def draw_mosaic_image(ax, aligner, img):
if img is not None:
if sys.version_info[0] != 2:
warnings.warn('ModestImage module (required for image display)'
' is only compatible with Python 2')
img = None
elif modest_image is None:
warnings.warn('Please install ModestImage for image display')
img = None
if img is not None:
modest_image.imshow(ax, img)
else:
h, w = aligner.mosaic_shape
# Draw a single-pixel image in the lowest color in the colormap,
# stretched across the same extent that the real image would be.
# This makes the graph edge colors visible even if there's no image.
ax.imshow([[0]], extent=(-0.5, w - 0.5, h - 0.5, -0.5))
def plot_waterfallc(wat, qindex=1, aspect = None,vmax=None, vmin=None, interpolation = 'none',
save=False, return_fig=False, cmap='viridis',*argv,**kwargs):
'''plot waterfall for a giving compressed file
FD: class object, the compressed file handler
labeled_array: np.array, a ROI mask
qindex: the index number of q, will calculate where( labeled_array == qindex)
aspect: the aspect ratio of the plot
Return waterfall
Plot the waterfall
'''
#wat = cal_waterfallc( FD, labeled_array, qindex=qindex)
if RUN_GUI:
fig = Figure(figsize=(8,6))
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots(figsize=(8,6))
if 'uid' in kwargs:
uid = kwargs['uid']
else:
uid = 'uid'
#fig, ax = plt.subplots(figsize=(8,6))
ax.set_ylabel('Pixel')
ax.set_xlabel('Frame')
ax.set_title('%s_Waterfall_Plot_@qind=%s'%(uid, qindex) )
if 'beg' in kwargs:
beg = kwargs['beg']
else:
beg=0
extent = [ beg, len(wat)+beg, 0, len( wat.T) ]
if vmax is None:
vmax=wat.max()
if vmin is None:
vmin = wat.min()
if aspect is None:
aspect = wat.shape[0]/wat.shape[1]
im = imshow(ax, wat.T, cmap=cmap, vmax=vmax,extent= extent,interpolation = interpolation )
#im = ax.imshow(wat.T, cmap='viridis', vmax=vmax,extent= extent,interpolation = interpolation )
fig.colorbar( im )
ax.set_aspect( aspect)
if save:
#dt =datetime.now()
#CurTime = '%s%02d%02d-%02d%02d-' % (dt.year, dt.month, dt.day,dt.hour,dt.minute)
path = kwargs['path']
#fp = path + "uid= %s--Waterfall-"%uid + CurTime + '.png'
fp = path + "%s_waterfall"%uid + '.png'
plt.savefig( fp, dpi=fig.dpi)
#plt.show()
if return_fig:
return fig,ax, im
def test_imshow_creates_modest_image():
"""returns a modestImage"""
data = default_data()
fig = plt.figure()
ax = fig.add_subplot(111)
artist = imshow(ax, data)
assert isinstance(artist, ModestImage)
assert artist in artist.axes.images
def main(argv=sys.argv):
try:
import modest_image
except ImportError:
print(
"Please install ModestImage (Python 2.7 only) to use this script.")
exit()
filepath = argv[1]
channel = 0
if len(argv) >= 3:
channel = int(argv[2])
reader = reg.BioformatsReader(filepath)
metadata = reader.metadata
positions = metadata.positions - metadata.origin
mshape = (
(metadata.positions + metadata.size - metadata.origin).max(axis=0) +
1).astype(int)
mosaic = np.zeros(mshape, dtype=np.uint16)
total = reader.metadata.num_images
for i in range(total):
sys.stdout.write("\rLoading %d/%d" % (i + 1, total))
sys.stdout.flush()
reg.paste(mosaic, reader.read(c=channel, series=i), positions[i],
np.maximum)
print()
ax = plt.gca()
modest_image.imshow(ax, mosaic)
h, w = metadata.size
for xy in np.fliplr(positions):
ax.add_patch(mpatches.Rectangle(xy, w, h, color='black', fill=False))
plt.show()
def show_img(img, save=False, vmin=None, vmax=None,cmap='winter',fontsize=24,
axis_on=True,title_on=True,xlabel=None,ylabel=None,aspect=1.0,
title='img_', show=True, logs=False,outDir=None, sizex=9,sizey=9 ):
"""show a two-D image"""
#show img
ax = plt.gca()
F = plt.gcf()
#print F.get_size_inches()
F.set_size_inches(sizex,sizey)
#plt.figure(figsize=(12, 12))
if vmin==None:vmin=img.min()
if vmax==None:vmax=img.max()
if not logs:
artist = imshow(ax, img,cmap=plt.get_cmap(cmap), vmin=vmin,vmax=vmax )
if logs:
img= log(img)
if vmin==None:vmin=img[nonzero(img)].min() #img.min()
artist = imshow(ax, img,cmap=plt.get_cmap(cmap),vmin=vmin, vmax=vmax,)
dx,dy=img.shape
#print dx,dy
ax.set_ylim(0,dx-1)
ax.set_xlim(0,dy-1)
#ax.set_aspect('auto')
if aspect!=1.0:
im = ax.get_images()
extent = im[0].get_extent()
ax.set_aspect(abs((extent[1]-extent[0])/(extent[3]-extent[2]))/aspect)
if title_on:plt.title( title,fontsize=fontsize )
#else:plt.title( None)
if not axis_on:plt.axis('off')
if xlabel!=None:plt.xlabel(xlabel,fontsize=18)
if ylabel!=None:plt.ylabel(ylabel,fontsize=18)
plt.setp(ax.get_xticklabels(), fontsize=fontsize)
plt.setp(ax.get_yticklabels(), fontsize=fontsize)
if save:
if outDir!=None:fp=outDir + title + '_.png'
else:fp= title + '_.png'
plt.savefig( fp )
if show:plt.show()
def test_imshow_mimics_mpl():
"""properties of two axes and artists objects should be same"""
data = default_data()
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
artist1 = ax1.imshow(data)
artist2 = imshow(ax2, data)
check_artist_props(artist1, artist2)
check_axes_props(ax1, ax2)
def _generate_plot(ax, power_data, title, min_db, max_db):
"""
Generate a ZPLS plot for an individual channel
:param ax: matplotlib axis to receive the plot image
:param power_data: Transducer data array
:param title: plot title
:param min_db: minimum power level
:param max_db: maximum power level
"""
# only generate plots for the transducers that have data
if power_data.size <= 0:
return
ax.set_title(title, fontsize=ZPLSPlot.font_size_large)
return imshow(ax, power_data, interpolation='none', aspect='auto', cmap='jet', vmin=min_db, vmax=max_db)
def show_g12q_taus( g12q, taus, slice_width=10, timeperframe=1,vmin= 1, vmax= 1.25 ):
'''
Dec 16, 2015, Y.G.@CHX
Plot tau-lines as a function of age with two correlation function
Parameters:
g12q: a 2-D array, one-q two correlation function, shape as ( imgs_length, imgs_length )
tau, a dict, tau lines
the keys of dict is tau(slice center) in unit of pixel
dict[key]:
a 1-D array, shape as ( tau_line-length ),
obtained by: for example,
taus = get_tau_from_g12q( g12b_norm[:,:,0], slice_num = 5, slice_width=1,
slice_start=3, slice_end= 5000-1 ))
Options:
slice_width: int, each slice width in unit of pixel, for line width of a plot
timeperframe: float, time per frame for axis unit
vmin, float, matplot vmin
vmax, float, matplot vmax
Return:
two plots, one for tau lines~ages, g12q,
One example:
show_g12q_taus( g12b_norm[:,:,0], taus, slice_width=50,
timeperframe=1,vmin=1.01,vmax=1.55 )
'''
age_center = list( taus.keys() )
print ('the cut tau centers are: ' +str(age_center) )
M,N = g12q.shape
#fig, ax = plt.subplots( figsize = (8,8) )
figw =10
figh = 10
fig = plt.figure(figsize=(figw,figh))
gs = gridspec.GridSpec(1, 2, width_ratios=[10, 8],height_ratios=[8,8] )
ax = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
im = imshow(ax, g12q, origin='lower' , cmap='viridis',
norm= LogNorm( vmin= vmin, vmax= vmax ) , extent=[0, N, 0, N ] )
linS = []
linE=[]
linS.append( zip( np.int_(age_center) -1, [0]*len(age_center) ))
linE.append( zip( [N -1]*len(age_center), N - np.int_(age_center) ))
for i, [ps,pe] in enumerate(zip(linS[0],linE[0])):
lined= slice_width #/2. *draw_scale_tau #in data width
linewidth= (lined * (figh*72./N)) * 0.8
#print (ps,pe)
ax.plot( [ps[0],pe[0]],[ps[1],pe[1]], linewidth=linewidth ) #, color= )
ax.set_title( '%s_frames'%(N) )
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
ax1.set_title("Tau_Cuts_in_G12")
for i in sorted(taus.keys()):
gx= np.arange(len(taus[i])) * timeperframe
marker = next(markers)
ax1.plot( gx,taus[i], '-%s'%marker, label=r"$tau= %.1f s$"%(i*timeperframe))
ax1.set_ylim( vmin,vmax )
ax1.set_xlabel(r'$t (s)$',fontsize=5)
ax1.set_ylabel("g2")
ax1.set_xscale('log')
ax1.legend(fontsize='small', loc='best' )
def show_g12q_aged_g2( g12q, g2_aged, taus_aged = None, slice_width=10, timeperframe=1,vmin= 1, vmax= 1.25,
save=True, uid='uid', path='', *argv,**kwargs):
'''
Octo 20, 2017, add taus_aged option
Dec 16, 2015, Y.G.@CHX
Plot one-time correlation function of different age with two correlation function
Parameters:
g12q: a 2-D array, one-q two correlation function, shape as ( imgs_length, imgs_length )
tau_aged: a dict, taus for different age
g2_aged: a dict, one time correlation function at different age
obtained by: for example,
g2_aged = get_aged_g2_from_g12q( g12q, slice_num =3, slice_width= 500,
slice_start=4000, slice_end= 20000-4000 )
the keys of dict is ages in unit of pixel
dict[key]:
a 1-D array, shape as ( imgs_length ),
a one-q one-time correlation function
Options:
slice_width: int, each slice width in unit of pixel, for line width of a plot
timeperframe: float, time per frame for axis unit
vmin, float, matplot vmin
vmax, float, matplot vmax
Return:
two plots, one for the two-time correlation, g12q,
One example:
show_g12q_aged_g2( g12q, g2_aged,timeperframe=1,vmin= 1, vmax= 1.22 )
'''
age_center = np.array( list( sorted( g2_aged.keys() ) ) )
print ('the cut age centers are: ' + str(age_center) )
age_center = np.int_(np.array( list( sorted( g2_aged.keys() ) ) )/timeperframe) *2 #in pixel
M,N = g12q.shape
#fig, ax = plt.subplots( figsize = (8,8) )
figw =10
figh = 8
fig = plt.figure(figsize=(figw,figh))
#gs = gridspec.GridSpec(1, 2, width_ratios=[10, 8],height_ratios=[8,8] )
gs = gridspec.GridSpec(1, 2 )
ax = plt.subplot(gs[0])
im = imshow(ax, g12q, origin='lower' , cmap='viridis',
norm= LogNorm( vmin, vmax ), extent=[0, N,0,N])
#plt.gca().set_xticks(ticks)
ticks = np.round( plt.gca().get_xticks() * timeperframe, 2)
#print( ticks )
ax.set_xticklabels(ticks )
ax.set_yticklabels(ticks )
#plt.xticks(ticks, fontsize=9)
#), extent=[0, g12q.shape[0]*timeperframe, 0, g12q.shape[0]*timeperframe ] )
ax1 = plt.subplot(gs[1])
linS1 = [ [0]*len(age_center ), np.int_(age_center - slice_width//2 ) ]
linS2 = [ [0]*len(age_center ), np.int_(age_center + slice_width//2 ) ]
linE1 = [ np.int_(age_center - slice_width//2 ), [0]*len(age_center) ]
linE2 = [ np.int_(age_center + slice_width//2 ), [0]*len(age_center) ]
linC = [ [0]*len(age_center ), np.int_(age_center ) ]
for i in range( len(age_center ) ):
ps = linS1[1][i]
pe = linE1[0][i]
if ps>=N:s0=ps - N;s1=N
else:s0=0;s1=ps
e0 = s1;e1=s0
#if pe>=N:e0=N;e1=pe - N
#else:e0=pe;e1=0
ps = linS2[1][i]
pe = linE2[0][i]
if ps>=N:S0=ps - N;S1=N
else:S0=0;S1=ps
#if pe>=N:e0=N;E1=pe - N
#else:E0=pe;E1=0
E0=S1;E1=S0
ps = linC[1][i]
if ps>=N:C0=ps - N;C1=N
else:C0=0;C1=ps
#if pe>=N:e0=N;E1=pe - N
#else:E0=pe;E1=0
D0=C1;D1=C0
lined= slice_width/2. #in data width
linewidthc= (lined * (figh*72./N)) * 0.5
#print( s0,e0, s1,e1, S0,E0, S1, E1)
#lined= slice_width/2. #in data width
#linewidth= (lined * (figh*72./N)) * 0.8
linewidth = 1
ax.plot( [s0,e0],[s1,e1], linewidth=linewidth , ls = '--', alpha=1 , color= colors_array[i] )
ax.plot( [S0,E0],[S1,E1], linewidth=linewidth , ls = '--', alpha=1 , color= colors_array[i] )
#print( i, [s0,e0],[s1,e1], [S0,E0],[S1,E1], colors_array[i] )
ax.plot( [C0,D0],[C1,D1], linewidth=linewidthc , ls = '-', alpha=.0 , color= colors_array[i] )
#ax.set_title( '%s_frames'%(N) )
ax.set_title( "%s_two_time"%uid )
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
ax1.set_title("%s_aged_g2"%uid)
ki=0
for i in sorted(g2_aged.keys()):
#ax = fig.add_subplot(sx,sy,sn+1 )
if taus_aged is None:
gx= np.arange(len(g2_aged[i])) * timeperframe
else:
gx = taus_aged[i]
#marker = next(markers)
#print( g2_aged[i], marker )
#print(i)
ax1.plot( gx,g2_aged[i], marker = '%s'%markers_array[ki], ls='-', color= colors_array[ki], label=r"$t_a= %.1f s$"%i)
#print( i, ki, colors_array[ki] )
ki += 1
ax1.set_ylim( vmin, vmax )
ax1.set_xlabel(r"$\tau $ $(s)$", fontsize=18)
ax1.set_ylabel("g2")
ax1.set_xscale('log')
ax1.legend(fontsize='small', loc='best' )
if save:
#fp = path + "uid= %s--Waterfall-"%uid + CurTime + '.png'
fp = path + "%s_aged_g2"%uid + '.png'
#print( fp )
fig.savefig( fp, dpi=fig.dpi)
def show_one_C12( C12, fig_ax=None, return_fig=False,interpolation = 'none',cmap='viridis',
*argv,**kwargs):
'''
plot one-q of two-time correlation function
C12: two-time correlation function, with shape as [ time, time, qs]
q_ind: if integer, for a SAXS q, the nth of q to be plotted
if a list: for a GiSAXS [qz_ind, qr_ind]
kwargs: support
timeperframe: the time interval
N1: the start frame(time)
N2: the end frame(time)
vmin/vmax: for plot
title: if True, show the tile
e.g.,
show_C12(g12b, q_ind=1, N1=0, N2=500, vmin=1.05, vmax=1.07, )
'''
#strs = [ 'timeperframe', 'N1', 'N2', 'vmin', 'vmax', 'title']
if 'uid' in kwargs:
uid=kwargs['uid']
else:
uid='uid'
shape = C12.shape
if 'timeperframe' in kwargs.keys():
timeperframe = kwargs['timeperframe']
else:
timeperframe=1
if 'vmin' in kwargs.keys():
vmin = kwargs['vmin']
else:
vmin=1
if 'vmax' in kwargs.keys():
vmax = kwargs['vmax']
else:
vmax=1.05
if 'N1' in kwargs.keys():
N1 = kwargs['N1']
else:
N1=0
if 'N2' in kwargs.keys():
N2 = kwargs['N2']
else:
N2= shape[0]
if 'title' in kwargs.keys():
title = kwargs['title']
else:
title=True
data = C12[N1:N2,N1:N2]
if fig_ax is None:
if RUN_GUI:
fig = Figure()
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots()
else:
fig,ax=fig_ax
im = imshow(ax, data, origin='lower' , cmap=cmap,
norm= LogNorm( vmin, vmax ),
extent=[0, data.shape[0]*timeperframe, 0, data.shape[0]*timeperframe ],
interpolation = interpolation)
if title:
tit = '%s-[%s-%s] frames'%(uid,N1,N2)
ax.set_title( tit )
else:
tit=''
#ax.set_title('%s-%s frames--Qth= %s'%(N1,N2,g12_num))
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
save=False
if 'save' in kwargs:
save=kwargs['save']
if save:
path=kwargs['path']
#fp = path + 'Two-time--uid=%s'%(uid) + tit + CurTime + '.png'
fp = path + '%s_Two_time'%(uid) + '.png'
plt.savefig( fp, dpi=fig.dpi)
if return_fig:
return fig, ax, im
def generate_plots(trans_array, trans_array_time, td_f, td_dr, title, filename):
"""
Generate plots for a transducer
@param trans_array Transducer data array
@param trans_array_time Transducer internal time array
@param td_f Transducer frequency
@param td_dr Transducer's sample thickness (in range)
@param title Transducer title
@param filename png file name to save the figure to
"""
# only generate plots for the transducers that have data
if np.size(trans_array_time) <= 0:
return
# determine size of the data array
max_depth, max_time = np.shape(trans_array)
min_depth = 0
min_time = 0
# subset/decimate the x & y ticks so that we don't plot everyone
num_xticks = 7
num_yticks = 10
min_db = -180
max_db = -59
cbar_ticks = np.arange(min_db, max_db, 20)
# convert time, which represents the number of 100-nanosecond intervals that
# have elapsed since 12:00 A.M. January 1, 1601 Coordinated Universal Time (UTC)
# to unix time, i.e. seconds since 1970-01-01 00:00:00.
# 11644473600 == difference between 1601 and 1970
# 1e7 == divide by 10 million to convert to seconds
trans_array_time = np.array(trans_array_time) / 1e7 - 11644473600
trans_array_time = (trans_array_time / (60*60*24)) + REF_TIME
# subset the xticks so that we don't plot every one
xticks = np.linspace(0, max_time, num_xticks)
# format trans_array_time array so that it can be used to label the x-axis
xticklabels = [i.strftime('%Y-%m-%d %H:%M:%S')
for i in num2date(trans_array_time[::round(xticks[1])])]
# subset the yticks so that we don't plot everyone
yticks = np.linspace(0, max_depth, num_yticks)
# create range vector (depth in meters)
yticklabels = np.round(np.arange(0, max_depth, round(yticks[1])) * td_dr)
fig, ax = plt.subplots()
ax.grid(False)
figure_title = 'Converted Power: ' + title + 'Frequency: ' + str(td_f)
ax.set_title(figure_title, fontsize=12)
ax.set_xlabel('time (UTC)', fontsize=10)
ax.set_ylabel('depth (m)', fontsize=10)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels, rotation=25, horizontalalignment='right', fontsize=10)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, fontsize=10)
ax.tick_params(axis="both", labelcolor="k", pad=4)
# set the x and y limits
ax.set_ylim(max_depth, min_depth)
ax.set_xlim(min_time, max_time)
# plot the colorbar
cax = imshow(ax, trans_array, interpolation='none', aspect='auto', cmap='jet', vmin=min_db, vmax=max_db)
cb = fig.colorbar(cax, orientation='horizontal', ticks=cbar_ticks, shrink=.6)
cb.ax.set_xticklabels(cbar_ticks, fontsize=8) # horizontally oriented colorbar
cb.set_label('dB', fontsize=10)
cb.ax.set_xlim(-180, -60)
fig.tight_layout(pad=1.2)
# adjust the subplot so that the x-tick labels will fit on the canvas
fig.subplots_adjust(bottom=0.1)
plt.figtext(0.01, 0.01, '*Note: Strictly sequential time tags are not guaranteed.',
axes=ax, fontsize=7)
# reposition the cbar
cb.ax.set_position([.4, .05, .4, .1])
# save the figure
fig.savefig(filename, dpi=300)
# close the figure
plt.close()
def show_g12q_taus( g12q, taus, slice_width=10, timeperframe=1,vmin= 1, vmax= 1.25 ):
'''
Dec 16, 2015, Y.G.@CHX
Plot tau-lines as a function of age with two correlation function
Parameters:
g12q: a 2-D array, one-q two correlation function, shape as ( imgs_length, imgs_length )
tau, a dict, tau lines
the keys of dict is tau(slice center) in unit of pixel
dict[key]:
a 1-D array, shape as ( tau_line-length ),
obtained by: for example,
taus = get_tau_from_g12q( g12b_norm[:,:,0], slice_num = 5, slice_width=1,
slice_start=3, slice_end= 5000-1 ))
Options:
slice_width: int, each slice width in unit of pixel, for line width of a plot
timeperframe: float, time per frame for axis unit
vmin, float, matplot vmin
vmax, float, matplot vmax
Return:
two plots, one for tau lines~ages, g12q,
One example:
show_g12q_taus( g12b_norm[:,:,0], taus, slice_width=50,
timeperframe=1,vmin=1.01,vmax=1.55 )
'''
age_center = list( taus.keys() )
print ('the cut tau centers are: ' +str(age_center) )
M,N = g12q.shape
#fig, ax = plt.subplots( figsize = (8,8) )
figw =10
figh = 10
fig = plt.figure(figsize=(figw,figh))
gs = gridspec.GridSpec(1, 2, width_ratios=[10, 8],height_ratios=[8,8] )
ax = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
im = imshow(ax, g12q, origin='lower' , cmap='viridis',
norm= LogNorm( vmin= vmin, vmax= vmax ) , extent=[0, N, 0, N ] )
linS = []
linE=[]
linS.append( zip( np.int_(age_center) -1, [0]*len(age_center) ))
linE.append( zip( [N -1]*len(age_center), N - np.int_(age_center) ))
for i, [ps,pe] in enumerate(zip(linS[0],linE[0])):
lined= slice_width #/2. *draw_scale_tau #in data width
linewidth= (lined * (figh*72./N)) * 0.8
#print (ps,pe)
ax.plot( [ps[0],pe[0]],[ps[1],pe[1]], linewidth=linewidth ) #, color= )
ax.set_title( '%s_frames'%(N) )
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
ax1.set_title("Tau_Cuts_in_G12")
for i in sorted(taus.keys()):
gx= np.arange(len(taus[i])) * timeperframe
marker = next(markers)
ax1.plot( gx,taus[i], '-%s'%marker, label=r"$tau= %.1f s$"%(i*timeperframe))
ax1.set_ylim( vmin,vmax )
ax1.set_xlabel(r'$t (s)$',fontsize=5)
ax1.set_ylabel("g2")
ax1.set_xscale('log')
ax1.legend(fontsize='small', loc='best' )
def show_g12q_aged_g2( g12q, g2_aged, taus_aged = None, slice_width=10, timeperframe=1,vmin= 1, vmax= 1.25,
save=True, uid='uid', path='', *argv,**kwargs):
'''
Octo 20, 2017, add taus_aged option
Dec 16, 2015, Y.G.@CHX
Plot one-time correlation function of different age with two correlation function
Parameters:
g12q: a 2-D array, one-q two correlation function, shape as ( imgs_length, imgs_length )
tau_aged: a dict, taus for different age
g2_aged: a dict, one time correlation function at different age
obtained by: for example,
g2_aged = get_aged_g2_from_g12q( g12q, slice_num =3, slice_width= 500,
slice_start=4000, slice_end= 20000-4000 )
the keys of dict is ages in unit of pixel
dict[key]:
a 1-D array, shape as ( imgs_length ),
a one-q one-time correlation function
Options:
slice_width: int, each slice width in unit of pixel, for line width of a plot
timeperframe: float, time per frame for axis unit
vmin, float, matplot vmin
vmax, float, matplot vmax
Return:
two plots, one for the two-time correlation, g12q,
One example:
show_g12q_aged_g2( g12q, g2_aged,timeperframe=1,vmin= 1, vmax= 1.22 )
'''
age_center = np.array( list( sorted( g2_aged.keys() ) ) )
print ('the cut age centers are: ' + str(age_center) )
age_center = np.int_(np.array( list( sorted( g2_aged.keys() ) ) )/timeperframe) *2 #in pixel
M,N = g12q.shape
#fig, ax = plt.subplots( figsize = (8,8) )
figw =10
figh = 8
fig = plt.figure(figsize=(figw,figh))
#gs = gridspec.GridSpec(1, 2, width_ratios=[10, 8],height_ratios=[8,8] )
gs = gridspec.GridSpec(1, 2 )
ax = plt.subplot(gs[0])
im = imshow(ax, g12q, origin='lower' , cmap='viridis',
norm= LogNorm( vmin, vmax ), extent=[0, N,0,N])
#plt.gca().set_xticks(ticks)
ticks = np.round( plt.gca().get_xticks() * timeperframe, 2)
#print( ticks )
ax.set_xticklabels(ticks )
ax.set_yticklabels(ticks )
#plt.xticks(ticks, fontsize=9)
#), extent=[0, g12q.shape[0]*timeperframe, 0, g12q.shape[0]*timeperframe ] )
ax1 = plt.subplot(gs[1])
linS1 = [ [0]*len(age_center ), np.int_(age_center - slice_width//2 ) ]
linS2 = [ [0]*len(age_center ), np.int_(age_center + slice_width//2 ) ]
linE1 = [ np.int_(age_center - slice_width//2 ), [0]*len(age_center) ]
linE2 = [ np.int_(age_center + slice_width//2 ), [0]*len(age_center) ]
linC = [ [0]*len(age_center ), np.int_(age_center ) ]
for i in range( len(age_center ) ):
ps = linS1[1][i]
pe = linE1[0][i]
if ps>=N:s0=ps - N;s1=N
else:s0=0;s1=ps
e0 = s1;e1=s0
#if pe>=N:e0=N;e1=pe - N
#else:e0=pe;e1=0
ps = linS2[1][i]
pe = linE2[0][i]
if ps>=N:S0=ps - N;S1=N
else:S0=0;S1=ps
#if pe>=N:e0=N;E1=pe - N
#else:E0=pe;E1=0
E0=S1;E1=S0
ps = linC[1][i]
if ps>=N:C0=ps - N;C1=N
else:C0=0;C1=ps
#if pe>=N:e0=N;E1=pe - N
#else:E0=pe;E1=0
D0=C1;D1=C0
lined= slice_width/2. #in data width
linewidthc= (lined * (figh*72./N)) * 0.5
#print( s0,e0, s1,e1, S0,E0, S1, E1)
#lined= slice_width/2. #in data width
#linewidth= (lined * (figh*72./N)) * 0.8
linewidth = 1
ax.plot( [s0,e0],[s1,e1], linewidth=linewidth , ls = '--', alpha=1 , color= colors_array[i] )
ax.plot( [S0,E0],[S1,E1], linewidth=linewidth , ls = '--', alpha=1 , color= colors_array[i] )
#print( i, [s0,e0],[s1,e1], [S0,E0],[S1,E1], colors_array[i] )
ax.plot( [C0,D0],[C1,D1], linewidth=linewidthc , ls = '-', alpha=.0 , color= colors_array[i] )
#ax.set_title( '%s_frames'%(N) )
ax.set_title( "%s_two_time"%uid )
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
ax1.set_title("%s_aged_g2"%uid)
ki=0
for i in sorted(g2_aged.keys()):
#ax = fig.add_subplot(sx,sy,sn+1 )
if taus_aged is None:
gx= np.arange(len(g2_aged[i])) * timeperframe
else:
gx = taus_aged[i]
#marker = next(markers)
#print( g2_aged[i], marker )
#print(i)
ax1.plot( gx,g2_aged[i], marker = '%s'%markers_array[ki], ls='-', color= colors_array[ki], label=r"$t_a= %.1f s$"%i)
#print( i, ki, colors_array[ki] )
ki += 1
ax1.set_ylim( vmin, vmax )
ax1.set_xlabel(r"$\tau $ $(s)$", fontsize=18)
ax1.set_ylabel("g2")
ax1.set_xscale('log')
ax1.legend(fontsize='small', loc='best' )
if save:
#fp = path + "uid= %s--Waterfall-"%uid + CurTime + '.png'
fp = path + "%s_aged_g2"%uid + '.png'
#print( fp )
fig.savefig( fp, dpi=fig.dpi)
def show_C12(C12, fig_ax=None, q_ind=1, return_fig=False, interpolation = 'none', cmap='viridis',
logs=True, qlabel=None, *argv,**kwargs):
'''
plot one-q of two-time correlation function
C12: two-time correlation function, with shape as [ time, time, qs]
q_ind: if integer, for a SAXS q, the nth of q to be plotted, starting from 1,
if a list: for a GiSAXS [qz_ind, qr_ind]
kwargs: support
timeperframe: the time interval
N1: the start frame(time)
N2: the end frame(time)
vmin/vmax: for plot
title: if True, show the tile
e.g.,
show_C12(g12b, q_ind=1, N1=0, N2=500, vmin=1.05, vmax=1.07, )
'''
#strs = [ 'timeperframe', 'N1', 'N2', 'vmin', 'vmax', 'title']
if 'uid' in kwargs:
uid=kwargs['uid']
else:
uid='uid'
shape = C12.shape
if (q_ind<0) or (q_ind>shape[2]-1):
raise Exceptions("Error: qind starts from 0 (corresponding to python array index 0, but in the plot it will show as 1) to the max Q-length of two time funcs-1 %s."%shape[2]-1)
if isinstance(q_ind, int):
C12_num = q_ind #-1
else:
qz_ind, qr_ind = q_ind #-1
C12_num = qz_ind * num_qr + qr_ind
if 'timeperframe' in kwargs.keys():
timeperframe = kwargs['timeperframe']
else:
timeperframe=1
if 'vmin' in kwargs.keys():
vmin = kwargs['vmin']
else:
vmin=1
if 'vmax' in kwargs.keys():
vmax = kwargs['vmax']
else:
vmax=1.05
if 'N1' in kwargs.keys():
N1 = kwargs['N1']
if N1<0:
N1 = 0
else:
N1=0
if 'N2' in kwargs.keys():
N2 = kwargs['N2']
if N2>shape[0]:
N2 = shape[0]
else:
N2= shape[0]
if 'title' in kwargs.keys():
title = kwargs['title']
else:
title=True
data = C12[N1:N2,N1:N2,C12_num]
if fig_ax is None:
if RUN_GUI:
fig = Figure()
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots()
else:
fig,ax=fig_ax
#extent=[0, data.shape[0]*timeperframe, 0, data.shape[0]*timeperframe ]
extent= np.array( [N1, N2, N1, N2]) *timeperframe
if logs:
im = imshow(ax, data, origin='lower' , cmap=cmap,
norm= LogNorm( vmin, vmax ), interpolation = interpolation,
extent=extent )
else:
im = imshow(ax, data, origin='lower' , cmap=cmap,
vmin=vmin, vmax=vmax, interpolation = interpolation,
extent=extent )
if qlabel is not None:
if isinstance(q_ind, int):
qstr = 'Qth= %s-qval=%s'%(C12_num+1, qlabel[C12_num])
else:
qstr = 'Qth= %s'%(C12_num+1)
if title:
if isinstance(q_ind, int):
tit = '%s-[%s-%s] frames--'%(uid,N1,N2) + qstr
else:
tit = '%s-[%s-%s] frames--Qzth= %s--Qrth= %s'%(uid,N1,N2, qz_ind, qr_ind )
ax.set_title( tit )
else:
tit=''
#ax.set_title('%s-%s frames--Qth= %s'%(N1,N2,g12_num))
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
save=False
if 'save' in kwargs:
save=kwargs['save']
if save:
path=kwargs['path']
#fp = path + 'Two-time--uid=%s'%(uid) + tit + CurTime + '.png'
fp = path + '%s_Two_time'%(uid) + '.png'
plt.savefig( fp, dpi=fig.dpi)
if return_fig:
return fig, ax, im
def show_one_C12( C12, fig_ax=None, return_fig=False,interpolation = 'none',cmap='viridis',
*argv,**kwargs):
'''
plot one-q of two-time correlation function
C12: two-time correlation function, with shape as [ time, time, qs]
q_ind: if integer, for a SAXS q, the nth of q to be plotted
if a list: for a GiSAXS [qz_ind, qr_ind]
kwargs: support
timeperframe: the time interval
N1: the start frame(time)
N2: the end frame(time)
vmin/vmax: for plot
title: if True, show the tile
e.g.,
show_C12(g12b, q_ind=1, N1=0, N2=500, vmin=1.05, vmax=1.07, )
'''
#strs = [ 'timeperframe', 'N1', 'N2', 'vmin', 'vmax', 'title']
if 'uid' in kwargs:
uid=kwargs['uid']
else:
uid='uid'
shape = C12.shape
if 'timeperframe' in kwargs.keys():
timeperframe = kwargs['timeperframe']
else:
timeperframe=1
if 'vmin' in kwargs.keys():
vmin = kwargs['vmin']
else:
vmin=1
if 'vmax' in kwargs.keys():
vmax = kwargs['vmax']
else:
vmax=1.05
if 'N1' in kwargs.keys():
N1 = kwargs['N1']
else:
N1=0
if 'N2' in kwargs.keys():
N2 = kwargs['N2']
else:
N2= shape[0]
if 'title' in kwargs.keys():
title = kwargs['title']
else:
title=True
data = C12[N1:N2,N1:N2]
if fig_ax is None:
if RUN_GUI:
fig = Figure()
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots()
else:
fig,ax=fig_ax
im = imshow(ax, data, origin='lower' , cmap=cmap,
norm= LogNorm( vmin, vmax ),
extent=[0, data.shape[0]*timeperframe, 0, data.shape[0]*timeperframe ],
interpolation = interpolation)
if title:
tit = '%s-[%s-%s] frames'%(uid,N1,N2)
ax.set_title( tit )
else:
tit=''
#ax.set_title('%s-%s frames--Qth= %s'%(N1,N2,g12_num))
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
save=False
if 'save' in kwargs:
save=kwargs['save']
if save:
path=kwargs['path']
#fp = path + 'Two-time--uid=%s'%(uid) + tit + CurTime + '.png'
fp = path + '%s_Two_time'%(uid) + '.png'
plt.savefig( fp, dpi=fig.dpi)
if return_fig:
return fig, ax, im
def generate_plots(trans_array, trans_array_time, td_f, td_dR, title,
filename):
"""
Generate plots for a transducer
@param trans_array Transducer data array
@param trans_array_time Transducer internal time array
@param td_f Transducer frequency
@param td_dR Transducer's sample thickness (in range)
@param title Transducer title
@param filename png file name to save the figure to
"""
# only generate plots for the transducers that have data
if np.size(trans_array_time) <= 0:
return
# determine size of the data array
max_depth, max_time = np.shape(trans_array)
min_depth = 0
min_time = 0
# subset/decimate the x & y ticks so that we don't plot everyone
num_xticks = 7
num_yticks = 10
min_db = -180
max_db = -59
cbar_ticks = np.arange(min_db, max_db, 20)
# convert time, which represents the number of 100-nanosecond intervals that
# have elapsed since 12:00 A.M. January 1, 1601 Coordinated Universal Time (UTC)
# to unix time, i.e. seconds since 1970-01-01 00:00:00.
# 11644473600 == difference between 1601 and 1970
# 1e7 == divide by 10 million to convert to seconds
trans_array_time = np.array(trans_array_time) / 1e7 - 11644473600
trans_array_time = (trans_array_time / (60 * 60 * 24)) + REF_TIME
# subset the xticks so that we don't plot every one
xticks = np.linspace(0, max_time, num_xticks)
# format trans_array_time array so that it can be used to label the x-axis
xticklabels = [
i.strftime('%Y-%m-%d %H:%M:%S')
for i in num2date(trans_array_time[::round(xticks[1])])
]
# subset the yticks so that we don't plot everyone
yticks = np.linspace(0, max_depth, num_yticks)
# create range vector (depth in meters)
yticklabels = np.round(np.arange(0, max_depth, round(yticks[1])) * td_dR)
fig, ax = plt.subplots()
ax.grid(False)
figure_title = 'Converted Power: ' + title + 'Frequency: ' + str(td_f)
ax.set_title(figure_title, fontsize=12)
ax.set_xlabel('time (UTC)', fontsize=10)
ax.set_ylabel('depth (m)', fontsize=10)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels,
rotation=25,
horizontalalignment='right',
fontsize=10)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels, fontsize=10)
ax.tick_params(axis="both", labelcolor="k", pad=4)
# set the x and y limits
ax.set_ylim(max_depth, min_depth)
ax.set_xlim(min_time, max_time)
# plot the colorbar
cax = imshow(ax,
trans_array,
interpolation='none',
aspect='auto',
cmap='jet',
vmin=min_db,
vmax=max_db)
cb = fig.colorbar(cax,
orientation='horizontal',
ticks=cbar_ticks,
shrink=.6)
cb.ax.set_xticklabels(cbar_ticks,
fontsize=8) # horizontally oriented colorbar
cb.set_label('dB', fontsize=10)
cb.ax.set_xlim(-180, -60)
fig.tight_layout(pad=1.2)
# adjust the subplot so that the x-tick labels will fit on the canvas
fig.subplots_adjust(bottom=0.1)
plt.figtext(0.01,
0.01,
'*Note: Strictly sequential time tags are not guaranteed.',
axes=ax,
fontsize=7)
# reposition the cbar
cb.ax.set_position([.4, .05, .4, .1])
# save the figure
fig.savefig(filename, dpi=300)
# close the figure
plt.close()
def plot_waterfallc(wat,
qindex=1,
aspect=None,
vmax=None,
vmin=None,
interpolation='none',
save=False,
return_fig=False,
cmap='viridis',
*argv,
**kwargs):
'''plot waterfall for a giving compressed file
FD: class object, the compressed file handler
labeled_array: np.array, a ROI mask
qindex: the index number of q, will calculate where( labeled_array == qindex)
aspect: the aspect ratio of the plot
Return waterfall
Plot the waterfall
'''
#wat = cal_waterfallc( FD, labeled_array, qindex=qindex)
if RUN_GUI:
fig = Figure(figsize=(8, 6))
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots(figsize=(8, 6))
if 'uid' in kwargs:
uid = kwargs['uid']
else:
uid = 'uid'
#fig, ax = plt.subplots(figsize=(8,6))
ax.set_ylabel('Pixel')
ax.set_xlabel('Frame')
ax.set_title('%s_Waterfall_Plot_@qind=%s' % (uid, qindex))
if 'beg' in kwargs:
beg = kwargs['beg']
else:
beg = 0
extent = [beg, len(wat) + beg, 0, len(wat.T)]
if vmax is None:
vmax = wat.max()
if vmin is None:
vmin = wat.min()
if aspect is None:
aspect = wat.shape[0] / wat.shape[1]
im = imshow(ax,
wat.T,
cmap=cmap,
vmax=vmax,
extent=extent,
interpolation=interpolation)
#im = ax.imshow(wat.T, cmap='viridis', vmax=vmax,extent= extent,interpolation = interpolation )
fig.colorbar(im)
ax.set_aspect(aspect)
if save:
#dt =datetime.now()
#CurTime = '%s%02d%02d-%02d%02d-' % (dt.year, dt.month, dt.day,dt.hour,dt.minute)
path = kwargs['path']
#fp = path + "uid= %s--Waterfall-"%uid + CurTime + '.png'
fp = path + "%s_waterfall" % uid + '.png'
plt.savefig(fp, dpi=fig.dpi)
#plt.show()
if return_fig:
return fig, ax, im
def show_C12(C12, fig_ax=None, q_ind=1, return_fig=False, interpolation = 'none', cmap='viridis',
logs=True, qlabel=None, *argv,**kwargs):
'''
plot one-q of two-time correlation function
C12: two-time correlation function, with shape as [ time, time, qs]
q_ind: if integer, for a SAXS q, the nth of q to be plotted, starting from 1,
if a list: for a GiSAXS [qz_ind, qr_ind]
kwargs: support
timeperframe: the time interval
N1: the start frame(time)
N2: the end frame(time)
vmin/vmax: for plot
title: if True, show the tile
e.g.,
show_C12(g12b, q_ind=1, N1=0, N2=500, vmin=1.05, vmax=1.07, )
'''
#strs = [ 'timeperframe', 'N1', 'N2', 'vmin', 'vmax', 'title']
if 'uid' in kwargs:
uid=kwargs['uid']
else:
uid='uid'
shape = C12.shape
if (q_ind<1) or (q_ind>shape[2]):
raise Exceptions("Error: qind starts from 1 (corresponding to python array index 0, but in the plot it will show as 1) to the max Q-length of two time funcs %s."%shape[2])
if isinstance(q_ind, int):
C12_num = q_ind -1
else:
qz_ind, qr_ind = q_ind -1
C12_num = qz_ind * num_qr + qr_ind
if 'timeperframe' in kwargs.keys():
timeperframe = kwargs['timeperframe']
else:
timeperframe=1
if 'vmin' in kwargs.keys():
vmin = kwargs['vmin']
else:
vmin=1
if 'vmax' in kwargs.keys():
vmax = kwargs['vmax']
else:
vmax=1.05
if 'N1' in kwargs.keys():
N1 = kwargs['N1']
if N1<0:
N1 = 0
else:
N1=0
if 'N2' in kwargs.keys():
N2 = kwargs['N2']
if N2>shape[0]:
N2 = shape[0]
else:
N2= shape[0]
if 'title' in kwargs.keys():
title = kwargs['title']
else:
title=True
data = C12[N1:N2,N1:N2,C12_num]
if fig_ax is None:
if RUN_GUI:
fig = Figure()
ax = fig.add_subplot(111)
else:
fig, ax = plt.subplots()
else:
fig,ax=fig_ax
#extent=[0, data.shape[0]*timeperframe, 0, data.shape[0]*timeperframe ]
extent= np.array( [N1, N2, N1, N2]) *timeperframe
if logs:
im = imshow(ax, data, origin='lower' , cmap=cmap,
norm= LogNorm( vmin, vmax ), interpolation = interpolation,
extent=extent )
else:
im = imshow(ax, data, origin='lower' , cmap=cmap,
vmin=vmin, vmax=vmax, interpolation = interpolation,
extent=extent )
if qlabel is not None:
if isinstance(q_ind, int):
qstr = 'Qth= %s-qval=%s'%(C12_num+1, qlabel[C12_num])
else:
qstr = 'Qth= %s'%(C12_num+1)
if title:
if isinstance(q_ind, int):
tit = '%s-[%s-%s] frames--'%(uid,N1,N2) + qstr
else:
tit = '%s-[%s-%s] frames--Qzth= %s--Qrth= %s'%(uid,N1,N2, qz_ind, qr_ind )
ax.set_title( tit )
else:
tit=''
#ax.set_title('%s-%s frames--Qth= %s'%(N1,N2,g12_num))
ax.set_xlabel( r'$t_1$ $(s)$', fontsize = 18)
ax.set_ylabel( r'$t_2$ $(s)$', fontsize = 18)
fig.colorbar(im)
save=False
if 'save' in kwargs:
save=kwargs['save']
if save:
path=kwargs['path']
#fp = path + 'Two-time--uid=%s'%(uid) + tit + CurTime + '.png'
fp = path + '%s_Two_time'%(uid) + '.png'
plt.savefig( fp, dpi=fig.dpi)
if return_fig:
return fig, ax, im