def olr_r(): n_colors=36 olr_map =[cm.gist_yarg(cc) for cc in range(5*n_colors,0,-5)] olr_coltbl = ListedColormap(olr_map,name = 'orl_coltbl',N=n_colors) #olr_coltbl.set_bad(color='gray') olr_coltbl.set_bad(color= olr_coltbl.colors[0]) return olr_coltbl
def olr_r():
n_colors = 36
olr_map = [cm.gist_yarg(cc) for cc in range(5 * n_colors, 0, -5)]
olr_coltbl = ListedColormap(olr_map, name='orl_coltbl', N=n_colors)
#olr_coltbl.set_bad(color='gray')
olr_coltbl.set_bad(color=olr_coltbl.colors[0])
return olr_coltbl
def load_colormap(filename):
"""Load a colormap defined in a text file
filename is the .txt file name located in the
data/ path, not the full path.
list_available_colormaps() lists the available color tables
"""
try:
if rank == 0:
vals = np.loadtxt(os.path.join(DATA_PATH, filename))/255.0
n_vals = len(vals)
else:
vals = None
if par.use_mpi:
vals = mpi.COMM_WORLD.bcast(vals, root=0)
colormap = ListedColormap(vals)
except exceptions.IOError:
print("Cannot load colormap, available colormaps: \n* " + "\n* ".join(list_available_colormaps()))
raise
# color of missing pixels
colormap.set_bad("gray")
# color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colormap)
colormap.set_under("white")
return colormap
def beta_plot(fig, ax, data, params, direction, depth=None, cax=None, fontsize=None, mccomas=False, limits=None, refinement=0, titlesize=25, labelsize=20, ticklabelsize=15, skip_labeling=False, cbar_orientation='vertical'):
X, Y, dslice = plot_setup(ax, data, params, direction, depth, fontsize=fontsize, mccomas=mccomas, titlesize=titlesize, labelsize=labelsize, ticklabelsize=ticklabelsize, skip_labeling=skip_labeling)
if limits is None:
limits = (-1,2)
# Setup custom colorbar
levels = np.logspace(limits[0] - .5,limits[1] + .5, (limits[1]-limits[0]+1)*(refinement+1)+1)
ticks = np.logspace(limits[0],limits[1],limits[1]-limits[0]+1)
viridis = cm.get_cmap('viridis', len(levels)+2)
cmap = ListedColormap(viridis.colors[1:-1],'beta_cmap')
cmap.set_over(viridis.colors[-1])
cmap.set_bad(viridis.colors[0])
# Catch the stupid warnings I don't care about
with warnings.catch_warnings():
warnings.simplefilter('ignore')
mappable = ax.contourf(X.T, Y.T, dslice, levels=levels, norm=MyLogNorm(),
cmap=cmap, extend='both',
vmin=levels[0], vmax=levels[-1])
if cax != 'None':
cb = fig.colorbar(mappable, ax=ax, cax=cax, orientation=cbar_orientation)
cb.set_ticks(ticks)
cb.set_ticklabels(ticks)
return mappable, X, Y, dslice
def planck_color_map(self):
'''
Itab = [ 0, 13, 26, 39, 52, 65, 76, 77, 88,
101, 114, 127, 140, 153, 166, 179, 192,
205, 218, 231, 255]
Rtab = [ 0, 10, 30, 80, 191, 228, 241, 241, 245,
248, 249.9, 242.25,204, 165, 114, 127.5, 178.5,
204, 229.5, 242.25, 252.45]
Gtab = [ 0, 20, 184, 235, 239, 240, 241, 241, 240,
235, 204, 153, 76.5, 32, 0, 127.5, 178.5,
204, 229.5, 242.25, 252.45]
Btab = [ 255, 255, 255, 255, 250, 245, 212, 212, 175,
130, 38.25, 12.75, 0, 32, 32, 153, 204,
229.5, 242.25, 249.9, 255]
ncolors = 256
ii = np.arange(ncolors, dtype=np.float32)
R = np.interp(ii, Itab, Rtab)
G = np.interp(ii, Itab, Rtab)
B = np.interp(ii, Itab, Rtab)
'''
cmap = ListedColormap(np.loadtxt("Planck_FreqMap_RGB.txt")/255.)
cmap.set_bad("darkgray")
cmap.set_under("white")
return cmap
def planck_color_map(self):
cmap = ListedColormap(np.loadtxt("Planck_FreqMap_RGB.txt")/255.)
cmap.set_bad("darkgray")
cmap.set_under("white")
return cmap
def get_planck_cmap():
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt")/255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under("white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
return colombi1_cmap
def get_planck_cmap():
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(
np.loadtxt("Planck_Parchment_RGB.txt") / 255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under(
"white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
return colombi1_cmap
def plot_skymap(skymap, smooth=None, decmax=None, scale=None, color_bins=40,
color_palette='viridis', symmetric=False, cbar_min=None,
cbar_max=None, cbar_title='Skymap', llabel=None, polar=False,
fig=None, sub=None):
cpalette = sns.color_palette(color_palette, color_bins)
cmap = ListedColormap(cpalette.as_hex())
cmap.set_under('white')
cmap.set_bad('gray')
# if cbar_max and cbar_max and symmetric and (cbar_max != -cbar_min):
# raise ValueError('The max/min colorbar values can\'t be symmetric')
# elif cbar_max and cbar_max:
# pass
# elif:
# skymap_min = skymap.min()
# skymap_max = skymap.max()
# maximum = np.max(np.abs([skymap_min, skymap_max]))
# cbar_min = np.sign(skymap_min) * maximum
# cbar_max = np.sign(skymap_max) * maximum
# else:
# cbar_min = cbar_min if cbar_min else skymap.min()
# cbar_max = cbar_max if cbar_max else skymap.max()
if polar:
shrink = 0.6
rot = [0,-90,180]
hp.orthview(skymap, half_sky=True, rot=rot, coord='C', title='',
min=cbar_min, max=cbar_max, cbar=False, cmap=cmap,
fig=fig, sub=sub)
else:
shrink = 1.0
hp.mollview(skymap, rot=180, coord='C', title='', min=cbar_min,
max=cbar_max, cbar=False, cmap=cmap, fig=fig, sub=sub)
hp.graticule(verbose=False)
fig = plt.gcf()
ax = plt.gca()
image = ax.get_images()[0]
cbar = fig.colorbar(image, orientation='horizontal', aspect=50,
pad=0.01, fraction=0.1, ax=ax,
format=FormatStrFormatter('%g'),
shrink=shrink)
if cbar_title:
cbar.set_label(cbar_title, size=14)
if not polar:
ax.set_ylim(-1, 0.005)
ax.annotate('0$^\circ$', xy=(1.8, -0.75), size=14)
ax.annotate('360$^\circ$', xy=(-1.99, -0.75), size=14)
if llabel:
ax.annotate(llabel, xy=(-1.85,-0.24), size=20, color='white')
return fig, ax
def planck_cmap():
"""
Generates the Planck CMB colormap from an input file, which stores the color values
for the complete gradient. The colormap values was obtained from the following link:
- https://github.com/zonca/paperplots/raw/master/data/Planck_Parchment_RGB.txt
"""
cmap = ListedColormap(np.loadtxt(data + 'Planck_Parchment_RGB.txt') / 255.)
cmap.set_bad('black') # color of missing pixels
cmap.set_under('white') # color of background
return cmap
def pycmap(gacmap): from matplotlib.colors import ListedColormap from numpy import vstack r = gacmap.table['r'] g = gacmap.table['g'] b = gacmap.table['b'] rt = ListedColormap(vstack((r, g, b)).T) rt.set_over((r[-1], g[-1], b[-1])) rt.set_under((r[0], g[0], b[0])) rt.set_bad(color='k', alpha=0) return rt
def pycmap(gacmap):
from matplotlib.colors import ListedColormap
from numpy import vstack
r = gacmap.table['r']
g = gacmap.table['g']
b = gacmap.table['b']
rt = ListedColormap(vstack((r, g, b)).T)
rt.set_over((r[-1], g[-1], b[-1]))
rt.set_under((r[0], g[0], b[0]))
rt.set_bad(color='k', alpha=0)
return rt
def load_planck_cmap(cmap_fname="../misc/Planck_Parchment_RGB.txt"):
'''
https://zonca.github.io/2013/09/Planck-CMB-map-at-high-resolution.html
'''
from matplotlib.colors import ListedColormap
import numpy as np
colombi1_cmap = ListedColormap(np.loadtxt(cmap_fname)/255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under("white") # color of background, necessary if you want to use
cmap = colombi1_cmap
return cmap
def rdm_colormap(n_cols=256, monitor=None):
"""this function provides a convenient colormap for visualizing
dissimilarity matrices. it goes from blue to yellow and has grey for
intermediate values.
Args:
n_cols (int, optional): precision of the colormap.
Defaults to 256.
Returns:
[matplotlib ListedColormap]: this matplotlib color object can be
used as a cmap in any plot.
Example:
.. code-block:: python
import numpy as np
import matplotlib.pyplot as plt
from rsatoolbox.vis.colors import rdm_colormap
plt.imshow(np.random.rand(10,10),cmap=rdm_colormap())
plt.colorbar()
plt.show()
(ported from Niko Kriegeskorte's RDMcolormap.m)
"""
# blue-cyan-gray-red-yellow with increasing V (BCGRYincV)
anchor_cols = np.array([
[0, 0, 1],
[0, 1, 1],
[.5, .5, .5],
[1, 0, 0],
[1, 1, 0],
])
# skimage rgb2hsv is intended for 3d images (RGB)
# here we add a new axis to our 2d anchorCols to satisfy
# skimage, and then squeeze
anchor_cols_hsv = rgb2hsv(anchor_cols[np.newaxis, :]).squeeze()
inc_v_weight = 1
anchor_cols_hsv[:, 2] = (1 - inc_v_weight) * anchor_cols_hsv[:, 2] + \
inc_v_weight * np.linspace(0.5, 1, anchor_cols.shape[0]).T
# anchorCols = brightness(anchorCols)
anchor_cols = hsv2rgb(anchor_cols_hsv[np.newaxis, :]).squeeze()
cols = color_scale(n_cols, anchor_cols, monitor)
cmap = ListedColormap(cols)
cmap.set_bad('white')
return cmap
def fplanckparchmentcmap():
'''
Creates the Planck matplotlib colormap.
'''
planck_cmap = ListedColormap(
np.loadtxt(
"/Users/campbell/Documents/PhD/data/functions/Planck_Parchment_RGB.txt"
) / 255.)
planck_cmap.set_bad("gray") # color of missing pixels
planck_cmap.set_under(
"white"
) # color of background, necessary if you want to use with mollview
return planck_cmap
def plot_mwd(RA,
Dec,
Color,
ifFillRect,
org=0,
title='Mollweide projection',
projection='mollweide'):
''' RA, Dec are arrays of the same length.
RA takes values in [0,360), Dec in [-90,90],
which represent angles in degrees.
org is the origin of the plot, 0 or a multiple of 30 degrees in [0,360).
title is the title of the figure.
projection is the kind of projection: 'mollweide', 'aitoff', 'hammer', 'lambert'
'''
x = [np.remainder(n + 360 - org, 360) for n in RA] # shift RA values
for i in range(len(x)):
if x[i] > 180:
x[i] -= 360 # scale conversion to [-180, 180]
x[i] = -x[i] # reverse the scale: East to the left
colombi1_cmap = ListedColormap(np.loadtxt("CMBColorMap.txt") / 255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under(
"white") # color of background, necessary if you want to use
tick_labels = np.array([150, 120, 90, 60, 30, 0, 330, 300, 270, 240, 210])
tick_labels = np.remainder(tick_labels + 360 + org, 360)
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111, projection=projection, facecolor='darkgray')
rand = np.random.random_sample((8651, ))
ax.scatter(np.radians(x),
np.radians(Dec),
c=Color,
s=1,
alpha=1,
cmap=colombi1_cmap) # convert degrees to radians
#fig.colorbar(ax, orientation='horizontal', fraction=.1)
#ax.scatter(0, 0, c='red', s = 10)
if ifFillRect:
fillRect(x, Dec, Color, 6, 3, ax)
ax.set_xticklabels(tick_labels) # we add the scale on the x axis
ax.set_title(title)
ax.title.set_fontsize(15)
ax.set_xlabel("RA")
ax.xaxis.label.set_fontsize(12)
ax.set_ylabel("Dec")
ax.yaxis.label.set_fontsize(12)
ax.grid(True)
plt.show()
def load_colormap(filename):
"""Load a colormap defined in a text file
filename is the .txt file name located in the
data/ path, not the full path.
list_available_colormaps() lists the available color tables
"""
try:
colormap = ListedColormap(np.loadtxt(os.path.join(DATA_PATH, filename))/255.)
except IOError:
print("Cannot load colormap, available colormaps: \n* " + "\n* ".join(list_available_colormaps()))
raise
colormap.set_bad("gray") # color of missing pixels
colormap.set_under("white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colormap)
return colormap
def load_colormap(filename):
"""Load a colormap defined in a text file
filename is the .txt file name located in the
data/ path, not the full path.
list_available_colormaps() lists the available color tables
"""
try:
colormap = ListedColormap(
np.loadtxt(os.path.join(DATA_PATH, filename)) / 255.)
except IOError:
print("Cannot load colormap, available colormaps: \n* " +
"\n* ".join(list_available_colormaps()))
raise
colormap.set_bad("gray") # color of missing pixels
colormap.set_under(
"white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colormap)
return colormap
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
from glob import glob
def GetMapName(freq):
indir = '/data/NEVERCOMMIT/'
f = np.int(freq)
filename = glob('/data/NEVERCOMMIT/*_%d_*fits' % f)[0]
return filename
############### Universal colormap
# setup linear colormap
from matplotlib.colors import ListedColormap
planck_freqmap_cmap = ListedColormap(np.loadtxt("Planck_FreqMap_RGB.txt")/255.)
planck_freqmap_cmap.set_bad("gray") # color of missing pixels
planck_freqmap_cmap.set_under("white") # color of background, necessary if you want to use
# Graticule spacing in degrees
grat_spacing = 10.0
imgsize = 750
imgresolution = 5.
faces_to_do = np.arange(32)
freq = sys.argv[1]
# load a map
mapfname = GetMapName(freq)
I = hp.ma(hp.read_map(mapfname,field=0,verbose=True))
if np.int(freq)<500:
I = I * 1e6
plt.close('all')
fig,ax=plt.subplots(1,1)
ax.hist(phase_rot.ravel())
ax.set_title('Gridded: Mask - 0 = Cloud,1 = 66% prob.\n Clear,2 = 95% prob. Clear,3 = 99% prob. Clear')
# In[112]:
plt.close('all')
from matplotlib.colors import Normalize
from numpy import ma
fig,ax=plt.subplots(1,1,figsize=(12,12))
colors=sns.color_palette('coolwarm')
pal=LinearSegmentedColormap.from_list('test',colors)
pal.set_bad('0.75') #75% grey
pal.set_over('r')
pal.set_under('k')
vmin= -5.
vmax= 5.
ch29 = np.rot90(file_dict['ch29'],2)
ch31 = np.rot90(file_dict['ch31'],2)
the_norm=Normalize(vmin=vmin,vmax=vmax,clip=False)
tdiff= ch31 - ch29
tdiff=ma.array(tdiff,mask=np.isnan(tdiff))
CS= ax.imshow(tdiff,cmap=pal,norm=the_norm)
cax=plt.colorbar(CS, ax = ax,extend='both')
cax.set_label('ch31 - ch29 brightnes temp (K)')
ax.set_title('TB 11 micron - TB 8 micron')
[231,255,0,255],
[231, 66, 0, 255],
[226,143,122,255],
[228,0,145,255],
[226,164,43,255],
[255,0,0,255],
[255,151,0,255],
[206,73,255,255],
[142,5,10,255],
[18,95,0,255],
[8,212,125,255],
[0,255,59,255],
])/255.1
from matplotlib.colors import ListedColormap
mycmap = ListedColormap(colorlist[:,:3], 'segmentation')
mycmap.set_bad(color=(0,0,0,0))
vols = config.vols.keys()
class GetDices(object):
def __init__(self, dir, title):
self.dir = dir
self.title = title
self.dices = {'title': self.title}
self.nsample = 0
def __call__(self, args, dirnames, fnames):
dir = self.dir
file_dice = args #args.pop('file_dice','dice_labels.txt')
if file_dice in fnames:
path = os.path.normpath(dirnames).split(os.sep)
if dir in path:
print(a)
a.loc["one", 5:7]
b = make_tuple(a)
a.one
# In[40]:
plt.close("all")
from matplotlib.colors import Normalize
from numpy import ma
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
colors = sns.color_palette("coolwarm")
pal = LinearSegmentedColormap.from_list("test", colors)
pal.set_bad("0.75") # 75% grey
pal.set_over("r")
pal.set_under("k")
vmin = -5.0
vmax = 5.0
ch29 = np.rot90(file_dict["ch29"], 2)
ch31 = np.rot90(file_dict["ch31"], 2)
the_norm = Normalize(vmin=vmin, vmax=vmax, clip=False)
tdiff = ch29 - ch31
tdiff = ma.array(tdiff, mask=np.isnan(tdiff))
CS = ax.imshow(tdiff, cmap=pal, norm=the_norm)
cax = plt.colorbar(CS, ax=ax, extend="both")
cax.set_label("ch29 - ch31 brightnes temp (K)")
ax.set_title("TB 8 micron - TB 11 micron")
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import ListedColormap
#
colors = [(), (), (), ()]
N = 256
blu_vals = np.ones((N, 4))
blu_vals[:, 0] = np.linspace(39 / 256, 1, N)
blu_vals[:, 1] = np.linspace(102 / 256, 1, N)
blu_vals[:, 2] = np.linspace(199 / 256, 1, N)
bluecmp = ListedColormap(blu_vals)
bluecmp.set_bad(color='w')
def colorFader(
c1,
c2,
mix=0
): #fade (linear interpolate) from color c1 (at mix=0) to c2 (mix=1)
c1 = np.array(mpl.colors.to_rgb(c1))
c2 = np.array(mpl.colors.to_rgb(c2))
return (1 - mix) * c1 + mix * c2 #mpl.colors.to_hex()#
north = '#1f77b4' #blue "#50a2d5"
east = "#4bb900" #"#76bb4b" #green
south = '#ffe200' # yellow
west = "#eb3920" # red
def publication_plot_old(save=False):
from matplotlib.gridspec import GridSpec
freqs = get_freqs(gag=True)
N_samples, L, _ = freqs.shape
#freqs_rm = get_freqs(gag=True, remove_rm=True)
freqs_rm = freqs.copy()
#for i in range(L):
# if i in GAG_RESISTANCE: continue
# freqs_rm[:, i-1] = 0.
for i in range(L):
for xi, ch in enumerate(ALPHABET[:20]):
if str(i+1) + ch in GAG_RESISTANCE_MUTS_SIMP: continue
freqs_rm[:, i, xi] = 0.
freqs = freqs.sum(axis=2)
freqs.sort(axis=0)
freqs = freqs[::-1]
freqs_rm = freqs_rm.sum(axis=2)
freqs_rm.sort(axis=0)
freqs_rm = freqs_rm[::-1]
#lanl_freqs = np.genfromtxt('lanl/gag_freqs_nowt_experienced.txt')
lanl_freqs = np.genfromtxt('lanl/gag_freqs_nowt_naive.txt')
lanl_freqs = lanl_freqs.reshape(-1,500,20)
lanl_rm = lanl_freqs.copy()
for i in range(L):
for xi, ch in enumerate(ALPHABET[:20]):
if str(i+1) + ch in GAG_RESISTANCE_MUTS_SIMP: continue
lanl_rm[:, i, xi] = 0.
lanl_freqs = lanl_freqs.sum(axis=2)
lanl_rm = lanl_rm.sum(axis=2)
print lanl_freqs.shape, lanl_rm.shape
print freqs.shape
#fig = plt.figure(figsize=(24, 12))
fig = plt.figure(figsize=(7.5, 4))
fig.suptitle('Variant Frequencies in Gag', size=20)
gs = GridSpec(3, 2, height_ratios=[10,1,10], width_ratios=[100, 1])
ax1 = plt.subplot(gs[0, :-1])
ax3 = plt.subplot(gs[1, :-1])
ax2 = plt.subplot(gs[2, :-1])
matplotlib.rc('font', size=24)
matplotlib.rc('axes', linewidth=5)
[ax.set_xlabel('Sequence Position') for ax in (ax1, ax2)]
ax1.set_ylabel('Samples with variants')
ax2.set_ylabel('LANL PI-naive\nsequences with variants')
red_cmap = ListedColormap(['red'])
black_cmap = ListedColormap(['black'])
red_cmap.set_bad((0,0,0,0))
freqs_ma = np.ma.masked_where(freqs < 0.01, freqs)
freqs_rm_ma = np.ma.masked_where(freqs_rm < 0.01, freqs_rm)
plt1 = ax1.pcolor(freqs_ma, cmap=black_cmap, edgecolors='none')
plt2 = ax1.pcolor(freqs_rm_ma, cmap=red_cmap, alpha=0.75, edgecolors='none')
plt3 = ax2.bar(np.arange(L), lanl_freqs.sum(axis=0), width=1, fc='0.5', ec='0.5')
plt4 = ax2.bar(np.arange(L), lanl_rm.sum(axis=0), width=1, fc='r', ec='none', alpha=0.75)
plt.setp(ax1.get_xticklabels(), visible=False)
quart1 = N_samples / 4.
quart2 = lanl_freqs.shape[0] / 4.
ax1.set_yticks(np.arange(0, N_samples+quart1, quart1))
ax2.set_yticks(np.arange(0, N_samples+quart1, quart1))
ax2.set_yticks(np.arange(0, lanl_freqs.shape[0]+quart2, quart2))
quarter = N_samples / 4.
#[ax.set_yticks(np.arange(0, N_samples + quarter, quarter)) for ax in (ax1, ax2)]
[ax.set_yticklabels(['0%', '25%', '50%', '75%', '100%']) for ax in (ax1, ax2)]
ax1.set_ylim(0, 1.5*N_samples)
#ax2.set_ylim(0, 0.8*N_samples)
ax2.set_ylim(0, 0.9*lanl_freqs.shape[0])
locs = [(132, 'MA'), (363, 'CA'), (377, 'p2'), (432, 'NC'), (448, 'p1'), (500, 'p6')]
x_start = 0
colors = ('#AAAAAA', '#EEEEEE')
for i, (junc, name) in enumerate(locs):
color = colors[i%2]
width = junc - x_start
rect = Rectangle((x_start, 0), width, 1, edgecolor='None', color=color)
ax3.add_patch(rect)
ax3.text(x_start + width/2., 1/2., name, ha='center', va='center')
ax1.axvline(x_start, color='k', ls='--', lw=1.0)
ax2.axvline(x_start, color='k', ls='--', lw=1.0)
x_start = junc
ax3.set_xlim(0, L)
plt.setp(ax3.get_xticklabels(), visible=False)
plt.setp(ax3.get_yticklabels(), visible=False)
[ax.tick_params(top=False, left=False, right=False, bottom=False) for ax in (ax1, ax2, ax3)]
ax2.invert_yaxis()
fig.subplots_adjust(hspace=0., wspace=0.02, left=0.08, right=0.95, top=0.95, bottom=0.06)
if not save:
plt.show()
else:
fig.savefig('PUB_VARIANTS_PLOT.tiff', dpi=600)
for i, r in enumerate(greens_half + [1] + list(reversed(greens_half)))
]),
'blue':
tuple([(0.1 * i, r, r) for i, r in enumerate(reversed(reds))])
}
CMAP_ASSOCIATION = LinearSegmentedColormap('association', colordict)
CMAP_ASSOCIATION.set_bad(C_BAD)
# Categorical
CMAP_CATEGORICAL = Paired
CMAP_CATEGORICAL_2 = Dark2
CMAP_CATEGORICAL.set_bad(C_BAD)
# Binary
CMAP_BINARY = ListedColormap(['#cdcdcd', '#404040'])
CMAP_BINARY.set_bad(C_BAD)
DPI = 100
# ======================================================================================================================
# Functions
# ======================================================================================================================
def plot_points(*args,
title='',
xlabel='',
ylabel='',
filepath=None,
**kwargs):
"""
from astropy.wcs import WCS
wcs = WCS(hdu_npeaks.header)
dxlist = np.array([20, 25, -60, 35, -45])
dylist = np.array([5, -25, -40, 35, 30])
ra_list, dec_list = wcs.all_pix2world(xlist, ylist, 0)
ra_list_text, dec_list_text = wcs.all_pix2world(xlist + dxlist, ylist + dylist,
0)
dra_list = ra_list - ra_list_text
ddec_list = dec_list - dec_list_text
fig = plt.figure(figsize=(4, 6))
ax = plt.subplot(projection=wcs)
cmaplst = ['#f1f7d2', '#7fcdbb', '#2c7fb8'] # stolen from colorbrewer2
lcmap = ListedColormap(cmaplst)
lcmap.set_bad('#f0f0f0')
plt.imshow(hdu_npeaks.data, vmin=-0.5, vmax=2.5, origin='lower', cmap=lcmap)
for (x, y) in zip(xlist, ylist):
r = Circle((x, y), radius=.75, zorder=1, fc="0.8", ec="k", lw=0.2)
ax.add_patch(r)
for i in range(len(labels)):
plt.annotate(
labels[i],
xy=(xlist[i], ylist[i]),
xycoords='data',
xytext=(xlist[i] + dxlist[i],
ylist[i] + dylist[i]), #textcoords='data',
bbox=dict(boxstyle="circle", fc="0.8", alpha=0.8),
arrowprops=dict(arrowstyle="-",
shrinkA=0,
[233,150,122], [165, 42, 42], # Label 8-19 (T1-12)
[255,250,250], [147,112,219], [218,112,214], [ 75, 0,130], [255,182,193],
[ 60,179,113], [255,235,205], # Label 20-26 (L1-6, sacrum)
[255,235,205], [255,228,196], # Label 27 cocc, 28 T13,
[218,165, 32], [ 0,128,128], [188,143,143], [255,105,180],
[255, 0, 0], [ 0,255, 0], [ 0, 0,255], [255,255, 0], [ 0,255,255],
[255, 0,255], [255,239,213], # 29-39 unused
[ 0, 0,205], [205,133, 63], [210,180,140], [102,205,170], [ 0, 0,128],
[ 0,139,139], [ 46,139, 87], [255,228,225], [106, 90,205], [221,160,221],
[233,150,122], # Label 40-50 (subregions)
[255,250,250], [147,112,219], [218,112,214], [ 75, 0,130], [255,182,193],
[ 60,179,113], [255,235,205], [255,105,180], [165, 42, 42], [188,143,143],
[255,235,205], [255,228,196], [218,165, 32], [ 0,128,128] # rest unused
])
cm_itk = ListedColormap(colors_itk)
cm_itk.set_bad(color='w', alpha=0) # set NaN to full opacity for overlay
# define HU windows
wdw_sbone = Normalize(vmin=-500, vmax=1300, clip=True)
wdw_hbone = Normalize(vmin=-200, vmax=1000, clip=True)
#########################
# Resample and reorient #
def reorient_to(img, axcodes_to=('P', 'I', 'R'), verb=False):
"""Reorients the nifti from its original orientation to another specified orientation
Parameters:
----------
img: nibabel image
def olr():
olr_map = [cm.gist_yarg(cc) for cc in range(0, 255, 5)]
olr_coltbl = ListedColormap(olr_map, name='orl_coltbl', N=36)
olr_coltbl.set_bad(color=olr_coltbl.colors[-1])
return olr_coltbl
def olr(): olr_map =[cm.gist_yarg(cc) for cc in range(0,255,5)] olr_coltbl = ListedColormap(olr_map,name = 'orl_coltbl',N=36) olr_coltbl.set_bad(color= olr_coltbl.colors[-1]) return olr_coltbl
"""This file contains GUI related variables."""
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
# Colormap for mask overlays
# palette = plt.cm.Reds
palette = ListedColormap([[0, 0, 0, 0], [1, 0, 1, 1]]) # white and magenta improve the visibility
palette.set_over('r', 1.0)
palette.set_under('w', 0)
palette.set_bad('m', 1.0)
# Slider colors
axcolor = '0.875'
hovcolor = '0.975'
]
grids = get_grids(envs_to_plot)
color_map = {
'place_cell': LINCLAB_COLS['red'],
'sr': LINCLAB_COLS['red'],
'unstructured': LINCLAB_COLS['blue']
}
N = 256
blu_vals = np.ones((N, 4))
blu_vals[:, 0] = np.linspace(39 / 256, 1, N)
blu_vals[:, 1] = np.linspace(102 / 256, 1, N)
blu_vals[:, 2] = np.linspace(199 / 256, 1, N)
bluecmp = ListedColormap(blu_vals)
bluecmp.set_bad(color='w')
red_vals = np.ones((N, 4))
red_vals[:, 0] = np.linspace(235 / 256, 1, N)
red_vals[:, 1] = np.linspace(77 / 256, 1, N)
red_vals[:, 2] = np.linspace(57 / 256, 1, N)
redcmp = ListedColormap(red_vals)
redcmp.set_bad(color='w')
blu_r_vals = np.ones((N, 4))
blu_r_vals[:, 0] = np.linspace(1, 39 / 256, N) #np.linspace(1,80/256, N)
blu_r_vals[:, 1] = np.linspace(1, 102 / 256, N) #np.linspace(1,162/256,N)
blu_r_vals[:, 2] = np.linspace(1, 199 / 256, N) #np.linspace(1,213/256,N)
bluecmp_r = ListedColormap(blu_r_vals)
bluecmp_r.set_bad(color='w')
[0.9937714286, 0.7454571429, 0.240347619],
[0.9990428571, 0.7653142857, 0.2164142857],
[0.9955333333, 0.7860571429, 0.196652381],
[0.988, 0.8066, 0.1793666667],
[0.9788571429, 0.8271428571, 0.1633142857],
[0.9697, 0.8481380952, 0.147452381],
[0.9625857143, 0.8705142857, 0.1309],
[0.9588714286, 0.8949, 0.1132428571],
[0.9598238095, 0.9218333333, 0.0948380952],
[0.9661, 0.9514428571, 0.0755333333],
[0.9763, 0.9831, 0.0538]]
parula = ListedColormap(_parula_data, name='parula')
parula_zero = _parula_data[0]
parula_0 = ListedColormap(_parula_data, name='parula_0')
parula_0.set_bad((1,1,1))
parula_r = ListedColormap(_parula_data[::-1], name='parula_r')
willi_blau = [0.0722666667, 0.4886666667, 0.8467]
# In[ ]:
debug = False
# ### Galpy initialization
#
# We are using the McMillan17 potential from McMillan, 2017, MNRAS, 465, 76.
# Contrary to galpy, its normalisation parameters are:
def makeMap(timegrid,method,datadir,popfile,popcolormap,stationdict,citylist,elats,elons):
figwidth = 8.0
bounds = timegrid.getRange()
bounds = list(bounds)
if bounds[1] < 0 and bounds[0] > bounds[1]:
bounds[1] = bounds[1] + 360
clat = bounds[2] + (bounds[3] - bounds[2])/2
clon = bounds[0] + (bounds[1] - bounds[0])/2
dx = (bounds[1] - bounds[0])*111191 * np.cos(np.degrees(clat))
dy = (bounds[3] - bounds[2])*111191
aspect = np.abs(dy/dx)
figheight = aspect * figwidth
fig = plt.figure(figsize=(figwidth,figheight),edgecolor='g',facecolor='g')
ax1 = fig.add_axes([0,0,1.0,1.0])
m = Basemap(llcrnrlon=bounds[0],llcrnrlat=bounds[2],
urcrnrlon=bounds[1],urcrnrlat=bounds[3],
resolution='h',projection='merc',lat_ts=clat)
#get the population grid
popgrid = EsriGrid(popfile)
popgrid.load(bounds=bounds)
popdata = np.flipud(popgrid.griddata)
cmap = GMTColormap(popcolormap)
clist = cmap.getColorList()
boundaries = cmap.getZValues()
palette = ListedColormap(clist,'my_colormap')
i = np.where(np.isnan(popdata))
popdata[i] = -1
popdatam = np.ma.masked_values(popdata, -1)
palette.set_bad(WATER_COLOR,1.0)
ncolors = len(boundaries)
am = m.imshow(popdatam,cmap=palette,norm=BoundaryNorm(boundaries,ncolors),interpolation='nearest')
statgrid = np.flipud(timegrid.griddata)
(lons,lats) = getLatLonGrids(timegrid)
(x,y) = m(lons,lats)
clevels = np.arange(MINTIME,MAXTIME+DTIME,DTIME)
cs = m.contour(x,y,statgrid,clevels)
#plt.clabel(cs, inline=1, fontsize=10)
proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_color()[0]) for pc in cs.collections]
labels = [str(c)+' sec' for c in clevels]
sx,sy = m(stationdict['lon'],stationdict['lat'])
m.plot(sx,sy,'rD')
plt.text(sx,sy,stationdict['code'])
#plot the various epicenters
for elat,elon in zip(elats,elons):
ex,ey = m(elon,elat)
m.plot(ex,ey,'k*')
#plot the cities
for city in citylist:
cx,cy = m(city['lon'],city['lat'])
m.plot(cx,cy,'.',color=CITY_COLOR)
plt.text(cx,cy,city['name'],color=CITY_COLOR)
m.drawrivers(color=WATER_COLOR)
m.drawcountries(color='k',linewidth=2.0)
mer = getMapLines(bounds[0],bounds[1])
par = getMapLines(bounds[2],bounds[3])
xmap_range = m.xmax-m.xmin
ymap_range = m.ymax-m.ymin
xoff = -0.09*(xmap_range)
yoff = -0.04*(ymap_range)
m.drawmeridians(mer,labels=[0,0,1,0],fontsize=8,
linewidth=0.5,color='black',yoffset=yoff,xoffset=xoff,dashes=[1,0.01])
m.drawparallels(par,labels=[0,1,0,0],fontsize=8,
linewidth=0.5,color='black',yoffset=yoff,xoffset=xoff,dashes=[1,0.01])
m.drawmapboundary(color='k',linewidth=2.0)
plt.legend(proxy,labels)
outfile = os.path.join(datadir,method+'.pdf')
plt.savefig(outfile)
plt.close()
import numpy as np
############### CMB colormap
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(np.loadtxt("../../data/Planck_Parchment_RGB.txt")/255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under("white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
############### Universal colormap
# setup linear colormap
from matplotlib.colors import ListedColormap
planck_freqmap_cmap = ListedColormap(np.loadtxt("../../data/Planck_FreqMap_RGB.txt")/255.)
planck_freqmap_cmap.set_bad("gray") # color of missing pixels
planck_freqmap_cmap.set_under("white") # color of background, necessary if you want to use
# setup nonlinear colormap
from matplotlib.colors import LinearSegmentedColormap
class PlanckUniversalColormap(LinearSegmentedColormap):
name = "planckuniv"
def __init__(self, cmap):
self.cmap = cmap
self.N = self.cmap.N
def is_gray(self):
return False
def __call__(self, xi, alpha=1.0, **kw):
x = xi * (1e7+1e3) - 1e3
yi = self.modsinh(x)
# range 0-1
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
from scipy import special
from matplotlib.colors import ListedColormap
#matplotlib.use("agg")
colombi1_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt") / 255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under(
"white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
cmap_planck = colombi1_cmap
import matplotlib
matplotlib.use("Agg")
Nside = 512
cl = hp.fitsfunc.read_cl("cl.fits")
gauss_map = hp.synfast(cl[0],
512,
lmax=3 * Nside - 1,
fwhm=0,
pixwin=True,
verbose=False,
pol=False)
dpi = 800
fig_size_inch = 6, 4.5
fig = plt.figure(1, figsize=(6, 4.5))
# M = np.sum(hits)
# print("total time samples: {}".format(M))
# Load input map
from s4cmb.input_sky import HealpixFitsMap
#Sky in
nside_out = 512
path_to_in = "/global/homes/e/elbouha/toast/examples/data/ffp10_lensed_scl_100_nside0512.fits"
sky_in = 1e6 * hp.fitsfunc.read_map(path_to_in, field=None, nest=True)
# Define Planck color map
from matplotlib.colors import ListedColormap
planck_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt") / 255.)
planck_cmap.set_bad("gray")
planck_cmap.set_under("white")
cmap = planck_cmap
# Plot the maps
# pl.figure(2,figsize=(8, 8))
xsize = 700
# hp.gnomview(np.log10(hits+1), rot=[0, -30], xsize=xsize, reso=6.9,
# title='nhits', notext=True, cmap=cmap)
# hp.gnomview(1e-1*hits, rot=[0, -30], xsize=xsize, reso = 1,
# title='hits', notext=True, cmap=cmap, nest=True)
# pl.show()
# hp.gnomview(cond, rot=[0, -30], xsize=xsize, reso = 1.5,
# title='rcond', notext=True, cmap=cmap, nest=True)
# pl.show()
def get_cmap_planck():
"""
Function to get Planck colormap
https://zonca.github.io/2013/09/Planck-CMB-map-at-high-resolution.html
"""
cm_data = [[0., 0., 255.], [0., 2., 255.], [0., 5., 255.], [0., 8., 255.],
[0., 10., 255.], [0., 13., 255.], [0., 16., 255.],
[0., 18., 255.], [0., 21., 255.], [0., 24., 255.],
[0., 26., 255.], [0., 29., 255.], [0., 32., 255.],
[0., 34., 255.], [0., 37., 255.], [0., 40., 255.],
[0., 42., 255.], [0., 45., 255.], [0., 48., 255.],
[0., 50., 255.], [0., 53., 255.], [0., 56., 255.],
[0., 58., 255.], [0., 61., 255.], [0., 64., 255.],
[0., 66., 255.], [0., 69., 255.], [0., 72., 255.],
[0., 74., 255.], [0., 77., 255.], [0., 80., 255.],
[0., 82., 255.], [0., 85., 255.], [0., 88., 255.],
[0., 90., 255.], [0., 93., 255.], [0., 96., 255.],
[0., 98., 255.], [0., 101., 255.], [0., 104., 255.],
[0., 106., 255.], [0., 109., 255.], [0., 112., 255.],
[0., 114., 255.], [0., 117., 255.], [0., 119., 255.],
[0., 122., 255.], [0., 124., 255.], [0., 127., 255.],
[0., 129., 255.], [0., 132., 255.], [0., 134., 255.],
[0., 137., 255.], [0., 139., 255.], [0., 142., 255.],
[0., 144., 255.], [0., 147., 255.], [0., 150., 255.],
[0., 152., 255.], [0., 155., 255.], [0., 157., 255.],
[0., 160., 255.], [0., 162., 255.], [0., 165., 255.],
[0., 167., 255.], [0., 170., 255.], [0., 172., 255.],
[0., 175., 255.], [0., 177., 255.], [0., 180., 255.],
[0., 182., 255.], [0., 185., 255.], [0., 188., 255.],
[0., 190., 255.], [0., 193., 255.], [0., 195., 255.],
[0., 198., 255.], [0., 200., 255.], [0., 203., 255.],
[0., 205., 255.], [0., 208., 255.], [0., 210., 255.],
[0., 213., 255.], [0., 215., 255.], [0., 218., 255.],
[0., 221., 255.], [6., 221., 254.], [12., 221., 253.],
[18., 222., 252.], [24., 222., 251.], [30., 222., 250.],
[36., 223., 249.], [42., 223., 248.], [48., 224., 247.],
[54., 224., 246.], [60., 224., 245.], [66., 225., 245.],
[72., 225., 244.], [78., 225., 243.], [85., 226., 242.],
[91., 226., 241.], [97., 227., 240.], [103., 227., 239.],
[109., 227., 238.], [115., 228., 237.], [121., 228., 236.],
[127., 229., 236.], [133., 229., 235.], [139., 229., 234.],
[145., 230., 233.], [151., 230., 232.], [157., 230., 231.],
[163., 231., 230.], [170., 231., 229.], [176., 232., 228.],
[182., 232., 227.], [188., 232., 226.], [194., 233., 226.],
[200., 233., 225.], [206., 233., 224.], [212., 234., 223.],
[218., 234., 222.], [224., 235., 221.], [230., 235., 220.],
[236., 235., 219.], [242., 236., 218.], [248., 236., 217.],
[255., 237., 217.], [255., 235., 211.], [255., 234., 206.],
[255., 233., 201.], [255., 231., 196.], [255., 230., 191.],
[255., 229., 186.], [255., 227., 181.], [255., 226., 176.],
[255., 225., 171.], [255., 223., 166.], [255., 222., 161.],
[255., 221., 156.], [255., 219., 151.], [255., 218., 146.],
[255., 217., 141.], [255., 215., 136.], [255., 214., 131.],
[255., 213., 126.], [255., 211., 121.], [255., 210., 116.],
[255., 209., 111.], [255., 207., 105.], [255., 206., 100.],
[255., 205., 95.], [255., 203., 90.], [255., 202., 85.],
[255., 201., 80.], [255., 199., 75.], [255., 198., 70.],
[255., 197., 65.], [255., 195., 60.], [255., 194., 55.],
[255., 193., 50.], [255., 191., 45.], [255., 190., 40.],
[255., 189., 35.], [255., 187., 30.], [255., 186., 25.],
[255., 185., 20.], [255., 183., 15.], [255., 182., 10.],
[255., 181., 5.], [255., 180., 0.], [255., 177., 0.],
[255., 175., 0.], [255., 172., 0.], [255., 170., 0.],
[255., 167., 0.], [255., 165., 0.], [255., 162., 0.],
[255., 160., 0.], [255., 157., 0.], [255., 155., 0.],
[255., 152., 0.], [255., 150., 0.], [255., 147., 0.],
[255., 145., 0.], [255., 142., 0.], [255., 140., 0.],
[255., 137., 0.], [255., 135., 0.], [255., 132., 0.],
[255., 130., 0.], [255., 127., 0.], [255., 125., 0.],
[255., 122., 0.], [255., 120., 0.], [255., 117., 0.],
[255., 115., 0.], [255., 112., 0.], [255., 110., 0.],
[255., 107., 0.], [255., 105., 0.], [255., 102., 0.],
[255., 100., 0.], [255., 97., 0.], [255., 95., 0.],
[255., 92., 0.], [255., 90., 0.], [255., 87., 0.],
[255., 85., 0.], [255., 82., 0.], [255., 80., 0.],
[255., 77., 0.], [255., 75., 0.], [251., 73., 0.],
[247., 71., 0.], [244., 69., 0.], [240., 68., 0.],
[236., 66., 0.], [233., 64., 0.], [229., 62., 0.],
[226., 61., 0.], [222., 59., 0.], [218., 57., 0.],
[215., 55., 0.], [211., 54., 0.], [208., 52., 0.],
[204., 50., 0.], [200., 48., 0.], [197., 47., 0.],
[193., 45., 0.], [190., 43., 0.], [186., 41., 0.],
[182., 40., 0.], [179., 38., 0.], [175., 36., 0.],
[172., 34., 0.], [168., 33., 0.], [164., 31., 0.],
[161., 29., 0.], [157., 27., 0.], [154., 26., 0.],
[150., 24., 0.], [146., 22., 0.], [143., 20., 0.],
[139., 19., 0.], [136., 17., 0.], [132., 15., 0.],
[128., 13., 0.], [125., 12., 0.], [121., 10., 0.],
[118., 8., 0.], [114., 6., 0.], [110., 5., 0.], [107., 3., 0.],
[103., 1., 0.], [100., 0., 0.]]
cmap = ListedColormap(np.array(cm_data) / 255.0, name='planck')
# color of missing pixels
cmap.set_bad('gray')
# color of background, necessary if you want to use
cmap.set_under('white')
return cmap
plt.close('all')
fig, ax = plt.subplots(1, 1)
ax.hist(phase_rot.ravel())
ax.set_title(
'Gridded: Mask - 0 = Cloud,1 = 66% prob.\n Clear,2 = 95% prob. Clear,3 = 99% prob. Clear'
)
# In[112]:
plt.close('all')
from matplotlib.colors import Normalize
from numpy import ma
fig, ax = plt.subplots(1, 1, figsize=(12, 12))
colors = sns.color_palette('coolwarm')
pal = LinearSegmentedColormap.from_list('test', colors)
pal.set_bad('0.75') #75% grey
pal.set_over('r')
pal.set_under('k')
vmin = -5.
vmax = 5.
ch29 = np.rot90(file_dict['ch29'], 2)
ch31 = np.rot90(file_dict['ch31'], 2)
the_norm = Normalize(vmin=vmin, vmax=vmax, clip=False)
tdiff = ch31 - ch29
tdiff = ma.array(tdiff, mask=np.isnan(tdiff))
CS = ax.imshow(tdiff, cmap=pal, norm=the_norm)
cax = plt.colorbar(CS, ax=ax, extend='both')
cax.set_label('ch31 - ch29 brightnes temp (K)')
ax.set_title('TB 11 micron - TB 8 micron')
# ### For next Tuesday: check in a notebook that
def publication_plot_old(save=False):
from matplotlib.gridspec import GridSpec
freqs = get_freqs(gag=True)
N_samples, L, _ = freqs.shape
#freqs_rm = get_freqs(gag=True, remove_rm=True)
freqs_rm = freqs.copy()
#for i in range(L):
# if i in GAG_RESISTANCE: continue
# freqs_rm[:, i-1] = 0.
for i in range(L):
for xi, ch in enumerate(ALPHABET[:20]):
if str(i + 1) + ch in GAG_RESISTANCE_MUTS_SIMP: continue
freqs_rm[:, i, xi] = 0.
freqs = freqs.sum(axis=2)
freqs.sort(axis=0)
freqs = freqs[::-1]
freqs_rm = freqs_rm.sum(axis=2)
freqs_rm.sort(axis=0)
freqs_rm = freqs_rm[::-1]
#lanl_freqs = np.genfromtxt('lanl/gag_freqs_nowt_experienced.txt')
lanl_freqs = np.genfromtxt('lanl/gag_freqs_nowt_naive.txt')
lanl_freqs = lanl_freqs.reshape(-1, 500, 20)
lanl_rm = lanl_freqs.copy()
for i in range(L):
for xi, ch in enumerate(ALPHABET[:20]):
if str(i + 1) + ch in GAG_RESISTANCE_MUTS_SIMP: continue
lanl_rm[:, i, xi] = 0.
lanl_freqs = lanl_freqs.sum(axis=2)
lanl_rm = lanl_rm.sum(axis=2)
print lanl_freqs.shape, lanl_rm.shape
print freqs.shape
#fig = plt.figure(figsize=(24, 12))
fig = plt.figure(figsize=(7.5, 4))
fig.suptitle('Variant Frequencies in Gag', size=20)
gs = GridSpec(3, 2, height_ratios=[10, 1, 10], width_ratios=[100, 1])
ax1 = plt.subplot(gs[0, :-1])
ax3 = plt.subplot(gs[1, :-1])
ax2 = plt.subplot(gs[2, :-1])
matplotlib.rc('font', size=24)
matplotlib.rc('axes', linewidth=5)
[ax.set_xlabel('Sequence Position') for ax in (ax1, ax2)]
ax1.set_ylabel('Samples with variants')
ax2.set_ylabel('LANL PI-naive\nsequences with variants')
red_cmap = ListedColormap(['red'])
black_cmap = ListedColormap(['black'])
red_cmap.set_bad((0, 0, 0, 0))
freqs_ma = np.ma.masked_where(freqs < 0.01, freqs)
freqs_rm_ma = np.ma.masked_where(freqs_rm < 0.01, freqs_rm)
plt1 = ax1.pcolor(freqs_ma, cmap=black_cmap, edgecolors='none')
plt2 = ax1.pcolor(freqs_rm_ma,
cmap=red_cmap,
alpha=0.75,
edgecolors='none')
plt3 = ax2.bar(np.arange(L),
lanl_freqs.sum(axis=0),
width=1,
fc='0.5',
ec='0.5')
plt4 = ax2.bar(np.arange(L),
lanl_rm.sum(axis=0),
width=1,
fc='r',
ec='none',
alpha=0.75)
plt.setp(ax1.get_xticklabels(), visible=False)
quart1 = N_samples / 4.
quart2 = lanl_freqs.shape[0] / 4.
ax1.set_yticks(np.arange(0, N_samples + quart1, quart1))
ax2.set_yticks(np.arange(0, N_samples + quart1, quart1))
ax2.set_yticks(np.arange(0, lanl_freqs.shape[0] + quart2, quart2))
quarter = N_samples / 4.
#[ax.set_yticks(np.arange(0, N_samples + quarter, quarter)) for ax in (ax1, ax2)]
[
ax.set_yticklabels(['0%', '25%', '50%', '75%', '100%'])
for ax in (ax1, ax2)
]
ax1.set_ylim(0, 1.5 * N_samples)
#ax2.set_ylim(0, 0.8*N_samples)
ax2.set_ylim(0, 0.9 * lanl_freqs.shape[0])
locs = [(132, 'MA'), (363, 'CA'), (377, 'p2'), (432, 'NC'), (448, 'p1'),
(500, 'p6')]
x_start = 0
colors = ('#AAAAAA', '#EEEEEE')
for i, (junc, name) in enumerate(locs):
color = colors[i % 2]
width = junc - x_start
rect = Rectangle((x_start, 0), width, 1, edgecolor='None', color=color)
ax3.add_patch(rect)
ax3.text(x_start + width / 2., 1 / 2., name, ha='center', va='center')
ax1.axvline(x_start, color='k', ls='--', lw=1.0)
ax2.axvline(x_start, color='k', ls='--', lw=1.0)
x_start = junc
ax3.set_xlim(0, L)
plt.setp(ax3.get_xticklabels(), visible=False)
plt.setp(ax3.get_yticklabels(), visible=False)
[
ax.tick_params(top=False, left=False, right=False, bottom=False)
for ax in (ax1, ax2, ax3)
]
ax2.invert_yaxis()
fig.subplots_adjust(hspace=0.,
wspace=0.02,
left=0.08,
right=0.95,
top=0.95,
bottom=0.06)
if not save:
plt.show()
else:
fig.savefig('PUB_VARIANTS_PLOT.tiff', dpi=600)
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
############### CMB colormap
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt") / 255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under(
"gray") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
############### CMB colormap
planck_parchment_cmap = ListedColormap(
np.loadtxt("Planck_Parchment_RGB.txt") / 255.)
#planck_parchment_cmap.set_bad("gray") # color of missing pixels
planck_parchment_cmap.set_bad("lightslategray") # color of missing pixels
#planck_parchment_cmap.set_under("white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=planck_parchment_cmap)
planck_grey_cmap = cm.get_cmap('gray')
############### Universal colormap
# setup linear colormap
planck_freqmap_cmap = ListedColormap(
np.loadtxt("Planck_FreqMap_RGB.txt") / 255.)
planck_freqmap_cmap.set_bad("gray") # color of missing pixels
planck_freqmap_cmap.set_under(
"white") # color of background, necessary if you want to use
# setup nonlinear colormap
mag_1 = mag.format(band_1.upper())
mag_2 = mag.format(band_2.upper())
mag_err_1 = mag_err.format(band_1.upper())
mag_err_2 = mag_err.format(band_2.upper())
mag_dered_1 = mag_dered.format(band_1.upper())
mag_dered_2 = mag_dered.format(band_2.upper())
################################################################################
# Set colors
cmap_gray = matplotlib.cm.Greys
cmap_color = matplotlib.cm.viridis
c_color = cmap_color(0.25)
p_color = cmap_color(1.0)
cmap_gray_mask = ListedColormap(cmap_gray(np.linspace(0.1, 1.0, 100)))
cmap_gray_mask.set_bad('white')
################################################################################
def analysis(ra, dec, mod, mc_source_id):
"""Analyze a candidate"""
pix_nside_select = ugali.utils.healpix.angToPix(nside, ra, dec)
ra_select, dec_select = ugali.utils.healpix.pixToAng(
nside, pix_nside_select)
pix_nside_neighbors = np.concatenate([[pix_nside_select],
healpy.get_all_neighbours(
nside, pix_nside_select)])
# Construct data
[231, 255, 0, 255],
[231, 66, 0, 255],
[226, 143, 122, 255],
[228, 0, 145, 255],
[226, 164, 43, 255],
[255, 0, 0, 255],
[255, 151, 0, 255],
[206, 73, 255, 255],
[142, 5, 10, 255],
[18, 95, 0, 255],
[8, 212, 125, 255],
[0, 255, 59, 255],
]) / 255.1
from matplotlib.colors import ListedColormap
mycmap = ListedColormap(colorlist[:, :3], 'segmentation')
mycmap.set_bad(color=(0, 0, 0, 0))
vols = config.vols.keys()
class GetDices(object):
def __init__(self, dir, title):
self.dir = dir
self.title = title
self.dices = {'title': self.title}
self.nsample = 0
def __call__(self, args, dirnames, fnames):
dir = self.dir
file_dice = args #args.pop('file_dice','dice_labels.txt')
if file_dice in fnames:
from setup_matplotlib import *
import healpy as hp
m = hp.ma(hp.read_map("../../data/wmap_band_iqumap_r9_7yr_W_v4.fits", 0)) * 1e3 # muK
nside = hp.npix2nside(len(m))
# setup colormap
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(np.loadtxt("../../data/parchment1.dat")/255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
# using directly matplotlib instead of mollview has higher
# quality output, I plan to merge this into healpy
# ratio is always 1/2
xsize = 2000
ysize = xsize/2.
unit = r"$\mathrm{\mu K}$"
# this is the mollview min and max
vmin = -1e3; vmax = 1e3
theta = np.linspace(np.pi, 0, ysize)
phi = np.linspace(-np.pi, np.pi, xsize)
longitude = np.radians(np.linspace(-180, 180, xsize))
latitude = np.radians(np.linspace(-90, 90, ysize))
# project the map to a rectangular matrix xsize x ysize
PHI, THETA = np.meshgrid(phi, theta)
grid_pix = hp.ang2pix(nside, THETA, PHI)
for i, r in enumerate(greens_half + [1] + list(reversed(greens_half)))
]),
'blue':
tuple([(0.1 * i, r, r) for i, r in enumerate(reversed(reds))])
}
CMAP_CONTINUOUS_ASSOCIATION = LinearSegmentedColormap('association', colordict)
CMAP_CONTINUOUS_ASSOCIATION.set_bad(C_BAD)
# Categorical colormap
CMAP_CATEGORICAL_PAIRED = Paired
CMAP_CATEGORICAL_PAIRED.set_bad(C_BAD)
CMAP_CATEGORICAL_SET3 = Set3
CMAP_CATEGORICAL_SET3.set_bad(C_BAD)
CMAP_CATEGORICAL_TAB20 = tab20
CMAP_CATEGORICAL_TAB20.set_bad(C_BAD)
CMAP_CATEGORICAL_TAB20B = tab20b
CMAP_CATEGORICAL_TAB20B.set_bad(C_BAD)
CMAP_CATEGORICAL_TAB20C = tab20c
CMAP_CATEGORICAL_TAB20C.set_bad(C_BAD)
# Binary colormap
CMAP_BINARY_WB = ListedColormap(['#E8E8E8', '#080808'])
CMAP_BINARY_WB.set_bad(C_BAD)
CMAP_BINARY_RE = ListedColormap(['#F80088', '#20D9BA'])
CMAP_BINARY_RE.set_bad(C_BAD)
def full_scan(self):
'''
creates a heat map of detection efficiency for every
point in MIDAS test stand scan
'''
# magma_r.colors gives us the colormap values below
reversed_magma = np.array(
[[0.987053, 0.991438, 0.749504, 1.00000],
[0.995131, 0.827052, 0.585701, 1.00000],
[0.996341, 0.660969, 0.451160, 1.00000],
[0.979645, 0.491014, 0.367783, 1.00000],
[0.913354, 0.330052, 0.382563, 1.00000],
[0.786212, 0.241514, 0.450184, 1.00000],
[0.639216, 0.189921, 0.494150, 1.00000],
[0.494258, 0.141462, 0.507988, 1.00000],
[0.347636, 0.0829460, 0.494121, 1.00000],
[0.198177, 0.0638620, 0.404009, 1.00000],
[0.0697640, 0.0497260, 0.193735, 1.00000],
[0.00146200, 0.000466000, 0.013866, 1.00000]])
# using the magma_r.color values we are able to create the
# whole color map using ListedColormap()
newcmp = ListedColormap(reversed_magma)
# we then set nan values to show up as 'white'
# which we will use to help us see errors
newcmp.set_bad(color='w')
def calc_cutoff(lst):
'''
calculates the single PE ADC cutoff value from the minimum pulse
histogram bar values. specifically, it picks the ADC value
directly between the pedastal and single PE peak.
'''
# assumes pedestal is tallest point on minimum
# pulse histogram
pedastal = max(lst)
# assumes single PE peak is second tallest
# point on minimum pulse historgam
ind = lst.index(pedastal)
lst.pop(ind)
pe_peak = max(lst)
# if the single PE peak is too close to the
# pedestal it recalculates the single PE peak
while pe_peak > pedastal * 0.98:
ind = lst.index(pe_peak)
lst.pop(ind) #loop this?
pe_peak = max(lst)
# calculates the cutoff to be the point between
# the single PE peak and the pedastal
cutoff = (pedastal + pe_peak) / 2
return cutoff
def position_vals():
'''
creates a list of the xy position values
at each event # using MOTO bank if motors
are running, and the SCAN bank if not
'''
# open the hdf5 file for reading
hdf5_file = h5py.File(
''.join([self.file_name, 'ScanEvents', '.hdf5']), 'r')
# save the name of all the groups (events)
# stored in the hdf5 file
keys = hdf5_file.keys()
# since the groups are python dictorinary
# keys they are not sorted in the order that they
# were stored so they need to be formatted
# and then properly stored
keys = list(map(int, list(keys)))
keys.sort()
# make a list containing all the groups in the file
groups = []
for key in keys:
groups.append(hdf5_file[str(key)])
# initialize the list to store all the
# position values from the scan
scan_vals = []
# loop through each group and store the
# position data in scan_vals
#
# when the motors are not running this code
# uses the SCAN bank to simulate xy data that
# would normally come from the MOTO bank
for group in groups:
for data_set_name in group.keys():
if data_set_name == "ch0":
data_set = group[data_set_name].value
dset = group[data_set_name]
moto_exists = dset.attrs["motors running"]
if moto_exists:
pos = dset.attrs["moto position"]
else:
pos = dset.attrs["scan position"]
scan_vals.append(pos)
return moto_exists, scan_vals
def detection_eff(scan_vals):
'''
calculates the detection efficiency at each xy position
and creates a list of all the xy positions without duplicates
'''
# initialize counter
i = 0
# initialize list to store xy positions
x_pos = []
y_pos = []
# initialize list to save xy position and
# minimum pulse values associated with that position
collective = []
# initialize list to use as temporary storage in loop
temp_lst = []
# if the motors are running use real xy position values
#
# for each position, associate a list of all the
# minimum pulse values at that position
if moto_exists:
old_pos = [0, 0]
for pos in scan_vals:
if pos[0] == old_pos[0] and pos[1] == old_pos[1]:
temp_lst.append(min_pulses[i])
else:
x_pos.append(old_pos[0])
y_pos.append(old_pos[1])
collective.append([old_pos, temp_lst])
temp_lst = []
temp_lst.append(min_pulses[i])
i += 1
old_pos = pos
# if motos aren't running, use simulated xy data from SCAN bank
#
# for each position, associate a list of all the
# minimum pulse values at that position
else:
old_pos = 0
for pos in scan_vals:
if pos == old_pos:
temp_lst.append(min_pulses[i])
else:
x_pos.append(old_pos)
y_pos.append(old_pos)
collective.append([old_pos, temp_lst])
temp_lst = []
temp_lst.append(min_pulses[i])
i += 1
old_pos = pos
x_pos = list(map(lambda x: x // 10, x_pos))
y_pos = list(map(lambda y: y % 10, y_pos))
# initilize list of detection efficiency at each point
d_eff_list = []
# for each xy position, calculate the pourcentage of
# associated minimum pulse values which lie below
# the cutoff ADC val (which mean a PE was seen by the PMT)
# then save these pourcentages in d_eff_list
for i in range(len(collective)):
loc = collective[i][0]
pulse_list = collective[i][1]
hits = 0
numof_pulses = len(pulse_list)
for pulse in pulse_list:
if pulse < cutoff:
hits += 1
hits1 = float(hits)
pulses = float(numof_pulses)
if pulses > 0:
d_eff = hits1 / pulses
else:
d_eff = 0.0
d_eff_list.append(d_eff)
return d_eff_list, x_pos, y_pos
def plotting():
'''
using the list of detection efficincies and the xy positions
this function plots a heat map of the detection efficiencies
on the xy positions of the scan
'''
side = int(np.sqrt(len(d_eff_l)))
Z = np.resize(d_eff_l, (side, side))
# plots heat map for detection efficiency and saves photo of it
# under the midas2hdf5 folder
plt.figure(figsize=(5, 5))
plt.imshow(Z,
cmap=newcmp,
interpolation='nearest',
extent=[min(x), max(x), min(y),
max(y)])
plt.colorbar()
plt.xlabel('X positon [m]')
plt.ylabel('Y positon [m]')
plt.title(''.join([self.file_name, ' detection efficency']))
plt.savefig(''.join([self.file_name, '_detectionEfficency']))
# opens the hdf5 file for reading
hdf5_file = h5py.File(''.join([self.file_name, 'ScanEvents', '.hdf5']),
'r')
# print the progress of the program
print("progress:")
y_hist, min_pulses = self.min_vals_histo(False)
cutoff = calc_cutoff(list(y_hist))
print("1/4")
moto_exists, scan_lst = position_vals()
print("2/4")
d_eff_l, x, y = detection_eff(scan_lst)
# closes the hdf5 file for reading
hdf5_file.close()
print("3/4")
# setting values of exactly zero equal to nan
# so they show up as white on the plot
for i in range(len(d_eff_l)):
if d_eff_l[i] == 0.0:
d_eff_l[i] = np.nan
plotting()
print("4/4 \nplot saved in midas2hdf5 folder")
import numpy as np
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
############### CMB colormap
from matplotlib.colors import ListedColormap
colombi1_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt")/255.)
colombi1_cmap.set_bad("gray") # color of missing pixels
colombi1_cmap.set_under("gray") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=colombi1_cmap)
############### CMB colormap
planck_parchment_cmap = ListedColormap(np.loadtxt("Planck_Parchment_RGB.txt")/255.)
#planck_parchment_cmap.set_bad("gray") # color of missing pixels
planck_parchment_cmap.set_bad("lightslategray") # color of missing pixels
#planck_parchment_cmap.set_under("white") # color of background, necessary if you want to use
# this colormap directly with hp.mollview(m, cmap=planck_parchment_cmap)
planck_grey_cmap = cm.get_cmap('gray')
############### Universal colormap
# setup linear colormap
planck_freqmap_cmap = ListedColormap(np.loadtxt("Planck_FreqMap_RGB.txt")/255.)
planck_freqmap_cmap.set_bad("gray") # color of missing pixels
planck_freqmap_cmap.set_under("white") # color of background, necessary if you want to use
# setup nonlinear colormap
from matplotlib.colors import LinearSegmentedColormap
class PlanckUniversalColormap(LinearSegmentedColormap):
name = "planckuniv"
