Code Duplication - sabiharustam/voltcycle - Measure and Improve Code Quality continuously with Scrutinizer


    return index_list


def peak_values(DataFrame_x, DataFrame_y):
    """Outputs x (potentials) and y (currents) values from data indices
        given by peak_detection function.

       ----------
       Parameters
       ----------
       DataFrame_x : should be in the form of a pandas DataFrame column.
         For example, df['potentials'] could be input as the column of x
         data.

        DataFrame_y : should be in the form of a pandas DataFrame column.
          For example, df['currents'] could be input as the column of y
          data.

       Returns
       -------
       Result : numpy array of coordinates at peaks in the following order:
         potential of peak on top curve, current of peak on top curve,
         potential of peak on bottom curve, current of peak on bottom curve"""
    index = peak_detection_fxn(DataFrame_y)
    potential1, potential2 = split(DataFrame_x)
    current1, current2 = split(DataFrame_y)
    Peak_values = []
    Peak_values.append(potential2[(index[0])])  # TOPX (bottom part of curve is
    # the first part of DataFrame)
    Peak_values.append(current2[(index[0])])  # TOPY
    Peak_values.append(potential1[(index[1])])  # BOTTOMX
    Peak_values.append(current1[(index[1])])  # BOTTOMY
    Peak_array = np.array(Peak_values)
    return Peak_array


def del_potential(DataFrame_x, DataFrame_y):


    return index_list


def peak_values(DataFrame_x, DataFrame_y):
    """Outputs x (potentials) and y (currents) values from data indices
        given by peak_detection function.

       ----------
       Parameters
       ----------
       DataFrame_x : should be in the form of a pandas DataFrame column.
         For example, df['potentials'] could be input as the column of x
         data.

        DataFrame_y : should be in the form of a pandas DataFrame column.
          For example, df['currents'] could be input as the column of y
          data.

       Returns
       -------
       Result : numpy array of coordinates at peaks in the following order:
         potential of peak on top curve, current of peak on top curve,
         potential of peak on bottom curve, current of peak on bottom curve"""
    index = peak_detection_fxn(DataFrame_y)
    potential1, potential2 = split(DataFrame_x)
    current1, current2 = split(DataFrame_y)
    Peak_values = []
    Peak_values.append(potential2[(index[0])])  # TOPX (bottom part of curve is
    # the first part of DataFrame)
    Peak_values.append(current2[(index[0])])  # TOPY
    Peak_values.append(potential1[(index[1])])  # BOTTOMX
    Peak_values.append(current1[(index[1])])  # BOTTOMY
    Peak_array = np.array(Peak_values)
    return Peak_array


def del_potential(DataFrame_x, DataFrame_y):


    return index_list


def peak_values(DataFrame_x, DataFrame_y):
    """Outputs x (potentials) and y (currents) values from data indices
        given by peak_detection function.

       ----------
       Parameters
       ----------
       DataFrame_x : should be in the form of a pandas DataFrame column.
         For example, df['potentials'] could be input as the column of x
         data.

        DataFrame_y : should be in the form of a pandas DataFrame column.
          For example, df['currents'] could be input as the column of y
          data.

       Returns
       -------
       Result : numpy array of coordinates at peaks in the following order:
         potential of peak on top curve, current of peak on top curve,
         potential of peak on bottom curve, current of peak on bottom curve"""
    index = peak_detection_fxn(DataFrame_y)
    potential1, potential2 = split(DataFrame_x)
    current1, current2 = split(DataFrame_y)
    Peak_values = []
    Peak_values.append(potential2[(index[0])])  # TOPX (bottom part of curve is
    # the first part of DataFrame)
    Peak_values.append(current2[(index[0])])  # TOPY
    Peak_values.append(potential1[(index[1])])  # BOTTOMX
    Peak_values.append(current1[(index[1])])  # BOTTOMY
    Peak_array = np.array(Peak_values)
    return Peak_array


def del_potential(DataFrame_x, DataFrame_y):


import pandas as pd


def peak_values(DataFrame_x, DataFrame_y):
    """Outputs x (potentials) and y (currents) values from data indices
        given by peak_detection function.

       ----------
       Parameters
       ----------
       DataFrame_x : should be in the form of a pandas DataFrame column.
         For example, df['potentials'] could be input as the column of x
         data.

        DataFrame_y : should be in the form of a pandas DataFrame column.
          For example, df['currents'] could be input as the column of y
          data.

       Returns
       -------
       Result : numpy array of coordinates at peaks in the following order:
         potential of peak on top curve, current of peak on top curve,
         potential of peak on bottom curve, current of peak on bottom curve"""
    index = peak_detection(DataFrame_y)
    potential1, potential2 = split(DataFrame_x)
    current1, current2 = split(DataFrame_y)
    Peak_values = []
    Peak_values.append(potential2[(index[0])])  # TOPX (bottom part of curve is
    # the first part of DataFrame)
    Peak_values.append(current2[(index[0])])  # TOPY
    Peak_values.append(potential1[(index[1])])  # BOTTOMX
    Peak_values.append(current1[(index[1])])  # BOTTOMY
    Peak_array = np.array(Peak_values)
    return Peak_array


def del_potential(DataFrame_x, DataFrame_y):

		@@ 404-434 (lines=31) @@
401		return index_list
402
403
404		def peak_values(DataFrame_x, DataFrame_y):
405		"""Outputs x (potentials) and y (currents) values from data indices
406		given by peak_detection function.
407
408		----------
409		Parameters
410		----------
411		DataFrame_x : should be in the form of a pandas DataFrame column.
412		For example, df['potentials'] could be input as the column of x
413		data.
414
415		DataFrame_y : should be in the form of a pandas DataFrame column.
416		For example, df['currents'] could be input as the column of y
417		data.
418
419		Returns
420		-------
421		Result : numpy array of coordinates at peaks in the following order:
422		potential of peak on top curve, current of peak on top curve,
423		potential of peak on bottom curve, current of peak on bottom curve"""
424		index = peak_detection_fxn(DataFrame_y)
425		potential1, potential2 = split(DataFrame_x)
426		current1, current2 = split(DataFrame_y)
427		Peak_values = []
428		Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is
429		# the first part of DataFrame)
430		Peak_values.append(current2[(index[0])]) # TOPY
431		Peak_values.append(potential1[(index[1])]) # BOTTOMX
432		Peak_values.append(current1[(index[1])]) # BOTTOMY
433		Peak_array = np.array(Peak_values)
434		return Peak_array
435
436
437		def del_potential(DataFrame_x, DataFrame_y):

		@@ 316-346 (lines=31) @@
313		return index_list
314
315
316		def peak_values(DataFrame_x, DataFrame_y):
317		"""Outputs x (potentials) and y (currents) values from data indices
318		given by peak_detection function.
319
320		----------
321		Parameters
322		----------
323		DataFrame_x : should be in the form of a pandas DataFrame column.
324		For example, df['potentials'] could be input as the column of x
325		data.
326
327		DataFrame_y : should be in the form of a pandas DataFrame column.
328		For example, df['currents'] could be input as the column of y
329		data.
330
331		Returns
332		-------
333		Result : numpy array of coordinates at peaks in the following order:
334		potential of peak on top curve, current of peak on top curve,
335		potential of peak on bottom curve, current of peak on bottom curve"""
336		index = peak_detection_fxn(DataFrame_y)
337		potential1, potential2 = split(DataFrame_x)
338		current1, current2 = split(DataFrame_y)
339		Peak_values = []
340		Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is
341		# the first part of DataFrame)
342		Peak_values.append(current2[(index[0])]) # TOPY
343		Peak_values.append(potential1[(index[1])]) # BOTTOMX
344		Peak_values.append(current1[(index[1])]) # BOTTOMY
345		Peak_array = np.array(Peak_values)
346		return Peak_array
347
348
349		def del_potential(DataFrame_x, DataFrame_y):

		@@ 316-346 (lines=31) @@
313		return index_list
314
315
316		def peak_values(DataFrame_x, DataFrame_y):
317		"""Outputs x (potentials) and y (currents) values from data indices
318		given by peak_detection function.
319
320		----------
321		Parameters
322		----------
323		DataFrame_x : should be in the form of a pandas DataFrame column.
324		For example, df['potentials'] could be input as the column of x
325		data.
326
327		DataFrame_y : should be in the form of a pandas DataFrame column.
328		For example, df['currents'] could be input as the column of y
329		data.
330
331		Returns
332		-------
333		Result : numpy array of coordinates at peaks in the following order:
334		potential of peak on top curve, current of peak on top curve,
335		potential of peak on bottom curve, current of peak on bottom curve"""
336		index = peak_detection_fxn(DataFrame_y)
337		potential1, potential2 = split(DataFrame_x)
338		current1, current2 = split(DataFrame_y)
339		Peak_values = []
340		Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is
341		# the first part of DataFrame)
342		Peak_values.append(current2[(index[0])]) # TOPY
343		Peak_values.append(potential1[(index[1])]) # BOTTOMX
344		Peak_values.append(current1[(index[1])]) # BOTTOMY
345		Peak_array = np.array(Peak_values)
346		return Peak_array
347
348
349		def del_potential(DataFrame_x, DataFrame_y):

GitHub Access Token became invalid

Code Duplication Length = 31-31 lines in 4 locations

voltcycle/core.py 1 location

practice/core.py 1 location

practice/dash_resumable_upload.py 1 location

voltcycle/submodule/calculations.py 1 location

		@@ 5-35 (lines=31) @@
2		import pandas as pd
3
4
5		def peak_values(DataFrame_x, DataFrame_y):
6		"""Outputs x (potentials) and y (currents) values from data indices
7		given by peak_detection function.
8
9		----------
10		Parameters
11		----------
12		DataFrame_x : should be in the form of a pandas DataFrame column.
13		For example, df['potentials'] could be input as the column of x
14		data.
15
16		DataFrame_y : should be in the form of a pandas DataFrame column.
17		For example, df['currents'] could be input as the column of y
18		data.
19
20		Returns
21		-------
22		Result : numpy array of coordinates at peaks in the following order:
23		potential of peak on top curve, current of peak on top curve,
24		potential of peak on bottom curve, current of peak on bottom curve"""
25		index = peak_detection(DataFrame_y)
26		potential1, potential2 = split(DataFrame_x)
27		current1, current2 = split(DataFrame_y)
28		Peak_values = []
29		Peak_values.append(potential2[(index[0])]) # TOPX (bottom part of curve is
30		# the first part of DataFrame)
31		Peak_values.append(current2[(index[0])]) # TOPY
32		Peak_values.append(potential1[(index[1])]) # BOTTOMX
33		Peak_values.append(current1[(index[1])]) # BOTTOMY
34		Peak_array = np.array(Peak_values)
35		return Peak_array
36
37
38		def del_potential(DataFrame_x, DataFrame_y):

sabiharustam / voltcycle

GitHub Access Token became invalid

Code Duplication Length = 31-31 lines in 4 locations

voltcycle/core.py 1 location

practice/core.py 1 location

practice/dash_resumable_upload.py 1 location

voltcycle/submodule/calculations.py 1 location