def restore_logaxes_labels(subplot, xaxis=True, yaxis=True): ''' This script reformats the default labeling scheme for logarithmic axis labels to the "regular" format. e.g. axis labels of "10**-1" and "10**3" will be changed to "0.1" and "1000" respectively. ''' xticks = subplot.get_xticks() yticks = subplot.get_yticks() if xaxis: xticks_new = [] for xi in xticks: base = pylab.log10(xi) if base >= 0: xi_new = '%i' % xi else: formatter = '%.' + str(int(abs(base))) + 'f' xi_new = formatter % xi xticks_new.append(xi_new) subplot.xaxis.set_ticklabels(xticks_new) if yaxis: yticks_new = [] for yi in yticks: base = pylab.log10(yi) if base >= 0: yi_new = '%i' % yi else: formatter = '%.' + str(int(abs(base))) + 'f' yi_new = formatter % yi yticks_new.append(yi_new) subplot.yaxis.set_ticklabels(yticks_new) return subplot
def value(self, data, image=None):
cam = data['name']
if type(cam) == N.recarray:
cam = cam[0]
pix_size = {"sky": N.sqrt(202000/4), "sbc": 11.3}[cam]
ex = data['exposure']
sky = data['sky'].clip(0.0001, data['sky'].max())
mean = data['mean']
pix_mag = 2.5*log10(pix_size**2)
temp = data['temp'].copy()
if type(cam) == N.recarray:
temp[temp < -35] = -40
temp[temp >= -35] = -30
offset = ex.copy()
offset[temp == -30] = 2.25*ex[temp == -30]
offset[temp == -40] = 0.59*ex[temp == -40]
else:
offset = 0
if cam == "sbc":
offset = 68
else:
offset += 77.1
return -2.5*log10(sky - offset) + data['zmag'] + pix_mag
def main():
Z = 17.4
pix_size = 11.3
pix_mag = 2.5*log10(pix_size**2)
sky = 148
offset = 68
print -2.5*log10(sky - offset) + Z + pix_mag
def get_Kmag_true(jflux_instr, kflux_instr): a = 1. b = -0.0257698323 c = 29.4494709 if jflux_instr < 0 or kflux_instr < 0: return -99. else: jmag_instr = -2.5 * pylab.log10(jflux_instr) kmag_instr = -2.5 * pylab.log10(kflux_instr) return a * kmag_instr + b * (jmag_instr - kmag_instr) + c
def get_Iplusmag_true(rflux_instr, iflux_instr): a = 1. b = 0.00013198858 c = 27.3378864 if rflux_instr < 0 or iflux_instr < 0: return -99. else: rmag_instr = -2.5 * pylab.log10(rflux_instr) imag_instr = -2.5 * pylab.log10(iflux_instr) return a * imag_instr + b * (rmag_instr - imag_instr) + c
def get_rmag_true(rflux_instr, iflux_instr): a = 1. b = 0.05033836 c = 26.52083003 if rflux_instr < 0 or iflux_instr < 0: return -99. else: rmag_instr = -2.5 * pylab.log10(rflux_instr) imag_instr = -2.5 * pylab.log10(iflux_instr) return a * rmag_instr + b * (rmag_instr - imag_instr) + c
def get_imag_true(rflux_instr, iflux_instr): a = 1. b = 0.021948947 c = 26.2291181 if rflux_instr < 0 or iflux_instr < 0: return -99. else: rmag_instr = -2.5 * pylab.log10(rflux_instr) imag_instr = -2.5 * pylab.log10(iflux_instr) return a * imag_instr + b * (rmag_instr - imag_instr) + c
def get_Jmag_true(jflux_instr, kflux_instr): a = 1. b = -0.04157139 c = 29.07118567 if jflux_instr < 0 or kflux_instr < 0: return -99. else: jmag_instr = -2.5 * pylab.log10(jflux_instr) kmag_instr = -2.5 * pylab.log10(kflux_instr) return a * jmag_instr + b * (jmag_instr - kmag_instr) + c
def get_zmag_true(iflux_instr, zflux_instr): a = 1. b = -0.03321189 c = 25.02113495 if iflux_instr < 0 or zflux_instr < 0: return -99. else: imag_instr = -2.5 * pylab.log10(iflux_instr) zmag_instr = -2.5 * pylab.log10(zflux_instr) return a * zmag_instr + b * (imag_instr - zmag_instr) + c
def get_Rplusmag_true(rflux_instr, iflux_instr): a = 1. b = 0.03185165 c = 27.45145076 if rflux_instr < 0 or iflux_instr < 0: return -99. else: rmag_instr = -2.5 * pylab.log10(rflux_instr) imag_instr = -2.5 * pylab.log10(iflux_instr) return a * rmag_instr + b * (rmag_instr - imag_instr) + c
def create_figure():
psd = test_eigenfre_music()
f = linspace(-0.5, 0.5, len(psd))
plot(f, 10 * log10(psd/max(psd)), '--',label='MUSIC 15')
savefig('psd_eigenfre_music.png')
psd = test_eigenfre_ev()
f = linspace(-0.5, 0.5, len(psd))
plot(f, 10 * log10(psd/max(psd)), '--',label='EV 15')
savefig('psd_eigenfre_ev.png')
def on_release(self, event):
"""When we release the mouse, if we were dragging the line, recompute the filter and draw it."""
if self.being_dragged is None: return
self.being_dragged = None
xy = self.design_line.get_data()
self.wp = [xy[0][2], xy[0][3]]
self.ws = [xy[0][1], xy[0][4]]
self.gpass = -20*pylab.log10(xy[1][2])
self.gstop = -20*pylab.log10(xy[1][1])
self.update_design()
def getDR(self):
#this function should return the dynamic range
#this should be the noiselevel of the fft
noiselevel=py.sqrt(py.mean(abs(py.fft(self._tdData.getAllPrecNoise()[0]))**2))
#apply a moving average filter on log
window_size=5
window=py.ones(int(window_size))/float(window_size)
hlog=py.convolve(20*py.log10(self.getFAbs()), window, 'valid')
one=py.ones((2,))
hlog=py.concatenate((hlog[0]*one,hlog,hlog[-1]*one))
return hlog-20*py.log10(noiselevel)
def create_figure():
psd = test_correlog()
f = linspace(-0.5, 0.5, len(psd))
psd = cshift(psd, len(psd)/2)
plot(f, 10*log10(psd/max(psd)))
savefig('psd_corr.png')
def plotsvd(in_filename, out_filename=None):
"""Plot heatmap of orthogonal template components."""
from gstlal.gstlal_svd_bank import read_bank
bank = read_bank(in_filename)
ntemplates = 0
for bf in bank.bank_fragments:
next_ntemplates = ntemplates + bf.orthogonal_template_bank.shape[0]
pylab.imshow(pylab.log10(abs(bf.orthogonal_template_bank[::-1, :])),
extent=(bf.end, bf.start, ntemplates, next_ntemplates),
hold=True,
aspect='auto')
pylab.text(bf.end + bank.filter_length / 30,
ntemplates + 0.5 * bf.orthogonal_template_bank.shape[0],
'%d Hz' % bf.rate,
size='x-small')
ntemplates = next_ntemplates
pylab.xlim(0, 1.15 * bank.filter_length)
pylab.ylim(0, 1.05 * ntemplates)
pylab.colorbar().set_label('$\mathrm{log}_{10} |u_{i}(t)|$')
pylab.xlabel(r"Time $t$ until coalescence (seconds)")
pylab.ylabel(r"Basis index $i$")
pylab.title(r"Orthonormal basis templates $u_{i}(t)$")
if out_filename is None:
pylab.show()
else:
pylab.savefig(out_filename)
pylab.close()
def compute_response(self, **kargs):
"""Compute the window data frequency response
:param norm: True by default. normalised the frequency data.
:param int NFFT: total length of the final data sets( 2048 by default. if less than data length, then
NFFT is set to the data length*2).
The response is stored in :attr:`response`.
.. note:: Units are dB (20 log10) since we plot the frequency response)
"""
from pylab import fft, fftshift, log10
norm = kargs.get('norm', self.norm)
# do some padding. Default is max(2048, data.len*2)
NFFT = kargs.get('NFFT', 2048)
if NFFT < len(self.data):
NFFT = self.data.size * 2
# compute the fft modulus
A = fft(self.data, NFFT)
mag = abs(fftshift(A))
# do we want to normalise the data
if norm is True:
mag = mag / max(mag)
response = 20. * log10(mag) # factor 20 we are looking at the response
# not the powe
#response = clip(response,mindB,100)
self.__response = response
def create_figure():
psd = test_correlog()
f = linspace(-0.5, 0.5, len(psd))
psd = cshift(psd, len(psd) / 2)
plot(f, 10 * log10(psd / max(psd)))
savefig('psd_corr.png')
def _fetch_normalisation(self):
# x is training
# y is gold
filename = self._pj([self._path2data, 'data', 'common_training.csv'])
training = pd.read_csv(filename)
filename = self._pj([self._path2data, 'data', 'common_gold.csv'])
goldfile = pd.read_csv(filename)
#"""function [m b rho] = fetch_normalization(jj)
#%% jj is the index of the molecule (1-7)
colnames = self.species
self.norm_training = pylab.log10(training[colnames] + 1)
self.norm_gold = pylab.log10(goldfile[colnames] + 1)
def quality_from_error_probability(pe):
"""
A quality value Q is an integer mapping of :math:`pe` (i.e., the probability
that the corresponding base call is incorrect). Two different equations have
been in use but we will only use the standard Sanger variant
otherwise known as Phred quality score:
The PHRED software assigns a non-negative quality value
to each base using a logged transformation of the error probability:
:math:`Q = -10 \log_{10}( P_e )`
::
>>> Pe = 1e-9
>>> Q = quality_from_error_probability(Pe)
>>> Q
90
>>> quality_from_error_probability(0.1)
0.1
>>> quality_from_error_probability(0)
1
"""
assert pe >=0 and pe <= 1, "probability expected to be between 0 and 1"
return -10 * pylab.log10(pe)
def pcolor(self, mode="mean", vmin=None, vmax=None):
"""
If you loaded the pickle data sets with only mean and sigma, the D and
pvalue mode cannot be used.
"""
from pylab import clf, xticks, yticks, pcolor, colorbar, flipud, log10
if mode == "mean":
data = self.get_mean()
elif mode == "sigma":
data = self.get_sigma()
elif mode == "D":
data = self.get_ks()[0]
elif mode == "pvalue":
data = self.get_ks()[1]
clf();
if mode == "pvalue":
pcolor(log10(flipud(data)), vmin=vmin, vmax=vmax);
else:
pcolor(flipud(data), vmin=vmin,vmax=vmax);
colorbar()
xticks([x+0.5 for x in range(0,8)], self.ligands, rotation=90)
cellLines = self.cellLines[:]
cellLines.reverse()
yticks([x+0.5 for x in range(0,4)], cellLines, rotation=0)
def create_figure():
newpsd = test_modcovar()
pylab.plot(pylab.linspace(-0.5, 0.5, 4096),
10 * pylab.log10(newpsd/max(newpsd)))
pylab.axis([-0.5,0.5,-60,0])
pylab.savefig('psd_modcovar.png')
def create_figure():
psd = test_yule()
psd = cshift(psd, len(psd) / 2) # switch positive and negative freq
plot(linspace(-0.5, 0.5, 4096), 10 * log10(psd / max(psd)))
axis([-0.5, 0.5, -60, 0])
savefig('psd_yulewalker.png')
def clim_fun(CO2,S): #CO2 in bar, S relative to S0
coeff=np.array([ -12004.26119591, -5991.89177414, 57658.4976358 , 9530.78716653, -44418.02337535, 77509.34762597, -101088.63476418, -51352.32727931, 28881.81475008, 5933.93289355, -37758.42567241, 78609.89563769, 48737.04765816, -59869.92585153, -27824.46745061, 40288.32796231, 832.50331297, 1538.94623985, -22839.47675961, -3910.4065741 , 6857.37036956, -5317.5699878 , -11783.38289392, 17273.58930254, 10919.9212031 ])
x=pylab.log10(CO2)
y=S
inputs=[x*0+1, x, y, x**2, x**2*y, x**2*y**2, y**2, x*y**2, x*y,x**3,x**3*y**3,y**3,x**3*y**2,x**2*y**3,x**3*y,x*y**3, x**4,x**4*y**4,y**4,x**4*y,x**4*y**2,x**4*y**3,y**4*x,y**4*x**2,y**4*x**3]
return np.sum(coeff*inputs)
def update_design(self):
ax = self.ax
ax.cla()
ax2 = self.ax2
ax2.cla()
wp = self.wp
ws = self.ws
gpass = self.gpass
gstop = self.gstop
b, a = ss.iirdesign(wp, ws, gpass, gstop, ftype=self.ftype, output='ba')
self.a = a
self.b = b
#b = [1,2]; a = [1,2]
#Print this on command line so we can use it in our programs
print 'b = ', pylab.array_repr(b)
print 'a = ', pylab.array_repr(a)
my_w = pylab.logspace(pylab.log10(.1*self.ws[0]), 0.0, num=512)
#import pdb;pdb.set_trace()
w, h = freqz(b, a, worN=my_w*pylab.pi)
gp = 10**(-gpass/20.)#Go from db to regular
gs = 10**(-gstop/20.)
self.design_line, = ax.plot([.1*self.ws[0], self.ws[0], wp[0], wp[1], ws[1], 1.0], [gs, gs, gp, gp, gs, gs], 'ko:', lw=2, picker=5)
ax.semilogx(w/pylab.pi, pylab.absolute(h),lw=2)
ax.text(.5,1.0, '{:d}/{:d}'.format(len(b), len(a)))
pylab.setp(ax, 'xlim', [.1*self.ws[0], 1.2], 'ylim', [-.1, max(1.1,1.1*pylab.absolute(h).max())], 'xticklabels', [])
ax2.semilogx(w/pylab.pi, pylab.unwrap(pylab.angle(h)),lw=2)
pylab.setp(ax2, 'xlim', [.1*self.ws[0], 1.2])
ax2.set_xlabel('Normalized frequency')
pylab.draw()
def toggle_logscale(self,label):
self.log_flag = not self.log_flag
if self.log_flag:
self.data = log10(self.data+1e-9)
else:
self.data = self.raw_data
self.update_img()
def hacerfft(channel):
tamanho = len(channel)
fdata = fft(channel)
print('> FFT realizada')
nUniquePts = int(ceil((tamanho)/2))
fdata = fdata[0:nUniquePts]
fdata = abs(fdata)
fdata = fdata/float(leng)
fdata = fdata**2
if leng % 2 > 0:
fdata[1:int(ceil(len(fdata)))] = fdata[1:int(ceil(len(fdata)))] * 2
else:
fdata[1:int(ceil(len(fdata)))-1] = fdata[1:int(ceil(len(fdata)))-1] * 2
freqArray = arange(0, nUniquePts, 1.0)*(fs/tamanho)
plot(freqArray/1000, 10*log10(fdata[0:tamanho:1]))
xlabel('Frequency (kHz)')
ylabel('Power (dB)')
plt.show()
print('> FFT graficada')
return fdata
def hz2mel(hz):
"""Convert a value in Hertz to Mels
:param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.
:returns: a value in Mels. If an array was passed in, an identical sized array is returned.
"""
return 2595 * pylab.log10(1+hz/700.0)
def create_figure():
psd = test_yule()
psd = cshift(psd, len(psd)/2) # switch positive and negative freq
plot(linspace(-0.5, 0.5, 4096),
10 * log10(psd/max(psd)))
axis([-0.5,0.5,-60,0])
savefig('psd_yulewalker.png')
def plot_freqz_response(b,a=1):
w,h = scipy.signal.freqz(b,a)
h_dB = 20 * pylab.log10 (abs(h))
pylab.plot(w/max(w),h_dB)
pylab.ylim(-150, 5)
pylab.ylabel('Magnitude (db)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Frequency response')
def _fetch_normalisation(self):
# x is training
# y is gold
filename = self._pj([self.classpath, 'data', 'common_training.csv'])
training = pd.read_csv(filename)
#filename = self._pj([self.classpath, 'data', 'common_gold.csv'])
self.download_goldstandard()
goldfile = pd.read_csv(filename)
#"""function [m b rho] = fetch_normalization(jj)
#%% jj is the index of the molecule (1-7)
colnames = self.species
self.norm_training = pylab.log10(training[colnames] + 1)
self.norm_gold = pylab.log10(goldfile[colnames] + 1)
def plot_freqz_response(b, a=1):
w, h = scipy.signal.freqz(b, a)
h_dB = 20 * pylab.log10(abs(h))
pylab.plot(w / max(w), h_dB)
pylab.ylim(-150, 5)
pylab.ylabel('Magnitude (db)')
pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
pylab.title(r'Frequency response')
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 __init__(self, name, version, zclust, sigmaz, alpha=50):
self.name = name # e.g. "NEP 200"
self.version = version # e.g. "nep200_v0.0.4"
self.zclust = zclust # cluster redshift
self.sigmaz = sigmaz # 1sigma scatter in (zphot-zspec)/(1+zspec)
self.zspec_lo = 0 # lower redshift bound for specz
self.zspec_hi = 0 # upper redshift bound for specz
self.substructures = []
self.cat = gunzip_read_gzip('%s/%s/%s.cat.gz' %
(data_dir, version, version),
readcat=1)
#self.zout = gunzip_read_gzip('%s/%s/%s.zout.gz' % (data_dir, version, version), readzout=1)
#self.fout = gunzip_read_gzip('%s/%s/%s.fout.gz' % (data_dir, version, version), readcat=1)
xyrd1 = self.cat.x[0], self.cat.y[0], self.cat.ra[0], self.cat.dec[0]
xyrd2 = self.cat.x[1], self.cat.y[1], self.cat.ra[1], self.cat.dec[1]
d_arcsec = mypy.radec_sep(xyrd1[2], xyrd1[3], xyrd2[2], xyrd2[3])
d_pixel = ((xyrd1[0] - xyrd2[0])**2 + (xyrd1[1] - xyrd2[1])**2)**0.5
self.px_scale = d_arcsec / d_pixel
### getting z band magnitude
try:
self.zmagnitude = 25 - 2.5 * pylab.log10(self.cat.fluxauto_z)
except:
pass
try:
self.zmagnitude = 25 - 2.5 * pylab.log10(self.cat.fluxauto_Zplus)
except:
pass
### setting spatial flag based on Convex Hull method
#zspecinds = pylab.find(self.cat.z_spec > 0)
#self.spatial_flag = checkPoint(self.cat.ra[zspecinds], self.cat.dec[zspecinds], self.cat.ra, self.cat.dec)
#self.inds_spatial = pylab.find(self.spatial_flag == 1)
### setting spatial flag based on Concave Hull method (creates a "tighter" spatial selection than Cnvex Hull)
zspecinds = pylab.find(self.cat.z_spec > 0)
points = pylab.array(zip(self.cat.x[zspecinds], self.cat.y[zspecinds]))
self.concave_hull, self.edge_points = alpha_shape(points, alpha)
self.inds_spatial = CheckPoints(self.concave_hull.buffer(10),
self.cat.x, self.cat.y)
self.area_pix2 = self.concave_hull.buffer(10).area
self.area_arcmin2 = self.area_pix2 * (self.px_scale / 60.)**2
print ''
def on_key(event):
#print 'you pressed', event.key, event.xdata, event.ydata
if (event.key == 'q'):
sys.exit(0)
#
elif (event.key == 'w'):
pylab.close(event.canvas.figure)
#
elif (event.key == 'd'):
print "##############################################################"
print "Please click two points to get the distance and slope."
from matplotlib.widgets import Cursor
cursor = Cursor(event.inaxes, useblit=False, color='red', linewidth=1)
points = pylab.ginput(n=2, show_clicks=True, timeout=0)
xs = pylab.array([points[0][0], points[1][0]])
ys = pylab.array([points[0][1], points[1][1]])
dx = xs[1] - xs[0]
dy = ys[1] - ys[0]
dy_log = pylab.log10(ys[0]) - pylab.log10(ys[1])
dy_ln = pylab.log(ys[0]) - pylab.log(ys[1])
angle = pylab.arctan2(dy, dx)
print "points = ", points
print "distance (x) =", dx
print "distance (y) =", dy
print "distance =", pylab.sqrt( dx**2 + dy**2 )
print "Ratio: x0/x1 =", xs[0] / xs[1], " x1/x0 =", xs[1] / xs[0], " y0/y1 =", ys[0] / ys[1], " y1/y0 =", ys[1] / ys[0]
print "dy/y0 = ", dy/ys[0]
print "dx/x0 = ", dx/xs[0]
print "Angle: theta = atan2(y/x) =", angle, "rad =", angle*180.0/pylab.pi, "deg"
print "Slope: ", dy/dx, " 1/slope =", dx/dy
print "Slope (log10 scale):", dy_log/dx
print "Slope (ln scale):", dy_ln/dx
event.inaxes.plot([points[0][0], points[1][0]], [points[0][1], points[1][1]], '--r', lw=1.0)
event.inaxes.plot([points[0][0], points[1][0]], [points[0][1], points[1][1]], '+r', lw=1.0)
pylab.draw()
#
elif (event.key == 'a'):
print "##############################################################"
print "Please click a point to get the position."
from matplotlib.widgets import Cursor
cursor = Cursor(event.inaxes, useblit=False, color='red', linewidth=1)
point = pylab.ginput(n=1, show_clicks=False, timeout=0)
print "point = ", point
event.inaxes.plot(point[0][0], point[0][1], '+r', lw=1.0)
pylab.draw()
def _set_scatter(self):
filename = self.caller.prefix + "results.csv"
df = pd.read_csv(filename)
df["log_ttest"] = -pylab.log10(df["ttest"])
df["markersize"] = 20
js = ScatterJS(df, x="Rp", y="log_ttest", color="bayes", size="markersize" )
js.xlabel = "Regression coefficient"
js.ylabel = "log10(ttest pvalue)"
self.jinja['volcano_jsdata'] = js.get_html()
def getBandwidth(self,dbDistancetoNoise=15):
#this function should return the lowest trustable and highest trustable
#frequency, along with the resulting bandwidth
absdata=-20*py.log10(self.getFAbs()/max(self.getFAbs()))
ix=self.maxDR-dbDistancetoNoise>absdata #dangerous, due to sidelobes, there might be some high freq component!
tfr=self.getfreqs()[ix]
return min(tfr),max(tfr)
def error_label(norm, maxnorm, xpos, ypos):
vexp = pylab.floor( pylab.log10( norm ) )
if norm == 0: vexp = 0
vmant = norm / 10**vexp
thistext = r'$\Vert$E$\Vert_1 = \, %4.2f' % vmant
if vexp != 0:
thistext += r' \times \, 10^{%2d}' % vexp
thistext += '$\n'
vexp = pylab.floor( pylab.log10( maxnorm ) )
if maxnorm == 0: vexp = 0
vmant = maxnorm / 10**vexp
thistext += r'$\Vert$E$\Vert_\infty \! = \, %4.2f' % vmant
if vexp != 0:
thistext += r' \times \, 10^{%2d}' % vexp
thistext += '$'
pylab.text(xpos, ypos, thistext, va='top')
def mu_ev(t):
"""
Remaining mass fraction due to stellar evolution
(equations 2 & 3 and Table 1 in L05)
"""
a_ev, b_ev, c_ev = 7, 0.255, -1.805
q_ev = 0 if t < 12.5e6 else 10**((log10(t) - a_ev)**b_ev + c_ev)
mu_ev = 1 - q_ev
return mu_ev
def plotFrequency(series, sampRate):
""" Plot a frequency to a graph """
s = series / (2.**15)
p, freqArray = _fastFourier(s, sampRate)
np.plot(freqArray / 1000, 10 * pl.log10(p), color='k')
np.xlabel('Frequency (kHz)')
np.ylabel('Power (dB)')
np.plt.show()
return
def deltac(self, z):
"""
calculate delta_collapse (the number that's ~1.686 for E-dS)
Taken from Nakamura and Suto, 1997
"""
deltac0 = 3.0 * (12.0 * M.pi) ** (2.0 / 3.0) / 20.0 * (1.0 + 0.0123 * M.log10(self.om0z(z)))
deltac = deltac0 * self.d1(0) / self.d1(z)
return deltac
def plot_fit(pop1, pop2):
pop_fraction_selected = [ [pop,fraction] for pop,fraction in zip(bins,n) if pop > pop1 and pop < pop2]
pop_selected = [ p for p,f in pop_fraction_selected ]
fraction_selected = [ f for p,f in pop_fraction_selected ]
pop_selected = pop_selected[1:]
fraction_selected = fraction_selected[1:]
x = P.log10(pop_selected)
y = P.log10(fraction_selected)
a,b = np.polyfit(x,y,1)
print a,b
x = pop_selected
y = [10**b*xx**a for xx in x]
P.loglog(x,y)
def main(nbits):
"""Main Program"""
import pylab
nbits = int(nbits)
m = mls(nbits)
fm = pylab.fft(m)
fig, ax = pylab.subplots(2, sharex=True)
ax[0].plot(20 * pylab.log10(numpy.abs(fm)))
ax[1].plot(numpy.angle(fm))
def flux_density(source='3C147', f=1.4):
'''
This returns the flux density for the calibrator sources listed below. 3C147 is a default.
source='3C147', f=frequency in GHz.
The flux density of 3C147 and 3C286 is taken from the VLA calibrators manual
( http://www.vla.nrao.edu/astro/calib/manual/baars.html ):
Source A B C D
3C48 1.31752 -0.74090 -0.16708 +0.01525
3C138 1.00761 -0.55629 -0.11134 -0.01460
3C147 1.44856 -0.67252 -0.21124 +0.04077
3C286 1.23734 -0.43276 -0.14223 +0.00345
3C295 1.46744 -0.77350 -0.25912 +0.00752
'''
if source.upper() == '3C147':
A = 1.44856
B = -0.67252
C = -0.21124
D = +0.04077
logs = A + B * pl.log10(f) + C * pl.log10(f)**2 + D * pl.log10(f)**3
return 10**logs
def Sol_prod(T):
bo = -.77712
b1 = 0.0028426
b2 = 178.34
co = -.07711
do = .0041249
S = 35
(bo + b1 * T + b2 / T) * S**0.5 + co * S + do * S**1.5
logK0 = -171.9065 - 0.077993 * T + 2839.319 / T + 71.595 * pylab.log10(T)
logK = logK0 + (bo + b1 * T + b2 / T) * S**0.5 + co * S + do * S**1.5
return 10**logK
def zipf(file, length):
words = word_count(file, length)
x = [x + 1 for x in range(length)]
y1 = [math.log10(word[1]) for word in words]
pylab.plot(x, y1, 'b-', label='Actual Frrequency')
y2 = y1[0] - pylab.log10(x)
pylab.plot(x, y2, 'r--', label='Theoretical Frequency')
pylab.legend()
pylab.show
def __init__(self, name, version, zclust, sigmaz):
self.name = name # e.g. "NEP 200"
self.version = version # e.g. "nep200_v0.0.4"
self.zclust = zclust # cluster redshift
self.sigmaz = sigmaz # 1sigma scatter in (zphot-zspec)/(1+zspec)
self.zspec_lo = 0 # lower redshift bound for specz
self.zspec_hi = 0 # upper redshift bound for specz
self.substructures = []
self.cat = gunzip_read_gzip('%s/%s/%s.cat.gz' % (data_dir, version, version), readcat=1)
self.zout = gunzip_read_gzip('%s/%s/%s.zout.gz' % (data_dir, version, version), readzout=1)
self.fout = gunzip_read_gzip('%s/%s/%s.fout.gz' % (data_dir, version, version), readcat=1)
### getting z band magnitude
try: self.zmagnitude = 25 - 2.5 * pylab.log10(self.cat.fluxauto_z)
except: pass
try: self.zmagnitude = 25 - 2.5 * pylab.log10(self.cat.fluxauto_Zplus)
except: pass
print ''
def save_2D_movie(frame_list, filename, frame_duration):
"""
Saves a list of 2D numpy arrays of gray shades between 0 and 1 to a zipped tree of PNG files.
Inputs:
frame_list - a list of 2D numpy arrays of floats between 0 and 1
filename - string specifying the filename where to save the data, has to end on '.zip'
frame_duration - specifier for the duration per frame, will be stored as additional meta-data
Example:
>> import numpy
>> framelist = []
>> for i in range(100): framelist.append(numpy.random.random([100,100])) # creates a list of 2D numpy arrays with random values between 0. and 1.
>> save_2D_movie(framelist, 'randommovie100x100x100.zip', 0.1)
"""
try:
import zipfile
except ImportError:
raise ImportError(
"ERROR: Python module zipfile not found! Needed by NeuroTools.plotting.save_2D_movie(...)!"
)
try:
import StringIO
except ImportError:
raise ImportError(
"ERROR: Python module StringIO not found! Needed by NeuroTools.plotting.save_2D_movie(...)!"
)
assert PILIMAGEUSE, "ERROR: Since PIL has not been detected, the function NeuroTools.plotting.save_2D_movie(...) is not supported!"
filenameConditionStr = "ERROR: Second argument of function NeuroTools.plotting.save_2D_movie(...) must be a string ending on \".zip\"!"
assert (type(filename) == str) and (len(filename) > 4) and (
filename[-4:].lower() == '.zip'), filenameConditionStr
zf = zipfile.ZipFile(filename, 'w', zipfile.ZIP_DEFLATED)
container = filename[:-4] # remove .zip
frame_name_format = "frame%s.%dd.png" % (
"%", pylab.ceil(pylab.log10(len(frame_list))))
for frame_num, frame in enumerate(frame_list):
frame_data = [(p, p, p) for p in frame.flat]
im = Image.new('RGB', frame.shape, 'white')
im.putdata(frame_data)
io = StringIO.StringIO()
im.save(io, format='png')
pngname = frame_name_format % frame_num
arcname = "%s/%s" % (container, pngname)
io.seek(0)
zf.writestr(arcname, io.read())
progress_bar(float(frame_num) / len(frame_list))
# add 'parameters' and 'frames' files to the zip archive
zf.writestr("%s/parameters" % container,
'frame_duration = %s' % frame_duration)
zf.writestr(
"%s/frames" % container,
'\n'.join(["frame%.3d.png" % i for i in range(len(frame_list))]))
zf.close()
def plot_bode_simple(freq, p2p, amp, ptitle='Bode'):
gain = nu.array(p2p) / amp
cutoff = nu.array([-3] *
len(gain)) #70.7% : 10^-.15 : 20*log10(10^-.15)=-3
semilogx(freq, 20 * pylab.log10(gain))
semilogx(freq, cutoff, 'r-')
ylabel('Mag. Ratio (dB)')
xlabel('Freq. (Hz)')
title(ptitle)
grid(True)
show()
def make_histogram(self):
edges = []
# Set up evenly spaced bins and determine their centers
if (self.binstyle == "log"):
edges = 10.0**N.linspace(P.log10(self.lowedge),
P.log10(self.highedge), self.bincount + 1)
self.centers = N.sqrt(edges[1:] * edges[:-1])
else:
edges = N.linspace(self.lowedge, self.highedge, self.bincount + 1)
self.centers = 0.5 * (edges[1:] + edges[:-1])
self.widths = (edges[1:] - edges[:-1])
# Make the histogram and error histogram
self.ndata, temp = N.histogram(self.data,
bins=edges,
weights=self.weights)
sqweights = [i * i for i in self.weights]
self.nweights, temp = N.histogram(self.data,
bins=edges,
weights=sqweights)
self.errors = N.sqrt(self.nweights)
def plot_A_transitions(A_transitions, Amin, Amax, color=True, logA=True):
"""
Plot the radiative decays in the list A_transitions. Amin and Amax are the
rates for the bottom and top of the color bar. By default the logarithm of
the rates are printed as colors. If color is false, the rate or logarithm
of the rate is plotted as a black line with the linewidth proportional to
the rate.
"""
for trans in A_transitions:
Elo = float(trans['E lower'])
f = float(trans['freq'])
Eup = Elo + f * MHz2invCm
A = koln.einstein_a(f, float(trans['str']), int(trans['g upper']))
if logA == True:
rate = py.log10(A)
else:
rate = A
lineColor = C.strRgb(rate, Amin, Amax)
# Plot separated lines for A+ and A- states
# lower level
if trans['q lower'][1] == '0':
# for K=0 there is no distinction. Plot from/to level centers
klo = int(trans['q lower'][1])
elif trans['q lower'][3] == '+0':
# A+ on the left
klo = int(trans['q lower'][1]) - 0.2
elif trans['q lower'][3] == '-0':
# A- on the right
klo = int(trans['q lower'][1]) + 0.2
# upper level
if trans['q upper'][1] == '0':
# K=0, plot between line centers
kup = int(trans['q upper'][1])
elif trans['q upper'][3] == '+0':
# A- on the left
kup = int(trans['q upper'][1]) - 0.2
elif trans['q upper'][3] == '-0':
# A+ on the right
kup = int(trans['q upper'][1]) + 0.2
if color == True:
if rate > Amin:
# double width lines above the minimum value
py.plot([kup, klo], [Eup, Elo], '-', lw=2, c=lineColor)
else:
# single width lines below the minimum value
py.plot([kup, klo], [Eup, Elo],
'-',
lw=1,
c=C.strRgb(Amin, Amin, Amax))
else:
if rate > Amin:
linew = 3 * (rate - Amin) / (Amax - Amin)
py.plot([kup, klo], [Eup, Elo], '-', lw=linew, c='k')
def plot_upd_mfreqz(self, fig, taps, a=1):
if not signal:
return
self.plot_init_mfreqz(fig)
ax1, ax2 = fig.get_axes()
w, h = signal.freqz(taps, a)
if sum(abs(h)) == 0:
return
h_dB = 20 * pylab.log10(abs(h))
ax1.plot(w / max(w), h_dB)
h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h)))
ax2.plot(w / max(w), h_Phase)
def plot_scores(self, filename=None, savefig=False):
# scores
from pylab import log10
pylab.clf()
pylab.plot(log10(self.df.query('score>540')['rep1_signal']),
log10(self.df.query('score>540')['rep2_signal']),
'ko',
alpha=0.5,
label='<0.05 IDR')
pylab.plot(log10(self.df.query('score<540')['rep1_signal']),
log10(self.df.query('score<540')['rep2_signal']),
'ro',
alpha=0.5,
label='>=0.05 IDR')
N = pylab.ylim()[1]
pylab.plot([0, N], [0, N], color='blue', alpha=0.5, ls='--')
pylab.xlabel("Rep1 log10 score")
pylab.ylabel("Rep2 log10 score")
pylab.legend(loc='lower right')
if savefig:
pylab.savefig(filename)
def save_sonogram(self, replace=False, n_fft=settings.N_FFT, \
min_freq=settings.MIN_FREQ, \
max_freq=settings.MAX_FREQ, \
dpi=100,
width=1000,
height=350,
max_framerate=settings.MAX_FRAMERATE):
filename = self.get_sonogram_name()
name = os.path.join(settings.SONOGRAM_DIR, filename)
path = os.path.join(settings.MEDIA_ROOT, name)
try:
if not os.path.exists(path):
replace = True
except (ValueError, SuspiciousOperation, AttributeError):
replace = True
if replace:
audio, framerate = self.get_audio(max_framerate=max_framerate)
Pxx, freqs, bins, im = specgram(audio, NFFT=n_fft, Fs=framerate)
f = where(logical_and(freqs > min_freq, freqs <= max_freq))[0]
Pxx[where(Pxx > percentile(Pxx[f].flatten(), 99.99))] = percentile(
Pxx[f].flatten(), 99.99)
Pxx[where(Pxx < percentile(Pxx[f].flatten(), 0.01))] = percentile(
Pxx[f].flatten(), 0.01)
clf()
fig = figure(figsize=(float(width) / dpi, float(height) / dpi),
dpi=dpi)
imshow(flipud(10 * log10(Pxx[f, ])),
extent=(bins[0], bins[-1], freqs[f][0], freqs[f][-1]),
aspect='auto',
cmap=cm.gray)
gca().set_ylabel('Frequency (Hz)')
gca().set_xlabel('Time (s)')
axis_pixels = gca().transData.transform(
np.array((gca().get_xlim(), gca().get_ylim())).T)
st, created = SonogramTransform.objects.get_or_create(
n_fft=n_fft,
framerate=framerate,
min_freq=min_freq,
max_freq=max_freq,
duration=self.duration,
width=width,
height=height,
dpi=dpi,
top_px=max(axis_pixels[:, 1]),
bottom_px=min(axis_pixels[:, 1]),
left_px=min(axis_pixels[:, 0]),
right_px=max(axis_pixels[:, 0]),
)
savefig(open(path, 'wb'), format='jpg', dpi=dpi)
sonogram, created = Sonogram.objects.get_or_create(snippet=self,
transform=st,
path=name)
close()
def Sol_prod(
T
): # from appendix G Pilson book, reproduces table G.1 for salinity=35
bo = -.77712
b1 = 0.0028426
b2 = 178.34
co = -.07711
do = .0041249
S = 35
(bo + b1 * T + b2 / T) * S**0.5 + co * S + do * S**1.5
logK0 = -171.9065 - 0.077993 * T + 2839.319 / T + 71.595 * pylab.log10(T)
logK = logK0 + (bo + b1 * T + b2 / T) * S**0.5 + co * S + do * S**1.5
return 10**logK
def test_periodogram_real_vs_octave():
# the periodogram is tested against the octave output that is "identical"
# for the following real example
import numpy as np
PSDs = []
for this in range(100):
xx = data_two_freqs()
p = Periodogram(xx, 4, window="hanning")
p()
PSDs.append(p.psd)
M = 10 * log10(np.mean(PSDs, axis=0))
assert max(M) > 10 # 10.939020375396096
assert np.mean(M[M < -35]) > -50
assert np.mean(M[M < -35]) < -40
def EllipticSlope():
rho_list = [0.5]
Roell_list = pl.logspace(pl.log10(0.1), pl.log10(50), 20)
markers = ["bo-", "gv-", "r>-"]
for i, rho in enumerate(rho_list):
tf_list = []
te_list = []
for Roell in Roell_list:
ell = 1 / Roell
te, tf = find_data(
["material.ell", "problem.rho", "problem.hsize"],
[ell, rho, 1e-3])
te_list.append(te)
tf_list.append(tf)
pl.figure(i)
pl.loglog(Roell_list, tf_list, markers[i], label=r"$\rho=%g$" % (rho))
ind1 = 8
ind2 = 5
coeff = pl.polyfit(pl.log(Roell_list[ind1:-ind2]),
pl.log(tf_list[ind1:-ind2]), 1)
poly = pl.poly1d(coeff)
fit = pl.exp(poly(pl.log(Roell_list[ind1:-ind2])))
print("The slope = %.2e" % (coeff[0]))
pl.loglog(Roell_list[ind1:-ind2], fit, "k-", linewidth=3.0, alpha=0.2)
pl.xlabel(r"Relative defect size $a/\ell$")
pl.ylabel("$\sigma/\sigma_0$ at fracture")
pl.legend(loc="best")
pl.xlim([9e-2, 1e2])
pl.grid(True)
pl.savefig("elliptic_rho_" + str(rho).replace(".", "x") + ".pdf",
bbox_inches='tight')
def schechter_mf_log(xaxis, alpha, mstar, phistar):
"""
# DESCRIPTION:
# Returns the values for a Schechter mass function
# from a given mass-axis and Schechter parameters.
#
# INPUTS:
# xaxis = input mass value(s)
# alpha = Schechter parameters
# mstar = Schechter parameters
# phistar = Schechter parameters
"""
return pylab.log10(
pylab.log(10) * phistar * 10**((xaxis - mstar) * (1 + alpha)) *
pylab.exp(-10**(xaxis - mstar)))
def imshow(self, log=True, **kwargs):
"""show the detector science frame as an image"""
v = self.sci.copy()
v[v == 0] = 1.0
if log is True:
v = log10(v)
pylab.imshow(v,
cmap=cm.gray,
origin='lower',
interpolation='nearest',
**kwargs)
pylab.colorbar()
def clim_fun_highCH4(CO2, S):
# polynomial coefficients from fit to 1-D climate model:
coeff = np.array([
-7.54993474e+00, -1.56537857e+02, 1.09569449e+03, -4.23166796e+01,
3.33328419e+02, -5.36250637e+02, -1.24044934e+03, -1.54156844e+03,
1.04071399e+03, -1.46500650e+00, 2.45480651e+01, 5.13587132e+02,
-4.87777690e+01, 2.51789370e+02, 2.63929259e+01, 6.86937462e+02
])
x = pylab.log10(CO2) # polynomial fit done in log space
y = S
inputs = [
x * 0 + 1, x, y, x**2, x**2 * y, x**2 * y**2, y**2, x * y**2, x * y,
x**3, x**3 * y**3, y**3, x**3 * y**2, x**2 * y**3, x**3 * y, x * y**3
]
return np.sum(coeff * inputs)
def main():
path = "../../example/"
#samplingRate, s1 = wavfile.read(path + '440_sine.wav')
samplingRate, s1 = wavfile.read(path + '12000hz.wav')
if len(s1.shape) == 2:
s1 = s1[:,0]
if len(s1) > 8192:
s1 = s1[:256]
n = float(len(s1))
print "DType %s" % s1.dtype
print "Sound File Shape " + str(s1.shape)
print "Sample Frequency / Entries: %.2f / %.2f" % (samplingRate, n)
print "Duration %.2f ms" % ((n / samplingRate)*1000)
s1 = s1 / (2.**15)
# Plotting the Tone
timeArray = arange(0, n, 1)
timeArray = timeArray / samplingRate
timeArray = timeArray * 1000 #scale to milliseconds
_, (axTone, axFreq, axLogFreq) = subplots(3)
axTone.plot(timeArray, s1, color='k')
ylabel('Amplitude')
xlabel('Time (ms)')
#Plotting the Frequency Content
nUniquePts, p = getFFT(s1, n)
#nUniquePts, p = getFFTUtil(s1, n)
freqArray = arange(0, nUniquePts, 1.0) * (samplingRate / n);
print "FreqMax %fHz" % freqArray[np.argmax(p)]
axFreq.plot(freqArray/1000, p, color='k')
axLogFreq.plot(freqArray/1000, 10*log10(p), color='k')
xlabel('Frequency (kHz)')
ylabel('Power (dB)')
show()
