def plotDensityMap(bins,binnedData,xscale='log',yscale='log',normaliseX=True,logScale=True):
if logScale:
l, b, r, t = 0.1, 0.12, 1.0, 0.970
else:
l, b, r, t = 0.1, 0.12, 1.05, 0.970
axes_rect = [l,b,r-l,t-b]
fig=p.figure()
fig.subplots_adjust(left=0.01, bottom=.05, right=.985, top=.95, wspace=.005, hspace=.05)
ax = fig.add_axes(axes_rect)
ax.set_xscale(xscale)
ax.set_yscale(yscale)
X,Y=bins.edge_grids
if normaliseX:
ySum=p.sum(binnedData,1)
ySum=p.ma.masked_array(ySum,ySum==0)
Z=binnedData.transpose()/ySum
else:
Z=binnedData.transpose()
if logScale:
mappable=ax.pcolor(X,Y,p.ma.array(Z,mask=Z==0),cmap=p.get_cmap("jet"),norm=matplotlib.colors.LogNorm())
else:
mappable=ax.pcolor(X,Y,p.ma.array(Z,mask=Z==0),cmap=p.get_cmap("jet"))
markersize=5.0
linewidth=2.0
fig.colorbar(mappable)
#ax.set_ylim((T/2.0/bins2D.centers[0][-1],T/2.0*2))
#ax.set_xlim(bins2D.centers[0][0]*day,bins2D.centers[0][-1]*day)
return fig,ax
def visibility(vis,pol):
"""
Helper function for plotMP. Defines the visibility/transparency of scatter
symbols.
@param vis: keyword among 'Visible', 'Invisible', and 'Transparent'.
@type vis: string
"""
if vis == "Visible":
c = [pol]
cmap = P.get_cmap('jet')
v = True
col = "0.0"
elif vis == "Invisible":
c = [0.0]
cmap = P.get_cmap('gist_yarg')
v = False
col = "1.0"
elif vis == "Transparent":
c = [0.20]
cmap = P.get_cmap('gist_yarg')
v = True
col = "0.80"
else:
c = None
cmap = None
v = None
col = None
return [c, cmap, v, col]
def show_matrix_with_names(matrix, vert_names, horiz_names, colormap='b_jet', overlay=None, force_range=False):
def full_rename(namelist, subtypes):
def rename(list, subtype, character):
ret_list = []
for bearer in list:
modded = False
for mod_decider in subtype:
if mod_decider in bearer:
modded = True
ret_list.append(bearer+' '+str(character))
break
if not modded:
ret_list.append(bearer)
return ret_list
new_vert_names = namelist
for idux, subtype in enumerate(subtypes, 1):
new_vert_names = rename(new_vert_names, subtype, ''.join(['*']*idux))
return new_vert_names
if colormap == 'b_jet':
prism_cmap = get_cmap('jet')
prism_vals = prism_cmap(np.arange(0, 1, 0.01))
prism_vals[99] = [0, 0, 0, 1]
costum_cmap = colors.LinearSegmentedColormap.from_list('my_colormap', prism_vals)
else:
costum_cmap = get_cmap(colormap)
if force_range:
plt.imshow(matrix, interpolation='nearest', cmap=costum_cmap, vmin=force_range[0], vmax=force_range[1])
else:
plt.imshow(matrix, interpolation='nearest', cmap=costum_cmap)
plt.colorbar()
if overlay:
print overlay[0]
print overlay[1]
overlay_y, overlay_x = np.nonzero(overlay[0])
plt.scatter(overlay_x, overlay_y, c='k', marker='*', label=overlay[1])
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, mode="expand", borderaxespad=0.)
plt.tick_params(axis='both', labelsize=10)
if type(vert_names) == list:
plt.yticks(range(0, len(vert_names)), vert_names, rotation='horizontal')
if type(horiz_names) == list:
plt.xticks(range(0, len(horiz_names)), horiz_names, rotation='vertical')
if type(vert_names) == tuple:
vert_names = full_rename(vert_names[0], vert_names[1:])
plt.yticks(range(0, len(vert_names)), vert_names, rotation='horizontal')
if type(horiz_names) == tuple:
horiz_names = full_rename(horiz_names[0], horiz_names[1:])
plt.xticks(range(0, len(horiz_names)), horiz_names, rotation='vertical')
plt.subplots_adjust(left=0.2, bottom=0.2)
plt.show()
def __call__(self, inputs):
from numpy import outer, arange, ones
from pylab import figure, axis, imshow, title, show, subplot, text, clf, subplots_adjust
maps = self.get_input('colormap')
a=outer(arange(0,1,0.01),ones(10))
if self.get_input('showall') is True:
figure(figsize=(10,5))
clf()
l = len(tools.cmaps)
subplots_adjust(top=0.9,bottom=0.05,left=0.01,right=0.99)
for index, m in enumerate(tools.cmaps):
#print index
subplot(int(l/2)+l%2+1, 2, index+1)
#print int(l/2)+l%2, 2, (index+1)/2+(index+1)%2+1
axis("off")
imshow(a.transpose(),aspect='auto',cmap=tools.get_cmap(m),origin="lower")
#title(m,rotation=0,fontsize=10)
text(0.5,0.5, m)
show()
elif self.get_input('show') is True:
figure(figsize=(10,5))
clf()
axis("off")
imshow(a.transpose(),aspect='auto',cmap=get_cmap(maps),origin="lower")
title(maps,rotation=0,fontsize=10)
show()
from pylab import get_cmap
res = get_cmap(maps)
return res
def plot_stats(X,Y,model,costs):
#two plots, the decision fcn and points and the cost over time
y_onehot = Trainer.class_to_onehot(Y)
f,(p1,p2) = plot.subplots(1,2)
p2.plot(range(len(costs)),costs)
p2.set_title("Cost over time")
#plot points/centroids/decision fcn
cls_ct = y_onehot.shape[1]
y_cls = Trainer.onehot_to_int(y_onehot)
colors = get_cmap("RdYlGn")(np.linspace(0,1,cls_ct))
#model_cents = model.c.get_value()
#p1.scatter(model_cents[:,0], model_cents[:,1], c='black', s=81)
for curclass,curcolor in zip(range(cls_ct),colors):
inds = [i for i,yi in enumerate(y_cls) if yi==curclass]
p1.scatter(X[inds,0], X[inds,1], c=curcolor)
nx,ny = 200, 200
x = np.linspace(X[:,0].min()-1,X[:,0].max()+1,nx)
y = np.linspace(X[:,1].min()-1,X[:,1].max()+1,ny)
xv,yv = np.meshgrid(x,y)
Z = np.array([z for z in np.c_[xv.ravel(), yv.ravel()]])
Zp = Trainer.onehot_to_int(np.array(model.probability(Z)))
Zp = Zp.reshape(xv.shape)
p1.imshow(Zp, interpolation='nearest',
extent=(xv.min(), xv.max(), yv.min(), yv.max()),
origin = 'lower', cmap=get_cmap("Set1"))
p1.set_title("Decision boundaries and centroids")
f.tight_layout()
plot.show()
def compare(hdf5_in, hdf5_out, pdf_out, n_events):
"""Plots original and cropped img side by side."""
_, original = load_dataset(hdf5_in) # original dataset
_, cropped = load_dataset(hdf5_out) # cropped dataset
# labels
pid_ticks = [0, 1, 2, 3, 4, 5, 6, 7]
pid_labels = ['nth', 'EM', 'mu', 'pi+', 'pi-', 'n', 'p', 'oth']
with PdfPages(pdf_out) as pdf:
for _ in range(n_events):
# grab random event
i = np.random.randint(0, cropped.shape[0])
fig, axes = plt.subplots(1, 2)
cmap = 'tab10'
cbt = 'pid'
fig.suptitle("Event: " + str(i))
# plot pid image
im = axes[0].imshow(original[i][0], cmap=pylab.get_cmap(cmap),
vmin=0, vmax=7)
# plot pid image
im = axes[1].imshow(cropped[i], cmap=pylab.get_cmap(cmap),
vmin=0, vmax=7)
# just plot settings
cbar = pylab.colorbar(im, fraction=0.04, ticks=pid_ticks)
cbar.ax.set_yticklabels(pid_labels)
cbar.set_label("pid", size=9)
cbar.ax.tick_params(labelsize=6)
pdf.savefig()
def create_plot_2d_speeches(self, withLabels = True):
if withLabels:
font = { 'fontname':'Tahoma', 'fontsize':0.5, 'verticalalignment': 'top', 'horizontalalignment':'center' }
labels= [ (i['who'], i['date']) for i in self.data ]
pylab.subplots_adjust(bottom =0.1)
pylab.scatter(self.speech_2d[:,0], self.speech_2d[:,1], marker = '.' ,cmap = pylab.get_cmap('Spectral'))
for label, x, y in zip(labels, self.speech_2d[:, 0], self.speech_2d[:, 1]):
pylab.annotate(
label,
xy = (x, y), xytext = None,
ha = 'right', va = 'bottom', **font)
#,textcoords = 'offset points',bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),
#arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))
pylab.title('U.S. Presidential Speeches(1790-2006)')
pylab.xlabel('X')
pylab.ylabel('Y')
pylab.savefig('plot_with_labels', bbox_inches ='tight', dpi = 1000, orientation = 'landscape', papertype = 'a0')
else:
pylab.subplots_adjust(bottom =0.1)
pylab.scatter(self.speech_2d[:,0], self.speech_2d[:,1], marker = 'o' ,cmap = pylab.get_cmap('Spectral'))
pylab.title('U.S. Presidential Speeches(1790-2006)')
pylab.xlabel('X')
pylab.ylabel('Y')
pylab.savefig('plot_without_labels', bbox_inches ='tight', dpi = 1000, orientation = 'landscape', papertype = 'a0')
pylab.close()
def run_main(options):
try:
inp = open(options.input_file,'rb')
geo_input = pickle.load(inp)
inp.close()
except:
print 'Problems in opening the %s. Check the file.' % options.input_file
exit()
width,height = map(int,options.size.split(','))
mode_colormap = options.mode
n_connections = options.n_connections
#Create image
im = Image.new('RGBA', (width, height), (0, 0, 0, 0))
draw = ImageDraw.Draw(im)
geo_final = {}
#Remove the same start == end and repeated cities (start,end) == (end,start).
for origem,destino in geo_input:
if origem != destino:
if not geo_final.has_key((origem,destino)):
if not geo_final.has_key((destino,origem)):
geo_final.setdefault((origem,destino),0)
geo_final[(origem,destino)] += geo_input[(origem,destino)]
else:
geo_final.setdefault((destino,origem),0)
geo_final[(destino,origem)] += geo_input[(origem,destino)]
matrix_geo = {}
#Find all valid cities and calculate the distances
max_distance = -10000000
for origem,destino in geo_final:
if geo_final[(origem,destino)] > n_connections:
x1,y1 = geo2pixel(origem[0],origem[1],width,height)
x2,y2 = geo2pixel(destino[0],destino[1],width,height)
distance = sqrt((y2-y1)**2 + (x2-x1)**2)*40000/360.0
if distance > max_distance: max_distance = distance
matrix_geo[(x1,y1),(x2,y2)] = distance
#Sort by distance.
sorted_matrix = sorted(matrix_geo.items(),key= lambda x: x[1], reverse=True)
cmap = get_cmap('jet') if mode_colormap == 'all' else get_cmap('jet')
dist_bins = logspace(1,log(max_distance)/log(10),255)
#Plot using the correct colormap
for ((x1,y1),(x2,y2)),distance in sorted_matrix:
for i in range(len(dist_bins)-1):
if distance > dist_bins[i] and distance < dist_bins[i+1]:
break
p = rgb2hex(converter.to_rgb(cmap(255-i)))
draw.line([(y1,x1),(y2,x2)], p )
#Save the image.
im.save(options.output_file)
def flamePlot(mapZ, noNanZ, markRegions, flatten, colorCeil):
print '\nPlotting flameview'
ratios=[4,1,4]
gs = gridspec.GridSpec(len(ratios), 1, height_ratios=ratios)
plt.figure(figsize=(24,9))
# Direct
plt.subplot(gs[0])
plt.imshow(mapZ, cmap=get_cmap(fColorMap), interpolation='nearest', aspect='auto')
plt.clim(-colorCeil,colorCeil)
plt.title('Overview')
plt.xlabel('Probe number')
plt.ylabel('Window Step')
# Markers
plt.subplot(gs[1])
flatMarkers = [0]*len(markRegions[0])
for i, val in enumerate(markRegions[0]):
if abs(val) > flatten:
flatMarkers[i] = val
markRegions.append(flatMarkers)
plt.imshow(markRegions, cmap=get_cmap(fColorMap), interpolation='nearest', aspect='auto')
plt.clim(-colorCeil,colorCeil)
plt.title('Maximum values')
plt.xlabel('Probe number')
plt.ylabel('Post|Pre')
# Flattened
for i in range(len(mapZ)):
for j in range(len(noNanZ)):
stouff=mapZ[i][j]
if abs(stouff) < flatten:
stouff = 0
mapZ[i][j]=stouff
plt.subplot(gs[2])
plt.imshow(mapZ, cmap=get_cmap(fColorMap), interpolation='nearest', aspect='auto')
plt.clim(-colorCeil,colorCeil)
plt.title('Filtered at abs(z) > ' + str(flatten))
plt.xlabel('Probe number')
plt.ylabel('Window Step')
plt.tight_layout()
return plt
def _get__pl(self):
'''return the LinearSegmentedColormap describing this CustomColormap'''
if self.cmap=='file' and self.fname:
colors = lut_manager.parse_lut_file(self.fname)
if self.reverse:
colors.reverse()
return LinearSegmentedColormap.from_list('file',colors)
elif self.cmap=='custom_heat':
return gen_heatmap(t=self.threshold,reverse=self.reverse)
elif self.reverse:
return get_cmap(self.cmap+'_r')
else:
return get_cmap(self.cmap)
def getColorMapPlotSettings(trackFile, colorPropertyName, settings):
if 'colorSettings' in settings:
colorSettings = settings['colorSettings']
vmin = colorSettings['vmin']
vmax = colorSettings['vmax']
separation = colorSettings['separation']
colormap = P.get_cmap(colorSettings['colorMap'])
else:
vmin = (trackFile.axisLimits[colorPropertyName])[0]
vmax = (trackFile.axisLimits[colorPropertyName])[1]
separation = 35
colormap = P.get_cmap('jet')
return vmin, vmax, separation, colormap
def embed_dat_matrix_two_dimensions(low_dimension_data_matrix,
y=None,
labels=None,
density_colormap='Blues',
instance_colormap='YlOrRd'):
from sklearn.preprocessing import scale
low_dimension_data_matrix = scale(low_dimension_data_matrix)
# make mesh
x_min, x_max = low_dimension_data_matrix[:, 0].min(), low_dimension_data_matrix[:, 0].max()
y_min, y_max = low_dimension_data_matrix[:, 1].min(), low_dimension_data_matrix[:, 1].max()
step_num = 50
h = min((x_max - x_min) / step_num, (y_max - y_min) / step_num) # step size in the mesh
b = h * 10 # border size
x_min, x_max = low_dimension_data_matrix[:, 0].min() - b, low_dimension_data_matrix[:, 0].max() + b
y_min, y_max = low_dimension_data_matrix[:, 1].min() - b, low_dimension_data_matrix[:, 1].max() + b
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# induce a one class model to estimate densities
from sklearn.svm import OneClassSVM
gamma = max(x_max - x_min, y_max - y_min)
clf = OneClassSVM(gamma=gamma, nu=0.1)
clf.fit(low_dimension_data_matrix)
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max] . [y_min, y_max].
if hasattr(clf, "decision_function"):
score_matrix = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
score_matrix = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
# Put the result into a color plot
levels = np.linspace(min(score_matrix), max(score_matrix), 40)
score_matrix = score_matrix.reshape(xx.shape)
if y is None:
y = 'white'
plt.contourf(xx, yy, score_matrix, cmap=plt.get_cmap(density_colormap), alpha=0.9, levels=levels)
plt.scatter(low_dimension_data_matrix[:, 0], low_dimension_data_matrix[:, 1],
alpha=.5,
s=70,
edgecolors='gray',
c=y,
cmap=plt.get_cmap(instance_colormap))
# labels
if labels is not None:
for id in range(low_dimension_data_matrix.shape[0]):
label = labels[id]
x = low_dimension_data_matrix[id, 0]
y = low_dimension_data_matrix[id, 1]
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')
def plot_vapor(self,imagename='vapor.png', title='',timestep=0):
"""plot qvapor
Optional input:
--------
imagename = 'your file name.png', defaults to vapor.png'
title = 'your title' defaults to none
timestep = integer. If there are multiple time periods per file then
choose your timestep, defaults to 0'
Returns:
--------
contour plot of qvapor
"""
pl.figure(2)
pl.clf()
try:
vapor_full=self.variable_dict['QVAPOR']
except:
vapor_full=self.get_var('QVAPOR')
qvapor=vapor_full[timestep,0,:,:].copy()
custom_map=pl.get_cmap('jet',lut=22)
try:
m=self.map_proj
x=self.map_x
y=self.map_y
except:
self.bmap()
m=self.map_proj
x=self.map_x
y=self.map_y
custom_map=pl.get_cmap('jet',lut=10)
contour_levs=n.arange(0.012,0.025, 0.001)
vapor_plot=m.contourf(x,y,qvapor[:,:],contour_levs,cmap=custom_map, title=title)#,cmap=custom_map)
vapor_plot.set_clim((0.012,0.025))
self.map_lines()
pl.colorbar(orientation='horizontal')
pl.title(title)
pl.savefig(self.plot_directory+'/'+imagename)
def color_iterator(grouping, cmap='jet', n=None):
'''
Given a Matplotlib colormap, iterate through the color range in equal-sized
increments based on the size of the group.
Parameters
----------
grouping : iterable
Values to return on each iteration
cmap : string
Matplotlib colormap to use
n : int
Size of the group. If not provided, will attempt to estimate the size
from the iterable.
'''
# Attempt to get the length of the iterator first. If we can't get the
# length, then we need to convert the iterator into a list so we can get
# the number of elements.
if n is None:
try:
n = len(grouping)
except:
grouping = list(grouping)
n = len(grouping)
if isinstance(cmap, basestring):
cmap = pylab.get_cmap(cmap)
for i, g in enumerate(grouping):
if n == 1:
yield cmap(1), g
else:
yield cmap(i/(n-1)), g
def makeimg(wav):
global callpath
global imgpath
fs, frames = wavfile.read(os.path.join(callpath, wav))
pylab.ion()
# generate specgram
pylab.figure(1)
# generate specgram
pylab.specgram(
frames,
NFFT=256,
Fs=22050,
detrend=pylab.detrend_none,
window=numpy.hamming(256),
noverlap=192,
cmap=pylab.get_cmap('Greys'))
x_width = len(frames)/fs
pylab.ylim([0,11025])
pylab.xlim([0,round(x_width,3)-0.006])
img_path = os.path.join(imgpath, wav.replace(".wav",".png"))
pylab.savefig(img_path)
return img_path
def main():
print "Importing data from: {0}".format(DIRPATH)
# get the output file of interest from each of the experiment folders
path_lists = natsort.natsorted([os.path.join(dp, f) \
for (dp, dn, fn) in os.walk(DIRPATH) \
for f in fn \
if re.search(r'(^l.*csv)', f)])
print "Loading data ..."
canopy_data = [import_spa_canopy(exp_paths, '30min') for exp_paths in path_lists]
col1_map = get_cmap("rainbow")(np.linspace(0, 1, len(canopy_data)))
plt.figure(figsize=(10, 8))
mygrid = gridspec.GridSpec(5, 2, hspace=1)
for (i, cdata) in enumerate(canopy_data):
ax = plt.subplot(mygrid[i])
gpp = cdata['Ag'].resample('D', how=lambda x: integrate.trapz(x, dx=1800)*1e-6*12)
ax.plot(gpp, '-', lw=1.5, c=col1_map[i], label='layer {0}'.format(i+1))
ax.set_title('layer {0}'.format(i+1))
#plt.legend(loc='upper center', bbox_to_anchor=(1.0, 1),prop={'size':10})
plt.show()
print "Done"
return None
def ST_xA(r,steps = 99):
"""
Makes a heat map for x over ST space for a given r using the full model.
inputs
======
r : float
Value of relatedness
steps: int {99}
Number of decrete points at which to sample S and T
"""
Ss = np.linspace(-1,2,steps)
Ts = np.linspace(0,3,steps)
dataFull = np.zeros( (steps,steps) )
for i,S in enumerate(Ss):
for j,T in enumerate(Ts):
mod = ESS_class(r,S,T)
state = mod.getESS()
dataFull[i,j] = mod.xA(state,r)
#pl.figure()
cmap = pl.get_cmap('Reds')
pl.imshow(dataFull, origin = [Ts[0], Ss[0]],\
extent = [ Ts[0],Ts[-1],Ss[0],Ss[-1] ], vmin = 0,vmax = 1, cmap = cmap)
pl.plot( [ Ts[0], Ts[-1] ],[ 0,0 ],color='black', linewidth = 2.5 )
pl.plot( [ 1, 1 ],[ Ss[0],Ss[-1] ],'--',color='black', linewidth = 2.5 )
pl.plot( [ 0, 3 ],[ 2, -1 ],'--',color='black', linewidth = 2.5 )
def ST_pi_pure(r,steps = 99):
"""
Makes a heat map over ST space for a given value of r, using the pure strategy model, plotting
fitness.
Inputs
======
r : float
Value of relatedness
steps : int {99}
Number of points to sample S and T at.
"""
Ss = np.linspace(-1,2,steps)
Ts = np.linspace(0,3,steps)
dataFull = np.zeros( (steps,steps) )
for i,S in enumerate(Ss):
for j,T in enumerate(Ts):
x = ESS_pure(r,S,T)
dataFull[i,j] = fit_pure(x,r,S,T)/maximal_possible_fitness(S,T)
#pl.figure()
cmap = pl.get_cmap('Reds')
pl.imshow(dataFull, origin = [Ts[0], Ss[0]], interpolation = 'nearest',\
extent = [ Ts[0],Ts[-1],Ss[0],Ss[-1] ], cmap = cmap, vmin = 0, vmax = .5*(Ss[-1]+Ts[-1]))
pl.plot( [ Ts[0], Ts[-1] ],[ 0,0 ],color='black', linewidth = 2.5 )
pl.plot( [ 1, 1 ],[ Ss[0],Ss[-1] ],'--',color='black', linewidth = 2.5 )
pl.plot( [ 0, 3 ],[ 2, -1 ],'--',color='black', linewidth = 2.5 )
def plot_rels(data, labels=None, colors=None, outfile="rels", latent=None, alpha=0.5):
ns, n = data.shape
if labels is None:
labels = map(str, range(n))
ncol = 5
nrow = int(np.ceil(float(n * (n - 1) / 2) / ncol))
fig, axs = pylab.subplots(nrow, ncol)
fig.set_size_inches(5 * ncol, 5 * nrow)
pairs = list(combinations(range(n), 2))
if colors is not None:
colors = (colors - np.min(colors)) / (np.max(colors) - np.min(colors))
for ax, pair in zip(axs.flat, pairs):
ax.scatter(data[:, pair[0]], data[:, pair[1]], c=colors, cmap=pylab.get_cmap("jet"),
marker='.', alpha=alpha, edgecolors='none', vmin=0, vmax=1)
ax.set_xlabel(shorten(labels[pair[0]]))
ax.set_ylabel(shorten(labels[pair[1]]))
for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
ax.scatter(data[:, 0], data[:, 1], marker='.')
pylab.rcParams['font.size'] = 12 #6
pylab.draw()
#fig.set_tight_layout(True)
fig.tight_layout()
for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
ax.set_visible(False)
filename = outfile + '.png'
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
fig.savefig(outfile + '.png')
pylab.close('all')
return True
def draw_adjacency_graph(adjacency_matrix,
node_color=None,
size=10,
layout='graphviz',
prog='neato',
node_size=80,
colormap='autumn'):
"""draw_adjacency_graph."""
graph = nx.from_scipy_sparse_matrix(adjacency_matrix)
plt.figure(figsize=(size, size))
plt.grid(False)
plt.axis('off')
if layout == 'graphviz':
pos = nx.graphviz_layout(graph, prog=prog)
else:
pos = nx.spring_layout(graph)
if len(node_color) == 0:
node_color = 'gray'
nx.draw_networkx_nodes(graph, pos,
node_color=node_color,
alpha=0.6,
node_size=node_size,
cmap=plt.get_cmap(colormap))
nx.draw_networkx_edges(graph, pos, alpha=0.5)
plt.show()
def ST_beta(r,steps = 99):
"""
Makes a heat map of beta over ST space, for a given value of r.
Uses the full model.
Steps : int {99}
Number of steps to sample S and T at
Returns
=======
None
"""
Ss = np.linspace(-1,3,steps)
Ts = np.linspace(0,4,steps)
dataFull = np.zeros( (steps,steps) )
for i,S in enumerate(Ss):
for j,T in enumerate(Ts):
mod = ESS_class(r,S,T)
state = mod.getESS()
dataFull[i,j] = mod.beta(state,r)
#pl.figure()
cmap = pl.get_cmap('coolwarm')
pl.imshow(dataFull, origin = [Ts[0], Ss[0]],\
extent = [ Ts[0],Ts[-1],Ss[0],Ss[-1] ], vmin = -1,vmax = 1, cmap = cmap)
pl.plot( [ Ts[0], Ts[-1] ],[ 0,0 ],color='black', linewidth = 2.5 )
pl.plot( [ 1, 1 ],[ Ss[0],Ss[-1] ],'--',color='black', linewidth = 2.5 )
pl.plot( [ 0, 3 ],[ 2, -1 ],'--',color='black', linewidth = 2.5 )
def apply_cmap(img, cmap, min_value=None, max_value=None):
"""Applies the colormap to the image and returns the result.
If min_value is None or max_value is None, the min and max values from img are used.
:param img: image
:param cmap: string that specifies the colormap
:param min_value: min value of the colormap
:param max_value: max value of the colormap
:return: the image with the applied colormap
"""
img = numpy.squeeze(img).astype(numpy.float32)
if len(img.shape) != 2:
raise Exception("The given image must be 2-dimensional.")
# Check min and max values.
if min_value is None:
min_value = numpy.min(img)
if max_value is None:
max_value = numpy.max(img)
# Convert img to fit into the values 0 to 255.
img = ((img - min_value) * 255 / max_value).astype(numpy.uint8)
# Apply the colormap.
cmap = pylab.get_cmap(cmap)
return cmap(img)
def _plot_annotation_on_ax(self, signal, ax, autoscale=False, colourmap="flag"):
if autoscale:
xstart = 0
xdelta = signal.duration.total_seconds()
else:
xstart,ystart,xdelta,ydelta = ax.viewLim.bounds
if xstart <0:
start_time = timedelta()
else:
start_time = timedelta(seconds=xstart)
stop_time = timedelta(seconds=xdelta) + timedelta(seconds=xstart)
sub_sig = signal[start_time:stop_time]
xs =np.linspace(0, sub_sig.duration.total_seconds() ,sub_sig.size) + start_time.total_seconds()
ys = sub_sig.values
probs = sub_sig.probas
ys = ys.reshape((1,ys.size))
zoom_f = float(self.max_point_amplitude_plot)/ sub_sig.size
ys = zoom(ys,[1, zoom_f], order=0)
ax.imshow(ys, extent=[np.min(xs), np.max(xs), 1.5, -0.5], aspect="auto",
cmap=colourmap, vmin=0, vmax=255, origin='lower')
ax.plot(xs,probs,"-", color="k", linewidth=3)
ax.plot(xs,probs,"-", color="y", linewidth=1,alpha=0.5)
jet = cm = pl.get_cmap(colourmap)
cNorm = colors.Normalize(vmin=0, vmax=255)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
states = np.unique(ys)
boxes = [pl.Rectangle((0, 0), 1, 1, fc=scalarMap.to_rgba(col)) for col in states]
labels = [chr(s) for s in states]
pl.legend(boxes,labels, loc='lower right')
n_labels = 8 #fixme magic number
if len(xs) > n_labels:
trimming = int(float(len(xs)) / float(n_labels))
xs_trimmed = np.round(xs[::trimming])
else:
xs_trimmed = xs
time_strings = [str(timedelta(seconds=s)) for s in xs_trimmed]
ax.set_xticks(xs_trimmed)
ax.set_xticklabels(time_strings, rotation=70)
return
def make_heatmap(data, col_labels, row_labels, make_grid=True,
grid_kwargs=None, colormap=None, **kwargs):
if make_grid and (grid_kwargs is None):
grid_kwargs = {'color': 'w', 'lw': 2}
fig = plt.figure(**kwargs)
plt.hold(True)
if colormap is None:
cmap = None
elif type(colormap) == StringType:
cmap = get_cmap(colormap)
else:
cmap = colormap
plt.imshow(data, interpolation='nearest', cmap=cmap, aspect='auto')
plt.yticks(range(len(row_labels)), row_labels)
plt.xticks(range(len(col_labels)), col_labels, rotation=90)
if grid_kwargs:
xpos = np.arange(len(col_labels))+0.5
ypos = np.arange(len(row_labels))+0.5
add_grid(plt.gca(), xpos, ypos, **grid_kwargs)
plt.hold(True)
return fig
def view(solution):
"""
Plot a 2D projection of the original points and the discovered solution. Each
original point is drawn as a square. Each "center" associated with a point
is drawn as a circle and has a line drawn between it and its corresponding
point. Points/Centers/Lines with the same color are in the same cluster.
"""
X = solution.problem.X
C = solution.u
labels = solution.clusters
maxlabel = np.max(labels).astype(float) + 1
if X.shape[1] > 2:
X, U = pca(X, n=2)
C = C.dot(U)
if X.shape[1] < 2:
X = np.hstack([X, np.zeros(len(X))])
U = np.hstack([U, np.zeros(len(X))])
cmap = pl.get_cmap("Paired")
# draw original points
pl.scatter(X[:,0], X[:,1], c=labels/maxlabel, cmap=cmap, marker='s')
# draw centers
pl.scatter(C[:,0], C[:,1], c=labels/maxlabel, cmap=cmap, marker='o')
# draw line between points, centers
for i in range(len(X)):
x = X[i]
c = C[i]
l = labels[i]
s = np.vstack([x, c])
pl.plot(s[:,0], s[:,1], color=cmap(l / maxlabel))
def val2hex(v, scale=1.0):
if not v:
return "000044"
j = pylab.get_cmap("jet")
v = v / scale
nums = [int(255 * i) for i in j(v)][:3]
return ''.join(["00" if i == 0 else hex(i)[2:] for i in nums])
def LevelColormap(levels, cmap=None):
"""Make a colormap based on an increasing sequence of levels"""
# Start with an existing colormap
if cmap == None:
cmap = pl.get_cmap()
# Spread the colours maximally
nlev = len(levels)
S = pl.arange(nlev, dtype='float')/(nlev-1)
A = cmap(S)
# Normalize the levels to interval [0,1]
levels = pl.array(levels, dtype='float')
L = (levels-levels[0])/(levels[-1]-levels[0])
# Make the colour dictionary
R = [(L[i], A[i,0], A[i,0]) for i in xrange(nlev)]
G = [(L[i], A[i,1], A[i,1]) for i in xrange(nlev)]
B = [(L[i], A[i,2], A[i,2]) for i in xrange(nlev)]
cdict = dict(red=tuple(R),green=tuple(G),blue=tuple(B))
# Use
return matplotlib.colors.LinearSegmentedColormap(
'%s_levels' % cmap.name, cdict, 256)
def cluster_show(_3D_chan1, _3D_chan2, _3D_labels, n_clusters_, means, std, hulls):
"""
:param _3D_chan1:
:param _3D_chan2:
:param _3D_labels:
"""
red = (1.0, 0.0, 0.0)
green = (0.0, 1.0, 0.1)
mlab.pipeline.volume(mlab.pipeline.scalar_field(_3D_chan1), color=green)
mlab.pipeline.volume(mlab.pipeline.scalar_field(_3D_chan2), color=red)
mlab.show()
cm = get_cmap('gist_rainbow')
for i in range(0, n_clusters_):
re_img = np.zeros(_3D_chan2.shape)
re_img[_3D_labels == i] = _3D_chan2[_3D_labels == i]
mlab.pipeline.volume(mlab.pipeline.scalar_field(re_img), color=tuple(cm(1.*i/n_clusters_)[:-1]))
# mean_arr = np.zeros((n_clusters_, 3))
# std_arr = np.zeros((n_clusters_))
# for i in range(0, n_clusters_):
# mean_arr[i, :] = means[i]
# std_arr[i] = std[i]
# x,y,z = mean_arr.T
# mlab.points3d(x,y,z, std_arr)
for hull in hulls:
x,y,z = hull.points.T
triangles = hull.simplices
mlab.triangular_mesh(x, y, z, triangles, representation='wireframe', color=(0, 0, 0))
mlab.show()
def calibration_summary(data, calname = "p_filt_value_tdc"):
ds1 = data.first_good_dataset
cal1 = ds1.calibration[calname]
res = np.zeros((data.num_good_channels, len(cal1.elements)))
plt.figure()
cmap = plt.get_cmap()
cmap = [cmap(i/float(len(cal1.elements))) for i in xrange(len(cal1.elements))]
for j, feature in enumerate(cal1.elements):
for k, ds in enumerate(data):
#plt.subplot(np.ceil(np.sqrt(len(cal1.elements))), np.ceil(np.sqrt(len(cal1.elements))), j)
res[k, j] = ds.calibration[calname].energy_resolutions[j]
plt.hist(res[:,j], np.arange(0,40,1), histtype="step", label=str(feature+" %0.2f"%np.median(res[:,j])),color=cmap[j])
plt.xlabel("energy resolution (eV)")
plt.ylabel("num channels per bin")
plt.legend()
plt.grid("on")
elements = [ds.calibration[calname].elements for ds in data]
energy_resolution = [ds.calibration[calname].energy_resolutions for ds in data]
energies = [mass.energy_calibration.STANDARD_FEATURES[name] for name in elements[0]]
plt.figure()
for j in xrange(len(elements)):
plt.plot(energies, energy_resolution[j],'.')
plt.plot(energies, np.median(res, axis=0),'s',markersize=10)
plt.xlabel("energy (eV)")
plt.ylabel("fwhm res from calibration (eV)")
plt.ylim(0,20)
plt.grid("on")
def _peek(self, lat, alt, data, latrange, dataname, vmin, vmax, axis_datetime=False, ymin=0, ymax=25, cmap=None):
import niceplots
import matplotlib.pyplot as plt
from pylab import get_cmap
print('Showing ' + dataname)
print('Lat range : ', latrange)
if cmap is None:
cmap = get_cmap('gist_stern_r')
plt.ioff()
fig = plt.figure(figsize=(20, 6))
ax = plt.gca()
plt.pcolormesh(lat, alt, data.T, vmin=vmin, vmax=vmax, cmap=cmap)
plt.xlim(latrange[0], latrange[1])
plt.ylim(ymin, ymax)
if axis_datetime:
niceplots.axis_set_date_format(ax, format='%H:%M')
fig.autofmt_xdate()
else:
plt.xlabel('Latitude')
plt.colorbar().set_label(dataname)
plt.ylabel('Altitude [km]')
plt.title(dataname + ' - CALIOP ' + str(self.date))
phi_km[0] = phit_km[-1]
S_km = np.zeros((len(phit_km) + 1, len(phit_km[0])), float)
S_km[1:] = St_km
S_km[0] = St_km[-1]
# S_km += 1
S_km /= 2
Nm = len(phi_km[0])
phi_km = np.tile(phi_km, (1, 2))
phi_km[:, Nm:] += 2 * np.pi
S_km = np.tile(S_km, (1, 2))
plt.figure()
plt.scatter(np.tile(np.arange(len(phi_km)), len(phi_km.T)),
phi_km.T.reshape(-1),
cmap=plt.get_cmap('viridis'),
c=S_km.T.reshape(-1),
s=5,
marker='o')
cbar = plt.colorbar()
cbar.set_label(r'$\langle S_z\rangle/\hbar$', size=20)
# plt.xlabel(r'$k_\mathrm{y}$', size=24)
plt.ylabel(r'$\gamma_x$', size=24)
plt.xticks([0, Nk / 2, Nk], [r'$-\mathrm{M}$', r'$\Gamma$', r'$\mathrm{M}$'],
size=20)
plt.yticks([0, np.pi, 2 * np.pi], [r'$0$', r'$\pi$', r'$2\pi$'], size=20)
plt.axis([0, Nk, 0, 2 * np.pi])
plt.tight_layout()
plt.savefig('phases.png')
absprob = np.exp(0.5 * (weights.min() - weights))
k = 0
for i, j in enumerate(vals1):
for m, n in enumerate(vals2):
prob[k] = np.sum(absprob[np.where((x == j) & (y == n))])
vals_x[k] = j
vals_y[k] = n
k += 1
#pdb.set_trace()
prob /= prob.sum()
return vals_x, vals_y, prob, np.log(prob)
if __name__ == '__main__':
cm = plt.get_cmap('Blues')
# name of match console input file
name = sys.argv[1]
# read in match ssp file
data = np.loadtxt(name)
plt.close('all')
# 1-d distributions for av, age, and 2d distribution for age-av
plt.figure()
res = makepdfs(data[:, 0], data[:, 5])
plt.hist(res[0],
weights=res[1],
bins=len(res[0]),
def make_plots_seg(data_dict, max_events, normed_img, pred_dict):
"""
Copy of make_plots adapted for pid plots
"""
target_plane_codes = {9: 1, 18: 2, 27: 3, 36: 6, 45: 4, 50: 5}
pkeys = []
for k in data_dict.keys():
if len(data_dict[k]) > 0:
pkeys.append(k)
print('Data dictionary present keys: {}'.format(pkeys))
types = ['energy', 'time']
views = ['x', 'u', 'v'] # TODO? build dynamically?
# only working with two-deep imgs these days
# plotting_two_tensors = True
def get_maybe_missing(data_dict, key, counter):
try:
return data_dict[key][counter]
except KeyError:
pass
return -1
evt_plotted = 0
with PdfPages("evt_all.pdf") as pdf:
for counter in range(len(data_dict[EVENTIDS])):
evtid = data_dict[EVENTIDS][counter]
segment = get_maybe_missing(data_dict, SEGMENTS, counter)
planecode = get_maybe_missing(data_dict, PLANECODES, counter)
n_hadmultmeas = get_maybe_missing(data_dict, N_HADMULTMEAS, counter)
(run, subrun, gate, phys_evt) = decode_eventid(evtid)
if evt_plotted > max_events:
break
status_string = 'Plotting entry %d: %d: ' % (counter, evtid)
title_string = '{}/{}/{}/{}'
title_elems = [run, subrun, gate, phys_evt]
if segment != -1 and planecode != -1:
title_string = title_string + ', segment {}, planecode {}'
title_elems.extend([segment, planecode])
if planecode in tuple(target_plane_codes.keys()):
title_string = title_string + ', targ {}'
title_elems.append(target_plane_codes[planecode[0]])
if n_hadmultmeas != -1:
title_string = title_string + ', n_chghad {}'
title_elems.append(n_hadmultmeas)
if pred_dict is not None:
try:
prediction = pred_dict[str(evtid)]
title_string = title_string + ', pred={}'
title_elems.append(prediction)
except KeyError:
pass
print(status_string + title_string.format(*title_elems))
# run, subrun, gate, phys_evt = decode_eventid(evtid)
fig_wid = 9
fig_height = 9
grid_height = 3
fig = pylab.figure(figsize=(fig_wid, fig_height))
fig.suptitle(title_string.format(*title_elems))
gs = pylab.GridSpec(grid_height, 3)
for i, t in enumerate(types):
datatyp = 'energies+times'
# set the bounds on the color scale
if normed_img:
minv = 0 if t == 'energy' else -1
maxv = 1
else:
maxes = []
mins = []
for v in views:
maxes.append(
np.abs(np.max(data_dict[datatyp][v][counter, i, :, :]))
)
mins.append(
np.abs(np.max(data_dict[datatyp][v][counter, i, :, :]))
)
minv = np.max(mins)
maxv = np.max(maxes)
maxex = maxv if maxv > minv else minv
minv = 0 if minv < 0.0001 else 0 if t == 'energy' else -maxv
maxv = maxex
for j, view in enumerate(views):
gs_pos = i * 3 + j
ax = pylab.subplot(gs[gs_pos])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
cmap = 'Reds' if t == 'energy' else 'bwr'
cbt = 'energy' if t == 'energy' else 'times'
datap = data_dict[datatyp][view][counter, i, :, :]
# make the plot
im = ax.imshow(
datap,
cmap=pylab.get_cmap(cmap),
interpolation='nearest',
vmin=minv, vmax=maxv
)
cbar = pylab.colorbar(im, fraction=0.04)
cbar.set_label(cbt, size=9)
cbar.ax.tick_params(labelsize=6)
pylab.title(t + ' - ' + view, fontsize=12)
pylab.xlabel('plane', fontsize=10)
pylab.ylabel('strip', fontsize=10)
# plot pid
for j, view in enumerate(views):
gs_pos = 6 + j
ax = pylab.subplot(gs[gs_pos])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
cmap = 'tab10'
cbt = 'pid'
datap = data_dict["pid"][view][counter, 0, :, :]
# make the plot
im = ax.imshow(
datap,
cmap=pylab.get_cmap(cmap),
interpolation='nearest',
vmin=0, vmax=7
)
cbar = pylab.colorbar(im, fraction=0.04, ticks=[0, 1, 2, 3, 4, 5, 6, 7])
cbar.ax.set_yticklabels(['nth',
'EM',
'mu',
'pi+',
'pi-',
'n',
'p',
'oth'])
cbar.set_label("pid", size=9)
cbar.ax.tick_params(labelsize=6)
pylab.title("pid" + ' - ' + view, fontsize=12)
pylab.xlabel('plane', fontsize=10)
pylab.ylabel('strip', fontsize=10)
pdf.savefig()
evt_plotted += 1
from math import log10, ceil
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from numpy.random import randint
mpl.rcParams['font.size'] = 8
mpl.rcParams['lines.linewidth'] = 1.0
mpl.rcParams['legend.frameon'] = False
mpl.rcParams['legend.fontsize'] = 'small'
vmin = log10(1e-04)
vmax = log10(1e-02)
cmap = 'jet'
jet = cm = pylab.get_cmap(cmap)
cNorm = colors.Normalize(vmin=vmin, vmax=vmax)
pi_scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
################################################################################
#
# Standard header to read in argument information
#
################################################################################
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("species_name", help="name of species to process")
parser.add_argument("--debug",
help="Loads only a subset of SNPs for speed",
action="store_true")
parser.add_argument("--chunk-size",
Z_REF = NodInf_Data_REF[0][:, 1]
dZ_REF = numpy.zeros_like(Z_REF)
dZ_REF0 = Z_REF[1:] - Z_REF[:-1]
dZ_REF[0] = dZ_REF0[0] / 2
dZ_REF[1:-1] = (dZ_REF0[1:] + dZ_REF0[:-1]) / 2
dZ_REF[-1] = dZ_REF0[-1] / 2
CAS_All_Z = CAS_All * dZ_REF[None, None, None, :]
CPRS_All_Z = CPRS_All * dZ_REF[None, None, None, :]
CTRS_All_Z = CTRS_All * dZ_REF[None, None, None, :]
fig = pylab.figure()
ax = fig.add_subplot(111)
V_T = numpy.logspace(-3, 12, 15)
V = numpy.concatenate((-V_T[::-1], V_T))
cmap = pylab.get_cmap('bwr')
cols = cmap(numpy.linspace(0, 1, V.size))
im = ax.contourf(TMesh, ZMesh, CAS_All[0, 0, :, :].transpose(), V, colors=cols)
# ax.contour(TMesh,ZMesh,CAS_All[10,0,:,:].transpose(),V,colors='k')
ax.set_xlabel('Time (d)')
ax.set_ylabel('Z (cm)')
# ax.set_title('%s - h = %1.2e cm'%(NodInf_Header[iNI],hSens))
# fig.colorbar(im)
for iC in range(nC):
# fig = pylab.figure()
# ax =fig.add_subplot(111)
# ax.plot(-hSens_All,numpy.nanmean(numpy.abs(CAS_All_Z[:,iC,:,:]).reshape((nH,-1))))
# ax.set_xscale('log')
# ax.set_xlabel('Matric Head (cm)')
# ax.set_ylabel('CAS')
return welch1(p,alpha)[1:] - 1
def rconv2(x,y):
return n.fft.irfft2(n.fft.rfft2(x) * n.conj(n.fft.rfft2(y)))
def arconv2(x):
return n.fft.irfft2(n.abs(n.fft.rfft2(x))**2)
def recenter(x, null_auto=True):
if null_auto: x[0,0] = 0
return a.img.recenter(x, n.array(x.shape)/2)
if __name__ == '__main__':
import sys
import pylab
cmap = pylab.get_cmap('gist_yarg')
prms = {'cmap':pylab.get_cmap('gist_yarg'), 'interpolation':'nearest', 'origin':'lower'}
if False:
rs = reps_from_file(sys.argv[-1])
else:
p = 13
#p = 37
#p = 71
rs = []
for alpha in range(2,p):
try: rs.append(welch1(p,alpha)); print alpha
except(AssertionError): pass
rs = n.array(rs)
print 'Welch method generated %d solutions for p=%d' % (rs.shape[0], p)
N = rs.shape[-1]
_d = []
def cartesian_map_array(self,
fn,
vmin=None,
vmax=None,
bands=8,
title='',
cblabel='',
ecliptic=False,
equatorial=False,
nocolorbar=False,
cmap=plt.get_cmap('coolwarm')):
"""
Plot an array of cartesian maps
fn : function object
fn(iband) returns nside=12 HEALPix array
has attributes vmin, vmax, title, cblabel
"""
if vmin is None: vmin = fn.vmin
if vmax is None: vmax = fn.vmax
nrows, ncols = ((bands + 1) // 4, 4) if bands >= 4 else (1, bands)
fig, axx = plt.subplots(nrows,
ncols,
figsize=(3 + 3 * ncols, 1 + 3 * nrows),
sharex=True,
sharey=True)
plt.subplots_adjust(left=0.1,
right=0.92,
hspace=0.15,
wspace=0.01,
bottom=0.15)
if ecliptic:
lon, sinlat = self.ecliptic_coords()
elif equatorial:
lon, sinlat = self.equatorial_coords()
else:
lon = self.df.glon
sinlat = self.singlat
for iband, energy in enumerate(self.energy[:bands]):
ax = axx.flatten()[iband] if bands > 1 else axx
scat = self.basic_skyplot(ax,
lon,
sinlat,
fn(iband).clip(vmin, vmax),
title='%d MeV' % energy,
vmin=vmin,
vmax=vmax,
s=30,
edgecolor='none',
colorbar=False,
labels=False,
cmap=cmap)
fig.text(0.5, 0.95, getattr(fn, 'title', title), ha='center', size=14)
if nocolorbar: return fig
#put colorbar at right
cbax = fig.add_axes((0.92, 0.15, 0.02, 0.7))
cb = plt.colorbar(scat, cbax, orientation='vertical')
cb.set_label(getattr(fn, 'cblabel', cblabel))
fig.text(0.5, 0.025, 'longitude', ha='center', fontsize=14)
fig.text(0.05,
0.5,
'sin(latitude)',
rotation='vertical',
va='center',
fontsize=14)
return fig
def __init__(self,
fig,
xpos,
ypos,
num=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
xl=None,
yl=None,
title=None,
xticks=None,
yticks=None,
xlog=False,
xticklabels=None,
yticklabels=None,
xticklabels_rotation=0,
yticklabels_rotation=0,
ylog=False,
plot_width=None,
plot_height=None,
dashes=None,
lc=None,
lw=None,
pt=None,
ps=None,
pc=None,
errorbar_area=None,
legend_xpos=None,
legend_ypos=None,
hlines=None,
vlines=None,
hspans=None,
vspans=None,
show_colormap=False,
colormap=None,
zmin=None,
zmax=None,
zticks=None,
zticklabels=None,
zticklabels_rotation=0,
zl=None):
self.fig = fig
self.xpos = float(xpos)
self.ypos = float(ypos)
self.plot_width = plot_width
self.plot_height = plot_height
self.lc = lc
self.lw = lw
self.dashes = dashes
self.pt = pt
self.ps = ps
self.pc = pc
self.show_colormap = show_colormap
self.errorbar_area = errorbar_area
if fig.auto_panel_letters is True and num != '':
if num is None:
num = chr(fig.autonum)
else:
fig.autonum = ord(num)
fig.autonum += 1
self.xmin = xmin
self.xmax = xmax
self.ymin = ymin
self.ymax = ymax
self.zmin = zmin
self.zmax = zmax
self.zticks = zticks
self.zticklabels = zticklabels
self.zticklabels_rotation = zticklabels_rotation
self.colormap = colormap
self.zl = zl
self.legend_xpos = legend_xpos
self.legend_ypos = legend_ypos
if self.plot_width is None:
self.plot_width = fig.plot_width
else:
self.plot_width = float(self.plot_width)
if self.plot_height is None:
self.plot_height = fig.plot_height
else:
self.plot_height = float(self.plot_height)
if self.lc is None:
self.lc = fig.lc
if self.lw is None:
self.lw = fig.lw
if self.ps is None:
self.ps = fig.ps
if self.pt is None:
self.pt = fig.pt
if self.pc is None:
self.pc = fig.pc
if self.dashes is None:
self.dashes = fig.dashes
if self.errorbar_area is None:
self.errorbar_area = fig.errorbar_area
if self.legend_xpos is None:
self.legend_xpos = self.xpos + self.plot_width
if self.legend_ypos is None:
self.legend_ypos = self.ypos + self.plot_height
self.ax = self.fig.fig.add_axes([
self.xpos / self.fig.fig_width, self.ypos / self.fig.fig_height,
self.plot_width / self.fig.fig_width,
self.plot_height / self.fig.fig_height
])
#self.ax.set_facecolor("none")
self.ax.spines['right'].set_color('none')
self.ax.spines['top'].set_color('none')
self.ax.spines['left'].set_linewidth(self.lw / 2.)
self.ax.spines['bottom'].set_linewidth(self.lw / 2.)
self.ax.spines['left'].set_color(self.fig.textcolor)
self.ax.spines['bottom'].set_color(self.fig.textcolor)
self.ax.xaxis.set_ticks_position('bottom')
self.ax.yaxis.set_ticks_position('left')
self.ax.tick_params('both',
width=self.lw / 2.,
which='major',
tickdir="out",
color=self.fig.textcolor)
self.ax.tick_params('both',
width=self.lw / 2.,
which='minor',
tickdir="out",
color=self.fig.textcolor)
#self.ax.set_autoscalex_on(False)
if xmin is not None:
self.ax.set_xlim([xmin, xmax]) #, ymin, ymax])
if ymin is not None:
self.ax.set_ylim([ymin, ymax]) #, ymin, ymax])
if xl is not None:
self.ax.set_xlabel(xl,
horizontalalignment='center',
color=self.fig.textcolor)
self.ax.xaxis.set_label_coords(0.5, -0.6 / self.plot_height)
if yl is not None:
self.ax.set_ylabel(yl,
verticalalignment='center',
horizontalalignment='center',
color=self.fig.textcolor)
distance = -1.3 if yticks is not None else -0.5
if '\n' in yl:
self.ax.yaxis.set_label_coords(
distance / self.plot_width * self.fig.fontsize_plots / 9.,
0.5)
else:
self.ax.yaxis.set_label_coords(
distance / self.plot_width * self.fig.fontsize_plots / 9.,
0.5)
if xlog is True:
self.ax.set_xscale("log")
if ylog is True:
self.ax.set_yscale("log")
if xticks is not None:
self.ax.set_xticks(xticks)
if xticklabels is None:
xticklabels = [str(lbl) for lbl in xticks]
if xticklabels is not None:
xticklabels = xticklabels.copy(
) # make sure we do not modifiy the original
for i in range(len(xticklabels)):
xticklabels[i] = xticklabels[i].replace("-", '–')
if xticklabels_rotation == 0:
self.ax.set_xticklabels(xticklabels,
rotation=xticklabels_rotation,
horizontalalignment='center',
color=self.fig.textcolor)
else:
self.ax.set_xticklabels(xticklabels,
rotation=xticklabels_rotation,
horizontalalignment='right',
color=self.fig.textcolor)
else:
self.ax.spines['bottom'].set_visible(False)
self.ax.tick_params(axis='x', which='minor', bottom='off')
self.ax.tick_params(axis='x', which='major', bottom='off')
self.ax.get_xaxis().set_ticks([])
if yticks is not None:
self.ax.set_yticks(yticks)
if yticklabels is None:
yticklabels = [str(lbl) for lbl in yticks]
if yticklabels is not None:
yticklabels = yticklabels.copy(
) # make sure we do not modifiy the original
for i in range(len(yticklabels)):
yticklabels[i] = yticklabels[i].replace("-", '–')
self.ax.set_yticklabels(yticklabels,
rotation=yticklabels_rotation,
horizontalalignment='right',
color=self.fig.textcolor)
else:
self.ax.spines['left'].set_visible(False)
self.ax.tick_params(axis='y', which='minor', left='off')
self.ax.tick_params(axis='y', which='major', left='off')
self.ax.get_yaxis().set_ticks([])
if hlines is not None:
for hline in hlines:
self.ax.axhline(hline,
linewidth=0.25 * self.lw,
color=self.fig.textcolor,
dashes=self.fig.dashes,
solid_capstyle="round",
dash_capstyle="round",
zorder=0)
if vlines is not None:
for vline in vlines:
self.ax.axvline(vline,
linewidth=0.25 * self.lw,
color=self.fig.textcolor,
dashes=self.fig.dashes,
solid_capstyle="round",
dash_capstyle="round",
zorder=0)
if vspans is not None:
for vspan in vspans:
self.ax.axvspan(vspan[0],
vspan[1],
lw=0,
edgecolor='none',
facecolor=vspan[2],
zorder=0,
alpha=vspan[3])
if hspans is not None:
for hspan in hspans:
self.ax.axhspan(hspan[0],
hspan[1],
lw=0,
edgecolor='none',
facecolor=hspan[2],
zorder=0,
alpha=hspan[3])
if title is not None:
self.ax.set_title(title,
color=self.fig.textcolor,
fontsize=self.fig.fontsize_plots)
if num is not None:
if self.ax.spines['left'].get_visible():
pl.figtext(
(self.xpos - 1.8 * self.fig.fontsize_plots / 9.) /
self.fig.fig_width,
(self.ypos + self.plot_height + 0.5) / self.fig.fig_height,
num,
weight='bold',
fontsize=self.fig.fontsize_labels,
ha='center',
va='center',
color=self.fig.textcolor)
else:
pl.figtext(
(self.xpos - 0.3 * self.fig.fontsize_plots / 9.) /
self.fig.fig_width,
(self.ypos + self.plot_height + 0.5) / self.fig.fig_height,
num,
weight='bold',
fontsize=self.fig.fontsize_labels,
ha='center',
va='center',
color=self.fig.textcolor)
if self.show_colormap is True:
cbar_ax = fig.fig.add_axes([
(self.xpos + self.plot_width + self.plot_width / 20.) /
self.fig.fig_width, self.ypos / self.fig.fig_height,
(self.plot_width / 6.) / self.fig.fig_width,
self.plot_height / self.fig.fig_height
])
cbar_ax.set_facecolor("none")
cbar_ax2 = cbar_ax.twinx()
cbar_ax2.tick_params('both',
width=self.lw / 2.,
which='major',
tickdir="out")
cbar_ax2.imshow(np.c_[np.linspace(self.zmin, self.zmax, 500)],
extent=(0, 1, self.zmin, self.zmax),
aspect='auto',
origin='lower',
cmap=pl.get_cmap(self.colormap))
cbar_ax.yaxis.set_ticks([])
cbar_ax.xaxis.set_ticks([])
if self.zticks is not None:
cbar_ax2.set_yticks(self.zticks)
if self.zticklabels is None:
self.zticklabels = [str(lbl) for lbl in self.zticks]
for i in range(len(self.zticklabels)):
self.zticklabels[i] = self.zticklabels[i].replace("-", '–')
cbar_ax2.set_yticklabels(
self.zticklabels,
rotation=zticklabels_rotation,
horizontalalignment='left',
color=self.fig.textcolor) #self.fig.textcolor)
else:
print("need define zticks...")
if zl is not None:
cbar_ax2.set_ylabel(zl, color=self.fig.textcolor)
def themis_plot_phasespace_helistyle(vlsvReader, cellID, plane_x=np.array([1.,0,0]), plane_y=np.array([0,0,1.]), smooth=True, xlabel="Vx", ylabel="Vy"):
''' Plots a view of phasespace, as seen by a themis detector, at the given cellID, in the style that heli likes.
:param vlsvReader: Some VlsvReader class with a file open
:type vlsvReader: :class:`vlsvfile.VlsvReader`
:param cellid: The cell id where the distribution is supposet to be sampled NOTE: The cell id must have a velocity distribution!
:param smooth: Smooth re-gridded phasespace before plotting
:param plane_x and plane_y: x and y direction of the resulting plot plane
'''
matrix = simulation_to_observation_frame(plane_x,plane_y)
angles, energies, vmin, vmax, values = themis_observation_from_file( vlsvReader=vlsvReader, cellid=cellID, matrix=matrix, countrates=False)
if vmin == 0:
vmin = 1e-15
if vmax < vmin:
vmax = vmin*10
# Regrid into cartesian space, 256x256:
grid_r, grid_theta = np.meshgrid(energies,angles)
grid_x = -grid_r * np.sin(grid_theta) # turn radial grid points into (x, y)
grid_y = -grid_r * np.cos(grid_theta)
hires_x = np.linspace(-2200,2200,256);
hires_y = np.linspace(-2200,2200,256);
xi,yi = np.meshgrid(hires_x,hires_y);
vi = griddata( (grid_x.flatten(),grid_y.flatten()), values.flatten(), (xi,yi), method='linear')
if smooth:
# Convolve the grid data with a gaussian kernel
blurkernel = np.exp(-.17*np.power([6,5,4,3,2,1,0,1,2,3,4,5,6],2))
vi = sepfir2d(vi, blurkernel, blurkernel) / 4.2983098411528502
fig,ax=pl.subplots(figsize=(12,10))
ax.set_aspect('equal')
ax.set_title("Phasespace at cell " + str(cellID))
ax.set_xlabel(xlabel+" (km/s)")
ax.set_ylabel(ylabel+" (km/s)")
cax = ax.pcolormesh(xi,yi,vi.T, norm=matplotlib.colors.LogNorm(vmin=vmin,vmax=vmax), vmin=vmin, vmax=vmax, cmap=pl.get_cmap("Blues"), shading='flat')
cax2 = ax.contourf(xi,yi,vi.T, levels=np.logspace(np.log10(vmin),np.log10(vmax),20), norm=matplotlib.colors.LogNorm(vmin=vmin,vmax=vmax), vmin=vmin, vmax=vmax, cmap=pl.get_cmap("Blues"))
#cax3 = ax.contour(xi,yi,vi.T, levels=np.logspace(np.log10(vmin),np.log10(vmax),20), norm=matplotlib.colors.LogNorm(vmin=vmin,vmax=vmax), cmap=pl.get_cmap("binary"))
ax.grid(True)
fig.colorbar(cax)
pl.show()
def correction_plots(self,
cc,
vmin=0.5,
vmax=1.5,
title=None,
hist=False,
start=0,
cmap='coolwarm',
cbtext='correction factor',
**kwargs):
from matplotlib import patches
nrows = cc.shape[1] / 4
#assert cc.shape[1]==8, 'Found shape {}'.format(cc.shape)
if hist:
hkw = dict(bins=np.linspace(vmin, vmax, 21), lw=1, histtype='step')
fig, axx = plt.subplots(nrows,
4,
figsize=(14, 3 * nrows + 1),
sharex=True,
sharey=False)
plt.subplots_adjust(wspace=0.3, hspace=0.15)
else:
fig, axx = plt.subplots(nrows,
4,
figsize=(12, 3 * nrows),
sharex=True,
sharey=True)
plt.subplots_adjust(left=0.10,
wspace=0.1,
hspace=0.15,
right=0.92,
top=0.90)
for i, ax in enumerate(axx.flatten()):
if i < start:
ax.set_visible(False)
continue
if hist:
h = np.array(cc[:, i], float)
ax.hist(h.clip(vmin, vmax), **hkw)
ax.axvline(1.0, color='grey', ls='--')
mypatch = patches.Patch(
fill=False,
lw=0,
facecolor='none',
label='{:4.1f} {:4.1f}'.format(100 * (h.mean() - 1),
100 * h.std()),
)
ax.legend(handles=[mypatch],
facecolor='none',
edgecolor='none')
else:
t, scat = self.skyplot(cc[:, i],
ax=ax,
vmin=vmin,
vmax=vmax,
title='{:0f}'.format(self.energy[i]),
cmap=plt.get_cmap(cmap),
colorbar=False,
labels=False,
**kwargs)
ax.set_title('{:.0f} MeV'.format(self.energy[i]), fontsize=12)
if not hist:
cbax = fig.add_axes((0.94, 0.15, 0.015, 0.7))
fig.colorbar(scat, cbax,
orientation='vertical').set_label(cbtext, fontsize=12)
fig.suptitle(title, fontsize=16)
return fig
#! /usr/bin/env python
import aipy as a, pylab as p, numpy as n, sys, optparse
o = optparse.OptionParser()
o.set_usage('beamcal.py [options]')
a.scripting.add_standard_options(o, cal=True, pol=True, cmap=True)
o.add_option('-f', '--freq', dest='freq', type='float', default=.150,
help='Frequency to plot beam for. Default .150 GHz')
o.add_option('--nside', dest='nside', type='int', default=32,
help='NSIDE parameter for HEALPix map of beam.')
o.add_option('-o', '--outfile', dest='outfile',
help='The name of the fits file to create.')
opts,args = o.parse_args(sys.argv[1:])
cmap = p.get_cmap(opts.cmap)
print 'Modelling beam at %f GHz' % (opts.freq)
afreqs = n.load(args[0])['afreqs']
srclist = [f.split('__')[0] for f in args]
aa = a.cal.get_aa(opts.cal, afreqs)
cat = a.cal.get_catalog(opts.cal,srclist)
cat.compute(aa)
beam = a.map.Map(opts.nside,interp=True)
fluxcal = 'cyg'
srctimes = {}
srcfluxes = {}
srcgains = {}
# Error bar:
# example data
x = np.arange(0.1, 4, 0.5)
y = np.exp(-x)
plt.errorbar(x, y, xerr=0.2, yerr=0.4, capthick=2,
capsize=5, ecolor='g', fmt='--o')
plt.title("Simplest errorbars, 0.2 in x, 0.4 in y")
# xticks interval
ax1.set_xticks(np.arange(0, 6, 0.5))
NUM_COLORS = 10
cm = pl.get_cmap('gist_rainbow') # , gist_yarg
for i in range(3):
axs[i].set_prop_cycle(cycler('color', [cm(1.*ii/NUM_COLORS)
for ii in range(NUM_COLORS)]))
import matplotlib.gridspec as gridspec
fig = pl.figure(figsize=(12, 9))
gs1 = gridspec.GridSpec(3, 1, hspace=0.0)
axs = []
axs.append(fig.add_subplot(gs1[0]))
axs.append(fig.add_subplot(gs1[1]))
axs.append(fig.add_subplot(gs1[2]))
x = np.linspace(0, 2*np.pi, 100)
corr_r = np.corrcoef(c_mat);
corr_c = np.corrcoef(np.transpose(c_mat));
total_corr = corr_r + corr_c;
thresh_vec = np.linspace(0,1,20)
all_groups = []
all_entropy= []
all_blocks = []
all_global_dists = []
count = 1
for thresh in thresh_vec:
plt.subplot(4,5,count)
#test_mat, pred_group, order_vec = blockify(total_corr, thresh)
test_mat, pred_group, order_vec = blockify(this_dist, thresh,'dis')
all_blocks.append(test_mat)
plt.imshow(test_mat,cmap = plt.get_cmap('viridis'))
this_entropy = entropy_proxy(test_mat)
#this_global_dist = global_distance(test_mat,pred_group)
#all_global_dists.append(this_global_dist)
all_entropy.append(this_entropy)
all_groups.append(pred_group)
count += 1
#plt.figure()
#plt.imshow(all_blocks[np.argmax(all_entropy)])
#print(all_groups[np.argmax(all_entropy)])
print(np.argsort(all_entropy,)[-5:])
print(np.sort(all_entropy)[-5:])
#print(np.argsort(all_global_dists))
#print(np.sort(all_global_dists))
def check_cube(cubename, showlamrms=False, savefig=False, no_stamp=False):
"""Plot a datacube.
This function can produce a PDF file of the data cube.
Args:
cubename (str): name of cube numpy file
showlamrms (bool): display the rms of the wavelength fit,
otherwise display average trace width
savefig (bool): save a pdf of the plot
no_stamp (bool): set to prevent printing DRP version stamp on plot
Returns:
None
"""
if not os.path.isfile(cubename):
sys.exit("No such file: %s" % cubename)
cc, meta = np.load(cubename)
fid_wave = meta['fiducial_wavelength']
if 'drp_version' in meta:
drp_ver = meta['drp_version']
else:
drp_ver = ''
xs = [c.X_as for c in cc]
ys = [c.Y_as for c in cc]
if showlamrms:
ss = [0.] * len(cc)
for i in range(len(cc)):
if cc[i].lamrms is not None:
if np.isfinite(cc[i].lamrms):
ss[i] = cc[i].lamrms
c, low, upp = sigmaclip(ss)
smdn = np.nanmedian(c)
sstd = np.nanstd(c)
print("Nspax: %d, Nclip: %d, <RMS>: %f, RMS(std): %f" %
(len(cc), (len(cc) - len(c)), float(smdn), float(sstd)))
# smx = smdn + 3. * sstd
# smn = smdn - 3. * sstd
# if smn < 0.:
# smn = 0.
smn = 0.
smx = 1.2
cbtitle = "Wavelength RMS [nm]"
outf = "cube_lambdarms.pdf"
else:
ss = [c.trace_sigma for c in cc]
smx = 2
smn = 0.8
cbtitle = "RMS trace width [pix]"
outf = "cube_trace_sigma.pdf"
pl.figure(1)
pl.scatter(xs,
ys,
marker='H',
linewidth=0,
s=35,
c=ss,
vmin=smn,
vmax=smx,
cmap=pl.get_cmap('jet'))
if not no_stamp:
pl.title("Hexagonal Grid of Cube Positions")
pl.xlim(15, -15)
pl.ylim(-15, 15)
pl.colorbar(label=cbtitle)
pl.xlabel("RA offset [asec] @ %6.1f nm" % fid_wave)
pl.ylabel("Dec offset [asec]")
# Add drp version
if len(drp_ver) > 0 and not no_stamp:
ax = pl.gca()
ax.annotate('DRP: ' + drp_ver,
xy=(0.0, 0.01),
xytext=(0, 0),
xycoords=('axes fraction', 'figure fraction'),
textcoords='offset points',
size=6,
ha='center',
va='bottom')
pl.grid(True)
pl.ioff()
if savefig:
pl.savefig(outf)
print("Figure saved to " + outf)
else:
pl.show()
print 'BpCMB: %.8e %.8e' % (bpCMB.max(), bpCMB.min())
print 'reconstruct BpCMB: %.8e %.8e' % (bpsurf.max(), bpsurf.min())
fig = P.figure(figsize=(12, 4))
P.subplots_adjust(right=0.99,
left=0.01,
top=0.99,
bottom=0.01,
wspace=0.02,
hspace=0.02)
ax = fig.add_subplot(231, frameon=False)
im = ax.contourf(x,
y,
symmetrize(brCMB, g.minc),
16,
cmap=P.get_cmap('RdYlBu_r'))
P.axis('off')
ax = fig.add_subplot(232, frameon=False)
im = ax.contourf(x,
y,
symmetrize(btCMB, g.minc),
16,
cmap=P.get_cmap('RdYlBu_r'))
P.axis('off')
ax = fig.add_subplot(233, frameon=False)
im = ax.contourf(x,
y,
symmetrize(bpCMB, g.minc),
16,
cmap=P.get_cmap('RdYlBu_r'))
P.axis('off')
#Textos para la legenda
lab = np.linspace(args.vmin, args.vmax, 4)
texto = ['Bajo', 'Medio', 'Alto', 'Muy alto']
labText = ['%dmm\n%s' % (i, j) for i, j in zip(lab, texto)]
#se fija si si hay algo para graficar
if len(pos) > 0:
#Acumula la lluvia para el periodo
Vsum = np.zeros(cu.ncells)
for i in pos:
v, r = wmf.models.read_int_basin(rutebin, i, cu.ncells)
v = v.astype(float)
v = v / 1000.0
Vsum += v
#Genera la figura
c = cu.Plot_basinClean(Vsum,
cmap=pl.get_cmap('viridis', 10),
show_cbar=True,
vmin=args.vmin,
vmax=args.vmax,
cbar_ticksize=16,
cbar_ticks=lab,
cbar_ticklabels=labText,
cbar_aspect=17,
ruta=args.rutaFigura,
show=False,
figsize=(10, 12))
c[1].set_title('Mapa Lluvia de Radar Acumulada', fontsize=16)
if args.verbose:
print 'Aviso: Se ha producido una grafica nueva con valores diferentes de cero para ' + args.rutaFigura[
49:-4]
print fecha_f - fecha_i
# print np.max(evt)
for i in range(len(evt)):
ax = pylab.subplot(gs[i])
ax.axis('on')
ax.xaxis.set_major_locator(pylab.NullLocator())
ax.yaxis.set_major_locator(pylab.NullLocator())
# images are normalized such the max e-dep has val 1, independent
# of view, so set vmin, vmax here to keep matplotlib from
# normalizing each view on its own
minv = 0
cmap = 'jet'
if have_times:
minv = -1
cmap = 'bwr'
im = ax.imshow(evt[i][0],
cmap=pylab.get_cmap(cmap),
interpolation='nearest',
vmin=minv,
vmax=1)
cbar = pylab.colorbar(im, fraction=0.04)
cbar.set_label(colorbar_tile, size=9)
cbar.ax.tick_params(labelsize=6)
pylab.title(titles[i], fontsize=12)
pylab.xlabel("plane", fontsize=10)
pylab.ylabel("strip", fontsize=10)
if labels_shp is not None:
figname = 'evt_%s_%s_%s_%s_targ_%d_pcode_%d.pdf' % \
(run, subrun, gate, phys_evt, targ, pcode)
else:
figname = 'evt_%s_%s_%s_%s.pdf' % \
(run, subrun, gate, phys_evt)
(at your option) any later version.
Astaroth is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with Astaroth. If not, see <http://www.gnu.org/licenses/>.
'''
import pylab as plt
import numpy as np
import matplotlib.gridspec as gridspec
import matplotlib.colors as colors
CM_INFERNO = plt.get_cmap('inferno')
def plot_3(mesh, input_grid, title = '', fname = 'default', bitmap=False,
slicetype = 'middle', colrange=None, colormap=CM_INFERNO ,
contourplot=False, points_from_centre = -1, bfieldlines=False, velfieldlines=False, trimghost = 0):
fig = plt.figure(figsize=(8, 8))
grid = gridspec.GridSpec(2, 3, wspace=0.4, hspace=0.4, width_ratios=[1,1, 0.15])
ax00 = fig.add_subplot( grid[0,0] )
ax10 = fig.add_subplot( grid[0,1] )
ax11 = fig.add_subplot( grid[1,1] )
axcbar = fig.add_subplot( grid[:,2] )
print(mesh.minfo.contents.keys())
if slicetype == 'middle':
def get_colors(k):
cm = pylab.get_cmap('gist_rainbow')
colors = []
for i in range(k):
colors.append(cm(1. * i / k))
return colors
wave = fixup_wave(wave, f.samplerate)
# Apply noise cancellation to signal
wave = filter_signal(wave, f.samplerate)
# Generate plot of wave, spectrogram, and power and save as image file
pl.subplot(3, 1, 1)
pl.plot(wave)
pl.title('start={}, dur={:.1f}, sig={:.1f}'.format(
hhmmss(d[0]), d[1], d[2]))
# start_time, duration, significance
pl.xticks([])
pl.subplot(3, 1, 2)
pl.specgram(wave,
SPECTROGRAM_NFFT,
f.samplerate,
noverlap=SPECTROGRAM_NFFT - SPECTROGRAM_STEP,
cmap=pl.get_cmap('inferno'))
pl.xticks([])
pl.ylim([750, 1750])
pl.subplot(3, 1, 3)
pl.plot(t[start_i:stop_i], meteor_power[start_i:stop_i])
pl.savefig(
os.path.join(output_path,
source_name + " - " + hhmmss(d[0]) + ".png"))
pl.show()
# pl.close()
# Generate wav file for detection
# Renormalise for WAV format (+/-1. limits)
peak = max(np.max(wave), -np.min(wave))
wave = np.divide(wave, 1.01 * peak)
sf.write(
os.path.join(output_path,
def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
from pylab import get_cmap
data = NP_D
print data.min(), data.max()
plt.pcolor(data, cmap=get_cmap("Reds"))
#cmap=get_cmap("Rlues"))
#cmap=hm)
#norm = MidpointNormalize(midpoint=100),
ax.set_frame_on(False)
ax.xaxis.tick_top()
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticks(np.arange(len(X_names)) + 0.5, minor=False)
ax.set_yticks(np.arange(len(Y_names)) + 0.5, minor=False)
ax.set_xticklabels(X_names, rotation=90)
i = 0
for xtick in ax.get_xticklabels():
xtick.set_color(X_colors[i])
def draw_graph(graph,
vertex_label='label',
secondary_vertex_label=None,
edge_label='label',
secondary_edge_label=None,
vertex_color=None,
vertex_alpha=0.6,
edge_color=None,
edge_alpha=0.5,
size=10,
size_x_to_y_ratio=1,
node_size=600,
font_size=9,
layout='graphviz',
prog='neato',
node_border=False,
colormap='YlOrRd',
vmin=0,
vmax=1,
invert_colormap=False,
verbose=True,
file_name=None,
title_key='id',
ignore_for_layout="edge_attribute",
logscale=False):
"""Plot graph layout."""
if size is not None:
size_x = size
size_y = int(float(size) / size_x_to_y_ratio)
plt.figure(figsize=(size_x, size_y))
plt.grid(False)
plt.axis('off')
if vertex_label is not None:
if secondary_vertex_label:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label1 = _serialize_list(d.get(vertex_label, 'N/A'))
label2 = _serialize_list(d.get(secondary_vertex_label, 'N/A'))
vertex_labels[u] = '%s\n%s' % (label1, label2)
else:
vertex_labels = dict()
for u, d in graph.nodes(data=True):
label = d.get(vertex_label, 'N/A')
vertex_labels[u] = _serialize_list(label)
edges_normal = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is False]
edges_nesting = [(u, v) for (u, v, d) in graph.edges(data=True)
if d.get('nesting', False) is True]
if edge_label is not None:
if secondary_edge_label:
edge_labels = dict([(
(
u,
v,
), '%s\n%s' %
(d.get(edge_label, 'N/A'), d.get(secondary_edge_label, 'N/A')))
for u, v, d in graph.edges(data=True)])
else:
edge_labels = dict([((
u,
v,
), d.get(edge_label, 'N/A'))
for u, v, d in graph.edges(data=True)])
if vertex_color is None:
node_color = 'white'
elif vertex_color in ['_labels_', '_label_', '__labels__', '__label__']:
node_color = []
for u, d in graph.nodes(data=True):
label = d.get('label', '.')
node_color.append(hash(_serialize_list(label)))
color_set = set(node_color)
color_map = {c: i for i, c in enumerate(color_set)}
node_color = [color_map[c] for c in node_color]
else:
if invert_colormap:
node_color = [
-d.get(vertex_color, 0) for u, d in graph.nodes(data=True)
]
else:
node_color = [
d.get(vertex_color, 0) for u, d in graph.nodes(data=True)
]
if logscale is True:
log_threshold = 0.01
node_color = [
math.log(c) if c > log_threshold else math.log(log_threshold)
for c in node_color
]
if edge_color is None:
edge_colors = 'black'
elif edge_color in ['_labels_', '_label_', '__labels__', '__label__']:
edge_colors = [
hash(str(d.get('label', '.')))
for u, v, d in graph.edges(data=True) if 'nesting' not in d
]
else:
if invert_colormap:
edge_colors = [
-d.get(edge_color, 0) for u, v, d in graph.edges(data=True)
if 'nesting' not in d
]
else:
edge_colors = [
d.get(edge_color, 0) for u, v, d in graph.edges(data=True)
if 'nesting' not in d
]
tmp_edge_set = [(a, b, d) for (a, b, d) in graph.edges(data=True)
if ignore_for_layout in d]
graph.remove_edges_from(tmp_edge_set)
if layout == 'graphviz':
pos = nx.graphviz_layout(graph, prog=prog, args="-Gstart=rand")
elif layout == "RNA":
import RNA
rna_object = RNA.get_xy_coordinates(graph.graph['structure'])
pos = {
i: (rna_object.get(i).X, rna_object.get(i).Y)
for i in range(len(graph.graph['structure']))
}
elif layout == 'circular':
pos = nx.circular_layout(graph)
elif layout == 'random':
pos = nx.random_layout(graph)
elif layout == 'spring':
pos = nx.spring_layout(graph)
elif layout == 'shell':
pos = nx.shell_layout(graph)
elif layout == 'spectral':
pos = nx.spectral_layout(graph)
else:
raise Exception('Unknown layout format: %s' % layout)
if node_border is False:
linewidths = 0.001
else:
linewidths = 1
graph.add_edges_from(tmp_edge_set)
nx.draw_networkx_nodes(graph,
pos,
node_color=node_color,
alpha=vertex_alpha,
node_size=node_size,
linewidths=linewidths,
cmap=plt.get_cmap(colormap))
if vertex_label is not None:
nx.draw_networkx_labels(graph,
pos,
vertex_labels,
font_size=font_size,
font_color='black')
nx.draw_networkx_edges(graph,
pos,
edgelist=edges_normal,
width=2,
edge_color=edge_colors,
cmap=plt.get_cmap(colormap),
alpha=edge_alpha)
nx.draw_networkx_edges(graph,
pos,
edgelist=edges_nesting,
width=1,
edge_color='k',
style='dotted',
alpha=0.3)
if edge_label is not None:
nx.draw_networkx_edge_labels(graph,
pos,
edge_labels=edge_labels,
font_size=font_size)
if title_key:
title = str(graph.graph.get(title_key, ''))
font = FontProperties()
font.set_family('monospace')
plt.title(title, fontproperties=font)
if size is not None:
# here we decide if we output the image.
# note: if size is not set, the canvas has been created outside
# of this function.
# we wont write on a canvas that we didn't create ourselves.
if file_name is None:
plt.show()
else:
plt.savefig(file_name,
bbox_inches='tight',
transparent=True,
pad_inches=0)
plt.close()
def _plotFunction(self):
if self.evalFunction is None:
return
self.lock.acquire()
# Clean up old plot
for patch in self.plottedPatches:
patch.remove()
self.plottedPatches = []
self.colorMapping = dict()
self.colors = cycle(["b", "g", "r", "c", "m", "y"])
cmap = pylab.get_cmap("jet")
# Check if the observed function returns discrete or continuous value
discreteFunction = isinstance(self.functionObservable,
FunctionOverStateSpaceObservable) \
and self.functionObservable.discreteValues
if not discreteFunction:
# The values of the observed function over the 2d state space
values = numpy.ma.array(numpy.zeros(
(self.maze.getColumns(), self.maze.getRows())),
mask=numpy.zeros((self.maze.getColumns(),
self.maze.getRows())))
# Iterate over all states and compute the value of the observed function
dimensions = [
self.stateSpace[dimName] for dimName in ["column", "row"]
]
for column in range(self.maze.getColumns()):
for row in range(self.maze.getRows()):
# Create state object
state = State((column, row), dimensions)
# Evaluate function for this state
if isinstance(self.functionObservable,
FunctionOverStateSpaceObservable):
functionValue = self.evalFunction(state)
else: # StateActionValuesObservable
# Determine chosen option first
selectedOption = None
for option in self.actions:
selectedOptionName = str(
self.suboptionComboBox.currentText())
if str(option) == selectedOptionName:
selectedOption = option
break
assert selectedOption is not None
functionValue = self.evalFunction(state, option)
# Map function value onto color value
if discreteFunction:
# Deal with situations where the function is only defined over
# part of the state space
if functionValue == None or functionValue in [
numpy.nan, numpy.inf, -numpy.inf
]:
continue
# Determine color value for function value
if not functionValue in self.colorMapping:
# Choose value for function value that occurrs for the
# first time
self.colorMapping[functionValue] = self.colors.next()
patch = self.maze.plotSquare(
self.axis, (column, row),
self.colorMapping[functionValue])
self.plottedPatches.append(patch[0])
else:
# Remember values since we have to know the min and max value
# before we can plot
values[column, row] = functionValue
if functionValue == None or functionValue in [
numpy.nan, numpy.inf, -numpy.inf
]:
values.mask[column, row] = True
# Do the actual plotting for functions with continuous values
if not discreteFunction:
minValue = values.min()
maxValue = values.max()
for column in range(self.maze.getColumns()):
for row in range(self.maze.getRows()):
if values.mask[column, row]: continue
value = (values[column, row] - minValue) / (maxValue -
minValue)
patch = self.maze.plotSquare(self.axis, (column, row),
cmap(value),
zorder=0)
self.plottedPatches.append(patch[0])
# Set limits
self.axis.set_xlim(0, len(self.maze.structure[0]) - 1)
self.axis.set_ylim(0, len(self.maze.structure) - 1)
# Update legend
self.legendWidget.clear()
if discreteFunction:
for functionValue, colorValue in self.colorMapping.items():
if isinstance(functionValue, tuple):
functionValue = functionValue[0] # deal with '(action,)'
rgbaColor = matplotlib.colors.ColorConverter().to_rgba(
colorValue)
item = QtGui.QListWidgetItem(str(functionValue),
self.legendWidget)
color = QtGui.QColor(int(rgbaColor[0] * 255),
int(rgbaColor[1] * 255),
int(rgbaColor[2] * 255))
item.setTextColor(color)
self.legendWidget.addItem(item)
else:
for value in numpy.linspace(values.min(), values.max(), 10):
rgbaColor = cmap(
(value - values.min()) / (values.max() - values.min()))
item = QtGui.QListWidgetItem(str(value), self.legendWidget)
color = QtGui.QColor(int(rgbaColor[0] * 255),
int(rgbaColor[1] * 255),
int(rgbaColor[2] * 255))
item.setTextColor(color)
self.legendWidget.addItem(item)
self.canvas.draw()
self.lock.release()
"Sunday": 6
}
processed_data['day'] = raw_data['DayOfWeek'].replace(weekdays)
districts = {
raw_data['PdDistrict'].unique()[i]: i
for i in range(0, len(raw_data['PdDistrict'].unique()))
}
processed_data['district'] = raw_data['PdDistrict'].replace(districts)
processed_data['PdDistrict'] = raw_data['PdDistrict']
processed_data['time'] = processed_data['date'].apply(lambda x: x.time())
processed_data['hour'] = processed_data['time'].apply(maketime)
return (processed_data)
jet = P.get_cmap('jet')
greys = P.get_cmap('pink')
#Print a plot of where prostitution took place by year.
def crime_loc_plot(types="all", y="all"):
print("printing...")
printdata = processed_data
if types != "all":
printdata = printdata[printdata['Category'].isin(types)]
if y != "all":
printdata = printdata[(printdata['year'] == y)]
printdata.info()
P.figure(figsize=(10, 10))
P.scatter(printdata['X'],
def plot(self):
hfname = 'OmFrag.html'
hfile = lib_file.htmlfile(hfname)
hfile.pre('Electron-hole correlation plots')
hfile.write(
'<h2>Electron-hole correlation plots of the Omega matrices for the individual states.</h2>'
)
htable = lib_file.htmltable(ncol=4)
matplotlib.rc('font', size=self['fsize'])
if self['grid']:
edgecolors = 'k'
else:
edgecolors = None
for state in self.state_list:
if self['plot_type'] == 1:
plot_arr = state['OmFrag']
elif self['plot_type'] == 2:
plot_arr = numpy.sqrt(state['OmFrag'])
else:
raise error_handler.ElseError(str(self['plot_type']),
'plot_type')
if self['sscale']:
vmin = self['vmin']
vmax = self['vmax']
else:
vmin = 0.
vmax = state['OmFrag'].max()
# Completely delete the small elements
# for x in numpy.nditer(plot_arr, op_flags = ['readwrite']):
# if x < vmin:
# x[...] = -1. # numpy.nan
pylab.figure(figsize=(2, 2))
pylab.pcolor(plot_arr,
cmap=pylab.get_cmap(name=self['cmap']),
vmin=vmin,
vmax=vmax,
edgecolors=edgecolors)
# *** Different colouring of different parts ***
# frag_lists = [[0, 2, 4, 6], [1, 3, 5]]
# cmaps = ['Reds', 'Blues']
# OmDim = len(plot_arr)
# for frag in frag_lists:
# tmp_arr = numpy.array([[numpy.nan for i in range(OmDim)] for j in range(OmDim)])
# for i in frag:
# tmp_arr[i,i] = plot_arr[i,i]
# pylab.pcolor(tmp_arr, cmap=pylab.get_cmap(cmaps.pop(0)), vmin=0., vmax=vmax, edgecolors=edgecolors)
if self['axis']:
pylab.axis('on')
if self['ticks']:
pylab.tick_params(which='both', length=0)
if self['cticks']:
pylab.xticks([x + 0.5 for x in range(len(plot_arr))],
self['xticks'])
pylab.yticks([y + 0.5 for y in range(len(plot_arr))],
self['yticks'])
else:
pylab.xticks([x + 0.5 for x in range(len(plot_arr))],
[x + 1 for x in range(len(plot_arr))])
pylab.yticks([y + 0.5 for y in range(len(plot_arr))],
[y + 1 for y in range(len(plot_arr))])
else:
pylab.xticks([])
pylab.yticks([])
else:
pylab.axis('off')
if self['cbar']: pylab.colorbar()
pname = 'pcolor_%s.%s' % (state['name'], self['output_format'])
print("Writing %s ..." % pname)
pylab.tight_layout()
pylab.savefig(pname, dpi=self['plot_dpi'])
pylab.close()
tel = '<img src="%s", border="1" width="200">\n' % pname
tel += '<br>%s' % state['name']
htable.add_el(tel)
# create a plot with the e/h axes and optionally the scale
pylab.figure(figsize=(3, 2))
matplotlib.rc('font', size=14)
ax = pylab.axes()
ax.arrow(0.15,
0.15,
0.5,
0.,
head_width=0.05,
head_length=0.1,
fc='r',
ec='r')
ax.text(0.20, 0.03, 'hole', color='r')
ax.arrow(0.15,
0.15,
0.,
0.5,
head_width=0.05,
head_length=0.1,
fc='b',
ec='b')
ax.text(0.02, 0.20, 'electron', rotation='vertical', color='b')
pylab.axis('off')
if self['sscale']:
pylab.savefig('axes_no.%s' % self['output_format'],
dpi=self['plot_dpi'])
# pylab.figure(figsize=(2,2))
pylab.pcolor(numpy.zeros([1, 1]),
cmap=pylab.get_cmap(name=self['cmap']),
vmin=self['vmin'],
vmax=self['vmax'])
pylab.colorbar()
pylab.savefig('axes.%s' % self['output_format'], dpi=self['plot_dpi'])
tel = '<img src="axes.%s", border="1" width="200">\n' % self[
'output_format']
tel += '<br>Axes / Scale'
htable.add_el(tel)
hfile.write(htable.ret_table())
hfile.post()
print(
" HTML file %s containing the electron-hole correlation plots written."
% hfname)
def plot_rels(data,
labels=None,
colors=None,
outfile="rels",
latent=None,
alpha=0.8):
ns, n = data.shape
if labels is None:
labels = list(map(str, range(n)))
ncol = 5
# ncol = 4
nrow = int(np.ceil(float(n * (n - 1) / 2) / ncol))
#nrow=1
#pylab.rcParams.update({'figure.autolayout': True})
fig, axs = pylab.subplots(nrow, ncol)
fig.set_size_inches(5 * ncol, 5 * nrow)
#fig.set_canvas(pylab.gcf().canvas)
pairs = list(combinations(range(n), 2)) #[:4]
pairs = sorted(
pairs,
key=lambda q: q[0]**2 + q[1]**2) # Puts stronger relationships first
if colors is not None:
colors = (colors - np.min(colors)) / (np.max(colors) -
np.min(colors)).clip(1e-7)
for ax, pair in zip(axs.flat, pairs):
if latent is None:
ax.scatter(data[:, pair[0]],
data[:, pair[1]],
marker='.',
edgecolors='none',
alpha=alpha)
else:
# cs = 'rgbcmykrgbcmyk'
markers = 'x+.o,<>^^<>,+x.'
for j, ind in enumerate(np.unique(latent)):
inds = (latent == ind)
ax.scatter(data[inds, pair[0]],
data[inds, pair[1]],
c=colors[inds],
cmap=pylab.get_cmap("jet"),
marker=markers[j],
alpha=0.5,
edgecolors='none',
vmin=0,
vmax=1)
ax.set_xlabel(shorten(labels[pair[0]]))
ax.set_ylabel(shorten(labels[pair[1]]))
for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
ax.scatter(data[:, 0], data[:, 1], marker='.')
pylab.rcParams['font.size'] = 12 #6
pylab.draw()
#fig.set_tight_layout(True)
fig.tight_layout()
for ax in axs.flat[axs.size - 1:len(pairs) - 1:-1]:
ax.set_visible(False)
filename = outfile + '.png'
if not os.path.exists(os.path.dirname(filename)):
os.makedirs(os.path.dirname(filename))
fig.savefig(outfile + '.png') #df')
pylab.close('all')
return True
def show(self):
Z = np.array(self.to_matrix(self.randoms, self.size))
imshow(Z, cmap=get_cmap("Spectral"), interpolation='nearest')
show()
def Plot_CV_Inacc(self, coords, threshold, labels, limits, savename):
import matplotlib.gridspec as gridspec
if labels == None or len(labels) != coords.shape[1]:
new_labels = []
for i in range(coords.shape[0]):
new_labels.append('X%s' % i)
labels = new_labels
statistic = 100 * (self.y_GP / self.y_compare - 1)
if len(statistic.shape) > 1:
statistic_avg = np.zeros(statistic.shape[0])
for i in range(len(statistic_avg)):
statistic_avg[i] = np.mean(statistic[i, :])
statistic = statistic_avg
count_inacc = len(np.where(statistic > threshold)[0])
cmap = plt.get_cmap('jet')
colors = [cmap(i) for i in np.linspace(0, 1, count_inacc)]
fig = plt.figure(figsize=(12, 10)) #figsize = (20,14)
gs1 = gridspec.GridSpec(coords.shape[1] - 1, coords.shape[1] - 1)
p = 0 # subplot number
for i in range(coords.shape[1] - 1):
l = i + 1 # which y-axis statistic is plotted on each row.
for j in range(coords.shape[1] - 1):
ax1 = plt.subplot(gs1[p])
if j > i:
ax1.axis('off')
else:
ax1.scatter(coords[:, j],
coords[:, l],
color='dimgrey',
s=20)
color_idx = 0
for s in range(coords.shape[0]):
if statistic[s] > threshold:
ax1.scatter(coords[s, j],
coords[s, l],
color=colors[color_idx],
s=30)
color_idx += 1
# Decide what the axis limits should be. If limits=None, it doesn't set any.
# If limits is [a,b], limits are set to be a*min and b*max in each dimension.
# Else, limits is interpreted as [ [x1,x2],[y1,y2],[z1,z2]...] for each dimension.
if limits != None:
if len(np.array(limits).shape) == 1:
ax1.set_xlim([
limits[0] * coords[:, j].min(),
limits[1] * coords[:, j].max()
])
ax1.set_ylim([
limits[0] * coords[:, l].min(),
limits[1] * coords[:, l].max()
])
else:
ax1.set_xlim([limits[j][0], limits[j][1]])
ax1.set_ylim([limits[l][0], limits[l][1]])
# If the axes tick labels are too busy, uncomment the following!:
# Set every other x/y-tick to be invisible
#if len(ax1.get_xticklabels()) > 5: # Too many things will plot on x-axis,
# so only plot every third
# for thing in ax1.get_xticklabels():
# if thing not in ax1.get_xticklabels()[::3]:
# thing.set_visible(False)
#else:
# for thing in ax1.get_xticklabels()[::2]:
# thing.set_visible(False)
#for thing in ax1.get_yticklabels()[::2]:
# thing.set_visible(False)
# Get rid of x/y ticks completely for subplots not at the edge
if j == 0:
ax1.set_ylabel(labels[l])
else:
ax1.set_yticks([])
if i == coords.shape[1] - 2:
ax1.set_xlabel(labels[j])
else:
ax1.set_xticks([])
p += 1
fig.suptitle(
r'Training nodes for which mean accuracy worse than %s per cent (coloured points)'
% threshold)
plt.savefig(savename)
plt.show()
return
# Creation of Array
A=np.zeros(t,dtype=np.uint8)
for i in range(h):
for j in range(w):
A[i,j]=[i%256,j%256,(i+j)%256] # Assigning Colors to Each pixel
i=Image.fromarray(A,"RGB")
i.show()
pyplot.imshow(i)
pyplot.show()
from pylab import imshow, show, get_cmap
from numpy import random
Z = random.random((50,50)) # Test data
imshow(Z, cmap=get_cmap("Spectral"), interpolation='nearest')
show()
import pylab as plt
import numpy as np
Z = np.random.random((500,500)) # Test data
plt.imshow(Z, cmap='gray', interpolation='nearest')
plt.show()
!pip install -q matplotlib-venn
!apt-get -qq install -y libfluidsynth1
import matplotlib.pyplot as plt
import numpy as np
Z= random.random((size,size))
#-------------------------------------------------filtering------------------------------------------------------------
for i in range(ITERATIONS):
Z = scipy.ndimage.filters.median_filter(Z, size=(size/10,size/10))
Z = scipy.ndimage.filters.median_filter(Z, size=(size/10, size/10))
Z = scipy.ndimage.filters.gaussian_gradient_magnitude(Z, 1)
#Z = scipy.ndimage.filters.sobel(Z)
#---------------------------------------------------plotting-----------------------------------------------------------
plt.subplot(2, 2, 1)
imshow(Z, cmap=get_cmap("Spectral"), interpolation='bicubic')
plt.subplot(2, 2, 2)
plt.imshow(Z, cmap=get_cmap('cubehelix'), interpolation='bicubic')
plt.subplot(2, 2, 3)
imshow(Z, cmap=get_cmap("copper"), interpolation='bicubic')
plt.subplot(2, 2, 4)
imshow(Z, cmap='gray', interpolation='bicubic')
#----------------------------------------------------saving------------------------------------------------------------
if SAVE:
filename = 'output'
i = 0
while os.path.exists('{}{:d}.png'.format(filename, i)):
i += 1
plt.savefig('{}{:d}.png'.format(filename, i))
