def plotScores(inputPath, outPath, mapping):
with open(inputPath) as f:
data = f.readlines()
headers = data[0].split("\t")[1::3]
distances = []
scores = [ [] for i in range(len(headers))]
for line in data[1:]:
tokens = line.strip().split("\t")
distances.append(int(tokens[0]))
scoreTokens = tokens[1::3]
for i in range(len(scoreTokens)):
scores[i].append(scoreTokens[i])
# set colors
cm = plt.get_cmap('rainbow')
ax = plt.gca()
ax.set_color_cycle([cm(1.*i/len(headers)) for i in range(len(headers))])
for i in range(len(headers)):
if i % 2 == 0:
plt.plot(distances, scores[i], linewidth=2)
else:
plt.plot(distances, scores[i], linewidth=2, linestyle="--")
# map headerto factor names
factors = []
for head in headers:
factors.append(mapping[head.replace("\\","/").split("/")[-1].split("-")[0]])
ax.legend(factors)
plt.ylabel("ChIP Coverage (per bp per peak)")
plt.xlabel("Distance from Peak Center")
plt.savefig(outPath)
plt.close()
def planck_cmap(ncolors=256):
from matplotlib.colors import LinearSegmentedColormap as cm
"""
Returns a color map similar to the one used for the "Planck CMB Map".
Parameters
----------
ncolors : int, *optional*
Number of color segments (default: 256).
Returns
-------
cmap : matplotlib.colors.LinearSegmentedColormap instance
Linear segmented color map.
"""
segmentdata = {"red": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00),
(0.2, 0.00, 0.00), (0.3, 0.00, 0.00),
(0.4, 0.00, 0.00), (0.5, 1.00, 1.00),
(0.6, 1.00, 1.00), (0.7, 1.00, 1.00),
(0.8, 0.83, 0.83), (0.9, 0.67, 0.67),
(1.0, 0.50, 0.50)],
"green": [(0.0, 0.00, 0.00), (0.1, 0.00, 0.00),
(0.2, 0.00, 0.00), (0.3, 0.30, 0.30),
(0.4, 0.70, 0.70), (0.5, 1.00, 1.00),
(0.6, 0.70, 0.70), (0.7, 0.30, 0.30),
(0.8, 0.00, 0.00), (0.9, 0.00, 0.00),
(1.0, 0.00, 0.00)],
"blue": [(0.0, 0.50, 0.50), (0.1, 0.67, 0.67),
(0.2, 0.83, 0.83), (0.3, 1.00, 1.00),
(0.4, 1.00, 1.00), (0.5, 1.00, 1.00),
(0.6, 0.00, 0.00), (0.7, 0.00, 0.00),
(0.8, 0.00, 0.00), (0.9, 0.00, 0.00),
(1.0, 0.00, 0.00)]}
return cm("Planck-like", segmentdata, N=int(ncolors), gamma=1.0)
def gmmClustering(X, k = 2, maxiter = 3):
my_io.startLog(__name__)
logger = logging.getLogger(__name__)
X, r = kmeans.kmeansClustering(X, 2, 1)
(N, D) = np.shape(X)
pi_k_old = [np.divide(len(np.where(r==kth)[0]), float(N))
for kth in range(k) ]
# mu_old = compute_muk(X, k, r)
cova_old, mu_old = compute_cova(k, X, r)
for i in range(maxiter):
#if i==1:
# pdb.set_trace()
logger.info('ite: %d loss: %f',i,
loss(X, mu_old, pi_k_old, cova_old) )
p = gmm_Esteps(X, pi_k_old, k, cova_old, mu_old)
mu_new, cova_new, pi_k_new = gmm_Msteps(mu_old, X, p)
pi_k_old = pi_k_new
mu_old = mu_new
cova_old = cova_new
#matplotlib.cm=get_cmap("jet")
cm = plt.get_cmap('jet')
ax = plt.gca()
# colors = ['r' if i==0 else 'g' for i in ?]
#colors = 'r'
#ax.scatter(X[:,0], X[:,1], c=colors,alpha=0.8)
# plt.show()
for j in range(N):
likehood = p[1][j]
color = cm(likehood)
plt.plot(X[j,0], X[j,1] ,"o", color=color)
plt.show()
for j in range(N):
likehood = p[0][j]
color = cm(likehood)
plt.plot(X[j,0], X[j,1] ,"o", color=color)
plt.show()
def gen_color3(N):
import matplotlib.cm
import random
cm = matplotlib.cm.get_cmap('Accent')
N = float(N)
colors = [map(lambda x:int(x * 256), cm(i/N)) for i in range(int(N))]
random.shuffle(colors)
for i in range(int(N)):
yield colors[i]
def colors_list(self):
'''
Returns a list of matplotlib color tuples in the ordered
by the index of the clonofilter
'''
import pylab
cm = pylab.get_cmap('gist_rainbow')
clonofilters = sorted(self.clonofilters_all())
returnable = [cm(1. * i / len(clonofilters))
for i in range(len(clonofilters))]
return returnable
def colors_dict(self):
'''
Returns a dict of colors indexed with clonofilters as keys and
matplotlib colors and values
'''
import pylab
returnable = {}
clonofilters = sorted(self.clonofilters_all())
cm = pylab.get_cmap('gist_rainbow')
for index, clonofilter in enumerate(clonofilters):
returnable[clonofilter] = cm(1. * index / len(clonofilters))
return returnable
def plot_connectivity_legend(num_lines=5, vmin=0, vmax=1,ax=P,cm=cm.jet):
"""Plot a legend that explains arrows in connectivity plot"""
#TODO: Make it handle symmetric measures as well
vals = N.linspace(vmin,vmax,num_lines)
pvals = N.linspace(0,1,num_lines)
fig = ax.gca()
fig.axis('off')
for i in range(num_lines):
#ax.xlim([0,1])
ax.ylim([0,1])
ax.xlim([0,1])
arr1 = ax.Arrow(0.1, i*0.1 ,0.3,0, width=(pvals[i]*5+1)/50,ec=cm(pvals[i]),fc=cm(pvals[i]))
fig.add_patch(arr1)
ax.text(0.5,i*0.1,"%.3f"%vals[i], va="center")
def export_colormap(cm, filename, entries=20):
"""
Export a matplotlib colormap to a JSON file.
The colormap is exported as a list of ``entries`` RGBA tuples for
linearly spaced indices between 0 and 1 (inclusive). The list is
part of a dictionary that also contains the minimum and maximum
rent.
"""
data = {
'min': MIN_RENT,
'max': MAX_RENT,
'colors': cm(np.linspace(0, 1, entries)).tolist(),
}
with codecs.open(filename, 'w', encoding='utf8') as f:
json.dump(data, f)
def get_line_style(default='o-',
label=None, label_lookup=None,
i=None, i_max=None, i_markers=None, colormap=None):
"""
colormap: str, default None
colormap name. Try gist_rainbow. Requires i and i_max
"""
# ls, marker, color
kwargs = { }
style = default
# Do an initial update to get starting values of kwargs for use in
# future configuration.
if label and label in label_lookup:
kwargs.update(label_lookup[label])
else:
print(label)
#print label, label_lookup
# Custom config of this function through kwargs
config_vars = ['i', 'i_max', 'colormap']
if 'i' in kwargs: i = kwargs.pop('i')
if 'i_max' in kwargs: i_max = kwargs.pop('i_max')
if 'colormap' in kwargs: colormap = kwargs.pop('colormap')
#print colormap, i, i_max
if i is not None:
if colormap:
import pylab as plt
import numpy
cm = getattr(plt.cm, colormap)
colors = cm(numpy.linspace(0, 1, i_max))
color = colors[i%i_max]
kwargs['color'] = color
if i_markers is None:
kwargs['marker'] = 'o+x<>*^vsh.'[i%11]
else:
kwargs['marker'] = i_markers[i%len(i_markers)]
# This overrides anything else we may set.
if label and label in label_lookup:
kwargs.update(label_lookup[label])
# Remove function-local configuration again:
for var in config_vars:
if var in kwargs: kwargs.pop(var)
return kwargs
def powerHist():
"""
Plots number of hits occured at each power level
"""
# Get DETPOW/MEANPOW
relpow = [row[0]/row[1] for table in data for row in table]
# Plot histogram
plt.figure()
plt.clf()
cm = plt.cm.get_cmap('jet')
Y,X = np.histogram(relpow, bins = 1e3) # arbitrary bin amount
# Y,X = np.histogram(relpow, bins = 100) # arbitrary bin amount
x_span = X.max()-X.min()
C = [cm(((x-X.min())/x_span)) for x in X]
plt.bar(X[:-1],Y,color=C,width=X[1]-X[0],edgecolor = "none")
# plt.colorbar(orientation='vertical')
plt.title('Power Histogram')
plt.xlabel('Relative Power (DETPOW/MEANPOW)')
plt.ylabel('Number of Hits')
plt.autoscale(enable=True, axis='x', tight=True)
plt.yscale('log', nonposy='clip')
def to_rgba(self):
data = self.raster_data
if self.is_enumerated:
if self.enumeration_colors:
cmap, norm = self.colormap()
data2 = norm(data) # np.clip((data - MIN) / (MAX - MIN), 0, 1)
rgba = (cmap(data2) * 255).astype(int)
rgba[:, :, 3] = np.logical_not(data.mask).astype(int) * 255
return rgba
else:
raise NotImplementedError()
# Not enumerated...
if self.scalar_c_lims:
MIN, MAX = self.scalar_c_lims
else:
MIN, MAX = data.min(), data.max()
cm = self.colormap()
data2 = np.clip((data - MIN) / (MAX - MIN), 0, 1)
rgba = (cm(data2) * 255).astype(int)
rgba[:, :, 3] = np.logical_not(data.mask).astype(int) * 255
return rgba
def gray_scale_to_color_ramp(gray_scale,
colormap,
min_colormap_cut=None,
max_colormap_cut=None,
alpha=False,
output_8bit=True):
"""
Turns normalized gray scale np.array to rgba (np.array of 4 np.arrays r, g, b, a).
Parameters
----------
gray_scale : np.array (2D)
Normalized gray_scale img as np.array (0-1)
colormap : str
Colormap form matplotlib (https://matplotlib.org/3.3.2/tutorials/colors/colormaps.html)
min_colormap_cut : float
What lower part of colormap to cut to select part of colormap.
Valid values are between 0 and 1, if 0.2 it cuts off (deletes) 20% of lower colors in colormap.
If None cut is not applied.
max_colormap_cut : float
What upper part of colormap to cut to select part of colormap.
Valid values are between 0 and 1, if 0.8 it cuts off (deletes) 20% of upper colors in colormap.
If None cut is not applied.
alpha : bool
If True outputs 4D array RGBA, if False outputs 3D array RGB
output_8bit : bool
If true output values will be int 0-255 instead of normalized values.
Returns
-------
rgba_out : np.array (3D: red 0-255, green 0-255, blue 0-255)
If alpha False: np.array (4D: red 0-255, green 0-255, blue 0-255, alpha 0-255)
"""
cm = mpl.cm.get_cmap(colormap)
if min_colormap_cut is not None or max_colormap_cut is not None:
if min_colormap_cut is None:
min_colormap_cut = 0.0
if max_colormap_cut is None:
max_colormap_cut = 1.0
if min_colormap_cut > 1 or min_colormap_cut < 0 or max_colormap_cut > 1 or max_colormap_cut < 0:
raise Exception(
"rvt.blend_funct.gray_scale_to_color_ramp: min_colormap_cut and max_colormap_cut must be"
" between 0 and 1!")
if min_colormap_cut >= max_colormap_cut:
raise Exception(
"rvt.blend_funct.gray_scale_to_color_ramp: min_colormap_cut can't be smaller than"
" max_colormap_cut!")
cm = truncate_colormap(cmap=cm,
minval=min_colormap_cut,
maxval=max_colormap_cut)
rgba_mtpl_out = cm(gray_scale) # normalized rgb
if output_8bit:
rgba_mtpl_out = np.uint8(
rgba_mtpl_out * 255) # 0-1 scale to 0-255 and change type to uint8
if not alpha:
rgba_out = np.array([
rgba_mtpl_out[:, :, 0], rgba_mtpl_out[:, :, 1], rgba_mtpl_out[:, :,
2]
])
else:
rgba_out = np.array([
rgba_mtpl_out[:, :, 0], rgba_mtpl_out[:, :, 1],
rgba_mtpl_out[:, :, 2], rgba_mtpl_out[:, :, 3]
])
return rgba_out
import random
import matplotlib.cm as cm
import matplotlib.colors as colors
NUM_COLORS = 2
cm = plt.get_cmap('Paired')
file_list = sys.argv[1:]
fig = plt.figure()
#plt.suptitle(r'Pdf per i tempi di collisione in funzione di $\eta$',fontsize=16)
ax = fig.add_subplot(111)
ax.grid(True)
plt.xlabel(r'$t_c$', fontsize='15')
plt.ylabel(r'$P(t_c)$', fontsize='15')
ax.set_color_cycle([cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
i = 0
Markers = [
'x', '+', '*', 's', 'd', 'v', '^', '<', '>', 'p', 'h', '.', '+', '*', 'o',
'x', '^', '<', 'h', '.', '>', 'p', 's', 'd', 'v', 'o', 'x', '+', '*', 's',
'd', 'v', '^', '<', '>', 'p', 'h', '.'
]
col = ['r', 'b']
#labels for cfr between fit and mean
labels = ['fit', 'calcolato']
for f in file_list:
#eta_temp=float(f[-13:-4])
eta, mfp = np.loadtxt(f, unpack=True, usecols=(0, 1))
width = 0.7 * (eta[1] - eta[0])
color = cm(1. * i / NUM_COLORS)
#plt.bar(eta, mfp, width=width,label=r'$\eta=%.5lf$'%(eta_temp), alpha=(0.8),color=color,linewidth=0)
def plot_minmax_levels(var_list, ID, times):
print('computing min/max at each level')
cm = plt.cm.get_cmap('coolwarm')
zrange = dx[2] * krange
minmax = {}
minmax['time'] = times
for var_name in var_list:
minmax[var_name] = {}
minmax[var_name]['max'] = np.zeros((len(times), kmax), dtype=np.double)
minmax[var_name]['min'] = np.zeros((len(times), kmax), dtype=np.double)
# # # minmax['w'] = {}
# # # minmax['s'] = {}
# # # minmax['temp'] = {}
for var_name in var_list:
print('')
print('variable: ' + var_name)
fig, (ax1, ax2) = plt.subplots(1, 2, sharey='all', figsize=(12, 6))
for it, t0 in enumerate(times):
count_color = np.double(it) / len(times)
if var_name == 'theta':
s_var = read_in_netcdf_fields(
's', os.path.join(path_fields,
str(t0) + '.nc'))
var = theta_s(s_var)
else:
var = read_in_netcdf_fields(
var_name, os.path.join(path_fields,
str(t0) + '.nc'))
for k in range(kmax):
minmax[var_name]['max'][it, k] = np.amax(var[:, :, k])
minmax[var_name]['min'][it, k] = np.amin(var[:, :, k])
del var
minn = ax1.plot(minmax[var_name]['min'][it, :],
zrange,
'-',
color=cm(count_color),
label='t=' + str(it) + 's')
maxx = ax2.plot(minmax[var_name]['max'][it, :],
zrange,
'-',
color=cm(count_color),
label='t=' + str(it) + 's')
ax1.legend(loc='upper center',
bbox_to_anchor=(2.75, 0.75),
fancybox=True,
shadow=True,
ncol=2,
fontsize=10)
fig.subplots_adjust(bottom=0.12,
right=.75,
left=0.1,
top=0.95,
wspace=0.25)
ax1.set_xlim(np.amin(minmax[var_name]['min']),
np.amax(minmax[var_name]['min']))
ax2.set_xlim(np.amin(minmax[var_name]['max']),
np.amax(minmax[var_name]['max']))
ax1.set_xlabel('min(' + var_name + ') [m/s]')
ax2.set_xlabel('max(' + var_name + ') [m/s]')
ax1.set_ylabel('height z [m]')
fig.suptitle(ID)
fig_name = var_name + '_' + str(ID) + '_minmax_levels.png'
fig.savefig(os.path.join(path_out_figs, fig_name))
plt.close(fig)
print('')
return minmax
data_types = args.data.split(",")
bound_types = args.bounds.split(",")
fig = plt.figure(figsize=(11, 3.6), dpi=120)
ax = fig.add_subplot()
max_bound = 0
data = {}
for data_type in sorted(data_types):
lines = load_data(data_type)
bounds = create_real(lines, args.N, args.d)
data[data_type] = lines
ax.step(range(args.d),
bounds,
label=data_type + " (sample)",
where="post",
linestyle=":",
color=cm(0 / 10)) # + f" ({sum(bounds)}b)"
if max(bounds) > max_bound:
max_bound = max(bounds)
for data_type in sorted(data_types):
for bound_type in sorted(bound_types):
bound_name = "geom" if bound_type == "geomN" else bound_type
bounds = create_bounds(data_type, bound_type, args.N, args.d)
mean_err, std_err = estimate_errors(data[data_type], args.N,
args.d, bounds)
residuals = estimate_residuals(data[data_type], args.N, args.d,
bounds)
ax.boxplot(residuals,
manage_ticks=False,
notch=True,
bootstrap=1000)
def plotTrace(data, stage = True):
mxstage = 5;
cm = plt.get_cmap('rainbow');
cdat = cm(data[:,-1] / mxstage);
plt.scatter(data[:, 0], data[:,1], c = cdat);
def plot_unrealisticness(self,
curves,
file_name,
time=None,
show=True,
save=True,
title=False,
timeseries=False,
evaluation=None,
eval_label='MSE'):
fig = plt.figure(figsize=(5, 4))
ax = fig.add_subplot(111)
ax.grid(True)
if timeseries:
print("curves.shape", curves.shape, curves.shape[1])
NUM_COLORS = curves.shape[1]
cm = plt.get_cmap('coolwarm')
ax.set_prop_cycle(color=[
cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)[::-1]
])
if time is None:
ax.plot(curves, linestyle='None', marker='o')
else:
ax.plot(time, curves, linestyle='None', marker='o')
plt.xticks(rotation=20)
custom_lines = [
Line2D([0], [0], color=cm(1.), lw=4),
Line2D([0], [0], color=cm(0.), lw=4)
]
# Shrink current axis's height by 10% on the bottom
box = ax.get_position()
ax.set_position([
box.x0, box.y0 - box.height * 0.1, box.width, box.height * 0.9
])
# Put a legend below current axis
ax.legend(custom_lines, ['Short End', 'Long End'],
loc='upper center',
bbox_to_anchor=(0.5, 1.15),
fancybox=True,
shadow=False,
ncol=5)
fig.subplots_adjust(bottom=0.2)
# ax.legend(custom_lines, ['Short End', 'Long End'])
else:
if time is None:
ax.plot(curves, linestyle='None', marker='o')
ax.set_xlabel('Time (days)')
else:
ax.plot(time, curves, linestyle='None', marker='o')
plt.xticks(rotation=20)
ax.set_xlabel('Date')
ax.set_ylabel('Latent Value')
if evaluation is not None:
ax2 = ax.twinx()
ax2.plot(evaluation, 'k', label='eval', alpha=1)
# ax2.bar(np.arange(len(evaluation)), evaluation, color='k', alpha=0.5)
ax.set_ylabel("Price ($\$$)")
ax2.set_ylabel(eval_label)
box2 = ax2.get_position()
ax2.set_position([
box2.x0, box2.y0 - box2.height * 0.1, box2.width,
box2.height * 0.9
])
if title:
plt.title(file_name)
if save:
plt.savefig(self.config.get_filepath_img(file_name),
dpi=300,
bbox_inches='tight')
plt.savefig(self.config.get_filepath_pgf(file_name),
dpi=300,
transparent=True) # , bbox_inches='tight'
if show:
plt.show()
plt.close()
nonzeroE = np.where(magE > eps_plot)
zeroE = np.where(magE <= eps_plot)
E[nonzeroE, 0] = E[nonzeroE, 0] / magE[nonzeroE]
E[nonzeroE, 1] = E[nonzeroE, 1] / magE[nonzeroE]
magE[nonzeroE] = np.log10(magE[nonzeroE])
magE[np.where(magE < 0)] = 0
magE[zeroE] = 0
norm = colors.Normalize()
norm.autoscale(magE)
cm = cm.get_cmap("turbo")
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm.set_array([])
fig1, ax1 = plt.subplots(dpi=400)
ax1.set_aspect('equal')
quiv_scale = np.max(E) * 75
plt.quiver(C[:, 0],
C[:, 1],
E[:, 0],
E[:, 1],
color=cm(magE),
scale=quiv_scale,
headwidth=2,
headlength=3)
ax1.set_title('Electric Field Lines')
cb = fig1.colorbar(sm)
cb.ax.set_ylabel("log(|E [V/um]|), saturated at 0", rotation=90)
plt.show()
def plot_tree(ax, tree, cm='cividis', median=None, ignore=None):
def maybe_expand(x):
a = np.asanyarray(x)
return a if len(a.shape) else a[np.newaxis]
tree = tree.map(maybe_expand)
min_v, max_v, depth = tree.min(), tree.max(), tree.depth()
extra_ticks = [min_v, max_v]
min_v, max_v = get_value_range(median, min_v, max_v)
# Normalize tree values to [0, 1]
rng = max_v - min_v
# if rng one of 0, +inf, -inf, or nan
if rng == 0 or rng == float('inf') or rng == float('-inf') or rng != rng:
rng = float('nan')
tree = tree.map(lambda xs: [(x - min_v) / rng for x in xs])
acc = []
acc.append([0.5, 0, 1, 0, 2 * math.pi,
0]) # makes sure 2pi = one revolution
draw_tree(acc,
tree,
1,
range_theta[0],
range_theta[1],
depth,
False,
False,
ignore=ignore)
acc = sorted(acc, key=lambda x: x[-1]) # sort by depth
df = pd.DataFrame(acc,
columns=['v', 'r', 'r0', 'theta', 'dtheta', 'depth'])
cm = plt.cm.get_cmap(cm) if isinstance(cm, str) else cm
plot = ax.bar(df['theta'],
df['r'],
width=df['dtheta'],
bottom=df['r0'],
color=cm(df['v']),
align='edge')
sm = plt.cm.ScalarMappable(cmap=cm, norm=plt.Normalize(min_v, max_v))
sm.set_array(df['v'])
if display_colorbar:
cbar = plt.colorbar(sm,
ax=ax,
shrink=0.8,
orientation='horizontal',
pad=0.0)
cbar.ax.tick_params(labelsize='small')
cbar.ax.ticklabel_format(style='sci', axis='x', scilimits=(-3, 3))
else:
cbar = None
ax.set_thetamin(range_theta[0] / 2 / math.pi * 360)
ax.set_thetamax(range_theta[1] / 2 / math.pi * 360)
ax.set_thetagrids([])
ax.set_rgrids([])
ax.grid(False)
ax.set_axis_off()
return plot, cbar
def getColors():
COLORS = []
cm = plt.cm.get_cmap('hsv', n)
for i in np.arange(n):
COLORS.append(cm(i))
return COLORS
# reshape so it can be a rectangle
kpagecount_data = np.append(kpagecount, np.zeros((WIDTH - kpagecount.size % WIDTH,), dtype=np.int64))
kpagecount = np.reshape(kpagecount_data, (-1, WIDTH))
hilbert_curve(kpagecount_data, t=kpagecount, N=256)
kpageflags_data = np.append(kpageflags, np.zeros((WIDTH - kpageflags.size % WIDTH,), dtype=np.uint64))
kpageflags = np.reshape(kpageflags_data, (-1, WIDTH))
hilbert_curve(kpageflags_data, t=kpageflags, N=256)
#hilbert_curve(kpageflags_data, t=kpageflags, N=256, offset=65536, t_offset=[0, 256])
kpageflags_bounds = np.unique(kpageflags)
kpageflags_bounds = np.append(kpageflags_bounds, kpageflags_bounds[-1] + np.uint64(1))
kpageflags_colors = ['black']
cm = plt.get_cmap('gist_rainbow')
for i in range(kpageflags_bounds.size):
color = cm(1. * i / (kpageflags_bounds.size))
kpageflags_colors.append(color)
# np.random.shuffle(kpageflags_colors)
kpageflags_cm = colors.ListedColormap(kpageflags_colors)
kpageflags_norm = colors.BoundaryNorm(kpageflags_bounds, kpageflags_bounds.size)
kpageflags_str = []
for b in np.nditer(kpageflags_bounds):
s = ""
for i in range(64):
if b & np.uint64(1 << i):
s += "LERUDlASWIBMasbHTGuXnxtrmdPpOhcIPAE______________________________"[i]
kpageflags_str.append(s)
kpagecount_bounds = np.unique(kpagecount)
kpagecount_bounds = np.append(kpagecount_bounds, kpagecount_bounds[-1] + 1)
kpagecount_colors = ['white', 'black', 'yellow']
def get_features():
"""
scanpy examples:
http://127.0.0.1:8000/features?db_name=1_scanpy_10xpbmc&feature=louvain
http://127.0.0.1:8000/features?db_name=1_scanpy_10xpbmc&feature=expression&gene=SUMO3
seurat examples:
http://127.0.0.1:8000/features?db_name=4_seurat_10xpbmc&feature=expression&gene=SUMO3
http://127.0.0.1:8000/features?db_name=4_seurat_10xpbmc&feature=expression&gene=SUMO3
velocity examples:
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=clusters
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=expression&gene=Rbbp7
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=velocity&embed=umap&time=None
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=velocity&embed=umap&time=1
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=velocity&embed=umap&time=10
velocity grid examples:
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=velocity_grid&embed=umap&time=10
http://127.0.0.1:8000/features?db_name=3_velocity_pancrease&feature=velocity_grid&embed=umap&time=100
"""
database = request.args.get("db_name")
feature = request.args.get("feature")
filename = glob(os.path.join(DATASET_DIRECTORY, f"{database}.*"))[0]
db_type = get_dataset_type_adata(filename)
if feature.lower() == "velocity":
embed = request.args.get("embed")
try:
del adata
except:
pass
if get_dataset_type_adata(database).lower() in [
"scanpy", "velocity", "seurat", "paga"
]:
adata = sc.read(filename)
else:
adata = st.read(filename, file_format="pkl", workdir="./")
list_metadata = []
if feature in get_available_annotations_adata(adata): # cluster columns
adata.obs[feature] = adata.obs[feature].astype('category')
if f"{feature}_colors" in adata.uns.keys():
dict_colors = {
feature:
dict(
zip(adata.obs[feature].cat.categories,
adata.uns[f"{feature}_colors"]))
}
else:
dict_colors = {
feature:
dict(
zip(adata.obs[feature].cat.categories,
get_colors(adata, feature)))
}
for i in range(adata.shape[0]):
dict_metadata = dict()
dict_metadata["cell_id"] = adata.obs_names[i]
dict_metadata["label"] = adata.obs[feature].tolist()[i]
dict_metadata["clusters"] = adata.obs[feature].tolist()[i]
dict_metadata["clusters_color"] = dict_colors[feature][
dict_metadata["clusters"]]
list_metadata.append(dict_metadata)
elif feature in ["expression", "rna"]: # pseudotime or latent_time columns
gene = request.args.get("gene")
if gene not in adata.var_names:
return jsonify({})
else:
if "time" in feature:
values = adata.obs[feature]
else:
if db_type == "seurat":
values = (adata[:, gene].layers["norm_data"].toarray()[:,
0]
if isspmatrix(adata.layers["norm_data"]) else
adata[:, gene].layers["norm_data"][:, 0])
else:
values = (adata[:, gene].X.toarray()[:, 0] if isspmatrix(
adata.X) else adata[:, gene].X[:, 0])
cm = mpl.cm.get_cmap("viridis", 512)
norm = mpl.colors.Normalize(vmin=0, vmax=max(values), clip=True)
list_metadata = []
for i, x in enumerate(adata.obs_names):
dict_genes = dict()
dict_genes["cell_id"] = x
dict_genes["color"] = mpl.colors.to_hex(cm(norm(values[i])))
list_metadata.append(dict_genes)
elif feature == "velocity":
list_metadata = []
time = request.args.get("time")
for i in range(adata.shape[0]):
dict_coord_cells = dict()
if isinstance(adata.obs_names[i], bytes):
dict_coord_cells["cell_id"] = adata.obs_names[i].decode(
"utf-8")
else:
dict_coord_cells["cell_id"] = adata.obs_names[i]
dict_coord_cells["x0"] = str(adata.obsm[f"X_{embed}"][i, 0])
dict_coord_cells["y0"] = str(adata.obsm[f"X_{embed}"][i, 1])
dict_coord_cells["z0"] = str(adata.obsm[f"X_{embed}"][i, 2])
if time == "None":
dict_coord_cells["x1"] = str(
adata.obsm[f"velocity_{embed}"][i, 0])
dict_coord_cells["y1"] = str(
adata.obsm[f"velocity_{embed}"][i, 1])
dict_coord_cells["z1"] = str(
adata.obsm[f"velocity_{embed}"][i, 2])
elif time in list(map(str,
[0.01, 0.1, 1, 5, 10, 20, 30, 50, 100])):
dict_coord_cells["x1"] = str(
adata.obsm[f"absolute_velocity_{embed}_{time}s"][i, 0])
dict_coord_cells["y1"] = str(
adata.obsm[f"absolute_velocity_{embed}_{time}s"][i, 1])
dict_coord_cells["z1"] = str(
adata.obsm[f"absolute_velocity_{embed}_{time}s"][i, 2])
else:
return jsonify({})
list_metadata.append(dict_coord_cells)
elif feature == "velocity_grid":
list_metadata = []
time = request.args.get("time")
p_mass = adata.uns['p_mass']
for i in np.where(p_mass >= 1)[0]:
dict_coord_cells = dict()
if time == "None":
dict_coord_cells["x0"] = str(adata.uns[f"X_grid"][i, 0])
dict_coord_cells["y0"] = str(adata.uns[f"X_grid"][i, 1])
dict_coord_cells["z0"] = str(adata.uns[f"X_grid"][i, 2])
dict_coord_cells["x1"] = str(adata.uns[f"V_grid"][i, 0])
dict_coord_cells["y1"] = str(adata.uns[f"V_grid"][i, 1])
dict_coord_cells["z1"] = str(adata.uns[f"V_grid"][i, 2])
elif time in list(map(str,
[0.01, 0.1, 1, 5, 10, 20, 50, 80, 100])):
dict_coord_cells["x0"] = str(adata.uns[f"X_grid_{time}"][i, 0])
dict_coord_cells["y0"] = str(adata.uns[f"X_grid_{time}"][i, 1])
dict_coord_cells["z0"] = str(adata.uns[f"X_grid_{time}"][i, 2])
dict_coord_cells["x1"] = str(adata.uns[f"V_grid_{time}"][i, 0])
dict_coord_cells["y1"] = str(adata.uns[f"V_grid_{time}"][i, 1])
dict_coord_cells["z1"] = str(adata.uns[f"V_grid_{time}"][i, 2])
else:
return jsonify({})
list_metadata.append(dict_coord_cells)
elif feature == "paga":
G = nx.from_numpy_matrix(adata.uns["paga"]["connectivities"].toarray())
adata.uns["paga"]["pos"] = get_paga3d_pos(adata)
## output coordinates of paga graph
list_lines = []
for edge_i in G.edges():
dict_coord_lines = dict()
dict_coord_lines["branch_id"] = [[str(edge_i[0]), str(edge_i[1])]]
dict_coord_lines["xyz"] = [{
"x": pos[0],
"y": pos[1],
"z": pos[2]
} for pos in adata.uns["paga"]["pos"][[edge_i[0], edge_i[1]], :]]
list_lines.append(dict_coord_lines)
## output topology of paga graph
dict_nodes = dict()
list_edges = []
dict_nodename = {
i: adata.obs[adata.uns["paga"]["groups"]].cat.categories[i]
for i in G.nodes()
}
for node_i in G.nodes():
dict_nodes_i = dict()
dict_nodes_i["node_name"] = dict_nodename[node_i]
dict_nodes_i["xyz"] = {
"x": adata.uns["paga"]["pos"][:, 0][node_i],
"y": adata.uns["paga"]["pos"][:, 1][node_i],
"z": adata.uns["paga"]["pos"][:, 2][node_i],
}
dict_nodes[node_i] = dict_nodes_i
for edge_i in G.edges():
dict_edges = dict()
dict_edges["nodes"] = [str(edge_i[0]), str(edge_i[1])]
dict_edges["weight"] = adata.uns["paga"]["connectivities"][
edge_i[0], edge_i[1]]
list_edges.append(dict_edges)
list_metadata = {"nodes": dict_nodes, "edges": list_edges}
elif feature == "curves":
flat_tree = adata.uns['flat_tree']
epg = adata.uns['epg']
epg_node_pos = nx.get_node_attributes(epg, 'pos')
ft_node_label = nx.get_node_attributes(flat_tree, 'label')
ft_node_pos = nx.get_node_attributes(flat_tree, 'pos')
list_curves = []
for edge_i in flat_tree.edges():
branch_i_pos = np.array(
[epg_node_pos[i] for i in flat_tree.edges[edge_i]['nodes']])
df_coord_curve_i = pd.DataFrame(branch_i_pos)
dict_coord_curves = dict()
dict_coord_curves['branch_id'] = ft_node_label[
edge_i[0]] + '_' + ft_node_label[edge_i[1]]
dict_coord_curves['xyz'] = [{
'x': df_coord_curve_i.iloc[j, 0],
'y': df_coord_curve_i.iloc[j, 1],
'z': df_coord_curve_i.iloc[j, 2]
} for j in range(df_coord_curve_i.shape[0])]
list_curves.append(dict_coord_curves)
list_metadata = list_curves
del adata
gc.collect()
return jsonify({feature: list_metadata})
return np.nan
else:
return dt.datetime(y, m, d).timetuple().tm_yday / dt.datetime(
y, 12, 31).timetuple().tm_yday * 2 * np.pi
full_df['degree'] = full_df.apply(year_frac2, axis=1)
full_df = full_df.reset_index()
# full_df.to_csv('/workspace/Shared/Users/jschroder/TMP/full_df.csv')
# full_df = pd.read_csv( '/workspace/Shared/Users/jschroder/TMP/full_df.csv', index_col=0 )
cm = plt.get_cmap('rainbow')
#cm = sns.color_palette("cubehelix", 8)
length = full_df.degree.__len__()
color = [cm(i / (length - 1)) for i in range(length)]
fig = plt.figure()
ax = plt.subplot(111, projection='polar')
period = 1
for i in range(length):
print(period)
ax.plot(full_df.degree[i * period:((i + 1) * period + 1)],
full_df.anomalies[i * period:((i + 1) * period + 1)],
color=color[i * period],
alpha=0.4,
linewidth=0.8)
ax.set_thetagrids(np.linspace(360 / 24, 360 * 23 / 24, 12))
ax.set_theta_direction('clockwise')
ax.set_theta_offset(np.pi / 2)
ax.xaxis.set_ticklabels([
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
def plot_2d(self,
curves,
file_name,
curve2=None,
time=None,
show=True,
save=True,
title=False,
timeseries=False,
evaluation=None):
fig = plt.figure(figsize=(5, 4))
ax = fig.add_subplot(111)
ax.grid(True)
if timeseries:
print("curves.shape", curves.shape, curves.shape[1])
NUM_COLORS = curves.shape[1]
cm = plt.get_cmap('coolwarm')
ax.set_prop_cycle(color=[
cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)[::-1]
])
if time is None:
ax.plot(curves)
else:
ax.plot(time, curves)
plt.xticks(rotation=20)
custom_lines = [
Line2D([0], [0], color=cm(1.), lw=4),
Line2D([0], [0], color=cm(0.), lw=4)
]
ax.legend(custom_lines, ['Short End', 'Long End'])
ax.set_ylabel("Price ($\$$)")
else:
if isinstance(curves, pd.DataFrame):
ax.plot(curves)
plt.xticks(rotation=20)
ax.set_xlabel('Date')
elif time is None:
ax.plot(curves)
ax.set_xlabel('Time (days)')
else:
ax.plot(time, curves)
plt.xticks(rotation=20)
ax.set_xlabel('Date')
if curve2 is not None:
ax.plot(curve2, color='tab:orange')
ax.set_ylabel('Latent Value')
if evaluation is not None:
# if isinstance(evaluation, pd.DataFrame):
# _evaluation = evaluation
# else:
# _evaluation = pd.DataFrame(evaluation)
ax2 = ax.twinx()
ax2.plot(curves.index.values,
evaluation,
'k',
label='eval',
alpha=0.5)
ax2.set_ylabel("SMAPE")
if title:
plt.title(file_name)
if save:
plt.savefig(self.config.get_filepath_img(file_name),
dpi=300,
bbox_inches='tight')
plt.savefig(self.config.get_filepath_pgf(file_name),
dpi=300,
transparent=True) # , bbox_inches='tight'
if show:
plt.show()
plt.close()
if ( plot_flow_flag ) or ( plot_lobes_flag):
# Create plot
fig = plt.figure()
ax = fig.add_subplot(111)
if ( len(shape_name) > 0 ):
# Read the shapefile
sf = shapefile.Reader(shape_name)
recs = sf.records()
shapes = sf.shapes()
Nshp = len(shapes)
cm = plt.get_cmap('Dark2')
cccol = cm(1.*np.arange(Nshp)/Nshp)
for nshp in xrange(Nshp):
ptchs = []
pts = np.array(shapes[nshp].points)
prt = shapes[nshp].parts
par = list(prt) + [pts.shape[0]]
for pij in xrange(len(prt)):
ptchs.append(Polygon(pts[par[pij]:par[pij+1]]))
ax.add_collection(PatchCollection(ptchs,facecolor=cccol[nshp,:],edgecolor='k', linewidths=.1))
print ('')
def measure_lum_funcs(catl, path_to_mocks):
volume = temp_dict.get('volume')
mag_cen_arr = []
mag_n_arr = []
mag_err_arr = []
# box_id_arr = np.linspace(5001,5008,8)
box_id_arr = np.array([0, 1, 2, 3])
mock_dir_arr = glob.glob(path_to_mocks + 'ECO_mvir*')
for mock_dir in mock_dir_arr:
for num in range(temp_dict.get('num_mocks')):
filename = mock_dir + '/{0}_cat_{1}_Planck_memb_cat.hdf5'.\
format(temp_dict.get('mock_name'), num)
mock_pd = reading_catls(filename)
# Using the same survey definition as in mcmc smf
# i.e excluding the buffer
mock_pd = mock_pd.loc[
(mock_pd.cz.values >= temp_dict.get('min_cz'))
& (mock_pd.cz.values <= temp_dict.get('max_cz')) &
(mock_pd.M_r.values <= temp_dict.get('mag_limit')) &
(mock_pd.logmstar.values >= temp_dict.get('mstar_limit'))]
mag_cen, mag_edg, mag_n, mag_err, bw = cumu_num_dens(
mock_pd.M_r.values, None, volume, True)
mag_cen_arr.append(mag_cen)
mag_n_arr.append(mag_n)
mag_err_arr.append(mag_err)
mag_cen_arr = np.array(mag_cen_arr)
mag_n_arr = np.array(mag_n_arr)
mag_err_arr = np.array(mag_err_arr)
# Added this max cut so that it matches the data luminosity function used
# for abundance matching in Victor's mock-making script
catl = catl.loc[catl.absrmag.values >= -23.5]
mag_cen, mag_edg, mag_n, mag_err, bw = cumu_num_dens(
catl.absrmag.values, None, volume, True)
cm = plt.get_cmap('Spectral')
n_catls = temp_dict.get('num_mocks') * len(box_id_arr)
col_arr = [cm(idx / float(n_catls)) for idx in range(n_catls)]
fig6 = plt.figure(figsize=(10, 10))
for idx in range(n_catls):
plt.errorbar(mag_cen_arr[idx],
mag_n_arr[idx],
yerr=mag_err_arr[idx],
color=col_arr[idx],
marker='o',
markersize=4,
capsize=5,
capthick=0.5)
plt.errorbar(mag_cen,
mag_n,
yerr=mag_err,
markersize=4,
capsize=5,
capthick=2,
marker='o',
label='data',
color='k',
fmt='-s',
ecolor='k',
linewidth=2,
zorder=10)
plt.yscale('log')
plt.gca().invert_xaxis()
plt.xlabel(r'\boldmath $M_{r}$')
plt.ylabel(r'\boldmath $\mathrm{n(< M_{r})} [\mathrm{h}^{3}'
r'\mathrm{Mpc}^{-3}]$')
if survey == 'resolvea' or survey == 'resolveb':
plt.title(r'RESOLVE-{0} Luminosity Function'.\
format(temp_dict.get('mock_name')))
else:
plt.title(r'{0} Luminosity Function'.format(
temp_dict.get('mock_name')))
plt.legend(loc='lower left', prop={'size': 20})
plt.show()
def relax_label_two(orf, print_layers, tkagg=False, view_axis=False):
"""
Perform relaxation labeling of a two-class orientation field 'orf'
"""
assert orf.shape == print_layers.shape
do_update_view = tkagg and view_axis
# Used to determine convergence of labels
EPSILON = 0.1
olpd_blks = print_layers[:,:,0] & print_layers[:,:,1]
any_prnt_blks = print_layers[:,:,0] | print_layers[:,:,1]
no_print_i, no_print_j = np.nonzero(~any_prnt_blks)
sorted_coords = _sort_overlapped_coords(olpd_blks)
n_overlapped = len(sorted_coords)
p0 = print_layers[:,:,0] & (~olpd_blks)
p0 = p0.astype(np.float)
p0[olpd_blks] = 0.5
p1 = print_layers[:,:,1] & (~ olpd_blks)
p1 = p1.astype(np.float)
p1[olpd_blks] = 0.5
pk_next = np.dstack((p0, p1))
def unpack_and_zip_data(varg):
"""
Helper function which zips all data from the sorted overlapped pixels
into one convenient iterable
"""
cntr, idx = varg
imin = max(0, cntr[0] - 2)
jmin = max(0, cntr[1] - 2)
imax = min(orf.shape[0], cntr[0] + 3)
jmax = min(orf.shape[1], cntr[1] + 3)
nbri, nbrj = np.meshgrid(np.arange(imin,imax), \
np.arange(jmin, jmax), \
indexing="ij")
not_cntr = (nbri.flat != cntr[0]) | (nbrj.flat != cntr[1])
has_print_inf = any_prnt_blks[nbri.flat, nbrj.flat]
nbri = nbri.flat[not_cntr & has_print_inf]
nbrj = nbrj.flat[not_cntr & has_print_inf]
offsti = cntr[0] - nbri
offstj = cntr[1] - nbrj
epnt = -0.5 * (offsti ** 2 + offstj ** 2)
wt = np.exp(epnt)
wt = wt / np.sum(wt)
return (cntr, (nbri, nbrj), wt, idx)
ovlp_data = map(unpack_and_zip_data, \
zip(sorted_coords, range(n_overlapped)))
def compat_func(lab_cnt, lab_nbr, cnt_coords, nbr_coords):
""" Returns compatability between a center pixel and a neighbor. Used
as a helper function. """
thta_cnt = orf[cnt_coords]
thta_nbr = orf[nbr_coords]
is_nbr_olpd = olpd_blks[nbr_coords]
if not is_nbr_olpd:
thta_diff = thta_cnt[lab_cnt] - thta_nbr[lab_cnt]
r = 2 * abs(cos(thta_diff)) - 1.0
if not ( -1 <= r <= 1 ):
print "BAD R!"
raise RuntimeError
if lab_cnt == lab_nbr:
thta_diff = thta_cnt - thta_nbr[::1]
else:
thta_diff = thta_cnt - thta_nbr[::-1]
abs_cos_thta = np.abs(np.cos(thta_diff))
r = np.sum(abs_cos_thta) - 1
if not (-1 <= r <= 1):
print "BAD R!"
raise RuntimeError
return r
def get_sop_rp(nbr_crds, cnt_crds, lab_cnt, pk):
"""Helper function which returns the sum of products term of
the support update function """
r0 = compat_func(lab_cnt=lab_cnt, lab_nbr=0, \
cnt_coords=cnt_crds, nbr_coords=nbr_crds)
sop0 = r0 * pk[nbr_crds][lab_cnt]
r1 = compat_func(lab_cnt=lab_cnt, lab_nbr=1, \
cnt_coords=cnt_crds, nbr_coords=nbr_crds)
sop1 = r1 * pk[nbr_crds][lab_cnt]
return (r0 * sop0) + (r1 + sop1)
k = 0
did_converge = False
while (not did_converge) and k < 50:
k = k + 1
pk = pk_next.copy()
q = np.zeros((n_overlapped, 2))
if do_update_view:
try:
pkshow = np.copy(pk_next[:,:,0])
minpk = np.min(pkshow)
maxpk = np.max(pkshow)
pkshow = (pkshow-minpk)/(maxpk-minpk)
cm = matplotlib.cm.get_cmap('rainbow')
pkrgb = cm(pkshow)
pkrgb = pkrgb[:,:,0:3]
pkrgb[no_print_i, no_print_j] = 1.0
view_axis.imshow(pkrgb, interpolation="nearest")
tkagg.show()
except IndexError as err:
print "pkshow.shape:" + str(pkshow.shape)
print "cm:" + str(cm)
print "pkrgb.shape:" + str(pkrgb.shape)
print "any_prnt_blks:" + str(any_prnt_blks)
raise err
for crds, nbrs, wts, idx in ovlp_data:
assert olpd_blks[crds]
for lbl in [0, 1]:
get_rp = partial(get_sop_rp, cnt_crds=crds, lab_cnt=lbl, pk=pk)
sums_prod_rp = map(get_rp, zip(*nbrs))
wtd_sop = wts * sums_prod_rp
q_i = np.sum(wtd_sop)
q[idx, lbl] = q_i
pk_i_nxt_unnorm = pk[crds] * (1 + q[idx])
pk_i_nxt = pk_i_nxt_unnorm / np.sum(pk_i_nxt_unnorm)
pk_next[crds] = pk_i_nxt
p_diff = np.sum(np.abs(pk - pk_next))
did_converge = p_diff < EPSILON
print "p_diff:" + str(p_diff)
if did_converge:
print "CONVERGED!!"
# extract labeled orientations from overlapped area
p0_gt_p1 = pk[:,:,0] >= pk[:,:,1]
print_0_orf = np.where(p0_gt_p1, orf[:,:,0], orf[:,:,1])
print_1_orf = np.where(~p0_gt_p1, orf[:,:,0], orf[:,:,1])
# extract primary orientation into known print blocks
p0_only = print_layers[:,:,0] & (~olpd_blks)
p1_only = print_layers[:,:,1] & (~olpd_blks)
print_0_orf[p0_only] = orf[:,:,0][p0_only]
print_1_orf[p1_only] = orf[:,:,1][p1_only]
# set all other blocks (those without a part of a print) to NaN
no_info = (~print_layers[:,:,0]) & (~print_layers[:,:,1])
print_0_orf[no_info] = np.nan
print_1_orf[no_info] = np.nan
return np.dstack((print_0_orf, print_1_orf))
def binary_diagonal(method_names,
true_prevs,
estim_prevs,
pos_class=1,
title=None,
show_std=True,
legend=True,
train_prev=None,
savepath=None,
method_order=None):
"""
The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence
values (along the x-axis). The optimal quantifier is described by the diagonal (0,0)-(1,1) of the plot (hence the
name). It is convenient for binary quantification problems, though it can be used for multiclass problems by
indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots
like the :meth:`error_by_drift` might be preferable though).
:param method_names: array-like with the method names for each experiment
:param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for
each experiment
:param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)
for each experiment
:param pos_class: index of the positive class
:param title: the title to be displayed in the plot
:param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient
for cases in which many methods are compared, or when the standard deviations are high -- default True)
:param legend: whether or not to display the leyend (default True)
:param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted
in the plot. This is convenient when all the experiments have been conducted in the same dataset.
:param savepath: path where to save the plot. If not indicated (as default), the plot is shown.
:param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,
listed in the legend and associated with matplotlib colors).
"""
fig, ax = plt.subplots()
ax.set_aspect('equal')
ax.grid()
ax.plot([0, 1], [0, 1], '--k', label='ideal', zorder=1)
method_names, true_prevs, estim_prevs = _merge(method_names, true_prevs,
estim_prevs)
order = list(zip(method_names, true_prevs, estim_prevs))
if method_order is not None:
table = {
method_name: [true_prev, estim_prev]
for method_name, true_prev, estim_prev in order
}
order = [(method_name, *table[method_name])
for method_name in method_order]
cm = plt.get_cmap('tab20')
NUM_COLORS = len(method_names)
ax.set_prop_cycle(
color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
for method, true_prev, estim_prev in order:
true_prev = true_prev[:, pos_class]
estim_prev = estim_prev[:, pos_class]
x_ticks = np.unique(true_prev)
x_ticks.sort()
y_ave = np.asarray(
[estim_prev[true_prev == x].mean() for x in x_ticks])
y_std = np.asarray([estim_prev[true_prev == x].std() for x in x_ticks])
ax.errorbar(x_ticks,
y_ave,
fmt='-',
marker='o',
label=method,
markersize=3,
zorder=2)
if show_std:
ax.fill_between(x_ticks, y_ave - y_std, y_ave + y_std, alpha=0.25)
if train_prev is not None:
train_prev = train_prev[pos_class]
ax.scatter(train_prev,
train_prev,
c='c',
label='tr-prev',
linewidth=2,
edgecolor='k',
s=100,
zorder=3)
ax.set(xlabel='true prevalence',
ylabel='estimated prevalence',
title=title)
ax.set_ylim(0, 1)
ax.set_xlim(0, 1)
if legend:
# box = ax.get_position()
# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
# ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='lower center',
bbox_to_anchor=(1, -0.5),
ncol=(len(method_names) + 1) // 2)
_save_or_show(savepath)
def main_routine (wd="./",cfg="./python/parameter.cfg",gammaCnt=11,bloch=0,myMap="custom",project=0):
### read config file ###
print ("load from config file: " + cfg)
configParser = cp.ConfigParser()
configParser.read(cfg)
print (configParser.sections())
cfg=configParser.__dict__['_sections'].copy()
#for src, target in cfg['NVSETUP'].items():
# print(src + " : " + target)
omega_c = float(cfg['NVSETUP']['{omega_c}'])
nWrite=int(cfg['OCFourier']['{write_harmonic}'])
nRead =int(cfg['OCFourier']['{read_harmonic}'])
nStore=int(cfg['MEFourier']['{storage_harmonic}'])
nDown =nRead+nWrite
nUp =nDown+nWrite
### read config file ###
### read data ###
cavityWrite,cavityMemo,cavityRead =IOHelper.harmonics_readwrite(**cfg)
time =IOHelper.functionaltimes_readwrite(**cfg)
time['write'][:] *= 1e9
time['read'][:] *= 1e9
ti = int(time['idx_ti'])
tf = int(time['idx_tf'])
functime = time['read'][ti:tf]
dt = float(time['delta_t'])
filename =IOHelper.getVectorOverlap(**cfg)
reGamma,imGamma=sp.loadtxt(filename).T
alphaR =reGamma[0:nRead] -1j*imGamma[0:nRead]
alphaD =reGamma[nRead:nDown]-1j*imGamma[nRead:nDown]
alphaU =reGamma[nDown:nUp] -1j*imGamma[nDown:nUp]
### read data ###
### plotting
Reg1Up = sp.dot(alphaU.conj(),cavityWrite)
Reg1Down = sp.dot(alphaD.conj(),cavityWrite)
Reg2Down = sp.dot(alphaD.conj(),cavityMemo)
Reg2Up = sp.dot(alphaU.conj(),cavityMemo)
Reg2Read = sp.dot(alphaR.conj(),cavityRead)
Reg2DownRead = Reg2Down + Reg2Read
Reg2UpRead = Reg2Up + Reg2Read
myDensity = sp.zeros([functime.size,gammaCnt])
FuncCavity = sp.zeros([functime.size,gammaCnt],complex)
spos = sp.zeros([2,gammaCnt])
FuncInfoOlap = sp.zeros([2,gammaCnt],complex)
I00 = cumtrapz( sp.real(Reg2DownRead[ti:tf] * Reg2DownRead[ti:tf].conj()), x=None, dx=dt )[-1]
I11 = cumtrapz( sp.real(Reg2UpRead[ti:tf] * Reg2UpRead[ti:tf].conj()), x=None, dx=dt )[-1]
for g in sp.arange(0.0,gammaCnt):
gamma = g/(gammaCnt-1.0)
myDensity[:,g]=1.0-gamma
spos[0,g] = gamma
spos[1,g] = 1.0-gamma
FuncCavity[:,g] = spos[0,g]*Reg2Down[ti:tf] + spos[1,g]*Reg2Up[ti:tf]
FuncCavity[:,g] += Reg2Read [ti:tf]
FuncInfoOlap [0,g] = cumtrapz( (FuncCavity[:,g] * Reg2DownRead[ti:tf].conj()).real, x=None, dx=dt )[-1]
FuncInfoOlap [0,g] += 1j*cumtrapz( (FuncCavity[:,g] * Reg2DownRead[ti:tf].conj()).imag, x=None, dx=dt )[-1]
FuncInfoOlap [0,g] /= I00
FuncInfoOlap [0,g] = sp.absolute(FuncInfoOlap [0,g])
FuncInfoOlap [1,g] = cumtrapz( (FuncCavity[:,g] * Reg2UpRead[ti:tf].conj()).real, x=None, dx=dt )[-1]
FuncInfoOlap [1,g] += 1j*cumtrapz( (FuncCavity[:,g] * Reg2UpRead[ti:tf].conj()).imag, x=None, dx=dt )[-1]
FuncInfoOlap [1,g] /= I11
FuncInfoOlap [1,g] = sp.absolute(FuncInfoOlap [1,g])
# FuncInfoOlap [0,g] = cumtrapz( sp.absolute(FuncCavity[:,g]) * sp.absolute(Reg2DownRead[ti:tf].conj()), x=None, dx=dt )[-1]
# FuncInfoOlap [0,g] /= I00
# FuncInfoOlap [1,g] = cumtrapz( sp.absolute(FuncCavity[:,g]) * sp.absolute(Reg2UpRead[ti:tf].conj()), x=None, dx=dt )[-1]
# FuncInfoOlap [1,g] /= I11
fig = plt.figure()
fs = 22
label_size = 20
plt.rcParams['xtick.labelsize'] = label_size
plt.rcParams['ytick.labelsize'] = label_size
xx, yy = sp.meshgrid(sp.linspace(0.0,1.0,gammaCnt),functime)
zmax = (sp.absolute(FuncCavity)**2).max()
c = mcolors.ColorConverter().to_rgb
if myMap =="custom" :
cm = make_colormap(
[c('blue'), c('purple'), c('red')])
else:
cm = plt.cm.get_cmap(myMap)
myColors = cm(myDensity)
fig1 = fig.add_subplot(111, projection='3d')
fig1.plot(spos[0,:], FuncInfoOlap [0,:].real, zs=functime[ project], zdir='y',lw=2.5, color="blue",label="$\gamma^{\prime}$",zorder=0.1)
fig1.plot(spos[0,:], FuncInfoOlap [1,:].real, zs=functime[ project], zdir='y',lw=2.5, color="red" ,label="$\delta^{\prime}$",zorder=0.1)
fig1.plot_surface(xx, yy, sp.absolute(FuncCavity)**2/zmax,rstride=1, cstride=1,alpha=1,facecolors=myColors, antialiased=False)
fig1.plot_wireframe(xx, yy, sp.absolute(FuncCavity)**2/zmax, rstride=15, cstride=3,alpha=1,linewidth=1,color="black")
fig1.legend(fontsize=fs)
fig1.set_zlim(0,1.1)
fig1.set_ylim(functime[0],functime[-1])
fig1.set_xticks([0,0.5,1])
fig1.set_yticks([55,75,95])
fig1.set_zticks([0,0.5,1])
fig1.set_xlim(0,1)
fig1.set_zlabel("$|A(t)|^2$",fontsize=fs)
fig1.set_ylabel("$t$ in ns",fontsize=fs)
fig1.set_xlabel("$\gamma$",fontsize=fs)
fig1.xaxis._axinfo['label']['space_factor'] = 2.0
fig1.yaxis._axinfo['label']['space_factor'] = 2.0
fig1.zaxis._axinfo['label']['space_factor'] = 2.0
# m.set_array(myDensity)
# plt.colorbar(m,shrink=0.5,aspect=7)
plt.show()
def _set_colors(ax, n_methods):
NUM_COLORS = n_methods
cm = plt.get_cmap('tab20')
ax.set_prop_cycle(
color=[cm(1. * i / NUM_COLORS) for i in range(NUM_COLORS)])
def plot(self, outputDirectory):
# Skip this step if matplotlib is not installed
try:
import pylab
except ImportError:
return
import matplotlib.cm
cm = matplotlib.cm.jet
Nreac = self.network.Nisom + self.network.Nreac
Nprod = Nreac + self.network.Nprod
Tlist = self.Tlist.value_si
Plist = self.Plist.value_si
Tcount = Tlist.shape[0]
Pcount = Plist.shape[0]
K = self.K
count = 0
for prod in range(Nprod):
for reac in range(Nreac):
if reac == prod: continue
reaction = self.network.netReactions[count]
count += 1
reaction_str = '{0} {1} {2}'.format(
' + '.join([reactant.label for reactant in reaction.reactants]),
'<=>' if prod < Nreac else '-->',
' + '.join([product.label for product in reaction.products]),
)
fig = pylab.figure(figsize=(10,6))
K2 = numpy.zeros((Tcount, Pcount))
if reaction.kinetics is not None:
for t in range(Tcount):
for p in range(Pcount):
K2[t,p] = reaction.kinetics.getRateCoefficient(Tlist[t], Plist[p])
K = self.K[:,:,prod,reac].copy()
order = len(reaction.reactants)
K *= 1e6 ** (order-1)
K2 *= 1e6 ** (order-1)
kunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
pylab.subplot(1,2,1)
for p in range(Pcount):
pylab.semilogy(1000.0 / Tlist, K[:,p], color=cm(1.*p/(Pcount-1)), marker='o', linestyle='')
if reaction.kinetics is not None:
pylab.semilogy(1000.0 / Tlist, K2[:,p], color=cm(1.*p/(Pcount-1)), marker='', linestyle='-')
pylab.xlabel('1000 / Temperature (1000/K)')
pylab.ylabel('Rate coefficient ({0})'.format(kunits))
pylab.title(reaction_str)
pylab.subplot(1,2,2)
for t in range(Tcount):
pylab.loglog(Plist*1e-5, K[t,:], color=cm(1.*t/(Tcount-1)), marker='o', linestyle='')
pylab.loglog(Plist*1e-5, K2[t,:], color=cm(1.*t/(Tcount-1)), marker='', linestyle='-')
pylab.xlabel('Pressure (bar)')
pylab.ylabel('Rate coefficient ({0})'.format(kunits))
pylab.title(reaction_str)
fig.subplots_adjust(left=0.10, bottom=0.13, right=0.95, top=0.92, wspace=0.3, hspace=0.3)
pylab.savefig(os.path.join(outputDirectory, 'kinetics_{0:d}.pdf'.format(count)))
pylab.close()
def make_plot(X, Y, Z, Z_init, h_min, h_max, simtime, cr_angle, run_name, iter,
plot_show_flag):
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib import colors
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib.pyplot as plt
import numpy as np
import imp
try:
imp.find_module('mayavi')
found = True
except ImportError:
found = False
if found:
from mayavi import mlab
plt.ion()
plt.rcParams.update({'font.size': 8})
plt.close('all')
Z_diff = Z - Z_init
fig = plt.figure()
fig.set_size_inches(11, 7)
ax1 = fig.add_subplot(2, 2, 1, projection='3d')
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax4 = fig.add_subplot(2, 2, 4)
plt.tight_layout(pad=4, w_pad=4, h_pad=4)
time_text = ax3.text(0, 0, 'time =' + "{:8.2f}".format(simtime) + 's')
delta_x = X[0, 1] - X[0, 0]
delta_y = Y[1, 0] - Y[0, 0]
Z_x, Z_y = np.gradient(Z, delta_x, delta_y)
grad_Z = np.sqrt(Z_x**2 + Z_y**2)
slope = (np.arctan(grad_Z) * 180.0 / np.pi)
my_col = cm.jet(slope / cr_angle)
norm = colors.Normalize(vmin=0.0, vmax=cr_angle)
ax1.plot_surface(X,
Y,
Z,
rstride=1,
cstride=1,
facecolors=my_col,
linewidth=0,
antialiased=False,
alpha=0.7)
#m = cm.ScalarMappable(cmap=plt.cm.jet, norm=norm)
#m.set_array([])
#plt.colorbar(m,ax=ax1,location='bottom')
# clb1.set_label('Slope [°]')
nx = X.shape[1]
ny = X.shape[0]
idx1 = int(np.floor(nx / 4))
idx2 = int(np.floor(nx / 2))
idx3 = int(np.floor(3 * nx / 4))
dh = h_max - h_min
ax1.plot3D(X[idx1, :], Y[idx1, :], Z[idx1, :] + 0.01 * dh, 'blue')
ax1.plot3D(X[idx2, :], Y[idx2, :], Z[idx2, :] + 0.01 * dh, 'red')
ax1.plot3D(X[idx3, :], Y[idx3, :], Z[idx3, :] + 0.01 * dh, 'green')
if (np.min(Z_diff) == np.max(Z_diff)):
Z_diff[0, 0] = 0.001
Z_diff[-1, -1] = -0.001
ax1.set_xlabel('x [m]')
ax1.set_xlim(np.amin(X), np.amax(X))
ax1.set_ylabel('y [m]')
ax1.set_ylim(np.amin(Y), np.amax(Y))
ax1.set_zlabel('z [m]')
ax1.set_zlim(h_min, h_max)
extent = [np.amin(X), np.amax(X), np.amin(Y), np.amax(Y)]
z_range = np.amax(np.abs(Z_diff))
cnt = ax2.imshow(Z_diff,
cmap='seismic',
extent=extent,
vmin=-z_range,
vmax=z_range)
clb = plt.colorbar(cnt, ax=ax2)
clb.set_label('Delta h [m]')
# colorbar(cnt);
ax2.set_xlabel('x [m]')
ax2.set_xlim(np.amin(X), np.amax(X))
ax2.set_ylabel('y [m]')
ax2.set_ylim(np.amin(Y), np.amax(Y))
l1, = ax3.plot(X[idx1, :], Z[idx1, :], 'b-')
l2, = ax3.plot(X[idx2, :], Z[idx2, :], 'r-')
l3, = ax3.plot(X[idx3, :], Z[idx3, :], 'g-')
ax3.legend((l1, l2, l3), ("y="+str(Y[idx1,0])+' m',"y="+str(Y[idx2,0])+' m', \
"y="+str(Y[idx3,0])+' m'), loc='upper right', shadow=True)
ax3.plot(X[idx1, :], Z_init[idx1, :], 'b--')
ax3.plot(X[idx2, :], Z_init[idx2, :], 'r--')
ax3.plot(X[idx3, :], Z_init[idx3, :], 'g--')
ax3.set_xlabel('x [m]')
ax3.set_ylabel('z [m]')
x_min, x_max = ax3.get_xlim()
y_min, y_max = ax3.get_ylim()
time_text.set_position(
(x_min + 0.05 * (x_max - x_min), y_min + 0.9 * (y_max - y_min)))
time_text.set_text('time =' + "{:8.2f}".format(simtime) + 's')
slope_flat = slope.flatten()
"""
n, bins, patches = ax4.hist(slope_flat, weights=100.0*np.ones(len(slope_flat)) / len(slope_flat),\
histtype='stepfilled', alpha=0.2)
bin_centers = 0.5 * (bins[:-1] + bins[1:])
# scale values to interval [0,1]
col = bin_centers - min(bin_centers)
col /= max(col)
cm = plt.cm.get_cmap('RdYlBu_r')
for c, p in zip(col, patches):
plt.setp(p, 'facecolor', cm(c))
"""
# Get the histogramp
cm = plt.cm.get_cmap('jet')
Yh, Xh = np.histogram(slope_flat, density=True)
x_span = Xh.max() - Xh.min()
C = [cm(x / cr_angle) for x in Xh]
ax4.bar(Xh[:-1] + 0.5 * (Xh[1] - Xh[0]), Yh, color=C, width=Xh[1] - Xh[0])
ax4b = ax4.twinx()
# plot the cumulative histogram
n_bins = 50
ax4b.hist(slope_flat,
n_bins,
density=True,
histtype='step',
cumulative=True,
label='Empirical')
ax4.set_xlabel('slope [degrees]')
ax4.set_ylabel('probability density function')
ax4b.set_ylabel('cumulative distribution function')
frame_name = run_name + '_{0:03}'.format(iter) + '.png'
plt.savefig(frame_name, dpi=200)
frame_name = run_name + '_{0:03}'.format(iter) + '.pdf'
plt.savefig(frame_name)
if plot_show_flag:
plt.show()
plt.pause(0.01)
return time_text
def make_tsne(session_data,
data,
labels,
fig_filename='tsne.svg',
batch_size=128,
image_size=32):
data_to_use = [x for x in data if x[1] in labels]
data_generator = get_batches(data_to_use,
batch_size,
image_size,
word2vec=True,
send_raw_str=True)
sess = session_data['session']
best_model = session_data['best_model']
model_output = session_data['model_output']
x = session_data['x']
restorer = tf.train.Saver()
restorer.restore(sess, best_model)
points = []
p_labels = []
for batch_x, batch_y, batch_labels in data_generator:
output = sess.run(model_output, {x: batch_x})
for i, o in enumerate(output):
label = normalize_label(batch_labels[i])
points.append(o)
p_labels.append(label)
labels = [normalize_label(L) for L in labels]
label_points = [find_word_vec(L) for L in labels]
for i, L in enumerate(labels):
points.append(label_points[i])
p_labels.append('LABEL-' + normalize_label(L))
print('RUNNING TSNE')
manifold = TSNE(n_components=2, metric='cosine').fit_transform(points)
print('DONE')
output_points = [
a for i, a in enumerate(manifold) if 'LABEL' not in p_labels[i]
]
class_points = [[a, p_labels[i]] for i, a in enumerate(manifold)
if 'LABEL' in p_labels[i]]
output_labels = [L for L in p_labels if 'LABEL' not in L]
output_labels = [labels.index(normalize_label(L)) for L in output_labels]
x_output = [a[0] for a in output_points]
y_output = [a[1] for a in output_points]
x_class_points = [a[0][0] for a in class_points]
y_class_points = [a[0][1] for a in class_points]
l_class_points = [a[1].split('-')[1] for a in class_points]
fig, ax = plt.subplots()
num_colors = len(labels)
cm = plt.get_cmap('rainbow')
ax.set_color_cycle([cm(1. * i / num_colors) for i in range(num_colors)])
for c in range(len(labels)):
x_L = [x for i, x in enumerate(x_output) if output_labels[i] == c]
y_L = [y for i, y in enumerate(y_output) if output_labels[i] == c]
ax.scatter(x_L, y_L, marker='x', label=labels[c])
lgd = ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5))
font0 = FontProperties()
font0.set_weight('bold')
font0.set_size(7)
for i, L in enumerate(l_class_points):
ax.text(x_class_points[i],
y_class_points[i],
L,
fontproperties=font0,
bbox=dict(facecolor='white', edgecolor='black', alpha=0.8))
ax.set_xlim([-50, 50])
fig.savefig(fig_filename,
bbox_extra_artists=(lgd, ),
bbox_inches='tight',
format='svg')
plt.show(block=False)
COLOR='blue'
#RESFACT=10
MAP='jet' # choose carefully, or color transitions will not appear smoooth
# create random data
np.random.seed(101)
x = y2009_class['NDVI']
y = y2009_class['TRMM']
z = y2009_class['LST']
%matplotlib inline
fig = plt.figure(figsize=(8,8))
ax = plt.axes(projection='3d')
cm = plt.get_cmap(MAP)
ax.set_color_cycle([cm(1.*i/(NPOINTS-1)) for i in range(NPOINTS-1)])
for i in range(NPOINTS-1):
ax.plot(x[i:i+2],y[i:i+2],z[i:i+2])
# ax.scatter(x[i:i+2],y[i:i+2],z[i:i+2])
#ax.plot(x,y,z)
ax.scatter(x,y,z,c=np.linspace(0,1,NPOINTS), s=40)
#ax.text(.05,1.05,'Reg. Res - Color Map')
ax.set_xlim(0,5)
ax.set_ylim(0,5)
ax.set_zlim(0,5)
qtls_NDVI_lst = qtls_NDVI.tolist()
qtls_NDVI_lst.insert(0,' ')
def plot_minmax_levels_binned(var_list, ID, times, time_bins, it_bins, ic, jc,
di, dj):
print('computing binned min/max at each level')
cm = plt.cm.get_cmap('coolwarm')
cm2 = plt.cm.get_cmap('bwr')
zrange = dx[2] * krange
c1 = -1.
c2 = -1.
c3 = -1.
minmax = {}
minmax['time'] = times
max_single = np.zeros((kmax), dtype=np.double)
max_double = np.zeros((kmax), dtype=np.double)
max_triple = np.zeros(
(2, kmax), dtype=np.double
) # 0: total domain, (2*di)x(2*dj) gridpoints around collision point
for var_name in var_list:
minmax[var_name] = {}
minmax[var_name]['max'] = np.zeros((len(times), kmax), dtype=np.double)
minmax[var_name]['min'] = np.zeros((len(times), kmax), dtype=np.double)
# # # minmax['w'] = {}
# # # minmax['s'] = {}
# # # minmax['temp'] = {}
for var_name in var_list:
print('')
print('variable: ' + var_name)
fig2, axes2 = plt.subplots(3, 2, sharey='all', figsize=(10, 15))
for it, t0 in enumerate(times):
count_color = np.double(it) / len(times)
if var_name == 'theta':
s_var = read_in_netcdf_fields(
's', os.path.join(path_fields,
str(t0) + '.nc'))
var = theta_s(s_var)
else:
var = read_in_netcdf_fields(
var_name, os.path.join(path_fields,
str(t0) + '.nc'))
for k in range(kmax):
minmax[var_name]['max'][it, k] = np.amax(var[:, :, k])
minmax[var_name]['min'][it, k] = np.amin(var[:, :, k])
if t0 < time_bins[1]:
ax = axes2[0, :]
c1 += 1.
c = c1 / (it_bins[1] - it_bins[0])
max_single += np.amax(np.amax(var[:, :, :kmax], axis=0),
axis=0)
elif t0 < time_bins[2]:
ax = axes2[1, :]
c2 += 1.
c = c2 / (it_bins[2] - it_bins[1])
max_double += np.amax(np.amax(var[:, :, :kmax], axis=0),
axis=0)
elif t0 < time_bins[3]:
ax = axes2[2, :]
c3 += 1.
c = c3 / (it_bins[3] - it_bins[2])
max_triple[0, :] += np.amax(np.amax(var[:, :, :kmax], axis=0),
axis=0)
max_triple[1, :] += np.amax(np.amax(var[ic - di:ic + di, jc -
dj:jc + dj, :kmax],
axis=0),
axis=0)
else:
continue
del var
ax[0].plot(minmax[var_name]['min'][it, :],
zrange,
'-',
color=cm(c),
label='t=' + str(it) + 's')
ax[1].plot(minmax[var_name]['max'][it, :],
zrange,
'-',
color=cm(c),
label='t=' + str(it) + 's')
max_single /= c1
max_double /= c2
max_triple /= c3
fig2.subplots_adjust(bottom=0.05,
right=.85,
left=0.1,
top=.95,
wspace=0.25)
for i in range(3):
axes2[i, 0].set_xlim(np.amin(minmax[var_name]['min']),
np.amax(minmax[var_name]['min']))
axes2[i, 1].set_xlim(np.amin(minmax[var_name]['max']),
np.amax(minmax[var_name]['max']))
axes2[i, 0].set_ylabel('height z [m]')
axes2[i, 1].set_title('t=' + str(time_bins[i]) + ' - ' +
str(time_bins[i + 1]) + 's',
fontsize=18)
axes2[0, 0].set_title('single CP', fontsize=18)
axes2[1, 0].set_title('2-CP collision', fontsize=18)
axes2[2, 0].set_title('3-CP collision', fontsize=18)
for i in range(2):
axes2[2, i].set_xlabel('min(' + var_name + ') [m/s]')
axes2[2, i].set_xlabel('max(' + var_name + ') [m/s]')
fig2.suptitle('min/max of ' + var_name, fontsize=24)
fig_name2 = var_name + '_' + str(ID) + '_minmax_binned.png'
fig2.savefig(os.path.join(path_out_figs, fig_name2))
plt.close(fig2)
print('')
fig, (ax1, ax2, ax3) = plt.subplots(1,
3,
sharey='none',
figsize=(15, 5))
k0 = 25
# s_var = read_in_netcdf_fields('s', os.path.join(path_fields, str(1500) + '.nc'))
# var = theta_s(s_var)[:,:,22]
# del s_var
w_var = read_in_netcdf_fields(
'w', os.path.join(path_fields,
str(1200) + '.nc'))
ax1.contourf(w_var[:, :, k0].T)
w_var = read_in_netcdf_fields(
'w', os.path.join(path_fields,
str(1500) + '.nc'))
aux = np.amax(np.abs(w_var[:, :, k0]))
ax2.contourf(w_var[:, :, 20].T,
levels=np.linspace(-aux, aux, 1e2),
cmap=cm2)
rect_double = mpatches.Rectangle((ic - 50, jc - 50),
2 * 50,
2 * 50,
linewidth=1,
edgecolor='grey',
facecolor='none')
rect = mpatches.Rectangle((ic - di, jc - dj),
2 * di,
2 * dj,
linewidth=1,
edgecolor='k',
facecolor='none')
ax1.add_patch(rect)
ax1.add_patch(rect_double)
ax2.add_patch(rect)
ax2.add_patch(rect_double)
ax3.plot(max_single, zrange, label='single')
ax3.plot(max_double, zrange, label='double')
ax3.plot(max_triple[0, :], zrange, label='triple')
ax3.plot(max_triple[1, :], zrange, label='triple subdomain')
ax3.legend()
ax1.set_xlabel('min(' + var_name + ') [m/s]')
ax3.set_xlabel('max(' + var_name + ') [m/s]')
ax1.set_ylabel('height z [m]')
fig.suptitle('min/max of ' + var_name, fontsize=24)
fig_name = var_name + '_' + str(ID) + '_max_binned.png'
fig.savefig(os.path.join(path_out_figs, fig_name))
plt.close(fig)
return
motion_inx = 0
for motion in os.listdir(args.bag_dir):
if os.path.isdir(os.path.join(args.bag_dir, motion)):
bag_dir = os.path.join(args.bag_dir, motion)
for fov in os.listdir(bag_dir):
if os.path.isdir(os.path.join(bag_dir, fov)):
chi, alpha, fl = parse('chi{:f}_alpha{:f}_fl{:f}',
fov)
print "Motion type {}, chi={}, alpha={}, focal_length={}".format(
motion, chi, alpha, fl)
ax = fig.add_subplot(
num_rows, num_cols,
motion_inx * num_cols + fov_dict[fov] + 1)
cm = plt.get_cmap('nipy_spectral')
ax.set_prop_cycle(color=[
cm(1. * i / len(descriptor_list))
for i in range(len(descriptor_list))
])
if args.detector == '':
handles = []
for det, desc in d_list:
print "Detector+Descriptor {}".format(det +
'+' +
desc)
matcher = Matcher(args.image_topic,
args.depth_image_topic,
args.pose_topic,
det,
desc,
fl,
chi,
default="jet",
help="Colormap used to pick a color.")
args = parser.parse_args()
cm = colors.cm_mapper(0, 1, args.colormap)
fig, axs = get_axs()
fnames = args.INPUT_DIRECTORIES
for i, fname in enumerate(fnames):
hackfactor = None
if fname == "run-transient4":
hackfactor = 2.0
if args.colors is None:
if len(fnames) > 1:
c = cm(float(i) / (len(fnames) - 1))
else:
c = 'b'
else:
if type(args.colors[i]) is float:
c = cm(args.colors[i])
else:
c = args.colors[i]
ktd = parse_ats.readATS(fname, "visdump_surface_data.h5")
plot_surface_balance(ktd, axs, c, label=fname, hackfactor=hackfactor)
ktd[2].close()
plt.tight_layout()
axs[0].legend(bbox_to_anchor=(0., 1., 3.6, .05),
loc=3,
def sample_colours_from_colourmap(n_colours, colour_map):
import matplotlib.pyplot as plt
cm = plt.get_cmap(colour_map)
return [cm(1. * i / n_colours)[:3] for i in range(n_colours)]
def plot(self, outputDirectory):
# Skip this step if matplotlib is not installed
try:
import matplotlib.pyplot as plt
except ImportError:
return
import matplotlib.cm
cm = matplotlib.cm.jet
Nreac = self.network.Nisom + self.network.Nreac
Nprod = Nreac + self.network.Nprod
Tlist = self.Tlist.value_si
Plist = self.Plist.value_si
Tcount = Tlist.shape[0]
Pcount = Plist.shape[0]
K = self.K
count = 0
for prod in range(Nprod):
for reac in range(Nreac):
if reac == prod: continue
reaction = self.network.netReactions[count]
count += 1
reaction_str = '{0} {1} {2}'.format(
' + '.join([reactant.label for reactant in reaction.reactants]),
'<=>' if prod < Nreac else '-->',
' + '.join([product.label for product in reaction.products]),
)
fig = plt.figure(figsize=(10,6))
K2 = numpy.zeros((Tcount, Pcount))
if reaction.kinetics is not None:
for t in range(Tcount):
for p in range(Pcount):
K2[t,p] = reaction.kinetics.getRateCoefficient(Tlist[t], Plist[p])
K = self.K[:,:,prod,reac].copy()
order = len(reaction.reactants)
K *= 1e6 ** (order-1)
K2 *= 1e6 ** (order-1)
kunits = {1: 's^-1', 2: 'cm^3/(mol*s)', 3: 'cm^6/(mol^2*s)'}[order]
plt.subplot(1,2,1)
for p in xrange(Pcount):
plt.semilogy(1000.0 / Tlist, K[:,p], color=cm(1.*p/(Pcount-1)), marker='o', linestyle='',
label=str('%.2e' % (Plist[p]/1e+5)) + ' bar')
if reaction.kinetics is not None:
plt.semilogy(1000.0 / Tlist, K2[:,p], color=cm(1.*p/(Pcount-1)), marker='', linestyle='-')
plt.xlabel('1000 / Temperature (1000/K)')
plt.ylabel('Rate coefficient ({0})'.format(kunits))
plt.title(reaction_str)
plt.legend()
plt.subplot(1,2,2)
for t in xrange(Tcount):
plt.loglog(Plist*1e-5, K[t,:], color=cm(1.*t/(Tcount-1)), marker='o', linestyle='',
label=str('%.0d' % Tlist[t]) + ' K')
plt.loglog(Plist*1e-5, K2[t,:], color=cm(1.*t/(Tcount-1)), marker='', linestyle='-')
plt.xlabel('Pressure (bar)')
plt.ylabel('Rate coefficient ({0})'.format(kunits))
plt.title(reaction_str)
plt.legend()
fig.subplots_adjust(left=0.10, bottom=0.13, right=0.95, top=0.92, wspace=0.3, hspace=0.3)
if not os.path.exists('plots'):
os.mkdir('plots')
plt.savefig(os.path.join(outputDirectory, 'plots/kinetics_{0:d}.pdf'.format(count)))
plt.close()
def plot_head_connectivity(connectivity, sensorlocations, plotsensors=False,
vmin=None, vmax=None, cm=cm.jet, plothead=True,
plothead_kwargs=None, ax=P, view='top', **kwargs):
"""Plot connectivity on a head surface, derived from some sensor locations.
The sensor locations are first projected onto the best fitting sphere and
finally projected onto a circle (by simply ignoring the z-axis).
:param connectivity: Connectivity matrix
:type connectivity: matrix
:param sensorlocations: array (nsensors x 3), 3D coordinates of each sensor. The order of the sensors has to match with the `connectivity` matrix.
:param plotsensors: bool; if True, sensor will be plotted on their projected coordinates. No sensors are shown otherwise.
:param plothead: bool; If True, a head outline is plotted.
:param plothead_kwargs: Additional keyword arguments passed to `plot_head_outline()`.
:param vmin,vmax: Minimum and maximum value to be used for graphics.
:param ax: matplotlib axes to plot to. Standard is pylab.
:param view: one of 'top' and 'rear'; Defines from where the head is viewed.
:param kwargs: All additional arguments will be passed to `P.imshow()`.
:returns: (map, head, sensors)
The corresponding matplotlib objects are returned if plotted, i.e.,
if plothead is set to `False`, head will be `None`.
map
The colormap that makes the actual plot, a
matplotlib.image.AxesImage instance.
head
What is returned by :py:meth:`plot_head_outline`.
sensors
The dots marking the electrodes, a matplotlib.lines.Line2d
instance.
..seealso: :py:meth:`plot_head_topography`
"""
#Some assertions:
assert len(connectivity.shape)==2 and connectivity.shape[0]==connectivity.shape[1], "connectivity must be a quadratic matrix"
assert connectivity.shape[0] == sensorlocations.shape[0], "connectivity and sensorlocations must have same length"
if vmin!=None and vmax != None:
assert vmin<vmax, "vmin(=%f) must be smaller than vmax(=%f)" % (vmin,vmax)
# give sane defaults
if plothead_kwargs is None:
plothead_kwargs = {}
#assert sensorlocations is an numpy-arrays and swap x and y coordinates
#swapping is necessary as the eeglab .ced files have another interpretation of this
sensorlocations = N.array(sensorlocations)
tmp = sensorlocations[:,1].copy()
sensorlocations[:,1] = sensorlocations[:,0]
sensorlocations[:,0]=tmp[:]
sensorlocations[:,0]*=-1
del tmp
# error function to fit the sensor locations to a sphere
def err(params):
r, cx, cy, cz = params
#print r,cx,cy,cz
rv = (sensorlocations[:, 0] - cx) ** 2 + (sensorlocations[:, 1] - cy) ** 2 + (sensorlocations[:, 2] - cz) ** 2 - r ** 2
rv = abs(rv.sum())
#print "rv: ",rv
return rv
# initial guess of sphere parameters (radius and center)
params = N.array([1.0, 0.0, 0.0, 0.0])
# do fit
r, cx, cy, cz = fmin(err,params,disp=0)#leastsq(err, params)#
#print "Results of fit:", r, cx,cy,cz
# project the sensor locations onto the sphere
sphere_center = N.array((cx, cy, cz))
sproj = sensorlocations - sphere_center
sproj = r * sproj / N.c_[N.sqrt(N.sum(sproj ** 2, axis=1))]
sproj += sphere_center
#print "sproj.shape:",sproj.shape
#vmin, vmax: give sane defaults
#first, make copy of connectivity and set diagonal elements to zero
conn = connectivity.copy()
conn *= N.ones((conn.shape[0]),"d")-N.diag(N.ones((conn.shape[0]),"d"))
if vmin==None:
vmin=conn.min()
if vmax==None:
vmax=conn.max()
#Now transform values of conn to be between 0 and 1
conn = (conn-vmin)/(vmax-vmin)
conn[conn>1] = N.ones(conn[conn>1].shape,"d")
conn[conn<0] = N.zeros(conn[conn<0].shape,"d")
if view == 'top':
#
fig = ax.gca()
for i in range(connectivity.shape[0]):
for j in range(i):
dx=sproj[j,0]-sproj[i,0]
dy=sproj[j,1]-sproj[i,1]
if conn[i,j] != conn[j,i]:
if conn[i,j]>0.0:
#ax.arrow(sproj[i,0],sproj[i,1],dx,dy,lw=conn[i,j]*5+1,ec=cm(conn[i,j]),head_width=N.sqrt(dx**2+dy**2)*conn[i,j]*0.03,zorder=100-conn[i,j])
arr1 = P.Arrow(sproj[i,0], sproj[i,1], dx, dy, width=(conn[i,j]*5+1)/30,ec=cm(conn[i,j]),fc=cm(conn[i,j]),zorder=100+conn[i,j])
fig.add_patch(arr1)
if conn[j,i]>0.0:
#ax.arrow(sproj[i,0],sproj[i,1],dx,dy,lw=conn[j,i]*5+1,ec=cm(conn[j,i]),head_width=N.sqrt(dx**2+dy**2)*conn[j,i]*0.03,zorder=100-conn[j,i])
arr1 = P.Arrow(sproj[i,0], sproj[i,1], dx, dy, width=(conn[j,i]*5+1)/30,ec=cm(conn[j,i]),fc=cm(conn[j,i]),zorder=100+conn[j,i])
fig.add_patch(arr1)
else:
if conn[i,j]>0.0:
ax.arrow(sproj[i,0],sproj[i,1],sproj[j,0]-sproj[i,0],sproj[j,1]-sproj[i,1],lw=conn[i,j]*5+1,ec=cm(conn[i,j]),zorder=100-conn[i,j])
#elif view=='rear':
# pass
else:
raise ValueError("view must be one of 'top' and 'rear'")
# show surface
#map = ax.imshow(topo, origin="lower", extent=(-r, r, -r, r), **kwargs)
ax.axis('off')
ax.axis('equal')
if plothead:
# plot scaled head outline
head = plot_head_outline(scale=r, shift=(cx, cy), view=view, **plothead_kwargs)
else:
head = None
if plotsensors:
sensors = plot_sensors(sproj,ax,"wo",view=view)
else:
sensors = None
if view == 'top':
ax.xlim((cx-(r*1.2),cx+(r*1.2)))
ax.ylim((cy-(r*1.2),cy+(r*1.2)))
elif view=='rear':
ax.xlim((cx-(r*1.2),cx+(r*1.2)))
ax.ylim((cz-(r*0.4),cz+(r*1.2)))
return map, head, sensors
chosen_pic_depth_image = Image.fromarray(numpy.uint8(chosen_pic_depth_image_array))
# chosen_pic_depth_image = chosen_pic_depth_image.convert('P', palette = Image.ADAPTIVE, colors = 2)
# chosen_pic_depth_image.show()
# Making the images smaller for faster processing.
chosen_pic_image.thumbnail((350, 350), Image.ANTIALIAS)
chosen_pic_depth_image.thumbnail((350, 350), Image.ANTIALIAS)
# When the colormap mode is enabled, we use the colormapped depth data as a texture.
chosen_pic_photo_image_buffer = io.BytesIO()
chosen_pic_colormap_image_buffer = io.BytesIO()
arrx = numpy.array(chosen_pic_depth_image.convert('L')).astype(int)
pre_cmap_array = (255*(arrx - numpy.min(arrx))/numpy.ptp(arrx)).astype(int)
cm = matplotlib.cm.get_cmap('jet')
post_cmap_array = numpy.uint8(numpy.rint(cm(pre_cmap_array)*255))[:, :, :3]
cmap_img = Image.fromarray(post_cmap_array)
cmap_img.save(chosen_pic_colormap_image_buffer, format = 'PNG')
chosen_pic_image.save(chosen_pic_photo_image_buffer, format = 'PNG')
rgbData = chosen_pic_photo_image_buffer.getvalue()
chosen_pic_depth_image_buffer = io.BytesIO()
chosen_pic_depth_image.save(chosen_pic_depth_image_buffer, format = 'PNG')
depthData = chosen_pic_depth_image_buffer.getvalue()
s = Server()
s.start_server()
modeSelector = ui.SegmentedControl(alpha = 0, corner_radius = 5)
modeSelector.segments = ('Mesh' , 'Wireframe', 'Point Cloud')
modeSelector.selected_index = 0
def plot_2D_SWC(tree=None, file_name=None, cs=None, synapses=None, locs=None, syn_cs=None, outN=None, draw_cbar=True,
draw_scale=True, color_scale=None, num_ticks=5, no_axon=True, my_color='k', special_syn=None,
syn_labels=None, cbar_orientation='horizontal', cbar_label='Vm', lwidth_factor=1, show_axes=False,
radial_projection=False, alpha=1.):
'''
Colors can be
None: uniform/default matplotlib color
Any color code: uniform but specified color
array of values:
colormap?
'''
import matplotlib.patheffects as PathEffects
xlim = [0,0]
ylim = [0,0]
frame1 = plt.gca()
if file_name != None:
# read the SWC into a dictionary: key=index, value=(x,y,z,d,parent)
x = open(file_name,'r')
SWC = {}
for line in x :
if(not line.startswith('#')) :
splits = line.split()
index = int(splits[0])
n_type = int(splits[1])
x = float(splits[2])
y = float(splits[3])
z = float(splits[4])
r = float(splits[5])
parent = int(splits[-1])
SWC[index] = (x,y,z,r,parent,n_type)
if x > xlim[1]: xlim[1] = x
if x < xlim[0]: xlim[0] = x
if y > ylim[1]: ylim[1] = y
if y < ylim[0]: xlim[0] = y
elif tree != None :
SWC = {}
nodes = tree.get_nodes()
for node in nodes:
p3d = node.get_content()['p3d']
index = p3d.index
n_type = p3d.type
x = p3d.x
y = p3d.y
z = p3d.z
r = p3d.radius
parent = p3d.parent_index
SWC[index] = (x,y,z,r,parent,n_type)
if x > xlim[1]: xlim[1] = x
if x < xlim[0]: xlim[0] = x
if y > ylim[1]: ylim[1] = y
if y < ylim[0]: xlim[0] = y
else:
print 'Error: input is either \'tree\' or \'filename\''
exit(1)
if locs != None:
# reshape location list
loc_dict = {}
for loc in locs:
loc_dict[loc['node']] = loc['x']
#if use_colors:
#my_color_list = ['r','g','b','c','m','y','r--','b--','g--', 'y--']
#else:
#my_color_list = ['k','k','k','k','k','k','k--','k--','k--', 'k--']
# for color scale plotting
if cs == None:
pass
elif color_scale != None:
max_cs = color_scale[1]
min_cs = color_scale[0]
norm_cs = (max_cs - min_cs) * (1. + 1./100.)
elif isinstance(cs, np.ndarray):
max_cs = np.max(cs)
min_cs = np.min(cs)
norm_cs = (max_cs - min_cs) * (1. + 1./100.)
elif isinstance(cs, list):
max_cs = max(cs)
min_cs = min(cs)
norm_cs = (max_cs - min_cs) * (1. + 1./100.)
elif isinstance(cs, dict):
arr = np.array([cs[key] for key in cs.keys()])
max_cs = np.max(arr)
min_cs = np.min(arr)
norm_cs = (max_cs - min_cs) * (1. + 1./100.)
else:
raise Exception('cs type is invalid')
if cs != None:
cm = plt.get_cmap('jet')
Z = [[0,0],[0,0]]
levels = np.linspace(min_cs, max_cs, 100)
CS3 = plt.contourf(Z, levels, cmap=cm)
min_y = 100000.0
for index in SWC.keys() : # not ordered but that has little importance here
# draw a line segment from parent to current point
current_SWC = SWC[index]
#print 'index: ', index, ' -> ', current_SWC
c_x = current_SWC[0]
c_y = current_SWC[1]
c_z = current_SWC[2]
c_r = current_SWC[3]*2.
parent_index = current_SWC[4]
if(c_y < min_y) :
min_y = c_y
if(index <= 3) :
print 'do not draw the soma and its CNG, 2 point descriptions'
else :
if (not no_axon) or (current_SWC[5] !=2):
parent_SWC = SWC[parent_index]
p_x = parent_SWC[0]
p_y = parent_SWC[1]
p_z = parent_SWC[2]
p_r = parent_SWC[3]
if(p_y < min_y) :
min_y= p_y
# print 'index:', index, ', len(cs)=', len(cs)
if(cs == None) :
if radial_projection:
pl = plt.plot([np.sqrt(p_x**2+p_z**2), np.sqrt(c_x**2+c_z**2)], [p_y,c_y], c=my_color, linewidth=c_r*lwidth_factor, alpha=alpha)
else:
pl = plt.plot([p_x,c_x], [p_y,c_y], c=my_color, linewidth=c_r*lwidth_factor, alpha=alpha)
else :
if radial_projection:
pl = plt.plot([np.sqrt(p_x**2+p_z**2), np.sqrt(c_x**2+c_z**2)], [p_y,c_y], c=cm((cs[index]-min_cs)/norm_cs), linewidth=c_r*lwidth_factor, alpha=alpha)
else:
pl = plt.plot([p_x,c_x], [p_y,c_y], c=cm((cs[index]-min_cs)/norm_cs), linewidth=c_r*lwidth_factor, alpha=alpha)
# add the synapses
if synapses != None:
if index in synapses:
# plot synapse marker
if syn_cs == None:
if radial_projection:
plt.plot(np.sqrt(c_x**2+c_z**2),c_y,'ro', markersize=5*lwidth_factor)
else:
plt.plot(c_x,c_y,'ro', markersize=5*lwidth_factor)
else:
if radial_projection:
plt.plot(np.sqrt(c_x**2+c_z**2),c_y, 'o', mfc=syn_cs[index], markersize=5*lwidth_factor)
else:
plt.plot(c_x,c_y, 'o', mfc=syn_cs[index], markersize=5*lwidth_factor)
# plot synapse label
if syn_labels != None and index in syn_labels.keys():
txt = frame1.annotate(syn_labels[index], xy=(c_x, c_y), xycoords='data', xytext=(5,5), textcoords='offset points', fontsize='large')
txt.set_path_effects([PathEffects.withStroke(foreground="w", linewidth=2)])
if locs != None:
if index in loc_dict.keys():
# plot synapse marker
p_x = SWC[parent_index][0]
p_y = SWC[parent_index][1]
p_z = SWC[parent_index][2]
if radial_projection:
x_plot = p_x + (c_x - p_x) * loc_dict[index]
z_plot = p_z + (c_z - p_z) * loc_dict[index]
point_plot = np.sqrt(x_plot**2 + z_plot**2)
else:
point_plot = p_x + (c_x - p_x) * loc_dict[index]
y_plot = p_y + (c_y - p_y) * loc_dict[index]
if syn_cs == None:
plt.plot(point_plot, y_plot, 'ro', markersize=5*lwidth_factor)
else:
plt.plot(point_plot, y_plot, 'o', mfc=syn_cs[index], markersize=5*lwidth_factor)
# plot synapse label
if syn_labels != None and index in syn_labels.keys():
txt = frame1.annotate(syn_labels[index], xy=(x_plot, y_plot), xycoords='data', xytext=(5,5), textcoords='offset points', fontsize='large')
txt.set_path_effects([PathEffects.withStroke(foreground="w", linewidth=2)])
if not show_axes:
frame1.axes.get_xaxis().set_ticks([])
frame1.axes.get_yaxis().set_ticks([])
frame1.set_xlabel('X')
frame1.set_ylabel('Y')
frame1.axes.get_xaxis().set_visible(show_axes)
frame1.axes.get_yaxis().set_visible(show_axes)
frame1.axison = show_axes
# draw a scale bar
if draw_scale:
scale = 100
plt.plot([0,scale],[min_y*1.1,min_y*1.1],'k',linewidth=5) # 250 for MN, 100 for granule
txt = frame1.annotate(r'' + str(scale) + ' $\mu$m', xy=(scale/2., min_y*1.1), xycoords='data', xytext=(-28,8), textcoords='offset points', fontsize='medium')
txt.set_path_effects([PathEffects.withStroke(foreground="w", linewidth=2)])
#~ frame1.text(, min_y+(ylim[1]-ylim[0])/30, str(scale) + ' um')
if(cs != None and draw_cbar) :
# so that colorbar works with tight_layout
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(frame1)
if cbar_orientation=='horizontal':
cax = divider.append_axes("bottom", "5%", pad="3%")
else:
cax = divider.append_axes("right", "5%", pad="3%")
cb = plt.colorbar(None, cax=cax, orientation=cbar_orientation)
ticks_f = np.round(np.linspace(min_cs, max_cs, num_ticks+2), decimals=1)
#~ print ticks_f
ticks_i = ticks_f
cb.set_ticks(ticks_i)
if cbar_orientation=='horizontal':
cb.ax.xaxis.set_ticks_position('bottom')
cb.set_label(r'$V_m$ (mV)')
if(outN != None) :
plt.savefig(outN)
#~ if ax != None:
#~ ax = frame1
return frame1
parser.add_argument("--colors", "-c", type=colors.float_list_type,
default=None,
help="List of color indices to use, of the form: --colors=[0,0.1,1], where the doubles are in the range (0,1) and are mapped to a color using the colormap.")
parser.add_argument("--colormap", "-m", type=str,
default="jet",
help="Colormap used to pick a color.")
args = parser.parse_args()
cm = colors.cm_mapper(0,1,args.colormap)
fig, axs = get_axs()
fnames = args.INPUT_DIRECTORIES
for i,fname in enumerate(fnames):
if args.colors is None:
if len(fnames) > 1:
c = cm(float(i)/(len(fnames)-1))
else:
c = 'b'
else:
if type(args.colors[i]) is float:
c = cm(args.colors[i])
else:
c = args.colors[i]
times,td = thaw_depth(fname)
plot_thaw_depth(times, td, axs, color=c, label=fname)
plt.tight_layout()
axs.legend()
plt.show()
import numpy as np
import pandas as pd
import matplotlib.pyplot as pylab
# NOTE: Either freefall_Omukai.py or freefall_Omukai_Temperature.py must be run
# first to generate the data for this script.
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from matplotlib import cm
cm = cm.get_cmap('coolwarm', 4)
cols = [cm(0.2), cm(0.4), cm(0.6), cm(0.8)]
# Set colors
colors = ["blue", "brown", "magenta", "purple"]
# Set metallicity
logZ = [-6., -4., -2., 0.]
for i in range(len(logZ)):
filenamefH2 = 'logfH2_v_logN_[Z]=%d_2005.csv' % logZ[i]
n = []
fH2 = []
with open(filenamefH2, 'r') as datafH2:
lines = datafH2.readlines()
for line in lines[1:]:
p = line.split(',')
n.append(float(p[0]))
fH2.append(float(p[1]))
pylab.plot(n,
fH2,
def main_routine (wd="./",cfg="./python/parameter.cfg",cnt=11,sptype=1,myCmap="RdYlBu"):
### read config file ###
print ("load from config file: " + cfg)
configParser = cp.ConfigParser()
configParser.read(cfg)
print (configParser.sections())
cfg=configParser.__dict__['_sections'].copy()
#for src, target in cfg['NVSETUP'].items():
# print(src + " : " + target)
omega_c = float(cfg['NVSETUP']['{omega_c}'])
nWrite=int(cfg['OCFourier']['{write_harmonic}'])
nRead =int(cfg['OCFourier']['{read_harmonic}'])
nStore=int(cfg['MEFourier']['{storage_harmonic}'])
nDown =nRead+nWrite
nUp =nDown+nWrite
### read config file ###
### read data ###
cavityWrite,cavityMemo,cavityRead =IOHelper.harmonics_readwrite(**cfg)
time =IOHelper.functionaltimes_readwrite(**cfg)
time['write'][:] *= 1e9
time['read'][:] *= 1e9
ti = int(time['idx_ti'])
tf = int(time['idx_tf'])
functime = time['read'][ti:tf]
dt = float(time['delta_t'])
filename =IOHelper.getVectorOverlap(**cfg)
reGamma,imGamma=sp.loadtxt(filename).T
alphaR =reGamma[0:nRead] -1j*imGamma[0:nRead]
alphaD =reGamma[nRead:nDown]-1j*imGamma[nRead:nDown]
alphaU =reGamma[nDown:nUp] -1j*imGamma[nDown:nUp]
### read data ###
### plotting
Reg1Up = sp.dot(alphaU.conj(),cavityWrite)
Reg1Down = sp.dot(alphaD.conj(),cavityWrite)
Reg2Down = sp.dot(alphaD.conj(),cavityMemo)
Reg2Up = sp.dot(alphaU.conj(),cavityMemo)
Reg2Read = sp.dot(alphaR.conj(),cavityRead)
Reg2DownRead = Reg2Down + Reg2Read
Reg2UpRead = Reg2Up + Reg2Read
FuncInfo = sp.zeros([cnt,2])
FuncInfoPhase = sp.zeros([cnt,2])
FuncSuperRead = sp.zeros([cnt,functime.size],complex)
denom = sp.zeros([2])
denom[0] = cumtrapz( sp.absolute(Reg2DownRead[ti:tf])**2, x=None, dx=dt )[-1]
denom[1] = cumtrapz( sp.absolute(Reg2UpRead[ti:tf])**2, x=None, dx=dt )[-1]
for i in sp.arange(0.0,cnt):
phi = i*2.0/(cnt-1.0)
Reg1Super = superimpose(Reg1Down,Reg1Up,phi,sptype)
Reg2Super = superimpose(Reg2Down,Reg2Up,phi,sptype)
Reg2SuperRead = Reg2Super + Reg2Read
FuncSuperRead[i,:] = Reg2SuperRead[ti:tf]
FuncInfoIntegrand = sp.absolute(FuncSuperRead[i,:]) * sp.absolute(Reg2DownRead[ti:tf])
FuncInfo[i,0] = cumtrapz( FuncInfoIntegrand, x=None, dx=dt )[-1]/denom[0]
FuncInfoIntegrand = sp.absolute(FuncSuperRead[i,:]) * sp.absolute(Reg2UpRead[ti:tf])
FuncInfo[i,1] = cumtrapz( FuncInfoIntegrand, x=None, dx=dt )[-1]/denom[1]
FuncInfoIntegrand = FuncSuperRead[i,:].conj() * Reg2UpRead[ti:tf]
FuncInfoPhase[i,0] = cumtrapz( FuncInfoIntegrand.real, x=None, dx=dt )[-1]/denom[1]
FuncInfoPhase[i,1] = cumtrapz( FuncInfoIntegrand.imag, x=None, dx=dt )[-1]/denom[1]
xx, yy = sp.meshgrid(functime,sp.linspace(0.0,2.0,cnt))
zzR = FuncSuperRead.real/FuncSuperRead.real.max()
zzI = FuncSuperRead.imag/FuncSuperRead.imag.max()
zzA = sp.absolute(FuncSuperRead)**2/((sp.absolute(FuncSuperRead)**2).max())
zmin = -1.5
zmax = +1.0
fs = 20
cm = plt.cm.get_cmap(myCmap)
myColors = cm(zzR)
fig = plt.figure()
fig0 = fig.add_subplot(231, projection='3d')
fig0.plot_surface(xx, yy, zzA, rstride=10, cstride=5, cmap=cm, alpha=0.5,zorder=11.0,vmin=-1, vmax=1)
fig0.contourf(xx, yy, zzA, zdir='z', offset=zmin, cmap=cm, vmin=-1, vmax=1,zorder=1.0)
fig0.set_zlim(zmin,zmax)
fig0.set_title("a) normalized $|A_0(t;\phi_0)|^2$",fontsize=fs)
fig0.set_xlabel("$t$ in ns",fontsize=fs)
fig0.set_ylabel("$\phi_0/\pi$",fontsize=fs)
fig1 = fig.add_subplot(232, projection='3d')
fig1.plot_surface(xx, yy, zzR, rstride=10, cstride=5, cmap=cm, alpha=0.5,zorder=11.0,vmin=-1, vmax=1)
fig1.contourf(xx, yy, zzR, zdir='z', offset=zmin, cmap=cm, vmin=-1, vmax=1,zorder=1.0)
fig1.set_zlim(zmin,zmax)
fig1.set_title("b) normalized $Re[\,A_0(t;\phi_0)\,]$",fontsize=fs)
fig1.set_xlabel("$t$ in ns",fontsize=fs)
fig1.set_ylabel("$\phi_0/\pi$",fontsize=fs)
fig2 = fig.add_subplot(233, projection='3d')
fig2.plot_surface(xx, yy, zzI, rstride=10, cstride=5, cmap=cm, alpha=0.5,zorder=11.0,vmin=-1, vmax=1)
fig2.contourf(xx, yy, zzI, zdir='z', offset=zmin, cmap=cm, vmin=-1, vmax=1,zorder=1.0)
fig2.set_zlim(zmin,zmax)
fig2.set_title("c) normalized $Im[\,A_0(t;\phi_0)\,]$",fontsize=fs)
fig2.set_xlabel("$t$ in ns",fontsize=fs)
fig2.set_ylabel("$\phi_0/\pi$",fontsize=fs)
plt.subplot2grid((2,3),(1,0),colspan=1,rowspan=1)
plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfo[:,0],label="overlap with $i=$'$0$'",linewidth="2",color="blue")
plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfo[:,1],label="overlap with $i=$'$1$'",linewidth="2",color="red")
plt.ylim(0,1.1)
plt.xlabel("$\phi_0/\pi$",fontsize=fs)
plt.legend(bbox_to_anchor=(0.52, 0.0), loc=3, borderaxespad=0.)
plt.title("d) classical overlap, $O_{\mathbb{R}}(i)$",fontsize=fs)
# plt.title("$\\frac{1}{N}\int_{T_{{\cal F}1}}^{T_{{\cal F}2}}dt\,|A_0(t;\phi_0)|\cdot|A_i(t)|$",fontsize=fs)
# FuncInfoPhase[:,0]-=min(FuncInfoPhase[:,0])
# FuncInfoPhase[:,0]/=0.5*max(sp.absolute(FuncInfoPhase[:,0]))
# FuncInfoPhase[:,0]-=1.0
# FuncInfoPhase[:,1]-=min(FuncInfoPhase[:,1])
# FuncInfoPhase[:,1]/=0.5*max(sp.absolute(FuncInfoPhase[:,1]))
# FuncInfoPhase[:,1]-=1.0
plt.subplot2grid((2,3),(1,1),colspan=1,rowspan=1)
plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfoPhase[:,0],label="Re[ $O_{\mathbb{C}}($'$1$'$)$ ]",linewidth="2",color="red")
plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfoPhase[:,1],label="Im[ $O_{\mathbb{C}}($'$1$'$)$ ]",linewidth="2",color="magenta")
plt.title("e) complex overlap, $O_{\mathbb{C}}($'$1$'$)$",fontsize=fs)
# plt.title("$\\frac{1}{N}\int_{T_{{\cal F}1}}^{T_{{\cal F}2}}dt\,A_0(t;\phi_0)^*\cdot A_1(t)$",fontsize=fs)
plt.xlabel("$\phi_0/\pi$",fontsize=fs)
plt.legend(bbox_to_anchor=(0.66, 0.0), loc=3, borderaxespad=0.)
plt.ylim(-1,1.1)
FuncInfoPhase[:,0]-=min(FuncInfoPhase[:,0])
FuncInfoPhase[:,0]/=0.5*max(sp.absolute(FuncInfoPhase[:,0]))
FuncInfoPhase[:,0]-=1.0
FuncInfoPhase[:,1]-=min(FuncInfoPhase[:,1])
FuncInfoPhase[:,1]/=0.5*max(sp.absolute(FuncInfoPhase[:,1]))
FuncInfoPhase[:,1]-=1.0
# plt.subplot2grid((2,3),(1,2),colspan=1,rowspan=1)
# plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfoPhase[:,0],linewidth="2",color="red")
# plt.plot(sp.linspace(0.0,2.0,cnt),FuncInfoPhase[:,1],linewidth="2",color="magenta")
# plt.title("f) $O_{\mathbb{C}}$ scaled & translated, $(O_{\mathbb{C}}($'$1$'$)-I_R)/I_0$",fontsize=fs)
# plt.xlabel("$\phi_0/\pi$",fontsize=fs)
# plt.ylim(-1,1)
plt.show()
def get_colors(self, num, cmap='jet'):
import matplotlib.cm as ccmm
from numpy import linspace
cm = getattr(ccmm, cmap)
return cm(linspace(0, 1, num))
import matplotlib.cm as cm
import matplotlib.colors as colors
NUM_COLORS = 2
cm = plt.get_cmap('Paired')
file_list = sys.argv[1:]
fig = plt.figure()
#plt.suptitle(r'Pdf per i tempi di collisione in funzione di $\eta$',fontsize=16)
ax = fig.add_subplot(111)
ax.grid(True)
plt.xlabel(r'$t_c$',fontsize='15')
plt.ylabel(r'$P(t_c)$',fontsize='15')
ax.set_color_cycle([cm(1.*i/NUM_COLORS) for i in range(NUM_COLORS)])
i=0
Markers=['x','+','*','s','d','v','^','<','>','p','h','.','+','*','o','x','^','<','h','.','>','p','s','d','v','o','x','+','*','s','d','v','^','<','>','p','h','.']
col=['r','b']
#labels for cfr between fit and mean
labels=['fit','calcolato']
for f in file_list:
#eta_temp=float(f[-13:-4])
eta,mfp=np.loadtxt(f,unpack=True,usecols=(0,1))
width = 0.7 * (eta[1] - eta[0])
color = cm(1.*i/NUM_COLORS)
#plt.bar(eta, mfp, width=width,label=r'$\eta=%.5lf$'%(eta_temp), alpha=(0.8),color=color,linewidth=0)
plt.plot(eta,mfp,Markers[i], label=labels[i],color=col[i])
i+=1
mean=0
if (calc_distance):
ax1.set_title('Electric Field Magnitude \n Mean Point Distance %0.4e mm' % mean_point_distance)
else:
ax1.set_title('Electric Field Magnitude')
plt.show()
else:
eps_plot = 1e-10
nonzeroE = np.where(magE > eps_plot)
zeroE = np.where(magE <= eps_plot)
E[nonzeroE, 0] = E[nonzeroE, 0] / magE[nonzeroE]
E[nonzeroE, 1] = E[nonzeroE, 1] / magE[nonzeroE]
magE[nonzeroE] = np.log10(magE[nonzeroE])
magE[np.where(magE<0)] = 0
magE[zeroE] = 0
norm = colors.Normalize()
norm.autoscale(magE)
cm = cm.get_cmap("turbo")
sm = plt.cm.ScalarMappable(cmap=cm, norm=norm)
sm.set_array([])
fig1, ax1 = plt.subplots(dpi=400)
ax1.set_aspect('equal')
quiv_scale = np.max(E)*75
plt.quiver(C[:, 0], C[:, 1], E[:, 0], E[:, 1], color=cm(magE), scale=quiv_scale, headwidth=2, headlength=3)
ax1.set_title('Electric Field Lines')
cb = fig1.colorbar(sm)
cb.ax.set_ylabel("log(|E [V/mm]|), saturated at 0", rotation=90)
plt.show()
def show_distance_plot(stream, event, inventory, starttime, endtime,
plot_travel_times=True):
"""
Plots distance dependent seismogramm sections.
:param stream: The waveforms.
:type stream: :class:`obspy.core.stream.Stream`
:param event: The event.
:type event: :class:`obspy.core.event.Event`
:param inventory: The station information.
:type inventory: :class:`obspy.station.inventory.Inventory`
:param starttime: starttime of traces to be plotted
:type starttime: UTCDateTime
:param endttime: endttime of traces to be plotted
:type endttime: UTCDateTime
:param plot_travel_times: flag whether phases are marked as traveltime plots
in the section obspy.taup is used to calculate the phases
:type pot_travel_times: bool
"""
stream = stream.slice(starttime=starttime, endtime=endtime).copy()
event_depth_in_km = event.origins[0].depth / 1000.0
event_time = event.origins[0].time
attach_coordinates_to_traces(stream, inventory, event=event)
cm = plt.cm.jet
stream.traces = sorted(stream.traces, key=lambda x: x.stats.distance)[::-1]
# One color for each trace.
colors = [cm(_i) for _i in np.linspace(0, 1, len(stream))]
# Relative event times.
times_array = stream[0].times() + (stream[0].stats.starttime - event_time)
distances = [tr.stats.distance for tr in stream]
min_distance = min(distances)
max_distance = max(distances)
distance_range = max_distance - min_distance
stream_range = distance_range / 10.0
# Normalize data and "shift to distance".
stream.normalize()
for tr in stream:
tr.data *= stream_range
tr.data += tr.stats.distance
plt.figure(figsize=(18, 10))
for _i, tr in enumerate(stream):
plt.plot(times_array, tr.data, label="%s.%s" % (tr.stats.network,
tr.stats.station), color=colors[_i])
plt.grid()
plt.ylabel("Distance in degree to event")
plt.xlabel("Time in seconds since event")
plt.legend()
dist_min, dist_max = plt.ylim()
if plot_travel_times:
distances = defaultdict(list)
ttimes = defaultdict(list)
for i in np.linspace(dist_min, dist_max, 1000):
tts = getTravelTimes(i, event_depth_in_km, "ak135")
for phase in tts:
name = phase["phase_name"]
distances[name].append(i)
ttimes[name].append(phase["time"])
for key in distances.iterkeys():
min_distance = min(distances[key])
max_distance = max(distances[key])
min_tt_time = min(ttimes[key])
max_tt_time = max(ttimes[key])
if min_tt_time >= times_array[-1] or \
max_tt_time <= times_array[0] or \
(max_distance - min_distance) < 0.8 * (dist_max - dist_min):
continue
ttime = ttimes[key]
dist = distances[key]
if max(ttime) > times_array[0] + 0.9 * times_array.ptp():
continue
plt.scatter(ttime, dist, s=0.5, zorder=-10, color="black", alpha=0.8)
plt.text(max(ttime) + 0.005 * times_array.ptp(),
dist_max - 0.02 * (dist_max - dist_min),
key)
plt.ylim(dist_min, dist_max)
plt.xlim(times_array[0], times_array[-1])
plt.title(event.short_str())
plt.show()
#print(df)
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
x = list(range(2009, 2020))
univs = df.university.unique()
cm = plt.cm.get_cmap('tab20')
print(len(univs))
for i, univ in enumerate(sorted(univs)):
y = [
len(df[np.logical_and(df.university == univ, df.year == year)])
for year in x
]
ax.plot(x,
y,
label=univ,
alpha=0.95,
lw=5 if univ == 'bucknell' else 2,
color=cm(i / len(univs)))
ax.xaxis.set_major_locator(MultipleLocator(1))
ax.set_ylabel('dblp count')
ax.set_xlabel('year')
ax.set_ylim(bottom=0)
ax.legend()
plt.tight_layout()
plt.show()
def main_routine (wd="./",cfg="./python/parameter.cfg",thetaCnt=11,phiCnt=11,bloch=0,myMap="jet",minOlap=0,fortranCheck=0,gridR=5,gridC=5):
### read config file ###
print ("load from config file: " + cfg)
cfg=IOHelper.loadCfg(wd,cfg)
#for src, target in cfg['NVSETUP'].items():
# print(src + " : " + target)
omega_c = float(cfg['NVSETUP']['{omega_c}'])
nWrite=int(cfg['OCFourier']['{write_harmonic}'])
nRead =int(cfg['OCFourier']['{read_harmonic}'])
nStore=int(cfg['MEFourier']['{storage_harmonic}'])
nDown =nRead+nWrite
nUp =nDown+nWrite
### read config file ###
### read data ###
cavityWrite,cavityMemo,cavityRead =IOHelper.harmonics_readwrite(**cfg)
time =IOHelper.functionaltimes_readwrite(**cfg)
time['write'][:] *= 1e9
time['read'][:] *= 1e9
ti = int(time['idx_ti'])
tf = int(time['idx_tf'])
functime = time['read'][ti:tf]
dt = float(time['delta_t'])
filename =IOHelper.getVectorOverlap(**cfg)
reGamma,imGamma=sp.loadtxt(filename).T
alphaR =reGamma[0:nRead] -1j*imGamma[0:nRead]
alphaD =reGamma[nRead:nDown]-1j*imGamma[nRead:nDown]
alphaU =reGamma[nDown:nUp] -1j*imGamma[nDown:nUp]
### read data ###
### plotting
Reg1Up = sp.dot(alphaU.conj(),cavityWrite)
Reg1Down = sp.dot(alphaD.conj(),cavityWrite)
Reg2Down = sp.dot(alphaD.conj(),cavityMemo)
Reg2Up = sp.dot(alphaU.conj(),cavityMemo)
Reg2Read = sp.dot(alphaR.conj(),cavityRead)
Reg2DownRead = Reg2Down + Reg2Read
Reg2UpRead = Reg2Up + Reg2Read
spos = sp.zeros([2,thetaCnt,phiCnt],complex)
FuncInfoOlap = sp.zeros([2,thetaCnt,phiCnt],complex)
gamma = sp.zeros([thetaCnt,phiCnt],complex)
delta = sp.zeros([thetaCnt,phiCnt],complex)
# I00 = 1j*cumtrapz( (Reg2Down[ti:tf].conj() * Reg2Down[ti:tf]).imag, x=None, dx=dt )[-1]
I00 = cumtrapz( (Reg2Down[ti:tf] * Reg2Down[ti:tf].conj()).real, x=None, dx=dt )[-1]
I01 = 1j*cumtrapz( (Reg2Up [ti:tf] * Reg2Down[ti:tf].conj()).imag, x=None, dx=dt )[-1]
I01 += cumtrapz( (Reg2Up [ti:tf] * Reg2Down[ti:tf].conj()).real, x=None, dx=dt )[-1]
I0R = 1j*cumtrapz( (Reg2Read[ti:tf] * Reg2Down[ti:tf].conj()).imag, x=None, dx=dt )[-1]
I0R += cumtrapz( (Reg2Read[ti:tf] * Reg2Down[ti:tf].conj()).real, x=None, dx=dt )[-1]
# I11 = 1j*cumtrapz( (Reg2Up [ti:tf].conj() * Reg2Up [ti:tf]).imag, x=None, dx=dt )[-1]
I11 = cumtrapz( (Reg2Up [ti:tf] * Reg2Up [ti:tf].conj()).real, x=None, dx=dt )[-1]
I10 = 1j*cumtrapz( (Reg2Down[ti:tf] * Reg2Up [ti:tf].conj()).imag, x=None, dx=dt )[-1]
I10 += cumtrapz( (Reg2Down[ti:tf] * Reg2Up [ti:tf].conj()).real, x=None, dx=dt )[-1]
I1R = 1j*cumtrapz( (Reg2Read[ti:tf] * Reg2Up [ti:tf].conj()).imag, x=None, dx=dt )[-1]
I1R += cumtrapz( (Reg2Read[ti:tf] * Reg2Up [ti:tf].conj()).real, x=None, dx=dt )[-1]
for i in sp.arange(0.0,thetaCnt):
theta = i/(thetaCnt-1.0) # from 0 to 1 * pi
for j in sp.arange(0.0,phiCnt):
phi = j/(phiCnt-1.0)*2.0 # from 0 to 2 * pi
spos[0,i,j] = sp.cos(theta*sp.pi/2.0)
spos[1,i,j] = sp.sin(theta*sp.pi/2.0)*sp.exp(1j*phi*sp.pi)
if fortranCheck == 1:
cmd = "./scripts/ifort-checkBloch.sh " + wd
print ("compile fortran routines: "+cmd)
call(cmd.split())
print ("### call checkBloch")
cmd=wd+"checkBloch"
generateSuperposition = Popen(cmd.split(), stdin=PIPE) # run fortran program with piped standard input
cmd = "echo {:}".format(thetaCnt) # communication with fortran-routine: chose superposition parameter
generateInput = Popen(cmd.split(), stdout=generateSuperposition.stdin) # send action to fortran program
cmd = "echo {:}".format(phiCnt) # communication with fortran-routine: chose superposition parameter
generateInput = Popen(cmd.split(), stdout=generateSuperposition.stdin) # send action to fortran program
output = generateSuperposition.communicate()[0]
generateInput.wait()
FuncInfoOlap [0,:,:],FuncInfoOlap [1,:,:]=IOHelper.read_MtrxProjection(thetaCnt,phiCnt,**cfg['FILES'])
else:
for i in sp.arange(0.0,thetaCnt):
for j in sp.arange(0.0,phiCnt):
FuncCavity = spos[0,i,j]*Reg2Down[ti:tf] + spos[1,i,j]*Reg2Up[ti:tf]
FuncCavity[:] += Reg2Read [ti:tf]
FuncInfoOlap [0,i,j] = cumtrapz( (FuncCavity[:] * Reg2Down[ti:tf].conj()).real, x=None, dx=dt )[-1]
FuncInfoOlap [0,i,j] += 1j*cumtrapz( (FuncCavity[:] * Reg2Down[ti:tf].conj()).imag, x=None, dx=dt )[-1]
FuncInfoOlap [1,i,j] = cumtrapz( (FuncCavity[:] * Reg2Up[ti:tf].conj()).real, x=None, dx=dt )[-1]
FuncInfoOlap [1,i,j] += 1j*cumtrapz( (FuncCavity[:] * Reg2Up[ti:tf].conj()).imag, x=None, dx=dt )[-1]
if minOlap==0:
gamma[:,:]=((FuncInfoOlap [0,:,:]-I0R)*I11+(I1R-FuncInfoOlap [1,:,:])*I01)/(I11*I00-I01*I10)
delta[:,:]=((FuncInfoOlap [1,:,:]-I1R)*I00+(I0R-FuncInfoOlap [0,:,:])*I10)/(I11*I00-I01*I10)
else :
gamma[:,:]= (FuncInfoOlap [0,:,:]-I0R)/I00
delta[:,:]= (FuncInfoOlap [1,:,:]-I1R)/I11
fs = 22
label_size = 12
plt.rcParams['xtick.labelsize'] = label_size
plt.rcParams['ytick.labelsize'] = label_size
plt.rcParams['xtick.major.pad']='20'
plt.rcParams['ytick.major.pad']='20'
fig = plt.figure()
if bloch == 0:
xx, yy = sp.meshgrid(sp.linspace(0.0,2.0,phiCnt),sp.linspace(0.0,1.0,thetaCnt))
zmin = 0.0
zmax = +1.0
zzOlapR0 = gamma [:,:].real
zzOlapI0 = gamma [:,:].imag
zzErr0 = sp.absolute(gamma[:,:]-spos[0,:,:])
zzOlapR1 = delta [:,:].real
zzOlapI1 = delta [:,:].imag
zzErr1 = sp.absolute(delta[:,:]-spos[1,:,:])
fig1 = fig.add_subplot(321, projection='3d')
fig1.plot_surface(xx, yy, zzOlapR0, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzOlapR0.min(), vmax=zzOlapR0.max())
fig1.set_zlim(0,1)
# fig1.set_title("Re$[\,\gamma_R\,]\\approx\cos(\\theta/2)$",fontsize=fs)
fig1.set_title("Re$[\,\gamma_R\,]$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W / \pi$",fontsize=fs)
fig1 = fig.add_subplot(323, projection='3d')
fig1.plot_surface(xx, yy, zzOlapI0, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzOlapI0.min(), vmax=zzOlapI0.max())
# fig1.set_zlim(-0.01,0.01)
# fig1.set_title("Im$[\,\gamma_R\,]\\approx\cos(\\theta/2)$",fontsize=fs)
fig1.set_title("Im$[\,\gamma_R\,]$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W/\pi$",fontsize=fs)
fig1 = fig.add_subplot(322, projection='3d')
fig1.plot_surface(xx, yy, zzOlapR1, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzOlapR1.min(), vmax=zzOlapR1.max())
fig1.set_zlim(-1,1)
# fig1.set_title("Re$[\,\delta_R\,]\\approx\sin(\\theta/2)\cos(\phi)$",fontsize=fs)
fig1.set_title("Re$[\,\delta_R\,]$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W / \pi$",fontsize=fs)
fig1 = fig.add_subplot(324, projection='3d')
fig1.plot_surface(xx, yy, zzOlapI1, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzOlapI1.min(), vmax=zzOlapI1.max())
fig1.set_zlim(-1,1)
# fig1.set_title("Im$[\,\delta_R\,]\\approx\sin(\\theta/2)\sin(\phi)$",fontsize=fs)
fig1.set_title("Im$[\,\delta_R\,]$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W/\pi$",fontsize=fs)
fig1 = fig.add_subplot(325, projection='3d')
fig1.plot_surface(xx, yy, zzErr0, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzErr0.min(), vmax=zzErr0.max())
# fig1.set_zlim(-1,1)
# fig1.set_title("Re$[\,\delta_R\,]\\approx\sin(\\theta/2)\cos(\phi)$",fontsize=fs)
fig1.set_title("$\epsilon_\gamma$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W / \pi$",fontsize=fs)
fig1 = fig.add_subplot(326, projection='3d')
fig1.plot_surface(xx, yy, zzErr1, rstride=gridR, cstride=gridC, cmap=myMap, alpha=0.5,zorder=11.0,vmin=zzErr1.min(), vmax=zzErr1.max())
# fig1.set_zlim(-1,1)
# fig1.set_title("Re$[\,\delta_R\,]\\approx\sin(\\theta/2)\cos(\phi)$",fontsize=fs)
fig1.set_title("$\epsilon_\delta$",fontsize=fs)
fig1.set_ylabel("$\\theta_W/\pi$",fontsize=fs)
fig1.set_xlabel("$\phi_W / \pi$",fontsize=fs)
else:
# x_expect = 2e0*sp.real(FuncInfoOlap [0,:,:]*FuncInfoOlap [1,:,:])
#
x = 2e0*sp.real(spos[0,:,:].conj()*spos [1,:,:])
y = 2e0*sp.real(spos[0,:,:].conj()*spos [1,:,:]/1.0j)
z = sp.absolute(spos[0,:,:])**2 -sp.absolute(spos [1,:,:])**2
xprime = 2e0*sp.real(gamma [:,:].conj()*delta [:,:])
yprime = 2e0*sp.real(gamma [:,:].conj()*delta [:,:]/1.0j)
zprime = sp.absolute(gamma [:,:])**2 -sp.absolute(delta [:,:])**2
myDensity = sp.sqrt(sp.absolute(x-xprime)**2 + sp.absolute(y-yprime)**2 + sp.absolute(z-zprime)**2)
# myDensity = 1.0 - (x*xprime + y*yprime + z*zprime)
cm = plt.cm.get_cmap(myMap)
myMin = myDensity.min()
myMax = max(sp.absolute(myDensity.max()),sp.absolute(myDensity.min()))
myColors = cm(myDensity/myMax)
m = plt.cm.ScalarMappable(cmap=myMap)
fig3D = fig.add_subplot(1, 1, 1, projection='3d')
surf = fig3D.plot_surface(xprime, yprime, zprime, rstride=1, cstride=1, linewidth=1, color="black",
facecolors=myColors,shade=False,antialiased=True,
vmin=myMin, vmax=myMax)
# fig3D.plot_wireframe(x*1.01, y*1.01, z*1.01, rstride=1, cstride=1,alpha=1,linewidth=1,color="black")
tick2=(myMin+myMax)/2.0
tick1=round_sig(myMin+(myMax-myMin)*0.1,2)
tick3=round_sig(myMax-(myMax-myMin)*0.1,2)
tick2=round_sig(tick2,2)
print "### error boundaries:", tick1,tick2,tick3
m.set_array(myDensity)
cb= plt.colorbar(m,shrink=0.5,aspect=7,ticks=([tick1,tick2,tick3]))
cb.formatter.set_scientific(True)
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
fig3D.set_xlabel("$\langle \sigma_x(\gamma^\prime,\delta^\prime) \\rangle$", fontsize=fs)
fig3D.set_ylabel("$\langle \sigma_y(\gamma^\prime,\delta^\prime) \\rangle$", fontsize=fs)
fig3D.set_zlabel("$\langle \sigma_z(\gamma^\prime,\delta^\prime) \\rangle$", fontsize=fs)
fig3D.set_xticks([-1,0,1])
fig3D.set_xlim([-1.01,1.01])
fig3D.xaxis._axinfo['label']['space_factor'] = 2.0
fig3D.set_yticks([-1,0,1])
fig3D.set_ylim([-1.01,1.01])
fig3D.yaxis._axinfo['label']['space_factor'] = 2.0
fig3D.set_zticks([-1,0,1])
fig3D.set_zlim([-1.01,1.01])
fig3D.zaxis._axinfo['label']['space_factor'] = 2.0
plt.show()
def get_color(i, n):
spaces = np.linspace(0, 1 - 1 / n, n)
cm = plt.get_cmap('hsv')
return cm(spaces[i])
def main():
# starting position (x, y)
start1 = [3, 3]
start2 = [1, 3]
start3 = [1, 2]
# goal point (x, y)
goal1 = [1, 7]
goal2 = [3, 7]
goal3 = [7, 8]
if not os.path.exists('Output'):
os.makedirs('Output')
# safe distance
s = 0.5
# Initial Planning
rrt_star1 = RRTStar(start1, goal1, s=s)
traj1 = rrt_star1.plan("Output/plan1.txt", "Output/explored1.png")
rrt_star2 = RRTStar(start2, goal2, s=s)
traj2 = rrt_star2.plan("Output/plan2.txt", "Output/explored2.png")
rrt_star3 = RRTStar(start3, goal3, s=s)
traj3 = rrt_star3.plan("Output/plan3.txt", "Output/explored3.png")
# Plot planned trajectories
fig, ax = plt.subplots()
ax.set_xlim([0, 10])
ax.set_ylim([0, 10])
ax.plot(traj1[0], traj1[1], color='b', linewidth=1)
ax.plot(traj2[0], traj2[1], color='r', linewidth=1)
ax.plot(traj3[0], traj3[1], color='g', linewidth=1)
plt.savefig("Output/Plan0.png")
replan = [True, True, True]
traj_all = [traj1, traj2, traj3]
rrt_star = [rrt_star1, rrt_star2, rrt_star3]
# replan2 = True
# replan3 = True
flag = True
for i in range(2):
col, t = check_for_replanning(traj1, traj2, traj3, s, flag)
flag = False
print("Collision: ", col)
pd1 = float('inf')
pd2 = float('inf')
pd3 = float('inf')
if col[0] and replan[0]:
new_traj1 = rrt_star1.replan(
[traj2, traj3], t[0], "Output/replanned" + str(i) + "_1.txt",
"Output/re_explored" + str(i) + "_1.png")
pd1 = (len(new_traj1[0]) - len(traj1[0])) / float(len(
traj1[0])) * 100
if col[1] and replan[1]:
new_traj2 = rrt_star2.replan(
[traj1, traj3], t[1], "Output/replanned" + str(i) + "_2.txt",
"Output/re_explored" + str(i) + "_2.png")
pd2 = (len(new_traj2[0]) - len(traj2[0])) / float(len(
traj2[0])) * 100
if col[2] and replan[2]:
new_traj3 = rrt_star3.replan(
[traj1, traj2], t[2], "Output/replanned" + str(i) + "_3.txt",
"Output/re_explored" + str(i) + "_3.png")
pd3 = (len(new_traj3[0]) - len(traj3[0])) / float(len(
traj3[0])) * 100
m = min(pd1, pd2, pd3)
if m == float('inf'):
print("Final trajectories found at iteration: " + str(i + 1))
break
if m == pd1:
print("Trajectory 1 changed at iteration " + str(i + 1))
traj1 = new_traj1
replan[0] = False
elif m == pd2:
print("Trajectory 2 changed at iteration " + str(i + 1))
traj2 = new_traj2
replan[1] = False
else:
print("Trajectory 3 changed at iteration " + str(i + 1))
traj3 = new_traj3
replan[2] = False
fig6, ax6 = plt.subplots()
ax6.set_xlim([0, 10])
ax6.set_ylim([0, 10])
ax6.plot(traj1[0], traj1[1], color='b', linewidth=1)
ax6.plot(traj2[0], traj2[1], color='r', linewidth=1)
ax6.plot(traj3[0], traj3[1], color='g', linewidth=1)
plt.savefig("Output/Plan" + str(i + 1) + ".png")
fig2, ax2 = plt.subplots()
ax2.set_xlim([0, 10])
ax2.set_ylim([0, 10])
obs = plt.Rectangle((4, 3.5), 2, 3, fill=True, color='k')
ax2.add_patch(obs)
L1 = len(traj1[0])
L2 = len(traj2[0])
L3 = len(traj3[0])
L = max(L1, L2, L3)
cm = plt.get_cmap('plasma')
ax2.set_color_cycle([cm(1. * i / (L)) for i in range(L)])
for i in range(L1 - 1):
ax2.plot(traj1[0][i:i + 2], traj1[1][i:i + 2])
cm = plt.get_cmap('plasma')
ax2.set_color_cycle([cm(1. * i / (L)) for i in range(L)])
for i in range(L2 - 1):
ax2.plot(traj2[0][i:i + 2], traj2[1][i:i + 2])
cm = plt.get_cmap('plasma')
ax2.set_color_cycle([cm(1. * i / (L)) for i in range(L)])
for i in range(L3 - 1):
ax2.plot(traj3[0][i:i + 2], traj3[1][i:i + 2])
tr = [traj1, traj2, traj3]
# for i in range(3):
# print("xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx")
# print("For iteration: " + str(i+3))
# l2 = min(len(tr[i][0]), len(tr[(i+1)%3][0]))
# for j in range(l2):
# dist = np.sqrt((tr[i][0][j]-tr[(i+1)%3][0][j])**2 + (tr[i][1][j]-tr[(i+1)%3][1][j])**2)
# # print(dist)
# if dist < s:
# print("Collision detected at: " + str(i))
# print(dist)
# save data in txt file
out1 = traj1.T
if os.path.exists("Output/final_path1.txt"):
os.remove("Output/final_path1.txt")
final1 = open("Output/final_path1.txt", "a")
for i in range(len(out1)):
np.savetxt(final1, out1[i], fmt="%s", newline=' ')
final1.write("\n")
out2 = traj2.T
if os.path.exists("Output/final_path2.txt"):
os.remove("Output/final_path2.txt")
final2 = open("Output/final_path2.txt", "a")
for i in range(len(out2)):
np.savetxt(final2, out2[i], fmt="%s", newline=' ')
final2.write("\n")
out3 = traj3.T
if os.path.exists("Output/final_path3.txt"):
os.remove("Output/final_path3.txt")
final3 = open("Output/final_path3.txt", "a")
for i in range(len(out3)):
np.savetxt(final3, out3[i], fmt="%s", newline=' ')
final3.write("\n")
plt.savefig("Output/final_path.png")
plt.show()
plt.pause(15)
plt.close()
def colorize(value, normalize=True, vmin=None, vmax=None, cmap=None, vals=255):
"""
A utility function for TensorFlow that maps a grayscale image to a matplotlib
colormap for use with TensorBoard image summaries.
By default it will normalize the input value to the range 0..1 before mapping
to a grayscale colormap.
Arguments:
- value: 2D Tensor of shape [height, width] or 3D Tensor of shape
[height, width, 1].
- vmin: the minimum value of the range used for normalization.
(Default: value minimum)
- vmax: the maximum value of the range used for normalization.
(Default: value maximum)
- cmap: a valid cmap named for use with matplotlib's `get_cmap`.
(Default: 'gray')
- vals: the number of values in the cmap minus one
Example usage:
```
output = tf.random_uniform(shape=[256, 256, 1])
output_color = colorize(output, vmin=0.0, vmax=1.0, cmap='viridis')
tf.summary.image('output', output_color)
```
Returns a 3D tensor of shape [height, width, 3].
"""
value = tf.squeeze(value, axis=3)
if normalize:
vmin = tf.reduce_min(value) if vmin is None else vmin
vmax = tf.reduce_max(value) if vmax is None else vmax
value = (value - vmin) / (vmax - vmin) # vmin..vmax
# dma = tf.reduce_max(value)
# dma = tf.Print(dma, [dma], 'dma', summarize=16)
# tf.summary.histogram('dma', dma) # just so tf.Print works
# quantize
indices = tf.to_int32(tf.round(value * float(vals)))
else:
# quantize
indices = tf.to_int32(value)
# 00 Unknown 0 0 0
# 01 Terrain 210 0 200
# 02 Sky 90 200 255
# 03 Tree 0 199 0
# 04 Vegetation 90 240 0
# 05 Building 140 140 140
# 06 Road 100 60 100
# 07 GuardRail 255 100 255
# 08 TrafficSign 255 255 0
# 09 TrafficLight 200 200 0
# 10 Pole 255 130 0
# 11 Misc 80 80 80
# 12 Truck 160 60 60
# 13 Car:0 200 200 200
if cmap == 'vkitti':
colors = np.array([
0, 0, 0, 210, 0, 200, 90, 200, 255, 0, 199, 0, 90, 240, 0, 140,
140, 140, 100, 60, 100, 255, 100, 255, 255, 255, 0, 200, 200, 0,
255, 130, 0, 80, 80, 80, 160, 60, 60, 200, 200, 200, 230, 208, 202
])
colors = np.reshape(colors, [15, 3]).astype(np.float32) / 255.0
colors = tf.constant(colors)
else:
# gather
cm = matplotlib.cm.get_cmap(cmap if cmap is not None else 'gray')
if cmap == 'RdBu' or cmap == 'RdYlGn':
colors = cm(np.arange(256))[:, :3]
else:
colors = cm.colors
colors = np.array(colors).astype(np.float32)
colors = np.reshape(colors, [-1, 3])
colors = tf.constant(colors, dtype=tf.float32)
value = tf.gather(colors, indices)
# value is float32, in [0,1]
return value
def chroma_fill_between(x, y1, y2=None, c=None, cm=cm.gist_rainbow, axes=None):
"""Plot y vs. x but fill in between y and 0 with a colormap.
Arguments
---------
x, y1, y2 -- curves to plot between
c -- array congruent to x and y indicating which color in the
color map to fill in at that point. The range should be from
0.0 to 1.0
Keyword Arguments
-----------------
cm -- Matplotlib Colormap Object. The c array above picks colors
from this array. (default matplotlib.cm.gist_rainbow, which
covers roygbiv with red at c=0.0 and violet at c=1.0)
axes -- Matplotlib Axes object to use to make the plot.
"""
if axes is None: axes = plt.gca()
if y2 is None:
y2 = np.zeros(len(y1), dtype=float)
elif isinstance(y2, (int, float)):
y2 *= np.ones(len(y1), dtype=float)
cc = c.copy()
cc[cc > 1.0] = 1.0
cc[cc < 0.0] = 0.0
for i in xrange(cm.N):
cmin = 1.0*i/cm.N
cmax = 1.0*(i+1)/cm.N
w = np.int_(np.logical_and(cc >= cmin, cc <= cmax))
if not w.any() : continue
starts = np.nonzero(np.diff(w) == 1)[0]
if w[0] == True :
starts = np.insert(starts, 0, 0)
ends = np.nonzero(np.diff(w) == -1)[0]
if w[-1] == True :
ends = np.append(ends, 0)
for start, end in zip(starts, ends):
axes.fill_between(x[start:end+1], y1[start:end+1], y2[start:end+1], color=cm(i))
def plot_snapshots(stg,
var,
cvar,
times,
varname='',
varunit='',
fvar=lambda v: '%g' % v,
cvarname='',
cvarunit='',
fcvar=lambda v: '%g' % v):
fig, axs = plt.subplots(1, len(times))
fig.subplots_adjust(wspace=0.1, hspace=0.05)
cm = plt.get_cmap('tab10')
colors = [cm(i) for i in (0, 4, 6, 9)]
uv = array(sorted(stg[var].unique()))
ucv = array(sorted(stg[cvar].unique()))
csp = linspace(-0.075 * len(ucv), 0.075 * len(ucv), len(ucv))
legh, legl = [], []
for i, v in enumerate(uv):
for j, t in enumerate(times):
axs[j].set_yscale('log')
for k, cv in enumerate(ucv):
#for rmax in [0,1]:
sl = stg[(stg[var] == v) & (stg[cvar] == cv)]
#sl = sl[sl['Rmax'] == rmax]
es = [Evap(tdir, gdir) for tdir in sl.index]
e = array([interp(t, e.tf, e.evapf, right=nan) for e in es])
e = e[~isnan(e)]
x = array([i + csp[k]] * len(e))
y = lake_evap(e, L_lake, A_lake) * yrsec
if len(y) > 0:
#axs[j].scatter(x+0.1, y, s=2.5, c=colors[k])
r = axs[j].violinplot(y, positions=[x[0]])
r['bodies'][0].set_alpha(0)
for s in ['cmins', 'cmaxes', 'cbars']:
r[s].set_color(colors[k])
r[s].set_linewidth(1.5)
if i == 0 and j == 0:
label = '%s = %s %s' % (cvarname, fcvar(cv), cvarunit)
legh.append(r['cbars'])
legl.append(label)
ymin = min([ax.get_ylim()[0] for ax in axs])
ymax = max([ax.get_ylim()[1] for ax in axs])
for j in range(len(axs)):
axs[j].set_ylim(ymin, ymax)
axs[j].set_title('{:,g} {}'.format(times[j] / TU, TL))
axs[j].grid(False, axis='x')
axs[j].set_xticks(range(len(var)))
axs[j].tick_params(axis='x', length=0)
axs[j].set_xticklabels([fvar(v) for v in uv])
axs[j].set_xlim(-0.2 * len(uv), (len(uv) - 1) + 0.2 * len(uv))
axs[j].spines['top'].set_visible(True)
axs[j].spines['right'].set_visible(True)
for i in range(len(uv) - 1):
axs[j].plot([i + 1 / 2] * 2, [ymin, ymax], 'k', alpha=0.5)
for j in range(1, len(axs)):
axs[j].set_yticklabels([])
axs[j].tick_params(axis='y', length=0)
axs[0].set_ylabel('Balancing Evaporation Rate (m/yr)\n4000 km$^2$ surface')
fig.text(0.5, 0.04, '%s (%s)' % (varname, varunit), ha='center')
axs[0].legend(legh, legl, loc='upper left')
def sample_colours_from_colourmap(n_colours, colour_map):
import matplotlib.pyplot as plt
cm = plt.get_cmap(colour_map)
return [cm(1.*i/n_colours)[:3] for i in range(n_colours)]
p.new(title='Trajectories', aspect='equal',xlabel='x-coordinate',ylabel='y-coordinate')
traj -= np.floor(traj/L)*L
p.plot([0,L,L,0,0],[0,0,L,L,0],'b-', lw=2)
p.plot(traj[-1,0,:],traj[-1,1,:],'wo', alpha=0.1 ,ms=7, mew = 2)
p.plot(traj[0,0,:],traj[0,1,:],'+', c=[0.8,0.8,0.8], alpha=0.1)
i = range(traj.shape[2])
np.random.shuffle(i)
tpart = np.array(traj[:,:,i[:3]])
for n in range(1,tpart.shape[0]):
i = (tpart[n-1,0,:] - tpart[n,0,:])**2+(tpart[n-1,1,:] - tpart[n,1,:])**2 > 50
tpart[n,:,i] = [None,None,None]
nmax = tpart.shape[2]
cm = cm.get_cmap('Dark2')
colors=[cm(1.*i/nmax) for i in range(nmax)]
for n in range(nmax):
p.plot(tpart[:,0,n],tpart[:,1,n],'-', c = colors[n], alpha=0.8)
p.plot(tpart[-1,0,n],tpart[-1,1,n],'o', c = colors[n], alpha=0.8 ,ms=7, mew = 2)
# Energies
p.new(title='Energies (from t=0 to t=10)',xlabel='time',ylabel='energy')
i = ts < 10
p.plot(ts[i],Ekins[i], label='Ekin')
p.plot(ts[i ],Es[i], label='Eges')
p.plot(ts[i ],Epots[i], label='Epot')
# Energies
p.new(title='Energies (from t=10 to t=100)',xlabel='time',ylabel='energy')
i = np.all([ts < 100, ts > 10], axis=0)
p.plot(ts[i],Ekins[i], label='Ekin')