本文整理汇总了Python中astroML.plotting.setup_text_plots函数的典型用法代码示例。如果您正苦于以下问题：Python setup_text_plots函数的具体用法？Python setup_text_plots怎么用？Python setup_text_plots使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了setup_text_plots函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: plot
def plot(fname):
setup_text_plots(fontsize=10, usetex=True)
data = dl.read_dat(fname,',')
n = len(data[0]) - 4
f,plots = plt.subplots(int(np.ceil(n/2))+n%2,2, sharex=False,sharey=False)
sigmas = []
for sigma in range(0,n):
sigmas.append(dl.get_column(data,4+sigma))
for j in range(0,len(sigmas)):
sigma = sigmas[j]
good_sigmas = []
for i in range(0,len(sigma)):
if sigma[i] != 'nan':
good_sigmas.append((float(sigma[i])))
good_sigmas = np.sort(good_sigmas)
good_sigmas = good_sigmas[0:int(.8*len(good_sigmas))]
hist = np.histogram(good_sigmas,bins = 175)
plots[np.ceil((j)/2)][(j)%2].bar(hist[1][:-1],hist[0],width=hist[1][1]-hist[1][0])
plots[np.ceil((j)/2)][(j)%2].set_xlabel('$\sigma_{'+str(j+1)+'}$',fontsize=40)
plots[np.ceil((j)/2)][(j)%2].set_ylabel('$n$',fontsize=40)
plots[np.ceil((j)/2)][(j)%2].text(plots[np.ceil((j)/2)][(j)%2].get_xlim()[1]*0.9,plots[np.ceil((j)/2)][(j)%2].get_ylim()[1]*0.75,'$\sigma_{'+str(j+1)+'}$',fontsize=40)
plots[np.ceil((j)/2)][(j)%2].tick_params(axis='both', which='major', labelsize=20)
f.set_size_inches(32,20)
f.savefig('histograms/sigma_histo_' + fname+'.png', dpi = 300)示例2: compute_Power
def compute_Power():
from astroML.time_series import generate_power_law
from astroML.fourier import PSD_continuous
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
N = 1024
dt = 0.01
factor = 100
t = dt * np.arange(N)
random_state = np.random.RandomState(1)
fig = plt.figure(figsize=(5, 3.75))
fig.subplots_adjust(wspace=0.05)
for i, beta in enumerate([1.0, 2.0]):
# Generate the light curve and compute the PSD
x = factor * generate_power_law(N, dt, beta, random_state=random_state)
f, PSD = PSD_continuous(t, x)
# First axes: plot the time series
ax1 = fig.add_subplot(221 + i)
ax1.plot(t, x, '-k')
ax1.text(0.95, 0.05, r"$P(f) \propto f^{-%i}$" % beta,
ha='right', va='bottom', transform=ax1.transAxes)
ax1.set_xlim(0, 10.24)
ax1.set_ylim(-1.5, 1.5)
ax1.set_xlabel(r'$t$')
# Second axes: plot the PSD
ax2 = fig.add_subplot(223 + i, xscale='log', yscale='log')
ax2.plot(f, PSD, '-k')
ax2.plot(f[1:], (factor * dt) ** 2 * (2 * np.pi * f[1:]) ** -beta, '--k')
ax2.set_xlim(1E-1, 60)
ax2.set_ylim(1E-6, 1E1)
ax2.set_xlabel(r'$f$')
if i == 1:
ax1.yaxis.set_major_formatter(plt.NullFormatter())
ax2.yaxis.set_major_formatter(plt.NullFormatter())
else:
ax1.set_ylabel(r'${\rm counts}$')
ax2.set_ylabel(r'$PSD(f)$')
plt.show()示例3: plot_num_density
def plot_num_density(zbins_list, dist_z_list, err_z_list, titles=None,
markers=None):
''' Plots the number density of galaxies for multiple samples.
Specifically, rho(z) / [z/0.08]^2 vs z where rho(z) is the number density
of galaxies at a given redshift. Reproduces the top right plot of Figure
4.10.
Parameters
----------
zbins_list : array-like
Tuple of absolute redshift bin centers.
dist_z_list : tuple, list
Tuple of redshift count distributions.
err_z_list : tuple, list
Tuple of redshift count errors.
titles : tuple, list
Tuple of titles for the different distributions.
markers : tuple, list
Tuple of markers for the different distributions.
Returns
-------
None
'''
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(1, 1, 1)
for i in xrange(len(dist_z_list)):
factor = 0.08 ** 2 / (0.5 * (zbins_list[i][1:] + \
zbins_list[i][:-1])) ** 2
ax.errorbar(0.5 * (zbins_list[i][1:] + zbins_list[i][:-1]),
factor * dist_z_list[i], factor * err_z_list[i],
fmt='-k' + markers[i], ecolor='gray', lw=1, ms=4,
label=titles[i])
ax.legend(loc=1)
ax.xaxis.set_major_locator(plt.MultipleLocator(0.01))
ax.set_xlabel(r'$z$')
ax.set_ylabel(r'$\rho(z) / [z / 0.08]^2$')
ax.set_xlim(0.075, 0.125)
ax.set_ylim(10, 25)
plt.show()示例4: plot_separation
def plot_separation(test_R, sample_R):
plt.clf()
setup_text_plots(fontsize = 16, usetex = True)
fig = plt.figure(figsize = (12,8))
plt.hist(test_R, 50, histtype = 'step', color = 'red', lw = 2,
label = 'Test', range = (-2.5,2.5))
plt.hist(sample_R, 50, histtype = 'step', color = 'blue',lw = 2,
label = 'Sample', range = (-2.5,2.5))
plt.legend(loc="best")
plt.xlabel("$\log(R/R_e)$", fontsize=18)
plt.ylabel("Number", fontsize=18)
plt.xlim(-2.5, 2.5)
plt.show()示例5: plot_luminosity_function
def plot_luminosity_function(Mbins_list, dist_M_list, err_M_list, titles=None,
markers=None):
''' Plots the normalized luminosity functions Phi(M) vs (M) for multiple
samples. Reproduces the bottom right plot of Figure 4.10.
Parameters
----------
Mbins_list : array-like
Tuple of absolute magnitude bin centers.
dist_M_list : tuple, list
Tuple of absolute magnitude count distributions.
err_M_list : tuple, list
Tuple of absolute magnitude count errors.
titles : tuple, list
Tuple of titles for the different distributions.
markers : tuple, list
Tuple of markers for the different distributions.
Returns
-------
None
'''
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, yscale='log')
# truncate the bins so the plot looks better
for i in xrange(len(dist_M_list)):
Mbins = Mbins_list[i][3:-1]
dist_M = dist_M_list[i][3:-1]
err_M = err_M_list[i][3:-1]
ax.errorbar(0.5 * (Mbins[1:] + Mbins[:-1]), dist_M, err_M,
fmt='-k' + markers[i], ecolor='gray', lw=1, ms=4,
label=titles[i])
ax.legend(loc=3)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.set_xlabel(r'$M$')
ax.set_ylabel(r'$\Phi(M)$')
ax.set_xlim(-20, -23.5)
ax.set_ylim(1E-5, 2)
plt.show()示例6: plot
def plot(fname):
setup_text_plots(fontsize=10, usetex=True)
data = dl.read_dat(fname,',')
print data[0]
n = len(data[0]) - 4
print int(np.ceil(n/2))+n%2
f,plots = plt.subplots(int(np.ceil(n/2))+n%2,2, sharex=False,sharey=False)
dwarf_flags = dl.get_column(data,1)
kepmags = dl.get_column(data,3)
Teffs = dl.get_column(data,2)
sigmas = []
for sigma in range(0,n):
sigmas.append(dl.get_column(data,4+sigma))
length = len(dl.get_column(data,0))
for i in range(0,length):
done = []
for seg in range(0,n):
done.append(seg)
is_dwarf = dwarf_flags[i]
if is_dwarf == '1.0' or is_dwarf == '1':
symbol = 'd'
elif is_dwarf == '0.0' or is_dwarf == '0':
symbol = 'o'
else:
symbol = 'x'
x = int(np.ceil((seg)/2))
y = (seg)%2
plots[x][y].set_ylabel('$\log{\sigma_{' + str(seg+1) +'}}$',fontsize=40)
plots[x][y].set_xlabel('Kepler band magnitude',fontsize=20)
plots[x][y].tick_params(axis='both', which='major', labelsize=20)
plots[x][y].grid(True)
plots[x][y].set_xlim([8.25,17])
plots[x][y].set_ylim([-4.5,0])
plots[x][y].text(15.25,-0.5,'Segment ' + str(seg+1),fontsize=30)
if sigma != 'nan' and kepmags[i] != 'nan' and sigmas[seg][i] != 'nan' and sigmas[seg][i] != '':
plots[x][y].scatter(float(kepmags[i]),np.log10(float(sigmas[seg][i])),marker=symbol,color=color_T(Teffs[i]))
print fname + ' : ' + str(done) + ' : ' + str((i*100.0)/length) + '%'
f.set_size_inches(32,32)
f.savefig(fname+'.png', dpi = 300)示例7: plot
def plot(fname):
setup_text_plots(fontsize=10, usetex=True)
data = dl.read_dat(fname,',')
mags = dl.get_column(data,3)
for i in range(0,len(mags)):
if mags[i] == 'nan':
del mags[i]
else:
mags[i] = float(mags[i])
hist = np.histogram(mags,bins=100)
plt.bar(hist[1][:-1],hist[0],width=hist[1][1]-hist[1][0])
plt.xlabel('Kepler band magnitude',fontsize=50)
plt.ylabel('$n$',fontsize=50)
plt.tick_params(axis='both', which='major', labelsize=40)
plt.gcf().set_size_inches(32,20)
plt.gcf().savefig(fname.replace('evaluations/','histograms/mag/mag_histo_') +'.png', dpi = 300)示例8: plot_bic
def plot_bic(param_range,bics,lowest_comp):
plt.clf()
setup_text_plots(fontsize=16, usetex=True)
fig = plt.figure(figsize=(12, 6))
plt.plot(param_range,bics,color='blue',lw=2, marker='o')
plt.text(lowest_comp, bics.min() * 0.97 + .03 * bics.max(), '*',
fontsize=14, ha='center')
plt.xticks(param_range)
plt.ylim(bics.min() - 0.05 * (bics.max() - bics.min()),
bics.max() + 0.05 * (bics.max() - bics.min()))
plt.xlim(param_range.min() - 1, param_range.max() + 1)
plt.xticks(param_range,fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel('Number of components',fontsize=18)
plt.ylabel('BIC score',fontsize=18)
plt.show()示例9: medians
def medians(fname):
setup_text_plots(fontsize=10, usetex=True)
data = dl.read_dat(fname,',')
n = len(data[0]) - 4
sigmas = []
for sigma in range(0,n):
sigmas.append(dl.get_column(data,4+sigma))
medians = []
for j in range(0,len(sigmas)):
sigma = sigmas[j]
good_sigmas = []
for i in range(0,len(sigma)):
if sigma[i] != 'nan':
good_sigmas.append((float(sigma[i])))
good_sigmas = np.sort(good_sigmas)
good_sigmas = good_sigmas[0:int(.8*len(good_sigmas))]
medians.append(np.median(good_sigmas))
return medians示例10: setup_text_plots
"""
Code to generate 2D histograms of embedded spectral data.
Authored by OGT 3/15/16
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
from astroML.plotting import setup_text_plots
import matplotlib as mpl
plt.ion()
setup_text_plots(fontsize=18)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
mpl.rc('font', size=18, family='serif', style='normal', variant='normal', stretch='normal', weight='bold')
mpl.rc('legend', labelspacing=0.1, handlelength=2, fontsize=12)
def get_contours(x, y, bins=(50, 50), ranges=None):
if ranges:
H, xedges, yedges = np.histogram2d(x, y, bins=bins, range=ranges)
else:
H, xedges, yedges = np.histogram2d(x, y, bins=bins)
xmid = (xedges[1:] + xedges[:-1]) / 2.0
ymid = (yedges[1:] + yedges[:-1]) / 2.0
return xmid, ymid, H.T
def plot_2Dhist(x, y, xlabel=None, ylabel=None, cblabel=None, ranges=[[-0.007, 0.002],[-0.014, 0.005]], vmin=1, vmax=10**5, normed=False,
filename=None, annotate_string=None, annotate_loc=None):
xmid, ymid, H = get_contours(x, y, bins=(50, 50), ranges=ranges)示例11: plot_cont_galaxy
def plot_cont_galaxy(filename, N_bulge, N_disk, bin_no=100): #If you have enough particle resolution, choose 1000
tmp = filename.split('.npy')
t = tmp[0].split('galaxy_')[1]
galaxy = np.load('%s'%(filename))
x_gal = np.zeros_like(galaxy[0])
y_gal = np.zeros_like(galaxy[1])
z_gal = np.zeros_like(galaxy[2])
cs_gal = galaxy[5]
cont = galaxy[4]/np.sum(galaxy[4])
# Bulge (Spherical to Cartesian)
r_gal = galaxy[0,:N_bulge]
theta_gal = galaxy[1,:N_bulge]
phi_gal_sph = galaxy[2,:N_bulge]
x_gal[:N_bulge] = r_gal*np.sin(theta_gal)*np.cos(phi_gal_sph)
y_gal[:N_bulge] = r_gal*np.sin(theta_gal)*np.sin(phi_gal_sph)
z_gal[:N_bulge] = r_gal*np.cos(theta_gal)
# Disk (Cylindrical to Cartesian)
rho_gal = galaxy[0,N_bulge:N_bulge+N_disk]
phi_gal_cyl = galaxy[1,N_bulge:N_bulge+N_disk]
z_gal_cyl = galaxy[2,N_bulge:N_bulge+N_disk]
x_gal[N_bulge:N_bulge+N_disk] = rho_gal*np.cos(phi_gal_cyl)
y_gal[N_bulge:N_bulge+N_disk] = rho_gal*np.sin(phi_gal_cyl)
z_gal[N_bulge:N_bulge+N_disk] = z_gal_cyl
# Halo (Spherical to Cartesian)
r_gal = galaxy[0,N_bulge+N_disk:]
theta_gal = galaxy[1,N_bulge+N_disk:]
phi_gal_sph = galaxy[2,N_bulge+N_disk:]
x_gal[N_bulge+N_disk:] = r_gal*np.sin(theta_gal)*np.cos(phi_gal_sph)
y_gal[N_bulge+N_disk:] = r_gal*np.sin(theta_gal)*np.sin(phi_gal_sph)
z_gal[N_bulge+N_disk:] = r_gal*np.cos(theta_gal)
# Spot the colonizer
inds = np.where(cs_gal!=0)[0]
x_col = x_gal[inds]
y_col = y_gal[inds]
z_col = z_gal[inds]
colonized_fraction = abs(np.sum(cs_gal)/len(cs_gal))
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=16, usetex=False)
from astroML.stats import binned_statistic_2d
fig = plt.figure(figsize=(20, 10))
# Face-on
axfo = plt.subplot(121)
cmap = plt.cm.spectral_r
cmap.set_bad('w', 0.)
N, xedges, yedges = binned_statistic_2d(x_gal*1e-3, y_gal*1e-3, cont, 'sum', bins=bin_no)
plt.imshow((N.T), origin='lower',
extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]],
aspect='equal', interpolation='nearest', cmap=cmap)
plt.xlabel(r'X (kpc)')
plt.ylabel(r'Y (kpc)')
plt.xlim([-1e1, 1e1])
plt.ylim([-1e1, 1e1])
cb = plt.colorbar(pad=0.2,
orientation='horizontal')
cb.set_label(r'$\mathrm{log(L/L_{total})}$')
clim_min = min(cont)
clim_max = max(cont)
plt.title("time = %s Myr"%(t))
# plt.clim(clim_min, clim_max)
# Edge-on
axeo = plt.subplot(122)
cmap = plt.cm.spectral_r
cmap.set_bad('w', 0.)
N, xedges, zedges=binned_statistic_2d(x_gal*1e-3, z_gal*1e-3, cont, 'sum', bins=bin_no)
plt.imshow((N.T), origin='lower',
extent=[xedges[0], xedges[-1], zedges[0], zedges[-1]],
aspect='equal', interpolation='nearest', cmap=cmap)
plt.xlabel(r'X (kpc)')
plt.ylabel(r'Z (kpc)')
plt.xlim([-1e1, 1e1])
plt.ylim([-1e1, 1e1])
cb = plt.colorbar(pad=0.2,
orientation='horizontal')
cb.set_label(r'$\mathrm{log(L/L_{total})}$')
clim_min = min(cont)
clim_max = max(cont)
# print ("Colonized fraction = %.2f"%(colonized_fraction))
# plt.clim(clim_min, clim_max)
# plt.title("Colonized fraction = %.2f"%(abs(colonized_fraction)))
plt.savefig("%s.svg"%(filename))
plt.savefig("%s.png"%(filename))
# plt.show()
return galaxy示例12: compute_Weiner
def compute_Weiner():
from scipy import optimize, fftpack
from astroML.filters import savitzky_golay, wiener_filter
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#------------------------------------------------------------
# Create the noisy data
np.random.seed(5)
N = 2000
dt = 0.05
t = dt * np.arange(N)
h = np.exp(-0.5 * ((t - 20.) / 1.0) ** 2)
hN = h + np.random.normal(0, 0.5, size=h.shape)
Df = 1. / N / dt
f = fftpack.ifftshift(Df * (np.arange(N) - N / 2))
HN = fftpack.fft(hN)
#------------------------------------------------------------
# Set up the Wiener filter:
# fit a model to the PSD consisting of the sum of a
# gaussian and white noise
h_smooth, PSD, P_S, P_N, Phi = wiener_filter(t, hN, return_PSDs=True)
#------------------------------------------------------------
# Use the Savitzky-Golay filter to filter the values
h_sg = savitzky_golay(hN, window_size=201, order=4, use_fft=False)
#------------------------------------------------------------
# Plot the results
N = len(t)
Df = 1. / N / (t[1] - t[0])
f = fftpack.ifftshift(Df * (np.arange(N) - N / 2))
HN = fftpack.fft(hN)
fig = plt.figure(figsize=(5, 3.75))
fig.subplots_adjust(wspace=0.05, hspace=0.25,
bottom=0.1, top=0.95,
left=0.12, right=0.95)
# First plot: noisy signal
ax = fig.add_subplot(221)
ax.plot(t, hN, '-', c='gray')
ax.plot(t, np.zeros_like(t), ':k')
ax.text(0.98, 0.95, "Input Signal", ha='right', va='top',
transform=ax.transAxes, bbox=dict(fc='w', ec='none'))
ax.set_xlim(0, 90)
ax.set_ylim(-0.5, 1.5)
ax.xaxis.set_major_locator(plt.MultipleLocator(20))
ax.set_xlabel(r'$\lambda$')
ax.set_ylabel('flux')
# Second plot: filtered signal
ax = plt.subplot(222)
ax.plot(t, np.zeros_like(t), ':k', lw=1)
ax.plot(t, h_smooth, '-k', lw=1.5, label='Wiener')
ax.plot(t, h_sg, '-', c='gray', lw=1, label='Savitzky-Golay')
ax.text(0.98, 0.95, "Filtered Signal", ha='right', va='top',
transform=ax.transAxes)
ax.legend(loc='upper right', bbox_to_anchor=(0.98, 0.9), frameon=False)
ax.set_xlim(0, 90)
ax.set_ylim(-0.5, 1.5)
ax.yaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_major_locator(plt.MultipleLocator(20))
ax.set_xlabel(r'$\lambda$')
# Third plot: Input PSD
ax = fig.add_subplot(223)
ax.scatter(f[:N / 2], PSD[:N / 2], s=9, c='k', lw=0)
ax.plot(f[:N / 2], P_S[:N / 2], '-k')
ax.plot(f[:N / 2], P_N[:N / 2], '-k')
ax.text(0.98, 0.95, "Input PSD", ha='right', va='top',
transform=ax.transAxes)
ax.set_ylim(-100, 3500)
ax.set_xlim(0, 0.9)
ax.yaxis.set_major_locator(plt.MultipleLocator(1000))
ax.xaxis.set_major_locator(plt.MultipleLocator(0.2))
ax.set_xlabel('$f$')
ax.set_ylabel('$PSD(f)$')
# Fourth plot: Filtered PSD
ax = fig.add_subplot(224)
#.........这里部分代码省略.........
示例13: compute_Convolution
def compute_Convolution():
from scipy.signal import fftconvolve
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#------------------------------------------------------------
# Generate random x, y with a given covariance length
np.random.seed(1)
x = np.linspace(0, 1, 500)
h = 0.01
C = np.exp(-0.5 * (x - x[:, None]) ** 2 / h ** 2)
y = 0.8 + 0.3 * np.random.multivariate_normal(np.zeros(len(x)), C)
#------------------------------------------------------------
# Define a normalized top-hat window function
w = np.zeros_like(x)
w[(x > 0.12) & (x < 0.28)] = 1
#------------------------------------------------------------
# Perform the convolution
y_norm = np.convolve(np.ones_like(y), w, mode='full')
valid_indices = (y_norm != 0)
y_norm = y_norm[valid_indices]
y_w = np.convolve(y, w, mode='full')[valid_indices] / y_norm
# trick: convolve with x-coordinate to find the center of the window at
# each point.
x_w = np.convolve(x, w, mode='full')[valid_indices] / y_norm
#------------------------------------------------------------
# Compute the Fourier transforms of the signal and window
y_fft = np.fft.fft(y)
w_fft = np.fft.fft(w)
yw_fft = y_fft * w_fft
yw_final = np.fft.ifft(yw_fft)
#------------------------------------------------------------
# Set up the plots
fig = plt.figure(figsize=(5, 5))
fig.subplots_adjust(left=0.09, bottom=0.09, right=0.95, top=0.95,
hspace=0.05, wspace=0.05)
#----------------------------------------
# plot the data and window function
ax = fig.add_subplot(221)
ax.plot(x, y, '-k', label=r'data $D(x)$')
ax.fill(x, w, color='gray', alpha=0.5,
label=r'window $W(x)$')
ax.fill(x, w[::-1], color='gray', alpha=0.5)
ax.legend()
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.set_ylabel('$D$')
ax.set_xlim(0.01, 0.99)
ax.set_ylim(0, 2.0)
#----------------------------------------
# plot the convolution
ax = fig.add_subplot(223)
ax.plot(x_w, y_w, '-k')
ax.text(0.5, 0.95, "Convolution:\n" + r"$[D \ast W](x)$",
ha='center', va='top', transform=ax.transAxes,
bbox=dict(fc='w', ec='k', pad=8), zorder=2)
ax.text(0.5, 0.05,
(r'$[D \ast W](x)$' +
r'$= \mathcal{F}^{-1}\{\mathcal{F}[D] \cdot \mathcal{F}[W]\}$'),
ha='center', va='bottom', transform=ax.transAxes)
for x_loc in (0.2, 0.8):
y_loc = y_w[x_w <= x_loc][-1]
ax.annotate('', (x_loc, y_loc), (x_loc, 2.0), zorder=1,
arrowprops=dict(arrowstyle='->', color='gray', lw=2))
ax.set_xlabel('$x$')
ax.set_ylabel('$D_W$')
ax.set_xlim(0.01, 0.99)
ax.set_ylim(0, 1.99)
#----------------------------------------
# plot the Fourier transforms
N = len(x)
k = - 0.5 * N + np.arange(N) * 1. / N / (x[1] - x[0])
ax = fig.add_subplot(422)
ax.plot(k, abs(np.fft.fftshift(y_fft)), '-k')
#.........这里部分代码省略.........
示例14: setup_text_plots
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
# To report a bug or issue, use the following forum:
# https://groups.google.com/forum/#!forum/astroml-general
from matplotlib import pyplot as plt
import numpy as np
from sklearn.mixture import GMM
import math
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#------------------------------------------------------------
# Set up the dataset.
# We'll use scikit-learn's Gaussian Mixture Model to sample
# data from a mixture of Gaussians. The usual way of using
# this involves fitting the mixture to data: we'll see that
# below. Here we'll set the internal means, covariances,
# and weights by-hand.
#np.random.seed(1)
#gmm = GMM(4, n_iter=1)
#gmm.means_ = np.array([[-4], [-1], [4], [1]])
#gmm.covars_ = np.array([[1.5], [0.5], [0.5], [1.0]]) ** 2
#gmm.weights_ = np.array([0.3, 0.3, 0.2, 0.2])
示例15: compute_FFT
def compute_FFT():
from scipy.fftpack import fft
from scipy.stats import norm
from astroML.fourier import PSD_continuous
#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX. This may
# result in an error if LaTeX is not installed on your system. In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=False)
#------------------------------------------------------------
# Draw the data
np.random.seed(1)
tj = np.linspace(-25, 25, 512)
hj = np.sin(tj)
hj *= norm(0, 10).pdf(tj)
#------------------------------------------------------------
# plot the results
fig = plt.figure(figsize=(5, 3.75))
fig.subplots_adjust(hspace=0.25)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
offsets = (0, 0.15)
colors = ('black', 'gray')
linewidths = (1, 2)
errors = (0.005, 0.05)
for (offset, color, error, linewidth) in zip(offsets, colors,
errors, linewidths):
# compute the PSD
err = np.random.normal(0, error, size=hj.shape)
hj_N = hj + err + offset
fk, PSD = PSD_continuous(tj, hj_N)
# plot the data and PSD
ax1.scatter(tj, hj_N, s=4, c=color, lw=0)
ax1.plot(tj, 0 * tj + offset, '-', c=color, lw=1)
ax2.plot(fk, PSD, '-', c=color, lw=linewidth)
# vertical line marking the expected peak location
ax2.plot([0.5 / np.pi, 0.5 / np.pi], [-0.1, 1], ':k', lw=1)
ax1.set_xlim(-25, 25)
ax1.set_ylim(-0.1, 0.3001)
ax1.set_xlabel('$t$')
ax1.set_ylabel('$h(t)$')
ax1.yaxis.set_major_locator(plt.MultipleLocator(0.1))
ax2.set_xlim(0, 0.8)
ax2.set_ylim(-0.101, 0.801)
ax2.set_xlabel('$f$')
ax2.set_ylabel('$PSD(f)$')
plt.show()