def process(self):
"""rearranges the ping data into a matrix of max amplitude of
dimensions corrisponding to the power, gain and beam sections."""
MINSAMPLES = 5
datadim = self.pingdata.shape
self.pingmax = pl.zeros((len(self.settings['power']), len(self.settings['gain']), datadim[2]))
for i, power in enumerate(self.settings['power']):
for j, gain in enumerate(self.settings['gain']):
for k in xrange(datadim[2]):
sampleindx = pl.find((self.pingdata[:, 1, k] == power) & (self.pingdata[:, 2, k] == gain))
if len(sampleindx) > MINSAMPLES:
temp = self.pingdata[sampleindx[-MINSAMPLES:], 0, k]
tempmax = temp.max()
if tempmax == 0:
self.pingmax[i, j, k] = pl.NaN
else:
self.pingmax[i, j, k] = temp.max()
else:
self.pingmax[i, j, k] = pl.NaN
#The following section removes settings that were collected erroniously.
#gain settings first
null = pl.zeros((len(self.settings['gain']), datadim[2]))
powershortlist = []
self.havedata = True # this is an ugly workaround...
for i, power in enumerate(self.settings['power']):
test = pl.isnan(self.pingmax[i, :, :] )
if test.all():
powershortlist.append(i)
print 'removing ' + str(power) + ' power setting.'
for i in powershortlist:
try:
self.settings['power'].pop(i)
except IndexError:
self.havedata = False
if self.havedata:
self.pingmax = pl.delete(self.pingmax, powershortlist, 0)
#then power settings
null = pl.zeros((len(self.settings['power']), datadim[2]))
gainshortlist = []
for i, gain in enumerate(self.settings['gain']):
test = pl.isnan(self.pingmax[:, i, :])
if test.all():
gainshortlist.append(i)
print 'removing ' + str(gain) + ' gain setting.'
for i in gainshortlist:
try:
self.settings['gain'].pop(i)
except IndexError:
self.havedata = False
if self.havedata:
self.pingmax = pl.delete(self.pingmax, gainshortlist, 1)
#remove the power and gain to normalize
self.pingmax = 20*pl.log10(self.pingmax)
for i, power in enumerate(self.settings['power']):
for j, gain in enumerate(self.settings['gain']):
self.pingmax[i, j, :] = self.pingmax[i, j, :] - power - gain
def _create_cropped_cmap(n_segments = 12, cmap='hot'):
new_cmap = plt.cm.get_cmap(cmap, n_segments)(range(n_segments))
new_cmap = plt.delete(new_cmap, (0, -1), 0)
palette = matplotlib.colors.LinearSegmentedColormap.from_list("palette", new_cmap)
return palette
def delta_E(pos):
t0 = time.time()
neighbors = (pos + neighbor_kernel) % N
tpos = tuple(pos)
dE = n.zeros(6) # calc. dE for each neighbor exchange, return array
for k, neigh in enumerate(neighbors):
tneigh = tuple(neigh)
if lattice[tneigh] != lattice[tpos]:
n_center = [neighbors[j] for j in p.delete(p.arange(6), k)]
n_neigh = ((pos + neighbor_neighbors[k]) % N)
# n_neigh = [ne for j, ne in enumerate(n_neigh) if j != reverse_neigh_indices[k]]
for temp in n_center:
temp = tuple(temp)
dE[k] -= lattice[temp] * lattice[tpos] * u11 + u00 * (
(not lattice[temp]) * (not lattice[tpos])
) #+ u10*(lattice[temp] + lattice[tpos])*(not lattice[temp]*lattice[tpos])
dE[k] += lattice[temp] * lattice[tneigh] * u11 + u00 * (
(not lattice[temp]) * (not lattice[tneigh])
) #+ u10*(lattice[temp] + lattice[tneigh])*(not lattice[temp]*lattice[tneigh])
for temp in n_neigh:
temp = tuple(temp)
dE[k] += lattice[temp] * lattice[tpos] * u11 + u00 * (
(not lattice[temp]) * (not lattice[tpos])
) #+ u10*(lattice[temp] + lattice[tpos])*(not lattice[temp]*lattice[tpos])
dE[k] -= lattice[temp] * lattice[tneigh] * u11 + u00 * (
(not lattice[temp]) * (not lattice[tneigh])
) #+ u10*(lattice[temp] + lattice[tneigh])*(not lattice[temp]*lattice[tneigh])
t1 = time.time()
return dE, t1 - t0
def calculate_activity_histogram(spikes, total_neurons, bin=0.1):
"""Calculates histogram and bins specifically for neurons.
Bins are provided in seconds instead of milliseconds."""
hist, bin_edges = pylab.histogram(
[spike[0] for spike in spikes],
bins=pylab.ceil(max([spike[0] for spike in spikes])/bin))
bin_edges = pylab.delete(bin_edges, len(bin_edges)-1) / 1000.
return [[float(i)/total_neurons for i in hist], bin_edges]
def findAbsorptionLines(self):
#no interpolation here, just return the measured data peak index
tresh=6 #db treshhold to detect a line
#filter first
#bring to logarithmic scale
movav=self.getmovingAveragedData()
hlog=20*py.log10(movav[:,2])
#smooth data
#remove etalon minima
ixlines_prob=signal.argrelmin(hlog)[0]
ixlines=[]
ixmax=signal.argrelmax(hlog)[0]
#check depth of minima compared two the two adjacent maxima
if ixmax[0]>ixlines_prob[0]:
#true if starts with minimum, treat this case separatedly
if hlog[ixmax[0]]-hlog[ixlines_prob[0]]>tresh:
ixlines.append(ixlines_prob[0])
#and remove first minimum from ixlines_prob
ixlines_prob=py.delete(ixlines_prob,0)
if ixmax[-1]<ixlines_prob[-1]: #treat this case separatedly
if hlog[ixmax[-1]]-hlog[ixlines_prob[-1]]>tresh:
ixlines.append(ixlines_prob[0])
ixlines_prob=py.delete(ixlines_prob,-1)
#now the remaining elements of ixlines, ixmax should be in the order of max min max ... min max
leftdist=hlog[ixmax[:-1]]-hlog[ixlines_prob]
rightdist=hlog[ixmax[1:]]-hlog[ixlines_prob]
for i in range(len(ixlines_prob)):
#check if distance is higher than treshhold
if (leftdist[i]>tresh or rightdist[i]>tresh):
ixlines.append(ixlines_prob[i])
#remove inappropriate lines (by distance so far, at least 5 datapoints!
ixlines=py.asarray(ixlines)
ixlines=py.concatenate(([ixlines[0]],ixlines[py.diff(ixlines)>5]))
return ixlines
def variability(r_times):
"""
Get HRV from RR occurance times array.
In:
r_times : ndarray, Relative time in seconds
Out:
time : ndarray, Relative time vector in seconds
hrv : ndarray, HRV vector in milliseconds
"""
time = pl.delete(r_times,-1)
hrv = pl.zeros(len(r_times)-1, dtype='float')
for i in range(0, len(r_times)-1):
hrv[i] = (r_times[i+1]-r_times[i])* 1000
assert pl.shape(time) == pl.shape(hrv)
return time, hrv
def data_remove_constant_cols(array):
(nrows, ncols) = array.shape
delete_cols = []
non_delete_cols = []
for c in range(ncols):
same = True
for r in range(1, nrows):
# Non constant
if array[r][c] != array[r - 1][c]:
same = False
break
# If constant, save column number to be deleted
if same:
delete_cols.append(c)
else:
non_delete_cols.append(c)
# Delete columns
ndeleted_cols = 0
for c in delete_cols:
array = pylab.delete(array, c - ndeleted_cols, axis=1)
# Reduce future column numbers now that array shape has changed
ndeleted_cols += 1
return array
filters.low_pass_filter(data_ecog[i, :], Fsampling=f_sampling, Fcutoff=f_lp_cutoff), int(f_sampling / f_subsample))
data_ecog_lp_ss.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_ecog_lp_ss.npy'), data_ecog_lp_ss)
spike_samples = tf.spikedetect(data_probe_hp, threshold_multiplier=6.5, bad_channels=probe_bad_channels)
pl.save(os.path.join(memap_folder, 'spike_samples.npy'), spike_samples)
spike_samples_clean = spike_samples
for i in pl.arange(pl.size(spike_samples_clean)-1,-1,-1):
data = data_probe_hp[:, spike_samples[i]-60:spike_samples[i]+60]
stdevs = sp.std(data,1)
if np.max(data) > 3000 or pl.any(stdevs>600):
spike_samples_clean = pl.delete(spike_samples_clean, i)
if i%100==0:
print(i)
spike_samples_clean = pl.delete(spike_samples_clean, 0)
pl.save(os.path.join(memap_folder, 'spike_samples_clean.npy'), spike_samples_clean)
channels = np.empty(0)
for i in pl.arange(0, pl.size(spike_samples_clean)):
data = np.array(data_probe_hp[:, spike_samples_clean[i]].tolist())
channels = np.append(channels, np.argmax(data))
if i%100==0:
print(i)
channels_spikes_df = pd.DataFrame([(channels, spike_samples_clean)], columns=['Channels', 'Samples'])
spike_times_shaftA = channels_spikes_df.Samples[0][channels_spikes_df.Channels[0]>7][channels_spikes_df.Channels[0]<16]
spike_times_shaftB = channels_spikes_df.Samples[0][channels_spikes_df.Channels[0]>23]
def crunchData(estimTypes, colNums, observables):
'''
Takes a list of pimc file types ['estimator', 'super', etc..]
and a list of list of column numbers (in order of file types) as
[[0,1], [1,4,6], etc...] and will combine data from jobs that were
the same but had different random number seeds all into one
file.
'''
args = parseCMD()
reduceType = args.reduceType
numToDelete = args.deleteNum
deleteList = []
for n in range(numToDelete):
deleteList.append(n)
numLinesToSkip = 2+numToDelete
print 'deleting elements indexed by: ',deleteList
print 'and skipping',numLinesToSkip,'lines of data file.'
# make list of data file names
estimFiles = glob.glob('*%s*' % estimTypes[0])
# check which ensemble
canonical = ensembleCheck(estimFiles[0][0])
# make list of all reduced variable, in order, based on ensemble
indList = indepVarList(estimFiles, canonical, reduceType)
for nType, estType in enumerate(estimTypes):
# dict to hold all estType data for all independent variables (T,..)
allTemps = {}
# dict to hold numBins, average, stdErr for each data file
statTemps = {}
# loop over independent variable for given estType
for numTemp, temp in enumerate(indList):
# Get filenames. The chemical potential always has a sign
# in front and the temperature always comes after the
# file type and a dash.
if reduceType == 'T':
bipFiles = glob.glob('*%s-%s*' % (estimTypes[nType], temp))
elif reduceType == 'u':
bipFiles = glob.glob('*%s*%s*' % (estimTypes[nType], temp))
arrs = {}
arrs2 = {}
for nf, f in enumerate(bipFiles):
if checkIfEmpty(f,numLinesToSkip):
print f,' is empty.'
pass
else:
dat = pl.genfromtxt(f, unpack=True, usecols=colNums[nType])
# skip a few
for d in dat:
d = pl.delete(d, deleteList)
# define key that can be sorted properly
if numTemp < 10:
arrName = 'arr00'+str(numTemp)
elif (numTemp >= 10 and numTemp < 100):
arrName = 'arr0'+str(numTemp)
else:
arrName = 'arr'+str(numTemp)
# treat data from one column
if int(len(colNums[nType])) == 1:
if nf == 0:
arrs[arrName] = dat
arrs2[arrName] = [[pl.average(dat),
pl.std(dat)/pl.sqrt(1.0*int(len(dat))),
int(len(dat))]]
else:
arrs[arrName] = pl.append(arrs[arrName], dat)
arrs2[arrName] = pl.append(
arrs2[arrName], [[pl.average(dat),
pl.std(dat)/pl.sqrt(1.0*int(len(dat))),
int(len(dat))]])
# treat data from multiple columns
else:
if nf == 0:
for n, arr in enumerate(dat):
arrs[arrName+'_'+str(n)] = arr
arrs2[arrName+'_'+str(n)] = [[pl.average(arr),
pl.std(arr)/pl.sqrt(1.0*int(len(arr))),
int(len(arr))]]
else:
for n, arr in enumerate(dat):
arrs[arrName+'_'+str(n)] = pl.append(
arrs[arrName+'_'+str(n)], arr)
arrs2[arrName+'_'+str(n)] = pl.append(
arrs2[arrName+'_'+str(n)], [[pl.average(arr),
pl.std(arr)/pl.sqrt(1.0*int(len(arr))),
int(len(arr))]])
# construct key name. This assumes <1000 temperatures.
if numTemp < 10:
allArrName = 'allTemps00'+str(numTemp)
elif (10 <= numTemp and numTemp < 100):
allArrName = 'allTemps0'+str(numTemp)
else:
allArrName = 'allTemps'+str(numTemp)
if nType < 10:
allArrName += '00'+str(nType)
elif (10 <= nType and nType < 100):
allArrName += '0'+str(nType)
else:
allArrName += str(nType)
allTemps[allArrName] = arrs
statTemps[allArrName] = arrs2
# ---- Write all data to disk ----
# length of max. sized array for all data
maxLen = 0
for t in allTemps:
for g in allTemps[t]:
arrayLen = len(allTemps[t][g])
if arrayLen > maxLen:
maxLen = arrayLen
# open file to hold all of estType data
newName = 'Reduced'+str(estType.capitalize())+'Data.dat'
fout = open(newName, 'w')
# write independent variable as top header line
fout.write('#%15s\t' % indList[0])
for n in range(len(colNums[nType])-1):
fout.write('%16s\t'% '')
for temp in indList[1:]:
fout.write('%16s\t'% temp)
for n in range(len(colNums[nType])-1):
fout.write('%16s\t'% '')
fout.write('\n')
# write observable names as second header line
fout.write('#%15s\t' % observables[nType][0])
for n in range(len(observables[nType])-1):
fout.write('%16s\t' % observables[nType][n+1])
for temp in indList[1:]:
for obs in observables[nType]:
fout.write('%16s\t' % obs)
fout.write('\n')
# write data arrays to disk
for line in range(maxLen):
for a in sorted(allTemps.iterkeys()):
for aa in sorted(allTemps[a]):
try:
fout.write('%16.8E,\t' % (
float(allTemps[a][aa][line]) ))
except:
fout.write('%16s,\t' % '')
fout.write('\n')
fout.close()
# ---- Average individual files and write to disk ----
# get length of max. sized array for averages
maxLen2 = 0
for t in statTemps:
for g in statTemps[t]:
arrayLen = len(statTemps[t][g])
if arrayLen > maxLen2:
maxLen2 = arrayLen
maxLen2 /= 3
# open file for standard error
newName = 'zAveraged'+str(estType.capitalize())+'Data.dat'
fout = open(newName, 'w')
# write independent variable as top header line
fout.write('#%15s\t%16s\t%16s\t' % (
indList[0], '',''))
for n in range(len(colNums[nType])-1):
fout.write('%16s\t%16s\t%16s\t'% ('','',''))
for temp in indList[1:]:
fout.write('%16s\t%16s\t%16s\t'% (temp,'',''))
for n in range(len(colNums[nType])-1):
fout.write('%16s\t%16s\t%16s\t'% ('','',''))
fout.write('\n')
# write observable names as second header line
fout.write('#%15s\t%16s\t%16s\t' % (
observables[nType][0],
'std Err',
'bins'))
for n in range(len(observables[nType])-1):
fout.write('%16s\t%16s\t%16s\t' % (
observables[nType][n+1],
'std Err',
'bins'))
for temp in indList[1:]:
for obs in observables[nType]:
fout.write('%16s\t%16s\t%16s\t' % (
obs, 'std Err', 'bins'))
fout.write('\n')
# write data arrays to disk
en = 0
for line in range(maxLen2):
for a in sorted(statTemps.iterkeys()):
for aa in sorted(statTemps[a]):
try:
fout.write('%15.8E,\t%15.8E,\t%15.8E,\t' % (
float(statTemps[a][aa][en]),
float(statTemps[a][aa][en+1]),
float(statTemps[a][aa][en+2])))
except:
fout.write('%15s,\t%15s,\t%15s,\t' % ('','',''))
fout.write('\n')
en += 3
fout.close()
ll = 1.5
ul = 4
bins_x = pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x >= xmin, x < xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH * 100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
pbp(f1s1, icd, indexer=list(pyl.delete(bins_x, -1) + 0.25))
pbp(f1s2, icd, indexer=list(pyl.delete(bins_x, -1) + 0.25))
# Add the box and whiskers
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies2 = filter(lambda galaxy: galaxy.ston_V > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul = 4
bins_x = pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle','rb'))
# galaxies = filter(lambda galaxy: 0.06 * galaxy.halflight *\
# astCalc.da(galaxy.z)*1000/206265. > 2, galaxies)
# Make figure
f1 = pyl.figure(1, figsize=(6,4))
f1s1 = f1.add_subplot(121)
f1s2 = f1.add_subplot(122)
# f1s3 = f1.add_subplot(223)
# f1s4 = f1.add_subplot(224)
#Upper and Lower limit arrow verts
arrowup_verts = [[0.,0.], [-1., -1], [0.,0.],
[0.,-2.], [0.,0.], [1,-1], [0,0]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
for galaxy in galaxies:
if galaxy.ston_I > 10. and galaxy.ICD_IH != None:
# Add arrows first
if galaxy.ICD_IH > 0.5:
f1s1.scatter(galaxy.Mass, 0.5*100, s=100, marker=None,
verts=arrowup_verts)
else:
f1s1.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s2.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 10., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile_width import percentile_box_plot as pbp
#bp1 = f1s1.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s1, icd, indexer=list(index), width=width)
pbp(f1s2, icd, indexer=list(index), width=width)
f1s1.set_xlim(8,12)
f1s2.set_xlim(8,12)
f1s1.set_ylim(-10,50)
f1s2.set_ylim(0,15)
f1s1.set_xticks([8,9,10,11,12])
f1s2.set_xticks([8,9,10,11,12])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s1.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s2.set_xlabel(r"Log Mass ($M_{\odot})$")
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='#348ABD', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), 'lower center', prop=prop, ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (12, 15)
xy2 = (8, 15)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
xy = (12, 0)
xy2 = (8, 0)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (12, 3)
xy2 = (8, 3)
xy = (12, -1)
xy2 = (8, -1)
pyl.draw()
pyl.show()
import pylab as pl
from scipy.spatial import ConvexHull
# On the 2-Sphere, the Voronoi tesselation is equivalent to the convex hull projected on the sphere
# (Sugihara, Journal for Geometry and Graphics Volume 6 (2002), No. 1, 69-81.)
# I assume that the same is true in 4D.... [This has to be checked!]
R = 1.6180339887498949 #magic number by straley for 120 particles
import sys
polar = pl.load(sys.argv[1])
from spheretools import *
cartesian = convert(polar, R)
CHull = ConvexHull(cartesian)
with open("bonds.txt", 'w') as fw:
for p in range(cartesian.shape[0]):
# print p
which_simplex, position = pl.where(CHull.simplices == p)
# print which_simplex
all_neighs = pl.unique(CHull.simplices[which_simplex].flatten())
# print "all_neighs",all_neighs
index_of_p = pl.where(all_neighs == p)
# print "p is at",index_of_p
neighs = pl.delete(all_neighs, index_of_p)
# print"neighs after ", neighs
fw.write(str(len(neighs)) + " " + " ".join(map(str, neighs)) + "\n")
def read_bunch_data(file_list, format='ascii'):
A, R = [], []
if format == 'ascii':
data_list = [f.replace('.cfg', '_prt.dat') for f in file_list]
for i, d in enumerate(data_list):
try:
A.append(plt.loadtxt(d).T)
except IOError:
print '*** WARNING: no file ', d
# R.append((i, file_list[i]))
A.append(A[-1])
except ValueError:
print i, d
raise ValueError
elif format == 'hdf5':
data_list = [f.replace('.cfg', '.prt.h5') for f in file_list]
for i, d in enumerate(data_list):
try:
hf = h5py.File(d, 'r')
A.append(
plt.insert(hf['Fields']['Data'], 0, hf['Time']['time'], axis=1).T)
except IOError:
print '*** WARNING: no file ', d
R.append((i, file_list[i]))
elif format == 'h5':
data_list = file_list
A = None
for i, d in enumerate(data_list):
try:
hf = h5py.File(d, 'r')['Bunch']
if A == None:
keys = hf.keys()
a = hf[keys[0]]
A = plt.zeros((len(data_list), len(keys) + 1, len(a)))
A[i, 0, :] = plt.arange(len(hf['mean_x']))
A[i, 1, :] = hf['mean_x']
A[i, 2, :] = hf['mean_xp']
A[i, 3, :] = hf['mean_y']
A[i, 4, :] = hf['mean_yp']
A[i, 5, :] = hf['mean_z']
A[i, 6, :] = hf['mean_dp']
A[i, 7, :] = hf['sigma_x']
A[i, 8, :] = hf['sigma_y']
A[i, 9, :] = hf['sigma_z']
A[i, 10, :] = hf['sigma_dp']
A[i, 11, :] = hf['epsn_x']
A[i, 12, :] = hf['epsn_y']
A[i, 13, :] = hf['epsn_z']
A[i, 14, :] = hf['n_macroparticles']
# A.append(plt.array(
# [,
# hf['mean_x'], hf['mean_xp'], hf['mean_y'], hf['mean_yp'], hf['mean_dz'], hf['mean_dp'],
# hf['sigma_x'], hf['sigma_y'], hf['sigma_dz'], hf['sigma_dp'],
# hf['epsn_x'], hf['epsn_y'], hf['epsn_z'], hf['n_macroparticles']]).T)
except IOError:
print '*** WARNING: no file ', d
R.append((i, file_list[i]))
except KeyError:
print '*** WARNING: damaged file ', d
R.append((i, file_list[i]))
else:
raise(ValueError('*** Unknown format: ', format))
for r in R:
file_list.remove(r[1])
delete_from_data_array = [r[0] for r in R]
A = plt.delete(A, delete_from_data_array, axis=0)
A = plt.array(A)
A = plt.rollaxis(A, 0, 3)
# [n_cols, n_turns, n_files] = A.shape
return A, file_list
def sim(vx0,y0,L01,a0,k1,dE,m,ygrd,t,fs,steps):
# function for using fmin
# def optfunc_dE(par):
# return abs(-.5*k1*c**2+.5*(k1+par)*(c-(c*par/(k1+par)))**2-dE)
# function for using fsolve
def optfunc_dE(par, spring_par):
k1 = spring_par['k']
c = spring_par['c']
return abs(-.5*k1*c**2+.5*(k1+par)*(c-(c*par/(k1+par)))**2-dE)
g = -9.81 # gravitational force
betaTO_sim = zeros(steps)
xF=0
print 'x'
x_sim = [0]
y_sim = [0]
vx_sim = [0]
vy_sim = [0]
t_sim = [0]
k2 = 0
L02 = 0
skip_all = False
st_lo_idx = -1
# leg orientation
xL = L01 * cos(a0 * pi/180.)
yL = L01 * sin(a0 * pi/180.)
# first ground contact
t_gc = sqrt(2.*(L01*sin(a0*pi/180.)-y0)/g)
if isnan(t_gc) == True:
skip_all = True
if skip_all == False:
x_gc = vx0*t_gc
y_gc = .5*g*t_gc**2 + y0
# system parameter for first step
s = zeros(4)
s[0] = 0
s[1] = y0
s[2] = vx0
s[3] = 0
# definitions for calculation
end_sim = False
res = zeros([1,4])
res_app = []
t_v = [0]
skip_stance = False
# start loop
while end_sim == False:
st_lo_idx += 1
if st_lo_idx != 0: # if not first step
t_gc = (-res[-1,3]/g) + \
sqrt( ((res[-1,3]**2)/(g**2)) - \
((2*res[-1,1])/g-(2*L01*sin(a0*pi/180)/g)) )
if isnan(t_gc) == False and isinf(t_gc) == False:
y_gc = 0.5*g*t_gc**2 + res[-1,3]*t_gc + res[-1,1]
t_vec = linspace(0,t_gc,ceil(t_gc*fs)+1)
#print len(t_vec)
s[0] = res[-1,2]*t_gc + res[-1,0]
s[1] = y_gc
s[2] = res[-1,2]
s[3] = g*t_gc +res[-1,3]
else:
t_gc = 1
t_vec = linspace(0,t_gc,ceil(t_gc*fs)+1)
end_sim = True
skip_stance = True
#break
vx = zeros_like(t_vec)
vy = zeros_like(t_vec)
x = zeros_like(t_vec)
y = zeros_like(t_vec)
vx[0] = res[-1,2]
vy[0] = res[-1,3]
x[0] = res[-1,0]
y[0] = res[-1,1]
else: # if first step
t_vec = linspace(0,t_gc,ceil(t_gc*fs)+1)
#print t_vec
vx = zeros_like(t_vec)
vy = zeros_like(t_vec)
x = zeros_like(t_vec)
y = zeros_like(t_vec)
#print vx
vx[0] = s[2]
vy[0] = s[3]
x[0] = s[0]
y[0] = s[1]
### flight phase
x[1:] = vx[0]*t_vec[1:] + x[0]
y[1:] = .5*g*(t_vec[1:]**2)+vy[0]*t_vec[1:] + y[0]
vy[1:] = g*t_vec[1:] + vy[0]
vx[1:] = vx[0]*ones_like(t_vec[1:])
# end at apex
if st_lo_idx == steps or skip_stance == True:
tapex = vy[0]/-g
#print 'y0=',y0,'vy0=',vy[0],' tapex=',tapex
tvec_apex = linspace(0,tapex,ceil(tapex*fs)+1)
vyapex = g*tvec_apex+vy[0]
#print 'vyapex=',vyapex
yapex = .5*g*(tvec_apex**2) + vyapex[0]*tvec_apex + y[0]
xapex = vx[0] * tvec_apex + x[0]
vxapex = vx[0] * ones_like(tvec_apex)
#print 'lvy:',len(vyapex),' lvx:',len(vxapex)
x_sim = append(x_sim,xapex[1:])
y_sim = append(y_sim,yapex[1:])
vx_sim = append(vx_sim,vxapex[1:])
vy_sim = append(vy_sim,vyapex[1:])
t_sim = append(t_sim,tvec_apex[1:]+t_sim[-1])
skip_stance = True
"""
for kk in arange(2,len(y)):
if y[kk-2]<y[kk-1] and y[kk-1]>y[kk]:
end_sim = True
x_sim = append(x_sim,x[1:kk])
y_sim = append(y_sim,y[1:kk])
vx_sim = append(vx_sim,vx[1:kk])
vy_sim = append(vy_sim,vy[1:kk])
t_sim = append(t_sim,t_vec[1:kk]+t_sim[-1])
"""
### stance phase
if skip_stance == False:
# system parameter
if st_lo_idx == 0: # if first step
s[0] = x_gc # x
s[1] = y_gc # y
s[2] = vx0 # vx
s[3] = g*t_gc + 0 # vy
xF = s[0]+xL
yF = ygrd
parameter = {'k':k1,'L0':L01,'m':m,'g':g,'xF':xF,'yF':yF}
calc_length = 0.1*fs
find_pos = False
round_prec = 11 # precision of rounding
#print 'round_prec = ', round_prec
step_size = 1./fs
res = zeros([1,4])
t_v = [0]
res_app = False
skip = False
L0 = L01
FinMin = False
# start integration of stance phase
while find_pos == False:
t_vint = (step_size) * arange(calc_length)
res_int = odeint(s_dot,s,t_vint, args=(parameter,), rtol=1e-11)
# stop condition vx or y < 0
if res_int[-1,1] < 0 or res_int[-1,2] < 0:
find_pos = True
end_sim = True
skip = True
# if ymin: calculation of new k and L for energy changes
for idx in arange(1,len(res_int[:,1])-1):
if res_int[idx,1]<res_int[idx-1,1] and res_int[idx,1]<res_int[idx+1,1]:
res_int=delete(res_int,s_[idx+1::],0)
t_vint = delete(t_vint,s_[idx+1::],0)
c= L0-(sqrt(res_int[-1,1]**2 + (xF-res_int[-1,0])**2))
#dk = fmin(optfunc_dE,0,full_output=1,disp=0,xtol=1e-12)
spring_par = {'k':k1,'c':c}
dk = fsolve(optfunc_dE,x0=[0],args=(spring_par,))
#print '###### dk ####',dk
# if not find a minimum stop loop --> no step
#if dk[-1] <> 0:
# find_pos = True
# end_sim = True
# skip = True
# raise ValueError, 'no energy management possible'
# break
k2 = k1+dk[0]
L02 = L01 - c*dk[0]/(k1+dk[0])
#print 'delta L = ',L0 - L02, ' delta k = ', dk[0]
L0 = L02
parameter = {'k':k2,'L0':L02,'m':m,'g':g,'xF':xF,'yF':yF}
FinMin = True
break
# if not ymin: start one before to avoid ignoring while change
if FinMin == False:
res_int = res_int[:-1]
t_vint = t_vint[:-1]
# virtual leg after integration
L_virt = sqrt(res_int[-1,1]**2 + (xF-res_int[-1,0])**2)
if L_virt < L0 and skip == False:
s[0] = res_int[-1,0]
s[1] = res_int[-1,1]
s[2] = res_int[-1,2]
s[3] = res_int[-1,3]
elif L_virt >= L0 and skip == False:
for jj in arange(1,len(res_int)):
L_virt_1 = sqrt(res_int[jj,1]**2 + abs(xF-res_int[jj,0])**2)
L_virt_2 = sqrt(res_int[jj-1,1]**2 + abs(xF-res_int[jj-1,0])**2)
if round(L_virt_2,round_prec) == round(L0,round_prec):
find_pos = True
res_int = res_int[0:jj,:]
res_app = True
t_vint = t_vint[0:jj]
if res_int[-1,3] <=0: end_sim = True
break
elif L_virt_2 < L0 and L_virt_1 > L0:
s[0] = res_int[jj-1,0]
s[1] = res_int[jj-1,1]
s[2] = res_int[jj-1,2]
s[3] = res_int[jj-1,3]
res_int = res_int[0:jj,:]
res_app = True
t_vint = t_vint[0:jj]
step_size = step_size / 10.
break
# append arrays
if res_app == False:
res = res_int
t_v = t_vint
res_app = True
else:
res = append(res,res_int[1::],0)
t_v = append(t_v,t_vint[1::]+t_v[-1])
# take of angle
#print st_lo_idx
betaTO_sim[st_lo_idx] = arctan(res[-1,1]/(res[-1,0]-xF))*180./pi
# append arrays
x_sim = append(x_sim,append(x,res[:,0]))
y_sim = append(y_sim,append(y,res[:,1]))
vx_sim = append(vx_sim,append(vx,res[:,2]))
vy_sim = append(vy_sim,append(vy,res[:,3]))
t_sim = append(t_sim,append(t_vec,t_v+t_gc)+t_sim[-1])
else: #skip_stance == True because of ending at apex
end_sim = True
# cut array
x_sim = x_sim[1::]
y_sim = y_sim[1::]
vx_sim = vx_sim[1::]
vy_sim = vy_sim[1::]
t_sim = t_sim[1::]
#print 'vor return'
return x_sim,y_sim,vx_sim,vy_sim,t_sim,st_lo_idx,betaTO_sim,xF,k2,L02
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle','rb'))
arrow_up = [[0,0], [-1,-1], [0,0], [0,-2], [0,0], [1,-1], [0,0]]
# Make figure
f1 = pyl.figure(1, figsize=(4,6))
f1s1 = f1.add_subplot(211)
f1s2 = f1.add_subplot(212)
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
f1s1.scatter(galaxy.z, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
if galaxy.ston_V > 30. and galaxy.ICD_VJ != None:
f1s2.scatter(galaxy.z, galaxy.ICD_VJ * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
pbp(f1s1, icd, indexer=list(pyl.delete(bins_x,-1)+0.25), whisker_top=None,
whisker_bottom=None)
# Add the box and whiskers
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies2 = filter(lambda galaxy: galaxy.ston_V > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_VJ*100 for galaxy in grid[i]])
pbp(f1s2, icd, indexer=list(pyl.delete(bins_x,-1)+0.25), whisker_top=None,
whisker_bottom=None)
f1s1.set_xlim(1.5, 3.5)
f1s2.set_xlim(1.5, 3.5)
f1s1.set_ylim(-1, 10)
f1s2.set_ylim(-1, 10)
f1s1.set_xticklabels([])
f1s2.set_xticks([1.5,2,2.5,3,3.5])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s2.set_xlabel("Redshift")
f1s2.set_ylabel(r"$\xi[V_{606},J_{125}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='#348ABD', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), 'lower center', prop=prop, ncol=3)
pyl.show()
def calculate_activity_histogram_one_neu(spikes, bins):
"""Calculates histogram of one neuron"""
hist, bin_edges = pylab.histogram(spikes, bins=bins)
bin_edges = pylab.delete(bin_edges, len(bin_edges)-1) / 1000.
return [[float(i) for i in hist], bin_edges]
def plot_uvj_vs_icd():
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies = filter(lambda galaxy: galaxy.ICD_IH != None, galaxies)
galaxies = filter(lambda galaxy: galaxy.sersic != None and \
galaxy.ston_I > 30, galaxies)
#Upper and Lower limit arrow verts
arrowup_verts = [[0.,0.], [-1., -1], [0.,0.],
[0.,-2.], [0.,0.], [1,-1]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
F = pyl.figure(1,figsize=(8,3))
grid = AxesGrid(F, 111,
nrows_ncols=(1,4),
axes_pad = 0.1,
add_all=True,
aspect=False,
share_all = True)
ax1 = grid[0]
ax2 = grid[1]
ax3 = grid[2]
ax4 = grid[3]
for galaxy in galaxies:
if galaxy.sersic < 1.:
col1 =ax1.scatter(galaxy.Mass, galaxy.ICD_IH * 100.,
s=25, c='0.8', edgecolor='0.8')
if 1. < galaxy.sersic < 2.:
col2 =ax2.scatter(galaxy.Mass, galaxy.ICD_IH * 100.,
s=25, c='0.8', edgecolor='0.8')
if 2. < galaxy.sersic < 3.:
col3 =ax3.scatter(galaxy.Mass, galaxy.ICD_IH * 100.,
s=25, c='0.8', edgecolor='0.8')
if 3. < galaxy.sersic:
if galaxy.ICD_IH*100 < 50:
col4 =ax4.scatter(galaxy.Mass, galaxy.ICD_IH * 100.,
s=25, c='0.8', edgecolor='0.8')
else:
col4 = ax4.scatter(galaxy.Mass, 50, marker=None, s=100,
verts=arrowup_verts)
# Add the box and whiskers
galaxies1 = filter(lambda galaxy: galaxy.ston_I > 30. and \
galaxy.sersic < 1, galaxies)
galaxies1 = pyl.asarray(galaxies1)
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30. and \
1 < galaxy.sersic < 2, galaxies)
galaxies2 = pyl.asarray(galaxies2)
galaxies3 = filter(lambda galaxy: galaxy.ston_I > 30. and \
2 < galaxy.sersic < 3, galaxies)
galaxies3 = pyl.asarray(galaxies3)
galaxies4 = filter(lambda galaxy: galaxy.ston_I > 30. and \
3 < galaxy.sersic, galaxies)
galaxies4 = pyl.asarray(galaxies4)
x1 = [galaxy.Mass for galaxy in galaxies1]
x2 = [galaxy.Mass for galaxy in galaxies2]
x3 = [galaxy.Mass for galaxy in galaxies3]
x4 = [galaxy.Mass for galaxy in galaxies4]
grid1 = []
grid2 = []
grid3 = []
grid4 = []
from boxplot_percentile_width import percentile_box_plot as pbp
bins_x =pyl.array([8.5, 9., 9.5, 10., 11])
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x1>=xmin, x1<xmax)]
grid1.append(galaxies1.compress(cond))
icd1 = []
for i in range(len(grid1)):
icd1.append([galaxy.ICD_IH*100 for galaxy in grid1[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
bp1 = pbp(ax1, icd1, indexer=list(index), width=width)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5 ,11.5])
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x2>=xmin, x2<xmax)]
grid2.append(galaxies2.compress(cond))
icd2 = []
for i in range(len(grid2)):
icd2.append([galaxy.ICD_IH*100 for galaxy in grid2[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
bp2 = pbp(ax2, icd2, indexer=list(index), width=width)
bins_x =pyl.array([8.5, 9.5, 10., 11.])
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x3>=xmin, x3<xmax)]
grid3.append(galaxies3.compress(cond))
icd3 = []
for i in range(len(grid3)):
icd3.append([galaxy.ICD_IH*100 for galaxy in grid3[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
index[0] = index[0] + 0.25
bp3 = pbp(ax3, icd3, indexer=list(index), width=width)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x4>=xmin, x4<xmax)]
grid4.append(galaxies4.compress(cond))
icd4 = []
for i in range(len(grid4)):
icd4.append([galaxy.ICD_IH*100 for galaxy in grid4[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x,-1) + 0.25
index[-1] = index[-1] + 0.25
print 'ajsdf'
bp4 = pbp(ax4, icd4, indexer=list(index), width=width)
ax1.set_xticks([8, 9, 10, 11])
ax2.set_xticks([8, 9, 10, 11])
ax3.set_xticks([8, 9, 10, 11])
ax4.set_xticks([8, 9, 10, 11])
ax1.set_ylim(0, 50)
ax2.set_ylim(0, 50)
ax3.set_ylim(0, 50)
ax4.set_ylim(0, 50)
ax1.set_ylabel(r'$\xi[i_{775},H_{160}]$ (%)')
ax1.set_title('n < 1')
ax2.set_title('1 < n < 2')
ax3.set_title('2 < n < 3')
ax4.set_title('3 < n')
pyl.figtext(.5, .05, r'Log Mass $(M_{\odot})$',fontsize=18,
horizontalalignment='center')
ax1.axhline(0, lw=2, zorder=0)
ax2.axhline(0, lw=2, zorder=0)
ax3.axhline(0, lw=2, zorder=0)
ax4.axhline(0, lw=2, zorder=0)
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='#348ABD', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
ax3.legend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), loc='upper center', prop=prop, ncol=1)
pyl.tight_layout()
pyl.subplots_adjust(bottom=0.21, left=0.11)
pyl.show()
def crunchData(estimTypes, colNums, observables):
'''
Takes a list of pimc file types ['estimator', 'super', etc..]
and a list of list of column numbers (in order of file types) as
[[0,1], [1,4,6], etc...] and will combine data from jobs that were
the same but had different random number seeds all into one
file.
'''
args = parseCMD()
reduceType = args.reduceType
numToDelete = args.deleteNum
deleteList = []
for n in range(numToDelete):
deleteList.append(n)
numLinesToSkip = 2 + numToDelete
print 'deleting elements indexed by: ', deleteList
print 'and skipping', numLinesToSkip, 'lines of data file.'
# make list of data file names
estimFiles = glob.glob('*%s*' % estimTypes[0])
# check which ensemble
canonical = ensembleCheck(estimFiles[0][0])
# make list of all reduced variable, in order, based on ensemble
indList = indepVarList(estimFiles, canonical, reduceType)
for nType, estType in enumerate(estimTypes):
# dict to hold all estType data for all independent variables (T,..)
allTemps = {}
# dict to hold numBins, average, stdErr for each data file
statTemps = {}
# loop over independent variable for given estType
for numTemp, temp in enumerate(indList):
# Get filenames. The chemical potential always has a sign
# in front and the temperature always comes after the
# file type and a dash.
if reduceType == 'T':
bipFiles = glob.glob('*%s-%s*' % (estimTypes[nType], temp))
elif reduceType == 'u':
bipFiles = glob.glob('*%s*%s*' % (estimTypes[nType], temp))
arrs = {}
arrs2 = {}
for nf, f in enumerate(bipFiles):
if checkIfEmpty(f, numLinesToSkip):
print f, ' is empty.'
pass
else:
dat = pl.genfromtxt(f, unpack=True, usecols=colNums[nType])
# skip a few
for d in dat:
d = pl.delete(d, deleteList)
# define key that can be sorted properly
if numTemp < 10:
arrName = 'arr00' + str(numTemp)
elif (numTemp >= 10 and numTemp < 100):
arrName = 'arr0' + str(numTemp)
else:
arrName = 'arr' + str(numTemp)
# treat data from one column
if int(len(colNums[nType])) == 1:
if nf == 0:
arrs[arrName] = dat
arrs2[arrName] = [[
pl.average(dat),
pl.std(dat) / pl.sqrt(1.0 * int(len(dat))),
int(len(dat))
]]
else:
arrs[arrName] = pl.append(arrs[arrName], dat)
arrs2[arrName] = pl.append(arrs2[arrName], [[
pl.average(dat),
pl.std(dat) / pl.sqrt(1.0 * int(len(dat))),
int(len(dat))
]])
# treat data from multiple columns
else:
if nf == 0:
for n, arr in enumerate(dat):
arrs[arrName + '_' + str(n)] = arr
arrs2[arrName + '_' + str(n)] = [[
pl.average(arr),
pl.std(arr) / pl.sqrt(1.0 * int(len(arr))),
int(len(arr))
]]
else:
for n, arr in enumerate(dat):
arrs[arrName + '_' + str(n)] = pl.append(
arrs[arrName + '_' + str(n)], arr)
arrs2[arrName + '_' + str(n)] = pl.append(
arrs2[arrName + '_' + str(n)], [[
pl.average(arr),
pl.std(arr) /
pl.sqrt(1.0 * int(len(arr))),
int(len(arr))
]])
# construct key name. This assumes <1000 temperatures.
if numTemp < 10:
allArrName = 'allTemps00' + str(numTemp)
elif (10 <= numTemp and numTemp < 100):
allArrName = 'allTemps0' + str(numTemp)
else:
allArrName = 'allTemps' + str(numTemp)
if nType < 10:
allArrName += '00' + str(nType)
elif (10 <= nType and nType < 100):
allArrName += '0' + str(nType)
else:
allArrName += str(nType)
allTemps[allArrName] = arrs
statTemps[allArrName] = arrs2
# ---- Write all data to disk ----
# length of max. sized array for all data
maxLen = 0
for t in allTemps:
for g in allTemps[t]:
arrayLen = len(allTemps[t][g])
if arrayLen > maxLen:
maxLen = arrayLen
# open file to hold all of estType data
newName = 'Reduced' + str(estType.capitalize()) + 'Data.dat'
fout = open(newName, 'w')
# write independent variable as top header line
fout.write('#%15s\t' % indList[0])
for n in range(len(colNums[nType]) - 1):
fout.write('%16s\t' % '')
for temp in indList[1:]:
fout.write('%16s\t' % temp)
for n in range(len(colNums[nType]) - 1):
fout.write('%16s\t' % '')
fout.write('\n')
# write observable names as second header line
fout.write('#%15s\t' % observables[nType][0])
for n in range(len(observables[nType]) - 1):
fout.write('%16s\t' % observables[nType][n + 1])
for temp in indList[1:]:
for obs in observables[nType]:
fout.write('%16s\t' % obs)
fout.write('\n')
# write data arrays to disk
for line in range(maxLen):
for a in sorted(allTemps.iterkeys()):
for aa in sorted(allTemps[a]):
try:
fout.write('%16.8E,\t' %
(float(allTemps[a][aa][line])))
except:
fout.write('%16s,\t' % '')
fout.write('\n')
fout.close()
# ---- Average individual files and write to disk ----
# get length of max. sized array for averages
maxLen2 = 0
for t in statTemps:
for g in statTemps[t]:
arrayLen = len(statTemps[t][g])
if arrayLen > maxLen2:
maxLen2 = arrayLen
maxLen2 /= 3
# open file for standard error
newName = 'zAveraged' + str(estType.capitalize()) + 'Data.dat'
fout = open(newName, 'w')
# write independent variable as top header line
fout.write('#%15s\t%16s\t%16s\t' % (indList[0], '', ''))
for n in range(len(colNums[nType]) - 1):
fout.write('%16s\t%16s\t%16s\t' % ('', '', ''))
for temp in indList[1:]:
fout.write('%16s\t%16s\t%16s\t' % (temp, '', ''))
for n in range(len(colNums[nType]) - 1):
fout.write('%16s\t%16s\t%16s\t' % ('', '', ''))
fout.write('\n')
# write observable names as second header line
fout.write('#%15s\t%16s\t%16s\t' %
(observables[nType][0], 'std Err', 'bins'))
for n in range(len(observables[nType]) - 1):
fout.write('%16s\t%16s\t%16s\t' %
(observables[nType][n + 1], 'std Err', 'bins'))
for temp in indList[1:]:
for obs in observables[nType]:
fout.write('%16s\t%16s\t%16s\t' % (obs, 'std Err', 'bins'))
fout.write('\n')
# write data arrays to disk
en = 0
for line in range(maxLen2):
for a in sorted(statTemps.iterkeys()):
for aa in sorted(statTemps[a]):
try:
fout.write('%15.8E,\t%15.8E,\t%15.8E,\t' %
(float(statTemps[a][aa][en]),
float(statTemps[a][aa][en + 1]),
float(statTemps[a][aa][en + 2])))
except:
fout.write('%15s,\t%15s,\t%15s,\t' % ('', '', ''))
fout.write('\n')
en += 3
fout.close()
def plot_uvj_vs_icd():
galaxies = pickle.load(open("galaxies.pickle", "rb"))
galaxies = filter(lambda galaxy: galaxy.ICD_IH != None, galaxies)
galaxies = filter(lambda galaxy: galaxy.sersic != None and galaxy.ston_I > 30, galaxies)
# Upper and Lower limit arrow verts
arrowup_verts = [[0.0, 0.0], [-1.0, -1], [0.0, 0.0], [0.0, -2.0], [0.0, 0.0], [1, -1]]
# arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
F = pyl.figure(1, figsize=(8, 3))
grid = AxesGrid(F, 111, nrows_ncols=(1, 4), axes_pad=0.1, add_all=True, aspect=False, share_all=True)
ax1 = grid[0]
ax2 = grid[1]
ax3 = grid[2]
ax4 = grid[3]
for galaxy in galaxies:
if galaxy.sersic < 1.0:
col1 = ax1.scatter(galaxy.Mass, galaxy.Color_grad, s=25, c="0.8", edgecolor="0.8")
if 1.0 < galaxy.sersic < 2.0:
col2 = ax2.scatter(galaxy.Mass, galaxy.Color_grad, s=25, c="0.8", edgecolor="0.8")
if 2.0 < galaxy.sersic < 3.0:
col3 = ax3.scatter(galaxy.Mass, galaxy.Color_grad, s=25, c="0.8", edgecolor="0.8")
if 3.0 < galaxy.sersic:
if galaxy.Color_grad < 50:
col4 = ax4.scatter(galaxy.Mass, galaxy.Color_grad, s=25, c="0.8", edgecolor="0.8")
else:
col4 = ax4.scatter(galaxy.Mass, 50, marker=None, s=100, verts=arrowup_verts)
# Add the box and whiskers
galaxies1 = filter(lambda galaxy: galaxy.ston_I > 30.0 and galaxy.sersic < 1, galaxies)
galaxies1 = pyl.asarray(galaxies1)
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30.0 and 1 < galaxy.sersic < 2, galaxies)
galaxies2 = pyl.asarray(galaxies2)
galaxies3 = filter(lambda galaxy: galaxy.ston_I > 30.0 and 2 < galaxy.sersic < 3, galaxies)
galaxies3 = pyl.asarray(galaxies3)
galaxies4 = filter(lambda galaxy: galaxy.ston_I > 30.0 and 3 < galaxy.sersic, galaxies)
galaxies4 = pyl.asarray(galaxies4)
x1 = [galaxy.Mass for galaxy in galaxies1]
x2 = [galaxy.Mass for galaxy in galaxies2]
x3 = [galaxy.Mass for galaxy in galaxies3]
x4 = [galaxy.Mass for galaxy in galaxies4]
ll = 8.5
ul = 12
bins_x = pyl.arange(8.5, 12.5, 0.5)
grid1 = []
grid2 = []
grid3 = []
grid4 = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x1 >= xmin, x1 < xmax)]
grid1.append(galaxies1.compress(cond))
cond = [cond1 and cond2 for cond1, cond2 in zip(x2 >= xmin, x2 < xmax)]
grid2.append(galaxies2.compress(cond))
cond = [cond1 and cond2 for cond1, cond2 in zip(x3 >= xmin, x3 < xmax)]
grid3.append(galaxies3.compress(cond))
cond = [cond1 and cond2 for cond1, cond2 in zip(x4 >= xmin, x4 < xmax)]
grid4.append(galaxies4.compress(cond))
icd1 = []
icd2 = []
icd3 = []
icd4 = []
for i in range(len(grid1)):
icd1.append([galaxy.Color_grad for galaxy in grid1[i]])
icd2.append([galaxy.Color_grad for galaxy in grid2[i]])
icd3.append([galaxy.Color_grad for galaxy in grid3[i]])
icd4.append([galaxy.Color_grad for galaxy in grid4[i]])
from boxplot_percentile import percentile_box_plot as pbp
bp1 = pbp(ax1, icd1, indexer=list(pyl.delete(bins_x, -1) + 0.25))
bp2 = pbp(ax2, icd2, indexer=list(pyl.delete(bins_x, -1) + 0.25))
bp3 = pbp(ax3, icd3, indexer=list(pyl.delete(bins_x, -1) + 0.25))
bp4 = pbp(ax4, icd4, indexer=list(pyl.delete(bins_x, -1) + 0.25))
ax1.set_xticks([8, 9, 10, 11])
ax2.set_xticks([8, 9, 10, 11])
ax3.set_xticks([8, 9, 10, 11])
ax4.set_xticks([8, 9, 10, 11])
# ax1.set_xlim(8, 12)
# ax2.set_xlim(8, 12)
# ax3.set_xlim(8, 12)
# ax4.set_xlim(8, 12)
# ax1.set_ylim(0, 50)
# ax2.set_ylim(0, 50)
# ax3.set_ylim(0, 50)
# ax4.set_ylim(0, 50)
ax1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
ax1.set_title("n < 1")
ax2.set_title("1 < n < 2")
ax3.set_title("2 < n < 3")
ax4.set_title("3 < n")
pyl.figtext(0.5, 0.05, r"Log Mass $(M_{\odot})$", fontsize=18, horizontalalignment="center")
ax1.axhline(0, lw=2, zorder=0)
ax2.axhline(0, lw=2, zorder=0)
ax3.axhline(0, lw=2, zorder=0)
ax4.axhline(0, lw=2, zorder=0)
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker="o", mfc="0.8", mec="0.8", markersize=8, linewidth=0)
line2 = pyl.Line2D([], [], marker="s", mec="blue", mfc="None", markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color="r", linewidth=2)
prop = matplotlib.font_manager.FontProperties(size="small")
ax3.legend((line1, line2, line3), ("Data", "Quartiles", "Medians"), "upper center", prop=prop, ncol=1)
pyl.tight_layout()
pyl.subplots_adjust(bottom=0.21, left=0.11)
pyl.show()
data_ecog_lp_ss.flush()
print(i)
pl.save(os.path.join(memap_folder, 'data_ecog_lp_ss.npy'), data_ecog_lp_ss)
spike_samples = tf.spikedetect(data_probe_hp, threshold_multiplier=6.5, bad_channels=probe_bad_channels)
pl.save(os.path.join(memap_folder, 'spike_samples.npy'), spike_samples)
spike_samples_clean = spike_samples
for i in pl.arange(pl.size(spike_samples_clean)-1,-1,-1):
data = data_probe_hp[:, spike_samples[i]-60:spike_samples[i]+60]
stdevs = sp.std(data,1)
if np.max(data) > 3000 or pl.any(stdevs>600):
spike_samples_clean = pl.delete(spike_samples_clean, i)
if i%100==0:
print(i)
spike_samples_clean = pl.delete(spike_samples_clean, 0)
pl.save(os.path.join(memap_folder, 'spike_samples_clean.npy'), spike_samples_clean)
channels = np.empty(0)
for i in pl.arange(0, pl.size(spike_samples_clean)):
data = np.array(data_probe_hp[:, spike_samples_clean[i]].tolist())
channels = np.append(channels, np.argmax(data))
if i%100==0:
print(i)
channels_spikes_df = pd.DataFrame([(channels, spike_samples_clean)], columns=['Channels', 'Samples'])
spike_times_shaftA = channels_spikes_df.Samples[0][channels_spikes_df.Channels[0]>7][channels_spikes_df.Channels[0]<16]
spike_times_shaftB = channels_spikes_df.Samples[0][channels_spikes_df.Channels[0]>23]
def plot_uvj_vs_icd():
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies = filter(lambda galaxy: galaxy.ICD_IH != None, galaxies)
galaxies = filter(lambda galaxy: galaxy.sersic != None and \
galaxy.ston_I > 10, galaxies)
#Upper and Lower limit arrow verts
arrowup_verts = [[0., 0.], [-1., -1], [0., 0.], [0., -2.], [0., 0.],
[1, -1]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
F = pyl.figure(1, figsize=(8, 3))
grid = AxesGrid(F,
111,
nrows_ncols=(1, 4),
axes_pad=0.1,
add_all=True,
aspect=False,
share_all=False)
ax1 = grid[0]
ax2 = grid[1]
ax3 = grid[2]
ax4 = grid[3]
for galaxy in galaxies:
if galaxy.sersic < 1.:
if galaxy.ICD_IH * 100 < 50:
col1 = ax1.scatter(galaxy.Mass,
galaxy.ICD_IH * 100.,
s=25,
c='0.8',
edgecolor='0.8')
else:
col1 = ax1.scatter(galaxy.Mass,
50,
marker=None,
s=100,
verts=arrowup_verts)
if 1. < galaxy.sersic < 2.:
if galaxy.ICD_IH * 100 < 50:
col2 = ax2.scatter(galaxy.Mass,
galaxy.ICD_IH * 100.,
s=25,
c='0.8',
edgecolor='0.8')
else:
col2 = ax2.scatter(galaxy.Mass,
50,
marker=None,
s=100,
verts=arrowup_verts)
if 2. < galaxy.sersic < 3.:
if galaxy.ICD_IH * 100 < 50:
col3 = ax3.scatter(galaxy.Mass,
galaxy.ICD_IH * 100.,
s=25,
c='0.8',
edgecolor='0.8')
else:
col3 = ax3.scatter(galaxy.Mass,
50,
marker=None,
s=100,
verts=arrowup_verts)
if 3. < galaxy.sersic:
if galaxy.ICD_IH * 100 < 50:
col4 = ax4.scatter(galaxy.Mass,
galaxy.ICD_IH * 100.,
s=25,
c='0.8',
edgecolor='0.8')
else:
col4 = ax4.scatter(galaxy.Mass,
50,
marker=None,
s=100,
verts=arrowup_verts)
# Add the box and whiskers
galaxies1 = filter(lambda galaxy: galaxy.ston_I > 10. and \
galaxy.sersic < 1, galaxies)
galaxies1 = pyl.asarray(galaxies1)
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 10. and \
1 < galaxy.sersic < 2, galaxies)
galaxies2 = pyl.asarray(galaxies2)
galaxies3 = filter(lambda galaxy: galaxy.ston_I > 10. and \
2 < galaxy.sersic < 3, galaxies)
galaxies3 = pyl.asarray(galaxies3)
galaxies4 = filter(lambda galaxy: galaxy.ston_I > 10. and \
3 < galaxy.sersic, galaxies)
galaxies4 = pyl.asarray(galaxies4)
x1 = [galaxy.Mass for galaxy in galaxies1]
x2 = [galaxy.Mass for galaxy in galaxies2]
x3 = [galaxy.Mass for galaxy in galaxies3]
x4 = [galaxy.Mass for galaxy in galaxies4]
grid1 = []
grid2 = []
grid3 = []
grid4 = []
from boxplot_percentile_width import percentile_box_plot as pbp
bins_x = pyl.array([8.5, 9., 9.5, 10., 11])
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x1 >= xmin, x1 < xmax)]
grid1.append(galaxies1.compress(cond))
icd1 = []
for i in range(len(grid1)):
icd1.append([galaxy.ICD_IH * 100 for galaxy in grid1[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
bp1 = pbp(ax1, icd1, indexer=list(index), width=width)
bins_x = pyl.array([8.5, 9., 9.5, 10., 10.5, 11.5])
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x2 >= xmin, x2 < xmax)]
grid2.append(galaxies2.compress(cond))
icd2 = []
for i in range(len(grid2)):
icd2.append([galaxy.ICD_IH * 100 for galaxy in grid2[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
bp2 = pbp(ax2, icd2, indexer=list(index), width=width)
bins_x = pyl.array([8.5, 9.5, 10., 11.])
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x3 >= xmin, x3 < xmax)]
grid3.append(galaxies3.compress(cond))
icd3 = []
for i in range(len(grid3)):
icd3.append([galaxy.ICD_IH * 100 for galaxy in grid3[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
index[0] = index[0] + 0.25
bp3 = pbp(ax3, icd3, indexer=list(index), width=width)
bins_x = pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x4 >= xmin, x4 < xmax)]
grid4.append(galaxies4.compress(cond))
icd4 = []
for i in range(len(grid4)):
icd4.append([galaxy.ICD_IH * 100 for galaxy in grid4[i]])
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
print 'ajsdf'
bp4 = pbp(ax4, icd4, indexer=list(index), width=width)
ax1.set_xticks([8, 9, 10, 11])
ax2.set_xticks([8, 9, 10, 11, 12])
ax3.set_xticks([8, 9, 10, 11])
ax4.set_xticks([8, 9, 10, 11, 12])
ax1.set_ylim(0, 50)
ax2.set_ylim(0, 50)
ax3.set_ylim(0, 50)
ax4.set_ylim(0, 50)
ax1.set_xlim(8, 12.5)
ax2.set_xlim(8, 12.5)
ax3.set_xlim(8, 12.5)
ax4.set_xlim(8, 12.5)
ax1.set_ylabel(r'$\xi[i_{775},H_{160}]$ (%)')
ax1.set_title('n < 1')
ax2.set_title('1 < n < 2')
ax3.set_title('2 < n < 3')
ax4.set_title('3 < n')
pyl.figtext(.5,
.05,
r'Log Mass $(M_{\odot})$',
fontsize=18,
horizontalalignment='center')
ax1.axhline(0, lw=2, zorder=0)
ax2.axhline(0, lw=2, zorder=0)
ax3.axhline(0, lw=2, zorder=0)
ax4.axhline(0, lw=2, zorder=0)
import matplotlib.font_manager
line1 = pyl.Line2D([], [],
marker='o',
mfc='0.8',
mec='0.8',
markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [],
marker='s',
mec='#348ABD',
mfc='None',
markersize=10,
linewidth=0,
markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
ax3.legend((line1, line2, line3), ('Data', 'Quartiles', 'Medians'),
loc='upper center',
prop=prop,
ncol=1)
pyl.tight_layout()
pyl.subplots_adjust(bottom=0.21, left=0.11)
pyl.show()
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle','rb'))
arrow_up = [[0,0], [-1,-1], [0,0], [0,-2], [0,0], [1,-1], [0,0]]
# Make figure
f1 = pyl.figure(1, figsize=(6,4))
f1s1 = f1.add_subplot(221)
f1s2 = f1.add_subplot(222)
f1s3 = f1.add_subplot(223)
f1s4 = f1.add_subplot(224)
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
if galaxy.ICD_IH*100 < 50:
f1s1.scatter(galaxy.z, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
else:
f1s1.scatter(galaxy.z, 50, s=100, marker=None, verts=arrow_up)
f1s2.scatter(galaxy.z, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
if galaxy.ston_V > 30. and galaxy.ICD_VJ != None:
f1s3.scatter(galaxy.z, galaxy.ICD_VJ * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s4.scatter(galaxy.z, galaxy.ICD_VJ * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
pbp(f1s1, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
pbp(f1s2, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
# Add the box and whiskers
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies2 = filter(lambda galaxy: galaxy.ston_V > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_VJ*100 for galaxy in grid[i]])
pbp(f1s3, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
pbp(f1s4, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
f1s1.set_xlim(1.5, 3.5)
f1s2.set_xlim(1.5, 3.5)
f1s3.set_xlim(1.5, 3.5)
f1s4.set_xlim(1.5, 3.5)
f1s1.set_ylim(-5, 50)
f1s2.set_ylim(-1, 10)
f1s3.set_ylim(-5, 50)
f1s4.set_ylim(-1, 10)
f1s1.set_xticks([1.5,2,2.5,3,3.5])
f1s2.set_xticks([1.5,2,2.5,3,3.5])
f1s1.set_xticklabels([])
f1s2.set_xticklabels([])
f1s3.set_xticks([1.5,2,2.5,3,3.5])
f1s4.set_xticks([1.5,2,2.5,3,3.5])
f1s1.axhline(0.0, lw=2, c='b', zorder=0)
f1s2.axhline(0.0, lw=2, c='b', zorder=0)
f1s3.axhline(0.0, lw=2, c='b', zorder=0)
f1s4.axhline(0.0, lw=2, c='b', zorder=0)
f1s3.set_xlabel("Redshift")
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s4.set_xlabel("Redshift")
f1s3.set_ylabel(r"$\xi[V_{606},J_{125}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='blue', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='r', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), 'lower center', prop=prop, ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (3.5, 10)
xy2 = (1.5, 10)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
xy = (3.5, -1)
xy2 = (1.5, -1)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (3.5, 10)
xy2 = (1.5, 10)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s3, axesB=f1s4)
xy = (3.5, -1)
xy2 = (1.5, -1)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s3, axesB=f1s4)
f1s3.add_artist(con)
f1s3.add_artist(con2)
pyl.draw()
pyl.show()
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
# galaxies = filter(lambda galaxy: 0.06 * galaxy.halflight *\
# astCalc.da(galaxy.z)*1000/206265. > 2, galaxies)
# Make figure
f1 = pyl.figure(1, figsize=(6, 4))
f1s1 = f1.add_subplot(121)
f1s2 = f1.add_subplot(122)
# f1s3 = f1.add_subplot(223)
# f1s4 = f1.add_subplot(224)
#Upper and Lower limit arrow verts
arrowup_verts = [[0., 0.], [-1., -1], [0., 0.], [0., -2.], [0., 0.],
[1, -1], [0, 0]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
for galaxy in galaxies:
if galaxy.ston_I > 10. and galaxy.ICD_IH != None:
# Add arrows first
if galaxy.ICD_IH > 0.5:
f1s1.scatter(galaxy.Mass,
0.5 * 100,
s=100,
marker=None,
verts=arrowup_verts)
else:
f1s1.scatter(galaxy.Mass,
galaxy.ICD_IH * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
f1s2.scatter(galaxy.Mass,
galaxy.ICD_IH * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 10., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul = 12
bins_x = pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x >= xmin, x < xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH * 100 for galaxy in grid[i]])
from boxplot_percentile_width import percentile_box_plot as pbp
#bp1 = f1s1.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
width = pyl.diff(bins_x)
index = pyl.delete(bins_x, -1) + 0.25
index[-1] = index[-1] + 0.25
pbp(f1s1, icd, indexer=list(index), width=width)
pbp(f1s2, icd, indexer=list(index), width=width)
f1s1.set_xlim(8, 12)
f1s2.set_xlim(8, 12)
f1s1.set_ylim(-10, 50)
f1s2.set_ylim(0, 15)
f1s1.set_xticks([8, 9, 10, 11, 12])
f1s2.set_xticks([8, 9, 10, 11, 12])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s1.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s2.set_xlabel(r"Log Mass ($M_{\odot})$")
import matplotlib.font_manager
line1 = pyl.Line2D([], [],
marker='o',
mfc='0.8',
mec='0.8',
markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [],
marker='s',
mec='#348ABD',
mfc='None',
markersize=10,
linewidth=0,
markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles', 'Medians'),
'lower center',
prop=prop,
ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (12, 15)
xy2 = (8, 15)
con = ConnectionPatch(xyA=xy,
xyB=xy2,
coordsA='data',
coordsB='data',
axesA=f1s1,
axesB=f1s2)
xy = (12, 0)
xy2 = (8, 0)
con2 = ConnectionPatch(xyA=xy,
xyB=xy2,
coordsA='data',
coordsB='data',
axesA=f1s1,
axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (12, 3)
xy2 = (8, 3)
xy = (12, -1)
xy2 = (8, -1)
pyl.draw()
pyl.show()
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle','rb'))
# Make figure
f1 = pyl.figure(1, figsize=(6,4))
f1s1 = f1.add_subplot(221)
f1s2 = f1.add_subplot(222)
f1s3 = f1.add_subplot(223)
f1s4 = f1.add_subplot(224)
#Upper and Lower limit arrow verts
arrowup_verts = [[0.,0.], [-1., -1], [0.,0.],
[0.,-2.], [0.,0.], [1,-1], [0,0]]
#arrowdown_verts = [[0.,0.], [-1., 1], [0.,0.],
# [0.,2.], [0.,0.], [1, 1]]
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
# Add arrows first
if galaxy.ICD_IH > 0.5:
f1s1.scatter(galaxy.Mass, 0.5*100, s=100, marker=None,
verts=arrowup_verts)
else:
f1s1.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s2.scatter(galaxy.Mass, galaxy.ICD_IH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
if galaxy.ston_J > 30. and galaxy.ICD_JH != None:
# Add arrows first
if galaxy.ICD_JH > 0.12:
f1s3.scatter(galaxy.Mass, 12, s=100, marker=None,
verts=arrowup_verts)
else:
f1s3.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
f1s4.scatter(galaxy.Mass, galaxy.ICD_JH * 100, c='0.8',
marker='o', s=25, edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
#bp1 = f1s1.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
pbp(f1s1, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
pbp(f1s2, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_J > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.Mass for galaxy in galaxies2]
ll = 8.5
ul= 12
#bins_x =pyl.linspace(ll, ul, 7)
#bins_x =pyl.arange(8.5, 12.5, 0.5)
bins_x =pyl.array([8.5, 9., 9.5, 10., 10.5, 11., 12.])
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_JH*100 for galaxy in grid[i]])
#bp2 = f1s2.boxplot(icd, positions=pyl.delete(bins_x,-1)+0.25, sym='')
pbp(f1s3, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
pbp(f1s4, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
# Finish Plot
# Tweak colors on the boxplot
#pyl.setp(bp1['boxes'], lw=2)
#pyl.setp(bp1['whiskers'], lw=2)
#pyl.setp(bp1['medians'], lw=2)
#pyl.setp(bp2['boxes'], lw=2)
#pyl.setp(bp2['whiskers'], lw=2)
#pyl.setp(bp2['medians'], lw=2)
#pyl.setp(bp['fliers'], color='#8CFF6F', marker='+')
#f1s1.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s1.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(7.477, 9, facecolor='#FFFDD0', ec='None', zorder=0)
#f1s2.axvspan(11, 12, facecolor='#FFFDD0', ec='None', zorder=0)
f1s1.set_xlim(8,12)
f1s2.set_xlim(8,12)
f1s3.set_xlim(8,12)
f1s4.set_xlim(8,12)
f1s1.set_ylim(-10,50)
f1s2.set_ylim(0,15)
f1s3.set_ylim(-5,12)
f1s4.set_ylim(-1,3)
f1s1.set_xticks([8,9,10,11,12])
f1s1.set_xticklabels([])
f1s2.set_xticks([8,9,10,11,12])
f1s2.set_xticklabels([])
f1s3.set_xticks([8,9,10,11,12])
f1s4.set_xticks([8,9,10,11,12])
f1s4.set_yticks([-1, 0, 1, 2, 3])
f1s3.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s4.set_xlabel(r"Log Mass ($M_{\odot})$")
f1s3.set_ylabel(r"$\xi[J_{125},H_{160}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [], marker='o', mfc='0.8', mec='0.8', markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [], marker='s', mec='blue', mfc='None',
markersize=10, linewidth=0, markeredgewidth=2)
line3 = pyl.Line2D([], [], color='r', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles',
'Medians'), 'lower center', prop=prop, ncol=3)
from matplotlib.patches import ConnectionPatch
xy = (12, 15)
xy2 = (8, 15)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
xy = (12, 0)
xy2 = (8, 0)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s1, axesB=f1s2)
f1s1.add_artist(con)
f1s1.add_artist(con2)
xy = (12, 3)
xy2 = (8, 3)
con = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s3, axesB=f1s4)
xy = (12, -1)
xy2 = (8, -1)
con2 = ConnectionPatch(xyA=xy, xyB=xy2, coordsA='data', coordsB='data',
axesA=f1s3, axesB=f1s4)
f1s3.add_artist(con)
f1s3.add_artist(con2)
pyl.draw()
pyl.show()
from scipy.spatial import ConvexHull
# On the 2-Sphere, the Voronoi tesselation is equivalent to the convex hull projected on the sphere
# (Sugihara, Journal for Geometry and Graphics Volume 6 (2002), No. 1, 69-81.)
# I assume that the same is true in 4D.... [This has to be checked!]
R= 1.6180339887498949 #magic number by Straley for 120 particles
import sys
if (sys.argv[1][-3:]=="npy"):
polar=pl.load(sys.argv[1])
else:
polar=pl.loadtxt(sys.argv[1])
from spheretools import *
cartesian=convert(polar, R)
CHull=ConvexHull(cartesian)
with open("bonds.txt",'w') as fw:
for p in range(cartesian.shape[0]):
# print p
which_simplex,position=pl.where(CHull.simplices==p)
# print which_simplex
all_neighs=pl.unique(CHull.simplices[which_simplex].flatten())
# print "all_neighs",all_neighs
index_of_p=pl.where(all_neighs==p)
# print "p is at",index_of_p
neighs=pl.delete(all_neighs,index_of_p)
# print"neighs after ", neighs
fw.write(str(len(neighs))+" "+" ".join(map(str, neighs))+"\n" )
def plot_icd_vs_mass():
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
arrow_up = [[0, 0], [-1, -1], [0, 0], [0, -2], [0, 0], [1, -1], [0, 0]]
# Make figure
f1 = pyl.figure(1, figsize=(4, 6))
f1s1 = f1.add_subplot(211)
f1s2 = f1.add_subplot(212)
for galaxy in galaxies:
if galaxy.ston_I > 30. and galaxy.ICD_IH != None:
f1s1.scatter(galaxy.z,
galaxy.ICD_IH * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
if galaxy.ston_V > 30. and galaxy.ICD_VJ != None:
f1s2.scatter(galaxy.z,
galaxy.ICD_VJ * 100,
c='0.8',
marker='o',
s=25,
edgecolor='0.8')
# Add the box and whiskers
galaxies2 = filter(lambda galaxy: galaxy.ston_I > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul = 4
bins_x = pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x >= xmin, x < xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH * 100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
pbp(f1s1,
icd,
indexer=list(pyl.delete(bins_x, -1) + 0.25),
whisker_top=None,
whisker_bottom=None)
# Add the box and whiskers
galaxies = pickle.load(open('galaxies.pickle', 'rb'))
galaxies2 = filter(lambda galaxy: galaxy.ston_V > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul = 4
bins_x = pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size - 1):
xmin = bins_x[i]
xmax = bins_x[i + 1]
cond = [cond1 and cond2 for cond1, cond2 in zip(x >= xmin, x < xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_VJ * 100 for galaxy in grid[i]])
pbp(f1s2,
icd,
indexer=list(pyl.delete(bins_x, -1) + 0.25),
whisker_top=None,
whisker_bottom=None)
f1s1.set_xlim(1.5, 3.5)
f1s2.set_xlim(1.5, 3.5)
f1s1.set_ylim(-1, 10)
f1s2.set_ylim(-1, 10)
f1s1.set_xticklabels([])
f1s2.set_xticks([1.5, 2, 2.5, 3, 3.5])
f1s1.set_ylabel(r"$\xi[i_{775},H_{160}]$ (%)")
f1s2.set_xlabel("Redshift")
f1s2.set_ylabel(r"$\xi[V_{606},J_{125}]$ (%)")
import matplotlib.font_manager
line1 = pyl.Line2D([], [],
marker='o',
mfc='0.8',
mec='0.8',
markersize=8,
linewidth=0)
line2 = pyl.Line2D([], [],
marker='s',
mec='#348ABD',
mfc='None',
markersize=10,
linewidth=0,
markeredgewidth=2)
line3 = pyl.Line2D([], [], color='#A60628', linewidth=2)
prop = matplotlib.font_manager.FontProperties(size='small')
pyl.figlegend((line1, line2, line3), ('Data', 'Quartiles', 'Medians'),
'lower center',
prop=prop,
ncol=3)
pyl.show()
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
cond=[cond1 and cond2 for cond1, cond2 in zip(x>=xmin, x<xmax)]
grid.append(galaxies2.compress(cond))
icd = []
for i in range(len(grid)):
icd.append([galaxy.ICD_IH*100 for galaxy in grid[i]])
from boxplot_percentile import percentile_box_plot as pbp
pbp(f1s1, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
pbp(f1s2, icd, indexer=list(pyl.delete(bins_x,-1)+0.25))
# Add the box and whiskers
galaxies = pickle.load(open('galaxies.pickle','rb'))
galaxies2 = filter(lambda galaxy: galaxy.ston_V > 30., galaxies)
galaxies2 = pyl.asarray(galaxies2)
x = [galaxy.z for galaxy in galaxies2]
ll = 1.5
ul= 4
bins_x =pyl.arange(ll, ul, 0.5)
grid = []
for i in range(bins_x.size-1):
xmin = bins_x[i]
xmax = bins_x[i+1]
