def _plot_output(self, type, latpositions, longpositions, data, media_file):
fig = plt.figure(figsize=plt.figaspect(0.3))
image = self.input_config.image
impositions = self.input_config.positions
imfactors = self.input_config.factors
intensities = self.input_config.intensities
writer = FFMpegFileWriter(fps=5, codec='mpeg4')
writer.setup(
fig, media_file, dpi=80,
frame_prefix=os.path.splitext(media_file)[0]+'_')
writer.frame_format = 'png'
step = 100
plt.hold(False)
#TODO keep DRY
U, V = np.mgrid[0:np.pi/2:complex(0, self.PARALLELS),
0:2*np.pi:complex(0, self.MERIDIANS)]
X = np.cos(V)*np.sin(U)
Y = np.sin(V)*np.sin(U)
Z = np.cos(U)
for i, d in enumerate(data):
if i % step == 0:
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_title('Image')
if type == 'image':
ax1.imshow(image, cmap=cm.Greys_r)
elif type == 'video':
# TODO show red rectangle
ax1 = fig.add_subplot(1, 3, 1)
xind = impositions[i, 0:2]
yind = impositions[i, 2:4]
ax1.imshow(image[int(xind[0]):int(xind[1]),
int(yind[0]):int(yind[1])],
cmap=cm.Greys_r)
else:
raise ValueError('Invalid value for media type: {}'
', valid values image, video'.format(type))
colors = self.compute_colors(U, V, latpositions,
longpositions, intensities[i])
ax2 = fig.add_subplot(1, 3, 2, projection='3d')
ax2.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.gray(colors),
antialiased=False, shade=False)
ax2.set_title('Intensities')
colors = self.compute_colors(U, V, latpositions,
longpositions, data[i])
ax3 = fig.add_subplot(1, 3, 3, projection='3d')
ax3.plot_surface(X, Y, Z, rstride=1, cstride=1, facecolors=cm.gray(colors),
antialiased=False, shade=False)
ax3.set_title('Photoreceptor outputs')
fig.canvas.draw()
writer.grab_frame()
writer.finish()
def get_colors(self, qty):
qty = np.power(qty / qty.max(), 1.0 / CONTRAST)
if COLORMAP == 0:
rgba = cm.gray(qty, alpha=ALPHA)
elif COLORMAP == 1:
rgba = cm.afmhot(qty, alpha=ALPHA)
elif COLORMAP == 2:
rgba = cm.hot(qty, alpha=ALPHA)
elif COLORMAP == 3:
rgba = cm.gist_heat(qty, alpha=ALPHA)
elif COLORMAP == 4:
rgba = cm.copper(qty, alpha=ALPHA)
elif COLORMAP == 5:
rgba = cm.gnuplot2(qty, alpha=ALPHA)
elif COLORMAP == 6:
rgba = cm.gnuplot(qty, alpha=ALPHA)
elif COLORMAP == 7:
rgba = cm.gist_stern(qty, alpha=ALPHA)
elif COLORMAP == 8:
rgba = cm.gist_earth(qty, alpha=ALPHA)
elif COLORMAP == 9:
rgba = cm.spectral(qty, alpha=ALPHA)
return rgba
def generateGreyTile(x, y, zoom, allRecords):
timestamp = int(time.time())
nw_corner = num2deg(x, y, zoom)
se_corner = num2deg(x+1, y+1, zoom)
lats = [min(nw_corner[0], se_corner[0]), max(nw_corner[0], se_corner[0])]
lngs = [min(nw_corner[1], se_corner[1]), max(nw_corner[1], se_corner[1])]
lats2 = np.around([lats[0] - .0002, lats[1] + .0002], decimals=4)
lngs2 = np.around([lngs[0] - .0002, lngs[1] + .0002], decimals=4)
timestamp = int(time.time())
df = allRecords[
(allRecords.lat >= lats2[0]) &
(allRecords.lat <= lats2[1]) &
(allRecords.lng >= lngs2[0]) &
(allRecords.lng <= lngs2[1])
]
df = df.reset_index(drop=True)
timestamp = int(time.time())
if len(df) != 0:
bins = [
np.arange(lngs2[0]-.00005, lngs2[1]+.00005, .0001),
np.arange(lats2[0]-.00005, lats2[1]+.00005, .0001)
]
zi, xi, yi = np.histogram2d(
df['lng'], df['lat'],
bins=bins, normed=False
)
zi = np.ma.masked_equal(zi, 0)
zi = ((np.clip(zi, -90, -29) + 91) * 4.25).astype(int)
zi = np.rot90(zi)
s = 1024
color = np.uint8(cm.gray(zi/225.0) * 225)
color = imresize(color, size=(s,s), interp='nearest')
lat_len = zi.shape[0]*.0001
lng_len = zi.shape[1]*.0001
x1 = ((lngs[0]-(lngs2[0]-.00005))/lng_len)*s
x2 = s-(((lngs2[1]+.00005) - lngs[1])/lng_len)*s
y1 = (((lats2[1]+.00005) - lats[1])/lat_len)*s
y2 = s-((lats[0]-(lats2[0]-.00005))/lat_len)*s
color = color[y1:y2+1,x1:x2+1]
color = imresize(color, size=(256,256), interp='nearest')
return Image.fromarray(color)
def play_rates(rates, patches, num_levels=255, time=None,
repeat=True, frametime=100):
"""
Plays a movie representation of the firing rate of a list of cells, by
coloring a list of patches with a color proportional to the firing rate. This
is useful, for example, in conjunction with ``plot_cells``, to color the
ellipses fitted to a set of receptive fields proportional to the firing rate.
Parameters
----------
rates : array_like
An ``(N, T)`` matrix of firing rates. ``N`` is the number of cells, and
``T`` gives the firing rate at a each time point.
patches : list
A list of ``N`` matplotlib patch elements. The facecolor of these patches is
altered according to the rates values.
Returns
-------
anim : matplotlib.animation.Animation
The object representing the full animation.
"""
# Validate input
if rates.ndim == 1:
rates = rates.reshape(1, -1)
if isinstance(patches, Ellipse):
patches = [patches]
N, T = rates.shape
# Approximate necessary colormap
colors = cm.gray(np.arange(num_levels))
rscale = np.round((num_levels - 1) * (rates - rates.min()) /
(rates.max() - rates.min())).astype('int').reshape(N, T)
# set up
fig = plt.gcf()
ax = plt.gca()
if time is None:
time = np.arange(T)
# Animation function (called sequentially)
def animate(t):
for i in range(N):
patches[i].set_facecolor(colors[rscale[i, t]])
ax.set_title('Time: %0.2f seconds' % (time[t]), fontsize=20)
# Call the animator
anim = animation.FuncAnimation(fig, animate,
np.arange(T), interval=frametime, repeat=repeat)
return anim
def PlotBands(procar):
# Iterate over bands to collect data.
for i in range(1, procar.Nb+1):
# Iterate over k-points to build (kx, energy) pairs for this band.
# Entries in surface are 0 (bulk states) or 1 (surface states).
# surface controls the color of the markers.
kx, energy, surface = [], [], []
for k in procar.kPoints:
kx.append(k.kx)
b = k.Band(i)
energy.append(b.energy)
if hasattr(b, 'surface') and b.surface:
print(str(k.kx) + " " + str(i))
surface.append(cm.gray(0.0))
else:
surface.append(cm.gray(0.99))
# plot band as thin green line (to clarify connection btwn markers)
plt.plot(kx, energy, 'g-', linewidth=1)
# plot band as markers: bulk -> white, surface -> black
plt.scatter(kx, energy, facecolors=surface, edgecolor='b',
marker='o', cmap=cm.gray, linewidth=1, s=50)
# Display the plot
plt.show()
def drawShape(m, ax, paraTable):
for conshpfn in paraTable["ShapeFile"].unique():
for i, conShape in enumerate(getPointArray(conshpfn)):
tempParaTable = concat([paraTable[paraTable["ShapeFile"]==conshpfn]], ignore_index = True)
partLimit = tempParaTable.ix[i,"PartLimit"]
normal = tempParaTable.ix[i,"Normal"]
shpsegs = []
for j, conShapePart in enumerate(conShape):
if j < partLimit:
lons, lats = zip(*conShapePart)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(cm.gray(1 - normal))
lines.set_linewidth(0.01)
ax.add_collection(lines)
def plotDelay(resultFilenames):
# Size-Delay values from each file
sizeValues = []
delayValues = []
# Loop over files and parse size=delay values
for resultFilename in resultFilenames :
# Append a new array for this file's size-delay values
sizeValues.append ([])
delayValues.append ([])
index = len (sizeValues) - 1
resultFile = open (resultFilename)
inSizeDelayBlock = False
for line in resultFile :
if not inSizeDelayBlock :
if "(Packet Size, Delay) Values:" in line :
inSizeDelayBlock = True
else :
if re.search ("\([-+]?[0-9]*\.?[0-9]+, [-+]?[0-9]*\.?[0-9]+\)", line) :
splitLine = line.split (",")
packetSize = int (splitLine[0][1:])
delay = float (splitLine[1][1:-2]) / 1000.0;
sizeValues[index].append (packetSize)
delayValues[index].append (delay)
else :
inSizeDelayBlock = False
maxDelay = 0.0
for i in range (0, len (delayValues)) :
for delayValue in delayValues[i] :
if delayValue > maxDelay :
maxDelay = delayValue
matplotlib.rcParams.update({'font.size':17})
matplotlib.rcParams.update({'figure.autolayout': True})
colors = cm.gray ( np.linspace (0, 1, len (delayValues)))
shortenedfilenames = []
for name in resultFilenames:
shortenedfilenames.append(name.split('.')[0])
plt.hist(delayValues, 20, color=colors, label=shortenedfilenames)
plt.legend()
plt.xlabel ("Packet Delay (us)")
plt.ylabel ("Occurrences")
plt.savefig ("delay.pdf")
plt.close()
def write_lfs(datadict, max_lfs=2):
with open('lfs_out.off', 'w') as f:
f.write("COFF\n")
# max_lfs = nanmax(datadict['lfs'])
m,n = datadict['coords'].shape
f.write("{} 0 0\n".format(m))
for p, lfs in zip(datadict['coords'], datadict['lfs']):
if not isnan(lfs):
red=green=blue=0
colorval = 255-int(255 * (lfs/max_lfs))
if colorval > 255: colorval = 255
if colorval < 0: colorval = 0
rgba = cm.gray([colorval])[0]
f.write("{0} {1} {2} {3} {4} {5} {6}\n".format(p[0], p[1], p[2], int(rgba[0]*255),int(rgba[1]*255),int(rgba[2]*255),int(rgba[3]*255)))
def plot_countries(m, ax):
global cfg
global args
colors = {}
countries = []
country_index = []
# create temp color index and country index
for ncountry, shapedict in enumerate(m.countries_info):
country_name = shapedict['NAME'].decode('utf-8')
if country_name in cfg['iso_atwo_fixes'].keys():
country_code = cfg['iso_atwo_fixes'][country_name]
else:
country_code = shapedict['ISO_A2']
if cfg['color_random']:
colors[country_code] = rgb2hex((random.uniform(0,1),random.uniform(0,1),random.uniform(0,1)))
else:
colors[country_code] = rgb2hex(cm.gray(cfg['country_color_map'].get(country_code, 255) * cfg['color_step']))
if cfg['country_color_map'].get(country_code, 255) == 255:
if args.verbose:
print "cannot map {country_name}({country_code})".format(country_name= country_name, country_code=country_code)
if country_code not in countries:
if args.verbose:
print "adding {country_name}({country_code}) using color {country_color} ({country_color_raw}) to index.".format(country_name=country_name, country_code=country_code, country_color=colors[country_code], country_color_raw=cfg['country_color_map'].get(country_code, 255))
countries.append(country_code)
country_index.append(country_code)
if args.verbose:
print "added {country_count} countries to index.".format(country_count=len(countries))
# plot countries
for cindex, cc in enumerate(country_index):
xx,yy = zip(*m.countries[cindex])
if cc in cfg['color_overwrites'].keys():
color = cfg['color_overwrites'][cc]
if cfg['fill_overwrites']:
ax.fill(xx,yy,color,edgecolor=color)
else:
color = colors[cc]
if cfg['auto_fill_country']:
ax.fill(xx,yy,color,edgecolor=color)
def save_image_no_border(img):
fig, ax = plt.subplots(figsize=(1, 1), dpi=224)
ax.imshow(img, cmap='jet')
ax.imshow(CAMs, alpha=0.4, cmap='jet')
ax.axis('off')
fig.frameon = False
#with open("/home/cbrengman/Documents/Travel/AGU_2019/Talk/CAM_comb.tif",'wb') as outfile:
# fig.canvas.print_tif(outfile)
from matplotlib import cm
img = np.asarray(img)
img = Image.fromarray(np.uint8(cm.gray(img) * 255))
cam = Image.fromarray(np.uint8(cm.jet(CAMs[0]) * 255))
ogcam = Image.fromarray(np.uint8(cm.jet(OGCAMs[0]) * 255))
comb = Image.blend(img, cam, alpha=0.4)
img.save('/home/cbrengman/Documents/transfer/img3.tif')
cam.save('/home/cbrengman/Documents/transfer/img33_cam.tif')
comb.save('/home/cbrengman/Documents/transfer/img3_comb.tif')
ogcam.save('/home/cbrengman/Documents/transfer/img3_ogcam.tif', dpi=(7, 7))
def calculate_auto_corr(container, in_dir, opt_end="", s_color=False):
"""Calculate a matrix of autocorrs for each unit in a container"""
ax1, fig1 = nc_plot._make_ax_if_none(None)
auto_corr_matrix = np.empty((len(container), 20), dtype=float)
color = iter(cm.gray(np.linspace(0, 0.8, len(container))))
for i, ndata in enumerate(container):
auto_corr_data = ndata.isi_auto_corr(bins=1, bound=[0, 20])
auto_corr_matrix[i] = (auto_corr_data["isiCorr"] /
ndata.spike.get_unit_stamp().size)
bins = auto_corr_data['isiAllCorrBins']
bin_centres = bins[:-1] + np.mean(np.diff(bins)) / 2
c = next(color) if s_color else "k"
ax1.plot(bin_centres / 1000, auto_corr_matrix[i], c=c)
ax1.set_xlim([0.000, 0.02])
ax1.set_xticks([0.000, 0.005, 0.01, 0.015, 0.02])
ax1.axvline(x=0.006, c="r", ls="--")
plot_loc = os.path.join(in_dir, "nc_plots", "autocorr" + opt_end + ".png")
fig1.savefig(plot_loc, dpi=400)
return auto_corr_matrix
def calculate_isi_hist(container, in_dir, opt_end="", s_color=False):
"""Calculate a matrix of isi_hists for each unit in a container"""
ax1, fig1 = nc_plot._make_ax_if_none(None)
isi_hist_matrix = np.empty((len(container), 60), dtype=float)
color = iter(cm.gray(np.linspace(0, 0.8, len(container))))
for i, ndata in enumerate(container):
res_isi, bins = log_isi(ndata)
isi_hist_matrix[i] = res_isi
bin_centres = bins[:-1] + np.mean(np.diff(bins)) / 2
c = next(color) if s_color else "k"
ax1.plot(bin_centres, res_isi, c=c)
ax1.set_xlim([-3, 1])
ax1.set_xticks([-3, -2, -1, 0])
ax1.axvline(x=np.log10(0.006), c="r", ls="--")
plot_loc = os.path.join(
in_dir, "nc_plots", "logisi" + opt_end + ".png")
fig1.savefig(plot_loc, dpi=400)
return isi_hist_matrix
def drawLimits(useObjects,filt="Ks",useGrayscale=False):
colorDict={}
for ii,obj in enumerate(useObjects):
if obj not in limitDict[filt].keys():
print obj + " not found"
else:
if useGrayscale:
colormap = cm.gray((ii)/(float(len(useObjects))),1)
else:
colormap = cm.spectral((ii)/(float(len(useObjects))),1)
dists = []; deltaMag = []
for xx in range(len(apertures)):
if limitDict[filt][obj][xx][1] != "-":
dists.append(eval(apertures[xx]))
deltaMag.append(eval(limitDict[filt][obj][xx]))
# print "\n\n",dists,"\n",deltaMag,"\n\n"
pylab.semilogx(dists,deltaMag,linewidth=2,linestyle='solid',color=colormap,label=obj+" "+instrUsed)
colorDict[obj]=colormap
return colorDict
def plot_sphere(beta, alpha, f):
alpha = np.concatenate((alpha, alpha[:, :1]), axis=1)
beta = np.concatenate((beta, beta[:, :1]), axis=1)
f = np.concatenate((f, f[:, :1]), axis=1)
x = np.sin(beta) * np.cos(alpha)
y = np.sin(beta) * np.sin(alpha)
z = np.cos(beta)
fc = cm.gray(f)
fc = plt.get_cmap("bwr")(f)
ax = plt.gca()
ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=fc)
ax.view_init(azim=-90, elev=90)
ax.set_axis_off()
a = 0.6
ax.set_xlim3d(-a, a)
ax.set_ylim3d(-a, a)
ax.set_zlim3d(-a, a)
def plot_fb8(fb8, npts):
"""
Plot fb8 on 3D sphere
"""
xs = fb8.spherical_coordinates_to_nu(*grid(npts))
pdfs = fb8.pdf(xs)
z,x,y = xs.T
fig = plt.figure(figsize=plt.figaspect(1.))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x.reshape(npts, npts),
y.reshape(npts, npts),
z.reshape(npts, npts),
alpha=0.5,
rstride=1, cstride=1,
facecolors=cm.gray(pdfs.reshape(npts, npts)/pdfs.max()))
# ax.set_xticks([])
# ax.set_yticks([])
# ax.set_zticks([])
ax.set_axis_off()
ax.set_title(make_title(fb8), fontsize=12, y=0.18)
plt.tight_layout(-5)
def test():
import numpy as np
from matplotlib import cm
#from pylab import cm, rand
from PYME.DSView.LUT import lut
lut1 = (255 *
cm.gray(np.linspace(0, 1, 256))[:, :3].T).astype('uint8').copy()
print((lut1.shape))
def testLut():
d = (100 * np.random.rand(5, 5)).astype('uint16')
o = np.zeros((5, 5, 3), 'uint8')
lut.applyLUTu8(d.astype('uint8'), .01, 0, lut1, o)
lut.applyLUTu16(d, .01, 0, lut1, o)
lut.applyLUTf(d.astype('uint16'), .01, 0, lut1, o)
print(o)
print((lut1.T[((d + 0) * .01 * 256).astype('i')]))
testLut()
def surf_plot(mg,
surface='topographic__elevation',
title='Surface plot of topography'):
fig = plt.figure()
ax = fig.gca(projection='3d')
# Plot the surface.
Z = mg.at_node[surface].reshape(mg.shape)
color = cm.gray((Z - Z.min()) / (Z.max() - Z.min()))
surf = ax.plot_surface(mg.node_x.reshape(mg.shape),
mg.node_y.reshape(mg.shape),
Z,
rstride=1,
cstride=1,
facecolors=color,
linewidth=0.,
antialiased=False)
ax.view_init(elev=35, azim=-120)
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
ax.set_zlabel('Elevation')
plt.title(title)
plt.show()
def image_update(Qdata, fig, maxrep, Nplot, dBf, stop_flag):
renderer = fig.select(dict(name="echoes", type=GlyphRenderer))
source = renderer[0].data_source
img = source.data["image"][0]
while (not stop_flag.is_set()):
Xrcv_seg = Qdata.get()
# convert Xcrv_seg to dBf
Xrcv_seg_dB = 10.0 * log10(Xrcv_seg)
# enforce minimum dBf
Xrcv_seg_dB[Xrcv_seg_dB < -dBf] = -dBf
# convert to image intensity out of 1
new_line = (Xrcv_seg_dB + dBf) / dBf
img = np.roll(img, 1, 0)
view = img.view(dtype=np.uint8).reshape((maxrep, Nplot, 4))
view[0, :, :] = cm.gray(new_line) * 255
source.data["image"] = [img]
push_notebook()
Qdata.queue.clear()
def PlotMultiBar(xticks, ArrayBarTuples, inTitle, yLabel, inwidth=0.2, colorbounds=[0.3,0.6]):
locs = numpy.arange(1, len(xticks) + 1)
width = inwidth
colors = [cm.gray(i) for i in numpy.linspace(colorbounds[0],colorbounds[1], len(ArrayBarTuples))]
colorcycler = cycle(colors)
fig = plt.figure()
ax = plt.subplot(111)
locAdj = 0
for datatuple in ArrayBarTuples:
locAdj += 1
plt.bar(locs + locAdj*width, datatuple[0], yerr=datatuple[1], color=next(colorcycler), width=width,\
antialiased=True, label=datatuple[3], ecolor='k') # color=datatuple[2]
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
ax.legend(loc='upper center', bbox_to_anchor = (0.5, -0.05), fancybox=True, shadow=True, ncol=locAdj)
plt.title(inTitle)
plt.xticks((locs + (locAdj * (width/2)) + width), xticks)
plt.ylabel(yLabel)
return fig
def cb_mouse_motion(self, event):
"""
Callback to process a mouse movement event.
If the group selection option is enabled, then any points with
Y-coordinate less than the cursor's Y-coordinate will be marked in a
different opacity level, but not highlighted. If the user clicks with
the mouse, then the points will be highlighted, but this event is
processed in another method.
This method also processes tooltip-related events when the group
selection is disabled. If the user hovers the mouse cursor over a data
point, then the name associated to that point will be shown in a
tooltip.
Parameters
----------
event: matplotlib.backend_bases.MouseEvent
Data about the event.
"""
# We remove the horizonal line here (if any), regardless of the mouse
# cursor position. We also restore the points colors.
if self._hthresh_line:
lines = self.axes.get_lines()
for l in lines:
self.axes.lines.remove(l)
del l
self._hthresh_line = None
for i, p in enumerate(self.axes.collections):
if i not in self._reference_idx:
p.set_facecolor(self._points_colors[i])
self.draw()
# Restoring the points' original size.
if self._point_artists:
for art in self._point_artists:
if art is None:
continue
art.set_sizes([self._plot_params['s']])
if event.xdata is None or event.ydata is None or self._points is None:
return False
if self.group_selection_enabled:
# We must get only the points above the cursor.
diff = self._points - event.ydata
above = []
above = [i for i in reversed(self._yidx_points) if diff[i] >= 0]
for a in above:
if a in self._reference_idx:
continue
self.axes.collections[a].set_facecolor(cm.gray(200))
self._hthresh_line = self.axes.axhline(y=event.ydata,
c='b',
linewidth=2)
else:
# Testing if the cursor is over a point. If it is, we plot the
# tooltip and notify this event by calling the registered
# callback, if any.
if not self.curvenames:
return False
else:
hover_idx = None
for i, art in enumerate(self._point_artists):
if not art:
continue
contains, _ = art.contains(event)
if contains:
art.set_sizes([self._plot_params['s'] * 3])
if i > len(self.curvenames):
return False
hover_idx = i
break
if hover_idx is not None:
palette = QPalette()
palette.setColor(QPalette.ToolTipBase,
QColor(252, 243, 207))
palette.setColor(QPalette.ToolTipText, QColor(0, 0, 0))
QToolTip.setPalette(palette)
QToolTip.setFont(QFont('Arial', 14, QFont.Bold))
pos = self.mapToGlobal(
QPoint(event.x,
self.height() - event.y))
QToolTip.showText(pos,
'{}'.format(self.curvenames[hover_idx]))
else:
QToolTip.hideText()
if self._cb_notify_tooltip:
self._cb_notify_tooltip(self.name, hover_idx)
self.draw()
def grayhist(img, stats=False):
"""
入力 : BGR画像, 統計量の表示の有無(Optional)
出力 : グレースケールのimshow, Histgram(+統計量)
"""
# スタイルの設定。seabornの設定は残るため、常に最初に書いておく
sns.set()
sns.set_style(style='ticks')
# プロット全体と個々のグラフのインスタンス
fig = plt.figure(figsize=[15, 4])
ax1 = fig.add_subplot(1, 2, 1)
sns.set_style(style='whitegrid')
ax2 = fig.add_subplot(1, 2, 2)
# グレースケール画像化→三重配列に戻す
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
ax1.imshow(img)
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 一次元配列化
img = np.array(img).flatten()
# 本来rangeは[0,255]となるはずだが、255に値が集まりすぎたので弾いた
img = img[img != 255]
# 軸の刻み目
ax2.set_xticks(np.linspace(0, 255, 9).astype(int))
# ヒストグラムを計算→色をつける
N, bins, patches = ax2.hist(img, range=(0, 255), bins=256)
for (i, patch) in enumerate(patches):
color = cm.gray(bins[i] / 256)
patch.set_facecolor(color)
if stats == True: # 統計量を表示する
mean = img.mean()
std = np.std(img)
median = np.median(img)
mode = sstats.mode(img)[0][0]
# 統計量のラインをひく
ax2.axvline(mean, color='#d95763', linestyle='solid', linewidth=3)
ax2.axvline(median, color='#6abe30', linestyle='solid', linewidth=2)
ax2.axvline(mode, color='#ba9641', linestyle='solid', linewidth=2)
ax2.axvline(mean + std,
color='#d95763',
linestyle='dashdot',
linewidth=1)
ax2.axvline(mean - std,
color='#d95763',
linestyle='dashdot',
linewidth=1)
# 統計量の説明の文字
ax2.text(mean,
N.max() * 1.075,
"$\mu$",
color='#d95763',
horizontalalignment='center')
ax2.text(median,
N.max() * 1.18,
"median",
color='#6abe30',
rotation=45)
ax2.text(mode, N.max() * 1.15, "mode", color='#ba9641', rotation=45)
ax2.text(mean + std,
N.max() * 1.075,
"$\mu+\sigma$",
color='#d95763',
horizontalalignment='center')
ax2.text(mean - std,
N.max() * 1.075,
"$\mu-\sigma$",
color='#d95763',
horizontalalignment='center')
fig.tight_layout()
plt.show()
print("mean : ", mean)
print("stddev : ", std)
print("median : ", median)
print("mode : ", mode)
else:
fig.tight_layout()
plt.show()
def plot_spectra_grid(file_set, ligands, *args, **kwargs):
#Example Syntax:
# plot_spectra_grid(file_set,ligands,protein=['Src'],ligand=['Bosutinib'])
# plot_spectra_grid(file_set,ligands)
# plot_spectra_grid(file_set,ligands,output='Figure1')
protein = kwargs.get('protein', None)
ligand = kwargs.get('ligand', None)
output = kwargs.get('output', None)
if protein != None:
these_proteins = protein
else:
these_proteins = file_set.keys()
if ligand != None:
these_ligands = ligand
else:
these_ligands = ligands
print(these_proteins)
print(len(these_proteins))
print(these_ligands)
print(len(these_ligands))
grid = len(these_proteins) + len(these_ligands)
if grid == 2:
my_figsize = (12, 8)
else:
my_figsize = (24, 18)
fig, axes = plt.subplots(nrows=len(these_ligands),
ncols=len(these_proteins),
figsize=my_figsize,
sharey=True,
sharex=True)
for j, protein in enumerate(these_proteins):
for k, ligand in enumerate(these_ligands):
index = ligands.index(ligand)
file = file_set[protein][index]
if grid == 2:
my_axes = None
else:
my_axes = axes[k, j]
# pick a title
title = "%s - %s" % (protein, ligand)
# make a dataframe
df = xml2df(file)
# plot the spectra
#fig = plt.figure();
for i in range(11):
df['fluorescence'].iloc[:, i].plot(ylim=(0, 100000),
xlim=(400, 600),
linewidth=3,
ax=my_axes,
c=cm.hsv(i * 15),
title=title)
df['fluorescence'].iloc[:, 11 + i].plot(ylim=(0, 100000),
xlim=(400, 600),
legend=False,
ax=my_axes,
linewidth=4,
c=cm.gray(i * 15 + 50),
fontsize=20,
title=title)
sns.despine()
plt.yticks([])
plt.tight_layout()
if output != None:
plt.savefig('%s.png' % output)
def main():
backgroundimage = plt.imread("worldmap.jpg", format='jpg')
inputfile = "normalizedcoordinates.txt"
latitude= np.loadtxt(inputfile, dtype=float, delimiter=",", usecols=[0])
longitude= np.loadtxt(inputfile, dtype=float, delimiter=",", usecols=[1])
X = []
for i in range(len(latitude)):
X.append((longitude[i], latitude[i]))
X = np.asarray(X)
######### Alkuperäinen data ##############
fig, ax = plt.subplots()
x0,x1 = ax.get_xlim()
y0,y1 = ax.get_ylim()
for j in range(len(X)):
plt.scatter(X[j, 0], X[j, 1], c=cm.jet(0),s=7, marker='o')
ax.imshow(backgroundimage, extent=[x0,x1,y0,y1], aspect='auto', alpha=0.8)
plt.title("Earthquakes around the world in July 2017 (Original data)")
plt.xlabel("Latitude")
plt.ylabel("Longitude")
######## Silhouette score ################
num_clusters = Silhouette_score(X)
########## K-Means ########################
fig1, ax1 = plt.subplots()
x0,x1 = ax1.get_xlim()
y0,y1 = ax1.get_ylim()
print("Best value for number of clusters based on silhouette_score was {}".format(num_clusters))
labels, cluster_centers = Kmeans(X, num_clusters)
for j in range(len(labels)):
plt.scatter(X[j, 0], X[j, 1], c=cm.jet(labels[j]/num_clusters),s=7, marker='o') #Piirretään näytteet kuvaajaan eri väreillä kmeans luokittelutiedon perusteella
for i in range(len(cluster_centers)):
plt.scatter(cluster_centers[i,0], cluster_centers[i,1], marker='x', s=140, linewidth=3, c = cm.jet(i/num_clusters), zorder = 10) #Piirretään myös kuvaajan kmeans algoritmin klusterikeskipisteet
ax1.imshow(backgroundimage, extent=[x0,x1,y0,y1], aspect='auto', alpha=0.8)
plt.title("Clustering with K-means")
plt.xlabel("Latitude")
plt.ylabel("Longitude")
################# DBSCAN ###########################
fig2, ax2 = plt.subplots()
x0,x1 = ax2.get_xlim()
y0,y1 = ax2.get_ylim()
labels = Dbscan(X)
n_clusters = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters for DBSCAN: {}'.format(n_clusters))
unique_labels = set(labels)
for k in range(len(labels)):
if labels[k] == -1:
plt.scatter(X[k, 0], X[k, 1], c=cm.gray(0), s=7,marker = 'o')
else:
plt.scatter(X[k, 0], X[k, 1], c=cm.jet(labels[k]/len(unique_labels)), s=5,marker = 'o')
ax2.imshow(backgroundimage, extent=[x0,x1,y0,y1], aspect='auto', alpha=0.8)
plt.title("Clustering with DBSCAN")
plt.xlabel("Latitude")
plt.ylabel("Longitude")
############## Kokoava hierarkinen klusterointi ###########################
fig3, ax3 = plt.subplots()
x0,x1 = ax3.get_xlim()
y0,y1 = ax3.get_ylim()
labels = Agglomerative_clustering(X, num_clusters)
for k in range(len(labels)):
plt.scatter(X[k, 0], X[k, 1], c=cm.jet(labels[k]/num_clusters),s=7, marker='o')
ax3.imshow(backgroundimage, extent=[x0,x1,y0,y1], aspect='auto', alpha=0.8)
plt.title("Clustering with AgglomerativeClustering")
plt.xlabel("Latitude")
plt.ylabel("Longitude")
plt.show()
def scatterEuler3d(fig, angs, cnt, color_map='cool', hide_zero_marker=False, **extra):
''' Plot the angular histogram using a 3D scatter plot
:Parameters:
fig : Figure
Matplotlib figure handle
angs : array
Array of view angles
cnt : array
Histogram for each view angle
color_map : str
Name of color map
hide_zero_marker : bool
If true, hide the zero projection count marker
extra : dict
Unused keyword arguments
'''
cmap = getattr(cm, color_map)
cnt = cnt.astype(numpy.float)
nhist = cnt.copy()
if nhist.min() != nhist.max():
nhist-=nhist.min()
nhist/=nhist.max()
ax = mplot3d.Axes3D(fig)
data = numpy.zeros((len(angs), 3))
for i in xrange(len(angs)):
if i == 0: print angs[i, :]
data[i, :] = spider_transforms.euler_to_vector(*angs[i, :])
nonzero = numpy.nonzero(cnt)
ax.scatter3D(data[nonzero, 0].ravel(), data[nonzero, 1].ravel(), data[nonzero, 2].ravel(), c=nhist, cmap=cmap)
if not hide_zero_marker:
nonzero = numpy.nonzero(cnt==0)
if len(nonzero) > 0:
ax.scatter3D(data[nonzero, 0].ravel(), data[nonzero, 1].ravel(), data[nonzero, 2].ravel(), color=cm.gray(0.5), marker='x') # @UndefinedVariable
h5 = h5py.File(sys.argv[1])
print(h5.attrs['cluttercount'])
print(h5.attrs['range'])
for i in range(0,150,10):
print(i)
# Thresholded images for the radars
binary = {}
for radar in ['NL60', 'NL61']:
binary[radar] = np.greater(h5[radar],i)
binary['overlap'] = np.product(binary.values(), 0)
for k, v in binary.items():
Image.fromarray(
cm.gray(
colors.Normalize()(v),
bytes=True,
),
).save('threshold_{:03.0f}_{}.png'.format(float(i), k))
rgba = np.zeros(binary['overlap'].shape + (4,), dtype=np.uint8)
mask = ~np.bool8(binary['overlap'])
rgba[..., 0] = binary['NL60'] * mask
rgba[..., 1] = 0
rgba[..., 2] = binary['NL61'] * mask
rgba[..., 3] = np.logical_or(binary['NL60'], binary['NL61'])
Image.fromarray(
rgba * 255,
).save('threshold_{:03.0f}_{}.png'.format(float(i), 'color'))
for val in val_loader:
#X_val = next(iter(val))
X_val = val.type(dtype)
X_val = X_val.to(device)
X_hat_val = decoder(X_val)
X_hat_val = X_hat_val.cpu().view(10 * 9, 160)
X_hat_val = X_hat_val.detach().numpy()
list_of_arrays.append(X_hat_val)
return list_of_arrays
reconstruct(decoder, device, dtype, val_loader)
cnt = 0
for item in list_of_arrays:
#print(item)
im = Image.fromarray(np.uint8(cm.gray(item) * 255))
new_img = im.resize((480, 270))
new_img = new_img.convert('RGB')
if cnt in range(10):
new_img.save(
"C:/Users/Manda/Documents/NCF/Practical_Data_Science/critters/latent_images/0"
+ str(cnt) + ".jpg")
else:
new_img.save(
"C:/Users/Manda/Documents/NCF/Practical_Data_Science/critters/latent_images/"
+ str(cnt) + ".jpg")
cnt += 1
image_folder = 'C:/Users/Manda/Documents/NCF/Practical_Data_Science/critters/latent_images/'
fps = 5
image_files = [
def plot_angles(angs, hist, mapargs, color_map='cool', area_mult=1.0, alpha=0.9, hide_zero_marker=False, use_scale=False, label_view=[], **extra):
''' Plot the angular histogram using a map projection from basemap
.. note::
Basemap uses longitude latitude conventions, but the given angles are in
colatitude, longitude convention.
:Parameters:
angs : array
Array of view angles
cnt : array
Histogram for each view angle
mapargs : dict
Arguments specific to a map projection in basemap
color_map : str
Name of color map
area_mult : float
Scaling factor for size display
alpha : float
Transparency factor
hide_zero_marker : bool
If true, hide the zero projection count marker
use_scale : bool
If true, then display scale for size and color
label_view : list
Label each view with text
extra : dict
Unused keyword arguments
'''
cmap = getattr(cm, color_map)
m = basemap.Basemap(**mapargs)
# Y -> latitude
# Z -> longitude
#longitude, latitude = 90-colatitude
x, y = m(angs[:, 1], 90.0-angs[:, 0])
sel = hist < 1
hist = hist.astype(numpy.float)
s = numpy.sqrt(hist)*area_mult
nhist = hist.copy()
nhist-=nhist.min()
nhist/=nhist.max()
m.drawparallels(numpy.arange(-90.,120.,30.))
m.drawmeridians(numpy.arange(0.,420.,60.))
im = m.scatter(x, y, s=s, marker="o", c=cmap(nhist), alpha=alpha, edgecolors='none')
font_tiny=matplotlib.font_manager.FontProperties()
font_tiny.set_size('xx-small')
if len(label_view) > 0:
for i in label_view:
if i > len(angs):
_logger.warn("Cannot label view: %d when there are only %d views (skipping)"%(i, len(angs)))
continue
ytext = -15 if i%2==0 else 15
pylab.annotate('%d: %.1f,%.1f'%(i+1, angs[i, 0], angs[i, 1]), xy=(x[i],y[i]), xycoords='data',
xytext=(-15, ytext), textcoords='offset points',
arrowprops=dict(arrowstyle="->"), fontproperties =font_tiny
)
if numpy.sum(sel) > 0 and not hide_zero_marker:
im = m.scatter(x[sel], y[sel], numpy.max(s), marker="x", c=cm.gray(0.5))#@UndefinedVariable
if not use_scale:
im = matplotlib.cm.ScalarMappable(cmap=cmap, norm=matplotlib.colors.Normalize())
im.set_array(hist)
m.colorbar(im, "right", size="3%", pad='1%')
else:
fontP=matplotlib.font_manager.FontProperties()
fontP.set_size('small')
inc = len(hist)/10
lines = []
labels = []
idx = numpy.argsort(hist)[::-1]
for j in xrange(0, len(hist), inc):
i=idx[j]
labels.append("%s"%hist[i])
lines.append(matplotlib.lines.Line2D(range(1), range(1), color=cmap(nhist[i]), marker='o', markersize=s[i]/2, linestyle='none', markeredgecolor='white'))
if numpy.sum(sel) > 0 and not hide_zero_marker:
i=idx[len(idx)-1]
labels.append("%s"%hist[i])
lines.append(matplotlib.lines.Line2D(range(1), range(1), color=cm.gray(0.5), marker='x', markersize=numpy.max(s)/5, linestyle='none')) #@UndefinedVariable
pylab.legend(tuple(lines),tuple(labels), numpoints=1, frameon=False, loc='center left', bbox_to_anchor=(1, 0.5), prop = fontP)
plt.scatter(train_x, train_y, color="RED")
# 簡単に学習させてみる
# エポックで回してみる
epoch = 1000
for ep in range(epoch):
# まずはデータをシャッフル
perm = numpy.random.permutation(len(train_x))
train_x = numpy.array(train_x)[perm]
train_y = numpy.array(train_y)[perm]
for (x, y) in zip(train_x, train_y):
# 完璧なChainer風に仕上げた
model.zerograds()
loss = model([x], [y])
loss.backward()
optimizer.update()
# 最終結果をプロット
plot_y = model(plot_x).data
plt.plot(plot_x,
plot_y,
label=str(ep + 1) + " epoch score",
color=cm.gray(1 - ep / epoch))
# 表示
plt.ylim([-5, 5])
plt.legend()
plt.title("learning rate = " + str(alpha))
plt.savefig("images/dim" + str(dim) + ".png")
######
ax = fig.add_subplot(131)
import matplotlib.cm as cm
i = 3 #Picked team number 3
d = max_depth(C[i])
depth_colors = []
for j in C[i].nodes():
depth_percentage = C[i].node[j]['depth']/d
depth_colors.append(cm.gray(depth_percentage))
same_age_colors = []
same_media_colors = []
same_relationship_colors = []
for e in C[i].edges():
if C[i].node[e[0]]['age']=='unknown' or C[i].node[e[1]]['age']=='unknown':
same_age_colors.append('green')
elif C[i].node[e[0]]['age']==C[i].node[e[1]]['age']:
same_age_colors.append('blue')
else:
same_age_colors.append('red')
if C[i].node[e[0]]['source_through']==0 or C[i].node[e[1]]['source_through']==0:
same_media_colors.append('green')
elif C[i].node[e[0]]['source_through']==C[i].node[e[1]]['source_through']:
same_media_colors.append('blue')
# N.B. for a transparent background -> 4th column == 1.
cmap = cm.jet(range(256))
cmap[0, :3] = 1.0
cmap = mpl.colors.ListedColormap(cmap)
try:
plt.style.use(["goose", "goose-latex"])
except OSError:
pass
fig, axes = plt.subplots(figsize=(18, 6), nrows=1, ncols=3)
ax = axes[0]
im = ax.imshow(img,
clim=(0, 1),
cmap=mpl.colors.ListedColormap(cm.gray([0, 255])))
ax.xaxis.set_ticks([0, 20])
ax.yaxis.set_ticks([0, 20])
ax.set_xlim([-0.5, 20.5])
ax.set_ylim([-0.5, 20.5])
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$y$")
ax.set_title(r"image")
div = make_axes_locatable(ax)
cax = div.append_axes("right", size="5%", pad=0.1)
cbar = plt.colorbar(im, cax=cax)
cbar.set_ticks([0, 1])
ax = axes[1]
im = ax.imshow(CD, clim=(0, np.max(C) + 1), cmap=cmap)
ax.xaxis.set_ticks([0, 20])
def plot_multiple_traces(obj, ida):
for i, trace in enumerate(obj.traces[obj.current_position]):
c = cm.gray(i/len(obj.traces[obj.current_position]),1)
obj.ax[ida].plot(obj.t, trace, color=c)
obj.last_position = len(obj.traces) - 1
title='Presidential Approval Rating')
days_func = lambda x: x - x.iloc[0]
pres_41_45['Days in Office']=pres_41_45.groupby('President')['End Date']\
.transform(days_func)
pres_41_45.head()
pres_41_45.groupby('President').head(3)
pres_41_45.dtypes
pres_41_45['Days in Office'] = pres_41_45['Days in Office'].dt.days
pres_41_45['Days in Office'].head()
pres_pivot = pres_41_45.pivot(index='Days in Office',
columns='President',
values='Approving')
pres_pivot.head()
plot_kwargs = dict(figsize=(16, 6),
color=cm.gray([.3, .7]),
style=['-', '--'],
title='Approval Rating')
pres_pivot.loc[:250,
['Donald J. Trump', 'Barack Obama']].ffill().plot(**plot_kwargs)
pres_rm = pres_41_45.groupby('President', sort=False).rolling('90D', on='End Date')['Approving']\
.mean()
pres_rm.head()
styles = ['-.', '-', ':', '-', ':']
colors = [.9, .3, .7, .3, .9]
color = cm.Greys(colors)
title = '90 Day Approval Rating Rolling Average'
plot_kwargs = dict(figsize=(16, 6), style=styles, color=color, title=title)
correct_col_order = pres_41_45.President.unique()
pres_rm.unstack('President')[correct_col_order].plot(**plot_kwargs)
def get_random_plot(name, direc):
"""
Random plot generation method.
Inputs:
name: (string) name of the plot which will be saved.
Outputs:
ax : (matplotlib obj) Matplotlib object of the axes of the plot
fig : (matplotlib obj) Matplotlib object of the figure of the plot
x, y : (list, list) Actuall x and y coordinates of the points.
s : (list) sizes of the points.
categories : (list) categories of the points.
tick_size : (list) Tick size on the plot. [width, length]
axes_x_pos, axes_y_pos: (float, float) Position of the labels of the axis.
"""
# PLOT STYLE
style = random.choice(styles)
plt.style.use(style)
# POINT DISTRIBUTION
distribution = random.choice(point_dist)
# RESOLUTION AND TICK SIZE
dpi = int(dpi_min + np.random.rand(1)[0] * (dpi_max - dpi_min))
figsize = (figsize_min + np.random.rand(2) *
(figsize_max - figsize_min)).astype(int)
tick_size = [(tick_size_width_min + np.random.rand(1)[0] *
(tick_size_width_max - tick_size_width_min)),
(tick_size_length_min + np.random.rand(1)[0] *
(tick_size_length_max - tick_size_length_min))]
tick_size.sort()
fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
# ACTUAL POINTS
points_nb = int(points_nb_min + (np.random.rand(1)[0]**1.5) *
(points_nb_max - points_nb_min))
#print points_nb
x_scale = int(x_min_top + np.random.rand(1)[0] * (x_max_top - x_min_top))
#print x_scale
y_scale = int(y_min_top + np.random.rand(1)[0] * (y_max_top - y_min_top))
#print y_scale
x_scale_range = x_scale + int(np.random.rand(1)[0] * x_scale_range_max)
#print x_scale_range
y_scale_range = y_scale + int(np.random.rand(1)[0] * y_scale_range_max)
#print y_scale_range
x_min = (-np.random.rand(1)[0] + np.random.rand(1)[0]) * 10**(x_scale)
x_max = (-np.random.rand(1)[0] +
np.random.rand(1)[0]) * 10**(x_scale_range)
x_min, x_max = min(x_min, x_max), max(x_min, x_max)
y_min = (-np.random.rand(1)[0] + np.random.rand(1)[0]) * 10**(y_scale)
y_max = (-np.random.rand(1)[0] +
np.random.rand(1)[0]) * 10**(y_scale_range)
y_min, y_max = min(y_min, y_max), max(y_min, y_max)
if distribution == 'uniform':
x = x_min + np.random.rand(points_nb) * (x_max - x_min)
y = y_min + np.random.rand(points_nb) * (y_max - y_min)
elif distribution == 'linear':
x = x_min + np.random.rand(points_nb) * (x_max - x_min)
y = x * (max(y_max, -y_min) / (max(x_max, -x_min))) * random.choice(
[-1.0, 1.0]) + (y_min + np.random.rand(points_nb) *
(y_max - y_min)) * np.random.rand(1)[0] / 2.0
elif distribution == 'quadratic':
x = x_min + np.random.rand(points_nb) * (x_max - x_min)
y = x**2 * (1.0 / (max(x_max, -x_min)))**2 * max(
y_max, -y_min) * random.choice(
[-1.0, 1.0]) + (y_min + np.random.rand(points_nb) *
(y_max - y_min)) * np.random.rand(1)[0] / 2.0
# POINTS VARIATION
nb_points_var = 1 + int(np.random.rand(1)[0] * max_points_variations)
nb_points_var_colors = 1 + int(np.random.rand(1)[0] * nb_points_var)
nb_points_var_markers = 1 + int(
np.random.rand(1)[0] * (nb_points_var - nb_points_var_colors))
nb_points_var_size = max(
1, 1 + nb_points_var - nb_points_var_colors - nb_points_var_markers)
rand_color_number = np.random.rand(1)[0]
if rand_color_number <= 0.5:
colors = cm.rainbow(np.random.rand(nb_points_var_colors))
elif rand_color_number > 0.5 and rand_color_number <= 0.7:
colors = cm.gnuplot(np.random.rand(nb_points_var_colors))
elif rand_color_number > 0.7 and rand_color_number <= 0.8:
colors = cm.copper(np.random.rand(nb_points_var_colors))
else:
colors = cm.gray(np.linspace(0, 0.6, nb_points_var_colors))
s_set = (size_points_min + np.random.rand(nb_points_var_size) *
(size_points_max - size_points_min))**2
markers_subset = list(np.random.choice(markers,
size=nb_points_var_markers))
markers_empty = np.random.rand(1)[0] > 0.75
markers_empty_ratio = random.choice([0.0, 0.5, 0.7])
# BUILDING THE PLOT
s = []
categories = []
cat_dict = {}
index_cat = 0
for _x, _y, in zip(x, y):
s_ = random.choice(s_set)
c_ = random.choice(colors)
m_ = random.choice(markers_subset)
if m_ in markers_with_full and markers_empty:
e_ = np.random.rand(1)[0] > markers_empty_ratio
else:
e_ = False
cat = [s_, c_, m_, e_]
if cat_in_dict(cat, cat_dict) is False:
cat_dict[index_cat] = cat
index_cat += 1
categories.append(cat_in_dict(cat, cat_dict))
s.append(s_)
if e_:
plt.scatter(_x, _y, s=s_, color=c_, marker=m_, facecolors='none')
else:
plt.scatter(_x, _y, s=s_, color=c_, marker=m_)
# PAD BETWEEN TICKS AND LABELS
#labs = [item.get_text() for item in ax.get_xticklabels()]
#print labs
pad_x = max(tick_size[1] + 0.5,
int(pad_min + np.random.rand(1)[0] * (pad_max - pad_min)))
pad_y = max(tick_size[1] + 0.5,
int(pad_min + np.random.rand(1)[0] * (pad_max - pad_min)))
direction_ticks_x = random.choice(direction_ticks)
direction_ticks_y = random.choice(direction_ticks)
# NON-DEFAULT TICKS PROB, WITH THRESHOLD OF 0.6
weid_ticks_prob = np.random.rand(1)[0]
# TICKS STYLE AND LOCATION (X AXIS)
if np.random.rand(1)[0] > 0.5:
axes_x_pos = 1
ax.xaxis.tick_top()
ax.xaxis.set_label_position("top")
if weid_ticks_prob > 0.6:
ax.xaxis.set_tick_params(width=tick_size[0],
length=tick_size[1],
color='black',
pad=pad_x,
direction=direction_ticks_x,
bottom=np.random.rand(1)[0] > 0.5,
top=True)
else:
ax.xaxis.set_tick_params(bottom=np.random.rand(1)[0] > 0.5,
top=True)
if np.random.rand(1)[0] > 0.5:
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_tick_params(bottom=False)
if np.random.rand(1)[0] > 0.5:
axes_x_pos = np.random.rand(1)[0]
ax.spines['top'].set_position(('axes', axes_x_pos))
else:
axes_x_pos = 0
if weid_ticks_prob > 0.6:
ax.xaxis.set_tick_params(width=tick_size[0],
length=tick_size[1],
color='black',
pad=pad_x,
direction=direction_ticks_x,
bottom=True,
top=np.random.rand(1)[0] > 0.5)
else:
ax.xaxis.set_tick_params(bottom=True,
top=np.random.rand(1)[0] > 0.5)
if np.random.rand(1)[0] > 0.5:
ax.spines['top'].set_visible(False)
ax.xaxis.set_tick_params(top=False)
if np.random.rand(1)[0] > 0.5:
axes_x_pos = np.random.rand(1)[0]
ax.spines['bottom'].set_position(('axes', axes_x_pos))
# TICKS STYLE AND LOCATION (Y AXIS)
if np.random.rand(1)[0] > 0.5:
axes_y_pos = 1
ax.yaxis.tick_right()
ax.yaxis.set_label_position("right")
if weid_ticks_prob > 0.6:
ax.yaxis.set_tick_params(width=tick_size[0],
length=tick_size[1],
color='black',
pad=pad_y,
direction=direction_ticks_y,
left=np.random.rand(1)[0] > 0.5,
right=True)
else:
ax.yaxis.set_tick_params(left=np.random.rand(1)[0] > 0.5,
right=True)
if np.random.rand(1)[0] > 0.5:
ax.spines['left'].set_visible(False)
ax.yaxis.set_tick_params(left=False)
if np.random.rand(1)[0] > 0.5:
axes_y_pos = np.random.rand(1)[0]
ax.spines['right'].set_position(('axes', axes_y_pos))
else:
axes_y_pos = 0
if weid_ticks_prob > 0.6:
ax.yaxis.set_tick_params(width=tick_size[0],
length=tick_size[1],
color='black',
pad=pad_y,
direction=direction_ticks_y,
left=True,
right=np.random.rand(1)[0] > 0.5)
else:
ax.yaxis.set_tick_params(left=True,
right=np.random.rand(1)[0] > 0.5)
if np.random.rand(1)[0] > 0.5:
ax.spines['right'].set_visible(False)
ax.yaxis.set_tick_params(right=False)
if np.random.rand(1)[0] > 0.5:
axes_y_pos = np.random.rand(1)[0]
ax.spines['left'].set_position(('axes', axes_y_pos))
# LABEL ROTATION
if np.random.rand(1)[0] > 0.77:
plt.xticks(rotation=int(np.random.rand(1)[0] * 90))
if np.random.rand(1)[0] > 0.77:
plt.yticks(rotation=int(np.random.rand(1)[0] * 90))
# SUB-TICKs
if weid_ticks_prob > 0.6:
color_subtick = random.choice(color_subtick_list)
length_subtick = 0.75 * np.random.rand(1)[0] * tick_size[1]
if np.random.rand(1)[0] > 0.7:
minorLocator = AutoMinorLocator()
ax.xaxis.set_minor_locator(minorLocator)
ax.xaxis.set_tick_params(which='minor',
length=length_subtick,
direction=direction_ticks_x,
color=color_subtick,
bottom=ax.spines['bottom'].get_visible(),
top=ax.spines['top'].get_visible())
if np.random.rand(1)[0] > 0.7:
minorLocator = AutoMinorLocator()
ax.yaxis.set_minor_locator(minorLocator)
ax.yaxis.set_tick_params(which='minor',
length=length_subtick,
direction=direction_ticks_y,
color=color_subtick,
left=ax.spines['left'].get_visible(),
right=ax.spines['right'].get_visible())
# FONT AND SIZE FOR LABELS (tick labels, axes labels and title)
font = random.choice(font_list)
size_ticks = int(tick_label_size_min + np.random.rand(1)[0] *
(tick_label_size_max - tick_label_size_min))
size_axes = int(axes_label_size_min + np.random.rand(1)[0] *
(axes_label_size_max - axes_label_size_min))
size_title = int(title_size_min + np.random.rand(1)[0] *
(title_size_max - title_size_min))
ticks_font = font_manager.FontProperties(fname=font,
style='normal',
size=size_ticks,
weight='normal',
stretch='normal')
axes_font = font_manager.FontProperties(fname=font,
style='normal',
size=size_axes,
weight='normal',
stretch='normal')
title_font = font_manager.FontProperties(fname=font,
style='normal',
size=size_title,
weight='normal',
stretch='normal')
# TEXTS FOR AXIS LABELS AND TITLE
label_x_length = int(axes_label_length_min + np.random.rand(1)[0] *
(axes_label_length_max - axes_label_length_min))
#print label_x_length
label_y_length = int(axes_label_length_min + np.random.rand(1)[0] *
(axes_label_length_max - axes_label_length_min))
title_length = int(title_length_min + np.random.rand(1)[0] *
(title_length_max - title_length_min))
x_label = ("".join([
random.choice(string.ascii_letters + ' ')
for i in range(label_x_length)
])).strip()
#print x_label
y_label = ("".join([
random.choice(string.ascii_letters + ' ')
for i in range(label_y_length)
])).strip()
title = ("".join([
random.choice(string.ascii_letters + ' ')
for i in range(title_length)
])).strip()
plt.xlabel(x_label, fontproperties=axes_font)
plt.ylabel(y_label, fontproperties=axes_font, color='black')
if axes_x_pos == 1:
plt.title(title, fontproperties=title_font, color='black', y=1.1)
else:
plt.title(title, fontproperties=title_font, color='black')
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# GRID
if np.random.rand(1)[0] > 0.7:
plt.grid(b=True,
which='major',
color=random.choice(color_grid),
linestyle=random.choice(linestyles))
# AXIS LIMITS
xmin = min(x)
xmax = max(x)
deltax = 0.05 * abs(xmax - xmin)
plt.xlim(xmin - deltax, xmax + deltax)
ymin = min(y)
ymax = max(y)
deltay = 0.05 * abs(ymax - ymin)
plt.ylim(ymin - deltay, ymax + deltay)
# BACKGROUND AND PATCH COLORS
if np.random.rand(1)[0] > 0.75:
color_bg = (1 - colorbg_transparant_max
) + colorbg_transparant_max * np.random.rand(3)
ax.set_axis_bgcolor(color_bg)
if np.random.rand(1)[0] > 0.75:
color_bg = (1 - colorbg_transparant_max
) + colorbg_transparant_max * np.random.rand(3)
fig.patch.set_facecolor(color_bg)
# MAKE SURE THE PLOT FITS INSIDE THE FIGURES
try:
plt.tight_layout()
plt.savefig("./data/{}/".format(direc) + name,
dpi='figure',
facecolor=fig.get_facecolor())
except RuntimeError:
pass
return ax, fig, x, y, s, categories, tick_size, axes_x_pos, axes_y_pos
cd = CarbonData(
"/home/bahnsen/carbon_nn/carbondata/MixedCarbon/non_relaxed_single0.8_non_relaxed_single0.2/",
random_seed=None,
with_forces=True)
index = 500
positions = cd.data_positions[:index]
E_real = cd.data_energies[:index]
E_nn, outside = ml.get_energy_of_structures(positions)
print(E_real)
print(E_nn)
import matplotlib.cm as cm
colors = cm.gray((1 / 13) * outside)
for i in range(len(E_nn)):
x = E_real[i]
y = E_nn[i]
c = colors[i]
plt.scatter(x, y, color=c, edgecolor='k')
plt.plot([-180, -165], [-180, -165], '--k')
plt.show()
exit()
# ------------
_, step, AME = ml.get_scalar('loss/mean_absolute_error_1')
_, step, AME_test = ml.get_scalar('loss/mean_absolute_error_test_1')
def datevec2datetime(d_vec):
'''
Returns datetime list from a datevec matrix
d_vec = [[y1 m1 d1 H1 M1],[y2 ,2 d2 H2 M2],..]
'''
return [datetime.datetime(d[0], d[1], d[2], d[3], d[4]) for d in d_vec]
dwtDates = np.array(datevec2datetime(DWT['DWT']['dates']))
dwtBmus = DWT['DWT']['bmus']
dwtBMUS = dwtBmus
order = DWT['DWT']['order']
import matplotlib.cm as cm
etcolors = cm.rainbow(np.linspace(0, 1, numDWTs-5))
tccolors = np.flipud(cm.gray(np.linspace(0,1,6)))
dwtcolors = np.vstack((etcolors,tccolors[1:,:]))
repmatDesviacion = np.tile(DWT['PCA']['Desviacion'], (25,1))
repmatMedia = np.tile(DWT['PCA']['Media'], (25,1))
Km_ = np.multiply(DWT['KMA']['centroids'],repmatDesviacion) + repmatMedia
[mK, nK] = np.shape(Km_)
zOuterInd = np.where((np.round(2 * xFRFtrough) / 2 == bathyX))
zOuterTrough[tt] = bathy[zOuterInd[0]]
#barPatch = mpatches.Patch(color='red', label='sandbar')
#troughPatch = mpatches.Patch(color='blue', label='trough')
fig2 = plt.figure(figsize=(8, 8))
plt.plot(time, innerBar, 'bo')
plt.plot(time, outerBar, 'ro')
plt.show()
fig11, ax11 = plt.subplots(2, 1)
#ax10 = fig.add_subplot(111)
#ax10.set_prop_cycle(plt.cycler('color', plt.cm.jet(np.linspace(0, 1, numClusters))))
import matplotlib.cm as cm
colors = cm.rainbow(np.linspace(0, 1, numClusters))
colors2 = cm.gray(np.linspace(0, 1, numClusters))
for i in range(numClusters):
bmuIndex = np.where((KMA.bmus == sortedPeakInd[i]))
ax11[0].scatter(time[bmuIndex],
outerBar[bmuIndex],
label=time[i],
color=colors[i])
ax11[1].scatter(time[bmuIndex], innerBar[bmuIndex], color=colors[i])
ax11[0].set_ylabel('xFRF')
ax11[1].set_ylabel('xFRF')
plt.show()
fig3 = plt.figure(figsize=(10, 10))
gs = gridspec.GridSpec(10, 10, wspace=0.0, hspace=0.0)
CMAP_JET = glumpy.colormap.Colormap(
(value[0], (r[0], g[0], b[0])), (value[16], (r[16], g[16], b[16])),
(value[32], (r[32], g[32], b[32])), (value[48], (r[48], g[48], b[48])),
(value[64], (r[64], g[64], b[64])), (value[96], (r[96], g[96], b[96])),
(value[112], (r[112], g[112], b[112])), (value[128],
(r[128], g[128], b[128])),
(value[144], (r[144], g[144], b[144])), (value[160],
(r[160], g[160], b[160])),
(value[176], (r[176], g[176], b[176])), (value[192],
(r[192], g[192], b[192])),
(value[208], (r[208], g[208], b[208])), (value[224],
(r[224], g[224], b[224])),
(value[240], (r[240], g[240], b[240])), (value[255],
(r[255], g[255], b[255])))
jet = cm.gray(range(0, 256))
r, g, b, a = jet[:, 0], jet[:, 1], jet[:, 2], jet[:, 3]
value = np.linspace(0.0, 1.0, 256, endpoint=True)
CMAP_GRAY = glumpy.colormap.Colormap(
(value[0], (r[0], g[0], b[0])), (value[16], (r[16], g[16], b[16])),
(value[32], (r[32], g[32], b[32])), (value[48], (r[48], g[48], b[48])),
(value[64], (r[64], g[64], b[64])), (value[96], (r[96], g[96], b[96])),
(value[112], (r[112], g[112], b[112])), (value[128],
(r[128], g[128], b[128])),
(value[144], (r[144], g[144], b[144])), (value[160],
(r[160], g[160], b[160])),
(value[176], (r[176], g[176], b[176])), (value[192],
(r[192], g[192], b[192])),
(value[208], (r[208], g[208], b[208])), (value[224],
(r[224], g[224], b[224])),
(value[240], (r[240], g[240], b[240])), (value[255],
def compute(self):
from matplotlib import cm
# make a copy for changes
data = self.getData('in').copy()
# get extra dimension parameters and modify data
dimfunc = self.getVal('Extra Dimension')
dimval = self.getVal('Slice/Tile Dimension')
if data.ndim == 3 and dimfunc < 2:
if dimfunc == 0: # slice data
slval = self.getVal('Slice') - 1
if dimval == 0:
data = data[slval, ...]
elif dimval == 1:
data = data[:, slval, :]
else:
data = data[..., slval]
else: # tile data
ncol = self.getVal('# Columns')
nrow = self.getVal('# Rows')
# add some blank tiles
data = np.rollaxis(data, dimval)
N, xres, yres = data.shape
N_new = ncol * nrow
pad_vals = ((0, N_new - N), (0, 0), (0, 0))
data = np.pad(data, pad_vals, mode='constant')
# from http://stackoverflow.com/a/13990648/333308
data = np.reshape(data, (nrow, ncol, xres, yres))
data = np.swapaxes(data, 1, 2)
data = np.reshape(data, (nrow * xres, ncol * yres))
# Read in parameters, make a little floor:ceiling adjustment
gamma = self.getVal('Gamma')
lval = self.getAttr('L W F C:', 'val')
cval = self.getVal('Complex Display')
if 'Complex Display' in self.widgetEvents():
if cval == 4:
self.setAttr('Color Map',
buttons=self.complex_cmaps,
collapsed=self.getAttr('Color Map', 'collapsed'),
val=0)
# elif self.getAttr('Color Map', 'buttons') != self.real_cmaps:
# there is no "get_buttons" method, so for now this will reset the
# colormap whenever "Complex Display" is changed
# this could/will be added in a future framework update
else:
self.setAttr('Color Map',
buttons=self.real_cmaps,
collapsed=self.getAttr('Color Map', 'collapsed'),
val=0)
cmap = self.getVal('Color Map')
sval = self.getVal('Scalar Display')
zval = self.getVal('Zero Ref')
fval = self.getVal('Fix Range')
rmin = self.getVal('Range Min')
rmax = self.getVal('Range Max')
flor = 0.01 * lval['floor']
ceil = 0.01 * lval['ceiling']
if ceil == flor:
if ceil == 1.:
flor = 0.999
else:
ceil += 0.001
# SHOW COMPLEX DATA
if np.iscomplexobj(data) and cval == 4:
mag = np.abs(data)
phase = np.angle(data, deg=True)
# normalize the mag
data_min = 0.
if fval:
data_max = rmax
else:
data_max = mag.max()
self.setAttr('Range Max', val=data_max)
data_range = data_max - data_min
dmask = np.ones(data.shape)
new_min = data_range * flor + data_min
new_max = data_range * ceil + data_min
mag = np.clip(mag, new_min, new_max)
if new_max > new_min:
if (
gamma == 1
): # Put in check for gamma=1, the common use case, just to save time
mag = (mag - new_min) / (new_max - new_min)
else:
mag = pow((mag - new_min) / (new_max - new_min), gamma)
else:
mag = np.ones(mag.shape)
# ADD BORDERS
edgpix = self.getVal('Edge Pixels')
blkpix = self.getVal('Black Pixels')
if (edgpix + blkpix) > 0:
# new image will be h2 x w2
# frame defines edge pixels to paint with phase table
h, w = mag.shape
h2 = h + 2 * (edgpix + blkpix)
w2 = w + 2 * (edgpix + blkpix)
mag2 = np.zeros((h2, w2))
phase2 = np.zeros((h2, w2))
frame = np.zeros((h2, w2)) == 1
frame[0:edgpix, :] = frame[h2 - edgpix:h2, :] = True
frame[:, 0:edgpix] = frame[:, w2 - edgpix:w2] = True
mag2[edgpix + blkpix:edgpix + blkpix + h,
edgpix + blkpix:edgpix + blkpix + w] = mag
mag2[frame] = 1
phase2[edgpix + blkpix:edgpix + blkpix + h,
edgpix + blkpix:edgpix + blkpix + w] = phase
xloc = np.tile(np.linspace(-1., 1., w2), (h2, 1))
yloc = np.transpose(np.tile(np.linspace(1., -1., h2), (w2, 1)))
phase2[frame] = np.degrees(np.arctan2(yloc[frame],
xloc[frame]))
mag = mag2
phase = phase2
# now colorize!
if cmap == 0: # HSV
phase_cmap = cm.hsv
elif cmap == 1: # HSL
try:
import seaborn as sns
except:
self.log.warn(
"Seaborn (required for HSL map) not available! Falling back on HSV."
)
phase_cmap = cm.hsv
else: # from http://stackoverflow.com/a/34557535/333308
import matplotlib.colors as col
hlsmap = col.ListedColormap(sns.color_palette("hls", 256))
phase_cmap = hlsmap
elif cmap == 2: #HUSL
try:
import seaborn as sns
except:
self.log.warn(
"Seaborn (required for HUSL map) not available! Falling back on HSV."
)
phase_cmap = cm.hsv
else: # from http://stackoverflow.com/a/34557535/333308
import matplotlib.colors as col
huslmap = col.ListedColormap(sns.color_palette(
"husl", 256))
phase_cmap = huslmap
elif cmap == 3: # coolwarm
phase_cmap = cm.coolwarm
mag_norm = mag
phase_norm = (phase + 180) / 360
# phase shift to match old look better
if cmap != 3:
phase_norm = (phase_norm - 1 / 3) % 1
colorized = 255 * cm.gray(mag_norm) * phase_cmap(phase_norm)
red = colorized[..., 0]
green = colorized[..., 1]
blue = colorized[..., 2]
alpha = colorized[..., 3]
# DISPLAY SCALAR DATA
elif dimfunc != 2:
if np.iscomplexobj(data):
if cval == 0: # Real
data = np.real(data)
elif cval == 1: # Imag
data = np.imag(data)
elif cval == 2: # Mag
data = np.abs(data)
elif cval == 3: # Phase
data = np.angle(data, deg=True)
if sval == 1: # Mag
data = np.abs(data)
elif sval == 2: # Sign
sign = np.sign(data)
data = np.abs(data)
# normalize the data
if fval:
data_min = rmin
data_max = rmax
else:
data_min = data.min()
data_max = data.max()
if sval != 2:
if zval == 1:
data_min = 0.
elif zval == 2:
data_max = max(abs(data_min), abs(data_max))
data_min = -data_max
elif zval == 3:
data_max = 0.
data_range = data_max - data_min
self.setAttr('Range Min', val=data_min)
self.setAttr('Range Max', val=data_max)
else:
data_min = 0.
data_max = max(abs(data_min), abs(data_max))
data_range = data_max
self.setAttr('Range Min', val=-data_range)
self.setAttr('Range Max', val=data_range)
dmask = np.ones(data.shape)
new_min = data_range * flor + data_min
new_max = data_range * ceil + data_min
data = np.minimum(np.maximum(data, new_min * dmask),
new_max * dmask)
if new_max > new_min:
if (
gamma == 1
): # Put in check for gamma=1, the common use case, just to save time
data = 255. * (data - new_min) / (new_max - new_min)
else:
data = 255. * pow(
(data - new_min) / (new_max - new_min), gamma)
else:
data = 255. * np.ones(data.shape)
if sval != 2: #Not Signed Data (Pass or Mag)
# Show based on a color map
if cmap == 0: # Grayscale
red = green = blue = np.uint8(data)
alpha = 255. * np.ones(blue.shape)
else:
rd = np.zeros(data.shape)
gn = np.zeros(data.shape)
be = np.zeros(data.shape)
zmask = np.zeros(data.shape)
fmask = np.ones(data.shape)
if cmap == 1: # IceFire
hue = 4. * (data / 256.)
hindex0 = hue < 1.
hindex1 = np.logical_and(hue >= 1., hue < 2.)
hindex2 = np.logical_and(hue >= 2., hue < 3.)
hindex3 = np.logical_and(hue >= 3., hue < 4.)
be[hindex0] = hue[hindex0]
gn[hindex0] = zmask[hindex0]
rd[hindex0] = zmask[hindex0]
gn[hindex1] = (hue - 1.)[hindex1]
rd[hindex1] = (hue - 1.)[hindex1]
be[hindex1] = fmask[hindex1]
gn[hindex2] = fmask[hindex2]
rd[hindex2] = fmask[hindex2]
be[hindex2] = (3. - hue)[hindex2]
rd[hindex3] = fmask[hindex3]
gn[hindex3] = (4. - hue)[hindex3]
be[hindex3] = zmask[hindex3]
elif cmap == 2: # Fire
hue = 4. * (data / 256.)
hindex0 = hue < 1.
hindex1 = np.logical_and(hue >= 1., hue < 2.)
hindex2 = np.logical_and(hue >= 2., hue < 3.)
hindex3 = np.logical_and(hue >= 3., hue < 4.)
be[hindex0] = hue[hindex0]
rd[hindex0] = zmask[hindex0]
gn[hindex0] = zmask[hindex0]
be[hindex1] = (2. - hue)[hindex1]
rd[hindex1] = (hue - 1.)[hindex1]
gn[hindex1] = zmask[hindex1]
rd[hindex2] = fmask[hindex2]
gn[hindex2] = (hue - 2.)[hindex2]
be[hindex2] = zmask[hindex2]
rd[hindex3] = fmask[hindex3]
gn[hindex3] = fmask[hindex3]
be[hindex3] = (hue - 3.)[hindex3]
elif cmap == 3: # Hot
hue = 3. * (data / 256.)
hindex0 = hue < 1.
hindex1 = np.logical_and(hue >= 1., hue < 2.)
hindex2 = np.logical_and(hue >= 2., hue < 3.)
rd[hindex0] = hue[hindex0]
be[hindex0] = zmask[hindex0]
gn[hindex0] = zmask[hindex0]
gn[hindex1] = (hue - 1.)[hindex1]
rd[hindex1] = fmask[hindex1]
be[hindex1] = zmask[hindex1]
rd[hindex2] = fmask[hindex2]
gn[hindex2] = fmask[hindex2]
be[hindex2] = (hue - 2.)[hindex2]
if cmap == 4: # Hot2, from ASIST (http://asist.umin.jp/index-e.htm)
rindex0 = data < 20.0
rindex1 = np.logical_and(data >= 20.0, data <= 100.0)
rindex2 = np.logical_and(data > 100.0, data < 128.0)
rindex3 = np.logical_and(data >= 128.0, data <= 191.0)
rindex4 = data > 191.0
rd[rindex0] = data[rindex0] * 4.0
rd[rindex1] = 80.0 - (data[rindex1] - 20.0)
rd[rindex3] = (data[rindex3] - 128.0) * 4.0
rd[rindex4] = data[rindex4] * 0.0 + 255.0
rd = rd / 255.0
gindex0 = data < 45.0
gindex1 = np.logical_and(data >= 45.0, data <= 130.0)
gindex2 = np.logical_and(data > 130.0, data < 192.0)
gindex3 = data >= 192.0
gn[gindex1] = (data[gindex1] - 45.0) * 3.0
gn[gindex2] = data[gindex2] * 0.0 + 255.0
gn[gindex3] = 252.0 - (data[gindex3] - 192.0) * 4.0
gn = gn / 255.0
bindex0 = (data < 1.0)
bindex1 = np.logical_and(data >= 1.0, data < 86.0)
bindex2 = np.logical_and(data >= 86.0, data <= 137.0)
bindex3 = data > 137.0
be[bindex1] = (data[bindex1] - 1.0) * 3.0
be[bindex2] = 255.0 - (data[bindex2] - 86.0) * 5.0
be = be / 255.0
elif cmap == 5: # BGR
hue = 4. * (data / 256.)
hindex0 = hue < 1.
hindex1 = np.logical_and(hue >= 1., hue < 2.)
hindex2 = np.logical_and(hue >= 2., hue < 3.)
hindex3 = np.logical_and(hue >= 3., hue < 4.)
be[hindex0] = hue[hindex0]
gn[hindex0] = zmask[hindex0]
rd[hindex0] = zmask[hindex0]
gn[hindex1] = (hue - 1.)[hindex1]
rd[hindex1] = zmask[hindex1]
be[hindex1] = fmask[hindex1]
gn[hindex2] = fmask[hindex2]
rd[hindex2] = (hue - 2.)[hindex2]
be[hindex2] = (3. - hue)[hindex2]
rd[hindex3] = fmask[hindex3]
gn[hindex3] = (4. - hue)[hindex3]
be[hindex3] = zmask[hindex3]
blue = np.uint8(255. * rd)
red = np.uint8(255. * be)
green = np.uint8(255. * gn)
alpha = np.uint8(255. * np.ones(blue.shape))
else: #Signed data, positive numbers green, negative numbers magenta
red = np.zeros(data.shape)
green = np.zeros(data.shape)
blue = np.zeros(data.shape)
red[sign <= 0] = data[sign <= 0]
blue[sign <= 0] = data[sign <= 0]
green[sign >= 0] = data[sign >= 0]
red = red.astype(np.uint8)
green = green.astype(np.uint8)
blue = blue.astype(np.uint8)
alpha = np.uint8(data)
# DISPLAY RGB image
else:
if data.shape[-1] > 3:
red = data[:, :, 0].astype(np.uint8)
green = data[:, :, 1].astype(np.uint8)
blue = data[:, :, 2].astype(np.uint8)
if (data.ndim == 3 and data.shape[-1] == 4):
alpha = data[:, :, 3].astype(np.uint8)
else:
alpha = 255. * np.ones(blue.shape)
else:
self.log.warn("input veclen of " + str(data.shape[-1]) +
" is incompatible")
return 1
h, w = red.shape[:2]
image1 = np.zeros((h, w, 4), dtype=np.uint8)
image1[:, :, 0] = red
image1[:, :, 1] = green
image1[:, :, 2] = blue
image1[:, :, 3] = alpha
format_ = QtGui.QImage.Format_RGB32
image = QtGui.QImage(image1.data, w, h, format_)
#send the RGB values to the output port
imageTru = np.zeros((h, w, 4), dtype=np.uint8)
imageTru[:, :, 0] = red
imageTru[:, :, 1] = green
imageTru[:, :, 2] = blue
imageTru[:, :, 3] = alpha
image.ndarray = imageTru
if image.isNull():
self.log.warn("Image Viewer: cannot load image")
self.setAttr('Viewport:', val=image)
self.setData('out', imageTru)
return 0
import matplotlib.cm as cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
try:
plt.style.use(["goose", "goose-latex"])
except OSError:
pass
cmap = mpl.colors.ListedColormap(
np.array([[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 1.0]], dtype="float64")
)
fig, axes = plt.subplots(figsize=(18, 12), nrows=2, ncols=3)
ax = axes[0, 0]
im = ax.imshow(img, clim=(0, 1), cmap=mpl.colors.ListedColormap(cm.gray([0, 255])))
D = ax.imshow(W, clim=(0, 1), cmap=cmap)
ax.xaxis.set_ticks([0, 500])
ax.yaxis.set_ticks([0, 500])
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$y$")
ax.set_title(r"$\mathcal{I}$ (black/white) + $\mathcal{W}$ (red)")
div = make_axes_locatable(ax)
cax = div.append_axes("right", size="5%", pad=0.1)
cbar = plt.colorbar(im, cax=cax)
cbar.set_ticks([0, 1])
ax = axes[0, 1]
im = ax.imshow(WI, clim=(0, 1), cmap="jet", extent=(-50, 50, -50, 50))
ax.xaxis.set_ticks([-50, 0, +50])
ax.yaxis.set_ticks([-50, 0, +50])
scaled.append(new_value)
#set scaled colour values
cmap_dem = make_colormap([
c('#010071'), scaled[0],
c('#010071'),
c('#1f78b4'), scaled[1],
c('#1f78b4'),
c("#89e2ff"), scaled[2],
c("#0b6e24"),
c("#dfc485"), scaled[3],
c("#dfc485"),
c('#8d7c58'), scaled[4],
c("#8d7c58"),
c("#a49fa3"), scaled[5],
c("#a49fa3")
])
im = Image.fromarray(np.uint8(cmap_dem(rs_dem) * 255))
#create hillshade
ls = LightSource(azdeg=315, altdeg=45)
hs = ls.hillshade(read_dem, vert_exag=0.04, fraction=1.35)
hsimg = Image.fromarray(np.uint8(cm.gray(hs) * 255))
#blend hillshade to RGB image using multiplication
blend = ImageChops.multiply(im, hsimg)
#write image to disc
blend.save('OUTFILEPATH.tif')
def plot_spectra_grid(file_set,protein,ligands,ligand):
grid = len(protein) + len(ligand)
# pick the correct file
proteins = file_set.keys()
index = ligands.index(ligand)
file = file_set[protein][index]
# pick a title
title = "%s - %s" %(protein, ligand)
# make a dataframe
df = xml2df(file)
# plot the spectra
fig = plt.figure();
ax = df['fluorescence'].iloc[:,12].plot(ylim=(0,100000),legend=False, linewidth=4,color='m');
ax.axvline(x=480,color='0.7',linestyle='--');
for i in range(11):
#s = df['fluorescence'].iloc[:,i].plot(ylim=(0,100000),linewidth=3,c=cm.hsv(i*15), ax = ax, title=title);
df['fluorescence'].iloc[:,i].plot(ylim=(0,100000),linewidth=3,c=cm.hsv(i*15), ax = ax);
df['fluorescence'].iloc[:,11+i].plot(ylim=(0,100000),legend=False, linewidth=4,c=cm.gray(i*15+50), ax = ax, fontsize =20);
sns.despine()
plt.xlim(320,600)
plt.yticks([])
plt.xlabel('wavelength (nm)', fontsize=20)
plt.tight_layout();
plt.savefig('%s.eps'%title, type='eps', dpi=1000)
def cb_mouse_motion(self, event):
"""
Callback to process a mouse movement event.
If the group selection option is enabled, then any points with
Y-coordinate less than the cursor's Y-coordinate will be marked in a
different opacity level, but not highlighted. If the user clicks with
the mouse, then the points will be highlighted, but this event is
processed in another method.
This method also processes tooltip-related events when the group
selection is disabled. If the user hovers the mouse cursor over a data
point, then the name associated to that point will be shown in a
tooltip.
Parameters
----------
event: matplotlib.backend_bases.MouseEvent
Data about the event.
"""
# We remove the horizonal line here (if any), regardless of the mouse
# cursor position. We also restore the lines colors.
if self._hthresh_line:
for i, l in enumerate(self.axes.lines):
if l == self._hthresh_line:
del self.axes.lines[i]
break
self._hthresh_line = None
for i, line in enumerate(self.axes.lines):
if self._is_normal_curve_idx(i):
line.set_color(self._curves_colors[i])
if self._ts_line:
for i, l in enumerate(self.axes.lines):
if l == self._ts_line:
del self.axes.lines[i]
break
# Restoring the lines' widths.
if self._plotted_series:
for art in self._plotted_series:
if not art:
continue
art[0].set_linewidth(self._plot_params['linewidth'])
self.draw()
if event.xdata is None or event.ydata is None or self._rank_series is None:
return True
self._ts_line = self.axes.axvline(x=event.xdata, c='b', ls='--')
# If the group selection is enabled, we show a preview of all curves
# that will be highlighted should the user click the mouse button.
if self.group_selection_enabled:
ts = int(event.xdata - self.time_range[0] + 0.5)
if self.rank_inverted:
for i, series in enumerate(self._rank_series):
if series[ts] < event.ydata and self._is_normal_curve_idx(
i):
self.axes.lines[i].set_color(cm.gray(200))
else:
for i, series in enumerate(self._rank_series):
if series[ts] > event.ydata and self._is_normal_curve_idx(
i):
self.axes.lines[i].set_color(cm.gray(200))
self._hthresh_line = self.axes.axhline(y=event.ydata,
c='b',
linewidth=2)
else:
hover_idx = None
for i, art in enumerate(self._plotted_series):
if not art:
continue
contains, _ = art[0].contains(event)
if contains:
art[0].set_linewidth(self._plot_params['linewidth'] * 2)
if not self.curvenames or i > len(self.curvenames):
return False
hover_idx = i
break
if hover_idx is not None:
palette = QPalette()
palette.setColor(QPalette.ToolTipBase, QColor(252, 243, 207))
palette.setColor(QPalette.ToolTipText, QColor(0, 0, 0))
QToolTip.setPalette(palette)
QToolTip.setFont(QFont('Arial', 14, QFont.Bold))
pos = self.mapToGlobal(QPoint(event.x,
self.height() - event.y))
QToolTip.showText(pos, '{}'.format(self.curvenames[hover_idx]))
else:
QToolTip.hideText()
self.update()
if self._cb_notify_tooltip:
self._cb_notify_tooltip(self.name, hover_idx)
self.draw()
def bg2RGBA(bg):
img = bg / 4 + 0.25 # rescale to range of approx. 0..1 float
img = np.uint8(cm.gray(img) * 255)
return img
def summary_bar_plot(summary, sig_ref=None, sig_thresh='>0.05', ymin=0.5,
show_data=[0,1,2], legend=False):
"""Bar plot of summary statistics with SEM errorbars
Arguments:
summary -- summary results object
ymin -- y-axis minimum
show_data -- which data to show: list of integers, where 0=remapping,
1=rate_remapping, and 2=turnover
sig_ref -- index number of condition that will be reference for determining
statistical significance
sig_thresh -- string indicating threshold relationship for placing a
significance mark (star/asterisk): either '<' or '>' followed by float
threshold value
legend -- whether to display a legend
"""
from pylab import figure, axes, rcParams
from matplotlib import cm
# Plot parameters
fig_size = 9, 6
show_data = array(show_data)
num_data = len(show_data)
bar_w = 1/float(num_data+1)
# Significance parameters
sig_less = True
if sig_thresh[0] == '>':
sig_less = False
sig_value = float(sig_thresh[1:])
if sig_ref is not None:
print '* Significance indicates p%s%.5f.'%(sig_thresh[0], sig_value)
# Create the figure and axes
old_fig_size = rcParams['figure.figsize']
rcParams['figure.figsize'] = fig_size
f = figure()
f.set_size_inches(fig_size)
f.suptitle('Remapping Sample Summary Data')
ax = axes()
# Create the bar data
left = []
height = []
yerr = []
xticklabels = []
sig_x = []
sig_y = []
c = 0
for i,row in enumerate(summary['data']):
if not summary['visible'][i]:
continue
if num_data == 1:
left.extend([c-0.5*bar_w])
elif num_data == 2:
left.extend([c-bar_w, c])
elif num_data == 3:
left.extend([c-1.5*bar_w, c-0.5*bar_w, c+0.5*bar_w])
height.extend(list(row[show_data]))
yerr.extend(list(row[3+show_data]/1.96))
xticklabels.append(summary['labels'][i])
if sig_ref is not None and sig_ref <> i:
for n in xrange(num_data):
sig_mark = False
if sig_less:
if summary['pvals'][show_data[n], sig_ref, i] < sig_value:
sig_mark = True
else:
if summary['pvals'][show_data[n], sig_ref, i] >= sig_value:
sig_mark = True
if sig_mark:
sig_x.append(left[n-num_data]+0.5*bar_w)
sig_y.append(height[n-num_data]+yerr[n-num_data]+0.02)
c += 1
# Create the bar chart and legend
bar_cols = cm.gray(([0.25, 0.6, 0.8][:num_data])*c)
bar_h = ax.bar(left, height, width=bar_w, yerr=yerr,
ec='k', color=bar_cols, linewidth=0, ecolor='k', capsize=3, aa=False)
if legend:
labels = ['Remapping', 'Rate Remapping', 'Turnover']
data_labels = [labels[i] for i in show_data]
ax.legend(bar_h[:num_data], data_labels)
if sig_ref is not None:
ax.plot(sig_x, sig_y, 'k*', ms=8)
ax.hlines(1.0, xmin=-0.5, xmax=c-0.5, linestyle=':', color='k')
ax.set_xlim(-0.5, c-0.5)
ax.set_ylim(ymin, 1.1)
ax.set_xticks(arange(c))
ax.set_xticklabels(xticklabels)
rcParams['figure.figsize'] = old_fig_size
return f
def _make_gray(self, data):
data = data.astype(np.float) - np.nanmin(data)
data /= np.nanmax(data)
return (255 * plt_cm.gray(data)).astype('uint8')
def make_gif(h5name,dielectric_bg = True,data_name = None,normalize = False):
"""makes a gif animation of the real part of the field in the file h5name.
if dielectric_bg is a h5 name, will also plot the dielectric landscape. in case dielectric_bg = True
will look for a file dielectric.h5 as well"""
f = h5py.File(h5name)
if data_name == None:
data_name = f.keys()[-1]
d = f[data_name]
if normalize:
d = d/abs(array(d).flatten()).max()
if dielectric_bg == True:
where = "./" + os.path.dirname(h5name) + "/dielectric.h5"
if os.path.exists(where):
dielectric_bg = where
else:
dielectric_bg = None
if dielectric_bg != None:
diel = h5py.File(dielectric_bg)
diel_val = transpose(array(diel["eps"]))
imdiel = diel_val
diel.close()
else:
imdiel = transpose(ones(shape(d[:,:,0])))
#m = max(imdiel.flatten())
di = uint8(dstack((imdiel,imdiel,imdiel,imdiel)))
images2gif.writeGif(h5name[:-3] + ".gif",[Image.fromarray(cm.jet(0.5+transpose(d[:,:,i]), bytes=True)*(di==1.0) + cm.gray(0.5+transpose(d[:,:,i]) , bytes=True)*(di!=1.0)) for i in range(shape(d)[-1])])
f.close()
layer_index = utils.find_layer_idx(model, 'visualized_layer')
# Swap softmax with linear
model.layers[layer_index].activation = activations.linear
model = utils.apply_modifications(model)
# Numbers to visualize
indices_to_visualize = [0, 12, 38, 83, 112, 74, 190]
# Visualize
for index_to_visualize in indices_to_visualize:
input_image = input_test[index_to_visualize]
input_class = np.argmax(target_test[index_to_visualize])
# Matplotlib preparations
fig, axes = plt.subplots(1, 3)
# Generate visualization
visualization = visualize_cam(model,
layer_index,
filter_indices=input_class,
seed_input=input_image)
axes[0].imshow(input_image[..., 0], cmap='gray')
axes[0].set_title('Input')
axes[1].imshow(visualization)
axes[1].set_title('Grad-CAM')
heatmap = np.uint8(cm.jet(visualization)[..., :3] * 255)
original = np.uint8(cm.gray(input_image[..., 0])[..., :3] * 255)
axes[2].imshow(overlay(heatmap, original))
axes[2].set_title('Overlay')
fig.suptitle(f'MNIST target = {input_class}')
plt.show()
def flow_gray(data):
v = ((data > 0)*(data < 1)).reshape(list(data.shape) + [1])*cm.gray(data)
v += (data == 0).reshape(list(data.shape) + [1]) * np.array([0, 1., 0, 0]).reshape(list(np.ones(data.ndim) + [3]))
v += (data == 1).reshape(list(data.shape) + [1]) * np.array([1., 0, 1., 0]).reshape(list(np.ones(data.ndim) + [3]))
return v
