try using entry_points

deepclustering/manager.py

@@ -11,7 +11,7 @@ class ConfigManger:
     DEFAULT_CONFIG = ""
 
     def __init__(
-        self, DEFAULT_CONFIG_PATH: str = None, verbose=True, integrality_check=True
+            self, DEFAULT_CONFIG_PATH: str = None, verbose=True, integrality_check=True
     ) -> None:
         self.parsed_args: Dict[str, Any] = YAMLArgParser(verbose=verbose)
         if DEFAULT_CONFIG_PATH is None:

deepclustering/utils/__init__.py

@@ -1,3 +1,4 @@
 from .general import *
+from .type_check import *
 from .warnings import _warnings
 from .yaml_parser import *

deepclustering/utils/type_check.py

@@ -0,0 +1,234 @@
+import collections
+import numbers
+import sys
+import types
+
+import numpy as np
+import six
+
+
+def is_np_array(val):
+    """
+    Checks whether a variable is a numpy array.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    out : bool
+        True if the variable is a numpy array. Otherwise False.
+
+    """
+    # using np.generic here via isinstance(val, (np.ndarray, np.generic)) seems to also fire for scalar numpy values
+    # even though those are not arrays
+    return isinstance(val, np.ndarray)
+
+
+def is_np_scalar(val):
+    """
+    Checks whether a variable is a numpy scalar.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    out : bool
+        True if the variable is a numpy scalar. Otherwise False.
+
+    """
+    # Note that isscalar() alone also fires for thinks like python strings
+    # or booleans.
+    # The isscalar() was added to make this function not fire for non-scalar
+    # numpy types. Not sure if it is necessary.
+    return isinstance(val, np.generic) and np.isscalar(val)
+
+
+def is_single_integer(val):
+    """
+    Checks whether a variable is an integer.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is an integer. Otherwise False.
+
+    """
+    return isinstance(val, numbers.Integral) and not isinstance(val, bool)
+
+
+def is_single_float(val):
+    """
+    Checks whether a variable is a float.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a float. Otherwise False.
+
+    """
+    return isinstance(val, numbers.Real) and not is_single_integer(val) and not isinstance(val, bool)
+
+
+def is_single_number(val):
+    """
+    Checks whether a variable is a number, i.e. an integer or float.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a number. Otherwise False.
+
+    """
+    return is_single_integer(val) or is_single_float(val)
+
+
+def is_iterable(val):
+    """
+    Checks whether a variable is iterable.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is an iterable. Otherwise False.
+
+    """
+    return isinstance(val, collections.Iterable)
+
+
+# TODO convert to is_single_string() or rename is_single_integer/float/number()
+def is_string(val):
+    """
+    Checks whether a variable is a string.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a string. Otherwise False.
+
+    """
+    return isinstance(val, six.string_types)
+
+
+def is_single_bool(val):
+    """
+    Checks whether a variable is a boolean.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a boolean. Otherwise False.
+
+    """
+    return type(val) == type(True)
+
+
+def is_integer_array(val):
+    """
+    Checks whether a variable is a numpy integer array.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a numpy integer array. Otherwise False.
+
+    """
+    return is_np_array(val) and issubclass(val.dtype.type, np.integer)
+
+
+def is_float_array(val):
+    """
+    Checks whether a variable is a numpy float array.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a numpy float array. Otherwise False.
+
+    """
+    return is_np_array(val) and issubclass(val.dtype.type, np.floating)
+
+
+def is_callable(val):
+    """
+    Checks whether a variable is a callable, e.g. a function.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True if the variable is a callable. Otherwise False.
+
+    """
+    # python 3.x with x <= 2 does not support callable(), apparently
+    if sys.version_info[0] == 3 and sys.version_info[1] <= 2:
+        return hasattr(val, '__call__')
+    else:
+        return callable(val)
+
+
+def is_generator(val):
+    """
+    Checks whether a variable is a generator.
+
+    Parameters
+    ----------
+    val
+        The variable to check.
+
+    Returns
+    -------
+    bool
+        True is the variable is a generator. Otherwise False.
+
+    """
+    return isinstance(val, types.GeneratorType)

deepclustering/viewer/Viewer.py

@@ -278,6 +278,21 @@ def _next_slice(ax):
         ax.set_title(f'{ax.subject_name} @ plane:{ax.index} with {ax.mask_name}')
 
 
+def main():
+    args = get_parser()
+    V = Volume(
+        args.img_source,
+        args.gt_folders,
+        group_pattern=args.group_pattern,
+        img_extension=args.img_extension,
+        crop=args.crop,
+        mapping=args.mapping
+    )
+
+    Viewer = Multi_Slice_Viewer(V, shuffle_subject=args.shuffle, n_subject=args.n_subject)
+    Viewer.show()
+
+
 if __name__ == '__main__':
     '''
     python admm_research/postprocessing/Viewer.py --img_source=admm_research/dataset/ACDC-2D-All/val/Img --gt_folders admm_research/dataset/ACDC-2D-All/val/GT archives/LV_prior/Livia/fs/iter1000/best/ archives/LV_prior/Livia/gc_size/iter1000/best/ archives/LV_prior/Livia/size/iter1000/best/ --group_pattern='patient\d+_\d+' --crop 70

setup.py

@@ -1,12 +1,17 @@
+import pathlib
+
 from setuptools import setup, find_packages
 
+HERE = pathlib.Path(__file__).parent
+README = (HERE / "README.md").read_text()
+
 setup(
     name="deepclustering",
     version="1.0.0",
     packages=find_packages(),
     url="https://github.com/jizongFox/deep-clustering-toolbox",
-    license="",
-    author="jizong",
+    license="MIT    ",
+    author="Jizong Peng",
     author_email="jizong.peng.1@etsmtl.net",
     description="",
     install_requires=[
@@ -36,4 +41,9 @@
         "pyyaml",
         "termcolor"
     ],
+    entry_points = {
+        "console_script":[
+            "viewer=deepclustering.viewer.Viewer:main",
+        ]
+    },
 )