Denis641/AdvTestNodocstring · Datasets at Hugging Face

query string	positive string	id_ int64	task_name string	negative string
Return either the full or truncated version of a QIIME-formatted taxonomy string. :type p: str :param p: A QIIME-formatted taxonomy string: k__Foo; p__Bar; ... :type level: str :param level: The different level of identification are kingdom (k), phylum (p), class (c),order (o), family (f), genus (g) and species (s). If level is not provided, the default level of identification is species. :rtype: str :return: A QIIME-formatted taxonomy string up to the classification given by param level.	def split_phylogeny(p, level="s"): level = level+"__" result = p.split(level) return result[0]+level+result[1].split(";")[0]	0	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L159-L177	def reset_local_buffers(self): agent_ids = list(self.keys()) for k in agent_ids: self[k].reset_agent()
Check to make sure the supplied directory path does not exist, if so, create it. The method catches OSError exceptions and returns a descriptive message instead of re-raising the error. :type d: str :param d: It is the full path to a directory. :return: Does not return anything, but creates a directory path if it doesn't exist already.	def ensure_dir(d): if not os.path.exists(d): try: os.makedirs(d) except OSError as oe: # should not happen with os.makedirs # ENOENT: No such file or directory if os.errno == errno.ENOENT: msg = twdd("""One or more directories in the path ({}) do not exist. If you are specifying a new directory for output, please ensure all other directories in the path currently exist.""") return msg.format(d) else: msg = twdd("""An error occurred trying to create the output directory ({}) with message: {}""") return msg.format(d, oe.strerror)	1	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L180-L206	def on_change(self, value): self._modifier(self.inst, self.prop, value)
Takes either a file path or an open file handle, checks validity and returns an open file handle or raises an appropriate Exception. :type fnh: str :param fnh: It is the full path to a file, or open file handle :type mode: str :param mode: The way in which this file will be used, for example to read or write or both. By default, file will be opened in rU mode. :return: Returns an opened file for appropriate usage.	def file_handle(fnh, mode="rU"): handle = None if isinstance(fnh, file): if fnh.closed: raise ValueError("Input file is closed.") handle = fnh elif isinstance(fnh, str): handle = open(fnh, mode) return handle	2	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L209-L231	def merge_partition_offsets(*partition_offsets): output = dict() for partition_offset in partition_offsets: for partition, offset in six.iteritems(partition_offset): prev_offset = output.get(partition, 0) output[partition] = max(prev_offset, offset) return output
Find the user specified categories in the map and create a dictionary to contain the relevant data for each type within the categories. Multiple categories will have their types combined such that each possible combination will have its own entry in the dictionary. :type imap: dict :param imap: The input mapping file data keyed by SampleID :type header: list :param header: The header line from the input mapping file. This will be searched for the user-specified categories :type categories: list :param categories: The list of user-specified category column name from mapping file :rtype: dict :return: A sorted dictionary keyed on the combinations of all the types found within the user-specified categories. Each entry will contain an empty DataCategory namedtuple. If no categories are specified, a single entry with the key 'default' will be returned	def gather_categories(imap, header, categories=None): # If no categories provided, return all SampleIDs if categories is None: return {"default": DataCategory(set(imap.keys()), {})} cat_ids = [header.index(cat) for cat in categories if cat in header and "=" not in cat] table = OrderedDict() conditions = defaultdict(set) for i, cat in enumerate(categories): if "=" in cat and cat.split("=")[0] in header: cat_name = header[header.index(cat.split("=")[0])] conditions[cat_name].add(cat.split("=")[1]) # If invalid categories or conditions identified, return all SampleIDs if not cat_ids and not conditions: return {"default": DataCategory(set(imap.keys()), {})} #If only category column given, return column-wise SampleIDs if cat_ids and not conditions: for sid, row in imap.items(): cat_name = "_".join([row[cid] for cid in cat_ids]) if cat_name not in table: table[cat_name] = DataCategory(set(), {}) table[cat_name].sids.add(sid) return table # Collect all condition names cond_ids = set() for k in conditions: try: cond_ids.add(header.index(k)) except ValueError: continue idx_to_test = set(cat_ids).union(cond_ids) # If column name and condition given, return overlapping SampleIDs of column and # condition combinations for sid, row in imap.items(): if all([row[header.index(c)] in conditions[c] for c in conditions]): key = "_".join([row[idx] for idx in idx_to_test]) try: assert key in table.keys() except AssertionError: table[key] = DataCategory(set(), {}) table[key].sids.add(sid) try: assert len(table) > 0 except AssertionError: return {"default": DataCategory(set(imap.keys()), {})} else: return table	3	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L238-L309	def elapsed_time_from(start_time): time_then = make_time(start_time) time_now = datetime.utcnow().replace(microsecond=0) if time_then is None: return delta_t = time_now - time_then return delta_t
Parses the unifrac results file into a dictionary :type unifracFN: str :param unifracFN: The path to the unifrac results file :rtype: dict :return: A dictionary with keys: 'pcd' (principle coordinates data) which is a dictionary of the data keyed by sample ID, 'eigvals' (eigenvalues), and 'varexp' (variation explained)	def parse_unifrac(unifracFN): with open(unifracFN, "rU") as uF: first = uF.next().split("\t") lines = [line.strip() for line in uF] unifrac = {"pcd": OrderedDict(), "eigvals": [], "varexp": []} if first[0] == "pc vector number": return parse_unifrac_v1_8(unifrac, lines) elif first[0] == "Eigvals": return parse_unifrac_v1_9(unifrac, lines) else: raise ValueError("File format not supported/recognized. Please check input " "unifrac file.")	4	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L311-L334	def set_classes(self): # Custom field classes on field wrapper if self.attrs.get("_field_class"): self.values["class"].append(escape(self.attrs.get("_field_class"))) # Inline class if self.attrs.get("_inline"): self.values["class"].append("inline") # Disabled class if self.field.field.disabled: self.values["class"].append("disabled") # Required class if self.field.field.required and not self.attrs.get("_no_required"): self.values["class"].append("required") elif self.attrs.get("_required") and not self.field.field.required: self.values["class"].append("required")
Function to parse data from older version of unifrac file obtained from Qiime version 1.8 and earlier. :type unifrac: dict :param unifracFN: The path to the unifrac results file :type file_data: list :param file_data: Unifrac data lines after stripping whitespace characters.	def parse_unifrac_v1_8(unifrac, file_data): for line in file_data: if line == "": break line = line.split("\t") unifrac["pcd"][line[0]] = [float(e) for e in line[1:]] unifrac["eigvals"] = [float(entry) for entry in file_data[-2].split("\t")[1:]] unifrac["varexp"] = [float(entry) for entry in file_data[-1].split("\t")[1:]] return unifrac	5	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L337-L356	async def stop_bridges(self): for task in self.sleep_tasks: task.cancel() for bridge in self.bridges: bridge.stop()
Function to parse data from newer version of unifrac file obtained from Qiime version 1.9 and later. :type unifracFN: str :param unifracFN: The path to the unifrac results file :type file_data: list :param file_data: Unifrac data lines after stripping whitespace characters.	def parse_unifrac_v1_9(unifrac, file_data): unifrac["eigvals"] = [float(entry) for entry in file_data[0].split("\t")] unifrac["varexp"] = [float(entry)*100 for entry in file_data[3].split("\t")] for line in file_data[8:]: if line == "": break line = line.split("\t") unifrac["pcd"][line[0]] = [float(e) for e in line[1:]] return unifrac	6	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L359-L378	def is_charge_balanced(reaction): charge = 0 for metabolite, coefficient in iteritems(reaction.metabolites): if metabolite.charge is None: return False charge += coefficient * metabolite.charge return charge == 0
Determine color-category mapping. If color_column was specified, then map the category names to color values. Otherwise, use the palettable colors to automatically generate a set of colors for the group values. :type sample_map: dict :param unifracFN: Map associating each line of the mapping file with the appropriate sample ID (each value of the map also contains the sample ID) :type header: tuple :param A tuple of header line for mapping file :type group_column: str :param group_column: String denoting the column name for sample groups. :type color_column: str :param color_column: String denoting the column name for sample colors. :type return: dict :param return: {SampleID: Color}	def color_mapping(sample_map, header, group_column, color_column=None): group_colors = OrderedDict() group_gather = gather_categories(sample_map, header, [group_column]) if color_column is not None: color_gather = gather_categories(sample_map, header, [color_column]) # match sample IDs between color_gather and group_gather for group in group_gather: for color in color_gather: # allow incomplete assignment of colors, if group sids overlap at # all with the color sids, consider it a match if group_gather[group].sids.intersection(color_gather[color].sids): group_colors[group] = color else: bcolors = itertools.cycle(Set3_12.hex_colors) for group in group_gather: group_colors[group] = bcolors.next() return group_colors	7	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L380-L419	def is_balance_proof_safe_for_onchain_operations( balance_proof: BalanceProofSignedState, ) -> bool: total_amount = balance_proof.transferred_amount + balance_proof.locked_amount return total_amount <= UINT256_MAX
return reverse completment of read	def rev_c(read): rc = [] rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'} for base in read: rc.extend(rc_nucs[base.upper()]) return rc[::-1]	8	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/shuffle_genome.py#L27-L35	def hypermedia_out(): request = cherrypy.serving.request request._hypermedia_inner_handler = request.handler # If handler has been explicitly set to None, don't override. if request.handler is not None: request.handler = hypermedia_handler
randomly shuffle genome	def shuffle_genome(genome, cat, fraction = float(100), plot = True, \ alpha = 0.1, beta = 100000, \ min_length = 1000, max_length = 200000): header = '>randomized_%s' % (genome.name) sequence = list(''.join([i[1] for i in parse_fasta(genome)])) length = len(sequence) shuffled = [] # break genome into pieces while sequence is not False: s = int(random.gammavariate(alpha, beta)) if s <= min_length or s >= max_length: continue if len(sequence) < s: seq = sequence[0:] else: seq = sequence[0:s] sequence = sequence[s:] # if bool(random.getrandbits(1)) is True: # seq = rev_c(seq) # print('fragment length: %s reverse complement: True' % ('{:,}'.format(s)), file=sys.stderr) # else: # print('fragment length: %s reverse complement: False' % ('{:,}'.format(s)), file=sys.stderr) shuffled.append(''.join(seq)) if sequence == []: break # shuffle pieces random.shuffle(shuffled) # subset fragments if fraction == float(100): subset = shuffled else: max_pieces = int(length * fraction/100) subset, total = [], 0 for fragment in shuffled: length = len(fragment) if total + length <= max_pieces: subset.append(fragment) total += length else: diff = max_pieces - total subset.append(fragment[0:diff]) break # combine sequences, if requested if cat is True: yield [header, ''.join(subset)] else: for i, seq in enumerate(subset): yield ['%s fragment:%s' % (header, i), seq]	9	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/shuffle_genome.py#L37-L87	def GetEntries(self, parser_mediator, match=None, **unused_kwargs): stores = match.get('Stores', {}) for volume_name, volume in iter(stores.items()): datetime_value = volume.get('CreationDate', None) if not datetime_value: continue partial_path = volume['PartialPath'] event_data = plist_event.PlistTimeEventData() event_data.desc = 'Spotlight Volume {0:s} ({1:s}) activated.'.format( volume_name, partial_path) event_data.key = '' event_data.root = '/Stores' event = time_events.PythonDatetimeEvent( datetime_value, definitions.TIME_DESCRIPTION_WRITTEN) parser_mediator.ProduceEventWithEventData(event, event_data)
If the fit contains statistically insignificant parameters, remove them. Returns a pruned fit where all parameters have p-values of the t-statistic below p_max Parameters ---------- fit: fm.ols fit object Can contain insignificant parameters p_max : float Maximum allowed probability of the t-statistic Returns ------- fit: fm.ols fit object Won't contain any insignificant parameters	def _prune(self, fit, p_max): def remove_from_model_desc(x, model_desc): """ Return a model_desc without x """ rhs_termlist = [] for t in model_desc.rhs_termlist: if not t.factors: # intercept, add anyway rhs_termlist.append(t) elif not x == t.factors[0]._varname: # this is not the term with x rhs_termlist.append(t) md = ModelDesc(model_desc.lhs_termlist, rhs_termlist) return md corrected_model_desc = ModelDesc(fit.model.formula.lhs_termlist[:], fit.model.formula.rhs_termlist[:]) pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist() try: pars_to_prune.remove('Intercept') except: pass while pars_to_prune: corrected_model_desc = remove_from_model_desc(pars_to_prune[0], corrected_model_desc) fit = fm.ols(corrected_model_desc, data=self.df).fit() pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist() try: pars_to_prune.remove('Intercept') except: pass return fit	10	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L222-L272	def hexblock_word(cls, data, address = None, bits = None, separator = ' ', width = 8): return cls.hexblock_cb(cls.hexa_word, data, address, bits, width * 2, cb_kwargs = {'separator': separator})
Return the best fit, based on rsquared	def find_best_rsquared(list_of_fits): res = sorted(list_of_fits, key=lambda x: x.rsquared) return res[-1]	11	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L275-L278	def _skip_trampoline(handler): data_event, self = (yield None) delegate = handler event = None depth = 0 while True: def pass_through(): _trans = delegate.send(Transition(data_event, delegate)) return _trans, _trans.delegate, _trans.event if data_event is not None and data_event.type is ReadEventType.SKIP: while True: trans, delegate, event = pass_through() if event is not None: if event.event_type is IonEventType.CONTAINER_END and event.depth <= depth: break if event is None or event.event_type is IonEventType.INCOMPLETE: data_event, _ = yield Transition(event, self) else: trans, delegate, event = pass_through() if event is not None and (event.event_type is IonEventType.CONTAINER_START or event.event_type is IonEventType.CONTAINER_END): depth = event.depth data_event, _ = yield Transition(event, self)
Return a df with predictions and confidence interval Notes ----- The df will contain the following columns: - 'predicted': the model output - 'interval_u', 'interval_l': upper and lower confidence bounds. The result will depend on the following attributes of self: confint : float (default=0.95) Confidence level for two-sided hypothesis allow_negative_predictions : bool (default=True) If False, correct negative predictions to zero (typically for energy consumption predictions) Parameters ---------- fit : Statsmodels fit df : pandas DataFrame or None (default) If None, use self.df Returns ------- df_res : pandas DataFrame Copy of df with additional columns 'predicted', 'interval_u' and 'interval_l'	def _predict(self, fit, df): # Add model results to data as column 'predictions' df_res = df.copy() if 'Intercept' in fit.model.exog_names: df_res['Intercept'] = 1.0 df_res['predicted'] = fit.predict(df_res) if not self.allow_negative_predictions: df_res.loc[df_res['predicted'] < 0, 'predicted'] = 0 prstd, interval_l, interval_u = wls_prediction_std(fit, df_res[fit.model.exog_names], alpha=1 - self.confint) df_res['interval_l'] = interval_l df_res['interval_u'] = interval_u if 'Intercept' in df_res: df_res.drop(labels=['Intercept'], axis=1, inplace=True) return df_res	12	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L292-L338	def detach(self, listener): if listener in self.listeners: self.listeners.remove(listener)
Calculate the relative abundance of each OTUID in a Sample. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: list :param sampleIDs: A list of sample id's from BIOM format OTU table. :rtype: dict :return: Returns a keyed on SampleIDs, and the values are dictionaries keyed on OTUID's and their values represent the relative abundance of that OTUID in that SampleID.	def relative_abundance(biomf, sampleIDs=None): if sampleIDs is None: sampleIDs = biomf.ids() else: try: for sid in sampleIDs: assert sid in biomf.ids() except AssertionError: raise ValueError( "\nError while calculating relative abundances: The sampleIDs provided do" " not match the sampleIDs in biom file. Please double check the sampleIDs" " provided.\n") otuIDs = biomf.ids(axis="observation") norm_biomf = biomf.norm(inplace=False) return {sample: {otuID: norm_biomf.get_value_by_ids(otuID, sample) for otuID in otuIDs} for sample in sampleIDs}	13	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L11-L41	def clean(jail=None, chroot=None, root=None, clean_all=False, dryrun=False): opts = '' if clean_all: opts += 'a' if dryrun: opts += 'n' else: opts += 'y' cmd = _pkg(jail, chroot, root) cmd.append('clean') if opts: cmd.append('-' + opts) return __salt__['cmd.run']( cmd, output_loglevel='trace', python_shell=False )
Calculate the mean OTU abundance percentage. :type ra: Dict :param ra: 'ra' refers to a dictionary keyed on SampleIDs, and the values are dictionaries keyed on OTUID's and their values represent the relative abundance of that OTUID in that SampleID. 'ra' is the output of relative_abundance() function. :type otuIDs: List :param otuIDs: A list of OTUID's for which the percentage abundance needs to be measured. :rtype: dict :return: A dictionary of OTUID and their percent relative abundance as key/value pair.	def mean_otu_pct_abundance(ra, otuIDs): sids = ra.keys() otumeans = defaultdict(int) for oid in otuIDs: otumeans[oid] = sum([ra[sid][oid] for sid in sids if oid in ra[sid]]) / len(sids) * 100 return otumeans	14	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L44-L67	def change_frozen_attr(self): # Selections are not supported if self.grid.selection: statustext = _("Freezing selections is not supported.") post_command_event(self.main_window, self.StatusBarMsg, text=statustext) cursor = self.grid.actions.cursor frozen = self.grid.code_array.cell_attributes[cursor]["frozen"] if frozen: # We have an frozen cell that has to be unfrozen # Delete frozen cache content self.grid.code_array.frozen_cache.pop(repr(cursor)) else: # We have an non-frozen cell that has to be frozen # Add frozen cache content res_obj = self.grid.code_array[cursor] self.grid.code_array.frozen_cache[repr(cursor)] = res_obj # Set the new frozen state / code selection = Selection([], [], [], [], [cursor[:2]]) self.set_attr("frozen", not frozen, selection=selection)
Calculate the mean relative abundance percentage. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: list :param sampleIDs: A list of sample id's from BIOM format OTU table. :param transform: Mathematical function which is used to transform smax to another format. By default, the function has been set to None. :rtype: dict :return: A dictionary keyed on OTUID's and their mean relative abundance for a given number of sampleIDs.	def MRA(biomf, sampleIDs=None, transform=None): ra = relative_abundance(biomf, sampleIDs) if transform is not None: ra = {sample: {otuID: transform(abd) for otuID, abd in ra[sample].items()} for sample in ra.keys()} otuIDs = biomf.ids(axis="observation") return mean_otu_pct_abundance(ra, otuIDs)	15	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L70-L92	def close(self): if self.device: usb.util.dispose_resources(self.device) self.device = None
Calculate the total number of sequences in each OTU or SampleID. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: List :param sampleIDs: A list of column id's from BIOM format OTU table. By default, the list has been set to None. :type sample_abd: Boolean :param sample_abd: A boolean operator to provide output for OTUID's or SampleID's. By default, the output will be provided for SampleID's. :rtype: dict :return: Returns a dictionary keyed on either OTUID's or SampleIDs and their respective abundance as values.	def raw_abundance(biomf, sampleIDs=None, sample_abd=True): results = defaultdict(int) if sampleIDs is None: sampleIDs = biomf.ids() else: try: for sid in sampleIDs: assert sid in biomf.ids() except AssertionError: raise ValueError( "\nError while calculating raw total abundances: The sampleIDs provided " "do not match the sampleIDs in biom file. Please double check the " "sampleIDs provided.\n") otuIDs = biomf.ids(axis="observation") for sampleID in sampleIDs: for otuID in otuIDs: abd = biomf.get_value_by_ids(otuID, sampleID) if sample_abd: results[sampleID] += abd else: results[otuID] += abd return results	16	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L95-L135	def upgrade_api(request, client, version): min_ver, max_ver = api_versions._get_server_version_range(client) if min_ver <= api_versions.APIVersion(version) <= max_ver: client = _nova.novaclient(request, version) return client
Function to transform the total abundance calculation for each sample ID to another format based on user given transformation function. :type biomf: A BIOM file. :param biomf: OTU table format. :param fn: Mathematical function which is used to transform smax to another format. By default, the function has been given as base 10 logarithm. :rtype: dict :return: Returns a dictionary similar to output of raw_abundance function but with the abundance values modified by the mathematical operation. By default, the operation performed on the abundances is base 10 logarithm.	def transform_raw_abundance(biomf, fn=math.log10, sampleIDs=None, sample_abd=True): totals = raw_abundance(biomf, sampleIDs, sample_abd) return {sid: fn(abd) for sid, abd in totals.items()}	17	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L138-L155	def close(self): if self.device: usb.util.dispose_resources(self.device) self.device = None
Compute the Mann-Whitney U test for unequal group sample sizes.	def print_MannWhitneyU(div_calc): try: x = div_calc.values()[0].values() y = div_calc.values()[1].values() except: return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\ "significance testing." T, p = stats.mannwhitneyu(x, y) print "\nMann-Whitney U test statistic:", T print "Two-tailed p-value: {}".format(2 * p)	18	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L54-L66	def ParseFileObject(self, parser_mediator, file_object): try: file_header = self._ReadFileHeader(file_object) except (ValueError, errors.ParseError): raise errors.UnableToParseFile('Unable to parse file header.') tables = self._ReadTablesArray(file_object, file_header.tables_array_offset) table = tables.get(self._RECORD_TYPE_APPLICATION_PASSWORD, None) if table: for record in table.records: self._ParseApplicationPasswordRecord(parser_mediator, record) table = tables.get(self._RECORD_TYPE_INTERNET_PASSWORD, None) if table: for record in table.records: self._ParseInternetPasswordRecord(parser_mediator, record)
Compute the Kruskal-Wallis H-test for independent samples. A typical rule is that each group must have at least 5 measurements.	def print_KruskalWallisH(div_calc): calc = defaultdict(list) try: for k1, v1 in div_calc.iteritems(): for k2, v2 in v1.iteritems(): calc[k1].append(v2) except: return "Error setting up input arrays for Kruskal-Wallis H-Test. Skipping "\ "significance testing." h, p = stats.kruskal(*calc.values()) print "\nKruskal-Wallis H-test statistic for {} groups: {}".format(str(len(div_calc)), h) print "p-value: {}".format(p)	19	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L69-L84	def _record_offset(self): offset = self.blob_file.tell() self.event_offsets.append(offset)
Parses the given options passed in at the command line.	def handle_program_options(): parser = argparse.ArgumentParser(description="Calculate the alpha diversity\ of a set of samples using one or more \ metrics and output a kernal density \ estimator-smoothed histogram of the \ results.") parser.add_argument("-m", "--map_file", help="QIIME mapping file.") parser.add_argument("-i", "--biom_fp", help="Path to the BIOM table") parser.add_argument("-c", "--category", help="Specific category from the mapping file.") parser.add_argument("-d", "--diversity", default=["shannon"], nargs="+", help="The alpha diversity metric. Default \ value is 'shannon', which will calculate the Shannon\ entropy. Multiple metrics can be specified (space separated).\ The full list of metrics is available at:\ http://scikit-bio.org/docs/latest/generated/skbio.diversity.alpha.html.\ Beta diversity metrics will be supported in the future.") parser.add_argument("--x_label", default=[None], nargs="+", help="The name of the diversity metric to be displayed on the\ plot as the X-axis label. If multiple metrics are specified,\ then multiple entries for the X-axis label should be given.") parser.add_argument("--color_by", help="A column name in the mapping file containing\ hexadecimal (#FF0000) color values that will\ be used to color the groups. Each sample ID must\ have a color entry.") parser.add_argument("--plot_title", default="", help="A descriptive title that will appear at the top \ of the output plot. Surround with quotes if there are\ spaces in the title.") parser.add_argument("-o", "--output_dir", default=".", help="The directory plots will be saved to.") parser.add_argument("--image_type", default="png", help="The type of image to save: png, svg, pdf, eps, etc...") parser.add_argument("--save_calculations", help="Path and name of text file to store the calculated " "diversity metrics.") parser.add_argument("--suppress_stats", action="store_true", help="Do not display " "significance testing results which are shown by default.") parser.add_argument("--show_available_metrics", action="store_true", help="Supply this parameter to see which alpha diversity metrics " " are available for usage. No calculations will be performed" " if this parameter is provided.") return parser.parse_args()	20	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L122-L168	def fingerprint(self): if self.num_vertices == 0: return np.zeros(20, np.ubyte) else: return sum(self.vertex_fingerprints)
make blast db	def blastdb(fasta, maxfile = 10000000): db = fasta.rsplit('.', 1)[0] type = check_type(fasta) if type == 'nucl': type = ['nhr', type] else: type = ['phr', type] if os.path.exists('%s.%s' % (db, type[0])) is False \ and os.path.exists('%s.00.%s' % (db, type[0])) is False: print('# ... making blastdb for: %s' % (fasta), file=sys.stderr) os.system('makeblastdb \ -in %s -out %s -dbtype %s -max_file_sz %s >> log.txt' \ % (fasta, db, type[1], maxfile)) else: print('# ... database found for: %s' % (fasta), file=sys.stderr) return db	21	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/search.py#L28-L46	def writln(line, unit): lineP = stypes.stringToCharP(line) unit = ctypes.c_int(unit) line_len = ctypes.c_int(len(line)) libspice.writln_(lineP, ctypes.byref(unit), line_len)
make usearch db	def usearchdb(fasta, alignment = 'local', usearch_loc = 'usearch'): if '.udb' in fasta: print('# ... database found: %s' % (fasta), file=sys.stderr) return fasta type = check_type(fasta) db = '%s.%s.udb' % (fasta.rsplit('.', 1)[0], type) if os.path.exists(db) is False: print('# ... making usearch db for: %s' % (fasta), file=sys.stderr) if alignment == 'local': os.system('%s -makeudb_ublast %s -output %s >> log.txt' % (usearch_loc, fasta, db)) elif alignment == 'global': os.system('%s -makeudb_usearch %s -output %s >> log.txt' % (usearch_loc, fasta, db)) else: print('# ... database found for: %s' % (fasta), file=sys.stderr) return db	22	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/search.py#L68-L85	def point_to_line(point, segment_start, segment_end): # TODO: Needs unittests. segment_vec = segment_end - segment_start # t is distance along line t = -(segment_start - point).dot(segment_vec) / ( segment_vec.length_squared()) closest_point = segment_start + scale_v3(segment_vec, t) return point - closest_point
Pretty print.	def _pp(dict_data): for key, val in dict_data.items(): # pylint: disable=superfluous-parens print('{0:<11}: {1}'.format(key, val))	23	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L11-L15	def _reserve(self, key): self.assign(key, RESERVED) try: yield finally: del self._cache[key]
Print licenses. :param argparse.Namespace params: parameter :param bootstrap_py.classifier.Classifiers metadata: package metadata	def print_licences(params, metadata): if hasattr(params, 'licenses'): if params.licenses: _pp(metadata.licenses_desc()) sys.exit(0)	24	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L27-L36	def vrel(v1, v2): v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)
Check repository existence. :param argparse.Namespace params: parameters	def check_repository_existence(params): repodir = os.path.join(params.outdir, params.name) if os.path.isdir(repodir): raise Conflict( 'Package repository "{0}" has already exists.'.format(repodir))	25	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L39-L47	def context(self): stats = status_codes_by_date_stats() attacks_data = [{ 'type': 'line', 'zIndex': 9, 'name': _('Attacks'), 'data': [(v[0], v[1]['attacks']) for v in stats] }] codes_data = [{ 'zIndex': 4, 'name': '2xx', 'data': [(v[0], v[1][200]) for v in stats] }, { 'zIndex': 5, 'name': '3xx', 'data': [(v[0], v[1][300]) for v in stats] }, { 'zIndex': 6, 'name': '4xx', 'data': [(v[0], v[1][400]) for v in stats] }, { 'zIndex': 8, 'name': '5xx', 'data': [(v[0], v[1][500]) for v in stats] }] return {'generic_chart': json.dumps(status_codes_by_date_chart()), 'attacks_data': json.dumps(attacks_data), 'codes_data': json.dumps(codes_data)}
Generate package repository. :param argparse.Namespace params: parameters	def generate_package(params): pkg_data = package.PackageData(params) pkg_tree = package.PackageTree(pkg_data) pkg_tree.generate() pkg_tree.move() VCS(os.path.join(pkg_tree.outdir, pkg_tree.name), pkg_tree.pkg_data)	26	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L59-L68	def startResponse(self, status, headers, excInfo=None): self.status = status self.headers = headers self.reactor.callInThread( responseInColor, self.request, status, headers ) return self.write
print single reads to stderr	def print_single(line, rev): if rev is True: seq = rc(['', line[9]])[1] qual = line[10][::-1] else: seq = line[9] qual = line[10] fq = ['@%s' % line[0], seq, '+%s' % line[0], qual] print('\n'.join(fq), file = sys.stderr)	27	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/sam2fastq.py#L13-L24	def set_cache_dir(directory): global cache_dir if directory is None: cache_dir = None return if not os.path.exists(directory): os.makedirs(directory) if not os.path.isdir(directory): raise ValueError("not a directory") cache_dir = directory
convert sam to fastq	def sam2fastq(sam, singles = False, force = False): L, R = None, None for line in sam: if line.startswith('@') is True: continue line = line.strip().split() bit = [True if i == '1' else False \ for i in bin(int(line[1])).split('b')[1][::-1]] while len(bit) < 8: bit.append(False) pair, proper, na, nap, rev, mrev, left, right = bit # make sure read is paired if pair is False: if singles is True: print_single(line, rev) continue # check if sequence is reverse-complemented if rev is True: seq = rc(['', line[9]])[1] qual = line[10][::-1] else: seq = line[9] qual = line[10] # check if read is forward or reverse, return when both have been found if left is True: if L is not None and force is False: print('sam file is not sorted', file = sys.stderr) print('\te.g.: %s' % (line[0]), file = sys.stderr) exit() if L is not None: L = None continue L = ['@%s' % line[0], seq, '+%s' % line[0], qual] if R is not None: yield L yield R L, R = None, None if right is True: if R is not None and force is False: print('sam file is not sorted', file = sys.stderr) print('\te.g.: %s' % (line[0]), file = sys.stderr) exit() if R is not None: R = None continue R = ['@%s' % line[0], seq, '+%s' % line[0], qual] if L is not None: yield L yield R L, R = None, None	28	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/sam2fastq.py#L26-L78	def cublasGetStream(handle): id = ctypes.c_int() status = _libcublas.cublasGetStream_v2(handle, ctypes.byref(id)) cublasCheckStatus(status) return id.value
sort sam file	def sort_sam(sam, sort): tempdir = '%s/' % (os.path.abspath(sam).rsplit('/', 1)[0]) if sort is True: mapping = '%s.sorted.sam' % (sam.rsplit('.', 1)[0]) if sam != '-': if os.path.exists(mapping) is False: os.system("\ sort -k1 --buffer-size=%sG -T %s -o %s %s\ " % (sbuffer, tempdir, mapping, sam)) else: mapping = 'stdin-sam.sorted.sam' p = Popen("sort -k1 --buffer-size=%sG -T %s -o %s" \ % (sbuffer, tempdir, mapping), stdin = sys.stdin, shell = True) p.communicate() mapping = open(mapping) else: if sam == '-': mapping = sys.stdin else: mapping = open(sam) return mapping	29	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/subset_sam.py#L14-L37	def ssn(self) -> str: area = self.random.randint(1, 899) if area == 666: area = 665 return '{:03}-{:02}-{:04}'.format( area, self.random.randint(1, 99), self.random.randint(1, 9999), )
randomly subset sam file	def sub_sam(sam, percent, sort = True, sbuffer = False): mapping = sort_sam(sam, sort) pool = [1 for i in range(0, percent)] + [0 for i in range(0, 100 - percent)] c = cycle([1, 2]) for line in mapping: line = line.strip().split() if line[0].startswith('@'): # get the sam header yield line continue if int(line[1]) <= 20: # is this from a single read? if random.choice(pool) == 1: yield line else: n = next(c) if n == 1: prev = line if n == 2 and random.choice(pool) == 1: yield prev yield line	30	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/subset_sam.py#L39-L60	def get_max_port_count_for_storage_bus(self, bus): if not isinstance(bus, StorageBus): raise TypeError("bus can only be an instance of type StorageBus") max_port_count = self._call("getMaxPortCountForStorageBus", in_p=[bus]) return max_port_count
convert fq to fa	def fq2fa(fq): c = cycle([1, 2, 3, 4]) for line in fq: n = next(c) if n == 1: seq = ['>%s' % (line.strip().split('@', 1)[1])] if n == 2: seq.append(line.strip()) yield seq	31	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/fastq2fasta.py#L11-L22	def query_under_condition(condition, kind='2'): if DB_CFG['kind'] == 's': return TabPost.select().where( (TabPost.kind == kind) & (TabPost.valid == 1) ).order_by( TabPost.time_update.desc() ) return TabPost.select().where( (TabPost.kind == kind) & (TabPost.valid == 1) & TabPost.extinfo.contains(condition) ).order_by(TabPost.time_update.desc())
Converts the returned value of wrapped function to the type of the first arg or to the type specified by a kwarg key return_type's value.	def change_return_type(f): @wraps(f) def wrapper(args, kwargs): if kwargs.has_key('return_type'): return_type = kwargs['return_type'] kwargs.pop('return_type') return return_type(f(args, *kwargs)) elif len(args) > 0: return_type = type(args[0]) return return_type(f(args, *kwargs)) else: return f(args, **kwargs) return wrapper	32	https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/decorators.py#L11-L27	def _timing_representation(message): s = _encode_to_binary_string(message, on="=", off=".") N = len(s) s += '\n' + _numbers_decades(N) s += '\n' + _numbers_units(N) s += '\n' s += '\n' + _timing_char(message) return s
Converts all args to 'set' type via self.setify function.	def convert_args_to_sets(f): @wraps(f) def wrapper(args, kwargs): args = (setify(x) for x in args) return f(args, **kwargs) return wrapper	33	https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/decorators.py#L30-L38	def list_publications(): publications = search_publications( DBPublication(is_public=True) ) return SimpleTemplate(INDEX_TEMPLATE).render( publications=publications, compose_path=web_tools.compose_path, delimiter=":", )
Membuat objek-objek entri dari laman yang diambil. :param laman: Laman respons yang dikembalikan oleh KBBI daring. :type laman: Response	def _init_entri(self, laman): sup = BeautifulSoup(laman.text, 'html.parser') estr = '' for label in sup.find('hr').next_siblings: if label.name == 'hr': self.entri.append(Entri(estr)) break if label.name == 'h2': if estr: self.entri.append(Entri(estr)) estr = '' estr += str(label).strip()	34	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L46-L63	def diff_text(candidate_config=None, candidate_path=None, running_config=None, running_path=None, saltenv='base'): candidate_text = clean(config=candidate_config, path=candidate_path, saltenv=saltenv) running_text = clean(config=running_config, path=running_path, saltenv=saltenv) return _get_diff_text(running_text, candidate_text)
Memproses kata dasar yang ada dalam nama entri. :param dasar: ResultSet untuk label HTML dengan class="rootword" :type dasar: ResultSet	def _init_kata_dasar(self, dasar): for tiap in dasar: kata = tiap.find('a') dasar_no = kata.find('sup') kata = ambil_teks_dalam_label(kata) self.kata_dasar.append( kata + ' [{}]'.format(dasar_no.text.strip()) if dasar_no else kata )	35	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L126-L139	def runWizard( self ): plugin = self.currentPlugin() if ( plugin and plugin.runWizard(self) ): self.accept()
Mengembalikan hasil serialisasi objek Entri ini. :returns: Dictionary hasil serialisasi :rtype: dict	def serialisasi(self): return { "nama": self.nama, "nomor": self.nomor, "kata_dasar": self.kata_dasar, "pelafalan": self.pelafalan, "bentuk_tidak_baku": self.bentuk_tidak_baku, "varian": self.varian, "makna": [makna.serialisasi() for makna in self.makna] }	36	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L141-L156	def escape_for_cmd_exe(arg): meta_chars = '()%!^"<>&\|' meta_re = re.compile('(' + '\|'.join(re.escape(char) for char in list(meta_chars)) + ')') meta_map = {char: "^{0}".format(char) for char in meta_chars} def escape_meta_chars(m): char = m.group(1) return meta_map[char] return meta_re.sub(escape_meta_chars, arg)
Mengembalikan representasi string untuk semua makna entri ini. :returns: String representasi makna-makna :rtype: str	def _makna(self): if len(self.makna) > 1: return '\n'.join( str(i) + ". " + str(makna) for i, makna in enumerate(self.makna, 1) ) return str(self.makna[0])	37	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L158-L170	def controlMsg(self, requestType, request, buffer, value = 0, index = 0, timeout = 100): return self.dev.ctrl_transfer( requestType, request, wValue = value, wIndex = index, data_or_wLength = buffer, timeout = timeout)
Mengembalikan representasi string untuk nama entri ini. :returns: String representasi nama entri :rtype: str	def _nama(self): hasil = self.nama if self.nomor: hasil += " [{}]".format(self.nomor) if self.kata_dasar: hasil = " » ".join(self.kata_dasar) + " » " + hasil return hasil	38	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L172-L184	def _setup(self): self.log.info("Adding reader to prepare to receive.") self.loop.add_reader(self.dev.fd, self.read) self.log.info("Flushing the RFXtrx buffer.") self.flushSerialInput() self.log.info("Writing the reset packet to the RFXtrx. (blocking)") yield from self.sendRESET() self.log.info("Wating 0.4s") yield from asyncio.sleep(0.4) self.log.info("Write the status packet (blocking)") yield from self.sendSTATUS() # TODO receive status response, compare it with the needed MODE and # request a new MODE if required. Currently MODE is always sent. self.log.info("Adding mode packet to the write queue (blocking)") yield from self.sendMODE()
Mengembalikan representasi string untuk varian entri ini. Dapat digunakan untuk "Varian" maupun "Bentuk tidak baku". :param varian: List bentuk tidak baku atau varian :type varian: list :returns: String representasi varian atau bentuk tidak baku :rtype: str	def _varian(self, varian): if varian == self.bentuk_tidak_baku: nama = "Bentuk tidak baku" elif varian == self.varian: nama = "Varian" else: return '' return nama + ': ' + ', '.join(varian)	39	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L186-L202	def compile_all(): # print("Compiling for Qt: style.qrc -> style.rcc") # os.system("rcc style.qrc -o style.rcc") print("Compiling for PyQt4: style.qrc -> pyqt_style_rc.py") os.system("pyrcc4 -py3 style.qrc -o pyqt_style_rc.py") print("Compiling for PyQt5: style.qrc -> pyqt5_style_rc.py") os.system("pyrcc5 style.qrc -o pyqt5_style_rc.py") print("Compiling for PySide: style.qrc -> pyside_style_rc.py") os.system("pyside-rcc -py3 style.qrc -o pyside_style_rc.py")
Memproses kelas kata yang ada dalam makna. :param makna_label: BeautifulSoup untuk makna yang ingin diproses. :type makna_label: BeautifulSoup	def _init_kelas(self, makna_label): kelas = makna_label.find(color='red') lain = makna_label.find(color='darkgreen') info = makna_label.find(color='green') if kelas: kelas = kelas.find_all('span') if lain: self.kelas = {lain.text.strip(): lain['title'].strip()} self.submakna = lain.next_sibling.strip() self.submakna += ' ' + makna_label.find(color='grey').text.strip() else: self.kelas = { k.text.strip(): k['title'].strip() for k in kelas } if kelas else {} self.info = info.text.strip() if info else ''	40	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L239-L259	def sync(self, since=None, timeout_ms=30000, filter=None, full_state=None, set_presence=None): request = { # non-integer timeouts appear to cause issues "timeout": int(timeout_ms) } if since: request["since"] = since if filter: request["filter"] = filter if full_state: request["full_state"] = json.dumps(full_state) if set_presence: request["set_presence"] = set_presence return self._send("GET", "/sync", query_params=request, api_path=MATRIX_V2_API_PATH)
Memproses contoh yang ada dalam makna. :param makna_label: BeautifulSoup untuk makna yang ingin diproses. :type makna_label: BeautifulSoup	def _init_contoh(self, makna_label): indeks = makna_label.text.find(': ') if indeks != -1: contoh = makna_label.text[indeks + 2:].strip() self.contoh = contoh.split('; ') else: self.contoh = []	41	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L261-L273	def delete_events(self, event_collection, timeframe=None, timezone=None, filters=None): params = self.get_params(timeframe=timeframe, timezone=timezone, filters=filters) return self.api.delete_events(event_collection, params)
Mengembalikan hasil serialisasi objek Makna ini. :returns: Dictionary hasil serialisasi :rtype: dict	def serialisasi(self): return { "kelas": self.kelas, "submakna": self.submakna, "info": self.info, "contoh": self.contoh }	42	https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L275-L287	def diff_fromDelta(self, text1, delta): diffs = [] pointer = 0 # Cursor in text1 tokens = delta.split("\t") for token in tokens: if token == "": # Blank tokens are ok (from a trailing \t). continue # Each token begins with a one character parameter which specifies the # operation of this token (delete, insert, equality). param = token[1:] if token[0] == "+": param = urllib.parse.unquote(param) diffs.append((self.DIFF_INSERT, param)) elif token[0] == "-" or token[0] == "=": try: n = int(param) except ValueError: raise ValueError("Invalid number in diff_fromDelta: " + param) if n < 0: raise ValueError("Negative number in diff_fromDelta: " + param) text = text1[pointer : pointer + n] pointer += n if token[0] == "=": diffs.append((self.DIFF_EQUAL, text)) else: diffs.append((self.DIFF_DELETE, text)) else: # Anything else is an error. raise ValueError("Invalid diff operation in diff_fromDelta: " + token[0]) if pointer != len(text1): raise ValueError( "Delta length (%d) does not equal source text length (%d)." % (pointer, len(text1))) return diffs
Build sphinx documentation. :rtype: int :return: subprocess.call return code :param `bootstrap_py.control.PackageData` pkg_data: package meta data :param str projectdir: project root directory	def build_sphinx(pkg_data, projectdir): try: version, _minor_version = pkg_data.version.rsplit('.', 1) except ValueError: version = pkg_data.version args = ' '.join(('sphinx-quickstart', '--sep', '-q', '-p "{name}"', '-a "{author}"', '-v "{version}"', '-r "{release}"', '-l en', '--suffix=.rst', '--master=index', '--ext-autodoc', '--ext-viewcode', '--makefile', '{projectdir}')).format(name=pkg_data.name, author=pkg_data.author, version=version, release=pkg_data.version, projectdir=projectdir) if subprocess.call(shlex.split(args)) == 0: _touch_gitkeep(projectdir)	43	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/docs.py#L8-L40	def cli(env, volume_id): file_manager = SoftLayer.FileStorageManager(env.client) snapshot_schedules = file_manager.list_volume_schedules(volume_id) table = formatting.Table(['id', 'active', 'type', 'replication', 'date_created', 'minute', 'hour', 'day', 'week', 'day_of_week', 'date_of_month', 'month_of_year', 'maximum_snapshots']) for schedule in snapshot_schedules: if 'REPLICATION' in schedule['type']['keyname']: replication = '' else: replication = formatting.blank() file_schedule_type = schedule['type']['keyname'].replace('REPLICATION_', '') file_schedule_type = file_schedule_type.replace('SNAPSHOT_', '') property_list = ['MINUTE', 'HOUR', 'DAY', 'WEEK', 'DAY_OF_WEEK', 'DAY_OF_MONTH', 'MONTH_OF_YEAR', 'SNAPSHOT_LIMIT'] schedule_properties = [] for prop_key in property_list: item = formatting.blank() for schedule_property in schedule.get('properties', []): if schedule_property['type']['keyname'] == prop_key: if schedule_property['value'] == '-1': item = '' else: item = schedule_property['value'] break schedule_properties.append(item) table_row = [ schedule['id'], '*' if schedule.get('active', '') else '', file_schedule_type, replication, schedule.get('createDate', '') ] table_row.extend(schedule_properties) table.add_row(table_row) env.fout(table)
make bowtie db	def bowtiedb(fa, keepDB): btdir = '%s/bt2' % (os.getcwd()) # make directory for if not os.path.exists(btdir): os.mkdir(btdir) btdb = '%s/%s' % (btdir, fa.rsplit('/', 1)[-1]) if keepDB is True: if os.path.exists('%s.1.bt2' % (btdb)): return btdb p = subprocess.Popen('bowtie2-build -q %s %s' \ % (fa, btdb), shell = True) p.communicate() return btdb	44	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L16-L31	def parse_resource_data_entry(self, rva): try: # If the RVA is invalid all would blow up. Some EXEs seem to be # specially nasty and have an invalid RVA. data = self.get_data(rva, Structure(self.__IMAGE_RESOURCE_DATA_ENTRY_format__).sizeof() ) except PEFormatError as excp: self.__warnings.append( 'Error parsing a resource directory data entry, ' 'the RVA is invalid: 0x%x' % ( rva ) ) return None data_entry = self.__unpack_data__( self.__IMAGE_RESOURCE_DATA_ENTRY_format__, data, file_offset = self.get_offset_from_rva(rva) ) return data_entry
generate bowtie2 command	def bowtie(sam, btd, f, r, u, opt, no_shrink, threads): bt2 = 'bowtie2 -x %s -p %s ' % (btd, threads) if f is not False: bt2 += '-1 %s -2 %s ' % (f, r) if u is not False: bt2 += '-U %s ' % (u) bt2 += opt if no_shrink is False: if f is False: bt2 += ' \| shrinksam -u -k %s-shrunk.sam ' % (sam) else: bt2 += ' \| shrinksam -k %s-shrunk.sam ' % (sam) else: bt2 += ' > %s.sam' % (sam) return bt2	45	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L33-L50	def format_string(self, s, args, kwargs): if isinstance(s, Markup): formatter = SandboxedEscapeFormatter(self, s.escape) else: formatter = SandboxedFormatter(self) kwargs = _MagicFormatMapping(args, kwargs) rv = formatter.vformat(s, args, kwargs) return type(s)(rv)
map all read sets against all fasta files	def crossmap(fas, reads, options, no_shrink, keepDB, threads, cluster, nodes): if cluster is True: threads = '48' btc = [] for fa in fas: btd = bowtiedb(fa, keepDB) F, R, U = reads if F is not False: if U is False: u = False for i, f in enumerate(F): r = R[i] if U is not False: u = U[i] sam = '%s/%s-vs-%s' % (os.getcwd(), \ fa.rsplit('/', 1)[-1], f.rsplit('/', 1)[-1].rsplit('.', 3)[0]) btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads)) else: f = False r = False for u in U: sam = '%s/%s-vs-%s' % (os.getcwd(), \ fa.rsplit('/', 1)[-1], u.rsplit('/', 1)[-1].rsplit('.', 3)[0]) btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads)) if cluster is False: for i in btc: p = subprocess.Popen(i, shell = True) p.communicate() else: ID = ''.join(random.choice([str(i) for i in range(0, 9)]) for _ in range(5)) for node, commands in enumerate(chunks(btc, nodes), 1): bs = open('%s/crossmap-qsub.%s.%s.sh' % (os.getcwd(), ID, node), 'w') print('\n'.join(commands), file=bs) bs.close() p = subprocess.Popen(\ 'qsub -V -N crossmap %s' \ % (bs.name), \ shell = True) p.communicate()	46	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L55-L96	def with_division(self, division): if division is None: division = '' division = slugify(division) self._validate_division(division) self.division = division return self
Returns a connection object from the router given ``args``. Useful in cases where a connection cannot be automatically determined during all steps of the process. An example of this would be Redis pipelines.	def get_conn(self, args, *kwargs): connections = self.__connections_for('get_conn', args=args, kwargs=kwargs) if len(connections) is 1: return connections[0] else: return connections	47	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/base.py#L100-L113	def write_comment(self, comment): self._FITS.write_comment(self._ext+1, str(comment))
return the non-direct init if the direct algorithm has been selected.	def __get_nondirect_init(self, init): crc = init for i in range(self.Width): bit = crc & 0x01 if bit: crc^= self.Poly crc >>= 1 if bit: crc \|= self.MSB_Mask return crc & self.Mask	48	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L98-L110	def from_bytes(self, string): msg = srsly.msgpack_loads(gzip.decompress(string)) self.attrs = msg["attrs"] self.strings = set(msg["strings"]) lengths = numpy.fromstring(msg["lengths"], dtype="int32") flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool) flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64") shape = (flat_tokens.size // len(self.attrs), len(self.attrs)) flat_tokens = flat_tokens.reshape(shape) flat_spaces = flat_spaces.reshape((flat_spaces.size, 1)) self.tokens = NumpyOps().unflatten(flat_tokens, lengths) self.spaces = NumpyOps().unflatten(flat_spaces, lengths) for tokens in self.tokens: assert len(tokens.shape) == 2, tokens.shape return self
reflect a data word, i.e. reverts the bit order.	def reflect(self, data, width): x = data & 0x01 for i in range(width - 1): data >>= 1 x = (x << 1) \| (data & 0x01) return x	49	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L115-L123	def linkify_templates(self): self.hosts.linkify_templates() self.contacts.linkify_templates() self.services.linkify_templates() self.servicedependencies.linkify_templates() self.hostdependencies.linkify_templates() self.timeperiods.linkify_templates() self.hostsextinfo.linkify_templates() self.servicesextinfo.linkify_templates() self.escalations.linkify_templates() # But also old srv and host escalations self.serviceescalations.linkify_templates() self.hostescalations.linkify_templates()
Classic simple and slow CRC implementation. This function iterates bit by bit over the augmented input message and returns the calculated CRC value at the end.	def bit_by_bit(self, in_data): # If the input data is a string, convert to bytes. if isinstance(in_data, str): in_data = [ord(c) for c in in_data] register = self.NonDirectInit for octet in in_data: if self.ReflectIn: octet = self.reflect(octet, 8) for i in range(8): topbit = register & self.MSB_Mask register = ((register << 1) & self.Mask) \| ((octet >> (7 - i)) & 0x01) if topbit: register ^= self.Poly for i in range(self.Width): topbit = register & self.MSB_Mask register = ((register << 1) & self.Mask) if topbit: register ^= self.Poly if self.ReflectOut: register = self.reflect(register, self.Width) return register ^ self.XorOut	50	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L128-L156	def create_cvmfs_persistent_volume_claim(cvmfs_volume): from kubernetes.client.rest import ApiException from reana_commons.k8s.api_client import current_k8s_corev1_api_client try: current_k8s_corev1_api_client.\ create_namespaced_persistent_volume_claim( "default", render_cvmfs_pvc(cvmfs_volume) ) except ApiException as e: if e.status != 409: raise e
This function generates the CRC table used for the table_driven CRC algorithm. The Python version cannot handle tables of an index width other than 8. See the generated C code for tables with different sizes instead.	def gen_table(self): table_length = 1 << self.TableIdxWidth tbl = [0] * table_length for i in range(table_length): register = i if self.ReflectIn: register = self.reflect(register, self.TableIdxWidth) register = register << (self.Width - self.TableIdxWidth + self.CrcShift) for j in range(self.TableIdxWidth): if register & (self.MSB_Mask << self.CrcShift) != 0: register = (register << 1) ^ (self.Poly << self.CrcShift) else: register = (register << 1) if self.ReflectIn: register = self.reflect(register >> self.CrcShift, self.Width) << self.CrcShift tbl[i] = register & (self.Mask << self.CrcShift) return tbl	51	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L190-L212	def cancel_broadcast(self, broadcast_guid): subpath = 'broadcasts/%s/update' % broadcast_guid broadcast = {'status': 'CANCELED'} bcast_dict = self._call(subpath, method='POST', data=broadcast, content_type='application/json') return bcast_dict
The Standard table_driven CRC algorithm.	def table_driven(self, in_data): # If the input data is a string, convert to bytes. if isinstance(in_data, str): in_data = [ord(c) for c in in_data] tbl = self.gen_table() register = self.DirectInit << self.CrcShift if not self.ReflectIn: for octet in in_data: tblidx = ((register >> (self.Width - self.TableIdxWidth + self.CrcShift)) ^ octet) & 0xff register = ((register << (self.TableIdxWidth - self.CrcShift)) ^ tbl[tblidx]) & (self.Mask << self.CrcShift) register = register >> self.CrcShift else: register = self.reflect(register, self.Width + self.CrcShift) << self.CrcShift for octet in in_data: tblidx = ((register >> self.CrcShift) ^ octet) & 0xff register = ((register >> self.TableIdxWidth) ^ tbl[tblidx]) & (self.Mask << self.CrcShift) register = self.reflect(register, self.Width + self.CrcShift) & self.Mask if self.ReflectOut: register = self.reflect(register, self.Width) return register ^ self.XorOut	52	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L217-L242	def _prune_subdirs(dir_: str) -> None: for logdir in [path.join(dir_, f) for f in listdir(dir_) if is_train_dir(path.join(dir_, f))]: for subdir in [path.join(logdir, f) for f in listdir(logdir) if path.isdir(path.join(logdir, f))]: _safe_rmtree(subdir)
parse masked sequence into non-masked and masked regions	def parse_masked(seq, min_len): nm, masked = [], [[]] prev = None for base in seq[1]: if base.isupper(): nm.append(base) if masked != [[]] and len(masked[-1]) < min_len: nm.extend(masked[-1]) del masked[-1] prev = False elif base.islower(): if prev is False: masked.append([]) masked[-1].append(base) prev = True return nm, masked	53	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_masked.py#L13-L31	def handle_event(self, event): subscription_id = event.subscription_id if subscription_id in self._subscriptions: # FIXME: [1] should be a constant handler = self._subscriptions[subscription_id][SUBSCRIPTION_CALLBACK] WampSubscriptionWrapper(self,handler,event).start()
remove masked regions from fasta file as long as they are longer than min_len	def strip_masked(fasta, min_len, print_masked): for seq in parse_fasta(fasta): nm, masked = parse_masked(seq, min_len) nm = ['%s removed_masked >=%s' % (seq[0], min_len), ''.join(nm)] yield [0, nm] if print_masked is True: for i, m in enumerate([i for i in masked if i != []], 1): m = ['%s insertion:%s' % (seq[0], i), ''.join(m)] yield [1, m]	54	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_masked.py#L33-L45	def websocket_connect(self, message): self.session_id = self.scope['url_route']['kwargs']['subscriber_id'] super().websocket_connect(message) # Create new subscriber object. Subscriber.objects.get_or_create(session_id=self.session_id)
Return arcsine transformed relative abundance from a BIOM format file. :type biomfile: BIOM format file :param biomfile: BIOM format file used to obtain relative abundances for each OTU in a SampleID, which are used as node sizes in network plots. :type return: Dictionary of dictionaries. :return: Dictionary keyed on SampleID whose value is a dictionarykeyed on OTU Name whose value is the arc sine tranfsormed relative abundance value for that SampleID-OTU Name pair.	def get_relative_abundance(biomfile): biomf = biom.load_table(biomfile) norm_biomf = biomf.norm(inplace=False) rel_abd = {} for sid in norm_biomf.ids(): rel_abd[sid] = {} for otuid in norm_biomf.ids("observation"): otuname = oc.otu_name(norm_biomf.metadata(otuid, axis="observation")["taxonomy"]) otuname = " ".join(otuname.split("_")) abd = norm_biomf.get_value_by_ids(otuid, sid) rel_abd[sid][otuname] = abd ast_rel_abd = bc.arcsine_sqrt_transform(rel_abd) return ast_rel_abd	55	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/network_plots_gephi.py#L33-L57	def update_context(self, context, update_mask=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None): # Wrap the transport method to add retry and timeout logic. if 'update_context' not in self._inner_api_calls: self._inner_api_calls[ 'update_context'] = google.api_core.gapic_v1.method.wrap_method( self.transport.update_context, default_retry=self._method_configs['UpdateContext'].retry, default_timeout=self._method_configs['UpdateContext'] .timeout, client_info=self._client_info, ) request = context_pb2.UpdateContextRequest( context=context, update_mask=update_mask, ) return self._inner_api_calls['update_context']( request, retry=retry, timeout=timeout, metadata=metadata)
Find an OTU ID in a Newick-format tree. Return the starting position of the ID or None if not found.	def find_otu(otuid, tree): for m in re.finditer(otuid, tree): before, after = tree[m.start()-1], tree[m.start()+len(otuid)] if before in ["(", ",", ")"] and after in [":", ";"]: return m.start() return None	56	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/iTol.py#L17-L26	def set_keyspace(self, keyspace): self.keyspace = keyspace dfrds = [] for p in self._protos: dfrds.append(p.submitRequest(ManagedThriftRequest( 'set_keyspace', keyspace))) return defer.gatherResults(dfrds)
Replace the OTU ids in the Newick phylogenetic tree format with truncated OTU names	def newick_replace_otuids(tree, biomf): for val, id_, md in biomf.iter(axis="observation"): otu_loc = find_otu(id_, tree) if otu_loc is not None: tree = tree[:otu_loc] + \ oc.otu_name(md["taxonomy"]) + \ tree[otu_loc + len(id_):] return tree	57	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/iTol.py#L29-L40	def led_changed(self, addr, group, val): _LOGGER.debug("Button %d LED changed from %d to %d", self._group, self._value, val) led_on = bool(val) if led_on != bool(self._value): self._update_subscribers(int(led_on))
return genome info for choosing representative if ggKbase table provided - choose rep based on SCGs and genome length - priority for most SCGs - extra SCGs, then largest genome otherwise, based on largest genome	def genome_info(genome, info): try: scg = info['#SCGs'] dups = info['#SCG duplicates'] length = info['genome size (bp)'] return [scg - dups, length, genome] except: return [False, False, info['genome size (bp)'], genome]	58	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L97-L112	def reset_env(exclude=[]): if os.getenv(env.INITED): wandb_keys = [key for key in os.environ.keys() if key.startswith( 'WANDB_') and key not in exclude] for key in wandb_keys: del os.environ[key] return True else: return False
choose represenative genome and print cluster information *if ggKbase table is provided, use SCG info to choose best genome	def print_clusters(fastas, info, ANI): header = ['#cluster', 'num. genomes', 'rep.', 'genome', '#SCGs', '#SCG duplicates', \ 'genome size (bp)', 'fragments', 'list'] yield header in_cluster = [] for cluster_num, cluster in enumerate(connected_components(ANI)): cluster = sorted([genome_info(genome, info[genome]) \ for genome in cluster], \ key = lambda x: x[0:], reverse = True) rep = cluster[0][-1] cluster = [i[-1] for i in cluster] size = len(cluster) for genome in cluster: in_cluster.append(genome) try: stats = [size, rep, genome, \ info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \ info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster] except: stats = [size, rep, genome, \ 'n/a', 'n/a', \ info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster] if rep == genome: stats = ['%s' % (cluster_num)] + stats else: stats = [cluster_num] + stats yield stats # print singletons try: start = cluster_num + 1 except: start = 0 fastas = set([i.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] for i in fastas]) for cluster_num, genome in \ enumerate(fastas.difference(set(in_cluster)), start): try: stats = ['%s' % (cluster_num), 1, genome, genome, \ info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \ info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]] except: stats = ['*%s' % (cluster_num), 1, genome, genome, \ 'n/a', 'n/a', \ info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]] yield stats	59	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L114-L163	def _apply_index_days(self, i, roll): nanos = (roll % 2) * Timedelta(days=self.day_of_month - 1).value return i + nanos.astype('timedelta64[ns]')
convert ggKbase genome info tables to dictionary	def parse_ggKbase_tables(tables, id_type): g2info = {} for table in tables: for line in open(table): line = line.strip().split('\t') if line[0].startswith('name'): header = line header[4] = 'genome size (bp)' header[12] = '#SCGs' header[13] = '#SCG duplicates' continue name, code, info = line[0], line[1], line info = [to_int(i) for i in info] if id_type is False: # try to use name and code ID if 'UNK' in code or 'unknown' in code: code = name if (name != code) and (name and code in g2info): print('# duplicate name or code in table(s)', file=sys.stderr) print('# %s and/or %s' % (name, code), file=sys.stderr) exit() if name not in g2info: g2info[name] = {item:stat for item, stat in zip(header, info)} if code not in g2info: g2info[code] = {item:stat for item, stat in zip(header, info)} else: if id_type == 'name': ID = name elif id_type == 'code': ID = code else: print('# specify name or code column using -id', file=sys.stderr) exit() ID = ID.replace(' ', '') g2info[ID] = {item:stat for item, stat in zip(header, info)} if g2info[ID]['genome size (bp)'] == '': g2info[ID]['genome size (bp)'] = 0 return g2info	60	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L174-L213	def _cleanSessions(self): tooOld = extime.Time() - timedelta(seconds=PERSISTENT_SESSION_LIFETIME) self.store.query( PersistentSession, PersistentSession.lastUsed < tooOld).deleteFromStore() self._lastClean = self._clock.seconds()
convert checkM genome info tables to dictionary	def parse_checkM_tables(tables): g2info = {} for table in tables: for line in open(table): line = line.strip().split('\t') if line[0].startswith('Bin Id'): header = line header[8] = 'genome size (bp)' header[5] = '#SCGs' header[6] = '#SCG duplicates' continue ID, info = line[0], line info = [to_int(i) for i in info] ID = ID.replace(' ', '') g2info[ID] = {item:stat for item, stat in zip(header, info)} if g2info[ID]['genome size (bp)'] == '': g2info[ID]['genome size (bp)'] = 0 return g2info	61	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L215-L235	def slanted_triangular(max_rate, num_steps, cut_frac=0.1, ratio=32, decay=1, t=0.0): cut = int(num_steps * cut_frac) while True: t += 1 if t < cut: p = t / cut else: p = 1 - ((t - cut) / (cut * (1 / cut_frac - 1))) learn_rate = max_rate * (1 + p * (ratio - 1)) * (1 / ratio) yield learn_rate
get genome lengths	def genome_lengths(fastas, info): if info is False: info = {} for genome in fastas: name = genome.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] if name in info: continue length = 0 fragments = 0 for seq in parse_fasta(genome): length += len(seq[1]) fragments += 1 info[name] = {'genome size (bp)':length, '# contigs':fragments} return info	62	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L237-L253	def save_reg(data): reg_dir = _reg_dir() regfile = os.path.join(reg_dir, 'register') try: if not os.path.exists(reg_dir): os.makedirs(reg_dir) except OSError as exc: if exc.errno == errno.EEXIST: pass else: raise try: with salt.utils.files.fopen(regfile, 'a') as fh_: salt.utils.msgpack.dump(data, fh_) except Exception: log.error('Could not write to msgpack file %s', __opts__['outdir']) raise
Returns a list of db keys to route the given call to. :param attr: Name of attribute being called on the connection. :param args: List of arguments being passed to ``attr``. :param kwargs: Dictionary of keyword arguments being passed to ``attr``. >>> redis = Cluster(router=BaseRouter) >>> router = redis.router >>> router.get_dbs('incr', args=('key name', 1)) [0,1,2]	def get_dbs(self, attr, args, kwargs, fkwargs): if not self._ready: if not self.setup_router(args=args, kwargs=kwargs, fkwargs): raise self.UnableToSetupRouter() retval = self._pre_routing(attr=attr, args=args, kwargs=kwargs, fkwargs) if retval is not None: args, kwargs = retval if not (args or kwargs): return self.cluster.hosts.keys() try: db_nums = self._route(attr=attr, args=args, kwargs=kwargs, fkwargs) except Exception as e: self._handle_exception(e) db_nums = [] return self._post_routing(attr=attr, db_nums=db_nums, args=args, kwargs=kwargs, **fkwargs)	63	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/routers/base.py#L50-L81	def fullLoad(self): self._parseDirectories(self.ntHeaders.optionalHeader.dataDirectory, self.PE_TYPE)
Call method to perform any setup	def setup_router(self, args, kwargs, fkwargs): self._ready = self._setup_router(args=args, kwargs=kwargs, fkwargs) return self._ready	64	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/routers/base.py#L87-L93	def world_series_logs(): file_name = 'GLWS.TXT' z = get_zip_file(world_series_url) data = pd.read_csv(z.open(file_name), header=None, sep=',', quotechar='"') data.columns = gamelog_columns return data
Perform routing and return db_nums	def _route(self, attr, args, kwargs, **fkwargs): return self.cluster.hosts.keys()	65	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/routers/base.py#L111-L115	def static_stability(pressure, temperature, axis=0): theta = potential_temperature(pressure, temperature) return - mpconsts.Rd * temperature / pressure * first_derivative(np.log(theta / units.K), x=pressure, axis=axis)
Iterates through all connections which were previously listed as unavailable and marks any that have expired their retry_timeout as being up.	def check_down_connections(self): now = time.time() for db_num, marked_down_at in self._down_connections.items(): if marked_down_at + self.retry_timeout <= now: self.mark_connection_up(db_num)	66	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/routers/base.py#L175-L184	def _CalculateDigestHash(self, file_entry, data_stream_name): file_object = file_entry.GetFileObject(data_stream_name=data_stream_name) if not file_object: return None try: file_object.seek(0, os.SEEK_SET) hasher_object = hashers_manager.HashersManager.GetHasher('sha256') data = file_object.read(self._READ_BUFFER_SIZE) while data: hasher_object.Update(data) data = file_object.read(self._READ_BUFFER_SIZE) finally: file_object.close() return hasher_object.GetStringDigest()
Marks all connections which were previously listed as unavailable as being up.	def flush_down_connections(self): self._get_db_attempts = 0 for db_num in self._down_connections.keys(): self.mark_connection_up(db_num)	67	https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/routers/base.py#L186-L192	def _CalculateDigestHash(self, file_entry, data_stream_name): file_object = file_entry.GetFileObject(data_stream_name=data_stream_name) if not file_object: return None try: file_object.seek(0, os.SEEK_SET) hasher_object = hashers_manager.HashersManager.GetHasher('sha256') data = file_object.read(self._READ_BUFFER_SIZE) while data: hasher_object.Update(data) data = file_object.read(self._READ_BUFFER_SIZE) finally: file_object.close() return hasher_object.GetStringDigest()
Compute standby power Parameters ---------- df : pandas.DataFrame or pandas.Series Electricity Power resolution : str, default='d' Resolution of the computation. Data will be resampled to this resolution (as mean) before computation of the minimum. String that can be parsed by the pandas resample function, example ='h', '15min', '6h' time_window : tuple with start-hour and end-hour, default=None Specify the start-time and end-time for the analysis. Only data within this time window will be considered. Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects Returns ------- df : pandas.Series with DateTimeIndex in the given resolution	def standby(df, resolution='24h', time_window=None): if df.empty: raise EmptyDataFrame() df = pd.DataFrame(df) # if df was a pd.Series, convert to DataFrame def parse_time(t): if isinstance(t, numbers.Number): return pd.Timestamp.utcfromtimestamp(t).time() else: return pd.Timestamp(t).time() # first filter based on the time-window if time_window is not None: t_start = parse_time(time_window[0]) t_end = parse_time(time_window[1]) if t_start > t_end: # start before midnight df = df[(df.index.time >= t_start) \| (df.index.time < t_end)] else: df = df[(df.index.time >= t_start) & (df.index.time < t_end)] return df.resample(resolution).min()	68	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/analysis.py#L72-L115	def retract(self): if lib.EnvRetract(self._env, self._fact) != 1: raise CLIPSError(self._env)
Compute the share of the standby power in the total consumption. Parameters ---------- df : pandas.DataFrame or pandas.Series Power (typically electricity, can be anything) resolution : str, default='d' Resolution of the computation. Data will be resampled to this resolution (as mean) before computation of the minimum. String that can be parsed by the pandas resample function, example ='h', '15min', '6h' time_window : tuple with start-hour and end-hour, default=None Specify the start-time and end-time for the analysis. Only data within this time window will be considered. Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects Returns ------- fraction : float between 0-1 with the share of the standby consumption	def share_of_standby(df, resolution='24h', time_window=None): p_sb = standby(df, resolution, time_window) df = df.resample(resolution).mean() p_tot = df.sum() p_standby = p_sb.sum() share_standby = p_standby / p_tot res = share_standby.iloc[0] return res	69	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/analysis.py#L118-L146	def bulk_delete(handler, request): ids = request.GET.getall('ids') Message.delete().where(Message.id << ids).execute() raise muffin.HTTPFound(handler.url)
Toggle counter for gas boilers Counts the number of times the gas consumption increases with more than 3kW Parameters ---------- ts: Pandas Series Gas consumption in minute resolution Returns ------- int	def count_peaks(ts): on_toggles = ts.diff() > 3000 shifted = np.logical_not(on_toggles.shift(1)) result = on_toggles & shifted count = result.sum() return count	70	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/analysis.py#L149-L169	def FindProxies(): sc = objc.SystemConfiguration() # Get the dictionary of network proxy settings settings = sc.dll.SCDynamicStoreCopyProxies(None) if not settings: return [] try: cf_http_enabled = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPEnable") if cf_http_enabled and bool(sc.CFNumToInt32(cf_http_enabled)): # Proxy settings for HTTP are enabled cfproxy = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPProxy") cfport = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPPort") if cfproxy and cfport: proxy = sc.CFStringToPystring(cfproxy) port = sc.CFNumToInt32(cfport) return ["http://%s:%d/" % (proxy, port)] cf_auto_enabled = sc.CFDictRetrieve( settings, "kSCPropNetProxiesProxyAutoConfigEnable") if cf_auto_enabled and bool(sc.CFNumToInt32(cf_auto_enabled)): cfurl = sc.CFDictRetrieve(settings, "kSCPropNetProxiesProxyAutoConfigURLString") if cfurl: unused_url = sc.CFStringToPystring(cfurl) # TODO(amoser): Auto config is enabled, what is the plan here? # Basically, all we get is the URL of a javascript file. To get the # correct proxy for a given URL, browsers call a Javascript function # that returns the correct proxy URL. The question is now, do we really # want to start running downloaded js on the client? return [] finally: sc.dll.CFRelease(settings) return []
Calculate the ratio of input vs. norm over a given interval. Parameters ---------- ts : pandas.Series timeseries resolution : str, optional interval over which to calculate the ratio default: resolution of the input timeseries norm : int \| float, optional denominator of the ratio default: the maximum of the input timeseries Returns ------- pandas.Series	def load_factor(ts, resolution=None, norm=None): if norm is None: norm = ts.max() if resolution is not None: ts = ts.resample(rule=resolution).mean() lf = ts / norm return lf	71	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/analysis.py#L172-L199	def inject_basic_program(self, ascii_listing): program_start = self.cpu.memory.read_word( self.machine_api.PROGRAM_START_ADDR ) tokens = self.machine_api.ascii_listing2program_dump(ascii_listing) self.cpu.memory.load(program_start, tokens) log.critical("BASIC program injected into Memory.") # Update the BASIC addresses: program_end = program_start + len(tokens) self.cpu.memory.write_word(self.machine_api.VARIABLES_START_ADDR, program_end) self.cpu.memory.write_word(self.machine_api.ARRAY_START_ADDR, program_end) self.cpu.memory.write_word(self.machine_api.FREE_SPACE_START_ADDR, program_end) log.critical("BASIC addresses updated.")
get top hits after sorting by column number	def top_hits(hits, num, column, reverse): hits.sort(key = itemgetter(column), reverse = reverse) for hit in hits[0:num]: yield hit	72	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/besthits.py#L17-L23	def weld_udf(weld_template, mapping): weld_obj = create_empty_weld_object() for k, v in mapping.items(): if isinstance(v, (np.ndarray, WeldObject)): obj_id = get_weld_obj_id(weld_obj, v) mapping.update({k: obj_id}) weld_obj.weld_code = weld_template.format(**mapping) return weld_obj
parse b6 output with sorting	def numBlast_sort(blast, numHits, evalueT, bitT): header = ['#query', 'target', 'pident', 'alen', 'mismatch', 'gapopen', 'qstart', 'qend', 'tstart', 'tend', 'evalue', 'bitscore'] yield header hmm = {h:[] for h in header} for line in blast: if line.startswith('#'): continue line = line.strip().split('\t') # Evalue and Bitscore thresholds line[10], line[11] = float(line[10]), float(line[11]) evalue, bit = line[10], line[11] if evalueT is not False and evalue > evalueT: continue if bitT is not False and bit < bitT: continue for i, h in zip(line, header): hmm[h].append(i) hmm = pd.DataFrame(hmm) for query, df in hmm.groupby(by = ['#query']): df = df.sort_values(by = ['bitscore'], ascending = False) for hit in df[header].values[0:numHits]: yield hit	73	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/besthits.py#L25-L50	def delete(self): try: return self._server.query('/library/sections/%s' % self.key, method=self._server._session.delete) except BadRequest: # pragma: no cover msg = 'Failed to delete library %s' % self.key msg += 'You may need to allow this permission in your Plex settings.' log.error(msg) raise
parse b6 output	def numBlast(blast, numHits, evalueT = False, bitT = False, sort = False): if sort is True: for hit in numBlast_sort(blast, numHits, evalueT, bitT): yield hit return header = ['#query', 'target', 'pident', 'alen', 'mismatch', 'gapopen', 'qstart', 'qend', 'tstart', 'tend', 'evalue', 'bitscore'] yield header prev, hits = None, [] for line in blast: line = line.strip().split('\t') ID = line[0] line[10], line[11] = float(line[10]), float(line[11]) evalue, bit = line[10], line[11] if ID != prev: if len(hits) > 0: # column is 1 + line index for hit in top_hits(hits, numHits, 11, True): yield hit hits = [] if evalueT == False and bitT == False: hits.append(line) elif evalue <= evalueT and bitT == False: hits.append(line) elif evalue <= evalueT and bit >= bitT: hits.append(line) elif evalueT == False and bit >= bitT: hits.append(line) prev = ID for hit in top_hits(hits, numHits, 11, True): yield hit	74	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/besthits.py#L52-L85	def CloseHandle(self): if hasattr(self, 'handle'): ret = vmGuestLib.VMGuestLib_CloseHandle(self.handle.value) if ret != VMGUESTLIB_ERROR_SUCCESS: raise VMGuestLibException(ret) del(self.handle)
parse hmm domain table output this version is faster but does not work unless the table is sorted	def numDomtblout(domtblout, numHits, evalueT, bitT, sort): if sort is True: for hit in numDomtblout_sort(domtblout, numHits, evalueT, bitT): yield hit return header = ['#target name', 'target accession', 'tlen', 'query name', 'query accession', 'qlen', 'full E-value', 'full score', 'full bias', 'domain #', '# domains', 'domain c-Evalue', 'domain i-Evalue', 'domain score', 'domain bias', 'hmm from', 'hmm to', 'seq from', 'seq to', 'env from', 'env to', 'acc', 'target description'] yield header prev, hits = None, [] for line in domtblout: if line.startswith('#'): continue # parse line and get description line = line.strip().split() desc = ' '.join(line[18:]) line = line[0:18] line.append(desc) # create ID based on query name and domain number ID = line[0] + line[9] # domain c-Evalue and domain score thresholds line[11], line[13] = float(line[11]), float(line[13]) evalue, bitscore = line[11], line[13] line[11], line[13] = evalue, bitscore if ID != prev: if len(hits) > 0: for hit in top_hits(hits, numHits, 13, True): yield hit hits = [] if evalueT == False and bitT == False: hits.append(line) elif evalue <= evalueT and bitT == False: hits.append(line) elif evalue <= evalueT and bit >= bitT: hits.append(line) elif evalueT == False and bit >= bitT: hits.append(line) prev = ID for hit in top_hits(hits, numHits, 13, True): yield hit	75	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/besthits.py#L121-L168	def account_unblock(self, id): id = self.__unpack_id(id) url = '/api/v1/accounts/{0}/unblock'.format(str(id)) return self.__api_request('POST', url)
convert stockholm to fasta	def stock2fa(stock): seqs = {} for line in stock: if line.startswith('#') is False and line.startswith(' ') is False and len(line) > 3: id, seq = line.strip().split() id = id.rsplit('/', 1)[0] id = re.split('[0-9]\\|', id, 1)[-1] if id not in seqs: seqs[id] = [] seqs[id].append(seq) if line.startswith('//'): break return seqs	76	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/stockholm2fa.py#L11-L26	def put_lifecycle_configuration(Bucket, Rules, region=None, key=None, keyid=None, profile=None): try: conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) if Rules is not None and isinstance(Rules, six.string_types): Rules = salt.utils.json.loads(Rules) conn.put_bucket_lifecycle_configuration(Bucket=Bucket, LifecycleConfiguration={'Rules': Rules}) return {'updated': True, 'name': Bucket} except ClientError as e: return {'updated': False, 'error': __utils__['boto3.get_error'](e)}
Return boolean time series following given week schedule. Parameters ---------- index : pandas.DatetimeIndex Datetime index on_time : str or datetime.time Daily opening time. Default: '09:00' off_time : str or datetime.time Daily closing time. Default: '17:00' off_days : list of str List of weekdays. Default: ['Sunday', 'Monday'] Returns ------- pandas.Series of bool True when on, False otherwise for given datetime index Examples -------- >>> import pandas as pd >>> from opengrid.library.utils import week_schedule >>> index = pd.date_range('20170701', '20170710', freq='H') >>> week_schedule(index)	def week_schedule(index, on_time=None, off_time=None, off_days=None): if on_time is None: on_time = '9:00' if off_time is None: off_time = '17:00' if off_days is None: off_days = ['Sunday', 'Monday'] if not isinstance(on_time, datetime.time): on_time = pd.to_datetime(on_time, format='%H:%M').time() if not isinstance(off_time, datetime.time): off_time = pd.to_datetime(off_time, format='%H:%M').time() times = (index.time >= on_time) & (index.time < off_time) & (~index.weekday_name.isin(off_days)) return pd.Series(times, index=index)	77	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/utils.py#L10-L47	def deregister_entity_from_group(self, entity, group): if entity in self._entities: if entity in self._groups[group]: self._groups[group].remove(entity) else: raise UnmanagedEntityError(entity)
Draw a carpet plot of a pandas timeseries. The carpet plot reads like a letter. Every day one line is added to the bottom of the figure, minute for minute moving from left (morning) to right (evening). The color denotes the level of consumption and is scaled logarithmically. If vmin and vmax are not provided as inputs, the minimum and maximum of the colorbar represent the minimum and maximum of the (resampled) timeseries. Parameters ---------- timeseries : pandas.Series vmin, vmax : If not None, either or both of these values determine the range of the z axis. If None, the range is given by the minimum and/or maximum of the (resampled) timeseries. zlabel, title : If not None, these determine the labels of z axis and/or title. If None, the name of the timeseries is used if defined. cmap : matplotlib.cm instance, default coolwarm Examples -------- >>> import numpy as np >>> import pandas as pd >>> from opengrid.library import plotting >>> plt = plotting.plot_style() >>> index = pd.date_range('2015-1-1','2015-12-31',freq='h') >>> ser = pd.Series(np.random.normal(size=len(index)), index=index, name='abc') >>> im = plotting.carpet(ser)	def carpet(timeseries, kwargs): # define optional input parameters cmap = kwargs.pop('cmap', cm.coolwarm) norm = kwargs.pop('norm', LogNorm()) interpolation = kwargs.pop('interpolation', 'nearest') cblabel = kwargs.pop('zlabel', timeseries.name if timeseries.name else '') title = kwargs.pop('title', 'carpet plot: ' + timeseries.name if timeseries.name else '') # data preparation if timeseries.dropna().empty: print('skipped {} - no data'.format(title)) return ts = timeseries.resample('15min').interpolate() vmin = max(0.1, kwargs.pop('vmin', ts[ts > 0].min())) vmax = max(vmin, kwargs.pop('vmax', ts.quantile(.999))) # convert to dataframe with date as index and time as columns by # first replacing the index by a MultiIndex mpldatetimes = date2num(ts.index.to_pydatetime()) ts.index = pd.MultiIndex.from_arrays( [np.floor(mpldatetimes), 2 + mpldatetimes % 1]) # '2 +': matplotlib bug workaround. # and then unstacking the second index level to columns df = ts.unstack() # data plotting fig, ax = plt.subplots() # define the extent of the axes (remark the +- 0.5 for the y axis in order to obtain aligned date ticks) extent = [df.columns[0], df.columns[-1], df.index[-1] + 0.5, df.index[0] - 0.5] im = plt.imshow(df, vmin=vmin, vmax=vmax, extent=extent, cmap=cmap, aspect='auto', norm=norm, interpolation=interpolation, kwargs) # figure formatting # x axis ax.xaxis_date() ax.xaxis.set_major_locator(HourLocator(interval=2)) ax.xaxis.set_major_formatter(DateFormatter('%H:%M')) ax.xaxis.grid(True) plt.xlabel('UTC Time') # y axis ax.yaxis_date() dmin, dmax = ax.yaxis.get_data_interval() number_of_days = (num2date(dmax) - num2date(dmin)).days # AutoDateLocator is not suited in case few data is available if abs(number_of_days) <= 35: ax.yaxis.set_major_locator(DayLocator()) else: ax.yaxis.set_major_locator(AutoDateLocator()) ax.yaxis.set_major_formatter(DateFormatter("%a, %d %b %Y")) # plot colorbar cbticks = np.logspace(np.log10(vmin), np.log10(vmax), 11, endpoint=True) cb = plt.colorbar(format='%.0f', ticks=cbticks) cb.set_label(cblabel) # plot title plt.title(title) return im	78	https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/plotting.py#L34-L125	def num_gpus(): count = ctypes.c_int() check_call(_LIB.MXGetGPUCount(ctypes.byref(count))) return count.value
calculate percent identity	def calc_pident_ignore_gaps(a, b): m = 0 # matches mm = 0 # mismatches for A, B in zip(list(a), list(b)): if A == '-' or A == '.' or B == '-' or B == '.': continue if A == B: m += 1 else: mm += 1 try: return float(float(m)/float((m + mm))) * 100 except: return 0	79	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L34-L50	def get_local_file(file): try: with open(file.path): yield file.path except NotImplementedError: _, ext = os.path.splitext(file.name) with NamedTemporaryFile(prefix='wagtailvideo-', suffix=ext) as tmp: try: file.open('rb') for chunk in file.chunks(): tmp.write(chunk) finally: file.close() tmp.flush() yield tmp.name
skip column if either is a gap	def remove_gaps(A, B): a_seq, b_seq = [], [] for a, b in zip(list(A), list(B)): if a == '-' or a == '.' or b == '-' or b == '.': continue a_seq.append(a) b_seq.append(b) return ''.join(a_seq), ''.join(b_seq)	80	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L52-L62	def read_legacy(filename): reader = vtk.vtkDataSetReader() reader.SetFileName(filename) # Ensure all data is fetched with poorly formated legacy files reader.ReadAllScalarsOn() reader.ReadAllColorScalarsOn() reader.ReadAllNormalsOn() reader.ReadAllTCoordsOn() reader.ReadAllVectorsOn() # Perform the read reader.Update() output = reader.GetOutputDataObject(0) if output is None: raise AssertionError('No output when using VTKs legacy reader') return vtki.wrap(output)
compare pairs of sequences	def compare_seqs(seqs): A, B, ignore_gaps = seqs a, b = A[1], B[1] # actual sequences if len(a) != len(b): print('# reads are not the same length', file=sys.stderr) exit() if ignore_gaps is True: pident = calc_pident_ignore_gaps(a, b) else: pident = calc_pident(a, b) return A[0], B[0], pident	81	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L64-L77	def get_s3_origin_conf_class(): if LooseVersion(troposphere.__version__) > LooseVersion('2.4.0'): return cloudfront.S3OriginConfig if LooseVersion(troposphere.__version__) == LooseVersion('2.4.0'): return S3OriginConfig return cloudfront.S3Origin
calculate Levenshtein ratio of sequences	def compare_seqs_leven(seqs): A, B, ignore_gaps = seqs a, b = remove_gaps(A[1], B[1]) # actual sequences if len(a) != len(b): print('# reads are not the same length', file=sys.stderr) exit() pident = lr(a, b) * 100 return A[0], B[0], pident	82	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L79-L89	def del_unused_keyframes(self): skl = self.key_frame_list.sorted_key_list() unused_keys = [k for k in self.dct['keys'] if k not in skl] for k in unused_keys: del self.dct['keys'][k]
make pairwise sequence comparisons between aligned sequences	def pairwise_compare(afa, leven, threads, print_list, ignore_gaps): # load sequences into dictionary seqs = {seq[0]: seq for seq in nr_fasta([afa], append_index = True)} num_seqs = len(seqs) # define all pairs pairs = ((i[0], i[1], ignore_gaps) for i in itertools.combinations(list(seqs.values()), 2)) pool = multithread(threads) # calc percent identity between all pairs - parallelize if leven is True: pident = pool.map(compare_seqs_leven, pairs) else: compare = pool.imap_unordered(compare_seqs, pairs) pident = [i for i in tqdm(compare, total = (num_seqs*num_seqs)/2)] pool.close() pool.terminate() pool.join() return to_dictionary(pident, print_list)	83	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L91-L110	def makeCubiccFunc(self,mNrm,cNrm): EndOfPrdvPP = self.DiscFacEffself.Rfreeself.Rfreeself.PermGroFac(-self.CRRA-1.0) \ np.sum(self.PermShkVals_temp*(-self.CRRA-1.0) self.vPPfuncNext(self.mNrmNext)self.ShkPrbs_temp,axis=0) dcda = EndOfPrdvPP/self.uPP(np.array(cNrm[1:])) MPC = dcda/(dcda+1.) MPC = np.insert(MPC,0,self.MPCmaxNow) cFuncNowUnc = CubicInterp(mNrm,cNrm,MPC,self.MPCminNowself.hNrmNow,self.MPCminNow) return cFuncNowUnc
print matrix of pidents to stdout	def print_pairwise(pw, median = False): names = sorted(set([i for i in pw])) if len(names) != 0: if '>' in names[0]: yield ['#'] + [i.split('>')[1] for i in names if '>' in i] else: yield ['#'] + names for a in names: if '>' in a: yield [a.split('>')[1]] + [pw[a][b] for b in names] else: out = [] for b in names: if b in pw[a]: if median is False: out.append(max(pw[a][b])) else: out.append(np.median(pw[a][b])) else: out.append('-') yield [a] + out	84	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L132-L155	def delete_index(self, cardinality): DatabaseConnector.delete_index(self, cardinality) query = "DROP INDEX IF EXISTS idx_{0}_gram_varchar;".format(cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_normalized_varchar;".format( cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_lower_varchar;".format( cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_lower_normalized_varchar;".\ format(cardinality) self.execute_sql(query) for i in reversed(range(cardinality)): if i != 0: query = "DROP INDEX IF EXISTS idx_{0}_gram_{1}_lower;".format( cardinality, i) self.execute_sql(query)
print stats for comparisons	def print_comps(comps): if comps == []: print('n/a') else: print('# min: %s, max: %s, mean: %s' % \ (min(comps), max(comps), np.mean(comps)))	85	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L157-L165	def _load_vertex_buffers(self): fd = gzip.open(cache_name(self.file_name), 'rb') for buff in self.meta.vertex_buffers: mat = self.wavefront.materials.get(buff['material']) if not mat: mat = Material(name=buff['material'], is_default=True) self.wavefront.materials[mat.name] = mat mat.vertex_format = buff['vertex_format'] self.load_vertex_buffer(fd, mat, buff['byte_length']) fd.close()
print min. pident within each clade and then matrix of between-clade max.	def compare_clades(pw): names = sorted(set([i for i in pw])) for i in range(0, 4): wi, bt = {}, {} for a in names: for b in pw[a]: if ';' not in a or ';' not in b: continue pident = pw[a][b] cA, cB = a.split(';')[i], b.split(';')[i] if i == 0 and '_' in cA and '_' in cB: cA = cA.rsplit('_', 1)[1] cB = cB.rsplit('_', 1)[1] elif '>' in cA or '>' in cB: cA = cA.split('>')[1] cB = cB.split('>')[1] if cA == cB: if cA not in wi: wi[cA] = [] wi[cA].append(pident) else: if cA not in bt: bt[cA] = {} if cB not in bt[cA]: bt[cA][cB] = [] bt[cA][cB].append(pident) print('\n# min. within') for clade, pidents in list(wi.items()): print('\t'.join(['wi:%s' % str(i), clade, str(min(pidents))])) # print matrix of maximum between groups comps = [] print('\n# max. between') for comp in print_pairwise(bt): if comp is not None: print('\t'.join(['bt:%s' % str(i)] + [str(j) for j in comp])) if comp[0] != '#': comps.extend([j for j in comp[1:] if j != '-']) print_comps(comps) # print matrix of median between groups comps = [] print('\n# median between') for comp in print_pairwise(bt, median = True): if comp is not None: print('\t'.join(['bt:%s' % str(i)] + [str(j) for j in comp])) if comp[0] != '#': comps.extend([j for j in comp[1:] if j != '-']) print_comps(comps)	86	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L167-L216	def setGroups(self, groups, kerningGroupConversionRenameMaps=None): skipping = [] for name, members in groups.items(): checked = [] for m in members: if m in self.font: checked.append(m) else: skipping.append(m) if checked: self.font.groups[name] = checked if skipping: if self.verbose and self.logger: self.logger.info("\tNote: some glyphnames were removed from groups: %s (unavailable in the font)", ", ".join(skipping)) if kerningGroupConversionRenameMaps: # in case the sources were UFO2, # and defcon upconverted them to UFO3 # and now we have to down convert them again, # we don't want the UFO3 public prefixes in the group names self.font.kerningGroupConversionRenameMaps = kerningGroupConversionRenameMaps
convert matrix to dictionary of comparisons	def matrix2dictionary(matrix): pw = {} for line in matrix: line = line.strip().split('\t') if line[0].startswith('#'): names = line[1:] continue a = line[0] for i, pident in enumerate(line[1:]): b = names[i] if a not in pw: pw[a] = {} if b not in pw: pw[b] = {} if pident != '-': pident = float(pident) pw[a][b] = pident pw[b][a] = pident return pw	87	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L218-L239	def _sampleLocationOnSide(self): z = random.uniform(-1, 1) * self.height / 2. sampledAngle = 2 * random.random() * pi x, y = self.radius * cos(sampledAngle), self.radius * sin(sampledAngle) return [x, y, z]
Set argument parser option.	def setoption(parser, metadata=None): parser.add_argument('-v', action='version', version=__version__) subparsers = parser.add_subparsers(help='sub commands help') create_cmd = subparsers.add_parser('create') create_cmd.add_argument('name', help='Specify Python package name.') create_cmd.add_argument('-d', dest='description', action='store', help='Short description about your package.') create_cmd.add_argument('-a', dest='author', action='store', required=True, help='Python package author name.') create_cmd.add_argument('-e', dest='email', action='store', required=True, help='Python package author email address.') create_cmd.add_argument('-l', dest='license', choices=metadata.licenses().keys(), default='GPLv3+', help='Specify license. (default: %(default)s)') create_cmd.add_argument('-s', dest='status', choices=metadata.status().keys(), default='Alpha', help=('Specify development status. ' '(default: %(default)s)')) create_cmd.add_argument('--no-check', action='store_true', help='No checking package name in PyPI.') create_cmd.add_argument('--with-samples', action='store_true', help='Generate package with sample code.') group = create_cmd.add_mutually_exclusive_group(required=True) group.add_argument('-U', dest='username', action='store', help='Specify GitHub username.') group.add_argument('-u', dest='url', action='store', type=valid_url, help='Python package homepage url.') create_cmd.add_argument('-o', dest='outdir', action='store', default=os.path.abspath(os.path.curdir), help='Specify output directory. (default: $PWD)') list_cmd = subparsers.add_parser('list') list_cmd.add_argument('-l', dest='licenses', action='store_true', help='show license choices.')	88	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L12-L51	def swd_sync(self, pad=False): if pad: self._dll.JLINK_SWD_SyncBytes() else: self._dll.JLINK_SWD_SyncBits() return None
Parse argument options.	def parse_options(metadata): parser = argparse.ArgumentParser(description='%(prog)s usage:', prog=__prog__) setoption(parser, metadata=metadata) return parser	89	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L72-L77	def vrel(v1, v2): v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)
Execute main processes.	def main(): try: pkg_version = Update() if pkg_version.updatable(): pkg_version.show_message() metadata = control.retreive_metadata() parser = parse_options(metadata) argvs = sys.argv if len(argvs) <= 1: parser.print_help() sys.exit(1) args = parser.parse_args() control.print_licences(args, metadata) control.check_repository_existence(args) control.check_package_existence(args) control.generate_package(args) except (RuntimeError, BackendFailure, Conflict) as exc: sys.stderr.write('{0}\n'.format(exc)) sys.exit(1)	90	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L80-L99	def price(self, minimum: float = 10.00, maximum: float = 1000.00) -> str: price = self.random.uniform(minimum, maximum, precision=2) return '{0} {1}'.format(price, self.currency_symbol())
Check key and set default vaule when it does not exists.	def _check_or_set_default_params(self): if not hasattr(self, 'date'): self._set_param('date', datetime.utcnow().strftime('%Y-%m-%d')) if not hasattr(self, 'version'): self._set_param('version', self.default_version) # pylint: disable=no-member if not hasattr(self, 'description') or self.description is None: getattr(self, '_set_param')('description', self.warning_message)	91	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L44-L52	def _get_files_modified(): cmd = "git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD" _, files_modified, _ = run(cmd) extensions = [re.escape(ext) for ext in list(SUPPORTED_FILES) + [".rst"]] test = "(?:{0})$".format("\|".join(extensions)) return list(filter(lambda f: re.search(test, f), files_modified))
Move directory from working directory to output directory.	def move(self): if not os.path.isdir(self.outdir): os.makedirs(self.outdir) shutil.move(self.tmpdir, os.path.join(self.outdir, self.name))	92	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L169-L173	def init(args, kwargs): global _initial_client client = Client(args, **kwargs) Hub.current.bind_client(client) rv = _InitGuard(client) if client is not None: _initial_client = weakref.ref(client) return rv
Initialize VCS repository.	def vcs_init(self): VCS(os.path.join(self.outdir, self.name), self.pkg_data)	93	https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L185-L187	def group_experiments_greedy(tomo_expt: TomographyExperiment): diag_sets = _max_tpb_overlap(tomo_expt) grouped_expt_settings_list = list(diag_sets.values()) grouped_tomo_expt = TomographyExperiment(grouped_expt_settings_list, program=tomo_expt.program) return grouped_tomo_expt
Finds the location of the current Steam installation on Windows machines. Returns None for any non-Windows machines, or for Windows machines where Steam is not installed.	def find_steam_location(): if registry is None: return None key = registry.CreateKey(registry.HKEY_CURRENT_USER,"Software\Valve\Steam") return registry.QueryValueEx(key,"SteamPath")[0]	94	https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/winutils.py#L10-L20	def _merge(*args): return re.compile(r'^' + r'[/-]'.join(args) + r'(?:\s+' + _dow + ')?$')
Plot PCoA principal coordinates scaled by the relative abundances of otu_name.	def plot_PCoA(cat_data, otu_name, unifrac, names, colors, xr, yr, outDir, save_as, plot_style): fig = plt.figure(figsize=(14, 8)) ax = fig.add_subplot(111) for i, cat in enumerate(cat_data): plt.scatter(cat_data[cat]["pc1"], cat_data[cat]["pc2"], cat_data[cat]["size"], color=colors[cat], alpha=0.85, marker="o", edgecolor="black", label=cat) lgnd = plt.legend(loc="best", scatterpoints=3, fontsize=13) for i in range(len(colors.keys())): lgnd.legendHandles[i]._sizes = [80] # Change the legend marker size manually plt.title(" ".join(otu_name.split("_")), style="italic") plt.ylabel("PC2 (Percent Explained Variance {:.3f}%)".format(float(unifrac["varexp"][1]))) plt.xlabel("PC1 (Percent Explained Variance {:.3f}%)".format(float(unifrac["varexp"][0]))) plt.xlim(round(xr[0]1.5, 1), round(xr[1]1.5, 1)) plt.ylim(round(yr[0]1.5, 1), round(yr[1]1.5, 1)) if plot_style: gu.ggplot2_style(ax) fc = "0.8" else: fc = "none" fig.savefig(os.path.join(outDir, "_".join(otu_name.split())) + "." + save_as, facecolor=fc, edgecolor="none", format=save_as, bbox_inches="tight", pad_inches=0.2) plt.close(fig)	95	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/PCoA_bubble.py#L36-L65	def reset(cls): cls.debug = False cls.disabled = False cls.overwrite = False cls.playback_only = False cls.recv_timeout = 5 cls.recv_endmarkers = [] cls.recv_size = None
Split up the column data in a biom table by mapping category value.	def split_by_category(biom_cols, mapping, category_id): columns = defaultdict(list) for i, col in enumerate(biom_cols): columns[mapping[col['id']][category_id]].append((i, col)) return columns	96	https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/transpose_biom.py#L17-L25	def update_prompt(self): prefix = "" if self._local_endpoint is not None: prefix += "(%s:%d) " % self._local_endpoint prefix += self.engine.region if self.engine.partial: self.prompt = len(prefix) * " " + "> " else: self.prompt = prefix + "> "
print line if starts with ...	def print_line(l): print_lines = ['# STOCKHOLM', '#=GF', '#=GS', ' '] if len(l.split()) == 0: return True for start in print_lines: if l.startswith(start): return True return False	97	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/stockholm2oneline.py#L11-L21	def setOverlayTransformTrackedDeviceRelative(self, ulOverlayHandle, unTrackedDevice): fn = self.function_table.setOverlayTransformTrackedDeviceRelative pmatTrackedDeviceToOverlayTransform = HmdMatrix34_t() result = fn(ulOverlayHandle, unTrackedDevice, byref(pmatTrackedDeviceToOverlayTransform)) return result, pmatTrackedDeviceToOverlayTransform
convert stockholm to single line format	def stock2one(stock): lines = {} for line in stock: line = line.strip() if print_line(line) is True: yield line continue if line.startswith('//'): continue ID, seq = line.rsplit(' ', 1) if ID not in lines: lines[ID] = '' else: # remove preceding white space seq = seq.strip() lines[ID] += seq for ID, line in lines.items(): yield '\t'.join([ID, line]) yield '\n//'	98	https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/stockholm2oneline.py#L23-L44	def describe_event_source_mapping(UUID=None, EventSourceArn=None, FunctionName=None, region=None, key=None, keyid=None, profile=None): ids = _get_ids(UUID, EventSourceArn=EventSourceArn, FunctionName=FunctionName) if not ids: return {'event_source_mapping': None} UUID = ids[0] try: conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) desc = conn.get_event_source_mapping(UUID=UUID) if desc: keys = ('UUID', 'BatchSize', 'EventSourceArn', 'FunctionArn', 'LastModified', 'LastProcessingResult', 'State', 'StateTransitionReason') return {'event_source_mapping': dict([(k, desc.get(k)) for k in keys])} else: return {'event_source_mapping': None} except ClientError as e: return {'error': __utils__['boto3.get_error'](e)}
Statics the methods. wut.	def math_func(f): @wraps(f) def wrapper(args, kwargs): if len(args) > 0: return_type = type(args[0]) if kwargs.has_key('return_type'): return_type = kwargs['return_type'] kwargs.pop('return_type') return return_type(f(args, *kwargs)) args = list((setify(x) for x in args)) return return_type(f(args, **kwargs)) return wrapper	99	https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/helpers.py#L8-L22	def PublishMultipleEvents(cls, events, token=None): event_name_map = registry.EventRegistry.EVENT_NAME_MAP for event_name, messages in iteritems(events): if not isinstance(event_name, string_types): raise ValueError( "Event names should be string, got: %s" % type(event_name)) for msg in messages: if not isinstance(msg, rdfvalue.RDFValue): raise ValueError("Can only publish RDFValue instances.") for event_cls in event_name_map.get(event_name, []): event_cls().ProcessMessages(messages, token=token)

Return either the full or truncated version of a QIIME-formatted taxonomy string. :type p: str :param p: A QIIME-formatted taxonomy string: k__Foo; p__Bar; ... :type level: str :param level: The different level of identification are kingdom (k), phylum (p), class (c),order (o), family (f), genus (g) and species (s). If level is not provided, the default level of identification is species. :rtype: str :return: A QIIME-formatted taxonomy string up to the classification given by param level.

def split_phylogeny(p, level="s"): level = level+"__" result = p.split(level) return result[0]+level+result[1].split(";")[0]

0

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L159-L177

def reset_local_buffers(self): agent_ids = list(self.keys()) for k in agent_ids: self[k].reset_agent()

Check to make sure the supplied directory path does not exist, if so, create it. The method catches OSError exceptions and returns a descriptive message instead of re-raising the error. :type d: str :param d: It is the full path to a directory. :return: Does not return anything, but creates a directory path if it doesn't exist already.

def ensure_dir(d): if not os.path.exists(d): try: os.makedirs(d) except OSError as oe: # should not happen with os.makedirs # ENOENT: No such file or directory if os.errno == errno.ENOENT: msg = twdd("""One or more directories in the path ({}) do not exist. If you are specifying a new directory for output, please ensure all other directories in the path currently exist.""") return msg.format(d) else: msg = twdd("""An error occurred trying to create the output directory ({}) with message: {}""") return msg.format(d, oe.strerror)

1

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L180-L206

def on_change(self, value): self._modifier(self.inst, self.prop, value)

Takes either a file path or an open file handle, checks validity and returns an open file handle or raises an appropriate Exception. :type fnh: str :param fnh: It is the full path to a file, or open file handle :type mode: str :param mode: The way in which this file will be used, for example to read or write or both. By default, file will be opened in rU mode. :return: Returns an opened file for appropriate usage.

def file_handle(fnh, mode="rU"): handle = None if isinstance(fnh, file): if fnh.closed: raise ValueError("Input file is closed.") handle = fnh elif isinstance(fnh, str): handle = open(fnh, mode) return handle

2

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L209-L231

def merge_partition_offsets(*partition_offsets): output = dict() for partition_offset in partition_offsets: for partition, offset in six.iteritems(partition_offset): prev_offset = output.get(partition, 0) output[partition] = max(prev_offset, offset) return output

Find the user specified categories in the map and create a dictionary to contain the relevant data for each type within the categories. Multiple categories will have their types combined such that each possible combination will have its own entry in the dictionary. :type imap: dict :param imap: The input mapping file data keyed by SampleID :type header: list :param header: The header line from the input mapping file. This will be searched for the user-specified categories :type categories: list :param categories: The list of user-specified category column name from mapping file :rtype: dict :return: A sorted dictionary keyed on the combinations of all the types found within the user-specified categories. Each entry will contain an empty DataCategory namedtuple. If no categories are specified, a single entry with the key 'default' will be returned

def gather_categories(imap, header, categories=None): # If no categories provided, return all SampleIDs if categories is None: return {"default": DataCategory(set(imap.keys()), {})} cat_ids = [header.index(cat) for cat in categories if cat in header and "=" not in cat] table = OrderedDict() conditions = defaultdict(set) for i, cat in enumerate(categories): if "=" in cat and cat.split("=")[0] in header: cat_name = header[header.index(cat.split("=")[0])] conditions[cat_name].add(cat.split("=")[1]) # If invalid categories or conditions identified, return all SampleIDs if not cat_ids and not conditions: return {"default": DataCategory(set(imap.keys()), {})} #If only category column given, return column-wise SampleIDs if cat_ids and not conditions: for sid, row in imap.items(): cat_name = "_".join([row[cid] for cid in cat_ids]) if cat_name not in table: table[cat_name] = DataCategory(set(), {}) table[cat_name].sids.add(sid) return table # Collect all condition names cond_ids = set() for k in conditions: try: cond_ids.add(header.index(k)) except ValueError: continue idx_to_test = set(cat_ids).union(cond_ids) # If column name and condition given, return overlapping SampleIDs of column and # condition combinations for sid, row in imap.items(): if all([row[header.index(c)] in conditions[c] for c in conditions]): key = "_".join([row[idx] for idx in idx_to_test]) try: assert key in table.keys() except AssertionError: table[key] = DataCategory(set(), {}) table[key].sids.add(sid) try: assert len(table) > 0 except AssertionError: return {"default": DataCategory(set(imap.keys()), {})} else: return table

3

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L238-L309

def elapsed_time_from(start_time): time_then = make_time(start_time) time_now = datetime.utcnow().replace(microsecond=0) if time_then is None: return delta_t = time_now - time_then return delta_t

Parses the unifrac results file into a dictionary :type unifracFN: str :param unifracFN: The path to the unifrac results file :rtype: dict :return: A dictionary with keys: 'pcd' (principle coordinates data) which is a dictionary of the data keyed by sample ID, 'eigvals' (eigenvalues), and 'varexp' (variation explained)

def parse_unifrac(unifracFN): with open(unifracFN, "rU") as uF: first = uF.next().split("\t") lines = [line.strip() for line in uF] unifrac = {"pcd": OrderedDict(), "eigvals": [], "varexp": []} if first[0] == "pc vector number": return parse_unifrac_v1_8(unifrac, lines) elif first[0] == "Eigvals": return parse_unifrac_v1_9(unifrac, lines) else: raise ValueError("File format not supported/recognized. Please check input " "unifrac file.")

4

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L311-L334

def set_classes(self): # Custom field classes on field wrapper if self.attrs.get("_field_class"): self.values["class"].append(escape(self.attrs.get("_field_class"))) # Inline class if self.attrs.get("_inline"): self.values["class"].append("inline") # Disabled class if self.field.field.disabled: self.values["class"].append("disabled") # Required class if self.field.field.required and not self.attrs.get("_no_required"): self.values["class"].append("required") elif self.attrs.get("_required") and not self.field.field.required: self.values["class"].append("required")

Function to parse data from older version of unifrac file obtained from Qiime version 1.8 and earlier. :type unifrac: dict :param unifracFN: The path to the unifrac results file :type file_data: list :param file_data: Unifrac data lines after stripping whitespace characters.

def parse_unifrac_v1_8(unifrac, file_data): for line in file_data: if line == "": break line = line.split("\t") unifrac["pcd"][line[0]] = [float(e) for e in line[1:]] unifrac["eigvals"] = [float(entry) for entry in file_data[-2].split("\t")[1:]] unifrac["varexp"] = [float(entry) for entry in file_data[-1].split("\t")[1:]] return unifrac

5

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L337-L356

async def stop_bridges(self): for task in self.sleep_tasks: task.cancel() for bridge in self.bridges: bridge.stop()

Function to parse data from newer version of unifrac file obtained from Qiime version 1.9 and later. :type unifracFN: str :param unifracFN: The path to the unifrac results file :type file_data: list :param file_data: Unifrac data lines after stripping whitespace characters.

def parse_unifrac_v1_9(unifrac, file_data): unifrac["eigvals"] = [float(entry) for entry in file_data[0].split("\t")] unifrac["varexp"] = [float(entry)*100 for entry in file_data[3].split("\t")] for line in file_data[8:]: if line == "": break line = line.split("\t") unifrac["pcd"][line[0]] = [float(e) for e in line[1:]] return unifrac

6

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L359-L378

def is_charge_balanced(reaction): charge = 0 for metabolite, coefficient in iteritems(reaction.metabolites): if metabolite.charge is None: return False charge += coefficient * metabolite.charge return charge == 0

Determine color-category mapping. If color_column was specified, then map the category names to color values. Otherwise, use the palettable colors to automatically generate a set of colors for the group values. :type sample_map: dict :param unifracFN: Map associating each line of the mapping file with the appropriate sample ID (each value of the map also contains the sample ID) :type header: tuple :param A tuple of header line for mapping file :type group_column: str :param group_column: String denoting the column name for sample groups. :type color_column: str :param color_column: String denoting the column name for sample colors. :type return: dict :param return: {SampleID: Color}

def color_mapping(sample_map, header, group_column, color_column=None): group_colors = OrderedDict() group_gather = gather_categories(sample_map, header, [group_column]) if color_column is not None: color_gather = gather_categories(sample_map, header, [color_column]) # match sample IDs between color_gather and group_gather for group in group_gather: for color in color_gather: # allow incomplete assignment of colors, if group sids overlap at # all with the color sids, consider it a match if group_gather[group].sids.intersection(color_gather[color].sids): group_colors[group] = color else: bcolors = itertools.cycle(Set3_12.hex_colors) for group in group_gather: group_colors[group] = bcolors.next() return group_colors

7

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/util.py#L380-L419

def is_balance_proof_safe_for_onchain_operations( balance_proof: BalanceProofSignedState, ) -> bool: total_amount = balance_proof.transferred_amount + balance_proof.locked_amount return total_amount <= UINT256_MAX

return reverse completment of read

def rev_c(read): rc = [] rc_nucs = {'A':'T', 'T':'A', 'G':'C', 'C':'G', 'N':'N'} for base in read: rc.extend(rc_nucs[base.upper()]) return rc[::-1]

8

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/shuffle_genome.py#L27-L35

def hypermedia_out(): request = cherrypy.serving.request request._hypermedia_inner_handler = request.handler # If handler has been explicitly set to None, don't override. if request.handler is not None: request.handler = hypermedia_handler

randomly shuffle genome

def shuffle_genome(genome, cat, fraction = float(100), plot = True, \ alpha = 0.1, beta = 100000, \ min_length = 1000, max_length = 200000): header = '>randomized_%s' % (genome.name) sequence = list(''.join([i[1] for i in parse_fasta(genome)])) length = len(sequence) shuffled = [] # break genome into pieces while sequence is not False: s = int(random.gammavariate(alpha, beta)) if s <= min_length or s >= max_length: continue if len(sequence) < s: seq = sequence[0:] else: seq = sequence[0:s] sequence = sequence[s:] # if bool(random.getrandbits(1)) is True: # seq = rev_c(seq) # print('fragment length: %s reverse complement: True' % ('{:,}'.format(s)), file=sys.stderr) # else: # print('fragment length: %s reverse complement: False' % ('{:,}'.format(s)), file=sys.stderr) shuffled.append(''.join(seq)) if sequence == []: break # shuffle pieces random.shuffle(shuffled) # subset fragments if fraction == float(100): subset = shuffled else: max_pieces = int(length * fraction/100) subset, total = [], 0 for fragment in shuffled: length = len(fragment) if total + length <= max_pieces: subset.append(fragment) total += length else: diff = max_pieces - total subset.append(fragment[0:diff]) break # combine sequences, if requested if cat is True: yield [header, ''.join(subset)] else: for i, seq in enumerate(subset): yield ['%s fragment:%s' % (header, i), seq]

9

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/shuffle_genome.py#L37-L87

def GetEntries(self, parser_mediator, match=None, **unused_kwargs): stores = match.get('Stores', {}) for volume_name, volume in iter(stores.items()): datetime_value = volume.get('CreationDate', None) if not datetime_value: continue partial_path = volume['PartialPath'] event_data = plist_event.PlistTimeEventData() event_data.desc = 'Spotlight Volume {0:s} ({1:s}) activated.'.format( volume_name, partial_path) event_data.key = '' event_data.root = '/Stores' event = time_events.PythonDatetimeEvent( datetime_value, definitions.TIME_DESCRIPTION_WRITTEN) parser_mediator.ProduceEventWithEventData(event, event_data)

If the fit contains statistically insignificant parameters, remove them. Returns a pruned fit where all parameters have p-values of the t-statistic below p_max Parameters ---------- fit: fm.ols fit object Can contain insignificant parameters p_max : float Maximum allowed probability of the t-statistic Returns ------- fit: fm.ols fit object Won't contain any insignificant parameters

def _prune(self, fit, p_max): def remove_from_model_desc(x, model_desc): """ Return a model_desc without x """ rhs_termlist = [] for t in model_desc.rhs_termlist: if not t.factors: # intercept, add anyway rhs_termlist.append(t) elif not x == t.factors[0]._varname: # this is not the term with x rhs_termlist.append(t) md = ModelDesc(model_desc.lhs_termlist, rhs_termlist) return md corrected_model_desc = ModelDesc(fit.model.formula.lhs_termlist[:], fit.model.formula.rhs_termlist[:]) pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist() try: pars_to_prune.remove('Intercept') except: pass while pars_to_prune: corrected_model_desc = remove_from_model_desc(pars_to_prune[0], corrected_model_desc) fit = fm.ols(corrected_model_desc, data=self.df).fit() pars_to_prune = fit.pvalues.where(fit.pvalues > p_max).dropna().index.tolist() try: pars_to_prune.remove('Intercept') except: pass return fit

10

https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L222-L272

def hexblock_word(cls, data, address = None, bits = None, separator = ' ', width = 8): return cls.hexblock_cb(cls.hexa_word, data, address, bits, width * 2, cb_kwargs = {'separator': separator})

Return the best fit, based on rsquared

def find_best_rsquared(list_of_fits): res = sorted(list_of_fits, key=lambda x: x.rsquared) return res[-1]

11

https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L275-L278

def _skip_trampoline(handler): data_event, self = (yield None) delegate = handler event = None depth = 0 while True: def pass_through(): _trans = delegate.send(Transition(data_event, delegate)) return _trans, _trans.delegate, _trans.event if data_event is not None and data_event.type is ReadEventType.SKIP: while True: trans, delegate, event = pass_through() if event is not None: if event.event_type is IonEventType.CONTAINER_END and event.depth <= depth: break if event is None or event.event_type is IonEventType.INCOMPLETE: data_event, _ = yield Transition(event, self) else: trans, delegate, event = pass_through() if event is not None and (event.event_type is IonEventType.CONTAINER_START or event.event_type is IonEventType.CONTAINER_END): depth = event.depth data_event, _ = yield Transition(event, self)

Return a df with predictions and confidence interval Notes ----- The df will contain the following columns: - 'predicted': the model output - 'interval_u', 'interval_l': upper and lower confidence bounds. The result will depend on the following attributes of self: confint : float (default=0.95) Confidence level for two-sided hypothesis allow_negative_predictions : bool (default=True) If False, correct negative predictions to zero (typically for energy consumption predictions) Parameters ---------- fit : Statsmodels fit df : pandas DataFrame or None (default) If None, use self.df Returns ------- df_res : pandas DataFrame Copy of df with additional columns 'predicted', 'interval_u' and 'interval_l'

def _predict(self, fit, df): # Add model results to data as column 'predictions' df_res = df.copy() if 'Intercept' in fit.model.exog_names: df_res['Intercept'] = 1.0 df_res['predicted'] = fit.predict(df_res) if not self.allow_negative_predictions: df_res.loc[df_res['predicted'] < 0, 'predicted'] = 0 prstd, interval_l, interval_u = wls_prediction_std(fit, df_res[fit.model.exog_names], alpha=1 - self.confint) df_res['interval_l'] = interval_l df_res['interval_u'] = interval_u if 'Intercept' in df_res: df_res.drop(labels=['Intercept'], axis=1, inplace=True) return df_res

12

https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/library/regression.py#L292-L338

def detach(self, listener): if listener in self.listeners: self.listeners.remove(listener)

Calculate the relative abundance of each OTUID in a Sample. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: list :param sampleIDs: A list of sample id's from BIOM format OTU table. :rtype: dict :return: Returns a keyed on SampleIDs, and the values are dictionaries keyed on OTUID's and their values represent the relative abundance of that OTUID in that SampleID.

def relative_abundance(biomf, sampleIDs=None): if sampleIDs is None: sampleIDs = biomf.ids() else: try: for sid in sampleIDs: assert sid in biomf.ids() except AssertionError: raise ValueError( "\nError while calculating relative abundances: The sampleIDs provided do" " not match the sampleIDs in biom file. Please double check the sampleIDs" " provided.\n") otuIDs = biomf.ids(axis="observation") norm_biomf = biomf.norm(inplace=False) return {sample: {otuID: norm_biomf.get_value_by_ids(otuID, sample) for otuID in otuIDs} for sample in sampleIDs}

13

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L11-L41

def clean(jail=None, chroot=None, root=None, clean_all=False, dryrun=False): opts = '' if clean_all: opts += 'a' if dryrun: opts += 'n' else: opts += 'y' cmd = _pkg(jail, chroot, root) cmd.append('clean') if opts: cmd.append('-' + opts) return __salt__['cmd.run']( cmd, output_loglevel='trace', python_shell=False )

Calculate the mean OTU abundance percentage. :type ra: Dict :param ra: 'ra' refers to a dictionary keyed on SampleIDs, and the values are dictionaries keyed on OTUID's and their values represent the relative abundance of that OTUID in that SampleID. 'ra' is the output of relative_abundance() function. :type otuIDs: List :param otuIDs: A list of OTUID's for which the percentage abundance needs to be measured. :rtype: dict :return: A dictionary of OTUID and their percent relative abundance as key/value pair.

def mean_otu_pct_abundance(ra, otuIDs): sids = ra.keys() otumeans = defaultdict(int) for oid in otuIDs: otumeans[oid] = sum([ra[sid][oid] for sid in sids if oid in ra[sid]]) / len(sids) * 100 return otumeans

14

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L44-L67

def change_frozen_attr(self): # Selections are not supported if self.grid.selection: statustext = _("Freezing selections is not supported.") post_command_event(self.main_window, self.StatusBarMsg, text=statustext) cursor = self.grid.actions.cursor frozen = self.grid.code_array.cell_attributes[cursor]["frozen"] if frozen: # We have an frozen cell that has to be unfrozen # Delete frozen cache content self.grid.code_array.frozen_cache.pop(repr(cursor)) else: # We have an non-frozen cell that has to be frozen # Add frozen cache content res_obj = self.grid.code_array[cursor] self.grid.code_array.frozen_cache[repr(cursor)] = res_obj # Set the new frozen state / code selection = Selection([], [], [], [], [cursor[:2]]) self.set_attr("frozen", not frozen, selection=selection)

Calculate the mean relative abundance percentage. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: list :param sampleIDs: A list of sample id's from BIOM format OTU table. :param transform: Mathematical function which is used to transform smax to another format. By default, the function has been set to None. :rtype: dict :return: A dictionary keyed on OTUID's and their mean relative abundance for a given number of sampleIDs.

def MRA(biomf, sampleIDs=None, transform=None): ra = relative_abundance(biomf, sampleIDs) if transform is not None: ra = {sample: {otuID: transform(abd) for otuID, abd in ra[sample].items()} for sample in ra.keys()} otuIDs = biomf.ids(axis="observation") return mean_otu_pct_abundance(ra, otuIDs)

15

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L70-L92

def close(self): if self.device: usb.util.dispose_resources(self.device) self.device = None

Calculate the total number of sequences in each OTU or SampleID. :type biomf: A BIOM file. :param biomf: OTU table format. :type sampleIDs: List :param sampleIDs: A list of column id's from BIOM format OTU table. By default, the list has been set to None. :type sample_abd: Boolean :param sample_abd: A boolean operator to provide output for OTUID's or SampleID's. By default, the output will be provided for SampleID's. :rtype: dict :return: Returns a dictionary keyed on either OTUID's or SampleIDs and their respective abundance as values.

def raw_abundance(biomf, sampleIDs=None, sample_abd=True): results = defaultdict(int) if sampleIDs is None: sampleIDs = biomf.ids() else: try: for sid in sampleIDs: assert sid in biomf.ids() except AssertionError: raise ValueError( "\nError while calculating raw total abundances: The sampleIDs provided " "do not match the sampleIDs in biom file. Please double check the " "sampleIDs provided.\n") otuIDs = biomf.ids(axis="observation") for sampleID in sampleIDs: for otuID in otuIDs: abd = biomf.get_value_by_ids(otuID, sampleID) if sample_abd: results[sampleID] += abd else: results[otuID] += abd return results

16

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L95-L135

def upgrade_api(request, client, version): min_ver, max_ver = api_versions._get_server_version_range(client) if min_ver <= api_versions.APIVersion(version) <= max_ver: client = _nova.novaclient(request, version) return client

Function to transform the total abundance calculation for each sample ID to another format based on user given transformation function. :type biomf: A BIOM file. :param biomf: OTU table format. :param fn: Mathematical function which is used to transform smax to another format. By default, the function has been given as base 10 logarithm. :rtype: dict :return: Returns a dictionary similar to output of raw_abundance function but with the abundance values modified by the mathematical operation. By default, the operation performed on the abundances is base 10 logarithm.

def transform_raw_abundance(biomf, fn=math.log10, sampleIDs=None, sample_abd=True): totals = raw_abundance(biomf, sampleIDs, sample_abd) return {sid: fn(abd) for sid, abd in totals.items()}

17

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/phylotoast/biom_calc.py#L138-L155

def close(self): if self.device: usb.util.dispose_resources(self.device) self.device = None

Compute the Mann-Whitney U test for unequal group sample sizes.

def print_MannWhitneyU(div_calc): try: x = div_calc.values()[0].values() y = div_calc.values()[1].values() except: return "Error setting up input arrays for Mann-Whitney U Test. Skipping "\ "significance testing." T, p = stats.mannwhitneyu(x, y) print "\nMann-Whitney U test statistic:", T print "Two-tailed p-value: {}".format(2 * p)

18

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L54-L66

def ParseFileObject(self, parser_mediator, file_object): try: file_header = self._ReadFileHeader(file_object) except (ValueError, errors.ParseError): raise errors.UnableToParseFile('Unable to parse file header.') tables = self._ReadTablesArray(file_object, file_header.tables_array_offset) table = tables.get(self._RECORD_TYPE_APPLICATION_PASSWORD, None) if table: for record in table.records: self._ParseApplicationPasswordRecord(parser_mediator, record) table = tables.get(self._RECORD_TYPE_INTERNET_PASSWORD, None) if table: for record in table.records: self._ParseInternetPasswordRecord(parser_mediator, record)

Compute the Kruskal-Wallis H-test for independent samples. A typical rule is that each group must have at least 5 measurements.

def print_KruskalWallisH(div_calc): calc = defaultdict(list) try: for k1, v1 in div_calc.iteritems(): for k2, v2 in v1.iteritems(): calc[k1].append(v2) except: return "Error setting up input arrays for Kruskal-Wallis H-Test. Skipping "\ "significance testing." h, p = stats.kruskal(*calc.values()) print "\nKruskal-Wallis H-test statistic for {} groups: {}".format(str(len(div_calc)), h) print "p-value: {}".format(p)

19

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L69-L84

def _record_offset(self): offset = self.blob_file.tell() self.event_offsets.append(offset)

Parses the given options passed in at the command line.

def handle_program_options(): parser = argparse.ArgumentParser(description="Calculate the alpha diversity\ of a set of samples using one or more \ metrics and output a kernal density \ estimator-smoothed histogram of the \ results.") parser.add_argument("-m", "--map_file", help="QIIME mapping file.") parser.add_argument("-i", "--biom_fp", help="Path to the BIOM table") parser.add_argument("-c", "--category", help="Specific category from the mapping file.") parser.add_argument("-d", "--diversity", default=["shannon"], nargs="+", help="The alpha diversity metric. Default \ value is 'shannon', which will calculate the Shannon\ entropy. Multiple metrics can be specified (space separated).\ The full list of metrics is available at:\ http://scikit-bio.org/docs/latest/generated/skbio.diversity.alpha.html.\ Beta diversity metrics will be supported in the future.") parser.add_argument("--x_label", default=[None], nargs="+", help="The name of the diversity metric to be displayed on the\ plot as the X-axis label. If multiple metrics are specified,\ then multiple entries for the X-axis label should be given.") parser.add_argument("--color_by", help="A column name in the mapping file containing\ hexadecimal (#FF0000) color values that will\ be used to color the groups. Each sample ID must\ have a color entry.") parser.add_argument("--plot_title", default="", help="A descriptive title that will appear at the top \ of the output plot. Surround with quotes if there are\ spaces in the title.") parser.add_argument("-o", "--output_dir", default=".", help="The directory plots will be saved to.") parser.add_argument("--image_type", default="png", help="The type of image to save: png, svg, pdf, eps, etc...") parser.add_argument("--save_calculations", help="Path and name of text file to store the calculated " "diversity metrics.") parser.add_argument("--suppress_stats", action="store_true", help="Do not display " "significance testing results which are shown by default.") parser.add_argument("--show_available_metrics", action="store_true", help="Supply this parameter to see which alpha diversity metrics " " are available for usage. No calculations will be performed" " if this parameter is provided.") return parser.parse_args()

20

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/diversity.py#L122-L168

def fingerprint(self): if self.num_vertices == 0: return np.zeros(20, np.ubyte) else: return sum(self.vertex_fingerprints)

make blast db

def blastdb(fasta, maxfile = 10000000): db = fasta.rsplit('.', 1)[0] type = check_type(fasta) if type == 'nucl': type = ['nhr', type] else: type = ['phr', type] if os.path.exists('%s.%s' % (db, type[0])) is False \ and os.path.exists('%s.00.%s' % (db, type[0])) is False: print('# ... making blastdb for: %s' % (fasta), file=sys.stderr) os.system('makeblastdb \ -in %s -out %s -dbtype %s -max_file_sz %s >> log.txt' \ % (fasta, db, type[1], maxfile)) else: print('# ... database found for: %s' % (fasta), file=sys.stderr) return db

21

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/search.py#L28-L46

def writln(line, unit): lineP = stypes.stringToCharP(line) unit = ctypes.c_int(unit) line_len = ctypes.c_int(len(line)) libspice.writln_(lineP, ctypes.byref(unit), line_len)

make usearch db

def usearchdb(fasta, alignment = 'local', usearch_loc = 'usearch'): if '.udb' in fasta: print('# ... database found: %s' % (fasta), file=sys.stderr) return fasta type = check_type(fasta) db = '%s.%s.udb' % (fasta.rsplit('.', 1)[0], type) if os.path.exists(db) is False: print('# ... making usearch db for: %s' % (fasta), file=sys.stderr) if alignment == 'local': os.system('%s -makeudb_ublast %s -output %s >> log.txt' % (usearch_loc, fasta, db)) elif alignment == 'global': os.system('%s -makeudb_usearch %s -output %s >> log.txt' % (usearch_loc, fasta, db)) else: print('# ... database found for: %s' % (fasta), file=sys.stderr) return db

22

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/search.py#L68-L85

def point_to_line(point, segment_start, segment_end): # TODO: Needs unittests. segment_vec = segment_end - segment_start # t is distance along line t = -(segment_start - point).dot(segment_vec) / ( segment_vec.length_squared()) closest_point = segment_start + scale_v3(segment_vec, t) return point - closest_point

Pretty print.

def _pp(dict_data): for key, val in dict_data.items(): # pylint: disable=superfluous-parens print('{0:<11}: {1}'.format(key, val))

23

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L11-L15

def _reserve(self, key): self.assign(key, RESERVED) try: yield finally: del self._cache[key]

Print licenses. :param argparse.Namespace params: parameter :param bootstrap_py.classifier.Classifiers metadata: package metadata

def print_licences(params, metadata): if hasattr(params, 'licenses'): if params.licenses: _pp(metadata.licenses_desc()) sys.exit(0)

24

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L27-L36

def vrel(v1, v2): v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)

Check repository existence. :param argparse.Namespace params: parameters

def check_repository_existence(params): repodir = os.path.join(params.outdir, params.name) if os.path.isdir(repodir): raise Conflict( 'Package repository "{0}" has already exists.'.format(repodir))

25

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L39-L47

def context(self): stats = status_codes_by_date_stats() attacks_data = [{ 'type': 'line', 'zIndex': 9, 'name': _('Attacks'), 'data': [(v[0], v[1]['attacks']) for v in stats] }] codes_data = [{ 'zIndex': 4, 'name': '2xx', 'data': [(v[0], v[1][200]) for v in stats] }, { 'zIndex': 5, 'name': '3xx', 'data': [(v[0], v[1][300]) for v in stats] }, { 'zIndex': 6, 'name': '4xx', 'data': [(v[0], v[1][400]) for v in stats] }, { 'zIndex': 8, 'name': '5xx', 'data': [(v[0], v[1][500]) for v in stats] }] return {'generic_chart': json.dumps(status_codes_by_date_chart()), 'attacks_data': json.dumps(attacks_data), 'codes_data': json.dumps(codes_data)}

Generate package repository. :param argparse.Namespace params: parameters

def generate_package(params): pkg_data = package.PackageData(params) pkg_tree = package.PackageTree(pkg_data) pkg_tree.generate() pkg_tree.move() VCS(os.path.join(pkg_tree.outdir, pkg_tree.name), pkg_tree.pkg_data)

26

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/control.py#L59-L68

def startResponse(self, status, headers, excInfo=None): self.status = status self.headers = headers self.reactor.callInThread( responseInColor, self.request, status, headers ) return self.write

print single reads to stderr

def print_single(line, rev): if rev is True: seq = rc(['', line[9]])[1] qual = line[10][::-1] else: seq = line[9] qual = line[10] fq = ['@%s' % line[0], seq, '+%s' % line[0], qual] print('\n'.join(fq), file = sys.stderr)

27

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/sam2fastq.py#L13-L24

def set_cache_dir(directory): global cache_dir if directory is None: cache_dir = None return if not os.path.exists(directory): os.makedirs(directory) if not os.path.isdir(directory): raise ValueError("not a directory") cache_dir = directory

convert sam to fastq

def sam2fastq(sam, singles = False, force = False): L, R = None, None for line in sam: if line.startswith('@') is True: continue line = line.strip().split() bit = [True if i == '1' else False \ for i in bin(int(line[1])).split('b')[1][::-1]] while len(bit) < 8: bit.append(False) pair, proper, na, nap, rev, mrev, left, right = bit # make sure read is paired if pair is False: if singles is True: print_single(line, rev) continue # check if sequence is reverse-complemented if rev is True: seq = rc(['', line[9]])[1] qual = line[10][::-1] else: seq = line[9] qual = line[10] # check if read is forward or reverse, return when both have been found if left is True: if L is not None and force is False: print('sam file is not sorted', file = sys.stderr) print('\te.g.: %s' % (line[0]), file = sys.stderr) exit() if L is not None: L = None continue L = ['@%s' % line[0], seq, '+%s' % line[0], qual] if R is not None: yield L yield R L, R = None, None if right is True: if R is not None and force is False: print('sam file is not sorted', file = sys.stderr) print('\te.g.: %s' % (line[0]), file = sys.stderr) exit() if R is not None: R = None continue R = ['@%s' % line[0], seq, '+%s' % line[0], qual] if L is not None: yield L yield R L, R = None, None

28

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/sam2fastq.py#L26-L78

def cublasGetStream(handle): id = ctypes.c_int() status = _libcublas.cublasGetStream_v2(handle, ctypes.byref(id)) cublasCheckStatus(status) return id.value

sort sam file

def sort_sam(sam, sort): tempdir = '%s/' % (os.path.abspath(sam).rsplit('/', 1)[0]) if sort is True: mapping = '%s.sorted.sam' % (sam.rsplit('.', 1)[0]) if sam != '-': if os.path.exists(mapping) is False: os.system("\ sort -k1 --buffer-size=%sG -T %s -o %s %s\ " % (sbuffer, tempdir, mapping, sam)) else: mapping = 'stdin-sam.sorted.sam' p = Popen("sort -k1 --buffer-size=%sG -T %s -o %s" \ % (sbuffer, tempdir, mapping), stdin = sys.stdin, shell = True) p.communicate() mapping = open(mapping) else: if sam == '-': mapping = sys.stdin else: mapping = open(sam) return mapping

29

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/subset_sam.py#L14-L37

def ssn(self) -> str: area = self.random.randint(1, 899) if area == 666: area = 665 return '{:03}-{:02}-{:04}'.format( area, self.random.randint(1, 99), self.random.randint(1, 9999), )

randomly subset sam file

def sub_sam(sam, percent, sort = True, sbuffer = False): mapping = sort_sam(sam, sort) pool = [1 for i in range(0, percent)] + [0 for i in range(0, 100 - percent)] c = cycle([1, 2]) for line in mapping: line = line.strip().split() if line[0].startswith('@'): # get the sam header yield line continue if int(line[1]) <= 20: # is this from a single read? if random.choice(pool) == 1: yield line else: n = next(c) if n == 1: prev = line if n == 2 and random.choice(pool) == 1: yield prev yield line

30

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/subset_sam.py#L39-L60

def get_max_port_count_for_storage_bus(self, bus): if not isinstance(bus, StorageBus): raise TypeError("bus can only be an instance of type StorageBus") max_port_count = self._call("getMaxPortCountForStorageBus", in_p=[bus]) return max_port_count

convert fq to fa

def fq2fa(fq): c = cycle([1, 2, 3, 4]) for line in fq: n = next(c) if n == 1: seq = ['>%s' % (line.strip().split('@', 1)[1])] if n == 2: seq.append(line.strip()) yield seq

31

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/fastq2fasta.py#L11-L22

def query_under_condition(condition, kind='2'): if DB_CFG['kind'] == 's': return TabPost.select().where( (TabPost.kind == kind) & (TabPost.valid == 1) ).order_by( TabPost.time_update.desc() ) return TabPost.select().where( (TabPost.kind == kind) & (TabPost.valid == 1) & TabPost.extinfo.contains(condition) ).order_by(TabPost.time_update.desc())

Converts the returned value of wrapped function to the type of the first arg or to the type specified by a kwarg key return_type's value.

def change_return_type(f): @wraps(f) def wrapper(*args, **kwargs): if kwargs.has_key('return_type'): return_type = kwargs['return_type'] kwargs.pop('return_type') return return_type(f(*args, **kwargs)) elif len(args) > 0: return_type = type(args[0]) return return_type(f(*args, **kwargs)) else: return f(*args, **kwargs) return wrapper

32

https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/decorators.py#L11-L27

def _timing_representation(message): s = _encode_to_binary_string(message, on="=", off=".") N = len(s) s += '\n' + _numbers_decades(N) s += '\n' + _numbers_units(N) s += '\n' s += '\n' + _timing_char(message) return s

Converts all args to 'set' type via self.setify function.

def convert_args_to_sets(f): @wraps(f) def wrapper(*args, **kwargs): args = (setify(x) for x in args) return f(*args, **kwargs) return wrapper

33

https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/decorators.py#L30-L38

def list_publications(): publications = search_publications( DBPublication(is_public=True) ) return SimpleTemplate(INDEX_TEMPLATE).render( publications=publications, compose_path=web_tools.compose_path, delimiter=":", )

Membuat objek-objek entri dari laman yang diambil. :param laman: Laman respons yang dikembalikan oleh KBBI daring. :type laman: Response

def _init_entri(self, laman): sup = BeautifulSoup(laman.text, 'html.parser') estr = '' for label in sup.find('hr').next_siblings: if label.name == 'hr': self.entri.append(Entri(estr)) break if label.name == 'h2': if estr: self.entri.append(Entri(estr)) estr = '' estr += str(label).strip()

34

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L46-L63

def diff_text(candidate_config=None, candidate_path=None, running_config=None, running_path=None, saltenv='base'): candidate_text = clean(config=candidate_config, path=candidate_path, saltenv=saltenv) running_text = clean(config=running_config, path=running_path, saltenv=saltenv) return _get_diff_text(running_text, candidate_text)

Memproses kata dasar yang ada dalam nama entri. :param dasar: ResultSet untuk label HTML dengan class="rootword" :type dasar: ResultSet

def _init_kata_dasar(self, dasar): for tiap in dasar: kata = tiap.find('a') dasar_no = kata.find('sup') kata = ambil_teks_dalam_label(kata) self.kata_dasar.append( kata + ' [{}]'.format(dasar_no.text.strip()) if dasar_no else kata )

35

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L126-L139

def runWizard( self ): plugin = self.currentPlugin() if ( plugin and plugin.runWizard(self) ): self.accept()

Mengembalikan hasil serialisasi objek Entri ini. :returns: Dictionary hasil serialisasi :rtype: dict

def serialisasi(self): return { "nama": self.nama, "nomor": self.nomor, "kata_dasar": self.kata_dasar, "pelafalan": self.pelafalan, "bentuk_tidak_baku": self.bentuk_tidak_baku, "varian": self.varian, "makna": [makna.serialisasi() for makna in self.makna] }

36

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L141-L156

def escape_for_cmd_exe(arg): meta_chars = '()%!^"<>&|' meta_re = re.compile('(' + '|'.join(re.escape(char) for char in list(meta_chars)) + ')') meta_map = {char: "^{0}".format(char) for char in meta_chars} def escape_meta_chars(m): char = m.group(1) return meta_map[char] return meta_re.sub(escape_meta_chars, arg)

Mengembalikan representasi string untuk semua makna entri ini. :returns: String representasi makna-makna :rtype: str

def _makna(self): if len(self.makna) > 1: return '\n'.join( str(i) + ". " + str(makna) for i, makna in enumerate(self.makna, 1) ) return str(self.makna[0])

37

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L158-L170

def controlMsg(self, requestType, request, buffer, value = 0, index = 0, timeout = 100): return self.dev.ctrl_transfer( requestType, request, wValue = value, wIndex = index, data_or_wLength = buffer, timeout = timeout)

Mengembalikan representasi string untuk nama entri ini. :returns: String representasi nama entri :rtype: str

def _nama(self): hasil = self.nama if self.nomor: hasil += " [{}]".format(self.nomor) if self.kata_dasar: hasil = " » ".join(self.kata_dasar) + " » " + hasil return hasil

38

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L172-L184

def _setup(self): self.log.info("Adding reader to prepare to receive.") self.loop.add_reader(self.dev.fd, self.read) self.log.info("Flushing the RFXtrx buffer.") self.flushSerialInput() self.log.info("Writing the reset packet to the RFXtrx. (blocking)") yield from self.sendRESET() self.log.info("Wating 0.4s") yield from asyncio.sleep(0.4) self.log.info("Write the status packet (blocking)") yield from self.sendSTATUS() # TODO receive status response, compare it with the needed MODE and # request a new MODE if required. Currently MODE is always sent. self.log.info("Adding mode packet to the write queue (blocking)") yield from self.sendMODE()

Mengembalikan representasi string untuk varian entri ini. Dapat digunakan untuk "Varian" maupun "Bentuk tidak baku". :param varian: List bentuk tidak baku atau varian :type varian: list :returns: String representasi varian atau bentuk tidak baku :rtype: str

def _varian(self, varian): if varian == self.bentuk_tidak_baku: nama = "Bentuk tidak baku" elif varian == self.varian: nama = "Varian" else: return '' return nama + ': ' + ', '.join(varian)

39

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L186-L202

def compile_all(): # print("Compiling for Qt: style.qrc -> style.rcc") # os.system("rcc style.qrc -o style.rcc") print("Compiling for PyQt4: style.qrc -> pyqt_style_rc.py") os.system("pyrcc4 -py3 style.qrc -o pyqt_style_rc.py") print("Compiling for PyQt5: style.qrc -> pyqt5_style_rc.py") os.system("pyrcc5 style.qrc -o pyqt5_style_rc.py") print("Compiling for PySide: style.qrc -> pyside_style_rc.py") os.system("pyside-rcc -py3 style.qrc -o pyside_style_rc.py")

Memproses kelas kata yang ada dalam makna. :param makna_label: BeautifulSoup untuk makna yang ingin diproses. :type makna_label: BeautifulSoup

def _init_kelas(self, makna_label): kelas = makna_label.find(color='red') lain = makna_label.find(color='darkgreen') info = makna_label.find(color='green') if kelas: kelas = kelas.find_all('span') if lain: self.kelas = {lain.text.strip(): lain['title'].strip()} self.submakna = lain.next_sibling.strip() self.submakna += ' ' + makna_label.find(color='grey').text.strip() else: self.kelas = { k.text.strip(): k['title'].strip() for k in kelas } if kelas else {} self.info = info.text.strip() if info else ''

40

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L239-L259

def sync(self, since=None, timeout_ms=30000, filter=None, full_state=None, set_presence=None): request = { # non-integer timeouts appear to cause issues "timeout": int(timeout_ms) } if since: request["since"] = since if filter: request["filter"] = filter if full_state: request["full_state"] = json.dumps(full_state) if set_presence: request["set_presence"] = set_presence return self._send("GET", "/sync", query_params=request, api_path=MATRIX_V2_API_PATH)

Memproses contoh yang ada dalam makna. :param makna_label: BeautifulSoup untuk makna yang ingin diproses. :type makna_label: BeautifulSoup

def _init_contoh(self, makna_label): indeks = makna_label.text.find(': ') if indeks != -1: contoh = makna_label.text[indeks + 2:].strip() self.contoh = contoh.split('; ') else: self.contoh = []

41

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L261-L273

def delete_events(self, event_collection, timeframe=None, timezone=None, filters=None): params = self.get_params(timeframe=timeframe, timezone=timezone, filters=filters) return self.api.delete_events(event_collection, params)

Mengembalikan hasil serialisasi objek Makna ini. :returns: Dictionary hasil serialisasi :rtype: dict

def serialisasi(self): return { "kelas": self.kelas, "submakna": self.submakna, "info": self.info, "contoh": self.contoh }

42

https://github.com/laymonage/kbbi-python/blob/1a52ba8bcc6dc4c5c1215f9e00207aca264287d6/kbbi/kbbi.py#L275-L287

def diff_fromDelta(self, text1, delta): diffs = [] pointer = 0 # Cursor in text1 tokens = delta.split("\t") for token in tokens: if token == "": # Blank tokens are ok (from a trailing \t). continue # Each token begins with a one character parameter which specifies the # operation of this token (delete, insert, equality). param = token[1:] if token[0] == "+": param = urllib.parse.unquote(param) diffs.append((self.DIFF_INSERT, param)) elif token[0] == "-" or token[0] == "=": try: n = int(param) except ValueError: raise ValueError("Invalid number in diff_fromDelta: " + param) if n < 0: raise ValueError("Negative number in diff_fromDelta: " + param) text = text1[pointer : pointer + n] pointer += n if token[0] == "=": diffs.append((self.DIFF_EQUAL, text)) else: diffs.append((self.DIFF_DELETE, text)) else: # Anything else is an error. raise ValueError("Invalid diff operation in diff_fromDelta: " + token[0]) if pointer != len(text1): raise ValueError( "Delta length (%d) does not equal source text length (%d)." % (pointer, len(text1))) return diffs

Build sphinx documentation. :rtype: int :return: subprocess.call return code :param `bootstrap_py.control.PackageData` pkg_data: package meta data :param str projectdir: project root directory

def build_sphinx(pkg_data, projectdir): try: version, _minor_version = pkg_data.version.rsplit('.', 1) except ValueError: version = pkg_data.version args = ' '.join(('sphinx-quickstart', '--sep', '-q', '-p "{name}"', '-a "{author}"', '-v "{version}"', '-r "{release}"', '-l en', '--suffix=.rst', '--master=index', '--ext-autodoc', '--ext-viewcode', '--makefile', '{projectdir}')).format(name=pkg_data.name, author=pkg_data.author, version=version, release=pkg_data.version, projectdir=projectdir) if subprocess.call(shlex.split(args)) == 0: _touch_gitkeep(projectdir)

43

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/docs.py#L8-L40

def cli(env, volume_id): file_manager = SoftLayer.FileStorageManager(env.client) snapshot_schedules = file_manager.list_volume_schedules(volume_id) table = formatting.Table(['id', 'active', 'type', 'replication', 'date_created', 'minute', 'hour', 'day', 'week', 'day_of_week', 'date_of_month', 'month_of_year', 'maximum_snapshots']) for schedule in snapshot_schedules: if 'REPLICATION' in schedule['type']['keyname']: replication = '*' else: replication = formatting.blank() file_schedule_type = schedule['type']['keyname'].replace('REPLICATION_', '') file_schedule_type = file_schedule_type.replace('SNAPSHOT_', '') property_list = ['MINUTE', 'HOUR', 'DAY', 'WEEK', 'DAY_OF_WEEK', 'DAY_OF_MONTH', 'MONTH_OF_YEAR', 'SNAPSHOT_LIMIT'] schedule_properties = [] for prop_key in property_list: item = formatting.blank() for schedule_property in schedule.get('properties', []): if schedule_property['type']['keyname'] == prop_key: if schedule_property['value'] == '-1': item = '*' else: item = schedule_property['value'] break schedule_properties.append(item) table_row = [ schedule['id'], '*' if schedule.get('active', '') else '', file_schedule_type, replication, schedule.get('createDate', '') ] table_row.extend(schedule_properties) table.add_row(table_row) env.fout(table)

make bowtie db

def bowtiedb(fa, keepDB): btdir = '%s/bt2' % (os.getcwd()) # make directory for if not os.path.exists(btdir): os.mkdir(btdir) btdb = '%s/%s' % (btdir, fa.rsplit('/', 1)[-1]) if keepDB is True: if os.path.exists('%s.1.bt2' % (btdb)): return btdb p = subprocess.Popen('bowtie2-build -q %s %s' \ % (fa, btdb), shell = True) p.communicate() return btdb

44

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L16-L31

def parse_resource_data_entry(self, rva): try: # If the RVA is invalid all would blow up. Some EXEs seem to be # specially nasty and have an invalid RVA. data = self.get_data(rva, Structure(self.__IMAGE_RESOURCE_DATA_ENTRY_format__).sizeof() ) except PEFormatError as excp: self.__warnings.append( 'Error parsing a resource directory data entry, ' 'the RVA is invalid: 0x%x' % ( rva ) ) return None data_entry = self.__unpack_data__( self.__IMAGE_RESOURCE_DATA_ENTRY_format__, data, file_offset = self.get_offset_from_rva(rva) ) return data_entry

generate bowtie2 command

def bowtie(sam, btd, f, r, u, opt, no_shrink, threads): bt2 = 'bowtie2 -x %s -p %s ' % (btd, threads) if f is not False: bt2 += '-1 %s -2 %s ' % (f, r) if u is not False: bt2 += '-U %s ' % (u) bt2 += opt if no_shrink is False: if f is False: bt2 += ' | shrinksam -u -k %s-shrunk.sam ' % (sam) else: bt2 += ' | shrinksam -k %s-shrunk.sam ' % (sam) else: bt2 += ' > %s.sam' % (sam) return bt2

45

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L33-L50

def format_string(self, s, args, kwargs): if isinstance(s, Markup): formatter = SandboxedEscapeFormatter(self, s.escape) else: formatter = SandboxedFormatter(self) kwargs = _MagicFormatMapping(args, kwargs) rv = formatter.vformat(s, args, kwargs) return type(s)(rv)

map all read sets against all fasta files

def crossmap(fas, reads, options, no_shrink, keepDB, threads, cluster, nodes): if cluster is True: threads = '48' btc = [] for fa in fas: btd = bowtiedb(fa, keepDB) F, R, U = reads if F is not False: if U is False: u = False for i, f in enumerate(F): r = R[i] if U is not False: u = U[i] sam = '%s/%s-vs-%s' % (os.getcwd(), \ fa.rsplit('/', 1)[-1], f.rsplit('/', 1)[-1].rsplit('.', 3)[0]) btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads)) else: f = False r = False for u in U: sam = '%s/%s-vs-%s' % (os.getcwd(), \ fa.rsplit('/', 1)[-1], u.rsplit('/', 1)[-1].rsplit('.', 3)[0]) btc.append(bowtie(sam, btd, f, r, u, options, no_shrink, threads)) if cluster is False: for i in btc: p = subprocess.Popen(i, shell = True) p.communicate() else: ID = ''.join(random.choice([str(i) for i in range(0, 9)]) for _ in range(5)) for node, commands in enumerate(chunks(btc, nodes), 1): bs = open('%s/crossmap-qsub.%s.%s.sh' % (os.getcwd(), ID, node), 'w') print('\n'.join(commands), file=bs) bs.close() p = subprocess.Popen(\ 'qsub -V -N crossmap %s' \ % (bs.name), \ shell = True) p.communicate()

46

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/crossmap.py#L55-L96

def with_division(self, division): if division is None: division = '' division = slugify(division) self._validate_division(division) self.division = division return self

Returns a connection object from the router given ``args``. Useful in cases where a connection cannot be automatically determined during all steps of the process. An example of this would be Redis pipelines.

def get_conn(self, *args, **kwargs): connections = self.__connections_for('get_conn', args=args, kwargs=kwargs) if len(connections) is 1: return connections[0] else: return connections

47

https://github.com/disqus/nydus/blob/9b505840da47a34f758a830c3992fa5dcb7bb7ad/nydus/db/base.py#L100-L113

def write_comment(self, comment): self._FITS.write_comment(self._ext+1, str(comment))

return the non-direct init if the direct algorithm has been selected.

def __get_nondirect_init(self, init): crc = init for i in range(self.Width): bit = crc & 0x01 if bit: crc^= self.Poly crc >>= 1 if bit: crc |= self.MSB_Mask return crc & self.Mask

48

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L98-L110

def from_bytes(self, string): msg = srsly.msgpack_loads(gzip.decompress(string)) self.attrs = msg["attrs"] self.strings = set(msg["strings"]) lengths = numpy.fromstring(msg["lengths"], dtype="int32") flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool) flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64") shape = (flat_tokens.size // len(self.attrs), len(self.attrs)) flat_tokens = flat_tokens.reshape(shape) flat_spaces = flat_spaces.reshape((flat_spaces.size, 1)) self.tokens = NumpyOps().unflatten(flat_tokens, lengths) self.spaces = NumpyOps().unflatten(flat_spaces, lengths) for tokens in self.tokens: assert len(tokens.shape) == 2, tokens.shape return self

reflect a data word, i.e. reverts the bit order.

def reflect(self, data, width): x = data & 0x01 for i in range(width - 1): data >>= 1 x = (x << 1) | (data & 0x01) return x

49

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L115-L123

def linkify_templates(self): self.hosts.linkify_templates() self.contacts.linkify_templates() self.services.linkify_templates() self.servicedependencies.linkify_templates() self.hostdependencies.linkify_templates() self.timeperiods.linkify_templates() self.hostsextinfo.linkify_templates() self.servicesextinfo.linkify_templates() self.escalations.linkify_templates() # But also old srv and host escalations self.serviceescalations.linkify_templates() self.hostescalations.linkify_templates()

Classic simple and slow CRC implementation. This function iterates bit by bit over the augmented input message and returns the calculated CRC value at the end.

def bit_by_bit(self, in_data): # If the input data is a string, convert to bytes. if isinstance(in_data, str): in_data = [ord(c) for c in in_data] register = self.NonDirectInit for octet in in_data: if self.ReflectIn: octet = self.reflect(octet, 8) for i in range(8): topbit = register & self.MSB_Mask register = ((register << 1) & self.Mask) | ((octet >> (7 - i)) & 0x01) if topbit: register ^= self.Poly for i in range(self.Width): topbit = register & self.MSB_Mask register = ((register << 1) & self.Mask) if topbit: register ^= self.Poly if self.ReflectOut: register = self.reflect(register, self.Width) return register ^ self.XorOut

50

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L128-L156

def create_cvmfs_persistent_volume_claim(cvmfs_volume): from kubernetes.client.rest import ApiException from reana_commons.k8s.api_client import current_k8s_corev1_api_client try: current_k8s_corev1_api_client.\ create_namespaced_persistent_volume_claim( "default", render_cvmfs_pvc(cvmfs_volume) ) except ApiException as e: if e.status != 409: raise e

This function generates the CRC table used for the table_driven CRC algorithm. The Python version cannot handle tables of an index width other than 8. See the generated C code for tables with different sizes instead.

def gen_table(self): table_length = 1 << self.TableIdxWidth tbl = [0] * table_length for i in range(table_length): register = i if self.ReflectIn: register = self.reflect(register, self.TableIdxWidth) register = register << (self.Width - self.TableIdxWidth + self.CrcShift) for j in range(self.TableIdxWidth): if register & (self.MSB_Mask << self.CrcShift) != 0: register = (register << 1) ^ (self.Poly << self.CrcShift) else: register = (register << 1) if self.ReflectIn: register = self.reflect(register >> self.CrcShift, self.Width) << self.CrcShift tbl[i] = register & (self.Mask << self.CrcShift) return tbl

51

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L190-L212

def cancel_broadcast(self, broadcast_guid): subpath = 'broadcasts/%s/update' % broadcast_guid broadcast = {'status': 'CANCELED'} bcast_dict = self._call(subpath, method='POST', data=broadcast, content_type='application/json') return bcast_dict

The Standard table_driven CRC algorithm.

def table_driven(self, in_data): # If the input data is a string, convert to bytes. if isinstance(in_data, str): in_data = [ord(c) for c in in_data] tbl = self.gen_table() register = self.DirectInit << self.CrcShift if not self.ReflectIn: for octet in in_data: tblidx = ((register >> (self.Width - self.TableIdxWidth + self.CrcShift)) ^ octet) & 0xff register = ((register << (self.TableIdxWidth - self.CrcShift)) ^ tbl[tblidx]) & (self.Mask << self.CrcShift) register = register >> self.CrcShift else: register = self.reflect(register, self.Width + self.CrcShift) << self.CrcShift for octet in in_data: tblidx = ((register >> self.CrcShift) ^ octet) & 0xff register = ((register >> self.TableIdxWidth) ^ tbl[tblidx]) & (self.Mask << self.CrcShift) register = self.reflect(register, self.Width + self.CrcShift) & self.Mask if self.ReflectOut: register = self.reflect(register, self.Width) return register ^ self.XorOut

52

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/_crc_algorithms.py#L217-L242

def _prune_subdirs(dir_: str) -> None: for logdir in [path.join(dir_, f) for f in listdir(dir_) if is_train_dir(path.join(dir_, f))]: for subdir in [path.join(logdir, f) for f in listdir(logdir) if path.isdir(path.join(logdir, f))]: _safe_rmtree(subdir)

parse masked sequence into non-masked and masked regions

def parse_masked(seq, min_len): nm, masked = [], [[]] prev = None for base in seq[1]: if base.isupper(): nm.append(base) if masked != [[]] and len(masked[-1]) < min_len: nm.extend(masked[-1]) del masked[-1] prev = False elif base.islower(): if prev is False: masked.append([]) masked[-1].append(base) prev = True return nm, masked

53

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_masked.py#L13-L31

def handle_event(self, event): subscription_id = event.subscription_id if subscription_id in self._subscriptions: # FIXME: [1] should be a constant handler = self._subscriptions[subscription_id][SUBSCRIPTION_CALLBACK] WampSubscriptionWrapper(self,handler,event).start()

remove masked regions from fasta file as long as they are longer than min_len

def strip_masked(fasta, min_len, print_masked): for seq in parse_fasta(fasta): nm, masked = parse_masked(seq, min_len) nm = ['%s removed_masked >=%s' % (seq[0], min_len), ''.join(nm)] yield [0, nm] if print_masked is True: for i, m in enumerate([i for i in masked if i != []], 1): m = ['%s insertion:%s' % (seq[0], i), ''.join(m)] yield [1, m]

54

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_masked.py#L33-L45

def websocket_connect(self, message): self.session_id = self.scope['url_route']['kwargs']['subscriber_id'] super().websocket_connect(message) # Create new subscriber object. Subscriber.objects.get_or_create(session_id=self.session_id)

Return arcsine transformed relative abundance from a BIOM format file. :type biomfile: BIOM format file :param biomfile: BIOM format file used to obtain relative abundances for each OTU in a SampleID, which are used as node sizes in network plots. :type return: Dictionary of dictionaries. :return: Dictionary keyed on SampleID whose value is a dictionarykeyed on OTU Name whose value is the arc sine tranfsormed relative abundance value for that SampleID-OTU Name pair.

def get_relative_abundance(biomfile): biomf = biom.load_table(biomfile) norm_biomf = biomf.norm(inplace=False) rel_abd = {} for sid in norm_biomf.ids(): rel_abd[sid] = {} for otuid in norm_biomf.ids("observation"): otuname = oc.otu_name(norm_biomf.metadata(otuid, axis="observation")["taxonomy"]) otuname = " ".join(otuname.split("_")) abd = norm_biomf.get_value_by_ids(otuid, sid) rel_abd[sid][otuname] = abd ast_rel_abd = bc.arcsine_sqrt_transform(rel_abd) return ast_rel_abd

55

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/network_plots_gephi.py#L33-L57

def update_context(self, context, update_mask=None, retry=google.api_core.gapic_v1.method.DEFAULT, timeout=google.api_core.gapic_v1.method.DEFAULT, metadata=None): # Wrap the transport method to add retry and timeout logic. if 'update_context' not in self._inner_api_calls: self._inner_api_calls[ 'update_context'] = google.api_core.gapic_v1.method.wrap_method( self.transport.update_context, default_retry=self._method_configs['UpdateContext'].retry, default_timeout=self._method_configs['UpdateContext'] .timeout, client_info=self._client_info, ) request = context_pb2.UpdateContextRequest( context=context, update_mask=update_mask, ) return self._inner_api_calls['update_context']( request, retry=retry, timeout=timeout, metadata=metadata)

Find an OTU ID in a Newick-format tree. Return the starting position of the ID or None if not found.

def find_otu(otuid, tree): for m in re.finditer(otuid, tree): before, after = tree[m.start()-1], tree[m.start()+len(otuid)] if before in ["(", ",", ")"] and after in [":", ";"]: return m.start() return None

56

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/iTol.py#L17-L26

def set_keyspace(self, keyspace): self.keyspace = keyspace dfrds = [] for p in self._protos: dfrds.append(p.submitRequest(ManagedThriftRequest( 'set_keyspace', keyspace))) return defer.gatherResults(dfrds)

Replace the OTU ids in the Newick phylogenetic tree format with truncated OTU names

def newick_replace_otuids(tree, biomf): for val, id_, md in biomf.iter(axis="observation"): otu_loc = find_otu(id_, tree) if otu_loc is not None: tree = tree[:otu_loc] + \ oc.otu_name(md["taxonomy"]) + \ tree[otu_loc + len(id_):] return tree

57

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/iTol.py#L29-L40

def led_changed(self, addr, group, val): _LOGGER.debug("Button %d LED changed from %d to %d", self._group, self._value, val) led_on = bool(val) if led_on != bool(self._value): self._update_subscribers(int(led_on))

return genome info for choosing representative if ggKbase table provided - choose rep based on SCGs and genome length - priority for most SCGs - extra SCGs, then largest genome otherwise, based on largest genome

def genome_info(genome, info): try: scg = info['#SCGs'] dups = info['#SCG duplicates'] length = info['genome size (bp)'] return [scg - dups, length, genome] except: return [False, False, info['genome size (bp)'], genome]

58

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L97-L112

def reset_env(exclude=[]): if os.getenv(env.INITED): wandb_keys = [key for key in os.environ.keys() if key.startswith( 'WANDB_') and key not in exclude] for key in wandb_keys: del os.environ[key] return True else: return False

choose represenative genome and print cluster information *if ggKbase table is provided, use SCG info to choose best genome

def print_clusters(fastas, info, ANI): header = ['#cluster', 'num. genomes', 'rep.', 'genome', '#SCGs', '#SCG duplicates', \ 'genome size (bp)', 'fragments', 'list'] yield header in_cluster = [] for cluster_num, cluster in enumerate(connected_components(ANI)): cluster = sorted([genome_info(genome, info[genome]) \ for genome in cluster], \ key = lambda x: x[0:], reverse = True) rep = cluster[0][-1] cluster = [i[-1] for i in cluster] size = len(cluster) for genome in cluster: in_cluster.append(genome) try: stats = [size, rep, genome, \ info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \ info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster] except: stats = [size, rep, genome, \ 'n/a', 'n/a', \ info[genome]['genome size (bp)'], info[genome]['# contigs'], cluster] if rep == genome: stats = ['*%s' % (cluster_num)] + stats else: stats = [cluster_num] + stats yield stats # print singletons try: start = cluster_num + 1 except: start = 0 fastas = set([i.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] for i in fastas]) for cluster_num, genome in \ enumerate(fastas.difference(set(in_cluster)), start): try: stats = ['*%s' % (cluster_num), 1, genome, genome, \ info[genome]['#SCGs'], info[genome]['#SCG duplicates'], \ info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]] except: stats = ['*%s' % (cluster_num), 1, genome, genome, \ 'n/a', 'n/a', \ info[genome]['genome size (bp)'], info[genome]['# contigs'], [genome]] yield stats

59

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L114-L163

def _apply_index_days(self, i, roll): nanos = (roll % 2) * Timedelta(days=self.day_of_month - 1).value return i + nanos.astype('timedelta64[ns]')

convert ggKbase genome info tables to dictionary

def parse_ggKbase_tables(tables, id_type): g2info = {} for table in tables: for line in open(table): line = line.strip().split('\t') if line[0].startswith('name'): header = line header[4] = 'genome size (bp)' header[12] = '#SCGs' header[13] = '#SCG duplicates' continue name, code, info = line[0], line[1], line info = [to_int(i) for i in info] if id_type is False: # try to use name and code ID if 'UNK' in code or 'unknown' in code: code = name if (name != code) and (name and code in g2info): print('# duplicate name or code in table(s)', file=sys.stderr) print('# %s and/or %s' % (name, code), file=sys.stderr) exit() if name not in g2info: g2info[name] = {item:stat for item, stat in zip(header, info)} if code not in g2info: g2info[code] = {item:stat for item, stat in zip(header, info)} else: if id_type == 'name': ID = name elif id_type == 'code': ID = code else: print('# specify name or code column using -id', file=sys.stderr) exit() ID = ID.replace(' ', '') g2info[ID] = {item:stat for item, stat in zip(header, info)} if g2info[ID]['genome size (bp)'] == '': g2info[ID]['genome size (bp)'] = 0 return g2info

60

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L174-L213

def _cleanSessions(self): tooOld = extime.Time() - timedelta(seconds=PERSISTENT_SESSION_LIFETIME) self.store.query( PersistentSession, PersistentSession.lastUsed < tooOld).deleteFromStore() self._lastClean = self._clock.seconds()

convert checkM genome info tables to dictionary

def parse_checkM_tables(tables): g2info = {} for table in tables: for line in open(table): line = line.strip().split('\t') if line[0].startswith('Bin Id'): header = line header[8] = 'genome size (bp)' header[5] = '#SCGs' header[6] = '#SCG duplicates' continue ID, info = line[0], line info = [to_int(i) for i in info] ID = ID.replace(' ', '') g2info[ID] = {item:stat for item, stat in zip(header, info)} if g2info[ID]['genome size (bp)'] == '': g2info[ID]['genome size (bp)'] = 0 return g2info

61

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L215-L235

def slanted_triangular(max_rate, num_steps, cut_frac=0.1, ratio=32, decay=1, t=0.0): cut = int(num_steps * cut_frac) while True: t += 1 if t < cut: p = t / cut else: p = 1 - ((t - cut) / (cut * (1 / cut_frac - 1))) learn_rate = max_rate * (1 + p * (ratio - 1)) * (1 / ratio) yield learn_rate

get genome lengths

def genome_lengths(fastas, info): if info is False: info = {} for genome in fastas: name = genome.rsplit('.', 1)[0].rsplit('/', 1)[-1].rsplit('.contigs')[0] if name in info: continue length = 0 fragments = 0 for seq in parse_fasta(genome): length += len(seq[1]) fragments += 1 info[name] = {'genome size (bp)':length, '# contigs':fragments} return info

62

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/cluster_ani.py#L237-L253

def save_reg(data): reg_dir = _reg_dir() regfile = os.path.join(reg_dir, 'register') try: if not os.path.exists(reg_dir): os.makedirs(reg_dir) except OSError as exc: if exc.errno == errno.EEXIST: pass else: raise try: with salt.utils.files.fopen(regfile, 'a') as fh_: salt.utils.msgpack.dump(data, fh_) except Exception: log.error('Could not write to msgpack file %s', __opts__['outdir']) raise

Returns a list of db keys to route the given call to. :param attr: Name of attribute being called on the connection. :param args: List of arguments being passed to ``attr``. :param kwargs: Dictionary of keyword arguments being passed to ``attr``. >>> redis = Cluster(router=BaseRouter) >>> router = redis.router >>> router.get_dbs('incr', args=('key name', 1)) [0,1,2]

def get_dbs(self, attr, args, kwargs, **fkwargs): if not self._ready: if not self.setup_router(args=args, kwargs=kwargs, **fkwargs): raise self.UnableToSetupRouter() retval = self._pre_routing(attr=attr, args=args, kwargs=kwargs, **fkwargs) if retval is not None: args, kwargs = retval if not (args or kwargs): return self.cluster.hosts.keys() try: db_nums = self._route(attr=attr, args=args, kwargs=kwargs, **fkwargs) except Exception as e: self._handle_exception(e) db_nums = [] return self._post_routing(attr=attr, db_nums=db_nums, args=args, kwargs=kwargs, **fkwargs)

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def fullLoad(self): self._parseDirectories(self.ntHeaders.optionalHeader.dataDirectory, self.PE_TYPE)

Call method to perform any setup

def setup_router(self, args, kwargs, **fkwargs): self._ready = self._setup_router(args=args, kwargs=kwargs, **fkwargs) return self._ready

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def world_series_logs(): file_name = 'GLWS.TXT' z = get_zip_file(world_series_url) data = pd.read_csv(z.open(file_name), header=None, sep=',', quotechar='"') data.columns = gamelog_columns return data

Perform routing and return db_nums

def _route(self, attr, args, kwargs, **fkwargs): return self.cluster.hosts.keys()

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def static_stability(pressure, temperature, axis=0): theta = potential_temperature(pressure, temperature) return - mpconsts.Rd * temperature / pressure * first_derivative(np.log(theta / units.K), x=pressure, axis=axis)

Iterates through all connections which were previously listed as unavailable and marks any that have expired their retry_timeout as being up.

def check_down_connections(self): now = time.time() for db_num, marked_down_at in self._down_connections.items(): if marked_down_at + self.retry_timeout <= now: self.mark_connection_up(db_num)

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def _CalculateDigestHash(self, file_entry, data_stream_name): file_object = file_entry.GetFileObject(data_stream_name=data_stream_name) if not file_object: return None try: file_object.seek(0, os.SEEK_SET) hasher_object = hashers_manager.HashersManager.GetHasher('sha256') data = file_object.read(self._READ_BUFFER_SIZE) while data: hasher_object.Update(data) data = file_object.read(self._READ_BUFFER_SIZE) finally: file_object.close() return hasher_object.GetStringDigest()

Marks all connections which were previously listed as unavailable as being up.

def flush_down_connections(self): self._get_db_attempts = 0 for db_num in self._down_connections.keys(): self.mark_connection_up(db_num)

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def _CalculateDigestHash(self, file_entry, data_stream_name): file_object = file_entry.GetFileObject(data_stream_name=data_stream_name) if not file_object: return None try: file_object.seek(0, os.SEEK_SET) hasher_object = hashers_manager.HashersManager.GetHasher('sha256') data = file_object.read(self._READ_BUFFER_SIZE) while data: hasher_object.Update(data) data = file_object.read(self._READ_BUFFER_SIZE) finally: file_object.close() return hasher_object.GetStringDigest()

Compute standby power Parameters ---------- df : pandas.DataFrame or pandas.Series Electricity Power resolution : str, default='d' Resolution of the computation. Data will be resampled to this resolution (as mean) before computation of the minimum. String that can be parsed by the pandas resample function, example ='h', '15min', '6h' time_window : tuple with start-hour and end-hour, default=None Specify the start-time and end-time for the analysis. Only data within this time window will be considered. Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects Returns ------- df : pandas.Series with DateTimeIndex in the given resolution

def standby(df, resolution='24h', time_window=None): if df.empty: raise EmptyDataFrame() df = pd.DataFrame(df) # if df was a pd.Series, convert to DataFrame def parse_time(t): if isinstance(t, numbers.Number): return pd.Timestamp.utcfromtimestamp(t).time() else: return pd.Timestamp(t).time() # first filter based on the time-window if time_window is not None: t_start = parse_time(time_window[0]) t_end = parse_time(time_window[1]) if t_start > t_end: # start before midnight df = df[(df.index.time >= t_start) | (df.index.time < t_end)] else: df = df[(df.index.time >= t_start) & (df.index.time < t_end)] return df.resample(resolution).min()

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def retract(self): if lib.EnvRetract(self._env, self._fact) != 1: raise CLIPSError(self._env)

Compute the share of the standby power in the total consumption. Parameters ---------- df : pandas.DataFrame or pandas.Series Power (typically electricity, can be anything) resolution : str, default='d' Resolution of the computation. Data will be resampled to this resolution (as mean) before computation of the minimum. String that can be parsed by the pandas resample function, example ='h', '15min', '6h' time_window : tuple with start-hour and end-hour, default=None Specify the start-time and end-time for the analysis. Only data within this time window will be considered. Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects Returns ------- fraction : float between 0-1 with the share of the standby consumption

def share_of_standby(df, resolution='24h', time_window=None): p_sb = standby(df, resolution, time_window) df = df.resample(resolution).mean() p_tot = df.sum() p_standby = p_sb.sum() share_standby = p_standby / p_tot res = share_standby.iloc[0] return res

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def bulk_delete(handler, request): ids = request.GET.getall('ids') Message.delete().where(Message.id << ids).execute() raise muffin.HTTPFound(handler.url)

Toggle counter for gas boilers Counts the number of times the gas consumption increases with more than 3kW Parameters ---------- ts: Pandas Series Gas consumption in minute resolution Returns ------- int

def count_peaks(ts): on_toggles = ts.diff() > 3000 shifted = np.logical_not(on_toggles.shift(1)) result = on_toggles & shifted count = result.sum() return count

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def FindProxies(): sc = objc.SystemConfiguration() # Get the dictionary of network proxy settings settings = sc.dll.SCDynamicStoreCopyProxies(None) if not settings: return [] try: cf_http_enabled = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPEnable") if cf_http_enabled and bool(sc.CFNumToInt32(cf_http_enabled)): # Proxy settings for HTTP are enabled cfproxy = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPProxy") cfport = sc.CFDictRetrieve(settings, "kSCPropNetProxiesHTTPPort") if cfproxy and cfport: proxy = sc.CFStringToPystring(cfproxy) port = sc.CFNumToInt32(cfport) return ["http://%s:%d/" % (proxy, port)] cf_auto_enabled = sc.CFDictRetrieve( settings, "kSCPropNetProxiesProxyAutoConfigEnable") if cf_auto_enabled and bool(sc.CFNumToInt32(cf_auto_enabled)): cfurl = sc.CFDictRetrieve(settings, "kSCPropNetProxiesProxyAutoConfigURLString") if cfurl: unused_url = sc.CFStringToPystring(cfurl) # TODO(amoser): Auto config is enabled, what is the plan here? # Basically, all we get is the URL of a javascript file. To get the # correct proxy for a given URL, browsers call a Javascript function # that returns the correct proxy URL. The question is now, do we really # want to start running downloaded js on the client? return [] finally: sc.dll.CFRelease(settings) return []

Calculate the ratio of input vs. norm over a given interval. Parameters ---------- ts : pandas.Series timeseries resolution : str, optional interval over which to calculate the ratio default: resolution of the input timeseries norm : int | float, optional denominator of the ratio default: the maximum of the input timeseries Returns ------- pandas.Series

def load_factor(ts, resolution=None, norm=None): if norm is None: norm = ts.max() if resolution is not None: ts = ts.resample(rule=resolution).mean() lf = ts / norm return lf

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def inject_basic_program(self, ascii_listing): program_start = self.cpu.memory.read_word( self.machine_api.PROGRAM_START_ADDR ) tokens = self.machine_api.ascii_listing2program_dump(ascii_listing) self.cpu.memory.load(program_start, tokens) log.critical("BASIC program injected into Memory.") # Update the BASIC addresses: program_end = program_start + len(tokens) self.cpu.memory.write_word(self.machine_api.VARIABLES_START_ADDR, program_end) self.cpu.memory.write_word(self.machine_api.ARRAY_START_ADDR, program_end) self.cpu.memory.write_word(self.machine_api.FREE_SPACE_START_ADDR, program_end) log.critical("BASIC addresses updated.")

get top hits after sorting by column number

def top_hits(hits, num, column, reverse): hits.sort(key = itemgetter(column), reverse = reverse) for hit in hits[0:num]: yield hit

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def weld_udf(weld_template, mapping): weld_obj = create_empty_weld_object() for k, v in mapping.items(): if isinstance(v, (np.ndarray, WeldObject)): obj_id = get_weld_obj_id(weld_obj, v) mapping.update({k: obj_id}) weld_obj.weld_code = weld_template.format(**mapping) return weld_obj

parse b6 output with sorting

def numBlast_sort(blast, numHits, evalueT, bitT): header = ['#query', 'target', 'pident', 'alen', 'mismatch', 'gapopen', 'qstart', 'qend', 'tstart', 'tend', 'evalue', 'bitscore'] yield header hmm = {h:[] for h in header} for line in blast: if line.startswith('#'): continue line = line.strip().split('\t') # Evalue and Bitscore thresholds line[10], line[11] = float(line[10]), float(line[11]) evalue, bit = line[10], line[11] if evalueT is not False and evalue > evalueT: continue if bitT is not False and bit < bitT: continue for i, h in zip(line, header): hmm[h].append(i) hmm = pd.DataFrame(hmm) for query, df in hmm.groupby(by = ['#query']): df = df.sort_values(by = ['bitscore'], ascending = False) for hit in df[header].values[0:numHits]: yield hit

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def delete(self): try: return self._server.query('/library/sections/%s' % self.key, method=self._server._session.delete) except BadRequest: # pragma: no cover msg = 'Failed to delete library %s' % self.key msg += 'You may need to allow this permission in your Plex settings.' log.error(msg) raise

parse b6 output

def numBlast(blast, numHits, evalueT = False, bitT = False, sort = False): if sort is True: for hit in numBlast_sort(blast, numHits, evalueT, bitT): yield hit return header = ['#query', 'target', 'pident', 'alen', 'mismatch', 'gapopen', 'qstart', 'qend', 'tstart', 'tend', 'evalue', 'bitscore'] yield header prev, hits = None, [] for line in blast: line = line.strip().split('\t') ID = line[0] line[10], line[11] = float(line[10]), float(line[11]) evalue, bit = line[10], line[11] if ID != prev: if len(hits) > 0: # column is 1 + line index for hit in top_hits(hits, numHits, 11, True): yield hit hits = [] if evalueT == False and bitT == False: hits.append(line) elif evalue <= evalueT and bitT == False: hits.append(line) elif evalue <= evalueT and bit >= bitT: hits.append(line) elif evalueT == False and bit >= bitT: hits.append(line) prev = ID for hit in top_hits(hits, numHits, 11, True): yield hit

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def CloseHandle(self): if hasattr(self, 'handle'): ret = vmGuestLib.VMGuestLib_CloseHandle(self.handle.value) if ret != VMGUESTLIB_ERROR_SUCCESS: raise VMGuestLibException(ret) del(self.handle)

parse hmm domain table output this version is faster but does not work unless the table is sorted

def numDomtblout(domtblout, numHits, evalueT, bitT, sort): if sort is True: for hit in numDomtblout_sort(domtblout, numHits, evalueT, bitT): yield hit return header = ['#target name', 'target accession', 'tlen', 'query name', 'query accession', 'qlen', 'full E-value', 'full score', 'full bias', 'domain #', '# domains', 'domain c-Evalue', 'domain i-Evalue', 'domain score', 'domain bias', 'hmm from', 'hmm to', 'seq from', 'seq to', 'env from', 'env to', 'acc', 'target description'] yield header prev, hits = None, [] for line in domtblout: if line.startswith('#'): continue # parse line and get description line = line.strip().split() desc = ' '.join(line[18:]) line = line[0:18] line.append(desc) # create ID based on query name and domain number ID = line[0] + line[9] # domain c-Evalue and domain score thresholds line[11], line[13] = float(line[11]), float(line[13]) evalue, bitscore = line[11], line[13] line[11], line[13] = evalue, bitscore if ID != prev: if len(hits) > 0: for hit in top_hits(hits, numHits, 13, True): yield hit hits = [] if evalueT == False and bitT == False: hits.append(line) elif evalue <= evalueT and bitT == False: hits.append(line) elif evalue <= evalueT and bit >= bitT: hits.append(line) elif evalueT == False and bit >= bitT: hits.append(line) prev = ID for hit in top_hits(hits, numHits, 13, True): yield hit

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def account_unblock(self, id): id = self.__unpack_id(id) url = '/api/v1/accounts/{0}/unblock'.format(str(id)) return self.__api_request('POST', url)

convert stockholm to fasta

def stock2fa(stock): seqs = {} for line in stock: if line.startswith('#') is False and line.startswith(' ') is False and len(line) > 3: id, seq = line.strip().split() id = id.rsplit('/', 1)[0] id = re.split('[0-9]\|', id, 1)[-1] if id not in seqs: seqs[id] = [] seqs[id].append(seq) if line.startswith('//'): break return seqs

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def put_lifecycle_configuration(Bucket, Rules, region=None, key=None, keyid=None, profile=None): try: conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) if Rules is not None and isinstance(Rules, six.string_types): Rules = salt.utils.json.loads(Rules) conn.put_bucket_lifecycle_configuration(Bucket=Bucket, LifecycleConfiguration={'Rules': Rules}) return {'updated': True, 'name': Bucket} except ClientError as e: return {'updated': False, 'error': __utils__['boto3.get_error'](e)}

Return boolean time series following given week schedule. Parameters ---------- index : pandas.DatetimeIndex Datetime index on_time : str or datetime.time Daily opening time. Default: '09:00' off_time : str or datetime.time Daily closing time. Default: '17:00' off_days : list of str List of weekdays. Default: ['Sunday', 'Monday'] Returns ------- pandas.Series of bool True when on, False otherwise for given datetime index Examples -------- >>> import pandas as pd >>> from opengrid.library.utils import week_schedule >>> index = pd.date_range('20170701', '20170710', freq='H') >>> week_schedule(index)

def week_schedule(index, on_time=None, off_time=None, off_days=None): if on_time is None: on_time = '9:00' if off_time is None: off_time = '17:00' if off_days is None: off_days = ['Sunday', 'Monday'] if not isinstance(on_time, datetime.time): on_time = pd.to_datetime(on_time, format='%H:%M').time() if not isinstance(off_time, datetime.time): off_time = pd.to_datetime(off_time, format='%H:%M').time() times = (index.time >= on_time) & (index.time < off_time) & (~index.weekday_name.isin(off_days)) return pd.Series(times, index=index)

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def deregister_entity_from_group(self, entity, group): if entity in self._entities: if entity in self._groups[group]: self._groups[group].remove(entity) else: raise UnmanagedEntityError(entity)

Draw a carpet plot of a pandas timeseries. The carpet plot reads like a letter. Every day one line is added to the bottom of the figure, minute for minute moving from left (morning) to right (evening). The color denotes the level of consumption and is scaled logarithmically. If vmin and vmax are not provided as inputs, the minimum and maximum of the colorbar represent the minimum and maximum of the (resampled) timeseries. Parameters ---------- timeseries : pandas.Series vmin, vmax : If not None, either or both of these values determine the range of the z axis. If None, the range is given by the minimum and/or maximum of the (resampled) timeseries. zlabel, title : If not None, these determine the labels of z axis and/or title. If None, the name of the timeseries is used if defined. cmap : matplotlib.cm instance, default coolwarm Examples -------- >>> import numpy as np >>> import pandas as pd >>> from opengrid.library import plotting >>> plt = plotting.plot_style() >>> index = pd.date_range('2015-1-1','2015-12-31',freq='h') >>> ser = pd.Series(np.random.normal(size=len(index)), index=index, name='abc') >>> im = plotting.carpet(ser)

def carpet(timeseries, **kwargs): # define optional input parameters cmap = kwargs.pop('cmap', cm.coolwarm) norm = kwargs.pop('norm', LogNorm()) interpolation = kwargs.pop('interpolation', 'nearest') cblabel = kwargs.pop('zlabel', timeseries.name if timeseries.name else '') title = kwargs.pop('title', 'carpet plot: ' + timeseries.name if timeseries.name else '') # data preparation if timeseries.dropna().empty: print('skipped {} - no data'.format(title)) return ts = timeseries.resample('15min').interpolate() vmin = max(0.1, kwargs.pop('vmin', ts[ts > 0].min())) vmax = max(vmin, kwargs.pop('vmax', ts.quantile(.999))) # convert to dataframe with date as index and time as columns by # first replacing the index by a MultiIndex mpldatetimes = date2num(ts.index.to_pydatetime()) ts.index = pd.MultiIndex.from_arrays( [np.floor(mpldatetimes), 2 + mpldatetimes % 1]) # '2 +': matplotlib bug workaround. # and then unstacking the second index level to columns df = ts.unstack() # data plotting fig, ax = plt.subplots() # define the extent of the axes (remark the +- 0.5 for the y axis in order to obtain aligned date ticks) extent = [df.columns[0], df.columns[-1], df.index[-1] + 0.5, df.index[0] - 0.5] im = plt.imshow(df, vmin=vmin, vmax=vmax, extent=extent, cmap=cmap, aspect='auto', norm=norm, interpolation=interpolation, **kwargs) # figure formatting # x axis ax.xaxis_date() ax.xaxis.set_major_locator(HourLocator(interval=2)) ax.xaxis.set_major_formatter(DateFormatter('%H:%M')) ax.xaxis.grid(True) plt.xlabel('UTC Time') # y axis ax.yaxis_date() dmin, dmax = ax.yaxis.get_data_interval() number_of_days = (num2date(dmax) - num2date(dmin)).days # AutoDateLocator is not suited in case few data is available if abs(number_of_days) <= 35: ax.yaxis.set_major_locator(DayLocator()) else: ax.yaxis.set_major_locator(AutoDateLocator()) ax.yaxis.set_major_formatter(DateFormatter("%a, %d %b %Y")) # plot colorbar cbticks = np.logspace(np.log10(vmin), np.log10(vmax), 11, endpoint=True) cb = plt.colorbar(format='%.0f', ticks=cbticks) cb.set_label(cblabel) # plot title plt.title(title) return im

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def num_gpus(): count = ctypes.c_int() check_call(_LIB.MXGetGPUCount(ctypes.byref(count))) return count.value

calculate percent identity

def calc_pident_ignore_gaps(a, b): m = 0 # matches mm = 0 # mismatches for A, B in zip(list(a), list(b)): if A == '-' or A == '.' or B == '-' or B == '.': continue if A == B: m += 1 else: mm += 1 try: return float(float(m)/float((m + mm))) * 100 except: return 0

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def get_local_file(file): try: with open(file.path): yield file.path except NotImplementedError: _, ext = os.path.splitext(file.name) with NamedTemporaryFile(prefix='wagtailvideo-', suffix=ext) as tmp: try: file.open('rb') for chunk in file.chunks(): tmp.write(chunk) finally: file.close() tmp.flush() yield tmp.name

skip column if either is a gap

def remove_gaps(A, B): a_seq, b_seq = [], [] for a, b in zip(list(A), list(B)): if a == '-' or a == '.' or b == '-' or b == '.': continue a_seq.append(a) b_seq.append(b) return ''.join(a_seq), ''.join(b_seq)

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def read_legacy(filename): reader = vtk.vtkDataSetReader() reader.SetFileName(filename) # Ensure all data is fetched with poorly formated legacy files reader.ReadAllScalarsOn() reader.ReadAllColorScalarsOn() reader.ReadAllNormalsOn() reader.ReadAllTCoordsOn() reader.ReadAllVectorsOn() # Perform the read reader.Update() output = reader.GetOutputDataObject(0) if output is None: raise AssertionError('No output when using VTKs legacy reader') return vtki.wrap(output)

compare pairs of sequences

def compare_seqs(seqs): A, B, ignore_gaps = seqs a, b = A[1], B[1] # actual sequences if len(a) != len(b): print('# reads are not the same length', file=sys.stderr) exit() if ignore_gaps is True: pident = calc_pident_ignore_gaps(a, b) else: pident = calc_pident(a, b) return A[0], B[0], pident

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def get_s3_origin_conf_class(): if LooseVersion(troposphere.__version__) > LooseVersion('2.4.0'): return cloudfront.S3OriginConfig if LooseVersion(troposphere.__version__) == LooseVersion('2.4.0'): return S3OriginConfig return cloudfront.S3Origin

calculate Levenshtein ratio of sequences

def compare_seqs_leven(seqs): A, B, ignore_gaps = seqs a, b = remove_gaps(A[1], B[1]) # actual sequences if len(a) != len(b): print('# reads are not the same length', file=sys.stderr) exit() pident = lr(a, b) * 100 return A[0], B[0], pident

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def del_unused_keyframes(self): skl = self.key_frame_list.sorted_key_list() unused_keys = [k for k in self.dct['keys'] if k not in skl] for k in unused_keys: del self.dct['keys'][k]

make pairwise sequence comparisons between aligned sequences

def pairwise_compare(afa, leven, threads, print_list, ignore_gaps): # load sequences into dictionary seqs = {seq[0]: seq for seq in nr_fasta([afa], append_index = True)} num_seqs = len(seqs) # define all pairs pairs = ((i[0], i[1], ignore_gaps) for i in itertools.combinations(list(seqs.values()), 2)) pool = multithread(threads) # calc percent identity between all pairs - parallelize if leven is True: pident = pool.map(compare_seqs_leven, pairs) else: compare = pool.imap_unordered(compare_seqs, pairs) pident = [i for i in tqdm(compare, total = (num_seqs*num_seqs)/2)] pool.close() pool.terminate() pool.join() return to_dictionary(pident, print_list)

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https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L91-L110

def makeCubiccFunc(self,mNrm,cNrm): EndOfPrdvPP = self.DiscFacEff*self.Rfree*self.Rfree*self.PermGroFac**(-self.CRRA-1.0)* \ np.sum(self.PermShkVals_temp**(-self.CRRA-1.0)* self.vPPfuncNext(self.mNrmNext)*self.ShkPrbs_temp,axis=0) dcda = EndOfPrdvPP/self.uPP(np.array(cNrm[1:])) MPC = dcda/(dcda+1.) MPC = np.insert(MPC,0,self.MPCmaxNow) cFuncNowUnc = CubicInterp(mNrm,cNrm,MPC,self.MPCminNow*self.hNrmNow,self.MPCminNow) return cFuncNowUnc

print matrix of pidents to stdout

def print_pairwise(pw, median = False): names = sorted(set([i for i in pw])) if len(names) != 0: if '>' in names[0]: yield ['#'] + [i.split('>')[1] for i in names if '>' in i] else: yield ['#'] + names for a in names: if '>' in a: yield [a.split('>')[1]] + [pw[a][b] for b in names] else: out = [] for b in names: if b in pw[a]: if median is False: out.append(max(pw[a][b])) else: out.append(np.median(pw[a][b])) else: out.append('-') yield [a] + out

84

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L132-L155

def delete_index(self, cardinality): DatabaseConnector.delete_index(self, cardinality) query = "DROP INDEX IF EXISTS idx_{0}_gram_varchar;".format(cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_normalized_varchar;".format( cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_lower_varchar;".format( cardinality) self.execute_sql(query) query = "DROP INDEX IF EXISTS idx_{0}_gram_lower_normalized_varchar;".\ format(cardinality) self.execute_sql(query) for i in reversed(range(cardinality)): if i != 0: query = "DROP INDEX IF EXISTS idx_{0}_gram_{1}_lower;".format( cardinality, i) self.execute_sql(query)

print stats for comparisons

def print_comps(comps): if comps == []: print('n/a') else: print('# min: %s, max: %s, mean: %s' % \ (min(comps), max(comps), np.mean(comps)))

85

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L157-L165

def _load_vertex_buffers(self): fd = gzip.open(cache_name(self.file_name), 'rb') for buff in self.meta.vertex_buffers: mat = self.wavefront.materials.get(buff['material']) if not mat: mat = Material(name=buff['material'], is_default=True) self.wavefront.materials[mat.name] = mat mat.vertex_format = buff['vertex_format'] self.load_vertex_buffer(fd, mat, buff['byte_length']) fd.close()

print min. pident within each clade and then matrix of between-clade max.

def compare_clades(pw): names = sorted(set([i for i in pw])) for i in range(0, 4): wi, bt = {}, {} for a in names: for b in pw[a]: if ';' not in a or ';' not in b: continue pident = pw[a][b] cA, cB = a.split(';')[i], b.split(';')[i] if i == 0 and '_' in cA and '_' in cB: cA = cA.rsplit('_', 1)[1] cB = cB.rsplit('_', 1)[1] elif '>' in cA or '>' in cB: cA = cA.split('>')[1] cB = cB.split('>')[1] if cA == cB: if cA not in wi: wi[cA] = [] wi[cA].append(pident) else: if cA not in bt: bt[cA] = {} if cB not in bt[cA]: bt[cA][cB] = [] bt[cA][cB].append(pident) print('\n# min. within') for clade, pidents in list(wi.items()): print('\t'.join(['wi:%s' % str(i), clade, str(min(pidents))])) # print matrix of maximum between groups comps = [] print('\n# max. between') for comp in print_pairwise(bt): if comp is not None: print('\t'.join(['bt:%s' % str(i)] + [str(j) for j in comp])) if comp[0] != '#': comps.extend([j for j in comp[1:] if j != '-']) print_comps(comps) # print matrix of median between groups comps = [] print('\n# median between') for comp in print_pairwise(bt, median = True): if comp is not None: print('\t'.join(['bt:%s' % str(i)] + [str(j) for j in comp])) if comp[0] != '#': comps.extend([j for j in comp[1:] if j != '-']) print_comps(comps)

86

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L167-L216

def setGroups(self, groups, kerningGroupConversionRenameMaps=None): skipping = [] for name, members in groups.items(): checked = [] for m in members: if m in self.font: checked.append(m) else: skipping.append(m) if checked: self.font.groups[name] = checked if skipping: if self.verbose and self.logger: self.logger.info("\tNote: some glyphnames were removed from groups: %s (unavailable in the font)", ", ".join(skipping)) if kerningGroupConversionRenameMaps: # in case the sources were UFO2, # and defcon upconverted them to UFO3 # and now we have to down convert them again, # we don't want the UFO3 public prefixes in the group names self.font.kerningGroupConversionRenameMaps = kerningGroupConversionRenameMaps

convert matrix to dictionary of comparisons

def matrix2dictionary(matrix): pw = {} for line in matrix: line = line.strip().split('\t') if line[0].startswith('#'): names = line[1:] continue a = line[0] for i, pident in enumerate(line[1:]): b = names[i] if a not in pw: pw[a] = {} if b not in pw: pw[b] = {} if pident != '-': pident = float(pident) pw[a][b] = pident pw[b][a] = pident return pw

87

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/compare_aligned.py#L218-L239

def _sampleLocationOnSide(self): z = random.uniform(-1, 1) * self.height / 2. sampledAngle = 2 * random.random() * pi x, y = self.radius * cos(sampledAngle), self.radius * sin(sampledAngle) return [x, y, z]

Set argument parser option.

def setoption(parser, metadata=None): parser.add_argument('-v', action='version', version=__version__) subparsers = parser.add_subparsers(help='sub commands help') create_cmd = subparsers.add_parser('create') create_cmd.add_argument('name', help='Specify Python package name.') create_cmd.add_argument('-d', dest='description', action='store', help='Short description about your package.') create_cmd.add_argument('-a', dest='author', action='store', required=True, help='Python package author name.') create_cmd.add_argument('-e', dest='email', action='store', required=True, help='Python package author email address.') create_cmd.add_argument('-l', dest='license', choices=metadata.licenses().keys(), default='GPLv3+', help='Specify license. (default: %(default)s)') create_cmd.add_argument('-s', dest='status', choices=metadata.status().keys(), default='Alpha', help=('Specify development status. ' '(default: %(default)s)')) create_cmd.add_argument('--no-check', action='store_true', help='No checking package name in PyPI.') create_cmd.add_argument('--with-samples', action='store_true', help='Generate package with sample code.') group = create_cmd.add_mutually_exclusive_group(required=True) group.add_argument('-U', dest='username', action='store', help='Specify GitHub username.') group.add_argument('-u', dest='url', action='store', type=valid_url, help='Python package homepage url.') create_cmd.add_argument('-o', dest='outdir', action='store', default=os.path.abspath(os.path.curdir), help='Specify output directory. (default: $PWD)') list_cmd = subparsers.add_parser('list') list_cmd.add_argument('-l', dest='licenses', action='store_true', help='show license choices.')

88

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L12-L51

def swd_sync(self, pad=False): if pad: self._dll.JLINK_SWD_SyncBytes() else: self._dll.JLINK_SWD_SyncBits() return None

Parse argument options.

def parse_options(metadata): parser = argparse.ArgumentParser(description='%(prog)s usage:', prog=__prog__) setoption(parser, metadata=metadata) return parser

89

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L72-L77

def vrel(v1, v2): v1 = stypes.toDoubleVector(v1) v2 = stypes.toDoubleVector(v2) return libspice.vrel_c(v1, v2)

Execute main processes.

def main(): try: pkg_version = Update() if pkg_version.updatable(): pkg_version.show_message() metadata = control.retreive_metadata() parser = parse_options(metadata) argvs = sys.argv if len(argvs) <= 1: parser.print_help() sys.exit(1) args = parser.parse_args() control.print_licences(args, metadata) control.check_repository_existence(args) control.check_package_existence(args) control.generate_package(args) except (RuntimeError, BackendFailure, Conflict) as exc: sys.stderr.write('{0}\n'.format(exc)) sys.exit(1)

90

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/commands.py#L80-L99

def price(self, minimum: float = 10.00, maximum: float = 1000.00) -> str: price = self.random.uniform(minimum, maximum, precision=2) return '{0} {1}'.format(price, self.currency_symbol())

Check key and set default vaule when it does not exists.

def _check_or_set_default_params(self): if not hasattr(self, 'date'): self._set_param('date', datetime.utcnow().strftime('%Y-%m-%d')) if not hasattr(self, 'version'): self._set_param('version', self.default_version) # pylint: disable=no-member if not hasattr(self, 'description') or self.description is None: getattr(self, '_set_param')('description', self.warning_message)

91

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L44-L52

def _get_files_modified(): cmd = "git diff-index --cached --name-only --diff-filter=ACMRTUXB HEAD" _, files_modified, _ = run(cmd) extensions = [re.escape(ext) for ext in list(SUPPORTED_FILES) + [".rst"]] test = "(?:{0})$".format("|".join(extensions)) return list(filter(lambda f: re.search(test, f), files_modified))

Move directory from working directory to output directory.

def move(self): if not os.path.isdir(self.outdir): os.makedirs(self.outdir) shutil.move(self.tmpdir, os.path.join(self.outdir, self.name))

92

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L169-L173

def init(*args, **kwargs): global _initial_client client = Client(*args, **kwargs) Hub.current.bind_client(client) rv = _InitGuard(client) if client is not None: _initial_client = weakref.ref(client) return rv

Initialize VCS repository.

def vcs_init(self): VCS(os.path.join(self.outdir, self.name), self.pkg_data)

93

https://github.com/mkouhei/bootstrap-py/blob/95d56ed98ef409fd9f019dc352fd1c3711533275/bootstrap_py/package.py#L185-L187

def group_experiments_greedy(tomo_expt: TomographyExperiment): diag_sets = _max_tpb_overlap(tomo_expt) grouped_expt_settings_list = list(diag_sets.values()) grouped_tomo_expt = TomographyExperiment(grouped_expt_settings_list, program=tomo_expt.program) return grouped_tomo_expt

Finds the location of the current Steam installation on Windows machines. Returns None for any non-Windows machines, or for Windows machines where Steam is not installed.

def find_steam_location(): if registry is None: return None key = registry.CreateKey(registry.HKEY_CURRENT_USER,"Software\Valve\Steam") return registry.QueryValueEx(key,"SteamPath")[0]

94

https://github.com/scottrice/pysteam/blob/1eb2254b5235a053a953e596fa7602d0b110245d/pysteam/winutils.py#L10-L20

def _merge(*args): return re.compile(r'^' + r'[/-]'.join(args) + r'(?:\s+' + _dow + ')?$')

Plot PCoA principal coordinates scaled by the relative abundances of otu_name.

def plot_PCoA(cat_data, otu_name, unifrac, names, colors, xr, yr, outDir, save_as, plot_style): fig = plt.figure(figsize=(14, 8)) ax = fig.add_subplot(111) for i, cat in enumerate(cat_data): plt.scatter(cat_data[cat]["pc1"], cat_data[cat]["pc2"], cat_data[cat]["size"], color=colors[cat], alpha=0.85, marker="o", edgecolor="black", label=cat) lgnd = plt.legend(loc="best", scatterpoints=3, fontsize=13) for i in range(len(colors.keys())): lgnd.legendHandles[i]._sizes = [80] # Change the legend marker size manually plt.title(" ".join(otu_name.split("_")), style="italic") plt.ylabel("PC2 (Percent Explained Variance {:.3f}%)".format(float(unifrac["varexp"][1]))) plt.xlabel("PC1 (Percent Explained Variance {:.3f}%)".format(float(unifrac["varexp"][0]))) plt.xlim(round(xr[0]*1.5, 1), round(xr[1]*1.5, 1)) plt.ylim(round(yr[0]*1.5, 1), round(yr[1]*1.5, 1)) if plot_style: gu.ggplot2_style(ax) fc = "0.8" else: fc = "none" fig.savefig(os.path.join(outDir, "_".join(otu_name.split())) + "." + save_as, facecolor=fc, edgecolor="none", format=save_as, bbox_inches="tight", pad_inches=0.2) plt.close(fig)

95

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/PCoA_bubble.py#L36-L65

def reset(cls): cls.debug = False cls.disabled = False cls.overwrite = False cls.playback_only = False cls.recv_timeout = 5 cls.recv_endmarkers = [] cls.recv_size = None

Split up the column data in a biom table by mapping category value.

def split_by_category(biom_cols, mapping, category_id): columns = defaultdict(list) for i, col in enumerate(biom_cols): columns[mapping[col['id']][category_id]].append((i, col)) return columns

96

https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/transpose_biom.py#L17-L25

def update_prompt(self): prefix = "" if self._local_endpoint is not None: prefix += "(%s:%d) " % self._local_endpoint prefix += self.engine.region if self.engine.partial: self.prompt = len(prefix) * " " + "> " else: self.prompt = prefix + "> "

print line if starts with ...

def print_line(l): print_lines = ['# STOCKHOLM', '#=GF', '#=GS', ' '] if len(l.split()) == 0: return True for start in print_lines: if l.startswith(start): return True return False

97

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/stockholm2oneline.py#L11-L21

def setOverlayTransformTrackedDeviceRelative(self, ulOverlayHandle, unTrackedDevice): fn = self.function_table.setOverlayTransformTrackedDeviceRelative pmatTrackedDeviceToOverlayTransform = HmdMatrix34_t() result = fn(ulOverlayHandle, unTrackedDevice, byref(pmatTrackedDeviceToOverlayTransform)) return result, pmatTrackedDeviceToOverlayTransform

convert stockholm to single line format

def stock2one(stock): lines = {} for line in stock: line = line.strip() if print_line(line) is True: yield line continue if line.startswith('//'): continue ID, seq = line.rsplit(' ', 1) if ID not in lines: lines[ID] = '' else: # remove preceding white space seq = seq.strip() lines[ID] += seq for ID, line in lines.items(): yield '\t'.join([ID, line]) yield '\n//'

98

https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/stockholm2oneline.py#L23-L44

def describe_event_source_mapping(UUID=None, EventSourceArn=None, FunctionName=None, region=None, key=None, keyid=None, profile=None): ids = _get_ids(UUID, EventSourceArn=EventSourceArn, FunctionName=FunctionName) if not ids: return {'event_source_mapping': None} UUID = ids[0] try: conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) desc = conn.get_event_source_mapping(UUID=UUID) if desc: keys = ('UUID', 'BatchSize', 'EventSourceArn', 'FunctionArn', 'LastModified', 'LastProcessingResult', 'State', 'StateTransitionReason') return {'event_source_mapping': dict([(k, desc.get(k)) for k in keys])} else: return {'event_source_mapping': None} except ClientError as e: return {'error': __utils__['boto3.get_error'](e)}

Statics the methods. wut.

def math_func(f): @wraps(f) def wrapper(*args, **kwargs): if len(args) > 0: return_type = type(args[0]) if kwargs.has_key('return_type'): return_type = kwargs['return_type'] kwargs.pop('return_type') return return_type(f(*args, **kwargs)) args = list((setify(x) for x in args)) return return_type(f(*args, **kwargs)) return wrapper

99

https://github.com/elbow-jason/Uno-deprecated/blob/4ad07d7b84e5b6e3e2b2c89db69448906f24b4e4/uno/helpers.py#L8-L22

def PublishMultipleEvents(cls, events, token=None): event_name_map = registry.EventRegistry.EVENT_NAME_MAP for event_name, messages in iteritems(events): if not isinstance(event_name, string_types): raise ValueError( "Event names should be string, got: %s" % type(event_name)) for msg in messages: if not isinstance(msg, rdfvalue.RDFValue): raise ValueError("Can only publish RDFValue instances.") for event_cls in event_name_map.get(event_name, []): event_cls().ProcessMessages(messages, token=token)