diff options
Diffstat (limited to 'stwcs/distortion/mutil.py')
| -rw-r--r-- | stwcs/distortion/mutil.py | 315 |
1 files changed, 161 insertions, 154 deletions
diff --git a/stwcs/distortion/mutil.py b/stwcs/distortion/mutil.py index ed6a1ea..0e193c8 100644 --- a/stwcs/distortion/mutil.py +++ b/stwcs/distortion/mutil.py @@ -1,25 +1,22 @@ -from __future__ import division, print_function # confidence high +from __future__ import division, print_function from stsci.tools import fileutil import numpy as np import calendar -# Set up IRAF-compatible Boolean values -yes = True -no = False - # This function read the IDC table and generates the two matrices with # the geometric correction coefficients. # # INPUT: FITS object of open IDC table # OUTPUT: coefficient matrices for Fx and Fy # -#### If 'tabname' == None: This should return a default, undistorted -#### solution. +# If 'tabname' == None: This should return a default, undistorted +# solution. # -def readIDCtab (tabname, chip=1, date=None, direction='forward', - filter1=None,filter2=None, offtab=None): + +def readIDCtab(tabname, chip=1, date=None, direction='forward', + filter1=None, filter2=None, offtab=None): """ Read IDCTAB, and optional OFFTAB if sepcified, and generate @@ -30,17 +27,17 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', """ - # Return a default geometry model if no IDCTAB filename + # Return a default geometry model if no IDCTAB filename # is given. This model will not distort the data in any way. - if tabname == None: + if tabname is None: print('Warning: No IDCTAB specified! No distortion correction will be applied.') return defaultModel() # Implement default values for filters here to avoid the default # being overwritten by values of None passed by user. - if filter1 == None or filter1.find('CLEAR') == 0 or filter1.strip() == '': + if filter1 is None or filter1.find('CLEAR') == 0 or filter1.strip() == '': filter1 = 'CLEAR' - if filter2 == None or filter2.find('CLEAR') == 0 or filter2.strip() == '': + if filter2 is None or filter2.find('CLEAR') == 0 or filter2.strip() == '': filter2 = 'CLEAR' # Insure that tabname is full filename with fully expanded @@ -51,7 +48,7 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', try: ftab = fileutil.openImage(tabname) except: - err_str = "------------------------------------------------------------------------ \n" + err_str = "------------------------------------------------------------------------ \n" err_str += "WARNING: the IDCTAB geometric distortion file specified in the image \n" err_str += "header was not found on disk. Please verify that your environment \n" err_str += "variable ('jref'/'uref'/'oref'/'nref') has been correctly defined. If \n" @@ -63,7 +60,7 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', err_str += "------------------------------------------------------------------------ \n" raise IOError(err_str) - #First thing we need, is to read in the coefficients from the IDC + # First thing we need, is to read in the coefficients from the IDC # table and populate the Fx and Fy matrices. if 'DETECTOR' in ftab['PRIMARY'].header: @@ -96,10 +93,10 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', else: order = norder - fx = np.zeros(shape=(order+1,order+1),dtype=np.float64) - fy = np.zeros(shape=(order+1,order+1),dtype=np.float64) + fx = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) + fy = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) - #Determine row from which to get the coefficients. + # Determine row from which to get the coefficients. # How many rows do we have in the table... fshape = ftab[1].data.shape colnames = ftab[1].data.names @@ -111,14 +108,14 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', try: # Match FILTER combo to appropriate row, - #if there is a filter column in the IDCTAB... + # if there is a filter column in the IDCTAB... if 'FILTER1' in colnames and 'FILTER2' in colnames: filt1 = ftab[1].data.field('FILTER1')[i] - if filt1.find('CLEAR') > -1: filt1 = filt1[:5] + if filt1.find('CLEAR') > -1: filt1 = filt1[: 5] filt2 = ftab[1].data.field('FILTER2')[i] - if filt2.find('CLEAR') > -1: filt2 = filt2[:5] + if filt2.find('CLEAR') > -1: filt2 = filt2[: 5] else: if 'OPT_ELEM' in colnames: filt1 = ftab[1].data.field('OPT_ELEM') @@ -170,16 +167,17 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', joinstr = '' else: f2str = filter2.strip() - filtstr = (joinstr.join([f1str,f2str])).strip() + filtstr = (joinstr.join([f1str, f2str])).strip() if row < 0: err_str = '\nProblem finding row in IDCTAB! Could not find row matching:\n' - err_str += ' CHIP: '+str(detchip)+'\n' - err_str += ' FILTERS: '+filtstr+'\n' + err_str += ' CHIP: ' + str(detchip) + '\n' + err_str += ' FILTERS: ' + filtstr + '\n' ftab.close() del ftab raise LookupError(err_str) else: - print('- IDCTAB: Distortion model from row',str(row+1),'for chip',detchip,':',filtstr) + print('- IDCTAB: Distortion model from row', str(row + 1), 'for chip', + detchip, ':', filtstr) # Read in V2REF and V3REF: this can either come from current table, # or from an OFFTAB if time-dependent (i.e., for WFPC2) @@ -190,12 +188,12 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', else: # Read V2REF/V3REF from offset table (OFFTAB) if offtab: - v2ref,v3ref,theta = readOfftab(offtab, date, chip=detchip) + v2ref, v3ref, theta = readOfftab(offtab, date, chip=detchip) else: v2ref = 0.0 v3ref = 0.0 - if theta == None: + if theta is None: if 'THETA' in colnames: theta = ftab[1].data.field('THETA')[row] else: @@ -206,14 +204,14 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', refpix['YREF'] = ftab[1].data.field('YREF')[row] refpix['XSIZE'] = ftab[1].data.field('XSIZE')[row] refpix['YSIZE'] = ftab[1].data.field('YSIZE')[row] - refpix['PSCALE'] = round(ftab[1].data.field('SCALE')[row],8) + refpix['PSCALE'] = round(ftab[1].data.field('SCALE')[row], 8) refpix['V2REF'] = v2ref refpix['V3REF'] = v3ref refpix['THETA'] = theta refpix['XDELTA'] = 0.0 refpix['YDELTA'] = 0.0 - refpix['DEFAULT_SCALE'] = yes - refpix['centered'] = no + refpix['DEFAULT_SCALE'] = True + refpix['centered'] = False refpix['skew_coeffs'] = skew_coeffs # Now that we know which row to look at, read coefficients into the # numeric arrays we have set up... @@ -226,13 +224,13 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', cxstr = 'A' cystr = 'B' - for i in range(norder+1): + for i in range(norder + 1): if i > 0: - for j in range(i+1): - xcname = cxstr+str(i)+str(j) - ycname = cystr+str(i)+str(j) - fx[i,j] = ftab[1].data.field(xcname)[row] - fy[i,j] = ftab[1].data.field(ycname)[row] + for j in range(i + 1): + xcname = cxstr + str(i) + str(j) + ycname = cystr + str(i) + str(j) + fx[i, j] = ftab[1].data.field(xcname)[row] + fy[i, j] = ftab[1].data.field(ycname)[row] ftab.close() del ftab @@ -240,21 +238,21 @@ def readIDCtab (tabname, chip=1, date=None, direction='forward', # If CX11 is 1.0 and not equal to the PSCALE, then the # coeffs need to be scaled - if fx[1,1] == 1.0 and abs(fx[1,1]) != refpix['PSCALE']: + if fx[1, 1] == 1.0 and abs(fx[1, 1]) != refpix['PSCALE']: fx *= refpix['PSCALE'] fy *= refpix['PSCALE'] # Return arrays and polynomial order read in from table. # NOTE: XREF and YREF are stored in Fx,Fy arrays respectively. - return fx,fy,refpix,order -# -# -# Time-dependent skew correction coefficients (only ACS/WFC) -# -# + return fx, fy, refpix, order + + def read_tdd_coeffs(phdr, chip=1): - ''' Read in the TDD related keywords from the PRIMARY header of the IDCTAB - ''' + """ + Time-dependent skew correction coefficients (only ACS/WFC). + + Read in the TDD related keywords from the PRIMARY header of the IDCTAB + """ # Insure we have an integer form of chip ic = int(chip) @@ -269,7 +267,7 @@ def read_tdd_coeffs(phdr, chip=1): skew_coeffs['TDD_CX_ALPHA'] = None # Skew-based TDD coefficients - skew_terms = ['TDD_CTB','TDD_CTA','TDD_CYA','TDD_CYB','TDD_CXA','TDD_CXB'] + skew_terms = ['TDD_CTB', 'TDD_CTA', 'TDD_CYA', 'TDD_CYB', 'TDD_CXA', 'TDD_CXB'] for s in skew_terms: skew_coeffs[s] = None @@ -280,7 +278,7 @@ def read_tdd_coeffs(phdr, chip=1): print("Using 2015-calibrated VAFACTOR-corrected TDD correction...") skew_coeffs['TDD_DATE'] = phdr['TDD_DATE'] for s in skew_terms: - skew_coeffs[s] = phdr.get('{0}{1}'.format(s,ic),None) + skew_coeffs[s] = phdr.get('{0}{1}'.format(s, ic), None) elif "TDD_CYB1" in phdr: # We have 2014-calibrated TDD correction to apply, not J.A.-derived values @@ -288,17 +286,17 @@ def read_tdd_coeffs(phdr, chip=1): skew_coeffs['TDD_DATE'] = phdr['TDD_DATE'] # Read coefficients for TDD Y coefficient cyb_kw = 'TDD_CYB{0}'.format(int(chip)) - skew_coeffs['TDD_CY_BETA'] = phdr.get(cyb_kw,None) + skew_coeffs['TDD_CY_BETA'] = phdr.get(cyb_kw, None) cya_kw = 'TDD_CYA{0}'.format(int(chip)) - tdd_cya = phdr.get(cya_kw,None) + tdd_cya = phdr.get(cya_kw, None) if tdd_cya == 0 or tdd_cya == 'N/A': tdd_cya = None skew_coeffs['TDD_CY_ALPHA'] = tdd_cya # Read coefficients for TDD X coefficient cxb_kw = 'TDD_CXB{0}'.format(int(chip)) - skew_coeffs['TDD_CX_BETA'] = phdr.get(cxb_kw,None) + skew_coeffs['TDD_CX_BETA'] = phdr.get(cxb_kw, None) cxa_kw = 'TDD_CXA{0}'.format(int(chip)) - tdd_cxa = phdr.get(cxa_kw,None) + tdd_cxa = phdr.get(cxa_kw, None) if tdd_cxa == 0 or tdd_cxa == 'N/A': tdd_cxa = None skew_coeffs['TDD_CX_ALPHA'] = tdd_cxa @@ -309,12 +307,12 @@ def read_tdd_coeffs(phdr, chip=1): print('TDDORDER kw not present, using default TDD correction') return None - a = np.zeros((n+1,), np.float64) - b = np.zeros((n+1,), np.float64) - for i in range(n+1): + a = np.zeros((n + 1,), np.float64) + b = np.zeros((n + 1,), np.float64) + for i in range(n + 1): a[i] = phdr.get(("TDD_A%d" % i), 0.0) b[i] = phdr.get(("TDD_B%d" % i), 0.0) - if (a==0).all() and (b==0).all(): + if (a == 0).all() and (b == 0).all(): print('Warning: TDD_A and TDD_B coeffiecients have values of 0, \n \ but TDDORDER is %d.' % TDDORDER) @@ -325,15 +323,15 @@ def read_tdd_coeffs(phdr, chip=1): return skew_coeffs -def readOfftab(offtab, date, chip=None): - -#Read V2REF,V3REF from a specified offset table (OFFTAB). -# Return a default geometry model if no IDCTAB filenam e -# is given. This model will not distort the data in any way. - - if offtab == None: - return 0.,0. +def readOfftab(offtab, date, chip=None): + """ + Read V2REF,V3REF from a specified offset table (OFFTAB). + Return a default geometry model if no IDCTAB filenam e + is given. This model will not distort the data in any way. + """ + if offtab is None: + return 0., 0. # Provide a default value for chip if chip: @@ -347,7 +345,7 @@ def readOfftab(offtab, date, chip=None): except: raise IOError("Offset table '%s' not valid as specified!" % offtab) - #Determine row from which to get the coefficients. + # Determine row from which to get the coefficients. # How many rows do we have in the table... fshape = ftab[1].data.shape colnames = ftab[1].data.names @@ -374,7 +372,7 @@ def readOfftab(offtab, date, chip=None): obsdate = convertDate(ftab[1].data.field('OBSDATE')[i]) # If the row is appropriate for the chip... - # Interpolate between dates + # Interpolate between dates if int(detchip) == int(chip) or int(detchip) == -999: if num_date <= obsdate: date_end = obsdate @@ -384,7 +382,7 @@ def readOfftab(offtab, date, chip=None): row_end = i continue - if row_end == None and (num_date > obsdate): + if row_end is None and (num_date > obsdate): date_end = obsdate v2end = ftab[1].data.field('V2REF')[i] v3end = ftab[1].data.field('V3REF')[i] @@ -403,16 +401,17 @@ def readOfftab(offtab, date, chip=None): ftab.close() del ftab - if row_start == None and row_end == None: - print('Row corresponding to DETCHIP of ',detchip,' was not found!') + if row_start is None and row_end is None: + print('Row corresponding to DETCHIP of ', detchip, ' was not found!') raise LookupError - elif row_start == None: - print('- OFFTAB: Offset defined by row',str(row_end+1)) + elif row_start is None: + print('- OFFTAB: Offset defined by row', str(row_end + 1)) else: - print('- OFFTAB: Offset interpolated from rows',str(row_start+1),'and',str(row_end+1)) + print('- OFFTAB: Offset interpolated from rows', str(row_start + 1), + 'and', str(row_end + 1)) # Now, do the interpolation for v2ref, v3ref, and theta - if row_start == None or row_end == row_start: + if row_start is None or row_end is row_start: # We are processing an observation taken after the last calibration date_start = date_end v2start = v2end @@ -426,31 +425,32 @@ def readOfftab(offtab, date, chip=None): v3ref = _fraction * (v3end - v3start) + v3start theta = _fraction * (theta_end - theta_start) + theta_start - return v2ref,v3ref,theta + return v2ref, v3ref, theta -def readWCSCoeffs(header): - - #Read distortion coeffs from WCS header keywords and - #populate distortion coeffs arrays. +def readWCSCoeffs(header): + """ + Read distortion coeffs from WCS header keywords and + populate distortion coeffs arrays. + """ # Read in order for polynomials _xorder = header['a_order'] _yorder = header['b_order'] - order = max(max(_xorder,_yorder),3) + order = max(max(_xorder, _yorder), 3) - fx = np.zeros(shape=(order+1,order+1),dtype=np.float64) - fy = np.zeros(shape=(order+1,order+1),dtype=np.float64) + fx = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) + fy = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) # Read in CD matrix _cd11 = header['cd1_1'] _cd12 = header['cd1_2'] _cd21 = header['cd2_1'] _cd22 = header['cd2_2'] - _cdmat = np.array([[_cd11,_cd12],[_cd21,_cd22]]) - _theta = np.arctan2(-_cd12,_cd22) - _rotmat = np.array([[np.cos(_theta),np.sin(_theta)], - [-np.sin(_theta),np.cos(_theta)]]) - _rCD = np.dot(_rotmat,_cdmat) + _cdmat = np.array([[_cd11, _cd12], [_cd21, _cd22]]) + _theta = np.arctan2(-_cd12, _cd22) + _rotmat = np.array([[np.cos(_theta), np.sin(_theta)], + [-np.sin(_theta), np.cos(_theta)]]) + _rCD = np.dot(_rotmat, _cdmat) _skew = np.arcsin(-_rCD[1][0] / _rCD[0][0]) _scale = _rCD[0][0] * np.cos(_skew) * 3600. _scale2 = _rCD[1][1] * 3600. @@ -467,40 +467,40 @@ def readWCSCoeffs(header): refpix['THETA'] = np.rad2deg(_theta) refpix['XDELTA'] = 0.0 refpix['YDELTA'] = 0.0 - refpix['DEFAULT_SCALE'] = yes - refpix['centered'] = yes - + refpix['DEFAULT_SCALE'] = True + refpix['centered'] = True # Set up template for coeffs keyword names cxstr = 'A_' cystr = 'B_' # Read coeffs into their own matrix - for i in range(_xorder+1): - for j in range(i+1): - xcname = cxstr+str(j)+'_'+str(i-j) + for i in range(_xorder + 1): + for j in range(i + 1): + xcname = cxstr + str(j) + '_' + str(i - j) if xcname in header: - fx[i,j] = header[xcname] + fx[i, j] = header[xcname] # Extract Y coeffs separately as a different order may # have been used to fit it. - for i in range(_yorder+1): - for j in range(i+1): - ycname = cystr+str(j)+'_'+str(i-j) + for i in range(_yorder + 1): + for j in range(i + 1): + ycname = cystr + str(j) + '_' + str(i - j) if ycname in header: - fy[i,j] = header[ycname] + fy[i, j] = header[ycname] # Now set the linear terms fx[0][0] = 1.0 fy[0][0] = 1.0 - return fx,fy,refpix,order + return fx, fy, refpix, order -def readTraugerTable(idcfile,wavelength): - - # Return a default geometry model if no coefficients filename - # is given. This model will not distort the data in any way. - if idcfile == None: +def readTraugerTable(idcfile, wavelength): + """ + Return a default geometry model if no coefficients filename + is given. This model will not distort the data in any way. + """ + if idcfile is None: return fileutil.defaultModel() # Trauger coefficients only result in a cubic file... @@ -510,10 +510,10 @@ def readTraugerTable(idcfile,wavelength): b_coeffs = [0] * numco indx = _MgF2(wavelength) - ifile = open(idcfile,'r') + ifile = open(idcfile, 'r') # Search for the first line of the coefficients _line = fileutil.rAsciiLine(ifile) - while _line[:7].lower() != 'trauger': + while _line[: 7].lower() != 'trauger': _line = fileutil.rAsciiLine(ifile) # Read in each row of coefficients,split them into their values, # and convert them into cubic coefficients based on @@ -525,9 +525,11 @@ def readTraugerTable(idcfile,wavelength): if _line == '': continue _lc = _line.split() if j < 10: - a_coeffs[j] = float(_lc[0])+float(_lc[1])*(indx-1.5)+float(_lc[2])*(indx-1.5)**2 + a_coeffs[j] = float(_lc[0]) + float(_lc[1]) * (indx - 1.5) + \ + float(_lc[2]) * (indx - 1.5) ** 2 else: - b_coeffs[j-10] = float(_lc[0])+float(_lc[1])*(indx-1.5)+float(_lc[2])*(indx-1.5)**2 + b_coeffs[j - 10] = float(_lc[0]) + float(_lc[1]) * (indx - 1.5) + \ + float(_lc[2]) * (indx - 1.5) ** 2 j = j + 1 ifile.close() @@ -536,17 +538,17 @@ def readTraugerTable(idcfile,wavelength): # Now, convert the coefficients into a Numeric array # with the right coefficients in the right place. # Populate output values now... - fx = np.zeros(shape=(order+1,order+1),dtype=np.float64) - fy = np.zeros(shape=(order+1,order+1),dtype=np.float64) + fx = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) + fy = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) # Assign the coefficients to their array positions - fx[0,0] = 0. - fx[1] = np.array([a_coeffs[2],a_coeffs[1],0.,0.],dtype=np.float64) - fx[2] = np.array([a_coeffs[5],a_coeffs[4],a_coeffs[3],0.],dtype=np.float64) - fx[3] = np.array([a_coeffs[9],a_coeffs[8],a_coeffs[7],a_coeffs[6]],dtype=np.float64) - fy[0,0] = 0. - fy[1] = np.array([b_coeffs[2],b_coeffs[1],0.,0.],dtype=np.float64) - fy[2] = np.array([b_coeffs[5],b_coeffs[4],b_coeffs[3],0.],dtype=np.float64) - fy[3] = np.array([b_coeffs[9],b_coeffs[8],b_coeffs[7],b_coeffs[6]],dtype=np.float64) + fx[0, 0] = 0. + fx[1] = np.array([a_coeffs[2], a_coeffs[1], 0., 0.], dtype=np.float64) + fx[2] = np.array([a_coeffs[5], a_coeffs[4], a_coeffs[3], 0.], dtype=np.float64) + fx[3] = np.array([a_coeffs[9], a_coeffs[8], a_coeffs[7], a_coeffs[6]], dtype=np.float64) + fy[0, 0] = 0. + fy[1] = np.array([b_coeffs[2], b_coeffs[1], 0., 0.], dtype=np.float64) + fy[2] = np.array([b_coeffs[5], b_coeffs[4], b_coeffs[3], 0.], dtype=np.float64) + fy[3] = np.array([b_coeffs[9], b_coeffs[8], b_coeffs[7], b_coeffs[6]], dtype=np.float64) # Used in Pattern.computeOffsets() refpix = {} @@ -557,10 +559,10 @@ def readTraugerTable(idcfile,wavelength): refpix['XDELTA'] = 0. refpix['YDELTA'] = 0. refpix['PSCALE'] = None - refpix['DEFAULT_SCALE'] = no - refpix['centered'] = yes + refpix['DEFAULT_SCALE'] = False + refpix['centered'] = True - return fx,fy,refpix,order + return fx, fy, refpix, order def readCubicTable(idcfile): @@ -572,17 +574,17 @@ def readCubicTable(idcfile): # Return a default geometry model if no coefficients filename # is given. This model will not distort the data in any way. - if idcfile == None: + if idcfile is None: return fileutil.defaultModel() - ifile = open(idcfile,'r') + ifile = open(idcfile, 'r') # Search for the first line of the coefficients _line = fileutil.rAsciiLine(ifile) - _found = no - while _found == no: - if _line[:7] in ['cubic','quartic','quintic'] or _line[:4] == 'poly': - found = yes + _found = False + while not _found: + if _line[:7] in ['cubic', 'quartic', 'quintic'] or _line[: 4] == 'poly': + found = True break _line = fileutil.rAsciiLine(ifile) @@ -613,17 +615,17 @@ def readCubicTable(idcfile): # Now, convert the coefficients into a Numeric array # with the right coefficients in the right place. # Populate output values now... - fx = np.zeros(shape=(order+1,order+1),dtype=np.float64) - fy = np.zeros(shape=(order+1,order+1),dtype=np.float64) + fx = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) + fy = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) # Assign the coefficients to their array positions - fx[0,0] = 0. - fx[1] = np.array([a_coeffs[2],a_coeffs[1],0.,0.],dtype=np.float64) - fx[2] = np.array([a_coeffs[5],a_coeffs[4],a_coeffs[3],0.],dtype=np.float64) - fx[3] = np.array([a_coeffs[9],a_coeffs[8],a_coeffs[7],a_coeffs[6]],dtype=np.float64) - fy[0,0] = 0. - fy[1] = np.array([b_coeffs[2],b_coeffs[1],0.,0.],dtype=np.float64) - fy[2] = np.array([b_coeffs[5],b_coeffs[4],b_coeffs[3],0.],dtype=np.float64) - fy[3] = np.array([b_coeffs[9],b_coeffs[8],b_coeffs[7],b_coeffs[6]],dtype=np.float64) + fx[0, 0] = 0. + fx[1] = np.array([a_coeffs[2], a_coeffs[1], 0., 0.], dtype=np.float64) + fx[2] = np.array([a_coeffs[5], a_coeffs[4], a_coeffs[3], 0.], dtype=np.float64) + fx[3] = np.array([a_coeffs[9], a_coeffs[8], a_coeffs[7], a_coeffs[6]], dtype=np.float64) + fy[0, 0] = 0. + fy[1] = np.array([b_coeffs[2], b_coeffs[1], 0., 0.], dtype=np.float64) + fy[2] = np.array([b_coeffs[5], b_coeffs[4], b_coeffs[3], 0.], dtype=np.float64) + fy[3] = np.array([b_coeffs[9], b_coeffs[8], b_coeffs[7], b_coeffs[6]], dtype=np.float64) # Used in Pattern.computeOffsets() refpix = {} @@ -634,21 +636,23 @@ def readCubicTable(idcfile): refpix['XDELTA'] = 0. refpix['YDELTA'] = 0. refpix['PSCALE'] = None - refpix['DEFAULT_SCALE'] = no - refpix['centered'] = yes + refpix['DEFAULT_SCALE'] = False + refpix['centered'] = True + + return fx, fy, refpix, order - return fx,fy,refpix,order def factorial(n): """ Compute a factorial for integer n. """ m = 1 for i in range(int(n)): - m = m * (i+1) + m = m * (i + 1) return m -def combin(j,n): + +def combin(j, n): """ Return the combinatorial factor for j in n.""" - return (factorial(j) / (factorial(n) * factorial( (j-n) ) ) ) + return (factorial(j) / (factorial(n) * factorial((j - n)))) def defaultModel(): @@ -657,15 +661,15 @@ def defaultModel(): """ order = 3 - fx = np.zeros(shape=(order+1,order+1),dtype=np.float64) - fy = np.zeros(shape=(order+1,order+1),dtype=np.float64) + fx = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) + fy = np.zeros(shape=(order + 1, order + 1), dtype=np.float64) - fx[1,1] = 1. - fy[1,0] = 1. + fx[1, 1] = 1. + fy[1, 0] = 1. # Used in Pattern.computeOffsets() refpix = {} - refpix['empty_model'] = yes + refpix['empty_model'] = True refpix['XREF'] = None refpix['YREF'] = None refpix['V2REF'] = 0. @@ -675,17 +679,20 @@ def defaultModel(): refpix['XDELTA'] = 0. refpix['YDELTA'] = 0. refpix['PSCALE'] = None - refpix['DEFAULT_SCALE'] = no + refpix['DEFAULT_SCALE'] = False refpix['THETA'] = 0. - refpix['centered'] = yes - return fx,fy,refpix,order + refpix['centered'] = True + return fx, fy, refpix, order + -# Function to compute the index of refraction for MgF2 at -# the specified wavelength for use with Trauger coefficients def _MgF2(lam): - _sig = pow((1.0e7/lam),2) - return np.sqrt(1.0 + 2.590355e10/(5.312993e10-_sig) + - 4.4543708e9/(11.17083e9-_sig) + 4.0838897e5/(1.766361e5-_sig)) + """ + Function to compute the index of refraction for MgF2 at + the specified wavelength for use with Trauger coefficients + """ + _sig = pow((1.0e7 / lam), 2) + return np.sqrt(1.0 + 2.590355e10 / (5.312993e10 - _sig) + + 4.4543708e9 / (11.17083e9 - _sig) + 4.0838897e5 / (1.766361e5 - _sig)) def convertDate(date): |