def plot_overlay_withcolorbar(imgbase, imgcolor, title=None, figsize=None, vmin=None, vmax=None, overlay_alpha=1.0,
overlay_colormap='viridis', overlay_steps=None, display_contours=False, contour_fmt=None,
contour_steps=None, contour_alpha=None, show=True):
''' Plot an image with a colorbar '''
fig, axis = plt.subplots(1, 1, figsize=figsize, squeeze=False)
base = axis[0][0].imshow(imgbase)
if overlay_steps is not None:
overlay_colormap = cm.get_cmap(overlay_colormap, overlay_steps)
rad2 = axis[0][0].imshow(imgcolor, vmin=vmin, vmax=vmax, alpha=overlay_alpha, cmap=overlay_colormap)
if display_contours:
if contour_steps is None:
contour_steps = overlay_steps
if contour_alpha is None:
contour_alpha = overlay_alpha
contour_cmap = cm.get_cmap(overlay_colormap, contour_steps)
contour_list = np.arange(vmin, vmax, (vmax - vmin) / contour_steps)
rad3 = axis[0][0].contour(imgcolor, contour_list, cmap=contour_cmap, alpha=contour_alpha)
fontsize = 8 + (max(figsize) / 10) * 2
axis[0][0].clabel(rad3, rad3.levels, inline=True, fontsize=fontsize, fmt=contour_fmt)
axis[0][0].set_title(title)
divider = make_axes_locatable(axis[0][0])
cax = divider.append_axes("right", size="3%", pad=0.05)
fig.colorbar(rad2, cax=cax)
plt.tight_layout()
if show:
plt.show()
return fig, axis[0][0]
def plot_trips(self, trips, title='Trips', newplot=True):
n = len(trips) + 2
# create the new map
cmap = cm.get_cmap('winter')
colors = [cmap(1. * i / n) for i in range(n)]
if newplot:
plt.figure(title, figsize=(12, 8))
plt.subplot(211)
else:
plt.subplot(212)
plt.scatter(self.gifts.Longitude, gifts.Latitude, color='gray')
c = 0
coords = [360, 0, 180, 0]
for tripid in trips:
#range(510,530):#
self.plot_trip(tripid, newplot=False, color=colors[c + 1])
c += 1
# Get boundries
if self.T[tripid].Longitude.min() + 180 < coords[0]:
coords[0] = self.T[tripid].Longitude.min() + 180
if self.T[tripid].Longitude.max() + 180 > coords[1]:
coords[1] = self.T[tripid].Longitude.max() + 180
if self.T[tripid].Latitude.min() + 90 < coords[2]:
coords[2] = self.T[tripid].Latitude.min() + 90
if self.T[tripid].Latitude.max() + 90 > coords[3]:
coords[3] = self.T[tripid].Latitude.max() + 90
band = .1
plt.xlim(coords[0] * (1 - band) - 180, coords[1] * (1 + band) - 180)
plt.ylim(coords[2] * (1 - band) - 90, coords[3] * (1 + band) - 90)
plt.title(title, fontsize=20)
plt.tight_layout()
def plot_trips(trips, title=''):
n = len(trips) + 2
# create the new map
cmap = cm.get_cmap('winter')
colors = [cmap(1. * i / n) for i in range(n)]
plt.figure('Smallest Trips', figsize=(17, 11))
plt.scatter(gifts.Longitude, gifts.Latitude, color='gray')
c = 0
coords = [360, 0, 180, 0]
for tripid in trips:
#range(510,530):#
plot_trip(T, tripid, newplot=False, color=colors[c + 1])
c += 1
# Get boundries
if T[tripid].iloc[1:-1].Longitude.min() + 180 < coords[0]:
coords[0] = T[tripid].iloc[1:-1].Longitude.min() + 180
if T[tripid].iloc[1:-1].Longitude.max() + 180 > coords[1]:
coords[1] = T[tripid].iloc[1:-1].Longitude.max() + 180
if T[tripid].iloc[1:-1].Latitude.min() + 90 < coords[2]:
coords[2] = T[tripid].iloc[1:-1].Latitude.min() + 90
if T[tripid].iloc[1:-1].Latitude.max() + 90 > coords[3]:
coords[3] = T[tripid].iloc[1:-1].Latitude.max() + 90
band = .1
plt.xlim(coords[0] * (1 - band) - 180, coords[1] * (1 + band) - 180)
plt.ylim(coords[2] * (1 - band) - 90, coords[3] * (1 + band) - 90)
plt.title(title, fontsize=20)
plt.tight_layout()
def plot_worst_trips(n_trips=10, lightest=False):
## Plots
############################################
# Find Worst Trips
if lightest:
headroom = Santa.trip_metrics.Weight.copy()
headroom.sort_values(inplace=True)
else:
headroom = (Santa.trip_metrics.WRW - Santa.trip_metrics.LB) #-1
headroom.sort_values(inplace=True, ascending=False)
# create the new map
cmap = cm.get_cmap('winter')
colors = [cmap(1. * i / (n_trips + 2)) for i in range(n_trips + 2)]
# create the new map
cmap = cm.get_cmap('winter', n_trips + 2)
plt.figure('Worst Trips', figsize=(17, 11))
plt.scatter(Santa.gifts.Longitude, gifts.Latitude, color='gray')
c = 0
coords = [360, 0, 180, 0]
for tripid in headroom.index[:n_trips]:
#range(510,530):#
plot_trip(Santa.T, tripid, newplot=False, color=colors[c + 1])
c += 1
# Get boundries
if Santa.T[tripid].Longitude.min() + 182 < coords[0]:
coords[0] = Santa.T[tripid].Longitude.min() + 182
if Santa.T[tripid].Longitude.max() + 182 > coords[1]:
coords[1] = Santa.T[tripid].Longitude.max() + 182
if Santa.T[tripid].Latitude.min() + 92 < coords[2]:
coords[2] = Santa.T[tripid].Latitude.min() + 92
if Santa.T[tripid].Latitude.max() + 92 > coords[3]:
coords[3] = Santa.T[tripid].Latitude.max() + 92
band = .1
plt.xlim(coords[0] * (1 - band) - 182,
min(coords[1] * (1 + band) - 182, 180))
plt.ylim(coords[2] * (1 - band) - 92, min(coords[3] * (1 + band) - 92, 90))
plt.title('Worst Trips - Most Wasted Movement', fontsize=20)
plt.tight_layout()
def visualize_save(test_image, heatmaps, prediction, param, model):
directory = '../results/'
if not os.path.exists(directory):
os.makedirs(directory)
colormap = cm.jet_r
oriImg = cv.imread(test_image)
prediction = prediction.astype(np.int)
prefix = os.path.basename(test_image).split('.')[0]
npart = model['np']
# save heatmap superimposed and marked images
for c in range(heatmaps.shape[2]):
colorized = colormap(heatmaps[:, :, c])
img_superimposed = (colorized[:, :, 0:3] * 255 + oriImg) / 2
img_superimposed = img_superimposed.astype(np.uint8)
#print img_superimposed
if c != npart:
img_superimposed = drawCross(img_superimposed, prediction[c])
cv.imwrite('%s/%s_heatmap%d.png' % (directory, prefix, c + 1),
img_superimposed)
# draw limbs
limbs = model['limbs']
colormap = cm.get_cmap('hsv_r')
facealpha = 0.6
bodyHeight = np.max(prediction[:, 0]) - np.min(prediction[:, 0])
stickwidth = oriImg.shape[0] / 30.0
thre = 0.05
img_stickman = oriImg.copy()
for p in range(limbs.shape[0]):
x1 = prediction[limbs[p, 0] - 1, 0]
y1 = prediction[limbs[p, 0] - 1, 1]
if heatmaps[x1, y1, limbs[p, 0] - 1] < thre:
continue
x2 = prediction[limbs[p, 1] - 1, 0]
y2 = prediction[limbs[p, 1] - 1, 1]
if heatmaps[x2, y2, limbs[p, 1] - 1] < thre:
continue
X = prediction[limbs[p, :] - 1, 0]
Y = prediction[limbs[p, :] - 1, 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1])**2 + (Y[0] - Y[1])**2)**0.5
if Y[0] - Y[1] == 0:
angle = 90
else:
angle = math.degrees(
math.atan(float(X[0] - X[1]) / float(Y[0] - Y[1])))
polygon = cv.ellipse2Poly(
(int(mY), int(mX)), (int(length / 2), int(stickwidth)), int(angle),
0, 360, 1)
color = [x * 255 for x in list(colormap(float(p) / limbs.shape[0]))]
color = tuple(color[0:3])
cv.fillConvexPoly(img_stickman, polygon, color)
for c in range(heatmaps.shape[2] - 1):
img_stickman = drawDot(img_stickman, prediction[c])
cv.addWeighted(img_stickman, facealpha, oriImg, 1 - facealpha, 0,
img_stickman)
cv.imwrite('%s/%s_stickman.png' % (directory, prefix), img_stickman)
def visualize_save(test_image, heatmaps, prediction, param, model):
directory = '../results/'
if not os.path.exists(directory):
os.makedirs(directory)
colormap = cm.jet_r
oriImg = cv.imread(test_image)
prediction = prediction.astype(np.int)
prefix = os.path.basename(test_image).split('.')[0]
npart = model['np']
# save heatmap superimposed and marked images
for c in range(heatmaps.shape[2]):
colorized = colormap(heatmaps[:,:,c])
img_superimposed = (colorized[:,:,0:3] * 255 + oriImg)/2
img_superimposed = img_superimposed.astype(np.uint8)
#print img_superimposed
if c != npart:
img_superimposed = drawCross(img_superimposed, prediction[c])
cv.imwrite('%s/%s_heatmap%d.png' % (directory, prefix, c+1), img_superimposed)
# draw limbs
limbs = model['limbs']
colormap = cm.get_cmap('hsv_r')
facealpha = 0.6
bodyHeight = np.max(prediction[:,0]) - np.min(prediction[:,0])
stickwidth = oriImg.shape[0]/30.0
thre = 0.05
img_stickman = oriImg.copy()
for p in range(limbs.shape[0]):
x1 = prediction[limbs[p,0]-1, 0]
y1 = prediction[limbs[p,0]-1, 1]
if heatmaps[x1,y1,limbs[p,0]-1] < thre:
continue
x2 = prediction[limbs[p,1]-1, 0]
y2 = prediction[limbs[p,1]-1, 1]
if heatmaps[x2,y2,limbs[p,1]-1] < thre:
continue
X = prediction[limbs[p,:]-1, 0]
Y = prediction[limbs[p,:]-1, 1]
mX = np.mean(X)
mY = np.mean(Y)
length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
if Y[0] - Y[1] == 0:
angle = 90
else:
angle = math.degrees(math.atan(float(X[0] - X[1]) / float(Y[0] - Y[1])))
polygon = cv.ellipse2Poly((int(mY),int(mX)), (int(length/2), int(stickwidth)), int(angle), 0, 360, 1)
color = [x * 255 for x in list(colormap(float(p)/limbs.shape[0]))]
color = tuple(color[0:3])
cv.fillConvexPoly(img_stickman, polygon, color)
for c in range(heatmaps.shape[2]-1):
img_stickman = drawDot(img_stickman, prediction[c])
cv.addWeighted(img_stickman, facealpha, oriImg, 1 - facealpha, 0, img_stickman)
cv.imwrite('%s/%s_stickman.png' % (directory, prefix), img_stickman)
matplotlib.use(
'Agg') #makes sure matplotlib doesn't look for a display when plotting
import matplotlib.pylab as plt
import tangos
import pynbody
from tangos.examples import mergers
from pynbody.plot import image
from matplotlib.pylab import cm
from scipy.stats import binned_statistic
import gc
plot_rain = False
plot_zoom = False
plot_mixing = True
cmap = cm.get_cmap('magma')
#load particles
basename = '/nobackupp2/mtremmel/Romulus/'
datadir = basename + 'h1.cosmo50/h1.cosmo50PLK.1536gst1bwK1BH.004096'
#load halo database
db = tangos.get_simulation('h1.cosmo50')
steps = db.timesteps
stepname = [step.relative_filename for step in steps]
steptime = [step.time_gyr for step in steps]
stepnum = -1
halonum = 0 #generalize these later
halo = steps[stepnum].halos[halonum]
bh = halo['BH_central'][0]
halonum_pb = halo.halo_number
g_LB = (g_temp.LB * g_temp.Weight).sum() + Dmax
room = g_LB - WRW_start
g1, g2 = split_trip(g_temp)
WRW1 = wrw_trip(g1)
WRW2 = wrw_trip(g2)
print 'WRW 2 seperate trips =', WRW2 + WRW1
##################################
# Plot
n = 3
# create the new map
cmap = cm.get_cmap('winter')
colors = [cmap(1. * i / n) for i in range(n)]
# create the new map
cmap = cm.get_cmap('winter', 33)
plt.figure('2 Trips', figsize=(17, 11))
plt.scatter(gifts.Longitude, gifts.Latitude, color='gray')
plt.scatter(g1['Longitude'], g1['Latitude'], s=g1['Weight'], color='red')
plt.plot(g1['Longitude'], g1['Latitude'], color=colors[1])
plt.scatter(g2['Longitude'], g2['Latitude'], s=g2['Weight'], color='red')
plt.plot(g2['Longitude'], g2['Latitude'], color=colors[2])
coords = [
g_temp.Longitude.min(),
