JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Declare the view as a JSON API method This converts view return value into a :cls:JsonResponse. The following return types are supported: - tuple: a tuple of (response, status, headers) - any other object is converted to JSON def jsonapi(f): """ Declare the view as a JSON ...
Download + unpack given package into temp dir ``tmp``. Return ``(real_version, source)`` where ``real_version`` is the "actual" version downloaded (e.g. if a Git master was indicated, it will be the SHA of master HEAD) and ``source`` is the source directory (relative to unpacked source) to import into ...
Vendorize Python package ``distribution`` at version/SHA ``version``. Specify the vendor folder (e.g. ``<mypackage>/vendor``) as ``vendor_dir``. For Crate/PyPI releases, ``package`` should be the name of the software entry on those sites, and ``version`` should be a specific version number. E.g. ``ven...
Create a passworded sudo-capable user. Used by other tasks to execute the test suite so sudo tests work. def make_sudouser(c): """ Create a passworded sudo-capable user. Used by other tasks to execute the test suite so sudo tests work. """ user = c.travis.sudo.user password = c.travis.sud...
Set up passwordless SSH keypair & authorized_hosts access to localhost. def make_sshable(c): """ Set up passwordless SSH keypair & authorized_hosts access to localhost. """ user = c.travis.sudo.user home = "~{0}".format(user) # Run sudo() as the new sudo user; means less chown'ing, etc. c.c...
Run some command under Travis-oriented sudo subshell/virtualenv. :param str command: Command string to run, e.g. ``inv coverage``, ``inv integration``, etc. (Does not necessarily need to be an Invoke task, but...) def sudo_run(c, command): """ Run some command under Travis-oriented sudo su...
Install and execute ``black`` under appropriate circumstances, with diffs. Installs and runs ``black`` under Python 3.6 (the first version it supports). Since this sort of CI based task only needs to run once per commit (formatting is not going to change between interpreters) this seems like a worthwhi...
List based implementation of binary tree algorithm for concordance measure after :cite:`Christensen2005`. def _calc(self, x, y): """ List based implementation of binary tree algorithm for concordance measure after :cite:`Christensen2005`. """ x = np.array(x) y =...
Wrapper function to decorate a function def decorator(self, func): """ Wrapper function to decorate a function """ if inspect.isfunction(func): func._methodview = self elif inspect.ismethod(func): func.__func__._methodview = self else: raise Assertion...
Test if the method matches the provided set of arguments :param verb: HTTP verb. Uppercase :type verb: str :param params: Existing route parameters :type params: set :returns: Whether this view matches :rtype: bool def matches(self, verb, params): """ Test if th...
Detect a view matching the query :param method: HTTP method :param route_params: Route parameters dict :return: Method :rtype: Callable\|None def _match_view(self, method, route_params): """ Detect a view matching the query :param method: HTTP method :param rout...
Register the view with an URL route :param app: Flask application :type app: flask.Flask\|flask.Blueprint :param name: Unique view name :type name: str :param rules: List of route rules to use :type rules: Iterable[str\|werkzeug.routing.Rule] :param class_args: Args...
Calculates the steady state probability vector for a regular Markov transition matrix P. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- : array (k, ), steady state distribution. Exa...
Calculates the matrix of first mean passage times for an ergodic transition probability matrix. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- M : array (k, k), elements are the expected value for the num...
Variances of first mean passage times for an ergodic transition probability matrix. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- : array (k, k), elements are the variances for the number of in...
Examine world state, returning data on what needs updating for release. :param c: Invoke ``Context`` object or subclass. :returns: Two dicts (technically, dict subclasses, which allow attribute access), ``actions`` and ``state`` (in that order.) ``actions`` maps release component name...
Print current release (version, changelog, tag, etc) status. Doubles as a subroutine, returning the return values from its inner call to ``_converge`` (an ``(actions, state)`` two-tuple of Lexicons). def status(c): """ Print current release (version, changelog, tag, etc) status. Doubles as a subr...
Edit changelog & version, git commit, and git tag, to set up for release. def prepare(c): """ Edit changelog & version, git commit, and git tag, to set up for release. """ # Print dry-run/status/actions-to-take data & grab programmatic result # TODO: maybe expand the enum-based stuff to have values...
Examine current repo state to determine what type of release to prep. :returns: A two-tuple of ``(branch-name, line-type)`` where: - ``branch-name`` is the current branch name, e.g. ``1.1``, ``master``, ``gobbledygook`` (or, usually, ``HEAD`` if not on a branch). - ``line-type`` ...
Return all released versions from given ``changelog``, sorted. :param dict changelog: A changelog dict as returned by ``releases.util.parse_changelog``. :returns: A sorted list of `semantic_version.Version` objects. def _versions_from_changelog(changelog): """ Return all released versions fro...
Return most recent branch-appropriate release, if any, and its contents. :param dict changelog: Changelog contents, as returned by ``releases.util.parse_changelog``. :param str branch: Branch name. :param release_type: Member of `Release`, e.g. `Release.FEATURE`. :returns: ...
Return sorted list of release-style tags as semver objects. def _get_tags(c): """ Return sorted list of release-style tags as semver objects. """ tags_ = [] for tagstr in c.run("git tag", hide=True).stdout.strip().split("\n"): try: tags_.append(Version(tagstr)) # Ignore ...
Try to find 'the' One True Package for this project. Mostly for obtaining the ``_version`` file within it. Uses the ``packaging.package`` config setting if defined. If not defined, fallback is to look for a single top-level Python package (directory containing ``__init__.py``). (This search ignores a ...
Build sdist and/or wheel archives, optionally in a temp base directory. All parameters save ``directory`` honor config settings of the same name, under the ``packaging`` tree. E.g. say ``.configure({'packaging': {'wheel': True}})`` to force building wheel archives by default. :param bool sdist: ...
Publish code to PyPI or index of choice. All parameters save ``dry_run`` and ``directory`` honor config settings of the same name, under the ``packaging`` tree. E.g. say ``.configure({'packaging': {'wheel': True}})`` to force building wheel archives by default. :param bool sdist: Whether t...
Upload (potentially also signing) all artifacts in ``directory``. :param str index: Custom upload index/repository name. By default, uses whatever the invoked ``pip`` is configured to use. Modify your ``pypirc`` file to add new named repositories. :param bool sign: Whether to ...
Context-manage a temporary directory. Can be given ``skip_cleanup`` to skip cleanup, and ``explicit`` to choose a specific location. (If both are given, this is basically not doing anything, but it allows code that normally requires a secure temporary directory to 'dry run' instead.) def tmpdir(s...
Generate ransom spatial permutations for inference on LISA vectors. Parameters ---------- permutations : int, optional Number of random permutations of observations. alternative : string, optional Type of alternative to form in generating p-values. Op...
Plot the rose diagram. Parameters ---------- attribute : (n,) ndarray, optional Variable to specify colors of the colorbars. ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. Note, this axis ...
Plot vectors of positional transition of LISA values starting from the same origin. def plot_origin(self): # TODO add attribute option to color vectors """ Plot vectors of positional transition of LISA values starting from the same origin. """ import matplotlib.cm as cm...
Plot vectors of positional transition of LISA values within quadrant in scatterplot in a polar plot. Parameters ---------- ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. arrows : boolean, opti...
Nuke docs build target directory so next build is clean. def _clean(c): """ Nuke docs build target directory so next build is clean. """ if isdir(c.sphinx.target): rmtree(c.sphinx.target)
Open build target's index.html in a browser (using 'open'). def _browse(c): """ Open build target's index.html in a browser (using 'open'). """ index = join(c.sphinx.target, c.sphinx.target_file) c.run("open {0}".format(index))
Build the project's Sphinx docs. def build( c, clean=False, browse=False, nitpick=False, opts=None, source=None, target=None, ): """ Build the project's Sphinx docs. """ if clean: _clean(c) if opts is None: opts = "" if nitpick: opts += " -n -...
Display documentation contents with the 'tree' program. def tree(c): """ Display documentation contents with the 'tree' program. """ ignore = ".git\|.pyc\|.swp\|dist\|*.egg-info\|_static\|_build\|_templates" c.run('tree -Ca -I "{0}" {1}'.format(ignore, c.sphinx.source))
Build both doc sites w/ maxed nitpicking. def sites(c): """ Build both doc sites w/ maxed nitpicking. """ # TODO: This is super lolzy but we haven't actually tackled nontrivial # in-Python task calling yet, so we do this to get a copy of 'our' context, # which has been updated with the per-coll...
Watch both doc trees & rebuild them if files change. This includes e.g. rebuilding the API docs if the source code changes; rebuilding the WWW docs if the README changes; etc. Reuses the configuration values ``packaging.package`` or ``tests.package`` (the former winning over the latter if both defined...
Random permutation of rows and columns of a matrix Parameters ---------- X : array (k, k), array to be permutated. ids : array range (k, ). Returns ------- X : array (k, k) with rows and columns randomly shuffled. Examples -------- >>> import ...
Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector. Parameters ---------- matrix : array (n, n) numpy array, a distance matrix. Returns ------- lowvec : array numpy array, the lower half of the distance matrix flattened into a ve...
r""" Run black on the current source tree (all ``.py`` files). .. warning:: ``black`` only runs on Python 3.6 or above. (However, it can be executed against Python 2 compatible code.) :param int line_length: Line length argument. Default: ``79``. :param list folders: Li...
Normalize the response value into a 3-tuple (rv, status, headers) :type rv: tuple\|* :returns: tuple(rv, status, headers) :rtype: tuple(Response\|JsonResponse\|*, int\|None, dict\|None) def normalize_response_value(rv): """ Normalize the response value into a 3-tuple (rv, status, headers) ...
Make JsonResponse :param rv: Response: the object to encode, or tuple (response, status, headers) :type rv: tuple\|* :rtype: JsonResponse def make_json_response(rv): """ Make JsonResponse :param rv: Response: the object to encode, or tuple (response, status, headers) :type rv: tuple\|* :rtype...
Markov-based mobility index. Parameters ---------- p : array (k, k), Markov transition probability matrix. measure : string If measure= "P", :math:`M_{P} = \\frac{m-\sum_{i=1}^m P_{ii}}{m-1}`; if measure = "D", :math:`M_{D} = 1...
chi-squared test of difference between two transition matrices. Parameters ---------- T1 : array (k, k), matrix of transitions (counts). T2 : array (k, k), matrix of transitions (counts) to use to form the probabilities under the null. Returns ------- ...
Kullback information based test of Markov Homogeneity. Parameters ---------- F : array (s, r, r), values are transitions (not probabilities) for s strata, r initial states, r terminal states. Returns ------- Results : dictionary (key - value) Condit...
Prais conditional mobility measure. Parameters ---------- pmat : matrix (k, k), Markov probability transition matrix. Returns ------- pr : matrix (1, k), conditional mobility measures for each of the k classes. Notes ----- Prais' conditional mobility measur...
Test for homogeneity of Markov transition probabilities across regimes. Parameters ---------- transition_matrices : list of transition matrices for regimes, all matrices must have same size (r, c). r is the number of rows in the ...
Calculate sojourn time based on a given transition probability matrix. Parameters ---------- p : array (k, k), a Markov transition probability matrix. Returns ------- : array (k, ), sojourn times. Each element is the expected time a Markov ...
Helper to estimate spatial lag conditioned Markov transition probability matrices based on maximum likelihood techniques. def _calc(self, y, w): '''Helper to estimate spatial lag conditioned Markov transition probability matrices based on maximum likelihood techniques. ''' if s...
A summary method to call the Markov homogeneity test to test for temporally lagged spatial dependence. To learn more about the properties of the tests, refer to :cite:`Rey2016a` and :cite:`Kang2018`. def summary(self, file_name=None): """ A summary method to call the Markov hom...
Helper method for classifying continuous data. def _maybe_classify(self, y, k, cutoffs): '''Helper method for classifying continuous data. ''' rows, cols = y.shape if cutoffs is None: if self.fixed: mcyb = mc.Quantiles(y.flatten(), k=k) yb =...
Detect spillover locations for diffusion in LISA Markov. Parameters ---------- quadrant : int which quadrant in the scatterplot should form the core of a cluster. neighbors_on : binary If false, then only the 1st o...
Get entity property names :param entity: Entity :type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta :returns: Set of entity property names :rtype: set def get_entity_propnames(entity): """ Get entity property names :param entity: Entity :type entity: sqlal...
Get entity property names that are loaded (e.g. won't produce new queries) :param entity: Entity :type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta :returns: Set of entity property names :rtype: set def get_entity_loaded_propnames(entity): """ Get entity property names th...
Return a Version whose minor number is one greater than self's. .. note:: The new Version will always have a zeroed-out bugfix/tertiary version number, because the "next minor release" of e.g. 1.2.1 is 1.3.0, not 1.3.1. def next_minor(self): """ Return a Version whose minor number ...
Check that ``encoding`` is a valid Python encoding :param name: name under which the encoding is known to the user, e.g. 'default encoding' :param encoding_to_check: name of the encoding to check, e.g. 'utf-8' :param source: source where the encoding has been set, e.g. option name :raise pygount.common....
Generator function to yield lines (delimited with ``'\n'``) stored in ``text``. This is useful when a regular expression should only match on a per line basis in a memory efficient way. def lines(text): """ Generator function to yield lines (delimited with ``'\n'``) stored in ``text``. This is usef...
The first line and its number (starting with 0) in the source code that indicated that the source code is generated. :param source_lines: lines of text to scan :param generated_regexes: regular expressions a line must match to indicate the source code is generated. :param max_line_count: maximum...
Similar to tokens but converts strings after a colon (:) to comments. def _pythonized_comments(tokens): """ Similar to tokens but converts strings after a colon (:) to comments. """ is_after_colon = True for token_type, token_text in tokens: if is_after_colon and (token_type in pygments.tok...
The encoding used by the text file stored in ``source_path``. The algorithm used is: * If ``encoding`` is ``'automatic``, attempt the following: 1. Check BOM for UTF-8, UTF-16 and UTF-32. 2. Look for XML prolog or magic heading like ``# -- coding: cp1252 --`` 3. Read the file using UTF-8. ...
Initial quick check if there is a lexer for ``source_path``. This removes the need for calling :py:func:`pygments.lexers.guess_lexer_for_filename()` which fully reads the source file. def has_lexer(source_path): """ Initial quick check if there is a lexer for ``source_path``. This removes the need ...
Analysis for line counts in source code stored in ``source_path``. :param source_path: :param group: name of a logical group the sourc code belongs to, e.g. a package. :param encoding: encoding according to :func:`encoding_for` :param fallback_encoding: fallback encoding according to :func:...
replace all masked values calculate flatField from 2d-polynomal fit filling all high gradient areas within averaged fit-image returns flatField, average background level, fitted image, valid indices mask def polynomial(img, mask, inplace=False, replace_all=False, max_dev=1e-5, max_iter...
TODO def errorDist(scale, measExpTime, n_events_in_expTime, event_duration, std, points_per_time=100, n_repetitions=300): ''' TODO ''' ntimes = 10 s1 = measExpTime * scale * 10 # exp. time factor 1/16-->16: p2 = np.logspace(-4, 4, 18, base=2) t = ...
std ... standard deviation of every signal dur1...dur3 --> event duration per second n1...n3 --> number of events per second def exampleSignals(std=1, dur1=1, dur2=3, dur3=0.2, n1=0.5, n2=0.5, n3=2): ''' std ... standard deviation of every signal dur1...dur3 --> even...
returns Gaussian shaped signal fluctuations [events] t --> times to calculate event for n --> numbers of events per sec duration --> event duration per sec std --> std of event if averaged over time offs --> event offset def _flux(t, n, duration, std, offs=1): ''' returns Gauss...
capture signal and return its standard deviation #TODO: more detail def _capture(f, t, t0, factor): ''' capture signal and return its standard deviation #TODO: more detail ''' n_per_sec = len(t) / t[-1] # len of one split: n = int(t0 * factor * n_per_sec) s = len(f) // n ...
Return a generic camera matrix [[fx, 0, cx], [ 0, fy, cy], [ 0, 0, 1]] for a given image shape def genericCameraMatrix(shape, angularField=60): ''' Return a generic camera matrix [[fx, 0, cx], [ 0, fy, cy], [ 0, 0, 1]] for a given image shape ''' # http:/...
calculate the spatial resolved standard deviation for a given 2d array ksize -> kernel size blurred(optional) -> with same ksize gaussian filtered image setting this parameter reduces processing time def standardDeviation2d(img, ksize=5, blurred=None): ''' ...
fn['mean', 'median'] fill_mask=True: replaced masked areas with filtered results fill_mask=False: masked areas are ignored def maskedFilter(arr, mask, ksize=30, fill_mask=True, fn='median'): ''' fn['mean', 'median'] fill_mask=True: replaced masked...
Extract vignetting from a set of images containing different devices The devices spatial inhomogeneities are averaged This method is referred as 'Method C' in --- K.Bedrich, M.Bokalic et al.: ELECTROLUMINESCENCE IMAGING OF PV DEVICES: ADVANCED FLAT FIELD CALIBRATION,2017 --- ...
Calculate the averaged signal-to-noise ratio SNR50 as defined by IEC NP 60904-13 needs 2 reference EL images and one background image def SNR_IEC(i1, i2, ibg=0, allow_color_images=False): ''' Calculate the averaged signal-to-noise ratio SNR50 as defined by IEC NP 60904-13 needs 2 refe...
angle [DEG] def _rotate(img, angle): ''' angle [DEG] ''' s = img.shape if angle == 0: return img else: M = cv2.getRotationMatrix2D((s[1] // 2, s[0] // 2), angle, 1) return cv2.warpAffi...
return offset(x,y) which fit best self._base_img through template matching def _findOverlap(self, img_rgb, overlap, overlapDeviation, rotation, rotationDeviation): ''' return offset(x,y) which fit best self._base_img through template matching ''' ...
estimate the noise level function as stDev over image intensity from a set of 2 image groups images at the same position have to show the identical setup, so imgs1[i] - imgs2[i] = noise def estimateFromImages(imgs1, imgs2=None, mn_mx=None, nbins=100): ''' estimate the noise level functio...
get the parameters of the, needed by 'function' through curve fitting def _evaluate(x, y, weights): ''' get the parameters of the, needed by 'function' through curve fitting ''' i = _validI(x, y, weights) xx = x[i] y = y[i] try: fitParams = _fit(xx, y) #...
limit [function] to a minimum y value def boundedFunction(x, minY, ax, ay): ''' limit [function] to a minimum y value ''' y = function(x, ax, ay) return np.maximum(np.nan_to_num(y), minY)
general square root function def function(x, ax, ay): ''' general square root function ''' with np.errstate(invalid='ignore'): return ay * (x - ax)**0.5
return indices that have enough data points and are not erroneous def _validI(x, y, weights): ''' return indices that have enough data points and are not erroneous ''' # density filter: i = np.logical_and(np.isfinite(y), weights > np.median(weights)) # filter outliers: try: ...
in case the NLF cannot be described by a square root function commit bounded polynomial interpolation def smooth(x, y, weights): ''' in case the NLF cannot be described by a square root function commit bounded polynomial interpolation ''' # Spline hard to smooth properly, ther...
Estimate the NLF from one or two images of the same kind def oneImageNLF(img, img2=None, signal=None): ''' Estimate the NLF from one or two images of the same kind ''' x, y, weights, signal = calcNLF(img, img2, signal) _, fn, _ = _evaluate(x, y, weights) return fn, signal
Get the a range of image intensities that most pixels are in with def _getMinMax(img): ''' Get the a range of image intensities that most pixels are in with ''' av = np.mean(img) std = np.std(img) # define range for segmentation: mn = av - 3 * std mx = av + 3 * std ...
Calculate the noise level function (NLF) as f(intensity) using one or two image. The approach for this work is published in JPV########## img2 - 2nd image taken under same conditions used to estimate noise via image difference signalFromMultipleImages - whether the signal is an avera...
fit unstructured data def polyfit2d(x, y, z, order=3 #bounds=None ): ''' fit unstructured data ''' ncols = (order + 1)**2 G = np.zeros((x.size, ncols)) ij = itertools.product(list(range(order+1)), list(range(order+1))) for k, (i,j) in enumerate(ij): G[:,k] = ...
replace all masked values with polynomial fitted ones def polyfit2dGrid(arr, mask=None, order=3, replace_all=False, copy=True, outgrid=None): ''' replace all masked values with polynomial fitted ones ''' s0,s1 = arr.shape if mask is None: if outgrid is None: ...
find closest minimum position next to middle line relative: return position relative to middle line f: relative decrease (0...1) - setting this value close to one will discriminate positions further away from the center ##order: 2 for cubic refinement def minimumLineInArray(arr, relative=False, ...
remove all low frequencies by setting a square in the middle of the Fourier transformation of the size (2*threshold)^2 to zero threshold = 0...1 def highPassFilter(self, threshold): ''' remove all low frequencies by setting a square in the middle of the Fourier transformati...
remove all high frequencies by setting boundary around a quarry in the middle of the size (2threshold)^2 to zero threshold = 0...1 def lowPassFilter(self, threshold): ''' remove all high frequencies by setting boundary around a quarry in the middle of the size (2threshold...
do inverse Fourier transform and return result def reconstructImage(self): ''' do inverse Fourier transform and return result ''' f_ishift = np.fft.ifftshift(self.fshift) return np.real(np.fft.ifft2(f_ishift))
x,y,v --> 1d numpy.array grid --> 2d numpy.array fast if number of given values is small relative to grid resolution def interpolate2dUnstructuredIDW(x, y, v, grid, power=2): ''' x,y,v --> 1d numpy.array grid --> 2d numpy.array fast if number of given values is small relative to grid ...
returns the Histogram of Oriented Gradients :param ksize: convolution kernel size as (y,x) - needs to be odd :param orientations: number of orientations in between rad=0 and rad=pi similar to http://scikit-image.org/docs/dev/auto_examples/plot_hog.html but faster and with less options def hog(i...
visualize HOG as polynomial around cell center for [grid] * cells def visualize(hog, grid=(10, 10), radCircle=None): ''' visualize HOG as polynomial around cell center for [grid] * cells ''' s0, s1, nang = hog.shape angles = np.linspace(0, np.pi, nang + 1)[:-1] # center ...
Post process measured flat field [arr]. Depending on the measurement, different post processing [method]s are beneficial. The available methods are presented in --- K.Bedrich, M.Bokalic et al.: ELECTROLUMINESCENCE IMAGING OF PV DEVICES: ADVANCED FLAT FIELD CALI...
border [None], if images are corrected and device ends at image border [one number] (like 50), if there is an equally spaced border around the device [two tu...
######### mask -- optional def addImage(self, image, mask=None): ''' ######### mask -- optional ''' self._last_diff = diff = image - self.noSTE ste = diff > self.threshold removeSinglePixels(ste) self.mask_clean = clean = ~ste ...
return STE area - relative to image area def relativeAreaSTE(self): ''' return STE area - relative to image area ''' s = self.noSTE.shape return np.sum(self.mask_STE) / (s[0] * s[1])
return distribution of STE intensity def intensityDistributionSTE(self, bins=10, range=None): ''' return distribution of STE intensity ''' v = np.abs(self._last_diff[self.mask_STE]) return np.histogram(v, bins, range)
transform a float to an unsigned integer array of a fitting dtype adds an offset, to get rid of negative values range = (min, max) - scale values between given range cutNegative - all values <0 will be set to 0 cutHigh - set to False to rather scale values to fit def toUIntArray(img, dtype=None...
transform an unsigned integer array into a float array of the right size def toFloatArray(img): ''' transform an unsigned integer array into a float array of the right size ''' _D = {1: np.float32, # uint8 2: np.float32, # uint16 4: np.float64, # uint32 ...
cast array to the next higher integer array if dtype=unsigned integer def toNoUintArray(arr): ''' cast array to the next higher integer array if dtype=unsigned integer ''' d = arr.dtype if d.kind == 'u': arr = arr.astype({1: np.int16, 2: np.int32, ...

Declare the view as a JSON API method This converts view return value into a :cls:JsonResponse. The following return types are supported: - tuple: a tuple of (response, status, headers) - any other object is converted to JSON def jsonapi(f): """ Declare the view as a JSON ...

Download + unpack given package into temp dir ``tmp``. Return ``(real_version, source)`` where ``real_version`` is the "actual" version downloaded (e.g. if a Git master was indicated, it will be the SHA of master HEAD) and ``source`` is the source directory (relative to unpacked source) to import into ...

Vendorize Python package ``distribution`` at version/SHA ``version``. Specify the vendor folder (e.g. ``<mypackage>/vendor``) as ``vendor_dir``. For Crate/PyPI releases, ``package`` should be the name of the software entry on those sites, and ``version`` should be a specific version number. E.g. ``ven...

Create a passworded sudo-capable user. Used by other tasks to execute the test suite so sudo tests work. def make_sudouser(c): """ Create a passworded sudo-capable user. Used by other tasks to execute the test suite so sudo tests work. """ user = c.travis.sudo.user password = c.travis.sud...

Set up passwordless SSH keypair & authorized_hosts access to localhost. def make_sshable(c): """ Set up passwordless SSH keypair & authorized_hosts access to localhost. """ user = c.travis.sudo.user home = "~{0}".format(user) # Run sudo() as the new sudo user; means less chown'ing, etc. c.c...

Run some command under Travis-oriented sudo subshell/virtualenv. :param str command: Command string to run, e.g. ``inv coverage``, ``inv integration``, etc. (Does not necessarily need to be an Invoke task, but...) def sudo_run(c, command): """ Run some command under Travis-oriented sudo su...

Install and execute ``black`` under appropriate circumstances, with diffs. Installs and runs ``black`` under Python 3.6 (the first version it supports). Since this sort of CI based task only needs to run once per commit (formatting is not going to change between interpreters) this seems like a worthwhi...

List based implementation of binary tree algorithm for concordance measure after :cite:`Christensen2005`. def _calc(self, x, y): """ List based implementation of binary tree algorithm for concordance measure after :cite:`Christensen2005`. """ x = np.array(x) y =...

Wrapper function to decorate a function def decorator(self, func): """ Wrapper function to decorate a function """ if inspect.isfunction(func): func._methodview = self elif inspect.ismethod(func): func.__func__._methodview = self else: raise Assertion...

Test if the method matches the provided set of arguments :param verb: HTTP verb. Uppercase :type verb: str :param params: Existing route parameters :type params: set :returns: Whether this view matches :rtype: bool def matches(self, verb, params): """ Test if th...

Detect a view matching the query :param method: HTTP method :param route_params: Route parameters dict :return: Method :rtype: Callable|None def _match_view(self, method, route_params): """ Detect a view matching the query :param method: HTTP method :param rout...

Register the view with an URL route :param app: Flask application :type app: flask.Flask|flask.Blueprint :param name: Unique view name :type name: str :param rules: List of route rules to use :type rules: Iterable[str|werkzeug.routing.Rule] :param class_args: Args...

Calculates the steady state probability vector for a regular Markov transition matrix P. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- : array (k, ), steady state distribution. Exa...

Calculates the matrix of first mean passage times for an ergodic transition probability matrix. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- M : array (k, k), elements are the expected value for the num...

Variances of first mean passage times for an ergodic transition probability matrix. Parameters ---------- P : array (k, k), an ergodic Markov transition probability matrix. Returns ------- : array (k, k), elements are the variances for the number of in...

Examine world state, returning data on what needs updating for release. :param c: Invoke ``Context`` object or subclass. :returns: Two dicts (technically, dict subclasses, which allow attribute access), ``actions`` and ``state`` (in that order.) ``actions`` maps release component name...

Print current release (version, changelog, tag, etc) status. Doubles as a subroutine, returning the return values from its inner call to ``_converge`` (an ``(actions, state)`` two-tuple of Lexicons). def status(c): """ Print current release (version, changelog, tag, etc) status. Doubles as a subr...

Edit changelog & version, git commit, and git tag, to set up for release. def prepare(c): """ Edit changelog & version, git commit, and git tag, to set up for release. """ # Print dry-run/status/actions-to-take data & grab programmatic result # TODO: maybe expand the enum-based stuff to have values...

Examine current repo state to determine what type of release to prep. :returns: A two-tuple of ``(branch-name, line-type)`` where: - ``branch-name`` is the current branch name, e.g. ``1.1``, ``master``, ``gobbledygook`` (or, usually, ``HEAD`` if not on a branch). - ``line-type`` ...

Return all released versions from given ``changelog``, sorted. :param dict changelog: A changelog dict as returned by ``releases.util.parse_changelog``. :returns: A sorted list of `semantic_version.Version` objects. def _versions_from_changelog(changelog): """ Return all released versions fro...

Return most recent branch-appropriate release, if any, and its contents. :param dict changelog: Changelog contents, as returned by ``releases.util.parse_changelog``. :param str branch: Branch name. :param release_type: Member of `Release`, e.g. `Release.FEATURE`. :returns: ...

Return sorted list of release-style tags as semver objects. def _get_tags(c): """ Return sorted list of release-style tags as semver objects. """ tags_ = [] for tagstr in c.run("git tag", hide=True).stdout.strip().split("\n"): try: tags_.append(Version(tagstr)) # Ignore ...

Try to find 'the' One True Package for this project. Mostly for obtaining the ``_version`` file within it. Uses the ``packaging.package`` config setting if defined. If not defined, fallback is to look for a single top-level Python package (directory containing ``__init__.py``). (This search ignores a ...

Build sdist and/or wheel archives, optionally in a temp base directory. All parameters save ``directory`` honor config settings of the same name, under the ``packaging`` tree. E.g. say ``.configure({'packaging': {'wheel': True}})`` to force building wheel archives by default. :param bool sdist: ...

Publish code to PyPI or index of choice. All parameters save ``dry_run`` and ``directory`` honor config settings of the same name, under the ``packaging`` tree. E.g. say ``.configure({'packaging': {'wheel': True}})`` to force building wheel archives by default. :param bool sdist: Whether t...

Upload (potentially also signing) all artifacts in ``directory``. :param str index: Custom upload index/repository name. By default, uses whatever the invoked ``pip`` is configured to use. Modify your ``pypirc`` file to add new named repositories. :param bool sign: Whether to ...

Context-manage a temporary directory. Can be given ``skip_cleanup`` to skip cleanup, and ``explicit`` to choose a specific location. (If both are given, this is basically not doing anything, but it allows code that normally requires a secure temporary directory to 'dry run' instead.) def tmpdir(s...

Generate ransom spatial permutations for inference on LISA vectors. Parameters ---------- permutations : int, optional Number of random permutations of observations. alternative : string, optional Type of alternative to form in generating p-values. Op...

Plot the rose diagram. Parameters ---------- attribute : (n,) ndarray, optional Variable to specify colors of the colorbars. ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. Note, this axis ...

Plot vectors of positional transition of LISA values starting from the same origin. def plot_origin(self): # TODO add attribute option to color vectors """ Plot vectors of positional transition of LISA values starting from the same origin. """ import matplotlib.cm as cm...

Plot vectors of positional transition of LISA values within quadrant in scatterplot in a polar plot. Parameters ---------- ax : Matplotlib Axes instance, optional If given, the figure will be created inside this axis. Default =None. arrows : boolean, opti...

Nuke docs build target directory so next build is clean. def _clean(c): """ Nuke docs build target directory so next build is clean. """ if isdir(c.sphinx.target): rmtree(c.sphinx.target)

Open build target's index.html in a browser (using 'open'). def _browse(c): """ Open build target's index.html in a browser (using 'open'). """ index = join(c.sphinx.target, c.sphinx.target_file) c.run("open {0}".format(index))

Build the project's Sphinx docs. def build( c, clean=False, browse=False, nitpick=False, opts=None, source=None, target=None, ): """ Build the project's Sphinx docs. """ if clean: _clean(c) if opts is None: opts = "" if nitpick: opts += " -n -...

Display documentation contents with the 'tree' program. def tree(c): """ Display documentation contents with the 'tree' program. """ ignore = ".git|*.pyc|*.swp|dist|*.egg-info|_static|_build|_templates" c.run('tree -Ca -I "{0}" {1}'.format(ignore, c.sphinx.source))

Build both doc sites w/ maxed nitpicking. def sites(c): """ Build both doc sites w/ maxed nitpicking. """ # TODO: This is super lolzy but we haven't actually tackled nontrivial # in-Python task calling yet, so we do this to get a copy of 'our' context, # which has been updated with the per-coll...

Watch both doc trees & rebuild them if files change. This includes e.g. rebuilding the API docs if the source code changes; rebuilding the WWW docs if the README changes; etc. Reuses the configuration values ``packaging.package`` or ``tests.package`` (the former winning over the latter if both defined...

Random permutation of rows and columns of a matrix Parameters ---------- X : array (k, k), array to be permutated. ids : array range (k, ). Returns ------- X : array (k, k) with rows and columns randomly shuffled. Examples -------- >>> import ...

Flattens the lower part of an n x n matrix into an n*(n-1)/2 x 1 vector. Parameters ---------- matrix : array (n, n) numpy array, a distance matrix. Returns ------- lowvec : array numpy array, the lower half of the distance matrix flattened into a ve...

r""" Run black on the current source tree (all ``.py`` files). .. warning:: ``black`` only runs on Python 3.6 or above. (However, it can be executed against Python 2 compatible code.) :param int line_length: Line length argument. Default: ``79``. :param list folders: Li...

Normalize the response value into a 3-tuple (rv, status, headers) :type rv: tuple|* :returns: tuple(rv, status, headers) :rtype: tuple(Response|JsonResponse|*, int|None, dict|None) def normalize_response_value(rv): """ Normalize the response value into a 3-tuple (rv, status, headers) ...

Make JsonResponse :param rv: Response: the object to encode, or tuple (response, status, headers) :type rv: tuple|* :rtype: JsonResponse def make_json_response(rv): """ Make JsonResponse :param rv: Response: the object to encode, or tuple (response, status, headers) :type rv: tuple|* :rtype...

Markov-based mobility index. Parameters ---------- p : array (k, k), Markov transition probability matrix. measure : string If measure= "P", :math:`M_{P} = \\frac{m-\sum_{i=1}^m P_{ii}}{m-1}`; if measure = "D", :math:`M_{D} = 1...

chi-squared test of difference between two transition matrices. Parameters ---------- T1 : array (k, k), matrix of transitions (counts). T2 : array (k, k), matrix of transitions (counts) to use to form the probabilities under the null. Returns ------- ...

Kullback information based test of Markov Homogeneity. Parameters ---------- F : array (s, r, r), values are transitions (not probabilities) for s strata, r initial states, r terminal states. Returns ------- Results : dictionary (key - value) Condit...

Prais conditional mobility measure. Parameters ---------- pmat : matrix (k, k), Markov probability transition matrix. Returns ------- pr : matrix (1, k), conditional mobility measures for each of the k classes. Notes ----- Prais' conditional mobility measur...

Test for homogeneity of Markov transition probabilities across regimes. Parameters ---------- transition_matrices : list of transition matrices for regimes, all matrices must have same size (r, c). r is the number of rows in the ...

Calculate sojourn time based on a given transition probability matrix. Parameters ---------- p : array (k, k), a Markov transition probability matrix. Returns ------- : array (k, ), sojourn times. Each element is the expected time a Markov ...

Helper to estimate spatial lag conditioned Markov transition probability matrices based on maximum likelihood techniques. def _calc(self, y, w): '''Helper to estimate spatial lag conditioned Markov transition probability matrices based on maximum likelihood techniques. ''' if s...

A summary method to call the Markov homogeneity test to test for temporally lagged spatial dependence. To learn more about the properties of the tests, refer to :cite:`Rey2016a` and :cite:`Kang2018`. def summary(self, file_name=None): """ A summary method to call the Markov hom...

Helper method for classifying continuous data. def _maybe_classify(self, y, k, cutoffs): '''Helper method for classifying continuous data. ''' rows, cols = y.shape if cutoffs is None: if self.fixed: mcyb = mc.Quantiles(y.flatten(), k=k) yb =...

Detect spillover locations for diffusion in LISA Markov. Parameters ---------- quadrant : int which quadrant in the scatterplot should form the core of a cluster. neighbors_on : binary If false, then only the 1st o...

Get entity property names :param entity: Entity :type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta :returns: Set of entity property names :rtype: set def get_entity_propnames(entity): """ Get entity property names :param entity: Entity :type entity: sqlal...

Get entity property names that are loaded (e.g. won't produce new queries) :param entity: Entity :type entity: sqlalchemy.ext.declarative.api.DeclarativeMeta :returns: Set of entity property names :rtype: set def get_entity_loaded_propnames(entity): """ Get entity property names th...

Return a Version whose minor number is one greater than self's. .. note:: The new Version will always have a zeroed-out bugfix/tertiary version number, because the "next minor release" of e.g. 1.2.1 is 1.3.0, not 1.3.1. def next_minor(self): """ Return a Version whose minor number ...

Check that ``encoding`` is a valid Python encoding :param name: name under which the encoding is known to the user, e.g. 'default encoding' :param encoding_to_check: name of the encoding to check, e.g. 'utf-8' :param source: source where the encoding has been set, e.g. option name :raise pygount.common....

Generator function to yield lines (delimited with ``'\n'``) stored in ``text``. This is useful when a regular expression should only match on a per line basis in a memory efficient way. def lines(text): """ Generator function to yield lines (delimited with ``'\n'``) stored in ``text``. This is usef...

The first line and its number (starting with 0) in the source code that indicated that the source code is generated. :param source_lines: lines of text to scan :param generated_regexes: regular expressions a line must match to indicate the source code is generated. :param max_line_count: maximum...

Similar to tokens but converts strings after a colon (:) to comments. def _pythonized_comments(tokens): """ Similar to tokens but converts strings after a colon (:) to comments. """ is_after_colon = True for token_type, token_text in tokens: if is_after_colon and (token_type in pygments.tok...

The encoding used by the text file stored in ``source_path``. The algorithm used is: * If ``encoding`` is ``'automatic``, attempt the following: 1. Check BOM for UTF-8, UTF-16 and UTF-32. 2. Look for XML prolog or magic heading like ``# -*- coding: cp1252 -*-`` 3. Read the file using UTF-8. ...

Initial quick check if there is a lexer for ``source_path``. This removes the need for calling :py:func:`pygments.lexers.guess_lexer_for_filename()` which fully reads the source file. def has_lexer(source_path): """ Initial quick check if there is a lexer for ``source_path``. This removes the need ...

Analysis for line counts in source code stored in ``source_path``. :param source_path: :param group: name of a logical group the sourc code belongs to, e.g. a package. :param encoding: encoding according to :func:`encoding_for` :param fallback_encoding: fallback encoding according to :func:...

replace all masked values calculate flatField from 2d-polynomal fit filling all high gradient areas within averaged fit-image returns flatField, average background level, fitted image, valid indices mask def polynomial(img, mask, inplace=False, replace_all=False, max_dev=1e-5, max_iter...

TODO def errorDist(scale, measExpTime, n_events_in_expTime, event_duration, std, points_per_time=100, n_repetitions=300): ''' TODO ''' ntimes = 10 s1 = measExpTime * scale * 10 # exp. time factor 1/16-->16: p2 = np.logspace(-4, 4, 18, base=2) t = ...

std ... standard deviation of every signal dur1...dur3 --> event duration per second n1...n3 --> number of events per second def exampleSignals(std=1, dur1=1, dur2=3, dur3=0.2, n1=0.5, n2=0.5, n3=2): ''' std ... standard deviation of every signal dur1...dur3 --> even...

returns Gaussian shaped signal fluctuations [events] t --> times to calculate event for n --> numbers of events per sec duration --> event duration per sec std --> std of event if averaged over time offs --> event offset def _flux(t, n, duration, std, offs=1): ''' returns Gauss...

capture signal and return its standard deviation #TODO: more detail def _capture(f, t, t0, factor): ''' capture signal and return its standard deviation #TODO: more detail ''' n_per_sec = len(t) / t[-1] # len of one split: n = int(t0 * factor * n_per_sec) s = len(f) // n ...

Return a generic camera matrix [[fx, 0, cx], [ 0, fy, cy], [ 0, 0, 1]] for a given image shape def genericCameraMatrix(shape, angularField=60): ''' Return a generic camera matrix [[fx, 0, cx], [ 0, fy, cy], [ 0, 0, 1]] for a given image shape ''' # http:/...

calculate the spatial resolved standard deviation for a given 2d array ksize -> kernel size blurred(optional) -> with same ksize gaussian filtered image setting this parameter reduces processing time def standardDeviation2d(img, ksize=5, blurred=None): ''' ...

fn['mean', 'median'] fill_mask=True: replaced masked areas with filtered results fill_mask=False: masked areas are ignored def maskedFilter(arr, mask, ksize=30, fill_mask=True, fn='median'): ''' fn['mean', 'median'] fill_mask=True: replaced masked...

Extract vignetting from a set of images containing different devices The devices spatial inhomogeneities are averaged This method is referred as 'Method C' in --- K.Bedrich, M.Bokalic et al.: ELECTROLUMINESCENCE IMAGING OF PV DEVICES: ADVANCED FLAT FIELD CALIBRATION,2017 --- ...

Calculate the averaged signal-to-noise ratio SNR50 as defined by IEC NP 60904-13 needs 2 reference EL images and one background image def SNR_IEC(i1, i2, ibg=0, allow_color_images=False): ''' Calculate the averaged signal-to-noise ratio SNR50 as defined by IEC NP 60904-13 needs 2 refe...

angle [DEG] def _rotate(img, angle): ''' angle [DEG] ''' s = img.shape if angle == 0: return img else: M = cv2.getRotationMatrix2D((s[1] // 2, s[0] // 2), angle, 1) return cv2.warpAffi...

return offset(x,y) which fit best self._base_img through template matching def _findOverlap(self, img_rgb, overlap, overlapDeviation, rotation, rotationDeviation): ''' return offset(x,y) which fit best self._base_img through template matching ''' ...

estimate the noise level function as stDev over image intensity from a set of 2 image groups images at the same position have to show the identical setup, so imgs1[i] - imgs2[i] = noise def estimateFromImages(imgs1, imgs2=None, mn_mx=None, nbins=100): ''' estimate the noise level functio...

get the parameters of the, needed by 'function' through curve fitting def _evaluate(x, y, weights): ''' get the parameters of the, needed by 'function' through curve fitting ''' i = _validI(x, y, weights) xx = x[i] y = y[i] try: fitParams = _fit(xx, y) #...

limit [function] to a minimum y value def boundedFunction(x, minY, ax, ay): ''' limit [function] to a minimum y value ''' y = function(x, ax, ay) return np.maximum(np.nan_to_num(y), minY)

general square root function def function(x, ax, ay): ''' general square root function ''' with np.errstate(invalid='ignore'): return ay * (x - ax)**0.5

return indices that have enough data points and are not erroneous def _validI(x, y, weights): ''' return indices that have enough data points and are not erroneous ''' # density filter: i = np.logical_and(np.isfinite(y), weights > np.median(weights)) # filter outliers: try: ...

in case the NLF cannot be described by a square root function commit bounded polynomial interpolation def smooth(x, y, weights): ''' in case the NLF cannot be described by a square root function commit bounded polynomial interpolation ''' # Spline hard to smooth properly, ther...

Estimate the NLF from one or two images of the same kind def oneImageNLF(img, img2=None, signal=None): ''' Estimate the NLF from one or two images of the same kind ''' x, y, weights, signal = calcNLF(img, img2, signal) _, fn, _ = _evaluate(x, y, weights) return fn, signal

Get the a range of image intensities that most pixels are in with def _getMinMax(img): ''' Get the a range of image intensities that most pixels are in with ''' av = np.mean(img) std = np.std(img) # define range for segmentation: mn = av - 3 * std mx = av + 3 * std ...

Calculate the noise level function (NLF) as f(intensity) using one or two image. The approach for this work is published in JPV########## img2 - 2nd image taken under same conditions used to estimate noise via image difference signalFromMultipleImages - whether the signal is an avera...

fit unstructured data def polyfit2d(x, y, z, order=3 #bounds=None ): ''' fit unstructured data ''' ncols = (order + 1)**2 G = np.zeros((x.size, ncols)) ij = itertools.product(list(range(order+1)), list(range(order+1))) for k, (i,j) in enumerate(ij): G[:,k] = ...

replace all masked values with polynomial fitted ones def polyfit2dGrid(arr, mask=None, order=3, replace_all=False, copy=True, outgrid=None): ''' replace all masked values with polynomial fitted ones ''' s0,s1 = arr.shape if mask is None: if outgrid is None: ...

find closest minimum position next to middle line relative: return position relative to middle line f: relative decrease (0...1) - setting this value close to one will discriminate positions further away from the center ##order: 2 for cubic refinement def minimumLineInArray(arr, relative=False, ...

remove all low frequencies by setting a square in the middle of the Fourier transformation of the size (2*threshold)^2 to zero threshold = 0...1 def highPassFilter(self, threshold): ''' remove all low frequencies by setting a square in the middle of the Fourier transformati...

remove all high frequencies by setting boundary around a quarry in the middle of the size (2*threshold)^2 to zero threshold = 0...1 def lowPassFilter(self, threshold): ''' remove all high frequencies by setting boundary around a quarry in the middle of the size (2*threshold...

do inverse Fourier transform and return result def reconstructImage(self): ''' do inverse Fourier transform and return result ''' f_ishift = np.fft.ifftshift(self.fshift) return np.real(np.fft.ifft2(f_ishift))

x,y,v --> 1d numpy.array grid --> 2d numpy.array fast if number of given values is small relative to grid resolution def interpolate2dUnstructuredIDW(x, y, v, grid, power=2): ''' x,y,v --> 1d numpy.array grid --> 2d numpy.array fast if number of given values is small relative to grid ...

returns the Histogram of Oriented Gradients :param ksize: convolution kernel size as (y,x) - needs to be odd :param orientations: number of orientations in between rad=0 and rad=pi similar to http://scikit-image.org/docs/dev/auto_examples/plot_hog.html but faster and with less options def hog(i...

visualize HOG as polynomial around cell center for [grid] * cells def visualize(hog, grid=(10, 10), radCircle=None): ''' visualize HOG as polynomial around cell center for [grid] * cells ''' s0, s1, nang = hog.shape angles = np.linspace(0, np.pi, nang + 1)[:-1] # center ...

Post process measured flat field [arr]. Depending on the measurement, different post processing [method]s are beneficial. The available methods are presented in --- K.Bedrich, M.Bokalic et al.: ELECTROLUMINESCENCE IMAGING OF PV DEVICES: ADVANCED FLAT FIELD CALI...

border [None], if images are corrected and device ends at image border [one number] (like 50), if there is an equally spaced border around the device [two tu...

######### mask -- optional def addImage(self, image, mask=None): ''' ######### mask -- optional ''' self._last_diff = diff = image - self.noSTE ste = diff > self.threshold removeSinglePixels(ste) self.mask_clean = clean = ~ste ...

return STE area - relative to image area def relativeAreaSTE(self): ''' return STE area - relative to image area ''' s = self.noSTE.shape return np.sum(self.mask_STE) / (s[0] * s[1])

return distribution of STE intensity def intensityDistributionSTE(self, bins=10, range=None): ''' return distribution of STE intensity ''' v = np.abs(self._last_diff[self.mask_STE]) return np.histogram(v, bins, range)

transform a float to an unsigned integer array of a fitting dtype adds an offset, to get rid of negative values range = (min, max) - scale values between given range cutNegative - all values <0 will be set to 0 cutHigh - set to False to rather scale values to fit def toUIntArray(img, dtype=None...

transform an unsigned integer array into a float array of the right size def toFloatArray(img): ''' transform an unsigned integer array into a float array of the right size ''' _D = {1: np.float32, # uint8 2: np.float32, # uint16 4: np.float64, # uint32 ...

cast array to the next higher integer array if dtype=unsigned integer def toNoUintArray(arr): ''' cast array to the next higher integer array if dtype=unsigned integer ''' d = arr.dtype if d.kind == 'u': arr = arr.astype({1: np.int16, 2: np.int32, ...