Python numpy.nanquantile() Examples
The following are 29 code examples for showing how to use numpy.nanquantile(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
| Project: hvplot Author: holoviz File: converter.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
|
def _process_symmetric(self, symmetric, clim, check_symmetric_max):
if symmetric is not None or clim is not None:
return symmetric
if is_xarray(self.data):
# chunks mean it's lazily loaded; nanquantile will eagerly load
if self.data.chunks:
return False
data = self.data[self.z]
if is_xarray_dataarray(data):
if data.size > check_symmetric_max:
return False
else:
return False
elif self._color_dim:
data = self.data[self._color_dim]
else:
return
cmin = np.nanquantile(data, 0.05)
cmax = np.nanquantile(data, 0.95)
return bool(cmin < 0 and cmax > 0)
Example 2
| Project: recruit Author: Frank-qlu File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 3
| Project: recruit Author: Frank-qlu File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 4
| Project: recruit Author: Frank-qlu File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 5
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 6
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 7
| Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 8
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 9
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 10
| Project: GraphicDesignPatternByPython Author: Relph1119 File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 11
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 12
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 13
| Project: predictive-maintenance-using-machine-learning Author: awslabs File: test_nanfunctions.py License: Apache License 2.0 | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 14
| Project: pySINDy Author: luckystarufo File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 15
| Project: pySINDy Author: luckystarufo File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 16
| Project: pySINDy Author: luckystarufo File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 17
| Project: coffeegrindsize Author: jgagneastro File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 18
| Project: coffeegrindsize Author: jgagneastro File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 19
| Project: coffeegrindsize Author: jgagneastro File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 20
| Project: Carnets Author: holzschu File: test_quantity_non_ufuncs.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_nanquantile(self):
self.check(np.nanquantile, 0.5)
o = np.nanquantile(self.q, 50 * u.percent)
expected = np.nanquantile(self.q.value, 0.5) * u.m
assert np.all(o == expected)
Example 21
| Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 22
| Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 23
| Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda Author: PacktPublishing File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 24
| Project: twitter-stock-recommendation Author: alvarobartt File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_regression(self):
ar = np.arange(24).reshape(2, 3, 4).astype(float)
ar[0][1] = np.nan
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
np.nanpercentile(ar, q=50, axis=0))
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
np.nanpercentile(ar, q=50, axis=1))
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
np.nanpercentile(ar, q=[50], axis=1))
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
Example 25
| Project: twitter-stock-recommendation Author: alvarobartt File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_basic(self):
x = np.arange(8) * 0.5
assert_equal(np.nanquantile(x, 0), 0.)
assert_equal(np.nanquantile(x, 1), 3.5)
assert_equal(np.nanquantile(x, 0.5), 1.75)
Example 26
| Project: twitter-stock-recommendation Author: alvarobartt File: test_nanfunctions.py License: MIT License | 5 votes |
|
def test_no_p_overwrite(self):
# this is worth retesting, because quantile does not make a copy
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
p = p0.copy()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
p0 = p0.tolist()
p = p.tolist()
np.nanquantile(np.arange(100.), p, interpolation="midpoint")
assert_array_equal(p, p0)
Example 27
| Project: HiCExplorer Author: deeptools File: hicCompartmentalization.py License: GNU General Public License v3.0 | 4 votes |
|
def main(args=None):
"""
Main function to generate the polarization plot.
"""
args = parse_arguments().parse_args(args)
pc1 = pd.read_table(args.pca, header=None, sep="\t",
dtype={0: "object", 1: "Int64", 2: "Int64", 3: "float32"})
pc1 = pc1.rename(columns={0: "chr", 1: "start", 2: "end", 3: "pc1"})
if args.outliers != 0:
quantile = [args.outliers / 100, (100 - args.outliers) / 100]
boundaries = np.nanquantile(pc1['pc1'].values.astype(float), quantile)
quantiled_bins = np.linspace(boundaries[0], boundaries[1],
args.quantile)
else:
quantile = [j / (args.quantile - 1) for j in range(0, args.quantile)]
quantiled_bins = np.nanquantile(pc1['pc1'].values.astype(float),
quantile)
pc1["quantile"] = np.searchsorted(quantiled_bins,
pc1['pc1'].values.astype(float),
side="right")
pc1.loc[pc1["pc1"] == np.nan]["quantile"] = args.quantile + 1
polarization_ratio = []
output_matrices = []
labels = []
for matrix in args.obsexp_matrices:
obs_exp = hm.hiCMatrix(matrix)
name = ".".join(matrix.split("/")[-1].split(".")[0:-1])
labels.append(name)
normalised_sum_per_quantile = count_interactions(obs_exp, pc1,
args.quantile,
args.offset)
normalised_sum_per_quantile = np.nan_to_num(normalised_sum_per_quantile)
if args.outputMatrix:
output_matrices.append(normalised_sum_per_quantile)
polarization_ratio.append(within_vs_between_compartments(
normalised_sum_per_quantile,
args.quantile))
if args.outputMatrix:
np.savez(args.outputMatrix, [matrix for matrix in output_matrices])
plot_polarization_ratio(
polarization_ratio, args.outputFileName, labels, args.quantile)
Example 28
| Project: scikit-lego Author: koaning File: columncapper.py License: MIT License | 4 votes |
|
def fit(self, X, y=None):
"""
Computes the quantiles for each column of ``X``.
:type X: pandas.DataFrame or numpy.ndarray
:param X: The column(s) from which the capping limit(s) will be computed.
:param y: Ignored.
:rtype: sklego.preprocessing.ColumnCapper
:returns: The fitted object.
:raises:
``ValueError`` if ``X`` contains non-numeric columns
"""
X = check_array(
X, copy=True, force_all_finite=False, dtype=FLOAT_DTYPES, estimator=self
)
# If X contains infs, we need to replace them by nans before computing quantiles
np.putmask(X, (X == np.inf) | (X == -np.inf), np.nan)
# There should be no column containing only nan cells at this point. If that's not the case,
# it means that the user asked ColumnCapper to fit some column containing only nan or inf cells.
nans_mask = np.isnan(X)
invalid_columns_mask = (
nans_mask.sum(axis=0) == X.shape[0]
) # Contains as many nans as rows
if invalid_columns_mask.any():
raise ValueError(
"ColumnCapper cannot fit columns containing only inf/nan values"
)
q = [quantile_limit / 100 for quantile_limit in self.quantile_range]
self.quantiles_ = np.nanquantile(
a=X, q=q, axis=0, overwrite_input=True, interpolation=self.interpolation
)
# Saving the number of columns to ensure coherence between fit and transform inputs
self.n_columns_ = X.shape[1]
return self
Example 29
| Project: xclim Author: Ouranosinc File: utils.py License: Apache License 2.0 | 4 votes |
|
def map_cdf(
x: xr.DataArray,
y: xr.DataArray,
y_value: xr.DataArray,
*,
group: Union[str, Grouper] = "time",
skipna: bool = False,
):
"""Return the value in `x` with the same CDF as `y_value` in `y`.
Parameters
----------
x : xr.DataArray
Values from which to pick
y : xr.DataArray
Reference values giving the ranking
y_value : float, array
Value within the support of `y`.
dim : str
Dimension along which to compute quantile.
Returns
-------
array
Quantile of `x` with the same CDF as `y_value` in `y`.
"""
def _map_cdf_1d(x, y, y_value, skipna=False):
q = _ecdf_1d(y, y_value)
_func = np.nanquantile if skipna else np.quantile
return _func(x, q=q)
def _map_cdf_group(gr, y_value, dim=["time"], skipna=False):
return xr.apply_ufunc(
_map_cdf_1d,
gr.x,
gr.y,
input_core_dims=[dim] * 2,
output_core_dims=[["x"]],
vectorize=True,
keep_attrs=True,
kwargs={"y_value": y_value, "skipna": skipna},
dask="parallelized",
output_dtypes=[gr.x.dtype],
)
return group.apply(
_map_cdf_group, {"x": x, "y": y}, y_value=np.atleast_1d(y_value), skipna=skipna,
)
