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Correct requirements
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@iagolops
iagolops committed Jul 3, 2024
1 parent 6c28514 commit 8ceb28a
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36 changes: 29 additions & 7 deletions lcanalyzer/models.py
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Expand Up @@ -11,40 +11,62 @@
import numpy as np
from astropy.timeseries import LombScargle


def load_dataset(filename):
"""Load a table from CSV file.
:param filename: The name of the .csv file to load
:returns: pd.DataFrame with the data from the file.
"""
return pd.read_csv(filename)


def mean_mag(data,mag_col):
def mean_mag(data, mag_col):
"""Calculate the mean magnitude of a lightcurve periods
:param data: The data frame
:param mag_col: a string with the name of the column for calculating the mean value
:returns: A float with the mean value of the column.
"""
return data[mag_col].mean()


def max_mag(data,mag_col):
def max_mag(data, mag_col):
"""Calculate the max magnitude of a lightcurve periods
:param data: pd.DataFrame with the magnitudes
:param mag_col: a string with the name of the column for calculating the max value
:returns: A float with the max value of the column.
"""
return data[mag_col].max()


def min_mag(data,mag_col):
def min_mag(data, mag_col):
"""Calculate the min magnitude of a lightcurve periods
:param data: pd.DataFrame with observed magnitudes for a single source
:param mag_col: a string with the name of the column for calculating the min value
:returns: A float with the min value of the column.
"""
return data[mag_col].min()


def calc_stats(lc, bands, mag_col):
# Calculate max, mean and min values for all bands of a light curve
stats = {}
for b in bands:
stat = {}
stat["max"] = max_mag(lc[b], mag_col)
stat["mean"] = mean_mag(lc[b], mag_col)
stat["min"] = min_mag(lc[b], mag_col)
stats[b] = stat
return pd.DataFrame.from_records(stats)


def normalize_lc(df, mag_col):
# Normalize a single light curve
min = min_mag(df, mag_col)
max = max_mag((df - min), mag_col)
lc = (df[mag_col] - min) / max
lc = lc.fillna(0)
return lc
59 changes: 31 additions & 28 deletions requirements.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -16,22 +16,22 @@ black==24.4.2
blacken-docs==1.18.0
bleach==6.1.0
blosc2==2.5.1
boltons @ file:///home/conda/feedstock_root/build_artifacts/boltons_1677499911949/work
Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1693583441880/work
boltons==23.0.0
Brotli==1.1.0
ceci==1.10.1
certifi==2023.7.22
cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1671179353105/work
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1688813409104/work
cffi==1.15.1
charset-normalizer==3.2.0
click==8.1.7
colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
colorama==0.4.6
comm==0.2.1
conda==23.3.1
conda-libmamba-solver @ file:///home/conda/feedstock_root/build_artifacts/conda-libmamba-solver_1680508672016/work/src
conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1691048088238/work
conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1691009212940/work
conda-libmamba-solver==23.3.0
conda-package-handling==2.2.0
conda_package_streaming==0.9.0
contourpy==1.2.0
coverage==7.5.4
cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1691444160254/work
cryptography==41.0.3
cycler==0.12.1
debugpy==1.8.1
decorator==5.1.1
Expand All @@ -46,7 +46,7 @@ flexcode==0.2.1
fonttools==4.49.0
fqdn==1.5.1
h5py==3.10.0
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work
idna==3.4
iniconfig==2.0.0
ipykernel==6.29.2
ipython==8.21.0
Expand All @@ -58,26 +58,26 @@ jedi==0.19.1
Jinja2==3.1.2
joblib==1.3.2
json5==0.9.14
jsonpatch @ file:///home/conda/feedstock_root/build_artifacts/jsonpatch_1632759296524/work
jsonpatch==1.32
jsonpointer==2.0
jsonschema==4.19.1
jsonschema-specifications==2023.7.1
jupyter==1.0.0
jupyter_client==8.6.0
jupyter-console==6.6.3
jupyter_core==5.7.1
jupyter-events==0.8.0
jupyter-lsp==2.2.0
jupyter_client==8.6.0
jupyter_core==5.7.1
jupyter_server==2.8.0
jupyter_server_terminals==0.4.4
jupyterlab==4.0.7
jupyterlab-pygments==0.2.2
jupyterlab-widgets==3.0.9
jupyterlab_server==2.25.0
jupyterlab-widgets==3.0.9
jupytext==1.16.2
kiwisolver==1.4.5
libmambapy @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1680791035685/work/libmambapy
mamba @ file:///home/conda/feedstock_root/build_artifacts/mamba-split_1680791035685/work/mamba
libmambapy==1.4.2
mamba==1.4.2
markdown-it-py==3.0.0
MarkupSafe==2.1.3
matplotlib==3.8.3
Expand Down Expand Up @@ -108,6 +108,7 @@ pathspec==0.12.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==10.1.0
pip==23.2.1
platformdirs==4.2.0
pluggy==1.5.0
prometheus-client==0.17.1
Expand All @@ -118,15 +119,15 @@ pure-eval==0.2.2
py-cpuinfo==9.0.0
pyarrow==15.0.0
pycodestyle==2.12.0
pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1666836542287/work
pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work
pycosat==0.6.4
pycparser==2.21
pyerfa==2.0.1.1
pyflakes==3.2.0
Pygments==2.17.2
pylint==3.2.5
pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1685514481738/work
pyOpenSSL==23.2.0
pyparsing==3.1.1
PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work
PySocks==1.7.1
pytest==8.2.2
pytest-cov==5.0.0
python-dateutil==2.8.2
Expand All @@ -136,24 +137,25 @@ pyupgrade==3.16.0
PyWavelets==1.5.0
PyYAML==6.0.1
pyzmq==25.1.2
pz-rail @ file:///home/iago/rail
pz-rail==1.0.0
pz-rail-base==0.1.9
pz-rail-flexzboost==0.1.4
qp-flexzboost==0.1.2
qp-prob==0.8.5
qtconsole==5.4.4
QtPy==2.4.0
referencing==0.30.2
requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1684774241324/work
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.10.6
ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1686993901728/work
ruamel.yaml.clib @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml.clib_1670412719074/work
ruamel.yaml==0.17.32
ruamel.yaml.clib==0.2.7
ruff==0.5.0
scikit-learn==1.4.1.post1
scipy==1.12.0
Send2Trash==1.8.2
setuptools==68.1.2
six==1.16.0
sniffio==1.3.0
soupsieve==2.5
Expand All @@ -166,20 +168,21 @@ tinycss2==1.2.1
tokenize-rt==5.2.0
tomli==2.0.1
tomlkit==0.12.5
toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work
toolz==0.12.0
tornado==6.4
tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1691671248568/work
tqdm==4.66.1
traitlets==5.14.1
types-python-dateutil==2.8.19.14
typing_extensions==4.8.0
tzdata==2024.1
uri-template==1.3.0
urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1689789803562/work
urllib3==2.0.4
wcwidth==0.2.13
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.4
wheel==0.41.2
widgetsnbextension==4.0.9
wrapt==1.16.0
xgboost==2.0.3
zstandard @ file:///home/conda/feedstock_root/build_artifacts/zstandard_1667296087208/work
zstandard==0.19.0
84 changes: 82 additions & 2 deletions tests/test_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@

import pandas as pd
import pytest
import pandas.testing as pdt

def test_max_mag_strings():
# Test for TypeError when passing a string
Expand Down Expand Up @@ -44,14 +45,93 @@ def test_max_mag(test_df, test_colname, expected):
"a",
pytest.approx(3.66,0.1)),
(pd.DataFrame(data=[[0, 0, 0],
[0, 1, 0],
[0, 0, 0],
[0, 0, 0]],
columns=list("abc")),
"b",
1/3),
0),
])

def test_mean_mag(test_df, test_colname, expected):
from lcanalyzer.models import mean_mag
assert mean_mag(test_df, test_colname) == expected



test_cols = list('abc')
test_dict = {}
test_dict["df0"] = pd.DataFrame(
data=[[8, 8, 0],
[0, 1, 1],
[2, 3, 1],
[7, 9, 7]], columns=test_cols
)
test_dict["df1"] = pd.DataFrame(
data=[[3, 8, 2],
[3, 8, 0],
[3, 9, 8],
[8, 2, 5]], columns=test_cols
)
test_dict["df2"] = pd.DataFrame(
data=[[8, 4, 3],
[7, 6, 3],
[4, 2, 9],
[6, 4, 0]], columns=test_cols
)

@pytest.mark.parametrize(
"test_lc, test_bands, test_mag_col, expected",
[
(
test_dict,
['df0', 'df1', 'df2'],
'b',
pd.DataFrame(data=[[9, 9, 6],
[5.25, 6.75, 4.0],
[1, 2, 2]],
columns=['df0', 'df1', 'df2'],
index=['max', 'mean', 'min']).astype('float64')
)
]
)


def test_calc_stats(test_lc, test_bands, test_mag_col, expected):
from lcanalyzer.models import calc_stats
pdt.assert_frame_equal(calc_stats(test_lc, test_bands, 'b'),
expected,
check_exact=False,
atol=0.01)

# Parametrization for normalize_lc function testing
@pytest.mark.parametrize(
"test_input_df, test_input_colname, expected",
[
(pd.DataFrame(data=[[8, 9, 1],
[1, 4, 1],
[1, 2, 4],
[1, 4, 1]],
columns=list("abc")),
"b",
pd.Series(data=[1,0.285,0,0.285])),
(pd.DataFrame(data=[[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]],
columns=list("abc")),
"b",
pd.Series(data=[0.,0.,0.,0.])),
(pd.DataFrame(data=[[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]],
columns=list("abc")),
"b",
pd.Series(data=[0.,0.,0.,0.])),
])
def test_normalize_lc(test_input_df, test_input_colname, expected):
"""Test how normalize_lc function works for arrays of positive integers."""
from lcanalyzer.models import normalize_lc
import pandas.testing as pdt
pdt.assert_series_equal(normalize_lc(test_input_df,test_input_colname),expected,check_exact=False,atol=0.01,check_names=False)

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