using for loop to install conda package

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/__init__.py

@@ -0,0 +1,15 @@
+
+from __future__ import annotations
+from typing import List, Type 
+import numpy
+
+# list of numarray data types
+integer_types: List[Type] = [
+    numpy.int8, numpy.uint8, numpy.int16, numpy.uint16,
+    numpy.int32, numpy.uint32, numpy.int64, numpy.uint64]
+
+float_types: List[Type] = [numpy.float32, numpy.float64]
+
+complex_types: List[Type] = [numpy.complex64, numpy.complex128]
+
+types: List[Type] = integer_types + float_types

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/data/label_inputs.txt

@@ -0,0 +1,21 @@
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
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+1 0 1 1 1 0 1
+0 0 0 1 0 0 0
+1 0 0 1 0 0 1
+1 1 1 1 1 1 1
+1 0 0 1 0 0 1
+0 0 0 1 0 0 0
+1 0 1 1 1 0 1

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/data/label_results.txt

@@ -0,0 +1,294 @@
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+2 0 0 2 0 0 2
+0 0 0 2 0 0 0
+4 0 2 2 2 0 5

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/data/label_strels.txt

@@ -0,0 +1,42 @@
+0 0 1
+1 1 1
+1 0 0
+1 0 0
+1 1 1
+0 0 1
+0 0 0
+1 1 1
+0 0 0
+0 1 1
+0 1 0
+1 1 0
+0 0 0
+0 0 0
+0 0 0
+0 1 1
+1 1 1
+1 1 0
+0 1 0
+1 1 1
+0 1 0
+1 0 0
+0 1 0
+0 0 1
+0 1 0
+0 1 0
+0 1 0
+1 1 1
+1 1 1
+1 1 1
+1 1 0
+0 1 0
+0 1 1
+1 0 1
+0 1 0
+1 0 1
+0 0 1
+0 1 0
+1 0 0
+1 1 0
+1 1 1
+0 1 1

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/test_c_api.py

@@ -0,0 +1,94 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+from scipy import ndimage
+from scipy.ndimage import _ctest
+from scipy.ndimage import _cytest
+from scipy._lib._ccallback import LowLevelCallable
+
+FILTER1D_FUNCTIONS = [
+    lambda filter_size: _ctest.filter1d(filter_size),
+    lambda filter_size: _cytest.filter1d(filter_size, with_signature=False),
+    lambda filter_size: LowLevelCallable(_cytest.filter1d(filter_size, with_signature=True)),
+    lambda filter_size: LowLevelCallable.from_cython(_cytest, "_filter1d",
+                                                     _cytest.filter1d_capsule(filter_size)),
+]
+
+FILTER2D_FUNCTIONS = [
+    lambda weights: _ctest.filter2d(weights),
+    lambda weights: _cytest.filter2d(weights, with_signature=False),
+    lambda weights: LowLevelCallable(_cytest.filter2d(weights, with_signature=True)),
+    lambda weights: LowLevelCallable.from_cython(_cytest, "_filter2d", _cytest.filter2d_capsule(weights)),
+]
+
+TRANSFORM_FUNCTIONS = [
+    lambda shift: _ctest.transform(shift),
+    lambda shift: _cytest.transform(shift, with_signature=False),
+    lambda shift: LowLevelCallable(_cytest.transform(shift, with_signature=True)),
+    lambda shift: LowLevelCallable.from_cython(_cytest, "_transform", _cytest.transform_capsule(shift)),
+]
+
+
+def test_generic_filter():
+    def filter2d(footprint_elements, weights):
+        return (weights*footprint_elements).sum()
+
+    def check(j):
+        func = FILTER2D_FUNCTIONS[j]
+
+        im = np.ones((20, 20))
+        im[:10,:10] = 0
+        footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+        footprint_size = np.count_nonzero(footprint)
+        weights = np.ones(footprint_size)/footprint_size
+
+        res = ndimage.generic_filter(im, func(weights),
+                                     footprint=footprint)
+        std = ndimage.generic_filter(im, filter2d, footprint=footprint,
+                                     extra_arguments=(weights,))
+        assert_allclose(res, std, err_msg="#{} failed".format(j))
+
+    for j, func in enumerate(FILTER2D_FUNCTIONS):
+        check(j)
+
+
+def test_generic_filter1d():
+    def filter1d(input_line, output_line, filter_size):
+        for i in range(output_line.size):
+            output_line[i] = 0
+            for j in range(filter_size):
+                output_line[i] += input_line[i+j]
+        output_line /= filter_size
+
+    def check(j):
+        func = FILTER1D_FUNCTIONS[j]
+
+        im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
+        filter_size = 3
+
+        res = ndimage.generic_filter1d(im, func(filter_size),
+                                       filter_size)
+        std = ndimage.generic_filter1d(im, filter1d, filter_size,
+                                       extra_arguments=(filter_size,))
+        assert_allclose(res, std, err_msg="#{} failed".format(j))
+
+    for j, func in enumerate(FILTER1D_FUNCTIONS):
+        check(j)
+
+
+def test_geometric_transform():
+    def transform(output_coordinates, shift):
+        return output_coordinates[0] - shift, output_coordinates[1] - shift
+
+    def check(j):
+        func = TRANSFORM_FUNCTIONS[j]
+
+        im = np.arange(12).reshape(4, 3).astype(np.float64)
+        shift = 0.5
+
+        res = ndimage.geometric_transform(im, func(shift))
+        std = ndimage.geometric_transform(im, transform, extra_arguments=(shift,))
+        assert_allclose(res, std, err_msg="#{} failed".format(j))
+
+    for j, func in enumerate(TRANSFORM_FUNCTIONS):
+        check(j)

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/test_datatypes.py

@@ -0,0 +1,66 @@
+""" Testing data types for ndimage calls
+"""
+import sys
+
+import numpy as np
+from numpy.testing import assert_array_almost_equal, assert_
+import pytest
+
+from scipy import ndimage
+
+
+def test_map_coordinates_dts():
+    # check that ndimage accepts different data types for interpolation
+    data = np.array([[4, 1, 3, 2],
+                     [7, 6, 8, 5],
+                     [3, 5, 3, 6]])
+    shifted_data = np.array([[0, 0, 0, 0],
+                             [0, 4, 1, 3],
+                             [0, 7, 6, 8]])
+    idx = np.indices(data.shape)
+    dts = (np.uint8, np.uint16, np.uint32, np.uint64,
+           np.int8, np.int16, np.int32, np.int64,
+           np.intp, np.uintp, np.float32, np.float64)
+    for order in range(0, 6):
+        for data_dt in dts:
+            these_data = data.astype(data_dt)
+            for coord_dt in dts:
+                # affine mapping
+                mat = np.eye(2, dtype=coord_dt)
+                off = np.zeros((2,), dtype=coord_dt)
+                out = ndimage.affine_transform(these_data, mat, off)
+                assert_array_almost_equal(these_data, out)
+                # map coordinates
+                coords_m1 = idx.astype(coord_dt) - 1
+                coords_p10 = idx.astype(coord_dt) + 10
+                out = ndimage.map_coordinates(these_data, coords_m1, order=order)
+                assert_array_almost_equal(out, shifted_data)
+                # check constant fill works
+                out = ndimage.map_coordinates(these_data, coords_p10, order=order)
+                assert_array_almost_equal(out, np.zeros((3,4)))
+            # check shift and zoom
+            out = ndimage.shift(these_data, 1)
+            assert_array_almost_equal(out, shifted_data)
+            out = ndimage.zoom(these_data, 1)
+            assert_array_almost_equal(these_data, out)
+
+
+@pytest.mark.xfail(not sys.platform == 'darwin', reason="runs only on darwin")
+def test_uint64_max():
+    # Test interpolation respects uint64 max.  Reported to fail at least on
+    # win32 (due to the 32 bit visual C compiler using signed int64 when
+    # converting between uint64 to double) and Debian on s390x.
+    # Interpolation is always done in double precision floating point, so
+    # we use the largest uint64 value for which int(float(big)) still fits
+    # in a uint64.
+    big = 2**64 - 1025
+    arr = np.array([big, big, big], dtype=np.uint64)
+    # Tests geometric transform (map_coordinates, affine_transform)
+    inds = np.indices(arr.shape) - 0.1
+    x = ndimage.map_coordinates(arr, inds)
+    assert_(x[1] == int(float(big)))
+    assert_(x[2] == int(float(big)))
+    # Tests zoom / shift
+    x = ndimage.shift(arr, 0.1)
+    assert_(x[1] == int(float(big)))
+    assert_(x[2] == int(float(big)))

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/test_fourier.py

@@ -0,0 +1,151 @@
+import numpy
+from numpy import fft
+from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
+                           assert_equal)
+
+import pytest
+
+from scipy import ndimage
+
+
+class TestNdimageFourier:
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.float32, 6), (numpy.float64, 14)])
+    def test_fourier_gaussian_real01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.complex64, 6), (numpy.complex128, 14)])
+    def test_fourier_gaussian_complex01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.float32, 6), (numpy.float64, 14)])
+    def test_fourier_uniform_real01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.complex64, 6), (numpy.complex128, 14)])
+    def test_fourier_uniform_complex01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.float32, 4), (numpy.float64, 11)])
+    def test_fourier_shift_real01(self, shape, dtype, dec):
+        expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
+        expected.shape = shape
+        a = fft.rfft(expected, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1],
+                                  decimal=dec)
+        assert_array_almost_equal(a.imag, numpy.zeros(shape),
+                                  decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.complex64, 6), (numpy.complex128, 11)])
+    def test_fourier_shift_complex01(self, shape, dtype, dec):
+        expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
+        expected.shape = shape
+        a = fft.fft(expected, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_shift(a, [1, 1], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1],
+                                  decimal=dec)
+        assert_array_almost_equal(a.imag, numpy.zeros(shape),
+                                  decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.float32, 5), (numpy.float64, 14)])
+    def test_fourier_ellipsoid_real01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5],
+                                      shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec',
+                             [(numpy.complex64, 5), (numpy.complex128, 14)])
+    def test_fourier_ellipsoid_complex01(self, shape, dtype, dec):
+        a = numpy.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    def test_fourier_ellipsoid_unimplemented_ndim(self):
+        # arrays with ndim > 3 raise NotImplementedError
+        x = numpy.ones((4, 6, 8, 10), dtype=numpy.complex128)
+        with pytest.raises(NotImplementedError):
+            a = ndimage.fourier_ellipsoid(x, 3)
+
+    def test_fourier_ellipsoid_1d_complex(self):
+        # expected result of 1d ellipsoid is the same as for fourier_uniform
+        for shape in [(32, ), (31, )]:
+            for type_, dec in zip([numpy.complex64, numpy.complex128],
+                                  [5, 14]):
+                x = numpy.ones(shape, dtype=type_)
+                a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
+                b = ndimage.fourier_uniform(x, 5, -1, 0)
+                assert_array_almost_equal(a, b, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
+    @pytest.mark.parametrize('dtype',
+                             [numpy.float32, numpy.float64,
+                              numpy.complex64, numpy.complex128])
+    @pytest.mark.parametrize('test_func',
+                             [ndimage.fourier_ellipsoid,
+                              ndimage.fourier_gaussian,
+                              ndimage.fourier_uniform])
+    def test_fourier_zero_length_dims(self, shape, dtype, test_func):
+        a = numpy.ones(shape, dtype)
+        b = test_func(a, 3)
+        assert_equal(a, b)

.CondaPkg/env/Lib/site-packages/scipy/ndimage/tests/test_splines.py

@@ -0,0 +1,65 @@
+"""Tests for spline filtering."""
+import numpy as np
+import pytest
+
+from numpy.testing import assert_almost_equal
+
+from scipy import ndimage
+
+
+def get_spline_knot_values(order):
+    """Knot values to the right of a B-spline's center."""
+    knot_values = {0: [1],
+                   1: [1],
+                   2: [6, 1],
+                   3: [4, 1],
+                   4: [230, 76, 1],
+                   5: [66, 26, 1]}
+
+    return knot_values[order]
+
+
+def make_spline_knot_matrix(n, order, mode='mirror'):
+    """Matrix to invert to find the spline coefficients."""
+    knot_values = get_spline_knot_values(order)
+
+    matrix = np.zeros((n, n))
+    for diag, knot_value in enumerate(knot_values):
+        indices = np.arange(diag, n)
+        if diag == 0:
+            matrix[indices, indices] = knot_value
+        else:
+            matrix[indices, indices - diag] = knot_value
+            matrix[indices - diag, indices] = knot_value
+
+    knot_values_sum = knot_values[0] + 2 * sum(knot_values[1:])
+
+    if mode == 'mirror':
+        start, step = 1, 1
+    elif mode == 'reflect':
+        start, step = 0, 1
+    elif mode == 'grid-wrap':
+        start, step = -1, -1
+    else:
+        raise ValueError('unsupported mode {}'.format(mode))
+
+    for row in range(len(knot_values) - 1):
+        for idx, knot_value in enumerate(knot_values[row + 1:]):
+            matrix[row, start + step*idx] += knot_value
+            matrix[-row - 1, -start - 1 - step*idx] += knot_value
+
+    return matrix / knot_values_sum
+
+
+@pytest.mark.parametrize('order', [0, 1, 2, 3, 4, 5])
+@pytest.mark.parametrize('mode', ['mirror', 'grid-wrap', 'reflect'])
+def test_spline_filter_vs_matrix_solution(order, mode):
+    n = 100
+    eye = np.eye(n, dtype=float)
+    spline_filter_axis_0 = ndimage.spline_filter1d(eye, axis=0, order=order,
+                                                   mode=mode)
+    spline_filter_axis_1 = ndimage.spline_filter1d(eye, axis=1, order=order,
+                                                   mode=mode)
+    matrix = make_spline_knot_matrix(n, order, mode=mode)
+    assert_almost_equal(eye, np.dot(spline_filter_axis_0, matrix))
+    assert_almost_equal(eye, np.dot(spline_filter_axis_1, matrix.T))