`rwth_nb.misc`

Feedback 

class rwth_nb.misc.feedback.RWTHFeedbackCollector

RWTH Feedback Collector Class

Processes json feedback files in a folder or feedbacks from RWTHJupyter realm and creates dataframes for every: mentioned notebook.
Intended to be used with RWTHFeedbackEvaluator but also standalone possible as the collected feedbacks are stored: as pandas Dataframes.

Examples

>>> from rwth_nb.misc.feedback import RWTHFeedbackCollector
>>> path_to_folder = './Feedbacks' # path to folder with feedback json files
>>> collector = RWTHFeedbackCollector()
>>> data = collector.get_all_folder(path_to_folder) # get feedback for all notebooks from folder
>>> data = collector.get_all_jupyter(realm) # get feedback for all notebooks from RWTHJupyter realm

get_all_folder(folder_path: str) → DataFrame

Collect all DataFrames for every notebook from every json file in a path

Parameters

folder_path: str: Folder in which json feedback files are stored

Returns

pd.DataFrame: pandas dataframe of all submitted feedback files in a folder

class rwth_nb.misc.feedback.RWTHFeedbackEvaluator

RWTH Feedback Evaluator Class

Processes pandas dataframes created with the collector class above into an evaluation interface. Likert scale like answers are plotted in different styles (bar only for now) Free text answers are collected and displayed into a list for readability

Examples

>>> from rwth_nb.misc.feedback import RWTHFeedbackEvaluator
>>> eva = RWTHFeedbackEvaluator()
>>> eva.evaluate(data, lang='de') # data: see rwth_nb.misc.feedback.RWTHFeedbackCollector

evaluate(data, lang='en')

Actual evaluation

Parameters

data: pandas.Dataframe or List[pandas.Dataframe]: dataframe created by collector class using json files multiple dataframes can be passed in a list (note that all must be in the same language)
lang: str, optional: language to be used; the likert scale is chosen accordingly from RWTHFeedback class

class rwth_nb.misc.feedback.RWTHFeedbackJupyter(feedback_name, questions, lang='en', realm=None)

RWTH Feedback submission with RWTHJupyters submission service

Parameters

feedback_name: str: the feedbacks name
questions: dict: feedback options to be filled out
lang: str, optional: feedback language, scales are shown in that language
realm: str, optional: jupyter submission realm in which the feedback should be stored, is set automatically if None

class rwth_nb.misc.feedback.RWTHFeedbackMail(feedback_name, questions, lang='en', feedback_path='feedback.json', mail_to=None, mail_from='feedback@jupyter.rwth-aachen.de', mail_subject=None, mail_smtp_host='smarthost.rwth-aachen.de')

RWTH Feedback submission with mail

Parameters

feedback_name: str: the feedbacks name
questions: dict: feedback options to be filled out
lang: str, optional: feedback language, scales are shown in that language
feedback_path: str, optional: path in which a feedback json file should be stored
mail_to: str, optional: mail adress to which the feedback should be sent when submitted
mail_from: str, optional: mail adress from which the feedback should be sent when submitted
mail_subject: str, optional: subject of the mail
mail_smtp_host: str, optional: smtp host

save_entries()

Save entries into json file.

Not used if user is in jupyter cluster.

send_mail()

Sends JSON file as attachment of a mail to predefined recipient

Sets self.is_submitted to True if mail was sent successfully. False otherwise.

Media 

Signals 

This module defines functionality related to signal processing.

rwth_nb.misc.signals.dft(s, fs, NFFT=0): Calculate discrete Fourier transform of vector s Sampling frequency fs is used to calculate frequency vector f Number of frequency coefficients can be specified as well

rwth_nb.misc.signals.find_ind_least_diff(x, x0)

Find index of the value that’s nearest to x0

Parameters

xarray_like: Array to be inspected
x0float: Target value to be searched for

Returns

indexint or list: index(indices) of value(s) that is (are) nearest to x0

rwth_nb.misc.signals.find_intervals(s, t, thresh, delta)

Find intervals of signal s by searching for delta-functions in the second derivative of s

Parameters

sarray_like: The signal
tarray_like: Corresponding t-axis
threshfloat: Threshold for delta search
deltafloat: Sampling period

Returns

intervals_s, peaks, dd : intervals are the intervals, peaks the found peaks and dd the second derivative of s.

rwth_nb.misc.signals.idft(S, Ntime=0, NFFT=0): Calculate discrete inverse Fourier transform of vector S Number of time bins is used to crop the output of NumPy’s ifft function Number of frequency coefficients can be specified as well

Filters 

rwth_nb.misc.filters.butter(cutoff, fs, order=5, type='Tiefpass', fdelta=0)

Butterworth Filter of order n

Parameters

cutofffloat: cutoff frequency
fsfloat: sampling frequency, is used to calculate nyquist frequency
orderfloat, optional: order of filter
type: {‘Tiefpass’, ‘Bandpass’, ‘Hochpass’}, optional: type of filter.
fdelta: float, optional: bandwith of filter

Returns

bndarray: numerator polynomial of the IIR filter
andarray: denominator polynomial of the IIR filter

rwth_nb.misc.filters.butter_bandpass(f0, fdelta, fs, order=5)

Bandpass Butterworth Filter

This filter is set as a bandpass filter with f0, fdelta, fs and order set by user.

Parameters

sarray_like: n-dimensional input array
barray_like: numerator coefficient vector in a 1-D sequence.
aarray_like: denominator coefficient vector in a 1-D sequence.

Returns

garray: output of digital filter.

Transforms 

rwth_nb.misc.transforms.dft(s, fs, NFFT=0)

Calculate discrete fourier transform of vector s

Parameters

sarray_like: vector to be transformed
fsfloat: sampling frequency, is used to calculate frequency vector f
NFFTfloat, optional: number of frequency coefficients

Returns

Sndarray: resulting discrete fourier transform
fndarray: frequency vector

Examples

For examples see primer at rwth_nb/RWTH Transforms.ipynb

rwth_nb.misc.transforms.idft(S, Ntime=0, NFFT=0)

Calculate inverse discrete fourier transform of vector S

Parameters

Sarray_like: vector to be transformed
Ntimefloat, optional: number of time bins, is used to crop the output of NumPy’s ifft function
NFFTfloat, optional: number of frequency coefficients

Returns

sndarray: resulting inverse discrete fourier transform

Examples

For examples see primer at rwth_nb/RWTH Transforms.ipynb

rwth_nb.misc.transforms.ilaplace_Hf(f=array([-6., -5.98826979, -5.97653959, ..., 5.97653959, 5.98826979, 6.]), H0=1, pp=array([], dtype=float64), pz=array([], dtype=float64), ord_p=array([], dtype=float64), ord_z=array([], dtype=float64), dB=False)

Calculate frequency response H(f) of H(p) defined by its gain factor, poles and zeroes

Parameters

farray_like: array of f-domain to be transformed to
H0float: gain factor
pparray_like: poles on pz-plane (exclude conjugated poles)
pzarray_like: zeroes on pz-plane (exclude conjugated zeroes)
ord_parray_like: poles’ orders
ord_zarray_like: zeroes’ orders
dBbool: return frq response in dB if true

Returns

_ndarray: calculated frequency response (in dB if parameter dB is true)

Examples

Calculate frequency response of H(p) with gain H0 = 1

poles:: p_p1 = +j, -j (order 1) Note: Exclude conjugated poles!
zeroes:: p_n1 = 0 (order 1)

and return in dB

>>> from rwth_nb.misc.transforms import ilaplace_Hf
>>>
>>> f = numpy.linspace(-6, 6, 1024)
>>> H0 = 1
>>> poles = [1j]  # Exclude conjugated pole
>>> poles_order = [1]
>>> zeroes = [0]
>>> zeroes_order = [1]
>>> dB = True
>>>
>>> H_f = ilaplace_Hf(f, H0, poles, zeroes, poles_order, zeroes_order, dB)

For more examples see primer at rwth_nb/RWTH Transforms.ipynb

rwth_nb.misc.transforms.ilaplace_ht(t=array([-6., -5.98826979, -5.97653959, ..., 5.97653959, 5.98826979, 6.]), H0=1, pp=array([], dtype=float64), pz=array([], dtype=float64), ord_p=array([], dtype=float64), ord_z=array([], dtype=float64), roc=[-12, 12])

Calculate inverse laplace transform h(t) of H(p) defined by its gain factor, poles and zeroes

Parameters

tarray_like: array of t-domain to be transformed to
H0float: gain factor
pparray_like: poles on pz-plane (exclude conjugated poles)
pzarray_like: zeroes on pz-plane (exclude conjugated zeroes)
ord_parray_like: poles’ orders
ord_zarray_like: zeroes’ orders
rocarray_like: region of convergence (range from -infinity to infinity)

Returns

hndarray: calculated inverse Laplace-transform according to t-domain
td: ndarray: dirac’s x coordinate if existing, empty otherwise
sd: ndarray: dirac’s gain factor if existing, empty otherwise

Examples

Calculate inverse Laplace-transform with gain H0 = 1

poles:: p_p1 = -3 (order 1) p_p2 = +1 (order 2)
zeroes:: p_n1 = 0 (order 1)
region of convergence:: 1 to infinity

>>> from rwth_nb.misc.transforms import ilaplace_ht
>>>
>>> t = numpy.linspace(-5, 5, 1024)
>>> H0 = 1
>>> poles = [-3, 1]
>>> poles_order = [1, 2]
>>> zeroes = [0]
>>> zeroes_order = [1]
>>> roc = [1, numpy.inf]
>>>
>>> h_t, td, sd = ilaplace_ht(t, H0, poles, zeroes, poles_order, zeroes_order, roc)

For more examples see primer at rwth_nb/RWTH Transforms.ipynb

rwth_nb.misc.transforms.iz_Hf(f=array([-6., -5.98826979, -5.97653959, ..., 5.97653959, 5.98826979, 6.]), H0=1, pp=array([], dtype=float64), pz=array([], dtype=float64), ord_p=array([], dtype=float64), ord_z=array([], dtype=float64), dB=False)

Calculate frequency response H(f) of z-transformed H(z) defined by its gain factor, poles and zeroes

Parameters

farray_like: array of f-domain to be transformed to
H0float: gain factor
pparray_like: poles on pz-plane (exclude conjugated poles)
pzarray_like: zeroes on pz-plane (exclude conjugated zeroes)
ord_parray_like: poles’ orders
ord_zarray_like: zeroes’ orders
dBbool: return frq response in dB if true

Returns

_ndarray: calculated frequency response (in dB if parameter dB is true)

Examples

Calculate frequency response of H(z) with gain H0 = 1

poles:: z_p1/2 = +j, -j (order 1) Note: Exclude conjugated poles!
zeroes:: z_n1 = 0 (order 1)

and return in dB

>>> from rwth_nb.misc.transforms import iz_Hf
>>>
>>> f = numpy.linspace(-6, 6, 1024)
>>> H0 = 1
>>> poles = [1j]  # Exclude conjugated pole
>>> poles_order = [1]
>>> zeroes = [0]
>>> zeroes_order = [1]
>>> dB = True
>>>
>>> H_f = iz_Hf(f, H0, poles, zeroes, poles_order, zeroes_order, dB)

For more examples see primer at rwth_nb/RWTH Transforms.ipynb

rwth_nb.misc.transforms.iz_hn(n=array([-6., -5., -4., -3., -2., -1., 0., 1., 2., 3., 4., 5., 6.]), H0=1, pp=array([], dtype=float64), pz=array([], dtype=float64), ord_p=array([], dtype=float64), ord_z=array([], dtype=float64), roc=[0, 12])

Calculate inverse z-transform h(n) of H(z) defined by its gain factor, poles and zeroes

Parameters

narray_like: array of n-domain to be transformed to
H0float: gain factor
pparray_like: poles on pz-plane (exclude conjugated poles)
pzarray_like: zeroes on pz-plane (exclude conjugated zeroes)
ord_parray_like: poles’ orders
ord_zarray_like: zeroes’ orders
rocarray_like: region of convergence (range from 0 to infinity)

Returns

hndarray: calculated inverse z-transform according to n-domain

Examples

Calculate inverse z-transform with gain H0 = 1

poles:: z_p1 = +0.5 (order 1) z_p2 = +1 (order 2)
zeroes:: z_n1 = +2 (order 1)
region of convergence:: 0.5 to 1

>>> from rwth_nb.misc.transforms import iz_hn
>>>
>>> n = [-3, -2, -1, 0, 1, 2, 3]
>>> H0 = 1
>>> poles = [0.5, 1]
>>> poles_order = [1, 2]
>>> zeroes = [2]
>>> zeroes_order = [1]
>>> roc = [0.5, 1]
>>>
>>> h_n = iz_hn(n, H0, poles, zeroes, poles_order, zeroes_order, roc)

rwth_nb.misc

Feedback

Examples

Parameters

Returns

Examples

Parameters

Parameters

Parameters

Media

Signals

Parameters

Returns

Parameters

Returns

Filters

Parameters

Returns

See Also

See Also

See Also

Parameters

Returns

Transforms

Parameters

Returns

Examples

Parameters

Returns

Examples

Parameters

Returns

Examples

Parameters

Returns

Examples

Parameters

Returns

Examples

Parameters

Returns

Examples

`rwth_nb.misc`

Feedback 

Media 

Signals 

Filters 

Transforms 