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WIP: Wrap fitcircle #1550
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7c1ae68
add fitcircle.py
willschlitzer 41a3ded
add fitcircle imports
willschlitzer 7e2dfee
add test_fitcircle
willschlitzer a239841
add if statements and df column names
willschlitzer d526eab
update formatting in fixture_data
willschlitzer f02466e
formatting
willschlitzer 0355a7d
add functions
willschlitzer 9bf8331
add test info to test_fitcircle_no_outfile
willschlitzer 0360abc
add if statement for normalize
willschlitzer 1e86759
add tests
willschlitzer e5b7a91
add fitcircle to index.rst
willschlitzer 3cde6d8
run make format
willschlitzer c199d09
remove unused imports
willschlitzer 899a93f
Merge branch 'main' into wrap/fitcircle
willschlitzer d131d2f
change top docstring
willschlitzer 3e57da6
Merge remote-tracking branch 'origin/wrap/fitcircle' into wrap/fitcircle
willschlitzer d4eebb7
fix variable names
willschlitzer 913a70b
Merge branch 'main' into wrap/fitcircle
willschlitzer f0033fa
Merge branch 'main' into wrap/fitcircle
willschlitzer d41d90e
Apply suggestions from code review
willschlitzer 0c11f2c
Update pygmt/src/fitcircle.py
willschlitzer a6f6265
Update pygmt/src/fitcircle.py
willschlitzer 8ab458a
Merge branch 'main' into wrap/fitcircle
willschlitzer bbbb84d
run make format
willschlitzer 00ffffd
Apply suggestions from code review
willschlitzer af7c4f9
add normalize and small_circle parameters
willschlitzer 8803d43
Merge branch 'main' into wrap/fitcircle
willschlitzer d166335
Apply suggestions from code review
willschlitzer 9227dbe
change "normalize" to "norm"
willschlitzer a10ed09
Merge branch 'main' into wrap/fitcircle
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Apply suggestions from code review
willschlitzer bfa8fa6
Merge branch 'main' into wrap/fitcircle
willschlitzer fa0aa4b
add docstring for "data"
willschlitzer e885b54
Update pygmt/src/fitcircle.py
willschlitzer c890477
Merge branch 'main' into wrap/fitcircle
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Original file line number | Diff line number | Diff line change |
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|
@@ -118,6 +118,7 @@ Operations on tabular data | |
blockmedian | ||
blockmode | ||
filter1d | ||
fitcircle | ||
nearneighbor | ||
project | ||
select | ||
|
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Original file line number | Diff line number | Diff line change |
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|
@@ -35,6 +35,7 @@ | |
config, | ||
dimfilter, | ||
filter1d, | ||
fitcircle, | ||
grd2cpt, | ||
grd2xyz, | ||
grdclip, | ||
|
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,131 @@ | ||
""" | ||
fitcircle - Find mean position and great [or small] circle fit to points on | ||
sphere. | ||
""" | ||
import warnings | ||
|
||
import pandas as pd | ||
from pygmt.clib import Session | ||
from pygmt.exceptions import GMTInvalidInput | ||
from pygmt.helpers import GMTTempFile, build_arg_string, fmt_docstring, use_alias | ||
|
||
|
||
@fmt_docstring | ||
@use_alias( | ||
L="norm", | ||
S="small_circle", | ||
V="verbose", | ||
) | ||
def fitcircle(data, output_type="pandas", outfile=None, **kwargs): | ||
r""" | ||
Find mean position and great [or small] circle fit to points on sphere. | ||
|
||
**fitcircle** reads lon,lat [or lat,lon] values from the first two | ||
columns of the table. These are converted to | ||
Cartesian three-vectors on the unit sphere. Then two locations are | ||
found: the mean of the input positions, and the pole to the great circle | ||
which best fits the input positions. The user may choose one or both of | ||
two possible solutions to this problem. When the data are closely grouped | ||
along a great circle both solutions are similar. If the data have large | ||
dispersion, the pole to the great circle will be less well determined | ||
than the mean. Compare both solutions as a qualitative check. | ||
|
||
Setting ``norm`` to **1** approximates the | ||
minimization of the sum of absolute values of cosines of angular | ||
distances. This solution finds the mean position as the Fisher average | ||
of the data, and the pole position as the Fisher average of the | ||
cross-products between the mean and the data. Averaging cross-products | ||
gives weight to points in proportion to their distance from the mean, | ||
analogous to the "leverage" of distant points in linear regression in | ||
the plane. | ||
|
||
Setting ``norm`` to **2** approximates the | ||
minimization of the sum of squares of cosines of angular distances. It | ||
creates a 3 by 3 matrix of sums of squares of components of the data | ||
vectors. The eigenvectors of this matrix give the mean and pole | ||
locations. This method may be more subject to roundoff errors when there | ||
are thousands of data. The pole is given by the eigenvector | ||
corresponding to the smallest eigenvalue; it is the least-well | ||
represented factor in the data and is not easily estimated by either | ||
method. | ||
|
||
Full option list at :gmt-docs:`fitcircle.html` | ||
|
||
{aliases} | ||
|
||
Parameters | ||
---------- | ||
data : str or list or {table-like} | ||
Pass in either a file name to an ASCII data table, a Python list, a 2D | ||
{table-classes} containing longitude and latitude values. | ||
output_type : str | ||
Determine the format the xyz data will be returned in [Default is | ||
``pandas``]: | ||
|
||
- ``numpy`` - :class:`numpy.ndarray` | ||
- ``pandas``- :class:`pandas.DataFrame` | ||
- ``file`` - ASCII file (requires ``outfile``) | ||
outfile : str | ||
The file name for the output ASCII file. | ||
norm : int or bool | ||
Specify the desired *norm* as **1** or **2**\ , or use ``True`` | ||
or **3** to see both solutions. | ||
small_circle : float | ||
Attempt to fit a small circle instead of a great circle. The pole | ||
will be constrained to lie on the great circle connecting the pole | ||
of the best-fit great circle and the mean location of the data. | ||
Optionally append the desired fixed latitude of the small circle | ||
[Default will determine the optimal latitude]. | ||
{V} | ||
|
||
Returns | ||
------- | ||
ret : pandas.DataFrame or numpy.ndarray or None | ||
Return type depends on ``outfile`` and ``output_type``: | ||
|
||
- None if ``outfile`` is set (output will be stored in file set by | ||
``outfile``) | ||
- :class:`pandas.DataFrame` or :class:`numpy.ndarray` if ``outfile`` is | ||
not set (depends on ``output_type`` [Default is | ||
:class:`pandas.DataFrame`]) | ||
|
||
""" | ||
if kwargs.get("L") is None: | ||
raise GMTInvalidInput("Pass a required argument to 'norm'.") | ||
if output_type not in ["numpy", "pandas", "file"]: | ||
raise GMTInvalidInput("Must specify format as either numpy, pandas, or file.") | ||
if outfile is not None and output_type != "file": | ||
msg = ( | ||
f"Changing `output_type` of fitcirle from '{output_type}' to 'file' " | ||
"since `outfile` parameter is set. Please use `output_type='file'` " | ||
"to silence this warning." | ||
) | ||
warnings.warn(msg, category=RuntimeWarning, stacklevel=2) | ||
output_type = "file" | ||
elif output_type == "file" and outfile is None: | ||
raise GMTInvalidInput("Must specify outfile for ASCII output.") | ||
with GMTTempFile() as tmpfile: | ||
with Session() as lib: | ||
file_context = lib.virtualfile_from_data(check_kind="vector", data=data) | ||
with file_context as infile: | ||
if outfile is None: | ||
outfile = tmpfile.name | ||
lib.call_module( | ||
module="fitcircle", | ||
args=build_arg_string(kwargs, infile=infile, outfile=outfile), | ||
) | ||
|
||
# Read temporary csv output to a pandas table | ||
if outfile == tmpfile.name: # if user did not set outfile, return pd.DataFrame | ||
result = pd.read_csv( | ||
tmpfile.name, | ||
sep="\t", | ||
names=["longitude", "latitude", "method"], | ||
comment=">", | ||
) | ||
elif outfile != tmpfile.name: # return None if outfile set, output in outfile | ||
result = None | ||
|
||
if output_type == "numpy": | ||
result = result.to_numpy() | ||
return result |
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,111 @@ | ||
""" | ||
Tests for fitcircle. | ||
""" | ||
|
||
import os | ||
|
||
import numpy as np | ||
import numpy.testing as npt | ||
import pandas as pd | ||
import pytest | ||
from pygmt import fitcircle | ||
from pygmt.exceptions import GMTInvalidInput | ||
from pygmt.helpers import GMTTempFile | ||
from pygmt.src import which | ||
|
||
|
||
@pytest.fixture(scope="module", name="data") | ||
def fixture_data(): | ||
""" | ||
Load the sample data from the sat_03 remote file. | ||
""" | ||
fname = which("@sat_03.txt", download="c") | ||
data = pd.read_csv( | ||
fname, | ||
header=None, | ||
skiprows=1, | ||
sep="\t", | ||
names=["longitutde", "latitude", "z"], | ||
) | ||
return data | ||
|
||
|
||
def test_fitcircle_no_outfile(data): | ||
""" | ||
Test fitcircle with no set outfile. | ||
""" | ||
result = fitcircle(data=data, norm=True) | ||
assert result.shape == (7, 3) | ||
# Test longitude results | ||
npt.assert_allclose(result.longitude.min(), 52.7434273422) | ||
npt.assert_allclose(result.longitude.max(), 330.243649573) | ||
npt.assert_allclose(result.longitude.mean(), 223.078116476) | ||
npt.assert_allclose(result.longitude.median(), 232.7449849) | ||
# Test latitude results | ||
npt.assert_allclose(result.latitude.min(), -21.2085369093) | ||
npt.assert_allclose(result.latitude.max(), 21.2085369093) | ||
npt.assert_allclose(result.latitude.mean(), -7.8863683297) | ||
npt.assert_allclose(result.latitude.median(), -18.406777) | ||
|
||
|
||
def test_fitcircle_file_output(data): | ||
""" | ||
Test that fitcircle returns a file output when it is specified. | ||
""" | ||
with GMTTempFile(suffix=".txt") as tmpfile: | ||
result = fitcircle( | ||
data=data, norm=True, outfile=tmpfile.name, output_type="file" | ||
) | ||
assert result is None # return value is None | ||
assert os.path.exists(path=tmpfile.name) # check that outfile exists | ||
|
||
|
||
def test_fitcircle_invalid_format(data): | ||
""" | ||
Test that fitcircle fails with an incorrect format for output_type. | ||
""" | ||
with pytest.raises(GMTInvalidInput): | ||
fitcircle(data=data, norm=True, output_type="a") | ||
|
||
|
||
def test_fitcircle_no_normalize(data): | ||
""" | ||
Test that fitcircle fails with an argument is missing for normalize. | ||
""" | ||
with pytest.raises(GMTInvalidInput): | ||
fitcircle(data=data) | ||
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||
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||
def test_fitcircle_no_outfile_specified(data): | ||
""" | ||
Test that fitcircle fails when outpput_type is set to 'file' but no output | ||
file name is specified. | ||
""" | ||
with pytest.raises(GMTInvalidInput): | ||
fitcircle(data=data, norm=True, output_type="file") | ||
|
||
|
||
def test_filter1d_outfile_incorrect_output_type(data): | ||
""" | ||
Test that filter1d raises a warning when an outfile filename is set but the | ||
output_type is not set to 'file'. | ||
""" | ||
with pytest.warns(RuntimeWarning): | ||
with GMTTempFile(suffix=".txt") as tmpfile: | ||
result = fitcircle( | ||
data=data, norm=True, outfile=tmpfile.name, output_type="numpy" | ||
) | ||
assert result is None # return value is None | ||
assert os.path.exists(path=tmpfile.name) # check that outfile exists | ||
|
||
|
||
def test_fitcircle_format(data): | ||
""" | ||
Test that correct formats are returned. | ||
""" | ||
circle_default = fitcircle(data=data, norm=True) | ||
assert isinstance(circle_default, pd.DataFrame) | ||
circle_array = fitcircle(data=data, norm=True, output_type="numpy") | ||
assert isinstance(circle_array, np.ndarray) | ||
circle_df = fitcircle(data=data, norm=True, output_type="pandas") | ||
assert isinstance(circle_df, pd.DataFrame) |
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As you can see, the output of
fitcircle
is quite different with or without-F
option, so theread_csv
call doesn't work with-F
.I'm wondering should we return a dict instead, e.g.,:
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I can work on this, but I'm about to move so I may not be too speedy
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No hurry. Better to have more discussions about the output before working on it.