc="none",
ec="grey",
lw=0.5,
alpha=0.5)
if self.verbose:
print("Plotting: Starting contour", flush=True, end="\r")
ax2 = f.add_subplot(gs0[4:7, :-1])
PlotHelper.contour_own(ax_obj_=ax2,
nll_obj_=nll_vec,
m_obj_=mt_,
var1_="alpha",
var2_="mass")
if self.verbose: print("Plotting: End Contour", flush=True, end="\r")
ax3 = f.add_subplot(gs0[4:7, -1])
PlotHelper.plot_nll_scan_inside_window(
ax_obj_=ax3,
x_=mass_,
y_=nll_mass,
x_ticks_=p_mass_,
label_=
r"$- 2 \ln \left( \frac{\mathcal{L}}{\mathcal{L}_{\rm{min}}} \right)-\rm{profile}$",
x_label=r"$m_{\rm{H}}$",
y_label=
r"$- 2 \ln \left( \frac{\mathcal{L}}{\mathcal{L}_{\rm{min}}} \right)$"
)
file_ = os.path.join(
self.save_dir,
f"2D_scan_unbinned_{self.sfu.__name__}_{self.bfu.__name__}.png")
plt.savefig(file_) if to_file else plt.show()
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
axins = zoomed_inset_axes(axesb, 3.0, loc=4)
for i in nhj.keys():
axins.plot(nhjb[i][1], nhja[i][1], "o", markersize=4.0)
x1, x2, y1, y2 = 350, 470, 900, 1100 # specify the limits
axins.set_xlim(x1, x2) # apply the x-limits
axins.set_ylim(y1, y2) # apply the y-limits
plt.yticks(visible=False)
plt.xticks(visible=False)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
mark_inset(axesb, axins, loc1=1, loc2=2, fc="none", ec="0.0")
##############################################
# ax_inset=fig.add_axes([0.35,0.17,0.65,0.5])
# ax_inset.xaxis.set_tick_params(labelsize=11)
# ax_inset.yaxis.set_tick_params(labelsize=11)
# for i in nhj.keys():
# ax_inset.plot(nhjb[i][1],nhja[i][1],"o",markersize=4.0)
# ax_inset.set_ylim(900,1100)
# ax_inset.set_xlim(350,480)
######################################
# axesb.axvline(nhj[i][0][0],0.0,nhj[i][1][0], color='k', linestyle='--')
# axesb.stem(xtj,ytj,linefmt='--', markerfmt='bo', basefmt='r-')
weight="bold",
textcoords='offset points',
horizontalalignment='right',
verticalalignment='bottom',
)
#axins.annotate('NAA (24000 Hz)', xy=(29, 24000),
# xycoords='data',
# xytext=(0, 10),color="w",weight="bold", textcoords='offset points',
# horizontalalignment='right', verticalalignment='bottom',
# )
plt.xticks(visible=False)
plt.yticks(visible=False)
# draw a bbox of the region of the inset axes in the parent axes and
# connecting lines between the bbox and the inset axes area
mark_inset(ax, axins, loc1=1, loc2=2, fc="none", ec="1")
axins2 = inset_axes(
ax,
width="30%", # width = 30% of parent_bbox
height=1.5, # height : 1 inch
loc=3)
axins2.plot(Time, signal, color='r', linewidth=2)
t1, t2 = 22., 23.2
axins2.set_xlim(t1, t2)
axins2.annotate(
'Spherics',
xy=(22.4, 20000),
xycoords='data',
def roc_curve_bootstrap_with_readers(y, preds, savepath=None, n_bootstrap=5000, seed=12345, readers=None, inset=True):
"""Evaluates ROC curve using bootstrapping
Also reports confidence intervals and prints them.
Parameters
----------
y : numpy.array
Ground truth
preds : numpy.array
Predictions
savepath: str
Where to save the figure with ROC curve
n_bootstrap:
Number of bootstrap samples to draw
seed : int
Random seed
"""
auc = trunc(roc_auc_score(y, preds), 5)
np.random.seed(seed)
aucs = []
tprs = []
base_fpr = np.linspace(0, 1, 1001)
for i in range(n_bootstrap):
ind = np.random.choice(y.shape[0], y.shape[0])
if y[ind].sum() == 0:
continue
aucs.append(roc_auc_score(y[ind], preds[ind]))
fpr, tpr, _ = roc_curve(y[ind], preds[ind])
tpr = np.interp(base_fpr, fpr, tpr)
tpr[0] = 0.0
tprs.append(tpr)
tprs = np.array(tprs)
mean_tprs = np.mean(tprs, 0)
std = np.std(tprs, axis=0)
tprs_upper = np.minimum(mean_tprs + std, 1)
tprs_lower = mean_tprs - std
if savepath:
tmp = savepath.split('/')
tmp2 = tmp[-1]
tmp3 = tmp2.split('_')
ts = tmp3[0]
else:
ts = 'test'
with open(f'analysis/{ts}_roc.pkl', 'wb') as f:
pickle.dump(aucs, f)
pickle.dump(tpr, f)
pickle.dump(fpr, f)
# fig = plt.figure(figsize=(6, 6))
fig, ax = plt.subplots(figsize=(6,6))
fpr, tpr, _ = roc_curve(y, preds, drop_intermediate=False)
# plt.fill_between(base_fpr, tprs_lower, tprs_upper, color='grey', alpha=0.2)
ax.plot(fpr, tpr, color='midnightblue', label='DeepWrist, AUROC: %0.2f'%(trunc(auc, 2)))
ax.plot([0, 1], [0, 1], '--', color='black', label='Random Guess')
if readers is not None:
for key in readers:
data = readers[key]
ax.plot([1.0 - data[1]], [data[0]], marker=data[2], label=f'{key} ({trunc(data[1], 2)}, {trunc(data[0], 2)})', color=data[3])
ax.set_xlim(-0.01, 1)
ax.set_ylim(0, 1.01)
ax.legend(loc=0, bbox_to_anchor=(0.4, 0., 0.4, 0.4), fontsize=8)
plt.grid()
ax.set_xlabel('1 - Specificity')
ax.set_ylabel('Sensitivity')
if inset:
ax_ins = zoomed_inset_axes(ax, 1.8, loc=10)
ax_ins.plot(fpr, tpr, color='midnightblue')
ax_ins.plot([0, 1], [0, 1], '--', color='black')
if readers is not None:
for key in readers:
data = readers[key]
# ax.plot([data[1]], [data[0]], marker=data[2], label="%s (%0.2f, %0.2f)"%(key, trunc(data[1], 2), trunc(data[0], 2)), color=data[3])
if inset:
ax_ins.plot([1.0 - data[1]], [data[0]], marker=data[2], color=data[3])
if inset:
x1, x2, y1, y2 = -0.01, 0.46, 0.9, 1.01
ax_ins.set_xlim(x1, x2)
ax_ins.set_ylim(y1, y2)
ax_ins.set_xticks([])
ax_ins.set_yticks([])
plt.xticks(visible=True)
plt.yticks(visible=True)
if inset:
mark_inset(ax, ax_ins, loc1=1, loc2=2, fc='none', ec="0.5")
plt.draw()
# plt.tight_layout()
if savepath is not None:
plt.savefig(savepath, bbox_inches='tight')
plt.close(fig)
CI_l, CI_h = np.percentile(aucs, 2.5), np.percentile(aucs, 97.5)
return auc, CI_l, CI_h
def pr_curve_bootstrap_with_readers(y, preds, savepath=None, readers=None, n_bootstrap=5000, seed=12345, inset=True):
precision, recall, _ = precision_recall_curve(y, preds)
np.random.seed(seed)
auc = average_precision_score(y, preds)
aucs = []
for i in range(n_bootstrap):
ind = np.random.choice(y.shape[0], y.shape[0])
if y[ind].sum() == 0:
continue
aucs.append(average_precision_score(y[ind], preds[ind]))
# fig = plt.figure(figsize=(6, 6))
fig, ax = plt.subplots(figsize=(6,6))
positives = float(np.sum(y))
total = float(len(y))
baseline = positives / total
baseline = trunc(baseline, 2)
# plt.fill_between(base_fpr, tprs_lower, tprs_upper, color='grey', alpha=0.2)
ax.plot(recall, precision, color='midnightblue', label=f'DeepWrist, AUPR: {trunc(auc, 2)}')
ax.plot([0, 1], [baseline, baseline], '--', color='black', label=f'Random Guess (Prevalence: {baseline})')
if savepath:
tmp = savepath.split('/')
tmp2 = tmp[-1]
tmp3 = tmp2.split('_')
ts = tmp3[0]
else:
ts = 'test'
with open(f'analysis/{ts}_pr.pkl', 'wb') as f:
pickle.dump(aucs, f)
pickle.dump(precision, f)
pickle.dump(recall, f)
if readers is not None:
for key in readers:
data = readers[key]
ax.plot([data[1]], [data[0]], marker=data[2], label="%s (%0.2f, %0.2f)"%(key, trunc(data[1], 2), trunc(data[0], 2)), color=data[3])
# ax_ins.plot([data[1]], [data[0]], marker=data[2], color=data[3])
ax.set_xlim(0, 1.01)
ax.set_ylim(0, 1.01)
ax.set_xlabel('Recall')
ax.set_ylabel('Precision')
ax.grid()
ax.legend(fontsize=8)
if inset:
ax_ins = zoomed_inset_axes(ax, 2.3, loc=8)
ax_ins.plot(recall, precision, color='midnightblue')
ax_ins.plot([0, 1], [baseline, baseline], '--', color='black')
if readers is not None:
for key in readers:
data = readers[key]
# ax.plot([data[1]], [data[0]], marker=data[2], label="%s (%0.2f, %0.2f)"%(key, trunc(data[1], 2), trunc(data[0], 2)), color=data[3])
if inset:
ax_ins.plot([data[1]], [data[0]], marker=data[2], color=data[3])
if inset:
x1, x2, y1, y2 = 0.8, 1.01, 0.77, 1.01
ax_ins.set_xlim(x1, x2)
ax_ins.set_ylim(y1, y2)
ax_ins.set_xticks([])
ax_ins.set_yticks([])
plt.xticks(visible=True)
plt.yticks(visible=True)
if inset:
mark_inset(ax, ax_ins, loc1=2, loc2=4, fc='none', ec="0.5")
plt.draw()
# plt.tight_layout()
if savepath is not None:
plt.savefig(savepath, bbox_inches='tight', dpi=300)
plt.close(fig)
CI_l, CI_h = np.percentile(aucs, 2.5), np.percentile(aucs, 97.5)
return auc, CI_l, CI_h
1,
loc=2,
bbox_to_anchor=(0.22, 0.435),
bbox_transform=ax.figure.transFigure) # no zoom
axinsMg.plot(wavelengths, observed_flux, 'k', wavelengths, model_flux, 'r')
x1, x2, y1, y2 = 5150, 5200, 0.6, 1.2 # specify the limits
axinsMg.set_xlim(x1, x2) # apply the x-limits
axinsMg.set_ylim(y1, y2) # apply the y-limits
axinsMg.xaxis.set_major_locator(ticker.MaxNLocator(3))
plt.title('Mg')
#plt.yticks(visible=False)
#plt.xticks(visible=False)
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
mark_inset(ax, axinsMg, loc1=1, loc2=2, fc="none", ec="0.5")
#K
AxinsK = inset_axes(ax,
3,
1,
loc=2,
bbox_to_anchor=(0.5, 0.435),
bbox_transform=ax.figure.transFigure) # no zoom
AxinsK.plot(wavelengths, observed_flux, 'k', wavelengths, model_flux, 'r')
AxinsK.xaxis.set_major_locator(ticker.MaxNLocator(4))
x1, x2, y1, y2 = 7650, 7720, 0.6, 1.2 # specify the limits
AxinsK.set_xlim(x1, x2) # apply the x-limits
AxinsK.set_ylim(y1, y2) # apply the y-limits
plt.title('K')
def zoom_taliban_attack_types(attacks):
"""
Zooms in the particular location of the attacks perpetrated by the Taliban group
showing the different attack types.
"""
group = 'Taliban'
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(111)
cmap = plt.get_cmap('inferno')
plt.title('Attack types perpetrated by the Taliban group\n')
# create the map
map = Basemap(projection='cyl', lat_0=0, lon_0=0)
map.drawmapboundary()
map.fillcontinents(color='lightgray', zorder=0)
# create the plot structure
temp_markers, _MAX = get_group_markers(attacks, group)
xs, ys = map(temp_markers['Longitude'], temp_markers['Latitude'])
scat = map.scatter(xs,
ys,
s=temp_markers['Size'],
c=temp_markers['Color'],
cmap=cmap,
marker='o',
alpha=0.5,
zorder=10)
axins = zoomed_inset_axes(ax, 9, loc=2)
axins.set_xlim(25, 40)
axins.set_ylim(60, 75)
plt.xticks(visible=False)
plt.yticks(visible=False)
map2 = Basemap(llcrnrlon=55,
llcrnrlat=25,
urcrnrlon=75,
urcrnrlat=40,
ax=axins)
map2.drawmapboundary()
map2.fillcontinents(color='lightgray', zorder=0)
map2.drawcoastlines()
map2.drawcountries()
temp_markers, label_color_dict_attack_type = get_group_attack_types_markers(
attacks, group)
map2.scatter(xs,
ys,
s=temp_markers['Size'] / 5.,
c=temp_markers['Color'],
alpha=0.5)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
handles = create_handles(label_color_dict_attack_type, ax)
labels = [h.get_label() for h in handles]
ax.legend(loc='upper left',
bbox_to_anchor=(1, 1),
handles=handles,
labels=labels)
plt.savefig('taliban_zoom_attack_types.pdf', bbox_inches='tight')
plt.show()
def parse_encyclopedia(path, filename=None, q_value=0.05, IO=True):
"""Helper to generate a list of peptides an analyze from an encyclopedia output.
Filters the input to the q-value specified by the user (default of 0.05)
Typical use:
peptides = gen_peptide_list('./')
**assumes that the encyclopedia file you want to work with is the only file in the directory you are using
Optional arguments:
** q_value=[float] | specify the q-value cutoff to use to filter peptides
(0.0 will take all peptides in the input)
** filename | directly specify the full path and filename to analyze instead
of assuming you will use the first one (alphabetically) in the directory
** IO | True will show outputs, false will supress
Return:
Returns a pandas dataframe indexed by the peptide sequence and bearing columns of 'mz' and 'z' (mass/charge, charge)
"""
#check for optional arguments then read proper file
if filename is None:
filename = glob.glob(path + '/*.encyclopedia.txt')[0]
if IO:
print('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
print('reading peptides from encyclopedia file ' + filename)
#generate pandas file, filter, parse peptides, save encyc_rts values, save charge values, calculate mz, save proteins
encyc = pandas.read_csv(filename, sep='\t')
all_entries = encyc[encyc['q-value'] < q_value]
peptides = [i[2:-2] for i in all_entries['peptide']]
encyc_rts = [float(i.split(':')[1]) / 60.0 for i in all_entries['PSMId']]
zs = [i.split('+')[-1] for i in all_entries['PSMId']]
prot_names = [i for i in all_entries['proteinIds']]
mzs = []
for index, pepseq in enumerate(peptides):
mzs.append(calc_mz(pepseq, int(zs[index])))
#mzs = [calc_mz(pair[0], int(zs[pair[1]])) for pair in enumerate(peptides))]
assert len(peptides) == len(encyc_rts)
assert len(peptides) == len(zs)
assert len(peptides) == len(prot_names)
assert len(peptides) == len(mzs)
#convert to a dictionary
pdl = {}
for i, p in enumerate(peptides):
pdl[p] = [encyc_rts[i], zs[i], prot_names[i], mzs[i]]
# show plots for q-value criteria
if IO:
print(str(encyc.shape[0]) + ' peptides identified')
print(str(len(peptides)) + ' peptides meet q-value criteria')
print(
str(encyc.shape[0] - len(peptides)) +
' peptides fail q-value criteria')
fig, ax = pylab.subplots(
) # create a new figure with a default 111 subplot
ax.hist(encyc['q-value'].dropna(), bins=100)
axins = zoomed_inset_axes(
ax, 2, loc=1) # zoom-factor: 2.5, location: upper-left
axins.hist(encyc['q-value'].dropna(), bins=100)
axins.set_xlim(0, q_value * 2) # apply the x-limits
axins.set_ylim(0, ax.get_ylim()[1] / 4) # apply the x-limits
axins.vlines([q_value],
0,
ax.get_ylim()[1] / 4,
colors='red',
linestyle='dashed',
label='q-cuttoff')
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
ax.set_xlabel('q-value')
ax.set_ylabel('peptides')
pylab.show()
print('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
# convert to a pandas dataframe
mz_df = pandas.DataFrame.from_dict(pdl, orient='index')
mz_df = mz_df.rename(index=str,
columns={
0: 'encyc_rt',
1: 'z',
2: 'proteinIds',
3: 'mz'
})
return mz_df
def plot_corr_dist(
Xa,
Xp,
inset=True,
xlabel='$F_{dft}$' + r' $(kcal \times mol^{-1} \times \AA^{-1})$',
ylabel='$F_{dft}$' + r' $(kcal \times mol^{-1} \times \AA^{-1})$',
figsize=[13, 10],
cmap=mpl.cm.viridis):
Fmx = Xa.max()
Fmn = Xa.min()
label_size = 14
mpl.rcParams['xtick.labelsize'] = label_size
mpl.rcParams['ytick.labelsize'] = label_size
fig, ax = plt.subplots(figsize=figsize)
# Plot ground truth line
ax.plot([Fmn, Fmx], [Fmn, Fmx], '--', c='r', linewidth=3)
# Set labels
ax.set_xlabel(xlabel, fontsize=22)
ax.set_ylabel(ylabel, fontsize=22)
#cmap = mpl.cm.viridis
#cmap = mpl.cm.brg
# Plot 2d Histogram
bins = ax.hist2d(Xa,
Xp,
bins=200,
norm=LogNorm(),
range=[[Fmn, Fmx], [Fmn, Fmx]],
cmap=cmap)
# Build color bar
#cbaxes = fig.add_axes([0.91, 0.1, 0.03, 0.8])
cb1 = fig.colorbar(bins[-1], cmap=cmap)
cb1.set_label('Count', fontsize=16)
# Annotate with errors
PMAE = hdt.calculatemeanabserror(Xa, Xp)
PRMS = hdt.calculaterootmeansqrerror(Xa, Xp)
ax.text(0.75 * ((Fmx - Fmn)) + Fmn,
0.43 * ((Fmx - Fmn)) + Fmn,
'MAE=' + "{:.3f}".format(PMAE) + '\nRMSE=' + "{:.3f}".format(PRMS),
fontsize=20,
bbox={
'facecolor': 'white',
'alpha': 0.5,
'pad': 5
})
if inset:
axins = zoomed_inset_axes(ax, 2.2, loc=2) # zoom = 6
sz = 6
axins.hist2d(Xa,
Xp,
bins=50,
range=[[Fmn / sz, Fmx / sz], [Fmn / sz, Fmx / sz]],
norm=LogNorm(),
cmap=cmap)
axins.plot([Xa.min(), Xa.max()], [Xa.min(), Xa.max()],
'--',
c='r',
linewidth=3)
# sub region of the original image
x1, x2, y1, y2 = Fmn / sz, Fmx / sz, Fmn / sz, Fmx / sz
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.yaxis.tick_right()
plt.xticks(visible=True)
plt.yticks(visible=True)
mark_inset(ax, axins, loc1=1, loc2=3, fc="none", ec="0.5")
Ferr = Xa - Xp
std = np.std(Ferr)
men = np.mean(Ferr)
axh = plt.axes([.49, .16, .235, .235])
axh.hist(Ferr,
bins=75,
range=[men - 4 * std, men + 4 * std],
normed=True)
axh.set_title('Difference distribution')
#plt.draw()
plt.show()
def plot_road_network(net, subs, inset={}, path=None):
"""
"""
fig = plt.figure(figsize=(40, 40), dpi=72)
ax = fig.add_subplot(111)
sub_x = [subs[s]['cord'][0] for s in subs]
sub_y = [subs[s]['cord'][1] for s in subs]
# Draw nodes
ax.scatter(sub_x, sub_y, c='dodgerblue', s=2000)
DrawNodes(net, ax, label='T', color='green', size=25)
DrawNodes(net, ax, label='R', color='black', size=2.0)
# Draw edges
d = {'edges':list(net.edges()),
'geometry':[LineString((net.nodes[e[0]]['cord'],net.nodes[e[1]]['cord'])) \
for e in net.edges()]}
df_edges = gpd.GeoDataFrame(d, crs="EPSG:4326")
df_edges.plot(ax=ax, edgecolor="black", linewidth=2.0, linestyle="dashed")
ax.tick_params(left=False,
bottom=False,
labelleft=False,
labelbottom=False)
# Inset figures
for sub in inset:
axins = zoomed_inset_axes(ax,
inset[sub]['zoom'],
loc=inset[sub]['loc'])
axins.set_aspect(1.3)
# Draw nodes
ax.scatter([subs[sub]['cord'][0]], [subs[sub]['cord'][1]],
c='dodgerblue',
s=2000)
DrawNodes(inset[sub]['graph'],
axins,
label='T',
color='green',
size=25)
DrawNodes(inset[sub]['graph'],
axins,
label='R',
color='black',
size=2.0)
# Draw edges
d = {'edges':list(inset[sub]['graph'].edges()),
'geometry':[LineString((inset[sub]['graph'].nodes[e[0]]['cord'],
inset[sub]['graph'].nodes[e[1]]['cord'])) \
for e in inset[sub]['graph'].edges()]}
df_edges = gpd.GeoDataFrame(d, crs="EPSG:4326")
df_edges.plot(ax=axins,
edgecolor="black",
linewidth=2.0,
linestyle="dashed")
axins.tick_params(bottom=False,
left=False,
labelleft=False,
labelbottom=False)
mark_inset(ax,
axins,
loc1=inset[sub]['loc1'],
loc2=inset[sub]['loc2'],
fc="none",
ec="0.5")
# Legend for the plot
leghands = [
Line2D([0], [0],
color='black',
markerfacecolor='black',
marker='o',
markersize=0,
label='road network'),
Line2D([0], [0],
color='white',
markerfacecolor='green',
marker='o',
markersize=20,
label='transformer'),
Line2D([0], [0],
color='white',
markerfacecolor='black',
marker='o',
markersize=20,
label='road node'),
Line2D([0], [0],
color='white',
markerfacecolor='dodgerblue',
marker='o',
markersize=20,
label='substation')
]
ax.legend(handles=leghands, loc='best', ncol=1, prop={'size': 25})
if path != None:
fig.savefig("{}{}.png".format(path, '-51121-road'),
bbox_inches='tight')
return
def run_expr(
dens_func_P,
dens_func_Q,
log_likelihood_ratio_func,
num_composition,
use_cornish_fisher=False,
left=-np.inf,
right=np.inf,
title=None,
ax=None,
log_scale=False,
zoom_in=False,
):
print("$$$$$ n:{}".format(num_composition))
moments_P, errs = edgeworth.compute_moments(log_likelihood_ratio_func,
dens_func_P,
max_order=4,
left=left,
right=right)
kappas_P = edgeworth.compute_cumulants(moments_P)
moments_Q, errs = edgeworth.compute_moments(log_likelihood_ratio_func,
dens_func_Q,
max_order=4,
left=left,
right=right)
kappas_Q = edgeworth.compute_cumulants(moments_Q)
print("\t\tCumulants of Q: {}".format(kappas_Q))
print("\t\tCumulants of P: {}".format(kappas_P))
sigma_square_P = [kappas_P[1]] * num_composition
sigma_square_Q = [kappas_Q[1]] * num_composition
kappa_3_P = [kappas_P[2]] * num_composition
kappa_3_Q = [kappas_Q[2]] * num_composition
kappa_4_P = [kappas_P[3]] * num_composition
kappa_4_Q = [kappas_Q[3]] * num_composition
mu_f = ((moments_Q[0] - moments_P[0]) / np.sqrt(kappas_P[1]) *
np.sqrt(num_composition))
alpha = np.linspace(1e-7, 1 - 1e-7, 100)
print("\t\tmu_f:{}".format(mu_f))
start = time.time()
f_clt = edgeworth.compute_gdp_clt(alpha, mu_f)
clt_time = time.time() - start
print("\t\tCLT used {} seconds.".format(clt_time))
f_edgeworth = []
start = time.time()
for aa in alpha:
val = edgeworth.approx_f_edgeworth(
aa,
sigma_square_P,
sigma_square_Q,
kappa_3_P,
kappa_3_Q,
kappa_4_P,
kappa_4_Q,
mu_f,
use_cornish_fisher=use_cornish_fisher,
)
f_edgeworth.append(val)
edgeworth_time = time.time() - start
print("\t\tEdgeworth used {} seconds.".format(edgeworth_time))
f_edgeworth = np.array(f_edgeworth)
epsilon = np.linspace(-6, 6, 100)
start = time.time()
f_numerical, f_eps_delta, deltas = edgeworth.approx_f_numerical(
dens_func_Q,
log_likelihood_ratio_func,
num_composition,
epsilon,
alpha=alpha,
left=left,
right=right,
)
numerical_time = time.time() - start
print("\t\tNumerical used {} seconds.".format(numerical_time))
if ax is not None:
line1, = ax.plot(alpha,
f_numerical,
linewidth=4,
color="k",
linestyle="-")
line2, = ax.plot(alpha, f_edgeworth, linewidth=4, color="r")
line3, = ax.plot(alpha, f_clt, linewidth=4, color="b", linestyle="--")
ax.set_xlabel(r"Type I Error", fontsize=30)
ax.set_ylabel(r"Type II Error", fontsize=30)
ax.xaxis.set_tick_params(size=18, labelsize=30)
ax.yaxis.set_tick_params(size=18, labelsize=30)
if title is not None:
ax.set_title(title, fontdict={"fontsize": 30})
if log_scale:
ax.set_yscale("log")
if zoom_in:
from mpl_toolkits.axes_grid1.inset_locator import (
zoomed_inset_axes,
mark_inset,
)
axins = zoomed_inset_axes(
ax, 1.8, loc=1) # zoom-factor: 2.5, location: upper-left
axins.plot(alpha,
f_numerical,
linewidth=4,
color="k",
linestyle="-")
axins.plot(alpha, f_edgeworth, linewidth=4, color="r")
axins.plot(alpha, f_clt, linewidth=4, color="b", linestyle="--")
axins.set_xticks([])
axins.set_yticks([])
x1, x2, y1, y2 = 0.0, 0.35, 0, 0.4 # specify the limits
axins.set_xlim(x1, x2) # apply the x-limits
axins.set_ylim(y1, y2) # apply the y-limits
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
else:
if num_composition == 1:
ax.legend(["numerical", "Edgeworth", "CLT"], prop={"size": 30})
pickle_out = open("laplace_primal_n{}.pickle".format(num_composition),
"wb")
pickle.dump(
{
"f_numerical": f_numerical,
"f_edgeworth": f_edgeworth,
"f_eps_delta": f_eps_delta,
"f_clt": f_clt,
"epsilon": epsilon,
"deltas": deltas,
"clt_time": clt_time,
"edgeworth_time": edgeworth_time,
"numerical_time": numerical_time,
},
pickle_out,
)
pickle_out.close()
return clt_time, edgeworth_time, numerical_time, alpha, f_edgeworth
def plot_mean_pol(mPols, log_x=False, log_y=False, show=True):
txt = 'Polarization'
fig = plt.figure(num=None,
figsize=(8, 5),
dpi=150,
facecolor='w',
edgecolor='k')
ax1 = fig.add_subplot(111)
model = 'Vicsek' if mPols[0].input.neighbor_norm == True else 'Flying XY'
title = '{}, {} Model'.format(txt, model)
#ax1.set_title(title, fontsize=14, fontweight='bold')
ax1.set_xlabel('$D$', fontsize=22, fontweight='bold', labelpad=-2)
ax1.set_ylabel(r'$<\Pi>$', fontsize=22, fontweight='bold')
mPols_list = seperate_by_npart(mPols)
#ax1.set_xlim([0,0.6])
cmap = plt.get_cmap('jet')
colors = [cmap(i) for i in np.linspace(0, 0.9, len(mPols_list))]
#
#inset = True
inset = False
if inset:
colors = ['b'] * 10
axins = zoomed_inset_axes(ax1,
7,
loc=1,
bbox_to_anchor=(0.65, 0.9),
bbox_transform=ax1.figure.transFigure)
axins.axis([0.23, 0.5, 0, 0.1])
axins.get_xaxis().set_visible(False)
axins.get_yaxis().set_visible(False)
mark_inset(ax1, axins, loc1=2, loc2=4, fc="none", ec="0.5")
linestyles = ['-', '--', '-.', ':']
for n, mPols in enumerate(mPols_list):
dirs = [val.output_dir for val in mPols] # dirs
x = [val.input.noise for val in mPols] # noise
y = [val.mValue for val in mPols] # mPol
if inset:
ax1.plot(x,
y,
linestyle=linestyles[n % len(linestyles)],
linewidth=2.5,
label=r'N = {}'.format(mPols[0].input.npart),
color=colors[n],
picker=True)
else:
ax1.plot(x,
y,
linestyle=linestyles[n % len(linestyles)],
linewidth=0,
label=r'N = {}'.format(mPols[0].input.npart),
marker='o',
markersize=8,
color=colors[n],
picker=True)
if inset:
axins.plot(
x,
y,
linestyle=linestyles[n % len(linestyles)],
linewidth=3,
label=r'N = {}'.format(
mPols[0].input.npart), #marker='o', markersize = 4,
color=colors[n],
picker=False)
def onpick_polar(event):
ind = event.ind
thisline = event.artist
line_index = ax1.lines.index(thisline)
#print 'Plotting ', plot_output_dirs[line_index][ind]
time = None
direc = mPols_list[line_index][ind].output_dir
if not isdir(direc):
direc = direc.split(os.sep)[-1]
if not isdir(direc):
raise FileNotFoundError('Error: directory not found!')
if event.mouseevent.button == 2:
input = read_input_from_dir(direc)
pol = calc_property(direc, 'Pols', Pol_data_calc, read=False)
plot_property_vs_time([pol])
return True
if event.mouseevent.button == 3:
input = mPols_list[line_index][ind].input
time = get_value_box(
title='Drawing snapshot for D={}, maximum time = {:.2f}'.
format(input.noise, mPols_list[line_index][ind].simlen),
prompt='Choose time:')
try:
time = float(time)
if time == 0:
return
except:
time = None
snap = snapshot(mPols_list[line_index][ind].output_dir, time=time)
plot_snap(
snap,
save=True) #, color = 'w', backgroundcolor='k', clusters=True)
if (log_x):
ax1.set_xscale('log')
if (log_y):
ax1.set_yscale('log')
ax1.legend(loc='upper right')
ax1.set_ylim([0, 1])
if inset:
axins.xaxis.tick_top()
fig.canvas.mpl_connect('pick_event', onpick_polar)
filename = 'Polarization_vs_noise{}.png'.format(model)
fig.savefig(filename, dpi=fig.dpi)
if show:
plt.show()
return
axins.text(x,
r.f(x) - 0.05,
"{}".format(n),
color="r",
fontsize="10",
verticalalignment="top",
horizontalalignment="center",
clip_on=True,
zorder=100)
else:
axins.text(x,
r.f(x) + 0.05,
"{}".format(n),
color="r",
fontsize="10",
verticalalignment="bottom",
horizontalalignment="center",
clip_on=True,
zorder=100)
axins.plot(xfine, xfine * 0, ls=":", color="0.5")
mark_inset(ax, axins, loc1=3, loc2=4, fc="none", ec="0.5", zorder=10)
f = plt.gcf()
f.set_size_inches(7.20, 7.20)
plt.tight_layout()
plt.savefig("newton_%2.2d.pdf" % (n))
#y = np.linspace(0,1,128)
#X,Y = np.meshgrid(x,y,indexing='ij')
#x_skip = 8
#y_skip = 4
#plt.pcolormesh(x,y,sigma.T,cmap='RdBu_r')
#plt.quiver(X[::x_skip,::y_skip],Y[::x_skip,::y_skip],u[::x_skip,::y_skip],v[::x_skip,::y_skip],pivot='center',units='width',headaxislength=5,scale=7)
#axins.set_xlim(155*1./2,157.5*1./2)
#axins.set_ylim(0,1)
yfrac = yy-np.floor(yy)
mat = yfrac>0.5
axins.imshow(mat.T,vmin=-0.5, vmax=1.5, extent=[55,60,55,60],interpolation='nearest',origin='lower',cmap='binary',alpha=0.8)
#plt.pcolormesh(xx,yy,mat,cmap='bone')
#plt.clim([-1.5,1.5])
#axins.set_xlim(-10,-20)
#axins.set_ylim(-10,-20)
#axins.set_xlim(80,90)
#axins.set_ylim(80,90)
axins.xaxis.set_major_locator(nullloc)
axins.yaxis.set_major_locator(nullloc)
mark_inset(axmain[2],axins,2,4,fc='none',ec='0.5')
plt.suptitle('Acoustic wave propagation in layered periodic media',fontsize=25,color='#EEEEEE')
plt.figtext(0.5,0.05,'Units scaled to medium period', fontsize=15, horizontalalignment='center',color='#EEEEEE')
plt.figtext(0.5,0.075,'$x$', fontsize=25, horizontalalignment='center',color='#EEEEEE')
plt.figtext(0.05,0.5,'$y$', fontsize=25, verticalalignment='center',color='#EEEEEE')
plt.savefig('_dispersion.png',dpi=100, facecolor=fig.get_facecolor(), edgecolor='none')
plt.close()
bins=10,
normed=True,
alpha=0.7)
ax2.plot(p, x, 'k', linewidth=2)
ax2.set(xlabel='fraction of observations', ylabel='feet')
# cumulative
ax3.scatter(annual_highs['HT'], annual_highs['cumprob'])
ax3.plot(x, cp, 'k', linewidth=2)
ax3.set(xlabel='feet', ylabel='cumulative probability')
# zoom in for 2nd subplot
ax4.scatter(annual_highs['HT'], annual_highs['cumprob'])
ax4.plot(x, cp, 'k')
ax4.set(xlim=(50, 60),
ylim=(0.9, 1.0),
xlabel='feet',
ylabel='cumulative probability')
ax4.axhline(0.99, linestyle='dashed')
ax4.axvline(one_hundred_year_flood, linestyle='dashed')
ax4.annotate(xy=(one_hundred_year_flood + 0.1, 0.98),
s='100-year = ' + "%0.1f ft" % one_hundred_year_flood)
mark_inset(ax3, ax4, loc1=1, loc2=3, fc="none", ec="0.5")
# set title
plt.suptitle('Annual High Water Mark - Brays Bayou',
x=0.5,
y=0.925,
fontsize=15)
plt.show()
[datetime.date(i, j, 1) for i in range(2013, 2019) for j in [1, 5, 9]])
axins.plot(
model_data[model_data['Date'] < split_date]['Date'][window_len:].astype(
datetime.datetime),
training_set['cl_Close**'][window_len:],
label='Actual')
axins.plot(
model_data[model_data['Date'] < split_date]['Date'][window_len:].astype(
datetime.datetime),
((np.transpose(cl_model.predict(LSTM_training_inputs)) + 1) *
training_set['cl_Close**'].values[:-window_len])[0],
label='Predicted')
axins.set_xlim([datetime.date(2017, 3, 1), datetime.date(2017, 5, 1)])
axins.set_ylim([10, 60])
axins.set_xticklabels('')
mark_inset(ax1, axins, loc1=1, loc2=3, fc="none", ec="0.5")
plt.savefig(
"C:/wamp64/www/crypto-predict_website/images/trainset_timepoint.png")
#plt.show()
#TEST THIS
#date2017 = [datetime.time(2017, i+1, 1) for i in range(12)]
#date2018 = [datetime.time(2018, i+1, 1) for i in range(12)]
#total_date = [datetime.combine(date2017, date2018)]
fig, ax1 = plt.subplots(1, 1)
ax1.set_xticks(
([datetime.date(i, j, 1) for i in range(2013, 2019) for j in [1, 5, 9]]))
ax1.set_xticklabels(
([datetime.date(i, j, 1) for i in range(2013, 2019) for j in [1, 5, 9]]))
ax1.plot(
def map_ssp_view(self):
"""plot all the ssp in the database"""
view_rows = self._get_all_ssp_view_rows()
if view_rows is None:
raise DbError("Unable to retrieve ssp view rows > Empty database?")
if len(view_rows) == 0:
raise DbError("Unable to retrieve ssp view rows > Empty database?")
# prepare the data
ssp_x = list()
ssp_y = list()
ssp_label = list()
for vr in view_rows:
ssp_x.append(vr[b'cast_position'].x)
ssp_y.append(vr[b'cast_position'].y)
ssp_label.append(vr[b'pk'])
ssp_x_min = min(ssp_x)
ssp_x_max = max(ssp_x)
ssp_x_mean = (ssp_x_min + ssp_x_max) / 2
ssp_x_delta = max(0.05, abs(ssp_x_max - ssp_x_min)/5)
ssp_y_min = min(ssp_y)
ssp_y_max = max(ssp_y)
ssp_y_mean = (ssp_y_min + ssp_y_max) / 2
ssp_y_delta = max(0.05, abs(ssp_y_max - ssp_y_min)/5)
log.info("data boundary: %.4f, %.4f (%.4f) / %.4f, %.4f (%.4f)"
% (ssp_x_min, ssp_x_max, ssp_x_delta, ssp_y_min, ssp_y_max, ssp_y_delta))
# make the world map
fig = plt.figure()
#plt.title("SSP Map (%s profiles)" % len(view_rows))
ax = fig.add_subplot(111)
plt.ioff()
wm = self._world_draw_map()
# x, y = wm(ssp_x_mean, ssp_y_mean)
# wm.scatter(x, y, s=18, color='y')
if ssp_x_mean > 0:
ssp_loc = 2
else:
ssp_loc = 1
max_delta_range = max(abs(ssp_x_min-ssp_x_max),abs(ssp_y_min-ssp_y_max))
log.info("maximum delta range: %s" % max_delta_range)
if max_delta_range > 15:
ins_scale = 6
elif max_delta_range > 12:
ins_scale = 9
elif max_delta_range > 6:
ins_scale = 12
elif max_delta_range > 3:
ins_scale = 15
else:
ins_scale = 18
ax_ins = zoomed_inset_axes(ax, ins_scale, loc=ssp_loc)
ax_ins.set_xlim((ssp_x_min - ssp_x_delta), (ssp_x_max + ssp_x_delta))
ax_ins.set_ylim((ssp_y_min - ssp_y_delta), (ssp_y_max + ssp_y_delta))
m = self._inset_draw_map(llcrnrlon=(ssp_x_min - ssp_x_delta), llcrnrlat=(ssp_y_min - ssp_y_delta),
urcrnrlon=(ssp_x_max + ssp_x_delta), urcrnrlat=(ssp_y_max + ssp_y_delta),
ax_ins=ax_ins)
x, y = m(ssp_x, ssp_y)
m.scatter(x, y, marker='o', s=16, color='r')
m.scatter(x, y, marker='.', s=1, color='k')
if ssp_x_mean > 0:
mark_inset(ax, ax_ins, loc1=1, loc2=4, fc="none", ec='y')
else:
mark_inset(ax, ax_ins, loc1=2, loc2=3, fc="none", ec='y')
ax_ins.tick_params(color='y', labelcolor='y')
for spine in ax_ins.spines.values():
spine.set_edgecolor('y')
#fig.tight_layout()
plt.show()
def scatter_plot_energies(x2d,
energies,
visualization_options,
min_energy,
max_energy,
x1_sub=-2.06,
zoom_on_plot=True,
x2_sub=-1.70,
y1_sub=0.96,
y2_sub=1.37):
"""Plots the 2D PCA of the data with the associated energies
Args:
x2d (numpy array): 2d matrix containing the points given by a 2D PCA on the data matrix
energies (numpy array): array of all the Delta G(Rxn A) energies of the molecules
visualization_options (dict): contains the options for visualization (plotting)
zoom_on_plot (bool): if True, defines a region inside the actual plot on which it is possible to zoom to have better details
min_energy (float): minimum energy present in all the energies
max_energy (float): maximum energy present in all the energies
x1_sub (float): top left x coordinate for defining a box on which the plot is zoomed on
x2_sub (float): bottom right x coordinate for defining a zone on which the plot is zoomed on
y1_sub (float): top left y coordinate for defining a box on which the plot is zoomed on
y2_sub (float): bottom right y coordinate for defining a box on which the plot is zoomed on
"""
# Set given plot values locally
figsize = viz.get_option(visualization_options, 'figsize')
fontsize = viz.get_option(visualization_options, 'fontsize')
colormap = viz.get_option(visualization_options, 'colormap')
# === Main plot ===
# Title of plot
energy_fig = plt.figure(figsize=figsize)
ax = energy_fig.add_subplot(111)
ax.set_title(
r"2D PCA with the associated $\Delta G (\textrm{Rxn}$ $A)$ energies",
fontsize=fontsize)
scatter = ax.scatter(x2d[:, 0],
x2d[:, 1],
c=energies,
cmap=colormap,
alpha=0.05,
s=10,
vmin=min_energy,
vmax=max_energy)
colorbar = plt.colorbar(scatter,
label=r"$\Delta G (\textrm{Rxn}$ $A)$ [kcal/mol]")
colorbar.set_alpha(0.5)
colorbar.draw_all()
if zoom_on_plot:
# === Zoomed plot ===
axin = zoomed_inset_axes(ax, 4, loc=2)
axin.scatter(x2d[:, 0],
x2d[:, 1],
c=energies,
cmap=colormap,
alpha=0.1,
s=10,
vmin=min_energy,
vmax=max_energy)
# subregion of the original image
axin.set_xlim(x1_sub, x2_sub)
axin.set_ylim(y1_sub, y2_sub)
plt.xticks(visible=False)
plt.yticks(visible=False)
# Draw a frame around the subplot
mark_inset(ax, axin, loc1=1, loc2=3, fc="none", ec="0.5")
plt.tight_layout()
if viz.do_plot(visualization_options):
viz.visualize(energy_fig, 'energies', visualization_options)
def color_nodes(net, inset={}, path=None, vmax=1.05):
fig = plt.figure(figsize=(35, 30), dpi=72)
ax = fig.add_subplot(111)
# Draw edges
DrawEdges(net, ax, label=['P', 'E', 'S'], color='black', width=1.0)
# Draw nodes
d = {
'nodes': net.nodes(),
'geometry': [Point(net.nodes[n]['cord']) for n in net.nodes()],
'voltage': [net.nodes[n]['voltage'] for n in net.nodes()]
}
df_nodes = gpd.GeoDataFrame(d, crs="EPSG:4326")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.001)
df_nodes.plot(ax=ax,
column='voltage',
markersize=40.0,
cmap=cm.plasma,
vmin=0.80,
vmax=vmax,
cax=cax,
legend=True)
cax.set_ylabel("Voltage(in pu)", fontsize=30)
cax.tick_params(labelsize=30)
ax.tick_params(left=False,
bottom=False,
labelleft=False,
labelbottom=False)
# Inset figures
for sub in inset:
axins = zoomed_inset_axes(ax,
inset[sub]['zoom'],
loc=inset[sub]['loc'])
axins.set_aspect(1.3)
# Draw nodes and edges
DrawEdges(inset[sub]['graph'],
axins,
label=['P', 'E', 'S'],
color='black',
width=1.0)
d = {'nodes':inset[sub]['graph'].nodes(),
'geometry':[Point(inset[sub]['graph'].nodes[n]['cord']) \
for n in inset[sub]['graph'].nodes()],
'voltage':[inset[sub]['graph'].nodes[n]['voltage'] \
for n in inset[sub]['graph'].nodes()]}
df_nodes = gpd.GeoDataFrame(d, crs="EPSG:4326")
df_nodes.plot(ax=axins,
column='voltage',
markersize=30.0,
cmap=cm.plasma,
vmin=0.80,
vmax=vmax)
axins.tick_params(bottom=False,
left=False,
labelleft=False,
labelbottom=False)
mark_inset(ax,
axins,
loc1=inset[sub]['loc1'],
loc2=inset[sub]['loc2'],
fc="none",
ec="0.5")
if path != None:
fig.savefig("{}{}.png".format(path, '-dist-voltage'),
bbox_inches='tight')
return
color=[0.4660, 0.6740, 0.1880],
label="Frequenza di risonanza teorica")
plt.plot([rlc.f_ris_regressione, rlc.f_ris_regressione],
[min(rlc._amp_teo), max(rlc._amp_teo)],
'--',
linewidth=1.8,
color=[0, 0.4470, 0.7410],
label="Frequenza di risonanza per regressione")
if (idx == 2):
zoom.set_xlim(1e4, 3e4)
zoom.set_ylim(-10, 2)
else:
zoom.set_xlim(1.5e4, 2e4)
zoom.set_ylim(-15, 0.5)
plt.grid(which='both')
mark_inset(primary_ax, zoom, loc1=2, loc2=4, fc="none", ec="0.5")
# Grafico fase
plt.subplot(2, 1, 2)
rlc.plot_teorica_fase()
plt.errorbar(rlc.freq,
rlc.fase,
rlc.sigmaFase,
rlc.sigmaFreq,
'.',
ecolor=[0.6350, 0.0780, 0.1840],
color=[0.6350, 0.0780, 0.1840],
markersize=10,
linewidth=1.8,
label="Valori sperimentali")
plt.plot([rlc.f_ris_teo, rlc.f_ris_teo],
lw=.5,
linestyle='-',
label=label_legend
)
axins_a.plot(t, u_ssls[:, 1],
color=sto_color,
lw=1,
linestyle='-'
)
np.save('OneLongPathSolutionSto' + eps_prefix + '.npy',
np.transpose(np.array([t, u_ssls[:, 0], u_ssls[:, 1]])))
#
plt.yticks([y1-100, y2-100], visible=True)
plt.xticks([t1-200, t2-200], visible=True)
mark_inset(
ax2,
axins_a,
loc1=3,
loc2=4,
fc="none",
ec="0.5"
)
ax1.legend(loc='upper center', bbox_to_anchor=(0.5, 1.315), ncol=4,
fancybox=False, shadow=False)
plt.tight_layout()
plt.savefig(file_name1, resolution=1000)
def plotConc(concData, lonData, latData, saveLoc, modelRun, prtype, ip1, spc, cmapType, bins, minVal, maxVal, extension, name, removed,buff):
""" Plots and saves concentration data.
Will plot difference data if difference data was passed.
"""
# useful things for determining projection details
lowLon = np.amin(lonData)
lowLat = np.amin(latData)
highLon = np.amax(lonData)
highLat = np.amax(latData)
maxDim = maxDimensions(lowLon,highLon,lowLat,highLat,buff)
midLon = (highLon+lowLon)/2
midLat = (highLat + lowLat)/2
# Uncomment the following lines for an alternative midLatLon calculation
#mids = midLatLon(lonData,latData)
#midLon = mids['midLon']
#midLat = mids['midLat']
max_width = maxDim['xDist']
max_height = maxDim['yDist']
# Initialize the figure
# Create the map based on projection type
if (prtype=='ortho') or (prtype == 'nsper') or (prtype == 'laea') or (prtype == 'aeqd') or (prtype == 'gnom') or (prtype == 'lcc'):
concMap = Basemap(projection=prtype, resolution = 'c', lon_0=midLon, lat_0=midLat, width=max_width, height=max_height)
elif prtype == 'stere':
concMap = Basemap(projection=prtype, lon_0=midLon,lat_0=midLat,width=max_width,height=max_height)
elif (prtype == 'cyl') or (prtype == 'merc'):
concMap = Basemap(projection=prtype, resolution='c', llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)
elif (prtype == 'aea') or (prtype == 'eqdc'):
concMap = Basemap(projection = prtype, lon_0=midLon, lat_0=midLat, llcrnrlat=lowLat, urcrnrlat=highLat, llcrnrlon=lowLon, urcrnrlon=highLon)
else:
print('Error: Could not generate map. Try a different projection.')
# Can check the available basemap types and add to existing if statements
# Add in other map details
mapColor = cmapType
mapBins = bins
# if stripping the borders of the data....
if removed != 0:
ni = len(lonData)
nj = len(lonData[0])
n_pil = removed
x, y = concMap(lonData[n_pil:ni-n_pil,n_pil:nj-n_pil], latData[n_pil:ni-n_pil,n_pil:nj-n_pil])
concMap.pcolormesh(x,y,concDiff[n_pil:ni-n_pil,n_pil:nj-n_pil],cmap=plt.cm.get_cmap(mapColor,mapBins))
else:
x, y = concMap(lonData, latData)
concMap.pcolormesh(x, y, concData, cmap=plt.cm.get_cmap(mapColor, mapBins))
concMap.drawcoastlines(color='lightgray')
concMap.drawcountries(color='gray')
concMap.drawstates(color='gray')
# Comment out the following to remove lonlat lines
concMap.drawmeridians(np.arange(0,360,10), labels=[0,0,0,1], fontsize=6)
concMap.drawparallels(np.arange(-180,180,10), labels=[1,0,0,0],fontsize=6)
# Add colorbar and details
#TODO: Fix the set label option for the colorbar, right now the colorbar doesn't have a title
cbar = plt.colorbar(extend = extension, shrink=0.5)
cbar.set_label=('Concentration: '+spc)
plt.clim(minVal, maxVal)
# Add subplot
concData2 = [50:200, 50:200]
fig, ax = plt.subplots(figsize=[3,3])
axins = zoomed_inset_axes(ax, 5, loc=1)
axins.imshow(concData2, interpolation='nearest', origin='lower')
mark_inset(ax,axins,loc1=2,loc2=4,fc="none",ec="o.5")
# Name and save the figure
hy = ((os.popen('r.ip1 {}'.format(ip1))).read()).lstrip()
plt.title('{}, {} \n hy: {}, Spc: {}'.format(name,modelRun, hy,spc))
fig.savefig(os.path.join(saveLoc, modelRun) + '_' + spc + '_' + name + '.png', dpi=300, bbox_inches='tight', pad_inches=0.3)
plt.close('all')
levels=levels,
axes=axes,
cmap='coolwarm'),
ax=axes)
cbar.set_label(r"Dislocation $[\mathrm m]$")
op.annotate_plot(axes)
axes.axis(False)
# Add zoom
axins = zoomed_inset_axes(axes, 2.5, bbox_to_anchor=[750,
525]) # zoom-factor: 2.5
tricontourf(eta0, levels=51, axes=axins, cmap='coolwarm')
axins.axis(False)
axins.set_xlim(xlim)
axins.set_ylim(ylim)
mark_inset(axes, axins, loc1=1, loc2=1, fc="none", ec="0.5")
# Save
fname = "dislocation_{:d}_{:s}".format(level, categories)
if not loaded:
fname += '_ig'
savefig(fname, "plots", extensions=["jpg"], tight=False)
# --- Setup tsunami propagation problem
b = Function(P1).assign(op.set_bathymetry(P1))
g = Constant(op.g)
dtc = Constant(op.dt)
n = FacetNormal(mesh)
u, eta = TrialFunctions(TaylorHood)
def test_fill_facecolor():
fig, ax = plt.subplots(1, 5)
fig.set_size_inches(5, 5)
for i in range(1, 4):
ax[i].yaxis.set_visible(False)
ax[4].yaxis.tick_right()
bbox = Bbox.from_extents(0, 0.4, 1, 0.6)
# fill with blue by setting 'fc' field
bbox1 = TransformedBbox(bbox, ax[0].transData)
bbox2 = TransformedBbox(bbox, ax[1].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(bbox1,
bbox2,
loc1a=1,
loc2a=2,
loc1b=4,
loc2b=3,
ec="r",
fc="b")
p.set_clip_on(False)
ax[0].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[0], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5")
# fill with yellow by setting 'facecolor' field
bbox3 = TransformedBbox(bbox, ax[1].transData)
bbox4 = TransformedBbox(bbox, ax[2].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(bbox3,
bbox4,
loc1a=1,
loc2a=2,
loc1b=4,
loc2b=3,
ec="r",
facecolor="y")
p.set_clip_on(False)
ax[1].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[1], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5")
# fill with green by setting 'color' field
bbox5 = TransformedBbox(bbox, ax[2].transData)
bbox6 = TransformedBbox(bbox, ax[3].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(bbox5,
bbox6,
loc1a=1,
loc2a=2,
loc1b=4,
loc2b=3,
ec="r",
color="g")
p.set_clip_on(False)
ax[2].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[2], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5")
# fill with green but color won't show if set fill to False
bbox7 = TransformedBbox(bbox, ax[3].transData)
bbox8 = TransformedBbox(bbox, ax[4].transData)
# BboxConnectorPatch won't show green
p = BboxConnectorPatch(bbox7,
bbox8,
loc1a=1,
loc2a=2,
loc1b=4,
loc2b=3,
ec="r",
fc="g",
fill=False)
p.set_clip_on(False)
ax[3].add_patch(p)
# marked area won't show green
axins = zoomed_inset_axes(ax[3], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
axins.get_xaxis().set_ticks([])
axins.get_yaxis().set_ticks([])
mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False)
def color_edges(net, inset={}, path=None):
fig = plt.figure(figsize=(35, 30), dpi=72)
ax = fig.add_subplot(111)
# Draw nodes
DrawNodes(net, ax, label=['S', 'T', 'R', 'H'], color='black', size=2.0)
# Draw edges
d = {
'edges': net.edges(),
'geometry': [net[e[0]][e[1]]['geometry'] for e in net.edges()],
'flows': [net[e[0]][e[1]]['flow'] for e in net.edges()]
}
df_edges = gpd.GeoDataFrame(d, crs="EPSG:4326")
fmin = np.log(0.2)
fmax = np.log(800.0)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.001)
df_edges.plot(column='flows',
ax=ax,
cmap=cm.plasma,
vmin=fmin,
vmax=fmax,
cax=cax,
legend=True)
cax.set_ylabel('Flow along edge in kVA', size=30)
labels = [100, 200, 300, 400, 500, 600, 700, 800]
cax.set_yticklabels(labels)
cax.tick_params(labelsize=20)
ax.tick_params(left=False,
bottom=False,
labelleft=False,
labelbottom=False)
# Inset figures
for sub in inset:
axins = zoomed_inset_axes(ax,
inset[sub]['zoom'],
loc=inset[sub]['loc'])
axins.set_aspect(1.3)
# Draw nodes and edges
DrawNodes(inset[sub]['graph'],
axins,
label=['S', 'T', 'R', 'H'],
color='black',
size=2.0)
d = {'edges':inset[sub]['graph'].edges(),
'geometry':[inset[sub]['graph'][e[0]][e[1]]['geometry'] \
for e in inset[sub]['graph'].edges()],
'flows':[np.exp(inset[sub]['graph'][e[0]][e[1]]['flow']) \
for e in inset[sub]['graph'].edges()]}
df_edges = gpd.GeoDataFrame(d, crs="EPSG:4326")
df_edges.plot(column='flows', ax=axins, cmap=cm.plasma)
axins.tick_params(bottom=False,
left=False,
labelleft=False,
labelbottom=False)
mark_inset(ax,
axins,
loc1=inset[sub]['loc1'],
loc2=inset[sub]['loc2'],
fc="none",
ec="0.5")
if path != None:
fig.savefig("{}{}.png".format(path, '-dist-flows'),
bbox_inches='tight')
return
def cluster_membership(catalogfile, velcut, magcut, outdir, N_gauss, prob, rotate=True):
"""
Calculates cluster membership probabilities for each star given the population parameters
in parameter file (from plot_posteriors_1D). Assumes parameter file will be found
in <outdir>/plots/pop_parameters.txt
Assumes catalogfile is untrimmed
velcut: velocity error cut (mas/yr)
magcut: phot error cut (mag)
N_gauss: number of gaussians
Identifies stars with a cluster membership probability GREATER THAN prob as high-prob members
Produces new catalog cluster.fits which only contains cluster members. THIS HAS POSITIONS/PMs
IN PIXELS. Membership probabilities are added in last column. Also makes new catalog
*_prob_#.fits which is the original catalog with the cluster probabilities added on.
This is the catalog we will work with extensively in the analysis
Plots: Membership histogram, Spatial/velocity distributions of high-prob members
Assumes gaussian 1 is the cluster gaussian
If rotate = True, rotates velocities to RA/DEC to calculate membership probs.
NOT COMPATABLE WITH MAKING VPD!!!
"""
# Extract catalog data
d = loadData(catalogfile, velcut, magcut, rotate=rotate)
star_xpos_pix = d["x_2010_F160W"]
star_ypos_pix = d["y_2010_F160W"]
star_Vx = d["fit_vx"]
star_Vy = d["fit_vy"]
star_Sigx = d["fit_vxe"]
star_Sigy = d["fit_vye"]
# Transform velocities into reference frame of the cluster
# Using velocity determined from UPDATED doit.py:
# star_Vx -= cluster_Vx_doit
# star_Vy -= cluster_Vy_doit
# Extract the necessary parameters from the parameter file
params = Table.read(outdir + "/plots/pop_parameters.txt", format="ascii")
pscale = astrometry.scale["WFC"] * 1e3
pi_fit = np.zeros(N_gauss, dtype=float)
vx_fit = np.zeros(N_gauss, dtype=float)
vy_fit = np.zeros(N_gauss, dtype=float)
sigA_fit = np.zeros(N_gauss, dtype=float)
sigB_fit = np.zeros(N_gauss, dtype=float)
theta_fit = np.zeros(N_gauss, dtype=float)
for kk in range(N_gauss):
# Extract the necessary parameters from the parameter file
pname = params["Param"]
pi_idx = np.where(pname == ("pi_" + str(kk)))[0]
vx_idx = np.where(pname == ("vx_" + str(kk)))[0]
vy_idx = np.where(pname == ("vy_" + str(kk)))[0]
sigA_idx = np.where(pname == ("sigA_" + str(kk)))[0]
sigB_idx = np.where(pname == ("sigB_" + str(kk)))[0]
theta_idx = np.where(pname == ("theta_" + str(kk)))[0]
pi_fit[kk] = params["Mu"][pi_idx][0]
vx_fit[kk] = params["Mu"][vx_idx][0]
vy_fit[kk] = params["Mu"][vy_idx][0]
sigA_fit[kk] = params["Mu"][sigA_idx][0]
sigB_fit[kk] = params["Mu"][sigB_idx][0]
theta_fit[kk] = params["Mu"][theta_idx][0]
# Calculate cluster membership probability assuming
# 1st gaussian is the cluster.
prob_all = np.zeros((N_gauss, len(d)), dtype=float)
for kk in range(N_gauss):
prob_all[kk, :] = prob_ellipse(
star_Vx,
star_Vy,
star_Sigx,
star_Sigy,
pi_fit[kk],
vx_fit[kk],
vy_fit[kk],
sigA_fit[kk],
sigB_fit[kk],
theta_fit[kk],
)
# Calculate cluster membership probability
p_cluster = prob_all[0, :] / prob_all.sum(axis=0)
# Plot distribution of cluster membership probabilities
xbins = np.arange(0, 1.01, 0.05)
py.close("all")
fig, ax = py.subplots(num=1)
n, bins, patch = py.hist(p_cluster, bins=xbins)
py.axis([0, 1, 0, 4000])
py.xlabel("Cluster Membership Probability")
py.ylabel(r"N$_{stars}$")
axins = inset_axes(ax, width="75%", height=3, loc=1)
py.hist(p_cluster, bins=xbins)
py.axvline(prob, color="red", linestyle="--", linewidth=2)
axins.set_xlim(0.1, 1.0)
axins.set_ylim(0, 800)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
outfile = outdir + "/plots/membership_hist_rot.png"
py.savefig(outfile)
# Collect the stars where P(membership > prob)
# Highlight these stars in VPD
members_ind = np.array(np.where(bins >= prob)[0])
members_ind = members_ind[:-1]
members = n[members_ind].sum()
print "Number of cluster members: " + str(members)
# Plot spatial and VPD positions of ID'ed cluster members
memberID = np.where(p_cluster >= prob)[0]
# Change positions into arcsecs from cluster center for plotting purposes
cl_x = star_xpos_pix[memberID].mean()
cl_y = star_xpos_pix[memberID].mean()
star_xpos_plot = (star_xpos_pix - cl_x) * pscale / 1e3
star_ypos_plot = (star_ypos_pix - cl_y) * pscale / 1e3
# --Now for spatial/velocity positions of high prob members---#
outfile = outdir + "/plots/members_" + str(prob) + "_rot.png"
py.close(2)
py.figure(2, figsize=(12, 6))
py.clf()
py.subplot(121)
py.subplots_adjust(left=0.1)
ax = py.gca()
py.plot(star_xpos_plot, star_ypos_plot, "k.", ms=6, alpha=0.2)
py.plot(star_xpos_plot[memberID], star_ypos_plot[memberID], "r.", ms=8, alpha=0.5)
py.axis([125, -125, -125, 125])
py.xlabel("X Position Offset (arcsec)")
py.ylabel("Y Position Offset (arcsec)")
py.subplot(122)
py.plot(star_Vx, star_Vy, "k.", ms=4, alpha=0.5)
py.axis([6, -6, -6, 6])
py.xlabel(r"$\mu_{\alpha}$cos$\delta$ (mas yr$^{-1}$)")
py.ylabel(r"$\mu_{\delta}$ (mas yr$^{-1}$)")
py.plot(star_Vx[memberID], star_Vy[memberID], "r.", ms=4, alpha=0.3)
#
# Test orientation
#
# tmp1 = prob_all[2, :] / prob_all[2, :].sum()
# sdx = tmp1.argsort()
# tmp = tmp1[sdx].cumsum()
# sig_lev_1 = tmp1[sdx[np.where(tmp > 0.01)[0][0]]]
# sig_lev_2 = tmp1[sdx[np.where(tmp > 0.10)[0][0]]]
# sig_lev_3 = tmp1[sdx[np.where(tmp > 0.50)[0][0]]]
# f3 = np.where(tmp1 > sig_lev_1)[0]
# f2 = np.where(tmp1 > sig_lev_2)[0]
# f1 = np.where(tmp1 > sig_lev_3)[0]
# py.plot(star_Vx[f3], star_Vy[f3], 'g.', ms=4, alpha=0.2)
# py.plot(star_Vx[f2], star_Vy[f2], 'b.', ms=4, alpha=0.2)
# py.plot(star_Vx[f1], star_Vy[f1], 'c.', ms=4, alpha=0.2)
outfile = outdir + "/plots/membership_VPD_" + str(prob) + ".png"
py.savefig(outfile)
# Add column for membership probability in original cluster table
d["Membership"] = p_cluster
# HACK for BRITE cluster members.
mag = d["m_2005_F814W"]
color = d["m_2005_F814W"] - d["m_2013_F160W"]
idx = np.where((mag < 18.8) & ((color > 3.1) & (color < 4.8)))[0]
d["Membership"][idx] = 1.0
# Finally, make a new catalog with only cluster members
outfile = "{0}/catalog_membership_{1}_rot.fits".format(outdir, N_gauss)
print "Writing: ", outfile
d.write(outfile, format="fits", overwrite=True)
outfile = "{0}/catalog_cluster_only_{1}_{2:.1f}_rot.fits".format(outdir, N_gauss, prob)
print "Writing: ", outfile
d_clust = d[memberID]
d_clust.write(outfile, format="fits", overwrite=True)
return
plt.rcParams.update({'font.size': 16})
ret = plt.hist(log_returns,
bins=100,
density=True,
color="dodgerblue",
label="Empirical Distribution")
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'k', linewidth=2, label="Gaussian Fit")
plt.legend(loc=2)
plt.title("IBEX-35 Log Returns Distribution")
plt.xlabel("$R_{(t)}$")
axins1 = zoomed_inset_axes(ax, zoom=5, loc=1)
axins1.hist(log_returns,
bins=100,
density=True,
color="dodgerblue",
label="Fat Tails")
axins1.plot(x, p, 'k')
axins1.legend(loc=1)
x1, x2, y1, y2 = 0.035, 0.060, 0.05, 2
axins1.set_xlim(x1, x2)
axins1.set_ylim(y1, y2)
plt.yticks(visible=False)
mark_inset(ax, axins1, loc1=4, loc2=3, fc="none", ec="0.5")
plt.savefig("gaussian_distributionibex.pdf")
ax2.plot(porcurve(pordepth, porfit), pordepth, 'k-', label='Curve fit', linewidth=3)
ax3.plot(sedtemp(np.arange(concunique[-1,0]), bottom_temp), np.arange(concunique[-1,0]), 'k-', linewidth=3)
ax4.plot(picks[:,1]/1000000, picks[:,0], 'ro', label='Picks')
ax4.plot(sedtimes/1000000, seddepths, 'k-', label='Curve fit', linewidth=2)
# Inset in concentration plot
y2 = np.ceil(concunique[dp-1,0])
x2 = max(concunique[:dp,1])+2
x1 = min(concunique[:dp,1])-2
axins1 = inset_axes(ax1, width="50%", height="30%", loc=5)
axins1.plot(concunique[:,1], concunique[:,0], 'go')
axins1.plot(concunique[0:dp,1], concunique[0:dp,0], 'bo', label="Used for curve fit")
axins1.plot(conc_interp_fit_plot, np.linspace(concunique[0,0], concunique[dp-1,0], num=50), 'k-')
axins1.set_xlim(x1-1, x2+1)
axins1.set_ylim(0, y2)
mark_inset(ax1, axins1, loc1=1, loc2=2, fc="none", ec="0.5")
# Additional formatting
ax1.legend(loc='best', fontsize='small')
ax2.legend(loc='best', fontsize='small')
ax4.legend(loc='best', fontsize='small')
ax1.set_ylabel('Depth (mbsf)')
ax1.set_xlabel('Concentration (mM)')
ax2.set_xlabel('Porosity')
ax3.set_xlabel('Temperature (\u00b0C)')
ax4.set_xlabel('Age (Ma)')
ax1.locator_params(axis='x', nbins=4)
ax2.locator_params(axis='x', nbins=4)
ax3.locator_params(axis='x', nbins=4)
ax4.locator_params(axis='x', nbins=4)
axins1.locator_params(axis='x', nbins=3)
topp = 193
ax2 = plt.axes([ax2left, bott, ax2right - ax2left, topp])
ax2.xaxis.set_major_formatter(fmt)
ax2.yaxis.set_major_formatter(fmt)
ax2.yaxis.set_major_formatter(FormatStrFormatter('%.0f'))
ax2.xaxis.set_major_formatter(FormatStrFormatter('%.0f'))
ax2.set_xlim(left=ax2left, right=ax2right)
ax2.set_ylim(bottom=bott, top=topp)
# Manually set the position and relative size of the inset axes within ax1
ip = InsetPosition(ax1, [0.4, 0.1, 0.55, 0.4])
ax2.set_axes_locator(ip)
# Mark the region corresponding to the inset axes on ax1 and draw lines
# in grey linking the two axes.
mark_inset(ax1, ax2, loc1=1, loc2=2, fc="none", ec='0.5')
ax2.plot(1000 * ant, 1e6 * anr, label='Theoretical')
plt.plot(vof2['t'], vof2['x'], label='VOF')
plt.plot(vof4['t'], vof4['x'], label='VOF (2x ref.)')
plt.plot(vof6['t'], vof6['x'], label='VOF (3x ref.)')
ax2.legend(loc=4)
ax2.set_yticks(np.arange(bott, topp, 4))
ax2.set_xticks(np.arange(ax2left, ax2right, 3))
#ax2.set_xticklabels(ax2.get_xticks(), backgroundcolor='w')
leg = ax1.legend(loc=2, prop={'size': 17})
for line in leg.get_lines():
line.set_linewidth(3.0)
for item in ([ax1.title, ax1.xaxis.label, ax1.yaxis.label] +
ax1.get_xticklabels() + ax1.get_yticklabels()):
def plotmustarsigma(self, outputid = 0, zoomperc = 0.05, loc = 2):
'''
Plot the mu* vs sigma chart to interpret the combined effect of both.
Parameters
-----------
zoomperc : float (0-1)
the percentage of the output range to show in the zoomplot,
if 'none', no zoom plot is added
loc : int
matplotlib.pyplot.legend: location code (0-10)
outputid : int
the output to use whe multiple are compared; starts with 0
Returns
---------
fig : matplotlib.figure.Figure object
figure containing the output
ax1 : axes.AxesSubplot object
the subplot
txtobjects : list of textobjects
enbales the ad hoc replacement of labels when overlapping
Notes
-------
Visualization as proposed by [M2]_
'''
mustar2use = self.mustar[outputid*self._ndim:(outputid+1)*self._ndim]
sigma2use = self.sigma[outputid*self._ndim:(outputid+1)*self._ndim]
fig = plt.figure()
ax1 = fig.add_subplot(111)
axs, txtobjects = scatterwithtext(ax1, mustar2use, sigma2use,
self._namelist, 'ks', markersize = 8)
ax1.set_xlabel(r'$\mu^*$', fontsize=20)
ax1.set_ylabel(r'$\sigma$', fontsize=20)
ax1.grid()
ax1.yaxis.grid(linestyle = '--', color = '0.75')
ax1.xaxis.grid(linestyle = '--', color = '0.75')
ax1.set_xlim((0.0, mustar2use.max()+mustar2use.max()*0.1))
ax1.set_ylim((0.0, sigma2use.max()+sigma2use.max()*0.1))
majloc1 = MaxNLocator(nbins=4, prune='lower')
ax1.yaxis.set_major_locator(majloc1)
majloc2 = MaxNLocator(nbins=4)
ax1.xaxis.set_major_locator(majloc2)
if zoomperc != 'none':
#the zooming box size is ad hoc and can be improved
axins = zoomed_inset_axes(ax1, np.floor(1./zoomperc/2.5),
loc = loc)
axins.plot(mustar2use, sigma2use, 'ks', markersize = 3)
transOffset2 = offset_copy(axins.transData, fig=plt.gcf(),
x = -0.05, y=0.10, units='inches')
#plot in the subplot
ct2=0
txtobjects2=[]
for x, y in zip(mustar2use, sigma2use):
if x < mustar2use.max()*zoomperc and y < sigma2use.max()*zoomperc:
axins.plot((x,),(y,), 'ks', markersize = 3)
ls = axins.text(x, y, '%s' %self._namelist[ct2],
transform=transOffset2, color='k')
txtobjects2.append(ls)
ct2+=1
#zoomplot with labels right
axins.yaxis.set_ticks_position('right')
#set the limits of the zoom plot
axins.set_xlim((0.0, mustar2use.max()*zoomperc))
axins.set_ylim((0.0, sigma2use.max()*zoomperc))
#only minor number of ticks for cleaner overview
majloc3 = MaxNLocator(nbins=3, prune='lower')
axins.yaxis.set_major_locator(majloc3)
majloc4 = MaxNLocator(nbins=3, prune='lower')
axins.xaxis.set_major_locator(majloc4)
#smaller size for the ticklabels (different is actually not needed)
for tickx in axins.xaxis.get_major_ticks():
tickx.label.set_fontsize(10)
for label in axins.yaxis.get_majorticklabels():
label.set_fontsize(10)
label.set_rotation('vertical')
#create the subarea-plot in main frame and connect
mark_inset(ax1, axins, loc1=2, loc2=4, fc='none', ec='0.8')
axins.grid()
axins.yaxis.grid(linestyle = '--', color = '0.85')
axins.xaxis.grid(linestyle = '--', color = '0.85')
return fig, ax1, txtobjects
def plot_accuracy(self,
dataset,
net_name,
schemes,
groups,
cases,
inset=True):
"""
Plots accuracy over training for different experimental cases.
Args:
dataset
net_name
schemes
cases (list): Experimental cases to include in figure.
Returns:
None.
"""
# pull data
acc_df = self.stats_processor.load_accuracy_df(dataset, net_name,
schemes, cases)
# process a bit
acc_df = acc_df.query(f"group in {groups}")
index_cols = ["dataset", "net_name", "train_scheme", "case", "epoch"]
acc_df.set_index(index_cols, inplace=True)
df_stats = acc_df.groupby(index_cols).agg(
{"val_acc": [np.mean, np.std]})
# group
df_groups = df_stats.groupby(index_cols[:-1])
# plot
fig, ax = plt.subplots(figsize=(14, 8))
clrs = sns.color_palette("hls", len(df_groups.groups))
y_arr = []
yerr_arr = []
for group, clr in zip(df_groups.groups, clrs):
d, n, s, c = group
group_data = df_groups.get_group(group)
# error bars = 2 standard devs
yvals = group_data["val_acc"]["mean"].values * 100
yerr = group_data["val_acc"]["std"].values * 1.98 * 100
ax.plot(range(len(yvals)), yvals, label=f"{s} {c}", c=clr)
ax.fill_between(range(len(yvals)),
yvals - yerr,
yvals + yerr,
alpha=0.1,
facecolor=clr)
# for the insert...
y_arr.append(yvals)
yerr_arr.append(yerr)
# zoomed inset
if inset:
axins = zoomed_inset_axes(ax, zoom=10, loc=8)
for yvals, yerr, clr in zip(y_arr, yerr_arr, clrs):
nlast = 10
x = [i for i in range(len(yvals) - nlast, len(yvals))]
y_end = yvals[-nlast:]
yerr_end = yerr[-nlast:]
axins.plot(x, y_end, c=clr)
axins.fill_between(x,
y_end - yerr_end,
y_end + yerr_end,
alpha=0.1,
facecolor=clr)
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
axins.xaxis.set_ticks([])
ax.set_title(
f"Classification accuracy during training: {net_name} on {dataset}",
fontsize=20)
ax.set_xlabel("Epoch", fontsize=16)
ax.set_ylabel("Validation accuracy (%)", fontsize=16)
ax.set_ylim([0, 100])
ax.legend(fontsize=14)
plt.tight_layout()
# optional saving
if not self.save_fig:
print("Not saving.")
plt.show()
return
sub_dir = ensure_sub_dir(self.data_dir, f"figures/accuracy/")
case_names = ", ".join(cases)
schemes = ", ".join(schemes)
filename = f"{dataset}_{net_name}_{schemes}_{case_names} accuracy"
filename = os.path.join(sub_dir, filename)
print(f"Saving... {filename}")
plt.savefig(f"{filename}.svg")
plt.savefig(f"{filename}.png", dpi=300)
def test_fill_facecolor():
fig, ax = plt.subplots(1, 5)
fig.set_size_inches(5, 5)
for i in range(1, 4):
ax[i].yaxis.set_visible(False)
ax[4].yaxis.tick_right()
bbox = Bbox.from_extents(0, 0.4, 1, 0.6)
# fill with blue by setting 'fc' field
bbox1 = TransformedBbox(bbox, ax[0].transData)
bbox2 = TransformedBbox(bbox, ax[1].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox1, bbox2, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="b")
p.set_clip_on(False)
ax[0].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[0], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[0], axins, loc1=2, loc2=4, fc="b", ec="0.5")
# fill with yellow by setting 'facecolor' field
bbox3 = TransformedBbox(bbox, ax[1].transData)
bbox4 = TransformedBbox(bbox, ax[2].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox3, bbox4, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", facecolor="y")
p.set_clip_on(False)
ax[1].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[1], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[1], axins, loc1=2, loc2=4, facecolor="y", ec="0.5")
# fill with green by setting 'color' field
bbox5 = TransformedBbox(bbox, ax[2].transData)
bbox6 = TransformedBbox(bbox, ax[3].transData)
# set color to BboxConnectorPatch
p = BboxConnectorPatch(
bbox5, bbox6, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", color="g")
p.set_clip_on(False)
ax[2].add_patch(p)
# set color to marked area
axins = zoomed_inset_axes(ax[2], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
plt.gca().axes.get_xaxis().set_ticks([])
plt.gca().axes.get_yaxis().set_ticks([])
mark_inset(ax[2], axins, loc1=2, loc2=4, color="g", ec="0.5")
# fill with green but color won't show if set fill to False
bbox7 = TransformedBbox(bbox, ax[3].transData)
bbox8 = TransformedBbox(bbox, ax[4].transData)
# BboxConnectorPatch won't show green
p = BboxConnectorPatch(
bbox7, bbox8, loc1a=1, loc2a=2, loc1b=4, loc2b=3,
ec="r", fc="g", fill=False)
p.set_clip_on(False)
ax[3].add_patch(p)
# marked area won't show green
axins = zoomed_inset_axes(ax[3], 1, loc='upper right')
axins.set_xlim(0, 0.2)
axins.set_ylim(0, 0.2)
axins.get_xaxis().set_ticks([])
axins.get_yaxis().set_ticks([])
mark_inset(ax[3], axins, loc1=2, loc2=4, fc="g", ec="0.5", fill=False)
if matched:
axins = zoomed_inset_axes(ax, 5.5, loc=9)
axins.plot((np.arange(range_domain[0], range_domain[1]) -
EFFECTIVE_LENGTH // 2) * 0.375 / 1000,
rd_db4[::-1, slice_velocity],
label='with compensation')
axins.plot((np.arange(range_domain[0], range_domain[1]) -
EFFECTIVE_LENGTH // 2) * 0.375 / 1000,
dd_db3[::-1, slice_velocity],
label='without compensation')
axins.set_xlim([4.115, 4.135])
axins.set_ylim([69.5, 72.75])
axins.grid(ls=':')
plt.xticks(visible=False)
# axins.yaxis.tick_right()
mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
axins = zoomed_inset_axes(ax, 2.5, loc='upper left')
axins.plot(
(np.arange(range_domain[0], range_domain[1]) - EFFECTIVE_LENGTH // 2) *
0.375 / 1000,
rd_db4[::-1, slice_velocity],
label='with compensation')
axins.plot(
(np.arange(range_domain[0], range_domain[1]) - EFFECTIVE_LENGTH // 2) *
0.375 / 1000,
dd_db3[::-1, slice_velocity],
label='without compensation')
axins.set_xlim([3.56, 3.68])
axins.set_ylim([43.5, 51])
axins.grid(ls=':')
main_axes = grid[0]
main_axes.locator_params(nbins=5)
cb_axes = grid.cbar_axes[0] # colorbar axes
im = main_axes.imshow(d, origin="lower", cmap=plt.cm.gray_r,
vmin=4.e-05, vmax=0.00018,
interpolation="nearest")
cb_axes.colorbar(im)
cb_axes.axis["right"].toggle(ticklabels=False)
axins = zoomed_inset_axes(main_axes, zoom=3, loc=1,
axes_class=pywcsgrid2.Axes,
axes_kwargs=dict(wcs=h))
im2 = axins.imshow(d, origin="lower", interpolation="nearest",
vmin=9.e-05, vmax=18.e-05,
cmap=plt.cm.gray_r)
axins.set_xlim(120, 160)
axins.set_ylim(120, 160)
axins.set_ticklabel_type("delta")
axins.axis[:].invert_ticklabel_direction()
mark_inset(main_axes, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.show()
fontsize=18,
va='center',
ha='center',
clip_on=False)
# INSET VIP MAP
axins = zoomed_inset_axes(ax1, 9.2, loc=1, borderpad=.06)
axins.set_xlim(lons2[0], lons2[1])
axins.set_ylim(lats2[0], lats2[1])
m2 = Basemap(llcrnrlon=lons2[0],
llcrnrlat=lats2[0],
urcrnrlon=lons2[1],
urcrnrlat=lats2[1],
resolution='f')
m2.drawmapboundary(fill_color='azure', linewidth=2.0)
m2.pcolormesh(lons, lats, DHW_max, vmin=0, vmax=16, cmap=dhw_noaa, latlon=True)
polygon_patch(m2, axins)
axins.plot(121, 14.5, 'D', ms=8, mfc=dhw_noaa.colors[9, :], mec='k')
axins.text(121.09, 14.5, 'Manila', style='italic', fontsize=11, va='center')
axins.text(121.08, 13.1, 'Mindoro', fontsize=14, ha='center', va='center')
axins.text(121.2, 14.08, 'Luzon', fontsize=14, ha='center', va='center')
mark_inset(ax1, axins, loc1=2, loc2=3, fc="none", ec="0", lw=2)
plt_box(axins)
plt.savefig('Fig1_Max_DHW_98.png')
plt.show()
ax_n = plt.axes( [0.13, 0.275, 0.135, 0.45]) Qh_n = hf_n[:,4]/(-2 * hfi_n[:,4]) X_n = hf_n[:,1] Y_n = hf_n[:,2] Qh_n = np.rot90(np.resize(Qh_n, ( np.size(np.unique(X_n)), np.size(np.unique(Y_n))))) ax_n.set_yticks([]) ax_n.set_xticks([]) Data = np.log10(Qh_n) ax_n.imshow(Data, vmin=4, vmax=7, extent=[np.min(X_n),np.max(X_n),np.min(Y_n),np.max(Y_n)], interpolation='none') #ax.imshow(Data, vmin=0, vmax=10, extent=[np.min(X_n),np.max(X_n),np.min(Y_n),np.max(Y_n)]) mark_inset(ax, ax_n, loc1=1, loc2=4, fc='none', ec='1') ax_n.set_xlim([-0.06, -0.03]) ax_n.set_ylim([-0.03, 0.03]) #for i in range(len(pol[:,0])): # drawEllipse(pol[i,0],pol[i,1], pol[i,2], pol[i,3], 0.001, ax_n) ax_p = plt.axes([0.72, 0.275, 0.135, 0.45]) Qh_p = hf_p[:,4]/(-2 * hfi_p[:,4]) X_p = hf_p[:,1] Y_p = hf_p[:,2] E_p = hf_p[:,4] Qh_p = np.rot90(np.resize(Qh_p, ( np.size(np.unique(X_p)), np.size(np.unique(Y_p)))))
def plotProjection(kind, index, boxsize=1.):
#index is which SIGO it is, ihalo refers to the numbering in halo100_indices
ihalo = SIGOidx[index]
print ihalo
path = prefix + run + '/output/GasOnly_FOF/'
s = gadget_readsnap(snap,
snapbase='snap-groupordered_',
snappath=path,
hdf5=True,
loadonly=['pos', 'vel', 'mass', 'vol', 'rho', 'u'],
loadonlytype=[0],
forcesingleprec=False)
cms = cat_118kms.GroupPos
cms = cms / (s.hubbleparam / s.time)
cvel = cat_118kms.GroupVel
cvel = cvel / s.time
pxsize = 6.
pysize = 6.
psize = 2.
offsetx = 1.1
offsety = .4
offset = .33
fig = pylab.figure(figsize=(np.array([pxsize, pysize]) * toinch), dpi=300)
res = 256
#res = 128
#boxsize = .5 # 10kpc
fact = 0.5 # projection length will be 2.0 * fact * boxsize
iplot = 0
ix = iplot % 4
x = ix * (2. * psize + offsetx) / pxsize + offsetx / pysize
y = offsety / pysize
y = (2. * offsety) / pysize
ax1 = axes([x, y, 2. * psize / pxsize, 2. * psize / pysize], frameon=True)
y = (2. * psize + 3. * offset) / pysize + 0.15 * psize / pysize
cax = axes([x, y, 2. * psize / pxsize, psize / pysize / 15.],
frameon=False)
s.pos = s.pos - cms[ihalo]
s.vel = s.vel - cvel[ihalo]
#convert to kpc
igas, = np.where((np.abs(s.pos[:, 0]) < boxsize)
& (np.abs(s.pos[:, 1]) < boxsize)
& (np.abs(s.pos[:, 2]) < boxsize))
npart = len(igas)
print "Gas density plot selected particles in box", len(igas)
# this conversion seems strange but basically rho is first converted to 10 Msun / kpc^3 by
# multiplying by then and then in cm^-2 with all the other factors (this holds also for the
# other projection functions). The factor boxsize / res is the dl of the projection
#ne = s.data['ne'][:]
#metallicity = 0
#XH = s.data['gmet'][:, 0]
#yhelium = (1 - XH - metallicity) / (4. * XH);
#mu = (1 + 4 * yhelium) / (1 + yhelium + ne)
mu = 1.22 #neutral primordial gas
s.data['pos'] = s.pos[igas]
s.data['type'] = s.type[igas]
#s.data['rho'] = s.rho[igas].astype('float64') * 1.0e10 * MSUN / KPC**2.0 / mu / PROTONMASS * boxsize / res #put in 1/cm^2
s.data['rho'] = s.rho[igas].astype(
'float64') * 1.0e10 * MSUN / KPC**2.0 * boxsize / res #put in g/cm^2
s.data['vol'] = s.vol[igas]
s.data['mass'] = s.mass[igas]
#convert internal energy to temperature
u = 1.0e10 * s.data['u'][
igas] #it's a velocity squared to be converted in cgs
#turn into temp
temp = GAMMA_MINUS1 / BOLTZMANN * u * PROTONMASS * mu
if kind == "TEMP":
s.data['u'] = temp
#turn into sound speed
cs = np.sqrt(GAMMA * BOLTZMANN * temp / mu / PROTONMASS)
if kind == "CS":
s.data['u'] = cs / 1.0e5 #convert to km/s
s.data['vel'] = s.vel[igas] * 1.0e5 #convert to cgs
#Turn first v_x into |v|
velmag = np.linalg.norm(s.vel, axis=1)
if kind == "VEL":
s.data['u'] = velmag / 1.0e5 #convert to km/s
mach = velmag / cs
if kind == "MACH":
s.data['u'] = mach
if kind == "RHOC":
rhocrit = gp['rhocritSIGO'][index]
s.data['rho'] = s.rho / (rhocrit * boxsize / res * KPC)
if kind == "RHO":
s.data['rho'] = s.rho / mu / PROTONMASS # in 1/cm^2
if kind == "OVER":
critical_density = 3.0 * .1 * .1 / 8.0 / np.pi / GCONST #.1 is to convert 100/Mpc to 1/kpc, this is in units of h^2
Omega0 = s.omega0
OmegaLambda = s.omegalambda
atime = s.time
critical_density *= Omega0 + atime**3 * OmegaLambda
critical_density_gas = critical_density * baryonfraction
critical_density_gas *= hubbleparam**2 / atime**3 * 1.0e10 * MSUN / KPC**2.0 * boxsize / res # in units of g/cm^2
s.data['rho'] = s.rho / critical_density_gas - 1.
axes(ax1)
dextoshow = 6
numthreads = 4
#Plot mass weighted slice
#temperature
if kind == "TEMP":
s.plot_Aweightedslice("u",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
minimum=1.0,
newfig=False,
cmap='inferno',
numthreads=8,
vrange=[100, 10000])
#mach
if kind == "MACH":
s.plot_Aweightedslice("u",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
newfig=False,
cmap='inferno',
numthreads=8,
vrange=[0.0001, 20])
if kind == "CS":
s.plot_Aweightedslice("u",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
minimum=1.0,
newfig=False,
cmap='inferno',
numthreads=8,
vrange=[1, 50])
if kind == "VEL":
s.plot_Aweightedslice("u",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
minimum=1.0,
newfig=False,
cmap='inferno',
numthreads=8,
vrange=[1, 50])
if kind == "RHOC":
s.plot_Aweightedslice("rho",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
newfig=False,
cmap='Spectral',
numthreads=8,
vrange=[1, 40])
#s.plot_Aweightedslice( "rho", "mass", colorbar=False, res=res, proj=True,axes=[0,1], box=[boxsize,boxsize], center=np.array([0.,0.,0.]), proj_fact=fact,logplot=True, rasterized=True, newfig=False, cmap='Spectral',numthreads=8,minimum=.7)
if kind == "RHO":
s.plot_Aweightedslice("rho",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
newfig=False,
cmap='Spectral',
numthreads=8,
vrange=[1e17, 1e19])
#Make velocity field
denom = np.sqrt((s.data['vel'][:, :2]**2).sum(axis=1))
vnorm = s.data['vel'] / denom[:, None]
vmax = np.max(denom)
vmin = np.min(denom)
vnorm *= (denom[:, None] - vmin) / (vmax - vmin)
ngbs = s.get_Aslice('rho',
box=[boxsize, boxsize],
axes=[0, 1],
center=np.array([0., 0., 0.]),
proj=False,
proj_fact=fact,
res=res)["neighbours"]
ax1.quiver(s.pos[ngbs[:, :], 0],
s.pos[ngbs[:, :], 1],
vnorm[ngbs[:, :], 0],
vnorm[ngbs[:, :], 1],
scale_units='inches',
scale=5.,
pivot='middle',
width=0.002,
edgecolors=(''),
color='lightgray')
#Make inset of SIGO
axins = zoomed_inset_axes(ax1, 2, loc=1)
axes(axins)
s.plot_Aweightedslice("rho",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
newfig=False,
cmap='Spectral',
numthreads=8,
vrange=[1e17, 1e19])
x1, x2, y1, y2 = -0.2, 0.2, -0.2, 0.2
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax1, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.tick_params(axis='both',
which='both',
bottom=False,
left=False,
top=False,
labelbottom=False,
labelleft=False)
axes(ax1)
if kind == "OVER":
s.plot_Aweightedslice("rho",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
minimum=1.0,
newfig=False,
cmap='Spectral',
numthreads=8,
vrange=[1e-1, 1e2])
#Make inset of SIGO
axins = zoomed_inset_axes(ax1, 2, loc=1)
axes(axins)
s.plot_Aweightedslice("rho",
"mass",
colorbar=False,
res=res,
proj=True,
axes=[0, 1],
box=[boxsize, boxsize],
center=np.array([0., 0., 0.]),
proj_fact=fact,
logplot=True,
rasterized=True,
newfig=False,
cmap='Spectral',
numthreads=8,
vrange=[1e-1, 1e2])
x1, x2, y1, y2 = -0.2, 0.2, -0.2, 0.2
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
mark_inset(ax1, axins, loc1=2, loc2=4, fc="none", ec="0.5")
plt.tick_params(axis='both',
which='both',
bottom=False,
left=False,
top=False,
labelbottom=False,
labelleft=False)
axes(ax1)
#colorbar( cax=cax, orientation='horizontal')
colorbar(cax=cax,
orientation='horizontal',
format=matplotlib.ticker.LogFormatterMathtext())
if kind == "TEMP":
cax.set_title('$Temperature\\rm{\\,[K]}}$', size=8)
if kind == "MACH":
cax.set_title('$Mach$', size=8)
if kind == "VEL":
cax.set_title('$Vel\\rm{\\,[km/s]}}$', size=8)
if kind == "CS":
cax.set_title('$cs\\rm{\\,[km/s]}}$', size=8)
if kind == "RHOC":
cax.set_title('$\\rho/\\rho_{\\rm crit}$', size=8)
if kind == "RHO":
cax.set_title('$N\\rm{\\,[cm^{-2}]}$', size=8)
if kind == "OVER":
cax.set_title('$\\delta$', size=8)
for label in cax.xaxis.get_ticklabels():
label.set_fontsize(8)
#******* ******* ******* ******* ******* ******* ******* ******* ******* ******* *******
for label in cax.xaxis.get_ticklabels():
label.set_fontsize(6)
for label in ax1.xaxis.get_ticklabels():
label.set_fontsize(6)
for label in ax1.yaxis.get_ticklabels():
label.set_fontsize(6)
majorLocator = MultipleLocator(1.0)
minorLocator = MultipleLocator(0.5)
ax1.xaxis.set_major_locator(majorLocator)
ax1.yaxis.set_major_locator(majorLocator)
ax1.xaxis.set_minor_locator(minorLocator)
ax1.yaxis.set_minor_locator(minorLocator)
ax1.set_xlabel("$\\rm{x\\,[kpc]}$", size=7)
ax1.set_ylabel("$\\rm{y\\,[kpc]}$", size=7)
ax1.xaxis.labelpad = -0.25
ax1.yaxis.labelpad = -1
if kind == "TEMP":
fig.savefig(jobdir + 'Temp_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "MACH":
fig.savefig(jobdir + 'Mach_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "VEL":
fig.savefig(jobdir + 'velnorm_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "CS":
fig.savefig(jobdir + 'cs_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "RHOC":
fig.savefig(jobdir + 'Rhocrit_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "RHO":
fig.savefig(jobdir + 'Rho_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
if kind == "OVER":
fig.savefig(jobdir + 'Over_%s_%s_Cooling.pdf' % (name, str(ihalo)),
transparent=True,
dpi=300)
