def hist_plot(thetali, qt, ql, zrange, krange, time_field, path):
nk = len(krange)
plt.figure(figsize=(25, 5))
for k in range(nk):
plt.subplot(1, nk, k + 1)
plt.hist2d(thetali[k, :], qt[k, :], bins=100, normed=True)
plt.colorbar()
plt.title('z=' + str(zrange[k]) + 'm')
plt.xlabel(r'$\theta_l$')
plt.ylabel(r'$q_t$')
# plt.xlim([298, 305])
# plt.ylim([0.007,0.018])
plt.suptitle('LES Data (t=' + str(np.round((time_field / 3600), 1)) + 'h)')
plt.savefig(os.path.join(path, 'hist2d_plot_t' + str(time_field) + '.png'))
plt.close()
plt.figure(figsize=(25, 5))
for k in range(nk):
plt.subplot(1, nk, k + 1)
plt.hist2d(thetali[k, :],
qt[k, :],
bins=100,
norm=LogNorm(),
normed=True)
plt.colorbar()
plt.title('z=' + str(zrange[k]) + 'm')
plt.xlabel(r'$\theta_l$')
plt.ylabel(r'$q_t$')
# plt.xlim([298, 305])
# plt.ylim([0.007,0.018])
plt.suptitle('LES Data (t=' + str(np.round((time_field / 3600), 1)) + 'h)')
plt.savefig(
os.path.join(path, 'hist2d_plot_log_t' + str(time_field) + '.png'))
plt.close()
return
def shear_plot2d(env, **kwargs):
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', None)
src_index = kwargs.pop('src_index', None)
key = kwargs.pop('key', 'accepted')
obj_slice = index_to_slice(obj_index)
src_slice = index_to_slice(src_index)
s0 = [[] for o in env.objects]
s1 = [[] for o in env.objects]
for mi, m in enumerate(models):
# For H0 we only have to look at one model because the others are the same
for oi, [obj, data] in enumerate(m['obj,data'][obj_slice]):
if not data.has_key('shear'): continue
#s0[oi].append(90-np.degrees(np.arctan2(*data['shear'])))
s0[oi].append(data['shear'][0])
s1[oi].append(data['shear'][1])
#s0,s1 = data['shear']
#Log( 'Model %i Object %i Shear %.4f %.4f' % (mi, oi, s0,s1) )
kw = kwargs.copy()
kw.setdefault(
'bins',
max(11,
min(15, (int(np.sqrt(max(len(s0[0]), len(s1[0]))) // 2)) * 2 + 1)))
if s0[0] and s1[0]:
pl.hist2d(s0[0], s1[0], **kw)
pl.title(r'Shear')
pl.xlabel(r'$\varepsilon_1$')
pl.ylabel(r'$\varepsilon_2$')
def do_q2_sampling(): NSAMPLES = 10000 u = -1.0 + (2.0 * np.random.random(N)) v = np.random.normal(0.0, 1.0) p.hist2d(u, v)
def shear_plot2d(env, **kwargs):
models = kwargs.pop('models', env.models)
obj_index = kwargs.pop('obj_index', None)
src_index = kwargs.pop('src_index', None)
key = kwargs.pop('key', 'accepted')
obj_slice = index_to_slice(obj_index)
src_slice = index_to_slice(src_index)
s0 = [ [] for o in env.objects ]
s1 = [ [] for o in env.objects ]
for mi,m in enumerate(models):
# For H0 we only have to look at one model because the others are the same
for oi, [obj,data] in enumerate(m['obj,data'][obj_slice]):
if not data.has_key('shear'): continue
#s0[oi].append(90-np.degrees(np.arctan2(*data['shear'])))
s0[oi].append(data['shear'][0])
s1[oi].append(data['shear'][1])
#s0,s1 = data['shear']
#Log( 'Model %i Object %i Shear %.4f %.4f' % (mi, oi, s0,s1) )
kw = kwargs.copy()
kw.setdefault('bins', max(11, min(15,(int(np.sqrt(max(len(s0[0]),len(s1[0])))//2)) * 2 + 1)))
if s0[0] and s1[0]:
pl.hist2d(s0[0],s1[0], **kw)
pl.title(r'Shear')
pl.xlabel(r'$\varepsilon_1$')
pl.ylabel(r'$\varepsilon_2$')
def __init__(self, num, name, x_data, x_ax_label, x_nbins, y_data, y_ax_label, y_nbins, outputpath):
plt.close('all')
## The histogram plot.
plot = plt.figure(num, figsize=(5.0, 3.0), dpi=150, facecolor='w', edgecolor='w')
# Adjust the position of the axes.
plot.subplots_adjust(bottom=0.17, left=0.15)
## The plot axes.
plotax = plot.add_subplot(111)
# Set the y axis label.
plt.ylabel(y_ax_label)
# Set the x axis label.
plt.xlabel(x_ax_label)
# Add a grid.
plt.grid(1)
# Plot the 2D histogram.
plt.hist2d(x_data, y_data, bins=[x_nbins, y_nbins], norm=LogNorm())
# Add a colour bar.
plt.colorbar()
# Save the figure.
plot.savefig("%s/%s.png" % (outputpath, name))
def main(datafile, feature1, feature2, bins, percentile, copula, logscale):
X, features = read_sah_h5(datafile, just_good=False)
x = X[:, features.index(feature1)]
y = X[:, features.index(feature2)]
if percentile > 0 and not copula:
bx = np.linspace(
scoreatpercentile(x, percentile),
scoreatpercentile(x, 100-percentile),
bins)
by = np.linspace(
scoreatpercentile(y, percentile),
scoreatpercentile(y, 100-percentile),
bins)
bins = (bx, by)
if copula:
x = copula_transform(x)
y = copula_transform(y)
if logscale:
pl.hist2d(x, y, bins=bins, norm=LogNorm())
else:
pl.hist2d(x, y, bins=bins)
pl.xlabel(feature1)
pl.ylabel(feature2)
pl.show()
def draw5(x, y):
from matplotlib.colors import LogNorm
from pylab import hist2d, colorbar, show
hist2d(x, y, bins=140, norm=LogNorm())
colorbar()
show()
def plotgCAIsDomain(X,Y,xDiv=1,yDiv=20,figName=None):
fig = pylab.figure(figsize=(320,3))
pylab.hist2d(Y,X,bins=[len(np.unique(Y))/yDiv,xDiv], norm=mpl.colors.LogNorm(), cmap=mpl.cm.jet)
pylab.xlabel("Domain Sorted By Translation Level")
pylab.ylabel("gCAIs")
pylab.title("Desity Plot - Domains Grouped by [%d], gCAIs Divided by [%d], Top [%d] Domains (PS : values in brackets are manipulable)"%(xDiv,yDiv,len(np.unique(Y))))
fig.subplots_adjust(bottom=0.25)
cbar = pylab.colorbar()
if figName:
pylab.savefig(figName)
else:
pylab.show()
pylab.close(fig)
def plotgCAIsDomain4(X,Y,xDiv=5,yDiv=25,figName=None):
fig = pylab.figure(figsize=(200,5))
pylab.hist2d(X,Y,bins=[max(X),yDiv], norm=mpl.colors.LogNorm(), cmap=mpl.cm.jet)
pylab.xlabel("Expression Level")
pylab.ylabel("gCAIs")
pylab.title("Desity Plot - Expressoin Level Grouped by [%d], gCAIs Divided by [%d] (PS : values in brackets are manipulable)"%(xDiv,yDiv))
pylab.xlim(0,5000)
cbar = pylab.colorbar()
if figName:
pylab.savefig(figName)
else:
pylab.show()
pylab.close(fig)
def centroid_heatmap(c_samples, log_dir, x_min, x_max, y_min, y_max, bins=10):
for i in range(np.shape(np.array(c_samples))[1]):
cs = np.array(c_samples)[:, i, :]
cx, cy = cs[:, 0], cs[:, 1]
import matplotlib.colors as mcolors
plt.figure(figsize=(5, 4))
plt.hist2d(cx, cy, bins=bins, range=[[x_min, x_max], [y_min, y_max]])
plt.xlabel('$x$-coordinate')
plt.ylabel('$y$-coordinate')
cbar = plt.colorbar()
cbar.ax.set_ylabel('Number of samples')
plt.savefig(log_dir + 'centroid_heat_' + str(i) + '.pdf',
bbox_inches='tight')
def tVertexErrorHist2D(diffsList, values):
"""
Plots a 2D error histogram for the tVertex-genVertex z values along some other axis,
such as time smearing.
Usage: tVertexErrorHist2D(differencesList)
Differences are a 2D list of differences for each time smearing value.
"""
pylab.hist2d(diffsList, values, bins=(100, len(np.unique(values))))
pylab.xlabel("tVertexed $z$ - genVertex $z$ (mm)")
pylab.ylabel("Smearing $\sigma$ (ps)")
pylab.title("tVertexing Precision Over Time Smearing")
cbar = pylab.colorbar()
cbar.set_label("Counts (normalized per $\sigma$ value)")
plt.show()
def tVertexErrorHist2D(diffsList, values):
'''
Plots a 2D error histogram for the tVertex-genVertex z values along some other axis,
such as time smearing.
Usage: tVertexErrorHist2D(differencesList)
Differences are a 2D list of differences for each time smearing value.
'''
pylab.hist2d(diffsList, values, bins=(100, len(np.unique(values))))
pylab.xlabel("tVertexed $z$ - genVertex $z$ (mm)")
pylab.ylabel("Smearing $\sigma$ (ps)")
pylab.title("tVertexing Precision Over Time Smearing")
cbar = pylab.colorbar()
cbar.set_label("Counts (normalized per $\sigma$ value)")
plt.show()
def plot_responsability_2D_hist(
file_name,
save_img=False,
output_file_name='responsability_histogram.png'):
with h5py.File(file_name, 'r') as f:
responsabilities = numpy.transpose(f['data'])
max_o = []
unique_rots = []
for i in range(numpy.shape(responsabilities)[1]):
max_o.append(max(responsabilities[:, i]))
unique_rots.append(len(unique(responsabilities[:, i])))
fig = pylab.figure('responsability histogram')
pylab.hist2d(max_o, unique_rots, bins=100, norm=LogNorm())
if save_img:
pylab.savefig(output_file_name)
def draw_testing_result(in_situ_SSM,
predicted_ssm,
rmse,
r,
save_path,
show_figure=0):
plt.figure()
# label of the axis.
plt.xlabel(r'In-situ soil moisture [$cm^3/cm^3$]', fontsize=12)
plt.ylabel(r'Estimated soil moisture [$cm^3/cm^3$]', fontsize=12)
# plot the data with the density (number of pixels)
h = hist2d(list(in_situ_SSM),
list(predicted_ssm),
bins=40,
cmap='PuBu',
range=[[0, 0.4], [0, 0.4]])
cb = plt.colorbar()
cb.set_label('Numbers of points')
# Add 1:1 line
x = np.arange(0, 0.4, 0.1, dtype=float)
y = x
# Add the information of RMSE and r on the figure.
plt.text(0.1, 0.35, 'RMSE: %.2f' % rmse, fontdict={'size': 12})
plt.text(0.1, 0.3, 'r: %.2f' % r, fontdict={'size': 12})
plt.text(0.1, 0.25, 'Num: %d' % len(predicted_ssm), fontdict={'size': 12})
plt.plot(x, y, color='black')
plt.savefig(save_path, dpi=300)
if show_figure == 0:
plt.close()
def plot_distribution_2d(data, xpar, ypar, percentiles=[16, 50, 84]):
x, y = data[xpar], data[ypar]
xpc = np.percentile(x, percentiles, axis=0)
ypc = np.percentile(y, percentiles, axis=0)
pl.hist2d(x, y, bins=50, normed=True)
for i in xpc:
pl.axvline(x=i, ls='--', color='w')
for i in ypc:
pl.axhline(y=i, ls='--', color='w')
pl.xlabel(xpar)
pl.ylabel(ypar)
def do_hist2(self, s):
print s
el = self.get_this_command(s, min_args=2)
datareduced = self.reduce_data(data, el[0], self.limits)
if len(datareduced[0]) == 0:
print "No Data"
return
arg_dict = {
'bins': 25,
'range': [[0.0, 30.0], [0.0, 30.0]],
'normed': True
}
arg_dict.update(el[1])
#if ’bins’ in el[1]:
# arg_dict['bins'] = int(el[1]['bins']
#if ’normed’ in el[1]:
# arg_dict['normed'] = True
#if 'log' in el[1]:
# arg_dict['norm'] = mc.LogNorm()
pylab.hist2d(datareduced[0], datareduced[1], **arg_dict)
#pylab.hist2d(datareduced[0], datareduced[1], bins=bins, normed=True, range=[[0.0,30.0],[0.0,30.0]])
#bins = 25
# if len(el) > 1:
# bins = int(el[1])
#if len(datareduced[0]):
# if len(el) > 2:
# if el[2].lower() == "log":
# pylab.hist2d(datareduced[0], datareduced[1], bins=bins, norm=mc.LogNorm())#, normed=True)
# else:
# pylab.hist2d(datareduced[0], datareduced[1], bins=bins, normed=True, range=[[0.0,30.0],[0.0,30.0]])
pylab.colorbar()
pylab.show(block=False)
self.consume(s)
def main(datafile, feature1, feature2, clusterfile, clusterid,
bins, percentile, copula, logscale):
X, features = read_sah_h5(datafile, just_good=False)
x = X[:, features.index(feature1)]
y = X[:, features.index(feature2)]
if 'id' in features:
ids = X[:, features.index('id')]
else:
ids = np.arange(len(X)).astype(int)
include = {}
with open(clusterfile, 'r') as f:
for line in f:
i, c = map(float, line.strip().split(','))
include[i] = (c == clusterid)
include_mask = np.array([include.get(i, False) for i in ids])
x = x[include_mask]
y = y[include_mask]
if percentile > 0 and not copula:
bx = np.linspace(
scoreatpercentile(x, percentile),
scoreatpercentile(x, 100-percentile),
bins)
by = np.linspace(
scoreatpercentile(y, percentile),
scoreatpercentile(y, 100-percentile),
bins)
bins = (bx, by)
if copula:
x = copula_transform(x)
y = copula_transform(y)
if logscale:
pl.hist2d(x, y, bins=bins, norm=LogNorm())
else:
pl.hist2d(x, y, bins=bins)
pl.xlabel(feature1)
pl.ylabel(feature2)
pl.colorbar()
pl.show()
def Plot_Prof(med_log, bands, season, whata, whatb):
for band in bands:
idxb = med_log['band'] == band
sel = med_log[idxb]
"""
H, xedges, yedges = np.histogram2d(sel[whata],sel[whatb],bins=10,normed=True,range=[[np.min(sel[whata]),np.max(sel[whata])],[np.min(sel[whatb]),np.max(sel[whatb])]])
print(xedges,yedges)
"""
fig, ax = plt.subplots(ncols=1, nrows=1)
fig.suptitle('Season ' + str(season) + ' - ' + band + ' band')
plt.hist2d(sel[whata], sel[whatb], bins=20)
plt.colorbar(fraction=0.02, pad=0.04)
ax.set_xlabel(whata)
ax.set_ylabel(whatb)
#ax = fig.add_subplot(111)
#im = plt.imshow(H, interpolation='nearest', origin='low',
# extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
"""
def draw4(x, y):
from matplotlib.colors import LogNorm
from pylab import hist2d, contour, grid, colorbar, show
counts, ybins, xbins, image = hist2d(x, y, bins=100, norm=LogNorm())
contour(counts, extent=[xbins.min(), xbins.max(), ybins.min(), ybins.max()], linewidths=10)
grid(True)
colorbar()
show()
def plotD(self,PoI=None,DoW=None,ToD=None,uagent=None,nbins=500):
parkc = self.all
p_title = 'LA Parking Citation density'
if DoW != None:
if DoW == 'Weedays':
p_title += ' during weekdays'
parkc = parkc[(parkc.DoW != 'Saturday') & (parkc.DoW != 'Sunday')]
elif DoW == 'Weekend':
p_title+= ' during weekends'
parkc = parkc[(parkc.DoW == 'Saturday') | (parkc.DoW == 'Sunday')]
elif (DoW == 'Monday') | (DoW == 'Tuesday') | (DoW == 'Wednesday') | (DoW == 'Thursday') | (DoW == 'Friday') | (DoW == 'Saturday') | (DoW == 'Sunday'):
parkc = parkc[parkc.DoW == DoW]
p_title+= ' on '+DoW
if ToD != None:
if ToD == 'Early':
p_title+= ' in the early morning'
parkc = parkc[(parkc['Issue time'] >= 0) & (parkc['Issue time'] < 6)]
elif ToD == 'Morning':
p_title+= ' in the morning'
parkc = parkc[(parkc['Issue time'] >= 6) & (parkc['Issue time'] < 12)]
elif ToD == 'Afternoon':
p_title+= ' in the afternoon'
parkc = parkc[(parkc['Issue time'] >= 12) & (parkc['Issue time'] < 18)]
elif ToD == 'Evening':
p_title+= ' in the evening'
parkc = parkc[(parkc['Issue time'] >= 18) & (parkc['Issue time'] < 24)]
plt.hist2d(parkc.Longitude,parkc.Latitude,bins=nbins,norm=LogNorm())
if type(PoI) == str:
lon,lat = getLaLo(PoI,uagent=uagent)
plt.plot(lon,lat,'r*',markersize=10)
elif type(PoI) == list:
plt.plot(PoI[0],PoI[1],'r*',markersize=10)
plt.colorbar()
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.xlim([-118.7,-118.0])
plt.ylim([33.7,34.4])
plt.title(p_title)
plt.show()
def plot2DHist(points, name, x_dimension, y_dimension, fig):
#add points to data at each corner, truncate data past corners.
x = points[x_dimension]
y = points[y_dimension]
#truncate any data outside off -2,-2 2,2 borders
index = []
for i in range(0, len(x)):
if x[i] > 2 or x[i] < -2:
index = numpy.append(index, i)
if y[i] > 2 or y[i] < -2:
index = numpy.append(index, i)
x = numpy.delete(x, index)
y = numpy.delete(y, index)
#fix borders of histogram with edge points
x = numpy.append(x, -2)
y = numpy.append(y, -2)
x = numpy.append(x, -2)
y = numpy.append(y, 2)
x = numpy.append(x, 2)
y = numpy.append(y, -2)
x = numpy.append(x, 2)
y = numpy.append(y, 2)
#pylab.axis("off")
#fig = pylab.figure()
dpi = fig.get_dpi()
inches = 512.0 / dpi
fig.set_size_inches(inches,inches)
ax = pylab.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
pylab.hist2d(x, y, bins=100)
pylab.set_cmap('gray')
pylab.savefig(name + '.png')
pylab.clf()
def plot2DHist(points, name, x_dimension, y_dimension, fig):
#add points to data at each corner, truncate data past corners.
x = points[x_dimension]
y = points[y_dimension]
#truncate any data outside off -2,-2 2,2 borders
index = []
for i in range(0, len(x)):
if x[i] > 2 or x[i] < -2:
index = numpy.append(index, i)
if y[i] > 2 or y[i] < -2:
index = numpy.append(index, i)
x = numpy.delete(x, index)
y = numpy.delete(y, index)
#fix borders of histogram with edge points
x = numpy.append(x, -2)
y = numpy.append(y, -2)
x = numpy.append(x, -2)
y = numpy.append(y, 2)
x = numpy.append(x, 2)
y = numpy.append(y, -2)
x = numpy.append(x, 2)
y = numpy.append(y, 2)
#pylab.axis("off")
#fig = pylab.figure()
dpi = fig.get_dpi()
inches = 512.0 / dpi
fig.set_size_inches(inches, inches)
ax = pylab.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
pylab.hist2d(x, y, bins=100)
pylab.set_cmap('gray')
pylab.savefig(name + '.png')
pylab.clf()
def plot_hist2d(qt_, qt_env_tr, qt_up_tr, thetal_, th_env_tr, th_up_tr, ql_,
ql_env_tr, ql_up_tr, n_tot, n_env_tr, n_up_tr, type_, z, t,
path_, file_name):
# (B) Figure with ql-coloring
# ql_min = np.amin(ql_)
# ql_max = np.amax(ql_)
# x_min = np.amin(thetal_)
# x_max = np.amax(thetal_)
# y_min = np.amin(qt_)
# y_max = np.amax(qt_)
plt.figure(figsize=(12, 6))
plt.subplot(1, 3, 1)
plt.hist2d(
thetal_, qt_
) #, c=ql_[:], s=5, alpha=0.5, edgecolors='none', vmin=ql_min, vmax=ql_max) # , cmap = cm)
labeling(r'$\theta_l$', r'$q_t$', x_min, x_max, y_min, y_max)
plt.colorbar(shrink=0.75)
plt.title('all data (n=' + str(n_tot) + ')')
# plt.subplot(1, 3, 2)
# plt.scatter(th_env_tr, qt_env_tr, c=ql_env_tr[:], s=5, alpha=0.5, edgecolors='none', vmin=ql_min,
# vmax=ql_max) # , cmap = cm)
# plt.colorbar(shrink=0.75)
# labeling(r'$\theta_l$', r'$q_t$', x_min, x_max, y_min, y_max)
# plt.title('environment (n=' + str(n_env_tr) + ')')
#
# plt.subplot(1, 3, 3)
# plt.scatter(th_up_tr, qt_up_tr, c=ql_up_tr, s=5, alpha=0.5, edgecolors='none', vmin=ql_min, vmax=ql_max) # , cmap = cm)
# plt.colorbar(shrink=0.75)
# labeling(r'$\theta_l$', r'$q_t$', x_min, x_max, y_min, y_max)
# plt.title('updrafts (n=' + str(n_up_tr) + ')')
#
# plt.suptitle('Updraft selection ' + type_ + ' (z=' + str(z) + 'm, t=' + str(t) + ')')
# print('save:', os.path.join(path_, file_name))
# plt.savefig(os.path.join(path_, file_name))
# plt.show()
# print('saved')
plt.close()
return
def draw_colourmap(densmap, quantity, xlims, ylims):
"""
Draw a colour mesh map of a desired quantity for a given DataMap.
"""
# Set quantity keyword
for value, keyword in zip(['mass', 'number', 'temp', 'shear'], ['M', 'N', 'T', 'shear']):
if quantity == value:
_type = keyword
# Initiate empty lists for cell values
x = []; y = []; values = []
# Get shape of system
shape = datamap.cells.T.shape
# With cells as a flat list, read values for position and quantity
with DataMap.FlatArray(datamap.cells) as cells:
for cell in cells:
x.append(cell['X'])
y.append(cell['Y'])
values.append(cell[_type])
# Draw quantity as 2D histogram for speed
plt.hist2d(x, y, weights=values, bins=shape, vmin=args.Tmin, vmax=args.Tmax)#, xlim=xlims, ylim=ylims)
plt.axis(args.axis)
plt.xlim(xlims)
plt.ylim(ylims)
plt.colorbar()
if save:
plt.savefig(save, dpi=args.dpi)
if args.show:
plt.show()
plt.clf()
return None
def main(datafile, feature1, feature2, clusterfile, clusterid, bins,
percentile, copula, logscale):
X, features = read_sah_h5(datafile, just_good=False)
x = X[:, features.index(feature1)]
y = X[:, features.index(feature2)]
if 'id' in features:
ids = X[:, features.index('id')]
else:
ids = np.arange(len(X)).astype(int)
include = {}
with open(clusterfile, 'r') as f:
for line in f:
i, c = map(float, line.strip().split(','))
include[i] = (c == clusterid)
include_mask = np.array([include.get(i, False) for i in ids])
x = x[include_mask]
y = y[include_mask]
if percentile > 0 and not copula:
bx = np.linspace(scoreatpercentile(x, percentile),
scoreatpercentile(x, 100 - percentile), bins)
by = np.linspace(scoreatpercentile(y, percentile),
scoreatpercentile(y, 100 - percentile), bins)
bins = (bx, by)
if copula:
x = copula_transform(x)
y = copula_transform(y)
if logscale:
pl.hist2d(x, y, bins=bins, norm=LogNorm())
else:
pl.hist2d(x, y, bins=bins)
pl.xlabel(feature1)
pl.ylabel(feature2)
pl.colorbar()
pl.show()
def main(datafile, feature1, feature2, bins, percentile, copula, logscale):
X, features = read_sah_h5(datafile, just_good=False)
x = X[:, features.index(feature1)]
y = X[:, features.index(feature2)]
if percentile > 0 and not copula:
bx = np.linspace(scoreatpercentile(x, percentile),
scoreatpercentile(x, 100 - percentile), bins)
by = np.linspace(scoreatpercentile(y, percentile),
scoreatpercentile(y, 100 - percentile), bins)
bins = (bx, by)
if copula:
x = copula_transform(x)
y = copula_transform(y)
if logscale:
pl.hist2d(x, y, bins=bins, norm=LogNorm())
else:
pl.hist2d(x, y, bins=bins)
pl.xlabel(feature1)
pl.ylabel(feature2)
pl.show()
def __init__(self, num, name, x_data, x_ax_label, x_nbins, y_data,
y_ax_label, y_nbins, outputpath):
""" Constructor. """
plt.close('all')
## The histogram plot.
plot = plt.figure(num,
figsize=(5.0, 3.0),
dpi=150,
facecolor='w',
edgecolor='w')
# Adjust the position of the axes.
plot.subplots_adjust(bottom=0.17, left=0.15)
## The plot axes.
plotax = plot.add_subplot(111)
# Set the y axis label.
plt.ylabel(y_ax_label)
# Set the x axis label.
plt.xlabel(x_ax_label)
# Add a grid.
plt.grid(1)
# Plot the 2D histogram.
plt.hist2d(x_data, y_data, bins=[x_nbins, y_nbins], norm=LogNorm())
# Add a colour bar.
plt.colorbar()
# Save the figure.
plot.savefig("%s/%s.png" % (outputpath, name))
def pcr_poi(self,PoI,uagent=None,DoW=None,ToD=None,fmil=0.25,dyrs=4):
lat_to_mi = 68.92
lon_to_mi = 57.41
p_title = 'Ave. parking citation rate, at PoI given'
parkc = self.all
if type(PoI) == str:
lon,lat = getLaLo(PoI,uagent=uagent)
elif type(PoI) == list:
lon,lat = PoI[0], PoI[1]
if DoW != None:
if DoW == 'Weedays':
p_title += ', during weekdays'
parkc = parkc[(parkc.DoW != 'Saturday') & (parkc.DoW != 'Sunday')]
elif DoW == 'Weekend':
p_title+= ', during weekends'
parkc = parkc[(parkc.DoW == 'Saturday') | (parkc.DoW == 'Sunday')]
elif (DoW == 'Monday') | (DoW == 'Tuesday') | (DoW == 'Wednesday') | (DoW == 'Thursday') | (DoW == 'Friday') | (DoW == 'Saturday') | (DoW == 'Sunday'):
parkc = parkc[parkc.DoW == DoW]
p_title+= ', on '+DoW
if ToD != None:
if ToD == 'Early':
p_title+= ', in the early morning'
parkc = parkc[(parkc['Issue time'] >= 0) & (parkc['Issue time'] < 6)]
elif ToD == 'Morning':
p_title+= ', in the morning'
parkc = parkc[(parkc['Issue time'] >= 6) & (parkc['Issue time'] < 12)]
elif ToD == 'Afternoon':
p_title+= ', in the afternoon'
parkc = parkc[(parkc['Issue time'] >= 12) & (parkc['Issue time'] < 18)]
elif ToD == 'Evening':
p_title+= ', in the evening'
parkc = parkc[(parkc['Issue time'] >= 18) & (parkc['Issue time'] < 24)]
xbins = np.arange(np.min(parkc.Longitude),np.max(parkc.Longitude),fmil/lon_to_mi)
ybins = np.arange(np.min(parkc.Latitude),np.max(parkc.Latitude),fmil/lat_to_mi)
ahist,xbins,ybins,junk = plt.hist2d(parkc.Longitude,parkc.Latitude,bins=[xbins,ybins])
plt.close()
xdex = np.where([xbins < lon])[1][-1]
ydex = np.where([ybins < lat])[1][-1]
print(p_title+' : '+str(round(ahist[xdex,ydex]/(365.0*dyrs)/fmil**2,2))+' (per day/mil^2)')
print('Largest rate: '+str(int(np.max(ahist)/(365.0*dyrs)/fmil**2)))
def plotAgainstGFP_hist2d(self):
fig1 = pylab.figure(figsize = (20, 15))
print len(self.GFP)
for i in xrange(min(len(data.cat), 4)):
print len(self.GFP[self.categories == i])
vect = []
pylab.subplot(2,2,i+1)
pop = self.GFP[self.categories == i]
print "cat", i, "n pop", len(self.GFP[(self.categories == i) & (self.GFP > -np.log(12.5))])
H, xedges, yedges = np.histogram2d(self.angles[self.categories == i], self.GFP[self.categories == i], bins = 10)
hist = pylab.hist2d(self.GFP[self.categories == i], self.angles[self.categories == i], bins = 10, cmap = pylab.cm.Reds, normed = True)
pylab.clim(0.,0.035)
pylab.colorbar()
pylab.title(data.cat[i])
pylab.xlabel('GFP score')
pylab.ylabel('Angle (degree)')
pylab.xlim([-4.2, -1])
pylab.show()
pl.plot(prediction_error_history)
pl.title("Prediction Error History")
pl.xlabel("time")
pl.ylabel("Prediction Error")
save_figure.save(folder_name+"/Prediction Error History")
pl.figure(expert.cluster_num+2)
pl.hist(state1_history)
pl.title("Resultant State Histogram")
pl.xlabel("State")
pl.ylabel("# of Occurrence")
pl.autoscale(True)
save_figure.save(folder_name+"/Resultant State Histogram")
pl.figure(expert.cluster_num+3)
pl.hist2d(state0_history, action0_history)
pl.title("Initial State vs Initial Action Histogram")
pl.xlabel("State")
pl.ylabel("Action")
pl.colorbar()
save_figure.save(folder_name+"/Initial State vs Initial Action Histogram")
#print("Most common Action")
def most_common(lst):
return max(set(lst), key=lst.count)
bin_num = 100
bin_size = int(len(action0_history)/bin_num)
most_common_action = []
for i in range(bin_num):
start = i*bin_size
end = start + bin_size
def plot(self, bins=100, cmap="hot_r", fontsize=10, Nlevels=4,
xlabel=None, ylabel=None, norm=None, range=None, normed=False,
colorbar=True, contour=True, grid=True, **kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram (either a float or list
of 2 binning values).
:param cmap: a valid colormap (defaults to hot_r)
:param fontsize: fontsize for the labels
:param int Nlevels: must be more than 2
:param str xlabel: set the xlabel (overwrites content of the dataframe)
:param str ylabel: set the ylabel (overwrites content of the dataframe)
:param norm: set to 'log' to show the log10 of the values.
:param normed: normalise the data
:param range: as in pylab.Hist2D : a 2x2 shape [[-3,3],[-4,4]]
:param contour: show some contours (default to True)
:param bool grid: Show unerlying grid (defaults to True)
If the input is a dataframe, the xlabel and ylabel will be populated
with the column names of the dataframe.
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X, Y, bins=bins, normed=normed,
cmap=cmap, norm=colors.LogNorm())
else:
res = pylab.hist2d(X, Y, bins=bins, cmap=cmap,
normed=normed, range=range)
if colorbar is True:
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
if res[0].max().max() < 10 and norm == 'log':
pylab.contour(X, Y, res[0].transpose(), color="g")
else:
levels = [round(x) for x in
pylab.logspace(0, pylab.log10(res[0].max().max()), Nlevels)]
pylab.contour(X, Y, res[0].transpose(), levels[2:], color="g")
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel is None:
ylabel = self.df.columns[1]
if xlabel is None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
if grid is True:
pylab.grid(True)
return res
def corner(samples, labels):
N = len(labels)
from matplotlib.colors import LogNorm
py.figure(figsize=(12, 12))
axes = {}
for i, l1 in enumerate(labels):
for j, l2 in enumerate(labels):
if j > i:
continue
ax = py.subplot2grid((N, N), (i, j))
axes[(i, j)] = ax
idx_y = labels.index(l1)
idx_x = labels.index(l2)
x, y = samples[:, idx_x], samples[:, idx_y]
if i == j:
# plot distributions
xx, yy = histogram(x, bins=200, plot=False)
py.plot(xx, yy, '-o', markersize=3)
py.gca().set_yticklabels([])
if i == (N - 1):
py.xlabel(l2)
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
else:
counts, ybins, xbins, image = py.hist2d(x,
y,
bins=100,
norm=LogNorm())
#py.contour(counts,extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()],linewidths=3)
if i == (N - 1):
py.xlabel(l2)
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
if j == 0:
py.ylabel(l1)
[l.set_rotation(45) for l in ax.get_yticklabels()]
else:
ax.set_yticklabels([])
# make all the x- and y-lims the same
j = 0
lims = [0] * N
for i in range(1, N):
ax = axes[(i, 0)]
lims[i] = ax.get_ylim()
if i == N - 1:
lims[0] = ax.get_xlim()
for i, l1 in enumerate(labels):
for j, l2 in enumerate(labels):
if j > i:
continue
ax = axes[(i, j)]
if j == i:
ax.set_xlim(lims[i])
else:
ax.set_ylim(lims[i])
ax.set_xlim(lims[j])
df = df[df["Primary Type"].isin(["ROBBERY","ASSAULT","BATTERY"])]
df = df[df["Location Description"].isin(["STREET","SIDEWALK","ALLEY"])]
hours = [datetime.strptime(timestr,"%m/%d/%Y %I:%M:%S %p").hour for timestr in list(df.Date)]
df["hour"] = hours
for i in range(24):
df1 = df[df.hour==i]
plt.figure(figsize=(6, 5), dpi=80, facecolor='w', edgecolor='k')
y = list(df1["Latitude"])
x = list(df1["Longitude"])
plt.hist2d(x, y, bins=100, vmax=35,range=np.array([(xmin, xmax), (ymin, ymax)]))
#plt.title("Robbery, assualt, and battery\n on streets, sidewalks, and alleys")
plt.text(-87.95,41.65,"Hour: "+str(i),color="white")
plt.xlim([xmin,xmax])
plt.ylim([ymin,ymax])
plt.xlabel("Latitude")
plt.ylabel("Longtitude")
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.axes().set_aspect('equal')
# plt.show()
plt.savefig(str(i)+".gif")
def main():
#Set font size
font = {'size': 28}
matplotlib.rc('font', **font)
signalNames = [
"WR800N200", "WR800N400", "WR800N600", "WR800N700", "WR1000N200",
"WR1000N400", "WR1000N600", "WR1000N800", "WR1000N900", "WR1200N400",
"WR1200N600", "WR1200N800", "WR1200N1000", "WR1200N1100", "WR1400N400",
"WR1400N600", "WR1400N800", "WR1400N1000", "WR1400N1200",
"WR1400N1300", "WR1600N400", "WR1600N600", "WR1600N800", "WR1600N1000",
"WR1600N1200", "WR1600N1400", "WR1600N1500", "WR1800N400",
"WR1800N600", "WR1800N800", "WR1800N1000", "WR1800N1200",
"WR1800N1400", "WR1800N1600", "WR1800N1700", "WR2000N600",
"WR2000N800", "WR2000N1000", "WR2000N1200", "WR2000N1400",
"WR2000N1600", "WR2000N1800", "WR2000N1900"
]
#Load background files
dy = np.load("DY.npy")
ttbar = np.load("ttbar.npy")
#Modify colormap to be white at 0
viridis = cm.get_cmap('viridis', 256)
newcolors = viridis(np.linspace(0, 1, 256))
white = np.array([256 / 256, 256 / 256, 256 / 256, 1])
newcolors[:1, :] = white
cmap = ListedColormap(newcolors)
resolvedNdy = []
superResolvedNdy = []
leadNdy = []
subleadNdy = []
WRdy = []
weightdy = []
resolvedNttbar = []
superResolvedNttbar = []
leadNttbar = []
subleadNttbar = []
WRttbar = []
weightttbar = []
#Extract ttbar background
for x in range(len(ttbar)):
resolvedNttbar.append(ttbar[x][2])
superResolvedNttbar.append(ttbar[x][1])
WRttbar.append(ttbar[x][0])
leadNttbar.append(ttbar[x][5])
subleadNttbar.append(ttbar[x][6])
weightttbar.append(ttbar[x][-1] * 1000.0 * 35.9)
#Extract dy backgorund
for x in range(len(dy)):
resolvedNdy.append(dy[x][2])
superResolvedNdy.append(dy[x][1])
WRdy.append(dy[x][0])
leadNdy.append(dy[x][5])
subleadNdy.append(dy[x][6])
weightdy.append(dy[x][-1] * 1000.0 * 35.9)
resolvedN = resolvedNttbar + resolvedNdy
superResolvedN = superResolvedNttbar + superResolvedNdy
leadN = leadNttbar + leadNdy
subleadN = subleadNttbar + subleadNdy
WR = WRttbar + WRdy
weight = weightttbar + weightdy
#Iterate over the signal samples
for y in range(len(signalNames)):
signal = np.load(signalNames[y] + ".npy")
resolvedN2 = []
superResolvedN2 = []
leadN2 = []
subleadN2 = []
WR2 = []
weight2 = []
for x in range(len(signal)):
resolvedN2.append(signal[x][2])
superResolvedN2.append(signal[x][1])
WR2.append(signal[x][0])
leadN2.append(signal[x][5])
subleadN2.append(signal[x][6])
weight2.append(signal[x][-1] * 1000.0 * 35.9)
fSizeX = 13.5
fSizeY = 9
xMax = 4000
#Generate plots
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist(np.array(WR2),
weights=np.array(weight2),
bins=100,
range=(0, 3000))
plt.title("WR signal")
plt.xlabel("m_lljj (GeV)")
plt.savefig("signalBackground2/" + signalNames[y] + "/WR_signal.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(resolvedN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Resolved Leptons signal")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_signal.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(superResolvedN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Super Resolved Leptons signal")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_signal.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(resolvedN2),
np.array(WR2) - np.array(resolvedN2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [0, 3000]],
cmap=cmap)
plt.title("NN Resolved Leptons signal shifted part 1")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_signal_shift_p1.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(superResolvedN2),
np.array(WR2) - np.array(superResolvedN2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [0, 3000]],
cmap=cmap)
plt.title("NN Super Resolved Leptons signal shifted part 1")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_signal_shift_p1.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(resolvedN2),
np.log10(np.array(WR2) - np.array(resolvedN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Resolved Leptons signal shifted full\n")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_signal_shift.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(superResolvedN2),
np.log10(np.array(WR2) - np.array(superResolvedN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Super Resolved Leptons signal shifted full\n")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_signal_shift.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist([np.array(WRttbar), np.array(WRdy)],
weights=[np.array(weightttbar),
np.array(weightdy)],
bins=100,
range=(0, 3000),
stacked=True,
label=["ttbar", "dy"])
plt.title("WR background")
plt.xlabel("m_lljj (GeV)")
plt.legend()
plt.savefig("signalBackground2/" + signalNames[y] +
"/WR_background.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(resolvedN),
np.array(WR),
weights=np.array(weight),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Resolved Leptons background")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_background.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(superResolvedN),
np.array(WR),
weights=np.array(weight),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Super Resolved Leptons background")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_background.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(resolvedN),
np.log10(np.array(WR) - np.array(resolvedN)),
weights=np.array(weight),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Resolved Leptons background")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_background_shift.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(superResolvedN),
np.log10(np.array(WR) - np.array(superResolvedN)),
weights=np.array(weight),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Super Resolved Leptons background")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_background_shift.png")
plt.show()
fSizeX = 13.5
fSizeY = 9
xMax = 4000
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist(
[np.array(WRttbar),
np.array(WRdy), np.array(WR2)],
weights=[
np.array(weightttbar),
np.array(weightdy),
np.array(weight2)
],
bins=100,
range=(0, 3000),
stacked=True,
label=["ttbar", "dy", "signal"])
plt.title("WR signal + background")
plt.xlabel("m_lljj (GeV)")
plt.legend()
plt.savefig("signalBackground2/" + signalNames[y] +
"/WR_signal_plus_background.png")
plt.show()
resolvedN2 += resolvedN
superResolvedN2 += superResolvedN
leadN2 += leadN
subleadN2 += subleadN
WR2 += WR
weight2 += weight
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(resolvedN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Resolved Leptons combined")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_combined.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(superResolvedN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("NN Super Resolved Leptons combined")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_combined.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(resolvedN2),
np.log10(np.array(WR2) - np.array(resolvedN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Resolved Leptons combined shifted\n")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_Resolved_Leptons_combined_shift.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(superResolvedN2),
np.log10(np.array(WR2) - np.array(superResolvedN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("NN Super Resolved Leptons combined shifted\n")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/NN_SuperResolved_Leptons_combined_shift.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(leadN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("Lead Leptons combined")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/Lead_Leptons_combined.png")
plt.show()
plt.figure(figsize=(fSizeY * 2, fSizeY))
plt.hist2d(np.array(subleadN2),
np.array(WR2),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, 2500], [0, 3000]],
cmap=cmap)
plt.title("Sublead Leptons combined")
plt.ylabel("m_lljj (GeV)")
plt.xlabel("m_ljj (GeV)")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/Sublead_Leptons_combined.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(leadN2),
np.log10(np.array(WR2) - np.array(leadN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("Lead Leptons combined shifted")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/Lead_Leptons_combined_shift.png")
plt.show()
plt.figure(figsize=(fSizeX, fSizeY))
plt.hist2d(np.array(subleadN2),
np.log10(np.array(WR2) - np.array(subleadN2)),
weights=np.array(weight2),
bins=[100, 100],
range=[[0, xMax], [1, 5]],
cmap=cmap)
plt.title("Sublead Leptons combined shifted")
plt.colorbar()
plt.savefig("signalBackground2/" + signalNames[y] +
"/Sublead_Leptons_combined_shift.png")
plt.show()
if plot_1 == True:
hist_fig = pylab.figure()
x = energies
if weight_by_p_earth == True:
hist_fig.suptitle(
'Trigger = %0.1f (Weighted by $p_\mathrm{earth}$' %
trigger_level)
else:
hist_fig.suptitle('Trigger = %0.1f' % trigger_level)
ax1 = pylab.subplot(2, 3, 1)
pylab.hist2d(x,
info['observation_angle'],
bins=bins,
weights=w1,
norm=LogNorm())
pylab.ylabel('Observation Angle (deg)')
pylab.xlabel('E (GeV)')
pylab.colorbar()
pylab.subplot(2, 3, 4, sharex=ax1, sharey=ax1)
pylab.hist2d(x,
info['observation_angle'],
bins=bins,
weights=w2,
norm=LogNorm())
pylab.ylabel('Observation Angle (deg)')
pylab.xlabel(
pl.xticks([])
pl.yticks([])
pl.suptitle(str(cube_sizeX) + ' x ' + str(cube_sizeY) + ' slices comparison')
# Plot the 2D histograms
pl.figure()
prediction = predicted_distances[cube_slice].flatten()
results = out.flatten()
labels = true_distances[cube_slice].flatten()
pl.subplot(1,2,1)
pl.hist2d(labels, prediction, (max_dist+1, max_dist+1), cmap=pl.cm.jet, norm=pl.mpl.colors.LogNorm())
pl.xlabel('ground truth')
pl.ylabel('noisy distance map')
pl.title('2d histogram of noisy distance')
pl.subplot(1,2,2)
pl.hist2d(labels, results, (15, 15), cmap=pl.cm.jet, norm=pl.mpl.colors.LogNorm())
pl.xlabel('ground truth')
pl.ylabel('smoothed distance map')
pl.title('2d histogram of smoothed distance')
pl.show()
#####################e
# Some MC Plot to see what Energy We have
#####################e
#max_nrg_mc = ev_df.p_mc_mag.max()
#n, bins, patches = plt.hist(ev_df.p_mc_mag,bins=15,facecolor='blue',alpha = 0.8)
#plt.title('Shower Energy')
#n, bins, patches = plt.hist(ev_df.p_mc_mag,bins=max_nrg_mc,range=(0,max_nrg_mc),facecolor='red',alpha = 0.8)
#plt.show()
# Let's compare the totall charge deposited wrt all charge clustered
# Pre - factor fit
t = np.arange(0., 100000000, 1000)
plt.hist2d(ev_df.mc_qdep,
ev_df.q_tot_obj,
bins=100,
range=[[0, 100000000], [0, 100000000]],
cmap='gnuplot2',
norm=LogNorm())
plt.plot(t, t, 'k', label='y=x')
plt.plot(t, 2 * t, 'r-', label='y=2x')
plt.legend(loc='upper left')
plt.title("Reco_Charge vs MC_charge")
plt.xlabel("mc_q (e)")
plt.ylabel("reco_q(e)")
if _save:
plt.savefig('{}/recovmccharge.png'.format(fig_dir))
plt.show()
plt.close()
n, bins, patches = plt.hist((ev_df.mc_qdep - ev_df.q_tot_obj) / ev_df.mc_qdep,
bins=60,
def plot_PDF(data, x_, y_, z_, ZZ, var_name1, var_name2, var_name3, score, amp_qt, amp_w, z):
fig = plt.figure(figsize=(20, 15))
plt.subplot(3, 5, 1)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 2)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 3)
# xxx
ax1 = plt.contourf(x_, y_, np.sum(np.exp(ZZ),axis=2).T)
# xxx
# plt.text(0.1, 0.9, 'score='+str(score), ha='center', va='center')#, transform=ax.transAxes)
# text(x, y, s, fontdict=None, withdash=False, **kwargs)
# plt.text(1, 1, 'HOIHOI',color='r')
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 4)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True)
# xxx
ax2 = plt.contour(x_, y_, np.sum(np.exp(ZZ), axis=2).T, colors='w', linewidths=2)
plt.colorbar(ax2, shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 6)
plt.scatter(data[:, 0], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 7)
ax1 = plt.hist2d(data[:, 0], data[:, 2], bins=30, normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 8)
# xxx
ax1 = plt.contourf(x_, z_, np.sum(np.exp(ZZ), axis=1).T)
# xxx
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 9)
ax1 = plt.hist2d(data[:,0], data[:, 2], bins=30, normed=True)
# xxx
ax2 = plt.contour(x_, z_ , np.sum(np.exp(ZZ), axis=1).T, colors='w', linewidths=2)
plt.colorbar(ax2, shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 11)
plt.scatter(data[:, 1], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 12)
ax1 = plt.hist2d(data[:, 1], data[:, 2], bins=30, normed=True)
plt.colorbar(shrink=0.8)
plt.title('data histogram')
plt.subplot(3, 5, 13)
# xxx
ax1 = plt.contourf(y_, z_, np.sum(np.exp(ZZ), axis=0).T)
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.colorbar(ax1, shrink=0.8)
plt.subplot(3, 5, 14)
ax1 = plt.hist2d(data[:,1], data[:,2], bins=30, normed=True)
# xxx
ax2 = plt.contour(y_, z_, np.sum(np.exp(ZZ), axis=0).T, colors='w', linewidths=2)
plt.colorbar(ax2, shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
# plt.colorbar(ax1, shrink=0.8)
fig.suptitle('EM2 PDF: ' + var_name1 + var_name2 + var_name3 + ' (score='+str(score)+'; z=' + str(z) +'m)', fontsize=20)
plt.savefig(os.path.join(
fullpath_out,'EM2_trivar_alltimes_figures','EM2_PDF_trivariate_' + var_name1 + var_name2 + var_name3 + '_z' + str(
np.int(z)) + 'm.png')
)
plt.close()
return
#z reki
#pliki=["ot001", "ot002", "ot009", "ot019", "ot020" , "nt001"];
bin_width = 0.01;
rec = root2rec(sys.argv[1], "tdig")
#x = np.extract(np.absolute(rec.Teta) < 1.61, rec.Tbeta)
#x = np.extract(np.logical_and(np.logical_and(np.absolute(rec.Teta0) < 1.61, rec.Tlbx_1), rec.Tpt0 > 10), rec.Teta0);
#y = np.extract(np.logical_and(np.logical_and(np.absolute(rec.Teta0) < 1.61, rec.Tlbx_1), rec.Tpt0 > 10), rec.Tphi0);
for i in range(0, len(rec)):
print i
'''
print len(x), len(y)
pl.hist2d(x, y, bins=200, norm=LogNorm())
#pl.hist2d(x, y, bins=np.arange(0.,4,bin_width) , norm=LogNorm())
pl.colorbar()
#plt.xscale('log')
plt.ylabel(r'$\phi$')
plt.xlabel(r'$\eta$')
plt.draw()
pp.savefig()
pp.close()
plt.show()
'''
def plot_PDF(data, x_, y_, z_, ZZ, var_name1, var_name2, var_name3, amp_qt,
amp_w, time, z):
fig = plt.figure(figsize=(18, 15))
plt.subplot(3, 5, 1)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 2)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 3)
# xxx
ax1 = plt.contourf(x_, y_, np.sum(np.exp(ZZ), axis=2).T)
# xxx
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 4)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True)
# xxx
ax2 = plt.contour(x_,
y_,
np.sum(np.exp(ZZ), axis=2).T,
colors='w',
linewidths=2)
# xxx
plt.colorbar(ax2, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 6)
plt.scatter(data[:, 0], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 7)
ax1 = plt.hist2d(data[:, 0], data[:, 2], bins=30, normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 8)
# xxx
ax1 = plt.contourf(x_, z_, np.sum(np.exp(ZZ), axis=1).T)
# xxx
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 9)
ax1 = plt.hist2d(data[:, 0], data[:, 2], bins=30, normed=True)
# xxx
ax2 = plt.contour(x_,
z_,
np.sum(np.exp(ZZ), axis=1).T,
colors='w',
linewidths=2)
# xxx
plt.colorbar(ax2, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 11)
plt.scatter(data[:, 1], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 12)
ax1 = plt.hist2d(data[:, 1], data[:, 2], bins=30, normed=True)
plt.colorbar(shrink=0.8)
plt.title('data histogram')
plt.subplot(3, 5, 13)
# xxx
ax1 = plt.contourf(y_, z_, np.sum(np.exp(ZZ), axis=0).T)
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.colorbar(ax1, shrink=0.8)
plt.subplot(3, 5, 14)
ax1 = plt.hist2d(data[:, 1], data[:, 2], bins=30, normed=True)
# xxx
ax2 = plt.contour(y_,
z_,
np.sum(np.exp(ZZ), axis=0).T,
colors='w',
linewidths=2)
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.colorbar(ax2, shrink=0.8)
fig.suptitle('EM2 PDF: ' + var_name1 + var_name2 + var_name3 + ' (t=' +
str(time) + ', z=' + str(z) + 'm)',
fontsize=20)
plt.savefig(
os.path.join(
path, 'EM2_trivar_figures',
'EM2_PDF_trivariate_' + var_name1 + var_name2 + var_name3 + '_' +
str(time) + '_z' + str(np.int(z)) + 'm.png'))
plt.close()
return
def make_2d_plot(dat_joined: pd.DataFrame,
factor_1: str,
factor_2: str,
single_sentence_plots: bool = False) -> None:
"""Create 2D quilt plot from dataframe row columns ('word_' + factor_1) and ('word_' + factor_2).
:param dat_joined: Dataframe of the format returned by pull_mentions
:param factor_1: x-axis of the graph, possible entries 'virus', 'therapy', 'drug', 'exp'
:param factor_2: y-axis of the graph, possible entries 'virus', 'therapy', 'drug', 'exp'
:param single_sentece_plots: If true, plot only coocurrences in the same sentence
"""
if single_sentence_plots:
grouped = dat_joined[dat_joined.same_sentence is True].groupby(
['word_' + factor_1, 'word_' + factor_2])
else:
grouped = dat_joined.groupby(['word_' + factor_1, 'word_' + factor_2])
values = grouped.count().values[:, 0]
index = grouped.count().index
# Holds aggregated, in order appearences of factor_1 and factor_2 respectively
index_1, index_2 = zip(*index)
uniq_1 = np.unique(index_1)
uniq_2 = np.unique(index_2)
# Populates index_1 and index_2 with the index they appear in uniq_1 and uniq_2 respectively
for i in range(0, len(index_1)):
index_1[i] = np.where(index_1[i] == uniq_1)[0][0]
index_2[i] = np.where(index_2[i] == uniq_2)[0][0]
pylab.figure(figsize=(5, 5), dpi=200)
hist = pylab.hist2d(index_1,
index_2,
(range(0,
len(uniq_1) + 1), range(0,
len(uniq_2) + 1)),
weights=values,
cmap='Blues')
pylab.xticks(np.arange(0, len(uniq_1)) + 0.5, uniq_1, rotation=90)
pylab.yticks(np.arange(0, len(uniq_2)) + 0.5, uniq_2)
pylab.clim(0, np.max(hist[0]) * 1.5)
for i in range(0, len(uniq_1)):
for j in range(0, len(uniq_2)):
pylab.text(i + 0.5,
j + 0.5,
int(hist[0][i][j]),
ha='center',
va='center')
pylab.colorbar()
if single_sentence_plots:
pylab.title(factor_1 + " and " + factor_2 + " in One Sentence")
pylab.tight_layout()
pylab.savefig("Overlap" + factor_1 + "_Vs_" + factor_2 +
"_2D_sentence.png",
bbox_inches='tight',
dpi=200)
else:
pylab.title(factor_1 + " and " + factor_2 + " in One Block")
pylab.tight_layout()
pylab.savefig("Overlap" + factor_1 + "_Vs_" + factor_2 +
"_2D_block.png",
bbox_inches='tight',
dpi=200)
def plot_PDF_log(data, x_, y_, z_, ZZ, var_name1, var_name2, var_name3, score, amp_qt, amp_w, z):
fig = plt.figure(figsize=(20, 15))
plt.subplot(3, 5, 1)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 2)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, norm=LogNorm(), normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 3)
ax1 = plt.contourf(x_, y_, np.sum(np.exp(ZZ),axis=2).T, norm=LogNorm())
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 4)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True, norm=LogNorm())
# xxx
lvls = np.logspace(-1, 6, 10)
ax2 = plt.contour(x_, y_, np.sum(np.exp(ZZ), axis=2).T, levels=lvls, colors='w', norm=LogNorm(), linewidths=3)
plt.colorbar(ax2, shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name1, var_name2, amp_qt, amp_w)
plt.subplot(3, 5, 6)
plt.scatter(data[:, 0], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 7)
ax1 = plt.hist2d(data[:, 0], data[:, 2], bins=30, norm=LogNorm(), normed=True)
plt.colorbar(shrink=0.8)
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.title('data histogram')
plt.subplot(3, 5, 8)
# xxx
ax1 = plt.contourf(x_, z_, np.sum(np.exp(ZZ), axis=1).T, norm=LogNorm())
# xxx
plt.colorbar(ax1, shrink=0.8)
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 9)
ax1 = plt.hist2d(data[:,0], data[:, 2], bins=30, normed=True, norm=LogNorm())
# xxx
# lvls = np.exp(np.arange(-5,6,1))
lvls = np.logspace(-1, 5, 10)
ax2 = plt.contour(x_, z_ , np.sum(np.exp(ZZ), axis=1).T, levels=lvls, colors='w', norm=LogNorm(), linewidths=3)
plt.colorbar(ax2,shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name1, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 11)
plt.scatter(data[:, 1], data[:, 2], s=2, alpha=0.3)
plt.title('data')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.subplot(3, 5, 12)
ax1 = plt.hist2d(data[:, 1], data[:, 2], bins=30, norm=LogNorm(), normed=True)
plt.colorbar(shrink=0.8)
plt.title('data histogram')
plt.subplot(3, 5, 13)
# xxx
ax1 = plt.contourf(y_, z_, np.sum(np.exp(ZZ), axis=0).T, norm=LogNorm())
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
plt.colorbar(ax1, shrink=0.8)
plt.subplot(3, 5, 14)
ax1 = plt.hist2d(data[:,1], data[:,2], bins=30, normed=True, norm=LogNorm())
# xxx
lvls = np.logspace(-1, 6, 10)
ax2 = plt.contour(y_, z_, np.sum(np.exp(ZZ), axis=0).T, levels=lvls, colors='w', linewidths=3, norm=LogNorm())
plt.colorbar(ax2, shrink=0.8)
# xxx
plt.title('EM2 PDF')
axis_label(var_name2, var_name3, amp_qt, amp_w)
# plt.colorbar(ax1, shrink=0.8)
fig.suptitle('EM2 PDF: ' + var_name1 + var_name2 + var_name3 + ' (score=' + str(score) + '; z=' + str(z) + 'm)',
fontsize=20)
plt.savefig(os.path.join(
fullpath_out,'EM2_trivar_alltimes_figures','EM2_PDF_trivariate_' + var_name1 + var_name2 + var_name3 + '_z' + str(
np.int(z)) + 'm_log.png')
)
plt.close()
return
print round(np.mean(ecart_abs),3)
fig1 = plt.figure()
fig1.suptitle('histogramme (scores mesures - predictions)', fontsize=14, fontweight='bold')
plt.xlabel('valeur des ecarts')
plt.ylabel("# d'ecarts")
bins = np.arange(-1.0,1.0,0.05)
plt.hist(ecart, bins, normed=True)
fig1.savefig('spkshow_histo_ecart')
plt.show()
fig1 = plt.figure()
fig1.suptitle('predictions / scores mesures', fontsize=14, fontweight='bold')
plt.ylabel('prediction')
plt.xlabel("scores mesures")
bins = np.arange(0,1.01,0.05)
plt.plot(x, y, 'ro')
plt.axis([0,1,0,1])
plt.grid()
fig1.savefig('spkshow_nuage')
plt.show()
fig1 = plt.figure()
fig1.suptitle('predictions / scores mesures', fontsize=14, fontweight='bold')
plt.ylabel('prediction')
plt.xlabel("scores mesures")
plt.hist2d(x,y,bins=50);
fig1.savefig('spkshow_nuage2D')
plt.show()
pl.yticks([])
# Plot the 2D histograms
pl.figure()
prediction = predicted_distances[square_slice].flatten()
results = out.flatten()
labels = true_distances[square_slice].flatten()
n_binsX = np.max(labels)-np.min(labels)
n_binsY = np.max(prediction)-np.min(prediction)
pl.subplot(1,2,1)
pl.hist2d(labels, prediction, (n_binsX, n_binsY), cmap=pl.cm.jet, norm=pl.mpl.colors.LogNorm())
pl.xlabel('ground truth')
pl.ylabel('noisy distance map')
pl.title('2d histogram of noisy distance')
n_binsY = np.max(results)-np.min(results)
pl.subplot(1,2,2)
pl.hist2d(labels, results, (n_binsX, n_binsY), cmap=pl.cm.jet, norm=pl.mpl.colors.LogNorm())
pl.xlabel('ground truth')
pl.ylabel('smoothed distance map')
pl.title('2d histogram of smoothed distance')
pl.show()
def corner(samples,labels):
N=len(labels)
from matplotlib.colors import LogNorm
py.figure(figsize=(12,12))
axes={}
for i,l1 in enumerate(labels):
for j,l2 in enumerate(labels):
if j>i:
continue
ax = py.subplot2grid((N,N),(i, j))
axes[(i,j)]=ax
idx_y=labels.index(l1)
idx_x=labels.index(l2)
x,y=samples[:,idx_x],samples[:,idx_y]
if i==j:
# plot distributions
xx,yy=histogram(x,bins=200,plot=False)
py.plot(xx,yy,'-o',markersize=3)
py.gca().set_yticklabels([])
if i==(N-1):
py.xlabel(l2)
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
else:
counts,ybins,xbins,image = py.hist2d(x,y,bins=100,norm=LogNorm())
#py.contour(counts,extent=[xbins.min(),xbins.max(),ybins.min(),ybins.max()],linewidths=3)
if i==(N-1):
py.xlabel(l2)
[l.set_rotation(45) for l in ax.get_xticklabels()]
else:
ax.set_xticklabels([])
if j==0:
py.ylabel(l1)
[l.set_rotation(45) for l in ax.get_yticklabels()]
else:
ax.set_yticklabels([])
# make all the x- and y-lims the same
j=0
lims=[0]*N
for i in range(1,N):
ax=axes[(i,0)]
lims[i]=ax.get_ylim()
if i==N-1:
lims[0]=ax.get_xlim()
for i,l1 in enumerate(labels):
for j,l2 in enumerate(labels):
if j>i:
continue
ax=axes[(i,j)]
if j==i:
ax.set_xlim(lims[i])
else:
ax.set_ylim(lims[i])
ax.set_xlim(lims[j])
def plot_PDF_samples_log(data, var_name1, var_name2, clf, amp, time, z):
import matplotlib.mlab as mlab
import matplotlib.cm as cm
det1 = np.linalg.det(clf.covariances_[0, :, :])
det2 = np.linalg.det(clf.covariances_[1, :, :])
det_ = min(det1, det2)
fact_ = 1. / np.sqrt((2 * np.pi)**2 * det_)
fact = 1.1 * clf.weights_[0] * 1. / np.sqrt(
(2 * np.pi)**2 * det1) + clf.weights_[1] * 1. / np.sqrt(
(2 * np.pi)**2 * det2)
# Plotting
n_sample = 300
x1_max = np.amax(data[:, 0])
x1_min = np.amin(data[:, 0])
x2_max = np.amax(data[:, 1])
x2_min = np.amin(data[:, 1])
x = np.linspace(x1_min, x1_max, n_sample)
y = np.linspace(x2_min, x2_max, n_sample)
X, Y = np.meshgrid(x, y)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = clf.score_samples(XX).reshape(X.shape)
mx1 = clf.means_[0, 0]
my1 = clf.means_[0, 1]
sx1 = np.sqrt(clf.covariances_[0, 0, 0])
sy1 = np.sqrt(clf.covariances_[0, 1, 1])
sxy1 = clf.covariances_[0, 1, 0]
mx2 = clf.means_[1, 0]
my2 = clf.means_[1, 1]
sx2 = np.sqrt(clf.covariances_[1, 0, 0])
sy2 = np.sqrt(clf.covariances_[1, 1, 1])
sxy2 = clf.covariances_[1, 1, 0]
Z1 = mlab.bivariate_normal(X,
Y,
sigmax=sx1,
sigmay=sy1,
mux=mx1,
muy=my1,
sigmaxy=sxy1)
Z2 = mlab.bivariate_normal(X,
Y,
sigmax=sx2,
sigmay=sy2,
mux=mx2,
muy=my2,
sigmaxy=sxy2)
fig = plt.figure(figsize=(10, 12))
levels_tot = np.linspace(0, fact, 10)
if fact <= 2:
levels_cont = np.arange(0, fact, 0.2)
levels_contf = np.arange(0, fact, 0.2)
elif fact <= 10:
levels_cont = np.arange(0, fact, 0.5)
levels_contf = np.arange(0, fact, 0.5)
elif fact <= 20:
levels_cont = np.arange(0, fact, 2)
levels_contf = np.arange(0, fact, 2)
elif fact <= 50:
levels_cont = np.arange(0, fact, 5)
levels_contf = np.arange(0, fact, 5)
else:
levels_cont = np.arange(0, fact, 20)
levels_contf = np.arange(0, fact, 20)
levels_comp = np.linspace(0, fact_, 7)
plt.subplot(3, 2, 1)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.3)
ax1 = plt.contour(X, Y, Z, levels=np.linspace(10, 20, 2))
plt.colorbar(ax1, shrink=0.8)
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.title(var_name1 + var_name2 + ' (data), t=' + str(time) + ', z=' +
str(z))
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.title(var_name1 + var_name2 + ' (data)')
axis_label(var_name1, var_name2, amp)
plt.subplot(3, 2, 3)
ax1 = plt.hist2d(data[:, 0],
data[:, 1],
bins=30,
normed=True,
norm=colors.LogNorm())
plt.colorbar(shrink=0.8)
ax2 = plt.contour(X,
Y,
np.exp(Z),
norm=colors.LogNorm(),
linewidths=2,
colors='w')
axis_label(var_name1, var_name2, amp)
plt.title('data histogram')
plt.subplot(3, 2, 4)
ax1 = plt.contourf(X, Y, np.exp(Z), norm=colors.LogNorm())
plt.colorbar(ax1, shrink=0.8)
plt.title('EM PDF')
axis_label(var_name1, var_name2, amp)
plt.subplot(3, 2, 5)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.05)
ax1 = plt.contour(X, Y, Z1, norm=colors.LogNorm(), linewidths=1.5)
ax2 = plt.contour(X, Y, Z2, norm=colors.LogNorm(), linewidths=1.5)
plt.plot([clf.means_[0, 0]], [clf.means_[0, 1]], 'wo', markersize=6)
plt.plot([clf.means_[1, 0]], [clf.means_[1, 1]], 'wo', markersize=6)
plt.colorbar(ax1, shrink=0.8)
plt.title('f1, f2')
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
axis_label(var_name1, var_name2, amp)
plt.subplot(3, 2, 6)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.05)
ax1 = plt.contour(X, Y, np.exp(Z), norm=colors.LogNorm(), linewidths=1.5)
plt.plot([clf.means_[0, 0]], [clf.means_[0, 1]], 'wo', markersize=6)
plt.plot([clf.means_[1, 0]], [clf.means_[1, 1]], 'wo', markersize=6)
plt.colorbar(ax1, shrink=0.8)
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
axis_label(var_name1, var_name2, amp)
plt.title('f = f1 + f2')
fig.suptitle('EM2 PDF: ' + var_name1 + var_name2 + ' (t=' + str(time) +
', z=' + str(z) + 'm)',
fontsize=20)
plt.savefig(
os.path.join(
fullpath_out, 'EM2_bivar_figures',
'EM2_PDF_bivariate_' + var_name1 + '_' + var_name2 + '_' +
str(time) + '_z' + str(np.int(z)) + 'm_log.png'))
plt.close()
return
def singlehist2d(x,y, quad):
counts, xedges, yedges, Image = pylab.hist2d(x,y,bins=20)
quad.set_array(counts)
return quad
plt.savefig(os.path.join(folder_output, 'Feature_Importances.jpg'))
plt.close()
# show the validation of the test set
plt.figure()
# label of the axis.
plt.xlabel(
'In-situ soil moisture [$\mathregular{cm^3}$/$\mathregular{cm^3}$]',
fontsize=12)
plt.ylabel(
'Estimated soil moisture [$\mathregular{cm^3}$/$\mathregular{cm^3}$]',
fontsize=12)
# plot the data with the density (number of pixels)
h = hist2d(list(test_labels),
list(predictions),
bins=40,
cmap='PuBu',
range=[[0, 0.6], [0, 0.6]])
cb = plt.colorbar()
cb.set_label('Numbers of points')
# Add 1:1 line
x = np.arange(0, 0.6, 0.1, dtype=float)
y = x
# Add the information of RMSE and r on the figure.
plt.text(0.1, 0.54, 'RMSE: %.2f' % rmse, fontdict={'size': 12})
plt.text(0.1,
0.50,
'ubRMSE: %.2f' % cal_ubrmse(test_labels, predictions)[0],
fontdict={'size': 12})
plt.text(0.1, 0.46, 'r: %.2f' % r, fontdict={'size': 12})
plt.text(0.1, 0.42, 'Num: %d' % len(test_features), fontdict={'size': 12})
""" Example script for plotting histogram """ # import every function from numpy module # can use the function directly from now from numpy import * # import pylab module # use pylab.function to call import pylab # generating data # number of data points in each dimension N = 20000 # random number from "normal" distribution # default: sigma = 1.0, mean = 0.0 x = random.randn(N) y = random.randn(N) # short cut to plot a histogram pylab.hist2d(x,y,bins=50,cmin=0.0001) # show() is required if using in non-interactive environment pylab.show()
matplotlib.use('pdf')
import matplotlib.pyplot as plt
import glob
import sys,numpy,pylab
file_list = glob.glob(sys.argv[1])
mt_list = []
tt_list = []
for lfile in file_list:
try:
llh_file = pickle.load(open(lfile, "rb"))
mt_list.append(float("%.3f" % llh_file['result']['mt']))
tt_list.append(float("%.5f" % llh_file['result']['tt']))
except:
print lfile
my_bins =101
counts,ybins,xbins,image = pylab.hist2d(mt_list,tt_list,range=[[-0.015,0.015],[-0.1,0.1]],weights=numpy.array([float(1)/float(350) for j in mt_list]),bins=my_bins)
print xbins,ybins,counts.shape
#counts,ybins,xbins,image = pylab.hist2d(mt_list,tt_list,bins=10)
pylab.colorbar()
#pylab.xlim(-0.015,0.015)
#pylab.ylim(-0.1,0.1)
new_counts = numpy.array([[float(-1) for i in xrange(my_bins)] for j in xrange(my_bins)])
for i in xrange(((my_bins-1)/2)+1):
if(i>0):
p = ((my_bins-1)/2)-i
q = ((my_bins-1)/2)+(i+1)
tot_counts = 0
#Calculate total counts so far
for j in xrange(p,q):
for k in xrange(p,q):
tot_counts +=counts[j][k]
from numpy import *
import pylab
import matplotlib.animation as animation
def singlehist2d(x,y, quad):
counts, xedges, yedges, Image = pylab.hist2d(x,y,bins=20)
quad.set_array(counts)
return quad
fig = pylab.figure()
ax = pylab.subplot(111)
ims = []
for ind in linspace(0.0,4.0,10):
N = int(100*ind)
x = random.randn(N)
y = random.randn(N)
ax.set_title('N = %i'%N)
= pylab.hist2d(x,y,hold=False)
ims.append((pylab.pcolor(xedges, yedges, counts),))
im_ani = animation.ArtistAnimation(fig, ims, interval=100, repeat_delay=3000,
blit=True)
pylab.show()
#~ out = sr.solve_via_ILP(predicted_distances[cube], max_gradient=[1,1,2])
t1 = time()
elif isinstance(params,int):
t0 = time()
out = sr.reconstruct_surface(predicted_distances[cube], tmp_file1,tmp_file2,prog,overwrite=True, max_dist=max_dist, sampling = [1,1,2], clipping_dist=params, cost_fun=cost_function[:-2], verbose=True)
t1 = time()
elif isinstance(params,str):
f = h5py.File(os.path.join(ROOT_DATA,params), 'r')
predicted_distances_prior = f['inference_results'].value
true_distances_prior = f['labels'].value
f.close()
predicted_distances_prior = np.minimum(predicted_distances_prior,np.max(true_distances))
prior = pl.hist2d(predicted_distances_prior.flatten(), true_distances_prior.flatten(), range=[[-0.5,max_dist+0.5],[-0.5,max_dist+0.5]],bins=np.linspace(-0.5,max_dist + 0.5,max_dist+2))
prior = np.exp(-(prior[0] / prior[0].sum(axis=1)[:,np.newaxis].astype(np.float) ))*1000
pl.close()
t0 = time()
out = sr.reconstruct_surface(predicted_distances[cube], tmp_file1,tmp_file2,prog,overwrite=True, max_dist=max_dist, sampling = [1,1,2], clipping_dist=params, cost_fun=prior, verbose=True)
t1 = time()
imsave(output,out.astype(np.int32),compress=1)
os.remove(output_flag)
os.remove(tmp_file1)
os.remove(tmp_file2)
times[cost_function].append(t1-t0)
#z reki
#pliki=["ot001", "ot002", "ot009", "ot019", "ot020" , "nt001"];
PtBins=[0., 0.1,1.5, 2., 2.5, 3., 3.5, 4., 4.5, 5., 6., 7., 8.,10., 12., 14., 16., 18., 20., 25., 30., 35., 40., 45.,50., 60., 70., 80., 90., 100., 120., 140.,160.];
PtWidth=[(PtBins[j+1]-PtBins[j]) for j in range(len(PtBins)-1)]
bin_width = 0.01;
rec = root2rec(sys.argv[1], "tvec")
#x = np.extract(np.absolute(rec.Teta) < 1.61, rec.Tbeta)
x = np.extract(np.logical_and(np.logical_and(np.absolute(rec.Teta0) < 1.61, rec.Tlbx_1), rec.Tpt0 > 10), rec.Teta0);
y = np.extract(np.logical_and(np.logical_and(np.absolute(rec.Teta0) < 1.61, rec.Tlbx_1), rec.Tpt0 > 10), rec.Tphi0);
print len(x), len(y)
pl.hist2d(x, y, bins=200, norm=LogNorm())
#pl.hist2d(x, y, bins=np.arange(0.,4,bin_width) , norm=LogNorm())
pl.colorbar()
#plt.xscale('log')
plt.ylabel(r'$\phi$')
plt.xlabel(r'$\eta$')
plt.draw()
pp.savefig()
pp.close()
plt.show()
# if(math.fabs(mt)>0.001):
print dm2,th23,dm21,th231,2*(float("%.5f" % llh_file1['result']['llh'])-float("%.5f" % llh_file['result']['llh']))
llh_diff.append(2*(float("%.5f" % llh_file1['result']['llh'])-float("%.5f" % llh_file['result']['llh'])))
n_pull.append((float("%.5f" % llh_file['result']['norm'])-1)/0.5)
ne_pull.append((float("%.5f" % llh_file['result']['norm_e'])-1)/1)
g_pull.append((float("%.5f" % llh_file['result']['gamma'])-0)/5)
atm_pull.append((float("%.5f" % llh_file['result']['atmmu_fraction'])-0.01)/1)
de_pull.append((float("%.5f" % llh_file['result']['domeff'])-1)/0.3)
hi_pull.append((float("%.5f" % llh_file['result']['hole_ice'])-0.02)/0.02)
except:
pass
#print lfile
print len(llh_diff),len(n_pull)
from scipy import stats
print 'lists',count,count1,len(mt_list),len(mtp_list)
pylab.hist2d(mt_list+mtp_list,exp_rat,bins=20)
pylab.xlabel(r"Fitted $\theta_{23}$")
#pylab.xlabel(r"Fitted $sin^2(2 \theta_{23})$")
pylab.ylabel("Events/5174")
pylab.colorbar()
pylab.savefig("ElimPlot.pdf")
pylab.close()
pylab.hist2d(ttdiff_list+ttpdiff_list,exp1_rat,bins=20)
pylab.xlabel(r"Fitted $\theta_{23}$")
#pylab.xlabel(r"Fitted $sin^2(2 \theta_{23})$")
pylab.ylabel("Events/5174")
pylab.colorbar()
pylab.savefig("ElimPlot1.pdf")
pylab.close()
#pylab.hist(mt_list+mtp_list,bins=numpy.linspace(-0.01,0.01,30),color='k',histtype='step')
#pylab.hist(mt_list+mtp_list,bins=numpy.linspace(0.7,1.2,40),color='k',histtype='step')
numpy
matplotlib
'''
import argparse
from matplotlib.colors import LogNorm
import numpy as np
import pylab
import sys
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-i', '--input',
type = argparse.FileType('r', encoding = 'utf8'),
default = sys.stdin,
help = 'input file')
parser.add_argument('--num-bins',
type = int, default = 50,
help = 'number of bins in x and y direction')
parser.add_argument('-d',
type = str, default = ' ',
help = 'delimiter')
args = parser.parse_args()
x, y = np.loadtxt(args.input, delimiter = args.delimiter, unpack = True)
pylab.hist2d(x,y,bins=args.num_bins,norm=LogNorm())
pylab.colorbar()
pylab.show()
def plot_PDF_samples(data, var_name1, var_name2, clf, time, z):
import matplotlib.mlab as mlab
import matplotlib.cm as cm
det_ = np.linalg.det(clf.covariances_[0, :, :])
fact_ = 1. / np.sqrt((2 * np.pi)**2 * det_)
# Plotting
n_sample = 300
x1_max = np.amax(data[:, 0])
x1_min = np.amin(data[:, 0])
x2_max = np.amax(data[:, 1])
x2_min = np.amin(data[:, 1])
x = np.linspace(x1_min, x1_max, n_sample)
y = np.linspace(x2_min, x2_max, n_sample)
X, Y = np.meshgrid(x, y)
XX = np.array([X.ravel(), Y.ravel()]).T
Z = clf.score_samples(XX).reshape(X.shape)
mx1 = clf.means_[0, 0]
my1 = clf.means_[0, 1]
sx1 = np.sqrt(clf.covariances_[0, 0, 0])
sy1 = np.sqrt(clf.covariances_[0, 1, 1])
sxy1 = clf.covariances_[0, 1, 0]
Z1 = mlab.bivariate_normal(X,
Y,
sigmax=sx1,
sigmay=sy1,
mux=mx1,
muy=my1,
sigmaxy=sxy1)
plt.figure(figsize=(12, 12))
levels_tot = np.linspace(0, fact_, 10)
if fact_ <= 2:
levels_cont = np.arange(0, fact_, 0.2)
levels_contf = np.arange(0, fact_, 0.2)
elif fact_ <= 10:
levels_cont = np.arange(0, fact_, 0.5)
levels_contf = np.arange(0, fact_, 0.5)
elif fact_ <= 20:
levels_cont = np.arange(0, fact_, 2)
levels_contf = np.arange(0, fact_, 2)
elif fact_ <= 50:
levels_cont = np.arange(0, fact_, 5)
levels_contf = np.arange(0, fact_, 5)
else:
levels_cont = np.arange(0, fact_, 20)
levels_contf = np.arange(0, fact_, 20)
levels_comp = np.linspace(0, fact_, 7)
plt.subplot(3, 2, 1)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.05)
ax1 = plt.contour(X, Y, Z, levels=np.linspace(10, 20, 2))
plt.colorbar(ax1, shrink=0.8)
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.title(var_name1 + var_name2 + ' (data), t=' + str(time) + ', z=' +
str(z))
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.colorbar(ax1, shrink=0.8)
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.title(var_name1 + var_name2 + ' (data), t=' + str(time) + ', z=' +
str(z))
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.subplot(3, 2, 3)
ax1 = plt.hist2d(data[:, 0], data[:, 1], bins=30, normed=True)
plt.colorbar(shrink=0.8)
ax2 = plt.contour(X,
Y,
np.exp(Z),
levels=levels_cont,
linewidths=1,
colors='w')
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.title('data histogram')
plt.subplot(3, 2, 4)
ax1 = plt.contourf(X, Y, np.exp(Z), levels=levels_contf)
plt.colorbar(ax1, shrink=0.8)
plt.title('EM PDF')
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.subplot(3, 2, 5)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.05)
ax1 = plt.contour(X, Y, Z1, levels=levels_comp, linewidths=1.5)
plt.plot([clf.means_[0, 0]], [clf.means_[0, 1]], 'wo', markersize=6)
plt.colorbar(ax1, shrink=0.8)
plt.title('f1, f2')
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.subplot(3, 2, 6)
plt.scatter(data[:, 0], data[:, 1], s=2, alpha=0.05)
ax1 = plt.contour(X, Y, np.exp(Z), levels=levels_cont, linewidths=1.5)
# ax1 = plt.contour(X, Y, Z1+Z2, linewidths=1.5)
plt.plot([clf.means_[0, 0]], [clf.means_[0, 1]], 'wo', markersize=6)
plt.colorbar(ax1, shrink=0.8)
plt.xlim([x1_min, x1_max])
plt.ylim([x2_min, x2_max])
plt.xlabel(var_name1)
plt.ylabel(var_name2)
plt.title('f = f1 + f2')
plt.savefig(fullpath_out + 'CloudClosure_alltimes_figures/CC_bivariate_' +
var_name1 + '_' + var_name2 + '_z' + str(np.int(z)) +
'm_alltime.png')
plt.close()
return
def plot(self, bins=100, cmap="hot_r", fontsize=10, Nlevels=4,
xlabel=None, ylabel=None, norm=None, range=None,
contour=True, **kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram
:param fontsize: fontsize for the labels
:param contour: show some contours
:param int Nlevels: must be more than 2
:param range: as in pylab.hist2d : a 2x2 shape [[-3,3],[-4,4]]
.. plot::
:include-source:
:width: 50%
>>> from msdas import *
>>> r = replicates.ReplicatesYeast(get_yeast_raw_data())
>>> r.drop_na_count(54) # to speed up the plot creation
>>> r.hist2d_mu_versus_cv()
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X, Y, bins=bins,
cmap=cmap, norm=colors.LogNorm())
else:
res = pylab.hist2d(X, Y, bins=bins, cmap=cmap, range=range)
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
levels = [round(x) for x in pylab.logspace(0, pylab.log10(res[0].max().max()),Nlevels)]
pylab.contour(X, Y, res[0].transpose(), levels[2:], color="g")
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel == None:
ylabel = self.df.columns[1]
if xlabel == None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
pylab.grid(True)
return res
def plot(self,
bins=100,
cmap="hot_r",
fontsize=10,
Nlevels=4,
xlabel=None,
ylabel=None,
norm=None,
range=None,
normed=False,
colorbar=True,
contour=True,
grid=True,
**kargs):
"""plots histogram of mean across replicates versus coefficient variation
:param int bins: binning for the 2D histogram (either a float or list
of 2 binning values).
:param cmap: a valid colormap (defaults to hot_r)
:param fontsize: fontsize for the labels
:param int Nlevels: must be more than 2
:param str xlabel: set the xlabel (overwrites content of the dataframe)
:param str ylabel: set the ylabel (overwrites content of the dataframe)
:param norm: set to 'log' to show the log10 of the values.
:param normed: normalise the data
:param range: as in pylab.Hist2D : a 2x2 shape [[-3,3],[-4,4]]
:param contour: show some contours (default to True)
:param bool grid: Show unerlying grid (defaults to True)
If the input is a dataframe, the xlabel and ylabel will be populated
with the column names of the dataframe.
"""
X = self.df[self.df.columns[0]].values
Y = self.df[self.df.columns[1]].values
if len(X) > 10000:
print("Computing 2D histogram. Please wait")
pylab.clf()
if norm == 'log':
from matplotlib import colors
res = pylab.hist2d(X,
Y,
bins=bins,
density=normed,
cmap=cmap,
norm=colors.LogNorm())
else:
res = pylab.hist2d(X,
Y,
bins=bins,
cmap=cmap,
density=normed,
range=range)
if colorbar is True:
pylab.colorbar()
if contour:
try:
bins1 = bins[0]
bins2 = bins[1]
except:
bins1 = bins
bins2 = bins
X, Y = pylab.meshgrid(res[1][0:bins1], res[2][0:bins2])
if contour:
if res[0].max().max() < 10 and norm == 'log':
pylab.contour(X, Y, res[0].transpose())
else:
levels = [
round(x) for x in pylab.logspace(
0, pylab.log10(res[0].max().max()), Nlevels)
]
pylab.contour(X, Y, res[0].transpose(), levels[2:])
#pylab.clabel(C, fontsize=fontsize, inline=1)
if ylabel is None:
ylabel = self.df.columns[1]
if xlabel is None:
xlabel = self.df.columns[0]
pylab.xlabel(xlabel, fontsize=fontsize)
pylab.ylabel(ylabel, fontsize=fontsize)
if grid is True:
pylab.grid(True)
return res
se = good[np.in1d(good[OBJECT_ID],coadd[OBJECT_ID])]
uid,inv,cts = np.unique(se[OBJECT_ID],False,True,True)
if not (uid == coadd[OBJECT_ID]).all():
raise Exception("Object IDs do not match")
kwargs = dict(bins=100,histtype='step',lw=1.5)
# Spatial distribution
maglim = 30
plt.figure()
sel = (se['MAG_PSF'] < maglim) & (se['BAND'] == 'g')
plt.hist2d(se['RA'][sel],se['DEC'][sel],bins=250,norm=colors.LogNorm())
plt.colorbar(label='log(Counts)')
plt.title('HPX %05d (g < 30)'%opts.pix)
plt.xlabel('RA (deg)')
plt.ylabel('DEC (deg)')
plt.savefig(pltdir+'spatial_se_%05d.png'%opts.pix)
plt.figure()
sel = coadd['MAG_PSF_G'] < maglim
plt.hist2d(coadd['RA'][sel],coadd['DEC'][sel],bins=250,norm=colors.LogNorm())
plt.colorbar(label='log(Counts)')
plt.title('HPX %05d (g < %s)'%(opts.pix,maglim))
plt.xlabel('RA (deg)')
plt.ylabel('DEC (deg)')
plt.savefig(pltdir+'spatial_coadd_%05d.png'%opts.pix)
