def phrag_map(bgrn, wid=10):
""" Impose a model for phragmites on a scaled (R,G,B,N -> r,g,b,N/Nmax)
map """
# -- smooth the images
print("median filtering... ")
t0 = time.time()
imgsm = mf(bgrn, (1, wid, wid))
print(" {0}s".format(np.round(time.time() - t0, 2)))
imgusm = uf(bgrn, (0, wid, wid))
img2usm = uf(bgrn**2, (0, wid, wid))
# -- get the variance image
var = img2usm - imgusm**2
# -- get the pseudo NDVI
pndvi = bgrn[3] - bgrn[2]
# -- apply cuts
return (imgsm[0] - imgsm[1] > 0.09) & (var[3] < 0.005) & (pndvi > 0.0)
def show_spectrum(event):
if hold_spec[0]:
return
if event.inaxes==ax[0]:
cind = int(round(event.xdata))
rind = int(round(event.ydata))
mn = cube.data[:,rind,cind].mean()
sd = cube.data[:,rind,cind].std()
if not median_filter:
lin[0].set_data(waves,cube.data[:,rind,cind])
else:
lin[0].set_data(waves,mf(cube.data[:,rind,cind],
median_filter))
ax[1].set_ylim([min(-10,cube.data[:,rind,cind].min()),
max(20,1.2*cube.data[:,rind,cind].max())])
pos_text.set_y(ax[1].get_ylim()[1])
pos_text.set_text('(row,col) = ({0:4},{1:4})'.format(rind,cind))
fig.canvas.draw()
return
def show_spectrum(event):
if hold_spec[0]:
return
if event.inaxes == ax[0]:
cind = int(round(event.xdata))
rind = int(round(event.ydata))
mn = cube.data[:, rind, cind].mean()
sd = cube.data[:, rind, cind].std()
if not median_filter:
lin[0].set_data(waves, cube.data[:, rind, cind])
else:
lin[0].set_data(waves,
mf(cube.data[:, rind, cind], median_filter))
ax[1].set_ylim([
min(-10, cube.data[:, rind, cind].min()),
max(20, 1.2 * cube.data[:, rind, cind].max())
])
pos_text.set_y(ax[1].get_ylim()[1])
pos_text.set_text('(row,col) = ({0:4},{1:4})'.format(rind, cind))
fig.canvas.draw()
return
# Applied Data Science/ fall 2014 # # Video Project (Final) # # Dimas Rinarso Putro | [email protected] # # image_manipulation.py # ############################################## import os import sys import time import pylab import numpy as np import matplotlib.pyplot as plt import scipy.ndimage as nd from scipy.ndimage.filters import median_filter as mf #1-7 img_ml = mf( 1.0 * (255 - nd.imread(os.path.join('images', 'ml.jpg'))[::2, ::2, ::-1]), [8, 2, 1]).clip(0, 255).astype(np.uint8) #plot it ysize = 10. xsize = ysize * float(img_ml.shape[1]) / float(img_ml.shape[0]) fig0, ax0 = plt.subplots(num=0, figsize=[xsize, ysize]) fig0.canvas.set_window_title('modified Mona Lisa') fig0.subplots_adjust(0, 0, 1, 1, 0, 0) ax0.axis('off') im0 = ax0.imshow(img_ml) fig0.canvas.draw() plt.show()
def plot_cube(cube,cmap='bone',clim=None,median_filter=False,figsize=10):
"""
An interactive visualization of the data cube.
Parameters
----------
cube : HyperCube
An instance of the HyperCube class to be shown.
cmap : matplotlib color map, optional
The matplotlib color map to use.
clim : 2-element list, optional
The limits of the color stretch.
median_filter : float, optional
Set the width of the median filter on the spectra.
figsize : float, optional
The horizontal width of the figure in inches.
"""
# -- set the flag to freeze the spectrum
def toggle_hold(event):
if event.button==1 and event.inaxes==ax[0]:
hold_spec[0] = not hold_spec[0]
return
# -- mouseover event updates the spectrum
def show_spectrum(event):
if hold_spec[0]:
return
if event.inaxes==ax[0]:
cind = int(round(event.xdata))
rind = int(round(event.ydata))
mn = cube.data[:,rind,cind].mean()
sd = cube.data[:,rind,cind].std()
if not median_filter:
lin[0].set_data(waves,cube.data[:,rind,cind])
else:
lin[0].set_data(waves,mf(cube.data[:,rind,cind],
median_filter))
ax[1].set_ylim([min(-10,cube.data[:,rind,cind].min()),
max(20,1.2*cube.data[:,rind,cind].max())])
pos_text.set_y(ax[1].get_ylim()[1])
pos_text.set_text('(row,col) = ({0:4},{1:4})'.format(rind,cind))
fig.canvas.draw()
return
# -- utilities
waves = cube.wavelength*1e-3 if not cube.indexing else np.arange(cube.nwav)
xlab = 'wavelength [micron]' if not cube.indexing else 'index'
arat = float(cube.nrow)/float(cube.ncol)
rat = arat*3./5.
med = np.median(cube.img_L)
sig = cube.img_L.std()
scl = 0.2
if clim==None:
clim = [scl*max(med - 2*sig,cube.img_L.min()),
scl*min(med + 10*sig,cube.img_L.max())]
# -- initialize the figure
fig = plt.figure(figsize=[figsize,figsize*rat])
fig.subplots_adjust(0,0,1,1,0,0)
ax = []
ax.append(fig.add_axes([0.1,0.35,0.85,0.6]))
ax.append(fig.add_axes([0.1,0.1,0.85,0.2]))
fig.set_facecolor('ivory')
ax[0].axis('off')
ax[1].set_ylim([-10,20])
ax[1].grid(1,color='white',ls='-',lw=1.5)
ax[1].set_axis_bgcolor('lightgray')
ax[1].set_xlabel(xlab)
ax[1].set_xlim([waves.min(),waves.max()])
ax[1].set_ylabel('intensity\n[arb units]')
ax[1].set_axisbelow(True)
# -- show the grayscale iamge
ax[0].imshow(cube.img_L,clim=clim,cmap=cmap,aspect=0.6/0.85*rat/arat)
# -- plot the spectrum
if not median_filter:
lin = ax[1].plot(waves,cube.data[:,0,0],color='#E24A33',lw=1.5)
else:
lin = ax[1].plot(waves,mf(cube.data[:,0,0],median_filter),
color='#E24A33',lw=1.5)
# -- show the position of the spectrum
pos_text = ax[1].text(ax[1].get_xlim()[1],ax[1].get_ylim()[1],
'(row,col) = ({0:4},{1:4})'.format(0,0),
ha='right',va='bottom')
# -- initialize hold flag and connect events
hold_spec = [False]
fig.canvas.mpl_connect('button_press_event',toggle_hold)
fig.canvas.mpl_connect('motion_notify_event',show_spectrum)
return
def Fg_Extract(frame,type = 1,trun = 100): #extract foreground
global BG_old
global F_case
global M_case
global B_case
if type ==1: # training BG
mu[:] = alpha*frame + (1.0-alpha)*mu_old
mu_old[:] = mu
sig2[:] = alpha*(1.0*frame-mu)**2 + (1.0-alpha)*sig2_old
sig2_old[:] = sig2
sig = sig2**0.5
lmcs = lmc*sig
bmcs = bmc*sig
sig_factor = 1
fg= (np.abs(1.0*frame-mu)[:,:,0]-sig_factor*sig[:,:,0]>0.0) +\
(np.abs(1.0*frame-mu)[:,:,1]-sig_factor*sig[:,:,1]>0.0) +\
(np.abs(1.0*frame-mu)[:,:,2]-sig_factor*sig[:,:,2]>0.0)
elif type == 2: # avg total seq (need saved file)
print('bg')
BG = pickle.load(open("./BG/20141031.pkl","rb"))
BG = cv2.resize(BG,(0,0),fx = scale,fy=scale)
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>50.)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>50.)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>50.)
elif type == 2.5 : #avg total seq (need saved file) normalized image
BG = pickle.load(open("./BG/car accident.pkl","rb"))
BG = cv2.resize(BG,(0,0),fx = scale,fy=scale)
fcal = np.zeros(BG.shape)
BG_r_avg = BG[:,:,0][Rmask].mean()
BG_g_avg = BG[:,:,1][Rmask].mean()
BG_b_avg = BG[:,:,2][Rmask].mean()
BG_r_std = BG[:,:,0][Rmask].std()
BG_g_std = BG[:,:,1][Rmask].std()
BG_b_std = BG[:,:,2][Rmask].std()
f_r_avg = frame[:,:,0][Rmask].mean()
f_g_avg = frame[:,:,1][Rmask].mean()
f_b_avg = frame[:,:,2][Rmask].mean()
f_r_std = frame[:,:,0][Rmask].std()
f_g_std = frame[:,:,1][Rmask].std()
f_b_std = frame[:,:,2][Rmask].std()
fcal[:,:,0] = (frame[:,:,0]-f_r_avg)/f_r_std*BG_r_std+BG_r_avg
fcal[:,:,1] = (frame[:,:,1]-f_g_avg)/f_g_std*BG_g_std+BG_g_avg
fcal[:,:,2] = (frame[:,:,2]-f_b_avg)/f_b_std*BG_b_std+BG_b_avg
dif = abs(fcal-BG)
fg = ((dif[:,:,1]>20.)|(dif[:,:,2]>30.))
elif type == 3 : #truncation mean 1.
if len(vid)>trun:
if (vid_idx-round(trun/2))<0:
if F_case == 1:
BG = BG_old
else:
LB = 0
UB = trun-1
BG = array(vid[LB:UB+1]).mean(0)
F_case = 1
elif len(vid)-vid_idx<=(trun-1):
if B_case == 1:
BG = BG_old
else:
LB = len(vid)-(trun-1)
UB = len(vid)
BG = array(vid[LB:UB+1]).mean(0)
B_case = 1
else:
if M_case == 1:
BG = BG_old + array(vid[vid_idx+int(trun/2)])/trun\
- array(vid[vid_idx-int(trun/2)+1])/trun
else:
LB = vid_idx-int(trun/2)+1
UB = vid_idx+int(trun/2)
#pdb.set_trace()
BG = array(vid[LB:UB+1]).mean(0)
M_case = 1
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>25.0)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>25.0)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>25.0)
else:
print('select truncation is larger then the sequence....')
BG = array(vid).mean(0)
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>30.0)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>30.0)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>30.0)
elif type==4: #truncation mean 2.
if len(vid)>trun:
if len(vid)-vid_idx<=(trun-1):
if B_case == 1:
BG = BG_old
else:
LB = len(vid)-(trun-1)
UB = len(vid)
BG = array(vid[LB:UB+1]).mean(0)
B_case = 1
else:
if F_case == 1:
BG = BG_old + array(vid[vid_idx+trun-1])/trun\
- array(vid[vid_idx-1])/trun
else:
LB = 0
UB = trun-1
BG = array(vid[LB:UB+1]).mean(0)
F_case = 1
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>30.0)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>30.0)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>30.0)
else:
print('select truncation is larger then the sequence....')
BG = array(vid).mean(0)
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>30.0)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>30.0)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>30.0)
elif type == 5:
if (vid_idx-round(trun/2))<0:
if F_case == 1:
BG = BG_old
else:
LB = 0
UB = trun-1
BG = array(vid[LB:UB+1]).mean(0)
F_case = 1
elif len(vid)-vid_idx<=(trun-1):
if B_case == 1:
BG = BG_old
else:
LB = len(vid)-(trun-1)
UB = len(vid)
BG = array(vid[LB:UB+1]).mean(0)
B_case = 1
else:
if M_case == 1:
BG = BG_old + array(vid[vid_idx+int(trun/2)])/trun\
- array(vid[vid_idx-int(trun/2)+1])/trun
else:
LB = vid_idx-int(trun/2)+1
UB = vid_idx+int(trun/2)
BG = array(vid[LB:UB+1]).mean(0)
M_case = 1
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>25.0)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>25.0)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>25.0)
#fg = fg * ShadowRm(fg,frame,BG,5,frame.shape[0],frame.shape[1],20)
elif type == 6:
global buf,Tf
N = 25
trun = N*2+1
ind =range(trun)
ind.pop(trun/2)
buf[-1] = vid[vid_idx+N+1]
if len(vid)>trun:
BG = np.abs(buf[ind]-buf[trun/2]).mean(3).mean(0)
fg = (BG>40.)*Tf
buf=np.roll(buf,-1,0)
else:
print('select truncation is larger then the sequence....')
if maskon:
fg = fg*mask*mask2
fgd = ndm.binary_dilation(fg,np.ones([2,2]))
fgf = ndm.binary_fill_holes(fgd)
right.set_data(mf(fgf,5))
plt.draw()
if (type!=1)&(type!=5):
BG_old = BG
return fgf
def Fg_Extract(frame,type = 1,trun = 100): #extract foreground
global BG_old
global F_case
global M_case
global B_case
if type ==1: # training BG
mu[:] = alpha*frame + (1.0-alpha)*mu_old
mu_old[:] = mu
sig2[:] = alpha*(1.0*frame-mu)**2 + (1.0-alpha)*sig2_old
sig2_old[:] = sig2
sig = sig2**0.5
lmcs = lmc*sig
bmcs = bmc*sig
sig_factor = 1
#pdb.set_trace()
fg= (np.abs(1.0*frame-mu)[:,:,0]-sig_factor*sig[:,:,0]>0.0) +\
(np.abs(1.0*frame-mu)[:,:,1]-sig_factor*sig[:,:,1]>0.0) +\
(np.abs(1.0*frame-mu)[:,:,2]-sig_factor*sig[:,:,2]>0.0)
elif type == 2: # avg total seq (need saved file)
print('bg')
BG = pickle.load(open("./BG/TLC0005(15000-16000).pkl","rb"))
BG = cv2.resize(BG,(0,0),fx = scale,fy=scale)
fg = (np.abs(1.0*frame[:,:,0]-BG[:,:,0])>50.)+\
(np.abs(1.0*frame[:,:,1]-BG[:,:,1])>50.)+\
(np.abs(1.0*frame[:,:,2]-BG[:,:,2])>50.)
elif type == 2.5 : #avg total seq (need saved file) normalized image
BG = pickle.load(open("./BG/TLC0005(15000-16000).pkl","rb"))
BG = cv2.resize(BG,(0,0),fx = scale,fy=scale)
fcal = np.zeros(BG.shape)
#pdb.set_trace()
BG_r_avg = BG[:,:,0][Rmask].mean()
BG_g_avg = BG[:,:,1][Rmask].mean()
BG_b_avg = BG[:,:,2][Rmask].mean()
BG_r_std = BG[:,:,0][Rmask].std()
BG_g_std = BG[:,:,1][Rmask].std()
BG_b_std = BG[:,:,2][Rmask].std()
f_r_avg = frame[:,:,0][Rmask].mean()
f_g_avg = frame[:,:,1][Rmask].mean()
f_b_avg = frame[:,:,2][Rmask].mean()
f_r_std = frame[:,:,0][Rmask].std()
f_g_std = frame[:,:,1][Rmask].std()
f_b_std = frame[:,:,2][Rmask].std()
fcal[:,:,0] = (frame[:,:,0]-f_r_avg)/f_r_std*BG_r_std+BG_r_avg
fcal[:,:,1] = (frame[:,:,1]-f_g_avg)/f_g_std*BG_g_std+BG_g_avg
fcal[:,:,2] = (frame[:,:,2]-f_b_avg)/f_b_std*BG_b_std+BG_b_avg
dif = abs(fcal-BG)
fg = ((dif[:,:,1]>50.)|(dif[:,:,2]>50.))
Tf = (frame[:,:,1]>100) & (frame[:,:,0]<100)
Tf = ndm.binary_closing(Tf,structure=np.ones((4,4)))
Tf = ~ndm.binary_fill_holes(Tf)
df = (abs(1.0*vid[vid_idx-1][:,:,0]-1.0*vid[vid_idx][:,:,0])>50.)+\
(abs(1.0*vid[vid_idx-1][:,:,1]-1.0*vid[vid_idx][:,:,1])>50.)+\
(abs(1.0*vid[vid_idx-1][:,:,2]-1.0*vid[vid_idx][:,:,2])>50.)
if maskon:
fg = fg*mask*Tf*df#*mask2
#fgo = ndm.binary_opening(fg,np.ones([5,5]))
fgo = ndm.binary_dilation(fg,np.ones([2,2]))
fgf = ndm.binary_fill_holes(fgo)
right.set_data(mf(fgf,5))
plt.draw()
if (type!=1)&(type!=5):
BG_old = BG
return fgf
start = 15225
end = 15240
buf = double(vid[start-trun/2:start+trun/2+1])
ind = range(trun+1)
ind.pop(trun/2)
th =40
#build tree filter
Tf = (vid[0:100,:,:,1].mean(0)>100) & (vid[0:100,:,:,0].mean(0)<100)
Tf = ndm.binary_closing(Tf,structure=np.ones((4,4)))
Tf = ~ndm.binary_fill_holes(Tf)
for ii in range(start,end):
print(ii)
left.set_data(vid[ii][:,:,::-1])
#pdb.set_trace()
BG[:] = np.abs(buf[ind]-buf[trun/2]).mean(3).mean(0)>40.
#fg = np.abs(buf[ind].mean(3)-buf[trun/2].mean(2)).mean(0)>40.
fgo = ndm.binary_opening(BG*Tf)
fgf = ndm.binary_fill_holes(fgo)
right.set_data(mf(fgf,5))
plt.draw()
buf = np.roll(buf,-1,0)
buf[-1] = vid[ii+trun/2+1]
# In[1]:
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
####### Problem 1
# In[2]:
img = mf((255 - nd.imread('images/ml.jpg')[::1,::2,::-1]).astype(float), (8, 2, 1)).clip(0,255).astype(np.uint8)
### Explanation:
### 1. nd.imread reaturns a 3D array
### 2. Then subsetting [::2, ::1, ::-1] inverses colors and skips every other column
### 3. 255 - ... converts to negative
### 4. .astype(float)
### 5. mf() applies median filter
### 6. .clip() clips
### 7. .astype(np.uint8) converts to uint
nrow, ncol = img.shape[:2]
ysize = 10.
xsize = ysize * float(ncol)/float(nrow)
fig1, ax1 = plt.subplots(num = 1, figsize = [xsize, ysize])
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
img = mf(1.0*(255 - np.array([[e[2], e[1], e[0]] for e in nd.imread('images/ml.jpg').reshape(610560,3)[::2]]).\
reshape(954,320,3)),[8,2,1]).clip(0,255).astype(np.uint8)
ysize = 10.
xsize = ysize*float(img.shape[1])/float(img.shape[0])
fig5, ax5 = plt.subplots(num=5,figsize=[xsize,ysize])
fig5.subplots_adjust(0,0,1,1)
fig5.canvas.set_window_title("modified mona lisa")
ax5.axis('off')
im5 = ax5.imshow(img)
fig5.canvas.draw()
plt.show()
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
img_ml=mf(1.0*(255-nd.imread('images/ml.jpg')[::2,::2,::-1]),[8,2,1]).clip(0,255)
fig1, ax1 = plt.subplots(num=1,figsize=(6,1.0*img_ml.shape[0]/img_ml.shape[1]*6))
fig1.subplots_adjust(0,0,1,1);ax1.grid('off')
ax1.axis('off')
fig1.canvas.set_window_title('modified Mona Lisa')
im1 = ax1.imshow(img_ml.astype(np.uint8))
def med_filter(im, size = 10):
from scipy.ndimage.filters import median_filter as mf
import numpy as np
return mf(im, size = size)
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
# 1: read in and manipulate image
img = mf(
(255 -
(nd.imread(os.path.join('images', 'city_image.jpg'))[::2][:, :, ::-1])),
[8, 2, 1]).clip(0, 255).astype(np.uint8)
# 2: display it
ysize = 3.
xsize = ysize * float(img.shape[1]) / float(img.shape[0])
fig, ax = plt.subplots(1, figsize=[xsize, ysize])
fig.subplots_adjust(0, 0, 1, 1)
ax.axis("off")
im = ax.imshow(img)
fig.canvas.set_window_title('modified city image')
fig.canvas.draw()
plt.show()
def med_filter(im, size=10):
from scipy.ndimage.filters import median_filter as mf
import numpy as np
return mf(im, size=size)
def res_lc_plot():
# -- read in the residential data
lcs = []
for night in range(22):
fopen = open(os.path.join(os.environ['DST_WRITE'],'res_lc_night_' +
str(night).zfill(2) + '_2.pkl'),'rb')
lcs.append(pkl.load(fopen))
fopen.close()
# -- utilities
tcks, htimes = time_ticks()
# -- define fri/sat and all other days
# we = [0,6,7,13,14,20,21]
# wd = [1,2,3,4,5,8,9,10,11,12,15,16,17,18,19]
we = [0,6,7,13,14,20,21]
wd = [1,2,3,4,5,8,9,10,11,12,15,16,17,18,19]
# -- get mean averaged lc
tmax = min([len(i.mean(0)) for i in lcs if len(i.mean(0))>3000])
mnwd = np.zeros(tmax)
mnwe = np.zeros(tmax)
cnt = 0.0
for idy in wd:
if idy==8: continue
if len(lcs[idy].mean(0))>3000:
mnwd += lcs[idy].mean(0)[:tmax]
cnt += 1.0
mnwd /= cnt
cnt = 0.0
for idy in we:
if idy==21: continue
if len(lcs[idy].mean(0))>3000:
mnwe += lcs[idy].mean(0)[:tmax]
cnt += 1.0
mnwe /= cnt
plt.figure(1,figsize=[5,5])
plt.xticks(tcks,htimes,rotation=30)
plt.ylabel('intensity [arb units]',size=15)
plt.xlim([0,3600])
plt.grid(b=1)
plt.plot(mnwd,'k')
plt.plot(mnwe,'r')
plt.savefig('../output/res_lc_mean.png',clobber=True)
plt.close()
# -- open the figure
plt.figure(0,figsize=[15,5])
plt.subplots_adjust(0.05,0.1,0.95,0.95)
plt.subplot(131)
for idy in wd:
# plt.plot(mf(lcs[idy][0]/lcs[idy][0,0],6))
# plt.plot(mf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6))
# cdf = np.cumsum(lcs[idy].mean(0))
# plt.plot(cdf/cdf[360]/(np.arange(cdf.size)/360.))
lc_sm = gf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6)
norm = (lc_sm-lc_sm[-1])
# plt.plot(lc_sm/lc_sm[0])
plt.plot(norm/norm[0])
# plt.plot(gf((lc_sm-np.roll(lc_sm,1))[1:-1],360))
# plt.ylim([0.8,1.2])
# plt.ylim([0.6,1.4])
plt.ylim([-0.1,1.4])
plt.grid(b=1)
plt.xticks(tcks,htimes,rotation=30)
plt.xlim([0,3600])
plt.ylabel('intensity [arb units]',size=15)
plt.subplot(132)
for idy in we:
# plt.plot(mf(lcs[idy][0]/lcs[idy][0,0],6))
# plt.plot(mf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6))
# cdf = np.cumsum(lcs[idy].mean(0))
# plt.plot(cdf/cdf[360]/(np.arange(cdf.size)/360.))
lc_sm = gf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6)
norm = (lc_sm-lc_sm[-1])
# plt.plot(lc_sm/lc_sm[0])
plt.plot(norm/norm[0])
# plt.plot(gf((lc_sm-np.roll(lc_sm,1))[1:-1],360))
# plt.ylim([0.6,1.4])
# plt.ylim([0.8,1.2])
plt.ylim([-0.1,1.4])
plt.grid(b=1)
plt.xticks(tcks,htimes,rotation=30)
plt.xlim([0,3600])
plt.ylabel('intensity [arb units]',size=15)
plt.subplot(133)
for idy in wd:
# plt.plot(mf(lcs[idy][0]/lcs[idy][0,0],6),'k')
plt.plot(mf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6),'k')
for idy in we:
# plt.plot(mf(lcs[idy][0]/lcs[idy][0,0],6),'r')
plt.plot(mf(lcs[idy].mean(0)/lcs[idy].mean(0)[0],6),'r')
plt.ylim([0.6,1.4])
plt.grid(b=1)
plt.xticks(tcks,htimes,rotation=30)
plt.xlim([0,3600])
plt.ylabel('intensity [arb units]',size=15)
plt.savefig('../output/res_lc_all.png',clobber=True)
plt.close()
return
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.ndimage as nd
from scipy.ndimage.filters import median_filter as mf
if __name__ == '__main__':
dpath = 'images'
fname = 'ml.jpg'
infile = os.path.join(dpath,fname)
#fancy image read line
img_ml = np.uint8( mf( np.float64(255-nd.imread(infile)[::2,::2,::-1]), (8,2,1)) )
#display the image
ysize = 9.
xsize = ysize*float(img_ml.shape[1])/float(img_ml.shape[0])
plt.ion()
fig, ax = plt.subplots(figsize=(xsize,ysize))
fig.subplots_adjust(0,0,1,1)
fig.canvas.set_window_title('modified Mona Lisa')
ax.axis('off')
im = ax.imshow(img_ml)
fig.canvas.draw()
plt.show(block=True)
def plot_cube(cube, cmap='bone', clim=None, median_filter=False, figsize=10):
"""
An interactive visualization of the data cube.
Parameters
----------
cube : HyperCube
An instance of the HyperCube class to be shown.
cmap : matplotlib color map, optional
The matplotlib color map to use.
clim : 2-element list, optional
The limits of the color stretch.
median_filter : float, optional
Set the width of the median filter on the spectra.
figsize : float, optional
The horizontal width of the figure in inches.
"""
# -- set the flag to freeze the spectrum
def toggle_hold(event):
if event.button == 1 and event.inaxes == ax[0]:
hold_spec[0] = not hold_spec[0]
return
# -- mouseover event updates the spectrum
def show_spectrum(event):
if hold_spec[0]:
return
if event.inaxes == ax[0]:
cind = int(round(event.xdata))
rind = int(round(event.ydata))
mn = cube.data[:, rind, cind].mean()
sd = cube.data[:, rind, cind].std()
if not median_filter:
lin[0].set_data(waves, cube.data[:, rind, cind])
else:
lin[0].set_data(waves,
mf(cube.data[:, rind, cind], median_filter))
ax[1].set_ylim([
min(-10, cube.data[:, rind, cind].min()),
max(20, 1.2 * cube.data[:, rind, cind].max())
])
pos_text.set_y(ax[1].get_ylim()[1])
pos_text.set_text('(row,col) = ({0:4},{1:4})'.format(rind, cind))
fig.canvas.draw()
return
# -- utilities
waves = cube.wavelength * 1e-3 if not cube.indexing else np.arange(
cube.nwav)
xlab = 'wavelength [micron]' if not cube.indexing else 'index'
arat = float(cube.nrow) / float(cube.ncol)
rat = arat * 3. / 5.
med = np.median(cube.img_L)
sig = cube.img_L.std()
scl = 0.2
if clim == None:
clim = [
scl * max(med - 2 * sig, cube.img_L.min()),
scl * min(med + 10 * sig, cube.img_L.max())
]
# -- initialize the figure
fig = plt.figure(figsize=[figsize, figsize * rat])
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
ax = []
ax.append(fig.add_axes([0.1, 0.35, 0.85, 0.6]))
ax.append(fig.add_axes([0.1, 0.1, 0.85, 0.2]))
fig.set_facecolor('ivory')
ax[0].axis('off')
ax[1].set_ylim([-10, 20])
ax[1].grid(1, color='white', ls='-', lw=1.5)
ax[1].set_axis_bgcolor('lightgray')
ax[1].set_xlabel(xlab)
ax[1].set_xlim([waves.min(), waves.max()])
ax[1].set_ylabel('intensity\n[arb units]')
ax[1].set_axisbelow(True)
# -- show the grayscale iamge
ax[0].imshow(cube.img_L,
clim=clim,
cmap=cmap,
aspect=0.6 / 0.85 * rat / arat)
# -- plot the spectrum
if not median_filter:
lin = ax[1].plot(waves, cube.data[:, 0, 0], color='#E24A33', lw=1.5)
else:
lin = ax[1].plot(waves,
mf(cube.data[:, 0, 0], median_filter),
color='#E24A33',
lw=1.5)
# -- show the position of the spectrum
pos_text = ax[1].text(ax[1].get_xlim()[1],
ax[1].get_ylim()[1],
'(row,col) = ({0:4},{1:4})'.format(0, 0),
ha='right',
va='bottom')
# -- initialize hold flag and connect events
hold_spec = [False]
fig.canvas.mpl_connect('button_press_event', toggle_hold)
fig.canvas.mpl_connect('motion_notify_event', show_spectrum)
return