本文整理匯總了Python中MOSFIRE.IO.readfits_all方法的典型用法代碼示例。如果您正苦於以下問題：Python IO.readfits_all方法的具體用法？Python IO.readfits_all怎麽用？Python IO.readfits_all使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類MOSFIRE.IO的用法示例。
在下文中一共展示了IO.readfits_all方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_trace_edge
# 需要導入模塊: from MOSFIRE import IO [as 別名]
# 或者: from MOSFIRE.IO import readfits_all [as 別名]
def test_trace_edge(self):
(header, data1, targs, ssl, msl, asl) = \
IO.readfits_all("/users/npk/desktop/c9/m110326_3242.fits")
data = data1
ssl = ssl[ssl["Slit_Number"] != ' ']
numslits = np.round(np.array(ssl["Slit_length"],
dtype=np.float) / 7.02)
for i in range(len(ssl)):
print ssl[i]["Target_Name"], numslits[i]示例2: reload
# 需要導入模塊: from MOSFIRE import IO [as 別名]
# 或者: from MOSFIRE.IO import readfits_all [as 別名]
npk April 14th 2011
'''
import MOSFIRE
import time
from MOSFIRE import Fit, IO
import numpy as np, pylab as pl
reload(Fit)
reload(IO)
if __name__ == "__main__":
(header, data1, targs, ssl, msl, asl) = IO.readfits_all("/users/npk/desktop/c9/m110326_3242.fits")
data = data1
ssl = ssl[ssl["Slit_Number"] != ' ']
numslits = np.round(np.array(ssl["Slit_length"], dtype=np.float) / 7.02)
for i in range(len(ssl)):
print ssl[i]["Target_Name"], numslits[i]
Outputs:
xposs []: Array of x positions along the slit edge [pix]
yposs []: The fitted y positions of the "top" edge of the slit [pix]示例3: reload
# 需要導入模塊: from MOSFIRE import IO [as 別名]
# 或者: from MOSFIRE.IO import readfits_all [as 別名]
import pylab as pl
import scipy as sp
from MOSFIRE import IO, Fit, Bspline
reload(IO)
reload(Fit)
reload(Bspline)
np.set_printoptions(precision=3)
# use the following file and id'd spectrum to guess
(header, data, targs, ssl, msl, asl) = IO.readfits_all(
"/users/npk/desktop/c9/m110319_1949.fits")
band = 'H'
pl.ion()
DRAW = True
def fit_spec(data, pos, alpha, sinbeta, gamma, delta, band):
global DRAW
ar_h_lines = np.array([1.465435, 1.474317, 1.505062, 1.517684, 1.530607,
1.533334, 1.540685, 1.590403, 1.599386, 1.618444, 1.644107,
1.674465, 1.744967, 1.791961])
Ar_K_lines = np.array([1.982291, 1.997118, 2.032256, 2.057443,
2.062186, 2.099184, 2.133871, 2.15409, 2.204558, 2.208321,
2.313952, 2.385154])