Python AsdfFile.tree['model']方法代码示例

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def ote2asdf(otepcf, outname, ref_kw):
    """
    ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')

    ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
    """
    with open(otepcf) as f:
        lines = [l.strip() for l in f.readlines()]

    factors = lines[lines.index('*Factor 2 1') + 1].split()
    # this corresponds to modeling Rotation direction as is
    rotation_angle = float(lines[lines.index('*Rotation') + 1])
    input_rot_center = lines[lines.index('*InputRotationCentre 2 1') + 1].split()
    output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') + 1].split()

    mlinear = homothetic_det2sky(input_rot_center, rotation_angle, factors, output_rot_center)

    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1].split('\t')
    xcoeff_backward = coeffs_from_pcf(degree, xlines)
    x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1].split('\t')
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ylines = lines[ycoeff_index + 1].split('\t')
    ycoeff_backward = coeffs_from_pcf(degree, ylines)
    y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ylines = lines[ycoeff_index + 1].split('\t')
    ycoeff_forward = coeffs_from_pcf(degree, ylines)
    y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)

    x_poly_forward.inverse = x_poly_backward
    y_poly_forward.inverse = y_poly_backward

    output2poly_mapping = Identity(2, name='output_mapping')
    output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
    input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping

    model = model_poly | mlinear


    f = AsdfFile()
    f.tree = ref_kw.copy()
    f.tree['model'] = model
    f.add_history_entry("Build 6")
    f.write_to(outname)
    return model_poly, mlinear

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]

#.........这里部分代码省略.........
	print('Coefficient for x1_1: ', x1_1i)
	x0_2i = coeffxi[5]
	print('Coefficient for x0_2: ', x0_2i)
	x3_0i = coeffxi[6]
	print('Coefficient for x3_0: ', x3_0i)
	x2_1i = coeffxi[7]
	print('Coefficient for x2_1: ', x2_1i)
	x1_2i = coeffxi[8]
	print('Coefficient for x1_2: ', x1_2i)
	x0_3i = coeffxi[9]
	print('Coefficient for x0_3: ', x0_3i)
	x4_0i = coeffxi[10]
	print('Coefficient for x4_0: ', x4_0i)
	x3_1i = coeffxi[11]
	print('Coefficient for x3_1: ', x3_1i)
	x2_2i = coeffxi[12]
	print('Coefficient for x2_2: ', x2_2i)
	x1_3i = coeffxi[13]
	print('Coefficient for x1_3: ', x1_3i)
	x0_4i = coeffxi[14]
	print('Coefficient for x0_4: ', x0_4i)

	"Transform coefficients to get distorted, ideal, detector y."
	y0_0i = coeffyi[0]
	print('Coefficient for y0_0: ', y0_0i)
	y1_0i = coeffyi[1]
	print('Coefficient for y1_0: ', y1_0i)
	y0_1i = coeffyi[2]
	print('Coefficient for y0_1: ', y0_1i)
	y2_0i = coeffyi[3]
	print('Coefficient for y2_0: ', y2_0i)
	y1_1i = coeffyi[4]
	print('Coefficient for y1_1: ', y1_1i)
	y0_2i = coeffyi[5]
	print('Coefficient for y0_2: ', y0_2i)
	y3_0i = coeffyi[6]
	print('Coefficient for y3_0: ', y3_0i)
	y2_1i = coeffyi[7]
	print('Coefficient for y2_1: ', y2_1i)
	y1_2i = coeffyi[8]
	print('Coefficient for y1_2: ', y1_2i)
	y0_3i = coeffyi[9]
	print('Coefficient for y0_3: ', y0_3i)
	y4_0i = coeffyi[10]
	print('Coefficient for y4_0: ', y4_0i)
	y3_1i = coeffyi[11]
	print('Coefficient for y3_1: ', y3_1i)
	y2_2i = coeffyi[12]
	print('Coefficient for y2_2: ', y2_2i)
	y1_3i = coeffyi[13]
	print('Coefficient for y1_3: ', y1_3i)
	y0_4i = coeffyi[14]
	print('Coefficient for y0_4: ', y0_4i)

	"Create reference file using asdf formats."
	polyxr = models.Polynomial2D(4, c0_0=x0_0r, c1_0=x1_0r, c0_1=x0_1r, c2_0=x2_0r, c1_1=x1_1r, c0_2=x0_2r, c3_0=x3_0r, c2_1=x2_1r, c1_2=x1_2r, c0_3=x0_3r, c4_0=x4_0r, c3_1=x3_1r, c2_2=x2_2r, c1_3=x1_3r, c0_4=x0_4r)
	polyyr = models.Polynomial2D(4, c0_0=y0_0r, c1_0=y1_0r, c0_1=y0_1r, c2_0=y2_0r, c1_1=y1_1r, c0_2=y0_2r, c3_0=y3_0r, c2_1=y2_1r, c1_2=y1_2r, c0_3=y0_3r, c4_0=y4_0r, c3_1=y3_1r, c2_2=y2_2r, c1_3=y1_3r, c0_4=y0_4r)
	mapping = models.Mapping([0, 1, 0, 1])
	det2sky = mapping | polyxr & polyyr

	polyxi = models.Polynomial2D(4, c0_0=x0_0i, c1_0=x1_0i, c0_1=x0_1i, c2_0=x2_0i, c1_1=x1_1i, c0_2=x0_2i, c3_0=x3_0i, c2_1=x2_1i, c1_2=x1_2i, c0_3=x0_3i, c4_0=x4_0i, c3_1=x3_1i, c2_2=x2_2i, c1_3=x1_3i, c0_4=x0_4i)
	polyyi = models.Polynomial2D(4, c0_0=y0_0i, c1_0=y1_0i, c0_1=y0_1i, c2_0=y2_0i, c1_1=y1_1i, c0_2=y0_2i, c3_0=y3_0i, c2_1=y2_1i, c1_2=y1_2i, c0_3=y0_3i, c4_0=y4_0i, c3_1=y3_1i, c2_2=y2_2i, c1_3=y1_3i, c0_4=y0_4i)
	mapping = models.Mapping([0, 1, 0, 1])
	sky2det = mapping | polyxi & polyyi

	det2sky.inverse = sky2det

	f = AsdfFile()
	f.tree['model'] = det2sky
	f.tree['TITLE'] = "NIRISS CDP5 distortion reference data."
	f.tree['FILENAME'] = "niriss_ref_dist_coeff_image.asdf"
	f.tree['EXP_TYPE'] = "NIS_IMAGE"
	f.tree['REFTYPE'] = "DISTORTION"
	f.tree['AUTHOR'] = "Michael A. Wolfe, Alex Fullerton"
	f.tree['PEDIGREE'] = "GROUND"
	f.tree['INSTRUMENT'] = "NIRISS"
	f.tree['DETECTOR'] = "NIS"
	f.tree['USEAFTER'] = "Jan 01 2015 00:00:00.00"
	f.tree['DESCRIP'] = "This is a distortion correction reference file."
	f.tree['HISTORY'] = "This file is being delivered because it is the distortion correction file. This file was created from the python script niriss_ref_dist_coeff.py and data provied by Alex Fullerton. There is no documentation yet and the python script is titled: niriss_ref_dist_coeff.py. The data used to create the file can be found in the text files: NIS_Ideal2Real.txt & NIS_Real2Ideal.txt."
	f.write_to('niriss_ref_distortion_image.asdf')

	f = AsdfFile()
	f.tree['model'] = det2sky
	f.tree['TITLE'] = "NIRISS CDP5 distortion reference data."
	f.tree['FILENAME'] = "jwst_niriss_distortion_0001.asdf"
	f.tree['EXP_TYPE'] = "NIS_IMAGE"
	f.tree['REFTYPE'] = "DISTORTION"
	f.tree['AUTHOR'] = "Michael A. Wolfe, Alex Fullerton"
	f.tree['PEDIGREE'] = "GROUND"
	f.tree['INSTRUMENT'] = "NIRISS"
	f.tree['DETECTOR'] = "NIS"
	f.tree['USEAFTER'] = "Jan 01 2015 00:00:00.00"
	f.tree['DESCRIP'] = "This is a distortion correction reference file."
	f.tree['HISTORY'] = "This file is being delivered because it is the distortion correction file. This file was created from the python script niriss_ref_dist_coeff.py and data provied by Alex Fullerton. There is no documentation yet and the python script is titled: niriss_ref_dist_coeff.py. The data used to create the file can be found in the text files: NIS_Ideal2Real.txt & NIS_Real2Ideal.txt."
	f.write_to('jwst_niriss_distortion_0001.asdf')

	print('Done, done, and done with creating the disrtortion correction reference file. Git the bleep along little doggies!!!!!')

	dist_coeff()

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def fore2asdf(pcffore, outname, ref_kw):
    """
    forward direction : msa 2 ote
    backward_direction: msa 2 fpa

    """
    with open(pcffore) as f:
        lines = [l.strip() for l in f.readlines()]

    fore_det2sky = linear_from_pcf_det2sky(pcffore)
    fore_linear = fore_det2sky
    fore_linear.inverse = fore_det2sky.inverse & Identity(1)

    # compute the polynomial
    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    # Polynomial Correction in x
    x_poly_backward = models.Polynomial2D(degree, name="x_poly_backward", **xcoeff_forward)
    xlines_distortion = lines[xcoeff_index + 22: xcoeff_index + 43]
    xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
    x_poly_backward_distortion = models.Polynomial2D(degree, name="x_backward_distortion",
                                                     **xcoeff_forward_distortion)
    # do chromatic correction
    # the input is Xote, Yote, lam
    model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
                     ((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * \
                     (Mapping((2,)) | Identity(1)))

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_backward = models.Polynomial2D(degree, name="y_poly_backward",  **ycoeff_forward)

    ylines_distortion = lines[ycoeff_index + 22: ycoeff_index + 43]
    ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
    y_poly_backward_distortion = models.Polynomial2D(degree, name="y_backward_distortion",
                                                     **ycoeff_forward_distortion)

    # do chromatic correction
    # the input is Xote, Yote, lam
    model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
                     ((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * \
                     (Mapping((2,)) | Identity(1) ))

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
    x_poly_forward = models.Polynomial2D(degree,name="x_poly_forward", **xcoeff_backward)

    xcoeff_backward_distortion = coeffs_from_pcf(degree, lines[xcoeff_index + 22: xcoeff_index + 43])
    x_poly_forward_distortion = models.Polynomial2D(degree, name="x_forward_distortion", **xcoeff_backward_distortion)

    # the chromatic correction is done here
    # the input is Xmsa, Ymsa, lam
    model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
                    ((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * \
                    (Mapping((2,)) | Identity(1)))

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_forward = models.Polynomial2D(degree, name="y_poly_forward",**ycoeff_backward)

    ycoeff_backward_distortion = coeffs_from_pcf(degree, lines[ycoeff_index + 22: ycoeff_index + 43])
    y_poly_forward_distortion = models.Polynomial2D(degree, name="y_forward_distortion",
                                                    **ycoeff_backward_distortion)

    # do chromatic correction
    # the input is Xmsa, Ymsa, lam
    model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
                    ((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * \
                    (Mapping((2,)) | Identity(1)))

    #assign inverse transforms
    model_x = model_x_forward.copy()
    model_y = model_y_forward.copy()

    model_x.inverse = model_x_backward
    model_y.inverse = model_y_backward

    output2poly_mapping = Identity(2, name="output_mapping")
    output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
    input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping  | (model_x & model_y) | output2poly_mapping


    model = model_poly | fore_linear

    f = AsdfFile()
    f.tree = ref_kw.copy()
    f.tree['model'] = model
    asdffile = f.write_to(outname)
    return asdffile

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def pcf2asdf(pcffile, outname, ref_file_kw):
    """
    Create an asdf reference file with the transformation coded in a NIRSPEC
    Camera.pcf or Collimator*.pcf file.

    - forward (team): sky to detector
      - Shift inputs to input_rotation_center
      - Rotate inputs
      - Scale  inputs
      - Shift inputs to output_rot_center
      - Apply polynomial distortion
    - backward_team (team definition) detector to sky
      - Apply polynomial distortion
      - Shift inputs to output_rot_center
      - Scale  inputs
      - Rotate inputs
      - Shift inputs to input_rotation_center

    WCS implementation
    - forward: detector to sky
      - equivalent to backward_team
    - backward: sky to detector
      - equivalent to forward_team

    Parameters
    ----------
    pcffile : str
        one of the NIRSPEC ".pcf" reference files provided by the IDT team.
        "pcf" stands for "polynomial coefficients fit"
    outname : str
        Name of reference file to be wriiten to disk.

    Returns
    -------
    fasdf : AsdfFile
        AsdfFile object

    Examples
    --------
    >>> pcf2asdf("Camera.pcf", "camera.asdf")

    """
    linear_det2sky = linear_from_pcf_det2sky(pcffile)

    with open(pcffile) as f:
        lines = [l.strip() for l in f.readlines()]

    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
    x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)

    x_poly_forward.inverse = x_poly_backward
    y_poly_forward.inverse = y_poly_backward

    output2poly_mapping = Identity(2, name='output_mapping')
    output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
    input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping

    model = model_poly | linear_det2sky

    f = AsdfFile()
    f.tree = ref_file_kw.copy()
    f.tree['model'] = model
    f.write_to(outname)

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def ote2asdf(otepcf, outname, ref_kw):
    """
    ref_kw = common_reference_file_keywords('OTE', 'NIRSPEC OTE transform - CDP4')

    ote2asdf('Model/Ref_Files/CoordTransform/OTE.pcf', 'jwst_nirspec_ote_0001.asdf', ref_kw)
    """
    with open(otepcf) as f:
        lines = [l.strip() for l in f.readlines()]

    factors = lines[lines.index('*Factor 2 1') + 1].split()

    scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
          models.Scale(1 / float(factors[1]), name="y_scale")

    # this corresponds to modeling Rotation direction as is
    rotation_angle = lines[lines.index('*Rotation') + 1]
    rotation = models.Rotation2D(float(rotation_angle), name='rotation')

    input_rot_center = lines[lines.index('*InputRotationCentre 2 1') + 1].split()
    output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
                 models.Shift(float(input_rot_center[1]), name='output_y_shift')

    # Here the model is called "input_shift" but in the team version it is the "output_shift".
    output_rot_center = lines[lines.index('*OutputRotationCentre 2 1') + 1].split()
    input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
                  models.Shift(-float(output_rot_center[1]), name='input_y_shift')

    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1].split('\t')
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ylines = lines[ycoeff_index + 1].split('\t')
    ycoeff_forward = coeffs_from_pcf(degree, ylines)
    y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1].split('\t')
    xcoeff_backward = coeffs_from_pcf(degree, xlines)
    x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ylines = lines[ycoeff_index + 1].split('\t')
    ycoeff_backward = coeffs_from_pcf(degree, ylines)
    y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)

    x_poly_forward.inverse = x_poly_backward
    y_poly_forward.inverse = y_poly_backward

    mlinear = input_offset | rotation | scale | output_offset

    output2poly_mapping = Identity(2, name='output_mapping')
    output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
    input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping

    model = model_poly | mlinear


    f = AsdfFile()
    f.tree = ref_kw.copy()
    f.tree['model'] = model
    f.write_to(outname)

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def fore2asdf(pcffore, outname, ref_kw):
    """
    forward direction : msa 2 ote
    backward_direction: msa 2 fpa
    """
    with open(pcffore) as f:
        lines = [l.strip() for l in f.readlines()]

    factors = lines[lines.index('*Factor 2') + 1].split()
    # factor==1/factor in backward msa2ote direction and == factor in ote2msa direction
    scale = models.Scale(1. / float(factors[0]), name='scale_x') & \
          models.Scale(1 / float(factors[1]), name='scale_y')

    rotation_angle = lines[lines.index('*Rotation') + 1]
    rotation = models.Rotation2D(float(rotation_angle), name='rotation')

    input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
    output_offset = models.Shift(float(input_rot_center[0]), name="output_x_shift") & \
                 models.Shift(float(input_rot_center[1]), name="output_y_shift")

    output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
    input_offset = models.Shift(-float(output_rot_center[0]), name="input_x_shift") & \
                  models.Shift(-float(output_rot_center[1]), name="input_y_shift")

    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    # Polynomial Correction in x
    x_poly_backward = models.Polynomial2D(degree, name="x_poly_backward", **xcoeff_forward)
    xlines_distortion = lines[xcoeff_index + 22: xcoeff_index + 43]
    xcoeff_forward_distortion = coeffs_from_pcf(degree, xlines_distortion)
    x_poly_backward_distortion = models.Polynomial2D(degree, name="x_backward_distortion",
                                                     **xcoeff_forward_distortion)
    # do chromatic correction
    # the input is Xote, Yote, lam
    model_x_backward = (Mapping((0, 1), n_inputs=3) | x_poly_backward) + \
                     ((Mapping((0,1), n_inputs=3) | x_poly_backward_distortion) * Mapping((2,)))

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_backward = models.Polynomial2D(degree, name="y_poly_backward",  **ycoeff_forward)

    ylines_distortion = lines[ycoeff_index + 22: ycoeff_index + 43]
    ycoeff_forward_distortion = coeffs_from_pcf(degree, ylines_distortion)
    y_poly_backward_distortion = models.Polynomial2D(degree, name="y_backward_distortion",
                                                     **ycoeff_forward_distortion)

    # do chromatic correction
    # the input is Xote, Yote, lam
    model_y_backward = (Mapping((0,1), n_inputs=3) | y_poly_backward) + \
                     ((Mapping((0, 1), n_inputs=3) | y_poly_backward_distortion) * Mapping((2,)))

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
    x_poly_forward = models.Polynomial2D(degree,name="x_poly_forward", **xcoeff_backward)

    xcoeff_backward_distortion = coeffs_from_pcf(degree, lines[xcoeff_index + 22: xcoeff_index + 43])
    x_poly_forward_distortion = models.Polynomial2D(degree, name="x_forward_distortion", **xcoeff_backward_distortion)

    # the chromatic correction is done here
    # the input is Xmsa, Ymsa, lam
    model_x_forward = (Mapping((0,1), n_inputs=3) | x_poly_forward) + \
                    ((Mapping((0,1), n_inputs=3) | x_poly_forward_distortion) * Mapping((2,)))

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_forward = models.Polynomial2D(degree, name="y_poly_forward",**ycoeff_backward)

    ycoeff_backward_distortion = coeffs_from_pcf(degree, lines[ycoeff_index + 22: ycoeff_index + 43])
    y_poly_forward_distortion = models.Polynomial2D(degree, name="y_forward_distortion",
                                                    **ycoeff_backward_distortion)

    # do chromatic correction
    # the input is Xmsa, Ymsa, lam
    model_y_forward = (Mapping((0,1), n_inputs=3) | y_poly_forward) + \
                    ((Mapping((0,1), n_inputs=3) | y_poly_forward_distortion) * Mapping((2,)))

    #assign inverse transforms
    model_x = model_x_forward.copy()
    model_y = model_y_forward.copy()

    model_x.inverse = model_x_backward
    model_y.inverse = model_y_backward

    output2poly_mapping = Identity(2, name="output_mapping")
    output2poly_mapping.inverse = Mapping([0, 1, 2, 0, 1, 2])
    input2poly_mapping = Mapping([0, 1, 2, 0, 1, 2], name="input_mapping")
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping  | (model_x & model_y) | output2poly_mapping
    fore_linear = (input_offset | rotation | scale | output_offset)
    fore_linear_inverse  = fore_linear.inverse
    fore_linear.inverse = fore_linear_inverse & Identity(1)
    model = model_poly | fore_linear

    f = AsdfFile()
    f.tree = ref_kw.copy()
    f.tree['model'] = model
#.........这里部分代码省略.........

# 需要导入模块: from asdf import AsdfFile [as 别名]
# 或者: from asdf.AsdfFile import tree['model'] [as 别名]
def pcf2asdf(pcffile, outname, ref_file_kw):
    """
    Create an asdf reference file with the transformation coded in a NIRSPEC
    Camera.pcf or Collimator*.pcf file.

    - forward (team): sky to detector
      - Shift inputs to input_rotation_center
      - Rotate inputs
      - Scale  inputs
      - Shift inputs to output_rot_center
      - Apply polynomial distortion
    - backward_team (team definition) detector to sky
      - Apply polynomial distortion
      - Shift inputs to output_rot_center
      - Scale  inputs
      - Rotate inputs
      - Shift inputs to input_rotation_center

    WCS implementation
    - forward: detector to sky
      - equivalent to backward_team
    - backward: sky to detector
      - equivalent to forward_team

    Parameters
    ----------
    pcffile : str
        one of the NIRSPEC ".pcf" reference files provided by the IDT team.
        "pcf" stands for "polynomial coefficients fit"
    outname : str
        Name of reference file to be wriiten to disk.

    Returns
    -------
    fasdf : AsdfFile
        AsdfFile object

    Examples
    --------
    >>> pcf2asdf("Camera.pcf", "camera.asdf")

    """
    with open(pcffile) as f:
        lines = [l.strip() for l in f.readlines()]

    factors = lines[lines.index('*Factor 2') + 1].split()
    scale = models.Scale(1 / float(factors[0]), name="x_scale") & \
          models.Scale(1 / float(factors[1]), name="y_scale")

    rotation_angle = lines[lines.index('*Rotation') + 1]
    # Backward rotation is in the counter-clockwise direction as in modeling
    # Forward is clockwise
    backward_rotation = models.Rotation2D(float(rotation_angle), name='rotation')
    rotation = backward_rotation.copy()

    # Here the model is called "output_shift" but in the team version it is the "input_shift".
    input_rot_center = lines[lines.index('*InputRotationCentre 2') + 1].split()
    output_offset = models.Shift(float(input_rot_center[0]), name='output_x_shift') & \
                 models.Shift(float(input_rot_center[1]), name='output_y_shift')

    # Here the model is called "input_shift" but in the team version it is the "output_shift".
    output_rot_center = lines[lines.index('*OutputRotationCentre 2') + 1].split()
    input_offset = models.Shift(-float(output_rot_center[0]), name='input_x_shift') & \
                  models.Shift(-float(output_rot_center[1]), name='input_y_shift')

    degree = int(lines[lines.index('*FitOrder') + 1])

    xcoeff_index = lines.index('*xForwardCoefficients 21 2')
    xlines = lines[xcoeff_index + 1: xcoeff_index + 22]
    xcoeff_forward = coeffs_from_pcf(degree, xlines)
    x_poly_backward = models.Polynomial2D(degree, name='x_poly_backward', **xcoeff_forward)

    ycoeff_index = lines.index('*yForwardCoefficients 21 2')
    ycoeff_forward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_backward = models.Polynomial2D(degree, name='y_poly_backward', **ycoeff_forward)

    xcoeff_index = lines.index('*xBackwardCoefficients 21 2')
    xcoeff_backward = coeffs_from_pcf(degree, lines[xcoeff_index + 1: xcoeff_index + 22])
    x_poly_forward = models.Polynomial2D(degree, name='x_poly_forward', **xcoeff_backward)

    ycoeff_index = lines.index('*yBackwardCoefficients 21 2')
    ycoeff_backward = coeffs_from_pcf(degree, lines[ycoeff_index + 1: ycoeff_index + 22])
    y_poly_forward = models.Polynomial2D(degree, name='y_poly_forward', **ycoeff_backward)

    x_poly_forward.inverse = x_poly_backward
    y_poly_forward.inverse = y_poly_backward

    output2poly_mapping = Identity(2, name='output_mapping')
    output2poly_mapping.inverse = Mapping([0, 1, 0, 1])
    input2poly_mapping = Mapping([0, 1, 0, 1], name='input_mapping')
    input2poly_mapping.inverse = Identity(2)

    model_poly = input2poly_mapping | (x_poly_forward & y_poly_forward) | output2poly_mapping

    model = model_poly | input_offset |scale |rotation |output_offset

    f = AsdfFile()
    f.tree = ref_file_kw.copy()
    f.tree['model'] = model
    f.write_to(outname)