using for loop to install conda package

.CondaPkg/env/Lib/site-packages/skimage/data/README.txt

@@ -0,0 +1,9 @@
+This directory contains sample data from scikit-image.
+
+By default, it only contains a small subset of the entire dataset.
+
+The full detaset can be downloaded by using the following commands from
+a python console.
+
+  >>> from skimage.data import download_all
+  >>> download_all()

.CondaPkg/env/Lib/site-packages/skimage/data/__init__.py

@@ -0,0 +1,3 @@
+import lazy_loader as lazy
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

.CondaPkg/env/Lib/site-packages/skimage/data/__init__.pyi

@@ -0,0 +1,91 @@
+__all__ = [
+    'astronaut',
+    'binary_blobs',
+    'brain',
+    'brick',
+    'camera',
+    'cat',
+    'cell',
+    'cells3d',
+    'checkerboard',
+    'chelsea',
+    'clock',
+    'coffee',
+    'coins',
+    'colorwheel',
+    'create_image_fetcher',
+    'data_dir',
+    'download_all',
+    'eagle',
+    'file_hash',
+    'grass',
+    'gravel',
+    'horse',
+    'hubble_deep_field',
+    'human_mitosis',
+    'image_fetcher',
+    'immunohistochemistry',
+    'kidney',
+    'lbp_frontal_face_cascade_filename',
+    'lfw_subset',
+    'lily',
+    'logo',
+    'microaneurysms',
+    'moon',
+    'nickel_solidification',
+    'page',
+    'protein_transport',
+    'retina',
+    'rocket',
+    'shepp_logan_phantom',
+    'skin',
+    'stereo_motorcycle',
+    'text',
+    'vortex',
+]
+
+from ._binary_blobs import binary_blobs
+from ._fetchers import (
+    astronaut,
+    brain,
+    brick,
+    camera,
+    cat,
+    cell,
+    cells3d,
+    checkerboard,
+    chelsea,
+    clock,
+    coffee,
+    coins,
+    colorwheel,
+    create_image_fetcher,
+    data_dir,
+    download_all,
+    eagle,
+    file_hash,
+    grass,
+    gravel,
+    horse,
+    hubble_deep_field,
+    human_mitosis,
+    image_fetcher,
+    immunohistochemistry,
+    kidney,
+    lbp_frontal_face_cascade_filename,
+    lfw_subset,
+    lily,
+    logo,
+    microaneurysms,
+    moon,
+    nickel_solidification,
+    page,
+    protein_transport,
+    retina,
+    rocket,
+    shepp_logan_phantom,
+    skin,
+    stereo_motorcycle,
+    text,
+    vortex,
+)

.CondaPkg/env/Lib/site-packages/skimage/data/_binary_blobs.py

@@ -0,0 +1,63 @@
+import numpy as np
+
+from .._shared.filters import gaussian
+
+
+def binary_blobs(length=512, blob_size_fraction=0.1, n_dim=2,
+                 volume_fraction=0.5, seed=None):
+    """
+    Generate synthetic binary image with several rounded blob-like objects.
+
+    Parameters
+    ----------
+    length : int, optional
+        Linear size of output image.
+    blob_size_fraction : float, optional
+        Typical linear size of blob, as a fraction of ``length``, should be
+        smaller than 1.
+    n_dim : int, optional
+        Number of dimensions of output image.
+    volume_fraction : float, default 0.5
+        Fraction of image pixels covered by the blobs (where the output is 1).
+        Should be in [0, 1].
+    seed : {None, int, `numpy.random.Generator`}, optional
+        If `seed` is None the `numpy.random.Generator` singleton is used.
+        If `seed` is an int, a new ``Generator`` instance is used,
+        seeded with `seed`.
+        If `seed` is already a ``Generator`` instance then that instance is
+        used.
+
+    Returns
+    -------
+    blobs : ndarray of bools
+        Output binary image
+
+    Examples
+    --------
+    >>> from skimage import data
+    >>> data.binary_blobs(length=5, blob_size_fraction=0.2)  # doctest: +SKIP
+    array([[ True, False,  True,  True,  True],
+           [ True,  True,  True, False,  True],
+           [False,  True, False,  True,  True],
+           [ True, False, False,  True,  True],
+           [ True, False, False, False,  True]])
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.1)
+    >>> # Finer structures
+    >>> blobs = data.binary_blobs(length=256, blob_size_fraction=0.05)
+    >>> # Blobs cover a smaller volume fraction of the image
+    >>> blobs = data.binary_blobs(length=256, volume_fraction=0.3)
+
+    """
+    # filters is quite an expensive import since it imports all of scipy.signal
+    # We lazy import here
+
+    rs = np.random.default_rng(seed)
+    shape = tuple([length] * n_dim)
+    mask = np.zeros(shape)
+    n_pts = max(int(1. / blob_size_fraction) ** n_dim, 1)
+    points = (length * rs.random((n_dim, n_pts))).astype(int)
+    mask[tuple(indices for indices in points)] = 1
+    mask = gaussian(mask, sigma=0.25 * length * blob_size_fraction,
+                    preserve_range=False)
+    threshold = np.percentile(mask, 100 * (1 - volume_fraction))
+    return np.logical_not(mask < threshold)

.CondaPkg/env/Lib/site-packages/skimage/data/_registry.py

@@ -0,0 +1,186 @@
+# flake8: noqa
+
+# This minimal dataset was available as part of
+# scikit-image 0.15 and will be retained until
+# further notice.
+# Testing data and additional datasets should only
+# be made available by pooch
+legacy_datasets = [
+    'astronaut.png',
+    'brick.png',
+    'camera.png',
+    'chessboard_GRAY.png',
+    'chessboard_RGB.png',
+    'chelsea.png',
+    'clock_motion.png',
+    'coffee.png',
+    'coins.png',
+    'color.png',
+    'cell.png',
+    'grass.png',
+    'gravel.png',
+    'horse.png',
+    'hubble_deep_field.jpg',
+    'ihc.png',
+    'lbpcascade_frontalface_opencv.xml',
+    'lfw_subset.npy',
+    'logo.png',
+    'microaneurysms.png',
+    'moon.png',
+    'page.png',
+    'text.png',
+    'retina.jpg',
+    'rocket.jpg',
+    'phantom.png',
+    'motorcycle_disp.npz',
+    'motorcycle_left.png',
+    'motorcycle_right.png',
+]
+
+# Registry of datafiles that can be downloaded along with their SHA256 hashes
+# To generate the SHA256 hash, use the command
+# openssl sha256 filename
+registry = {
+    "color/tests/data/lab_array_a_10.npy": "a3ef76f1530e374f9121020f1f220bc89767dc866f4bbd1b1f47e5b84891a38c",
+    "color/tests/data/lab_array_a_2.npy": "793d5981cbffceb14b5fb589f998a2b1acdb5ff9c14d364c8e9e8bd45a80b275",
+    "color/tests/data/lab_array_a_r.npy": "3d3613da109d0c87827525fc49b58111aefc12438fa6426654979f66807b9227",
+    "color/tests/data/lab_array_b_10.npy": "e8d648b28077c1bfcef55ec6dc8679819612b56a01647f8c0a78625bb06f99b6",
+    "color/tests/data/lab_array_b_2.npy": "da9c6aa99e4ab3af8ec3107bbf11647cc483a0760285dd5c9fb66988be393ca1",
+    "color/tests/data/lab_array_b_r.npy": "d9eee96f4d65a2fbba82039508aac8c18304752ee8e33233e2a013e65bb91464",
+    "color/tests/data/lab_array_c_10.npy": "88b4ff2a2d2c4f48e7bb265609221d4b9ef439a4e2d8a86989696bfdb47790e6",
+    "color/tests/data/lab_array_c_2.npy": "e1b8acfdc7284ab9cd339de66948134304073b6f734ecf9ad42f8297b83d3405",
+    "color/tests/data/lab_array_c_r.npy": "09ffba2ed69e467864fea883493cd2d2706da028433464e3e858a8086842867e",
+    "color/tests/data/lab_array_d50_10.npy": "42e2ff26cb10e2a98fcf1bc06c2483302ff4fabf971fe8d49b530f490b5d24c7",
+    "color/tests/data/lab_array_d50_2.npy": "4aa03b7018ff7276643d3c082123cf07304f9d8d898ae92a5756a86955de4faf",
+    "color/tests/data/lab_array_d50_r.npy": "57db02009f9a68dade33ce1ecffead0418d8ac8113b2a589fc02a20e6bf7e799",
+    "color/tests/data/lab_array_d55_10.npy": "ab4f21368b6d8351578ab093381c44b49ae87a6b7f25c11aa094b07f215eed7d",
+    "color/tests/data/lab_array_d55_2.npy": "0319723de4632a252bae828b7c96d038fb075a7df05beadfbad653da05efe372",
+    "color/tests/data/lab_array_d55_r.npy": "060ebc446f7b4da4df58a60f0006133dbca735da87ba61854f4a75d28db67a3a",
+    "color/tests/data/lab_array_d65_10.npy": "5cb9e9c384d2577aaf8b7d2d21ff5b505708b80605a2f59d10e89d22c3d308d2",
+    "color/tests/data/lab_array_d65_2.npy": "16e847160f7ba4f19806d8194ed44a6654c9367e5a2cb240aa6e7eece44a6649",
+    "color/tests/data/lab_array_d65_r.npy": "82d0dd7a46741f627b8868793e64cdc2f9944fe1e049b573f752a93760a1577c",
+    "color/tests/data/lab_array_d75_10.npy": "c2d3de5422c785c925926b0c6223aeaf50b9393619d1c30830190d433606cbe1",
+    "color/tests/data/lab_array_d75_2.npy": "c94d53da398d36e076471ff7e0dafcaffc64ce4ba33b4d04849c32d19c87494a",
+    "color/tests/data/lab_array_e_2.npy": "ac05f17a83961b020ceccbdd46bddc86943d43e678dabcc898caf4a1e4be6165",
+    "color/tests/data/luv_array_a_10.npy": "c8af67f9fd64a6e9c610ac0c12c5315a49ca229363f048e5d851409d4a3ae5b6",
+    "color/tests/data/luv_array_a_2.npy": "eaf05dc61f4a70ece367d5e751a14d42b7c397c7b1c2df4cfecec9ddf26e1c1a",
+    "color/tests/data/luv_array_a_r.npy": "2c0891add787ec757601f9c61ad14dd9621dd969af4e32753f2e64df437081b7",
+    "color/tests/data/luv_array_b_10.npy": "a5407736b8a43071139ca178d12cdf930f32f52a0644f0b13f89d8895c8b43db",
+    "color/tests/data/luv_array_b_2.npy": "8e74173d54dc549b6c0ebd1f1d70489d2905cad87744e41ed74384f21f22986d",
+    "color/tests/data/luv_array_b_r.npy": "0a74c41df369cbb5fc0a00c16d60dc6f946ebf144bc5e506545b0d160fa53dfa",
+    "color/tests/data/luv_array_c_10.npy": "3a5f975ffa57f69a1be9e02b153e8161f83040ce3002ea1b0a05b9fbdd0d8ec4",
+    "color/tests/data/luv_array_c_2.npy": "32506cd50ea2181997cb88d3511e275740e8151d6c693cd178f5eafd8b0c6e47",
+    "color/tests/data/luv_array_c_r.npy": "c0fbf98cc0e62ed426ab4d228986d6660089444a7bbfcc64cbb1c632644067bb",
+    "color/tests/data/luv_array_d50_10.npy": "fe223db556222ce3a59198bed3a3324c2c719b8083fb84dc5b00f214b4773b16",
+    "color/tests/data/luv_array_d50_2.npy": "48e8989048904bdf2c3c1ada265c1c29c5eff60f02f848a25cde622982c84901",
+    "color/tests/data/luv_array_d50_r.npy": "f93f0def9c93f872dd10ce4a91fdb3f06eea61ddb6e72387b7669909827d4f9c",
+    "color/tests/data/luv_array_d55_10.npy": "d88d53d2bad230c2331442187712ec52ffdee62bf0f60b200c33411bfed76c60",
+    "color/tests/data/luv_array_d55_2.npy": "c761b40475df591ae9c0475d54ef712d067190ca4652efc6308b69080a652061",
+    "color/tests/data/luv_array_d55_r.npy": "05fbd57e3602ee4d5202b9f18f9b5fc05b545891a9b4456d2a88aa798a5a774a",
+    "color/tests/data/luv_array_d65_10.npy": "41a5452ffac4d31dd579d9528e725432c60d77b5f505d801898d9401429c89bf",
+    "color/tests/data/luv_array_d65_2.npy": "962ce180132c6c11798cbc423b2b204d1d10187670f6eb5dec1058eaad301e0e",
+    "color/tests/data/luv_array_d65_r.npy": "78db8c19af26dd802ce98b039a33855f7c8d6a103a2721d094b1d9c619717449",
+    "color/tests/data/luv_array_d75_10.npy": "e1cc70d56eb6789633d4c2a4059b9533f616a7c8592c9bd342403e41d72f45e4",
+    "color/tests/data/luv_array_d75_2.npy": "07db3bd59bd89de8e5ff62dad786fe5f4b299133495ba9bea30495b375133a98",
+    "color/tests/data/luv_array_e_2.npy": "41b1037d81b267305ffe9e8e97e0affa9fa54b18e60413b01b8f11861cb32213",
+    "color/tests/ciede2000_test_data.txt": "2e005c6f76ddfb7bbcc8f68490f1f7b4b4a2a4b06b36a80c985677a2799c0e40",
+    "data/astronaut.png": "88431cd9653ccd539741b555fb0a46b61558b301d4110412b5bc28b5e3ea6cb5",
+    "data/brick.png": "7966caf324f6ba843118d98f7a07746d22f6a343430add0233eca5f6eaaa8fcf",
+    "data/cell.png": "8d23a7fb81f7cc877cd09f330357fc7f595651306e84e17252f6e0a1b3f61515",
+    "data/camera.png": "b0793d2adda0fa6ae899c03989482bff9a42d3d5690fc7e3648f2795d730c23a",
+    "data/chessboard_GRAY.png": "3e51870774515af4d07d820bd8827364c70839bf9b573c746e485095e893df90",
+    "data/chessboard_RGB.png": "1ac01eff2d4e50f4eda55a2ddecdc28a6576623a58d7a7ef84513c5cc19a0331",
+    "data/chelsea.png": "596aa1e7cb875eb79f437e310381d26b338a81c2da23439704a73c4651e8c4bb",
+    "data/clock_motion.png": "f029226b28b642e80113d86622e9b215ee067a0966feaf5e60604a1e05733955",
+    "data/coffee.png": "cc02f8ca188b167c775a7101b5d767d1e71792cf762c33d6fa15a4599b5a8de7",
+    "data/coins.png": "f8d773fc9cfa6f4d8e5942dc34d0a0788fcaed2a4fefbbed0aef5398d7ef4cba",
+    "data/color.png": "7d2df993de2b4fa2a78e04e5df8050f49a9c511aa75e59ab3bd56ac9c98aef7e",
+    "data/eagle.png": "baba13e43e566c711023c0646ec614924a1ad854b57e70807f7e89d7ba86a9cf",
+    "data/horse.png": "c7fb60789fe394c485f842291ea3b21e50d140f39d6dcb5fb9917cc178225455",
+    "data/grass.png": "b6b6022426b38936c43a4ac09635cd78af074e90f42ffa8227ac8b7452d39f89",
+    "data/hubble_deep_field.jpg": "3a19c5dd8a927a9334bb1229a6d63711b1c0c767fb27e2286e7c84a3e2c2f5f4",
+    "data/ihc.png": "f8dd1aa387ddd1f49d8ad13b50921b237df8e9b262606d258770687b0ef93cef",
+    "data/logo.png": "f2c57fe8af089f08b5ba523d95573c26e62904ac5967f4c8851b27d033690168",
+    "data/lfw_subset.npy": "9560ec2f5edfac01973f63a8a99d00053fecd11e21877e18038fbe500f8e872c",
+    "data/microaneurysms.png": "a1e1be59aa447f8ce082f7fa809997ab369a2b137cb6c4202abc647c7ccf6456",
+    "data/moon.png": "78739619d11f7eb9c165bb5d2efd4772cee557812ec847532dbb1d92ef71f577",
+    "data/motorcycle_left.png": "db18e9c4157617403c3537a6ba355dfeafe9a7eabb6b9b94cb33f6525dd49179",
+    "data/motorcycle_right.png": "5fc913ae870e42a4b662314bc904d1786bcad8e2f0b9b67dba5a229406357797",
+    "data/motorcycle_disp.npz": "2e49c8cebff3fa20359a0cc6880c82e1c03bbb106da81a177218281bc2f113d7",
+    "data/mssim_matlab_output.npz": "cc11a14bfa040c75b02db32282439f2e2e3e96779196c171498afaa70528ed7a",
+    "data/page.png": "341a6f0a61557662b02734a9b6e56ec33a915b2c41886b97509dedf2a43b47a3",
+    "data/phantom.png": "552ff698167aa402cceb17981130607a228a0a0aa7c519299eaa4d5f301ba36c",
+    "data/retina.jpg": "38a07f36f27f095e818aea7b96d34202c05176d30253c66733f2e00379e9e0e6",
+    "data/rocket.jpg": "c2dd0de7c538df8d111e479619b129464d0269d0ae5fd18ca91d33a7fdfea95c",
+    "data/gravel.png": "c48615b451bf1e606fbd72c0aa9f8cc0f068ab7111ef7d93bb9b0f2586440c12",
+    "data/text.png": "bd84aa3a6e3c9887850d45d606c96b2e59433fbef50338570b63c319e668e6d1",
+    "data/chessboard_GRAY_U16.tif": "9fd3392c5b6cbc5f686d8ff83eb57ef91d038ee0852ac26817e5ac99df4c7f45",
+    "data/chessboard_GRAY_U16B.tif": "b0a9270751f0fc340c90b8b615b62b88187b9ab5995942717566735d523cddb2",
+    "data/chessboard_GRAY_U8.npy": "71f394694b721e8a33760a355b3666c9b7d7fc1188ff96b3cd23c2a1d73a38d8",
+    "data/lbpcascade_frontalface_opencv.xml": "03097789a3dcbb0e40d20b9ef82537dbc3b670b6a7f2268d735470f22e003a91",
+    "data/astronaut_GRAY_hog_L1.npy": "5d8ab22b166d1dd49c12caeff9d178ed28132efea3852b952e9d75f7f7f94954",
+    "data/astronaut_GRAY_hog_L2-Hys.npy": "c4dd6e50d1129aada358311cf8880ce8c775f31e0e550fc322c16e43a96d56fe",
+    "data/rank_filter_tests.npz": "efaf5699630f4a53255e91681dc72a965acd4a8aa1f84671c686fb93e7df046d",
+    "data/rank_filters_tests_3d.npz": "1741c2b978424e93558a07d345b2a0d9bfbb33c095c123da147fca066714ab16",
+    "data/multi.fits": "5c71a83436762a52b1925f2f0d83881af7765ed50aede155af2800e54bbd5040",
+    "data/simple.fits": "cd36087fdbb909b6ba506bbff6bcd4c5f4da3a41862608fbac5e8555ef53d40f",
+    "data/palette_color.png": "c4e817035fb9f7730fe95cff1da3866dea01728efc72b6e703d78f7ab9717bdd",
+    "data/palette_gray.png": "bace7f73783bf3ab3b7fdaf701707e4fa09f0dbd0ea72cf5b12ddc73d50b02a9",
+    "data/green_palette.png": "42d49d94be8f9bc76e50639d3701ed0484258721f6b0bd7f50bb1b9274a010f0",
+    "data/truncated.jpg": "4c226038acc78012d335efba29c6119a24444a886842182b7e18db378f4a557d",
+    "data/multipage.tif": "4da0ad0d3df4807a9847247d1b5e565b50d46481f643afb5c37c14802c78130f",
+    "data/multipage_rgb.tif": "1d23b844fd38dce0e2d06f30432817cdb85e52070d8f5460a2ba58aebf34a0de",
+    "data/no_time_for_that_tiny.gif": "20abe94ba9e45f18de416c5fbef8d1f57a499600be40f9a200fae246010eefce",
+    "data/foo3x5x4indexed.png": "48a64c25c6da000ffdb5fcc34ebafe9ba3b1c9b61d7984ea7ca6dc54f9312dfa",
+    "data/mssim_matlab_output.npz": "cc11a14bfa040c75b02db32282439f2e2e3e96779196c171498afaa70528ed7a",
+    "data/gray_morph_output.npz": "3012eb994e864e1dca1f66fada6b4375f84eac63658d049886b710488c2394d1",
+    "data/disk-matlab-output.npz": "8a39d5c866f6216d6a9c9166312aa4bbf4d18fab3d0dcd963c024985bde5856b",
+    "data/diamond-matlab-output.npz": "02fca68907e2b252b501dfe977eef71ae39fadaaa3702ebdc855195422ae1cc2",
+    "data/bw_text.png": "308c2b09f8975a69b212e103b18520e8cbb7a4eccfce0f757836cd371f1b9094",
+    "data/bw_text_skeleton.npy": "9ff4fc23c6a01497d7987f14e3a97cbcc39cce54b2b3b7ee33b84c1b661d0ae1",
+    "data/_blobs_3d_fiji_skeleton.tif": "e3449ad9819425959952050c147278555e5ffe1c2c4a30df29f6a1f9023e10c3",
+    "data/checker_bilevel.png": "2e207e486545874a2a3e69ba653b28fdef923157be9017559540e65d1bcb8e28",
+    "restoration/tests/astronaut_rl.npy":
+        "3f8373e2c6182a89366e51cef6624e3625deac75fdda1079cbdad2a33322152c",
+    "restoration/tests/camera_rl.npy": "fd4f59af84dd471fbbe79ee70c1b7e68a69864c461f0db5ac587e7975363f78f",
+    "restoration/tests/camera_unsup.npy": "3de10a0b97267352b18886b25d66a967f9e1d78ada61050577d78586cab82baa",
+    "restoration/tests/camera_unsup2.npy": "29cdc60605eb528c5f014baa8564d7d1ba0bd4b3170a66522058cbe5aed0960b",
+    "restoration/tests/camera_wiener.npy": "4505ea8b0d63d03250c6d756560d615751b76dd6ffc4a95972fa260c0c84633e",
+    "registration/tests/data/OriginalX-130Y130.png": "bf24a06d99ae131c97e582ef5e1cd0c648a8dad0caab31281f3564045492811f",
+    "registration/tests/data/OriginalX130Y130.png": "7fdd4c06d504fec35ee0703bd7ed2c08830b075a74c8506bae4a70d682f5a2db",
+    "registration/tests/data/OriginalX75Y75.png": "c5cd58893c93140df02896df80b13ecf432f5c86eeaaf8fb311aec52a65c7016",
+    "registration/tests/data/TransformedX-130Y130.png": "1cda90ed69c921eb7605b73b76d141cf4ea03fb8ce3336445ca08080e40d7375",
+    "registration/tests/data/TransformedX130Y130.png": "bb10c6ae3f91a313b0ac543efdb7ca69c4b95e55674c65a88472a6c4f4692a25",
+    "registration/tests/data/TransformedX75Y75.png": "a1e9ead5f8e4a0f604271e1f9c50e89baf53f068f1d19fab2876af4938e695ea",
+    "data/brain.tiff": "bcdbaf424fbad7b1fb0f855f608c68e5a838f35affc323ff04ea17f678eef5c6",
+    "data/cells3d.tif": "afc7c7d80d38bfde09788b4064ac1e64ec14e88454ab785ebdc8dbba5ca3b222",
+    "data/kidney.tif": "80c0799bc58b08cf6eaa53ecd202305eb42fd7bc73746cb6c5064dbeae7e8476",
+    "data/lily.tif": "395c2f0194c25b9824a8cd79266920362a0816bc9e906dd392adce2d8309af03",
+    "data/mitosis.tif": "2751ba667c4067c5d30817cff004aa06f6f6287f1cdbb5b8c9c6a500308cb456",
+    "data/skin.jpg": "8759fe080509712163453f4b17106582b8513e73b0788d80160abf840e272075",
+    "data/pivchallenge-B-B001_1.tif": "e95e09abbcecba723df283ac7d361766328abd943701a2ec2f345d4a2014da2a",
+    "data/pivchallenge-B-B001_2.tif": "4ceb5407e4e333476a0f264c14b7a3f6c0e753fcdc99ee1c4b8196e5f823805e",
+    "data/protein_transport.tif": "a8e24e8d187f33e92ee28508d5615286c850ca75374af7e74e527d290e8b06ea",
+    "data/solidification.tif": "50ef9a52c621b7c0c506ad1fe1b8ee8a158a4d7c8e50ddfce1e273a422dca3f9",
+}
+
+registry_urls = {
+    "data/brain.tiff": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/brain.tiff",
+    "data/cells3d.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/cells3d.tif",
+    "data/eagle.png": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/eagle.png",
+    "data/kidney.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/kidney-tissue-fluorescence.tif",
+    "data/lily.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/lily-of-the-valley-fluorescence.tif",
+    "data/mitosis.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/AS_09125_050116030001_D03f00d0.tif",
+    "data/rank_filters_tests_3d.npz": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/Tests_besides_Equalize_Otsu/add18_entropy/rank_filters_tests_3d.npz",
+    "data/skin.jpg": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/Normal_Epidermis_and_Dermis_with_Intradermal_Nevus_10x.JPG",
+    "data/pivchallenge-B-B001_1.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/pivchallenge/B/B001_1.tif",
+    "data/pivchallenge-B-B001_2.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/pivchallenge/B/B001_2.tif",
+    "data/protein_transport.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/NPCsingleNucleus.tif",
+    "data/solidification.tif": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/nickel_solidification.tif",
+    "restoration/tests/astronaut_rl.npy": "https://gitlab.com/scikit-image/data/-/raw/2cdc5ce89b334d28f06a58c9f0ca21aa6992a5ba/astronaut_rl.npy",
+}
+
+legacy_registry = {
+    ('data/' + filename): registry['data/' + filename]
+    for filename in legacy_datasets
+}

.CondaPkg/env/Lib/site-packages/skimage/data/tests/test_data.py

@@ -0,0 +1,209 @@
+import numpy as np
+import skimage.data as data
+from skimage.data._fetchers import image_fetcher
+from skimage import io
+from skimage._shared.testing import assert_equal, assert_almost_equal, fetch
+import os
+import pytest
+
+
+def test_data_dir():
+    # data_dir should be a directory people can use as a standard directory
+    # https://github.com/scikit-image/scikit-image/pull/3945#issuecomment-498141893
+    data_dir = data.data_dir
+    assert 'astronaut.png' in os.listdir(data_dir)
+
+
+def test_download_all_with_pooch():
+    # jni first wrote this test with the intention of
+    # fully deleting the files in the data_dir,
+    # then ensure that the data gets downloaded accordingly.
+    # hmaarrfk raised the concern that this test wouldn't
+    # play well with parallel testing since we
+    # may be breaking the global state that certain other
+    # tests require, especially in parallel testing
+
+    # The second concern is that this test essentially uses
+    # a lot of bandwidth, which is not fun for developers on
+    # lower speed connections.
+    # https://github.com/scikit-image/scikit-image/pull/4666/files/26d5138b25b958da6e97ebf979e9bc36f32c3568#r422604863
+    data_dir = data.data_dir
+    if image_fetcher is not None:
+        data.download_all()
+        assert len(os.listdir(data_dir)) > 50
+    else:
+        with pytest.raises(ModuleNotFoundError):
+            data.download_all()
+
+
+def test_astronaut():
+    """ Test that "astronaut" image can be loaded. """
+    astronaut = data.astronaut()
+    assert_equal(astronaut.shape, (512, 512, 3))
+
+
+def test_camera():
+    """ Test that "camera" image can be loaded. """
+    cameraman = data.camera()
+    assert_equal(cameraman.ndim, 2)
+
+
+def test_checkerboard():
+    """ Test that "checkerboard" image can be loaded. """
+    data.checkerboard()
+
+
+def test_chelsea():
+    """ Test that "chelsea" image can be loaded. """
+    data.chelsea()
+
+
+def test_clock():
+    """ Test that "clock" image can be loaded. """
+    data.clock()
+
+
+def test_coffee():
+    """ Test that "coffee" image can be loaded. """
+    data.coffee()
+
+
+def test_eagle():
+    """ Test that "eagle" image can be loaded. """
+    # Fetching the data through the testing module will
+    # cause the test to skip if pooch isn't installed.
+    fetch('data/eagle.png')
+    eagle = data.eagle()
+    assert_equal(eagle.ndim, 2)
+    assert_equal(eagle.dtype, np.dtype('uint8'))
+
+
+def test_horse():
+    """ Test that "horse" image can be loaded. """
+    horse = data.horse()
+    assert_equal(horse.ndim, 2)
+    assert_equal(horse.dtype, np.dtype('bool'))
+
+
+def test_hubble():
+    """ Test that "Hubble" image can be loaded. """
+    data.hubble_deep_field()
+
+
+def test_immunohistochemistry():
+    """ Test that "immunohistochemistry" image can be loaded. """
+    data.immunohistochemistry()
+
+
+def test_logo():
+    """ Test that "logo" image can be loaded. """
+    logo = data.logo()
+    assert_equal(logo.ndim, 3)
+    assert_equal(logo.shape[2], 4)
+
+
+def test_moon():
+    """ Test that "moon" image can be loaded. """
+    data.moon()
+
+
+def test_page():
+    """ Test that "page" image can be loaded. """
+    data.page()
+
+
+def test_rocket():
+    """ Test that "rocket" image can be loaded. """
+    data.rocket()
+
+
+def test_text():
+    """ Test that "text" image can be loaded. """
+    data.text()
+
+
+def test_stereo_motorcycle():
+    """ Test that "stereo_motorcycle" image can be loaded. """
+    data.stereo_motorcycle()
+
+
+def test_binary_blobs():
+    blobs = data.binary_blobs(length=128)
+    assert_almost_equal(blobs.mean(), 0.5, decimal=1)
+    blobs = data.binary_blobs(length=128, volume_fraction=0.25)
+    assert_almost_equal(blobs.mean(), 0.25, decimal=1)
+    blobs = data.binary_blobs(length=32, volume_fraction=0.25, n_dim=3)
+    assert_almost_equal(blobs.mean(), 0.25, decimal=1)
+    other_realization = data.binary_blobs(length=32, volume_fraction=0.25,
+                                          n_dim=3)
+    assert not np.all(blobs == other_realization)
+
+
+def test_lfw_subset():
+    """ Test that "lfw_subset" can be loaded."""
+    data.lfw_subset()
+
+
+def test_skin():
+    """Test that "skin" image can be loaded.
+
+    Needs internet connection.
+    """
+    skin = data.skin()
+    assert skin.ndim == 3
+
+
+def test_cell():
+    """ Test that "cell" image can be loaded."""
+    data.cell()
+
+
+def test_cells3d():
+    """Needs internet connection."""
+    path = fetch('data/cells3d.tif')
+    image = io.imread(path)
+    assert image.shape == (60, 2, 256, 256)
+
+
+def test_brain_3d():
+    """Needs internet connection."""
+    path = fetch('data/brain.tiff')
+    image = io.imread(path)
+    assert image.shape == (10, 256, 256)
+
+
+def test_kidney_3d_multichannel():
+    """Test that 3D multichannel image of kidney tissue can be loaded.
+
+    Needs internet connection.
+    """
+    fetch('data/kidney.tif')
+    kidney = data.kidney()
+    assert kidney.shape == (16, 512, 512, 3)
+
+
+def test_lily_multichannel():
+    """Test that microscopy image of lily of the valley can be loaded.
+
+    Needs internet connection.
+    """
+    fetch('data/lily.tif')
+    lily = data.lily()
+    assert lily.shape == (922, 922, 4)
+
+
+def test_vortex():
+    fetch('data/pivchallenge-B-B001_1.tif')
+    fetch('data/pivchallenge-B-B001_2.tif')
+    image0, image1 = data.vortex()
+    for image in [image0, image1]:
+        assert image.shape == (512, 512)
+
+
+@pytest.mark.parametrize(
+    'function_name', ['create_image_fetcher', 'file_hash', 'image_fetcher']
+)
+def test_fetchers_are_public(function_name):
+    # Check that the following functions that are only used indirectly in the
+    # above tests are public.
+    assert hasattr(data, function_name)