BoghdadyJR/codesearch_python · Data Studio

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def jc(result, reference): """ Jaccard coefficient Computes the Jaccard coefficient between the binary objects in two images. Parameters ---------- result: array_like Input data containing objects. Can be any type but will be converted into binary: background wh...
def precision(result, reference): """ Precison. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing object...
def recall(result, reference): """ Recall. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing objects. Ca...
def specificity(result, reference): """ Specificity. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing o...
def hd(result, reference, voxelspacing=None, connectivity=1): """ Hausdorff Distance. Computes the (symmetric) Hausdorff Distance (HD) between the binary objects in two images. It is defined as the maximum surface distance between the objects. Parameters ---------- result : array_l...
def hd95(result, reference, voxelspacing=None, connectivity=1): """ 95th percentile of the Hausdorff Distance. Computes the 95th percentile of the (symmetric) Hausdorff Distance (HD) between the binary objects in two images. Compared to the Hausdorff Distance, this metric is slightly more stable to sma...
def assd(result, reference, voxelspacing=None, connectivity=1): """ Average symmetric surface distance. Computes the average symmetric surface distance (ASD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objects. C...
def asd(result, reference, voxelspacing=None, connectivity=1): """ Average surface distance metric. Computes the average surface distance (ASD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objects. Can be any type but...
def ravd(result, reference): """ Relative absolute volume difference. Compute the relative absolute volume difference between the (joined) binary objects in the two images. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be ...
def volume_correlation(results, references): r""" Volume correlation. Computes the linear correlation in binary object volume between the contents of the successive binary images supplied. Measured through the Pearson product-moment correlation coefficient. Parameters ---------- ...
def volume_change_correlation(results, references): r""" Volume change correlation. Computes the linear correlation of change in binary object volume between the contents of the successive binary images supplied. Measured through the Pearson product-moment correlation coefficient. Par...
def obj_assd(result, reference, voxelspacing=None, connectivity=1): """ Average symmetric surface distance. Computes the average symmetric surface distance (ASSD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objec...
def obj_asd(result, reference, voxelspacing=None, connectivity=1): """ Average surface distance between objects. First correspondences between distinct binary objects in reference and result are established. Then the average surface distance is only computed between corresponding objects. Corre...
def obj_fpr(result, reference, connectivity=1): """ The false positive rate of distinct binary object detection. The false positive rates gives a percentage measure of how many distinct binary objects in the second array do not exists in the first array. A partial overlap (of minimum one voxel)...
def obj_tpr(result, reference, connectivity=1): """ The true positive rate of distinct binary object detection. The true positive rates gives a percentage measure of how many distinct binary objects in the first array also exists in the second array. A partial overlap (of minimum one voxel) is ...
def __distinct_binary_object_correspondences(reference, result, connectivity=1): """ Determines all distinct (where connectivity is defined by the connectivity parameter passed to scipy's `generate_binary_structure`) binary objects in both of the input parameters and returns a 1to1 mapping from the labe...
def __surface_distances(result, reference, voxelspacing=None, connectivity=1): """ The distances between the surface voxel of binary objects in result and their nearest partner surface voxel of a binary object in reference. """ result = numpy.atleast_1d(result.astype(numpy.bool)) reference = num...
def __combine_windows(w1, w2): """ Joins two windows (defined by tuple of slices) such that their maximum combined extend is covered by the new returned window. """ res = [] for s1, s2 in zip(w1, w2): res.append(slice(min(s1.start, s2.start), max(s1.stop, s2.stop))) return tuple(res)
def applyslicer(self, array, slicer, cval = None): r""" Apply a slicer returned by the iterator to a new array of the same dimensionality as the one used to initialize the iterator. Notes ----- If ``array`` has more dimensions than ``slicer`` and ``pmask``, the f...
def applyslicer(array, slicer, pmask, cval = 0): r""" Apply a slicer returned by the iterator to a new array of the same dimensionality as the one used to initialize the iterator. Notes ----- If ``array`` has more dimensions than ``slicer`` and ``pmask``, the fir...
def assembleimage(patches, pmasks, gridids): r""" Assemble an image from a number of patches, patch masks and their grid ids. Parameters ---------- patches : sequence Sequence of patches. pmasks : sequence Sequence of associated patch mask...
def size_threshold(img, thr, comp='lt', structure = None): r""" Removes binary objects from an image identified by a size threshold. The unconnected binary objects in an image are identified and all removed whose size compares (e.g. less-than) to a supplied threshold value. The threshold `...
def largest_connected_component(img, structure = None): r""" Select the largest connected binary component in an image. Treats all zero values in the input image as background and all others as foreground. The return value is an binary array of equal dimensions as the input array with TRUE valu...
def bounding_box(img): r""" Return the bounding box incorporating all non-zero values in the image. Parameters ---------- img : array_like An array containing non-zero objects. Returns ------- bbox : a list of slicer objects defining the bounding box """ loc...
def shape_based_slice_interpolation(img, dim, nslices): """ Adds `nslices` slices between all slices of the binary image `img` along dimension `dim` respecting the original slice values to be situated in the middle of each slice. Extrapolation situations are handled by simple repeating. Interpo...
def shape_based_slice_insertation_object_wise(sl1, sl2, dim, nslices, order=3): """ Wrapper to apply `shape_based_slice_insertation()` for each binary object separately to ensure correct extrapolation behaviour. """ out = None sandwich = numpy.concatenate((sl1[numpy.newaxis], sl2[numpy.newaxis])...
def shape_based_slice_insertation(sl1, sl2, dim, nslices, order=3): """ Insert `nslices` new slices between `sl1` and `sl2` along dimension `dim` using shape based binary interpolation. Extrapolation is handled adding `nslices`/2 step-wise eroded copies of the last slice in each direction. ...
def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if args.order < 0 or args.order > 5: parser.error('The order has to be a number between 0 and 5.') return args
def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() # check mutual exlusive and reaquired arguments if args.lmodel and args.smodel: parser.error('only one of --load-model and --save-model can be supplied, as they deci...
def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(description=__description__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('images', nargs='+', help='The images used for training (in the learning case) or to transform (in the tr...
def minowski(h1, h2, p = 2): # 46..45..14,11..43..44 / 45 us for p=int(-inf..-24..-1,1..24..inf) / float @array, +20 us @list \w 100 bins r""" Minowski distance. With :math:`p=2` equal to the Euclidean distance, with :math:`p=1` equal to the Manhattan distance, and the Chebyshev distance implementa...
def __minowski_low_positive_integer_p(h1, h2, p = 2): # 11..43 us for p = 1..24 \w 100 bins """ A faster implementation of the Minowski distance for positive integer < 25. @note do not use this function directly, but the general @link minowski() method. @note the passed histograms must be scipy arrays. ...
def manhattan(h1, h2): # # 7 us @array, 31 us @list \w 100 bins r""" Equal to Minowski distance with :math:`p=1`. See also -------- minowski """ h1, h2 = __prepare_histogram(h1, h2) return scipy.sum(scipy.absolute(h1 - h2))
def euclidean(h1, h2): # 9 us @array, 33 us @list \w 100 bins r""" Equal to Minowski distance with :math:`p=2`. See also -------- minowski """ h1, h2 = __prepare_histogram(h1, h2) return math.sqrt(scipy.sum(scipy.square(scipy.absolute(h1 - h2))))
def chebyshev(h1, h2): # 12 us @array, 36 us @list \w 100 bins r""" Chebyshev distance. Also Tchebychev distance, Maximum or :math:`L_{\infty}` metric; equal to Minowski distance with :math:`p=+\infty`. For the case of :math:`p=-\infty`, use `chebyshev_neg`. The Chebyshev distance between ...
def chebyshev_neg(h1, h2): # 12 us @array, 36 us @list \w 100 bins r""" Chebyshev negative distance. Also Tchebychev distance, Minimum or :math:`L_{-\infty}` metric; equal to Minowski distance with :math:`p=-\infty`. For the case of :math:`p=+\infty`, use `chebyshev`. The Chebyshev distanc...
def histogram_intersection(h1, h2): # 6 us @array, 30 us @list \w 100 bins r""" Calculate the common part of two histograms. The histogram intersection between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{\cap}(H, H') = \sum_{m=1}^M\min...
def relative_deviation(h1, h2): # 18 us @array, 42 us @list \w 100 bins r""" Calculate the deviation between two histograms. The relative deviation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{rd}(H, H') = \frac{ ...
def relative_bin_deviation(h1, h2): # 79 us @array, 104 us @list \w 100 bins r""" Calculate the bin-wise deviation between two histograms. The relative bin deviation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{rbd}(H, H') = \su...
def chi_square(h1, h2): # 23 us @array, 49 us @list \w 100 r""" Chi-square distance. Measure how unlikely it is that one distribution (histogram) was drawn from the other. The Chi-square distance between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math::...
def kullback_leibler(h1, h2): # 83 us @array, 109 us @list \w 100 bins r""" Kullback-Leibler divergence. Compute how inefficient it would to be code one histogram into another. Actually computes :math:`\frac{d_{KL}(h1, h2) + d_{KL}(h2, h1)}{2}` to achieve symmetry. The Kullback-Leibler div...
def __kullback_leibler(h1, h2): # 36.3 us """ The actual KL implementation. @see kullback_leibler() for details. Expects the histograms to be of type scipy.ndarray. """ result = h1.astype(scipy.float_) mask = h1 != 0 result[mask] = scipy.multiply(h1[mask], scipy.log(h1[mask] / h2[mask])) ...
def jensen_shannon(h1, h2): # 85 us @array, 110 us @list \w 100 bins r""" Jensen-Shannon divergence. A symmetric and numerically more stable empirical extension of the Kullback-Leibler divergence. The Jensen Shannon divergence between two histograms :math:`H` and :math:`H'` of size :ma...
def fidelity_based(h1, h2): # 25 us @array, 51 us @list \w 100 bins r""" Fidelity based distance. Also Bhattacharyya distance; see also the extensions `noelle_1` to `noelle_5`. The metric between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: ...
def noelle_4(h1, h2): # 26 us @array, 52 us @list \w 100 bins r""" Extension of `fidelity_based` proposed by [1]_. .. math:: d_{\arccos F}(H, H') = \frac{2}{\pi} \arccos d_{F}(H, H') See `fidelity_based` for the definition of :math:`d_{F}(H, H')`. Attributes: ...
def noelle_5(h1, h2): # 26 us @array, 52 us @list \w 100 bins r""" Extension of `fidelity_based` proposed by [1]_. .. math:: d_{\sin F}(H, H') = \sqrt{1 -d_{F}^2(H, H')} See `fidelity_based` for the definition of :math:`d_{F}(H, H')`. Attributes: - metr...
def cosine_alt(h1, h2): # 17 us @array, 42 us @list \w 100 bins r""" Alternative implementation of the `cosine` distance measure. Notes ----- Under development. """ h1, h2 = __prepare_histogram(h1, h2) return -1 * float(scipy.sum(h1 * h2)) / (scipy.sum(scipy.power(h1, 2)) * scipy.su...
def cosine(h1, h2): # 17 us @array, 42 us @list \w 100 bins r""" Cosine simmilarity. Compute the angle between the two histograms in vector space irrespective of their length. The cosine similarity between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math...
def correlate(h1, h2): # 31 us @array, 55 us @list \w 100 bins r""" Correlation between two histograms. The histogram correlation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{corr}(H, H') = \frac{ \sum_{m=1}...
def quadratic_forms(h1, h2): r""" Quadrativ forms metric. Notes ----- UNDER DEVELOPMENT This distance measure shows very strange behaviour. The expression transpose(h1-h2) * A * (h1-h2) yields egative values that can not be processed by the square root. Some examples:: ...
def __quadratic_forms_matrix_euclidean(h1, h2): r""" Compute the bin-similarity matrix for the quadratic form distance measure. The matric :math:`A` for two histograms :math:`H` and :math:`H'` of size :math:`m` and :math:`n` respectively is defined as .. math:: A_{m,n} = 1 - \frac{...
def __prepare_histogram(h1, h2): """Convert the histograms to scipy.ndarrays if required.""" h1 = h1 if scipy.ndarray == type(h1) else scipy.asarray(h1) h2 = h2 if scipy.ndarray == type(h2) else scipy.asarray(h2) if h1.shape != h2.shape or h1.size != h2.size: raise ValueError('h1 and h2 must be ...
def find(): """Find a local spark installation. Will first check the SPARK_HOME env variable, and otherwise search common installation locations, e.g. from homebrew """ spark_home = os.environ.get('SPARK_HOME', None) if not spark_home: for path in [ '/usr/local/opt/apache-s...
def change_rc(spark_home, spark_python, py4j): """Persists changes to environment by changing shell config. Adds lines to .bashrc to set environment variables including the adding of dependencies to the system path. Will only edit this file if they already exist. Currently only works for bash. Par...
def edit_ipython_profile(spark_home, spark_python, py4j): """Adds a startup file to the current IPython profile to import pyspark. The startup file sets the required environment variables and imports pyspark. Parameters ---------- spark_home : str Path to Spark installation. spark_pyth...
def init(spark_home=None, python_path=None, edit_rc=False, edit_profile=False): """Make pyspark importable. Sets environment variables and adds dependencies to sys.path. If no Spark location is provided, will try to find an installation. Parameters ---------- spark_home : str, optional, defaul...
def _add_to_submit_args(s): """Adds string s to the PYSPARK_SUBMIT_ARGS env var""" new_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "") + (" %s" % s) os.environ["PYSPARK_SUBMIT_ARGS"] = new_args return new_args
def add_packages(packages): """Add external packages to the pyspark interpreter. Set the PYSPARK_SUBMIT_ARGS properly. Parameters ---------- packages: list of package names in string format """ #if the parameter is a string, convert to a single element list if isinstance(packages,str)...
def add_jars(jars): """Add external jars to the pyspark interpreter. Set the PYSPARK_SUBMIT_ARGS properly. Parameters ---------- jars: list of path to jars in string format """ #if the parameter is a string, convert to a single element list if isinstance(jars,str): jars = [jar...
def parse(cls, expression): """ Parse the given console command definition into a dict. :param expression: The expression to parse :type expression: str :rtype: dict """ parsed = {"name": None, "arguments": [], "options": []} if not expression.strip(): ...
def _parameters(cls, tokens): """ Extract all of the parameters from the tokens. :param tokens: The tokens to extract the parameters from :type tokens: list :rtype: dict """ arguments = [] options = [] for token in tokens: if not tok...
def _parse_argument(cls, token): """ Parse an argument expression. :param token: The argument expression :type token: str :rtype: InputArgument """ description = "" validator = None if " : " in token: token, description = tuple(token...
def _parse_option(cls, token): """ Parse an option expression. :param token: The option expression :type token: str :rtype: InputOption """ description = "" validator = None if " : " in token: token, description = tuple(token.split("...
def add(self, command): # type: (BaseCommand) -> Application """ Adds a command object. """ self.add_command(command.config) command.set_application(self) return self
def _configure_using_fluent_definition(self): """ Configure the console command using a fluent definition. """ definition = Parser.parse(self.signature) self._config.set_name(definition["name"]) for name, flags, description, default in definition["arguments"]: ...
def call(self, name, args=None): # type: (str, Optional[str]) -> int """ Call another command. """ if args is None: args = "" args = StringArgs(args) command = self.application.get_command(name) return command.run(args, self.io)
def call_silent(self, name, args=None): # type: (str, Optional[str]) -> int """ Call another command. """ if args is None: args = "" args = StringArgs(args) command = self.application.get_command(name) return command.run(args, NullIO())
def argument(self, key=None): """ Get the value of a command argument. """ if key is None: return self._args.arguments() return self._args.argument(key)
def option(self, key=None): """ Get the value of a command option. """ if key is None: return self._args.options() return self._args.option(key)
def confirm(self, question, default=False, true_answer_regex="(?i)^y"): """ Confirm a question with the user. """ return self._io.confirm(question, default, true_answer_regex)
def ask(self, question, default=None): """ Prompt the user for input. """ if isinstance(question, Question): return self._io.ask_question(question) return self._io.ask(question, default)
def choice(self, question, choices, default=None, attempts=None, multiple=False): """ Give the user a single choice from an list of answers. """ question = ChoiceQuestion(question, choices, default) question.set_max_attempts(attempts) question.set_multi_select(multiple) ...
def create_question(self, question, type=None, kwargs): """ Returns a Question of specified type. """ if not type: return Question(question, kwargs) if type == "choice": return ChoiceQuestion(question, **kwargs) if type == "confirmation": ...
def table(self, header=None, rows=None, style=None): """ Return a Table instance. """ if style is not None: style = self.TABLE_STYLES[style] table = Table(style) if header: table.set_header_row(header) if rows: table.set_rows...
def render_table(self, headers, rows, style=None): """ Format input to textual table. """ table = self.table(headers, rows, style) table.render(self._io)
def write(self, text, style=None): """ Writes a string without a new line. Useful if you want to use overwrite(). """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.write(styled)
def line(self, text, style=None, verbosity=None): """ Write a string as information output. """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.write_line(styled, verbosity)
def line_error(self, text, style=None, verbosity=None): """ Write a string as information output to stderr. """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.error_line(styled, verbosity)
def progress_indicator(self, fmt=None, interval=100, values=None): """ Creates a new progress indicator. """ return ProgressIndicator(self.io, fmt, interval, values)
def spin(self, start_message, end_message, fmt=None, interval=100, values=None): """ Automatically spin a progress indicator. """ spinner = ProgressIndicator(self.io, fmt, interval, values) return spinner.auto(start_message, end_message)
def add_style(self, name, fg=None, bg=None, options=None): """ Adds a new style """ style = Style(name) if fg is not None: style.fg(fg) if bg is not None: style.bg(bg) if options is not None: if "bold" in options: ...
def overwrite(self, text, size=None): """ Overwrites the current line. It will not add a new line so use line('') if necessary. """ self._io.overwrite(text, size=size)
def get_login_password(site_name="github.com", netrc_file="~/.netrc", git_credential_file="~/.git-credentials"): """Read a .netrc file and return login/password for LWN.""" try: n = netrc.netrc(os.path.expanduser(netrc_file)) except OSError: pass...
def get_github_hostname_user_repo_from_url(url): """Return hostname, user and repository to fork from. :param url: The URL to parse :return: hostname, user, repository """ parsed = parse.urlparse(url) if parsed.netloc == '': # Probably ssh host, sep, path = parsed.path.partition...
def git_get_title_and_message(begin, end): """Get title and message summary for patches between 2 commits. :param begin: first commit to look at :param end: last commit to look at :return: number of commits, title, message """ titles = git_get_log_titles(begin, end) title = "Pull request fo...
def validate_term(func): """Compares current term and request term: if current term is stale: update current term & become follower if received term is stale: respond with False """ @functools.wraps(func) def on_receive_function(self, data): if self.stora...
def validate_commit_index(func): """Apply to State Machine everything up to commit index""" @functools.wraps(func) def wrapped(self, args, *kwargs): for not_applied in range(self.log.last_applied + 1, self.log.commit_index + 1): self.state_machine.apply(self.log[not_applied]['command'...
async def append_entries(self, destination=None): """AppendEntries RPC — replicate log entries / heartbeat Args: destination — destination id Request params: term — leader’s term leader_id — so follower can redirect clients prev_log_index — index ...
async def execute_command(self, command): """Write to log & send AppendEntries RPC""" self.apply_future = asyncio.Future(loop=self.loop) entry = self.log.write(self.storage.term, command) asyncio.ensure_future(self.append_entries(), loop=self.loop) await self.apply_future
def start(self): """Increment current term, vote for herself & send vote requests""" self.storage.update({ 'term': self.storage.term + 1, 'voted_for': self.id }) self.vote_count = 1 self.request_vote() self.election_timer.start()
def request_vote(self): """RequestVote RPC — gather votes Arguments: term — candidate’s term candidate_id — candidate requesting vote last_log_index — index of candidate’s last log entry last_log_term — term of candidate’s last log entry """ ...
def on_receive_request_vote_response(self, data): """Receives response for vote request. If the vote was granted then check if we got majority and may become Leader """ if data.get('vote_granted'): self.vote_count += 1 if self.state.is_majority(self.vote_count):...
def on_receive_append_entries(self, data): """If we discover a Leader with the same term — step down""" if self.storage.term == data['term']: self.state.to_follower()
def init_storage(self): """Set current term to zero upon initialization & voted_for to None""" if not self.storage.exists('term'): self.storage.update({ 'term': 0, }) self.storage.update({ 'voted_for': None })
async def wait_for_election_success(cls): """Await this function if your cluster must have a leader""" if cls.leader is None: cls.leader_future = asyncio.Future(loop=cls.loop) await cls.leader_future
async def wait_until_leader(cls, node_id): """Await this function if you want to do nothing until node_id becomes a leader""" if node_id is None: raise ValueError('Node id can not be None!') if cls.get_leader() != node_id: cls.wait_until_leader_id = node_id c...
async def register(*address_list, cluster=None, loop=None): """Start Raft node (server) Args: address_list — 127.0.0.1:8000 [, 127.0.0.1:8001 ...] cluster — [127.0.0.1:8001, 127.0.0.1:8002, ...] """ loop = loop or asyncio.get_event_loop() for address in address_list: host, p...
async def send(self, data, destination): """Sends data to destination Node Args: data — serializable object destination — <str> '127.0.0.1:8000' or <tuple> (127.0.0.1, 8000) """ if isinstance(destination, str): host, port = destination.split(':') ...
def broadcast(self, data): """Sends data to all Nodes in cluster (cluster list does not contain self Node)""" for destination in self.cluster: asyncio.ensure_future(self.send(data, destination), loop=self.loop)
def is_foreign_key(line): """ :param line: a line from the table definition :return: true if the line appears to be a foreign key definition """ arrow_position = line.find('->') return arrow_position >= 0 and not any(c in line[:arrow_position] for c in '"#\'')

def jc(result, reference): """ Jaccard coefficient Computes the Jaccard coefficient between the binary objects in two images. Parameters ---------- result: array_like Input data containing objects. Can be any type but will be converted into binary: background wh...

def precision(result, reference): """ Precison. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing object...

def recall(result, reference): """ Recall. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing objects. Ca...

def specificity(result, reference): """ Specificity. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be converted into binary: background where 0, object everywhere else. reference : array_like Input data containing o...

def hd(result, reference, voxelspacing=None, connectivity=1): """ Hausdorff Distance. Computes the (symmetric) Hausdorff Distance (HD) between the binary objects in two images. It is defined as the maximum surface distance between the objects. Parameters ---------- result : array_l...

def hd95(result, reference, voxelspacing=None, connectivity=1): """ 95th percentile of the Hausdorff Distance. Computes the 95th percentile of the (symmetric) Hausdorff Distance (HD) between the binary objects in two images. Compared to the Hausdorff Distance, this metric is slightly more stable to sma...

def assd(result, reference, voxelspacing=None, connectivity=1): """ Average symmetric surface distance. Computes the average symmetric surface distance (ASD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objects. C...

def asd(result, reference, voxelspacing=None, connectivity=1): """ Average surface distance metric. Computes the average surface distance (ASD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objects. Can be any type but...

def ravd(result, reference): """ Relative absolute volume difference. Compute the relative absolute volume difference between the (joined) binary objects in the two images. Parameters ---------- result : array_like Input data containing objects. Can be any type but will be ...

def volume_correlation(results, references): r""" Volume correlation. Computes the linear correlation in binary object volume between the contents of the successive binary images supplied. Measured through the Pearson product-moment correlation coefficient. Parameters ---------- ...

def volume_change_correlation(results, references): r""" Volume change correlation. Computes the linear correlation of change in binary object volume between the contents of the successive binary images supplied. Measured through the Pearson product-moment correlation coefficient. Par...

def obj_assd(result, reference, voxelspacing=None, connectivity=1): """ Average symmetric surface distance. Computes the average symmetric surface distance (ASSD) between the binary objects in two images. Parameters ---------- result : array_like Input data containing objec...

def obj_asd(result, reference, voxelspacing=None, connectivity=1): """ Average surface distance between objects. First correspondences between distinct binary objects in reference and result are established. Then the average surface distance is only computed between corresponding objects. Corre...

def obj_fpr(result, reference, connectivity=1): """ The false positive rate of distinct binary object detection. The false positive rates gives a percentage measure of how many distinct binary objects in the second array do not exists in the first array. A partial overlap (of minimum one voxel)...

def obj_tpr(result, reference, connectivity=1): """ The true positive rate of distinct binary object detection. The true positive rates gives a percentage measure of how many distinct binary objects in the first array also exists in the second array. A partial overlap (of minimum one voxel) is ...

def __distinct_binary_object_correspondences(reference, result, connectivity=1): """ Determines all distinct (where connectivity is defined by the connectivity parameter passed to scipy's `generate_binary_structure`) binary objects in both of the input parameters and returns a 1to1 mapping from the labe...

def __surface_distances(result, reference, voxelspacing=None, connectivity=1): """ The distances between the surface voxel of binary objects in result and their nearest partner surface voxel of a binary object in reference. """ result = numpy.atleast_1d(result.astype(numpy.bool)) reference = num...

def __combine_windows(w1, w2): """ Joins two windows (defined by tuple of slices) such that their maximum combined extend is covered by the new returned window. """ res = [] for s1, s2 in zip(w1, w2): res.append(slice(min(s1.start, s2.start), max(s1.stop, s2.stop))) return tuple(res)

def applyslicer(self, array, slicer, cval = None): r""" Apply a slicer returned by the iterator to a new array of the same dimensionality as the one used to initialize the iterator. Notes ----- If ``array`` has more dimensions than ``slicer`` and ``pmask``, the f...

def applyslicer(array, slicer, pmask, cval = 0): r""" Apply a slicer returned by the iterator to a new array of the same dimensionality as the one used to initialize the iterator. Notes ----- If ``array`` has more dimensions than ``slicer`` and ``pmask``, the fir...

def assembleimage(patches, pmasks, gridids): r""" Assemble an image from a number of patches, patch masks and their grid ids. Parameters ---------- patches : sequence Sequence of patches. pmasks : sequence Sequence of associated patch mask...

def size_threshold(img, thr, comp='lt', structure = None): r""" Removes binary objects from an image identified by a size threshold. The unconnected binary objects in an image are identified and all removed whose size compares (e.g. less-than) to a supplied threshold value. The threshold `...

def largest_connected_component(img, structure = None): r""" Select the largest connected binary component in an image. Treats all zero values in the input image as background and all others as foreground. The return value is an binary array of equal dimensions as the input array with TRUE valu...

def bounding_box(img): r""" Return the bounding box incorporating all non-zero values in the image. Parameters ---------- img : array_like An array containing non-zero objects. Returns ------- bbox : a list of slicer objects defining the bounding box """ loc...

def shape_based_slice_interpolation(img, dim, nslices): """ Adds `nslices` slices between all slices of the binary image `img` along dimension `dim` respecting the original slice values to be situated in the middle of each slice. Extrapolation situations are handled by simple repeating. Interpo...

def shape_based_slice_insertation_object_wise(sl1, sl2, dim, nslices, order=3): """ Wrapper to apply `shape_based_slice_insertation()` for each binary object separately to ensure correct extrapolation behaviour. """ out = None sandwich = numpy.concatenate((sl1[numpy.newaxis], sl2[numpy.newaxis])...

def shape_based_slice_insertation(sl1, sl2, dim, nslices, order=3): """ Insert `nslices` new slices between `sl1` and `sl2` along dimension `dim` using shape based binary interpolation. Extrapolation is handled adding `nslices`/2 step-wise eroded copies of the last slice in each direction. ...

def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() if args.order < 0 or args.order > 5: parser.error('The order has to be a number between 0 and 5.') return args

def getArguments(parser): "Provides additional validation of the arguments collected by argparse." args = parser.parse_args() # check mutual exlusive and reaquired arguments if args.lmodel and args.smodel: parser.error('only one of --load-model and --save-model can be supplied, as they deci...

def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(description=__description__, formatter_class=argparse.RawDescriptionHelpFormatter) parser.add_argument('images', nargs='+', help='The images used for training (in the learning case) or to transform (in the tr...

def minowski(h1, h2, p = 2): # 46..45..14,11..43..44 / 45 us for p=int(-inf..-24..-1,1..24..inf) / float @array, +20 us @list \w 100 bins r""" Minowski distance. With :math:`p=2` equal to the Euclidean distance, with :math:`p=1` equal to the Manhattan distance, and the Chebyshev distance implementa...

def __minowski_low_positive_integer_p(h1, h2, p = 2): # 11..43 us for p = 1..24 \w 100 bins """ A faster implementation of the Minowski distance for positive integer < 25. @note do not use this function directly, but the general @link minowski() method. @note the passed histograms must be scipy arrays. ...

def manhattan(h1, h2): # # 7 us @array, 31 us @list \w 100 bins r""" Equal to Minowski distance with :math:`p=1`. See also -------- minowski """ h1, h2 = __prepare_histogram(h1, h2) return scipy.sum(scipy.absolute(h1 - h2))

def euclidean(h1, h2): # 9 us @array, 33 us @list \w 100 bins r""" Equal to Minowski distance with :math:`p=2`. See also -------- minowski """ h1, h2 = __prepare_histogram(h1, h2) return math.sqrt(scipy.sum(scipy.square(scipy.absolute(h1 - h2))))

def chebyshev(h1, h2): # 12 us @array, 36 us @list \w 100 bins r""" Chebyshev distance. Also Tchebychev distance, Maximum or :math:`L_{\infty}` metric; equal to Minowski distance with :math:`p=+\infty`. For the case of :math:`p=-\infty`, use `chebyshev_neg`. The Chebyshev distance between ...

def chebyshev_neg(h1, h2): # 12 us @array, 36 us @list \w 100 bins r""" Chebyshev negative distance. Also Tchebychev distance, Minimum or :math:`L_{-\infty}` metric; equal to Minowski distance with :math:`p=-\infty`. For the case of :math:`p=+\infty`, use `chebyshev`. The Chebyshev distanc...

def histogram_intersection(h1, h2): # 6 us @array, 30 us @list \w 100 bins r""" Calculate the common part of two histograms. The histogram intersection between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{\cap}(H, H') = \sum_{m=1}^M\min...

def relative_deviation(h1, h2): # 18 us @array, 42 us @list \w 100 bins r""" Calculate the deviation between two histograms. The relative deviation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{rd}(H, H') = \frac{ ...

def relative_bin_deviation(h1, h2): # 79 us @array, 104 us @list \w 100 bins r""" Calculate the bin-wise deviation between two histograms. The relative bin deviation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{rbd}(H, H') = \su...

def chi_square(h1, h2): # 23 us @array, 49 us @list \w 100 r""" Chi-square distance. Measure how unlikely it is that one distribution (histogram) was drawn from the other. The Chi-square distance between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math::...

def kullback_leibler(h1, h2): # 83 us @array, 109 us @list \w 100 bins r""" Kullback-Leibler divergence. Compute how inefficient it would to be code one histogram into another. Actually computes :math:`\frac{d_{KL}(h1, h2) + d_{KL}(h2, h1)}{2}` to achieve symmetry. The Kullback-Leibler div...

def __kullback_leibler(h1, h2): # 36.3 us """ The actual KL implementation. @see kullback_leibler() for details. Expects the histograms to be of type scipy.ndarray. """ result = h1.astype(scipy.float_) mask = h1 != 0 result[mask] = scipy.multiply(h1[mask], scipy.log(h1[mask] / h2[mask])) ...

def jensen_shannon(h1, h2): # 85 us @array, 110 us @list \w 100 bins r""" Jensen-Shannon divergence. A symmetric and numerically more stable empirical extension of the Kullback-Leibler divergence. The Jensen Shannon divergence between two histograms :math:`H` and :math:`H'` of size :ma...

def fidelity_based(h1, h2): # 25 us @array, 51 us @list \w 100 bins r""" Fidelity based distance. Also Bhattacharyya distance; see also the extensions `noelle_1` to `noelle_5`. The metric between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: ...

def noelle_4(h1, h2): # 26 us @array, 52 us @list \w 100 bins r""" Extension of `fidelity_based` proposed by [1]_. .. math:: d_{\arccos F}(H, H') = \frac{2}{\pi} \arccos d_{F}(H, H') See `fidelity_based` for the definition of :math:`d_{F}(H, H')`. *Attributes:* ...

def noelle_5(h1, h2): # 26 us @array, 52 us @list \w 100 bins r""" Extension of `fidelity_based` proposed by [1]_. .. math:: d_{\sin F}(H, H') = \sqrt{1 -d_{F}^2(H, H')} See `fidelity_based` for the definition of :math:`d_{F}(H, H')`. *Attributes:* - metr...

def cosine_alt(h1, h2): # 17 us @array, 42 us @list \w 100 bins r""" Alternative implementation of the `cosine` distance measure. Notes ----- Under development. """ h1, h2 = __prepare_histogram(h1, h2) return -1 * float(scipy.sum(h1 * h2)) / (scipy.sum(scipy.power(h1, 2)) * scipy.su...

def cosine(h1, h2): # 17 us @array, 42 us @list \w 100 bins r""" Cosine simmilarity. Compute the angle between the two histograms in vector space irrespective of their length. The cosine similarity between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math...

def correlate(h1, h2): # 31 us @array, 55 us @list \w 100 bins r""" Correlation between two histograms. The histogram correlation between two histograms :math:`H` and :math:`H'` of size :math:`m` is defined as: .. math:: d_{corr}(H, H') = \frac{ \sum_{m=1}...

def quadratic_forms(h1, h2): r""" Quadrativ forms metric. Notes ----- UNDER DEVELOPMENT This distance measure shows very strange behaviour. The expression transpose(h1-h2) * A * (h1-h2) yields egative values that can not be processed by the square root. Some examples:: ...

def __quadratic_forms_matrix_euclidean(h1, h2): r""" Compute the bin-similarity matrix for the quadratic form distance measure. The matric :math:`A` for two histograms :math:`H` and :math:`H'` of size :math:`m` and :math:`n` respectively is defined as .. math:: A_{m,n} = 1 - \frac{...

def __prepare_histogram(h1, h2): """Convert the histograms to scipy.ndarrays if required.""" h1 = h1 if scipy.ndarray == type(h1) else scipy.asarray(h1) h2 = h2 if scipy.ndarray == type(h2) else scipy.asarray(h2) if h1.shape != h2.shape or h1.size != h2.size: raise ValueError('h1 and h2 must be ...

def find(): """Find a local spark installation. Will first check the SPARK_HOME env variable, and otherwise search common installation locations, e.g. from homebrew """ spark_home = os.environ.get('SPARK_HOME', None) if not spark_home: for path in [ '/usr/local/opt/apache-s...

def change_rc(spark_home, spark_python, py4j): """Persists changes to environment by changing shell config. Adds lines to .bashrc to set environment variables including the adding of dependencies to the system path. Will only edit this file if they already exist. Currently only works for bash. Par...

def edit_ipython_profile(spark_home, spark_python, py4j): """Adds a startup file to the current IPython profile to import pyspark. The startup file sets the required environment variables and imports pyspark. Parameters ---------- spark_home : str Path to Spark installation. spark_pyth...

def init(spark_home=None, python_path=None, edit_rc=False, edit_profile=False): """Make pyspark importable. Sets environment variables and adds dependencies to sys.path. If no Spark location is provided, will try to find an installation. Parameters ---------- spark_home : str, optional, defaul...

def _add_to_submit_args(s): """Adds string s to the PYSPARK_SUBMIT_ARGS env var""" new_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "") + (" %s" % s) os.environ["PYSPARK_SUBMIT_ARGS"] = new_args return new_args

def add_packages(packages): """Add external packages to the pyspark interpreter. Set the PYSPARK_SUBMIT_ARGS properly. Parameters ---------- packages: list of package names in string format """ #if the parameter is a string, convert to a single element list if isinstance(packages,str)...

def add_jars(jars): """Add external jars to the pyspark interpreter. Set the PYSPARK_SUBMIT_ARGS properly. Parameters ---------- jars: list of path to jars in string format """ #if the parameter is a string, convert to a single element list if isinstance(jars,str): jars = [jar...

def parse(cls, expression): """ Parse the given console command definition into a dict. :param expression: The expression to parse :type expression: str :rtype: dict """ parsed = {"name": None, "arguments": [], "options": []} if not expression.strip(): ...

def _parameters(cls, tokens): """ Extract all of the parameters from the tokens. :param tokens: The tokens to extract the parameters from :type tokens: list :rtype: dict """ arguments = [] options = [] for token in tokens: if not tok...

def _parse_argument(cls, token): """ Parse an argument expression. :param token: The argument expression :type token: str :rtype: InputArgument """ description = "" validator = None if " : " in token: token, description = tuple(token...

def _parse_option(cls, token): """ Parse an option expression. :param token: The option expression :type token: str :rtype: InputOption """ description = "" validator = None if " : " in token: token, description = tuple(token.split("...

def add(self, command): # type: (BaseCommand) -> Application """ Adds a command object. """ self.add_command(command.config) command.set_application(self) return self

def _configure_using_fluent_definition(self): """ Configure the console command using a fluent definition. """ definition = Parser.parse(self.signature) self._config.set_name(definition["name"]) for name, flags, description, default in definition["arguments"]: ...

def call(self, name, args=None): # type: (str, Optional[str]) -> int """ Call another command. """ if args is None: args = "" args = StringArgs(args) command = self.application.get_command(name) return command.run(args, self.io)

def call_silent(self, name, args=None): # type: (str, Optional[str]) -> int """ Call another command. """ if args is None: args = "" args = StringArgs(args) command = self.application.get_command(name) return command.run(args, NullIO())

def argument(self, key=None): """ Get the value of a command argument. """ if key is None: return self._args.arguments() return self._args.argument(key)

def option(self, key=None): """ Get the value of a command option. """ if key is None: return self._args.options() return self._args.option(key)

def confirm(self, question, default=False, true_answer_regex="(?i)^y"): """ Confirm a question with the user. """ return self._io.confirm(question, default, true_answer_regex)

def ask(self, question, default=None): """ Prompt the user for input. """ if isinstance(question, Question): return self._io.ask_question(question) return self._io.ask(question, default)

def choice(self, question, choices, default=None, attempts=None, multiple=False): """ Give the user a single choice from an list of answers. """ question = ChoiceQuestion(question, choices, default) question.set_max_attempts(attempts) question.set_multi_select(multiple) ...

def create_question(self, question, type=None, **kwargs): """ Returns a Question of specified type. """ if not type: return Question(question, **kwargs) if type == "choice": return ChoiceQuestion(question, **kwargs) if type == "confirmation": ...

def table(self, header=None, rows=None, style=None): """ Return a Table instance. """ if style is not None: style = self.TABLE_STYLES[style] table = Table(style) if header: table.set_header_row(header) if rows: table.set_rows...

def render_table(self, headers, rows, style=None): """ Format input to textual table. """ table = self.table(headers, rows, style) table.render(self._io)

def write(self, text, style=None): """ Writes a string without a new line. Useful if you want to use overwrite(). """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.write(styled)

def line(self, text, style=None, verbosity=None): """ Write a string as information output. """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.write_line(styled, verbosity)

def line_error(self, text, style=None, verbosity=None): """ Write a string as information output to stderr. """ if style: styled = "<%s>%s</>" % (style, text) else: styled = text self._io.error_line(styled, verbosity)

def progress_indicator(self, fmt=None, interval=100, values=None): """ Creates a new progress indicator. """ return ProgressIndicator(self.io, fmt, interval, values)

def spin(self, start_message, end_message, fmt=None, interval=100, values=None): """ Automatically spin a progress indicator. """ spinner = ProgressIndicator(self.io, fmt, interval, values) return spinner.auto(start_message, end_message)

def add_style(self, name, fg=None, bg=None, options=None): """ Adds a new style """ style = Style(name) if fg is not None: style.fg(fg) if bg is not None: style.bg(bg) if options is not None: if "bold" in options: ...

def overwrite(self, text, size=None): """ Overwrites the current line. It will not add a new line so use line('') if necessary. """ self._io.overwrite(text, size=size)

def get_login_password(site_name="github.com", netrc_file="~/.netrc", git_credential_file="~/.git-credentials"): """Read a .netrc file and return login/password for LWN.""" try: n = netrc.netrc(os.path.expanduser(netrc_file)) except OSError: pass...

def get_github_hostname_user_repo_from_url(url): """Return hostname, user and repository to fork from. :param url: The URL to parse :return: hostname, user, repository """ parsed = parse.urlparse(url) if parsed.netloc == '': # Probably ssh host, sep, path = parsed.path.partition...

def git_get_title_and_message(begin, end): """Get title and message summary for patches between 2 commits. :param begin: first commit to look at :param end: last commit to look at :return: number of commits, title, message """ titles = git_get_log_titles(begin, end) title = "Pull request fo...

def validate_term(func): """Compares current term and request term: if current term is stale: update current term & become follower if received term is stale: respond with False """ @functools.wraps(func) def on_receive_function(self, data): if self.stora...

def validate_commit_index(func): """Apply to State Machine everything up to commit index""" @functools.wraps(func) def wrapped(self, *args, **kwargs): for not_applied in range(self.log.last_applied + 1, self.log.commit_index + 1): self.state_machine.apply(self.log[not_applied]['command'...

async def append_entries(self, destination=None): """AppendEntries RPC — replicate log entries / heartbeat Args: destination — destination id Request params: term — leader’s term leader_id — so follower can redirect clients prev_log_index — index ...

async def execute_command(self, command): """Write to log & send AppendEntries RPC""" self.apply_future = asyncio.Future(loop=self.loop) entry = self.log.write(self.storage.term, command) asyncio.ensure_future(self.append_entries(), loop=self.loop) await self.apply_future

def start(self): """Increment current term, vote for herself & send vote requests""" self.storage.update({ 'term': self.storage.term + 1, 'voted_for': self.id }) self.vote_count = 1 self.request_vote() self.election_timer.start()

def request_vote(self): """RequestVote RPC — gather votes Arguments: term — candidate’s term candidate_id — candidate requesting vote last_log_index — index of candidate’s last log entry last_log_term — term of candidate’s last log entry """ ...

def on_receive_request_vote_response(self, data): """Receives response for vote request. If the vote was granted then check if we got majority and may become Leader """ if data.get('vote_granted'): self.vote_count += 1 if self.state.is_majority(self.vote_count):...

def on_receive_append_entries(self, data): """If we discover a Leader with the same term — step down""" if self.storage.term == data['term']: self.state.to_follower()

def init_storage(self): """Set current term to zero upon initialization & voted_for to None""" if not self.storage.exists('term'): self.storage.update({ 'term': 0, }) self.storage.update({ 'voted_for': None })

async def wait_for_election_success(cls): """Await this function if your cluster must have a leader""" if cls.leader is None: cls.leader_future = asyncio.Future(loop=cls.loop) await cls.leader_future

async def wait_until_leader(cls, node_id): """Await this function if you want to do nothing until node_id becomes a leader""" if node_id is None: raise ValueError('Node id can not be None!') if cls.get_leader() != node_id: cls.wait_until_leader_id = node_id c...

async def register(*address_list, cluster=None, loop=None): """Start Raft node (server) Args: address_list — 127.0.0.1:8000 [, 127.0.0.1:8001 ...] cluster — [127.0.0.1:8001, 127.0.0.1:8002, ...] """ loop = loop or asyncio.get_event_loop() for address in address_list: host, p...

async def send(self, data, destination): """Sends data to destination Node Args: data — serializable object destination — <str> '127.0.0.1:8000' or <tuple> (127.0.0.1, 8000) """ if isinstance(destination, str): host, port = destination.split(':') ...

def broadcast(self, data): """Sends data to all Nodes in cluster (cluster list does not contain self Node)""" for destination in self.cluster: asyncio.ensure_future(self.send(data, destination), loop=self.loop)

def is_foreign_key(line): """ :param line: a line from the table definition :return: true if the line appears to be a foreign key definition """ arrow_position = line.find('->') return arrow_position >= 0 and not any(c in line[:arrow_position] for c in '"#\'')