def basefit(hdu, mode='auto', *args, **kwargs):
import time
import numpy
import pyfits
import analyse
print('basefit: mode=%s'%(mode))
if mode.lower()=='simple':
fitfunc = basefit_simple
elif mode.lower()=='auto':
fitfunc = basefit_auto
v = analyse.generate_axis(hdu, axis=3)
fitted, emission_flag = fitfunc(hdu, v, *args, **kwargs)
header = hdu.header.copy()
header.add_history('pyanalyse.basefit: generate fitted data (mode:%s)'%mode)
header.add_history('pyanalyse.basefit: time stamp (%s)'%time.strftime('%Y/%m/%d %H:%M:%S'))
ef_header = hdu.header.copy()
ef_header.add_history('pyanalyse.basefit: generate emission flag (mode:%s)'%mode)
ef_header.add_history('pyanalyse.basefit: time stamp (%s)'%time.strftime('%Y/%m/%d %H:%M:%S'))
fitted_data = pyfits.PrimaryHDU(fitted, header)
flag = pyfits.PrimaryHDU(emission_flag, ef_header)
return fitted_data, flag
def draw_otf_spectrum(cube, figure=None, subplot=111, title='', grid=True,
font_family=None, tick_labels_size=9, xlim=None, ylim=None,
show=True, *args, **kwargs):
import time
import numpy
import analyse
import analyse.plotter
import matplotlib.figure
import matplotlib.colors
import pylab
t0 = time.time()
print('[%f] -- %f'%(time.time(), time.time()-t0))
if not isinstance(figure, matplotlib.figure.Figure):
if figure is None:
figure = pylab.figure()
else:
figure = pylab.figure(figsize=figure)
else:
show = False
if type(subplot) is int:
subplot = analyse.plotter.get_subplot(subplot)
elif type(subplot) in [list, tuple]:
if len(subplot) == 3:
subplot = analyse.plotter.get_subplot(subplot)
nz, ny, nx = cube.data.shape
spectra = cube.data.T.reshape(nx*ny, nz)
v = analyse.generate_axis(cube, axis=3) / 1000.
ax = figure.add_axes(subplot)
#[ax.plot(v, s, '.b', ms=1, mew=0, alpha=0.1) for s in spectra]
xd = numpy.array(list(v) * len(spectra))
yd = spectra.ravel()
yd[numpy.isnan(yd)] = 0
histmap, histx, histy = pylab.histogram2d(xd, yd, bins=501)
histmap = histmap.T
histmap[numpy.where(histmap==0)] = 0.3
histmap = numpy.log10(histmap)
histX, histY = numpy.meshgrid(histx, histy)
ax.pcolormesh(histX, histY, histmap, vmax=3.3)
ax.grid(grid)
ax.set_title(title, size=tick_labels_size+1)
if xlim is not None: ax.set_xlim(*xlim)
else: ax.set_xlim(xd.min(), xd.max())
if ylim is not None: ax.set_ylim(*ylim)
else: ax.set_ylim(yd.min(), yd.max())
tx = ax.get_xticks()
ty = ax.get_yticks()
ax.set_xticklabels(tx, size=tick_labels_size)
ax.set_yticklabels(ty, size=tick_labels_size)
if show:
pylab.show()
print('[%f] -- %f'%(time.time(), time.time()-t0))
return figure
def draw_ps_spectrum(cube, figure=None, subplot=111, title='', grid=True,
font_family=None, tick_labels_size=9, show=True, *args, **kwargs):
import analyse
import analyse.plotter
import pylab
import matplotlib
def_font_size = matplotlib.rcParams['font.size']
def_font_family = matplotlib.rcParams['font.family']
matplotlib.rcParams['font.size'] = tick_labels_size
if font_family is not None: matplotlib.rcParams['font.family'] = font_family
if figure is None:
figure = pylab.figure()
else:
show = False
if type(subplot) is int:
subplot = analyse.plotter.get_subplot(subplot)
elif type(subplot) in [list, tuple]:
if len(subplot) == 3:
subplot = analyse.plotter.get_subplot(subplot)
nz, ny, nx = cube.data.shape
spectra = cube.data.T.reshape(nx*ny, nz)
v = analyse.generate_axis(cube, axis=3) / 1000.
ax = figure.add_axes(subplot)
[ax.plot(v, s) for s in spectra]
ax.grid(grid)
ax.set_title(title)
if show:
pylab.show()
matplotlib.rcParams['font.size'] = def_font_size
matplotlib.rcParams['font.family'] = def_font_family
return figure
def draw_ss_spectrum(cube, flag, figure=None, subplot=111, title='', grid=True,
font_family=None, tick_labels_size=9, show=True, *args, **kwargs):
import analyse
import analyse.plotter
import pylab
import numpy
import matplotlib
tpeak, rms = analyse_ss(cube, flag)
def_font_size = matplotlib.rcParams['font.size']
def_font_family = matplotlib.rcParams['font.family']
matplotlib.rcParams['font.size'] = tick_labels_size
if font_family is not None: matplotlib.rcParams['font.family'] = font_family
if figure is None:
figure = pylab.figure()
else:
show = False
if type(subplot) is int:
subplot = analyse.plotter.get_subplot(subplot)
elif type(subplot) in [list, tuple]:
if len(subplot) == 3:
subplot = analyse.plotter.get_subplot(subplot)
nz, ny, nx = cube.data.shape
spectra = cube.data.T.reshape(nx*ny, nz)
v = analyse.generate_axis(cube, axis=3) / 1000.
ax = figure.add_axes(subplot)
[ax.plot(v, s) for s in spectra]
ax.grid(grid)
ax.set_title(title)
"""
if fits_params['MOLECULE']=='12CO': tmb = float(sslist['tmb12'])
elif fits_params['MOLECULE']=='13CO': tmb = float(sslist['tmb13'])
elif fits_params['MOLECULE']=='C18O': tmb = float(sslist['tmb18'])
else: pass
"""
# 判断の参考に参考にTmbが○％のところに線を引く
"""
xs = float(sslist['v_center']) - float(sslist['v_width'])/2.
xe = float(sslist['v_center']) + float(sslist['v_width'])/2.
"""
###### atodekesu #####
xs = -41. #
xe = 55. #
tmb = 40. #
###### atodekesu #####
percents = numpy.arange(50, 101, 10)
percent_lines = tmb * percents / 100.
[pylab.axhline(y=_y, color='r', linestyle='--', linewidth=2, alpha=0.5) for _y in percent_lines]
[ax.text(xs, _y, str(_p)+'%') for _y, _p in zip(percent_lines, percents)]
ax.set_ylim(-2, tmb*1.2)
ax.set_ylabel('Ta* [K]')
ax.set_xlabel('km/s')
ax.set_xlim(xs, xe)
#ax.set_title('%s %s' % (fits_params['OBJECT'], fits_params['DATE-OBS']))
ax.annotate('Tpeak=%.2f [K]\nTmb=%.2f [K]\neff.=%.2f\nTrms=%.2f [K]'%(tpeak, tmb, tpeak/tmb, rms), xy=(.65,.85), xycoords='axes fraction')
"""
if str(fits_params['BACKEND'])[-1]==1: direction='H'
elif str(fits_params['BACKEND'])[-1]==2: direction='V'
else: pass
"""
if show:
pylab.show()
matplotlib.rcParams['font.size'] = def_font_size
matplotlib.rcParams['font.family'] = def_font_family
return figure
