Transformations added in notebook (1367aff6) · Commits · aflab / astrogeology / Plio

notebooks/Socet2ISIS.ipynb

+0 −47

Original line number	Diff line number	Diff line
		%% Cell type:code id: tags:

		``` python
		import os
		import sys
		from functools import singledispatch
		import warnings

		import pandas as pd
		import numpy as np

		sys.path.insert(0, "/home/tthatcher/Desktop/Projects/Plio/plio")

		from plio.examples import get_path
		from plio.io.io_bae import read_gpf, read_ipf
		```

		%% Cell type:code id: tags:

		``` python
		# Reads a .atf file and outputs all of the
		# .ipf, .gpf, .sup, .prj, and path to locate the
		# .apf file (should be the same as all others)
		def read_atf(atf_file):
		with open(atf_file) as f:

		files = []
		ipf = []
		sup = []
		files_dict = []

		# Grabs every PRJ, GPF, SUP, and IPF image from the ATF file
		for line in f:
		if line[-4:-1] == 'prj' or line[-4:-1] == 'gpf' or line[-4:-1] == 'sup' or line[-4:-1] == 'ipf' or line[-4:-1] == 'atf':
		files.append(line)

		files = np.array(files)

		# Creates appropriate arrays for certain files in the right format
		for file in files:
		file = file.strip()
		file = file.split(' ')

		# Grabs all the IPF files
		if file[1].endswith('.ipf'):
		ipf.append(file[1])

		# Grabs all the SUP files
		if file[1].endswith('.sup'):
		sup.append(file[1])

		files_dict.append(file)

		# Creates a dict out of file lists for GPF, PRJ, IPF, and ATF
		files_dict = (dict(files_dict))

		# Sets the value of IMAGE_IPF to all IPF images
		files_dict['IMAGE_IPF'] = ipf

		# Sets the value of IMAGE_SUP to all SUP images
		files_dict['IMAGE_SUP'] = sup

		# Sets the value of PATH to the path of the ATF file
		files_dict['PATH'] = os.path.dirname(os.path.abspath(atf_file))

		return files_dict
		```

		%% Cell type:code id: tags:

		``` python
		atf_dict = read_atf(get_path('CTX_Athabasca_Middle_step0.atf'))
		print(atf_dict)

		gpf_file = os.path.join(atf_dict['PATH'], atf_dict['GP_FILE']);
		ipf_list = [os.path.join(atf_dict['PATH'], i) for i in atf_dict['IMAGE_IPF']]

		gpf_df = read_gpf(gpf_file).set_index('point_id')
		ipf_df = read_ipf(ipf_list).set_index('pt_id')

		point_diff = ipf_df.index.difference(gpf_df.index)

		if len(point_diff) != 0:
		warnings.warn("The following points found in ipf files missing from gpf file: \n\n{}. \
		\n\nContinuing, but these points will be missing from the control network".format(list(point_diff)))

		new_df = ipf_df.merge(gpf_df, left_index=True, right_index=True)
		list(new_df)
		```

		%% Cell type:code id: tags:

		``` python
		def sample_size(record):

		with open(atf_dict['PATH'] + '/' + record['ipf_file'] + '.sup') as f:
		sample_num = 0
		for i, line in enumerate(f):
		if i == 4:
		sample_num = line.split(' ')
		sample_num = sample_num[-1].strip()
		break
		assert int(sample_num) > 0, "Sample number {} from {} is a negative number: Invalid Data".format(sample_num, record['ipf_file'])

		sample_num = int(sample_num)/2.0 + record['s.'] + 1
		return sample_num

		def line_size(record):

		with open(atf_dict['PATH'] + '/' + record['ipf_file'] + '.sup') as f:
		sample_num = 0
		for i, line in enumerate(f):
		if i == 3:
		sample_num = line.split(' ')
		sample_num = sample_num[-1].strip()
		break
		assert int(sample_num) > 0, "Sample number {} from {} is a negative number: Invalid Data".format(sample_num, record['ipf_file'])

		sample_num = int(sample_num)/2.0 + record['l.'] + 1
		return sample_num

		def known(record):
		if record['known'] == 0:
		return 'Free'

		elif record['known'] == 1 or record['known'] == 2 or record['known'] == 3:
		return 'Constrained'

		# new_df['s.'] = new_df.apply(sample_size, axis=1)
		# new_df['l.'] = new_df.apply(line_size, axis=1)
		# new_df['known'] = new_df.apply(known, axis=1)
		display(new_df)
		# cmeas->SetCoordinate(img_samples/2.0+sample+1,img_lines/2.0+line+1);
		```

		%% Cell type:code id: tags:

		``` python
		import math
		import pyproj

		# converts +/- 180 system to 0 - 360 system
		def to_360(num):
		return num % 360

		# ocentric to ographic latitudes
		def oc2og(dlat, dMajorRadius, dMinorRadius):
		try:
		dlat = math.radians(dlat)
		dlat = math.atan(((dMajorRadius / dMinorRadius)*2) (math.tan(dlat)))
		dlat = math.degrees(dlat)
		except:
		print ("Error in oc2og conversion")
		return dlat

		# ographic to ocentric latitudes
		def og2oc(dlat, dMajorRadius, dMinorRadius):
		try:
		dlat = math.radians(dlat)
		dlat = math.atan((math.tan(dlat) / ((dMajorRadius / dMinorRadius)**2)))
		dlat = math.degrees(dlat)
		except:
		print ("Error in og2oc conversion")
		return dlat

		def get_axis():
		with open(atf_dict['PATH'] + '/' + 'CTX_Athabasca_Middle.prj') as f:
		from collections import defaultdict

		files = defaultdict(list)

		for line in f:

		ext = line.strip().split(' ')
		files[ext[0]].append(ext[-1])

		eRadius = float(files['A_EARTH'][0])
		pRadius = eRadius * (1 - float(files['E_EARTH'][0]))

		return eRadius, pRadius

		# function to convert lat_Y_North to ISIS_lat
		def lat_ISIS_coord(record):
		with open(atf_dict['PATH'] + '/' + 'CTX_Athabasca_Middle.prj') as f:
		from collections import defaultdict

		files = defaultdict(list)

		for line in f:

		ext = line.strip().split(' ')
		files[ext[0]].append(ext[-1])

		eRadius = float(files['A_EARTH'][0])
		pRadius = eRadius * (1 - float(files['E_EARTH'][0]))
		ocentric_coord = og2oc(record['lat_Y_North'], eRadius, pRadius)
		coord_360 = to_360(ocentric_coord)
		return coord_360

		# function to convert long_X_East to ISIS_lon
		def lon_ISIS_coord(record):
		with open(atf_dict['PATH'] + '/' + 'CTX_Athabasca_Middle.prj') as f:
		from collections import defaultdict

		files = defaultdict(list)

		for line in f:

		ext = line.strip().split(' ')
		files[ext[0]].append(ext[-1])

		eRadius = float(files['A_EARTH'][0])
		pRadius = eRadius * (1 - float(files['E_EARTH'][0]))
		ocentric_coord = og2oc(record['long_X_East'], eRadius, pRadius)
		coord_360 = to_360(ocentric_coord)
		return coord_360

		def body_fixed_lat(record):
		semi_major, semi_minor = get_axis()
		ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)
		lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)
		lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])
		return lat

		def body_fixed_lon(record):
		semi_major, semi_minor = get_axis()
		ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)
		lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)
		lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])
		return lon

		def body_fixed_height(record):
		semi_major, semi_minor = get_axis()
		ecef = pyproj.Proj(proj='geocent', a=semi_major, b=semi_minor)
		lla = pyproj.Proj(proj='latlon', a=semi_major, b=semi_minor)
		lon, lat, height = pyproj.transform(lla, ecef, record['long_X_East'], record['lat_Y_North'], record['ht'])
		return height


		new_df['lat_Y_North'] = new_df.apply(lat_ISIS_coord, axis=1)
		new_df['long_X_East'] = new_df.apply(lon_ISIS_coord, axis=1)

		new_df['lat_Y_North'] = new_df.apply(body_fixed_lat, axis=1)
		new_df['long_X_East'] = new_df.apply(body_fixed_lon, axis=1)

		new_df['ht'] = new_df.apply(body_fixed_height, axis=1)


		display(new_df)
		```

		%% Cell type:code id: tags:

		``` python
		# Reads a .atf file and outputs all of the
		# .ipf, .gpf, .sup, .prj, and path to locate the
		# .apf file (should be the same as all others)
		def read_atf(atf_file):
		with open(atf_file) as f:

		files = []
		ipf = []
		sup = []
		files_dict = []

		# Grabs every PRJ, GPF, SUP, and IPF image from the ATF file
		for line in f:
		if line[-4:-1] == 'prj' or line[-4:-1] == 'gpf' or line[-4:-1] == 'sup' or line[-4:-1] == 'ipf' or line[-4:-1] == 'atf':
		files.append(line)

		files = np.array(files)

		# Creates appropriate arrays for certain files in the right format
		for file in files:
		file = file.strip()
		file = file.split(' ')

		# Grabs all the IPF files
		if file[1].endswith('.ipf'):
		ipf.append(file[1])

		# Grabs all the SUP files
		if file[1].endswith('.sup'):
		sup.append(file[1])

		files_dict.append(file)

		# Creates a dict out of file lists for GPF, PRJ, IPF, and ATF
		files_dict = (dict(files_dict))

		# Sets the value of IMAGE_IPF to all IPF images
		files_dict['IMAGE_IPF'] = ipf

		# Sets the value of IMAGE_SUP to all SUP images
		files_dict['IMAGE_SUP'] = sup

		# Sets the value of PATH to the path of the ATF file
		files_dict['PATH'] = os.path.dirname(os.path.abspath(atf_file))

		return files_dict

		@singledispatch
		def read_ipf(arg):
		return str(arg)
		# new_df['known'] = new_df.apply(known, axis=1)

		@read_ipf.register(str)
		def read_ipf_str(input_data):
		"""
		Read a socet ipf file into a pandas data frame

		Parameters
		----------
		input_data : str
		path to the an input data file

		Returns
		-------
		df : pd.DataFrame
		containing the ipf data with appropriate column names and indices
		"""

		# Check that the number of rows is matching the expected number
		with open(input_data, 'r') as f:
		for i, l in enumerate(f):
		if i == 1:
		cnt = int(l)
		elif i == 2:
		col = l
		break

		columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',
		max_rows = 1, delimiter = ',')

		# TODO: Add unicode conversion
		d = [line.split() for line in open(input_data, 'r')]
		d = np.hstack(np.array(d[3:]))

		d = d.reshape(-1, 12)

		df = pd.DataFrame(d, columns=columns)
		file = os.path.split(os.path.splitext(input_data)[0])[1]
		df['ipf_file'] = pd.Series(np.full((len(df['pt_id'])), file), index = df.index)

		assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))

		# Soft conversion of numeric types to numerics, allows str in first col for point_id
		df = df.apply(pd.to_numeric, errors='ignore')

		return df

		@read_ipf.register(list)
		def read_ipf_list(input_data_list):
		"""
		Read a socet ipf file into a pandas data frame

		Parameters
		----------
		input_data_list : list
		list of paths to the a set of input data files

		Returns
		-------
		df : pd.DataFrame
		containing the ipf data with appropriate column names and indices
		"""
		frames = []

		for input_file in input_data_list:
		frames.append(read_ipf(input_file))

		df = pd.concat(frames)

		return df
		```

plio/io/io_bae.py

+49 −2

Original line number	Diff line number	Diff line
		@@ -85,11 +85,10 @@ def read_ipf_str(input_data):
		columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',
		max_rows = 1, delimiter = ',')

		# TODO: Add unicode conversion
		d = [line.split() for line in open(input_data, 'r')]
		d = np.hstack(np.array(d[3:]))

		d = d.reshape(-1, 12)
		d = d.reshape(-1, 12).astype('unicode')

		df = pd.DataFrame(d, columns=columns)
		file = os.path.split(os.path.splitext(input_data)[0])[1]
		@@ -126,6 +125,54 @@ def read_ipf_list(input_data_list):

		return df

		def save_ipf(df, output_path):
		"""
		Write a socet gpf file from a gpf-defined pandas dataframe

		Parameters
		----------
		df : pd.DataFrame
		Pandas DataFrame

		output_file : str
		path to the output data file

		Returns
		-------
		int : success value
		0 = success, 1 = errors
		"""

		for ipf_file, ipf_df in df.groupby('ipf_file'):

		output_file = os.path.join(output_path, ipf_file + '.ipf')

		# Check that file can be opened
		try:
		outIPF = open(output_file, 'w', newline='\r\n')
		except:
		print('Unable to open output ipf file: {0}'.format(output_file))
		return 1

		#grab number of rows in pandas dataframe ipf group
		numpts = len(ipf_df)

		#Output ipf header
		outIPF.write('IMAGE POINT FILE\n')
		outIPF.write('{0}\n'.format(numpts))
		outIPF.write('pt_id,val,fid_val,no_obs,l.,s.,sig_l,sig_s,res_l,res_s,fid_x,fid_y\n')

		for index, row in ipf_df.iterrows():
		#Output coordinates to ipf file
		outIPF.write('{0} {1} {2} {3}\n'.format(row['pt_id'], row['val'], row['fid_val'], row['no_obs']))
		outIPF.write('{:0.6f} {:0.6f}\n'.format(row['l.'], row['s.']))
		outIPF.write('{:0.6f} {:0.6f}\n'.format(row['sig_l'], row['sig_s']))
		outIPF.write('{:0.6f} {:0.6f}\n'.format(row['res_l'], row['res_s']))
		outIPF.write('{:0.6f} {:0.6f}\n\n'.format(row['fid_x'], row['fid_y']))

		outIPF.close()
		return

		def read_gpf(input_data):
		"""
		Read a socet gpf file into a pandas data frame

plio/io/tests/temp/P20_008845_1894_XN_09N203W.ipf

0 → 100644

+729 −0

Original line number	Diff line number	Diff line
		IMAGE POINT FILE
		121
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		-0.287801 -0.050820
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_10 1 0 1
		-1163.659546 -2110.948730
		0.054246 0.054246
		-0.209774 -0.028915
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_11 1 0 1
		-649.603027 -291.606293
		0.144968 0.144968
		0.179422 0.480350
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_12 1 0 1
		-1879.115479 1650.314575
		0.044342 0.044342
		0.194544 -0.551285
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_13 1 0 1
		1195.076782 -2043.631714
		0.081316 0.081316
		-0.184309 0.153021
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_14 1 0 1
		1826.979736 -2180.584961
		0.185789 0.185789
		0.208304 -0.026378
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_15 1 0 1
		1833.592407 -1740.516968
		0.238122 0.238122
		0.652892 -0.509659
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_16 1 0 1
		1370.180664 -273.589996
		0.113241 0.113241
		0.296132 -0.060485
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_17 1 0 1
		1198.482544 -429.786133
		0.055307 0.055307
		-0.091967 0.108114
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_18 1 0 1
		1516.067993 1830.024048
		0.009269 0.009269
		-0.002093 -0.264173
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_19 1 0 1
		1338.967651 1679.986084
		0.004901 0.004901
		-0.030115 -0.306342
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_20 1 0 1
		1350.758423 1952.026978
		0.019970 0.019970
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		0.000000 0.000000

		P20_008845_1894_XN_09N203W_1 1 0 0
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		0.000000 0.000000
		0.055124 0.018517
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_2 1 0 0
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		0.000000 0.000000
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		0.000000 0.000000

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		0.000000 0.000000
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		0.000000 0.000000

		P20_008845_1894_XN_09N203W_4 1 0 1
		-3226.000000 167.000000
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		-0.195774 -0.000066
		0.000000 0.000000

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		0.000000 0.000000
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		0.000000 0.000000

		P20_008845_1894_XN_09N203W_6 1 0 3
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		0.000000 0.000000
		0.026541 0.063346
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_7 1 0 3
		-3378.000000 1672.000000
		0.000000 0.000000
		0.055623 0.013456
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_8 1 0 3
		-2793.000000 1837.000000
		0.000000 0.000000
		-0.117483 -0.229337
		0.000000 0.000000

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		0.000000 0.000000

		P20_008845_1894_XN_09N203W_10 1 0 2
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		0.000000 0.000000

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		0.069550 -0.000458
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_12 1 0 1
		2885.000000 -2124.000000
		0.000000 0.000000
		0.254136 -0.273798
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_13 1 0 1
		2746.000000 -2253.000000
		0.000000 0.000000
		-0.407167 -0.176964
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_14 1 0 0
		2728.000000 -1980.000000
		0.000000 0.000000
		-0.885848 0.137668
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_15 0 0 0
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		0.000000 0.000000
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		0.000000 0.000000

		P20_008845_1894_XN_09N203W_16 1 0 2
		2865.000000 2024.000000
		0.000000 0.000000
		-0.053859 0.035981
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_17 1 0 2
		3175.000000 1974.000000
		0.000000 0.000000
		-0.064058 0.044374
		0.000000 0.000000

		P20_008845_1894_XN_09N203W_18 1 0 2
		2872.000000 1565.000000
		0.000000 0.000000
		0.256228 0.218860
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_49 1 0 1
		-2629.182861 1085.034424
		0.012761 0.012761
		0.208403 -0.004282
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_54 1 0 1
		-1095.406738 1127.088013
		0.274421 0.274421
		0.019106 -0.331897
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_55 1 0 1
		-941.496277 1247.825806
		0.055631 0.055631
		0.152919 -0.306029
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_56 1 0 1
		-1098.958984 1441.816895
		0.114842 0.114842
		-0.297223 -0.308728
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_63 1 0 1
		119.944458 1116.177612
		0.023727 0.023727
		-0.104057 -0.034587
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_64 1 0 1
		-19.255381 1270.651611
		0.007022 0.007022
		-0.294970 -0.136646
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_65 1 0 1
		-271.636139 1030.269287
		0.141314 0.141314
		-0.007378 0.127183
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_71 1 0 1
		1793.756836 1419.468140
		0.070811 0.070811
		0.008127 -0.059651
		0.000000 0.000000

		P03_002371_1888_XI_08N204W_77 1 0 1
		3770.185547 1056.433594
		0.051015 0.051015
		-0.344149 0.791881
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_3 1 0 0
		-819.994568 -476.940125
		0.000000 0.000000
		-0.010086 0.238933
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_4 1 0 0
		-1038.745972 -225.617737
		0.000000 0.000000
		-0.102515 0.293944
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_5 1 0 0
		-1043.086548 -229.290588
		0.000000 0.000000
		-0.103618 0.222174
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_6 1 0 0
		-1016.667114 -212.669418
		0.000000 0.000000
		0.098336 0.182504
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_7 1 0 0
		-1089.852539 -179.613083
		0.000000 0.000000
		-0.099015 0.247487
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_43 1 0 1
		231.430023 1286.035034
		0.179721 0.179721
		0.043216 0.007602
		0.000000 0.000000

		P01_001540_1889_XI_08N204W_44 1 0 1
		53.370300 1130.200195
		0.035373 0.035373
		-0.239021 0.051970
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_20 1 0 1
		-3490.634277 -1071.889893
		0.074986 0.074986
		-0.109513 0.027899
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_21 1 0 1
		-3489.029297 -1344.598267
		0.117221 0.117221
		0.119840 -0.073966
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_26 1 0 1
		-2843.921875 -1365.052490
		0.066368 0.066368
		0.053526 -0.129321
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_27 1 0 1
		-2872.278076 -1055.845581
		0.044944 0.044944
		0.014887 -0.175307
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_32 1 0 1
		-702.726318 -1309.760742
		0.097152 0.097152
		-0.121270 0.083388
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_33 1 0 1
		-425.879700 -1332.264404
		0.141272 0.141272
		0.278120 0.018378
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_39 1 0 1
		957.507324 -1314.205933
		0.214219 0.214219
		-0.088346 0.135983
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_40 1 0 1
		1286.863525 -1325.946045
		0.022701 0.022701
		0.025672 -0.015907
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_41 1 0 1
		1415.343628 -1585.812134
		0.062944 0.062944
		0.178247 0.100219
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_42 1 0 1
		-713.122681 -1899.271973
		0.109711 0.109711
		-0.247557 0.240463
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_48 1 0 1
		2357.769287 -1605.211670
		0.142259 0.142259
		-0.046652 -0.341859
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_49 1 0 1
		2368.677490 -1310.253662
		0.084816 0.084816
		0.168448 -0.178884
		0.000000 0.000000

		P01_001606_1897_XI_09N203W_50 1 0 1
		2938.904541 -1286.650635
		0.268181 0.268181
		0.222150 -0.289282
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_41 1 0 1
		-2964.822021 -752.729980
		0.130511 0.130511
		0.199895 0.181932
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_42 1 0 1
		-2676.941162 -549.997925
		0.015523 0.015523
		-0.189075 0.282047
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_43 1 0 1
		-2503.684570 -1243.333618
		0.091619 0.091619
		-0.183131 -0.034143
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_46 1 0 1
		-984.080139 -1301.115845
		0.028430 0.028430
		-0.103870 -0.014166
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_47 1 0 1
		-1119.626953 -926.132080
		0.195907 0.195907
		-0.217611 0.087415
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_53 1 0 1
		3304.798340 -528.821411
		0.090225 0.090225
		-0.127036 -0.243320
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_54 1 0 1
		3597.587646 -608.987488
		0.187463 0.187463
		0.132150 -0.402642
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_55 1 0 1
		3277.619873 -1106.364136
		0.141123 0.141123
		-0.258301 -0.487463
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_64 1 0 1
		-1488.927979 306.370270
		0.141123 0.141123
		0.113540 0.000408
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_67 1 0 1
		-1501.843384 294.863434
		0.141123 0.141123
		-0.038216 -0.001066
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_68 1 0 1
		-1487.291992 301.690338
		0.141123 0.141123
		-0.161360 0.000601
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_71 1 0 1
		-1476.824463 421.302856
		0.141123 0.141123
		0.045458 0.000436
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_66 1 0 1
		-1473.926636 445.164948
		0.141123 0.141123
		-0.039173 -0.000061
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_72 1 0 1
		-1478.191040 398.059052
		0.141123 0.141123
		0.039510 -0.000569
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_74 1 0 1
		-1463.276489 270.004364
		0.141123 0.141123
		0.097608 0.000572
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_76 1 0 1
		-1399.385376 210.815338
		0.141123 0.141123
		-0.021339 -0.000335
		0.000000 0.000000

		P03_002226_1895_XI_09N203W_82 1 0 1
		-1110.612061 -112.804382
		0.141123 0.141123
		-0.179249 0.431591
		0.000000 0.000000

		P19_008344_1894_XN_09N203W_2 1 0 1
		-3987.065674 -1756.185791
		0.141123 0.141123
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		0.000000 0.000000

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		-3991.173340 1967.117554
		0.141123 0.141123
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		0.000000 0.000000

		P03_002371_1888_XI_08N204W_76 1 0 1
		4066.573730 1038.447998
		0.141123 0.141123
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		P03_002371_1888_XI_08N204W_78 1 0 1
		4083.873291 1277.689209
		0.141123 0.141123
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		P01_001606_1897_XI_09N203W_19 1 0 1
		-3917.203125 -1175.195679
		0.141123 0.141123
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		P01_001606_1897_XI_09N203W_65 1 0 1
		4070.485840 -2315.992188
		0.141123 0.141123
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		P03_002226_1895_XI_09N203W_1 1 0 0
		-1197.647949 -569.925598
		0.000000 0.000000
		-0.070627 0.194853
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		P03_002226_1895_XI_09N203W_2 1 0 0
		-1241.685791 -499.006836
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		-0.066482 0.270447
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plio/io/tests/test_io_bae.py

+35 −4

Original line number	Diff line number	Diff line
		import json
		import os

		import numpy as np
		import pandas as pd
		from pandas.util.testing import assert_frame_equal

		from plio.io.io_bae import socetset_keywords_to_json, read_gpf, save_gpf, read_ipf
		from plio.io.io_bae import socetset_keywords_to_json, read_gpf, save_gpf, read_ipf, save_ipf
		from plio.examples import get_path

		import pytest
		@@ -25,15 +26,42 @@ def insight_ipf():
		def insight_expected_ipf():
		return pd.read_csv(get_path('P20_008845_1894_XN_09N203W.csv'))

		@pytest.mark.parametrize('ipf, expected', [([insight_ipf()],insight_expected_ipf())])
		def test_read_ifp(ipf, expected):
		df = read_ipf(ipf)
		assert_frame_equal(df, expected)

		@pytest.mark.parametrize('gpf, expected', [(insight_gpf(),insight_expected_gpf())])
		def test_read_gfp(gpf, expected):
		df = read_gpf(gpf)
		assert_frame_equal(df, expected)

		@pytest.mark.parametrize('ipf, expected', [([insight_ipf()],insight_expected_ipf())])
		def test_read_ifp(ipf, expected):
		@pytest.mark.parametrize('ipf, file', [(insight_ipf(), 'plio/io/tests/temp')])
		def test_write_ipf(ipf, file):
		df = read_ipf(ipf)
		assert_frame_equal(df, expected)
		save_ipf(df, file)

		file = os.path.join(file, 'P20_008845_1894_XN_09N203W.ipf')

		with open(ipf) as f:
		fl = f.readlines()

		with open(file) as f:
		fs = f.readlines()

		# Check that the header is the same
		for i in range(3):
		assert fl[i] == fs[i]

		truth_arr = [line.split() for line in open(ipf, 'r')][3:]
		truth_arr = np.hstack(np.array(truth_arr))
		truth_arr = truth_arr.reshape(-1, 12)

		test_arr = [line.split() for line in open(file, 'r')][3:]
		test_arr = np.hstack(np.array(test_arr))
		test_arr = test_arr.reshape(-1, 12)

		assert (truth_arr == test_arr).all()

		@pytest.mark.parametrize('gpf, file', [(insight_gpf(), 'out.gpf')])
		def test_write_gpf(gpf, file):
		@@ -56,8 +84,11 @@ def test_write_gpf(gpf, file):

		truth_arr = np.genfromtxt(gpf, skip_header=3)
		test_arr = np.genfromtxt(file, skip_header=3)

		np.testing.assert_array_almost_equal(truth_arr, test_arr)

		# np.testing.assert_array_almost_equal(truth_arr, test_arr)

		def test_create_from_socet_lis():
		socetlis = get_path('socet_isd.lis')
		socetell = get_path('ellipsoid.ell')