def vis_back_proj(ind=1, img_style="edge", pcd_style="intens"):
imgfile = "img/" + str(ind).zfill(
params["file_name_digits"]) + "." + params['image_format']
if img_style == "edge":
gray = cv2.imread(imgfile)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
img = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
elif img_style == "orig":
img = cv2.imread(imgfile)
else:
print "Please input the right image style"
csvfile = "pcd/" + str(ind).zfill(params["file_name_digits"]) + ".csv"
csv = np.genfromtxt(csvfile, delimiter=",", skip_header=1)
pcd = csv[:, :3]
dis_arr = np.linalg.norm(pcd, axis=1)
intens = csv[:, 3]
filels = os.listdir(".")
for file in filels:
if file.find("cali_result.txt") > -1:
r_t = np.genfromtxt(file, delimiter=',')
print "Load ", file, " as the extrinsic calibration parameters!"
break
else:
raise Exception("No calibration file is found!")
if pcd_style == "intens":
pcd_color = np.fliplr(
(cm.jet(intens.astype(np.float32) / intens.max()) * 255).astype(
np.int32)[:, :3])
elif pcd_style == "dis":
pcd_color = np.fliplr(
(cm.jet(dis_arr / 10) * 255).astype(np.int32)[:, :3])
else:
print "Please input the right pcd color style"
backproj_img = back_project_pcd(img, pcd, pcd_color, r_t)
resized_img_for_view = cv2.resize(backproj_img, (int(W / 4.), int(H / 4.)))
window_name = "ind: " + str(
ind) + " img_style: " + img_style + " pcd_style: " + pcd_style
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
cv2.imshow(window_name, resized_img_for_view)
k = cv2.waitKey(0)
if k == 27: # wait for ESC key to exit
cv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exit
save_file_name = str(ind).zfill(
params["file_name_digits"]
) + "_" + img_style + "_" + pcd_style + "." + params['image_format']
cv2.imwrite(save_file_name, img, [cv2.IMWRITE_JPEG_QUALITY, 70])
print "The image is saved to ", save_file_name
cv2.destroyAllWindows()
def compareVoteonBar():
total = 0
for elval1 in listt:
total += elval1[1]
for elval in listt:
c = "%.3f" % ((elval[1] / total) * 100)
chng.append([elval[0], float(c)])
perclist = sorted(chng, key=itemgetter(1))
for element in perclist:
names.append(element[0])
byvote.append(element[1])
byvote.reverse()
names.reverse()
length = len(names)
aaa = np.arange(length)
rex = list(map("{} %".format, byvote))
k = 0
for con in names:
plot.bar(k,
byvote[k],
align='center',
color=cm.jet(1. * k / length),
label=con)
k += 1
plot.xticks(aaa, rex)
plot.xlabel("Nominees")
plot.ylabel("Vote percentages")
plot.legend()
plot.autoscale()
plot.tight_layout()
plot.savefig("CompVotePercs.pdf")
plot.close()
def SetClassPoints(self, classCoords):
self.sizer4.DeleteWindows()
vals = np.arange(float(len(classCoords))) / len(classCoords)
if len(vals) > 0:
vals += (1.0 - vals[-1]) / 2
colors = [np.array(cm.jet(val)) * 255 for val in vals]
colors_dict = {}
i=0
for name in classCoords:
colors_dict[name] = colors[i]
i+=1
self.sizer4.Add(wx.StaticText(self.Parent, -1, 'Classes:'))
i=1
classCoords_ordered = [name for name in classCoords if name[:8] != 'training']
classCoords_ordered.extend([name for name in classCoords if name[:8] == 'training'])
for name in classCoords_ordered:
if i == len(classCoords)/2 + 1:
self.sizer4.Add(wx.StaticText(self.Parent, -1, 'Training:'))
checkBox = wx.CheckBox(self.Parent, wx.NewId(), '%d) %s'%(i,name))
#checkBox.SetForegroundColour(color) # Doesn't work on Mac. Works on Windows.
checkBox.SetBackgroundColour(colors_dict[name])
checkBox.SetValue(True)
self.sizer4.Add(checkBox, flag=wx.EXPAND)
def OnTogglePhenotype(evt):
className = evt.EventObject.Label
for listener in self.listeners:
listener.ToggleClass(className[3:], evt.Checked())
checkBox.Bind(wx.EVT_CHECKBOX, OnTogglePhenotype)
i+=1
def plot_mesh(self):
"""Plot the overall BEM mesh and domain geometry
Each domain can have different color by modifing line:
plt.plot(np.append([BE.xa for BE in BEMobj.BEs_edge], BEMobj.BEs_edge[0].xa),
np.append([BE.ya for BE in BEMobj.BEs_edge], BEMobj.BEs_edge[0].ya),
,color=c,markersize=5)
plt.scatter([BE.xc for BE in BEMobj.BEs_edge],
[BE.yc for BE in BEMobj.BEs_edge], color=c, s=25)
Author:Bin Wang([email protected])
Date: July. 2017
"""
plt.figure(figsize=(6, 6))
plt.axes().set_aspect('equal')
plt.axes().set_title('BEM Mesh', fontsize=15)
plt.axes().set_xlabel('x(m)')
plt.axes().set_ylabel('y(m)')
#plt.xticks(np.arange(-3, 9, 1))
#plt.yticks(np.arange(-2, 5,1))
#plt.grid(b=True, which='major', color='k', linestyle=':')
#plt.grid(b=True, which='minor', color='k', linestyle='--', alpha=0.2)
#plt.minorticks_on()
Total_Ele = 0
color = cm.jet(np.linspace(0, 1, self.NumObj + 1))
for i, c in zip(range(self.NumObj), color): #For each subdomain
BEMobj = self.BEMobjs[i]
#Boundary elements
plt.plot(np.append([BE.xa for BE in BEMobj.BEs_edge],
BEMobj.BEs_edge[0].xa),
np.append([BE.ya for BE in BEMobj.BEs_edge],
BEMobj.BEs_edge[0].ya),
'bo-',
markersize=5)
if (BEMobj.BEs_edge[0].element_type == "Quad"):
plt.scatter([BE.xc for BE in BEMobj.BEs_edge],
[BE.yc for BE in BEMobj.BEs_edge],
color='b',
s=25)
Total_Ele += len(BEMobj.BEs_edge)
#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.tight_layout()
#plt.savefig('Couplded_Fig.png', format='png', dpi=300)
print('-----Mesh Info-----')
print('Total Number of Elements:', Total_Ele)
print("Total Number of Subdomains:", self.NumObj)
plt.show()
def random_jet(bgcolor=(255, 255, 255)):
"""Generate random RGB lut for nice visually object distinguishing.
Return random uint8 cmap based on jet colors.
"""
lut = (cm.jet(np.arange(256))[...,:3] * 256).round().astype(np.uint8)
np.random.shuffle(lut)
lut[0] = bgcolor
return lut
def get_colors(self):
if self.colors:
colors = self.colors
elif max(list(map(len, self.x_points)))==0:
colors = []
else:
# Choose colors from jet colormap starting with light blue (0.28)
vals = np.arange(0.28, 1.28, 1. / len(self.x_points)) % 1.
colors = [colorConverter.to_rgba(cm.jet(val), alpha=0.75)
for val in vals]
return colors
def plotReflectance(root, dirpath, filename):
try:
referenceGroup = root.getGroup("Reflectance")
rrsData = referenceGroup.getDataset("Rrs")
font = {
'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 16,
}
x = []
for k in rrsData.data.dtype.names:
if Utilities.isFloat(k):
x.append(k)
total = rrsData.data.shape[0]
color = iter(cm.jet(np.linspace(0, 1, total)))
for i in range(total):
y = []
for k in x:
y.append(rrsData.data[k][i])
c = next(color)
plt.plot(x, y, 'k', c=c)
#if (i % 25) == 0:
# plt.plot(x, y, 'k', color=(i/total, 0, 1-i/total, 1))
#plt.title('Remote sensing reflectance', fontdict=font)
#plt.text(2, 0.65, r'$\cos(2 \pi t) \exp(-t)$', fontdict=font)
plt.xlabel('wavelength (nm)', fontdict=font)
plt.ylabel('Rrs ($sr^{-1}$)', fontdict=font)
# Tweak spacing to prevent clipping of ylabel
plt.subplots_adjust(left=0.15)
#plt.show()
# Create output directory
plotdir = os.path.join(dirpath, 'Plots')
os.makedirs(plotdir, exist_ok=True)
# Save the plot
fp = os.path.join(plotdir, filename + '.png')
plt.savefig(fp)
plt.close(
) # This prevents displaying the polt on screen with certain IDEs
except:
e = sys.exc_info()[0]
print("Error: %s" % e)
def SetClassPoints(self, classes):
if len(classes) == 0:
logging.warn('There are no objects to classify in this image.')
return
from matplotlib.pyplot import cm
self.classes = classes
vals = np.arange(0, 1, 1. / len(classes))
vals += (1.0 - vals[-1]) / 2
self.colors = [np.array(cm.jet(val)) * 255 for val in vals]
self.class_nums = [str(i + 1) for i, _ in enumerate(classes)]
self.classVisible = {}
for className in classes.keys():
self.classVisible[className] = True
self.Refresh()
def SetClassPoints(self, classes):
if len(classes) == 0:
logging.warn('There are no objects to classify in this image.')
return
from matplotlib.pyplot import cm
self.classes = classes
vals = np.arange(0, 1, 1. / len(classes))
vals += (1.0 - vals[-1]) / 2
self.colors = [np.array(cm.jet(val)) * 255 for val in vals]
self.class_nums = [str(i+1) for i,_ in enumerate(classes)]
self.classVisible = {}
for className in classes.keys():
self.classVisible[className] = True
self.Refresh()
def plotDataset(root, filename, name, label):
try:
referenceGroup = root.getGroup("Reflectance")
rrsData = referenceGroup.getDataset(name)
font = {
'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 16,
}
x = []
for k in rrsData.data.dtype.names:
x.append(k)
total = rrsData.data.shape[0]
color = iter(cm.jet(np.linspace(0, 1, total)))
for i in range(total):
y = []
for k in x:
y.append(rrsData.data[k][i])
c = next(color)
plt.plot(x, y, 'k', c=c)
#if (i % 25) == 0:
# plt.plot(x, y, 'k', color=(i/total, 0, 1-i/total, 1))
#plt.title('Remote sensing reflectance', fontdict=font)
#plt.text(2, 0.65, r'$\cos(2 \pi t) \exp(-t)$', fontdict=font)
plt.xlabel('wavelength (nm)', fontdict=font)
plt.ylabel(label, fontdict=font)
# Tweak spacing to prevent clipping of ylabel
plt.subplots_adjust(left=0.15)
#plt.show()
plt.savefig('Plots/' + filename + '.png')
plt.close(
) # This prevents displaying the polt on screen with certain IDEs
except:
e = sys.exc_info()[0]
print("Error: %s" % e)
def SetClassPoints(self, classCoords):
self.sizer4.DeleteWindows()
vals = np.arange(float(len(classCoords))) / len(classCoords)
if len(vals) > 0:
vals += (1.0 - vals[-1]) / 2
colors = [np.array(cm.jet(val)) * 255 for val in vals]
self.sizer4.Add(wx.StaticText(self.Parent, -1, 'Classes:'))
i=1
for (name, keys), color in zip(classCoords.items(), colors):
checkBox = wx.CheckBox(self.Parent, wx.NewId(), '%d) %s'%(i,name))
checkBox.SetForegroundColour(color) # Doesn't work on Mac. Works on Windows.
checkBox.SetValue(True)
self.sizer4.Add(checkBox, flag=wx.EXPAND)
def OnTogglePhenotype(evt):
className = evt.EventObject.Label
for listener in self.listeners:
listener.ToggleClass(className[3:], evt.Checked())
checkBox.Bind(wx.EVT_CHECKBOX, OnTogglePhenotype)
i+=1
def SetClassPoints(self, classCoords):
self.sizer4.DeleteWindows()
vals = np.arange(float(len(classCoords))) / len(classCoords)
if len(vals) > 0:
vals += (1.0 - vals[-1]) / 2
colors = [np.array(cm.jet(val)) * 255 for val in vals]
colors_dict = {}
i = 0
for name in classCoords:
colors_dict[name] = colors[i]
i += 1
self.sizer4.Add(wx.StaticText(self.Parent, -1, 'Classes:'))
i = 1
classCoords_ordered = [
name for name in classCoords if name[:8] != 'training'
]
classCoords_ordered.extend(
[name for name in classCoords if name[:8] == 'training'])
for name in classCoords_ordered:
if i == len(classCoords) / 2 + 1:
self.sizer4.Add(wx.StaticText(self.Parent, -1,
'Training:'))
checkBox = wx.CheckBox(self.Parent, wx.NewId(),
'%d) %s' % (i, name))
#checkBox.SetForegroundColour(color) # Doesn't work on Mac. Works on Windows.
checkBox.SetBackgroundColour(colors_dict[name])
checkBox.SetValue(True)
self.sizer4.Add(checkBox, flag=wx.EXPAND)
def OnTogglePhenotype(evt):
className = evt.EventObject.Label
for listener in self.listeners:
listener.ToggleClass(className[3:], evt.Checked())
checkBox.Bind(wx.EVT_CHECKBOX, OnTogglePhenotype)
i += 1
def plot_evol(z_nums, masses, bins, avg=False, normalized=False):
fig, ax = plt.subplots()
color = iter(cm.jet(np.linspace(0, 1, len(masses))))
if avg == True:
for m in np.arange(len(masses)):
ax.plot(
z_nums[m],
masses[m],
color=next(color),
label=("bin " + str(m + 1) + ": (" + "{:.2e}".format(bins[m]) +
" to " + "{:.2e}".format(bins[m + 1]) + ")"))
ax.legend()
if normalized == True:
ax.set_title("Normalized averaged mass evolution of halos in " +
str(int(len(bins) - 1)) + " bins")
elif normalized == False:
ax.set_title("Averaged mass evolution of halos in " +
str(int(len(bins) - 1)) + " bins")
elif avg == False:
for m in np.arange(len(masses)):
ax.plot(z_nums[m], masses[m], color=next(color))
if extremum == 'max':
ax.set_title("Mass evolution of " + str(n) + " most massive halos")
elif extremum == 'min':
ax.set_title("Mass evolution of " + str(n) +
" least massive halos")
elif extremum == '':
ax.set_title("Mass evolution of halos in range " +
my_range_str[0] + " to " + my_range_str[1])
ax.set_yscale('log', nonpositive='clip')
ax.set_xlabel('Redshift')
ax.set_ylabel('Mass [M_sun/h]')
plt.savefig('full_mass_evol_bins_z_norm.png')
plt.show()
def SetClassPoints(self, classCoords):
self.sizer4.DeleteWindows()
vals = np.arange(float(len(classCoords))) / len(classCoords)
if len(vals) > 0:
vals += (1.0 - vals[-1]) / 2
colors = [np.array(cm.jet(val)) * 255 for val in vals]
self.sizer4.Add(wx.StaticText(self.Parent, -1, 'Classes:'))
i = 1
for (name, keys), color in zip(classCoords.items(), colors):
checkBox = wx.CheckBox(self.Parent, wx.NewId(),
'%d) %s' % (i, name))
checkBox.SetForegroundColour(
color) # Doesn't work on Mac. Works on Windows.
checkBox.SetValue(True)
self.sizer4.Add(checkBox, flag=wx.EXPAND)
def OnTogglePhenotype(evt):
className = evt.EventObject.Label
for listener in self.listeners:
listener.ToggleClass(className[3:], evt.Checked())
checkBox.Bind(wx.EVT_CHECKBOX, OnTogglePhenotype)
i += 1
from matplotlib.markers import MarkerStyle as mk
markers = mk.filled_markers
from matplotlib.pyplot import cm
import matplotlib.pyplot as plt
import glob as g
import numpy as np
from astropy.coordinates import SkyCoord as SC
from astropy import units as u
from astropy.io import ascii
cats = sorted(g.glob('Catalogues/v*dat'))
visits = np.array(cats).reshape((len(cats) / 4, 4))
color = cm.jet(np.linspace(0, 1, len(visits)))
markers = markers[:len(visits)]
for v, c in zip(visits, color):
alpha, delta = np.loadtxt(v[0], usecols=(0, 1), unpack=True)
plt.scatter(alpha, delta, color=c, s=2, label=str(v[0]).split('/')[1][:3])
alpha, delta = np.loadtxt(v[2], usecols=(0, 1), unpack=True)
plt.scatter(alpha, delta, color=c, s=2)
plt.scatter(194.8987875,
27.9593889,
marker='^',
color='black',
s=50,
label='NGC4874')
plt.scatter(195.0337375,
27.9770250,
marker='v',
color='black',
s=50,
label='NGC4889')
data = ascii.read('Catalogues/vandokkum15.csv',
thr=0.9,
colors='yellow',
vmax=0.75,
display_numbers=False,
cmap='gray')
a4 = plot_contours(A_gt.tocsc()[:, idx_components_gt[fn_gt_online]],
Cn,
thr=0.9,
vmax=0.85,
colors='r',
display_numbers=False,
cmap='gray')
# %% FIGURE 4 c and d
from matplotlib.pyplot import cm as cmap
pl.figure("Figure 4c and 4d")
color = cmap.jet(np.linspace(0, 1, 10))
i = 0
legs = []
all_ys = []
SNRs = np.arange(0, 10)
pl.subplot(1, 3, 1)
for k, fl_results in all_results.items():
print(k)
nm = k[:]
nm = nm.replace('neurofinder', 'NF')
nm = nm.replace('final_map', '')
nm = nm.replace('.', '')
nm = nm.replace('Data', '')
idx_components_gt = fl_results['idx_components_gt']
tp_gt = fl_results['tp_gt']
def plot_grouped_by_mica(self,
train,
params,
i,
ylim=[0, 1],
force_ylim=True,
fs=24,
fs_tick=18):
# if mica_library is not loaded, load it
self.load_mica_library(train)
# compute mica features
mica_attributes = train.m.F_att(self.mica_library)
mica_latents = train.m.F_lat(self.mica_library)
self.mica_features = torch.cat([mica_latents, mica_attributes],
dim=1).cpu().data.numpy()
# generate samples and compute their features
samples = torch.cat(
[train.m.get_fake(64, params['z_dim']) for i in range(10)], dim=0)
attributes = train.m.F_att(samples)
latents = train.m.F_lat(samples)
features = torch.cat([latents, attributes], dim=1).cpu().data.numpy()
# compute cosine similarity
similarity = cosine_similarity(features, self.mica_features)
# matches = argmax cosine similarity
matches = np.argmax(similarity, axis=1)
# group spectra by matches
groups = {}
for idx, match in enumerate(matches):
if match not in groups:
groups[match] = set([(idx, similarity[idx][match])])
else:
groups[match].add((idx, similarity[idx][match]))
# for each class in generated spectra:
# plot spectra and save plot with filename as mica match
samples = samples.cpu().data.numpy()
# sim_min = np.cos(np.pi/4.)
sim_min = max(np.cos(np.pi / 4.), np.min(np.max(similarity, axis=1)))
sim_max = np.max(similarity)
for endmember, sample_idxs in groups.items():
n = 0
avg_sim = 0.
for idx, sim in list(sample_idxs):
if sim >= sim_min:
# alpha = np.clip(0.5 * (sim + 1.), 0., 1.)
sim_norm = (sim - sim_min) / (sim_max - sim_min)
alpha = np.clip(sim_norm, 0., 1.)
plt.plot(self.waves,
samples[idx],
'--',
color=cm.jet(alpha))
avg_sim += sim
n += 1
if n >= 1:
avg_sim /= float(n)
plt.plot(self.waves,
self.mica_library[endmember].cpu().data.numpy(), 'k-')
plt.title(
'{:s}: avg_sim={:1.4f} global_min/max={:1.4f}/{:1.4f}'.
format(self.mica_names[endmember], avg_sim, sim_min,
sim_max),
fontsize=fs_tick - 6)
plt.xlabel('Channels', fontsize=fs)
plt.ylabel('Intensities', fontsize=fs)
plt.tick_params(axis='both', which='major', labelsize=fs_tick)
plt.grid(True, axis='x')
if force_ylim:
plt.gca().set_ylim(ylim)
filename = ''.join([
c for c in self.mica_names[endmember]
if c.isalpha() or c.isdigit()
]).rstrip()
plt.savefig(params['saveto'] +
'mica/{}_{}.png'.format(i, filename))
plt.close()
def plot_3D(*opts, fxn_name, animate_gif=True, f_name='v1'):
def init():
for line in lines:
line.set_data([], [])
line.set_3d_properties([])
return lines
def animate(i):
num1 = int(x2[i])
for lnum, (line, dot) in enumerate(zip(lines, dots)):
if num1 < dp[lnum]:
line.set_data(
xlist[lnum][:num1],
ylist[lnum][:num1]) # set data for each line separately.
line.set_3d_properties(zlist[lnum][:num1])
line.set_label(nn[lnum]) # set the label and draw the legend
plt.legend(loc="upper center", ncol=4)
return lines
# Get data
xlist, ylist, zlist, nn, dp = get_dlists(opts)
n = len(opts)
if fxn_name == 'rosenbrock':
x2 = np.rint(np.linspace(0, len(xlist[1]), endpoint=False, num=200))
c = cm.jet(np.linspace(0, 1, n))
g = 0.4
rot_val = 245
nr = colors.PowerNorm(gamma=g)
elif fxn_name == 'saddle':
rot_val = -60
nr = None
x2 = np.rint(np.linspace(0, len(xlist[1]), endpoint=False, num=100))
c = cm.rainbow(np.linspace(0, 1, n))
else:
x2 = np.rint(np.linspace(0, len(xlist[1]), endpoint=False, num=200))
c = cm.rainbow(np.linspace(0, 1, n))
g = 0.25
rot_val = 245
nr = colors.PowerNorm(gamma=g)
# Plot 3D Surface
fig = plt.figure(figsize=(9.6, 6.4))
ax = plt.axes(projection='3d', azim=rot_val)
plt.tight_layout()
data, minima, x_lims, y_lims = plot_fxn(fxn_name, '3D')
ax.plot_surface(*data,
rstride=1,
cstride=1,
norm=nr,
cmap='viridis',
edgecolor='none',
alpha=1.0)
ax.plot(*minima, 'x', markersize=12, mew=2, color='k')
ax.set_xlabel('x')
ax.set_ylabel('y')
# ax.set_xlim(x_lims)
# ax.set_ylim(y_lims)
# c = new_cmap(np.linspace(0,1,n))
# c = cm.jet(np.linspace(0,1,n))
if animate_gif == False:
for k in range(n):
x_history = np.array(xlist[k])
y_history = np.array(ylist[k])
path = np.concatenate(
(np.expand_dims(x_history, 1), np.expand_dims(y_history, 1)),
axis=1).T
ax.quiver(path[0, :-1],
path[1, :-1],
beale_fxn(path[::, :-1][0], path[::, :-1][1]),
path[0, 1:] - path[0, :-1],
path[1, 1:] - path[1, :-1],
beale_fxn(path[::, :-1][0], path[::, :-1][1]) -
beale_fxn(path[::, :-1][0], path[::, :-1][1]),
color=c[k],
label=nn[k],
length=1,
normalize=False,
lw=5)
plt.legend(loc="upper left")
else:
line, = ax.plot([], [], [], lw=2)
# dot, = ax.plot([], [], 'o', lw=2)
lines = []
dots = []
for index in range(n):
l_obj = ax.plot([], [], [], lw=2, color=c[index])[0]
lines.append(l_obj)
d_obj = ax.scatter([], [], [], marker='o', color=c[index])
dots.append(d_obj)
# print(x2)
# blit=True --> only re-draw the parts that have changed.
# dot = ax.scatter([],[],[]) # Set the dot at some arbitrary position initially
anim = animation.FuncAnimation(fig,
animate,
init_func=init,
frames=len(x2),
interval=1,
blit=True)
anim.save('images/{}_3D_{}.gif'.format(fxn_name, f_name),
dpi=60,
writer="imagemagick")
# ax.invert_yaxis()
plt.show()
def get_network_from_bursts(self, bursts_count_threshold=1, user_interaction_weight_threshold=1):
"""
get bursts in activity for units of information, get network of connected users that parcipate in these bursts
:param bursts_count_threshold: threshold for the number of bursts in activity for an information to be considered
:param user_interaction_weight_threshold: threshold for the number of burts two users must participate in together in order for them to be connected
"""
def get_burst_user_connections_df(content_id, content_df, burst_interval, userid_col='from_id', timestamp_col='date'):
'''connect users who participate in the same burst'''
burst_df = content_df[(content_df[self.timestamp_col].between(burst_interval[0], burst_interval[1], inclusive=True)) & (~content_df[self.userid_col].isna())].copy()
uids = list(sorted(burst_df[self.userid_col].unique()))
if len(uids) < 2:
return []
content_user_connections = []
for i, uid1 in enumerate(uids):
for uid2 in uids[i+1:]:
content_user_connections.append({'content': content_id,
'uid1': uid1,
'uid2': uid2,
'weight': 1})
return content_user_connections
user_connections = []
n_ids = self.dataset_df[self.content_col].nunique()
max_plots_to_show = 5
num_plots = 0
for content_id, content_df in self.dataset_df.groupby(self.content_col):
if num_plots < max_plots_to_show:
show = True
else:
show = False
if self.selected_content is not None and content_id not in self.selected_content:
continue
burstDetection = BurstDetection(dataset_df=content_df, metadata=self.metadata, id_col=self.id_col,
timestamp_col=self.timestamp_col, platform_col=self.platform_col,
time_granularity=self.time_granularity,
min_date=self.min_date, max_date=self.max_date)
burst_intervals = burstDetection.detect_bursts(self.gammas[content_id])
if len(burst_intervals) < bursts_count_threshold:
continue
if self.plot_bursts:
plot_df = self.dataset_df.copy()
plot_df.set_index(self.timestamp_col, inplace=True)
new_df = plot_df.groupby(pd.Grouper(freq=self.time_granularity))[[self.content_col]].count()
new_df.reset_index(inplace=True)
plt.figure()
plt.plot(new_df[self.timestamp_col], new_df[self.content_col])
for burst_interval in burst_intervals:
user_connections.extend(get_burst_user_connections_df(content_id, content_df, burst_interval))
if self.plot_bursts:
plt.axvspan(xmin=burst_interval[0], xmax=burst_interval[1], color="red", alpha=0.25)
if show and self.plot_bursts:
plt.show()
if self.plot_bursts and self.save_plots:
plt.savefig(self.plot_dir + str(content_id) + "_persistent_groups_with_bursts.png", bbox_inches='tight')
plt.close()
num_plots += 1
user_network_df = pd.DataFrame(user_connections)
if 'uid1' not in user_network_df.columns:
warnings.warn("No bursts detected in any information IDs. Persistent group measurements cannot be run. They will fail with uid1 KeyError.")
user_network_df = user_network_df.groupby(['uid1', 'uid2'])['weight'].sum().reset_index()
self.user_network_df = user_network_df[user_network_df['weight']>=user_interaction_weight_threshold]
edgelist = self.user_network_df[['uid1', 'uid2', 'weight']].apply(tuple, axis=1).tolist()
if len(edgelist) == 0:
return
user_interaction_graph = ig.Graph.TupleList(edgelist, directed=False)
self.groups = self.community_detection_algorithm(user_interaction_graph)
print('Number of groups: ', len(self.groups))
if self.plot:
figsize = (10, 10)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(1, 1, 1)
ax.axis('off')
ax.set_frame_on(False)
colors = iter(cm.jet(np.linspace(0, 1, len(self.groups))))
node_color = {}
node_labels = {}
for g_id, g in enumerate(self.groups):
c = next(colors)
for i in g:
node_color[i] = c
node_labels[i] = g_id
# Draw graph of users by group membership
node_c = [node_color[node['name']] for node in user_interaction_graph.vs]
ig.plot(user_interaction_graph, layout=user_interaction_graph.layout_fruchterman_reingold(), vertex_size=30, edge_color='black', vertex_color=node_c)
plt.show()
if self.save_plots:
plt.savefig(self.plot_dir+ "network_of_users_by_groups.png",bbox_inches='tight')
dec_min=np.min(data_cat["DELTA_J2000_1"][~np.isnan(data_cat["DELTA_J2000_1"])])
ra_slices=20
dec_slices=20
for i,ii in enumerate(np.linspace(ra_min,ra_max,ra_slices)):
for j,jj in enumerate(np.linspace(dec_min,dec_max,dec_slices)):
coords.append([ii,jj])
'''filter_names="ugrizJHK"
filters=[]
for f in filter_names:
filters.append((g.glob("synth/*"+f+"*Mega*")+g.glob("synth/"+f+"wircam*"))[0])
'''
filters=sorted(g.glob("../../Coding/synth/F*W.fil.txt"))
filtercolor=cm.jet(np.linspace(0,1,len(filters)))
#print filters
wl=np.empty(0)
filts=[]
wls=[]
ax1=plt.subplot2grid((3,1),(0,0))
ax2=plt.subplot2grid((3,1),(1,0),sharex=ax1)
ax3=plt.subplot2grid((3,1),(2,0))
ax1.set_xlim([2900,17100])
ax1.set_ylim([0,0.6])
spec=0.5
for i,f in enumerate(filters):
wlr,eff=np.loadtxt(f,unpack=True)
wls.append(wlr)
wl=np.concatenate((wl,wlr))
filts.append(eff)
ax2 = fig1.add_subplot(312)
ax3 = fig1.add_subplot(313)
plt.tight_layout(pad=3.0, w_pad=3.0, h_pad=3.0)
fig1.subplots_adjust(left=0.15)
# fig1.subplots_adjust(bottom=0.25)
# fig1.subplots_adjust(top=0.90)
# fig1.subplots_adjust(right=0.95)
for axis in ['top', 'bottom', 'left', 'right']:
ax1.spines[axis].set_linewidth(1.)
ax2.spines[axis].set_linewidth(1.)
ax3.spines[axis].set_linewidth(1.)
plt_title = ax1.set_title(' ')
color = cycle(cm.jet(np.linspace(0, 1, 100)))
ax1_hdl, = ax1.plot([], [])
ax1_c_hdl, = ax1.plot([], [])
ax2_hdl, = ax2.plot([], [])
ax2_c_hdl, = ax2.plot([], [])
ax3_hdl, = ax3.plot([], [])
set_line_hdl = ax3.axvline(1.5, color='red')
act_line_hdl = ax3.axvline(1.5, color='black')
# ax1.set_ylabel('Amplitude of TM modes (norm.)',**axis_font)
ax1.set_ylabel('Power of TM modes (norm.)', **axis_font)
ax1.set_xlabel('Time (ms)', **axis_font)
ax2.set_ylabel('Power of TE modes (norm.)', **axis_font)
# ax2.set_ylabel('Amplitude of Fabry Perot (norm.)',**axis_font)
galq[6] = 4341
galq[7] = 4862
galq[8] = 5008
galq[9] = 6564
lab = [
'OVI: 1034 $\AA$', 'Ly-alpha: 1215 $\AA$', 'NV: 1240 $\AA$',
'CIV: 1549 $\AA$', 'CIII: 1908 $\AA$', 'MgII: 2799 $\AA$',
'H-gama: 4341 $\AA$', 'H-beta: 4863 $\AA$', 'OIII: 5008 $\AA$',
'H-alpha: 6564 $\AA$'
]
# Redshift
z = N.arange(0., 10.01, 0.01)
colores = list(cm.jet(N.linspace(0, 1, 10)))
colores = colores[::-1]
plt.figure(1, figsize=(11, 9), dpi=80, facecolor='w', edgecolor='k')
plt.clf()
for ii in range(10):
plt.plot(galq[ii] * (1 + z), z, '-', alpha=0.5, lw=3., color=colores[ii])
for ii in range(7):
x, y = U.get_data(root_to_filts + filters[ii], (0, 1))
plt.plot(x, 8. * (y / y.max()), '-', lw=1, color='grey', alpha=0.5)
plt.fill_betweenx(8. * (y / y.max()), x, 0, alpha=0.1)
plt.xlabel('Wavelength [$\AA$]', size=25, labelpad=5)
plt.ylabel('$z$', size=35)
for ii in range(10):
def plot_contour(*opts,
fxn_name,
animate_gif=True,
fig_size=None,
save_f=True,
f_name='v1',
in_type=None):
def init():
for line in lines:
line.set_data([], [])
return lines
def animate(i):
num1 = int(x2[i])
for lnum, (line, scat) in enumerate(zip(lines, scats)):
if num1 < dp[lnum]:
# print(i, dp[lnum])
line.set_data(
xlist[lnum][:num1],
ylist[lnum][:num1]) # set data for each line separately.
scat.set_offsets([xlist[lnum][num1], ylist[lnum][num1]])
line.set_label(nn[lnum]) # set the label and draw the legend
plt.legend(loc="upper left")
return lines
if fig_size == 'small':
fig, ax = plt.subplots(figsize=(6, 4))
else:
fig, ax = plt.subplots(figsize=(8.5, 6.8))
plt.tight_layout()
data, minima, x_lims, y_lims = plot_fxn(fxn_name, 'contour')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_xlim(x_lims)
ax.set_ylim(y_lims)
ax.contour(*data,
levels=np.logspace(-.5, 5, 35),
norm=LogNorm(),
cmap='viridis',
alpha=0.7) #cmap=plt.cm.jet)
ax.plot(*minima[0:2], 'x', markersize=12, mew=2, color='k')
xlist, ylist, zlist, nn, dp = get_dlists(opts, in_type)
n = len(xlist)
# print(len(xlist))
if fxn_name == 'rosenbrock':
if in_type == 'sdict':
c = cm.rainbow_r(np.linspace(0, 1, n))
else:
c = cm.jet(np.linspace(0, 1, n))
else:
c = cm.rainbow(np.linspace(0, 1, n))
if animate_gif == False:
for k in range(n):
x_history = np.array(xlist[k])
y_history = np.array(ylist[k])
path = np.concatenate(
(np.expand_dims(x_history, 1), np.expand_dims(y_history, 1)),
axis=1).T
ax.quiver(path[0, :-1],
path[1, :-1],
path[0, 1:] - path[0, :-1],
path[1, 1:] - path[1, :-1],
scale_units='xy',
angles='xy',
width=0.003,
scale=1,
color=c[k],
label=nn[k])
plt.legend(loc="upper left")
if save_f == True:
plt.savefig('images/{}_path.png'.format(fxn_name))
else:
line, = ax.plot([], [], lw=2) #, markersize=12)
lines = []
scats = []
for index in range(n):
l_obj = ax.plot([], [], lw=2, color=c[index])[0]
lines.append(l_obj)
s_obj = ax.scatter([], [], lw=2, color=c[index])
scats.append(s_obj)
num_min = int(len(xlist[0]) / 50)
# print(num_min)
x2 = np.rint(np.linspace(0, len(xlist[1]), endpoint=False,
num=200)) # fix this
# print(x2)
# x2 = np.arange(len(xlist[0]))
# blit=True --> only re-draw the parts that have changed.
scat = ax.scatter(
[], []) # Set the dot at some arbitrary position initially
anim = animation.FuncAnimation(fig,
animate,
init_func=init,
frames=len(x2),
interval=1,
blit=True)
anim.save('images/{}_contour_{}.gif'.format(fxn_name, f_name),
dpi=60,
writer="imagemagick")
plt.show()
#plot slices responsible for reconstruction
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 8))
plt.gray()
plt.tight_layout()
maxLines = len(lines)
#maxLines = 12
ax[0].imshow(powerSpect)
ax[1].imshow(powerSpect)
#color=iter(cm.rainbow(np.linspace(0,1,len(lines))))
color=iter(cm.jet(np.linspace(0,1,maxLines+1)))
fareyImage = np.zeros_like(powerSpect)
for i, line in enumerate(lines):
u, v = line
c=next(color)
ax[0].plot(u, v, '.', c=c)
ax[1].plot(u, v, '.r',markersize=1)
fareyImage[u,v] = 255
if i == maxLines:
break
#ax[0].set_title('Sampling (colour per line) for prime size:'+str(p))
#ax[1].set_title('Sampling (same colour per line) for prime size:'+str(p))
ax[0].set_title('Sampling (colour per line) for dyadic size:'+str(M))
ax[1].set_title('Sampling (same colour per line) for dyadic size:'+str(M))
#ax[0].set_xlim([0,M])
#ax[0].set_ylim([0,M])
def vis_back_proj(ind=1,
img_style="edge",
pcd_style="intens",
hide_occlussion_by_marker=False,
save_without_show=False):
imgfile = os.path.join(
params['base_dir'],
"img/" + str(ind).zfill(params["file_name_digits"]) + "." +
params['image_format'])
if img_style == "edge":
gray = cv2.imread(imgfile)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
img = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
elif img_style == "orig":
img = cv2.imread(imgfile)
else:
raise Exception("Please input the right image style")
csvfile = os.path.join(
params['base_dir'],
"pcd/" + str(ind).zfill(params["file_name_digits"]) + ".csv")
csv = np.genfromtxt(csvfile, delimiter=",", skip_header=1)
pcd = csv[:, :3]
dis_arr = np.linalg.norm(pcd, axis=1)
intens = csv[:, params['intensity_col_ind']]
filels = os.listdir(params['base_dir'])
cali_file_ls = []
for file in filels:
if file.find("cali_result.txt") > -1:
cali_file_ls.append(file)
if len(cali_file_ls) > 1:
warnings.warn(
"More than one calibration file exit! Load the latest file.",
UserWarning)
latest_cali = find_latest(cali_file_ls)
r_t = np.genfromtxt(os.path.join(params['base_dir'], latest_cali),
delimiter=',')
print("Load ", latest_cali,
" as the extrinsic calibration parameters!")
elif len(cali_file_ls) == 1:
r_t = np.genfromtxt(os.path.join(params['base_dir'], cali_file_ls[0]),
delimiter=',')
print("Load ", cali_file_ls[0],
" as the extrinsic calibration parameters!")
else:
raise Exception("No calibration file is found!")
if pcd_style == "intens":
pcd_color = np.fliplr(
(cm.jet(intens.astype(np.float32) / intens.max()) * 255).astype(
np.int32)[:, :3])
elif pcd_style == "dis":
pcd_color = np.fliplr(
(cm.jet(dis_arr / 42) * 255).astype(np.int32)[:, :3])
else:
print("Please input the right pcd color style")
backproj_img = back_project_pcd(img, pcd, pcd_color, r_t, ind,
hide_occlussion_by_marker)
if max(backproj_img.shape[0], backproj_img.shape[1]) > 1000:
resize_factor = 1000. / max(backproj_img.shape[0],
backproj_img.shape[1])
resized_img_for_view = cv2.resize(backproj_img, (0, 0),
fx=resize_factor,
fy=resize_factor)
else:
resized_img_for_view = backproj_img
if save_without_show:
window_name = "ind: " + str(
ind) + " img_style: " + img_style + " pcd_style: " + pcd_style + (
" hide_occlusion " if hide_occlussion_by_marker else "")
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
cv2.imshow(window_name, resized_img_for_view)
k = cv2.waitKey(0)
if k == 27: # wait for ESC key to exit
cv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exit
save_file_name = os.path.join(
params['base_dir'],
str(ind).zfill(params["file_name_digits"])
) + "_" + img_style + "_" + pcd_style + (
"_hide_occlusion" if hide_occlussion_by_marker else
"") + "." + params['image_format']
cv2.imwrite(save_file_name, img, [cv2.IMWRITE_JPEG_QUALITY, 70])
print("The image is saved to ", save_file_name)
cv2.destroyAllWindows()
else:
save_file_name = os.path.join(
params['base_dir'],
str(ind).zfill(params["file_name_digits"])
) + "_" + img_style + "_" + pcd_style + (
"_hide_occlusion" if hide_occlussion_by_marker else
"") + "." + params['image_format']
cv2.imwrite(save_file_name, img, [cv2.IMWRITE_JPEG_QUALITY, 70])
print("The image is saved to ", save_file_name)
cv2.destroyAllWindows()
tmp['log_C1_true'] = -5
tmp['log_C2_true'] = -5
tmp['r1_true'] = 100. * (.3)
tmp['r2_true'] = 100. * (.3)
tmp['log_alpha1_true'] = np.array(log_alpha)
tmp['log_alpha2_true'] = np.array(log_alpha)
tmp['log_gamma1_true'] = 0.
tmp['log_gamma2_true'] = 0.
T = T.append(tmp, ignore_index=True)
except:
print d
#Figure B, compare pso fitting
plt.figure(figsize=(3, 5))
ax = []
cols = cm.jet(np.linspace(0, 1, len(T['expt'].unique())))
fils = glob.glob('*/*/*.h5')
df = pd.DataFrame([])
x = {}
for tmp in T['expt'].unique():
ky = tmp.split('_')[-1].split('x')[0]
x[ky] = np.array([])
x[ky + '_iter'] = np.array([])
for fil in fils:
try:
f = h5py.File(fil, 'r')
if 'pso' in f.keys():
ky = fil.split('_')[1].split('x')[0]
x[ky] = np.concatenate(
def load_labelsLinesMarkers(experiments, varied_values, colormap=None):
''' Load labels, lines, color and markers for the (simulation, groups) combinations.
Returns
-------
line_style, line_color, marker_color, marker_style : dictionaries with keys [simlutation] and [groups]
'''
# Initiate the dictionaries
line_label = initiate_nesteddict()
marker_label = initiate_nesteddict()
line_style = initiate_nesteddict()
line_color = initiate_nesteddict()
marker_color = initiate_nesteddict()
marker_style = initiate_nesteddict()
# Initiate colors, lines, markers to chose from
if colormap == "rainbow":
l_colors = cm.rainbow(np.linspace(0, 1, len(experiments) + 1))
if colormap == "jet":
l_colors = cm.jet(np.linspace(0, 1, len(experiments) + 1))
if colormap == None:
l_colors = [
'navy', 'crimson', 'black', 'darkgreen', 'purple', 'magenta'
] * 5
m_colors = cm.jet(np.linspace(0, 1, len(varied_values)))
l_styles = [
'-', '--', ':', '-.', 'densely dotted', 'densely dasged', 'densely'
] * 5
m_styles = ['o', 'X', 'D', 'P', 's', 'x', 'd', 'p'] * 10
# Find the common prefix of the experiments
common_prefix = ''.join(c[0] for c in takewhile(
lambda x: all(x[0] == y for y in x), zip(*experiments)))
# If no specific case was chosen, given random colors
for experiment in experiments:
if common_prefix == experiments[0] or common_prefix == "":
line_label[experiment] = str(experiment).replace(
"nl_", "").replace("th_", "").replace("_",
" ") #.split("_")[0]
if "=" in common_prefix:
line_label[experiment] = str(experiment)
else:
try:
line_label[experiment] = str(experiment).replace("_", " ")
# line_label[experiment] = str(experiment).split(common_prefix)[-1].replace("_", " ")#.split("_")[0]
except:
line_label[experiment] = str(experiment).replace("_", " ")
marker_label[experiment] = str(experiment).replace("__", ": ").replace(
"_", " ")
line_color[experiment] = l_colors[experiments.index(experiment)]
line_style[experiment] = l_styles[experiments.index(experiment)]
marker_color[experiment] = l_colors[experiments.index(experiment)]
marker_style[experiment] = m_styles[experiments.index(experiment)]
for variation in varied_values:
line_label[variation] = str(variation)
marker_label[variation] = str(variation)
line_color[variation] = m_colors[varied_values.index(variation)]
line_style[variation] = '-'
marker_color[variation] = m_colors[varied_values.index(variation)]
marker_style[variation] = m_styles[varied_values.index(variation)]
if experiments == varied_values and len(experiments) == 1:
return line_label[varied_values[0]], marker_label[
varied_values[0]], line_style[varied_values[0]], line_color[
varied_values[0]], marker_style[
varied_values[0]], marker_color[varied_values[0]]
return line_label, marker_label, line_style, line_color, marker_style, marker_color
for i in range(0, 3, 1):
m, b = get_ls_line(plot_data[i][:, 0], plot_data[i][:, 2])
ls_fit[i] = [b + m * x for x in plot_data[i][:, 0]]
fits[i] = [m, b]
#%% Plotting Average
# Build Plot
plot.style.use('ggplot')
plot.title("Hall Effect at Different Magnetic Field Strengths (Cr)", color='k')
plot.xlabel("Charge Density($J_x$) [$A/m^2$]")
plot.ylabel("Electric Field Strength($E_y$) [J]")
# Plot data
for i in range(0, 3, 1):
colors = cm.jet((0.85, 0.25, 0.45))
plot.errorbar(plot_data[i][:, 0],
plot_data[i][:, 2],
yerr=plot_data[i][:, 3],
xerr=plot_data[i][:, 1],
fmt='o',
color=colors[i])
plot.plot(plot_data[i][:, 0], ls_fit[i], color=colors[i])
plot.legend(['Field Strength: {}G'.format(x) for x in trial_field_strengths])
# Display
fig = plot.gcf()
plot.figure()
#%% Save Figure
support_types = np.unique(support)
'''
Based on support
'''
category = support.copy()
types = support_types.copy()
legend_labels = [r'$\rm CeO_{2}(100)$', r'$\rm CeO_{2}(111)$', 'Graphene', 'MgO(100)', '2H-'+r'$\rm MoS_{2}(0001)$', r'$\rm SrTiO_{3}(100)$',
'Steps of ' + r'$\rm CeO_{2}$', r'$\rm TiO_{2}(110)$', 'ZnO(100)']
term_index = np.where(np.array(x_features_poly_combined) == 'Ec_-1Ebind_2')[0][0]
x_plot = X_before_scaling[:,term_index]
y_plot = y.copy()
sns.set_style("ticks")
fig, ax = plt.subplots(figsize=(6, 6))
color_set = cm.jet(np.linspace(0,1,len(types)))
for type_i, ci, label_i in zip(types, color_set, legend_labels):
indices = np.where(np.array(category) == type_i)[0]
ax.scatter(x_plot[indices],
y_plot[indices],
label=label_i,
facecolor = ci,
alpha = 0.8,
s = 100)
ax.plot([x_plot.min(), x_plot.max()], [DSL.predict(X_DSL).min(), DSL.predict(X_DSL).max()], 'k--', lw=2)
ax.set_xlabel(r'$\rm E_{bind}^2/E_c$ ' + '(eV)')
ax.set_ylabel(r'$\rm E_a$' +'(eV)')
plt.text(1.6,0, r'$\rm E_a$ = ' + str(np.around(u1, decimals = 3)) + r'$\rm E_{bind}^2/E_c$ ' + str(np.around(u0, decimals = 3)))
plt.text(4,0.4, r'$\rm R^2$ = ' + str(np.around(DSL_r2, decimals = 3)) )
['PC%i' % (w + 1) for w in range(nDescriptors)],
rotation=60)
plt.legend(loc='right')
plt.tight_layout()
#%% Plot the normalized desciptor loading
'''
Need to be completed
'''
ind = 0
yvals = []
ylabels = []
bar_vals = []
space = 0.2
cm_cur = cm.jet(np.linspace(0, 1, nDescriptors))
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
n = len(descriptors)
width = (1 - space) / (len(descriptors))
indeces = np.arange(0, pc_reg) + 0.5
PC_loadings = []
# Create a set of bars at each position
for i, pci in enumerate(eig_vecs[:pc_reg]):
vals = pci / np.sum(np.absolute(pci))
PC_loadings.append(vals)
pos = width * np.arange(n) + i
ax.bar(pos, vals, width=width, label=str(i + 1), color=cm_cur, alpha=1)
plotDir3 = '../plots/gws/Ka2017_TrPi2018_FixZPT0/F606W_F160W_K/0_10/GRB160821B/1.00/' pcklFile = os.path.join(plotDir3,"data.pkl") f = open(pcklFile, 'r') (data_out, data3, tmag3, lbol3, mag3, t0_best3, zp_best3, n_params3, labels3, best3, truths3) = pickle.load(f) f.close() tmag1 = tmag1 + t0_best1 tmag2 = tmag2 + t0_best2 tmag3 = tmag3 + t0_best3 title_fontsize = 30 label_fontsize = 30 #filts = ["u","g","r","i","z","y","J","H","K"] filts = ["F606W","F160W","K"] colors=cm.jet(np.linspace(0,1,len(filts))) tini, tmax, dt = 0.0, 21.0, 0.1 tt = np.arange(tini,tmax,dt) color1 = 'coral' color2 = 'cornflowerblue' color3 = 'palegreen' plotName = "%s/models_panels.pdf"%(plotDir) #plt.figure(figsize=(20,18)) plt.figure(figsize=(20,24)) tini, tmax, dt = 0.0, 10.0, 0.1 tt = np.arange(tini,tmax,dt) cnt = 0
import imageio
from diffusion_model import grid
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import numpy as np
gr = grid(200, 200)
gr.initiate_atoms_in_line(quantitiy=5000)
images = []
for i in range(800):
gr.simulate_move()
images.append(np.uint8(cm.jet(gr.grid) * 255))
imageio.mimsave('diffusion.gif', images, duration=0.1)
def createFractal4(reduction, N, proportion):
#parameters
M = 2*N
twoQuads = True
angles, lengths = mojette.angleSet_Symmetric(N,N,1,True,50)
perpAngle = farey.farey(1,0)
angles.append(perpAngle)
powerSpect = np.zeros((M,M))
#compute lines
centered = True
mLines = []
sLines = []
mValues = []
sValues = []
pValues = []
qValues = []
for angle in angles:
m, s, p, q, inv = farey.toFinite(angle, M)
pValues.append(p)
qValues.append(q)
if m not in mValues and m < M:
u, v = radon.getSliceCoordinates2(m, powerSpect, centered, M)
mLines.append((u,v))
mValues.append(m)
#second quadrant
if twoQuads:
if m != 0 and m != M: #dont repeat these
m = M-m
if m not in mValues and m < M:
u, v = radon.getSliceCoordinates2(m, powerSpect, centered, M)
mLines.append((u,v))
mValues.append(m)
i = (len(mValues)+len(sValues))/float(M)
if i >= 1:
break
ss = []
ss.append(M)
ss.extend(range(0, N/2))
maxLines = len(ss)
for i,s in enumerate(ss):
u, v = radon.getSliceCoordinates2(s, powerSpect, centered, M)
sLines.append((u,v))
sValues.append(s)
i = (len(mValues)+len(sValues))/float(M)
if i >= 1.5:
break
length = 0
fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 8))
plt.gray()
plt.tight_layout()
maxLines = len(sLines+mLines)
i = 0
ax[0].imshow(powerSpect)
ax[1].imshow(powerSpect)
color=iter(cm.jet(np.linspace(0,1,maxLines+1)))
fareyImage = np.zeros_like(powerSpect)
fareyImage1 = np.zeros_like(powerSpect)
for i, sLine in enumerate(sLines):
u, v = sLine
ax[1].plot(u, v, '.w',markersize=1)
fareyImage[u,v] = 1
length = length + 1
i = np.count_nonzero(fareyImage)/float((M*M))
if i >= reduction*proportion:
break
maxLines = len(mLines)
for i, mLine in enumerate(mLines):
u, v = mLine
ax[0].plot(u, v, '.r', markersize=1)
ax[1].plot(u, v, '.r',markersize=1)
fareyImage[u,v] = 1
fareyImage1[u,v] = 1
length = length + 1
i = np.count_nonzero(fareyImage)/float((M*M))
if i >= reduction:
break
print("Proportion of M:", (length/float(M)))
print("Non-zero elements with holes: ", np.count_nonzero(fareyImage1)/float((M*M)) * 100)
print("Non-zero elements without holes: ", np.count_nonzero(fareyImage)/float((M*M)) * 100)
print("Absolute difference percentage extra filled in is ", (np.count_nonzero(fareyImage)- np.count_nonzero(fareyImage1))/float((M*M)) *100)
withHoles = np.count_nonzero(fareyImage1)/float((M*M)) * 100
withoutHoles = np.count_nonzero(fareyImage)/float((M*M)) * 100
percentage = (withoutHoles - withHoles)/float(withHoles) * 100
print("Percentage difference percentage extra filled in is ", percentage)
ax[0].set_title('Sampling (colour per line) for dyadic size:'+str(M))
ax[1].set_title('Sampling (same colour per line) for dyadic size:'+str(M))
imageio.imsave("farey_image_"+str(M)+"_"+".png", fareyImage)
plt.show()
lines = mLines + sLines
return fareyImage, lines
plt.legend(lab, loc='upper right', fontsize=21, frameon=False) # plt.xlim(3000,14500) plt.xlim(3000, 10500) plt.ylim(0.001, 0.999) plt.xticks(fontsize=22) plt.yticks(fontsize=22) #### Separated in two windows ruta = '/Users/albertomolino/codigos/bpz-1.99.2/FILTER/SPLUS_July2017/' filtros = U.get_str(ruta + 'SPLUS.list', 0) bbs = [0, 5, 7, 9, 11] nbs = [1, 2, 3, 4, 6, 8, 10] splus_colors = list(cm.jet(N.linspace(0, 1, 12))) colores_BB = N.zeros((3, 5), float) colores_NB = N.zeros((3, 7), float) colores_BB[:, 0] = (0.00, 0.00, 1.00) colores_BB[:, 1] = (0.00, 0.50, 0.00) colores_BB[:, 2] = (0.85, 0.65, 0.00) colores_BB[:, 3] = (1.00, 0.00, 0.00) colores_BB[:, 4] = (0.35, 0.00, 0.00) colores_NB[:, 0] = (0.00, 0.25, 1.00) colores_NB[:, 1] = (0.00, 0.65, 1.00) colores_NB[:, 2] = (0.00, 0.50, 0.00) colores_NB[:, 3] = (0.85, 0.65, 0.00) colores_NB[:, 4] = (0.80, 0.25, 0.00) colores_NB[:, 5] = (0.85, 0.00, 0.00)