def get_iono_cb(ct_name='bwr'):
'''
Several custom colorbars used by RIM and AMIE have become standard when
visualizing data from these models. These are 'blue_white_red' and
'white_red', used for data that have positive and negative values and
for data that have only positive values, respectively. This function
builds and returns these colorbars when called with the initials of the
color table name as the only argument.
Other Parameters
================
ct_name : str
Select the color table. Can be 'bwr' for blue-white-red or 'wr' for
white-red. Defaults to 'bwr'.
Examples
========
>>> bwr_map = get_iono_cb('bwr')
>>> wr_map = get_iono_cb('wr')
'''
from matplotlib.colors import LinearSegmentedColormap as lsc
if ct_name == 'bwr':
table = {
'red': [(0., 0., .0), (.34, 0., 0.), (.5, 1., 1.), (1., 1., 1.)],
'green': [(0., 0., 0.), (.35, 1., 1.), (.66, 1., 1.),
(1., 0., 0.)],
'blue': [(0., 1., 1.), (.5, 1., 1.), (.66, 0., 0.), (.85, 0., 0.),
(1., .1, .1)]
}
cmap = lsc('blue_white_red', table)
elif ct_name == 'wr':
table = {
'red': [(0., 1., 1.), (1., 1., 1.)],
'green': [(0., 1., 1.), (1., 0., 0.)],
'blue': [(0., 1., 1.), (1., .0, .0)]
}
cmap = lsc('white_red', table)
return cmap
def get_iono_cb(ct_name='bwr'):
'''
Several custom colorbars used by RIM and AMIE have become standard when
visualizing data from these models. These are 'blue_white_red' and
'white_red', used for data that have positive and negative values and
for data that have only positive values, respectively. This function
builds and returns these colorbars when called with the initials of the
color table name as the only argument.
Other Parameters
================
ct_name : str
Select the color table. Can be 'bwr' for blue-white-red or 'wr' for
white-red. Defaults to 'bwr'.
Examples
========
>>> bwr_map = get_iono_cb('bwr')
>>> wr_map = get_iono_cb('wr')
'''
from matplotlib.colors import LinearSegmentedColormap as lsc
if ct_name=='bwr':
table = {
'red': [(0.,0.,.0),(.34,0.,0.),(.5,1.,1.),(1.,1.,1.)],
'green':[(0.,0.,0.),(.35,1.,1.),(.66,1.,1.),(1.,0.,0.)],
'blue' :[(0.,1.,1.),(.5,1.,1.),(.66,0.,0.),(.85,0.,0.),(1.,.1,.1)]
}
cmap = lsc('blue_white_red',table)
elif ct_name=='wr':
table = {
'red': [(0.,1.,1.),(1.,1.,1.)],
'green':[(0.,1.,1.),(1.,0.,0.)],
'blue' :[(0.,1.,1.),(1.,.0,.0)]
}
cmap = lsc('white_red',table)
return cmap
def cmap_map(function, cmap, name='colormap_mod', N=None, gamma=None):
"""
Modify a colormap using `function` which must operate on 3-element
arrays of [r, g, b] values.
You may specify the number of colors, `N`, and the opacity, `gamma`,
value of the returned colormap. These values default to the ones in
the input `cmap`.
You may also specify a `name` for the colormap, so that it can be
loaded using plt.get_cmap(name).
"""
from matplotlib.colors import LinearSegmentedColormap as lsc
if N is None:
N = cmap.N
if gamma is None:
gamma = cmap._gamma
cdict = cmap._segmentdata
# Cast the steps into lists:
step_dict = {key: list(map(lambda x: x[0], cdict[key])) for key in cdict}
# Now get the unique steps (first column of the arrays):
step_dicts = np.array(list(step_dict.values()))
step_list = np.unique(step_dicts)
# 'y0', 'y1' are as defined in LinearSegmentedColormap docstring:
y0 = cmap(step_list)[:, :3]
y1 = y0.copy()[:, :3]
# Go back to catch the discontinuities, and place them into y0, y1
for iclr, key in enumerate(['red', 'green', 'blue']):
for istp, step in enumerate(step_list):
try:
ind = step_dict[key].index(step)
except ValueError:
# This step is not in this color
continue
y0[istp, iclr] = cdict[key][ind][1]
y1[istp, iclr] = cdict[key][ind][2]
# Map the colors to their new values:
y0 = np.array(list(map(function, y0)))
y1 = np.array(list(map(function, y1)))
# Build the new colormap (overwriting step_dict):
for iclr, clr in enumerate(['red', 'green', 'blue']):
step_dict[clr] = np.vstack((step_list, y0[:, iclr], y1[:, iclr])).T
# Remove alpha, otherwise crashes...
step_dict.pop('alpha', None)
return lsc(name, step_dict, N=N, gamma=gamma)
from matplotlib.colors import LinearSegmentedColormap as lsc
cdict = {
'red': ((0., 1, 1), (0.05, 1, 1), (0.20, 0, 0), (0.66, 1, 1), (0.89, 1, 1),
(1, 0.5, 0.5)),
'green': ((0., 1, 1), (0.05, 1, 1), (0.20, 0, 0), (0.375, 1, 1),
(0.64, 1, 1), (0.91, 0, 0), (1, 0, 0)),
'blue': ((0., 1, 1), (0.075, 1, 1), (0.20, 1, 1), (0.34, 1, 1),
(0.65, 0, 0), (1, 0, 0))
}
whitejet = lsc('whitejet', cdict, 256)
def plot_1sta(sim_path, staname, hypocenter, stafile, tlims=[0, 30]):
'''
Plot one station and it's predicted arrivals from ray tracing
'''
from matplotlib import pyplot as plt
from obspy.taup import TauPyModel
from numpy import genfromtxt, where, rad2deg
from glob import glob
from pyproj import Geod
#load data
afile = glob(sim_path + '*.' + staname + '.acc*')[0]
a = genfromtxt(afile)
vfile = glob(sim_path + '*.' + staname + '.vel*')[0]
v = genfromtxt(vfile)
#Station coordinates
all_stations = genfromtxt(sim_path + 'param_files/' + stafile,
