added optimize function to DataProcessing

README.rst

@@ -36,6 +36,8 @@ Introduction
 
 This software works on a pipeline basis.
 
+To install use: pip install submovements
+
 Input
 ~~~~~~~~~~~~~~~
 The input is a directory of trials which are saved as CSV files with the following file names:

setup.py

@@ -1,41 +1,41 @@
-"""The setup script."""
-
-from setuptools import setup, find_packages
-
-with open('README.rst') as readme_file:
-    readme = readme_file.read()
-
-with open('HISTORY.rst') as history_file:
-    history = history_file.read()
-
-requirements = [ ]
-
-setup_requirements = ['pytest-runner', ]
-
-test_requirements = ['pytest', ]
-
-setup(
-    author="Stas Steinberg, Liz Izakson, Meyrav Dayan",
-    author_email='stanislavs1@mail.tau.ac.il, lizizakson@mail.tau.ac.il, meyravdayan@mail.tau.ac.il',
-    classifiers=[
-        'Development Status :: 4 - Beta',
-        'Intended Audience :: Developers',
-        'License :: OSI Approved :: MIT License',
-        'Natural Language :: English',
-        'Programming Language :: Python :: 3.7',
-    ],
-    description="Our goal is to detect sub-movements from motion data.",
-    install_requires=requirements,
-    license="MIT license",
-    long_description=readme + '\n\n' + history,
-    include_package_data=True,
-    keywords='submovements',
-    name='submovements',
-    packages=find_packages(include=['submovements']),
-    setup_requires=setup_requirements,
-    test_suite='tests',
-    tests_require=test_requirements,
-    url='https://github.com/sstbrg/submovements',
-    version='0.1.1',
-    zip_safe=False,
-)
+"""The setup script."""
+
+from setuptools import setup, find_packages
+
+with open('README.rst') as readme_file:
+    readme = readme_file.read()
+
+with open('HISTORY.rst') as history_file:
+    history = history_file.read()
+
+requirements = [ ]
+
+setup_requirements = ['pytest-runner', ]
+
+test_requirements = ['pytest', ]
+
+setup(
+    author="Stas Steinberg, Liz Izakson, Meyrav Dayan",
+    author_email='stanislavs1@mail.tau.ac.il, lizizakson@mail.tau.ac.il, meyravdayan@mail.tau.ac.il',
+    classifiers=[
+        'Development Status :: 4 - Beta',
+        'Intended Audience :: Developers',
+        'License :: OSI Approved :: MIT License',
+        'Natural Language :: English',
+        'Programming Language :: Python :: 3.7',
+    ],
+    description='Our goal is to detect submovements from motion data',
+    install_requires=requirements,
+    license="MIT license",
+    long_description='',
+    include_package_data=True,
+    keywords='submovements',
+    name='submovements',
+    packages=find_packages(include=['submovements']),
+    setup_requires=setup_requirements,
+    test_suite='tests',
+    tests_require=test_requirements,
+    url='https://github.com/sstbrg/submovements',
+    version='0.1.1',
+    zip_safe=False,
+)

submovements/DataProcessing.py

@@ -7,6 +7,7 @@
 from scipy.signal import butter, filtfilt
 from pathlib import Path
 import seaborn as sns
+from fit_submovements import *
 
 sns.set()
 
@@ -184,6 +185,10 @@ class Preprocessor():
     raw_headers = attr.ib(default=['SampleNum', 'x', 'y', 'z',
                                    'phi', 'theta', 'psi', 'Time', 'Event'])
 
+    def do_fit(self, trial):
+        fit_result = optimize_jerk(trial)
+        return fit_result
+
     def load_df_from_directory_gen(self, dir_path, cols=('x', 'y')):
         ###
         # This is a generator which takes csv

submovements/fit_submovements.py

@@ -30,7 +30,7 @@ def func(t, *params):
     return Y
 
 
-def optimize_jerk(trial, plot_fit):
+def optimize_jerk(trial):
     ###
     # Creates the cost jerk function for x and y
     # TODO: add support for all dimensions (currently fits only X data
@@ -49,30 +49,28 @@ def optimize_jerk(trial, plot_fit):
 
     n = 3
 
-    guess_x = [0, 0.9, 180]
+    guess_vx = [0, 0.9, 180]
     for n in range(1,n):
-        guess_x += [t0, D/n, 180]
+        guess_vx += [t0, D/n, 180]
 
+    guess_vy = [0, 0.9, 180]
+    for n in range(1,n):
+        guess_vy += [t0, D/n, 180]
 
     # TODO: calculate bounds
     #  bounds_low = [0, 0.1, -50, -50]
     #  bounds_high = [t.max(), t.max()-t.min(), 200, 200]
 
-    popt_x, pcov_x = curve_fit(func, t, Vx, p0=guess_x)
-    perr_x = np.sqrt(np.diag(pcov_x))
-
-    print(popt_x)
-    print(perr_x)
-
-    fit = func(t, *popt_x)
+    popt_vx, pcov_vx = curve_fit(func, t, Vx, p0=guess_vx)
+    perr_vx = np.sqrt(np.diag(pcov_vx))
 
-    if plot_fit:
-        plt.plot(t, Vx)
-        plt.plot(t, fit, 'r-')
-        plt.show()
+    popt_vy, pcov_vy = curve_fit(func, t, Vy, p0=guess_vy)
+    perr_vy = np.sqrt(np.diag(pcov_vy))
 
+    fit_vx = func(t, *popt_vx)
+    fit_vy = func(t, *popt_vy)
 
-    return fit, popt_x, perr_x
+    return fit_vx, fit_vy, popt_vx, perr_vx, popt_vy, perr_vy
 
 if __name__ == '__main__':
     path_to_raw_data = '..\\data\\results\\simonvisual\\1'