BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def makeHist(x_val, y_val, fit=spline_base.fit2d, bins=[np.linspace(-36.5,36.5,74),np.linspace(-180,180,361)]): """ Constructs a (fitted) histogram of the given data. Parameters: x_val : array The data to be histogrammed along the x-axis. y_val : array ...
def firstSacDist(fm): """ Computes the distribution of angle and length combinations that were made as first saccades Parameters: fm : ocupy.fixmat The fixation data to be analysed """ ang, leng, ad, ld = anglendiff(fm, return_abs=True) ...
def trajLenDist(fm): """ Computes the distribution of trajectory lengths, i.e. the number of saccades that were made as a part of one trajectory Parameters: fm : ocupy.fixmat The fixation data to be analysed """ trajLen = np.roll(fm.fix, 1...
def reshift(I): """ Transforms the given number element into a range of [-180, 180], which covers all possible angle differences. This method reshifts larger or smaller numbers that might be the output of other angular calculations into that range by adding or subtracting 360, respectively. To...
def initializeData(self, fit = None, full_H1=None, max_length = 40, in_deg = True): """ Prepares the data to be replicated. Calculates the second-order length and angle dependencies between saccades and stores them in a fitted histogram. Parameters: ...
def _calc_xy(self, xxx_todo_changeme, angle, length): """ Calculates the coordinates after a specific saccade was made. Parameters: (x,y) : tuple of floats or ints The coordinates before the saccade was made angle : float or int Th...
def _draw(self, prev_angle = None, prev_length = None): """ Draws a new length- and angle-difference pair and calculates length and angle absolutes matching the last saccade drawn. Parameters: prev_angle : float, optional The last angle that was drawn in the ...
def sample_many(self, num_samples = 2000): """ Generates a given number of trajectories, using the method sample(). Returns a fixmat with the generated data. Parameters: num_samples : int, optional The number of trajectories that shall be generated. ...
def sample(self): """ Draws a trajectory length, first coordinates, lengths, angles and length-angle-difference pairs according to the empirical distribution. Each call creates one complete trajectory. """ lenghts = [] angles = [] coordinates = [] ...
def drawFrom(self, cumsum, r): """ Draws a value from a cumulative sum. Parameters: cumsum : array Cumulative sum from which shall be drawn. Returns: int : Index of the cumulative sum element drawn. """ a = cumsum.rsplit(...
def load(path): """ Load fixmat at path. Parameters: path : string Absolute path of the file to load from. """ f = h5py.File(path,'r') if 'Fixmat' in f: fm_group = f['Fixmat'] else: fm_group = f['Datamat'] fields = {} params = {} for field, va...
def compute_fdm(fixmat, fwhm=2, scale_factor=1): """ Computes a fixation density map for the calling fixmat. Creates a map the size of the image fixations were recorded on. Every pixel contains the frequency of fixations for this image. The fixation map is smoothed by convolution with a ...
def relative_bias(fm, scale_factor = 1, estimator = None): """ Computes the relative bias, i.e. the distribution of saccade angles and amplitudes. Parameters: fm : DataMat The fixation data to use scale_factor : double Returns: 2D probability distribution of s...
def DirectoryFixmatFactory(directory, categories = None, glob_str = '*.mat', var_name = 'fixmat'): """ Concatenates all fixmats in dir and returns the resulting single fixmat. Parameters: directory : string Path from which the fixmats should be loaded categories : instan...
def FixmatFactory(fixmatfile, categories = None, var_name = 'fixmat', field_name='x'): """ Loads a single fixmat (fixmatfile). Parameters: fixmatfile : string The matlab fixmat that should be loaded. categories : instance of stimuli.Categories, optional Links dat...
def add_feature_values(self, features): """ Adds feature values of feature 'feature' to all fixations in the calling fixmat. For fixations out of the image boundaries, NaNs are returned. The function generates a new attribute field named with the string in featu...
def make_reg_data(self, feature_list=None, all_controls=False): """ Generates two M x N matrices with M feature values at fixations for N features. Controls are a random sample out of all non-fixated regions of an image or fixations of the same subject group on a randomly chosen ...
def get_velocity(samplemat, Hz, blinks=None): ''' Compute velocity of eye-movements. Samplemat must contain fields 'x' and 'y', specifying the x,y coordinates of gaze location. The function assumes that the values in x,y are sampled continously at a rate specified by 'Hz'. ''' Hz = float(Hz...
def saccade_detection(samplemat, Hz=200, threshold=30, acc_thresh=2000, min_duration=21, min_movement=.35, ignore_blinks=False): ''' Detect saccades in a stream of gaze location samples. Coordinates in samplemat are assumed to be in degrees. Saccades are det...
def fixation_detection(samplemat, saccades, Hz=200, samples2fix=None, respect_trial_borders=False, sample_times=None): ''' Detect Fixation from saccades. Fixations are defined as intervals between saccades. This function also calcuates start and end times (in ms) for each fixatio...
def parse(parse_obj, agent=None, etag=None, modified=None, inject=False): """Parse a subscription list and return a dict containing the results. :param parse_obj: A file-like object or a string containing a URL, an absolute or relative filename, or an XML document. :type parse_obj: str or file ...
def FixmatStimuliFactory(fm, loader): """ Constructs an categories object for all image / category combinations in the fixmat. Parameters: fm: FixMat Used for extracting valid category/image combination. loader: loader Loader that accesses the stimuli for th...
def DirectoryStimuliFactory(loader): """ Takes an input path to the images folder of an experiment and generates automatically the category - filenumber list needed to construct an appropriate _categories object. Parameters : loader : Loader object which contains impath : s...
def fixations(self): ''' Filter the fixmat such that it only contains fixations on images in categories that are also in the categories object''' if not self._fixations: raise RuntimeError('This Images object does not have' +' an associated fixmat') if len(lis...
def data(self, value): """ Saves a new image to disk """ self.loader.save_image(self.category, self.image, value)
def fixations(self): """ Returns all fixations that are on this image. A precondition for this to work is that a fixmat is associated with this Image object. """ if not self._fixations: raise RuntimeError('This Images object does not have' +' ...
def generate(self): """ Generator for creating the cross-validation slices. Returns A tuple of that contains two fixmats (training and test) and two Category objects (test and train). """ for _ in range(0, self.num_slices): #1. separate fixm...
def prepare_data(fm, max_back, dur_cap=700): ''' Computes angle and length differences up to given order and deletes suspiciously long fixations. Input fm: Fixmat Fixmat for which to comput angle and length differences max_back: Int Computes delta angle and ampli...
def saccadic_momentum_effect(durations, forward_angle, summary_stat=nanmean): """ Computes the mean fixation duration at forward angles. """ durations_per_da = np.nan * np.ones((len(e_angle) - 1,)) for i, (bo, b1) in enumerate(zip(e_angle[:-1], e_angle[1:])): idx...
def ior_effect(durations, angle_diffs, length_diffs, summary_stat=np.mean, parallel=True, min_samples=20): """ Computes a measure of fixation durations at delta angle and delta length combinations. """ raster = np.empty((len(e_dist) - 1, len(e_angle) - 1), dtype=object) for a, (a_...
def predict_fixation_duration( durations, angles, length_diffs, dataset=None, params=None): """ Fits a non-linear piecewise regression to fixtaion durations for a fixmat. Returns corrected fixation durations. """ if dataset is None: dataset = np.ones(durations.shape) corrected_d...
def subject_predictions(fm, field='SUBJECTINDEX', method=predict_fixation_duration, data=None): ''' Calculates the saccadic momentum effect for individual subjects. Removes any effect of amplitude differences. The parameters are fitted on unbinned data. The effects are comp...
def intersubject_scores(fm, category, predicting_filenumbers, predicting_subjects, predicted_filenumbers, predicted_subjects, controls = True, scale_factor = 1): """ Calculates how well the fixations from a set of subjects on a set of images can be predicted w...
def intersubject_scores_random_subjects(fm, category, filenumber, n_train, n_predict, controls=True, scale_factor = 1): """ Calculates how well the fixations of n random subjects on one image can be predicted with the fixations ...
def upper_bound(fm, nr_subs = None, scale_factor = 1): """ compute the inter-subject consistency upper bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1...
def lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1): """ Compute the spatial bias lower bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minu...
def ind2sub(ind, dimensions): """ Calculates subscripts for indices into regularly spaced matrixes. """ # check that the index is within range if ind >= np.prod(dimensions): raise RuntimeError("ind2sub: index exceeds array size") cum_dims = list(dimensions) cum_dims.reverse() m =...
def sub2ind(indices, dimensions): """ An exemplary sub2ind implementation to create randomization scripts. This function calculates indices from subscripts into regularly spaced matrixes. """ # check that none of the indices exceeds the size of the array if any([i > j for i, j in zip(...
def RestoreTaskStoreFactory(store_class, chunk_size, restore_file, save_file): """ Restores a task store from file. """ intm_results = np.load(restore_file) intm = intm_results[intm_results.files[0]] idx = np.isnan(intm).flatten().nonzero()[0] partitions = math.ceil(len(idx) / float(chunk_si...
def xmlrpc_reschedule(self): """ Reschedule all running tasks. """ if not len(self.scheduled_tasks) == 0: self.reschedule = list(self.scheduled_tasks.items()) self.scheduled_tasks = {} return True
def xmlrpc_get_task(self): """ Return a new task description: ID and necessary parameters, all are given in a dictionary """ try: if len(self.reschedule) == 0: (task_id, cur_task) = next(self.task_iterator) else: (task_id, ...
def xmlrpc_task_done(self, result): """ Take the results of a computation and put it into the results list. """ (task_id, task_results) = result del self.scheduled_tasks[task_id] self.task_store.update_results(task_id, task_results) self.results += 1 retur...
def xmlrpc_status(self): """ Return a status message """ return (""" %i Jobs are still wating for execution %i Jobs are being processed %i Jobs are done """ %(self.task_store.partitions - self.results - len(self.scheduled_...
def xmlrpc_save2file(self, filename): """ Save results and own state into file. """ savefile = open(filename,'wb') try: pickle.dump({'scheduled':self.scheduled_tasks, 'reschedule':self.reschedule},savefile) except pickle.PicklingError...
def run(self): """This function needs to be called to start the computation.""" (task_id, tasks) = self.server.get_task() self.task_store.from_dict(tasks) for (index, task) in self.task_store: result = self.compute(index, task) self.results.append(result) ...
def from_dict(self, description): """Configures the task store to be the task_store described in description""" assert(self.ident == description['ident']) self.partitions = description['partitions'] self.indices = description['indices']
def partition(self): """Partitions all tasks into groups of tasks. A group is represented by a task_store object that indexes a sub- set of tasks.""" step = int(math.ceil(self.num_tasks / float(self.partitions))) if self.indices == None: slice_ind = list(range(0...
def fit3d(samples, e_x, e_y, e_z, remove_zeros = False, **kw): """Fits a 3D distribution with splines. Input: samples: Array Array of samples from a probability distribution e_x: Array Edges that define the events in the probability distribution along the x ...
def fit2d(samples,e_x, e_y, remove_zeros = False, p_est = None, **kw): """Fits a 2D distribution with splines. Input: samples: Matrix or list of arrays If matrix, it must be of size Nx2, where N is the number of observations. If list, it must contain two arrays of length ...
def fit1d(samples, e, remove_zeros = False, **kw): """Fits a 1D distribution with splines. Input: samples: Array Array of samples from a probability distribution e: Array Edges that define the events in the probability distribution. For example, e[0] < x <= ...
def knots_from_marginal(marginal, nr_knots, spline_order): """ Determines knot placement based on a marginal distribution. It places knots such that each knot covers the same amount of probability mass. Two of the knots are reserved for the borders which are treated seperatly. For example, a uni...
def spline_base1d(length, nr_knots = 20, spline_order = 5, marginal = None): """Computes a 1D spline basis Input: length: int length of each basis nr_knots: int Number of knots, i.e. number of basis functions. spline_order: int Order of the splin...
def spline_base2d(width, height, nr_knots_x = 20.0, nr_knots_y = 20.0, spline_order = 5, marginal_x = None, marginal_y = None): """Computes a set of 2D spline basis functions. The basis functions cover the entire space in height*width and can for example be used to create fixation density ma...
def spline_base3d( width, height, depth, nr_knots_x = 10.0, nr_knots_y = 10.0, nr_knots_z=10, spline_order = 3, marginal_x = None, marginal_y = None, marginal_z = None): """Computes a set of 3D spline basis functions. For a description of the parameters see spline_base2d. """ if...
def spline(x,knots,p,i=0.0): """Evaluates the ith spline basis given by knots on points in x""" assert(p+1<len(knots)) return np.array([N(float(u),float(i),float(p),knots) for u in x])
def spcol(x,knots,spline_order): """Computes the spline colocation matrix for knots in x. The spline collocation matrix contains all m-p-1 bases defined by knots. Specifically it contains the ith basis in the ith column. Input: x: vector to evaluate the bases on knots: vec...
def augknt(knots,order): """Augment knot sequence such that some boundary conditions are met.""" a = [] [a.append(knots[0]) for t in range(0,order)] [a.append(k) for k in knots] [a.append(knots[-1]) for t in range(0,order)] return np.array(a)
def N(u,i,p,knots): """Compute Spline Basis Evaluates the spline basis of order p defined by knots at knot i and point u. """ if p == 0: if knots[i] < u and u <=knots[i+1]: return 1.0 else: return 0.0 else: try: k = (( float((u-kn...
def prediction_scores(prediction, fm, **kw): """ Evaluates a prediction against fixations in a fixmat with different measures. The default measures which are used are AUC, NSS and KL-divergence. This can be changed by setting the list of measures with set_scores. As different measures need potentia...
def kldiv_model(prediction, fm): """ wraps kldiv functionality for model evaluation input: prediction: 2D matrix the model salience map fm : fixmat Should be filtered for the image corresponding to the prediction """ (_, r_x) = calc_resize_factor(prediction, ...
def kldiv(p, q, distp = None, distq = None, scale_factor = 1): """ Computes the Kullback-Leibler divergence between two distributions. Parameters p : Matrix The first probability distribution q : Matrix The second probability distribution distp : fixmat ...
def kldiv_cs_model(prediction, fm): """ Computes Chao-Shen corrected KL-divergence between prediction and fdm made from fixations in fm. Parameters : prediction : np.ndarray a fixation density map fm : FixMat object """ # compute histogram of fixations needed for Cha...
def chao_shen(q): """ Computes some terms needed for the Chao-Shen KL correction. """ yx = q[q > 0] # remove bins with zero counts n = np.sum(yx) p = yx.astype(float)/n f1 = np.sum(yx == 1) # number of singletons in the sample if f1 == n: # avoid C == 0 f1 -= 1 C = 1 - (f1/n)...
def correlation_model(prediction, fm): """ wraps numpy.corrcoef functionality for model evaluation input: prediction: 2D Matrix the model salience map fm: fixmat Used to compute a FDM to which the prediction is compared. """ (_, r_x) = calc_resize_factor(pred...
def nss_model(prediction, fm): """ wraps nss functionality for model evaluation input: prediction: 2D matrix the model salience map fm : fixmat Fixations that define the actuals """ (r_y, r_x) = calc_resize_factor(prediction, fm.image_size) fix = ((np.arr...
def nss(prediction, fix): """ Compute the normalized scanpath salience input: fix : list, l[0] contains y, l[1] contains x """ prediction = prediction - np.mean(prediction) prediction = prediction / np.std(prediction) return np.mean(prediction[fix[0], fix[1]])
def roc_model(prediction, fm, ctr_loc = None, ctr_size = None): """ wraps roc functionality for model evaluation Parameters: prediction: 2D array the model salience map fm : fixmat Fixations that define locations of the actuals ctr_loc : tuple of (y.x) coordi...
def fast_roc(actuals, controls): """ approximates the area under the roc curve for sets of actuals and controls. Uses all values appearing in actuals as thresholds and lower sum interpolation. Also returns arrays of the true positive rate and the false positive rate that can be used for plotting the...
def faster_roc(actuals, controls): """ Histogram based implementation of AUC unde ROC curve. Parameters: actuals : list A list of numeric values for positive observations. controls : list A list of numeric values for negative observations. """ assert(type(actu...
def emd_model(prediction, fm): """ wraps emd functionality for model evaluation requires: OpenCV python bindings input: prediction: the model salience map fm : fixmat filtered for the image corresponding to the prediction """ (_, r_x) = calc_resize_factor(prediction, fm...
def emd(prediction, ground_truth): """ Compute the Eart Movers Distance between prediction and model. This implementation uses opencv for doing the actual work. Unfortunately, at the time of implementation only the SWIG bindings werer available and the numpy arrays have to converted by hand. Th...
def _rfc822(date): """Parse RFC 822 dates and times http://tools.ietf.org/html/rfc822#section-5 There are some formatting differences that are accounted for: 1. Years may be two or four digits. 2. The month and day can be swapped. 3. Additional timezone names are supported. 4. A default tim...
def _to_rfc822(date): """_to_rfc822(datetime.datetime) -> str The datetime `strftime` method is subject to locale-specific day and month names, so this function hardcodes the conversion.""" months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] da...
def format(self, sql, params): """ Formats the SQL query to use ordinal parameters instead of named parameters. sql (\|string\|) is the SQL query. params (\|dict\|) maps each named parameter (\|string\|) to value (\|object\|). If \|self.named\| is "numeric", then params can be simply a \|sequence\| of values ...
def formatmany(self, sql, many_params): """ Formats the SQL query to use ordinal parameters instead of named parameters. sql (\|string\|) is the SQL query. many_params (\|iterable\|) contains each params to format. - params (\|dict\|) maps each named parameter (\|string\|) to value (\|object\|). If \|se...
def _get_parser(f): """ Gets the parser for the command f, if it not exists it creates a new one """ _COMMAND_GROUPS[f.__module__].load() if f.__name__ not in _COMMAND_GROUPS[f.__module__].parsers: parser = _COMMAND_GROUPS[f.__module__].parser_generator.add_parser(f.__name__, help=f.__doc__...
def findMentions(sourceURL, targetURL=None, exclude_domains=[], content=None, test_urls=True, headers={}, timeout=None): """Find all <a /> elements in the given html for a post. Only scan html element matching all criteria in look_in. optionally the content to be scanned can be given as an argument. If an...
def findEndpoint(html): """Search the given html content for all <link /> elements and return any discovered WebMention URL. :param html: html content :rtype: WebMention URL """ poss_rels = ['webmention', 'http://webmention.org', 'http://webmention.org/', 'https://webmention.org', 'https://webm...
def discoverEndpoint(url, test_urls=True, headers={}, timeout=None, request=None, debug=False): """Discover any WebMention endpoint for a given URL. :param link: URL to discover WebMention endpoint :param test_urls: optional flag to test URLs for validation :param headers: optional headers to send with...
def sendWebmention(sourceURL, targetURL, webmention=None, test_urls=True, vouchDomain=None, headers={}, timeout=None, debug=False): """Send to the :targetURL: a WebMention for the :sourceURL: The WebMention will be discovered if not given in the :webmention: parameter. :param source...
def parse_link_header(link): """takes the link header as a string and returns a dictionary with rel values as keys and urls as values :param link: link header as a string :rtype: dictionary {rel_name: rel_value} """ rel_dict = {} for rels in link.split(','): rel_break = quoted_split(rels...
def findRelMe(sourceURL): """Find all <a /> elements in the given html for a post. If any have an href attribute that is rel="me" then include it in the result. :param sourceURL: the URL for the post we are scanning :rtype: dictionary of RelMe references """ r = requests.get(sourceURL) ...
def confirmRelMe(profileURL, resourceURL, profileRelMes=None, resourceRelMes=None): """Determine if a given :resourceURL: is authoritative for the :profileURL: TODO add https/http filtering for those who wish to limit/restrict urls to match fully TODO add code to ensure that each item in the redirect chain...
def indent_text(string, indent_level=2): """Indent every line of text in a newline-delimited string""" indented_lines = [] indent_spaces = ' ' * indent_level for line in string.split('\n'): indented_lines.append(indent_spaces + line) return '\n'.join(indented_lines)
def download(url, target, headers=None, trackers=()): """Download a file using requests. This is like urllib.request.urlretrieve, but: - requests validates SSL certificates by default - you can pass tracker objects to e.g. display a progress bar or calculate a file hash. """ if headers i...
def write(parsed_obj, spec=None, filename=None): """Writes an object created by `parse` to either a file or a bytearray. If the object doesn't end on a byte boundary, zeroes are appended to it until it does. """ if not isinstance(parsed_obj, BreadStruct): raise ValueError( 'Obje...
def deploy_file(file_path, bucket): """ Uploads a file to an S3 bucket, as a public file. """ # Paths look like: # index.html # css/bootstrap.min.css logger.info("Deploying {0}".format(file_path)) # Upload the actual file to file_path k = Key(bucket) k.key = file_path try: ...
def deploy(www_dir, bucket_name): """ Deploy to the configured S3 bucket. """ # Set up the connection to an S3 bucket. conn = boto.connect_s3() bucket = conn.get_bucket(bucket_name) # Deploy each changed file in www_dir os.chdir(www_dir) for root, dirs, files in os.walk('.'): for f...
def has_changed_since_last_deploy(file_path, bucket): """ Checks if a file has changed since the last time it was deployed. :param file_path: Path to file which should be checked. Should be relative from root of bucket. :param bucket_name: Name of S3 bucket to check against. :...
def main(): """ Entry point for the package, as defined in setup.py. """ # Log info and above to console logging.basicConfig( format='%(levelname)s: %(message)s', level=logging.INFO) # Get command line input/output arguments msg = 'Instantly deploy static HTML sites to S3 at the command li...
def start_sikuli_process(self, port=None): """ This keyword is used to start sikuli java process. If library is inited with mode "OLD", sikuli java process is started automatically. If library is inited with mode "NEW", this keyword should be used. :param port: port of sikuli ja...
def post(self, request): """Respond to POSTed username/password with token.""" serializer = AuthTokenSerializer(data=request.data) if serializer.is_valid(): token, _ = ExpiringToken.objects.get_or_create( user=serializer.validated_data['user'] ) ...
def EXPIRING_TOKEN_LIFESPAN(self): """ Return the allowed lifespan of a token as a TimeDelta object. Defaults to 30 days. """ try: val = settings.EXPIRING_TOKEN_LIFESPAN except AttributeError: val = timedelta(days=30) return val
def expired(self): """Return boolean indicating token expiration.""" now = timezone.now() if self.created < now - token_settings.EXPIRING_TOKEN_LIFESPAN: return True return False
def unicode_is_punctuation(text): """ Test if a token is made entirely of Unicode characters of the following classes: - P: punctuation - S: symbols - Z: separators - M: combining marks - C: control characters >>> unicode_is_punctuation('word') False >>> unicode_is_punctuat...
def process(self): """ Store the actual process in _process. If it doesn't exist yet, create it. """ if hasattr(self, '_process'): return self._process else: self._process = self._get_process() return self._process
def _get_process(self): """ Create the process by running the specified command. """ command = self._get_command() return subprocess.Popen(command, bufsize=-1, close_fds=True, stdout=subprocess.PIPE, stdin=subprocess...
def tokenize_list(self, text): """ Split a text into separate words. """ return [self.get_record_token(record) for record in self.analyze(text)]
def is_stopword(self, text): """ Determine whether a single word is a stopword, or whether a short phrase is made entirely of stopwords, disregarding context. Use of this function should be avoided; it's better to give the text in context and let the process determine which word...
def normalize_list(self, text, cache=None): """ Get a canonical list representation of text, with words separated and reduced to their base forms. TODO: use the cache. """ words = [] analysis = self.analyze(text) for record in analysis: if not...

def makeHist(x_val, y_val, fit=spline_base.fit2d, bins=[np.linspace(-36.5,36.5,74),np.linspace(-180,180,361)]): """ Constructs a (fitted) histogram of the given data. Parameters: x_val : array The data to be histogrammed along the x-axis. y_val : array ...

def firstSacDist(fm): """ Computes the distribution of angle and length combinations that were made as first saccades Parameters: fm : ocupy.fixmat The fixation data to be analysed """ ang, leng, ad, ld = anglendiff(fm, return_abs=True) ...

def trajLenDist(fm): """ Computes the distribution of trajectory lengths, i.e. the number of saccades that were made as a part of one trajectory Parameters: fm : ocupy.fixmat The fixation data to be analysed """ trajLen = np.roll(fm.fix, 1...

def reshift(I): """ Transforms the given number element into a range of [-180, 180], which covers all possible angle differences. This method reshifts larger or smaller numbers that might be the output of other angular calculations into that range by adding or subtracting 360, respectively. To...

def initializeData(self, fit = None, full_H1=None, max_length = 40, in_deg = True): """ Prepares the data to be replicated. Calculates the second-order length and angle dependencies between saccades and stores them in a fitted histogram. Parameters: ...

def _calc_xy(self, xxx_todo_changeme, angle, length): """ Calculates the coordinates after a specific saccade was made. Parameters: (x,y) : tuple of floats or ints The coordinates before the saccade was made angle : float or int Th...

def _draw(self, prev_angle = None, prev_length = None): """ Draws a new length- and angle-difference pair and calculates length and angle absolutes matching the last saccade drawn. Parameters: prev_angle : float, optional The last angle that was drawn in the ...

def sample_many(self, num_samples = 2000): """ Generates a given number of trajectories, using the method sample(). Returns a fixmat with the generated data. Parameters: num_samples : int, optional The number of trajectories that shall be generated. ...

def sample(self): """ Draws a trajectory length, first coordinates, lengths, angles and length-angle-difference pairs according to the empirical distribution. Each call creates one complete trajectory. """ lenghts = [] angles = [] coordinates = [] ...

def drawFrom(self, cumsum, r): """ Draws a value from a cumulative sum. Parameters: cumsum : array Cumulative sum from which shall be drawn. Returns: int : Index of the cumulative sum element drawn. """ a = cumsum.rsplit(...

def load(path): """ Load fixmat at path. Parameters: path : string Absolute path of the file to load from. """ f = h5py.File(path,'r') if 'Fixmat' in f: fm_group = f['Fixmat'] else: fm_group = f['Datamat'] fields = {} params = {} for field, va...

def compute_fdm(fixmat, fwhm=2, scale_factor=1): """ Computes a fixation density map for the calling fixmat. Creates a map the size of the image fixations were recorded on. Every pixel contains the frequency of fixations for this image. The fixation map is smoothed by convolution with a ...

def relative_bias(fm, scale_factor = 1, estimator = None): """ Computes the relative bias, i.e. the distribution of saccade angles and amplitudes. Parameters: fm : DataMat The fixation data to use scale_factor : double Returns: 2D probability distribution of s...

def DirectoryFixmatFactory(directory, categories = None, glob_str = '*.mat', var_name = 'fixmat'): """ Concatenates all fixmats in dir and returns the resulting single fixmat. Parameters: directory : string Path from which the fixmats should be loaded categories : instan...

def FixmatFactory(fixmatfile, categories = None, var_name = 'fixmat', field_name='x'): """ Loads a single fixmat (fixmatfile). Parameters: fixmatfile : string The matlab fixmat that should be loaded. categories : instance of stimuli.Categories, optional Links dat...

def add_feature_values(self, features): """ Adds feature values of feature 'feature' to all fixations in the calling fixmat. For fixations out of the image boundaries, NaNs are returned. The function generates a new attribute field named with the string in featu...

def make_reg_data(self, feature_list=None, all_controls=False): """ Generates two M x N matrices with M feature values at fixations for N features. Controls are a random sample out of all non-fixated regions of an image or fixations of the same subject group on a randomly chosen ...

def get_velocity(samplemat, Hz, blinks=None): ''' Compute velocity of eye-movements. Samplemat must contain fields 'x' and 'y', specifying the x,y coordinates of gaze location. The function assumes that the values in x,y are sampled continously at a rate specified by 'Hz'. ''' Hz = float(Hz...

def saccade_detection(samplemat, Hz=200, threshold=30, acc_thresh=2000, min_duration=21, min_movement=.35, ignore_blinks=False): ''' Detect saccades in a stream of gaze location samples. Coordinates in samplemat are assumed to be in degrees. Saccades are det...

def fixation_detection(samplemat, saccades, Hz=200, samples2fix=None, respect_trial_borders=False, sample_times=None): ''' Detect Fixation from saccades. Fixations are defined as intervals between saccades. This function also calcuates start and end times (in ms) for each fixatio...

def parse(parse_obj, agent=None, etag=None, modified=None, inject=False): """Parse a subscription list and return a dict containing the results. :param parse_obj: A file-like object or a string containing a URL, an absolute or relative filename, or an XML document. :type parse_obj: str or file ...

def FixmatStimuliFactory(fm, loader): """ Constructs an categories object for all image / category combinations in the fixmat. Parameters: fm: FixMat Used for extracting valid category/image combination. loader: loader Loader that accesses the stimuli for th...

def DirectoryStimuliFactory(loader): """ Takes an input path to the images folder of an experiment and generates automatically the category - filenumber list needed to construct an appropriate _categories object. Parameters : loader : Loader object which contains impath : s...

def fixations(self): ''' Filter the fixmat such that it only contains fixations on images in categories that are also in the categories object''' if not self._fixations: raise RuntimeError('This Images object does not have' +' an associated fixmat') if len(lis...

def data(self, value): """ Saves a new image to disk """ self.loader.save_image(self.category, self.image, value)

def fixations(self): """ Returns all fixations that are on this image. A precondition for this to work is that a fixmat is associated with this Image object. """ if not self._fixations: raise RuntimeError('This Images object does not have' +' ...

def generate(self): """ Generator for creating the cross-validation slices. Returns A tuple of that contains two fixmats (training and test) and two Category objects (test and train). """ for _ in range(0, self.num_slices): #1. separate fixm...

def prepare_data(fm, max_back, dur_cap=700): ''' Computes angle and length differences up to given order and deletes suspiciously long fixations. Input fm: Fixmat Fixmat for which to comput angle and length differences max_back: Int Computes delta angle and ampli...

def saccadic_momentum_effect(durations, forward_angle, summary_stat=nanmean): """ Computes the mean fixation duration at forward angles. """ durations_per_da = np.nan * np.ones((len(e_angle) - 1,)) for i, (bo, b1) in enumerate(zip(e_angle[:-1], e_angle[1:])): idx...

def ior_effect(durations, angle_diffs, length_diffs, summary_stat=np.mean, parallel=True, min_samples=20): """ Computes a measure of fixation durations at delta angle and delta length combinations. """ raster = np.empty((len(e_dist) - 1, len(e_angle) - 1), dtype=object) for a, (a_...

def predict_fixation_duration( durations, angles, length_diffs, dataset=None, params=None): """ Fits a non-linear piecewise regression to fixtaion durations for a fixmat. Returns corrected fixation durations. """ if dataset is None: dataset = np.ones(durations.shape) corrected_d...

def subject_predictions(fm, field='SUBJECTINDEX', method=predict_fixation_duration, data=None): ''' Calculates the saccadic momentum effect for individual subjects. Removes any effect of amplitude differences. The parameters are fitted on unbinned data. The effects are comp...

def intersubject_scores(fm, category, predicting_filenumbers, predicting_subjects, predicted_filenumbers, predicted_subjects, controls = True, scale_factor = 1): """ Calculates how well the fixations from a set of subjects on a set of images can be predicted w...

def intersubject_scores_random_subjects(fm, category, filenumber, n_train, n_predict, controls=True, scale_factor = 1): """ Calculates how well the fixations of n random subjects on one image can be predicted with the fixations ...

def upper_bound(fm, nr_subs = None, scale_factor = 1): """ compute the inter-subject consistency upper bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minus 1...

def lower_bound(fm, nr_subs = None, nr_imgs = None, scale_factor = 1): """ Compute the spatial bias lower bound for a fixmat. Input: fm : a fixmat instance nr_subs : the number of subjects used for the prediction. Defaults to the total number of subjects in the fixmat minu...

def ind2sub(ind, dimensions): """ Calculates subscripts for indices into regularly spaced matrixes. """ # check that the index is within range if ind >= np.prod(dimensions): raise RuntimeError("ind2sub: index exceeds array size") cum_dims = list(dimensions) cum_dims.reverse() m =...

def sub2ind(indices, dimensions): """ An exemplary sub2ind implementation to create randomization scripts. This function calculates indices from subscripts into regularly spaced matrixes. """ # check that none of the indices exceeds the size of the array if any([i > j for i, j in zip(...

def RestoreTaskStoreFactory(store_class, chunk_size, restore_file, save_file): """ Restores a task store from file. """ intm_results = np.load(restore_file) intm = intm_results[intm_results.files[0]] idx = np.isnan(intm).flatten().nonzero()[0] partitions = math.ceil(len(idx) / float(chunk_si...

def xmlrpc_reschedule(self): """ Reschedule all running tasks. """ if not len(self.scheduled_tasks) == 0: self.reschedule = list(self.scheduled_tasks.items()) self.scheduled_tasks = {} return True

def xmlrpc_get_task(self): """ Return a new task description: ID and necessary parameters, all are given in a dictionary """ try: if len(self.reschedule) == 0: (task_id, cur_task) = next(self.task_iterator) else: (task_id, ...

def xmlrpc_task_done(self, result): """ Take the results of a computation and put it into the results list. """ (task_id, task_results) = result del self.scheduled_tasks[task_id] self.task_store.update_results(task_id, task_results) self.results += 1 retur...

def xmlrpc_status(self): """ Return a status message """ return (""" %i Jobs are still wating for execution %i Jobs are being processed %i Jobs are done """ %(self.task_store.partitions - self.results - len(self.scheduled_...

def xmlrpc_save2file(self, filename): """ Save results and own state into file. """ savefile = open(filename,'wb') try: pickle.dump({'scheduled':self.scheduled_tasks, 'reschedule':self.reschedule},savefile) except pickle.PicklingError...

def run(self): """This function needs to be called to start the computation.""" (task_id, tasks) = self.server.get_task() self.task_store.from_dict(tasks) for (index, task) in self.task_store: result = self.compute(index, task) self.results.append(result) ...

def from_dict(self, description): """Configures the task store to be the task_store described in description""" assert(self.ident == description['ident']) self.partitions = description['partitions'] self.indices = description['indices']

def partition(self): """Partitions all tasks into groups of tasks. A group is represented by a task_store object that indexes a sub- set of tasks.""" step = int(math.ceil(self.num_tasks / float(self.partitions))) if self.indices == None: slice_ind = list(range(0...

def fit3d(samples, e_x, e_y, e_z, remove_zeros = False, **kw): """Fits a 3D distribution with splines. Input: samples: Array Array of samples from a probability distribution e_x: Array Edges that define the events in the probability distribution along the x ...

def fit2d(samples,e_x, e_y, remove_zeros = False, p_est = None, **kw): """Fits a 2D distribution with splines. Input: samples: Matrix or list of arrays If matrix, it must be of size Nx2, where N is the number of observations. If list, it must contain two arrays of length ...

def fit1d(samples, e, remove_zeros = False, **kw): """Fits a 1D distribution with splines. Input: samples: Array Array of samples from a probability distribution e: Array Edges that define the events in the probability distribution. For example, e[0] < x <= ...

def knots_from_marginal(marginal, nr_knots, spline_order): """ Determines knot placement based on a marginal distribution. It places knots such that each knot covers the same amount of probability mass. Two of the knots are reserved for the borders which are treated seperatly. For example, a uni...

def spline_base1d(length, nr_knots = 20, spline_order = 5, marginal = None): """Computes a 1D spline basis Input: length: int length of each basis nr_knots: int Number of knots, i.e. number of basis functions. spline_order: int Order of the splin...

def spline_base2d(width, height, nr_knots_x = 20.0, nr_knots_y = 20.0, spline_order = 5, marginal_x = None, marginal_y = None): """Computes a set of 2D spline basis functions. The basis functions cover the entire space in height*width and can for example be used to create fixation density ma...

def spline_base3d( width, height, depth, nr_knots_x = 10.0, nr_knots_y = 10.0, nr_knots_z=10, spline_order = 3, marginal_x = None, marginal_y = None, marginal_z = None): """Computes a set of 3D spline basis functions. For a description of the parameters see spline_base2d. """ if...

def spline(x,knots,p,i=0.0): """Evaluates the ith spline basis given by knots on points in x""" assert(p+1<len(knots)) return np.array([N(float(u),float(i),float(p),knots) for u in x])

def spcol(x,knots,spline_order): """Computes the spline colocation matrix for knots in x. The spline collocation matrix contains all m-p-1 bases defined by knots. Specifically it contains the ith basis in the ith column. Input: x: vector to evaluate the bases on knots: vec...

def augknt(knots,order): """Augment knot sequence such that some boundary conditions are met.""" a = [] [a.append(knots[0]) for t in range(0,order)] [a.append(k) for k in knots] [a.append(knots[-1]) for t in range(0,order)] return np.array(a)

def N(u,i,p,knots): """Compute Spline Basis Evaluates the spline basis of order p defined by knots at knot i and point u. """ if p == 0: if knots[i] < u and u <=knots[i+1]: return 1.0 else: return 0.0 else: try: k = (( float((u-kn...

def prediction_scores(prediction, fm, **kw): """ Evaluates a prediction against fixations in a fixmat with different measures. The default measures which are used are AUC, NSS and KL-divergence. This can be changed by setting the list of measures with set_scores. As different measures need potentia...

def kldiv_model(prediction, fm): """ wraps kldiv functionality for model evaluation input: prediction: 2D matrix the model salience map fm : fixmat Should be filtered for the image corresponding to the prediction """ (_, r_x) = calc_resize_factor(prediction, ...

def kldiv(p, q, distp = None, distq = None, scale_factor = 1): """ Computes the Kullback-Leibler divergence between two distributions. Parameters p : Matrix The first probability distribution q : Matrix The second probability distribution distp : fixmat ...

def kldiv_cs_model(prediction, fm): """ Computes Chao-Shen corrected KL-divergence between prediction and fdm made from fixations in fm. Parameters : prediction : np.ndarray a fixation density map fm : FixMat object """ # compute histogram of fixations needed for Cha...

def chao_shen(q): """ Computes some terms needed for the Chao-Shen KL correction. """ yx = q[q > 0] # remove bins with zero counts n = np.sum(yx) p = yx.astype(float)/n f1 = np.sum(yx == 1) # number of singletons in the sample if f1 == n: # avoid C == 0 f1 -= 1 C = 1 - (f1/n)...

def correlation_model(prediction, fm): """ wraps numpy.corrcoef functionality for model evaluation input: prediction: 2D Matrix the model salience map fm: fixmat Used to compute a FDM to which the prediction is compared. """ (_, r_x) = calc_resize_factor(pred...

def nss_model(prediction, fm): """ wraps nss functionality for model evaluation input: prediction: 2D matrix the model salience map fm : fixmat Fixations that define the actuals """ (r_y, r_x) = calc_resize_factor(prediction, fm.image_size) fix = ((np.arr...

def nss(prediction, fix): """ Compute the normalized scanpath salience input: fix : list, l[0] contains y, l[1] contains x """ prediction = prediction - np.mean(prediction) prediction = prediction / np.std(prediction) return np.mean(prediction[fix[0], fix[1]])

def roc_model(prediction, fm, ctr_loc = None, ctr_size = None): """ wraps roc functionality for model evaluation Parameters: prediction: 2D array the model salience map fm : fixmat Fixations that define locations of the actuals ctr_loc : tuple of (y.x) coordi...

def fast_roc(actuals, controls): """ approximates the area under the roc curve for sets of actuals and controls. Uses all values appearing in actuals as thresholds and lower sum interpolation. Also returns arrays of the true positive rate and the false positive rate that can be used for plotting the...

def faster_roc(actuals, controls): """ Histogram based implementation of AUC unde ROC curve. Parameters: actuals : list A list of numeric values for positive observations. controls : list A list of numeric values for negative observations. """ assert(type(actu...

def emd_model(prediction, fm): """ wraps emd functionality for model evaluation requires: OpenCV python bindings input: prediction: the model salience map fm : fixmat filtered for the image corresponding to the prediction """ (_, r_x) = calc_resize_factor(prediction, fm...

def emd(prediction, ground_truth): """ Compute the Eart Movers Distance between prediction and model. This implementation uses opencv for doing the actual work. Unfortunately, at the time of implementation only the SWIG bindings werer available and the numpy arrays have to converted by hand. Th...

def _rfc822(date): """Parse RFC 822 dates and times http://tools.ietf.org/html/rfc822#section-5 There are some formatting differences that are accounted for: 1. Years may be two or four digits. 2. The month and day can be swapped. 3. Additional timezone names are supported. 4. A default tim...

def _to_rfc822(date): """_to_rfc822(datetime.datetime) -> str The datetime `strftime` method is subject to locale-specific day and month names, so this function hardcodes the conversion.""" months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'] da...

def _get_parser(f): """ Gets the parser for the command f, if it not exists it creates a new one """ _COMMAND_GROUPS[f.__module__].load() if f.__name__ not in _COMMAND_GROUPS[f.__module__].parsers: parser = _COMMAND_GROUPS[f.__module__].parser_generator.add_parser(f.__name__, help=f.__doc__...

def findMentions(sourceURL, targetURL=None, exclude_domains=[], content=None, test_urls=True, headers={}, timeout=None): """Find all <a /> elements in the given html for a post. Only scan html element matching all criteria in look_in. optionally the content to be scanned can be given as an argument. If an...

def findEndpoint(html): """Search the given html content for all <link /> elements and return any discovered WebMention URL. :param html: html content :rtype: WebMention URL """ poss_rels = ['webmention', 'http://webmention.org', 'http://webmention.org/', 'https://webmention.org', 'https://webm...

def discoverEndpoint(url, test_urls=True, headers={}, timeout=None, request=None, debug=False): """Discover any WebMention endpoint for a given URL. :param link: URL to discover WebMention endpoint :param test_urls: optional flag to test URLs for validation :param headers: optional headers to send with...

def sendWebmention(sourceURL, targetURL, webmention=None, test_urls=True, vouchDomain=None, headers={}, timeout=None, debug=False): """Send to the :targetURL: a WebMention for the :sourceURL: The WebMention will be discovered if not given in the :webmention: parameter. :param source...

def parse_link_header(link): """takes the link header as a string and returns a dictionary with rel values as keys and urls as values :param link: link header as a string :rtype: dictionary {rel_name: rel_value} """ rel_dict = {} for rels in link.split(','): rel_break = quoted_split(rels...

def findRelMe(sourceURL): """Find all <a /> elements in the given html for a post. If any have an href attribute that is rel="me" then include it in the result. :param sourceURL: the URL for the post we are scanning :rtype: dictionary of RelMe references """ r = requests.get(sourceURL) ...

def confirmRelMe(profileURL, resourceURL, profileRelMes=None, resourceRelMes=None): """Determine if a given :resourceURL: is authoritative for the :profileURL: TODO add https/http filtering for those who wish to limit/restrict urls to match fully TODO add code to ensure that each item in the redirect chain...

def indent_text(string, indent_level=2): """Indent every line of text in a newline-delimited string""" indented_lines = [] indent_spaces = ' ' * indent_level for line in string.split('\n'): indented_lines.append(indent_spaces + line) return '\n'.join(indented_lines)

def download(url, target, headers=None, trackers=()): """Download a file using requests. This is like urllib.request.urlretrieve, but: - requests validates SSL certificates by default - you can pass tracker objects to e.g. display a progress bar or calculate a file hash. """ if headers i...

def write(parsed_obj, spec=None, filename=None): """Writes an object created by `parse` to either a file or a bytearray. If the object doesn't end on a byte boundary, zeroes are appended to it until it does. """ if not isinstance(parsed_obj, BreadStruct): raise ValueError( 'Obje...

def deploy_file(file_path, bucket): """ Uploads a file to an S3 bucket, as a public file. """ # Paths look like: # index.html # css/bootstrap.min.css logger.info("Deploying {0}".format(file_path)) # Upload the actual file to file_path k = Key(bucket) k.key = file_path try: ...

def deploy(www_dir, bucket_name): """ Deploy to the configured S3 bucket. """ # Set up the connection to an S3 bucket. conn = boto.connect_s3() bucket = conn.get_bucket(bucket_name) # Deploy each changed file in www_dir os.chdir(www_dir) for root, dirs, files in os.walk('.'): for f...

def has_changed_since_last_deploy(file_path, bucket): """ Checks if a file has changed since the last time it was deployed. :param file_path: Path to file which should be checked. Should be relative from root of bucket. :param bucket_name: Name of S3 bucket to check against. :...

def main(): """ Entry point for the package, as defined in setup.py. """ # Log info and above to console logging.basicConfig( format='%(levelname)s: %(message)s', level=logging.INFO) # Get command line input/output arguments msg = 'Instantly deploy static HTML sites to S3 at the command li...

def start_sikuli_process(self, port=None): """ This keyword is used to start sikuli java process. If library is inited with mode "OLD", sikuli java process is started automatically. If library is inited with mode "NEW", this keyword should be used. :param port: port of sikuli ja...

def post(self, request): """Respond to POSTed username/password with token.""" serializer = AuthTokenSerializer(data=request.data) if serializer.is_valid(): token, _ = ExpiringToken.objects.get_or_create( user=serializer.validated_data['user'] ) ...

def EXPIRING_TOKEN_LIFESPAN(self): """ Return the allowed lifespan of a token as a TimeDelta object. Defaults to 30 days. """ try: val = settings.EXPIRING_TOKEN_LIFESPAN except AttributeError: val = timedelta(days=30) return val

def expired(self): """Return boolean indicating token expiration.""" now = timezone.now() if self.created < now - token_settings.EXPIRING_TOKEN_LIFESPAN: return True return False

def unicode_is_punctuation(text): """ Test if a token is made entirely of Unicode characters of the following classes: - P: punctuation - S: symbols - Z: separators - M: combining marks - C: control characters >>> unicode_is_punctuation('word') False >>> unicode_is_punctuat...

def process(self): """ Store the actual process in _process. If it doesn't exist yet, create it. """ if hasattr(self, '_process'): return self._process else: self._process = self._get_process() return self._process

def _get_process(self): """ Create the process by running the specified command. """ command = self._get_command() return subprocess.Popen(command, bufsize=-1, close_fds=True, stdout=subprocess.PIPE, stdin=subprocess...

def tokenize_list(self, text): """ Split a text into separate words. """ return [self.get_record_token(record) for record in self.analyze(text)]

def is_stopword(self, text): """ Determine whether a single word is a stopword, or whether a short phrase is made entirely of stopwords, disregarding context. Use of this function should be avoided; it's better to give the text in context and let the process determine which word...

def normalize_list(self, text, cache=None): """ Get a canonical list representation of text, with words separated and reduced to their base forms. TODO: use the cache. """ words = [] analysis = self.analyze(text) for record in analysis: if not...