def plot_stats_pctl(x, stat, pctl, xlabel, ylabel, ftitle, fname):
mean = stat["mean"]
std = stat["std"]
p10 = pctl['p10']
p90 = pctl['p90']
plt.switch_backend('agg')
fig = plt.figure(figsize=(12,9))
ax1 = fig.add_subplot(111)
ax1.plot(x, mean, 'g-', alpha=0.75, label='Mean')
ax1.plot(x, p10, 'b-', alpha=0.25)
ax1.plot(x, p90, 'b-', alpha=0.25)
ax1.fill_between(x, p10, p90, alpha=0.25, label='90% prediction interval')
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel, color='b')
ax1.tick_params('y', colors='b')
ax1.grid()
ax1.legend()
ax2 = ax1.twinx()
ax2.plot(x, std, 'r-', alpha=0.5)
ax2.set_ylabel('Standard deviation', color='r')
ax2.tick_params('y', colors='r')
ax2 = format_exponent(ax2, axis='y')
plt.title(ftitle)
fig.savefig(fname)
plt.close(fig)
def plot_stats(x, stat, xlabel, ylabel, ftitle, fname):
mean = np.array(stat["mean"])
var = stat["var"]
std = np.array(stat['std'])
plt.switch_backend('agg')
fig = plt.figure(figsize=(12,9))
ax1 = fig.add_subplot(111)
ax1.plot(x, mean, 'g-', alpha=0.75, label='Mean')
ax1.plot(x, mean-std, 'b-', alpha=0.25)
ax1.plot(x, mean+std, 'b-', alpha=0.25)
ax1.fill_between(x, mean-std, mean+std, alpha=0.25, label=r'Mean $\pm$ deviation')
ax1.set_xlabel(xlabel)
ax1.set_ylabel(ylabel, color='b')
ax1.tick_params('y', colors='b')
ax1.grid()
ax1.legend()
ax2 = ax1.twinx()
ax2.plot(x, var, 'r-', alpha=0.5)
ax2.set_ylabel('Variance', color='r')
ax2.tick_params('y', colors='r')
ax2 = format_exponent(ax2, axis='y')
plt.title(ftitle)
fig.savefig(fname)
plt.close(fig)
def plot_mesh(mesh, key, plot_title, color, *args, **kwargs):
import matplotlib as mtpl
mtpl.use('pdf')
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pylab as plt
plt.switch_backend('pdf')
import seaborn as sns
import os
print('plotting {0} - {1}'.format(key, plot_title))
path = myfs.setup_output_directory(kwargs.get('expt_dir', ''),
kwargs.get('dir_path_full', ''), *args)
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=1.5)
fmt = 'f' if mesh.dtype == np.float64 else 'd'
h = sns.heatmap(mesh, annot=True, fmt=fmt, cmap=color)
h.set(xlabel="Mesh map X")
h.set(ylabel="Mesh map Y")
h.set(title=plot_title + " heat map - " + key)
h_fig = h.get_figure()
pp = PdfPages(
os.path.join(path, plot_title + key + kwargs.get('name', '') + '.pdf'))
h_fig.savefig(pp, format='pdf')
plt.close()
pp.close()
def gromov_wasserstein_distance_TSNE_test(data_path, num_labels, num_clusters,
result_path):
import scipy as sp
import matplotlib.pylab as pl
pl.switch_backend('agg') # FIXME: add this line if executed on server.
import ot
d_t = np.load(data_path + config.statistic_name4d_t)
d_s = np.load(data_path + config.statistic_name4d_s)
# Compute distance kernels, normalize them and then display
xs = d_s.item().get('0')
xt = d_t.item().get('0')
print(xt.shape)
print(xs.shape)
n_samples = min(100, xs.shape[0], xt.shape[0])
xs = xs[:n_samples]
xt = xt[:n_samples]
C1 = sp.spatial.distance.cdist(xs, xs)
C2 = sp.spatial.distance.cdist(xt, xt)
C1 /= C1.max()
C2 /= C2.max()
p = ot.unif(n_samples)
q = ot.unif(n_samples)
gw0, log0 = ot.gromov.gromov_wasserstein(C1,
C2,
p,
q,
'square_loss',
verbose=True,
log=True)
gw, log = ot.gromov.entropic_gromov_wasserstein(C1,
C2,
p,
q,
'square_loss',
epsilon=5e-4,
log=True,
verbose=True)
print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
pl.figure(1, (10, 5))
pl.subplot(1, 2, 1)
pl.imshow(gw0, cmap='jet')
pl.title('Gromov Wasserstein')
pl.subplot(1, 2, 2)
pl.imshow(gw, cmap='jet')
pl.title('Entropic Gromov Wasserstein')
pl.savefig(result_path + "/WD_TSNE.jpg")
def plot_sobols_all(x, sobols, params, ftitle, fname):
plt.switch_backend('agg')
npar = len(params)
plt.switch_backend('agg')
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
for i in range(npar):
s = sobols[params[i]]
ax.plot(x, s, label=params[i])
ax.set_xlabel(r'$\rho_{tor} ~ [m]$')
ax.set_ylabel('Sobol index')
ax.set_title(ftitle)
plt.legend()
fig.savefig(fname)
plt.close(fig)
def plot_sobols(x, sobols, params, ftitle, fname):
plt.switch_backend('agg')
npar = len(params)
if npar==2:
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
s = sobols[params[0]]
ax.plot(x, s, label=params[0])
s = sobols[params[1]]
ax.plot(x, s, label=params[1])
ax.legend()
ax.grid()
ax.set_title(ftitle)
fig.savefig(fname)
plt.close(fig)
if npar==4:
fig, axs = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for i in range(npar):
ax = axs[i//2, i%2]
s = sobols[params[i]]
ax.plot(x, s)
ax.grid()
ax.set_title(params[i])
fig.suptitle(ftitle)
fig.savefig(fname)
plt.close(fig)
if npar==6:
fig, axs = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)
for i in range(npar):
ax = axs[i//3, i%3]
s = sobols[params[i]]
ax.plot(x, s)
ax.grid()
ax.set_title(params[i])
fig.suptitle(ftitle)
fig.savefig(fname)
plt.close(fig)
def __init__(self, gdf_map, path_to_centroids=''):
"""
Initialize a TrajectoryClustermap objects
Parameters:
gdf_map: GeoDataFrame to be plotted (typically the municipalities
for all Italy)
path_to_centroids: str, path to a file containing the controids of
each municipality
"""
plt.switch_backend('agg')
self.fig = plt.figure(figsize=(13, 15))
self.ax = self.fig.add_subplot(1, 1, 1)
self.fontsize = 20
self.city_markersize = 6
self.city_marker = 'o'
self.city_markercolor = 'k'
self.map = gdf_map
self.df_centroids = pd.read_csv(path_to_centroids)
def plot_passage(df, plot_title, path, key, *kwargs):
import os
import matplotlib as mtpl
mtpl.use('pdf')
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pylab as plt
plt.switch_backend('pdf')
import seaborn as sns
print('plotting {}'.format(plot_title))
phi, t = get_cgs_camera_positions()
x, y = np.meshgrid(phi, t)
columns = [
"longitude", "latitude", "sum_refine", "sum_match", "mean_refine",
"mean_match", "mean_ratio", "mesh_num"
]
def plot_data(title, data):
plt.figure(figsize=(16, 12))
sns.set("paper", "whitegrid", "dark", font_scale=1.5)
pp = PdfPages(
os.path.join(path,
plot_title + key + kwargs.get('name', '') + '.pdf'))
import numpy as np
import matplotlib.pylab as plt
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
# ---------------------------------------------------------
# Simple finite difference solver
#
# Acoustic wave equation p_tt = c^2 p_xx + src
# 2-D regular grid
# ---------------------------------------------------------
nx = 200 # grid points in x
nz = 200 # grid points in z
nt = 1000 # number of time steps
dx = 10.0 # grid increment in x
dt = 0.001 # Time step
c0 = 3000.0 # velocity (can be an array)
isx = nx / 2 # source index x
isz = nz / 2 # source index z
ist = 100 # shifting of source time function
f0 = 100.0 # dominant frequency of source (Hz)
isnap = 10 # snapshot frequency
T = 1.0 / f0 # dominant period
nop = 3 # length of operator
# Model type, available are "homogeneous", "fault_zone",
# "surface_low_velocity_zone", "random", "topography",
# "slab"
model_type = "fault_zone"
def plot_reputation_networks_fs(network, filename='output/final_state.png', dpi=80, width=15, height=15, alpha=1.,
colors_dict=None, sfdp_C=2, sfdp_p=6, node_shrink_fac=0.25, font_size=28,
binary_decision=None):
# calc values
color_converter = ColorConverter()
agents_pmap = network.vp['agents']
if colors_dict is None:
colors_dict = defaultdict(lambda: color_converter.to_rgba('blue')) # covers cases with #words > 3
colors_dict[1] = color_converter.to_rgba('green') # one word
colors_dict[2] = color_converter.to_rgba('blue') # two words
colors_dict[3] = color_converter.to_rgba('red') # three and more words
for key, val in colors_dict.iteritems():
if isinstance(val, str):
val = color_converter.to_rgba(val)
colors_dict[key] = val[:3] + tuple([alpha])
colors_pmap = network.new_vertex_property('vector<double>')
shape_pmap = network.new_vertex_property('int')
used_keys = set()
for v in network.vertices():
agent_size = len(agents_pmap[v])
if binary_decision is not None:
agent_size = 1 if agent_size > 1 else 0
used_keys.add(agent_size)
colors_pmap[v] = colors_dict[agent_size]
shape_pmap[v] = agent_size
colors_dict = {key: val for key, val in colors_dict.iteritems() if key in used_keys}
# calc node-size
res = (dpi * height, dpi * width)
tmp_output_size = min(res) * 0.7
num_nodes = network.num_vertices()
if num_nodes < 10:
num_nodes = 10
max_vertex_size = np.sqrt((np.pi * tmp_output_size ** 2) / num_nodes)
if max_vertex_size < 1:
max_vertex_size = 1
min_vertex_size = max(max_vertex_size * node_shrink_fac, 1)
deg_pmap = network.degree_property_map('total')
verterx_size = prop_to_size(deg_pmap, mi=min_vertex_size, ma=max_vertex_size, power=1)
# plot
plt.close('all')
plt.switch_backend('cairo')
f, ax = plt.subplots(figsize=(width, height))
tmp = ["o", "^", "s", "p", "h", "H", "8", "double_circle", "double_triangle", "double_square",
"double_pentagon", "double_hexagon", "double_heptagon", "double_octagon"]
if binary_decision:
gt_shape_to_plt_shape = {idx: val for idx, val in enumerate(tmp)}
else:
gt_shape_to_plt_shape = {idx+1: val for idx, val in enumerate(tmp)}
for key, val in sorted(colors_dict.iteritems(), key=lambda x: x[0]):
label = str(key) + ' ' + ('word' if key == 1 else 'words')
if binary_decision is not None:
label = binary_decision[key]
ax.plot(None, label=label, color=val, ms=font_size, marker=gt_shape_to_plt_shape[key], lw=10, alpha=alpha, ls='')
pos = sfdp_layout(network, C=sfdp_C, p=sfdp_p)
graph_draw(network, pos=pos, vertex_shape=shape_pmap, edge_color=[0.179, 0.203, 0.210, alpha / 6], vertex_size=verterx_size, output_size=res, mplfig=ax, vertex_fill_color=colors_pmap) # , bg_color=color_converter.to_rgba('white'))
plt.legend(loc='upper right', prop={'size': font_size})
plt.axis('off')
plt.savefig(filename, bbox_inches='tight', dpi=dpi)
plt.close('all')
plt.switch_backend('Agg')
import numpy as np
import matplotlib.pylab as plt
import sys
#import pickle
import uproot
plt.switch_backend("Agg")
import plotting_tools as pt
################################################################################
# RUN OVER FILES IN THE PID_assignment subdirectory
# Variables of interest
pars = pt.get_variable_parameters_for_plotting()
voi = ['ncharged', 'pp', 'ep', 'mup']
values = {}
for v in voi:
values[v] = []
infilenames = sys.argv[1:]
sptag = pt.get_sptag(infilenames[0])
print(sptag)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import flask
from flask.ext.cache import Cache
import matplotlib.pylab as plt
from matplotlib.colors import hex2color
plt.switch_backend("agg")
import collections
import copy
import geojson
import json
from obspy.imaging.mopad_wrapper import Beach
import io
import inspect
import numpy as np
import os
import lasif
WEBSERVER_DIRECTORY = os.path.dirname(os.path.abspath(inspect.getfile(
inspect.currentframe())))
STATIC_DIRECTORY = os.path.join(WEBSERVER_DIRECTORY, "static")
app = flask.Flask("LASIF Webinterface", static_folder=STATIC_DIRECTORY)
cache = Cache()
def make_cache_key(*args, **kwargs):
def freqtor(yr, mo, dy, hr, mn, sc, duration, ndays, datdir, freq1, freq2,
thresholdv, deltaf, masktimes, madtimes, time_thres,
distance_thres):
#control plot behavior
import matplotlib.pylab as plt
plt.switch_backend("nbagg")
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = 18, 12 #width,then height
plt.rcParams['savefig.dpi'] = 80
from obspy import UTCDateTime
import numpy as np
import matplotlib.dates as mdates
import matplotlib.tri as tri
from obspy.signal.trigger import recursive_sta_lta as recSTALTA
from obspy.signal.trigger import trigger_onset as triggerOnset
import copy, os, bisect, scipy, datetime, itertools
import pandas as pd
#suppress the chained assignment warning
pd.options.mode.chained_assignment = None
from mpl_toolkits.basemap import Basemap
from obspy.taup import TauPyModel as TauP
model = TauP(model="iasp91")
from obspy.geodetics import locations2degrees as loc2d
import Util as Ut
import geopy.distance as pydist
from obspy.core import read
#############################
homedir = ''
wb = 5 #which basin # are we working on for station list import
maketemplates = 1
tlength = 7200 #nsamples on either side of detection time for template
counter = datetime.date(int(yr), int(mo), int(dy)).timetuple().tm_yday
edgebuffer = 00
duration = duration + edgebuffer
#ndays= 2 #however many days you want to generate images for
dayat = int(dy)
#set parameter values; k = area threshold for detections:
#thresholdv= 2.0
#deltaf = 250.0
nseconds = 7200
npts = int(deltaf * (nseconds + edgebuffer))
fftsize = 256
overlap = 4
hop = fftsize / overlap
w = scipy.hanning(fftsize + 1)[:-1]
#delta=250.0
if duration == 86400:
im = 12
elif duration == 7200:
im = 1
else:
im = 1
#parse the datetime
counter_3char = str(counter).zfill(3)
datest = yr + str('-') + mo + str('-') + str(dayat) + str('T') + hr + str(
':') + mn + str('.') + sc
tt = UTCDateTime(datest)
#####################################################################
# Now start making the detections, in 2 hour data chunks, 1 day at a time
print(os.getcwd())
for days in range(ndays):
plt.close('all')
print(str(tt))
sacyear = str(tt.date.year)
sacmonth = str(tt.date.month)
sacday = str(tt.date.day)
if len(sacmonth) == 1:
sacmonth = str(0) + sacmonth
if len(sacday) == 1:
sacday = str(0) + sacday
sacname = str(sacyear) + str(sacmonth) + str(sacday)
sacdir = datdir + sacyear + sacmonth + sacday + '/' + sacname + '*.sac'
#############################
s = homedir + 'basin%s/' % wb + yr + str('_') + counter_3char
if not os.path.exists(s):
os.makedirs(s)
sz = read(sacdir)
sz.sort()
sz.detrend()
sz.trim(starttime=tt,
endtime=tt + duration,
pad=True,
fill_value=000,
nearest_sample=False)
sz.filter('highpass', freq=1.0)
alltimes = Ut.gettvals(sz[0], sz[1], sz[2])
#############################
#########################
#%%
nptsf = edgebuffer * deltaf
blockette = 0
d = {
'Contributor': 'NA',
'Latitude': 'NA',
'Longitude': 'NA',
'S1': 'NA',
'S1time': 'NA',
'Magnitude': -999.00,
'mag_error': -999.00,
'cent_er': -999.00,
'Confidence': 0,
'S2': 'NA',
'S3': 'NA',
'S4': 'NA',
'S5': 'NA',
'S2time': 'NA',
'S3time': 'NA',
'S4time': 'NA',
'S5time': 'NA',
'Type': 'Event'
}
index = [0]
df1 = pd.DataFrame(data=d, index=index)
stations, latitudes, longitudes, distances = [], [], [], []
snames, latitudes, longitudes = [], [], []
for i in range(len(sz)):
snames.append(str(sz[i].stats.station))
latitudes.append(sz[i].stats.sac['stla'])
longitudes.append(sz[i].stats.sac['stlo'])
latmin = min(latitudes)
lonmin = max(longitudes)
newlat = np.empty([len(snames)])
newlon = np.empty([len(snames)])
stations = copy.copy(snames)
for i in range(len(snames)):
reindex = stations.index(snames[i])
newlat[i] = latitudes[reindex]
newlon[i] = longitudes[reindex]
distances.append(
pydist.vincenty([newlat[i], newlon[i]],
[latmin, lonmin]).meters)
#####this is where maths happends and arrays are created
for block in range(im):
print(blockette, tt)
ll, lo, stalist, vizray, dist = [], [], [], [], []
shorty = 0
for z in range(len(snames)):
szdata = sz[z].data[blockette:blockette + npts]
# if len(szdata)==npts:
vizray.append([])
Bwhite = Ut.w_spec(szdata, deltaf, fftsize, freq1, freq2)
vizray[shorty].append(np.sum(Bwhite[:, :], axis=0))
ll.append(newlat[z])
lo.append(newlon[z])
dist.append(distances[z])
stalist.append(snames[z])
shorty = shorty + 1
rays = np.vstack(np.array(vizray))
ix = np.where(np.isnan(rays))
rays[ix] = 0
rayz = np.copy(rays)
latudes = copy.copy(ll)
longitudes = copy.copy(lo)
slist = copy.copy(stalist)
#sort the array orders by distance from lomin,latmin
for i in range(len(slist)):
junk = np.where(np.array(dist) == max(dist))
rayz[i] = rays[junk[0][0]]
ll[i] = latudes[junk[0][0]]
lo[i] = longitudes[junk[0][0]]
slist[i] = stalist[junk[0][0]]
dist[junk[0][0]] = -9999999999
timevector = Ut.getfvals(tt, Bwhite, nseconds, edgebuffer)
#clean up the array
rayz = Ut.saturateArray(rayz, masktimes)
ix = np.where(np.isnan(rayz))
rayz[ix] = 0
#determine which level to use as detections 4* MAD
levels = [Ut.get_levels(rayz, madtimes)]
#get the ANF catalog events and get closest station
localE, globalE, closesti = Ut.getCatalogData(tt, nseconds, lo, ll)
#closesti = np.flipud(closesti)
#unstructured triangular mesh with stations as verticies, mask out the long edges
triang = tri.Triangulation(lo, ll)
mask, edgeL = Ut.long_edges(lo, ll, triang.triangles)
triang.set_mask(mask)
kval = Ut.get_k(lo, ll, triang.triangles, thresholdv)
#%%
#get contour areas by frame
av,aa,xc,yc,centroids,ctimes,ctimesdate,junkx,junky=[],[],[],[],[],[],[],[],[]
for each in range(len(rayz[0, :])):
# refiner = tri.UniformTriRefiner(triang)
# tri_refi, z_refi = refiner.refine_field(rayz[0:,each], subdiv=0)
cs = plt.tricontour(triang,
rayz[0:, each],
mask=mask,
levels=levels,
colors='c',
linewidths=[1.5])
contour = cs.collections[0].get_paths()
for alls in range(len(contour)):
vs = contour[alls].vertices
a = Ut.PolygonArea(vs)
aa.append(a)
x = vs[:, 0]
y = vs[:, 1]
points = np.array([x, y])
points = points.transpose()
sx = sy = sL = 0
for i in range(
len(points)): # counts from 0 to len(points)-1
x0, y0 = points[
i -
1] # in Python points[-1] is last element of points
x1, y1 = points[i]
L = ((x1 - x0)**2 + (y1 - y0)**2)**0.5
sx += (x0 + x1) / 2 * L
sy += (y0 + y1) / 2 * L
sL += L
xc.append(sx / sL)
yc.append(sy / sL)
if aa != []:
idi = np.where(np.array(aa) > kval)
filler = np.where(np.array(aa) <= kval)
chained = itertools.chain.from_iterable(filler)
chain = itertools.chain.from_iterable(idi)
idi = list(chain)
filler = list(chained)
for alls in range(len(aa)):
if aa[alls] > kval:
centroids.append([xc[idi[0]], yc[idi[0]]])
ctimes.append(timevector[each])
ctimesdate.append(timevector[each])
av.append(aa[idi[0]])
else:
centroids.append([0, 0])
ctimes.append(timevector[each])
ctimesdate.append(timevector[each])
av.append(0)
aa, yc, xc = [], [], []
#%% Filter peaks in av above threshold by time and distance to remove redundant.
idxx, idx, regionals, localev = [], [], [], []
coordinatesz = np.transpose(centroids)
avz = av
abovek = np.where(np.array(avz) > 0)
idxx = abovek[0]
iii = []
for i in range(len(abovek[0]) - 1):
junk = ctimes[idxx[i + 1]] - ctimes[idxx[i]]
junk1 = centroids[idxx[i]]
junk2 = centroids[idxx[i + 1]]
if junk.seconds < time_thres and pydist.vincenty(
junk2, junk1).meters < distance_thres:
iii.append(idxx[i + 1])
idxx = set(idxx) - set(iii)
idxx = list(idxx)
idxx.sort()
idx = idxx
ltxlocal, ltxlocalexist = [], []
ltxglobal = []
ltxglobalexist = []
doubles, localev = [], []
dit2 = []
#%%
#if there are no picks but cataloged events exist, make null arrays
if len(idx) == 0 and len(globalE) > 0:
ltxglobalexist = np.ones(len(globalE))
if len(idx) == 0 and len(localE) > 0:
ltxlocalexist = np.ones(len(localE))
#try to match detections with known catalog events based on time and location
if len(idx) > 0:
distarray = []
dmin = np.zeros([5])
dval = np.zeros([5])
closestl = np.empty([len(idx), 5])
dvals = np.empty([len(idx), 5])
closestl = closestl.astype(np.int64)
for i in range(len(idx)):
#find distance to the 5 nearest stations and save them for plotting templates
for each in range(len(ll)):
distarray.append(
pydist.vincenty([
coordinatesz[1][idx[i]],
coordinatesz[0][idx[i]]
], [ll[each], lo[each]]).meters)
for all5 in range(5):
dmin[all5] = np.argmin(distarray)
dmin = dmin.astype(np.int64)
dval[all5] = distarray[dmin[all5]]
distarray[dmin[all5]] = 9e10
closestl[i][:] = dmin
dvals[i][:] = dval
dmin = np.zeros_like(dmin)
distarray = []
#get timeseries for this pick
stg = slist[closestl[i][0]]
timeindex = bisect.bisect_left(alltimes, ctimes[idx[i]])
sss = sz.select(station=stg)
av = sss[0].data[timeindex - tlength:timeindex + tlength]
cf = recSTALTA(av, int(40), int(1200))
peaks = triggerOnset(cf, 3, .2)
#get rid of peaks that are way off LTX times
peaksi = []
for peak in peaks:
peak = peak[0]
junk = alltimes[timeindex] - alltimes[timeindex -
tlength + peak]
if abs(junk.seconds) > 45:
peaksi.append(i)
peaks = np.delete(peaks, peaksi, axis=0)
#look for overlap with ANF global
for j in range(len(globalE)):
#get distance between stations and depth for theoretical ttime calc
# the time buffers are somewhat arbitrary
dep = globalE.depth[j]
dit = loc2d(centroids[idx[i]][1], centroids[idx[i]][0],
globalE.Lat[j], globalE.Lon[j])
arrivals = model.get_travel_times(dep,
dit,
phase_list=['P'])
#if no calculated tt but sta/lta peak
if len(arrivals) == 0 and len(peaks) != 0:
junk = UTCDateTime(
alltimes[timeindex - tlength +
peaks[0][0]]) - UTCDateTime(
globalE.DateString[j])
if junk > -40 and junk < 40:
doubles.append(idx[i])
ltxglobal.append(
UTCDateTime(alltimes[timeindex - tlength +
peaks[0][0]]))
ltxglobalexist.append(0)
else:
ltxglobalexist.append(1)
#if no calculated tt and no sta/lta peak use ltx time
elif len(arrivals) == 0 and len(peaks) == 0:
junk = UTCDateTime(
alltimes[timeindex]) - UTCDateTime(
globalE.DateString[j])
if junk > -40 and junk < 40:
doubles.append(idx[i])
ltxglobal.append(
UTCDateTime(alltimes[timeindex]))
ltxglobalexist.append(0)
else:
ltxglobalexist.append(1)
#if there are calculated arrivals and sta/lta peak
elif len(peaks) != 0:
junk = UTCDateTime(
alltimes[timeindex - tlength + peaks[0][0]]
) - (UTCDateTime(globalE.DateString[j]) +
datetime.timedelta(seconds=arrivals[0].time))
if junk > -30 and junk < 30:
doubles.append(idx[i])
ltxglobal.append(
UTCDateTime(alltimes[timeindex - tlength +
peaks[0][0]]))
ltxglobalexist.append(0)
else:
ltxglobalexist.append(1)
#if there are calculated arrivals and no sta/lta peaks
else:
junk = UTCDateTime(alltimes[timeindex]) - (
UTCDateTime(globalE.DateString[j]) +
datetime.timedelta(seconds=arrivals[0].time))
if junk > -60 and junk < 60:
doubles.append(idx[i])
ltxglobalexist.append(0)
else:
ltxglobalexist.append(1)
#look for overlap with ANF local
if len(localE) > 0 and len(peaks) != 0:
for eachlocal in range(len(localE)):
#junk= UTCDateTime(alltimes[timeindex-tlength+peaks[0][0]]) - UTCDateTime(localE.DateString[eachlocal])
#took this out because faulty picks disassociated too many events
#calculate with LTX pick time instead
dep = localE.depth[eachlocal]
dit = pydist.vincenty(
[centroids[idx[i]][1], centroids[idx[i]][0]],
[localE.Lat[eachlocal], localE.Lon[eachlocal]
]).meters
junk = UTCDateTime(
alltimes[timeindex]) - UTCDateTime(
localE.DateString[eachlocal])
if junk > -60 and junk < 60 and dit < 2.0 * edgeL:
localev.append(idx[i])
ltxlocal.append(
UTCDateTime(alltimes[timeindex - tlength +
peaks[0][0]]))
ltxlocalexist.append(0)
else:
ltxlocalexist.append(1)
if len(localE) > 0 and len(peaks) == 0:
for eachlocal in range(len(localE)):
dep = localE.depth[eachlocal]
dit = pydist.vincenty(
[centroids[idx[i]][1], centroids[idx[i]][0]],
[localE.Lat[eachlocal], localE.Lon[eachlocal]
]).meters
junk = UTCDateTime(
alltimes[timeindex]) - UTCDateTime(
localE.DateString[eachlocal])
if junk > -60 and junk < 60 and dit < 2.0 * edgeL:
localev.append(idx[i])
ltxlocal.append(
UTCDateTime(alltimes[timeindex]))
ltxlocalexist.append(0)
else:
ltxlocalexist.append(1)
#if it goes with a local- don't let it go with a double too
dupe = []
for dl in range(len(doubles)):
if localev.count(doubles[dl]) > 0:
dupe.append(doubles[dl])
for repeats in range(len(dupe)):
doubles.remove(dupe[repeats])
#
detections = []
detections = set(idx) #-set(doubles)
detections = list(detections)
#or if there are more locals LTX detections than ANF locals, fix it
#by assuming double pick on closest pair
pdist = []
if len(localev) > len(localE):
for i in range(len(localev) - 1):
pdist.append(localev[i + 1] - localev[i])
junk = np.where(pdist == min(pdist))
localev.pop(junk[0][0] + 1)
#detections.remove(localev[junk[0][0]+1])
detections.sort()
idx = detections
dtype, cents = Ut.markType(detections, centroids, localev,
localE, ctimes, doubles)
#get the nearest station also for cataloged events
closestd = np.zeros([len(doubles)])
distarray = np.zeros([len(ll)])
for event in range(len(doubles)):
for each in range(len(ll)):
distarray[each] = pydist.vincenty([
coordinatesz[1][doubles[event]],
coordinatesz[0][doubles[event]]
], [ll[each], lo[each]]).meters
finder = np.argmin(distarray)
closestd[event] = finder
distarray[finder] = 9e10
closestd = closestd.astype(np.int64)
closestp = []
distarray = np.zeros([len(ll)])
for event in range(len(localev)):
for each in range(len(ll)):
distarray[each] = pydist.vincenty([
coordinatesz[1][localev[event]],
coordinatesz[0][localev[event]]
], [ll[each], lo[each]]).meters
finder = np.argmin(distarray)
closestp.append(finder)
distarray[finder] = 9e10
#%%#save templates from this round of picks to verify on closest station
ss = str(tt)
ss = ss[0:13]
if 'detections' in locals():
index = range(len(detections))
else:
index = [0]
detections = []
df = pd.DataFrame(data=d, index=index)
if maketemplates == 1 and len(detections) > 0:
ptimes, confidence = [], []
magi = np.zeros_like(dvals)
dum = 0
for fi in range(len(detections)):
if localev.count(detections[fi]) == 0:
df.Contributor[fi] = 'LTX'
else:
df.Contributor[fi] = 'ANF,LTX'
allmags = [
localE.ms[dum], localE.mb[dum], localE.ml[dum]
]
df.Magnitude[fi] = np.max(allmags)
dum = dum + 1
#df.Latitude[fi] = coordinatesz[1][detections[fi]]
#df.Longitude[fi]=coordinatesz[0][detections[fi]]
df.Latitude[fi] = cents[fi][0]
df.Longitude[fi] = cents[fi][1]
df.Type[fi] = dtype[fi]
plt.cla()
ax = plt.gca()
timeindex = bisect.bisect_left(alltimes,
(ctimes[detections[fi]]))
sss = np.zeros([5, tlength * 2])
for stas in range(5):
stg = slist[closestl[fi][stas]]
tr = sz.select(station=stg)
if ctimes[detections[fi]] - datetime.timedelta(
seconds=80) < tt.datetime:
sss[stas][tlength:] = tr[0].data[
timeindex:timeindex + tlength]
elif ctimes[detections[fi]] + datetime.timedelta(
seconds=80) > tt.datetime + datetime.timedelta(
seconds=nseconds + edgebuffer):
sss[stas][0:tlength] = tr[0].data[
timeindex - tlength:timeindex]
else:
sss[stas][:] = tr[0].data[timeindex -
tlength:timeindex +
tlength]
sss = np.nan_to_num(sss)
stg = slist[closestl[0][0]]
#plt.figure(fi)
peak = None
plt.suptitle('nearest station:' + stg + ' ' +
str(ctimes[detections[fi]]) + 'TYPE = ' +
dtype[fi])
for plots in range(5):
plt.subplot(5, 1, plots + 1)
cf = recSTALTA(sss[plots][:], int(80), int(500))
peaks = triggerOnset(cf, 3, .1)
peaksi = []
dummy = 0
for pk in peaks:
endi = pk[1]
peak = pk[0]
mcdur = alltimes[timeindex - tlength +
endi] - alltimes[timeindex -
tlength + peak]
mdur = mcdur.total_seconds()
if alltimes[timeindex] > alltimes[timeindex -
tlength + peak]:
junk = alltimes[timeindex] - alltimes[
timeindex - tlength + peak]
else:
junk = alltimes[timeindex - tlength +
peak] - alltimes[timeindex]
if (junk.seconds) > 40:
peaksi.append(dummy)
dummy = dummy + 1
peaks = np.delete(peaks, peaksi, axis=0)
sss[plots] = np.nan_to_num(sss[plots])
#if the channel is blank underflow problems occur plotting station name
sss = np.round(sss, decimals=10)
plt.plot(
Ut.templatetimes(alltimes[timeindex], tlength,
deltaf), sss[plots][:], 'black')
plt.axvline(x=alltimes[timeindex])
plt.text(alltimes[timeindex],
0,
slist[closestl[fi][plots]],
color='red',
fontsize=20)
plt.axis('tight')
for arc in range(len(peaks)):
plt.axvline(x=alltimes[timeindex - tlength - 10 +
peaks[arc][0]],
color='orange')
plt.axvline(x=alltimes[timeindex - tlength - 10 +
peaks[arc][1]],
color='purple')
if len(peaks) > 0:
ptimes.append(
UTCDateTime(alltimes[timeindex - tlength - 10 +
peaks[0][0]]))
confidence.append(len(peaks))
magi[fi][plots] = (
-2.25 + 2.32 * np.log10(mdur) +
0.0023 * dvals[fi][plots] / 1000)
#magi[fi][plots]=(1.86*np.log10(mdur)-0.85)
else:
ptimes.append(UTCDateTime(alltimes[timeindex]))
confidence.append(2)
magi = np.round(magi, decimals=2)
magii = pd.DataFrame(magi)
magu = magii[magii != 0]
if df.Contributor[fi] == 'ANF,LTX':
df.mag_error[fi] = np.round(np.max(allmags) -
np.mean(magu, axis=1)[fi],
decimals=2)
df.Magnitude[fi] = str(
str(df.Magnitude[fi]) + ',' +
str(np.round(np.mean(magu, axis=1)[fi],
decimals=2)))
df.cent_er[fi] = np.round(pydist.vincenty([
coordinatesz[1][detections[fi]],
coordinatesz[0][detections[fi]]
], [cents[fi][0], cents[fi][1]]).meters / 1000.00,
decimals=2)
else:
df.Magnitude[fi] = np.round(np.mean(magu, axis=1)[fi],
decimals=2)
#ptimes = np.reshape(ptimes,[len(ptimes)/5,5])
df.S1[fi] = slist[closestl[fi][0]]
df.S1time[fi] = ptimes[0]
df.S2[fi] = slist[closestl[fi][1]]
df.S2time[fi] = (ptimes[1])
df.S3[fi] = slist[closestl[fi][2]]
df.S3time[fi] = (ptimes[2])
df.S4[fi] = slist[closestl[fi][3]]
df.S4time[fi] = (ptimes[3])
df.S5[fi] = slist[closestl[fi][4]]
df.S5time[fi] = (ptimes[4])
#df.Confidence[fi]= confidence[0]
ptimes = []
if dtype[fi] == 'earthquake':
svname = homedir + str(s) + "/image" + ss[
11:13] + "_pick_" + str(fi + 1) + ".png"
plt.savefig(svname, format='png')
plt.clf()
#%%
df1 = [df1, df]
df1 = pd.concat(df1)
################################################
#%%
fig = plt.figure()
plt.cla()
ax = plt.gca()
#plot it all
for i in range(len(detections)):
if localev.count(detections[i]) == 1:
color = 'c'
elif doubles.count(detections[i]) == 1:
color = 'blue'
else:
color = 'white'
if dtype[i] == 'blast':
facecolor = 'none'
else:
facecolor = color
plt.scatter(mdates.date2num(ctimes[detections[i]]),
closestl[i][0],
s=200,
color=color,
facecolor=facecolor)
#
for i in range(len(globalE)):
plt.scatter(mdates.date2num(UTCDateTime(globalE.time[i])),
1,
s=100,
color='b',
alpha=.8)
for i in range(len(localE)):
plt.scatter(mdates.date2num(UTCDateTime(localE.time[i])),
closesti[i],
s=100,
facecolor='c',
edgecolor='grey')
plt.imshow(np.flipud(rayz),
extent=[
mdates.date2num(tt.datetime),
mdates.date2num(
(tt + nseconds + edgebuffer).datetime), 0,
len(slist)
],
aspect='auto',
interpolation='nearest',
cmap='bone',
vmin=np.min(rayz) / 2,
vmax=np.max(rayz) * 2)
ax.set_adjustable('box-forced')
ax.xaxis_date()
plt.yticks(np.arange(len(ll)))
ax.set_yticklabels(slist)
tdate = yr + '-' + mo + '-' + str(dayat).zfill(2)
plt.title(tdate)
ax.grid(color='black')
ss = str(tt)
ss = ss[0:13]
kurs = "%s/" % s + "%s.png" % ss
svpath = homedir + kurs
plt.savefig(svpath, format='png')
plt.close()
#%%
blockette = blockette + (npts - nptsf)
tt = tt + nseconds
detections = []
localev = []
doubles = []
#############################
svpath = homedir + '%s' % s + "/picktable.html"
df1.to_html(open(svpath, 'w'), index=False)
svpath = homedir + '%s' % s + "/picktable.pkl"
df1.to_pickle(svpath)
dayat = dayat + 1
counter = counter + 1
counter_3char = str(counter).zfill(3)
#############################
if __name__ == '__main__':
detection_function()
# assignement: Find Black Hole
import pynbody
import matplotlib.pylab as plt
plt.switch_backend("agg")
# loading the snapshot
s =pynbody.load('/mnt/cptmarvel/cptmarvel.cosmo25cmb.4096g5HbwK1BH.004096/cptmarvel.cosmo25cmb.4096g5HbwK1BH.004096')
# convert the units
s.physical_units()
# the halo that I need is h[5]
h = s.halos()
h5=h[5]
# put your galaxy that you care about in the center of the simulation
pynbody.analysis.angmom.faceon(h5)
# convert the units
s.physical_units()
with pynbody.analysis.halo.center(h[5], mode='hyb'):
print (h[5]['pos'][0])
print (h[5]['pos'][1])
print (h[5]['pos'][2])
print ('pos')
def findBH(s):
import os
import torch
import random
import itertools
import numpy as np
import matplotlib.pylab as plt
plt.switch_backend("qt5agg")
from sklearn.metrics import confusion_matrix
def accuracy(output,label,topk=(0,)):
# compute accuracy for precision@k for the specified k and each class
# output : BatchSize x n_classes
maxk = max(topk)
_, pred_index = torch.topk(output,maxk+1,dim=1,largest=True,sorted=True) # descending order
correct = pred_index.eq(label.view(len(label),-1).expand_as(pred_index))
for i in range(len(label)):
for j in range(3):
if correct[i,j] == 1 :
for k in range(j+1,3):
correct[i,k] = 1
break
res=[]
for k in topk:
correct_k = float(correct[:,k].sum()) / float(len(label))
res.append(correct_k)
cls1, cls3 = list(), list()
for i in range(len(label)):
import numpy as np
import chainer
import os
from chainer import serializers, optimizers, cuda, training
from chainer.training import extension, extensions, updaters
import argparse
import glob
from matplotlib import pylab as plt
plt.switch_backend('agg')
from chainer.datasets import TransformDataset
from user_dataset_3class import UserDataset3Class
from guinness_net_yolov2 import GUINNESS_YOLOv2
from yolo_predictor import YOLOv2Predictor
from transform_sg import convert_sg, Transform
parser = argparse.ArgumentParser(description='YOLOv2 trainer')
parser.add_argument('--batch_size',
'-b',
type=int,
default=6,
help='Mini batch size')
parser.add_argument('--img_size',
'-s',
type=int,
default=213,
help='test image size')
parser.add_argument('--gpu',
'-g',
type=int,
import numpy as np
from numpy import exp
from numpy import pi
from numpy import sqrt
from numpy import log
import numpy.testing as nut
import scipy.integrate as si
import mpmath as mp
import matplotlib.pylab as plt
# these three lines are in case you work with a specific software
try:
plt.switch_backend("Qt5Agg")
except:
raise Exception("Failed to set matplotlib backend to Qt5Agg")
#DEFINITION OF MATHEMATICAL OBJECTS
#Definition of the transformation matrices
def matrixFlight(L): # For a flight path of length
return np.array([[1,-L,0],[0,1,0],[0,0,1]])
def matrixMonoPlane(b, ThetaB): # For a perfect flat crystal monochromator
return np.array([[b,0,0],[0,1/b,(1-1/b)*np.tan(ThetaB)],[0,0,1]])
def matrixMonoBent(b, Fc, ThetaB): # For a perfect curved crystal monochromator (meridionally and sagitally focusing)
return np.array([[b,0,0],[1/Fc,1/b,(1-1/b)*np.tan(ThetaB)],[0,0,1]])
def matrixMonoMosaic(ThetaB): # For a mosaic monochromator
return np.array([[1,0,0],[0,-1,2*np.tan(ThetaB)],[0,0,1]])
def draw_graph(network, color, min_color=None, max_color=None, groups=None, sizep=None, colormap_name='bwr', min_vertex_size_shrinking_factor=4, output='graph.png', output_size=(15, 15), dpi=80, standardize=False, color_bar=True, crop=True, **kwargs):
output_splitted = output.rsplit('/', 1)[-1].split('_graph_')
net_name, prop_key = output_splitted[0], output_splitted[-1]
print_prefix = utils.color_string('[' + net_name + '] ') + '[' + prop_key + '] [' + str(
datetime.datetime.now().replace(microsecond=0)) + '] draw graph'
print print_prefix
print_prefix += ': '
num_nodes = network.num_vertices()
min_vertex_size_shrinking_factor = min_vertex_size_shrinking_factor
if num_nodes < 10:
num_nodes = 10
max_vertex_size = np.sqrt((np.pi * (min(output_size) * dpi / 2) ** 2) / num_nodes)
if max_vertex_size < min_vertex_size_shrinking_factor:
max_vertex_size = min_vertex_size_shrinking_factor
min_vertex_size = max_vertex_size / min_vertex_size_shrinking_factor
if sizep is None:
sizep = max_vertex_size + min_vertex_size
sizep /= 3
else:
sizep = prop_to_size(sizep, mi=min_vertex_size / 3 * 2, ma=max_vertex_size / 3 * 2, power=2)
v_shape = 'circle'
if isinstance(groups, str):
try:
v_shape = network.vp[groups].copy()
#groups = network.vp[groups]
#unique_groups = set(np.array(groups.a))
#num_groups = len(unique_groups)
#groups_c_map = colormap.get_cmap('gist_rainbow')
#groups_c_map = {i: groups_c_map(idx / (num_groups - 1)) for idx, i in enumerate(unique_groups)}
#v_pen_color = network.new_vertex_property('vector<float>')
#for v in network.vertices():
# v_pen_color = groups_c_map[groups[v]]
v_shape.a %= 14
except KeyError:
# print print_prefix + 'cannot find groups property:', groups
v_shape = 'circle'
cmap = colormap.get_cmap(colormap_name)
color = color.copy()
v_shape = network.new_vertex_property('int')
v_shape.a = np.array(
[0 if np.isclose(color[int(v)], 1.) else (1 if color[int(v)] > 1. else 4) for v in network.vertices()],
dtype='int')
try:
_ = color.a
except AttributeError:
c = network.new_vertex_property('float')
c.a = color
color = c
min_color = color.a.min() if min_color is None else min_color
max_color = color.a.max() if max_color is None else max_color
if np.isclose(min_color, max_color):
min_color = 0
max_color = 2
#orig_color = np.array(color.a)
if standardize:
color.a -= color.a.mean()
color.a /= color.a.var()
color.a += 1
color.a /= 2
else:
#color.a -= min_color
#color.a /= max_color
tmp = np.array(color.a)
tmp[tmp > 1] = 1 + (tmp[tmp > 1] / (max_color/1))
color.a = tmp
color.a /= 2
if not output.endswith('.png'):
output += '.png'
color_pmap = network.new_vertex_property('vector<float>')
tmp = np.array([np.array(cmap(i)) for i in color.a])
color_pmap.set_2d_array(tmp.T)
plt.switch_backend('cairo')
f, ax = plt.subplots(figsize=(15, 15))
output_size = (output_size[0], output_size[1]*.3) # make space for colorbar
edge_alpha = 0.3 if network.num_vertices() < 1000 else 0.01
pen_width = 0.8 if network.num_vertices() < 1000 else 0.1
v_pen_color = [0., 0., 0., 1] if network.num_vertices() < 1000 else [0.0, 0.0, 0.0, edge_alpha]
graph_draw(network, vertex_fill_color=color_pmap, mplfig=ax, vertex_pen_width=pen_width, vertex_shape=v_shape,
vertex_color=v_pen_color, edge_color=[0.179, 0.203, 0.210, edge_alpha], vertex_size=sizep,
output_size=output_size, output=output, **kwargs)
if color_bar:
cmap = plt.cm.ScalarMappable(cmap=cmap)
cmap.set_array([0., 2.])
cbar = f.colorbar(cmap, drawedges=False)
ticks = [0, 1.0, max_color / 1]
cbar.set_ticks([0., 1., 2.])
tick_labels = None
non_zero_dig = 1
for digi in range(10):
tick_labels = [str("{:2." + str(digi) + "f}").format(i) for i in ticks]
if any([len(i.replace('.', '').replace('0', '').replace(' ', '').replace('-', '').replace('+', '')) > 0 for
i in tick_labels]):
non_zero_dig -= 1
if non_zero_dig == 0:
break
cbar.ax.set_yticklabels(tick_labels)
cbar.ax.tick_params(labelsize=40)
#var = stats.tvar(orig_color)
cbar.set_label('Bias Factor', labelpad=+30)
matplotlib.rcParams.update({'font.size': 40})
plt.axis('off')
plt.savefig(output, bbox_tight=True, dpi=dpi)
plt.close('all')
plt.switch_backend('Agg')
if crop:
if output.endswith('.pdf'):
os.system('pdfcrop ' + output + ' ' + output)
else:
pass
#
# interpolate using Lagrangian interpolation
#
import numpy as np
from scipy import interpolate as int
if '__main__' in __name__:
import matplotlib as mpl
import matplotlib.pylab as plt
plt.switch_backend('TkAgg')
plt.ion()
x = lambda n: np.linspace(-1,1,n)
f = lambda x: np.cos(np.sin(np.pi*x))
xd = x(300); fd=f(xd)
plt.plot(xd,fd,'k')
spf = int.interp1d(x(300),fd,kind='cubic')
plt.plot(xd,spf(xd),'r')
#!/usr/bin/env python
"""Init file for pre and post processing"""
import os
import matplotlib.pylab as plt
# Set proper backend for the display
try:
os.environ["DISPLAY"]
except KeyError:
plt.switch_backend("Agg")
cos_anneling = (1 + math.cos(math.pi * T / self.phase2_iters)) / 2
for i in range(len(self.optimizer.param_groups)):
self.optimizer.param_groups[i][
'momentum'] = self.momentums[1] - self.mom_diff * cos_anneling
return [
final_lr + (base_lr - final_lr) * cos_anneling
for base_lr, final_lr in zip(self.base_lrs, self.final_lrs)
]
if __name__ == "__main__":
import torchvision
import torch
import matplotlib.pylab as plt
plt.switch_backend('Agg')
resnet = torchvision.models.resnet34()
params = {"lr": 0.01, "weight_decay": 0.001, "momentum": 0.9}
optimizer = torch.optim.SGD(params=resnet.parameters(), **params)
epochs = 2
iters_per_epoch = 100
lrs = []
mementums = []
# lr_scheduler = OneCycle(optimizer, epochs, iters_per_epoch)
lr_scheduler = Poly(optimizer, epochs, iters_per_epoch)
for epoch in range(epochs):
for i in range(iters_per_epoch):
lr_scheduler.step(epoch=epoch)
import numpy as np
import matplotlib.pylab as plt
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
# ---------------------------------------------------------
# Simple finite difference solver
#
# Acoustic wave equation p_tt = c^2 p_xx + src
# 2-D regular grid
# ---------------------------------------------------------
nx = 200 # grid points in x
nz = 200 # grid points in z
nt = 1000 # number of time steps
dx = 10.0 # grid increment in x
dt = 0.001 # Time step
c0 = 3000.0 # velocity (can be an array)
isx = nx / 2 # source index x
isz = nz / 2 # source index z
ist = 100 # shifting of source time function
f0 = 100.0 # dominant frequency of source (Hz)
isnap = 10 # snapshot frequency
T = 1.0 / f0 # dominant period
nop = 3 # length of operator
# Model type, available are "homogeneous", "fault_zone",
# "surface_low_velocity_zone", "random", "topography",
# "slab"
model_type = "fault_zone"
items = d.get_items_df()
skills = d.get_skills_df()
items = items.join(skills, on="skill_lvl_1")
concepts = items["name"].unique()
sts = {}
for concept in concepts:
print(concept)
its = list(items[items["name"] == concept].index)
students = answers[answers["item"].isin(its)].groupby("student").size()
students = students[students >= TRASHOLD]
print(len(students))
sts[concept] = students
data = pd.DataFrame(index=concepts, columns=concepts, dtype=float)
for concept1 in concepts:
for concept2 in concepts:
count = len(set(sts[concept1]) & set(sts[concept2]))
print(concept1, concept2, count)
data[concept1][concept2] = count
print(data)
plt.switch_backend('agg')
sns.heatmap(data, annot=True)
plt.show()
plt.title("Student with {} answer in concept".format(TRASHOLD))
plt.savefig("concepts crossolving - {}.png".format(TRASHOLD))
#
# calculate 2nd moments of the power spectrum for the TH and Gaussian filters
#
import math
import numpy as np
from scipy import interpolate as intp
from scipy import integrate as integrate
from matplotlib import pylab as plt
from socket import gethostname
if ( gethostname()[0:6] == 'midway' ):
plt.switch_backend('TkAgg')
k,Pk = np.loadtxt('matter_power_kmax10000.dat',usecols=(0,1), unpack=True)
lnk = np.log(k); lk = np.log10(k)
#
# set relevant cosmological parameters
#
h = 0.7; Omega_m = 0.276; rho_mean = 2.77e11*h*h*Omega_m # in Msun/Mpc^3
lr = np.arange(-1.0,2.5,0.01)
r = 10.0**lr
#
# set a desired grid of masses and corresponding radii
#
M = 4.0*math.pi*r**3*rho_mean/3.0; lM = np.log10(M)
# intial P(k) cutoff scale
import collections
import json
import sys
import os.path
import matplotlib
import matplotlib.pylab as pylab
import numpy as np
pylab.switch_backend('agg')
stats_file = sys.argv[1]
beg_range = int(sys.argv[2])
end_range = int(sys.argv[3])
with open(stats_file) as jsondata:
statistics = json.load(jsondata)
video_labels = {
"67GZuUxV27w_30.000.mkv.gz": "Rooster (C**k)",
"9PmzQI8ZYpg_30.000.mkv.gz": "Sewing Machine",
"_A30xsFBMXA_40.000.mkv.gz": "Fire Truck",
"BUGx2e7OgFE_30.000.mkv.gz": "Harmonica",
"eHIlPlNWISg_90.000.mkv.gz": "Polaroid Camera",
"eV5JX81GzqA_150.000.mkv.gz": "Race Car",
"-OAyRsvFGgc_30.000.mkv.gz": "Electric Guitar",
"rctt0dhCHxs_16.000.mkv.gz": "Tree Frog",
"rTh92nlG9io_30.000.mkv.gz": "Keyboard",
"-XilaFMUwng_50.000.mkv.gz": "Magpie"
