def scale_mtx(M, normalize=False, dbscale=False, norm=False, bels=False):
"""
::
Perform mutually-orthogonal scaling operations, otherwise return identity:
normalize [False]
dbscale [False]
norm [False]
"""
if not (normalize or dbscale or norm or bels):
return M
else:
X = M.copy() # don't alter the original
if norm:
nz_idx = (X * X).sum(1) > 0
X[nz_idx] = (X[nz_idx].T / np.sqrt(
(X[nz_idx] * X[nz_idx]).sum(1))).T
if normalize:
X = X - np.min(X)
X = X / np.max(X)
if dbscale or bels:
X = P.log10(P.clip(X, 0.0001, X.max()))
if dbscale:
X = 20 * X
return X
def _mfcc(self):
"""
::
DCT of the Log magnitude CQFT
"""
fp = self._check_feature_params()
if not self._cqft():
return False
self._make_dct()
AA = P.log10(P.clip(self.CQFT, 0.0001, self.CQFT.max()))
self.MFCC = P.dot(self.DCT, AA)
self._have_mfcc = True
if self.verbosity:
print("Extracted MFCC: lcoef=%d, ncoef=%d, intensified=%d" %
(self.lcoef, self.ncoef, self.intensify))
n = self.ncoef
l = self.lcoef
self.X = self.MFCC[l:l + n, :]
return True
def feature_scale(M, normalize=False, dbscale=False, norm=False, bels=False):
"""
::
Perform mutually-orthogonal scaling operations, otherwise return identity:
normalize [False]
dbscale [False]
norm [False]
"""
if not (normalize or dbscale or norm or bels):
return M
else:
X = M.copy() # don't alter the original
if norm:
X = X / P.tile(P.sqrt((X * X).sum(0)), (X.shape[0], 1))
if normalize:
X = _normalize(X)
if dbscale or bels:
X = P.log10(P.clip(X, 0.0001, X.max()))
if dbscale:
X = 20 * X
return X
def Make_Z_Plot(grbdict, z_key='grbox_z', Nbins=31, z_cutoff=4.0):
'''Given any dictionary which has the redshift as one of the keywords, plot
a histogram of those redshifts.
'''
z_list = []
zname_list = []
date_list = []
# 'un'zip all_grbs (is there a better way to do this? just assign rather than a for loop?)
for key, value in grbdict.iteritems():
# date_list.append(value['date'])
z_list.append(value[z_key])
zname_list.append(key)
print '***All***'
print ' '
print z_list
print len(z_list)
### Print out the most distant GRB as a function of time
zmax = 0.0
rr = []
for key, value in grbdict.iteritems():
if value[z_key] > zmax:
rr.append(key)
zmax = value[z_key]
# print value['date'].year + value['date'].timetuple().tm_yday/365.0, zmax, "# ", key
ax = plt.subplot(111)
n, bins, patches = plt.hist(plt.log10(z_list),
bins=Nbins,
facecolor='grey',
edgecolor='grey')
# Define pre-swift burst index as bursts before 041210
# high_z_i = plt.where(plt.array(date_list) < datetime.date(2004,12,10))
# high_z_list = [z_list[i] for i in list(high_z_i[0])]
#print high_z_list
# n, bins1, patches = plt.hist(plt.log10(high_z_list),bins=bins,facecolor='black',edgecolor='black',alpha=0.6)
if overplot_high_z:
high_z_list = [z for z in z_list if z > z_cutoff]
n, bins1, patches = plt.hist(plt.log10(high_z_list),
bins=bins,
facecolor='black',
edgecolor='black')
ay = ax.twinx()
argg = list(plt.ones(len(z_list)).cumsum().repeat(2))
zz = copy.copy(z_list)
zz.sort()
tmp = list(plt.log10(zz).repeat(2))
tmp.append(1)
yy = [0]
yy.extend(argg)
ay.plot(tmp, yy, aa=True, linewidth=4, color='black')
argg = list(plt.ones(len(high_z_list)).cumsum().repeat(2))
zz = copy.copy(high_z_list)
zz.sort()
tmp = list(plt.log10(zz).repeat(2))
tmp.append(1)
yy = [0]
yy.extend(argg)
ay.plot(tmp, yy, aa=True, linewidth=2, color='grey')
ay.set_ylim((0, len(z_list) * 1.05))
ay.set_ylabel("Cumulative Number", fontsize=20)
# formatter for bottom x axis
def ff(x, pos=None):
if x < -1:
return "%.2f" % (10**x)
elif x < 0:
return "%.1f" % (10**x)
elif 10**x == 8.5:
return "%.1f" % (10**x)
else:
return "%i" % (10**x)
formatter = FuncFormatter(ff)
ax.set_xticks([
-2, -1,
plt.log10(0.3), 0,
plt.log10(2),
plt.log10(3),
plt.log10(4),
plt.log10(6),
plt.log10(8.5)
])
ax.xaxis.set_major_formatter(formatter)
ax.set_xlabel("Redshift ($z$)", fontsize=20)
ax.set_ylabel("Number", fontsize=20)
ax.set_xlim((plt.log10(0.005), plt.log10(10)))
ax2 = ax.twiny()
xlim = ax.get_xlim()
#ax2.set_xscale("log")
ax2.set_xlim((xlim[0], xlim[1]))
# Define function for plotting the top X axis; time since big bang in Gyr
def rr(x, pos=None):
g = cosmocalc.cosmocalc(10.0**x, H0=71.0)
if g['zage_Gyr'] < 1:
return "%.2f" % g[
'zage_Gyr'] # Return 2 dec place if age < 1; e.g 0.62
else:
return "%.1f" % g[
'zage_Gyr'] # Return 1 dec place if age > 1; e.g. 1.5
ax2.set_xticks(
[-1.91, -1.3, -0.752, -0.283, 0.102, 0.349, 0.62,
plt.log10(8.3)])
formatter1 = FuncFormatter(rr)
ax2.xaxis.set_major_formatter(formatter1)
ax2.set_xlabel("Time since Big Bang (Gyr)", fontsize=20)
#plt.bar(l,a['yy'],width=w,log=False)
#ax.set_xscale("log",nonposx='clip')
## Now plot inset plot of GRBs greater than z=z_cutoff
axins = inset_axes(
ax2,
width="30%", # width = 30% of parent_bbox
height="30%") # height : 1 inch)
locator = axins.get_axes_locator()
locator.set_bbox_to_anchor((-0.8, -0.45, 1.35, 1.35), ax.transAxes)
locator.borderpad = 0.0
high_z_list = [z for z in z_list if z > z_cutoff]
if high_z_list: # if there are any high-z's, plot them
n, bins, patches = plt.hist(plt.array(high_z_list),
facecolor='black',
edgecolor='black')
axins.set_xlim(z_cutoff, 8.5)
axins.set_xlabel("z")
axins.set_ylabel("N")
# high_z_i = plt.where(plt.array(date_list) < datetime.date(2004,12,10))
# high_z_list = [z_list[i] for i in list(high_z_i[0]) if z_list[i] > z_cutoff]
n, bins, patches = plt.hist(plt.array(high_z_list),
bins=bins,
facecolor='black',
edgecolor='black')
high_z_list = [z for z in z_list if z > z_cutoff]
if high_z_list: # if there are any high-z's, plot them
n, bins, patches = plt.hist(plt.array(high_z_list),
facecolor='black',
edgecolor='black')
axins.set_xlim(z_cutoff, 9.0)
#mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
#axins2.set_xlabel("Time since Big Bang [Gyr]",fontsize=20)
ylabels = ax.get_yticklabels()
plt.setp(ylabels, size=14, name='times', weight='light', color='k')
xlabels = ax.get_xticklabels()
plt.setp(xlabels, size=14, name='times', weight='light', color='k')
xlabels = ax2.get_xticklabels()
plt.setp(xlabels, size=14, name='times', weight='light', color='k')
xlabels = ay.get_yticklabels()
plt.setp(xlabels, size=14, name='times', weight='light', color='k')
plt.savefig('z_plot.eps')
plt.draw()
return z_list
def Make_Z_Plot(grbdict, z_key="grbox_z", Nbins=31, z_cutoff=4.0):
"""Given any dictionary which has the redshift as one of the keywords, plot
a histogram of those redshifts.
"""
z_list = []
zname_list = []
date_list = []
# 'un'zip all_grbs (is there a better way to do this? just assign rather than a for loop?)
for key, value in grbdict.iteritems():
# date_list.append(value['date'])
z_list.append(value[z_key])
zname_list.append(key)
print "***All***"
print " "
print z_list
print len(z_list)
### Print out the most distant GRB as a function of time
zmax = 0.0
rr = []
for key, value in grbdict.iteritems():
if value[z_key] > zmax:
rr.append(key)
zmax = value[z_key]
# print value['date'].year + value['date'].timetuple().tm_yday/365.0, zmax, "# ", key
ax = plt.subplot(111)
n, bins, patches = plt.hist(plt.log10(z_list), bins=Nbins, facecolor="grey", edgecolor="grey")
# Define pre-swift burst index as bursts before 041210
# high_z_i = plt.where(plt.array(date_list) < datetime.date(2004,12,10))
# high_z_list = [z_list[i] for i in list(high_z_i[0])]
# print high_z_list
# n, bins1, patches = plt.hist(plt.log10(high_z_list),bins=bins,facecolor='black',edgecolor='black',alpha=0.6)
if overplot_high_z:
high_z_list = [z for z in z_list if z > z_cutoff]
n, bins1, patches = plt.hist(plt.log10(high_z_list), bins=bins, facecolor="black", edgecolor="black")
ay = ax.twinx()
argg = list(plt.ones(len(z_list)).cumsum().repeat(2))
zz = copy.copy(z_list)
zz.sort()
tmp = list(plt.log10(zz).repeat(2))
tmp.append(1)
yy = [0]
yy.extend(argg)
ay.plot(tmp, yy, aa=True, linewidth=4, color="black")
argg = list(plt.ones(len(high_z_list)).cumsum().repeat(2))
zz = copy.copy(high_z_list)
zz.sort()
tmp = list(plt.log10(zz).repeat(2))
tmp.append(1)
yy = [0]
yy.extend(argg)
ay.plot(tmp, yy, aa=True, linewidth=2, color="grey")
ay.set_ylim((0, len(z_list) * 1.05))
ay.set_ylabel("Cumulative Number", fontsize=20)
# formatter for bottom x axis
def ff(x, pos=None):
if x < -1:
return "%.2f" % (10 ** x)
elif x < 0:
return "%.1f" % (10 ** x)
elif 10 ** x == 8.5:
return "%.1f" % (10 ** x)
else:
return "%i" % (10 ** x)
formatter = FuncFormatter(ff)
ax.set_xticks([-2, -1, plt.log10(0.3), 0, plt.log10(2), plt.log10(3), plt.log10(4), plt.log10(6), plt.log10(8.5)])
ax.xaxis.set_major_formatter(formatter)
ax.set_xlabel("Redshift ($z$)", fontsize=20)
ax.set_ylabel("Number", fontsize=20)
ax.set_xlim((plt.log10(0.005), plt.log10(10)))
ax2 = ax.twiny()
xlim = ax.get_xlim()
# ax2.set_xscale("log")
ax2.set_xlim((xlim[0], xlim[1]))
# Define function for plotting the top X axis; time since big bang in Gyr
def rr(x, pos=None):
g = cosmocalc.cosmocalc(10.0 ** x, H0=71.0)
if g["zage_Gyr"] < 1:
return "%.2f" % g["zage_Gyr"] # Return 2 dec place if age < 1; e.g 0.62
else:
return "%.1f" % g["zage_Gyr"] # Return 1 dec place if age > 1; e.g. 1.5
ax2.set_xticks([-1.91, -1.3, -0.752, -0.283, 0.102, 0.349, 0.62, plt.log10(8.3)])
formatter1 = FuncFormatter(rr)
ax2.xaxis.set_major_formatter(formatter1)
ax2.set_xlabel("Time since Big Bang (Gyr)", fontsize=20)
# plt.bar(l,a['yy'],width=w,log=False)
# ax.set_xscale("log",nonposx='clip')
## Now plot inset plot of GRBs greater than z=z_cutoff
axins = inset_axes(ax2, width="30%", height="30%") # width = 30% of parent_bbox # height : 1 inch)
locator = axins.get_axes_locator()
locator.set_bbox_to_anchor((-0.8, -0.45, 1.35, 1.35), ax.transAxes)
locator.borderpad = 0.0
high_z_list = [z for z in z_list if z > z_cutoff]
if high_z_list: # if there are any high-z's, plot them
n, bins, patches = plt.hist(plt.array(high_z_list), facecolor="black", edgecolor="black")
axins.set_xlim(z_cutoff, 8.5)
axins.set_xlabel("z")
axins.set_ylabel("N")
# high_z_i = plt.where(plt.array(date_list) < datetime.date(2004,12,10))
# high_z_list = [z_list[i] for i in list(high_z_i[0]) if z_list[i] > z_cutoff]
n, bins, patches = plt.hist(plt.array(high_z_list), bins=bins, facecolor="black", edgecolor="black")
high_z_list = [z for z in z_list if z > z_cutoff]
if high_z_list: # if there are any high-z's, plot them
n, bins, patches = plt.hist(plt.array(high_z_list), facecolor="black", edgecolor="black")
axins.set_xlim(z_cutoff, 9.0)
# mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5")
# axins2.set_xlabel("Time since Big Bang [Gyr]",fontsize=20)
ylabels = ax.get_yticklabels()
plt.setp(ylabels, size=14, name="times", weight="light", color="k")
xlabels = ax.get_xticklabels()
plt.setp(xlabels, size=14, name="times", weight="light", color="k")
xlabels = ax2.get_xticklabels()
plt.setp(xlabels, size=14, name="times", weight="light", color="k")
xlabels = ay.get_yticklabels()
plt.setp(xlabels, size=14, name="times", weight="light", color="k")
plt.savefig("z_plot.eps")
plt.draw()
return z_list
# Get pixel coordinates of SN
wcs = pywcs.WCS(prihdr)
try:
target_pix = wcs.wcs_sky2pix([(np.array([ra,dec], np.float_))], 1)[0]
except:
print "ERROR when converting sky to wcs. Is astrometry in place? Default coordinates assigned."
target_pix = [+nx/2., ny/2.]
print target_pix
else:
target_pix = [+nx/2., ny/2.]
img = img - np.nanmin(img)
av = np.median(img.flatten())
mi, ma = zscale.zscale(img)
im = plt.imshow(plt.log10(img), aspect="equal", extent=(0, ny, 0, nx), \
origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=np.log10(av), vmax=np.log10(3*av)) #, interpolation="lanczos")
plt.scatter(target_pix[0], target_pix[1], marker="x", s=10, c="red")
plt.colorbar(im)
filename = os.path.basename(f)
plt.savefig(os.path.join(plot_dir, filename.replace("."+filename.split(".")[-1], "_{:}.png".format(i))), dpi=200)
plt.clf()
def move_to_discarded(mydir, myfilter, ra, dec):
import shutil
for f in glob.glob(os.path.join(mydir, myfilter)):
frames_with_target = get_frames_with_target(f, ra, dec)
if len(frames_with_target) == 0:
discarddir = os.path.join(mydir, "discarded")
if (not os.path.isdir(discarddir)):
wcs = pywcs.WCS(prihdr)
try:
target_pix = wcs.wcs_sky2pix(
[(np.array([ra, dec], np.float_))], 1)[0]
except:
print "ERROR when converting sky to wcs. Is astrometry in place? Default coordinates assigned."
target_pix = [+nx / 2., ny / 2.]
print target_pix
else:
target_pix = [+nx / 2., ny / 2.]
img = img - np.nanmin(img)
av = np.median(img.flatten())
mi, ma = zscale.zscale(img)
im = plt.imshow(plt.log10(img), aspect="equal", extent=(0, ny, 0, nx), \
origin="lower", cmap=matplotlib.cm.gray_r, interpolation="none", vmin=np.log10(av), vmax=np.log10(3*av)) #, interpolation="lanczos")
plt.scatter(target_pix[0],
target_pix[1],
marker="x",
s=10,
c="red")
plt.colorbar(im)
filename = os.path.basename(f)
plt.savefig(os.path.join(
plot_dir,
filename.replace("." + filename.split(".")[-1],
"_{:}.png".format(i))),
dpi=200)
plt.clf()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as plb
from scipy.stats import linregress
labels, values = zip(*aggCount.most_common(50))
indexes = np.arange(len(labels))
width = 1
plt.bar(indexes, values, width)
plt.xticks(indexes + width * 0.5, labels)
plt.show()
X = indexes + 1
Y = values
plt.plot(X, Y, 'bo')
plt.savefig('rawplot.png')
plt.loglog(X, Y, 'bo')
plt.savefig('loglogplot.png')
m, b = plb.polyfit(plb.log10(X), plb.log10(Y), 1)
slope, intercept, r_value, p_value, std_err = linregress(
plb.log10(X), plb.log10(Y))
rsquared = r_value**2
plt.plot(plb.log10(X), plb.log10(Y), 'bo', plb.log10(X),
plb.log10(X) * slope + intercept, '--k')
plt.savefig('loglogplot_fitted.png')
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pylab as plb
from scipy.stats import linregress
labels, values = zip(*aggCount.most_common(50))
indexes = np.arange(len(labels))
width = 1
plt.bar(indexes, values, width)
plt.xticks(indexes + width * 0.5, labels)
plt.show()
X=indexes +1
Y=values
plt.plot(X,Y, 'bo')
plt.savefig('rawplot.png')
plt.loglog(X,Y,'bo')
plt.savefig('loglogplot.png')
m,b=plb.polyfit(plb.log10(X),plb.log10(Y),1)
slope, intercept, r_value, p_value, std_err=linregress(plb.log10(X),plb.log10(Y))
rsquared=r_value**2
plt.plot(plb.log10(X),plb.log10(Y),'bo',plb.log10(X), plb.log10(X)*slope +intercept, '--k')
plt.savefig('loglogplot_fitted.png')
#!/usr/bin/env python
# coding: utf-8
import pickle
from pylab import *
import random
from matplotlib import pylab as pl
file = open('table_data.bin', 'rb')
table = pickle.load(file)
file.close()
chen_f = lambda Ree, jdd: (-2.0 * pl.log10(
jdd / 3.7065 - 5.0452 / Ree * pl.log10((jdd**1.1098) / 2.8257 + 5.8506 /
(Ree**0.8981))))**-2 #Re>2100일경우
minor_loss = {
'edged inlet': 0.5,
'Re-entrant inlet': 1,
'Well rounded inlet': 0.05,
'Exit': 1,
'90º Elbow': 1.4,
'45º Elbow': 0.35,
'Globe valve': 10,
'Gate valve': 0.15,
'Basket strainer': 1.3
}
# -------------------------------- 압력차 ---------------------------------------------
def Pressure_drop(Q, L, dz, liquid_type, pipe_standard, merterial, loss):
Q = float(Q)
