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Commit a14410e6 authored by Adam Paquette's avatar Adam Paquette
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Modifiction to ipf_read and added test.

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plio/examples/SocetSet/P20_008845_1894_XN_09N203W.csv 0 → 100644
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P01_001606_1897_XI_09N203W_65,1,0,1,4070.48584,-2315.9921879999997,0.141123,0.141123,-0.12137,-0.47907,0.0,0.0
P03_002226_1895_XI_09N203W_1,1,0,0,-1197.647949,-569.925598,0.0,0.0,-0.070627,0.194853,0.0,0.0
P03_002226_1895_XI_09N203W_2,1,0,0,-1241.685791,-499.006836,0.0,0.0,-0.066482,0.270447,0.0,0.0
plio/io/io_bae.py
+ 35
− 75
View file @ a14410e6
import json
import re
from functools import singledispatch
import numpy as np
import pandas as pd
......@@ -52,14 +53,19 @@ def socetset_keywords_to_json(keywords, ell=None):
parse(ell)
return json.dumps(stream)
def read_ipfs(input_data_list):
@singledispatch
def read_ipf(arg): # pragma: no cover
return str(arg)
@read_ipf.register(str)
def read_ipf_str(input_data):
"""
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
input_data : str
path to the an input data file
Returns
-------
......@@ -67,102 +73,56 @@ def read_ipfs(input_data_list):
containing the ipf data with appropriate column names and indices
"""
default_columns = np.genfromtxt(input_data_list[0], skip_header=2, dtype='unicode',
max_rows = 1, delimiter = ',')
columns = []
for column in default_columns:
if '(' in column and ')' in column:
column_name ,suffix = column.split('(')
num = int(suffix.split(')')[0])
# 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
for column_num in range(int(num)):
new_column = '{}{}'.format(column_name, column_num)
columns.append(new_column);
columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',
max_rows = 1, delimiter = ',')
else:
columns.append(column)
# TODO: Add unicode conversion
d = [line.split() for line in open(input_data, 'r')]
d = np.hstack(np.array(d[3:]))
d_total = []
d = d.reshape(-1, 12)
for input_file in input_data_list:
d = read_ipf(input_file)
for point in d:
d_total.append(point)
df = pd.DataFrame(d, columns=columns)
df = pd.DataFrame(d_total, columns=columns)
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')
# Validate the read data with the header point count
# assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))
return df
def read_ipf(input_data):
@read_ipf.register(list)
def read_ipf_list(input_data_list):
"""
Read a socet ipf file into a pandas data frame
Parameters
----------
input_data : str
path to the an input data file
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 = []
# 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
# default_columns = np.genfromtxt(input_data, skip_header=2, dtype='unicode',
# max_rows = 1, delimiter = ',')
#
# columns = []
#
# for column in default_columns:
#
# if '(' in column and ')' in column:
# column_name ,suffix = column.split('(')
# num = int(suffix.split(')')[0])
#
# for column_num in range(int(num)):
# new_column = '{}{}'.format(column_name, column_num)
# columns.append(new_column);
#
# else:
# columns.append(column)
# 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)
assert int(cnt) == len(d), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))
for input_file in input_data_list:
frames.append(read_ipf(input_file))
return d
df = pd.concat(frames)
# df = pd.DataFrame(d, columns=columns)
#
# # Soft conversion of numeric types to numerics, allows str in first col for point_id
# df = df.apply(pd.to_numeric, errors='ignore')
#
# # Validate the read data with the header point count
# assert int(cnt) == len(df), 'Dataframe length {} does not match point length {}.'.format(int(cnt), len(df))
#
# return df
return df
def read_gpf(input_data):
"""
......@@ -242,7 +202,7 @@ def save_gpf(df, output_file):
try:
outGPF = open(output_file, 'w', newline='\r\n')
except:
print ('Unable to open output gpf file: {0}'.format(output_file))
print('Unable to open output gpf file: {0}'.format(output_file))
return 1
#grab number of rows in pandas dataframe
......
plio/io/tests/test_io_bae.py
+ 24
− 10
View file @ a14410e6
import json
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
from plio.io.io_bae import socetset_keywords_to_json, read_gpf, save_gpf, read_ipf
from plio.examples import get_path
import pytest
......@@ -13,27 +14,40 @@ def insight_gpf():
return get_path('InSightE08_XW.gpf')
@pytest.fixture()
def insight_expected():
def insight_expected_gpf():
return pd.read_csv(get_path('InSightE08_XW.csv'))
@pytest.mark.parametrize('gpf, expected', [(insight_gpf(),insight_expected())])
@pytest.fixture
def insight_ipf():
return get_path('P20_008845_1894_XN_09N203W.ipf')
@pytest.fixture()
def insight_expected_ipf():
return pd.read_csv(get_path('P20_008845_1894_XN_09N203W.csv'))
@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('gpf', [(insight_gpf())])
def test_write_gpf(gpf):
@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, file', [(insight_gpf(), 'out.gpf')])
def test_write_gpf(gpf, file):
"""
We test by manually comparing files and not using filecmp so that we
are not testing float point precision differences, e.g. 0.0 == 0.00000000.
"""
df = read_gpf(gpf)
save_gpf(df, 'out.gpf')
save_gpf(df, file)
with open(gpf) as f:
fl = f.readlines()
with open('out.gpf') as f:
with open(file) as f:
fs = f.readlines()
# Check that the header is the same
......@@ -41,7 +55,7 @@ def test_write_gpf(gpf):
assert fl[i] == fs[i]
truth_arr = np.genfromtxt(gpf, skip_header=3)
test_arr = np.genfromtxt('out.gpf', skip_header=3)
test_arr = np.genfromtxt(file, skip_header=3)
np.testing.assert_array_almost_equal(truth_arr, test_arr)
def test_create_from_socet_lis():
......@@ -49,8 +63,8 @@ def test_create_from_socet_lis():
socetell = get_path('ellipsoid.ell')
js = json.loads(socetset_keywords_to_json(socetlis))
assert isinstance(js, dict) # This is essentially a JSON linter
# Manually validated
# Manually validated
assert 'RECTIFICATION_TERMS' in js.keys()
assert 'SEMI_MAJOR_AXIS' in js.keys() # From ellipsoid file
assert 'NUMBER_OF_EPHEM' in js.keys()
assert len(js['EPHEM_PTS']) / 3 == js['NUMBER_OF_EPHEM']
\ No newline at end of file
assert len(js['EPHEM_PTS']) / 3 == js['NUMBER_OF_EPHEM']
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