Docstring and PEP8 cleanup

pycroscopy/analysis/be_loop_model.py

@@ -30,31 +30,12 @@ from ..io.microdata import MicroDataset, MicroDataGroup
 '''
 Custom dtypes for the datasets created during fitting.
 '''
-# loop_metrics32 = np.dtype([('Area', np.float32),
-#                            ('Centroid x', np.float32),
-#                            ('Centroid y', np.float32),
-#                            ('Rotation Angle [rad]', np.float32),
-#                            ('Offset', np.float32)])
 loop_metrics32 = np.dtype({'names': ['Area', 'Centroid x', 'Centroid y', 'Rotation Angle [rad]', 'Offset'],
                            'formats': [np.float32, np.float32, np.float32, np.float32, np.float32]})
 
-# crit32 = np.dtype([('AIC_loop', np.float32),
-#                    ('BIC_loop', np.float32),
-#                    ('AIC_line', np.float32),
-#                    ('BIC_line', np.float32)])
 crit32 = np.dtype({'names': ['AIC_loop', 'BIC_loop', 'AIC_line', 'BIC_line'],
                    'formats': [np.float32, np.float32, np.float32, np.float32]})
 
-# loop_fit32 = np.dtype([('a_0', np.float32),
-#                        ('a_1', np.float32),
-#                        ('a_2', np.float32),
-#                        ('a_3', np.float32),
-#                        ('a_4', np.float32),
-#                        ('b_0', np.float32),
-#                        ('b_1', np.float32),
-#                        ('b_2', np.float32),
-#                        ('b_3', np.float32),
-#                        ('R2 Criterion', np.float32)])
 field_names = ['a_0', 'a_1', 'a_2', 'a_3', 'a_4', 'b_0', 'b_1', 'b_2', 'b_3', 'R2 Criterion']
 loop_fit32 = np.dtype({'names': field_names,
                        'formats': [np.float32 for name in field_names]})
@@ -104,6 +85,7 @@ class BELoopModel(Model):
         self._sho_all_but_dc_forc_inds = None
         self._met_all_but_forc_inds = None
         self._current_forc = 0
+        self._maxDataChunk = 1
 
     def _is_legal(self, h5_main, variables=['DC_Offset']):
         """

pycroscopy/analysis/be_sho_model.py

@@ -50,6 +50,8 @@ class BESHOmodel(Model):
         self.is_reshapable = True
         self.num_udvs_steps = None
         self.freq_vec = None
+        self._maxDataChunk = 1
+        self._max_pos_per_read = 1
 
     def _create_guess_datasets(self):
         """

pycroscopy/analysis/fit_methods.py

@@ -27,8 +27,13 @@ class Fit_Methods(object):
         Generates the single Harmonic Oscillator response over the given vector
 
         Parameters
-        -----------
+        ----------
+        guess : array-like
+            The set of guess parameters to be tested
+        data_vec : numpy.ndarray
+            The data vector to compare the current guess against
         freq_vector : numpy.ndarray
+            The frequencies that correspond to each data point in `data_vec`
         args : list or tuple
             SHO parameters=(Amp,w0,Q,phi,vector). vector: 1D np.array of frequency values.
             Amp: amplitude.
@@ -70,12 +75,16 @@ class BE_Fit_Methods(object):
 
         Parameters
         ----------
+        coef_vec : numpy.ndarray
+        data_vec : numpy.ndarray
         dc_vec : numpy.ndarray
             The DC offset vector
         args : list
 
         Returns
         -------
+        fitness : float
+            The 1-r^2 value for the current set of loop coefficients
 
         """
 
@@ -94,38 +103,6 @@ class BE_Fit_Methods(object):
         return 1 - r_squared
 
 
-    # @staticmethod
-    # def BE_LOOP(dc_vec, *args):
-    #     """
-    #
-    #     Parameters
-    #     ----------
-    #     dc_vec : numpy.ndarray
-    #         The DC offset vector
-    #     args : list
-    #
-    #     Returns
-    #     -------
-    #
-    #     """
-    #     def loop_func(coef_vec, data_vec):
-    #         if coef_vec.size < 9:
-    #             raise ValueError('Error: The Loop Fit requires 9 parameter guesses!')
-    #
-    #         data_mean = np.mean(data_vec)
-    #
-    #         func = loop_fit_function(dc_vec, coef_vec)
-    #
-    #         ss_tot = sum(abs(data_vec - data_mean) ** 2)
-    #         ss_res = sum(abs(data_vec - func) ** 2)
-    #
-    #         r_squared = 1 - ss_res / ss_tot if ss_tot > 0 else 0
-    #
-    #         return 1 - r_squared
-    #
-    #     return loop_func
-
-
 class forc_iv_fit_methods(Fit_Methods):
     """
     Any fitting methods specific to FORC_IV should go here.

pycroscopy/analysis/optimize.py

@@ -58,6 +58,10 @@ class Optimize(object):
             warn('Error: data and guess must be numpy.ndarray. Exiting...')
             sys.exit()
         self._parallel = parallel
+        self.strategy = None
+        self.options = None
+        self.solver_type = None
+        self.solver_options = None
 
     def _guessFunc(self):
         gm = GuessMethods()

pycroscopy/io/translators/be_odf.py

@@ -30,10 +30,12 @@ class BEodfTranslator(Translator):
 
     def __init__(self, *args, **kwargs):
         super(BEodfTranslator, self).__init__(*args, **kwargs)
-
         self.hdf = None
         self.h5_raw = None
         self.num_rand_spectra = kwargs.pop('num_rand_spectra', 1000)
+        self.FFT_BE_wave = None
+        self.signal_type = None
+        self.expt_type = None
 
     def translate(self, file_path, show_plots=True, save_plots=True, do_histogram=False, verbose=False):
         """

pycroscopy/io/translators/be_odf_relaxation.py

@@ -31,6 +31,14 @@ class BEodfRelaxationTranslator(Translator):
     It will not work for in-field. This should be fixed at a later date.
     
     """
+    def __init__(self, max_mem_mb=1024):
+        super(BEodfRelaxationTranslator, self).__init__(max_mem_mb)
+        self.FFT_BE_wave = None
+        self.hdf = None
+        self.ds_main = None
+        self.mean_resp = None
+        self.max_resp = None
+        self.min_resp = None
 
     def translate(self, file_path, show_plots=True, save_plots=True, do_histogram=False):
         """
@@ -147,7 +155,7 @@ class BEodfRelaxationTranslator(Translator):
         num_actual_udvs_steps = int(num_actual_udvs_steps)
 
         stind = 0
-        for step_index in xrange(UDVS_mat.shape[0]):
+        for step_index in range(UDVS_mat.shape[0]):
             if UDVS_mat[step_index, 2] < 1E-3:  # invalid AC amplitude
                 continue  # skip
             spec_inds[0, stind:stind + bins_per_step] = np.arange(bins_per_step, dtype=np.uint32)  # Bin step

pycroscopy/io/translators/beps_data_generator.py

@@ -55,6 +55,12 @@ class FakeBEPSGenerator(Translator):
         self.n_spec_bins = None
         self.n_fields = None
         self.binning_func = no_bin
+        self.cycle_fraction = None
+        self.h5_path = None
+        self.image_ext = None
+        self.rebin = None
+        self.bin_factor = None
+        self.bin_func = None
 
     def _read_data(self, folder):
         """

pycroscopy/io/translators/beps_ndf.py

@@ -30,6 +30,28 @@ class BEPSndfTranslator(Translator):
     files to .h5
 
     """
+    def __init__(self, *args, **kwargs):
+        super(BEPSndfTranslator, self).__init__(*args, **kwargs)
+        self.debug = False
+        self.parm_dict = dict()
+        self.field_mode = None
+        self.spec_label = None
+        self.halve_udvs_steps = None
+        self.BE_wave = None
+        self.BE_wave_rev = None
+        self.BE_bin_inds = None
+        self.udvs_mat = None
+        self.udvs_labs = None
+        self.udvs_units = None
+        self.num_udvs_steps = None
+        self.excit_type_vec = None
+        self.__unique_waves__ = None
+        self.__num_wave_types__ = None
+        self.max_pixels = None
+        self.pos_labels = None
+        self.pos_mat = None
+        self.pos_units = None
+        self.hdf = None
 
     def translate(self, data_filepath, show_plots=True, save_plots=True, do_histogram=False, debug=False):
         """

pycroscopy/io/translators/df_utils/be_utils.py

@@ -1184,12 +1184,21 @@ BEHistogram Class and Functions
 
 
 class BEHistogram:
-    # TODO: Turn into proper class
-    # TODO: Parallelize Histogram generation
+    # TODO: Make into Process class
     """
     Class just functions as a container so we can have shared objects
     Chris Smith -- csmith55@utk.edu
     """
+    def __init__(self):
+        self.max_mem = None
+        self.max_response = None
+        self.min_response = None
+        self.num_udvs_steps = 1
+        self.N_spectral_steps = 1
+        self.N_bins = 1
+        self.N_freqs = 1
+        self.N_pixels = 1
+        self.N_y_bins = 1
 
     def addBEHist(self, h5_path, max_mem_mb=1024, show_plot=True, save_plot=True):
         """

pycroscopy/io/translators/df_utils/nanonispy/read.py

@@ -170,7 +170,7 @@ class Grid(NanonisFile):
 
     def __init__(self, fname):
         _is_valid_file(fname, ext='3ds')
-        super().__init__(fname)
+        super(NanonisFile, self).__init__(fname)
         self.header = _parse_3ds_header(self.header_raw)
         self.signals = self._load_data()
         self.signals['sweep_signal'] = self._derive_sweep_signal()
@@ -296,7 +296,7 @@ class Scan(NanonisFile):
 
     def __init__(self, fname):
         _is_valid_file(fname, ext='sxm')
-        super().__init__(fname)
+        super(NanonisFile, self).__init__(fname)
         self.header = _parse_sxm_header(self.header_raw)
 
         # data begins with 4 byte code, add 4 bytes to offset instead
@@ -368,7 +368,7 @@ class Spec(NanonisFile):
 
     def __init__(self, fname):
         _is_valid_file(fname, ext='dat')
-        super().__init__(fname)
+        super(NanonisFile, self).__init__(fname)
         self.header = _parse_dat_header(self.header_raw)
         self.signals = self._load_data()
 

pycroscopy/io/translators/forc_iv.py

@@ -21,6 +21,9 @@ class ForcIVTranslator(Translator):
     """
     Translates FORC IV datasets from .mat files to .h5
     """
+    def __init__(self, *args, **kwargs):
+        super(ForcIVTranslator, self).__init__(*args, **kwargs)
+        self.h5_read = None
 
     def _read_data(self):
         pass

pycroscopy/io/translators/gmode_iv.py

@@ -24,6 +24,9 @@ class GIVTranslator(Translator):
     """
     Translates G-mode Fast IV datasets from .mat files to .h5
     """
+    def __init__(self, *args, **kwargs):
+        super(GIVTranslator, self).__init__(*args, **kwargs)
+        self.raw_datasets = None
 
     def _parse_file_path(self, input_path):
         pass

pycroscopy/io/translators/gmode_line.py

@@ -24,7 +24,12 @@ class GLineTranslator(Translator):
     """
     Translated G-mode line (bigtimedata.dat) files from actual BE line experiments to HDF5
     """
-    
+    def __init__(self, *args, **kwargs):
+        super(Translator, self).__init__(*args, **kwargs)
+        self.points_per_pixel = 1
+        self.num_rows = 1
+        self.__bytes_per_row__ = 1
+
     def translate(self, file_path):
         """
         The main function that translates the provided file into a .h5 file

pycroscopy/io/translators/gmode_tune.py

@@ -27,6 +27,9 @@ class GTuneTranslator(GLineTranslator):
     Translates G-mode Tune (bigtimedata.dat) files from actual BE line experiments to HDF5
     """
 
+    def __init__(self, *args, **kwargs):
+        super(GLineTranslator, self).__init__(*args, **kwargs)
+
     def translate(self, file_path):
         """
         The main function that translates the provided file into a .h5 file

pycroscopy/io/translators/labview_h5_patcher.py

@@ -31,7 +31,7 @@ class LabViewH5Patcher(Translator):
     def _read_data(self):
         pass
 
-    def translate(self, h5_path, force_patch=False):
+    def translate(self, h5_path, force_patch=False, **kwargs):
         """
         Add the needed references and attributes to the h5 file that are not created by the
         LabView data aquisition program.

pycroscopy/io/translators/trKPFM_translator.py

@@ -23,6 +23,9 @@ class TRKPFMTranslator(Translator):
     """
     Translates  trKPFM datasets from .mat and .dat files to .h5
     """
+    def __init__(self, *args, **kwargs):
+        super(TRKPFMTranslator, self).__init__(*args, **kwargs)
+        self.raw_datasets = None
 
     def _parse_file_path(self, input_path):
         folder_path, base_name = path.split(input_path)

pycroscopy/processing/feature_extraction.py

@@ -108,7 +108,7 @@ class FeatureExtractorParallel(object):
 
         Parameters
         ----------
-        processors : int, optional
+        processes : int, optional
                     Number of processors to use, default = 1.
         mask : boolean, optional, default False.
             Whether to use

pycroscopy/processing/giv_bayesian.py

@@ -66,6 +66,9 @@ class GIVBayesian(Process):
         self.roll_pts = int(self.single_ao.size * roll_cyc_fract)
         self.rolled_bias = np.roll(self.single_ao, self.roll_pts)
 
+        self.reverse_results = None
+        self.forward_results = None
+
     def _set_memory_and_cores(self, cores=1, mem=1024):
         """
         Checks hardware limitations such as memory, # cpus and sets the recommended datachunk sizes and the
@@ -238,7 +241,6 @@ class GIVBayesian(Process):
 
         Parameters
         ----------
-        None
 
         Returns
         -------

pycroscopy/processing/image_processing.py

@@ -20,12 +20,8 @@ from ..io.io_hdf5 import ioHDF5
 from ..io.io_utils import getAvailableMem
 from ..io.microdata import MicroDataGroup, MicroDataset
 from ..io.translators.utils import get_position_slicing, make_position_mat, get_spectral_slicing
-from .svd_utils import _get_component_slice
+from .svd_utils import get_component_slice
 
-# windata32 = np.dtype([('Image Data', np.float32)])
-# absfft32 = np.dtype([('FFT Magnitude', np.float32)])
-# winabsfft32 = np.dtype([('Image Data', np.float32), ('FFT Magnitude', np.float32)])
-# wincompfft32 = np.dtype([('Image Data', np.float32), ('FFT Real', np.float32), ('FFT Imag', np.float32)])
 windata32 = np.dtype({'names': ['Image Data'],
                       'formats': [np.float32]})
 absfft32 = np.dtype({'names': ['FFT Magnitude'],
@@ -799,7 +795,7 @@ class ImageWindow(object):
 
         print('Cleaning the image by removing unwanted components.')
 
-        comp_slice = _get_component_slice(components)
+        comp_slice = get_component_slice(components)
 
         '''
         Read the 1st n_comp components from the SVD results
@@ -970,7 +966,7 @@ class ImageWindow(object):
             return
 
         print('Cleaning the image by removing unwanted components.')
-        comp_slice = _get_component_slice(components)
+        comp_slice = get_component_slice(components)
 
         '''
         Read the 1st n_comp components from the SVD results

pycroscopy/processing/image_transformation.py

@@ -785,7 +785,8 @@ class geoTransformerParallel(object):
 
         Parameters
         ----------
-        input: h5py.dataset
+        dataset: h5py.dataset
+            The dataset to be corrected
 
         """
         if not isinstance(dataset, h5py.Dataset):
@@ -1048,7 +1049,7 @@ class geoTransformerParallel(object):
 
         Parameters
         ----------
-        Processors: int, optional
+        processes: int, optional
             Number of processors to use, default = 1.
 
         Returns
@@ -1197,7 +1198,7 @@ class geoTransformerSerial(object):
         return matches, filt_matches
 
     # TODO: Need Better Error Handling.
-    def findTransformation(self, transform, matches, processes, **kwargs):
+    def findTransformation(self, transform, matches, **kwargs):
         """
         This is a Method that finds the optimal transformation between two images
         given matching features using a random sample consensus.
@@ -1207,8 +1208,6 @@ class geoTransformerSerial(object):
         transform : skimage.transform object
         matches : list
             matches found through match_features method.
-        processors : int
-            Number of processors to use.
         **kwargs are passed to skimage.transform.ransac
 
         Returns
@@ -1351,7 +1350,7 @@ class geoTransformerSerial(object):
 
         Parameters
         ----------
-        Processors: int, optional
+        processes: int, optional
             Number of processors to use, default = 1.
 
         Returns