BoghdadyJR/codesearch_python · Datasets at Hugging Face

text stringlengths 75 104k
def encode(self): ''' Encode and store a CONNECT control packet. @raise e: C{ValueError} if any encoded topic string exceeds 65535 bytes. @raise e: C{ValueError} if encoded username string exceeds 65535 bytes. ''' header = bytearray(1) varHeader = bytearray() ...
def decode(self, packet): ''' Decode a CONNECT control packet. ''' self.encoded = packet # Strip the fixed header plus variable length field lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] # Var...
def encode(self): ''' Encode and store a CONNACK control packet. ''' header = bytearray(1) varHeader = bytearray(2) header[0] = 0x20 varHeader[0] = self.session varHeader[1] = self.resultCode header.extend(encodeLength(len(varHe...
def decode(self, packet): ''' Decode a CONNACK control packet. ''' self.encoded = packet # Strip the fixed header plus variable length field lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self....
def decode(self, packet): ''' Decode a SUBSCRIBE control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining[0:2]) self....
def encode(self): ''' Encode and store a SUBACK control packet. ''' header = bytearray(1) payload = bytearray() varHeader = encode16Int(self.msgId) header[0] = 0x90 for code in self.granted: payload.append(code[0] \| (0x80 if code[1] == Tru...
def encode(self): ''' Encode and store an UNSUBCRIBE control packet @raise e: C{ValueError} if any encoded topic string exceeds 65535 bytes ''' header = bytearray(1) payload = bytearray() varHeader = encode16Int(self.msgId) header[0] = 0xA2 # packe...
def decode(self, packet): ''' Decode a UNSUBACK control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining[0:2]) self.t...
def encode(self): ''' Encode and store an UNSUBACK control packet ''' header = bytearray(1) varHeader = encode16Int(self.msgId) header[0] = 0xB0 header.extend(encodeLength(len(varHeader))) header.extend(varHeader) self.encoded = header ...
def encode(self): ''' Encode and store a PUBLISH control packet. @raise e: C{ValueError} if encoded topic string exceeds 65535 bytes. @raise e: C{ValueError} if encoded packet size exceeds 268435455 bytes. @raise e: C{TypeError} if C{data} is not a string, bytearray, int, boolean...
def decode(self, packet): ''' Decode a PUBLISH control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.dup = (packet[0] & 0x08) == 0x08 self.qos = (p...
def decode(self, packet): ''' Decode a PUBREL control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining) self.dup = (pa...
def get_url(self, method=None, **kwargs): """Return url for call method. :param method (optional): `str` method name. :returns: `str` URL. """ kwargs.setdefault('v', self.__version) if self.__token is not None: kwargs.setdefault('access_token', self.__token)...
def request(self, method, **kwargs): """ Send request to API. :param method: `str` method name. :returns: `dict` response. """ kwargs.setdefault('v', self.__version) if self.__token is not None: kwargs.setdefault('access_token', self.__token) ...
def authentication(login, password): """ Authentication on vk.com. :param login: login on vk.com. :param password: password on vk.com. :returns: `requests.Session` session with cookies. """ session = requests.Session() response = session.get('https://m.vk.com') url = re.search(r'act...
def oauth(login, password, app_id=4729418, scope=2097151): """ OAuth on vk.com. :param login: login on vk.com. :param password: password on vk.com. :param app_id: vk.com application id (default: 4729418). :param scope: allowed actions (default: 2097151 (all)). :returns: OAuth2 access token ...
def create_from_array(self, blockname, array, Nfile=None, memorylimit=1024 * 1024 * 256): """ create a block from array like objects The operation is well defined only if array is at most 2d. Parameters ---------- array : array_like, array shall h...
def refresh(self): """ Refresh the list of blocks to the disk, collectively """ if self.comm.rank == 0: self._blocks = self.list_blocks() else: self._blocks = None self._blocks = self.comm.bcast(self._blocks)
def create_from_array(self, blockname, array, Nfile=None, memorylimit=1024 * 1024 * 256): """ create a block from array like objects The operation is well defined only if array is at most 2d. Parameters ---------- array : array_like, array shall h...
def maybebool(value): ''' If `value` is a string type, attempts to convert it to a boolean if it looks like it might be one, otherwise returns the value unchanged. The difference between this and :func:`pyramid.settings.asbool` is how non-bools are handled: this returns the original value, where...
def get_webassets_env_from_settings(settings, prefix='webassets'): """This function will take all webassets.* parameters, and call the ``Environment()`` constructor with kwargs passed in. The only two parameters that are not passed as keywords are: * base_dir * base_url which are passed in po...
def format_data(self, data, scale=True): """ Function for converting a dict to an array suitable for sklearn. Parameters ---------- data : dict A dict of data, containing all elements of `analytes` as items. scale : bool Whether or not...
def fitting_data(self, data): """ Function to format data for cluster fitting. Parameters ---------- data : dict A dict of data, containing all elements of `analytes` as items. Returns ------- A data array for initial cluster fitt...
def fit_kmeans(self, data, n_clusters, *kwargs): """ Fit KMeans clustering algorithm to data. Parameters ---------- data : array-like A dataset formatted by `classifier.fitting_data`. n_clusters : int The number of clusters in the data. ...
def fit_meanshift(self, data, bandwidth=None, bin_seeding=False, **kwargs): """ Fit MeanShift clustering algorithm to data. Parameters ---------- data : array-like A dataset formatted by `classifier.fitting_data`. bandwidth : float The bandwidth v...
def fit(self, data, method='kmeans', **kwargs): """ fit classifiers from large dataset. Parameters ---------- data : dict A dict of data for clustering. Must contain items with the same name as analytes used for clustering. method : st...
def predict(self, data): """ Label new data with cluster identities. Parameters ---------- data : dict A data dict containing the same analytes used to fit the classifier. sort_by : str The name of an analyte used to sort the resulting...
def map_clusters(self, size, sampled, clusters): """ Translate cluster identity back to original data size. Parameters ---------- size : int size of original dataset sampled : array-like integer array describing location of finite values ...
def sort_clusters(self, data, cs, sort_by): """ Sort clusters by the concentration of a particular analyte. Parameters ---------- data : dict A dataset containing sort_by as a key. cs : array-like An array of clusters, the same length as values of...
def get_date(datetime, time_format=None): """ Return a datetime oject from a string, with optional time format. Parameters ---------- datetime : str Date-time as string in any sensible format. time_format : datetime str (optional) String describing the datetime format. If missin...
def get_total_n_points(d): """ Returns the total number of data points in values of dict. Paramters --------- d : dict """ n = 0 for di in d.values(): n += len(di) return n
def get_total_time_span(d): """ Returns total length of analysis. """ tmax = 0 for di in d.values(): if di.uTime.max() > tmax: tmax = di.uTime.max() return tmax
def unitpicker(a, llim=0.1, denominator=None, focus_stage=None): """ Determines the most appropriate plotting unit for data. Parameters ---------- a : float or array-like number to optimise. If array like, the 25% quantile is optimised. llim : float minimum allowable value in sc...
def pretty_element(s): """ Returns formatted element name. Parameters ---------- s : str of format [A-Z][a-z]?[0-9]+ Returns ------- str LaTeX formatted string with superscript numbers. """ el = re.match('.?([A-z]{1,3}).?', s).groups()[0] m = re.match('.*?...
def analyte_2_namemass(s): """ Converts analytes in format '27Al' to 'Al27'. Parameters ---------- s : str of format [A-z]{1,3}[0-9]{1,3} Returns ------- str Name in format [0-9]{1,3}[A-z]{1,3} """ el = re.match('.?([A-z]{1,3}).?', s).groups()[0] m = re.ma...
def analyte_2_massname(s): """ Converts analytes in format 'Al27' to '27Al'. Parameters ---------- s : str of format [0-9]{1,3}[A-z]{1,3} Returns ------- str Name in format [A-z]{1,3}[0-9]{1,3} """ el = re.match('.?([A-z]{1,3}).?', s).groups()[0] m = re.ma...
def collate_data(in_dir, extension='.csv', out_dir=None): """ Copy all csvs in nested directroy to single directory. Function to copy all csvs from a directory, and place them in a new directory. Parameters ---------- in_dir : str Input directory containing csv files in subfolders ...
def bool_2_indices(a): """ Convert boolean array into a 2D array of (start, stop) pairs. """ if any(a): lims = [] lims.append(np.where(a[:-1] != a[1:])[0]) if a[0]: lims.append([0]) if a[-1]: lims.append([len(a) - 1]) lims = np.concatenate...
def enumerate_bool(bool_array, nstart=0): """ Consecutively numbers contiguous booleans in array. i.e. a boolean sequence, and resulting numbering T F T T T F T F F F T T F 0-1 1 1 - 2 ---3 3 - where ' - ' Parameters ---------- bool_array : array_like Array of booleans. ...
def tuples_2_bool(tuples, x): """ Generate boolean array from list of limit tuples. Parameters ---------- tuples : array_like [2, n] array of (start, end) values x : array_like x scale the tuples are mapped to Returns ------- array_like boolean array, True w...
def rolling_window(a, window, pad=None): """ Returns (win, len(a)) rolling - window array of data. Parameters ---------- a : array_like Array to calculate the rolling window of window : int Description of `window`. pad : same as dtype(a) Description of `pad`. Re...
def fastsmooth(a, win=11): """ Returns rolling - window smooth of a. Function to efficiently calculate the rolling mean of a numpy array using 'stride_tricks' to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win]. Parameters -----...
def fastgrad(a, win=11): """ Returns rolling - window gradient of a. Function to efficiently calculate the rolling gradient of a numpy array using 'stride_tricks' to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win]. Parameters -...
def calc_grads(x, dat, keys=None, win=5): """ Calculate gradients of values in dat. Parameters ---------- x : array like Independent variable for items in dat. dat : dict {key: dependent_variable} pairs keys : str or array-like Which keys in dict to calculate the...
def findmins(x, y): """ Function to find local minima. Parameters ---------- x, y : array_like 1D arrays of the independent (x) and dependent (y) variables. Returns ------- array_like Array of points in x where y has a local minimum. """ return x[np.r_[False, y[1:] ...
def stack_keys(ddict, keys, extra=None): """ Combine elements of ddict into an array of shape (len(ddict[key]), len(keys)). Useful for preparing data for sklearn. Parameters ---------- ddict : dict A dict containing arrays or lists to be stacked. Must be of equal length. ke...
def cluster_meanshift(data, bandwidth=None, bin_seeding=False, **kwargs): """ Identify clusters using Meanshift algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. bandwidth : float or None If None, bandwidth is estimated automatically using...
def cluster_kmeans(data, n_clusters, **kwargs): """ Identify clusters using K - Means algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. n_clusters : int The number of clusters expected in the data. Returns ------- dict ...
def cluster_DBSCAN(data, eps=None, min_samples=None, n_clusters=None, maxiter=200, **kwargs): """ Identify clusters using DBSCAN algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. eps : float The minimum 'distance' points...
def get_defined_srms(srm_file): """ Returns list of SRMS defined in the SRM database """ srms = read_table(srm_file) return np.asanyarray(srms.index.unique())
def read_configuration(config='DEFAULT'): """ Read LAtools configuration file, and return parameters as dict. """ # read configuration file _, conf = read_latoolscfg() # if 'DEFAULT', check which is the default configuration if config == 'DEFAULT': config = conf['DEFAULT']['config'] ...
def read_latoolscfg(): """ Reads configuration, returns a ConfigParser object. Distinct from read_configuration, which returns a dict. """ config_file = pkgrs.resource_filename('latools', 'latools.cfg') cf = configparser.ConfigParser() cf.read(config_file) return config_file, cf
def print_all(): """ Prints all currently defined configurations. """ # read configuration file _, conf = read_latoolscfg() default = conf['DEFAULT']['config'] pstr = '\nCurrently defined LAtools configurations:\n\n' for s in conf.sections(): if s == default: pstr +...
def copy_SRM_file(destination=None, config='DEFAULT'): """ Creates a copy of the default SRM table at the specified location. Parameters ---------- destination : str The save location for the SRM file. If no location specified, saves it as 'LAtools_[config]_SRMTable.csv' in the cur...
def create(config_name, srmfile=None, dataformat=None, base_on='DEFAULT', make_default=False): """ Adds a new configuration to latools.cfg. Parameters ---------- config_name : str The name of the new configuration. This should be descriptive (e.g. UC Davis Foram Group) srmfile :...
def change_default(config): """ Change the default configuration. """ config_file, cf = read_latoolscfg() if config not in cf.sections(): raise ValueError("\n'{:s}' is not a defined configuration.".format(config)) if config == 'REPRODUCE': pstr = ('Are you SURE you want to set ...
def threshold(values, threshold): """ Return boolean arrays where a >= and < threshold. Parameters ---------- values : array-like Array of real values. threshold : float Threshold value Returns ------- (below, above) : tuple or boolean arrays """ values ...
def exclude_downhole(filt, threshold=2): """ Exclude all data after the first excluded portion. This makes sense for spot measurements where, because of the signal mixing inherent in LA-ICPMS, once a contaminant is ablated, it will always be present to some degree in signals from further down t...
def defrag(filt, threshold=3, mode='include'): """ 'Defragment' a filter. Parameters ---------- filt : boolean array A filter threshold : int Consecutive values equal to or below this threshold length are considered fragments, and will be removed. mode : str ...
def trim(ind, start=1, end=0): """ Remove points from the start and end of True regions. Parameters ---------- start, end : int The number of points to remove from the start and end of the specified filter. ind : boolean array Which filter to trim. If True, applies t...
def setfocus(self, focus): """ Set the 'focus' attribute of the data file. The 'focus' attribute of the object points towards data from a particular stage of analysis. It is used to identify the 'working stage' of the data. Processing functions operate on the 'focus' sta...
def despike(self, expdecay_despiker=True, exponent=None, noise_despiker=True, win=3, nlim=12., maxiter=3): """ Applies expdecay_despiker and noise_despiker to data. Parameters ---------- expdecay_despiker : bool Whether or not to apply the exponential...
def autorange(self, analyte='total_counts', gwin=5, swin=3, win=30, on_mult=[1., 1.], off_mult=[1., 1.5], ploterrs=True, transform='log', **kwargs): """ Automatically separates signal and background data regions. Automatically detect signal and background reg...
def autorange_plot(self, analyte='total_counts', gwin=7, swin=None, win=20, on_mult=[1.5, 1.], off_mult=[1., 1.5], transform='log'): """ Plot a detailed autorange report for this sample. """ if analyte is None: # sig = self.focus[...
def mkrngs(self): """ Transform boolean arrays into list of limit pairs. Gets Time limits of signal/background boolean arrays and stores them as sigrng and bkgrng arrays. These arrays can be saved by 'save_ranges' in the analyse object. """ bbool = bool_2_indices...
def bkg_subtract(self, analyte, bkg, ind=None, focus_stage='despiked'): """ Subtract provided background from signal (focus stage). Results is saved in new 'bkgsub' focus stage Returns ------- None """ if 'bkgsub' not in self.data.keys(): sel...
def correct_spectral_interference(self, target_analyte, source_analyte, f): """ Correct spectral interference. Subtract interference counts from target_analyte, based on the intensity of a source_analayte and a known fractional contribution (f). Correction takes the form: ...
def ratio(self, internal_standard=None): """ Divide all analytes by a specified internal_standard analyte. Parameters ---------- internal_standard : str The analyte used as the internal_standard. Returns ------- None """ if in...
def calibrate(self, calib_ps, analytes=None): """ Apply calibration to data. The `calib_dict` must be calculated at the `analyse` level, and passed to this calibrate function. Parameters ---------- calib_dict : dict A dict of calibration values to ap...
def sample_stats(self, analytes=None, filt=True, stat_fns={}, eachtrace=True): """ Calculate sample statistics Returns samples, analytes, and arrays of statistics of shape (samples, analytes). Statistics are calculated from the 'focus' d...
def ablation_times(self): """ Function for calculating the ablation time for each ablation. Returns ------- dict of times for each ablation. """ ats = {} for n in np.arange(self.n) + 1: t = self.Time[self.ns == n] ats[n...
def filter_threshold(self, analyte, threshold): """ Apply threshold filter. Generates threshold filters for the given analytes above and below the specified threshold. Two filters are created with prefixes '_above' and '_below'. '_above' keeps all the data above the...
def filter_gradient_threshold(self, analyte, win, threshold, recalc=True): """ Apply gradient threshold filter. Generates threshold filters for the given analytes above and below the specified threshold. Two filters are created with prefixes '_above' and '_below'. '...
def filter_clustering(self, analytes, filt=False, normalise=True, method='meanshift', include_time=False, sort=None, min_data=10, **kwargs): """ Applies an n - dimensional clustering filter to the data. Available Clustering Algorithms ...
def calc_correlation(self, x_analyte, y_analyte, window=15, filt=True, recalc=True): """ Calculate local correlation between two analytes. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes to correlate. window : int, None ...
def filter_correlation(self, x_analyte, y_analyte, window=15, r_threshold=0.9, p_threshold=0.05, filt=True, recalc=False): """ Calculate correlation filter. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes ...
def correlation_plot(self, x_analyte, y_analyte, window=15, filt=True, recalc=False): """ Plot the local correlation between two analytes. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes to correlate. window : int, None ...
def filter_new(self, name, filt_str): """ Make new filter from combination of other filters. Parameters ---------- name : str The name of the new filter. Should be unique. filt_str : str A logical combination of partial strings which will create ...
def filter_trim(self, start=1, end=1, filt=True): """ Remove points from the start and end of filter regions. Parameters ---------- start, end : int The number of points to remove from the start and end of the specified filter. filt : vali...
def filter_exclude_downhole(self, threshold, filt=True): """ Exclude all points down-hole (after) the first excluded data. Parameters ---------- threhold : int The minimum number of contiguous excluded data points that must exist before downhole exclusion...
def signal_optimiser(self, analytes, min_points=5, threshold_mode='kde_first_max', threshold_mult=1., x_bias=0, weights=None, filt=True, mode='minimise'): """ Optimise data selection based on specified analytes. Identifi...
def tplot(self, analytes=None, figsize=[10, 4], scale='log', filt=None, ranges=False, stats=False, stat='nanmean', err='nanstd', focus_stage=None, err_envelope=False, ax=None): """ Plot analytes as a function of Time. Parameters ---------- analytes : ...
def gplot(self, analytes=None, win=5, figsize=[10, 4], ranges=False, focus_stage=None, ax=None): """ Plot analytes gradients as a function of Time. Parameters ---------- analytes : array_like list of strings containing names of analytes to plot. ...
def crossplot(self, analytes=None, bins=25, lognorm=True, filt=True, colourful=True, figsize=(12, 12)): """ Plot analytes against each other. Parameters ---------- analytes : optional, array_like or str The analyte(s) to plot. Defaults to all analytes. lognor...
def crossplot_filters(self, filter_string, analytes=None): """ Plot the results of a group of filters in a crossplot. Parameters ---------- filter_string : str A string that identifies a group of filters. e.g. 'test' would plot all filters with 'test' in ...
def filter_report(self, filt=None, analytes=None, savedir=None, nbin=5): """ Visualise effect of data filters. Parameters ---------- filt : str Exact or partial name of filter to plot. Supports partial matching. i.e. if 'cluster' is specified, all ...
def get_params(self): """ Returns paramters used to process data. Returns ------- dict dict of analysis parameters """ outputs = ['sample', 'ratio_params', 'despike_params', 'autorange_params', ...
def tplot(self, analytes=None, figsize=[10, 4], scale='log', filt=None, ranges=False, stats=False, stat='nanmean', err='nanstd', focus_stage=None, err_envelope=False, ax=None): """ Plot analytes as a function of Time. Parameters ---------- analytes : ...
def gplot(self, analytes=None, win=25, figsize=[10, 4], ranges=False, focus_stage=None, ax=None, recalc=True): """ Plot analytes gradients as a function of Time. Parameters ---------- analytes : array_like list of strings containing names of analytes to...
def crossplot(dat, keys=None, lognorm=True, bins=25, figsize=(12, 12), colourful=True, focus_stage=None, denominator=None, mode='hist2d', cmap=None, kwargs): """ Plot analytes against each other. The number of plots is n2 - n, where n = len(keys). Parameters -------...
def histograms(dat, keys=None, bins=25, logy=False, cmap=None, ncol=4): """ Plot histograms of all items in dat. Parameters ---------- dat : dict Data in {key: array} pairs. keys : arra-like The keys in dat that you want to plot. If None, all are plotted. bins : int ...
def autorange_plot(t, sig, gwin=7, swin=None, win=30, on_mult=(1.5, 1.), off_mult=(1., 1.5), nbin=10, thresh=None): """ Function for visualising the autorange mechanism. Parameters ---------- t : array-like Independent variable (usually time). sig :...
def calibration_plot(self, analytes=None, datarange=True, loglog=False, ncol=3, srm_group=None, save=True): """ Plot the calibration lines between measured and known SRM values. Parameters ---------- analytes : optional, array_like or str The analyte(s) to plot. Defaults to all analytes. ...
def filter_report(Data, filt=None, analytes=None, savedir=None, nbin=5): """ Visualise effect of data filters. Parameters ---------- filt : str Exact or partial name of filter to plot. Supports partial matching. i.e. if 'cluster' is specified, all filters with 'cluster' in t...
def pairwise_reproducibility(df, plot=False): """ Calculate the reproducibility of LA-ICPMS based on unique pairs of repeat analyses. Pairwise differences are fit with a half-Cauchy distribution, and the median and 95% confidence limits are returned for each analyte. Parameters ------...
def comparison_stats(df, els=['Mg', 'Sr', 'Ba', 'Al', 'Mn']): """ Compute comparison stats for test and LAtools data. Population-level similarity assessed by a Kolmogorov-Smirnov test. Individual similarity assessed by a pairwise Wilcoxon signed rank test. Trends in residuals assessed...
def summary_stats(x, y, nm=None): """ Compute summary statistics for paired x, y data. Tests ----- Parameters ---------- x, y : array-like Data to compare nm : str (optional) Index value of created dataframe. Returns ------- pandas dataframe of statistics. ...
def load_reference_data(name=None): """ Fetch LAtools reference data from online repository. Parameters ---------- name : str< Which data to download. Can be one of 'culture_reference', 'culture_test', 'downcore_reference', 'downcore_test', 'iolite_reference' or 'zircon_refe...
def lookup(self, TC: type, G: type) -> Optional[TypeClass]: ''' Find an instance of the type class `TC` for type `G`. Iterates `G`'s parent classes, looking up instances for each, checking whether the instance is a subclass of the target type class `TC`. ''' if isinstance...
def rangecalc(x, y=None, pad=0.05): """ Calculate padded range limits for axes. """ mn = np.nanmin([np.nanmin(x), np.nanmin(y)]) mx = np.nanmax([np.nanmax(x), np.nanmax(y)]) rn = mx - mn return (mn - pad * rn, mx + pad * rn)

def encode(self): ''' Encode and store a CONNECT control packet. @raise e: C{ValueError} if any encoded topic string exceeds 65535 bytes. @raise e: C{ValueError} if encoded username string exceeds 65535 bytes. ''' header = bytearray(1) varHeader = bytearray() ...

def decode(self, packet): ''' Decode a CONNECT control packet. ''' self.encoded = packet # Strip the fixed header plus variable length field lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] # Var...

def encode(self): ''' Encode and store a CONNACK control packet. ''' header = bytearray(1) varHeader = bytearray(2) header[0] = 0x20 varHeader[0] = self.session varHeader[1] = self.resultCode header.extend(encodeLength(len(varHe...

def decode(self, packet): ''' Decode a CONNACK control packet. ''' self.encoded = packet # Strip the fixed header plus variable length field lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self....

def decode(self, packet): ''' Decode a SUBSCRIBE control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining[0:2]) self....

def encode(self): ''' Encode and store a SUBACK control packet. ''' header = bytearray(1) payload = bytearray() varHeader = encode16Int(self.msgId) header[0] = 0x90 for code in self.granted: payload.append(code[0] | (0x80 if code[1] == Tru...

def encode(self): ''' Encode and store an UNSUBCRIBE control packet @raise e: C{ValueError} if any encoded topic string exceeds 65535 bytes ''' header = bytearray(1) payload = bytearray() varHeader = encode16Int(self.msgId) header[0] = 0xA2 # packe...

def decode(self, packet): ''' Decode a UNSUBACK control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining[0:2]) self.t...

def encode(self): ''' Encode and store an UNSUBACK control packet ''' header = bytearray(1) varHeader = encode16Int(self.msgId) header[0] = 0xB0 header.extend(encodeLength(len(varHeader))) header.extend(varHeader) self.encoded = header ...

def encode(self): ''' Encode and store a PUBLISH control packet. @raise e: C{ValueError} if encoded topic string exceeds 65535 bytes. @raise e: C{ValueError} if encoded packet size exceeds 268435455 bytes. @raise e: C{TypeError} if C{data} is not a string, bytearray, int, boolean...

def decode(self, packet): ''' Decode a PUBLISH control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.dup = (packet[0] & 0x08) == 0x08 self.qos = (p...

def decode(self, packet): ''' Decode a PUBREL control packet. ''' self.encoded = packet lenLen = 1 while packet[lenLen] & 0x80: lenLen += 1 packet_remaining = packet[lenLen+1:] self.msgId = decode16Int(packet_remaining) self.dup = (pa...

def get_url(self, method=None, **kwargs): """Return url for call method. :param method (optional): `str` method name. :returns: `str` URL. """ kwargs.setdefault('v', self.__version) if self.__token is not None: kwargs.setdefault('access_token', self.__token)...

def request(self, method, **kwargs): """ Send request to API. :param method: `str` method name. :returns: `dict` response. """ kwargs.setdefault('v', self.__version) if self.__token is not None: kwargs.setdefault('access_token', self.__token) ...

def authentication(login, password): """ Authentication on vk.com. :param login: login on vk.com. :param password: password on vk.com. :returns: `requests.Session` session with cookies. """ session = requests.Session() response = session.get('https://m.vk.com') url = re.search(r'act...

def oauth(login, password, app_id=4729418, scope=2097151): """ OAuth on vk.com. :param login: login on vk.com. :param password: password on vk.com. :param app_id: vk.com application id (default: 4729418). :param scope: allowed actions (default: 2097151 (all)). :returns: OAuth2 access token ...

def create_from_array(self, blockname, array, Nfile=None, memorylimit=1024 * 1024 * 256): """ create a block from array like objects The operation is well defined only if array is at most 2d. Parameters ---------- array : array_like, array shall h...

def refresh(self): """ Refresh the list of blocks to the disk, collectively """ if self.comm.rank == 0: self._blocks = self.list_blocks() else: self._blocks = None self._blocks = self.comm.bcast(self._blocks)

def create_from_array(self, blockname, array, Nfile=None, memorylimit=1024 * 1024 * 256): """ create a block from array like objects The operation is well defined only if array is at most 2d. Parameters ---------- array : array_like, array shall h...

def maybebool(value): ''' If `value` is a string type, attempts to convert it to a boolean if it looks like it might be one, otherwise returns the value unchanged. The difference between this and :func:`pyramid.settings.asbool` is how non-bools are handled: this returns the original value, where...

def get_webassets_env_from_settings(settings, prefix='webassets'): """This function will take all webassets.* parameters, and call the ``Environment()`` constructor with kwargs passed in. The only two parameters that are not passed as keywords are: * base_dir * base_url which are passed in po...

def format_data(self, data, scale=True): """ Function for converting a dict to an array suitable for sklearn. Parameters ---------- data : dict A dict of data, containing all elements of `analytes` as items. scale : bool Whether or not...

def fitting_data(self, data): """ Function to format data for cluster fitting. Parameters ---------- data : dict A dict of data, containing all elements of `analytes` as items. Returns ------- A data array for initial cluster fitt...

def fit_kmeans(self, data, n_clusters, **kwargs): """ Fit KMeans clustering algorithm to data. Parameters ---------- data : array-like A dataset formatted by `classifier.fitting_data`. n_clusters : int The number of clusters in the data. *...

def fit_meanshift(self, data, bandwidth=None, bin_seeding=False, **kwargs): """ Fit MeanShift clustering algorithm to data. Parameters ---------- data : array-like A dataset formatted by `classifier.fitting_data`. bandwidth : float The bandwidth v...

def fit(self, data, method='kmeans', **kwargs): """ fit classifiers from large dataset. Parameters ---------- data : dict A dict of data for clustering. Must contain items with the same name as analytes used for clustering. method : st...

def predict(self, data): """ Label new data with cluster identities. Parameters ---------- data : dict A data dict containing the same analytes used to fit the classifier. sort_by : str The name of an analyte used to sort the resulting...

def map_clusters(self, size, sampled, clusters): """ Translate cluster identity back to original data size. Parameters ---------- size : int size of original dataset sampled : array-like integer array describing location of finite values ...

def sort_clusters(self, data, cs, sort_by): """ Sort clusters by the concentration of a particular analyte. Parameters ---------- data : dict A dataset containing sort_by as a key. cs : array-like An array of clusters, the same length as values of...

def get_date(datetime, time_format=None): """ Return a datetime oject from a string, with optional time format. Parameters ---------- datetime : str Date-time as string in any sensible format. time_format : datetime str (optional) String describing the datetime format. If missin...

def get_total_n_points(d): """ Returns the total number of data points in values of dict. Paramters --------- d : dict """ n = 0 for di in d.values(): n += len(di) return n

def get_total_time_span(d): """ Returns total length of analysis. """ tmax = 0 for di in d.values(): if di.uTime.max() > tmax: tmax = di.uTime.max() return tmax

def unitpicker(a, llim=0.1, denominator=None, focus_stage=None): """ Determines the most appropriate plotting unit for data. Parameters ---------- a : float or array-like number to optimise. If array like, the 25% quantile is optimised. llim : float minimum allowable value in sc...

def pretty_element(s): """ Returns formatted element name. Parameters ---------- s : str of format [A-Z][a-z]?[0-9]+ Returns ------- str LaTeX formatted string with superscript numbers. """ el = re.match('.*?([A-z]{1,3}).*?', s).groups()[0] m = re.match('.*?...

def analyte_2_namemass(s): """ Converts analytes in format '27Al' to 'Al27'. Parameters ---------- s : str of format [A-z]{1,3}[0-9]{1,3} Returns ------- str Name in format [0-9]{1,3}[A-z]{1,3} """ el = re.match('.*?([A-z]{1,3}).*?', s).groups()[0] m = re.ma...

def analyte_2_massname(s): """ Converts analytes in format 'Al27' to '27Al'. Parameters ---------- s : str of format [0-9]{1,3}[A-z]{1,3} Returns ------- str Name in format [A-z]{1,3}[0-9]{1,3} """ el = re.match('.*?([A-z]{1,3}).*?', s).groups()[0] m = re.ma...

def collate_data(in_dir, extension='.csv', out_dir=None): """ Copy all csvs in nested directroy to single directory. Function to copy all csvs from a directory, and place them in a new directory. Parameters ---------- in_dir : str Input directory containing csv files in subfolders ...

def bool_2_indices(a): """ Convert boolean array into a 2D array of (start, stop) pairs. """ if any(a): lims = [] lims.append(np.where(a[:-1] != a[1:])[0]) if a[0]: lims.append([0]) if a[-1]: lims.append([len(a) - 1]) lims = np.concatenate...

def enumerate_bool(bool_array, nstart=0): """ Consecutively numbers contiguous booleans in array. i.e. a boolean sequence, and resulting numbering T F T T T F T F F F T T F 0-1 1 1 - 2 ---3 3 - where ' - ' Parameters ---------- bool_array : array_like Array of booleans. ...

def tuples_2_bool(tuples, x): """ Generate boolean array from list of limit tuples. Parameters ---------- tuples : array_like [2, n] array of (start, end) values x : array_like x scale the tuples are mapped to Returns ------- array_like boolean array, True w...

def rolling_window(a, window, pad=None): """ Returns (win, len(a)) rolling - window array of data. Parameters ---------- a : array_like Array to calculate the rolling window of window : int Description of `window`. pad : same as dtype(a) Description of `pad`. Re...

def fastsmooth(a, win=11): """ Returns rolling - window smooth of a. Function to efficiently calculate the rolling mean of a numpy array using 'stride_tricks' to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win]. Parameters -----...

def fastgrad(a, win=11): """ Returns rolling - window gradient of a. Function to efficiently calculate the rolling gradient of a numpy array using 'stride_tricks' to split up a 1D array into an ndarray of sub - sections of the original array, of dimensions [len(a) - win, win]. Parameters -...

def calc_grads(x, dat, keys=None, win=5): """ Calculate gradients of values in dat. Parameters ---------- x : array like Independent variable for items in dat. dat : dict {key: dependent_variable} pairs keys : str or array-like Which keys in dict to calculate the...

def findmins(x, y): """ Function to find local minima. Parameters ---------- x, y : array_like 1D arrays of the independent (x) and dependent (y) variables. Returns ------- array_like Array of points in x where y has a local minimum. """ return x[np.r_[False, y[1:] ...

def stack_keys(ddict, keys, extra=None): """ Combine elements of ddict into an array of shape (len(ddict[key]), len(keys)). Useful for preparing data for sklearn. Parameters ---------- ddict : dict A dict containing arrays or lists to be stacked. Must be of equal length. ke...

def cluster_meanshift(data, bandwidth=None, bin_seeding=False, **kwargs): """ Identify clusters using Meanshift algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. bandwidth : float or None If None, bandwidth is estimated automatically using...

def cluster_kmeans(data, n_clusters, **kwargs): """ Identify clusters using K - Means algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. n_clusters : int The number of clusters expected in the data. Returns ------- dict ...

def cluster_DBSCAN(data, eps=None, min_samples=None, n_clusters=None, maxiter=200, **kwargs): """ Identify clusters using DBSCAN algorithm. Parameters ---------- data : array_like array of size [n_samples, n_features]. eps : float The minimum 'distance' points...

def get_defined_srms(srm_file): """ Returns list of SRMS defined in the SRM database """ srms = read_table(srm_file) return np.asanyarray(srms.index.unique())

def read_configuration(config='DEFAULT'): """ Read LAtools configuration file, and return parameters as dict. """ # read configuration file _, conf = read_latoolscfg() # if 'DEFAULT', check which is the default configuration if config == 'DEFAULT': config = conf['DEFAULT']['config'] ...

def read_latoolscfg(): """ Reads configuration, returns a ConfigParser object. Distinct from read_configuration, which returns a dict. """ config_file = pkgrs.resource_filename('latools', 'latools.cfg') cf = configparser.ConfigParser() cf.read(config_file) return config_file, cf

def print_all(): """ Prints all currently defined configurations. """ # read configuration file _, conf = read_latoolscfg() default = conf['DEFAULT']['config'] pstr = '\nCurrently defined LAtools configurations:\n\n' for s in conf.sections(): if s == default: pstr +...

def copy_SRM_file(destination=None, config='DEFAULT'): """ Creates a copy of the default SRM table at the specified location. Parameters ---------- destination : str The save location for the SRM file. If no location specified, saves it as 'LAtools_[config]_SRMTable.csv' in the cur...

def create(config_name, srmfile=None, dataformat=None, base_on='DEFAULT', make_default=False): """ Adds a new configuration to latools.cfg. Parameters ---------- config_name : str The name of the new configuration. This should be descriptive (e.g. UC Davis Foram Group) srmfile :...

def change_default(config): """ Change the default configuration. """ config_file, cf = read_latoolscfg() if config not in cf.sections(): raise ValueError("\n'{:s}' is not a defined configuration.".format(config)) if config == 'REPRODUCE': pstr = ('Are you SURE you want to set ...

def threshold(values, threshold): """ Return boolean arrays where a >= and < threshold. Parameters ---------- values : array-like Array of real values. threshold : float Threshold value Returns ------- (below, above) : tuple or boolean arrays """ values ...

def exclude_downhole(filt, threshold=2): """ Exclude all data after the first excluded portion. This makes sense for spot measurements where, because of the signal mixing inherent in LA-ICPMS, once a contaminant is ablated, it will always be present to some degree in signals from further down t...

def defrag(filt, threshold=3, mode='include'): """ 'Defragment' a filter. Parameters ---------- filt : boolean array A filter threshold : int Consecutive values equal to or below this threshold length are considered fragments, and will be removed. mode : str ...

def trim(ind, start=1, end=0): """ Remove points from the start and end of True regions. Parameters ---------- start, end : int The number of points to remove from the start and end of the specified filter. ind : boolean array Which filter to trim. If True, applies t...

def setfocus(self, focus): """ Set the 'focus' attribute of the data file. The 'focus' attribute of the object points towards data from a particular stage of analysis. It is used to identify the 'working stage' of the data. Processing functions operate on the 'focus' sta...

def despike(self, expdecay_despiker=True, exponent=None, noise_despiker=True, win=3, nlim=12., maxiter=3): """ Applies expdecay_despiker and noise_despiker to data. Parameters ---------- expdecay_despiker : bool Whether or not to apply the exponential...

def autorange(self, analyte='total_counts', gwin=5, swin=3, win=30, on_mult=[1., 1.], off_mult=[1., 1.5], ploterrs=True, transform='log', **kwargs): """ Automatically separates signal and background data regions. Automatically detect signal and background reg...

def autorange_plot(self, analyte='total_counts', gwin=7, swin=None, win=20, on_mult=[1.5, 1.], off_mult=[1., 1.5], transform='log'): """ Plot a detailed autorange report for this sample. """ if analyte is None: # sig = self.focus[...

def mkrngs(self): """ Transform boolean arrays into list of limit pairs. Gets Time limits of signal/background boolean arrays and stores them as sigrng and bkgrng arrays. These arrays can be saved by 'save_ranges' in the analyse object. """ bbool = bool_2_indices...

def bkg_subtract(self, analyte, bkg, ind=None, focus_stage='despiked'): """ Subtract provided background from signal (focus stage). Results is saved in new 'bkgsub' focus stage Returns ------- None """ if 'bkgsub' not in self.data.keys(): sel...

def correct_spectral_interference(self, target_analyte, source_analyte, f): """ Correct spectral interference. Subtract interference counts from target_analyte, based on the intensity of a source_analayte and a known fractional contribution (f). Correction takes the form: ...

def ratio(self, internal_standard=None): """ Divide all analytes by a specified internal_standard analyte. Parameters ---------- internal_standard : str The analyte used as the internal_standard. Returns ------- None """ if in...

def calibrate(self, calib_ps, analytes=None): """ Apply calibration to data. The `calib_dict` must be calculated at the `analyse` level, and passed to this calibrate function. Parameters ---------- calib_dict : dict A dict of calibration values to ap...

def sample_stats(self, analytes=None, filt=True, stat_fns={}, eachtrace=True): """ Calculate sample statistics Returns samples, analytes, and arrays of statistics of shape (samples, analytes). Statistics are calculated from the 'focus' d...

def ablation_times(self): """ Function for calculating the ablation time for each ablation. Returns ------- dict of times for each ablation. """ ats = {} for n in np.arange(self.n) + 1: t = self.Time[self.ns == n] ats[n...

def filter_threshold(self, analyte, threshold): """ Apply threshold filter. Generates threshold filters for the given analytes above and below the specified threshold. Two filters are created with prefixes '_above' and '_below'. '_above' keeps all the data above the...

def filter_gradient_threshold(self, analyte, win, threshold, recalc=True): """ Apply gradient threshold filter. Generates threshold filters for the given analytes above and below the specified threshold. Two filters are created with prefixes '_above' and '_below'. '...

def filter_clustering(self, analytes, filt=False, normalise=True, method='meanshift', include_time=False, sort=None, min_data=10, **kwargs): """ Applies an n - dimensional clustering filter to the data. Available Clustering Algorithms ...

def calc_correlation(self, x_analyte, y_analyte, window=15, filt=True, recalc=True): """ Calculate local correlation between two analytes. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes to correlate. window : int, None ...

def filter_correlation(self, x_analyte, y_analyte, window=15, r_threshold=0.9, p_threshold=0.05, filt=True, recalc=False): """ Calculate correlation filter. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes ...

def correlation_plot(self, x_analyte, y_analyte, window=15, filt=True, recalc=False): """ Plot the local correlation between two analytes. Parameters ---------- x_analyte, y_analyte : str The names of the x and y analytes to correlate. window : int, None ...

def filter_new(self, name, filt_str): """ Make new filter from combination of other filters. Parameters ---------- name : str The name of the new filter. Should be unique. filt_str : str A logical combination of partial strings which will create ...

def filter_trim(self, start=1, end=1, filt=True): """ Remove points from the start and end of filter regions. Parameters ---------- start, end : int The number of points to remove from the start and end of the specified filter. filt : vali...

def filter_exclude_downhole(self, threshold, filt=True): """ Exclude all points down-hole (after) the first excluded data. Parameters ---------- threhold : int The minimum number of contiguous excluded data points that must exist before downhole exclusion...

def signal_optimiser(self, analytes, min_points=5, threshold_mode='kde_first_max', threshold_mult=1., x_bias=0, weights=None, filt=True, mode='minimise'): """ Optimise data selection based on specified analytes. Identifi...

def tplot(self, analytes=None, figsize=[10, 4], scale='log', filt=None, ranges=False, stats=False, stat='nanmean', err='nanstd', focus_stage=None, err_envelope=False, ax=None): """ Plot analytes as a function of Time. Parameters ---------- analytes : ...

def gplot(self, analytes=None, win=5, figsize=[10, 4], ranges=False, focus_stage=None, ax=None): """ Plot analytes gradients as a function of Time. Parameters ---------- analytes : array_like list of strings containing names of analytes to plot. ...

def crossplot(self, analytes=None, bins=25, lognorm=True, filt=True, colourful=True, figsize=(12, 12)): """ Plot analytes against each other. Parameters ---------- analytes : optional, array_like or str The analyte(s) to plot. Defaults to all analytes. lognor...

def crossplot_filters(self, filter_string, analytes=None): """ Plot the results of a group of filters in a crossplot. Parameters ---------- filter_string : str A string that identifies a group of filters. e.g. 'test' would plot all filters with 'test' in ...

def filter_report(self, filt=None, analytes=None, savedir=None, nbin=5): """ Visualise effect of data filters. Parameters ---------- filt : str Exact or partial name of filter to plot. Supports partial matching. i.e. if 'cluster' is specified, all ...

def get_params(self): """ Returns paramters used to process data. Returns ------- dict dict of analysis parameters """ outputs = ['sample', 'ratio_params', 'despike_params', 'autorange_params', ...

def tplot(self, analytes=None, figsize=[10, 4], scale='log', filt=None, ranges=False, stats=False, stat='nanmean', err='nanstd', focus_stage=None, err_envelope=False, ax=None): """ Plot analytes as a function of Time. Parameters ---------- analytes : ...

def gplot(self, analytes=None, win=25, figsize=[10, 4], ranges=False, focus_stage=None, ax=None, recalc=True): """ Plot analytes gradients as a function of Time. Parameters ---------- analytes : array_like list of strings containing names of analytes to...

def crossplot(dat, keys=None, lognorm=True, bins=25, figsize=(12, 12), colourful=True, focus_stage=None, denominator=None, mode='hist2d', cmap=None, **kwargs): """ Plot analytes against each other. The number of plots is n**2 - n, where n = len(keys). Parameters -------...

def histograms(dat, keys=None, bins=25, logy=False, cmap=None, ncol=4): """ Plot histograms of all items in dat. Parameters ---------- dat : dict Data in {key: array} pairs. keys : arra-like The keys in dat that you want to plot. If None, all are plotted. bins : int ...

def autorange_plot(t, sig, gwin=7, swin=None, win=30, on_mult=(1.5, 1.), off_mult=(1., 1.5), nbin=10, thresh=None): """ Function for visualising the autorange mechanism. Parameters ---------- t : array-like Independent variable (usually time). sig :...

def calibration_plot(self, analytes=None, datarange=True, loglog=False, ncol=3, srm_group=None, save=True): """ Plot the calibration lines between measured and known SRM values. Parameters ---------- analytes : optional, array_like or str The analyte(s) to plot. Defaults to all analytes. ...

def filter_report(Data, filt=None, analytes=None, savedir=None, nbin=5): """ Visualise effect of data filters. Parameters ---------- filt : str Exact or partial name of filter to plot. Supports partial matching. i.e. if 'cluster' is specified, all filters with 'cluster' in t...

def pairwise_reproducibility(df, plot=False): """ Calculate the reproducibility of LA-ICPMS based on unique pairs of repeat analyses. Pairwise differences are fit with a half-Cauchy distribution, and the median and 95% confidence limits are returned for each analyte. Parameters ------...

def comparison_stats(df, els=['Mg', 'Sr', 'Ba', 'Al', 'Mn']): """ Compute comparison stats for test and LAtools data. Population-level similarity assessed by a Kolmogorov-Smirnov test. Individual similarity assessed by a pairwise Wilcoxon signed rank test. Trends in residuals assessed...

def summary_stats(x, y, nm=None): """ Compute summary statistics for paired x, y data. Tests ----- Parameters ---------- x, y : array-like Data to compare nm : str (optional) Index value of created dataframe. Returns ------- pandas dataframe of statistics. ...

def load_reference_data(name=None): """ Fetch LAtools reference data from online repository. Parameters ---------- name : str< Which data to download. Can be one of 'culture_reference', 'culture_test', 'downcore_reference', 'downcore_test', 'iolite_reference' or 'zircon_refe...

def lookup(self, TC: type, G: type) -> Optional[TypeClass]: ''' Find an instance of the type class `TC` for type `G`. Iterates `G`'s parent classes, looking up instances for each, checking whether the instance is a subclass of the target type class `TC`. ''' if isinstance...

def rangecalc(x, y=None, pad=0.05): """ Calculate padded range limits for axes. """ mn = np.nanmin([np.nanmin(x), np.nanmin(y)]) mx = np.nanmax([np.nanmax(x), np.nanmax(y)]) rn = mx - mn return (mn - pad * rn, mx + pad * rn)