code stringlengths 17 6.64M |
|---|
class DeepFM(BaseModel):
'Instantiates the DeepFM Network architecture.\n\n :param linear_feature_columns: An iterable containing all the features used by linear part of the model.\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param use_fm: bool,... |
class DIN(BaseModel):
'Instantiates the Deep Interest Network architecture.\n\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param history_feature_list: list,to indicate sequence sparse field\n :param dnn_use_bn: bool. Whether use BatchNormalizat... |
class FiBiNET(BaseModel):
'Instantiates the Feature Importance and Bilinear feature Interaction NETwork architecture.\n\n :param linear_feature_columns: An iterable containing all the features used by linear part of the model.\n :param dnn_feature_columns: An iterable containing all the features used by dee... |
class MLR(BaseModel):
'Instantiates the Mixed Logistic Regression/Piece-wise Linear Model.\n\n :param region_feature_columns: An iterable containing all the features used by region part of the model.\n :param base_feature_columns: An iterable containing all the features used by base part of the model.\n ... |
class NFM(BaseModel):
'Instantiates the NFM Network architecture.\n\n :param linear_feature_columns: An iterable containing all the features used by linear part of the model.\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param dnn_hidden_units: l... |
class PNN(BaseModel):
'Instantiates the Product-based Neural Network architecture.\n\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep ... |
class WDL(BaseModel):
'Instantiates the Wide&Deep Learning architecture.\n\n :param linear_feature_columns: An iterable containing all the features used by linear part of the model.\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param dnn_hidden_u... |
class xDeepFM(BaseModel):
'Instantiates the xDeepFM architecture.\n\n :param linear_feature_columns: An iterable containing all the features used by linear part of the model.\n :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.\n :param dnn_hidden_units: l... |
class EncoderBase(object):
def __init__(self):
self.trans_ls = list()
def reset(self):
self.trans_ls = list()
def check_var(self, df):
for (_, _, target, _) in self.trans_ls:
if (target not in df.columns):
raise Exception('The columns to be transforme... |
class LabelEncoder(EncoderBase):
def __init__(self):
super(LabelEncoder, self).__init__()
def fit(self, df, targets):
self.reset()
for target in targets:
unique = df[target].unique()
index = range(len(unique))
mapping = dict(zip(unique, index))
... |
class NANEncoder(EncoderBase):
def __init__(self):
warnings.warn("This is a simple application in order to perform the simpliest imputation. It is strongly suggest to use R's mice package instead. ")
super().__init__()
def fit(self, df, targets, method='simple_impute'):
self.reset()
... |
class ScaleEncoder(EncoderBase):
def __init__(self):
super().__init__()
def fit(self, df, targets, configs):
'\n :param df: the dataframe to transform\n :param targets: a list of variables to perform the scaling\n :param configs: the scaling methods\n '
fo... |
class ClusteringEncoder(EncoderBase):
'\n '
def __init__(self):
super().__init__()
def fit(self, df, targets, configs):
'\n :param df: the dataframe to train the clustering algorithm.\n :param targets: a list of list of variables.\n :param config: configurations f... |
class CategoryEncoder(EncoderBase):
def __init__(self):
super().__init__()
def fit(self, df, y, targets, configurations):
"\n\n :param df: the data frame to be fitted; can be different from the transformed ones.\n :param y: the y variable\n :param targets: the variables ... |
class DiscreteEncoder(EncoderBase):
def __init__(self):
super(DiscreteEncoder, self).__init__()
def fit(self, df, targets, configurations):
"\n\n :param df: the dataframe to be fitted; can be different from the transformed one;\n :param targets: the variables to be transformed\... |
class UnaryContinuousVarEncoder(EncoderBase):
def __init__(self):
super(UnaryContinuousVarEncoder, self).__init__()
def fit(self, targets, config):
self.reset()
for target in targets:
for (method, parameter) in config:
if (method == 'power'):
... |
class BinaryContinuousVarEncoder(EncoderBase):
def __init__(self):
super(BinaryContinuousVarEncoder, self).__init__()
def fit(self, targets_pairs, config):
for (target1, target2) in targets_pairs:
for method in config:
if (method == 'add'):
sel... |
class BoostTreeEncoder(EncoderBase):
def __init__(self, nthread=None):
super(BoostTreeEncoder, self).__init__()
if nthread:
self.nthread = cpu_count
else:
self.nthread = nthread
def fit(self, df, y, targets_list, config):
self.reset()
for (meth... |
class AnomalyScoreEncoder(EncoderBase):
def __init__(self, nthread=None):
super(AnomalyScoreEncoder, self).__init__()
if nthread:
self.nthread = cpu_count
else:
self.nthread = nthread
def fit(self, df, y, targets_list, config):
self.reset()
for... |
class GroupbyEncoder(EncoderBase):
def __init__(self):
super(GroupbyEncoder, self).__init__()
def fit(self, df, targets, groupby_op_list):
self.reset()
for target in targets:
for (groupby, operations, param) in groupby_op_list:
for operation in operations:... |
def get_interval(x, sorted_intervals):
if pd.isnull(x):
return (- 1)
if (x == np.inf):
return (- 2)
if (x == (- np.inf)):
return (- 3)
interval = 0
found = False
sorted_intervals.append(np.inf)
if ((x < sorted_intervals[0]) or (x >= sorted_intervals[(len(sorted_inte... |
def encode_label(x):
x_copy = x.copy(deep=True)
unique = sorted(list(set([str(item) for item in x_copy.astype(str).unique()])))
kv = {unique[i]: i for i in range(len(unique))}
x_copy = x_copy.map((lambda x: kv[str(x)]))
return x_copy
|
def get_uniform_interval(minimum, maximum, nbins):
result = [minimum]
step_size = (float((maximum - minimum)) / nbins)
for index in range((nbins - 1)):
result.append((minimum + (step_size * (index + 1))))
result.append(maximum)
return result
|
def get_quantile_interval(data, nbins):
quantiles = get_uniform_interval(0, 1, nbins)
return list(data.quantile(quantiles))
|
def to_str(x):
if pd.isnull(x):
return '#NA#'
else:
return str(x)
|
def get_booster_leaf_condition(leaf_node, conditions, tree_info: pd.DataFrame):
start_node_info = tree_info[(tree_info['Node'] == leaf_node)]
if (start_node_info['Feature'].tolist()[0] == 'Leaf'):
conditions = []
if (str(leaf_node) in tree_info['Yes'].drop_duplicates().tolist()):
father_no... |
class tree_to_dataframe_for_lightgbm(object):
def __init__(self, model):
self.json_model = model.dump_model()
self.features = self.json_model['feature_names']
def get_root_nodes_count(self, tree, max_id):
tree_node_id = tree.get('split_index')
if tree_node_id:
if ... |
class StandardizeEncoder(EncoderBase):
def __init__(self):
super(StandardizeEncoder, self).__init__()
def fit(self, df, targets):
self.reset()
for target in targets:
mean = df[target].mean()
std = df[target].std()
new_name = (('continuous_' + remov... |
class InteractionEncoder():
def __init__(self):
self.level = list()
self.targets = None
def fit(self, targets, level='all'):
if (level == 'all'):
self.level = [2, 3, 4]
else:
self.level = level
self.targets = targets
def transform(self, df... |
class DimReducEncoder():
def __init__(self):
self.result = list()
def fit(self, df, targets, config):
for target in targets:
for (method, parameter) in config:
if (method == 'pca'):
n_comp = parameter['n_components']
pos = (... |
def is_missing(v):
return (v == np.NaN)
|
@dataclass
class TabDataOpt():
label: str = field(default='label', metadata={'help': "\n The name for the `y` variable.\n The default name is 'label'.\n "})
dis_vars_entity: list = field(default=None, metadata={'help': '\n A l... |
def permutate_selector(train_df, eval_df, y, variables=None, metric='acc', **kwargs):
'\n Return the importance of variables based on permutation loss\n\n :param train_df: training data set\n :param eval_df: eval data set\n :param y: name of the target variable\n :param variables: the variables to ... |
def tree_selector(train_df, eval_df, y, opt, metric='error', type='lgb'):
"\n This function select variable importance using built functions from xgboost or lightgbm\n :param train_df: training dataset,\n :param eval_df: evaluation dataset\n :param y: target variable\n :param opt: training operatio... |
def shap_selector(train_df, eval_df, y, opt, type='lgb', metric='error'):
"\n This returns the shap explainer so that one can use it for variable selection.\n The base tree model we use will select the best iterations\n :param train_df: training dataset\n :param eval_df: eval dataset\n :param y: th... |
def vif_selector(data_df, y):
'\n Calculate the VIF to select variables.\n :param data_df: the dataset\n :param y: the target variable\n '
ray.init()
ex_var = [var for var in data_df.columns if (var != y)]
data_df_id = ray.put(data_df)
@ray.remote()
def ls(data_df, target_var):
... |
@click.group()
def cli():
'FitsMap --- Convert FITS files and catalogs into LeafletJS maps.'
pass
|
@cli.command()
@click.argument('directory', type=str)
@click.option('--out_dir', default='.', help=HELP_OUT_DIR)
@click.option('--min_zoom', default=0, help=HELP_MIN_ZOOM)
@click.option('--title', default='FitsMap', help=HELP_TITLE)
@click.option('--task_procs', default=0, help=HELP_TASK_PROCS)
@click.option('--procs... |
@cli.command()
@click.argument('files', type=str)
@click.option('--out_dir', default='.', help=HELP_OUT_DIR)
@click.option('--min_zoom', default=0, help=HELP_MIN_ZOOM)
@click.option('--title', default='FitsMap', help=HELP_TITLE)
@click.option('--task_procs', default=0, help=HELP_TASK_PROCS)
@click.option('--procs_per... |
def __server(out_dir: str, port: int) -> None:
def f():
server = http.server.HTTPServer(('', port), functools.partial(http.server.SimpleHTTPRequestHandler, directory=out_dir))
server.serve_forever()
return f
|
def __opener(address: str) -> None:
def f():
print('Opening up FitsMap in browser')
webbrowser.open(address)
return f
|
@cli.command()
@click.option('--out_dir', default='.', help=HELP_OUT_DIR)
@click.option('--port', default=8000, help=HELP_OUT_DIR)
@click.option('--open_browser', default=True, help=HELP_OUT_DIR)
def serve(out_dir: str, port: int, open_browser: bool):
'Spins up a web server to serve a fitsmap. webservers are requ... |
class KDBush():
'Python port of https://github.com/mourner/kdbush'
def __init__(self, points, get_x: Callable=(lambda p: p[0]), get_y: Callable=(lambda p: p[1]), node_size: int=64, array_dtype=np.float64):
self.points = points
self.node_size = node_size
n_points = len(points)
... |
def _sort(ids: np.ndarray, coords: np.ndarray, node_size: int, left: int, right: int, axis: int) -> None:
if ((right - left) <= node_size):
return
m = ((left + right) >> 1)
_select(ids, coords, m, left, right, axis)
_sort(ids, coords, node_size, left, (m - 1), (1 - axis))
_sort(ids, coords... |
def _select(ids: np.ndarray, coords: np.ndarray, k: int, left: int, right: int, axis: int) -> None:
while (right > left):
if ((right - left) > 600):
n = ((right - left) + 1)
m = ((k - left) + 1)
z = np.log(n)
s = (0.5 * np.exp(((2 * z) / 3)))
sd ... |
def _swap_item(ids: np.ndarray, coords: np.ndarray, i: int, j: int) -> None:
_swap(ids, i, j)
_swap(coords, (2 * i), (2 * j))
_swap(coords, ((2 * i) + 1), ((2 * j) + 1))
|
def _swap(arr: np.ndarray, i: int, j: int) -> None:
tmp = arr[i]
arr[i] = arr[j]
arr[j] = tmp
|
def _range(ids: np.ndarray, coords: np.ndarray, min_x: int, min_y: int, max_x: int, max_y: int, node_size: int) -> List[int]:
stack = [0, (len(ids) - 1), 0]
result = []
while len(stack):
axis = stack.pop()
right = stack.pop()
left = stack.pop()
if ((right - left) <= node_si... |
def _within(ids: np.ndarray, coords: np.ndarray, qx: int, qy: int, r: int, node_size: int) -> List[int]:
stack = [0, (len(ids) - 1), 0]
result = []
r2 = (r * r)
while len(stack):
axis = stack.pop()
right = stack.pop()
left = stack.pop()
if ((right - left) <= node_size):... |
def __sq_dist(ax: float, ay: float, bx: float, by: float) -> float:
return (((ax - bx) ** 2) + ((ay - by) ** 2))
|
class OutputManager():
'Manages all FitsMap console output for tasks.'
SENTINEL = (- 1)
__instance = None
@staticmethod
def pbar_disabled():
return bool(os.getenv('DISBALE_TQDM', False))
def check_for_updates(func):
def f(*args, **kwargs):
func(*args, **kwargs)
... |
class PaddedArray():
def __init__(self, array: np.ndarray, pad: Tuple[(int, int)], tile_size=(256, 256)):
self.array = array
self.pad = pad
shape = [(array.shape[0] + pad[0]), (array.shape[1] + pad[1])]
empty_tile_shape = list(tile_size)
if (len(array.shape) == 3):
... |
def default_map(i):
return i
|
def default_udpate_f():
pass
|
def default_get_x(p):
return p['x']
|
def default_get_y(p):
return p['y']
|
class Supercluster():
def __init__(self, min_zoom: int=0, max_zoom: int=16, min_points: int=2, radius: float=40, extent: int=512, node_size: int=64, log: bool=False, generate_id: bool=False, reduce: Callable=None, map: Callable=default_map, alternate_CRS: Tuple[(int, int)]=(), update_f: Callable=default_udpate_f... |
class MockTQDM():
unit = ''
def update(self, n: int=1):
pass
def clear(self):
pass
def display(self, message):
pass
def set_description(self, desc):
pass
def reset(total):
pass
|
class MockWCS():
'Mock WCS object for testing'
def __init__(self, include_cd: bool):
if include_cd:
self.cd = np.array([[1, 0], [0, 1]])
else:
self.crpix = np.array([1, 1])
self.crval = np.array([1, 1])
def all_pix2world(self, *args, **kwargs):
... |
def setup(with_data=False):
'Builds testing structure'
if (not os.path.exists(TEST_PATH)):
os.mkdir(TEST_PATH)
if with_data:
with_data_path = (lambda f: os.path.join(DATA_DIR, f))
with_test_path = (lambda f: os.path.join(TEST_PATH, f))
copy_file = (lambda f: shutil.copy(wit... |
def tear_down(include_ray=False):
'Tears down testing structure'
if os.path.exists(TEST_PATH):
shutil.rmtree(TEST_PATH)
if include_ray:
ray.shutdown()
|
def disbale_tqdm():
os.environ[TQDM_ENV_VAR] = 'True'
|
def enable_tqdm():
os.environ[TQDM_ENV_VAR] = 'False'
|
def cat_to_json(fname):
with open(fname, 'r') as f:
lines = f.readlines()
data = json.loads((('[' + ''.join([l.strip() for l in lines[1:(- 1)]])) + ']'))
return (data, lines[0])
|
def __stable_idx_answer(shape, zoom, tile_size=256):
dim0_tile_fraction = (shape[0] / tile_size)
dim1_tile_fraction = (shape[1] / tile_size)
if ((dim0_tile_fraction < 1) or (dim1_tile_fraction < 1)):
raise StopIteration()
num_tiles_dim0 = int(np.ceil(dim0_tile_fraction))
num_tiles_dim1 = i... |
def covert_idx_to_hashable_tuple(idx):
'Converts idxs to hashable type for set, slice is not hashable'
return (idx[0], idx[1], idx[2], str(idx[3]), str(idx[4]))
|
def get_slice_idx_generator_solution(zoom: int):
'Gets proper idxs using a method that tests correctly.\n\n The data returned by this can be big at high zoom levels.\n TODO: Find particular cases to test for.\n '
return list(__stable_idx_answer((4305, 9791), zoom))
|
def compare_file_directories(dir1, dir2) -> bool:
is_file = (lambda x: x.is_file())
is_dir = (lambda x: x.is_dir())
get_name = (lambda x: x.name)
get_path = (lambda x: x.path)
def get_file_extension(fname):
return os.path.splitext(fname)[1]
def compare_file_contents(file1, file2) -> ... |
def get_version():
here = os.path.dirname(os.path.realpath(__file__))
version_lcocation = os.path.join(here, '../__version__.py')
with open(version_lcocation, 'r') as f:
return f.readline().strip().replace('"', '')
|
@pytest.mark.unit
@pytest.mark.cartographer
def test_layer_name_to_dict_image():
'test cartographer.layer_name_to_dict'
out_dir = '.'
min_zoom = 0
max_native_zoom = 2
name = 'test'
color = ''
actual_dict = c.layer_name_to_dict(out_dir, (max_native_zoom + 5), min_zoom, max_native_zoom, name... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_layer_name_to_dict_catalog():
'test cartographer.layer_name_to_dict'
helpers.setup()
out_dir = helpers.DATA_DIR
min_zoom = 0
max_native_zoom = 2
name = 'test'
color = '#4C72B0'
columns = 'a,b,c'
with open(os.path.join(out_dir, f'... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_img_layer_dict_to_str():
'test cartographer.layer_dict_to_str'
min_zoom = 0
max_zoom = 2
name = 'test'
layer_dict = dict(directory=(name + '/{z}/{y}/{x}.png'), name=name, min_zoom=min_zoom, max_zoom=(max_zoom + 5), max_native_zoom=max_zoom)
... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_cat_layer_dict_to_str():
'test cartographer.layer_dict_to_str'
min_zoom = 0
max_zoom = 2
name = 'test'
columns = 'a,b,c'
layer_dict = dict(directory=(name + '/{z}/{y}/{x}.png'), name=name, min_zoom=min_zoom, max_zoom=(max_zoom + 5), max_nati... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_leaflet_layer_control_declaration():
'test cartographer.add_layer_control'
min_zoom = 0
max_zoom = 2
name = 'test'
img_layer_dict = dict(directory=(name + '/{z}/{y}/{x}.png'), name=name, min_zoom=min_zoom, max_zoom=(max_zoom + 5), max_native_zoo... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_get_colors():
'test cartographer.colors_js'
expected = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860', '#DA8BC3', '#8C8C8C', '#CCB974', '#64B5CD']
color_iter = c.get_colors()
assert (expected == [next(color_iter) for _ in range(le... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_leaflet_crs_js():
'test cartographer.leaflet_crs_js'
min_zoom = 0
max_zoom = 2
name = 'test'
layer_dict = dict(directory=(name + '/{z}/{y}/{x}.png'), name=name, min_zoom=min_zoom, max_zoom=(max_zoom + 5), max_native_zoom=max_zoom)
actual = c... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_extract_cd_matrix_as_string_with_cd():
'test cartographer.extract_cd_matrix_as_string'
wcs = helpers.MockWCS(include_cd=True)
actual = c.extract_cd_matrix_as_string(wcs)
expected = '[[1, 0], [0, 1]]'
assert (actual == expected)
|
@pytest.mark.unit
@pytest.mark.cartographer
def test_extract_cd_matrix_as_string_without_cd():
'test cartographer.extract_cd_matrix_as_string'
wcs = helpers.MockWCS(include_cd=False)
actual = c.extract_cd_matrix_as_string(wcs)
expected = '[[0.0, 0.0], [0.0, 0.0]]'
assert (actual == expected)
|
@pytest.mark.unit
@pytest.mark.cartographer
def test_leaflet_map_js():
'test cartographer.leaflet_map_js'
min_zoom = 0
max_zoom = 2
name = 'test'
layer_dict = dict(directory=(name + '/{z}/{y}/{x}.png'), name=name, min_zoom=min_zoom, max_zoom=(max_zoom + 5), max_native_zoom=max_zoom)
acutal_map... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_build_conditional_css():
'test cartographer.build_conditional_css'
helpers.setup()
actual_css = c.build_conditional_css(helpers.TEST_PATH)
expected_css = '\n'.join([' <link rel=\'preload\' href=\'https://unpkg.com/leaflet-search@2.9.8/dist/leafle... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_build_conditional_js():
'test cartographer.build_conditional_js'
helpers.setup()
acutal_js = c.build_conditional_js(helpers.TEST_PATH, True)
expected_js = '\n'.join([" <script defer src='https://cdnjs.cloudflare.com/ajax/libs/leaflet-search/3.0.2... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_build_index_js():
'Tests cartographer.build_index_js'
img_layer_dict = [dict(name='img', directory='img/{z}/{y}/{x}.png', min_zoom=0, max_zoom=8, max_native_zoom=3)]
cat_layer_dict = [dict(name='cat', directory='cat/{z}/{y}/{x}.pbf', min_zoom=0, max_zoo... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_move_support_images():
'test cartographer.move_support_images'
helpers.setup()
actual_moved_images = c.move_support_images(helpers.TEST_PATH)
expected_moved_images = ['favicon.ico', 'loading-logo.svg']
helpers.tear_down()
assert (actual_move... |
@pytest.mark.unit
@pytest.mark.cartographer
def test_build_html():
'test cartographer.build_html'
title = 'test_title'
extra_js = 'test_extra_js'
extra_css = 'test_extra_css'
actual_html = c.build_html(title, extra_js, extra_css)
expected_html = '\n'.join(['<!DOCTYPE html>', '<html lang="en">'... |
@pytest.mark.integration
@pytest.mark.cartographer
def test_chart_no_wcs():
'test cartographer.chart'
helpers.setup(with_data=True)
out_dir = helpers.TEST_PATH
title = 'test'
map_layer_names = 'test_layer'
marker_file_names = 'test_marker'
wcs = None
columns = 'a,b,c'
with open(os.... |
@pytest.mark.integration
@pytest.mark.cartographer
def test_chart_with_wcs():
'test cartographer.chart'
helpers.setup(with_data=True)
out_dir = helpers.TEST_PATH
title = 'test'
map_layer_names = 'test_layer'
marker_file_names = 'test_marker'
wcs = WCS(os.path.join(out_dir, 'test_image.fits... |
@pytest.mark.unit
@pytest.mark.convert
def test_build_path():
'Test the convert.build_path function'
(z, y, x) = (1, 2, 3)
out_dir = helpers.TEST_PATH
img_name = convert.build_path(z, y, x, out_dir)
expected_img_name = os.path.join(out_dir, str(z), str(y), f'{x}.png')
expected_file_name_matche... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z0():
"Test convert.slice_idx_generator at zoom level 0.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 0
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z1():
"Test convert.slice_idx_generator at zoom level 1.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 1
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z2():
"Test convert.slice_idx_generator at zoom level 2.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 2
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z3():
"Test convert.slice_idx_generator at zoom level 3.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 3
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z4():
"Test convert.slice_idx_generator at zoom level 4.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 4
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_z5():
"Test convert.slice_idx_generator at zoom level 5.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (4305, 9791)
zoom = 5
tile_size = 256
given =... |
@pytest.mark.unit
@pytest.mark.convert
def test_slice_idx_generator_raises():
"Test convert.slice_idx_generator raises StopIteration.\n\n The given shape (4305, 9791) breaks iterative schemes that don't properly\n seperate tiles. Was a bug.\n "
shape = (250, 250)
zoom = 5
tile_size = 256
... |
@pytest.mark.unit
@pytest.mark.convert
def test_balance_array_2d():
'Test convert.balance_array'
in_shape = (10, 20)
expected_shape = (32, 32)
expected_num_nans = (np.prod(expected_shape) - np.prod(in_shape))
test_array = np.zeros(in_shape)
out_array = convert.balance_array(test_array)
ass... |
@pytest.mark.unit
@pytest.mark.convert
def test_balance_array_3d():
'Test convert.balance_array'
in_shape = (10, 20, 3)
expected_shape = (32, 32, 3)
expected_num_nans = (np.prod(expected_shape) - np.prod(in_shape))
test_array = np.zeros(in_shape)
out_array = convert.balance_array(test_array)
... |
@pytest.mark.unit
@pytest.mark.convert
def test_get_array_fits():
'Test convert.get_array'
helpers.setup()
tmp = np.zeros((3, 3), dtype=np.float32)
out_path = os.path.join(helpers.TEST_PATH, 'test.fits')
fits.PrimaryHDU(data=tmp).writeto(out_path)
pads = [[0, 1], [0, 1]]
expected_array = n... |
@pytest.mark.unit
@pytest.mark.convert
def test_get_array_fits_fails():
'Test convert.get_array'
helpers.setup()
tmp = np.zeros(3, dtype=np.float32)
out_path = os.path.join(helpers.TEST_PATH, 'test.fits')
fits.PrimaryHDU(data=tmp).writeto(out_path)
with pytest.raises(ValueError) as excinfo:
... |
@pytest.mark.unit
@pytest.mark.convert
def test_get_array_png():
'Test convert.get_array'
helpers.setup()
expected_array = camera()
out_path = os.path.join(helpers.TEST_PATH, 'test.png')
Image.fromarray(expected_array).save(out_path)
actual_array = convert.get_array(out_path)
helpers.tear_... |
@pytest.mark.unit
@pytest.mark.convert
def test_filter_on_extension_without_predicate():
'Test convert.filter_on_extension without a predicate argument'
test_files = ['file_one.fits', 'file_two.fits', 'file_three.exclude']
extensions = ['fits']
expected_list = test_files[:(- 1)]
actual_list = conv... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.