本文整理汇总了Python中MCUtils.get_fits_data方法的典型用法代码示例。如果您正苦于以下问题：Python MCUtils.get_fits_data方法的具体用法？Python MCUtils.get_fits_data怎么用？Python MCUtils.get_fits_data使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类MCUtils的用法示例。
在下文中一共展示了MCUtils.get_fits_data方法的3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: hashresponse
# 需要导入模块: import MCUtils [as 别名]
# 或者: from MCUtils import get_fits_data [as 别名]
def hashresponse(band,events,calpath='../cal/',verbose=0):
"""Given detector xi, eta, return the response at each position."""
# Hash out the response correction
if verbose:
mc.print_inline("Applying the response correction.")
flat = mc.get_fits_data(flat_filename(band, calpath))
events['col'], events['row'] = xieta2colrow(events['xi'], events['eta'],
flat_filename(band, calpath))
events['flat'] = flat[np.array(events['col'], dtype='int16'),
np.array(events['row'], dtype='int16')]
events['scale'] = gxt.compute_flat_scale(events['t'], band)
# TODO: Separately do the binlinearly interpolated response
events['response'] = (events['flat']*events['scale'])
return events示例2: xieta2colrow
# 需要导入模块: import MCUtils [as 别名]
# 或者: from MCUtils import get_fits_data [as 别名]
def xieta2colrow(xi, eta, calfile, detsize=1.25):
"""Convert detector xi, eta into col, row."""
flat = mc.get_fits_data(calfile)
flatinfo = mc.get_fits_header(calfile)
# should be able to get npix from the header...
npixx = flat.shape[0]
npixy = flat.shape[1]
pixsz = flatinfo['CDELT2']
flatfill = detsize/(npixx*pixsz)
col = ((( xi/36000.)/(detsize/2.)*flatfill + 1.)/2. * npixx)
row = (((eta/36000.)/(detsize/2.)*flatfill + 1.)/2. * npixy)
# You could theoretically drop a cut on detector position / detsize here...
# Also, is this cut absolutely necessary? I think it's already been taken
# care of by the flag==0 assertion in the SQL query.
#cut = ((col > 0.) & (col < flat.shape[0]-1) &
# (row > 0.) & (row < flat.shape[1]-1))
#cut = np.where(ix == True)
return col, row示例3: enumerate
# 需要导入模块: import MCUtils [as 别名]
# 或者: from MCUtils import get_fits_data [as 别名]
for i,band in enumerate(bands):
delta = mc.angularSeparation(data[band]['ra'],data[band]['dec'],
data[band]['racent'],data[band]['deccent'])
plt.subplot(1,2,i+1,yticks=[],xlim=[0.*a,0.002*a])
plt.title('{band} Angular Separation (arcsec)'.format(
band=band,d=r'$\Delta$'))
plt.hist(delta*a,bins=500,range=[0.*a,0.002*a],color='k')
fig.savefig('../calpaper/src/angSep({band}).png'.format(band=band))
###############################################################################
"""Deadtime Sanity Checks
According to the calibration paper, the FUV deadtime correction should be
small (~ a few percent), but it is actually bigger than the NUV correction.
"""
fig = plt.figure(figsize=(8,4))
fig.subplots_adjust(left=0.12,right=0.95,wspace=0.02,bottom=0.15,top=0.9)
for i,band in enumerate(bands):
plt.subplot(1,2,i+1,yticks=[])
plt.title('{band} Deadtime Ratio Histogram'.format(band=band))
plt.hist(data[band]['t_eff']/data[band]['t_raw'],bins=100,range=[0.2,1.2])
"""Response Sanity Checks"""
fig = plt.figure(figsize=(8,4))
fig.subplots_adjust(left=0.12,right=0.95,wspace=0.02,bottom=0.15,top=0.9)
for i,band in enumerate(bands):
flat = mc.get_fits_data(flat_filename(band,'../cal/'))
plt.subplot(1,2,i+1,yticks=[])
plt.title('{band} Response Histogram'.format(band=band))
plt.hist(flat.flatten(),bins=100,range=[0.2,1.2])
plt.hist(data[band]['response'],bins=100,range=[0.2,1.2])