def get_sweep(wfall):
zoom = 1
while zoom == 1:
pl.imshow(wfall, aspect=0.1, cmap='Greys')
pl.colorbar()
pl.xlabel('Bins')
pl.ylabel('Time')
click = input('Ready to click ? :')
if click == 'y':
pl.title('Click on peak for t=0')
t0 = pl.ginput(1)
pl.close()
break
else:
continue
while zoom == 1:
pl.imshow(wfall, aspect=0.1, cmap='Greys')
pl.xlabel('Bins')
pl.ylabel('Time')
click = input('Ready to click ? :')
if click == 'y':
pl.title('Click on peak for t=1')
t1 = pl.ginput(1)
pl.close()
break
else:
continue
sweep = np.int(np.rint(t0[0][0]) - np.rint(t1[0][0]))
return sweep
def getClickedPointsByImage(filename,nPoints):
im = np.array(Image.open(filename))
plt.imshow(im)
print 'Please click ' + str(nPoints) + 'points'
x = plt.ginput(nPoints,-1)
plt.show()
return x
def find_gates(mag1, mag2, param):
col = mag1 - mag2
lines = open(param, 'r').readlines()
colmin, colmax = map(float, lines[4].split()[3:-1])
mag1min, mag1max = map(float, lines[5].split()[:-1])
#mag2min, mag2max = map(float, lines[5].split()[:-1])
# click around
fig, ax = plt.subplots()
ax.plot(col, mag2, ',', color='k', alpha=0.2)
ax.set_ylim(mag1max, mag1min)
ax.set_xlim(colmin, colmax)
ok = 1
while ok == 1:
print 'click '
pts = np.asarray(plt.ginput(n=4, timeout=-1))
exclude_gate = '1 {} 0 \n'.format(' '.join(['%.4f' % p for p in pts.flatten()]))
pts = np.append(pts, pts[0]).reshape(5,2)
ax.plot(pts[:,0], pts[:,1], color='r', lw=3, alpha=0.3)
plt.draw()
ok = move_on(0)
lines[7] = exclude_gate
# not so simple ... need them to be parallelograms.
# PASS!
# write new param file with exclude/include gate
os.system('mv {0} {0}_bkup'.format(param))
with open(param, 'w') as outp:
[outp.write(l) for l in lines]
print('wrote %s' % param)
def get_Period(dds_t, tsamp):
N = np.int(nearest_power_of_2(dds_t.size))
print('Padding ', dds_t.size, 'length array with ', N-dds_t.size, 'zeros.')
DDS_T = np.abs(ft.rfft(dds_t-dds_t.mean(), N))
nu = ft.rfftfreq(N, tsamp)
zoom = 1
while zoom == 1:
pl.plot(nu, DDS_T)
pl.xlabel('Frequency [Hz]')
pl.ylabel('Flux')
click = input('Ready to click ? :')
if click == 'y':
pl.title('Click on peak for F0')
F0 = pl.ginput(1)
zoom = 0
break
else:
continue
F0 = F0[0][0]
P0 = 1.0 / F0
pl.close()
pl.plot(nu, DDS_T)
pl.xlim(0.0, 12.5*F0)
pl.xlabel('Frequency [Hz]')
pl.ylabel('Flux')
pl.title('First 12 harmonics')
pl.savefig('First-12-harmonics.eps')
pl.savefig('First-12-harmonics.png')
return P0, F0
def pick_particles(segmentation):
"""
Allow user to locate a particle in a segmented image interactively.
Args
----------
segmentation : Numpy array
Segmented label image
Returns
----------
particles : int
Value associated with the chosen particle
"""
numpoints = segmentation.max()
# warnings.filterwarnings('ignore')
plt.figure(frameon=False)
plt.imshow(segmentation, cmap='nipy_spectral')
plt.title('Choose particles ...')
coords = np.array(plt.ginput(numpoints, timeout=0, show_clicks=True))
plt.close()
particles = np.zeros_like(segmentation)
for i, _ in enumerate(coords):
val = segmentation[np.int32(coords[i][1]), np.int32(coords[i][0])]
particles[segmentation == val] = val
return particles
def remove_particles(segmentation):
"""
Remove particles from segmentation via user interaction.
Args
----------
segmentation : Numpy array
Segmented label image
Returns
----------
segmentation : Numpy array
Segmented label image after particle removal
removed_any : bool
True if any particles were removed. Otherwise, False
n_removed : int
Number of particles removed from image
"""
numpoints = segmentation.max()
removed_any = False
plt.figure(frameon=False)
plt.imshow(segmentation, cmap='nipy_spectral')
plt.title('Choose particles to remove...')
coords = np.array(plt.ginput(numpoints, timeout=0, show_clicks=True))
plt.close()
n_removed = len(coords)
if n_removed > 0:
removed_any = True
for i, _ in enumerate(coords):
val = segmentation[np.int32(coords[i][1]), np.int32(coords[i][0])]
segmentation[segmentation == val] = 0
return segmentation, removed_any, n_removed
def interaction():
"""
Interaction with plots
"""
print("Please click 3 points")
x = plt.ginput(3)
print("you clicked: ", x)
def find_gates(mag1, mag2, param):
"""Click 4 points to make an exclude gate -- does not work in calcsfh!"""
print('not supported')
sys.exit()
col = mag1 - mag2
lines = open(param, 'r').readlines()
colmin, colmax = map(float, lines[4].split()[3:-1])
mag1min, mag1max = map(float, lines[5].split()[:-1])
# mag2min, mag2max = map(float, lines[5].split()[:-1])
# click around
_, ax = plt.subplots()
ax.plot(col, mag2, ',', color='k', alpha=0.2)
ax.set_ylim(mag1max, mag1min)
ax.set_xlim(colmin, colmax)
okay = 1
while okay != 0:
print('click ')
pts = np.asarray(plt.ginput(n=4, timeout=-1))
exclude_gate = '1 {} 0 \n'.format(' '.join(
['%.4f' % p for p in pts.flatten()]))
pts = np.append(pts, pts[0]).reshape(5, 2)
ax.plot(pts[:, 0], pts[:, 1], color='r', lw=3, alpha=0.3)
plt.draw()
okay = move_on(0)
lines[7] = exclude_gate
# not so simple ... need them to be parallelograms.
# PASS!
# write new param file with exclude/include gate
os.system('mv {0} {0}_bkup'.format(param))
with open(param, 'w') as outp:
_ = [outp.write(l) for l in lines]
print('wrote %s' % param)
def get_cursor_id(self, x, y):
c = zip(*(pylab.ginput(n=0, mouse_add=1, mouse_pop=2, mouse_stop=3)))
lonpt, latpt = self(c[0], c[1], inverse=True)
for enum in enumerate(zip(*(lonpt, latpt))):
dst = calcul_distance(enum[1][1], enum[1][0], x, y)
if enum[0] == 0: i = [np.argmin(dst).tolist()]
else: i.append(np.argmin(dst))
return i
def screen_cut_and_get_point():
screen_img = screen_capture()
pylab.figure(1, tight_layout=True)
pylab.axis('off')
pylab.imshow(screen_img)
mng = pylab.get_current_fig_manager()
mng.window.showMaximized()
mng.set_window_title('è¯·ç”¨é¼ æ ‡ç‚¹å‡»å¸Œæœ›æ¨¡æ‹Ÿé¼ æ ‡ç‚¹å‡»çš„åŒºåŸŸ')
point = pylab.ginput(1)[0]
pylab.close()
return point
def main():
# paths: change to folder selection
imageFolder = sys.argv[1]
imageFile = sys.argv[2]
imagePath = imageFolder + imageFile
original = cv.LoadImageM(imagePath)
img = cv.LoadImageM(imagePath, cv.CV_LOAD_IMAGE_GRAYSCALE)
eig_image = cv.CreateMat(img.rows, img.cols, cv.CV_32FC1)
temp_image = cv.CreateMat(img.rows, img.cols, cv.CV_32FC1)
tracker_coords = []
for (x, y) in cv.GoodFeaturesToTrack(img, eig_image, temp_image, 2000, 0.001, 5.0, useHarris=True):
tracker_coords.append([x, y])
tracker_coords = np.array(tracker_coords)
# crop image
while True:
plt.cla()
plt.imshow(original)
plt.plot(tracker_coords[:, 0], tracker_coords[:, 1], "ro", markersize=3)
a = ginput(2)
xmin = min([a[0][0], a[1][0]])
xmax = max([a[0][0], a[1][0]])
ymin = min([a[0][1], a[1][1]])
ymax = max([a[0][1], a[1][1]])
plt.plot([xmin, xmax, xmax, xmin, xmin], [ymin, ymin, ymax, ymax, ymin], "-bo")
plt.draw()
if tkMessageBox.askyesno("Save Grid", "Keep this selection?"):
break
# filter out points
output_trackers = []
for point in tracker_coords:
if (point[0] > xmin and point[0] < xmax and point[1] > ymin and point[1] < ymax).all():
output_trackers.append(point)
output_trackers = np.array(output_trackers)
# save data as grid
np.savetxt(imageFolder + "grid_x.dat", output_trackers[:, 0])
np.savetxt(imageFolder + "grid_y.dat", output_trackers[:, 1])
print a
plt.show()
def get_cursor(self, *args):
c = zip(*(pylab.ginput(n=0, mouse_add=1, mouse_pop=2, mouse_stop=3)))
lonpt, latpt = self(c[0], c[1], inverse=True)
for enum in enumerate(zip(*(lonpt, latpt))):
dst = calcul_distance(enum[1][1], enum[1][0], args[0], args[1])
i = np.argmin(dst)
xdum = args[0][i]
ydum = args[1][i]
if len(args) == 3: zdum = args[2][i]
if enum[0] == 0:
xout = xdum
yout = ydum
if len(args) == 3: zout = zdum
else:
xout = np.append(xout, xdum)
yout = np.append(yout, ydum)
if len(args) == 3: zout = np.append(zout, zdum)
if len(args) == 3: return xout, yout, zout
else: return xout, yout
def find_gates(target):
import glob
here = os.getcwd()
os.chdir(target)
# read match file for mag1, mag2
phot = glob.glob1('.', '*match')
params = glob.glob1('.', '*param')
for i, param in enumerate(params):
# read param file
mag1, mag2 = np.genfromtxt(phot[i], unpack=True)
col = mag1 - mag2
lines = open(param, 'r').readlines()
colmin, colmax = map(float, lines[4].split()[3:-1])
mag1min, mag1max = map(float, lines[5].split()[:-1])
#mag2min, mag2max = map(float, lines[5].split()[:-1])
# click around
fig, ax = plt.subplots()
ax.plot(col, mag2, ',', color='k', alpha=0.2)
ax.set_ylim(mag1max, mag1min)
ax.set_xlim(colmin, colmax)
ok = 1
while ok == 1:
print 'click '
pts = np.asarray(plt.ginput(n=4, timeout=-1))
exclude_gate = '1 {} 0 \n'.format(' '.join(['%.4f' % p for p in pts.flatten()]))
pts = np.append(pts, pts[0]).reshape(5,2)
ax.plot(pts[:,0], pts[:,1], color='r', lw=3, alpha=0.3)
plt.draw()
ok = move_on(0)
lines[7] = exclude_gate
# not so simple ... need them to be parallelograms.
# PASS!
# write new param file with exclude/include gate
os.system('mv {0} {0}_bkup'.format(param))
with open(param, 'w') as outp:
[outp.write(l) for l in lines]
print('wrote %s' % param)
os.chdir(here)
def select_gate(wfall):
g_on = [0, 0]
profile = wfall.mean(axis=0)
pl.plot(profile)
zoom = 1
while zoom == 1:
pl.plot(profile,'kx', lw=1.0)
pl.xlabel('Bin #')
pl.ylabel('Flux')
click = input('Ready to click ? :')
if click == 'y':
pl.title('Select gate for dynamic spectrum')
gate = pl.ginput(2)
zoom = 0
break
else:
continue
g_on[0] = np.int(np.floor(gate[0][0]))
g_on[1] = np.int(np.ceil(gate[1][0]))
return g_on
def main():
# paths: change to folder selection
imageFolder = sys.argv[1]
imageFile = sys.argv[2]
imagePath = imageFolder + imageFile
original = cv.LoadImageM(imagePath)
tracker_coords = []
tracker_coords = np.array(tracker_coords)
nr_points = int(sys.argv[3])
plt.imshow(original)
tracker_coords = ginput(nr_points)
tracker_coords = np.array(tracker_coords)
plt.plot(tracker_coords[:,0], tracker_coords[:,1],'ro',markersize=3)
# save data as grid
np.savetxt(imageFolder + 'grid_x.dat',tracker_coords[:,0])
np.savetxt(imageFolder + 'grid_y.dat',tracker_coords[:,1])
plt.show()
def online_orica_spike_sorting(recording,
n_comp='all',
pca_window=0,
ica_window=0,
skew_window=5,
step=1,
skew_thresh=0.5,
online=False,
detect=True,
calibPCA=True,
ff='cooling',
lambda_val=0.995,
dtype='int16',
verbose=True,
detect_thresh=10,
white_mode='pca',
pca_block=2000,
ica_block=800):
import matplotlib.pylab as plt
if not isinstance(recording, BaseRecording):
raise Exception("Input a RecordingExtractor object!")
if n_comp == 'all':
n_comp = recording.get_num_channels()
fs = recording.get_sampling_frequency()
traces = recording.get_traces().astype(dtype).T
t_init = time.time()
ori = orica.onlineORICAss(traces,
fs=fs,
onlineWhitening=online,
calibratePCA=calibPCA,
ndim=n_comp,
forgetfac=ff,
lambda_0=lambda_val,
numpass=1,
step_size=step,
skew_window=skew_window,
pca_window=pca_window,
ica_window=ica_window,
verbose=True,
detect_trheshold=detect_thresh,
onlineDetection=False,
white_mode=white_mode,
pca_block=pca_block,
ica_block=ica_block)
if verbose:
t_orica_online = time.time() - t_init
print('Online ORICA completed in: ', t_orica_online)
last_idxs = find_consistent_sorces(ori.source_idx, thresh=0.5)
# last_idxs = ori.all_sources
last_idxs = last_idxs[np.argsort(
np.abs(stats.skew(ori.y[last_idxs], axis=1)))[::-1]]
n_id = len(last_idxs)
y_on_selected = ori.y_on[last_idxs]
y_on = np.array([
-np.sign(sk) * s
for (sk, s) in zip(stats.skew(y_on_selected, axis=1), y_on_selected)
])
if verbose:
print('Rough spike detection to choose thresholds')
t_start = 0 * pq.s
t_stop = recording.get_num_frames() / float(fs) * pq.s
detected_spikes = detect_and_align(y_on,
fs,
traces,
t_start=t_start,
t_stop=t_stop,
n_std=5,
upsample=1)
# Manual thresholding
thresholds = []
for i, st in enumerate(detected_spikes):
fig = plt.figure(figsize=(10, 10))
# only plot 10% of the spikes
perm = np.random.permutation(len(st))
nperm = int(0.2 * len(st))
plt.plot(st.annotations['ica_wf'][perm[:nperm]].T, lw=0.1, color='g')
plt.title('IC ' + str(i + 1))
coord = plt.ginput()
th = coord[0][1]
plt.close(fig)
thresholds.append(th)
## User defined thresholds and detection
if verbose:
print('Detecting spikes based on user-defined thresholds')
spikes = threshold_spike_sorting(y_on, thresholds)
# Convert spikes to neo
spike_trains = []
for i, k in enumerate(sorted(spikes.keys())):
times = spikes[k]
tt = np.array(times) / float(fs)
# st = neo.SpikeTrain(times=tt, t_start=(pca_window + ica_window) * pq.s, t_stop=t_stop)
st = neo.SpikeTrain(times=tt * pq.s, t_start=t_start, t_stop=t_stop)
st.annotate(ica_source=last_idxs[i])
spike_trains.append(st)
sst, independent_spike_idx, dup = reject_duplicate_spiketrains(
spike_trains)
if verbose:
print('Number of spiky sources: ', len(spike_trains))
print('Number of spike trains after duplicate rejection: ', len(sst))
ica_spike_sources_idx = last_idxs[independent_spike_idx]
ica_spike_sources = ori.y[ica_spike_sources_idx]
A_spike_sources = ori.mixing[-1, ica_spike_sources_idx]
W_spike_sources = ori.unmixing[-1, ica_spike_sources_idx]
if verbose:
print('Elapsed time: ', time.time() - t_init)
sorting = ss.set_times_labels(sst, fs)
return sorting # , ica_spike_sources
import numpy as np
import Image
import matplotlib.pylab as plt
from im2array import *
im = Image.open('cam1.10000')
imnp = image2array(im)
plt.figure()
plt.imshow(imnp,cmap = plt.cm.gray)
# now press 2 corners: top-left, right-bottom
x = plt.ginput(2)
# parsing
a,b=x[0]; c,d=x[1]
box1 = (a,b,c,d)
# now press again 2 corners: top-left, right-bottom
x = plt.ginput(2)
# parsing
a,b=x[0]; c,d=x[1]
box2 = (a,b,c,d)
# crop using PIL
ball1 = im.crop(box1)
ball2 = im.crop(box2)
# resize to get integer sizes for sure
ball1 = ball1.resize((int(ball1.size[0]),int(ball1.size[1])))
def find_match_limits(phot, phot_ext, comp1=99., comp2=None, color_only=False,
xlim=None, ylim=None):
"""
click color limits on a cmd and mag1 mag2 limits on a plot of mag1 vs mag2
"""
mag1 = phot['mag1_%s' % phot_ext]
mag2 = phot['mag2_%s' % phot_ext]
col = mag1 - mag2
fig, ax = plt.subplots()
ax.plot(col, mag2, 'o', color='k', ms=3, alpha=0.3, mec='none')
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
else:
ax.set_ylim(ax.get_ylim()[::-1])
if comp2 is not None:
ax.hlines(comp2, *ax.get_xlim())
else:
comp2 = 99.
ok = 1
while ok == 1:
print 'click on color min then color max'
pts = plt.ginput(2, timeout=-1)
colmin, colmax = [pts[i][0] for i in range(2)]
ax.vlines(colmin, *ax.get_ylim())
ax.vlines(colmax, *ax.get_ylim())
plt.draw()
ok = move_on(0)
plt.close()
inds, = np.nonzero((col < colmax) & (col > colmin))
data = (colmin, colmax)
if not color_only:
fig, ax = plt.subplots()
ax.plot(mag1, mag2, '.', color='k')
ok = 1
while ok == 1:
print 'click the bright mag value of mag1 and mag2, click a second time to finish'
pts = plt.ginput(2, timeout=-1)
mag1max, mag2max = pts[0]
ax.plot(mag1max, mag2max, 'o', color='r')
plt.draw()
ok = move_on(ok)
plt.close()
inds, = np.nonzero((mag1 < comp1) & (mag1 > mag1max) &
(mag2 < comp2) & (mag2 > mag2max) &
(col < colmax) & (col > colmin))
fig, ax = plt.subplots()
ax.plot(col, mag2, '.', color='k')
ax.plot(col[inds], mag2[inds], '.', color='r')
ax.set_ylim(ax.get_ylim()[::-1])
ax.hlines(comp2, *ax.get_xlim(), lw=2)
ax.vlines(colmin, *ax.get_ylim(), lw=2)
ax.vlines(colmax, *ax.get_ylim(), lw=2)
if not color_only:
ax.hlines(mag2max, *ax.get_xlim(), lw=2)
data = (colmin, colmax, mag1max, mag2max)
plt.draw()
return data
def find_match_limits(mag1, mag2, comp1=90., comp2=90., color_only=False,
xlim=None, ylim=None):
"""
click color limits on a cmd and mag1 mag2 limits on a plot of mag1 vs mag2
"""
col = mag1 - mag2
fig, ax = plt.subplots()
ax.plot(col, mag2, 'o', color='k', ms=3, alpha=0.3, mec='none')
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
else:
ax.set_ylim(ax.get_ylim()[::-1])
if comp1 < 90.:
ax.hlines(comp2, *ax.get_xlim())
ok = 1
while ok == 1:
print 'click color extrema'
pts = plt.ginput(2, timeout=-1)
colmin, colmax = [pts[i][0] for i in range(2)]
if colmin > colmax:
colmin, colmax = colmax, colmin
ax.vlines(colmin, *ax.get_ylim())
ax.vlines(colmax, *ax.get_ylim())
plt.draw()
ok = move_on(0)
plt.close()
inds, = np.nonzero((col < colmax) & (col > colmin))
data = (colmin, colmax)
if not color_only:
fig, ax = plt.subplots()
ax.plot(mag1, mag2, '.', color='k')
ok = 1
while ok == 1:
print 'click mag extrema'
pts = plt.ginput(2, timeout=-1)
mag1max, mag2max = pts[0]
mag1min, mag2min = pts[1]
if mag1min > mag1max:
mag1min, mag1max = mag1max, mag1min
if mag2min > mag2max:
mag2min, mag2max = mag2max, mag2min
ax.plot(mag1max, mag2max, 'o', color='r')
ax.plot(mag1min, mag2min, 'o', color='r')
plt.draw()
ok = move_on(ok)
plt.close()
inds, = np.nonzero((mag1 < mag1min) & (mag1 > mag1max) &
(mag2 < mag2min) & (mag2 > mag2max) &
(col < colmax) & (col > colmin))
fig, ax = plt.subplots()
ax.plot(col, mag2, '.', color='k')
ax.plot(col[inds], mag2[inds], '.', color='r')
ax.set_ylim(ax.get_ylim()[::-1])
if comp2 < 90.:
ax.hlines(comp2, *ax.get_xlim(), lw=2)
ax.vlines(colmin, *ax.get_ylim(), lw=2)
ax.vlines(colmax, *ax.get_ylim(), lw=2)
if not color_only:
ax.hlines(mag2max, *ax.get_xlim(), lw=2)
data = (colmin, colmax, mag1min, mag1max, mag2min, mag2max)
plt.draw()
print data
return data
print 'reading', ImageToLoad
# Read the image and convert it to grayscale rightaway
ImageRGB = plt.imread(ImageToLoad)
Image = rgb2gray(ImageRGB)
ImageWidth = Image.shape[0]
ImageHeight = Image.shape[1]
print 'The image we loaded is', ImageWidth, 'by', ImageHeight, \
'pixels big. That is', round(ImageWidth * ImageHeight / 1e6, 3), 'MPx.'
plt.subplot(221)
plt.imshow(ImageRGB, origin='lower')
plt.title('Pick point for drawing\n horizontal and vertical profile')
if SelectStartPointManually:
PickPoint = plt.ginput(1)
else:
if Camera == 'iPhone':
PickPoint = [[1500, 1000]]
elif Camera[:6] == 'tiscam':
# Select middle of image...
PickPoint = [[ImageHeight / 2, ImageWidth / 2]]
elif Camera == 'Elphel':
PickPoint = [[ImageHeight / 2, ImageWidth / 2]]
plt.title('Original image')
Horizon = int(PickPoint[0][1])
Vertigo = int(PickPoint[0][0])
if SelectStartPointManually:
print 'You selected horizontal line', Horizon, 'and vertical line', Vertigo
else:
print 'I selected horizontal line', Horizon, 'and vertical line', Vertigo
import numpy as np
import Image
import matplotlib.pylab as plt
from im2array import *
im = Image.open('cam1.10000')
imnp = image2array(im)
plt.figure()
plt.imshow(imnp, cmap=plt.cm.gray)
# now press 2 corners: top-left, right-bottom
x = plt.ginput(2)
# parsing
a, b = x[0]
c, d = x[1]
box1 = (a, b, c, d)
# now press again 2 corners: top-left, right-bottom
x = plt.ginput(2)
# parsing
a, b = x[0]
c, d = x[1]
box2 = (a, b, c, d)
# crop using PIL
ball1 = im.crop(box1)
ball2 = im.crop(box2)
# resize to get integer sizes for sure
# nonlinear
print('getting filenames...')
abs_exper = sorted(glob(basepath + 'abs*'))
relu_exper = sorted(glob(basepath + 'relu*'))
select_exper = sorted(glob(basepath + 'select-*'))
select_max_exper = sorted(glob(basepath + 'select_max*'))
print('loading data...')
abs_data = load_experiment_data(abs_exper)
relu_data = load_experiment_data(relu_exper)
select_data = load_experiment_data(select_exper)
select_max_data = load_experiment_data(select_max_exper)
print('displying data...')
display_griddata([abs_data, relu_data, select_data, select_max_data],
savepath + 'nonlinearity')
# aggregation
avg_exper = sorted(glob(basepath + 'avg*'))
max_norm_exper = sorted(glob(basepath + 'max_norm*'))
max_exper = sorted(glob(basepath + 'max-*'))
avg_data = load_experiment_data(avg_exper)
max_norm_data = load_experiment_data(max_norm_exper)
max_data = load_experiment_data(max_exper)
display_griddata([avg_data, max_data, max_norm_data],
savepath + 'aggregation')
plt.ginput()
balloon = dphi_norm
attachment = dot(dphi, dg)
dphi_t = smoothing + balloon + attachment
phi = phi + dphi_t
return phi
# np.set_printoptions(threshold=np.inf)
im = Image.open('Moon.jpg')
im2 = np.array(Image.open('background2.jpg'))
im = im.resize((im2.shape[1], im2.shape[0]))
im_copy = im.copy()
im1 = im_copy.resize((im2.shape[1] // 4, im2.shape[0] // 4))
plt.imshow(im1)
X = plt.ginput(4)
X = np.array(X)
plt.close()
a = np.max(X, axis=0)
b = np.min(X, axis=0)
contour = active_contour(np.array(im1.convert('L')), b, a, 100, 0.05)
mask = np.clip(contour, 0, 1)
mask1 = 1 - mask
mask1 = cv2.resize(mask1, (im2.shape[1], im2.shape[0]))
mask1 = np.dstack((mask1, mask1, mask1))
p1 = mask1 * im
p1 = p1.astype(np.uint8)
plt.figure()
def study_dndz_interactive():
x, z, type = match_wise_cosmos(quick=True)
w1=x[:,0]
w2=x[:,1]
w3=x[:,2]
w4=x[:,3]
j=x[:,4]
h=x[:,5]
k=x[:,6]
cA = w1
cB = w2-w4-0.7*w1
xlab = 'W1'
ylab = 'W2-W4-0.7W1'
fs = 16
from matplotlib import rc
rc('xtick', labelsize=17)
rc('ytick', labelsize=17)
fcolor='green'
fcolor='blue'
pl.figure(1, figsize=(12,8))
pl.clf()
pl.subplot(2,1,1)
pl.plot(cA,cB,'k.')
pl.xlabel(xlab,fontsize=fs);pl.ylabel(ylab,fontsize=fs)
keep_going=True
from matplotlib.pylab import ginput
import cosmolopy as cp
while keep_going:
pt = ginput(2,timeout=100000)
pl.clf()
pl.subplot(2,1,1)
pl.plot(cA,cB,'k.')
pl.xlabel(xlab,fontsize=fs);pl.ylabel(ylab,fontsize=fs)
if pt[0]==pt[1]:
keep_going=False
else:
x0 = pt[0][0]
y0 = pt[0][1]
x1 = pt[1][0]
y1 = pt[1][1]
cA_min = np.min([x0,x1])
cA_max = np.max([x0,x1])
cB_min = np.min([y0,y1])
cB_max = np.max([y0,y1])
wh_cut = np.where((cA>=cA_min) & (cA<=cA_max) & (cB>=cB_min) & (cB<=cB_max) & (type==0))[0]
n_cut = len(wh_cut)
pl.subplot(2,1,1)
pl.plot(cA[wh_cut], cB[wh_cut],'r.')
pl.xlabel(xlab,fontsize=fs);pl.ylabel(ylab,fontsize=fs)
meanz = np.mean(z[wh_cut])
medz = np.median(z[wh_cut])
pl.title('[%0.2f, %0.2f], [%0.2f, %0.2f], %i gals, <z>=%0.2f, |z|=%0.2f, sig=%0.2f'%(cA_min, cA_max, cB_min, cB_max, n_cut, meanz, medz,np.std(z[wh_cut])), fontsize=16)
# pl.legend(['%0.2f, %0.2f'%(cA_min, cA_max), '%0.2f, %0.2f'%(cB_min, cB_max)], 'upper left')
pl.subplot(2,1,2)
zbins = np.linspace(0.1,2.0,15)
n, bins, patches = pl.hist(z[wh_cut], zbins, normed=0, facecolor=fcolor, alpha=0.5)
zcen = 0.5*(zbins[0:-1]+zbins[1:])
rsound_com = 153.
rsound_phs = rsound_com/(1.+zcen)
dang = cp.distance.angular_diameter_distance(zcen,**cp.fidcosmo)
ang_sound = rsound_phs/dang
ang_sound /= np.median(ang_sound)
pl.xlabel('Redshift (z)',fontsize=20)
pl.ylabel('N',fontsize=20)
print 'reading', ImageToLoad
# Read the image and convert it to grayscale rightaway
ImageRGB = plt.imread(ImageToLoad)
Image = rgb2gray(ImageRGB)
ImageWidth = Image.shape[0]
ImageHeight = Image.shape[1]
print 'The image we loaded is', ImageWidth, 'by', ImageHeight, \
'pixels big. That is', round(ImageWidth * ImageHeight / 1e6, 3), 'MPx.'
plt.subplot(221)
plt.imshow(ImageRGB, origin='lower')
plt.title('Pick point for drawing\n horizontal and vertical profile')
if SelectStartPointManually:
PickPoint = plt.ginput(1)
else:
if Camera == 'iPhone':
PickPoint = [[1500, 1000]]
elif Camera[:6] == 'tiscam':
# Select middle of image...
PickPoint = [[ImageHeight / 2, ImageWidth / 2]]
elif Camera == 'Elphel':
PickPoint = [[ImageHeight / 2, ImageWidth / 2]]
plt.title('Original image')
Horizon = int(PickPoint[0][1])
Vertigo = int(PickPoint[0][0])
if SelectStartPointManually:
print 'You selected horizontal line', Horizon, 'and vertical line', Vertigo
else:
print 'I selected horizontal line', Horizon, 'and vertical line', Vertigo
# Load projection matrices for views p, q, r
P1 = data['P'][:, :, p] # Reprojection matrix of view p
P2 = data['P'][:, :, q] # Reprojection matrix of view q
P3 = data['P'][:, :, r] # Reprojection matrix of view r
P = np.vstack([P1, P2, P3]) # Join all projection matrices
Ip = image_set.load_image(44, p)
Iq = image_set.load_image(44, q)
Ir = image_set.load_image(44, r)
# Plot lines and plot on figures
fig1, ax1 = plt.subplots(1, 1, subplot_kw=dict(title='Figure p'))
ax1.imshow(Ip, cmap='gray')
ax1.axis('off')
print('Click first and second points in Figure 1 ...')
mp = np.hstack([np.array(plt.ginput(2)), np.ones((2, 1))]).T # Click
ax1.plot(mp[0, :], mp[1, :], 'ro')
ax1.plot(mp[0, :], mp[1, :], 'g', linewidth=1.0)
fig1.canvas.draw()
fig2, ax2 = plt.subplots(1, 1, subplot_kw=dict(title='Figure q'))
ax2.imshow(Iq, cmap='gray')
ax2.axis('off')
print('Click first and second points in Figure 2 ...')
mq = np.hstack([np.array(plt.ginput(2)), np.ones((2, 1))]).T # Click
ax2.plot(mq[0, :], mq[1, :], 'ro')
ax2.plot(mq[0, :], mq[1, :], 'g', linewidth=1.0)
fig2.canvas.draw()
fig3, ax3 = plt.subplots(1, 1, subplot_kw=dict(title='Figure r'))
ax3.imshow(Ir, cmap='gray')
#use filtered
#d1=data1_filt[start_time_index:end_time_index]
#d2=data2_filt[start_time_index:end_time_index]
if plotshots:
plt.plot(np.array(timeB_us),data1mod_norm)
plt.plot(np.array(timeB_us),data2mod_norm)
plt.title(tde_pair[0]+tde_pair[1]+' Shot '+str(shot))
plt.xlim(0.0,40.0)
plt.ylim(0,0.3)
# Bind the button_press_event with the onclick() method
fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
plt.ginput(10,timeout=0)
plt.clf()
plt.plot(tau,corr)
plt.title(tde_pair[0]+tde_pair[1]+' Shot '+str(shot))
fig.canvas.mpl_connect('button_press_event', onclick)
plt.show()
plt.ginput(20,timeout=0)
plt.clf()
#savedirectory='C:\\Users\\dschaffner\\Dropbox\\Data\\BMPL\\BMX\\2022\\01122022\\QuickPlots\\timeseries\\probepairs\\'+tde_pair[0]+tde_pair[1]+'\\mod\\'
#savefilename=tde_pair[0]+tde_pair[1]+'_shot_'+str(shot+1).zfill(2)+'mod.png'
#savefile = savedirectory+savefilename
#plt.savefig(savefile,dpi=300,facecolor='w',edgecolor='k')
#plt.clf()
#use unfiltered
#d1=data1_norm[start_time_index:end_time_index]
Pq = data['P'][:, :, q] # Projection matrix of view q
F = estimate_fundamental_matrix(Pp, Pq, method='pseudo')
colors = 'bgrcmykw' # Colors for each point-line pair
fig1, ax1 = plt.subplots(1, 1)
fig1.suptitle('Figure p')
ax1.imshow(Ip, cmap='gray')
ax1.axis('off')
fig2, ax2 = plt.subplots(1,
1,
subplot_kw=dict(xlim=(0, Ip.shape[1]),
ylim=(Iq.shape[0], 1)))
fig2.suptitle('Figure q')
ax2.imshow(Iq, cmap='gray')
ax2.axis('off')
fig2.show()
for i in range(8):
plt.figure(fig1.number) # Focus on fig1 and get the mouse locations
m = np.hstack([np.array(plt.ginput(1)), np.ones((1, 1))]).T # Click
ax1.plot(m[0, 0], m[1, 0],
f'{colors[i]}*') # Plot lines and plot on figures
fig1.canvas.draw()
ax2 = plot_epipolar_line(F, m, line_color=colors[i], ax=ax2)
fig2.canvas.draw()
plt.show()
def select_outlets(dem,
fd,
prefix,
hs=None,
outlet_filename='outlets.p',
color='r'):
if hs is None:
hs = d.Hillshade(elevation=dem, azimuth=320, inclination=20)
plot_dem(dem, hs)
keep_going = True
while keep_going:
zoom_ok = False
print(
'\nZoom or pan to view, \npress spacebar when ready to select point upstream of outlet:\n'
)
while not zoom_ok:
zoom_ok = plt.waitforbuttonpress()
print('\nClick on point upstream of outlet.')
xy = plt.ginput(1)[0]
xy_path = fd.search_down_flow_direction_from_xy_location(xy)
plt.plot(xy)
plt.plot(xy_path)
xy_path_plot = list(zip(*xy_path))
path = plt.plot(xy_path_plot[0], xy_path_plot[1], color + '-')
print('\nClick on the outlet location.')
xy = plt.ginput(1)[0]
min_distance = 1E12
for loc in xy_path:
if (np.sqrt(
np.power(loc[0] - xy[0], 2) + np.power(loc[1] - xy[1], 2))
< min_distance) or min_distance == 1E12:
outlet_loc = loc
min_distance = np.sqrt(
np.power(loc[0] - xy[0], 2) + np.power(loc[1] - xy[1], 2))
plt.figure(1)
plt.plot(outlet_loc[0], outlet_loc[1], color + 'o')
path.pop(0).remove()
outlet_prefix = raw_input(
'Type a name for this outlet (leaving this blank will prevent outlet from being saved and will complete the selection process: '
)
if outlet_prefix != '':
import os.path
if os.path.isfile(outlet_filename):
outlets = p.load(open(outlet_filename, 'rb'))
else:
outlets = dict()
this_tile = outlets.get(prefix, dict())
this_tile[outlet_prefix] = outlet_loc
outlets[prefix] = this_tile
p.dump(outlets, open(outlet_filename, 'wb'))
else:
keep_going = False
def find_match_limits(mag1,
mag2,
comp1=90.,
comp2=90.,
color_only=False,
xlim=None,
ylim=None):
"""
click color limits on a cmd and mag1 mag2 limits on a plot of mag1 vs mag2
"""
col = mag1 - mag2
_, ax = plt.subplots()
ax.plot(col, mag2, 'o', color='k', ms=3, alpha=0.3, mec='none')
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
else:
ax.set_ylim(ax.get_ylim()[::-1])
if comp1 < 90.:
ax.hlines(comp2, *ax.get_xlim())
okay = 1
while okay == 1:
print('click color extrema')
pts = plt.ginput(2, timeout=-1)
colmin, colmax = [pts[i][0] for i in range(2)]
if colmin > colmax:
colmin, colmax = colmax, colmin
ax.vlines(colmin, *ax.get_ylim())
ax.vlines(colmax, *ax.get_ylim())
plt.draw()
okay = move_on(0)
plt.close()
inds, = np.nonzero((col < colmax) & (col > colmin))
data = (colmin, colmax)
if not color_only:
_, ax = plt.subplots()
ax.plot(mag1, mag2, '.', color='k')
okay = 1
while okay == 1:
print('click mag extrema')
pts = plt.ginput(2, timeout=-1)
mag1max, mag2max = pts[0]
mag1min, mag2min = pts[1]
if mag1min > mag1max:
mag1min, mag1max = mag1max, mag1min
if mag2min > mag2max:
mag2min, mag2max = mag2max, mag2min
ax.plot(mag1max, mag2max, 'o', color='r')
ax.plot(mag1min, mag2min, 'o', color='r')
plt.draw()
okay = move_on(okay)
plt.close()
inds, = np.nonzero((mag1 < mag1min) & (mag1 > mag1max)
& (mag2 < mag2min) & (mag2 > mag2max)
& (col < colmax) & (col > colmin))
_, ax = plt.subplots()
ax.plot(col, mag2, '.', color='k')
ax.plot(col[inds], mag2[inds], '.', color='r')
ax.set_ylim(ax.get_ylim()[::-1])
if comp2 < 90.:
ax.hlines(comp2, *ax.get_xlim(), lw=2)
ax.vlines(colmin, *ax.get_ylim(), lw=2)
ax.vlines(colmax, *ax.get_ylim(), lw=2)
if not color_only:
ax.hlines(mag2max, *ax.get_xlim(), lw=2)
data = (colmin, colmax, mag1min, mag1max, mag2min, mag2max)
plt.draw()
print(data)
return data
# Do ROI if desired
plt.figure(1)
plt.imshow(Image)
plt.title('Original')
if options.ROI:
# make the ROI-Coordinates into a double tuple, so we can use less code
# afterwards
options.ROI = ((int(options.ROI.split(',')[0]),
int(options.ROI.split(',')[1])),
(int(options.ROI.split(',')[2]),
int(options.ROI.split(',')[3])))
else:
plt.title('Please select two corners of the ROI. Top left, bottom right')
options.ROI = plt.ginput(2)
# swap ROI coordinates if necessary (if user clicked right/left instead of
# left/right)
if options.ROI[0][0] > options.ROI[1][0]:
plt.title('Select top left, then bottom right!')
options.ROI = plt.ginput(2)
if options.ROI[0][0] > options.ROI[1][0]:
plt.title('TOP LEFT, BOTTOM RIGHT!')
options.ROI = plt.ginput(2)
if options.ROI[0][0] > 0:
plt.subplot(211)
plt.imshow(Image)
plt.title('Original')
plt.hlines(options.ROI[0][1], options.ROI[0][0], options.ROI[1][0], 'r',
linewidth=3)
