Installable using Pip and Conda

.gitignore

@@ -4,10 +4,11 @@
 *.project
 *.pydevproject
 .idea
-dev
+dev/
 doc/build/
 doc/source/_static/
 doc/source/_templates/
 build/
 dist/
-*egg-info
+*egg-info
+recipe/

README.md

@@ -40,11 +40,21 @@ Install
     * Open command prompt, navigate to the source folder, run the following 
     commands:
         ```commandline
-        $ conda create -n discorpy
-        $ conda activate discorpy
-        $ conda install python
-        $ python setup.py install
+        conda create -n discorpy
+        conda activate discorpy
+        conda install python
+        python setup.py install
         ```
+ - Using conda:
+    + Install Miniconda as instructed above.
+    + Open terminal or command prompt and run the following command:     
+      `conda install -c algotom discorpy`
+
+- Using pip:
+    + Install Miniconda as instructed above.
+    + Open terminal or command prompt and run the following command:  
+      `pip install discorpy`
+
 
 How to use
 ==========
@@ -62,31 +72,31 @@ Demonstrations
 - Apply to a visible dot-target collected at [Beamline I12](https://www.diamond.ac.uk/Instruments/Imaging-and-Microscopy/I12/Detectors-at-I12.html),
 Diamond Light Source, UK:
 
-![I12_before_after1](data/demo/i12_data_1.jpg)
+![I12_before_after1](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/i12_data_1.jpg)
 
-![I12_before_after2](data/demo/i12_data_2.jpg)
+![I12_before_after2](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/i12_data_2.jpg)
 
 - Apply to an X-ray dot-target collected at [Beamline I13](https://www.diamond.ac.uk/Instruments/Imaging-and-Microscopy/I13/Diamond-Manchester_Imaging_Branchline/Facilities_and_equipment_Imaging.html),
 Diamond Light Source, UK:
 
-![I13_before_after1](data/demo/i13_data_1.jpg)
+![I13_before_after1](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/i13_data_1.jpg)
 
-![I13_before_after2](data/demo/i13_data_2.jpg)
+![I13_before_after2](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/i13_data_2.jpg)
 
 - Improvement of a tomographic reconstructed image after distortion correction.
   + Before the correction:
 
-    ![tomo_before](data/demo/recon_before.jpg)
+    ![tomo_before](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/recon_before.jpg)
 
   + After the correction:
 
-    ![tomo_before](data/demo/recon_after.jpg)
+    ![tomo_before](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/recon_after.jpg)
 
 
 - Apply to a hazard camera of the [Mars Perseverance Rover](https://mars.nasa.gov/mars2020/multimedia/raw-images/).
 Details of how to estimate distortion coefficients of that camera without using
 a calibration target are shown [here](https://github.com/DiamondLightSource/discorpy/blob/master/examples/Perseverance_distortion_correction/Distortion_correction_for_Perseverance_camera.md)  
 
-![Mars_rover](data/demo/Mars_Rover_camera.jpg)
+![Mars_rover](https://github.com/DiamondLightSource/discorpy/raw/master/data/demo/Mars_Rover_camera.jpg)
 
        

discorpy/losa/loadersaver.py

@@ -85,6 +85,114 @@ def _get_key(name, obj):
                     return key_path
 
 
+def get_hdf_information(file_path):
+    """
+    Get information of datasets in a hdf/nxs file.
+
+    Parameters
+    ----------
+    file_path : str
+        Path to the file.
+
+    Returns
+    -------
+    list_key : str
+        Keys to the datasets.
+    list_shape : tuple of int
+        Shapes of the datasets.
+    list_type : str
+        Types of the datasets.
+    """
+    ifile = h5py.File(file_path, 'r')
+    keys = []
+    ifile.visit(keys.append)
+    list_key = []
+    list_shape = []
+    list_type = []
+    for key in keys:
+        data = ifile[key]
+        if isinstance(data, h5py.Group):
+            for key2, _ in list(data.items()):
+                list_key.append(key + "/" + key2)
+        else:
+            list_key.append(data.name)
+    for i, key in enumerate(list_key):
+        data = ifile[list_key[i]]
+        try:
+            shape = data.shape
+        except AttributeError:
+            shape = "None"
+        try:
+            dtype = data.dtype
+        except AttributeError:
+            dtype = "None"
+        if isinstance(data, list):
+            if len(data) == 1:
+                if not isinstance(data, np.ndarray):
+                    dtype = str(list(data)[0])
+                    dtype = dtype.replace("b'", "'")
+        list_shape.append(shape)
+        list_type.append(dtype)
+    ifile.close()
+    return list_key, list_shape, list_type
+
+
+def find_hdf_key(file_path, pattern):
+    """
+    Find datasets matching the pattern in a hdf/nxs file.
+
+    Parameters
+    ----------
+    file_path : str
+        Path to the file.
+    pattern : str
+        Pattern to find the full names of the datasets.
+
+    Returns
+    -------
+    list_key : str
+        Keys to the datasets.
+    list_shape : tuple of int
+        Shapes of the datasets.
+    list_type : str
+        Types of the datasets.
+    """
+    ifile = h5py.File(file_path, 'r')
+    list_key = []
+    keys = []
+    ifile.visit(keys.append)
+    for key in keys:
+        data = ifile[key]
+        if isinstance(data, h5py.Group):
+            for key2, _ in list(data.items()):
+                list_key.append(data.name + "/" + key2)
+        else:
+            list_key.append(data.name)
+    list_dkey = []
+    list_dshape = []
+    list_dtype = []
+    for _, key in enumerate(list_key):
+        if pattern in key:
+            list_dkey.append(key)
+            data = ifile[key]
+            try:
+                shape = data.shape
+            except AttributeError:
+                shape = "None"
+            list_dshape.append(shape)
+            try:
+                dtype = data.dtype
+            except AttributeError:
+                dtype = "None"
+            list_dtype.append(dtype)
+            if isinstance(data, list):
+                if len(data) == 1:
+                    dtype = str(list(data)[0])
+                    dtype = dtype.replace("b'", "'")
+    ifile.close()
+    return list_dkey, list_dshape, list_dtype
+
+
 def load_hdf_file(file_path, key_path=None, index=None, axis=0):
     """
     Load data from a hdf5/nxs file.

setup.py

@@ -1,11 +1,17 @@
+import pathlib
 import setuptools
 
+HERE = pathlib.Path(__file__).parent
+README = (HERE / "README.md").read_text()
+
 setuptools.setup(
     name="discorpy",
     version="1.3",
     author="Nghia Vo",
     author_email="nghia.vo@diamond.ac.uk",
     description='Radial lens distortion correction in Python',
+    long_description=README,
+    long_description_content_type="text/markdown",
     keywords=['Distortion correction', 'Tomography', 'Radial lens distortion',
               'Camera calibration'],
     url="https://github.com/DiamondLightSource/discorpy",