First commit of an initial working version.

.gitignore

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 # mypy
 .mypy_cache/
+
+# Compressed files
+.rar

CHANGES.txt

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+First commit to new repository.

MANIFEST.in

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+include LICENSE.txt README.rst CHANGES.txt contributors.txt

README.rst

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+=======
+lidartf
+=======
+
+The LiDAR Testing Framework (lidartf) aims to provide the tools necessary to assess of methodologies/tools developed to process LiDAR data.
+
+This is still a work in progress, requiring some polishing to improve user-friendliness and additional testing data. Also, new tests, for different types of processing,
+will be added over time.
+
+The LiDAR Testing Framework is being developed as part of my PhD research, supervised by Dr. Mat Disney, in the Department of Geography at University College London (UCL). My research 
+is funded through Science Without Borders from the National Council of Technological and Scientific Development (10.13039/501100003593) – Brazil (Process number 233849/2014-9). 
+
+Any questions or suggestions, feel free to contact me using one of the following e-mails: [email protected] or [email protected]
+
+------------
+Requirements
+------------
+mayavi>=4.5.0
+numpy>=1.11.3
+pandas>=0.19.2
+setuptools>=36.0.1
+
+
+-----------------
+Who do I talk to?
+-----------------
+
+* Matheus Boni Vicari ([email protected] or [email protected])

contributors.txt

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+Matheus Boni Vicari
+ ([email protected] or [email protected])

lidartf/runframework.py

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+# -*- coding: utf-8 -*-
+"""
+Module to perform the framework testing.
+
+@author: Matheus Boni Vicari (2017).
+"""
+
+import numpy as np
+import imp
+import os
+import pandas as pd
+import sys
+from tests.wlseparation import test_separation
+
+
+def run(fun, results_folder, plot_cloud, dataset, *args):
+
+    """
+    Function to run the testing framework. This is the main function in the
+    package and manages input, testing and output.
+
+    Parameters
+    ----------
+    fun: list of str
+        List containing module path and function name (in this order) to import
+        and test.
+    results_folder: str
+        Path of the directory to save the testing results. Path must finish
+        with a separator (e.g. / or \).
+    plot_cloud: bool
+        Option to plot or not the separated point clouds.
+    dataset: list
+        List of dataset paths to use as data for the testing.
+    args: list
+        List of arguments necessary to run the function to be tested.
+        These arguments should be inserted in the same order as required by
+        function to be tested. Even if using only single values for each
+        argument, it should be inserted inside a list.
+
+    Returns
+    -------
+    res: pandas.DataFrame
+        Set of results for the testing of the function.
+    params: list
+        List of parameters used to run the tested function.
+
+    Usage
+    -----
+    >>> dataset = ['path/to/dataset.txt']
+    >>> fun = ['path/to/module_file', 'function_name']
+    >>> res, t = run(fun, 'results/', True, dataset, [10, 20, 40, 100],\
+ [100, 200, 300])
+    """
+
+    # Importing module 'm' and function 'f' to test.
+    m = import_(fun[0])
+    f = getattr(m, fun[1])
+
+    # Initializing empty lists to store parameters and results of the test.
+    results = []
+    params = []
+
+    # Looping over every data in dataset.
+    for i in dataset:
+
+        # Importing current dataset.
+        data = np.loadtxt(i, delimiter=' ')
+
+        # Extracting the dataset filename.
+        filename = os.path.basename(i).split('.')[0]
+
+        # Running the separation test.
+        results, params = test_separation(data, f, filename, results_folder,
+                                          plot_cloud, *args)
+
+    # Creating a pandas.DataFrame from the test results.
+    res = pd.DataFrame(results, columns=['accuracy', 'tp', 'fp', 'tn', 'fn',
+                                         'F_wood', 'F_leaf', 'k', 'time'])
+
+    # Saving the testing parameters to a text file.
+    np.savetxt(results_folder + 'params_' + filename + '.txt', params,
+               fmt='%1.2f')
+
+    # Saving the testing results to a text file.
+    res.to_csv(results_folder + 'results_' + filename + '.txt',
+               float_format='%1.2f')
+
+    return res, params
+
+
+def import_(filename):
+
+    """
+    Function to import a Python module from a filename.
+
+    Parameters
+    ----------
+    filename: str
+        Path of the module to import without extension.
+
+    """
+
+    # Splitting filename to extract path and name of the module.
+    path, name = os.path.split(filename)
+    # Extracting name and extension.
+    name, ext = os.path.splitext(name)
+
+    # Appending modulo path to system path.
+    sys.path.append(path)
+
+    # Finding module and importing it.
+    file_, filename, data = imp.find_module(name, [path])
+    mod = imp.load_module(name, file_, filename, data)
+
+    return mod
+
+
+if __name__ == "__main__":
+
+    fun = [r'path_to_module', 'name_of_function_to_test']
+    dataset = [r'data_to_use_in_test_1',
+               r'data_to_use_in_test_1']
+    res, t = run(fun, 'path_to_results', True, dataset, [list_arguments_1], [list_of_arguments_2])