0:\\n if flat_tree[0][0] == 'Category':\\n start_loop_at = 1\\n inserted = [] # holds the entries already saved as categories\\n saved_categories = []\\n for i in range(start_loop_at, len(flat_tree)):\\n current_row = flat_tree[i]\\n saved_categories_row = []\\n for j in range(0, len(current_row)):\\n column = current_row[j]\\n # if the current row contains only one entry, it means it's a root node\\n if len(current_row) == 1:\\n category = Category(name=column)\\n category.save()\\n channel.category_set.add(category)\\n saved_categories_row.append(category)\\n else:\\n # check if the current entry was not saved yet.\\n path = current_row[0:j + 1]\\n if i - 2 < 0 or j - 1 < 0:\\n parent = None\\n else:\\n parent = utils.try_catch_index(saved_categories, [i - 2, j - 1])\\n\\n if not utils.is_in(path, inserted):\\n category = Category(name=column, parent=parent)\\n category.save()\\n channel.category_set.add(category)\\n saved_categories_row.append(category)\\n else:\\n previous_row = utils.try_catch_index(saved_categories, [i - 2], [])\\n for saved_category in previous_row:\\n if saved_category.name == column:\\n saved_categories_row.append(saved_category)\\n break\\n saved_categories.append(saved_categories_row)\\n inserted.append(current_row)\\n str_row = \\\" > \\\".join(current_row)\\n self.stdout.write(self.style.SUCCESS(\\n 'Successfully imported \\\"%s\\\" into \\\"%s\\\"' % (str_row, channel.name))\\n )\",\n \"def categories_menu():\\n categories = ['EU-affairs', 'Economy', 'Security', 'Society', 'World']\\n\\n for category in categories:\\n url = build_url({'mode': 'Topic', 'foldername': category, 'page': 1})\\n li = xbmcgui.ListItem(category, iconImage='DefaultFolder.png')\\n xbmcplugin.addDirectoryItem(handle=ADDON_HANDLE, url=url, listitem=li, isFolder=True)\\n\\n xbmcplugin.endOfDirectory(ADDON_HANDLE)\",\n \"def _init_channel_configs(self, recipe):\\n channel_decls = read_channel_declarations(recipe, self.loading_rules)\\n\\n if channel_decls:\\n self.channel_configs = channel_configs_from_decls(\\n channel_decls, self.project_config.keychain\\n )\\n elif self.bulk_mode == \\\"Serial\\\":\\n self.channel_configs = [\\n standard_channel_config(\\n self.org_config,\\n self.recipe_options,\\n 1,\\n 1,\\n )\\n ]\\n else:\\n self.channel_configs = [\\n standard_channel_config(\\n self.org_config,\\n self.recipe_options,\\n self.num_generator_workers,\\n None,\\n )\\n ]\",\n \"def handlerAddStyleCategorized(self):\\r\\n value = QtGui.QTableWidgetItem(self.lineEditStyleCategorizedValue.text())\\r\\n label = QtGui.QTableWidgetItem(self.lineEditStyleCategorizedLabel.text())\\r\\n color = self.styleCategorizedColor\\r\\n self.addStyleCategorized(color, value, label)\\r\\n self.lineEditStyleCategorizedValue.setText('')\\r\\n self.lineEditStyleCategorizedLabel.setText('')\",\n \"def OnCustomRender(self, dc, item, rect):\\r\\n \\r\\n pass\",\n \"def categories_id(self, categories_id):\\n\\n self._categories_id = categories_id\",\n \"def finalize_post_processing(self):\\n populate_enabled_tag_rows_with_limit(self.schema, self.enabled_tag_keys, Provider.PROVIDER_AWS)\\n create_enabled_categories(self.schema, self.enabled_categories)\",\n \"def init_items(self):\\r\\n raise NotImplementedError()\",\n \"def categories_taxonomy(self, categories_taxonomy):\\n\\n self._categories_taxonomy = categories_taxonomy\",\n \"def set_categorical_test_node(self, feature_id, left_categories,\\n default_left, left_child_key,\\n right_child_key):\\n if not self.empty:\\n try:\\n node_key = self.node_key\\n except AttributeError:\\n node_key = '_'\\n raise ValueError(\\n 'Cannot modify a non-empty node. ' + \\\\\\n 'If you meant to change type of node {}, '.format(node_key) + \\\\\\n 'delete it first and then add an empty node with ' + \\\\\\n 'the same key.')\\n try:\\n # automatically create child nodes that don't exist yet\\n if left_child_key not in self.tree:\\n self.tree[left_child_key] = ModelBuilder.Node()\\n if right_child_key not in self.tree:\\n self.tree[right_child_key] = ModelBuilder.Node()\\n _check_call(_LIB.TreeliteTreeBuilderSetCategoricalTestNode(\\n self.tree.handle,\\n ctypes.c_int(self.node_key),\\n ctypes.c_uint(feature_id),\\n c_array(ctypes.c_uint, left_categories),\\n ctypes.c_size_t(len(left_categories)),\\n ctypes.c_int(1 if default_left else 0),\\n ctypes.c_int(left_child_key),\\n ctypes.c_int(right_child_key)))\\n self.empty = False\\n except AttributeError:\\n raise TreeliteError('This node has never been inserted into a tree; '\\\\\\n + 'a node must be inserted before it can be a test node')\",\n \"def _get_derived_parameters(self) -> DerivedParameterCollection:\\n return DerivedParameterCollection([])\",\n \"def _get_derived_parameters(self) -> DerivedParameterCollection:\\n return DerivedParameterCollection([])\",\n \"def set_base_meals(self, base_meals):\\n\\n self.base_meals = u';'.join([u'%d-%d' % (item[0], item[1]) \\\\\\n for item in base_meals.items()])\\n return self.base_meals\",\n \"def assign_properties_to_quantities(self, materials):\\n for quantity in self.derived_quantities:\\n quantity.D = materials.D\\n quantity.S = materials.S\\n quantity.thermal_cond = materials.thermal_cond\\n quantity.H = materials.H\",\n \"def _configure_auxiliary_mask(self, auxiliary_mask):\\n indices = self.indices\\n\\n new = [\\n mask[\\n tuple(\\n [\\n (slice(None) if n == 1 else index)\\n for n, index in zip(mask.shape, indices)\\n ]\\n )\\n ]\\n for mask in auxiliary_mask\\n ]\\n\\n # # If the partition is to be parallelised then get rid of mask\\n # # components which are all False so the mask component does\\n # # not get copied to the child process\\n # if not config['serial']:\\n # new = [mask for mask in new if not mask.any()]\\n\\n self.config[\\\"auxiliary_mask\\\"] = new\",\n \"def init_layout(self):\\n super(WxTraitsItem, self).init_layout()\\n self.refresh_traits_widget(notify=False)\",\n \"def import_categories(self, all_categories):\\n\\n i = 0\\n while (\\n i < len(all_categories['tags'])\\n and all_categories['tags'][i]['products'] > 1000\\n ):\\n category = all_categories['tags'][i]\\n if self.test_category(category):\\n self.create_category(category)\\n i += 1\",\n \"def setItems(self, items):\\n self._nsObject.removeAllItems()\\n for item in items:\\n if isinstance(item, NSMenuItem):\\n menu = self._nsObject.menu()\\n menu.addItem_(item)\\n else:\\n self._nsObject.addItemWithTitle_(item)\",\n \"def buildcutlineset():\\r\\n cutlineset=[[[-3.2697,-3.2697],[-4.3304,-4.3304]],[[-3.2697,-4.3304],[-4.3304,-3.2697]]]\\r\\n cutlineset.extend([[[-3.2697,176.0104],[-4.3304,174.9497]],[[-3.2697,174.9497],[-4.3304,176.0104]]])\\r\\n cutlineset.extend([[[176.0104,176.0104],[174.9497,174.9497]],[[176.0104,174.9497],[174.9497,176.0104]]])\\r\\n cutlineset.extend([[[175.4800,-3.05],[175.4800,-4.55]],[[174.7300,-3.8],[176.2300,-3.8]]])\\r\\n \\r\\n for cutline in cutlineset:\\r\\n for pos in cutline:\\r\\n pos[0]=pos[0]+globalconfig.CUTLINE_X_OFFSET\\r\\n pos[1]=pos[1]+globalconfig.CUTLINE_Y_OFFSET\\r\\n \\r\\n for row in range(0,globalconfig.X_ARRAY_NUM):\\r\\n cutlineset.append([[globalconfig.X_BLANK+row*(globalconfig.X_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_X_OFFSET,0.0+globalconfig.CUTLINE_Y_OFFSET],[globalconfig.X_BLANK+row*(globalconfig.X_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_X_OFFSET,-3.0+globalconfig.CUTLINE_Y_OFFSET]])\\r\\n cutlineset.append([[globalconfig.X_BLANK+row*(globalconfig.X_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_X_OFFSET,171.68+globalconfig.CUTLINE_Y_OFFSET],[globalconfig.X_BLANK+row*(globalconfig.X_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_X_OFFSET,174.68+globalconfig.CUTLINE_Y_OFFSET]])\\r\\n for line in range(0,globalconfig.Y_ARRAY_NUM):\\r\\n cutlineset.append([[0.0+globalconfig.CUTLINE_X_OFFSET,globalconfig.Y_BLANK+line*(globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_Y_OFFSET],[-3.0+globalconfig.CUTLINE_X_OFFSET,globalconfig.Y_BLANK+line*(globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_Y_OFFSET]])\\r\\n cutlineset.append([[171.68+globalconfig.CUTLINE_X_OFFSET,globalconfig.Y_BLANK+line*(globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_Y_OFFSET],[174.68+globalconfig.CUTLINE_X_OFFSET,globalconfig.Y_BLANK+line*(globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO)+globalconfig.CUTLINE_Y_OFFSET]])\\r\\n return cutlineset\",\n \"def SetList(self, temp_list: list or tuple):\\n self.Clear()\\n for item in temp_list:\\n self.insert(Tags.End.value, item)\",\n \"def scsi_adapter_types(self, scsi_adapter_types):\\n\\n self._scsi_adapter_types = scsi_adapter_types\",\n \"def get_erasure_target_categories(self) -> List[str]:\\n erasure_categories = []\\n for rule in self.rules:\\n if rule.action_type == ActionType.erasure:\\n erasure_categories.extend(rule.get_target_data_categories())\\n\\n return erasure_categories\",\n \"def set_collidable(self, bCollidable):\\n\\n self._bCollidable = bCollidable\",\n \"def addToolBarButtons(self):\\n self.addNewCategoryButton()\",\n \"def buildTiles(self, items, attributes):\\n pass\",\n \"def clear_categories(self, subset=None, inclusive=None):\\n if inclusive is not None:\\n inc = inclusive\\n else:\\n inc = self.is_cat_inclusive\\n if subset is None:\\n # clear all categories\\n self.cats = None\\n else:\\n # Do not use self[subset].blah = 0 ! - SettingWithCopyWarning\\n if inc:\\n self.cats = self.cats.drop(\\n columns=[col for col in self.cats.columns.values if subset in col]\\n )\\n else:\\n self.cats = self.cats.drop(columns=subset)\\n if len(self.cat_labels) == 0:\\n self.is_categorised = False\\n self.is_cat_inclusive = False\",\n \"def block_and_item_tag(self, name_parts: ResourceIdentifier, *values: Union[ResourceIdentifier, JsonObject], replace: bool = None):\\n self.tag(name_parts, 'items', *values, replace=replace)\\n self.tag(name_parts, 'blocks', *values, replace=replace)\",\n \"def plot_categorical(self, plot_categorical_columns=None):\\n assert isinstance(\\n plot_categorical_columns, (list, type(None))\\n ), \\\"plot_categorical_columns should be of type list\\\"\\n\\n # Count distinct values in each column\\n col_nunique = self.df_baseline.nunique()\\n\\n # won't plot categoricals with more than 20 values\\n if plot_categorical_columns is None:\\n plot_categorical_columns = [\\n col\\n for col in col_nunique.index\\n if ((col_nunique[col] <= 20) & (col in self.categorical_columns))\\n ]\\n\\n logger.info(\\n \\\"Plotting the following categorical column(s):\\\", plot_categorical_columns\\n )\\n\\n fig, ax = plt.subplots(\\n len(plot_categorical_columns),\\n 2,\\n figsize=(10, 5 * len(plot_categorical_columns)),\\n )\\n\\n for i, col in enumerate(plot_categorical_columns):\\n\\n if len(plot_categorical_columns) == 1:\\n _ax0 = ax[0]\\n _ax1 = ax[1]\\n elif len(plot_categorical_columns) > 1:\\n _ax0 = ax[i, 0]\\n _ax1 = ax[i, 1]\\n\\n # Get all values and counts from baseline and sample dfs\\n df_baseline_values = (\\n self.df_baseline[col]\\n .value_counts(normalize=True, dropna=False)\\n .index.values\\n )\\n df_sample_values = (\\n self.df_sample[col]\\n .value_counts(normalize=True, dropna=False)\\n .index.values\\n )\\n\\n # Get all unique values in the union of both lists above\\n all_values = np.union1d(df_baseline_values, df_sample_values)\\n\\n # recount values in each df to include missing values in each - impute by zero\\n df_baseline_values = (\\n self.df_baseline[col].value_counts()[all_values].fillna(0)\\n )\\n df_sample_values = self.df_sample[col].value_counts()[all_values].fillna(0)\\n\\n # generate side-by-side barplots\\n (\\n df_baseline_values.rename(\\\"Proportion\\\")\\n .sort_index()\\n .reset_index()\\n .pipe((sns.barplot, \\\"data\\\"), x=\\\"index\\\", y=\\\"Proportion\\\", ax=_ax0)\\n )\\n _ax0.set_title(col + \\\", baseline\\\")\\n _ax0.set(xlabel=col)\\n (\\n df_sample_values.rename(\\\"Proportion\\\")\\n .sort_index()\\n .reset_index()\\n .pipe((sns.barplot, \\\"data\\\"), x=\\\"index\\\", y=\\\"Proportion\\\", ax=_ax1)\\n )\\n _ax1.set(xlabel=col)\\n _ax1.set_title(col + \\\", sample\\\")\\n\\n plt.close(fig)\\n\\n return fig\",\n \"def create_xblock(self, runtime, category, fields=None, block_id=None, definition_id=None, parent_xblock=None):\\r\\n xblock_class = runtime.load_block_type(category)\\r\\n json_data = {\\r\\n 'category': category,\\r\\n 'fields': fields or {},\\r\\n }\\r\\n if definition_id is not None:\\r\\n json_data['definition'] = definition_id\\r\\n if parent_xblock is not None:\\r\\n json_data['_inherited_settings'] = parent_xblock.xblock_kvs.inherited_settings.copy()\\r\\n if fields is not None:\\r\\n for field_name in inheritance.InheritanceMixin.fields:\\r\\n if field_name in fields:\\r\\n json_data['_inherited_settings'][field_name] = fields[field_name]\\r\\n\\r\\n new_block = runtime.xblock_from_json(xblock_class, block_id, json_data)\\r\\n if parent_xblock is not None:\\r\\n parent_xblock.children.append(new_block.scope_ids.usage_id)\\r\\n # decache pending children field settings\\r\\n parent_xblock.save()\\r\\n return new_block\",\n \"def add_bias(self, bias_types):\\n self.segments = deepcopy(self.segments)\\n # Add the biases specified in the argument.\\n if bias_types['final'] and len(self.segments) == 3:\\n self.segments[2].add_bias('voiceless')\\n if bias_types['medial'] and len(self.segments) == 4:\\n self.segments[2].add_bias('voiced')\",\n \"def update_mask(self):\\r\\n \\r\\n # Binary mask from ML detection\\r\\n if len(self.selected_ML_Index) > 0:\\r\\n # Delete items in dictionary that are not roi items\\r\\n roi_dict = self.selected_cells_infor_dict.copy()\\r\\n del_key_list=[]\\r\\n for key in roi_dict:\\r\\n print(key)\\r\\n if 'ROIitem' not in key:\\r\\n del_key_list.append(key)\\r\\n for key in del_key_list:\\r\\n del roi_dict[key]\\r\\n \\r\\n self.MLmask = ProcessImage.ROIitem2Mask(roi_dict, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n # Binary mask of added rois\\r\\n self.addedROIitemMask = ProcessImage.ROIitem2Mask(self.roi_list_freehandl_added, mask_resolution = (self.MLtargetedImg.shape[0], self.MLtargetedImg.shape[1]))\\r\\n \\r\\n self.intergrate_into_final_mask()\",\n \"def unmarkItems(self, items, appendchar = \\\"\\\"):\\n\\t\\tfor item in items:\\n\\t\\t\\tif appendchar != \\\"\\\":\\n\\t\\t\\t\\ttxt = self.tree.GetItemText(item)\\n\\t\\t\\t\\tif txt[-len(appendchar):] == appendchar:\\n\\t\\t\\t\\t\\tnewtext = txt[:-len(appendchar)]\\n\\t\\t\\t\\t\\tself.tree.SetItemText(item, newtext)\\n\\t\\t\\tself.tree.SetItemTextColour(item, self.itemColor)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.42504486","0.41763225","0.41738588","0.4147597","0.4145194","0.41436017","0.41436017","0.41436017","0.41436017","0.41305125","0.41266572","0.41144997","0.40753424","0.40561587","0.38878268","0.38473186","0.3840825","0.38260534","0.38147235","0.37995836","0.3703957","0.36765128","0.36682683","0.3618933","0.36005056","0.3581162","0.35684228","0.3550813","0.3550813","0.35282844","0.3521966","0.35214692","0.35213506","0.35100463","0.3491261","0.3490157","0.34895742","0.34881613","0.34825736","0.34780625","0.34677538","0.34639382","0.3461665","0.34607333","0.34606144","0.34599346","0.34488523","0.3436575","0.34363598","0.3428016","0.34238142","0.34143028","0.34138748","0.34137392","0.34124458","0.34110317","0.34038126","0.3391102","0.33867553","0.33857292","0.33828852","0.3381264","0.33789292","0.33789292","0.33789292","0.33704364","0.3365271","0.3365232","0.33635145","0.3356162","0.33501658","0.33488894","0.33482945","0.3347219","0.33453244","0.33408082","0.33345208","0.33332863","0.33313245","0.33313245","0.33276546","0.3319494","0.33166823","0.3315432","0.33099145","0.33075023","0.330483","0.32955196","0.32927","0.32858074","0.32833537","0.32833183","0.32802656","0.3278373","0.32783145","0.32737815","0.3270434","0.326992","0.32663766","0.32658434"],"string":"[\n \"0.42504486\",\n \"0.41763225\",\n \"0.41738588\",\n \"0.4147597\",\n \"0.4145194\",\n \"0.41436017\",\n \"0.41436017\",\n \"0.41436017\",\n \"0.41436017\",\n \"0.41305125\",\n \"0.41266572\",\n \"0.41144997\",\n \"0.40753424\",\n \"0.40561587\",\n \"0.38878268\",\n \"0.38473186\",\n \"0.3840825\",\n \"0.38260534\",\n \"0.38147235\",\n \"0.37995836\",\n \"0.3703957\",\n \"0.36765128\",\n \"0.36682683\",\n \"0.3618933\",\n \"0.36005056\",\n \"0.3581162\",\n \"0.35684228\",\n \"0.3550813\",\n \"0.3550813\",\n \"0.35282844\",\n \"0.3521966\",\n \"0.35214692\",\n \"0.35213506\",\n \"0.35100463\",\n \"0.3491261\",\n \"0.3490157\",\n \"0.34895742\",\n \"0.34881613\",\n \"0.34825736\",\n \"0.34780625\",\n \"0.34677538\",\n \"0.34639382\",\n \"0.3461665\",\n \"0.34607333\",\n \"0.34606144\",\n \"0.34599346\",\n \"0.34488523\",\n \"0.3436575\",\n \"0.34363598\",\n \"0.3428016\",\n \"0.34238142\",\n \"0.34143028\",\n \"0.34138748\",\n \"0.34137392\",\n \"0.34124458\",\n \"0.34110317\",\n \"0.34038126\",\n \"0.3391102\",\n \"0.33867553\",\n \"0.33857292\",\n \"0.33828852\",\n \"0.3381264\",\n \"0.33789292\",\n \"0.33789292\",\n \"0.33789292\",\n \"0.33704364\",\n \"0.3365271\",\n \"0.3365232\",\n \"0.33635145\",\n \"0.3356162\",\n \"0.33501658\",\n \"0.33488894\",\n \"0.33482945\",\n \"0.3347219\",\n \"0.33453244\",\n \"0.33408082\",\n \"0.33345208\",\n \"0.33332863\",\n \"0.33313245\",\n \"0.33313245\",\n \"0.33276546\",\n \"0.3319494\",\n \"0.33166823\",\n \"0.3315432\",\n \"0.33099145\",\n \"0.33075023\",\n \"0.330483\",\n \"0.32955196\",\n \"0.32927\",\n \"0.32858074\",\n \"0.32833537\",\n \"0.32833183\",\n \"0.32802656\",\n \"0.3278373\",\n \"0.32783145\",\n \"0.32737815\",\n \"0.3270434\",\n \"0.326992\",\n \"0.32663766\",\n \"0.32658434\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7910564"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79574,"cells":{"query":{"kind":"string","value":"Returns the model properties as a dict"},"document":{"kind":"string","value":"def to_dict(self):\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def to_dict(self):\n return self.properties","def to_dict(self):\n return self.properties","def get_properties(self):\n return self.properties","def asdict(self):\n return self._prop_dict","def json(self):\n rv = {\n prop: getattr(self, prop)\n for prop in self.__properties__\n if prop in vars(self)\n }\n rv.update(self._props)\n return rv","def get_properties(self):\n return self.properties","def get_properties():","def getProperties():","def properties(self):\r\n if self._properties is None:\r\n res = self._con.get(self._url, {'f':'json'})\r\n self._properties = PropertyMap(res)\r\n return self._properties","def properties(self):\r\n if self._properties is None:\r\n res = self._con.get(self._url, {'f':'json'})\r\n self._properties = PropertyMap(res)\r\n return self._properties","def getProperties(self):\n return self.properties","def __properties__(self) -> dict:\r\n parameters = [\r\n d for d in dir(self) if (d[0] != \"_\") and (d.count(\"set\") == 0)\r\n and (d.count(\"_c\") == 0) and (d.count(\"_f\") == 0)\r\n ]\r\n\r\n return self.__as_json__(parameters)","def json_properties(self):\n attributes = []\n all = vars(self)\n for var in all:\n if var[:1] != '_':\n attributes.append(var)\n if isinstance(self, db.Model):\n properties = self.properties().keys()\n for property in properties:\n if property[:1] != '_':\n attributes.append(property)\n return attributes","def properties(self) -> Any:\n return pulumi.get(self, \"properties\")","def _properties(self) -> dict[str, dict[str, str]]:\n schema = self.schema(by_alias=False)\n if schema.get('properties') is not None:\n return schema.get('properties', {})\n return schema.get('definitions', {}).get(self.__class__.__name__, {}).get('properties', {})","def get_model_properties(self):\n properties = {}\n\n filename = self._get_data_filename(\"modelargs.json\")\n with open(filename, \"r\") as f:\n results = json.loads(f.read())\n properties[\"image_size\"] = results.get(\"image_size\")\n properties[\"num_classes\"] = results.get(\"num_classes\")\n properties[\"model\"] = results.get(\"model\")\n properties[\"name\"] = results.get(\"name\")\n properties[\"filter_size\"] = results.get(\"filter_size\", 3)\n properties[\"increase_factor\"] = results.get(\"increase_factor\", 0)\n self.model = properties[\"name\"] # regardless of the name of the folder, this will get the proper model name (i.e. <modelname>.cntk)\n\n # optional property\n properties[\"trainer\"] = results.get(\"trainer\", \"CNTK 2.2\")\n\n self._ensure_model_file()\n properties[\"size_mb\"] = round(os.path.getsize(self.model_file) / (1000 * 1000))\n\n return properties","def as_dict(self):\n result = {}\n for attr in self.__attr:\n result[attr] = getattr(self, attr)\n return result","def to_dict_model(self) -> dict:\n return dict((key, getattr(self, key)) for key in self.__mapper__.c.keys())","def get_properties():\n properties = dict()\n properties['size'] = list()\n properties['color'] = list()\n properties['quality'] = list()\n u = models.Size.query.all()\n for i in u:\n properties['size'].append(i.size_name)\n u = models.Color.query.all()\n for i in u:\n properties['color'].append(i.color_name)\n u = models.Quality.query.all()\n for i in u:\n properties['quality'].append(i.quality_name)\n return make_response(jsonify(properties))","def get_modelDict(self):\n return self.__modelDict","def attributes(self):\n return dict(self.__attributes)","def properties(self):\n return self._properties","def properties(self):\n return self._properties","def to_dict(self):\n result = {}\n for p in self.json_properties():\n value = getattr(self, p)\n if isinstance(value, datetime.datetime):\n value = value.strftime('%s%f')[:-3]\n result[Jsonifiable.transform_to_camelcase(p)] = value\n return result","def properties(self):\n return self._props","def properties(self):\n pass","def to_dict(self):\n d = {}\n for attr in self.__class__.attributes:\n d[attr] = getattr(self, attr)\n return d","def properties_get(self):\n return self._get('properties')","def _collect_properties(self):\n properties = {\n 'userid': self.user_id,\n 'title': self.get_fullname()\n }\n if not self.ogds_user:\n return properties\n\n for attribute_name in self.ogds_user_attributes:\n value = getattr(self.ogds_user, attribute_name)\n properties[attribute_name] = value\n return properties","def getPropertyDict(self):\n \n d = self.getChild('__properties')\n if d:\n return d.getDict()\n else:\n return {}","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def to_dict(self, include=None):\n _MODEL = type(self)\n repr_dict = {}\n if include is None:\n include = []\n for name, prop in _MODEL._properties.iteritems():\n if hasattr(prop, 'public') and getattr(prop, 'public', False):\n include.append(name)\n\n for name in include:\n # check if this property is even allowed to be public\n # or has a value set\n if not hasattr(self, name):\n continue\n\n value = getattr(self, name)\n if type(getattr(_MODEL, name)) == ndb.StructuredProperty:\n if isinstance(value, list):\n items = []\n for item in value:\n items.append(item.to_dict(include=None))\n repr_dict[name] = items\n else:\n repr_dict[name] = value.to_dict(include=None)\n elif isinstance(value, date):\n repr_dict[name] = value.isoformat()\n elif isinstance(value, ndb.Key):\n repr_dict[name] = value.urlsafe()\n else:\n repr_dict[name] = value\n\n if self._key:\n repr_dict['key'] = self.get_key_urlsafe()\n return repr_dict","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k == 'POSSIBLE_METHODS':\n continue\n if k == 'keysamplers':\n properties[k] = [i.to_dict() for i in self.__dict__[k] if hasattr(i,'to_dict')]\n elif k in {'pooler'}:\n properties[k] = self.__dict__[k].to_dict()\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def get_all_properties(cls):\n return ['key', 'id'] + _.keys(cls._properties)","def get_properties(self):\n return self.name, self.author, self.description, self.fmu_type, self.version, self.guid, self.tool, self.numStates","def properties(self):\n\n return self._properties","def ToDict(self):\n atributes_dictionary = {}\n for key, value in self.__dict__.iteritems():\n atributes_dictionary[key] = value\n return atributes_dictionary","def properties(self):","def properties(self):","def properties(self):","def modelPropertiesDictionary(sql_row_list):\n \n properties_dictionary = \\\n {\n \"id\": sql_row_list[0],\n \"name\": sql_row_list[1],\n \"last_deploy_timestamp\": sql_row_list[2],\n \"active_version\": sql_row_list[3],\n \"build_id\": sql_row_list[4]\n };\n\n return properties_dictionary;","def as_dict(self):\n data = dict()\n for name in self.fields:\n val = getattr(self, name)\n if isinstance(val, Model):\n val = val.as_dict()\n elif isinstance(val, list) and val and isinstance(val[0], Model):\n val = [sub.as_dict() for sub in val]\n data[name] = val\n return data","def to_dict(self):\n if self._dict is not None:\n return self._dict\n\n result = {}\n for key in self.ATTRIBUTES:\n value = getattr(self, key)\n if value:\n result[key] = value\n self._dict = result\n return result","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def properties(self) -> Optional[Mapping[str, str]]:\n return pulumi.get(self, \"properties\")","def to_dict(self):\n _dict = {}\n for f in self._meta.fields:\n if f.name == 'created':\n _dict[f.name] = str(f.value_from_object(self))\n else:\n _dict[f.name] = f.value_from_object(self)\n\n return _dict","def to_dict(self):\r\n return self.__dict__","def properties(self):\n return None","def properties(self):\n return None","def to_dict(self):\n return attr.asdict(self)","def as_dict(self):\n return self.__dict__","def _get_model_state(self) -> dict:\n return dict(model=self.model, kwargs=self._model_kwargs)","def dictify(self):\n return {\n \"name\" : self.name,\n \"lastname\" : self.lastname,\n \"phone\" : self.phone,\n \"email\" : self.email\n }","def to_dict(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n # \"created_by\": self.created_by,\n # \"created_on\": self.created_on,\n # \"modified_by\": self.modified_by,\n # \"modified_on\": self.modified_on\n }","def properties(self):\r\n return resources.Properties(self)","def attributes(self):\n params = self.model.param_array\n return {'parameters': params}","def properties(self, pk):\n return JsonResponse(self._get_properties(pk))","def to_dict(self):\n return vars(self)","def to_dict(self):\n\n # Check if is the right instance.\n if isinstance(self, db.Model):\n # construct a dictionary from column names and values.\n dict_representation = {c.name: getattr(self, c.name) for c in self.__table__.columns}\n return dict_representation\n else:\n raise AttributeError(type(self).__name__ + \" is not instance of \" + db.Model.__name__)","def bson_properties(self):\n return []","def to_dict(self):\n return {\n \"id\": self.id,\n \"name\": self.name\n }","def get_dict(self):\n return","def to_dict(self):\n return to_dict(self.__dict__)","def to_json(self):\n properties = self.to_dict()\n if isinstance(self, db.Model):\n properties['id'] = unicode(self.key().id())\n return json.dumps(properties)","def to_dict(self):","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def to_dict(self):\n return self.__dict__","def get_attributes(self) -> Dict[str, str]:\n pass","def config(self) -> ModelConfigDict:\n return self.config_obj.to_dict()","def properties(self):\n return self.properties_with_uid[1:]","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def to_dict(self):\n properties = {}\n for k in self.__dict__:\n if k in {'idsSoFar'}:\n continue\n else:\n properties[k] = deepcopy(self.__dict__[k])\n return properties","def serialise(self):\n return {\n 'id': self.id,\n 'category_id': self.category_id,\n 'name': self.name,\n 'description': self.description,\n 'quantity': self.quantity,\n 'price': self.price,\n 'user_id': self.user_id\n }","def getPropertiesAll():","def get_all_properties(self) -> dict:\n return self._request(\n \"post\",\n URL,\n json=attr.asdict(\n Body(\"getAllProperties\", API_VERSION),\n filter=attr.filters.exclude(attr.fields(Body).params),\n ),\n )","def model_info(self):\n if not self._model_info:\n self._load_model_info()\n try:\n data = json.loads(self._model_info)\n except (TypeError, ValueError):\n data = {}\n return data","def to_dict(self):\n return {\n 'name': self.get_name(),\n 'description': self.get_description()\n }","def serialize(self):\n return {\n 'id' : self.id,\n 'name' : self.name,\n }","def serialize(self):\n return {\n 'id' : self.id,\n 'name' : self.name,\n }","def as_dict(self):\n return self.__dict__","def to_dict(self):\r\n\r\n return {\r\n 'product_id': self.product_id,\r\n 'product_name': self.product_name\r\n }","def serialize(self):\n return {\n 'name' : self.name,\n 'id' : self.id,\n }","def asdict(self):\n return attr.asdict(self)","def to_dict(self) -> dict:","def getDict(self):\n res = {}\n for attr, value in self.__dict__.iteritems():\n if type(attr) is IntType or type(attr) is StringType or type(attr) is LongType or type(attr) is UnicodeType:\n res[attr] = value\n elif isinstance(attr, datetime.datetime):\n res[attr] = value.isoformat('-')\n \n return res","def attributes(self):\n return self.__dict.keys()","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def dict(self):\n return self.__dict__","def as_dict(self):\n return {c.key: getattr(self, c.key)\n for c in inspect(self).mapper.column_attrs}"],"string":"[\n \"def to_dict(self):\\n return self.properties\",\n \"def to_dict(self):\\n return self.properties\",\n \"def get_properties(self):\\n return self.properties\",\n \"def asdict(self):\\n return self._prop_dict\",\n \"def json(self):\\n rv = {\\n prop: getattr(self, prop)\\n for prop in self.__properties__\\n if prop in vars(self)\\n }\\n rv.update(self._props)\\n return rv\",\n \"def get_properties(self):\\n return self.properties\",\n \"def get_properties():\",\n \"def getProperties():\",\n \"def properties(self):\\r\\n if self._properties is None:\\r\\n res = self._con.get(self._url, {'f':'json'})\\r\\n self._properties = PropertyMap(res)\\r\\n return self._properties\",\n \"def properties(self):\\r\\n if self._properties is None:\\r\\n res = self._con.get(self._url, {'f':'json'})\\r\\n self._properties = PropertyMap(res)\\r\\n return self._properties\",\n \"def getProperties(self):\\n return self.properties\",\n \"def __properties__(self) -> dict:\\r\\n parameters = [\\r\\n d for d in dir(self) if (d[0] != \\\"_\\\") and (d.count(\\\"set\\\") == 0)\\r\\n and (d.count(\\\"_c\\\") == 0) and (d.count(\\\"_f\\\") == 0)\\r\\n ]\\r\\n\\r\\n return self.__as_json__(parameters)\",\n \"def json_properties(self):\\n attributes = []\\n all = vars(self)\\n for var in all:\\n if var[:1] != '_':\\n attributes.append(var)\\n if isinstance(self, db.Model):\\n properties = self.properties().keys()\\n for property in properties:\\n if property[:1] != '_':\\n attributes.append(property)\\n return attributes\",\n \"def properties(self) -> Any:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def _properties(self) -> dict[str, dict[str, str]]:\\n schema = self.schema(by_alias=False)\\n if schema.get('properties') is not None:\\n return schema.get('properties', {})\\n return schema.get('definitions', {}).get(self.__class__.__name__, {}).get('properties', {})\",\n \"def get_model_properties(self):\\n properties = {}\\n\\n filename = self._get_data_filename(\\\"modelargs.json\\\")\\n with open(filename, \\\"r\\\") as f:\\n results = json.loads(f.read())\\n properties[\\\"image_size\\\"] = results.get(\\\"image_size\\\")\\n properties[\\\"num_classes\\\"] = results.get(\\\"num_classes\\\")\\n properties[\\\"model\\\"] = results.get(\\\"model\\\")\\n properties[\\\"name\\\"] = results.get(\\\"name\\\")\\n properties[\\\"filter_size\\\"] = results.get(\\\"filter_size\\\", 3)\\n properties[\\\"increase_factor\\\"] = results.get(\\\"increase_factor\\\", 0)\\n self.model = properties[\\\"name\\\"] # regardless of the name of the folder, this will get the proper model name (i.e. <modelname>.cntk)\\n\\n # optional property\\n properties[\\\"trainer\\\"] = results.get(\\\"trainer\\\", \\\"CNTK 2.2\\\")\\n\\n self._ensure_model_file()\\n properties[\\\"size_mb\\\"] = round(os.path.getsize(self.model_file) / (1000 * 1000))\\n\\n return properties\",\n \"def as_dict(self):\\n result = {}\\n for attr in self.__attr:\\n result[attr] = getattr(self, attr)\\n return result\",\n \"def to_dict_model(self) -> dict:\\n return dict((key, getattr(self, key)) for key in self.__mapper__.c.keys())\",\n \"def get_properties():\\n properties = dict()\\n properties['size'] = list()\\n properties['color'] = list()\\n properties['quality'] = list()\\n u = models.Size.query.all()\\n for i in u:\\n properties['size'].append(i.size_name)\\n u = models.Color.query.all()\\n for i in u:\\n properties['color'].append(i.color_name)\\n u = models.Quality.query.all()\\n for i in u:\\n properties['quality'].append(i.quality_name)\\n return make_response(jsonify(properties))\",\n \"def get_modelDict(self):\\n return self.__modelDict\",\n \"def attributes(self):\\n return dict(self.__attributes)\",\n \"def properties(self):\\n return self._properties\",\n \"def properties(self):\\n return self._properties\",\n \"def to_dict(self):\\n result = {}\\n for p in self.json_properties():\\n value = getattr(self, p)\\n if isinstance(value, datetime.datetime):\\n value = value.strftime('%s%f')[:-3]\\n result[Jsonifiable.transform_to_camelcase(p)] = value\\n return result\",\n \"def properties(self):\\n return self._props\",\n \"def properties(self):\\n pass\",\n \"def to_dict(self):\\n d = {}\\n for attr in self.__class__.attributes:\\n d[attr] = getattr(self, attr)\\n return d\",\n \"def properties_get(self):\\n return self._get('properties')\",\n \"def _collect_properties(self):\\n properties = {\\n 'userid': self.user_id,\\n 'title': self.get_fullname()\\n }\\n if not self.ogds_user:\\n return properties\\n\\n for attribute_name in self.ogds_user_attributes:\\n value = getattr(self.ogds_user, attribute_name)\\n properties[attribute_name] = value\\n return properties\",\n \"def getPropertyDict(self):\\n \\n d = self.getChild('__properties')\\n if d:\\n return d.getDict()\\n else:\\n return {}\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def to_dict(self, include=None):\\n _MODEL = type(self)\\n repr_dict = {}\\n if include is None:\\n include = []\\n for name, prop in _MODEL._properties.iteritems():\\n if hasattr(prop, 'public') and getattr(prop, 'public', False):\\n include.append(name)\\n\\n for name in include:\\n # check if this property is even allowed to be public\\n # or has a value set\\n if not hasattr(self, name):\\n continue\\n\\n value = getattr(self, name)\\n if type(getattr(_MODEL, name)) == ndb.StructuredProperty:\\n if isinstance(value, list):\\n items = []\\n for item in value:\\n items.append(item.to_dict(include=None))\\n repr_dict[name] = items\\n else:\\n repr_dict[name] = value.to_dict(include=None)\\n elif isinstance(value, date):\\n repr_dict[name] = value.isoformat()\\n elif isinstance(value, ndb.Key):\\n repr_dict[name] = value.urlsafe()\\n else:\\n repr_dict[name] = value\\n\\n if self._key:\\n repr_dict['key'] = self.get_key_urlsafe()\\n return repr_dict\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k == 'POSSIBLE_METHODS':\\n continue\\n if k == 'keysamplers':\\n properties[k] = [i.to_dict() for i in self.__dict__[k] if hasattr(i,'to_dict')]\\n elif k in {'pooler'}:\\n properties[k] = self.__dict__[k].to_dict()\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def get_all_properties(cls):\\n return ['key', 'id'] + _.keys(cls._properties)\",\n \"def get_properties(self):\\n return self.name, self.author, self.description, self.fmu_type, self.version, self.guid, self.tool, self.numStates\",\n \"def properties(self):\\n\\n return self._properties\",\n \"def ToDict(self):\\n atributes_dictionary = {}\\n for key, value in self.__dict__.iteritems():\\n atributes_dictionary[key] = value\\n return atributes_dictionary\",\n \"def properties(self):\",\n \"def properties(self):\",\n \"def properties(self):\",\n \"def modelPropertiesDictionary(sql_row_list):\\n \\n properties_dictionary = \\\\\\n {\\n \\\"id\\\": sql_row_list[0],\\n \\\"name\\\": sql_row_list[1],\\n \\\"last_deploy_timestamp\\\": sql_row_list[2],\\n \\\"active_version\\\": sql_row_list[3],\\n \\\"build_id\\\": sql_row_list[4]\\n };\\n\\n return properties_dictionary;\",\n \"def as_dict(self):\\n data = dict()\\n for name in self.fields:\\n val = getattr(self, name)\\n if isinstance(val, Model):\\n val = val.as_dict()\\n elif isinstance(val, list) and val and isinstance(val[0], Model):\\n val = [sub.as_dict() for sub in val]\\n data[name] = val\\n return data\",\n \"def to_dict(self):\\n if self._dict is not None:\\n return self._dict\\n\\n result = {}\\n for key in self.ATTRIBUTES:\\n value = getattr(self, key)\\n if value:\\n result[key] = value\\n self._dict = result\\n return result\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def properties(self) -> Optional[Mapping[str, str]]:\\n return pulumi.get(self, \\\"properties\\\")\",\n \"def to_dict(self):\\n _dict = {}\\n for f in self._meta.fields:\\n if f.name == 'created':\\n _dict[f.name] = str(f.value_from_object(self))\\n else:\\n _dict[f.name] = f.value_from_object(self)\\n\\n return _dict\",\n \"def to_dict(self):\\r\\n return self.__dict__\",\n \"def properties(self):\\n return None\",\n \"def properties(self):\\n return None\",\n \"def to_dict(self):\\n return attr.asdict(self)\",\n \"def as_dict(self):\\n return self.__dict__\",\n \"def _get_model_state(self) -> dict:\\n return dict(model=self.model, kwargs=self._model_kwargs)\",\n \"def dictify(self):\\n return {\\n \\\"name\\\" : self.name,\\n \\\"lastname\\\" : self.lastname,\\n \\\"phone\\\" : self.phone,\\n \\\"email\\\" : self.email\\n }\",\n \"def to_dict(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name,\\n # \\\"created_by\\\": self.created_by,\\n # \\\"created_on\\\": self.created_on,\\n # \\\"modified_by\\\": self.modified_by,\\n # \\\"modified_on\\\": self.modified_on\\n }\",\n \"def properties(self):\\r\\n return resources.Properties(self)\",\n \"def attributes(self):\\n params = self.model.param_array\\n return {'parameters': params}\",\n \"def properties(self, pk):\\n return JsonResponse(self._get_properties(pk))\",\n \"def to_dict(self):\\n return vars(self)\",\n \"def to_dict(self):\\n\\n # Check if is the right instance.\\n if isinstance(self, db.Model):\\n # construct a dictionary from column names and values.\\n dict_representation = {c.name: getattr(self, c.name) for c in self.__table__.columns}\\n return dict_representation\\n else:\\n raise AttributeError(type(self).__name__ + \\\" is not instance of \\\" + db.Model.__name__)\",\n \"def bson_properties(self):\\n return []\",\n \"def to_dict(self):\\n return {\\n \\\"id\\\": self.id,\\n \\\"name\\\": self.name\\n }\",\n \"def get_dict(self):\\n return\",\n \"def to_dict(self):\\n return to_dict(self.__dict__)\",\n \"def to_json(self):\\n properties = self.to_dict()\\n if isinstance(self, db.Model):\\n properties['id'] = unicode(self.key().id())\\n return json.dumps(properties)\",\n \"def to_dict(self):\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\n return self.__dict__\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def config(self) -> ModelConfigDict:\\n return self.config_obj.to_dict()\",\n \"def properties(self):\\n return self.properties_with_uid[1:]\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def to_dict(self):\\n properties = {}\\n for k in self.__dict__:\\n if k in {'idsSoFar'}:\\n continue\\n else:\\n properties[k] = deepcopy(self.__dict__[k])\\n return properties\",\n \"def serialise(self):\\n return {\\n 'id': self.id,\\n 'category_id': self.category_id,\\n 'name': self.name,\\n 'description': self.description,\\n 'quantity': self.quantity,\\n 'price': self.price,\\n 'user_id': self.user_id\\n }\",\n \"def getPropertiesAll():\",\n \"def get_all_properties(self) -> dict:\\n return self._request(\\n \\\"post\\\",\\n URL,\\n json=attr.asdict(\\n Body(\\\"getAllProperties\\\", API_VERSION),\\n filter=attr.filters.exclude(attr.fields(Body).params),\\n ),\\n )\",\n \"def model_info(self):\\n if not self._model_info:\\n self._load_model_info()\\n try:\\n data = json.loads(self._model_info)\\n except (TypeError, ValueError):\\n data = {}\\n return data\",\n \"def to_dict(self):\\n return {\\n 'name': self.get_name(),\\n 'description': self.get_description()\\n }\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'name' : self.name,\\n }\",\n \"def serialize(self):\\n return {\\n 'id' : self.id,\\n 'name' : self.name,\\n }\",\n \"def as_dict(self):\\n return self.__dict__\",\n \"def to_dict(self):\\r\\n\\r\\n return {\\r\\n 'product_id': self.product_id,\\r\\n 'product_name': self.product_name\\r\\n }\",\n \"def serialize(self):\\n return {\\n 'name' : self.name,\\n 'id' : self.id,\\n }\",\n \"def asdict(self):\\n return attr.asdict(self)\",\n \"def to_dict(self) -> dict:\",\n \"def getDict(self):\\n res = {}\\n for attr, value in self.__dict__.iteritems():\\n if type(attr) is IntType or type(attr) is StringType or type(attr) is LongType or type(attr) is UnicodeType:\\n res[attr] = value\\n elif isinstance(attr, datetime.datetime):\\n res[attr] = value.isoformat('-')\\n \\n return res\",\n \"def attributes(self):\\n return self.__dict.keys()\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def dict(self):\\n return self.__dict__\",\n \"def as_dict(self):\\n return {c.key: getattr(self, c.key)\\n for c in inspect(self).mapper.column_attrs}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7751993","0.7751993","0.73391134","0.7334895","0.7297356","0.727818","0.7159078","0.71578115","0.71494967","0.71494967","0.71283495","0.71275014","0.7122587","0.71079814","0.7060394","0.7043251","0.7034103","0.70233124","0.69635814","0.69586295","0.690053","0.6881568","0.6881568","0.6857664","0.68415916","0.68122137","0.680096","0.67914945","0.6757063","0.6753585","0.6741746","0.6741746","0.6741746","0.6735291","0.67126125","0.6697801","0.6695801","0.6689893","0.6680752","0.66802895","0.66802895","0.66802895","0.66547817","0.66495687","0.6633999","0.6619567","0.6619567","0.66156983","0.66049474","0.6590706","0.6590706","0.6590206","0.6587873","0.65861845","0.65822417","0.65794736","0.65792733","0.657747","0.6571183","0.65662557","0.65637356","0.6539919","0.65396816","0.65283066","0.65252614","0.6513477","0.65098846","0.65077883","0.65077883","0.65077883","0.65077883","0.65077883","0.65077883","0.6507418","0.6505772","0.65015876","0.64951885","0.64951885","0.64951885","0.64857763","0.6474329","0.6469453","0.64684683","0.6453606","0.6453024","0.6453024","0.6430734","0.6429058","0.6426903","0.64215595","0.64201874","0.6417152","0.6414739","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.6411571","0.64035517"],"string":"[\n \"0.7751993\",\n \"0.7751993\",\n \"0.73391134\",\n \"0.7334895\",\n \"0.7297356\",\n \"0.727818\",\n \"0.7159078\",\n \"0.71578115\",\n \"0.71494967\",\n \"0.71494967\",\n \"0.71283495\",\n \"0.71275014\",\n \"0.7122587\",\n \"0.71079814\",\n \"0.7060394\",\n \"0.7043251\",\n \"0.7034103\",\n \"0.70233124\",\n \"0.69635814\",\n \"0.69586295\",\n \"0.690053\",\n \"0.6881568\",\n \"0.6881568\",\n \"0.6857664\",\n \"0.68415916\",\n \"0.68122137\",\n \"0.680096\",\n \"0.67914945\",\n \"0.6757063\",\n \"0.6753585\",\n \"0.6741746\",\n \"0.6741746\",\n \"0.6741746\",\n \"0.6735291\",\n \"0.67126125\",\n \"0.6697801\",\n \"0.6695801\",\n \"0.6689893\",\n \"0.6680752\",\n \"0.66802895\",\n \"0.66802895\",\n \"0.66802895\",\n \"0.66547817\",\n \"0.66495687\",\n \"0.6633999\",\n \"0.6619567\",\n \"0.6619567\",\n \"0.66156983\",\n \"0.66049474\",\n \"0.6590706\",\n \"0.6590706\",\n \"0.6590206\",\n \"0.6587873\",\n \"0.65861845\",\n \"0.65822417\",\n \"0.65794736\",\n \"0.65792733\",\n \"0.657747\",\n \"0.6571183\",\n \"0.65662557\",\n \"0.65637356\",\n \"0.6539919\",\n \"0.65396816\",\n \"0.65283066\",\n \"0.65252614\",\n \"0.6513477\",\n \"0.65098846\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.65077883\",\n \"0.6507418\",\n \"0.6505772\",\n \"0.65015876\",\n \"0.64951885\",\n \"0.64951885\",\n \"0.64951885\",\n \"0.64857763\",\n \"0.6474329\",\n \"0.6469453\",\n \"0.64684683\",\n \"0.6453606\",\n \"0.6453024\",\n \"0.6453024\",\n \"0.6430734\",\n \"0.6429058\",\n \"0.6426903\",\n \"0.64215595\",\n \"0.64201874\",\n \"0.6417152\",\n \"0.6414739\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.6411571\",\n \"0.64035517\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79575,"cells":{"query":{"kind":"string","value":"Returns the string representation of the model"},"document":{"kind":"string","value":"def to_str(self):\n return pprint.pformat(self.to_dict())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __str__(self):\n return super().__str__() + self.model.__str__()","def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n model_fields = get_model_fields(\n opts.model,\n foreign=False,\n m2m=False,\n exclude=self.exclude_from_str\n )\n # TODO: replace the above with the below to remove the get_model_fields call:\n # model_fields = [\n # f for f in opts.get_fields()\n # if f.concrete\n # and not (f.primary_key or f.is_relation or f.name in self.exclude_from_str)\n # ]\n result = \" \".join(\n [\n str(fld.value_from_object(self))\n for fld in model_fields\n if fld.value_from_object(self)\n ]\n )\n return result.strip() or super().__str__()","def __str__(self):\n return '%s%s' % (self.name, ' - %s' % self.model if self.model else '')","def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"","def __str__(self):\n model = self._meta.verbose_name.title()\n return f\"{model:s}: {self.name:s}\"","def __repr__(self):\n\n mod = f\"{self.__class__.__name__} Model\"\n try:\n mod += f': {self.filename}'\n except AttributeError:\n pass\n s = [mod]\n for name, v in self.metadata.items():\n s += [f\"{name:16} : {v}\"]\n return '\\n'.join(s)","def __str__(self):\n \n res = ['>>> Model %(model_name)s <<<']\n res.append('')\n res.append('Independent parameters:')\n res.append('-----------------------')\n res.append('')","def __str__(self):\n return \"DataModel(name={},attributes={},description={})\".format(\n self.name, {a.name: str(a) for a in self.attributes}, self.description\n )","def model_info(self) -> str:\n return self._model_info(self.model).decode(\"utf-8\")","def __str__(self):\n return str(self.serialize())","def __str__ (self) :\n\n return self.as_string()","def __str__(self):\n\n return self.toString()","def __str__(self):\n msg = [\n f'{self.model=}',\n f'{self.field=}',\n f'{self.fxx=}',\n f'{self.date=}',\n f'{self.priority=}',\n ]\n return '\\n'.join(msg)","def __str__(self):\n model = self._meta.verbose_name.title()\n title = self.extended_object.get_title()\n return f\"{model:s}: {title:s}\"","def __repr__(self):\n return grid_search_to_str(self.model)","def __str__(self):\n return self.toString()","def __str__(self):\n return str(self.__dict__)","def __str__(self):\n return str(self.__dict__)","def to_representation(self) -> str:\n raise NotImplementedError()","def __str__(self):\n return str(self.obj)","def __str__(self):\n return self.make_flat()","def dump_model(self):","def __str__(self):\n return str(self.__dict__['_obj'])","def __str__(self) -> str:\n model_str = [\"\\nModel info:\\n\", \" Unimodal encoder:\\n\"]\n\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_encoder[modality]}\")\n\n model_str.append(\"\\n\\n Unimodal decoder:\\n\")\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_decoder[modality]}\")\n\n if self.multimodal_decoder is not None:\n model_str.append(\"\\n\\n Multimodal decoder:\\n\")\n model_str.append(f\" {self.multimodal_decoder}\")\n\n return \"\".join(model_str)","def __repr__(self):\n s = 'text model name: ' + self.name + '\\n'\n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\n'\n s += ' most common words: ' + str(self.common_word) + '\\n'\n\n return s","def to_string(self):\r\n return self.__str__()","def __repr__(self):\n return '<ModelSignature(model_name=%r)>' % self.model_name","def __repr__(self):\n return '<ModelSignature(model_name=%r)>' % self.model_name","def __str__(self):\n return str(self.get_data())","def __str__(self):\n\n return self.raw_field","def __str__(self):\n return f\"model {self._name}\"","def serialize(self):\n\n\t\treturn str(self)","def __repr__(self):\n \n s = 'text model name: ' + self.name + '\\n' \n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\n'\n s += ' number of word stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of commas counts: ' + str(len(self.commas_per_sentence)) + '\\n'\n return s","def __str__(self):\n return self.get_str()","def serialize(self):\n\n return str(self)","def __str__(self) -> str:\n if self.name_field:\n return str(getattr(self, self.name_field))\n # noinspection PyUnresolvedReferences\n data = [\n # Collect the string representations of related objects.\n # getattr(self, fk_field.attname) and\n # fk_field.value_from_object(self) would only return the primary\n # key of the related object.\n str(getattr(self, fk_field.name))\n for fk_field in get_model_fields(\n self._meta.model, base=False, foreign=True, m2m=False\n )\n if not fk_field.null\n ]\n if len(data) < 2:\n # Cannot build a more meaningful representation than the default.\n return super().__str__()\n else:\n template = \"{}\" + \" ({})\" * (len(data) - 1)\n return template.format(*data)","def __str__(self):\n return self.s","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __str__(self):\n return self.__repr__()","def __repr__(self):\n\n # info string\n info = self.model.__repr__()\n info += \"\\n=========================\\n\"\n info += f\"Train data length:\\t\\t{ len(self.train_dataset) }\\n\"\n info += f\"Eval sata length:\\t\\t{ len(self.eval_dataset) }\\n\"\n info += f\"Optimizer:\\t\\t\\t\\t{ str(self.optimizer).split('(')[0] }\\n\"\n info += f\"Criterion:\\t\\t\\t\\t{ str(self.criterion).split('(')[0] }\\n\"\n info += f\"Training Environment:\\t{ self.device.type }\\n\"\n info += f\"Show information:\\t\\t{ 'True' if self.info else 'False' }\\n\"\n info += \"=========================\\n\"\n\n return info","def __repr__(self):\n s = 'text model name: ' + self.name + '\\n'\n s += ' number of words: ' + str(len(self.words)) + '\\n'\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\n'\n s += ' number of stems: ' + str(len(self.stems)) + '\\n'\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths))\\\n + '\\n'\n s += ' number of punctuation types: ' + str(len(self.punctuation))\n return s","def dumps(self, indent=0):\n outstr = \" \"*indent + \"MewloDbModel object '{0}' attribute values:\\n\".format(self.__class__.__name__)\n public_props = (name for name in dir(object) if not name.startswith('_'))\n for name in public_props:\n outstr += \" \"*indent + \"{0}: {1}\\n\".format(name, str(getattr(self,name)))\n return outstr","def __str__(self):\n model = self._meta.verbose_name.title()\n title = self.title or str(_(\"Empty title\"))\n\n return f\"{model:s}: {title:s}\"","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()"],"string":"[\n \"def __str__(self):\\n return super().__str__() + self.model.__str__()\",\n \"def __str__(self) -> str:\\n # noinspection PyUnresolvedReferences\\n opts = self._meta\\n if self.name_field:\\n result = str(opts.get_field(self.name_field).value_from_object(self))\\n else:\\n model_fields = get_model_fields(\\n opts.model,\\n foreign=False,\\n m2m=False,\\n exclude=self.exclude_from_str\\n )\\n # TODO: replace the above with the below to remove the get_model_fields call:\\n # model_fields = [\\n # f for f in opts.get_fields()\\n # if f.concrete\\n # and not (f.primary_key or f.is_relation or f.name in self.exclude_from_str)\\n # ]\\n result = \\\" \\\".join(\\n [\\n str(fld.value_from_object(self))\\n for fld in model_fields\\n if fld.value_from_object(self)\\n ]\\n )\\n return result.strip() or super().__str__()\",\n \"def __str__(self):\\n return '%s%s' % (self.name, ' - %s' % self.model if self.model else '')\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n return f\\\"{model:s}: {self.name:s}\\\"\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n return f\\\"{model:s}: {self.name:s}\\\"\",\n \"def __repr__(self):\\n\\n mod = f\\\"{self.__class__.__name__} Model\\\"\\n try:\\n mod += f': {self.filename}'\\n except AttributeError:\\n pass\\n s = [mod]\\n for name, v in self.metadata.items():\\n s += [f\\\"{name:16} : {v}\\\"]\\n return '\\\\n'.join(s)\",\n \"def __str__(self):\\n \\n res = ['>>> Model %(model_name)s <<<']\\n res.append('')\\n res.append('Independent parameters:')\\n res.append('-----------------------')\\n res.append('')\",\n \"def __str__(self):\\n return \\\"DataModel(name={},attributes={},description={})\\\".format(\\n self.name, {a.name: str(a) for a in self.attributes}, self.description\\n )\",\n \"def model_info(self) -> str:\\n return self._model_info(self.model).decode(\\\"utf-8\\\")\",\n \"def __str__(self):\\n return str(self.serialize())\",\n \"def __str__ (self) :\\n\\n return self.as_string()\",\n \"def __str__(self):\\n\\n return self.toString()\",\n \"def __str__(self):\\n msg = [\\n f'{self.model=}',\\n f'{self.field=}',\\n f'{self.fxx=}',\\n f'{self.date=}',\\n f'{self.priority=}',\\n ]\\n return '\\\\n'.join(msg)\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n title = self.extended_object.get_title()\\n return f\\\"{model:s}: {title:s}\\\"\",\n \"def __repr__(self):\\n return grid_search_to_str(self.model)\",\n \"def __str__(self):\\n return self.toString()\",\n \"def __str__(self):\\n return str(self.__dict__)\",\n \"def __str__(self):\\n return str(self.__dict__)\",\n \"def to_representation(self) -> str:\\n raise NotImplementedError()\",\n \"def __str__(self):\\n return str(self.obj)\",\n \"def __str__(self):\\n return self.make_flat()\",\n \"def dump_model(self):\",\n \"def __str__(self):\\n return str(self.__dict__['_obj'])\",\n \"def __str__(self) -> str:\\n model_str = [\\\"\\\\nModel info:\\\\n\\\", \\\" Unimodal encoder:\\\\n\\\"]\\n\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_encoder[modality]}\\\")\\n\\n model_str.append(\\\"\\\\n\\\\n Unimodal decoder:\\\\n\\\")\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_decoder[modality]}\\\")\\n\\n if self.multimodal_decoder is not None:\\n model_str.append(\\\"\\\\n\\\\n Multimodal decoder:\\\\n\\\")\\n model_str.append(f\\\" {self.multimodal_decoder}\\\")\\n\\n return \\\"\\\".join(model_str)\",\n \"def __repr__(self):\\n s = 'text model name: ' + self.name + '\\\\n'\\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\\\n'\\n s += ' most common words: ' + str(self.common_word) + '\\\\n'\\n\\n return s\",\n \"def to_string(self):\\r\\n return self.__str__()\",\n \"def __repr__(self):\\n return '<ModelSignature(model_name=%r)>' % self.model_name\",\n \"def __repr__(self):\\n return '<ModelSignature(model_name=%r)>' % self.model_name\",\n \"def __str__(self):\\n return str(self.get_data())\",\n \"def __str__(self):\\n\\n return self.raw_field\",\n \"def __str__(self):\\n return f\\\"model {self._name}\\\"\",\n \"def serialize(self):\\n\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n \\n s = 'text model name: ' + self.name + '\\\\n' \\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths)) + '\\\\n'\\n s += ' number of word stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of commas counts: ' + str(len(self.commas_per_sentence)) + '\\\\n'\\n return s\",\n \"def __str__(self):\\n return self.get_str()\",\n \"def serialize(self):\\n\\n return str(self)\",\n \"def __str__(self) -> str:\\n if self.name_field:\\n return str(getattr(self, self.name_field))\\n # noinspection PyUnresolvedReferences\\n data = [\\n # Collect the string representations of related objects.\\n # getattr(self, fk_field.attname) and\\n # fk_field.value_from_object(self) would only return the primary\\n # key of the related object.\\n str(getattr(self, fk_field.name))\\n for fk_field in get_model_fields(\\n self._meta.model, base=False, foreign=True, m2m=False\\n )\\n if not fk_field.null\\n ]\\n if len(data) < 2:\\n # Cannot build a more meaningful representation than the default.\\n return super().__str__()\\n else:\\n template = \\\"{}\\\" + \\\" ({})\\\" * (len(data) - 1)\\n return template.format(*data)\",\n \"def __str__(self):\\n return self.s\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __str__(self):\\n return self.__repr__()\",\n \"def __repr__(self):\\n\\n # info string\\n info = self.model.__repr__()\\n info += \\\"\\\\n=========================\\\\n\\\"\\n info += f\\\"Train data length:\\\\t\\\\t{ len(self.train_dataset) }\\\\n\\\"\\n info += f\\\"Eval sata length:\\\\t\\\\t{ len(self.eval_dataset) }\\\\n\\\"\\n info += f\\\"Optimizer:\\\\t\\\\t\\\\t\\\\t{ str(self.optimizer).split('(')[0] }\\\\n\\\"\\n info += f\\\"Criterion:\\\\t\\\\t\\\\t\\\\t{ str(self.criterion).split('(')[0] }\\\\n\\\"\\n info += f\\\"Training Environment:\\\\t{ self.device.type }\\\\n\\\"\\n info += f\\\"Show information:\\\\t\\\\t{ 'True' if self.info else 'False' }\\\\n\\\"\\n info += \\\"=========================\\\\n\\\"\\n\\n return info\",\n \"def __repr__(self):\\n s = 'text model name: ' + self.name + '\\\\n'\\n s += ' number of words: ' + str(len(self.words)) + '\\\\n'\\n s += ' number of word lengths: ' + str(len(self.word_lengths)) + '\\\\n'\\n s += ' number of stems: ' + str(len(self.stems)) + '\\\\n'\\n s += ' number of sentence lengths: ' + str(len(self.sentence_lengths))\\\\\\n + '\\\\n'\\n s += ' number of punctuation types: ' + str(len(self.punctuation))\\n return s\",\n \"def dumps(self, indent=0):\\n outstr = \\\" \\\"*indent + \\\"MewloDbModel object '{0}' attribute values:\\\\n\\\".format(self.__class__.__name__)\\n public_props = (name for name in dir(object) if not name.startswith('_'))\\n for name in public_props:\\n outstr += \\\" \\\"*indent + \\\"{0}: {1}\\\\n\\\".format(name, str(getattr(self,name)))\\n return outstr\",\n \"def __str__(self):\\n model = self._meta.verbose_name.title()\\n title = self.title or str(_(\\\"Empty title\\\"))\\n\\n return f\\\"{model:s}: {title:s}\\\"\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8585799","0.7814791","0.77903897","0.7750947","0.7750947","0.7713712","0.7699211","0.76708376","0.76511395","0.7601015","0.75830185","0.7570755","0.7540716","0.7523477","0.75169474","0.7501407","0.7487798","0.7487798","0.7470098","0.74518627","0.7446157","0.744218","0.74369943","0.7413959","0.74064153","0.7383157","0.7362038","0.7362038","0.73310906","0.73273003","0.73245645","0.7311504","0.73108083","0.7302708","0.7300278","0.729456","0.72921836","0.72901046","0.72901046","0.72901046","0.72901046","0.72901046","0.72814804","0.72625434","0.7251249","0.7244838","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753","0.72263753"],"string":"[\n \"0.8585799\",\n \"0.7814791\",\n \"0.77903897\",\n \"0.7750947\",\n \"0.7750947\",\n \"0.7713712\",\n \"0.7699211\",\n \"0.76708376\",\n \"0.76511395\",\n \"0.7601015\",\n \"0.75830185\",\n \"0.7570755\",\n \"0.7540716\",\n \"0.7523477\",\n \"0.75169474\",\n \"0.7501407\",\n \"0.7487798\",\n \"0.7487798\",\n \"0.7470098\",\n \"0.74518627\",\n \"0.7446157\",\n \"0.744218\",\n \"0.74369943\",\n \"0.7413959\",\n \"0.74064153\",\n \"0.7383157\",\n \"0.7362038\",\n \"0.7362038\",\n \"0.73310906\",\n \"0.73273003\",\n \"0.73245645\",\n \"0.7311504\",\n \"0.73108083\",\n \"0.7302708\",\n \"0.7300278\",\n \"0.729456\",\n \"0.72921836\",\n \"0.72901046\",\n \"0.72901046\",\n \"0.72901046\",\n \"0.72901046\",\n \"0.72901046\",\n \"0.72814804\",\n \"0.72625434\",\n \"0.7251249\",\n \"0.7244838\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\",\n \"0.72263753\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79576,"cells":{"query":{"kind":"string","value":"For `print` and `pprint`"},"document":{"kind":"string","value":"def __repr__(self):\n return self.to_str()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)","def print_out():\n pass","def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)","def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)","def _printable(self):\n pass","def _print_custom(self):\n pass","def pypprint(*args, **kwargs): # type: ignore\n from typing import Iterable\n\n if len(args) != 1:\n print(*args, **kwargs)\n return\n x = args[0]\n if isinstance(x, dict):\n for k, v in x.items():\n print(f\"{k}:\", v, **kwargs)\n elif isinstance(x, Iterable) and not isinstance(x, str):\n for i in x:\n print(i, **kwargs)\n else:\n print(x, **kwargs)","def pprint(self):\n # just here for defining the interface; work is done in subclasses\n pass","def print(*args, **kwargs):\n with P_LOCK:\n __builtins__.print(*args, **kwargs)","def print(self):\n # Your implementation here","def p(value):\n pp.pprint(value)","def static_print(*args, __p=print, **kwargs):\n __p(*args, **kwargs)","def print(self, *args, **kwargs):\n print(*args, **kwargs)","def pprint(self):\n print(self.pprint_str())","def pprint(obj):\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\n # Skip callables\n if hasattr(getattr(obj, argname), '__call__'):\n continue\n print(\"{} : {}\".format(argname, getattr(obj, argname)))","def print_(self, s: str) -> None:","def my_pprint(obj, intend = 0):\n if isinstance(obj, dict):\n for key, value in obj.items():\n print(intend*\" \"+str(key)+\" : \")\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, list):\n for value in obj:\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, bytes):\n print(\"<binary data>\")\n \n else:\n try:\n print(intend*\" \"+str(obj))\n except UnicodeDecodeError:\n print(intend*\" \"\"<?>\")","def test_print(chikin):\n chikin.print()","def _print(self, text):\n\t\tif self.verbose:\n\t\t\tprint text","def out(*args):\r\n print(*args)","def __pprint(object, stream=None, indent=1, width=80, depth=None):\n printer = PrettyPrinterExt(\n stream=stream, indent=indent, width=width, depth=depth)\n printer.pprint(object)","def DumpPprint(data):\n #NOTE(g): Import is done here, instead of the top of the file, to not require this module if it is not used\n import pprint\n \n text = pprint.pformat(data)\n \n return text","def repl_print_statements():\n pass","def test_03_pass_print(self):\n print('Hello World!')","def p(self):\n self.printstdout = True","def print(*args, **kwargs):\n new_args = []\n for arg in args:\n if builtins.isinstance(arg, models.Point):\n new_args.append(\"({0}, {1})\".format(arg.x, arg.y))\n else:\n new_args.append(arg)\n\n builtins.print(*new_args, **kwargs)","def real_print(*args, **kwargs):\n\n kwargs.setdefault('file', real_stdout)\n _python_print_function(*args, **kwargs)","def to_print_out(self):\n self.error_throw('output')\n\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\n self.output_div('print')\n else:\n self.output('print')","def debug_print(debug_data):\n if DEBUG_MODE == \"true\":\n pp.pprint(debug_data)","def print(self):\r\n self.print_avec_separateur()","def pprint(self):\r\n for i in self.items():\r\n print '%s => %r'%i","def pprint(self, parameter_s=''):\n ptformatter = self.shell.display_formatter.formatters['text/plain']\n ptformatter.pprint = bool(1 - ptformatter.pprint)\n print('Pretty printing has been turned',\n ['OFF','ON'][ptformatter.pprint])","def print(self):\n print(self.pretty_str())","def test_print4(self):\n writer = StringIO()\n collatz_print(writer, 1, 1, 1)\n self.assertEqual(writer.getvalue(), \"1 1 1\\n\")","def eprint(*args, **kwargs):\n\tprint(*args, file=sys.stderr, **kwargs)","def _print(self, *args, **kwargs) -> None:\n # Only print in verbose mode\n if self._verbose:\n arglist = list(args)\n arglist[0] = f\"[buddy-{self._experiment_name}] {args[0]}\"\n print(*arglist, **kwargs)","def use_pypprint_for_implicit_print(self) -> None:\n if self.implicit_print is not None:\n self.implicit_print.func.id = \"pypprint\" # type: ignore\n # Make sure we import it later\n self.undefined.add(\"pypprint\")","def test_print(self):\n writer = StringIO()\n collatz_print(writer, 1, 10, 20)\n self.assertEqual(writer.getvalue(), \"1 10 20\\n\")","def pprint(self):\n return pformat(repr(self))","def printer(message):\n if VERBOSITY:\n pprint(message)","def rec_print(p):\n if len(p) == 0:\n return\n t = p.pop(0)\n print t\n rec_print(p)","def pformat(object):\r\n return PrettyPrinter().pformat(object)","def printc(*a, **kw):\n print(*a, **kw)","def pr(x):\n Card.print_pretty_cards(x)","def debug_print(self, *content):\n if self.debug:\n print(*content)","def pprint(object, stream=None):\r\n printer = PrettyPrinter(stream=stream)\r\n printer.pprint(object)","def safe_print(*objs, errors=\"replace\"):\n\tprint(*(to_stdout(str(o), errors) for o in objs))","def magic_Pprint(self, parameter_s=''):\n \n self.shell.outputcache.Pprint = 1 - self.shell.outputcache.Pprint\n print 'Pretty printing has been turned', \\\n ['OFF','ON'][self.shell.outputcache.Pprint]","def print_output(tree):\n print_value(tree)\n print_tree(tree)","def _Print(self, t):\n self.RaiseError(t, \"Print not supported\")","def vprint(*args, **kwargs ):\n\n forceprint = False\n for key in kwargs:\n if key == \"forceprint\":\n forceprint =kwargs[key]\n \n line = ''\n if debug or forceprint : \n for arg in args:\n line += str(arg) +\" \"\n log = open(exepath + 'pyframe.log', 'a') \n log.write(line + \"\\n\")\n log.close() \n print line","def eprint(*pargs, **kargs):\n print('\\u001b[31m', end='', file=sys.stderr)\n print(*pargs, file=sys.stderr, **kargs)\n print('\\u001b[0m', end='', file=sys.stderr)","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def printer(obj, ident=''):\n import inspect\n print ident + obj.__class__.__name__.upper()\n ident += ' '\n lists = []\n for name in dir(obj):\n elem = getattr(obj, name)\n if isinstance(elem, list) and name != u'decoded_content':\n lists.append(elem)\n elif not inspect.ismethod(elem):\n if not name.startswith('__'):\n if name == u'data' and elem:\n print ident + u'data = '\n printer(elem, ident + ' ')\n else:\n print ident + u'%s\\t= %s' % (name, getattr(obj, name))\n for l in lists:\n for i in l:\n printer(i, ident + ' ')","def _print(self, *args):\n return _ida_hexrays.qstring_printer_t__print(self, *args)","def pprint(self):\n def pprintStr(node):\n s = \"(\" + str(node.value) \n for action in node.children:\n s = s + \", \" + pprintStr(node.children[action])\n s = s + \")\"\n return s\n\n print pprintStr(self)","def hook_print():\n sys.stdout = PrintHook()","def cmdPrint( self, *args):\n return self.cmd( *args, **{ 'verbose': True } )","def print_list(self):\r\n pass","def debugprint(obj, depth=-1, print_type=False,\r\n file=None, ids='CHAR', stop_on_name=False):\r\n if file == 'str':\r\n _file = StringIO()\r\n elif file is None:\r\n _file = sys.stdout\r\n else:\r\n _file = file\r\n done = dict()\r\n results_to_print = []\r\n order = []\r\n if isinstance(obj, gof.Variable):\r\n results_to_print.append(obj)\r\n elif isinstance(obj, gof.Apply):\r\n results_to_print.extend(obj.outputs)\r\n elif isinstance(obj, Function):\r\n results_to_print.extend(obj.maker.fgraph.outputs)\r\n order = obj.maker.fgraph.toposort()\r\n elif isinstance(obj, (list, tuple)):\r\n results_to_print.extend(obj)\r\n elif isinstance(obj, gof.FunctionGraph):\r\n results_to_print.extend(obj.outputs)\r\n order = obj.toposort()\r\n elif isinstance(obj, (int, long, float, numpy.ndarray)):\r\n print obj\r\n else:\r\n raise TypeError(\"debugprint cannot print an object of this type\", obj)\r\n for r in results_to_print:\r\n debugmode.debugprint(r, depth=depth, done=done, print_type=print_type,\r\n file=_file, order=order, ids=ids,\r\n stop_on_name=stop_on_name)\r\n if file is _file:\r\n return file\r\n elif file == 'str':\r\n return _file.getvalue()\r\n else:\r\n _file.flush()","def _get_print_fn(file=sys.stdout):\n def _print_fn(op, xin,):\n for attr in op.attrs:\n temp = getattr(xin, attr)\n if callable(temp):\n pmsg = temp()\n else:\n pmsg = temp\n print(op.message, attr, '=', pmsg, file=file)\n return _print_fn","def test_print1(self):\n writer = StringIO()\n collatz_print(writer, 100, 200, 125)\n self.assertEqual(writer.getvalue(), \"100 200 125\\n\")","def printOutput(self):\n pass","def _print(self, *args):\n return _ida_hexrays.cnumber_t__print(self, *args)","def setPrint():\n (e,d,sr,sw) = codecs.lookup('utf-8')\n unicode_to_utf8 = sw(sys.stdout)\n sys.stdout = unicode_to_utf8","def pr(string, verbose):\n if(verbose):\n print(string)","def print(*args, sep=\" \"):\n pass","def printv(self, *arg):\n if self.verbose:\n print(*arg)","def print(self):\n\n print(self)","def _p(self, *args, level=2, **kwargs):\n if self._verbosity >= level:\n print(*args, **kwargs)","def test_print2(self):\n writer = StringIO()\n collatz_print(writer, 201, 210, 89)\n self.assertEqual(writer.getvalue(), \"201 210 89\\n\")","def print_pointers(self):\n\n ### FILL IN ###","def foo_printer(self):\n print(\"\\nHi I'm {}\".format(self.foo))","def printed(method):\n\t\tdef wrapper(cls, *args):\n\t\t\tif cls.verbose:\n\t\t\t\treturn method(cls, *args)\n\t\treturn wrapper","def print_me(self, tabs=0, tab=' '):\n pre = tab*tabs\n print(pre+'Producer:')\n print(pre+' produces:', self._produces)\n print(pre+' consumes:', self._consumes)\n print(pre+' transfer:', self._transfer)\n print(pre+' capacity:', self._capacity)","def _print(cls, quad):\n\t\tprint(\"\\nLIGHT OUTPUT:\\n<<<<{}>>>>\".format(ast.literal_eval(str(cls.get_address_value(quad.result)))))\n\t\tprint(\"END\")\n\n\t\tvar = cls.get_address_value(quad.result)\n\t\tif isinstance(var, collections.Iterable):\n\t\t\tprint(\"DEEP COPY\")\n\t\t\tcls.print_queue.enqueue(copy.deepcopy(var))\n\t\telse:\n\t\t\tcls.print_queue.enqueue(var)","def printout(*args, **kwargs):\n console_print(sys.stdout, *args, **kwargs)","def pprint(x):\n if is_theano_object(x):\n return _gettheano().printing.pprint(x)\n else:\n return str(x)","def PrettyPrint(self):\r\n print(self.data)\r\n return","def print(self):\n self.print_avec_separateur(\" \")","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def eprint(*args, **kwargs):\n print(*args, file=sys.stderr, **kwargs)","def pprint_helper(self, angle, indent):\n # just here for defining the interface; work is done in subclasses\n pass","def _pprint(params, offset=0, printer=repr):\n # Do a multi-line justified repr:\n param_names = [p for p in params.keys() if p is not \"cost\"]\n param_names.sort()\n\n params_list = list()\n this_line_length = offset\n line_sep = ',\\n' + (1 + offset // 2) * ' '\n for i, name in enumerate(param_names):\n value = params[name]\n if isinstance(value, float):\n this_repr = '%s=%s' % (name, str(value))\n else:\n this_repr = '%s=%s' % (name, printer(value))\n if len(this_repr) > 500:\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\n if i > 0:\n if (this_line_length + len(this_repr) >= 75 or '\\n' in this_repr):\n params_list.append(line_sep)\n this_line_length = len(line_sep)\n else:\n params_list.append(', ')\n this_line_length += 2\n params_list.append(this_repr)\n this_line_length += len(this_repr)\n # options = np.get_printoptions()\n # np.set_printoptions(**options)\n lines = ''.join(params_list)\n # Strip trailing space to avoid nightmare in doctests\n lines = '\\n'.join(l.rstrip(' ') for l in lines.split('\\n'))\n return lines","def init_printing(pretty_print=True, order=None, use_unicode=None):\n if pretty_print:\n stringify_func = lambda arg: pretty(arg, order=order, use_unicode=use_unicode)\n else:\n stringify_func = sstrrepr\n\n try:\n import IPython\n\n ip = IPython.ipapi.get()\n\n if ip is not None:\n def result_display(self, arg):\n \"\"\"IPython's pretty-printer display hook.\n\n This function was adapted from:\n\n ipython/IPython/hooks.py:155\n\n \"\"\"\n if self.rc.pprint:\n out = stringify_func(arg)\n\n if '\\n' in out:\n print\n\n print out\n else:\n print repr(arg)\n\n ip.set_hook('result_display', result_display)\n return\n except ImportError:\n pass\n\n import __builtin__, sys\n\n def displayhook(arg):\n \"\"\"Python's pretty-printer display hook.\n\n This function was adapted from:\n\n http://www.python.org/dev/peps/pep-0217/\n\n \"\"\"\n if arg is not None:\n __builtin__._ = None\n print stringify_func(arg)\n __builtin__._ = arg\n\n sys.displayhook = displayhook","def print_verbose(self) -> None:\n print(self)\n if self.meta is not None:\n print(self.meta.__repr__())","def _print(self, *args):\n return _ida_hexrays.cinsn_t__print(self, *args)","def my_print(self):\n if self.__size == 0:\n print(\"\")\n return\n [print(\"\") for x in range(0, self.__position[1])]\n for i in range(0, self.__size):\n [print(\" \", end=\"\") for i in range(0, self.__position[0])]\n [print(\"#\", end=\"\") for j in range(0, self.__size)]\n print(\"\")","def sequential_print_statements():\n pass","def print_post():\n print('| | |'),","def print_(*args, **kwargs):\r\n fp = kwargs.pop(\"file\", sys.stdout)\r\n if fp is None:\r\n return\r\n def write(data):\r\n if not isinstance(data, basestring):\r\n data = str(data)\r\n fp.write(data)\r\n want_unicode = False\r\n sep = kwargs.pop(\"sep\", None)\r\n if sep is not None:\r\n if isinstance(sep, unicode):\r\n want_unicode = True\r\n elif not isinstance(sep, str):\r\n raise TypeError(\"sep must be None or a string\")\r\n end = kwargs.pop(\"end\", None)\r\n if end is not None:\r\n if isinstance(end, unicode):\r\n want_unicode = True\r\n elif not isinstance(end, str):\r\n raise TypeError(\"end must be None or a string\")\r\n if kwargs:\r\n raise TypeError(\"invalid keyword arguments to print()\")\r\n if not want_unicode:\r\n for arg in args:\r\n if isinstance(arg, unicode):\r\n want_unicode = True\r\n break\r\n if want_unicode:\r\n newline = unicode(\"\\n\")\r\n space = unicode(\" \")\r\n else:\r\n newline = \"\\n\"\r\n space = \" \"\r\n if sep is None:\r\n sep = space\r\n if end is None:\r\n end = newline\r\n for i, arg in enumerate(args):\r\n if i:\r\n write(sep)\r\n write(arg)\r\n write(end)","def print_(*args, **kwargs):\r\n fp = kwargs.pop(\"file\", sys.stdout)\r\n if fp is None:\r\n return\r\n def write(data):\r\n if not isinstance(data, basestring):\r\n data = str(data)\r\n fp.write(data)\r\n want_unicode = False\r\n sep = kwargs.pop(\"sep\", None)\r\n if sep is not None:\r\n if isinstance(sep, unicode):\r\n want_unicode = True\r\n elif not isinstance(sep, str):\r\n raise TypeError(\"sep must be None or a string\")\r\n end = kwargs.pop(\"end\", None)\r\n if end is not None:\r\n if isinstance(end, unicode):\r\n want_unicode = True\r\n elif not isinstance(end, str):\r\n raise TypeError(\"end must be None or a string\")\r\n if kwargs:\r\n raise TypeError(\"invalid keyword arguments to print()\")\r\n if not want_unicode:\r\n for arg in args:\r\n if isinstance(arg, unicode):\r\n want_unicode = True\r\n break\r\n if want_unicode:\r\n newline = unicode(\"\\n\")\r\n space = unicode(\" \")\r\n else:\r\n newline = \"\\n\"\r\n space = \" \"\r\n if sep is None:\r\n sep = space\r\n if end is None:\r\n end = newline\r\n for i, arg in enumerate(args):\r\n if i:\r\n write(sep)\r\n write(arg)\r\n write(end)","def debugprint(r, prefix='', depth=-1, done=None, print_type=False,\r\n file=sys.stdout, print_destroy_map=False,\r\n print_view_map=False, order=None, ids='CHAR',\r\n stop_on_name=False, prefix_child=None):\r\n if depth == 0:\r\n return\r\n\r\n if order is None:\r\n order = []\r\n\r\n if done is None:\r\n done = dict()\r\n\r\n if print_type:\r\n type_str = ' <%s>' % r.type\r\n else:\r\n type_str = ''\r\n\r\n if prefix_child is None:\r\n prefix_child = prefix\r\n\r\n def get_id_str(obj):\r\n if obj in done:\r\n id_str = done[obj]\r\n elif ids == \"id\":\r\n id_str = \"[@%s]\" % str(id(r))\r\n elif ids == \"int\":\r\n id_str = \"[@%s]\" % str(len(done))\r\n elif ids == \"CHAR\":\r\n id_str = \"[@%s]\" % char_from_number(len(done))\r\n elif ids == \"\":\r\n id_str = \"\"\r\n done[obj] = id_str\r\n return id_str\r\n\r\n if hasattr(r.owner, 'op'):\r\n # this variable is the output of computation,\r\n # so just print out the apply\r\n a = r.owner\r\n\r\n r_name = getattr(r, 'name', '')\r\n # normally if the name isn't set, it'll be None, so\r\n # r_name is None here\r\n if r_name is None:\r\n r_name = ''\r\n\r\n if print_destroy_map:\r\n destroy_map_str = str(getattr(r.owner.op, 'destroy_map', ''))\r\n else:\r\n destroy_map_str = ''\r\n\r\n if print_view_map:\r\n view_map_str = str(getattr(r.owner.op, 'view_map', ''))\r\n else:\r\n view_map_str = ''\r\n if destroy_map_str and destroy_map_str != '{}':\r\n destroy_map_str = 'd=' + destroy_map_str\r\n if view_map_str and view_map_str != '{}':\r\n view_map_str = 'v=' + view_map_str\r\n\r\n o = ''\r\n if order:\r\n o = str(order.index(r.owner))\r\n already_printed = a in done # get_id_str put it in the dict\r\n id_str = get_id_str(a)\r\n\r\n if len(a.outputs) == 1:\r\n print >> file, '%s%s %s%s \\'%s\\' %s %s %s' % (prefix, a.op,\r\n id_str,\r\n type_str, r_name,\r\n destroy_map_str,\r\n view_map_str,\r\n o)\r\n else:\r\n print >> file, '%s%s.%i %s%s \\'%s\\' %s %s %s' % (prefix, a.op,\r\n a.outputs.index(r),\r\n id_str, type_str,\r\n r_name,\r\n destroy_map_str,\r\n view_map_str,\r\n o)\r\n if not already_printed:\r\n if (not stop_on_name or\r\n not (hasattr(r, 'name') and r.name is not None)):\r\n new_prefix = prefix_child + ' |'\r\n new_prefix_child = prefix_child + ' |'\r\n for idx, i in enumerate(a.inputs):\r\n if idx == len(a.inputs) - 1:\r\n new_prefix_child = prefix_child + ' '\r\n\r\n debugprint(i, new_prefix, depth=depth - 1, done=done,\r\n print_type=print_type, file=file, order=order,\r\n ids=ids, stop_on_name=stop_on_name,\r\n prefix_child=new_prefix_child)\r\n else:\r\n #this is an input variable\r\n id_str = get_id_str(r)\r\n print >> file, '%s%s %s%s' % (prefix, r, id_str, type_str)\r\n\r\n return file","def bpprint(self, out=None):\n if out is None:\n out = sys.stdout\n print(self.bpformat(), file=out)","def vprint(expr, **settings):\n\n outstr = vsprint(expr, **settings)\n\n import builtins\n if (outstr != 'None'):\n builtins._ = outstr\n print(outstr)","def _pprint(params, offset=0, printer=repr):\n # Do a multi-line justified repr:\n options = numpy.get_printoptions()\n numpy.set_printoptions(precision=5, threshold=64, edgeitems=2)\n params_list = list()\n this_line_length = offset\n line_sep = ',\\n' + (1 + offset // 2) * ' '\n for i, (k, v) in enumerate(sorted(params.items())):\n if isinstance(v, float):\n # use str for representing floating point numbers\n # this way we get consistent representation across\n # architectures and versions.\n this_repr = '%s=%s' % (k, str(v))\n else:\n # use repr of the rest\n this_repr = '%s=%s' % (k, printer(v))\n if len(this_repr) > 500:\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\n if i > 0:\n if this_line_length + len(this_repr) >= 75 or '\\n' in this_repr:\n params_list.append(line_sep)\n this_line_length = len(line_sep)\n else:\n params_list.append(', ')\n this_line_length += 2\n params_list.append(this_repr)\n this_line_length += len(this_repr)\n\n numpy.set_printoptions(**options)\n lines = ''.join(params_list)\n # Strip trailing space to avoid nightmare in doctests\n lines = '\\n'.join(l.rstrip(' ') for l in lines.split('\\n'))\n return lines","def print_(*args, **kwargs):\n fp = kwargs.pop(\"file\", sys.stdout)\n if fp is None:\n return\n\n def write(data):\n if not isinstance(data, basestring):\n data = str(data)\n fp.write(data)\n want_unicode = False\n sep = kwargs.pop(\"sep\", None)\n if sep is not None:\n if isinstance(sep, unicode):\n want_unicode = True\n elif not isinstance(sep, str):\n raise TypeError(\"sep must be None or a string\")\n end = kwargs.pop(\"end\", None)\n if end is not None:\n if isinstance(end, unicode):\n want_unicode = True\n elif not isinstance(end, str):\n raise TypeError(\"end must be None or a string\")\n if kwargs:\n raise TypeError(\"invalid keyword arguments to print()\")\n if not want_unicode:\n for arg in args:\n if isinstance(arg, unicode):\n want_unicode = True\n break\n if want_unicode:\n newline = unicode(\"\\n\")\n space = unicode(\" \")\n else:\n newline = \"\\n\"\n space = \" \"\n if sep is None:\n sep = space\n if end is None:\n end = newline\n for i, arg in enumerate(args):\n if i:\n write(sep)\n write(arg)\n write(end)"],"string":"[\n \"def pprint(*args, **kwargs):\\n if PRINTING:\\n print(*args, **kwargs)\",\n \"def print_out():\\n pass\",\n \"def custom_print(*objects):\\n print(*objects, sep=OFS, end=ORS)\",\n \"def _print(self, *args):\\n return _ida_hexrays.vd_printer_t__print(self, *args)\",\n \"def _printable(self):\\n pass\",\n \"def _print_custom(self):\\n pass\",\n \"def pypprint(*args, **kwargs): # type: ignore\\n from typing import Iterable\\n\\n if len(args) != 1:\\n print(*args, **kwargs)\\n return\\n x = args[0]\\n if isinstance(x, dict):\\n for k, v in x.items():\\n print(f\\\"{k}:\\\", v, **kwargs)\\n elif isinstance(x, Iterable) and not isinstance(x, str):\\n for i in x:\\n print(i, **kwargs)\\n else:\\n print(x, **kwargs)\",\n \"def pprint(self):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def print(*args, **kwargs):\\n with P_LOCK:\\n __builtins__.print(*args, **kwargs)\",\n \"def print(self):\\n # Your implementation here\",\n \"def p(value):\\n pp.pprint(value)\",\n \"def static_print(*args, __p=print, **kwargs):\\n __p(*args, **kwargs)\",\n \"def print(self, *args, **kwargs):\\n print(*args, **kwargs)\",\n \"def pprint(self):\\n print(self.pprint_str())\",\n \"def pprint(obj):\\n for argname in sorted([x for x in dir(obj) if not x.startswith('__')]):\\n # Skip callables\\n if hasattr(getattr(obj, argname), '__call__'):\\n continue\\n print(\\\"{} : {}\\\".format(argname, getattr(obj, argname)))\",\n \"def print_(self, s: str) -> None:\",\n \"def my_pprint(obj, intend = 0):\\n if isinstance(obj, dict):\\n for key, value in obj.items():\\n print(intend*\\\" \\\"+str(key)+\\\" : \\\")\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, list):\\n for value in obj:\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, bytes):\\n print(\\\"<binary data>\\\")\\n \\n else:\\n try:\\n print(intend*\\\" \\\"+str(obj))\\n except UnicodeDecodeError:\\n print(intend*\\\" \\\"\\\"<?>\\\")\",\n \"def test_print(chikin):\\n chikin.print()\",\n \"def _print(self, text):\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint text\",\n \"def out(*args):\\r\\n print(*args)\",\n \"def __pprint(object, stream=None, indent=1, width=80, depth=None):\\n printer = PrettyPrinterExt(\\n stream=stream, indent=indent, width=width, depth=depth)\\n printer.pprint(object)\",\n \"def DumpPprint(data):\\n #NOTE(g): Import is done here, instead of the top of the file, to not require this module if it is not used\\n import pprint\\n \\n text = pprint.pformat(data)\\n \\n return text\",\n \"def repl_print_statements():\\n pass\",\n \"def test_03_pass_print(self):\\n print('Hello World!')\",\n \"def p(self):\\n self.printstdout = True\",\n \"def print(*args, **kwargs):\\n new_args = []\\n for arg in args:\\n if builtins.isinstance(arg, models.Point):\\n new_args.append(\\\"({0}, {1})\\\".format(arg.x, arg.y))\\n else:\\n new_args.append(arg)\\n\\n builtins.print(*new_args, **kwargs)\",\n \"def real_print(*args, **kwargs):\\n\\n kwargs.setdefault('file', real_stdout)\\n _python_print_function(*args, **kwargs)\",\n \"def to_print_out(self):\\n self.error_throw('output')\\n\\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\\n self.output_div('print')\\n else:\\n self.output('print')\",\n \"def debug_print(debug_data):\\n if DEBUG_MODE == \\\"true\\\":\\n pp.pprint(debug_data)\",\n \"def print(self):\\r\\n self.print_avec_separateur()\",\n \"def pprint(self):\\r\\n for i in self.items():\\r\\n print '%s => %r'%i\",\n \"def pprint(self, parameter_s=''):\\n ptformatter = self.shell.display_formatter.formatters['text/plain']\\n ptformatter.pprint = bool(1 - ptformatter.pprint)\\n print('Pretty printing has been turned',\\n ['OFF','ON'][ptformatter.pprint])\",\n \"def print(self):\\n print(self.pretty_str())\",\n \"def test_print4(self):\\n writer = StringIO()\\n collatz_print(writer, 1, 1, 1)\\n self.assertEqual(writer.getvalue(), \\\"1 1 1\\\\n\\\")\",\n \"def eprint(*args, **kwargs):\\n\\tprint(*args, file=sys.stderr, **kwargs)\",\n \"def _print(self, *args, **kwargs) -> None:\\n # Only print in verbose mode\\n if self._verbose:\\n arglist = list(args)\\n arglist[0] = f\\\"[buddy-{self._experiment_name}] {args[0]}\\\"\\n print(*arglist, **kwargs)\",\n \"def use_pypprint_for_implicit_print(self) -> None:\\n if self.implicit_print is not None:\\n self.implicit_print.func.id = \\\"pypprint\\\" # type: ignore\\n # Make sure we import it later\\n self.undefined.add(\\\"pypprint\\\")\",\n \"def test_print(self):\\n writer = StringIO()\\n collatz_print(writer, 1, 10, 20)\\n self.assertEqual(writer.getvalue(), \\\"1 10 20\\\\n\\\")\",\n \"def pprint(self):\\n return pformat(repr(self))\",\n \"def printer(message):\\n if VERBOSITY:\\n pprint(message)\",\n \"def rec_print(p):\\n if len(p) == 0:\\n return\\n t = p.pop(0)\\n print t\\n rec_print(p)\",\n \"def pformat(object):\\r\\n return PrettyPrinter().pformat(object)\",\n \"def printc(*a, **kw):\\n print(*a, **kw)\",\n \"def pr(x):\\n Card.print_pretty_cards(x)\",\n \"def debug_print(self, *content):\\n if self.debug:\\n print(*content)\",\n \"def pprint(object, stream=None):\\r\\n printer = PrettyPrinter(stream=stream)\\r\\n printer.pprint(object)\",\n \"def safe_print(*objs, errors=\\\"replace\\\"):\\n\\tprint(*(to_stdout(str(o), errors) for o in objs))\",\n \"def magic_Pprint(self, parameter_s=''):\\n \\n self.shell.outputcache.Pprint = 1 - self.shell.outputcache.Pprint\\n print 'Pretty printing has been turned', \\\\\\n ['OFF','ON'][self.shell.outputcache.Pprint]\",\n \"def print_output(tree):\\n print_value(tree)\\n print_tree(tree)\",\n \"def _Print(self, t):\\n self.RaiseError(t, \\\"Print not supported\\\")\",\n \"def vprint(*args, **kwargs ):\\n\\n forceprint = False\\n for key in kwargs:\\n if key == \\\"forceprint\\\":\\n forceprint =kwargs[key]\\n \\n line = ''\\n if debug or forceprint : \\n for arg in args:\\n line += str(arg) +\\\" \\\"\\n log = open(exepath + 'pyframe.log', 'a') \\n log.write(line + \\\"\\\\n\\\")\\n log.close() \\n print line\",\n \"def eprint(*pargs, **kargs):\\n print('\\\\u001b[31m', end='', file=sys.stderr)\\n print(*pargs, file=sys.stderr, **kargs)\\n print('\\\\u001b[0m', end='', file=sys.stderr)\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def printer(obj, ident=''):\\n import inspect\\n print ident + obj.__class__.__name__.upper()\\n ident += ' '\\n lists = []\\n for name in dir(obj):\\n elem = getattr(obj, name)\\n if isinstance(elem, list) and name != u'decoded_content':\\n lists.append(elem)\\n elif not inspect.ismethod(elem):\\n if not name.startswith('__'):\\n if name == u'data' and elem:\\n print ident + u'data = '\\n printer(elem, ident + ' ')\\n else:\\n print ident + u'%s\\\\t= %s' % (name, getattr(obj, name))\\n for l in lists:\\n for i in l:\\n printer(i, ident + ' ')\",\n \"def _print(self, *args):\\n return _ida_hexrays.qstring_printer_t__print(self, *args)\",\n \"def pprint(self):\\n def pprintStr(node):\\n s = \\\"(\\\" + str(node.value) \\n for action in node.children:\\n s = s + \\\", \\\" + pprintStr(node.children[action])\\n s = s + \\\")\\\"\\n return s\\n\\n print pprintStr(self)\",\n \"def hook_print():\\n sys.stdout = PrintHook()\",\n \"def cmdPrint( self, *args):\\n return self.cmd( *args, **{ 'verbose': True } )\",\n \"def print_list(self):\\r\\n pass\",\n \"def debugprint(obj, depth=-1, print_type=False,\\r\\n file=None, ids='CHAR', stop_on_name=False):\\r\\n if file == 'str':\\r\\n _file = StringIO()\\r\\n elif file is None:\\r\\n _file = sys.stdout\\r\\n else:\\r\\n _file = file\\r\\n done = dict()\\r\\n results_to_print = []\\r\\n order = []\\r\\n if isinstance(obj, gof.Variable):\\r\\n results_to_print.append(obj)\\r\\n elif isinstance(obj, gof.Apply):\\r\\n results_to_print.extend(obj.outputs)\\r\\n elif isinstance(obj, Function):\\r\\n results_to_print.extend(obj.maker.fgraph.outputs)\\r\\n order = obj.maker.fgraph.toposort()\\r\\n elif isinstance(obj, (list, tuple)):\\r\\n results_to_print.extend(obj)\\r\\n elif isinstance(obj, gof.FunctionGraph):\\r\\n results_to_print.extend(obj.outputs)\\r\\n order = obj.toposort()\\r\\n elif isinstance(obj, (int, long, float, numpy.ndarray)):\\r\\n print obj\\r\\n else:\\r\\n raise TypeError(\\\"debugprint cannot print an object of this type\\\", obj)\\r\\n for r in results_to_print:\\r\\n debugmode.debugprint(r, depth=depth, done=done, print_type=print_type,\\r\\n file=_file, order=order, ids=ids,\\r\\n stop_on_name=stop_on_name)\\r\\n if file is _file:\\r\\n return file\\r\\n elif file == 'str':\\r\\n return _file.getvalue()\\r\\n else:\\r\\n _file.flush()\",\n \"def _get_print_fn(file=sys.stdout):\\n def _print_fn(op, xin,):\\n for attr in op.attrs:\\n temp = getattr(xin, attr)\\n if callable(temp):\\n pmsg = temp()\\n else:\\n pmsg = temp\\n print(op.message, attr, '=', pmsg, file=file)\\n return _print_fn\",\n \"def test_print1(self):\\n writer = StringIO()\\n collatz_print(writer, 100, 200, 125)\\n self.assertEqual(writer.getvalue(), \\\"100 200 125\\\\n\\\")\",\n \"def printOutput(self):\\n pass\",\n \"def _print(self, *args):\\n return _ida_hexrays.cnumber_t__print(self, *args)\",\n \"def setPrint():\\n (e,d,sr,sw) = codecs.lookup('utf-8')\\n unicode_to_utf8 = sw(sys.stdout)\\n sys.stdout = unicode_to_utf8\",\n \"def pr(string, verbose):\\n if(verbose):\\n print(string)\",\n \"def print(*args, sep=\\\" \\\"):\\n pass\",\n \"def printv(self, *arg):\\n if self.verbose:\\n print(*arg)\",\n \"def print(self):\\n\\n print(self)\",\n \"def _p(self, *args, level=2, **kwargs):\\n if self._verbosity >= level:\\n print(*args, **kwargs)\",\n \"def test_print2(self):\\n writer = StringIO()\\n collatz_print(writer, 201, 210, 89)\\n self.assertEqual(writer.getvalue(), \\\"201 210 89\\\\n\\\")\",\n \"def print_pointers(self):\\n\\n ### FILL IN ###\",\n \"def foo_printer(self):\\n print(\\\"\\\\nHi I'm {}\\\".format(self.foo))\",\n \"def printed(method):\\n\\t\\tdef wrapper(cls, *args):\\n\\t\\t\\tif cls.verbose:\\n\\t\\t\\t\\treturn method(cls, *args)\\n\\t\\treturn wrapper\",\n \"def print_me(self, tabs=0, tab=' '):\\n pre = tab*tabs\\n print(pre+'Producer:')\\n print(pre+' produces:', self._produces)\\n print(pre+' consumes:', self._consumes)\\n print(pre+' transfer:', self._transfer)\\n print(pre+' capacity:', self._capacity)\",\n \"def _print(cls, quad):\\n\\t\\tprint(\\\"\\\\nLIGHT OUTPUT:\\\\n<<<<{}>>>>\\\".format(ast.literal_eval(str(cls.get_address_value(quad.result)))))\\n\\t\\tprint(\\\"END\\\")\\n\\n\\t\\tvar = cls.get_address_value(quad.result)\\n\\t\\tif isinstance(var, collections.Iterable):\\n\\t\\t\\tprint(\\\"DEEP COPY\\\")\\n\\t\\t\\tcls.print_queue.enqueue(copy.deepcopy(var))\\n\\t\\telse:\\n\\t\\t\\tcls.print_queue.enqueue(var)\",\n \"def printout(*args, **kwargs):\\n console_print(sys.stdout, *args, **kwargs)\",\n \"def pprint(x):\\n if is_theano_object(x):\\n return _gettheano().printing.pprint(x)\\n else:\\n return str(x)\",\n \"def PrettyPrint(self):\\r\\n print(self.data)\\r\\n return\",\n \"def print(self):\\n self.print_avec_separateur(\\\" \\\")\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def eprint(*args, **kwargs):\\n print(*args, file=sys.stderr, **kwargs)\",\n \"def pprint_helper(self, angle, indent):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def _pprint(params, offset=0, printer=repr):\\n # Do a multi-line justified repr:\\n param_names = [p for p in params.keys() if p is not \\\"cost\\\"]\\n param_names.sort()\\n\\n params_list = list()\\n this_line_length = offset\\n line_sep = ',\\\\n' + (1 + offset // 2) * ' '\\n for i, name in enumerate(param_names):\\n value = params[name]\\n if isinstance(value, float):\\n this_repr = '%s=%s' % (name, str(value))\\n else:\\n this_repr = '%s=%s' % (name, printer(value))\\n if len(this_repr) > 500:\\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\\n if i > 0:\\n if (this_line_length + len(this_repr) >= 75 or '\\\\n' in this_repr):\\n params_list.append(line_sep)\\n this_line_length = len(line_sep)\\n else:\\n params_list.append(', ')\\n this_line_length += 2\\n params_list.append(this_repr)\\n this_line_length += len(this_repr)\\n # options = np.get_printoptions()\\n # np.set_printoptions(**options)\\n lines = ''.join(params_list)\\n # Strip trailing space to avoid nightmare in doctests\\n lines = '\\\\n'.join(l.rstrip(' ') for l in lines.split('\\\\n'))\\n return lines\",\n \"def init_printing(pretty_print=True, order=None, use_unicode=None):\\n if pretty_print:\\n stringify_func = lambda arg: pretty(arg, order=order, use_unicode=use_unicode)\\n else:\\n stringify_func = sstrrepr\\n\\n try:\\n import IPython\\n\\n ip = IPython.ipapi.get()\\n\\n if ip is not None:\\n def result_display(self, arg):\\n \\\"\\\"\\\"IPython's pretty-printer display hook.\\n\\n This function was adapted from:\\n\\n ipython/IPython/hooks.py:155\\n\\n \\\"\\\"\\\"\\n if self.rc.pprint:\\n out = stringify_func(arg)\\n\\n if '\\\\n' in out:\\n print\\n\\n print out\\n else:\\n print repr(arg)\\n\\n ip.set_hook('result_display', result_display)\\n return\\n except ImportError:\\n pass\\n\\n import __builtin__, sys\\n\\n def displayhook(arg):\\n \\\"\\\"\\\"Python's pretty-printer display hook.\\n\\n This function was adapted from:\\n\\n http://www.python.org/dev/peps/pep-0217/\\n\\n \\\"\\\"\\\"\\n if arg is not None:\\n __builtin__._ = None\\n print stringify_func(arg)\\n __builtin__._ = arg\\n\\n sys.displayhook = displayhook\",\n \"def print_verbose(self) -> None:\\n print(self)\\n if self.meta is not None:\\n print(self.meta.__repr__())\",\n \"def _print(self, *args):\\n return _ida_hexrays.cinsn_t__print(self, *args)\",\n \"def my_print(self):\\n if self.__size == 0:\\n print(\\\"\\\")\\n return\\n [print(\\\"\\\") for x in range(0, self.__position[1])]\\n for i in range(0, self.__size):\\n [print(\\\" \\\", end=\\\"\\\") for i in range(0, self.__position[0])]\\n [print(\\\"#\\\", end=\\\"\\\") for j in range(0, self.__size)]\\n print(\\\"\\\")\",\n \"def sequential_print_statements():\\n pass\",\n \"def print_post():\\n print('| | |'),\",\n \"def print_(*args, **kwargs):\\r\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\r\\n if fp is None:\\r\\n return\\r\\n def write(data):\\r\\n if not isinstance(data, basestring):\\r\\n data = str(data)\\r\\n fp.write(data)\\r\\n want_unicode = False\\r\\n sep = kwargs.pop(\\\"sep\\\", None)\\r\\n if sep is not None:\\r\\n if isinstance(sep, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(sep, str):\\r\\n raise TypeError(\\\"sep must be None or a string\\\")\\r\\n end = kwargs.pop(\\\"end\\\", None)\\r\\n if end is not None:\\r\\n if isinstance(end, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(end, str):\\r\\n raise TypeError(\\\"end must be None or a string\\\")\\r\\n if kwargs:\\r\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\r\\n if not want_unicode:\\r\\n for arg in args:\\r\\n if isinstance(arg, unicode):\\r\\n want_unicode = True\\r\\n break\\r\\n if want_unicode:\\r\\n newline = unicode(\\\"\\\\n\\\")\\r\\n space = unicode(\\\" \\\")\\r\\n else:\\r\\n newline = \\\"\\\\n\\\"\\r\\n space = \\\" \\\"\\r\\n if sep is None:\\r\\n sep = space\\r\\n if end is None:\\r\\n end = newline\\r\\n for i, arg in enumerate(args):\\r\\n if i:\\r\\n write(sep)\\r\\n write(arg)\\r\\n write(end)\",\n \"def print_(*args, **kwargs):\\r\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\r\\n if fp is None:\\r\\n return\\r\\n def write(data):\\r\\n if not isinstance(data, basestring):\\r\\n data = str(data)\\r\\n fp.write(data)\\r\\n want_unicode = False\\r\\n sep = kwargs.pop(\\\"sep\\\", None)\\r\\n if sep is not None:\\r\\n if isinstance(sep, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(sep, str):\\r\\n raise TypeError(\\\"sep must be None or a string\\\")\\r\\n end = kwargs.pop(\\\"end\\\", None)\\r\\n if end is not None:\\r\\n if isinstance(end, unicode):\\r\\n want_unicode = True\\r\\n elif not isinstance(end, str):\\r\\n raise TypeError(\\\"end must be None or a string\\\")\\r\\n if kwargs:\\r\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\r\\n if not want_unicode:\\r\\n for arg in args:\\r\\n if isinstance(arg, unicode):\\r\\n want_unicode = True\\r\\n break\\r\\n if want_unicode:\\r\\n newline = unicode(\\\"\\\\n\\\")\\r\\n space = unicode(\\\" \\\")\\r\\n else:\\r\\n newline = \\\"\\\\n\\\"\\r\\n space = \\\" \\\"\\r\\n if sep is None:\\r\\n sep = space\\r\\n if end is None:\\r\\n end = newline\\r\\n for i, arg in enumerate(args):\\r\\n if i:\\r\\n write(sep)\\r\\n write(arg)\\r\\n write(end)\",\n \"def debugprint(r, prefix='', depth=-1, done=None, print_type=False,\\r\\n file=sys.stdout, print_destroy_map=False,\\r\\n print_view_map=False, order=None, ids='CHAR',\\r\\n stop_on_name=False, prefix_child=None):\\r\\n if depth == 0:\\r\\n return\\r\\n\\r\\n if order is None:\\r\\n order = []\\r\\n\\r\\n if done is None:\\r\\n done = dict()\\r\\n\\r\\n if print_type:\\r\\n type_str = ' <%s>' % r.type\\r\\n else:\\r\\n type_str = ''\\r\\n\\r\\n if prefix_child is None:\\r\\n prefix_child = prefix\\r\\n\\r\\n def get_id_str(obj):\\r\\n if obj in done:\\r\\n id_str = done[obj]\\r\\n elif ids == \\\"id\\\":\\r\\n id_str = \\\"[@%s]\\\" % str(id(r))\\r\\n elif ids == \\\"int\\\":\\r\\n id_str = \\\"[@%s]\\\" % str(len(done))\\r\\n elif ids == \\\"CHAR\\\":\\r\\n id_str = \\\"[@%s]\\\" % char_from_number(len(done))\\r\\n elif ids == \\\"\\\":\\r\\n id_str = \\\"\\\"\\r\\n done[obj] = id_str\\r\\n return id_str\\r\\n\\r\\n if hasattr(r.owner, 'op'):\\r\\n # this variable is the output of computation,\\r\\n # so just print out the apply\\r\\n a = r.owner\\r\\n\\r\\n r_name = getattr(r, 'name', '')\\r\\n # normally if the name isn't set, it'll be None, so\\r\\n # r_name is None here\\r\\n if r_name is None:\\r\\n r_name = ''\\r\\n\\r\\n if print_destroy_map:\\r\\n destroy_map_str = str(getattr(r.owner.op, 'destroy_map', ''))\\r\\n else:\\r\\n destroy_map_str = ''\\r\\n\\r\\n if print_view_map:\\r\\n view_map_str = str(getattr(r.owner.op, 'view_map', ''))\\r\\n else:\\r\\n view_map_str = ''\\r\\n if destroy_map_str and destroy_map_str != '{}':\\r\\n destroy_map_str = 'd=' + destroy_map_str\\r\\n if view_map_str and view_map_str != '{}':\\r\\n view_map_str = 'v=' + view_map_str\\r\\n\\r\\n o = ''\\r\\n if order:\\r\\n o = str(order.index(r.owner))\\r\\n already_printed = a in done # get_id_str put it in the dict\\r\\n id_str = get_id_str(a)\\r\\n\\r\\n if len(a.outputs) == 1:\\r\\n print >> file, '%s%s %s%s \\\\'%s\\\\' %s %s %s' % (prefix, a.op,\\r\\n id_str,\\r\\n type_str, r_name,\\r\\n destroy_map_str,\\r\\n view_map_str,\\r\\n o)\\r\\n else:\\r\\n print >> file, '%s%s.%i %s%s \\\\'%s\\\\' %s %s %s' % (prefix, a.op,\\r\\n a.outputs.index(r),\\r\\n id_str, type_str,\\r\\n r_name,\\r\\n destroy_map_str,\\r\\n view_map_str,\\r\\n o)\\r\\n if not already_printed:\\r\\n if (not stop_on_name or\\r\\n not (hasattr(r, 'name') and r.name is not None)):\\r\\n new_prefix = prefix_child + ' |'\\r\\n new_prefix_child = prefix_child + ' |'\\r\\n for idx, i in enumerate(a.inputs):\\r\\n if idx == len(a.inputs) - 1:\\r\\n new_prefix_child = prefix_child + ' '\\r\\n\\r\\n debugprint(i, new_prefix, depth=depth - 1, done=done,\\r\\n print_type=print_type, file=file, order=order,\\r\\n ids=ids, stop_on_name=stop_on_name,\\r\\n prefix_child=new_prefix_child)\\r\\n else:\\r\\n #this is an input variable\\r\\n id_str = get_id_str(r)\\r\\n print >> file, '%s%s %s%s' % (prefix, r, id_str, type_str)\\r\\n\\r\\n return file\",\n \"def bpprint(self, out=None):\\n if out is None:\\n out = sys.stdout\\n print(self.bpformat(), file=out)\",\n \"def vprint(expr, **settings):\\n\\n outstr = vsprint(expr, **settings)\\n\\n import builtins\\n if (outstr != 'None'):\\n builtins._ = outstr\\n print(outstr)\",\n \"def _pprint(params, offset=0, printer=repr):\\n # Do a multi-line justified repr:\\n options = numpy.get_printoptions()\\n numpy.set_printoptions(precision=5, threshold=64, edgeitems=2)\\n params_list = list()\\n this_line_length = offset\\n line_sep = ',\\\\n' + (1 + offset // 2) * ' '\\n for i, (k, v) in enumerate(sorted(params.items())):\\n if isinstance(v, float):\\n # use str for representing floating point numbers\\n # this way we get consistent representation across\\n # architectures and versions.\\n this_repr = '%s=%s' % (k, str(v))\\n else:\\n # use repr of the rest\\n this_repr = '%s=%s' % (k, printer(v))\\n if len(this_repr) > 500:\\n this_repr = this_repr[:300] + '...' + this_repr[-100:]\\n if i > 0:\\n if this_line_length + len(this_repr) >= 75 or '\\\\n' in this_repr:\\n params_list.append(line_sep)\\n this_line_length = len(line_sep)\\n else:\\n params_list.append(', ')\\n this_line_length += 2\\n params_list.append(this_repr)\\n this_line_length += len(this_repr)\\n\\n numpy.set_printoptions(**options)\\n lines = ''.join(params_list)\\n # Strip trailing space to avoid nightmare in doctests\\n lines = '\\\\n'.join(l.rstrip(' ') for l in lines.split('\\\\n'))\\n return lines\",\n \"def print_(*args, **kwargs):\\n fp = kwargs.pop(\\\"file\\\", sys.stdout)\\n if fp is None:\\n return\\n\\n def write(data):\\n if not isinstance(data, basestring):\\n data = str(data)\\n fp.write(data)\\n want_unicode = False\\n sep = kwargs.pop(\\\"sep\\\", None)\\n if sep is not None:\\n if isinstance(sep, unicode):\\n want_unicode = True\\n elif not isinstance(sep, str):\\n raise TypeError(\\\"sep must be None or a string\\\")\\n end = kwargs.pop(\\\"end\\\", None)\\n if end is not None:\\n if isinstance(end, unicode):\\n want_unicode = True\\n elif not isinstance(end, str):\\n raise TypeError(\\\"end must be None or a string\\\")\\n if kwargs:\\n raise TypeError(\\\"invalid keyword arguments to print()\\\")\\n if not want_unicode:\\n for arg in args:\\n if isinstance(arg, unicode):\\n want_unicode = True\\n break\\n if want_unicode:\\n newline = unicode(\\\"\\\\n\\\")\\n space = unicode(\\\" \\\")\\n else:\\n newline = \\\"\\\\n\\\"\\n space = \\\" \\\"\\n if sep is None:\\n sep = space\\n if end is None:\\n end = newline\\n for i, arg in enumerate(args):\\n if i:\\n write(sep)\\n write(arg)\\n write(end)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.75577617\",\n \"0.73375154\",\n \"0.6986672\",\n \"0.698475\",\n \"0.6944995\",\n \"0.692333\",\n \"0.6899106\",\n \"0.6898902\",\n \"0.68146646\",\n \"0.6806209\",\n \"0.6753795\",\n \"0.67497987\",\n \"0.6744008\",\n \"0.6700308\",\n \"0.6691256\",\n \"0.6674591\",\n \"0.6658083\",\n \"0.66091245\",\n \"0.6606931\",\n \"0.6601862\",\n \"0.6563738\",\n \"0.6561717\",\n \"0.65549695\",\n \"0.6494838\",\n \"0.6473391\",\n \"0.64491546\",\n \"0.6411177\",\n \"0.6340302\",\n \"0.6339321\",\n \"0.6335031\",\n \"0.6332035\",\n \"0.6315847\",\n \"0.631272\",\n \"0.6297732\",\n \"0.62969106\",\n \"0.6283717\",\n \"0.6279154\",\n \"0.6271603\",\n \"0.62673396\",\n \"0.6265511\",\n \"0.62629336\",\n \"0.6258366\",\n \"0.6258278\",\n \"0.62501305\",\n \"0.6248315\",\n \"0.62459755\",\n \"0.6244254\",\n \"0.6242083\",\n \"0.62393075\",\n \"0.62156516\",\n \"0.6208198\",\n \"0.62068796\",\n \"0.62062824\",\n \"0.62062824\",\n \"0.6194123\",\n \"0.6189738\",\n \"0.6183852\",\n \"0.6183035\",\n 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PreviewObjectAutofill):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return equal(self, other)\",\n \"def __eq__(self, other: Any) -> bool:\\n return self.__class__ is other.__class__ and self.identifier == other.identifier\",\n \"def __eq__(self, other):\\n return self.__id == other.get_id()\",\n \"def __eq__ (self, other):\\n if type(self) == type(other):\\n return self._m == other._m\\n else:\\n return False\",\n \"def __eq__(self, other):\\n if not isinstance(other, Referent):\\n return False\\n\\n return self.__dict__ == other.__dict__\",\n \"def __eq__(self, other):\\n return self.properties == other.properties\",\n \"def __eq__(self, other):\\n return self.items() == other.items()\",\n \"def __eq__(self, other):\\n return hash(self) == hash(other)\",\n \"def __eq__(self, other):\\n return hash(self) == hash(other)\",\n \"def __eq__(self, other):\\n return hash(self) == hash(other)\",\n \"def __eq__(self, other):\\n return self.x == other.x and self.y == other.y\",\n \"def __eq__(self, other):\\n\\n if self is other:\\n return True\\n return hash(self) == hash(other)\",\n \"def __eq__(self, other):\\n if other._field1 == self._field1:\\n return True\\n return False\",\n \"def same_as(self, other):\\n return super().__eq__(other)\",\n \"def __eq__(self, other):\\n try:\\n return other and \\\\\\n self.id == other.id\\n\\n except AttributeError:\\n return False\",\n \"def __eq__(self, other):\\r\\n\\t\\treturn self._to_pylist() == other._to_pylist()\",\n \"def __eq__(self, other):\\n if not isinstance(other, Fiddle):\\n return False\\n\\n return self.__dict__ == other.__dict__\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8088132","0.8088132","0.8054589","0.7982687","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.79670393","0.7961088","0.7961088","0.79433626","0.79303336","0.7926563","0.7897525","0.78826123","0.78826123","0.78806067","0.7872423","0.7868354","0.78668815","0.7825702","0.7819993","0.78162885","0.78078854","0.78068274","0.7796298","0.7794721","0.7784825","0.77790844","0.7769397","0.77534705","0.7746211","0.7741107","0.77282816","0.7725766","0.7719537","0.770273","0.7685999","0.7677552","0.76739407","0.7664857","0.76557016","0.7655046","0.76282835","0.7625795","0.76242626","0.76237214","0.76237214","0.76237214","0.7617347","0.7600536","0.7599156","0.7595863","0.75945824","0.7594092","0.75899327"],"string":"[\n \"0.8088132\",\n \"0.8088132\",\n \"0.8054589\",\n \"0.7982687\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.79670393\",\n \"0.7961088\",\n \"0.7961088\",\n \"0.79433626\",\n \"0.79303336\",\n \"0.7926563\",\n \"0.7897525\",\n \"0.78826123\",\n \"0.78826123\",\n \"0.78806067\",\n \"0.7872423\",\n \"0.7868354\",\n \"0.78668815\",\n \"0.7825702\",\n \"0.7819993\",\n \"0.78162885\",\n \"0.78078854\",\n \"0.78068274\",\n \"0.7796298\",\n \"0.7794721\",\n \"0.7784825\",\n \"0.77790844\",\n \"0.7769397\",\n \"0.77534705\",\n \"0.7746211\",\n \"0.7741107\",\n \"0.77282816\",\n \"0.7725766\",\n \"0.7719537\",\n \"0.770273\",\n \"0.7685999\",\n \"0.7677552\",\n \"0.76739407\",\n \"0.7664857\",\n \"0.76557016\",\n \"0.7655046\",\n \"0.76282835\",\n \"0.7625795\",\n \"0.76242626\",\n \"0.76237214\",\n \"0.76237214\",\n \"0.76237214\",\n \"0.7617347\",\n \"0.7600536\",\n \"0.7599156\",\n \"0.7595863\",\n \"0.75945824\",\n \"0.7594092\",\n \"0.75899327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79578,"cells":{"query":{"kind":"string","value":"Returns true if both objects are not equal"},"document":{"kind":"string","value":"def __ne__(self, other):\n return not self == other"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True","def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)","def __ne__(self, other) -> bool:\n return not self.__eq__(other)","def __eq__(self, other):\n return not self.__ne__(other)","def __ne__(self, other):\n if self.__eq__(other):\n return False\n return True","def __ne__(self, other):\n return not self == other","def __ne__(self, other):\n # type: (object) -> bool\n return not self == other","def __ne__(self, other):\n # type: (object) -> bool\n return not self == other","def __ne__(self, other):\n # type: (object) -> bool\n return not self == other","def __ne__(self, other):\r\n return not self == other","def __ne__(self, other):\r\n return not self == other","def __ne__(self, other):\n return not self == other","def __ne__(self, other):\n return not self == other","def __ne__ (self, other):\n return not self == other"],"string":"[\n \"def __ne__(self, other: object) -> bool:\\n if self.__eq__(other):\\n return False\\n return True\",\n \"def __ne__(self, other: object) -> bool:\\n return not self.__eq__(other)\",\n \"def __ne__(self, other) -> bool:\\n return not self.__eq__(other)\",\n \"def __eq__(self, other):\\n return not self.__ne__(other)\",\n \"def __ne__(self, other):\\n if self.__eq__(other):\\n return False\\n return True\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n # type: (object) -> bool\\n return not self == other\",\n \"def __ne__(self, other):\\n # type: (object) -> bool\\n return not self == other\",\n \"def __ne__(self, other):\\n # type: (object) -> bool\\n return not self == other\",\n \"def __ne__(self, other):\\r\\n return not self == other\",\n \"def __ne__(self, other):\\r\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__(self, other):\\n return not self == other\",\n \"def __ne__ (self, other):\\n return not self == other\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.845611","0.8391477","0.8144138","0.81410587","0.8132492","0.8093973","0.80920255","0.80920255","0.80920255","0.8085325","0.8085325","0.8076365","0.8076365","0.8065748"],"string":"[\n \"0.845611\",\n \"0.8391477\",\n \"0.8144138\",\n \"0.81410587\",\n \"0.8132492\",\n \"0.8093973\",\n \"0.80920255\",\n \"0.80920255\",\n \"0.80920255\",\n \"0.8085325\",\n \"0.8085325\",\n \"0.8076365\",\n \"0.8076365\",\n \"0.8065748\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79579,"cells":{"query":{"kind":"string","value":"Return list of recIDs matching query for PVALUE and FVALUE."},"document":{"kind":"string","value":"def get_recids_matching_query(pvalue, fvalue):\n rec_id = list(search_pattern(p=pvalue, f=fvalue, m='e') - INTBITSET_OF_DELETED_RECORDS)\n return rec_id"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_values(self, value):\n return [\n obj for obj in self if obj == value\n ]","def values(self):\n if '%' in self.starid:\n query = \"\"\"SELECT * from ngc2236 where starid like '%s'\"\"\" % self.starid\n else:\n query = \"\"\"SELECT * from ngc2236 where starid = '%s'\"\"\" % self.starid\n result = self.wifsip.query(query)\n values = [r for r in result[0]]\n if '%' in self.starid:\n self.starid=values[0]\n return values","def get_drupal_fc_ids(db_obj, db_cur, entity_type, bundle, entity_id,\n revision_id, fc_cv):\n\n # fc details\n fc_ident = fc_cv[0]\n fc_value_type = fc_cv[1]\n fc_value = fc_cv[2]\n fc_type = fc_ident[1]\n fc_id_type = fc_ident[2]\n\n # handle fc ID types\n if fc_id_type == 'id':\n fc_ident_column = 'fci.item_id'\n elif fc_id_type == 'label':\n fc_ident_column = 'fci.label'\n\n # query string and arguments\n query_str = (\n'''\nSELECT fci.item_id, fci.revision_id\nFROM field_data_field_{0} as fcf\nLEFT JOIN field_collection_item as fci\nON fci.item_id = fcf.field_{0}_value\nAND fci.revision_id = fcf.field_{0}_revision_id\nWHERE fcf.entity_type = %s\nAND fcf.bundle = %s\nAND fcf.entity_id = %s\nAND fcf.revision_id = %s\nAND fcf.deleted = 0\nAND fci.revision_id IN\n (SELECT MAX(revision_id)\n FROM field_collection_item_revision\n GROUP BY item_id)\nAND fci.archived = 0\nAND {1} = %s\n''' .\n format(fc_type, fc_ident_column)\n )\n query_args = [entity_type, bundle, entity_id, revision_id, fc_value]\n\n # execute the query\n if not db_obj.execute(db_cur, query_str.strip(), query_args,\n has_results=True):\n return None\n ret = db_obj.fetchall(db_cur)\n if not ret[0]:\n return None\n if not ret[1]:\n return []\n return ret[1]","def values(rbt, keylo, keyhi):\n try:\n lstvalues = lt.newList('SINGLELINKED', rbt['cmpfunction'])\n lstvalues = valuesRange(rbt['root'], keylo, keyhi, lstvalues,\n rbt['cmpfunction'])\n return lstvalues\n except Exception as exp:\n error.reraise(exp, 'RBT:Values')","def get_searches(self,redcap,boolean,val):\n\t\tmatches = []\n\t\ttry:\n\t\t\tfor eachdict in self.get_data(redcap):\n\t\t\t\tif (boolean):\n\t\t\t\t\tif (re.search(str(val),eachdict[redcap].strip(),re.I)):\n\t\t\t\t\t\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\n\t\t\t\telse:\n\t\t\t\t\tif not (re.search(str(val),eachdict[redcap].strip(),re.I)):\n\t\t\t\t\t\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\n\t\texcept KeyError as e:\n\t\t\tprint(\"redcap field: '{}'\\nnot found. did you mean: '{}'?\\nverify and try again\".format(redcap,get_close_matches(redcap,[d['field_name'] for d in self.get_metadata()])))\n\n\t\treturn matches","def find_matches(ktree, val):\n collection = []\n\n ktree.pre_order(lambda n: collection.append(n) if n.val == val else None)\n\n if collection == []:\n raise ValueError('Your value did not match any values in the tree')\n\n return collection","def _filter_search_values(key: str, values: list, collection: list):\n return_data = []\n for item in collection:\n if any(val in values for val in item[key]):\n return_data.append(item)\n return return_data","def getKIDsFP():\n\tdb = MySQLdb.connect(host='tddb.astro.washington.edu', user='tddb', passwd='tddb', db='Kepler')\n\tcursor = db.cursor()\n\tfoo1 = 'select KID from KEPFP'\n\tcursor.execute(foo1)\n\tresults = cursor.fetchall()\n\tr1 = ([el[0] for el in results])\n\tresults=set(r1)\n \n\treturn results","def get_matches(self,redcap,boolean,val):\n\t\tmatches = []\n\t\ttry:\n\t\t\tfor eachdict in self.get_data(redcap):\n\t\t\t\tif (boolean):\n\t\t\t\t\tif (eachdict[redcap].strip() == str(val)):\n\t\t\t\t\t\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\n\t\t\t\telse:\n\t\t\t\t\tif (eachdict[redcap].strip() != str(val)):\n\t\t\t\t\t\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\n\t\texcept KeyError as e:\n\t\t\tprint(\"redcap field: '{}'\\nnot found. did you mean: '{}'?\\nverify and try again\".format(redcap,get_close_matches(redcap,[d['field_name'] for d in self.get_metadata()])))\n\t\treturn matches","def task_3_find_item_via_value(data: DT, value) -> DT:\n find = [find for find in data for n in find.values() if n == value]\n return find","def getOpaqueValues(valueIDs):\n store = getMainStore()\n return store.find(OpaqueValue,\n OpaqueValue.fileID == OpaqueValueLink.fileID,\n OpaqueValueLink.valueID.is_in(valueIDs))","def itervaluerefs(self):\r\n return self.data.itervalues()","def getListFieldValue(self, vocab_value_id):\n try:\n value = ''\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('qiime_assets.get_list_field_value', [vocab_value_id, results])\n for row in results:\n value = row[0]\n return value\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)","def _get_f(couplelist, dpplist, bpm_name, value):\n lst = []\n x = []\n for dpp in dpplist:\n x.append(dpp)\n couplefile = couplelist[dpp]\n lst.append(getattr(couplefile, value)[couplefile.indx[bpm_name]])\n\n lreg = linreg(x, lst)\n\n return lreg[0], lreg[3]","def get_value_list(self, insupdel=0):\n #NOTE: statement = [record, {...}]\n result = []\n if insupdel == StatementType.INSERT:\n statements = self.statements_insert\n elif insupdel == StatementType.UPDATE:\n statements = self.statements_update\n elif insupdel == StatementType.DELETE:\n statements = self.statements_delete\n for statement in statements:\n result.append([key for key, val in statement[1].items()])\n return result","def return_values(self):\r\n\r\n values = list(self.piDD.values())\r\n return values","def values(self):\n store = getMainStore()\n tagIDs = self._getTagIDs()\n where = self._getWhereClause(tagIDs)\n return store.find((Tag, TagValue), *where)","def task_3_find_item_via_value(data: DT, value) -> DT:\n return [dic for dic in data if value in dic.values()]","def process_result_value(self, value, dialect):\n masks = list()\n if value:\n for e in enums.CryptographicUsageMask:\n if e.value & value:\n masks.append(e)\n return masks","def getvaluelist(doclist, fieldname):\n\tl = []\n\tfor d in doclist:\n\t\tl.append(d.fields[fieldname])\n\treturn l","def get_matches(q_id, res):\n tokens = res[0]\n opt_dict = res[1]\n ret_val = []\n for opt in opt_dict.keys():\n if opt == q_id:\n continue\n if float(opt_dict[opt]) > float(4)/5 * tokens:\n ret_val.append(opt)\n return ret_val","def get_key_from_values(f, catalog):\n L_keys = []\n L_values = []\n for path, files in catalog.items():\n if f in catalog[path]:\n return path","def queryByAttributeIn(table, attribute, values, access=None, addtl=\"\"):\n if len(values) > MAX_IN_ELEMENTS:\n values1 = values[:MAX_IN_ELEMENTS]\n values2 = values[MAX_IN_ELEMENTS:]\n records1 = queryByAttributeIn(table, attribute, values1, access, addtl)\n records2 = queryByAttributeIn(table, attribute, values2, access, addtl)\n records1.extend(records2)\n return records1\n\n valueString = u\",\".join(u\"'\" + sqlapi.quote(val) + u\"'\" for val in values)\n condition = u\"%s IN (%s)\" % (attribute, valueString)\n records = sqlapi.RecordSet2(table, condition,\n access=access, access_persno=auth.persno,\n addtl=addtl)\n return [records]","def get_fvalues(self, positions):\n if all(self._measures.has_key(p) for p in positions):\n return [ self._measures[p] for p in positions ]\n else:\n raise Exception(\"Trying to obtain focus value at\"\n \" non-visted positions %s\" % str(positions))","def return_poll_id(self, value_id:int) -> set:\n try:\n value_coordinates = self.cursor.execute(f\"SELECT id_user, latitude, longitude FROM {table_poll} WHERE id=?;\", (value_id,)).fetchall()\n value_lists = self.cursor.execute(f\"SELECT id_group FROM {table_poll_groups} WHERE id_poll=?;\", (value_id,)).fetchall()\n value_coordinates = value_coordinates[-1] if value_coordinates else []\n value_lists = [f[0] for f in value_lists] if value_lists else []\n return value_coordinates, value_lists\n except Exception as e:\n msg = f\"We faced problems with getting values from the database via poll id; Mistake: {e}\"\n self.proceed_error(msg)\n return [], []","def sick_patients(lab: str, gt_lt: str, value: float) -> set[int]:\r\n cur = con.cursor()\r\n output = cur.execute(\r\n f\"\"\"SELECT DISTINCT(Patient_ID)\r\n FROM Lab\r\n WHERE Lab_Name = ?\r\n AND Lab_Value {gt_lt} {value}\"\"\",\r\n [lab],\r\n ).fetchall()\r\n\r\n patient_IDs: set[str] = set() # O(1)\r\n for row in output:\r\n patient_IDs.add(row[0])\r\n\r\n return patient_IDs # O(1)\r","def values_list(self):\n if self.values_number() == 0:\n raise RegistryStructureDoesNotExist(\"NK Record has no associated values.\")\n\n values_list_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(0x28))\n\n d = HBINCell(self._buf, values_list_offset, self)\n return ValuesList(self._buf, d.data_offset(), self, self.values_number())","def itervaluerefs(self):\n for value in self.itervalues():\n yield ref(value)","def get_field_names_fuzzy(self, value: str, key: str = \"name_qual\") -> List[str]:\n splits = self.split_searches(value=value)\n fields = self.get()\n\n matches = []\n\n for adapter_name, names in splits:\n adapter = self.get_adapter_name(value=adapter_name)\n for name in names:\n schemas = fields[adapter]\n amatches = self.fuzzy_filter(search=name, schemas=schemas, key=key, root_only=True)\n matches += [x for x in amatches if x not in matches]\n\n return matches","def getFiList():\n fi = getFi()\n fiList = []\n for arg,val in fi.items():\n fiList.append(val)\n return fiList","def get_params_from_pids(self, frompid, topid, syselem=None):\n\n with self.__connection.cursor() as cursor:\n query = ''\n if not syselem:\n query = (\"SELECT NAME FROM %s WHERE \" +\n \" PID BETWEEN %s AND %s\") % (self.__schema, frompid, topid)\n else:\n query = (\"SELECT NAME FROM %s WHERE \" +\n \" SYSTEM_ELEMENT='%s'\" +\n \" AND PID BETWEEN %s AND %s\") % (self.__schema, syselem, frompid, topid)\n cursor.execute(query)\n result = cursor.fetchall()\n\n return result","def parameter_values(self) -> List[Tuple[str, Any]]:\n pvs = [(param, getattr(self, variable))\n for variable, param in self.variable_name_to_query_param.items()]\n return [(p, v) for p, v in pvs if v is not None]","def find_qualifications_by_ids(\n self,\n qualification_ids: List[str] = None,\n task_run_ids: Optional[List[str]] = None,\n ) -> List[dict]:\n if not qualification_ids:\n return []\n\n with self.table_access_condition, self._get_connection() as conn:\n c = conn.cursor()\n\n qualification_ids_block = \"\"\n if qualification_ids:\n qualification_ids_block = \" OR \".join(\n \"qualification_ids LIKE '%\\\"\" + str(_id) + \"\\\"%'\" for _id in qualification_ids\n )\n qualification_ids_block = f\"({qualification_ids_block})\"\n\n task_run_ids_block = \"\"\n if task_run_ids:\n task_run_ids_str = \",\".join([f'\"{tid}\"' for tid in task_run_ids])\n task_run_ids_block = f\"AND task_run_id IN ({task_run_ids_str})\"\n\n c.execute(\n f\"\"\"\n SELECT * FROM qualifications\n WHERE {qualification_ids_block} {task_run_ids_block};\n \"\"\"\n )\n results = c.fetchall()\n return results","def _rr_yun0_sqf_list(self, f):\n if f.is_ground:\n return []\n\n result, count = [], 1\n qs = [f.diff(x) for x in self.gens]\n\n g = f\n for q in qs:\n g = self.gcd(g, q)\n\n while f != 1:\n qs = [q // g for q in qs]\n f //= g\n qs = [q - f.diff(x) for x, q in zip(self.gens, qs)]\n\n g = f\n for q in qs:\n g = self.gcd(g, q)\n if g != 1:\n result.append((g, count))\n\n count += 1\n\n return result","def matching(self, pids):\n for pid in pids:\n if self.matches(pid):\n yield pid","def map_values_to_value_list(value_list, values):\n return [value_list.index(x) for x in values]","def filter_by_compare(self, diclist, fidname, value, comparison, outputfidlist=[]):\n if comparison == 'GT':\n result = [item for item in diclist if item[fidname] > value]\n elif comparison == 'LT':\n result = [item for item in diclist if item[fidname] < value]\n elif comparison == 'EQ':\n result = [item for item in diclist if item[fidname] == value]\n else:\n result = []\n \n return self.filter_output(result, outputfidlist)","def get_existing_values(self): #DONE\n return (value.value for value in self.address.values() if value.value)","def wql_values_query(self, start_node, path):\n self.tr_id = get_tr_id()\n xml_msg = self._create_wql_values_msg(self.tr_id, start_node, path)\n self.conn.connect()\n self.conn.send(xml_msg)\n response = self.conn.receive()\n self._check_error(response)\n if \"results\" in response:\n node_list = parse_URI_list(response[\"results\"])\n return node_list\n else:\n raise SIBError(M3_SIB_ERROR)","def get_field_names_eq(\n self,\n value: Union[str, List[str]],\n key: str = \"name_qual\",\n fields_error: bool = True,\n selectable_only: bool = True,\n ) -> List[str]:\n splits = self.split_searches(value=value)\n fields = self.get()\n\n matches = []\n\n for adapter_name, names in splits:\n adapter = self.get_adapter_name(value=adapter_name)\n for name in names:\n schemas = fields[adapter]\n schema = self.get_field_schema(\n value=name,\n schemas=schemas,\n fields_error=fields_error,\n selectable_only=selectable_only,\n )\n\n match = schema[key] if key else schema\n if match not in matches:\n matches.append(match)\n\n return matches","def contains(self, value):\n n = self.search(value)\n return (n.value==value, n)","def queryByAttributePair(table, attribute1, attribute2, values):\n if len(values) > MAX_PAIRS:\n values1 = values[:MAX_PAIRS]\n values2 = values[MAX_PAIRS:]\n records1 = queryByAttributePair(table, attribute1, attribute2, values1)\n records2 = queryByAttributePair(table, attribute1, attribute2, values2)\n records1.extend(records2)\n return records1\n\n condition = u\"1=0\"\n for val1, val2 in values:\n condition += \" OR %s = '%s' AND %s = '%s'\" % (attribute1, sqlapi.quote(val1), attribute2, sqlapi.quote(val2))\n records = sqlapi.RecordSet2(table, condition, access_persno=auth.persno)\n return [records]","def extract_q_values(results):\n return results['max_q_values'].tolist()","def valuerefs(self):\n return [ref(value) for value in self.itervalues()]","def _find_ids(self,\r\n data_list,\r\n prop,\r\n lookup_index,\r\n lookup_doc_type,\r\n lookup_field):\r\n lg = logging.getLogger(\"%s.%s\" % (self.ln, inspect.stack()[0][3]))\r\n lg.setLevel(self.log_level)\r\n\r\n rtn_list = []\r\n first_time = IsFirst()\r\n for item in data_list:\r\n # the Dot class will retive and set dictionary values via dot\r\n # notation\r\n val = Dot(item).get(prop)\r\n if val.startswith(\"#;lookup#;\"):\r\n lookup_val = val.replace(\"#;lookup#;\", \"\")\r\n lookup_obj = self.get_item(lookup_val, lookup_field)\r\n if first_time.first():\r\n lg.debug(\" lookup_obj:\\n%s\", pp.pformat(lookup_obj))\r\n if lookup_obj:\r\n rtn_list.append(Dot(item).set(prop, lookup_obj['_id']))\r\n return rtn_list","def lookup_rows(self, key, values, fields=None):\n\n s3db = current.s3db\n ntable = s3db.need_need\n nttable = s3db.need_tag\n\n left = nttable.on((nttable.need_id == ntable.id) & \\\n (nttable.tag == \"req_number\"))\n\n qty = len(values)\n if qty == 1:\n query = (ntable.id == values[0])\n limitby = (0, 1)\n else:\n query = (ntable.id.belongs(values))\n limitby = (0, qty)\n\n rows = current.db(query).select(ntable.id,\n ntable.name,\n nttable.value,\n left = left,\n limitby = limitby,\n )\n self.queries += 1\n return rows","def get_criteria_values(self) -> list or None:\n\n # query_string = 'SELECT * FROM [{}];'.format(self.settings.excelSheetName)\n query_string = 'SELECT DISTINCT [{}] FROM [{}];'.format(self.settings.excelCriteriaSelector,\n self.settings.excelSheetName)\n\n if self.connected and self.settings.excelSheetName:\n cursor = self.workbook.cursor()\n if cursor:\n criteria_values = []\n for row in cursor.execute(query_string):\n criteria_values.append(row[self.settings.excelCriteriaSelector.lower()])\n cursor.close()\n return criteria_values\n\n return None","def _extract_subtable_by_attribute_values(self, values, \n mode=\"and\"):\n self._check_attribute_names(list(values.keys()))\n if mode == \"and\":\n indices = [i for i in range(len(self)) if self._has_values(i, values)]\n elif mode == \"or\":\n indices = [i for i in range(len(self)) if self._has_at_least_one_value(i, values)]\n return ([self.objects[i] for i in indices],\n [self.table[i] for i in indices])","def check_granted_qualifications(\n self,\n qualification_id: Optional[str] = None,\n worker_id: Optional[str] = None,\n value: Optional[int] = None,\n ) -> List[GrantedQualification]:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from granted_qualifications\n WHERE (?1 IS NULL OR qualification_id = ?1)\n AND (?2 IS NULL OR worker_id = ?2)\n AND (?3 IS NULL OR value = ?3)\n \"\"\",\n (qualification_id, worker_id, value),\n )\n rows = c.fetchall()\n return [\n GrantedQualification(\n self, str(r[\"qualification_id\"]), str(r[\"worker_id\"])\n )\n for r in rows\n ]","def get_value(self, key, getter=lambda x: x):\n record_trie = self._record_trie\n assert key in record_trie\n value = record_trie[key]\n # Record trie allows keys to have multiple values and returns a list of values for each key.\n # As we make the value for each key a list already (to control order/not have to sort again),\n # we need to assert there is only a single value\n assert len(value) == 1\n value = value[0]\n return_value = inverse_qid_cand_with_score(value=value, itos=self._itos)\n res = list(map(getter, return_value))\n assert len(res) <= self._max_value\n return res","def get_ids_values(path):\n \n def read_txt(path):\n \"\"\"read text file from path.\"\"\"\n \n with open(path, \"r\") as f:\n return f.read().splitlines()\n \n raw_data = read_txt(path)[1:]\n \n def deal_line(line):\n ids, values = line.split(',')\n return ids, values\n \n ids_values = [deal_line(line) for line in raw_data]\n ids = [x[0] for x in ids_values]\n values = [x[1] for x in ids_values]\n values = list(map(int, values))\n \n return ids, values","def list_reference_images_in_filter(conn,primary_ref,f,log):\n\n log.info('Identifying all current reference image in filter '+str(f))\n\n query = 'SELECT * FROM reference_images WHERE filter=\"'+str(primary_ref[f])+\\\n '\" AND software=\"'+str(primary_ref['software_id'])+\\\n '\" AND facility!=\"'+str(primary_ref['facility_id'])+'\"'\n\n ref_image_list = phot_db.query_to_astropy_table(conn, query, args=())\n\n log.info(repr(ref_image_list))\n\n return ref_image_list","def _list(self, val, fld):\n if isinstance(val, (list, tuple)):\n if len(val) == 1:\n return fld == val[0]\n else:\n return fld.in_(val)\n else:\n return fld == val","def get_values(self, *fields):\n result = []\n for field in fields:\n if isinstance(field, (str, basestring)):\n result.append(self.get_value(field))\n elif isinstance(field, (tuple, list)):\n result.append(self.get_values(*field))\n else:\n result.append(None)\n return result","def values(self):\n return [entry.value for entry in self.table if entry.value is not None]","def get_sub_values(self):\n return list()","def bind_kpis(self, data_values):\n kpi_names = [kpi.name for kpi in self.kpis]\n\n kpi_results = [\n dv\n for kpi_name in kpi_names\n for dv in data_values\n if dv.name == kpi_name\n ]\n\n return kpi_results","def values(self):\n return [i.value for i in self.value]","def getRecordsByAttributePair(table, attribute1, attribute2, values):\n recordSets = queryByAttributePair(table, attribute1, attribute2, values)\n records = []\n for recordSet in recordSets:\n records.extend(list(recordSet))\n return records","def query_by_field(cls, field: str, value: str)->List:\n database.cursor.execute(\n \"SELECT * FROM {0} WHERE {1} = %s\".format(cls.table_name, field), (value,))\n items = database.cursor.fetchall()\n return [cls.to_object(item) for item in items]","def _policy_values(self, p_map, v_note):\n pitches = None\n for p in self.v_policy_map[v_note]:\n p_values = p.values(p_map, v_note)\n if p_values is None:\n returned_pitches = OrderedSet() # None means p cannot be satisfied!\n else:\n returned_pitches = OrderedSet()\n for n in p_values:\n returned_pitches.add(n.diatonic_pitch)\n pitches = returned_pitches if pitches is None else pitches.intersection(returned_pitches)\n\n retset = OrderedSet()\n for p in pitches:\n retset.add(Note(p, v_note.base_duration, v_note.num_dots))\n return retset","def getRecordsByAttributeIn(table, attribute, values, access=None, addtl=\"\"):\n recordSets = queryByAttributeIn(table, attribute, values, access, addtl)\n records = []\n for recordSet in recordSets:\n records.extend(list(recordSet))\n return records","def value_in(table_rows, values=[], col_name=\"\", col_num=-1):\n key = col_name\n if(key==\"\"):\n key = table_rows[0].keys[col_num]\n rst = True\n lst = []\n for i in range(len(table_rows)):\n value = table_rows[i].get_d_value(key)\n if(value not in values):\n rst = False\n lst.append(\"(col:{0},row:{1}:value:{2}\".format(\n key, i, value\n ))\n return rst,\",\".join(lst)","def values():","def search(self, values):\n if values is False:\n return False\n if all(len(values[square]) == 1 for square in self.squares):\n return values\n n, square = min((len(values[square]), square)\n for square in self.squares if len(values[square]) > 1)\n\n return self.possible_values(self.search(self.assign(values.copy(), square, dig))\n for dig in values[square])","def _get_split_field_values(self, fname):\r\n files = [os.path.splitext(os.path.basename(f))[0] for f in glob.glob(fname)]\r\n filt = [self.filter_filename(f) for f in files]\r\n values = [f.split(self.sep)[self.field_index(self.split_by)] for f in filt]\r\n return sorted(values)","def _equality_fishing_queries(self, f, dist):\n query_dicts = []\n for q in xrange(len(self.__queries)):\n query_dicts = []\n for count in xrange(self.__queries[q]['no_queries']*OVER_GENERATION_RATIO):\n self.__count += 1\n logger.info('EQ: Created %d out of %d queries' % (self.__count, self.__total))\n field = f\n r_lower_cdf = self.__queries[q]['rss_lower_cdf']\n r_upper_cdf = self.__queries[q]['rss_upper_cdf'] \n (search_value, lbound, ubound, enum) = \\\n self._get_fishing_values(f, dist, r_lower_cdf, r_upper_cdf)\n \n query_dicts.append({qs.QRY_ENUM : enum, \n qs.QRY_QID : qids.query_id(),\n qs.QRY_DBNUMRECORDS : self.__db_size,\n qs.QRY_DBRECORDSIZE : self.__row_width, \n qs.QRY_PERF : self.__perf,\n qs.QRY_CAT : self.__cat,\n qs.QRY_SUBCAT : '', \n qs.QRY_WHERECLAUSE : '',\n qs.QRY_FIELD : sv.sql_info[field][0],\n qs.QRY_NEGATE : False,\n qs.QRY_FIELDTYPE : sv.sql_info[field][1],\n qs.QRY_LRSS : self.__queries[q][qs.QRY_LRSS],\n qs.QRY_URSS : self.__queries[q][qs.QRY_URSS],\n qs.QRY_VALUE : None,\n qs.QRY_SEARCHFOR : search_value,\n qs.QRY_LBOUND : lbound,\n qs.QRY_UBOUND : ubound })\n \n self.__bobs.append(aqb.EqualityFishingQueryBatch(query_dicts,count, \n int((count+1)/OVER_GENERATION_RATIO),\n True))","def get_values(self):\n \n return []","def list_value(self) -> global___Expression.RepeatedValue:","def get_param_vals(self, parseq):\n\n vals = []\n for conf in self.sim_confs:\n val = conf[parseq]\n if val not in vals:\n vals.append(val)\n return vals","def get_field_values(self, req, query_field=None,\n multi_query_field=None, multi=True):\n\n if not multi_query_field:\n multi_query_field = '{}s[]'.format(query_field)\n\n if multi:\n values = req.get_param_as_list(multi_query_field) or []\n else:\n values = []\n\n single = req.get_param(query_field)\n\n if single:\n values.append(single)\n\n return values","def find(self, query, fields=None):\n # get records matching query\n records = [rec for rec in self.__data.values()\n if rec.check_condition(query)]\n\n # handle `fields` argument\n if fields is None:\n # return raw results if fields not given\n results = dict(rec.to_id_dict() for rec in records)\n elif isinstance(fields, str):\n # chose one value from each record\n results = [rec[fields] for rec in records]\n elif isinstance(fields, list):\n # chose only values matching given fields\n results = [rec.get_fields_list(fields) for rec in records]\n else:\n raise TypeError(f\"`fields` should be of one of types: \"\n f\"`None`, `str` or `list`. got: {type(fields)}\")\n\n return results","def getCriteriaItems( self ):\n # filter out empty strings\n result = []\n\n value = tuple( filter( None, self.value ) )\n if not value:\n return ()\n result.append( ( self.field, self.value ), )\n\n if self.operator is not None:\n result.append( ( '%s_operator' % self.field, self.operator ) )\n\n return tuple( result )","def list_of_gene_IDs_and_symbols_from_file(input_seq_file):\n print(\"Find entrezgene IDs for reporter genes in RNAseq run\")\n lines = []\n with open(input_seq_file, 'U') as input_seq_file:\n next(input_seq_file)\n for line in input_seq_file:\n new_line = line.strip().split('\\t')[0]\n new_line = re.sub('\"', '', new_line) # inputs contained quotes\n lines.append(new_line)\n\n query_list = pd.Series(data=lines).tolist()\n\n print(\"length of query list: \", len(query_list))\n return query_list","def lookups(self, request, model_admin):\n list_id = [[None, \"All\"]]\n for firm in Firm.objects.all():\n list_id.append([firm.pk, firm.name])\n\n return list_id","def find_by_value(cls, field, value, as_dict=False):\n _field = cls.get_property_by_name(field)\n _label = cls.get_label()\n\n value_type = False\n if isinstance(value, integer_types + float_types):\n value_type = True\n results = cls._find_vertex_by_value(\n value_type=value_type,\n vlabel=_label,\n field=_field,\n val=value\n )\n\n if as_dict: # pragma: no cover\n return {v._id: v for v in results}\n\n return results","def valuerefs(self):\r\n return self.data.values()","def get_attr_val_ids(self, ext_ids):\n\n value_ids = []\n\n attr_val_prefix = 'product_configurator.product_attribute_value_%s'\n\n for ext_id in ext_ids:\n if ext_id in self.attr_val_ext_ids:\n value_ids.append(self.attr_val_ext_ids[ext_id])\n elif attr_val_prefix % ext_id in self.attr_val_ext_ids:\n value_ids.append(\n self.attr_val_ext_ids[attr_val_prefix % ext_id]\n )\n\n return value_ids","def custom_lookup_rows(self, key, values, fields=None):\n\n s3db = current.s3db\n ntable = s3db.req_need\n nttable = s3db.req_need_tag\n\n left = nttable.on((nttable.need_id == ntable.id) & \\\n (nttable.tag == \"req_number\"))\n\n qty = len(values)\n if qty == 1:\n query = (ntable.id == values[0])\n limitby = (0, 1)\n else:\n query = (ntable.id.belongs(values))\n limitby = (0, qty)\n\n rows = current.db(query).select(ntable.id,\n ntable.name,\n nttable.value,\n left=left,\n limitby=limitby)\n self.queries += 1\n return rows","def custom_lookup_rows(self, key, values, fields=None):\n\n s3db = current.s3db\n ntable = s3db.req_need\n nttable = s3db.req_need_tag\n\n left = nttable.on((nttable.need_id == ntable.id) & \\\n (nttable.tag == \"req_number\"))\n\n qty = len(values)\n if qty == 1:\n query = (ntable.id == values[0])\n limitby = (0, 1)\n else:\n query = (ntable.id.belongs(values))\n limitby = (0, qty)\n\n rows = current.db(query).select(ntable.id,\n ntable.name,\n nttable.value,\n left=left,\n limitby=limitby)\n self.queries += 1\n return rows","def _build_potential_values(self, p_map, v_notes):\n ranked_list = list() # A list of tuples (v_note, {solution values})\n for v_note in v_notes:\n if p_map[v_note] is not None and p_map[v_note].note is not None:\n continue\n if v_note not in self.v_policy_map.keys():\n continue\n values = self._policy_values(p_map, v_note)\n if len(values) == 0:\n continue\n ranked_list.append((v_note, values))\n\n ranked_list = sorted(ranked_list, key=lambda x: len(x[1]))\n return ranked_list","def doFindAllMatching(self, str):\n matches = []\n for value in self.doId2do.values():\n if re.search(str,repr(value)):\n matches.append(value)\n return matches","def transform_to_fqids(\n value: Optional[\n Union[\n int,\n str,\n FullQualifiedId,\n Sequence[int],\n Sequence[str],\n Sequence[FullQualifiedId],\n ]\n ],\n collection: Collection,\n) -> List[FullQualifiedId]:\n id_list: IdentifierList\n if value is None:\n id_list = [] # type: ignore # see https://github.com/python/mypy/issues/2164\n elif not isinstance(value, list):\n value_arr = cast(IdentifierList, [value])\n id_list = value_arr\n else:\n id_list = value\n\n fqid_list = []\n for id in id_list:\n if isinstance(id, int):\n fqid_list.append(fqid_from_collection_and_id(collection, id))\n else:\n fqid_list.append(cast(FullQualifiedId, id))\n return fqid_list","def dict_list(query_id, qrels):\n rel_list = []\n for query_dict in qrels:\n if int(query_dict['query_num']) == query_id:\n rel_list.append(query_dict)\n\n return rel_list","def get_selection(self):\n return [\n self.id_value_map[item]\n for item in self.selection()\n if item in self.id_value_map\n ]","def values_list(self, *args, **kwargs):\n flat = kwargs.pop(\"flat\", False)\n if kwargs:\n raise AttributeError(f\"Unknown kwargs: {kwargs}\")\n if flat and len(args) != 1:\n raise ValueError(\"flat=True requires exactly one field name\")\n try:\n only_fields = tuple(self._get_field_path(arg) for arg in args)\n except ValueError as e:\n raise ValueError(f\"{e.args[0]} in values_list()\")\n new_qs = self._copy_self()\n new_qs.only_fields = only_fields\n new_qs.return_format = self.FLAT if flat else self.VALUES_LIST\n return new_qs","def values(\n self, *keys: _TResultKey\n ) -> t.List[t.List[t.Any]]:\n return [record.values(*keys) for record in self]","def obtain_DyndbProtein_id_list(dyn_id):\n model=DyndbModel.objects.select_related(\"id_protein\",\"id_complex_molecule\").get(dyndbdynamics__id=dyn_id)\n pdbid=model.pdbid\n prot_li_gpcr=[]\n dprot_li_all=[]\n dprot_li_all_info=[]\n if model.id_protein:\n dprot=model.id_protein\n dprot_li_all=[dprot]\n (prot_li_gpcr,dprot_li_all_info)=retrieve_prot_info(dprot,prot_li_gpcr,dprot_li_all_info)\n else:\n dprot_li_all=DyndbProtein.objects.select_related(\"receptor_id_protein\").filter(dyndbcomplexprotein__id_complex_exp__dyndbcomplexmolecule=model.id_complex_molecule.id)\n for dprot in dprot_li_all:\n (prot_li_gpcr,dprot_li_all_info)=retrieve_prot_info(dprot,prot_li_gpcr,dprot_li_all_info)\n return (prot_li_gpcr, dprot_li_all, dprot_li_all_info,pdbid)","def get_filter_values(self):\n return [f.get() for f in self._filters[:-1]] # Ignore placeholder","def values(self):\n return [p.value for p in self]","def search(self, *args, **kwargs):\n ret = []\n for record in Handler.ALL_VERS_DATA.values():\n matchArgs = list(kwargs.keys())\n for k,v in iteritems(kwargs): # restrict records based on key-value match requirement\n try:\n if record[k] != v: break # a non-matching requirement means this record doesn't match\n except: break # record doesn't have required key 'k'\n matchArgs.remove(k)\n if matchArgs: continue # didn't match all required kwargs\n matchArgs = list(args)\n for k,v in iteritems(record): # find any record with a <value> in it\n if k in matchArgs: matchArgs.remove(k)\n if v in matchArgs: matchArgs.remove(v)\n if matchArgs: continue # didn't match all required args\n ret.append(record)\n return ret","def AND(self, values: pdarray) -> Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]:\n if values.dtype not in [akint64, akuint64, bigint]:\n raise TypeError(\"AND is only supported for pdarrays of dtype int64, uint64, or bigint\")\n\n return self.aggregate(values, \"and\") # type: ignore","def return_patids_val_folds(fld_number):\n fold_idx = np.arange(6)\n fld_idx_train_val = np.delete(fold_idx, fld_number)\n fld_idx_1 = fld_idx_train_val[0]\n fld_idx_2 = fld_idx_train_val[1]\n fld_idx_3 = fld_idx_train_val[2]\n fld_idx_4 = fld_idx_train_val[3]\n fld_idx_5 = fld_idx_train_val[4]\n fln_pat_ids_1 = 'fold_' + str(fld_idx_1) + '_pat_ids.npy'\n fln_pat_ids_2 = 'fold_' + str(fld_idx_2) + '_pat_ids.npy'\n fln_pat_ids_3 = 'fold_' + str(fld_idx_3) + '_pat_ids.npy'\n fln_pat_ids_4 = 'fold_' + str(fld_idx_4) + '_pat_ids.npy'\n fln_pat_ids_5 = 'fold_' + str(fld_idx_5) + '_pat_ids.npy'\n pat_ids_1 = af.load_with(fln_pat_ids_1)\n pat_ids_2 = af.load_with(fln_pat_ids_2)\n pat_ids_3 = af.load_with(fln_pat_ids_3)\n pat_ids_4 = af.load_with(fln_pat_ids_4)\n pat_ids_5 = af.load_with(fln_pat_ids_5)\n pat_ids_run_1 = np.hstack((pat_ids_1, pat_ids_2))\n pat_ids_run_2 = np.hstack((pat_ids_3, pat_ids_4))\n pat_ids_5_run_1 = pat_ids_5[:5]\n pat_ids_5_run_2 = pat_ids_5[5:]\n pat_ids_run_1 = np.hstack((pat_ids_run_1, pat_ids_5_run_1))\n pat_ids_run_2 = np.hstack((pat_ids_run_2, pat_ids_5_run_2))\n return pat_ids_run_1, pat_ids_run_2","def getValuesFromField(self, table_fullpath, where_clause, field):\r\n \r\n try:\r\n rows = arcpy.SearchCursor(table_fullpath, where_clause, \"\", field, \"\")\r\n return [str(row.getValue(field)) for row in rows]\r\n finally:\r\n del rows","def choices(self, typename, value_field='description'):\n rows = self.type(typename).values('id', value_field)\n return [(r['id'], r[value_field]) for r in rows]","def _get_values(self) -> ty.List[float]:\r\n ...","def create_recordid_list(rec_ids):\n rec_list = []\n for row in rec_ids:\n rec_list.append(row[0])\n return rec_list","def values(self):\n\t\treturn self.myVals","def value(self):\n return [ f.value() for f in self.fields ]","def get_patient_lookup_results(form):\n args = form.data\n data = {k: v for (k, v) in args.items() if not is_empty_form_value(v)}\n if (not form.validate()) or (len(data) == 0):\n return {\"has_form_data\": False, \"tb\": None}\n where = \"where \" + (\" and \".join(\"(X.[{}] = ?)\".format(k) for k, _ in data.items()))\n params = [v for _, v in data.items()]\n q = (\"select top 1000 X.* \" +\n \"from {schema}.PersonCombined X \".format(schema=app_schema) +\n where)\n tb = pd.read_sql(q, engine, params=params)\n return {\"has_form_data\": True, \"tb\": tb}"],"string":"[\n \"def get_values(self, value):\\n return [\\n obj for obj in self if obj == value\\n ]\",\n \"def values(self):\\n if '%' in self.starid:\\n query = \\\"\\\"\\\"SELECT * from ngc2236 where starid like '%s'\\\"\\\"\\\" % self.starid\\n else:\\n query = \\\"\\\"\\\"SELECT * from ngc2236 where starid = '%s'\\\"\\\"\\\" % self.starid\\n result = self.wifsip.query(query)\\n values = [r for r in result[0]]\\n if '%' in self.starid:\\n self.starid=values[0]\\n return values\",\n \"def get_drupal_fc_ids(db_obj, db_cur, entity_type, bundle, entity_id,\\n revision_id, fc_cv):\\n\\n # fc details\\n fc_ident = fc_cv[0]\\n fc_value_type = fc_cv[1]\\n fc_value = fc_cv[2]\\n fc_type = fc_ident[1]\\n fc_id_type = fc_ident[2]\\n\\n # handle fc ID types\\n if fc_id_type == 'id':\\n fc_ident_column = 'fci.item_id'\\n elif fc_id_type == 'label':\\n fc_ident_column = 'fci.label'\\n\\n # query string and arguments\\n query_str = (\\n'''\\nSELECT fci.item_id, fci.revision_id\\nFROM field_data_field_{0} as fcf\\nLEFT JOIN field_collection_item as fci\\nON fci.item_id = fcf.field_{0}_value\\nAND fci.revision_id = fcf.field_{0}_revision_id\\nWHERE fcf.entity_type = %s\\nAND fcf.bundle = %s\\nAND fcf.entity_id = %s\\nAND fcf.revision_id = %s\\nAND fcf.deleted = 0\\nAND fci.revision_id IN\\n (SELECT MAX(revision_id)\\n FROM field_collection_item_revision\\n GROUP BY item_id)\\nAND fci.archived = 0\\nAND {1} = %s\\n''' .\\n format(fc_type, fc_ident_column)\\n )\\n query_args = [entity_type, bundle, entity_id, revision_id, fc_value]\\n\\n # execute the query\\n if not db_obj.execute(db_cur, query_str.strip(), query_args,\\n has_results=True):\\n return None\\n ret = db_obj.fetchall(db_cur)\\n if not ret[0]:\\n return None\\n if not ret[1]:\\n return []\\n return ret[1]\",\n \"def values(rbt, keylo, keyhi):\\n try:\\n lstvalues = lt.newList('SINGLELINKED', rbt['cmpfunction'])\\n lstvalues = valuesRange(rbt['root'], keylo, keyhi, lstvalues,\\n rbt['cmpfunction'])\\n return lstvalues\\n except Exception as exp:\\n error.reraise(exp, 'RBT:Values')\",\n \"def get_searches(self,redcap,boolean,val):\\n\\t\\tmatches = []\\n\\t\\ttry:\\n\\t\\t\\tfor eachdict in self.get_data(redcap):\\n\\t\\t\\t\\tif (boolean):\\n\\t\\t\\t\\t\\tif (re.search(str(val),eachdict[redcap].strip(),re.I)):\\n\\t\\t\\t\\t\\t\\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif not (re.search(str(val),eachdict[redcap].strip(),re.I)):\\n\\t\\t\\t\\t\\t\\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\\n\\t\\texcept KeyError as e:\\n\\t\\t\\tprint(\\\"redcap field: '{}'\\\\nnot found. did you mean: '{}'?\\\\nverify and try again\\\".format(redcap,get_close_matches(redcap,[d['field_name'] for d in self.get_metadata()])))\\n\\n\\t\\treturn matches\",\n \"def find_matches(ktree, val):\\n collection = []\\n\\n ktree.pre_order(lambda n: collection.append(n) if n.val == val else None)\\n\\n if collection == []:\\n raise ValueError('Your value did not match any values in the tree')\\n\\n return collection\",\n \"def _filter_search_values(key: str, values: list, collection: list):\\n return_data = []\\n for item in collection:\\n if any(val in values for val in item[key]):\\n return_data.append(item)\\n return return_data\",\n \"def getKIDsFP():\\n\\tdb = MySQLdb.connect(host='tddb.astro.washington.edu', user='tddb', passwd='tddb', db='Kepler')\\n\\tcursor = db.cursor()\\n\\tfoo1 = 'select KID from KEPFP'\\n\\tcursor.execute(foo1)\\n\\tresults = cursor.fetchall()\\n\\tr1 = ([el[0] for el in results])\\n\\tresults=set(r1)\\n \\n\\treturn results\",\n \"def get_matches(self,redcap,boolean,val):\\n\\t\\tmatches = []\\n\\t\\ttry:\\n\\t\\t\\tfor eachdict in self.get_data(redcap):\\n\\t\\t\\t\\tif (boolean):\\n\\t\\t\\t\\t\\tif (eachdict[redcap].strip() == str(val)):\\n\\t\\t\\t\\t\\t\\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif (eachdict[redcap].strip() != str(val)):\\n\\t\\t\\t\\t\\t\\tmatches.append((eachdict['article_doi'],eachdict['record_id'],eachdict[redcap]))\\n\\t\\texcept KeyError as e:\\n\\t\\t\\tprint(\\\"redcap field: '{}'\\\\nnot found. did you mean: '{}'?\\\\nverify and try again\\\".format(redcap,get_close_matches(redcap,[d['field_name'] for d in self.get_metadata()])))\\n\\t\\treturn matches\",\n \"def task_3_find_item_via_value(data: DT, value) -> DT:\\n find = [find for find in data for n in find.values() if n == value]\\n return find\",\n \"def getOpaqueValues(valueIDs):\\n store = getMainStore()\\n return store.find(OpaqueValue,\\n OpaqueValue.fileID == OpaqueValueLink.fileID,\\n OpaqueValueLink.valueID.is_in(valueIDs))\",\n \"def itervaluerefs(self):\\r\\n return self.data.itervalues()\",\n \"def getListFieldValue(self, vocab_value_id):\\n try:\\n value = ''\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('qiime_assets.get_list_field_value', [vocab_value_id, results])\\n for row in results:\\n value = row[0]\\n return value\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\",\n \"def _get_f(couplelist, dpplist, bpm_name, value):\\n lst = []\\n x = []\\n for dpp in dpplist:\\n x.append(dpp)\\n couplefile = couplelist[dpp]\\n lst.append(getattr(couplefile, value)[couplefile.indx[bpm_name]])\\n\\n lreg = linreg(x, lst)\\n\\n return lreg[0], lreg[3]\",\n \"def get_value_list(self, insupdel=0):\\n #NOTE: statement = [record, {...}]\\n result = []\\n if insupdel == StatementType.INSERT:\\n statements = self.statements_insert\\n elif insupdel == StatementType.UPDATE:\\n statements = self.statements_update\\n elif insupdel == StatementType.DELETE:\\n statements = self.statements_delete\\n for statement in statements:\\n result.append([key for key, val in statement[1].items()])\\n return result\",\n \"def return_values(self):\\r\\n\\r\\n values = list(self.piDD.values())\\r\\n return values\",\n \"def values(self):\\n store = getMainStore()\\n tagIDs = self._getTagIDs()\\n where = self._getWhereClause(tagIDs)\\n return store.find((Tag, TagValue), *where)\",\n \"def task_3_find_item_via_value(data: DT, value) -> DT:\\n return [dic for dic in data if value in dic.values()]\",\n \"def process_result_value(self, value, dialect):\\n masks = list()\\n if value:\\n for e in enums.CryptographicUsageMask:\\n if e.value & value:\\n masks.append(e)\\n return masks\",\n \"def getvaluelist(doclist, fieldname):\\n\\tl = []\\n\\tfor d in doclist:\\n\\t\\tl.append(d.fields[fieldname])\\n\\treturn l\",\n \"def get_matches(q_id, res):\\n tokens = res[0]\\n opt_dict = res[1]\\n ret_val = []\\n for opt in opt_dict.keys():\\n if opt == q_id:\\n continue\\n if float(opt_dict[opt]) > float(4)/5 * tokens:\\n ret_val.append(opt)\\n return ret_val\",\n \"def get_key_from_values(f, catalog):\\n L_keys = []\\n L_values = []\\n for path, files in catalog.items():\\n if f in catalog[path]:\\n return path\",\n \"def queryByAttributeIn(table, attribute, values, access=None, addtl=\\\"\\\"):\\n if len(values) > MAX_IN_ELEMENTS:\\n values1 = values[:MAX_IN_ELEMENTS]\\n values2 = values[MAX_IN_ELEMENTS:]\\n records1 = queryByAttributeIn(table, attribute, values1, access, addtl)\\n records2 = queryByAttributeIn(table, attribute, values2, access, addtl)\\n records1.extend(records2)\\n return records1\\n\\n valueString = u\\\",\\\".join(u\\\"'\\\" + sqlapi.quote(val) + u\\\"'\\\" for val in values)\\n condition = u\\\"%s IN (%s)\\\" % (attribute, valueString)\\n records = sqlapi.RecordSet2(table, condition,\\n access=access, access_persno=auth.persno,\\n addtl=addtl)\\n return [records]\",\n \"def get_fvalues(self, positions):\\n if all(self._measures.has_key(p) for p in positions):\\n return [ self._measures[p] for p in positions ]\\n else:\\n raise Exception(\\\"Trying to obtain focus value at\\\"\\n \\\" non-visted positions %s\\\" % str(positions))\",\n \"def return_poll_id(self, value_id:int) -> set:\\n try:\\n value_coordinates = self.cursor.execute(f\\\"SELECT id_user, latitude, longitude FROM {table_poll} WHERE id=?;\\\", (value_id,)).fetchall()\\n value_lists = self.cursor.execute(f\\\"SELECT id_group FROM {table_poll_groups} WHERE id_poll=?;\\\", (value_id,)).fetchall()\\n value_coordinates = value_coordinates[-1] if value_coordinates else []\\n value_lists = [f[0] for f in value_lists] if value_lists else []\\n return value_coordinates, value_lists\\n except Exception as e:\\n msg = f\\\"We faced problems with getting values from the database via poll id; Mistake: {e}\\\"\\n self.proceed_error(msg)\\n return [], []\",\n \"def sick_patients(lab: str, gt_lt: str, value: float) -> set[int]:\\r\\n cur = con.cursor()\\r\\n output = cur.execute(\\r\\n f\\\"\\\"\\\"SELECT DISTINCT(Patient_ID)\\r\\n FROM Lab\\r\\n WHERE Lab_Name = ?\\r\\n AND Lab_Value {gt_lt} {value}\\\"\\\"\\\",\\r\\n [lab],\\r\\n ).fetchall()\\r\\n\\r\\n patient_IDs: set[str] = set() # O(1)\\r\\n for row in output:\\r\\n patient_IDs.add(row[0])\\r\\n\\r\\n return patient_IDs # O(1)\\r\",\n \"def values_list(self):\\n if self.values_number() == 0:\\n raise RegistryStructureDoesNotExist(\\\"NK Record has no associated values.\\\")\\n\\n values_list_offset = self.abs_offset_from_hbin_offset(self.unpack_dword(0x28))\\n\\n d = HBINCell(self._buf, values_list_offset, self)\\n return ValuesList(self._buf, d.data_offset(), self, self.values_number())\",\n \"def itervaluerefs(self):\\n for value in self.itervalues():\\n yield ref(value)\",\n \"def get_field_names_fuzzy(self, value: str, key: str = \\\"name_qual\\\") -> List[str]:\\n splits = self.split_searches(value=value)\\n fields = self.get()\\n\\n matches = []\\n\\n for adapter_name, names in splits:\\n adapter = self.get_adapter_name(value=adapter_name)\\n for name in names:\\n schemas = fields[adapter]\\n amatches = self.fuzzy_filter(search=name, schemas=schemas, key=key, root_only=True)\\n matches += [x for x in amatches if x not in matches]\\n\\n return matches\",\n \"def getFiList():\\n fi = getFi()\\n fiList = []\\n for arg,val in fi.items():\\n fiList.append(val)\\n return fiList\",\n \"def get_params_from_pids(self, frompid, topid, syselem=None):\\n\\n with self.__connection.cursor() as cursor:\\n query = ''\\n if not syselem:\\n query = (\\\"SELECT NAME FROM %s WHERE \\\" +\\n \\\" PID BETWEEN %s AND %s\\\") % (self.__schema, frompid, topid)\\n else:\\n query = (\\\"SELECT NAME FROM %s WHERE \\\" +\\n \\\" SYSTEM_ELEMENT='%s'\\\" +\\n \\\" AND PID BETWEEN %s AND %s\\\") % (self.__schema, syselem, frompid, topid)\\n cursor.execute(query)\\n result = cursor.fetchall()\\n\\n return result\",\n \"def parameter_values(self) -> List[Tuple[str, Any]]:\\n pvs = [(param, getattr(self, variable))\\n for variable, param in self.variable_name_to_query_param.items()]\\n return [(p, v) for p, v in pvs if v is not None]\",\n \"def find_qualifications_by_ids(\\n self,\\n qualification_ids: List[str] = None,\\n task_run_ids: Optional[List[str]] = None,\\n ) -> List[dict]:\\n if not qualification_ids:\\n return []\\n\\n with self.table_access_condition, self._get_connection() as conn:\\n c = conn.cursor()\\n\\n qualification_ids_block = \\\"\\\"\\n if qualification_ids:\\n qualification_ids_block = \\\" OR \\\".join(\\n \\\"qualification_ids LIKE '%\\\\\\\"\\\" + str(_id) + \\\"\\\\\\\"%'\\\" for _id in qualification_ids\\n )\\n qualification_ids_block = f\\\"({qualification_ids_block})\\\"\\n\\n task_run_ids_block = \\\"\\\"\\n if task_run_ids:\\n task_run_ids_str = \\\",\\\".join([f'\\\"{tid}\\\"' for tid in task_run_ids])\\n task_run_ids_block = f\\\"AND task_run_id IN ({task_run_ids_str})\\\"\\n\\n c.execute(\\n f\\\"\\\"\\\"\\n SELECT * FROM qualifications\\n WHERE {qualification_ids_block} {task_run_ids_block};\\n \\\"\\\"\\\"\\n )\\n results = c.fetchall()\\n return results\",\n \"def _rr_yun0_sqf_list(self, f):\\n if f.is_ground:\\n return []\\n\\n result, count = [], 1\\n qs = [f.diff(x) for x in self.gens]\\n\\n g = f\\n for q in qs:\\n g = self.gcd(g, q)\\n\\n while f != 1:\\n qs = [q // g for q in qs]\\n f //= g\\n qs = [q - f.diff(x) for x, q in zip(self.gens, qs)]\\n\\n g = f\\n for q in qs:\\n g = self.gcd(g, q)\\n if g != 1:\\n result.append((g, count))\\n\\n count += 1\\n\\n return result\",\n \"def matching(self, pids):\\n for pid in pids:\\n if self.matches(pid):\\n yield pid\",\n \"def map_values_to_value_list(value_list, values):\\n return [value_list.index(x) for x in values]\",\n \"def filter_by_compare(self, diclist, fidname, value, comparison, outputfidlist=[]):\\n if comparison == 'GT':\\n result = [item for item in diclist if item[fidname] > value]\\n elif comparison == 'LT':\\n result = [item for item in diclist if item[fidname] < value]\\n elif comparison == 'EQ':\\n result = [item for item in diclist if item[fidname] == value]\\n else:\\n result = []\\n \\n return self.filter_output(result, outputfidlist)\",\n \"def get_existing_values(self): #DONE\\n return (value.value for value in self.address.values() if value.value)\",\n \"def wql_values_query(self, start_node, path):\\n self.tr_id = get_tr_id()\\n xml_msg = self._create_wql_values_msg(self.tr_id, start_node, path)\\n self.conn.connect()\\n self.conn.send(xml_msg)\\n response = self.conn.receive()\\n self._check_error(response)\\n if \\\"results\\\" in response:\\n node_list = parse_URI_list(response[\\\"results\\\"])\\n return node_list\\n else:\\n raise SIBError(M3_SIB_ERROR)\",\n \"def get_field_names_eq(\\n self,\\n value: Union[str, List[str]],\\n key: str = \\\"name_qual\\\",\\n fields_error: bool = True,\\n selectable_only: bool = True,\\n ) -> List[str]:\\n splits = self.split_searches(value=value)\\n fields = self.get()\\n\\n matches = []\\n\\n for adapter_name, names in splits:\\n adapter = self.get_adapter_name(value=adapter_name)\\n for name in names:\\n schemas = fields[adapter]\\n schema = self.get_field_schema(\\n value=name,\\n schemas=schemas,\\n fields_error=fields_error,\\n selectable_only=selectable_only,\\n )\\n\\n match = schema[key] if key else schema\\n if match not in matches:\\n matches.append(match)\\n\\n return matches\",\n \"def contains(self, value):\\n n = self.search(value)\\n return (n.value==value, n)\",\n \"def queryByAttributePair(table, attribute1, attribute2, values):\\n if len(values) > MAX_PAIRS:\\n values1 = values[:MAX_PAIRS]\\n values2 = values[MAX_PAIRS:]\\n records1 = queryByAttributePair(table, attribute1, attribute2, values1)\\n records2 = queryByAttributePair(table, attribute1, attribute2, values2)\\n records1.extend(records2)\\n return records1\\n\\n condition = u\\\"1=0\\\"\\n for val1, val2 in values:\\n condition += \\\" OR %s = '%s' AND %s = '%s'\\\" % (attribute1, sqlapi.quote(val1), attribute2, sqlapi.quote(val2))\\n records = sqlapi.RecordSet2(table, condition, access_persno=auth.persno)\\n return [records]\",\n \"def extract_q_values(results):\\n return results['max_q_values'].tolist()\",\n \"def valuerefs(self):\\n return [ref(value) for value in self.itervalues()]\",\n \"def _find_ids(self,\\r\\n data_list,\\r\\n prop,\\r\\n lookup_index,\\r\\n lookup_doc_type,\\r\\n lookup_field):\\r\\n lg = logging.getLogger(\\\"%s.%s\\\" % (self.ln, inspect.stack()[0][3]))\\r\\n lg.setLevel(self.log_level)\\r\\n\\r\\n rtn_list = []\\r\\n first_time = IsFirst()\\r\\n for item in data_list:\\r\\n # the Dot class will retive and set dictionary values via dot\\r\\n # notation\\r\\n val = Dot(item).get(prop)\\r\\n if val.startswith(\\\"#;lookup#;\\\"):\\r\\n lookup_val = val.replace(\\\"#;lookup#;\\\", \\\"\\\")\\r\\n lookup_obj = self.get_item(lookup_val, lookup_field)\\r\\n if first_time.first():\\r\\n lg.debug(\\\" lookup_obj:\\\\n%s\\\", pp.pformat(lookup_obj))\\r\\n if lookup_obj:\\r\\n rtn_list.append(Dot(item).set(prop, lookup_obj['_id']))\\r\\n return rtn_list\",\n \"def lookup_rows(self, key, values, fields=None):\\n\\n s3db = current.s3db\\n ntable = s3db.need_need\\n nttable = s3db.need_tag\\n\\n left = nttable.on((nttable.need_id == ntable.id) & \\\\\\n (nttable.tag == \\\"req_number\\\"))\\n\\n qty = len(values)\\n if qty == 1:\\n query = (ntable.id == values[0])\\n limitby = (0, 1)\\n else:\\n query = (ntable.id.belongs(values))\\n limitby = (0, qty)\\n\\n rows = current.db(query).select(ntable.id,\\n ntable.name,\\n nttable.value,\\n left = left,\\n limitby = limitby,\\n )\\n self.queries += 1\\n return rows\",\n \"def get_criteria_values(self) -> list or None:\\n\\n # query_string = 'SELECT * FROM [{}];'.format(self.settings.excelSheetName)\\n query_string = 'SELECT DISTINCT [{}] FROM [{}];'.format(self.settings.excelCriteriaSelector,\\n self.settings.excelSheetName)\\n\\n if self.connected and self.settings.excelSheetName:\\n cursor = self.workbook.cursor()\\n if cursor:\\n criteria_values = []\\n for row in cursor.execute(query_string):\\n criteria_values.append(row[self.settings.excelCriteriaSelector.lower()])\\n cursor.close()\\n return criteria_values\\n\\n return None\",\n \"def _extract_subtable_by_attribute_values(self, values, \\n mode=\\\"and\\\"):\\n self._check_attribute_names(list(values.keys()))\\n if mode == \\\"and\\\":\\n indices = [i for i in range(len(self)) if self._has_values(i, values)]\\n elif mode == \\\"or\\\":\\n indices = [i for i in range(len(self)) if self._has_at_least_one_value(i, values)]\\n return ([self.objects[i] for i in indices],\\n [self.table[i] for i in indices])\",\n \"def check_granted_qualifications(\\n self,\\n qualification_id: Optional[str] = None,\\n worker_id: Optional[str] = None,\\n value: Optional[int] = None,\\n ) -> List[GrantedQualification]:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from granted_qualifications\\n WHERE (?1 IS NULL OR qualification_id = ?1)\\n AND (?2 IS NULL OR worker_id = ?2)\\n AND (?3 IS NULL OR value = ?3)\\n \\\"\\\"\\\",\\n (qualification_id, worker_id, value),\\n )\\n rows = c.fetchall()\\n return [\\n GrantedQualification(\\n self, str(r[\\\"qualification_id\\\"]), str(r[\\\"worker_id\\\"])\\n )\\n for r in rows\\n ]\",\n \"def get_value(self, key, getter=lambda x: x):\\n record_trie = self._record_trie\\n assert key in record_trie\\n value = record_trie[key]\\n # Record trie allows keys to have multiple values and returns a list of values for each key.\\n # As we make the value for each key a list already (to control order/not have to sort again),\\n # we need to assert there is only a single value\\n assert len(value) == 1\\n value = value[0]\\n return_value = inverse_qid_cand_with_score(value=value, itos=self._itos)\\n res = list(map(getter, return_value))\\n assert len(res) <= self._max_value\\n return res\",\n \"def get_ids_values(path):\\n \\n def read_txt(path):\\n \\\"\\\"\\\"read text file from path.\\\"\\\"\\\"\\n \\n with open(path, \\\"r\\\") as f:\\n return f.read().splitlines()\\n \\n raw_data = read_txt(path)[1:]\\n \\n def deal_line(line):\\n ids, values = line.split(',')\\n return ids, values\\n \\n ids_values = [deal_line(line) for line in raw_data]\\n ids = [x[0] for x in ids_values]\\n values = [x[1] for x in ids_values]\\n values = list(map(int, values))\\n \\n return ids, values\",\n \"def list_reference_images_in_filter(conn,primary_ref,f,log):\\n\\n log.info('Identifying all current reference image in filter '+str(f))\\n\\n query = 'SELECT * FROM reference_images WHERE filter=\\\"'+str(primary_ref[f])+\\\\\\n '\\\" AND software=\\\"'+str(primary_ref['software_id'])+\\\\\\n '\\\" AND facility!=\\\"'+str(primary_ref['facility_id'])+'\\\"'\\n\\n ref_image_list = phot_db.query_to_astropy_table(conn, query, args=())\\n\\n log.info(repr(ref_image_list))\\n\\n return ref_image_list\",\n \"def _list(self, val, fld):\\n if isinstance(val, (list, tuple)):\\n if len(val) == 1:\\n return fld == val[0]\\n else:\\n return fld.in_(val)\\n else:\\n return fld == val\",\n \"def get_values(self, *fields):\\n result = []\\n for field in fields:\\n if isinstance(field, (str, basestring)):\\n result.append(self.get_value(field))\\n elif isinstance(field, (tuple, list)):\\n result.append(self.get_values(*field))\\n else:\\n result.append(None)\\n return result\",\n \"def values(self):\\n return [entry.value for entry in self.table if entry.value is not None]\",\n \"def get_sub_values(self):\\n return list()\",\n \"def bind_kpis(self, data_values):\\n kpi_names = [kpi.name for kpi in self.kpis]\\n\\n kpi_results = [\\n dv\\n for kpi_name in kpi_names\\n for dv in data_values\\n if dv.name == kpi_name\\n ]\\n\\n return kpi_results\",\n \"def values(self):\\n return [i.value for i in self.value]\",\n \"def getRecordsByAttributePair(table, attribute1, attribute2, values):\\n recordSets = queryByAttributePair(table, attribute1, attribute2, values)\\n records = []\\n for recordSet in recordSets:\\n records.extend(list(recordSet))\\n return records\",\n \"def query_by_field(cls, field: str, value: str)->List:\\n database.cursor.execute(\\n \\\"SELECT * FROM {0} WHERE {1} = %s\\\".format(cls.table_name, field), (value,))\\n items = database.cursor.fetchall()\\n return [cls.to_object(item) for item in items]\",\n \"def _policy_values(self, p_map, v_note):\\n pitches = None\\n for p in self.v_policy_map[v_note]:\\n p_values = p.values(p_map, v_note)\\n if p_values is None:\\n returned_pitches = OrderedSet() # None means p cannot be satisfied!\\n else:\\n returned_pitches = OrderedSet()\\n for n in p_values:\\n returned_pitches.add(n.diatonic_pitch)\\n pitches = returned_pitches if pitches is None else pitches.intersection(returned_pitches)\\n\\n retset = OrderedSet()\\n for p in pitches:\\n retset.add(Note(p, v_note.base_duration, v_note.num_dots))\\n return retset\",\n \"def getRecordsByAttributeIn(table, attribute, values, access=None, addtl=\\\"\\\"):\\n recordSets = queryByAttributeIn(table, attribute, values, access, addtl)\\n records = []\\n for recordSet in recordSets:\\n records.extend(list(recordSet))\\n return records\",\n \"def value_in(table_rows, values=[], col_name=\\\"\\\", col_num=-1):\\n key = col_name\\n if(key==\\\"\\\"):\\n key = table_rows[0].keys[col_num]\\n rst = True\\n lst = []\\n for i in range(len(table_rows)):\\n value = table_rows[i].get_d_value(key)\\n if(value not in values):\\n rst = False\\n lst.append(\\\"(col:{0},row:{1}:value:{2}\\\".format(\\n key, i, value\\n ))\\n return rst,\\\",\\\".join(lst)\",\n \"def values():\",\n \"def search(self, values):\\n if values is False:\\n return False\\n if all(len(values[square]) == 1 for square in self.squares):\\n return values\\n n, square = min((len(values[square]), square)\\n for square in self.squares if len(values[square]) > 1)\\n\\n return self.possible_values(self.search(self.assign(values.copy(), square, dig))\\n for dig in values[square])\",\n \"def _get_split_field_values(self, fname):\\r\\n files = [os.path.splitext(os.path.basename(f))[0] for f in glob.glob(fname)]\\r\\n filt = [self.filter_filename(f) for f in files]\\r\\n values = [f.split(self.sep)[self.field_index(self.split_by)] for f in filt]\\r\\n return sorted(values)\",\n \"def _equality_fishing_queries(self, f, dist):\\n query_dicts = []\\n for q in xrange(len(self.__queries)):\\n query_dicts = []\\n for count in xrange(self.__queries[q]['no_queries']*OVER_GENERATION_RATIO):\\n self.__count += 1\\n logger.info('EQ: Created %d out of %d queries' % (self.__count, self.__total))\\n field = f\\n r_lower_cdf = self.__queries[q]['rss_lower_cdf']\\n r_upper_cdf = self.__queries[q]['rss_upper_cdf'] \\n (search_value, lbound, ubound, enum) = \\\\\\n self._get_fishing_values(f, dist, r_lower_cdf, r_upper_cdf)\\n \\n query_dicts.append({qs.QRY_ENUM : enum, \\n qs.QRY_QID : qids.query_id(),\\n qs.QRY_DBNUMRECORDS : self.__db_size,\\n qs.QRY_DBRECORDSIZE : self.__row_width, \\n qs.QRY_PERF : self.__perf,\\n qs.QRY_CAT : self.__cat,\\n qs.QRY_SUBCAT : '', \\n qs.QRY_WHERECLAUSE : '',\\n qs.QRY_FIELD : sv.sql_info[field][0],\\n qs.QRY_NEGATE : False,\\n qs.QRY_FIELDTYPE : sv.sql_info[field][1],\\n qs.QRY_LRSS : self.__queries[q][qs.QRY_LRSS],\\n qs.QRY_URSS : self.__queries[q][qs.QRY_URSS],\\n qs.QRY_VALUE : None,\\n qs.QRY_SEARCHFOR : search_value,\\n qs.QRY_LBOUND : lbound,\\n qs.QRY_UBOUND : ubound })\\n \\n self.__bobs.append(aqb.EqualityFishingQueryBatch(query_dicts,count, \\n int((count+1)/OVER_GENERATION_RATIO),\\n True))\",\n \"def get_values(self):\\n \\n return []\",\n \"def list_value(self) -> global___Expression.RepeatedValue:\",\n \"def get_param_vals(self, parseq):\\n\\n vals = []\\n for conf in self.sim_confs:\\n val = conf[parseq]\\n if val not in vals:\\n vals.append(val)\\n return vals\",\n \"def get_field_values(self, req, query_field=None,\\n multi_query_field=None, multi=True):\\n\\n if not multi_query_field:\\n multi_query_field = '{}s[]'.format(query_field)\\n\\n if multi:\\n values = req.get_param_as_list(multi_query_field) or []\\n else:\\n values = []\\n\\n single = req.get_param(query_field)\\n\\n if single:\\n values.append(single)\\n\\n return values\",\n \"def find(self, query, fields=None):\\n # get records matching query\\n records = [rec for rec in self.__data.values()\\n if rec.check_condition(query)]\\n\\n # handle `fields` argument\\n if fields is None:\\n # return raw results if fields not given\\n results = dict(rec.to_id_dict() for rec in records)\\n elif isinstance(fields, str):\\n # chose one value from each record\\n results = [rec[fields] for rec in records]\\n elif isinstance(fields, list):\\n # chose only values matching given fields\\n results = [rec.get_fields_list(fields) for rec in records]\\n else:\\n raise TypeError(f\\\"`fields` should be of one of types: \\\"\\n f\\\"`None`, `str` or `list`. got: {type(fields)}\\\")\\n\\n return results\",\n \"def getCriteriaItems( self ):\\n # filter out empty strings\\n result = []\\n\\n value = tuple( filter( None, self.value ) )\\n if not value:\\n return ()\\n result.append( ( self.field, self.value ), )\\n\\n if self.operator is not None:\\n result.append( ( '%s_operator' % self.field, self.operator ) )\\n\\n return tuple( result )\",\n \"def list_of_gene_IDs_and_symbols_from_file(input_seq_file):\\n print(\\\"Find entrezgene IDs for reporter genes in RNAseq run\\\")\\n lines = []\\n with open(input_seq_file, 'U') as input_seq_file:\\n next(input_seq_file)\\n for line in input_seq_file:\\n new_line = line.strip().split('\\\\t')[0]\\n new_line = re.sub('\\\"', '', new_line) # inputs contained quotes\\n lines.append(new_line)\\n\\n query_list = pd.Series(data=lines).tolist()\\n\\n print(\\\"length of query list: \\\", len(query_list))\\n return query_list\",\n \"def lookups(self, request, model_admin):\\n list_id = [[None, \\\"All\\\"]]\\n for firm in Firm.objects.all():\\n list_id.append([firm.pk, firm.name])\\n\\n return list_id\",\n \"def find_by_value(cls, field, value, as_dict=False):\\n _field = cls.get_property_by_name(field)\\n _label = cls.get_label()\\n\\n value_type = False\\n if isinstance(value, integer_types + float_types):\\n value_type = True\\n results = cls._find_vertex_by_value(\\n value_type=value_type,\\n vlabel=_label,\\n field=_field,\\n val=value\\n )\\n\\n if as_dict: # pragma: no cover\\n return {v._id: v for v in results}\\n\\n return results\",\n \"def valuerefs(self):\\r\\n return self.data.values()\",\n \"def get_attr_val_ids(self, ext_ids):\\n\\n value_ids = []\\n\\n attr_val_prefix = 'product_configurator.product_attribute_value_%s'\\n\\n for ext_id in ext_ids:\\n if ext_id in self.attr_val_ext_ids:\\n value_ids.append(self.attr_val_ext_ids[ext_id])\\n elif attr_val_prefix % ext_id in self.attr_val_ext_ids:\\n value_ids.append(\\n self.attr_val_ext_ids[attr_val_prefix % ext_id]\\n )\\n\\n return value_ids\",\n \"def custom_lookup_rows(self, key, values, fields=None):\\n\\n s3db = current.s3db\\n ntable = s3db.req_need\\n nttable = s3db.req_need_tag\\n\\n left = nttable.on((nttable.need_id == ntable.id) & \\\\\\n (nttable.tag == \\\"req_number\\\"))\\n\\n qty = len(values)\\n if qty == 1:\\n query = (ntable.id == values[0])\\n limitby = (0, 1)\\n else:\\n query = (ntable.id.belongs(values))\\n limitby = (0, qty)\\n\\n rows = current.db(query).select(ntable.id,\\n ntable.name,\\n nttable.value,\\n left=left,\\n limitby=limitby)\\n self.queries += 1\\n return rows\",\n \"def custom_lookup_rows(self, key, values, fields=None):\\n\\n s3db = current.s3db\\n ntable = s3db.req_need\\n nttable = s3db.req_need_tag\\n\\n left = nttable.on((nttable.need_id == ntable.id) & \\\\\\n (nttable.tag == \\\"req_number\\\"))\\n\\n qty = len(values)\\n if qty == 1:\\n query = (ntable.id == values[0])\\n limitby = (0, 1)\\n else:\\n query = (ntable.id.belongs(values))\\n limitby = (0, qty)\\n\\n rows = current.db(query).select(ntable.id,\\n ntable.name,\\n nttable.value,\\n left=left,\\n limitby=limitby)\\n self.queries += 1\\n return rows\",\n \"def _build_potential_values(self, p_map, v_notes):\\n ranked_list = list() # A list of tuples (v_note, {solution values})\\n for v_note in v_notes:\\n if p_map[v_note] is not None and p_map[v_note].note is not None:\\n continue\\n if v_note not in self.v_policy_map.keys():\\n continue\\n values = self._policy_values(p_map, v_note)\\n if len(values) == 0:\\n continue\\n ranked_list.append((v_note, values))\\n\\n ranked_list = sorted(ranked_list, key=lambda x: len(x[1]))\\n return ranked_list\",\n \"def doFindAllMatching(self, str):\\n matches = []\\n for value in self.doId2do.values():\\n if re.search(str,repr(value)):\\n matches.append(value)\\n return matches\",\n \"def transform_to_fqids(\\n value: Optional[\\n Union[\\n int,\\n str,\\n FullQualifiedId,\\n Sequence[int],\\n Sequence[str],\\n Sequence[FullQualifiedId],\\n ]\\n ],\\n collection: Collection,\\n) -> List[FullQualifiedId]:\\n id_list: IdentifierList\\n if value is None:\\n id_list = [] # type: ignore # see https://github.com/python/mypy/issues/2164\\n elif not isinstance(value, list):\\n value_arr = cast(IdentifierList, [value])\\n id_list = value_arr\\n else:\\n id_list = value\\n\\n fqid_list = []\\n for id in id_list:\\n if isinstance(id, int):\\n fqid_list.append(fqid_from_collection_and_id(collection, id))\\n else:\\n fqid_list.append(cast(FullQualifiedId, id))\\n return fqid_list\",\n \"def dict_list(query_id, qrels):\\n rel_list = []\\n for query_dict in qrels:\\n if int(query_dict['query_num']) == query_id:\\n rel_list.append(query_dict)\\n\\n return rel_list\",\n \"def get_selection(self):\\n return [\\n self.id_value_map[item]\\n for item in self.selection()\\n if item in self.id_value_map\\n ]\",\n \"def values_list(self, *args, **kwargs):\\n flat = kwargs.pop(\\\"flat\\\", False)\\n if kwargs:\\n raise AttributeError(f\\\"Unknown kwargs: {kwargs}\\\")\\n if flat and len(args) != 1:\\n raise ValueError(\\\"flat=True requires exactly one field name\\\")\\n try:\\n only_fields = tuple(self._get_field_path(arg) for arg in args)\\n except ValueError as e:\\n raise ValueError(f\\\"{e.args[0]} in values_list()\\\")\\n new_qs = self._copy_self()\\n new_qs.only_fields = only_fields\\n new_qs.return_format = self.FLAT if flat else self.VALUES_LIST\\n return new_qs\",\n \"def values(\\n self, *keys: _TResultKey\\n ) -> t.List[t.List[t.Any]]:\\n return [record.values(*keys) for record in self]\",\n \"def obtain_DyndbProtein_id_list(dyn_id):\\n model=DyndbModel.objects.select_related(\\\"id_protein\\\",\\\"id_complex_molecule\\\").get(dyndbdynamics__id=dyn_id)\\n pdbid=model.pdbid\\n prot_li_gpcr=[]\\n dprot_li_all=[]\\n dprot_li_all_info=[]\\n if model.id_protein:\\n dprot=model.id_protein\\n dprot_li_all=[dprot]\\n (prot_li_gpcr,dprot_li_all_info)=retrieve_prot_info(dprot,prot_li_gpcr,dprot_li_all_info)\\n else:\\n dprot_li_all=DyndbProtein.objects.select_related(\\\"receptor_id_protein\\\").filter(dyndbcomplexprotein__id_complex_exp__dyndbcomplexmolecule=model.id_complex_molecule.id)\\n for dprot in dprot_li_all:\\n (prot_li_gpcr,dprot_li_all_info)=retrieve_prot_info(dprot,prot_li_gpcr,dprot_li_all_info)\\n return (prot_li_gpcr, dprot_li_all, dprot_li_all_info,pdbid)\",\n \"def get_filter_values(self):\\n return [f.get() for f in self._filters[:-1]] # Ignore placeholder\",\n \"def values(self):\\n return [p.value for p in self]\",\n \"def search(self, *args, **kwargs):\\n ret = []\\n for record in Handler.ALL_VERS_DATA.values():\\n matchArgs = list(kwargs.keys())\\n for k,v in iteritems(kwargs): # restrict records based on key-value match requirement\\n try:\\n if record[k] != v: break # a non-matching requirement means this record doesn't match\\n except: break # record doesn't have required key 'k'\\n matchArgs.remove(k)\\n if matchArgs: continue # didn't match all required kwargs\\n matchArgs = list(args)\\n for k,v in iteritems(record): # find any record with a <value> in it\\n if k in matchArgs: matchArgs.remove(k)\\n if v in matchArgs: matchArgs.remove(v)\\n if matchArgs: continue # didn't match all required args\\n ret.append(record)\\n return ret\",\n \"def AND(self, values: pdarray) -> Tuple[Union[pdarray, List[Union[pdarray, Strings]]], pdarray]:\\n if values.dtype not in [akint64, akuint64, bigint]:\\n raise TypeError(\\\"AND is only supported for pdarrays of dtype int64, uint64, or bigint\\\")\\n\\n return self.aggregate(values, \\\"and\\\") # type: ignore\",\n \"def return_patids_val_folds(fld_number):\\n fold_idx = np.arange(6)\\n fld_idx_train_val = np.delete(fold_idx, fld_number)\\n fld_idx_1 = fld_idx_train_val[0]\\n fld_idx_2 = fld_idx_train_val[1]\\n fld_idx_3 = fld_idx_train_val[2]\\n fld_idx_4 = fld_idx_train_val[3]\\n fld_idx_5 = fld_idx_train_val[4]\\n fln_pat_ids_1 = 'fold_' + str(fld_idx_1) + '_pat_ids.npy'\\n fln_pat_ids_2 = 'fold_' + str(fld_idx_2) + '_pat_ids.npy'\\n fln_pat_ids_3 = 'fold_' + str(fld_idx_3) + '_pat_ids.npy'\\n fln_pat_ids_4 = 'fold_' + str(fld_idx_4) + '_pat_ids.npy'\\n fln_pat_ids_5 = 'fold_' + str(fld_idx_5) + '_pat_ids.npy'\\n pat_ids_1 = af.load_with(fln_pat_ids_1)\\n pat_ids_2 = af.load_with(fln_pat_ids_2)\\n pat_ids_3 = af.load_with(fln_pat_ids_3)\\n pat_ids_4 = af.load_with(fln_pat_ids_4)\\n pat_ids_5 = af.load_with(fln_pat_ids_5)\\n pat_ids_run_1 = np.hstack((pat_ids_1, pat_ids_2))\\n pat_ids_run_2 = np.hstack((pat_ids_3, pat_ids_4))\\n pat_ids_5_run_1 = pat_ids_5[:5]\\n pat_ids_5_run_2 = pat_ids_5[5:]\\n pat_ids_run_1 = np.hstack((pat_ids_run_1, pat_ids_5_run_1))\\n pat_ids_run_2 = np.hstack((pat_ids_run_2, pat_ids_5_run_2))\\n return pat_ids_run_1, pat_ids_run_2\",\n \"def getValuesFromField(self, table_fullpath, where_clause, field):\\r\\n \\r\\n try:\\r\\n rows = arcpy.SearchCursor(table_fullpath, where_clause, \\\"\\\", field, \\\"\\\")\\r\\n return [str(row.getValue(field)) for row in rows]\\r\\n finally:\\r\\n del rows\",\n \"def choices(self, typename, value_field='description'):\\n rows = self.type(typename).values('id', value_field)\\n return [(r['id'], r[value_field]) for r in rows]\",\n \"def _get_values(self) -> ty.List[float]:\\r\\n ...\",\n \"def create_recordid_list(rec_ids):\\n rec_list = []\\n for row in rec_ids:\\n rec_list.append(row[0])\\n return rec_list\",\n \"def values(self):\\n\\t\\treturn self.myVals\",\n \"def value(self):\\n return [ f.value() for f in self.fields ]\",\n \"def get_patient_lookup_results(form):\\n args = form.data\\n data = {k: v for (k, v) in args.items() if not is_empty_form_value(v)}\\n if (not form.validate()) or (len(data) == 0):\\n return {\\\"has_form_data\\\": False, \\\"tb\\\": None}\\n where = \\\"where \\\" + (\\\" and \\\".join(\\\"(X.[{}] = ?)\\\".format(k) for k, _ in data.items()))\\n params = [v for _, v in data.items()]\\n q = (\\\"select top 1000 X.* \\\" +\\n \\\"from {schema}.PersonCombined X \\\".format(schema=app_schema) +\\n where)\\n tb = pd.read_sql(q, engine, params=params)\\n return {\\\"has_form_data\\\": True, \\\"tb\\\": tb}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5373899","0.51960784","0.51567584","0.5128959","0.5087617","0.50727177","0.49959168","0.49717903","0.49696368","0.49286002","0.49211693","0.4917529","0.48955002","0.48827276","0.48814398","0.48741","0.48672736","0.4864506","0.48131928","0.48071274","0.48001948","0.47940686","0.4768048","0.47583073","0.4746422","0.47421122","0.47415566","0.47342786","0.4729942","0.47278118","0.4713565","0.47122383","0.47110197","0.4704848","0.46935946","0.4681815","0.46777558","0.4665715","0.46575972","0.4655597","0.46555418","0.46502072","0.46391663","0.46299735","0.4617857","0.46167716","0.46119505","0.45975596","0.45974606","0.45908144","0.45895723","0.45823118","0.45797795","0.4579018","0.45748597","0.45742196","0.45671636","0.45651078","0.45594424","0.45576268","0.45545056","0.45541027","0.45486858","0.4546709","0.45436302","0.4537276","0.45317122","0.4530614","0.45244253","0.4523577","0.45221993","0.4516944","0.45168966","0.4505433","0.45051384","0.4505111","0.45038277","0.44937542","0.44934118","0.44934118","0.44887203","0.4486339","0.44862545","0.44859582","0.4477478","0.44732782","0.44730338","0.44712198","0.44690666","0.44643316","0.4462311","0.4456818","0.44445774","0.44353834","0.44345596","0.44319966","0.4429747","0.44266453","0.44260898","0.4421897"],"string":"[\n \"0.5373899\",\n \"0.51960784\",\n \"0.51567584\",\n \"0.5128959\",\n \"0.5087617\",\n \"0.50727177\",\n \"0.49959168\",\n \"0.49717903\",\n \"0.49696368\",\n \"0.49286002\",\n \"0.49211693\",\n \"0.4917529\",\n \"0.48955002\",\n \"0.48827276\",\n \"0.48814398\",\n \"0.48741\",\n \"0.48672736\",\n \"0.4864506\",\n \"0.48131928\",\n \"0.48071274\",\n \"0.48001948\",\n \"0.47940686\",\n \"0.4768048\",\n \"0.47583073\",\n \"0.4746422\",\n \"0.47421122\",\n \"0.47415566\",\n \"0.47342786\",\n \"0.4729942\",\n \"0.47278118\",\n \"0.4713565\",\n \"0.47122383\",\n \"0.47110197\",\n \"0.4704848\",\n \"0.46935946\",\n \"0.4681815\",\n \"0.46777558\",\n \"0.4665715\",\n \"0.46575972\",\n \"0.4655597\",\n \"0.46555418\",\n \"0.46502072\",\n \"0.46391663\",\n \"0.46299735\",\n \"0.4617857\",\n \"0.46167716\",\n \"0.46119505\",\n \"0.45975596\",\n \"0.45974606\",\n \"0.45908144\",\n \"0.45895723\",\n \"0.45823118\",\n \"0.45797795\",\n \"0.4579018\",\n \"0.45748597\",\n \"0.45742196\",\n \"0.45671636\",\n \"0.45651078\",\n \"0.45594424\",\n \"0.45576268\",\n \"0.45545056\",\n \"0.45541027\",\n \"0.45486858\",\n \"0.4546709\",\n \"0.45436302\",\n \"0.4537276\",\n \"0.45317122\",\n \"0.4530614\",\n \"0.45244253\",\n \"0.4523577\",\n \"0.45221993\",\n \"0.4516944\",\n \"0.45168966\",\n \"0.4505433\",\n \"0.45051384\",\n \"0.4505111\",\n \"0.45038277\",\n \"0.44937542\",\n \"0.44934118\",\n \"0.44934118\",\n \"0.44887203\",\n \"0.4486339\",\n \"0.44862545\",\n \"0.44859582\",\n \"0.4477478\",\n \"0.44732782\",\n \"0.44730338\",\n \"0.44712198\",\n \"0.44690666\",\n \"0.44643316\",\n \"0.4462311\",\n \"0.4456818\",\n \"0.44445774\",\n \"0.44353834\",\n \"0.44345596\",\n \"0.44319966\",\n \"0.4429747\",\n \"0.44266453\",\n \"0.44260898\",\n \"0.4421897\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7764384"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79580,"cells":{"query":{"kind":"string","value":"return a dictionary which is used by bibrank daemon for generating the index of sorted research results by citation information"},"document":{"kind":"string","value":"def get_citation_weight(rank_method_code, config):\n begin_time = time.time()\n last_update_time = get_bibrankmethod_lastupdate(rank_method_code)\n\n if task_get_option(\"quick\") == \"no\":\n last_update_time = \"0000-00-00 00:00:00\"\n write_message(\"running thorough indexing since quick option not used\", verbose=3)\n\n last_modified_records = get_last_modified_rec(last_update_time)\n #id option forces re-indexing a certain range even if there are no new recs\n if last_modified_records or task_get_option(\"id\"):\n if task_get_option(\"id\"):\n #construct a range of records to index\n taskid = task_get_option(\"id\")\n first = taskid[0][0]\n last = taskid[0][1]\n #make range, last+1 so that e.g. -i 1-2 really means [1,2] not [1]\n updated_recid_list = range(first, last+1)\n else:\n updated_recid_list = create_recordid_list(last_modified_records)\n\n write_message(\"Last update \"+str(last_update_time)+\" records: \"+ \\\n str(len(last_modified_records))+\" updates: \"+ \\\n str(len(updated_recid_list)))\n\n #write_message(\"updated_recid_list: \"+str(updated_recid_list))\n result_intermediate = last_updated_result(rank_method_code)\n\n #result_intermed should be warranted to exists!\n #but if the user entered a \"-R\" (do all) option, we need to\n #make an empty start set\n if task_get_option(\"quick\") == \"no\":\n result_intermediate = [{}, {}, {}]\n else:\n # check indexing times of `journal' and `reportnumber`\n # indexes, since if they are not up to date yet, then we\n # should wait and not run citation indexing as of yet:\n last_timestamp_bibrec = run_sql(\"SELECT DATE_FORMAT(MAX(modification_date), '%%Y-%%m-%%d %%H:%%i:%%s') FROM bibrec\", (), 1)[0][0]\n last_timestamp_indexes = run_sql(\"SELECT DATE_FORMAT(MAX(last_updated), '%%Y-%%m-%%d %%H:%%i:%%s') FROM idxINDEX WHERE name IN (%s,%s)\", ('journal', 'reportnumber'), 1)[0][0]\n if not last_timestamp_indexes or \\\n not last_timestamp_bibrec or \\\n last_timestamp_bibrec > last_timestamp_indexes:\n write_message(\"Not running citation indexer since journal/reportnumber indexes are not up to date yet.\")\n return {}\n\n citation_weight_dic_intermediate = result_intermediate[0]\n citation_list_intermediate = result_intermediate[1]\n reference_list_intermediate = result_intermediate[2]\n\n # Enrich updated_recid_list so that it would contain also\n # records citing or referring to updated records, so that\n # their citation information would be updated too. Not the\n # most efficient way to treat this problem, but the one that\n # requires least code changes until ref_analyzer() is more\n # nicely re-factored.\n updated_recid_list_set = intbitset(updated_recid_list)\n for somerecid in updated_recid_list:\n # add both citers and citees:\n updated_recid_list_set |= intbitset(citation_list_intermediate.get(somerecid, []))\n updated_recid_list_set |= intbitset(reference_list_intermediate.get(somerecid, []))\n updated_recid_list = list(updated_recid_list_set)\n\n #call the procedure that does the hard work by reading fields of\n #citations and references in the updated_recid's (but nothing else)!\n if task_get_task_param('verbose') >= 9:\n write_message(\"Entering get_citation_informations\")\n citation_informations = get_citation_informations(updated_recid_list,\n config)\n #write_message(\"citation_informations: \"+str(citation_informations))\n #create_analysis_tables() #temporary..\n #test how much faster in-mem indexing is\n write_message(\"Entering ref_analyzer\", verbose=9)\n #call the analyser that uses the citation_informations to really\n #search x-cites-y in the coll..\n dic = ref_analyzer(citation_informations,\n citation_weight_dic_intermediate,\n citation_list_intermediate,\n reference_list_intermediate,\n config,updated_recid_list)\n #dic is docid-numberofreferences like {1: 2, 2: 0, 3: 1}\n #write_message(\"Docid-number of known references \"+str(dic))\n end_time = time.time()\n write_message(\"Total time of get_citation_weight(): %.2f sec\" % (end_time - begin_time))\n task_update_progress(\"citation analysis done\")\n else:\n dic = {}\n write_message(\"No new records added since last time this rank method was executed\")\n return dic"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_dict(results, chunk):\n from math import inf\n from collections import defaultdict\n chunk = [int(au) for au in chunk]\n d = defaultdict(\n lambda: {\"first_year\": inf, \"pubs\": set(), \"coauth\": set(),\n \"n_coauth\": inf, \"n_pubs\": inf})\n for pub in results:\n if not pub.author_ids:\n continue\n authors = set([int(au) for au in pub.author_ids.split(\";\")])\n for focal in authors.intersection(chunk):\n d[focal][\"coauth\"].update(authors)\n d[focal][\"coauth\"].remove(focal)\n d[focal][\"pubs\"].add(pub.eid)\n d[focal][\"n_pubs\"] = len(d[focal][\"pubs\"])\n d[focal][\"n_coauth\"] = len(d[focal][\"coauth\"])\n if not pub.coverDate:\n continue\n first_year = min(d[focal][\"first_year\"], int(pub.coverDate[:4]))\n d[focal][\"first_year\"] = first_year\n return d","def get_citation_informations(recid_list, config):\n begin_time = os.times()[4]\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\n d_records_s = {} #dict of recid -> this_records_publication_info\n citation_informations = []\n\n write_message(\"config function \"+config.get(\"rank_method\", \"function\"), verbose=9)\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attribute called function\", alert_admin=True)\n #we cannot continue\n return [ {}, {}, {}, {} ]\n record_pri_number_tag = \"\"\n try:\n record_pri_number_tag = config.get(function, \"primary_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attribute primary_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n record_add_number_tag = \"\"\n try:\n record_add_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"additional_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute additional_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_number_tag = \"\"\n try:\n reference_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_tag = \"\"\n try:\n reference_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_pubinfo\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\n #037a: contains (often) the \"hep-ph/0501084\" tag of THIS record\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\n #088a: additional short identifier for the record\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\n p_reference_tag = tagify(parse_tag(reference_tag))\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\n #fields needed to construct the pubinfo for this record\n publication_pages_tag = \"\"\n publication_year_tag = \"\"\n publication_journal_tag = \"\"\n publication_volume_tag = \"\"\n publication_format_string = \"p v (y) c\"\n try:\n tag = config.get(function, \"pubinfo_journal_page\")\n publication_pages_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_year\")\n publication_year_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_title\")\n publication_journal_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_volume\")\n publication_volume_tag = tagify(parse_tag(tag))\n publication_format_string = config.get(function, \"pubinfo_journal_format\")\n except:\n pass\n\n #print values for tags for debugging\n if task_get_task_param('verbose') >= 9:\n write_message(\"tag values\")\n write_message(\"p_record_pri_number_tag \"+str(p_record_pri_number_tag))\n write_message(\"p_reference_tag \"+str(p_reference_tag))\n write_message(\"publication_journal_tag \"+str(publication_journal_tag))\n write_message(\"publication_format_string is \"+publication_format_string)\n done = 0 #for status reporting\n numrecs = len(recid_list)\n\n # perform quick check to see if there are some records with\n # reference tags, because otherwise get.cit.inf would be slow even\n # if there is nothing to index:\n if run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_tag[0:2],\n (p_reference_tag,)) or \\\n run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_number_tag[0:2],\n (p_reference_number_tag,)):\n for recid in recid_list:\n if (done % 10 == 0):\n task_sleep_now_if_required()\n #in fact we can sleep any time here\n\n if (done % 1000 == 0):\n mesg = \"get cit.inf done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n if recid in INTBITSET_OF_DELETED_RECORDS:\n # do not treat this record since it was deleted; we\n # skip it like this in case it was only soft-deleted\n # e.g. via bibedit (i.e. when collection tag 980 is\n # DELETED but other tags like report number or journal\n # publication info remained the same, so the calls to\n # get_fieldvalues() below would return old values)\n continue\n\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\n references_s = get_fieldvalues(recid, p_reference_tag)\n\n l_report_numbers = pri_report_numbers\n l_report_numbers.extend(add_report_numbers)\n d_reports_numbers[recid] = l_report_numbers\n\n if reference_report_numbers:\n write_message(str(recid)+\"'s report number values \"+str(l_report_numbers), verbose=9)\n d_references_report_numbers[recid] = reference_report_numbers\n\n references_s = get_fieldvalues(recid, p_reference_tag)\n if references_s:\n write_message(str(recid)+\"'s \"+str(p_reference_tag)+\" values \"+str(references_s), verbose=9)\n d_references_s[recid] = references_s\n\n #get a combination of\n #journal vol (year) pages\n if publication_pages_tag and publication_journal_tag and \\\n publication_volume_tag and publication_year_tag and publication_format_string:\n tagsvalues = {} #we store the tags and their values here\n #like c->444 y->1999 p->\"journal of foo\",v->20\n tagsvalues[\"p\"] = \"\"\n tagsvalues[\"y\"] = \"\"\n tagsvalues[\"c\"] = \"\"\n tagsvalues[\"v\"] = \"\"\n tmp = get_fieldvalues(recid, publication_journal_tag)\n if tmp:\n tagsvalues[\"p\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_volume_tag)\n if tmp:\n tagsvalues[\"v\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_year_tag)\n if tmp:\n tagsvalues[\"y\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_pages_tag)\n if tmp:\n #if the page numbers have \"x-y\" take just x\n pages = tmp[0]\n hpos = pages.find(\"-\")\n if hpos > 0:\n pages = pages[:hpos]\n tagsvalues[\"c\"] = pages\n #format the publ infostring according to the format\n publ = \"\"\n ok = 1\n for i in range (0, len(publication_format_string)):\n current = publication_format_string[i]\n #these are supported\n if current == \"p\" or current == \"c\" or current == \"v\" \\\n or current == \"y\":\n if tagsvalues[current]:\n #add the value in the string\n publ += tagsvalues[current]\n else:\n ok = 0\n break #it was needed and not found\n else:\n publ += current #just add the character in the format string\n if ok:\n write_message(\"d_records_s (publication info) for \"+str(recid)+\" is \"+publ, verbose=9)\n d_records_s[recid] = publ\n else:\n mesg = \"Warning: there are no records with tag values for \"\n mesg += p_reference_number_tag+\" or \"+p_reference_tag+\". Nothing to do.\"\n write_message(mesg)\n\n mesg = \"get cit.inf done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n citation_informations.append(d_reports_numbers)\n citation_informations.append(d_references_report_numbers)\n citation_informations.append(d_references_s)\n citation_informations.append(d_records_s)\n end_time = os.times()[4]\n write_message(\"Execution time for generating citation info from record: %.2f sec\" % \\\n (end_time - begin_time))\n return citation_informations","def get_search_index(self):\n with self._conn.begin():\n return {\n rec.package: (rec.downloads_recent, rec.downloads_all)\n for rec in self._conn.execute(\n \"SELECT package, downloads_recent, downloads_all \"\n \"FROM get_search_index()\")\n }","def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\n dict, dict, dict, dict,\n dict, dict]:\n name_table = create_name_table(citelist)\n unique_names = list()\n nameset = set()\n # total_binomial_year_cnts = {}\n total_binomial_year_cnts = collections.Counter()\n for c in citelist:\n if c.name != \".\":\n clean = clean_name(c.name)\n if not clean.lower() in nameset:\n nameset |= {clean.lower()}\n unique_names.append(clean)\n y = refdict[c.cite_key].year()\n if y is not None:\n total_binomial_year_cnts.update([y])\n # if y in total_binomial_year_cnts:\n # total_binomial_year_cnts[y] += 1\n # else:\n # total_binomial_year_cnts[y] = 1\n unique_names.sort(key=lambda s: s.lower())\n\n # identify genera used per paper\n genera_per_paper = {}\n for c in citelist:\n if c.name != \".\":\n if c.cite_key not in genera_per_paper:\n genera_per_paper[c.cite_key] = set()\n genera_set = genera_per_paper[c.cite_key]\n genera_set |= {extract_genus(clean_name(c.name))}\n genus_cnts = {}\n for c in genera_per_paper:\n y = refdict[c].year()\n if y is not None:\n if init_data().start_year <= y <= init_data().current_year:\n genera_set = genera_per_paper[c]\n for genus in genera_set:\n genus = clean_genus(genus)\n if genus != \"\":\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\n init_data().current_year + 1)})[y] += 1\n # if genus not in genus_cnts:\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\n # init_data().current_year + 1)}\n # gcnts = genus_cnts[genus]\n # gcnts[y] += 1\n\n binomial_usage_cnts_by_year = {}\n binomial_location_applications = {}\n binomial_usage_cnts = {}\n for name in unique_names:\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\n if tmptotal > 0:\n binomial_usage_cnts[name] = tmptotal\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\n\n # specific_year_cnts = {}\n specific_year_cnts = collections.Counter()\n specific_usage_cnts_by_year = {}\n specific_location_applications = {}\n specific_usage_cnts = {}\n for name in specific_names:\n (specific_usage_cnts_by_year[name.name],\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\n if tmptotal > 0:\n specific_usage_cnts[name.name] = tmptotal\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\n binomial_location_applications)\n tmpkey = name.priority_source\n if tmpkey != \".\":\n y = refdict[tmpkey].year()\n if y is not None:\n specific_year_cnts.update([y])\n # if y in specific_year_cnts:\n # specific_year_cnts[y] += 1\n # else:\n # specific_year_cnts[y] = 1\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\n binomial_usage_cnts, specific_usage_cnts)","def createIndex(pages): \n index = defaultdict(list)\n for url, content, links in pages:\n counts = getNumberTerms(content)\n for term, count in counts.items():\n index[term].append((url, count))\n return index","def build_query_dict(self, term_list, issn_list, year_list, jlist):\n journal_frame = self.make_jlist(jlist)\n\n search_terms = self.build_search_terms(term_list)\n dict1 = {}\n #This loop goes through and sets up a dictionary key with an ISSN number\n\n for issn in issn_list:\n\n issn_terms = ' AND ISSN(' + issn + ')'\n dict2 = {}\n #This loop goes and attaches all the years to the outer loop's key.\n for year in year_list:\n\n year_terms = \"AND PUBYEAR IS \" + str(year)\n querystring = search_terms + year_terms + issn_terms\n\n dict2[year] = querystring\n\n dict1[issn] = dict2\n\n return dict1","def indexes(self):\n return {'status': self._status_sort, 'rms': self._rms_sort}","def fill(self):\n if self.source == 'citations':\n url = self._scholarly.URLS(\"CITATIONPUB\").format(self.id_citations)\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\n\n if soup.find('a', class_='gsc_vcd_title_link'):\n self.bib['url'] = soup.find(\n 'a', class_='gsc_vcd_title_link')['href']\n\n for item in soup.find_all('div', class_='gs_scl'):\n key = item.find(class_='gsc_vcd_field').text\n val = item.find(class_='gsc_vcd_value')\n if key == 'Authors':\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\n elif key == 'Journal':\n self.bib['journal'] = val.text\n elif key == 'Volume':\n self.bib['volume'] = val.text\n elif key == 'Issue':\n self.bib['number'] = val.text\n elif key == 'Pages':\n self.bib['pages'] = val.text\n elif key == 'Publisher':\n self.bib['publisher'] = val.text\n elif key == 'Publication date':\n self.bib['year'] = arrow.get(val.text).year\n elif key == 'Description':\n if val.text[0:8].lower() == 'abstract':\n val = val.text[9:].strip()\n self.bib['abstract'] = val\n elif key == 'Total citations':\n self.id_scholarcitedby = re.findall(\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\n\n # number of citation per year\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\n self.cites_per_year = dict(zip(years, cites))\n\n if soup.find('div', class_='gsc_vcd_title_ggi'):\n self.bib['eprint'] = soup.find(\n 'div', class_='gsc_vcd_title_ggi').a['href']\n self._filled = True\n\n elif self.source == 'scholar':\n self.bib['add_to_lib'] = self.url_add_sclib\n\n try:\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\n bibtex = bibtex.find('pre').string\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\n self.bib['author_count'] = str(\n len(self.bib['author'].split('and')))\n self.bib['age'] = str(\n int(date.today().year) - int(self.bib['year']))\n except:\n # did not find year\n pass\n\n self._filled = True\n return self","def __generate_dict_of_keys_to_classification__(self):\n dict_of_assigned_citations = {}\n # duplicating citation dataset to filter as matches go on meaning\n # it should result in quicker allocation\n # can be removed to reduce memory load at expense of speed\n list_of_unassigned = []\n for key in self.dict_of_keywords:\n list_of_current_key = []\n for citation_instance in self.array_of_citations:\n if key == citation_instance.get_classification():\n list_of_current_key.append(citation_instance)\n if \"Unassigned\" == citation_instance.get_classification():\n list_of_unassigned.append(citation_instance)\n dict_of_assigned_citations[key] = list_of_current_key\n dict_of_assigned_citations[\"Unassigned\"] = list_of_unassigned\n return dict_of_assigned_citations","def return_dict_of_assigned_citations_classifications(self):\n for citation_instance in self.array_of_citations:\n citation_string = \"\"\n citation_string = citation_string.join(\n citation_instance.get_author()) + \\\n citation_instance.get_title()\\\n + citation_instance.get_journal()\n max_keyword_matches = 0\n for key in self.dict_of_keywords:\n current_key_matches = 0\n for keyword in self.dict_of_keywords.get(key):\n pattern = re.compile(keyword)\n if pattern.search(citation_string):\n current_key_matches += 1\n if current_key_matches > max_keyword_matches:\n max_keyword_matches = current_key_matches\n citation_instance.set_classification(key)\n return self.__generate_dict_of_keys_to_classification__()","def index():\n rows=db().select(db.titles.ALL,orderby=db.titles.title_name)\n \n #print type(value)\n \n coloms=db().select(db.titles.ALL,orderby=db.titles.title_name)\n #print coloms\n return dict(rows=rows,coloms=coloms)","def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in","def generate_dict(self):\n dict = defaultdict(list)\n for i in range(self.no_of_docs-1):\n doc_txt = self.doc_to_df(i)\n #assign key to index in dictionary and its locations as tuples(docid,line,wordpos) as the values\n for j in range(len(doc_txt)):\n for k in range(doc_txt.shape[1]):\n key = doc_txt[k][j]\n dict[key].append((i,j,k))","def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def ref_analyzer(citation_informations, initialresult, initial_citationlist,\n initial_referencelist,config, updated_rec_list ):\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attr function\", alert_admin=True)\n return {}\n\n pubrefntag = \"\"\n try:\n pubrefntag = config.get(function, \"reference_via_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_report_number\", alert_admin=True)\n return {}\n\n pubreftag = \"\"\n try:\n pubreftag = config.get(function, \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_pubinfo\", alert_admin=True)\n return {}\n\n #pubrefntag is often 999C5r, pubreftag 999C5s\n if task_get_task_param('verbose') >= 9:\n write_message(\"pubrefntag \"+pubrefntag)\n write_message(\"pubreftag \"+pubreftag)\n\n citation_list = initial_citationlist\n reference_list = initial_referencelist\n result = initialresult\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\n d_references_s = citation_informations[2]\n #dict of recid -> publication_infos_in_its_bibliography\n d_records_s = citation_informations[3] #recid -> its publication inf\n t1 = os.times()[4]\n\n write_message(\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\")\n for somerecid in updated_rec_list:\n try:\n del citation_list[somerecid]\n except KeyError:\n pass\n try:\n del reference_list[somerecid]\n except KeyError:\n pass\n\n write_message(\"Phase 1: d_references_report_numbers\")\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\n #meaning: rec 8 contains these in bibliography\n\n done = 0\n numrecs = len(d_references_report_numbers)\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n done = done+1\n\n for refnumber in refnumbers:\n if refnumber:\n p = refnumber\n f = 'reportnumber'\n #sanitise p\n p.replace(\"\\n\",'')\n #search for \"hep-th/5644654 or such\" in existing records\n rec_ids = get_recids_matching_query(p, f)\n\n if len(rec_ids) > 1:\n msg = \"Whoops: record '%d' report number value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_citer_cited(thisrecid, rec_ids[0])\n remove_from_missing(p)\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n # Citation list should have rec_ids[0] but check anyway\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n #append unless this key already has the item\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid)\n #and update result\n result[rec_ids[0]] += 1\n\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n else:\n #the reference we wanted was not found among our records.\n #put the reference in the \"missing\".. however, it will look\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\n #This should really be done in the next loop d_references_s\n #but the 999C5s fields are not yet normalized\n\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\n rectext = \"\" # print_record() call disabled to speed things up\n lines = rectext.split(\"\\n\")\n rpart = p #to be used..\n for l in lines:\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\n st = l.find('$s')\n if (st > 0):\n end = l.find('$', st)\n if (end == st):\n end = len(l)\n rpart = l[st+2:end]\n insert_into_missing(thisrecid, rpart)\n\n mesg = \"d_references_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t2 = os.times()[4]\n\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\n write_message(\"Phase 2: d_references_s\")\n done = 0\n numrecs = len(d_references_s)\n for thisrecid, refss in d_references_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n\n done = done+1\n\n for refs in refss:\n if refs:\n p = refs\n #remove the latter page number if it is like 67-74\n matches = re.compile(\"(.*)(-\\d+$)\").findall(p)\n if matches and matches[0]:\n p = matches[0][0]\n\n # check reference value to see whether it is well formed:\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"is not well formed; skipping it.\" % (thisrecid, p)\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n continue # skip this ill-formed value\n\n # look for reference value:\n rec_id = None\n try:\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\n except:\n rec_ids = None\n if len(rec_ids) > 1:\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_message(\"These match searching \"+p+\" in journal: \"+repr(rec_ids), verbose=9)\n #the refered publication is in our collection, remove\n #from missing\n remove_from_missing(p)\n else:\n #it was not found so add in missing\n insert_into_missing(thisrecid, p)\n #check citation and reference for this..\n if rec_ids and rec_ids[0]:\n #the above should always hold\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\n result[rec_ids[0]] += 1 #add count for this..\n\n #update reference_list accordingly\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n mesg = \"d_references_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t3 = os.times()[4]\n done = 0\n numrecs = len(d_reports_numbers)\n write_message(\"Phase 3: d_reports_numbers\")\n\n #search for stuff like CERN-TH-4859/87 in list of refs\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n for reportcode in reportcodes:\n if reportcode:\n rec_ids = []\n try:\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\n except:\n rec_ids = []\n\n if rec_ids:\n for recid in rec_ids:\n #normal checks..\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(recid):\n reference_list[recid] = []\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n\n #normal updates\n if not recid in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(recid)\n if not thisrecid in reference_list[recid]:\n reference_list[recid].append(thisrecid)\n\n mesg = \"d_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #find this record's pubinfo in other records' bibliography\n write_message(\"Phase 4: d_records_s\")\n done = 0\n numrecs = len(d_records_s)\n t4 = os.times()[4]\n for thisrecid, recs in d_records_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_records_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n p = recs.replace(\"\\\"\",\"\")\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\n write_message(\"These records match \"+p+\" in \"+pubreftag+\" : \"+str(rec_ids), verbose=9)\n if rec_ids:\n for rec_id in rec_ids:\n #normal checks\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(rec_id):\n reference_list[rec_id] = []\n\n if not rec_id in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(rec_id)\n if not thisrecid in reference_list[rec_id]:\n reference_list[rec_id].append(thisrecid)\n\n mesg = \"d_records_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n write_message(\"Phase 5: reverse lists\")\n\n #remove empty lists in citation and reference\n keys = citation_list.keys()\n for k in keys:\n if not citation_list[k]:\n del citation_list[k]\n\n keys = reference_list.keys()\n for k in keys:\n if not reference_list[k]:\n del reference_list[k]\n\n write_message(\"Phase 6: self-citations\")\n selfdic = {}\n #get the initial self citation dict\n initial_self_dict = get_cit_dict(\"selfcitdict\")\n selfdic = initial_self_dict\n #add new records to selfdic\n acit = task_get_option(\"author-citations\")\n if not acit:\n write_message(\"Self cite processing disabled. Use -A option to enable it.\")\n else:\n write_message(\"self cite and author citations enabled\")\n selfdic = get_self_citations(updated_rec_list, citation_list,\n initial_self_dict, config)\n #selfdic consists of\n #key k -> list of values [v1,v2,..]\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\n\n #create a reverse \"x cited by y\" self cit dict\n selfcitedbydic = {}\n for k in selfdic.keys():\n vlist = selfdic[k]\n for v in vlist:\n if selfcitedbydic.has_key(v):\n tmplist = selfcitedbydic[v]\n if not k in tmplist:\n tmplist.append(k)\n else:\n tmplist = [k]\n selfcitedbydic[v] = tmplist\n\n write_message(\"Getting author citations\")\n\n #get author citations for records in updated_rec_list\n initial_author_dict = get_initial_author_dict()\n authorcitdic = initial_author_dict\n acit = task_get_option(\"author-citations\")\n if not acit:\n print \"Author cites disabled. Use -A option to enable it.\"\n else:\n write_message(\"author citations enabled\")\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\n initial_author_dict, config)\n\n\n if task_get_task_param('verbose') >= 3:\n #print only X first to prevent flood\n tmpdict = {}\n tmp = citation_list.keys()[0:10]\n for t in tmp:\n tmpdict[t] = citation_list[t]\n write_message(\"citation_list (x is cited by y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(citation_list.keys())))\n tmp = reference_list.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = reference_list[t]\n write_message(\"reference_list (x cites y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(reference_list.keys())))\n tmp = selfcitedbydic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfcitedbydic[t]\n mesg = \"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\"\n mesg += str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfcitedbydic.keys())))\n tmp = selfdic.keys()[0:100]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfdic[t]\n mesg = \"selfdic (x cites y and one of the authors of x same as y's): \"+str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfdic.keys())))\n tmp = authorcitdic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = authorcitdic[t]\n write_message(\"authorcitdic (author is cited in recs): \"+str(tmpdict))\n write_message(\"size: \"+str(len(authorcitdic.keys())))\n insert_cit_ref_list_intodb(citation_list, reference_list,\n selfcitedbydic, selfdic, authorcitdic)\n\n t5 = os.times()[4]\n\n write_message(\"Execution time for analyzing the citation information generating the dictionary:\")\n write_message(\"... checking ref number: %.2f sec\" % (t2-t1))\n write_message(\"... checking ref ypvt: %.2f sec\" % (t3-t2))\n write_message(\"... checking rec number: %.2f sec\" % (t4-t3))\n write_message(\"... checking rec ypvt: %.2f sec\" % (t5-t4))\n write_message(\"... total time of ref_analyze: %.2f sec\" % (t5-t1))\n\n return result","def build_index(self):\r\n date_time('Building indexes in citations table')\r\n self.cursor.execute('DROP INDEX IF EXISTS IDX_citations ;')\r\n self.cursor.execute('CREATE INDEX IDX_citations ON citations (citation);')\r\n self.conn.commit()\r\n gc.collect()","def get_book_data(isbn: int):\n try:\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\n except StopIteration:\n pass # actually, I could put the whole rest of the function here\n else:\n data = core.Book.view_str(book.id)\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\n del data['borrowed_by_id'], data['__str__']\n return data\n\n try:\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\n r.raise_for_status()\n except requests.exceptions.RequestException:\n raise core.BuchSchlossError('no_connection', 'no_connection')\n\n person_re = re.compile(r'(\\w*, \\w*) \\((\\w*)\\)')\n results = {'concerned_people': []}\n\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n if table is None:\n # see if we got multiple results\n link_to_first = page.select_one('#recordLink_0')\n if link_to_first is None:\n raise core.BuchSchlossError(\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n\n for tr in table.select('tr'):\n td = [x.get_text('\\n').strip() for x in tr.select('td')]\n if len(td) == 2:\n if td[0] == 'Titel':\n results['title'] = td[1].split('/')[0].strip()\n elif td[0] == 'Person(en)':\n for p in td[1].split('\\n'):\n g = person_re.search(p)\n if g is None:\n continue\n g = g.groups()\n if g[1] == 'Verfasser':\n results['author'] = g[0]\n else:\n results['concerned_people'].append(g[1]+': '+g[0])\n elif td[0] == 'Verlag':\n results['publisher'] = td[1].split(':')[1].strip()\n elif td[0] == 'Zeitliche Einordnung':\n results['year'] = td[1].split(':')[1].strip()\n elif td[0] == 'Sprache(n)':\n results['language'] = td[1].split(',')[0].split()[0].strip()\n\n results['concerned_people'] = '; '.join(results['concerned_people'])\n return results","def index(self):\n path = self.path.format('index')\n \n with open(path, 'r', newline='') as file:\n l = list(csv.reader(file))\n \n index = [v for _ in l for v in _]\n index = dict((v, i) for (i, v) in enumerate(index))\n \n return index","def index():\n\n return dict()","def relevant_docs_from_posting(self, query):\n relevant_docs = {}\n # postingLists = [self.FindPostingByTerm(term) for term in query] #list of posting file -->[idx,tweet id,tfi]\n for term in query:\n post = self.FindPostingByTerm_Binary(term)\n for p in post:\n tweet_id = p[1]\n if tweet_id not in relevant_docs.keys():\n relevant_docs[tweet_id] = {}\n relevant_docs[tweet_id][term] = p[2] * self.inverted_index[term][1] # wiq\n return relevant_docs","def index(self):\n return dict(data='index')","def __citationsFromFile(file):\n\n # Tokens for parsing\n titleToken = '#*'\n indexToken = '#index'\n citationToken = '#%'\n\n # Predicates for error checking\n noneNone = lambda *items: all([item is not None for item in items])\n allNone = lambda *items: all([item is None for item in items])\n\n # Next entry data\n title = None\n index = None\n citations = []\n\n # Basic stats\n withCitations = 0\n withoutCitations = 0\n totalPapers = 0\n\n for line in file:\n line = line.strip()\n\n # Parse entry, enforcing that data appears in title -> index -> citations order\n if line.startswith(titleToken):\n assert allNone(title, index) and len(citations) == 0\n title = line[len(titleToken):].strip('.')\n\n elif line.startswith(indexToken):\n assert noneNone(title) and allNone(index) and len(citations) == 0\n index = int(line[len(indexToken):])\n\n elif line.startswith(citationToken):\n assert noneNone(title, index)\n newCitationId = int(line[len(citationToken):])\n assert newCitationId >= 0\n citations.append(newCitationId)\n\n elif len(line) == 0:\n totalPapers += 1\n\n # Yield this entry if it has any citations,\n if noneNone(title, index):\n if len(citations) > 0:\n withCitations += 1\n yield title, index, citations\n else:\n withoutCitations += 1\n\n title = None\n index = None\n citations = []\n\n # Output some basic statistics about papers with/without citations\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\n print \"\\n\\nTotal Papers: %d\" % totalPapers\n print \" With References: %d (%2.2f%%)\\n Without References: %d (%2.2f%%)\" % (\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\n )","def results(self):\n out = []\n fields = 'eid doi pii pubmed_id title subtype creator afid affilname '\\\n 'affiliation_city affiliation_country author_count '\\\n 'author_names author_ids author_afids coverDate '\\\n 'coverDisplayDate publicationName issn source_id eIssn '\\\n 'aggregationType volume issueIdentifier article_number '\\\n 'pageRange description authkeywords citedby_count '\\\n 'openaccess fund_acr fund_no fund_sponsor'\n doc = namedtuple('Document', fields)\n for item in self._json:\n info = {}\n # Parse affiliations\n try:\n info[\"affilname\"] = _join(item['affiliation'], 'affilname')\n info[\"afid\"] = _join(item['affiliation'], 'afid')\n info[\"aff_city\"] = _join(item['affiliation'], 'affiliation-city')\n info[\"aff_country\"] = _join(item['affiliation'],\n 'affiliation-country')\n except KeyError:\n pass\n # Parse authors\n try:\n # Deduplicate list of authors\n authors = _deduplicate(item['author'])\n # Extract information\n surnames = _replace_none([d['surname'] for d in authors])\n firstnames = _replace_none([d['given-name'] for d in authors])\n info[\"auth_names\"] = \";\".join([\", \".join([t[0], t[1]]) for t in\n zip(surnames, firstnames)])\n info[\"auth_ids\"] = \";\".join([d['authid'] for d in authors])\n affs = []\n for auth in authors:\n aff = listify(_deduplicate(auth.get('afid', [])))\n affs.append('-'.join([d['$'] for d in aff]))\n info[\"auth_afid\"] = (';'.join(affs) or None)\n except KeyError:\n pass\n date = item.get('prism:coverDate')\n if isinstance(date, list):\n date = date[0].get('$')\n new = doc(article_number=item.get('article-number'),\n title=item.get('dc:title'), fund_sponsor=item.get('fund-sponsor'),\n subtype=item.get('subtype'), issn=item.get('prism:issn'),\n creator=item.get('dc:creator'), affilname=info.get(\"affilname\"),\n author_names=info.get(\"auth_names\"), doi=item.get('prism:doi'),\n coverDate=date, volume=item.get('prism:volume'),\n coverDisplayDate=item.get('prism:coverDisplayDate'),\n publicationName=item.get('prism:publicationName'),\n source_id=item.get('source-id'), author_ids=info.get(\"auth_ids\"),\n aggregationType=item.get('prism:aggregationType'),\n issueIdentifier=item.get('prism:issueIdentifier'),\n pageRange=item.get('prism:pageRange'),\n author_afids=info.get(\"auth_afid\"), fund_no=item.get('fund-no'),\n affiliation_country=info.get(\"aff_country\"),\n citedby_count=item.get('citedby-count'),\n openaccess=item.get('openaccess'), eIssn=item.get('prism:eIssn'),\n author_count=item.get('author-count', {}).get('$'),\n affiliation_city=info.get(\"aff_city\"), afid=info.get(\"afid\"),\n description=item.get('dc:description'), pii=item.get('pii'),\n authkeywords=item.get('authkeywords'), eid=item.get('eid'),\n fund_acr=item.get('fund-acr'), pubmed_id=item.get('pubmed-id'))\n out.append(new)\n return out or None","def h_index(citations):\n citations.sort(reverse=True)\n for i, citation in enumerate(citations):\n if i <= citation:\n return i","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def get_books(mapping, cidlist):\n havecache = _c.cacheisactive(_c.CACHE)\n\n # Don't want to keep hammering their servers, so check if available\n if havecache:\n cache, nocache = _c.check(_c.CACHE, mapping, [parse_cid(cid) for cid in cidlist])\n\n BASE = \"http://cmu.verbacompare.com/comparison?id={}\"\n \n # If cache is available, still need to check for uncached stuff\n if havecache:\n sections = [cmu_to_verba(mapping, cid) for cid in nocache]\n else: \n sections = [cmu_to_verba(mapping, cid) for cid in cidlist] \n sections = [s for s in sections if s != False]\n \n verba_info = [cmu_to_verba(mapping, cid) for cid in cidlist] \n verba_info = [s for s in verba_info if s != False]\n \n if verba_info:\n verba_ids = [section['id'] for section in reduce(list.__add__, verba_info)]\n URL = BASE.format(\",\".join(verba_ids))\n \n if sections: \n print \"Downloading from {}\".format(URL)\n # Download and parse if needed \n parser = BeautifulSoup(requests.get(URL).content)\n raw_data = [el.getText() for el in parser.findAll(\"script\")\n if 'Verba.Compare' in el.getText()][0] \n \n # Parse the extracted JS into an AST to extract the correct variable\n tree = Parser().parse(raw_data)\n objects = ObjVisitor()\n # Oh god why\n objects.visit(tree)\n\n # Finally\n data = [json.loads(d) for d in [i for i in objects.vardump if \"isbn\" in i]]\n\n # Bring in the cached data if it exists, otherwise just initialize empty result\n if havecache and cache:\n print \"Grabbing cache...\"\n _courses = [_c.retrieve(_c.CACHE, cid, cmu_to_verba(mapping, cid)) for cid in cache]\n _courses = reduce(list.__add__, _courses)\n summary = {\n 'url': URL,\n 'courses': _courses\n }\n else: \n summary = {\n 'url': URL,\n 'courses': []\n }\n \n # If we had to grab anything, now put it into the result\n if sections:\n for course in data:\n if course.get('title'):\n info = {\n 'name': course['title'],\n 'instructor': course.get('instructor'),\n 'sections': course['name'],\n 'books': []\n }\n if 'books' in course:\n for book in course['books']:\n bookinfo = {\n 'title': book['title'],\n 'author': book['author'],\n 'isbn': book['isbn'],\n 'citation': book['citation'],\n 'required': book['required'].lower() == 'required',\n }\n info['books'].append(bookinfo) \n \n summary['courses'].append(info)\n \n if havecache:\n # Store in cache for future use\n _c.store(_c.CACHE, info)\n \n return summary","def dict_of_recs_for_run (ins, exp, runnum) :\n return calibration_runs(ins, exp)[runnum]","def index_records(vr):\n return collections.OrderedDict((record2key(rec), clean_sample_index(rec))\n for rec in vr)","def get_data(self):\n with LoggingTime(\"Fetching data took: \"):\n try:\n with open(self.__io_params.input_file_path, 'r', encoding=\"utf8\") as csvFiles:\n reader = csv.reader(csvFiles, delimiter=',', quotechar='|')\n dictionary = {}\n for data in reader:\n if len(data) > 0 and data[0]: # ignore malformed data lines\n word_base = data[0].strip().lower()\n if self.word_is_eligible(word_base) and word_base not in dictionary:\n dictionary[word_base] = ProcessingEntry(word_base)\n logging.info(\"Loaded %i search terms\" % (len(dictionary)))\n return dictionary\n except IOError as e:\n logging.error(\"IO Error has occurred. Check input file\")\n logging.error(e)\n raise\n except:\n logging.error(\"Reading data failed. Check input file format\")\n raise","def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp': [] }\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n\n if author['dblp'].__contains__('publications'):\n publications['dblp'] = author['dblp']['publications']\n# for pub in author['dblp']['publications']:\n# print(pub)\n\n if author['cdblp'].__contains__('publications'):\n publications['cdblp'] = author['cdblp']['publications']\n# for pub in author['cdblp']['publications']:\n# print(pub)\n return publications","def solr_dict(self):\n raise NotImplementedError","def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\n dictionary_key_two):\n list_of_one_citations = []\n list_of_two_citations = []\n list_of_unassigned_citations = []\n for citation in self.array_of_citations:\n citation_string = self.__generate_author_string__(\n citation.get_author()) + \" \" + citation.get_title() \\\n + \" \" + citation.get_journal()\n one_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_one)\n two_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_two)\n if one_key_matches > two_key_matches:\n citation.set_classification(dictionary_key_one)\n list_of_one_citations.append(citation)\n elif two_key_matches > one_key_matches:\n citation.set_classification(dictionary_key_two)\n list_of_two_citations.append(citation)\n else:\n citation.set_classification(\"Unassigned\")\n list_of_unassigned_citations.append(citation)\n\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\n list_of_two_citations, \"Unassigned\":\n list_of_unassigned_citations}","def gen_search_index(record, link_content=None):\n document_id = record[\"document_id\"]\n record_index = {\n \"document_name\": record[\"document_name\"],\n \"document_type\": record[\"document_type\"].__name__,\n \"content\": record[\"content\"],\n \"authors\": record[\"authors\"],\n \"publish_date\": record[\"publish_date\"],\n \"link_content\": link_content,\n }\n return (document_id, record_index)","def tr_nc_dict(dfin):\n\n\ttr_nc_index_dict = OrderedDict()\n\t\n\ttrCount = 0\n\tpreviousTrIndex = 0\n\n\tcurrent_id=\"\"\n\tcurrent_tr=\"\"\n\n\tfor i in range(len(dfin)):\n# print dfin.loc[i]\n\n\t\tif dfin.loc[i,'feature'] == 'transcript':\n\t\t\ttrdict = parse_mod_entry(dfin.loc[i,'transcript_id'])\n\n\t\t\tcurGeneID = trdict['gene_id'][0]\n\n\t\t\tif trCount != 0:\n\t\t\t\tnewTrIndex = i\n\t\t\t\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\n\t\t\t\tpreviousTrIndex = i\n\t\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\t\ttrCount += 1\n\n\t\t\telse:\n\t\t\t\tnewTrIndex = 0\n\t\t\t\ttrCount +=1\n\t\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\t\t\n\t\tif i == (len(dfin)-1):\n\t\t\tnewTrIndex = i+1\n\t\t\tcurrent_id = trdict['gene_id'][0]\n\t\t\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\n\t\t\t\n\treturn tr_nc_index_dict","def get_Pre_Succ(I):\n #Docs = I.docs\n #Docs_id = Docs.keys()\n Docs = I.getIndex().all_ids_\n Docs_id = [ int(float(k)) for k in Docs] \n N_pgs = len(Docs_id)\n Index_P = { id:idx for idx,id in enumerate(Docs_id)}\n Counter_Index_P = { idx:id for idx,id in enumerate(Docs_id)}\n \n print \"\\nBuilding Pi...\"\n Succ = { Index_P[p]:(I.getLinksForDoc(p),len(I.getLinksForDoc(p))) for p in Docs_id }\n P = {}\n for e in Succ:\n succ_e,l_e = Succ[e]\n for s in succ_e: \n if Index_P.get(s,\"Unknown_Doc_id\") not in P:\n P[Index_P.get(s,\"Unknown_Doc_id\")] = set()\n P[Index_P.get(s,\"Unknown_Doc_id\")].add(e) \n \n return P,Succ,Index_P,Counter_Index_P,N_pgs","def __find_correlations(self, results):\n\n for result in results[:self.__result_limit]:\n\n # pub without venue\n if len(result['ven']) == 0:\n result['alternative'] = []\n\n with self.vix.searcher(weighting=Frequency) as vs:\n vq_parse = QueryParser('key', self.vix.schema).parse(result['pub']['crossref'])\n tresult = vs.search(vq_parse, limit=None, )\n if len(tresult) != 0:\n result['ven'] = {}\n result['added'] = 1\n for attr in tresult[0].items():\n result['ven'][attr[0]] = attr[1]\n\n self.__output.append(result)\n\n # venue without pub or venue with a list of pubs\n elif len(result['pub']) == 0 or (\n isinstance(result['pub'], list) and len(result['pub']) > 1):\n result['alternative'] = []\n\n with self.pix.searcher(weighting=Frequency) as ps:\n pq_parse = QueryParser('crossref', self.pix.schema).parse(result['ven']['key'])\n tresult = ps.search(pq_parse, limit=None, )\n\n if len(tresult):\n plist = []\n tmp = dict()\n for el in tresult:\n for attr in el.items():\n if attr[0] == 'title' and attr[1] not in [x['title'] for x in result['pub']]:\n plist.append(attr[1])\n break\n\n result['alternative'] = plist\n self.__output.append(result)\n\n # mixed case\n elif len(self.__output) == 0 or not result['ven']['key'] in [x['key'] for x in self.__output]:\n lis = [x for x in results if len(x['ven']) and x['ven']['key'] == result['ven']['key']]\n tmp = {}\n if len(lis) <= 1:\n tmp = {'key': result['pub']['key'],\n 'score': result['score'],\n 'pub': [x['pub'] for x in lis],\n 'ven': result['ven'],\n 'alternative': list()}\n else:\n tmp = {'key': result['ven']['key'],\n 'score': result['score'],\n 'pub': [x['pub'] for x in lis],\n 'ven': result['ven'],\n 'alternative': list()}\n plist = []\n with self.pix.searcher() as ps:\n pq_parse = QueryParser('crossref', self.pix.schema).parse(tmp['key'])\n tresult = ps.search(pq_parse, limit=None, )\n if len(tresult):\n for el in tresult:\n for attr in el.items():\n if attr[0] == 'title' and attr[1] not in [x['title'] for x in tmp['pub']]:\n plist.append(attr[1])\n break\n\n tmp['alternative'] = plist\n self.__output.append(tmp)","def get_coauthors_dict(name_url_list, schl_name):\n\n\tpaper_info_dict = {}\n\n\tfor name_url in name_url_list:\n\t\t# Get a sub papers_info dictfor an individual author\n\t\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\n\t\t# add sub dict to full dict\n\t\tpaper_info_dict.update(papers_info)\n\t\n\tif \"Humanities\" in schl_name:\n\t\tschl_name = \"School of Humanities\"\n\n\treturn paper_info_dict","def get_results(self):\r\n r ={\r\n 'Gp_s' :np.array(self.Gp_guess_s), #Means of the posteriors\r\n 'Gm_s' :np.array(self.Gm_guess_s), #Means of the posteriors\r\n 'eGp_s':np.array(self.eGp_guess_s) , #Standard deviations of the posteriors\r\n 'eGm_s':np.array(self.eGm_guess_s) , #Standard deviations of the posteriors\r\n 'covGpGm_s':np.array(self.cov_GpGm_s) , #Covariances of the posteriors\r\n 't_tot_pulseSequences_s':np.array(self.t_tot_pulseSequences_s), #Time it takes for each batch of pulse sequences\r\n 't_tot_process_s':np.array(self.t_tot_process_s), #CPU time it takes for processing the Bayes stuff at each iteration\r\n 't_probe_p_s':np.array(self.t_probe_p_s), # Times probed for the diff p\r\n 't_probe_m_s':np.array(self.t_probe_m_s), # Times probed for the diff m\r\n 't_pulseSequences_s':np.array(self.t_pulseSequences_s),\r\n 't_process_s':np.array(self.t_process_s),\r\n 'iteration_s':np.array(self.nb_iteration_s), #Number of iterations performed\r\n 'R_tot_s':np.array(self.R_tot_s) #Total number of readout performed\r\n }\r\n return r","def get_relevance_dict(corpus):\n global RELEVANCE_CORPUS\n global RELEVANCE_DICT\n if RELEVANCE_DICT and corpus == RELEVANCE_CORPUS:\n return RELEVANCE_DICT\n RELEVANCE_CORPUS = corpus\n RELEVANCE_DICT = read_relevance_from_csv(corpus)\n return RELEVANCE_DICT","def qasmCircuitResults(self):\n returnedDictionary={}\n self.circutDrawing = self.draw()\n self.blochSpheres=self.separatedBlochSpheres()\n returnedDictionary[\"wires\"]=self.num_qubits\n returnedDictionary[\"probabilities\"] = self.separatedProbabilities()\n #returnedDictionary[\"blochSpheres\"] = self.separatedBlochSpheres()\n returnedDictionary[\"diracNotation\"] = self.diracNotation()\n returnedDictionary['chart'] = self.graph()\n returnedDictionary[\"link\"] = \"\"\n #returnedDictionary[\"qasmRows\"] = np.transpose(cols).tolist()\n \n if self.API_TOKEN != \"\":\n returnedDictionary[\"link\"] = self.runOnIBMQ()\n \n return returnedDictionary","def _search_hits_dict(self, query):\n return docs_to_dict(docs_from_result(self.adapter.search(query)))","def getCrowDistDict(self):\n retDict = Distribution.getCrowDistDict(self)\n retDict['n'] = self.n\n retDict['p'] = self.p\n return retDict","def index_terms(self, terms):\n index = dict()\n for term in terms:\n links = [cell.metadata[\"nbpages\"][\"link\"] for nb in self.notebooks\n for cell in nb.content.cells if re.search(term, cell.source) if \"nbpages\" in cell.metadata.keys()]\n index[term] = list(dict.fromkeys(links))\n return index","def data_from_result():\n return dict(\n gene=\"BRAF\",\n strand=\"-\",\n tx_pos_range=(1802, 1921),\n alt_pos_range=(140453074, 140453193),\n alt_aln_method=\"splign\",\n tx_exon_id=780494,\n alt_exon_id=1927263\n )","def get_data_dict(self):\n\n print('---------repo creation date------------------')\n print('start date', self.start_date)\n print('---------------------------------------------')\n print('------Commits----------')\n print(self.commits)\n print('--------------------------------------------')\n\n df = pd.DataFrame.from_records(self.commits) # pylint: disable=invalid-name\n df.date = pd.to_datetime(df.date, utc=True, unit='s')\n df.set_index('date', inplace=True)\n df.index = df.index.floor('D')\n\n date_range = pd.date_range(start=self.start_date, end=self.end_date, freq=self.date_unit)\n date_range = date_range.floor('D')\n\n grouped = df.groupby('author')\n new_df = pd.DataFrame(index=date_range)\n for name, group in grouped:\n new_df[name] = group.groupby('date').size()\n new_df.fillna(0, inplace=True)\n\n return {\n 'x': new_df.index.strftime('%Y-%m-%d').tolist(),\n 'y': new_df.columns.tolist(),\n 'z': new_df.T.values.astype('int32').tolist()\n }","def formula_search_to_dict(raw_result):\n return [\n {\n \"id\": item.cluster_id,\n \"text\": item.text,\n \"n_entries\": item.n_entries,\n \"n_texts\": item.unique_text,\n \"verb_text\": item.verb_text,\n }\n for item in raw_result\n ]","def list_publications(bib_format=\"dict\"):\n\n def get_bibtex(key, value):\n total_keys = [\n \"title\",\n \"journal\",\n \"volume\",\n \"issue\",\n \"number\",\n \"pages\",\n \"numpages\",\n \"year\",\n \"month\",\n \"publisher\",\n \"url\",\n \"doi\",\n \"issn\",\n ]\n bibtex_str = (\n \"@article{\"\n + key\n + \",\\n\"\n + \" author={\"\n + \" and \".join(value[\"author\"])\n + \"},\\n\"\n )\n for key in total_keys:\n if key in value.keys():\n bibtex_str += \" \" + key + \"={\" + value[key] + \"},\\n\"\n bibtex_str += \"}\\n\"\n return bibtex_str\n\n def get_apa(value):\n apa_str = \" & \".join(value[\"author\"])\n if \"year\" in value.keys():\n apa_str += \" (\" + value[\"year\"] + \"). \"\n if \"title\" in value.keys():\n apa_str += value[\"title\"] + \". \"\n if \"journal\" in value.keys():\n apa_str += value[\"journal\"] + \", \"\n if \"volume\" in value.keys():\n apa_str += value[\"volume\"] + \", \"\n if \"pages\" in value.keys():\n apa_str += value[\"pages\"] + \". \"\n if \"doi\" in value.keys():\n apa_str += \"doi: \" + value[\"doi\"] + \"\\n\"\n return apa_str\n\n publication_dict = s.publication_lst\n if bib_format.lower() == \"dict\":\n return publication_dict\n elif bib_format.lower() == \"bibtex\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_bibtex(key, value)\n return total_str\n elif bib_format.lower() == \"apa\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_apa(value)\n return total_str\n else:\n raise ValueError(\"Supported Bibformats are ['dict', 'bibtex', 'apa']\")","def results(self):\n \n results = {}\n if self.descriptors is not None:\n for i in range(len(self.items)):\n results[self.items[i]] = {'I': self.ivalue(i),\n 'J': self.jvalue(i)}\n else:\n for i in range(len(self.items)):\n results[self.items[i]] = {'I': self.ivalue(i)}\n return results","def construct_occurrence_dico(data) :\n print('Constructing occurence dictionnaries...')\n\n p_kw_dico = dict()\n kw_p_dico = dict()\n full_stem_dico = {}\n for patent in data :\n patent_id = patent['id']\n #[keywords,stem_dico] = extract_keywords(patent[1]+\". \"+patent[2],patent_id)\n [keywords,stem_dico] = extract_keywords(patent['title']+\". \"+patent['abstract'],patent_id)\n #print(keywords)\n\n for k in keywords :\n # add to p_kw dico\n if k in kw_p_dico :\n kw_p_dico[k].append(patent_id)\n else :\n kw_p_dico[k]= [patent_id]\n #\n if patent_id in p_kw_dico :\n p_kw_dico[patent_id].append(k)\n else :\n p_kw_dico[patent_id] = [k]\n\n for k in stem_dico.keys():\n if k in full_stem_dico :\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\n else :\n full_stem_dico[k] = stem_dico[k]\n\n return([p_kw_dico,kw_p_dico,full_stem_dico])","def blast_to_dict():\n print(\"Parsing the BLAST results...\")\n GeneIDs = []\n blast_dict = defaultdict(lambda: defaultdict())\n for record in NCBIXML.parse(open(\"Log_Directory/blast_results.xml\")):\n for align in record.alignments:\n for hsp in align.hsps:\n percent_identity = round(100 * float(hsp.identities) / float(hsp.align_length),2) # https://www.dnastar.com/megalign_help/index.html#!Documents/calculationofpercentidentity.htm\n hit_id = align.title.split('|')\n # this uses NCBI's gi number (GenInfo Identifier) which is reliable now but getting phased out, so might\n # need to change to hit_id[3] at some point\n GeneIDs.append(hit_id[1])\n blast_dict[record.query]['Hit_ID'] = align.title\n blast_dict[record.query]['Gene_ID'] = hit_id[1]\n blast_dict[record.query]['Sequence'] = hsp.query\n blast_dict[record.query]['SeqLength'] = len(hsp.query)\n blast_dict[record.query]['Description'] = hit_id[4]\n blast_dict[record.query]['Accession'] = hit_id[3]\n blast_dict[record.query]['Db'] = hit_id[2]\n blast_dict[record.query]['Score'] = hsp.score\n blast_dict[record.query]['E_value'] = hsp.expect\n blast_dict[record.query]['Percent_Identity'] = percent_identity\n break\n \n GeneIDs = list(set(GeneIDs))\n if not GeneIDs:\n print('\\nYour BLAST query was rejected. Please enter a smaller sample size or try running this script \\\n at a better time.\\nNCBI asks that you run scripts on weekends or between 9pm and 5am Eastern \\\n time on weekdays if more than 50 searches will be submitted.')\n sys.exit()\n\n return blast_dict, GeneIDs,","def generate_data(cls):\n cls.results = {}\n cls.pre_process()\n today = datetime.today()\n skip = None\n values = map(lambda a: a[0], cls.data)\n if len(values) == 0 and 'citation' not in cls.config_data_name:\n skip = True\n weights= map(lambda a: a[1], cls.data)\n if cls.config_data_name == 'reads_histogram':\n bins = range(1996, today.year+2)\n elif cls.min_year:\n bins = range(cls.min_year, today.year+2)\n else:\n try:\n bins = range(min(values),max(values)+2)\n except:\n skip = True\n if not skip:\n refereed_values = map(lambda a: a[0], cls.refereed_data)\n refereed_weights= map(lambda a: a[1], cls.refereed_data)\n # get the regular histogram\n cls.value_histogram = histogram(values,bins=bins)\n cls.refereed_value_histogram = histogram(refereed_values,bins=bins)\n # get the normalized histogram\n cls.normalized_value_histogram = histogram(values,bins=bins,weights=weights)\n cls.refereed_normalized_value_histogram = histogram(refereed_values,bins=bins,weights=refereed_weights)\n else:\n cls.value_histogram = False\n cls.results[str(today.year)] = \"0:0:0:0\"\n cls.post_process()","def build_index(text: Iterable) -> Dict[str, List[Tuple[int, int]]]:\n index = defaultdict(list)\n for line_no, line in enumerate(text, 1):\n for match in WORD_RE.finditer(line):\n word = match.group()\n column_no = match.start() + 1\n location = (line_no, column_no)\n index[word].append(location)\n return index","def get_coauthors(self):\n # Get number of authors to search for\n res = download(url=self.coauthor_link, accept='json')\n data = loads(res.text)['search-results']\n N = int(data.get('opensearch:totalResults', 0))\n # Store information in namedtuples\n fields = 'surname given_name id areas affiliation_id name city country'\n coauth = namedtuple('Coauthor', fields)\n coauthors = []\n # Iterate over search results in chunks of 25 results\n count = 0\n while count < N:\n params = {'start': count, 'count': 25}\n res = download(url=self.coauthor_link, params=params, accept='json')\n data = loads(res.text)['search-results'].get('entry', [])\n # Extract information for each coauthor\n for entry in data:\n aff = entry.get('affiliation-current', {})\n try:\n areas = [a['$'] for a in entry.get('subject-area', [])]\n except TypeError: # Only one subject area given\n areas = [entry['subject-area']['$']]\n new = coauth(surname=entry['preferred-name']['surname'],\n given_name=entry['preferred-name'].get('given-name'),\n id=entry['dc:identifier'].split(':')[-1],\n areas='; '.join(areas),\n affiliation_id=aff.get('affiliation-id'),\n name=aff.get('affiliation-name'),\n city=aff.get('affiliation-city'),\n country=aff.get('affiliation-country'))\n coauthors.append(new)\n count += 25\n return coauthors","def _transform_search_database(self):\n # TODO: Create this and link with protein object when fasta file is provided\n return {\n \"file_format\": \"fasta format\",\n \"name\": \"\",\n \"id\": 1,\n \"location\": \"\",\n \"params\": [],\n }","def result_dict(self):\n result = {}\n for k, v in self._parser_result.items():\n result[k] = ' '.join(map(lambda x: x[0], v.most_common(3)))\n return result","def codonComposition(self):#works\n return {codon: self.countDicNuc.get(codon) for codon in self.rnaCodonTable.keys()}","def get_search_result_info(self):\n result = {}\n href = self.driver.find_element_by_xpath(locators.search_result_computer_name).get_attribute('href')\n name = self.driver.find_element_by_xpath(locators.search_result_computer_name).text\n introduced_date = self.driver.find_element_by_xpath(locators.search_result_introduced_date).text\n discontinued_date = self.driver.find_element_by_xpath(locators.search_result_discontinued_date).text\n company = self.driver.find_element_by_xpath(locators.search_result_company).text\n result[\"href\"] = href\n result[\"name\"] = name\n result[\"introduced_date\"] = introduced_date\n result[\"discontinued_date\"] = discontinued_date\n result[\"company\"] = company\n return result","def getCitations (self, doc, verbose = False):\r\n if verbose is True:\r\n print \"getting citations for document \" +str(doc) \r\n return self.citations[int(doc)]","def extract_metadata(res):\n d = OrderedDict()\n\n d['type'] = res['type']\n d['authors'] = make_author_list(res)\n d['citation-authors'] = make_citation_authors(res)\n d['citation'] = make_citation(res)\n d['source-title'] = res['title'].strip(\" \").capitalize()\n d['title'] = \"Notes on '\" + d['source-title'] + \"', by \" + \", \".join(d['authors'][0:-1])\n if len(d['authors']) > 1:\n d['title'] = d['title'] + \", and \" + d['authors'][-1] \n d['citation-authors']\n d['container'] = make_container(res)\n d['publisher'] = res['publisher'] if 'publisher' in res.keys() else '' \n d['issn'] = res['issn'] if 'issn' in res.keys() else ''\n d['year'] = make_year(res)\n d['issue'] = res['issue'] if \"issue\" in res.keys() else \"\"\n d['volume'] = res['volume'] if \"volume\" in res.keys() else \"\"\n d['pages'] = res['page'] if \"page\" in res.keys() else \"\"\n d['subjects'] = make_subject(res)\n d['doi'] = res['DOI']\n d['link'] = res['URL']\n d['citationkey'] = make_citation_key(res)\n d['fetched'] = date.today().strftime(\"%Y%m%d\")\n return d","def get_results_for_init(self):\n return dict(init=self.centroids, n_clusters=self.centroids.shape[0])","def get_initial_author_dict():\n adict = {}\n try:\n ah = run_sql(\"select aterm,hitlist from rnkAUTHORDATA\")\n for (a, h) in ah:\n adict[a] = deserialize_via_marshal(h)\n return adict\n except:\n register_exception(prefix=\"could not read rnkAUTHORDATA\", alert_admin=True)\n return {}","def get_meta_information() -> Dict:\n return {'name': 'NAS-Bench-201',\n 'references': ['Xuanyi Dong, Yi Yang',\n 'NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search',\n 'https://openreview.net/forum?id=HJxyZkBKDr',\n 'https://github.com/D-X-Y/AutoDL-Projects'],\n }","def provincias_distritos():\n\n rv = OrderedDict()\n q = \"\"\"\n SELECT da.id_distrito,\n da.desc_distrito,\n da.id_seccion,\n da.desc_seccion,\n count(e.id) AS estab_count,\n count(wm.id) AS matches_count\n FROM divisiones_administrativas da\n INNER JOIN establecimientos e\n USING (id_distrito, id_seccion)\n LEFT OUTER JOIN weighted_matches wm\n ON wm.establecimiento_id = e.id\n AND wm.score = 1 and wm.match_source = 1\n GROUP BY da.id_distrito,\n da.desc_distrito,\n da.id_seccion,\n da.desc_seccion\n ORDER BY id_distrito,\n id_seccion\n \"\"\"\n\n for d in db.query(q):\n k = (d['id_distrito'], d['desc_distrito'],)\n if k not in rv:\n rv[k] = []\n rv[k].append((\n d['id_distrito'],\n d['id_seccion'],\n d['desc_seccion'],\n d['estab_count'],\n d['matches_count']))\n return rv","def create_index_dict(vcb_file):\n index_dict = {}\n vcb = open(vcb_file).readlines()\n for line in vcb:\n line = line.split()\n index_dict[int(line[0])] = line[1]\n return index_dict","def tag_dict_values (self):\r\n\r\n if self.using_database:\r\n value_tuple = (notebookname,)\r\n db_cursor.execute(\"SELECT keyword \"\r\n +\"FROM keys_to_indexes\"\r\n +\" WHERE notebook=?;\",\r\n value_tuple)\r\n\r\n fetched = db_cursor.fetchall()\r\n if fetched:\r\n return {index[0].strip() for index in fetched}\r\n return set()\r\n\r\n return self.tag_dict.values()","def fetch_to_dict(blast_dictionary):\n blast_dict = blast_dictionary.copy()\n fetch_dict = defaultdict(lambda: defaultdict())\n for record in SeqIO.parse(\"Log_Directory/fetch_results.txt\", \"genbank\"):\n fetch_dict[record.id]['Organism'] = record.annotations['organism']\n fetch_dict[record.id]['Source'] = record.annotations['source']\n fetch_dict[record.id]['Domain'] = record.annotations['taxonomy'][0]\n fetch_dict[record.id]['Taxonomy'] = record.annotations['taxonomy']\n for record in blast_dict.keys():\n for accession in fetch_dict.keys():\n if accession in blast_dict[record][\"Accession\"]:\n for accession_item in next(iter(fetch_dict.values())).keys():\n blast_dict[record][accession_item] = fetch_dict[accession][accession_item]\n\n return blast_dict","def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\n\n course_dictionary = {} # placeholder dictionary\n\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\n full_title_desc_list = {}\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\n\n return course_dictionary","def covid19(request):\n\tip = get_ip(request, right_most_proxy=True)\n\tIpAddressInformation.objects.create(ip_address=ip)\n\n\tcontextres =[]\n\t#build elasticsearch query to search data\n\tquery={\"query\": {\n\t\t\"bool\": {\n\t\t\t\"must\": [\n\t\t\t\t{\"match\": {\"Associated with COVID-19\": \"Yes\"}},\n\t\t\t\t{\"match\": {\"UniprotKb entry status\": \"Yes\"}}\n\t\t\t]\n\t\t}\n\t}\n\t}\n\t#generate random file name to store search result in json format\n\tnameFIle=names.get_first_name()\n\tjsonfilename=nameFIle+'_basic_search_covid19.json'\n\tjsonfilepath=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'results', jsonfilename)\n\tjsonfileoutput= open(jsonfilepath,'w')\n\tjfinaldata=[]\n\tes.indices.refresh(index=\"mrmassaydb-index\")\n\t#elasticsearch will search data\n\tres=helpers.scan(client=es,scroll='2m',index=\"mrmassaydb-index\", doc_type=\"mrmassaydb-type\",query=query,request_timeout=30)\n\tjfinaldata=[]\n\tpepSeqList=[]\n\tproteinList=[]\n\t#if data is valid based on uniprotkb release then it will display\n\tfor i in res:\n\t\tjdic=i['_source']\n\t\tjdic={str(tkey):force_text(tvalue) for tkey,tvalue in jdic.items()}\n\t\tif jdic[\"UniprotKb entry status\"] ==\"Yes\" and jdic['UniProtKB Accession'] !='502':\n\t\t\tjdic[\"PPI\"] =\"View\"\n\t\t\tjdic[\"sel\"] =\"\"\n\t\t\tjdic[\"Drug Bank\"]=jdic[\"Drug Bank\"].replace('\\\\','')\n\t\t\tjdic[\"Drug Bank\"]=jdic[\"Drug Bank\"].replace('<br>','|')\n\t\t\tjdic[\"SRMAtlas URL\"]=jdic[\"SRMAtlas URL\"].replace('\\\\','')\n\t\t\tjdic[\"Passel URL\"]=jdic[\"Passel URL\"].replace('\\\\','')\n\t\t\tjdic[\"CPTAC URL\"]=jdic[\"CPTAC URL\"].replace('\\\\','')\n\t\t\tjdic[\"Panoramaweb URL\"]=jdic[\"Panoramaweb URL\"].replace('\\\\','')\n\t\t\tjdic[\"PeptideTracker URL\"]=jdic[\"PeptideTracker URL\"].replace('\\\\','')\n\t\t\t#if jdic[\"Pathway Name\"].lower() !='na':\n\t\t\t#\tjdic[\"Pathway Name\"]=re.sub(r\"(\\w)([A-Z])\",r\"\\1|\\2\",jdic[\"Pathway Name\"])\n\t\t\tjdic[\"Mean Concentration\"] =jdic[\"Mean Concentration\"].replace('fmol/','fmol/µ')\n\t\t\tjdic[\"Concentration\"] =jdic[\"Concentration\"].replace('fmol/','fmol/µ')\n\t\t\tif str(jdic[\"Associated with COVID-19\"]).strip().upper() =='YES':\n\t\t\t\tpepSeqList.append(jdic[\"Peptide Sequence\"].strip())\n\t\t\t\tproteinList.append(jdic[\"UniProtKB Accession\"].strip().split('-')[0])\n\t\t\tjfinaldata.append(jdic)\n\tes.indices.refresh(index=\"mrmassaydb-index\")\n\t#checking any result generated by database\n\tfoundHits=len(jfinaldata)\n\t#storing only 10000 rows in json format\n\tjson.dump(jfinaldata[:10000],jsonfileoutput)\n\tjsonfileoutput.close()\n\t# if result found then do other job\n\tif foundHits >0:\n\t\tstatsummary=summaryStatcal(jfinaldata) # sent data to this funcation for generating stat\n\t\tpathwaychart=statsummary['pathwaychart']\n\t\tpathwaychart=[i[:2] for i in pathwaychart]\n\t\tspecieslist=statsummary['specieslist']\n\t\ttotallist=statsummary['total']\n\t\tsubcell=statsummary['subcell']\n\t\tgodic=statsummary['godic']\n\t\tjvennprot=statsummary['jevennstat'][0]\n\t\tjvennpep=statsummary['jevennstat'][1]\n\t\tmrmdatabase=statsummary['jevennstat'][2]\n\t\tsortedgodic=OrderedDict(sorted(godic.items(), key=lambda t: t[1])) # sorting GO data\n\t\tupdatedgodic=dict(list(sortedgodic.items()))\n\t\tpepseqdataseries=ast.literal_eval(json.dumps(statsummary['pepseqdataseries'])) #dumping data into json format\n\t\tprodataseries=statsummary['prodataseries']\n\t\tunqisostat=statsummary['unqisostat']\n\t\tjsonfilepathStat=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'statsummary', jsonfilename) #storing stat result in json format\n\t\tjsonfileoutputStat= open(jsonfilepathStat,'w')\n\t\tjson.dump(statsummary,jsonfileoutputStat)\n\t\tjsonfileoutputStat.close()\n\t\turlname=\"'/resultFile/jsonData/resultJson/basicsearch/results/\"+jsonfilename+\"'\"\n\n\t\tcontextindex={\n\t\t\t\"filename\":urlname,\"colname\":json.dumps(colname),'foundHits':foundHits,\n\t\t\t'pathwaychart':pathwaychart[:11],'specieslist':specieslist,\n\t\t\t'totallist':totallist,'subcell':subcell,\n\t\t\t'updatedgodic':updatedgodic,'pepseqdataseries':pepseqdataseries,\n\t\t\t'prodataseries':prodataseries,'unqisostat':unqisostat,\n\t\t\t'uniquePepSeq':len(set(pepSeqList)),'uniqueProtein':len(set(proteinList)),\n\t\t\t'jvennprot':json.dumps(jvennprot),'jvennpep':json.dumps(jvennpep),'jvennmrmdb':json.dumps(mrmdatabase)\n\t\t\t}\n\t\treturn render(request,'covid19.html',contextindex)\n\telse:\n\t\treturn render(request,'covid19.html',{'foundHits':foundHits})","def key_dict_values (self):\r\n\r\n if self.using_database:\r\n value_tuple = (notebookname,)\r\n db_cursor.execute(\"SELECT note_index \"\r\n +\"FROM keys_to_indexes\"\r\n +\" WHERE notebook=?;\",\r\n value_tuple)\r\n\r\n fetched = db_cursor.fetchall()\r\n if fetched:\r\n return {index[0].strip() for index in fetched}\r\n return set()\r\n\r\n return self.key_dict.values()","def get_titles_dict(name_url_list):\n\tbib_dict = {}\n\t\n\tfor name_url in name_url_list:\n\t\ttitles = get_author_titles(name_url[1])\n\t\tbib_dict[name_url] = titles\n\n\treturn bib_dict","def search_for_redbooks(book_codes: tuple):\n\n book_dict = {}\n\n global setup\n\n for book_code in book_codes:\n URI_string = build_URI_string(book_code)\n search_web_page = requests.get(URI_string)\n if search_web_page.status_code != 200:\n print(\"Book with code {} not found! Continuing...\".format(book_code))\n continue\n web_page_content = search_web_page.content\n soup = BS(web_page_content, 'html.parser')\n book_name = soup.find('h1',{'class':'ibm-h1','id':'ibm-pagetitle-h1'}).text\n book_dict[book_code] = book_name\n\n return book_dict","def citations(request, format='csv'):\n \n filtered = False # make sure we're filtering by something\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\n \n \n # accession number\n if request.GET.get('gabi_acc_number'):\n filtered = True\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\n \n # species AND bentity\n if request.GET.get('species'):\n filtered = True\n if request.GET.get('species'): \n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\n if request.GET.get('bentity_id'):\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\n \n # lat and lon\n if request.GET.get('lat') and request.GET.get('lon'):\n filtered = True\n if request.GET.get('lat'):\n records = records.filter(lat=request.GET.get('lat'))\n if request.GET.get('lon'):\n records = records.filter(lon=request.GET.get('lon'))\n \n # status\n if request.GET.get('status'):\n records = records.filter(status=request.GET.get('status')[0].upper())\n \n \n # error message if the user didn't supply an argument to filter the records\n if not filtered: \n return errorResponse(\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\", format, {'records': []})\n \n \n # fetch all the bentitites at once, so we don't have to hit the database once for each record\n records = records.prefetch_related('bentity') \n \n output_objects = [{\n 'gabi_acc_number': r.gabi_acc_number,\n 'species': r.valid_species_name_id,\n 'bentity_id': r.bentity_id,\n 'bentity_name': r.bentity.bentity,\n 'status': r.status,\n 'type_of_data': r.type_of_data,\n 'lat': r.lat,\n 'lon': r.lon, \n 'citation': r.citation,\n } for r in records]\n \n \n \n if format == 'csv':\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\n \n else:\n return JSONResponse({'records': output_objects})","def index_feats_dict(self):\n doc_features_dict = {}\n\n for index, doc in zip(self.index, self.series):\n # Sets for a doc and feature words\n doc_set = set(doc.split())\n feat_set = set(self.features)\n\n # Shared words between the two sets\n interset_words = doc_set.intersection(feat_set)\n\n # Append to doc_features_dict\n doc_features_dict[index] = list(interset_words)\n\n return doc_features_dict","def last_updated_result(rank_method_code):\n result = [{}, {}, {}]\n query = \"\"\"select relevance_data from rnkMETHOD, rnkMETHODDATA where\n rnkMETHOD.id = rnkMETHODDATA.id_rnkMETHOD\n and rnkMETHOD.Name = '%s'\"\"\"% rank_method_code\n rdict = run_sql(query)\n if rdict and rdict[0] and rdict[0][0]:\n #has to be prepared for corrupted data!\n try:\n dic = deserialize_via_marshal(rdict[0][0])\n except zlib.error:\n return [{}, {}, {}]\n query = \"select object_value from rnkCITATIONDATA where object_name='citationdict'\"\n cit_compressed = run_sql(query)\n cit = []\n if cit_compressed and cit_compressed[0] and cit_compressed[0][0]:\n cit = deserialize_via_marshal(cit_compressed[0][0])\n if cit:\n query = \"\"\"select object_value from rnkCITATIONDATA\n where object_name='reversedict'\"\"\"\n ref_compressed = run_sql(query)\n if ref_compressed and ref_compressed[0] and ref_compressed[0][0]:\n ref = deserialize_via_marshal(ref_compressed[0][0])\n result = (dic, cit, ref)\n return result","def baby_search_engine(name):\r\n \r\n name_ranking = []\r\n \r\n for publication_name, name_list in baby_names.items():\r\n publication = {}\r\n if name.capitalize() in name_list:\r\n publication['list'] = publication_name\r\n publication['rank'] = name_list.index(name.capitalize()) + 1\r\n name_ranking.append(publication)\r\n\r\n \r\n return sorted(name_ranking, key=lambda k: k['rank'])","def _group_by_bank(self):\n rslt = {}\n for company in self:\n if not company.indexa_currency_provider:\n continue\n\n if rslt.get(company.indexa_currency_provider):\n rslt[company.indexa_currency_provider] += company\n else:\n rslt[company.indexa_currency_provider] = company\n return rslt","def get_complementary_infos_disc(browser, dict_disc: dict, url_disc: str) -> dict:\n try:\n browser.get_url(url_disc)\n soup = browser.get_soup()\n dict_complementary = {}\n table = soup.find(\"table\", {\"class\": \"album_info\"})\n table_descriptors = [x.find(\"th\").text.strip() for x in table.find_all(\"tr\")]\n table_values = [\n \" \".join(x.find(\"td\").text.strip().replace(\"\\n\", \", \").split())\n for x in table.find_all(\"tr\")\n ]\n for d, v in zip(table_descriptors, table_values):\n dict_complementary[d] = v\n\n try:\n dict_complementary[\"Rank Overall\"] = [\n x.replace(\"#\", \"\").replace(\",\", \"\")\n for y in dict_complementary[\"Ranked\"].split(\", \")\n if \"overall\" in y\n for x in y.split()\n ][0]\n except Exception as e:\n logger.debug(\"No overall rank found : %s\", e)\n try:\n dict_complementary[\"Rank Year\"] = [\n x.replace(\"#\", \"\").replace(\",\", \"\")\n for y in dict_complementary[\"Ranked\"].split(\", \")\n if dict_disc[\"Year\"] in y\n for x in y.split()\n ][0]\n except Exception as e:\n logger.debug(\"No year rank found : %s\", e)\n dict_disc.update(dict_complementary)\n except Exception as e:\n logger.error(e)\n return dict_disc","def getCrowDistDict(self):\n retDict = Distribution.getCrowDistDict(self)\n retDict['lambda'] = self.lambdaVar\n retDict['k'] = self.k\n retDict['low'] = self.low\n return retDict","def main():\n records = get_block_of_records({\"keyword\": \"food\"})\n print (\"returned items: {}\".format(len(records)))\n\n processed_records = {}\n for item in records:\n meta = item[\"meta\"]\n umm = item[\"umm\"]\n cid = meta[\"concept-id\"]\n short_name = umm[\"ShortName\"]\n processed_records[cid] = short_name\n\n print (\"uniq keys: {}\".format(len(processed_records.keys())))","def getUniChemData(self, inchiKeyList):\n mapD = {\n 1: {\"name\": \"chembl\", \"baseUrl\": \"https://www.ebi.ac.uk/chembl/\", \"entryUrl\": \"https://www.ebi.ac.uk/chembldb/compound/inspect/\"},\n 3: {\"name\": \"pdb\", \"baseUrl\": \"http://www.ebi.ac.uk/pdbe/\", \"entryUrl\": \"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\"},\n 2: {\"name\": \"drugbank\", \"baseUrl\": \"http://drugbank.ca/\", \"entryUrl\": \"http://www.drugbank.ca/drugs/\"},\n 5: {\"name\": \"pubchem_dotf\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 4: {\"name\": \"gtopdb\", \"baseUrl\": \"http://www.guidetopharmacology.org\", \"entryUrl\": \"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\"},\n 11: {\"name\": \"ibm\", \"baseUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\", \"entryUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\"},\n 6: {\"name\": \"kegg_ligand\", \"baseUrl\": \"http://www.genome.jp/kegg/ligand.html\", \"entryUrl\": \"http://www.genome.jp/dbget-bin/www_bget?\"},\n 9: {\"name\": \"zinc\", \"baseUrl\": \"http://zinc15.docking.org\", \"entryUrl\": \"http://zinc15.docking.org/substances/\"},\n 8: {\"name\": \"nih_ncc\", \"baseUrl\": \"http://nihsmr.evotec.com/evotec/\", \"entryUrl\": \"\"},\n 10: {\"name\": \"emolecules\", \"baseUrl\": \"https://www.emolecules.com/\", \"entryUrl\": \"https://www.emolecules.com/cgi-bin/more?vid=\"},\n 12: {\"name\": \"atlas\", \"baseUrl\": \"http://www.ebi.ac.uk/gxa/home\", \"entryUrl\": \"http://www.ebi.ac.uk/gxa/query?conditionQuery=\"},\n 7: {\"name\": \"chebi\", \"baseUrl\": \"http://www.ebi.ac.uk/chebi/downloadsForward.do\", \"entryUrl\": \"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\"},\n 14: {\n \"name\": \"fdasrs\",\n \"baseUrl\": \"http://fdasis.nlm.nih.gov/srs/srs.jsp\",\n \"entryUrl\": \"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\",\n },\n 15: {\"name\": \"surechembl\", \"baseUrl\": \"https://www.surechembl.org/search/\", \"entryUrl\": \"https://www.surechembl.org/chemical/\"},\n 21: {\"name\": \"pubchem_tpharma\", \"baseUrl\": \"http://www.thomson-pharma.com/\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 22: {\"name\": \"pubchem\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/compound/\"},\n 27: {\"name\": \"recon\", \"baseUrl\": \"https://vmh.uni.lu\", \"entryUrl\": \"https://vmh.uni.lu/\"},\n 28: {\"name\": \"molport\", \"baseUrl\": \"https://www.molport.com/shop/index\", \"entryUrl\": \"https://www.molport.com/shop/molecule-link/\"},\n 31: {\n \"name\": \"bindingdb\",\n \"baseUrl\": \"https://www.bindingdb.org/bind/index.jsp\",\n \"entryUrl\": \"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\",\n },\n 41: {\"name\": \"swisslipids\", \"baseUrl\": \"http://www.swisslipids.org/\", \"entryUrl\": \"http://www.swisslipids.org/\"},\n 29: {\"name\": \"nikkaji\", \"baseUrl\": \"http://jglobal.jst.go.jp/en/\", \"entryUrl\": \"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\"},\n 32: {\"name\": \"comptox\", \"baseUrl\": \"https://comptox.epa.gov/dashboard/\", \"entryUrl\": \"https://comptox.epa.gov/dashboard/\"},\n 33: {\"name\": \"lipidmaps\", \"baseUrl\": \"http://www.lipidmaps.org\", \"entryUrl\": \"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\"},\n 35: {\"name\": \"carotenoiddb\", \"baseUrl\": \"http://carotenoiddb.jp/index.html\", \"entryUrl\": \"http://carotenoiddb.jp/Entries/\"},\n 36: {\"name\": \"metabolights\", \"baseUrl\": \"http://www.ebi.ac.uk/metabolights/\", \"entryUrl\": \"http://www.ebi.ac.uk/metabolights/\"},\n 37: {\"name\": \"brenda\", \"baseUrl\": \"https://www.brenda-enzymes.org/index.php\", \"entryUrl\": \"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\"},\n 17: {\"name\": \"pharmgkb\", \"baseUrl\": \"https://www.pharmgkb.org\", \"entryUrl\": \"https://www.pharmgkb.org/drug/\"},\n 18: {\"name\": \"hmdb\", \"baseUrl\": \"http://www.hmdb.ca\", \"entryUrl\": \"http://www.hmdb.ca/metabolites/\"},\n 24: {\n \"name\": \"nmrshiftdb2\",\n \"baseUrl\": \"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\",\n \"entryUrl\": \"http://nmrshiftdb.org/molecule/\",\n },\n 25: {\"name\": \"lincs\", \"baseUrl\": \"http://www.lincsproject.org/\", \"entryUrl\": \"http://identifiers.org/lincs.smallmolecule/\"},\n 39: {\"name\": \"chemicalbook\", \"baseUrl\": \"https://www.chemicalbook.com\", \"entryUrl\": \"https://www.chemicalbook.com/ChemicalProductProperty_EN_\"},\n 20: {\"name\": \"selleck\", \"baseUrl\": \"http://www.selleckchem.com\", \"entryUrl\": \"http://www.selleckchem.com/products/\"},\n 23: {\"name\": \"mcule\", \"baseUrl\": \"https://mcule.com\", \"entryUrl\": \"https://mcule.com/\"},\n 26: {\"name\": \"actor\", \"baseUrl\": \"https://actor.epa.gov\", \"entryUrl\": \"http://actor.epa.gov/actor/chemical.xhtml?casrn=\"},\n 34: {\"name\": \"drugcentral\", \"baseUrl\": \"http://drugcentral.org\", \"entryUrl\": \"http://drugcentral.org/drugcard/\"},\n 38: {\"name\": \"rhea\", \"baseUrl\": \"http://www.rhea-db.org\", \"entryUrl\": \"http://www.rhea-db.org/searchresults?q=CHEBI:\"},\n }\n oD = {}\n try:\n for ky in inchiKeyList:\n unc = unichem_client # pylint: disable=no-member\n # unc.set_format(\"json\")\n uDL = unc.get(ky)\n if uDL:\n qD = {}\n for uD in uDL:\n if \"src_id\" in uD and int(uD[\"src_id\"]) in mapD:\n qD[mapD[int(uD[\"src_id\"])][\"name\"]] = uD[\"src_compound_id\"]\n if qD:\n oD[ky] = qD\n\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n return oD","def get_papers_by_institution(inst_names):\n papers = list(ads.SearchQuery(aff=\"IATE\"))\n for p in papers:\n print(p.author)\n print(f\"Paper from year {p.year} with \"\n f\"{len(p.author)} authors and \"\n f\"{p.citation_count} citations.\")\n input()","def index_authors(data_file=DATA_FILE_AUTHORS, index_file=INDEX_FILE_AUTHORS):\n context = etree.iterparse(\n gzip.open(data_file),\n tag=\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description\",\n )\n\n indexed = 0\n authors_idx = SqliteDict(index_file, autocommit=False)\n\n log.info(\"Indexing GND authors (~13.6 Million)\")\n\n for event, elem in context:\n if not elem.attrib.get(\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\"):\n continue\n if elem.getparent().tag == \"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}RDF\":\n name = extract_creator(elem)\n elif elem.getparent().tag == \"{http://www.w3.org/2002/07/owl#}sameAs\":\n name = extract_creator(elem)\n else:\n continue\n\n if name is not None:\n creator_id = str(\n elem.attrib.get(\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\")\n ).split(\"http://d-nb.info/gnd/\")[1]\n authors_idx[creator_id] = name\n log.debug(\"Indexing GND Author {} -> {}\".format(creator_id, name))\n\n indexed += 1\n if not indexed % 100000:\n log.info(\"Indexed {:,} authors\".format(indexed))\n\n # It's safe to call clear() here because no descendants will be\n # accessed\n elem.clear()\n # Also eliminate now-empty references from the root node to elem\n for ancestor in elem.xpath(\"ancestor-or-self::*\"):\n while ancestor.getprevious() is not None:\n del ancestor.getparent()[0]\n\n authors_idx.commit()\n log.info(\"Indexed {:,} authors\".format(len(authors_idx)))\n\n del context\n return authors_idx","def build_index(path, limit=None):\n\n documents = {}\n doc_lengths = {}\n index = {}\n j = 0 # Counter for articles\n for i in range(0, 22):\n if i >= 10:\n file = open(path + \"reut2-0\" + str(i) + \".sgm\", encoding='latin-1')\n else:\n file = open(path + \"reut2-00\" + str(i) + \".sgm\", encoding='latin-1')\n\n # Parsing html pages and getting reuters tagged once\n soup = BeautifulSoup(file, \"html.parser\")\n articles = soup.find_all('reuters')\n\n for article in articles:\n\n body = \"\"\n title = \"\"\n newid = int(article['newid'])\n\n try:\n body = article.body.get_text()\n except AttributeError:\n pass\n\n try:\n title = article.title.get_text()\n except AttributeError:\n pass\n\n words_list = title + \"\\n\" + body\n\n # Adding title+body to documents dictionary\n documents[newid] = words_list\n\n # Processing document and adding document lengths to dictionary\n processed_doc = preprocess(documents[newid])\n doc_lengths[newid] = len(processed_doc)\n\n # Adding word to index\n for term in processed_doc:\n if term in index:\n term_freq, docs_dict = index[term]\n\n term_freq += 1\n if newid in docs_dict:\n docs_dict[newid] += 1\n else:\n docs_dict[newid] = 1\n\n index[term] = (term_freq, docs_dict)\n else:\n docs_dict = {newid: 1}\n index[term] = (1, docs_dict)\n j += 1\n # Checking limit on articles\n if limit is not None:\n if j == limit:\n break\n\n # Checking limit on articles\n if limit is not None:\n if j == limit:\n break\n\n for term in index:\n term_freq, docs_dict = index[term]\n index[term] = [term_freq] + list(docs_dict.items())\n\n if limit is None:\n save_obj(index, \"reuters_index\")\n save_obj(documents, \"reuters_documents\")\n save_obj(doc_lengths, \"reuters_doc_length\")\n\n return index","def get_search_results(query):\n global index, doc_names\n result = ranked = list()\n doc_list = set(doc_names.keys())\n flag = 0\n for word in query:\n if word in index:\n flag = 1\n doc_list = doc_list.intersection(index[word].keys())\n else:\n return []\n\n if flag != 0:\n for doc_id in doc_list:\n positions = list()\n for word in query:\n positions.append(index[word][doc_id])\n doc_result = [(doc_id, x) for x in position_merge(positions)]\n result += doc_result\n ranked = sorted(result, key=lambda x: (x[0], x[1]))\n return ranked","def _docMapping(self):\n doc2quests = defaultdict(list)\n for q, d in self.quest2doc.items():\n doc2quests[d].append(q)\n return doc2quests","def pypi_indexes() -> IndexesDefinition:\n return {}","def shopee_dict(quote, results=100):\n\n brands = {}\n total_items = 0\n for brand in get_brands(quote, results):\n if brand in brands:\n brands[brand] += 1\n else:\n brands[brand] = 1\n total_items += 1\n return brands, total_items","def fetch_index_data():\n top_voters = Student.objects.annotate(num_ratings=Count('rating')).order_by('-num_ratings')[:6]\n top_voters_json = [[e.user.username, e.num_ratings] for e in top_voters]\n return {\n 'study_space_list': StudySpace.objects.order_by('-avg_rating'),\n 'top_student_voters': top_voters,\n 'top_student_voters_list': top_voters_json,\n 'login_form': LoginForm(),\n 'user_form': UserForm(),\n 'student_form': StudentForm()\n }","def get_matching_citizens():\n try:\n volunteer = request.headers.get('X-volunteer')\n except:\n return jsonify(\"X-volunteer header is missing\")\n logger.info(\"X-volunteer header is missing\")\n \n vaibhav_interests = ['sleeping','home building','garden walks']\n arsalan_interests = ['music','politics','science','reading']\n senior_list = table.scan()[\"Items\"]\n if request.headers['X-volunteer'] == \"Vaibhav\":\n dummy_volunteer_interest_list = vaibhav_interests\n matching_list = []\n for senior in senior_list:\n match = len(set(dummy_volunteer_interest_list) & set(senior['interests'])) / float(len(set(dummy_volunteer_interest_list) | set(senior['interests']))) * 100\n if match >= 20:\n matching_list.append(senior)\n if len(matching_list) == 0:\n return(jsonify(\"No matches found!\"))\n logger.info(\"Vaibhav Matching citizens returned\")\n elif request.headers['X-volunteer'] == \"Arsalan\":\n dummy_volunteer_interest_list = arsalan_interests\n matching_list = []\n # senior_list = [post for post in posts.find()]\n for senior in senior_list:\n match = len(set(dummy_volunteer_interest_list) & set(senior['interests'])) / float(len(set(dummy_volunteer_interest_list) | set(senior['interests']))) * 100\n if match >= 20:\n matching_list.append(senior)\n if len(matching_list) == 0:\n return jsonify(\"No matches found!\")\n logger.info(\"Arsalan Matching citizens returned\")\n else:\n return jsonify(\"Send a valid user header!\")\n return jsonify(matching_list)","def build_index():\n pass","def getCrowDistDict(self):\n retDict = Distribution.getCrowDistDict(self)\n retDict['p'] = self.p\n return retDict","def getCrowDistDict(self):\n retDict = Distribution.getCrowDistDict(self)\n retDict['p'] = self.p\n return retDict","def blastresults(br_naam1, br_naam2, br_pos1, br_pos2):\n singles = {}\n twogdeellijst = openfile(br_naam1 + br_naam2)\n print(br_pos1, \"pos1\", br_pos2, \"pos2\")\n for zin in twogdeellijst:\n singles[(zin.split()[br_pos1] + \" \" + zin.split()[br_pos2])] = 0\n return singles.keys()","def get_data(self):\n def _clean_search_hit(search_hit):\n \"\"\"\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\n\n :param search_hit:\n :return Dictionary: A dictionary with the cleaned data\n \"\"\"\n\n hit_name = search_hit.find(class_='hit-name')\n hit_url = hit_name.get('href')\n hit_id = hit_url.split('/')[-1]\n name = hit_name.get_text().split(',')[0].title().split()\n\n current_city = search_hit.find(class_='hit-location').get_text().upper()\n\n # Find all Addresses for search result.\n try:\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\n address = list({a.text.upper().replace('.', '') for a in address})\n except AttributeError:\n address = list()\n\n # find the address that is most likely the current main address.\n try:\n address.insert(0, address.pop(address.index(current_city)))\n except ValueError:\n address.insert(0, current_city)\n\n address = [\n {\n '@type': 'PostalAddress',\n 'addressLocality': locality.title(),\n 'addressRegion': region\n } for locality, region in [a.split(', ') for a in address]]\n\n work_location = {'@type': 'Place'}\n try:\n work_location['name'] = search_hit\\\n .find(class_='hit-work')\\\n .find(class_='hit-values')\\\n .get_text()\\\n .title()\n except AttributeError:\n work_location['name'] = ''\n\n alumni_of = {'@type': 'EducationalOrganization'}\n try:\n alumni_of['name'] = search_hit\\\n .find(class_='hit-high-school')\\\n .find(class_='hit-values')\\\n .get_text().title()\n except AttributeError:\n pass\n\n return {\n '@id': hit_id,\n '@type': 'Person',\n 'name': ' '.join(name),\n 'givenName': name[0],\n 'middleName': ' '.join(name[1:-1]),\n 'familyName': name[-1],\n 'url': hit_url,\n 'address': address,\n 'workLocation': work_location,\n 'alumniOf': alumni_of,\n }\n\n def _refine_search(search_str, options):\n \"\"\"\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\n press the option if found. Returns Boolean for found status\n\n :param search_str: str of the desired option.\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\n :return:\n \"\"\"\n search_str = search_str.upper()\n logging.info(f'Looking for \\'{search_str}\\'')\n try:\n for option in options:\n option_text = option.text.upper()\n logging.info(f'Option Checked: {option_text}')\n if search_str in option_text:\n option.click()\n time.sleep(2)\n logging.info(f'Option Selected: {option_text}')\n return True\n else:\n return False\n except AttributeError:\n return True\n except StaleElementReferenceException as e:\n ChromeCrash(e)\n\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\n driver.get(self.url)\n\n \"\"\"\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \n \"\"\"\n driver.fullscreen_window()\n\n # Refine the search by State\n address_region = self.person.get('addressRegion', '')\n address_region = STATES.get(address_region.upper(), address_region.upper())\n region_options = driver\\\n .find_element_by_class_name(\"STATE\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_region, region_options):\n return False\n\n # Narrow the search by pressing a City option\n address_locality = self.person.get('addressLocality').title()\n locality_options = driver\\\n .find_element_by_class_name(\"CITY\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_locality, locality_options):\n return False\n\n \"\"\"\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\n be done in steps with a pause between movements to allow for loading. \n Here it will first get the current location on the page, attempt to move down the page, and then check to\n see if the location changed.\n \"\"\"\n\n if self.auto_scroll and len(driver.find_elements_by_class_name(\"ais-InfiniteHits-item\")) > 15:\n current_height, new_height = 0, driver.execute_script(\"return document.body.scrollHeight\")\n\n while new_height != current_height:\n # Scroll down to the bottom of the page\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # Wait to load page\n time.sleep(SCROLL_PAUSE_TIME)\n\n # Calculate new scroll height and compare with last scroll height\n current_height, new_height = new_height, driver.execute_script(\"return document.body.scrollHeight\")\n\n page_source = driver.page_source\n page_soup = bs(page_source, 'html.parser')\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\n for i, search_result in enumerate(search_results):\n search_results[i] = _clean_search_hit(search_result)\n\n self.data_from_website = pd.DataFrame(search_results)\n self.data_from_website.set_index('@id', inplace=True)\n return True","def newAnalyzer():\n analyzer = {'crimes': None,\n 'dateIndex': None,\n 'autors': None,\n 'instrumentalness': None,\n 'tempo':None,\n 'liveness':None,\n 'speechiness':None,\n 'danceability':None,\n 'valence':None,\n 'loudness':None,\n 'acousticness':None,\n 'energy':None,\n 'generos':None\n }\n\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\n comparefunction=compareDates)\n\n analyzer['autors'] = om.newMap(omaptype='RBT',\n comparefunction=compareAUTOR)\n\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['tempo'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['liveness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['danceability'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n analyzer['valence'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['loudness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt)\n analyzer['energy'] = om.newMap(omaptype='RBT',\n comparefunction=compareInt) \n\n analyzer['generos']= m.newMap(11,\n maptype='CHAINING',\n loadfactor=4.0)\n \n return analyzer","def get_data(inp):\n movies = __get_movies(inp)\n series = __get_series(inp)\n\n exist_title(movies, series)\n is_response_larger_than_max_results(movies, series)\n\n search_dict = {}\n\n if movies['Response'] != 'False':\n for movie in movies['Search']:\n search_dict.update({'movie': __get_title_info(movie['imdbID'])})\n\n if series['Response'] != 'False':\n for show in series['Search']:\n search_dict.update({'series': __get_title_info(show['imdbID'])})\n\n return search_dict","def get_data(self):\n\n self.cur.execute('SELECT year, sex, education, score from vocabulary_scores;')\n scores = dict()\n education = dict()\n count = dict()\n\n for row in self.cur :\n if row[0] in scores:\n if row[1] in scores[row[0]]:\n scores[row[0]][row[1]] += int(row[3])\n education[row[0]][row[1]] += int(row[2])\n count[row[0]][row[1]] += 1\n else:\n scores[row[0]][row[1]] = int(row[3])\n education[row[0]][row[1]] = int(row[2])\n count[row[0]][row[1]] = 1\n else:\n # scores[year] = {gender: score}\n scores[row[0]] = {row[1]: int(row[3])}\n education[row[0]] = {row[1]: int(row[2])}\n count[row[0]] = {row[1]: 1}\n\n scores, education = self.average_scores(scores, education, count)\n\n return scores, education","def _FindBibEntries(self):\n bibs = \" \".join(glob.glob(\"*.bib\"))\n cat_process = subprocess.Popen(shlex.split(\"cat %s\" % bibs),\n stdout=subprocess.PIPE)\n grep_process = subprocess.Popen(shlex.split(\"grep ^@\"),\n stdin=cat_process.stdout,\n stdout=subprocess.PIPE)\n cat_process.stdout.close()\n grep2_process = subprocess.Popen(shlex.split(\"grep -vi @string\"),\n stdin=grep_process.stdout,\n stdout=subprocess.PIPE)\n grep_process.stdout.close()\n\n lines = grep2_process.communicate()[0]\n\n ret = []\n for l in lines.split(\"\\n\"):\n ret.append(responses.BuildCompletionData(\n re.sub(r\"@([A-Za-z]*)\\s*{\\s*([^,]*),.*\", r\"\\2\", l)\n )\n )\n return ret","def stats(self):\r\n return {}"],"string":"[\n \"def build_dict(results, chunk):\\n from math import inf\\n from collections import defaultdict\\n chunk = [int(au) for au in chunk]\\n d = defaultdict(\\n lambda: {\\\"first_year\\\": inf, \\\"pubs\\\": set(), \\\"coauth\\\": set(),\\n \\\"n_coauth\\\": inf, \\\"n_pubs\\\": inf})\\n for pub in results:\\n if not pub.author_ids:\\n continue\\n authors = set([int(au) for au in pub.author_ids.split(\\\";\\\")])\\n for focal in authors.intersection(chunk):\\n d[focal][\\\"coauth\\\"].update(authors)\\n d[focal][\\\"coauth\\\"].remove(focal)\\n d[focal][\\\"pubs\\\"].add(pub.eid)\\n d[focal][\\\"n_pubs\\\"] = len(d[focal][\\\"pubs\\\"])\\n d[focal][\\\"n_coauth\\\"] = len(d[focal][\\\"coauth\\\"])\\n if not pub.coverDate:\\n continue\\n first_year = min(d[focal][\\\"first_year\\\"], int(pub.coverDate[:4]))\\n d[focal][\\\"first_year\\\"] = first_year\\n return d\",\n \"def get_citation_informations(recid_list, config):\\n begin_time = os.times()[4]\\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\\n d_records_s = {} #dict of recid -> this_records_publication_info\\n citation_informations = []\\n\\n write_message(\\\"config function \\\"+config.get(\\\"rank_method\\\", \\\"function\\\"), verbose=9)\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attribute called function\\\", alert_admin=True)\\n #we cannot continue\\n return [ {}, {}, {}, {} ]\\n record_pri_number_tag = \\\"\\\"\\n try:\\n record_pri_number_tag = config.get(function, \\\"primary_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attribute primary_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n record_add_number_tag = \\\"\\\"\\n try:\\n record_add_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"additional_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute additional_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_number_tag = \\\"\\\"\\n try:\\n reference_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_tag = \\\"\\\"\\n try:\\n reference_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_pubinfo\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\\n #037a: contains (often) the \\\"hep-ph/0501084\\\" tag of THIS record\\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\\n #088a: additional short identifier for the record\\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\\n p_reference_tag = tagify(parse_tag(reference_tag))\\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\\n #fields needed to construct the pubinfo for this record\\n publication_pages_tag = \\\"\\\"\\n publication_year_tag = \\\"\\\"\\n publication_journal_tag = \\\"\\\"\\n publication_volume_tag = \\\"\\\"\\n publication_format_string = \\\"p v (y) c\\\"\\n try:\\n tag = config.get(function, \\\"pubinfo_journal_page\\\")\\n publication_pages_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_year\\\")\\n publication_year_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_title\\\")\\n publication_journal_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_volume\\\")\\n publication_volume_tag = tagify(parse_tag(tag))\\n publication_format_string = config.get(function, \\\"pubinfo_journal_format\\\")\\n except:\\n pass\\n\\n #print values for tags for debugging\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"tag values\\\")\\n write_message(\\\"p_record_pri_number_tag \\\"+str(p_record_pri_number_tag))\\n write_message(\\\"p_reference_tag \\\"+str(p_reference_tag))\\n write_message(\\\"publication_journal_tag \\\"+str(publication_journal_tag))\\n write_message(\\\"publication_format_string is \\\"+publication_format_string)\\n done = 0 #for status reporting\\n numrecs = len(recid_list)\\n\\n # perform quick check to see if there are some records with\\n # reference tags, because otherwise get.cit.inf would be slow even\\n # if there is nothing to index:\\n if run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_tag[0:2],\\n (p_reference_tag,)) or \\\\\\n run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_number_tag[0:2],\\n (p_reference_number_tag,)):\\n for recid in recid_list:\\n if (done % 10 == 0):\\n task_sleep_now_if_required()\\n #in fact we can sleep any time here\\n\\n if (done % 1000 == 0):\\n mesg = \\\"get cit.inf done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n if recid in INTBITSET_OF_DELETED_RECORDS:\\n # do not treat this record since it was deleted; we\\n # skip it like this in case it was only soft-deleted\\n # e.g. via bibedit (i.e. when collection tag 980 is\\n # DELETED but other tags like report number or journal\\n # publication info remained the same, so the calls to\\n # get_fieldvalues() below would return old values)\\n continue\\n\\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n\\n l_report_numbers = pri_report_numbers\\n l_report_numbers.extend(add_report_numbers)\\n d_reports_numbers[recid] = l_report_numbers\\n\\n if reference_report_numbers:\\n write_message(str(recid)+\\\"'s report number values \\\"+str(l_report_numbers), verbose=9)\\n d_references_report_numbers[recid] = reference_report_numbers\\n\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n if references_s:\\n write_message(str(recid)+\\\"'s \\\"+str(p_reference_tag)+\\\" values \\\"+str(references_s), verbose=9)\\n d_references_s[recid] = references_s\\n\\n #get a combination of\\n #journal vol (year) pages\\n if publication_pages_tag and publication_journal_tag and \\\\\\n publication_volume_tag and publication_year_tag and publication_format_string:\\n tagsvalues = {} #we store the tags and their values here\\n #like c->444 y->1999 p->\\\"journal of foo\\\",v->20\\n tagsvalues[\\\"p\\\"] = \\\"\\\"\\n tagsvalues[\\\"y\\\"] = \\\"\\\"\\n tagsvalues[\\\"c\\\"] = \\\"\\\"\\n tagsvalues[\\\"v\\\"] = \\\"\\\"\\n tmp = get_fieldvalues(recid, publication_journal_tag)\\n if tmp:\\n tagsvalues[\\\"p\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_volume_tag)\\n if tmp:\\n tagsvalues[\\\"v\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_year_tag)\\n if tmp:\\n tagsvalues[\\\"y\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_pages_tag)\\n if tmp:\\n #if the page numbers have \\\"x-y\\\" take just x\\n pages = tmp[0]\\n hpos = pages.find(\\\"-\\\")\\n if hpos > 0:\\n pages = pages[:hpos]\\n tagsvalues[\\\"c\\\"] = pages\\n #format the publ infostring according to the format\\n publ = \\\"\\\"\\n ok = 1\\n for i in range (0, len(publication_format_string)):\\n current = publication_format_string[i]\\n #these are supported\\n if current == \\\"p\\\" or current == \\\"c\\\" or current == \\\"v\\\" \\\\\\n or current == \\\"y\\\":\\n if tagsvalues[current]:\\n #add the value in the string\\n publ += tagsvalues[current]\\n else:\\n ok = 0\\n break #it was needed and not found\\n else:\\n publ += current #just add the character in the format string\\n if ok:\\n write_message(\\\"d_records_s (publication info) for \\\"+str(recid)+\\\" is \\\"+publ, verbose=9)\\n d_records_s[recid] = publ\\n else:\\n mesg = \\\"Warning: there are no records with tag values for \\\"\\n mesg += p_reference_number_tag+\\\" or \\\"+p_reference_tag+\\\". Nothing to do.\\\"\\n write_message(mesg)\\n\\n mesg = \\\"get cit.inf done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n citation_informations.append(d_reports_numbers)\\n citation_informations.append(d_references_report_numbers)\\n citation_informations.append(d_references_s)\\n citation_informations.append(d_records_s)\\n end_time = os.times()[4]\\n write_message(\\\"Execution time for generating citation info from record: %.2f sec\\\" % \\\\\\n (end_time - begin_time))\\n return citation_informations\",\n \"def get_search_index(self):\\n with self._conn.begin():\\n return {\\n rec.package: (rec.downloads_recent, rec.downloads_all)\\n for rec in self._conn.execute(\\n \\\"SELECT package, downloads_recent, downloads_all \\\"\\n \\\"FROM get_search_index()\\\")\\n }\",\n \"def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\\n dict, dict, dict, dict,\\n dict, dict]:\\n name_table = create_name_table(citelist)\\n unique_names = list()\\n nameset = set()\\n # total_binomial_year_cnts = {}\\n total_binomial_year_cnts = collections.Counter()\\n for c in citelist:\\n if c.name != \\\".\\\":\\n clean = clean_name(c.name)\\n if not clean.lower() in nameset:\\n nameset |= {clean.lower()}\\n unique_names.append(clean)\\n y = refdict[c.cite_key].year()\\n if y is not None:\\n total_binomial_year_cnts.update([y])\\n # if y in total_binomial_year_cnts:\\n # total_binomial_year_cnts[y] += 1\\n # else:\\n # total_binomial_year_cnts[y] = 1\\n unique_names.sort(key=lambda s: s.lower())\\n\\n # identify genera used per paper\\n genera_per_paper = {}\\n for c in citelist:\\n if c.name != \\\".\\\":\\n if c.cite_key not in genera_per_paper:\\n genera_per_paper[c.cite_key] = set()\\n genera_set = genera_per_paper[c.cite_key]\\n genera_set |= {extract_genus(clean_name(c.name))}\\n genus_cnts = {}\\n for c in genera_per_paper:\\n y = refdict[c].year()\\n if y is not None:\\n if init_data().start_year <= y <= init_data().current_year:\\n genera_set = genera_per_paper[c]\\n for genus in genera_set:\\n genus = clean_genus(genus)\\n if genus != \\\"\\\":\\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\\n init_data().current_year + 1)})[y] += 1\\n # if genus not in genus_cnts:\\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\\n # init_data().current_year + 1)}\\n # gcnts = genus_cnts[genus]\\n # gcnts[y] += 1\\n\\n binomial_usage_cnts_by_year = {}\\n binomial_location_applications = {}\\n binomial_usage_cnts = {}\\n for name in unique_names:\\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\\n if tmptotal > 0:\\n binomial_usage_cnts[name] = tmptotal\\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\\n\\n # specific_year_cnts = {}\\n specific_year_cnts = collections.Counter()\\n specific_usage_cnts_by_year = {}\\n specific_location_applications = {}\\n specific_usage_cnts = {}\\n for name in specific_names:\\n (specific_usage_cnts_by_year[name.name],\\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\\n if tmptotal > 0:\\n specific_usage_cnts[name.name] = tmptotal\\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\\n binomial_location_applications)\\n tmpkey = name.priority_source\\n if tmpkey != \\\".\\\":\\n y = refdict[tmpkey].year()\\n if y is not None:\\n specific_year_cnts.update([y])\\n # if y in specific_year_cnts:\\n # specific_year_cnts[y] += 1\\n # else:\\n # specific_year_cnts[y] = 1\\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\\n binomial_usage_cnts, specific_usage_cnts)\",\n \"def createIndex(pages): \\n index = defaultdict(list)\\n for url, content, links in pages:\\n counts = getNumberTerms(content)\\n for term, count in counts.items():\\n index[term].append((url, count))\\n return index\",\n \"def build_query_dict(self, term_list, issn_list, year_list, jlist):\\n journal_frame = self.make_jlist(jlist)\\n\\n search_terms = self.build_search_terms(term_list)\\n dict1 = {}\\n #This loop goes through and sets up a dictionary key with an ISSN number\\n\\n for issn in issn_list:\\n\\n issn_terms = ' AND ISSN(' + issn + ')'\\n dict2 = {}\\n #This loop goes and attaches all the years to the outer loop's key.\\n for year in year_list:\\n\\n year_terms = \\\"AND PUBYEAR IS \\\" + str(year)\\n querystring = search_terms + year_terms + issn_terms\\n\\n dict2[year] = querystring\\n\\n dict1[issn] = dict2\\n\\n return dict1\",\n \"def indexes(self):\\n return {'status': self._status_sort, 'rms': self._rms_sort}\",\n \"def fill(self):\\n if self.source == 'citations':\\n url = self._scholarly.URLS(\\\"CITATIONPUB\\\").format(self.id_citations)\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\\n\\n if soup.find('a', class_='gsc_vcd_title_link'):\\n self.bib['url'] = soup.find(\\n 'a', class_='gsc_vcd_title_link')['href']\\n\\n for item in soup.find_all('div', class_='gs_scl'):\\n key = item.find(class_='gsc_vcd_field').text\\n val = item.find(class_='gsc_vcd_value')\\n if key == 'Authors':\\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\\n elif key == 'Journal':\\n self.bib['journal'] = val.text\\n elif key == 'Volume':\\n self.bib['volume'] = val.text\\n elif key == 'Issue':\\n self.bib['number'] = val.text\\n elif key == 'Pages':\\n self.bib['pages'] = val.text\\n elif key == 'Publisher':\\n self.bib['publisher'] = val.text\\n elif key == 'Publication date':\\n self.bib['year'] = arrow.get(val.text).year\\n elif key == 'Description':\\n if val.text[0:8].lower() == 'abstract':\\n val = val.text[9:].strip()\\n self.bib['abstract'] = val\\n elif key == 'Total citations':\\n self.id_scholarcitedby = re.findall(\\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\\n\\n # number of citation per year\\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\\n self.cites_per_year = dict(zip(years, cites))\\n\\n if soup.find('div', class_='gsc_vcd_title_ggi'):\\n self.bib['eprint'] = soup.find(\\n 'div', class_='gsc_vcd_title_ggi').a['href']\\n self._filled = True\\n\\n elif self.source == 'scholar':\\n self.bib['add_to_lib'] = self.url_add_sclib\\n\\n try:\\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\\n bibtex = bibtex.find('pre').string\\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\\n self.bib['author_count'] = str(\\n len(self.bib['author'].split('and')))\\n self.bib['age'] = str(\\n int(date.today().year) - int(self.bib['year']))\\n except:\\n # did not find year\\n pass\\n\\n self._filled = True\\n return self\",\n \"def __generate_dict_of_keys_to_classification__(self):\\n dict_of_assigned_citations = {}\\n # duplicating citation dataset to filter as matches go on meaning\\n # it should result in quicker allocation\\n # can be removed to reduce memory load at expense of speed\\n list_of_unassigned = []\\n for key in self.dict_of_keywords:\\n list_of_current_key = []\\n for citation_instance in self.array_of_citations:\\n if key == citation_instance.get_classification():\\n list_of_current_key.append(citation_instance)\\n if \\\"Unassigned\\\" == citation_instance.get_classification():\\n list_of_unassigned.append(citation_instance)\\n dict_of_assigned_citations[key] = list_of_current_key\\n dict_of_assigned_citations[\\\"Unassigned\\\"] = list_of_unassigned\\n return dict_of_assigned_citations\",\n \"def return_dict_of_assigned_citations_classifications(self):\\n for citation_instance in self.array_of_citations:\\n citation_string = \\\"\\\"\\n citation_string = citation_string.join(\\n citation_instance.get_author()) + \\\\\\n citation_instance.get_title()\\\\\\n + citation_instance.get_journal()\\n max_keyword_matches = 0\\n for key in self.dict_of_keywords:\\n current_key_matches = 0\\n for keyword in self.dict_of_keywords.get(key):\\n pattern = re.compile(keyword)\\n if pattern.search(citation_string):\\n current_key_matches += 1\\n if current_key_matches > max_keyword_matches:\\n max_keyword_matches = current_key_matches\\n citation_instance.set_classification(key)\\n return self.__generate_dict_of_keys_to_classification__()\",\n \"def index():\\n rows=db().select(db.titles.ALL,orderby=db.titles.title_name)\\n \\n #print type(value)\\n \\n coloms=db().select(db.titles.ALL,orderby=db.titles.title_name)\\n #print coloms\\n return dict(rows=rows,coloms=coloms)\",\n \"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\\n\\n #sorry bout repeated code to get the tags\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n tagvals = {}\\n for t in tags:\\n try:\\n x = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n tagvals[t] = x\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_author_dict\\n\\n #parse the tags\\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"mainauthortag \\\"+mainauthortag)\\n write_message(\\\"coauthortag \\\"+coauthortag)\\n write_message(\\\"extauthortag \\\"+extauthortag)\\n\\n author_cited_in = initial_author_dict\\n if citedbydict:\\n i = 0 #just a counter for debug\\n write_message(\\\"Checking records referred to in new records\\\")\\n for u in updated_redic_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Author ref done \\\"+str(i)+\\\" of \\\"+str(len(updated_redic_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n if citedbydict.has_key(u):\\n these_cite_k = citedbydict[u]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n authors = get_fieldvalues(u, mainauthortag)\\n coauthl = get_fieldvalues(u, coauthortag)\\n extauthl = get_fieldvalues(u, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author ref done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #go through the dictionary again: all keys but search only if new records are cited\\n write_message(\\\"Checking authors in new records\\\")\\n i = 0\\n for k in citedbydict.keys():\\n if (i % 1000 == 0):\\n mesg = \\\"Author cit done \\\"+str(i)+\\\" of \\\"+str(len(citedbydict.keys()))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n these_cite_k = citedbydict[k]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n #do things only if these_cite_k contains any new stuff\\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\\n if intersec_list:\\n authors = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author cit done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return author_cited_in\",\n \"def generate_dict(self):\\n dict = defaultdict(list)\\n for i in range(self.no_of_docs-1):\\n doc_txt = self.doc_to_df(i)\\n #assign key to index in dictionary and its locations as tuples(docid,line,wordpos) as the values\\n for j in range(len(doc_txt)):\\n for k in range(doc_txt.shape[1]):\\n key = doc_txt[k][j]\\n dict[key].append((i,j,k))\",\n \"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\\n i = 0 #just for debugging ..\\n #get the tags for main author, coauthors, ext authors from config\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n for t in tags:\\n try:\\n dummy = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_selfcitdict\\n\\n r_mainauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"first_author\\\")\\n r_coauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"additional_author\\\")\\n r_extauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"alternative_author_name\\\")\\n #parse the tags\\n mainauthortag = tagify(parse_tag(r_mainauthortag))\\n coauthortag = tagify(parse_tag(r_coauthortag))\\n extauthortag = tagify(parse_tag(r_extauthortag))\\n\\n selfcites = initial_selfcitdict\\n for k in new_record_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Selfcites done \\\"+str(i)+\\\" of \\\"+str(len(new_record_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i+1\\n #get the author of k\\n authorlist = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authorlist.append(coauthl)\\n authorlist.append(extauthl)\\n #author tag\\n #print \\\"record \\\"+str(k)+\\\" by \\\"+str(authorlist)\\n #print \\\"is cited by\\\"\\n #get the \\\"x-cites-this\\\" list\\n if citationdic.has_key(k):\\n xct = citationdic[k]\\n for c in xct:\\n #get authors of c\\n cauthorlist = get_fieldvalues(c, mainauthortag)\\n coauthl = get_fieldvalues(c, coauthortag)\\n extauthl = get_fieldvalues(c, extauthortag)\\n cauthorlist.extend(coauthl)\\n cauthorlist.extend(extauthl)\\n #print str(c)+\\\" by \\\"+str(cauthorlist)\\n for ca in cauthorlist:\\n if (ca in authorlist):\\n #found!\\n if selfcites.has_key(k):\\n val = selfcites[k]\\n #add only if not there already\\n if val:\\n if not c in val:\\n val.append(c)\\n selfcites[k] = val\\n else:\\n #new key for selfcites\\n selfcites[k] = [c]\\n\\n mesg = \\\"Selfcites done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return selfcites\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def ref_analyzer(citation_informations, initialresult, initial_citationlist,\\n initial_referencelist,config, updated_rec_list ):\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attr function\\\", alert_admin=True)\\n return {}\\n\\n pubrefntag = \\\"\\\"\\n try:\\n pubrefntag = config.get(function, \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_report_number\\\", alert_admin=True)\\n return {}\\n\\n pubreftag = \\\"\\\"\\n try:\\n pubreftag = config.get(function, \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_pubinfo\\\", alert_admin=True)\\n return {}\\n\\n #pubrefntag is often 999C5r, pubreftag 999C5s\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"pubrefntag \\\"+pubrefntag)\\n write_message(\\\"pubreftag \\\"+pubreftag)\\n\\n citation_list = initial_citationlist\\n reference_list = initial_referencelist\\n result = initialresult\\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\\n d_references_s = citation_informations[2]\\n #dict of recid -> publication_infos_in_its_bibliography\\n d_records_s = citation_informations[3] #recid -> its publication inf\\n t1 = os.times()[4]\\n\\n write_message(\\\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\\\")\\n for somerecid in updated_rec_list:\\n try:\\n del citation_list[somerecid]\\n except KeyError:\\n pass\\n try:\\n del reference_list[somerecid]\\n except KeyError:\\n pass\\n\\n write_message(\\\"Phase 1: d_references_report_numbers\\\")\\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\\n #meaning: rec 8 contains these in bibliography\\n\\n done = 0\\n numrecs = len(d_references_report_numbers)\\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n done = done+1\\n\\n for refnumber in refnumbers:\\n if refnumber:\\n p = refnumber\\n f = 'reportnumber'\\n #sanitise p\\n p.replace(\\\"\\\\n\\\",'')\\n #search for \\\"hep-th/5644654 or such\\\" in existing records\\n rec_ids = get_recids_matching_query(p, f)\\n\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops: record '%d' report number value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_citer_cited(thisrecid, rec_ids[0])\\n remove_from_missing(p)\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n # Citation list should have rec_ids[0] but check anyway\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n #append unless this key already has the item\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid)\\n #and update result\\n result[rec_ids[0]] += 1\\n\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n else:\\n #the reference we wanted was not found among our records.\\n #put the reference in the \\\"missing\\\".. however, it will look\\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\\n #This should really be done in the next loop d_references_s\\n #but the 999C5s fields are not yet normalized\\n\\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\\n rectext = \\\"\\\" # print_record() call disabled to speed things up\\n lines = rectext.split(\\\"\\\\n\\\")\\n rpart = p #to be used..\\n for l in lines:\\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\\n st = l.find('$s')\\n if (st > 0):\\n end = l.find('$', st)\\n if (end == st):\\n end = len(l)\\n rpart = l[st+2:end]\\n insert_into_missing(thisrecid, rpart)\\n\\n mesg = \\\"d_references_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t2 = os.times()[4]\\n\\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\\n write_message(\\\"Phase 2: d_references_s\\\")\\n done = 0\\n numrecs = len(d_references_s)\\n for thisrecid, refss in d_references_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n\\n done = done+1\\n\\n for refs in refss:\\n if refs:\\n p = refs\\n #remove the latter page number if it is like 67-74\\n matches = re.compile(\\\"(.*)(-\\\\d+$)\\\").findall(p)\\n if matches and matches[0]:\\n p = matches[0][0]\\n\\n # check reference value to see whether it is well formed:\\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"is not well formed; skipping it.\\\" % (thisrecid, p)\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n continue # skip this ill-formed value\\n\\n # look for reference value:\\n rec_id = None\\n try:\\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\\n except:\\n rec_ids = None\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_message(\\\"These match searching \\\"+p+\\\" in journal: \\\"+repr(rec_ids), verbose=9)\\n #the refered publication is in our collection, remove\\n #from missing\\n remove_from_missing(p)\\n else:\\n #it was not found so add in missing\\n insert_into_missing(thisrecid, p)\\n #check citation and reference for this..\\n if rec_ids and rec_ids[0]:\\n #the above should always hold\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\\n result[rec_ids[0]] += 1 #add count for this..\\n\\n #update reference_list accordingly\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n mesg = \\\"d_references_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t3 = os.times()[4]\\n done = 0\\n numrecs = len(d_reports_numbers)\\n write_message(\\\"Phase 3: d_reports_numbers\\\")\\n\\n #search for stuff like CERN-TH-4859/87 in list of refs\\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n for reportcode in reportcodes:\\n if reportcode:\\n rec_ids = []\\n try:\\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\\n except:\\n rec_ids = []\\n\\n if rec_ids:\\n for recid in rec_ids:\\n #normal checks..\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(recid):\\n reference_list[recid] = []\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n\\n #normal updates\\n if not recid in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(recid)\\n if not thisrecid in reference_list[recid]:\\n reference_list[recid].append(thisrecid)\\n\\n mesg = \\\"d_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #find this record's pubinfo in other records' bibliography\\n write_message(\\\"Phase 4: d_records_s\\\")\\n done = 0\\n numrecs = len(d_records_s)\\n t4 = os.times()[4]\\n for thisrecid, recs in d_records_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_records_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n p = recs.replace(\\\"\\\\\\\"\\\",\\\"\\\")\\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\\n write_message(\\\"These records match \\\"+p+\\\" in \\\"+pubreftag+\\\" : \\\"+str(rec_ids), verbose=9)\\n if rec_ids:\\n for rec_id in rec_ids:\\n #normal checks\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(rec_id):\\n reference_list[rec_id] = []\\n\\n if not rec_id in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(rec_id)\\n if not thisrecid in reference_list[rec_id]:\\n reference_list[rec_id].append(thisrecid)\\n\\n mesg = \\\"d_records_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n write_message(\\\"Phase 5: reverse lists\\\")\\n\\n #remove empty lists in citation and reference\\n keys = citation_list.keys()\\n for k in keys:\\n if not citation_list[k]:\\n del citation_list[k]\\n\\n keys = reference_list.keys()\\n for k in keys:\\n if not reference_list[k]:\\n del reference_list[k]\\n\\n write_message(\\\"Phase 6: self-citations\\\")\\n selfdic = {}\\n #get the initial self citation dict\\n initial_self_dict = get_cit_dict(\\\"selfcitdict\\\")\\n selfdic = initial_self_dict\\n #add new records to selfdic\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n write_message(\\\"Self cite processing disabled. Use -A option to enable it.\\\")\\n else:\\n write_message(\\\"self cite and author citations enabled\\\")\\n selfdic = get_self_citations(updated_rec_list, citation_list,\\n initial_self_dict, config)\\n #selfdic consists of\\n #key k -> list of values [v1,v2,..]\\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\\n\\n #create a reverse \\\"x cited by y\\\" self cit dict\\n selfcitedbydic = {}\\n for k in selfdic.keys():\\n vlist = selfdic[k]\\n for v in vlist:\\n if selfcitedbydic.has_key(v):\\n tmplist = selfcitedbydic[v]\\n if not k in tmplist:\\n tmplist.append(k)\\n else:\\n tmplist = [k]\\n selfcitedbydic[v] = tmplist\\n\\n write_message(\\\"Getting author citations\\\")\\n\\n #get author citations for records in updated_rec_list\\n initial_author_dict = get_initial_author_dict()\\n authorcitdic = initial_author_dict\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n print \\\"Author cites disabled. Use -A option to enable it.\\\"\\n else:\\n write_message(\\\"author citations enabled\\\")\\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\\n initial_author_dict, config)\\n\\n\\n if task_get_task_param('verbose') >= 3:\\n #print only X first to prevent flood\\n tmpdict = {}\\n tmp = citation_list.keys()[0:10]\\n for t in tmp:\\n tmpdict[t] = citation_list[t]\\n write_message(\\\"citation_list (x is cited by y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(citation_list.keys())))\\n tmp = reference_list.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = reference_list[t]\\n write_message(\\\"reference_list (x cites y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(reference_list.keys())))\\n tmp = selfcitedbydic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfcitedbydic[t]\\n mesg = \\\"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\\\"\\n mesg += str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfcitedbydic.keys())))\\n tmp = selfdic.keys()[0:100]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfdic[t]\\n mesg = \\\"selfdic (x cites y and one of the authors of x same as y's): \\\"+str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfdic.keys())))\\n tmp = authorcitdic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = authorcitdic[t]\\n write_message(\\\"authorcitdic (author is cited in recs): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(authorcitdic.keys())))\\n insert_cit_ref_list_intodb(citation_list, reference_list,\\n selfcitedbydic, selfdic, authorcitdic)\\n\\n t5 = os.times()[4]\\n\\n write_message(\\\"Execution time for analyzing the citation information generating the dictionary:\\\")\\n write_message(\\\"... checking ref number: %.2f sec\\\" % (t2-t1))\\n write_message(\\\"... checking ref ypvt: %.2f sec\\\" % (t3-t2))\\n write_message(\\\"... checking rec number: %.2f sec\\\" % (t4-t3))\\n write_message(\\\"... checking rec ypvt: %.2f sec\\\" % (t5-t4))\\n write_message(\\\"... total time of ref_analyze: %.2f sec\\\" % (t5-t1))\\n\\n return result\",\n \"def build_index(self):\\r\\n date_time('Building indexes in citations table')\\r\\n self.cursor.execute('DROP INDEX IF EXISTS IDX_citations ;')\\r\\n self.cursor.execute('CREATE INDEX IDX_citations ON citations (citation);')\\r\\n self.conn.commit()\\r\\n gc.collect()\",\n \"def get_book_data(isbn: int):\\n try:\\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\\n except StopIteration:\\n pass # actually, I could put the whole rest of the function here\\n else:\\n data = core.Book.view_str(book.id)\\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\\n del data['borrowed_by_id'], data['__str__']\\n return data\\n\\n try:\\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\\n r.raise_for_status()\\n except requests.exceptions.RequestException:\\n raise core.BuchSchlossError('no_connection', 'no_connection')\\n\\n person_re = re.compile(r'(\\\\w*, \\\\w*) \\\\((\\\\w*)\\\\)')\\n results = {'concerned_people': []}\\n\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n if table is None:\\n # see if we got multiple results\\n link_to_first = page.select_one('#recordLink_0')\\n if link_to_first is None:\\n raise core.BuchSchlossError(\\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n\\n for tr in table.select('tr'):\\n td = [x.get_text('\\\\n').strip() for x in tr.select('td')]\\n if len(td) == 2:\\n if td[0] == 'Titel':\\n results['title'] = td[1].split('/')[0].strip()\\n elif td[0] == 'Person(en)':\\n for p in td[1].split('\\\\n'):\\n g = person_re.search(p)\\n if g is None:\\n continue\\n g = g.groups()\\n if g[1] == 'Verfasser':\\n results['author'] = g[0]\\n else:\\n results['concerned_people'].append(g[1]+': '+g[0])\\n elif td[0] == 'Verlag':\\n results['publisher'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Zeitliche Einordnung':\\n results['year'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Sprache(n)':\\n results['language'] = td[1].split(',')[0].split()[0].strip()\\n\\n results['concerned_people'] = '; '.join(results['concerned_people'])\\n return results\",\n \"def index(self):\\n path = self.path.format('index')\\n \\n with open(path, 'r', newline='') as file:\\n l = list(csv.reader(file))\\n \\n index = [v for _ in l for v in _]\\n index = dict((v, i) for (i, v) in enumerate(index))\\n \\n return index\",\n \"def index():\\n\\n return dict()\",\n \"def relevant_docs_from_posting(self, query):\\n relevant_docs = {}\\n # postingLists = [self.FindPostingByTerm(term) for term in query] #list of posting file -->[idx,tweet id,tfi]\\n for term in query:\\n post = self.FindPostingByTerm_Binary(term)\\n for p in post:\\n tweet_id = p[1]\\n if tweet_id not in relevant_docs.keys():\\n relevant_docs[tweet_id] = {}\\n relevant_docs[tweet_id][term] = p[2] * self.inverted_index[term][1] # wiq\\n return relevant_docs\",\n \"def index(self):\\n return dict(data='index')\",\n \"def __citationsFromFile(file):\\n\\n # Tokens for parsing\\n titleToken = '#*'\\n indexToken = '#index'\\n citationToken = '#%'\\n\\n # Predicates for error checking\\n noneNone = lambda *items: all([item is not None for item in items])\\n allNone = lambda *items: all([item is None for item in items])\\n\\n # Next entry data\\n title = None\\n index = None\\n citations = []\\n\\n # Basic stats\\n withCitations = 0\\n withoutCitations = 0\\n totalPapers = 0\\n\\n for line in file:\\n line = line.strip()\\n\\n # Parse entry, enforcing that data appears in title -> index -> citations order\\n if line.startswith(titleToken):\\n assert allNone(title, index) and len(citations) == 0\\n title = line[len(titleToken):].strip('.')\\n\\n elif line.startswith(indexToken):\\n assert noneNone(title) and allNone(index) and len(citations) == 0\\n index = int(line[len(indexToken):])\\n\\n elif line.startswith(citationToken):\\n assert noneNone(title, index)\\n newCitationId = int(line[len(citationToken):])\\n assert newCitationId >= 0\\n citations.append(newCitationId)\\n\\n elif len(line) == 0:\\n totalPapers += 1\\n\\n # Yield this entry if it has any citations,\\n if noneNone(title, index):\\n if len(citations) > 0:\\n withCitations += 1\\n yield title, index, citations\\n else:\\n withoutCitations += 1\\n\\n title = None\\n index = None\\n citations = []\\n\\n # Output some basic statistics about papers with/without citations\\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\\n print \\\"\\\\n\\\\nTotal Papers: %d\\\" % totalPapers\\n print \\\" With References: %d (%2.2f%%)\\\\n Without References: %d (%2.2f%%)\\\" % (\\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\\n )\",\n \"def results(self):\\n out = []\\n fields = 'eid doi pii pubmed_id title subtype creator afid affilname '\\\\\\n 'affiliation_city affiliation_country author_count '\\\\\\n 'author_names author_ids author_afids coverDate '\\\\\\n 'coverDisplayDate publicationName issn source_id eIssn '\\\\\\n 'aggregationType volume issueIdentifier article_number '\\\\\\n 'pageRange description authkeywords citedby_count '\\\\\\n 'openaccess fund_acr fund_no fund_sponsor'\\n doc = namedtuple('Document', fields)\\n for item in self._json:\\n info = {}\\n # Parse affiliations\\n try:\\n info[\\\"affilname\\\"] = _join(item['affiliation'], 'affilname')\\n info[\\\"afid\\\"] = _join(item['affiliation'], 'afid')\\n info[\\\"aff_city\\\"] = _join(item['affiliation'], 'affiliation-city')\\n info[\\\"aff_country\\\"] = _join(item['affiliation'],\\n 'affiliation-country')\\n except KeyError:\\n pass\\n # Parse authors\\n try:\\n # Deduplicate list of authors\\n authors = _deduplicate(item['author'])\\n # Extract information\\n surnames = _replace_none([d['surname'] for d in authors])\\n firstnames = _replace_none([d['given-name'] for d in authors])\\n info[\\\"auth_names\\\"] = \\\";\\\".join([\\\", \\\".join([t[0], t[1]]) for t in\\n zip(surnames, firstnames)])\\n info[\\\"auth_ids\\\"] = \\\";\\\".join([d['authid'] for d in authors])\\n affs = []\\n for auth in authors:\\n aff = listify(_deduplicate(auth.get('afid', [])))\\n affs.append('-'.join([d['$'] for d in aff]))\\n info[\\\"auth_afid\\\"] = (';'.join(affs) or None)\\n except KeyError:\\n pass\\n date = item.get('prism:coverDate')\\n if isinstance(date, list):\\n date = date[0].get('$')\\n new = doc(article_number=item.get('article-number'),\\n title=item.get('dc:title'), fund_sponsor=item.get('fund-sponsor'),\\n subtype=item.get('subtype'), issn=item.get('prism:issn'),\\n creator=item.get('dc:creator'), affilname=info.get(\\\"affilname\\\"),\\n author_names=info.get(\\\"auth_names\\\"), doi=item.get('prism:doi'),\\n coverDate=date, volume=item.get('prism:volume'),\\n coverDisplayDate=item.get('prism:coverDisplayDate'),\\n publicationName=item.get('prism:publicationName'),\\n source_id=item.get('source-id'), author_ids=info.get(\\\"auth_ids\\\"),\\n aggregationType=item.get('prism:aggregationType'),\\n issueIdentifier=item.get('prism:issueIdentifier'),\\n pageRange=item.get('prism:pageRange'),\\n author_afids=info.get(\\\"auth_afid\\\"), fund_no=item.get('fund-no'),\\n affiliation_country=info.get(\\\"aff_country\\\"),\\n citedby_count=item.get('citedby-count'),\\n openaccess=item.get('openaccess'), eIssn=item.get('prism:eIssn'),\\n author_count=item.get('author-count', {}).get('$'),\\n affiliation_city=info.get(\\\"aff_city\\\"), afid=info.get(\\\"afid\\\"),\\n description=item.get('dc:description'), pii=item.get('pii'),\\n authkeywords=item.get('authkeywords'), eid=item.get('eid'),\\n fund_acr=item.get('fund-acr'), pubmed_id=item.get('pubmed-id'))\\n out.append(new)\\n return out or None\",\n \"def h_index(citations):\\n citations.sort(reverse=True)\\n for i, citation in enumerate(citations):\\n if i <= citation:\\n return i\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def get_books(mapping, cidlist):\\n havecache = _c.cacheisactive(_c.CACHE)\\n\\n # Don't want to keep hammering their servers, so check if available\\n if havecache:\\n cache, nocache = _c.check(_c.CACHE, mapping, [parse_cid(cid) for cid in cidlist])\\n\\n BASE = \\\"http://cmu.verbacompare.com/comparison?id={}\\\"\\n \\n # If cache is available, still need to check for uncached stuff\\n if havecache:\\n sections = [cmu_to_verba(mapping, cid) for cid in nocache]\\n else: \\n sections = [cmu_to_verba(mapping, cid) for cid in cidlist] \\n sections = [s for s in sections if s != False]\\n \\n verba_info = [cmu_to_verba(mapping, cid) for cid in cidlist] \\n verba_info = [s for s in verba_info if s != False]\\n \\n if verba_info:\\n verba_ids = [section['id'] for section in reduce(list.__add__, verba_info)]\\n URL = BASE.format(\\\",\\\".join(verba_ids))\\n \\n if sections: \\n print \\\"Downloading from {}\\\".format(URL)\\n # Download and parse if needed \\n parser = BeautifulSoup(requests.get(URL).content)\\n raw_data = [el.getText() for el in parser.findAll(\\\"script\\\")\\n if 'Verba.Compare' in el.getText()][0] \\n \\n # Parse the extracted JS into an AST to extract the correct variable\\n tree = Parser().parse(raw_data)\\n objects = ObjVisitor()\\n # Oh god why\\n objects.visit(tree)\\n\\n # Finally\\n data = [json.loads(d) for d in [i for i in objects.vardump if \\\"isbn\\\" in i]]\\n\\n # Bring in the cached data if it exists, otherwise just initialize empty result\\n if havecache and cache:\\n print \\\"Grabbing cache...\\\"\\n _courses = [_c.retrieve(_c.CACHE, cid, cmu_to_verba(mapping, cid)) for cid in cache]\\n _courses = reduce(list.__add__, _courses)\\n summary = {\\n 'url': URL,\\n 'courses': _courses\\n }\\n else: \\n summary = {\\n 'url': URL,\\n 'courses': []\\n }\\n \\n # If we had to grab anything, now put it into the result\\n if sections:\\n for course in data:\\n if course.get('title'):\\n info = {\\n 'name': course['title'],\\n 'instructor': course.get('instructor'),\\n 'sections': course['name'],\\n 'books': []\\n }\\n if 'books' in course:\\n for book in course['books']:\\n bookinfo = {\\n 'title': book['title'],\\n 'author': book['author'],\\n 'isbn': book['isbn'],\\n 'citation': book['citation'],\\n 'required': book['required'].lower() == 'required',\\n }\\n info['books'].append(bookinfo) \\n \\n summary['courses'].append(info)\\n \\n if havecache:\\n # Store in cache for future use\\n _c.store(_c.CACHE, info)\\n \\n return summary\",\n \"def dict_of_recs_for_run (ins, exp, runnum) :\\n return calibration_runs(ins, exp)[runnum]\",\n \"def index_records(vr):\\n return collections.OrderedDict((record2key(rec), clean_sample_index(rec))\\n for rec in vr)\",\n \"def get_data(self):\\n with LoggingTime(\\\"Fetching data took: \\\"):\\n try:\\n with open(self.__io_params.input_file_path, 'r', encoding=\\\"utf8\\\") as csvFiles:\\n reader = csv.reader(csvFiles, delimiter=',', quotechar='|')\\n dictionary = {}\\n for data in reader:\\n if len(data) > 0 and data[0]: # ignore malformed data lines\\n word_base = data[0].strip().lower()\\n if self.word_is_eligible(word_base) and word_base not in dictionary:\\n dictionary[word_base] = ProcessingEntry(word_base)\\n logging.info(\\\"Loaded %i search terms\\\" % (len(dictionary)))\\n return dictionary\\n except IOError as e:\\n logging.error(\\\"IO Error has occurred. Check input file\\\")\\n logging.error(e)\\n raise\\n except:\\n logging.error(\\\"Reading data failed. Check input file format\\\")\\n raise\",\n \"def get_publications_by_author(cached_list, cached_set, author_name):\\n publications = { 'dblp': [], 'cdblp': [] }\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n\\n if author['dblp'].__contains__('publications'):\\n publications['dblp'] = author['dblp']['publications']\\n# for pub in author['dblp']['publications']:\\n# print(pub)\\n\\n if author['cdblp'].__contains__('publications'):\\n publications['cdblp'] = author['cdblp']['publications']\\n# for pub in author['cdblp']['publications']:\\n# print(pub)\\n return publications\",\n \"def solr_dict(self):\\n raise NotImplementedError\",\n \"def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\\n dictionary_key_two):\\n list_of_one_citations = []\\n list_of_two_citations = []\\n list_of_unassigned_citations = []\\n for citation in self.array_of_citations:\\n citation_string = self.__generate_author_string__(\\n citation.get_author()) + \\\" \\\" + citation.get_title() \\\\\\n + \\\" \\\" + citation.get_journal()\\n one_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_one)\\n two_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_two)\\n if one_key_matches > two_key_matches:\\n citation.set_classification(dictionary_key_one)\\n list_of_one_citations.append(citation)\\n elif two_key_matches > one_key_matches:\\n citation.set_classification(dictionary_key_two)\\n list_of_two_citations.append(citation)\\n else:\\n citation.set_classification(\\\"Unassigned\\\")\\n list_of_unassigned_citations.append(citation)\\n\\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\\n list_of_two_citations, \\\"Unassigned\\\":\\n list_of_unassigned_citations}\",\n \"def gen_search_index(record, link_content=None):\\n document_id = record[\\\"document_id\\\"]\\n record_index = {\\n \\\"document_name\\\": record[\\\"document_name\\\"],\\n \\\"document_type\\\": record[\\\"document_type\\\"].__name__,\\n \\\"content\\\": record[\\\"content\\\"],\\n \\\"authors\\\": record[\\\"authors\\\"],\\n \\\"publish_date\\\": record[\\\"publish_date\\\"],\\n \\\"link_content\\\": link_content,\\n }\\n return (document_id, record_index)\",\n \"def tr_nc_dict(dfin):\\n\\n\\ttr_nc_index_dict = OrderedDict()\\n\\t\\n\\ttrCount = 0\\n\\tpreviousTrIndex = 0\\n\\n\\tcurrent_id=\\\"\\\"\\n\\tcurrent_tr=\\\"\\\"\\n\\n\\tfor i in range(len(dfin)):\\n# print dfin.loc[i]\\n\\n\\t\\tif dfin.loc[i,'feature'] == 'transcript':\\n\\t\\t\\ttrdict = parse_mod_entry(dfin.loc[i,'transcript_id'])\\n\\n\\t\\t\\tcurGeneID = trdict['gene_id'][0]\\n\\n\\t\\t\\tif trCount != 0:\\n\\t\\t\\t\\tnewTrIndex = i\\n\\t\\t\\t\\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\\n\\t\\t\\t\\tpreviousTrIndex = i\\n\\t\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\t\\ttrCount += 1\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnewTrIndex = 0\\n\\t\\t\\t\\ttrCount +=1\\n\\t\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\t\\t\\n\\t\\tif i == (len(dfin)-1):\\n\\t\\t\\tnewTrIndex = i+1\\n\\t\\t\\tcurrent_id = trdict['gene_id'][0]\\n\\t\\t\\ttr_nc_index_dict[current_id] = [previousTrIndex,newTrIndex]\\n\\t\\t\\t\\n\\treturn tr_nc_index_dict\",\n \"def get_Pre_Succ(I):\\n #Docs = I.docs\\n #Docs_id = Docs.keys()\\n Docs = I.getIndex().all_ids_\\n Docs_id = [ int(float(k)) for k in Docs] \\n N_pgs = len(Docs_id)\\n Index_P = { id:idx for idx,id in enumerate(Docs_id)}\\n Counter_Index_P = { idx:id for idx,id in enumerate(Docs_id)}\\n \\n print \\\"\\\\nBuilding Pi...\\\"\\n Succ = { Index_P[p]:(I.getLinksForDoc(p),len(I.getLinksForDoc(p))) for p in Docs_id }\\n P = {}\\n for e in Succ:\\n succ_e,l_e = Succ[e]\\n for s in succ_e: \\n if Index_P.get(s,\\\"Unknown_Doc_id\\\") not in P:\\n P[Index_P.get(s,\\\"Unknown_Doc_id\\\")] = set()\\n P[Index_P.get(s,\\\"Unknown_Doc_id\\\")].add(e) \\n \\n return P,Succ,Index_P,Counter_Index_P,N_pgs\",\n \"def __find_correlations(self, results):\\n\\n for result in results[:self.__result_limit]:\\n\\n # pub without venue\\n if len(result['ven']) == 0:\\n result['alternative'] = []\\n\\n with self.vix.searcher(weighting=Frequency) as vs:\\n vq_parse = QueryParser('key', self.vix.schema).parse(result['pub']['crossref'])\\n tresult = vs.search(vq_parse, limit=None, )\\n if len(tresult) != 0:\\n result['ven'] = {}\\n result['added'] = 1\\n for attr in tresult[0].items():\\n result['ven'][attr[0]] = attr[1]\\n\\n self.__output.append(result)\\n\\n # venue without pub or venue with a list of pubs\\n elif len(result['pub']) == 0 or (\\n isinstance(result['pub'], list) and len(result['pub']) > 1):\\n result['alternative'] = []\\n\\n with self.pix.searcher(weighting=Frequency) as ps:\\n pq_parse = QueryParser('crossref', self.pix.schema).parse(result['ven']['key'])\\n tresult = ps.search(pq_parse, limit=None, )\\n\\n if len(tresult):\\n plist = []\\n tmp = dict()\\n for el in tresult:\\n for attr in el.items():\\n if attr[0] == 'title' and attr[1] not in [x['title'] for x in result['pub']]:\\n plist.append(attr[1])\\n break\\n\\n result['alternative'] = plist\\n self.__output.append(result)\\n\\n # mixed case\\n elif len(self.__output) == 0 or not result['ven']['key'] in [x['key'] for x in self.__output]:\\n lis = [x for x in results if len(x['ven']) and x['ven']['key'] == result['ven']['key']]\\n tmp = {}\\n if len(lis) <= 1:\\n tmp = {'key': result['pub']['key'],\\n 'score': result['score'],\\n 'pub': [x['pub'] for x in lis],\\n 'ven': result['ven'],\\n 'alternative': list()}\\n else:\\n tmp = {'key': result['ven']['key'],\\n 'score': result['score'],\\n 'pub': [x['pub'] for x in lis],\\n 'ven': result['ven'],\\n 'alternative': list()}\\n plist = []\\n with self.pix.searcher() as ps:\\n pq_parse = QueryParser('crossref', self.pix.schema).parse(tmp['key'])\\n tresult = ps.search(pq_parse, limit=None, )\\n if len(tresult):\\n for el in tresult:\\n for attr in el.items():\\n if attr[0] == 'title' and attr[1] not in [x['title'] for x in tmp['pub']]:\\n plist.append(attr[1])\\n break\\n\\n tmp['alternative'] = plist\\n self.__output.append(tmp)\",\n \"def get_coauthors_dict(name_url_list, schl_name):\\n\\n\\tpaper_info_dict = {}\\n\\n\\tfor name_url in name_url_list:\\n\\t\\t# Get a sub papers_info dictfor an individual author\\n\\t\\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\\n\\t\\t# add sub dict to full dict\\n\\t\\tpaper_info_dict.update(papers_info)\\n\\t\\n\\tif \\\"Humanities\\\" in schl_name:\\n\\t\\tschl_name = \\\"School of Humanities\\\"\\n\\n\\treturn paper_info_dict\",\n \"def get_results(self):\\r\\n r ={\\r\\n 'Gp_s' :np.array(self.Gp_guess_s), #Means of the posteriors\\r\\n 'Gm_s' :np.array(self.Gm_guess_s), #Means of the posteriors\\r\\n 'eGp_s':np.array(self.eGp_guess_s) , #Standard deviations of the posteriors\\r\\n 'eGm_s':np.array(self.eGm_guess_s) , #Standard deviations of the posteriors\\r\\n 'covGpGm_s':np.array(self.cov_GpGm_s) , #Covariances of the posteriors\\r\\n 't_tot_pulseSequences_s':np.array(self.t_tot_pulseSequences_s), #Time it takes for each batch of pulse sequences\\r\\n 't_tot_process_s':np.array(self.t_tot_process_s), #CPU time it takes for processing the Bayes stuff at each iteration\\r\\n 't_probe_p_s':np.array(self.t_probe_p_s), # Times probed for the diff p\\r\\n 't_probe_m_s':np.array(self.t_probe_m_s), # Times probed for the diff m\\r\\n 't_pulseSequences_s':np.array(self.t_pulseSequences_s),\\r\\n 't_process_s':np.array(self.t_process_s),\\r\\n 'iteration_s':np.array(self.nb_iteration_s), #Number of iterations performed\\r\\n 'R_tot_s':np.array(self.R_tot_s) #Total number of readout performed\\r\\n }\\r\\n return r\",\n \"def get_relevance_dict(corpus):\\n global RELEVANCE_CORPUS\\n global RELEVANCE_DICT\\n if RELEVANCE_DICT and corpus == RELEVANCE_CORPUS:\\n return RELEVANCE_DICT\\n RELEVANCE_CORPUS = corpus\\n RELEVANCE_DICT = read_relevance_from_csv(corpus)\\n return RELEVANCE_DICT\",\n \"def qasmCircuitResults(self):\\n returnedDictionary={}\\n self.circutDrawing = self.draw()\\n self.blochSpheres=self.separatedBlochSpheres()\\n returnedDictionary[\\\"wires\\\"]=self.num_qubits\\n returnedDictionary[\\\"probabilities\\\"] = self.separatedProbabilities()\\n #returnedDictionary[\\\"blochSpheres\\\"] = self.separatedBlochSpheres()\\n returnedDictionary[\\\"diracNotation\\\"] = self.diracNotation()\\n returnedDictionary['chart'] = self.graph()\\n returnedDictionary[\\\"link\\\"] = \\\"\\\"\\n #returnedDictionary[\\\"qasmRows\\\"] = np.transpose(cols).tolist()\\n \\n if self.API_TOKEN != \\\"\\\":\\n returnedDictionary[\\\"link\\\"] = self.runOnIBMQ()\\n \\n return returnedDictionary\",\n \"def _search_hits_dict(self, query):\\n return docs_to_dict(docs_from_result(self.adapter.search(query)))\",\n \"def getCrowDistDict(self):\\n retDict = Distribution.getCrowDistDict(self)\\n retDict['n'] = self.n\\n retDict['p'] = self.p\\n return retDict\",\n \"def index_terms(self, terms):\\n index = dict()\\n for term in terms:\\n links = [cell.metadata[\\\"nbpages\\\"][\\\"link\\\"] for nb in self.notebooks\\n for cell in nb.content.cells if re.search(term, cell.source) if \\\"nbpages\\\" in cell.metadata.keys()]\\n index[term] = list(dict.fromkeys(links))\\n return index\",\n \"def data_from_result():\\n return dict(\\n gene=\\\"BRAF\\\",\\n strand=\\\"-\\\",\\n tx_pos_range=(1802, 1921),\\n alt_pos_range=(140453074, 140453193),\\n alt_aln_method=\\\"splign\\\",\\n tx_exon_id=780494,\\n alt_exon_id=1927263\\n )\",\n \"def get_data_dict(self):\\n\\n print('---------repo creation date------------------')\\n print('start date', self.start_date)\\n print('---------------------------------------------')\\n print('------Commits----------')\\n print(self.commits)\\n print('--------------------------------------------')\\n\\n df = pd.DataFrame.from_records(self.commits) # pylint: disable=invalid-name\\n df.date = pd.to_datetime(df.date, utc=True, unit='s')\\n df.set_index('date', inplace=True)\\n df.index = df.index.floor('D')\\n\\n date_range = pd.date_range(start=self.start_date, end=self.end_date, freq=self.date_unit)\\n date_range = date_range.floor('D')\\n\\n grouped = df.groupby('author')\\n new_df = pd.DataFrame(index=date_range)\\n for name, group in grouped:\\n new_df[name] = group.groupby('date').size()\\n new_df.fillna(0, inplace=True)\\n\\n return {\\n 'x': new_df.index.strftime('%Y-%m-%d').tolist(),\\n 'y': new_df.columns.tolist(),\\n 'z': new_df.T.values.astype('int32').tolist()\\n }\",\n \"def formula_search_to_dict(raw_result):\\n return [\\n {\\n \\\"id\\\": item.cluster_id,\\n \\\"text\\\": item.text,\\n \\\"n_entries\\\": item.n_entries,\\n \\\"n_texts\\\": item.unique_text,\\n \\\"verb_text\\\": item.verb_text,\\n }\\n for item in raw_result\\n ]\",\n \"def list_publications(bib_format=\\\"dict\\\"):\\n\\n def get_bibtex(key, value):\\n total_keys = [\\n \\\"title\\\",\\n \\\"journal\\\",\\n \\\"volume\\\",\\n \\\"issue\\\",\\n \\\"number\\\",\\n \\\"pages\\\",\\n \\\"numpages\\\",\\n \\\"year\\\",\\n \\\"month\\\",\\n \\\"publisher\\\",\\n \\\"url\\\",\\n \\\"doi\\\",\\n \\\"issn\\\",\\n ]\\n bibtex_str = (\\n \\\"@article{\\\"\\n + key\\n + \\\",\\\\n\\\"\\n + \\\" author={\\\"\\n + \\\" and \\\".join(value[\\\"author\\\"])\\n + \\\"},\\\\n\\\"\\n )\\n for key in total_keys:\\n if key in value.keys():\\n bibtex_str += \\\" \\\" + key + \\\"={\\\" + value[key] + \\\"},\\\\n\\\"\\n bibtex_str += \\\"}\\\\n\\\"\\n return bibtex_str\\n\\n def get_apa(value):\\n apa_str = \\\" & \\\".join(value[\\\"author\\\"])\\n if \\\"year\\\" in value.keys():\\n apa_str += \\\" (\\\" + value[\\\"year\\\"] + \\\"). \\\"\\n if \\\"title\\\" in value.keys():\\n apa_str += value[\\\"title\\\"] + \\\". \\\"\\n if \\\"journal\\\" in value.keys():\\n apa_str += value[\\\"journal\\\"] + \\\", \\\"\\n if \\\"volume\\\" in value.keys():\\n apa_str += value[\\\"volume\\\"] + \\\", \\\"\\n if \\\"pages\\\" in value.keys():\\n apa_str += value[\\\"pages\\\"] + \\\". \\\"\\n if \\\"doi\\\" in value.keys():\\n apa_str += \\\"doi: \\\" + value[\\\"doi\\\"] + \\\"\\\\n\\\"\\n return apa_str\\n\\n publication_dict = s.publication_lst\\n if bib_format.lower() == \\\"dict\\\":\\n return publication_dict\\n elif bib_format.lower() == \\\"bibtex\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_bibtex(key, value)\\n return total_str\\n elif bib_format.lower() == \\\"apa\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_apa(value)\\n return total_str\\n else:\\n raise ValueError(\\\"Supported Bibformats are ['dict', 'bibtex', 'apa']\\\")\",\n \"def results(self):\\n \\n results = {}\\n if self.descriptors is not None:\\n for i in range(len(self.items)):\\n results[self.items[i]] = {'I': self.ivalue(i),\\n 'J': self.jvalue(i)}\\n else:\\n for i in range(len(self.items)):\\n results[self.items[i]] = {'I': self.ivalue(i)}\\n return results\",\n \"def construct_occurrence_dico(data) :\\n print('Constructing occurence dictionnaries...')\\n\\n p_kw_dico = dict()\\n kw_p_dico = dict()\\n full_stem_dico = {}\\n for patent in data :\\n patent_id = patent['id']\\n #[keywords,stem_dico] = extract_keywords(patent[1]+\\\". \\\"+patent[2],patent_id)\\n [keywords,stem_dico] = extract_keywords(patent['title']+\\\". \\\"+patent['abstract'],patent_id)\\n #print(keywords)\\n\\n for k in keywords :\\n # add to p_kw dico\\n if k in kw_p_dico :\\n kw_p_dico[k].append(patent_id)\\n else :\\n kw_p_dico[k]= [patent_id]\\n #\\n if patent_id in p_kw_dico :\\n p_kw_dico[patent_id].append(k)\\n else :\\n p_kw_dico[patent_id] = [k]\\n\\n for k in stem_dico.keys():\\n if k in full_stem_dico :\\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\\n else :\\n full_stem_dico[k] = stem_dico[k]\\n\\n return([p_kw_dico,kw_p_dico,full_stem_dico])\",\n \"def blast_to_dict():\\n print(\\\"Parsing the BLAST results...\\\")\\n GeneIDs = []\\n blast_dict = defaultdict(lambda: defaultdict())\\n for record in NCBIXML.parse(open(\\\"Log_Directory/blast_results.xml\\\")):\\n for align in record.alignments:\\n for hsp in align.hsps:\\n percent_identity = round(100 * float(hsp.identities) / float(hsp.align_length),2) # https://www.dnastar.com/megalign_help/index.html#!Documents/calculationofpercentidentity.htm\\n hit_id = align.title.split('|')\\n # this uses NCBI's gi number (GenInfo Identifier) which is reliable now but getting phased out, so might\\n # need to change to hit_id[3] at some point\\n GeneIDs.append(hit_id[1])\\n blast_dict[record.query]['Hit_ID'] = align.title\\n blast_dict[record.query]['Gene_ID'] = hit_id[1]\\n blast_dict[record.query]['Sequence'] = hsp.query\\n blast_dict[record.query]['SeqLength'] = len(hsp.query)\\n blast_dict[record.query]['Description'] = hit_id[4]\\n blast_dict[record.query]['Accession'] = hit_id[3]\\n blast_dict[record.query]['Db'] = hit_id[2]\\n blast_dict[record.query]['Score'] = hsp.score\\n blast_dict[record.query]['E_value'] = hsp.expect\\n blast_dict[record.query]['Percent_Identity'] = percent_identity\\n break\\n \\n GeneIDs = list(set(GeneIDs))\\n if not GeneIDs:\\n print('\\\\nYour BLAST query was rejected. Please enter a smaller sample size or try running this script \\\\\\n at a better time.\\\\nNCBI asks that you run scripts on weekends or between 9pm and 5am Eastern \\\\\\n time on weekdays if more than 50 searches will be submitted.')\\n sys.exit()\\n\\n return blast_dict, GeneIDs,\",\n \"def generate_data(cls):\\n cls.results = {}\\n cls.pre_process()\\n today = datetime.today()\\n skip = None\\n values = map(lambda a: a[0], cls.data)\\n if len(values) == 0 and 'citation' not in cls.config_data_name:\\n skip = True\\n weights= map(lambda a: a[1], cls.data)\\n if cls.config_data_name == 'reads_histogram':\\n bins = range(1996, today.year+2)\\n elif cls.min_year:\\n bins = range(cls.min_year, today.year+2)\\n else:\\n try:\\n bins = range(min(values),max(values)+2)\\n except:\\n skip = True\\n if not skip:\\n refereed_values = map(lambda a: a[0], cls.refereed_data)\\n refereed_weights= map(lambda a: a[1], cls.refereed_data)\\n # get the regular histogram\\n cls.value_histogram = histogram(values,bins=bins)\\n cls.refereed_value_histogram = histogram(refereed_values,bins=bins)\\n # get the normalized histogram\\n cls.normalized_value_histogram = histogram(values,bins=bins,weights=weights)\\n cls.refereed_normalized_value_histogram = histogram(refereed_values,bins=bins,weights=refereed_weights)\\n else:\\n cls.value_histogram = False\\n cls.results[str(today.year)] = \\\"0:0:0:0\\\"\\n cls.post_process()\",\n \"def build_index(text: Iterable) -> Dict[str, List[Tuple[int, int]]]:\\n index = defaultdict(list)\\n for line_no, line in enumerate(text, 1):\\n for match in WORD_RE.finditer(line):\\n word = match.group()\\n column_no = match.start() + 1\\n location = (line_no, column_no)\\n index[word].append(location)\\n return index\",\n \"def get_coauthors(self):\\n # Get number of authors to search for\\n res = download(url=self.coauthor_link, accept='json')\\n data = loads(res.text)['search-results']\\n N = int(data.get('opensearch:totalResults', 0))\\n # Store information in namedtuples\\n fields = 'surname given_name id areas affiliation_id name city country'\\n coauth = namedtuple('Coauthor', fields)\\n coauthors = []\\n # Iterate over search results in chunks of 25 results\\n count = 0\\n while count < N:\\n params = {'start': count, 'count': 25}\\n res = download(url=self.coauthor_link, params=params, accept='json')\\n data = loads(res.text)['search-results'].get('entry', [])\\n # Extract information for each coauthor\\n for entry in data:\\n aff = entry.get('affiliation-current', {})\\n try:\\n areas = [a['$'] for a in entry.get('subject-area', [])]\\n except TypeError: # Only one subject area given\\n areas = [entry['subject-area']['$']]\\n new = coauth(surname=entry['preferred-name']['surname'],\\n given_name=entry['preferred-name'].get('given-name'),\\n id=entry['dc:identifier'].split(':')[-1],\\n areas='; '.join(areas),\\n affiliation_id=aff.get('affiliation-id'),\\n name=aff.get('affiliation-name'),\\n city=aff.get('affiliation-city'),\\n country=aff.get('affiliation-country'))\\n coauthors.append(new)\\n count += 25\\n return coauthors\",\n \"def _transform_search_database(self):\\n # TODO: Create this and link with protein object when fasta file is provided\\n return {\\n \\\"file_format\\\": \\\"fasta format\\\",\\n \\\"name\\\": \\\"\\\",\\n \\\"id\\\": 1,\\n \\\"location\\\": \\\"\\\",\\n \\\"params\\\": [],\\n }\",\n \"def result_dict(self):\\n result = {}\\n for k, v in self._parser_result.items():\\n result[k] = ' '.join(map(lambda x: x[0], v.most_common(3)))\\n return result\",\n \"def codonComposition(self):#works\\n return {codon: self.countDicNuc.get(codon) for codon in self.rnaCodonTable.keys()}\",\n \"def get_search_result_info(self):\\n result = {}\\n href = self.driver.find_element_by_xpath(locators.search_result_computer_name).get_attribute('href')\\n name = self.driver.find_element_by_xpath(locators.search_result_computer_name).text\\n introduced_date = self.driver.find_element_by_xpath(locators.search_result_introduced_date).text\\n discontinued_date = self.driver.find_element_by_xpath(locators.search_result_discontinued_date).text\\n company = self.driver.find_element_by_xpath(locators.search_result_company).text\\n result[\\\"href\\\"] = href\\n result[\\\"name\\\"] = name\\n result[\\\"introduced_date\\\"] = introduced_date\\n result[\\\"discontinued_date\\\"] = discontinued_date\\n result[\\\"company\\\"] = company\\n return result\",\n \"def getCitations (self, doc, verbose = False):\\r\\n if verbose is True:\\r\\n print \\\"getting citations for document \\\" +str(doc) \\r\\n return self.citations[int(doc)]\",\n \"def extract_metadata(res):\\n d = OrderedDict()\\n\\n d['type'] = res['type']\\n d['authors'] = make_author_list(res)\\n d['citation-authors'] = make_citation_authors(res)\\n d['citation'] = make_citation(res)\\n d['source-title'] = res['title'].strip(\\\" \\\").capitalize()\\n d['title'] = \\\"Notes on '\\\" + d['source-title'] + \\\"', by \\\" + \\\", \\\".join(d['authors'][0:-1])\\n if len(d['authors']) > 1:\\n d['title'] = d['title'] + \\\", and \\\" + d['authors'][-1] \\n d['citation-authors']\\n d['container'] = make_container(res)\\n d['publisher'] = res['publisher'] if 'publisher' in res.keys() else '' \\n d['issn'] = res['issn'] if 'issn' in res.keys() else ''\\n d['year'] = make_year(res)\\n d['issue'] = res['issue'] if \\\"issue\\\" in res.keys() else \\\"\\\"\\n d['volume'] = res['volume'] if \\\"volume\\\" in res.keys() else \\\"\\\"\\n d['pages'] = res['page'] if \\\"page\\\" in res.keys() else \\\"\\\"\\n d['subjects'] = make_subject(res)\\n d['doi'] = res['DOI']\\n d['link'] = res['URL']\\n d['citationkey'] = make_citation_key(res)\\n d['fetched'] = date.today().strftime(\\\"%Y%m%d\\\")\\n return d\",\n \"def get_results_for_init(self):\\n return dict(init=self.centroids, n_clusters=self.centroids.shape[0])\",\n \"def get_initial_author_dict():\\n adict = {}\\n try:\\n ah = run_sql(\\\"select aterm,hitlist from rnkAUTHORDATA\\\")\\n for (a, h) in ah:\\n adict[a] = deserialize_via_marshal(h)\\n return adict\\n except:\\n register_exception(prefix=\\\"could not read rnkAUTHORDATA\\\", alert_admin=True)\\n return {}\",\n \"def get_meta_information() -> Dict:\\n return {'name': 'NAS-Bench-201',\\n 'references': ['Xuanyi Dong, Yi Yang',\\n 'NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search',\\n 'https://openreview.net/forum?id=HJxyZkBKDr',\\n 'https://github.com/D-X-Y/AutoDL-Projects'],\\n }\",\n \"def provincias_distritos():\\n\\n rv = OrderedDict()\\n q = \\\"\\\"\\\"\\n SELECT da.id_distrito,\\n da.desc_distrito,\\n da.id_seccion,\\n da.desc_seccion,\\n count(e.id) AS estab_count,\\n count(wm.id) AS matches_count\\n FROM divisiones_administrativas da\\n INNER JOIN establecimientos e\\n USING (id_distrito, id_seccion)\\n LEFT OUTER JOIN weighted_matches wm\\n ON wm.establecimiento_id = e.id\\n AND wm.score = 1 and wm.match_source = 1\\n GROUP BY da.id_distrito,\\n da.desc_distrito,\\n da.id_seccion,\\n da.desc_seccion\\n ORDER BY id_distrito,\\n id_seccion\\n \\\"\\\"\\\"\\n\\n for d in db.query(q):\\n k = (d['id_distrito'], d['desc_distrito'],)\\n if k not in rv:\\n rv[k] = []\\n rv[k].append((\\n d['id_distrito'],\\n d['id_seccion'],\\n d['desc_seccion'],\\n d['estab_count'],\\n d['matches_count']))\\n return rv\",\n \"def create_index_dict(vcb_file):\\n index_dict = {}\\n vcb = open(vcb_file).readlines()\\n for line in vcb:\\n line = line.split()\\n index_dict[int(line[0])] = line[1]\\n return index_dict\",\n \"def tag_dict_values (self):\\r\\n\\r\\n if self.using_database:\\r\\n value_tuple = (notebookname,)\\r\\n db_cursor.execute(\\\"SELECT keyword \\\"\\r\\n +\\\"FROM keys_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n fetched = db_cursor.fetchall()\\r\\n if fetched:\\r\\n return {index[0].strip() for index in fetched}\\r\\n return set()\\r\\n\\r\\n return self.tag_dict.values()\",\n \"def fetch_to_dict(blast_dictionary):\\n blast_dict = blast_dictionary.copy()\\n fetch_dict = defaultdict(lambda: defaultdict())\\n for record in SeqIO.parse(\\\"Log_Directory/fetch_results.txt\\\", \\\"genbank\\\"):\\n fetch_dict[record.id]['Organism'] = record.annotations['organism']\\n fetch_dict[record.id]['Source'] = record.annotations['source']\\n fetch_dict[record.id]['Domain'] = record.annotations['taxonomy'][0]\\n fetch_dict[record.id]['Taxonomy'] = record.annotations['taxonomy']\\n for record in blast_dict.keys():\\n for accession in fetch_dict.keys():\\n if accession in blast_dict[record][\\\"Accession\\\"]:\\n for accession_item in next(iter(fetch_dict.values())).keys():\\n blast_dict[record][accession_item] = fetch_dict[accession][accession_item]\\n\\n return blast_dict\",\n \"def build_course_dictionary(title_result_set, desc_result_set) -> Dict[str, List[str]]:\\n\\n course_dictionary = {} # placeholder dictionary\\n\\n for (tagged_title, tagged_description) in zip(title_result_set, desc_result_set): # iterate through multiple result sets\\n full_title_desc_list = {}\\n full_title_desc_list = [str(tagged_title.text)] + str(tagged_description.text).strip().splitlines() # remove trailing whitespace, then get list of lines\\n course_dictionary[str(tagged_title.text)[:8]] = full_title_desc_list\\n\\n return course_dictionary\",\n \"def covid19(request):\\n\\tip = get_ip(request, right_most_proxy=True)\\n\\tIpAddressInformation.objects.create(ip_address=ip)\\n\\n\\tcontextres =[]\\n\\t#build elasticsearch query to search data\\n\\tquery={\\\"query\\\": {\\n\\t\\t\\\"bool\\\": {\\n\\t\\t\\t\\\"must\\\": [\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"Associated with COVID-19\\\": \\\"Yes\\\"}},\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"UniprotKb entry status\\\": \\\"Yes\\\"}}\\n\\t\\t\\t]\\n\\t\\t}\\n\\t}\\n\\t}\\n\\t#generate random file name to store search result in json format\\n\\tnameFIle=names.get_first_name()\\n\\tjsonfilename=nameFIle+'_basic_search_covid19.json'\\n\\tjsonfilepath=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'results', jsonfilename)\\n\\tjsonfileoutput= open(jsonfilepath,'w')\\n\\tjfinaldata=[]\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#elasticsearch will search data\\n\\tres=helpers.scan(client=es,scroll='2m',index=\\\"mrmassaydb-index\\\", doc_type=\\\"mrmassaydb-type\\\",query=query,request_timeout=30)\\n\\tjfinaldata=[]\\n\\tpepSeqList=[]\\n\\tproteinList=[]\\n\\t#if data is valid based on uniprotkb release then it will display\\n\\tfor i in res:\\n\\t\\tjdic=i['_source']\\n\\t\\tjdic={str(tkey):force_text(tvalue) for tkey,tvalue in jdic.items()}\\n\\t\\tif jdic[\\\"UniprotKb entry status\\\"] ==\\\"Yes\\\" and jdic['UniProtKB Accession'] !='502':\\n\\t\\t\\tjdic[\\\"PPI\\\"] =\\\"View\\\"\\n\\t\\t\\tjdic[\\\"sel\\\"] =\\\"\\\"\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('<br>','|')\\n\\t\\t\\tjdic[\\\"SRMAtlas URL\\\"]=jdic[\\\"SRMAtlas URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Passel URL\\\"]=jdic[\\\"Passel URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"CPTAC URL\\\"]=jdic[\\\"CPTAC URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Panoramaweb URL\\\"]=jdic[\\\"Panoramaweb URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"PeptideTracker URL\\\"]=jdic[\\\"PeptideTracker URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\t#if jdic[\\\"Pathway Name\\\"].lower() !='na':\\n\\t\\t\\t#\\tjdic[\\\"Pathway Name\\\"]=re.sub(r\\\"(\\\\w)([A-Z])\\\",r\\\"\\\\1|\\\\2\\\",jdic[\\\"Pathway Name\\\"])\\n\\t\\t\\tjdic[\\\"Mean Concentration\\\"] =jdic[\\\"Mean Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tjdic[\\\"Concentration\\\"] =jdic[\\\"Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tif str(jdic[\\\"Associated with COVID-19\\\"]).strip().upper() =='YES':\\n\\t\\t\\t\\tpepSeqList.append(jdic[\\\"Peptide Sequence\\\"].strip())\\n\\t\\t\\t\\tproteinList.append(jdic[\\\"UniProtKB Accession\\\"].strip().split('-')[0])\\n\\t\\t\\tjfinaldata.append(jdic)\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#checking any result generated by database\\n\\tfoundHits=len(jfinaldata)\\n\\t#storing only 10000 rows in json format\\n\\tjson.dump(jfinaldata[:10000],jsonfileoutput)\\n\\tjsonfileoutput.close()\\n\\t# if result found then do other job\\n\\tif foundHits >0:\\n\\t\\tstatsummary=summaryStatcal(jfinaldata) # sent data to this funcation for generating stat\\n\\t\\tpathwaychart=statsummary['pathwaychart']\\n\\t\\tpathwaychart=[i[:2] for i in pathwaychart]\\n\\t\\tspecieslist=statsummary['specieslist']\\n\\t\\ttotallist=statsummary['total']\\n\\t\\tsubcell=statsummary['subcell']\\n\\t\\tgodic=statsummary['godic']\\n\\t\\tjvennprot=statsummary['jevennstat'][0]\\n\\t\\tjvennpep=statsummary['jevennstat'][1]\\n\\t\\tmrmdatabase=statsummary['jevennstat'][2]\\n\\t\\tsortedgodic=OrderedDict(sorted(godic.items(), key=lambda t: t[1])) # sorting GO data\\n\\t\\tupdatedgodic=dict(list(sortedgodic.items()))\\n\\t\\tpepseqdataseries=ast.literal_eval(json.dumps(statsummary['pepseqdataseries'])) #dumping data into json format\\n\\t\\tprodataseries=statsummary['prodataseries']\\n\\t\\tunqisostat=statsummary['unqisostat']\\n\\t\\tjsonfilepathStat=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'statsummary', jsonfilename) #storing stat result in json format\\n\\t\\tjsonfileoutputStat= open(jsonfilepathStat,'w')\\n\\t\\tjson.dump(statsummary,jsonfileoutputStat)\\n\\t\\tjsonfileoutputStat.close()\\n\\t\\turlname=\\\"'/resultFile/jsonData/resultJson/basicsearch/results/\\\"+jsonfilename+\\\"'\\\"\\n\\n\\t\\tcontextindex={\\n\\t\\t\\t\\\"filename\\\":urlname,\\\"colname\\\":json.dumps(colname),'foundHits':foundHits,\\n\\t\\t\\t'pathwaychart':pathwaychart[:11],'specieslist':specieslist,\\n\\t\\t\\t'totallist':totallist,'subcell':subcell,\\n\\t\\t\\t'updatedgodic':updatedgodic,'pepseqdataseries':pepseqdataseries,\\n\\t\\t\\t'prodataseries':prodataseries,'unqisostat':unqisostat,\\n\\t\\t\\t'uniquePepSeq':len(set(pepSeqList)),'uniqueProtein':len(set(proteinList)),\\n\\t\\t\\t'jvennprot':json.dumps(jvennprot),'jvennpep':json.dumps(jvennpep),'jvennmrmdb':json.dumps(mrmdatabase)\\n\\t\\t\\t}\\n\\t\\treturn render(request,'covid19.html',contextindex)\\n\\telse:\\n\\t\\treturn render(request,'covid19.html',{'foundHits':foundHits})\",\n \"def key_dict_values (self):\\r\\n\\r\\n if self.using_database:\\r\\n value_tuple = (notebookname,)\\r\\n db_cursor.execute(\\\"SELECT note_index \\\"\\r\\n +\\\"FROM keys_to_indexes\\\"\\r\\n +\\\" WHERE notebook=?;\\\",\\r\\n value_tuple)\\r\\n\\r\\n fetched = db_cursor.fetchall()\\r\\n if fetched:\\r\\n return {index[0].strip() for index in fetched}\\r\\n return set()\\r\\n\\r\\n return self.key_dict.values()\",\n \"def get_titles_dict(name_url_list):\\n\\tbib_dict = {}\\n\\t\\n\\tfor name_url in name_url_list:\\n\\t\\ttitles = get_author_titles(name_url[1])\\n\\t\\tbib_dict[name_url] = titles\\n\\n\\treturn bib_dict\",\n \"def search_for_redbooks(book_codes: tuple):\\n\\n book_dict = {}\\n\\n global setup\\n\\n for book_code in book_codes:\\n URI_string = build_URI_string(book_code)\\n search_web_page = requests.get(URI_string)\\n if search_web_page.status_code != 200:\\n print(\\\"Book with code {} not found! Continuing...\\\".format(book_code))\\n continue\\n web_page_content = search_web_page.content\\n soup = BS(web_page_content, 'html.parser')\\n book_name = soup.find('h1',{'class':'ibm-h1','id':'ibm-pagetitle-h1'}).text\\n book_dict[book_code] = book_name\\n\\n return book_dict\",\n \"def citations(request, format='csv'):\\n \\n filtered = False # make sure we're filtering by something\\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\\n \\n \\n # accession number\\n if request.GET.get('gabi_acc_number'):\\n filtered = True\\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\\n \\n # species AND bentity\\n if request.GET.get('species'):\\n filtered = True\\n if request.GET.get('species'): \\n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\\n if request.GET.get('bentity_id'):\\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\\n \\n # lat and lon\\n if request.GET.get('lat') and request.GET.get('lon'):\\n filtered = True\\n if request.GET.get('lat'):\\n records = records.filter(lat=request.GET.get('lat'))\\n if request.GET.get('lon'):\\n records = records.filter(lon=request.GET.get('lon'))\\n \\n # status\\n if request.GET.get('status'):\\n records = records.filter(status=request.GET.get('status')[0].upper())\\n \\n \\n # error message if the user didn't supply an argument to filter the records\\n if not filtered: \\n return errorResponse(\\\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\\\", format, {'records': []})\\n \\n \\n # fetch all the bentitites at once, so we don't have to hit the database once for each record\\n records = records.prefetch_related('bentity') \\n \\n output_objects = [{\\n 'gabi_acc_number': r.gabi_acc_number,\\n 'species': r.valid_species_name_id,\\n 'bentity_id': r.bentity_id,\\n 'bentity_name': r.bentity.bentity,\\n 'status': r.status,\\n 'type_of_data': r.type_of_data,\\n 'lat': r.lat,\\n 'lon': r.lon, \\n 'citation': r.citation,\\n } for r in records]\\n \\n \\n \\n if format == 'csv':\\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\\n \\n else:\\n return JSONResponse({'records': output_objects})\",\n \"def index_feats_dict(self):\\n doc_features_dict = {}\\n\\n for index, doc in zip(self.index, self.series):\\n # Sets for a doc and feature words\\n doc_set = set(doc.split())\\n feat_set = set(self.features)\\n\\n # Shared words between the two sets\\n interset_words = doc_set.intersection(feat_set)\\n\\n # Append to doc_features_dict\\n doc_features_dict[index] = list(interset_words)\\n\\n return doc_features_dict\",\n \"def last_updated_result(rank_method_code):\\n result = [{}, {}, {}]\\n query = \\\"\\\"\\\"select relevance_data from rnkMETHOD, rnkMETHODDATA where\\n rnkMETHOD.id = rnkMETHODDATA.id_rnkMETHOD\\n and rnkMETHOD.Name = '%s'\\\"\\\"\\\"% rank_method_code\\n rdict = run_sql(query)\\n if rdict and rdict[0] and rdict[0][0]:\\n #has to be prepared for corrupted data!\\n try:\\n dic = deserialize_via_marshal(rdict[0][0])\\n except zlib.error:\\n return [{}, {}, {}]\\n query = \\\"select object_value from rnkCITATIONDATA where object_name='citationdict'\\\"\\n cit_compressed = run_sql(query)\\n cit = []\\n if cit_compressed and cit_compressed[0] and cit_compressed[0][0]:\\n cit = deserialize_via_marshal(cit_compressed[0][0])\\n if cit:\\n query = \\\"\\\"\\\"select object_value from rnkCITATIONDATA\\n where object_name='reversedict'\\\"\\\"\\\"\\n ref_compressed = run_sql(query)\\n if ref_compressed and ref_compressed[0] and ref_compressed[0][0]:\\n ref = deserialize_via_marshal(ref_compressed[0][0])\\n result = (dic, cit, ref)\\n return result\",\n \"def baby_search_engine(name):\\r\\n \\r\\n name_ranking = []\\r\\n \\r\\n for publication_name, name_list in baby_names.items():\\r\\n publication = {}\\r\\n if name.capitalize() in name_list:\\r\\n publication['list'] = publication_name\\r\\n publication['rank'] = name_list.index(name.capitalize()) + 1\\r\\n name_ranking.append(publication)\\r\\n\\r\\n \\r\\n return sorted(name_ranking, key=lambda k: k['rank'])\",\n \"def _group_by_bank(self):\\n rslt = {}\\n for company in self:\\n if not company.indexa_currency_provider:\\n continue\\n\\n if rslt.get(company.indexa_currency_provider):\\n rslt[company.indexa_currency_provider] += company\\n else:\\n rslt[company.indexa_currency_provider] = company\\n return rslt\",\n \"def get_complementary_infos_disc(browser, dict_disc: dict, url_disc: str) -> dict:\\n try:\\n browser.get_url(url_disc)\\n soup = browser.get_soup()\\n dict_complementary = {}\\n table = soup.find(\\\"table\\\", {\\\"class\\\": \\\"album_info\\\"})\\n table_descriptors = [x.find(\\\"th\\\").text.strip() for x in table.find_all(\\\"tr\\\")]\\n table_values = [\\n \\\" \\\".join(x.find(\\\"td\\\").text.strip().replace(\\\"\\\\n\\\", \\\", \\\").split())\\n for x in table.find_all(\\\"tr\\\")\\n ]\\n for d, v in zip(table_descriptors, table_values):\\n dict_complementary[d] = v\\n\\n try:\\n dict_complementary[\\\"Rank Overall\\\"] = [\\n x.replace(\\\"#\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\")\\n for y in dict_complementary[\\\"Ranked\\\"].split(\\\", \\\")\\n if \\\"overall\\\" in y\\n for x in y.split()\\n ][0]\\n except Exception as e:\\n logger.debug(\\\"No overall rank found : %s\\\", e)\\n try:\\n dict_complementary[\\\"Rank Year\\\"] = [\\n x.replace(\\\"#\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\")\\n for y in dict_complementary[\\\"Ranked\\\"].split(\\\", \\\")\\n if dict_disc[\\\"Year\\\"] in y\\n for x in y.split()\\n ][0]\\n except Exception as e:\\n logger.debug(\\\"No year rank found : %s\\\", e)\\n dict_disc.update(dict_complementary)\\n except Exception as e:\\n logger.error(e)\\n return dict_disc\",\n \"def getCrowDistDict(self):\\n retDict = Distribution.getCrowDistDict(self)\\n retDict['lambda'] = self.lambdaVar\\n retDict['k'] = self.k\\n retDict['low'] = self.low\\n return retDict\",\n \"def main():\\n records = get_block_of_records({\\\"keyword\\\": \\\"food\\\"})\\n print (\\\"returned items: {}\\\".format(len(records)))\\n\\n processed_records = {}\\n for item in records:\\n meta = item[\\\"meta\\\"]\\n umm = item[\\\"umm\\\"]\\n cid = meta[\\\"concept-id\\\"]\\n short_name = umm[\\\"ShortName\\\"]\\n processed_records[cid] = short_name\\n\\n print (\\\"uniq keys: {}\\\".format(len(processed_records.keys())))\",\n \"def getUniChemData(self, inchiKeyList):\\n mapD = {\\n 1: {\\\"name\\\": \\\"chembl\\\", \\\"baseUrl\\\": \\\"https://www.ebi.ac.uk/chembl/\\\", \\\"entryUrl\\\": \\\"https://www.ebi.ac.uk/chembldb/compound/inspect/\\\"},\\n 3: {\\\"name\\\": \\\"pdb\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/pdbe/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\\\"},\\n 2: {\\\"name\\\": \\\"drugbank\\\", \\\"baseUrl\\\": \\\"http://drugbank.ca/\\\", \\\"entryUrl\\\": \\\"http://www.drugbank.ca/drugs/\\\"},\\n 5: {\\\"name\\\": \\\"pubchem_dotf\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 4: {\\\"name\\\": \\\"gtopdb\\\", \\\"baseUrl\\\": \\\"http://www.guidetopharmacology.org\\\", \\\"entryUrl\\\": \\\"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\\\"},\\n 11: {\\\"name\\\": \\\"ibm\\\", \\\"baseUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\\\", \\\"entryUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\\\"},\\n 6: {\\\"name\\\": \\\"kegg_ligand\\\", \\\"baseUrl\\\": \\\"http://www.genome.jp/kegg/ligand.html\\\", \\\"entryUrl\\\": \\\"http://www.genome.jp/dbget-bin/www_bget?\\\"},\\n 9: {\\\"name\\\": \\\"zinc\\\", \\\"baseUrl\\\": \\\"http://zinc15.docking.org\\\", \\\"entryUrl\\\": \\\"http://zinc15.docking.org/substances/\\\"},\\n 8: {\\\"name\\\": \\\"nih_ncc\\\", \\\"baseUrl\\\": \\\"http://nihsmr.evotec.com/evotec/\\\", \\\"entryUrl\\\": \\\"\\\"},\\n 10: {\\\"name\\\": \\\"emolecules\\\", \\\"baseUrl\\\": \\\"https://www.emolecules.com/\\\", \\\"entryUrl\\\": \\\"https://www.emolecules.com/cgi-bin/more?vid=\\\"},\\n 12: {\\\"name\\\": \\\"atlas\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/gxa/home\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/gxa/query?conditionQuery=\\\"},\\n 7: {\\\"name\\\": \\\"chebi\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/chebi/downloadsForward.do\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\\\"},\\n 14: {\\n \\\"name\\\": \\\"fdasrs\\\",\\n \\\"baseUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/srs.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\\\",\\n },\\n 15: {\\\"name\\\": \\\"surechembl\\\", \\\"baseUrl\\\": \\\"https://www.surechembl.org/search/\\\", \\\"entryUrl\\\": \\\"https://www.surechembl.org/chemical/\\\"},\\n 21: {\\\"name\\\": \\\"pubchem_tpharma\\\", \\\"baseUrl\\\": \\\"http://www.thomson-pharma.com/\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 22: {\\\"name\\\": \\\"pubchem\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/compound/\\\"},\\n 27: {\\\"name\\\": \\\"recon\\\", \\\"baseUrl\\\": \\\"https://vmh.uni.lu\\\", \\\"entryUrl\\\": \\\"https://vmh.uni.lu/\\\"},\\n 28: {\\\"name\\\": \\\"molport\\\", \\\"baseUrl\\\": \\\"https://www.molport.com/shop/index\\\", \\\"entryUrl\\\": \\\"https://www.molport.com/shop/molecule-link/\\\"},\\n 31: {\\n \\\"name\\\": \\\"bindingdb\\\",\\n \\\"baseUrl\\\": \\\"https://www.bindingdb.org/bind/index.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\\\",\\n },\\n 41: {\\\"name\\\": \\\"swisslipids\\\", \\\"baseUrl\\\": \\\"http://www.swisslipids.org/\\\", \\\"entryUrl\\\": \\\"http://www.swisslipids.org/\\\"},\\n 29: {\\\"name\\\": \\\"nikkaji\\\", \\\"baseUrl\\\": \\\"http://jglobal.jst.go.jp/en/\\\", \\\"entryUrl\\\": \\\"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\\\"},\\n 32: {\\\"name\\\": \\\"comptox\\\", \\\"baseUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\", \\\"entryUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\"},\\n 33: {\\\"name\\\": \\\"lipidmaps\\\", \\\"baseUrl\\\": \\\"http://www.lipidmaps.org\\\", \\\"entryUrl\\\": \\\"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\\\"},\\n 35: {\\\"name\\\": \\\"carotenoiddb\\\", \\\"baseUrl\\\": \\\"http://carotenoiddb.jp/index.html\\\", \\\"entryUrl\\\": \\\"http://carotenoiddb.jp/Entries/\\\"},\\n 36: {\\\"name\\\": \\\"metabolights\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\"},\\n 37: {\\\"name\\\": \\\"brenda\\\", \\\"baseUrl\\\": \\\"https://www.brenda-enzymes.org/index.php\\\", \\\"entryUrl\\\": \\\"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\\\"},\\n 17: {\\\"name\\\": \\\"pharmgkb\\\", \\\"baseUrl\\\": \\\"https://www.pharmgkb.org\\\", \\\"entryUrl\\\": \\\"https://www.pharmgkb.org/drug/\\\"},\\n 18: {\\\"name\\\": \\\"hmdb\\\", \\\"baseUrl\\\": \\\"http://www.hmdb.ca\\\", \\\"entryUrl\\\": \\\"http://www.hmdb.ca/metabolites/\\\"},\\n 24: {\\n \\\"name\\\": \\\"nmrshiftdb2\\\",\\n \\\"baseUrl\\\": \\\"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\\\",\\n \\\"entryUrl\\\": \\\"http://nmrshiftdb.org/molecule/\\\",\\n },\\n 25: {\\\"name\\\": \\\"lincs\\\", \\\"baseUrl\\\": \\\"http://www.lincsproject.org/\\\", \\\"entryUrl\\\": \\\"http://identifiers.org/lincs.smallmolecule/\\\"},\\n 39: {\\\"name\\\": \\\"chemicalbook\\\", \\\"baseUrl\\\": \\\"https://www.chemicalbook.com\\\", \\\"entryUrl\\\": \\\"https://www.chemicalbook.com/ChemicalProductProperty_EN_\\\"},\\n 20: {\\\"name\\\": \\\"selleck\\\", \\\"baseUrl\\\": \\\"http://www.selleckchem.com\\\", \\\"entryUrl\\\": \\\"http://www.selleckchem.com/products/\\\"},\\n 23: {\\\"name\\\": \\\"mcule\\\", \\\"baseUrl\\\": \\\"https://mcule.com\\\", \\\"entryUrl\\\": \\\"https://mcule.com/\\\"},\\n 26: {\\\"name\\\": \\\"actor\\\", \\\"baseUrl\\\": \\\"https://actor.epa.gov\\\", \\\"entryUrl\\\": \\\"http://actor.epa.gov/actor/chemical.xhtml?casrn=\\\"},\\n 34: {\\\"name\\\": \\\"drugcentral\\\", \\\"baseUrl\\\": \\\"http://drugcentral.org\\\", \\\"entryUrl\\\": \\\"http://drugcentral.org/drugcard/\\\"},\\n 38: {\\\"name\\\": \\\"rhea\\\", \\\"baseUrl\\\": \\\"http://www.rhea-db.org\\\", \\\"entryUrl\\\": \\\"http://www.rhea-db.org/searchresults?q=CHEBI:\\\"},\\n }\\n oD = {}\\n try:\\n for ky in inchiKeyList:\\n unc = unichem_client # pylint: disable=no-member\\n # unc.set_format(\\\"json\\\")\\n uDL = unc.get(ky)\\n if uDL:\\n qD = {}\\n for uD in uDL:\\n if \\\"src_id\\\" in uD and int(uD[\\\"src_id\\\"]) in mapD:\\n qD[mapD[int(uD[\\\"src_id\\\"])][\\\"name\\\"]] = uD[\\\"src_compound_id\\\"]\\n if qD:\\n oD[ky] = qD\\n\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n return oD\",\n \"def get_papers_by_institution(inst_names):\\n papers = list(ads.SearchQuery(aff=\\\"IATE\\\"))\\n for p in papers:\\n print(p.author)\\n print(f\\\"Paper from year {p.year} with \\\"\\n f\\\"{len(p.author)} authors and \\\"\\n f\\\"{p.citation_count} citations.\\\")\\n input()\",\n \"def index_authors(data_file=DATA_FILE_AUTHORS, index_file=INDEX_FILE_AUTHORS):\\n context = etree.iterparse(\\n gzip.open(data_file),\\n tag=\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description\\\",\\n )\\n\\n indexed = 0\\n authors_idx = SqliteDict(index_file, autocommit=False)\\n\\n log.info(\\\"Indexing GND authors (~13.6 Million)\\\")\\n\\n for event, elem in context:\\n if not elem.attrib.get(\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\\\"):\\n continue\\n if elem.getparent().tag == \\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}RDF\\\":\\n name = extract_creator(elem)\\n elif elem.getparent().tag == \\\"{http://www.w3.org/2002/07/owl#}sameAs\\\":\\n name = extract_creator(elem)\\n else:\\n continue\\n\\n if name is not None:\\n creator_id = str(\\n elem.attrib.get(\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\\\")\\n ).split(\\\"http://d-nb.info/gnd/\\\")[1]\\n authors_idx[creator_id] = name\\n log.debug(\\\"Indexing GND Author {} -> {}\\\".format(creator_id, name))\\n\\n indexed += 1\\n if not indexed % 100000:\\n log.info(\\\"Indexed {:,} authors\\\".format(indexed))\\n\\n # It's safe to call clear() here because no descendants will be\\n # accessed\\n elem.clear()\\n # Also eliminate now-empty references from the root node to elem\\n for ancestor in elem.xpath(\\\"ancestor-or-self::*\\\"):\\n while ancestor.getprevious() is not None:\\n del ancestor.getparent()[0]\\n\\n authors_idx.commit()\\n log.info(\\\"Indexed {:,} authors\\\".format(len(authors_idx)))\\n\\n del context\\n return authors_idx\",\n \"def build_index(path, limit=None):\\n\\n documents = {}\\n doc_lengths = {}\\n index = {}\\n j = 0 # Counter for articles\\n for i in range(0, 22):\\n if i >= 10:\\n file = open(path + \\\"reut2-0\\\" + str(i) + \\\".sgm\\\", encoding='latin-1')\\n else:\\n file = open(path + \\\"reut2-00\\\" + str(i) + \\\".sgm\\\", encoding='latin-1')\\n\\n # Parsing html pages and getting reuters tagged once\\n soup = BeautifulSoup(file, \\\"html.parser\\\")\\n articles = soup.find_all('reuters')\\n\\n for article in articles:\\n\\n body = \\\"\\\"\\n title = \\\"\\\"\\n newid = int(article['newid'])\\n\\n try:\\n body = article.body.get_text()\\n except AttributeError:\\n pass\\n\\n try:\\n title = article.title.get_text()\\n except AttributeError:\\n pass\\n\\n words_list = title + \\\"\\\\n\\\" + body\\n\\n # Adding title+body to documents dictionary\\n documents[newid] = words_list\\n\\n # Processing document and adding document lengths to dictionary\\n processed_doc = preprocess(documents[newid])\\n doc_lengths[newid] = len(processed_doc)\\n\\n # Adding word to index\\n for term in processed_doc:\\n if term in index:\\n term_freq, docs_dict = index[term]\\n\\n term_freq += 1\\n if newid in docs_dict:\\n docs_dict[newid] += 1\\n else:\\n docs_dict[newid] = 1\\n\\n index[term] = (term_freq, docs_dict)\\n else:\\n docs_dict = {newid: 1}\\n index[term] = (1, docs_dict)\\n j += 1\\n # Checking limit on articles\\n if limit is not None:\\n if j == limit:\\n break\\n\\n # Checking limit on articles\\n if limit is not None:\\n if j == limit:\\n break\\n\\n for term in index:\\n term_freq, docs_dict = index[term]\\n index[term] = [term_freq] + list(docs_dict.items())\\n\\n if limit is None:\\n save_obj(index, \\\"reuters_index\\\")\\n save_obj(documents, \\\"reuters_documents\\\")\\n save_obj(doc_lengths, \\\"reuters_doc_length\\\")\\n\\n return index\",\n \"def get_search_results(query):\\n global index, doc_names\\n result = ranked = list()\\n doc_list = set(doc_names.keys())\\n flag = 0\\n for word in query:\\n if word in index:\\n flag = 1\\n doc_list = doc_list.intersection(index[word].keys())\\n else:\\n return []\\n\\n if flag != 0:\\n for doc_id in doc_list:\\n positions = list()\\n for word in query:\\n positions.append(index[word][doc_id])\\n doc_result = [(doc_id, x) for x in position_merge(positions)]\\n result += doc_result\\n ranked = sorted(result, key=lambda x: (x[0], x[1]))\\n return ranked\",\n \"def _docMapping(self):\\n doc2quests = defaultdict(list)\\n for q, d in self.quest2doc.items():\\n doc2quests[d].append(q)\\n return doc2quests\",\n \"def pypi_indexes() -> IndexesDefinition:\\n return {}\",\n \"def shopee_dict(quote, results=100):\\n\\n brands = {}\\n total_items = 0\\n for brand in get_brands(quote, results):\\n if brand in brands:\\n brands[brand] += 1\\n else:\\n brands[brand] = 1\\n total_items += 1\\n return brands, total_items\",\n \"def fetch_index_data():\\n top_voters = Student.objects.annotate(num_ratings=Count('rating')).order_by('-num_ratings')[:6]\\n top_voters_json = [[e.user.username, e.num_ratings] for e in top_voters]\\n return {\\n 'study_space_list': StudySpace.objects.order_by('-avg_rating'),\\n 'top_student_voters': top_voters,\\n 'top_student_voters_list': top_voters_json,\\n 'login_form': LoginForm(),\\n 'user_form': UserForm(),\\n 'student_form': StudentForm()\\n }\",\n \"def get_matching_citizens():\\n try:\\n volunteer = request.headers.get('X-volunteer')\\n except:\\n return jsonify(\\\"X-volunteer header is missing\\\")\\n logger.info(\\\"X-volunteer header is missing\\\")\\n \\n vaibhav_interests = ['sleeping','home building','garden walks']\\n arsalan_interests = ['music','politics','science','reading']\\n senior_list = table.scan()[\\\"Items\\\"]\\n if request.headers['X-volunteer'] == \\\"Vaibhav\\\":\\n dummy_volunteer_interest_list = vaibhav_interests\\n matching_list = []\\n for senior in senior_list:\\n match = len(set(dummy_volunteer_interest_list) & set(senior['interests'])) / float(len(set(dummy_volunteer_interest_list) | set(senior['interests']))) * 100\\n if match >= 20:\\n matching_list.append(senior)\\n if len(matching_list) == 0:\\n return(jsonify(\\\"No matches found!\\\"))\\n logger.info(\\\"Vaibhav Matching citizens returned\\\")\\n elif request.headers['X-volunteer'] == \\\"Arsalan\\\":\\n dummy_volunteer_interest_list = arsalan_interests\\n matching_list = []\\n # senior_list = [post for post in posts.find()]\\n for senior in senior_list:\\n match = len(set(dummy_volunteer_interest_list) & set(senior['interests'])) / float(len(set(dummy_volunteer_interest_list) | set(senior['interests']))) * 100\\n if match >= 20:\\n matching_list.append(senior)\\n if len(matching_list) == 0:\\n return jsonify(\\\"No matches found!\\\")\\n logger.info(\\\"Arsalan Matching citizens returned\\\")\\n else:\\n return jsonify(\\\"Send a valid user header!\\\")\\n return jsonify(matching_list)\",\n \"def build_index():\\n pass\",\n \"def getCrowDistDict(self):\\n retDict = Distribution.getCrowDistDict(self)\\n retDict['p'] = self.p\\n return retDict\",\n \"def getCrowDistDict(self):\\n retDict = Distribution.getCrowDistDict(self)\\n retDict['p'] = self.p\\n return retDict\",\n \"def blastresults(br_naam1, br_naam2, br_pos1, br_pos2):\\n singles = {}\\n twogdeellijst = openfile(br_naam1 + br_naam2)\\n print(br_pos1, \\\"pos1\\\", br_pos2, \\\"pos2\\\")\\n for zin in twogdeellijst:\\n singles[(zin.split()[br_pos1] + \\\" \\\" + zin.split()[br_pos2])] = 0\\n return singles.keys()\",\n \"def get_data(self):\\n def _clean_search_hit(search_hit):\\n \\\"\\\"\\\"\\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\\n\\n :param search_hit:\\n :return Dictionary: A dictionary with the cleaned data\\n \\\"\\\"\\\"\\n\\n hit_name = search_hit.find(class_='hit-name')\\n hit_url = hit_name.get('href')\\n hit_id = hit_url.split('/')[-1]\\n name = hit_name.get_text().split(',')[0].title().split()\\n\\n current_city = search_hit.find(class_='hit-location').get_text().upper()\\n\\n # Find all Addresses for search result.\\n try:\\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\\n address = list({a.text.upper().replace('.', '') for a in address})\\n except AttributeError:\\n address = list()\\n\\n # find the address that is most likely the current main address.\\n try:\\n address.insert(0, address.pop(address.index(current_city)))\\n except ValueError:\\n address.insert(0, current_city)\\n\\n address = [\\n {\\n '@type': 'PostalAddress',\\n 'addressLocality': locality.title(),\\n 'addressRegion': region\\n } for locality, region in [a.split(', ') for a in address]]\\n\\n work_location = {'@type': 'Place'}\\n try:\\n work_location['name'] = search_hit\\\\\\n .find(class_='hit-work')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text()\\\\\\n .title()\\n except AttributeError:\\n work_location['name'] = ''\\n\\n alumni_of = {'@type': 'EducationalOrganization'}\\n try:\\n alumni_of['name'] = search_hit\\\\\\n .find(class_='hit-high-school')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text().title()\\n except AttributeError:\\n pass\\n\\n return {\\n '@id': hit_id,\\n '@type': 'Person',\\n 'name': ' '.join(name),\\n 'givenName': name[0],\\n 'middleName': ' '.join(name[1:-1]),\\n 'familyName': name[-1],\\n 'url': hit_url,\\n 'address': address,\\n 'workLocation': work_location,\\n 'alumniOf': alumni_of,\\n }\\n\\n def _refine_search(search_str, options):\\n \\\"\\\"\\\"\\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\\n press the option if found. Returns Boolean for found status\\n\\n :param search_str: str of the desired option.\\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\\n :return:\\n \\\"\\\"\\\"\\n search_str = search_str.upper()\\n logging.info(f'Looking for \\\\'{search_str}\\\\'')\\n try:\\n for option in options:\\n option_text = option.text.upper()\\n logging.info(f'Option Checked: {option_text}')\\n if search_str in option_text:\\n option.click()\\n time.sleep(2)\\n logging.info(f'Option Selected: {option_text}')\\n return True\\n else:\\n return False\\n except AttributeError:\\n return True\\n except StaleElementReferenceException as e:\\n ChromeCrash(e)\\n\\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\\n driver.get(self.url)\\n\\n \\\"\\\"\\\"\\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \\n \\\"\\\"\\\"\\n driver.fullscreen_window()\\n\\n # Refine the search by State\\n address_region = self.person.get('addressRegion', '')\\n address_region = STATES.get(address_region.upper(), address_region.upper())\\n region_options = driver\\\\\\n .find_element_by_class_name(\\\"STATE\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_region, region_options):\\n return False\\n\\n # Narrow the search by pressing a City option\\n address_locality = self.person.get('addressLocality').title()\\n locality_options = driver\\\\\\n .find_element_by_class_name(\\\"CITY\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_locality, locality_options):\\n return False\\n\\n \\\"\\\"\\\"\\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\\n be done in steps with a pause between movements to allow for loading. \\n Here it will first get the current location on the page, attempt to move down the page, and then check to\\n see if the location changed.\\n \\\"\\\"\\\"\\n\\n if self.auto_scroll and len(driver.find_elements_by_class_name(\\\"ais-InfiniteHits-item\\\")) > 15:\\n current_height, new_height = 0, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n while new_height != current_height:\\n # Scroll down to the bottom of the page\\n driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n\\n # Wait to load page\\n time.sleep(SCROLL_PAUSE_TIME)\\n\\n # Calculate new scroll height and compare with last scroll height\\n current_height, new_height = new_height, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n page_source = driver.page_source\\n page_soup = bs(page_source, 'html.parser')\\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\\n for i, search_result in enumerate(search_results):\\n search_results[i] = _clean_search_hit(search_result)\\n\\n self.data_from_website = pd.DataFrame(search_results)\\n self.data_from_website.set_index('@id', inplace=True)\\n return True\",\n \"def newAnalyzer():\\n analyzer = {'crimes': None,\\n 'dateIndex': None,\\n 'autors': None,\\n 'instrumentalness': None,\\n 'tempo':None,\\n 'liveness':None,\\n 'speechiness':None,\\n 'danceability':None,\\n 'valence':None,\\n 'loudness':None,\\n 'acousticness':None,\\n 'energy':None,\\n 'generos':None\\n }\\n\\n analyzer['crimes'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['ids'] = lt.newList('ARRAY_LIST', compareIds)\\n analyzer['dateIndex'] = om.newMap(omaptype='RBT',\\n comparefunction=compareDates)\\n\\n analyzer['autors'] = om.newMap(omaptype='RBT',\\n comparefunction=compareAUTOR)\\n\\n analyzer['instrumentalness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['tempo'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['liveness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['speechiness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['danceability'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n analyzer['valence'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['loudness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['acousticness'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt)\\n analyzer['energy'] = om.newMap(omaptype='RBT',\\n comparefunction=compareInt) \\n\\n analyzer['generos']= m.newMap(11,\\n maptype='CHAINING',\\n loadfactor=4.0)\\n \\n return analyzer\",\n \"def get_data(inp):\\n movies = __get_movies(inp)\\n series = __get_series(inp)\\n\\n exist_title(movies, series)\\n is_response_larger_than_max_results(movies, series)\\n\\n search_dict = {}\\n\\n if movies['Response'] != 'False':\\n for movie in movies['Search']:\\n search_dict.update({'movie': __get_title_info(movie['imdbID'])})\\n\\n if series['Response'] != 'False':\\n for show in series['Search']:\\n search_dict.update({'series': __get_title_info(show['imdbID'])})\\n\\n return search_dict\",\n \"def get_data(self):\\n\\n self.cur.execute('SELECT year, sex, education, score from vocabulary_scores;')\\n scores = dict()\\n education = dict()\\n count = dict()\\n\\n for row in self.cur :\\n if row[0] in scores:\\n if row[1] in scores[row[0]]:\\n scores[row[0]][row[1]] += int(row[3])\\n education[row[0]][row[1]] += int(row[2])\\n count[row[0]][row[1]] += 1\\n else:\\n scores[row[0]][row[1]] = int(row[3])\\n education[row[0]][row[1]] = int(row[2])\\n count[row[0]][row[1]] = 1\\n else:\\n # scores[year] = {gender: score}\\n scores[row[0]] = {row[1]: int(row[3])}\\n education[row[0]] = {row[1]: int(row[2])}\\n count[row[0]] = {row[1]: 1}\\n\\n scores, education = self.average_scores(scores, education, count)\\n\\n return scores, education\",\n \"def _FindBibEntries(self):\\n bibs = \\\" \\\".join(glob.glob(\\\"*.bib\\\"))\\n cat_process = subprocess.Popen(shlex.split(\\\"cat %s\\\" % bibs),\\n stdout=subprocess.PIPE)\\n grep_process = subprocess.Popen(shlex.split(\\\"grep ^@\\\"),\\n stdin=cat_process.stdout,\\n stdout=subprocess.PIPE)\\n cat_process.stdout.close()\\n grep2_process = subprocess.Popen(shlex.split(\\\"grep -vi @string\\\"),\\n stdin=grep_process.stdout,\\n stdout=subprocess.PIPE)\\n grep_process.stdout.close()\\n\\n lines = grep2_process.communicate()[0]\\n\\n ret = []\\n for l in lines.split(\\\"\\\\n\\\"):\\n ret.append(responses.BuildCompletionData(\\n re.sub(r\\\"@([A-Za-z]*)\\\\s*{\\\\s*([^,]*),.*\\\", r\\\"\\\\2\\\", l)\\n )\\n )\\n return ret\",\n \"def stats(self):\\r\\n return {}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.615179","0.5961631","0.59024155","0.58764994","0.5853894","0.5837746","0.5777888","0.573996","0.573696","0.5724125","0.5716872","0.56960845","0.5678323","0.5668764","0.56393546","0.5624318","0.5615255","0.5601827","0.5591958","0.55661815","0.55574524","0.5556497","0.55068237","0.5500237","0.54860747","0.5484418","0.54466873","0.5434954","0.5432653","0.54213023","0.5400136","0.53496546","0.5337897","0.5329234","0.5318188","0.53164685","0.5309765","0.530708","0.5302689","0.52806056","0.52723897","0.5272117","0.5268731","0.5263384","0.52572715","0.5255348","0.5248582","0.52428734","0.52339774","0.5223772","0.5218746","0.5204554","0.51944315","0.5194311","0.51887697","0.5180489","0.5178314","0.51766753","0.5175247","0.5170875","0.51630956","0.51592517","0.51579756","0.5148958","0.5138004","0.5136308","0.51311946","0.51309675","0.512815","0.51251453","0.51247865","0.5116475","0.51132053","0.51083463","0.5108079","0.5102357","0.50959766","0.50860703","0.50860065","0.50859314","0.5085657","0.50844216","0.5079753","0.5076935","0.5074586","0.5071434","0.5070397","0.5068535","0.5065374","0.5065227","0.50641197","0.50544286","0.50544286","0.5053695","0.50517255","0.504732","0.5046039","0.50414944","0.503697","0.5031389"],"string":"[\n \"0.615179\",\n \"0.5961631\",\n \"0.59024155\",\n \"0.58764994\",\n \"0.5853894\",\n \"0.5837746\",\n \"0.5777888\",\n \"0.573996\",\n \"0.573696\",\n \"0.5724125\",\n \"0.5716872\",\n \"0.56960845\",\n \"0.5678323\",\n \"0.5668764\",\n \"0.56393546\",\n \"0.5624318\",\n \"0.5615255\",\n \"0.5601827\",\n \"0.5591958\",\n \"0.55661815\",\n \"0.55574524\",\n \"0.5556497\",\n \"0.55068237\",\n \"0.5500237\",\n \"0.54860747\",\n \"0.5484418\",\n \"0.54466873\",\n \"0.5434954\",\n \"0.5432653\",\n \"0.54213023\",\n \"0.5400136\",\n \"0.53496546\",\n \"0.5337897\",\n \"0.5329234\",\n \"0.5318188\",\n \"0.53164685\",\n \"0.5309765\",\n \"0.530708\",\n \"0.5302689\",\n \"0.52806056\",\n \"0.52723897\",\n \"0.5272117\",\n \"0.5268731\",\n \"0.5263384\",\n \"0.52572715\",\n \"0.5255348\",\n \"0.5248582\",\n \"0.52428734\",\n \"0.52339774\",\n \"0.5223772\",\n \"0.5218746\",\n \"0.5204554\",\n \"0.51944315\",\n \"0.5194311\",\n \"0.51887697\",\n \"0.5180489\",\n \"0.5178314\",\n \"0.51766753\",\n \"0.5175247\",\n \"0.5170875\",\n \"0.51630956\",\n \"0.51592517\",\n \"0.51579756\",\n \"0.5148958\",\n \"0.5138004\",\n \"0.5136308\",\n \"0.51311946\",\n \"0.51309675\",\n \"0.512815\",\n \"0.51251453\",\n \"0.51247865\",\n \"0.5116475\",\n \"0.51132053\",\n \"0.51083463\",\n \"0.5108079\",\n \"0.5102357\",\n \"0.50959766\",\n \"0.50860703\",\n \"0.50860065\",\n \"0.50859314\",\n \"0.5085657\",\n \"0.50844216\",\n \"0.5079753\",\n \"0.5076935\",\n \"0.5074586\",\n \"0.5071434\",\n \"0.5070397\",\n \"0.5068535\",\n \"0.5065374\",\n \"0.5065227\",\n \"0.50641197\",\n \"0.50544286\",\n \"0.50544286\",\n \"0.5053695\",\n \"0.50517255\",\n \"0.504732\",\n \"0.5046039\",\n \"0.50414944\",\n \"0.503697\",\n \"0.5031389\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55858314"},"document_rank":{"kind":"string","value":"19"}}},{"rowIdx":79581,"cells":{"query":{"kind":"string","value":"return the last excution date of bibrank method"},"document":{"kind":"string","value":"def get_bibrankmethod_lastupdate(rank_method_code):\n query = \"\"\"select last_updated from rnkMETHOD where name ='%s'\"\"\" % rank_method_code\n last_update_time = run_sql(query)\n r = last_update_time[0][0]\n if r is None:\n return \"0000-00-00 00:00:00\"\n return r"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lastdate(self):\n if hasattr(self, \"_lastdate\"):\n return self._lastdate\n else:\n return None","def _format_last_br_date(self, data):\n raise NotImplementedError","def _get_eur_gbp_last_daily(self) -> None:\n data = _get_ecb_data(FREQUENCY_DAILY, _ten_days_ago(), _today())\n\n self.eur_gbp_last_day = _get_latest_ecb_rate(data)","def last_update(blank):\n today = date.today()\n return today.strftime('%A %B %d')","def _get_eur_gbp_last_month(self) -> None:\n last_month = _last_month()\n data = _get_ecb_data(FREQUENCY_MONTHLY, last_month, last_month)\n\n self.eur_gbp_last_month = _get_latest_ecb_rate(data)","def getLastDDtDate(self,cr,uid,brwseId):\n \n sql=\"\"\"SELECT ddt_number,ddt_date FROM STOCK_PICKING WHERE DDT_NUMBER IS NOT NULL AND ddt_sequence=%s ORDER BY DDT_DATE DESC;\"\"\" %(brwseId)\n cr.execute(sql)\n results=cr.dictfetchall()\n for result in results:\n return datetime.strptime(result.get('ddt_date','2000-01-01'),'%Y-%m-%d')\n return datetime.strptime('2000-01-01','%Y-%m-%d')","def bday(self):\n return self._bday","def getLastFunction(self) -> ghidra.program.model.listing.Function:\n ...","def last(self):\n rows = sorted(self, key=lambda x: x.date)\n return rows[-1]","def lastTick():","def end(self) -> datetime:\n return self.df.index[-1].to_pydatetime()","def max_drawdown_date(self) -> dt.date:\n mdd_date = (self.tsdf / self.tsdf.expanding(min_periods=1).max()).idxmin().values[0].astype(dt.datetime)\n return dt.datetime.fromtimestamp(mdd_date / 1e9).date()","def bios_date(self):\n\t\treturn self.__info_dict['info']['bios_date']['value']","def get_last_modified_rec(bibrank_method_lastupdate):\n query = \"\"\"SELECT id FROM bibrec\n WHERE modification_date >= '%s' \"\"\" % bibrank_method_lastupdate\n query += \"order by id ASC\"\n ilist = run_sql(query)\n return ilist","def last(self) -> 'outputs.CommitmentPeriodResponse':\n return pulumi.get(self, \"last\")","def last_contribution_date(self):\n from kitsune.customercare.models import Reply\n from kitsune.questions.models import Answer\n from kitsune.wiki.models import Revision\n\n dates = []\n\n # Latest Army of Awesome reply:\n try:\n aoa_reply = Reply.objects.filter(\n user=self.user).latest('created')\n dates.append(aoa_reply.created)\n except Reply.DoesNotExist:\n pass\n\n # Latest Support Forum answer:\n try:\n answer = Answer.objects.filter(\n creator=self.user).latest('created')\n dates.append(answer.created)\n except Answer.DoesNotExist:\n pass\n\n # Latest KB Revision edited:\n try:\n revision = Revision.objects.filter(\n creator=self.user).latest('created')\n dates.append(revision.created)\n except Revision.DoesNotExist:\n pass\n\n # Latest KB Revision reviewed:\n try:\n revision = Revision.objects.filter(\n reviewer=self.user).latest('reviewed')\n # Old revisions don't have the reviewed date.\n dates.append(revision.reviewed or revision.created)\n except Revision.DoesNotExist:\n pass\n\n if len(dates) == 0:\n return None\n\n return max(dates)","def testLastBillable(self):\n months = range(1, 13)\n first_days = [utils.add_timezone(datetime.datetime(2011, month, 1))\n for month in months]\n last_billable = [utils.get_last_billable_day(day).day \\\n for day in first_days]\n #should equal the last saturday of every month in 2011\n self.assertEqual(last_billable,\n [30, 27, 27, 24, 29, 26, 31, 28, 25, 30, 27, 25])","def date():\r\n try:\r\n curr_date = datetime.datetime.now().strftime(\"%b %d %Y\")\r\n except Exception as e:\r\n print(e)\r\n curr_date = False\r\n return curr_date","def get_latest_bar_datetime(self, symbol):\n raise NotImplementedError(\"Should implement get_latest_bar_datetime()\")","def first_blood_time(self):\n return self._get(\"first_blood_time\")","def dt_last_update(self):\n return self.last_update","def get_last_image_date(self) -> datetime.datetime:\n\n soup = self.load_page()\n header = soup.select('#comicwrap > div.comicnav.top > div')\n\n lst = header[0].text.split(\" \")[-3:]\n lst[0] = lst[0][lst[0].find('\\n') + 1:]\n\n return datetime.datetime.strptime(\" \".join(lst), '%B %d, %Y')","def getLastDDtDate(self):\n sql = \"\"\"SELECT ddt_number,ddt_date FROM STOCK_PICKING WHERE DDT_NUMBER IS NOT NULL ORDER BY DDT_DATE DESC LIMIT 1;\"\"\"\n self.env.cr.execute(sql)\n results = self.env.cr.dictfetchall()\n failReturnDate = datetime.strptime('2000-01-01', '%Y-%m-%d')\n for result in results:\n ddtDate = result.get('ddt_date', '2000-01-01')\n if not ddtDate:\n return failReturnDate\n return datetime.strptime(ddtDate, '%Y-%m-%d')\n return failReturnDate","def last_run(self):\n return self._last_run","def last_date(self):\n if self._last_date is None:\n raise ValueError(\"Run pick() method before access this property\")\n return self._last_date","def last_value():\n return blockchain[-1]","def completedPrecoveryMaxDate(instance):\n # Get a cursor from the DB connection.\n cursor = Conection.connect(DB_USER, DB_PASSWD, instance, DB_HOST)\n \n # Fetch the maximum MJD precovery has processed.\n sql = 'select max(epoch_mjd) from detections d, tracklet_attrib ta '\n sql += 'where d.det_id = ta.det_id and ta.tracklet_id in '\n sql += '(select tracklet_id from history_precoveries)'\n nRes = cursor.execute(sql)\n return(cursor.fetchone()[0])","def last_value(self):\n return 0","def get_last_result(self):\n return self.last_result","def last_change_date(self) -> Optional[str]:\n return pulumi.get(self, \"last_change_date\")","def last_tick_time(self):\n return self.last_tick_","def last_date_method(self) -> datetime.datetime:\n\n try:\n with open(os.path.join(THIS_DIR, \"data_file.json\"), mode='r') as json_file:\n try:\n data = datetime.datetime.strptime(json.load(json_file)[self.site],\n '%Y-%m-%d %H:%M:%S')\n return data\n\n except:\n return datetime.datetime(1970, 1, 1)\n except:\n with open(os.path.join(THIS_DIR, \"data_file.json\"), mode='a') as json_file:\n json.dump({}, json_file)\n json_file.close()\n\n return datetime.datetime(1970, 1, 1)","def Besuchsende(self):\n return self.getEnddate()","def get_date(self):\n raise Unimplemented()","def last_month():\n return datetime.now() + relativedelta(months=-1)","def date(self):","def get_last_dynamic_bias(self, x: np.ndarray,\n country_df: pd.DataFrame) -> np.ndarray:","def get_bundle_end_date(self):\n if not self._bundle_end_date:\n max_date = self.engine.execute(\n \"\"\"\n SELECT\n MAX(end_date)\n FROM (\n SELECT\n end_date\n FROM\n equities\n UNION\n SELECT\n end_date\n FROM\n futures_contracts\n )\n \"\"\"\n ).scalar()\n self._bundle_end_date = pd.Timestamp(max_date, tz=\"UTC\")\n\n return self._bundle_end_date","def end_date(self):\n return self.__end_date","def getSubmitTime():","def last_bloodwork(self):\n if hasattr(self, '_prior_bloodwork'):\n #in memory lookup\n return self._prior_bloodwork\n\n #memcached lookup\n last_bw = cache.get('%s_bloodwork' % (self._id), None)\n if last_bw == None:\n #it's null, so it's a cache miss.\n #do a requery\n pass\n elif last_bw == '[##Null##]':\n return None\n else:\n self._prior_bloodwork = CBloodwork.wrap(simplejson.loads(last_bw))\n return self._prior_bloodwork\n\n\n #requery\n bw_docs = CBloodwork.view('pactcarehq/patient_bloodwork', key=self.pact_id).all()\n bw_docs = sorted(bw_docs, key=lambda x: x['test_date'])\n if len(bw_docs) > 0:\n self._prior_bloodwork = bw_docs[0]\n cache.set('%s_bloodwork' % (self._id), simplejson.dumps(bw_docs[0].to_json()))\n return bw_docs[0]\n if self.prior_bloodwork.test_date == None:\n #this is a bit hacky, it should really be null, but since this is an added on object, to PactPatient, it doesn't show up as None\n #so we need to do an explicit check for the\n cache.set('%s_bloodwork' % (self._id), '[##Null##]')\n pass\n else:\n self._prior_bloodwork = self.prior_bloodwork\n return self.prior_bloodwork\n return None","def finish(self):\n return self.this_evaluation.moment","def get_latest_date(cls):\n\n return cls.query.order_by(desc(cls.date)).first().date","def get_last_blockchain_value():\n return blockchain[-1]","def get_last(self):\n self.accumulated_time_last = pg.time.get_ticks() - self.start_time_last\n return self.accumulated_time_last","def last_percept(self):\n return self.percept","def get_last(self, count):","def getLastData(self) -> ghidra.program.model.listing.Data:\n ...","def get_last_event(self):\n return self.last_event_code","def getLastInstruction(self) -> ghidra.program.model.listing.Instruction:\n ...","def getFirmwareDate(self): \n return self.firmware_date","def get_rates_grid_last_modified_date(self):\n return self.get_specific_column_value_from_grid(self.rates_grid_div_id, self.rates_grid_row_count, self.last_modified_column_name)","def get_last_run_date():\n last_date = demisto.getLastRun().get('last_date')\n demisto.debug('[BigQuery Debug] last_date is: {}'.format(last_date))\n\n if last_date is None:\n first_fetch_time = demisto.params().get('first_fetch_time', '1 days')\n first_fetch, _ = parse_date_range(first_fetch_time, date_format='%Y-%m-%d %H:%M:%S.%f')\n last_date = first_fetch\n demisto.debug('[BigQuery Debug] FIRST RUN - last_date is: {}'.format(last_date))\n\n return last_date","def last_update(self):\n return self._last_update","def last_update(self):\n return self._last_update","def getLastUpdate():\n swDB = switchdb.DB()\n lastupdate = swDB.getLastUpdate()\n swDB.close()\n return lastupdate","def get_last_time(self):\n \n return self._last","def get_last_observation(data):\n date, time = data[0], data[1]\n \n last_observation = \"{} at {} GMT\".format(date, time)\n return last_observation","def get_last_image_date(self) -> datetime.datetime:\n\n soup = self.load_page()\n header = soup.select('.posted-on')\n data = header[0].getText()\n return datetime.datetime.strptime(data, \" %A, %B %d, %Y at %I:%M%p\")","def _get_last_exported_date(self, cr, uid, external_session, context=None):\n return False","def last(cls):\n return Session.query(cls).order_by(desc(cls.date)).first()","def get_last_update(self):\n return self.ticker.all().order_by('-created').first()","def last_job_run(self):\n try:\n date_ = parse(self._data.get('last_job_run'))\n except (ValueError, TypeError):\n date_ = None\n return date_","def get_yara_after(self, date):\n return self.__make_api_call('get/yara/after/{}'.format(date))","def getDate(delta):\r\n debug.p(\"FUNC:check_db_alarm.getDate\")\r\n db = connectToDB()\r\n debug.p('Get Max Date in DB')\r\n date_entry = db.query(func.max(StatLogSCP1.date))\r\n debug.p(date_entry)\r\n #max_Date = c.fetchone() #This return a tuple, 0 item is a datetime.datetime object\r\n #maxDate = max_Date[0]\r\n deltaDate = date_entry - timedelta(minutes= delta)\r\n debug.p(\"**Leaving FUNC:check_db_alarm.getDate\")\r\n return deltaDate","def last_commit_date():\n return subprocess.check_output(['git', 'log', '-1', '--pretty=%ad',\n '--date=format:%d %b %H:%M', 'py/calendon']).decode().strip()","def lastsave(self):\n self.connect()\n self._write('LASTSAVE\\r\\n')\n return self._get_numeric_response()","def latest(self):\n return self.series.tail(1)[0]","def test_last_output_date(self):\n delta = datetime.timedelta(days=30)\n input_ = [\n self.indicator_record(date=datetime.date(2014, 7, 10), value=0.041063),\n self.indicator_record(date=datetime.date(2014, 7, 11), value=0.041063),\n self.indicator_record(date=datetime.date(2014, 7, 14), value=0.041063),\n ]\n output = self.expander._daily_workday_indicator_expander(input_)\n last_input_date = input_[-1].date + delta\n last_output_date = output[-1].date\n\n self.assertTrue(last_input_date < last_output_date)","def get_run_last_date(casename, run_path):\n # ---------------------------------------------------------------------\n logger.debug(\"get_run_last_date\")\n\n pattern = \"{0}.cpl.r.*.nc\".format(casename)\n cpl_files = sorted(glob.glob(os.path.join(run_path, pattern)))\n\n if cpl_files:\n _, cpl_file = os.path.split(cpl_files[-1])\n fparts = cpl_file.split(\".\")\n return fparts[-2]\n\n return \"0000-00-00\"","def __last_commit_date(self):\n return utils.run('git', ['log', '--all', '-1', '--format=%cI'],\n self.__project.location).rstrip()","def getLastFinishedBuild():","def _get_first_end_date(self):\n ins = acm.FInstrument['SACPI']\n market = \"internal\"\n start_year = acm.Time.DateAddDelta(acm.Time.FirstDayOfYear(self.start_date), 0, 0, -1)\n this_year_prices = acm.FPrice.Select(\"instrument='%s' and market='%s' and day>'%s' and day<'%s'\" \n % (ins.Name(), market, start_year, self.start_date))\n\n prices = sorted(this_year_prices, key=lambda price: price.Day(), reverse=True)\n last_sacpi_day = prices[0].Day()\n sacpi_plus_five_m = acm.Time.FirstDayOfMonth(acm.Time.DateAddDelta(last_sacpi_day, 0, 5, 0))\n return sacpi_plus_five_m","def get_latest_bt(self):\n data = self.collection.find({\"sensor_type\": \"bt\"}, {\"_id\": False}).sort(\n [(\"timestamp\", -1)]).limit(1)\n\n if self.is_empty(data):\n return None\n else:\n return data[0]","def last_month(self):\r\n return RecordsLastMonth(self)","def model_end_date(self):\n return self._model_end_date","def test_last_tracking():\n yoga_last_day = analytics.last_day(yoga_habits_trackings())\n run_last_day = analytics.last_day(run_habits_trackings())\n read_last_day = analytics.last_day(read_habits_trackings())\n meditation_last_day = analytics.last_day(meditation_habits_trackings())\n french_last_day = analytics.last_day(french_habits_trackings(), 5)\n\n assert yoga_last_day == date(2021, 7, 23)\n assert run_last_day == date(2021, 7, 12)\n assert read_last_day == date(2021, 7, 22)\n assert meditation_last_day == date(2021, 7, 26)\n assert french_last_day == date(2021, 7, 26)","def max_time(self):\n #{{{ function to return time of last sample\n\n if self.maxtime == -1:\n return stock.now()\n\n return self.maxtime","def event_date(self):\r\n return self.end_date","def getlastmod():\n raise NotImplementedError()","def get_latest_bar_datetime(self, symbol):\n try:\n bars_list = self.latest_symbol_data[symbol]\n except KeyError:\n print(\"That symbol is not available in the historical data\")\n raise\n else:\n return getattr(bars_list[-1][1], 'Time')","def get_last_init() -> str:\n return db[\"last_init\"]","def get_last_execution(self):\n try:\n base_path = \"/tmp/last.bkp\"\n # Open backup file for read and gets the string the number os saved objects\n # from last execution\n file = open(base_path, \"r\")\n result = file.read()\n file.close()\n except FileNotFoundError as err:\n logging.error(f\"[ERROR] File last.bkp not found. Traceback: {err}\")\n return False\n else:\n # Remove the EOL and convert to int before returning\n return int(result.strip(\"\\n\"))","def latest_close(df):\n return df['close'].iloc[-1]","def get_last_price(args):\n\tmarket = get_market(args)\n\trequest = api.get_ticker(market)\n\tif not request['message']:\n\t\tlast = str(request['result']['Last'])\n\t\treturn (last)\n\telse:\n\t\tprint(request['message'])\n\t\tsys.exit(0)","def last_accessed_date(self) -> Optional[str]:\n return pulumi.get(self, \"last_accessed_date\")","def date_rappro(self):\n try:\n opes = Ope.objects.filter(compte__id=self.id).filter(rapp__isnull=False).select_related('rapp').latest(\n 'date')\n return opes.rapp.date\n except Ope.DoesNotExist:\n return None","def get_last_lr(self):\n return self._lr_scheduler.get_last_lr()","def _latest_date(self):\n with self.connection as db:\n latest = db.execute(\n \"SELECT MIN(creation_date) FROM cache WHERE size IS NULL\"\n ).fetchone()[0]\n if latest is None:\n latest = db.execute(\n \"SELECT MAX(creation_date) FROM cache WHERE size IS NOT NULL\"\n ).fetchone()[0]\n if latest is None:\n latest = datetime.datetime.utcnow()\n return latest","def GetDownLast(self, *args, **kwargs):\n pass","def end_date(self):\n return self._end_date","def end_date(self):\n return self._end_date","def end_date(self):\n return self._end_date","def _get_date():\n return datetime.datetime.now()","def function(self):\n return self.generator.module.bounce_back","def last_time(self) -> datetime:\n return self.activities[-1].timestamp","def cal_end(self):\n return self.datetime_end","def get_date():\n return datetime.datetime.now()","def last_error_time(self) -> str:\n return pulumi.get(self, \"last_error_time\")","def next_birthday(self):\n if self.birthday == datetime.date.today():\n print(\"Happy Birthday!\")\n\n else:\n current_year = datetime.date.today().year\n birth_month = self.birthday.month\n birth_day = self.birthday.day\n print(datetime.date(current_year, birth_month, birth_day))"],"string":"[\n \"def lastdate(self):\\n if hasattr(self, \\\"_lastdate\\\"):\\n return self._lastdate\\n else:\\n return None\",\n \"def _format_last_br_date(self, data):\\n raise NotImplementedError\",\n \"def _get_eur_gbp_last_daily(self) -> None:\\n data = _get_ecb_data(FREQUENCY_DAILY, _ten_days_ago(), _today())\\n\\n self.eur_gbp_last_day = _get_latest_ecb_rate(data)\",\n \"def last_update(blank):\\n today = date.today()\\n return today.strftime('%A %B %d')\",\n \"def _get_eur_gbp_last_month(self) -> None:\\n last_month = _last_month()\\n data = _get_ecb_data(FREQUENCY_MONTHLY, last_month, last_month)\\n\\n self.eur_gbp_last_month = _get_latest_ecb_rate(data)\",\n \"def getLastDDtDate(self,cr,uid,brwseId):\\n \\n sql=\\\"\\\"\\\"SELECT ddt_number,ddt_date FROM STOCK_PICKING WHERE DDT_NUMBER IS NOT NULL AND ddt_sequence=%s ORDER BY DDT_DATE DESC;\\\"\\\"\\\" %(brwseId)\\n cr.execute(sql)\\n results=cr.dictfetchall()\\n for result in results:\\n return datetime.strptime(result.get('ddt_date','2000-01-01'),'%Y-%m-%d')\\n return datetime.strptime('2000-01-01','%Y-%m-%d')\",\n \"def bday(self):\\n return self._bday\",\n \"def getLastFunction(self) -> ghidra.program.model.listing.Function:\\n ...\",\n \"def last(self):\\n rows = sorted(self, key=lambda x: x.date)\\n return rows[-1]\",\n \"def lastTick():\",\n \"def end(self) -> datetime:\\n return self.df.index[-1].to_pydatetime()\",\n \"def max_drawdown_date(self) -> dt.date:\\n mdd_date = (self.tsdf / self.tsdf.expanding(min_periods=1).max()).idxmin().values[0].astype(dt.datetime)\\n return dt.datetime.fromtimestamp(mdd_date / 1e9).date()\",\n \"def bios_date(self):\\n\\t\\treturn self.__info_dict['info']['bios_date']['value']\",\n \"def get_last_modified_rec(bibrank_method_lastupdate):\\n query = \\\"\\\"\\\"SELECT id FROM bibrec\\n WHERE modification_date >= '%s' \\\"\\\"\\\" % bibrank_method_lastupdate\\n query += \\\"order by id ASC\\\"\\n ilist = run_sql(query)\\n return ilist\",\n \"def last(self) -> 'outputs.CommitmentPeriodResponse':\\n return pulumi.get(self, \\\"last\\\")\",\n \"def last_contribution_date(self):\\n from kitsune.customercare.models import Reply\\n from kitsune.questions.models import Answer\\n from kitsune.wiki.models import Revision\\n\\n dates = []\\n\\n # Latest Army of Awesome reply:\\n try:\\n aoa_reply = Reply.objects.filter(\\n user=self.user).latest('created')\\n dates.append(aoa_reply.created)\\n except Reply.DoesNotExist:\\n pass\\n\\n # Latest Support Forum answer:\\n try:\\n answer = Answer.objects.filter(\\n creator=self.user).latest('created')\\n dates.append(answer.created)\\n except Answer.DoesNotExist:\\n pass\\n\\n # Latest KB Revision edited:\\n try:\\n revision = Revision.objects.filter(\\n creator=self.user).latest('created')\\n dates.append(revision.created)\\n except Revision.DoesNotExist:\\n pass\\n\\n # Latest KB Revision reviewed:\\n try:\\n revision = Revision.objects.filter(\\n reviewer=self.user).latest('reviewed')\\n # Old revisions don't have the reviewed date.\\n dates.append(revision.reviewed or revision.created)\\n except Revision.DoesNotExist:\\n pass\\n\\n if len(dates) == 0:\\n return None\\n\\n return max(dates)\",\n \"def testLastBillable(self):\\n months = range(1, 13)\\n first_days = [utils.add_timezone(datetime.datetime(2011, month, 1))\\n for month in months]\\n last_billable = [utils.get_last_billable_day(day).day \\\\\\n for day in first_days]\\n #should equal the last saturday of every month in 2011\\n self.assertEqual(last_billable,\\n [30, 27, 27, 24, 29, 26, 31, 28, 25, 30, 27, 25])\",\n \"def date():\\r\\n try:\\r\\n curr_date = datetime.datetime.now().strftime(\\\"%b %d %Y\\\")\\r\\n except Exception as e:\\r\\n print(e)\\r\\n curr_date = False\\r\\n return curr_date\",\n \"def get_latest_bar_datetime(self, symbol):\\n raise NotImplementedError(\\\"Should implement get_latest_bar_datetime()\\\")\",\n \"def first_blood_time(self):\\n return self._get(\\\"first_blood_time\\\")\",\n \"def dt_last_update(self):\\n return self.last_update\",\n \"def get_last_image_date(self) -> datetime.datetime:\\n\\n soup = self.load_page()\\n header = soup.select('#comicwrap > div.comicnav.top > div')\\n\\n lst = header[0].text.split(\\\" \\\")[-3:]\\n lst[0] = lst[0][lst[0].find('\\\\n') + 1:]\\n\\n return datetime.datetime.strptime(\\\" \\\".join(lst), '%B %d, %Y')\",\n \"def getLastDDtDate(self):\\n sql = \\\"\\\"\\\"SELECT ddt_number,ddt_date FROM STOCK_PICKING WHERE DDT_NUMBER IS NOT NULL ORDER BY DDT_DATE DESC LIMIT 1;\\\"\\\"\\\"\\n self.env.cr.execute(sql)\\n results = self.env.cr.dictfetchall()\\n failReturnDate = datetime.strptime('2000-01-01', '%Y-%m-%d')\\n for result in results:\\n ddtDate = result.get('ddt_date', '2000-01-01')\\n if not ddtDate:\\n return failReturnDate\\n return datetime.strptime(ddtDate, '%Y-%m-%d')\\n return failReturnDate\",\n \"def last_run(self):\\n return self._last_run\",\n \"def last_date(self):\\n if self._last_date is None:\\n raise ValueError(\\\"Run pick() method before access this property\\\")\\n return self._last_date\",\n \"def last_value():\\n return blockchain[-1]\",\n \"def completedPrecoveryMaxDate(instance):\\n # Get a cursor from the DB connection.\\n cursor = Conection.connect(DB_USER, DB_PASSWD, instance, DB_HOST)\\n \\n # Fetch the maximum MJD precovery has processed.\\n sql = 'select max(epoch_mjd) from detections d, tracklet_attrib ta '\\n sql += 'where d.det_id = ta.det_id and ta.tracklet_id in '\\n sql += '(select tracklet_id from history_precoveries)'\\n nRes = cursor.execute(sql)\\n return(cursor.fetchone()[0])\",\n \"def last_value(self):\\n return 0\",\n \"def get_last_result(self):\\n return self.last_result\",\n \"def last_change_date(self) -> Optional[str]:\\n return pulumi.get(self, \\\"last_change_date\\\")\",\n \"def last_tick_time(self):\\n return self.last_tick_\",\n \"def last_date_method(self) -> datetime.datetime:\\n\\n try:\\n with open(os.path.join(THIS_DIR, \\\"data_file.json\\\"), mode='r') as json_file:\\n try:\\n data = datetime.datetime.strptime(json.load(json_file)[self.site],\\n '%Y-%m-%d %H:%M:%S')\\n return data\\n\\n except:\\n return datetime.datetime(1970, 1, 1)\\n except:\\n with open(os.path.join(THIS_DIR, \\\"data_file.json\\\"), mode='a') as json_file:\\n json.dump({}, json_file)\\n json_file.close()\\n\\n return datetime.datetime(1970, 1, 1)\",\n \"def Besuchsende(self):\\n return self.getEnddate()\",\n \"def get_date(self):\\n raise Unimplemented()\",\n \"def last_month():\\n return datetime.now() + relativedelta(months=-1)\",\n \"def date(self):\",\n \"def get_last_dynamic_bias(self, x: np.ndarray,\\n country_df: pd.DataFrame) -> np.ndarray:\",\n \"def get_bundle_end_date(self):\\n if not self._bundle_end_date:\\n max_date = self.engine.execute(\\n \\\"\\\"\\\"\\n SELECT\\n MAX(end_date)\\n FROM (\\n SELECT\\n end_date\\n FROM\\n equities\\n UNION\\n SELECT\\n end_date\\n FROM\\n futures_contracts\\n )\\n \\\"\\\"\\\"\\n ).scalar()\\n self._bundle_end_date = pd.Timestamp(max_date, tz=\\\"UTC\\\")\\n\\n return self._bundle_end_date\",\n \"def end_date(self):\\n return self.__end_date\",\n \"def getSubmitTime():\",\n \"def last_bloodwork(self):\\n if hasattr(self, '_prior_bloodwork'):\\n #in memory lookup\\n return self._prior_bloodwork\\n\\n #memcached lookup\\n last_bw = cache.get('%s_bloodwork' % (self._id), None)\\n if last_bw == None:\\n #it's null, so it's a cache miss.\\n #do a requery\\n pass\\n elif last_bw == '[##Null##]':\\n return None\\n else:\\n self._prior_bloodwork = CBloodwork.wrap(simplejson.loads(last_bw))\\n return self._prior_bloodwork\\n\\n\\n #requery\\n bw_docs = CBloodwork.view('pactcarehq/patient_bloodwork', key=self.pact_id).all()\\n bw_docs = sorted(bw_docs, key=lambda x: x['test_date'])\\n if len(bw_docs) > 0:\\n self._prior_bloodwork = bw_docs[0]\\n cache.set('%s_bloodwork' % (self._id), simplejson.dumps(bw_docs[0].to_json()))\\n return bw_docs[0]\\n if self.prior_bloodwork.test_date == None:\\n #this is a bit hacky, it should really be null, but since this is an added on object, to PactPatient, it doesn't show up as None\\n #so we need to do an explicit check for the\\n cache.set('%s_bloodwork' % (self._id), '[##Null##]')\\n pass\\n else:\\n self._prior_bloodwork = self.prior_bloodwork\\n return self.prior_bloodwork\\n return None\",\n \"def finish(self):\\n return self.this_evaluation.moment\",\n \"def get_latest_date(cls):\\n\\n return cls.query.order_by(desc(cls.date)).first().date\",\n \"def get_last_blockchain_value():\\n return blockchain[-1]\",\n \"def get_last(self):\\n self.accumulated_time_last = pg.time.get_ticks() - self.start_time_last\\n return self.accumulated_time_last\",\n \"def last_percept(self):\\n return self.percept\",\n \"def get_last(self, count):\",\n \"def getLastData(self) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def get_last_event(self):\\n return self.last_event_code\",\n \"def getLastInstruction(self) -> ghidra.program.model.listing.Instruction:\\n ...\",\n \"def getFirmwareDate(self): \\n return self.firmware_date\",\n \"def get_rates_grid_last_modified_date(self):\\n return self.get_specific_column_value_from_grid(self.rates_grid_div_id, self.rates_grid_row_count, self.last_modified_column_name)\",\n \"def get_last_run_date():\\n last_date = demisto.getLastRun().get('last_date')\\n demisto.debug('[BigQuery Debug] last_date is: {}'.format(last_date))\\n\\n if last_date is None:\\n first_fetch_time = demisto.params().get('first_fetch_time', '1 days')\\n first_fetch, _ = parse_date_range(first_fetch_time, date_format='%Y-%m-%d %H:%M:%S.%f')\\n last_date = first_fetch\\n demisto.debug('[BigQuery Debug] FIRST RUN - last_date is: {}'.format(last_date))\\n\\n return last_date\",\n \"def last_update(self):\\n return self._last_update\",\n \"def last_update(self):\\n return self._last_update\",\n \"def getLastUpdate():\\n swDB = switchdb.DB()\\n lastupdate = swDB.getLastUpdate()\\n swDB.close()\\n return lastupdate\",\n \"def get_last_time(self):\\n \\n return self._last\",\n \"def get_last_observation(data):\\n date, time = data[0], data[1]\\n \\n last_observation = \\\"{} at {} GMT\\\".format(date, time)\\n return last_observation\",\n \"def get_last_image_date(self) -> datetime.datetime:\\n\\n soup = self.load_page()\\n header = soup.select('.posted-on')\\n data = header[0].getText()\\n return datetime.datetime.strptime(data, \\\" %A, %B %d, %Y at %I:%M%p\\\")\",\n \"def _get_last_exported_date(self, cr, uid, external_session, context=None):\\n return False\",\n \"def last(cls):\\n return Session.query(cls).order_by(desc(cls.date)).first()\",\n \"def get_last_update(self):\\n return self.ticker.all().order_by('-created').first()\",\n \"def last_job_run(self):\\n try:\\n date_ = parse(self._data.get('last_job_run'))\\n except (ValueError, TypeError):\\n date_ = None\\n return date_\",\n \"def get_yara_after(self, date):\\n return self.__make_api_call('get/yara/after/{}'.format(date))\",\n \"def getDate(delta):\\r\\n debug.p(\\\"FUNC:check_db_alarm.getDate\\\")\\r\\n db = connectToDB()\\r\\n debug.p('Get Max Date in DB')\\r\\n date_entry = db.query(func.max(StatLogSCP1.date))\\r\\n debug.p(date_entry)\\r\\n #max_Date = c.fetchone() #This return a tuple, 0 item is a datetime.datetime object\\r\\n #maxDate = max_Date[0]\\r\\n deltaDate = date_entry - timedelta(minutes= delta)\\r\\n debug.p(\\\"**Leaving FUNC:check_db_alarm.getDate\\\")\\r\\n return deltaDate\",\n \"def last_commit_date():\\n return subprocess.check_output(['git', 'log', '-1', '--pretty=%ad',\\n '--date=format:%d %b %H:%M', 'py/calendon']).decode().strip()\",\n \"def lastsave(self):\\n self.connect()\\n self._write('LASTSAVE\\\\r\\\\n')\\n return self._get_numeric_response()\",\n \"def latest(self):\\n return self.series.tail(1)[0]\",\n \"def test_last_output_date(self):\\n delta = datetime.timedelta(days=30)\\n input_ = [\\n self.indicator_record(date=datetime.date(2014, 7, 10), value=0.041063),\\n self.indicator_record(date=datetime.date(2014, 7, 11), value=0.041063),\\n self.indicator_record(date=datetime.date(2014, 7, 14), value=0.041063),\\n ]\\n output = self.expander._daily_workday_indicator_expander(input_)\\n last_input_date = input_[-1].date + delta\\n last_output_date = output[-1].date\\n\\n self.assertTrue(last_input_date < last_output_date)\",\n \"def get_run_last_date(casename, run_path):\\n # ---------------------------------------------------------------------\\n logger.debug(\\\"get_run_last_date\\\")\\n\\n pattern = \\\"{0}.cpl.r.*.nc\\\".format(casename)\\n cpl_files = sorted(glob.glob(os.path.join(run_path, pattern)))\\n\\n if cpl_files:\\n _, cpl_file = os.path.split(cpl_files[-1])\\n fparts = cpl_file.split(\\\".\\\")\\n return fparts[-2]\\n\\n return \\\"0000-00-00\\\"\",\n \"def __last_commit_date(self):\\n return utils.run('git', ['log', '--all', '-1', '--format=%cI'],\\n self.__project.location).rstrip()\",\n \"def getLastFinishedBuild():\",\n \"def _get_first_end_date(self):\\n ins = acm.FInstrument['SACPI']\\n market = \\\"internal\\\"\\n start_year = acm.Time.DateAddDelta(acm.Time.FirstDayOfYear(self.start_date), 0, 0, -1)\\n this_year_prices = acm.FPrice.Select(\\\"instrument='%s' and market='%s' and day>'%s' and day<'%s'\\\" \\n % (ins.Name(), market, start_year, self.start_date))\\n\\n prices = sorted(this_year_prices, key=lambda price: price.Day(), reverse=True)\\n last_sacpi_day = prices[0].Day()\\n sacpi_plus_five_m = acm.Time.FirstDayOfMonth(acm.Time.DateAddDelta(last_sacpi_day, 0, 5, 0))\\n return sacpi_plus_five_m\",\n \"def get_latest_bt(self):\\n data = self.collection.find({\\\"sensor_type\\\": \\\"bt\\\"}, {\\\"_id\\\": False}).sort(\\n [(\\\"timestamp\\\", -1)]).limit(1)\\n\\n if self.is_empty(data):\\n return None\\n else:\\n return data[0]\",\n \"def last_month(self):\\r\\n return RecordsLastMonth(self)\",\n \"def model_end_date(self):\\n return self._model_end_date\",\n \"def test_last_tracking():\\n yoga_last_day = analytics.last_day(yoga_habits_trackings())\\n run_last_day = analytics.last_day(run_habits_trackings())\\n read_last_day = analytics.last_day(read_habits_trackings())\\n meditation_last_day = analytics.last_day(meditation_habits_trackings())\\n french_last_day = analytics.last_day(french_habits_trackings(), 5)\\n\\n assert yoga_last_day == date(2021, 7, 23)\\n assert run_last_day == date(2021, 7, 12)\\n assert read_last_day == date(2021, 7, 22)\\n assert meditation_last_day == date(2021, 7, 26)\\n assert french_last_day == date(2021, 7, 26)\",\n \"def max_time(self):\\n #{{{ function to return time of last sample\\n\\n if self.maxtime == -1:\\n return stock.now()\\n\\n return self.maxtime\",\n \"def event_date(self):\\r\\n return self.end_date\",\n \"def getlastmod():\\n raise NotImplementedError()\",\n \"def get_latest_bar_datetime(self, symbol):\\n try:\\n bars_list = self.latest_symbol_data[symbol]\\n except KeyError:\\n print(\\\"That symbol is not available in the historical data\\\")\\n raise\\n else:\\n return getattr(bars_list[-1][1], 'Time')\",\n \"def get_last_init() -> str:\\n return db[\\\"last_init\\\"]\",\n \"def get_last_execution(self):\\n try:\\n base_path = \\\"/tmp/last.bkp\\\"\\n # Open backup file for read and gets the string the number os saved objects\\n # from last execution\\n file = open(base_path, \\\"r\\\")\\n result = file.read()\\n file.close()\\n except FileNotFoundError as err:\\n logging.error(f\\\"[ERROR] File last.bkp not found. Traceback: {err}\\\")\\n return False\\n else:\\n # Remove the EOL and convert to int before returning\\n return int(result.strip(\\\"\\\\n\\\"))\",\n \"def latest_close(df):\\n return df['close'].iloc[-1]\",\n \"def get_last_price(args):\\n\\tmarket = get_market(args)\\n\\trequest = api.get_ticker(market)\\n\\tif not request['message']:\\n\\t\\tlast = str(request['result']['Last'])\\n\\t\\treturn (last)\\n\\telse:\\n\\t\\tprint(request['message'])\\n\\t\\tsys.exit(0)\",\n \"def last_accessed_date(self) -> Optional[str]:\\n return pulumi.get(self, \\\"last_accessed_date\\\")\",\n \"def date_rappro(self):\\n try:\\n opes = Ope.objects.filter(compte__id=self.id).filter(rapp__isnull=False).select_related('rapp').latest(\\n 'date')\\n return opes.rapp.date\\n except Ope.DoesNotExist:\\n return None\",\n \"def get_last_lr(self):\\n return self._lr_scheduler.get_last_lr()\",\n \"def _latest_date(self):\\n with self.connection as db:\\n latest = db.execute(\\n \\\"SELECT MIN(creation_date) FROM cache WHERE size IS NULL\\\"\\n ).fetchone()[0]\\n if latest is None:\\n latest = db.execute(\\n \\\"SELECT MAX(creation_date) FROM cache WHERE size IS NOT NULL\\\"\\n ).fetchone()[0]\\n if latest is None:\\n latest = datetime.datetime.utcnow()\\n return latest\",\n \"def GetDownLast(self, *args, **kwargs):\\n pass\",\n \"def end_date(self):\\n return self._end_date\",\n \"def end_date(self):\\n return self._end_date\",\n \"def end_date(self):\\n return self._end_date\",\n \"def _get_date():\\n return datetime.datetime.now()\",\n \"def function(self):\\n return self.generator.module.bounce_back\",\n \"def last_time(self) -> datetime:\\n return self.activities[-1].timestamp\",\n \"def cal_end(self):\\n return self.datetime_end\",\n \"def get_date():\\n return datetime.datetime.now()\",\n \"def last_error_time(self) -> str:\\n return pulumi.get(self, \\\"last_error_time\\\")\",\n \"def next_birthday(self):\\n if self.birthday == datetime.date.today():\\n print(\\\"Happy Birthday!\\\")\\n\\n else:\\n current_year = datetime.date.today().year\\n birth_month = self.birthday.month\\n birth_day = self.birthday.day\\n print(datetime.date(current_year, birth_month, birth_day))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62563515","0.6135235","0.5993334","0.59568703","0.58971405","0.5813312","0.58071977","0.57964516","0.577513","0.576336","0.5753354","0.57206935","0.5710407","0.5695503","0.56912684","0.56911653","0.56623274","0.5656828","0.56515944","0.5618803","0.56027627","0.5596017","0.5579425","0.5576252","0.557067","0.5547065","0.55360794","0.5533532","0.55265355","0.55151415","0.55095947","0.55090326","0.54995924","0.5499414","0.5492734","0.5481941","0.54735726","0.5467783","0.54578274","0.5453471","0.5453367","0.5428897","0.5426773","0.5418759","0.5413057","0.5408673","0.54083145","0.5403297","0.53921396","0.53916734","0.53825","0.53800356","0.5376654","0.53705454","0.53705454","0.5369532","0.5365745","0.53618723","0.5359117","0.53498125","0.5345495","0.5337614","0.53269154","0.532477","0.5323501","0.53227216","0.5316404","0.5309105","0.5307316","0.5301697","0.5296476","0.5295525","0.5293719","0.5293011","0.5289533","0.52864075","0.5283054","0.5275315","0.52624303","0.52619624","0.52571774","0.5255123","0.52486926","0.5246608","0.52434534","0.52347016","0.5229427","0.52247053","0.5218606","0.5214345","0.5213454","0.5213454","0.5213454","0.5212387","0.5209532","0.52066153","0.5203819","0.520144","0.52004415","0.5194895"],"string":"[\n \"0.62563515\",\n \"0.6135235\",\n \"0.5993334\",\n \"0.59568703\",\n \"0.58971405\",\n \"0.5813312\",\n \"0.58071977\",\n \"0.57964516\",\n \"0.577513\",\n \"0.576336\",\n \"0.5753354\",\n \"0.57206935\",\n \"0.5710407\",\n \"0.5695503\",\n \"0.56912684\",\n \"0.56911653\",\n \"0.56623274\",\n \"0.5656828\",\n \"0.56515944\",\n \"0.5618803\",\n \"0.56027627\",\n \"0.5596017\",\n \"0.5579425\",\n \"0.5576252\",\n \"0.557067\",\n \"0.5547065\",\n \"0.55360794\",\n \"0.5533532\",\n \"0.55265355\",\n \"0.55151415\",\n \"0.55095947\",\n \"0.55090326\",\n \"0.54995924\",\n \"0.5499414\",\n \"0.5492734\",\n \"0.5481941\",\n \"0.54735726\",\n \"0.5467783\",\n \"0.54578274\",\n \"0.5453471\",\n \"0.5453367\",\n \"0.5428897\",\n \"0.5426773\",\n \"0.5418759\",\n \"0.5413057\",\n \"0.5408673\",\n \"0.54083145\",\n \"0.5403297\",\n \"0.53921396\",\n \"0.53916734\",\n \"0.53825\",\n \"0.53800356\",\n \"0.5376654\",\n \"0.53705454\",\n \"0.53705454\",\n \"0.5369532\",\n \"0.5365745\",\n \"0.53618723\",\n \"0.5359117\",\n \"0.53498125\",\n \"0.5345495\",\n \"0.5337614\",\n \"0.53269154\",\n \"0.532477\",\n \"0.5323501\",\n \"0.53227216\",\n \"0.5316404\",\n \"0.5309105\",\n \"0.5307316\",\n \"0.5301697\",\n \"0.5296476\",\n \"0.5295525\",\n \"0.5293719\",\n \"0.5293011\",\n \"0.5289533\",\n \"0.52864075\",\n \"0.5283054\",\n \"0.5275315\",\n \"0.52624303\",\n \"0.52619624\",\n \"0.52571774\",\n \"0.5255123\",\n \"0.52486926\",\n \"0.5246608\",\n \"0.52434534\",\n \"0.52347016\",\n \"0.5229427\",\n \"0.52247053\",\n \"0.5218606\",\n \"0.5214345\",\n \"0.5213454\",\n \"0.5213454\",\n \"0.5213454\",\n \"0.5212387\",\n \"0.5209532\",\n \"0.52066153\",\n \"0.5203819\",\n \"0.520144\",\n \"0.52004415\",\n \"0.5194895\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6489557"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79582,"cells":{"query":{"kind":"string","value":"return the list of recods which have been modified after the last exec of bibrank method. The result is expected to have ascending num order."},"document":{"kind":"string","value":"def get_last_modified_rec(bibrank_method_lastupdate):\n query = \"\"\"SELECT id FROM bibrec\n WHERE modification_date >= '%s' \"\"\" % bibrank_method_lastupdate\n query += \"order by id ASC\"\n ilist = run_sql(query)\n return ilist"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def updateResorts(self):\n self.resorts = []\n for x in self._dfs_non_recursive(self.V):\n pass\n #print(\"visited\", x)","def getChanges():","def reset(self) -> List[int]:","def get_blists(self):\n return self.blists[:]","def getchangableobslist(self):\n return self.__changableobslist","def get_items_changed(self, base_ref='HEAD'):\n command = ['diff-index', '--name-only',\n '--cached', base_ref]\n res = self.run(command)\n items = res.split('\\n') if res else []\n return items","def order_bfs(self) -> List[Nodo]:\n\n # Nodos por buscar, es una cola\n pending: List[Nodo] = [self.root]\n # Nodos ya visitados\n visited: List[Nodo] = []\n\n # Mientras la cola tenga items\n while len(pending) > 0:\n # Procesar el primer elemento\n curr = pending.pop(0)\n visited.append(curr)\n\n # Agregar los hijos no visitados del nodo a la cola\n for child in curr.children:\n if child in visited:\n continue\n pending.append(child)\n\n return visited","def set_rank_order(self):\n for k in self._run:\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\n tot_res = len(self._run[k])\n for r in self._run[k]:\n r.set_score(tot_res - int(r.get_rank()) + 1)\n print r.get_str()","def get_change_list():\n today = date.today()\n current_month = today.month\n current_year = today.year\n current_month_pred = dict()\n previous_month_pred = dict()\n change = list()\n commodity_list = [\"arhar\", \"bajra\", \"barley\", \"copra\", \"cotton\", \"sesamum\", \"gram\", \"groundnut\",\n \"jowar\", \"maize\", \"masoor\", \"moong\", \"niger\", \"paddy\", \"ragi\", \"rape\", \"jute\",\n \"safflower\", \"soyabean\", \"sugarcane\", \"sunflower\", \"urad\", \"wheat\"]\n if current_month == 1:\n previous_month = 12\n previous_year = current_year - 1\n else:\n previous_month = current_month - 1\n previous_year = current_year\n for crop in commodity_list:\n model_path = \"static/models/\" + crop + \".joblib\"\n model = load(model_path)\n current_month_wpi = model.predict(pd.DataFrame([current_month, current_year]).T)[0]\n current_month_pred[crop] = current_month_wpi\n previous_month_wpi = model.predict(pd.DataFrame([previous_month, previous_year]).T)[0]\n previous_month_pred[crop] = previous_month_wpi\n change.append((((current_month_wpi - previous_month_wpi) * 100 / previous_month_wpi), crop))\n sorted_change = change\n return sorted_change, current_month_pred, previous_month_pred","def reversesort(self):\n ...","def reset(self) -> List[int]:\n return self.nums","def check_order(racers):\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\n a = 1\n b = 1\n for r in racers_orderd:\n if r.group == \"ALL\":\n r.rank = str(a)\n a += 1\n else:\n r.rank = str(b)\n b += 1\n return racers_orderd","def reset(self) -> List[int]:\n return self.orignial #Return the original copy.","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def banks(self):\n return self.__banks","def renumber_cells_ordering(self):\n Nactive = sum(~self.cells['deleted'])\n return np.argsort( self.cells['deleted'],kind='mergesort')[:Nactive]","def get_flank_modifications(self):\n\n return self.modifications","def rank_hist(self):\n self.ranks = {}\n for card in self.cards:\n self.ranks[card.rank] = self.ranks.get(card.rank, 0) + 1","def reset(self) -> List[int]:\n return self.nums","def dirty_branches(self):\n # If no course index has been set, then no branches have changed\n if self.index is None:\n return []\n\n # If there was no index in the database to start with, then all branches\n # are dirty by definition\n if self.initial_index is None:\n return list(self.index.get('versions', {}).keys())\n\n # Return branches whose ids differ between self.index and self.initial_index\n return [\n branch\n for branch, _id\n in self.index.get('versions', {}).items()\n if self.initial_index.get('versions', {}).get(branch) != _id\n ]","def module_ids(self, rev=False):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tids = sorted(list(self.shutit_map.keys()),key=lambda module_id: self.shutit_map[module_id].run_order)\n\t\tif rev:\n\t\t\treturn list(reversed(ids))\n\t\treturn ids","def get_compact(self):\n c = []\n m = len(self.revs)\n a = self.revs[0]\n i = 1\n while i <= m:\n if i == m or self.revs[i-1]+1 < self.revs[i]:\n if a == self.revs[i-1]:\n c += [a]\n else:\n c += [\"%s-%s\" % (a, self.revs[i-1])]\n if i < m:\n a = self.revs[i]\n i += 1\n return c","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def madebanks(self):\n made = []\n for bank in self.__banks:\n if bank.made():\n made.append(bank)\n return made","def recorders(self):\n return self.__recorders","def initialized_revs(self):\n initialized_revs = []\n old_len = len(self._initial_value)\n for rev, val in zip(self._changed_revs, self._changed_values):\n if len(val) != old_len:\n initialized_revs.append(rev)\n old_len = len(val)\n return initialized_revs","def gather_referring_orders (gdbval):\n# TODO: Somehow also note speculative references and attributes in\n# general\n vec = gdbval[\"referring\"]\n return [int(i[\"referring\"][\"order\"]) for i in vec_iter(vec)]","def modified_stats(self):\n return self._counts_per_month('last_modified')","def get_reward_list(self):\n return self._gameRewards","def get_reward_list(self):\n return self._gameRewards","def reset(self) -> List[int]:\n pass","def posisions(self):\n return self.preorder() # return entire preorder iteration","def copie_goban(self):\n new_goban = []\n for old_lines in self.game.goban.cell:\n new_goban.append(list(old_lines))\n return(new_goban)","def reset(self):\n return self.nums","def _addReferees(self):\n a_ranks = self.division.GetGroupsRanks(['A'])\n b_ranks = self.division.GetGroupsRanks(['B'])\n c_ranks = self.division.GetGroupsRanks(['C'])\n d_ranks = self.division.GetGroupsRanks(['D'])\n\n self._GroupAddReferees('7th', [d_ranks[1]])\n self._GroupAddReferees('5th', [d_ranks[2]])\n\n self._GroupAddReferees('3rd',[d_ranks[0]])\n self._GroupAddReferees('final',[c_ranks[2]])","def get_reporeted_casks():\n return load_formula_list(REPORTED_CASKS_FILE)","def reset(self) -> List[int]:\n return self.origin","def gather_references_orders (gdbval):\n# TODO: Somehow also note speculative references and attributes in\n# general\n vec = gdbval[\"references\"]\n return [int(i[\"referred\"][\"order\"]) for i in vec_iter(vec)]","def backfill_revlist(buildername, revisions, times=1, dry_run=False):\n new_revisions_list = []\n repo_name = query_repo_name_from_buildername(buildername)\n LOG.info(\"We want to find a successful job for '%s' in this range: [%s:%s]\" %\n (buildername, revisions[0], revisions[-1]))\n for rev in revisions:\n jobs = query_jobs(repo_name, rev)\n matching_jobs = _matching_jobs(buildername, jobs)\n successful_jobs = _status_summary(matching_jobs)[0]\n if successful_jobs > 0:\n LOG.info(\"The last succesful job for buildername '%s' is on %s\" %\n (buildername, rev))\n # We don't need to look any further in the list of revisions\n break\n else:\n new_revisions_list.append(rev)\n\n LOG.info(\"We only need to backfill %s\" % new_revisions_list)\n return new_revisions_list","def backorders(self):\n return self._backorders","def positions(self):\n return self.preorder() # return entire preorder iteration","def reset(self):\n return self.nums","def getOldCodeList(self):\n tmp = []\n for child in self.children:\n tmp.extend(child.getOldCodeList())\n return tmp","async def check_back_refreshes(self):\n\n remove_indexes = []\n updated_pairs = set()\n refreshes = 0\n\n for index, refresh in enumerate(self.back_refreshes):\n pair = refresh['pair']\n end_time = refresh['end']\n start_time = refresh['start']\n\n last_time = self.close_times[config['base_pairs'][0]][-1]\n if last_time > refresh['time']:\n refresh_num = int(last_time - start_time) // config['tick_interval_secs']\n remove_indexes.append(index)\n\n if pair in self.close_times:\n refreshes += 1\n ticks = await self.api.get_ticks(pair, refresh_num)\n overwritten = await self._overwrite_tick_data(pair, start_time, end_time, ticks)\n if overwritten:\n self.log.info(\"{} back-refreshed {} ticks.\", pair, overwritten)\n updated_pairs.add(pair)\n\n if refreshes >= config['back_refresh_max_per_tick']:\n break\n\n for index in reversed(remove_indexes):\n del self.back_refreshes[index]\n\n if remove_indexes:\n self.save_attr('back_refreshes')\n\n return updated_pairs","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def returnedbanks(self):\n returned = []\n for bank in self.__banks:\n if bank.returned():\n returned.append(bank)\n return returned","def get_changed_nodes(self):\n\n return self._changed_nodes","def _shuffle_roidb_inds(self):\n ## 设置cur来指向现在访问的长度\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\n self._cur = 0","def reset(self) -> List[int]:\n self.q = self.nums[::]\n return self.q","def reset(self) -> List[int]:\n self.q = self.nums[::]\n return self.q","def reset(self) -> List[int]:\n self.q = self.nums[::]\n return self.q","def get_last_calls(self):\r\n last_calls = self.last_calls\r\n self.last_calls = []\r\n return last_calls","def _update_revision_list_indexes(self):\n for i, rev in enumerate(self.revisions):\n rev.list_index = i\n for i in xrange(len(self.revisions)):\n if i:\n self.revisions[i].previous_revision = self.revisions[i - 1]\n if i < len(self.revisions) - 1:\n self.revisions[i].next_revision = self.revisions[i + 1]","def ranks(cls):\n ranked = []\n for team in sorted(dbsession.query(cls).order_by(desc(cls.money)).all()):\n if not team.locked:\n ranked.append(team)\n return ranked","def getModifiedBlockList(self):\n if self.binary:\n return []\n block_list = []\n for child in self.children:\n old_line_list = [line.strip() for line, color in child.getOldCodeList()\n if line is not None and color in (MODIFIED_DIFF_COLOR,\n DELETED_DIFF_COLOR)]\n new_line_list = [line.strip() for line, color in child.getNewCodeList()\n if line is not None and color in (MODIFIED_DIFF_COLOR,\n ADDITION_DIFF_COLOR)]\n if old_line_list or new_line_list:\n block_list.append((child,(old_line_list, new_line_list)))\n return block_list","def history(self):\n return _spacegrant_swig.NRZI_sptr_history(self)","def getModificationNumber(self) -> long:\n ...","def get_biases(self):\n return []","def unsaved_changes(self) -> int:\n return (\n abs(self._snapshot_index - self._last_save_snapshot)\n + self._branch_save_count\n )","def findRelativeRanks(nums):\n compare_lst = copy.deepcopy(nums)\n compare_lst.sort(reverse=True)\n for i in nums:\n compare_index = compare_lst.index(i)\n nums_index = nums.index(i)\n if compare_index > 2:\n nums[nums_index] = str(compare_index + 1)\n elif compare_index == 0:\n nums[nums_index] = 'Gold Medal'\n elif compare_index == 1:\n nums[nums_index] = 'Silver Medal'\n else:\n nums[nums_index] = 'Bronze Medal'\n return nums","def getPriorityList(self):","def order_dfs(self) -> List[Nodo]:\n # Nodos por buscar, es una pila\n pending: List[Nodo] = [self.root]\n # Nodos ya visitados\n visited: List[Nodo] = []\n\n # Mientras la pila tenga items\n while len(pending) > 0:\n # Procesar el primer elemento\n curr = pending.pop()\n visited.append(curr)\n\n # Agregar los hijos no visitados del nodo a la pila\n for child in reversed(curr.children):\n if child in visited:\n continue\n pending.append(child)\n\n return visited","def get_history(self):\n return self.__history[:]","def findRanks(toBeRanked, values):\n\treturn list(map(lambda e: findRank(e, values), toBeRanked))","def keys(self):\r\n if self.indexchanged_key or not self.sortedkeys:\r\n self.indexchanged_key = False\r\n self.sortedkeys = sorted(self.get_keys())\r\n return self.sortedkeys\r\n return self.sortedkeys","def get_rexps(self):\n return self.rexps","def get_ranks(d): \n raise NotImplementedError(\"Problem 3 Incomplete\")","def reorder( self ):\n self.sorted.sort(self.compareFunction)","def reset(self) -> List[int]:\n self.array = deepcopy(self.original)\n return self.array","def reindex(self):","def reindex(self):","def get_modified_list(yearly_footprint, modified_footprint, age):\n # modification_factor = yearly_footprint = modified_footprint\n modified_list = [yearly_footprint]\n\n for i in range(1, 73):\n if i <= age - 18:\n modified_list.append(modified_list[i-1] + yearly_footprint)\n else:\n modified_list.append(modified_list[i-1] + modified_footprint)\n\n return modified_list","def reward_buffer(self) -> Deque[float]:\n return self._reward_buffer","def historial(self):\n return self.revision_set.order_by('-revision')[1:]","def redo_act(self):\n\n return self.history[self.position + 1]","def get_history(self):\n msg_ids = self._records.keys()\n # Remove any that do not have a submitted timestamp.\n # This is extremely unlikely to happen,\n # but it seems to come up in some tests on VMs.\n msg_ids = [m for m in msg_ids if self._records[m]['submitted'] is not None]\n return sorted(msg_ids, key=lambda m: self._records[m]['submitted'])","def reset(self):\n self.init_nums = copy.deepcopy(self.orig)\n return(self.init_nums)","def shuffle(self) -> List[int]:","def get_bc_lens(ids_bcs_added_field):\r\n\r\n bc_index = 0\r\n bc_lens = [len(curr_bc[bc_index])\r\n for curr_bc in ids_bcs_added_field.keys()]\r\n bc_lens = list(set(bc_lens))\r\n bc_lens.sort(reverse=True)\r\n return bc_lens","def restore(self, removals):\r\n for B, b in removals:\r\n self.curr_domains[B].append(b)","def positions(self):\n return self.preorder()","def history(self):\n return _spacegrant_swig.invert_bit_sptr_history(self)","def force_no_ordering(self):\n return []","def getListModifiedDates(self):\n return _libsbml.ModelHistory_getListModifiedDates(self)","def tanks(self):\n return self._node_reg.tanks"],"string":"[\n \"def updateResorts(self):\\n self.resorts = []\\n for x in self._dfs_non_recursive(self.V):\\n pass\\n #print(\\\"visited\\\", x)\",\n \"def getChanges():\",\n \"def reset(self) -> List[int]:\",\n \"def get_blists(self):\\n return self.blists[:]\",\n \"def getchangableobslist(self):\\n return self.__changableobslist\",\n \"def get_items_changed(self, base_ref='HEAD'):\\n command = ['diff-index', '--name-only',\\n '--cached', base_ref]\\n res = self.run(command)\\n items = res.split('\\\\n') if res else []\\n return items\",\n \"def order_bfs(self) -> List[Nodo]:\\n\\n # Nodos por buscar, es una cola\\n pending: List[Nodo] = [self.root]\\n # Nodos ya visitados\\n visited: List[Nodo] = []\\n\\n # Mientras la cola tenga items\\n while len(pending) > 0:\\n # Procesar el primer elemento\\n curr = pending.pop(0)\\n visited.append(curr)\\n\\n # Agregar los hijos no visitados del nodo a la cola\\n for child in curr.children:\\n if child in visited:\\n continue\\n pending.append(child)\\n\\n return visited\",\n \"def set_rank_order(self):\\n for k in self._run:\\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\\n tot_res = len(self._run[k])\\n for r in self._run[k]:\\n r.set_score(tot_res - int(r.get_rank()) + 1)\\n print r.get_str()\",\n \"def get_change_list():\\n today = date.today()\\n current_month = today.month\\n current_year = today.year\\n current_month_pred = dict()\\n previous_month_pred = dict()\\n change = list()\\n commodity_list = [\\\"arhar\\\", \\\"bajra\\\", \\\"barley\\\", \\\"copra\\\", \\\"cotton\\\", \\\"sesamum\\\", \\\"gram\\\", \\\"groundnut\\\",\\n \\\"jowar\\\", \\\"maize\\\", \\\"masoor\\\", \\\"moong\\\", \\\"niger\\\", \\\"paddy\\\", \\\"ragi\\\", \\\"rape\\\", \\\"jute\\\",\\n \\\"safflower\\\", \\\"soyabean\\\", \\\"sugarcane\\\", \\\"sunflower\\\", \\\"urad\\\", \\\"wheat\\\"]\\n if current_month == 1:\\n previous_month = 12\\n previous_year = current_year - 1\\n else:\\n previous_month = current_month - 1\\n previous_year = current_year\\n for crop in commodity_list:\\n model_path = \\\"static/models/\\\" + crop + \\\".joblib\\\"\\n model = load(model_path)\\n current_month_wpi = model.predict(pd.DataFrame([current_month, current_year]).T)[0]\\n current_month_pred[crop] = current_month_wpi\\n previous_month_wpi = model.predict(pd.DataFrame([previous_month, previous_year]).T)[0]\\n previous_month_pred[crop] = previous_month_wpi\\n change.append((((current_month_wpi - previous_month_wpi) * 100 / previous_month_wpi), crop))\\n sorted_change = change\\n return sorted_change, current_month_pred, previous_month_pred\",\n \"def reversesort(self):\\n ...\",\n \"def reset(self) -> List[int]:\\n return self.nums\",\n \"def check_order(racers):\\n racers_orderd = sorted(racers, key=lambda x: (x.group, x.time))\\n a = 1\\n b = 1\\n for r in racers_orderd:\\n if r.group == \\\"ALL\\\":\\n r.rank = str(a)\\n a += 1\\n else:\\n r.rank = str(b)\\n b += 1\\n return racers_orderd\",\n \"def reset(self) -> List[int]:\\n return self.orignial #Return the original copy.\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def banks(self):\\n return self.__banks\",\n \"def renumber_cells_ordering(self):\\n Nactive = sum(~self.cells['deleted'])\\n return np.argsort( self.cells['deleted'],kind='mergesort')[:Nactive]\",\n \"def get_flank_modifications(self):\\n\\n return self.modifications\",\n \"def rank_hist(self):\\n self.ranks = {}\\n for card in self.cards:\\n self.ranks[card.rank] = self.ranks.get(card.rank, 0) + 1\",\n \"def reset(self) -> List[int]:\\n return self.nums\",\n \"def dirty_branches(self):\\n # If no course index has been set, then no branches have changed\\n if self.index is None:\\n return []\\n\\n # If there was no index in the database to start with, then all branches\\n # are dirty by definition\\n if self.initial_index is None:\\n return list(self.index.get('versions', {}).keys())\\n\\n # Return branches whose ids differ between self.index and self.initial_index\\n return [\\n branch\\n for branch, _id\\n in self.index.get('versions', {}).items()\\n if self.initial_index.get('versions', {}).get(branch) != _id\\n ]\",\n \"def module_ids(self, rev=False):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tids = sorted(list(self.shutit_map.keys()),key=lambda module_id: self.shutit_map[module_id].run_order)\\n\\t\\tif rev:\\n\\t\\t\\treturn list(reversed(ids))\\n\\t\\treturn ids\",\n \"def get_compact(self):\\n c = []\\n m = len(self.revs)\\n a = self.revs[0]\\n i = 1\\n while i <= m:\\n if i == m or self.revs[i-1]+1 < self.revs[i]:\\n if a == self.revs[i-1]:\\n c += [a]\\n else:\\n c += [\\\"%s-%s\\\" % (a, self.revs[i-1])]\\n if i < m:\\n a = self.revs[i]\\n i += 1\\n return c\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def madebanks(self):\\n made = []\\n for bank in self.__banks:\\n if bank.made():\\n made.append(bank)\\n return made\",\n \"def recorders(self):\\n return self.__recorders\",\n \"def initialized_revs(self):\\n initialized_revs = []\\n old_len = len(self._initial_value)\\n for rev, val in zip(self._changed_revs, self._changed_values):\\n if len(val) != old_len:\\n initialized_revs.append(rev)\\n old_len = len(val)\\n return initialized_revs\",\n \"def gather_referring_orders (gdbval):\\n# TODO: Somehow also note speculative references and attributes in\\n# general\\n vec = gdbval[\\\"referring\\\"]\\n return [int(i[\\\"referring\\\"][\\\"order\\\"]) for i in vec_iter(vec)]\",\n \"def modified_stats(self):\\n return self._counts_per_month('last_modified')\",\n \"def get_reward_list(self):\\n return self._gameRewards\",\n \"def get_reward_list(self):\\n return self._gameRewards\",\n \"def reset(self) -> List[int]:\\n pass\",\n \"def posisions(self):\\n return self.preorder() # return entire preorder iteration\",\n \"def copie_goban(self):\\n new_goban = []\\n for old_lines in self.game.goban.cell:\\n new_goban.append(list(old_lines))\\n return(new_goban)\",\n \"def reset(self):\\n return self.nums\",\n \"def _addReferees(self):\\n a_ranks = self.division.GetGroupsRanks(['A'])\\n b_ranks = self.division.GetGroupsRanks(['B'])\\n c_ranks = self.division.GetGroupsRanks(['C'])\\n d_ranks = self.division.GetGroupsRanks(['D'])\\n\\n self._GroupAddReferees('7th', [d_ranks[1]])\\n self._GroupAddReferees('5th', [d_ranks[2]])\\n\\n self._GroupAddReferees('3rd',[d_ranks[0]])\\n self._GroupAddReferees('final',[c_ranks[2]])\",\n \"def get_reporeted_casks():\\n return load_formula_list(REPORTED_CASKS_FILE)\",\n \"def reset(self) -> List[int]:\\n return self.origin\",\n \"def gather_references_orders (gdbval):\\n# TODO: Somehow also note speculative references and attributes in\\n# general\\n vec = gdbval[\\\"references\\\"]\\n return [int(i[\\\"referred\\\"][\\\"order\\\"]) for i in vec_iter(vec)]\",\n \"def backfill_revlist(buildername, revisions, times=1, dry_run=False):\\n new_revisions_list = []\\n repo_name = query_repo_name_from_buildername(buildername)\\n LOG.info(\\\"We want to find a successful job for '%s' in this range: [%s:%s]\\\" %\\n (buildername, revisions[0], revisions[-1]))\\n for rev in revisions:\\n jobs = query_jobs(repo_name, rev)\\n matching_jobs = _matching_jobs(buildername, jobs)\\n successful_jobs = _status_summary(matching_jobs)[0]\\n if successful_jobs > 0:\\n LOG.info(\\\"The last succesful job for buildername '%s' is on %s\\\" %\\n (buildername, rev))\\n # We don't need to look any further in the list of revisions\\n break\\n else:\\n new_revisions_list.append(rev)\\n\\n LOG.info(\\\"We only need to backfill %s\\\" % new_revisions_list)\\n return new_revisions_list\",\n \"def backorders(self):\\n return self._backorders\",\n \"def positions(self):\\n return self.preorder() # return entire preorder iteration\",\n \"def reset(self):\\n return self.nums\",\n \"def getOldCodeList(self):\\n tmp = []\\n for child in self.children:\\n tmp.extend(child.getOldCodeList())\\n return tmp\",\n \"async def check_back_refreshes(self):\\n\\n remove_indexes = []\\n updated_pairs = set()\\n refreshes = 0\\n\\n for index, refresh in enumerate(self.back_refreshes):\\n pair = refresh['pair']\\n end_time = refresh['end']\\n start_time = refresh['start']\\n\\n last_time = self.close_times[config['base_pairs'][0]][-1]\\n if last_time > refresh['time']:\\n refresh_num = int(last_time - start_time) // config['tick_interval_secs']\\n remove_indexes.append(index)\\n\\n if pair in self.close_times:\\n refreshes += 1\\n ticks = await self.api.get_ticks(pair, refresh_num)\\n overwritten = await self._overwrite_tick_data(pair, start_time, end_time, ticks)\\n if overwritten:\\n self.log.info(\\\"{} back-refreshed {} ticks.\\\", pair, overwritten)\\n updated_pairs.add(pair)\\n\\n if refreshes >= config['back_refresh_max_per_tick']:\\n break\\n\\n for index in reversed(remove_indexes):\\n del self.back_refreshes[index]\\n\\n if remove_indexes:\\n self.save_attr('back_refreshes')\\n\\n return updated_pairs\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def returnedbanks(self):\\n returned = []\\n for bank in self.__banks:\\n if bank.returned():\\n returned.append(bank)\\n return returned\",\n \"def get_changed_nodes(self):\\n\\n return self._changed_nodes\",\n \"def _shuffle_roidb_inds(self):\\n ## 设置cur来指向现在访问的长度\\n self._perm = np.random.permutation(np.arange(len(self._roidb)))\\n self._cur = 0\",\n \"def reset(self) -> List[int]:\\n self.q = self.nums[::]\\n return self.q\",\n \"def reset(self) -> List[int]:\\n self.q = self.nums[::]\\n return self.q\",\n \"def reset(self) -> List[int]:\\n self.q = self.nums[::]\\n return self.q\",\n \"def get_last_calls(self):\\r\\n last_calls = self.last_calls\\r\\n self.last_calls = []\\r\\n return last_calls\",\n \"def _update_revision_list_indexes(self):\\n for i, rev in enumerate(self.revisions):\\n rev.list_index = i\\n for i in xrange(len(self.revisions)):\\n if i:\\n self.revisions[i].previous_revision = self.revisions[i - 1]\\n if i < len(self.revisions) - 1:\\n self.revisions[i].next_revision = self.revisions[i + 1]\",\n \"def ranks(cls):\\n ranked = []\\n for team in sorted(dbsession.query(cls).order_by(desc(cls.money)).all()):\\n if not team.locked:\\n ranked.append(team)\\n return ranked\",\n \"def getModifiedBlockList(self):\\n if self.binary:\\n return []\\n block_list = []\\n for child in self.children:\\n old_line_list = [line.strip() for line, color in child.getOldCodeList()\\n if line is not None and color in (MODIFIED_DIFF_COLOR,\\n DELETED_DIFF_COLOR)]\\n new_line_list = [line.strip() for line, color in child.getNewCodeList()\\n if line is not None and color in (MODIFIED_DIFF_COLOR,\\n ADDITION_DIFF_COLOR)]\\n if old_line_list or new_line_list:\\n block_list.append((child,(old_line_list, new_line_list)))\\n return block_list\",\n \"def history(self):\\n return _spacegrant_swig.NRZI_sptr_history(self)\",\n \"def getModificationNumber(self) -> long:\\n ...\",\n \"def get_biases(self):\\n return []\",\n \"def unsaved_changes(self) -> int:\\n return (\\n abs(self._snapshot_index - self._last_save_snapshot)\\n + self._branch_save_count\\n )\",\n \"def findRelativeRanks(nums):\\n compare_lst = copy.deepcopy(nums)\\n compare_lst.sort(reverse=True)\\n for i in nums:\\n compare_index = compare_lst.index(i)\\n nums_index = nums.index(i)\\n if compare_index > 2:\\n nums[nums_index] = str(compare_index + 1)\\n elif compare_index == 0:\\n nums[nums_index] = 'Gold Medal'\\n elif compare_index == 1:\\n nums[nums_index] = 'Silver Medal'\\n else:\\n nums[nums_index] = 'Bronze Medal'\\n return nums\",\n \"def getPriorityList(self):\",\n \"def order_dfs(self) -> List[Nodo]:\\n # Nodos por buscar, es una pila\\n pending: List[Nodo] = [self.root]\\n # Nodos ya visitados\\n visited: List[Nodo] = []\\n\\n # Mientras la pila tenga items\\n while len(pending) > 0:\\n # Procesar el primer elemento\\n curr = pending.pop()\\n visited.append(curr)\\n\\n # Agregar los hijos no visitados del nodo a la pila\\n for child in reversed(curr.children):\\n if child in visited:\\n continue\\n pending.append(child)\\n\\n return visited\",\n \"def get_history(self):\\n return self.__history[:]\",\n \"def findRanks(toBeRanked, values):\\n\\treturn list(map(lambda e: findRank(e, values), toBeRanked))\",\n \"def keys(self):\\r\\n if self.indexchanged_key or not self.sortedkeys:\\r\\n self.indexchanged_key = False\\r\\n self.sortedkeys = sorted(self.get_keys())\\r\\n return self.sortedkeys\\r\\n return self.sortedkeys\",\n \"def get_rexps(self):\\n return self.rexps\",\n \"def get_ranks(d): \\n raise NotImplementedError(\\\"Problem 3 Incomplete\\\")\",\n \"def reorder( self ):\\n self.sorted.sort(self.compareFunction)\",\n \"def reset(self) -> List[int]:\\n self.array = deepcopy(self.original)\\n return self.array\",\n \"def reindex(self):\",\n \"def reindex(self):\",\n \"def get_modified_list(yearly_footprint, modified_footprint, age):\\n # modification_factor = yearly_footprint = modified_footprint\\n modified_list = [yearly_footprint]\\n\\n for i in range(1, 73):\\n if i <= age - 18:\\n modified_list.append(modified_list[i-1] + yearly_footprint)\\n else:\\n modified_list.append(modified_list[i-1] + modified_footprint)\\n\\n return modified_list\",\n \"def reward_buffer(self) -> Deque[float]:\\n return self._reward_buffer\",\n \"def historial(self):\\n return self.revision_set.order_by('-revision')[1:]\",\n \"def redo_act(self):\\n\\n return self.history[self.position + 1]\",\n \"def get_history(self):\\n msg_ids = self._records.keys()\\n # Remove any that do not have a submitted timestamp.\\n # This is extremely unlikely to happen,\\n # but it seems to come up in some tests on VMs.\\n msg_ids = [m for m in msg_ids if self._records[m]['submitted'] is not None]\\n return sorted(msg_ids, key=lambda m: self._records[m]['submitted'])\",\n \"def reset(self):\\n self.init_nums = copy.deepcopy(self.orig)\\n return(self.init_nums)\",\n \"def shuffle(self) -> List[int]:\",\n \"def get_bc_lens(ids_bcs_added_field):\\r\\n\\r\\n bc_index = 0\\r\\n bc_lens = [len(curr_bc[bc_index])\\r\\n for curr_bc in ids_bcs_added_field.keys()]\\r\\n bc_lens = list(set(bc_lens))\\r\\n bc_lens.sort(reverse=True)\\r\\n return bc_lens\",\n \"def restore(self, removals):\\r\\n for B, b in removals:\\r\\n self.curr_domains[B].append(b)\",\n \"def positions(self):\\n return self.preorder()\",\n \"def history(self):\\n return _spacegrant_swig.invert_bit_sptr_history(self)\",\n \"def force_no_ordering(self):\\n return []\",\n \"def getListModifiedDates(self):\\n return _libsbml.ModelHistory_getListModifiedDates(self)\",\n \"def tanks(self):\\n return self._node_reg.tanks\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5999729","0.5646804","0.55687803","0.55413765","0.5502476","0.54501814","0.54318994","0.5424124","0.5380979","0.53601855","0.53181535","0.5303631","0.52810645","0.52623254","0.52623254","0.52623254","0.52623254","0.52623254","0.52623254","0.52623254","0.5259917","0.52596205","0.52525544","0.52483755","0.524111","0.52206385","0.5172728","0.5169178","0.5169178","0.5169178","0.5169178","0.5169178","0.5169178","0.5169178","0.5165756","0.5159323","0.515568","0.5153713","0.5151367","0.5151367","0.5144529","0.5114401","0.50990665","0.50949645","0.5076718","0.5067158","0.5062339","0.5062131","0.5044184","0.5043177","0.50414634","0.5031532","0.5030836","0.5030308","0.50111824","0.50111824","0.50111824","0.50111824","0.50111824","0.50111824","0.50111824","0.5008096","0.49950966","0.49916804","0.49916804","0.49916804","0.49900344","0.49884543","0.49841672","0.4972955","0.49683073","0.49534944","0.49461395","0.4937285","0.49271357","0.49222308","0.4921581","0.49187535","0.4917752","0.4914163","0.49117693","0.4903481","0.48967177","0.48921126","0.4885114","0.4885114","0.4884407","0.4883649","0.48764282","0.48633984","0.48591924","0.48551542","0.48491192","0.48490512","0.48479292","0.48461568","0.48435348","0.48424226","0.4840584","0.48387396"],"string":"[\n \"0.5999729\",\n \"0.5646804\",\n \"0.55687803\",\n \"0.55413765\",\n \"0.5502476\",\n \"0.54501814\",\n \"0.54318994\",\n \"0.5424124\",\n \"0.5380979\",\n \"0.53601855\",\n \"0.53181535\",\n \"0.5303631\",\n \"0.52810645\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.52623254\",\n \"0.5259917\",\n \"0.52596205\",\n \"0.52525544\",\n \"0.52483755\",\n \"0.524111\",\n \"0.52206385\",\n \"0.5172728\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5169178\",\n \"0.5165756\",\n \"0.5159323\",\n \"0.515568\",\n \"0.5153713\",\n \"0.5151367\",\n \"0.5151367\",\n \"0.5144529\",\n \"0.5114401\",\n \"0.50990665\",\n \"0.50949645\",\n \"0.5076718\",\n \"0.5067158\",\n \"0.5062339\",\n \"0.5062131\",\n \"0.5044184\",\n \"0.5043177\",\n \"0.50414634\",\n \"0.5031532\",\n \"0.5030836\",\n \"0.5030308\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.50111824\",\n \"0.5008096\",\n \"0.49950966\",\n \"0.49916804\",\n \"0.49916804\",\n \"0.49916804\",\n \"0.49900344\",\n \"0.49884543\",\n \"0.49841672\",\n \"0.4972955\",\n \"0.49683073\",\n \"0.49534944\",\n \"0.49461395\",\n \"0.4937285\",\n \"0.49271357\",\n \"0.49222308\",\n \"0.4921581\",\n \"0.49187535\",\n \"0.4917752\",\n \"0.4914163\",\n \"0.49117693\",\n \"0.4903481\",\n \"0.48967177\",\n \"0.48921126\",\n \"0.4885114\",\n \"0.4885114\",\n \"0.4884407\",\n \"0.4883649\",\n \"0.48764282\",\n \"0.48633984\",\n \"0.48591924\",\n \"0.48551542\",\n \"0.48491192\",\n \"0.48490512\",\n \"0.48479292\",\n \"0.48461568\",\n \"0.48435348\",\n \"0.48424226\",\n \"0.4840584\",\n \"0.48387396\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54004663"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":79583,"cells":{"query":{"kind":"string","value":"Create a list of record ids out of RECIDS. The result is expected to have ascending numerical order."},"document":{"kind":"string","value":"def create_recordid_list(rec_ids):\n rec_list = []\n for row in rec_ids:\n rec_list.append(row[0])\n return rec_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_recordIds(self):\n record_ids = []\n for item in self.order_items:\n record_ids.append(item.get_recordId())\n \n return record_ids","def get_rsids(input_file: str) -> list:\n\n column_names: list = [\"rsid\", \"chr\"]\n # catching the error if the file does not have the column names\n try:\n rsid_file: pd.DataFrame = pd.read_csv(input_file, usecols=column_names)\n except KeyError:\n print(\"The expected header was not found within the file\")\n print(\n \"Please ensure that the file has at least two columns: 'rsid' and 'chr'\"\n )\n sys.exit(1)\n\n rsid_list: list = rsid_file.rsid.values.tolist()\n chr_list: list = rsid_file[\"chr\"].values.tolist()\n\n total_rsid_list: list = list(zip(rsid_list, chr_list))\n\n return total_rsid_list","def get_rls_ids(self, table: \"BaseDatasource\") -> List[int]:\n ids = [f.id for f in self.get_rls_filters(table)]\n ids.sort() # Combinations rather than permutations\n return ids","def get_ids(self) -> List[str]:","def getIDs():","def seq_xref_ids(entry):\n\n xref_ids = []\n exon_data = exons(entry)\n for ids in xref_data(entry).values():\n for exon in exon_data:\n for xref_id in ids:\n key = \"{xref_id}-{gene_id}-{chr}:{start}..{stop}\".format(\n xref_id=xref_id,\n gene_id=primary_id(entry),\n chr=exon.chromosome_name,\n start=exon.primary_start,\n stop=exon.primary_end,\n )\n xref_ids.append((key, exon))\n\n return xref_ids","def rel_id_list(rel_list):\n\n rel_ids = []\n for i in range(0, len(rel_list)):\n rel_id = rel_list[i][\"id\"]\n rel_ids.append(rel_id)\n return rel_ids","def list_ids(self, start: int = None, end: int = None) -> List:\n return [i.unique_id for i in self.data[start:end]]","def ids(self):\n return (x[\"_id\"] for x in self.document._meta.collection.find(self.spec, fields = (\"_id\",)))","def get_ids(self, start_id, num):\n \n rows = self.get_rows()\n # Sort by restaurant ID\n all_ids = sorted(\n list(\n set(\n map(lambda k: int(k['id']), \n filter(lambda k: int(k['id']) >= start_id, rows)))))\n return all_ids[:num]","def to_object_id_list(data):\n \n if not data:\n return []\n return [ObjectId(i) for i in data if i]","def ids(filename):\n with open(filename) as file:\n contents = file.read()\n return [int(x) for x in contents.split(\",\")]","def get_literature_recids_for_orcid(orcid):\n orcid_object = f'[{{\"schema\": \"ORCID\", \"value\": \"{orcid}\"}}]'\n # this first query is written in a way that can use the index on (json -> ids)\n\n try:\n author_rec_uuid = (\n db.session.query(RecordMetadata.id)\n .filter(\n type_coerce(RecordMetadata.json, JSONB)[\"ids\"].contains(orcid_object)\n )\n .one()\n .id\n )\n except NoResultFound:\n LOGGER.warning(\"No profile is associated with this account!\", orcid=orcid)\n return []\n\n author_record = (\n db.session.query(PersistentIdentifier)\n .filter(\n PersistentIdentifier.object_type == \"rec\",\n PersistentIdentifier.object_uuid == author_rec_uuid,\n PersistentIdentifier.pid_type == \"aut\",\n )\n .one()\n )\n\n author_recid = (\n author_record.pid_value\n if not author_record.is_redirected()\n else InspireRedirect.get_redirect(author_record).pid_value\n )\n\n query = Q(\"match\", authors__curated_relation=True) & Q(\n \"match\", **{\"authors.record.$ref\": author_recid}\n )\n search_by_curated_author = (\n LiteratureSearch()\n .query(\"nested\", path=\"authors\", query=query)\n .params(_source=[\"control_number\"], size=9999)\n )\n\n return [el[\"control_number\"] for el in search_by_curated_author]","def genIdList(numId, idSize):\n\tiDs = []\n\tfor i in range(numId):\n\t\tiDs.append(genID(idSize))\n\treturn iDs","def get_orcids_for_push(record):\n orcids_on_record = []\n author_recids_with_claims = []\n for author in record.get(\"authors\", []):\n orcids_in_author = get_values_for_schema(author.get(\"ids\", []), \"ORCID\")\n if orcids_in_author:\n orcids_on_record.extend(orcids_in_author)\n if author.get(\"curated_relation\") is True and \"record\" in author:\n author_recids_with_claims.append(get_recid_from_ref(author[\"record\"]))\n author_records = AuthorsRecord.get_records_by_pids(\n (\"aut\", str(recid)) for recid in author_recids_with_claims\n )\n\n all_ids = (author.get(\"ids\", []) for author in author_records)\n orcids_in_authors = chain.from_iterable(\n get_values_for_schema(ids, \"ORCID\") for ids in all_ids\n )\n\n return chain(orcids_on_record, orcids_in_authors)","def genNumIdList(numId, idSize):\n\tiDs = []\n\tfor i in range(numId):\n\t\tiDs.append(genNumID(idSize))\n\treturn iDs","def get_objects_id(claims: Dict) -> List:\n ids = set()\n for prop in claims:\n for claim in claims[prop]:\n try:\n datatype = claim['mainsnak']['datavalue']['type']\n if datatype == \"wikibase-entityid\":\n d_id = claim['mainsnak']['datavalue']['value']['id']\n ids.add(d_id)\n elif datatype == \"quantity\":\n d_id = claim['mainsnak']['datavalue']['value']['unit'].split(\"/\")[-1]\n ids.add(d_id)\n else:\n continue\n except:\n traceback.print_exc()\n\n return list(ids)","def get_RSOPuids_in_RIS(seg):\n \n rsopuids = [] \n \n sequences = seg.ReferencedSeriesSequence[0]\\\n .ReferencedInstanceSequence\n \n for sequence in sequences:\n uid = sequence.ReferencedSOPInstanceUID\n \n rsopuids.append(uid)\n \n return rsopuids","def ids(self):\n return list(self._id_generator())","def ids(self):\n return list(self._id_generator())","def ids(self):\n\n if not hasattr(self, \"_ids\"):\n query = db.Query(\"pub_proc_cg c\", \"c.id\").unique().order(\"c.id\")\n query.join(\"query_term t\", \"t.doc_id = c.id\")\n query.join(\"query_term s\", \"s.doc_id = t.int_val\")\n query.where(\"t.path = '/Term/SemanticType/@cdr:ref'\")\n query.where(\"s.path = '/Term/PreferredName'\")\n query.where(\"s.value = 'Drug/agent'\")\n rows = query.execute(self.cdr_cursor).fetchall()\n self._ids = [row.id for row in rows]\n self.logger.info(\"found %d drug terms\", len(self._ids))\n return self._ids","def get_ids():\n # Filename for SALAMI IA metadata\n metadata_file = os.path.join(\n dpath.SALAMI, 'metadata', 'id_index_internetarchive.csv')\n\n ids = []\n\n with open(metadata_file, \"r\") as rwc_file:\n reader = csv.reader(rwc_file)\n next(reader) #skip header\n for row in reader:\n ids.append(int(row[0]))\n\n return ids","def _getRecordBatch(idList) :\n handle = Entrez.efetch(db = \"nuccore\", rettype = \"gbwithparts\",\n retmode = \"text\", id = \",\".join(idList))\n r = handle.read()\n handle.close()\n return r","def get_ids(self):\n return [item.id for item in self.items]","def _id_seq(self):\n return list(self.keys())","def get_ids(self, criteria: Dict = None):\n\n if criteria is None:\n criteria = {}\n\n ids = self.m.query(\n criteria=criteria,\n properties=[\"material_id\"])\n print(\"number %s\" % len(ids))\n ids = pd.DataFrame(ids).values.ravel().tolist()\n self.ids = ids\n return ids","def get_generator(strains, reference_id, start, end):\n primary_keys = []\n vals = range(start, end+1)\n for val in vals:\n for strain in strains:\n primary_keys.append(strain+\"_\"+reference_id+\"_\"+str(val))\n return primary_keys","def get_seq_ids(self, acl, howmany):\n ids = []\n it = 0\n for seq_id in moves.range(self._min_search_seqid,\n self._max_search_seqid):\n ids.append(seq_id)\n it += 1\n if(it >= howmany):\n break\n return ids","def batches2IDs(batches):\n l = [ np.array( [ char2id(x) for x in characters(b) ] ) for b in batches ]\n return l","def get_RSOPuidsByRoi(rts):\n \n RSOPuidsByRoi = []\n \n roiCntSeqs = rts.ROIContourSequence\n \n for r in range(len(roiCntSeqs)):\n cntSeqs = roiCntSeqs[r].ContourSequence\n \n # Initialise the list of ReferencedSOPInstanceUIDs for this ROI:\n RSOPuids = []\n \n for seq in cntSeqs:\n uid = seq.ContourImageSequence[0].ReferencedSOPInstanceUID\n \n RSOPuids.append(uid)\n \n RSOPuidsByRoi.append(RSOPuids)\n \n return RSOPuidsByRoi","def get_all_party_id() -> List[int]:\n\n # Open a new connection\n db, cursor = db_connector.cursor()\n\n query = \"select id from party order by id\"\n cursor.execute(query)\n data = cursor.fetchall()\n r_list = [x[0] for x in data]\n db.disconnect()\n return r_list","def get_ids(self):\n for object_query in self.query:\n objects = self._get_objects(object_query)\n objects = self._apply_order_by_and_limit(\n objects,\n order_by=object_query.get(\"order_by\"),\n limit=object_query.get(\"limit\"),\n )\n object_query[\"ids\"] = [o.id for o in objects]\n return self.query","def get_id_list(fname):\n id_list = []\n with open(fname, 'r') as f:\n for line in f:\n line = line.strip()\n if line:\n id_list.append(line)\n return id_list","def user_created_id_array(self, uid):\n result = self.find({ pair_data.STATISTICS + '.' + pair_data.CREATOR: self._uid2ref(uid) }, \n { pair_data.SEQUENCE: True } ).sort(pair_data.SEQUENCE, pymongo.ASCENDING)\n\n if result is not None:\n return [data[pair_data.SEQUENCE] for data in result]\n else:\n return []","def get_doc_ids(self):\n cursor = self.connection.cursor()\n cursor.execute(\"SELECT id FROM documents\")\n results = [r[0] for r in cursor.fetchall()]\n cursor.close()\n return results","def get_doc_ids(self):\n cursor = self.connection.cursor()\n cursor.execute(\"SELECT id FROM documents\")\n results = [r[0] for r in cursor.fetchall()]\n cursor.close()\n return results","def ids_from_fasta_lines(lines):\r\n ids = []\r\n for line in lines:\r\n if not line.startswith(\">\"):\r\n continue\r\n id = id_from_fasta_label_line(line)\r\n ids.append(id)\r\n\r\n return ids","def _convert_ids(self, ids):\n ids_list_int = []\n\n for id_ in ids:\n if not self._validate_identifier(id_):\n raise PhabfiveDataException(f\"Identifier '{id_}' is not valid\")\n\n id_ = id_.replace(\"P\", \"\")\n # constraints takes int\n id_ = int(id_)\n ids_list_int.append(id_)\n\n return ids_list_int","def ids_from_file(filepath):\n\n result = [[]] # A list containing an empty list.\n count = 0\n\n with open(filepath, 'r') as f_in:\n for line in f_in:\n if count < 200:\n # Append to the last sublist in the list\n result[-1].append(line.strip())\n count += 1\n else:\n # Create a new sublist once a group of 200 is reached.\n result.append([line.strip()])\n count = 1\n\n return result","def _iter_rIds(self):\n sldMasterId_lst = self._sldMasterIdLst.sldMasterId_lst\n for sldMasterId in sldMasterId_lst:\n yield sldMasterId.rId","def IDs(self, default=[{}]):\n tmp = self.data.get('ids', default)\n return [HEP.IDObject(i) for i in tmp]","def createRUID_List(rowIdxList, headerStr):\r\n RUID_List = []\r\n for aRowIdx in rowIdxList:\r\n workingRUID=df[headerStr].iloc[aRowIdx]\r\n RUID_List.append(workingRUID)\r\n return RUID_List","def ids(self):\n return self.obj_to_id.values()","def get_sids_by_uuids(self, rids):\n if not rids:\n return []\n session = self.DBSession()\n results = baked_query_sids(session).params(rids=rids).all()\n # check res.name to skip sids for supplementary rows, like 'downloads'\n data = {str(res.rid): res.sid for res in results if res.name == ''}\n return data","def getVaccinesIds():\n with driver.session() as s:\n ids = s.write_transaction(getVaccinesId)\n\n vIds = []\n for idEl in ids:\n vIds.append(idEl[\"ID(v)\"])\n\n return vIds","def test_get_ids(self):\r\n lines = \"\"\">S74_1 E86FECS01CEVAV orig_bc=ACATGTCACGTG new_bc=ACATGTCACGTG bc_diffs=0\r\nCTCCTC\r\n>Unassigned_2 E86FECS01EKKMF orig_bc=AGCGCTGATGTA new_bc=None bc_diffs=1\r\nGGTGCCTCCCTCGC\r\n>S80_3 E86FECS01EKKMF orig_bc=AGCGCTGATGTA new_bc=None bc_diffs=1\r\nGGTGCCTCCCTCGC\r\n>S80_4 E86FECS01CW66X orig_bc=AGTCCATAGCTG new_bc=AGTCCATAGCTG bc_diffs=0\r\nGTCCTGGCAG\"\"\".splitlines()\r\n result = get_ids(lines, 1)\r\n self.assertEqual(\r\n dict(result),\r\n {'S74': ['E86FECS01CEVAV'],\r\n 'Unassigned': ['E86FECS01EKKMF'],\r\n 'S80': ['E86FECS01EKKMF',\r\n 'E86FECS01CW66X']})","def get_matches_id(match_number):\n database = TinyDB('db.json')\n matches_table = database.table('match')\n # recuperation de tous les tours\n match_id_list = []\n for i in range(1, match_number + 1):\n data = matches_table.all()[-i]\n match_id_list.append(data.doc_id)\n return match_id_list","def fasta_ids(fasta_files, verbose=False):\r\n all_ids = set([])\r\n for fasta_in in fasta_files:\r\n for label, seq in parse_fasta(fasta_in):\r\n rid = label.split()[0]\r\n if rid in all_ids:\r\n raise ValueError(\r\n \"Duplicate ID found in FASTA/qual file: %s\" %\r\n label)\r\n all_ids.add(rid)\r\n return all_ids","def build_segment_ids(input_ids): \n segment_ids = [] \n for seq in input_ids: \n segment_id = []\n id_ = 0\n for token_id in seq: \n segment_id.append(id_)\n # 102 : [SEP]\n if token_id == 102: \n id_ +=1 \n id_ %= 2 \n segment_ids.append(segment_id)\n return segment_ids","def ids(self):\n\n if len(self._id_ranges) % 2 != 0:\n raise AutoIDException(message=ExceptionsMessage.AutoIDIllegalRanges)\n\n ids = []\n for i in range(int(len(self._id_ranges) / 2)):\n begin = self._id_ranges[i * 2]\n end = self._id_ranges[i * 2 + 1]\n for j in range(begin, end):\n ids.append(j)\n\n return ids","def getLigandResIds(ligchemid:str, struct: Structure)->List[Residue]:\n \"\"\"*ligchemids are of type https://www.rcsb.org/ligand/IDS\"\"\"\n ligandResidues: List[Residue] = list(filter(lambda x: x.get_resname() == ligchemid, list( struct.get_residues() )))\n return ligandResidues","def list_of_gene_IDs_and_symbols_from_file(input_seq_file):\n print(\"Find entrezgene IDs for reporter genes in RNAseq run\")\n lines = []\n with open(input_seq_file, 'U') as input_seq_file:\n next(input_seq_file)\n for line in input_seq_file:\n new_line = line.strip().split('\\t')[0]\n new_line = re.sub('\"', '', new_line) # inputs contained quotes\n lines.append(new_line)\n\n query_list = pd.Series(data=lines).tolist()\n\n print(\"length of query list: \", len(query_list))\n return query_list","def convert2seq(self,seq_int):\n\t\treturn [self.aminoacids[i] for i in seq_int]","def get_training_ids(mydb):\n\treturn list(mydb.fetch_table(table_name='training_set',field_names=['CaseId']).CaseId)","def get_gi_ids_from_files(filenames):\n idlst = []\n for filename in filenames:\n idlst += get_gi_ids(filename)\n return idlst","def get_ids(corpus):\n corpusids = []\n for chunk in corpus:\n for _id in chunk.keys():\n corpusids.append(_id)\n return corpusids","def compress_doc_ids(doc_ids: list):\r\n int_doc_ids = [int(x) for x in doc_ids]\r\n sorted_doc_ids = sorted(int_doc_ids)\r\n return sorted_doc_ids","def get_ids_from_list_file(list_file):\n res = []\n with open(list_file, 'r') as file:\n for line in file:\n res.append(line.strip())\n return res","def IDLIST(cls, tablename, **kwargs):\n limit = \"LIMIT \" + kwargs.get ( \"limit\", \"ALL\" ) \n offset = \"OFFSET \" + kwargs.get ( \"offset\", \"0\" )\n \n order = kwargs.get ( \"order\", ['objectid ASC'] )\n order = \",\".join (order)\n where = kwargs.get ( \"where\", ['TRUE'] )\n where = \" AND \".join (where)\n query = \"SELECT objectid FROM {0}.{1} WHERE {2} ORDER BY {3} {4} {5}\".format (\n CFG.DB.SCHEMA, tablename, where, order, limit, offset )\n rowset = map(lambda x: x[0], CFG.CX.query ( query ).getresult() )\n return rowset","def texts2ids(self, texts: list, length: int):\n return [self.text2ids(text, length) for text in texts]","def get_all_supplier_id() -> List[int]:\n\n # Open a new connection\n db, cursor = db_connector.cursor()\n\n query = \"select id from supplier order by id\"\n cursor.execute(query)\n data = cursor.fetchall()\n r_list = [x[0] for x in data]\n db.disconnect()\n return r_list","def get_instances_ids(self):\n reservations = self.__get_reservations()\n instances_ids = []\n instances,_ = self.__get_multi_instances(reservations)\n for instance in instances:\n instances_ids.append(instance.id.encode(\"latin-1\"))\n return instances_ids","def scrapeRecordIds(self):\n data = self._getJason(config.SOLD_URL)\n stored_records = self._queryScrapedIds('records', 'sale')\n records = [(d['_id'], 'sale', False) for d in data\n if d['_id'] not in stored_records]\n if records:\n self._storeData(records, 'records')\n else:\n print ('No new records')\n stored_records = self._queryScrapedIds('records', 'list')\n data = self._getJason(config.LIST_URL)\n listings = [(d['_id'], 'list', False) for d in data\n if d['_id'] not in stored_records]\n if listings:\n self._storeData(listings, 'records')\n else:\n print ('No new listings')\n return","def getLocationsIds():\n with driver.session() as s:\n ids = s.write_transaction(getLocationsId)\n\n lIds = []\n for idEl in ids:\n lIds.append(idEl[\"ID(l)\"])\n\n return lIds","def _load_output(buf):\n id_lists = []\n for line in buf:\n id_lists.append([int(id) for id in line.split()])\n\n for id_list in id_lists:\n assert sorted(id_list) == id_list\n\n return id_lists","def patron_holds(session, record_id):\n r = session.get('{}/patrons/{}/holds'.format(api_url_base, record_id))\n entries = json.loads(r.text)['entries']\n ids = [x['id'].split('/')[-1] for x in entries]\n hold_ids = []\n for i in ids:\n hold_ids.append(i)\n return hold_ids","def get_ids(self, text):\n\n tokens = [token.orth for token in self.tokenizer(text)]\n ids = []\n for token in tokens:\n try:\n id = self.vocab.vectors.key2row[token]\n except KeyError:\n id = self.oov_id\n\n ids.append(id)\n\n return ids","def get_job_ids(self, offset=0, length=-1):\n start = offset\n if length >= 0:\n end = offset + (length - 1)\n else:\n end = length\n return [as_text(job_id) for job_id in\n self.connection._lrange(self.key, start, end)]","def docs2ids(self):\n self.docs = [ [self.vocab[word] for word in doc] for doc in self.docs]","def ids(self):\n return frozenset([seq.id for seq in self])","def object_ids(self):\n return self._extract_set('id')","def get_list(str_scu):\n\tscu_list = str_scu.split(',')\n\tscu_list_int = []\n\tif len(str_scu) > 0:\n\t\tscu_list_int = [int(scu_id) for scu_id in scu_list]\n\treturn scu_list_int","def get_ids(self, text):\n\n tokens = [token.orth for token in self.tokenizer(text)]\n ids = []\n for token in tokens:\n try:\n id = self._vocab.vectors.key2row[token]\n except KeyError:\n id = self.oov_id\n\n ids.append(id)\n\n return ids","def patron_checkouts(session, record_id):\n r = session.get('{}/patrons/{}/checkouts'.format(api_url_base, record_id))\n entries = json.loads(r.text)['entries']\n ids = [x['id'].split('/')[-1] for x in entries]\n return ids","def sequence_to_list_ids(sequence, vocab):\n pass","def get_party_ids_by_list(party_names: List[str]) -> List[int]:\n\n id_list = []\n\n for names in party_names:\n id_list.append(get_party_id_by_name(names))\n\n return id_list","def serve_recos(ids, ref_catalog):\r\n desc_list = []\r\n for desc_id in ids:\r\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\r\n return desc_list","def get_ids(directory):\n return (f[:-4] for f in os.listdir(directory))","def list_ids (self, key):\n\n list_of_key_values = [str(x[key]) for x in self.result]\n\n self.result = list(dict.fromkeys([re.findall(r'\\b\\d+\\b', x)[0] for x in list_of_key_values if len(re.findall(r'\\b\\d+\\b', x)) !=0]))\n\n return self","def build_indices(review_ids):\n\n review_indices = {}\n\n # Load qrel_abs_train txt file\n clef_data = pd.read_csv(config.TRAIN_QREL_LOCATION, sep=\"\\s+\", names=['review_id', 'q0', 'pmid', 'included'])\n\n # Get index of documents for each review\n for review_id in review_ids:\n index = clef_data.index[clef_data['review_id'] == review_id].tolist()\n\n # Get the range of index for all documents within each review\n review_indices[review_id] = (min(index), max(index) + 1)\n\n return review_indices","def get_ids(self):\n return self._ids","def format(self, result):\n results = DBFormatter.format(self, result)\n\n subIDs = []\n for row in results:\n if len(row[1].split('/')) <= 3: # remember, first item is empty\n subIDs.append(row[0])\n\n return subIDs","def get_ids(voc_path):\n ids = []\n print(\"voc\")\n\n files_images = glob.iglob(os.path.join(voc_path, \"*.JPEG\"))\n for x in files_images:\n name = os.path.splitext(os.path.basename(x))[0]\n ids.append(name)\n print(\"names: \", ids)\n return ids","def __ui_convert_ids_string_to_list(string_of_ids):\n if string_of_ids == \"\":\n return []\n string_of_ids = string_of_ids.strip()\n string_of_ids = string_of_ids.replace(\",\", \" \")\n\n done = False\n while not done:\n if string_of_ids.find(\" \") == -1:\n done = True\n else:\n string_of_ids = string_of_ids.replace(\" \", \" \")\n list_of_ids = string_of_ids.split(\" \")\n for id_index in range(len(list_of_ids)):\n list_of_ids[id_index] = int(list_of_ids[id_index])\n return list_of_ids","def get_es_ids(self):\n search = self.search.source(['uri']).sort(['uri'])\n es_ids = [item.meta.id for item in search.scan()]\n return es_ids","def get_primary_dids(self, rows):\n\n\t\tprimary_dids = {}\n\t\tfor row in rows:\n\t\t\t(account_number, phone_number) = (int(row[0]), row[1])\n\t\t\tif primary_dids.has_key(account_number):\n\t\t\t\tprimary_dids[account_number].append(phone_number)\n\t\t\telse:\n\t\t\t\tprimary_dids[account_number] = [phone_number]\n\n\t\treturn primary_dids","def get_atten_IDs(filename):\n serialnos = loadtxt(filename, delimiter=',', skiprows=3, dtype=str)[0,1:]\n rxs = loadtxt(filename, delimiter=',', skiprows=4, dtype=str)[0,1:]\n headers = loadtxt(filename, delimiter=',', skiprows=5, dtype=str)[0,1:]\n ID = {}\n for index in range(0, len(headers), 2):\n chanIDa = rxs[index]+'-'+headers[index].replace(' ','-')\n chanIDb = rxs[index]+'-'+headers[index+1].replace(' ','-')\n ID[chanIDa] = serialnos[index]+'A'\n ID[chanIDb] = serialnos[index]+'B'\n return ID","def id_lst(self):\n id_set = set()\n for key in self.forward:\n for val in self.forward[key]:\n id_set.add(val)\n return list(id_set)","def _make_identifiers(self) -> Identifiers:\n identifier_list = []\n for meta_column in self._meta_columns:\n idf_string = meta_column.reflected_column_idf\n idf = Identifier.from_string(idf_string)\n identifier_list.append(idf)\n return identifier_list","def convert_to_ids(self, terms):\n vec = [self.get_id(label) for label in terms]\n return vec","def getMatchIds(self):\n return sorted(self._matches.keys())","def without_fmt(queries, chunk_size=2000):\n sql = queries['missing']\n recids = intbitset()\n max_id = run_sql(\"SELECT max(id) FROM bibrec\")[0][0]\n for start in xrange(1, max_id + 1, chunk_size):\n end = start + chunk_size\n recids += intbitset(run_sql(sql, (start, end)))\n return recids","def get_phone_numbers(r):\n phone_match = re.findall(r'\\d\\d\\d-\\d\\d\\d-\\d\\d\\d\\d', r)\n phone_list = []\n if phone_match:\n for match in phone_match:\n if match not in phone_list:\n phone_list.append(match)\n phone_list = set(phone_list)\n return phone_list","def sortId(dicS):\n idList = {}\n count = 0\n for k,v in sorted(dicS.items(), key=lambda x:x[1]):\n idList[count]=k\n count = count + 1\n return idList","def get_ids(self):\n page = r.get(self.url)\n tree = html.fromstring(page.content)\n ids_elements = tree.xpath(\"//div[@id='selectedcontent']/div/ul/li/a\")\n return [self._e_to_id(e) for e in ids_elements]","def get_ids(self, instances):\n instance_ids = []\n for instance in instances:\n instance_ids.append(instance.id)\n return instance_ids","def getEventIds(self):\n eventIdsLst = []\n for event in self.eventsLst:\n eventIdsLst.append(event['id'])\n return eventIdsLst","def ids(self) -> Sequence[str]:\n return pulumi.get(self, \"ids\")","def ids(self) -> Sequence[str]:\n return pulumi.get(self, \"ids\")","def mr2bib(id_list):\n d = mr2bib_dict(id_list)\n l = []\n for id in id_list:\n try:\n l.append(d[id])\n except:\n l.append(ReferenceErrorInfo(\"Not found\", id))\n\n return l"],"string":"[\n \"def get_recordIds(self):\\n record_ids = []\\n for item in self.order_items:\\n record_ids.append(item.get_recordId())\\n \\n return record_ids\",\n \"def get_rsids(input_file: str) -> list:\\n\\n column_names: list = [\\\"rsid\\\", \\\"chr\\\"]\\n # catching the error if the file does not have the column names\\n try:\\n rsid_file: pd.DataFrame = pd.read_csv(input_file, usecols=column_names)\\n except KeyError:\\n print(\\\"The expected header was not found within the file\\\")\\n print(\\n \\\"Please ensure that the file has at least two columns: 'rsid' and 'chr'\\\"\\n )\\n sys.exit(1)\\n\\n rsid_list: list = rsid_file.rsid.values.tolist()\\n chr_list: list = rsid_file[\\\"chr\\\"].values.tolist()\\n\\n total_rsid_list: list = list(zip(rsid_list, chr_list))\\n\\n return total_rsid_list\",\n \"def get_rls_ids(self, table: \\\"BaseDatasource\\\") -> List[int]:\\n ids = [f.id for f in self.get_rls_filters(table)]\\n ids.sort() # Combinations rather than permutations\\n return ids\",\n \"def get_ids(self) -> List[str]:\",\n \"def getIDs():\",\n \"def seq_xref_ids(entry):\\n\\n xref_ids = []\\n exon_data = exons(entry)\\n for ids in xref_data(entry).values():\\n for exon in exon_data:\\n for xref_id in ids:\\n key = \\\"{xref_id}-{gene_id}-{chr}:{start}..{stop}\\\".format(\\n xref_id=xref_id,\\n gene_id=primary_id(entry),\\n chr=exon.chromosome_name,\\n start=exon.primary_start,\\n stop=exon.primary_end,\\n )\\n xref_ids.append((key, exon))\\n\\n return xref_ids\",\n \"def rel_id_list(rel_list):\\n\\n rel_ids = []\\n for i in range(0, len(rel_list)):\\n rel_id = rel_list[i][\\\"id\\\"]\\n rel_ids.append(rel_id)\\n return rel_ids\",\n \"def list_ids(self, start: int = None, end: int = None) -> List:\\n return [i.unique_id for i in self.data[start:end]]\",\n \"def ids(self):\\n return (x[\\\"_id\\\"] for x in self.document._meta.collection.find(self.spec, fields = (\\\"_id\\\",)))\",\n \"def get_ids(self, start_id, num):\\n \\n rows = self.get_rows()\\n # Sort by restaurant ID\\n all_ids = sorted(\\n list(\\n set(\\n map(lambda k: int(k['id']), \\n filter(lambda k: int(k['id']) >= start_id, rows)))))\\n return all_ids[:num]\",\n \"def to_object_id_list(data):\\n \\n if not data:\\n return []\\n return [ObjectId(i) for i in data if i]\",\n \"def ids(filename):\\n with open(filename) as file:\\n contents = file.read()\\n return [int(x) for x in contents.split(\\\",\\\")]\",\n \"def get_literature_recids_for_orcid(orcid):\\n orcid_object = f'[{{\\\"schema\\\": \\\"ORCID\\\", \\\"value\\\": \\\"{orcid}\\\"}}]'\\n # this first query is written in a way that can use the index on (json -> ids)\\n\\n try:\\n author_rec_uuid = (\\n db.session.query(RecordMetadata.id)\\n .filter(\\n type_coerce(RecordMetadata.json, JSONB)[\\\"ids\\\"].contains(orcid_object)\\n )\\n .one()\\n .id\\n )\\n except NoResultFound:\\n LOGGER.warning(\\\"No profile is associated with this account!\\\", orcid=orcid)\\n return []\\n\\n author_record = (\\n db.session.query(PersistentIdentifier)\\n .filter(\\n PersistentIdentifier.object_type == \\\"rec\\\",\\n PersistentIdentifier.object_uuid == author_rec_uuid,\\n PersistentIdentifier.pid_type == \\\"aut\\\",\\n )\\n .one()\\n )\\n\\n author_recid = (\\n author_record.pid_value\\n if not author_record.is_redirected()\\n else InspireRedirect.get_redirect(author_record).pid_value\\n )\\n\\n query = Q(\\\"match\\\", authors__curated_relation=True) & Q(\\n \\\"match\\\", **{\\\"authors.record.$ref\\\": author_recid}\\n )\\n search_by_curated_author = (\\n LiteratureSearch()\\n .query(\\\"nested\\\", path=\\\"authors\\\", query=query)\\n .params(_source=[\\\"control_number\\\"], size=9999)\\n )\\n\\n return [el[\\\"control_number\\\"] for el in search_by_curated_author]\",\n \"def genIdList(numId, idSize):\\n\\tiDs = []\\n\\tfor i in range(numId):\\n\\t\\tiDs.append(genID(idSize))\\n\\treturn iDs\",\n \"def get_orcids_for_push(record):\\n orcids_on_record = []\\n author_recids_with_claims = []\\n for author in record.get(\\\"authors\\\", []):\\n orcids_in_author = get_values_for_schema(author.get(\\\"ids\\\", []), \\\"ORCID\\\")\\n if orcids_in_author:\\n orcids_on_record.extend(orcids_in_author)\\n if author.get(\\\"curated_relation\\\") is True and \\\"record\\\" in author:\\n author_recids_with_claims.append(get_recid_from_ref(author[\\\"record\\\"]))\\n author_records = AuthorsRecord.get_records_by_pids(\\n (\\\"aut\\\", str(recid)) for recid in author_recids_with_claims\\n )\\n\\n all_ids = (author.get(\\\"ids\\\", []) for author in author_records)\\n orcids_in_authors = chain.from_iterable(\\n get_values_for_schema(ids, \\\"ORCID\\\") for ids in all_ids\\n )\\n\\n return chain(orcids_on_record, orcids_in_authors)\",\n \"def genNumIdList(numId, idSize):\\n\\tiDs = []\\n\\tfor i in range(numId):\\n\\t\\tiDs.append(genNumID(idSize))\\n\\treturn iDs\",\n \"def get_objects_id(claims: Dict) -> List:\\n ids = set()\\n for prop in claims:\\n for claim in claims[prop]:\\n try:\\n datatype = claim['mainsnak']['datavalue']['type']\\n if datatype == \\\"wikibase-entityid\\\":\\n d_id = claim['mainsnak']['datavalue']['value']['id']\\n ids.add(d_id)\\n elif datatype == \\\"quantity\\\":\\n d_id = claim['mainsnak']['datavalue']['value']['unit'].split(\\\"/\\\")[-1]\\n ids.add(d_id)\\n else:\\n continue\\n except:\\n traceback.print_exc()\\n\\n return list(ids)\",\n \"def get_RSOPuids_in_RIS(seg):\\n \\n rsopuids = [] \\n \\n sequences = seg.ReferencedSeriesSequence[0]\\\\\\n .ReferencedInstanceSequence\\n \\n for sequence in sequences:\\n uid = sequence.ReferencedSOPInstanceUID\\n \\n rsopuids.append(uid)\\n \\n return rsopuids\",\n \"def ids(self):\\n return list(self._id_generator())\",\n \"def ids(self):\\n return list(self._id_generator())\",\n \"def ids(self):\\n\\n if not hasattr(self, \\\"_ids\\\"):\\n query = db.Query(\\\"pub_proc_cg c\\\", \\\"c.id\\\").unique().order(\\\"c.id\\\")\\n query.join(\\\"query_term t\\\", \\\"t.doc_id = c.id\\\")\\n query.join(\\\"query_term s\\\", \\\"s.doc_id = t.int_val\\\")\\n query.where(\\\"t.path = '/Term/SemanticType/@cdr:ref'\\\")\\n query.where(\\\"s.path = '/Term/PreferredName'\\\")\\n query.where(\\\"s.value = 'Drug/agent'\\\")\\n rows = query.execute(self.cdr_cursor).fetchall()\\n self._ids = [row.id for row in rows]\\n self.logger.info(\\\"found %d drug terms\\\", len(self._ids))\\n return self._ids\",\n \"def get_ids():\\n # Filename for SALAMI IA metadata\\n metadata_file = os.path.join(\\n dpath.SALAMI, 'metadata', 'id_index_internetarchive.csv')\\n\\n ids = []\\n\\n with open(metadata_file, \\\"r\\\") as rwc_file:\\n reader = csv.reader(rwc_file)\\n next(reader) #skip header\\n for row in reader:\\n ids.append(int(row[0]))\\n\\n return ids\",\n \"def _getRecordBatch(idList) :\\n handle = Entrez.efetch(db = \\\"nuccore\\\", rettype = \\\"gbwithparts\\\",\\n retmode = \\\"text\\\", id = \\\",\\\".join(idList))\\n r = handle.read()\\n handle.close()\\n return r\",\n \"def get_ids(self):\\n return [item.id for item in self.items]\",\n \"def _id_seq(self):\\n return list(self.keys())\",\n \"def get_ids(self, criteria: Dict = None):\\n\\n if criteria is None:\\n criteria = {}\\n\\n ids = self.m.query(\\n criteria=criteria,\\n properties=[\\\"material_id\\\"])\\n print(\\\"number %s\\\" % len(ids))\\n ids = pd.DataFrame(ids).values.ravel().tolist()\\n self.ids = ids\\n return ids\",\n \"def get_generator(strains, reference_id, start, end):\\n primary_keys = []\\n vals = range(start, end+1)\\n for val in vals:\\n for strain in strains:\\n primary_keys.append(strain+\\\"_\\\"+reference_id+\\\"_\\\"+str(val))\\n return primary_keys\",\n \"def get_seq_ids(self, acl, howmany):\\n ids = []\\n it = 0\\n for seq_id in moves.range(self._min_search_seqid,\\n self._max_search_seqid):\\n ids.append(seq_id)\\n it += 1\\n if(it >= howmany):\\n break\\n return ids\",\n \"def batches2IDs(batches):\\n l = [ np.array( [ char2id(x) for x in characters(b) ] ) for b in batches ]\\n return l\",\n \"def get_RSOPuidsByRoi(rts):\\n \\n RSOPuidsByRoi = []\\n \\n roiCntSeqs = rts.ROIContourSequence\\n \\n for r in range(len(roiCntSeqs)):\\n cntSeqs = roiCntSeqs[r].ContourSequence\\n \\n # Initialise the list of ReferencedSOPInstanceUIDs for this ROI:\\n RSOPuids = []\\n \\n for seq in cntSeqs:\\n uid = seq.ContourImageSequence[0].ReferencedSOPInstanceUID\\n \\n RSOPuids.append(uid)\\n \\n RSOPuidsByRoi.append(RSOPuids)\\n \\n return RSOPuidsByRoi\",\n \"def get_all_party_id() -> List[int]:\\n\\n # Open a new connection\\n db, cursor = db_connector.cursor()\\n\\n query = \\\"select id from party order by id\\\"\\n cursor.execute(query)\\n data = cursor.fetchall()\\n r_list = [x[0] for x in data]\\n db.disconnect()\\n return r_list\",\n \"def get_ids(self):\\n for object_query in self.query:\\n objects = self._get_objects(object_query)\\n objects = self._apply_order_by_and_limit(\\n objects,\\n order_by=object_query.get(\\\"order_by\\\"),\\n limit=object_query.get(\\\"limit\\\"),\\n )\\n object_query[\\\"ids\\\"] = [o.id for o in objects]\\n return self.query\",\n \"def get_id_list(fname):\\n id_list = []\\n with open(fname, 'r') as f:\\n for line in f:\\n line = line.strip()\\n if line:\\n id_list.append(line)\\n return id_list\",\n \"def user_created_id_array(self, uid):\\n result = self.find({ pair_data.STATISTICS + '.' + pair_data.CREATOR: self._uid2ref(uid) }, \\n { pair_data.SEQUENCE: True } ).sort(pair_data.SEQUENCE, pymongo.ASCENDING)\\n\\n if result is not None:\\n return [data[pair_data.SEQUENCE] for data in result]\\n else:\\n return []\",\n \"def get_doc_ids(self):\\n cursor = self.connection.cursor()\\n cursor.execute(\\\"SELECT id FROM documents\\\")\\n results = [r[0] for r in cursor.fetchall()]\\n cursor.close()\\n return results\",\n \"def get_doc_ids(self):\\n cursor = self.connection.cursor()\\n cursor.execute(\\\"SELECT id FROM documents\\\")\\n results = [r[0] for r in cursor.fetchall()]\\n cursor.close()\\n return results\",\n \"def ids_from_fasta_lines(lines):\\r\\n ids = []\\r\\n for line in lines:\\r\\n if not line.startswith(\\\">\\\"):\\r\\n continue\\r\\n id = id_from_fasta_label_line(line)\\r\\n ids.append(id)\\r\\n\\r\\n return ids\",\n \"def _convert_ids(self, ids):\\n ids_list_int = []\\n\\n for id_ in ids:\\n if not self._validate_identifier(id_):\\n raise PhabfiveDataException(f\\\"Identifier '{id_}' is not valid\\\")\\n\\n id_ = id_.replace(\\\"P\\\", \\\"\\\")\\n # constraints takes int\\n id_ = int(id_)\\n ids_list_int.append(id_)\\n\\n return ids_list_int\",\n \"def ids_from_file(filepath):\\n\\n result = [[]] # A list containing an empty list.\\n count = 0\\n\\n with open(filepath, 'r') as f_in:\\n for line in f_in:\\n if count < 200:\\n # Append to the last sublist in the list\\n result[-1].append(line.strip())\\n count += 1\\n else:\\n # Create a new sublist once a group of 200 is reached.\\n result.append([line.strip()])\\n count = 1\\n\\n return result\",\n \"def _iter_rIds(self):\\n sldMasterId_lst = self._sldMasterIdLst.sldMasterId_lst\\n for sldMasterId in sldMasterId_lst:\\n yield sldMasterId.rId\",\n \"def IDs(self, default=[{}]):\\n tmp = self.data.get('ids', default)\\n return [HEP.IDObject(i) for i in tmp]\",\n \"def createRUID_List(rowIdxList, headerStr):\\r\\n RUID_List = []\\r\\n for aRowIdx in rowIdxList:\\r\\n workingRUID=df[headerStr].iloc[aRowIdx]\\r\\n RUID_List.append(workingRUID)\\r\\n return RUID_List\",\n \"def ids(self):\\n return self.obj_to_id.values()\",\n \"def get_sids_by_uuids(self, rids):\\n if not rids:\\n return []\\n session = self.DBSession()\\n results = baked_query_sids(session).params(rids=rids).all()\\n # check res.name to skip sids for supplementary rows, like 'downloads'\\n data = {str(res.rid): res.sid for res in results if res.name == ''}\\n return data\",\n \"def getVaccinesIds():\\n with driver.session() as s:\\n ids = s.write_transaction(getVaccinesId)\\n\\n vIds = []\\n for idEl in ids:\\n vIds.append(idEl[\\\"ID(v)\\\"])\\n\\n return vIds\",\n \"def test_get_ids(self):\\r\\n lines = \\\"\\\"\\\">S74_1 E86FECS01CEVAV orig_bc=ACATGTCACGTG new_bc=ACATGTCACGTG bc_diffs=0\\r\\nCTCCTC\\r\\n>Unassigned_2 E86FECS01EKKMF orig_bc=AGCGCTGATGTA new_bc=None bc_diffs=1\\r\\nGGTGCCTCCCTCGC\\r\\n>S80_3 E86FECS01EKKMF orig_bc=AGCGCTGATGTA new_bc=None bc_diffs=1\\r\\nGGTGCCTCCCTCGC\\r\\n>S80_4 E86FECS01CW66X orig_bc=AGTCCATAGCTG new_bc=AGTCCATAGCTG bc_diffs=0\\r\\nGTCCTGGCAG\\\"\\\"\\\".splitlines()\\r\\n result = get_ids(lines, 1)\\r\\n self.assertEqual(\\r\\n dict(result),\\r\\n {'S74': ['E86FECS01CEVAV'],\\r\\n 'Unassigned': ['E86FECS01EKKMF'],\\r\\n 'S80': ['E86FECS01EKKMF',\\r\\n 'E86FECS01CW66X']})\",\n \"def get_matches_id(match_number):\\n database = TinyDB('db.json')\\n matches_table = database.table('match')\\n # recuperation de tous les tours\\n match_id_list = []\\n for i in range(1, match_number + 1):\\n data = matches_table.all()[-i]\\n match_id_list.append(data.doc_id)\\n return match_id_list\",\n \"def fasta_ids(fasta_files, verbose=False):\\r\\n all_ids = set([])\\r\\n for fasta_in in fasta_files:\\r\\n for label, seq in parse_fasta(fasta_in):\\r\\n rid = label.split()[0]\\r\\n if rid in all_ids:\\r\\n raise ValueError(\\r\\n \\\"Duplicate ID found in FASTA/qual file: %s\\\" %\\r\\n label)\\r\\n all_ids.add(rid)\\r\\n return all_ids\",\n \"def build_segment_ids(input_ids): \\n segment_ids = [] \\n for seq in input_ids: \\n segment_id = []\\n id_ = 0\\n for token_id in seq: \\n segment_id.append(id_)\\n # 102 : [SEP]\\n if token_id == 102: \\n id_ +=1 \\n id_ %= 2 \\n segment_ids.append(segment_id)\\n return segment_ids\",\n \"def ids(self):\\n\\n if len(self._id_ranges) % 2 != 0:\\n raise AutoIDException(message=ExceptionsMessage.AutoIDIllegalRanges)\\n\\n ids = []\\n for i in range(int(len(self._id_ranges) / 2)):\\n begin = self._id_ranges[i * 2]\\n end = self._id_ranges[i * 2 + 1]\\n for j in range(begin, end):\\n ids.append(j)\\n\\n return ids\",\n \"def getLigandResIds(ligchemid:str, struct: Structure)->List[Residue]:\\n \\\"\\\"\\\"*ligchemids are of type https://www.rcsb.org/ligand/IDS\\\"\\\"\\\"\\n ligandResidues: List[Residue] = list(filter(lambda x: x.get_resname() == ligchemid, list( struct.get_residues() )))\\n return ligandResidues\",\n \"def list_of_gene_IDs_and_symbols_from_file(input_seq_file):\\n print(\\\"Find entrezgene IDs for reporter genes in RNAseq run\\\")\\n lines = []\\n with open(input_seq_file, 'U') as input_seq_file:\\n next(input_seq_file)\\n for line in input_seq_file:\\n new_line = line.strip().split('\\\\t')[0]\\n new_line = re.sub('\\\"', '', new_line) # inputs contained quotes\\n lines.append(new_line)\\n\\n query_list = pd.Series(data=lines).tolist()\\n\\n print(\\\"length of query list: \\\", len(query_list))\\n return query_list\",\n \"def convert2seq(self,seq_int):\\n\\t\\treturn [self.aminoacids[i] for i in seq_int]\",\n \"def get_training_ids(mydb):\\n\\treturn list(mydb.fetch_table(table_name='training_set',field_names=['CaseId']).CaseId)\",\n \"def get_gi_ids_from_files(filenames):\\n idlst = []\\n for filename in filenames:\\n idlst += get_gi_ids(filename)\\n return idlst\",\n \"def get_ids(corpus):\\n corpusids = []\\n for chunk in corpus:\\n for _id in chunk.keys():\\n corpusids.append(_id)\\n return corpusids\",\n \"def compress_doc_ids(doc_ids: list):\\r\\n int_doc_ids = [int(x) for x in doc_ids]\\r\\n sorted_doc_ids = sorted(int_doc_ids)\\r\\n return sorted_doc_ids\",\n \"def get_ids_from_list_file(list_file):\\n res = []\\n with open(list_file, 'r') as file:\\n for line in file:\\n res.append(line.strip())\\n return res\",\n \"def IDLIST(cls, tablename, **kwargs):\\n limit = \\\"LIMIT \\\" + kwargs.get ( \\\"limit\\\", \\\"ALL\\\" ) \\n offset = \\\"OFFSET \\\" + kwargs.get ( \\\"offset\\\", \\\"0\\\" )\\n \\n order = kwargs.get ( \\\"order\\\", ['objectid ASC'] )\\n order = \\\",\\\".join (order)\\n where = kwargs.get ( \\\"where\\\", ['TRUE'] )\\n where = \\\" AND \\\".join (where)\\n query = \\\"SELECT objectid FROM {0}.{1} WHERE {2} ORDER BY {3} {4} {5}\\\".format (\\n CFG.DB.SCHEMA, tablename, where, order, limit, offset )\\n rowset = map(lambda x: x[0], CFG.CX.query ( query ).getresult() )\\n return rowset\",\n \"def texts2ids(self, texts: list, length: int):\\n return [self.text2ids(text, length) for text in texts]\",\n \"def get_all_supplier_id() -> List[int]:\\n\\n # Open a new connection\\n db, cursor = db_connector.cursor()\\n\\n query = \\\"select id from supplier order by id\\\"\\n cursor.execute(query)\\n data = cursor.fetchall()\\n r_list = [x[0] for x in data]\\n db.disconnect()\\n return r_list\",\n \"def get_instances_ids(self):\\n reservations = self.__get_reservations()\\n instances_ids = []\\n instances,_ = self.__get_multi_instances(reservations)\\n for instance in instances:\\n instances_ids.append(instance.id.encode(\\\"latin-1\\\"))\\n return instances_ids\",\n \"def scrapeRecordIds(self):\\n data = self._getJason(config.SOLD_URL)\\n stored_records = self._queryScrapedIds('records', 'sale')\\n records = [(d['_id'], 'sale', False) for d in data\\n if d['_id'] not in stored_records]\\n if records:\\n self._storeData(records, 'records')\\n else:\\n print ('No new records')\\n stored_records = self._queryScrapedIds('records', 'list')\\n data = self._getJason(config.LIST_URL)\\n listings = [(d['_id'], 'list', False) for d in data\\n if d['_id'] not in stored_records]\\n if listings:\\n self._storeData(listings, 'records')\\n else:\\n print ('No new listings')\\n return\",\n \"def getLocationsIds():\\n with driver.session() as s:\\n ids = s.write_transaction(getLocationsId)\\n\\n lIds = []\\n for idEl in ids:\\n lIds.append(idEl[\\\"ID(l)\\\"])\\n\\n return lIds\",\n \"def _load_output(buf):\\n id_lists = []\\n for line in buf:\\n id_lists.append([int(id) for id in line.split()])\\n\\n for id_list in id_lists:\\n assert sorted(id_list) == id_list\\n\\n return id_lists\",\n \"def patron_holds(session, record_id):\\n r = session.get('{}/patrons/{}/holds'.format(api_url_base, record_id))\\n entries = json.loads(r.text)['entries']\\n ids = [x['id'].split('/')[-1] for x in entries]\\n hold_ids = []\\n for i in ids:\\n hold_ids.append(i)\\n return hold_ids\",\n \"def get_ids(self, text):\\n\\n tokens = [token.orth for token in self.tokenizer(text)]\\n ids = []\\n for token in tokens:\\n try:\\n id = self.vocab.vectors.key2row[token]\\n except KeyError:\\n id = self.oov_id\\n\\n ids.append(id)\\n\\n return ids\",\n \"def get_job_ids(self, offset=0, length=-1):\\n start = offset\\n if length >= 0:\\n end = offset + (length - 1)\\n else:\\n end = length\\n return [as_text(job_id) for job_id in\\n self.connection._lrange(self.key, start, end)]\",\n \"def docs2ids(self):\\n self.docs = [ [self.vocab[word] for word in doc] for doc in self.docs]\",\n \"def ids(self):\\n return frozenset([seq.id for seq in self])\",\n \"def object_ids(self):\\n return self._extract_set('id')\",\n \"def get_list(str_scu):\\n\\tscu_list = str_scu.split(',')\\n\\tscu_list_int = []\\n\\tif len(str_scu) > 0:\\n\\t\\tscu_list_int = [int(scu_id) for scu_id in scu_list]\\n\\treturn scu_list_int\",\n \"def get_ids(self, text):\\n\\n tokens = [token.orth for token in self.tokenizer(text)]\\n ids = []\\n for token in tokens:\\n try:\\n id = self._vocab.vectors.key2row[token]\\n except KeyError:\\n id = self.oov_id\\n\\n ids.append(id)\\n\\n return ids\",\n \"def patron_checkouts(session, record_id):\\n r = session.get('{}/patrons/{}/checkouts'.format(api_url_base, record_id))\\n entries = json.loads(r.text)['entries']\\n ids = [x['id'].split('/')[-1] for x in entries]\\n return ids\",\n \"def sequence_to_list_ids(sequence, vocab):\\n pass\",\n \"def get_party_ids_by_list(party_names: List[str]) -> List[int]:\\n\\n id_list = []\\n\\n for names in party_names:\\n id_list.append(get_party_id_by_name(names))\\n\\n return id_list\",\n \"def serve_recos(ids, ref_catalog):\\r\\n desc_list = []\\r\\n for desc_id in ids:\\r\\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\\r\\n return desc_list\",\n \"def get_ids(directory):\\n return (f[:-4] for f in os.listdir(directory))\",\n \"def list_ids (self, key):\\n\\n list_of_key_values = [str(x[key]) for x in self.result]\\n\\n self.result = list(dict.fromkeys([re.findall(r'\\\\b\\\\d+\\\\b', x)[0] for x in list_of_key_values if len(re.findall(r'\\\\b\\\\d+\\\\b', x)) !=0]))\\n\\n return self\",\n \"def build_indices(review_ids):\\n\\n review_indices = {}\\n\\n # Load qrel_abs_train txt file\\n clef_data = pd.read_csv(config.TRAIN_QREL_LOCATION, sep=\\\"\\\\s+\\\", names=['review_id', 'q0', 'pmid', 'included'])\\n\\n # Get index of documents for each review\\n for review_id in review_ids:\\n index = clef_data.index[clef_data['review_id'] == review_id].tolist()\\n\\n # Get the range of index for all documents within each review\\n review_indices[review_id] = (min(index), max(index) + 1)\\n\\n return review_indices\",\n \"def get_ids(self):\\n return self._ids\",\n \"def format(self, result):\\n results = DBFormatter.format(self, result)\\n\\n subIDs = []\\n for row in results:\\n if len(row[1].split('/')) <= 3: # remember, first item is empty\\n subIDs.append(row[0])\\n\\n return subIDs\",\n \"def get_ids(voc_path):\\n ids = []\\n print(\\\"voc\\\")\\n\\n files_images = glob.iglob(os.path.join(voc_path, \\\"*.JPEG\\\"))\\n for x in files_images:\\n name = os.path.splitext(os.path.basename(x))[0]\\n ids.append(name)\\n print(\\\"names: \\\", ids)\\n return ids\",\n \"def __ui_convert_ids_string_to_list(string_of_ids):\\n if string_of_ids == \\\"\\\":\\n return []\\n string_of_ids = string_of_ids.strip()\\n string_of_ids = string_of_ids.replace(\\\",\\\", \\\" \\\")\\n\\n done = False\\n while not done:\\n if string_of_ids.find(\\\" \\\") == -1:\\n done = True\\n else:\\n string_of_ids = string_of_ids.replace(\\\" \\\", \\\" \\\")\\n list_of_ids = string_of_ids.split(\\\" \\\")\\n for id_index in range(len(list_of_ids)):\\n list_of_ids[id_index] = int(list_of_ids[id_index])\\n return list_of_ids\",\n \"def get_es_ids(self):\\n search = self.search.source(['uri']).sort(['uri'])\\n es_ids = [item.meta.id for item in search.scan()]\\n return es_ids\",\n \"def get_primary_dids(self, rows):\\n\\n\\t\\tprimary_dids = {}\\n\\t\\tfor row in rows:\\n\\t\\t\\t(account_number, phone_number) = (int(row[0]), row[1])\\n\\t\\t\\tif primary_dids.has_key(account_number):\\n\\t\\t\\t\\tprimary_dids[account_number].append(phone_number)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprimary_dids[account_number] = [phone_number]\\n\\n\\t\\treturn primary_dids\",\n \"def get_atten_IDs(filename):\\n serialnos = loadtxt(filename, delimiter=',', skiprows=3, dtype=str)[0,1:]\\n rxs = loadtxt(filename, delimiter=',', skiprows=4, dtype=str)[0,1:]\\n headers = loadtxt(filename, delimiter=',', skiprows=5, dtype=str)[0,1:]\\n ID = {}\\n for index in range(0, len(headers), 2):\\n chanIDa = rxs[index]+'-'+headers[index].replace(' ','-')\\n chanIDb = rxs[index]+'-'+headers[index+1].replace(' ','-')\\n ID[chanIDa] = serialnos[index]+'A'\\n ID[chanIDb] = serialnos[index]+'B'\\n return ID\",\n \"def id_lst(self):\\n id_set = set()\\n for key in self.forward:\\n for val in self.forward[key]:\\n id_set.add(val)\\n return list(id_set)\",\n \"def _make_identifiers(self) -> Identifiers:\\n identifier_list = []\\n for meta_column in self._meta_columns:\\n idf_string = meta_column.reflected_column_idf\\n idf = Identifier.from_string(idf_string)\\n identifier_list.append(idf)\\n return identifier_list\",\n \"def convert_to_ids(self, terms):\\n vec = [self.get_id(label) for label in terms]\\n return vec\",\n \"def getMatchIds(self):\\n return sorted(self._matches.keys())\",\n \"def without_fmt(queries, chunk_size=2000):\\n sql = queries['missing']\\n recids = intbitset()\\n max_id = run_sql(\\\"SELECT max(id) FROM bibrec\\\")[0][0]\\n for start in xrange(1, max_id + 1, chunk_size):\\n end = start + chunk_size\\n recids += intbitset(run_sql(sql, (start, end)))\\n return recids\",\n \"def get_phone_numbers(r):\\n phone_match = re.findall(r'\\\\d\\\\d\\\\d-\\\\d\\\\d\\\\d-\\\\d\\\\d\\\\d\\\\d', r)\\n phone_list = []\\n if phone_match:\\n for match in phone_match:\\n if match not in phone_list:\\n phone_list.append(match)\\n phone_list = set(phone_list)\\n return phone_list\",\n \"def sortId(dicS):\\n idList = {}\\n count = 0\\n for k,v in sorted(dicS.items(), key=lambda x:x[1]):\\n idList[count]=k\\n count = count + 1\\n return idList\",\n \"def get_ids(self):\\n page = r.get(self.url)\\n tree = html.fromstring(page.content)\\n ids_elements = tree.xpath(\\\"//div[@id='selectedcontent']/div/ul/li/a\\\")\\n return [self._e_to_id(e) for e in ids_elements]\",\n \"def get_ids(self, instances):\\n instance_ids = []\\n for instance in instances:\\n instance_ids.append(instance.id)\\n return instance_ids\",\n \"def getEventIds(self):\\n eventIdsLst = []\\n for event in self.eventsLst:\\n eventIdsLst.append(event['id'])\\n return eventIdsLst\",\n \"def ids(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"ids\\\")\",\n \"def ids(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"ids\\\")\",\n \"def mr2bib(id_list):\\n d = mr2bib_dict(id_list)\\n l = []\\n for id in id_list:\\n try:\\n l.append(d[id])\\n except:\\n l.append(ReferenceErrorInfo(\\\"Not found\\\", id))\\n\\n return l\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71660554","0.6755276","0.6567358","0.6502151","0.6431556","0.6394535","0.6390275","0.6199647","0.61650753","0.603711","0.5996257","0.5975088","0.5970256","0.5936337","0.5927022","0.58693117","0.5824793","0.58174443","0.5816194","0.5816194","0.58141625","0.5810133","0.57896507","0.5786759","0.5762522","0.57623506","0.5742798","0.5739587","0.57032496","0.56928533","0.566833","0.5646448","0.5619867","0.56164604","0.56126636","0.56126636","0.56003255","0.5595381","0.55631053","0.5559799","0.5557139","0.55398846","0.5528371","0.5526106","0.5524437","0.55158246","0.5511256","0.55017406","0.5497897","0.5486435","0.54773694","0.54747885","0.5474039","0.546609","0.54636055","0.54621685","0.5453546","0.5435215","0.54261154","0.54174143","0.5415114","0.5403847","0.5398919","0.53971857","0.539444","0.5387337","0.5379996","0.5377547","0.53753495","0.53711325","0.5368788","0.5363221","0.5361122","0.5360325","0.5352557","0.5340444","0.5329295","0.53277355","0.53270453","0.5317923","0.53159946","0.5310676","0.53090096","0.5307747","0.5298005","0.5289631","0.5286101","0.52855134","0.5280782","0.52711695","0.52696955","0.5268314","0.52641976","0.5259186","0.5258332","0.52529067","0.5248725","0.5247253","0.5247253","0.52443624"],"string":"[\n \"0.71660554\",\n \"0.6755276\",\n \"0.6567358\",\n \"0.6502151\",\n \"0.6431556\",\n \"0.6394535\",\n \"0.6390275\",\n \"0.6199647\",\n \"0.61650753\",\n \"0.603711\",\n \"0.5996257\",\n \"0.5975088\",\n \"0.5970256\",\n \"0.5936337\",\n \"0.5927022\",\n \"0.58693117\",\n \"0.5824793\",\n \"0.58174443\",\n \"0.5816194\",\n \"0.5816194\",\n \"0.58141625\",\n \"0.5810133\",\n \"0.57896507\",\n \"0.5786759\",\n \"0.5762522\",\n \"0.57623506\",\n \"0.5742798\",\n \"0.5739587\",\n \"0.57032496\",\n \"0.56928533\",\n \"0.566833\",\n \"0.5646448\",\n \"0.5619867\",\n \"0.56164604\",\n \"0.56126636\",\n \"0.56126636\",\n \"0.56003255\",\n \"0.5595381\",\n \"0.55631053\",\n \"0.5559799\",\n \"0.5557139\",\n \"0.55398846\",\n \"0.5528371\",\n \"0.5526106\",\n \"0.5524437\",\n \"0.55158246\",\n \"0.5511256\",\n \"0.55017406\",\n \"0.5497897\",\n \"0.5486435\",\n \"0.54773694\",\n \"0.54747885\",\n \"0.5474039\",\n \"0.546609\",\n \"0.54636055\",\n \"0.54621685\",\n \"0.5453546\",\n \"0.5435215\",\n \"0.54261154\",\n \"0.54174143\",\n \"0.5415114\",\n \"0.5403847\",\n \"0.5398919\",\n \"0.53971857\",\n \"0.539444\",\n \"0.5387337\",\n \"0.5379996\",\n \"0.5377547\",\n \"0.53753495\",\n \"0.53711325\",\n \"0.5368788\",\n \"0.5363221\",\n \"0.5361122\",\n \"0.5360325\",\n \"0.5352557\",\n \"0.5340444\",\n \"0.5329295\",\n \"0.53277355\",\n \"0.53270453\",\n \"0.5317923\",\n \"0.53159946\",\n \"0.5310676\",\n \"0.53090096\",\n \"0.5307747\",\n \"0.5298005\",\n \"0.5289631\",\n \"0.5286101\",\n \"0.52855134\",\n \"0.5280782\",\n \"0.52711695\",\n \"0.52696955\",\n \"0.5268314\",\n \"0.52641976\",\n \"0.5259186\",\n \"0.5258332\",\n \"0.52529067\",\n \"0.5248725\",\n \"0.5247253\",\n \"0.5247253\",\n \"0.52443624\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7744344"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79584,"cells":{"query":{"kind":"string","value":"Creates a tuple of record id from a list of id. The result is expected to have ascending numerical order."},"document":{"kind":"string","value":"def create_record_tuple(ilist):\n list_length = len(ilist)\n if list_length:\n rec_tuple = '('\n for row in list[0:list_length-1]:\n rec_tuple += str(row)\n rec_tuple += ','\n rec_tuple += str(list[list_length-1])\n rec_tuple += ')'\n else: rec_tuple = '()'\n return rec_tuple"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_featseltuple(ids):\n newlist = []\n for part_id in ids:\n newlist.extend([part_id, part_id + 91])\n return tuple(sorted(newlist))","def create_recordid_list(rec_ids):\n rec_list = []\n for row in rec_ids:\n rec_list.append(row[0])\n return rec_list","def create_id(elements: Iterable) -> str:\r\n i = 1\r\n while str(i) in elements:\r\n i += 1\r\n return str(i)","def make_int_ids(paths: List[Path]) -> List[Tuple[int, Path]]:\n\n id_results = []\n\n for id_path in paths:\n id_path = Path(id_path)\n\n try:\n id_results.append((int(id_path.name, fn_base), id_path))\n except ValueError:\n pass\n\n return id_results","def as_ids(lst, sort=True):\n ids = []\n for x in lst:\n if isinstance(x, dict):\n ids.append(x['id'])\n else:\n ids.append(x.id)\n if sort:\n ids = sorted(ids)\n return ids","def maybe_readable_combine_ids(cls, ids):\r\n ids = list(ids) # We can't len a generator.\r\n return ids[0] if len(ids) == 1 else cls.combine_ids(ids)","def _parse_rec_id(rec_id: str) -> Tuple[int, bool, Optional[int]]:\n\n if m := re.match(ORIGINAL_RECORD_PATTERN, rec_id):\n return [int(m.group(1)), \"A\", None]\n elif m := re.match(DUPE_RECORD_PATTERN, rec_id):\n return [int(m.group(1)), \"B\", m.group(2)]\n else:\n raise Exception(f\"Unable to parse rec_id: {rec_id}\")","def combine_ids(ids):\r\n return hash_all(sorted(ids)) # We sort so that the id isn't sensitive to order.\r","def _convert_ids(self, ids):\n ids_list_int = []\n\n for id_ in ids:\n if not self._validate_identifier(id_):\n raise PhabfiveDataException(f\"Identifier '{id_}' is not valid\")\n\n id_ = id_.replace(\"P\", \"\")\n # constraints takes int\n id_ = int(id_)\n ids_list_int.append(id_)\n\n return ids_list_int","def ids_to_string(idlist):\r\n if len(idlist) == 1:\r\n return \"{:d}\".format(idlist[0])\r\n else:\r\n return \"-\".join([\"{:d}\".format(id) for id in idlist])","def rel_id_list(rel_list):\n\n rel_ids = []\n for i in range(0, len(rel_list)):\n rel_id = rel_list[i][\"id\"]\n rel_ids.append(rel_id)\n return rel_ids","def readPidList(pidlist):\n assert len(pidlist) % 2 == 0 and len(pidlist) >= 2\n ret = [(pidlist[0], pidlist[1])]\n for i in xrange(2, len(pidlist), 2):\n ret.append((ret[-1][0] + pidlist[i], ret[-1][1] + pidlist[i + 1]))\n return ret","def parse_ids(\n ids: List[str],\n) -> List[Union[WorkflowId, CromwellWorkflowLabel, ExperimentId]]:\n return [parse_id(i) for i in ids]","def _format_ids(self, args):\n ids = []\n\n if isinstance(args, (int, long)):\n ids.append(args)\n elif isinstance(args, (str, unicode)):\n for item in re.split(u'[ ,]+', args):\n if len(item) == 0:\n continue\n addition = None\n try:\n # handle index\n addition = [int(item)]\n except ValueError:\n pass\n if not addition:\n # handle hashes\n try:\n int(item, 16)\n addition = [item]\n except:\n pass\n if not addition:\n # handle index ranges i.e. 5:10\n match = re.match(u'^(\\d+):(\\d+)$', item)\n if match:\n try:\n idx_from = int(match.group(1))\n idx_to = int(match.group(2))\n addition = range(idx_from, idx_to + 1)\n except:\n pass\n if not addition:\n raise ValueError(u'Invalid torrent id, \\\"%s\\\"' % item)\n ids.extend(addition)\n elif isinstance(args, (list)):\n for item in args:\n ids.extend(self._format_ids(item))\n else:\n raise ValueError(u'Invalid torrent id')\n return ids","def _get_id(self, item, prefix, item_list):\r\n try:\r\n index = item_list.index(item)\r\n except ValueError:\r\n index = len(item_list)\r\n item_list.append(item)\r\n\r\n return self._id_for_index(prefix, index)","def ids(filename):\n with open(filename) as file:\n contents = file.read()\n return [int(x) for x in contents.split(\",\")]","def convert_ids(docs: List) -> List:\r\n try:\r\n for doc in docs:\r\n doc[\"_id\"] = str(doc[\"_id\"])\r\n except TypeError:\r\n pass\r\n return docs","def getIDs():","def indices_to_ids(splits, ids):\n return [[[ids[i] for i in ids_group] for ids_group in split] for split in splits]","def get_list_id(collection, index = \"id\", ignore_filter = False):\n\tif (not ignore_filter):\n\t\tcollection = [register[index] for register in list(collection)]\n\treturn \"'\" + (\"', '\".join(collection)) + \"'\"","def test_sort_ids(self):\r\n\r\n mapping = {\"1\": [\"0\", \"2\", \"5\", \"6\"],\r\n \"3\": [],\r\n \"4\": [],\r\n \"11\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\r\n \"8\": [\"7\"]}\r\n\r\n self.assertEqual(sort_ids([\"1\", \"3\", \"4\", \"8\", \"11\"], mapping),\r\n [\"11\", \"1\", \"8\", \"4\", \"3\"])","def test_sort_ids(self):\r\n\r\n mapping = {\"1\": [\"0\", \"2\", \"5\", \"6\"],\r\n \"3\": [],\r\n \"4\": [],\r\n \"11\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\r\n \"8\": [\"7\"]}\r\n\r\n self.assertEqual(sort_ids([\"1\", \"3\", \"4\", \"8\", \"11\"], mapping),\r\n [\"11\", \"1\", \"8\", \"4\", \"3\"])","def IDs(self, default=[{}]):\n tmp = self.data.get('ids', default)\n return [HEP.IDObject(i) for i in tmp]","def batches2IDs(batches):\n l = [ np.array( [ char2id(x) for x in characters(b) ] ) for b in batches ]\n return l","def get_members_ids(members):\n members_list = []\n for member in members:\n identity = member.get('@odata.id')\n if not identity:\n continue\n members_list.append(os.path.basename(identity))\n\n return tuple(members_list)","def tuple_from_expandable(expandable, field_descriptions):\n list_ = [expandable.__dict__[name] for name, _ in field_descriptions]\n return tuple([expandable.id_] + list_)","def getNextID(self, d):\n try:\n listOrdered = d.keys()\n listOrdered = funcs.sortStringList(listOrdered)\n lastID = int(listOrdered[-1])\n nextID = str(lastID + 1)\n for i in range(1,int(nextID)):\n if str(i) not in listOrdered:\n return str(i)\n return nextID\n except:\n return '1'","def extarct_id_tf(docs):\n\n if len(docs) == 0:\n return []\n docs = docs.split(',')\n ret = []\n for doc in docs:\n doc = doc.split('|')\n # doc_id, tf\n ret.append((int(doc[0]), int(doc[1])))\n return ret","def _get_id(self):\n self.unix_time = self.__get_unix_now()\n self.mac_addr = self.__get_random_12_str() # self.__get_mac_address()\n self.pid_hex = self.__get_pid()\n self.sequence_num = self.__get_sequence_number()\n\n # uuid format: 8 + 4 + 4 + 4 + 12\n _tmp_list = [self.unix_time[:8],\n self.unix_time[8:],\n self.mac_addr[:4],\n self.mac_addr[4:8],\n self.mac_addr[8:] + self.pid_hex + self.sequence_num]\n return _tmp_list","def get_recordIds(self):\n record_ids = []\n for item in self.order_items:\n record_ids.append(item.get_recordId())\n \n return record_ids","def incr_id(id, n):\n return id[:-1] + (id[-1] + n,)","def get_from_id(self,id=None):\n if id is None:\n return(self.items)\n if type(id) is int:\n for item in self.items:\n if item.id == id: return(item)\n items=[]\n if type(id) is list:\n return ([item.id for item in self.items if (item.id in id)])","def mkVtkIdList(self, it):\n vil = vtk.vtkIdList()\n for i in it:\n vil.InsertNextId(int(i))\n return vil","def urls2ids(self, urls):\r\n\t\tids = []\r\n\t\tfor url in urls:\r\n\t\t\tif check_for_sqlite(url):\r\n\t\t\t\tid_ = self.get_urlid(url)\r\n\t\t\t\tif id_ == 0: continue\r\n\t\t\t\tids.append(id_)\r\n\t\treturn ids","def compress_doc_ids(doc_ids: list):\r\n int_doc_ids = [int(x) for x in doc_ids]\r\n sorted_doc_ids = sorted(int_doc_ids)\r\n return sorted_doc_ids","def create_list(self, table, indata_list):\n try:\n _ids = []\n with self.lock:\n for indata in indata_list:\n _id = indata.get(\"_id\")\n if not _id:\n _id = str(uuid4())\n indata[\"_id\"] = _id\n with self.lock:\n if table not in self.db:\n self.db[table] = []\n self.db[table].append(deepcopy(indata))\n _ids.append(_id)\n return _ids\n except Exception as e: # TODO refine\n raise DbException(str(e))","def _gen_ids(argnames, argvalues, idgen):\n if not callable(idgen):\n # idgen is a new-style string formatting template\n if not isinstance(idgen, string_types):\n raise TypeError(\"idgen should be a callable or a string, found: %r\" % idgen)\n\n _formatter = idgen\n\n def gen_id_using_str_formatter(**params):\n try:\n # format using the idgen template\n return _formatter.format(**params)\n except Exception as e:\n raise InvalidIdTemplateException(_formatter, params, e)\n\n idgen = gen_id_using_str_formatter\n\n if len(argnames) > 1:\n ids = [idgen(**{n: v for n, v in zip(argnames, _argvals)}) for _argvals in argvalues]\n else:\n _only_name = argnames[0]\n ids = [idgen(**{_only_name: v}) for v in argvalues]\n\n return ids","def get_ids(self) -> List[str]:","def find_id(self):\n\t\tx , y = self.id.split(':')\n\t\treturn int(x), int(y)","def get_ids_strings(self, ids):\n #Split ids by list no longer than 1000 units,\n #because vk api can only gets 1000 ids per one call \n splitted_ids = list(self.chunks(ids, 1000))\n ids_in_list = []\n \n #crate list of strings with ids\n for split_ids in splitted_ids:\n user_ids = ''\n #make string ids list. Do it because of api requirement\n for id in split_ids:\n user_ids += str(id) + \",\"\n #remove last \",\"\n user_ids = user_ids[:-1]\n ids_in_list.append(user_ids)\n\n return ids_in_list","def sort_ids(ids, mapping):\r\n\r\n def _lookup(id):\r\n try:\r\n return len(mapping[id])\r\n except (KeyError):\r\n return 0\r\n\r\n deco = [(_lookup(id), id) for id in ids]\r\n deco.sort(reverse=True)\r\n return [id for _, id in deco]","def comma_list(s):\n\n return tuple(int(v) for v in s.split(\",\"))","def getIdentity(cls):\n l = []\n for i in range(0,len(cls.id2items)):\n item = (cls.id2items[i],cls.id2items[i])\n l.append(item)\n return Relation(*l)","def get_locations_by_ids(self, id_list):","def __ui_convert_ids_string_to_list(string_of_ids):\n if string_of_ids == \"\":\n return []\n string_of_ids = string_of_ids.strip()\n string_of_ids = string_of_ids.replace(\",\", \" \")\n\n done = False\n while not done:\n if string_of_ids.find(\" \") == -1:\n done = True\n else:\n string_of_ids = string_of_ids.replace(\" \", \" \")\n list_of_ids = string_of_ids.split(\" \")\n for id_index in range(len(list_of_ids)):\n list_of_ids[id_index] = int(list_of_ids[id_index])\n return list_of_ids","def test_first_id(self):\r\n ids = \\\r\n \"R27DLI_4812 R27DLI_600 R27DLI_727 U1PLI_403 U1PLI_8969\".split(\r\n )\r\n self.assertEqual(first_id(ids, {}), 'R27DLI_4812')","def id_extract(ids, db_location, database_file):\n # For exact searching, need to be adapted for ignored letter\n # SeqIO.write((seq for seq in seqiter if seq.id in ids), temp_seq_file[1]+'.txt', \"fasta\")\n\n os.chdir(db_location)\n temp_seq_file = tempfile.mkstemp()\n f_results = open(temp_seq_file[1] + '.txt', 'w')\n\n seqiter = SeqIO.parse(open(database_file), 'fasta')\n\n for id_to_find in ids:\n for sequence in seqiter:\n if sequence.id.find(id_to_find) is not -1:\n f_results.write('>' + sequence.id + '\\n' + str(sequence.seq) + '\\n')\n f_results.close()\n\n return temp_seq_file[1] + '.txt'","def genIdList(numId, idSize):\n\tiDs = []\n\tfor i in range(numId):\n\t\tiDs.append(genID(idSize))\n\treturn iDs","def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\n return tuple([l_[x] for x in i])","def convert_to_pj_id(id_nat_plus):\n if not is_natural_plus(id_nat_plus):\n raise ValueError(\"convert_to_pj_id: Not given Natural+\")\n\n return [ID_STR, id_nat_plus]","def paths_to_ids(paths: List[Path]) -> List[int]:\n\n ids = []\n for path in paths:\n try:\n ids.append(int(path.name))\n except ValueError:\n raise ValueError(\"Invalid dir_db path '{}'\".format(path.as_posix()))\n return ids","def sort_id(self):\n return self.sort('id')","def _CreateRecordId(self):\n self._record_count += 1\n return '%s_%s' % (self._unique_id, self._record_count)","def _tuple_from_one_or_two_ints(self, v):\n try:\n a, b = [int(x) for x in v]\n except TypeError:\n a, b = int(v), int(v)\n return (a,b)","def _params_to_int(self, qs):\n return [int(str_id) for str_id in qs.split(',')]","def compute_sort_id_assignment(lang, start=0):\n bounds, ids = {}, {}\n _compute_id_assignment(lang, lang.Object, ids, bounds, compute_direct_sort_map(lang), start=start)\n return bounds, ids","def data_by_id(id_list: List[str]) -> Generator[GameData, None, None]:\n\ttarget = collection.find({\"_id\": {\"$in\": [ObjectId[id_] for id_ in id_list]}})\n\tfor data in target:\n\t\tyield GameData(data)","def to_object_id_list(data):\n \n if not data:\n return []\n return [ObjectId(i) for i in data if i]","def ilist_to_string(ilist):\n return ','.join([str(i.id) for i in ilist])","def create_download_id(*files):\n return str(obscure(bytes(str({'filenames': files}), 'utf-8')), 'utf-8')","def _getRecordBatch(idList) :\n handle = Entrez.efetch(db = \"nuccore\", rettype = \"gbwithparts\",\n retmode = \"text\", id = \",\".join(idList))\n r = handle.read()\n handle.close()\n return r","def parse_record(record_wrapper_elem) -> (int, int, int):\n records = record_wrapper_elem.contents[2].split('-')\n return int(records[0]), int(records[1]), int(records[2])","def doc2id(self, doc):\n doc = map(self.process_token, doc)\n return [self.token_to_id(token) for token in doc]","def str_to_id(string, block):\n if len(string) % block != 0:\n raise Exception('String length not a multiple of block={}'.format(block))\n num_blocks = len(string) // block\n return tuple([int(string[i*block: (i+1)*block]) for i in range(num_blocks)])","def create_obj_id_for_query(id_dict: Dict) -> Text:\n return \",\".join([f\"{key}={value}\" for key, value in id_dict.items()])","def convert_to_ids(self, terms):\n vec = [self.get_id(label) for label in terms]\n return vec","def _load_output(buf):\n id_lists = []\n for line in buf:\n id_lists.append([int(id) for id in line.split()])\n\n for id_list in id_lists:\n assert sorted(id_list) == id_list\n\n return id_lists","def word2tuple(word, char_id):\n tup = []\n for c in word:\n if str(c) in char_id:\n tup.append(char_id[c])\n else:\n tup.append(len(char_id))\n return tuple(tup)","def get_party_ids_by_list(party_names: List[str]) -> List[int]:\n\n id_list = []\n\n for names in party_names:\n id_list.append(get_party_id_by_name(names))\n\n return id_list","def __generate_id(self):\n ids = [int(fd.get('id')) for fd in self.favorite_drinks]\n return str(max(ids)+1)","def element_to_tuple(list_of_elements):\n return list(map(lambda x: tuple(x), list_of_elements))","def words_to_id(text, is_list=False, old_word_to_id=None):\n if is_list:\n x = \"\"\n for line in text:\n x += line + \" \"\n text = x\n \n uniq_words = set(text.split(\" \"))\n \n if old_word_to_id:\n word_to_id = old_word_to_id\n start = len(old_word_to_id)\n for word in uniq_words:\n if word not in word_to_id:\n word_to_id[word] = start\n start += 1\n else:\n word_to_id = {word:i for i, word in enumerate(uniq_words)}\n \n id_to_word = {str(v):k for k,v in word_to_id.items()}\n return word_to_id, id_to_word","def id_generator2(self, collection):\n max_id = collection.aggregate(\n [\n {'$replaceWith': {'$toInt': '$Id'}},\n {\n '$group': {\n \"_id\": None,\n \"max_id\": {'$max': '$Id'}\n }\n }\n ]\n )\n\n for id in max_id:\n return str(max_id+1)","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def getId(*args):","def find_non_unique_ids(ids: np.array) -> Tuple[np.array]:\n\n args = ids.argsort()\n mask = np.empty(args.shape, dtype=bool)\n sorted_ids = ids[args]\n\n # By definition, the first element should not be a repeat of itself.\n mask[0] = False\n # The actual duplicate check; works because sorted_ids is, well, sorted.\n mask[1:] = sorted_ids[1:] == sorted_ids[:-1]\n\n # sorted_ids may be very large and we no longer need it\n del sorted_ids\n\n # Now we need to put everything back where it belongs.\n position_corrected_mask = np.empty_like(mask)\n position_corrected_mask[args] = mask\n\n # Now we can compress everything to a nice integer array\n duplicate_positions = np.where(position_corrected_mask)[0]\n duplicate_ids = ids[duplicate_positions]\n\n return duplicate_ids, duplicate_positions","def generate_id(table, pk):\n query = \"SELECT \" + pk + \" FROM \" + table\n cursor.execute(query)\n records = cursor.fetchall()\n if records == []:\n return \"000000\"\n pks = [x[0] for x in records]\n print(pks)\n pks.sort(key=int)\n max_add1 = str(int(pks[-1]) + 1)\n return max_add1.rjust(6, '0')","def list_ids(self, start: int = None, end: int = None) -> List:\n return [i.unique_id for i in self.data[start:end]]","def get_ordered_ids(tree):\n ordered_ids = []\n ordered_ids.extend(id(node) for node in tree.gen_tips())\n ordered_ids.extend(id(node) for node in tree.gen_internal_nodes())\n return ordered_ids","def list_to_tuple(list):\n templist = []\n for i in list:\n try:\n templist.append(int(i))\n except ValueError:\n print('Error. Please enter only integers.')\n return ()\n return tuple(templist)","def neco__tuple_update_pids(tup, new_pid_dict):\n new_iterable = []\n for tok in tup:\n if isinstance(tok, Pid):\n new_tok = Pid.from_list(new_pid_dict[tuple(tok.data)])\n elif isinstance(tok, tuple):\n new_tok = neco__tuple_update_pids(tok, new_pid_dict)\n else:\n new_tok = tok\n new_iterable.append(new_tok)\n return tuple(new_iterable)","def get_tag_ids(tag_names):\n\ttag_names = tuple(tag_names)\n\twith postgres, postgres.cursor() as cur:\n\t\tcur.execute(\"select id from mustard.tags where english_name in %s\", (tag_names,))\n\t\treturn [row[0] for row in cur]","def get_ids_values(path):\n \n def read_txt(path):\n \"\"\"read text file from path.\"\"\"\n \n with open(path, \"r\") as f:\n return f.read().splitlines()\n \n raw_data = read_txt(path)[1:]\n \n def deal_line(line):\n ids, values = line.split(',')\n return ids, values\n \n ids_values = [deal_line(line) for line in raw_data]\n ids = [x[0] for x in ids_values]\n values = [x[1] for x in ids_values]\n values = list(map(int, values))\n \n return ids, values","def _expand_priority_order(self, id_list):\n res = self.id_priority_list.copy()\n for key in id_list:\n if key not in self.id_priority_list:\n res.append(key)\n return res","def compare_ids(ids_in_json_list, ids_in_file_list):\n new_pipeline_id = []\n if ids_in_json_list == ids_in_file_list:\n new_pipeline_id = []\n\n else:\n new_pipeline_id = [pipe_id for pipe_id in ids_in_json_list if pipe_id not in ids_in_file_list]\n return new_pipeline_id","def _params_to_ints(self, qs):\n return [int(str_id) for str_id in qs.split(',')]","def _params_to_ints(self, qs):\n return [int(str_id) for str_id in qs.split(',')]","def _params_to_ints(self, qs):\n return [int(str_id) for str_id in qs.split(',')]","def _params_to_ints(self, qs):\n return [int(str_id) for str_id in qs.split(',')]","def _params_to_ints(self, qs):\n return [int(str_id) for str_id in qs.split(',')]"],"string":"[\n \"def create_featseltuple(ids):\\n newlist = []\\n for part_id in ids:\\n newlist.extend([part_id, part_id + 91])\\n return tuple(sorted(newlist))\",\n \"def create_recordid_list(rec_ids):\\n rec_list = []\\n for row in rec_ids:\\n rec_list.append(row[0])\\n return rec_list\",\n \"def create_id(elements: Iterable) -> str:\\r\\n i = 1\\r\\n while str(i) in elements:\\r\\n i += 1\\r\\n return str(i)\",\n \"def make_int_ids(paths: List[Path]) -> List[Tuple[int, Path]]:\\n\\n id_results = []\\n\\n for id_path in paths:\\n id_path = Path(id_path)\\n\\n try:\\n id_results.append((int(id_path.name, fn_base), id_path))\\n except ValueError:\\n pass\\n\\n return id_results\",\n \"def as_ids(lst, sort=True):\\n ids = []\\n for x in lst:\\n if isinstance(x, dict):\\n ids.append(x['id'])\\n else:\\n ids.append(x.id)\\n if sort:\\n ids = sorted(ids)\\n return ids\",\n \"def maybe_readable_combine_ids(cls, ids):\\r\\n ids = list(ids) # We can't len a generator.\\r\\n return ids[0] if len(ids) == 1 else cls.combine_ids(ids)\",\n \"def _parse_rec_id(rec_id: str) -> Tuple[int, bool, Optional[int]]:\\n\\n if m := re.match(ORIGINAL_RECORD_PATTERN, rec_id):\\n return [int(m.group(1)), \\\"A\\\", None]\\n elif m := re.match(DUPE_RECORD_PATTERN, rec_id):\\n return [int(m.group(1)), \\\"B\\\", m.group(2)]\\n else:\\n raise Exception(f\\\"Unable to parse rec_id: {rec_id}\\\")\",\n \"def combine_ids(ids):\\r\\n return hash_all(sorted(ids)) # We sort so that the id isn't sensitive to order.\\r\",\n \"def _convert_ids(self, ids):\\n ids_list_int = []\\n\\n for id_ in ids:\\n if not self._validate_identifier(id_):\\n raise PhabfiveDataException(f\\\"Identifier '{id_}' is not valid\\\")\\n\\n id_ = id_.replace(\\\"P\\\", \\\"\\\")\\n # constraints takes int\\n id_ = int(id_)\\n ids_list_int.append(id_)\\n\\n return ids_list_int\",\n \"def ids_to_string(idlist):\\r\\n if len(idlist) == 1:\\r\\n return \\\"{:d}\\\".format(idlist[0])\\r\\n else:\\r\\n return \\\"-\\\".join([\\\"{:d}\\\".format(id) for id in idlist])\",\n \"def rel_id_list(rel_list):\\n\\n rel_ids = []\\n for i in range(0, len(rel_list)):\\n rel_id = rel_list[i][\\\"id\\\"]\\n rel_ids.append(rel_id)\\n return rel_ids\",\n \"def readPidList(pidlist):\\n assert len(pidlist) % 2 == 0 and len(pidlist) >= 2\\n ret = [(pidlist[0], pidlist[1])]\\n for i in xrange(2, len(pidlist), 2):\\n ret.append((ret[-1][0] + pidlist[i], ret[-1][1] + pidlist[i + 1]))\\n return ret\",\n \"def parse_ids(\\n ids: List[str],\\n) -> List[Union[WorkflowId, CromwellWorkflowLabel, ExperimentId]]:\\n return [parse_id(i) for i in ids]\",\n \"def _format_ids(self, args):\\n ids = []\\n\\n if isinstance(args, (int, long)):\\n ids.append(args)\\n elif isinstance(args, (str, unicode)):\\n for item in re.split(u'[ ,]+', args):\\n if len(item) == 0:\\n continue\\n addition = None\\n try:\\n # handle index\\n addition = [int(item)]\\n except ValueError:\\n pass\\n if not addition:\\n # handle hashes\\n try:\\n int(item, 16)\\n addition = [item]\\n except:\\n pass\\n if not addition:\\n # handle index ranges i.e. 5:10\\n match = re.match(u'^(\\\\d+):(\\\\d+)$', item)\\n if match:\\n try:\\n idx_from = int(match.group(1))\\n idx_to = int(match.group(2))\\n addition = range(idx_from, idx_to + 1)\\n except:\\n pass\\n if not addition:\\n raise ValueError(u'Invalid torrent id, \\\\\\\"%s\\\\\\\"' % item)\\n ids.extend(addition)\\n elif isinstance(args, (list)):\\n for item in args:\\n ids.extend(self._format_ids(item))\\n else:\\n raise ValueError(u'Invalid torrent id')\\n return ids\",\n \"def _get_id(self, item, prefix, item_list):\\r\\n try:\\r\\n index = item_list.index(item)\\r\\n except ValueError:\\r\\n index = len(item_list)\\r\\n item_list.append(item)\\r\\n\\r\\n return self._id_for_index(prefix, index)\",\n \"def ids(filename):\\n with open(filename) as file:\\n contents = file.read()\\n return [int(x) for x in contents.split(\\\",\\\")]\",\n \"def convert_ids(docs: List) -> List:\\r\\n try:\\r\\n for doc in docs:\\r\\n doc[\\\"_id\\\"] = str(doc[\\\"_id\\\"])\\r\\n except TypeError:\\r\\n pass\\r\\n return docs\",\n \"def getIDs():\",\n \"def indices_to_ids(splits, ids):\\n return [[[ids[i] for i in ids_group] for ids_group in split] for split in splits]\",\n \"def get_list_id(collection, index = \\\"id\\\", ignore_filter = False):\\n\\tif (not ignore_filter):\\n\\t\\tcollection = [register[index] for register in list(collection)]\\n\\treturn \\\"'\\\" + (\\\"', '\\\".join(collection)) + \\\"'\\\"\",\n \"def test_sort_ids(self):\\r\\n\\r\\n mapping = {\\\"1\\\": [\\\"0\\\", \\\"2\\\", \\\"5\\\", \\\"6\\\"],\\r\\n \\\"3\\\": [],\\r\\n \\\"4\\\": [],\\r\\n \\\"11\\\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\\r\\n \\\"8\\\": [\\\"7\\\"]}\\r\\n\\r\\n self.assertEqual(sort_ids([\\\"1\\\", \\\"3\\\", \\\"4\\\", \\\"8\\\", \\\"11\\\"], mapping),\\r\\n [\\\"11\\\", \\\"1\\\", \\\"8\\\", \\\"4\\\", \\\"3\\\"])\",\n \"def test_sort_ids(self):\\r\\n\\r\\n mapping = {\\\"1\\\": [\\\"0\\\", \\\"2\\\", \\\"5\\\", \\\"6\\\"],\\r\\n \\\"3\\\": [],\\r\\n \\\"4\\\": [],\\r\\n \\\"11\\\": [1, 2, 3, 4, 5, 6, 7, 8, 9],\\r\\n \\\"8\\\": [\\\"7\\\"]}\\r\\n\\r\\n self.assertEqual(sort_ids([\\\"1\\\", \\\"3\\\", \\\"4\\\", \\\"8\\\", \\\"11\\\"], mapping),\\r\\n [\\\"11\\\", \\\"1\\\", \\\"8\\\", \\\"4\\\", \\\"3\\\"])\",\n \"def IDs(self, default=[{}]):\\n tmp = self.data.get('ids', default)\\n return [HEP.IDObject(i) for i in tmp]\",\n \"def batches2IDs(batches):\\n l = [ np.array( [ char2id(x) for x in characters(b) ] ) for b in batches ]\\n return l\",\n \"def get_members_ids(members):\\n members_list = []\\n for member in members:\\n identity = member.get('@odata.id')\\n if not identity:\\n continue\\n members_list.append(os.path.basename(identity))\\n\\n return tuple(members_list)\",\n \"def tuple_from_expandable(expandable, field_descriptions):\\n list_ = [expandable.__dict__[name] for name, _ in field_descriptions]\\n return tuple([expandable.id_] + list_)\",\n \"def getNextID(self, d):\\n try:\\n listOrdered = d.keys()\\n listOrdered = funcs.sortStringList(listOrdered)\\n lastID = int(listOrdered[-1])\\n nextID = str(lastID + 1)\\n for i in range(1,int(nextID)):\\n if str(i) not in listOrdered:\\n return str(i)\\n return nextID\\n except:\\n return '1'\",\n \"def extarct_id_tf(docs):\\n\\n if len(docs) == 0:\\n return []\\n docs = docs.split(',')\\n ret = []\\n for doc in docs:\\n doc = doc.split('|')\\n # doc_id, tf\\n ret.append((int(doc[0]), int(doc[1])))\\n return ret\",\n \"def _get_id(self):\\n self.unix_time = self.__get_unix_now()\\n self.mac_addr = self.__get_random_12_str() # self.__get_mac_address()\\n self.pid_hex = self.__get_pid()\\n self.sequence_num = self.__get_sequence_number()\\n\\n # uuid format: 8 + 4 + 4 + 4 + 12\\n _tmp_list = [self.unix_time[:8],\\n self.unix_time[8:],\\n self.mac_addr[:4],\\n self.mac_addr[4:8],\\n self.mac_addr[8:] + self.pid_hex + self.sequence_num]\\n return _tmp_list\",\n \"def get_recordIds(self):\\n record_ids = []\\n for item in self.order_items:\\n record_ids.append(item.get_recordId())\\n \\n return record_ids\",\n \"def incr_id(id, n):\\n return id[:-1] + (id[-1] + n,)\",\n \"def get_from_id(self,id=None):\\n if id is None:\\n return(self.items)\\n if type(id) is int:\\n for item in self.items:\\n if item.id == id: return(item)\\n items=[]\\n if type(id) is list:\\n return ([item.id for item in self.items if (item.id in id)])\",\n \"def mkVtkIdList(self, it):\\n vil = vtk.vtkIdList()\\n for i in it:\\n vil.InsertNextId(int(i))\\n return vil\",\n \"def urls2ids(self, urls):\\r\\n\\t\\tids = []\\r\\n\\t\\tfor url in urls:\\r\\n\\t\\t\\tif check_for_sqlite(url):\\r\\n\\t\\t\\t\\tid_ = self.get_urlid(url)\\r\\n\\t\\t\\t\\tif id_ == 0: continue\\r\\n\\t\\t\\t\\tids.append(id_)\\r\\n\\t\\treturn ids\",\n \"def compress_doc_ids(doc_ids: list):\\r\\n int_doc_ids = [int(x) for x in doc_ids]\\r\\n sorted_doc_ids = sorted(int_doc_ids)\\r\\n return sorted_doc_ids\",\n \"def create_list(self, table, indata_list):\\n try:\\n _ids = []\\n with self.lock:\\n for indata in indata_list:\\n _id = indata.get(\\\"_id\\\")\\n if not _id:\\n _id = str(uuid4())\\n indata[\\\"_id\\\"] = _id\\n with self.lock:\\n if table not in self.db:\\n self.db[table] = []\\n self.db[table].append(deepcopy(indata))\\n _ids.append(_id)\\n return _ids\\n except Exception as e: # TODO refine\\n raise DbException(str(e))\",\n \"def _gen_ids(argnames, argvalues, idgen):\\n if not callable(idgen):\\n # idgen is a new-style string formatting template\\n if not isinstance(idgen, string_types):\\n raise TypeError(\\\"idgen should be a callable or a string, found: %r\\\" % idgen)\\n\\n _formatter = idgen\\n\\n def gen_id_using_str_formatter(**params):\\n try:\\n # format using the idgen template\\n return _formatter.format(**params)\\n except Exception as e:\\n raise InvalidIdTemplateException(_formatter, params, e)\\n\\n idgen = gen_id_using_str_formatter\\n\\n if len(argnames) > 1:\\n ids = [idgen(**{n: v for n, v in zip(argnames, _argvals)}) for _argvals in argvalues]\\n else:\\n _only_name = argnames[0]\\n ids = [idgen(**{_only_name: v}) for v in argvalues]\\n\\n return ids\",\n \"def get_ids(self) -> List[str]:\",\n \"def find_id(self):\\n\\t\\tx , y = self.id.split(':')\\n\\t\\treturn int(x), int(y)\",\n \"def get_ids_strings(self, ids):\\n #Split ids by list no longer than 1000 units,\\n #because vk api can only gets 1000 ids per one call \\n splitted_ids = list(self.chunks(ids, 1000))\\n ids_in_list = []\\n \\n #crate list of strings with ids\\n for split_ids in splitted_ids:\\n user_ids = ''\\n #make string ids list. Do it because of api requirement\\n for id in split_ids:\\n user_ids += str(id) + \\\",\\\"\\n #remove last \\\",\\\"\\n user_ids = user_ids[:-1]\\n ids_in_list.append(user_ids)\\n\\n return ids_in_list\",\n \"def sort_ids(ids, mapping):\\r\\n\\r\\n def _lookup(id):\\r\\n try:\\r\\n return len(mapping[id])\\r\\n except (KeyError):\\r\\n return 0\\r\\n\\r\\n deco = [(_lookup(id), id) for id in ids]\\r\\n deco.sort(reverse=True)\\r\\n return [id for _, id in deco]\",\n \"def comma_list(s):\\n\\n return tuple(int(v) for v in s.split(\\\",\\\"))\",\n \"def getIdentity(cls):\\n l = []\\n for i in range(0,len(cls.id2items)):\\n item = (cls.id2items[i],cls.id2items[i])\\n l.append(item)\\n return Relation(*l)\",\n \"def get_locations_by_ids(self, id_list):\",\n \"def __ui_convert_ids_string_to_list(string_of_ids):\\n if string_of_ids == \\\"\\\":\\n return []\\n string_of_ids = string_of_ids.strip()\\n string_of_ids = string_of_ids.replace(\\\",\\\", \\\" \\\")\\n\\n done = False\\n while not done:\\n if string_of_ids.find(\\\" \\\") == -1:\\n done = True\\n else:\\n string_of_ids = string_of_ids.replace(\\\" \\\", \\\" \\\")\\n list_of_ids = string_of_ids.split(\\\" \\\")\\n for id_index in range(len(list_of_ids)):\\n list_of_ids[id_index] = int(list_of_ids[id_index])\\n return list_of_ids\",\n \"def test_first_id(self):\\r\\n ids = \\\\\\r\\n \\\"R27DLI_4812 R27DLI_600 R27DLI_727 U1PLI_403 U1PLI_8969\\\".split(\\r\\n )\\r\\n self.assertEqual(first_id(ids, {}), 'R27DLI_4812')\",\n \"def id_extract(ids, db_location, database_file):\\n # For exact searching, need to be adapted for ignored letter\\n # SeqIO.write((seq for seq in seqiter if seq.id in ids), temp_seq_file[1]+'.txt', \\\"fasta\\\")\\n\\n os.chdir(db_location)\\n temp_seq_file = tempfile.mkstemp()\\n f_results = open(temp_seq_file[1] + '.txt', 'w')\\n\\n seqiter = SeqIO.parse(open(database_file), 'fasta')\\n\\n for id_to_find in ids:\\n for sequence in seqiter:\\n if sequence.id.find(id_to_find) is not -1:\\n f_results.write('>' + sequence.id + '\\\\n' + str(sequence.seq) + '\\\\n')\\n f_results.close()\\n\\n return temp_seq_file[1] + '.txt'\",\n \"def genIdList(numId, idSize):\\n\\tiDs = []\\n\\tfor i in range(numId):\\n\\t\\tiDs.append(genID(idSize))\\n\\treturn iDs\",\n \"def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\\n return tuple([l_[x] for x in i])\",\n \"def convert_to_pj_id(id_nat_plus):\\n if not is_natural_plus(id_nat_plus):\\n raise ValueError(\\\"convert_to_pj_id: Not given Natural+\\\")\\n\\n return [ID_STR, id_nat_plus]\",\n \"def paths_to_ids(paths: List[Path]) -> List[int]:\\n\\n ids = []\\n for path in paths:\\n try:\\n ids.append(int(path.name))\\n except ValueError:\\n raise ValueError(\\\"Invalid dir_db path '{}'\\\".format(path.as_posix()))\\n return ids\",\n \"def sort_id(self):\\n return self.sort('id')\",\n \"def _CreateRecordId(self):\\n self._record_count += 1\\n return '%s_%s' % (self._unique_id, self._record_count)\",\n \"def _tuple_from_one_or_two_ints(self, v):\\n try:\\n a, b = [int(x) for x in v]\\n except TypeError:\\n a, b = int(v), int(v)\\n return (a,b)\",\n \"def _params_to_int(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\",\n \"def compute_sort_id_assignment(lang, start=0):\\n bounds, ids = {}, {}\\n _compute_id_assignment(lang, lang.Object, ids, bounds, compute_direct_sort_map(lang), start=start)\\n return bounds, ids\",\n \"def data_by_id(id_list: List[str]) -> Generator[GameData, None, None]:\\n\\ttarget = collection.find({\\\"_id\\\": {\\\"$in\\\": [ObjectId[id_] for id_ in id_list]}})\\n\\tfor data in target:\\n\\t\\tyield GameData(data)\",\n \"def to_object_id_list(data):\\n \\n if not data:\\n return []\\n return [ObjectId(i) for i in data if i]\",\n \"def ilist_to_string(ilist):\\n return ','.join([str(i.id) for i in ilist])\",\n \"def create_download_id(*files):\\n return str(obscure(bytes(str({'filenames': files}), 'utf-8')), 'utf-8')\",\n \"def _getRecordBatch(idList) :\\n handle = Entrez.efetch(db = \\\"nuccore\\\", rettype = \\\"gbwithparts\\\",\\n retmode = \\\"text\\\", id = \\\",\\\".join(idList))\\n r = handle.read()\\n handle.close()\\n return r\",\n \"def parse_record(record_wrapper_elem) -> (int, int, int):\\n records = record_wrapper_elem.contents[2].split('-')\\n return int(records[0]), int(records[1]), int(records[2])\",\n \"def doc2id(self, doc):\\n doc = map(self.process_token, doc)\\n return [self.token_to_id(token) for token in doc]\",\n \"def str_to_id(string, block):\\n if len(string) % block != 0:\\n raise Exception('String length not a multiple of block={}'.format(block))\\n num_blocks = len(string) // block\\n return tuple([int(string[i*block: (i+1)*block]) for i in range(num_blocks)])\",\n \"def create_obj_id_for_query(id_dict: Dict) -> Text:\\n return \\\",\\\".join([f\\\"{key}={value}\\\" for key, value in id_dict.items()])\",\n \"def convert_to_ids(self, terms):\\n vec = [self.get_id(label) for label in terms]\\n return vec\",\n \"def _load_output(buf):\\n id_lists = []\\n for line in buf:\\n id_lists.append([int(id) for id in line.split()])\\n\\n for id_list in id_lists:\\n assert sorted(id_list) == id_list\\n\\n return id_lists\",\n \"def word2tuple(word, char_id):\\n tup = []\\n for c in word:\\n if str(c) in char_id:\\n tup.append(char_id[c])\\n else:\\n tup.append(len(char_id))\\n return tuple(tup)\",\n \"def get_party_ids_by_list(party_names: List[str]) -> List[int]:\\n\\n id_list = []\\n\\n for names in party_names:\\n id_list.append(get_party_id_by_name(names))\\n\\n return id_list\",\n \"def __generate_id(self):\\n ids = [int(fd.get('id')) for fd in self.favorite_drinks]\\n return str(max(ids)+1)\",\n \"def element_to_tuple(list_of_elements):\\n return list(map(lambda x: tuple(x), list_of_elements))\",\n \"def words_to_id(text, is_list=False, old_word_to_id=None):\\n if is_list:\\n x = \\\"\\\"\\n for line in text:\\n x += line + \\\" \\\"\\n text = x\\n \\n uniq_words = set(text.split(\\\" \\\"))\\n \\n if old_word_to_id:\\n word_to_id = old_word_to_id\\n start = len(old_word_to_id)\\n for word in uniq_words:\\n if word not in word_to_id:\\n word_to_id[word] = start\\n start += 1\\n else:\\n word_to_id = {word:i for i, word in enumerate(uniq_words)}\\n \\n id_to_word = {str(v):k for k,v in word_to_id.items()}\\n return word_to_id, id_to_word\",\n \"def id_generator2(self, collection):\\n max_id = collection.aggregate(\\n [\\n {'$replaceWith': {'$toInt': '$Id'}},\\n {\\n '$group': {\\n \\\"_id\\\": None,\\n \\\"max_id\\\": {'$max': '$Id'}\\n }\\n }\\n ]\\n )\\n\\n for id in max_id:\\n return str(max_id+1)\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def getId(*args):\",\n \"def find_non_unique_ids(ids: np.array) -> Tuple[np.array]:\\n\\n args = ids.argsort()\\n mask = np.empty(args.shape, dtype=bool)\\n sorted_ids = ids[args]\\n\\n # By definition, the first element should not be a repeat of itself.\\n mask[0] = False\\n # The actual duplicate check; works because sorted_ids is, well, sorted.\\n mask[1:] = sorted_ids[1:] == sorted_ids[:-1]\\n\\n # sorted_ids may be very large and we no longer need it\\n del sorted_ids\\n\\n # Now we need to put everything back where it belongs.\\n position_corrected_mask = np.empty_like(mask)\\n position_corrected_mask[args] = mask\\n\\n # Now we can compress everything to a nice integer array\\n duplicate_positions = np.where(position_corrected_mask)[0]\\n duplicate_ids = ids[duplicate_positions]\\n\\n return duplicate_ids, duplicate_positions\",\n \"def generate_id(table, pk):\\n query = \\\"SELECT \\\" + pk + \\\" FROM \\\" + table\\n cursor.execute(query)\\n records = cursor.fetchall()\\n if records == []:\\n return \\\"000000\\\"\\n pks = [x[0] for x in records]\\n print(pks)\\n pks.sort(key=int)\\n max_add1 = str(int(pks[-1]) + 1)\\n return max_add1.rjust(6, '0')\",\n \"def list_ids(self, start: int = None, end: int = None) -> List:\\n return [i.unique_id for i in self.data[start:end]]\",\n \"def get_ordered_ids(tree):\\n ordered_ids = []\\n ordered_ids.extend(id(node) for node in tree.gen_tips())\\n ordered_ids.extend(id(node) for node in tree.gen_internal_nodes())\\n return ordered_ids\",\n \"def list_to_tuple(list):\\n templist = []\\n for i in list:\\n try:\\n templist.append(int(i))\\n except ValueError:\\n print('Error. Please enter only integers.')\\n return ()\\n return tuple(templist)\",\n \"def neco__tuple_update_pids(tup, new_pid_dict):\\n new_iterable = []\\n for tok in tup:\\n if isinstance(tok, Pid):\\n new_tok = Pid.from_list(new_pid_dict[tuple(tok.data)])\\n elif isinstance(tok, tuple):\\n new_tok = neco__tuple_update_pids(tok, new_pid_dict)\\n else:\\n new_tok = tok\\n new_iterable.append(new_tok)\\n return tuple(new_iterable)\",\n \"def get_tag_ids(tag_names):\\n\\ttag_names = tuple(tag_names)\\n\\twith postgres, postgres.cursor() as cur:\\n\\t\\tcur.execute(\\\"select id from mustard.tags where english_name in %s\\\", (tag_names,))\\n\\t\\treturn [row[0] for row in cur]\",\n \"def get_ids_values(path):\\n \\n def read_txt(path):\\n \\\"\\\"\\\"read text file from path.\\\"\\\"\\\"\\n \\n with open(path, \\\"r\\\") as f:\\n return f.read().splitlines()\\n \\n raw_data = read_txt(path)[1:]\\n \\n def deal_line(line):\\n ids, values = line.split(',')\\n return ids, values\\n \\n ids_values = [deal_line(line) for line in raw_data]\\n ids = [x[0] for x in ids_values]\\n values = [x[1] for x in ids_values]\\n values = list(map(int, values))\\n \\n return ids, values\",\n \"def _expand_priority_order(self, id_list):\\n res = self.id_priority_list.copy()\\n for key in id_list:\\n if key not in self.id_priority_list:\\n res.append(key)\\n return res\",\n \"def compare_ids(ids_in_json_list, ids_in_file_list):\\n new_pipeline_id = []\\n if ids_in_json_list == ids_in_file_list:\\n new_pipeline_id = []\\n\\n else:\\n new_pipeline_id = [pipe_id for pipe_id in ids_in_json_list if pipe_id not in ids_in_file_list]\\n return new_pipeline_id\",\n \"def _params_to_ints(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\",\n \"def _params_to_ints(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\",\n \"def _params_to_ints(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\",\n \"def _params_to_ints(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\",\n \"def _params_to_ints(self, qs):\\n return [int(str_id) for str_id in qs.split(',')]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70673424","0.67906827","0.6282754","0.60380954","0.5777089","0.577077","0.56931186","0.56780326","0.5641889","0.5606788","0.55963814","0.5531544","0.55299294","0.5516861","0.55163807","0.5484265","0.5466873","0.5397534","0.53723544","0.5363456","0.53095704","0.53095704","0.5305502","0.5295378","0.52923506","0.5284601","0.5269888","0.525582","0.5246886","0.52442026","0.52410954","0.5221023","0.5210731","0.52105904","0.5206013","0.5205292","0.51963156","0.5175922","0.5170738","0.51702064","0.51569605","0.51510316","0.5145216","0.5137433","0.5125439","0.5124514","0.51239043","0.5123838","0.5122291","0.5120015","0.51150864","0.51058203","0.51050025","0.5101206","0.50892276","0.5062672","0.5049149","0.50466853","0.5035627","0.50355816","0.50192857","0.5019193","0.5017443","0.5005457","0.50027084","0.49950346","0.49846715","0.49796858","0.4976119","0.49756557","0.49560723","0.49522102","0.49521032","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.4951431","0.49511606","0.49480668","0.4947122","0.49260774","0.49244285","0.49205735","0.49131992","0.48974207","0.48931116","0.4889568","0.48891217","0.48891217","0.48891217","0.48891217","0.48891217"],"string":"[\n \"0.70673424\",\n \"0.67906827\",\n \"0.6282754\",\n \"0.60380954\",\n \"0.5777089\",\n \"0.577077\",\n \"0.56931186\",\n \"0.56780326\",\n \"0.5641889\",\n \"0.5606788\",\n \"0.55963814\",\n \"0.5531544\",\n \"0.55299294\",\n \"0.5516861\",\n \"0.55163807\",\n \"0.5484265\",\n \"0.5466873\",\n \"0.5397534\",\n \"0.53723544\",\n \"0.5363456\",\n \"0.53095704\",\n \"0.53095704\",\n \"0.5305502\",\n \"0.5295378\",\n \"0.52923506\",\n \"0.5284601\",\n \"0.5269888\",\n \"0.525582\",\n \"0.5246886\",\n \"0.52442026\",\n \"0.52410954\",\n \"0.5221023\",\n \"0.5210731\",\n \"0.52105904\",\n \"0.5206013\",\n \"0.5205292\",\n \"0.51963156\",\n \"0.5175922\",\n \"0.5170738\",\n \"0.51702064\",\n \"0.51569605\",\n \"0.51510316\",\n \"0.5145216\",\n \"0.5137433\",\n \"0.5125439\",\n \"0.5124514\",\n \"0.51239043\",\n \"0.5123838\",\n \"0.5122291\",\n \"0.5120015\",\n \"0.51150864\",\n \"0.51058203\",\n \"0.51050025\",\n \"0.5101206\",\n \"0.50892276\",\n \"0.5062672\",\n \"0.5049149\",\n \"0.50466853\",\n \"0.5035627\",\n \"0.50355816\",\n \"0.50192857\",\n \"0.5019193\",\n \"0.5017443\",\n \"0.5005457\",\n \"0.50027084\",\n \"0.49950346\",\n \"0.49846715\",\n \"0.49796858\",\n \"0.4976119\",\n \"0.49756557\",\n \"0.49560723\",\n \"0.49522102\",\n \"0.49521032\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.4951431\",\n \"0.49511606\",\n \"0.49480668\",\n \"0.4947122\",\n \"0.49260774\",\n \"0.49244285\",\n \"0.49205735\",\n \"0.49131992\",\n \"0.48974207\",\n \"0.48931116\",\n \"0.4889568\",\n \"0.48891217\",\n \"0.48891217\",\n \"0.48891217\",\n \"0.48891217\",\n \"0.48891217\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5790029"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":79585,"cells":{"query":{"kind":"string","value":"return the last value of dictionary in rnkMETHODDATA table if it exists and initialize the value of last updated records by zero, otherwise an initial dictionary with zero as value for all recids"},"document":{"kind":"string","value":"def last_updated_result(rank_method_code):\n result = [{}, {}, {}]\n query = \"\"\"select relevance_data from rnkMETHOD, rnkMETHODDATA where\n rnkMETHOD.id = rnkMETHODDATA.id_rnkMETHOD\n and rnkMETHOD.Name = '%s'\"\"\"% rank_method_code\n rdict = run_sql(query)\n if rdict and rdict[0] and rdict[0][0]:\n #has to be prepared for corrupted data!\n try:\n dic = deserialize_via_marshal(rdict[0][0])\n except zlib.error:\n return [{}, {}, {}]\n query = \"select object_value from rnkCITATIONDATA where object_name='citationdict'\"\n cit_compressed = run_sql(query)\n cit = []\n if cit_compressed and cit_compressed[0] and cit_compressed[0][0]:\n cit = deserialize_via_marshal(cit_compressed[0][0])\n if cit:\n query = \"\"\"select object_value from rnkCITATIONDATA\n where object_name='reversedict'\"\"\"\n ref_compressed = run_sql(query)\n if ref_compressed and ref_compressed[0] and ref_compressed[0][0]:\n ref = deserialize_via_marshal(ref_compressed[0][0])\n result = (dic, cit, ref)\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_rec(self):\n return {'cal': 0}","def _update(d):\n newd = copy.deepcopy(default)\n\n if 'lastdir' in d:\n newd['lastdir'] = d['lastdir']\n\n return newd","def last_dict():\n\n # newest_d = {}\n # my_dic = pd.read_excel('grad_gen.xlsx', index_col=0).to_dict()\n # # new_d = pickle.load(open('new_d.pickle', \"rb\"))\n # for item in my_dic:\n # newest_d = my_dic[item]\n # pickle.dump(newest_d,open('grad_g.pickle', 'wb'))\n\n new_d = pickle.load(open('grad_g.pickle', \"rb\"))\n # new_d.pop('Biology')\n # new_d['Obaid Hussen'] = 'male'\n # new_d['Faisal Tanvir R.'] = 'male'\n new_d = simplejson.loads(simplejson.dumps(new_d, ignore_nan=True))\n pickle.dump(new_d, open('grad_g.pickle', \"wb\"))\n\n return new_d","def get_last_record_dict(self):\n sql = (\"SELECT \" + ', '.join(self.labels) +\n \" FROM snapshot_log WHERE id = (SELECT MAX(id) FROM \"\n \"snapshot_log);\")\n try:\n cur = self.cursor()\n cur.execute(sql)\n rows = cur.fetchall()\n res = dict_factory(cur, rows[0])\n #cur.close()\n self.close()\n return res\n except AttributeError as ae:\n print (\"Can't get last record: {0}\".format(ae.message))\n except Exception as e:\n print (\"Can't get last record: {0}\".format(e.message))\n return None","def update_total_fpmu_dict(self):\n # identical for each long-range connection\n # extract parameters\n deltat = self.dt\n trise = self.tau_r\n tdamp = self.tau_d\n\n tr = deltat/trise\n etr = np.exp(-tr) \n td = deltat/tdamp\n etd = np.exp(-td)\n cst = trise/(tdamp-trise)\n\n # nmda should keep in memory which could not be reset to zerooooooo!!!\n \"\"\"\n no resetting to zero --> go directly to refreshing !!! based on pre-value\n \"\"\"\n for c in self.source_connection_list:\n if (c.conn_type == 'LongRange'):\n self.total_INMDA_dict[c.connection_distribution] = self.total_INMDA_dict[c.connection_distribution] * etd + self.total_HNMDA_dict[c.connection_distribution] * cst\n self.total_HNMDA_dict[c.connection_distribution] = self.total_HNMDA_dict[c.connection_distribution] * etr + c.curr_firing_rate * c.nsyn * c.weights * self.tau_r\n\n print 'Change HNMDA: ', c.curr_firing_rate * c.nsyn ,' \\n'\n print 'Inputlr dict: ', self.total_inputlr_dict[c.connection_distribution]\n\n\n\n # for curr_CD in self.source_connection_list:\n # have already exist\n for c in self.source_connection_list:\n if(c.conn_type == 'ShortRange'):\n self.total_fpmu_dict[c.connection_distribution] = 0.0\n # have already clear up all the short range connections\n for c in self.source_connection_list:\n if(c.conn_type == 'ShortRange'):\n self.total_fpmu_dict[c.connection_distribution] += c.curr_firing_rate * c.nsyn * c.weights\n\n # summation\n self.total_fp_vslave = 0.0\n for key,val in self.total_fpmu_dict.items():\n \n try:\n self.total_fp_vslave += val\n except:\n key.initialize()\n self.total_fp_vslave += val\n # and then, summation of Inmda\n for key,val in self.total_INMDA_dict.items():\n try:\n self.total_fp_vslave += val\n except:\n key.initialize()\n self.total_fp_vslave += val\n # and then divided by gL or multiply tau_m\n self.total_fp_vslave = self.total_fp_vslave * self.tau_m","def getLastData(self) -> ghidra.program.model.listing.Data:\n ...","def sweep_record(r):\n return dict_sweep(r, vals=[None])","def last_value(self):\n return 0","def get_data_updating(self):\n # add some special keywords, if they are specified in self.initialize\n special_keywords = ['size', 'bounds', 'primitive_type']\n for keyword in special_keywords:\n val = getattr(self, keyword)\n if val is not None and keyword not in self.data_updating:\n self.data_updating[keyword] = val\n return self.data_updating","def get_data_to_update_object(self):\n return {}","def GetUpdated(self, vals=[], default=None):\n partData, attribNames = vals\n \n ids = self.GetLatestIds(partData)\n return packDictionary(self.read(getCleanList(ids), attribNames))","def helper_retrieve_last_request_post_dict_key_val_index_zero_or_return_none(self, key_name):\n try:\n return self.last_request_post_data_dict[key_name][0]\n except:\n return None","def update_dict(new,old):","def helper_retrieve_last_request_get_dict_key_val_index_zero_or_return_none(self, key_name):\n try:\n return self.last_request_get_dict[key_name][0]\n except:\n return None","def _retention_rate(self):\n res = {}\n for ret_line in self.browse(self.ids):\n if ret_line.invoice_id:\n pass\n else:\n res[ret_line.id] = 0.0\n return res","def _reset_changes(self):\r\n self._original = {}\r\n if self.last_updated is not None:\r\n self._original['last_updated'] = self.last_updated","def get_bibrankmethod_lastupdate(rank_method_code):\n query = \"\"\"select last_updated from rnkMETHOD where name ='%s'\"\"\" % rank_method_code\n last_update_time = run_sql(query)\n r = last_update_time[0][0]\n if r is None:\n return \"0000-00-00 00:00:00\"\n return r","def update_data(self):\n for sai_id_key in self.if_id_map:\n namespace, sai_id = mibs.split_sai_id_key(sai_id_key)\n if_idx = mibs.get_index_from_str(self.if_id_map[sai_id_key])\n counter_table = self.namespace_db_map[namespace].get_all(mibs.COUNTERS_DB, \\\n mibs.counter_table(sai_id))\n if counter_table is None:\n counter_table = {}\n self.if_counters[if_idx] = counter_table\n\n\n self.lag_name_if_name_map, \\\n self.if_name_lag_name_map, \\\n self.oid_lag_name_map, _, _ = Namespace.get_sync_d_from_all_namespace(mibs.init_sync_d_lag_tables, self.db_conn)\n\n self.if_range = sorted(list(self.oid_name_map.keys()) + list(self.oid_lag_name_map.keys()))\n self.if_range = [(i,) for i in self.if_range]","def dd_recovery_return_map_nb(record, ts):\n end_val = dd_end_value_map_nb(record, ts)\n valley_val = dd_valley_value_map_nb(record, ts)\n return (end_val - valley_val) / valley_val","def update(self,d:dict):\n for name,(value,n) in d.items():\n if n==0:\n continue\n self.meters[name].update(value,n)","def updated(self, key=None):\n if key:\n sql = u\"\"\"\n SELECT `updated` FROM `{table}` WHERE `key` = ?\n \"\"\".format(table=self.name)\n\n row = self.conn.execute(sql, (key,)).fetchone()\n if row:\n return row['updated']\n\n return 0.0\n\n # Return latest updated\n sql = u\"\"\"\n SELECT MAX(`updated`) AS `updated` FROM `{table}`\n \"\"\".format(table=self.name)\n\n row = self.conn.execute(sql).fetchone()\n return row['updated'] if row['updated'] else 0.0","def last_bloodwork(self):\n if hasattr(self, '_prior_bloodwork'):\n #in memory lookup\n return self._prior_bloodwork\n\n #memcached lookup\n last_bw = cache.get('%s_bloodwork' % (self._id), None)\n if last_bw == None:\n #it's null, so it's a cache miss.\n #do a requery\n pass\n elif last_bw == '[##Null##]':\n return None\n else:\n self._prior_bloodwork = CBloodwork.wrap(simplejson.loads(last_bw))\n return self._prior_bloodwork\n\n\n #requery\n bw_docs = CBloodwork.view('pactcarehq/patient_bloodwork', key=self.pact_id).all()\n bw_docs = sorted(bw_docs, key=lambda x: x['test_date'])\n if len(bw_docs) > 0:\n self._prior_bloodwork = bw_docs[0]\n cache.set('%s_bloodwork' % (self._id), simplejson.dumps(bw_docs[0].to_json()))\n return bw_docs[0]\n if self.prior_bloodwork.test_date == None:\n #this is a bit hacky, it should really be null, but since this is an added on object, to PactPatient, it doesn't show up as None\n #so we need to do an explicit check for the\n cache.set('%s_bloodwork' % (self._id), '[##Null##]')\n pass\n else:\n self._prior_bloodwork = self.prior_bloodwork\n return self.prior_bloodwork\n return None","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def create_data_record(self, data_dict):\n source_dict = deepcopy(data_dict)\n assert not self.is_conflicting_keys(data_dict,\n self.default_values), \"Conflicting keys between default_values and extra_values\"\n source_dict.update(self.default_values)\n return {\n '_index': self.get_full_index(),\n '_type': 'python_log',\n '_source': source_dict\n }","def __getHoldingsCurrent(self, dirPath=None):\n rD = {}\n retD = {}\n dirPath = dirPath if dirPath else self.__sandboxPath\n try:\n updateTypeList = [\"all\"]\n contentTypeList = [\"pdb\", \"pdb-format\", \"mr\", \"cs\", \"sf\", \"nef\", \"nmr-str\"]\n contentNameD = {\n \"pdb\": \"coordinates\",\n \"pdb-format\": \"PDB format coordinates\",\n \"mr\": \"NMR restraints\",\n \"cs\": \"NMR chemical shifts\",\n \"sf\": \"structure factors\",\n \"nef\": \"Combined NMR data (NEF)\",\n \"nmr-str\": \"Combined NMR data (NMR-STAR)\",\n }\n #\n tD = {}\n for updateType in updateTypeList:\n for contentType in contentTypeList:\n fp = os.path.join(dirPath, \"update-lists\", updateType + \"-\" + contentType + \"-list\")\n if not self.__mU.exists(fp):\n continue\n entryIdL = self.__mU.doImport(fp, \"list\")\n #\n for entryId in entryIdL:\n entryId = entryId.strip().upper()\n if entryId not in tD:\n tD[entryId.upper()] = {}\n tD[entryId.upper()][contentNameD[contentType]] = True\n #\n fp = os.path.join(dirPath, \"status\", \"biounit_file_list.tsv\")\n lines = self.__mU.doImport(fp, \"list\")\n assemD = {}\n for line in lines:\n fields = line.split(\"\\t\")\n entryId = fields[0].strip().upper()\n assemId = fields[1].strip()\n if entryId not in assemD:\n assemD[entryId.upper()] = []\n assemD[entryId.upper()].append(assemId)\n #\n #\n fp = os.path.join(dirPath, \"status\", \"pdb_bundle_index_list.tsv\")\n bundleIdList = self.__mU.doImport(fp, \"list\")\n bundleD = {}\n for entryId in bundleIdList:\n bundleD[entryId.strip().upper()] = True\n #\n fp = os.path.join(dirPath, \"status\", \"validation_report_list_new.tsv\")\n vList = self.__mU.doImport(fp, \"list\")\n valD = {}\n valImageD = {}\n valCifD = {}\n for line in vList:\n fields = line.split(\"\\t\")\n entryId = fields[0].strip().upper()\n imageFlag = fields[1].strip().upper()\n valD[entryId] = True\n valImageD[entryId] = imageFlag == \"Y\"\n if len(fields) > 2:\n valCifD[entryId] = fields[2].strip().upper() == \"Y\"\n #\n #\n fp = os.path.join(dirPath, \"status\", \"entries_without_polymers.tsv\")\n pList = self.__mU.doImport(fp, \"list\")\n pD = {}\n for entryId in pList:\n pD[entryId.strip().upper()] = False\n #\n #\n fp = os.path.join(dirPath, \"status\", \"nmr_restraints_v2_list.tsv\")\n nmrV2List = self.__mU.doImport(fp, \"list\")\n nmrV2D = {}\n for entryId in nmrV2List:\n nmrV2D[entryId.strip().upper()] = False\n #\n if self.__cfgOb:\n configName = self.__cfgOb.getDefaultSectionName()\n fp = self.__cfgOb.getPath(\"RCSB_EDMAP_LIST_PATH\", sectionName=configName)\n else:\n fp = os.path.join(dirPath, \"status\", \"edmaps.json\")\n qD = self.__mU.doImport(fp, \"json\")\n edD = {}\n for entryId in qD:\n edD[entryId.upper()] = qD[entryId]\n #\n fp = os.path.join(dirPath, \"status\", \"obsolete_entry.json_2\")\n oL = self.__mU.doImport(fp, \"json\")\n obsD = {}\n for dD in oL:\n obsD[dD[\"entryId\"].upper()] = True\n logger.info(\"Removed entry length %d\", len(obsD))\n #\n #\n # Revise content types bundles and assemblies\n #\n for qId, dD in tD.items():\n entryId = qId.strip().upper()\n if entryId in obsD:\n continue\n rD[entryId] = []\n if entryId in bundleD:\n rD[entryId].append(\"entry PDB bundle\")\n if \"coordinates\" in dD:\n rD[entryId].append(\"entry mmCIF\")\n rD[entryId].append(\"entry PDBML\")\n if \"PDB format coordinates\" in dD:\n rD[entryId].append(\"entry PDB\")\n if entryId in assemD:\n if entryId in bundleD:\n rD[entryId].append(\"assembly mmCIF\")\n else:\n rD[entryId].append(\"assembly PDB\")\n #\n for cType in dD:\n if cType not in [\"coordinates\", \"PDB format coordinates\", \"NMR restraints\"]:\n rD[entryId].append(cType)\n if cType == \"NMR restraints\":\n rD[entryId].append(\"NMR restraints V1\")\n\n if entryId in nmrV2D:\n rD[entryId].append(\"NMR restraints V2\")\n #\n if entryId in valD:\n rD[entryId].append(\"validation report\")\n if entryId in valImageD and valImageD[entryId]:\n rD[entryId].append(\"validation slider image\")\n if entryId in valCifD and valCifD[entryId]:\n rD[entryId].append(\"validation data mmCIF\")\n if entryId in edD:\n rD[entryId].append(\"2fo-fc Map\")\n rD[entryId].append(\"fo-fc Map\")\n rD[entryId].append(\"Map Coefficients\")\n if entryId not in pD:\n rD[entryId].append(\"FASTA sequence\")\n #\n for entryId in rD:\n if entryId in assemD:\n retD[entryId] = {\"assembly_ids\": assemD[entryId], \"repository_content_types\": rD[entryId]}\n else:\n retD[entryId] = {\"repository_content_types\": rD[entryId]}\n return retD\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n\n return retD","def getDefaultData(dmd):","def reinit (self):\n #for name, obj in inspect.getmembers (self):\n ## if isinstance (obj, RField):\n # self.keyvals[name] = obj.default\n inspect.getmembers (self)","def _update(self):\n if self._dirty:\n return self._to_flattr_dict()\n return False","def update_stored(unchanged, new_found, ex_dic, dat_tim):\n\n for sensor in unchanged:\n for dev in ex_dic['devices']:\n if sensor == dev['id']:\n dev['last_seen'] = str(dat_tim)\n\n for newbie in new_found:\n new_dict = dict()\n new_dict['id'] = newbie\n new_dict['first_seen'] = str(dat_tim)\n new_dict['last_seen'] = str(dat_tim)\n ex_dic['devices'].append(new_dict)\n if len(new_found) > 0:\n send_tweet(new_found)\n pass\n with open('last_seen.json', 'w') as out_file:\n json.dump(ex_dic, out_file, indent=4)","def _refresh_cache(self, data_dict):\r\n pass","def processRecord(c,collection,VarMap,totalVariables,uniqueIndexes,versionNumber,specialKeyInds,incremental,sliceDB,sliceColTuples,ContentCols):\n\n vNInd = VarMap['__versionNumber__']\n retInd = VarMap['__retained__']\n aKInd = VarMap['__addedKeys__']\n origInd = VarMap['__originalVersion__']\n \n c = dict([(VarMap[k],c[k]) for k in c.keys()])\n c[vNInd] = versionNumber\n s = dict([(VarMap[k],c[VarMap[k]]) for k in uniqueIndexes])\n s[vNInd] = versionNumber - 1\n \n H = collection.find_one(s)\n \n if H:\n if incremental:\n diff = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and k in c.keys() and notEqual(H[k],c[k]) ])\n newc = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and k not in c.keys() ])\n newc.update(c)\n c = newc\n else:\n diff = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and (k not in c.keys() or notEqual(H[k],c[k])) ])\n \n c[origInd] = H[origInd]\n \n if diff:\n diff[retInd] = True\n newkeys = [k for k in c.keys() if k not in H.keys()]\n if newkeys:\n diff[aKInd] = newkeys\n \n if diff: \n DIFF = ContentCols.intersection([totalVariables[int(k)] for k in diff.keys()])\n diff.update(s)\n collection.update(s,diff)\n print 'Diff:' , diff\n else:\n DIFF = False\n c['_id'] = H['_id']\n collection.remove(s)\n \n else:\n c[origInd] = versionNumber\n diff = c\n DIFF = True\n \n id = collection.insert(c) \n if DIFF:\n sliceInsert(c,collection,sliceColTuples,VarMap,sliceDB,versionNumber)\n \n return id, c","def fill_wbwc_dict(wdict, fill='auto'):\n global all_kfs\n for k in wdict.keys():\n if ((wdict[k]['dg'] == 'nan') and (wdict[k]['ddg'] == 'nan')) or (fill == 'rates'):\n wdict[k]['dg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/wdict[k]['hr'])\n #wdict[k]['dg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/wdict[k]['hr'] / (wdict[k]['hf']/wdict[k]['hr'] + wdict[k]['hr']/wdict[k]['hf'])) #### NORMALIZE!!!!\n #wdict[k]['ddg'] = - (Rconst*temperature) * ((np.log(wdict[k]['hf'])/np.log((scon.k * temperature/scon.h))))\n wdict[k]['ddg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/(scon.k * temperature/scon.h))\n elif ((wdict[k]['hf'] == 'nan') and (wdict[k]['hr'] == 'nan')) or (fill == 'barriers'):\n wdict[k]['hf'] = (scon.k * temperature/scon.h) * np.exp(-wdict[k]['ddg']/(Rconst*temperature))\n wdict[k]['hr'] = (scon.k * temperature/scon.h) * np.exp(-(wdict[k]['ddg'] - wdict[k]['dg'])/(Rconst*temperature))\n ## add hf and hr as full name rate consts to the full dict\n for s in wdict.keys():\n for k in ['hf', 'hr']:\n all_kfs[k+s[0]] = wdict[s][k]\n return wdict","def get_updated_state(self, current_stream_state: MutableMapping[str, Any], latest_record: Mapping[str, Any]) -> Mapping[str, Any]:\n\n if self.first_run:\n self.first_run = False\n return {self.cursor_field: self.initial_state}\n else:\n self.max_state = max(self.max_state, latest_record[self.cursor_field])\n return {self.cursor_field: self.max_state if self.current_slice == self.last_slice else self.initial_state}","def default_setting(self):\n\t\tunknown_as_zero = False\n\t\tbinary_rele = False # using the original values\n\t\tpresort = False # a default setting\n\n\t\tscale_data, scaler_id, scaler_level = get_default_scaler_setting(data_id=self.data_id)\n\n\t\t# more data settings that are rarely changed\n\t\tself.data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\n\t\t\t\t\t\t sample_rankings_per_q=1, unknown_as_zero=unknown_as_zero, binary_rele=binary_rele,\n\t\t\t\t\t\t presort=presort, scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\n\n\t\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\n\t\tself.data_dict.update(data_meta)\n\n\t\treturn self.data_dict","def __getHoldingsUpdate(self, dirPath=None):\n retD = {}\n dirPath = dirPath if dirPath else self.__sandboxPath\n try:\n updateTypeList = [\"added\", \"modified\", \"obsolete\"]\n contentTypeList = [\"entries\", \"mr\", \"cs\", \"sf\", \"nef\", \"nmr-str\"]\n contentNameD = {\n \"entries\": \"coordinates\",\n \"mr\": \"NMR restraints\",\n \"cs\": \"NMR chemical shifts\",\n \"sf\": \"structure factors\",\n \"nef\": \"Combined NMR data (NEF)\",\n \"nmr-str\": \"Combined NMR data (NMR-STAR)\",\n }\n #\n for updateType in updateTypeList:\n uD = {}\n for contentType in contentTypeList:\n fp = os.path.join(dirPath, \"update-lists\", updateType + \"-\" + contentType)\n if not self.__mU.exists(fp):\n continue\n entryIdL = self.__mU.doImport(fp, \"list\")\n #\n for entryId in entryIdL:\n entryId = entryId.strip().upper()\n uD.setdefault(entryId, []).append(contentNameD[contentType])\n for entryId in uD:\n uType = \"removed\" if updateType == \"obsolete\" else updateType\n # retD[entryId] = {\"update_id\": updateId, \"entry_id\": entryId, \"update_type\": uType, \"repository_content_types\": uD[entryId]}\n retD[entryId] = {\"update_type\": uType, \"repository_content_types\": uD[entryId]}\n return retD\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n return retD","def update_crispval(self, val_dict):\n for v in val_dict:\n if v in self.variables_default_val:\n self.variables_default_val[v] = val_dict[v]","def updated_full_record(full_record):\n full_record[\"access\"][\"status\"] = \"embargoed\"\n full_record[\"created\"] = \"2023-03-23T00:00:00.000000+00:00\"\n full_record[\"id\"] = \"abcde-fghij\"\n full_record[\"metadata\"][\"resource_type\"][\"id\"] = \"other\"\n\n return full_record","def get_last_modified_rec(bibrank_method_lastupdate):\n query = \"\"\"SELECT id FROM bibrec\n WHERE modification_date >= '%s' \"\"\" % bibrank_method_lastupdate\n query += \"order by id ASC\"\n ilist = run_sql(query)\n return ilist","def _dictfetchone(self):\n data = self._dictfetchall()\n if data:\n return data[0]\n return {}","def initialize_log_data(ids_bcs_added_field):\r\n\r\n log_data = {}\r\n\r\n for curr_key in ids_bcs_added_field.keys():\r\n base_key = \"\"\r\n if curr_key[0]:\r\n base_key += curr_key[0] + \",\"\r\n if curr_key[1]:\r\n base_key += curr_key[1] + \",\"\r\n base_key += ids_bcs_added_field[curr_key]\r\n log_data[base_key] = 0\r\n\r\n return log_data","def rec_default(self):\n pass","def _compute_last_observations(self):\n observations = {}\n for ts in self.ts_ids:\n observations[ts] = self.traffic_signals[ts]._compute_observation()\n return observations","def latestValue(self):\n if len(self.values) > 0:\n return self.values[-1]\n else:\n return 0","def re_zero_to_datum(self):\n self.device.re_zero_to_datum()\n return \"OK\"","def getLastUpdate():\n swDB = switchdb.DB()\n lastupdate = swDB.getLastUpdate()\n swDB.close()\n return lastupdate","def update_graph(data, keep_last):\n if data is None or not data['val_list']:\n raise PreventUpdate\n if keep_last is None:\n keep_last = 1024\n return {'x': [data['time_list']], 'y': [data['val_list']]}, [0], keep_last","def last_update(self):\r\n request = http.Request('GET', '/metadata/last_update.json')\r\n return request, parsers.parse_json","def getChanged(self,key,default=None):\n if default != None and key not in self.data:\n self.data[key] = default\n self.setChanged(key)\n return self.data.get(key)","def last_value():\n return blockchain[-1]","def getLastAverage(self):\n lastAve=dict()\n lastAve['identifier']=self.lastWaveIdentifier\n lastAve['averageCalculated']=self.lastAverageCalculated \n lastAve['lastAverageArray']=self.lastAverageArray\n return lastAve","def resetlast(self):\n self.last = None\n self.dlast = 0","def req_latest_data(self) -> dict:\n latest_data = {\n \"distance_measurements\": self._last_sensor_dist_measurements,\n \"speed_measurements\": self._last_sensor_speed_measurements,\n \"navigation\": self._last_navi_state_dict,\n \"collision\": self._collision,\n \"progress\": self._progress,\n \"reached_goal\": self._reached_goal,\n # \"max_progress\": \n }\n\n if self.config.vessel.sensor_use_feasibility_pooling:\n latest_data[\"feasible_distances\"] = self._last_sector_feasible_dists\n\n return latest_data","def get_remittance(self):\n\n #kinko dict to be updated in Kinko Collection.\n kdict = {\n \"amt\": 0.0,\n \"cl\": unicode(self.cl),\n \"dt\": datetime.datetime.today(),\n \"typ\": TYPE_MAP[self.tab_type],\n \"range\": {\"lt\": self.q_dict[\"cs.sd\"].get(\"$lt\", None),\n \"gt\": self.q_dict[\"cs.sd\"].get(\"$gte\", None),\n }\n }\n\n# old_rem = connection.Kinko.find_one({\"cl\": self.cl, \"tid\": None,\n# \"typ\": TYPE_MAP[self.tab_type]})\n\n old_rem = None\n\n if old_rem:\n old_rem.dt = kdict['dt']\n old_rem.range.lt = kdict['range']['lt']\n old_rem.save()\n\n connection.Package.collection.update(self.q_dict,\n {\"$set\": {\"inv.rnum\": old_rem.num}}, multi=True, safe=True)\n kinko_number = old_rem.num\n\n else:\n k = Kinko(kdict)\n k_count = 1\n #the get num method of Kinko model generates the unique no for new kinko\n k[\"num\"] = self.get_knum(1)\n while connection.Kinko.collection.find({\"num\": k.num}).count() > 0:\n k[\"num\"] = self.get_knum(k_count+1)\n k_count += 1\n\n connection.Kinko(k).save(safe=True)\n #after creating a new document in Kinko all packages are updated.\n connection.Package.collection.update(self.q_dict,\n {\"$set\": {\"inv.rnum\": k[\"num\"]}}, multi=True, safe=True)\n kinko_number = k[\"num\"]\n #Aggrigation of remitted amount for requested client\n non_remitted = kinko_map_reduce(kinko_number, TYPE_MAP[self.tab_type])\n\n \"\"\"non remitted is none if the data for required query dict is\n already remitted. If none it returns False to notify user.\"\"\"\n if len(non_remitted) == 0:\n return False\n\n if old_rem:\n old_rem.amt = non_remitted[0]['value']['cod']\n old_rem.save()\n return old_rem\n else:\n new_rem = connection.Kinko.find_one({\"num\": k[\"num\"]})\n new_rem.amt = non_remitted[0]['value']['cod']\n new_rem.save()\n return new_rem","def refresh(self):\n new = self.table.records_updated_since(self.updated.max()).set_index(\"operator\")\n new = new.rename(columns={\"operator_alias\": \"alias\", \"fscore\": \"confidence\"})\n\n if not new.empty: # TODO: this is clunky. need to fix later\n self.update(new)\n for idx, values in new.iterrows():\n try:\n self.loc[\n idx\n ] # try to lookup the index. Insert record if the lookup fails.\n except KeyError:\n self.loc[idx] = values","def refresh(self):\n new = self.table.records_updated_since(self.updated.max()).set_index(\"operator\")\n new = new.rename(columns={\"operator_alias\": \"alias\", \"fscore\": \"confidence\"})\n\n if not new.empty: # TODO: this is clunky. need to fix later\n self.update(new)\n for idx, values in new.iterrows():\n try:\n self.loc[\n idx\n ] # try to lookup the index. Insert record if the lookup fails.\n except KeyError:\n self.loc[idx] = values","def updated_minimal_record(minimal_record):\n minimal_record[\"access\"][\"status\"] = \"open\"\n minimal_record[\"created\"] = \"2023-03-09T00:00:00.000000+00:00\"\n minimal_record[\"id\"] = \"abcde-fghij\"\n\n for creator in minimal_record[\"metadata\"][\"creators\"]:\n name = creator[\"person_or_org\"].get(\"name\")\n if not name:\n creator[\"person_or_org\"][\"name\"] = \"Name\"\n\n return minimal_record","def _get_last_read_id():\n webservice_url_initial = 'http://' + host_cmr + ':' + str(host_cmr_port) + '/rest/data/invocations/overview?latestReadId=' \\\n + str(MaxIdInDB[0])\n\n print('Web Service Url Initial for Last Read id is ', webservice_url_initial)\n response_summary = requests.get(webservice_url_initial)\n\n data = response_summary.json()\n df = pd.DataFrame(json_normalize(data))\n lastreadid_max = df[['id']].max()\n lastreadid_min = df[['id']].min()\n print('Last Read id VALUE in apm is ', lastreadid_max['id'])\n print('the min id VALUE in apm this json ', lastreadid_min['id'])\n\n if int(lastreadid_max) >= MaxIdInDB[0]:\n print(\"Send data to influx and MaxIDINDB[0] is from \", MaxIdInDB[0], ' to LastReadId:', int(lastreadid_max))\n a = lastreadid_max['id']\n print('a is ', a)\n return a\n time.sleep(1)","def setLastData(self, value):\n if value is not None:\n pdata_wrapper = PdataHelper(self.getPortalObject(), value)\n self._setLastData(pdata_wrapper)\n else:\n self._setLastData(None)","def find_latest_checkpoint(self) -> Tuple[str, str]:\n return {}","def update_model(self,update_dict,eta=None):\n\n A = self.A.get_value()\n param_max = np.max(np.abs(A),axis=0)\n\n if eta is None:\n update_max = np.max(np.abs(update_dict['dA']),axis=0)\n A -= update_dict['dA']\n else:\n update_max = eta*np.max(np.abs(update_dict['dA']),axis=0)\n A -= eta*update_dict['dA']\n\n update_max = np.max(update_max/param_max)\n\n A = self.normalize_A(A)\n self.A.set_value(A)\n\n return update_max","def originalIncrementalUpdate(self, pdffilename):\n original = ReadBinaryFile(pdffilename)\n fPDF = open(self.filename, 'wb')\n if sys.version_info[0] == 2:\n fPDF.write(original)\n else:\n fPDF.write(bytes(original, 'ascii'))\n fPDF.close()\n startxrefs = re.findall(r'startxref\\s+(\\d+)', original)\n if startxrefs == []:\n return None, None, None\n oMatch = re.search(r'trailer\\s+', original[int(startxrefs[-1]):])\n if oMatch == None:\n return None, None, None\n positionDictionaryTrailer = oMatch.end() + int(startxrefs[-1])\n dictionaryTrailer = self.MatchDictionary(original[positionDictionaryTrailer:])\n if dictionaryTrailer == None:\n return None, None, None\n oDictionaryTrailer = cDictionary(dictionaryTrailer)\n idRoot = oDictionaryTrailer.GetID('Root')\n if idRoot == None:\n return None, None, None\n oMatch = re.search(r'\\s+%d\\s+0\\s+obj\\s+' % idRoot, original)\n if oMatch == None:\n return None, None, None\n dictionaryRoot = self.MatchDictionary(original[oMatch.end():])\n if dictionaryRoot == None:\n return None, None, None\n oDictionaryRoot = cDictionary(dictionaryRoot)\n return oDictionaryTrailer, oDictionaryRoot, int(startxrefs[-1])","def _empty_normalize(self):\n return {'method':'self', 'custom_value':None, 'custom_max_count':0, 'global_value':None, 'global_max_count':0, 'processed':set(), 'global_bins':[]}","def reset_stat_obj(self, nn_id):\n try:\n obj = models.NN_DEF_LIST_INFO.objects.get(nn_id=nn_id)\n data_set = {}\n data_set['bygen'] = []\n data_set['best'] = []\n setattr(obj, \"automl_stat\", data_set)\n obj.save()\n return data_set\n except Exception as e:\n raise Exception(e)","def mod_last_column(outDictExclu):\n\t\n\toutDictMod = OrderedDict()\n\n\tfor key in outDictExclu:\n\t\tk = key\n\t\tdfIn = outDictExclu[key].copy()\n\t\tdfMod = edit_col8(dfIn)\n\t\toutDictMod[k] = dfMod\n\t\n\treturn outDictMod","def process_ubern(fdict,cdmd,cdm,fn,proflist,campaigns,data):\n good=False\n for d in data:\n if 'Min' in d:\n good=True\n if 'ri_name' in d:\n key=d.split()[-1]\n if key not in fdict.keys():\n fdict[key]={'observations_table':{},'header_table':{}}\n \n if 'COMP018' in fn:\n print('018')\n if not good:\n print(fn,'could not be processed')\n return\n if(not fdict[key]['observations_table']):\n \n for k in cdmd['observations_table'].element_name.values:\n fdict[key]['observations_table'][k]=[]\n for k in cdmd['header_table'].element_name.values:\n fdict[key]['header_table'][k]=[]\n refdate=datetime(year=1900,month=1,day=1)\n header=True\n aux={}\n l=0\n comp=fn[:7]\n for d in data:\n if 'Min' in d:\n head=d.split()\n break\n l+=1\n m=0\n for d in data[l+1:]:\n dlist=d.split()\n if dlist:\n if dlist[0]=='NA':\n continue\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\n \n try:\n x=float(dlist[1])*100\n except ValueError:\n try: \n if 'PPPcorr' in head:\n x=float(dlist[2])*100\n else:\n continue\n except ValueError:\n continue\n m+=1\n \n if m==0:\n print(fn,'no valid obs')\n return\n \n try:\n idx=np.where(cdm['iri_rstype_map'].ri_name==key.split('_')[0])[0][0]\n fdict[key]['sensor_id']=numpy.string_(cdm['iri_rstype_map'].vapor_name[idx]) #.split(b',')[0])\n except:\n fdict[key]['sensor_id']=numpy.string_('NA')\n \n l=0\n for d in data:\n if 'ri_name' in d:\n key=d.split()[1]\n print(comp,fn,key)\n if 'measurement_programme' not in fdict[key]['header_table'].keys():\n fdict[key]['header_table']['measurement_programme']=[]\n idx=campaigns.index[campaigns.ID==comp]\n fdict[key]['header_table']['measurement_programme'].append(campaigns.Name[idx].values[0]) \n\n id_ascent=fn.split('/')[-1][:11]\n #key=np.string_('0-20100-0-0000'+fn[-8])\n sidx=np.where(cdm['station_configuration'].measuring_system_id==numpy.string_(key))[0][0]\n pidx=np.where(proflist['id_ascent']==id_ascent)[0][0]\n fdict[key]['header_table']['primary_station_id'].append(cdm['station_configuration'].primary_id.values[sidx])\n fdict[key]['header_table']['report_id'].append(fdict[key]['header_table']['primary_station_id'][-1]+\n b'-'+numpy.string_(fn.split('_')[-2]))\n print(key,fdict[key]['header_table']['report_id'])\n fdict[key]['header_table']['latitude'].append(cdm['station_configuration'].latitude.values[sidx])\n fdict[key]['header_table']['longitude'].append(cdm['station_configuration'].longitude.values[sidx])\n fdict[key]['header_table']['height_of_station_above_sea_level'].append(\n proflist['alt'].values[pidx])\n\n for k in 'latitude','longitude':\n aux[k]=fdict[key]['header_table'][k][-1]\n\n if 'date ' in d:\n dat=d.split()[-1]\n if 'hour_utc' in d:\n hour=d.split()[-1]\n hh=hour.split(':')\n if len(hh)==2:\n hour=hour+':00'\n relset=datetime.strptime(dat+' '+hour,'%Y-%m-%d %H:%M:%S')\n fdict[key]['header_table']['report_timestamp'].append(int((relset-refdate).total_seconds()))\n fdict[key]['header_table']['record_timestamp'].append(int((relset-refdate).total_seconds()))\n if 'Min' in d:\n head=d.split()\n break\n l+=1\n\n m=0\n dlen=len(head)-2\n ddlen=dlen\n dstart=2\n if 'Notes' in head:\n dlen-=1\n ddlen-=1\n if 'PPPcorr' in head:\n dstart+=1\n ddlen-=2\n ovar={'TTT':85,'TTTcorr':85,'UU':38,'H':117,'DEWPT':36,'DIR':106,'WSPEED':107}\n offset={'TTT':273.15,'TTTcorr':273.15,'UU':0,'H':0,'DEWPT':273.15,'DIR':0,'WSPEED':0}\n scale={'TTT':1,'TTTcorr':1,'UU':1,'H':9.80655,'DEWPT':1,'DIR':1,'WSPEED':1}\n units={'TTT':5,'TTTcorr':5,'UU':0,'H':806,'DEWPT':5,'DIR':110,'WSPEED':731}\n for d in data[l+1:]:\n dlist=d.split()\n if dlist:\n if dlist[0]=='NA':\n continue\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\n \n try:\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[1])*100]\n except ValueError:\n try: \n if 'PPPcorr' in head:\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[2])*100]\n else:\n continue\n except ValueError:\n continue\n #fdict[key]['observations_table']['z_coordinate']+=ddlen*[numpy.nan]\n \n fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]+int(float(dlist[0])*60)]\n if ddlen<len(head[dstart:dlen+2]):\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart+1:dlen+2]]\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart+1:dlen+2]]\n else:\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart:dlen+2]]\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart:dlen+2]]\n for i in range(dstart,dlen+2):\n \n if head[i]!='TTTcorr': \n try:\n fdict[key]['observations_table']['observation_value'].append(offset[head[i]]+scale[head[i]]*float(dlist[i]))\n except ValueError:\n fdict[key]['observations_table']['observation_value'].append(numpy.nan)\n else:\n try:\n fdict[key]['observations_table']['observation_value'][-1]=offset[head[i]]+scale[head[i]]*float(dlist[i])\n except ValueError:\n pass\n \n fdict[key]['observations_table']['observation_id']+=[numpy.string_('{:0>8}'.format(m+i+1)) for i in range(ddlen)]\n m+=ddlen\n else:\n break\n\n aux['sensor_id']='NA'\n try:\n idx=np.where(cdm['iri_rstype_map'].ri_name==key)[0][0]\n aux['sensor_id']=cdm['iri_rstype_map'].vapor_name[idx]\n except:\n print('could not find rs type key for '+key+'!')\n pass\n aux['report_id']=fdict[key]['header_table']['report_id'][-1]\n\n for k,v in aux.items():\n fdict[key]['observations_table'][k]+=[v]*m\n \n return","def getData(self):\n data = {}\n data.update ( self._original_values, self._modified_values )\n return data","def data_dict_update(self, change):\n self.data_dict = change['value']","def _fill_day_dicts(self):\n today = datetime.date.today()\n for i, record in enumerate(self._dataset):\n if (record[\"createdAt\"] / 1000) > time.mktime((today - datetime.timedelta(days=30)).timetuple()):\n self._add_record(self._all30_dict, record, key=i)\n\n elif (record[\"createdAt\"] / 1000) > time.mktime((today - datetime.timedelta(days=60)).timetuple()):\n self._add_record(self._all60_dict, record, key=i)\n\n else:\n self._add_record(self._all90_dict, record, key=i)","def generic(self):\n result = {}\n result.update(self.__dict__)\n self.lastModified = datetime.now()\n result[self.LAST_MODIFIED] = self.lastModified\n del result[self.ID]\n return result","def before_index(self, data_dict):\n return data_dict","def update_all_dict_bywhereclause(cls, updatedict, whereclause):\n return cls.dbm().modelclass_update_all_dict_bywhereclause(cls, updatedict, whereclause)","def latest_data(self):\n if self._data:\n return self._data\n return None","def getLastWave(self):\n lastWave=dict()\n lastWave['identifier']=self.lastWaveIdentifier\n lastWave['samplingTime']=self.lastSamplingTime\n lastWave['transferedChannels']=self.lastTransferredChannel\n lastWave['transferAverage']=self.lastTransferAverage\n lastWave['nbrSegmentArray']=self.lastNbrSegmentsArray\n lastWave['nbrSamplesPerSeg']=self.lastNbrSamplesPerSeg\n lastWave['waveSizes']=self.lastWaveformArraySizes\n lastWave['wave']=self.lastWaveformArray\n lastWave['timeStampsSize']=self.lastTimeStampsArraySize\n lastWave['timeStamps']=self.lastTimeStampsArray\n lastWave['horPosSize']=self.lastHorPositionsArraySize\n lastWave['horPos']=self.lastHorPositionsArray\n lastWave['averageCalculated']=self.lastAverageCalculated \n lastWave['lastAverageArray']=self.lastAverageArray \n return lastWave","def update_dict(self, total_dict: dict, iter_dict: dict) -> dict:\n for k in iter_dict:\n if k not in total_dict:\n total_dict[k] = iter_dict[k]\n else:\n total_dict[k] += iter_dict[k]\n \n return total_dict","def _metrics_update(orig, new):\n revsd = orig\n for k, v in orig.items():\n if not v:\n revsd[k] = new[k]\n elif new[k]:\n if new[k] != v:\n # LOG ME, do something\n print(orig)\n print(new)\n elif not new[k] or v:\n pass\n else:\n raise Exception(\"_metrics_update error\")\n return revsd","def DefaultData():\n return copy.deepcopy(DEFAULT_DATA)","def get_update_number( self ):","def gen_update(params, data):\n result = {}\n for key, value in data.iteritems():\n if key in params:\n result[key] = value\n return result","def latest_data(self):\n if self._data:\n return self._data[0]\n return None","def _reset(self):\n self._values = {}","def last_update(self): # TOFIX model the job and return an object instead of dictionary\n return self._data.get('summary_fields', {}).get('last_update')","def _add_record(days_dict, record, key):\n days_dict[key] = {\n \"Name\": record[\"title\"],\n \"Owner\": record[\"owner\"],\n \"Severity\": record[\"severity\"],\n \"Created\": (time.strftime(SIRPPipeline.TIME_FMT, time.gmtime(record[\"createdAt\"] / 1000.0))),\n }\n if \"endDate\" in record:\n days_dict[key].update(\n {\n \"Closed\": (time.strftime(SIRPPipeline.TIME_FMT, time.gmtime(record[\"endDate\"] / 1000.0),)),\n \"Resolution\": record[\"resolutionStatus\"],\n }\n )","def npdict(self):\n\n d = {}\n\n # per profile\n d['cruise'] = self.cruise()\n d['day'] = self.day()\n d['latitude'] = self.latitude()\n d['latitude_unc'] = self.latitude_unc()\n d['longitude'] = self.longitude()\n d['longitude_unc'] = self.longitude_unc()\n d['month'] = self.month()\n d['n_levels'] = self.n_levels()\n d['primary_header_keys'] = self.primary_header_keys()\n d['probe_type'] = self.probe_type()\n d['time'] = self.time()\n d['uid'] = self.uid()\n d['year'] = self.year()\n d['PIs'] = self.PIs()\n d['originator_station'] = self.originator_station()\n d['originator_cruise'] = self.originator_cruise()\n d['originator_flag_type'] = self.originator_flag_type()\n d['t_metadata'] = self.t_metadata()\n d['s_metadata'] = self.s_metadata()\n # per level\n d['s'] = self.s()\n d['s_unc'] = self.s_unc()\n d['s_level_qc'] = self.s_level_qc()\n d['s_profile_qc'] = self.s_profile_qc()\n d['s_qc_mask'] = self.s_qc_mask()\n d['t'] = self.t()\n d['t_unc'] = self.t_unc()\n d['t_level_qc'] = self.t_level_qc()\n d['t_profile_qc'] = self.t_profile_qc()\n d['t_qc_mask'] = self.t_qc_mask()\n d['z'] = self.z()\n d['z_unc'] = self.z_unc()\n d['z_level_qc'] = self.z_level_qc()\n d['oxygen'] = self.oxygen()\n d['phosphate'] = self.phosphate()\n d['silicate'] = self.silicate()\n d['pH'] = self.pH()\n d['p'] = self.p()\n\n return d","def __add_missing_entries_to_dict(self, member_dict: dict) -> dict:\n try:\n self.dict = member_dict\n self.dict['Voorletters'] = member_dict['Voornaam'][0].upper() + '.'\n self.dict['Dag'] = member_dict['GeboorteDatum'].split('-')[0]\n self.dict['Maand'] = member_dict['GeboorteDatum'].split('-')[1]\n self.dict['Jaar'] = member_dict['GeboorteDatum'].split('-')[2]\n self.dict['Postcode'] = member_dict['Postcode'].replace(' ', '')\n r = member_dict['Adres'].split(' ')\n self.dict['Huisnr'] = r[len(r) - 1]\n return self.dict\n\n except Exception as e:\n raise Exception(\"Toevoegen lid in NAS mislukt: \" + str(e))","def update_existing_records(self,tickr=''):\n stock_master_df=self.__dbconn.get_tickr(tickr)\n #print(stock_master_df.head())\n if stock_master_df.empty:\n raise ValueError(f\"data does not exist for {tickr}\")\n\n last_updated_date=stock_master_df.iloc[0].LastUpdatedDate.strftime('%Y-%m-%d')\n print(f'Last updated date for {tickr},{last_updated_date}')\n current_date=datetime.now().strftime('%Y-%m-%d')\n\n if current_date!=last_updated_date and current_date>last_updated_date:\n df=self.__get_all_data(tickr)\n if not df.empty:\n mask = df.loc[last_updated_date:current_date, :]\n mask=mask.iloc[1:]\n if not mask.empty:\n \"\"\"we found records to be updated\"\"\"\n self.__endDate=datetime(2012,5,12)\n d1=self.__dbconn.get_stock_details(stock_master_df.iloc[0].Id,last_updated_date,self.__endDate.strftime(\"%Y-%m-%d\"))\n d1['Date'] = pd.to_datetime(d1['Date'], format = '%Y-%m-%d')\n d1.set_index(['Date'], inplace=True)\n result=pd.concat([d1,mask])\n df=self.__calculate_all_indicators(result)\n self.__dbconn.delete_stock_details(int(stock_master_df.iloc[0].Id))\n df['stockId']=int(stock_master_df.iloc[0].Id)\n self.__dbconn.save_data('stockdetails',df)\n sql = \"\"\" UPDATE public.stockmaster SET \"LastUpdatedDate\" = %s WHERE \"Id\" = %s\"\"\"\n self.__dbconn.update_stock_master(sql,current_date,int(stock_master_df.iloc[0].Id))\n print(f\"Records updated for {tickr}\")\n else:\n print(\"no rows found for update\")","def init_cur_stats(self):\n self._cur_stats = defaultdict(lambda: defaultdict(int))\n self._cur_stats[\"writes\"][\"/\"] = 0\n self._cur_stats[\"reads\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writes\"] = 0\n self._cur_stats[\"total\"][\"/reads\"] = 0\n\n if self._include_bytes:\n self._cur_stats[\"writesBytes\"][\"/\"] = 0\n self._cur_stats[\"readsBytes\"][\"/\"] = 0\n self._cur_stats[\"total\"][\"/writeBytes\"] = 0\n self._cur_stats[\"total\"][\"/readBytes\"] = 0","def get(self, key):\n if key is None or key not in self.cache_data:\n return None\n else:\n for x in self.LFU:\n if x.key == key:\n x.age = 0\n x.freq += 1\n else:\n x.age += 1\n return self.cache_data[key]","def update_from_latest_data(self) -> None:\n data = self.coordinator.data[self.entity_description.uid]\n\n self._attr_is_on = bool(data[\"state\"])\n\n attrs = {\n ATTR_CURRENT_CYCLE: data[\"cycle\"],\n ATTR_ID: data[\"uid\"],\n ATTR_NO_CYCLES: data[\"noOfCycles\"],\n ATTR_RESTRICTIONS: data[\"restriction\"],\n ATTR_SLOPE: SLOPE_TYPE_MAP.get(data[\"slope\"], 99),\n ATTR_SOIL_TYPE: SOIL_TYPE_MAP.get(data[\"soil\"], 99),\n ATTR_SPRINKLER_TYPE: SPRINKLER_TYPE_MAP.get(data[\"group_id\"], 99),\n ATTR_STATUS: RUN_STATE_MAP[data[\"state\"]],\n ATTR_SUN_EXPOSURE: SUN_EXPOSURE_MAP.get(data.get(\"sun\")),\n ATTR_VEGETATION_TYPE: VEGETATION_MAP.get(data[\"type\"], 99),\n }\n\n if \"waterSense\" in data:\n if \"area\" in data[\"waterSense\"]:\n attrs[ATTR_AREA] = round(data[\"waterSense\"][\"area\"], 2)\n if \"fieldCapacity\" in data[\"waterSense\"]:\n attrs[ATTR_FIELD_CAPACITY] = round(\n data[\"waterSense\"][\"fieldCapacity\"], 2\n )\n if \"precipitationRate\" in data[\"waterSense\"]:\n attrs[ATTR_PRECIP_RATE] = round(\n data[\"waterSense\"][\"precipitationRate\"], 2\n )\n\n if self._entry.options[CONF_USE_APP_RUN_TIMES]:\n provision_data = self._data.coordinators[DATA_PROVISION_SETTINGS].data\n if zone_durations := provision_data.get(\"system\", {}).get(\"zoneDuration\"):\n attrs[ATTR_ZONE_RUN_TIME] = zone_durations[\n list(self.coordinator.data).index(self.entity_description.uid)\n ]\n\n self._attr_extra_state_attributes.update(attrs)","def inverse_update(self, data):\n if not isinstance(data, dict) or not isinstance(self, transforms.MapTransform):\n return data\n d = dict(data)\n for k in self.key_iterator(data):\n transform_key = transforms.TraceableTransform.trace_key(k)\n if transform_key not in data or not data[transform_key]:\n continue\n d = transforms.sync_meta_info(k, data, t=False)\n return d","def valuecall(key, atom_dict):\n if key not in atom_dict:\n return 0\n else:\n return atom_dict[key]","def blankUpdateRange(dayHour):\n return { 'position': dayHour, 'updateHistory': [dayHour], 'width': Predictor.rangeWidth }","def pre_virtual_DNS_record_update(self, resource_id, resource_dict):\n pass","def populate_dyn(self, table):\n myrow = table.row\n myrow[\"sample_time\"] = int(time.time() - glob.base_time)\n myrow[\"available_bike_stands\"] = self.available_bike_stands\n myrow[\"available_bikes\"] = self.available_bikes\n myrow[\"last_update\"] = self.last_update\n myrow[\"status\"] = self.status\n myrow.append()\n table.flush()","def dd_end_value_map_nb(record, ts):\n return ts[record['end_idx'], record['col']]","def generate_update_dict(self, input_data, input_labels=None, batch_step=0):\n update_dict = dict()\n return update_dict","def get_update_request_no_data() -> Dict[str, Any]:\n payload_dict = {}\n return payload_dict","def _add_empty_record(\n self, recordarray, recname, kstpkper=None, totim=None\n ):\n if kstpkper is not None:\n if len(self.cbc_times) > 0:\n totim = self.cbc_times[self.cbc_kstpkper.index(kstpkper)]\n else:\n totim = 0.0\n elif totim is not None:\n if len(self.cbc_times) > 0:\n kstpkper = self.cbc_kstpkper[self.cbc_times.index(totim)]\n else:\n kstpkper = (0, 0)\n\n row = [totim, kstpkper[0], kstpkper[1], recname]\n row += [0.0 for _ in self._zonenamedict.values()]\n recs = np.array(tuple(row), dtype=recordarray.dtype)\n recordarray = np.append(recordarray, recs)\n return recordarray","def _initializeDoneDict(self):\n\t\tself.donedictfile = os.path.join(self.params['rundir'] , self.functionname+\".donedict\")\n\t\tif os.path.isfile(self.donedictfile) and self.params['continue'] == True:\n\t\t\t### unpickle previously done dictionary\n\t\t\tapDisplay.printMsg(\"Reading old done dictionary: \"+os.path.basename(self.donedictfile))\n\t\t\tf = open(self.donedictfile,'r')\n\t\t\tself.donedict = cPickle.load(f)\n\t\t\tf.close()\n\t\t\tif not 'commit' in self.donedict or self.donedict['commit'] == self.params['commit']:\n\t\t\t\t### all is well\n\t\t\t\tapDisplay.printMsg(\"Found \"+str(len(self.donedict))+\" done dictionary entries\")\n\t\t\t\treturn\n\t\t\telif self.donedict['commit'] is True and self.params['commit'] is not True:\n\t\t\t\t### die\n\t\t\t\tapDisplay.printError(\"Commit flag was enabled and is now disabled, create a new runname\")\n\t\t\telse:\n\t\t\t\t### set up fresh dictionary\n\t\t\t\tapDisplay.printWarning(\"'--commit' flag was changed, creating new done dictionary\")\n\n\t\t### set up fresh dictionary\n\t\tself.donedict = {}\n\t\tself.donedict['commit'] = self.params['commit']\n\t\tapDisplay.printMsg(\"Creating new done dictionary: \"+os.path.basename(self.donedictfile))\n\n\t\t### write donedict to file\n\t\tf = open(self.donedictfile, 'w', 0666)\n\t\tcPickle.dump(self.donedict, f)\n\t\tf.close()\n\t\treturn","def __missing__(self,key):\n # First time the dictionary is in actual use, so we do some setup\n if not self.sortedkeys:\n # If there are no keys, then we have an empty dictionary\n # which will raise an exception when searching for keys.\n # Since we are assuming no change once created, we will\n # set a lambda function to replace this one because we're\n # not using the method anyway.\n if not len(self.keys()):\n self.__missing__ = lambda x: (0.0,)\n return (0.0,)\n else:\n # ASSUMPTION: This dictionary is not changed once created.\n # TODO: We need to make this immutable by modifying __getattr__\n self.sortedkeys = sorted(self.keys())\n\n # Find the previous and next available keys\n ind = bisect_right(self.sortedkeys, key)-1\n x0 = int(self.sortedkeys[ind])\n x1 = int(self.sortedkeys[ind+1])\n\n # Find the previous and next available values\n y0 = self[x0]\n y1 = self[x1]\n val = None\n if isinstance(y0, tuple):\n if not len(y0): return () # We have nothing in the tuple, so return a blank tuple (not 'val', which is None)\n for i in xrange(len(y0)):\n # Try to add value to the tuple of values\n try: val += y0[i] + ((y1[i]-y0[i])*(key-x0))/(x1-x0),\n # Unless we have a value of None, then we make a tuple\n except TypeError: val = y0[i] + ((y1[i]-y0[i])*(key-x0))/(x1-x0),\n else: val = y0 + ((y1-y0)*(key-x0))/(x1-x0)\n # This can store the value in the dictionary to prevent us from\n # interpolating later, however, it is too much data for big models\n # and will fill the memory like a leak. It's commented here, but\n # kept in case people want to play.\n #self[key] = val\n return val","def get_latest_update(data_type=\"national\"):\n query_menu = get_query_menu()\n collection = query_menu[data_type][\"collection\"]\n try:\n doc = next(collection.find({}).sort([(DATE_KEY, -1)]).limit(1))\n latest_update = format_datetime(datetime=doc[DATE_KEY], format='short')\n except StopIteration:\n app.logger.error(\"While getting latest update: no data\")\n latest_update = \"n/a\"\n return latest_update"],"string":"[\n \"def get_rec(self):\\n return {'cal': 0}\",\n \"def _update(d):\\n newd = copy.deepcopy(default)\\n\\n if 'lastdir' in d:\\n newd['lastdir'] = d['lastdir']\\n\\n return newd\",\n \"def last_dict():\\n\\n # newest_d = {}\\n # my_dic = pd.read_excel('grad_gen.xlsx', index_col=0).to_dict()\\n # # new_d = pickle.load(open('new_d.pickle', \\\"rb\\\"))\\n # for item in my_dic:\\n # newest_d = my_dic[item]\\n # pickle.dump(newest_d,open('grad_g.pickle', 'wb'))\\n\\n new_d = pickle.load(open('grad_g.pickle', \\\"rb\\\"))\\n # new_d.pop('Biology')\\n # new_d['Obaid Hussen'] = 'male'\\n # new_d['Faisal Tanvir R.'] = 'male'\\n new_d = simplejson.loads(simplejson.dumps(new_d, ignore_nan=True))\\n pickle.dump(new_d, open('grad_g.pickle', \\\"wb\\\"))\\n\\n return new_d\",\n \"def get_last_record_dict(self):\\n sql = (\\\"SELECT \\\" + ', '.join(self.labels) +\\n \\\" FROM snapshot_log WHERE id = (SELECT MAX(id) FROM \\\"\\n \\\"snapshot_log);\\\")\\n try:\\n cur = self.cursor()\\n cur.execute(sql)\\n rows = cur.fetchall()\\n res = dict_factory(cur, rows[0])\\n #cur.close()\\n self.close()\\n return res\\n except AttributeError as ae:\\n print (\\\"Can't get last record: {0}\\\".format(ae.message))\\n except Exception as e:\\n print (\\\"Can't get last record: {0}\\\".format(e.message))\\n return None\",\n \"def update_total_fpmu_dict(self):\\n # identical for each long-range connection\\n # extract parameters\\n deltat = self.dt\\n trise = self.tau_r\\n tdamp = self.tau_d\\n\\n tr = deltat/trise\\n etr = np.exp(-tr) \\n td = deltat/tdamp\\n etd = np.exp(-td)\\n cst = trise/(tdamp-trise)\\n\\n # nmda should keep in memory which could not be reset to zerooooooo!!!\\n \\\"\\\"\\\"\\n no resetting to zero --> go directly to refreshing !!! based on pre-value\\n \\\"\\\"\\\"\\n for c in self.source_connection_list:\\n if (c.conn_type == 'LongRange'):\\n self.total_INMDA_dict[c.connection_distribution] = self.total_INMDA_dict[c.connection_distribution] * etd + self.total_HNMDA_dict[c.connection_distribution] * cst\\n self.total_HNMDA_dict[c.connection_distribution] = self.total_HNMDA_dict[c.connection_distribution] * etr + c.curr_firing_rate * c.nsyn * c.weights * self.tau_r\\n\\n print 'Change HNMDA: ', c.curr_firing_rate * c.nsyn ,' \\\\n'\\n print 'Inputlr dict: ', self.total_inputlr_dict[c.connection_distribution]\\n\\n\\n\\n # for curr_CD in self.source_connection_list:\\n # have already exist\\n for c in self.source_connection_list:\\n if(c.conn_type == 'ShortRange'):\\n self.total_fpmu_dict[c.connection_distribution] = 0.0\\n # have already clear up all the short range connections\\n for c in self.source_connection_list:\\n if(c.conn_type == 'ShortRange'):\\n self.total_fpmu_dict[c.connection_distribution] += c.curr_firing_rate * c.nsyn * c.weights\\n\\n # summation\\n self.total_fp_vslave = 0.0\\n for key,val in self.total_fpmu_dict.items():\\n \\n try:\\n self.total_fp_vslave += val\\n except:\\n key.initialize()\\n self.total_fp_vslave += val\\n # and then, summation of Inmda\\n for key,val in self.total_INMDA_dict.items():\\n try:\\n self.total_fp_vslave += val\\n except:\\n key.initialize()\\n self.total_fp_vslave += val\\n # and then divided by gL or multiply tau_m\\n self.total_fp_vslave = self.total_fp_vslave * self.tau_m\",\n \"def getLastData(self) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def sweep_record(r):\\n return dict_sweep(r, vals=[None])\",\n \"def last_value(self):\\n return 0\",\n \"def get_data_updating(self):\\n # add some special keywords, if they are specified in self.initialize\\n special_keywords = ['size', 'bounds', 'primitive_type']\\n for keyword in special_keywords:\\n val = getattr(self, keyword)\\n if val is not None and keyword not in self.data_updating:\\n self.data_updating[keyword] = val\\n return self.data_updating\",\n \"def get_data_to_update_object(self):\\n return {}\",\n \"def GetUpdated(self, vals=[], default=None):\\n partData, attribNames = vals\\n \\n ids = self.GetLatestIds(partData)\\n return packDictionary(self.read(getCleanList(ids), attribNames))\",\n \"def helper_retrieve_last_request_post_dict_key_val_index_zero_or_return_none(self, key_name):\\n try:\\n return self.last_request_post_data_dict[key_name][0]\\n except:\\n return None\",\n \"def update_dict(new,old):\",\n \"def helper_retrieve_last_request_get_dict_key_val_index_zero_or_return_none(self, key_name):\\n try:\\n return self.last_request_get_dict[key_name][0]\\n except:\\n return None\",\n \"def _retention_rate(self):\\n res = {}\\n for ret_line in self.browse(self.ids):\\n if ret_line.invoice_id:\\n pass\\n else:\\n res[ret_line.id] = 0.0\\n return res\",\n \"def _reset_changes(self):\\r\\n self._original = {}\\r\\n if self.last_updated is not None:\\r\\n self._original['last_updated'] = self.last_updated\",\n \"def get_bibrankmethod_lastupdate(rank_method_code):\\n query = \\\"\\\"\\\"select last_updated from rnkMETHOD where name ='%s'\\\"\\\"\\\" % rank_method_code\\n last_update_time = run_sql(query)\\n r = last_update_time[0][0]\\n if r is None:\\n return \\\"0000-00-00 00:00:00\\\"\\n return r\",\n \"def update_data(self):\\n for sai_id_key in self.if_id_map:\\n namespace, sai_id = mibs.split_sai_id_key(sai_id_key)\\n if_idx = mibs.get_index_from_str(self.if_id_map[sai_id_key])\\n counter_table = self.namespace_db_map[namespace].get_all(mibs.COUNTERS_DB, \\\\\\n mibs.counter_table(sai_id))\\n if counter_table is None:\\n counter_table = {}\\n self.if_counters[if_idx] = counter_table\\n\\n\\n self.lag_name_if_name_map, \\\\\\n self.if_name_lag_name_map, \\\\\\n self.oid_lag_name_map, _, _ = Namespace.get_sync_d_from_all_namespace(mibs.init_sync_d_lag_tables, self.db_conn)\\n\\n self.if_range = sorted(list(self.oid_name_map.keys()) + list(self.oid_lag_name_map.keys()))\\n self.if_range = [(i,) for i in self.if_range]\",\n \"def dd_recovery_return_map_nb(record, ts):\\n end_val = dd_end_value_map_nb(record, ts)\\n valley_val = dd_valley_value_map_nb(record, ts)\\n return (end_val - valley_val) / valley_val\",\n \"def update(self,d:dict):\\n for name,(value,n) in d.items():\\n if n==0:\\n continue\\n self.meters[name].update(value,n)\",\n \"def updated(self, key=None):\\n if key:\\n sql = u\\\"\\\"\\\"\\n SELECT `updated` FROM `{table}` WHERE `key` = ?\\n \\\"\\\"\\\".format(table=self.name)\\n\\n row = self.conn.execute(sql, (key,)).fetchone()\\n if row:\\n return row['updated']\\n\\n return 0.0\\n\\n # Return latest updated\\n sql = u\\\"\\\"\\\"\\n SELECT MAX(`updated`) AS `updated` FROM `{table}`\\n \\\"\\\"\\\".format(table=self.name)\\n\\n row = self.conn.execute(sql).fetchone()\\n return row['updated'] if row['updated'] else 0.0\",\n \"def last_bloodwork(self):\\n if hasattr(self, '_prior_bloodwork'):\\n #in memory lookup\\n return self._prior_bloodwork\\n\\n #memcached lookup\\n last_bw = cache.get('%s_bloodwork' % (self._id), None)\\n if last_bw == None:\\n #it's null, so it's a cache miss.\\n #do a requery\\n pass\\n elif last_bw == '[##Null##]':\\n return None\\n else:\\n self._prior_bloodwork = CBloodwork.wrap(simplejson.loads(last_bw))\\n return self._prior_bloodwork\\n\\n\\n #requery\\n bw_docs = CBloodwork.view('pactcarehq/patient_bloodwork', key=self.pact_id).all()\\n bw_docs = sorted(bw_docs, key=lambda x: x['test_date'])\\n if len(bw_docs) > 0:\\n self._prior_bloodwork = bw_docs[0]\\n cache.set('%s_bloodwork' % (self._id), simplejson.dumps(bw_docs[0].to_json()))\\n return bw_docs[0]\\n if self.prior_bloodwork.test_date == None:\\n #this is a bit hacky, it should really be null, but since this is an added on object, to PactPatient, it doesn't show up as None\\n #so we need to do an explicit check for the\\n cache.set('%s_bloodwork' % (self._id), '[##Null##]')\\n pass\\n else:\\n self._prior_bloodwork = self.prior_bloodwork\\n return self.prior_bloodwork\\n return None\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def create_data_record(self, data_dict):\\n source_dict = deepcopy(data_dict)\\n assert not self.is_conflicting_keys(data_dict,\\n self.default_values), \\\"Conflicting keys between default_values and extra_values\\\"\\n source_dict.update(self.default_values)\\n return {\\n '_index': self.get_full_index(),\\n '_type': 'python_log',\\n '_source': source_dict\\n }\",\n \"def __getHoldingsCurrent(self, dirPath=None):\\n rD = {}\\n retD = {}\\n dirPath = dirPath if dirPath else self.__sandboxPath\\n try:\\n updateTypeList = [\\\"all\\\"]\\n contentTypeList = [\\\"pdb\\\", \\\"pdb-format\\\", \\\"mr\\\", \\\"cs\\\", \\\"sf\\\", \\\"nef\\\", \\\"nmr-str\\\"]\\n contentNameD = {\\n \\\"pdb\\\": \\\"coordinates\\\",\\n \\\"pdb-format\\\": \\\"PDB format coordinates\\\",\\n \\\"mr\\\": \\\"NMR restraints\\\",\\n \\\"cs\\\": \\\"NMR chemical shifts\\\",\\n \\\"sf\\\": \\\"structure factors\\\",\\n \\\"nef\\\": \\\"Combined NMR data (NEF)\\\",\\n \\\"nmr-str\\\": \\\"Combined NMR data (NMR-STAR)\\\",\\n }\\n #\\n tD = {}\\n for updateType in updateTypeList:\\n for contentType in contentTypeList:\\n fp = os.path.join(dirPath, \\\"update-lists\\\", updateType + \\\"-\\\" + contentType + \\\"-list\\\")\\n if not self.__mU.exists(fp):\\n continue\\n entryIdL = self.__mU.doImport(fp, \\\"list\\\")\\n #\\n for entryId in entryIdL:\\n entryId = entryId.strip().upper()\\n if entryId not in tD:\\n tD[entryId.upper()] = {}\\n tD[entryId.upper()][contentNameD[contentType]] = True\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"biounit_file_list.tsv\\\")\\n lines = self.__mU.doImport(fp, \\\"list\\\")\\n assemD = {}\\n for line in lines:\\n fields = line.split(\\\"\\\\t\\\")\\n entryId = fields[0].strip().upper()\\n assemId = fields[1].strip()\\n if entryId not in assemD:\\n assemD[entryId.upper()] = []\\n assemD[entryId.upper()].append(assemId)\\n #\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"pdb_bundle_index_list.tsv\\\")\\n bundleIdList = self.__mU.doImport(fp, \\\"list\\\")\\n bundleD = {}\\n for entryId in bundleIdList:\\n bundleD[entryId.strip().upper()] = True\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"validation_report_list_new.tsv\\\")\\n vList = self.__mU.doImport(fp, \\\"list\\\")\\n valD = {}\\n valImageD = {}\\n valCifD = {}\\n for line in vList:\\n fields = line.split(\\\"\\\\t\\\")\\n entryId = fields[0].strip().upper()\\n imageFlag = fields[1].strip().upper()\\n valD[entryId] = True\\n valImageD[entryId] = imageFlag == \\\"Y\\\"\\n if len(fields) > 2:\\n valCifD[entryId] = fields[2].strip().upper() == \\\"Y\\\"\\n #\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"entries_without_polymers.tsv\\\")\\n pList = self.__mU.doImport(fp, \\\"list\\\")\\n pD = {}\\n for entryId in pList:\\n pD[entryId.strip().upper()] = False\\n #\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"nmr_restraints_v2_list.tsv\\\")\\n nmrV2List = self.__mU.doImport(fp, \\\"list\\\")\\n nmrV2D = {}\\n for entryId in nmrV2List:\\n nmrV2D[entryId.strip().upper()] = False\\n #\\n if self.__cfgOb:\\n configName = self.__cfgOb.getDefaultSectionName()\\n fp = self.__cfgOb.getPath(\\\"RCSB_EDMAP_LIST_PATH\\\", sectionName=configName)\\n else:\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"edmaps.json\\\")\\n qD = self.__mU.doImport(fp, \\\"json\\\")\\n edD = {}\\n for entryId in qD:\\n edD[entryId.upper()] = qD[entryId]\\n #\\n fp = os.path.join(dirPath, \\\"status\\\", \\\"obsolete_entry.json_2\\\")\\n oL = self.__mU.doImport(fp, \\\"json\\\")\\n obsD = {}\\n for dD in oL:\\n obsD[dD[\\\"entryId\\\"].upper()] = True\\n logger.info(\\\"Removed entry length %d\\\", len(obsD))\\n #\\n #\\n # Revise content types bundles and assemblies\\n #\\n for qId, dD in tD.items():\\n entryId = qId.strip().upper()\\n if entryId in obsD:\\n continue\\n rD[entryId] = []\\n if entryId in bundleD:\\n rD[entryId].append(\\\"entry PDB bundle\\\")\\n if \\\"coordinates\\\" in dD:\\n rD[entryId].append(\\\"entry mmCIF\\\")\\n rD[entryId].append(\\\"entry PDBML\\\")\\n if \\\"PDB format coordinates\\\" in dD:\\n rD[entryId].append(\\\"entry PDB\\\")\\n if entryId in assemD:\\n if entryId in bundleD:\\n rD[entryId].append(\\\"assembly mmCIF\\\")\\n else:\\n rD[entryId].append(\\\"assembly PDB\\\")\\n #\\n for cType in dD:\\n if cType not in [\\\"coordinates\\\", \\\"PDB format coordinates\\\", \\\"NMR restraints\\\"]:\\n rD[entryId].append(cType)\\n if cType == \\\"NMR restraints\\\":\\n rD[entryId].append(\\\"NMR restraints V1\\\")\\n\\n if entryId in nmrV2D:\\n rD[entryId].append(\\\"NMR restraints V2\\\")\\n #\\n if entryId in valD:\\n rD[entryId].append(\\\"validation report\\\")\\n if entryId in valImageD and valImageD[entryId]:\\n rD[entryId].append(\\\"validation slider image\\\")\\n if entryId in valCifD and valCifD[entryId]:\\n rD[entryId].append(\\\"validation data mmCIF\\\")\\n if entryId in edD:\\n rD[entryId].append(\\\"2fo-fc Map\\\")\\n rD[entryId].append(\\\"fo-fc Map\\\")\\n rD[entryId].append(\\\"Map Coefficients\\\")\\n if entryId not in pD:\\n rD[entryId].append(\\\"FASTA sequence\\\")\\n #\\n for entryId in rD:\\n if entryId in assemD:\\n retD[entryId] = {\\\"assembly_ids\\\": assemD[entryId], \\\"repository_content_types\\\": rD[entryId]}\\n else:\\n retD[entryId] = {\\\"repository_content_types\\\": rD[entryId]}\\n return retD\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n\\n return retD\",\n \"def getDefaultData(dmd):\",\n \"def reinit (self):\\n #for name, obj in inspect.getmembers (self):\\n ## if isinstance (obj, RField):\\n # self.keyvals[name] = obj.default\\n inspect.getmembers (self)\",\n \"def _update(self):\\n if self._dirty:\\n return self._to_flattr_dict()\\n return False\",\n \"def update_stored(unchanged, new_found, ex_dic, dat_tim):\\n\\n for sensor in unchanged:\\n for dev in ex_dic['devices']:\\n if sensor == dev['id']:\\n dev['last_seen'] = str(dat_tim)\\n\\n for newbie in new_found:\\n new_dict = dict()\\n new_dict['id'] = newbie\\n new_dict['first_seen'] = str(dat_tim)\\n new_dict['last_seen'] = str(dat_tim)\\n ex_dic['devices'].append(new_dict)\\n if len(new_found) > 0:\\n send_tweet(new_found)\\n pass\\n with open('last_seen.json', 'w') as out_file:\\n json.dump(ex_dic, out_file, indent=4)\",\n \"def _refresh_cache(self, data_dict):\\r\\n pass\",\n \"def processRecord(c,collection,VarMap,totalVariables,uniqueIndexes,versionNumber,specialKeyInds,incremental,sliceDB,sliceColTuples,ContentCols):\\n\\n vNInd = VarMap['__versionNumber__']\\n retInd = VarMap['__retained__']\\n aKInd = VarMap['__addedKeys__']\\n origInd = VarMap['__originalVersion__']\\n \\n c = dict([(VarMap[k],c[k]) for k in c.keys()])\\n c[vNInd] = versionNumber\\n s = dict([(VarMap[k],c[VarMap[k]]) for k in uniqueIndexes])\\n s[vNInd] = versionNumber - 1\\n \\n H = collection.find_one(s)\\n \\n if H:\\n if incremental:\\n diff = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and k in c.keys() and notEqual(H[k],c[k]) ])\\n newc = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and k not in c.keys() ])\\n newc.update(c)\\n c = newc\\n else:\\n diff = dict([(k,H[k]) for k in H.keys() if k != '_id' and k not in specialKeyInds and (k not in c.keys() or notEqual(H[k],c[k])) ])\\n \\n c[origInd] = H[origInd]\\n \\n if diff:\\n diff[retInd] = True\\n newkeys = [k for k in c.keys() if k not in H.keys()]\\n if newkeys:\\n diff[aKInd] = newkeys\\n \\n if diff: \\n DIFF = ContentCols.intersection([totalVariables[int(k)] for k in diff.keys()])\\n diff.update(s)\\n collection.update(s,diff)\\n print 'Diff:' , diff\\n else:\\n DIFF = False\\n c['_id'] = H['_id']\\n collection.remove(s)\\n \\n else:\\n c[origInd] = versionNumber\\n diff = c\\n DIFF = True\\n \\n id = collection.insert(c) \\n if DIFF:\\n sliceInsert(c,collection,sliceColTuples,VarMap,sliceDB,versionNumber)\\n \\n return id, c\",\n \"def fill_wbwc_dict(wdict, fill='auto'):\\n global all_kfs\\n for k in wdict.keys():\\n if ((wdict[k]['dg'] == 'nan') and (wdict[k]['ddg'] == 'nan')) or (fill == 'rates'):\\n wdict[k]['dg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/wdict[k]['hr'])\\n #wdict[k]['dg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/wdict[k]['hr'] / (wdict[k]['hf']/wdict[k]['hr'] + wdict[k]['hr']/wdict[k]['hf'])) #### NORMALIZE!!!!\\n #wdict[k]['ddg'] = - (Rconst*temperature) * ((np.log(wdict[k]['hf'])/np.log((scon.k * temperature/scon.h))))\\n wdict[k]['ddg'] = - (Rconst*temperature) * np.log(wdict[k]['hf']/(scon.k * temperature/scon.h))\\n elif ((wdict[k]['hf'] == 'nan') and (wdict[k]['hr'] == 'nan')) or (fill == 'barriers'):\\n wdict[k]['hf'] = (scon.k * temperature/scon.h) * np.exp(-wdict[k]['ddg']/(Rconst*temperature))\\n wdict[k]['hr'] = (scon.k * temperature/scon.h) * np.exp(-(wdict[k]['ddg'] - wdict[k]['dg'])/(Rconst*temperature))\\n ## add hf and hr as full name rate consts to the full dict\\n for s in wdict.keys():\\n for k in ['hf', 'hr']:\\n all_kfs[k+s[0]] = wdict[s][k]\\n return wdict\",\n \"def get_updated_state(self, current_stream_state: MutableMapping[str, Any], latest_record: Mapping[str, Any]) -> Mapping[str, Any]:\\n\\n if self.first_run:\\n self.first_run = False\\n return {self.cursor_field: self.initial_state}\\n else:\\n self.max_state = max(self.max_state, latest_record[self.cursor_field])\\n return {self.cursor_field: self.max_state if self.current_slice == self.last_slice else self.initial_state}\",\n \"def default_setting(self):\\n\\t\\tunknown_as_zero = False\\n\\t\\tbinary_rele = False # using the original values\\n\\t\\tpresort = False # a default setting\\n\\n\\t\\tscale_data, scaler_id, scaler_level = get_default_scaler_setting(data_id=self.data_id)\\n\\n\\t\\t# more data settings that are rarely changed\\n\\t\\tself.data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\\n\\t\\t\\t\\t\\t\\t sample_rankings_per_q=1, unknown_as_zero=unknown_as_zero, binary_rele=binary_rele,\\n\\t\\t\\t\\t\\t\\t presort=presort, scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\\n\\n\\t\\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\\n\\t\\tself.data_dict.update(data_meta)\\n\\n\\t\\treturn self.data_dict\",\n \"def __getHoldingsUpdate(self, dirPath=None):\\n retD = {}\\n dirPath = dirPath if dirPath else self.__sandboxPath\\n try:\\n updateTypeList = [\\\"added\\\", \\\"modified\\\", \\\"obsolete\\\"]\\n contentTypeList = [\\\"entries\\\", \\\"mr\\\", \\\"cs\\\", \\\"sf\\\", \\\"nef\\\", \\\"nmr-str\\\"]\\n contentNameD = {\\n \\\"entries\\\": \\\"coordinates\\\",\\n \\\"mr\\\": \\\"NMR restraints\\\",\\n \\\"cs\\\": \\\"NMR chemical shifts\\\",\\n \\\"sf\\\": \\\"structure factors\\\",\\n \\\"nef\\\": \\\"Combined NMR data (NEF)\\\",\\n \\\"nmr-str\\\": \\\"Combined NMR data (NMR-STAR)\\\",\\n }\\n #\\n for updateType in updateTypeList:\\n uD = {}\\n for contentType in contentTypeList:\\n fp = os.path.join(dirPath, \\\"update-lists\\\", updateType + \\\"-\\\" + contentType)\\n if not self.__mU.exists(fp):\\n continue\\n entryIdL = self.__mU.doImport(fp, \\\"list\\\")\\n #\\n for entryId in entryIdL:\\n entryId = entryId.strip().upper()\\n uD.setdefault(entryId, []).append(contentNameD[contentType])\\n for entryId in uD:\\n uType = \\\"removed\\\" if updateType == \\\"obsolete\\\" else updateType\\n # retD[entryId] = {\\\"update_id\\\": updateId, \\\"entry_id\\\": entryId, \\\"update_type\\\": uType, \\\"repository_content_types\\\": uD[entryId]}\\n retD[entryId] = {\\\"update_type\\\": uType, \\\"repository_content_types\\\": uD[entryId]}\\n return retD\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n return retD\",\n \"def update_crispval(self, val_dict):\\n for v in val_dict:\\n if v in self.variables_default_val:\\n self.variables_default_val[v] = val_dict[v]\",\n \"def updated_full_record(full_record):\\n full_record[\\\"access\\\"][\\\"status\\\"] = \\\"embargoed\\\"\\n full_record[\\\"created\\\"] = \\\"2023-03-23T00:00:00.000000+00:00\\\"\\n full_record[\\\"id\\\"] = \\\"abcde-fghij\\\"\\n full_record[\\\"metadata\\\"][\\\"resource_type\\\"][\\\"id\\\"] = \\\"other\\\"\\n\\n return full_record\",\n \"def get_last_modified_rec(bibrank_method_lastupdate):\\n query = \\\"\\\"\\\"SELECT id FROM bibrec\\n WHERE modification_date >= '%s' \\\"\\\"\\\" % bibrank_method_lastupdate\\n query += \\\"order by id ASC\\\"\\n ilist = run_sql(query)\\n return ilist\",\n \"def _dictfetchone(self):\\n data = self._dictfetchall()\\n if data:\\n return data[0]\\n return {}\",\n \"def initialize_log_data(ids_bcs_added_field):\\r\\n\\r\\n log_data = {}\\r\\n\\r\\n for curr_key in ids_bcs_added_field.keys():\\r\\n base_key = \\\"\\\"\\r\\n if curr_key[0]:\\r\\n base_key += curr_key[0] + \\\",\\\"\\r\\n if curr_key[1]:\\r\\n base_key += curr_key[1] + \\\",\\\"\\r\\n base_key += ids_bcs_added_field[curr_key]\\r\\n log_data[base_key] = 0\\r\\n\\r\\n return log_data\",\n \"def rec_default(self):\\n pass\",\n \"def _compute_last_observations(self):\\n observations = {}\\n for ts in self.ts_ids:\\n observations[ts] = self.traffic_signals[ts]._compute_observation()\\n return observations\",\n \"def latestValue(self):\\n if len(self.values) > 0:\\n return self.values[-1]\\n else:\\n return 0\",\n \"def re_zero_to_datum(self):\\n self.device.re_zero_to_datum()\\n return \\\"OK\\\"\",\n \"def getLastUpdate():\\n swDB = switchdb.DB()\\n lastupdate = swDB.getLastUpdate()\\n swDB.close()\\n return lastupdate\",\n \"def update_graph(data, keep_last):\\n if data is None or not data['val_list']:\\n raise PreventUpdate\\n if keep_last is None:\\n keep_last = 1024\\n return {'x': [data['time_list']], 'y': [data['val_list']]}, [0], keep_last\",\n \"def last_update(self):\\r\\n request = http.Request('GET', '/metadata/last_update.json')\\r\\n return request, parsers.parse_json\",\n \"def getChanged(self,key,default=None):\\n if default != None and key not in self.data:\\n self.data[key] = default\\n self.setChanged(key)\\n return self.data.get(key)\",\n \"def last_value():\\n return blockchain[-1]\",\n \"def getLastAverage(self):\\n lastAve=dict()\\n lastAve['identifier']=self.lastWaveIdentifier\\n lastAve['averageCalculated']=self.lastAverageCalculated \\n lastAve['lastAverageArray']=self.lastAverageArray\\n return lastAve\",\n \"def resetlast(self):\\n self.last = None\\n self.dlast = 0\",\n \"def req_latest_data(self) -> dict:\\n latest_data = {\\n \\\"distance_measurements\\\": self._last_sensor_dist_measurements,\\n \\\"speed_measurements\\\": self._last_sensor_speed_measurements,\\n \\\"navigation\\\": self._last_navi_state_dict,\\n \\\"collision\\\": self._collision,\\n \\\"progress\\\": self._progress,\\n \\\"reached_goal\\\": self._reached_goal,\\n # \\\"max_progress\\\": \\n }\\n\\n if self.config.vessel.sensor_use_feasibility_pooling:\\n latest_data[\\\"feasible_distances\\\"] = self._last_sector_feasible_dists\\n\\n return latest_data\",\n \"def get_remittance(self):\\n\\n #kinko dict to be updated in Kinko Collection.\\n kdict = {\\n \\\"amt\\\": 0.0,\\n \\\"cl\\\": unicode(self.cl),\\n \\\"dt\\\": datetime.datetime.today(),\\n \\\"typ\\\": TYPE_MAP[self.tab_type],\\n \\\"range\\\": {\\\"lt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$lt\\\", None),\\n \\\"gt\\\": self.q_dict[\\\"cs.sd\\\"].get(\\\"$gte\\\", None),\\n }\\n }\\n\\n# old_rem = connection.Kinko.find_one({\\\"cl\\\": self.cl, \\\"tid\\\": None,\\n# \\\"typ\\\": TYPE_MAP[self.tab_type]})\\n\\n old_rem = None\\n\\n if old_rem:\\n old_rem.dt = kdict['dt']\\n old_rem.range.lt = kdict['range']['lt']\\n old_rem.save()\\n\\n connection.Package.collection.update(self.q_dict,\\n {\\\"$set\\\": {\\\"inv.rnum\\\": old_rem.num}}, multi=True, safe=True)\\n kinko_number = old_rem.num\\n\\n else:\\n k = Kinko(kdict)\\n k_count = 1\\n #the get num method of Kinko model generates the unique no for new kinko\\n k[\\\"num\\\"] = self.get_knum(1)\\n while connection.Kinko.collection.find({\\\"num\\\": k.num}).count() > 0:\\n k[\\\"num\\\"] = self.get_knum(k_count+1)\\n k_count += 1\\n\\n connection.Kinko(k).save(safe=True)\\n #after creating a new document in Kinko all packages are updated.\\n connection.Package.collection.update(self.q_dict,\\n {\\\"$set\\\": {\\\"inv.rnum\\\": k[\\\"num\\\"]}}, multi=True, safe=True)\\n kinko_number = k[\\\"num\\\"]\\n #Aggrigation of remitted amount for requested client\\n non_remitted = kinko_map_reduce(kinko_number, TYPE_MAP[self.tab_type])\\n\\n \\\"\\\"\\\"non remitted is none if the data for required query dict is\\n already remitted. If none it returns False to notify user.\\\"\\\"\\\"\\n if len(non_remitted) == 0:\\n return False\\n\\n if old_rem:\\n old_rem.amt = non_remitted[0]['value']['cod']\\n old_rem.save()\\n return old_rem\\n else:\\n new_rem = connection.Kinko.find_one({\\\"num\\\": k[\\\"num\\\"]})\\n new_rem.amt = non_remitted[0]['value']['cod']\\n new_rem.save()\\n return new_rem\",\n \"def refresh(self):\\n new = self.table.records_updated_since(self.updated.max()).set_index(\\\"operator\\\")\\n new = new.rename(columns={\\\"operator_alias\\\": \\\"alias\\\", \\\"fscore\\\": \\\"confidence\\\"})\\n\\n if not new.empty: # TODO: this is clunky. need to fix later\\n self.update(new)\\n for idx, values in new.iterrows():\\n try:\\n self.loc[\\n idx\\n ] # try to lookup the index. Insert record if the lookup fails.\\n except KeyError:\\n self.loc[idx] = values\",\n \"def refresh(self):\\n new = self.table.records_updated_since(self.updated.max()).set_index(\\\"operator\\\")\\n new = new.rename(columns={\\\"operator_alias\\\": \\\"alias\\\", \\\"fscore\\\": \\\"confidence\\\"})\\n\\n if not new.empty: # TODO: this is clunky. need to fix later\\n self.update(new)\\n for idx, values in new.iterrows():\\n try:\\n self.loc[\\n idx\\n ] # try to lookup the index. Insert record if the lookup fails.\\n except KeyError:\\n self.loc[idx] = values\",\n \"def updated_minimal_record(minimal_record):\\n minimal_record[\\\"access\\\"][\\\"status\\\"] = \\\"open\\\"\\n minimal_record[\\\"created\\\"] = \\\"2023-03-09T00:00:00.000000+00:00\\\"\\n minimal_record[\\\"id\\\"] = \\\"abcde-fghij\\\"\\n\\n for creator in minimal_record[\\\"metadata\\\"][\\\"creators\\\"]:\\n name = creator[\\\"person_or_org\\\"].get(\\\"name\\\")\\n if not name:\\n creator[\\\"person_or_org\\\"][\\\"name\\\"] = \\\"Name\\\"\\n\\n return minimal_record\",\n \"def _get_last_read_id():\\n webservice_url_initial = 'http://' + host_cmr + ':' + str(host_cmr_port) + '/rest/data/invocations/overview?latestReadId=' \\\\\\n + str(MaxIdInDB[0])\\n\\n print('Web Service Url Initial for Last Read id is ', webservice_url_initial)\\n response_summary = requests.get(webservice_url_initial)\\n\\n data = response_summary.json()\\n df = pd.DataFrame(json_normalize(data))\\n lastreadid_max = df[['id']].max()\\n lastreadid_min = df[['id']].min()\\n print('Last Read id VALUE in apm is ', lastreadid_max['id'])\\n print('the min id VALUE in apm this json ', lastreadid_min['id'])\\n\\n if int(lastreadid_max) >= MaxIdInDB[0]:\\n print(\\\"Send data to influx and MaxIDINDB[0] is from \\\", MaxIdInDB[0], ' to LastReadId:', int(lastreadid_max))\\n a = lastreadid_max['id']\\n print('a is ', a)\\n return a\\n time.sleep(1)\",\n \"def setLastData(self, value):\\n if value is not None:\\n pdata_wrapper = PdataHelper(self.getPortalObject(), value)\\n self._setLastData(pdata_wrapper)\\n else:\\n self._setLastData(None)\",\n \"def find_latest_checkpoint(self) -> Tuple[str, str]:\\n return {}\",\n \"def update_model(self,update_dict,eta=None):\\n\\n A = self.A.get_value()\\n param_max = np.max(np.abs(A),axis=0)\\n\\n if eta is None:\\n update_max = np.max(np.abs(update_dict['dA']),axis=0)\\n A -= update_dict['dA']\\n else:\\n update_max = eta*np.max(np.abs(update_dict['dA']),axis=0)\\n A -= eta*update_dict['dA']\\n\\n update_max = np.max(update_max/param_max)\\n\\n A = self.normalize_A(A)\\n self.A.set_value(A)\\n\\n return update_max\",\n \"def originalIncrementalUpdate(self, pdffilename):\\n original = ReadBinaryFile(pdffilename)\\n fPDF = open(self.filename, 'wb')\\n if sys.version_info[0] == 2:\\n fPDF.write(original)\\n else:\\n fPDF.write(bytes(original, 'ascii'))\\n fPDF.close()\\n startxrefs = re.findall(r'startxref\\\\s+(\\\\d+)', original)\\n if startxrefs == []:\\n return None, None, None\\n oMatch = re.search(r'trailer\\\\s+', original[int(startxrefs[-1]):])\\n if oMatch == None:\\n return None, None, None\\n positionDictionaryTrailer = oMatch.end() + int(startxrefs[-1])\\n dictionaryTrailer = self.MatchDictionary(original[positionDictionaryTrailer:])\\n if dictionaryTrailer == None:\\n return None, None, None\\n oDictionaryTrailer = cDictionary(dictionaryTrailer)\\n idRoot = oDictionaryTrailer.GetID('Root')\\n if idRoot == None:\\n return None, None, None\\n oMatch = re.search(r'\\\\s+%d\\\\s+0\\\\s+obj\\\\s+' % idRoot, original)\\n if oMatch == None:\\n return None, None, None\\n dictionaryRoot = self.MatchDictionary(original[oMatch.end():])\\n if dictionaryRoot == None:\\n return None, None, None\\n oDictionaryRoot = cDictionary(dictionaryRoot)\\n return oDictionaryTrailer, oDictionaryRoot, int(startxrefs[-1])\",\n \"def _empty_normalize(self):\\n return {'method':'self', 'custom_value':None, 'custom_max_count':0, 'global_value':None, 'global_max_count':0, 'processed':set(), 'global_bins':[]}\",\n \"def reset_stat_obj(self, nn_id):\\n try:\\n obj = models.NN_DEF_LIST_INFO.objects.get(nn_id=nn_id)\\n data_set = {}\\n data_set['bygen'] = []\\n data_set['best'] = []\\n setattr(obj, \\\"automl_stat\\\", data_set)\\n obj.save()\\n return data_set\\n except Exception as e:\\n raise Exception(e)\",\n \"def mod_last_column(outDictExclu):\\n\\t\\n\\toutDictMod = OrderedDict()\\n\\n\\tfor key in outDictExclu:\\n\\t\\tk = key\\n\\t\\tdfIn = outDictExclu[key].copy()\\n\\t\\tdfMod = edit_col8(dfIn)\\n\\t\\toutDictMod[k] = dfMod\\n\\t\\n\\treturn outDictMod\",\n \"def process_ubern(fdict,cdmd,cdm,fn,proflist,campaigns,data):\\n good=False\\n for d in data:\\n if 'Min' in d:\\n good=True\\n if 'ri_name' in d:\\n key=d.split()[-1]\\n if key not in fdict.keys():\\n fdict[key]={'observations_table':{},'header_table':{}}\\n \\n if 'COMP018' in fn:\\n print('018')\\n if not good:\\n print(fn,'could not be processed')\\n return\\n if(not fdict[key]['observations_table']):\\n \\n for k in cdmd['observations_table'].element_name.values:\\n fdict[key]['observations_table'][k]=[]\\n for k in cdmd['header_table'].element_name.values:\\n fdict[key]['header_table'][k]=[]\\n refdate=datetime(year=1900,month=1,day=1)\\n header=True\\n aux={}\\n l=0\\n comp=fn[:7]\\n for d in data:\\n if 'Min' in d:\\n head=d.split()\\n break\\n l+=1\\n m=0\\n for d in data[l+1:]:\\n dlist=d.split()\\n if dlist:\\n if dlist[0]=='NA':\\n continue\\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\\n \\n try:\\n x=float(dlist[1])*100\\n except ValueError:\\n try: \\n if 'PPPcorr' in head:\\n x=float(dlist[2])*100\\n else:\\n continue\\n except ValueError:\\n continue\\n m+=1\\n \\n if m==0:\\n print(fn,'no valid obs')\\n return\\n \\n try:\\n idx=np.where(cdm['iri_rstype_map'].ri_name==key.split('_')[0])[0][0]\\n fdict[key]['sensor_id']=numpy.string_(cdm['iri_rstype_map'].vapor_name[idx]) #.split(b',')[0])\\n except:\\n fdict[key]['sensor_id']=numpy.string_('NA')\\n \\n l=0\\n for d in data:\\n if 'ri_name' in d:\\n key=d.split()[1]\\n print(comp,fn,key)\\n if 'measurement_programme' not in fdict[key]['header_table'].keys():\\n fdict[key]['header_table']['measurement_programme']=[]\\n idx=campaigns.index[campaigns.ID==comp]\\n fdict[key]['header_table']['measurement_programme'].append(campaigns.Name[idx].values[0]) \\n\\n id_ascent=fn.split('/')[-1][:11]\\n #key=np.string_('0-20100-0-0000'+fn[-8])\\n sidx=np.where(cdm['station_configuration'].measuring_system_id==numpy.string_(key))[0][0]\\n pidx=np.where(proflist['id_ascent']==id_ascent)[0][0]\\n fdict[key]['header_table']['primary_station_id'].append(cdm['station_configuration'].primary_id.values[sidx])\\n fdict[key]['header_table']['report_id'].append(fdict[key]['header_table']['primary_station_id'][-1]+\\n b'-'+numpy.string_(fn.split('_')[-2]))\\n print(key,fdict[key]['header_table']['report_id'])\\n fdict[key]['header_table']['latitude'].append(cdm['station_configuration'].latitude.values[sidx])\\n fdict[key]['header_table']['longitude'].append(cdm['station_configuration'].longitude.values[sidx])\\n fdict[key]['header_table']['height_of_station_above_sea_level'].append(\\n proflist['alt'].values[pidx])\\n\\n for k in 'latitude','longitude':\\n aux[k]=fdict[key]['header_table'][k][-1]\\n\\n if 'date ' in d:\\n dat=d.split()[-1]\\n if 'hour_utc' in d:\\n hour=d.split()[-1]\\n hh=hour.split(':')\\n if len(hh)==2:\\n hour=hour+':00'\\n relset=datetime.strptime(dat+' '+hour,'%Y-%m-%d %H:%M:%S')\\n fdict[key]['header_table']['report_timestamp'].append(int((relset-refdate).total_seconds()))\\n fdict[key]['header_table']['record_timestamp'].append(int((relset-refdate).total_seconds()))\\n if 'Min' in d:\\n head=d.split()\\n break\\n l+=1\\n\\n m=0\\n dlen=len(head)-2\\n ddlen=dlen\\n dstart=2\\n if 'Notes' in head:\\n dlen-=1\\n ddlen-=1\\n if 'PPPcorr' in head:\\n dstart+=1\\n ddlen-=2\\n ovar={'TTT':85,'TTTcorr':85,'UU':38,'H':117,'DEWPT':36,'DIR':106,'WSPEED':107}\\n offset={'TTT':273.15,'TTTcorr':273.15,'UU':0,'H':0,'DEWPT':273.15,'DIR':0,'WSPEED':0}\\n scale={'TTT':1,'TTTcorr':1,'UU':1,'H':9.80655,'DEWPT':1,'DIR':1,'WSPEED':1}\\n units={'TTT':5,'TTTcorr':5,'UU':0,'H':806,'DEWPT':5,'DIR':110,'WSPEED':731}\\n for d in data[l+1:]:\\n dlist=d.split()\\n if dlist:\\n if dlist[0]=='NA':\\n continue\\n #fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]]\\n \\n try:\\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[1])*100]\\n except ValueError:\\n try: \\n if 'PPPcorr' in head:\\n fdict[key]['observations_table']['z_coordinate']+=ddlen*[float(dlist[2])*100]\\n else:\\n continue\\n except ValueError:\\n continue\\n #fdict[key]['observations_table']['z_coordinate']+=ddlen*[numpy.nan]\\n \\n fdict[key]['observations_table']['date_time']+=ddlen*[fdict[key]['header_table']['report_timestamp'][-1]+int(float(dlist[0])*60)]\\n if ddlen<len(head[dstart:dlen+2]):\\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart+1:dlen+2]]\\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart+1:dlen+2]]\\n else:\\n fdict[key]['observations_table']['observed_variable']+=[ovar[i] for i in head[dstart:dlen+2]]\\n fdict[key]['observations_table']['units']+=[units[i] for i in head[dstart:dlen+2]]\\n for i in range(dstart,dlen+2):\\n \\n if head[i]!='TTTcorr': \\n try:\\n fdict[key]['observations_table']['observation_value'].append(offset[head[i]]+scale[head[i]]*float(dlist[i]))\\n except ValueError:\\n fdict[key]['observations_table']['observation_value'].append(numpy.nan)\\n else:\\n try:\\n fdict[key]['observations_table']['observation_value'][-1]=offset[head[i]]+scale[head[i]]*float(dlist[i])\\n except ValueError:\\n pass\\n \\n fdict[key]['observations_table']['observation_id']+=[numpy.string_('{:0>8}'.format(m+i+1)) for i in range(ddlen)]\\n m+=ddlen\\n else:\\n break\\n\\n aux['sensor_id']='NA'\\n try:\\n idx=np.where(cdm['iri_rstype_map'].ri_name==key)[0][0]\\n aux['sensor_id']=cdm['iri_rstype_map'].vapor_name[idx]\\n except:\\n print('could not find rs type key for '+key+'!')\\n pass\\n aux['report_id']=fdict[key]['header_table']['report_id'][-1]\\n\\n for k,v in aux.items():\\n fdict[key]['observations_table'][k]+=[v]*m\\n \\n return\",\n \"def getData(self):\\n data = {}\\n data.update ( self._original_values, self._modified_values )\\n return data\",\n \"def data_dict_update(self, change):\\n self.data_dict = change['value']\",\n \"def _fill_day_dicts(self):\\n today = datetime.date.today()\\n for i, record in enumerate(self._dataset):\\n if (record[\\\"createdAt\\\"] / 1000) > time.mktime((today - datetime.timedelta(days=30)).timetuple()):\\n self._add_record(self._all30_dict, record, key=i)\\n\\n elif (record[\\\"createdAt\\\"] / 1000) > time.mktime((today - datetime.timedelta(days=60)).timetuple()):\\n self._add_record(self._all60_dict, record, key=i)\\n\\n else:\\n self._add_record(self._all90_dict, record, key=i)\",\n \"def generic(self):\\n result = {}\\n result.update(self.__dict__)\\n self.lastModified = datetime.now()\\n result[self.LAST_MODIFIED] = self.lastModified\\n del result[self.ID]\\n return result\",\n \"def before_index(self, data_dict):\\n return data_dict\",\n \"def update_all_dict_bywhereclause(cls, updatedict, whereclause):\\n return cls.dbm().modelclass_update_all_dict_bywhereclause(cls, updatedict, whereclause)\",\n \"def latest_data(self):\\n if self._data:\\n return self._data\\n return None\",\n \"def getLastWave(self):\\n lastWave=dict()\\n lastWave['identifier']=self.lastWaveIdentifier\\n lastWave['samplingTime']=self.lastSamplingTime\\n lastWave['transferedChannels']=self.lastTransferredChannel\\n lastWave['transferAverage']=self.lastTransferAverage\\n lastWave['nbrSegmentArray']=self.lastNbrSegmentsArray\\n lastWave['nbrSamplesPerSeg']=self.lastNbrSamplesPerSeg\\n lastWave['waveSizes']=self.lastWaveformArraySizes\\n lastWave['wave']=self.lastWaveformArray\\n lastWave['timeStampsSize']=self.lastTimeStampsArraySize\\n lastWave['timeStamps']=self.lastTimeStampsArray\\n lastWave['horPosSize']=self.lastHorPositionsArraySize\\n lastWave['horPos']=self.lastHorPositionsArray\\n lastWave['averageCalculated']=self.lastAverageCalculated \\n lastWave['lastAverageArray']=self.lastAverageArray \\n return lastWave\",\n \"def update_dict(self, total_dict: dict, iter_dict: dict) -> dict:\\n for k in iter_dict:\\n if k not in total_dict:\\n total_dict[k] = iter_dict[k]\\n else:\\n total_dict[k] += iter_dict[k]\\n \\n return total_dict\",\n \"def _metrics_update(orig, new):\\n revsd = orig\\n for k, v in orig.items():\\n if not v:\\n revsd[k] = new[k]\\n elif new[k]:\\n if new[k] != v:\\n # LOG ME, do something\\n print(orig)\\n print(new)\\n elif not new[k] or v:\\n pass\\n else:\\n raise Exception(\\\"_metrics_update error\\\")\\n return revsd\",\n \"def DefaultData():\\n return copy.deepcopy(DEFAULT_DATA)\",\n \"def get_update_number( self ):\",\n \"def gen_update(params, data):\\n result = {}\\n for key, value in data.iteritems():\\n if key in params:\\n result[key] = value\\n return result\",\n \"def latest_data(self):\\n if self._data:\\n return self._data[0]\\n return None\",\n \"def _reset(self):\\n self._values = {}\",\n \"def last_update(self): # TOFIX model the job and return an object instead of dictionary\\n return self._data.get('summary_fields', {}).get('last_update')\",\n \"def _add_record(days_dict, record, key):\\n days_dict[key] = {\\n \\\"Name\\\": record[\\\"title\\\"],\\n \\\"Owner\\\": record[\\\"owner\\\"],\\n \\\"Severity\\\": record[\\\"severity\\\"],\\n \\\"Created\\\": (time.strftime(SIRPPipeline.TIME_FMT, time.gmtime(record[\\\"createdAt\\\"] / 1000.0))),\\n }\\n if \\\"endDate\\\" in record:\\n days_dict[key].update(\\n {\\n \\\"Closed\\\": (time.strftime(SIRPPipeline.TIME_FMT, time.gmtime(record[\\\"endDate\\\"] / 1000.0),)),\\n \\\"Resolution\\\": record[\\\"resolutionStatus\\\"],\\n }\\n )\",\n \"def npdict(self):\\n\\n d = {}\\n\\n # per profile\\n d['cruise'] = self.cruise()\\n d['day'] = self.day()\\n d['latitude'] = self.latitude()\\n d['latitude_unc'] = self.latitude_unc()\\n d['longitude'] = self.longitude()\\n d['longitude_unc'] = self.longitude_unc()\\n d['month'] = self.month()\\n d['n_levels'] = self.n_levels()\\n d['primary_header_keys'] = self.primary_header_keys()\\n d['probe_type'] = self.probe_type()\\n d['time'] = self.time()\\n d['uid'] = self.uid()\\n d['year'] = self.year()\\n d['PIs'] = self.PIs()\\n d['originator_station'] = self.originator_station()\\n d['originator_cruise'] = self.originator_cruise()\\n d['originator_flag_type'] = self.originator_flag_type()\\n d['t_metadata'] = self.t_metadata()\\n d['s_metadata'] = self.s_metadata()\\n # per level\\n d['s'] = self.s()\\n d['s_unc'] = self.s_unc()\\n d['s_level_qc'] = self.s_level_qc()\\n d['s_profile_qc'] = self.s_profile_qc()\\n d['s_qc_mask'] = self.s_qc_mask()\\n d['t'] = self.t()\\n d['t_unc'] = self.t_unc()\\n d['t_level_qc'] = self.t_level_qc()\\n d['t_profile_qc'] = self.t_profile_qc()\\n d['t_qc_mask'] = self.t_qc_mask()\\n d['z'] = self.z()\\n d['z_unc'] = self.z_unc()\\n d['z_level_qc'] = self.z_level_qc()\\n d['oxygen'] = self.oxygen()\\n d['phosphate'] = self.phosphate()\\n d['silicate'] = self.silicate()\\n d['pH'] = self.pH()\\n d['p'] = self.p()\\n\\n return d\",\n \"def __add_missing_entries_to_dict(self, member_dict: dict) -> dict:\\n try:\\n self.dict = member_dict\\n self.dict['Voorletters'] = member_dict['Voornaam'][0].upper() + '.'\\n self.dict['Dag'] = member_dict['GeboorteDatum'].split('-')[0]\\n self.dict['Maand'] = member_dict['GeboorteDatum'].split('-')[1]\\n self.dict['Jaar'] = member_dict['GeboorteDatum'].split('-')[2]\\n self.dict['Postcode'] = member_dict['Postcode'].replace(' ', '')\\n r = member_dict['Adres'].split(' ')\\n self.dict['Huisnr'] = r[len(r) - 1]\\n return self.dict\\n\\n except Exception as e:\\n raise Exception(\\\"Toevoegen lid in NAS mislukt: \\\" + str(e))\",\n \"def update_existing_records(self,tickr=''):\\n stock_master_df=self.__dbconn.get_tickr(tickr)\\n #print(stock_master_df.head())\\n if stock_master_df.empty:\\n raise ValueError(f\\\"data does not exist for {tickr}\\\")\\n\\n last_updated_date=stock_master_df.iloc[0].LastUpdatedDate.strftime('%Y-%m-%d')\\n print(f'Last updated date for {tickr},{last_updated_date}')\\n current_date=datetime.now().strftime('%Y-%m-%d')\\n\\n if current_date!=last_updated_date and current_date>last_updated_date:\\n df=self.__get_all_data(tickr)\\n if not df.empty:\\n mask = df.loc[last_updated_date:current_date, :]\\n mask=mask.iloc[1:]\\n if not mask.empty:\\n \\\"\\\"\\\"we found records to be updated\\\"\\\"\\\"\\n self.__endDate=datetime(2012,5,12)\\n d1=self.__dbconn.get_stock_details(stock_master_df.iloc[0].Id,last_updated_date,self.__endDate.strftime(\\\"%Y-%m-%d\\\"))\\n d1['Date'] = pd.to_datetime(d1['Date'], format = '%Y-%m-%d')\\n d1.set_index(['Date'], inplace=True)\\n result=pd.concat([d1,mask])\\n df=self.__calculate_all_indicators(result)\\n self.__dbconn.delete_stock_details(int(stock_master_df.iloc[0].Id))\\n df['stockId']=int(stock_master_df.iloc[0].Id)\\n self.__dbconn.save_data('stockdetails',df)\\n sql = \\\"\\\"\\\" UPDATE public.stockmaster SET \\\"LastUpdatedDate\\\" = %s WHERE \\\"Id\\\" = %s\\\"\\\"\\\"\\n self.__dbconn.update_stock_master(sql,current_date,int(stock_master_df.iloc[0].Id))\\n print(f\\\"Records updated for {tickr}\\\")\\n else:\\n print(\\\"no rows found for update\\\")\",\n \"def init_cur_stats(self):\\n self._cur_stats = defaultdict(lambda: defaultdict(int))\\n self._cur_stats[\\\"writes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"reads\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/reads\\\"] = 0\\n\\n if self._include_bytes:\\n self._cur_stats[\\\"writesBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"readsBytes\\\"][\\\"/\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/writeBytes\\\"] = 0\\n self._cur_stats[\\\"total\\\"][\\\"/readBytes\\\"] = 0\",\n \"def get(self, key):\\n if key is None or key not in self.cache_data:\\n return None\\n else:\\n for x in self.LFU:\\n if x.key == key:\\n x.age = 0\\n x.freq += 1\\n else:\\n x.age += 1\\n return self.cache_data[key]\",\n \"def update_from_latest_data(self) -> None:\\n data = self.coordinator.data[self.entity_description.uid]\\n\\n self._attr_is_on = bool(data[\\\"state\\\"])\\n\\n attrs = {\\n ATTR_CURRENT_CYCLE: data[\\\"cycle\\\"],\\n ATTR_ID: data[\\\"uid\\\"],\\n ATTR_NO_CYCLES: data[\\\"noOfCycles\\\"],\\n ATTR_RESTRICTIONS: data[\\\"restriction\\\"],\\n ATTR_SLOPE: SLOPE_TYPE_MAP.get(data[\\\"slope\\\"], 99),\\n ATTR_SOIL_TYPE: SOIL_TYPE_MAP.get(data[\\\"soil\\\"], 99),\\n ATTR_SPRINKLER_TYPE: SPRINKLER_TYPE_MAP.get(data[\\\"group_id\\\"], 99),\\n ATTR_STATUS: RUN_STATE_MAP[data[\\\"state\\\"]],\\n ATTR_SUN_EXPOSURE: SUN_EXPOSURE_MAP.get(data.get(\\\"sun\\\")),\\n ATTR_VEGETATION_TYPE: VEGETATION_MAP.get(data[\\\"type\\\"], 99),\\n }\\n\\n if \\\"waterSense\\\" in data:\\n if \\\"area\\\" in data[\\\"waterSense\\\"]:\\n attrs[ATTR_AREA] = round(data[\\\"waterSense\\\"][\\\"area\\\"], 2)\\n if \\\"fieldCapacity\\\" in data[\\\"waterSense\\\"]:\\n attrs[ATTR_FIELD_CAPACITY] = round(\\n data[\\\"waterSense\\\"][\\\"fieldCapacity\\\"], 2\\n )\\n if \\\"precipitationRate\\\" in data[\\\"waterSense\\\"]:\\n attrs[ATTR_PRECIP_RATE] = round(\\n data[\\\"waterSense\\\"][\\\"precipitationRate\\\"], 2\\n )\\n\\n if self._entry.options[CONF_USE_APP_RUN_TIMES]:\\n provision_data = self._data.coordinators[DATA_PROVISION_SETTINGS].data\\n if zone_durations := provision_data.get(\\\"system\\\", {}).get(\\\"zoneDuration\\\"):\\n attrs[ATTR_ZONE_RUN_TIME] = zone_durations[\\n list(self.coordinator.data).index(self.entity_description.uid)\\n ]\\n\\n self._attr_extra_state_attributes.update(attrs)\",\n \"def inverse_update(self, data):\\n if not isinstance(data, dict) or not isinstance(self, transforms.MapTransform):\\n return data\\n d = dict(data)\\n for k in self.key_iterator(data):\\n transform_key = transforms.TraceableTransform.trace_key(k)\\n if transform_key not in data or not data[transform_key]:\\n continue\\n d = transforms.sync_meta_info(k, data, t=False)\\n return d\",\n \"def valuecall(key, atom_dict):\\n if key not in atom_dict:\\n return 0\\n else:\\n return atom_dict[key]\",\n \"def blankUpdateRange(dayHour):\\n return { 'position': dayHour, 'updateHistory': [dayHour], 'width': Predictor.rangeWidth }\",\n \"def pre_virtual_DNS_record_update(self, resource_id, resource_dict):\\n pass\",\n \"def populate_dyn(self, table):\\n myrow = table.row\\n myrow[\\\"sample_time\\\"] = int(time.time() - glob.base_time)\\n myrow[\\\"available_bike_stands\\\"] = self.available_bike_stands\\n myrow[\\\"available_bikes\\\"] = self.available_bikes\\n myrow[\\\"last_update\\\"] = self.last_update\\n myrow[\\\"status\\\"] = self.status\\n myrow.append()\\n table.flush()\",\n \"def dd_end_value_map_nb(record, ts):\\n return ts[record['end_idx'], record['col']]\",\n \"def generate_update_dict(self, input_data, input_labels=None, batch_step=0):\\n update_dict = dict()\\n return update_dict\",\n \"def get_update_request_no_data() -> Dict[str, Any]:\\n payload_dict = {}\\n return payload_dict\",\n \"def _add_empty_record(\\n self, recordarray, recname, kstpkper=None, totim=None\\n ):\\n if kstpkper is not None:\\n if len(self.cbc_times) > 0:\\n totim = self.cbc_times[self.cbc_kstpkper.index(kstpkper)]\\n else:\\n totim = 0.0\\n elif totim is not None:\\n if len(self.cbc_times) > 0:\\n kstpkper = self.cbc_kstpkper[self.cbc_times.index(totim)]\\n else:\\n kstpkper = (0, 0)\\n\\n row = [totim, kstpkper[0], kstpkper[1], recname]\\n row += [0.0 for _ in self._zonenamedict.values()]\\n recs = np.array(tuple(row), dtype=recordarray.dtype)\\n recordarray = np.append(recordarray, recs)\\n return recordarray\",\n \"def _initializeDoneDict(self):\\n\\t\\tself.donedictfile = os.path.join(self.params['rundir'] , self.functionname+\\\".donedict\\\")\\n\\t\\tif os.path.isfile(self.donedictfile) and self.params['continue'] == True:\\n\\t\\t\\t### unpickle previously done dictionary\\n\\t\\t\\tapDisplay.printMsg(\\\"Reading old done dictionary: \\\"+os.path.basename(self.donedictfile))\\n\\t\\t\\tf = open(self.donedictfile,'r')\\n\\t\\t\\tself.donedict = cPickle.load(f)\\n\\t\\t\\tf.close()\\n\\t\\t\\tif not 'commit' in self.donedict or self.donedict['commit'] == self.params['commit']:\\n\\t\\t\\t\\t### all is well\\n\\t\\t\\t\\tapDisplay.printMsg(\\\"Found \\\"+str(len(self.donedict))+\\\" done dictionary entries\\\")\\n\\t\\t\\t\\treturn\\n\\t\\t\\telif self.donedict['commit'] is True and self.params['commit'] is not True:\\n\\t\\t\\t\\t### die\\n\\t\\t\\t\\tapDisplay.printError(\\\"Commit flag was enabled and is now disabled, create a new runname\\\")\\n\\t\\t\\telse:\\n\\t\\t\\t\\t### set up fresh dictionary\\n\\t\\t\\t\\tapDisplay.printWarning(\\\"'--commit' flag was changed, creating new done dictionary\\\")\\n\\n\\t\\t### set up fresh dictionary\\n\\t\\tself.donedict = {}\\n\\t\\tself.donedict['commit'] = self.params['commit']\\n\\t\\tapDisplay.printMsg(\\\"Creating new done dictionary: \\\"+os.path.basename(self.donedictfile))\\n\\n\\t\\t### write donedict to file\\n\\t\\tf = open(self.donedictfile, 'w', 0666)\\n\\t\\tcPickle.dump(self.donedict, f)\\n\\t\\tf.close()\\n\\t\\treturn\",\n \"def __missing__(self,key):\\n # First time the dictionary is in actual use, so we do some setup\\n if not self.sortedkeys:\\n # If there are no keys, then we have an empty dictionary\\n # which will raise an exception when searching for keys.\\n # Since we are assuming no change once created, we will\\n # set a lambda function to replace this one because we're\\n # not using the method anyway.\\n if not len(self.keys()):\\n self.__missing__ = lambda x: (0.0,)\\n return (0.0,)\\n else:\\n # ASSUMPTION: This dictionary is not changed once created.\\n # TODO: We need to make this immutable by modifying __getattr__\\n self.sortedkeys = sorted(self.keys())\\n\\n # Find the previous and next available keys\\n ind = bisect_right(self.sortedkeys, key)-1\\n x0 = int(self.sortedkeys[ind])\\n x1 = int(self.sortedkeys[ind+1])\\n\\n # Find the previous and next available values\\n y0 = self[x0]\\n y1 = self[x1]\\n val = None\\n if isinstance(y0, tuple):\\n if not len(y0): return () # We have nothing in the tuple, so return a blank tuple (not 'val', which is None)\\n for i in xrange(len(y0)):\\n # Try to add value to the tuple of values\\n try: val += y0[i] + ((y1[i]-y0[i])*(key-x0))/(x1-x0),\\n # Unless we have a value of None, then we make a tuple\\n except TypeError: val = y0[i] + ((y1[i]-y0[i])*(key-x0))/(x1-x0),\\n else: val = y0 + ((y1-y0)*(key-x0))/(x1-x0)\\n # This can store the value in the dictionary to prevent us from\\n # interpolating later, however, it is too much data for big models\\n # and will fill the memory like a leak. It's commented here, but\\n # kept in case people want to play.\\n #self[key] = val\\n return val\",\n \"def get_latest_update(data_type=\\\"national\\\"):\\n query_menu = get_query_menu()\\n collection = query_menu[data_type][\\\"collection\\\"]\\n try:\\n doc = next(collection.find({}).sort([(DATE_KEY, -1)]).limit(1))\\n latest_update = format_datetime(datetime=doc[DATE_KEY], format='short')\\n except StopIteration:\\n app.logger.error(\\\"While getting latest update: no data\\\")\\n latest_update = \\\"n/a\\\"\\n return latest_update\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58107096","0.5758537","0.5744504","0.5732967","0.53722936","0.5282241","0.5257113","0.5206331","0.520421","0.5142212","0.5122019","0.5088636","0.50742084","0.5067339","0.5063233","0.50564384","0.50432706","0.50253505","0.5007381","0.50047207","0.49860212","0.49807265","0.49640658","0.49588057","0.49425665","0.49398118","0.4939152","0.4903353","0.4896105","0.48862806","0.48773336","0.48724753","0.4871625","0.48604846","0.48558402","0.48557886","0.48472548","0.47984096","0.47899085","0.47844443","0.47806883","0.4779418","0.47571245","0.47568664","0.47536853","0.47251225","0.4717016","0.47156465","0.47137913","0.4706819","0.4703834","0.47021362","0.4701958","0.46910876","0.46910876","0.46792346","0.46769845","0.4673799","0.46539754","0.4653662","0.46524203","0.4647628","0.4641663","0.46391788","0.46388954","0.46338546","0.46311343","0.46267244","0.46213996","0.4620806","0.46125838","0.46009794","0.45954114","0.45902792","0.45902005","0.45860696","0.45837045","0.4580399","0.45794365","0.45656174","0.4564012","0.45572096","0.45567447","0.45564812","0.45558158","0.45550483","0.45489296","0.45451936","0.45450497","0.4543318","0.45414683","0.4541255","0.45408008","0.45405206","0.4536776","0.45366704","0.45363688","0.45346355","0.45344806","0.45319352"],"string":"[\n \"0.58107096\",\n \"0.5758537\",\n \"0.5744504\",\n \"0.5732967\",\n \"0.53722936\",\n \"0.5282241\",\n \"0.5257113\",\n \"0.5206331\",\n \"0.520421\",\n \"0.5142212\",\n \"0.5122019\",\n \"0.5088636\",\n \"0.50742084\",\n \"0.5067339\",\n \"0.5063233\",\n \"0.50564384\",\n \"0.50432706\",\n \"0.50253505\",\n \"0.5007381\",\n \"0.50047207\",\n \"0.49860212\",\n \"0.49807265\",\n \"0.49640658\",\n \"0.49588057\",\n \"0.49425665\",\n \"0.49398118\",\n \"0.4939152\",\n \"0.4903353\",\n \"0.4896105\",\n \"0.48862806\",\n \"0.48773336\",\n \"0.48724753\",\n \"0.4871625\",\n \"0.48604846\",\n \"0.48558402\",\n \"0.48557886\",\n \"0.48472548\",\n \"0.47984096\",\n \"0.47899085\",\n \"0.47844443\",\n \"0.47806883\",\n \"0.4779418\",\n \"0.47571245\",\n \"0.47568664\",\n \"0.47536853\",\n \"0.47251225\",\n \"0.4717016\",\n \"0.47156465\",\n \"0.47137913\",\n \"0.4706819\",\n \"0.4703834\",\n \"0.47021362\",\n \"0.4701958\",\n \"0.46910876\",\n \"0.46910876\",\n \"0.46792346\",\n \"0.46769845\",\n \"0.4673799\",\n \"0.46539754\",\n \"0.4653662\",\n \"0.46524203\",\n \"0.4647628\",\n \"0.4641663\",\n \"0.46391788\",\n \"0.46388954\",\n \"0.46338546\",\n \"0.46311343\",\n \"0.46267244\",\n \"0.46213996\",\n \"0.4620806\",\n \"0.46125838\",\n \"0.46009794\",\n \"0.45954114\",\n \"0.45902792\",\n \"0.45902005\",\n \"0.45860696\",\n \"0.45837045\",\n \"0.4580399\",\n \"0.45794365\",\n \"0.45656174\",\n \"0.4564012\",\n \"0.45572096\",\n \"0.45567447\",\n \"0.45564812\",\n \"0.45558158\",\n \"0.45550483\",\n \"0.45489296\",\n \"0.45451936\",\n \"0.45450497\",\n \"0.4543318\",\n \"0.45414683\",\n \"0.4541255\",\n \"0.45408008\",\n \"0.45405206\",\n \"0.4536776\",\n \"0.45366704\",\n \"0.45363688\",\n \"0.45346355\",\n \"0.45344806\",\n \"0.45319352\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5143988"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":79586,"cells":{"query":{"kind":"string","value":"scans the collections searching references (999C5x fields) and citations for items in the recid_list returns a 4 list of dictionaries that contains the citation information of cds records"},"document":{"kind":"string","value":"def get_citation_informations(recid_list, config):\n begin_time = os.times()[4]\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\n d_records_s = {} #dict of recid -> this_records_publication_info\n citation_informations = []\n\n write_message(\"config function \"+config.get(\"rank_method\", \"function\"), verbose=9)\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attribute called function\", alert_admin=True)\n #we cannot continue\n return [ {}, {}, {}, {} ]\n record_pri_number_tag = \"\"\n try:\n record_pri_number_tag = config.get(function, \"primary_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attribute primary_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n record_add_number_tag = \"\"\n try:\n record_add_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"additional_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute additional_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_number_tag = \"\"\n try:\n reference_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_tag = \"\"\n try:\n reference_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_pubinfo\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\n #037a: contains (often) the \"hep-ph/0501084\" tag of THIS record\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\n #088a: additional short identifier for the record\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\n p_reference_tag = tagify(parse_tag(reference_tag))\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\n #fields needed to construct the pubinfo for this record\n publication_pages_tag = \"\"\n publication_year_tag = \"\"\n publication_journal_tag = \"\"\n publication_volume_tag = \"\"\n publication_format_string = \"p v (y) c\"\n try:\n tag = config.get(function, \"pubinfo_journal_page\")\n publication_pages_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_year\")\n publication_year_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_title\")\n publication_journal_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_volume\")\n publication_volume_tag = tagify(parse_tag(tag))\n publication_format_string = config.get(function, \"pubinfo_journal_format\")\n except:\n pass\n\n #print values for tags for debugging\n if task_get_task_param('verbose') >= 9:\n write_message(\"tag values\")\n write_message(\"p_record_pri_number_tag \"+str(p_record_pri_number_tag))\n write_message(\"p_reference_tag \"+str(p_reference_tag))\n write_message(\"publication_journal_tag \"+str(publication_journal_tag))\n write_message(\"publication_format_string is \"+publication_format_string)\n done = 0 #for status reporting\n numrecs = len(recid_list)\n\n # perform quick check to see if there are some records with\n # reference tags, because otherwise get.cit.inf would be slow even\n # if there is nothing to index:\n if run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_tag[0:2],\n (p_reference_tag,)) or \\\n run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_number_tag[0:2],\n (p_reference_number_tag,)):\n for recid in recid_list:\n if (done % 10 == 0):\n task_sleep_now_if_required()\n #in fact we can sleep any time here\n\n if (done % 1000 == 0):\n mesg = \"get cit.inf done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n if recid in INTBITSET_OF_DELETED_RECORDS:\n # do not treat this record since it was deleted; we\n # skip it like this in case it was only soft-deleted\n # e.g. via bibedit (i.e. when collection tag 980 is\n # DELETED but other tags like report number or journal\n # publication info remained the same, so the calls to\n # get_fieldvalues() below would return old values)\n continue\n\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\n references_s = get_fieldvalues(recid, p_reference_tag)\n\n l_report_numbers = pri_report_numbers\n l_report_numbers.extend(add_report_numbers)\n d_reports_numbers[recid] = l_report_numbers\n\n if reference_report_numbers:\n write_message(str(recid)+\"'s report number values \"+str(l_report_numbers), verbose=9)\n d_references_report_numbers[recid] = reference_report_numbers\n\n references_s = get_fieldvalues(recid, p_reference_tag)\n if references_s:\n write_message(str(recid)+\"'s \"+str(p_reference_tag)+\" values \"+str(references_s), verbose=9)\n d_references_s[recid] = references_s\n\n #get a combination of\n #journal vol (year) pages\n if publication_pages_tag and publication_journal_tag and \\\n publication_volume_tag and publication_year_tag and publication_format_string:\n tagsvalues = {} #we store the tags and their values here\n #like c->444 y->1999 p->\"journal of foo\",v->20\n tagsvalues[\"p\"] = \"\"\n tagsvalues[\"y\"] = \"\"\n tagsvalues[\"c\"] = \"\"\n tagsvalues[\"v\"] = \"\"\n tmp = get_fieldvalues(recid, publication_journal_tag)\n if tmp:\n tagsvalues[\"p\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_volume_tag)\n if tmp:\n tagsvalues[\"v\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_year_tag)\n if tmp:\n tagsvalues[\"y\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_pages_tag)\n if tmp:\n #if the page numbers have \"x-y\" take just x\n pages = tmp[0]\n hpos = pages.find(\"-\")\n if hpos > 0:\n pages = pages[:hpos]\n tagsvalues[\"c\"] = pages\n #format the publ infostring according to the format\n publ = \"\"\n ok = 1\n for i in range (0, len(publication_format_string)):\n current = publication_format_string[i]\n #these are supported\n if current == \"p\" or current == \"c\" or current == \"v\" \\\n or current == \"y\":\n if tagsvalues[current]:\n #add the value in the string\n publ += tagsvalues[current]\n else:\n ok = 0\n break #it was needed and not found\n else:\n publ += current #just add the character in the format string\n if ok:\n write_message(\"d_records_s (publication info) for \"+str(recid)+\" is \"+publ, verbose=9)\n d_records_s[recid] = publ\n else:\n mesg = \"Warning: there are no records with tag values for \"\n mesg += p_reference_number_tag+\" or \"+p_reference_tag+\". Nothing to do.\"\n write_message(mesg)\n\n mesg = \"get cit.inf done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n citation_informations.append(d_reports_numbers)\n citation_informations.append(d_references_report_numbers)\n citation_informations.append(d_references_s)\n citation_informations.append(d_records_s)\n end_time = os.times()[4]\n write_message(\"Execution time for generating citation info from record: %.2f sec\" % \\\n (end_time - begin_time))\n return citation_informations"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_references(cls, pmids):\n\n references = cls.query.filter(cls.pmid.in_(pmids)).all()\n citations = {}\n\n for reference in references:\n citation_text = reference.authors + \". (\" + str(\n reference.year) + \"). \" + reference.title + \" \" + reference.journal + \".\"\n citations[reference.pmid] = citation_text\n\n return citations","def get_citations_ids_map(id_list):\n create_unverified_context()\n logging.debug('============== IN get_citations_ids_map: ================')\n logging.debug('============== ID LIST: ================')\n logging.debug(id_list)\n linked = {}\n for i in range(0, len(id_list)):\n handle = Entrez.elink(\n dbfrom=\"pubmed\", id=id_list[i], linkname=\"pubmed_pubmed_refs\")\n results = Entrez.read(handle)\n logging.debug('============== RESULTS: ================')\n logging.debug(results)\n handle.close()\n if len(results[0][\"LinkSetDb\"]) != 0:\n linked[id_list[i]] = [\n link[\"Id\"] for link in results[0][\"LinkSetDb\"][0][\"Link\"]\n ]\n logging.debug('============== LINKED ARTICLES: ================')\n logging.debug(linked)\n logging.debug('============== ARTICLE ID: ================')\n logging.debug(id_list[i])\n return linked","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def fill(self):\n if self.source == 'citations':\n url = self._scholarly.URLS(\"CITATIONPUB\").format(self.id_citations)\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\n\n if soup.find('a', class_='gsc_vcd_title_link'):\n self.bib['url'] = soup.find(\n 'a', class_='gsc_vcd_title_link')['href']\n\n for item in soup.find_all('div', class_='gs_scl'):\n key = item.find(class_='gsc_vcd_field').text\n val = item.find(class_='gsc_vcd_value')\n if key == 'Authors':\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\n elif key == 'Journal':\n self.bib['journal'] = val.text\n elif key == 'Volume':\n self.bib['volume'] = val.text\n elif key == 'Issue':\n self.bib['number'] = val.text\n elif key == 'Pages':\n self.bib['pages'] = val.text\n elif key == 'Publisher':\n self.bib['publisher'] = val.text\n elif key == 'Publication date':\n self.bib['year'] = arrow.get(val.text).year\n elif key == 'Description':\n if val.text[0:8].lower() == 'abstract':\n val = val.text[9:].strip()\n self.bib['abstract'] = val\n elif key == 'Total citations':\n self.id_scholarcitedby = re.findall(\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\n\n # number of citation per year\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\n self.cites_per_year = dict(zip(years, cites))\n\n if soup.find('div', class_='gsc_vcd_title_ggi'):\n self.bib['eprint'] = soup.find(\n 'div', class_='gsc_vcd_title_ggi').a['href']\n self._filled = True\n\n elif self.source == 'scholar':\n self.bib['add_to_lib'] = self.url_add_sclib\n\n try:\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\n bibtex = bibtex.find('pre').string\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\n self.bib['author_count'] = str(\n len(self.bib['author'].split('and')))\n self.bib['age'] = str(\n int(date.today().year) - int(self.bib['year']))\n except:\n # did not find year\n pass\n\n self._filled = True\n return self","def get_license_refs_dict(license_refs_list):\n license_ref_dict = {}\n if license_refs_list:\n for ref_dict in license_refs_list:\n license_ref_dict[ref_dict['licenseId']] = ref_dict['extractedText']\n return license_ref_dict","def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites","def find_references(doi):\n if doi is None:\n return None\n\n references = []\n if doi:\n response = requests.get(f\"https://opencitations.net/index/api/v1/references/{doi}\").json()\n if response:\n references = [{\"doi\": r['cited'].replace(\"coci =>\", \"\")} for r in response]\n\n if references:\n return references\n else:\n return None","def getCitations (self, doc, verbose = False):\r\n if verbose is True:\r\n print \"getting citations for document \" +str(doc) \r\n return self.citations[int(doc)]","def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\n selfdic, authorcitdic):\n insert_into_cit_db(reference_dic,\"reversedict\")\n insert_into_cit_db(citation_dic,\"citationdict\")\n insert_into_cit_db(selfcbdic,\"selfcitedbydict\")\n insert_into_cit_db(selfdic,\"selfcitdict\")\n\n for a in authorcitdic.keys():\n lserarr = (serialize_via_marshal(authorcitdic[a]))\n #author name: replace \" with something else\n a.replace('\"', '\\'')\n a = unicode(a, 'utf-8')\n try:\n ablob = run_sql(\"select hitlist from rnkAUTHORDATA where aterm = %s\", (a,))\n if not (ablob):\n #print \"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\" , (a,lserarr)\n run_sql(\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\",\n (a,lserarr))\n else:\n #print \"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\"\"\" , (lserarr,a)\n run_sql(\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\",\n (lserarr,a))\n except:\n register_exception(prefix=\"could not read/write rnkAUTHORDATA aterm=\"+a+\" hitlist=\"+str(lserarr), alert_admin=True)","def ref_analyzer(citation_informations, initialresult, initial_citationlist,\n initial_referencelist,config, updated_rec_list ):\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attr function\", alert_admin=True)\n return {}\n\n pubrefntag = \"\"\n try:\n pubrefntag = config.get(function, \"reference_via_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_report_number\", alert_admin=True)\n return {}\n\n pubreftag = \"\"\n try:\n pubreftag = config.get(function, \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_pubinfo\", alert_admin=True)\n return {}\n\n #pubrefntag is often 999C5r, pubreftag 999C5s\n if task_get_task_param('verbose') >= 9:\n write_message(\"pubrefntag \"+pubrefntag)\n write_message(\"pubreftag \"+pubreftag)\n\n citation_list = initial_citationlist\n reference_list = initial_referencelist\n result = initialresult\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\n d_references_s = citation_informations[2]\n #dict of recid -> publication_infos_in_its_bibliography\n d_records_s = citation_informations[3] #recid -> its publication inf\n t1 = os.times()[4]\n\n write_message(\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\")\n for somerecid in updated_rec_list:\n try:\n del citation_list[somerecid]\n except KeyError:\n pass\n try:\n del reference_list[somerecid]\n except KeyError:\n pass\n\n write_message(\"Phase 1: d_references_report_numbers\")\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\n #meaning: rec 8 contains these in bibliography\n\n done = 0\n numrecs = len(d_references_report_numbers)\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n done = done+1\n\n for refnumber in refnumbers:\n if refnumber:\n p = refnumber\n f = 'reportnumber'\n #sanitise p\n p.replace(\"\\n\",'')\n #search for \"hep-th/5644654 or such\" in existing records\n rec_ids = get_recids_matching_query(p, f)\n\n if len(rec_ids) > 1:\n msg = \"Whoops: record '%d' report number value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_citer_cited(thisrecid, rec_ids[0])\n remove_from_missing(p)\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n # Citation list should have rec_ids[0] but check anyway\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n #append unless this key already has the item\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid)\n #and update result\n result[rec_ids[0]] += 1\n\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n else:\n #the reference we wanted was not found among our records.\n #put the reference in the \"missing\".. however, it will look\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\n #This should really be done in the next loop d_references_s\n #but the 999C5s fields are not yet normalized\n\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\n rectext = \"\" # print_record() call disabled to speed things up\n lines = rectext.split(\"\\n\")\n rpart = p #to be used..\n for l in lines:\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\n st = l.find('$s')\n if (st > 0):\n end = l.find('$', st)\n if (end == st):\n end = len(l)\n rpart = l[st+2:end]\n insert_into_missing(thisrecid, rpart)\n\n mesg = \"d_references_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t2 = os.times()[4]\n\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\n write_message(\"Phase 2: d_references_s\")\n done = 0\n numrecs = len(d_references_s)\n for thisrecid, refss in d_references_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n\n done = done+1\n\n for refs in refss:\n if refs:\n p = refs\n #remove the latter page number if it is like 67-74\n matches = re.compile(\"(.*)(-\\d+$)\").findall(p)\n if matches and matches[0]:\n p = matches[0][0]\n\n # check reference value to see whether it is well formed:\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"is not well formed; skipping it.\" % (thisrecid, p)\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n continue # skip this ill-formed value\n\n # look for reference value:\n rec_id = None\n try:\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\n except:\n rec_ids = None\n if len(rec_ids) > 1:\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_message(\"These match searching \"+p+\" in journal: \"+repr(rec_ids), verbose=9)\n #the refered publication is in our collection, remove\n #from missing\n remove_from_missing(p)\n else:\n #it was not found so add in missing\n insert_into_missing(thisrecid, p)\n #check citation and reference for this..\n if rec_ids and rec_ids[0]:\n #the above should always hold\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\n result[rec_ids[0]] += 1 #add count for this..\n\n #update reference_list accordingly\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n mesg = \"d_references_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t3 = os.times()[4]\n done = 0\n numrecs = len(d_reports_numbers)\n write_message(\"Phase 3: d_reports_numbers\")\n\n #search for stuff like CERN-TH-4859/87 in list of refs\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n for reportcode in reportcodes:\n if reportcode:\n rec_ids = []\n try:\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\n except:\n rec_ids = []\n\n if rec_ids:\n for recid in rec_ids:\n #normal checks..\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(recid):\n reference_list[recid] = []\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n\n #normal updates\n if not recid in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(recid)\n if not thisrecid in reference_list[recid]:\n reference_list[recid].append(thisrecid)\n\n mesg = \"d_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #find this record's pubinfo in other records' bibliography\n write_message(\"Phase 4: d_records_s\")\n done = 0\n numrecs = len(d_records_s)\n t4 = os.times()[4]\n for thisrecid, recs in d_records_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_records_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n p = recs.replace(\"\\\"\",\"\")\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\n write_message(\"These records match \"+p+\" in \"+pubreftag+\" : \"+str(rec_ids), verbose=9)\n if rec_ids:\n for rec_id in rec_ids:\n #normal checks\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(rec_id):\n reference_list[rec_id] = []\n\n if not rec_id in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(rec_id)\n if not thisrecid in reference_list[rec_id]:\n reference_list[rec_id].append(thisrecid)\n\n mesg = \"d_records_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n write_message(\"Phase 5: reverse lists\")\n\n #remove empty lists in citation and reference\n keys = citation_list.keys()\n for k in keys:\n if not citation_list[k]:\n del citation_list[k]\n\n keys = reference_list.keys()\n for k in keys:\n if not reference_list[k]:\n del reference_list[k]\n\n write_message(\"Phase 6: self-citations\")\n selfdic = {}\n #get the initial self citation dict\n initial_self_dict = get_cit_dict(\"selfcitdict\")\n selfdic = initial_self_dict\n #add new records to selfdic\n acit = task_get_option(\"author-citations\")\n if not acit:\n write_message(\"Self cite processing disabled. Use -A option to enable it.\")\n else:\n write_message(\"self cite and author citations enabled\")\n selfdic = get_self_citations(updated_rec_list, citation_list,\n initial_self_dict, config)\n #selfdic consists of\n #key k -> list of values [v1,v2,..]\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\n\n #create a reverse \"x cited by y\" self cit dict\n selfcitedbydic = {}\n for k in selfdic.keys():\n vlist = selfdic[k]\n for v in vlist:\n if selfcitedbydic.has_key(v):\n tmplist = selfcitedbydic[v]\n if not k in tmplist:\n tmplist.append(k)\n else:\n tmplist = [k]\n selfcitedbydic[v] = tmplist\n\n write_message(\"Getting author citations\")\n\n #get author citations for records in updated_rec_list\n initial_author_dict = get_initial_author_dict()\n authorcitdic = initial_author_dict\n acit = task_get_option(\"author-citations\")\n if not acit:\n print \"Author cites disabled. Use -A option to enable it.\"\n else:\n write_message(\"author citations enabled\")\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\n initial_author_dict, config)\n\n\n if task_get_task_param('verbose') >= 3:\n #print only X first to prevent flood\n tmpdict = {}\n tmp = citation_list.keys()[0:10]\n for t in tmp:\n tmpdict[t] = citation_list[t]\n write_message(\"citation_list (x is cited by y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(citation_list.keys())))\n tmp = reference_list.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = reference_list[t]\n write_message(\"reference_list (x cites y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(reference_list.keys())))\n tmp = selfcitedbydic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfcitedbydic[t]\n mesg = \"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\"\n mesg += str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfcitedbydic.keys())))\n tmp = selfdic.keys()[0:100]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfdic[t]\n mesg = \"selfdic (x cites y and one of the authors of x same as y's): \"+str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfdic.keys())))\n tmp = authorcitdic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = authorcitdic[t]\n write_message(\"authorcitdic (author is cited in recs): \"+str(tmpdict))\n write_message(\"size: \"+str(len(authorcitdic.keys())))\n insert_cit_ref_list_intodb(citation_list, reference_list,\n selfcitedbydic, selfdic, authorcitdic)\n\n t5 = os.times()[4]\n\n write_message(\"Execution time for analyzing the citation information generating the dictionary:\")\n write_message(\"... checking ref number: %.2f sec\" % (t2-t1))\n write_message(\"... checking ref ypvt: %.2f sec\" % (t3-t2))\n write_message(\"... checking rec number: %.2f sec\" % (t4-t3))\n write_message(\"... checking rec ypvt: %.2f sec\" % (t5-t4))\n write_message(\"... total time of ref_analyze: %.2f sec\" % (t5-t1))\n\n return result","def process_citation(style, reference_list, citation):\n processed_citation = Element(\"span\", attrib={\"class\":\"citation\"})\n\n for reference in reference_list:\n citeref = SubElement(processed_citation, \"span\")\n for style_node in style.citation.layout:\n process_node(citeref, style_node, style.macros, reference) \n\n return(processed_citation)","def mr2bib(id_list):\n d = mr2bib_dict(id_list)\n l = []\n for id in id_list:\n try:\n l.append(d[id])\n except:\n l.append(ReferenceErrorInfo(\"Not found\", id))\n\n return l","def find_book_dois_in_crossref(isbn_list):\n ret_value = {\n \"success\": False,\n \"dois\": []\n }\n if type(isbn_list) != type([]) or len(isbn_list) == 0:\n ret_value['error_msg'] = \"Parameter must be a non-empty list!\"\n return ret_value\n filter_list = [\"isbn:\" + isbn.strip() for isbn in isbn_list]\n filters = \",\".join(filter_list)\n api_url = \"https://api.crossref.org/works?filter=\"\n url = api_url + filters + \"&rows=500\"\n request = Request(url)\n request.add_header(\"User-Agent\", USER_AGENT)\n try:\n ret = urlopen(request)\n content = ret.read()\n data = json.loads(content)\n if data[\"message\"][\"total-results\"] == 0:\n ret_value[\"success\"] = True\n else:\n for item in data[\"message\"][\"items\"]:\n if item[\"type\"] in [\"monograph\", \"book\"] and item[\"DOI\"] not in ret_value[\"dois\"]:\n ret_value[\"dois\"].append(item[\"DOI\"])\n if len(ret_value[\"dois\"]) == 0:\n msg = \"No monograph/book DOI type found in Crossref ISBN search result ({})!\"\n raise ValueError(msg.format(url))\n else:\n ret_value[\"success\"] = True\n except HTTPError as httpe:\n ret_value['error_msg'] = \"HTTPError: {} - {}\".format(httpe.code, httpe.reason)\n except URLError as urle:\n ret_value['error_msg'] = \"URLError: {}\".format(urle.reason)\n except ValueError as ve:\n ret_value['error_msg'] = str(ve)\n return ret_value","def documents(pmid_23982599, civic_aid6_document):\n return [pmid_23982599, civic_aid6_document]","def compute_species_from_citation_linking(citelist: list) -> None:\n # for i, cite in enumerate(tqdm(citelist)):\n unrecorded_xrefs = []\n recorded_refs = set()\n for i, cite in enumerate(citelist):\n recorded_refs.add(cite.cite_key)\n if cite.actual == \"=\":\n cname = \"\"\n crossnames = collections.Counter()\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n cname = tmp.name\n crossnames.update([tmp.actual])\n if len(crossnames) == 0:\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\n elif len(crossnames) == 1:\n cite.actual = list(crossnames.keys())[0]\n else:\n # find name(s) with largest count\n mcnt = max(crossnames.values())\n keylist = []\n for key in crossnames:\n if crossnames[key] == mcnt:\n keylist.append(key)\n\n if len(keylist) == 1:\n cite.actual = keylist[0]\n else:\n cite_name = cite.name.lower()\n while cite_name.find(\" \") > -1:\n cite_name = cite_name[cite_name.find(\" \")+1:]\n while cname.find(\" \") > -1:\n cname = cname[cname.find(\" \")+1:]\n\n if cite_name in keylist:\n cite.actual = cite_name\n elif cname in keylist:\n cite.actual = cname\n else:\n cite.actual = keylist[0]\n\n if cite.name_note == \".\":\n cite.name_note = \"in part\"\n else:\n cite.name_note = \"in part; \" + cite.name_note\n\n for x in unrecorded_xrefs:\n if x[1] in recorded_refs:\n report_error(\"Reference {} ({}) does not appear until after citation from {} ({})\".format(x[1], x[3],\n x[0], x[2]))","def parse_references(pubmed_article, reference_list):\n references = []\n reference_list_data = pubmed_article.find(\"PubmedData/ReferenceList\")\n if reference_list_data is not None:\n for ref in reference_list_data.findall(\"Reference\"):\n citation = ref.find(\"Citation\")\n if citation is not None:\n if citation.text is not None:\n citation = citation.text.strip()\n else:\n citation = \"\"\n else:\n citation = \"\"\n article_ids = ref.find(\"ArticleIdList\")\n pmid = (\n article_ids.find('ArticleId[@IdType=\"pubmed\"]')\n if article_ids is not None\n else None\n )\n if pmid is not None:\n if pmid.text is not None:\n pmid = pmid.text.strip()\n else:\n pmid = \"\"\n else:\n pmid = \"\"\n references.append({\"citation\": citation, \"pmid\": pmid})\n\n if reference_list:\n return references\n else:\n references = \";\".join(\n [ref[\"pmid\"] for ref in references if ref[\"pmid\"] != \"\"]\n )\n return references","def retrieve(dbxrefs, basics=True, hierarchy=True, wikipedia=True, literature=True, cross_references=True, overlaps=True):\n resolved = dbxref.resolver.resolve(dbxrefs, check_existence=False)\n documents = []\n for entry in resolved:\n # Construct URL for retrieve\n json_url = entry['locations']['json'][0]\n logger.debug('URL: %s', json_url)\n r = requests.get(json_url)\n logger.debug('Content: %s', r.text)\n ipro = json.loads(r.text)\n\n # Parse retrieved json file by selected Options\n output = {\"id\": entry[\"dbxref\"]}\n if basics:\n try:\n output.update(accession=ipro[\"metadata\"][\"accession\"], entry_type=ipro[\"metadata\"][\"type\"],\n description=ipro[\"metadata\"][\"description\"], counters=ipro[\"metadata\"][\"counters\"],\n entry_id=ipro[\"metadata\"][\"entry_id\"], name=ipro[\"metadata\"][\"name\"],\n source_database=ipro[\"metadata\"][\"source_database\"])\n except KeyError:\n logger.warning(\"One or more basic information were not available for the given entry. Please check your output.\")\n if hierarchy:\n try:\n output.update(hierarchy=ipro[\"metadata\"][\"hierarchy\"])\n except KeyError:\n logger.warning(\"Hierarchy information was not available for the given entry.\")\n if wikipedia:\n try:\n output.update(wikipedia=ipro[\"metadata\"][\"wikipedia\"])\n except KeyError:\n logger.warning(\"Wikipedia articles were not available for the given entry.\")\n if literature:\n try:\n output.update(literature=ipro[\"metadata\"][\"literature\"])\n except KeyError:\n logger.warning(\"Literature was not available for the given entry.\")\n if cross_references:\n try:\n output.update(cross_references=ipro[\"metadata\"][\"cross_references\"])\n except KeyError:\n logger.warning(\"Cross_references were not available for the given entry.\")\n if overlaps:\n try:\n output.update(overlaps=ipro[\"metadata\"][\"overlaps_with\"])\n except KeyError:\n logger.warning(\"Overlap information was not available for the given entry.\")\n documents.append(output)\n return documents","def results(self):\n out = []\n fields = 'eid doi pii pubmed_id title subtype creator afid affilname '\\\n 'affiliation_city affiliation_country author_count '\\\n 'author_names author_ids author_afids coverDate '\\\n 'coverDisplayDate publicationName issn source_id eIssn '\\\n 'aggregationType volume issueIdentifier article_number '\\\n 'pageRange description authkeywords citedby_count '\\\n 'openaccess fund_acr fund_no fund_sponsor'\n doc = namedtuple('Document', fields)\n for item in self._json:\n info = {}\n # Parse affiliations\n try:\n info[\"affilname\"] = _join(item['affiliation'], 'affilname')\n info[\"afid\"] = _join(item['affiliation'], 'afid')\n info[\"aff_city\"] = _join(item['affiliation'], 'affiliation-city')\n info[\"aff_country\"] = _join(item['affiliation'],\n 'affiliation-country')\n except KeyError:\n pass\n # Parse authors\n try:\n # Deduplicate list of authors\n authors = _deduplicate(item['author'])\n # Extract information\n surnames = _replace_none([d['surname'] for d in authors])\n firstnames = _replace_none([d['given-name'] for d in authors])\n info[\"auth_names\"] = \";\".join([\", \".join([t[0], t[1]]) for t in\n zip(surnames, firstnames)])\n info[\"auth_ids\"] = \";\".join([d['authid'] for d in authors])\n affs = []\n for auth in authors:\n aff = listify(_deduplicate(auth.get('afid', [])))\n affs.append('-'.join([d['$'] for d in aff]))\n info[\"auth_afid\"] = (';'.join(affs) or None)\n except KeyError:\n pass\n date = item.get('prism:coverDate')\n if isinstance(date, list):\n date = date[0].get('$')\n new = doc(article_number=item.get('article-number'),\n title=item.get('dc:title'), fund_sponsor=item.get('fund-sponsor'),\n subtype=item.get('subtype'), issn=item.get('prism:issn'),\n creator=item.get('dc:creator'), affilname=info.get(\"affilname\"),\n author_names=info.get(\"auth_names\"), doi=item.get('prism:doi'),\n coverDate=date, volume=item.get('prism:volume'),\n coverDisplayDate=item.get('prism:coverDisplayDate'),\n publicationName=item.get('prism:publicationName'),\n source_id=item.get('source-id'), author_ids=info.get(\"auth_ids\"),\n aggregationType=item.get('prism:aggregationType'),\n issueIdentifier=item.get('prism:issueIdentifier'),\n pageRange=item.get('prism:pageRange'),\n author_afids=info.get(\"auth_afid\"), fund_no=item.get('fund-no'),\n affiliation_country=info.get(\"aff_country\"),\n citedby_count=item.get('citedby-count'),\n openaccess=item.get('openaccess'), eIssn=item.get('prism:eIssn'),\n author_count=item.get('author-count', {}).get('$'),\n affiliation_city=info.get(\"aff_city\"), afid=info.get(\"afid\"),\n description=item.get('dc:description'), pii=item.get('pii'),\n authkeywords=item.get('authkeywords'), eid=item.get('eid'),\n fund_acr=item.get('fund-acr'), pubmed_id=item.get('pubmed-id'))\n out.append(new)\n return out or None","def mr2bib_dict(key_list):\n keys = []\n d = {}\n\n # validate keys\n for key in key_list:\n if is_valid(key):\n keys.append(key)\n else:\n d[key] = ReferenceErrorInfo(\"Invalid Mathematical Reviews identifier\", key)\n\n if len(keys) == 0:\n return d\n\n # make the api call\n entries = {}\n for key in keys:\n try:\n entry = mr_request(key)\n d[key] = Reference(entry)\n except NotFoundError as error:\n message, id = error.args\n d[key] = ReferenceErrorInfo(message, id)\n\n return d","def fetch(id_list):\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\n print(database)\n for sublist in id_list:\n with Entrez.efetch(db=database, id=','.join(sublist),\n retmode='xml') as handle:\n records = Entrez.read(handle)\n\n write_csv_file(records, output_file)","def __citationsFromFile(file):\n\n # Tokens for parsing\n titleToken = '#*'\n indexToken = '#index'\n citationToken = '#%'\n\n # Predicates for error checking\n noneNone = lambda *items: all([item is not None for item in items])\n allNone = lambda *items: all([item is None for item in items])\n\n # Next entry data\n title = None\n index = None\n citations = []\n\n # Basic stats\n withCitations = 0\n withoutCitations = 0\n totalPapers = 0\n\n for line in file:\n line = line.strip()\n\n # Parse entry, enforcing that data appears in title -> index -> citations order\n if line.startswith(titleToken):\n assert allNone(title, index) and len(citations) == 0\n title = line[len(titleToken):].strip('.')\n\n elif line.startswith(indexToken):\n assert noneNone(title) and allNone(index) and len(citations) == 0\n index = int(line[len(indexToken):])\n\n elif line.startswith(citationToken):\n assert noneNone(title, index)\n newCitationId = int(line[len(citationToken):])\n assert newCitationId >= 0\n citations.append(newCitationId)\n\n elif len(line) == 0:\n totalPapers += 1\n\n # Yield this entry if it has any citations,\n if noneNone(title, index):\n if len(citations) > 0:\n withCitations += 1\n yield title, index, citations\n else:\n withoutCitations += 1\n\n title = None\n index = None\n citations = []\n\n # Output some basic statistics about papers with/without citations\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\n print \"\\n\\nTotal Papers: %d\" % totalPapers\n print \" With References: %d (%2.2f%%)\\n Without References: %d (%2.2f%%)\" % (\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\n )","def get_doc_prov(j, gcis_url, refList, orgList):\n doc = ProvEsDocument()\n \n org = requests.get(j['href']).json()\n \n doc_attrs = [\n (\"prov:type\", 'gcis:organization'),\n (\"prov:label\", j['name']),\n (\"prov:location\", \"%s%s\"%(gcis_url, j['uri'])),\n (\"gcis:organization_type_identifier\", j['organization_type_identifier']),\n (\"gcis:country_code\", j['country_code']),\n ]\n orgID = 'bibo:%s' % j['identifier']\n doc.agent(orgID, doc_attrs)\n\n for child in org['children']:\n cOrgURI = child['organization']\n rel = child['relationship']\n\n cOrg = next(o for o in orgList if o['uri'] == cOrgURI)\n cOrgID = 'bibo:%s'%cOrg['identifier']\n\n #cOrgAttrs = [\n # (\"prov:type\", 'gcis:organization'),\n # (\"prov:label\", cOrg['name']),\n # (\"prov:location\", cOrg['uri']),\n # (\"gcis:organization_type_identifier\", cOrg['organization_type_identifier']),\n # (\"gcis:country_code\", cOrg['country_code']),\n # ]\n #doc.entity(cOrgID, cOrgAttrs)\n #doc.hadMember(orgID, cOrgID)\n #for parent in org['parents']:\n # pOrgURI = parent['organization']\n # rel = parent['relationship']\n # pOrg = next(o for o in orgList if o['uri'] == pOrgURI)\n # pOrgID = 'bibo:%s'%pOrg['identifier']\n # doc.hadMember(pOrgID, orgID)\n\n prov_json = json.loads(doc.serialize())\n\n return prov_json","def get_books(mapping, cidlist):\n havecache = _c.cacheisactive(_c.CACHE)\n\n # Don't want to keep hammering their servers, so check if available\n if havecache:\n cache, nocache = _c.check(_c.CACHE, mapping, [parse_cid(cid) for cid in cidlist])\n\n BASE = \"http://cmu.verbacompare.com/comparison?id={}\"\n \n # If cache is available, still need to check for uncached stuff\n if havecache:\n sections = [cmu_to_verba(mapping, cid) for cid in nocache]\n else: \n sections = [cmu_to_verba(mapping, cid) for cid in cidlist] \n sections = [s for s in sections if s != False]\n \n verba_info = [cmu_to_verba(mapping, cid) for cid in cidlist] \n verba_info = [s for s in verba_info if s != False]\n \n if verba_info:\n verba_ids = [section['id'] for section in reduce(list.__add__, verba_info)]\n URL = BASE.format(\",\".join(verba_ids))\n \n if sections: \n print \"Downloading from {}\".format(URL)\n # Download and parse if needed \n parser = BeautifulSoup(requests.get(URL).content)\n raw_data = [el.getText() for el in parser.findAll(\"script\")\n if 'Verba.Compare' in el.getText()][0] \n \n # Parse the extracted JS into an AST to extract the correct variable\n tree = Parser().parse(raw_data)\n objects = ObjVisitor()\n # Oh god why\n objects.visit(tree)\n\n # Finally\n data = [json.loads(d) for d in [i for i in objects.vardump if \"isbn\" in i]]\n\n # Bring in the cached data if it exists, otherwise just initialize empty result\n if havecache and cache:\n print \"Grabbing cache...\"\n _courses = [_c.retrieve(_c.CACHE, cid, cmu_to_verba(mapping, cid)) for cid in cache]\n _courses = reduce(list.__add__, _courses)\n summary = {\n 'url': URL,\n 'courses': _courses\n }\n else: \n summary = {\n 'url': URL,\n 'courses': []\n }\n \n # If we had to grab anything, now put it into the result\n if sections:\n for course in data:\n if course.get('title'):\n info = {\n 'name': course['title'],\n 'instructor': course.get('instructor'),\n 'sections': course['name'],\n 'books': []\n }\n if 'books' in course:\n for book in course['books']:\n bookinfo = {\n 'title': book['title'],\n 'author': book['author'],\n 'isbn': book['isbn'],\n 'citation': book['citation'],\n 'required': book['required'].lower() == 'required',\n }\n info['books'].append(bookinfo) \n \n summary['courses'].append(info)\n \n if havecache:\n # Store in cache for future use\n _c.store(_c.CACHE, info)\n \n return summary","def get_doc_prov(j, gcis_url, refList):\n gcis_ns = \"https://gcis-search-stage.jpl.net:3000/gcis.owl#\"\n doc = ProvEsDocument()\n bndl = None\n \n#to get people attributed to, you need to grab article -> jornal_identifier -> look up in references\n# for ref in refList:\n# if ref['child_publication'] == j['uri']:\n \n\n\n doc_attrs = [\n (\"prov:type\", 'gcis:Article'),\n (\"prov:label\", j['title']),\n (\"prov:location\", j['uri']),\n #(\"prov:wasAttributedTo\", j['']),\n ]\n doc.entity('bibo:%s' % j['identifier'], doc_attrs)\n\n prov_json = json.loads(doc.serialize())\n\n return prov_json","def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp': [] }\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n\n if author['dblp'].__contains__('publications'):\n publications['dblp'] = author['dblp']['publications']\n# for pub in author['dblp']['publications']:\n# print(pub)\n\n if author['cdblp'].__contains__('publications'):\n publications['cdblp'] = author['cdblp']['publications']\n# for pub in author['cdblp']['publications']:\n# print(pub)\n return publications","def get_coauthors_dict(name_url_list, schl_name):\n\n\tpaper_info_dict = {}\n\n\tfor name_url in name_url_list:\n\t\t# Get a sub papers_info dictfor an individual author\n\t\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\n\t\t# add sub dict to full dict\n\t\tpaper_info_dict.update(papers_info)\n\t\n\tif \"Humanities\" in schl_name:\n\t\tschl_name = \"School of Humanities\"\n\n\treturn paper_info_dict","def _FindBibEntries(self):\n bibs = \" \".join(glob.glob(\"*.bib\"))\n cat_process = subprocess.Popen(shlex.split(\"cat %s\" % bibs),\n stdout=subprocess.PIPE)\n grep_process = subprocess.Popen(shlex.split(\"grep ^@\"),\n stdin=cat_process.stdout,\n stdout=subprocess.PIPE)\n cat_process.stdout.close()\n grep2_process = subprocess.Popen(shlex.split(\"grep -vi @string\"),\n stdin=grep_process.stdout,\n stdout=subprocess.PIPE)\n grep_process.stdout.close()\n\n lines = grep2_process.communicate()[0]\n\n ret = []\n for l in lines.split(\"\\n\"):\n ret.append(responses.BuildCompletionData(\n re.sub(r\"@([A-Za-z]*)\\s*{\\s*([^,]*),.*\", r\"\\2\", l)\n )\n )\n return ret","def extract_all_references(dig_parent_dir, readfile):\n dig_parent_path_obj = Path(dig_parent_dir)\n extracted = {\"refs\": {}, \"hrefsToRefs\": {}}\n for split_page_num in [282, 283, 284]:\n split_page_dir = dig_parent_path_obj / \"dig/html/split\"\n refs_html = readfile(\n \"report\" + str(split_page_num) + \"b.html\", split_page_dir\n )\n data = extract_references_page(refs_html)\n extracted['refs'].update(data['refs'])\n extracted['hrefsToRefs'].update(data['hrefsToRefs'])\n\n return extracted","def getcitations(self, pmcid, source):\n import requests\n r = requests.get((self.citationurl).format(source=source, pmcid=pmcid))\n return r.content","def getcontacts():\n contacts = {}\n\n try:\n #get list of contact ids\n contactids = r.smembers(\"contacts\")\n\n #for each contact id get data\n for contactid in contactids:\n contacts.update(_getcontact(str(contactid)))\n return contacts\n except:\n print \"Unexpected error:\", sys.exc_info()[0]\n raise","def extract_references(text, preprocessor, model):\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\n reference_text = reference_section if has_reference_section else non_reference_section\n fragment = preprocessor(reference_text)\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\n\n buffer = ''\n found_citations = []\n in_citation = False\n for char, pred in zip(fragment.text, out[0, :]):\n if pred == 2:\n in_citation = True\n if pred == 3:\n in_citation = False\n if pred in [2, 3]:\n if buffer:\n found_citations.append(buffer)\n buffer = char\n elif pred == 1:\n buffer += char\n elif pred == 0:\n if in_citation:\n buffer += char\n else:\n if buffer:\n found_citations.append(buffer)\n buffer = ''\n return found_citations","def getConcertsByID(mbID, loc, min, max):\n \n skID = 'musichackdayboston'\n\n # fixme: use proper python URL helpers for this.\n if loc:\n myURL='http://api.songkick.com/api/3.0/artists/mbid:%s/events.json?apikey=%s&location=%s&min_date=%s&max_date=%s' % (mbID, skID, loc, min, max )\n else:\n myURL='http://api.songkick.com/api/3.0/artists/mbid:%s/events.json?apikey=%s&min_date=%s&max_date=%s' % (mbID, skID, min, max )\n\n data = urllib.urlopen(myURL).read()\n data = json.loads(data)\n \n \"\"\"\n format for this data:\n resultsPage\n totalEntries\n page (maximum 50 entries per page, navigated with &page=n)\n results\n event\n type (concert, festival)\n popularity\n status\n uri\n location\n lat\n long\n city\n start\n date\n time\n datetime\n venue\n lat\n uri\n lng\n displayName\n id\n metroArea\n state\n displayName\n country\n displayName\n displayName\n id\n displayName (pretty string for concert)\n performance (may be a list of artists if a festival)\n billingIndex\n billing\n displayName (for the band)\n id\n artist\n displayName\n identifier\n href\n mbid\n id\n id\n \"\"\"\n\n concerts = {}\n\n # fixme: totally disregarding case where results span multiple pages.\n # no matches will return an empty results set.\n if data[u'resultsPage'][u'results']:\n for concert in data[u'resultsPage'][u'results'][u'event']:\n # not extracting mbID from the returned JSON, because performance is \n # a list which can contain a number of artists, if a festival, etc.\n concerts[concert[u'id']] = {u'date':concert[u'start'][u'date'], \n u'venue':concert[u'venue'][u'displayName'],\n u'name':concert[u'displayName'],\n u'mbid':mbID}\n \n\n return concerts","def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\n publications = { 'cdblp': [], 'dblp': [] }\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\n for cdblp_pub in pub1.get('cdblp', []):\n authors = set(cdblp_pub.get('authors', []))\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\n publications['cdblp'].append(cdblp_pub)\n\n for dblp_pub in pub1.get('dblp', []):\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\n publications['dblp'].append(dblp_pub)\n\n return publications","def get_ill_borrowing_requests(document_pid):\n referenced = []\n brw_req_record_cls = current_ils_ill.borrowing_request_record_cls\n brw_req_search_cls = current_ils_ill.borrowing_request_search_cls\n search = brw_req_search_cls().search_by_document_pid(document_pid)\n for hit in search.scan():\n brw_req = brw_req_record_cls.get_record_by_pid(hit[\"pid\"])\n referenced.append(dict(pid_type=BORROWING_REQUEST_PID_TYPE, record=brw_req))\n return referenced","def find_cited_by(doi):\n if doi is None:\n return None\n\n citations = []\n if doi:\n response = requests.get(f\"https://opencitations.net/index/api/v1/citations/{doi}\").json()\n if response:\n citations = [{\"doi\": r['citing'].replace(\"coci =>\", \"\")} for r in response]\n if citations:\n return citations\n else:\n return None","def get_citations(self):\n url = self._get_url() + 'citations'\n citations = self._request(url)\n return citations.json()","def populate_crossreference_dictionary(self):\n master_crossreference_dictionary = dict()\n\n # If additional crossreferences need to be used to find interactors, they can be added here.\n # Use the crossreference prefix as the dictionary name.\n # Also add a regex entry to the resolve_identifier function.\n master_crossreference_dictionary['UniProtKB'] = dict()\n master_crossreference_dictionary['ENSEMBL'] = dict()\n master_crossreference_dictionary['NCBI_Gene'] = dict()\n master_crossreference_dictionary['RefSeq'] = dict()\n\n for key in master_crossreference_dictionary:\n self.logger.info('Querying for %s cross references.', key)\n with Neo4jHelper().run_single_parameter_query(self.query_xrefs, key) as result:\n for record in result:\n cross_ref_record = None\n # Modify the cross reference ID to match the PSI MITAB format if necessary.\n # So far, this is just converting 'NCBI_Gene' to 'entrez gene/locuslink'.\n if record['cr.globalCrossRefId'].startswith('NCBI_Gene'):\n cross_ref_record_split = record['cr.globalCrossRefId'].split(':')[1]\n cross_ref_record = 'entrez gene/locuslink:' + cross_ref_record_split\n else:\n cross_ref_record = record['cr.globalCrossRefId']\n\n # The crossreference dictionary is a list of genes\n # linked to a single crossreference.\n # Append the gene if the crossref dict entry exists.\n # Otherwise, create a list and append the entry.\n if cross_ref_record.lower() in master_crossreference_dictionary[key]:\n master_crossreference_dictionary[key][cross_ref_record.lower()].append(record['g.primaryKey'])\n else:\n master_crossreference_dictionary[key][cross_ref_record.lower()] = []\n master_crossreference_dictionary[key][cross_ref_record.lower()].append(record['g.primaryKey'])\n\n # The ids in PSI-MITAB files are lower case, hence the .lower() used above.\n\n return master_crossreference_dictionary","def process_and_dispatch(self):\n references = []\n for raw_block_references in self.raw_references:\n bibcode = raw_block_references['bibcode']\n block_references = raw_block_references['block_references']\n item_nums = raw_block_references.get('item_nums', [])\n\n parsed_references = []\n for i, reference in enumerate(block_references):\n reference = self.latex_reference.cleanup(reference)\n logger.debug(\"confTEX: parsing %s\" % reference)\n parsed_references.append(self.merge({'refstr': reference, 'refraw': reference}, self.any_item_num(item_nums, i)))\n\n references.append({'bibcode': bibcode, 'references': parsed_references})\n logger.debug(\"%s: parsed %d references\" % (bibcode, len(references)))\n\n return references","def extract_references(content):\n references = {}\n content = content.replace(\"\\n\", \"\\\\n\")\n matches = re.findall('(\\[[0-9]+\\][^\\[]*?(?=\\[|Acknowledge|Fig|Table|Conclusion|pdf))', content)\n if matches:\n for match in matches:\n ref_id = get_reference_id(match)\n # No reference id exist -- skip it\n if ref_id != -1:\n value = match.replace('\\\\n', '\\n')\n references[ref_id] = value\n return references","def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\n dict, dict, dict, dict,\n dict, dict]:\n name_table = create_name_table(citelist)\n unique_names = list()\n nameset = set()\n # total_binomial_year_cnts = {}\n total_binomial_year_cnts = collections.Counter()\n for c in citelist:\n if c.name != \".\":\n clean = clean_name(c.name)\n if not clean.lower() in nameset:\n nameset |= {clean.lower()}\n unique_names.append(clean)\n y = refdict[c.cite_key].year()\n if y is not None:\n total_binomial_year_cnts.update([y])\n # if y in total_binomial_year_cnts:\n # total_binomial_year_cnts[y] += 1\n # else:\n # total_binomial_year_cnts[y] = 1\n unique_names.sort(key=lambda s: s.lower())\n\n # identify genera used per paper\n genera_per_paper = {}\n for c in citelist:\n if c.name != \".\":\n if c.cite_key not in genera_per_paper:\n genera_per_paper[c.cite_key] = set()\n genera_set = genera_per_paper[c.cite_key]\n genera_set |= {extract_genus(clean_name(c.name))}\n genus_cnts = {}\n for c in genera_per_paper:\n y = refdict[c].year()\n if y is not None:\n if init_data().start_year <= y <= init_data().current_year:\n genera_set = genera_per_paper[c]\n for genus in genera_set:\n genus = clean_genus(genus)\n if genus != \"\":\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\n init_data().current_year + 1)})[y] += 1\n # if genus not in genus_cnts:\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\n # init_data().current_year + 1)}\n # gcnts = genus_cnts[genus]\n # gcnts[y] += 1\n\n binomial_usage_cnts_by_year = {}\n binomial_location_applications = {}\n binomial_usage_cnts = {}\n for name in unique_names:\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\n if tmptotal > 0:\n binomial_usage_cnts[name] = tmptotal\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\n\n # specific_year_cnts = {}\n specific_year_cnts = collections.Counter()\n specific_usage_cnts_by_year = {}\n specific_location_applications = {}\n specific_usage_cnts = {}\n for name in specific_names:\n (specific_usage_cnts_by_year[name.name],\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\n if tmptotal > 0:\n specific_usage_cnts[name.name] = tmptotal\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\n binomial_location_applications)\n tmpkey = name.priority_source\n if tmpkey != \".\":\n y = refdict[tmpkey].year()\n if y is not None:\n specific_year_cnts.update([y])\n # if y in specific_year_cnts:\n # specific_year_cnts[y] += 1\n # else:\n # specific_year_cnts[y] = 1\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\n binomial_usage_cnts, specific_usage_cnts)","def parse_citations(s: str):\n print(\"parse_citations(%s)\" % s)\n if pandas.isnull(s) or not s:\n return []\n terms = [e.strip() for e in s.split(\";\")]\n print(\"parse_citations: \" + str(terms))\n out = []\n for term in terms:\n if term.startswith(\"PMID\"):\n out.append({\"db\": \"PubMed\", \"id\": term})\n else:\n raise RuntimeError(\"Unknown citation format: \" + term)\n return out","def parse_citation_web(doc_id, id_type='PMC'):\n\n doc_id_dict = convert_document_id(doc_id, id_type=id_type)\n pmc = doc_id_dict['pmc']\n link = \"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/\" % pmc\n page = requests.get(link)\n tree = html.fromstring(page.content)\n n_citations = extract_citations(tree)\n n_pages = int(n_citations/30) + 1\n\n pmc_cited_all = list() # all PMC cited\n citations = tree.xpath('//div[@class=\"rprt\"]/div[@class=\"title\"]/a/@href')[1::]\n pmc_cited = list(map(extract_pmc, citations))\n pmc_cited_all.extend(pmc_cited)\n if n_pages >= 2:\n for i in range(2, n_pages+1):\n link = \"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/?page=%s\" % (pmc, str(i))\n page = requests.get(link)\n tree = html.fromstring(page.content)\n citations = tree.xpath('//div[@class=\"rprt\"]/div[@class=\"title\"]/a/@href')[1::]\n pmc_cited = list(map(extract_pmc, citations))\n pmc_cited_all.extend(pmc_cited)\n pmc_cited_all = [p for p in pmc_cited_all if p is not pmc]\n dict_out = {'n_citations': n_citations,\n 'pmid': doc_id_dict['pmid'],\n 'pmc': re.sub('PMC', '', doc_id_dict['pmc']),\n 'doi': doc_id_dict['doi'],\n 'pmc_cited': pmc_cited_all}\n return dict_out","def connect_refs_to_species(species: list, citelist: list) -> dict:\n # create a dictionary with empty reference lists\n species_refs = {s.species: set() for s in species}\n # go through all citations\n # for c in tqdm(citelist):\n for c in citelist:\n if c.actual in species_refs:\n reflist = species_refs[c.actual]\n reflist |= {c.cite_key}\n return species_refs","def fetch_fulltext(id_list):\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\n print(database)\n for sublist in id_list:\n with Entrez.efetch(db=database, id=','.join(sublist),\n retmode='xml') as handle:\n records = Entrez.read(handle)\n\n write_csv_fulltext(records, output_file)","def make_crossref_metadata(dataset: ObservatoryDataset) -> List[Dict]:\n\n records = []\n\n for paper in dataset.papers:\n # Create funders\n funders = []\n for funder in paper.funders:\n funders.append({\"name\": funder.name, \"DOI\": funder.doi, \"award\": None, \"doi_asserted_by\": None})\n\n # Add Crossref record\n records.append(\n {\n \"type\": paper.type,\n \"title\": [paper.title],\n \"DOI\": paper.doi,\n \"is_referenced_by_count\": len(paper.cited_by),\n \"issued\": {\n \"date_parts\": [paper.published_date.year, paper.published_date.month, paper.published_date.day]\n },\n \"funder\": funders,\n \"publisher\": paper.publisher.name,\n }\n )\n\n return records","def _process_dbxref(self):\n\n raw = '/'.join((self.rawdir, 'dbxref'))\n logger.info(\"processing dbxrefs\")\n line_counter = 0\n\n with open(raw, 'r') as f:\n filereader = csv.reader(f, delimiter='\\t', quotechar='\\\"')\n f.readline() # read the header row; skip\n for line in filereader:\n (dbxref_id, db_id, accession, version, description, url) = line\n # dbxref_id\tdb_id\taccession\tversion\tdescription\turl\n # 1\t2\tSO:0000000\t\"\"\n\n db_ids = { # the databases to fetch\n 50: 'PMID', # pubmed\n 68: 'RO', # obo-rel\n 71: 'FBdv', # FBdv\n 74: 'FBbt', # FBbt\n # 28:, # genbank\n 30: 'OMIM', # MIM\n # 38, # ncbi\n 75: 'ISBN', # ISBN\n 46: 'PMID', # PUBMED\n 51: 'ISBN', # isbn\n 52: 'SO', # so\n # 76, # http\n 77: 'PMID', # PMID\n 80: 'FBcv', # FBcv\n # 95, # MEDLINE\n 98: 'REACT', # Reactome\n 103: 'CHEBI', # Chebi\n 102: 'MESH', # MeSH\n 106: 'OMIM', # OMIM\n 105: 'KEGG-path', # KEGG pathway\n 107: 'DOI', # doi\n 108: 'CL', # CL\n 114: 'CHEBI', # CHEBI\n 115: 'KEGG', # KEGG\n 116: 'PubChem', # PubChem\n # 120, # MA???\n 3: 'GO', # GO\n 4: 'FlyBase', # FlyBase\n # 126, # URL\n 128: 'PATO', # PATO\n # 131, # IMG\n 2: 'SO', # SO\n 136: 'MESH', # MESH\n 139: 'CARO', # CARO\n 140: 'NCBITaxon', # NCBITaxon\n # 151, # MP ???\n 161: 'DOI', # doi\n 36: 'BDGP', # BDGP\n # 55, # DGRC\n # 54, # DRSC\n # 169, # Transgenic RNAi project???\n 231: 'RO', # RO ???\n 180: 'NCBIGene', # entrezgene\n # 192, # Bloomington stock center\n 197: 'UBERON', # Uberon\n 212: 'ENSEMBL', # Ensembl\n # 129, # GenomeRNAi\n 275: 'PMID', # PubMed\n 286: 'PMID', # pmid\n 264: 'HGNC',\n # 265: 'OMIM', # OMIM_Gene\n 266: 'OMIM', # OMIM_Phenotype\n 300: 'DOID', # DOID\n 302: 'MESH', # MSH\n 347: 'PMID', # Pubmed\n }\n\n if accession.strip() != '' and int(db_id) in db_ids:\n # scrub some identifiers here\n m = re.match(\n r'(doi|SO|GO|FBcv|FBbt_root|FBdv|FBgn|FBdv_root|FlyBase|FBbt):',\n accession)\n if m:\n accession = re.sub(m.group(1)+r'\\:', '', accession)\n elif re.match(\n r'(FlyBase miscellaneous CV|cell_lineprop|relationship type|FBgn$)',\n accession):\n continue\n elif re.match(r'\\:', accession): # starts with a colon\n accession = re.sub(r'\\:', '', accession)\n elif re.search(r'\\s', accession):\n # skip anything with a space\n # logger.debug(\n # 'dbxref %s accession has a space: %s',\n # dbxref_id, accession)\n continue\n\n if re.match(r'http', accession):\n did = accession.strip()\n else:\n prefix = db_ids.get(int(db_id))\n did = ':'.join((prefix, accession.strip()))\n if re.search(r'\\:', accession) and prefix != 'DOI':\n logger.warning(\n 'id %s may be malformed; skipping', did)\n\n self.dbxrefs[dbxref_id] = {db_id: did}\n\n elif url != '':\n self.dbxrefs[dbxref_id] = {db_id: url.strip()}\n else:\n continue\n\n # the following are some special cases that we scrub\n if int(db_id) == 2 \\\n and accession.strip() == 'transgenic_transposon':\n # transgenic_transposable_element\n self.dbxrefs[dbxref_id] = {db_id: 'SO:0000796'}\n\n line_counter += 1\n\n return","def to_cff_reference(bib_entry: pybtex.database.Entry) -> dict:\n\n def _cff_transform(cff_field, bib_value):\n if cff_field == \"type\":\n if bib_value == \"inproceedings\":\n return \"article\"\n elif bib_value == \"incollection\":\n return \"article\"\n elif cff_field == \"publisher\":\n return {\"name\": bib_value}\n elif cff_field == \"month\":\n try:\n return int(bib_value)\n except ValueError:\n return {\n \"jan\": 1,\n \"feb\": 2,\n \"mar\": 3,\n \"apr\": 4,\n \"may\": 5,\n \"jun\": 6,\n \"jul\": 7,\n \"aug\": 8,\n \"sep\": 9,\n \"oct\": 10,\n \"nov\": 11,\n \"dec\": 12,\n }[bib_value[:3].lower()]\n return bib_value\n\n cff_reference = {\n \"type\": _cff_transform(cff_field=\"type\", bib_value=bib_entry.type),\n \"authors\": [\n to_cff_person(person) for person in bib_entry.persons[\"author\"]\n ],\n }\n # Map BibTeX to CFF fields. This is just a subset of the most relevant\n # fields.\n fields = {\n \"doi\": \"doi\",\n \"edition\": \"edition\",\n \"isbn\": \"isbn\",\n \"license\": \"license\",\n \"month\": \"month\",\n \"number\": \"number\",\n \"pages\": \"pages\",\n \"publisher\": \"publisher\",\n \"title\": \"title\",\n \"url\": \"url\",\n \"version\": \"version\",\n \"volume\": \"volume\",\n \"year\": \"year\",\n \"booktitle\": \"collection-title\",\n }\n for bibtex_field, value in bib_entry.fields.items():\n bibtex_field = bibtex_field.lower()\n if bibtex_field in fields:\n cff_field = fields[bibtex_field]\n cff_reference[cff_field] = _cff_transform(\n cff_field=cff_field, bib_value=value\n )\n return cff_reference","def get_crefs_from( ea ):\r\n\tret = []\r\n\txrf = get_first_cref_from( ea )\r\n\tif xrf != BADADDR:\r\n\t\tret.append( xrf )\r\n\txrf = get_next_cref_from( ea, xrf )\r\n\twhile xrf != BADADDR:\r\n\t\tret.append( xrf )\r\n\t\txrf = get_next_cref_from( ea, xrf )\r\n\treturn ret","def get_citizens():\n response = table.scan()[\"Items\"]\n logger.info(\"All citizens returned\")\n return jsonify(response)","def list():\n\n return {\"cncs\": [{\"id\": id.split(\"/\")[-1]} for id in sorted(flask.current_app.redis.keys(\"/cnc/*\"))]}","def process_and_dispatch(self):\n references = []\n for raw_block_references in self.raw_references:\n bibcode = raw_block_references['bibcode']\n block_references = raw_block_references['block_references']\n item_nums = raw_block_references.get('item_nums', [])\n\n parsed_references = []\n for i, raw_reference in enumerate(block_references):\n reference = self.cleanup(raw_reference)\n\n logger.debug(\"arXivTXT: parsing %s\" % reference)\n parsed_references.append(self.merge({'refstr': reference, 'refraw': raw_reference}, self.any_item_num(item_nums, i)))\n\n references.append({'bibcode': bibcode, 'references': parsed_references})\n logger.debug(\"%s: parsed %d references\" % (bibcode, len(references)))\n\n return references","def recs_to_dict(keyfldname, recs):\n d = dict()\n for rec in recs:\n d[rec[keyfldname]] = rec\n return d","def return_dict_of_assigned_citations_classifications(self):\n for citation_instance in self.array_of_citations:\n citation_string = \"\"\n citation_string = citation_string.join(\n citation_instance.get_author()) + \\\n citation_instance.get_title()\\\n + citation_instance.get_journal()\n max_keyword_matches = 0\n for key in self.dict_of_keywords:\n current_key_matches = 0\n for keyword in self.dict_of_keywords.get(key):\n pattern = re.compile(keyword)\n if pattern.search(citation_string):\n current_key_matches += 1\n if current_key_matches > max_keyword_matches:\n max_keyword_matches = current_key_matches\n citation_instance.set_classification(key)\n return self.__generate_dict_of_keys_to_classification__()","def get_cuisine_recipes(search_cuisisnes, cuisines):\n cuisine_df = pd.DataFrame()\n for cuisine in search_cuisisnes:\n cuisine_dict = {}\n cuisine_dict['cuisine'] = cuisine\n cuisine_dict['source'] = 'BBC Good Food'\n cuisine_no_space = cuisine.lower().replace(' & ', '-').replace(' ', '-')\n recipes_cuisine_search = get_number_of_search_recipes(cuisine_no_space)\n cuisine_dict['pages'] = int(ceil(recipes_cuisine_search /\n NUMBER_OF_RECIPES_PER_SEARCH_PAGE))\n collection = False\n if cuisine in cuisines:\n cuisine_dict['pages'] += 1\n collection = True\n cuisine_dict['recipes_details'] = get_recipe_links(cuisine_no_space,\n cuisine_dict['pages']-1, collection)\n cuisine_dict['num_recipes'] = len(cuisine_dict['recipes_links'])\n print '#####'\n print \"Cuisine: %s \\t Number of recipes: %d \\t\\t Number of pages: %d\" \\\n % (cuisine, cuisine_dict['num_recipes'], cuisine_dict['pages'])\n coll.insert_one(cuisine_dict)\n cuisine_df = cuisine_df.append(pd.DataFrame.from_dict(cuisine_dict, orient='columns'),\n ignore_index=True)\n return cuisine_df","def get_citation_child_list(self):\n return self.address_list","def make_doi_institutions(author_list: AuthorList) -> List[Dict]:\n\n institutions = {}\n\n for author in author_list:\n # Institution\n inst = author.institution\n if inst.ror_id not in institutions:\n institutions[inst.ror_id] = {\n \"identifier\": inst.ror_id,\n \"types\": [inst.types],\n \"name\": inst.name,\n \"country\": inst.country,\n \"country_code\": inst.country_code,\n \"country_code_2\": inst.country_code_2,\n \"region\": inst.region,\n \"subregion\": inst.subregion,\n \"coordinates\": inst.coordinates,\n \"members\": [],\n }\n\n return to_affiliations_list(institutions)","def get_document_requests(document_pid):\n referenced = []\n docreq_search_cls = current_app_ils.document_request_search_cls\n docreq_record_cls = current_app_ils.document_request_record_cls\n for request in (\n docreq_search_cls().search_by_document_pid(document_pid=document_pid).scan()\n ):\n docreq = docreq_record_cls.get_record_by_pid(request[\"pid\"])\n referenced.append(dict(pid_type=DOCUMENT_REQUEST_PID_TYPE, record=docreq))\n return referenced","def createCID(self,\n project_set_list=[],\n header_image=True,\n list_cr_type=[],\n list_cr_status=[],\n list_cr_doamin=[],\n **kwargs):\n # Launch clock\n dico_timestamp={}\n dico_timestamp[\"begin_script\"] = datetime.now()\n for key in kwargs:\n self.__dict__[key] = kwargs[key]\n\n self.list_type_src = self.getSrcType()\n # get CID template name\n template_name = self._getTemplate(self.cid_type)\n # test presence of template\n if not os.path.exists(template_name):\n # warn user that template is not found\n self.ihm.popup('Specified template in docid.ini configuration file does not exists.')\n # abort cid generation\n return \"\",None\n self.ihm.log(\"Use template {:s}.\".format(template_name))\n if self.cid_type in (\"HCMR_PLD\",\"HCMR_BOARD\"):\n template_type = \"HCMR\"\n else:\n template_type = self.cid_type\n # Prepare output file\n docx_filename = self._setOuptutFilename(template_type)\n eoc_filename = None\n #\n # Documentations\n #\n self.ihm.log(\"Items query in progress...\")\n #self.ihm.defill()\n self._initTables()\n self._initTablesSrc()\n # Header for documents\n cid_type = self.getCIDType()\n if cid_type not in (\"SCI\"):\n cr_parent= True\n header = [\"Release:Project\",\"Document\",\"Issue\",\"Description\",\"Tasks\"]\n line_empty_input = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_cid_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n header_input = [\"Release:Project\",\"Document\",\"Issue\",\"Description\",\"Tasks\"]\n # Header for sources\n # FPGA\n header_soft_sources = [\"Release:Project\",\"Data\",\"Issue\",\"Tasks\",\"Change Request\"]\n header_prr = header\n line_sw_eoc_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_src_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_ccb_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_other_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n tbl_build = [header]\n fmt = self.setCellBorder([1000,2300,200,1000,500,500,500])\n fmt_ccb = self.setCellBorder([1000,2300,200,1000,1500])\n fmt_cid = self.setCellBorder([1000,2300,200,500,500])\n fmt_src = self.setCellBorder([2500,500,500,500,500,500])\n fmt_build = self.setCellBorder([3000,500,500,500,500])\n fmt_prr = self.setCellBorder([1000,2300,200,1000,500,500,500])\n fmt_small = self.setCellBorder([500,2000,500,500,500,500,500,500])\n fmt_tiny = self.setCellBorder([4000,500,500])\n fmt_tiny_sw = self.setCellBorder([3000,500,500,500,500])\n else:\n cr_parent= False\n line_empty_input = [\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_empty = [\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\"]\n header = [\"Title\",\"Reference\",\"Synergy Name\",\"Version\",\"Type\",\"Instance\",\"Release\",\"CR\"]\n header_input = [\"Title\",\"Reference\",\"Synergy Name\",\"Version\",\"Type\",\"Instance\",\"Release\"]\n # software\n header_soft_sources = [\"File Name\",\"Version\",\"Type\",\"Instance\",\"Release\",\"CR\"]\n header_prr = [\"Name\",\"Version\",\"Release\"]\n line_sw_eoc_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_cid_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_src_empty = [\"--\",\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_ccb_empty = [\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\"]\n line_other_empty = [\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\",\"--\"]\n tbl_build = [header_soft_sources]\n fmt = self.setCellBorder([1000,2300,200,500,500,500,500,500])\n fmt_ccb = self.setCellBorder([1000,2300,200,500,500,500,500])\n fmt_cid = self.setCellBorder([1000,2300,200,500,500])\n fmt_src = self.setCellBorder([2500,500,500,500,500,500])\n fmt_build = self.setCellBorder([2500,500,500,500,500,500])\n fmt_prr = self.setCellBorder([4000,500,500])\n fmt_small = self.setCellBorder([500,2500,500,500,500,500,500])\n fmt_tiny = self.setCellBorder([4000,500,500])\n fmt_tiny_sw = self.setCellBorder([3000,500,500,500,500])\n line_empty_three_columns = [\"--\",\"--\",\"--\"]\n table_input_data = []\n table_peer_reviews = []\n table_verif = []\n table_exclude = []\n tableau_sources_finduse = []\n tbl_build_finduse = []\n # Split table of items with sources\n items_filter_src = [self.sources_filter]\n items_filter_build = [self.build_filter]\n items_filter = [self.input_data_filter,\n self.peer_reviews_filter,\n self.verif_filter,\n self.exclude_filter]\n\n # Document part\n include_code = False\n if project_set_list != []:\n # Projects are available in GUI\n self.ihm.log(\"Use project set list to create CID for documents\",False)\n # Project set in GUI\n list_projects = project_set_list #self.ihm.project_set_list\n # List of projects from GUI\n #release_text,baseline_text,project_text = self.getContext(list_projects,\n # cid_type)\n else:\n project = self.project\n # [self.release,self.baseline,self.project]]\n if Tool.isAttributeValid(project):\n find_sub_projects = True\n list_projects = [[self.release,self.baseline,project]]\n prj_name, prj_version = self.getProjectInfo(project)\n self.findSubProjects(prj_name,\n prj_version,\n list_projects,\n mute = True)\n #print \"TBL\",list_projects\n for sub_release,sub_baseline,sub_project in list_projects:\n if project != sub_project:\n self.ihm.log(\"Find sub project {:s}\".format(sub_project))\n else:\n #Valid baseline ?\n if Tool.isAttributeValid(self.baseline):\n list_projects = self.getProjectsInBaseline(self.baseline)\n else:\n list_projects = [[self.release,\"\",\"\"]]\n # No project nor baseline\n # Patch: Looking for only release\n print \"INCLUDE_CODE\"\n include_code = True\n release_text,baseline_text,project_text = self.getContext(list_projects,\n cid_type)\n\n tbl_plans = [header]\n tbl_life_cycle_data = [header]\n tbl_verif = [header]\n tbl_peer_reviews = [header_prr]\n\n tbl_src = [header_soft_sources]\n header = [\"Ref\", \"Name\", \"Reference\", \"Version\", \"Description\"]\n self.tbl_cid = [header]\n tbl_input_data = [header_input]\n dico_tags = {\"part_number\":self.part_number,\n \"eoc_id\":\"\",\n \"checksum\":self.checksum,\n \"hw_sw_compatibility\":\"\"}\n #print \"Type CID\",self.cid_type\n if cid_type == \"HCMR_BOARD\" or cid_type == \"CID\":\n # HCMR BOARD or ECMR\n self._clearDicofound()\n tbl_upper_doc = [header]\n self.tbl_sas.append([\"R1\",\"Design Assurance Guidance for AEH\",\"DO-254/ED-80\",\"April 19th 2000\",\"\"])\n self.tbl_sas.append([\"R2\",\"Guidelines for Development of Civil Aircraft and Systems\",\"ARP-4754A/ED-79A\",\"December 6th 2010\",\"\"])\n\n link_id = 3\n list_llr_document = []\n for release,baseline,project in list_projects:\n if Tool.isAttributeValid(release) or Tool.isAttributeValid(baseline) or Tool.isAttributeValid(project):\n self.ihm.log(\"Use release \" + release,False)\n self.ihm.log(\"Use baseline \" + baseline,False)\n self.ihm.log(\"Use project \" + project,False)\n # Patch\n if project == \"All\":\n project = \"\"\n type_objects = self.list_type_doc\n type_objects.extend(self.list_type_src)\n type_objects.extend(self.list_type_prog)\n output = self.getArticles(type_objects,\n release,\n baseline,\n project,\n source = False,\n recursive=False)\n #print \"list_type_doc\",self.list_type_doc\n #print \"TEST HCI:\",output\n link_id = self.getBoardData(self.tableau_items,\n self.tbl_ccb,\n self.tbl_plans,\n self.tbl_sas,\n self.tbl_cid,\n output,\n link_id)\n # finduse\n l_table_input_data,l_table_peer_reviews,l_table_verif,l_table_exclude = self.getSpecificData(release,\n baseline,\n project,\n items_filter,\n False)\n l_tbl_program_file = self.getSpecificBuild(release,\n baseline,\n project,\n filters=items_filter_build)\n print \"l_tbl_program_file\",l_tbl_program_file\n tbl_build_finduse.extend(l_tbl_program_file)\n table_input_data.extend(l_table_input_data)\n table_peer_reviews.extend(l_table_peer_reviews)\n table_verif.extend(l_table_verif)\n table_exclude.extend(l_table_exclude)\n else:\n list_llr_document = []\n # self.tableau_items array is filled by invoking\n #\n # - _getAllDocuments (class BuildDoc)\n # - _getArticles (class Synergy)\n #\n table_life_cycle_data = []\n for tag in (\"INPUT_DATA\",\"CODE\",\"TEST\",\"SPEC\",\"CCB\"):\n if tag in self.dico_onw_user_cid_tags:\n table_life_cycle_data[tag] = self.dico_onw_user_cid_tags[tag]\n else:\n print \"X:\",tag\n print \"Y:\",self.dico_onw_user_cid_tags\n for release,baseline,project in list_projects:\n if Tool.isAttributeValid(release) or Tool.isAttributeValid(baseline) or Tool.isAttributeValid(project):\n self.ihm.log(\"Use release \" + release,False)\n self.ihm.log(\"Use baseline \" + baseline,False)\n self.ihm.log(\"Use project \" + project,False)\n\n baseline_code_only = self.isCodeOnly(baseline)\n project_code_only = self.isCodeOnly(project)\n\n if not baseline_code_only and not project_code_only:\n # baseline or project name begin with CODE\n # populates self.tbl_plans\n self._getAllDocuments(release,\n baseline,\n project)\n # finduse\n l_table_input_data,l_table_peer_reviews,l_table_verif,l_table_exclude = self.getSpecificData(release,\n baseline,\n project,\n items_filter,\n False)\n table_input_data.extend(l_table_input_data)\n table_peer_reviews.extend(l_table_peer_reviews)\n #print \"l_table_peer_reviews\",l_table_peer_reviews\n table_verif.extend(l_table_verif)\n table_exclude.extend(l_table_exclude)\n #print \"l_table_exclude\",l_table_exclude\n\n if cid_type in (\"SCI\"):\n #Input Data\n if self._is_array(table_life_cycle_data[\"INPUT_DATA\"][\"keyword\"]):\n for keyword in table_life_cycle_data[\"INPUT_DATA\"][\"keyword\"]:\n self.getObjectsPerFolder(keyword=keyword,\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"INPUT_DATA\"][\"table\"]\n )\n else:\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\"INPUT_DATA\"][\"keyword\"],\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"INPUT_DATA\"][\"table\"]\n )\n #CCB Minutes\n if self._is_array(table_life_cycle_data[\"CCB\"][\"keyword\"]):\n for keyword in table_life_cycle_data[\"CCB\"][\"keyword\"]:\n self.getObjectsPerFolder(keyword=keyword,\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"CCB\"][\"table\"])\n else:\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\"CCB\"][\"keyword\"],\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"CCB\"][\"table\"])\n # Specifications\n if self._is_array(table_life_cycle_data[\"SPEC\"][\"keyword\"]):\n for keyword in table_life_cycle_data[\"SPEC\"][\"keyword\"]:\n print \"keyword\",keyword\n self.getObjectsPerFolder(keyword=keyword,\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"SPEC\"][\"table\"],\n header = [\"Reference\",\"Synergy Name\",\"Version\",\"Type\",\"Instance\",\"Release\",\"CR\"],\n with_cr=True,\n cr_included=self.list_only_include_cr\n )\n else:\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\"SPEC\"][\"keyword\"],\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"SPEC\"][\"table\"],\n header = [\"Reference\",\"Synergy Name\",\"Version\",\"Type\",\"Instance\",\"Release\",\"CR\"],\n with_cr=True,\n cr_included=self.list_only_include_cr\n )\n # Get LLR\n design_keyword = \"S[w|W]DD\"\n self.ihm.log(\"Looking for Low Level requirement in SwDD folder ...\")\n #self.ihm.defill()\n self.getItemsInFolder(design_keyword,\n project=project,\n baseline=baseline,\n release=release,\n only_name=True,\n #exclude=[\"SwDD_\"],\n with_extension=True,\n mute=False,\n recur=True,\n converted_list=list_llr_document)\n\n\n\n\n if baseline_code_only or project_code_only or include_code:\n # Specific command for source code. TODO: To be optimized\n self.display_attr = ' -f \"%release;%name;%version;%task;%change_request;%type;%project;%instance\"' # %task_synopsis\n # self.display_attr is used in _getAllSources\n # _getAllSources populates self.tableau_src\n # Source code\n if Tool.isAttributeValid(project) and cid_type in (\"SCI\"):\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\"CODE\"][\"keyword\"],\n project = project,\n baseline = baseline,\n release = release,\n list_tbl = table_life_cycle_data[\"CODE\"][\"table\"],\n header = [\"Synergy Name\",\"Version\",\"Type\",\"Instance\",\"Release\",\"CR\"],\n with_cr=True,\n code=True,\n cr_included=self.list_only_include_cr\n )\n tableau_sources_finduse = table_life_cycle_data[\"CODE\"][\"table\"]\n else:\n tableau_sources_finduse = []\n self._getAllSources(release,\n baseline,\n project)\n\n # _getAllProg populates self.tableau_prog, self.tbl_sw_outputs and self.tbl_sw_eoc methods\n self._getAllProg(release,\n baseline,\n project)\n if cid_type not in (\"SCI\"):\n # TODO: Replace with self.getSpecificBuild(release,baseline,project,filters=[\"BIN\"])\n # Second chance to find sources in specific folder like SRC\n l_table_sources = self.getSpecificData(release,\n baseline,\n project,\n filters=items_filter_src,\n source=True)\n tableau_sources_finduse.extend(l_table_sources)\n # For software get build script in specific folder BUILD\n list_found_build_items = []\n l_tbl_program_file = self.getSpecificBuild(release,\n baseline,\n project,\n filters=[\"BUILD\"],\n list_found_items=list_found_build_items)\n tbl_build_finduse.extend(l_tbl_program_file)\n #print \"tbl_build_finduse:\",tbl_build_finduse\n #print \"list_found_build_items\",list_found_build_items\n list_found_items = []\n l_tbl_bin_file = self.getSpecificBuild(release,\n baseline,\n project,\n filters=[\"BIN\",\"EXE\"],\n list_found_items=list_found_items)\n\n eoc_filename = self.get_eoc_infos(list_found_items,dico_tags)\n\n if list_llr_document != []:\n for llr in list_llr_document:\n self.ihm.log(\"Found LLR: {:s}\".format(llr),display_gui=False)\n #self.ihm.defill()\n else:\n self.ihm.log(\"Found no LLR.\",display_gui=False)\n #self.ihm.defill()\n self.sortData(table_input_data=table_input_data,\n table_verif=table_verif,\n list_llr_document=list_llr_document,\n table_peer_reviews=table_peer_reviews,\n table_exclude=table_exclude,\n tbl_build_finduse=tbl_build_finduse,\n tableau_sources_finduse=tableau_sources_finduse,\n tbl_input_data=tbl_input_data,\n tbl_life_cycle_data=tbl_life_cycle_data,\n tbl_plans=tbl_plans,\n tbl_verif=tbl_verif,\n tbl_src=tbl_src,\n tbl_peer_reviews=tbl_peer_reviews,\n tbl_build=tbl_build)\n\n # TODO: Split input data and life cycle data for board and hardware\n # TODO: Make 2 query for SACR and HCR\n\n # Manage Change Requests and Problem Reports\n self.ihm.log(\"Change Request query in progress...\")\n ccb = CCB(self.ihm)\n\n dico_tableau_pr = {\"all\":[],\n \"open\":[],\n \"closed\":[]}\n\n ccb.getPR(dico_tableau_pr,\n #self.detect, # derprecated\n #self.target_release, # derprecated\n #self.cr_type, # derprecated\n cr_with_parent=cr_parent,\n list_cr_type=list_cr_type,\n list_cr_status=list_cr_status,\n list_cr_doamin=list_cr_doamin)\n\n self.tbl_sas = self._removeDoublons(self.tbl_sas)\n self.tbl_seci = self._removeDoublons(self.tbl_seci)\n self.tbl_scod = self._removeDoublons(self.tbl_scod)\n self.tbl_sw_eoc = self._removeDoublons(self.tbl_sw_eoc)\n self.tbl_sw_outputs = self._removeDoublons(self.tbl_sw_outputs)\n self.tableau_prog = self._removeDoublons(self.tableau_prog)\n self.tbl_stds = self._removeDoublons(self.tbl_stds)\n self.tbl_ccb = self._removeDoublons(self.tbl_ccb)\n self.tbl_program_file = self._removeDoublons(self.tbl_program_file)\n self.tbl_synthesis_file = self._removeDoublons(self.tbl_synthesis_file)\n\n if len(self.tbl_program_file) == 1:\n self.tbl_program_file.append(line_empty_three_columns)\n if len(self.tbl_synthesis_file) == 1:\n self.tbl_synthesis_file.append(line_empty_three_columns)\n if len(self.tbl_inspection_sheets) == 1:\n self.tbl_inspection_sheets.append(line_empty)\n if len(self.tbl_sas) == 1:\n self.tbl_sas.append(line_other_empty)\n if len(self.tbl_seci) == 1:\n self.tbl_seci.append(line_other_empty)\n if len(self.tbl_scod) == 1:\n self.tbl_scod.append(line_other_empty)\n if len(self.tableau_prog) == 1:\n self.tableau_prog.append(line_empty)\n if len(self.tableau_src) == 1:\n self.tableau_src.append(line_empty)\n if len(self.tbl_sw_outputs) == 1:\n line_sw_output_empty = [\"--\",\"--\",\"--\",\"--\",\"--\"]\n self.tbl_sw_outputs.append(line_sw_output_empty)\n if len(self.tbl_sw_eoc) == 1:\n self.tbl_sw_eoc.append(line_sw_eoc_empty)\n if len(self.tbl_build) == 1:\n self.tbl_build.append(line_src_empty)\n if len(self.tbl_sources) == 1:\n self.tbl_sources.append(line_src_empty)\n if len(tbl_plans) == 1:\n tbl_plans.append(line_other_empty)\n if len(self.tbl_stds) == 1:\n self.tbl_stds.append(line_other_empty)\n if len(self.tbl_ccb) == 1:\n self.tbl_ccb.append(line_ccb_empty)\n if len(self.tbl_constraint_file) == 1:\n self.tbl_constraint_file.append(line_empty_three_columns)\n if len(tbl_input_data) == 1:\n tbl_input_data.append(line_empty_input)\n if len(self.tbl_verif) == 1:\n self.tbl_verif.append(line_empty)\n if len(self.tbl_items_filtered) == 1:\n self.tbl_items_filtered.append(line_empty)\n if len(self.tbl_cid) == 1:\n self.tbl_cid.append(line_cid_empty)\n\n # Prepare information to put instead of tags\n title,subject = self.getSubject(self.system,\n self.item,\n self.component,\n template_type)\n doc_type = self.getTypeDocDescription(template_type)\n\n author,item,database,aircraft,item_description,ci_identification,program = self._getInfo()\n\n # Replace some tags\n self.protocol_compat = \"TDB\"\n self.data_compat = \"TDB\"\n if self.revision == \"\":\n version = \"1\"\n else:\n version = self.revision\n # Patch new ECMR template\n tbl_input_data_new = []\n tbl_life_cycle_data_new = []\n tbl_verif_new = []\n tbl_plans_new = []\n tbl_peer_reviews_new = []\n\n if cid_type == \"HCMR_BOARD\" or cid_type == \"CID\":\n fmt_life_cycle_data = self.formatDocTable(tbl_life_cycle_data_new,\n tbl_life_cycle_data)\n fmt_input_data = self.formatDocTable(tbl_input_data_new,\n tbl_input_data)\n fmt_verif = self.formatDocTable(tbl_verif_new,\n tbl_verif)\n fmt_plans = self.formatDocTable(tbl_plans_new,\n tbl_plans)\n fmt_prr = self.formatDocTable(tbl_peer_reviews_new,\n tbl_peer_reviews)\n\n list_tags = {\n 'TABLEITEMS':{'type':'tab','text':tbl_life_cycle_data_new,'fmt':fmt_life_cycle_data},\n 'TABLEINPUTDATA':{'type':'tab','text':tbl_input_data_new,'fmt':fmt_input_data},\n 'TABLEPEERREVIEWS':{'type':'tab','text':tbl_peer_reviews_new,'fmt':fmt_prr},\n 'TABLESOURCE':{'type':'tab','text':tbl_src,'fmt':fmt_src},\n 'TABLEBUILD':{'type':'tab','text':tbl_build,'fmt':fmt_build},\n 'TABLEEOC':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\n 'TABLEEOCID':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\n 'TABLEOUPUTS':{'type':'tab','text':self.tbl_sw_outputs,'fmt':fmt_tiny_sw},\n 'TABLEVERIF':{'type':'tab','text':tbl_verif_new,'fmt':fmt_verif},\n 'TABLEPLAN':{'type':'tab','text':tbl_plans_new,'fmt':fmt_plans},\n #'TABLESTD':{'type':'tab','text':self.tbl_stds,'fmt':fmt},\n #'TABLECCB':{'type':'tab','text':self.tbl_ccb,'fmt':fmt_ccb},\n #'TABLESAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt},\n #'TABLESECI':{'type':'tab','text':self.tbl_seci,'fmt':fmt},\n #'TABLESCOD':{'type':'tab','text':self.tbl_scod,'fmt':fmt},\n 'TABLECID':{'type':'tab','text':self.tbl_cid,'fmt':fmt_cid},\n 'TABLEPRS':{'type':'tab','text':dico_tableau_pr[\"all\"],'fmt':fmt_small},\n 'TABLECLOSEPRS':{'type':'tab','text':dico_tableau_pr[\"closed\"],'fmt':fmt_small},\n 'TABLEOPR':{'type':'tab','text':dico_tableau_pr[\"open\"],'fmt':fmt_small},\n 'PROGRAMING_FILE':{'type':'tab','text':self.tbl_program_file,'fmt':fmt_tiny},\n 'SYNTHESIS_FILES':{'type':'tab','text':self.tbl_synthesis_file,'fmt':fmt_tiny},\n 'CONSTRAINT_FILES':{'type':'tab','text':self.tbl_constraint_file,'fmt':fmt_tiny}\n }\n else:\n # Software\n tbl_life_cycle_data_new = self._removeDoublons(tbl_life_cycle_data)\n tbl_input_data_new = self._removeDoublons(tbl_input_data)\n tbl_verif_new = self._removeDoublons(tbl_verif)\n tbl_plans_new = self._removeDoublons(tbl_plans)\n tbl_peer_reviews_new = self._removeDoublons(tbl_peer_reviews)\n fmt_input_data = fmt\n fmt_life_cycle_data = fmt\n fmt_verif = fmt\n fmt_plans = fmt\n\n list_tags = {\n 'TABLEPEERREVIEWS':{'type':'tab','text':tbl_peer_reviews_new,'fmt':fmt_prr},\n 'TABLEBUILD':{'type':'tab','text':tbl_build,'fmt':fmt_build},\n 'TABLEEOC':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\n 'TABLEEOCID':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\n 'TABLEOUPUTS':{'type':'tab','text':self.tbl_sw_outputs,'fmt':fmt_tiny_sw},\n 'TABLEVERIF':{'type':'list','text':table_life_cycle_data[\"TEST\"][\"table\"],'fmt':fmt_verif},\n 'TABLEPLAN':{'type':'tab','text':tbl_plans_new,'fmt':fmt_plans},\n #'TABLESTD':{'type':'tab','text':self.tbl_stds,'fmt':fmt},\n #'TABLESAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt},\n #'TABLESECI':{'type':'tab','text':self.tbl_seci,'fmt':fmt},\n #'TABLESCOD':{'type':'tab','text':self.tbl_scod,'fmt':fmt},\n 'TABLECID':{'type':'tab','text':self.tbl_cid,'fmt':fmt_cid},\n 'TABLEPRS':{'type':'tab','text':dico_tableau_pr[\"all\"],'fmt':fmt_small},\n 'TABLECLOSEPRS':{'type':'tab','text':dico_tableau_pr[\"closed\"],'fmt':fmt_small},\n 'TABLEOPR':{'type':'tab','text':dico_tableau_pr[\"open\"],'fmt':fmt_small},\n 'PROGRAMING_FILE':{'type':'tab','text':self.tbl_program_file,'fmt':fmt_tiny},\n 'SYNTHESIS_FILES':{'type':'tab','text':self.tbl_synthesis_file,'fmt':fmt_tiny},\n 'CONSTRAINT_FILES':{'type':'tab','text':self.tbl_constraint_file,'fmt':fmt_tiny}\n }\n if table_life_cycle_data[\"INPUT_DATA\"][\"table\"] != []:\n list_tags.update({'TABLEINPUTDATA':{'type':'list','text':table_life_cycle_data[\"INPUT_DATA\"][\"table\"],'fmt':fmt_input_data}})\n else:\n list_tags.update({'TABLEINPUTDATA':{'type':'tab','text':tbl_input_data_new,'fmt':fmt_input_data}})\n if table_life_cycle_data[\"SPEC\"][\"table\"] != []:\n list_tags.update({'TABLEITEMS':{'type':'list','text':table_life_cycle_data[\"SPEC\"][\"table\"],'fmt':fmt_life_cycle_data}})\n else:\n list_tags.update({'TABLEITEMS':{'type':'tab','text':tbl_life_cycle_data_new,'fmt':fmt_life_cycle_data}})\n if table_life_cycle_data[\"CCB\"][\"table\"] != []:\n list_tags.update({'CCB':{'type':'list','text':table_life_cycle_data[\"CCB\"][\"table\"],'fmt':fmt_ccb}})\n else:\n list_tags.update({'CCB':{'type':'tab','text':self.tbl_ccb,'fmt':fmt_ccb}})\n if table_life_cycle_data[\"CODE\"][\"table\"] != []:\n list_tags.update({'TABLESOURCE':{'type':'list','text':table_life_cycle_data[\"CODE\"][\"table\"],'fmt':fmt_src}})\n else:\n list_tags.update({'TABLESOURCE':{'type':'tab','text':tbl_src,'fmt':fmt_src}})\n #print \"TEST1\",self.tbl_ccb\n #print 'TEST2',fmt_ccb\n if eoc_filename is not None:\n self.ihm.displayEOC_Info((dico_tags[\"hw_sw_compatibility\"],\n dico_tags[\"part_number\"],\n dico_tags[\"checksum\"],dico_tags[\"failed\"]))\n if dico_tags[\"part_number\"] == self.part_number:\n self.ihm.log(\"PN found in the {:s} EOC matches the PN given.\".format(eoc_filename),color=\"white\")\n else:\n self.ihm.log(\"Warning: PN found in the {:s} EOC mismatches the given PN.\".format(eoc_filename),color=\"red\")\n if dico_tags[\"checksum\"] == self.checksum:\n self.ihm.log(\"Checksum found in the {:s} EOC matches the given checksum.\".format(eoc_filename),color=\"white\")\n else:\n self.ihm.log(\"Warning: Checksum found in the {:s} EOC mismatches the given checksum.\".format(eoc_filename),color=\"red\")\n #if self.part_number != \"\":\n # dico_tags[\"part_number\"] = self.part_number\n #if self.checksum != \"\":\n # dico_tags[\"checksum\"] = self.checksum # Ex: 0x6b62\n list_tags.update({\n 'SYSTEM':{'type':'str','text':self.system,'fmt':{}},\n 'SUBJECT':{'type':'str','text':subject,'fmt':{}},\n 'DOCID':{'type':'str','text':\"Generated by doCID version {:s}\".format(VERSION),'fmt':{}},\n 'TYPE':{'type':'str','text':doc_type,'fmt':{}},\n 'TITLE':{'type':'str','text':title,'fmt':{}},\n 'CI_ID':{'type':'str','text':ci_identification,'fmt':{}},\n 'REFERENCE':{'type':'str','text':self.reference,'fmt':{}},\n 'ISSUE':{'type':'str','text':version,'fmt':{}},\n 'ITEM':{'type':'str','text':item,'fmt':{}},\n 'COMPONENT':{'type':'str','text':self.component,'fmt':{}},\n 'ITEM_DESCRIPTION':{'type':'str','text':item_description,'fmt':{}},\n 'DATE':{'type':'str','text':time.strftime(\"%d %b %Y\", time.localtime()),'fmt':{}},\n 'PROJECT':{'type':'str','text':project_text,'fmt':{}},\n 'RELEASE':{'type':'str','text':release_text,'fmt':{}},\n 'PREVIOUS_BASELINE':{'type':'str','text':self.previous_baseline,'fmt':{}},\n 'BASELINE':{'type':'str','text':baseline_text,'fmt':{}},\n 'WRITER':{'type':'str','text':author,'fmt':{}},\n 'PART_NUMBER':{'type':'str','text':dico_tags[\"part_number\"],'fmt':{}},\n 'BOARD_PART_NUMBER':{'type':'str','text':self.board_part_number,'fmt':{}},\n 'MAIN_BOARD_ PART_NUMBER':{'type':'str','text':\"\",'fmt':{}},\n 'MEZA_BOARD_ PART_NUMBER':{'type':'str','text':\"\",'fmt':{}},\n 'TGT_REL': {'type': 'str', 'text': self.target_release, 'fmt': {}},\n 'CHECKSUM':{'type':'str','text':dico_tags[\"checksum\"],'fmt':{}},\n 'DATABASE':{'type':'str','text':database,'fmt':{}},\n 'PROGRAM':{'type':'str','text':program,'fmt':{}},\n 'FUNCCHG':{'type':'par','text':self.func_chg,'fmt':{}},\n 'OPCHG':{'type':'par','text':self.oper_chg,'fmt':{}},\n 'PROTOCOL_COMPAT':{'type':'str','text':self.protocol_interface_index,'fmt':{}},\n 'DATA_COMPAT':{'type':'str','text':self.data_interface_index,'fmt':{}},\n 'HW_COMPAT':{'type':'str','text':dico_tags[\"hw_sw_compatibility\"],'fmt':{}},\n 'TOP_PLD_PRJ':{'type':'str','text':\"\",'fmt':{}}})\n for dico in self.dico_own_user_cid_tags[\"Documents\"]:\n #print \"X:\",dico\n tag = dico[\"tag\"]\n if tag in self.tbl_user:\n #tbl_cleanup = self._removeDoublons(self.tbl_user[tag])\n list_tags.update({tag:{'type':'tab','text':self.tbl_user[tag],'fmt':fmt}})\n for x,y in self.tbl_user.iteritems():\n print \"C:\",x,y\n if u'SAS' not in self.tbl_user:\n list_tags.update({'SAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt}})\n if header_image:\n image_name = self.get_image(self.aircraft)\n else:\n image_name = None\n self.ihm.docx_filename = docx_filename\n for x,y in list_tags.iteritems():\n print \"TAG:\",x,y\n self.docx_filename,exception = self._createDico2Word(list_tags,\n template_name,\n docx_filename,\n image_name)\n\n dico_timestamp[\"end_script\"] = datetime.now()\n duree_execution_script = dico_timestamp[\"end_script\"] - dico_timestamp[\"begin_script\"]\n self.ihm.log(\"Execution time for script: {:d} seconds\".format(duree_execution_script.seconds))\n return self.docx_filename,exception","def serve_recos(ids, ref_catalog):\r\n desc_list = []\r\n for desc_id in ids:\r\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\r\n return desc_list","def get_literature_recids_for_orcid(orcid):\n orcid_object = f'[{{\"schema\": \"ORCID\", \"value\": \"{orcid}\"}}]'\n # this first query is written in a way that can use the index on (json -> ids)\n\n try:\n author_rec_uuid = (\n db.session.query(RecordMetadata.id)\n .filter(\n type_coerce(RecordMetadata.json, JSONB)[\"ids\"].contains(orcid_object)\n )\n .one()\n .id\n )\n except NoResultFound:\n LOGGER.warning(\"No profile is associated with this account!\", orcid=orcid)\n return []\n\n author_record = (\n db.session.query(PersistentIdentifier)\n .filter(\n PersistentIdentifier.object_type == \"rec\",\n PersistentIdentifier.object_uuid == author_rec_uuid,\n PersistentIdentifier.pid_type == \"aut\",\n )\n .one()\n )\n\n author_recid = (\n author_record.pid_value\n if not author_record.is_redirected()\n else InspireRedirect.get_redirect(author_record).pid_value\n )\n\n query = Q(\"match\", authors__curated_relation=True) & Q(\n \"match\", **{\"authors.record.$ref\": author_recid}\n )\n search_by_curated_author = (\n LiteratureSearch()\n .query(\"nested\", path=\"authors\", query=query)\n .params(_source=[\"control_number\"], size=9999)\n )\n\n return [el[\"control_number\"] for el in search_by_curated_author]","def cite_report(self):\n\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\n print(\"number of citations = \", num_cites)\n\n for n, p in enumerate(self.mypapers):\n clean_tags = re.compile(\"<.*?>\")\n title = re.sub(clean_tags, \"\", p.title[0])\n tt = textwrap.wrap(title, 60)\n print(f\"{n+1:3} | {p.citation_count:4} {tt[0]}\")\n if len(tt) > 1:\n for line in tt[1:]:\n print(\"{:3} | {:4} {}\".format(\"\", \"\", line))\n print(\"{:3} | {:4} {}\".format(\"\", \"\", \"\"))","def make_open_citations(dataset: ObservatoryDataset) -> List[Dict]:\n\n records = []\n\n def make_oc_timespan(cited_date: pendulum.Date, citing_date: pendulum.Date):\n ts = \"P\"\n delta = citing_date - cited_date\n years = delta.in_years()\n months = delta.in_months() - years * 12\n\n if years > 0:\n ts += f\"{years}Y\"\n\n if months > 0 or years == 0:\n ts += f\"{months}M\"\n\n return ts\n\n def is_author_sc(cited_: Paper, citing_: Paper):\n for cited_author in cited_.authors:\n for citing_author in citing_.authors:\n if cited_author.name == citing_author.name:\n return True\n return False\n\n def is_journal_sc(cited_: Paper, citing_: Paper):\n return cited_.journal.name == citing_.journal.name\n\n for cited in dataset.papers:\n for citing in cited.cited_by:\n records.append(\n {\n \"oci\": \"\",\n \"citing\": citing.doi,\n \"cited\": cited.doi,\n \"creation\": citing.published_date.strftime(\"%Y-%m\"),\n \"timespan\": make_oc_timespan(cited.published_date, citing.published_date),\n \"journal_sc\": is_author_sc(cited, citing),\n \"author_sc\": is_journal_sc(cited, citing),\n }\n )\n\n return records","def process_bibliography(style, reference_list):\n processed_bibliography = Element(\"ol\", attrib={\"class\":\"bibliography\"})\n\n for reference in reference_list:\n ref = SubElement(processed_bibliography, \"li\", \n attrib={\"property\":\"dc:references\"})\n\n for style_node in style.bibliography.layout:\n process_node(ref, style_node, style.macros, reference) \n\n return(processed_bibliography)","def get_collection_details_sqlite(self, collid):\n\n # print \"collid : >> \",collid\n child_colls = self.get_child_collections(str(collid))\n coll_ids = [str(each_child[COL_NAME_COLL_COLLID]) for each_child in child_colls]\n coll_ids.append(collid)\n coll_ids = [str(x) for x in coll_ids]\n #Include collids of shared collections\n # print \"coll_ids :> 1\", coll_ids\n shared_ids = self.get_shared_collids(coll_ids)\n # print \"shared_ids :: \", shared_ids\n shared_collids = list(set([str(each_child[COL_NANE_SHARECOLL_CHILD_COLLID]) for each_child in shared_ids]))\n coll_ids.extend(shared_collids)\n # print \"shared_collids : \",shared_collids\n coll_ids = [str(x) for x in coll_ids]\n # print \"coll_ids :> 2\", coll_ids\n if len(shared_collids) != 0:\n #Get shared child collections\n for each_sharedid in shared_collids:\n shared_child_colls = self.get_child_collections(each_sharedid)\n shared_child_ids = [str(each_child[COL_NAME_COLL_COLLID]) for each_child in shared_child_colls]\n coll_ids.extend(str(shared_child_ids))\n coll_ids = [str(x) for x in coll_ids]\n # print \"coll_ids >> 3\", coll_ids\n return coll_ids","def get_coauthors(self):\n # Get number of authors to search for\n res = download(url=self.coauthor_link, accept='json')\n data = loads(res.text)['search-results']\n N = int(data.get('opensearch:totalResults', 0))\n # Store information in namedtuples\n fields = 'surname given_name id areas affiliation_id name city country'\n coauth = namedtuple('Coauthor', fields)\n coauthors = []\n # Iterate over search results in chunks of 25 results\n count = 0\n while count < N:\n params = {'start': count, 'count': 25}\n res = download(url=self.coauthor_link, params=params, accept='json')\n data = loads(res.text)['search-results'].get('entry', [])\n # Extract information for each coauthor\n for entry in data:\n aff = entry.get('affiliation-current', {})\n try:\n areas = [a['$'] for a in entry.get('subject-area', [])]\n except TypeError: # Only one subject area given\n areas = [entry['subject-area']['$']]\n new = coauth(surname=entry['preferred-name']['surname'],\n given_name=entry['preferred-name'].get('given-name'),\n id=entry['dc:identifier'].split(':')[-1],\n areas='; '.join(areas),\n affiliation_id=aff.get('affiliation-id'),\n name=aff.get('affiliation-name'),\n city=aff.get('affiliation-city'),\n country=aff.get('affiliation-country'))\n coauthors.append(new)\n count += 25\n return coauthors","def citations(request, format='csv'):\n \n filtered = False # make sure we're filtering by something\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\n \n \n # accession number\n if request.GET.get('gabi_acc_number'):\n filtered = True\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\n \n # species AND bentity\n if request.GET.get('species'):\n filtered = True\n if request.GET.get('species'): \n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\n if request.GET.get('bentity_id'):\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\n \n # lat and lon\n if request.GET.get('lat') and request.GET.get('lon'):\n filtered = True\n if request.GET.get('lat'):\n records = records.filter(lat=request.GET.get('lat'))\n if request.GET.get('lon'):\n records = records.filter(lon=request.GET.get('lon'))\n \n # status\n if request.GET.get('status'):\n records = records.filter(status=request.GET.get('status')[0].upper())\n \n \n # error message if the user didn't supply an argument to filter the records\n if not filtered: \n return errorResponse(\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\", format, {'records': []})\n \n \n # fetch all the bentitites at once, so we don't have to hit the database once for each record\n records = records.prefetch_related('bentity') \n \n output_objects = [{\n 'gabi_acc_number': r.gabi_acc_number,\n 'species': r.valid_species_name_id,\n 'bentity_id': r.bentity_id,\n 'bentity_name': r.bentity.bentity,\n 'status': r.status,\n 'type_of_data': r.type_of_data,\n 'lat': r.lat,\n 'lon': r.lon, \n 'citation': r.citation,\n } for r in records]\n \n \n \n if format == 'csv':\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\n \n else:\n return JSONResponse({'records': output_objects})","def find_authors(code):\n url = baseurl(code)\n page = req(url)\n soup = BeautifulSoup(page, 'lxml')\n addr = [t.attrs.get('content', None) \n for t in soup.find_all(\"meta\", {\"name\": \"citation_author_email\"})]\n \n # corresponding authors will have their email under another tag too\n corr = [t.find('a').attrs.get('href', None)\n for t in soup.find_all(None, {\"class\": \"author-corresp-email-link\"})]\n\n addr = [a for a in addr if a is not None]\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\n\n return dict(corr=list(set(corr)), all=list(set(addr)))","def list_publications(bib_format=\"dict\"):\n\n def get_bibtex(key, value):\n total_keys = [\n \"title\",\n \"journal\",\n \"volume\",\n \"issue\",\n \"number\",\n \"pages\",\n \"numpages\",\n \"year\",\n \"month\",\n \"publisher\",\n \"url\",\n \"doi\",\n \"issn\",\n ]\n bibtex_str = (\n \"@article{\"\n + key\n + \",\\n\"\n + \" author={\"\n + \" and \".join(value[\"author\"])\n + \"},\\n\"\n )\n for key in total_keys:\n if key in value.keys():\n bibtex_str += \" \" + key + \"={\" + value[key] + \"},\\n\"\n bibtex_str += \"}\\n\"\n return bibtex_str\n\n def get_apa(value):\n apa_str = \" & \".join(value[\"author\"])\n if \"year\" in value.keys():\n apa_str += \" (\" + value[\"year\"] + \"). \"\n if \"title\" in value.keys():\n apa_str += value[\"title\"] + \". \"\n if \"journal\" in value.keys():\n apa_str += value[\"journal\"] + \", \"\n if \"volume\" in value.keys():\n apa_str += value[\"volume\"] + \", \"\n if \"pages\" in value.keys():\n apa_str += value[\"pages\"] + \". \"\n if \"doi\" in value.keys():\n apa_str += \"doi: \" + value[\"doi\"] + \"\\n\"\n return apa_str\n\n publication_dict = s.publication_lst\n if bib_format.lower() == \"dict\":\n return publication_dict\n elif bib_format.lower() == \"bibtex\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_bibtex(key, value)\n return total_str\n elif bib_format.lower() == \"apa\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_apa(value)\n return total_str\n else:\n raise ValueError(\"Supported Bibformats are ['dict', 'bibtex', 'apa']\")","def process_and_dispatch(self):\n references = []\n for raw_block_references in self.raw_references:\n bibcode = raw_block_references['bibcode']\n block_references = raw_block_references['block_references']\n item_nums = raw_block_references.get('item_nums', [])\n\n parsed_references = []\n for i, raw_reference in enumerate(block_references):\n reference = self.cleanup(raw_reference)\n\n logger.debug(\"SpringerXML: parsing %s\" % reference)\n try:\n springer_reference = SPRINGERreference(reference)\n parsed_references.append(self.merge({**springer_reference.get_parsed_reference(), 'refraw': raw_reference}, self.any_item_num(item_nums, i)))\n except ReferenceError as error_desc:\n logger.error(\"SPRINGERxml: error parsing reference: %s\" %error_desc)\n\n references.append({'bibcode': bibcode, 'references': parsed_references})\n logger.debug(\"%s: parsed %d references\" % (bibcode, len(references)))\n\n return references","def get_references(input, verbose=False):\n\n # TODO: Make this a class reference parser\n\n # *** These tags are mobile-site specific\n\n # When we don't have proper access rights, this is present in the html\n GUEST_TAG_TUPLE = (\"li\", {\"id\": \"menuGuest\"})\n\n # Entries are \"li\" tags with classes of the form:\n # article-reference-article\n # article-reference-other-ref\n REFERENCE_TAG_TUPLE = (\"li\", {\"class\": re.compile('article-reference-*')})\n\n # This is the URL to the page that contains the document info, including\n # reference material\n BASE_URL = _SD_URL + '/science/article/pii/'\n\n # This URL was found first via Fiddler, then via closer inspection of the script\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\n # resolveReferences\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\n\n # Return the BeautifulSoup result, the requests session, and the requests response\n if _is_url(input):\n pii = _extract_pii(input)\n else:\n pii = input\n\n sess = requests.Session()\n\n if verbose:\n print('Requesting main page for pii: %s' % pii)\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\n\n # Step 2 - Get the reference tags\n\n soup = BeautifulSoup(resp.text)\n\n reference_section = soup.find(\"ol\", {\"class\": \"article-references\"})\n\n if reference_section is None:\n # Then we might be a guest. In other words, we might not have sufficient\n # privileges to access the data we want. Generally this is protected via\n # IP mask. When I'm working from home I need to VPN into work so\n # that I can access the data :/\n print(\"reference_section is None\")\n temp = soup.find(*GUEST_TAG_TUPLE)\n if temp is None:\n # We might have no references ... (Doubtful)\n raise ParseException(\"References were not found ..., code error likely\")\n else:\n raise InsufficientCredentialsException(\n \"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\")\n\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\n\n n_refs = len(ref_tags)\n\n if n_refs == 0:\n return None\n\n # Step 3 - Resolve reference links\n # --------------------------------------------------------------------------\n # The returned html code contains javascript which returns more information\n # about each reference, such as:\n #\n # - links to the full text\n # - DOI \n\n\n # Step 3.1 - Make the request for the information\n # --------------------------------------------------------------------------\n # We need the eid of the current entry, it is of the form:\n #\n # SDM.pm.eid = \"1-s2.0-0006899387903726\"\n #\n # * I think this entry gets deleted after the requests so it may not be\n # visible if looking for it in Chrome. \n match = re.search('SDM\\.pm\\.eid\\s*=\\s*\"([^\"]+)\"', resp.text)\n #eid = match.group(1)\n\n # This list comes from the resolveReferences function in article_catalyst.js\n payload = {\n '_pii': pii,\n '_refCnt': n_refs,\n '_docType': 'article', # yikes, this might change ...\n '_refRangeStart': '1',\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\n # I'm not sure if it is important to limit this. The browser then\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\n # It always goes by 20 even if there aren't 20 left\n\n if verbose:\n print('Requesting reference links')\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\n\n # Step 3.2 - Parse the returned information into single entries\n # --------------------------------------------------------------------------\n # This could probably be optimized in terms of execution time. We basically\n # get back a single script tag. Inside is some sort of hash map for links\n # for each reference.\n #\n # The script tag is of the form:\n # myMap['bibsbref11']['refHtml']= \"<some html stuffs>\"; \n # myMap['bibsbref11']['absUrl']= \"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\";\n # etc.\n #\n # - Each entry is quite long.\n # - Normally contains html\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \"\";\n # - the refHtml is quite interesting\n # - the absolute url is not always present (and currently not parsed)\n more_soup = BeautifulSoup(r2.text)\n script_tag = more_soup.find('script')\n\n # We unquote the script text as it is transmitted with characters escaped\n # and we want the parsed data to contain the non-escaped text\n #\n # We might eventually want to move this to being after the regular expression ...\n script_text = urllib_unquote(script_tag.text)\n\n ref_match_result = re.findall(\"myMap\\['bibsbref(\\d+)'\\]\\['refHtml'\\]=\\s?\" + '\"([^\"]*)\";', script_text)\n # Tokens:\n # 0 - the # from bibsbref#\n # 1 - the html content from the 'refHtml' entry\n # \n # NOTE: We don't really use the #, so we might remove the () around\n # \\d+ which would shift the index from 1 to 0\n if verbose:\n print('Creating reference objects')\n\n if len(ref_match_result) > 0:\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\n ref_tag, ref_link_info, ref_id in zipped]\n else:\n zipped = zip(ref_tags, range(n_refs))\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\n ref_tag, ref_id in zipped]\n\n # Step 4:\n # --------------------------------------------------------------------------\n # TODO: Improve documentation for this step\n\n if verbose:\n print('Retrieving Scopus Counts')\n\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\n # but with a particular formatting ...\n ref_count = 0 # Number of references we haven't resolved\n\n ref_count_list = []\n # NOTE: Browser requests these in the reverse order ...\n for ref_id, ref in enumerate(ref_objects):\n\n if ref._data_sceid is not None:\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\n ref_count += 1\n\n # If we've got enough, then update the counts\n # The 20 may be arbitrary but it was what was used in original JS\n if ref_count > 20:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n ref_count = 0\n ref_scopus_eids = []\n\n # Get any remaining reference counts\n if ref_count != 0:\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\n\n # Take the raw data and set the citation count for each object\n for ref_tuple in ref_count_list:\n ref_id = int(ref_tuple[0]) - 1\n ref_count = int(ref_tuple[1])\n ref_objects[ref_id].scopus_cite_count = ref_count\n\n # All done!\n # ---------\n return ref_objects","def get_tagged_corefs(xml, ordered=False):\n nps = {}\n if ordered:\n nps = []\n\n xml = _normalize_malformed_xml(xml)\n\n try:\n corefs = parseString(xml).getElementsByTagName('COREF')\n except ExpatError:\n return nps\n\n for coref in corefs:\n try:\n cid = coref.attributes['ID'].value\n if ordered:\n data = {}\n for npid, np in nps:\n if npid == cid:\n data = np\n break\n else:\n data = nps.get(cid, {})\n except KeyError:\n continue\n\n try:\n data['ref'] = coref.attributes['REF'].value\n except KeyError:\n data['ref'] = None\n\n data['text'] = coref.firstChild.data\n if ordered:\n nps.append((cid, data))\n else:\n nps[cid] = data\n\n\n return nps","def findall():\n\n dataset = {\n \"curso\": [],\n \"materia\": [],\n \"professor\": [],\n \"horas\": [],\n \"ids\": []\n }\n request_data_get = cursos_collections.find()\n\n for result in request_data_get:\n dataset['curso'].append(result[\"curso\"])\n dataset['materia'].append(result[\"materia\"])\n dataset['professor'].append(result[\"professor\"])\n dataset['horas'].append(result[\"horas\"])\n dataset['ids'].append(str(result[\"_id\"]))\n\n return dataset","def recursion_search_citations(self, paper_pmid, k):\n if k == 0: return\n print('recursion degree = ' + str(k))\n original_paper = self.papers_dict[paper_pmid]\n if original_paper == None or original_paper.pm_cited == None: return\n print('paper ' + original_paper.pmid + ' has ' + str(len(original_paper.pm_cited)) + ' citations')\n for citing_paper_pmid in original_paper.pm_cited:\n citing_paper = self.paper_builder.build_paper(citing_paper_pmid)\n if citing_paper_pmid not in self.papers_dict and citing_paper_pmid not in self.csv_papers_dict:\n self.papers_dict[citing_paper_pmid] = citing_paper\n self.recursion_search_citations(citing_paper.pmid, k - 1)\n else:\n citing_paper = self.papers_dict[citing_paper_pmid]\n #add citation info\n citation_year_gap = str(int(citing_paper.year) - int(original_paper.year))\n if citation_year_gap in original_paper.citations:\n (sum, count) = original_paper.citations[citation_year_gap]\n original_paper.citations[citation_year_gap] = (sum + citing_paper.h_index, count +1)\n else:\n original_paper.citations[citation_year_gap]=(citing_paper.h_index, 1)\n citing_paper.add_to_pm_cite(paper_pmid)\n citation_weighted_avg = 0\n gap_sum = 0\n for year_gap, (sum, counter) in original_paper.citations.items():\n weight = 20 - int(year_gap)\n citation_weighted_avg += weight * (sum/counter)\n gap_sum += weight\n original_paper.citation_weighted_avg = citation_weighted_avg/gap_sum","def get_book_data(isbn: int):\n try:\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\n except StopIteration:\n pass # actually, I could put the whole rest of the function here\n else:\n data = core.Book.view_str(book.id)\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\n del data['borrowed_by_id'], data['__str__']\n return data\n\n try:\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\n r.raise_for_status()\n except requests.exceptions.RequestException:\n raise core.BuchSchlossError('no_connection', 'no_connection')\n\n person_re = re.compile(r'(\\w*, \\w*) \\((\\w*)\\)')\n results = {'concerned_people': []}\n\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n if table is None:\n # see if we got multiple results\n link_to_first = page.select_one('#recordLink_0')\n if link_to_first is None:\n raise core.BuchSchlossError(\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n\n for tr in table.select('tr'):\n td = [x.get_text('\\n').strip() for x in tr.select('td')]\n if len(td) == 2:\n if td[0] == 'Titel':\n results['title'] = td[1].split('/')[0].strip()\n elif td[0] == 'Person(en)':\n for p in td[1].split('\\n'):\n g = person_re.search(p)\n if g is None:\n continue\n g = g.groups()\n if g[1] == 'Verfasser':\n results['author'] = g[0]\n else:\n results['concerned_people'].append(g[1]+': '+g[0])\n elif td[0] == 'Verlag':\n results['publisher'] = td[1].split(':')[1].strip()\n elif td[0] == 'Zeitliche Einordnung':\n results['year'] = td[1].split(':')[1].strip()\n elif td[0] == 'Sprache(n)':\n results['language'] = td[1].split(',')[0].split()[0].strip()\n\n results['concerned_people'] = '; '.join(results['concerned_people'])\n return results","def parse(self, lines):\n # convert file to string deleting end of line charcters\n citations_string = self.prepare_file(lines)\n # extract the entries from the string\n entries = list(self.find_entries(citations_string))\n entries.append(len(citations_string))\n # parse each entry to generate a citation\n for idx, jdx in zip(entries[:-1], entries[1:]):\n self.parse_entry(citations_string[idx:jdx])\n return self.force_field.citations","def _filter_cid(self, cids):\n return [cid for cid in cids if cid is not None]","def read_contacts(contacts_file, uidlist, used_uid_names):\n # Read in out values to a list\n lines = read_contacts_to_list(contacts_file)\n\n # Convert our list to dict\n contactsdict = contacts_from_list_to_dict(lines, uidlist, used_uid_names)\n\n #logging.debug(\"Aliasdict: %s\" % aliasdict)\n return contactsdict","def references(self):\n ref_nodes = self.root.xpath(\".//bib-reference\")\n return list(\n itertools.chain.from_iterable(\n self.get_reference_iter(node) for node in ref_nodes\n )\n )","def critics_list(catalog):\n critics_url = list()\n for ix, letter_pages in enumerate(catalog, 1):\n for a in fetch(website, letter_pages).find_all(\"a\", {\"class\": \"a critic-authors__name\"}):\n href_critic = a['href']\n if str(href_critic)[:7] != \"/source\":\n critics_url.append(href_critic + \"/movies\")\n print('1/4 — of movie critic URLs scraped.'.format(ix/len(catalog)), end=' ')\n print('\\r{} pages of movie critic URLs successfully scraped.'.format(ix), end=' '); print()\n return critics_url","def references_list( self, theWeaver ):\n return [ (c.name, c.seq) \n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]","def councils(self):\n catalog = getToolByName(self.context, 'portal_catalog')\n\n return [dict(url=council.getURL(), title=council.Title,\n address=council.Description) for council in\n catalog({'object_provides': ICouncil.__identifier__,\n 'path': dict(query='/'.join(self.context.getPhysicalPath()),\n depth=1), 'sort_on': 'sortable_title'})]","def parse_references(article):\n reference_list = []\n references = article.find(\"text\").find(\"div\", attrs={\"type\": \"references\"})\n references = references.find_all(\"biblstruct\") if references is not None else []\n reference_list = []\n for reference in references:\n title = reference.find(\"title\", attrs={\"level\": \"a\"})\n if title is None:\n title = reference.find(\"title\", attrs={\"level\": \"m\"})\n title = title.text if title is not None else \"\"\n journal = reference.find(\"title\", attrs={\"level\": \"j\"})\n journal = journal.text if journal is not None else \"\"\n if journal is \"\":\n journal = reference.find(\"publisher\")\n journal = journal.text if journal is not None else \"\"\n year = reference.find(\"date\")\n year = year.attrs.get(\"when\") if year is not None else \"\"\n authors = []\n for author in reference.find_all(\"author\"):\n firstname = author.find(\"forename\", {\"type\": \"first\"})\n firstname = firstname.text.strip() if firstname is not None else \"\"\n middlename = author.find(\"forename\", {\"type\": \"middle\"})\n middlename = middlename.text.strip() if middlename is not None else \"\"\n lastname = author.find(\"surname\")\n lastname = lastname.text.strip() if lastname is not None else \"\"\n if middlename is not \"\":\n authors.append(firstname + \" \" + middlename + \" \" + lastname)\n else:\n authors.append(firstname + \" \" + lastname)\n authors = \"; \".join(authors)\n reference_list.append(\n {\"title\": title, \"journal\": journal, \"year\": year, \"authors\": authors}\n )\n return reference_list","def get_complex_with_ncbi(self, ncbi):\n result = deque()\n query = {'ncbi_id': ncbi}\n projection = {'_id': 0}\n docs = self.collection.find(filter=query, projection=projection)\n for doc in docs:\n result.append(doc)\n return result","def get_curations(self, corpus_id, reader):\n logger.info('Getting curations for corpus %s' % corpus_id)\n corpus = self.get_corpus(corpus_id, check_s3=True, use_cache=True)\n corpus_curations = corpus.get_curations(look_in_cache=True)\n # Get all statements that have curations\n curated_stmts = {}\n for uuid in corpus_curations:\n curated_stmts[uuid] = corpus.statements[uuid]\n if reader and reader != 'all':\n # Filter out statements and curations that don't contain material\n # from provided reader (in source api of statement)\n filtered_curations = {}\n filtered_stmts = {}\n for uuid, stmt in curated_stmts.items():\n # Check if any of the evidences are from the provided reader\n for ev in stmt.evidence:\n if ev.source_api == reader.lower():\n filtered_stmts[uuid] = stmt\n filtered_curations[uuid] = corpus_curations[uuid]\n break\n data = {'curations': filtered_curations,\n 'statements': {uuid: st.to_json() for uuid, st in\n filtered_stmts.items()}}\n else:\n data = {'curations': corpus_curations,\n 'statements': {uuid: st.to_json() for uuid, st in\n curated_stmts.items()}}\n return data","def construct_occurrence_dico(data) :\n print('Constructing occurence dictionnaries...')\n\n p_kw_dico = dict()\n kw_p_dico = dict()\n full_stem_dico = {}\n for patent in data :\n patent_id = patent['id']\n #[keywords,stem_dico] = extract_keywords(patent[1]+\". \"+patent[2],patent_id)\n [keywords,stem_dico] = extract_keywords(patent['title']+\". \"+patent['abstract'],patent_id)\n #print(keywords)\n\n for k in keywords :\n # add to p_kw dico\n if k in kw_p_dico :\n kw_p_dico[k].append(patent_id)\n else :\n kw_p_dico[k]= [patent_id]\n #\n if patent_id in p_kw_dico :\n p_kw_dico[patent_id].append(k)\n else :\n p_kw_dico[patent_id] = [k]\n\n for k in stem_dico.keys():\n if k in full_stem_dico :\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\n else :\n full_stem_dico[k] = stem_dico[k]\n\n return([p_kw_dico,kw_p_dico,full_stem_dico])","def reduce_article_list(self, dfi, institution_keys=None):\n self.sanity_check()\n\n if institution_keys is None:\n institution_keys = self.config.inst_strings\n\n Ps = []\n for a, x in zip(dfi.authors, dfi.ppr_list):\n for p in x:\n\n isinst = False\n for aff in p.aff:\n ncoinc = sum([word in aff for word in institution_keys])\n thisis = ncoinc > 1\n isinst = isinst or thisis\n\n if isinst:\n t = [a, p.id, p.bibcode, p.title, p.aff, p.author,\n p.citation, p.pub, p.reference, p.year, p.pubdate,\n p.citation_count, p.read_count]\n Ps.append(t)\n\n names = ['author1', 'id', 'bibcode', 'title', 'aff', 'authors',\n 'citation', 'pub', 'reference', 'year', 'pubdate',\n 'citation_count', 'read_count']\n dfo = pd.DataFrame(Ps, columns=names)\n\n self.pub_auth_all = dfo","def getreferences(self, pmcid, source):\n import requests\n r = requests.get(\n (self.referencesurl).format(source=source, pmcid=pmcid))\n return r.content","def get_crefs_to( ea ):\r\n\tret = []\r\n\txrf = get_first_cref_to( ea )\r\n\tif xrf != BADADDR:\r\n\t\tret.append( xrf )\r\n\txrf = get_next_cref_to( ea, xrf )\r\n\twhile xrf != BADADDR:\r\n\t\tret.append( xrf )\r\n\t\txrf = get_next_cref_to( ea, xrf )\r\n\treturn ret","def make_crossref_fundref(dataset: ObservatoryDataset) -> List[Dict]:\n\n records = []\n\n for funder in dataset.funders:\n records.append(\n {\n \"pre_label\": funder.name,\n \"funder\": f\"http://dx.doi.org/{funder.doi}\",\n \"country_code\": funder.country_code,\n \"region\": funder.region,\n \"funding_body_type\": funder.funding_body_type,\n \"funding_body_sub_type\": funder.funding_body_subtype,\n }\n )\n\n return records","def references(md5):\n u = Upload.objects.filter(md5=md5).first()\n if not u:\n abort(404)\n # first, is this searchable?\n is_searchable = False\n count = elastic.count('page', filter={'md5': md5})\n if count > 0:\n is_searchable = True\n #annotations = Reference.objects.filter(upload=u, ref_url__exists=True)\n annotations = Reference.objects.filter(upload=u).order_by('ref_pos')\n # create a list of referenced things\n references = {'references':[], 'searchable': is_searchable}\n for a in annotations:\n try:\n references['references'].append({\n 'pos_x': a.pos_x, \n 'pos': a.pos, \n 'ref': a.ref_upload.md5, \n 'ref_pos': a.ref_pos\n })\n except:\n pass\n return jsonify(references)","def testGetRelatedIdentifiers(self):\n try:\n # --- Get related identifiers ---\n pcdcP = PubChemDataCacheProvider(self.__cfgOb, self.__cachePath)\n rD = pcdcP.getRelatedMapping(self.__cidList)\n logger.info(\"rD %r\", rD)\n self.assertGreaterEqual(len(rD), len(self.__cidList))\n #\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n self.fail()","def build_dict(results, chunk):\n from math import inf\n from collections import defaultdict\n chunk = [int(au) for au in chunk]\n d = defaultdict(\n lambda: {\"first_year\": inf, \"pubs\": set(), \"coauth\": set(),\n \"n_coauth\": inf, \"n_pubs\": inf})\n for pub in results:\n if not pub.author_ids:\n continue\n authors = set([int(au) for au in pub.author_ids.split(\";\")])\n for focal in authors.intersection(chunk):\n d[focal][\"coauth\"].update(authors)\n d[focal][\"coauth\"].remove(focal)\n d[focal][\"pubs\"].add(pub.eid)\n d[focal][\"n_pubs\"] = len(d[focal][\"pubs\"])\n d[focal][\"n_coauth\"] = len(d[focal][\"coauth\"])\n if not pub.coverDate:\n continue\n first_year = min(d[focal][\"first_year\"], int(pub.coverDate[:4]))\n d[focal][\"first_year\"] = first_year\n return d","def getcontactcongressdict(ccdump):\n d = {}\n for line in ccdump.strip().split('\\n'):\n (district, name, party, dc_office, dc_voice, district_voice, email_form, website) = line.split('\\t')\n dist = ''.join( (district[:2], '-', district[2:]) )\n d[dist] = email_form\n return d","def fetchRefSeq(genome = 'hg18',lookupval = 'name'):\n cursor=gbdbConnect(gbdbname=genome)\n select=\"SELECT * FROM refGene\"\n cursor.execute(select)\n rows=cursor.fetchall()\n output={}\n for chr in genomelib.chr_names:\n output[chr]={}\n output[chr]['+']={}\n output[chr]['-']={}\n for row in rows:\n if row['chrom'] in genomelib.chr_names:\n output[row['chrom']][row['strand']][row[lookupval]]=row\n return output","def seq_xref_ids(entry):\n\n xref_ids = []\n exon_data = exons(entry)\n for ids in xref_data(entry).values():\n for exon in exon_data:\n for xref_id in ids:\n key = \"{xref_id}-{gene_id}-{chr}:{start}..{stop}\".format(\n xref_id=xref_id,\n gene_id=primary_id(entry),\n chr=exon.chromosome_name,\n start=exon.primary_start,\n stop=exon.primary_end,\n )\n xref_ids.append((key, exon))\n\n return xref_ids","def search_for_redbooks(book_codes: tuple):\n\n book_dict = {}\n\n global setup\n\n for book_code in book_codes:\n URI_string = build_URI_string(book_code)\n search_web_page = requests.get(URI_string)\n if search_web_page.status_code != 200:\n print(\"Book with code {} not found! Continuing...\".format(book_code))\n continue\n web_page_content = search_web_page.content\n soup = BS(web_page_content, 'html.parser')\n book_name = soup.find('h1',{'class':'ibm-h1','id':'ibm-pagetitle-h1'}).text\n book_dict[book_code] = book_name\n\n return book_dict","def identify_primary_reference_datasets(conn, log):\n\n primary_ref = {}\n\n primary_ref['refimg_id_ip'] = phot_db.find_primary_reference_image_for_field(conn)\n\n query = 'SELECT facility, filter, software FROM reference_images WHERE refimg_id=\"'+str(primary_ref['refimg_id_ip'])+'\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n\n primary_ref['facility_id'] = t['facility'][0]\n primary_ref['software_id'] = t['software'][0]\n\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\"ip\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n primary_ref['ip'] = t['filter_id'][0]\n\n for f in ['rp', 'gp']:\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\"'+f+'\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n primary_ref[f] = t['filter_id'][0]\n\n query = 'SELECT refimg_id FROM reference_images WHERE facility=\"'+str(primary_ref['facility_id'])+\\\n '\" AND software=\"'+str(primary_ref['software_id'])+\\\n '\" AND filter=\"'+str(t['filter_id'][0])+'\"'\n qs = phot_db.query_to_astropy_table(conn, query, args=())\n\n if len(qs) > 0:\n primary_ref['refimg_id_'+f] = qs['refimg_id'][0]\n else:\n log.info('WARNING: Database contains no primary reference image data in filter '+f)\n\n log.info('Identified the primary reference datasets for this field as:')\n for key, value in primary_ref.items():\n log.info(str(key)+' = '+str(value))\n\n return primary_ref","def get_collections_details(self, collection, recurse=True):\n if type(collection) == str:\n collid = self.get_collection_id(collection)\n else:\n collid = collection\n\n col_list = [COL_NAME_COLL_COLLID, COL_NAME_COLL_NAME, COL_NAME_COLL_IS_ACTIVE, COL_NAME_COLL_PRID,\n COL_NAME_COLL_COLLCOMMENT, COL_NAME_COLL_PARENTID]\n if recurse:\n rec_list, col_list = self.get_collection_tree(collid, incl_shared=True, col_list=col_list)\n # exclude the first record it was not expect as per previous implementation and convert to list of dict\n records = [dict(zip(col_list, rec)) for rec in rec_list[1:]]\n else:\n\n cond = SQLBinaryExpr(COL_NAME_COLL_PARENTID, OP_EQ, \":1\")\n records = self.select_generic_data(col_list, table_list=[TABLE_NAME_COLL],\n where=cond, sqlparams={\"1\": collid})\n return records"],"string":"[\n \"def get_references(cls, pmids):\\n\\n references = cls.query.filter(cls.pmid.in_(pmids)).all()\\n citations = {}\\n\\n for reference in references:\\n citation_text = reference.authors + \\\". (\\\" + str(\\n reference.year) + \\\"). \\\" + reference.title + \\\" \\\" + reference.journal + \\\".\\\"\\n citations[reference.pmid] = citation_text\\n\\n return citations\",\n \"def get_citations_ids_map(id_list):\\n create_unverified_context()\\n logging.debug('============== IN get_citations_ids_map: ================')\\n logging.debug('============== ID LIST: ================')\\n logging.debug(id_list)\\n linked = {}\\n for i in range(0, len(id_list)):\\n handle = Entrez.elink(\\n dbfrom=\\\"pubmed\\\", id=id_list[i], linkname=\\\"pubmed_pubmed_refs\\\")\\n results = Entrez.read(handle)\\n logging.debug('============== RESULTS: ================')\\n logging.debug(results)\\n handle.close()\\n if len(results[0][\\\"LinkSetDb\\\"]) != 0:\\n linked[id_list[i]] = [\\n link[\\\"Id\\\"] for link in results[0][\\\"LinkSetDb\\\"][0][\\\"Link\\\"]\\n ]\\n logging.debug('============== LINKED ARTICLES: ================')\\n logging.debug(linked)\\n logging.debug('============== ARTICLE ID: ================')\\n logging.debug(id_list[i])\\n return linked\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\\n\\n #sorry bout repeated code to get the tags\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n tagvals = {}\\n for t in tags:\\n try:\\n x = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n tagvals[t] = x\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_author_dict\\n\\n #parse the tags\\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"mainauthortag \\\"+mainauthortag)\\n write_message(\\\"coauthortag \\\"+coauthortag)\\n write_message(\\\"extauthortag \\\"+extauthortag)\\n\\n author_cited_in = initial_author_dict\\n if citedbydict:\\n i = 0 #just a counter for debug\\n write_message(\\\"Checking records referred to in new records\\\")\\n for u in updated_redic_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Author ref done \\\"+str(i)+\\\" of \\\"+str(len(updated_redic_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n if citedbydict.has_key(u):\\n these_cite_k = citedbydict[u]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n authors = get_fieldvalues(u, mainauthortag)\\n coauthl = get_fieldvalues(u, coauthortag)\\n extauthl = get_fieldvalues(u, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author ref done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #go through the dictionary again: all keys but search only if new records are cited\\n write_message(\\\"Checking authors in new records\\\")\\n i = 0\\n for k in citedbydict.keys():\\n if (i % 1000 == 0):\\n mesg = \\\"Author cit done \\\"+str(i)+\\\" of \\\"+str(len(citedbydict.keys()))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n these_cite_k = citedbydict[k]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n #do things only if these_cite_k contains any new stuff\\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\\n if intersec_list:\\n authors = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author cit done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return author_cited_in\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def fill(self):\\n if self.source == 'citations':\\n url = self._scholarly.URLS(\\\"CITATIONPUB\\\").format(self.id_citations)\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\\n\\n if soup.find('a', class_='gsc_vcd_title_link'):\\n self.bib['url'] = soup.find(\\n 'a', class_='gsc_vcd_title_link')['href']\\n\\n for item in soup.find_all('div', class_='gs_scl'):\\n key = item.find(class_='gsc_vcd_field').text\\n val = item.find(class_='gsc_vcd_value')\\n if key == 'Authors':\\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\\n elif key == 'Journal':\\n self.bib['journal'] = val.text\\n elif key == 'Volume':\\n self.bib['volume'] = val.text\\n elif key == 'Issue':\\n self.bib['number'] = val.text\\n elif key == 'Pages':\\n self.bib['pages'] = val.text\\n elif key == 'Publisher':\\n self.bib['publisher'] = val.text\\n elif key == 'Publication date':\\n self.bib['year'] = arrow.get(val.text).year\\n elif key == 'Description':\\n if val.text[0:8].lower() == 'abstract':\\n val = val.text[9:].strip()\\n self.bib['abstract'] = val\\n elif key == 'Total citations':\\n self.id_scholarcitedby = re.findall(\\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\\n\\n # number of citation per year\\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\\n self.cites_per_year = dict(zip(years, cites))\\n\\n if soup.find('div', class_='gsc_vcd_title_ggi'):\\n self.bib['eprint'] = soup.find(\\n 'div', class_='gsc_vcd_title_ggi').a['href']\\n self._filled = True\\n\\n elif self.source == 'scholar':\\n self.bib['add_to_lib'] = self.url_add_sclib\\n\\n try:\\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\\n bibtex = bibtex.find('pre').string\\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\\n self.bib['author_count'] = str(\\n len(self.bib['author'].split('and')))\\n self.bib['age'] = str(\\n int(date.today().year) - int(self.bib['year']))\\n except:\\n # did not find year\\n pass\\n\\n self._filled = True\\n return self\",\n \"def get_license_refs_dict(license_refs_list):\\n license_ref_dict = {}\\n if license_refs_list:\\n for ref_dict in license_refs_list:\\n license_ref_dict[ref_dict['licenseId']] = ref_dict['extractedText']\\n return license_ref_dict\",\n \"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\\n i = 0 #just for debugging ..\\n #get the tags for main author, coauthors, ext authors from config\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n for t in tags:\\n try:\\n dummy = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_selfcitdict\\n\\n r_mainauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"first_author\\\")\\n r_coauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"additional_author\\\")\\n r_extauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"alternative_author_name\\\")\\n #parse the tags\\n mainauthortag = tagify(parse_tag(r_mainauthortag))\\n coauthortag = tagify(parse_tag(r_coauthortag))\\n extauthortag = tagify(parse_tag(r_extauthortag))\\n\\n selfcites = initial_selfcitdict\\n for k in new_record_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Selfcites done \\\"+str(i)+\\\" of \\\"+str(len(new_record_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i+1\\n #get the author of k\\n authorlist = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authorlist.append(coauthl)\\n authorlist.append(extauthl)\\n #author tag\\n #print \\\"record \\\"+str(k)+\\\" by \\\"+str(authorlist)\\n #print \\\"is cited by\\\"\\n #get the \\\"x-cites-this\\\" list\\n if citationdic.has_key(k):\\n xct = citationdic[k]\\n for c in xct:\\n #get authors of c\\n cauthorlist = get_fieldvalues(c, mainauthortag)\\n coauthl = get_fieldvalues(c, coauthortag)\\n extauthl = get_fieldvalues(c, extauthortag)\\n cauthorlist.extend(coauthl)\\n cauthorlist.extend(extauthl)\\n #print str(c)+\\\" by \\\"+str(cauthorlist)\\n for ca in cauthorlist:\\n if (ca in authorlist):\\n #found!\\n if selfcites.has_key(k):\\n val = selfcites[k]\\n #add only if not there already\\n if val:\\n if not c in val:\\n val.append(c)\\n selfcites[k] = val\\n else:\\n #new key for selfcites\\n selfcites[k] = [c]\\n\\n mesg = \\\"Selfcites done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return selfcites\",\n \"def find_references(doi):\\n if doi is None:\\n return None\\n\\n references = []\\n if doi:\\n response = requests.get(f\\\"https://opencitations.net/index/api/v1/references/{doi}\\\").json()\\n if response:\\n references = [{\\\"doi\\\": r['cited'].replace(\\\"coci =>\\\", \\\"\\\")} for r in response]\\n\\n if references:\\n return references\\n else:\\n return None\",\n \"def getCitations (self, doc, verbose = False):\\r\\n if verbose is True:\\r\\n print \\\"getting citations for document \\\" +str(doc) \\r\\n return self.citations[int(doc)]\",\n \"def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\\n selfdic, authorcitdic):\\n insert_into_cit_db(reference_dic,\\\"reversedict\\\")\\n insert_into_cit_db(citation_dic,\\\"citationdict\\\")\\n insert_into_cit_db(selfcbdic,\\\"selfcitedbydict\\\")\\n insert_into_cit_db(selfdic,\\\"selfcitdict\\\")\\n\\n for a in authorcitdic.keys():\\n lserarr = (serialize_via_marshal(authorcitdic[a]))\\n #author name: replace \\\" with something else\\n a.replace('\\\"', '\\\\'')\\n a = unicode(a, 'utf-8')\\n try:\\n ablob = run_sql(\\\"select hitlist from rnkAUTHORDATA where aterm = %s\\\", (a,))\\n if not (ablob):\\n #print \\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\" , (a,lserarr)\\n run_sql(\\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\",\\n (a,lserarr))\\n else:\\n #print \\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\"\\\"\\\" , (lserarr,a)\\n run_sql(\\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\",\\n (lserarr,a))\\n except:\\n register_exception(prefix=\\\"could not read/write rnkAUTHORDATA aterm=\\\"+a+\\\" hitlist=\\\"+str(lserarr), alert_admin=True)\",\n \"def ref_analyzer(citation_informations, initialresult, initial_citationlist,\\n initial_referencelist,config, updated_rec_list ):\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attr function\\\", alert_admin=True)\\n return {}\\n\\n pubrefntag = \\\"\\\"\\n try:\\n pubrefntag = config.get(function, \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_report_number\\\", alert_admin=True)\\n return {}\\n\\n pubreftag = \\\"\\\"\\n try:\\n pubreftag = config.get(function, \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attr reference_via_pubinfo\\\", alert_admin=True)\\n return {}\\n\\n #pubrefntag is often 999C5r, pubreftag 999C5s\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"pubrefntag \\\"+pubrefntag)\\n write_message(\\\"pubreftag \\\"+pubreftag)\\n\\n citation_list = initial_citationlist\\n reference_list = initial_referencelist\\n result = initialresult\\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\\n d_references_s = citation_informations[2]\\n #dict of recid -> publication_infos_in_its_bibliography\\n d_records_s = citation_informations[3] #recid -> its publication inf\\n t1 = os.times()[4]\\n\\n write_message(\\\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\\\")\\n for somerecid in updated_rec_list:\\n try:\\n del citation_list[somerecid]\\n except KeyError:\\n pass\\n try:\\n del reference_list[somerecid]\\n except KeyError:\\n pass\\n\\n write_message(\\\"Phase 1: d_references_report_numbers\\\")\\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\\n #meaning: rec 8 contains these in bibliography\\n\\n done = 0\\n numrecs = len(d_references_report_numbers)\\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n done = done+1\\n\\n for refnumber in refnumbers:\\n if refnumber:\\n p = refnumber\\n f = 'reportnumber'\\n #sanitise p\\n p.replace(\\\"\\\\n\\\",'')\\n #search for \\\"hep-th/5644654 or such\\\" in existing records\\n rec_ids = get_recids_matching_query(p, f)\\n\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops: record '%d' report number value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_citer_cited(thisrecid, rec_ids[0])\\n remove_from_missing(p)\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n # Citation list should have rec_ids[0] but check anyway\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n #append unless this key already has the item\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid)\\n #and update result\\n result[rec_ids[0]] += 1\\n\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n else:\\n #the reference we wanted was not found among our records.\\n #put the reference in the \\\"missing\\\".. however, it will look\\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\\n #This should really be done in the next loop d_references_s\\n #but the 999C5s fields are not yet normalized\\n\\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\\n rectext = \\\"\\\" # print_record() call disabled to speed things up\\n lines = rectext.split(\\\"\\\\n\\\")\\n rpart = p #to be used..\\n for l in lines:\\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\\n st = l.find('$s')\\n if (st > 0):\\n end = l.find('$', st)\\n if (end == st):\\n end = len(l)\\n rpart = l[st+2:end]\\n insert_into_missing(thisrecid, rpart)\\n\\n mesg = \\\"d_references_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t2 = os.times()[4]\\n\\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\\n write_message(\\\"Phase 2: d_references_s\\\")\\n done = 0\\n numrecs = len(d_references_s)\\n for thisrecid, refss in d_references_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_references_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n task_sleep_now_if_required()\\n\\n done = done+1\\n\\n for refs in refss:\\n if refs:\\n p = refs\\n #remove the latter page number if it is like 67-74\\n matches = re.compile(\\\"(.*)(-\\\\d+$)\\\").findall(p)\\n if matches and matches[0]:\\n p = matches[0][0]\\n\\n # check reference value to see whether it is well formed:\\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"is not well formed; skipping it.\\\" % (thisrecid, p)\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n continue # skip this ill-formed value\\n\\n # look for reference value:\\n rec_id = None\\n try:\\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\\n except:\\n rec_ids = None\\n if len(rec_ids) > 1:\\n msg = \\\"Whoops, record '%d' reference value '%s' \\\" \\\\\\n \\\"matches many records; taking only the first one. %s\\\" % \\\\\\n (thisrecid, p, repr(rec_ids))\\n write_message(msg, stream=sys.stderr)\\n try:\\n raise ValueError(msg)\\n except ValueError:\\n register_exception(alert_admin=True)\\n\\n if rec_ids and rec_ids[0]:\\n write_message(\\\"These match searching \\\"+p+\\\" in journal: \\\"+repr(rec_ids), verbose=9)\\n #the refered publication is in our collection, remove\\n #from missing\\n remove_from_missing(p)\\n else:\\n #it was not found so add in missing\\n insert_into_missing(thisrecid, p)\\n #check citation and reference for this..\\n if rec_ids and rec_ids[0]:\\n #the above should always hold\\n if not result.has_key(rec_ids[0]):\\n result[rec_ids[0]] = 0\\n if not citation_list.has_key(rec_ids[0]):\\n citation_list[rec_ids[0]] = []\\n if not thisrecid in citation_list[rec_ids[0]]:\\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\\n result[rec_ids[0]] += 1 #add count for this..\\n\\n #update reference_list accordingly\\n if not reference_list.has_key(thisrecid):\\n reference_list[thisrecid] = []\\n if not rec_ids[0] in reference_list[thisrecid]:\\n reference_list[thisrecid].append(rec_ids[0])\\n mesg = \\\"d_references_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n t3 = os.times()[4]\\n done = 0\\n numrecs = len(d_reports_numbers)\\n write_message(\\\"Phase 3: d_reports_numbers\\\")\\n\\n #search for stuff like CERN-TH-4859/87 in list of refs\\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_report_numbers done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n for reportcode in reportcodes:\\n if reportcode:\\n rec_ids = []\\n try:\\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\\n except:\\n rec_ids = []\\n\\n if rec_ids:\\n for recid in rec_ids:\\n #normal checks..\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(recid):\\n reference_list[recid] = []\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n\\n #normal updates\\n if not recid in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(recid)\\n if not thisrecid in reference_list[recid]:\\n reference_list[recid].append(thisrecid)\\n\\n mesg = \\\"d_report_numbers done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #find this record's pubinfo in other records' bibliography\\n write_message(\\\"Phase 4: d_records_s\\\")\\n done = 0\\n numrecs = len(d_records_s)\\n t4 = os.times()[4]\\n for thisrecid, recs in d_records_s.iteritems():\\n if (done % 1000 == 0):\\n mesg = \\\"d_records_s done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n p = recs.replace(\\\"\\\\\\\"\\\",\\\"\\\")\\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\\n write_message(\\\"These records match \\\"+p+\\\" in \\\"+pubreftag+\\\" : \\\"+str(rec_ids), verbose=9)\\n if rec_ids:\\n for rec_id in rec_ids:\\n #normal checks\\n if not result.has_key(thisrecid):\\n result[thisrecid] = 0\\n if not citation_list.has_key(thisrecid):\\n citation_list[thisrecid] = []\\n if not reference_list.has_key(rec_id):\\n reference_list[rec_id] = []\\n\\n if not rec_id in citation_list[thisrecid]:\\n result[thisrecid] += 1\\n citation_list[thisrecid].append(rec_id)\\n if not thisrecid in reference_list[rec_id]:\\n reference_list[rec_id].append(thisrecid)\\n\\n mesg = \\\"d_records_s done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n write_message(\\\"Phase 5: reverse lists\\\")\\n\\n #remove empty lists in citation and reference\\n keys = citation_list.keys()\\n for k in keys:\\n if not citation_list[k]:\\n del citation_list[k]\\n\\n keys = reference_list.keys()\\n for k in keys:\\n if not reference_list[k]:\\n del reference_list[k]\\n\\n write_message(\\\"Phase 6: self-citations\\\")\\n selfdic = {}\\n #get the initial self citation dict\\n initial_self_dict = get_cit_dict(\\\"selfcitdict\\\")\\n selfdic = initial_self_dict\\n #add new records to selfdic\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n write_message(\\\"Self cite processing disabled. Use -A option to enable it.\\\")\\n else:\\n write_message(\\\"self cite and author citations enabled\\\")\\n selfdic = get_self_citations(updated_rec_list, citation_list,\\n initial_self_dict, config)\\n #selfdic consists of\\n #key k -> list of values [v1,v2,..]\\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\\n\\n #create a reverse \\\"x cited by y\\\" self cit dict\\n selfcitedbydic = {}\\n for k in selfdic.keys():\\n vlist = selfdic[k]\\n for v in vlist:\\n if selfcitedbydic.has_key(v):\\n tmplist = selfcitedbydic[v]\\n if not k in tmplist:\\n tmplist.append(k)\\n else:\\n tmplist = [k]\\n selfcitedbydic[v] = tmplist\\n\\n write_message(\\\"Getting author citations\\\")\\n\\n #get author citations for records in updated_rec_list\\n initial_author_dict = get_initial_author_dict()\\n authorcitdic = initial_author_dict\\n acit = task_get_option(\\\"author-citations\\\")\\n if not acit:\\n print \\\"Author cites disabled. Use -A option to enable it.\\\"\\n else:\\n write_message(\\\"author citations enabled\\\")\\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\\n initial_author_dict, config)\\n\\n\\n if task_get_task_param('verbose') >= 3:\\n #print only X first to prevent flood\\n tmpdict = {}\\n tmp = citation_list.keys()[0:10]\\n for t in tmp:\\n tmpdict[t] = citation_list[t]\\n write_message(\\\"citation_list (x is cited by y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(citation_list.keys())))\\n tmp = reference_list.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = reference_list[t]\\n write_message(\\\"reference_list (x cites y): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(reference_list.keys())))\\n tmp = selfcitedbydic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfcitedbydic[t]\\n mesg = \\\"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\\\"\\n mesg += str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfcitedbydic.keys())))\\n tmp = selfdic.keys()[0:100]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = selfdic[t]\\n mesg = \\\"selfdic (x cites y and one of the authors of x same as y's): \\\"+str(tmpdict)\\n write_message(mesg)\\n write_message(\\\"size: \\\"+str(len(selfdic.keys())))\\n tmp = authorcitdic.keys()[0:10]\\n tmpdict = {}\\n for t in tmp:\\n tmpdict[t] = authorcitdic[t]\\n write_message(\\\"authorcitdic (author is cited in recs): \\\"+str(tmpdict))\\n write_message(\\\"size: \\\"+str(len(authorcitdic.keys())))\\n insert_cit_ref_list_intodb(citation_list, reference_list,\\n selfcitedbydic, selfdic, authorcitdic)\\n\\n t5 = os.times()[4]\\n\\n write_message(\\\"Execution time for analyzing the citation information generating the dictionary:\\\")\\n write_message(\\\"... checking ref number: %.2f sec\\\" % (t2-t1))\\n write_message(\\\"... checking ref ypvt: %.2f sec\\\" % (t3-t2))\\n write_message(\\\"... checking rec number: %.2f sec\\\" % (t4-t3))\\n write_message(\\\"... checking rec ypvt: %.2f sec\\\" % (t5-t4))\\n write_message(\\\"... total time of ref_analyze: %.2f sec\\\" % (t5-t1))\\n\\n return result\",\n \"def process_citation(style, reference_list, citation):\\n processed_citation = Element(\\\"span\\\", attrib={\\\"class\\\":\\\"citation\\\"})\\n\\n for reference in reference_list:\\n citeref = SubElement(processed_citation, \\\"span\\\")\\n for style_node in style.citation.layout:\\n process_node(citeref, style_node, style.macros, reference) \\n\\n return(processed_citation)\",\n \"def mr2bib(id_list):\\n d = mr2bib_dict(id_list)\\n l = []\\n for id in id_list:\\n try:\\n l.append(d[id])\\n except:\\n l.append(ReferenceErrorInfo(\\\"Not found\\\", id))\\n\\n return l\",\n \"def find_book_dois_in_crossref(isbn_list):\\n ret_value = {\\n \\\"success\\\": False,\\n \\\"dois\\\": []\\n }\\n if type(isbn_list) != type([]) or len(isbn_list) == 0:\\n ret_value['error_msg'] = \\\"Parameter must be a non-empty list!\\\"\\n return ret_value\\n filter_list = [\\\"isbn:\\\" + isbn.strip() for isbn in isbn_list]\\n filters = \\\",\\\".join(filter_list)\\n api_url = \\\"https://api.crossref.org/works?filter=\\\"\\n url = api_url + filters + \\\"&rows=500\\\"\\n request = Request(url)\\n request.add_header(\\\"User-Agent\\\", USER_AGENT)\\n try:\\n ret = urlopen(request)\\n content = ret.read()\\n data = json.loads(content)\\n if data[\\\"message\\\"][\\\"total-results\\\"] == 0:\\n ret_value[\\\"success\\\"] = True\\n else:\\n for item in data[\\\"message\\\"][\\\"items\\\"]:\\n if item[\\\"type\\\"] in [\\\"monograph\\\", \\\"book\\\"] and item[\\\"DOI\\\"] not in ret_value[\\\"dois\\\"]:\\n ret_value[\\\"dois\\\"].append(item[\\\"DOI\\\"])\\n if len(ret_value[\\\"dois\\\"]) == 0:\\n msg = \\\"No monograph/book DOI type found in Crossref ISBN search result ({})!\\\"\\n raise ValueError(msg.format(url))\\n else:\\n ret_value[\\\"success\\\"] = True\\n except HTTPError as httpe:\\n ret_value['error_msg'] = \\\"HTTPError: {} - {}\\\".format(httpe.code, httpe.reason)\\n except URLError as urle:\\n ret_value['error_msg'] = \\\"URLError: {}\\\".format(urle.reason)\\n except ValueError as ve:\\n ret_value['error_msg'] = str(ve)\\n return ret_value\",\n \"def documents(pmid_23982599, civic_aid6_document):\\n return [pmid_23982599, civic_aid6_document]\",\n \"def compute_species_from_citation_linking(citelist: list) -> None:\\n # for i, cite in enumerate(tqdm(citelist)):\\n unrecorded_xrefs = []\\n recorded_refs = set()\\n for i, cite in enumerate(citelist):\\n recorded_refs.add(cite.cite_key)\\n if cite.actual == \\\"=\\\":\\n cname = \\\"\\\"\\n crossnames = collections.Counter()\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n cname = tmp.name\\n crossnames.update([tmp.actual])\\n if len(crossnames) == 0:\\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\\n elif len(crossnames) == 1:\\n cite.actual = list(crossnames.keys())[0]\\n else:\\n # find name(s) with largest count\\n mcnt = max(crossnames.values())\\n keylist = []\\n for key in crossnames:\\n if crossnames[key] == mcnt:\\n keylist.append(key)\\n\\n if len(keylist) == 1:\\n cite.actual = keylist[0]\\n else:\\n cite_name = cite.name.lower()\\n while cite_name.find(\\\" \\\") > -1:\\n cite_name = cite_name[cite_name.find(\\\" \\\")+1:]\\n while cname.find(\\\" \\\") > -1:\\n cname = cname[cname.find(\\\" \\\")+1:]\\n\\n if cite_name in keylist:\\n cite.actual = cite_name\\n elif cname in keylist:\\n cite.actual = cname\\n else:\\n cite.actual = keylist[0]\\n\\n if cite.name_note == \\\".\\\":\\n cite.name_note = \\\"in part\\\"\\n else:\\n cite.name_note = \\\"in part; \\\" + cite.name_note\\n\\n for x in unrecorded_xrefs:\\n if x[1] in recorded_refs:\\n report_error(\\\"Reference {} ({}) does not appear until after citation from {} ({})\\\".format(x[1], x[3],\\n x[0], x[2]))\",\n \"def parse_references(pubmed_article, reference_list):\\n references = []\\n reference_list_data = pubmed_article.find(\\\"PubmedData/ReferenceList\\\")\\n if reference_list_data is not None:\\n for ref in reference_list_data.findall(\\\"Reference\\\"):\\n citation = ref.find(\\\"Citation\\\")\\n if citation is not None:\\n if citation.text is not None:\\n citation = citation.text.strip()\\n else:\\n citation = \\\"\\\"\\n else:\\n citation = \\\"\\\"\\n article_ids = ref.find(\\\"ArticleIdList\\\")\\n pmid = (\\n article_ids.find('ArticleId[@IdType=\\\"pubmed\\\"]')\\n if article_ids is not None\\n else None\\n )\\n if pmid is not None:\\n if pmid.text is not None:\\n pmid = pmid.text.strip()\\n else:\\n pmid = \\\"\\\"\\n else:\\n pmid = \\\"\\\"\\n references.append({\\\"citation\\\": citation, \\\"pmid\\\": pmid})\\n\\n if reference_list:\\n return references\\n else:\\n references = \\\";\\\".join(\\n [ref[\\\"pmid\\\"] for ref in references if ref[\\\"pmid\\\"] != \\\"\\\"]\\n )\\n return references\",\n \"def retrieve(dbxrefs, basics=True, hierarchy=True, wikipedia=True, literature=True, cross_references=True, overlaps=True):\\n resolved = dbxref.resolver.resolve(dbxrefs, check_existence=False)\\n documents = []\\n for entry in resolved:\\n # Construct URL for retrieve\\n json_url = entry['locations']['json'][0]\\n logger.debug('URL: %s', json_url)\\n r = requests.get(json_url)\\n logger.debug('Content: %s', r.text)\\n ipro = json.loads(r.text)\\n\\n # Parse retrieved json file by selected Options\\n output = {\\\"id\\\": entry[\\\"dbxref\\\"]}\\n if basics:\\n try:\\n output.update(accession=ipro[\\\"metadata\\\"][\\\"accession\\\"], entry_type=ipro[\\\"metadata\\\"][\\\"type\\\"],\\n description=ipro[\\\"metadata\\\"][\\\"description\\\"], counters=ipro[\\\"metadata\\\"][\\\"counters\\\"],\\n entry_id=ipro[\\\"metadata\\\"][\\\"entry_id\\\"], name=ipro[\\\"metadata\\\"][\\\"name\\\"],\\n source_database=ipro[\\\"metadata\\\"][\\\"source_database\\\"])\\n except KeyError:\\n logger.warning(\\\"One or more basic information were not available for the given entry. Please check your output.\\\")\\n if hierarchy:\\n try:\\n output.update(hierarchy=ipro[\\\"metadata\\\"][\\\"hierarchy\\\"])\\n except KeyError:\\n logger.warning(\\\"Hierarchy information was not available for the given entry.\\\")\\n if wikipedia:\\n try:\\n output.update(wikipedia=ipro[\\\"metadata\\\"][\\\"wikipedia\\\"])\\n except KeyError:\\n logger.warning(\\\"Wikipedia articles were not available for the given entry.\\\")\\n if literature:\\n try:\\n output.update(literature=ipro[\\\"metadata\\\"][\\\"literature\\\"])\\n except KeyError:\\n logger.warning(\\\"Literature was not available for the given entry.\\\")\\n if cross_references:\\n try:\\n output.update(cross_references=ipro[\\\"metadata\\\"][\\\"cross_references\\\"])\\n except KeyError:\\n logger.warning(\\\"Cross_references were not available for the given entry.\\\")\\n if overlaps:\\n try:\\n output.update(overlaps=ipro[\\\"metadata\\\"][\\\"overlaps_with\\\"])\\n except KeyError:\\n logger.warning(\\\"Overlap information was not available for the given entry.\\\")\\n documents.append(output)\\n return documents\",\n \"def results(self):\\n out = []\\n fields = 'eid doi pii pubmed_id title subtype creator afid affilname '\\\\\\n 'affiliation_city affiliation_country author_count '\\\\\\n 'author_names author_ids author_afids coverDate '\\\\\\n 'coverDisplayDate publicationName issn source_id eIssn '\\\\\\n 'aggregationType volume issueIdentifier article_number '\\\\\\n 'pageRange description authkeywords citedby_count '\\\\\\n 'openaccess fund_acr fund_no fund_sponsor'\\n doc = namedtuple('Document', fields)\\n for item in self._json:\\n info = {}\\n # Parse affiliations\\n try:\\n info[\\\"affilname\\\"] = _join(item['affiliation'], 'affilname')\\n info[\\\"afid\\\"] = _join(item['affiliation'], 'afid')\\n info[\\\"aff_city\\\"] = _join(item['affiliation'], 'affiliation-city')\\n info[\\\"aff_country\\\"] = _join(item['affiliation'],\\n 'affiliation-country')\\n except KeyError:\\n pass\\n # Parse authors\\n try:\\n # Deduplicate list of authors\\n authors = _deduplicate(item['author'])\\n # Extract information\\n surnames = _replace_none([d['surname'] for d in authors])\\n firstnames = _replace_none([d['given-name'] for d in authors])\\n info[\\\"auth_names\\\"] = \\\";\\\".join([\\\", \\\".join([t[0], t[1]]) for t in\\n zip(surnames, firstnames)])\\n info[\\\"auth_ids\\\"] = \\\";\\\".join([d['authid'] for d in authors])\\n affs = []\\n for auth in authors:\\n aff = listify(_deduplicate(auth.get('afid', [])))\\n affs.append('-'.join([d['$'] for d in aff]))\\n info[\\\"auth_afid\\\"] = (';'.join(affs) or None)\\n except KeyError:\\n pass\\n date = item.get('prism:coverDate')\\n if isinstance(date, list):\\n date = date[0].get('$')\\n new = doc(article_number=item.get('article-number'),\\n title=item.get('dc:title'), fund_sponsor=item.get('fund-sponsor'),\\n subtype=item.get('subtype'), issn=item.get('prism:issn'),\\n creator=item.get('dc:creator'), affilname=info.get(\\\"affilname\\\"),\\n author_names=info.get(\\\"auth_names\\\"), doi=item.get('prism:doi'),\\n coverDate=date, volume=item.get('prism:volume'),\\n coverDisplayDate=item.get('prism:coverDisplayDate'),\\n publicationName=item.get('prism:publicationName'),\\n source_id=item.get('source-id'), author_ids=info.get(\\\"auth_ids\\\"),\\n aggregationType=item.get('prism:aggregationType'),\\n issueIdentifier=item.get('prism:issueIdentifier'),\\n pageRange=item.get('prism:pageRange'),\\n author_afids=info.get(\\\"auth_afid\\\"), fund_no=item.get('fund-no'),\\n affiliation_country=info.get(\\\"aff_country\\\"),\\n citedby_count=item.get('citedby-count'),\\n openaccess=item.get('openaccess'), eIssn=item.get('prism:eIssn'),\\n author_count=item.get('author-count', {}).get('$'),\\n affiliation_city=info.get(\\\"aff_city\\\"), afid=info.get(\\\"afid\\\"),\\n description=item.get('dc:description'), pii=item.get('pii'),\\n authkeywords=item.get('authkeywords'), eid=item.get('eid'),\\n fund_acr=item.get('fund-acr'), pubmed_id=item.get('pubmed-id'))\\n out.append(new)\\n return out or None\",\n \"def mr2bib_dict(key_list):\\n keys = []\\n d = {}\\n\\n # validate keys\\n for key in key_list:\\n if is_valid(key):\\n keys.append(key)\\n else:\\n d[key] = ReferenceErrorInfo(\\\"Invalid Mathematical Reviews identifier\\\", key)\\n\\n if len(keys) == 0:\\n return d\\n\\n # make the api call\\n entries = {}\\n for key in keys:\\n try:\\n entry = mr_request(key)\\n d[key] = Reference(entry)\\n except NotFoundError as error:\\n message, id = error.args\\n d[key] = ReferenceErrorInfo(message, id)\\n\\n return d\",\n \"def fetch(id_list):\\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\\n print(database)\\n for sublist in id_list:\\n with Entrez.efetch(db=database, id=','.join(sublist),\\n retmode='xml') as handle:\\n records = Entrez.read(handle)\\n\\n write_csv_file(records, output_file)\",\n \"def __citationsFromFile(file):\\n\\n # Tokens for parsing\\n titleToken = '#*'\\n indexToken = '#index'\\n citationToken = '#%'\\n\\n # Predicates for error checking\\n noneNone = lambda *items: all([item is not None for item in items])\\n allNone = lambda *items: all([item is None for item in items])\\n\\n # Next entry data\\n title = None\\n index = None\\n citations = []\\n\\n # Basic stats\\n withCitations = 0\\n withoutCitations = 0\\n totalPapers = 0\\n\\n for line in file:\\n line = line.strip()\\n\\n # Parse entry, enforcing that data appears in title -> index -> citations order\\n if line.startswith(titleToken):\\n assert allNone(title, index) and len(citations) == 0\\n title = line[len(titleToken):].strip('.')\\n\\n elif line.startswith(indexToken):\\n assert noneNone(title) and allNone(index) and len(citations) == 0\\n index = int(line[len(indexToken):])\\n\\n elif line.startswith(citationToken):\\n assert noneNone(title, index)\\n newCitationId = int(line[len(citationToken):])\\n assert newCitationId >= 0\\n citations.append(newCitationId)\\n\\n elif len(line) == 0:\\n totalPapers += 1\\n\\n # Yield this entry if it has any citations,\\n if noneNone(title, index):\\n if len(citations) > 0:\\n withCitations += 1\\n yield title, index, citations\\n else:\\n withoutCitations += 1\\n\\n title = None\\n index = None\\n citations = []\\n\\n # Output some basic statistics about papers with/without citations\\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\\n print \\\"\\\\n\\\\nTotal Papers: %d\\\" % totalPapers\\n print \\\" With References: %d (%2.2f%%)\\\\n Without References: %d (%2.2f%%)\\\" % (\\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\\n )\",\n \"def get_doc_prov(j, gcis_url, refList, orgList):\\n doc = ProvEsDocument()\\n \\n org = requests.get(j['href']).json()\\n \\n doc_attrs = [\\n (\\\"prov:type\\\", 'gcis:organization'),\\n (\\\"prov:label\\\", j['name']),\\n (\\\"prov:location\\\", \\\"%s%s\\\"%(gcis_url, j['uri'])),\\n (\\\"gcis:organization_type_identifier\\\", j['organization_type_identifier']),\\n (\\\"gcis:country_code\\\", j['country_code']),\\n ]\\n orgID = 'bibo:%s' % j['identifier']\\n doc.agent(orgID, doc_attrs)\\n\\n for child in org['children']:\\n cOrgURI = child['organization']\\n rel = child['relationship']\\n\\n cOrg = next(o for o in orgList if o['uri'] == cOrgURI)\\n cOrgID = 'bibo:%s'%cOrg['identifier']\\n\\n #cOrgAttrs = [\\n # (\\\"prov:type\\\", 'gcis:organization'),\\n # (\\\"prov:label\\\", cOrg['name']),\\n # (\\\"prov:location\\\", cOrg['uri']),\\n # (\\\"gcis:organization_type_identifier\\\", cOrg['organization_type_identifier']),\\n # (\\\"gcis:country_code\\\", cOrg['country_code']),\\n # ]\\n #doc.entity(cOrgID, cOrgAttrs)\\n #doc.hadMember(orgID, cOrgID)\\n #for parent in org['parents']:\\n # pOrgURI = parent['organization']\\n # rel = parent['relationship']\\n # pOrg = next(o for o in orgList if o['uri'] == pOrgURI)\\n # pOrgID = 'bibo:%s'%pOrg['identifier']\\n # doc.hadMember(pOrgID, orgID)\\n\\n prov_json = json.loads(doc.serialize())\\n\\n return prov_json\",\n \"def get_books(mapping, cidlist):\\n havecache = _c.cacheisactive(_c.CACHE)\\n\\n # Don't want to keep hammering their servers, so check if available\\n if havecache:\\n cache, nocache = _c.check(_c.CACHE, mapping, [parse_cid(cid) for cid in cidlist])\\n\\n BASE = \\\"http://cmu.verbacompare.com/comparison?id={}\\\"\\n \\n # If cache is available, still need to check for uncached stuff\\n if havecache:\\n sections = [cmu_to_verba(mapping, cid) for cid in nocache]\\n else: \\n sections = [cmu_to_verba(mapping, cid) for cid in cidlist] \\n sections = [s for s in sections if s != False]\\n \\n verba_info = [cmu_to_verba(mapping, cid) for cid in cidlist] \\n verba_info = [s for s in verba_info if s != False]\\n \\n if verba_info:\\n verba_ids = [section['id'] for section in reduce(list.__add__, verba_info)]\\n URL = BASE.format(\\\",\\\".join(verba_ids))\\n \\n if sections: \\n print \\\"Downloading from {}\\\".format(URL)\\n # Download and parse if needed \\n parser = BeautifulSoup(requests.get(URL).content)\\n raw_data = [el.getText() for el in parser.findAll(\\\"script\\\")\\n if 'Verba.Compare' in el.getText()][0] \\n \\n # Parse the extracted JS into an AST to extract the correct variable\\n tree = Parser().parse(raw_data)\\n objects = ObjVisitor()\\n # Oh god why\\n objects.visit(tree)\\n\\n # Finally\\n data = [json.loads(d) for d in [i for i in objects.vardump if \\\"isbn\\\" in i]]\\n\\n # Bring in the cached data if it exists, otherwise just initialize empty result\\n if havecache and cache:\\n print \\\"Grabbing cache...\\\"\\n _courses = [_c.retrieve(_c.CACHE, cid, cmu_to_verba(mapping, cid)) for cid in cache]\\n _courses = reduce(list.__add__, _courses)\\n summary = {\\n 'url': URL,\\n 'courses': _courses\\n }\\n else: \\n summary = {\\n 'url': URL,\\n 'courses': []\\n }\\n \\n # If we had to grab anything, now put it into the result\\n if sections:\\n for course in data:\\n if course.get('title'):\\n info = {\\n 'name': course['title'],\\n 'instructor': course.get('instructor'),\\n 'sections': course['name'],\\n 'books': []\\n }\\n if 'books' in course:\\n for book in course['books']:\\n bookinfo = {\\n 'title': book['title'],\\n 'author': book['author'],\\n 'isbn': book['isbn'],\\n 'citation': book['citation'],\\n 'required': book['required'].lower() == 'required',\\n }\\n info['books'].append(bookinfo) \\n \\n summary['courses'].append(info)\\n \\n if havecache:\\n # Store in cache for future use\\n _c.store(_c.CACHE, info)\\n \\n return summary\",\n \"def get_doc_prov(j, gcis_url, refList):\\n gcis_ns = \\\"https://gcis-search-stage.jpl.net:3000/gcis.owl#\\\"\\n doc = ProvEsDocument()\\n bndl = None\\n \\n#to get people attributed to, you need to grab article -> jornal_identifier -> look up in references\\n# for ref in refList:\\n# if ref['child_publication'] == j['uri']:\\n \\n\\n\\n doc_attrs = [\\n (\\\"prov:type\\\", 'gcis:Article'),\\n (\\\"prov:label\\\", j['title']),\\n (\\\"prov:location\\\", j['uri']),\\n #(\\\"prov:wasAttributedTo\\\", j['']),\\n ]\\n doc.entity('bibo:%s' % j['identifier'], doc_attrs)\\n\\n prov_json = json.loads(doc.serialize())\\n\\n return prov_json\",\n \"def get_publications_by_author(cached_list, cached_set, author_name):\\n publications = { 'dblp': [], 'cdblp': [] }\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n\\n if author['dblp'].__contains__('publications'):\\n publications['dblp'] = author['dblp']['publications']\\n# for pub in author['dblp']['publications']:\\n# print(pub)\\n\\n if author['cdblp'].__contains__('publications'):\\n publications['cdblp'] = author['cdblp']['publications']\\n# for pub in author['cdblp']['publications']:\\n# print(pub)\\n return publications\",\n \"def get_coauthors_dict(name_url_list, schl_name):\\n\\n\\tpaper_info_dict = {}\\n\\n\\tfor name_url in name_url_list:\\n\\t\\t# Get a sub papers_info dictfor an individual author\\n\\t\\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\\n\\t\\t# add sub dict to full dict\\n\\t\\tpaper_info_dict.update(papers_info)\\n\\t\\n\\tif \\\"Humanities\\\" in schl_name:\\n\\t\\tschl_name = \\\"School of Humanities\\\"\\n\\n\\treturn paper_info_dict\",\n \"def _FindBibEntries(self):\\n bibs = \\\" \\\".join(glob.glob(\\\"*.bib\\\"))\\n cat_process = subprocess.Popen(shlex.split(\\\"cat %s\\\" % bibs),\\n stdout=subprocess.PIPE)\\n grep_process = subprocess.Popen(shlex.split(\\\"grep ^@\\\"),\\n stdin=cat_process.stdout,\\n stdout=subprocess.PIPE)\\n cat_process.stdout.close()\\n grep2_process = subprocess.Popen(shlex.split(\\\"grep -vi @string\\\"),\\n stdin=grep_process.stdout,\\n stdout=subprocess.PIPE)\\n grep_process.stdout.close()\\n\\n lines = grep2_process.communicate()[0]\\n\\n ret = []\\n for l in lines.split(\\\"\\\\n\\\"):\\n ret.append(responses.BuildCompletionData(\\n re.sub(r\\\"@([A-Za-z]*)\\\\s*{\\\\s*([^,]*),.*\\\", r\\\"\\\\2\\\", l)\\n )\\n )\\n return ret\",\n \"def extract_all_references(dig_parent_dir, readfile):\\n dig_parent_path_obj = Path(dig_parent_dir)\\n extracted = {\\\"refs\\\": {}, \\\"hrefsToRefs\\\": {}}\\n for split_page_num in [282, 283, 284]:\\n split_page_dir = dig_parent_path_obj / \\\"dig/html/split\\\"\\n refs_html = readfile(\\n \\\"report\\\" + str(split_page_num) + \\\"b.html\\\", split_page_dir\\n )\\n data = extract_references_page(refs_html)\\n extracted['refs'].update(data['refs'])\\n extracted['hrefsToRefs'].update(data['hrefsToRefs'])\\n\\n return extracted\",\n \"def getcitations(self, pmcid, source):\\n import requests\\n r = requests.get((self.citationurl).format(source=source, pmcid=pmcid))\\n return r.content\",\n \"def getcontacts():\\n contacts = {}\\n\\n try:\\n #get list of contact ids\\n contactids = r.smembers(\\\"contacts\\\")\\n\\n #for each contact id get data\\n for contactid in contactids:\\n contacts.update(_getcontact(str(contactid)))\\n return contacts\\n except:\\n print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n raise\",\n \"def extract_references(text, preprocessor, model):\\n has_reference_section, reference_section, non_reference_section = preprocess_reference_text(text)\\n reference_text = reference_section if has_reference_section else non_reference_section\\n fragment = preprocessor(reference_text)\\n out = model(fragment.idx).argmax(dim=2).cpu().numpy()\\n\\n buffer = ''\\n found_citations = []\\n in_citation = False\\n for char, pred in zip(fragment.text, out[0, :]):\\n if pred == 2:\\n in_citation = True\\n if pred == 3:\\n in_citation = False\\n if pred in [2, 3]:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = char\\n elif pred == 1:\\n buffer += char\\n elif pred == 0:\\n if in_citation:\\n buffer += char\\n else:\\n if buffer:\\n found_citations.append(buffer)\\n buffer = ''\\n return found_citations\",\n \"def getConcertsByID(mbID, loc, min, max):\\n \\n skID = 'musichackdayboston'\\n\\n # fixme: use proper python URL helpers for this.\\n if loc:\\n myURL='http://api.songkick.com/api/3.0/artists/mbid:%s/events.json?apikey=%s&location=%s&min_date=%s&max_date=%s' % (mbID, skID, loc, min, max )\\n else:\\n myURL='http://api.songkick.com/api/3.0/artists/mbid:%s/events.json?apikey=%s&min_date=%s&max_date=%s' % (mbID, skID, min, max )\\n\\n data = urllib.urlopen(myURL).read()\\n data = json.loads(data)\\n \\n \\\"\\\"\\\"\\n format for this data:\\n resultsPage\\n totalEntries\\n page (maximum 50 entries per page, navigated with &page=n)\\n results\\n event\\n type (concert, festival)\\n popularity\\n status\\n uri\\n location\\n lat\\n long\\n city\\n start\\n date\\n time\\n datetime\\n venue\\n lat\\n uri\\n lng\\n displayName\\n id\\n metroArea\\n state\\n displayName\\n country\\n displayName\\n displayName\\n id\\n displayName (pretty string for concert)\\n performance (may be a list of artists if a festival)\\n billingIndex\\n billing\\n displayName (for the band)\\n id\\n artist\\n displayName\\n identifier\\n href\\n mbid\\n id\\n id\\n \\\"\\\"\\\"\\n\\n concerts = {}\\n\\n # fixme: totally disregarding case where results span multiple pages.\\n # no matches will return an empty results set.\\n if data[u'resultsPage'][u'results']:\\n for concert in data[u'resultsPage'][u'results'][u'event']:\\n # not extracting mbID from the returned JSON, because performance is \\n # a list which can contain a number of artists, if a festival, etc.\\n concerts[concert[u'id']] = {u'date':concert[u'start'][u'date'], \\n u'venue':concert[u'venue'][u'displayName'],\\n u'name':concert[u'displayName'],\\n u'mbid':mbID}\\n \\n\\n return concerts\",\n \"def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\\n publications = { 'cdblp': [], 'dblp': [] }\\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\\n for cdblp_pub in pub1.get('cdblp', []):\\n authors = set(cdblp_pub.get('authors', []))\\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\\n publications['cdblp'].append(cdblp_pub)\\n\\n for dblp_pub in pub1.get('dblp', []):\\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\\n publications['dblp'].append(dblp_pub)\\n\\n return publications\",\n \"def get_ill_borrowing_requests(document_pid):\\n referenced = []\\n brw_req_record_cls = current_ils_ill.borrowing_request_record_cls\\n brw_req_search_cls = current_ils_ill.borrowing_request_search_cls\\n search = brw_req_search_cls().search_by_document_pid(document_pid)\\n for hit in search.scan():\\n brw_req = brw_req_record_cls.get_record_by_pid(hit[\\\"pid\\\"])\\n referenced.append(dict(pid_type=BORROWING_REQUEST_PID_TYPE, record=brw_req))\\n return referenced\",\n \"def find_cited_by(doi):\\n if doi is None:\\n return None\\n\\n citations = []\\n if doi:\\n response = requests.get(f\\\"https://opencitations.net/index/api/v1/citations/{doi}\\\").json()\\n if response:\\n citations = [{\\\"doi\\\": r['citing'].replace(\\\"coci =>\\\", \\\"\\\")} for r in response]\\n if citations:\\n return citations\\n else:\\n return None\",\n \"def get_citations(self):\\n url = self._get_url() + 'citations'\\n citations = self._request(url)\\n return citations.json()\",\n \"def populate_crossreference_dictionary(self):\\n master_crossreference_dictionary = dict()\\n\\n # If additional crossreferences need to be used to find interactors, they can be added here.\\n # Use the crossreference prefix as the dictionary name.\\n # Also add a regex entry to the resolve_identifier function.\\n master_crossreference_dictionary['UniProtKB'] = dict()\\n master_crossreference_dictionary['ENSEMBL'] = dict()\\n master_crossreference_dictionary['NCBI_Gene'] = dict()\\n master_crossreference_dictionary['RefSeq'] = dict()\\n\\n for key in master_crossreference_dictionary:\\n self.logger.info('Querying for %s cross references.', key)\\n with Neo4jHelper().run_single_parameter_query(self.query_xrefs, key) as result:\\n for record in result:\\n cross_ref_record = None\\n # Modify the cross reference ID to match the PSI MITAB format if necessary.\\n # So far, this is just converting 'NCBI_Gene' to 'entrez gene/locuslink'.\\n if record['cr.globalCrossRefId'].startswith('NCBI_Gene'):\\n cross_ref_record_split = record['cr.globalCrossRefId'].split(':')[1]\\n cross_ref_record = 'entrez gene/locuslink:' + cross_ref_record_split\\n else:\\n cross_ref_record = record['cr.globalCrossRefId']\\n\\n # The crossreference dictionary is a list of genes\\n # linked to a single crossreference.\\n # Append the gene if the crossref dict entry exists.\\n # Otherwise, create a list and append the entry.\\n if cross_ref_record.lower() in master_crossreference_dictionary[key]:\\n master_crossreference_dictionary[key][cross_ref_record.lower()].append(record['g.primaryKey'])\\n else:\\n master_crossreference_dictionary[key][cross_ref_record.lower()] = []\\n master_crossreference_dictionary[key][cross_ref_record.lower()].append(record['g.primaryKey'])\\n\\n # The ids in PSI-MITAB files are lower case, hence the .lower() used above.\\n\\n return master_crossreference_dictionary\",\n \"def process_and_dispatch(self):\\n references = []\\n for raw_block_references in self.raw_references:\\n bibcode = raw_block_references['bibcode']\\n block_references = raw_block_references['block_references']\\n item_nums = raw_block_references.get('item_nums', [])\\n\\n parsed_references = []\\n for i, reference in enumerate(block_references):\\n reference = self.latex_reference.cleanup(reference)\\n logger.debug(\\\"confTEX: parsing %s\\\" % reference)\\n parsed_references.append(self.merge({'refstr': reference, 'refraw': reference}, self.any_item_num(item_nums, i)))\\n\\n references.append({'bibcode': bibcode, 'references': parsed_references})\\n logger.debug(\\\"%s: parsed %d references\\\" % (bibcode, len(references)))\\n\\n return references\",\n \"def extract_references(content):\\n references = {}\\n content = content.replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n matches = re.findall('(\\\\[[0-9]+\\\\][^\\\\[]*?(?=\\\\[|Acknowledge|Fig|Table|Conclusion|pdf))', content)\\n if matches:\\n for match in matches:\\n ref_id = get_reference_id(match)\\n # No reference id exist -- skip it\\n if ref_id != -1:\\n value = match.replace('\\\\\\\\n', '\\\\n')\\n references[ref_id] = value\\n return references\",\n \"def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\\n dict, dict, dict, dict,\\n dict, dict]:\\n name_table = create_name_table(citelist)\\n unique_names = list()\\n nameset = set()\\n # total_binomial_year_cnts = {}\\n total_binomial_year_cnts = collections.Counter()\\n for c in citelist:\\n if c.name != \\\".\\\":\\n clean = clean_name(c.name)\\n if not clean.lower() in nameset:\\n nameset |= {clean.lower()}\\n unique_names.append(clean)\\n y = refdict[c.cite_key].year()\\n if y is not None:\\n total_binomial_year_cnts.update([y])\\n # if y in total_binomial_year_cnts:\\n # total_binomial_year_cnts[y] += 1\\n # else:\\n # total_binomial_year_cnts[y] = 1\\n unique_names.sort(key=lambda s: s.lower())\\n\\n # identify genera used per paper\\n genera_per_paper = {}\\n for c in citelist:\\n if c.name != \\\".\\\":\\n if c.cite_key not in genera_per_paper:\\n genera_per_paper[c.cite_key] = set()\\n genera_set = genera_per_paper[c.cite_key]\\n genera_set |= {extract_genus(clean_name(c.name))}\\n genus_cnts = {}\\n for c in genera_per_paper:\\n y = refdict[c].year()\\n if y is not None:\\n if init_data().start_year <= y <= init_data().current_year:\\n genera_set = genera_per_paper[c]\\n for genus in genera_set:\\n genus = clean_genus(genus)\\n if genus != \\\"\\\":\\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\\n init_data().current_year + 1)})[y] += 1\\n # if genus not in genus_cnts:\\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\\n # init_data().current_year + 1)}\\n # gcnts = genus_cnts[genus]\\n # gcnts[y] += 1\\n\\n binomial_usage_cnts_by_year = {}\\n binomial_location_applications = {}\\n binomial_usage_cnts = {}\\n for name in unique_names:\\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\\n if tmptotal > 0:\\n binomial_usage_cnts[name] = tmptotal\\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\\n\\n # specific_year_cnts = {}\\n specific_year_cnts = collections.Counter()\\n specific_usage_cnts_by_year = {}\\n specific_location_applications = {}\\n specific_usage_cnts = {}\\n for name in specific_names:\\n (specific_usage_cnts_by_year[name.name],\\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\\n if tmptotal > 0:\\n specific_usage_cnts[name.name] = tmptotal\\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\\n binomial_location_applications)\\n tmpkey = name.priority_source\\n if tmpkey != \\\".\\\":\\n y = refdict[tmpkey].year()\\n if y is not None:\\n specific_year_cnts.update([y])\\n # if y in specific_year_cnts:\\n # specific_year_cnts[y] += 1\\n # else:\\n # specific_year_cnts[y] = 1\\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\\n binomial_usage_cnts, specific_usage_cnts)\",\n \"def parse_citations(s: str):\\n print(\\\"parse_citations(%s)\\\" % s)\\n if pandas.isnull(s) or not s:\\n return []\\n terms = [e.strip() for e in s.split(\\\";\\\")]\\n print(\\\"parse_citations: \\\" + str(terms))\\n out = []\\n for term in terms:\\n if term.startswith(\\\"PMID\\\"):\\n out.append({\\\"db\\\": \\\"PubMed\\\", \\\"id\\\": term})\\n else:\\n raise RuntimeError(\\\"Unknown citation format: \\\" + term)\\n return out\",\n \"def parse_citation_web(doc_id, id_type='PMC'):\\n\\n doc_id_dict = convert_document_id(doc_id, id_type=id_type)\\n pmc = doc_id_dict['pmc']\\n link = \\\"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/\\\" % pmc\\n page = requests.get(link)\\n tree = html.fromstring(page.content)\\n n_citations = extract_citations(tree)\\n n_pages = int(n_citations/30) + 1\\n\\n pmc_cited_all = list() # all PMC cited\\n citations = tree.xpath('//div[@class=\\\"rprt\\\"]/div[@class=\\\"title\\\"]/a/@href')[1::]\\n pmc_cited = list(map(extract_pmc, citations))\\n pmc_cited_all.extend(pmc_cited)\\n if n_pages >= 2:\\n for i in range(2, n_pages+1):\\n link = \\\"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/?page=%s\\\" % (pmc, str(i))\\n page = requests.get(link)\\n tree = html.fromstring(page.content)\\n citations = tree.xpath('//div[@class=\\\"rprt\\\"]/div[@class=\\\"title\\\"]/a/@href')[1::]\\n pmc_cited = list(map(extract_pmc, citations))\\n pmc_cited_all.extend(pmc_cited)\\n pmc_cited_all = [p for p in pmc_cited_all if p is not pmc]\\n dict_out = {'n_citations': n_citations,\\n 'pmid': doc_id_dict['pmid'],\\n 'pmc': re.sub('PMC', '', doc_id_dict['pmc']),\\n 'doi': doc_id_dict['doi'],\\n 'pmc_cited': pmc_cited_all}\\n return dict_out\",\n \"def connect_refs_to_species(species: list, citelist: list) -> dict:\\n # create a dictionary with empty reference lists\\n species_refs = {s.species: set() for s in species}\\n # go through all citations\\n # for c in tqdm(citelist):\\n for c in citelist:\\n if c.actual in species_refs:\\n reflist = species_refs[c.actual]\\n reflist |= {c.cite_key}\\n return species_refs\",\n \"def fetch_fulltext(id_list):\\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\\n print(database)\\n for sublist in id_list:\\n with Entrez.efetch(db=database, id=','.join(sublist),\\n retmode='xml') as handle:\\n records = Entrez.read(handle)\\n\\n write_csv_fulltext(records, output_file)\",\n \"def make_crossref_metadata(dataset: ObservatoryDataset) -> List[Dict]:\\n\\n records = []\\n\\n for paper in dataset.papers:\\n # Create funders\\n funders = []\\n for funder in paper.funders:\\n funders.append({\\\"name\\\": funder.name, \\\"DOI\\\": funder.doi, \\\"award\\\": None, \\\"doi_asserted_by\\\": None})\\n\\n # Add Crossref record\\n records.append(\\n {\\n \\\"type\\\": paper.type,\\n \\\"title\\\": [paper.title],\\n \\\"DOI\\\": paper.doi,\\n \\\"is_referenced_by_count\\\": len(paper.cited_by),\\n \\\"issued\\\": {\\n \\\"date_parts\\\": [paper.published_date.year, paper.published_date.month, paper.published_date.day]\\n },\\n \\\"funder\\\": funders,\\n \\\"publisher\\\": paper.publisher.name,\\n }\\n )\\n\\n return records\",\n \"def _process_dbxref(self):\\n\\n raw = '/'.join((self.rawdir, 'dbxref'))\\n logger.info(\\\"processing dbxrefs\\\")\\n line_counter = 0\\n\\n with open(raw, 'r') as f:\\n filereader = csv.reader(f, delimiter='\\\\t', quotechar='\\\\\\\"')\\n f.readline() # read the header row; skip\\n for line in filereader:\\n (dbxref_id, db_id, accession, version, description, url) = line\\n # dbxref_id\\tdb_id\\taccession\\tversion\\tdescription\\turl\\n # 1\\t2\\tSO:0000000\\t\\\"\\\"\\n\\n db_ids = { # the databases to fetch\\n 50: 'PMID', # pubmed\\n 68: 'RO', # obo-rel\\n 71: 'FBdv', # FBdv\\n 74: 'FBbt', # FBbt\\n # 28:, # genbank\\n 30: 'OMIM', # MIM\\n # 38, # ncbi\\n 75: 'ISBN', # ISBN\\n 46: 'PMID', # PUBMED\\n 51: 'ISBN', # isbn\\n 52: 'SO', # so\\n # 76, # http\\n 77: 'PMID', # PMID\\n 80: 'FBcv', # FBcv\\n # 95, # MEDLINE\\n 98: 'REACT', # Reactome\\n 103: 'CHEBI', # Chebi\\n 102: 'MESH', # MeSH\\n 106: 'OMIM', # OMIM\\n 105: 'KEGG-path', # KEGG pathway\\n 107: 'DOI', # doi\\n 108: 'CL', # CL\\n 114: 'CHEBI', # CHEBI\\n 115: 'KEGG', # KEGG\\n 116: 'PubChem', # PubChem\\n # 120, # MA???\\n 3: 'GO', # GO\\n 4: 'FlyBase', # FlyBase\\n # 126, # URL\\n 128: 'PATO', # PATO\\n # 131, # IMG\\n 2: 'SO', # SO\\n 136: 'MESH', # MESH\\n 139: 'CARO', # CARO\\n 140: 'NCBITaxon', # NCBITaxon\\n # 151, # MP ???\\n 161: 'DOI', # doi\\n 36: 'BDGP', # BDGP\\n # 55, # DGRC\\n # 54, # DRSC\\n # 169, # Transgenic RNAi project???\\n 231: 'RO', # RO ???\\n 180: 'NCBIGene', # entrezgene\\n # 192, # Bloomington stock center\\n 197: 'UBERON', # Uberon\\n 212: 'ENSEMBL', # Ensembl\\n # 129, # GenomeRNAi\\n 275: 'PMID', # PubMed\\n 286: 'PMID', # pmid\\n 264: 'HGNC',\\n # 265: 'OMIM', # OMIM_Gene\\n 266: 'OMIM', # OMIM_Phenotype\\n 300: 'DOID', # DOID\\n 302: 'MESH', # MSH\\n 347: 'PMID', # Pubmed\\n }\\n\\n if accession.strip() != '' and int(db_id) in db_ids:\\n # scrub some identifiers here\\n m = re.match(\\n r'(doi|SO|GO|FBcv|FBbt_root|FBdv|FBgn|FBdv_root|FlyBase|FBbt):',\\n accession)\\n if m:\\n accession = re.sub(m.group(1)+r'\\\\:', '', accession)\\n elif re.match(\\n r'(FlyBase miscellaneous CV|cell_lineprop|relationship type|FBgn$)',\\n accession):\\n continue\\n elif re.match(r'\\\\:', accession): # starts with a colon\\n accession = re.sub(r'\\\\:', '', accession)\\n elif re.search(r'\\\\s', accession):\\n # skip anything with a space\\n # logger.debug(\\n # 'dbxref %s accession has a space: %s',\\n # dbxref_id, accession)\\n continue\\n\\n if re.match(r'http', accession):\\n did = accession.strip()\\n else:\\n prefix = db_ids.get(int(db_id))\\n did = ':'.join((prefix, accession.strip()))\\n if re.search(r'\\\\:', accession) and prefix != 'DOI':\\n logger.warning(\\n 'id %s may be malformed; skipping', did)\\n\\n self.dbxrefs[dbxref_id] = {db_id: did}\\n\\n elif url != '':\\n self.dbxrefs[dbxref_id] = {db_id: url.strip()}\\n else:\\n continue\\n\\n # the following are some special cases that we scrub\\n if int(db_id) == 2 \\\\\\n and accession.strip() == 'transgenic_transposon':\\n # transgenic_transposable_element\\n self.dbxrefs[dbxref_id] = {db_id: 'SO:0000796'}\\n\\n line_counter += 1\\n\\n return\",\n \"def to_cff_reference(bib_entry: pybtex.database.Entry) -> dict:\\n\\n def _cff_transform(cff_field, bib_value):\\n if cff_field == \\\"type\\\":\\n if bib_value == \\\"inproceedings\\\":\\n return \\\"article\\\"\\n elif bib_value == \\\"incollection\\\":\\n return \\\"article\\\"\\n elif cff_field == \\\"publisher\\\":\\n return {\\\"name\\\": bib_value}\\n elif cff_field == \\\"month\\\":\\n try:\\n return int(bib_value)\\n except ValueError:\\n return {\\n \\\"jan\\\": 1,\\n \\\"feb\\\": 2,\\n \\\"mar\\\": 3,\\n \\\"apr\\\": 4,\\n \\\"may\\\": 5,\\n \\\"jun\\\": 6,\\n \\\"jul\\\": 7,\\n \\\"aug\\\": 8,\\n \\\"sep\\\": 9,\\n \\\"oct\\\": 10,\\n \\\"nov\\\": 11,\\n \\\"dec\\\": 12,\\n }[bib_value[:3].lower()]\\n return bib_value\\n\\n cff_reference = {\\n \\\"type\\\": _cff_transform(cff_field=\\\"type\\\", bib_value=bib_entry.type),\\n \\\"authors\\\": [\\n to_cff_person(person) for person in bib_entry.persons[\\\"author\\\"]\\n ],\\n }\\n # Map BibTeX to CFF fields. This is just a subset of the most relevant\\n # fields.\\n fields = {\\n \\\"doi\\\": \\\"doi\\\",\\n \\\"edition\\\": \\\"edition\\\",\\n \\\"isbn\\\": \\\"isbn\\\",\\n \\\"license\\\": \\\"license\\\",\\n \\\"month\\\": \\\"month\\\",\\n \\\"number\\\": \\\"number\\\",\\n \\\"pages\\\": \\\"pages\\\",\\n \\\"publisher\\\": \\\"publisher\\\",\\n \\\"title\\\": \\\"title\\\",\\n \\\"url\\\": \\\"url\\\",\\n \\\"version\\\": \\\"version\\\",\\n \\\"volume\\\": \\\"volume\\\",\\n \\\"year\\\": \\\"year\\\",\\n \\\"booktitle\\\": \\\"collection-title\\\",\\n }\\n for bibtex_field, value in bib_entry.fields.items():\\n bibtex_field = bibtex_field.lower()\\n if bibtex_field in fields:\\n cff_field = fields[bibtex_field]\\n cff_reference[cff_field] = _cff_transform(\\n cff_field=cff_field, bib_value=value\\n )\\n return cff_reference\",\n \"def get_crefs_from( ea ):\\r\\n\\tret = []\\r\\n\\txrf = get_first_cref_from( ea )\\r\\n\\tif xrf != BADADDR:\\r\\n\\t\\tret.append( xrf )\\r\\n\\txrf = get_next_cref_from( ea, xrf )\\r\\n\\twhile xrf != BADADDR:\\r\\n\\t\\tret.append( xrf )\\r\\n\\t\\txrf = get_next_cref_from( ea, xrf )\\r\\n\\treturn ret\",\n \"def get_citizens():\\n response = table.scan()[\\\"Items\\\"]\\n logger.info(\\\"All citizens returned\\\")\\n return jsonify(response)\",\n \"def list():\\n\\n return {\\\"cncs\\\": [{\\\"id\\\": id.split(\\\"/\\\")[-1]} for id in sorted(flask.current_app.redis.keys(\\\"/cnc/*\\\"))]}\",\n \"def process_and_dispatch(self):\\n references = []\\n for raw_block_references in self.raw_references:\\n bibcode = raw_block_references['bibcode']\\n block_references = raw_block_references['block_references']\\n item_nums = raw_block_references.get('item_nums', [])\\n\\n parsed_references = []\\n for i, raw_reference in enumerate(block_references):\\n reference = self.cleanup(raw_reference)\\n\\n logger.debug(\\\"arXivTXT: parsing %s\\\" % reference)\\n parsed_references.append(self.merge({'refstr': reference, 'refraw': raw_reference}, self.any_item_num(item_nums, i)))\\n\\n references.append({'bibcode': bibcode, 'references': parsed_references})\\n logger.debug(\\\"%s: parsed %d references\\\" % (bibcode, len(references)))\\n\\n return references\",\n \"def recs_to_dict(keyfldname, recs):\\n d = dict()\\n for rec in recs:\\n d[rec[keyfldname]] = rec\\n return d\",\n \"def return_dict_of_assigned_citations_classifications(self):\\n for citation_instance in self.array_of_citations:\\n citation_string = \\\"\\\"\\n citation_string = citation_string.join(\\n citation_instance.get_author()) + \\\\\\n citation_instance.get_title()\\\\\\n + citation_instance.get_journal()\\n max_keyword_matches = 0\\n for key in self.dict_of_keywords:\\n current_key_matches = 0\\n for keyword in self.dict_of_keywords.get(key):\\n pattern = re.compile(keyword)\\n if pattern.search(citation_string):\\n current_key_matches += 1\\n if current_key_matches > max_keyword_matches:\\n max_keyword_matches = current_key_matches\\n citation_instance.set_classification(key)\\n return self.__generate_dict_of_keys_to_classification__()\",\n \"def get_cuisine_recipes(search_cuisisnes, cuisines):\\n cuisine_df = pd.DataFrame()\\n for cuisine in search_cuisisnes:\\n cuisine_dict = {}\\n cuisine_dict['cuisine'] = cuisine\\n cuisine_dict['source'] = 'BBC Good Food'\\n cuisine_no_space = cuisine.lower().replace(' & ', '-').replace(' ', '-')\\n recipes_cuisine_search = get_number_of_search_recipes(cuisine_no_space)\\n cuisine_dict['pages'] = int(ceil(recipes_cuisine_search /\\n NUMBER_OF_RECIPES_PER_SEARCH_PAGE))\\n collection = False\\n if cuisine in cuisines:\\n cuisine_dict['pages'] += 1\\n collection = True\\n cuisine_dict['recipes_details'] = get_recipe_links(cuisine_no_space,\\n cuisine_dict['pages']-1, collection)\\n cuisine_dict['num_recipes'] = len(cuisine_dict['recipes_links'])\\n print '#####'\\n print \\\"Cuisine: %s \\\\t Number of recipes: %d \\\\t\\\\t Number of pages: %d\\\" \\\\\\n % (cuisine, cuisine_dict['num_recipes'], cuisine_dict['pages'])\\n coll.insert_one(cuisine_dict)\\n cuisine_df = cuisine_df.append(pd.DataFrame.from_dict(cuisine_dict, orient='columns'),\\n ignore_index=True)\\n return cuisine_df\",\n \"def get_citation_child_list(self):\\n return self.address_list\",\n \"def make_doi_institutions(author_list: AuthorList) -> List[Dict]:\\n\\n institutions = {}\\n\\n for author in author_list:\\n # Institution\\n inst = author.institution\\n if inst.ror_id not in institutions:\\n institutions[inst.ror_id] = {\\n \\\"identifier\\\": inst.ror_id,\\n \\\"types\\\": [inst.types],\\n \\\"name\\\": inst.name,\\n \\\"country\\\": inst.country,\\n \\\"country_code\\\": inst.country_code,\\n \\\"country_code_2\\\": inst.country_code_2,\\n \\\"region\\\": inst.region,\\n \\\"subregion\\\": inst.subregion,\\n \\\"coordinates\\\": inst.coordinates,\\n \\\"members\\\": [],\\n }\\n\\n return to_affiliations_list(institutions)\",\n \"def get_document_requests(document_pid):\\n referenced = []\\n docreq_search_cls = current_app_ils.document_request_search_cls\\n docreq_record_cls = current_app_ils.document_request_record_cls\\n for request in (\\n docreq_search_cls().search_by_document_pid(document_pid=document_pid).scan()\\n ):\\n docreq = docreq_record_cls.get_record_by_pid(request[\\\"pid\\\"])\\n referenced.append(dict(pid_type=DOCUMENT_REQUEST_PID_TYPE, record=docreq))\\n return referenced\",\n \"def createCID(self,\\n project_set_list=[],\\n header_image=True,\\n list_cr_type=[],\\n list_cr_status=[],\\n list_cr_doamin=[],\\n **kwargs):\\n # Launch clock\\n dico_timestamp={}\\n dico_timestamp[\\\"begin_script\\\"] = datetime.now()\\n for key in kwargs:\\n self.__dict__[key] = kwargs[key]\\n\\n self.list_type_src = self.getSrcType()\\n # get CID template name\\n template_name = self._getTemplate(self.cid_type)\\n # test presence of template\\n if not os.path.exists(template_name):\\n # warn user that template is not found\\n self.ihm.popup('Specified template in docid.ini configuration file does not exists.')\\n # abort cid generation\\n return \\\"\\\",None\\n self.ihm.log(\\\"Use template {:s}.\\\".format(template_name))\\n if self.cid_type in (\\\"HCMR_PLD\\\",\\\"HCMR_BOARD\\\"):\\n template_type = \\\"HCMR\\\"\\n else:\\n template_type = self.cid_type\\n # Prepare output file\\n docx_filename = self._setOuptutFilename(template_type)\\n eoc_filename = None\\n #\\n # Documentations\\n #\\n self.ihm.log(\\\"Items query in progress...\\\")\\n #self.ihm.defill()\\n self._initTables()\\n self._initTablesSrc()\\n # Header for documents\\n cid_type = self.getCIDType()\\n if cid_type not in (\\\"SCI\\\"):\\n cr_parent= True\\n header = [\\\"Release:Project\\\",\\\"Document\\\",\\\"Issue\\\",\\\"Description\\\",\\\"Tasks\\\"]\\n line_empty_input = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_cid_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n header_input = [\\\"Release:Project\\\",\\\"Document\\\",\\\"Issue\\\",\\\"Description\\\",\\\"Tasks\\\"]\\n # Header for sources\\n # FPGA\\n header_soft_sources = [\\\"Release:Project\\\",\\\"Data\\\",\\\"Issue\\\",\\\"Tasks\\\",\\\"Change Request\\\"]\\n header_prr = header\\n line_sw_eoc_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_src_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_ccb_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_other_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n tbl_build = [header]\\n fmt = self.setCellBorder([1000,2300,200,1000,500,500,500])\\n fmt_ccb = self.setCellBorder([1000,2300,200,1000,1500])\\n fmt_cid = self.setCellBorder([1000,2300,200,500,500])\\n fmt_src = self.setCellBorder([2500,500,500,500,500,500])\\n fmt_build = self.setCellBorder([3000,500,500,500,500])\\n fmt_prr = self.setCellBorder([1000,2300,200,1000,500,500,500])\\n fmt_small = self.setCellBorder([500,2000,500,500,500,500,500,500])\\n fmt_tiny = self.setCellBorder([4000,500,500])\\n fmt_tiny_sw = self.setCellBorder([3000,500,500,500,500])\\n else:\\n cr_parent= False\\n line_empty_input = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n header = [\\\"Title\\\",\\\"Reference\\\",\\\"Synergy Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\",\\\"CR\\\"]\\n header_input = [\\\"Title\\\",\\\"Reference\\\",\\\"Synergy Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\"]\\n # software\\n header_soft_sources = [\\\"File Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\",\\\"CR\\\"]\\n header_prr = [\\\"Name\\\",\\\"Version\\\",\\\"Release\\\"]\\n line_sw_eoc_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_cid_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_src_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_ccb_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n line_other_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n tbl_build = [header_soft_sources]\\n fmt = self.setCellBorder([1000,2300,200,500,500,500,500,500])\\n fmt_ccb = self.setCellBorder([1000,2300,200,500,500,500,500])\\n fmt_cid = self.setCellBorder([1000,2300,200,500,500])\\n fmt_src = self.setCellBorder([2500,500,500,500,500,500])\\n fmt_build = self.setCellBorder([2500,500,500,500,500,500])\\n fmt_prr = self.setCellBorder([4000,500,500])\\n fmt_small = self.setCellBorder([500,2500,500,500,500,500,500])\\n fmt_tiny = self.setCellBorder([4000,500,500])\\n fmt_tiny_sw = self.setCellBorder([3000,500,500,500,500])\\n line_empty_three_columns = [\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n table_input_data = []\\n table_peer_reviews = []\\n table_verif = []\\n table_exclude = []\\n tableau_sources_finduse = []\\n tbl_build_finduse = []\\n # Split table of items with sources\\n items_filter_src = [self.sources_filter]\\n items_filter_build = [self.build_filter]\\n items_filter = [self.input_data_filter,\\n self.peer_reviews_filter,\\n self.verif_filter,\\n self.exclude_filter]\\n\\n # Document part\\n include_code = False\\n if project_set_list != []:\\n # Projects are available in GUI\\n self.ihm.log(\\\"Use project set list to create CID for documents\\\",False)\\n # Project set in GUI\\n list_projects = project_set_list #self.ihm.project_set_list\\n # List of projects from GUI\\n #release_text,baseline_text,project_text = self.getContext(list_projects,\\n # cid_type)\\n else:\\n project = self.project\\n # [self.release,self.baseline,self.project]]\\n if Tool.isAttributeValid(project):\\n find_sub_projects = True\\n list_projects = [[self.release,self.baseline,project]]\\n prj_name, prj_version = self.getProjectInfo(project)\\n self.findSubProjects(prj_name,\\n prj_version,\\n list_projects,\\n mute = True)\\n #print \\\"TBL\\\",list_projects\\n for sub_release,sub_baseline,sub_project in list_projects:\\n if project != sub_project:\\n self.ihm.log(\\\"Find sub project {:s}\\\".format(sub_project))\\n else:\\n #Valid baseline ?\\n if Tool.isAttributeValid(self.baseline):\\n list_projects = self.getProjectsInBaseline(self.baseline)\\n else:\\n list_projects = [[self.release,\\\"\\\",\\\"\\\"]]\\n # No project nor baseline\\n # Patch: Looking for only release\\n print \\\"INCLUDE_CODE\\\"\\n include_code = True\\n release_text,baseline_text,project_text = self.getContext(list_projects,\\n cid_type)\\n\\n tbl_plans = [header]\\n tbl_life_cycle_data = [header]\\n tbl_verif = [header]\\n tbl_peer_reviews = [header_prr]\\n\\n tbl_src = [header_soft_sources]\\n header = [\\\"Ref\\\", \\\"Name\\\", \\\"Reference\\\", \\\"Version\\\", \\\"Description\\\"]\\n self.tbl_cid = [header]\\n tbl_input_data = [header_input]\\n dico_tags = {\\\"part_number\\\":self.part_number,\\n \\\"eoc_id\\\":\\\"\\\",\\n \\\"checksum\\\":self.checksum,\\n \\\"hw_sw_compatibility\\\":\\\"\\\"}\\n #print \\\"Type CID\\\",self.cid_type\\n if cid_type == \\\"HCMR_BOARD\\\" or cid_type == \\\"CID\\\":\\n # HCMR BOARD or ECMR\\n self._clearDicofound()\\n tbl_upper_doc = [header]\\n self.tbl_sas.append([\\\"R1\\\",\\\"Design Assurance Guidance for AEH\\\",\\\"DO-254/ED-80\\\",\\\"April 19th 2000\\\",\\\"\\\"])\\n self.tbl_sas.append([\\\"R2\\\",\\\"Guidelines for Development of Civil Aircraft and Systems\\\",\\\"ARP-4754A/ED-79A\\\",\\\"December 6th 2010\\\",\\\"\\\"])\\n\\n link_id = 3\\n list_llr_document = []\\n for release,baseline,project in list_projects:\\n if Tool.isAttributeValid(release) or Tool.isAttributeValid(baseline) or Tool.isAttributeValid(project):\\n self.ihm.log(\\\"Use release \\\" + release,False)\\n self.ihm.log(\\\"Use baseline \\\" + baseline,False)\\n self.ihm.log(\\\"Use project \\\" + project,False)\\n # Patch\\n if project == \\\"All\\\":\\n project = \\\"\\\"\\n type_objects = self.list_type_doc\\n type_objects.extend(self.list_type_src)\\n type_objects.extend(self.list_type_prog)\\n output = self.getArticles(type_objects,\\n release,\\n baseline,\\n project,\\n source = False,\\n recursive=False)\\n #print \\\"list_type_doc\\\",self.list_type_doc\\n #print \\\"TEST HCI:\\\",output\\n link_id = self.getBoardData(self.tableau_items,\\n self.tbl_ccb,\\n self.tbl_plans,\\n self.tbl_sas,\\n self.tbl_cid,\\n output,\\n link_id)\\n # finduse\\n l_table_input_data,l_table_peer_reviews,l_table_verif,l_table_exclude = self.getSpecificData(release,\\n baseline,\\n project,\\n items_filter,\\n False)\\n l_tbl_program_file = self.getSpecificBuild(release,\\n baseline,\\n project,\\n filters=items_filter_build)\\n print \\\"l_tbl_program_file\\\",l_tbl_program_file\\n tbl_build_finduse.extend(l_tbl_program_file)\\n table_input_data.extend(l_table_input_data)\\n table_peer_reviews.extend(l_table_peer_reviews)\\n table_verif.extend(l_table_verif)\\n table_exclude.extend(l_table_exclude)\\n else:\\n list_llr_document = []\\n # self.tableau_items array is filled by invoking\\n #\\n # - _getAllDocuments (class BuildDoc)\\n # - _getArticles (class Synergy)\\n #\\n table_life_cycle_data = []\\n for tag in (\\\"INPUT_DATA\\\",\\\"CODE\\\",\\\"TEST\\\",\\\"SPEC\\\",\\\"CCB\\\"):\\n if tag in self.dico_onw_user_cid_tags:\\n table_life_cycle_data[tag] = self.dico_onw_user_cid_tags[tag]\\n else:\\n print \\\"X:\\\",tag\\n print \\\"Y:\\\",self.dico_onw_user_cid_tags\\n for release,baseline,project in list_projects:\\n if Tool.isAttributeValid(release) or Tool.isAttributeValid(baseline) or Tool.isAttributeValid(project):\\n self.ihm.log(\\\"Use release \\\" + release,False)\\n self.ihm.log(\\\"Use baseline \\\" + baseline,False)\\n self.ihm.log(\\\"Use project \\\" + project,False)\\n\\n baseline_code_only = self.isCodeOnly(baseline)\\n project_code_only = self.isCodeOnly(project)\\n\\n if not baseline_code_only and not project_code_only:\\n # baseline or project name begin with CODE\\n # populates self.tbl_plans\\n self._getAllDocuments(release,\\n baseline,\\n project)\\n # finduse\\n l_table_input_data,l_table_peer_reviews,l_table_verif,l_table_exclude = self.getSpecificData(release,\\n baseline,\\n project,\\n items_filter,\\n False)\\n table_input_data.extend(l_table_input_data)\\n table_peer_reviews.extend(l_table_peer_reviews)\\n #print \\\"l_table_peer_reviews\\\",l_table_peer_reviews\\n table_verif.extend(l_table_verif)\\n table_exclude.extend(l_table_exclude)\\n #print \\\"l_table_exclude\\\",l_table_exclude\\n\\n if cid_type in (\\\"SCI\\\"):\\n #Input Data\\n if self._is_array(table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"keyword\\\"]):\\n for keyword in table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"keyword\\\"]:\\n self.getObjectsPerFolder(keyword=keyword,\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"table\\\"]\\n )\\n else:\\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"keyword\\\"],\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"table\\\"]\\n )\\n #CCB Minutes\\n if self._is_array(table_life_cycle_data[\\\"CCB\\\"][\\\"keyword\\\"]):\\n for keyword in table_life_cycle_data[\\\"CCB\\\"][\\\"keyword\\\"]:\\n self.getObjectsPerFolder(keyword=keyword,\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"CCB\\\"][\\\"table\\\"])\\n else:\\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\\\"CCB\\\"][\\\"keyword\\\"],\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"CCB\\\"][\\\"table\\\"])\\n # Specifications\\n if self._is_array(table_life_cycle_data[\\\"SPEC\\\"][\\\"keyword\\\"]):\\n for keyword in table_life_cycle_data[\\\"SPEC\\\"][\\\"keyword\\\"]:\\n print \\\"keyword\\\",keyword\\n self.getObjectsPerFolder(keyword=keyword,\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"SPEC\\\"][\\\"table\\\"],\\n header = [\\\"Reference\\\",\\\"Synergy Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\",\\\"CR\\\"],\\n with_cr=True,\\n cr_included=self.list_only_include_cr\\n )\\n else:\\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\\\"SPEC\\\"][\\\"keyword\\\"],\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"SPEC\\\"][\\\"table\\\"],\\n header = [\\\"Reference\\\",\\\"Synergy Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\",\\\"CR\\\"],\\n with_cr=True,\\n cr_included=self.list_only_include_cr\\n )\\n # Get LLR\\n design_keyword = \\\"S[w|W]DD\\\"\\n self.ihm.log(\\\"Looking for Low Level requirement in SwDD folder ...\\\")\\n #self.ihm.defill()\\n self.getItemsInFolder(design_keyword,\\n project=project,\\n baseline=baseline,\\n release=release,\\n only_name=True,\\n #exclude=[\\\"SwDD_\\\"],\\n with_extension=True,\\n mute=False,\\n recur=True,\\n converted_list=list_llr_document)\\n\\n\\n\\n\\n if baseline_code_only or project_code_only or include_code:\\n # Specific command for source code. TODO: To be optimized\\n self.display_attr = ' -f \\\"%release;%name;%version;%task;%change_request;%type;%project;%instance\\\"' # %task_synopsis\\n # self.display_attr is used in _getAllSources\\n # _getAllSources populates self.tableau_src\\n # Source code\\n if Tool.isAttributeValid(project) and cid_type in (\\\"SCI\\\"):\\n self.getObjectsPerFolder(keyword=table_life_cycle_data[\\\"CODE\\\"][\\\"keyword\\\"],\\n project = project,\\n baseline = baseline,\\n release = release,\\n list_tbl = table_life_cycle_data[\\\"CODE\\\"][\\\"table\\\"],\\n header = [\\\"Synergy Name\\\",\\\"Version\\\",\\\"Type\\\",\\\"Instance\\\",\\\"Release\\\",\\\"CR\\\"],\\n with_cr=True,\\n code=True,\\n cr_included=self.list_only_include_cr\\n )\\n tableau_sources_finduse = table_life_cycle_data[\\\"CODE\\\"][\\\"table\\\"]\\n else:\\n tableau_sources_finduse = []\\n self._getAllSources(release,\\n baseline,\\n project)\\n\\n # _getAllProg populates self.tableau_prog, self.tbl_sw_outputs and self.tbl_sw_eoc methods\\n self._getAllProg(release,\\n baseline,\\n project)\\n if cid_type not in (\\\"SCI\\\"):\\n # TODO: Replace with self.getSpecificBuild(release,baseline,project,filters=[\\\"BIN\\\"])\\n # Second chance to find sources in specific folder like SRC\\n l_table_sources = self.getSpecificData(release,\\n baseline,\\n project,\\n filters=items_filter_src,\\n source=True)\\n tableau_sources_finduse.extend(l_table_sources)\\n # For software get build script in specific folder BUILD\\n list_found_build_items = []\\n l_tbl_program_file = self.getSpecificBuild(release,\\n baseline,\\n project,\\n filters=[\\\"BUILD\\\"],\\n list_found_items=list_found_build_items)\\n tbl_build_finduse.extend(l_tbl_program_file)\\n #print \\\"tbl_build_finduse:\\\",tbl_build_finduse\\n #print \\\"list_found_build_items\\\",list_found_build_items\\n list_found_items = []\\n l_tbl_bin_file = self.getSpecificBuild(release,\\n baseline,\\n project,\\n filters=[\\\"BIN\\\",\\\"EXE\\\"],\\n list_found_items=list_found_items)\\n\\n eoc_filename = self.get_eoc_infos(list_found_items,dico_tags)\\n\\n if list_llr_document != []:\\n for llr in list_llr_document:\\n self.ihm.log(\\\"Found LLR: {:s}\\\".format(llr),display_gui=False)\\n #self.ihm.defill()\\n else:\\n self.ihm.log(\\\"Found no LLR.\\\",display_gui=False)\\n #self.ihm.defill()\\n self.sortData(table_input_data=table_input_data,\\n table_verif=table_verif,\\n list_llr_document=list_llr_document,\\n table_peer_reviews=table_peer_reviews,\\n table_exclude=table_exclude,\\n tbl_build_finduse=tbl_build_finduse,\\n tableau_sources_finduse=tableau_sources_finduse,\\n tbl_input_data=tbl_input_data,\\n tbl_life_cycle_data=tbl_life_cycle_data,\\n tbl_plans=tbl_plans,\\n tbl_verif=tbl_verif,\\n tbl_src=tbl_src,\\n tbl_peer_reviews=tbl_peer_reviews,\\n tbl_build=tbl_build)\\n\\n # TODO: Split input data and life cycle data for board and hardware\\n # TODO: Make 2 query for SACR and HCR\\n\\n # Manage Change Requests and Problem Reports\\n self.ihm.log(\\\"Change Request query in progress...\\\")\\n ccb = CCB(self.ihm)\\n\\n dico_tableau_pr = {\\\"all\\\":[],\\n \\\"open\\\":[],\\n \\\"closed\\\":[]}\\n\\n ccb.getPR(dico_tableau_pr,\\n #self.detect, # derprecated\\n #self.target_release, # derprecated\\n #self.cr_type, # derprecated\\n cr_with_parent=cr_parent,\\n list_cr_type=list_cr_type,\\n list_cr_status=list_cr_status,\\n list_cr_doamin=list_cr_doamin)\\n\\n self.tbl_sas = self._removeDoublons(self.tbl_sas)\\n self.tbl_seci = self._removeDoublons(self.tbl_seci)\\n self.tbl_scod = self._removeDoublons(self.tbl_scod)\\n self.tbl_sw_eoc = self._removeDoublons(self.tbl_sw_eoc)\\n self.tbl_sw_outputs = self._removeDoublons(self.tbl_sw_outputs)\\n self.tableau_prog = self._removeDoublons(self.tableau_prog)\\n self.tbl_stds = self._removeDoublons(self.tbl_stds)\\n self.tbl_ccb = self._removeDoublons(self.tbl_ccb)\\n self.tbl_program_file = self._removeDoublons(self.tbl_program_file)\\n self.tbl_synthesis_file = self._removeDoublons(self.tbl_synthesis_file)\\n\\n if len(self.tbl_program_file) == 1:\\n self.tbl_program_file.append(line_empty_three_columns)\\n if len(self.tbl_synthesis_file) == 1:\\n self.tbl_synthesis_file.append(line_empty_three_columns)\\n if len(self.tbl_inspection_sheets) == 1:\\n self.tbl_inspection_sheets.append(line_empty)\\n if len(self.tbl_sas) == 1:\\n self.tbl_sas.append(line_other_empty)\\n if len(self.tbl_seci) == 1:\\n self.tbl_seci.append(line_other_empty)\\n if len(self.tbl_scod) == 1:\\n self.tbl_scod.append(line_other_empty)\\n if len(self.tableau_prog) == 1:\\n self.tableau_prog.append(line_empty)\\n if len(self.tableau_src) == 1:\\n self.tableau_src.append(line_empty)\\n if len(self.tbl_sw_outputs) == 1:\\n line_sw_output_empty = [\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\",\\\"--\\\"]\\n self.tbl_sw_outputs.append(line_sw_output_empty)\\n if len(self.tbl_sw_eoc) == 1:\\n self.tbl_sw_eoc.append(line_sw_eoc_empty)\\n if len(self.tbl_build) == 1:\\n self.tbl_build.append(line_src_empty)\\n if len(self.tbl_sources) == 1:\\n self.tbl_sources.append(line_src_empty)\\n if len(tbl_plans) == 1:\\n tbl_plans.append(line_other_empty)\\n if len(self.tbl_stds) == 1:\\n self.tbl_stds.append(line_other_empty)\\n if len(self.tbl_ccb) == 1:\\n self.tbl_ccb.append(line_ccb_empty)\\n if len(self.tbl_constraint_file) == 1:\\n self.tbl_constraint_file.append(line_empty_three_columns)\\n if len(tbl_input_data) == 1:\\n tbl_input_data.append(line_empty_input)\\n if len(self.tbl_verif) == 1:\\n self.tbl_verif.append(line_empty)\\n if len(self.tbl_items_filtered) == 1:\\n self.tbl_items_filtered.append(line_empty)\\n if len(self.tbl_cid) == 1:\\n self.tbl_cid.append(line_cid_empty)\\n\\n # Prepare information to put instead of tags\\n title,subject = self.getSubject(self.system,\\n self.item,\\n self.component,\\n template_type)\\n doc_type = self.getTypeDocDescription(template_type)\\n\\n author,item,database,aircraft,item_description,ci_identification,program = self._getInfo()\\n\\n # Replace some tags\\n self.protocol_compat = \\\"TDB\\\"\\n self.data_compat = \\\"TDB\\\"\\n if self.revision == \\\"\\\":\\n version = \\\"1\\\"\\n else:\\n version = self.revision\\n # Patch new ECMR template\\n tbl_input_data_new = []\\n tbl_life_cycle_data_new = []\\n tbl_verif_new = []\\n tbl_plans_new = []\\n tbl_peer_reviews_new = []\\n\\n if cid_type == \\\"HCMR_BOARD\\\" or cid_type == \\\"CID\\\":\\n fmt_life_cycle_data = self.formatDocTable(tbl_life_cycle_data_new,\\n tbl_life_cycle_data)\\n fmt_input_data = self.formatDocTable(tbl_input_data_new,\\n tbl_input_data)\\n fmt_verif = self.formatDocTable(tbl_verif_new,\\n tbl_verif)\\n fmt_plans = self.formatDocTable(tbl_plans_new,\\n tbl_plans)\\n fmt_prr = self.formatDocTable(tbl_peer_reviews_new,\\n tbl_peer_reviews)\\n\\n list_tags = {\\n 'TABLEITEMS':{'type':'tab','text':tbl_life_cycle_data_new,'fmt':fmt_life_cycle_data},\\n 'TABLEINPUTDATA':{'type':'tab','text':tbl_input_data_new,'fmt':fmt_input_data},\\n 'TABLEPEERREVIEWS':{'type':'tab','text':tbl_peer_reviews_new,'fmt':fmt_prr},\\n 'TABLESOURCE':{'type':'tab','text':tbl_src,'fmt':fmt_src},\\n 'TABLEBUILD':{'type':'tab','text':tbl_build,'fmt':fmt_build},\\n 'TABLEEOC':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\\n 'TABLEEOCID':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\\n 'TABLEOUPUTS':{'type':'tab','text':self.tbl_sw_outputs,'fmt':fmt_tiny_sw},\\n 'TABLEVERIF':{'type':'tab','text':tbl_verif_new,'fmt':fmt_verif},\\n 'TABLEPLAN':{'type':'tab','text':tbl_plans_new,'fmt':fmt_plans},\\n #'TABLESTD':{'type':'tab','text':self.tbl_stds,'fmt':fmt},\\n #'TABLECCB':{'type':'tab','text':self.tbl_ccb,'fmt':fmt_ccb},\\n #'TABLESAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt},\\n #'TABLESECI':{'type':'tab','text':self.tbl_seci,'fmt':fmt},\\n #'TABLESCOD':{'type':'tab','text':self.tbl_scod,'fmt':fmt},\\n 'TABLECID':{'type':'tab','text':self.tbl_cid,'fmt':fmt_cid},\\n 'TABLEPRS':{'type':'tab','text':dico_tableau_pr[\\\"all\\\"],'fmt':fmt_small},\\n 'TABLECLOSEPRS':{'type':'tab','text':dico_tableau_pr[\\\"closed\\\"],'fmt':fmt_small},\\n 'TABLEOPR':{'type':'tab','text':dico_tableau_pr[\\\"open\\\"],'fmt':fmt_small},\\n 'PROGRAMING_FILE':{'type':'tab','text':self.tbl_program_file,'fmt':fmt_tiny},\\n 'SYNTHESIS_FILES':{'type':'tab','text':self.tbl_synthesis_file,'fmt':fmt_tiny},\\n 'CONSTRAINT_FILES':{'type':'tab','text':self.tbl_constraint_file,'fmt':fmt_tiny}\\n }\\n else:\\n # Software\\n tbl_life_cycle_data_new = self._removeDoublons(tbl_life_cycle_data)\\n tbl_input_data_new = self._removeDoublons(tbl_input_data)\\n tbl_verif_new = self._removeDoublons(tbl_verif)\\n tbl_plans_new = self._removeDoublons(tbl_plans)\\n tbl_peer_reviews_new = self._removeDoublons(tbl_peer_reviews)\\n fmt_input_data = fmt\\n fmt_life_cycle_data = fmt\\n fmt_verif = fmt\\n fmt_plans = fmt\\n\\n list_tags = {\\n 'TABLEPEERREVIEWS':{'type':'tab','text':tbl_peer_reviews_new,'fmt':fmt_prr},\\n 'TABLEBUILD':{'type':'tab','text':tbl_build,'fmt':fmt_build},\\n 'TABLEEOC':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\\n 'TABLEEOCID':{'type':'tab','text':self.tbl_sw_eoc,'fmt':fmt_tiny_sw},\\n 'TABLEOUPUTS':{'type':'tab','text':self.tbl_sw_outputs,'fmt':fmt_tiny_sw},\\n 'TABLEVERIF':{'type':'list','text':table_life_cycle_data[\\\"TEST\\\"][\\\"table\\\"],'fmt':fmt_verif},\\n 'TABLEPLAN':{'type':'tab','text':tbl_plans_new,'fmt':fmt_plans},\\n #'TABLESTD':{'type':'tab','text':self.tbl_stds,'fmt':fmt},\\n #'TABLESAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt},\\n #'TABLESECI':{'type':'tab','text':self.tbl_seci,'fmt':fmt},\\n #'TABLESCOD':{'type':'tab','text':self.tbl_scod,'fmt':fmt},\\n 'TABLECID':{'type':'tab','text':self.tbl_cid,'fmt':fmt_cid},\\n 'TABLEPRS':{'type':'tab','text':dico_tableau_pr[\\\"all\\\"],'fmt':fmt_small},\\n 'TABLECLOSEPRS':{'type':'tab','text':dico_tableau_pr[\\\"closed\\\"],'fmt':fmt_small},\\n 'TABLEOPR':{'type':'tab','text':dico_tableau_pr[\\\"open\\\"],'fmt':fmt_small},\\n 'PROGRAMING_FILE':{'type':'tab','text':self.tbl_program_file,'fmt':fmt_tiny},\\n 'SYNTHESIS_FILES':{'type':'tab','text':self.tbl_synthesis_file,'fmt':fmt_tiny},\\n 'CONSTRAINT_FILES':{'type':'tab','text':self.tbl_constraint_file,'fmt':fmt_tiny}\\n }\\n if table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"table\\\"] != []:\\n list_tags.update({'TABLEINPUTDATA':{'type':'list','text':table_life_cycle_data[\\\"INPUT_DATA\\\"][\\\"table\\\"],'fmt':fmt_input_data}})\\n else:\\n list_tags.update({'TABLEINPUTDATA':{'type':'tab','text':tbl_input_data_new,'fmt':fmt_input_data}})\\n if table_life_cycle_data[\\\"SPEC\\\"][\\\"table\\\"] != []:\\n list_tags.update({'TABLEITEMS':{'type':'list','text':table_life_cycle_data[\\\"SPEC\\\"][\\\"table\\\"],'fmt':fmt_life_cycle_data}})\\n else:\\n list_tags.update({'TABLEITEMS':{'type':'tab','text':tbl_life_cycle_data_new,'fmt':fmt_life_cycle_data}})\\n if table_life_cycle_data[\\\"CCB\\\"][\\\"table\\\"] != []:\\n list_tags.update({'CCB':{'type':'list','text':table_life_cycle_data[\\\"CCB\\\"][\\\"table\\\"],'fmt':fmt_ccb}})\\n else:\\n list_tags.update({'CCB':{'type':'tab','text':self.tbl_ccb,'fmt':fmt_ccb}})\\n if table_life_cycle_data[\\\"CODE\\\"][\\\"table\\\"] != []:\\n list_tags.update({'TABLESOURCE':{'type':'list','text':table_life_cycle_data[\\\"CODE\\\"][\\\"table\\\"],'fmt':fmt_src}})\\n else:\\n list_tags.update({'TABLESOURCE':{'type':'tab','text':tbl_src,'fmt':fmt_src}})\\n #print \\\"TEST1\\\",self.tbl_ccb\\n #print 'TEST2',fmt_ccb\\n if eoc_filename is not None:\\n self.ihm.displayEOC_Info((dico_tags[\\\"hw_sw_compatibility\\\"],\\n dico_tags[\\\"part_number\\\"],\\n dico_tags[\\\"checksum\\\"],dico_tags[\\\"failed\\\"]))\\n if dico_tags[\\\"part_number\\\"] == self.part_number:\\n self.ihm.log(\\\"PN found in the {:s} EOC matches the PN given.\\\".format(eoc_filename),color=\\\"white\\\")\\n else:\\n self.ihm.log(\\\"Warning: PN found in the {:s} EOC mismatches the given PN.\\\".format(eoc_filename),color=\\\"red\\\")\\n if dico_tags[\\\"checksum\\\"] == self.checksum:\\n self.ihm.log(\\\"Checksum found in the {:s} EOC matches the given checksum.\\\".format(eoc_filename),color=\\\"white\\\")\\n else:\\n self.ihm.log(\\\"Warning: Checksum found in the {:s} EOC mismatches the given checksum.\\\".format(eoc_filename),color=\\\"red\\\")\\n #if self.part_number != \\\"\\\":\\n # dico_tags[\\\"part_number\\\"] = self.part_number\\n #if self.checksum != \\\"\\\":\\n # dico_tags[\\\"checksum\\\"] = self.checksum # Ex: 0x6b62\\n list_tags.update({\\n 'SYSTEM':{'type':'str','text':self.system,'fmt':{}},\\n 'SUBJECT':{'type':'str','text':subject,'fmt':{}},\\n 'DOCID':{'type':'str','text':\\\"Generated by doCID version {:s}\\\".format(VERSION),'fmt':{}},\\n 'TYPE':{'type':'str','text':doc_type,'fmt':{}},\\n 'TITLE':{'type':'str','text':title,'fmt':{}},\\n 'CI_ID':{'type':'str','text':ci_identification,'fmt':{}},\\n 'REFERENCE':{'type':'str','text':self.reference,'fmt':{}},\\n 'ISSUE':{'type':'str','text':version,'fmt':{}},\\n 'ITEM':{'type':'str','text':item,'fmt':{}},\\n 'COMPONENT':{'type':'str','text':self.component,'fmt':{}},\\n 'ITEM_DESCRIPTION':{'type':'str','text':item_description,'fmt':{}},\\n 'DATE':{'type':'str','text':time.strftime(\\\"%d %b %Y\\\", time.localtime()),'fmt':{}},\\n 'PROJECT':{'type':'str','text':project_text,'fmt':{}},\\n 'RELEASE':{'type':'str','text':release_text,'fmt':{}},\\n 'PREVIOUS_BASELINE':{'type':'str','text':self.previous_baseline,'fmt':{}},\\n 'BASELINE':{'type':'str','text':baseline_text,'fmt':{}},\\n 'WRITER':{'type':'str','text':author,'fmt':{}},\\n 'PART_NUMBER':{'type':'str','text':dico_tags[\\\"part_number\\\"],'fmt':{}},\\n 'BOARD_PART_NUMBER':{'type':'str','text':self.board_part_number,'fmt':{}},\\n 'MAIN_BOARD_ PART_NUMBER':{'type':'str','text':\\\"\\\",'fmt':{}},\\n 'MEZA_BOARD_ PART_NUMBER':{'type':'str','text':\\\"\\\",'fmt':{}},\\n 'TGT_REL': {'type': 'str', 'text': self.target_release, 'fmt': {}},\\n 'CHECKSUM':{'type':'str','text':dico_tags[\\\"checksum\\\"],'fmt':{}},\\n 'DATABASE':{'type':'str','text':database,'fmt':{}},\\n 'PROGRAM':{'type':'str','text':program,'fmt':{}},\\n 'FUNCCHG':{'type':'par','text':self.func_chg,'fmt':{}},\\n 'OPCHG':{'type':'par','text':self.oper_chg,'fmt':{}},\\n 'PROTOCOL_COMPAT':{'type':'str','text':self.protocol_interface_index,'fmt':{}},\\n 'DATA_COMPAT':{'type':'str','text':self.data_interface_index,'fmt':{}},\\n 'HW_COMPAT':{'type':'str','text':dico_tags[\\\"hw_sw_compatibility\\\"],'fmt':{}},\\n 'TOP_PLD_PRJ':{'type':'str','text':\\\"\\\",'fmt':{}}})\\n for dico in self.dico_own_user_cid_tags[\\\"Documents\\\"]:\\n #print \\\"X:\\\",dico\\n tag = dico[\\\"tag\\\"]\\n if tag in self.tbl_user:\\n #tbl_cleanup = self._removeDoublons(self.tbl_user[tag])\\n list_tags.update({tag:{'type':'tab','text':self.tbl_user[tag],'fmt':fmt}})\\n for x,y in self.tbl_user.iteritems():\\n print \\\"C:\\\",x,y\\n if u'SAS' not in self.tbl_user:\\n list_tags.update({'SAS':{'type':'tab','text':self.tbl_sas,'fmt':fmt}})\\n if header_image:\\n image_name = self.get_image(self.aircraft)\\n else:\\n image_name = None\\n self.ihm.docx_filename = docx_filename\\n for x,y in list_tags.iteritems():\\n print \\\"TAG:\\\",x,y\\n self.docx_filename,exception = self._createDico2Word(list_tags,\\n template_name,\\n docx_filename,\\n image_name)\\n\\n dico_timestamp[\\\"end_script\\\"] = datetime.now()\\n duree_execution_script = dico_timestamp[\\\"end_script\\\"] - dico_timestamp[\\\"begin_script\\\"]\\n self.ihm.log(\\\"Execution time for script: {:d} seconds\\\".format(duree_execution_script.seconds))\\n return self.docx_filename,exception\",\n \"def serve_recos(ids, ref_catalog):\\r\\n desc_list = []\\r\\n for desc_id in ids:\\r\\n desc_list.append(ref_catalog[ref_catalog['id'] == desc_id].iloc[0]['description'])\\r\\n return desc_list\",\n \"def get_literature_recids_for_orcid(orcid):\\n orcid_object = f'[{{\\\"schema\\\": \\\"ORCID\\\", \\\"value\\\": \\\"{orcid}\\\"}}]'\\n # this first query is written in a way that can use the index on (json -> ids)\\n\\n try:\\n author_rec_uuid = (\\n db.session.query(RecordMetadata.id)\\n .filter(\\n type_coerce(RecordMetadata.json, JSONB)[\\\"ids\\\"].contains(orcid_object)\\n )\\n .one()\\n .id\\n )\\n except NoResultFound:\\n LOGGER.warning(\\\"No profile is associated with this account!\\\", orcid=orcid)\\n return []\\n\\n author_record = (\\n db.session.query(PersistentIdentifier)\\n .filter(\\n PersistentIdentifier.object_type == \\\"rec\\\",\\n PersistentIdentifier.object_uuid == author_rec_uuid,\\n PersistentIdentifier.pid_type == \\\"aut\\\",\\n )\\n .one()\\n )\\n\\n author_recid = (\\n author_record.pid_value\\n if not author_record.is_redirected()\\n else InspireRedirect.get_redirect(author_record).pid_value\\n )\\n\\n query = Q(\\\"match\\\", authors__curated_relation=True) & Q(\\n \\\"match\\\", **{\\\"authors.record.$ref\\\": author_recid}\\n )\\n search_by_curated_author = (\\n LiteratureSearch()\\n .query(\\\"nested\\\", path=\\\"authors\\\", query=query)\\n .params(_source=[\\\"control_number\\\"], size=9999)\\n )\\n\\n return [el[\\\"control_number\\\"] for el in search_by_curated_author]\",\n \"def cite_report(self):\\n\\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\\n print(\\\"number of citations = \\\", num_cites)\\n\\n for n, p in enumerate(self.mypapers):\\n clean_tags = re.compile(\\\"<.*?>\\\")\\n title = re.sub(clean_tags, \\\"\\\", p.title[0])\\n tt = textwrap.wrap(title, 60)\\n print(f\\\"{n+1:3} | {p.citation_count:4} {tt[0]}\\\")\\n if len(tt) > 1:\\n for line in tt[1:]:\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", line))\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", \\\"\\\"))\",\n \"def make_open_citations(dataset: ObservatoryDataset) -> List[Dict]:\\n\\n records = []\\n\\n def make_oc_timespan(cited_date: pendulum.Date, citing_date: pendulum.Date):\\n ts = \\\"P\\\"\\n delta = citing_date - cited_date\\n years = delta.in_years()\\n months = delta.in_months() - years * 12\\n\\n if years > 0:\\n ts += f\\\"{years}Y\\\"\\n\\n if months > 0 or years == 0:\\n ts += f\\\"{months}M\\\"\\n\\n return ts\\n\\n def is_author_sc(cited_: Paper, citing_: Paper):\\n for cited_author in cited_.authors:\\n for citing_author in citing_.authors:\\n if cited_author.name == citing_author.name:\\n return True\\n return False\\n\\n def is_journal_sc(cited_: Paper, citing_: Paper):\\n return cited_.journal.name == citing_.journal.name\\n\\n for cited in dataset.papers:\\n for citing in cited.cited_by:\\n records.append(\\n {\\n \\\"oci\\\": \\\"\\\",\\n \\\"citing\\\": citing.doi,\\n \\\"cited\\\": cited.doi,\\n \\\"creation\\\": citing.published_date.strftime(\\\"%Y-%m\\\"),\\n \\\"timespan\\\": make_oc_timespan(cited.published_date, citing.published_date),\\n \\\"journal_sc\\\": is_author_sc(cited, citing),\\n \\\"author_sc\\\": is_journal_sc(cited, citing),\\n }\\n )\\n\\n return records\",\n \"def process_bibliography(style, reference_list):\\n processed_bibliography = Element(\\\"ol\\\", attrib={\\\"class\\\":\\\"bibliography\\\"})\\n\\n for reference in reference_list:\\n ref = SubElement(processed_bibliography, \\\"li\\\", \\n attrib={\\\"property\\\":\\\"dc:references\\\"})\\n\\n for style_node in style.bibliography.layout:\\n process_node(ref, style_node, style.macros, reference) \\n\\n return(processed_bibliography)\",\n \"def get_collection_details_sqlite(self, collid):\\n\\n # print \\\"collid : >> \\\",collid\\n child_colls = self.get_child_collections(str(collid))\\n coll_ids = [str(each_child[COL_NAME_COLL_COLLID]) for each_child in child_colls]\\n coll_ids.append(collid)\\n coll_ids = [str(x) for x in coll_ids]\\n #Include collids of shared collections\\n # print \\\"coll_ids :> 1\\\", coll_ids\\n shared_ids = self.get_shared_collids(coll_ids)\\n # print \\\"shared_ids :: \\\", shared_ids\\n shared_collids = list(set([str(each_child[COL_NANE_SHARECOLL_CHILD_COLLID]) for each_child in shared_ids]))\\n coll_ids.extend(shared_collids)\\n # print \\\"shared_collids : \\\",shared_collids\\n coll_ids = [str(x) for x in coll_ids]\\n # print \\\"coll_ids :> 2\\\", coll_ids\\n if len(shared_collids) != 0:\\n #Get shared child collections\\n for each_sharedid in shared_collids:\\n shared_child_colls = self.get_child_collections(each_sharedid)\\n shared_child_ids = [str(each_child[COL_NAME_COLL_COLLID]) for each_child in shared_child_colls]\\n coll_ids.extend(str(shared_child_ids))\\n coll_ids = [str(x) for x in coll_ids]\\n # print \\\"coll_ids >> 3\\\", coll_ids\\n return coll_ids\",\n \"def get_coauthors(self):\\n # Get number of authors to search for\\n res = download(url=self.coauthor_link, accept='json')\\n data = loads(res.text)['search-results']\\n N = int(data.get('opensearch:totalResults', 0))\\n # Store information in namedtuples\\n fields = 'surname given_name id areas affiliation_id name city country'\\n coauth = namedtuple('Coauthor', fields)\\n coauthors = []\\n # Iterate over search results in chunks of 25 results\\n count = 0\\n while count < N:\\n params = {'start': count, 'count': 25}\\n res = download(url=self.coauthor_link, params=params, accept='json')\\n data = loads(res.text)['search-results'].get('entry', [])\\n # Extract information for each coauthor\\n for entry in data:\\n aff = entry.get('affiliation-current', {})\\n try:\\n areas = [a['$'] for a in entry.get('subject-area', [])]\\n except TypeError: # Only one subject area given\\n areas = [entry['subject-area']['$']]\\n new = coauth(surname=entry['preferred-name']['surname'],\\n given_name=entry['preferred-name'].get('given-name'),\\n id=entry['dc:identifier'].split(':')[-1],\\n areas='; '.join(areas),\\n affiliation_id=aff.get('affiliation-id'),\\n name=aff.get('affiliation-name'),\\n city=aff.get('affiliation-city'),\\n country=aff.get('affiliation-country'))\\n coauthors.append(new)\\n count += 25\\n return coauthors\",\n \"def citations(request, format='csv'):\\n \\n filtered = False # make sure we're filtering by something\\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\\n \\n \\n # accession number\\n if request.GET.get('gabi_acc_number'):\\n filtered = True\\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\\n \\n # species AND bentity\\n if request.GET.get('species'):\\n filtered = True\\n if request.GET.get('species'): \\n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\\n if request.GET.get('bentity_id'):\\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\\n \\n # lat and lon\\n if request.GET.get('lat') and request.GET.get('lon'):\\n filtered = True\\n if request.GET.get('lat'):\\n records = records.filter(lat=request.GET.get('lat'))\\n if request.GET.get('lon'):\\n records = records.filter(lon=request.GET.get('lon'))\\n \\n # status\\n if request.GET.get('status'):\\n records = records.filter(status=request.GET.get('status')[0].upper())\\n \\n \\n # error message if the user didn't supply an argument to filter the records\\n if not filtered: \\n return errorResponse(\\\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\\\", format, {'records': []})\\n \\n \\n # fetch all the bentitites at once, so we don't have to hit the database once for each record\\n records = records.prefetch_related('bentity') \\n \\n output_objects = [{\\n 'gabi_acc_number': r.gabi_acc_number,\\n 'species': r.valid_species_name_id,\\n 'bentity_id': r.bentity_id,\\n 'bentity_name': r.bentity.bentity,\\n 'status': r.status,\\n 'type_of_data': r.type_of_data,\\n 'lat': r.lat,\\n 'lon': r.lon, \\n 'citation': r.citation,\\n } for r in records]\\n \\n \\n \\n if format == 'csv':\\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\\n \\n else:\\n return JSONResponse({'records': output_objects})\",\n \"def find_authors(code):\\n url = baseurl(code)\\n page = req(url)\\n soup = BeautifulSoup(page, 'lxml')\\n addr = [t.attrs.get('content', None) \\n for t in soup.find_all(\\\"meta\\\", {\\\"name\\\": \\\"citation_author_email\\\"})]\\n \\n # corresponding authors will have their email under another tag too\\n corr = [t.find('a').attrs.get('href', None)\\n for t in soup.find_all(None, {\\\"class\\\": \\\"author-corresp-email-link\\\"})]\\n\\n addr = [a for a in addr if a is not None]\\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\\n\\n return dict(corr=list(set(corr)), all=list(set(addr)))\",\n \"def list_publications(bib_format=\\\"dict\\\"):\\n\\n def get_bibtex(key, value):\\n total_keys = [\\n \\\"title\\\",\\n \\\"journal\\\",\\n \\\"volume\\\",\\n \\\"issue\\\",\\n \\\"number\\\",\\n \\\"pages\\\",\\n \\\"numpages\\\",\\n \\\"year\\\",\\n \\\"month\\\",\\n \\\"publisher\\\",\\n \\\"url\\\",\\n \\\"doi\\\",\\n \\\"issn\\\",\\n ]\\n bibtex_str = (\\n \\\"@article{\\\"\\n + key\\n + \\\",\\\\n\\\"\\n + \\\" author={\\\"\\n + \\\" and \\\".join(value[\\\"author\\\"])\\n + \\\"},\\\\n\\\"\\n )\\n for key in total_keys:\\n if key in value.keys():\\n bibtex_str += \\\" \\\" + key + \\\"={\\\" + value[key] + \\\"},\\\\n\\\"\\n bibtex_str += \\\"}\\\\n\\\"\\n return bibtex_str\\n\\n def get_apa(value):\\n apa_str = \\\" & \\\".join(value[\\\"author\\\"])\\n if \\\"year\\\" in value.keys():\\n apa_str += \\\" (\\\" + value[\\\"year\\\"] + \\\"). \\\"\\n if \\\"title\\\" in value.keys():\\n apa_str += value[\\\"title\\\"] + \\\". \\\"\\n if \\\"journal\\\" in value.keys():\\n apa_str += value[\\\"journal\\\"] + \\\", \\\"\\n if \\\"volume\\\" in value.keys():\\n apa_str += value[\\\"volume\\\"] + \\\", \\\"\\n if \\\"pages\\\" in value.keys():\\n apa_str += value[\\\"pages\\\"] + \\\". \\\"\\n if \\\"doi\\\" in value.keys():\\n apa_str += \\\"doi: \\\" + value[\\\"doi\\\"] + \\\"\\\\n\\\"\\n return apa_str\\n\\n publication_dict = s.publication_lst\\n if bib_format.lower() == \\\"dict\\\":\\n return publication_dict\\n elif bib_format.lower() == \\\"bibtex\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_bibtex(key, value)\\n return total_str\\n elif bib_format.lower() == \\\"apa\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_apa(value)\\n return total_str\\n else:\\n raise ValueError(\\\"Supported Bibformats are ['dict', 'bibtex', 'apa']\\\")\",\n \"def process_and_dispatch(self):\\n references = []\\n for raw_block_references in self.raw_references:\\n bibcode = raw_block_references['bibcode']\\n block_references = raw_block_references['block_references']\\n item_nums = raw_block_references.get('item_nums', [])\\n\\n parsed_references = []\\n for i, raw_reference in enumerate(block_references):\\n reference = self.cleanup(raw_reference)\\n\\n logger.debug(\\\"SpringerXML: parsing %s\\\" % reference)\\n try:\\n springer_reference = SPRINGERreference(reference)\\n parsed_references.append(self.merge({**springer_reference.get_parsed_reference(), 'refraw': raw_reference}, self.any_item_num(item_nums, i)))\\n except ReferenceError as error_desc:\\n logger.error(\\\"SPRINGERxml: error parsing reference: %s\\\" %error_desc)\\n\\n references.append({'bibcode': bibcode, 'references': parsed_references})\\n logger.debug(\\\"%s: parsed %d references\\\" % (bibcode, len(references)))\\n\\n return references\",\n \"def get_references(input, verbose=False):\\n\\n # TODO: Make this a class reference parser\\n\\n # *** These tags are mobile-site specific\\n\\n # When we don't have proper access rights, this is present in the html\\n GUEST_TAG_TUPLE = (\\\"li\\\", {\\\"id\\\": \\\"menuGuest\\\"})\\n\\n # Entries are \\\"li\\\" tags with classes of the form:\\n # article-reference-article\\n # article-reference-other-ref\\n REFERENCE_TAG_TUPLE = (\\\"li\\\", {\\\"class\\\": re.compile('article-reference-*')})\\n\\n # This is the URL to the page that contains the document info, including\\n # reference material\\n BASE_URL = _SD_URL + '/science/article/pii/'\\n\\n # This URL was found first via Fiddler, then via closer inspection of the script\\n # 'article_catalyst.js' under sciencedirect.com/mobile/js in the function\\n # resolveReferences\\n REF_RESOLVER_URL = _SD_URL + '/science/referenceResolution/ajaxRefResol'\\n\\n # Return the BeautifulSoup result, the requests session, and the requests response\\n if _is_url(input):\\n pii = _extract_pii(input)\\n else:\\n pii = input\\n\\n sess = requests.Session()\\n\\n if verbose:\\n print('Requesting main page for pii: %s' % pii)\\n resp = sess.get(BASE_URL + pii, cookies={'Site': 'Mobile'})\\n\\n # Step 2 - Get the reference tags\\n\\n soup = BeautifulSoup(resp.text)\\n\\n reference_section = soup.find(\\\"ol\\\", {\\\"class\\\": \\\"article-references\\\"})\\n\\n if reference_section is None:\\n # Then we might be a guest. In other words, we might not have sufficient\\n # privileges to access the data we want. Generally this is protected via\\n # IP mask. When I'm working from home I need to VPN into work so\\n # that I can access the data :/\\n print(\\\"reference_section is None\\\")\\n temp = soup.find(*GUEST_TAG_TUPLE)\\n if temp is None:\\n # We might have no references ... (Doubtful)\\n raise ParseException(\\\"References were not found ..., code error likely\\\")\\n else:\\n raise InsufficientCredentialsException(\\n \\\"Insufficient access rights to get referencs, requires certain IP addresses (e.g. university based IP)\\\")\\n\\n ref_tags = reference_section.find_all(*REFERENCE_TAG_TUPLE)\\n\\n n_refs = len(ref_tags)\\n\\n if n_refs == 0:\\n return None\\n\\n # Step 3 - Resolve reference links\\n # --------------------------------------------------------------------------\\n # The returned html code contains javascript which returns more information\\n # about each reference, such as:\\n #\\n # - links to the full text\\n # - DOI \\n\\n\\n # Step 3.1 - Make the request for the information\\n # --------------------------------------------------------------------------\\n # We need the eid of the current entry, it is of the form:\\n #\\n # SDM.pm.eid = \\\"1-s2.0-0006899387903726\\\"\\n #\\n # * I think this entry gets deleted after the requests so it may not be\\n # visible if looking for it in Chrome. \\n match = re.search('SDM\\\\.pm\\\\.eid\\\\s*=\\\\s*\\\"([^\\\"]+)\\\"', resp.text)\\n #eid = match.group(1)\\n\\n # This list comes from the resolveReferences function in article_catalyst.js\\n payload = {\\n '_pii': pii,\\n '_refCnt': n_refs,\\n '_docType': 'article', # yikes, this might change ...\\n '_refRangeStart': '1',\\n '_refRangeCount': str(n_refs)} # This is normally in sets of 20's ...\\n # I'm not sure if it is important to limit this. The browser then\\n # makes a request fromr 1 count 20, 21 count 20, 41 count 20 etc,\\n # It always goes by 20 even if there aren't 20 left\\n\\n if verbose:\\n print('Requesting reference links')\\n r2 = sess.get(REF_RESOLVER_URL, params=payload)\\n\\n # Step 3.2 - Parse the returned information into single entries\\n # --------------------------------------------------------------------------\\n # This could probably be optimized in terms of execution time. We basically\\n # get back a single script tag. Inside is some sort of hash map for links\\n # for each reference.\\n #\\n # The script tag is of the form:\\n # myMap['bibsbref11']['refHtml']= \\\"<some html stuffs>\\\"; \\n # myMap['bibsbref11']['absUrl']= \\\"http://www.sciencedirect.com/science/absref/sd/0018506X7790068X\\\";\\n # etc.\\n #\\n # - Each entry is quite long.\\n # - Normally contains html\\n # - can be empty i.e. myMap['bibsbref11']['refHtml'] = \\\"\\\";\\n # - the refHtml is quite interesting\\n # - the absolute url is not always present (and currently not parsed)\\n more_soup = BeautifulSoup(r2.text)\\n script_tag = more_soup.find('script')\\n\\n # We unquote the script text as it is transmitted with characters escaped\\n # and we want the parsed data to contain the non-escaped text\\n #\\n # We might eventually want to move this to being after the regular expression ...\\n script_text = urllib_unquote(script_tag.text)\\n\\n ref_match_result = re.findall(\\\"myMap\\\\['bibsbref(\\\\d+)'\\\\]\\\\['refHtml'\\\\]=\\\\s?\\\" + '\\\"([^\\\"]*)\\\";', script_text)\\n # Tokens:\\n # 0 - the # from bibsbref#\\n # 1 - the html content from the 'refHtml' entry\\n # \\n # NOTE: We don't really use the #, so we might remove the () around\\n # \\\\d+ which would shift the index from 1 to 0\\n if verbose:\\n print('Creating reference objects')\\n\\n if len(ref_match_result) > 0:\\n zipped = zip(ref_tags, ref_match_result, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_link_info[1], ref_id) for\\n ref_tag, ref_link_info, ref_id in zipped]\\n else:\\n zipped = zip(ref_tags, range(n_refs))\\n ref_objects = [ScienceDirectRef(ref_tag, ref_id) for\\n ref_tag, ref_id in zipped]\\n\\n # Step 4:\\n # --------------------------------------------------------------------------\\n # TODO: Improve documentation for this step\\n\\n if verbose:\\n print('Retrieving Scopus Counts')\\n\\n ref_scopus_eids = [] # The Scopus IDs of the references to resolve\\n # but with a particular formatting ...\\n ref_count = 0 # Number of references we haven't resolved\\n\\n ref_count_list = []\\n # NOTE: Browser requests these in the reverse order ...\\n for ref_id, ref in enumerate(ref_objects):\\n\\n if ref._data_sceid is not None:\\n ref_scopus_eids.append(ref._data_sceid + ',' + str(ref_id + 1) + '~')\\n ref_count += 1\\n\\n # If we've got enough, then update the counts\\n # The 20 may be arbitrary but it was what was used in original JS\\n if ref_count > 20:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n ref_count = 0\\n ref_scopus_eids = []\\n\\n # Get any remaining reference counts\\n if ref_count != 0:\\n ref_count_list += _update_counts(sess, ref_scopus_eids, REF_RESOLVER_URL)\\n\\n # Take the raw data and set the citation count for each object\\n for ref_tuple in ref_count_list:\\n ref_id = int(ref_tuple[0]) - 1\\n ref_count = int(ref_tuple[1])\\n ref_objects[ref_id].scopus_cite_count = ref_count\\n\\n # All done!\\n # ---------\\n return ref_objects\",\n \"def get_tagged_corefs(xml, ordered=False):\\n nps = {}\\n if ordered:\\n nps = []\\n\\n xml = _normalize_malformed_xml(xml)\\n\\n try:\\n corefs = parseString(xml).getElementsByTagName('COREF')\\n except ExpatError:\\n return nps\\n\\n for coref in corefs:\\n try:\\n cid = coref.attributes['ID'].value\\n if ordered:\\n data = {}\\n for npid, np in nps:\\n if npid == cid:\\n data = np\\n break\\n else:\\n data = nps.get(cid, {})\\n except KeyError:\\n continue\\n\\n try:\\n data['ref'] = coref.attributes['REF'].value\\n except KeyError:\\n data['ref'] = None\\n\\n data['text'] = coref.firstChild.data\\n if ordered:\\n nps.append((cid, data))\\n else:\\n nps[cid] = data\\n\\n\\n return nps\",\n \"def findall():\\n\\n dataset = {\\n \\\"curso\\\": [],\\n \\\"materia\\\": [],\\n \\\"professor\\\": [],\\n \\\"horas\\\": [],\\n \\\"ids\\\": []\\n }\\n request_data_get = cursos_collections.find()\\n\\n for result in request_data_get:\\n dataset['curso'].append(result[\\\"curso\\\"])\\n dataset['materia'].append(result[\\\"materia\\\"])\\n dataset['professor'].append(result[\\\"professor\\\"])\\n dataset['horas'].append(result[\\\"horas\\\"])\\n dataset['ids'].append(str(result[\\\"_id\\\"]))\\n\\n return dataset\",\n \"def recursion_search_citations(self, paper_pmid, k):\\n if k == 0: return\\n print('recursion degree = ' + str(k))\\n original_paper = self.papers_dict[paper_pmid]\\n if original_paper == None or original_paper.pm_cited == None: return\\n print('paper ' + original_paper.pmid + ' has ' + str(len(original_paper.pm_cited)) + ' citations')\\n for citing_paper_pmid in original_paper.pm_cited:\\n citing_paper = self.paper_builder.build_paper(citing_paper_pmid)\\n if citing_paper_pmid not in self.papers_dict and citing_paper_pmid not in self.csv_papers_dict:\\n self.papers_dict[citing_paper_pmid] = citing_paper\\n self.recursion_search_citations(citing_paper.pmid, k - 1)\\n else:\\n citing_paper = self.papers_dict[citing_paper_pmid]\\n #add citation info\\n citation_year_gap = str(int(citing_paper.year) - int(original_paper.year))\\n if citation_year_gap in original_paper.citations:\\n (sum, count) = original_paper.citations[citation_year_gap]\\n original_paper.citations[citation_year_gap] = (sum + citing_paper.h_index, count +1)\\n else:\\n original_paper.citations[citation_year_gap]=(citing_paper.h_index, 1)\\n citing_paper.add_to_pm_cite(paper_pmid)\\n citation_weighted_avg = 0\\n gap_sum = 0\\n for year_gap, (sum, counter) in original_paper.citations.items():\\n weight = 20 - int(year_gap)\\n citation_weighted_avg += weight * (sum/counter)\\n gap_sum += weight\\n original_paper.citation_weighted_avg = citation_weighted_avg/gap_sum\",\n \"def get_book_data(isbn: int):\\n try:\\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\\n except StopIteration:\\n pass # actually, I could put the whole rest of the function here\\n else:\\n data = core.Book.view_str(book.id)\\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\\n del data['borrowed_by_id'], data['__str__']\\n return data\\n\\n try:\\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\\n r.raise_for_status()\\n except requests.exceptions.RequestException:\\n raise core.BuchSchlossError('no_connection', 'no_connection')\\n\\n person_re = re.compile(r'(\\\\w*, \\\\w*) \\\\((\\\\w*)\\\\)')\\n results = {'concerned_people': []}\\n\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n if table is None:\\n # see if we got multiple results\\n link_to_first = page.select_one('#recordLink_0')\\n if link_to_first is None:\\n raise core.BuchSchlossError(\\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n\\n for tr in table.select('tr'):\\n td = [x.get_text('\\\\n').strip() for x in tr.select('td')]\\n if len(td) == 2:\\n if td[0] == 'Titel':\\n results['title'] = td[1].split('/')[0].strip()\\n elif td[0] == 'Person(en)':\\n for p in td[1].split('\\\\n'):\\n g = person_re.search(p)\\n if g is None:\\n continue\\n g = g.groups()\\n if g[1] == 'Verfasser':\\n results['author'] = g[0]\\n else:\\n results['concerned_people'].append(g[1]+': '+g[0])\\n elif td[0] == 'Verlag':\\n results['publisher'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Zeitliche Einordnung':\\n results['year'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Sprache(n)':\\n results['language'] = td[1].split(',')[0].split()[0].strip()\\n\\n results['concerned_people'] = '; '.join(results['concerned_people'])\\n return results\",\n \"def parse(self, lines):\\n # convert file to string deleting end of line charcters\\n citations_string = self.prepare_file(lines)\\n # extract the entries from the string\\n entries = list(self.find_entries(citations_string))\\n entries.append(len(citations_string))\\n # parse each entry to generate a citation\\n for idx, jdx in zip(entries[:-1], entries[1:]):\\n self.parse_entry(citations_string[idx:jdx])\\n return self.force_field.citations\",\n \"def _filter_cid(self, cids):\\n return [cid for cid in cids if cid is not None]\",\n \"def read_contacts(contacts_file, uidlist, used_uid_names):\\n # Read in out values to a list\\n lines = read_contacts_to_list(contacts_file)\\n\\n # Convert our list to dict\\n contactsdict = contacts_from_list_to_dict(lines, uidlist, used_uid_names)\\n\\n #logging.debug(\\\"Aliasdict: %s\\\" % aliasdict)\\n return contactsdict\",\n \"def references(self):\\n ref_nodes = self.root.xpath(\\\".//bib-reference\\\")\\n return list(\\n itertools.chain.from_iterable(\\n self.get_reference_iter(node) for node in ref_nodes\\n )\\n )\",\n \"def critics_list(catalog):\\n critics_url = list()\\n for ix, letter_pages in enumerate(catalog, 1):\\n for a in fetch(website, letter_pages).find_all(\\\"a\\\", {\\\"class\\\": \\\"a critic-authors__name\\\"}):\\n href_critic = a['href']\\n if str(href_critic)[:7] != \\\"/source\\\":\\n critics_url.append(href_critic + \\\"/movies\\\")\\n print('1/4 — of movie critic URLs scraped.'.format(ix/len(catalog)), end=' ')\\n print('\\\\r{} pages of movie critic URLs successfully scraped.'.format(ix), end=' '); print()\\n return critics_url\",\n \"def references_list( self, theWeaver ):\\n return [ (c.name, c.seq) \\n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]\",\n \"def councils(self):\\n catalog = getToolByName(self.context, 'portal_catalog')\\n\\n return [dict(url=council.getURL(), title=council.Title,\\n address=council.Description) for council in\\n catalog({'object_provides': ICouncil.__identifier__,\\n 'path': dict(query='/'.join(self.context.getPhysicalPath()),\\n depth=1), 'sort_on': 'sortable_title'})]\",\n \"def parse_references(article):\\n reference_list = []\\n references = article.find(\\\"text\\\").find(\\\"div\\\", attrs={\\\"type\\\": \\\"references\\\"})\\n references = references.find_all(\\\"biblstruct\\\") if references is not None else []\\n reference_list = []\\n for reference in references:\\n title = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"a\\\"})\\n if title is None:\\n title = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"m\\\"})\\n title = title.text if title is not None else \\\"\\\"\\n journal = reference.find(\\\"title\\\", attrs={\\\"level\\\": \\\"j\\\"})\\n journal = journal.text if journal is not None else \\\"\\\"\\n if journal is \\\"\\\":\\n journal = reference.find(\\\"publisher\\\")\\n journal = journal.text if journal is not None else \\\"\\\"\\n year = reference.find(\\\"date\\\")\\n year = year.attrs.get(\\\"when\\\") if year is not None else \\\"\\\"\\n authors = []\\n for author in reference.find_all(\\\"author\\\"):\\n firstname = author.find(\\\"forename\\\", {\\\"type\\\": \\\"first\\\"})\\n firstname = firstname.text.strip() if firstname is not None else \\\"\\\"\\n middlename = author.find(\\\"forename\\\", {\\\"type\\\": \\\"middle\\\"})\\n middlename = middlename.text.strip() if middlename is not None else \\\"\\\"\\n lastname = author.find(\\\"surname\\\")\\n lastname = lastname.text.strip() if lastname is not None else \\\"\\\"\\n if middlename is not \\\"\\\":\\n authors.append(firstname + \\\" \\\" + middlename + \\\" \\\" + lastname)\\n else:\\n authors.append(firstname + \\\" \\\" + lastname)\\n authors = \\\"; \\\".join(authors)\\n reference_list.append(\\n {\\\"title\\\": title, \\\"journal\\\": journal, \\\"year\\\": year, \\\"authors\\\": authors}\\n )\\n return reference_list\",\n \"def get_complex_with_ncbi(self, ncbi):\\n result = deque()\\n query = {'ncbi_id': ncbi}\\n projection = {'_id': 0}\\n docs = self.collection.find(filter=query, projection=projection)\\n for doc in docs:\\n result.append(doc)\\n return result\",\n \"def get_curations(self, corpus_id, reader):\\n logger.info('Getting curations for corpus %s' % corpus_id)\\n corpus = self.get_corpus(corpus_id, check_s3=True, use_cache=True)\\n corpus_curations = corpus.get_curations(look_in_cache=True)\\n # Get all statements that have curations\\n curated_stmts = {}\\n for uuid in corpus_curations:\\n curated_stmts[uuid] = corpus.statements[uuid]\\n if reader and reader != 'all':\\n # Filter out statements and curations that don't contain material\\n # from provided reader (in source api of statement)\\n filtered_curations = {}\\n filtered_stmts = {}\\n for uuid, stmt in curated_stmts.items():\\n # Check if any of the evidences are from the provided reader\\n for ev in stmt.evidence:\\n if ev.source_api == reader.lower():\\n filtered_stmts[uuid] = stmt\\n filtered_curations[uuid] = corpus_curations[uuid]\\n break\\n data = {'curations': filtered_curations,\\n 'statements': {uuid: st.to_json() for uuid, st in\\n filtered_stmts.items()}}\\n else:\\n data = {'curations': corpus_curations,\\n 'statements': {uuid: st.to_json() for uuid, st in\\n curated_stmts.items()}}\\n return data\",\n \"def construct_occurrence_dico(data) :\\n print('Constructing occurence dictionnaries...')\\n\\n p_kw_dico = dict()\\n kw_p_dico = dict()\\n full_stem_dico = {}\\n for patent in data :\\n patent_id = patent['id']\\n #[keywords,stem_dico] = extract_keywords(patent[1]+\\\". \\\"+patent[2],patent_id)\\n [keywords,stem_dico] = extract_keywords(patent['title']+\\\". \\\"+patent['abstract'],patent_id)\\n #print(keywords)\\n\\n for k in keywords :\\n # add to p_kw dico\\n if k in kw_p_dico :\\n kw_p_dico[k].append(patent_id)\\n else :\\n kw_p_dico[k]= [patent_id]\\n #\\n if patent_id in p_kw_dico :\\n p_kw_dico[patent_id].append(k)\\n else :\\n p_kw_dico[patent_id] = [k]\\n\\n for k in stem_dico.keys():\\n if k in full_stem_dico :\\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\\n else :\\n full_stem_dico[k] = stem_dico[k]\\n\\n return([p_kw_dico,kw_p_dico,full_stem_dico])\",\n \"def reduce_article_list(self, dfi, institution_keys=None):\\n self.sanity_check()\\n\\n if institution_keys is None:\\n institution_keys = self.config.inst_strings\\n\\n Ps = []\\n for a, x in zip(dfi.authors, dfi.ppr_list):\\n for p in x:\\n\\n isinst = False\\n for aff in p.aff:\\n ncoinc = sum([word in aff for word in institution_keys])\\n thisis = ncoinc > 1\\n isinst = isinst or thisis\\n\\n if isinst:\\n t = [a, p.id, p.bibcode, p.title, p.aff, p.author,\\n p.citation, p.pub, p.reference, p.year, p.pubdate,\\n p.citation_count, p.read_count]\\n Ps.append(t)\\n\\n names = ['author1', 'id', 'bibcode', 'title', 'aff', 'authors',\\n 'citation', 'pub', 'reference', 'year', 'pubdate',\\n 'citation_count', 'read_count']\\n dfo = pd.DataFrame(Ps, columns=names)\\n\\n self.pub_auth_all = dfo\",\n \"def getreferences(self, pmcid, source):\\n import requests\\n r = requests.get(\\n (self.referencesurl).format(source=source, pmcid=pmcid))\\n return r.content\",\n \"def get_crefs_to( ea ):\\r\\n\\tret = []\\r\\n\\txrf = get_first_cref_to( ea )\\r\\n\\tif xrf != BADADDR:\\r\\n\\t\\tret.append( xrf )\\r\\n\\txrf = get_next_cref_to( ea, xrf )\\r\\n\\twhile xrf != BADADDR:\\r\\n\\t\\tret.append( xrf )\\r\\n\\t\\txrf = get_next_cref_to( ea, xrf )\\r\\n\\treturn ret\",\n \"def make_crossref_fundref(dataset: ObservatoryDataset) -> List[Dict]:\\n\\n records = []\\n\\n for funder in dataset.funders:\\n records.append(\\n {\\n \\\"pre_label\\\": funder.name,\\n \\\"funder\\\": f\\\"http://dx.doi.org/{funder.doi}\\\",\\n \\\"country_code\\\": funder.country_code,\\n \\\"region\\\": funder.region,\\n \\\"funding_body_type\\\": funder.funding_body_type,\\n \\\"funding_body_sub_type\\\": funder.funding_body_subtype,\\n }\\n )\\n\\n return records\",\n \"def references(md5):\\n u = Upload.objects.filter(md5=md5).first()\\n if not u:\\n abort(404)\\n # first, is this searchable?\\n is_searchable = False\\n count = elastic.count('page', filter={'md5': md5})\\n if count > 0:\\n is_searchable = True\\n #annotations = Reference.objects.filter(upload=u, ref_url__exists=True)\\n annotations = Reference.objects.filter(upload=u).order_by('ref_pos')\\n # create a list of referenced things\\n references = {'references':[], 'searchable': is_searchable}\\n for a in annotations:\\n try:\\n references['references'].append({\\n 'pos_x': a.pos_x, \\n 'pos': a.pos, \\n 'ref': a.ref_upload.md5, \\n 'ref_pos': a.ref_pos\\n })\\n except:\\n pass\\n return jsonify(references)\",\n \"def testGetRelatedIdentifiers(self):\\n try:\\n # --- Get related identifiers ---\\n pcdcP = PubChemDataCacheProvider(self.__cfgOb, self.__cachePath)\\n rD = pcdcP.getRelatedMapping(self.__cidList)\\n logger.info(\\\"rD %r\\\", rD)\\n self.assertGreaterEqual(len(rD), len(self.__cidList))\\n #\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n self.fail()\",\n \"def build_dict(results, chunk):\\n from math import inf\\n from collections import defaultdict\\n chunk = [int(au) for au in chunk]\\n d = defaultdict(\\n lambda: {\\\"first_year\\\": inf, \\\"pubs\\\": set(), \\\"coauth\\\": set(),\\n \\\"n_coauth\\\": inf, \\\"n_pubs\\\": inf})\\n for pub in results:\\n if not pub.author_ids:\\n continue\\n authors = set([int(au) for au in pub.author_ids.split(\\\";\\\")])\\n for focal in authors.intersection(chunk):\\n d[focal][\\\"coauth\\\"].update(authors)\\n d[focal][\\\"coauth\\\"].remove(focal)\\n d[focal][\\\"pubs\\\"].add(pub.eid)\\n d[focal][\\\"n_pubs\\\"] = len(d[focal][\\\"pubs\\\"])\\n d[focal][\\\"n_coauth\\\"] = len(d[focal][\\\"coauth\\\"])\\n if not pub.coverDate:\\n continue\\n first_year = min(d[focal][\\\"first_year\\\"], int(pub.coverDate[:4]))\\n d[focal][\\\"first_year\\\"] = first_year\\n return d\",\n \"def getcontactcongressdict(ccdump):\\n d = {}\\n for line in ccdump.strip().split('\\\\n'):\\n (district, name, party, dc_office, dc_voice, district_voice, email_form, website) = line.split('\\\\t')\\n dist = ''.join( (district[:2], '-', district[2:]) )\\n d[dist] = email_form\\n return d\",\n \"def fetchRefSeq(genome = 'hg18',lookupval = 'name'):\\n cursor=gbdbConnect(gbdbname=genome)\\n select=\\\"SELECT * FROM refGene\\\"\\n cursor.execute(select)\\n rows=cursor.fetchall()\\n output={}\\n for chr in genomelib.chr_names:\\n output[chr]={}\\n output[chr]['+']={}\\n output[chr]['-']={}\\n for row in rows:\\n if row['chrom'] in genomelib.chr_names:\\n output[row['chrom']][row['strand']][row[lookupval]]=row\\n return output\",\n \"def seq_xref_ids(entry):\\n\\n xref_ids = []\\n exon_data = exons(entry)\\n for ids in xref_data(entry).values():\\n for exon in exon_data:\\n for xref_id in ids:\\n key = \\\"{xref_id}-{gene_id}-{chr}:{start}..{stop}\\\".format(\\n xref_id=xref_id,\\n gene_id=primary_id(entry),\\n chr=exon.chromosome_name,\\n start=exon.primary_start,\\n stop=exon.primary_end,\\n )\\n xref_ids.append((key, exon))\\n\\n return xref_ids\",\n \"def search_for_redbooks(book_codes: tuple):\\n\\n book_dict = {}\\n\\n global setup\\n\\n for book_code in book_codes:\\n URI_string = build_URI_string(book_code)\\n search_web_page = requests.get(URI_string)\\n if search_web_page.status_code != 200:\\n print(\\\"Book with code {} not found! Continuing...\\\".format(book_code))\\n continue\\n web_page_content = search_web_page.content\\n soup = BS(web_page_content, 'html.parser')\\n book_name = soup.find('h1',{'class':'ibm-h1','id':'ibm-pagetitle-h1'}).text\\n book_dict[book_code] = book_name\\n\\n return book_dict\",\n \"def identify_primary_reference_datasets(conn, log):\\n\\n primary_ref = {}\\n\\n primary_ref['refimg_id_ip'] = phot_db.find_primary_reference_image_for_field(conn)\\n\\n query = 'SELECT facility, filter, software FROM reference_images WHERE refimg_id=\\\"'+str(primary_ref['refimg_id_ip'])+'\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n\\n primary_ref['facility_id'] = t['facility'][0]\\n primary_ref['software_id'] = t['software'][0]\\n\\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\\\"ip\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n primary_ref['ip'] = t['filter_id'][0]\\n\\n for f in ['rp', 'gp']:\\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\\\"'+f+'\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n primary_ref[f] = t['filter_id'][0]\\n\\n query = 'SELECT refimg_id FROM reference_images WHERE facility=\\\"'+str(primary_ref['facility_id'])+\\\\\\n '\\\" AND software=\\\"'+str(primary_ref['software_id'])+\\\\\\n '\\\" AND filter=\\\"'+str(t['filter_id'][0])+'\\\"'\\n qs = phot_db.query_to_astropy_table(conn, query, args=())\\n\\n if len(qs) > 0:\\n primary_ref['refimg_id_'+f] = qs['refimg_id'][0]\\n else:\\n log.info('WARNING: Database contains no primary reference image data in filter '+f)\\n\\n log.info('Identified the primary reference datasets for this field as:')\\n for key, value in primary_ref.items():\\n log.info(str(key)+' = '+str(value))\\n\\n return primary_ref\",\n \"def get_collections_details(self, collection, recurse=True):\\n if type(collection) == str:\\n collid = self.get_collection_id(collection)\\n else:\\n collid = collection\\n\\n col_list = [COL_NAME_COLL_COLLID, COL_NAME_COLL_NAME, COL_NAME_COLL_IS_ACTIVE, COL_NAME_COLL_PRID,\\n COL_NAME_COLL_COLLCOMMENT, COL_NAME_COLL_PARENTID]\\n if recurse:\\n rec_list, col_list = self.get_collection_tree(collid, incl_shared=True, col_list=col_list)\\n # exclude the first record it was not expect as per previous implementation and convert to list of dict\\n records = [dict(zip(col_list, rec)) for rec in rec_list[1:]]\\n else:\\n\\n cond = SQLBinaryExpr(COL_NAME_COLL_PARENTID, OP_EQ, \\\":1\\\")\\n records = self.select_generic_data(col_list, table_list=[TABLE_NAME_COLL],\\n where=cond, sqlparams={\\\"1\\\": collid})\\n return records\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64813673","0.6178384","0.60846525","0.6077381","0.6043557","0.59674054","0.59034324","0.58734137","0.58487546","0.58361","0.5798192","0.5778596","0.57374436","0.563813","0.5604259","0.5579555","0.556315","0.5559273","0.5546832","0.5529941","0.5516851","0.55154306","0.55049694","0.549372","0.54813164","0.54626495","0.5462463","0.54599965","0.54466647","0.54413235","0.54204345","0.5384264","0.53696454","0.5368723","0.53371775","0.53249556","0.53216976","0.5314342","0.5297894","0.52812946","0.5272743","0.52721137","0.52696985","0.52664566","0.52651083","0.52547985","0.52405787","0.52296257","0.5218404","0.5218079","0.52064127","0.5192495","0.5190941","0.51905155","0.5176135","0.51669484","0.515762","0.51361567","0.5135656","0.5135583","0.51317286","0.5128462","0.5120397","0.5118705","0.51186866","0.5115568","0.51132405","0.51119083","0.5105252","0.50982344","0.50957525","0.5089644","0.5083859","0.5083263","0.507681","0.50752497","0.50686944","0.50567377","0.5048877","0.5037924","0.50290626","0.50272155","0.5025133","0.50214005","0.5001902","0.49995482","0.4978698","0.49748108","0.4971304","0.49704564","0.49588233","0.49520472","0.494327","0.49425736","0.49225664","0.49185696","0.49113733","0.49096555","0.4907494","0.48996997"],"string":"[\n \"0.64813673\",\n \"0.6178384\",\n \"0.60846525\",\n \"0.6077381\",\n \"0.6043557\",\n \"0.59674054\",\n \"0.59034324\",\n \"0.58734137\",\n \"0.58487546\",\n \"0.58361\",\n \"0.5798192\",\n \"0.5778596\",\n \"0.57374436\",\n \"0.563813\",\n \"0.5604259\",\n \"0.5579555\",\n \"0.556315\",\n \"0.5559273\",\n \"0.5546832\",\n \"0.5529941\",\n \"0.5516851\",\n \"0.55154306\",\n \"0.55049694\",\n \"0.549372\",\n \"0.54813164\",\n \"0.54626495\",\n \"0.5462463\",\n \"0.54599965\",\n \"0.54466647\",\n \"0.54413235\",\n \"0.54204345\",\n \"0.5384264\",\n \"0.53696454\",\n \"0.5368723\",\n \"0.53371775\",\n \"0.53249556\",\n \"0.53216976\",\n \"0.5314342\",\n \"0.5297894\",\n \"0.52812946\",\n \"0.5272743\",\n \"0.52721137\",\n \"0.52696985\",\n \"0.52664566\",\n \"0.52651083\",\n \"0.52547985\",\n \"0.52405787\",\n \"0.52296257\",\n \"0.5218404\",\n \"0.5218079\",\n \"0.52064127\",\n \"0.5192495\",\n \"0.5190941\",\n \"0.51905155\",\n \"0.5176135\",\n \"0.51669484\",\n \"0.515762\",\n \"0.51361567\",\n \"0.5135656\",\n \"0.5135583\",\n \"0.51317286\",\n \"0.5128462\",\n \"0.5120397\",\n \"0.5118705\",\n \"0.51186866\",\n \"0.5115568\",\n \"0.51132405\",\n \"0.51119083\",\n \"0.5105252\",\n \"0.50982344\",\n \"0.50957525\",\n \"0.5089644\",\n \"0.5083859\",\n \"0.5083263\",\n \"0.507681\",\n \"0.50752497\",\n \"0.50686944\",\n \"0.50567377\",\n \"0.5048877\",\n \"0.5037924\",\n \"0.50290626\",\n \"0.50272155\",\n \"0.5025133\",\n \"0.50214005\",\n \"0.5001902\",\n \"0.49995482\",\n \"0.4978698\",\n \"0.49748108\",\n \"0.4971304\",\n \"0.49704564\",\n \"0.49588233\",\n \"0.49520472\",\n \"0.494327\",\n \"0.49425736\",\n \"0.49225664\",\n \"0.49185696\",\n \"0.49113733\",\n \"0.49096555\",\n \"0.4907494\",\n \"0.48996997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.690522"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79587,"cells":{"query":{"kind":"string","value":"Check which items have been cited by one of the authors of the"},"document":{"kind":"string","value":"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cross_check(context, authors, poscom):\n displaynames = [x['author']['displayname'] for x in poscom]\n\n for author in authors:\n if author.user.username not in displaynames:\n context.assertFalse(True, \"%s not in list\" %author.user.username)","def test_citedby_author(self):\n inv_search = 'citedby:author:doggy'\n spi_search = 'find citedby author doggy'\n self._compare_searches(inv_search, spi_search)","def author_ManyToMany_entry_check(): #Old it was OneToMany before adding multiple authors\n import itertools\n entry_author_ids = itertools.chain(*Entry.objects.all().values_list('author_ids', flat=True))\n entry_author_ids_set = set(entry_author_ids)\n user_ids = set(User.objects.all().values_list('id',flat=True))\n\n author_id_not_in_user = entry_author_ids_set - user_ids\n\n if author_id_not_in_user:\n return (\"Error: There are entries without a correct cross relation with user: {}\"\n .format(\",\".join(str(s) for s in author_id_not_in_user)))\n else:\n return \"OK\"","def authors_in_relation(context, data, authors):\n guids = [a.id for a in authors]\n guids = map( lambda x: str(x).replace('-', ''), guids)\n\n for guid in guids:\n context.assertTrue(unicode(guid) in data)","def authors(self, key, value):\n _authors = self.get(\"authors\", [])\n item = build_ils_contributor(value)\n if item and item not in _authors:\n _authors.append(item)\n try:\n if \"u\" in value:\n other = [\"et al.\", \"et al\"]\n val_u = list(force_list(value.get(\"u\")))\n if [i for i in other if i in val_u]:\n self[\"other_authors\"] = True\n except UnexpectedValue:\n pass\n return _authors","def test_citedby_refersto_author(self):\n inv_search = 'refersto:citedby:author:penguin'\n spi_search = 'find refersto citedby author penguin'\n self._compare_searches(inv_search, spi_search)","def all_authors( data ) :\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))","def test_search_author(self):\n\n\t\titem_id = mock_item(title='Dummy Title', author='Made Up Author')[0]\n\n\t\titem = models.search('Made')[0]\n\t\tself.assertEqual(item['id'], item_id)","def test_author_filtering(self):\n # Get a valid author\n entry = Entry.objects.get(id=1)\n params = {\"author\": entry.first_author.id}\n\n self._test_filtering(**params)","def test_search_by_author(self):\n s1 = System()\n b1 = Books(\"1984\", \"George Orwell\", \"Harvill Secker\", \"1949\", \"0123456789123\")\n self.assertEqual(s1.search_by_author(\"George Orwell\"), 0)\n s1.add_resource(b1)\n self.assertEqual(s1.search_by_author(\"George Orwell\"), 1)","def authors_completion(self, terms):\n return self.db.execute(u'''SELECT * FROM \"authors\" WHERE name LIKE ? LIMIT 50''', (u\"%{}%\".format(terms),)).fetchall()","def test_discussion_filter_author(self):\n author_vals = (\n ('DoesNotExist', 0),\n ('admin', 1),\n ('jsocol', 4),\n )\n\n for name, number in author_vals:\n u = UserFactory(username=name)\n for i in range(number):\n thread1 = ThreadFactory(title=u'audio')\n PostFactory(thread=thread1, author=u)\n\n self.refresh()\n\n qs = {'a': 1, 'w': 4, 'format': 'json'}\n\n for author, total in author_vals:\n qs.update({'author': author})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(total, json.loads(response.content)['total'])","def getAuthors(self): #$NON-NLS-1$\r","def test_by_author(self):\n self.assert_equal(MinusRecord.objects.count(),11)\n self.go200('minus_by_author', ['Test0'])\n self.find(CR % 'Test0')\n for i in xrange(10):\n if i % 2 == 0:\n self.find(CR % ('test'+str(i)))\n else:\n self.notfind(CR % ('test'+str(i)))\n self.find(CR % \"midi_rec\")","def citing_authors(self, entities):\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\n FROM \"entity_occurrences\"\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\n return [t[0] for t in result]","def test_search_client_by_author(self, mock_get):\n\n response = isbn_utils.search_by(self.filter_author, self.author)\n self.assertEqual(response.data, json.loads(open(UNIT_TEST_RESOURCES_FOLDER +\n FILE_NAME_AUTHOR_SEARCH_RESPONSE).read())[\"data\"])","def test_multiple_authors(self):\r\n args = self.page_kwargs.copy()\r\n content = Page(**args)\r\n assert content.authors == [content.author]\r\n args['metadata'].pop('author')\r\n args['metadata']['authors'] = ['First Author', 'Second Author']\r\n content = Page(**args)\r\n assert content.authors\r\n assert content.author == content.authors[0]","def seen(user, item):\n print(\"---\", item.seen_by(user))\n return item.seen_by(user)","def test_author_simple(self):\n invenio_search = 'author:\"ellis, j*\"'\n spires_search = 'find a ellis, j'\n self._compare_searches(invenio_search, spires_search)","def collection_special_author(user_id, author_id):\n\n another_user_id = author_id\n if (user_id == another_user_id):\n return \"self\"\n query = db_session.query(Collection_User).filter_by(\n user_id=user_id, another_user_id=another_user_id).all()\n if len(query) == 0:\n collect_usr = Collection_User(user_id=user_id,\n another_user_id=another_user_id,\n time=datetime.now())\n db_session.add(collect_usr)\n db_session.commit()\n update_collection_num(user_id, another_user_id, True)\n else:\n return \"already\"\n return \"success\"","def any_author_exists(self):\n return bool(self.mapper.count())","def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in","def test_absorbs_naked_author_search(self):\n invenio_search = \"author:ellis\"\n spi_search = \"author ellis\"\n self._compare_searches(invenio_search, spi_search)","def test_authors():\n assert(hasattr(tekel, '__authors__'))","def test_draft_list_only_display_authors_aids(client, contributor):\n\n AidFactory(name='Is this the real life?', author=contributor)\n AidFactory(name='Is this just fantasy?')\n\n client.force_login(contributor)\n drafts_url = reverse('aid_draft_list_view')\n res = client.get(drafts_url)\n\n content = res.content.decode('utf-8')\n assert 'Is this the real life?' in content\n assert 'Is this just fantasy?' not in content","def test_refersto_author(self):\n inv_search = 'refersto:author:kitty'\n spi_search = 'find refersto author kitty'\n self._compare_searches(inv_search, spi_search)","def assertAuthorsInComments(context, authors, comments):\n cross_check(context, authors, comments)","def assertAuthorsInPosts(context, authors, posts):\n cross_check(context, authors, posts)","def test_list_all_authors(self):\n response = self.client.get(reverse('authors') + '?page=2')\n self.assertEqual(response.status_code, 200)\n self.assertTrue('is_paginated' in response.context)\n self.assertTrue(response.context['is_paginated'] is True)\n self.assertTrue(len(response.context['author_list']) == 3)","def authors(self):\n return self.properties.get('Authors', ClientValueCollection(SharedWithMeDocumentUser))","def test_author_simplest(self):\n invenio_search = 'author:ellis'\n spires_search = 'find a ellis'\n self._compare_searches(invenio_search, spires_search)","def test_get_all_authors(self):\n self.register_user()\n token = self.login_user()\n response = self.client.get(self.user_author, format='json', HTTP_AUTHORIZATION='Token ' +token)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def authors():\n\tclick.clear()\n\trich.print(\"[bold]IDT[/bold] was initially made by [bold magenta]Deliton Junior[/bold magenta] and [bold red]Misael Kelviny[/bold red]\")","def display_authors(self, *args):\n return ', '.join(author.name for author in args[0].authors.all()[:3])","def test_author_full_initial(self):\n inv_search = 'author:\"klebanov, ig* r*\" or exactauthor:\"klebanov, i r\"'\n spi_search = \"find a klebanov, ig.r.\"\n self._compare_searches(inv_search, spi_search)","def authors(self):\n raise BookInfoNotImplementedError('authors', self.__class__.__name__)","def test_collection_author_year_filtering(self):\n # Create a collection\n entries = Entry.objects.filter(id__in=(1, 5, 10, 15))\n collection = CollectionFactory(entries=entries)\n entry = Entry.objects.get(id=1)\n\n # Get a valid collection\n params = {\n \"collection\": collection.id,\n \"author\": entry.first_author.id,\n \"year\": entry.publication_date.year,\n }\n self._test_filtering(**params)","def test_refersto_author_multi_name(self):\n inv_search = 'author:ellis refersto:author:\"parke, s. j.\"'\n spi_search = 'find a ellis and refersto author \"parke, s. j.\"'\n self._compare_searches(inv_search, spi_search)","def items_should_contain_owner_object(context):\n items = context.response.json()['items']\n existing_owner_fields = [\n 'accept_rate', 'display_name', 'link', 'profile_image', 'reputation',\n 'user_id', 'user_type'\n ]\n non_existing_owner_fields = ['display_name', 'user_type']\n\n for item in items:\n assert 'owner' in item\n assert isinstance(item['owner'], dict)\n owner = item['owner']\n if owner['user_type'] == 'does_not_exist':\n all(field in owner for field in non_existing_owner_fields)\n logging.debug(\n ('Item %d contains an non-existing Owner object for user \"%s\" '\n 'with all required fields: %s'), item['question_id'], \n owner['display_name'], ', '.join(non_existing_owner_fields))\n else:\n all(field in owner for field in existing_owner_fields)\n logging.debug(\n ('Item %d contains an existing Owner object for user ID \"%d\" '\n 'with all required fields: %s'), item['question_id'], \n owner['user_id'], ', '.join(existing_owner_fields))","def _on_authors_list(self, evt):\n \n # raise authors management dialog\n dlg = AuthorsView(self, self._library)\n response = dlg.ShowModal()\n dlg.Destroy()\n \n # check response\n if response != wx.ID_OK:\n return\n \n # refresh collections view\n self._collections_view.UpdateCounts()\n \n # refresh articles view\n self._articles_view.ShowArticles()","def test_asked_by(self):\n author_vals = (\n ('DoesNotExist', 0),\n ('jsocol', 2),\n ('pcraciunoiu', 2),\n )\n\n # Set up all the question data---creats users, creates the\n # questions, shove it all in the index, then query it and see\n # what happens.\n for name, number in author_vals:\n u = UserFactory(username=name)\n for i in range(number):\n ques = QuestionFactory(title=u'audio', creator=u)\n ques.tags.add(u'desktop')\n ans = AnswerFactory(question=ques)\n AnswerVoteFactory(answer=ans, helpful=True)\n\n self.refresh()\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n\n for author, total in author_vals:\n qs.update({'asked_by': author})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(total, json.loads(response.content)['total'])","def test_func(self):\n taxonomy = self.get_object()\n return self.request.user == taxonomy.author","def test_func(self):\n taxonomy = self.get_object()\n return self.request.user == taxonomy.author","def filter_publication(publication, cmp_authors=True):\n query = None\n isbn_query = False\n\n # there can be ISBN query or book title query\n if publication.optionals and publication.optionals.ISBN:\n query = aleph.ISBNQuery(publication.optionals.ISBN)\n isbn_query = True\n else:\n query = aleph.TitleQuery(publication.title)\n\n result = aleph.reactToAMQPMessage(aleph.SearchRequest(query), \"\")\n\n if not result.records:\n return publication # book is not in database\n\n # if there was results with this ISBN, compare titles of the books\n # (sometimes, there are different books with same ISBN because of human\n # errors)\n if isbn_query:\n for record in result.records:\n epub = record.epublication\n\n # try to match title of the book\n if compare_names(epub.nazev, publication.title) >= 80:\n return None # book already in database\n\n return publication\n\n # checks whether the details from returned EPublication match Publication's\n for record in result.records:\n epub = record.epublication\n\n # if the title doens't match, go to next record from aleph\n if not compare_names(epub.nazev, publication.title) >= 80:\n continue\n\n if not cmp_authors:\n return None # book already in database\n\n # compare authors names\n for author in epub.autori:\n # convert Aleph's author structure to string\n author_str = \"%s %s %s\" % (\n author.firstName,\n author.lastName,\n author.title\n )\n\n # normalize author data from `publication`\n pub_authors = map(lambda x: x.name, publication.authors)\n if type(pub_authors) not in [list, tuple, set]:\n pub_authors = [pub_authors]\n\n # try to compare authors from `publication` and Aleph\n for pub_author in pub_authors:\n if compare_names(author_str, pub_author) >= 50:\n return None # book already in database\n\n return publication # book is not in database","def test_author_full_first(self):\n invenio_search = 'author:\"ellis, john*\" or exactauthor:\"ellis, j *\" or exactauthor:\"ellis, j\" or exactauthor:\"ellis, jo\" or exactauthor:\"ellis, joh\" or author:\"ellis, john, *\"'\n spires_search = 'find a ellis, john'\n self._compare_searches(invenio_search, spires_search)","def popAuthors(self):\r\n# cur = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\")\r\n# res = cur.fetchall()\r\n res = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\").fetchall()\r\n\r\n self.authorList = [formatNameSQL(ln) for ln in res]\r\n self.quickAuthors = [ln[\"Lastname\"].lower() for ln in res]\r\n vals = [ln[\"PersonID\"] for ln in res]\r\n \r\n self.authorLookup = dict(zip(self.authorList,vals))","def is_one_degree_separated(self,author1, author2):\n self.cursor.execute(\"\"\"SELECT COUNT(*) FROM wrote W WHERE W.authorID = %s AND EXISTS\n (SELECT * FROM wrote W2 WHERE W2.authorID = %s AND W.ISBN = W2.ISBN)\"\"\", (author1, author2))\n if int(self.cursor.fetchone()[0]):\n return True\n return False","def added_by(self, user):\n return ChefsHasRecipes.objects.filter(recipe=self, chef=user).exists()","def liked_by(self, user):\n return Likes.objects.filter(recipe=self, chef=user).exists()","def test_author_year_filtering(self):\n # Get a valid date\n entry = Entry.objects.get(id=1)\n params = {\"author\": entry.first_author.id, \"year\": entry.publication_date.year}\n\n self._test_filtering(**params)","def userCanAffordItemObj(self, user : bbUser.bbUser, item : bbItem.bbItem) -> bool:\n return user.credits >= item.getValue()","def test_func(self):\n taxonomy = self.get_taxonomy()\n return self.request.user == taxonomy.author","def test_exactauthor_simple(self):\n invenio_search = 'exactauthor:\"ellis, j\"'\n spires_search = 'find ea ellis, j'\n self._compare_searches(invenio_search, spires_search)","def test_refersto_author_multi_name_no_quotes(self):\n inv_search = 'author:ellis refersto:(author:\"parke, sj*\" or exactauthor:\"parke, s *\" or exactauthor:\"parke, s\" or author:\"parke, sj, *\")'\n spi_search = \"find a ellis and refersto author parke, sj\"\n self._compare_searches(inv_search, spi_search)","def user(self, request):\n\t\treturn super(cbotManager, self).get_queryset().filter(author=request.user)","def __contains__(self, item):\n return item in self._fetch()","def __contains__(self, item):\n return item in self.contents","def test_super_short_author_name(self):\n spi_search = \"fin a er and cn cms\"\n inv_search = \"author:er collaboration:cms\"\n self._compare_searches(inv_search, spi_search)","def test_author_view(self):\n table = self.get_change_page_form(self.user_1_client, self.question_1.pk)\n self.assertInHTML('<label>Title:</label><div class=\"readonly\">Lorem</div>', table)\n # The label's `for` attribute is present only on editable fields\n self.assertInHTML('<label class=\"required\" for=\"id_body\">Body:</label>', table)\n self.assertInHTML(\n '<label>Author:</label><div class=\"readonly\"><a href=\"/auth/user/2/change/\">user_1</a></div>', table)\n self.assertInHTML(\n '<div><label>Is published:</label><div class=\"readonly\"><img src=\"/static/admin/img/icon-yes.svg\" '\n 'alt=\"True\"></div></div>', table)","def test_get_authors_from_data(self):\n responses.add(\n responses.GET,\n \"https://openlibrary.org/authors/OL382982A\",\n json={\n \"name\": \"George Elliott\",\n \"personal_name\": \"George Elliott\",\n \"last_modified\": {\n \"type\": \"/type/datetime\",\n \"value\": \"2008-08-31 10:09:33.413686\",\n },\n \"remote_ids\": {\n \"isni\": \"000111\",\n },\n \"key\": \"/authors/OL453734A\",\n \"type\": {\"key\": \"/type/author\"},\n \"id\": 1259965,\n \"revision\": 2,\n },\n status=200,\n )\n results = self.connector.get_authors_from_data(self.work_data)\n result = list(results)[0]\n self.assertIsInstance(result, models.Author)\n self.assertEqual(result.name, \"George Elliott\")\n self.assertEqual(result.openlibrary_key, \"OL453734A\")\n self.assertEqual(result.isni, \"000111\")","def __contains__(self, item):\n return item in self._terms","def getInCitesAuthorInfo (self, authorInfo):\n\t\tprint 'getInCitesAuthorInfo with ' + authorInfo.fullname\n\t\tid = authorInfo.id\n\t\t\n\t\ttogglerClass = authorInfo.matches and \"toggler\" or \"\"\n\t\t\n\t\ttoggler = DIV (id=id, klass=togglerClass)\n\t\t\n\t\tif authorInfo.numMatches > 0:\n\t\t\ttogglerLnkClass = \"inCitesAuthor togglerClosed\"\n\t\telse:\n\t\t\ttogglerLnkClass = \"inCitesAuthor noTogglerClosed\"\n\t\t# print \"%s %s\" % (authorInfo.inCitesName, authorInfo.numMatches)\n\t\t\t\t\t\t \n\t\ttogglerLnk = DIV(id='toggler-lnk-'+id, klass=togglerLnkClass)\n\t\ttoggler.append(togglerLnk)\n\t\t\n\t\tauthorTable = TABLE(klass=\"authorTable\")\n\t\ttogglerLnk.append(authorTable)\n\t\tmatchesContent = '(%d matches)' % authorInfo.numMatches\n\t\tauthorTable.append(\n\t\t\tTR(\n\t\t\t\tTD (authorInfo.fullname, klass=\"author\"),\n\t\t\t\tTD (matchesContent, klass=\"matches\")\n\t\t\t)\n\t\t)\n\n\t\tif authorInfo.numMatches > 0:\n\t\t\ttogglerCon = DIV(id='toggler-con-'+id, style=\"display:none\")\n\t\t\ttoggler.append(togglerCon)\n\t\t\tmatchTable = TABLE(klass=\"matchTable\")\n\t\t\ttogglerCon.append(matchTable)\n\t\t\t\n\t\t\tfor match in authorInfo.matches:\n\t\t\t\tmatch_row = TR (klass=\"peopleDBmatch\", id=match.upid)\n\t\t\t\tmatch_row.append (\n\t\t\t\t\tTD (match.getName(), klass=\"match-name\"),\n\t\t\t\t\tTD (match.upid, klass=\"match-upid\"))\n\t\t\t\tmatchTable.append(match_row);\n\t\treturn toggler","def checker(item):\n return item in INSTOCK","def test_author_many_initials(self):\n inv_search = 'author:\"bach, p* d* q*\"'\n spi_search = 'find a p d q bach'\n self._compare_searches(inv_search, spi_search)","def test_author_initials(self):\n inv_search = 'author:\"polyakov, a* m*\"'\n spi_search = 'find a a m polyakov'\n self._compare_searches(inv_search, spi_search)","def is_liked_by(self, user):\n return user.liked_articles.filter(pk=self.pk).exists()","def test_retrieve_authors(self):\n sample_author()\n sample_author()\n\n res = self.client.get(reverse('authors'))\n authors = Author.objects.all()\n serializer = AuthorSerializer(authors, many=True)\n self.assertEqual(res.data, serializer.data)\n self.assertEqual(res.status_code, status.HTTP_200_OK)","def test_find_first_author(self):\n inv_search = 'firstauthor:ellis'\n spi_search = 'find fa ellis'\n self._compare_searches(inv_search, spi_search)","def run(self):\n authors = sorted(set(self.load_files_info()))\n\n # Show list of authors\n click.echo('List of users:')\n for n, author in enumerate(authors, start=1):\n click.echo(f'{n}) {author}')\n\n # Clean list and return\n remove = self._ask_remove()\n\n return [x[1] for x in enumerate(authors) if x not in remove]","def popular_authors() :\n query = \"\"\"SELECT authors.name,count(*) AS total_views FROM authors,articles,log WHERE log.path like concat ('/article/',articles.slug)\n AND articles.author=authors.id group by authors.name order by total_views desc\"\"\"\n result = get_data(query)\n print(\" 2. The most popular articles authors of all time:\")\n print(\"\")\n for record in result :\n print(' ' +' ' + str(record[0]) + ' -' + ' ' + str(record[1]) + ' ' +'views')\n print(\" \")","def question_2():\n cursor.execute(mostPopAuthors)\n output = cursor.fetchall()\n return output","def test_search_client_by_author_page2(self, mock_get):\n\n response = isbn_utils.search_by(self.filter_author, self.author, page=\"2\")\n self.assertEqual(response.data, json.loads(open(UNIT_TEST_RESOURCES_FOLDER +\n FILE_NAME_AUTHOR_SEARCH_PAGE_RESPONSE).read())[\"data\"])","def has_items(self):\r\n return self.orderitem_set.exists() # pylint: disable=E1101\r","def test_author_many_lastnames(self):\n inv_search = 'author:\"alvarez gaume, j* r* r*\"'\n spi_search = 'find a alvarez gaume, j r r'\n self._compare_searches(inv_search, spi_search)","def get_authors(self):\n\n names = []\n rows = []\n\n try:\n rows = self.find_elements_in_owner(self.locators['author_row'])\n except NoSuchElementException:\n # there are no authors\n rows = []\n\n for rowEle in rows:\n authorname = self._get_author(rowEle)\n names.append(authorname)\n return names","def match(self, filter_text):\n\n return filter_text.lower() in self.author.lower() or \\\n super().match(filter_text)","def test_author_list_equality_with_invalid_authentication(self) -> None:\n\n # Let's check for a request with no authorization\n\n response: Response = self.client.get(self.url)\n data = u.get_json(response)\n\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n self.assertEqual(data, {\n 'detail': 'Authentication credentials were not provided.'\n })\n\n # Now lets check with an Author without permissions.\n\n # Select the underprivileged author randomly.\n author: Author = random.choice(self.authors)\n\n self.client.credentials(HTTP_AUTHORIZATION=u.auth_header(author.get_key()))\n\n response: Response = self.client.get(self.url)\n data: typing.Dict[typing.Any, typing.Any] = u.get_json(response)\n\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(data, {\n 'detail': 'You do not have permission to perform this action.'\n })","def find_relations_among_authors():\n for book in books:\n if len(books[book]) > 1:\n for i in range(len(books[book])):\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]","def query_by_author(cls,aid):\n gds = from_cache('VG_%s'%aid)\n if not gds:\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).filter('author =',aid).fetch(1000)]\n to_cache('VG_%s'%aid, gds)\n return SuiGoods.load_by_ids(gds)","def checkPaper(self,event=None):\r\n if self.title.getVal() not in self.paperList:\r\n self.paperList.append(self.title.getVal())\r\n self.paperList.sort()\r\n self.title.updateVals(self.paperList)\r\n return\r\n\r\n ## This section of code should probably go into setData. . .\r\n self.authorBox.clearData()\r\n\r\n cur = self.dbConn.execute(\"SELECT People.* FROM Papers JOIN People on Papers.PrimaryAuthor = People.PersonID WHERE Papers.Title = ?\",[self.title.getVal()])\r\n res = cur.fetchone()\r\n if res ==None:\r\n self.primeAuthor.setVal(\"No Author Found; Check database\")\r\n return\r\n self.primeAuthor.setVal(formatNameSQL(res))\r\n self.addPrimeAuthorFn()\r\n\r\n cur = self.dbConn.execute(\"SELECT People.* FROM Papers JOIN People on Papers.CorrespondingAuthor = People.PersonID WHERE Papers.Title = ?\",[self.title.getVal()])\r\n res = cur.fetchone()\r\n if res == None:\r\n self.correspond.setVal(self.primeAuthor.getVal())\r\n else:\r\n self.correspond.setVal(formatNameSQL(res))\r\n\r\n cur = self.dbConn.execute(\"SELECT People.* FROM Papers JOIN People JOIN CoAuthors ON Papers.paperID = CoAuthors.PaperID AND People.PersonID = CoAuthors.Author WHERE Papers.Title = ?\",[self.title.getVal()])\r\n res = cur.fetchall()\r\n if res == None:\r\n return\r\n for ln in res:\r\n curAuthor = str.format(formatNameSQL(ln))\r\n self.authorBox.addLine(curAuthor)\r\n self.coAuthor.setVal(curAuthor)","def is_valid_book(current_author, inputed_name, availale_books):\n\tbook_info = []\n\tauthor_book = {}\n\n\tfor book in availale_books:\n\t\tauthor = book.author.username\n\t\tauthor_book[author] = book.book_name\n\t\tbook_info.append(author_book)\n\t\tauthor_book = {}\n\n\tfor book in book_info:\n\t\tfor author, book_name in book.items():\n\t\t\tif book_name == inputed_name and author == current_author:\n\t\t\t\treturn False\n\n\treturn True","def print_popular_authors(authors):\n for (author, views) in authors:\n print \"%s - %d views\" % (author, views)","def get_popular_authors():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n query_popular_authors = \"\"\"\n SELECT aut.name, COUNT(lg.id) AS views\n FROM articles AS art\n JOIN log AS lg ON art.slug = SUBSTRING(lg.path,10)\n AND lg.status = '200 OK'\n JOIN authors AS aut ON aut.id = art.author\n GROUP BY aut.name\n ORDER BY views desc; \"\"\"\n c.execute(query_popular_authors)\n authors = from_db_cursor(c)\n db.close()\n return authors","def has_item(item: Item):\n async def _wrapper(ctx):\n if not (res := 0 < await ctx.db.get(\"items\", ctx.author, item.id)):\n name = (f\"an \" if any(item.name.startswith(vowel) for vowel in \"aeiou\") else \"a \") + f\"**{item}**\"\n await ctx.send(f\"You need to own {name} in order to use this command.\" + (\n f\" You can go buy one from the shop! (`{ctx.clean_prefix}shop`)\" if item.buyable else \"\"\n ))\n return res\n\n return discord.ext.commands.check(_wrapper)","def query_authors(cls):\n authors = from_cache('AuthorsList')\n if not authors:\n authors = SuiAuthor.all().order('name').fetch(400)\n to_cache('AuthorsList', authors)\n return authors","def _validate_contributors_summary(cls, item):\n contributor_ids_from_contributors_summary = (\n list(item.contributors_summary.keys()))\n if sorted(item.contributor_ids) != sorted(\n contributor_ids_from_contributors_summary):\n cls._add_error(\n 'contributors %s' % (\n base_model_validators.ERROR_CATEGORY_SUMMARY_CHECK),\n 'Entity id %s: Contributor ids: %s do not match the '\n 'contributor ids obtained using contributors summary: %s' % (\n item.id, sorted(item.contributor_ids),\n sorted(contributor_ids_from_contributors_summary)))","def has_item(self, item):\n return item in self.set","def has_item(self, item):\n return item in self.set","def test_user_can_change_as_author(self):\n self.assertTrue(self.story.user_can_change(self.user1))","def test_author_reverse(self):\n invenio_search = 'author:\"ellis, j*\"'\n spires_search = 'find a j ellis'\n self._compare_searches(invenio_search, spires_search)","def popular_authors():\n query = \"\"\"select authors.name, sum(views)\n from authors, articles, article_path_views\n where authors.id = articles.author\n and '/article/' || articles.slug = article_path_views.path\n group by authors.name\n order by sum desc\"\"\"\n result_table = execute_query(query)\n\n # generate a report from table_to_report() function\n report = table_to_report(result_table, ' views')\n return \"Authors and Their Articles' Total Views:\\n\" + report","def _validate_author_id(cls, item):\n if (\n item.author_id and\n not user_services.is_user_or_pseudonymous_id(item.author_id)\n ):\n cls._add_error(\n 'final %s' % (\n base_model_validators.ERROR_CATEGORY_AUTHOR_CHECK),\n 'Entity id %s: Author ID %s is in a wrong format. '\n 'It should be either pid_<32 chars> or uid_<32 chars>.'\n % (item.id, item.author_id))","def item_exists(item_id):\n return item_id in all_items","def count_by_author(all_articles):\n author_count = {}\n\n for title in all_articles:\n author = \", \".join(all_articles[title]['authors'])\n if author not in author_count:\n author_count[author] = 1\n else:\n author_count[author] = author_count[author] + 1\n \n print_all_items_in_dict(author_count)","def collection_special_author_cancel(user_id, author_id):\n\n another_user_id = author_id\n if (user_id == another_user_id):\n return \"self\"\n query = db_session.query(Collection_User).filter_by(\n user_id=user_id, another_user_id=another_user_id).all()\n if len(query) == 1:\n db_session.delete(query[0])\n db_session.commit()\n update_collection_num(user_id, another_user_id, False)\n else:\n return \"already\"\n return \"success\"","def find_author(author):\n case = author_case(author)\n queries = author_queries(case, author)\n author_uri_set = set([])\n for query in queries:\n result = vivo_sparql_query(query.encode('utf-8'))\n count = len(result['results']['bindings'])\n if count == 1:\n author_uri_set = set([result['results']['bindings'][0]\\\n ['uri']['value']])\n break\n elif count > 1 and count < len(author_uri_set):\n author_uri_set = set([])\n for row in result['results']['bindings']:\n author_uri_set.add(row['uri']['value'])\n return author_uri_set","def get_authors(self):\n return [aer.author for aer in self.authorentryrank_set.all()]","def test_func(self):\n post = self.get_object()\n\n return self.request.user == post.author","def test_func(self):\n post = self.get_object()\n\n return self.request.user == post.author","def under_three_opens(name, state):\n counter = 0\n for item in state['items']:\n if item['seller'] == name:\n counter += 1\n return counter < 3","def alt_authors(self, key, value):\n _authors = self.get(\"authors\", [])\n if _authors:\n for i, v in enumerate(force_list(value)):\n _authors[i].update({\"alternative_names\": clean_val(\"a\", v, str)})\n return _authors"],"string":"[\n \"def cross_check(context, authors, poscom):\\n displaynames = [x['author']['displayname'] for x in poscom]\\n\\n for author in authors:\\n if author.user.username not in displaynames:\\n context.assertFalse(True, \\\"%s not in list\\\" %author.user.username)\",\n \"def test_citedby_author(self):\\n inv_search = 'citedby:author:doggy'\\n spi_search = 'find citedby author doggy'\\n self._compare_searches(inv_search, spi_search)\",\n \"def author_ManyToMany_entry_check(): #Old it was OneToMany before adding multiple authors\\n import itertools\\n entry_author_ids = itertools.chain(*Entry.objects.all().values_list('author_ids', flat=True))\\n entry_author_ids_set = set(entry_author_ids)\\n user_ids = set(User.objects.all().values_list('id',flat=True))\\n\\n author_id_not_in_user = entry_author_ids_set - user_ids\\n\\n if author_id_not_in_user:\\n return (\\\"Error: There are entries without a correct cross relation with user: {}\\\"\\n .format(\\\",\\\".join(str(s) for s in author_id_not_in_user)))\\n else:\\n return \\\"OK\\\"\",\n \"def authors_in_relation(context, data, authors):\\n guids = [a.id for a in authors]\\n guids = map( lambda x: str(x).replace('-', ''), guids)\\n\\n for guid in guids:\\n context.assertTrue(unicode(guid) in data)\",\n \"def authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n item = build_ils_contributor(value)\\n if item and item not in _authors:\\n _authors.append(item)\\n try:\\n if \\\"u\\\" in value:\\n other = [\\\"et al.\\\", \\\"et al\\\"]\\n val_u = list(force_list(value.get(\\\"u\\\")))\\n if [i for i in other if i in val_u]:\\n self[\\\"other_authors\\\"] = True\\n except UnexpectedValue:\\n pass\\n return _authors\",\n \"def test_citedby_refersto_author(self):\\n inv_search = 'refersto:citedby:author:penguin'\\n spi_search = 'find refersto citedby author penguin'\\n self._compare_searches(inv_search, spi_search)\",\n \"def all_authors( data ) :\\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))\",\n \"def test_search_author(self):\\n\\n\\t\\titem_id = mock_item(title='Dummy Title', author='Made Up Author')[0]\\n\\n\\t\\titem = models.search('Made')[0]\\n\\t\\tself.assertEqual(item['id'], item_id)\",\n \"def test_author_filtering(self):\\n # Get a valid author\\n entry = Entry.objects.get(id=1)\\n params = {\\\"author\\\": entry.first_author.id}\\n\\n self._test_filtering(**params)\",\n \"def test_search_by_author(self):\\n s1 = System()\\n b1 = Books(\\\"1984\\\", \\\"George Orwell\\\", \\\"Harvill Secker\\\", \\\"1949\\\", \\\"0123456789123\\\")\\n self.assertEqual(s1.search_by_author(\\\"George Orwell\\\"), 0)\\n s1.add_resource(b1)\\n self.assertEqual(s1.search_by_author(\\\"George Orwell\\\"), 1)\",\n \"def authors_completion(self, terms):\\n return self.db.execute(u'''SELECT * FROM \\\"authors\\\" WHERE name LIKE ? LIMIT 50''', (u\\\"%{}%\\\".format(terms),)).fetchall()\",\n \"def test_discussion_filter_author(self):\\n author_vals = (\\n ('DoesNotExist', 0),\\n ('admin', 1),\\n ('jsocol', 4),\\n )\\n\\n for name, number in author_vals:\\n u = UserFactory(username=name)\\n for i in range(number):\\n thread1 = ThreadFactory(title=u'audio')\\n PostFactory(thread=thread1, author=u)\\n\\n self.refresh()\\n\\n qs = {'a': 1, 'w': 4, 'format': 'json'}\\n\\n for author, total in author_vals:\\n qs.update({'author': author})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(total, json.loads(response.content)['total'])\",\n \"def getAuthors(self): #$NON-NLS-1$\\r\",\n \"def test_by_author(self):\\n self.assert_equal(MinusRecord.objects.count(),11)\\n self.go200('minus_by_author', ['Test0'])\\n self.find(CR % 'Test0')\\n for i in xrange(10):\\n if i % 2 == 0:\\n self.find(CR % ('test'+str(i)))\\n else:\\n self.notfind(CR % ('test'+str(i)))\\n self.find(CR % \\\"midi_rec\\\")\",\n \"def citing_authors(self, entities):\\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\\n FROM \\\"entity_occurrences\\\"\\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\\n return [t[0] for t in result]\",\n \"def test_search_client_by_author(self, mock_get):\\n\\n response = isbn_utils.search_by(self.filter_author, self.author)\\n self.assertEqual(response.data, json.loads(open(UNIT_TEST_RESOURCES_FOLDER +\\n FILE_NAME_AUTHOR_SEARCH_RESPONSE).read())[\\\"data\\\"])\",\n \"def test_multiple_authors(self):\\r\\n args = self.page_kwargs.copy()\\r\\n content = Page(**args)\\r\\n assert content.authors == [content.author]\\r\\n args['metadata'].pop('author')\\r\\n args['metadata']['authors'] = ['First Author', 'Second Author']\\r\\n content = Page(**args)\\r\\n assert content.authors\\r\\n assert content.author == content.authors[0]\",\n \"def seen(user, item):\\n print(\\\"---\\\", item.seen_by(user))\\n return item.seen_by(user)\",\n \"def test_author_simple(self):\\n invenio_search = 'author:\\\"ellis, j*\\\"'\\n spires_search = 'find a ellis, j'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def collection_special_author(user_id, author_id):\\n\\n another_user_id = author_id\\n if (user_id == another_user_id):\\n return \\\"self\\\"\\n query = db_session.query(Collection_User).filter_by(\\n user_id=user_id, another_user_id=another_user_id).all()\\n if len(query) == 0:\\n collect_usr = Collection_User(user_id=user_id,\\n another_user_id=another_user_id,\\n time=datetime.now())\\n db_session.add(collect_usr)\\n db_session.commit()\\n update_collection_num(user_id, another_user_id, True)\\n else:\\n return \\\"already\\\"\\n return \\\"success\\\"\",\n \"def any_author_exists(self):\\n return bool(self.mapper.count())\",\n \"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\\n\\n #sorry bout repeated code to get the tags\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n tagvals = {}\\n for t in tags:\\n try:\\n x = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n tagvals[t] = x\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_author_dict\\n\\n #parse the tags\\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"mainauthortag \\\"+mainauthortag)\\n write_message(\\\"coauthortag \\\"+coauthortag)\\n write_message(\\\"extauthortag \\\"+extauthortag)\\n\\n author_cited_in = initial_author_dict\\n if citedbydict:\\n i = 0 #just a counter for debug\\n write_message(\\\"Checking records referred to in new records\\\")\\n for u in updated_redic_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Author ref done \\\"+str(i)+\\\" of \\\"+str(len(updated_redic_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n if citedbydict.has_key(u):\\n these_cite_k = citedbydict[u]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n authors = get_fieldvalues(u, mainauthortag)\\n coauthl = get_fieldvalues(u, coauthortag)\\n extauthl = get_fieldvalues(u, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author ref done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #go through the dictionary again: all keys but search only if new records are cited\\n write_message(\\\"Checking authors in new records\\\")\\n i = 0\\n for k in citedbydict.keys():\\n if (i % 1000 == 0):\\n mesg = \\\"Author cit done \\\"+str(i)+\\\" of \\\"+str(len(citedbydict.keys()))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n these_cite_k = citedbydict[k]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n #do things only if these_cite_k contains any new stuff\\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\\n if intersec_list:\\n authors = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author cit done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return author_cited_in\",\n \"def test_absorbs_naked_author_search(self):\\n invenio_search = \\\"author:ellis\\\"\\n spi_search = \\\"author ellis\\\"\\n self._compare_searches(invenio_search, spi_search)\",\n \"def test_authors():\\n assert(hasattr(tekel, '__authors__'))\",\n \"def test_draft_list_only_display_authors_aids(client, contributor):\\n\\n AidFactory(name='Is this the real life?', author=contributor)\\n AidFactory(name='Is this just fantasy?')\\n\\n client.force_login(contributor)\\n drafts_url = reverse('aid_draft_list_view')\\n res = client.get(drafts_url)\\n\\n content = res.content.decode('utf-8')\\n assert 'Is this the real life?' in content\\n assert 'Is this just fantasy?' not in content\",\n \"def test_refersto_author(self):\\n inv_search = 'refersto:author:kitty'\\n spi_search = 'find refersto author kitty'\\n self._compare_searches(inv_search, spi_search)\",\n \"def assertAuthorsInComments(context, authors, comments):\\n cross_check(context, authors, comments)\",\n \"def assertAuthorsInPosts(context, authors, posts):\\n cross_check(context, authors, posts)\",\n \"def test_list_all_authors(self):\\n response = self.client.get(reverse('authors') + '?page=2')\\n self.assertEqual(response.status_code, 200)\\n self.assertTrue('is_paginated' in response.context)\\n self.assertTrue(response.context['is_paginated'] is True)\\n self.assertTrue(len(response.context['author_list']) == 3)\",\n \"def authors(self):\\n return self.properties.get('Authors', ClientValueCollection(SharedWithMeDocumentUser))\",\n \"def test_author_simplest(self):\\n invenio_search = 'author:ellis'\\n spires_search = 'find a ellis'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def test_get_all_authors(self):\\n self.register_user()\\n token = self.login_user()\\n response = self.client.get(self.user_author, format='json', HTTP_AUTHORIZATION='Token ' +token)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def authors():\\n\\tclick.clear()\\n\\trich.print(\\\"[bold]IDT[/bold] was initially made by [bold magenta]Deliton Junior[/bold magenta] and [bold red]Misael Kelviny[/bold red]\\\")\",\n \"def display_authors(self, *args):\\n return ', '.join(author.name for author in args[0].authors.all()[:3])\",\n \"def test_author_full_initial(self):\\n inv_search = 'author:\\\"klebanov, ig* r*\\\" or exactauthor:\\\"klebanov, i r\\\"'\\n spi_search = \\\"find a klebanov, ig.r.\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def authors(self):\\n raise BookInfoNotImplementedError('authors', self.__class__.__name__)\",\n \"def test_collection_author_year_filtering(self):\\n # Create a collection\\n entries = Entry.objects.filter(id__in=(1, 5, 10, 15))\\n collection = CollectionFactory(entries=entries)\\n entry = Entry.objects.get(id=1)\\n\\n # Get a valid collection\\n params = {\\n \\\"collection\\\": collection.id,\\n \\\"author\\\": entry.first_author.id,\\n \\\"year\\\": entry.publication_date.year,\\n }\\n self._test_filtering(**params)\",\n \"def test_refersto_author_multi_name(self):\\n inv_search = 'author:ellis refersto:author:\\\"parke, s. j.\\\"'\\n spi_search = 'find a ellis and refersto author \\\"parke, s. j.\\\"'\\n self._compare_searches(inv_search, spi_search)\",\n \"def items_should_contain_owner_object(context):\\n items = context.response.json()['items']\\n existing_owner_fields = [\\n 'accept_rate', 'display_name', 'link', 'profile_image', 'reputation',\\n 'user_id', 'user_type'\\n ]\\n non_existing_owner_fields = ['display_name', 'user_type']\\n\\n for item in items:\\n assert 'owner' in item\\n assert isinstance(item['owner'], dict)\\n owner = item['owner']\\n if owner['user_type'] == 'does_not_exist':\\n all(field in owner for field in non_existing_owner_fields)\\n logging.debug(\\n ('Item %d contains an non-existing Owner object for user \\\"%s\\\" '\\n 'with all required fields: %s'), item['question_id'], \\n owner['display_name'], ', '.join(non_existing_owner_fields))\\n else:\\n all(field in owner for field in existing_owner_fields)\\n logging.debug(\\n ('Item %d contains an existing Owner object for user ID \\\"%d\\\" '\\n 'with all required fields: %s'), item['question_id'], \\n owner['user_id'], ', '.join(existing_owner_fields))\",\n \"def _on_authors_list(self, evt):\\n \\n # raise authors management dialog\\n dlg = AuthorsView(self, self._library)\\n response = dlg.ShowModal()\\n dlg.Destroy()\\n \\n # check response\\n if response != wx.ID_OK:\\n return\\n \\n # refresh collections view\\n self._collections_view.UpdateCounts()\\n \\n # refresh articles view\\n self._articles_view.ShowArticles()\",\n \"def test_asked_by(self):\\n author_vals = (\\n ('DoesNotExist', 0),\\n ('jsocol', 2),\\n ('pcraciunoiu', 2),\\n )\\n\\n # Set up all the question data---creats users, creates the\\n # questions, shove it all in the index, then query it and see\\n # what happens.\\n for name, number in author_vals:\\n u = UserFactory(username=name)\\n for i in range(number):\\n ques = QuestionFactory(title=u'audio', creator=u)\\n ques.tags.add(u'desktop')\\n ans = AnswerFactory(question=ques)\\n AnswerVoteFactory(answer=ans, helpful=True)\\n\\n self.refresh()\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n\\n for author, total in author_vals:\\n qs.update({'asked_by': author})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(total, json.loads(response.content)['total'])\",\n \"def test_func(self):\\n taxonomy = self.get_object()\\n return self.request.user == taxonomy.author\",\n \"def test_func(self):\\n taxonomy = self.get_object()\\n return self.request.user == taxonomy.author\",\n \"def filter_publication(publication, cmp_authors=True):\\n query = None\\n isbn_query = False\\n\\n # there can be ISBN query or book title query\\n if publication.optionals and publication.optionals.ISBN:\\n query = aleph.ISBNQuery(publication.optionals.ISBN)\\n isbn_query = True\\n else:\\n query = aleph.TitleQuery(publication.title)\\n\\n result = aleph.reactToAMQPMessage(aleph.SearchRequest(query), \\\"\\\")\\n\\n if not result.records:\\n return publication # book is not in database\\n\\n # if there was results with this ISBN, compare titles of the books\\n # (sometimes, there are different books with same ISBN because of human\\n # errors)\\n if isbn_query:\\n for record in result.records:\\n epub = record.epublication\\n\\n # try to match title of the book\\n if compare_names(epub.nazev, publication.title) >= 80:\\n return None # book already in database\\n\\n return publication\\n\\n # checks whether the details from returned EPublication match Publication's\\n for record in result.records:\\n epub = record.epublication\\n\\n # if the title doens't match, go to next record from aleph\\n if not compare_names(epub.nazev, publication.title) >= 80:\\n continue\\n\\n if not cmp_authors:\\n return None # book already in database\\n\\n # compare authors names\\n for author in epub.autori:\\n # convert Aleph's author structure to string\\n author_str = \\\"%s %s %s\\\" % (\\n author.firstName,\\n author.lastName,\\n author.title\\n )\\n\\n # normalize author data from `publication`\\n pub_authors = map(lambda x: x.name, publication.authors)\\n if type(pub_authors) not in [list, tuple, set]:\\n pub_authors = [pub_authors]\\n\\n # try to compare authors from `publication` and Aleph\\n for pub_author in pub_authors:\\n if compare_names(author_str, pub_author) >= 50:\\n return None # book already in database\\n\\n return publication # book is not in database\",\n \"def test_author_full_first(self):\\n invenio_search = 'author:\\\"ellis, john*\\\" or exactauthor:\\\"ellis, j *\\\" or exactauthor:\\\"ellis, j\\\" or exactauthor:\\\"ellis, jo\\\" or exactauthor:\\\"ellis, joh\\\" or author:\\\"ellis, john, *\\\"'\\n spires_search = 'find a ellis, john'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def popAuthors(self):\\r\\n# cur = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\")\\r\\n# res = cur.fetchall()\\r\\n res = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\").fetchall()\\r\\n\\r\\n self.authorList = [formatNameSQL(ln) for ln in res]\\r\\n self.quickAuthors = [ln[\\\"Lastname\\\"].lower() for ln in res]\\r\\n vals = [ln[\\\"PersonID\\\"] for ln in res]\\r\\n \\r\\n self.authorLookup = dict(zip(self.authorList,vals))\",\n \"def is_one_degree_separated(self,author1, author2):\\n self.cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM wrote W WHERE W.authorID = %s AND EXISTS\\n (SELECT * FROM wrote W2 WHERE W2.authorID = %s AND W.ISBN = W2.ISBN)\\\"\\\"\\\", (author1, author2))\\n if int(self.cursor.fetchone()[0]):\\n return True\\n return False\",\n \"def added_by(self, user):\\n return ChefsHasRecipes.objects.filter(recipe=self, chef=user).exists()\",\n \"def liked_by(self, user):\\n return Likes.objects.filter(recipe=self, chef=user).exists()\",\n \"def test_author_year_filtering(self):\\n # Get a valid date\\n entry = Entry.objects.get(id=1)\\n params = {\\\"author\\\": entry.first_author.id, \\\"year\\\": entry.publication_date.year}\\n\\n self._test_filtering(**params)\",\n \"def userCanAffordItemObj(self, user : bbUser.bbUser, item : bbItem.bbItem) -> bool:\\n return user.credits >= item.getValue()\",\n \"def test_func(self):\\n taxonomy = self.get_taxonomy()\\n return self.request.user == taxonomy.author\",\n \"def test_exactauthor_simple(self):\\n invenio_search = 'exactauthor:\\\"ellis, j\\\"'\\n spires_search = 'find ea ellis, j'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def test_refersto_author_multi_name_no_quotes(self):\\n inv_search = 'author:ellis refersto:(author:\\\"parke, sj*\\\" or exactauthor:\\\"parke, s *\\\" or exactauthor:\\\"parke, s\\\" or author:\\\"parke, sj, *\\\")'\\n spi_search = \\\"find a ellis and refersto author parke, sj\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def user(self, request):\\n\\t\\treturn super(cbotManager, self).get_queryset().filter(author=request.user)\",\n \"def __contains__(self, item):\\n return item in self._fetch()\",\n \"def __contains__(self, item):\\n return item in self.contents\",\n \"def test_super_short_author_name(self):\\n spi_search = \\\"fin a er and cn cms\\\"\\n inv_search = \\\"author:er collaboration:cms\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def test_author_view(self):\\n table = self.get_change_page_form(self.user_1_client, self.question_1.pk)\\n self.assertInHTML('<label>Title:</label><div class=\\\"readonly\\\">Lorem</div>', table)\\n # The label's `for` attribute is present only on editable fields\\n self.assertInHTML('<label class=\\\"required\\\" for=\\\"id_body\\\">Body:</label>', table)\\n self.assertInHTML(\\n '<label>Author:</label><div class=\\\"readonly\\\"><a href=\\\"/auth/user/2/change/\\\">user_1</a></div>', table)\\n self.assertInHTML(\\n '<div><label>Is published:</label><div class=\\\"readonly\\\"><img src=\\\"/static/admin/img/icon-yes.svg\\\" '\\n 'alt=\\\"True\\\"></div></div>', table)\",\n \"def test_get_authors_from_data(self):\\n responses.add(\\n responses.GET,\\n \\\"https://openlibrary.org/authors/OL382982A\\\",\\n json={\\n \\\"name\\\": \\\"George Elliott\\\",\\n \\\"personal_name\\\": \\\"George Elliott\\\",\\n \\\"last_modified\\\": {\\n \\\"type\\\": \\\"/type/datetime\\\",\\n \\\"value\\\": \\\"2008-08-31 10:09:33.413686\\\",\\n },\\n \\\"remote_ids\\\": {\\n \\\"isni\\\": \\\"000111\\\",\\n },\\n \\\"key\\\": \\\"/authors/OL453734A\\\",\\n \\\"type\\\": {\\\"key\\\": \\\"/type/author\\\"},\\n \\\"id\\\": 1259965,\\n \\\"revision\\\": 2,\\n },\\n status=200,\\n )\\n results = self.connector.get_authors_from_data(self.work_data)\\n result = list(results)[0]\\n self.assertIsInstance(result, models.Author)\\n self.assertEqual(result.name, \\\"George Elliott\\\")\\n self.assertEqual(result.openlibrary_key, \\\"OL453734A\\\")\\n self.assertEqual(result.isni, \\\"000111\\\")\",\n \"def __contains__(self, item):\\n return item in self._terms\",\n \"def getInCitesAuthorInfo (self, authorInfo):\\n\\t\\tprint 'getInCitesAuthorInfo with ' + authorInfo.fullname\\n\\t\\tid = authorInfo.id\\n\\t\\t\\n\\t\\ttogglerClass = authorInfo.matches and \\\"toggler\\\" or \\\"\\\"\\n\\t\\t\\n\\t\\ttoggler = DIV (id=id, klass=togglerClass)\\n\\t\\t\\n\\t\\tif authorInfo.numMatches > 0:\\n\\t\\t\\ttogglerLnkClass = \\\"inCitesAuthor togglerClosed\\\"\\n\\t\\telse:\\n\\t\\t\\ttogglerLnkClass = \\\"inCitesAuthor noTogglerClosed\\\"\\n\\t\\t# print \\\"%s %s\\\" % (authorInfo.inCitesName, authorInfo.numMatches)\\n\\t\\t\\t\\t\\t\\t \\n\\t\\ttogglerLnk = DIV(id='toggler-lnk-'+id, klass=togglerLnkClass)\\n\\t\\ttoggler.append(togglerLnk)\\n\\t\\t\\n\\t\\tauthorTable = TABLE(klass=\\\"authorTable\\\")\\n\\t\\ttogglerLnk.append(authorTable)\\n\\t\\tmatchesContent = '(%d matches)' % authorInfo.numMatches\\n\\t\\tauthorTable.append(\\n\\t\\t\\tTR(\\n\\t\\t\\t\\tTD (authorInfo.fullname, klass=\\\"author\\\"),\\n\\t\\t\\t\\tTD (matchesContent, klass=\\\"matches\\\")\\n\\t\\t\\t)\\n\\t\\t)\\n\\n\\t\\tif authorInfo.numMatches > 0:\\n\\t\\t\\ttogglerCon = DIV(id='toggler-con-'+id, style=\\\"display:none\\\")\\n\\t\\t\\ttoggler.append(togglerCon)\\n\\t\\t\\tmatchTable = TABLE(klass=\\\"matchTable\\\")\\n\\t\\t\\ttogglerCon.append(matchTable)\\n\\t\\t\\t\\n\\t\\t\\tfor match in authorInfo.matches:\\n\\t\\t\\t\\tmatch_row = TR (klass=\\\"peopleDBmatch\\\", id=match.upid)\\n\\t\\t\\t\\tmatch_row.append (\\n\\t\\t\\t\\t\\tTD (match.getName(), klass=\\\"match-name\\\"),\\n\\t\\t\\t\\t\\tTD (match.upid, klass=\\\"match-upid\\\"))\\n\\t\\t\\t\\tmatchTable.append(match_row);\\n\\t\\treturn toggler\",\n \"def checker(item):\\n return item in INSTOCK\",\n \"def test_author_many_initials(self):\\n inv_search = 'author:\\\"bach, p* d* q*\\\"'\\n spi_search = 'find a p d q bach'\\n self._compare_searches(inv_search, spi_search)\",\n \"def test_author_initials(self):\\n inv_search = 'author:\\\"polyakov, a* m*\\\"'\\n spi_search = 'find a a m polyakov'\\n self._compare_searches(inv_search, spi_search)\",\n \"def is_liked_by(self, user):\\n return user.liked_articles.filter(pk=self.pk).exists()\",\n \"def test_retrieve_authors(self):\\n sample_author()\\n sample_author()\\n\\n res = self.client.get(reverse('authors'))\\n authors = Author.objects.all()\\n serializer = AuthorSerializer(authors, many=True)\\n self.assertEqual(res.data, serializer.data)\\n self.assertEqual(res.status_code, status.HTTP_200_OK)\",\n \"def test_find_first_author(self):\\n inv_search = 'firstauthor:ellis'\\n spi_search = 'find fa ellis'\\n self._compare_searches(inv_search, spi_search)\",\n \"def run(self):\\n authors = sorted(set(self.load_files_info()))\\n\\n # Show list of authors\\n click.echo('List of users:')\\n for n, author in enumerate(authors, start=1):\\n click.echo(f'{n}) {author}')\\n\\n # Clean list and return\\n remove = self._ask_remove()\\n\\n return [x[1] for x in enumerate(authors) if x not in remove]\",\n \"def popular_authors() :\\n query = \\\"\\\"\\\"SELECT authors.name,count(*) AS total_views FROM authors,articles,log WHERE log.path like concat ('/article/',articles.slug)\\n AND articles.author=authors.id group by authors.name order by total_views desc\\\"\\\"\\\"\\n result = get_data(query)\\n print(\\\" 2. The most popular articles authors of all time:\\\")\\n print(\\\"\\\")\\n for record in result :\\n print(' ' +' ' + str(record[0]) + ' -' + ' ' + str(record[1]) + ' ' +'views')\\n print(\\\" \\\")\",\n \"def question_2():\\n cursor.execute(mostPopAuthors)\\n output = cursor.fetchall()\\n return output\",\n \"def test_search_client_by_author_page2(self, mock_get):\\n\\n response = isbn_utils.search_by(self.filter_author, self.author, page=\\\"2\\\")\\n self.assertEqual(response.data, json.loads(open(UNIT_TEST_RESOURCES_FOLDER +\\n FILE_NAME_AUTHOR_SEARCH_PAGE_RESPONSE).read())[\\\"data\\\"])\",\n \"def has_items(self):\\r\\n return self.orderitem_set.exists() # pylint: disable=E1101\\r\",\n \"def test_author_many_lastnames(self):\\n inv_search = 'author:\\\"alvarez gaume, j* r* r*\\\"'\\n spi_search = 'find a alvarez gaume, j r r'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_authors(self):\\n\\n names = []\\n rows = []\\n\\n try:\\n rows = self.find_elements_in_owner(self.locators['author_row'])\\n except NoSuchElementException:\\n # there are no authors\\n rows = []\\n\\n for rowEle in rows:\\n authorname = self._get_author(rowEle)\\n names.append(authorname)\\n return names\",\n \"def match(self, filter_text):\\n\\n return filter_text.lower() in self.author.lower() or \\\\\\n super().match(filter_text)\",\n \"def test_author_list_equality_with_invalid_authentication(self) -> None:\\n\\n # Let's check for a request with no authorization\\n\\n response: Response = self.client.get(self.url)\\n data = u.get_json(response)\\n\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n self.assertEqual(data, {\\n 'detail': 'Authentication credentials were not provided.'\\n })\\n\\n # Now lets check with an Author without permissions.\\n\\n # Select the underprivileged author randomly.\\n author: Author = random.choice(self.authors)\\n\\n self.client.credentials(HTTP_AUTHORIZATION=u.auth_header(author.get_key()))\\n\\n response: Response = self.client.get(self.url)\\n data: typing.Dict[typing.Any, typing.Any] = u.get_json(response)\\n\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(data, {\\n 'detail': 'You do not have permission to perform this action.'\\n })\",\n \"def find_relations_among_authors():\\n for book in books:\\n if len(books[book]) > 1:\\n for i in range(len(books[book])):\\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]\",\n \"def query_by_author(cls,aid):\\n gds = from_cache('VG_%s'%aid)\\n if not gds:\\n gds = [str(g.id()) for g in SuiGoods.all(keys_only=True).filter('author =',aid).fetch(1000)]\\n to_cache('VG_%s'%aid, gds)\\n return SuiGoods.load_by_ids(gds)\",\n \"def checkPaper(self,event=None):\\r\\n if self.title.getVal() not in self.paperList:\\r\\n self.paperList.append(self.title.getVal())\\r\\n self.paperList.sort()\\r\\n self.title.updateVals(self.paperList)\\r\\n return\\r\\n\\r\\n ## This section of code should probably go into setData. . .\\r\\n self.authorBox.clearData()\\r\\n\\r\\n cur = self.dbConn.execute(\\\"SELECT People.* FROM Papers JOIN People on Papers.PrimaryAuthor = People.PersonID WHERE Papers.Title = ?\\\",[self.title.getVal()])\\r\\n res = cur.fetchone()\\r\\n if res ==None:\\r\\n self.primeAuthor.setVal(\\\"No Author Found; Check database\\\")\\r\\n return\\r\\n self.primeAuthor.setVal(formatNameSQL(res))\\r\\n self.addPrimeAuthorFn()\\r\\n\\r\\n cur = self.dbConn.execute(\\\"SELECT People.* FROM Papers JOIN People on Papers.CorrespondingAuthor = People.PersonID WHERE Papers.Title = ?\\\",[self.title.getVal()])\\r\\n res = cur.fetchone()\\r\\n if res == None:\\r\\n self.correspond.setVal(self.primeAuthor.getVal())\\r\\n else:\\r\\n self.correspond.setVal(formatNameSQL(res))\\r\\n\\r\\n cur = self.dbConn.execute(\\\"SELECT People.* FROM Papers JOIN People JOIN CoAuthors ON Papers.paperID = CoAuthors.PaperID AND People.PersonID = CoAuthors.Author WHERE Papers.Title = ?\\\",[self.title.getVal()])\\r\\n res = cur.fetchall()\\r\\n if res == None:\\r\\n return\\r\\n for ln in res:\\r\\n curAuthor = str.format(formatNameSQL(ln))\\r\\n self.authorBox.addLine(curAuthor)\\r\\n self.coAuthor.setVal(curAuthor)\",\n \"def is_valid_book(current_author, inputed_name, availale_books):\\n\\tbook_info = []\\n\\tauthor_book = {}\\n\\n\\tfor book in availale_books:\\n\\t\\tauthor = book.author.username\\n\\t\\tauthor_book[author] = book.book_name\\n\\t\\tbook_info.append(author_book)\\n\\t\\tauthor_book = {}\\n\\n\\tfor book in book_info:\\n\\t\\tfor author, book_name in book.items():\\n\\t\\t\\tif book_name == inputed_name and author == current_author:\\n\\t\\t\\t\\treturn False\\n\\n\\treturn True\",\n \"def print_popular_authors(authors):\\n for (author, views) in authors:\\n print \\\"%s - %d views\\\" % (author, views)\",\n \"def get_popular_authors():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n query_popular_authors = \\\"\\\"\\\"\\n SELECT aut.name, COUNT(lg.id) AS views\\n FROM articles AS art\\n JOIN log AS lg ON art.slug = SUBSTRING(lg.path,10)\\n AND lg.status = '200 OK'\\n JOIN authors AS aut ON aut.id = art.author\\n GROUP BY aut.name\\n ORDER BY views desc; \\\"\\\"\\\"\\n c.execute(query_popular_authors)\\n authors = from_db_cursor(c)\\n db.close()\\n return authors\",\n \"def has_item(item: Item):\\n async def _wrapper(ctx):\\n if not (res := 0 < await ctx.db.get(\\\"items\\\", ctx.author, item.id)):\\n name = (f\\\"an \\\" if any(item.name.startswith(vowel) for vowel in \\\"aeiou\\\") else \\\"a \\\") + f\\\"**{item}**\\\"\\n await ctx.send(f\\\"You need to own {name} in order to use this command.\\\" + (\\n f\\\" You can go buy one from the shop! (`{ctx.clean_prefix}shop`)\\\" if item.buyable else \\\"\\\"\\n ))\\n return res\\n\\n return discord.ext.commands.check(_wrapper)\",\n \"def query_authors(cls):\\n authors = from_cache('AuthorsList')\\n if not authors:\\n authors = SuiAuthor.all().order('name').fetch(400)\\n to_cache('AuthorsList', authors)\\n return authors\",\n \"def _validate_contributors_summary(cls, item):\\n contributor_ids_from_contributors_summary = (\\n list(item.contributors_summary.keys()))\\n if sorted(item.contributor_ids) != sorted(\\n contributor_ids_from_contributors_summary):\\n cls._add_error(\\n 'contributors %s' % (\\n base_model_validators.ERROR_CATEGORY_SUMMARY_CHECK),\\n 'Entity id %s: Contributor ids: %s do not match the '\\n 'contributor ids obtained using contributors summary: %s' % (\\n item.id, sorted(item.contributor_ids),\\n sorted(contributor_ids_from_contributors_summary)))\",\n \"def has_item(self, item):\\n return item in self.set\",\n \"def has_item(self, item):\\n return item in self.set\",\n \"def test_user_can_change_as_author(self):\\n self.assertTrue(self.story.user_can_change(self.user1))\",\n \"def test_author_reverse(self):\\n invenio_search = 'author:\\\"ellis, j*\\\"'\\n spires_search = 'find a j ellis'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def popular_authors():\\n query = \\\"\\\"\\\"select authors.name, sum(views)\\n from authors, articles, article_path_views\\n where authors.id = articles.author\\n and '/article/' || articles.slug = article_path_views.path\\n group by authors.name\\n order by sum desc\\\"\\\"\\\"\\n result_table = execute_query(query)\\n\\n # generate a report from table_to_report() function\\n report = table_to_report(result_table, ' views')\\n return \\\"Authors and Their Articles' Total Views:\\\\n\\\" + report\",\n \"def _validate_author_id(cls, item):\\n if (\\n item.author_id and\\n not user_services.is_user_or_pseudonymous_id(item.author_id)\\n ):\\n cls._add_error(\\n 'final %s' % (\\n base_model_validators.ERROR_CATEGORY_AUTHOR_CHECK),\\n 'Entity id %s: Author ID %s is in a wrong format. '\\n 'It should be either pid_<32 chars> or uid_<32 chars>.'\\n % (item.id, item.author_id))\",\n \"def item_exists(item_id):\\n return item_id in all_items\",\n \"def count_by_author(all_articles):\\n author_count = {}\\n\\n for title in all_articles:\\n author = \\\", \\\".join(all_articles[title]['authors'])\\n if author not in author_count:\\n author_count[author] = 1\\n else:\\n author_count[author] = author_count[author] + 1\\n \\n print_all_items_in_dict(author_count)\",\n \"def collection_special_author_cancel(user_id, author_id):\\n\\n another_user_id = author_id\\n if (user_id == another_user_id):\\n return \\\"self\\\"\\n query = db_session.query(Collection_User).filter_by(\\n user_id=user_id, another_user_id=another_user_id).all()\\n if len(query) == 1:\\n db_session.delete(query[0])\\n db_session.commit()\\n update_collection_num(user_id, another_user_id, False)\\n else:\\n return \\\"already\\\"\\n return \\\"success\\\"\",\n \"def find_author(author):\\n case = author_case(author)\\n queries = author_queries(case, author)\\n author_uri_set = set([])\\n for query in queries:\\n result = vivo_sparql_query(query.encode('utf-8'))\\n count = len(result['results']['bindings'])\\n if count == 1:\\n author_uri_set = set([result['results']['bindings'][0]\\\\\\n ['uri']['value']])\\n break\\n elif count > 1 and count < len(author_uri_set):\\n author_uri_set = set([])\\n for row in result['results']['bindings']:\\n author_uri_set.add(row['uri']['value'])\\n return author_uri_set\",\n \"def get_authors(self):\\n return [aer.author for aer in self.authorentryrank_set.all()]\",\n \"def test_func(self):\\n post = self.get_object()\\n\\n return self.request.user == post.author\",\n \"def test_func(self):\\n post = self.get_object()\\n\\n return self.request.user == post.author\",\n \"def under_three_opens(name, state):\\n counter = 0\\n for item in state['items']:\\n if item['seller'] == name:\\n counter += 1\\n return counter < 3\",\n \"def alt_authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n if _authors:\\n for i, v in enumerate(force_list(value)):\\n _authors[i].update({\\\"alternative_names\\\": clean_val(\\\"a\\\", v, str)})\\n return _authors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71577066","0.69033414","0.64529437","0.6411667","0.62453896","0.62002295","0.61882716","0.6115645","0.605986","0.59958255","0.59832895","0.591335","0.5908707","0.59039694","0.58876854","0.5848254","0.5828852","0.5805382","0.58008856","0.5780458","0.5744204","0.57388073","0.573548","0.5724365","0.57063437","0.5706303","0.56907034","0.567742","0.56765306","0.5658401","0.56436676","0.5621472","0.56120086","0.56031454","0.55704916","0.5557176","0.5555082","0.5521086","0.5520845","0.5519768","0.5502287","0.55004764","0.55004764","0.54792273","0.5478203","0.5477812","0.54471207","0.5442892","0.54413074","0.5423266","0.54181933","0.5390595","0.53743297","0.537041","0.53472584","0.53457487","0.53244454","0.5321593","0.530756","0.5307274","0.53065085","0.5305902","0.528864","0.5287909","0.52857864","0.52833766","0.52805495","0.5279336","0.5274076","0.5268398","0.5268149","0.52654874","0.52616763","0.5259156","0.5257853","0.5254195","0.52525157","0.52513516","0.5249828","0.5249402","0.5247161","0.5244922","0.5243245","0.52411944","0.5236026","0.5233355","0.5231554","0.5231554","0.5220918","0.52178156","0.5215065","0.52147657","0.52017045","0.5199978","0.5190371","0.51746565","0.5174346","0.51668596","0.51668596","0.516096","0.5158172"],"string":"[\n \"0.71577066\",\n \"0.69033414\",\n \"0.64529437\",\n \"0.6411667\",\n \"0.62453896\",\n \"0.62002295\",\n \"0.61882716\",\n \"0.6115645\",\n \"0.605986\",\n \"0.59958255\",\n \"0.59832895\",\n \"0.591335\",\n \"0.5908707\",\n \"0.59039694\",\n \"0.58876854\",\n \"0.5848254\",\n \"0.5828852\",\n \"0.5805382\",\n \"0.58008856\",\n \"0.5780458\",\n \"0.5744204\",\n \"0.57388073\",\n \"0.573548\",\n \"0.5724365\",\n \"0.57063437\",\n \"0.5706303\",\n \"0.56907034\",\n \"0.567742\",\n \"0.56765306\",\n \"0.5658401\",\n \"0.56436676\",\n \"0.5621472\",\n \"0.56120086\",\n \"0.56031454\",\n \"0.55704916\",\n \"0.5557176\",\n \"0.5555082\",\n \"0.5521086\",\n \"0.5520845\",\n \"0.5519768\",\n \"0.5502287\",\n \"0.55004764\",\n \"0.55004764\",\n \"0.54792273\",\n \"0.5478203\",\n \"0.5477812\",\n \"0.54471207\",\n \"0.5442892\",\n \"0.54413074\",\n \"0.5423266\",\n \"0.54181933\",\n \"0.5390595\",\n \"0.53743297\",\n \"0.537041\",\n \"0.53472584\",\n \"0.53457487\",\n \"0.53244454\",\n \"0.5321593\",\n \"0.530756\",\n \"0.5307274\",\n \"0.53065085\",\n \"0.5305902\",\n \"0.528864\",\n \"0.5287909\",\n \"0.52857864\",\n \"0.52833766\",\n \"0.52805495\",\n \"0.5279336\",\n \"0.5274076\",\n \"0.5268398\",\n \"0.5268149\",\n \"0.52654874\",\n \"0.52616763\",\n \"0.5259156\",\n \"0.5257853\",\n \"0.5254195\",\n \"0.52525157\",\n \"0.52513516\",\n \"0.5249828\",\n \"0.5249402\",\n \"0.5247161\",\n \"0.5244922\",\n \"0.5243245\",\n \"0.52411944\",\n \"0.5236026\",\n \"0.5233355\",\n \"0.5231554\",\n \"0.5231554\",\n \"0.5220918\",\n \"0.52178156\",\n \"0.5215065\",\n \"0.52147657\",\n \"0.52017045\",\n \"0.5199978\",\n \"0.5190371\",\n \"0.51746565\",\n \"0.5174346\",\n \"0.51668596\",\n \"0.51668596\",\n \"0.516096\",\n \"0.5158172\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79588,"cells":{"query":{"kind":"string","value":"Traverses citedbydict in order to build \"which author is quoted where\" dict. The keys of this are author names. An entry like \"Apollinaire\">[1,2,3] means Apollinaire is cited in records 1,2 and 3."},"document":{"kind":"string","value":"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_citedby_author(self):\n inv_search = 'citedby:author:doggy'\n spi_search = 'find citedby author doggy'\n self._compare_searches(inv_search, spi_search)","def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp': [] }\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n\n if author['dblp'].__contains__('publications'):\n publications['dblp'] = author['dblp']['publications']\n# for pub in author['dblp']['publications']:\n# print(pub)\n\n if author['cdblp'].__contains__('publications'):\n publications['cdblp'] = author['cdblp']['publications']\n# for pub in author['cdblp']['publications']:\n# print(pub)\n return publications","def get_coauthors_by_author(cached_list, cached_set, author_name):\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n coauthors = {}\n if author['dblp'].__contains__('coauthors'):\n for author_key in author['dblp']['coauthors']:\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\n\n if author['cdblp'].__contains__('coauthors'):\n for author_key in author['cdblp']['coauthors']:\n if coauthors.__contains__(author_key['full_name']):\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\n else:\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\n\n return coauthors","def authors(self, key, value):\n _authors = self.get(\"authors\", [])\n item = build_ils_contributor(value)\n if item and item not in _authors:\n _authors.append(item)\n try:\n if \"u\" in value:\n other = [\"et al.\", \"et al\"]\n val_u = list(force_list(value.get(\"u\")))\n if [i for i in other if i in val_u]:\n self[\"other_authors\"] = True\n except UnexpectedValue:\n pass\n return _authors","def find_relations_among_authors():\n for book in books:\n if len(books[book]) > 1:\n for i in range(len(books[book])):\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]","def __convert_authors(self, author_dict: dict) -> str:\n if \"author\" not in author_dict.keys():\n return str(author_dict)\n res = \"\"\n for i, _ in enumerate(author_dict['author']):\n if \"given-name\" in author_dict[\"author\"][i].keys() and \"surname\" in author_dict[\"author\"][i].keys():\n res += author_dict['author'][i][\"given-name\"] + \" \" + author_dict['author'][i][\"surname\"] + \", \"\n else:\n res += str(author_dict['author'][i])\n return res","def authorSign(g = None):\n if not g:\n return\n authors = g.find(\"Author\")\n ret = dict()\n for author in authors:\n ret[author['name']] = author['citation'].replace('\\n','').lower()\n return ret","def get_author_info(self, author: str):\n for writer_word in self._writer_words:\n data = json.loads(requests.get(WIKIDATA_SEARCH + \"&srsearch=\" + author + \" \" + writer_word).text)\n pages = data.get(\"query\").get(\"search\")\n if len(pages) >= 1:\n pageid = pages[0].get(\"title\")\n author_details = self._reference.author_map.get(author)\n if author_details:\n return author_details\n if pageid == -1:\n continue\n\n else:\n response = requests.get(WIKIDATA_PARSE + pageid + \".json\")\n data = json.loads(response.text)\n if author.lower() not in data.get(\"entities\").get(pageid).get(\"labels\").get(\"en\").get(\"value\").lower():\n continue\n else:\n try:\n id = data.get(\"entities\").get(pageid).get(\"claims\").get(\"P31\")[0].get(\"mainsnak\").get(\"datavalue\").get(\"value\").get(\"id\")\n if str(id) != \"Q5\": # the id for human\n continue\n except IndexError:\n continue\n properties = data.get(\"entities\").get(pageid).get(\"claims\")\n author_details = {\"id\": pageid, \"gender\": self.get_gender(properties)}\n country_details = self.get_country(properties)\n author_details[\"country\"] = country_details\n self._reference.author_map[author] = author_details\n return author_details\n return {\"id\": \"Unknown\", \"gender\": \"Unknown\", \"country\": [{\"name\": \"Unknown\", \"region\": \"Unknown\"}]}","def extra_bibparse(db):\n for key,entry in db.entries.items():\n for auth in entry.persons[\"author\"]:\n if (\"Harrison\" not in auth.first_names or\n \"Chapman\" not in auth.last_names):\n entry.add_person(auth, \"otherauthor\")","def alt_authors(self, key, value):\n _authors = self.get(\"authors\", [])\n if _authors:\n for i, v in enumerate(force_list(value)):\n _authors[i].update({\"alternative_names\": clean_val(\"a\", v, str)})\n return _authors","def get_coauthors_dict(name_url_list, schl_name):\n\n\tpaper_info_dict = {}\n\n\tfor name_url in name_url_list:\n\t\t# Get a sub papers_info dictfor an individual author\n\t\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\n\t\t# add sub dict to full dict\n\t\tpaper_info_dict.update(papers_info)\n\t\n\tif \"Humanities\" in schl_name:\n\t\tschl_name = \"School of Humanities\"\n\n\treturn paper_info_dict","def get_movie_people_relation(title, people_dict, movie_people_dict):\n for item in title:\n for key in people_dict.keys():\n for movie_title in people_dict[key]:\n if item == movie_title:\n if item in movie_people_dict.keys():\n if key not in movie_people_dict[item]:\n movie_people_dict[item] += ',' + key\n else:\n movie_people_dict[item] = key\n else:\n if item not in movie_people_dict.keys():\n movie_people_dict[item] = ''\n return movie_people_dict","def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\n publications = { 'cdblp': [], 'dblp': [] }\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\n for cdblp_pub in pub1.get('cdblp', []):\n authors = set(cdblp_pub.get('authors', []))\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\n publications['cdblp'].append(cdblp_pub)\n\n for dblp_pub in pub1.get('dblp', []):\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\n publications['dblp'].append(dblp_pub)\n\n return publications","def popAuthors(self):\r\n# cur = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\")\r\n# res = cur.fetchall()\r\n res = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\").fetchall()\r\n\r\n self.authorList = [formatNameSQL(ln) for ln in res]\r\n self.quickAuthors = [ln[\"Lastname\"].lower() for ln in res]\r\n vals = [ln[\"PersonID\"] for ln in res]\r\n \r\n self.authorLookup = dict(zip(self.authorList,vals))","def make_citation_authors(res):\n if \"author\" in res.keys():\n first_author = res['author'][0]['family'] + \", \" + res['author'][0]['given']\n last_author = res['author'][-1]['given'] + \" \" + res['author'][-1]['family']\n middle_authors = \", \".join(\" \".join([x['given'], x['family']]) for x in res['author'][1:-1])\n #assemble authors\n author_string = first_author\n author_string = author_string + \", \" + middle_authors if middle_authors != '' else author_string\n author_string = author_string + \", and \" + last_author if len(res['author']) > 1 else author_string\n \n author_string = author_string + \".\" if author_string[-1] != \".\" else author_string\n else:\n author_string = \"Unknown Authors\"\n\n return clean_txt(author_string.capitalize())","def author_dictionary_italian(corpus_token_path):\n token_files = os.listdir(corpus_token_path)\n correct_author = [filename.split('_')[0] for filename in token_files]\n author_name_token_dict = {}\n for i in range(0, correct_author.__len__()):\n if correct_author[i] not in author_name_token_dict.keys():\n author_name_token_dict[correct_author[i]] = [token_files[i]]\n # print(author_name_token_dict[french_correct_author[i]])\n else:\n existing_token_files = author_name_token_dict[correct_author[i]]\n if existing_token_files is not None:\n author_name_token_dict[correct_author[i]].append(token_files[i])\n return author_name_token_dict","def author_dictionary (corpus_token_path, correct_author_path):\n token_files = os.listdir(corpus_token_path)\n correct_author = read_file(correct_author_path).split(\"\\n\")\n author_name_token_dict = {}\n for i in range(0, correct_author.__len__()):\n if correct_author[i] not in author_name_token_dict.keys():\n author_name_token_dict[correct_author[i]] = [token_files[i]]\n #print(author_name_token_dict[french_correct_author[i]])\n else:\n existing_token_files = author_name_token_dict[correct_author[i]]\n if existing_token_files is not None:\n author_name_token_dict[correct_author[i]].append(token_files[i])\n return author_name_token_dict","def age_of_cited_work(art_map, art_cit_map, cit_map):\n logging.debug('============== IN age_of_cited_work: ================')\n age = {}\n for article in art_cit_map:\n age_list = []\n logging.debug('========== ARTICLE: ============')\n logging.debug(article)\n for citation in art_cit_map[article]:\n logging.debug('========== CITATION: ============')\n logging.debug(citation)\n logging.debug(art_map[article]['pub_year'])\n if citation in cit_map:\n logging.debug(cit_map[citation]['pub_year'])\n age_list.append(\n int(art_map[article]['pub_year']) - int(cit_map[citation][\n 'pub_year']))\n age[art_map[article]['pub_year']] = age_list\n logging.debug('========== AGE OF CITED WORK: ============')\n logging.debug(age)\n return sort_map_by_keys(age)","def __get_citedby(self):\n if not hasattr(self, 'id_scholarcitedby'):\n self.fill()\n if hasattr(self, 'id_scholarcitedby'):\n url = self._scholarly.URLS('SCHOLARPUB').format(\n requests.utils.quote(self.id_scholarcitedby))\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n return self._scholarly._search_scholar_soup(soup)\n else:\n return []","def test_citedby_refersto_author(self):\n inv_search = 'refersto:citedby:author:penguin'\n spi_search = 'find refersto citedby author penguin'\n self._compare_searches(inv_search, spi_search)","def getInCitesAuthorInfo (self, authorInfo):\n\t\tprint 'getInCitesAuthorInfo with ' + authorInfo.fullname\n\t\tid = authorInfo.id\n\t\t\n\t\ttogglerClass = authorInfo.matches and \"toggler\" or \"\"\n\t\t\n\t\ttoggler = DIV (id=id, klass=togglerClass)\n\t\t\n\t\tif authorInfo.numMatches > 0:\n\t\t\ttogglerLnkClass = \"inCitesAuthor togglerClosed\"\n\t\telse:\n\t\t\ttogglerLnkClass = \"inCitesAuthor noTogglerClosed\"\n\t\t# print \"%s %s\" % (authorInfo.inCitesName, authorInfo.numMatches)\n\t\t\t\t\t\t \n\t\ttogglerLnk = DIV(id='toggler-lnk-'+id, klass=togglerLnkClass)\n\t\ttoggler.append(togglerLnk)\n\t\t\n\t\tauthorTable = TABLE(klass=\"authorTable\")\n\t\ttogglerLnk.append(authorTable)\n\t\tmatchesContent = '(%d matches)' % authorInfo.numMatches\n\t\tauthorTable.append(\n\t\t\tTR(\n\t\t\t\tTD (authorInfo.fullname, klass=\"author\"),\n\t\t\t\tTD (matchesContent, klass=\"matches\")\n\t\t\t)\n\t\t)\n\n\t\tif authorInfo.numMatches > 0:\n\t\t\ttogglerCon = DIV(id='toggler-con-'+id, style=\"display:none\")\n\t\t\ttoggler.append(togglerCon)\n\t\t\tmatchTable = TABLE(klass=\"matchTable\")\n\t\t\ttogglerCon.append(matchTable)\n\t\t\t\n\t\t\tfor match in authorInfo.matches:\n\t\t\t\tmatch_row = TR (klass=\"peopleDBmatch\", id=match.upid)\n\t\t\t\tmatch_row.append (\n\t\t\t\t\tTD (match.getName(), klass=\"match-name\"),\n\t\t\t\t\tTD (match.upid, klass=\"match-upid\"))\n\t\t\t\tmatchTable.append(match_row);\n\t\treturn toggler","def make_author_list(res):\n try:\n r = [\", \".join([clean_txt(x['family']).capitalize(), clean_txt(x['given']).capitalize()]) for x in res['author']]\n except KeyError as e:\n print(\"No 'author' key, using 'Unknown Author'. You should edit the markdown file to change the name and citationkey.\")\n r = [\"Unknown Authors\"]\n return r","def get_author_detail(body):\n user_id = get_user_id(body) # user id\n author_details = body.find('div', {'id': 'gsc_prf_i'})\n\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\n\n affiliation = name_attributes[0].text or ''\n interests = name_attributes[1].text or ''\n email = name_attributes[2].text or ''\n\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\n\n year_since_text = citation_indices.find_all('tr')[0]\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\n\n # citations\n citations = citation_indices.find_all('tr')[1]\n h_index = citation_indices.find_all('tr')[2]\n h_index_all = h_index.find_all('td')[1].text\n h_index_since = h_index.find_all('td')[2].text\n citation_all = citations.find_all('td')[1].text\n citation_since = citations.find_all('td')[2].text\n\n author_dict = {'user_id': user_id,\n 'name': name,\n 'affiliation': affiliation,\n 'interests': interests,\n 'email': email,\n 'h_index_all': h_index_all,\n 'h_index_since': h_index_since,\n 'citation_all': citation_all,\n 'citation_since': citation_since,\n 'year_since': year_since}\n return author_dict","def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites","def corporate_authors(self, key, value):\n _corporate_authors = self.get(\"authors\", [])\n\n for v in force_list(value):\n if key == \"710__\":\n if \"a\" in v:\n _corporate_authors.append(\n {\n \"full_name\": clean_val(\"a\", v, str),\n \"type\": \"ORGANISATION\",\n }\n )\n else:\n self[\"authors\"] = collaborations(self, key, value)\n raise IgnoreKey(\"corporate_authors\")\n else:\n _corporate_authors.append(\n {\"full_name\": clean_val(\"a\", v, str), \"type\": \"ORGANISATION\"}\n )\n return _corporate_authors","def count_by_author(all_articles):\n author_count = {}\n\n for title in all_articles:\n author = \", \".join(all_articles[title]['authors'])\n if author not in author_count:\n author_count[author] = 1\n else:\n author_count[author] = author_count[author] + 1\n \n print_all_items_in_dict(author_count)","def make_citation_key(res):\n year = str(make_year(res))\n\n try:\n last_names = [x['family'] for x in res['author']]\n except KeyError as e:\n last_names = [\"Unknown\"]\n if len(last_names) >= 3:\n key = last_names[0].capitalize() + \"ETAL\" + year\n else:\n key = \"\".join(last_names) + year\n\n return clean_txt(key.replace(\" \", \"\"))","def author_committer_facts(model, date):\n return dict(\n author=model.developer,\n author_date=date,\n committer=model.developer,\n commit_date=date,\n )","def get_venues_by_author(cached_list, cached_set, author_name):\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n venues = {}\n\n if author['dblp'].__contains__('publications'):\n for pub in author['dblp']['publications']:\n if venues.__contains__(pub['venue']):\n venues[pub['venue']]['count'] += 1\n else:\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\n\n if author['cdblp'].__contains__('publications'):\n for pub in author['cdblp']['publications']:\n if venues.__contains__(pub['venue']):\n venues[pub['venue']]['count'] += 1\n else:\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\n\n return venues","def author_case_query(qn, author):\n if qn == 1:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n ?uri foaf:firstName ?fn .\n FILTER(regex(?fn,\"^{{fni}}\"))\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fni}}', author['first'][0])\n elif qn == 2:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n ?uri foaf:firstName ?fn .\n FILTER(regex(?fn,\"^{{fni}}\"))\n ?uri bibo:middleName ?mn .\n FILTER(regex(?fn,\"^{{mni}}\"))\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fni}}', author['first'][0])\n query = query.replace('{{mni}}', author['middle'][0])\n elif qn == 3:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri bibo:middleName \"{{mn}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n ?uri foaf:firstName ?fn .\n FILTER(regex(?fn,\"^{{fni}}\"))\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fni}}', author['first'][0])\n query = query.replace('{{mn}}', author['middle'])\n elif qn == 4:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri foaf:firstName \"{{fn}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fn}}', author['first'])\n elif qn == 5:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri foaf:firstName \"{{fn}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n ?uri bibo:middleName ?mn .\n FILTER(regex(?mn,\"^{{mni}}\"))\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fn}}', author['first'])\n query = query.replace('{{mni}}', author['middle'][0]) \n elif qn == 6:\n query = \"\"\"\n SELECT ?uri\n WHERE {\n ?uri foaf:lastName \"{{ln}}\" .\n ?uri a ufVivo:UFCurrentEntity .\n ?uri foaf:firstName \"{{fn}}\" .\n ?uri bibo:middleName \"{{mn}}\" .\n }\n \"\"\"\n query = query.replace('{{ln}}', author['last'])\n query = query.replace('{{fn}}', author['first'])\n query = query.replace('{{mn}}', author['middle'])\n return query","def get_author_info_from_people_collection(info):\n # TODO: probably we will need to extract this somewhere else\n URL = ('https://cds.cern.ch/submit/get_authors?'\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\n if '0' in info or not info.get('a'):\n # There is already enough information or we don't have a name to query\n return info\n author_name = info.get('a')\n if PY2:\n # In Python 3, encoded name will change type to bytes and this will\n # cause query to CDS to fail\n author_name = author_name.encode('utf-8')\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\n if not author_info or len(author_info) > 1:\n # Didn't find anything or find to many matches\n return info\n\n # Prepare author name\n author_info = author_info[0]\n if 'name' not in author_info:\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\n author_info['firstname'])\n return MementoDict([\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])","def all_authors( data ) :\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))","def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\n selfdic, authorcitdic):\n insert_into_cit_db(reference_dic,\"reversedict\")\n insert_into_cit_db(citation_dic,\"citationdict\")\n insert_into_cit_db(selfcbdic,\"selfcitedbydict\")\n insert_into_cit_db(selfdic,\"selfcitdict\")\n\n for a in authorcitdic.keys():\n lserarr = (serialize_via_marshal(authorcitdic[a]))\n #author name: replace \" with something else\n a.replace('\"', '\\'')\n a = unicode(a, 'utf-8')\n try:\n ablob = run_sql(\"select hitlist from rnkAUTHORDATA where aterm = %s\", (a,))\n if not (ablob):\n #print \"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\" , (a,lserarr)\n run_sql(\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\",\n (a,lserarr))\n else:\n #print \"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\"\"\" , (lserarr,a)\n run_sql(\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\",\n (lserarr,a))\n except:\n register_exception(prefix=\"could not read/write rnkAUTHORDATA aterm=\"+a+\" hitlist=\"+str(lserarr), alert_admin=True)","def generate_id_for_authors():\n counter = 1\n for book in books:\n for author in books[book]:\n if not author in authors:\n authors[author] = f'Author/{counter}'\n counter += 1","def get_titles_dict(name_url_list):\n\tbib_dict = {}\n\t\n\tfor name_url in name_url_list:\n\t\ttitles = get_author_titles(name_url[1])\n\t\tbib_dict[name_url] = titles\n\n\treturn bib_dict","def author_queries(case, author):\n query_set = {\n \"0\": [],\n \"1\": [1],\n \"2\": [4,1],\n \"3\": [2,1],\n \"4\": [3,2,1],\n \"5\": [5,4,3,2,1],\n \"6\": [6,5,4,3,2,1]\n }\n qs = query_set[str(case)]\n query_list = []\n for q in qs:\n query_list.append(author_case_query(q, author))\n return query_list","def AuthorURLs(entry):\n a_URLs = ''\n for a in entry.getAuthors():\n url = a.get('homepage', ' ')\n a_URLs += \"%s and \" % url\n return a_URLs[:-5]","def get_papers_info(author_url, existing_papers):\n\n\tauthor_dict = {}\n\n\tauthor_page_tree = get_tree(author_url)\n\t# Get the <a> elements for the papers on the author's page\n\ta_elems = get_a_elems_for_papers(author_page_tree)\n\t# get the dates corresponding to each paper\n\tpaper_dates = get_dates_for_papers(author_page_tree)\n\t# zip into a list of (a_elem, date) pairs\n\ta_elem_dates = zip(a_elems, paper_dates)\n\t# Each a is a paper\n\tfor a, date in a_elem_dates:\n\t\t# Title of paper is the text content of the a element\n\t\tpaper_title = a.text_content()\n\t\t# Check if paper has already been checked, if so, move on to next paper\n\t\tif paper_title in existing_papers:\n\t\t\tcontinue\n\n\t\tpaper_url = a.get(\"href\")\n\n\t\tpaper_tree = get_tree(paper_url)\n\t\t# Get list of the paper's authors\n\t\tauthors = get_paper_authors(paper_tree)\n\t\t# Get paper abstract\n\t\tabstract = get_paper_abstract(paper_tree)\n\t\t# Get paper keywords\n\t\tkeywords = get_paper_keywords(paper_tree)\n\t\t# Get paper id number from its url\n\t\tpaper_id = re.search(\"[0-9]+\", paper_url).group()\n\t\t# Add paper to dictionary with id as key and metadata as values\n\t\tauthor_dict[paper_id] = {\n\t\t\t\t\t\t\"title\": paper_title,\n\t\t\t\t\t\t\"authors\": authors,\n\t\t\t\t\t\t\"abstract\": abstract,\n\t\t\t\t\t\t\"url\": paper_url,\n\t\t\t\t\t\t\"keywords\": keywords,\n\t\t\t\t\t\t'year': date\n\t\t} \n\n\treturn author_dict","def find_authors(code):\n url = baseurl(code)\n page = req(url)\n soup = BeautifulSoup(page, 'lxml')\n addr = [t.attrs.get('content', None) \n for t in soup.find_all(\"meta\", {\"name\": \"citation_author_email\"})]\n \n # corresponding authors will have their email under another tag too\n corr = [t.find('a').attrs.get('href', None)\n for t in soup.find_all(None, {\"class\": \"author-corresp-email-link\"})]\n\n addr = [a for a in addr if a is not None]\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\n\n return dict(corr=list(set(corr)), all=list(set(addr)))","def articleAuthorsByYear(g = None, year = None):\n if not g:\n return\n articles = g.find(\"Article\", property_key='year', property_value=str(year))\n ret = dict()\n for article in articles:\n ret[article['title']] = article['authors'].replace('\\n','').lower()\n return ret","def __add_author(self, key_name, others_names, personal_information):\n for name in others_names:\n self.author_to_authorID[name] = (key_name, personal_information)","def find_reader_relations():\n for reader in readers:\n d100 = random.randint(1, 100)\n if d100 <= 50:\n reader_favourite_book[readers[reader]] = random.choice(list(book_ids.values()))\n\n d100 = random.randint(1, 100)\n if d100 <= 5:\n #TODO: fix so that you cannot be friend of yourself\n reader_knows[readers[reader]] = [random.choice(list(authors.values()))] + [random.choice(list(readers.values()))]\n elif d100 > 5 and d100 <= 10:\n reader_knows[readers[reader]] = [random.choice(list(authors.values()))]\n elif d100 > 10 and d100 <= 25:\n reader_knows[readers[reader]] = [random.choice(list(readers.values()))] + [random.choice(list(readers.values()))]\n elif d100 > 25 and d100 <= 50:\n reader_knows[readers[reader]] = [random.choice(list(readers.values()))]","def main(repo: Repository) -> dict:\n data = {}\n for commit in repo.traverse_commits():\n\n if not author_is_bot(commit.author):\n if commit.author.name in data:\n data[commit.author.name] = data[commit.author.name] + commit.lines\n else:\n data[commit.author.name] = commit.lines\n\n # Sort authors name in the dict\n data = dict(sorted(data.items(), key=lambda x: x[0].lower()))\n return data","def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\n dictionary_key_two):\n list_of_one_citations = []\n list_of_two_citations = []\n list_of_unassigned_citations = []\n for citation in self.array_of_citations:\n citation_string = self.__generate_author_string__(\n citation.get_author()) + \" \" + citation.get_title() \\\n + \" \" + citation.get_journal()\n one_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_one)\n two_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_two)\n if one_key_matches > two_key_matches:\n citation.set_classification(dictionary_key_one)\n list_of_one_citations.append(citation)\n elif two_key_matches > one_key_matches:\n citation.set_classification(dictionary_key_two)\n list_of_two_citations.append(citation)\n else:\n citation.set_classification(\"Unassigned\")\n list_of_unassigned_citations.append(citation)\n\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\n list_of_two_citations, \"Unassigned\":\n list_of_unassigned_citations}","def citation_formatter(citation, title=False):\n # first we split the author-list\n citation_string = \"\"\n # the spaces around the and are required!\n for match in citation[\"author\"].split(\" and \"):\n last_name, first_names = match.split(\",\", 1)\n citation_string += last_name.strip() + \",\"\n for name in first_names.strip().split(' '):\n citation_string += \" \" + name.strip()[0]\n\n citation_string += \"; \"\n\n if \"journal\" in citation:\n citation_string += \" \" + citation[\"journal\"]\n\n citation_string += \" \" + citation[\"year\"]\n\n if \"doi\" in citation:\n citation_string += \"; \" + citation[\"doi\"]\n\n return citation_string","def extract_author(self, parsed_html, author_idx=0, author=None):\n\n if not author:\n author = {}\n\n\n # Find all links with this xpath and tries to classify them\n links = []\n xpath = '//div[@class=\"profile cf\"]/div/ul/li/a'\n\n for url in parsed_html.xpath(xpath):\n links += [url.get('href')]\n\n for social in self.classify_links(links):\n author[social[0]] = social[1]\n\n\n # Get description text provided by the author\n xpath = '//div[contains(@class, \"profile-text\")]/p'\n items = parsed_html.xpath(xpath)\n author['about'] = self.clear_text(items)\n\n\n # Get Crunchbase url profile\n xpath = '//div[contains(@class, \"profile-text\")]/a'\n website = parsed_html.xpath(xpath)\n\n if website:\n author['website'] = website[0].get('href')\n\n\n # Get his/her avatar url\n xpath = '//div[@class=\"profile cf\"]/div/img'\n avatar = parsed_html.xpath(xpath)\n\n if avatar:\n author['avatar'] = avatar[0].get('src')\n\n\n # Tries to get the author's profile url\n xpath = 'meta[@property=\"og:url\"]'\n author['profile'] = self.get_text_or_attr(\n parsed_html, xpath, 'content'\n )\n\n\n # If the author cannot be fetched from his generated url, we have to\n # check the article's page in order to find him/her.\n\n if 'twitter' not in author and 'flag' not in author:\n\n parsed = self.parse(self.article_url, self.article_page_type)\n return self.extract_author_from_page(parsed, author_idx)\n\n\n if 'flag' in author:\n del author['flag']\n\n\n return author","def dict_by_year(incoming_dict):\n by_year = {}\n for key in incoming_dict.keys():\n try:\n year = incoming_dict[key]['year']\n keywords = incoming_dict[key]['keywords']\n for word in keywords:\n if year in by_year.keys():\n if word in by_year[year].keys():\n by_year[year][word] += keywords[word]\n else:\n by_year[year][word] = keywords[word]\n else:\n by_year[year] = {word: keywords[word]}\n except KeyError:\n print \"KeyError in dict_by_year. Key = {0}\".format(key)\n return by_year","def count_word_usage(counters_by_author, word_list):\n specific_word_counter = {}\n for author in counters_by_author.keys():\n word_counter = Counter()\n for item in counters_by_author[author]:\n for word in word_list:\n if word in item:\n print(item)\n word_counter[word] += counters_by_author[author][item]\n specific_word_counter[author] = word_counter\n return specific_word_counter","def get_authors_from_papers(papers):\n auth_set = set()\n for p in papers:\n auth_set.update(p['authors'])\n return list(auth_set)","def get_initial_author_dict():\n adict = {}\n try:\n ah = run_sql(\"select aterm,hitlist from rnkAUTHORDATA\")\n for (a, h) in ah:\n adict[a] = deserialize_via_marshal(h)\n return adict\n except:\n register_exception(prefix=\"could not read rnkAUTHORDATA\", alert_admin=True)\n return {}","def lookups(self, request, model_admin):\n # lambda article: article.author # 接受一个article，返回对应作者对象\n # map(func, iterable) # 将可迭代对象的每个元素传入func\n # author_list 所有文章的作者列表，去重\n author_list = list(set(map(lambda article: article.author, Article.objects.all())))\n\n # author_list2 = []\n # for author in author_list:\n # author_list2.append((author.id, author.nickname or author.username))\n # return author_list2\n\n for author in author_list: # verbose value, 在过滤器里显示的名称\n yield (author.id, (author.nickname or author.username))","def citing_authors(self, entities):\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\n FROM \"entity_occurrences\"\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\n return [t[0] for t in result]","def to_internal_value(self, data):\n authors = []\n for author in data:\n path = urlparse(author).path\n resolved_func, __, resolved_kwargs = resolve(path)\n person = resolved_func.cls.queryset.get(pk=resolved_kwargs['pk'])\n authors.append(person)\n\n return {'authors': authors}","def get_commentary_for_chapter(location):\n chapter_commentary_dictionary = {}\n\n soup = Ripper(location, parser=\"html5lib\", save_path=save_path).soup\n text = soup.find(\"table\", class_=\"texttable\")\n\n for each in text.find_all(\"p\"):\n attributes = each.attrs\n if attributes:\n if \"desc\" in attributes[\"class\"]:\n pass\n elif \"note\" in attributes[\"class\"]:\n new_content = each.contents\n verse_header, text = export_commentary_text_as_dictionary(new_content)\n chapter_commentary_dictionary[verse_header] = text\n else:\n continue\n return chapter_commentary_dictionary","def build_dict(results, chunk):\n from math import inf\n from collections import defaultdict\n chunk = [int(au) for au in chunk]\n d = defaultdict(\n lambda: {\"first_year\": inf, \"pubs\": set(), \"coauth\": set(),\n \"n_coauth\": inf, \"n_pubs\": inf})\n for pub in results:\n if not pub.author_ids:\n continue\n authors = set([int(au) for au in pub.author_ids.split(\";\")])\n for focal in authors.intersection(chunk):\n d[focal][\"coauth\"].update(authors)\n d[focal][\"coauth\"].remove(focal)\n d[focal][\"pubs\"].add(pub.eid)\n d[focal][\"n_pubs\"] = len(d[focal][\"pubs\"])\n d[focal][\"n_coauth\"] = len(d[focal][\"coauth\"])\n if not pub.coverDate:\n continue\n first_year = min(d[focal][\"first_year\"], int(pub.coverDate[:4]))\n d[focal][\"first_year\"] = first_year\n return d","def ordered_list_by_last_name(d):\n\n ordered_d = dict(sorted(d.items(), key=lambda x: x[1]['Last name']))\n\n print(\"\\nPhone book ordered by last name: \")\n for pid, info in ordered_d.items():\n print('\\nPerson number: ', pid)\n for key in info:\n print(key + ':', info[key])","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def author_name(text):\n tag = text.split()\n\n \"\"\"\n We take the beginning of the text since the\n author name will likely be there\n \"\"\"\n\n tag = tag[:100]\n author = []\n\n current_tag = 0\n \"\"\"\n We go through each word until we find the first instance\n of the word 'by' or 'author', which should mean the author\n will be written right after that.\n We save the first word after 'by' or 'author' since it should\n be the authors first name\n \"\"\"\n\n for word in tag:\n if (word.lower() == ('by') or\n word.lower() == ('author') or\n word.lower() == ('author:')):\n\n author.append(tag[current_tag+1].decode(encoding='UTF8',\n errors='ignore'))\n current_tag += 1\n tag = tag[current_tag+1:]\n break\n current_tag += 1\n\n \"\"\"\n We go through each word after the first name of the author\n until we find a word that is not capitalized. We assume that\n it marks the end of the author name.\n We then return a list of the author's name split up.\n \"\"\"\n current_tag = 0\n for word in tag:\n if tag[current_tag].lower() == 'this':\n break\n if tag[current_tag].istitle():\n author.append(tag[current_tag].decode(encoding='UTF8',\n errors='ignore'))\n current_tag += 1\n\n return author","def make_doi_countries(author_list: AuthorList):\n\n countries = {}\n\n for author in author_list:\n inst = author.institution\n if inst.country not in countries:\n countries[inst.country] = {\n \"identifier\": inst.country_code,\n \"name\": inst.country,\n \"types\": [\"Country\"],\n \"country\": inst.country,\n \"country_code\": inst.country_code,\n \"country_code_2\": inst.country_code_2,\n \"region\": inst.region,\n \"subregion\": inst.subregion,\n \"coordinates\": None,\n \"count\": 0,\n \"members\": {inst.ror_id},\n \"rors\": {inst.ror_id},\n }\n else:\n countries[inst.country][\"members\"].add(inst.ror_id)\n countries[inst.country][\"rors\"].add(inst.ror_id)\n\n return to_affiliations_list(countries)","def get_paper_authors(tree):\n\tpath = '//table/tr/th[text() = \"Glasgow Author(s) Enlighten ID:\"]/following-sibling::td/a'\n\t# Get list of <a> elements, each an author\n\tauthors = tree.xpath(path)\n\t# Make list of (author name, author url) pairs to return\n\tauthors = [(author.text, author.get(\"href\")) for author in authors]\n\n\treturn authors","def extract_relevant(self):\n item_extraction = self.data\n my_dict = {'tweeted_time': item_extraction['created_at'],\n 'tweet_id': item_extraction['id'],\n # If the time comes when the below becomes more significant, it will be no trouble at all to make an\n # additional column for it, but delimiting it with a ` creates less clutter in the Database\n 'in_reply_to':\n \"NAME/\" + str(item_extraction['in_reply_to_screen_name']) + \"`\" +\n \"STATUSID/\" + str(item_extraction['in_reply_to_status_id_str']) + \"`\" +\n \"USERID/\" + str(item_extraction['in_reply_to_user_id_str']),\n 'lang': item_extraction['lang'],\n 'place': item_extraction['place'], 'source': item_extraction['source']}\n if item_extraction['place'] is not None:\n my_dict['place'] = item_extraction['place']['full_name']\n if 'retweeted_status' in item_extraction.keys():\n my_dict['original_author_id'] = item_extraction['retweeted_status']['user']['id']\n my_dict['original_author_handle'] = item_extraction['retweeted_status']['user']['screen_name']\n tester = item_extraction['retweeted_status']['text']\n cleaned = ' '.join(re.sub(\"(RT : )|(@[\\S]+)|(&\\S+)|(http\\S+)\", \" \", tester).split())\n removed_others = \" \".join(re.sub(\"(#\\S+)\", ' ', cleaned).split())\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\n # This final text will make it a lot easier to run NLP\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\n my_dict['plain_text'] = final_text.lower()\n my_dict['tweet'] = cleaned\n else:\n my_dict['original_author_id'] = item_extraction['user']['id']\n my_dict['original_author_handle'] = item_extraction['user']['screen_name']\n cleaned = ' '.join(re.sub(\"(@[\\S]+)|(&\\S+)|(http\\S+)\", \" \", item_extraction['text']).split())\n removed_others = \" \".join(re.sub(\"(#\\S+)\", ' ', cleaned).split())\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\n my_dict['plain_text'] = final_text.lower()\n my_dict['tweet'] = cleaned\n return my_dict","def _get_sorted_trend_setters(authors: list) -> dict:\n\n trend_setters = {author: authors.count(author) for author in authors}\n top_trend_setters = dict(sorted(\n trend_setters.items(), key=lambda item: item[1], reverse=True\n ))\n return top_trend_setters","def get_ars(self, author):\n return self.divided[author][:-1]","def galeshapley(suitor_pref_dict, reviewer_pref_dict, max_iteration):\n suitors = list(suitor_pref_dict.keys())\n matching = dict()\n rev_matching = dict()\n\n for i in range(max_iteration):\n if len(suitors) <= 0:\n break\n for s in suitors:\n r = suitor_pref_dict[s][0]\n if r not in matching.values():\n matching[s] = r\n rev_matching[r] = s\n else:\n r_partner = rev_matching.get(r)\n if reviewer_pref_dict[r].index(s) < reviewer_pref_dict[r].index(r_partner):\n del matching[r_partner]\n matching[s] = r\n rev_matching[r] = s\n else:\n suitor_pref_dict[s].remove(r)\n suitors = list(set(suitor_pref_dict.keys()) - set(matching.keys()))\n return matching","def get_commentary_for_book_chapters(book_file_name):\n if not os.path.exists(challoner_store):\n os.mkdir(challoner_store)\n with open(book_file_name, \"r+\") as rh:\n book = json.load(rh)\n chapter_text = {}\n\n for name, chapters_dictionary in book.items():\n \n for chap, location in chapters_dictionary.items():\n norm = normalize_filename(\"{}_{}\".format(name, chap))\n \n outfile = \"{}/{}.json\".format(challoner_store, norm)\n \n if os.path.exists(outfile):\n continue\n else:\n chapter_text[name + \"__\" + chap] = get_commentary_for_chapter(location)\n with open(outfile, \"w+\") as wh:\n json.dump(chapter_text, wh)\n chapter_text = {}","def giveAuthorsName(catalog, ConstituentsID):\n names = []\n\n for x in ConstituentsID:\n names.append(' '+model.giveAuthorName(catalog, x))\n return ','.join(names)","def critics_letters(letters):\n letters_url = list()\n for elem in letters:\n letters_url.append(\"/critics/authors?letter=\" + elem)\n return letters_url","def get_word_counts(messages_by_author):\n counters_by_author = {}\n for author in messages_by_author.keys():\n author_counter = Counter()\n for message in messages_by_author[author]:\n author_counter += Counter(get_words(string_to_onlyalpha(message.content)))\n counters_by_author[author] = author_counter\n return counters_by_author","def make_haystack(self):\n journal_data = [\" \".join(self.format_authors(style=\"full\")),\n self.journal_long,\n self.journal_short,\n \"\".join(c for c in self.journal_short if c.isupper()),\n self.title]\n return [unidecode(data) for data in journal_data]","def find_books_by_author_separation(self, name, degree):\n self.cursor.execute(\"\"\"SELECT ID FROM author WHERE name=%s\"\"\", (name,))\n original_author_id = int(self.cursor.fetchone()[0])\n self.cursor.execute(\"\"\"SELECT ISBN FROM wrote WHERE authorID=%s\"\"\", (original_author_id,))\n first_degree_authors = []\n for original_author_books in self.cursor.fetchall():\n self.cursor.execute(\"\"\"SELECT authorID FROM wrote WHERE ISBN=%s AND authorID <> %s\"\"\",\n (original_author_books[0], original_author_id))\n for author_id in self.cursor.fetchall():\n first_degree_authors.append(author_id[0])\n first_degree_results = {}\n for author in first_degree_authors:\n self.cursor.execute(\"\"\"SELECT ISBN FROM wrote WHERE authorID=%s\"\"\",(author,))\n for ISBN in self.cursor.fetchall():\n book, author_list = self.get_single_book_info(ISBN[0])\n first_degree_results[ISBN[0]] = [book, author_list]\n if int(degree) == 1:\n return first_degree_results\n\n second_degree_results = {}\n authors_to_check = []\n all_authors = []\n self.cursor.execute(\"\"\"SELECT authorID FROM wrote\"\"\")\n for author in self.cursor.fetchall():\n all_authors.append(int(author[0]))\n for author1 in first_degree_authors:\n for author2 in all_authors:\n if (self.is_one_degree_separated(author1, author2) and author1 != author2\n and author2 != original_author_id):\n authors_to_check.append(author2)\n second_degree_authors = [a for a in authors_to_check if a not in first_degree_authors]\n for author in second_degree_authors:\n self.cursor.execute(\"\"\"SELECT ISBN FROM wrote WHERE authorID=%s\"\"\", (author,))\n for ISBN in self.cursor.fetchall():\n book, author_list = self.get_single_book_info(ISBN[0])\n second_degree_results[ISBN[0]] = [book, author_list]\n return second_degree_results","def get_annotated_content_info(course_id, content, user, user_info):\r\n voted = ''\r\n if content['id'] in user_info['upvoted_ids']:\r\n voted = 'up'\r\n elif content['id'] in user_info['downvoted_ids']:\r\n voted = 'down'\r\n return {\r\n 'voted': voted,\r\n 'subscribed': content['id'] in user_info['subscribed_thread_ids'],\r\n 'ability': get_ability(course_id, content, user),\r\n }","def get_autor_with_papers(author_id):\n print(\"getting information from dblp about author {}\".format(author_id))\n data = get(\"https://dblp.org/pid/\" + author_id + \".xml\")[\"dblpperson\"]\n author_id = author_id.replace(\"/\", \"_\")\n author = {\n \"name\": data[\"@name\"],\n \"papers\": [get_paper(paper_container) for paper_container in data[\"r\"]],\n }\n\n with open(local.file_name(\"authors\", author_id), \"w\") as out:\n json.dump(author, out, ensure_ascii=False, indent=4)","def get_subdict(indict, phrase):\n\tsubdict = {}\n\tfor key in indict:\n\t\tif phrase in key:\n\t\t\tsubdict[key] = indict[key]\n\treturn subdict","def return_dict_of_assigned_citations_classifications(self):\n for citation_instance in self.array_of_citations:\n citation_string = \"\"\n citation_string = citation_string.join(\n citation_instance.get_author()) + \\\n citation_instance.get_title()\\\n + citation_instance.get_journal()\n max_keyword_matches = 0\n for key in self.dict_of_keywords:\n current_key_matches = 0\n for keyword in self.dict_of_keywords.get(key):\n pattern = re.compile(keyword)\n if pattern.search(citation_string):\n current_key_matches += 1\n if current_key_matches > max_keyword_matches:\n max_keyword_matches = current_key_matches\n citation_instance.set_classification(key)\n return self.__generate_dict_of_keys_to_classification__()","def getPopualrAuthors():\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(\" select count(*) as views , authors.name from articles \"\n + \" inner join \"\n + \"log on concat('/article/', articles.slug) = log.path \"\n + \" inner join authors on articles.author = authors.id \"\n + \"group by name order by views desc; \")\n authors = c.fetchall()\n db.close()\n return authors","def getAuthors(self): #$NON-NLS-1$\r","def construct_authorities_for_answers(answers):\n domains = [rr._dn for rr in answers]\n glue = {\"authority\": [], \"additional\": []}\n for domain in domains:\n auth = construct_ns_rr_from_cache(domain)\n additional = construct_a_rr_from_cache(auth._nsdn)\n if auth:\n glue[\"authority\"].append(auth)\n if additional:\n glue[\"additional\"].append(additional)\n return (glue[\"authority\"], glue[\"additional\"])","def Authors(self, default=[{}]):\n tmp = self.data.get('authors', default)\n return [HEP.AuthorReducedObject(i) for i in tmp]","def books_by_author(self, author):\n request_url = \"%s?author=%s\" % (self.API_URL, author)\n json_data = self.make_request(request_url)\n if not json_data:\n return []\n books = []\n for book in json_data['docs']:\n books.append(book['title_suggest'])\n return books","def addtoks(indict): # type: ({}) -> {}\n\n if 'Clause Text' not in indict:\n return indict\n\n cleantext = helpers.cleantext(indict['Clause Text'])\n indict['Tokens'] = helpers.allngrams(cleantext, 1, 3)\n\n return indict","def find_author(author):\n case = author_case(author)\n queries = author_queries(case, author)\n author_uri_set = set([])\n for query in queries:\n result = vivo_sparql_query(query.encode('utf-8'))\n count = len(result['results']['bindings'])\n if count == 1:\n author_uri_set = set([result['results']['bindings'][0]\\\n ['uri']['value']])\n break\n elif count > 1 and count < len(author_uri_set):\n author_uri_set = set([])\n for row in result['results']['bindings']:\n author_uri_set.add(row['uri']['value'])\n return author_uri_set","def _extract_json_ils_ids(info, provenence=\"source\"):\n SOURCES = {\n \"AUTHOR|(INSPIRE)\": \"INSPIRE ID\",\n \"AUTHOR|(CDS)\": \"CDS\",\n \"AUTHOR|(SzGeCERN)\": \"CERN\",\n }\n regex = re.compile(r\"(AUTHOR\\|\\((CDS|INSPIRE|SzGeCERN)\\))(.*)\")\n ids = []\n author_ids = force_list(info.get(\"0\", \"\"))\n for author_id in author_ids:\n match = regex.match(author_id)\n if match:\n ids.append(\n {\"value\": match.group(3), provenence: SOURCES[match.group(1)]}\n )\n try:\n ids.append({\"value\": info[\"inspireid\"], provenence: \"INSPIRE ID\"})\n except KeyError:\n pass\n\n return ids","def get_author_data():\n entry = mongo.db.Authors\n output = list()\n look_up_type = None\n if 'name' in request.args:\n look_up_type = 'name'\n print(request.args)\n if len(request.args['name']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['name'].strip('\"')\n name = entry.find({'name': {'$regex': value}})\n if name:\n for author in name:\n output.append({'name': author['name']})\n elif 'booktitle' in request.args:\n look_up_type = 'related_books'\n if len(request.args['booktitle']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['booktitle'].strip('\"')\n related_books = entry.find(\n {'author_books': {'$regex': value}})\n if related_books:\n for related in related_books:\n for title in related['author_books']:\n if value in title:\n output.append(({'related_books': title}))\n if len(output) == 0:\n return render_template('error.html', message=\"No Entries Found\"), 400\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200","def load_authors():\n\n ret = {}\n for token in util.git('log', '--format=%aE:::%aN').split('\\n'):\n email, name = token.split(':::')\n ret[email] = name\n return ret","def print_loc_acrnym():\n\n #Method2\n val = College.objects.values('acronym','contact')\n for i in val:\n print(i['acronym'],i['contact'])","def join_chapter_text(chapter_content_list):\n cleaned_chapter_content_list = [each for each in chapter_content_list if each != \"\\n\"]\n chap_text = {0 : \"\"}\n tracker = 0\n\n for each in chapter_content_list:\n each = str(each)\n if re.search(r'\\/x\\/d\\?b=drb', each):\n tracker += 1\n verse = re.search(r'\\[(\\d+)\\]', each).group(1)\n chap_text[tracker] = \"\"\n else:\n chap_text[tracker] = chap_text[tracker] + each.strip()\n \n chap_text.pop(0, None)\n return chap_text","def query_by_author(cls,author):\n bakey = 'BookByAuthor_%s'%author\n bks = from_cache(bakey)\n if not bks:\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).filter('authors =',author).fetch(100))\n to_cache(bakey,bks)\n return bks","def test_author_reverse(self):\n invenio_search = 'author:\"ellis, j*\"'\n spires_search = 'find a j ellis'\n self._compare_searches(invenio_search, spires_search)","def ordered_list_by_first_name(d):\n\n ordered_d = dict(sorted(d.items(), key=lambda x: x[1]['First name']))\n\n print(\"\\nPhone book ordered by first name: \")\n for pid, info in ordered_d.items():\n print('\\nPerson number: ', pid)\n for key in info:\n print(key + ':', info[key])","def build_query_dict(self, term_list, issn_list, year_list, jlist):\n journal_frame = self.make_jlist(jlist)\n\n search_terms = self.build_search_terms(term_list)\n dict1 = {}\n #This loop goes through and sets up a dictionary key with an ISSN number\n\n for issn in issn_list:\n\n issn_terms = ' AND ISSN(' + issn + ')'\n dict2 = {}\n #This loop goes and attaches all the years to the outer loop's key.\n for year in year_list:\n\n year_terms = \"AND PUBYEAR IS \" + str(year)\n querystring = search_terms + year_terms + issn_terms\n\n dict2[year] = querystring\n\n dict1[issn] = dict2\n\n return dict1","def test_author_many_initials(self):\n inv_search = 'author:\"bach, p* d* q*\"'\n spi_search = 'find a p d q bach'\n self._compare_searches(inv_search, spi_search)","def get_author_titles(author_url):\n\t# Get the html tree for the author page\n\tauthor_page_tree = get_tree(author_url)\n\t# Get the <a> elements for the papers on the author's page\n\ta_elems = get_a_elems_for_papers(author_page_tree)\n\n\tall_titles = []\n\tlinks = []\n\t# Loop through a elements and put associated href and text into respective lists\n\tfor a in a_elems:\n\t\tall_titles.append(a.text_content())\n\t\tlinks.append(a.get(\"href\"))\n\n\t# Create list of (title, url) tuples\n\ttitles_links = zip(all_titles, links)\n\t# Get the list of titles of papers that have been tagged with a subject\n\ttagged_titles = get_tagged_titles(titles_links)\n\t# Return the 2 lists in a tuple\n\treturn (all_titles, tagged_titles)","def get_authors(config: Config) -> dict:\n\n file_handle = load_csv_file(config.input)\n\n start = time.time()\n raw_authors = get_raw_authors(config, file_handle)\n end = time.time()\n print(f\"get_raw_authors took {end - start} seconds\")\n\n start = time.time()\n sanitized_authors = sanitize_authors(config, raw_authors)\n end = time.time()\n print(f\"sanitize_authors took {end - start} seconds\")\n\n return sanitized_authors","def test_author_initials(self):\n inv_search = 'author:\"polyakov, a* m*\"'\n spi_search = 'find a a m polyakov'\n self._compare_searches(inv_search, spi_search)","def get_author_name_urls(dept_name, dept_url):\n\t# Change to \"School of Humanities\" to match the name used in Enlighten\n\t# Done because the string obtained from http://www.gla.ac.uk/schools/ contains the Gaelic name as well\n\tif \"Humanities\" in dept_name:\n\t\tdept_name = \"School of Humanities\"\n\n\t# get list of names of researchers in department\n\tnames = get_names(dept_url)\n\n\twinning_name_urls = set()\n\n\t# loop through each name\n\tfor name in names:\n\t\tname = initialise_first_name(name)\n\t\t# Get Enlighten page on which author name will be found (page for the letter of author's last name)\n\t\tfull_url = author_list_base + \"index.\"+ name.split(\" \")[0][0] + \".html\"\n\t\ttree = get_tree(full_url)\n\t\t# Get all candidate authors which match the name\n\t\tname_urls = get_name_url_matches(name, tree)\n\t\t# If candidates were found\n\t\tif name_urls:\n\t\t\t# Filter out authors that have already been scraped\n\t\t\tname_urls = [name_url for name_url in name_urls if name_url not in winning_name_urls]\n\t\t\t# Get the first ranked (name, url) tuple for the target name from the remaining candidates\n\t\t\twinning_name_url = get_winning_url(name_urls, dept_name)\n\t\t\tif winning_name_url:\n\t\t\t\twinning_name_urls.add(winning_name_url)\n\n\treturn winning_name_urls","def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\n str:\n acc = []\n \n for keys in user_to_tweets:\n author_hashes = hashtag_seperator(user_to_tweets[keys])\n text_hashes = extract_hashtags(tweet_text)\n if set(text_hashes).issubset(author_hashes):\n acc.append(keys)\n if len(acc) == 1:\n return acc[0]\n return 'unknown'","def build_ils_contributor(value):\n if not value.get(\"a\"):\n return []\n\n contributor = {\n \"identifiers\": _extract_json_ils_ids(value, \"scheme\") or None,\n \"full_name\": value.get(\"name\") or clean_val(\"a\", value, str),\n \"roles\": [\n _get_correct_ils_contributor_role(\"e\", value.get(\"e\", \"author\"))\n ],\n }\n\n value_u = value.get(\"u\")\n if value_u:\n values_u_list = list(force_list(value_u))\n other = [\"et al.\", \"et al\"]\n for x in other:\n if x in values_u_list:\n values_u_list.remove(x)\n contributor[\"affiliations\"] = [{\"name\": x} for x in values_u_list]\n contributor = dict(\n (k, v) for k, v in iteritems(contributor) if v is not None\n )\n return contributor","def subject_info(intent, extra_info=[]):\n\n text = intent['inputTranscript'].lower()\n utterances = AS.load_file('sample_utterances.txt')\n\n # add \"book\" and \"books\" to every utterance\n for line in list(utterances):\n utterances.insert(0, line + \" book\")\n utterances.insert(0, line + \" books\")\n\n # tells how many characters needs to be dropped before the subject starts\n to_drop = 0\n\n for line in utterances:\n if text.startswith(line):\n to_drop = len(line)\n break\n\n # drops the characters and makes a list from the strings that are left\n text = text[to_drop:].strip()\n text_list = text.split(' ', len(text))\n\n subject_list = []\n keywords = [\"books\", \"book\", \"by\", \"published\", \"written\"]\n keyword = \"\"\n\n # Find out when the book name ends\n for word in text_list:\n if word not in keywords:\n subject_list.append(word)\n else:\n break\n\n subject = \" \".join(subject_list)\n\n # Get all the keywords in the middle, so they can be\n # all be dropped at once, eg written by, books by\n text_list = text_list[len(subject_list):]\n if text_list:\n word = text_list[0]\n while word in keywords:\n keyword += word + \" \"\n text_list = text_list[1:]\n if text_list:\n word = text_list[0]\n else:\n break\n\n # search for an author from the rest of the characters\n author_text = text[len(keyword):].strip()\n author = AS.search(author_text, False)\n if author is \"\":\n author = None\n\n # There might be old info in the extra_info (author), so \n # we need to clear it\n extra_info.clear()\n\n # add the author to extra info so it can be used in the Finna API call\n if author:\n extra_info += [\"author:\\\"\" + author + \"\\\"\"]\n elif intent['sessionAttributes'].get('author'):\n extra_info += [\n \"author:\\\"\" + intent['sessionAttributes']['author'] + \"\\\"\"\n ]\n\n # The Finna API call\n request = lookfor(term=subject, filter=extra_info)['json']\n\n return parse_subject(request, subject, {'author': author})","def test_author_simple(self):\n invenio_search = 'author:\"ellis, j*\"'\n spires_search = 'find a ellis, j'\n self._compare_searches(invenio_search, spires_search)","def get_author_info(path):\n author_df = pandas.read_table(path)\n\n # Detect missing full name\n missing_full_name = author_df[author_df['full_name'].isnull()].index.tolist()\n if missing_full_name:\n msg = f'Missing full name in authors.tsv for author(s): {missing_full_name}'\n raise ValueError(msg)\n\n # Detect missing author initials\n missing_initials = author_df[author_df['initials'].isnull()].index.tolist()\n if missing_initials:\n msg = f'Missing initials in authors.tsv for author(s): {missing_initials}'\n raise ValueError(msg)\n\n # Detect duplicate author initials\n duplicated_initials = list(\n author_df[author_df.duplicated('initials')].initials)\n if duplicated_initials:\n msg = f'Duplicated initials in authors.tsv: {duplicated_initials}'\n raise ValueError(msg)\n\n # Replace missing values with None\n author_df.where(pandas.notnull(author_df), None, inplace=True)\n\n authors = author_df.to_dict(orient='records')\n return authors"],"string":"[\n \"def test_citedby_author(self):\\n inv_search = 'citedby:author:doggy'\\n spi_search = 'find citedby author doggy'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_publications_by_author(cached_list, cached_set, author_name):\\n publications = { 'dblp': [], 'cdblp': [] }\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n\\n if author['dblp'].__contains__('publications'):\\n publications['dblp'] = author['dblp']['publications']\\n# for pub in author['dblp']['publications']:\\n# print(pub)\\n\\n if author['cdblp'].__contains__('publications'):\\n publications['cdblp'] = author['cdblp']['publications']\\n# for pub in author['cdblp']['publications']:\\n# print(pub)\\n return publications\",\n \"def get_coauthors_by_author(cached_list, cached_set, author_name):\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n coauthors = {}\\n if author['dblp'].__contains__('coauthors'):\\n for author_key in author['dblp']['coauthors']:\\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\\n\\n if author['cdblp'].__contains__('coauthors'):\\n for author_key in author['cdblp']['coauthors']:\\n if coauthors.__contains__(author_key['full_name']):\\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\\n else:\\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\\n\\n return coauthors\",\n \"def authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n item = build_ils_contributor(value)\\n if item and item not in _authors:\\n _authors.append(item)\\n try:\\n if \\\"u\\\" in value:\\n other = [\\\"et al.\\\", \\\"et al\\\"]\\n val_u = list(force_list(value.get(\\\"u\\\")))\\n if [i for i in other if i in val_u]:\\n self[\\\"other_authors\\\"] = True\\n except UnexpectedValue:\\n pass\\n return _authors\",\n \"def find_relations_among_authors():\\n for book in books:\\n if len(books[book]) > 1:\\n for i in range(len(books[book])):\\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]\",\n \"def __convert_authors(self, author_dict: dict) -> str:\\n if \\\"author\\\" not in author_dict.keys():\\n return str(author_dict)\\n res = \\\"\\\"\\n for i, _ in enumerate(author_dict['author']):\\n if \\\"given-name\\\" in author_dict[\\\"author\\\"][i].keys() and \\\"surname\\\" in author_dict[\\\"author\\\"][i].keys():\\n res += author_dict['author'][i][\\\"given-name\\\"] + \\\" \\\" + author_dict['author'][i][\\\"surname\\\"] + \\\", \\\"\\n else:\\n res += str(author_dict['author'][i])\\n return res\",\n \"def authorSign(g = None):\\n if not g:\\n return\\n authors = g.find(\\\"Author\\\")\\n ret = dict()\\n for author in authors:\\n ret[author['name']] = author['citation'].replace('\\\\n','').lower()\\n return ret\",\n \"def get_author_info(self, author: str):\\n for writer_word in self._writer_words:\\n data = json.loads(requests.get(WIKIDATA_SEARCH + \\\"&srsearch=\\\" + author + \\\" \\\" + writer_word).text)\\n pages = data.get(\\\"query\\\").get(\\\"search\\\")\\n if len(pages) >= 1:\\n pageid = pages[0].get(\\\"title\\\")\\n author_details = self._reference.author_map.get(author)\\n if author_details:\\n return author_details\\n if pageid == -1:\\n continue\\n\\n else:\\n response = requests.get(WIKIDATA_PARSE + pageid + \\\".json\\\")\\n data = json.loads(response.text)\\n if author.lower() not in data.get(\\\"entities\\\").get(pageid).get(\\\"labels\\\").get(\\\"en\\\").get(\\\"value\\\").lower():\\n continue\\n else:\\n try:\\n id = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\").get(\\\"P31\\\")[0].get(\\\"mainsnak\\\").get(\\\"datavalue\\\").get(\\\"value\\\").get(\\\"id\\\")\\n if str(id) != \\\"Q5\\\": # the id for human\\n continue\\n except IndexError:\\n continue\\n properties = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\")\\n author_details = {\\\"id\\\": pageid, \\\"gender\\\": self.get_gender(properties)}\\n country_details = self.get_country(properties)\\n author_details[\\\"country\\\"] = country_details\\n self._reference.author_map[author] = author_details\\n return author_details\\n return {\\\"id\\\": \\\"Unknown\\\", \\\"gender\\\": \\\"Unknown\\\", \\\"country\\\": [{\\\"name\\\": \\\"Unknown\\\", \\\"region\\\": \\\"Unknown\\\"}]}\",\n \"def extra_bibparse(db):\\n for key,entry in db.entries.items():\\n for auth in entry.persons[\\\"author\\\"]:\\n if (\\\"Harrison\\\" not in auth.first_names or\\n \\\"Chapman\\\" not in auth.last_names):\\n entry.add_person(auth, \\\"otherauthor\\\")\",\n \"def alt_authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n if _authors:\\n for i, v in enumerate(force_list(value)):\\n _authors[i].update({\\\"alternative_names\\\": clean_val(\\\"a\\\", v, str)})\\n return _authors\",\n \"def get_coauthors_dict(name_url_list, schl_name):\\n\\n\\tpaper_info_dict = {}\\n\\n\\tfor name_url in name_url_list:\\n\\t\\t# Get a sub papers_info dictfor an individual author\\n\\t\\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\\n\\t\\t# add sub dict to full dict\\n\\t\\tpaper_info_dict.update(papers_info)\\n\\t\\n\\tif \\\"Humanities\\\" in schl_name:\\n\\t\\tschl_name = \\\"School of Humanities\\\"\\n\\n\\treturn paper_info_dict\",\n \"def get_movie_people_relation(title, people_dict, movie_people_dict):\\n for item in title:\\n for key in people_dict.keys():\\n for movie_title in people_dict[key]:\\n if item == movie_title:\\n if item in movie_people_dict.keys():\\n if key not in movie_people_dict[item]:\\n movie_people_dict[item] += ',' + key\\n else:\\n movie_people_dict[item] = key\\n else:\\n if item not in movie_people_dict.keys():\\n movie_people_dict[item] = ''\\n return movie_people_dict\",\n \"def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\\n publications = { 'cdblp': [], 'dblp': [] }\\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\\n for cdblp_pub in pub1.get('cdblp', []):\\n authors = set(cdblp_pub.get('authors', []))\\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\\n publications['cdblp'].append(cdblp_pub)\\n\\n for dblp_pub in pub1.get('dblp', []):\\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\\n publications['dblp'].append(dblp_pub)\\n\\n return publications\",\n \"def popAuthors(self):\\r\\n# cur = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\")\\r\\n# res = cur.fetchall()\\r\\n res = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\").fetchall()\\r\\n\\r\\n self.authorList = [formatNameSQL(ln) for ln in res]\\r\\n self.quickAuthors = [ln[\\\"Lastname\\\"].lower() for ln in res]\\r\\n vals = [ln[\\\"PersonID\\\"] for ln in res]\\r\\n \\r\\n self.authorLookup = dict(zip(self.authorList,vals))\",\n \"def make_citation_authors(res):\\n if \\\"author\\\" in res.keys():\\n first_author = res['author'][0]['family'] + \\\", \\\" + res['author'][0]['given']\\n last_author = res['author'][-1]['given'] + \\\" \\\" + res['author'][-1]['family']\\n middle_authors = \\\", \\\".join(\\\" \\\".join([x['given'], x['family']]) for x in res['author'][1:-1])\\n #assemble authors\\n author_string = first_author\\n author_string = author_string + \\\", \\\" + middle_authors if middle_authors != '' else author_string\\n author_string = author_string + \\\", and \\\" + last_author if len(res['author']) > 1 else author_string\\n \\n author_string = author_string + \\\".\\\" if author_string[-1] != \\\".\\\" else author_string\\n else:\\n author_string = \\\"Unknown Authors\\\"\\n\\n return clean_txt(author_string.capitalize())\",\n \"def author_dictionary_italian(corpus_token_path):\\n token_files = os.listdir(corpus_token_path)\\n correct_author = [filename.split('_')[0] for filename in token_files]\\n author_name_token_dict = {}\\n for i in range(0, correct_author.__len__()):\\n if correct_author[i] not in author_name_token_dict.keys():\\n author_name_token_dict[correct_author[i]] = [token_files[i]]\\n # print(author_name_token_dict[french_correct_author[i]])\\n else:\\n existing_token_files = author_name_token_dict[correct_author[i]]\\n if existing_token_files is not None:\\n author_name_token_dict[correct_author[i]].append(token_files[i])\\n return author_name_token_dict\",\n \"def author_dictionary (corpus_token_path, correct_author_path):\\n token_files = os.listdir(corpus_token_path)\\n correct_author = read_file(correct_author_path).split(\\\"\\\\n\\\")\\n author_name_token_dict = {}\\n for i in range(0, correct_author.__len__()):\\n if correct_author[i] not in author_name_token_dict.keys():\\n author_name_token_dict[correct_author[i]] = [token_files[i]]\\n #print(author_name_token_dict[french_correct_author[i]])\\n else:\\n existing_token_files = author_name_token_dict[correct_author[i]]\\n if existing_token_files is not None:\\n author_name_token_dict[correct_author[i]].append(token_files[i])\\n return author_name_token_dict\",\n \"def age_of_cited_work(art_map, art_cit_map, cit_map):\\n logging.debug('============== IN age_of_cited_work: ================')\\n age = {}\\n for article in art_cit_map:\\n age_list = []\\n logging.debug('========== ARTICLE: ============')\\n logging.debug(article)\\n for citation in art_cit_map[article]:\\n logging.debug('========== CITATION: ============')\\n logging.debug(citation)\\n logging.debug(art_map[article]['pub_year'])\\n if citation in cit_map:\\n logging.debug(cit_map[citation]['pub_year'])\\n age_list.append(\\n int(art_map[article]['pub_year']) - int(cit_map[citation][\\n 'pub_year']))\\n age[art_map[article]['pub_year']] = age_list\\n logging.debug('========== AGE OF CITED WORK: ============')\\n logging.debug(age)\\n return sort_map_by_keys(age)\",\n \"def __get_citedby(self):\\n if not hasattr(self, 'id_scholarcitedby'):\\n self.fill()\\n if hasattr(self, 'id_scholarcitedby'):\\n url = self._scholarly.URLS('SCHOLARPUB').format(\\n requests.utils.quote(self.id_scholarcitedby))\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n return self._scholarly._search_scholar_soup(soup)\\n else:\\n return []\",\n \"def test_citedby_refersto_author(self):\\n inv_search = 'refersto:citedby:author:penguin'\\n spi_search = 'find refersto citedby author penguin'\\n self._compare_searches(inv_search, spi_search)\",\n \"def getInCitesAuthorInfo (self, authorInfo):\\n\\t\\tprint 'getInCitesAuthorInfo with ' + authorInfo.fullname\\n\\t\\tid = authorInfo.id\\n\\t\\t\\n\\t\\ttogglerClass = authorInfo.matches and \\\"toggler\\\" or \\\"\\\"\\n\\t\\t\\n\\t\\ttoggler = DIV (id=id, klass=togglerClass)\\n\\t\\t\\n\\t\\tif authorInfo.numMatches > 0:\\n\\t\\t\\ttogglerLnkClass = \\\"inCitesAuthor togglerClosed\\\"\\n\\t\\telse:\\n\\t\\t\\ttogglerLnkClass = \\\"inCitesAuthor noTogglerClosed\\\"\\n\\t\\t# print \\\"%s %s\\\" % (authorInfo.inCitesName, authorInfo.numMatches)\\n\\t\\t\\t\\t\\t\\t \\n\\t\\ttogglerLnk = DIV(id='toggler-lnk-'+id, klass=togglerLnkClass)\\n\\t\\ttoggler.append(togglerLnk)\\n\\t\\t\\n\\t\\tauthorTable = TABLE(klass=\\\"authorTable\\\")\\n\\t\\ttogglerLnk.append(authorTable)\\n\\t\\tmatchesContent = '(%d matches)' % authorInfo.numMatches\\n\\t\\tauthorTable.append(\\n\\t\\t\\tTR(\\n\\t\\t\\t\\tTD (authorInfo.fullname, klass=\\\"author\\\"),\\n\\t\\t\\t\\tTD (matchesContent, klass=\\\"matches\\\")\\n\\t\\t\\t)\\n\\t\\t)\\n\\n\\t\\tif authorInfo.numMatches > 0:\\n\\t\\t\\ttogglerCon = DIV(id='toggler-con-'+id, style=\\\"display:none\\\")\\n\\t\\t\\ttoggler.append(togglerCon)\\n\\t\\t\\tmatchTable = TABLE(klass=\\\"matchTable\\\")\\n\\t\\t\\ttogglerCon.append(matchTable)\\n\\t\\t\\t\\n\\t\\t\\tfor match in authorInfo.matches:\\n\\t\\t\\t\\tmatch_row = TR (klass=\\\"peopleDBmatch\\\", id=match.upid)\\n\\t\\t\\t\\tmatch_row.append (\\n\\t\\t\\t\\t\\tTD (match.getName(), klass=\\\"match-name\\\"),\\n\\t\\t\\t\\t\\tTD (match.upid, klass=\\\"match-upid\\\"))\\n\\t\\t\\t\\tmatchTable.append(match_row);\\n\\t\\treturn toggler\",\n \"def make_author_list(res):\\n try:\\n r = [\\\", \\\".join([clean_txt(x['family']).capitalize(), clean_txt(x['given']).capitalize()]) for x in res['author']]\\n except KeyError as e:\\n print(\\\"No 'author' key, using 'Unknown Author'. You should edit the markdown file to change the name and citationkey.\\\")\\n r = [\\\"Unknown Authors\\\"]\\n return r\",\n \"def get_author_detail(body):\\n user_id = get_user_id(body) # user id\\n author_details = body.find('div', {'id': 'gsc_prf_i'})\\n\\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\\n\\n affiliation = name_attributes[0].text or ''\\n interests = name_attributes[1].text or ''\\n email = name_attributes[2].text or ''\\n\\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\\n\\n year_since_text = citation_indices.find_all('tr')[0]\\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\\n\\n # citations\\n citations = citation_indices.find_all('tr')[1]\\n h_index = citation_indices.find_all('tr')[2]\\n h_index_all = h_index.find_all('td')[1].text\\n h_index_since = h_index.find_all('td')[2].text\\n citation_all = citations.find_all('td')[1].text\\n citation_since = citations.find_all('td')[2].text\\n\\n author_dict = {'user_id': user_id,\\n 'name': name,\\n 'affiliation': affiliation,\\n 'interests': interests,\\n 'email': email,\\n 'h_index_all': h_index_all,\\n 'h_index_since': h_index_since,\\n 'citation_all': citation_all,\\n 'citation_since': citation_since,\\n 'year_since': year_since}\\n return author_dict\",\n \"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\\n i = 0 #just for debugging ..\\n #get the tags for main author, coauthors, ext authors from config\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n for t in tags:\\n try:\\n dummy = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_selfcitdict\\n\\n r_mainauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"first_author\\\")\\n r_coauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"additional_author\\\")\\n r_extauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"alternative_author_name\\\")\\n #parse the tags\\n mainauthortag = tagify(parse_tag(r_mainauthortag))\\n coauthortag = tagify(parse_tag(r_coauthortag))\\n extauthortag = tagify(parse_tag(r_extauthortag))\\n\\n selfcites = initial_selfcitdict\\n for k in new_record_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Selfcites done \\\"+str(i)+\\\" of \\\"+str(len(new_record_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i+1\\n #get the author of k\\n authorlist = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authorlist.append(coauthl)\\n authorlist.append(extauthl)\\n #author tag\\n #print \\\"record \\\"+str(k)+\\\" by \\\"+str(authorlist)\\n #print \\\"is cited by\\\"\\n #get the \\\"x-cites-this\\\" list\\n if citationdic.has_key(k):\\n xct = citationdic[k]\\n for c in xct:\\n #get authors of c\\n cauthorlist = get_fieldvalues(c, mainauthortag)\\n coauthl = get_fieldvalues(c, coauthortag)\\n extauthl = get_fieldvalues(c, extauthortag)\\n cauthorlist.extend(coauthl)\\n cauthorlist.extend(extauthl)\\n #print str(c)+\\\" by \\\"+str(cauthorlist)\\n for ca in cauthorlist:\\n if (ca in authorlist):\\n #found!\\n if selfcites.has_key(k):\\n val = selfcites[k]\\n #add only if not there already\\n if val:\\n if not c in val:\\n val.append(c)\\n selfcites[k] = val\\n else:\\n #new key for selfcites\\n selfcites[k] = [c]\\n\\n mesg = \\\"Selfcites done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return selfcites\",\n \"def corporate_authors(self, key, value):\\n _corporate_authors = self.get(\\\"authors\\\", [])\\n\\n for v in force_list(value):\\n if key == \\\"710__\\\":\\n if \\\"a\\\" in v:\\n _corporate_authors.append(\\n {\\n \\\"full_name\\\": clean_val(\\\"a\\\", v, str),\\n \\\"type\\\": \\\"ORGANISATION\\\",\\n }\\n )\\n else:\\n self[\\\"authors\\\"] = collaborations(self, key, value)\\n raise IgnoreKey(\\\"corporate_authors\\\")\\n else:\\n _corporate_authors.append(\\n {\\\"full_name\\\": clean_val(\\\"a\\\", v, str), \\\"type\\\": \\\"ORGANISATION\\\"}\\n )\\n return _corporate_authors\",\n \"def count_by_author(all_articles):\\n author_count = {}\\n\\n for title in all_articles:\\n author = \\\", \\\".join(all_articles[title]['authors'])\\n if author not in author_count:\\n author_count[author] = 1\\n else:\\n author_count[author] = author_count[author] + 1\\n \\n print_all_items_in_dict(author_count)\",\n \"def make_citation_key(res):\\n year = str(make_year(res))\\n\\n try:\\n last_names = [x['family'] for x in res['author']]\\n except KeyError as e:\\n last_names = [\\\"Unknown\\\"]\\n if len(last_names) >= 3:\\n key = last_names[0].capitalize() + \\\"ETAL\\\" + year\\n else:\\n key = \\\"\\\".join(last_names) + year\\n\\n return clean_txt(key.replace(\\\" \\\", \\\"\\\"))\",\n \"def author_committer_facts(model, date):\\n return dict(\\n author=model.developer,\\n author_date=date,\\n committer=model.developer,\\n commit_date=date,\\n )\",\n \"def get_venues_by_author(cached_list, cached_set, author_name):\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n venues = {}\\n\\n if author['dblp'].__contains__('publications'):\\n for pub in author['dblp']['publications']:\\n if venues.__contains__(pub['venue']):\\n venues[pub['venue']]['count'] += 1\\n else:\\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\\n\\n if author['cdblp'].__contains__('publications'):\\n for pub in author['cdblp']['publications']:\\n if venues.__contains__(pub['venue']):\\n venues[pub['venue']]['count'] += 1\\n else:\\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\\n\\n return venues\",\n \"def author_case_query(qn, author):\\n if qn == 1:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n ?uri foaf:firstName ?fn .\\n FILTER(regex(?fn,\\\"^{{fni}}\\\"))\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fni}}', author['first'][0])\\n elif qn == 2:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n ?uri foaf:firstName ?fn .\\n FILTER(regex(?fn,\\\"^{{fni}}\\\"))\\n ?uri bibo:middleName ?mn .\\n FILTER(regex(?fn,\\\"^{{mni}}\\\"))\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fni}}', author['first'][0])\\n query = query.replace('{{mni}}', author['middle'][0])\\n elif qn == 3:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri bibo:middleName \\\"{{mn}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n ?uri foaf:firstName ?fn .\\n FILTER(regex(?fn,\\\"^{{fni}}\\\"))\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fni}}', author['first'][0])\\n query = query.replace('{{mn}}', author['middle'])\\n elif qn == 4:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri foaf:firstName \\\"{{fn}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fn}}', author['first'])\\n elif qn == 5:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri foaf:firstName \\\"{{fn}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n ?uri bibo:middleName ?mn .\\n FILTER(regex(?mn,\\\"^{{mni}}\\\"))\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fn}}', author['first'])\\n query = query.replace('{{mni}}', author['middle'][0]) \\n elif qn == 6:\\n query = \\\"\\\"\\\"\\n SELECT ?uri\\n WHERE {\\n ?uri foaf:lastName \\\"{{ln}}\\\" .\\n ?uri a ufVivo:UFCurrentEntity .\\n ?uri foaf:firstName \\\"{{fn}}\\\" .\\n ?uri bibo:middleName \\\"{{mn}}\\\" .\\n }\\n \\\"\\\"\\\"\\n query = query.replace('{{ln}}', author['last'])\\n query = query.replace('{{fn}}', author['first'])\\n query = query.replace('{{mn}}', author['middle'])\\n return query\",\n \"def get_author_info_from_people_collection(info):\\n # TODO: probably we will need to extract this somewhere else\\n URL = ('https://cds.cern.ch/submit/get_authors?'\\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\\n if '0' in info or not info.get('a'):\\n # There is already enough information or we don't have a name to query\\n return info\\n author_name = info.get('a')\\n if PY2:\\n # In Python 3, encoded name will change type to bytes and this will\\n # cause query to CDS to fail\\n author_name = author_name.encode('utf-8')\\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\\n if not author_info or len(author_info) > 1:\\n # Didn't find anything or find to many matches\\n return info\\n\\n # Prepare author name\\n author_info = author_info[0]\\n if 'name' not in author_info:\\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\\n author_info['firstname'])\\n return MementoDict([\\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])\",\n \"def all_authors( data ) :\\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))\",\n \"def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\\n selfdic, authorcitdic):\\n insert_into_cit_db(reference_dic,\\\"reversedict\\\")\\n insert_into_cit_db(citation_dic,\\\"citationdict\\\")\\n insert_into_cit_db(selfcbdic,\\\"selfcitedbydict\\\")\\n insert_into_cit_db(selfdic,\\\"selfcitdict\\\")\\n\\n for a in authorcitdic.keys():\\n lserarr = (serialize_via_marshal(authorcitdic[a]))\\n #author name: replace \\\" with something else\\n a.replace('\\\"', '\\\\'')\\n a = unicode(a, 'utf-8')\\n try:\\n ablob = run_sql(\\\"select hitlist from rnkAUTHORDATA where aterm = %s\\\", (a,))\\n if not (ablob):\\n #print \\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\" , (a,lserarr)\\n run_sql(\\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\",\\n (a,lserarr))\\n else:\\n #print \\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\"\\\"\\\" , (lserarr,a)\\n run_sql(\\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\",\\n (lserarr,a))\\n except:\\n register_exception(prefix=\\\"could not read/write rnkAUTHORDATA aterm=\\\"+a+\\\" hitlist=\\\"+str(lserarr), alert_admin=True)\",\n \"def generate_id_for_authors():\\n counter = 1\\n for book in books:\\n for author in books[book]:\\n if not author in authors:\\n authors[author] = f'Author/{counter}'\\n counter += 1\",\n \"def get_titles_dict(name_url_list):\\n\\tbib_dict = {}\\n\\t\\n\\tfor name_url in name_url_list:\\n\\t\\ttitles = get_author_titles(name_url[1])\\n\\t\\tbib_dict[name_url] = titles\\n\\n\\treturn bib_dict\",\n \"def author_queries(case, author):\\n query_set = {\\n \\\"0\\\": [],\\n \\\"1\\\": [1],\\n \\\"2\\\": [4,1],\\n \\\"3\\\": [2,1],\\n \\\"4\\\": [3,2,1],\\n \\\"5\\\": [5,4,3,2,1],\\n \\\"6\\\": [6,5,4,3,2,1]\\n }\\n qs = query_set[str(case)]\\n query_list = []\\n for q in qs:\\n query_list.append(author_case_query(q, author))\\n return query_list\",\n \"def AuthorURLs(entry):\\n a_URLs = ''\\n for a in entry.getAuthors():\\n url = a.get('homepage', ' ')\\n a_URLs += \\\"%s and \\\" % url\\n return a_URLs[:-5]\",\n \"def get_papers_info(author_url, existing_papers):\\n\\n\\tauthor_dict = {}\\n\\n\\tauthor_page_tree = get_tree(author_url)\\n\\t# Get the <a> elements for the papers on the author's page\\n\\ta_elems = get_a_elems_for_papers(author_page_tree)\\n\\t# get the dates corresponding to each paper\\n\\tpaper_dates = get_dates_for_papers(author_page_tree)\\n\\t# zip into a list of (a_elem, date) pairs\\n\\ta_elem_dates = zip(a_elems, paper_dates)\\n\\t# Each a is a paper\\n\\tfor a, date in a_elem_dates:\\n\\t\\t# Title of paper is the text content of the a element\\n\\t\\tpaper_title = a.text_content()\\n\\t\\t# Check if paper has already been checked, if so, move on to next paper\\n\\t\\tif paper_title in existing_papers:\\n\\t\\t\\tcontinue\\n\\n\\t\\tpaper_url = a.get(\\\"href\\\")\\n\\n\\t\\tpaper_tree = get_tree(paper_url)\\n\\t\\t# Get list of the paper's authors\\n\\t\\tauthors = get_paper_authors(paper_tree)\\n\\t\\t# Get paper abstract\\n\\t\\tabstract = get_paper_abstract(paper_tree)\\n\\t\\t# Get paper keywords\\n\\t\\tkeywords = get_paper_keywords(paper_tree)\\n\\t\\t# Get paper id number from its url\\n\\t\\tpaper_id = re.search(\\\"[0-9]+\\\", paper_url).group()\\n\\t\\t# Add paper to dictionary with id as key and metadata as values\\n\\t\\tauthor_dict[paper_id] = {\\n\\t\\t\\t\\t\\t\\t\\\"title\\\": paper_title,\\n\\t\\t\\t\\t\\t\\t\\\"authors\\\": authors,\\n\\t\\t\\t\\t\\t\\t\\\"abstract\\\": abstract,\\n\\t\\t\\t\\t\\t\\t\\\"url\\\": paper_url,\\n\\t\\t\\t\\t\\t\\t\\\"keywords\\\": keywords,\\n\\t\\t\\t\\t\\t\\t'year': date\\n\\t\\t} \\n\\n\\treturn author_dict\",\n \"def find_authors(code):\\n url = baseurl(code)\\n page = req(url)\\n soup = BeautifulSoup(page, 'lxml')\\n addr = [t.attrs.get('content', None) \\n for t in soup.find_all(\\\"meta\\\", {\\\"name\\\": \\\"citation_author_email\\\"})]\\n \\n # corresponding authors will have their email under another tag too\\n corr = [t.find('a').attrs.get('href', None)\\n for t in soup.find_all(None, {\\\"class\\\": \\\"author-corresp-email-link\\\"})]\\n\\n addr = [a for a in addr if a is not None]\\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\\n\\n return dict(corr=list(set(corr)), all=list(set(addr)))\",\n \"def articleAuthorsByYear(g = None, year = None):\\n if not g:\\n return\\n articles = g.find(\\\"Article\\\", property_key='year', property_value=str(year))\\n ret = dict()\\n for article in articles:\\n ret[article['title']] = article['authors'].replace('\\\\n','').lower()\\n return ret\",\n \"def __add_author(self, key_name, others_names, personal_information):\\n for name in others_names:\\n self.author_to_authorID[name] = (key_name, personal_information)\",\n \"def find_reader_relations():\\n for reader in readers:\\n d100 = random.randint(1, 100)\\n if d100 <= 50:\\n reader_favourite_book[readers[reader]] = random.choice(list(book_ids.values()))\\n\\n d100 = random.randint(1, 100)\\n if d100 <= 5:\\n #TODO: fix so that you cannot be friend of yourself\\n reader_knows[readers[reader]] = [random.choice(list(authors.values()))] + [random.choice(list(readers.values()))]\\n elif d100 > 5 and d100 <= 10:\\n reader_knows[readers[reader]] = [random.choice(list(authors.values()))]\\n elif d100 > 10 and d100 <= 25:\\n reader_knows[readers[reader]] = [random.choice(list(readers.values()))] + [random.choice(list(readers.values()))]\\n elif d100 > 25 and d100 <= 50:\\n reader_knows[readers[reader]] = [random.choice(list(readers.values()))]\",\n \"def main(repo: Repository) -> dict:\\n data = {}\\n for commit in repo.traverse_commits():\\n\\n if not author_is_bot(commit.author):\\n if commit.author.name in data:\\n data[commit.author.name] = data[commit.author.name] + commit.lines\\n else:\\n data[commit.author.name] = commit.lines\\n\\n # Sort authors name in the dict\\n data = dict(sorted(data.items(), key=lambda x: x[0].lower()))\\n return data\",\n \"def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\\n dictionary_key_two):\\n list_of_one_citations = []\\n list_of_two_citations = []\\n list_of_unassigned_citations = []\\n for citation in self.array_of_citations:\\n citation_string = self.__generate_author_string__(\\n citation.get_author()) + \\\" \\\" + citation.get_title() \\\\\\n + \\\" \\\" + citation.get_journal()\\n one_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_one)\\n two_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_two)\\n if one_key_matches > two_key_matches:\\n citation.set_classification(dictionary_key_one)\\n list_of_one_citations.append(citation)\\n elif two_key_matches > one_key_matches:\\n citation.set_classification(dictionary_key_two)\\n list_of_two_citations.append(citation)\\n else:\\n citation.set_classification(\\\"Unassigned\\\")\\n list_of_unassigned_citations.append(citation)\\n\\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\\n list_of_two_citations, \\\"Unassigned\\\":\\n list_of_unassigned_citations}\",\n \"def citation_formatter(citation, title=False):\\n # first we split the author-list\\n citation_string = \\\"\\\"\\n # the spaces around the and are required!\\n for match in citation[\\\"author\\\"].split(\\\" and \\\"):\\n last_name, first_names = match.split(\\\",\\\", 1)\\n citation_string += last_name.strip() + \\\",\\\"\\n for name in first_names.strip().split(' '):\\n citation_string += \\\" \\\" + name.strip()[0]\\n\\n citation_string += \\\"; \\\"\\n\\n if \\\"journal\\\" in citation:\\n citation_string += \\\" \\\" + citation[\\\"journal\\\"]\\n\\n citation_string += \\\" \\\" + citation[\\\"year\\\"]\\n\\n if \\\"doi\\\" in citation:\\n citation_string += \\\"; \\\" + citation[\\\"doi\\\"]\\n\\n return citation_string\",\n \"def extract_author(self, parsed_html, author_idx=0, author=None):\\n\\n if not author:\\n author = {}\\n\\n\\n # Find all links with this xpath and tries to classify them\\n links = []\\n xpath = '//div[@class=\\\"profile cf\\\"]/div/ul/li/a'\\n\\n for url in parsed_html.xpath(xpath):\\n links += [url.get('href')]\\n\\n for social in self.classify_links(links):\\n author[social[0]] = social[1]\\n\\n\\n # Get description text provided by the author\\n xpath = '//div[contains(@class, \\\"profile-text\\\")]/p'\\n items = parsed_html.xpath(xpath)\\n author['about'] = self.clear_text(items)\\n\\n\\n # Get Crunchbase url profile\\n xpath = '//div[contains(@class, \\\"profile-text\\\")]/a'\\n website = parsed_html.xpath(xpath)\\n\\n if website:\\n author['website'] = website[0].get('href')\\n\\n\\n # Get his/her avatar url\\n xpath = '//div[@class=\\\"profile cf\\\"]/div/img'\\n avatar = parsed_html.xpath(xpath)\\n\\n if avatar:\\n author['avatar'] = avatar[0].get('src')\\n\\n\\n # Tries to get the author's profile url\\n xpath = 'meta[@property=\\\"og:url\\\"]'\\n author['profile'] = self.get_text_or_attr(\\n parsed_html, xpath, 'content'\\n )\\n\\n\\n # If the author cannot be fetched from his generated url, we have to\\n # check the article's page in order to find him/her.\\n\\n if 'twitter' not in author and 'flag' not in author:\\n\\n parsed = self.parse(self.article_url, self.article_page_type)\\n return self.extract_author_from_page(parsed, author_idx)\\n\\n\\n if 'flag' in author:\\n del author['flag']\\n\\n\\n return author\",\n \"def dict_by_year(incoming_dict):\\n by_year = {}\\n for key in incoming_dict.keys():\\n try:\\n year = incoming_dict[key]['year']\\n keywords = incoming_dict[key]['keywords']\\n for word in keywords:\\n if year in by_year.keys():\\n if word in by_year[year].keys():\\n by_year[year][word] += keywords[word]\\n else:\\n by_year[year][word] = keywords[word]\\n else:\\n by_year[year] = {word: keywords[word]}\\n except KeyError:\\n print \\\"KeyError in dict_by_year. Key = {0}\\\".format(key)\\n return by_year\",\n \"def count_word_usage(counters_by_author, word_list):\\n specific_word_counter = {}\\n for author in counters_by_author.keys():\\n word_counter = Counter()\\n for item in counters_by_author[author]:\\n for word in word_list:\\n if word in item:\\n print(item)\\n word_counter[word] += counters_by_author[author][item]\\n specific_word_counter[author] = word_counter\\n return specific_word_counter\",\n \"def get_authors_from_papers(papers):\\n auth_set = set()\\n for p in papers:\\n auth_set.update(p['authors'])\\n return list(auth_set)\",\n \"def get_initial_author_dict():\\n adict = {}\\n try:\\n ah = run_sql(\\\"select aterm,hitlist from rnkAUTHORDATA\\\")\\n for (a, h) in ah:\\n adict[a] = deserialize_via_marshal(h)\\n return adict\\n except:\\n register_exception(prefix=\\\"could not read rnkAUTHORDATA\\\", alert_admin=True)\\n return {}\",\n \"def lookups(self, request, model_admin):\\n # lambda article: article.author # 接受一个article，返回对应作者对象\\n # map(func, iterable) # 将可迭代对象的每个元素传入func\\n # author_list 所有文章的作者列表，去重\\n author_list = list(set(map(lambda article: article.author, Article.objects.all())))\\n\\n # author_list2 = []\\n # for author in author_list:\\n # author_list2.append((author.id, author.nickname or author.username))\\n # return author_list2\\n\\n for author in author_list: # verbose value, 在过滤器里显示的名称\\n yield (author.id, (author.nickname or author.username))\",\n \"def citing_authors(self, entities):\\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\\n FROM \\\"entity_occurrences\\\"\\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\\n return [t[0] for t in result]\",\n \"def to_internal_value(self, data):\\n authors = []\\n for author in data:\\n path = urlparse(author).path\\n resolved_func, __, resolved_kwargs = resolve(path)\\n person = resolved_func.cls.queryset.get(pk=resolved_kwargs['pk'])\\n authors.append(person)\\n\\n return {'authors': authors}\",\n \"def get_commentary_for_chapter(location):\\n chapter_commentary_dictionary = {}\\n\\n soup = Ripper(location, parser=\\\"html5lib\\\", save_path=save_path).soup\\n text = soup.find(\\\"table\\\", class_=\\\"texttable\\\")\\n\\n for each in text.find_all(\\\"p\\\"):\\n attributes = each.attrs\\n if attributes:\\n if \\\"desc\\\" in attributes[\\\"class\\\"]:\\n pass\\n elif \\\"note\\\" in attributes[\\\"class\\\"]:\\n new_content = each.contents\\n verse_header, text = export_commentary_text_as_dictionary(new_content)\\n chapter_commentary_dictionary[verse_header] = text\\n else:\\n continue\\n return chapter_commentary_dictionary\",\n \"def build_dict(results, chunk):\\n from math import inf\\n from collections import defaultdict\\n chunk = [int(au) for au in chunk]\\n d = defaultdict(\\n lambda: {\\\"first_year\\\": inf, \\\"pubs\\\": set(), \\\"coauth\\\": set(),\\n \\\"n_coauth\\\": inf, \\\"n_pubs\\\": inf})\\n for pub in results:\\n if not pub.author_ids:\\n continue\\n authors = set([int(au) for au in pub.author_ids.split(\\\";\\\")])\\n for focal in authors.intersection(chunk):\\n d[focal][\\\"coauth\\\"].update(authors)\\n d[focal][\\\"coauth\\\"].remove(focal)\\n d[focal][\\\"pubs\\\"].add(pub.eid)\\n d[focal][\\\"n_pubs\\\"] = len(d[focal][\\\"pubs\\\"])\\n d[focal][\\\"n_coauth\\\"] = len(d[focal][\\\"coauth\\\"])\\n if not pub.coverDate:\\n continue\\n first_year = min(d[focal][\\\"first_year\\\"], int(pub.coverDate[:4]))\\n d[focal][\\\"first_year\\\"] = first_year\\n return d\",\n \"def ordered_list_by_last_name(d):\\n\\n ordered_d = dict(sorted(d.items(), key=lambda x: x[1]['Last name']))\\n\\n print(\\\"\\\\nPhone book ordered by last name: \\\")\\n for pid, info in ordered_d.items():\\n print('\\\\nPerson number: ', pid)\\n for key in info:\\n print(key + ':', info[key])\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def author_name(text):\\n tag = text.split()\\n\\n \\\"\\\"\\\"\\n We take the beginning of the text since the\\n author name will likely be there\\n \\\"\\\"\\\"\\n\\n tag = tag[:100]\\n author = []\\n\\n current_tag = 0\\n \\\"\\\"\\\"\\n We go through each word until we find the first instance\\n of the word 'by' or 'author', which should mean the author\\n will be written right after that.\\n We save the first word after 'by' or 'author' since it should\\n be the authors first name\\n \\\"\\\"\\\"\\n\\n for word in tag:\\n if (word.lower() == ('by') or\\n word.lower() == ('author') or\\n word.lower() == ('author:')):\\n\\n author.append(tag[current_tag+1].decode(encoding='UTF8',\\n errors='ignore'))\\n current_tag += 1\\n tag = tag[current_tag+1:]\\n break\\n current_tag += 1\\n\\n \\\"\\\"\\\"\\n We go through each word after the first name of the author\\n until we find a word that is not capitalized. We assume that\\n it marks the end of the author name.\\n We then return a list of the author's name split up.\\n \\\"\\\"\\\"\\n current_tag = 0\\n for word in tag:\\n if tag[current_tag].lower() == 'this':\\n break\\n if tag[current_tag].istitle():\\n author.append(tag[current_tag].decode(encoding='UTF8',\\n errors='ignore'))\\n current_tag += 1\\n\\n return author\",\n \"def make_doi_countries(author_list: AuthorList):\\n\\n countries = {}\\n\\n for author in author_list:\\n inst = author.institution\\n if inst.country not in countries:\\n countries[inst.country] = {\\n \\\"identifier\\\": inst.country_code,\\n \\\"name\\\": inst.country,\\n \\\"types\\\": [\\\"Country\\\"],\\n \\\"country\\\": inst.country,\\n \\\"country_code\\\": inst.country_code,\\n \\\"country_code_2\\\": inst.country_code_2,\\n \\\"region\\\": inst.region,\\n \\\"subregion\\\": inst.subregion,\\n \\\"coordinates\\\": None,\\n \\\"count\\\": 0,\\n \\\"members\\\": {inst.ror_id},\\n \\\"rors\\\": {inst.ror_id},\\n }\\n else:\\n countries[inst.country][\\\"members\\\"].add(inst.ror_id)\\n countries[inst.country][\\\"rors\\\"].add(inst.ror_id)\\n\\n return to_affiliations_list(countries)\",\n \"def get_paper_authors(tree):\\n\\tpath = '//table/tr/th[text() = \\\"Glasgow Author(s) Enlighten ID:\\\"]/following-sibling::td/a'\\n\\t# Get list of <a> elements, each an author\\n\\tauthors = tree.xpath(path)\\n\\t# Make list of (author name, author url) pairs to return\\n\\tauthors = [(author.text, author.get(\\\"href\\\")) for author in authors]\\n\\n\\treturn authors\",\n \"def extract_relevant(self):\\n item_extraction = self.data\\n my_dict = {'tweeted_time': item_extraction['created_at'],\\n 'tweet_id': item_extraction['id'],\\n # If the time comes when the below becomes more significant, it will be no trouble at all to make an\\n # additional column for it, but delimiting it with a ` creates less clutter in the Database\\n 'in_reply_to':\\n \\\"NAME/\\\" + str(item_extraction['in_reply_to_screen_name']) + \\\"`\\\" +\\n \\\"STATUSID/\\\" + str(item_extraction['in_reply_to_status_id_str']) + \\\"`\\\" +\\n \\\"USERID/\\\" + str(item_extraction['in_reply_to_user_id_str']),\\n 'lang': item_extraction['lang'],\\n 'place': item_extraction['place'], 'source': item_extraction['source']}\\n if item_extraction['place'] is not None:\\n my_dict['place'] = item_extraction['place']['full_name']\\n if 'retweeted_status' in item_extraction.keys():\\n my_dict['original_author_id'] = item_extraction['retweeted_status']['user']['id']\\n my_dict['original_author_handle'] = item_extraction['retweeted_status']['user']['screen_name']\\n tester = item_extraction['retweeted_status']['text']\\n cleaned = ' '.join(re.sub(\\\"(RT : )|(@[\\\\S]+)|(&\\\\S+)|(http\\\\S+)\\\", \\\" \\\", tester).split())\\n removed_others = \\\" \\\".join(re.sub(\\\"(#\\\\S+)\\\", ' ', cleaned).split())\\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\\n # This final text will make it a lot easier to run NLP\\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\\n my_dict['plain_text'] = final_text.lower()\\n my_dict['tweet'] = cleaned\\n else:\\n my_dict['original_author_id'] = item_extraction['user']['id']\\n my_dict['original_author_handle'] = item_extraction['user']['screen_name']\\n cleaned = ' '.join(re.sub(\\\"(@[\\\\S]+)|(&\\\\S+)|(http\\\\S+)\\\", \\\" \\\", item_extraction['text']).split())\\n removed_others = \\\" \\\".join(re.sub(\\\"(#\\\\S+)\\\", ' ', cleaned).split())\\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\\n my_dict['plain_text'] = final_text.lower()\\n my_dict['tweet'] = cleaned\\n return my_dict\",\n \"def _get_sorted_trend_setters(authors: list) -> dict:\\n\\n trend_setters = {author: authors.count(author) for author in authors}\\n top_trend_setters = dict(sorted(\\n trend_setters.items(), key=lambda item: item[1], reverse=True\\n ))\\n return top_trend_setters\",\n \"def get_ars(self, author):\\n return self.divided[author][:-1]\",\n \"def galeshapley(suitor_pref_dict, reviewer_pref_dict, max_iteration):\\n suitors = list(suitor_pref_dict.keys())\\n matching = dict()\\n rev_matching = dict()\\n\\n for i in range(max_iteration):\\n if len(suitors) <= 0:\\n break\\n for s in suitors:\\n r = suitor_pref_dict[s][0]\\n if r not in matching.values():\\n matching[s] = r\\n rev_matching[r] = s\\n else:\\n r_partner = rev_matching.get(r)\\n if reviewer_pref_dict[r].index(s) < reviewer_pref_dict[r].index(r_partner):\\n del matching[r_partner]\\n matching[s] = r\\n rev_matching[r] = s\\n else:\\n suitor_pref_dict[s].remove(r)\\n suitors = list(set(suitor_pref_dict.keys()) - set(matching.keys()))\\n return matching\",\n \"def get_commentary_for_book_chapters(book_file_name):\\n if not os.path.exists(challoner_store):\\n os.mkdir(challoner_store)\\n with open(book_file_name, \\\"r+\\\") as rh:\\n book = json.load(rh)\\n chapter_text = {}\\n\\n for name, chapters_dictionary in book.items():\\n \\n for chap, location in chapters_dictionary.items():\\n norm = normalize_filename(\\\"{}_{}\\\".format(name, chap))\\n \\n outfile = \\\"{}/{}.json\\\".format(challoner_store, norm)\\n \\n if os.path.exists(outfile):\\n continue\\n else:\\n chapter_text[name + \\\"__\\\" + chap] = get_commentary_for_chapter(location)\\n with open(outfile, \\\"w+\\\") as wh:\\n json.dump(chapter_text, wh)\\n chapter_text = {}\",\n \"def giveAuthorsName(catalog, ConstituentsID):\\n names = []\\n\\n for x in ConstituentsID:\\n names.append(' '+model.giveAuthorName(catalog, x))\\n return ','.join(names)\",\n \"def critics_letters(letters):\\n letters_url = list()\\n for elem in letters:\\n letters_url.append(\\\"/critics/authors?letter=\\\" + elem)\\n return letters_url\",\n \"def get_word_counts(messages_by_author):\\n counters_by_author = {}\\n for author in messages_by_author.keys():\\n author_counter = Counter()\\n for message in messages_by_author[author]:\\n author_counter += Counter(get_words(string_to_onlyalpha(message.content)))\\n counters_by_author[author] = author_counter\\n return counters_by_author\",\n \"def make_haystack(self):\\n journal_data = [\\\" \\\".join(self.format_authors(style=\\\"full\\\")),\\n self.journal_long,\\n self.journal_short,\\n \\\"\\\".join(c for c in self.journal_short if c.isupper()),\\n self.title]\\n return [unidecode(data) for data in journal_data]\",\n \"def find_books_by_author_separation(self, name, degree):\\n self.cursor.execute(\\\"\\\"\\\"SELECT ID FROM author WHERE name=%s\\\"\\\"\\\", (name,))\\n original_author_id = int(self.cursor.fetchone()[0])\\n self.cursor.execute(\\\"\\\"\\\"SELECT ISBN FROM wrote WHERE authorID=%s\\\"\\\"\\\", (original_author_id,))\\n first_degree_authors = []\\n for original_author_books in self.cursor.fetchall():\\n self.cursor.execute(\\\"\\\"\\\"SELECT authorID FROM wrote WHERE ISBN=%s AND authorID <> %s\\\"\\\"\\\",\\n (original_author_books[0], original_author_id))\\n for author_id in self.cursor.fetchall():\\n first_degree_authors.append(author_id[0])\\n first_degree_results = {}\\n for author in first_degree_authors:\\n self.cursor.execute(\\\"\\\"\\\"SELECT ISBN FROM wrote WHERE authorID=%s\\\"\\\"\\\",(author,))\\n for ISBN in self.cursor.fetchall():\\n book, author_list = self.get_single_book_info(ISBN[0])\\n first_degree_results[ISBN[0]] = [book, author_list]\\n if int(degree) == 1:\\n return first_degree_results\\n\\n second_degree_results = {}\\n authors_to_check = []\\n all_authors = []\\n self.cursor.execute(\\\"\\\"\\\"SELECT authorID FROM wrote\\\"\\\"\\\")\\n for author in self.cursor.fetchall():\\n all_authors.append(int(author[0]))\\n for author1 in first_degree_authors:\\n for author2 in all_authors:\\n if (self.is_one_degree_separated(author1, author2) and author1 != author2\\n and author2 != original_author_id):\\n authors_to_check.append(author2)\\n second_degree_authors = [a for a in authors_to_check if a not in first_degree_authors]\\n for author in second_degree_authors:\\n self.cursor.execute(\\\"\\\"\\\"SELECT ISBN FROM wrote WHERE authorID=%s\\\"\\\"\\\", (author,))\\n for ISBN in self.cursor.fetchall():\\n book, author_list = self.get_single_book_info(ISBN[0])\\n second_degree_results[ISBN[0]] = [book, author_list]\\n return second_degree_results\",\n \"def get_annotated_content_info(course_id, content, user, user_info):\\r\\n voted = ''\\r\\n if content['id'] in user_info['upvoted_ids']:\\r\\n voted = 'up'\\r\\n elif content['id'] in user_info['downvoted_ids']:\\r\\n voted = 'down'\\r\\n return {\\r\\n 'voted': voted,\\r\\n 'subscribed': content['id'] in user_info['subscribed_thread_ids'],\\r\\n 'ability': get_ability(course_id, content, user),\\r\\n }\",\n \"def get_autor_with_papers(author_id):\\n print(\\\"getting information from dblp about author {}\\\".format(author_id))\\n data = get(\\\"https://dblp.org/pid/\\\" + author_id + \\\".xml\\\")[\\\"dblpperson\\\"]\\n author_id = author_id.replace(\\\"/\\\", \\\"_\\\")\\n author = {\\n \\\"name\\\": data[\\\"@name\\\"],\\n \\\"papers\\\": [get_paper(paper_container) for paper_container in data[\\\"r\\\"]],\\n }\\n\\n with open(local.file_name(\\\"authors\\\", author_id), \\\"w\\\") as out:\\n json.dump(author, out, ensure_ascii=False, indent=4)\",\n \"def get_subdict(indict, phrase):\\n\\tsubdict = {}\\n\\tfor key in indict:\\n\\t\\tif phrase in key:\\n\\t\\t\\tsubdict[key] = indict[key]\\n\\treturn subdict\",\n \"def return_dict_of_assigned_citations_classifications(self):\\n for citation_instance in self.array_of_citations:\\n citation_string = \\\"\\\"\\n citation_string = citation_string.join(\\n citation_instance.get_author()) + \\\\\\n citation_instance.get_title()\\\\\\n + citation_instance.get_journal()\\n max_keyword_matches = 0\\n for key in self.dict_of_keywords:\\n current_key_matches = 0\\n for keyword in self.dict_of_keywords.get(key):\\n pattern = re.compile(keyword)\\n if pattern.search(citation_string):\\n current_key_matches += 1\\n if current_key_matches > max_keyword_matches:\\n max_keyword_matches = current_key_matches\\n citation_instance.set_classification(key)\\n return self.__generate_dict_of_keys_to_classification__()\",\n \"def getPopualrAuthors():\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(\\\" select count(*) as views , authors.name from articles \\\"\\n + \\\" inner join \\\"\\n + \\\"log on concat('/article/', articles.slug) = log.path \\\"\\n + \\\" inner join authors on articles.author = authors.id \\\"\\n + \\\"group by name order by views desc; \\\")\\n authors = c.fetchall()\\n db.close()\\n return authors\",\n \"def getAuthors(self): #$NON-NLS-1$\\r\",\n \"def construct_authorities_for_answers(answers):\\n domains = [rr._dn for rr in answers]\\n glue = {\\\"authority\\\": [], \\\"additional\\\": []}\\n for domain in domains:\\n auth = construct_ns_rr_from_cache(domain)\\n additional = construct_a_rr_from_cache(auth._nsdn)\\n if auth:\\n glue[\\\"authority\\\"].append(auth)\\n if additional:\\n glue[\\\"additional\\\"].append(additional)\\n return (glue[\\\"authority\\\"], glue[\\\"additional\\\"])\",\n \"def Authors(self, default=[{}]):\\n tmp = self.data.get('authors', default)\\n return [HEP.AuthorReducedObject(i) for i in tmp]\",\n \"def books_by_author(self, author):\\n request_url = \\\"%s?author=%s\\\" % (self.API_URL, author)\\n json_data = self.make_request(request_url)\\n if not json_data:\\n return []\\n books = []\\n for book in json_data['docs']:\\n books.append(book['title_suggest'])\\n return books\",\n \"def addtoks(indict): # type: ({}) -> {}\\n\\n if 'Clause Text' not in indict:\\n return indict\\n\\n cleantext = helpers.cleantext(indict['Clause Text'])\\n indict['Tokens'] = helpers.allngrams(cleantext, 1, 3)\\n\\n return indict\",\n \"def find_author(author):\\n case = author_case(author)\\n queries = author_queries(case, author)\\n author_uri_set = set([])\\n for query in queries:\\n result = vivo_sparql_query(query.encode('utf-8'))\\n count = len(result['results']['bindings'])\\n if count == 1:\\n author_uri_set = set([result['results']['bindings'][0]\\\\\\n ['uri']['value']])\\n break\\n elif count > 1 and count < len(author_uri_set):\\n author_uri_set = set([])\\n for row in result['results']['bindings']:\\n author_uri_set.add(row['uri']['value'])\\n return author_uri_set\",\n \"def _extract_json_ils_ids(info, provenence=\\\"source\\\"):\\n SOURCES = {\\n \\\"AUTHOR|(INSPIRE)\\\": \\\"INSPIRE ID\\\",\\n \\\"AUTHOR|(CDS)\\\": \\\"CDS\\\",\\n \\\"AUTHOR|(SzGeCERN)\\\": \\\"CERN\\\",\\n }\\n regex = re.compile(r\\\"(AUTHOR\\\\|\\\\((CDS|INSPIRE|SzGeCERN)\\\\))(.*)\\\")\\n ids = []\\n author_ids = force_list(info.get(\\\"0\\\", \\\"\\\"))\\n for author_id in author_ids:\\n match = regex.match(author_id)\\n if match:\\n ids.append(\\n {\\\"value\\\": match.group(3), provenence: SOURCES[match.group(1)]}\\n )\\n try:\\n ids.append({\\\"value\\\": info[\\\"inspireid\\\"], provenence: \\\"INSPIRE ID\\\"})\\n except KeyError:\\n pass\\n\\n return ids\",\n \"def get_author_data():\\n entry = mongo.db.Authors\\n output = list()\\n look_up_type = None\\n if 'name' in request.args:\\n look_up_type = 'name'\\n print(request.args)\\n if len(request.args['name']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['name'].strip('\\\"')\\n name = entry.find({'name': {'$regex': value}})\\n if name:\\n for author in name:\\n output.append({'name': author['name']})\\n elif 'booktitle' in request.args:\\n look_up_type = 'related_books'\\n if len(request.args['booktitle']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['booktitle'].strip('\\\"')\\n related_books = entry.find(\\n {'author_books': {'$regex': value}})\\n if related_books:\\n for related in related_books:\\n for title in related['author_books']:\\n if value in title:\\n output.append(({'related_books': title}))\\n if len(output) == 0:\\n return render_template('error.html', message=\\\"No Entries Found\\\"), 400\\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200\",\n \"def load_authors():\\n\\n ret = {}\\n for token in util.git('log', '--format=%aE:::%aN').split('\\\\n'):\\n email, name = token.split(':::')\\n ret[email] = name\\n return ret\",\n \"def print_loc_acrnym():\\n\\n #Method2\\n val = College.objects.values('acronym','contact')\\n for i in val:\\n print(i['acronym'],i['contact'])\",\n \"def join_chapter_text(chapter_content_list):\\n cleaned_chapter_content_list = [each for each in chapter_content_list if each != \\\"\\\\n\\\"]\\n chap_text = {0 : \\\"\\\"}\\n tracker = 0\\n\\n for each in chapter_content_list:\\n each = str(each)\\n if re.search(r'\\\\/x\\\\/d\\\\?b=drb', each):\\n tracker += 1\\n verse = re.search(r'\\\\[(\\\\d+)\\\\]', each).group(1)\\n chap_text[tracker] = \\\"\\\"\\n else:\\n chap_text[tracker] = chap_text[tracker] + each.strip()\\n \\n chap_text.pop(0, None)\\n return chap_text\",\n \"def query_by_author(cls,author):\\n bakey = 'BookByAuthor_%s'%author\\n bks = from_cache(bakey)\\n if not bks:\\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).filter('authors =',author).fetch(100))\\n to_cache(bakey,bks)\\n return bks\",\n \"def test_author_reverse(self):\\n invenio_search = 'author:\\\"ellis, j*\\\"'\\n spires_search = 'find a j ellis'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def ordered_list_by_first_name(d):\\n\\n ordered_d = dict(sorted(d.items(), key=lambda x: x[1]['First name']))\\n\\n print(\\\"\\\\nPhone book ordered by first name: \\\")\\n for pid, info in ordered_d.items():\\n print('\\\\nPerson number: ', pid)\\n for key in info:\\n print(key + ':', info[key])\",\n \"def build_query_dict(self, term_list, issn_list, year_list, jlist):\\n journal_frame = self.make_jlist(jlist)\\n\\n search_terms = self.build_search_terms(term_list)\\n dict1 = {}\\n #This loop goes through and sets up a dictionary key with an ISSN number\\n\\n for issn in issn_list:\\n\\n issn_terms = ' AND ISSN(' + issn + ')'\\n dict2 = {}\\n #This loop goes and attaches all the years to the outer loop's key.\\n for year in year_list:\\n\\n year_terms = \\\"AND PUBYEAR IS \\\" + str(year)\\n querystring = search_terms + year_terms + issn_terms\\n\\n dict2[year] = querystring\\n\\n dict1[issn] = dict2\\n\\n return dict1\",\n \"def test_author_many_initials(self):\\n inv_search = 'author:\\\"bach, p* d* q*\\\"'\\n spi_search = 'find a p d q bach'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_author_titles(author_url):\\n\\t# Get the html tree for the author page\\n\\tauthor_page_tree = get_tree(author_url)\\n\\t# Get the <a> elements for the papers on the author's page\\n\\ta_elems = get_a_elems_for_papers(author_page_tree)\\n\\n\\tall_titles = []\\n\\tlinks = []\\n\\t# Loop through a elements and put associated href and text into respective lists\\n\\tfor a in a_elems:\\n\\t\\tall_titles.append(a.text_content())\\n\\t\\tlinks.append(a.get(\\\"href\\\"))\\n\\n\\t# Create list of (title, url) tuples\\n\\ttitles_links = zip(all_titles, links)\\n\\t# Get the list of titles of papers that have been tagged with a subject\\n\\ttagged_titles = get_tagged_titles(titles_links)\\n\\t# Return the 2 lists in a tuple\\n\\treturn (all_titles, tagged_titles)\",\n \"def get_authors(config: Config) -> dict:\\n\\n file_handle = load_csv_file(config.input)\\n\\n start = time.time()\\n raw_authors = get_raw_authors(config, file_handle)\\n end = time.time()\\n print(f\\\"get_raw_authors took {end - start} seconds\\\")\\n\\n start = time.time()\\n sanitized_authors = sanitize_authors(config, raw_authors)\\n end = time.time()\\n print(f\\\"sanitize_authors took {end - start} seconds\\\")\\n\\n return sanitized_authors\",\n \"def test_author_initials(self):\\n inv_search = 'author:\\\"polyakov, a* m*\\\"'\\n spi_search = 'find a a m polyakov'\\n self._compare_searches(inv_search, spi_search)\",\n \"def get_author_name_urls(dept_name, dept_url):\\n\\t# Change to \\\"School of Humanities\\\" to match the name used in Enlighten\\n\\t# Done because the string obtained from http://www.gla.ac.uk/schools/ contains the Gaelic name as well\\n\\tif \\\"Humanities\\\" in dept_name:\\n\\t\\tdept_name = \\\"School of Humanities\\\"\\n\\n\\t# get list of names of researchers in department\\n\\tnames = get_names(dept_url)\\n\\n\\twinning_name_urls = set()\\n\\n\\t# loop through each name\\n\\tfor name in names:\\n\\t\\tname = initialise_first_name(name)\\n\\t\\t# Get Enlighten page on which author name will be found (page for the letter of author's last name)\\n\\t\\tfull_url = author_list_base + \\\"index.\\\"+ name.split(\\\" \\\")[0][0] + \\\".html\\\"\\n\\t\\ttree = get_tree(full_url)\\n\\t\\t# Get all candidate authors which match the name\\n\\t\\tname_urls = get_name_url_matches(name, tree)\\n\\t\\t# If candidates were found\\n\\t\\tif name_urls:\\n\\t\\t\\t# Filter out authors that have already been scraped\\n\\t\\t\\tname_urls = [name_url for name_url in name_urls if name_url not in winning_name_urls]\\n\\t\\t\\t# Get the first ranked (name, url) tuple for the target name from the remaining candidates\\n\\t\\t\\twinning_name_url = get_winning_url(name_urls, dept_name)\\n\\t\\t\\tif winning_name_url:\\n\\t\\t\\t\\twinning_name_urls.add(winning_name_url)\\n\\n\\treturn winning_name_urls\",\n \"def detect_author(user_to_tweets: Dict[str, List[tuple]], tweet_text: str) -> \\\\\\n str:\\n acc = []\\n \\n for keys in user_to_tweets:\\n author_hashes = hashtag_seperator(user_to_tweets[keys])\\n text_hashes = extract_hashtags(tweet_text)\\n if set(text_hashes).issubset(author_hashes):\\n acc.append(keys)\\n if len(acc) == 1:\\n return acc[0]\\n return 'unknown'\",\n \"def build_ils_contributor(value):\\n if not value.get(\\\"a\\\"):\\n return []\\n\\n contributor = {\\n \\\"identifiers\\\": _extract_json_ils_ids(value, \\\"scheme\\\") or None,\\n \\\"full_name\\\": value.get(\\\"name\\\") or clean_val(\\\"a\\\", value, str),\\n \\\"roles\\\": [\\n _get_correct_ils_contributor_role(\\\"e\\\", value.get(\\\"e\\\", \\\"author\\\"))\\n ],\\n }\\n\\n value_u = value.get(\\\"u\\\")\\n if value_u:\\n values_u_list = list(force_list(value_u))\\n other = [\\\"et al.\\\", \\\"et al\\\"]\\n for x in other:\\n if x in values_u_list:\\n values_u_list.remove(x)\\n contributor[\\\"affiliations\\\"] = [{\\\"name\\\": x} for x in values_u_list]\\n contributor = dict(\\n (k, v) for k, v in iteritems(contributor) if v is not None\\n )\\n return contributor\",\n \"def subject_info(intent, extra_info=[]):\\n\\n text = intent['inputTranscript'].lower()\\n utterances = AS.load_file('sample_utterances.txt')\\n\\n # add \\\"book\\\" and \\\"books\\\" to every utterance\\n for line in list(utterances):\\n utterances.insert(0, line + \\\" book\\\")\\n utterances.insert(0, line + \\\" books\\\")\\n\\n # tells how many characters needs to be dropped before the subject starts\\n to_drop = 0\\n\\n for line in utterances:\\n if text.startswith(line):\\n to_drop = len(line)\\n break\\n\\n # drops the characters and makes a list from the strings that are left\\n text = text[to_drop:].strip()\\n text_list = text.split(' ', len(text))\\n\\n subject_list = []\\n keywords = [\\\"books\\\", \\\"book\\\", \\\"by\\\", \\\"published\\\", \\\"written\\\"]\\n keyword = \\\"\\\"\\n\\n # Find out when the book name ends\\n for word in text_list:\\n if word not in keywords:\\n subject_list.append(word)\\n else:\\n break\\n\\n subject = \\\" \\\".join(subject_list)\\n\\n # Get all the keywords in the middle, so they can be\\n # all be dropped at once, eg written by, books by\\n text_list = text_list[len(subject_list):]\\n if text_list:\\n word = text_list[0]\\n while word in keywords:\\n keyword += word + \\\" \\\"\\n text_list = text_list[1:]\\n if text_list:\\n word = text_list[0]\\n else:\\n break\\n\\n # search for an author from the rest of the characters\\n author_text = text[len(keyword):].strip()\\n author = AS.search(author_text, False)\\n if author is \\\"\\\":\\n author = None\\n\\n # There might be old info in the extra_info (author), so \\n # we need to clear it\\n extra_info.clear()\\n\\n # add the author to extra info so it can be used in the Finna API call\\n if author:\\n extra_info += [\\\"author:\\\\\\\"\\\" + author + \\\"\\\\\\\"\\\"]\\n elif intent['sessionAttributes'].get('author'):\\n extra_info += [\\n \\\"author:\\\\\\\"\\\" + intent['sessionAttributes']['author'] + \\\"\\\\\\\"\\\"\\n ]\\n\\n # The Finna API call\\n request = lookfor(term=subject, filter=extra_info)['json']\\n\\n return parse_subject(request, subject, {'author': author})\",\n \"def test_author_simple(self):\\n invenio_search = 'author:\\\"ellis, j*\\\"'\\n spires_search = 'find a ellis, j'\\n self._compare_searches(invenio_search, spires_search)\",\n \"def get_author_info(path):\\n author_df = pandas.read_table(path)\\n\\n # Detect missing full name\\n missing_full_name = author_df[author_df['full_name'].isnull()].index.tolist()\\n if missing_full_name:\\n msg = f'Missing full name in authors.tsv for author(s): {missing_full_name}'\\n raise ValueError(msg)\\n\\n # Detect missing author initials\\n missing_initials = author_df[author_df['initials'].isnull()].index.tolist()\\n if missing_initials:\\n msg = f'Missing initials in authors.tsv for author(s): {missing_initials}'\\n raise ValueError(msg)\\n\\n # Detect duplicate author initials\\n duplicated_initials = list(\\n author_df[author_df.duplicated('initials')].initials)\\n if duplicated_initials:\\n msg = f'Duplicated initials in authors.tsv: {duplicated_initials}'\\n raise ValueError(msg)\\n\\n # Replace missing values with None\\n author_df.where(pandas.notnull(author_df), None, inplace=True)\\n\\n authors = author_df.to_dict(orient='records')\\n return authors\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5992074","0.59668666","0.5799877","0.57464516","0.5611583","0.5566841","0.5525199","0.55247015","0.548933","0.5444908","0.54439306","0.53777725","0.533273","0.53153","0.5309737","0.5296748","0.5278362","0.5263696","0.5235916","0.5218292","0.520415","0.51868385","0.51827806","0.5173201","0.5147504","0.513976","0.5014424","0.49972236","0.4993433","0.49712986","0.49665037","0.49489027","0.49471873","0.49417987","0.49410787","0.49204722","0.48885068","0.4885325","0.48832706","0.48796633","0.48482555","0.48468834","0.48170894","0.48159692","0.47971877","0.47803786","0.47794098","0.4770227","0.47476584","0.47279546","0.47176075","0.47008","0.46997944","0.46483845","0.46468645","0.4644841","0.46411732","0.4623201","0.46221602","0.46205956","0.4611271","0.46075785","0.46043685","0.4603209","0.46016595","0.45988032","0.45934394","0.458576","0.45743528","0.4559599","0.45589143","0.45582095","0.45534608","0.45509982","0.4549973","0.45427266","0.45426863","0.45393464","0.453216","0.45289442","0.45271572","0.45243","0.45221773","0.45130944","0.45100772","0.45060793","0.45039192","0.450345","0.44955176","0.4495128","0.44930568","0.4492853","0.44900423","0.44825518","0.4476491","0.44763005","0.44684988","0.44648084","0.44588286","0.44522205"],"string":"[\n \"0.5992074\",\n \"0.59668666\",\n \"0.5799877\",\n \"0.57464516\",\n \"0.5611583\",\n \"0.5566841\",\n \"0.5525199\",\n \"0.55247015\",\n \"0.548933\",\n \"0.5444908\",\n \"0.54439306\",\n \"0.53777725\",\n \"0.533273\",\n \"0.53153\",\n \"0.5309737\",\n \"0.5296748\",\n \"0.5278362\",\n \"0.5263696\",\n \"0.5235916\",\n \"0.5218292\",\n \"0.520415\",\n \"0.51868385\",\n \"0.51827806\",\n \"0.5173201\",\n \"0.5147504\",\n \"0.513976\",\n \"0.5014424\",\n \"0.49972236\",\n \"0.4993433\",\n \"0.49712986\",\n \"0.49665037\",\n \"0.49489027\",\n \"0.49471873\",\n \"0.49417987\",\n \"0.49410787\",\n \"0.49204722\",\n \"0.48885068\",\n \"0.4885325\",\n \"0.48832706\",\n \"0.48796633\",\n \"0.48482555\",\n \"0.48468834\",\n \"0.48170894\",\n \"0.48159692\",\n \"0.47971877\",\n \"0.47803786\",\n \"0.47794098\",\n \"0.4770227\",\n \"0.47476584\",\n \"0.47279546\",\n \"0.47176075\",\n \"0.47008\",\n \"0.46997944\",\n \"0.46483845\",\n \"0.46468645\",\n \"0.4644841\",\n \"0.46411732\",\n \"0.4623201\",\n \"0.46221602\",\n \"0.46205956\",\n \"0.4611271\",\n \"0.46075785\",\n \"0.46043685\",\n \"0.4603209\",\n \"0.46016595\",\n \"0.45988032\",\n \"0.45934394\",\n \"0.458576\",\n \"0.45743528\",\n \"0.4559599\",\n \"0.45589143\",\n \"0.45582095\",\n \"0.45534608\",\n \"0.45509982\",\n \"0.4549973\",\n \"0.45427266\",\n \"0.45426863\",\n \"0.45393464\",\n \"0.453216\",\n \"0.45289442\",\n \"0.45271572\",\n \"0.45243\",\n \"0.45221773\",\n \"0.45130944\",\n \"0.45100772\",\n \"0.45060793\",\n \"0.45039192\",\n \"0.450345\",\n \"0.44955176\",\n \"0.4495128\",\n \"0.44930568\",\n \"0.4492853\",\n \"0.44900423\",\n \"0.44825518\",\n \"0.4476491\",\n \"0.44763005\",\n \"0.44684988\",\n \"0.44648084\",\n \"0.44588286\",\n \"0.44522205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63774025"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79589,"cells":{"query":{"kind":"string","value":"Analyze the citation informations and calculate the citation weight and cited by list dictionary."},"document":{"kind":"string","value":"def ref_analyzer(citation_informations, initialresult, initial_citationlist,\n initial_referencelist,config, updated_rec_list ):\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attr function\", alert_admin=True)\n return {}\n\n pubrefntag = \"\"\n try:\n pubrefntag = config.get(function, \"reference_via_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_report_number\", alert_admin=True)\n return {}\n\n pubreftag = \"\"\n try:\n pubreftag = config.get(function, \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attr reference_via_pubinfo\", alert_admin=True)\n return {}\n\n #pubrefntag is often 999C5r, pubreftag 999C5s\n if task_get_task_param('verbose') >= 9:\n write_message(\"pubrefntag \"+pubrefntag)\n write_message(\"pubreftag \"+pubreftag)\n\n citation_list = initial_citationlist\n reference_list = initial_referencelist\n result = initialresult\n d_reports_numbers = citation_informations[0] #dict of recid -> institute_give_publ_id\n d_references_report_numbers = citation_informations[1] #dict of recid -> ['astro-ph/xyz'..]\n d_references_s = citation_informations[2]\n #dict of recid -> publication_infos_in_its_bibliography\n d_records_s = citation_informations[3] #recid -> its publication inf\n t1 = os.times()[4]\n\n write_message(\"Phase 0: temporarily remove changed records from citation dictionaries; they will be filled later\")\n for somerecid in updated_rec_list:\n try:\n del citation_list[somerecid]\n except KeyError:\n pass\n try:\n del reference_list[somerecid]\n except KeyError:\n pass\n\n write_message(\"Phase 1: d_references_report_numbers\")\n #d_references_report_numbers: e.g 8 -> ([astro-ph/9889],[hep-ph/768])\n #meaning: rec 8 contains these in bibliography\n\n done = 0\n numrecs = len(d_references_report_numbers)\n for thisrecid, refnumbers in d_references_report_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n done = done+1\n\n for refnumber in refnumbers:\n if refnumber:\n p = refnumber\n f = 'reportnumber'\n #sanitise p\n p.replace(\"\\n\",'')\n #search for \"hep-th/5644654 or such\" in existing records\n rec_ids = get_recids_matching_query(p, f)\n\n if len(rec_ids) > 1:\n msg = \"Whoops: record '%d' report number value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_citer_cited(thisrecid, rec_ids[0])\n remove_from_missing(p)\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n # Citation list should have rec_ids[0] but check anyway\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n #append unless this key already has the item\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid)\n #and update result\n result[rec_ids[0]] += 1\n\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n else:\n #the reference we wanted was not found among our records.\n #put the reference in the \"missing\".. however, it will look\n #bad.. gfhgf/1254312, so get the corresponding 999C5s (full ref) too\n #This should really be done in the next loop d_references_s\n #but the 999C5s fields are not yet normalized\n\n #rectext = print_record(thisrecid, format='hm', ot=pubreftag[:-1])\n rectext = \"\" # print_record() call disabled to speed things up\n lines = rectext.split(\"\\n\")\n rpart = p #to be used..\n for l in lines:\n if (l.find(p) > 0): #the gfhgf/1254312 was found.. get the s-part of it\n st = l.find('$s')\n if (st > 0):\n end = l.find('$', st)\n if (end == st):\n end = len(l)\n rpart = l[st+2:end]\n insert_into_missing(thisrecid, rpart)\n\n mesg = \"d_references_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t2 = os.times()[4]\n\n #try to find references based on 999C5s, like Phys.Rev.Lett. 53 (1986) 2285\n write_message(\"Phase 2: d_references_s\")\n done = 0\n numrecs = len(d_references_s)\n for thisrecid, refss in d_references_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_references_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n task_sleep_now_if_required()\n\n done = done+1\n\n for refs in refss:\n if refs:\n p = refs\n #remove the latter page number if it is like 67-74\n matches = re.compile(\"(.*)(-\\d+$)\").findall(p)\n if matches and matches[0]:\n p = matches[0][0]\n\n # check reference value to see whether it is well formed:\n if not re_CFG_JOURNAL_PUBINFO_STANDARD_FORM_REGEXP_CHECK.match(p):\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"is not well formed; skipping it.\" % (thisrecid, p)\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n continue # skip this ill-formed value\n\n # look for reference value:\n rec_id = None\n try:\n rec_ids = list(search_unit(p, 'journal') - INTBITSET_OF_DELETED_RECORDS)\n except:\n rec_ids = None\n if len(rec_ids) > 1:\n msg = \"Whoops, record '%d' reference value '%s' \" \\\n \"matches many records; taking only the first one. %s\" % \\\n (thisrecid, p, repr(rec_ids))\n write_message(msg, stream=sys.stderr)\n try:\n raise ValueError(msg)\n except ValueError:\n register_exception(alert_admin=True)\n\n if rec_ids and rec_ids[0]:\n write_message(\"These match searching \"+p+\" in journal: \"+repr(rec_ids), verbose=9)\n #the refered publication is in our collection, remove\n #from missing\n remove_from_missing(p)\n else:\n #it was not found so add in missing\n insert_into_missing(thisrecid, p)\n #check citation and reference for this..\n if rec_ids and rec_ids[0]:\n #the above should always hold\n if not result.has_key(rec_ids[0]):\n result[rec_ids[0]] = 0\n if not citation_list.has_key(rec_ids[0]):\n citation_list[rec_ids[0]] = []\n if not thisrecid in citation_list[rec_ids[0]]:\n citation_list[rec_ids[0]].append(thisrecid) #append actual list\n result[rec_ids[0]] += 1 #add count for this..\n\n #update reference_list accordingly\n if not reference_list.has_key(thisrecid):\n reference_list[thisrecid] = []\n if not rec_ids[0] in reference_list[thisrecid]:\n reference_list[thisrecid].append(rec_ids[0])\n mesg = \"d_references_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n t3 = os.times()[4]\n done = 0\n numrecs = len(d_reports_numbers)\n write_message(\"Phase 3: d_reports_numbers\")\n\n #search for stuff like CERN-TH-4859/87 in list of refs\n for thisrecid, reportcodes in d_reports_numbers.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_report_numbers done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n for reportcode in reportcodes:\n if reportcode:\n rec_ids = []\n try:\n rec_ids = get_recids_matching_query(reportcode, pubrefntag)\n except:\n rec_ids = []\n\n if rec_ids:\n for recid in rec_ids:\n #normal checks..\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(recid):\n reference_list[recid] = []\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n\n #normal updates\n if not recid in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(recid)\n if not thisrecid in reference_list[recid]:\n reference_list[recid].append(thisrecid)\n\n mesg = \"d_report_numbers done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #find this record's pubinfo in other records' bibliography\n write_message(\"Phase 4: d_records_s\")\n done = 0\n numrecs = len(d_records_s)\n t4 = os.times()[4]\n for thisrecid, recs in d_records_s.iteritems():\n if (done % 1000 == 0):\n mesg = \"d_records_s done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n p = recs.replace(\"\\\"\",\"\")\n #search the publication string like Phys. Lett., B 482 (2000) 417 in 999C5s\n rec_ids = list(search_unit(f=pubreftag, p=p, m='a') - INTBITSET_OF_DELETED_RECORDS)\n write_message(\"These records match \"+p+\" in \"+pubreftag+\" : \"+str(rec_ids), verbose=9)\n if rec_ids:\n for rec_id in rec_ids:\n #normal checks\n if not result.has_key(thisrecid):\n result[thisrecid] = 0\n if not citation_list.has_key(thisrecid):\n citation_list[thisrecid] = []\n if not reference_list.has_key(rec_id):\n reference_list[rec_id] = []\n\n if not rec_id in citation_list[thisrecid]:\n result[thisrecid] += 1\n citation_list[thisrecid].append(rec_id)\n if not thisrecid in reference_list[rec_id]:\n reference_list[rec_id].append(thisrecid)\n\n mesg = \"d_records_s done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n write_message(\"Phase 5: reverse lists\")\n\n #remove empty lists in citation and reference\n keys = citation_list.keys()\n for k in keys:\n if not citation_list[k]:\n del citation_list[k]\n\n keys = reference_list.keys()\n for k in keys:\n if not reference_list[k]:\n del reference_list[k]\n\n write_message(\"Phase 6: self-citations\")\n selfdic = {}\n #get the initial self citation dict\n initial_self_dict = get_cit_dict(\"selfcitdict\")\n selfdic = initial_self_dict\n #add new records to selfdic\n acit = task_get_option(\"author-citations\")\n if not acit:\n write_message(\"Self cite processing disabled. Use -A option to enable it.\")\n else:\n write_message(\"self cite and author citations enabled\")\n selfdic = get_self_citations(updated_rec_list, citation_list,\n initial_self_dict, config)\n #selfdic consists of\n #key k -> list of values [v1,v2,..]\n #where k is a record with author A and k cites v1,v2.. and A appears in v1,v2..\n\n #create a reverse \"x cited by y\" self cit dict\n selfcitedbydic = {}\n for k in selfdic.keys():\n vlist = selfdic[k]\n for v in vlist:\n if selfcitedbydic.has_key(v):\n tmplist = selfcitedbydic[v]\n if not k in tmplist:\n tmplist.append(k)\n else:\n tmplist = [k]\n selfcitedbydic[v] = tmplist\n\n write_message(\"Getting author citations\")\n\n #get author citations for records in updated_rec_list\n initial_author_dict = get_initial_author_dict()\n authorcitdic = initial_author_dict\n acit = task_get_option(\"author-citations\")\n if not acit:\n print \"Author cites disabled. Use -A option to enable it.\"\n else:\n write_message(\"author citations enabled\")\n authorcitdic = get_author_citations(updated_rec_list, citation_list,\n initial_author_dict, config)\n\n\n if task_get_task_param('verbose') >= 3:\n #print only X first to prevent flood\n tmpdict = {}\n tmp = citation_list.keys()[0:10]\n for t in tmp:\n tmpdict[t] = citation_list[t]\n write_message(\"citation_list (x is cited by y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(citation_list.keys())))\n tmp = reference_list.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = reference_list[t]\n write_message(\"reference_list (x cites y): \"+str(tmpdict))\n write_message(\"size: \"+str(len(reference_list.keys())))\n tmp = selfcitedbydic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfcitedbydic[t]\n mesg = \"selfcitedbydic (x is cited by y and one of the authors of x same as y's):\"\n mesg += str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfcitedbydic.keys())))\n tmp = selfdic.keys()[0:100]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = selfdic[t]\n mesg = \"selfdic (x cites y and one of the authors of x same as y's): \"+str(tmpdict)\n write_message(mesg)\n write_message(\"size: \"+str(len(selfdic.keys())))\n tmp = authorcitdic.keys()[0:10]\n tmpdict = {}\n for t in tmp:\n tmpdict[t] = authorcitdic[t]\n write_message(\"authorcitdic (author is cited in recs): \"+str(tmpdict))\n write_message(\"size: \"+str(len(authorcitdic.keys())))\n insert_cit_ref_list_intodb(citation_list, reference_list,\n selfcitedbydic, selfdic, authorcitdic)\n\n t5 = os.times()[4]\n\n write_message(\"Execution time for analyzing the citation information generating the dictionary:\")\n write_message(\"... checking ref number: %.2f sec\" % (t2-t1))\n write_message(\"... checking ref ypvt: %.2f sec\" % (t3-t2))\n write_message(\"... checking rec number: %.2f sec\" % (t4-t3))\n write_message(\"... checking rec ypvt: %.2f sec\" % (t5-t4))\n write_message(\"... total time of ref_analyze: %.2f sec\" % (t5-t1))\n\n return result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def process_citation(style, reference_list, citation):\n processed_citation = Element(\"span\", attrib={\"class\":\"citation\"})\n\n for reference in reference_list:\n citeref = SubElement(processed_citation, \"span\")\n for style_node in style.citation.layout:\n process_node(citeref, style_node, style.macros, reference) \n\n return(processed_citation)","def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\n dict, dict, dict, dict,\n dict, dict]:\n name_table = create_name_table(citelist)\n unique_names = list()\n nameset = set()\n # total_binomial_year_cnts = {}\n total_binomial_year_cnts = collections.Counter()\n for c in citelist:\n if c.name != \".\":\n clean = clean_name(c.name)\n if not clean.lower() in nameset:\n nameset |= {clean.lower()}\n unique_names.append(clean)\n y = refdict[c.cite_key].year()\n if y is not None:\n total_binomial_year_cnts.update([y])\n # if y in total_binomial_year_cnts:\n # total_binomial_year_cnts[y] += 1\n # else:\n # total_binomial_year_cnts[y] = 1\n unique_names.sort(key=lambda s: s.lower())\n\n # identify genera used per paper\n genera_per_paper = {}\n for c in citelist:\n if c.name != \".\":\n if c.cite_key not in genera_per_paper:\n genera_per_paper[c.cite_key] = set()\n genera_set = genera_per_paper[c.cite_key]\n genera_set |= {extract_genus(clean_name(c.name))}\n genus_cnts = {}\n for c in genera_per_paper:\n y = refdict[c].year()\n if y is not None:\n if init_data().start_year <= y <= init_data().current_year:\n genera_set = genera_per_paper[c]\n for genus in genera_set:\n genus = clean_genus(genus)\n if genus != \"\":\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\n init_data().current_year + 1)})[y] += 1\n # if genus not in genus_cnts:\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\n # init_data().current_year + 1)}\n # gcnts = genus_cnts[genus]\n # gcnts[y] += 1\n\n binomial_usage_cnts_by_year = {}\n binomial_location_applications = {}\n binomial_usage_cnts = {}\n for name in unique_names:\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\n if tmptotal > 0:\n binomial_usage_cnts[name] = tmptotal\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\n\n # specific_year_cnts = {}\n specific_year_cnts = collections.Counter()\n specific_usage_cnts_by_year = {}\n specific_location_applications = {}\n specific_usage_cnts = {}\n for name in specific_names:\n (specific_usage_cnts_by_year[name.name],\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\n if tmptotal > 0:\n specific_usage_cnts[name.name] = tmptotal\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\n binomial_location_applications)\n tmpkey = name.priority_source\n if tmpkey != \".\":\n y = refdict[tmpkey].year()\n if y is not None:\n specific_year_cnts.update([y])\n # if y in specific_year_cnts:\n # specific_year_cnts[y] += 1\n # else:\n # specific_year_cnts[y] = 1\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\n binomial_usage_cnts, specific_usage_cnts)","def calc_weights(query_freq, posting_list, N):\n \n # type(weight_query) == {term: tf-idf == ltc }\n weight_query = {}\n # type(doc_dict) == {doc_id: {term: tf-idf == lnc }}\n doc_dict = {}\n\n for term in query_freq.keys():\n # query = ltc\n query_tf = 1 + math.log( query_freq[term] )\n # print(query_tf)\n term_df = posting_list[term][0]\n # print(term_df)\n query_idf = math.log(N / term_df)\n # print(query_idf)\n weight_query[term] = query_tf * query_idf\n \n # document = lnc\n weight_doc = {}\n for doc_id in posting_list[term][1].keys():\n doc_tf = 1 + math.log( posting_list[term][1][doc_id] )\n doc_df = 1 # no\n weight_doc[term] = doc_tf * doc_df\n if doc_id in doc_dict:\n doc_dict[doc_id][term] = weight_doc[term]\n else:\n doc_dict[doc_id] = {}\n doc_dict[doc_id][term] = weight_doc[term]\n\n # normalize query, c = euclidean\n divide_by = math.sqrt( sum([ x**2 for x in weight_query.values() ]) )\n for term, weight in weight_query.items():\n weight_query[term] = weight / divide_by\n # normalize docs, c = euclidean\n for doc_id, weight_doc in doc_dict.items():\n divide_by = math.sqrt( sum( [ x**2 for x in weight_doc.values() ] ) )\n for term, weight in weight_doc.items():\n doc_dict[doc_id][term] = weight / divide_by\n\n return (weight_query, doc_dict)","def get_citation_informations(recid_list, config):\n begin_time = os.times()[4]\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\n d_records_s = {} #dict of recid -> this_records_publication_info\n citation_informations = []\n\n write_message(\"config function \"+config.get(\"rank_method\", \"function\"), verbose=9)\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attribute called function\", alert_admin=True)\n #we cannot continue\n return [ {}, {}, {}, {} ]\n record_pri_number_tag = \"\"\n try:\n record_pri_number_tag = config.get(function, \"primary_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attribute primary_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n record_add_number_tag = \"\"\n try:\n record_add_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"additional_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute additional_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_number_tag = \"\"\n try:\n reference_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_tag = \"\"\n try:\n reference_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_pubinfo\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\n #037a: contains (often) the \"hep-ph/0501084\" tag of THIS record\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\n #088a: additional short identifier for the record\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\n p_reference_tag = tagify(parse_tag(reference_tag))\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\n #fields needed to construct the pubinfo for this record\n publication_pages_tag = \"\"\n publication_year_tag = \"\"\n publication_journal_tag = \"\"\n publication_volume_tag = \"\"\n publication_format_string = \"p v (y) c\"\n try:\n tag = config.get(function, \"pubinfo_journal_page\")\n publication_pages_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_year\")\n publication_year_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_title\")\n publication_journal_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_volume\")\n publication_volume_tag = tagify(parse_tag(tag))\n publication_format_string = config.get(function, \"pubinfo_journal_format\")\n except:\n pass\n\n #print values for tags for debugging\n if task_get_task_param('verbose') >= 9:\n write_message(\"tag values\")\n write_message(\"p_record_pri_number_tag \"+str(p_record_pri_number_tag))\n write_message(\"p_reference_tag \"+str(p_reference_tag))\n write_message(\"publication_journal_tag \"+str(publication_journal_tag))\n write_message(\"publication_format_string is \"+publication_format_string)\n done = 0 #for status reporting\n numrecs = len(recid_list)\n\n # perform quick check to see if there are some records with\n # reference tags, because otherwise get.cit.inf would be slow even\n # if there is nothing to index:\n if run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_tag[0:2],\n (p_reference_tag,)) or \\\n run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_number_tag[0:2],\n (p_reference_number_tag,)):\n for recid in recid_list:\n if (done % 10 == 0):\n task_sleep_now_if_required()\n #in fact we can sleep any time here\n\n if (done % 1000 == 0):\n mesg = \"get cit.inf done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n if recid in INTBITSET_OF_DELETED_RECORDS:\n # do not treat this record since it was deleted; we\n # skip it like this in case it was only soft-deleted\n # e.g. via bibedit (i.e. when collection tag 980 is\n # DELETED but other tags like report number or journal\n # publication info remained the same, so the calls to\n # get_fieldvalues() below would return old values)\n continue\n\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\n references_s = get_fieldvalues(recid, p_reference_tag)\n\n l_report_numbers = pri_report_numbers\n l_report_numbers.extend(add_report_numbers)\n d_reports_numbers[recid] = l_report_numbers\n\n if reference_report_numbers:\n write_message(str(recid)+\"'s report number values \"+str(l_report_numbers), verbose=9)\n d_references_report_numbers[recid] = reference_report_numbers\n\n references_s = get_fieldvalues(recid, p_reference_tag)\n if references_s:\n write_message(str(recid)+\"'s \"+str(p_reference_tag)+\" values \"+str(references_s), verbose=9)\n d_references_s[recid] = references_s\n\n #get a combination of\n #journal vol (year) pages\n if publication_pages_tag and publication_journal_tag and \\\n publication_volume_tag and publication_year_tag and publication_format_string:\n tagsvalues = {} #we store the tags and their values here\n #like c->444 y->1999 p->\"journal of foo\",v->20\n tagsvalues[\"p\"] = \"\"\n tagsvalues[\"y\"] = \"\"\n tagsvalues[\"c\"] = \"\"\n tagsvalues[\"v\"] = \"\"\n tmp = get_fieldvalues(recid, publication_journal_tag)\n if tmp:\n tagsvalues[\"p\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_volume_tag)\n if tmp:\n tagsvalues[\"v\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_year_tag)\n if tmp:\n tagsvalues[\"y\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_pages_tag)\n if tmp:\n #if the page numbers have \"x-y\" take just x\n pages = tmp[0]\n hpos = pages.find(\"-\")\n if hpos > 0:\n pages = pages[:hpos]\n tagsvalues[\"c\"] = pages\n #format the publ infostring according to the format\n publ = \"\"\n ok = 1\n for i in range (0, len(publication_format_string)):\n current = publication_format_string[i]\n #these are supported\n if current == \"p\" or current == \"c\" or current == \"v\" \\\n or current == \"y\":\n if tagsvalues[current]:\n #add the value in the string\n publ += tagsvalues[current]\n else:\n ok = 0\n break #it was needed and not found\n else:\n publ += current #just add the character in the format string\n if ok:\n write_message(\"d_records_s (publication info) for \"+str(recid)+\" is \"+publ, verbose=9)\n d_records_s[recid] = publ\n else:\n mesg = \"Warning: there are no records with tag values for \"\n mesg += p_reference_number_tag+\" or \"+p_reference_tag+\". Nothing to do.\"\n write_message(mesg)\n\n mesg = \"get cit.inf done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n citation_informations.append(d_reports_numbers)\n citation_informations.append(d_references_report_numbers)\n citation_informations.append(d_references_s)\n citation_informations.append(d_records_s)\n end_time = os.times()[4]\n write_message(\"Execution time for generating citation info from record: %.2f sec\" % \\\n (end_time - begin_time))\n return citation_informations","def fill(self):\n if self.source == 'citations':\n url = self._scholarly.URLS(\"CITATIONPUB\").format(self.id_citations)\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\n\n if soup.find('a', class_='gsc_vcd_title_link'):\n self.bib['url'] = soup.find(\n 'a', class_='gsc_vcd_title_link')['href']\n\n for item in soup.find_all('div', class_='gs_scl'):\n key = item.find(class_='gsc_vcd_field').text\n val = item.find(class_='gsc_vcd_value')\n if key == 'Authors':\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\n elif key == 'Journal':\n self.bib['journal'] = val.text\n elif key == 'Volume':\n self.bib['volume'] = val.text\n elif key == 'Issue':\n self.bib['number'] = val.text\n elif key == 'Pages':\n self.bib['pages'] = val.text\n elif key == 'Publisher':\n self.bib['publisher'] = val.text\n elif key == 'Publication date':\n self.bib['year'] = arrow.get(val.text).year\n elif key == 'Description':\n if val.text[0:8].lower() == 'abstract':\n val = val.text[9:].strip()\n self.bib['abstract'] = val\n elif key == 'Total citations':\n self.id_scholarcitedby = re.findall(\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\n\n # number of citation per year\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\n self.cites_per_year = dict(zip(years, cites))\n\n if soup.find('div', class_='gsc_vcd_title_ggi'):\n self.bib['eprint'] = soup.find(\n 'div', class_='gsc_vcd_title_ggi').a['href']\n self._filled = True\n\n elif self.source == 'scholar':\n self.bib['add_to_lib'] = self.url_add_sclib\n\n try:\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\n bibtex = bibtex.find('pre').string\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\n self.bib['author_count'] = str(\n len(self.bib['author'].split('and')))\n self.bib['age'] = str(\n int(date.today().year) - int(self.bib['year']))\n except:\n # did not find year\n pass\n\n self._filled = True\n return self","def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites","def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in","def compute_applied_name_contexts(citelist: list) -> None:\n for i, cite in enumerate(citelist):\n if (cite.context == \"specimen\") or (cite.context == \"location\") or (cite.context == \"sequence\"):\n cite.applied_cites = {cite}\n elif cite.context == \"citation\":\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n if (tmp.context == \"specimen\") or (tmp.context == \"location\") or (tmp.context == \"sequence\"):\n cite.applied_cites |= {tmp}\n if len(cite.applied_cites) == 0:\n cite.applied_cites = None\n\n # for testing purposes only\n # if cite.applied_cites is None:\n # print(cite.cite_key, cite.name_key, cite.context, \"No application\")\n # else:\n # for c in cite.applied_cites:\n # print(cite.cite_key, cite.name_key, cite.context, c.cite_key, c.name_key, c.context)\n # input()","def age_of_cited_work(art_map, art_cit_map, cit_map):\n logging.debug('============== IN age_of_cited_work: ================')\n age = {}\n for article in art_cit_map:\n age_list = []\n logging.debug('========== ARTICLE: ============')\n logging.debug(article)\n for citation in art_cit_map[article]:\n logging.debug('========== CITATION: ============')\n logging.debug(citation)\n logging.debug(art_map[article]['pub_year'])\n if citation in cit_map:\n logging.debug(cit_map[citation]['pub_year'])\n age_list.append(\n int(art_map[article]['pub_year']) - int(cit_map[citation][\n 'pub_year']))\n age[art_map[article]['pub_year']] = age_list\n logging.debug('========== AGE OF CITED WORK: ============')\n logging.debug(age)\n return sort_map_by_keys(age)","def __citationsFromFile(file):\n\n # Tokens for parsing\n titleToken = '#*'\n indexToken = '#index'\n citationToken = '#%'\n\n # Predicates for error checking\n noneNone = lambda *items: all([item is not None for item in items])\n allNone = lambda *items: all([item is None for item in items])\n\n # Next entry data\n title = None\n index = None\n citations = []\n\n # Basic stats\n withCitations = 0\n withoutCitations = 0\n totalPapers = 0\n\n for line in file:\n line = line.strip()\n\n # Parse entry, enforcing that data appears in title -> index -> citations order\n if line.startswith(titleToken):\n assert allNone(title, index) and len(citations) == 0\n title = line[len(titleToken):].strip('.')\n\n elif line.startswith(indexToken):\n assert noneNone(title) and allNone(index) and len(citations) == 0\n index = int(line[len(indexToken):])\n\n elif line.startswith(citationToken):\n assert noneNone(title, index)\n newCitationId = int(line[len(citationToken):])\n assert newCitationId >= 0\n citations.append(newCitationId)\n\n elif len(line) == 0:\n totalPapers += 1\n\n # Yield this entry if it has any citations,\n if noneNone(title, index):\n if len(citations) > 0:\n withCitations += 1\n yield title, index, citations\n else:\n withoutCitations += 1\n\n title = None\n index = None\n citations = []\n\n # Output some basic statistics about papers with/without citations\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\n print \"\\n\\nTotal Papers: %d\" % totalPapers\n print \" With References: %d (%2.2f%%)\\n Without References: %d (%2.2f%%)\" % (\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\n )","def article_stats(s_list,subject):\n word_dicts = {languages[0]:{},languages[1]:{}}\n stats = [subject]\n for i,article in enumerate(s_list):\n word_dicts[languages[i]] = get_words(article)\n wc = total_wc(word_dicts[languages[i]])\n stats.append((wc,avg_word_length(article, wc),avg_par_length(article)))\n stats.append(compute_similarity(word_dicts[languages[0]],word_dicts[languages[1]]))\n return stats","def score_ngrams(ngram_docs, citations, pc=0.9):\n\n scores = dict()\n for ng in ngram_docs:\n # Build term citation graph.\n gt_nodes = ngram_docs[ng]\n gt_edges = {}\n for node in gt_nodes:\n dests = citations[node].intersection(gt_nodes)\n if dests:\n gt_edges[node] = dests\n\n # Terms and sets for the equations\n n = float(len(citations))\n na = float(len(gt_nodes))\n nca = float(len(list(gt_edges.keys())))\n connected_nodes = list(gt_edges.keys())\n other_nodes = gt_nodes - set(gt_edges.keys())\n\n # Skip ones where there aren't enough nodes to count.\n if na < 4.0:\n continue\n\n # Likelihood of observing a tightly connected term citation graph\n # if the term is a topic.\n oh1 = nca * log(pc) + (na - nca) * log(1 - pc)\n\n # Likelihood of observing a tightly connected term citation graph\n # if the term is not a topic.\n term1 = 0.0\n for i in connected_nodes:\n li = len(citations[i])\n term1 += log(1.0 - (1.0 - (na - 1.0)/(n - 1.0))**li)\n term2 = 0.0\n for i in other_nodes:\n li = len(citations[i])\n term2 += li * log(1.0 - (na - 1.0)/(n - 1.0))\n oh0 = term1 + term2\n\n scores[ng] = oh1 - oh0\n return scores","def collect_year_and_holder(detected_copyright):\n for copyr in detected_copyright:\n if isinstance(copyr, nltk_tree_Tree):\n # logger.debug('n: ' + str(copyr))\n node_text = CopyrightDetector_as_str(copyr)\n copyr_label = copyr.label()\n if 'YR-RANGE' in copyr_label:\n years_append(refine_date(node_text))\n elif 'NAME' == copyr_label or 'COMPANY' in copyr_label:\n # FIXME : this would wreck things like 23andme\n # where a company name contains numbers\n holders_append(refine_author(node_text))\n # logger.debug('CopyrightDetector: node_text: ' + node_text)\n collect_year_and_holder(copyr)","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def centrality_weights_retrieval(weighted=True):\n results = {'_is_weighted':weighted, '_evaluation':'retrieval'}\n graph_metrics = graph_representation.get_metrics(weighted)\n\n print '> Reading cases..'\n descriptions_path = '../data/air/problem_descriptions_dependencies'\n description_texts, labels = data.read_files(descriptions_path)\n\n solutions_path = '../data/air/solutions_preprocessed'\n solution_texts, labels = data.read_files(solutions_path)\n solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)\n\n rep = {}\n for metric in graph_metrics:\n rep[metric] = []\n\n print '> Creating graph representations..'\n for i, text in enumerate(description_texts):\n if i%10==0: print ' ',str(i)+'/'+str(len(description_texts))\n g = graph_representation.construct_dependency_network(text, weighted=weighted)\n for metric in graph_metrics:\n d = graph_representation.graph_to_dict(g, metric)\n rep[metric].append(d)\n g = None # just to make sure..\n if i%100==0:\n if weighted:\n postfix = '_weighted'\n else:\n postfix = '_unweighted'\n data.pickle_to_file(rep, 'output/dependencies/exp1_retr_tmp_'+str(i)+'_'+postfix)\n\n print '> Creating vector representations..'\n for metric in graph_metrics:\n rep[metric] = graph_representation.dicts_to_vectors(rep[metric])\n\n print '> Evaluating..'\n for metric in graph_metrics:\n vectors = rep[metric]\n score = evaluation.evaluate_retrieval(vectors, solution_vectors)\n print ' ', metric, score\n results[metric] = score\n\n if weighted:\n postfix = '_weighted'\n else:\n postfix = '_unweighted'\n data.pickle_to_file(results, 'output/dependencies/exp1_retr'+postfix)\n\n pp.pprint(results)\n return results","def extract_information(preprocessed_sentences):\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\n\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\n\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\n\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\n\n preperation_info = extract_preparation_information(parsed)\n prep_gpes = preperation_info[0].most_common(3)\n\n restore_info = extract_restoration_information(parsed)\n\n landfall_info = extract_landfall_information(parsed)\n\n wind_info = extract_wind_information(quantities)\n rain_info = extract_rain_information(quantities)\n size_info = extract_size_information(parsed)\n\n # formation_info = extract_formation_info(parsed)\n death_info = extract_death_damages_info(parsed)\n\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\n most_common(1)[0][0]))\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))","def buildDict(self, dict):\n self.all_words = set(dict)\n self.wc_dict = collections.defaultdict(int)\n for w in dict:\n for wc in self.get_wildcards(w):\n self.wc_dict[wc] += 1","def cite_report(self):\n\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\n print(\"number of citations = \", num_cites)\n\n for n, p in enumerate(self.mypapers):\n clean_tags = re.compile(\"<.*?>\")\n title = re.sub(clean_tags, \"\", p.title[0])\n tt = textwrap.wrap(title, 60)\n print(f\"{n+1:3} | {p.citation_count:4} {tt[0]}\")\n if len(tt) > 1:\n for line in tt[1:]:\n print(\"{:3} | {:4} {}\".format(\"\", \"\", line))\n print(\"{:3} | {:4} {}\".format(\"\", \"\", \"\"))","def calculate_weighted_results():\n pass","def score_candidates(self,\n cand_list: List[Union[CandidateEntry, Tuple[str, float]]],\n query_info_obj_or_dict: Union[DataEntryFields, dict]) -> Dict[str, float]:\n query_text = self.get_query_text(query_info_obj_or_dict)\n\n if self.text_proc_obj_query is not None:\n query_text = self.text_proc_obj_query(query_text)\n\n query_text = self.handle_case(query_text)\n query_toks = query_text.split()\n query_terms_idfs = {w: self.calc_idf(w) for w in set(query_toks)}\n\n res = {}\n\n for doc_id, score in cand_list:\n doc_text = self.fwd_indx.get_doc_text(doc_id)\n if self.text_proc_obj_doc is not None:\n doc_text = self.text_proc_obj_doc(doc_text)\n doc_text = self.handle_case(doc_text)\n doc_toks = doc_text.split()\n doc_len = len(doc_toks)\n counts = Counter(doc_toks)\n score = 0\n for qterm in query_toks:\n tf = counts[qterm]\n if tf > 0:\n qidf = query_terms_idfs[qterm]\n norm_tf = (tf * (self.k1 + 1)) / \\\n (tf + self.k1 * (1 - self.b + self.b * doc_len * self.inv_avg_doc_len))\n score += qidf * norm_tf\n\n res[doc_id] = score\n\n return res","def learn_distributions(file_lists_by_category):\n ### TODO: Write your code here\n\n #get word frequncies in each email category\n #key:word, value: number of occurences in this email loader\n spam_dict = util.get_word_freq(file_lists_by_category[0])\n ham_dict = util.get_word_freq(file_lists_by_category[1])\n\n #get total length of each email loader\n spam_length = sum(spam_dict.values())\n ham_length = sum(ham_dict.values())\n\n #get the length of the dictionary: D\n dict_D = util.Counter()\n for key in spam_dict:\n dict_D[key] += spam_dict[key]\n for key in ham_dict:\n dict_D[key] += ham_dict[key]\n D = len(dict_D)\n\n spam_distribution = {}\n ham_distribution = {}\n #get the distributions of two email loaders\n for i in dict_D:\n spam_distribution[i] = (spam_dict[i] + 1) / (D + spam_length)\n\n for i in dict_D:\n ham_distribution[i] = (ham_dict[i] + 1) / (D + ham_length)\n #create the required tuple\n probabilities_by_category = (spam_distribution, ham_distribution)\n return probabilities_by_category","def _get_graph_based_ic_dictionary(self):\n\n\t\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\n\n\t\t#ic_dict = {}\n\t\t#num_terms_in_ontology = len(self)\n\t\t#for term in self.terms():\n\t\t#\tdepth = self._depth_dict[term.id]\n\t\t#\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t#\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t#\tic_dict[term.id] = ic_value\n\t\t#return(ic_dict)\n\n\n\t\t# Getting the information content of each term in the ontology based on graph structure.\n\t\tic_dict = {}\n\t\tnum_terms_in_ontology = len(self)\n\t\tfor term in self.terms():\n\t\t\tdepth = self._depth_dict[term.id]\n\t\t\tnum_descendants = len(list(term.subclasses(with_self=False)))\n\t\t\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\n\t\t\tic_dict[term.id] = ic_value\n\n\n\t\t# Converting to weights based on information content rather than raw value.\n\t\tic_dict_as_weights = {}\n\t\tic_values = ic_dict.values()\n\t\tmin_ic = min(ic_values)\n\t\tmax_ic = max(ic_values)\n\t\tnew_max = 1.00\n\t\tnew_min = 0.00\n\t\tfor k,v in ic_dict.items():\n\t\t\told_range = max_ic-min_ic\n\t\t\tnew_range = new_max-new_min\n\t\t\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\n\t\t\tic_dict_as_weights[k] = new_value\n\n\t\treturn(ic_dict, ic_dict_as_weights)","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def citation_validation(self):\n for file in filter(lambda x: x.startswith('citation'), listdir(bs_directory)):\n print \"Processing\", file\n bs = BeautifulSoup(open(file), 'xml')\n # find ground_truth file\n ground_truth = BeautifulSoup(open(self.ground_truth_directory + file.split(\".\")[0] + '.xml'), 'xml')\n self.__citation_correction(ground_truth, bs)\n file = open(self.citation_output + file, \"wb\")\n file.write(bs.prettify().encode('utf-8'))","def compute_species_from_citation_linking(citelist: list) -> None:\n # for i, cite in enumerate(tqdm(citelist)):\n unrecorded_xrefs = []\n recorded_refs = set()\n for i, cite in enumerate(citelist):\n recorded_refs.add(cite.cite_key)\n if cite.actual == \"=\":\n cname = \"\"\n crossnames = collections.Counter()\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n cname = tmp.name\n crossnames.update([tmp.actual])\n if len(crossnames) == 0:\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\n elif len(crossnames) == 1:\n cite.actual = list(crossnames.keys())[0]\n else:\n # find name(s) with largest count\n mcnt = max(crossnames.values())\n keylist = []\n for key in crossnames:\n if crossnames[key] == mcnt:\n keylist.append(key)\n\n if len(keylist) == 1:\n cite.actual = keylist[0]\n else:\n cite_name = cite.name.lower()\n while cite_name.find(\" \") > -1:\n cite_name = cite_name[cite_name.find(\" \")+1:]\n while cname.find(\" \") > -1:\n cname = cname[cname.find(\" \")+1:]\n\n if cite_name in keylist:\n cite.actual = cite_name\n elif cname in keylist:\n cite.actual = cname\n else:\n cite.actual = keylist[0]\n\n if cite.name_note == \".\":\n cite.name_note = \"in part\"\n else:\n cite.name_note = \"in part; \" + cite.name_note\n\n for x in unrecorded_xrefs:\n if x[1] in recorded_refs:\n report_error(\"Reference {} ({}) does not appear until after citation from {} ({})\".format(x[1], x[3],\n x[0], x[2]))","def addCowWeight(list, cows):\r\n sum = 0.0\r\n for key in list:\r\n sum += cows[key]\r\n return sum","def extract_citations(tree):\n citations_text = tree.xpath('//form/h2[@class=\"head\"]/text()')[0]\n n_citations = re.sub(\"Is Cited by the Following \", \"\", citations_text).split(' ')[0]\n try:\n n_citations = int(n_citations)\n except:\n n_citations = 0\n return n_citations","def return_dict_of_assigned_citations_classifications(self):\n for citation_instance in self.array_of_citations:\n citation_string = \"\"\n citation_string = citation_string.join(\n citation_instance.get_author()) + \\\n citation_instance.get_title()\\\n + citation_instance.get_journal()\n max_keyword_matches = 0\n for key in self.dict_of_keywords:\n current_key_matches = 0\n for keyword in self.dict_of_keywords.get(key):\n pattern = re.compile(keyword)\n if pattern.search(citation_string):\n current_key_matches += 1\n if current_key_matches > max_keyword_matches:\n max_keyword_matches = current_key_matches\n citation_instance.set_classification(key)\n return self.__generate_dict_of_keys_to_classification__()","def analyzeWeights(self, occludedPortions):\r\n\t\treturn None","def recursion_search_citations(self, paper_pmid, k):\n if k == 0: return\n print('recursion degree = ' + str(k))\n original_paper = self.papers_dict[paper_pmid]\n if original_paper == None or original_paper.pm_cited == None: return\n print('paper ' + original_paper.pmid + ' has ' + str(len(original_paper.pm_cited)) + ' citations')\n for citing_paper_pmid in original_paper.pm_cited:\n citing_paper = self.paper_builder.build_paper(citing_paper_pmid)\n if citing_paper_pmid not in self.papers_dict and citing_paper_pmid not in self.csv_papers_dict:\n self.papers_dict[citing_paper_pmid] = citing_paper\n self.recursion_search_citations(citing_paper.pmid, k - 1)\n else:\n citing_paper = self.papers_dict[citing_paper_pmid]\n #add citation info\n citation_year_gap = str(int(citing_paper.year) - int(original_paper.year))\n if citation_year_gap in original_paper.citations:\n (sum, count) = original_paper.citations[citation_year_gap]\n original_paper.citations[citation_year_gap] = (sum + citing_paper.h_index, count +1)\n else:\n original_paper.citations[citation_year_gap]=(citing_paper.h_index, 1)\n citing_paper.add_to_pm_cite(paper_pmid)\n citation_weighted_avg = 0\n gap_sum = 0\n for year_gap, (sum, counter) in original_paper.citations.items():\n weight = 20 - int(year_gap)\n citation_weighted_avg += weight * (sum/counter)\n gap_sum += weight\n original_paper.citation_weighted_avg = citation_weighted_avg/gap_sum","def process(list_, dict_, keyword):\n if len(list_) == 4:\n name, val, type_, frac_ = list_[0], list_[1], list_[2], list_[3]\n elif list_[0] == 'direc':\n name, val = list_[0], [list_[i] for i in range(len(list_)) if i > 0]\n else:\n name, val = list_[0], list_[1]\n\n if name not in dict_[keyword].keys() and name in ['coeff']:\n dict_[keyword][name] = []\n if keyword in ['TREATED', 'UNTREATED', 'COST'] and 'types' not in dict_[keyword].keys():\n dict_[keyword]['types'] = []\n if keyword in ['TREATED', 'UNTREATED', 'COST']:\n if len(list_) == 4:\n dict_[keyword]['types'] += [[type_, float(frac_)]]\n else:\n dict_[keyword]['types'] += ['nonbinary']\n\n # Type conversion\n if name in ['agents', 'seed', 'maxiter', 'disp']:\n val = int(val)\n elif name in ['source', 'file', 'optimizer', 'start']:\n val = str(val)\n elif name in ['direc']:\n val = list(val)\n else:\n val = float(val)\n if name in ['coeff']:\n dict_[keyword][name] += [val]\n else:\n dict_[keyword][name] = val\n # Finishing.\n return dict_","def compute_dictionary(self, name, nb_process=1, process_number=0):\n self.mvc_check()\n with open(name, \"w\", encoding=\"utf-8\") as f:\n count_word = 0\n for word in self._distWords:\n if count_word % nb_process == process_number:\n dist, aligned_word = self.dist_word(word)\n f.write(\"{} ; {} ; {} ; {} ; {} \\n\".format(dist, self._txt1[word].freq, self._txt2[aligned_word].freq, word, aligned_word))\n count_word += 1\n \n # notify the views\n for view in self._views:\n view.notify_progress(1 / self._txt1.length)","def score(self, doc, c):\n # >>> YOUR ANSWER HERE\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\n sum = self.logprior[c]\n words = doc.split()\n for w in words:\n if w in self.vocabulary:\n sum += self.loglikelihood[(w, c)]\n return sum\n # >>> END YOUR ANSWER","def get_complementary_infos_disc(browser, dict_disc: dict, url_disc: str) -> dict:\n try:\n browser.get_url(url_disc)\n soup = browser.get_soup()\n dict_complementary = {}\n table = soup.find(\"table\", {\"class\": \"album_info\"})\n table_descriptors = [x.find(\"th\").text.strip() for x in table.find_all(\"tr\")]\n table_values = [\n \" \".join(x.find(\"td\").text.strip().replace(\"\\n\", \", \").split())\n for x in table.find_all(\"tr\")\n ]\n for d, v in zip(table_descriptors, table_values):\n dict_complementary[d] = v\n\n try:\n dict_complementary[\"Rank Overall\"] = [\n x.replace(\"#\", \"\").replace(\",\", \"\")\n for y in dict_complementary[\"Ranked\"].split(\", \")\n if \"overall\" in y\n for x in y.split()\n ][0]\n except Exception as e:\n logger.debug(\"No overall rank found : %s\", e)\n try:\n dict_complementary[\"Rank Year\"] = [\n x.replace(\"#\", \"\").replace(\",\", \"\")\n for y in dict_complementary[\"Ranked\"].split(\", \")\n if dict_disc[\"Year\"] in y\n for x in y.split()\n ][0]\n except Exception as e:\n logger.debug(\"No year rank found : %s\", e)\n dict_disc.update(dict_complementary)\n except Exception as e:\n logger.error(e)\n return dict_disc","def __init__(self):\n # self.organism = []\n self.weighting_dict = defaultdict(list)\n # self.codon_obj_dict = {}\n self.codon_dict = {\n 'UUU':'F','UUC':'F',\n 'UUA':'L','UUG':'L','CUU':'L','CUC':'L','CUA':'L','CUG':'L',\n 'AUU':'I','AUC':'I','AUA':'I',\n 'AUG':'M',\n 'GUU':'V', 'GUC':'V','GUA':'V','GUG':'V',\n 'UCU':'S','UCC':'S','UCA':'S','UCG':'S',\n 'CCU':'P','CCC':'P','CCA':'P','CCG':'P',\n 'ACU':'T','ACC':'T','ACA':'T','ACG':'T',\n 'GCU':'A','GCC':'A','GCA':'A','GCG':'A',\n 'UAU':'Y','UAC':'Y',\n 'UAA':'X','UAG':'X',\n 'CAU':'H','CAC':'H',\n 'CAA':'Q','CAG':'Q',\n 'AAU':'N','AAC':'N',\n 'AAA':'K','AAG':'K',\n 'GAU':'D','GAC':'D',\n 'GAA':'E','GAG':'E',\n 'UGU':'C','UGC':'C',\n 'UGA':'X',\n 'UGG':'W',\n 'CGU':'R','CGC':'R','CGA':'R','CGG':'R',\n 'AGU':'S','AGC':'S',\n 'AGA':'R','AGG':'R',\n 'GGU':'G','GGC':'G', 'GGA':'G','GGG':'G'\n }","def doit():\n myp = MyPapers()\n cites = Cites(myp.mypapers)\n #cites.cite_report()\n cites.compare_and_update()","def summarize_corpus():\n\t\n\t# get metadata\n\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \"metadata\", md_mode)\n\t\n\t# visualize some metadata\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"author-continent\")\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"author-country\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"language\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_hist\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_x\")\n\tvisualize_metadata.plot_pie(wdir, md_csv, \"subgenre\")\n\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"subgenre\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"gender\")\n\t\n\t# make some counts\n\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\n\tnum_texts = len(md_table)\n\t#num_language = len(md_table.groupby([\"language\"]))\n\t#num_continent = len(md_table.groupby([\"author-continent\"]))\n\t#num_countries = len(md_table.groupby([\"author-country\"]))\n\t#num_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_subgenre = len(md_table.groupby([\"subgenre\"]))\n\t#num_subgenre_x = len(md_table.groupby([\"subgenre_x\"]))\n\t#fr_subgenre_hist = md_table.groupby([\"subgenre_hist\"]).count()\n\t#num_historical = fr_subgenre_hist[\"idno\"][\"historical\"]\n\t#num_not_historical = fr_subgenre_hist[\"idno\"][\"not_historical\"]\n\t\n\t\n\td = {\"texts\":[num_texts], \n\t#\"languages\":[num_language],\n\t#\"continents\":[num_continent],\n\t#\"countries\":[num_countries],\n\t\"authors\":[num_authors],\n\t#\"subgenre_x\":[num_subgenre_x],\n\t\"subgenre\":[num_subgenre]}\n\t#\"num_historical\":[num_historical],\n\t#\"num_not_historical\":[num_not_historical]}\n\t\n\t\n\t\n\tcount_fr = pd.DataFrame(d)\n\tcount_fr.to_csv(os.path.join(wdir, \"corpus-description.csv\"), sep=\",\", header=True)\n\tprint(\"Done: summarize corpus\")","def weigthIndex(index, nPages): \n weighted_index = defaultdict(list)\n for term, docs in index.items():\n df = len(docs)\n for url, count in docs:\n weight = tf_idf(count, nPages, df)\n weighted_index[term].append((url, weight))\n return weighted_index","def frequencyAnalysis(article, dictionary):\r\n string = article #Sets the string to the article\r\n string = re.sub(\"[^a-zA-Z0-9’\\s]+\",'', string) #Takes the articles and removes all characters appart from apostrophes, spaces, digits, and leters\r\n string = re.sub(\"’\", \"'\", string) #Replaces ’ with '\r\n string = string.lower() #Ensures that all the charcters are lower case\r\n stringList = string.split() #Takes the article and turns it into a list\r\n \r\n print(\"\\nConverted article to list\\n\")\r\n \r\n print(\"Starting frequency analysis\\n\")\r\n\r\n #Started the frequency anaylsis\r\n for word in stringList:\r\n if \"'s\" in word: #Done to remove extra keys in the dictionary, removes the possessive such that it counts \"Trump\" and \"Trump's\" as one word\r\n word = word[0:-2]\r\n elif \"s'\" in word:\r\n word = word[0:-1]\r\n if word != \"advertisement\":\r\n if word in dictionary:\r\n dictionary[word] +=1 #If it finds the word in the dictionary, the frequency has to increase by one\r\n else:\r\n dictionary[word] = 1 #If it finds a new word, it needs to add the word so the frequency is one\r","def dictionary(cleaned_data,threshold):\n news = []\n for date in cleaned_data:\n for headlines in cleaned_data[date]:\n news.append(headlines)\n\n word_freq = nltk.FreqDist(itertools.chain(*news))\n id_to_word = ['<pad>'] + [word for word, cnt in word_freq.items() if cnt >= threshold] + ['<unk>']\n word_to_id = {word:idx for idx, word in enumerate(id_to_word)}\n \n return id_to_word, word_to_id","def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\n dictionary_key_two):\n list_of_one_citations = []\n list_of_two_citations = []\n list_of_unassigned_citations = []\n for citation in self.array_of_citations:\n citation_string = self.__generate_author_string__(\n citation.get_author()) + \" \" + citation.get_title() \\\n + \" \" + citation.get_journal()\n one_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_one)\n two_key_matches = self.__count_of_keyword_matches__(\n citation_string, dictionary_key_two)\n if one_key_matches > two_key_matches:\n citation.set_classification(dictionary_key_one)\n list_of_one_citations.append(citation)\n elif two_key_matches > one_key_matches:\n citation.set_classification(dictionary_key_two)\n list_of_two_citations.append(citation)\n else:\n citation.set_classification(\"Unassigned\")\n list_of_unassigned_citations.append(citation)\n\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\n list_of_two_citations, \"Unassigned\":\n list_of_unassigned_citations}","def getCitations (self, doc, verbose = False):\r\n if verbose is True:\r\n print \"getting citations for document \" +str(doc) \r\n return self.citations[int(doc)]","def preprocessing():\n english_dictionary = nltk.corpus.brown.words()\n slang_vocab = pickle.load(open('vocab_pattern_match_with_freq.pkl', 'rb'))\n\n normalize_english_dict = len(english_dictionary)\n normalize_slang_vocab = 0\n for w, n in slang_vocab.items():\n normalize_slang_vocab += n\n\n words = {}\n for w, n in Counter(english_dictionary).items():\n words[w] = n/normalize_english_dict\n \n for w, n in slang_vocab.items():\n if w not in words:\n words[w] = 0.\n words[w] += n/normalize_slang_vocab\n\n words_by_freq = [w for w,_ in sorted(words.items(), key=lambda x: x[1], reverse=True)]\n\n # Build a cost dictionary, assuming Zipf's law and cost = -math.log(probability).\n #words = open(\"words_by_frequency.txt\").read().split()\n wordcost = dict((k, log((i+1)*log(len(words_by_freq)))) for i,k in enumerate(words_by_freq))\n maxword = max(len(x) for x in words_by_freq)\n return wordcost,maxword","def weighted_score(counters, lst, weight):\n if counters == None:\n counters = {}\n\n\n for item in lst:\n if item in counters:\n counters[item] += weight\n else:\n counters[item] = weight\n\n return counters","def preparation(self):\n self.word_freq = defaultdict(int)\n\n for sentence in self.corpus:\n for word in sentence:\n self.word_freq[word] += 1\n\n # self.words decide the index of all the words\n self.words = list(self.word_freq.keys())\n self.T = len(self.words)\n\n # word_index will give index for a given word and vice versa for index_word\n self.word_index = dict([[word, i] for i, word in enumerate(self.words)])\n self.index_word = dict([[i, word] for i, word in enumerate(self.words)])","def join_cinfo(cooccur, percents):\n import math\n\n word1 = cooccur[0][0]\n word2 = cooccur[0][1]\n try:\n word1_percent = percents[word1]\n weight1 = 1 / word1_percent\n word2_percent = percents[word2]\n weight2 = 1 / word2_percent\n return (cooccur[0], cooccur[1], cooccur[1] *\n math.log(min(weight1, weight2)))\n except:\n return 0","def get_citation_weight(rank_method_code, config):\n begin_time = time.time()\n last_update_time = get_bibrankmethod_lastupdate(rank_method_code)\n\n if task_get_option(\"quick\") == \"no\":\n last_update_time = \"0000-00-00 00:00:00\"\n write_message(\"running thorough indexing since quick option not used\", verbose=3)\n\n last_modified_records = get_last_modified_rec(last_update_time)\n #id option forces re-indexing a certain range even if there are no new recs\n if last_modified_records or task_get_option(\"id\"):\n if task_get_option(\"id\"):\n #construct a range of records to index\n taskid = task_get_option(\"id\")\n first = taskid[0][0]\n last = taskid[0][1]\n #make range, last+1 so that e.g. -i 1-2 really means [1,2] not [1]\n updated_recid_list = range(first, last+1)\n else:\n updated_recid_list = create_recordid_list(last_modified_records)\n\n write_message(\"Last update \"+str(last_update_time)+\" records: \"+ \\\n str(len(last_modified_records))+\" updates: \"+ \\\n str(len(updated_recid_list)))\n\n #write_message(\"updated_recid_list: \"+str(updated_recid_list))\n result_intermediate = last_updated_result(rank_method_code)\n\n #result_intermed should be warranted to exists!\n #but if the user entered a \"-R\" (do all) option, we need to\n #make an empty start set\n if task_get_option(\"quick\") == \"no\":\n result_intermediate = [{}, {}, {}]\n else:\n # check indexing times of `journal' and `reportnumber`\n # indexes, since if they are not up to date yet, then we\n # should wait and not run citation indexing as of yet:\n last_timestamp_bibrec = run_sql(\"SELECT DATE_FORMAT(MAX(modification_date), '%%Y-%%m-%%d %%H:%%i:%%s') FROM bibrec\", (), 1)[0][0]\n last_timestamp_indexes = run_sql(\"SELECT DATE_FORMAT(MAX(last_updated), '%%Y-%%m-%%d %%H:%%i:%%s') FROM idxINDEX WHERE name IN (%s,%s)\", ('journal', 'reportnumber'), 1)[0][0]\n if not last_timestamp_indexes or \\\n not last_timestamp_bibrec or \\\n last_timestamp_bibrec > last_timestamp_indexes:\n write_message(\"Not running citation indexer since journal/reportnumber indexes are not up to date yet.\")\n return {}\n\n citation_weight_dic_intermediate = result_intermediate[0]\n citation_list_intermediate = result_intermediate[1]\n reference_list_intermediate = result_intermediate[2]\n\n # Enrich updated_recid_list so that it would contain also\n # records citing or referring to updated records, so that\n # their citation information would be updated too. Not the\n # most efficient way to treat this problem, but the one that\n # requires least code changes until ref_analyzer() is more\n # nicely re-factored.\n updated_recid_list_set = intbitset(updated_recid_list)\n for somerecid in updated_recid_list:\n # add both citers and citees:\n updated_recid_list_set |= intbitset(citation_list_intermediate.get(somerecid, []))\n updated_recid_list_set |= intbitset(reference_list_intermediate.get(somerecid, []))\n updated_recid_list = list(updated_recid_list_set)\n\n #call the procedure that does the hard work by reading fields of\n #citations and references in the updated_recid's (but nothing else)!\n if task_get_task_param('verbose') >= 9:\n write_message(\"Entering get_citation_informations\")\n citation_informations = get_citation_informations(updated_recid_list,\n config)\n #write_message(\"citation_informations: \"+str(citation_informations))\n #create_analysis_tables() #temporary..\n #test how much faster in-mem indexing is\n write_message(\"Entering ref_analyzer\", verbose=9)\n #call the analyser that uses the citation_informations to really\n #search x-cites-y in the coll..\n dic = ref_analyzer(citation_informations,\n citation_weight_dic_intermediate,\n citation_list_intermediate,\n reference_list_intermediate,\n config,updated_recid_list)\n #dic is docid-numberofreferences like {1: 2, 2: 0, 3: 1}\n #write_message(\"Docid-number of known references \"+str(dic))\n end_time = time.time()\n write_message(\"Total time of get_citation_weight(): %.2f sec\" % (end_time - begin_time))\n task_update_progress(\"citation analysis done\")\n else:\n dic = {}\n write_message(\"No new records added since last time this rank method was executed\")\n return dic","def sentiment(self) -> Dict[str, float]:","def clsWordCounts(wd_dict, wd_list, cl):\n\n # set up counter object for words in passed word list\n word_counts = Counter(wd_list)\n\n # for each term in counter object\n for term in word_counts:\n\n # if already in dictionary add to the count\n if term in wd_dict[cl]:\n\n wd_dict[cl][term] += word_counts[term]\n\n # else add to the dictionary\n else:\n\n wd_dict[cl][term] = word_counts[term]\n\n return wd_dict","def build(ctx, inputs, output, cs):\n click.echo('chemdataextractor.dict.build')\n dt = DictionaryTagger(lexicon=ChemLexicon(), case_sensitive=cs)\n names = []\n for input in inputs:\n for line in input:\n tokens = line.split()\n names.append(tokens)\n dt.build(words=names)\n dt.save(output)","def write_citation_page(refdict: dict) -> None:\n with open(WEBOUT_PATH + init_data().cite_url, \"w\", encoding=\"utf-8\") as outfile:\n common_html_header(outfile, \"Fiddler Crab Website Citation\")\n outfile.write(\" <header id=\\\"\" + init_data().cite_url + \"\\\">\\n\")\n outfile.write(\" <h1>Citation Info</h1>\\n\")\n outfile.write(\" </header>\\n\")\n outfile.write(\"\\n\")\n outfile.write(\" <p>\\n\")\n outfile.write(\" Generally it is best to cite the primary literature, whenever possible. However, the \"\n \"following paper describes much of the data that is unique to this website:\\n\")\n outfile.write(\" </p>\\n\")\n outfile.write(\" <div class=\\\"reference_list\\\">\\n\")\n outfile.write(\" <ul>\\n\")\n ref = refdict[\"Rosenberg2014\"] # citation describing the database\n outfile.write(\" <li><a href=\\\"references/Rosenberg2014.html\\\">\" + ref.formatted_html + \"</a></li>\\n\")\n outfile.write(\" </ul>\\n\")\n outfile.write(\" </div>\\n\")\n outfile.write(\" <ul class=\\\"fa-ul\\\">\\n\")\n outfile.write(\" <li>\" + fetch_fa_glyph(\"list pdf\") +\n \"<a href=\\\"https://dx.plos.org/10.1371/journal.pone.0101704\\\">Read paper online at \"\n \"PLoS ONE</a></li>\\n\")\n outfile.write(\" <li>\" + fetch_fa_glyph(\"list github\") +\n \"<a href=\\\"https://github.com/msrosenberg/fiddlercrab.info\\\">Website data repository on \"\n \"GitHub</a></li>\\n\")\n outfile.write(\" </ul>\\n\")\n common_html_footer(outfile)","def list_publications(bib_format=\"dict\"):\n\n def get_bibtex(key, value):\n total_keys = [\n \"title\",\n \"journal\",\n \"volume\",\n \"issue\",\n \"number\",\n \"pages\",\n \"numpages\",\n \"year\",\n \"month\",\n \"publisher\",\n \"url\",\n \"doi\",\n \"issn\",\n ]\n bibtex_str = (\n \"@article{\"\n + key\n + \",\\n\"\n + \" author={\"\n + \" and \".join(value[\"author\"])\n + \"},\\n\"\n )\n for key in total_keys:\n if key in value.keys():\n bibtex_str += \" \" + key + \"={\" + value[key] + \"},\\n\"\n bibtex_str += \"}\\n\"\n return bibtex_str\n\n def get_apa(value):\n apa_str = \" & \".join(value[\"author\"])\n if \"year\" in value.keys():\n apa_str += \" (\" + value[\"year\"] + \"). \"\n if \"title\" in value.keys():\n apa_str += value[\"title\"] + \". \"\n if \"journal\" in value.keys():\n apa_str += value[\"journal\"] + \", \"\n if \"volume\" in value.keys():\n apa_str += value[\"volume\"] + \", \"\n if \"pages\" in value.keys():\n apa_str += value[\"pages\"] + \". \"\n if \"doi\" in value.keys():\n apa_str += \"doi: \" + value[\"doi\"] + \"\\n\"\n return apa_str\n\n publication_dict = s.publication_lst\n if bib_format.lower() == \"dict\":\n return publication_dict\n elif bib_format.lower() == \"bibtex\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_bibtex(key, value)\n return total_str\n elif bib_format.lower() == \"apa\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_apa(value)\n return total_str\n else:\n raise ValueError(\"Supported Bibformats are ['dict', 'bibtex', 'apa']\")","def count_word_usage(counters_by_author, word_list):\n specific_word_counter = {}\n for author in counters_by_author.keys():\n word_counter = Counter()\n for item in counters_by_author[author]:\n for word in word_list:\n if word in item:\n print(item)\n word_counter[word] += counters_by_author[author][item]\n specific_word_counter[author] = word_counter\n return specific_word_counter","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def calculateWeights(self):\n return self.distances #En lo que encontramos una funcion que represente","def calc_charge_info(values,weights):\n\t\t\n\t# We use the standard IC procedure to calculate the statistical error for the\n\t# weighted average\n\t# There are three main terms\n\t# 1) The weighted sum of charges, and its variance\n\tif len(values) == 0 :\n\t\treturn 0.0, 1.0\n\twsc = sum([ weights[i]*values[i] for i in range(0,len(values))])\n\tsw = sum(weights)\n\tif sw == 0.0 or wsc == 0.0 :\n\t\treturn 0.0, 1.0 \n\t# Defining the weighted average\n\tmu = wsc/sw\n\tvar_wsc = sum([(weights[i]*values[i])**2 for i in range(0,len(values))]) \n\t# 2) The sum of weights and its variance \n\tvar_sw = sum([weights[i]**2 for i in range(0,len(values))])\n\t# 3) The covariance associated to both sums of weights \n\tcov = sum([values[i]*(weights[i]**2) for i in range(0,len(values))])\n\t# The error in the weighted average of charges is given by the variance of the quantity \n\t# wsc/sw; a division of two sums of weights (for two sets of weights that are correlated). \\\n\tstd_mu = var_wsc/(wsc)**2\n\tstd_mu += var_sw/(sw)**2\n\tstd_mu -= 2.*cov/(sw*wsc)\n\tstd_mu = std_mu**0.5\n\tstd_mu *= mu\n\n\treturn mu , std_mu","def similarity(query,word_dict,dictionary,number_of_docs,id):\n similarity = 0.0\n scalar_leng = 0.0\n for term in query:\n if term in dictionary:\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\n\n for term in dictionary:\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\n\n final_scalar_leng = math.sqrt(scalar_leng)\n similarity = similarity / final_scalar_leng\n #print(similarity)\n return similarity","def get_mutual_information(c_wic, c_wioc, c_owic, c_owioc):\n # total word count\n c_total = c_wic + c_wioc + c_owic + c_owioc\n\n mi_1 = (c_wic / float(c_total)) * log10((c_total * c_wic) /\n float((c_wic + c_wioc) * (c_wic + c_owic)))\n mi_2 = (c_owic / float(c_total)) * log10((c_total * c_owic) /\n float((c_owic + c_owioc) * (c_wic + c_owic)))\n mi_3 = (c_wioc / float(c_total)) * log10((c_total * c_wioc) /\n float((c_wic + c_wioc) * (c_wioc + c_owioc)))\n mi_4 = (c_owioc / float(c_total)) * log10((c_total * c_owioc) /\n float((c_owic + c_owioc) * (c_wioc + c_owioc)))\n\n return mi_1 + mi_2 + mi_3 + mi_4","def combine_calib(calib_lst, weight_lst):\n\n k = calib_lst[0]['k']\n offset = calib_lst[0]['offset']\n xorder = calib_lst[0]['xorder']\n yorder = calib_lst[0]['yorder']\n npixel = calib_lst[0]['npixel']\n\n for calib in calib_lst:\n if calib['k'] != k \\\n or calib['offset'] != offset \\\n or calib['xorder'] != xorder \\\n or calib['yorder'] != yorder \\\n or calib['npixel'] != npixel:\n print('Error: calib list is not self-consistent')\n raise ValueError\n\n\n # calculate the weighted average coefficients\n coeff = np.zeros_like(calib_lst[0]['coeff'], dtype=np.float64)\n for j, i in itertools.product(range(yorder+1), range(xorder+1)):\n for calib, weight in zip(calib_lst, weight_lst):\n coeff[j, i] += calib['coeff'][j, i]*weight\n\n return {'k': k, 'offset': offset, 'xorder': xorder, 'yorder': yorder,\n 'npixel': npixel, 'coeff': coeff}","def collectSamples(authors_analysis_dict,sectorialized_agents):\n def returnSignals(keep_dict,analysis_dict):\n def updateDict(dict_keep,new_dict)\n for var in new_dict:\n if var not in dict_keep:\n dict_keep[var]=new_dict[var]\n else:\n dict_keep[var]+=new_dict[var]\n samples={\"raw_analysis\":{\"texts_measures\":{}, \"text_measures\":{}},\n \"pos_analysis\":{\"texts_measures\":{}, \"text_measures\":{}},\n \"wordnet_analysis\":{\"texts_measures\":{}, \"text_measures\":{}},\n }\n for sector in sectorialized_agents:\n for author in sectorialized_agents[sector]:\n for analyses in authors_analysis:\n for analysis_grouping in authors_analysis[analyses][author]:\n if type(authors_analysis[analyses][author][analysis_grouping])==dict:\n for analysis in authors_analysis[analyses][author][analysis_grouping]:\n samples[sector][analyses][analysis_grouping][analysis]=updateDict(samples[sector][analyses][analysis_grouping][analysis],getSamples(authors_analysis[analyses][author][analysis_grouping][analysis]))\n else:\n samples[sector][analyses][analysis_grouping]=updateDict(samples[sector][analyses][analysis_grouping],authors_analysis[analyses][author][analysis_grouping])\n ############ OLD IMPLEMENTATION\n for analysis in authors_analysis[\"raw_analysis\"][author][\"texts_measures\"][\"each_text\"]:\n samples[sector][\"raw_analysis\"][\"texts_measures\"][\"chars\"]=updateDict(samples[sector][\"raw_analysis\"][\"texts_measures\"][\"chars\"],getSamples(authors_analysis[\"chars\"]))\n samples[sector][\"raw_analysis\"][\"texts_measures\"][\"tokens\"]=updateDict(samples[sector][\"texts_measures\"][\"tokens\"],getSamples(authors_analysis[\"tokens\"]))\n samples[sector][\"raw_analysis\"][\"texts_measures\"][\"sentences\"]=updateDict(samples[sector][\"texts_measures\"][\"sentences\"],getSamples(authors_analysis[\"sentences\"]))\n\n samples[sector][\"raw_analysis\"][\"texts_measures\"][\"all_texts\"]=updateDict(samples[sector][\"texts_measures\"][\"all_texts\"],getSamples(authors_analysis[author][\"texts_measures\"][\"all_texts\"]))\n ########### DIV\n samples[sector][\"raw_analysis\"][\"text_measures\"][\"chars\"]=updateDict(samples[sector][\"text_measures\"][\"chars\"],getSamples(authors_analysis[author][\"text_measures\"][\"chars\"]))\n samples[sector][\"raw_analysis\"][\"text_measures\"][\"tokens\"]=updateDict(samples[sector][\"text_measures\"][\"tokens\"],getSamples(authors_analysis[author][\"text_measures\"][\"tokens\"]))\n samples[sector][\"raw_analysis\"][\"text_measures\"][\"sentences\"]=updateDict(samples[sector][\"text_measures\"][\"sentences\"],getSamples(authors_analysis[author][\"text_measures\"][\"sentences\"]))\n # POS\n for analysis in authors_analysis[\"pos_analysis\"][author][\"texts_measures\"]:\n samples[sector][\"pos_analysis\"][\"texts_measures\"]=updateDict(samples[sector][\"pos_analysis\"][\"texts_measures\"],analysis[\"pos_measures\"][\"texts_measures\"]))\n samples[sector][\"pos_analysis\"][\"text_measures\"]=updateDict(samples[sector][\"pos_analysis\"][\"text_measures\"],analysis))\n # Wordnet\n for analysis in authors_analysis[\"wordnet_analysis\"][author][\"texts_measures\"]:\n samples[sector][\"wordnet_analysis\"][\"texts_measures\"][\"wordnet_context\"]=updateDict(samples[sector][\"wordnet_analysis\"][\"texts_measures\"][\"wordnet_context\"],analysis[\"wordnet_context\"])\n samples[sector][\"wordnet_analysis\"][\"texts_measures\"][\"wordnet_analysis\"]=updateDict(samples[sector][\"wordnet_analysis\"][\"texts_measures\"][\"wordnet_analysis\"],analysis[\"wordnet_analysis\"])\n samples[sector][\"wordnet_analysis\"][\"text_measures\"][\"wordnet_context\"]=updateDict(samples[sector][\"wordnet_analysis\"][\"text_measures\"][\"wordnet_context\"],authors_analysis[\"wordnet_analysis\"][author][\"text_measures\"][\"wordnet_context\"])\n samples[sector][\"wordnet_analysis\"][\"text_measures\"][\"wordnet_analysis\"]=updateDict(samples[sector][\"wordnet_analysis\"][\"text_measures\"][\"wordnet_analysis\"],authors_analysis[\"wordnet_analysis\"][author][\"text_measures\"][\"wordnet_analysis\"])\n return samples","def get_require_post_score(chosen_require_list: list) -> pmag.MagicDict:\n res = pmag.MagicDict()\n for w, cate in chosen_require_list:\n posts = MODEL[cate]['posts']\n for post in [*posts]:\n if w in posts[post]['require']:\n freq = posts[post]['require'][w]['freq']\n prob = posts[post]['require'][w]['prob']\n score = prob # freq * prob / REQUIRE_NF[cate][post]\n else:\n continue\n if post in res[cate]:\n res[cate][post] += score\n else:\n res[cate][post] = score\n return res","def extract_relevant(self):\n item_extraction = self.data\n my_dict = {'tweeted_time': item_extraction['created_at'],\n 'tweet_id': item_extraction['id'],\n # If the time comes when the below becomes more significant, it will be no trouble at all to make an\n # additional column for it, but delimiting it with a ` creates less clutter in the Database\n 'in_reply_to':\n \"NAME/\" + str(item_extraction['in_reply_to_screen_name']) + \"`\" +\n \"STATUSID/\" + str(item_extraction['in_reply_to_status_id_str']) + \"`\" +\n \"USERID/\" + str(item_extraction['in_reply_to_user_id_str']),\n 'lang': item_extraction['lang'],\n 'place': item_extraction['place'], 'source': item_extraction['source']}\n if item_extraction['place'] is not None:\n my_dict['place'] = item_extraction['place']['full_name']\n if 'retweeted_status' in item_extraction.keys():\n my_dict['original_author_id'] = item_extraction['retweeted_status']['user']['id']\n my_dict['original_author_handle'] = item_extraction['retweeted_status']['user']['screen_name']\n tester = item_extraction['retweeted_status']['text']\n cleaned = ' '.join(re.sub(\"(RT : )|(@[\\S]+)|(&\\S+)|(http\\S+)\", \" \", tester).split())\n removed_others = \" \".join(re.sub(\"(#\\S+)\", ' ', cleaned).split())\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\n # This final text will make it a lot easier to run NLP\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\n my_dict['plain_text'] = final_text.lower()\n my_dict['tweet'] = cleaned\n else:\n my_dict['original_author_id'] = item_extraction['user']['id']\n my_dict['original_author_handle'] = item_extraction['user']['screen_name']\n cleaned = ' '.join(re.sub(\"(@[\\S]+)|(&\\S+)|(http\\S+)\", \" \", item_extraction['text']).split())\n removed_others = \" \".join(re.sub(\"(#\\S+)\", ' ', cleaned).split())\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\n my_dict['plain_text'] = final_text.lower()\n my_dict['tweet'] = cleaned\n return my_dict","def learn(self, rythm_list): \n for rythm in rythm_list:\n # Get the rythm density, add it to the agent's density\n # distribution and then normalize it\n for ratio, d in rythm.density():\n if ratio not in self.density.keys():\n self.density[ratio] = d\n else:\n self.density[ratio] += d\n \n # NORMALIZE! ","def compute_wiki(self):\n\n self.__wiki_counts()\n self.__cross_wiki_counts()\n\n # Step 1: Calculate p(e|m) for wiki.\n print(\"Filtering candidates and calculating p(e|m) values for Wikipedia.\")\n for ent_mention in self.wiki_freq:\n if len(ent_mention) < 1:\n continue\n\n ent_wiki_names = sorted(\n self.wiki_freq[ent_mention].items(), key=lambda kv: kv[1], reverse=True\n )\n # Get the sum of at most 100 candidates, but less if less are available.\n total_count = np.sum([v for k, v in ent_wiki_names][:100])\n\n if total_count < 1:\n continue\n\n self.p_e_m[ent_mention] = {}\n\n for ent_name, count in ent_wiki_names:\n self.p_e_m[ent_mention][ent_name] = count / total_count\n\n if len(self.p_e_m[ent_mention]) >= 100:\n break\n\n del self.wiki_freq","def __init__(self,sentences):\n self.data_set = sentences\n self.sum1=0\n for sentence in self.data_set:\n sentence.weight = 1/len(self.data_set)\n self.sum1 += sentence.weight\n\n self.sum2=1","def tag():\n iso_list = []\n tags = [\"spatial_entity\", \"place\", \"motion\", \"location\", \"signal\", \"qslink\", \"olink\"]\n for token in doc:\n if token.norm_ in tags:\n iso_list.append(token.norm_)\n setList = list(set(iso_list))\n my_dict = {i: iso_list.count(i) for i in setList}\n\n for i in tags:\n if i.lower() not in my_dict:\n my_dict[i] = 0\n print(my_dict)","def get_citations_trend(body):\n user_id = get_user_id(body)\n citations_per_year = body.find('div', attrs={'id': 'gsc_g'})\n years = citations_per_year.find('div', attrs={'id': 'gsc_g_x'})\n n_citations = citations_per_year.find('div', attrs={'id': 'gsc_g_bars'})\n citations_list = list()\n for year, n_citation in zip(years, n_citations):\n citations_dict = {'user_id': user_id,\n 'n_citation': n_citation.text,\n 'year': year.text}\n citations_list.append(citations_dict)\n return citations_list","def portrayalCatalogueCitation(self) -> Sequence[Citation]:\n pass","def compare_and_update(self):\n\n # first read in the old cites\n try:\n with open(\"cites.json\") as f:\n cites_json = f.read()\n old_cites = json.loads(cites_json)\n except OSError:\n old_cites = {}\n\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\n print(\"number of citations = \", num_cites)\n\n h_index = 0\n for n, p in enumerate(self.mypapers):\n if p.citation_count >= n+1:\n h_index = n + 1\n print(\"h-index = \", h_index)\n\n # now create a dict of the cites and update the stored JSON\n cites = {}\n for p in self.mypapers:\n cites[p.bibcode] = (p.title[0], p.citation_count)\n\n cite_json = json.dumps(cites, indent=4)\n with open(\"cites.json\", \"w\") as f:\n f.write(cite_json)\n\n # now look for differences in cites\n for key, value in cites.items():\n if key not in old_cites:\n print(f\"new paper: {value[0]}\")\n else:\n if value[1] != old_cites[key][1]:\n print(f\"change of {value[1] - old_cites[key][1]:+d} for paper: {value[0]}\")","def crawl_url_links(input_dict):\n\n extractor_name=input_dict.get('extractor','DefaultExtractor')\n import requests\n label=input_dict['label']\n urls,_, source, source_date=_process_input(input_dict['input'],False)\n\n\n docs=[]\n titles=[]\n for url in urls:\n print(url)\n try:\n r = requests.get(url)\n except ConnectionError:\n continue\n if r.status_code==200:\n html=r.text\n from boilerpipe.extract import Extractor\n extractor = Extractor(extractor=extractor_name, html=html)\n\n titles.append(url)\n text=''\n if label:\n text+='!'+label+'\\t'\n text+=extractor.getText()\n docs.append(text)\n\n\n corpus_date = str(time.strftime(\"%d.%m.%Y %H:%M:%S\", time.localtime()))\n documents, labels = _process_adc(docs, False, label, titles)\n features = {\"Source\": source, \"SourceDate\": source_date, \"CorpusCreateDate\": corpus_date,\n \"Labels\": json.dumps([label]) if label else '[]'}\n\n return {\"adc\": DocumentCorpus(documents=documents, features=features)}","def update_weights(text):\n soup = bs4.BeautifulSoup(text, 'lxml')\n if ord_metric == OrderingMetric.BACK_LINK_COUNT:\n for l in soup.find_all('a', href=True):\n base = req_obj.get_base_url()\n link = urljoin(base, l['href'])\n if re.match(pattern, link) is not None:\n if link not in link_weights:\n link_weights[link] = 1\n else:\n link_weights[link] += 1\n\n elif ord_metric == OrderingMetric.FORWARD_LINK_COUNT:\n for l in soup.find_all('a', href=True):\n base = req_obj.get_base_url()\n link = urljoin(base, l['href'])\n if re.match(pattern, link) is not None:\n if link not in link_weights:\n link_weights[link] = get_num_forward_link(link)\n\n elif ord_metric == OrderingMetric.VSM:\n vsmObj = VectorSpaceModel()\n vsmObj.fetch_and_rank_summary(queue, link_weights)","def get_duty_cate_score(chosen_duty_list: list) -> pmag.MagicDict:\n res = pmag.MagicDict()\n for w, cate in chosen_duty_list:\n freq = MODEL[cate]['duty'][w]['freq']\n prob = MODEL[cate]['duty'][w]['prob']\n score = prob # freq * prob / DUTY_NF[cate]\n if cate in res:\n res[cate] += score\n else:\n res[cate] = score\n return res","def likelihood_weighting_estimate(self,var,e,n,sampling_weight='uniform'):\n\t\tncpt = self.variables[var].cpt\n\t\tncount = dict((value,0.0) for value in ncpt.values())\n\t\twsum = 0.0\n\t\tfor iter in xrange(n):\n (w,sample) = self.importance_sample(e,sampling_weight)\n assert all(sample[key]==value for (key,value) in e.iteritems())\n \n ncount[sample[var]] += w\n wsum += w\n if wsum==0: return 'Undefined'\n for value in ncount.iterkeys():\n ncount[value] = float(ncount[value])/float(wsum)\n return ncount","def get_mutual_information(filename):\n categories = {} #{category: speakers of this category}\n features = {} #{feat: speakers who use this feature}\n pos_categories_features = {} #{category: {feat: speakers of category who use this feat}}\n neg_categories_features = {} #{category: {feat: speakers of category who do not use this feat}}\n users = set() #set of all users in data\n \n for line in open(filename):\n userid, c, date, statusid, rawtweet, toktweet, tagtweet = line.split('\\t')\n users.add(userid)\n \n if c not in categories:\n categories[c] = set()\n pos_categories_features[c] = {}\n categories[c].add(userid)\n \n feats = set(toktweet.lower().split()) #lowercase tweet and split into words\n\n for feat in feats:\n if feat not in pos_categories_features[c]:\n pos_categories_features[c][feat] = set()\n pos_categories_features[c][feat].add(userid)\n \n if feat not in features:\n features[feat] = set()\n features[feat].add(userid)\n\n print \"Parsed data\"\n\n numfeats = len(features) #num of features\n print numfeats, \"features\"\n numusers = len(users) #num of users \n print numusers, \"users\"\n\n #keep sizes of sets, not sets themselves\n for feat in features:\n features[feat] = len(features[feat])\n for c in categories:\n categories[c] = len(categories[c])\n for c in pos_categories_features:\n for feat in features:\n if feat in pos_categories_features[c]:\n pos_categories_features[c][feat] = len(pos_categories_features[c][feat])\n else:\n pos_categories_features[c][feat] = 0\n\n for c in categories:\n print c, categories[c], \"users\"\n\n print \"Computed counts\"\n \n mi = {}\n for feat in features:\n mi[feat] = 0.0\n for c in categories:\n #print c, feat, features[feat], pos_categories_features[c][feat]\n \n catprob = categories[c]/numusers\n\n #prob of speakers of category c using feat\n featprob = features[feat]/numusers\n jointprob = pos_categories_features[c][feat]/numusers\n if jointprob > 0 and featprob > 0:\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\n \n #prob of speakers of category c NOT using feat\n featprob = 1 - featprob\n jointprob = (categories[c] - pos_categories_features[c][feat])/numusers\n if jointprob > 0 and featprob > 0:\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\n\n print \"Computed mutual information\"\n\n feature_scores = sorted(mi.items(), key=lambda x:x[1], reverse=True)\n refcat = categories.keys()[0] #pick one of the categories\n print 'Feature\\tMI\\tP({0}|Feature)\\tNum. users'.format(refcat)\n for feat, score in feature_scores[:200]:\n prob = pos_categories_features[refcat][feat]/features[feat]\n print '{0}\\t{1:.3f}\\t{2:.3f}\\t{3}'.format(feat, score, prob, features[feat])","def calcCountDict(TFdict):\n\n countDict = {}\n\n for doc in TFdict:\n for term in doc:\n if term in countDict:\n countDict[term] +=1\n else:\n countDict[term] = 1\n\n return countDict","def _extract_information(key, word, language_data):\n try:\n number = int(key)\n except ValueError:\n print(f\"The given key {key} is not an integer\")\n return\n\n word = word.replace(\";\", \"\")\n count_greater_than_sign = word.count(\">\")\n count_less_than_sign = word.count(\"<\")\n count_equal_to_sign = word.count(\"=\")\n if count_equal_to_sign != 0:\n return\n if count_greater_than_sign == 0 and count_less_than_sign == 0:\n _add_base_words(number, word, language_data)\n elif count_greater_than_sign == 2 and count_less_than_sign == 0:\n _add_compound_words(number, word, language_data)\n elif count_greater_than_sign == 2 and count_less_than_sign == 2:\n _add_multiplier_words(number, word, language_data)","def parse_citation_web(doc_id, id_type='PMC'):\n\n doc_id_dict = convert_document_id(doc_id, id_type=id_type)\n pmc = doc_id_dict['pmc']\n link = \"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/\" % pmc\n page = requests.get(link)\n tree = html.fromstring(page.content)\n n_citations = extract_citations(tree)\n n_pages = int(n_citations/30) + 1\n\n pmc_cited_all = list() # all PMC cited\n citations = tree.xpath('//div[@class=\"rprt\"]/div[@class=\"title\"]/a/@href')[1::]\n pmc_cited = list(map(extract_pmc, citations))\n pmc_cited_all.extend(pmc_cited)\n if n_pages >= 2:\n for i in range(2, n_pages+1):\n link = \"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/?page=%s\" % (pmc, str(i))\n page = requests.get(link)\n tree = html.fromstring(page.content)\n citations = tree.xpath('//div[@class=\"rprt\"]/div[@class=\"title\"]/a/@href')[1::]\n pmc_cited = list(map(extract_pmc, citations))\n pmc_cited_all.extend(pmc_cited)\n pmc_cited_all = [p for p in pmc_cited_all if p is not pmc]\n dict_out = {'n_citations': n_citations,\n 'pmid': doc_id_dict['pmid'],\n 'pmc': re.sub('PMC', '', doc_id_dict['pmc']),\n 'doi': doc_id_dict['doi'],\n 'pmc_cited': pmc_cited_all}\n return dict_out","def jaccard_sim(news_data_dict: dict, song_data_dict: dict):\n jaccard_dict = defaultdict(list)\n\n for news_yr, news_txt in news_data_dict.items():\n news_txt_flat = []\n for nt in news_txt:\n news_txt_flat += nt\n news_txt_flat = set(news_txt_flat)\n if news_yr <= 2011:\n song_txt_flat = set()\n for i in range(5):\n song_yr = news_yr+i\n for st in song_data_dict[song_yr]:\n for stw in st:\n song_txt_flat.add(stw)\n shared_words = news_txt_flat.intersection(song_txt_flat)\n jaccard = len(shared_words) / (len(song_txt_flat) + len(news_txt_flat) - len(shared_words))\n jaccard = round(jaccard, 3)\n jaccard_dict[news_yr].append(jaccard)\n\n return jaccard_dict","def parse(self, lines):\n # convert file to string deleting end of line charcters\n citations_string = self.prepare_file(lines)\n # extract the entries from the string\n entries = list(self.find_entries(citations_string))\n entries.append(len(citations_string))\n # parse each entry to generate a citation\n for idx, jdx in zip(entries[:-1], entries[1:]):\n self.parse_entry(citations_string[idx:jdx])\n return self.force_field.citations","def get_papers_by_institution(inst_names):\n papers = list(ads.SearchQuery(aff=\"IATE\"))\n for p in papers:\n print(p.author)\n print(f\"Paper from year {p.year} with \"\n f\"{len(p.author)} authors and \"\n f\"{p.citation_count} citations.\")\n input()","def get_calib_weight_lst(calib_lst, obsdate, exptime):\n input_datetime = dateutil.parser.parse(obsdate) \\\n + datetime.timedelta(seconds=exptime/2)\n datetime_lst = [dateutil.parser.parse(calib['date-obs']) \\\n + datetime.timedelta(seconds=calib['exptime']/2)\n for calib in calib_lst]\n\n dt_lst = [(dt - input_datetime).total_seconds() for dt in datetime_lst]\n dt_lst = np.array(dt_lst)\n\n if len(dt_lst)==1:\n # only one reference in datetime_lst\n weight_lst = [1.0]\n elif (dt_lst<0).sum()==0:\n # all elements in dt_lst > 0. means all references are after the input\n # datetime. then use the first reference\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\n weight_lst[0] = 1.0\n elif (dt_lst>0).sum()==0:\n # all elements in dt_lst < 0. means all references are before the input\n # datetime. then use the last reference\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\n weight_lst[-1] = 1.0\n else:\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\n i = np.searchsorted(dt_lst, 0.0)\n w1 = -dt_lst[i-1]\n w2 = dt_lst[i]\n weight_lst[i-1] = w2/(w1+w2)\n weight_lst[i] = w1/(w1+w2)\n\n return weight_lst","def test_tax_court_citation_extractor(self):\n\n test_pairs = (\n (\n \"\"\" 1 UNITED STATES TAX COURT REPORT (2018)\n\n\n\n UNITED STATES TAX COURT\n\n\n\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\n MATTERS PARTNER, Petitioner v.\n COMMISSIONER OF INTERNAL REVENUE, Respondent\n\n\n\n Docket No. 5393-04. Filed May 31, 2006.\n\n\n\n Nancy Ortmeyer Kuhn, for petitioner.\n \"\"\",\n \"1 T.C. 2018\",\n ),\n (\n \"\"\" T.C. Memo. 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Memo. 150\",\n ),\n (\n \"\"\" T.C. Summary Opinion 2003-150\n\n\n\n UNITED STATES TAX COURT\n\n\n\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\n TAX MATTERS PARTNER,\n\n\n \"\"\",\n \"2003 T.C. Summary Opinion 150\",\n ),\n (\n \"\"\"\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\n OF INTERNAL REVENUE, RESPONDENT\n\n Docket No. 5706–12. Filed May 6, 2013.\n\n P was divorced from his wife, and their 2003 ‘‘Judgment of\n Absolute Divorce’’ provided that his ex-wife would have pri-\n mary residential custody of their three minor children. The\n judgment provided that the dependency exemption deductions\n for the three children would be divided between the two ex-\n spouses according to various conditions but did not provide\n that the ex-wife must execute in P’s favor a Form 8332,\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\n rated Parents’’. The children resided with P’s ex-wife for more\n than half of 2009, and P’s ex-wife did not execute in P’s favor\n any Form 8332 or equivalent document for any year. For 2009\n P timely filed a Federal income tax return on which he\n claimed dependency exemption deductions and the child tax\n credit for two of the children, consistent with his under-\n standing of the terms of the judgment, but he did not attach\n any Form 8332 to his return. He also claimed head-of-house-\n hold filing status. His ex-wife, the custodial parent, timely\n filed a Federal income tax return for 2009 on which she also\n\n 200\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f (200) SHENK v. COMMISSIONER 201\n\n\n claimed two dependency exemption deductions, so that one\n child was claimed on both parents’ returns. R allowed to P the\n dependency exemption deduction for one of the children but\n disallowed his claim for the dependency exemption deduction\n for the child who had also been claimed by the custodial\n parent. At trial P contended he is entitled to a dependency\n exemption deduction for all three children. Held: Since the\n custodial parent did not execute, and P could not and did not\n attach to his return, any Form 8332 or equivalent release, P\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\n dependency exemption deduction or the child tax credit. Held,\n further, where both the custodial parent and the noncustodial\n parent have claimed for the same year a dependency exemp-\n tion deduction for the same child, a declaration signed by the\n custodial parent after the period of limitations for assess-\n ments has expired as to the custodial parent could not qualify\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\n to grant P’s request to leave the record open so that he may\n obtain and proffer such a declaration. Held, further, P is not\n entitled to head-of-household filing status under I.R.C. sec.\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\n\n Michael Keith Shenk, for himself.\n Shari Salu, for respondent.\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\n determined a deficiency of $3,136 in the 2009 Federal income\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\n this Court, pursuant to section 6213(a), 1 for redetermination\n of the deficiency. After Mr. Shenk’s concession that he\n received but did not report $254 in dividend income, the\n issue for decision is whether Mr. Shenk is entitled to a\n dependency exemption deduction for one of his children\n under section 151(c), a child tax credit for that child under\n section 24(a), and head-of-household filing status under sec-\n tion 2(b)(1). On these issues, we hold for the IRS.\n FINDINGS OF FACT\n\n The judgment of divorce\n Mr. Shenk was married to Julie Phillips, and they have\n three minor children—M.S., W.S., and L.S. They divorced in\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\n\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\n\n\n\n\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\FILES\\BOUND VOL. WITHOUT CROP MARKS\\B.V.140\\SHENK JAMIE\n\f 202 140 UNITED STATES TAX COURT REPORTS (200)\n\n\n vided: that Ms. Phillips was ‘‘awarded primary residential\n custody’’ of the parties’ three children; and that Mr. Shenk\n would be liable for child support payments; but that, as to\n dependency exemptions—\"\"\",\n \"140 T.C. 200\",\n ),\n )\n site = tax.Site()\n for q, a in test_pairs:\n results = site.extract_from_text(q)\n cite_string = \"%s %s %s\" % (\n results[\"Citation\"][\"volume\"],\n results[\"Citation\"][\"reporter\"],\n results[\"Citation\"][\"page\"],\n )\n\n self.assertEqual(cite_string, a)\n print \"✓\", cite_string","def build_query_dict(self, term_list, issn_list, year_list, jlist):\n journal_frame = self.make_jlist(jlist)\n\n search_terms = self.build_search_terms(term_list)\n dict1 = {}\n #This loop goes through and sets up a dictionary key with an ISSN number\n\n for issn in issn_list:\n\n issn_terms = ' AND ISSN(' + issn + ')'\n dict2 = {}\n #This loop goes and attaches all the years to the outer loop's key.\n for year in year_list:\n\n year_terms = \"AND PUBYEAR IS \" + str(year)\n querystring = search_terms + year_terms + issn_terms\n\n dict2[year] = querystring\n\n dict1[issn] = dict2\n\n return dict1","def get_feature_set_SC(tweet, sentimentvalues):\n pos_tag_freq = {}\n additional_freq = {}\n for phrase in tweet.tagged_words:\n for word in phrase:\n try:\n tag = word['pos']\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# if tag=='PRtinf':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJS':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='ADJ':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='NP':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='DET':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n# elif tag=='P':\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\n if tag in ADJECTIVES:\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\n elif tag in ADVERBS: \n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\n elif tag in PRONOUNS:\n additional_freq['pronoun'] = 1\n except KeyError:\n continue\n for key in pos_tag_freq.keys():\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \n #Number of exclamation marks, number of emoticons,\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\n if emoticons>0:\n additional_freq['emoticons'] = emoticons*1.0\n if tweet.nrof_exclamations>0:\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\n \n #Add lexicon values\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\n sub_score = 0.0\n obj_score = 0.0\n nrof_subwords = 0\n nrof_objwords = 0\n for word in sentimentvalues.keys():\n if sentimentvalues[word][0]>0:\n sub_score = sub_score + sentimentvalues[word][0]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][1]>0:\n sub_score = sub_score + sentimentvalues[word][1]\n nrof_subwords = nrof_subwords + 1\n if sentimentvalues[word][2]>0:\n obj_score = obj_score + sentimentvalues[word][2]\n nrof_objwords = nrof_objwords + 1\n if sub_score>0:\n additional_freq[\"sub_score\"] = sub_score+1.0\n if obj_score>0:\n additional_freq[\"obj_score\"] = obj_score+1.0\n if nrof_subwords>0:\n additional_freq[\"subjective_words\"] = nrof_subwords*1.0\n if nrof_objwords>0:\n additional_freq[\"objective_words\"] = nrof_objwords*1.0\n \n #Concatenate the dicts\n features= dict(pos_tag_freq.items() + additional_freq.items())\n \n return features","def weightKmers(self, weightDict):\n for k, w in weightDict.iteritems():\n assert k in self.kmers\n self.G.edge[k + \"_L\"][k + \"_R\"]['weight'] = w","def get_relevant_info(claim, articles, vectorizer, max_seq_len, spellcheck, use_ngrams):\n\n # Note: expects claim to be already cleaned\n vec_claim = vectorizer.transform_txt(claim, max_seq_len,\n use_ngrams=use_ngrams) # Claim vector - we'll use this to compare using cosine similarity\n similarities_and_sents = [] # Stores tuples of (cos sim, sentence)\n\n # Loop through all articles to construct supporting information\n for article in articles:\n sentences = tokenize_by_sentence(article)\n\n '''\n For each sentence, we clean and vectorize, then retrieve the cosine similarity of the claim vs the sentence\n '''\n for sentence in sentences:\n # Basic cleaning on sentence\n sentence = clean_txt(sentence, spellcheck)\n # Don't process for sentences less than 40 characters long - this usually means improper sentences/words\n if len(sentence) < 40:\n continue\n # Get vector of sentence and find cosine similarity\n vec_sent = vectorizer.transform_txt(sentence, max_seq_len,\n use_ngrams=use_ngrams)\n similarity = cos_sim(vec_claim, vec_sent)\n # Add to results\n similarities_and_sents.append((similarity, sentence))\n\n # Sort the similarities (in desc order) using their similarity\n sorted_sents = sorted(similarities_and_sents, key=lambda elem: elem[0], reverse=True)\n\n article_info = ''\n num_words = 0\n '''\n Construct relevant info - keep looping through sentences, adding sentences until we hit max_seq_len\n We'll surpass max_seq_len, but that's okay\n '''\n for similarity, sentence in sorted_sents:\n if num_words >= max_seq_len:\n break\n article_info += ' || ' + sentence # Add a separator\n num_words += len(sentence.split())\n return article_info","def weight_distribution(self):\n all_scores = []\n for zettel in self.lemma_tokens:\n scores = []\n for word in zettel:\n cur_tf_idf = self.tf_idf_scores[word[0]] / 3 #range: 0-3+\n if word[1] == 'NG':\n word_list = re.split(\" \", word[0])\n cur_word_score = 0\n i = 0\n for new_word in word_list:\n cur_word_score += self.word_scores[new_word]\n i += 1\n cur_word_score = cur_word_score / i / 2 #range: 0-2+\n else:\n cur_word_score = self.word_scores[word[0]] / 2 #range: 0-2+\n cur_keyword_score = self.keyword_scores[word[0]] / 4 #0-4+\n cur_text_rank = self.text_ranks[word[0]] / 10 #range: 0-12+\n cur_pos_score = self.pos_scores[word[0]]\n cur_area_score = self.z_area_scores[word[0]]\n cur_total_score = ((cur_tf_idf * self.score_weights[0]) + (cur_word_score * self.score_weights[1]) +\n (cur_keyword_score * self.score_weights[2]) + (cur_text_rank * self.score_weights[3]) +\n (cur_pos_score * self.score_weights[4]) + (cur_area_score * self.score_weights[5])) / 6\n scores.append(cur_total_score)\n all_scores.append(scores)\n return all_scores","def average():\n dict_ = {}\n lst = []\n average_cost = {}\n for book in root.findall('Book'):\n publisher = book.find('Publisher').text\n price = book.find('Price').text\n lst.append(publisher)\n if publisher not in dict_:\n dict_[publisher] = float(price)\n else:\n dict_[publisher] += float(price)\n publishers = {i: lst.count(i) for i in lst}\n for key1, value1 in dict_.items():\n for key2, value2 in publishers.items():\n if key1 == key2:\n average_cost[key1] = round(value1 / value2, 2)\n return average_cost","def analyze_edges_and_weight(list_of_nodes):\n edges_info = []\n for node in list_of_nodes:\n n_edge_of_node = len(node.neighbors) # Counts the kys in the dictionary 'Node.neighbors'\n total_weight_of_node = sum(list(map(lambda x: node.neighbors[x], node.neighbors))) # Sums values of the dict\n node_info = (node.name, n_edge_of_node, total_weight_of_node)\n edges_info.append(node_info)\n total_n_edges = sum([tup[1] for tup in edges_info]) # Sum total number of edges\n total_weight_of_graph = sum([tup[2] for tup in edges_info]) # Sum total weight of edges\n sorted_info = sorted(edges_info, key=lambda tup: tup[1], reverse=True)\n return \"Total number of edges is {},\\nTotal weight of the graph is {}:\\nNodes sorted by no. of edges: {}.\".format(total_n_edges, total_weight_of_graph, sorted_info)","def similarities (self, listOfWords):\n \n # building the query dictionary\n queryDict = collections.defaultdict(int)\n for w in listOfWords:\n queryDict [w] += + 1.0\n \n # normalizing the query\n length = float (len (listOfWords))\n for k in queryDict:\n queryDict [k] /= length\n \n # computing the list of similarities\n sims = []\n for doc in self.documents:\n score = 0.0\n docDict = doc [1]\n for k in queryDict:\n if docDict.has_key (k):\n score += (queryDict [k] / self.corpusDict [k]) + (docDict [k] / self.corpusDict [k])\n sims.append ([doc [0], score])\n \n return sims","def sentiment_analysis(name, dictionary):\n\ttone_analyzer = ToneAnalyzerV3(\n\t\t username='2ed2f0c6-1722-472d-9126-224897b991af',\n\t\t password='UcuSde1YmeK6',\n\t\t version='2016-05-19')\n\tl = open(name + '.txt')\n\tlines = l.readlines()\n\tfeel_dict = {'Anger':1.0,'Fear':2.0, 'Sadness':3.0, 'Disgust':4.0,'Joy':5.0, 'Excitement':6.0}\n\tdictionary[name] = []\n\tfor i in lines:\n\t\t#print('-----------------')\n\t\t#print(i)\n\t\tmax_score = 0.0\n\t\tmax_feel = ''\n\t\ttone = tone_analyzer.tone(i, 'emotion')\n\t\tfor feel in tone['document_tone']['tone_categories']:\n\t\t\tfor feeling in feel['tones']:\n\t\t\t\tif feeling['score'] > max_score:\n\t\t\t\t\tmax_score = feeling['score']\n\t\t\t\t\tmax_feel = feeling['tone_name']\n\t\t#print(max_score, max_feel)\n\t\t#blob1 = TextBlob(i, pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())\n\t\tif max_feel != '':\n\t\t\ttweet_tbu = db.Tweet.objects(rating=feel_dict[max_feel]).first()\n\t\t\tdict_tbu = {}\n\t\t\tif tweet_tbu:\n\t\t\t\tdict_tbu = mongo_to_dict(tweet_tbu)\n\t\t\t\tprint('exists')\n\t\t\t\tprint(dict_tbu)\n\t\t\t\tif max_feel != '':\n\t\t\t\t\tnew_dict = {}\n\t\t\t\t\tnew_dict['tweet'] = dict_tbu['tweet']\n\t\t\t\t\tnew_dict['tweet'].append(i[0:-2])\n\t\t\t\t\ttweet_tbu.update(**new_dict)\n\t\t\t\t\ttweet_tbu.reload()\n\t\t\telse:\n\t\t\t\tprint('not exists - with max')\n\t\t\t\tnew_dict = {}\n\t\t\t\tnew_dict['tweet'] = [i[0:-1]]\n\t\t\t\tif max_feel != '':\n\t\t\t\t\tnew_dict['rating'] = feel_dict[max_feel]\n\t\t\t\telse:\n\t\t\t\t\tnew_dict['rating'] = 0.0\n\t\t\t\tprint(new_dict)\n\t\t\t\tnew_tweet = db.Tweet(**new_dict)\n\t\t\t\tnew_tweet.save()\n\t\telse:\n\t\t\tprint('not exists - without')\n\t\t\tnew_dict = {}\n\t\t\tnew_dict['tweet'] = [i[0:-1]]\n\t\t\tif max_feel != '':\n\t\t\t\tnew_dict['rating'] = feel_dict[max_feel]\n\t\t\telse:\n\t\t\t\tnew_dict['rating'] = 0.0\n\t\t\tprint(new_dict)\n\t\t\tnew_tweet = db.Tweet(**new_dict)\n\t\t\tnew_tweet.save()\n\tresult = db.Tweet.objects()\n\treturn(result)","def get_distribution(doc):\n word_count = {}\n word_count = clean(doc, word_count)\n factor = 1.0 / sum(word_count.values())\n dist = {k: v * factor for k, v in word_count.items()}\n return dist","def get_shop_list_by_dishes(cook_dict, dishes, person_count):\n temp_dict = dict()\n for dish_name in dishes:\n for dish_type, ingredient_list in cook_dict.items():\n if dish_type == dish_name:\n for ingr_dict in ingredient_list:\n ingr_name = ingr_dict['ingredient_name']\n if temp_dict.setdefault(ingr_name) is None:\n temp_dict[ingr_name] = {\n 'quantity': ingr_dict['quantity'] * person_count, 'measure': ingr_dict['measure']\n }\n else:\n temp_dict[ingr_name]['quantity'] += ingr_dict['quantity'] * person_count\n return temp_dict","def get(soup, data, dictionary):\n\n # Steg 1: Ting som alltid er sant:\n dictionary['fb_like'] = None #0\n dictionary['others_share'] = None #0\n dictionary['fb_share'] = len(soup.select(\".share-facebook\"))\n dictionary['googleplus_share'] = len(soup.select(\".share-googleplus\"))\n dictionary['twitter_share'] = len(soup.select(\".share-twitter\"))\n dictionary['email_share'] = len(soup.select(\".share-mail\"))\n\n # tror ikke disse har noen aside...\n dictionary['related_stories_box_les'] = len(soup.select(\"aside.articlewidgets article\"))\n\n # related thematic (found in footer part of page)\n dictionary['related_stories_box_thematic'] = 0\n # grab that footer part with data-relation-limit attr\n related_thematic = soup.find_all(has_data_relation_limit)\n # loop\n for el in related_thematic:\n #check divs\n for div in el.select(\"div\"):\n if has_data_id(div):\n dictionary['related_stories_box_thematic'] +=1\n\n # re related stories is the combined previous two\n dictionary['related_stories'] = dictionary['related_stories_box_les'] + dictionary['related_stories_box_thematic']\n \n\n # antall js dokumenter\n dictionary['js'] = count_js(soup, data, dictionary) # = len(re.findall(\"<iframe src=\", data)) # .js\n # remove javascript.\n [s.decompose() for s in soup.body.article('script')]\n # I believe this is what creates the somewhat awkward line-breaks in the soup\n\n # Find author(s)\n byline = soup.find('div', 'byline')\n authors = []\n try:\n for address, li in izip(byline.find_all('address'), byline.find_all('li', 'icon-email')):\n authorName = address.strong.text #address.find(class_='fn').string.encode('utf-8')\n # NRK is still trying to hide the email address from spammers. #href = li.a['href']\n authorMail = None # 'abandon this? too hard?'#unquote(href[21:-1])[7:] # Antakelsen er at epost vil holde seg til ASCII. \n authorRole = address.span.text #address.find(class_='role').string.strip().encode('utf-8')\n author = [authorName, authorMail, authorRole]\n authors.append(author)\n # and remove author image (so not to count it later..) \n address.figure.decompose()\n except AttributeError:\n # Finner ingen forfatter(e)\n new_logger.warn(\"Ingen forfattere \\\"{0}\\\". Oppgir \\\"<UKJENT>\\\" som forfatter\".format(dictionary['url']))\n #print \n authors.append([None, None, None])\n dictionary['authors'] = authors\n \n # Find published datetime\n try:\n dictionary['published'] = strptime(soup.time['datetime'][0:19], \"%Y-%m-%dT%H:%M:%S\")\n except TypeError:\n new_logger.info(\"finner ikke publiseringsdato\")\n dictionary['published'] = None\n\n new_logger.debug(\"published: %s\", type(dictionary['published']))\n # Find update datetime\n try:\n updated = soup.find('span', 'update-date')\n dictionary['updated'] = datetime.strptime(updated.time['datetime'][0:19], \"%Y-%m-%dT%H:%M:%S\")\n except:\n new_logger.info(\"finner ikke oppdateringsdato\")\n dictionary['updated'] = None\n\n # Find headline\n try:\n dictionary['headline'] = soup.body.article.find('h1').text.strip()\n #dictionary['headline'] = soup.header.find('div', 'articletitle').h1.text # .text gived unicode, .string gives 'bs4.element.NavigableString'\n except AttributeError:\n new_logger.debug( \"NB: bruker doc-title...\" )\n dictionary['headline'] = soup.title.text\n\n # Find fact-boxes :\n # Should be removes from body, but includes in LIX. Right?\n faktabokser = []\n #for boks in soup.find_all(\"section\", class_=\"articlewidget cf facts lp_faktaboks\"):\n for boks in soup.find_all(\"section\", class_=\"facts\"):\n faktaboks_text = boks.text.strip()\n lix = Lix(faktaboks_text)\n faktaboks_analysis = lix.analyzeText(faktaboks_text)\n faktabokser.append({\"text\":faktaboks_text, \"links\":boks.find_all(\"a\"), \"wordcount\":faktaboks_analysis['wordCount']})\n # and remove from soup\n #boks.decompose() #ikke fjern boks'n fra suppa.\n # NB, this also removes pictures if any in the fact-box\n dictionary['factbox'] = faktabokser\n\n new_logger.debug(\"faktabokser: %s\", len(dictionary['factbox']))\n\n # Find full text \n # article MINUS .universes OR is it .lp_related ?\n # remove the related section\n # try:\n # soup.body.article.find('section', 'lp_related').decompose()\n # except:\n # pass\n # # remove div.published (the top-bar)\n # soup.body.article.find('div', 'published').decompose()\n # # remove div.shareurl (the sharebar)\n # soup.body.article.find('div', 'sharing').decompose()\n\n # Find self declared url # get this before decomposing the header this is found in..\n dictionary['url_self_link'] = soup.select(\"time > a\")[0]['href']\n\n # remove header with sharing links and date\n soup.select(\".bulletin-header\")[0].decompose()\n # store body text\n dictionary['body'] = soup.body.article.text.strip() \n # .stripped_strings option?\n # soup.get_text(\"|\", strip=True) perhaps?\n\n # Find char count, line count, word count and Lix\n lix = Lix(dictionary['body']) \n\n analyse = lix.analyzeText(dictionary['body'])\n try:\n dictionary['char_count'] = len(dictionary['body'])\n dictionary['word_count'] = analyse['wordCount']\n dictionary['line_count'] = analyse['sentenceCount']\n dictionary['lesbahet'] = analyse['lixScore']\n except TypeError:\n new_logger.error(\"Kunne ikke kjøre lix\", dictionary['body']) \n dictionary['line_count'] = None\n dictionary['word_count'] = None\n dictionary['char_count'] = None\n dictionary['lesbahet'] = -1.0\n\n # look through the last part of the body text to find news bureau\n # add more in settings.py\n dictionary['news_bureau'] = matches_pattern(dictionary['body'].strip()[-200:], syndicators)\n\n\n # Find language. Defaults can be tampered with in settings.py\n (language, certainty) = langid.classify(soup.body.article.text)\n new_logger.debug( \"(language, certainty) (%s, %s)\" % (language, certainty))\n language_code = uncertain_language_string\n if (certainty > language_identification_threshold):\n language_code = language\n\n dictionary['language'] = language_code\n\n\n get_video(soup.body.article, data, dictionary)\n\n # flash (untested)\n dictionary['flash_file'] = get_flash(soup.body.article, data, dictionary)\n\n # Tell opp iframe. \n dictionary['iframe'] = count_iframes(soup, data, dictionary)\n \n # Tell opp css (karakterer)\n dictionary['css'] = count_css(soup, data, dictionary)\n\n\n # Finnes det en form for kommentarer her? I de nyere NRK sidene er det tydeligvis kun det på Ytring.\n # Men vi søker generelt nå, og håper på det beste. I verste fall vil et interessant krasj fortelle meg at dette ikke er tilfellet. –Haakon\n dictionary['comment_fields'] = 0\n dictionary['comment_number'] = 0\n if len(re.findall('<div id=\"disqus_thread\"', data)) != 0:\n dictionary['comment_fields'] = 1\n dictionary['comment_number'] = None # -9999#num_comments(dictionary)\n \n # tar seg av lenker i siden\n count_links(soup, data, dictionary)\n\n # antall bilder.\n # Beautiful Soup teller feil her og. Noe er galt.\n # Regex matching gir riktig resultat så vi får gå for det.\n #result = soup.article.find_all('figure', 'image')\n #print len(result)\n #new_logger.debug( \"antall bilder: %s\", len(re.findall(\"<img src=\\\"http:\", data)) )\n \n dictionary['images'] = count_images(soup.body.article, data, dictionary)\n \n # bildesamlinger\n dictionary['image_collection'] = len(soup.select(\".slideshow\")) # er dette nok?\n # Som diskutert med Eirik, dette henter ut bildetekstene og deler dem med pipe symboler.\n\n imgtagger = re.findall(u\"<img src=\\\"http.*\\n.*\", str(soup.body.article) )\n bildetekst = \"\"\n for imgtag in imgtagger:\n funn = re.findall(\"alt=\\\".*\\\"\", imgtag)\n if len(funn) > 0:\n bildetekst += ((funn[0])[5:-1] + \" | \")\n bildetekst = bildetekst[:-3] # Fjerner siste pipen\n dictionary['image_captions'] = bildetekst\n\n\n\n dictionary['map'] = count_map(soup.body.article, data, dictionary)\n dictionary['poll'] = None # -9999\n dictionary['game'] = None # -9999\n \n dictionary['interactive_elements'] = count_interactive( \\\n dictionary['comment_fields'] , dictionary['image_collection'] , \\\n dictionary['video_files'] , dictionary['video_files_nrk'] , \\\n dictionary['fb_like'] , dictionary['fb_share'] , \\\n dictionary['googleplus_share'] , dictionary['twitter_share'] , \\\n dictionary['others_share'] , dictionary['email_share'] , \\\n dictionary['map'] , dictionary['poll'] , dictionary['game'])\n \n\n\n\n\n return dictionary","def calculate_wasserstein(structure_list : List[str]) -> Dict:\n from itertools import combinations\n\n # from collections import defaultdict\n # wd_1d = defaultdict(list)\n wd_1d = {}\n # fill list with all the combinations from structure_list\n combos = list(combinations(structure_list, 2))\n\n #parallelize to calculate wasserstein distance\n from multiprocessing import Pool\n with Pool(processes=4) as pool:\n wds_1d = list(tqdm(pool.imap(multiprocessing_func, combos), total=len(combos)))\n\n # Hash structure_list tuple combos as key, value: wasserstein_distance\n wd_dict = {combo: wd_1d for combo, wd_1d in zip(combos, wds_1d)}\n return wd_dict","def get_recipe_details(recipe_links):\n cuisine_recipes = {}\n for r in recipe_links:\n soup_recipe = BeautifulSoup(r)\n if \"www.chowhound.com\" in r.a[\"href\"]:\n recipe = {}\n recipe['r_link'] = r.a[\"href\"]\n print \"recipe link: \", recipe['r_link']\n soup_recipe = get_recipe(recipe['r_link'])\n recipe['recipe title'] = get_recipe_title(soup_recipe)\n recipe['chef'] = get_recipe_chef(soup_recipe)\n recipe['description'] = get_description(soup_recipe)\n recipe['ingredient list'] = get_recipe_ingredients(soup_recipe)\n recipe['preperation steps'] = get_recipe_preperation(soup_recipe)\n recipe['total_time'], recipe['active_time'] = get_recipe_time(soup_recipe)\n recipe['servings'] = get_servings(soup_recipe)\n recipe['skill_level'] = get_recipe_difficulty(soup_recipe)\n recipe['rating'], recipe['rating count'] = get_ratings(soup_recipe)\n recipe['nutritional_info'] = get_nutrition_per_serving(soup_recipe)\n recipe['image_source'] = get_image_source(soup_recipe)\n cuisine_recipes[recipe['recipe title']] = recipe\n return cuisine_recipes","def calc_assn_weights():\n\t\n\t\t\t#\n\t\t\t#\n\ttext(\"\"\"INSERT INTO assignments (mentor_id, course_id, cost)\n\t\t\tSELECT M.mentor_id, C.course_id, SUM(COALESCE(PW.weight_value,PT.def_weight_val))\n\t\t\tFROM mentors M, courses C\n\t\t\tJOIN course2pref C2P ON C2P.course_id = C.course_id\n\t\t\tJOIN prefs P ON P.pref_id = C2P.pref_id\n\t\t\tJOIN pref_types PT ON PT.pref_type_id = P.pref_type_id\n\t\t\tJOIN pref_weights PW ON PW.pref_type_id = P.pref_type_id\n\t\t\tLEFT JOIN choices Ch ON Ch.mentor_id = M.mentor_id AND Ch.weight_id = PW.pref_id","def multi_filter(self, characteristic_dict):\n\n corpus_copy = self.clone()\n corpus_copy.documents = []\n\n for metadata_field in characteristic_dict:\n if metadata_field not in self.metadata_fields:\n raise MissingMetadataError([metadata_field])\n\n for this_document in self.documents:\n add_document = True\n for metadata_field in characteristic_dict:\n if metadata_field == 'date':\n if this_document.date != int(characteristic_dict['date']):\n add_document = False\n else:\n if getattr(this_document, metadata_field) != characteristic_dict[metadata_field]:\n add_document = False\n if add_document:\n corpus_copy.documents.append(this_document)\n\n if not corpus_copy:\n # displays for possible errors in field.value\n err = f'This corpus is empty. You may have mistyped something.'\n raise AttributeError(err)\n\n return corpus_copy","def _calculate_feedback(self):\r\n age = self.age_field.value()\r\n height_cm = self.height_field.value()\r\n weight_kg = self.weight_field.value()\r\n body_fat = self.bodyFat_field.value()\r\n male = bool(self.gender_combobox.currentIndex())\r\n\r\n if self._mode != 'metric':\r\n height_cm *= 2.54\r\n weight_kg *= 0.454\r\n\r\n document = getWeightLogDocument(height_cm, weight_kg, age, body_fat, male, None, 1.2)\r\n self.feedback_field.setHtml(document)","def mutual_information(x, y, w):\r\n \r\n\r\n total_entropy = entropy(y,w)\r\n\r\n partitioned_x = partition(x)\r\n weighted_entropy = 0\r\n # calculate the weighted entropy over the partition of x\r\n vals,counts= np.unique(x,return_counts=True)\r\n for key in partitioned_x:\r\n weighted_entropy += np.sum([(np.sum(w[partitioned_x[key]])/np.sum(w)) * entropy(y[partitioned_x[key]],w[partitioned_x[key]])])\r\n\r\n information_gain = total_entropy - weighted_entropy\r\n return information_gain"],"string":"[\n \"def process_citation(style, reference_list, citation):\\n processed_citation = Element(\\\"span\\\", attrib={\\\"class\\\":\\\"citation\\\"})\\n\\n for reference in reference_list:\\n citeref = SubElement(processed_citation, \\\"span\\\")\\n for style_node in style.citation.layout:\\n process_node(citeref, style_node, style.macros, reference) \\n\\n return(processed_citation)\",\n \"def calculate_name_index_data(refdict: dict, citelist: list, specific_names: list) -> Tuple[list, dict, dict, dict,\\n dict, dict, dict, dict,\\n dict, dict]:\\n name_table = create_name_table(citelist)\\n unique_names = list()\\n nameset = set()\\n # total_binomial_year_cnts = {}\\n total_binomial_year_cnts = collections.Counter()\\n for c in citelist:\\n if c.name != \\\".\\\":\\n clean = clean_name(c.name)\\n if not clean.lower() in nameset:\\n nameset |= {clean.lower()}\\n unique_names.append(clean)\\n y = refdict[c.cite_key].year()\\n if y is not None:\\n total_binomial_year_cnts.update([y])\\n # if y in total_binomial_year_cnts:\\n # total_binomial_year_cnts[y] += 1\\n # else:\\n # total_binomial_year_cnts[y] = 1\\n unique_names.sort(key=lambda s: s.lower())\\n\\n # identify genera used per paper\\n genera_per_paper = {}\\n for c in citelist:\\n if c.name != \\\".\\\":\\n if c.cite_key not in genera_per_paper:\\n genera_per_paper[c.cite_key] = set()\\n genera_set = genera_per_paper[c.cite_key]\\n genera_set |= {extract_genus(clean_name(c.name))}\\n genus_cnts = {}\\n for c in genera_per_paper:\\n y = refdict[c].year()\\n if y is not None:\\n if init_data().start_year <= y <= init_data().current_year:\\n genera_set = genera_per_paper[c]\\n for genus in genera_set:\\n genus = clean_genus(genus)\\n if genus != \\\"\\\":\\n genus_cnts.setdefault(genus, {y: 0 for y in range(init_data().start_year,\\n init_data().current_year + 1)})[y] += 1\\n # if genus not in genus_cnts:\\n # genus_cnts[genus] = {y: 0 for y in range(init_data().start_year,\\n # init_data().current_year + 1)}\\n # gcnts = genus_cnts[genus]\\n # gcnts[y] += 1\\n\\n binomial_usage_cnts_by_year = {}\\n binomial_location_applications = {}\\n binomial_usage_cnts = {}\\n for name in unique_names:\\n binomial_usage_cnts_by_year[name], tmptotal = calculate_binomial_yearly_cnts(name, refdict, citelist)\\n if tmptotal > 0:\\n binomial_usage_cnts[name] = tmptotal\\n binomial_location_applications[name] = calculate_binomial_locations(name, citelist)\\n\\n # specific_year_cnts = {}\\n specific_year_cnts = collections.Counter()\\n specific_usage_cnts_by_year = {}\\n specific_location_applications = {}\\n specific_usage_cnts = {}\\n for name in specific_names:\\n (specific_usage_cnts_by_year[name.name],\\n tmptotal) = calculate_specific_name_yearly_cnts(name, unique_names, binomial_usage_cnts_by_year)\\n if tmptotal > 0:\\n specific_usage_cnts[name.name] = tmptotal\\n specific_location_applications[name] = calculate_specific_locations(name, unique_names,\\n binomial_location_applications)\\n tmpkey = name.priority_source\\n if tmpkey != \\\".\\\":\\n y = refdict[tmpkey].year()\\n if y is not None:\\n specific_year_cnts.update([y])\\n # if y in specific_year_cnts:\\n # specific_year_cnts[y] += 1\\n # else:\\n # specific_year_cnts[y] = 1\\n return (unique_names, binomial_usage_cnts_by_year, specific_usage_cnts_by_year, genus_cnts,\\n total_binomial_year_cnts, name_table, specific_location_applications, binomial_location_applications,\\n binomial_usage_cnts, specific_usage_cnts)\",\n \"def calc_weights(query_freq, posting_list, N):\\n \\n # type(weight_query) == {term: tf-idf == ltc }\\n weight_query = {}\\n # type(doc_dict) == {doc_id: {term: tf-idf == lnc }}\\n doc_dict = {}\\n\\n for term in query_freq.keys():\\n # query = ltc\\n query_tf = 1 + math.log( query_freq[term] )\\n # print(query_tf)\\n term_df = posting_list[term][0]\\n # print(term_df)\\n query_idf = math.log(N / term_df)\\n # print(query_idf)\\n weight_query[term] = query_tf * query_idf\\n \\n # document = lnc\\n weight_doc = {}\\n for doc_id in posting_list[term][1].keys():\\n doc_tf = 1 + math.log( posting_list[term][1][doc_id] )\\n doc_df = 1 # no\\n weight_doc[term] = doc_tf * doc_df\\n if doc_id in doc_dict:\\n doc_dict[doc_id][term] = weight_doc[term]\\n else:\\n doc_dict[doc_id] = {}\\n doc_dict[doc_id][term] = weight_doc[term]\\n\\n # normalize query, c = euclidean\\n divide_by = math.sqrt( sum([ x**2 for x in weight_query.values() ]) )\\n for term, weight in weight_query.items():\\n weight_query[term] = weight / divide_by\\n # normalize docs, c = euclidean\\n for doc_id, weight_doc in doc_dict.items():\\n divide_by = math.sqrt( sum( [ x**2 for x in weight_doc.values() ] ) )\\n for term, weight in weight_doc.items():\\n doc_dict[doc_id][term] = weight / divide_by\\n\\n return (weight_query, doc_dict)\",\n \"def get_citation_informations(recid_list, config):\\n begin_time = os.times()[4]\\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\\n d_records_s = {} #dict of recid -> this_records_publication_info\\n citation_informations = []\\n\\n write_message(\\\"config function \\\"+config.get(\\\"rank_method\\\", \\\"function\\\"), verbose=9)\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attribute called function\\\", alert_admin=True)\\n #we cannot continue\\n return [ {}, {}, {}, {} ]\\n record_pri_number_tag = \\\"\\\"\\n try:\\n record_pri_number_tag = config.get(function, \\\"primary_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attribute primary_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n record_add_number_tag = \\\"\\\"\\n try:\\n record_add_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"additional_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute additional_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_number_tag = \\\"\\\"\\n try:\\n reference_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_tag = \\\"\\\"\\n try:\\n reference_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_pubinfo\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\\n #037a: contains (often) the \\\"hep-ph/0501084\\\" tag of THIS record\\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\\n #088a: additional short identifier for the record\\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\\n p_reference_tag = tagify(parse_tag(reference_tag))\\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\\n #fields needed to construct the pubinfo for this record\\n publication_pages_tag = \\\"\\\"\\n publication_year_tag = \\\"\\\"\\n publication_journal_tag = \\\"\\\"\\n publication_volume_tag = \\\"\\\"\\n publication_format_string = \\\"p v (y) c\\\"\\n try:\\n tag = config.get(function, \\\"pubinfo_journal_page\\\")\\n publication_pages_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_year\\\")\\n publication_year_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_title\\\")\\n publication_journal_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_volume\\\")\\n publication_volume_tag = tagify(parse_tag(tag))\\n publication_format_string = config.get(function, \\\"pubinfo_journal_format\\\")\\n except:\\n pass\\n\\n #print values for tags for debugging\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"tag values\\\")\\n write_message(\\\"p_record_pri_number_tag \\\"+str(p_record_pri_number_tag))\\n write_message(\\\"p_reference_tag \\\"+str(p_reference_tag))\\n write_message(\\\"publication_journal_tag \\\"+str(publication_journal_tag))\\n write_message(\\\"publication_format_string is \\\"+publication_format_string)\\n done = 0 #for status reporting\\n numrecs = len(recid_list)\\n\\n # perform quick check to see if there are some records with\\n # reference tags, because otherwise get.cit.inf would be slow even\\n # if there is nothing to index:\\n if run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_tag[0:2],\\n (p_reference_tag,)) or \\\\\\n run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_number_tag[0:2],\\n (p_reference_number_tag,)):\\n for recid in recid_list:\\n if (done % 10 == 0):\\n task_sleep_now_if_required()\\n #in fact we can sleep any time here\\n\\n if (done % 1000 == 0):\\n mesg = \\\"get cit.inf done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n if recid in INTBITSET_OF_DELETED_RECORDS:\\n # do not treat this record since it was deleted; we\\n # skip it like this in case it was only soft-deleted\\n # e.g. via bibedit (i.e. when collection tag 980 is\\n # DELETED but other tags like report number or journal\\n # publication info remained the same, so the calls to\\n # get_fieldvalues() below would return old values)\\n continue\\n\\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n\\n l_report_numbers = pri_report_numbers\\n l_report_numbers.extend(add_report_numbers)\\n d_reports_numbers[recid] = l_report_numbers\\n\\n if reference_report_numbers:\\n write_message(str(recid)+\\\"'s report number values \\\"+str(l_report_numbers), verbose=9)\\n d_references_report_numbers[recid] = reference_report_numbers\\n\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n if references_s:\\n write_message(str(recid)+\\\"'s \\\"+str(p_reference_tag)+\\\" values \\\"+str(references_s), verbose=9)\\n d_references_s[recid] = references_s\\n\\n #get a combination of\\n #journal vol (year) pages\\n if publication_pages_tag and publication_journal_tag and \\\\\\n publication_volume_tag and publication_year_tag and publication_format_string:\\n tagsvalues = {} #we store the tags and their values here\\n #like c->444 y->1999 p->\\\"journal of foo\\\",v->20\\n tagsvalues[\\\"p\\\"] = \\\"\\\"\\n tagsvalues[\\\"y\\\"] = \\\"\\\"\\n tagsvalues[\\\"c\\\"] = \\\"\\\"\\n tagsvalues[\\\"v\\\"] = \\\"\\\"\\n tmp = get_fieldvalues(recid, publication_journal_tag)\\n if tmp:\\n tagsvalues[\\\"p\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_volume_tag)\\n if tmp:\\n tagsvalues[\\\"v\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_year_tag)\\n if tmp:\\n tagsvalues[\\\"y\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_pages_tag)\\n if tmp:\\n #if the page numbers have \\\"x-y\\\" take just x\\n pages = tmp[0]\\n hpos = pages.find(\\\"-\\\")\\n if hpos > 0:\\n pages = pages[:hpos]\\n tagsvalues[\\\"c\\\"] = pages\\n #format the publ infostring according to the format\\n publ = \\\"\\\"\\n ok = 1\\n for i in range (0, len(publication_format_string)):\\n current = publication_format_string[i]\\n #these are supported\\n if current == \\\"p\\\" or current == \\\"c\\\" or current == \\\"v\\\" \\\\\\n or current == \\\"y\\\":\\n if tagsvalues[current]:\\n #add the value in the string\\n publ += tagsvalues[current]\\n else:\\n ok = 0\\n break #it was needed and not found\\n else:\\n publ += current #just add the character in the format string\\n if ok:\\n write_message(\\\"d_records_s (publication info) for \\\"+str(recid)+\\\" is \\\"+publ, verbose=9)\\n d_records_s[recid] = publ\\n else:\\n mesg = \\\"Warning: there are no records with tag values for \\\"\\n mesg += p_reference_number_tag+\\\" or \\\"+p_reference_tag+\\\". Nothing to do.\\\"\\n write_message(mesg)\\n\\n mesg = \\\"get cit.inf done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n citation_informations.append(d_reports_numbers)\\n citation_informations.append(d_references_report_numbers)\\n citation_informations.append(d_references_s)\\n citation_informations.append(d_records_s)\\n end_time = os.times()[4]\\n write_message(\\\"Execution time for generating citation info from record: %.2f sec\\\" % \\\\\\n (end_time - begin_time))\\n return citation_informations\",\n \"def fill(self):\\n if self.source == 'citations':\\n url = self._scholarly.URLS(\\\"CITATIONPUB\\\").format(self.id_citations)\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\\n\\n if soup.find('a', class_='gsc_vcd_title_link'):\\n self.bib['url'] = soup.find(\\n 'a', class_='gsc_vcd_title_link')['href']\\n\\n for item in soup.find_all('div', class_='gs_scl'):\\n key = item.find(class_='gsc_vcd_field').text\\n val = item.find(class_='gsc_vcd_value')\\n if key == 'Authors':\\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\\n elif key == 'Journal':\\n self.bib['journal'] = val.text\\n elif key == 'Volume':\\n self.bib['volume'] = val.text\\n elif key == 'Issue':\\n self.bib['number'] = val.text\\n elif key == 'Pages':\\n self.bib['pages'] = val.text\\n elif key == 'Publisher':\\n self.bib['publisher'] = val.text\\n elif key == 'Publication date':\\n self.bib['year'] = arrow.get(val.text).year\\n elif key == 'Description':\\n if val.text[0:8].lower() == 'abstract':\\n val = val.text[9:].strip()\\n self.bib['abstract'] = val\\n elif key == 'Total citations':\\n self.id_scholarcitedby = re.findall(\\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\\n\\n # number of citation per year\\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\\n self.cites_per_year = dict(zip(years, cites))\\n\\n if soup.find('div', class_='gsc_vcd_title_ggi'):\\n self.bib['eprint'] = soup.find(\\n 'div', class_='gsc_vcd_title_ggi').a['href']\\n self._filled = True\\n\\n elif self.source == 'scholar':\\n self.bib['add_to_lib'] = self.url_add_sclib\\n\\n try:\\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\\n bibtex = bibtex.find('pre').string\\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\\n self.bib['author_count'] = str(\\n len(self.bib['author'].split('and')))\\n self.bib['age'] = str(\\n int(date.today().year) - int(self.bib['year']))\\n except:\\n # did not find year\\n pass\\n\\n self._filled = True\\n return self\",\n \"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\\n i = 0 #just for debugging ..\\n #get the tags for main author, coauthors, ext authors from config\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n for t in tags:\\n try:\\n dummy = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_selfcitdict\\n\\n r_mainauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"first_author\\\")\\n r_coauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"additional_author\\\")\\n r_extauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"alternative_author_name\\\")\\n #parse the tags\\n mainauthortag = tagify(parse_tag(r_mainauthortag))\\n coauthortag = tagify(parse_tag(r_coauthortag))\\n extauthortag = tagify(parse_tag(r_extauthortag))\\n\\n selfcites = initial_selfcitdict\\n for k in new_record_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Selfcites done \\\"+str(i)+\\\" of \\\"+str(len(new_record_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i+1\\n #get the author of k\\n authorlist = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authorlist.append(coauthl)\\n authorlist.append(extauthl)\\n #author tag\\n #print \\\"record \\\"+str(k)+\\\" by \\\"+str(authorlist)\\n #print \\\"is cited by\\\"\\n #get the \\\"x-cites-this\\\" list\\n if citationdic.has_key(k):\\n xct = citationdic[k]\\n for c in xct:\\n #get authors of c\\n cauthorlist = get_fieldvalues(c, mainauthortag)\\n coauthl = get_fieldvalues(c, coauthortag)\\n extauthl = get_fieldvalues(c, extauthortag)\\n cauthorlist.extend(coauthl)\\n cauthorlist.extend(extauthl)\\n #print str(c)+\\\" by \\\"+str(cauthorlist)\\n for ca in cauthorlist:\\n if (ca in authorlist):\\n #found!\\n if selfcites.has_key(k):\\n val = selfcites[k]\\n #add only if not there already\\n if val:\\n if not c in val:\\n val.append(c)\\n selfcites[k] = val\\n else:\\n #new key for selfcites\\n selfcites[k] = [c]\\n\\n mesg = \\\"Selfcites done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return selfcites\",\n \"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\\n\\n #sorry bout repeated code to get the tags\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n tagvals = {}\\n for t in tags:\\n try:\\n x = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n tagvals[t] = x\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_author_dict\\n\\n #parse the tags\\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"mainauthortag \\\"+mainauthortag)\\n write_message(\\\"coauthortag \\\"+coauthortag)\\n write_message(\\\"extauthortag \\\"+extauthortag)\\n\\n author_cited_in = initial_author_dict\\n if citedbydict:\\n i = 0 #just a counter for debug\\n write_message(\\\"Checking records referred to in new records\\\")\\n for u in updated_redic_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Author ref done \\\"+str(i)+\\\" of \\\"+str(len(updated_redic_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n if citedbydict.has_key(u):\\n these_cite_k = citedbydict[u]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n authors = get_fieldvalues(u, mainauthortag)\\n coauthl = get_fieldvalues(u, coauthortag)\\n extauthl = get_fieldvalues(u, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author ref done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #go through the dictionary again: all keys but search only if new records are cited\\n write_message(\\\"Checking authors in new records\\\")\\n i = 0\\n for k in citedbydict.keys():\\n if (i % 1000 == 0):\\n mesg = \\\"Author cit done \\\"+str(i)+\\\" of \\\"+str(len(citedbydict.keys()))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n these_cite_k = citedbydict[k]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n #do things only if these_cite_k contains any new stuff\\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\\n if intersec_list:\\n authors = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author cit done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return author_cited_in\",\n \"def compute_applied_name_contexts(citelist: list) -> None:\\n for i, cite in enumerate(citelist):\\n if (cite.context == \\\"specimen\\\") or (cite.context == \\\"location\\\") or (cite.context == \\\"sequence\\\"):\\n cite.applied_cites = {cite}\\n elif cite.context == \\\"citation\\\":\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n if (tmp.context == \\\"specimen\\\") or (tmp.context == \\\"location\\\") or (tmp.context == \\\"sequence\\\"):\\n cite.applied_cites |= {tmp}\\n if len(cite.applied_cites) == 0:\\n cite.applied_cites = None\\n\\n # for testing purposes only\\n # if cite.applied_cites is None:\\n # print(cite.cite_key, cite.name_key, cite.context, \\\"No application\\\")\\n # else:\\n # for c in cite.applied_cites:\\n # print(cite.cite_key, cite.name_key, cite.context, c.cite_key, c.name_key, c.context)\\n # input()\",\n \"def age_of_cited_work(art_map, art_cit_map, cit_map):\\n logging.debug('============== IN age_of_cited_work: ================')\\n age = {}\\n for article in art_cit_map:\\n age_list = []\\n logging.debug('========== ARTICLE: ============')\\n logging.debug(article)\\n for citation in art_cit_map[article]:\\n logging.debug('========== CITATION: ============')\\n logging.debug(citation)\\n logging.debug(art_map[article]['pub_year'])\\n if citation in cit_map:\\n logging.debug(cit_map[citation]['pub_year'])\\n age_list.append(\\n int(art_map[article]['pub_year']) - int(cit_map[citation][\\n 'pub_year']))\\n age[art_map[article]['pub_year']] = age_list\\n logging.debug('========== AGE OF CITED WORK: ============')\\n logging.debug(age)\\n return sort_map_by_keys(age)\",\n \"def __citationsFromFile(file):\\n\\n # Tokens for parsing\\n titleToken = '#*'\\n indexToken = '#index'\\n citationToken = '#%'\\n\\n # Predicates for error checking\\n noneNone = lambda *items: all([item is not None for item in items])\\n allNone = lambda *items: all([item is None for item in items])\\n\\n # Next entry data\\n title = None\\n index = None\\n citations = []\\n\\n # Basic stats\\n withCitations = 0\\n withoutCitations = 0\\n totalPapers = 0\\n\\n for line in file:\\n line = line.strip()\\n\\n # Parse entry, enforcing that data appears in title -> index -> citations order\\n if line.startswith(titleToken):\\n assert allNone(title, index) and len(citations) == 0\\n title = line[len(titleToken):].strip('.')\\n\\n elif line.startswith(indexToken):\\n assert noneNone(title) and allNone(index) and len(citations) == 0\\n index = int(line[len(indexToken):])\\n\\n elif line.startswith(citationToken):\\n assert noneNone(title, index)\\n newCitationId = int(line[len(citationToken):])\\n assert newCitationId >= 0\\n citations.append(newCitationId)\\n\\n elif len(line) == 0:\\n totalPapers += 1\\n\\n # Yield this entry if it has any citations,\\n if noneNone(title, index):\\n if len(citations) > 0:\\n withCitations += 1\\n yield title, index, citations\\n else:\\n withoutCitations += 1\\n\\n title = None\\n index = None\\n citations = []\\n\\n # Output some basic statistics about papers with/without citations\\n withCitationsPercent = 100.0 * (float(withCitations) / totalPapers)\\n withoutCitationsPercent = 100.0 * (float(withoutCitations) / totalPapers)\\n print \\\"\\\\n\\\\nTotal Papers: %d\\\" % totalPapers\\n print \\\" With References: %d (%2.2f%%)\\\\n Without References: %d (%2.2f%%)\\\" % (\\n withCitations, withCitationsPercent, withoutCitations, withoutCitationsPercent\\n )\",\n \"def article_stats(s_list,subject):\\n word_dicts = {languages[0]:{},languages[1]:{}}\\n stats = [subject]\\n for i,article in enumerate(s_list):\\n word_dicts[languages[i]] = get_words(article)\\n wc = total_wc(word_dicts[languages[i]])\\n stats.append((wc,avg_word_length(article, wc),avg_par_length(article)))\\n stats.append(compute_similarity(word_dicts[languages[0]],word_dicts[languages[1]]))\\n return stats\",\n \"def score_ngrams(ngram_docs, citations, pc=0.9):\\n\\n scores = dict()\\n for ng in ngram_docs:\\n # Build term citation graph.\\n gt_nodes = ngram_docs[ng]\\n gt_edges = {}\\n for node in gt_nodes:\\n dests = citations[node].intersection(gt_nodes)\\n if dests:\\n gt_edges[node] = dests\\n\\n # Terms and sets for the equations\\n n = float(len(citations))\\n na = float(len(gt_nodes))\\n nca = float(len(list(gt_edges.keys())))\\n connected_nodes = list(gt_edges.keys())\\n other_nodes = gt_nodes - set(gt_edges.keys())\\n\\n # Skip ones where there aren't enough nodes to count.\\n if na < 4.0:\\n continue\\n\\n # Likelihood of observing a tightly connected term citation graph\\n # if the term is a topic.\\n oh1 = nca * log(pc) + (na - nca) * log(1 - pc)\\n\\n # Likelihood of observing a tightly connected term citation graph\\n # if the term is not a topic.\\n term1 = 0.0\\n for i in connected_nodes:\\n li = len(citations[i])\\n term1 += log(1.0 - (1.0 - (na - 1.0)/(n - 1.0))**li)\\n term2 = 0.0\\n for i in other_nodes:\\n li = len(citations[i])\\n term2 += li * log(1.0 - (na - 1.0)/(n - 1.0))\\n oh0 = term1 + term2\\n\\n scores[ng] = oh1 - oh0\\n return scores\",\n \"def collect_year_and_holder(detected_copyright):\\n for copyr in detected_copyright:\\n if isinstance(copyr, nltk_tree_Tree):\\n # logger.debug('n: ' + str(copyr))\\n node_text = CopyrightDetector_as_str(copyr)\\n copyr_label = copyr.label()\\n if 'YR-RANGE' in copyr_label:\\n years_append(refine_date(node_text))\\n elif 'NAME' == copyr_label or 'COMPANY' in copyr_label:\\n # FIXME : this would wreck things like 23andme\\n # where a company name contains numbers\\n holders_append(refine_author(node_text))\\n # logger.debug('CopyrightDetector: node_text: ' + node_text)\\n collect_year_and_holder(copyr)\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def centrality_weights_retrieval(weighted=True):\\n results = {'_is_weighted':weighted, '_evaluation':'retrieval'}\\n graph_metrics = graph_representation.get_metrics(weighted)\\n\\n print '> Reading cases..'\\n descriptions_path = '../data/air/problem_descriptions_dependencies'\\n description_texts, labels = data.read_files(descriptions_path)\\n\\n solutions_path = '../data/air/solutions_preprocessed'\\n solution_texts, labels = data.read_files(solutions_path)\\n solution_vectors = freq_representation.text_to_vector(solution_texts, freq_representation.FrequencyMetrics.TF_IDF)\\n\\n rep = {}\\n for metric in graph_metrics:\\n rep[metric] = []\\n\\n print '> Creating graph representations..'\\n for i, text in enumerate(description_texts):\\n if i%10==0: print ' ',str(i)+'/'+str(len(description_texts))\\n g = graph_representation.construct_dependency_network(text, weighted=weighted)\\n for metric in graph_metrics:\\n d = graph_representation.graph_to_dict(g, metric)\\n rep[metric].append(d)\\n g = None # just to make sure..\\n if i%100==0:\\n if weighted:\\n postfix = '_weighted'\\n else:\\n postfix = '_unweighted'\\n data.pickle_to_file(rep, 'output/dependencies/exp1_retr_tmp_'+str(i)+'_'+postfix)\\n\\n print '> Creating vector representations..'\\n for metric in graph_metrics:\\n rep[metric] = graph_representation.dicts_to_vectors(rep[metric])\\n\\n print '> Evaluating..'\\n for metric in graph_metrics:\\n vectors = rep[metric]\\n score = evaluation.evaluate_retrieval(vectors, solution_vectors)\\n print ' ', metric, score\\n results[metric] = score\\n\\n if weighted:\\n postfix = '_weighted'\\n else:\\n postfix = '_unweighted'\\n data.pickle_to_file(results, 'output/dependencies/exp1_retr'+postfix)\\n\\n pp.pprint(results)\\n return results\",\n \"def extract_information(preprocessed_sentences):\\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\\n\\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\\n\\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\\n\\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\\n\\n preperation_info = extract_preparation_information(parsed)\\n prep_gpes = preperation_info[0].most_common(3)\\n\\n restore_info = extract_restoration_information(parsed)\\n\\n landfall_info = extract_landfall_information(parsed)\\n\\n wind_info = extract_wind_information(quantities)\\n rain_info = extract_rain_information(quantities)\\n size_info = extract_size_information(parsed)\\n\\n # formation_info = extract_formation_info(parsed)\\n death_info = extract_death_damages_info(parsed)\\n\\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\\n most_common(1)[0][0]))\\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))\",\n \"def buildDict(self, dict):\\n self.all_words = set(dict)\\n self.wc_dict = collections.defaultdict(int)\\n for w in dict:\\n for wc in self.get_wildcards(w):\\n self.wc_dict[wc] += 1\",\n \"def cite_report(self):\\n\\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\\n print(\\\"number of citations = \\\", num_cites)\\n\\n for n, p in enumerate(self.mypapers):\\n clean_tags = re.compile(\\\"<.*?>\\\")\\n title = re.sub(clean_tags, \\\"\\\", p.title[0])\\n tt = textwrap.wrap(title, 60)\\n print(f\\\"{n+1:3} | {p.citation_count:4} {tt[0]}\\\")\\n if len(tt) > 1:\\n for line in tt[1:]:\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", line))\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", \\\"\\\"))\",\n \"def calculate_weighted_results():\\n pass\",\n \"def score_candidates(self,\\n cand_list: List[Union[CandidateEntry, Tuple[str, float]]],\\n query_info_obj_or_dict: Union[DataEntryFields, dict]) -> Dict[str, float]:\\n query_text = self.get_query_text(query_info_obj_or_dict)\\n\\n if self.text_proc_obj_query is not None:\\n query_text = self.text_proc_obj_query(query_text)\\n\\n query_text = self.handle_case(query_text)\\n query_toks = query_text.split()\\n query_terms_idfs = {w: self.calc_idf(w) for w in set(query_toks)}\\n\\n res = {}\\n\\n for doc_id, score in cand_list:\\n doc_text = self.fwd_indx.get_doc_text(doc_id)\\n if self.text_proc_obj_doc is not None:\\n doc_text = self.text_proc_obj_doc(doc_text)\\n doc_text = self.handle_case(doc_text)\\n doc_toks = doc_text.split()\\n doc_len = len(doc_toks)\\n counts = Counter(doc_toks)\\n score = 0\\n for qterm in query_toks:\\n tf = counts[qterm]\\n if tf > 0:\\n qidf = query_terms_idfs[qterm]\\n norm_tf = (tf * (self.k1 + 1)) / \\\\\\n (tf + self.k1 * (1 - self.b + self.b * doc_len * self.inv_avg_doc_len))\\n score += qidf * norm_tf\\n\\n res[doc_id] = score\\n\\n return res\",\n \"def learn_distributions(file_lists_by_category):\\n ### TODO: Write your code here\\n\\n #get word frequncies in each email category\\n #key:word, value: number of occurences in this email loader\\n spam_dict = util.get_word_freq(file_lists_by_category[0])\\n ham_dict = util.get_word_freq(file_lists_by_category[1])\\n\\n #get total length of each email loader\\n spam_length = sum(spam_dict.values())\\n ham_length = sum(ham_dict.values())\\n\\n #get the length of the dictionary: D\\n dict_D = util.Counter()\\n for key in spam_dict:\\n dict_D[key] += spam_dict[key]\\n for key in ham_dict:\\n dict_D[key] += ham_dict[key]\\n D = len(dict_D)\\n\\n spam_distribution = {}\\n ham_distribution = {}\\n #get the distributions of two email loaders\\n for i in dict_D:\\n spam_distribution[i] = (spam_dict[i] + 1) / (D + spam_length)\\n\\n for i in dict_D:\\n ham_distribution[i] = (ham_dict[i] + 1) / (D + ham_length)\\n #create the required tuple\\n probabilities_by_category = (spam_distribution, ham_distribution)\\n return probabilities_by_category\",\n \"def _get_graph_based_ic_dictionary(self):\\n\\n\\t\\t# TODO find the literature reference or presentation where this equation is from instead of just the presentation.\\n\\n\\t\\t#ic_dict = {}\\n\\t\\t#num_terms_in_ontology = len(self)\\n\\t\\t#for term in self.terms():\\n\\t\\t#\\tdepth = self._depth_dict[term.id]\\n\\t\\t#\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t#\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t#\\tic_dict[term.id] = ic_value\\n\\t\\t#return(ic_dict)\\n\\n\\n\\t\\t# Getting the information content of each term in the ontology based on graph structure.\\n\\t\\tic_dict = {}\\n\\t\\tnum_terms_in_ontology = len(self)\\n\\t\\tfor term in self.terms():\\n\\t\\t\\tdepth = self._depth_dict[term.id]\\n\\t\\t\\tnum_descendants = len(list(term.subclasses(with_self=False)))\\n\\t\\t\\tic_value = float(depth)*(1-(math.log(num_descendants+1)/math.log(num_terms_in_ontology)))\\n\\t\\t\\tic_dict[term.id] = ic_value\\n\\n\\n\\t\\t# Converting to weights based on information content rather than raw value.\\n\\t\\tic_dict_as_weights = {}\\n\\t\\tic_values = ic_dict.values()\\n\\t\\tmin_ic = min(ic_values)\\n\\t\\tmax_ic = max(ic_values)\\n\\t\\tnew_max = 1.00\\n\\t\\tnew_min = 0.00\\n\\t\\tfor k,v in ic_dict.items():\\n\\t\\t\\told_range = max_ic-min_ic\\n\\t\\t\\tnew_range = new_max-new_min\\n\\t\\t\\tnew_value = (((v - min_ic) * new_range) / old_range) + new_min\\n\\t\\t\\tic_dict_as_weights[k] = new_value\\n\\n\\t\\treturn(ic_dict, ic_dict_as_weights)\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def citation_validation(self):\\n for file in filter(lambda x: x.startswith('citation'), listdir(bs_directory)):\\n print \\\"Processing\\\", file\\n bs = BeautifulSoup(open(file), 'xml')\\n # find ground_truth file\\n ground_truth = BeautifulSoup(open(self.ground_truth_directory + file.split(\\\".\\\")[0] + '.xml'), 'xml')\\n self.__citation_correction(ground_truth, bs)\\n file = open(self.citation_output + file, \\\"wb\\\")\\n file.write(bs.prettify().encode('utf-8'))\",\n \"def compute_species_from_citation_linking(citelist: list) -> None:\\n # for i, cite in enumerate(tqdm(citelist)):\\n unrecorded_xrefs = []\\n recorded_refs = set()\\n for i, cite in enumerate(citelist):\\n recorded_refs.add(cite.cite_key)\\n if cite.actual == \\\"=\\\":\\n cname = \\\"\\\"\\n crossnames = collections.Counter()\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n cname = tmp.name\\n crossnames.update([tmp.actual])\\n if len(crossnames) == 0:\\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\\n elif len(crossnames) == 1:\\n cite.actual = list(crossnames.keys())[0]\\n else:\\n # find name(s) with largest count\\n mcnt = max(crossnames.values())\\n keylist = []\\n for key in crossnames:\\n if crossnames[key] == mcnt:\\n keylist.append(key)\\n\\n if len(keylist) == 1:\\n cite.actual = keylist[0]\\n else:\\n cite_name = cite.name.lower()\\n while cite_name.find(\\\" \\\") > -1:\\n cite_name = cite_name[cite_name.find(\\\" \\\")+1:]\\n while cname.find(\\\" \\\") > -1:\\n cname = cname[cname.find(\\\" \\\")+1:]\\n\\n if cite_name in keylist:\\n cite.actual = cite_name\\n elif cname in keylist:\\n cite.actual = cname\\n else:\\n cite.actual = keylist[0]\\n\\n if cite.name_note == \\\".\\\":\\n cite.name_note = \\\"in part\\\"\\n else:\\n cite.name_note = \\\"in part; \\\" + cite.name_note\\n\\n for x in unrecorded_xrefs:\\n if x[1] in recorded_refs:\\n report_error(\\\"Reference {} ({}) does not appear until after citation from {} ({})\\\".format(x[1], x[3],\\n x[0], x[2]))\",\n \"def addCowWeight(list, cows):\\r\\n sum = 0.0\\r\\n for key in list:\\r\\n sum += cows[key]\\r\\n return sum\",\n \"def extract_citations(tree):\\n citations_text = tree.xpath('//form/h2[@class=\\\"head\\\"]/text()')[0]\\n n_citations = re.sub(\\\"Is Cited by the Following \\\", \\\"\\\", citations_text).split(' ')[0]\\n try:\\n n_citations = int(n_citations)\\n except:\\n n_citations = 0\\n return n_citations\",\n \"def return_dict_of_assigned_citations_classifications(self):\\n for citation_instance in self.array_of_citations:\\n citation_string = \\\"\\\"\\n citation_string = citation_string.join(\\n citation_instance.get_author()) + \\\\\\n citation_instance.get_title()\\\\\\n + citation_instance.get_journal()\\n max_keyword_matches = 0\\n for key in self.dict_of_keywords:\\n current_key_matches = 0\\n for keyword in self.dict_of_keywords.get(key):\\n pattern = re.compile(keyword)\\n if pattern.search(citation_string):\\n current_key_matches += 1\\n if current_key_matches > max_keyword_matches:\\n max_keyword_matches = current_key_matches\\n citation_instance.set_classification(key)\\n return self.__generate_dict_of_keys_to_classification__()\",\n \"def analyzeWeights(self, occludedPortions):\\r\\n\\t\\treturn None\",\n \"def recursion_search_citations(self, paper_pmid, k):\\n if k == 0: return\\n print('recursion degree = ' + str(k))\\n original_paper = self.papers_dict[paper_pmid]\\n if original_paper == None or original_paper.pm_cited == None: return\\n print('paper ' + original_paper.pmid + ' has ' + str(len(original_paper.pm_cited)) + ' citations')\\n for citing_paper_pmid in original_paper.pm_cited:\\n citing_paper = self.paper_builder.build_paper(citing_paper_pmid)\\n if citing_paper_pmid not in self.papers_dict and citing_paper_pmid not in self.csv_papers_dict:\\n self.papers_dict[citing_paper_pmid] = citing_paper\\n self.recursion_search_citations(citing_paper.pmid, k - 1)\\n else:\\n citing_paper = self.papers_dict[citing_paper_pmid]\\n #add citation info\\n citation_year_gap = str(int(citing_paper.year) - int(original_paper.year))\\n if citation_year_gap in original_paper.citations:\\n (sum, count) = original_paper.citations[citation_year_gap]\\n original_paper.citations[citation_year_gap] = (sum + citing_paper.h_index, count +1)\\n else:\\n original_paper.citations[citation_year_gap]=(citing_paper.h_index, 1)\\n citing_paper.add_to_pm_cite(paper_pmid)\\n citation_weighted_avg = 0\\n gap_sum = 0\\n for year_gap, (sum, counter) in original_paper.citations.items():\\n weight = 20 - int(year_gap)\\n citation_weighted_avg += weight * (sum/counter)\\n gap_sum += weight\\n original_paper.citation_weighted_avg = citation_weighted_avg/gap_sum\",\n \"def process(list_, dict_, keyword):\\n if len(list_) == 4:\\n name, val, type_, frac_ = list_[0], list_[1], list_[2], list_[3]\\n elif list_[0] == 'direc':\\n name, val = list_[0], [list_[i] for i in range(len(list_)) if i > 0]\\n else:\\n name, val = list_[0], list_[1]\\n\\n if name not in dict_[keyword].keys() and name in ['coeff']:\\n dict_[keyword][name] = []\\n if keyword in ['TREATED', 'UNTREATED', 'COST'] and 'types' not in dict_[keyword].keys():\\n dict_[keyword]['types'] = []\\n if keyword in ['TREATED', 'UNTREATED', 'COST']:\\n if len(list_) == 4:\\n dict_[keyword]['types'] += [[type_, float(frac_)]]\\n else:\\n dict_[keyword]['types'] += ['nonbinary']\\n\\n # Type conversion\\n if name in ['agents', 'seed', 'maxiter', 'disp']:\\n val = int(val)\\n elif name in ['source', 'file', 'optimizer', 'start']:\\n val = str(val)\\n elif name in ['direc']:\\n val = list(val)\\n else:\\n val = float(val)\\n if name in ['coeff']:\\n dict_[keyword][name] += [val]\\n else:\\n dict_[keyword][name] = val\\n # Finishing.\\n return dict_\",\n \"def compute_dictionary(self, name, nb_process=1, process_number=0):\\n self.mvc_check()\\n with open(name, \\\"w\\\", encoding=\\\"utf-8\\\") as f:\\n count_word = 0\\n for word in self._distWords:\\n if count_word % nb_process == process_number:\\n dist, aligned_word = self.dist_word(word)\\n f.write(\\\"{} ; {} ; {} ; {} ; {} \\\\n\\\".format(dist, self._txt1[word].freq, self._txt2[aligned_word].freq, word, aligned_word))\\n count_word += 1\\n \\n # notify the views\\n for view in self._views:\\n view.notify_progress(1 / self._txt1.length)\",\n \"def score(self, doc, c):\\n # >>> YOUR ANSWER HERE\\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\\n sum = self.logprior[c]\\n words = doc.split()\\n for w in words:\\n if w in self.vocabulary:\\n sum += self.loglikelihood[(w, c)]\\n return sum\\n # >>> END YOUR ANSWER\",\n \"def get_complementary_infos_disc(browser, dict_disc: dict, url_disc: str) -> dict:\\n try:\\n browser.get_url(url_disc)\\n soup = browser.get_soup()\\n dict_complementary = {}\\n table = soup.find(\\\"table\\\", {\\\"class\\\": \\\"album_info\\\"})\\n table_descriptors = [x.find(\\\"th\\\").text.strip() for x in table.find_all(\\\"tr\\\")]\\n table_values = [\\n \\\" \\\".join(x.find(\\\"td\\\").text.strip().replace(\\\"\\\\n\\\", \\\", \\\").split())\\n for x in table.find_all(\\\"tr\\\")\\n ]\\n for d, v in zip(table_descriptors, table_values):\\n dict_complementary[d] = v\\n\\n try:\\n dict_complementary[\\\"Rank Overall\\\"] = [\\n x.replace(\\\"#\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\")\\n for y in dict_complementary[\\\"Ranked\\\"].split(\\\", \\\")\\n if \\\"overall\\\" in y\\n for x in y.split()\\n ][0]\\n except Exception as e:\\n logger.debug(\\\"No overall rank found : %s\\\", e)\\n try:\\n dict_complementary[\\\"Rank Year\\\"] = [\\n x.replace(\\\"#\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\")\\n for y in dict_complementary[\\\"Ranked\\\"].split(\\\", \\\")\\n if dict_disc[\\\"Year\\\"] in y\\n for x in y.split()\\n ][0]\\n except Exception as e:\\n logger.debug(\\\"No year rank found : %s\\\", e)\\n dict_disc.update(dict_complementary)\\n except Exception as e:\\n logger.error(e)\\n return dict_disc\",\n \"def __init__(self):\\n # self.organism = []\\n self.weighting_dict = defaultdict(list)\\n # self.codon_obj_dict = {}\\n self.codon_dict = {\\n 'UUU':'F','UUC':'F',\\n 'UUA':'L','UUG':'L','CUU':'L','CUC':'L','CUA':'L','CUG':'L',\\n 'AUU':'I','AUC':'I','AUA':'I',\\n 'AUG':'M',\\n 'GUU':'V', 'GUC':'V','GUA':'V','GUG':'V',\\n 'UCU':'S','UCC':'S','UCA':'S','UCG':'S',\\n 'CCU':'P','CCC':'P','CCA':'P','CCG':'P',\\n 'ACU':'T','ACC':'T','ACA':'T','ACG':'T',\\n 'GCU':'A','GCC':'A','GCA':'A','GCG':'A',\\n 'UAU':'Y','UAC':'Y',\\n 'UAA':'X','UAG':'X',\\n 'CAU':'H','CAC':'H',\\n 'CAA':'Q','CAG':'Q',\\n 'AAU':'N','AAC':'N',\\n 'AAA':'K','AAG':'K',\\n 'GAU':'D','GAC':'D',\\n 'GAA':'E','GAG':'E',\\n 'UGU':'C','UGC':'C',\\n 'UGA':'X',\\n 'UGG':'W',\\n 'CGU':'R','CGC':'R','CGA':'R','CGG':'R',\\n 'AGU':'S','AGC':'S',\\n 'AGA':'R','AGG':'R',\\n 'GGU':'G','GGC':'G', 'GGA':'G','GGG':'G'\\n }\",\n \"def doit():\\n myp = MyPapers()\\n cites = Cites(myp.mypapers)\\n #cites.cite_report()\\n cites.compare_and_update()\",\n \"def summarize_corpus():\\n\\t\\n\\t# get metadata\\n\\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \\\"metadata\\\", md_mode)\\n\\t\\n\\t# visualize some metadata\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"author-continent\\\")\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"author-country\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"language\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_hist\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_x\\\")\\n\\tvisualize_metadata.plot_pie(wdir, md_csv, \\\"subgenre\\\")\\n\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"gender\\\")\\n\\t\\n\\t# make some counts\\n\\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\\n\\tnum_texts = len(md_table)\\n\\t#num_language = len(md_table.groupby([\\\"language\\\"]))\\n\\t#num_continent = len(md_table.groupby([\\\"author-continent\\\"]))\\n\\t#num_countries = len(md_table.groupby([\\\"author-country\\\"]))\\n\\t#num_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_subgenre = len(md_table.groupby([\\\"subgenre\\\"]))\\n\\t#num_subgenre_x = len(md_table.groupby([\\\"subgenre_x\\\"]))\\n\\t#fr_subgenre_hist = md_table.groupby([\\\"subgenre_hist\\\"]).count()\\n\\t#num_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"historical\\\"]\\n\\t#num_not_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"not_historical\\\"]\\n\\t\\n\\t\\n\\td = {\\\"texts\\\":[num_texts], \\n\\t#\\\"languages\\\":[num_language],\\n\\t#\\\"continents\\\":[num_continent],\\n\\t#\\\"countries\\\":[num_countries],\\n\\t\\\"authors\\\":[num_authors],\\n\\t#\\\"subgenre_x\\\":[num_subgenre_x],\\n\\t\\\"subgenre\\\":[num_subgenre]}\\n\\t#\\\"num_historical\\\":[num_historical],\\n\\t#\\\"num_not_historical\\\":[num_not_historical]}\\n\\t\\n\\t\\n\\t\\n\\tcount_fr = pd.DataFrame(d)\\n\\tcount_fr.to_csv(os.path.join(wdir, \\\"corpus-description.csv\\\"), sep=\\\",\\\", header=True)\\n\\tprint(\\\"Done: summarize corpus\\\")\",\n \"def weigthIndex(index, nPages): \\n weighted_index = defaultdict(list)\\n for term, docs in index.items():\\n df = len(docs)\\n for url, count in docs:\\n weight = tf_idf(count, nPages, df)\\n weighted_index[term].append((url, weight))\\n return weighted_index\",\n \"def frequencyAnalysis(article, dictionary):\\r\\n string = article #Sets the string to the article\\r\\n string = re.sub(\\\"[^a-zA-Z0-9’\\\\s]+\\\",'', string) #Takes the articles and removes all characters appart from apostrophes, spaces, digits, and leters\\r\\n string = re.sub(\\\"’\\\", \\\"'\\\", string) #Replaces ’ with '\\r\\n string = string.lower() #Ensures that all the charcters are lower case\\r\\n stringList = string.split() #Takes the article and turns it into a list\\r\\n \\r\\n print(\\\"\\\\nConverted article to list\\\\n\\\")\\r\\n \\r\\n print(\\\"Starting frequency analysis\\\\n\\\")\\r\\n\\r\\n #Started the frequency anaylsis\\r\\n for word in stringList:\\r\\n if \\\"'s\\\" in word: #Done to remove extra keys in the dictionary, removes the possessive such that it counts \\\"Trump\\\" and \\\"Trump's\\\" as one word\\r\\n word = word[0:-2]\\r\\n elif \\\"s'\\\" in word:\\r\\n word = word[0:-1]\\r\\n if word != \\\"advertisement\\\":\\r\\n if word in dictionary:\\r\\n dictionary[word] +=1 #If it finds the word in the dictionary, the frequency has to increase by one\\r\\n else:\\r\\n dictionary[word] = 1 #If it finds a new word, it needs to add the word so the frequency is one\\r\",\n \"def dictionary(cleaned_data,threshold):\\n news = []\\n for date in cleaned_data:\\n for headlines in cleaned_data[date]:\\n news.append(headlines)\\n\\n word_freq = nltk.FreqDist(itertools.chain(*news))\\n id_to_word = ['<pad>'] + [word for word, cnt in word_freq.items() if cnt >= threshold] + ['<unk>']\\n word_to_id = {word:idx for idx, word in enumerate(id_to_word)}\\n \\n return id_to_word, word_to_id\",\n \"def return_dictionary_of_two_comparable_citations(self, dictionary_key_one,\\n dictionary_key_two):\\n list_of_one_citations = []\\n list_of_two_citations = []\\n list_of_unassigned_citations = []\\n for citation in self.array_of_citations:\\n citation_string = self.__generate_author_string__(\\n citation.get_author()) + \\\" \\\" + citation.get_title() \\\\\\n + \\\" \\\" + citation.get_journal()\\n one_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_one)\\n two_key_matches = self.__count_of_keyword_matches__(\\n citation_string, dictionary_key_two)\\n if one_key_matches > two_key_matches:\\n citation.set_classification(dictionary_key_one)\\n list_of_one_citations.append(citation)\\n elif two_key_matches > one_key_matches:\\n citation.set_classification(dictionary_key_two)\\n list_of_two_citations.append(citation)\\n else:\\n citation.set_classification(\\\"Unassigned\\\")\\n list_of_unassigned_citations.append(citation)\\n\\n return {dictionary_key_one: list_of_one_citations, dictionary_key_two:\\n list_of_two_citations, \\\"Unassigned\\\":\\n list_of_unassigned_citations}\",\n \"def getCitations (self, doc, verbose = False):\\r\\n if verbose is True:\\r\\n print \\\"getting citations for document \\\" +str(doc) \\r\\n return self.citations[int(doc)]\",\n \"def preprocessing():\\n english_dictionary = nltk.corpus.brown.words()\\n slang_vocab = pickle.load(open('vocab_pattern_match_with_freq.pkl', 'rb'))\\n\\n normalize_english_dict = len(english_dictionary)\\n normalize_slang_vocab = 0\\n for w, n in slang_vocab.items():\\n normalize_slang_vocab += n\\n\\n words = {}\\n for w, n in Counter(english_dictionary).items():\\n words[w] = n/normalize_english_dict\\n \\n for w, n in slang_vocab.items():\\n if w not in words:\\n words[w] = 0.\\n words[w] += n/normalize_slang_vocab\\n\\n words_by_freq = [w for w,_ in sorted(words.items(), key=lambda x: x[1], reverse=True)]\\n\\n # Build a cost dictionary, assuming Zipf's law and cost = -math.log(probability).\\n #words = open(\\\"words_by_frequency.txt\\\").read().split()\\n wordcost = dict((k, log((i+1)*log(len(words_by_freq)))) for i,k in enumerate(words_by_freq))\\n maxword = max(len(x) for x in words_by_freq)\\n return wordcost,maxword\",\n \"def weighted_score(counters, lst, weight):\\n if counters == None:\\n counters = {}\\n\\n\\n for item in lst:\\n if item in counters:\\n counters[item] += weight\\n else:\\n counters[item] = weight\\n\\n return counters\",\n \"def preparation(self):\\n self.word_freq = defaultdict(int)\\n\\n for sentence in self.corpus:\\n for word in sentence:\\n self.word_freq[word] += 1\\n\\n # self.words decide the index of all the words\\n self.words = list(self.word_freq.keys())\\n self.T = len(self.words)\\n\\n # word_index will give index for a given word and vice versa for index_word\\n self.word_index = dict([[word, i] for i, word in enumerate(self.words)])\\n self.index_word = dict([[i, word] for i, word in enumerate(self.words)])\",\n \"def join_cinfo(cooccur, percents):\\n import math\\n\\n word1 = cooccur[0][0]\\n word2 = cooccur[0][1]\\n try:\\n word1_percent = percents[word1]\\n weight1 = 1 / word1_percent\\n word2_percent = percents[word2]\\n weight2 = 1 / word2_percent\\n return (cooccur[0], cooccur[1], cooccur[1] *\\n math.log(min(weight1, weight2)))\\n except:\\n return 0\",\n \"def get_citation_weight(rank_method_code, config):\\n begin_time = time.time()\\n last_update_time = get_bibrankmethod_lastupdate(rank_method_code)\\n\\n if task_get_option(\\\"quick\\\") == \\\"no\\\":\\n last_update_time = \\\"0000-00-00 00:00:00\\\"\\n write_message(\\\"running thorough indexing since quick option not used\\\", verbose=3)\\n\\n last_modified_records = get_last_modified_rec(last_update_time)\\n #id option forces re-indexing a certain range even if there are no new recs\\n if last_modified_records or task_get_option(\\\"id\\\"):\\n if task_get_option(\\\"id\\\"):\\n #construct a range of records to index\\n taskid = task_get_option(\\\"id\\\")\\n first = taskid[0][0]\\n last = taskid[0][1]\\n #make range, last+1 so that e.g. -i 1-2 really means [1,2] not [1]\\n updated_recid_list = range(first, last+1)\\n else:\\n updated_recid_list = create_recordid_list(last_modified_records)\\n\\n write_message(\\\"Last update \\\"+str(last_update_time)+\\\" records: \\\"+ \\\\\\n str(len(last_modified_records))+\\\" updates: \\\"+ \\\\\\n str(len(updated_recid_list)))\\n\\n #write_message(\\\"updated_recid_list: \\\"+str(updated_recid_list))\\n result_intermediate = last_updated_result(rank_method_code)\\n\\n #result_intermed should be warranted to exists!\\n #but if the user entered a \\\"-R\\\" (do all) option, we need to\\n #make an empty start set\\n if task_get_option(\\\"quick\\\") == \\\"no\\\":\\n result_intermediate = [{}, {}, {}]\\n else:\\n # check indexing times of `journal' and `reportnumber`\\n # indexes, since if they are not up to date yet, then we\\n # should wait and not run citation indexing as of yet:\\n last_timestamp_bibrec = run_sql(\\\"SELECT DATE_FORMAT(MAX(modification_date), '%%Y-%%m-%%d %%H:%%i:%%s') FROM bibrec\\\", (), 1)[0][0]\\n last_timestamp_indexes = run_sql(\\\"SELECT DATE_FORMAT(MAX(last_updated), '%%Y-%%m-%%d %%H:%%i:%%s') FROM idxINDEX WHERE name IN (%s,%s)\\\", ('journal', 'reportnumber'), 1)[0][0]\\n if not last_timestamp_indexes or \\\\\\n not last_timestamp_bibrec or \\\\\\n last_timestamp_bibrec > last_timestamp_indexes:\\n write_message(\\\"Not running citation indexer since journal/reportnumber indexes are not up to date yet.\\\")\\n return {}\\n\\n citation_weight_dic_intermediate = result_intermediate[0]\\n citation_list_intermediate = result_intermediate[1]\\n reference_list_intermediate = result_intermediate[2]\\n\\n # Enrich updated_recid_list so that it would contain also\\n # records citing or referring to updated records, so that\\n # their citation information would be updated too. Not the\\n # most efficient way to treat this problem, but the one that\\n # requires least code changes until ref_analyzer() is more\\n # nicely re-factored.\\n updated_recid_list_set = intbitset(updated_recid_list)\\n for somerecid in updated_recid_list:\\n # add both citers and citees:\\n updated_recid_list_set |= intbitset(citation_list_intermediate.get(somerecid, []))\\n updated_recid_list_set |= intbitset(reference_list_intermediate.get(somerecid, []))\\n updated_recid_list = list(updated_recid_list_set)\\n\\n #call the procedure that does the hard work by reading fields of\\n #citations and references in the updated_recid's (but nothing else)!\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"Entering get_citation_informations\\\")\\n citation_informations = get_citation_informations(updated_recid_list,\\n config)\\n #write_message(\\\"citation_informations: \\\"+str(citation_informations))\\n #create_analysis_tables() #temporary..\\n #test how much faster in-mem indexing is\\n write_message(\\\"Entering ref_analyzer\\\", verbose=9)\\n #call the analyser that uses the citation_informations to really\\n #search x-cites-y in the coll..\\n dic = ref_analyzer(citation_informations,\\n citation_weight_dic_intermediate,\\n citation_list_intermediate,\\n reference_list_intermediate,\\n config,updated_recid_list)\\n #dic is docid-numberofreferences like {1: 2, 2: 0, 3: 1}\\n #write_message(\\\"Docid-number of known references \\\"+str(dic))\\n end_time = time.time()\\n write_message(\\\"Total time of get_citation_weight(): %.2f sec\\\" % (end_time - begin_time))\\n task_update_progress(\\\"citation analysis done\\\")\\n else:\\n dic = {}\\n write_message(\\\"No new records added since last time this rank method was executed\\\")\\n return dic\",\n \"def sentiment(self) -> Dict[str, float]:\",\n \"def clsWordCounts(wd_dict, wd_list, cl):\\n\\n # set up counter object for words in passed word list\\n word_counts = Counter(wd_list)\\n\\n # for each term in counter object\\n for term in word_counts:\\n\\n # if already in dictionary add to the count\\n if term in wd_dict[cl]:\\n\\n wd_dict[cl][term] += word_counts[term]\\n\\n # else add to the dictionary\\n else:\\n\\n wd_dict[cl][term] = word_counts[term]\\n\\n return wd_dict\",\n \"def build(ctx, inputs, output, cs):\\n click.echo('chemdataextractor.dict.build')\\n dt = DictionaryTagger(lexicon=ChemLexicon(), case_sensitive=cs)\\n names = []\\n for input in inputs:\\n for line in input:\\n tokens = line.split()\\n names.append(tokens)\\n dt.build(words=names)\\n dt.save(output)\",\n \"def write_citation_page(refdict: dict) -> None:\\n with open(WEBOUT_PATH + init_data().cite_url, \\\"w\\\", encoding=\\\"utf-8\\\") as outfile:\\n common_html_header(outfile, \\\"Fiddler Crab Website Citation\\\")\\n outfile.write(\\\" <header id=\\\\\\\"\\\" + init_data().cite_url + \\\"\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <h1>Citation Info</h1>\\\\n\\\")\\n outfile.write(\\\" </header>\\\\n\\\")\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\" <p>\\\\n\\\")\\n outfile.write(\\\" Generally it is best to cite the primary literature, whenever possible. However, the \\\"\\n \\\"following paper describes much of the data that is unique to this website:\\\\n\\\")\\n outfile.write(\\\" </p>\\\\n\\\")\\n outfile.write(\\\" <div class=\\\\\\\"reference_list\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <ul>\\\\n\\\")\\n ref = refdict[\\\"Rosenberg2014\\\"] # citation describing the database\\n outfile.write(\\\" <li><a href=\\\\\\\"references/Rosenberg2014.html\\\\\\\">\\\" + ref.formatted_html + \\\"</a></li>\\\\n\\\")\\n outfile.write(\\\" </ul>\\\\n\\\")\\n outfile.write(\\\" </div>\\\\n\\\")\\n outfile.write(\\\" <ul class=\\\\\\\"fa-ul\\\\\\\">\\\\n\\\")\\n outfile.write(\\\" <li>\\\" + fetch_fa_glyph(\\\"list pdf\\\") +\\n \\\"<a href=\\\\\\\"https://dx.plos.org/10.1371/journal.pone.0101704\\\\\\\">Read paper online at \\\"\\n \\\"PLoS ONE</a></li>\\\\n\\\")\\n outfile.write(\\\" <li>\\\" + fetch_fa_glyph(\\\"list github\\\") +\\n \\\"<a href=\\\\\\\"https://github.com/msrosenberg/fiddlercrab.info\\\\\\\">Website data repository on \\\"\\n \\\"GitHub</a></li>\\\\n\\\")\\n outfile.write(\\\" </ul>\\\\n\\\")\\n common_html_footer(outfile)\",\n \"def list_publications(bib_format=\\\"dict\\\"):\\n\\n def get_bibtex(key, value):\\n total_keys = [\\n \\\"title\\\",\\n \\\"journal\\\",\\n \\\"volume\\\",\\n \\\"issue\\\",\\n \\\"number\\\",\\n \\\"pages\\\",\\n \\\"numpages\\\",\\n \\\"year\\\",\\n \\\"month\\\",\\n \\\"publisher\\\",\\n \\\"url\\\",\\n \\\"doi\\\",\\n \\\"issn\\\",\\n ]\\n bibtex_str = (\\n \\\"@article{\\\"\\n + key\\n + \\\",\\\\n\\\"\\n + \\\" author={\\\"\\n + \\\" and \\\".join(value[\\\"author\\\"])\\n + \\\"},\\\\n\\\"\\n )\\n for key in total_keys:\\n if key in value.keys():\\n bibtex_str += \\\" \\\" + key + \\\"={\\\" + value[key] + \\\"},\\\\n\\\"\\n bibtex_str += \\\"}\\\\n\\\"\\n return bibtex_str\\n\\n def get_apa(value):\\n apa_str = \\\" & \\\".join(value[\\\"author\\\"])\\n if \\\"year\\\" in value.keys():\\n apa_str += \\\" (\\\" + value[\\\"year\\\"] + \\\"). \\\"\\n if \\\"title\\\" in value.keys():\\n apa_str += value[\\\"title\\\"] + \\\". \\\"\\n if \\\"journal\\\" in value.keys():\\n apa_str += value[\\\"journal\\\"] + \\\", \\\"\\n if \\\"volume\\\" in value.keys():\\n apa_str += value[\\\"volume\\\"] + \\\", \\\"\\n if \\\"pages\\\" in value.keys():\\n apa_str += value[\\\"pages\\\"] + \\\". \\\"\\n if \\\"doi\\\" in value.keys():\\n apa_str += \\\"doi: \\\" + value[\\\"doi\\\"] + \\\"\\\\n\\\"\\n return apa_str\\n\\n publication_dict = s.publication_lst\\n if bib_format.lower() == \\\"dict\\\":\\n return publication_dict\\n elif bib_format.lower() == \\\"bibtex\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_bibtex(key, value)\\n return total_str\\n elif bib_format.lower() == \\\"apa\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_apa(value)\\n return total_str\\n else:\\n raise ValueError(\\\"Supported Bibformats are ['dict', 'bibtex', 'apa']\\\")\",\n \"def count_word_usage(counters_by_author, word_list):\\n specific_word_counter = {}\\n for author in counters_by_author.keys():\\n word_counter = Counter()\\n for item in counters_by_author[author]:\\n for word in word_list:\\n if word in item:\\n print(item)\\n word_counter[word] += counters_by_author[author][item]\\n specific_word_counter[author] = word_counter\\n return specific_word_counter\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def calculateWeights(self):\\n return self.distances #En lo que encontramos una funcion que represente\",\n \"def calc_charge_info(values,weights):\\n\\t\\t\\n\\t# We use the standard IC procedure to calculate the statistical error for the\\n\\t# weighted average\\n\\t# There are three main terms\\n\\t# 1) The weighted sum of charges, and its variance\\n\\tif len(values) == 0 :\\n\\t\\treturn 0.0, 1.0\\n\\twsc = sum([ weights[i]*values[i] for i in range(0,len(values))])\\n\\tsw = sum(weights)\\n\\tif sw == 0.0 or wsc == 0.0 :\\n\\t\\treturn 0.0, 1.0 \\n\\t# Defining the weighted average\\n\\tmu = wsc/sw\\n\\tvar_wsc = sum([(weights[i]*values[i])**2 for i in range(0,len(values))]) \\n\\t# 2) The sum of weights and its variance \\n\\tvar_sw = sum([weights[i]**2 for i in range(0,len(values))])\\n\\t# 3) The covariance associated to both sums of weights \\n\\tcov = sum([values[i]*(weights[i]**2) for i in range(0,len(values))])\\n\\t# The error in the weighted average of charges is given by the variance of the quantity \\n\\t# wsc/sw; a division of two sums of weights (for two sets of weights that are correlated). \\\\\\n\\tstd_mu = var_wsc/(wsc)**2\\n\\tstd_mu += var_sw/(sw)**2\\n\\tstd_mu -= 2.*cov/(sw*wsc)\\n\\tstd_mu = std_mu**0.5\\n\\tstd_mu *= mu\\n\\n\\treturn mu , std_mu\",\n \"def similarity(query,word_dict,dictionary,number_of_docs,id):\\n similarity = 0.0\\n scalar_leng = 0.0\\n for term in query:\\n if term in dictionary:\\n similarity += word_dict[term][1]*imp(term,word_dict,number_of_docs,id)\\n\\n for term in dictionary:\\n scalar_leng += imp(term, word_dict, number_of_docs, id) ** 2\\n\\n final_scalar_leng = math.sqrt(scalar_leng)\\n similarity = similarity / final_scalar_leng\\n #print(similarity)\\n return similarity\",\n \"def get_mutual_information(c_wic, c_wioc, c_owic, c_owioc):\\n # total word count\\n c_total = c_wic + c_wioc + c_owic + c_owioc\\n\\n mi_1 = (c_wic / float(c_total)) * log10((c_total * c_wic) /\\n float((c_wic + c_wioc) * (c_wic + c_owic)))\\n mi_2 = (c_owic / float(c_total)) * log10((c_total * c_owic) /\\n float((c_owic + c_owioc) * (c_wic + c_owic)))\\n mi_3 = (c_wioc / float(c_total)) * log10((c_total * c_wioc) /\\n float((c_wic + c_wioc) * (c_wioc + c_owioc)))\\n mi_4 = (c_owioc / float(c_total)) * log10((c_total * c_owioc) /\\n float((c_owic + c_owioc) * (c_wioc + c_owioc)))\\n\\n return mi_1 + mi_2 + mi_3 + mi_4\",\n \"def combine_calib(calib_lst, weight_lst):\\n\\n k = calib_lst[0]['k']\\n offset = calib_lst[0]['offset']\\n xorder = calib_lst[0]['xorder']\\n yorder = calib_lst[0]['yorder']\\n npixel = calib_lst[0]['npixel']\\n\\n for calib in calib_lst:\\n if calib['k'] != k \\\\\\n or calib['offset'] != offset \\\\\\n or calib['xorder'] != xorder \\\\\\n or calib['yorder'] != yorder \\\\\\n or calib['npixel'] != npixel:\\n print('Error: calib list is not self-consistent')\\n raise ValueError\\n\\n\\n # calculate the weighted average coefficients\\n coeff = np.zeros_like(calib_lst[0]['coeff'], dtype=np.float64)\\n for j, i in itertools.product(range(yorder+1), range(xorder+1)):\\n for calib, weight in zip(calib_lst, weight_lst):\\n coeff[j, i] += calib['coeff'][j, i]*weight\\n\\n return {'k': k, 'offset': offset, 'xorder': xorder, 'yorder': yorder,\\n 'npixel': npixel, 'coeff': coeff}\",\n \"def collectSamples(authors_analysis_dict,sectorialized_agents):\\n def returnSignals(keep_dict,analysis_dict):\\n def updateDict(dict_keep,new_dict)\\n for var in new_dict:\\n if var not in dict_keep:\\n dict_keep[var]=new_dict[var]\\n else:\\n dict_keep[var]+=new_dict[var]\\n samples={\\\"raw_analysis\\\":{\\\"texts_measures\\\":{}, \\\"text_measures\\\":{}},\\n \\\"pos_analysis\\\":{\\\"texts_measures\\\":{}, \\\"text_measures\\\":{}},\\n \\\"wordnet_analysis\\\":{\\\"texts_measures\\\":{}, \\\"text_measures\\\":{}},\\n }\\n for sector in sectorialized_agents:\\n for author in sectorialized_agents[sector]:\\n for analyses in authors_analysis:\\n for analysis_grouping in authors_analysis[analyses][author]:\\n if type(authors_analysis[analyses][author][analysis_grouping])==dict:\\n for analysis in authors_analysis[analyses][author][analysis_grouping]:\\n samples[sector][analyses][analysis_grouping][analysis]=updateDict(samples[sector][analyses][analysis_grouping][analysis],getSamples(authors_analysis[analyses][author][analysis_grouping][analysis]))\\n else:\\n samples[sector][analyses][analysis_grouping]=updateDict(samples[sector][analyses][analysis_grouping],authors_analysis[analyses][author][analysis_grouping])\\n ############ OLD IMPLEMENTATION\\n for analysis in authors_analysis[\\\"raw_analysis\\\"][author][\\\"texts_measures\\\"][\\\"each_text\\\"]:\\n samples[sector][\\\"raw_analysis\\\"][\\\"texts_measures\\\"][\\\"chars\\\"]=updateDict(samples[sector][\\\"raw_analysis\\\"][\\\"texts_measures\\\"][\\\"chars\\\"],getSamples(authors_analysis[\\\"chars\\\"]))\\n samples[sector][\\\"raw_analysis\\\"][\\\"texts_measures\\\"][\\\"tokens\\\"]=updateDict(samples[sector][\\\"texts_measures\\\"][\\\"tokens\\\"],getSamples(authors_analysis[\\\"tokens\\\"]))\\n samples[sector][\\\"raw_analysis\\\"][\\\"texts_measures\\\"][\\\"sentences\\\"]=updateDict(samples[sector][\\\"texts_measures\\\"][\\\"sentences\\\"],getSamples(authors_analysis[\\\"sentences\\\"]))\\n\\n samples[sector][\\\"raw_analysis\\\"][\\\"texts_measures\\\"][\\\"all_texts\\\"]=updateDict(samples[sector][\\\"texts_measures\\\"][\\\"all_texts\\\"],getSamples(authors_analysis[author][\\\"texts_measures\\\"][\\\"all_texts\\\"]))\\n ########### DIV\\n samples[sector][\\\"raw_analysis\\\"][\\\"text_measures\\\"][\\\"chars\\\"]=updateDict(samples[sector][\\\"text_measures\\\"][\\\"chars\\\"],getSamples(authors_analysis[author][\\\"text_measures\\\"][\\\"chars\\\"]))\\n samples[sector][\\\"raw_analysis\\\"][\\\"text_measures\\\"][\\\"tokens\\\"]=updateDict(samples[sector][\\\"text_measures\\\"][\\\"tokens\\\"],getSamples(authors_analysis[author][\\\"text_measures\\\"][\\\"tokens\\\"]))\\n samples[sector][\\\"raw_analysis\\\"][\\\"text_measures\\\"][\\\"sentences\\\"]=updateDict(samples[sector][\\\"text_measures\\\"][\\\"sentences\\\"],getSamples(authors_analysis[author][\\\"text_measures\\\"][\\\"sentences\\\"]))\\n # POS\\n for analysis in authors_analysis[\\\"pos_analysis\\\"][author][\\\"texts_measures\\\"]:\\n samples[sector][\\\"pos_analysis\\\"][\\\"texts_measures\\\"]=updateDict(samples[sector][\\\"pos_analysis\\\"][\\\"texts_measures\\\"],analysis[\\\"pos_measures\\\"][\\\"texts_measures\\\"]))\\n samples[sector][\\\"pos_analysis\\\"][\\\"text_measures\\\"]=updateDict(samples[sector][\\\"pos_analysis\\\"][\\\"text_measures\\\"],analysis))\\n # Wordnet\\n for analysis in authors_analysis[\\\"wordnet_analysis\\\"][author][\\\"texts_measures\\\"]:\\n samples[sector][\\\"wordnet_analysis\\\"][\\\"texts_measures\\\"][\\\"wordnet_context\\\"]=updateDict(samples[sector][\\\"wordnet_analysis\\\"][\\\"texts_measures\\\"][\\\"wordnet_context\\\"],analysis[\\\"wordnet_context\\\"])\\n samples[sector][\\\"wordnet_analysis\\\"][\\\"texts_measures\\\"][\\\"wordnet_analysis\\\"]=updateDict(samples[sector][\\\"wordnet_analysis\\\"][\\\"texts_measures\\\"][\\\"wordnet_analysis\\\"],analysis[\\\"wordnet_analysis\\\"])\\n samples[sector][\\\"wordnet_analysis\\\"][\\\"text_measures\\\"][\\\"wordnet_context\\\"]=updateDict(samples[sector][\\\"wordnet_analysis\\\"][\\\"text_measures\\\"][\\\"wordnet_context\\\"],authors_analysis[\\\"wordnet_analysis\\\"][author][\\\"text_measures\\\"][\\\"wordnet_context\\\"])\\n samples[sector][\\\"wordnet_analysis\\\"][\\\"text_measures\\\"][\\\"wordnet_analysis\\\"]=updateDict(samples[sector][\\\"wordnet_analysis\\\"][\\\"text_measures\\\"][\\\"wordnet_analysis\\\"],authors_analysis[\\\"wordnet_analysis\\\"][author][\\\"text_measures\\\"][\\\"wordnet_analysis\\\"])\\n return samples\",\n \"def get_require_post_score(chosen_require_list: list) -> pmag.MagicDict:\\n res = pmag.MagicDict()\\n for w, cate in chosen_require_list:\\n posts = MODEL[cate]['posts']\\n for post in [*posts]:\\n if w in posts[post]['require']:\\n freq = posts[post]['require'][w]['freq']\\n prob = posts[post]['require'][w]['prob']\\n score = prob # freq * prob / REQUIRE_NF[cate][post]\\n else:\\n continue\\n if post in res[cate]:\\n res[cate][post] += score\\n else:\\n res[cate][post] = score\\n return res\",\n \"def extract_relevant(self):\\n item_extraction = self.data\\n my_dict = {'tweeted_time': item_extraction['created_at'],\\n 'tweet_id': item_extraction['id'],\\n # If the time comes when the below becomes more significant, it will be no trouble at all to make an\\n # additional column for it, but delimiting it with a ` creates less clutter in the Database\\n 'in_reply_to':\\n \\\"NAME/\\\" + str(item_extraction['in_reply_to_screen_name']) + \\\"`\\\" +\\n \\\"STATUSID/\\\" + str(item_extraction['in_reply_to_status_id_str']) + \\\"`\\\" +\\n \\\"USERID/\\\" + str(item_extraction['in_reply_to_user_id_str']),\\n 'lang': item_extraction['lang'],\\n 'place': item_extraction['place'], 'source': item_extraction['source']}\\n if item_extraction['place'] is not None:\\n my_dict['place'] = item_extraction['place']['full_name']\\n if 'retweeted_status' in item_extraction.keys():\\n my_dict['original_author_id'] = item_extraction['retweeted_status']['user']['id']\\n my_dict['original_author_handle'] = item_extraction['retweeted_status']['user']['screen_name']\\n tester = item_extraction['retweeted_status']['text']\\n cleaned = ' '.join(re.sub(\\\"(RT : )|(@[\\\\S]+)|(&\\\\S+)|(http\\\\S+)\\\", \\\" \\\", tester).split())\\n removed_others = \\\" \\\".join(re.sub(\\\"(#\\\\S+)\\\", ' ', cleaned).split())\\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\\n # This final text will make it a lot easier to run NLP\\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\\n my_dict['plain_text'] = final_text.lower()\\n my_dict['tweet'] = cleaned\\n else:\\n my_dict['original_author_id'] = item_extraction['user']['id']\\n my_dict['original_author_handle'] = item_extraction['user']['screen_name']\\n cleaned = ' '.join(re.sub(\\\"(@[\\\\S]+)|(&\\\\S+)|(http\\\\S+)\\\", \\\" \\\", item_extraction['text']).split())\\n removed_others = \\\" \\\".join(re.sub(\\\"(#\\\\S+)\\\", ' ', cleaned).split())\\n final_text = ''.join(list(filter(lambda x: x.isalpha() or x is ' ', removed_others)))\\n final_text = final_text.strip().replace(' ', ' ').replace(' ', ' ')\\n my_dict['plain_text'] = final_text.lower()\\n my_dict['tweet'] = cleaned\\n return my_dict\",\n \"def learn(self, rythm_list): \\n for rythm in rythm_list:\\n # Get the rythm density, add it to the agent's density\\n # distribution and then normalize it\\n for ratio, d in rythm.density():\\n if ratio not in self.density.keys():\\n self.density[ratio] = d\\n else:\\n self.density[ratio] += d\\n \\n # NORMALIZE! \",\n \"def compute_wiki(self):\\n\\n self.__wiki_counts()\\n self.__cross_wiki_counts()\\n\\n # Step 1: Calculate p(e|m) for wiki.\\n print(\\\"Filtering candidates and calculating p(e|m) values for Wikipedia.\\\")\\n for ent_mention in self.wiki_freq:\\n if len(ent_mention) < 1:\\n continue\\n\\n ent_wiki_names = sorted(\\n self.wiki_freq[ent_mention].items(), key=lambda kv: kv[1], reverse=True\\n )\\n # Get the sum of at most 100 candidates, but less if less are available.\\n total_count = np.sum([v for k, v in ent_wiki_names][:100])\\n\\n if total_count < 1:\\n continue\\n\\n self.p_e_m[ent_mention] = {}\\n\\n for ent_name, count in ent_wiki_names:\\n self.p_e_m[ent_mention][ent_name] = count / total_count\\n\\n if len(self.p_e_m[ent_mention]) >= 100:\\n break\\n\\n del self.wiki_freq\",\n \"def __init__(self,sentences):\\n self.data_set = sentences\\n self.sum1=0\\n for sentence in self.data_set:\\n sentence.weight = 1/len(self.data_set)\\n self.sum1 += sentence.weight\\n\\n self.sum2=1\",\n \"def tag():\\n iso_list = []\\n tags = [\\\"spatial_entity\\\", \\\"place\\\", \\\"motion\\\", \\\"location\\\", \\\"signal\\\", \\\"qslink\\\", \\\"olink\\\"]\\n for token in doc:\\n if token.norm_ in tags:\\n iso_list.append(token.norm_)\\n setList = list(set(iso_list))\\n my_dict = {i: iso_list.count(i) for i in setList}\\n\\n for i in tags:\\n if i.lower() not in my_dict:\\n my_dict[i] = 0\\n print(my_dict)\",\n \"def get_citations_trend(body):\\n user_id = get_user_id(body)\\n citations_per_year = body.find('div', attrs={'id': 'gsc_g'})\\n years = citations_per_year.find('div', attrs={'id': 'gsc_g_x'})\\n n_citations = citations_per_year.find('div', attrs={'id': 'gsc_g_bars'})\\n citations_list = list()\\n for year, n_citation in zip(years, n_citations):\\n citations_dict = {'user_id': user_id,\\n 'n_citation': n_citation.text,\\n 'year': year.text}\\n citations_list.append(citations_dict)\\n return citations_list\",\n \"def portrayalCatalogueCitation(self) -> Sequence[Citation]:\\n pass\",\n \"def compare_and_update(self):\\n\\n # first read in the old cites\\n try:\\n with open(\\\"cites.json\\\") as f:\\n cites_json = f.read()\\n old_cites = json.loads(cites_json)\\n except OSError:\\n old_cites = {}\\n\\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\\n print(\\\"number of citations = \\\", num_cites)\\n\\n h_index = 0\\n for n, p in enumerate(self.mypapers):\\n if p.citation_count >= n+1:\\n h_index = n + 1\\n print(\\\"h-index = \\\", h_index)\\n\\n # now create a dict of the cites and update the stored JSON\\n cites = {}\\n for p in self.mypapers:\\n cites[p.bibcode] = (p.title[0], p.citation_count)\\n\\n cite_json = json.dumps(cites, indent=4)\\n with open(\\\"cites.json\\\", \\\"w\\\") as f:\\n f.write(cite_json)\\n\\n # now look for differences in cites\\n for key, value in cites.items():\\n if key not in old_cites:\\n print(f\\\"new paper: {value[0]}\\\")\\n else:\\n if value[1] != old_cites[key][1]:\\n print(f\\\"change of {value[1] - old_cites[key][1]:+d} for paper: {value[0]}\\\")\",\n \"def crawl_url_links(input_dict):\\n\\n extractor_name=input_dict.get('extractor','DefaultExtractor')\\n import requests\\n label=input_dict['label']\\n urls,_, source, source_date=_process_input(input_dict['input'],False)\\n\\n\\n docs=[]\\n titles=[]\\n for url in urls:\\n print(url)\\n try:\\n r = requests.get(url)\\n except ConnectionError:\\n continue\\n if r.status_code==200:\\n html=r.text\\n from boilerpipe.extract import Extractor\\n extractor = Extractor(extractor=extractor_name, html=html)\\n\\n titles.append(url)\\n text=''\\n if label:\\n text+='!'+label+'\\\\t'\\n text+=extractor.getText()\\n docs.append(text)\\n\\n\\n corpus_date = str(time.strftime(\\\"%d.%m.%Y %H:%M:%S\\\", time.localtime()))\\n documents, labels = _process_adc(docs, False, label, titles)\\n features = {\\\"Source\\\": source, \\\"SourceDate\\\": source_date, \\\"CorpusCreateDate\\\": corpus_date,\\n \\\"Labels\\\": json.dumps([label]) if label else '[]'}\\n\\n return {\\\"adc\\\": DocumentCorpus(documents=documents, features=features)}\",\n \"def update_weights(text):\\n soup = bs4.BeautifulSoup(text, 'lxml')\\n if ord_metric == OrderingMetric.BACK_LINK_COUNT:\\n for l in soup.find_all('a', href=True):\\n base = req_obj.get_base_url()\\n link = urljoin(base, l['href'])\\n if re.match(pattern, link) is not None:\\n if link not in link_weights:\\n link_weights[link] = 1\\n else:\\n link_weights[link] += 1\\n\\n elif ord_metric == OrderingMetric.FORWARD_LINK_COUNT:\\n for l in soup.find_all('a', href=True):\\n base = req_obj.get_base_url()\\n link = urljoin(base, l['href'])\\n if re.match(pattern, link) is not None:\\n if link not in link_weights:\\n link_weights[link] = get_num_forward_link(link)\\n\\n elif ord_metric == OrderingMetric.VSM:\\n vsmObj = VectorSpaceModel()\\n vsmObj.fetch_and_rank_summary(queue, link_weights)\",\n \"def get_duty_cate_score(chosen_duty_list: list) -> pmag.MagicDict:\\n res = pmag.MagicDict()\\n for w, cate in chosen_duty_list:\\n freq = MODEL[cate]['duty'][w]['freq']\\n prob = MODEL[cate]['duty'][w]['prob']\\n score = prob # freq * prob / DUTY_NF[cate]\\n if cate in res:\\n res[cate] += score\\n else:\\n res[cate] = score\\n return res\",\n \"def likelihood_weighting_estimate(self,var,e,n,sampling_weight='uniform'):\\n\\t\\tncpt = self.variables[var].cpt\\n\\t\\tncount = dict((value,0.0) for value in ncpt.values())\\n\\t\\twsum = 0.0\\n\\t\\tfor iter in xrange(n):\\n (w,sample) = self.importance_sample(e,sampling_weight)\\n assert all(sample[key]==value for (key,value) in e.iteritems())\\n \\n ncount[sample[var]] += w\\n wsum += w\\n if wsum==0: return 'Undefined'\\n for value in ncount.iterkeys():\\n ncount[value] = float(ncount[value])/float(wsum)\\n return ncount\",\n \"def get_mutual_information(filename):\\n categories = {} #{category: speakers of this category}\\n features = {} #{feat: speakers who use this feature}\\n pos_categories_features = {} #{category: {feat: speakers of category who use this feat}}\\n neg_categories_features = {} #{category: {feat: speakers of category who do not use this feat}}\\n users = set() #set of all users in data\\n \\n for line in open(filename):\\n userid, c, date, statusid, rawtweet, toktweet, tagtweet = line.split('\\\\t')\\n users.add(userid)\\n \\n if c not in categories:\\n categories[c] = set()\\n pos_categories_features[c] = {}\\n categories[c].add(userid)\\n \\n feats = set(toktweet.lower().split()) #lowercase tweet and split into words\\n\\n for feat in feats:\\n if feat not in pos_categories_features[c]:\\n pos_categories_features[c][feat] = set()\\n pos_categories_features[c][feat].add(userid)\\n \\n if feat not in features:\\n features[feat] = set()\\n features[feat].add(userid)\\n\\n print \\\"Parsed data\\\"\\n\\n numfeats = len(features) #num of features\\n print numfeats, \\\"features\\\"\\n numusers = len(users) #num of users \\n print numusers, \\\"users\\\"\\n\\n #keep sizes of sets, not sets themselves\\n for feat in features:\\n features[feat] = len(features[feat])\\n for c in categories:\\n categories[c] = len(categories[c])\\n for c in pos_categories_features:\\n for feat in features:\\n if feat in pos_categories_features[c]:\\n pos_categories_features[c][feat] = len(pos_categories_features[c][feat])\\n else:\\n pos_categories_features[c][feat] = 0\\n\\n for c in categories:\\n print c, categories[c], \\\"users\\\"\\n\\n print \\\"Computed counts\\\"\\n \\n mi = {}\\n for feat in features:\\n mi[feat] = 0.0\\n for c in categories:\\n #print c, feat, features[feat], pos_categories_features[c][feat]\\n \\n catprob = categories[c]/numusers\\n\\n #prob of speakers of category c using feat\\n featprob = features[feat]/numusers\\n jointprob = pos_categories_features[c][feat]/numusers\\n if jointprob > 0 and featprob > 0:\\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\\n \\n #prob of speakers of category c NOT using feat\\n featprob = 1 - featprob\\n jointprob = (categories[c] - pos_categories_features[c][feat])/numusers\\n if jointprob > 0 and featprob > 0:\\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\\n\\n print \\\"Computed mutual information\\\"\\n\\n feature_scores = sorted(mi.items(), key=lambda x:x[1], reverse=True)\\n refcat = categories.keys()[0] #pick one of the categories\\n print 'Feature\\\\tMI\\\\tP({0}|Feature)\\\\tNum. users'.format(refcat)\\n for feat, score in feature_scores[:200]:\\n prob = pos_categories_features[refcat][feat]/features[feat]\\n print '{0}\\\\t{1:.3f}\\\\t{2:.3f}\\\\t{3}'.format(feat, score, prob, features[feat])\",\n \"def calcCountDict(TFdict):\\n\\n countDict = {}\\n\\n for doc in TFdict:\\n for term in doc:\\n if term in countDict:\\n countDict[term] +=1\\n else:\\n countDict[term] = 1\\n\\n return countDict\",\n \"def _extract_information(key, word, language_data):\\n try:\\n number = int(key)\\n except ValueError:\\n print(f\\\"The given key {key} is not an integer\\\")\\n return\\n\\n word = word.replace(\\\";\\\", \\\"\\\")\\n count_greater_than_sign = word.count(\\\">\\\")\\n count_less_than_sign = word.count(\\\"<\\\")\\n count_equal_to_sign = word.count(\\\"=\\\")\\n if count_equal_to_sign != 0:\\n return\\n if count_greater_than_sign == 0 and count_less_than_sign == 0:\\n _add_base_words(number, word, language_data)\\n elif count_greater_than_sign == 2 and count_less_than_sign == 0:\\n _add_compound_words(number, word, language_data)\\n elif count_greater_than_sign == 2 and count_less_than_sign == 2:\\n _add_multiplier_words(number, word, language_data)\",\n \"def parse_citation_web(doc_id, id_type='PMC'):\\n\\n doc_id_dict = convert_document_id(doc_id, id_type=id_type)\\n pmc = doc_id_dict['pmc']\\n link = \\\"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/\\\" % pmc\\n page = requests.get(link)\\n tree = html.fromstring(page.content)\\n n_citations = extract_citations(tree)\\n n_pages = int(n_citations/30) + 1\\n\\n pmc_cited_all = list() # all PMC cited\\n citations = tree.xpath('//div[@class=\\\"rprt\\\"]/div[@class=\\\"title\\\"]/a/@href')[1::]\\n pmc_cited = list(map(extract_pmc, citations))\\n pmc_cited_all.extend(pmc_cited)\\n if n_pages >= 2:\\n for i in range(2, n_pages+1):\\n link = \\\"http://www.ncbi.nlm.nih.gov/pmc/articles/%s/citedby/?page=%s\\\" % (pmc, str(i))\\n page = requests.get(link)\\n tree = html.fromstring(page.content)\\n citations = tree.xpath('//div[@class=\\\"rprt\\\"]/div[@class=\\\"title\\\"]/a/@href')[1::]\\n pmc_cited = list(map(extract_pmc, citations))\\n pmc_cited_all.extend(pmc_cited)\\n pmc_cited_all = [p for p in pmc_cited_all if p is not pmc]\\n dict_out = {'n_citations': n_citations,\\n 'pmid': doc_id_dict['pmid'],\\n 'pmc': re.sub('PMC', '', doc_id_dict['pmc']),\\n 'doi': doc_id_dict['doi'],\\n 'pmc_cited': pmc_cited_all}\\n return dict_out\",\n \"def jaccard_sim(news_data_dict: dict, song_data_dict: dict):\\n jaccard_dict = defaultdict(list)\\n\\n for news_yr, news_txt in news_data_dict.items():\\n news_txt_flat = []\\n for nt in news_txt:\\n news_txt_flat += nt\\n news_txt_flat = set(news_txt_flat)\\n if news_yr <= 2011:\\n song_txt_flat = set()\\n for i in range(5):\\n song_yr = news_yr+i\\n for st in song_data_dict[song_yr]:\\n for stw in st:\\n song_txt_flat.add(stw)\\n shared_words = news_txt_flat.intersection(song_txt_flat)\\n jaccard = len(shared_words) / (len(song_txt_flat) + len(news_txt_flat) - len(shared_words))\\n jaccard = round(jaccard, 3)\\n jaccard_dict[news_yr].append(jaccard)\\n\\n return jaccard_dict\",\n \"def parse(self, lines):\\n # convert file to string deleting end of line charcters\\n citations_string = self.prepare_file(lines)\\n # extract the entries from the string\\n entries = list(self.find_entries(citations_string))\\n entries.append(len(citations_string))\\n # parse each entry to generate a citation\\n for idx, jdx in zip(entries[:-1], entries[1:]):\\n self.parse_entry(citations_string[idx:jdx])\\n return self.force_field.citations\",\n \"def get_papers_by_institution(inst_names):\\n papers = list(ads.SearchQuery(aff=\\\"IATE\\\"))\\n for p in papers:\\n print(p.author)\\n print(f\\\"Paper from year {p.year} with \\\"\\n f\\\"{len(p.author)} authors and \\\"\\n f\\\"{p.citation_count} citations.\\\")\\n input()\",\n \"def get_calib_weight_lst(calib_lst, obsdate, exptime):\\n input_datetime = dateutil.parser.parse(obsdate) \\\\\\n + datetime.timedelta(seconds=exptime/2)\\n datetime_lst = [dateutil.parser.parse(calib['date-obs']) \\\\\\n + datetime.timedelta(seconds=calib['exptime']/2)\\n for calib in calib_lst]\\n\\n dt_lst = [(dt - input_datetime).total_seconds() for dt in datetime_lst]\\n dt_lst = np.array(dt_lst)\\n\\n if len(dt_lst)==1:\\n # only one reference in datetime_lst\\n weight_lst = [1.0]\\n elif (dt_lst<0).sum()==0:\\n # all elements in dt_lst > 0. means all references are after the input\\n # datetime. then use the first reference\\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\\n weight_lst[0] = 1.0\\n elif (dt_lst>0).sum()==0:\\n # all elements in dt_lst < 0. means all references are before the input\\n # datetime. then use the last reference\\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\\n weight_lst[-1] = 1.0\\n else:\\n weight_lst = np.zeros_like(dt_lst, dtype=np.float64)\\n i = np.searchsorted(dt_lst, 0.0)\\n w1 = -dt_lst[i-1]\\n w2 = dt_lst[i]\\n weight_lst[i-1] = w2/(w1+w2)\\n weight_lst[i] = w1/(w1+w2)\\n\\n return weight_lst\",\n \"def test_tax_court_citation_extractor(self):\\n\\n test_pairs = (\\n (\\n \\\"\\\"\\\" 1 UNITED STATES TAX COURT REPORT (2018)\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n BENTLEY COURT II LIMITED PARTNERSHIP, B.F. BENTLEY, INC., TAX\\n MATTERS PARTNER, Petitioner v.\\n COMMISSIONER OF INTERNAL REVENUE, Respondent\\n\\n\\n\\n Docket No. 5393-04. Filed May 31, 2006.\\n\\n\\n\\n Nancy Ortmeyer Kuhn, for petitioner.\\n \\\"\\\"\\\",\\n \\\"1 T.C. 2018\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Memo. 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Memo. 150\\\",\\n ),\\n (\\n \\\"\\\"\\\" T.C. Summary Opinion 2003-150\\n\\n\\n\\n UNITED STATES TAX COURT\\n\\n\\n\\n RIVER CITY RANCHES #1 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #2 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n RIVER CITY RANCHES #3 LTD., LEON SHEPARD,\\n TAX MATTERS PARTNER,\\n\\n\\n \\\"\\\"\\\",\\n \\\"2003 T.C. Summary Opinion 150\\\",\\n ),\\n (\\n \\\"\\\"\\\"\\n MICHAEL KEITH SHENK, PETITIONER v. COMMISSIONER\\n OF INTERNAL REVENUE, RESPONDENT\\n\\n Docket No. 5706–12. Filed May 6, 2013.\\n\\n P was divorced from his wife, and their 2003 ‘‘Judgment of\\n Absolute Divorce’’ provided that his ex-wife would have pri-\\n mary residential custody of their three minor children. The\\n judgment provided that the dependency exemption deductions\\n for the three children would be divided between the two ex-\\n spouses according to various conditions but did not provide\\n that the ex-wife must execute in P’s favor a Form 8332,\\n ‘‘Release of Claim to Exemption for Child of Divorced or Sepa-\\n rated Parents’’. The children resided with P’s ex-wife for more\\n than half of 2009, and P’s ex-wife did not execute in P’s favor\\n any Form 8332 or equivalent document for any year. For 2009\\n P timely filed a Federal income tax return on which he\\n claimed dependency exemption deductions and the child tax\\n credit for two of the children, consistent with his under-\\n standing of the terms of the judgment, but he did not attach\\n any Form 8332 to his return. He also claimed head-of-house-\\n hold filing status. His ex-wife, the custodial parent, timely\\n filed a Federal income tax return for 2009 on which she also\\n\\n 200\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00001 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f (200) SHENK v. COMMISSIONER 201\\n\\n\\n claimed two dependency exemption deductions, so that one\\n child was claimed on both parents’ returns. R allowed to P the\\n dependency exemption deduction for one of the children but\\n disallowed his claim for the dependency exemption deduction\\n for the child who had also been claimed by the custodial\\n parent. At trial P contended he is entitled to a dependency\\n exemption deduction for all three children. Held: Since the\\n custodial parent did not execute, and P could not and did not\\n attach to his return, any Form 8332 or equivalent release, P\\n is not entitled under I.R.C. sec. 152(e)(2)(A) to claim the\\n dependency exemption deduction or the child tax credit. Held,\\n further, where both the custodial parent and the noncustodial\\n parent have claimed for the same year a dependency exemp-\\n tion deduction for the same child, a declaration signed by the\\n custodial parent after the period of limitations for assess-\\n ments has expired as to the custodial parent could not qualify\\n under I.R.C. sec. 152(e)(2)(A), and therefore there is no reason\\n to grant P’s request to leave the record open so that he may\\n obtain and proffer such a declaration. Held, further, P is not\\n entitled to head-of-household filing status under I.R.C. sec.\\n 2(b)(1) nor to the child tax credit under I.R.C. sec. 24.\\n\\n Michael Keith Shenk, for himself.\\n Shari Salu, for respondent.\\n GUSTAFSON, Judge: The Internal Revenue Service (IRS)\\n determined a deficiency of $3,136 in the 2009 Federal income\\n tax of petitioner Michael Keith Shenk. Mr. Shenk petitioned\\n this Court, pursuant to section 6213(a), 1 for redetermination\\n of the deficiency. After Mr. Shenk’s concession that he\\n received but did not report $254 in dividend income, the\\n issue for decision is whether Mr. Shenk is entitled to a\\n dependency exemption deduction for one of his children\\n under section 151(c), a child tax credit for that child under\\n section 24(a), and head-of-household filing status under sec-\\n tion 2(b)(1). On these issues, we hold for the IRS.\\n FINDINGS OF FACT\\n\\n The judgment of divorce\\n Mr. Shenk was married to Julie Phillips, and they have\\n three minor children—M.S., W.S., and L.S. They divorced in\\n 2003. The family court’s ‘‘Judgment of Absolute Divorce’’ pro-\\n 1 Unless otherwise indicated, all citations of sections refer to the Internal\\n\\n Revenue Code (26 U.S.C.) in effect for the tax year at issue, and all cita-\\n tions of Rules refer to the Tax Court Rules of Practice and Procedure.\\n\\n\\n\\n\\nVerDate Nov 24 2008 10:59 Jul 11, 2014 Jkt 372897 PO 20012 Frm 00002 Fmt 3857 Sfmt 3857 V:\\\\FILES\\\\BOUND VOL. WITHOUT CROP MARKS\\\\B.V.140\\\\SHENK JAMIE\\n\\f 202 140 UNITED STATES TAX COURT REPORTS (200)\\n\\n\\n vided: that Ms. Phillips was ‘‘awarded primary residential\\n custody’’ of the parties’ three children; and that Mr. Shenk\\n would be liable for child support payments; but that, as to\\n dependency exemptions—\\\"\\\"\\\",\\n \\\"140 T.C. 200\\\",\\n ),\\n )\\n site = tax.Site()\\n for q, a in test_pairs:\\n results = site.extract_from_text(q)\\n cite_string = \\\"%s %s %s\\\" % (\\n results[\\\"Citation\\\"][\\\"volume\\\"],\\n results[\\\"Citation\\\"][\\\"reporter\\\"],\\n results[\\\"Citation\\\"][\\\"page\\\"],\\n )\\n\\n self.assertEqual(cite_string, a)\\n print \\\"✓\\\", cite_string\",\n \"def build_query_dict(self, term_list, issn_list, year_list, jlist):\\n journal_frame = self.make_jlist(jlist)\\n\\n search_terms = self.build_search_terms(term_list)\\n dict1 = {}\\n #This loop goes through and sets up a dictionary key with an ISSN number\\n\\n for issn in issn_list:\\n\\n issn_terms = ' AND ISSN(' + issn + ')'\\n dict2 = {}\\n #This loop goes and attaches all the years to the outer loop's key.\\n for year in year_list:\\n\\n year_terms = \\\"AND PUBYEAR IS \\\" + str(year)\\n querystring = search_terms + year_terms + issn_terms\\n\\n dict2[year] = querystring\\n\\n dict1[issn] = dict2\\n\\n return dict1\",\n \"def get_feature_set_SC(tweet, sentimentvalues):\\n pos_tag_freq = {}\\n additional_freq = {}\\n for phrase in tweet.tagged_words:\\n for word in phrase:\\n try:\\n tag = word['pos']\\n pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# if tag=='PRtinf':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJS':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='ADJ':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='NP':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='DET':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n# elif tag=='P':\\n# pos_tag_freq[tag] = pos_tag_freq.get(tag, 0) + 1\\n if tag in ADJECTIVES:\\n additional_freq['adjectives'] = additional_freq.get(tag, 0) + 1\\n elif tag in ADVERBS: \\n additional_freq['adverbs'] = additional_freq.get(tag, 0) + 1\\n elif tag in PRONOUNS:\\n additional_freq['pronoun'] = 1\\n except KeyError:\\n continue\\n for key in pos_tag_freq.keys():\\n pos_tag_freq[key] = pos_tag_freq[key]*1.0\\n #number of adjectives in sentence, number of adverbs in sentence(except ikke), pronoun in sentence(binary) \\n #Number of exclamation marks, number of emoticons,\\n emoticons = tweet.nrof_happyemoticons+tweet.nrof_sademoticons\\n if emoticons>0:\\n additional_freq['emoticons'] = emoticons*1.0\\n if tweet.nrof_exclamations>0:\\n additional_freq['exclamations'] = tweet.nrof_exclamations*1.0\\n \\n #Add lexicon values\\n #total subjectivity score from word polarities, total objectivity score, number of subjective words, number of objective words, e\\n sub_score = 0.0\\n obj_score = 0.0\\n nrof_subwords = 0\\n nrof_objwords = 0\\n for word in sentimentvalues.keys():\\n if sentimentvalues[word][0]>0:\\n sub_score = sub_score + sentimentvalues[word][0]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][1]>0:\\n sub_score = sub_score + sentimentvalues[word][1]\\n nrof_subwords = nrof_subwords + 1\\n if sentimentvalues[word][2]>0:\\n obj_score = obj_score + sentimentvalues[word][2]\\n nrof_objwords = nrof_objwords + 1\\n if sub_score>0:\\n additional_freq[\\\"sub_score\\\"] = sub_score+1.0\\n if obj_score>0:\\n additional_freq[\\\"obj_score\\\"] = obj_score+1.0\\n if nrof_subwords>0:\\n additional_freq[\\\"subjective_words\\\"] = nrof_subwords*1.0\\n if nrof_objwords>0:\\n additional_freq[\\\"objective_words\\\"] = nrof_objwords*1.0\\n \\n #Concatenate the dicts\\n features= dict(pos_tag_freq.items() + additional_freq.items())\\n \\n return features\",\n \"def weightKmers(self, weightDict):\\n for k, w in weightDict.iteritems():\\n assert k in self.kmers\\n self.G.edge[k + \\\"_L\\\"][k + \\\"_R\\\"]['weight'] = w\",\n \"def get_relevant_info(claim, articles, vectorizer, max_seq_len, spellcheck, use_ngrams):\\n\\n # Note: expects claim to be already cleaned\\n vec_claim = vectorizer.transform_txt(claim, max_seq_len,\\n use_ngrams=use_ngrams) # Claim vector - we'll use this to compare using cosine similarity\\n similarities_and_sents = [] # Stores tuples of (cos sim, sentence)\\n\\n # Loop through all articles to construct supporting information\\n for article in articles:\\n sentences = tokenize_by_sentence(article)\\n\\n '''\\n For each sentence, we clean and vectorize, then retrieve the cosine similarity of the claim vs the sentence\\n '''\\n for sentence in sentences:\\n # Basic cleaning on sentence\\n sentence = clean_txt(sentence, spellcheck)\\n # Don't process for sentences less than 40 characters long - this usually means improper sentences/words\\n if len(sentence) < 40:\\n continue\\n # Get vector of sentence and find cosine similarity\\n vec_sent = vectorizer.transform_txt(sentence, max_seq_len,\\n use_ngrams=use_ngrams)\\n similarity = cos_sim(vec_claim, vec_sent)\\n # Add to results\\n similarities_and_sents.append((similarity, sentence))\\n\\n # Sort the similarities (in desc order) using their similarity\\n sorted_sents = sorted(similarities_and_sents, key=lambda elem: elem[0], reverse=True)\\n\\n article_info = ''\\n num_words = 0\\n '''\\n Construct relevant info - keep looping through sentences, adding sentences until we hit max_seq_len\\n We'll surpass max_seq_len, but that's okay\\n '''\\n for similarity, sentence in sorted_sents:\\n if num_words >= max_seq_len:\\n break\\n article_info += ' || ' + sentence # Add a separator\\n num_words += len(sentence.split())\\n return article_info\",\n \"def weight_distribution(self):\\n all_scores = []\\n for zettel in self.lemma_tokens:\\n scores = []\\n for word in zettel:\\n cur_tf_idf = self.tf_idf_scores[word[0]] / 3 #range: 0-3+\\n if word[1] == 'NG':\\n word_list = re.split(\\\" \\\", word[0])\\n cur_word_score = 0\\n i = 0\\n for new_word in word_list:\\n cur_word_score += self.word_scores[new_word]\\n i += 1\\n cur_word_score = cur_word_score / i / 2 #range: 0-2+\\n else:\\n cur_word_score = self.word_scores[word[0]] / 2 #range: 0-2+\\n cur_keyword_score = self.keyword_scores[word[0]] / 4 #0-4+\\n cur_text_rank = self.text_ranks[word[0]] / 10 #range: 0-12+\\n cur_pos_score = self.pos_scores[word[0]]\\n cur_area_score = self.z_area_scores[word[0]]\\n cur_total_score = ((cur_tf_idf * self.score_weights[0]) + (cur_word_score * self.score_weights[1]) +\\n (cur_keyword_score * self.score_weights[2]) + (cur_text_rank * self.score_weights[3]) +\\n (cur_pos_score * self.score_weights[4]) + (cur_area_score * self.score_weights[5])) / 6\\n scores.append(cur_total_score)\\n all_scores.append(scores)\\n return all_scores\",\n \"def average():\\n dict_ = {}\\n lst = []\\n average_cost = {}\\n for book in root.findall('Book'):\\n publisher = book.find('Publisher').text\\n price = book.find('Price').text\\n lst.append(publisher)\\n if publisher not in dict_:\\n dict_[publisher] = float(price)\\n else:\\n dict_[publisher] += float(price)\\n publishers = {i: lst.count(i) for i in lst}\\n for key1, value1 in dict_.items():\\n for key2, value2 in publishers.items():\\n if key1 == key2:\\n average_cost[key1] = round(value1 / value2, 2)\\n return average_cost\",\n \"def analyze_edges_and_weight(list_of_nodes):\\n edges_info = []\\n for node in list_of_nodes:\\n n_edge_of_node = len(node.neighbors) # Counts the kys in the dictionary 'Node.neighbors'\\n total_weight_of_node = sum(list(map(lambda x: node.neighbors[x], node.neighbors))) # Sums values of the dict\\n node_info = (node.name, n_edge_of_node, total_weight_of_node)\\n edges_info.append(node_info)\\n total_n_edges = sum([tup[1] for tup in edges_info]) # Sum total number of edges\\n total_weight_of_graph = sum([tup[2] for tup in edges_info]) # Sum total weight of edges\\n sorted_info = sorted(edges_info, key=lambda tup: tup[1], reverse=True)\\n return \\\"Total number of edges is {},\\\\nTotal weight of the graph is {}:\\\\nNodes sorted by no. of edges: {}.\\\".format(total_n_edges, total_weight_of_graph, sorted_info)\",\n \"def similarities (self, listOfWords):\\n \\n # building the query dictionary\\n queryDict = collections.defaultdict(int)\\n for w in listOfWords:\\n queryDict [w] += + 1.0\\n \\n # normalizing the query\\n length = float (len (listOfWords))\\n for k in queryDict:\\n queryDict [k] /= length\\n \\n # computing the list of similarities\\n sims = []\\n for doc in self.documents:\\n score = 0.0\\n docDict = doc [1]\\n for k in queryDict:\\n if docDict.has_key (k):\\n score += (queryDict [k] / self.corpusDict [k]) + (docDict [k] / self.corpusDict [k])\\n sims.append ([doc [0], score])\\n \\n return sims\",\n \"def sentiment_analysis(name, dictionary):\\n\\ttone_analyzer = ToneAnalyzerV3(\\n\\t\\t username='2ed2f0c6-1722-472d-9126-224897b991af',\\n\\t\\t password='UcuSde1YmeK6',\\n\\t\\t version='2016-05-19')\\n\\tl = open(name + '.txt')\\n\\tlines = l.readlines()\\n\\tfeel_dict = {'Anger':1.0,'Fear':2.0, 'Sadness':3.0, 'Disgust':4.0,'Joy':5.0, 'Excitement':6.0}\\n\\tdictionary[name] = []\\n\\tfor i in lines:\\n\\t\\t#print('-----------------')\\n\\t\\t#print(i)\\n\\t\\tmax_score = 0.0\\n\\t\\tmax_feel = ''\\n\\t\\ttone = tone_analyzer.tone(i, 'emotion')\\n\\t\\tfor feel in tone['document_tone']['tone_categories']:\\n\\t\\t\\tfor feeling in feel['tones']:\\n\\t\\t\\t\\tif feeling['score'] > max_score:\\n\\t\\t\\t\\t\\tmax_score = feeling['score']\\n\\t\\t\\t\\t\\tmax_feel = feeling['tone_name']\\n\\t\\t#print(max_score, max_feel)\\n\\t\\t#blob1 = TextBlob(i, pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())\\n\\t\\tif max_feel != '':\\n\\t\\t\\ttweet_tbu = db.Tweet.objects(rating=feel_dict[max_feel]).first()\\n\\t\\t\\tdict_tbu = {}\\n\\t\\t\\tif tweet_tbu:\\n\\t\\t\\t\\tdict_tbu = mongo_to_dict(tweet_tbu)\\n\\t\\t\\t\\tprint('exists')\\n\\t\\t\\t\\tprint(dict_tbu)\\n\\t\\t\\t\\tif max_feel != '':\\n\\t\\t\\t\\t\\tnew_dict = {}\\n\\t\\t\\t\\t\\tnew_dict['tweet'] = dict_tbu['tweet']\\n\\t\\t\\t\\t\\tnew_dict['tweet'].append(i[0:-2])\\n\\t\\t\\t\\t\\ttweet_tbu.update(**new_dict)\\n\\t\\t\\t\\t\\ttweet_tbu.reload()\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint('not exists - with max')\\n\\t\\t\\t\\tnew_dict = {}\\n\\t\\t\\t\\tnew_dict['tweet'] = [i[0:-1]]\\n\\t\\t\\t\\tif max_feel != '':\\n\\t\\t\\t\\t\\tnew_dict['rating'] = feel_dict[max_feel]\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tnew_dict['rating'] = 0.0\\n\\t\\t\\t\\tprint(new_dict)\\n\\t\\t\\t\\tnew_tweet = db.Tweet(**new_dict)\\n\\t\\t\\t\\tnew_tweet.save()\\n\\t\\telse:\\n\\t\\t\\tprint('not exists - without')\\n\\t\\t\\tnew_dict = {}\\n\\t\\t\\tnew_dict['tweet'] = [i[0:-1]]\\n\\t\\t\\tif max_feel != '':\\n\\t\\t\\t\\tnew_dict['rating'] = feel_dict[max_feel]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tnew_dict['rating'] = 0.0\\n\\t\\t\\tprint(new_dict)\\n\\t\\t\\tnew_tweet = db.Tweet(**new_dict)\\n\\t\\t\\tnew_tweet.save()\\n\\tresult = db.Tweet.objects()\\n\\treturn(result)\",\n \"def get_distribution(doc):\\n word_count = {}\\n word_count = clean(doc, word_count)\\n factor = 1.0 / sum(word_count.values())\\n dist = {k: v * factor for k, v in word_count.items()}\\n return dist\",\n \"def get_shop_list_by_dishes(cook_dict, dishes, person_count):\\n temp_dict = dict()\\n for dish_name in dishes:\\n for dish_type, ingredient_list in cook_dict.items():\\n if dish_type == dish_name:\\n for ingr_dict in ingredient_list:\\n ingr_name = ingr_dict['ingredient_name']\\n if temp_dict.setdefault(ingr_name) is None:\\n temp_dict[ingr_name] = {\\n 'quantity': ingr_dict['quantity'] * person_count, 'measure': ingr_dict['measure']\\n }\\n else:\\n temp_dict[ingr_name]['quantity'] += ingr_dict['quantity'] * person_count\\n return temp_dict\",\n \"def get(soup, data, dictionary):\\n\\n # Steg 1: Ting som alltid er sant:\\n dictionary['fb_like'] = None #0\\n dictionary['others_share'] = None #0\\n dictionary['fb_share'] = len(soup.select(\\\".share-facebook\\\"))\\n dictionary['googleplus_share'] = len(soup.select(\\\".share-googleplus\\\"))\\n dictionary['twitter_share'] = len(soup.select(\\\".share-twitter\\\"))\\n dictionary['email_share'] = len(soup.select(\\\".share-mail\\\"))\\n\\n # tror ikke disse har noen aside...\\n dictionary['related_stories_box_les'] = len(soup.select(\\\"aside.articlewidgets article\\\"))\\n\\n # related thematic (found in footer part of page)\\n dictionary['related_stories_box_thematic'] = 0\\n # grab that footer part with data-relation-limit attr\\n related_thematic = soup.find_all(has_data_relation_limit)\\n # loop\\n for el in related_thematic:\\n #check divs\\n for div in el.select(\\\"div\\\"):\\n if has_data_id(div):\\n dictionary['related_stories_box_thematic'] +=1\\n\\n # re related stories is the combined previous two\\n dictionary['related_stories'] = dictionary['related_stories_box_les'] + dictionary['related_stories_box_thematic']\\n \\n\\n # antall js dokumenter\\n dictionary['js'] = count_js(soup, data, dictionary) # = len(re.findall(\\\"<iframe src=\\\", data)) # .js\\n # remove javascript.\\n [s.decompose() for s in soup.body.article('script')]\\n # I believe this is what creates the somewhat awkward line-breaks in the soup\\n\\n # Find author(s)\\n byline = soup.find('div', 'byline')\\n authors = []\\n try:\\n for address, li in izip(byline.find_all('address'), byline.find_all('li', 'icon-email')):\\n authorName = address.strong.text #address.find(class_='fn').string.encode('utf-8')\\n # NRK is still trying to hide the email address from spammers. #href = li.a['href']\\n authorMail = None # 'abandon this? too hard?'#unquote(href[21:-1])[7:] # Antakelsen er at epost vil holde seg til ASCII. \\n authorRole = address.span.text #address.find(class_='role').string.strip().encode('utf-8')\\n author = [authorName, authorMail, authorRole]\\n authors.append(author)\\n # and remove author image (so not to count it later..) \\n address.figure.decompose()\\n except AttributeError:\\n # Finner ingen forfatter(e)\\n new_logger.warn(\\\"Ingen forfattere \\\\\\\"{0}\\\\\\\". Oppgir \\\\\\\"<UKJENT>\\\\\\\" som forfatter\\\".format(dictionary['url']))\\n #print \\n authors.append([None, None, None])\\n dictionary['authors'] = authors\\n \\n # Find published datetime\\n try:\\n dictionary['published'] = strptime(soup.time['datetime'][0:19], \\\"%Y-%m-%dT%H:%M:%S\\\")\\n except TypeError:\\n new_logger.info(\\\"finner ikke publiseringsdato\\\")\\n dictionary['published'] = None\\n\\n new_logger.debug(\\\"published: %s\\\", type(dictionary['published']))\\n # Find update datetime\\n try:\\n updated = soup.find('span', 'update-date')\\n dictionary['updated'] = datetime.strptime(updated.time['datetime'][0:19], \\\"%Y-%m-%dT%H:%M:%S\\\")\\n except:\\n new_logger.info(\\\"finner ikke oppdateringsdato\\\")\\n dictionary['updated'] = None\\n\\n # Find headline\\n try:\\n dictionary['headline'] = soup.body.article.find('h1').text.strip()\\n #dictionary['headline'] = soup.header.find('div', 'articletitle').h1.text # .text gived unicode, .string gives 'bs4.element.NavigableString'\\n except AttributeError:\\n new_logger.debug( \\\"NB: bruker doc-title...\\\" )\\n dictionary['headline'] = soup.title.text\\n\\n # Find fact-boxes :\\n # Should be removes from body, but includes in LIX. Right?\\n faktabokser = []\\n #for boks in soup.find_all(\\\"section\\\", class_=\\\"articlewidget cf facts lp_faktaboks\\\"):\\n for boks in soup.find_all(\\\"section\\\", class_=\\\"facts\\\"):\\n faktaboks_text = boks.text.strip()\\n lix = Lix(faktaboks_text)\\n faktaboks_analysis = lix.analyzeText(faktaboks_text)\\n faktabokser.append({\\\"text\\\":faktaboks_text, \\\"links\\\":boks.find_all(\\\"a\\\"), \\\"wordcount\\\":faktaboks_analysis['wordCount']})\\n # and remove from soup\\n #boks.decompose() #ikke fjern boks'n fra suppa.\\n # NB, this also removes pictures if any in the fact-box\\n dictionary['factbox'] = faktabokser\\n\\n new_logger.debug(\\\"faktabokser: %s\\\", len(dictionary['factbox']))\\n\\n # Find full text \\n # article MINUS .universes OR is it .lp_related ?\\n # remove the related section\\n # try:\\n # soup.body.article.find('section', 'lp_related').decompose()\\n # except:\\n # pass\\n # # remove div.published (the top-bar)\\n # soup.body.article.find('div', 'published').decompose()\\n # # remove div.shareurl (the sharebar)\\n # soup.body.article.find('div', 'sharing').decompose()\\n\\n # Find self declared url # get this before decomposing the header this is found in..\\n dictionary['url_self_link'] = soup.select(\\\"time > a\\\")[0]['href']\\n\\n # remove header with sharing links and date\\n soup.select(\\\".bulletin-header\\\")[0].decompose()\\n # store body text\\n dictionary['body'] = soup.body.article.text.strip() \\n # .stripped_strings option?\\n # soup.get_text(\\\"|\\\", strip=True) perhaps?\\n\\n # Find char count, line count, word count and Lix\\n lix = Lix(dictionary['body']) \\n\\n analyse = lix.analyzeText(dictionary['body'])\\n try:\\n dictionary['char_count'] = len(dictionary['body'])\\n dictionary['word_count'] = analyse['wordCount']\\n dictionary['line_count'] = analyse['sentenceCount']\\n dictionary['lesbahet'] = analyse['lixScore']\\n except TypeError:\\n new_logger.error(\\\"Kunne ikke kjøre lix\\\", dictionary['body']) \\n dictionary['line_count'] = None\\n dictionary['word_count'] = None\\n dictionary['char_count'] = None\\n dictionary['lesbahet'] = -1.0\\n\\n # look through the last part of the body text to find news bureau\\n # add more in settings.py\\n dictionary['news_bureau'] = matches_pattern(dictionary['body'].strip()[-200:], syndicators)\\n\\n\\n # Find language. Defaults can be tampered with in settings.py\\n (language, certainty) = langid.classify(soup.body.article.text)\\n new_logger.debug( \\\"(language, certainty) (%s, %s)\\\" % (language, certainty))\\n language_code = uncertain_language_string\\n if (certainty > language_identification_threshold):\\n language_code = language\\n\\n dictionary['language'] = language_code\\n\\n\\n get_video(soup.body.article, data, dictionary)\\n\\n # flash (untested)\\n dictionary['flash_file'] = get_flash(soup.body.article, data, dictionary)\\n\\n # Tell opp iframe. \\n dictionary['iframe'] = count_iframes(soup, data, dictionary)\\n \\n # Tell opp css (karakterer)\\n dictionary['css'] = count_css(soup, data, dictionary)\\n\\n\\n # Finnes det en form for kommentarer her? I de nyere NRK sidene er det tydeligvis kun det på Ytring.\\n # Men vi søker generelt nå, og håper på det beste. I verste fall vil et interessant krasj fortelle meg at dette ikke er tilfellet. –Haakon\\n dictionary['comment_fields'] = 0\\n dictionary['comment_number'] = 0\\n if len(re.findall('<div id=\\\"disqus_thread\\\"', data)) != 0:\\n dictionary['comment_fields'] = 1\\n dictionary['comment_number'] = None # -9999#num_comments(dictionary)\\n \\n # tar seg av lenker i siden\\n count_links(soup, data, dictionary)\\n\\n # antall bilder.\\n # Beautiful Soup teller feil her og. Noe er galt.\\n # Regex matching gir riktig resultat så vi får gå for det.\\n #result = soup.article.find_all('figure', 'image')\\n #print len(result)\\n #new_logger.debug( \\\"antall bilder: %s\\\", len(re.findall(\\\"<img src=\\\\\\\"http:\\\", data)) )\\n \\n dictionary['images'] = count_images(soup.body.article, data, dictionary)\\n \\n # bildesamlinger\\n dictionary['image_collection'] = len(soup.select(\\\".slideshow\\\")) # er dette nok?\\n # Som diskutert med Eirik, dette henter ut bildetekstene og deler dem med pipe symboler.\\n\\n imgtagger = re.findall(u\\\"<img src=\\\\\\\"http.*\\\\n.*\\\", str(soup.body.article) )\\n bildetekst = \\\"\\\"\\n for imgtag in imgtagger:\\n funn = re.findall(\\\"alt=\\\\\\\".*\\\\\\\"\\\", imgtag)\\n if len(funn) > 0:\\n bildetekst += ((funn[0])[5:-1] + \\\" | \\\")\\n bildetekst = bildetekst[:-3] # Fjerner siste pipen\\n dictionary['image_captions'] = bildetekst\\n\\n\\n\\n dictionary['map'] = count_map(soup.body.article, data, dictionary)\\n dictionary['poll'] = None # -9999\\n dictionary['game'] = None # -9999\\n \\n dictionary['interactive_elements'] = count_interactive( \\\\\\n dictionary['comment_fields'] , dictionary['image_collection'] , \\\\\\n dictionary['video_files'] , dictionary['video_files_nrk'] , \\\\\\n dictionary['fb_like'] , dictionary['fb_share'] , \\\\\\n dictionary['googleplus_share'] , dictionary['twitter_share'] , \\\\\\n dictionary['others_share'] , dictionary['email_share'] , \\\\\\n dictionary['map'] , dictionary['poll'] , dictionary['game'])\\n \\n\\n\\n\\n\\n return dictionary\",\n \"def calculate_wasserstein(structure_list : List[str]) -> Dict:\\n from itertools import combinations\\n\\n # from collections import defaultdict\\n # wd_1d = defaultdict(list)\\n wd_1d = {}\\n # fill list with all the combinations from structure_list\\n combos = list(combinations(structure_list, 2))\\n\\n #parallelize to calculate wasserstein distance\\n from multiprocessing import Pool\\n with Pool(processes=4) as pool:\\n wds_1d = list(tqdm(pool.imap(multiprocessing_func, combos), total=len(combos)))\\n\\n # Hash structure_list tuple combos as key, value: wasserstein_distance\\n wd_dict = {combo: wd_1d for combo, wd_1d in zip(combos, wds_1d)}\\n return wd_dict\",\n \"def get_recipe_details(recipe_links):\\n cuisine_recipes = {}\\n for r in recipe_links:\\n soup_recipe = BeautifulSoup(r)\\n if \\\"www.chowhound.com\\\" in r.a[\\\"href\\\"]:\\n recipe = {}\\n recipe['r_link'] = r.a[\\\"href\\\"]\\n print \\\"recipe link: \\\", recipe['r_link']\\n soup_recipe = get_recipe(recipe['r_link'])\\n recipe['recipe title'] = get_recipe_title(soup_recipe)\\n recipe['chef'] = get_recipe_chef(soup_recipe)\\n recipe['description'] = get_description(soup_recipe)\\n recipe['ingredient list'] = get_recipe_ingredients(soup_recipe)\\n recipe['preperation steps'] = get_recipe_preperation(soup_recipe)\\n recipe['total_time'], recipe['active_time'] = get_recipe_time(soup_recipe)\\n recipe['servings'] = get_servings(soup_recipe)\\n recipe['skill_level'] = get_recipe_difficulty(soup_recipe)\\n recipe['rating'], recipe['rating count'] = get_ratings(soup_recipe)\\n recipe['nutritional_info'] = get_nutrition_per_serving(soup_recipe)\\n recipe['image_source'] = get_image_source(soup_recipe)\\n cuisine_recipes[recipe['recipe title']] = recipe\\n return cuisine_recipes\",\n \"def calc_assn_weights():\\n\\t\\n\\t\\t\\t#\\n\\t\\t\\t#\\n\\ttext(\\\"\\\"\\\"INSERT INTO assignments (mentor_id, course_id, cost)\\n\\t\\t\\tSELECT M.mentor_id, C.course_id, SUM(COALESCE(PW.weight_value,PT.def_weight_val))\\n\\t\\t\\tFROM mentors M, courses C\\n\\t\\t\\tJOIN course2pref C2P ON C2P.course_id = C.course_id\\n\\t\\t\\tJOIN prefs P ON P.pref_id = C2P.pref_id\\n\\t\\t\\tJOIN pref_types PT ON PT.pref_type_id = P.pref_type_id\\n\\t\\t\\tJOIN pref_weights PW ON PW.pref_type_id = P.pref_type_id\\n\\t\\t\\tLEFT JOIN choices Ch ON Ch.mentor_id = M.mentor_id AND Ch.weight_id = PW.pref_id\",\n \"def multi_filter(self, characteristic_dict):\\n\\n corpus_copy = self.clone()\\n corpus_copy.documents = []\\n\\n for metadata_field in characteristic_dict:\\n if metadata_field not in self.metadata_fields:\\n raise MissingMetadataError([metadata_field])\\n\\n for this_document in self.documents:\\n add_document = True\\n for metadata_field in characteristic_dict:\\n if metadata_field == 'date':\\n if this_document.date != int(characteristic_dict['date']):\\n add_document = False\\n else:\\n if getattr(this_document, metadata_field) != characteristic_dict[metadata_field]:\\n add_document = False\\n if add_document:\\n corpus_copy.documents.append(this_document)\\n\\n if not corpus_copy:\\n # displays for possible errors in field.value\\n err = f'This corpus is empty. You may have mistyped something.'\\n raise AttributeError(err)\\n\\n return corpus_copy\",\n \"def _calculate_feedback(self):\\r\\n age = self.age_field.value()\\r\\n height_cm = self.height_field.value()\\r\\n weight_kg = self.weight_field.value()\\r\\n body_fat = self.bodyFat_field.value()\\r\\n male = bool(self.gender_combobox.currentIndex())\\r\\n\\r\\n if self._mode != 'metric':\\r\\n height_cm *= 2.54\\r\\n weight_kg *= 0.454\\r\\n\\r\\n document = getWeightLogDocument(height_cm, weight_kg, age, body_fat, male, None, 1.2)\\r\\n self.feedback_field.setHtml(document)\",\n \"def mutual_information(x, y, w):\\r\\n \\r\\n\\r\\n total_entropy = entropy(y,w)\\r\\n\\r\\n partitioned_x = partition(x)\\r\\n weighted_entropy = 0\\r\\n # calculate the weighted entropy over the partition of x\\r\\n vals,counts= np.unique(x,return_counts=True)\\r\\n for key in partitioned_x:\\r\\n weighted_entropy += np.sum([(np.sum(w[partitioned_x[key]])/np.sum(w)) * entropy(y[partitioned_x[key]],w[partitioned_x[key]])])\\r\\n\\r\\n information_gain = total_entropy - weighted_entropy\\r\\n return information_gain\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60321796","0.5845413","0.5744994","0.56930727","0.5662978","0.56200105","0.55414337","0.5474391","0.5392177","0.5307543","0.5281071","0.5271394","0.52353287","0.52269924","0.5194141","0.5171498","0.51695114","0.5145369","0.5134784","0.5106476","0.5103909","0.5089404","0.5066094","0.5065613","0.50632304","0.5058637","0.50579464","0.5025072","0.5017804","0.5015852","0.5010922","0.50071764","0.50049317","0.50045234","0.5000722","0.49978366","0.49792853","0.4973382","0.49524483","0.49456015","0.48857182","0.48849827","0.48830146","0.48730367","0.48612383","0.48582262","0.48524848","0.48513812","0.4851083","0.48438275","0.48228037","0.48222488","0.48207963","0.4817444","0.481709","0.48159483","0.48122767","0.48060247","0.48013356","0.47869658","0.47783086","0.4771876","0.47667015","0.47657004","0.47635144","0.47605404","0.4759255","0.47575566","0.47561604","0.47349486","0.47336265","0.4729211","0.47248772","0.47237793","0.47160187","0.4713388","0.47132757","0.47100708","0.47034988","0.47000423","0.46969068","0.46937153","0.46936452","0.4693102","0.46929416","0.4692612","0.46834347","0.4682221","0.46796528","0.46790597","0.46740386","0.46731523","0.46727106","0.46683016","0.4667983","0.46654913","0.46627596","0.46578965","0.46520817","0.46500203"],"string":"[\n \"0.60321796\",\n \"0.5845413\",\n \"0.5744994\",\n \"0.56930727\",\n \"0.5662978\",\n \"0.56200105\",\n \"0.55414337\",\n \"0.5474391\",\n \"0.5392177\",\n \"0.5307543\",\n \"0.5281071\",\n \"0.5271394\",\n \"0.52353287\",\n \"0.52269924\",\n \"0.5194141\",\n \"0.5171498\",\n \"0.51695114\",\n \"0.5145369\",\n \"0.5134784\",\n \"0.5106476\",\n \"0.5103909\",\n \"0.5089404\",\n \"0.5066094\",\n \"0.5065613\",\n \"0.50632304\",\n \"0.5058637\",\n \"0.50579464\",\n \"0.5025072\",\n \"0.5017804\",\n \"0.5015852\",\n \"0.5010922\",\n \"0.50071764\",\n \"0.50049317\",\n \"0.50045234\",\n \"0.5000722\",\n \"0.49978366\",\n \"0.49792853\",\n \"0.4973382\",\n \"0.49524483\",\n \"0.49456015\",\n \"0.48857182\",\n \"0.48849827\",\n \"0.48830146\",\n \"0.48730367\",\n \"0.48612383\",\n \"0.48582262\",\n \"0.48524848\",\n \"0.48513812\",\n \"0.4851083\",\n \"0.48438275\",\n \"0.48228037\",\n \"0.48222488\",\n \"0.48207963\",\n \"0.4817444\",\n \"0.481709\",\n \"0.48159483\",\n \"0.48122767\",\n \"0.48060247\",\n \"0.48013356\",\n \"0.47869658\",\n \"0.47783086\",\n \"0.4771876\",\n \"0.47667015\",\n \"0.47657004\",\n \"0.47635144\",\n \"0.47605404\",\n \"0.4759255\",\n \"0.47575566\",\n \"0.47561604\",\n \"0.47349486\",\n \"0.47336265\",\n \"0.4729211\",\n \"0.47248772\",\n \"0.47237793\",\n \"0.47160187\",\n \"0.4713388\",\n \"0.47132757\",\n \"0.47100708\",\n \"0.47034988\",\n \"0.47000423\",\n \"0.46969068\",\n \"0.46937153\",\n \"0.46936452\",\n \"0.4693102\",\n \"0.46929416\",\n \"0.4692612\",\n \"0.46834347\",\n \"0.4682221\",\n \"0.46796528\",\n \"0.46790597\",\n \"0.46740386\",\n \"0.46731523\",\n \"0.46727106\",\n \"0.46683016\",\n \"0.4667983\",\n \"0.46654913\",\n \"0.46627596\",\n \"0.46578965\",\n \"0.46520817\",\n \"0.46500203\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5646937"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":79590,"cells":{"query":{"kind":"string","value":"Insert the reference and citation list into the database"},"document":{"kind":"string","value":"def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\n selfdic, authorcitdic):\n insert_into_cit_db(reference_dic,\"reversedict\")\n insert_into_cit_db(citation_dic,\"citationdict\")\n insert_into_cit_db(selfcbdic,\"selfcitedbydict\")\n insert_into_cit_db(selfdic,\"selfcitdict\")\n\n for a in authorcitdic.keys():\n lserarr = (serialize_via_marshal(authorcitdic[a]))\n #author name: replace \" with something else\n a.replace('\"', '\\'')\n a = unicode(a, 'utf-8')\n try:\n ablob = run_sql(\"select hitlist from rnkAUTHORDATA where aterm = %s\", (a,))\n if not (ablob):\n #print \"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\" , (a,lserarr)\n run_sql(\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\",\n (a,lserarr))\n else:\n #print \"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\"\"\" , (lserarr,a)\n run_sql(\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\",\n (lserarr,a))\n except:\n register_exception(prefix=\"could not read/write rnkAUTHORDATA aterm=\"+a+\" hitlist=\"+str(lserarr), alert_admin=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def insert_into_citation_table(citations):\n cursor = connection.cursor()\n cursor.execute(\n 'DROP TABLE IF EXISTS Citations;'\n 'CREATE TABLE Citations(sourcePaperId INT, targetPaperId INT, citationId INT NOT NULL AUTO_INCREMENT PRIMARY KEY);'\n )\n cursor.close()\n i = 0\n\n for citation in citations:\n print i\n i+=1\n cursor = connection.cursor()\n cursor.execute(\n 'INSERT INTO Citations (sourcePaperId, targetPaperId) VALUES '\n '((SELECT PaperId FROM Papers WHERE DOI = %s),(SELECT PaperId FROM Papers WHERE DOI = %s));'\n ,[citation['source'].encode('UTF-8'),citation['target'].encode('UTF-8')])\n cursor.close()","def process_citations(conn: Connection, path: Path) -> None:\n sql = \"INSERT OR IGNORE INTO Citations (note, reference) VALUES (?, ?)\"\n run_sql_on_csv(conn, path, sql, (int, str))","def main():\n papers_with_references = add_references_to_papers(PAPERS_FILE, TEXT_DIR)\n citations = match_citations_with_papers(papers_with_references)\n insert_into_citation_table(citations)","def insert_books_data():\n # Get data from csv file\n print(\"Getting data from csv..\")\n file = open(\"books.csv\")\n reader = csv.reader(file)\n\n # Insert csv data into table\n print(\"Inserting data into 'books' table..\")\n for isbn, title, author, year in reader:\n try:\n db.execute(\"INSERT INTO books (isbn, title, author, year)\\\n VALUES (:isbn, :title, :author, :year)\", {\n \"isbn\": isbn, \"title\": title, \"author\": author, \"year\": year })\n except exc.DataError as err:\n print(\"Invalid entry in csv file\")\n db.commit()\n print(\"Data inserted\")","def store_news(list_of_news, connection, url):\n cursor = connection.cursor()\n list_of_values = []\n for item in list_of_news:\n new_date = item.date.strftime('%Y%m%d')\n new_article = [item.title, item.link, item.date, new_date, item.source, item.description, item.image]\n list_of_values.append(new_article)\n\n sql = \"INSERT OR REPLACE INTO news VALUES (?, ?, ?, ?, ?, ?, ?, ?)\"\n cursor.execute(sql, (new_article[0], new_article[1], new_article[2], new_article[3], new_article[4],\n new_article[5], new_article[6], url))\n connection.commit()","def insertionGenero (cur, conn, genre_list):\n for genre in genre_list :\n idGenero = genre[0]\n genero=genre[1]\n # print(generoInsert.format(idTitulo,genero))\n # REGISTER DATA IN GENERO TABLE\n cur.execute(generoInsert.format(idGenero,genero))\n conn.commit()","def insert_into_db(self, database):\n\n # insert person\n keys = \"\"\n values = \"\"\n for key, value in self.person.items():\n # location\n if key == \"location\":\n # ensure location is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \n VALUES((SELECT location_id \n FROM p21_cdm.location\n WHERE city='{self.person['location']['city']}' \n and zip='{self.person['location']['zip']}'), \n {values[:-1]})\"\"\")\n\n # insert visits\n for visit in self.visits:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n for key, value in visit.items():\n if key == \"care_site_name\":\n # ensure care site is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \n FROM p21_cdm.care_site \n WHERE care_site_name='{value}') \n THEN INSERT INTO p21_cdm.care_site (care_site_name) \n VALUES ('{value}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \n VALUES((SELECT care_site_id\n FROM p21_cdm.care_site\n WHERE care_site_name='{visit['care_site_name']}'),\n {values[:-1]}) \n RETURNING visit_occurrence_id\"\"\")\n\n # insert measurements, observations, conditions & procedures\n for data, tablename in [(self.measurements, \"measurement\"),\n (self.observations, \"observation\"),\n (self.conditions, \"condition_occurrence\"),\n (self.procedures, \"procedure_occurrence\")]:\n for entry in data:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n\n for key, value in entry.items():\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n entry[\"sql_id\"] = database.select(f\"\"\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\n VALUES({values[:-1]}) RETURNING {tablename}_id\"\"\")[0][0]\n\n # insert fact_relationships in both directions\n for table1, entry1, table2, entry2 in self.fact_relations:\n # 44818890 = Finding associated with (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\"\"\")\n # 44818792 = Associated with finding (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\"\"\")\n\n # make transactions persistent\n database.commit()","def insert(self, db):\n if not self.content:\n raise ValueError(\"SequenceList content property must be filled in before calling save.\")\n if not self.title:\n raise ValueError(\"SequenceList title property must be filled in before calling save.\")\n seq_item_list = SequenceListItems(self.content)\n cur = db.cursor()\n self._insert_data(cur, seq_item_list, self.title)\n cur.close()\n db.commit()","def main2():\n\t\n\tcu_locations = cu_locations_data();\n\t\n\tfor row in cu_locations:\n\t\tprint \"INSERT INTO contact ('ref_id') VALUES (%s);\" % ( row['location_id'] );","def fillTweetInDB(self):\n sqlInsertTweets = \"INSERT INTO tweet content VALUES %s\"\n mycursor.executemany(sqlInsertTweets,self.content)\n mydb.commit()","def insert(self, url, values):\n pass","def fill(self):\n if self.source == 'citations':\n url = self._scholarly.URLS(\"CITATIONPUB\").format(self.id_citations)\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\n\n if soup.find('a', class_='gsc_vcd_title_link'):\n self.bib['url'] = soup.find(\n 'a', class_='gsc_vcd_title_link')['href']\n\n for item in soup.find_all('div', class_='gs_scl'):\n key = item.find(class_='gsc_vcd_field').text\n val = item.find(class_='gsc_vcd_value')\n if key == 'Authors':\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\n elif key == 'Journal':\n self.bib['journal'] = val.text\n elif key == 'Volume':\n self.bib['volume'] = val.text\n elif key == 'Issue':\n self.bib['number'] = val.text\n elif key == 'Pages':\n self.bib['pages'] = val.text\n elif key == 'Publisher':\n self.bib['publisher'] = val.text\n elif key == 'Publication date':\n self.bib['year'] = arrow.get(val.text).year\n elif key == 'Description':\n if val.text[0:8].lower() == 'abstract':\n val = val.text[9:].strip()\n self.bib['abstract'] = val\n elif key == 'Total citations':\n self.id_scholarcitedby = re.findall(\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\n\n # number of citation per year\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\n self.cites_per_year = dict(zip(years, cites))\n\n if soup.find('div', class_='gsc_vcd_title_ggi'):\n self.bib['eprint'] = soup.find(\n 'div', class_='gsc_vcd_title_ggi').a['href']\n self._filled = True\n\n elif self.source == 'scholar':\n self.bib['add_to_lib'] = self.url_add_sclib\n\n try:\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\n bibtex = bibtex.find('pre').string\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\n self.bib['author_count'] = str(\n len(self.bib['author'].split('and')))\n self.bib['age'] = str(\n int(date.today().year) - int(self.bib['year']))\n except:\n # did not find year\n pass\n\n self._filled = True\n return self","def insert(self, *args):\n return _ida_frame.xreflist_t_insert(self, *args)","def insert_into_solr():\n solr = pysolr.Solr('http://localhost:8983/solr/mag_journals', always_commit=True)\n filepath = '/vol1/mag/data/2018-07-19/dumps/Journals.txt'\n\n list_for_solr = []\n with open(filepath, \"r\") as file:\n csv_reader = csv.reader(file, delimiter='\\t')\n for journal_id, rank, normalized_name, display_name, issn, publisher, webpage, paper_count, citation_count, created_date in csv_reader:\n solr_record = {}\n solr_record['journal_id'] = journal_id\n solr_record['rank'] = rank\n solr_record['normalized_name'] = normalized_name\n solr_record['display_name'] = display_name\n solr_record['issn'] = issn\n solr_record['publisher'] = publisher\n solr_record['webpage'] = webpage\n solr_record['paper_count'] = paper_count\n solr_record['citation_count'] = citation_count\n solr_record['created_date'] = created_date\n list_for_solr.append(solr_record)\n # Upload to Solr: 48000-odd rows\n solr.add(list_for_solr)","def insert_into_solr(filepath):\n #filepath = '/vol1/mag/data/2018-07-19/dumps/PaperCitationContexts.txt'\n record_number = 0\n list_for_solr = []\n with open(filepath, \"r\") as file:\n # THERE ARE NULL BYTES WHICH MAKE CSV THROW AN ERROR. Replace them \n csv_reader = csv.reader((line.replace('\\0', '') for line in file), delimiter='\\t', quoting=csv.QUOTE_NONE)\n for paper_id, paper_reference_id, citation_context in csv_reader:\n #for record in csv_reader:\n #paper_id, paper_reference_id, citation_context = record\n record_number += 1\n solr_record = {}\n solr_record['paper_id'] = paper_id\n solr_record['paper_reference_id'] = paper_reference_id\n solr_record['citation_context'] = citation_context\n # Chunks of 500000\n if record_number % 25000 == 0:\n list_for_solr.append(solr_record)\n try:\n solr.add(list_for_solr)\n except Exception as e:\n print(e, record_number, filepath)\n list_for_solr = []\n print(record_number)\n else:\n list_for_solr.append(solr_record)\n #print(record_number)\n try:\n solr.add(list_for_solr)\n except Exception as e:\n print(e, record_number, filepath)","def insert_db():\n populate_tables()","def add_student_data(connection,fname,lname,class_n,marks):\r\n with connection:\r\n connection.execute(INSERT_STUDENT,(fname,lname,class_n,marks))","def insert_values():\n pass","def insertValues(defined_symbols,mysql,list_of_files,last_name,name,newfile,rowd):\n\n\tcrs = mysql.cursor()\n\tfor j in list_of_files:\n\t\tfn = j.replace(os.sep,'=')\n\t\tif newfile and rowd != None:\n\t\t\tif fn in rowd:\n\t\t\t\tcontinue \n\t\ttry:\t\t\t\t\n\t\t\tltp = defined_symbols[name][j] # list of tuples (line number reference)\n\t\t\tsql1 = '\"%d\", \"%s\"' % (last_name,fn)\n\t\t\tsql = 'INSERT INTO files (name,filename) VALUES ('+sql1+');'\n\t\t\tcrs.execute(sql)\n\t\t\tlast_file = crs.lastrowid\n\t\t\tfor k in ltp:\n\t\t\t\tif k[1] == DEFINED:\n\t\t\t\t\ttp = \"defin\"\n\t\t\t\telif k[1] == USED:\n\t\t\t\t\ttp = \"refer\"\n\t\t\t\telif k[1] == MACRO:\n\t\t\t\t\ttp = \"macro\"\n\t\t\t\telse:\n\t\t\t\t\ttp = \"both \"\n\t\t\ts = str(k[0])\n\t\t\tsql1 = '\"%d\", %s, \"%s\"' % (last_file,s,tp)\n\t\t\tsql = 'INSERT INTO linesinfile (filename,number,ref) VALUES ('+sql1+');'\n\t\t\tcrs.execute(sql)\n\t\texcept TypeError as e:\n\t\t\tprint \"ERROR (\"+str(e)\n\t\t\tprint name + \" , \"+ j \n\t\t\tprint ltp\n\t\t\tsys.exit()\n\t\texcept mysql.Error as err:\n\t\t\tprint 'ERROR DataBase '+str(err)\n\t\t\tmysql.rollback() \n\tmysql.commit()\n\tcrs.close()","def importCrisis ( c, crisisInstance ): \n assert str(type(c)) == \"<type '_mysql.connection'>\"\n assert str(type(crisisInstance)) == \"<type 'instance'>\"\n crisisID = crisisInstance.attrib[\"crisisIdent\"]\n \n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\n allLocations = crisisInstance.findall(\"Location\")\n if len(allLocations) != 0:\n for instance in allLocations:\n if len ( instance ) == 2 :\n sqlQuery ( c, \"insert into CrisisLocations values ( '\"+crisisID+\"', '\"+instance[0].text+\"', 'Null', '\"+instance[1].text+\"');\")\n else :\n sqlQuery ( c, \"insert into CrisisLocations values ( '\"+crisisID+\"', '\"+instance[0].text+\"', '\"+instance[1].text+\"', '\"+instance[2].text+\"');\")\n\n #Gets list of all resources needed. Iterates through lists and inserts into ResourceNeeded table\n resourcesNeeded = crisisInstance.findall(\"ResourceNeeded\")\n\n if len(resourcesNeeded) != 0:\n for instance in resourcesNeeded:\n sqlQuery ( c, \"insert into ResourceNeeded values ( '\"+crisisID+\"', '\"+instance.text+\"');\")\n \n #Gets all URL's in a list. Indexes list, splices tag to get type, inserts data into table\n externalResources = crisisInstance.find(\"ExternalResources\")\n \n#Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\n if len(externalResources) != 0:\n for instance in externalResources:\n sqlQuery ( c, \"insert into CrisisExternalResources values ( '\"+crisisID+\"', '\"+instance.tag+\"', '\"+instance.text+\"');\")\n \n\n #Gets list of all RelatedPeople and inserts into CrisesToPeople table\n relatedPeople = crisisInstance.findall(\".//\" + \"RelatedPerson\")\n\n if len(relatedPeople) != 0:\n for instance in relatedPeople:\n sqlQuery ( c, \"insert into CrisesToPeople values ( '\"+crisisID+\"', '\"+instance.attrib[\"personIdent\"]+\"');\")\n\n #Gets list of all RelatedOrganizations and inserts into OrganizationsToCrises table\n relatedOrgs = crisisInstance.findall(\".//\" + \"RelatedOrganization\")\n\n if len(relatedOrgs) != 0:\n for instance in relatedOrgs:\n sqlQuery ( c, \"insert into OrganizationsToCrises values ( '\"+instance.attrib[\"organizationIdent\"]+\"', '\"+crisisID+\"');\")\n\n #Gets all tags titled WaysToHelp, and inserts into corresponding table by indexing the list they're stored in\n waysToHelp = crisisInstance.findall(\"WaysToHelp\")\n \n if len(waysToHelp) != 0:\n for instance in waysToHelp:\n sqlQuery ( c, \"insert into WaysToHelp values ( '\"+crisisID+\"', '\"+instance.text+\"');\")\n\n #Gets list of HumanImpact elements. Iterates through list and inserts into HumanImpact table\n humanImpact = crisisInstance.findall(\"HumanImpact\")\n\n if len(humanImpact) !=0:\n for instance in humanImpact:\n sqlQuery ( c, \"insert into HumanImpact values ( '\"+crisisID+\"', '\"+instance[0].text+\"', '\"+instance[1].text+\"');\")\n\n #Finds values of remaining elements and inserts into Crises table\n name = crisisInstance.find(\"Name\").text\n kind = crisisInstance.find(\"Kind\").attrib[\"crisisKindIdent\"]\n startDateTime = crisisInstance.find(\"StartDateTime\")\n startDate = startDateTime[0].text\n if len(startDateTime) > 1:\n startTime = startDateTime[1].text\n else:\n startTime = '00:00:00'\n endDateTime = crisisInstance.find(\"EndDateTime\")\n if endDateTime != None :\n endDate = endDateTime[0].text\n if len(endDateTime) > 1:\n endTime = endDateTime[1].text\n else:\n endTime = '00:00:00'\n else :\n endDate = startDate\n endTime = '23:59:59'\n econImpact = crisisInstance.find(\"EconomicImpact\").text\n\n sqlQuery ( c, \"insert into Crises values ( '\"+crisisID+\"', '\"+name+\"', '\"+kind+\"', '\"+startDate+\"', '\"+startTime+\"', '\"+endDate+\"', '\"+endTime+\"', '\"+econImpact+\"');\")","def put_in_db(dbase,defined_symbols,createDB):\n\t\n\tmysql = DB.connect(host=\"127.0.0.1\",user=myutils.mysqluser,passwd=myutils.mysqlpwd) \n\tif createDB:\n\t createDataBase(mysql,dbase)\n\ttry:\n\t mysql.select_db(dbase)\n\texcept : \n\t print 'DataBase '+dbase+ ' does not exist'\n\t return\n\treferenceKeys = sorted(defined_symbols.iterkeys())\n\n\tcr = mysql.cursor()\n\ttry:\n\t\tif createDB:\n\t\t\tfor i in referenceKeys:\n\t\t\t\tdict_of_files = defined_symbols[i]\t\t\t\t\t\t# dict of files:list of tuples\n\t\t\t\tif len(dict_of_files) > 0:\n\t\t\t\t\tlist_of_files = sorted(dict_of_files.iterkeys()) \t# list of tuples (file , list of tuples)\n\t\t\t\t\tsql1 = '\"%s\"' % (i)\n\t\t\t\t\tsql = \"INSERT INTO names (name) VALUES (\"+sql1+\");\"\n\t\t\t\t\tcr.execute(sql)\n\t\t\t\t\tmysql.commit()\n\t\t\t\t\tlast_name = cr.lastrowid\n\t\t\t\t\tinsertValues(defined_symbols,mysql,list_of_files,last_name,i,False,0)\n\t\t\tallfilesd = buildallfiles(defined_symbols)\n\t\t\tcrs = mysql.cursor()\n\t\t\tfor j in allfilesd.keys():\n\t\t\t\tfn = j.replace(os.sep,'=')\n\t\t\t\tsql1 = '\"%s\"' % (fn)\n\t\t\t\tsql = \"INSERT INTO allfiles (file) VALUES (\"+sql1+\");\"\n\t\t\t\tcr.execute(sql)\n\t\t\t\tmysql.commit()\n\t\t\t\tlast_file = cr.lastrowid\n\t\t\t\tfor n in allfilesd[j]:\n\t\t\t\t\tsql1 = '\"%d\", \"%s\"' % (last_file,n)\n\t\t\t\t\tsql = 'INSERT INTO allnames (file,name) VALUES ('+sql1+');'\n\t\t\t\t\tcrs.execute(sql)\n\t\t\tmysql.commit()\n\t\t\tcrs.close()\n\t\telse:\n\t\t\tfor i in referenceKeys:\n\t\t\t\tsql2 = ''\n\t\t\t\tdict_of_files = defined_symbols[i]\t\t\t\t\t# dict of files:list of tuples\n\t\t\t\tlist_of_files = sorted(dict_of_files.iterkeys()) \t# list of tuples (file , list of tuples)\n\t\t\t\tsql1= '\"%s\"' % (i)\n\t\t\t\tsql = \"SELECT * FROM names WHERE name=\"+ sql1+\";\"\n\t\t\t\tcr.execute(sql)\n\t\t\t\tmysql.commit()\n\t\t\t\trow = cr.fetchone()\n\t\t\t\tif row != None:\n\t\t\t\t\tsql2 = '\"%d\"' % (row[0])\n\t\t\t\t\tsql = 'SELECT * FROM files WHERE name='+ sql2+\";\"\n\t\t\t\t\tcr.execute(sql)\n\t\t\t\t\trowd = cr.fetchall()\n\t\t\t\t\tinsertValues(defined_symbols,mysql,list_of_files,row[0],i,True,rowd)\n\t\t\t\telse:\n\t\t\t\t\tsql = \"INSERT INTO names (name) VALUES (\"+sql1+\");\"\n\t\t\t\t\tcr.execute(sql)\n\t\t\t\t\tmysql.commit()\n\t\t\t\t\tlast_name = cr.lastrowid\n\t\t\t\t\tinsertValues(defined_symbols,mysql,list_of_files,last_name,i,False,0)\n\n\texcept mysql.Error as err:\n\t\tprint 'ERROR DataBase '+str(err)\n\t\tmysql.rollback() \n\t\treturn\n\n\tcr.close()\n\tmysql.close()","def insert(title, author, year, isbn,shelf,raw):\n\n conn_obj = mysql.connector.connect(host='localhost',database='mydb',user='root',password='kks')\n cur_obj = conn_obj.cursor()\n sql=\"INSERT INTO book (title, author, year, isbn,shelf,raw) VALUES(%s, %s, %s, %s, %s, %s)\"\n cur_obj.execute(sql,(title, author, year, isbn,shelf,raw))\n conn_obj.commit()\n conn_obj.close()","def insert(sql, clue):\n\t# clue is [game, airdate, round, category, value, clue, answer]\n\t# note that at this point, clue[4] is False if round is 3\n\tif \"\\\\\\'\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\'\", \"'\")\n\tif \"\\\\\\\"\" in clue[6]:\n\t\tclue[6] = clue[6].replace(\"\\\\\\\"\", \"\\\"\")\n\tif not sql:\n\t\tprint clue\n\t\treturn\n\tsql.execute(\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\", (clue[0], clue[1], ))\n\tsql.execute(\"INSERT OR IGNORE INTO categories(category) VALUES(?);\", (clue[3], ))\n\tcategory_id = sql.execute(\"SELECT id FROM categories WHERE category = ?;\", (clue[3], )).fetchone()[0]\n\tclue_id = sql.execute(\"INSERT INTO documents(clue, answer) VALUES(?, ?);\", (clue[5], clue[6], )).lastrowid\n\tsql.execute(\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\", (clue[0], clue[2], clue[4], ))\n\tsql.execute(\"INSERT INTO classifications VALUES(?, ?)\", (clue_id, category_id, ))","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def insertionAno(cur, conn, date_list):\n for date in date_list:\n idAno = date[0]\n ano=date[1][0]\n fechaEstreno=date[1][1]\n # print(anoInsert.format(idAno,ano,fechaEstreno))\n # REGISTER ANO IN ACTOR TABLE\n cur.execute(anoInsert.format(idAno,ano,fechaEstreno))\n conn.commit()","def on_submit(self):\n\n\t\tfor accounting_entry in self.get('accounting_entries'):\n\t\t\tledger_entry_doc = frappe.get_doc({\n\t\t\t\t'doctype': 'Ledger Entry',\n\t\t\t\t'posting_date': self.posting_date,\n\t\t\t\t'account': accounting_entry.account,\n\t\t\t\t'debit': accounting_entry.debit,\n\t\t\t\t'credit': accounting_entry.credit,\n\t\t\t\t'voucher_type': 'Journal Entry',\n\t\t\t\t'voucher_number': self.name,\n\t\t\t\t'company': self.company\n\t\t\t})\n\t\t\tledger_entry_doc.insert()","def saveData(self):\r\n newPaper = self.getData()\r\n\r\n cur = self.dbConn.execute(\"SELECT PaperID FROM Papers WHERE Title = ?\",[self.title.getVal()])\r\n res = cur.fetchone()\r\n if not res==None:\r\n self.dbConn.execute(\"DELETE FROM CoAuthors WHERE Paper = ?\",[res[\"PaperID\"]])\r\n self.dbConn.execute(\"DELETE FROM Papers WHERE Title = ?\",[self.title.getVal()])\r\n\r\n sqlStr = str.format(\"INSERT into {} ({}) VALUES ({})\",self.dataTable,\",\".join(self.dataCols),\"?\"+\",?\"*(len(self.dataCols)-1))\r\n newCur = self.dbConn.execute(sqlStr,[newPaper.symposium, newPaper.title,newPaper.primeAuthor,newPaper.correspond])\r\n\r\n newRow = newCur.lastrowid\r\n if len(newPaper.coauthors)>0:\r\n for coauth in newPaper.coauthors:\r\n sqlStr = str.format(\"INSERT into {} ({}) VALUES ({})\",self.coauthorTable,\",\".join(self.coauthorCols),\"?\"+\",?\"*(len(self.coauthorCols)-1))\r\n newCur = self.dbConn.execute(sqlStr,[newRow,coauth])\r\n\r\n self.dbConn.commit()\r\n self.clearData()","def addRow(conn, title, url, text):\n with conn.cursor() as cursor:\n sql = \"INSERT INTO comments(course_name, course_url, course_comment) VALUES (%s, %s, %s)\"\n cursor.execute(sql,(title, url, text))\n conn.commit()","def add_complaint():\n complaint = {\n 'complaint_timestamp': datetime.datetime.now(),\n 'complaint_type': 'objectionable',\n 'process_status': 'complaint',\n 'display_status': 'good',\n 'pinner_id': 4,\n 'content_id': 11\n } \n complaints = Table('complaints', metadata, autoload=True)\n i = complaints.insert()\n out = i.execute(complaint)\n complaint['complaint_id'] = out.lastrowid\n \n print(\"complaint_id {}\".format(complaint['complaint_id']))\n\n # get the value from the db\n rs = complaints.select().where(complaints.c.complaint_id == complaint['complaint_id'])\n rrs = rs.execute()\n for r in rrs:\n rcomplaint = dict(r)\n print(\"length {}, value {}\".format(len(rcomplaint), rcomplaint))\n \n # now update the value\n rs = complaints.update().where(complaints.c.complaint_id == complaint['complaint_id']).\\\n values(reviewer_id=2, process_status='done')\n rs.execute()\n \n rs = complaints.select()\n run(rs, text='complaints')\n \n # now delete all entries\n rs = complaints.delete()\n rs.execute()\n \n rs = complaints.select()\n run(rs, text='complaints')","def insert_supplier(conn, cursor, supplier: Supplier) -> None:\n cursor.execute(\n \"INSERT INTO supplier values(?, ?, ?)\",\n (supplier.name, supplier.pull_url, supplier.rating),\n )\n conn.commit()","def fill_target_table(new_data, curs, conn, overwrite=False):\n for i in new_data:\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\n conn.commit()","def populate_tables(self, data_book, data_author, datafile_name, initial_stock=20):\n\n print(\"\\nPopulating book table with input data from\", datafile_name, \"...\", end='')\n count = 0\n failed_books = []\n for book in data_book:\n try:\n date = datetime.datetime.strptime(book[7], '%m/%d/%Y').date()\n t = (book[0], book[1], book[8], book[3], date,\n int(book[4]), initial_stock, book[9])\n self.cursor.execute(\n \"\"\"INSERT INTO book (ISBN, title, publisher, lang, publicationDate, pageCount, stock, price) \n VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\"\"\", t)\n except Exception as e:\n count = count + 1\n failed_books.append(t[1])\n if failed_books:\n print(\"\\nSome books were not added to the database because they had an invalid format:\")\n for book in failed_books:\n print(book)\n print(\"\\nTotal books not included in database: \", count)\n self.cursor.execute(\n \"\"\"SELECT COUNT(*)\n FROM book\"\"\")\n num_successful = self.cursor.fetchall()\n print(num_successful[0][0], \"books successfully inserted into table \\\"Book\\\".\")\n self.db.commit()\n print(\"done\")\n # Now we populate authors. First need to get all ISBNs of books that were added to the book table\n print(\"\\nAdding authors to \\\"Author\\\" table...\", end='')\n self.cursor.execute(\"SELECT ISBN FROM Book\")\n list_books = [book[0] for book in self.cursor.fetchall()]\n\n for author in data_author:\n self.cursor.execute(\"INSERT INTO author (name) VALUES (%s)\", (author,))\n self.db.commit()\n for book in data_author[author]:\n if book in list_books:\n self.cursor.execute(\"SELECT ID FROM author WHERE name = %s\", (author,))\n auth_id = self.cursor.fetchone()[0]\n self.cursor.execute(\"INSERT IGNORE INTO wrote VALUES (%s,%s)\", (auth_id, book))\n self.db.commit()\n print(\"done\")\n # # Finally, populate HasKeyword table. For now just add words in title and author names\n # print(\"\\nGenerating keywords for \\\"HasKeyword\\\" table...\", end='')\n # for book in list_books:\n # self.cursor.execute(\"SELECT title from book WHERE ISBN = %s\", (book,))\n # keywords = [i[0].split(' ') for i in self.cursor.fetchall()]\n # self.cursor.execute(\"SELECT name FROM author A, wrote W WHERE A.ID = W.authorID AND W.ISBN = %s\", (book,))\n # authors = [i[0].split(' ') for i in self.cursor.fetchall()]\n #\n # keywords.extend(authors)\n # for word_subset in keywords:\n # for word in word_subset:\n # if not word.isspace() and word:\n # self.cursor.execute(\"INSERT IGNORE INTO HasKeyword VALUES(%s,%s)\", (book, word))\n # self.db.commit()\n # print(\"done\")","def save_data(data_tuple_list: list):\n con = sqlite3.connect(\"thecomradenews.db\")\n cur = con.cursor()\n try:\n cur.execute('''CREATE TABLE articles\n (source text,\n title text,\n link text,\n pubdate date,\n guid integer,\n description text,\n content text,\n categories text,\n image_link TEXT,\n UNIQUE(source, title, link, pubdate, guid, description, content, categories, image_link)\n )''')\n except sqlite3.OperationalError as e:\n print(e)\n for i in data_tuple_list:\n try:\n cur.execute(\"INSERT INTO articles values (?, ?, ?, ?, ?, ?, ?, ?, ?)\", i)\n print(f\"Article Added: '{i[1]}'\")\n con.commit()\n except sqlite3.OperationalError as e:\n print(e)\n except sqlite3.IntegrityError:\n print(f\"Article Already Exists: '{i[1]}'\")\n con.close()","def bulkInsert(self, url, values):\n pass","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def insert_book(self, title, author, year, isbn):\n self.cursor.execute(\"INSERT INTO Book VALUES(NULL, ?, ?, ?, ?)\",\n (title, author, year, isbn))\n self.connection.commit()","def drupal_db_insert(db_obj, db_cur, key_cv, value_cv):\n\n # sanity check\n if len(value_cv) != 1:\n nori.core.email_logger.error(\n'''Internal Error: multiple value_cv entries supplied in call to\ndrupal_db_insert(); call was (in expanded notation):\n\ndrupal_db_insert(\n db_obj={0},\n db_cur={1},\n key_cv={2},\n value_cv={3}\n)\n\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\n )\n sys.exit(nori.core.exitvals['internal']['num'])\n\n # get the chain type\n chain_type = get_drupal_chain_type(key_cv, value_cv)\n if not chain_type:\n nori.core.email_logger.error(\n'''Internal Error: invalid field list supplied in call to\ndrupal_db_insert(); call was (in expanded notation):\n\ndrupal_db_insert(\n db_obj={0},\n db_cur={1},\n key_cv={2},\n value_cv={3}\n)\n\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\n )\n sys.exit(nori.core.exitvals['internal']['num'])\n\n # don't insert NULLs\n if value_cv[0][2] is None:\n return True\n\n #\n # node -> field (including term references)\n #\n if chain_type == 'n-f':\n # node details\n node_cv = key_cv[0]\n node_ident = node_cv[0]\n node_value_type = node_cv[1]\n node_value = node_cv[2]\n node_type = node_ident[1]\n node_id_type = node_ident[2]\n\n # field details\n field_cv = value_cv[0]\n field_ident = field_cv[0]\n field_value_type = field_cv[1]\n field_value = field_cv[2]\n field_name = field_ident[1]\n\n # get the node IDs\n ret = get_drupal_node_ids(db_obj, db_cur, node_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following parent node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [node_type, node_id_type,\n node_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n nid, vid = ret[0]\n\n # insert the field entry\n return insert_drupal_field(db_obj, db_cur, 'node', node_type, nid,\n vid, field_cv)\n\n #\n # node -> relation -> node\n #\n if chain_type == 'n-r-n':\n # key-node details\n k_node_cv = key_cv[0]\n k_node_ident = k_node_cv[0]\n k_node_value_type = k_node_cv[1]\n k_node_value = k_node_cv[2]\n k_node_type = k_node_ident[1]\n k_node_id_type = k_node_ident[2]\n\n # relation details\n relation_cv = key_cv[1]\n relation_ident = relation_cv[0]\n relation_type = relation_ident[1]\n if len(relation_ident) > 2:\n relation_field_name = relation_ident[2]\n relation_value_type = relation_cv[1]\n relation_value = relation_cv[2]\n\n # value-node details\n v_node_cv = value_cv[0]\n v_node_ident = v_node_cv[0]\n v_node_value_type = v_node_cv[1]\n v_node_value = v_node_cv[2]\n v_node_type = v_node_ident[1]\n v_node_id_type = v_node_ident[2]\n\n # get the key-node ID\n if k_node_id_type == 'id':\n k_nid = k_node_value\n elif k_node_id_type == 'title':\n ret = get_drupal_node_ids(db_obj, db_cur, k_node_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following linked node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [k_node_type, k_node_id_type,\n k_node_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n k_nid, k_vid = ret[0]\n\n # get the value-node ID\n if v_node_id_type == 'id':\n v_nid = v_node_value\n elif v_node_id_type == 'title':\n ret = get_drupal_node_ids(db_obj, db_cur, v_node_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following linked node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [v_node_type, v_node_id_type,\n v_node_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n v_nid, v_vid = ret[0]\n\n # insert the relation\n return insert_drupal_relation(db_obj, db_cur, 'node', k_nid,\n relation_cv, 'node', v_nid)[0]\n\n #\n # node -> relation & node -> relation_field (incl. term refs)\n #\n if chain_type == 'n-rn-rf':\n partial = False\n\n # node1 details\n node1_cv = key_cv[0]\n node1_ident = node1_cv[0]\n node1_value_type = node1_cv[1]\n node1_value = node1_cv[2]\n node1_type = node1_ident[1]\n node1_id_type = node1_ident[2]\n\n # relation details\n relation_cv = key_cv[1]\n relation_ident = relation_cv[0]\n relation_type = relation_ident[1]\n if len(relation_ident) > 2:\n relation_field_name = relation_ident[2]\n relation_value_type = relation_cv[1]\n relation_value = relation_cv[2]\n\n # node2 details\n node2_cv = key_cv[2]\n node2_ident = node2_cv[0]\n node2_value_type = node2_cv[1]\n node2_value = node2_cv[2]\n node2_type = node2_ident[1]\n node2_id_type = node2_ident[2]\n\n # field details\n field_cv = value_cv[0]\n field_ident = field_cv[0]\n field_value_type = field_cv[1]\n field_value = field_cv[2]\n field_name = field_ident[1]\n\n # get node1's ID\n if node1_id_type == 'id':\n node1_nid = node1_value\n elif node1_id_type == 'title':\n ret = get_drupal_node_ids(db_obj, db_cur, node1_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following linked node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [node1_type, node1_id_type,\n node1_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n node1_nid, node1_vid = ret[0]\n\n # get node2's ID\n if node2_id_type == 'id':\n node2_nid = node2_value\n elif node2_id_type == 'title':\n ret = get_drupal_node_ids(db_obj, db_cur, node2_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following linked node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [node2_type, node2_id_type,\n node2_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n node2_nid, node2_vid = ret[0]\n\n # get the relation's IDs\n ret = get_drupal_relation_ids(db_obj, db_cur, 'node', node1_nid,\n relation_cv, 'node', node2_nid)\n if ret is None:\n if len(relation_ident) > 2:\n msg = (\n'''Warning: could not get the IDs of the following relation:\n type: {0}\n field_name: {1}\n field_value: {2}\nwith the following endpoints:\n''' .\n format(relation_type, relation_field_name,\n relation_value)\n )\n else:\n msg = (\n'''Warning: could not get the IDs of the {0} relation with\nthe following endpoints:\n''' .\n format(relation_type)\n )\n msg += (\n''' node1_type: {0}\n node1_id_type: {1}\n node1_value: {2}\n node2_type: {3}\n node2_id_type: {4}\n node2_value: {5}\nSkipping insert.''' .\n format(*map(nori.pps, [node1_type, node1_id_type,\n node1_value, node2_type,\n node2_id_type, node2_value]))\n )\n nori.core.email_logger.error(msg)\n return None\n if not ret:\n # the relation doesn't exist, so insert it\n ret = insert_drupal_relation(db_obj, db_cur, 'node', node1_nid,\n relation_cv, 'node', node2_nid)\n if ret[0] is None:\n return None\n if not ret[0]:\n # keep going, but return partial success when we're done\n partial = True\n rid = ret[1]\n vid = ret[2]\n else:\n # we may eventually want to / be able to handle multiple\n # rows here, but for now just take the first one\n rid, vid = ret[0]\n\n # insert the field entry\n ret = insert_drupal_field(db_obj, db_cur, 'relation',\n relation_type, rid, vid, field_cv)\n if ret is None:\n return None\n if (not ret) or partial:\n return False\n return True\n\n #\n # node -> fc -> field (including term references)\n #\n if chain_type == 'n-fc-f':\n partial = False\n\n # node details\n node_cv = key_cv[0]\n node_ident = node_cv[0]\n node_value_type = node_cv[1]\n node_value = node_cv[2]\n node_type = node_ident[1]\n node_id_type = node_ident[2]\n\n # fc details\n fc_cv = key_cv[1]\n fc_ident = fc_cv[0]\n fc_value_type = fc_cv[1]\n fc_value = fc_cv[2]\n fc_type = fc_ident[1]\n fc_id_type = fc_ident[2]\n\n # field details\n field_cv = value_cv[0]\n field_ident = field_cv[0]\n field_value_type = field_cv[1]\n field_value = field_cv[2]\n field_name = field_ident[1]\n\n # get the node IDs\n ret = get_drupal_node_ids(db_obj, db_cur, node_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following parent node:\n node_type: {0}\n node_id_type: {1}\n node_value: {2}\nSkipping insert.''' .\n format(*map(nori.pps, [node_type, node_id_type,\n node_value]))\n )\n return None\n if not ret:\n # eventually, we'll want to actually add the node;\n # for now, this shouldn't even be reached\n return None\n # similarly, we may eventually want to / be able to handle\n # multiple rows here, but for now just take the first one\n n_id, n_vid = ret[0]\n\n # get the field collection's IDs\n ret = get_drupal_fc_ids(db_obj, db_cur, 'node', node_type, n_id,\n n_vid, fc_cv)\n if ret is None:\n nori.core.email_logger.error(\n'''Warning: could not get the IDs of the following Drupal parent field\ncollection:\n fc_type: {0}\n fc_id_type: {1}\n fc_value: {2}\n node_type: {3}\n node_id_type: {4}\n node_value: {5}\nSkipping insert.''' .\n format(*map(nori.pps, [fc_type, fc_id_type, fc_value,\n node_type, node_id_type,\n node_value]))\n )\n return None\n if not ret:\n # the fc doesn't exist, so insert it\n ret = insert_drupal_fc(db_obj, db_cur, 'node', node_type, n_id,\n n_vid, fc_cv)\n if ret[0] is None:\n return None\n if not ret[0]:\n # keep going, but return partial success when we're done\n partial = True\n fc_id = ret[1]\n fc_vid = ret[2]\n else:\n # we may eventually want to / be able to handle multiple\n # rows here, but for now just take the first one\n fc_id, fc_vid = ret[0]\n\n # insert the field entry\n ret = insert_drupal_field(db_obj, db_cur, 'field_collection_item',\n 'field_' + fc_type, fc_id, fc_vid,\n field_cv)\n if ret is None:\n return None\n if (not ret) or partial:\n return False\n return True","def add_ref_tag(basicSeqs):\r\n\r\n formattedBasicSeqs=list(basicSeqs) \r\n for record in formattedBasicSeqs:\r\n record.id=record.id+'_Ref'\r\n record.name=record.name+'_Ref'\r\n record.description=record.description+'_Ref'\r\n return formattedBasicSeqs","def insert(self):\n self.getDbRecord().insert()\n\n return","def process_bibliography(conn: Connection, path: Path) -> None:\n sql = \"INSERT OR IGNORE INTO Bibliography (key, text) VALUES (?, ?)\"\n run_sql_on_csv(conn, path, sql, (str, str))","def addrow_from_list(self, list):\n database = managers.database_manager.get_database(self.owner_id, self.database_id)\n con = database.get_connection()\n cur = con.cursor()\n arg = \"(?\"\n for i in xrange(len(list) - 1):\n arg += \", ?\"\n arg += \")\"\n sql = \"INSERT INTO \\'%s\\' VALUES %s\" % (self.name, arg)\n cur.execute(sql, tuple(list))\n con.commit()","def add_registrations(conn, reglist):\n duplicate_registrations = [] # registrations which could not be inserted due to primary key constraints\n \n c = conn.cursor()\n new_regs_num = 0\n with conn:\n sql_insert_statement = \"\"\"INSERT INTO registrations(\n registration_id,\n first_name,\n last_name,\n gender,\n dob,\n club,\n email,\n medical_conditions,\n emergency_name,\n emergency_contact,\n created,\n last_updated\n ) VALUES (\n (SELECT IFNULL(MAX(registration_id), 0) + 1 FROM registrations),\n ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?\n )\"\"\"\n for reg in reglist:\n try:\n reginfo = (\n reg['first_name'].strip(),\n reg['last_name'].strip(),\n reg['gender'].strip(),\n reg['dob'],\n reg['club'].strip(),\n reg['email'].strip(),\n reg['medical_conditions'].strip(),\n reg['emergency_name'].strip(),\n reg['emergency_contact'].strip(),\n reg['created'],\n reg['last_updated']\n )\n c.execute(sql_insert_statement, reginfo)\n new_regs_num += 1\n \n except sqlite3.IntegrityError as e:\n # Likely a duplicate entry in the input file\n duplicate_registrations.append(reg)\n\n except sqlite3.Error as e:\n print(e)\n print(\"ERROR: Unable to insert row for: {}, {} ({})\".format(\n reg['last_name'].strip(), reg['first_name'].strip(), reg['dob']))\n \n print(\"Added {} registration(s)\".format(new_regs_num))\n return duplicate_registrations","def insertData(self, table, title, rating, authorinfo, pubinfo):\n\n\t\tsql = \"insert into %s (bookname, authorinfo, pubinfo, rating) \\\n\t\t\tvalues('%s', '%s', '%s', '%s')\" %(table, title, authorinfo,\n\t\t\tpubinfo, rating)\n\t\ttry:\n\t\t\tself.cursor.execute(sql)\n\t\t\tself.conn.commit()\n\t\texcept Exception, e:\n\t\t\tsys.exit()","def insert_supplier(conn: Connection, cursor: Cursor, supplier: Supplier) -> None:\n cursor.execute(\n \"INSERT INTO supplier values(?, ?, ?)\",\n (supplier.name, supplier.pull_url, supplier.rating),\n )\n conn.commit()","def run(self):\n self.db.table('points').insert({\n 'name': 'biblioteca',\n 'rfid': '123456'\n })","def dbinsert(db,table,**assignments):\n cols = \"\"\n vals = \"\"\n values = []\n for k,v in assignments.items():\n if cols!=\"\": cols += \",\"\n cols += k\n if vals!=\"\": vals += \",\"\n vals += \"?\"\n values.append(v)\n cmd = \"insert or replace into \"+table+\" ( \"+cols+\" ) values ( \"+vals+\" ) \"\n params = list(values)\n # print cmd,params\n cur = db.cursor()\n cur.execute(cmd,params)\n cur.close()\n del cur","def insert_data_category_into_bdd(self):\n for category in constant.LIST_CATEGORIES:\n data = Category(name=category)\n data.save()\n print(\"the category \" + str(category) + \" has been created\")","def write_submission_to_db(cls, file_name, obj_list):\n conn = sqlite3.connect('database.db')\n c = conn.cursor()\n query = \"DELETE FROM `submission`;\"\n c.execute(query)\n\n for obj in obj_list:\n params = [obj.send_date, obj.name, obj.grade, obj.github_link, obj.student_id]\n c.execute(\"INSERT INTO submission (send_date, grade, name, github_link, student_id) VALUES (?, ?, ?, ?, ?)\", params)\n conn.commit()\n conn.close()","def insert_boost(connection: DBConnection, documents: Sequence[Document]) -> None:\n max_ = len(documents)\n current = 0\n print() # print an extra line, because we will delete lines with printing \\r\n for chunk in chunks(documents):\n\n connection.execute(\"BEGIN TRANSACTION\")\n for doc in chunk:\n connection.execute(\n \"INSERT INTO boost(did, date, page) VALUES (?, ?, ?)\", (doc.id, doc.date, doc.page))\n connection.execute(\"COMMIT\")\n current += len(chunk)\n print(f\"\\r[{current}/{max_}] boost done\", end='')\n print()","def insert_to_db(self) -> None:\n query = '''INSERT INTO ESLReceipts(Transaction_Number, Date, Description, Memo,\n Amount_Debit, Amount_Credit, Balance, Check_Number, \n Fees, Card_Type, Is_Payment, Is_Transaction, User_id)\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?);'''\n self.db.commit(query, values=self.to_tuple())\n\n if self.is_transaction \\\n and self.transaction is not None \\\n and not self.transaction.exists_in_db():\n self.transaction.insert_to_db()","def submitAdoption2db(cursor, dog_id, adoption_fee, adoption_date, application_num):\n query = f\"INSERT INTO Adoption VALUES ({application_num}, {dog_id}, {adoption_fee}, '{adoption_date}');\"\n cursor.execute(query)","def insert_data(settings_filename, query_filename, row_list):\n conn = connect_to_db(settings_filename)\n cursor = conn.cursor()\n\n query = load_query(query_filename)\n cursor.execute(query,row_list)\n conn.commit()\n \n cursor.close()\n conn.close()\n\n return True","def insert(self,table,values):\n self.connect.execute(self.insert_disc[table],values)\n self.connect.commit()","def insert_data(self):\n\n pass","def save(self):\n db = DBStorage()\n p = self.createPatient()\n db.add_prescription(p)","def insert_book():\n \n\n if request.method == 'POST':\n new_author = mongo.db.authors.insert_one({\n 'author_name': request.form.to_dict()['author_name']\n })\n \n author_id = new_author.inserted_id\n \n # Create new book in mongo.db.books\n new_book = mongo.db.books.insert_one({\n 'title': request.form.to_dict()['title'],\n 'genre': request.form.to_dict()['genre'],\n 'pages': request.form.to_dict()['pages'],\n 'reviews': [],\n 'likes': [],\n 'dislikes': [],\n 'author_id': str(ObjectId(author_id)),\n 'isbn_num': request.form.to_dict()['isbn_num']\n })\n \n return redirect(url_for('library'))\n \n return render_template('insert_book.html', \n genres=[genre for genre in mongo.db.genres.find()])","def insert_data():\n\tBase.metadata.drop_all(engine)\n\tBase.metadata.create_all(engine)\n\tu1 = insert_user(\"233@B.com\", \"/static/image/avatar.JPG\")\n\tu2 = insert_user(\"fy@B.com\", \"/static/image/avatar.JPG\")\n\tc = insert_catalog(u1.id, \"Sichuan Dish\")\n\tinsert_catalog(u1.id, \"Fujian Dish\")\n\tinsert_catalog(u1.id, \"Guangdong Dish\")\n\tinsert_catalog(u2.id, \"Zhejiang Dish\")\n\tinsert_catalog(u2.id, \"Beijing Dish\")\n\tinsert_item(u1.id, \"Iphone 6 plus\", c, 'Is a phone', None)\n\tinsert_item(u1.id, \"Hot pot\", c, \"Hot hot hot\", None)\n\tinsert_item(u2.id, \"Kong Bao Chicken\", c, \"Classic\", None)","def insertLogList(listLog):\r\n try:\r\n if listLog:\r\n #print(str(listLog))\r\n coll = dataBase.log\r\n doc = coll.insert(listLog)\r\n #print(str(doc))\r\n else: \r\n return(\"No param\") \r\n except Exception as ex:\r\n #return except_handler(\"confInsc\", ex)\r\n print(\"error \" + str(doc))\r\n x=1","def add_entry(db, table, columns, values):\n mycursor = db.cursor()\n\n sql = \"INSERT INTO \" + table + \" (\" + parse_sql_param_from_array(columns) + \") VALUES (\" + parse_sql_param_from_array(values, escape=True) + \")\"\n mycursor.execute(sql)\n\n db.commit()","def add_from_file(writer):\n with open(\"to_test_db\", \"rb\") as reader:\n lines = reader.readlines()\n place = \"out\"\n codes = []\n code = []\n for i, line in enumerate(lines):\n if \"<--NEW CODE-->\" in line:\n if \"lang\" in place:\n codes += [(q_id, ans_id, \"\".join(code), lang)]\n place = \"q_id\"\n elif \"<--language\" in line:\n place = \"lang\"\n lang = line.split(\"<--language=\")[1].split(\"-->\")[0]\n elif \"q_id\" in place:\n q_id = line.split(\"<--question_id=\")[1].split(\"-->\")[0]\n place = \"a_id\"\n elif \"a_id\" in place:\n ans_id = line.split(\"<--ans_id=\")[1].split(\"-->\")[0]\n place = \"code\"\n code = []\n elif \"code\" in place:\n code += [line]\n codes += [(q_id, ans_id, \"\".join(code), lang)]\n for next_id, entry in enumerate(codes):\n (q_id, ans_id, code, lang) = entry\n print next_id, entry\n writer.add_document(question_id=return_unicode(int(q_id)), answer_id=return_unicode(int(ans_id)), code=return_unicode(code), language=return_unicode(lang),code_id=return_unicode(next_id))\n CR_DOCS_DB.insert({\"question_id\": return_unicode(int(q_id)), \"answer_id\": return_unicode(int(ans_id)), \"code\": return_unicode(code), \"language\": return_unicode(lang), \"code_id\": return_unicode(next_id)})\n return len(codes)","def add_reference(self,ref):\n \n master = None #for the table that is referenced\n slave = None #for the table that uses the reference\n new_root = None\n \n for child in ref:\n local_tag = child.tag.split(\"}\")[1] #gets the local part of the tag\n if local_tag == 'connections':\n new_root = child\n break\n \n #new_root == None means the connection exists but is not properly\n #connected to any table in the diagram\n if new_root == None: ###\n self.err.print_error(\"dia:ref_not_closed\") ###\n e_code = self.err.exit_code[\"diagram\"] ###\n ###\n exit(e_code) ###\n \n \n for child in new_root: \n #master table\n if child.attrib['handle'] == \"0\":\n master_id = child.attrib['to'] #gets the master id\n master = self.table_dict[master_id] \n \n #slave table\n elif child.attrib['handle'] == \"1\":\n slave_id = child.attrib['to'] #gets the slave id\n slave = self.table_dict[slave_id] \n \n \n #error check if either table not found\n if master == None or slave == None: ###\n self.err.print_error(\"dia:ref_not_closed\") ###\n e_code = self.err.exit_code[\"diagram\"] ###\n ###\n exit(e_code) ###\n \n \n #updating both tables\n master.add_slave(slave)\n slave.add_foreign_key(master)\n \n return","def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()","def run_insert_example():\n table = \"actors\"\n insert_values = {\n 'id': 3,\n 'name': \"Matt\",\n 'last_name': \"Smith\",\n 'country': \"England\"}\n print querify.insert_from_dict(table, insert_values)\n\n insert_col_list = [\"id\", \"name\", \"last_name\", \"country\"]\n insert_val_list = [\n [1, \"Chris\", \"Eccleston\", \"England\"],\n [2, \"David\", \"Tennant\", \"Scotland\"],\n [3, \"Matt\", \"Smith\", \"England\"]]\n print querify.insert_from_list(table, insert_col_list, insert_val_list)","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def insert_into_request_table(self, request_list):\n query = \"\"\"insert into\n requests(created_at, track_list, languages, locations, minimum_tweet_per_sec,\n time_frame, peak_detection_sensibility, analysis_sample_size, db_tweets_name)\n values(?, ?, ?, ?, ?, ?, ?, ?, ?);\"\"\"\n with sql.connect('./{}.db'.format(self.name)) as conn:\n try:\n conn.execute(query, request_list)\n except Exception:\n logger.exception(\"fail to insert peak data: {}\".format(request_list))","def ingest_sources(db, sources, ras, decs, references, comments=None, epochs=None,\n equinoxes=None, verbose=False, save_db=False):\n\n n_added = 0\n n_sources = len(sources)\n\n if epochs is None:\n epochs = [None] * n_sources\n if equinoxes is None:\n equinoxes = [None] * n_sources\n if comments is None:\n comments = [None] * n_sources\n\n for i, source in enumerate(sources):\n\n # Construct data to be added\n source_data = [{'source': sources[i],\n 'ra': ras[i],\n 'dec': decs[i],\n 'reference': references[i],\n 'epoch': epochs[i],\n 'equinox': equinoxes[i],\n 'comments': comments[i]}]\n verboseprint(source_data, verbose=verbose)\n\n try:\n db.Sources.insert().execute(source_data)\n n_added += 1\n except sqlalchemy.exc.IntegrityError:\n # try reference without last letter e.g.Smit04 instead of Smit04a\n if source_data[0]['reference'][-1] in ('a', 'b'):\n source_data[0]['reference'] = references[i][:-1]\n try:\n db.Sources.insert().execute(source_data)\n n_added += 1\n except sqlalchemy.exc.IntegrityError:\n raise SimpleError(\"Discovery reference may not exist in the Publications table. \"\n \"Add it with add_publication function. \")\n else:\n raise SimpleError(\"Discovery reference may not exist in the Publications table. \"\n \"Add it with add_publication function. \")\n\n if save_db:\n db.save_database(directory='data/')\n print(n_added, \"sources added to database and saved\")\n else:\n print(n_added, \"sources added to database\")\n\n return","def insert_into_cit_db(dic, name):\n ndate = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n try:\n s = serialize_via_marshal(dic)\n write_message(\"size of \"+name+\" \"+str(len(s)))\n #check that this column really exists\n testres = run_sql(\"select object_name from rnkCITATIONDATA where object_name = %s\",\n (name,))\n if testres:\n run_sql(\"UPDATE rnkCITATIONDATA SET object_value = %s where object_name = %s\",\n (s, name))\n else:\n #there was no entry for name, let's force..\n run_sql(\"INSERT INTO rnkCITATIONDATA(object_name,object_value) values (%s,%s)\",\n (name,s))\n run_sql(\"UPDATE rnkCITATIONDATA SET last_updated = %s where object_name = %s\",\n (ndate,name))\n except:\n register_exception(prefix=\"could not write \"+name+\" into db\", alert_admin=True)","def insert_data(stored_data, table_name):\r\n for item in stored_data:\r\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)","def add_phosphosites_to_db(phosphosites, db_cursor):\n\n for phosphosite in phosphosites:\n residue = phosphosite.get_residue()\n position = phosphosite.get_position()\n uniprotid = phosphosite.get_uniprotid()\n fold_change = phosphosite.get_fold_change()\n db_cursor.execute(\\\n \"INSERT INTO phosphositetb (residue,position,uniprotid,foldchange) VALUES(?,?,?,?);\"\\\n ,(residue,position,uniprotid,fold_change))","def insert_many(cursor, data):\n query = \"\"\"INSERT INTO hasil(label, arti, query_id)\n VALUES(?, ?, (\n SELECT id FROM pencarian\n WHERE query=?))\"\"\"\n\n cursor.executemany(query, data)","def enrich_textrefs(self, db):\n tr_list = db.select_all(db.TextRef,\n db.TextContent.text_ref_id == db.TextRef.id,\n db.TextContent.source == self.my_source,\n db.TextRef.manuscript_id.is_(None))\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\n if entry['PMID'] is not '0'}\n for tr in tr_list:\n if tr.pmcid is not None:\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\n db.commit(\"Could not update text refs with manuscript ids.\")\n return","def add_announcement(self, cid, c_name, content, date):\n query = \"INSERT INTO announcement(cid, c_name, content, date) VALUES (%s, %s, %s, %s)\"\n inputs = (cid, c_name, content, date, )\n return self.database_manager.execute_query(query, inputs)","def importOrg ( c, orgInstance ):\n assert str(type(c)) == \"<type '_mysql.connection'>\"\n assert str(type(orgInstance)) == \"<type 'instance'>\"\n orgID = orgInstance.attrib[\"organizationIdent\"]\n\n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\n allLocations = orgInstance.findall(\"Location\") \n if len(allLocations) != 0:\n for instance in allLocations:\n sqlQuery ( c, \"insert into OrganizationLocations values ( '\"+orgID+\"', '\"+instance[0].text+\"', '\"+instance[1].text+\"', '\"+instance[2].text+\"');\")\n\n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\n externalResources = orgInstance.find(\"ExternalResources\")\n if len(externalResources) != 0:\n for instance in externalResources:\n sqlQuery ( c, \"insert into OrganizationExternalResources values ( '\"+orgID+\"', '\"+instance.tag+\"', '\"+instance.text+\"');\")\n\n #Get all values for Organizations table and insert to DB\n postalAddress = orgInstance.find(\".//\" + \"PostalAddress\")\n name = orgInstance.find(\"Name\").text\n kind = orgInstance.find(\"Kind\").attrib[\"organizationKindIdent\"]\n history = orgInstance.find(\"History\").text\n phone = str(orgInstance.find(\".//\" + \"Telephone\").text)\n fax = str(orgInstance.find(\".//\" + \"Fax\").text)\n email = orgInstance.find(\".//\" + \"Email\").text\n address = postalAddress[0].text\n locality = postalAddress[1].text\n region = postalAddress[2].text\n postalCode = postalAddress[3].text\n country = postalAddress[4].text\n sqlQuery (c , \"insert into Organizations values ( '\"+orgID+\"', '\"+name+\"', '\"+kind+\"', '\"+history+\"', \"+phone+\", \"+fax+\", '\"+email+\"', '\"+address+\"', '\"+locality+\"', '\"+region+\"', '\"+postalCode+\"', '\"+country+\"');\")","def insert(self, documents, index=0):\n\n # Initialize graph backend\n self.initialize()\n\n for _, document, _ in documents:\n if isinstance(document, dict):\n # Require text or object field\n document = document.get(self.text, document.get(self.object))\n\n if document is not None:\n if isinstance(document, list):\n # Join tokens as text\n document = \" \".join(document)\n\n # Create node\n self.addnode(index, data=document)\n index += 1","def insert_characterlist_mongo(characterList):\n #MONGO_URI, DB_NAME and COLLECTION_NAME is from creds file\n client = MongoClient(MONGO_URI)\n db = client[MONGO_DB_NAME]\n Names = db[MONGO_COLLECTION_NAME]\n result = Names.insert_many(characterList)","def insert_documents(connection: DBConnection, documents: Sequence[Document]) -> None:\n max_ = len(documents)\n current = 0\n print() # print an extra line, because we will delete lines with printing \\r\n for chunk in chunks(documents):\n connection.execute(\"BEGIN TRANSACTION\")\n for doc in chunk:\n # python doesn't support prepared statements, but instead has a builtin sql cache\n connection.execute(\n \"INSERT INTO docs(did, title, url) VALUES (?, ?, ?)\", doc.convert_to_tuple())\n current += 1\n print(f\"\\r[{current}/{max_}] doc done\", end='')\n connection.execute(\"COMMIT\")","def insert_into_tweets(self, infos):\n query = \"insert into tweets(tweet_id, insert_date, created_at, hashtag) values(?, ?, ?, ?);\"\n with sql.connect('./{}.db'.format(self.name)) as conn:\n conn.executemany(query, infos)","def commit(self,form_list):\n raise NotImplementedError","def store_all_to_database(self, session):\n\n description = 'Established in 1974, JSM is a family-owned provider of quality apartments. We offer a variety of units from studios to five bedrooms with every location benefitting from our award winning amenities, responsive 24 hour maintenance, and friendly property management staff. JSM Development began in Champaign, IL, and manages roughly 1,500 apartments and 450,000 sq/ft of commercial space. JSM has been a major contributor to the development of Campustown in Champaign and the East Campus area in Urbana at the University of Illinois. These popular locations are now home to major national retailers such as Urban Outfitters, Chipotle, Panera, Cold Stone Creamery, and Noodles & Co.'\n\n # Insert a JSM company instance into the database\n current_company = Company(\n name='JSM',\n baseurl='https://apartments.jsmliving.com/',\n description = description\n )\n session.add(current_company)\n\n # Iterate over the apartments, storing each in the database\n for apartment in self.apartment_data:\n logging.info(\"Inserting %s to database\", apartment['name'])\n new_apartment = Apartment(\n company=current_company,\n url=apartment['url'],\n name=apartment['name'],\n bedrooms=apartment['bedrooms'],\n bathrooms=apartment['bathrooms'],\n price=apartment['price'],\n leasing_period=apartment['leasing_period'],\n description=apartment['description'],\n address=apartment['address'],\n lat=apartment['lat'],\n lng=apartment['lng']\n )\n session.add(new_apartment)\n\n # Insert images for the given apartment\n for index, image_url in enumerate(apartment['image_urls']):\n new_image = Image(\n url=image_url,\n apartment_id=new_apartment.id,\n type=0,\n image_index=index\n )\n session.add(new_image)\n\n # Connect images to apartment\n new_apartment.images.append(new_image)\n\n # Insert floorplan image, if it exists\n if apartment['floorplan_url'] != 0:\n new_floorplan_image = Image(\n url=apartment['floorplan_url'],\n apartment_id=new_apartment.id,\n type=1,\n image_index=len(apartment['image_urls'])\n )\n session.add(new_floorplan_image)\n\n # Connect images to apartment\n new_apartment.images.append(new_floorplan_image)\n\n # Insert amenities for the given apartment\n for amenity in apartment['amenities']:\n new_amenity = Amenity(\n apartment_id=new_apartment.id,\n amenity=amenity\n )\n session.add(new_amenity)\n\n # Connect amenity to apartment\n new_apartment.amenities.append(new_amenity)\n\n # Write all queries to the database\n session.commit()","def insert_values(listingid_to_text):\n sql = \"INSERT INTO listingid_to_text_english VALUES (%s, %s)\"\n args = [(key, val) for key, val in listingid_to_text.iteritems()]\n conn = None\n try:\n # read database configuration\n params = config()\n # connect to the PostgreSQL database\n conn = psycopg2.connect(**params)\n # create a new cursor\n cur = conn.cursor()\n \n print(\"here\")\n # execute the INSERT statement\n cur.executemany(sql, args)\n # commit the changes to the database\n conn.commit()\n # close communication with the database\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","def insert_vendor_list(vendor_list):\n sql = \"INSERT INTO vendors(vendor_name) VALUES(%s)\"\n conn = None\n try:\n # read database configuration\n params = config()\n # connect to the PostgreSQL database\n conn = psycopg2.connect(**params)\n # create a new cursor\n cur = conn.cursor()\n # execute the INSERT statement\n cur.executemany(sql, vendor_list)\n # commit the changes to the database\n conn.commit()\n # close communication with the database\n cur.close()\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if conn is not None:\n conn.close()","def add_teacher_data(connection,name,tsc_no,subjects,type_of_teacher):\r\n with connection:\r\n connection.execute(INSERT_TEACHER,(name,tsc_no,subjects,type_of_teacher))","def addRecord(self):\n\n ## Saving recorded entries to the CRM and Mailings Database\n print(\"Saving entries to the CRM and Mailings database...\")\n db_connection.executeQuery(\"INSERT INTO dbo.CRM (f_name, l_name, company, address, city, county, state, zip, primary_phone, secondary_phone, email_address) VALUES ('\" + self.first_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.last_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.crm_company_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.address.title() + \"', '\" + self.city.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.county.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.state_code.upper() + \"', '\" + str(self.zip_code) + \"', '\" + self.phone_number + \"', '\" + self.phone_number_2 + \"' , '\" + self.email_address + \"'); COMMIT\")\n db_connection.executeQuery(\"INSERT INTO dbo.Mailings (name, company, address) VALUES ('\" + self.first_name.replace(\"\\'\", \"\\'\\'\").title() + \" \" + self.last_name.replace(\"\\'\", \"\\'\\'\").title() + \"', '\" + self.company_name.replace(\"\\'\", \"\\'\\'\").title() + \"','\" + self.address + \" \" + self.city.title() + \" \" + self.county.title() + \" \" + self.state_code.upper() + \" \" + str(self.zip_code) + \"'); COMMIT\")","def add_to_db(car_list: list) -> dict:\n db = CarsDb() # pylint: disable=invalid-name\n db.create_table()\n for _cars in car_list:\n for car in _cars:\n db.add_cars(car)\n db.commit()\n db.close()","def save_in_db(self):\n self.sql_database.table_name = self.table_db\n self.sql_database.db_name = self.db\n if self.sql_database.insert_item(text_path=self.path, word_first=self.word_1.get(),\n word_second=self.word_2.get(),\n word_third=self.word_3.get(), word_fourth=self.word_4.get(),\n word_fifth=self.word_5.get()):\n msg.showinfo(message=\"Done\")","def insert_section(\n self, args: Union[List[Any], List[List[Any]]], many=False\n ) -> None:\n\n columns = \",\".join(column[0] for column in DBConnection.column_names)\n\n query = f\"\"\"\n INSERT INTO {self.table_name} ({columns})\n VALUES\n ({','.join('?' for _ in range(len(DBConnection.column_names)))})\n \"\"\"\n\n self.select_db()\n self._insert_or_update(query, args, many)","def create_db_post(entry, keys, like):\n h = get_hash(entry['link'])\n collection = pos if like else neg\n return collection.update(\n {'hash': h},\n {\n 'link': entry['link'],\n 'title': entry['title'],\n 'published': '',\n 'content': \" \".join(keys),\n 'hash': h,\n 'read': False\n }, upsert=True\n )","def create_publication(conn, publication):\n\tsql = ''' INSERT INTO publication(eid, doi, publicationDate,publicationYear,title,venueName)\n\t\t\t VALUES(?,?,?,?,?,?) '''\n\tcur = conn.cursor()\n\tcur.execute(sql, publication)\n\treturn cur.lastrowid","def sync(self):\n try:\n result = self.client.insert_rows(\n table=self.table, rows=map(lambda c: c.as_tuple, self.new_contributors())\n )\n if result:\n logging.error(\"failed to add new contributors\")\n logging.error(\"%s\", \"\\n\".join(result))\n exit(1)\n\n except exceptions.BadRequest as exception:\n if exception.errors[0].get(\"message\") != \"No rows present in the request.\":\n raise exception","def add_ref(doc_name, bib_name, keep_label=False, slides=False):\n bib_path = bib_name # os.path.join(static_path, \"tex\", bib_name)\n if not slides:\n keep_label = True\n with open(doc_name, \"r\") as f:\n _file = f.read()\n # find citations\n match = re.findall(r\"\\[@.*?\\]\", _file)\n references = []\n # print(match)\n for cit in match:\n tmp_cite_html = tempfile.NamedTemporaryFile(suffix=\".html\", delete=True)\n tmp = tempfile.NamedTemporaryFile(suffix=\".md\", delete=True)\n with open(tmp.name, \"w\") as md_file:\n # Open the file for writing.\n md_file.write(cit)\n\n pandoc_command = [\n \"pandoc\",\n \"-C\",\n tmp.name,\n \"--bibliography\",\n bib_path,\n \"--csl\",\n \"./templates/my-csl.csl\",\n \"-o\",\n tmp_cite_html.name,\n ]\n if not slides:\n for tag in [\"--metadata\", \"link-citations=true\"]:\n pandoc_command.append(tag)\n output = subprocess.check_output(\n pandoc_command, stderr=subprocess.STDOUT\n ).decode(\"utf-8\")\n print(output.rstrip())\n tmp.close()\n with open(tmp_cite_html.name, \"r\") as cite_html:\n # Open the file for writing.\n cit_new = cite_html.read()\n # print(cit_new)\n if not keep_label:\n # remove first line\n cit_new = \"\\n\".join(cit_new.splitlines()[1:])\n else:\n lines = cit_new.splitlines()\n # remove the <p> tags\n lines[0] = lines[0][3:-4]\n cit_new = \"\\n\".join(lines)\n if slides:\n _file = _file.replace(cit, cit_new)\n else:\n _file = _file.replace(cit, lines[0])\n # collect the references\n references.append(\"<p>\")\n references.append(\"\\n\".join(lines))\n references.append(\"</p>\")\n # paste_refs_end()\n tmp_cite_html.close()\n if slides:\n with open(doc_name, \"w\") as f:\n f.write(_file)\n if not slides:\n if references == []:\n _file=clean_html_refs(_file)\n else:\n _file = _file.replace(\"<bibliography-placeholder>\", \"\\n\".join(references))\n with open(doc_name, \"w\") as f:\n f.write(_file)\n return","def add_to_db(self, data_base):\n comment_data = self.get_comment(dictionary=True)\n comment_data.pop('id')\n cursor = data_base.cursor()\n cursor.execute(f\"INSERT INTO comment ({', '.join(comment_data.keys())}) VALUES {tuple(comment_data.values())}\")\n if self.commit_to_db:\n data_base.commit()\n cursor.close()","def insert(conn, table_info, table_data):\n\n sql = ''' INSERT INTO ''' + table_info \n + ''' VALUES(''' + \"?,\" * (len(table_data)-1) + \"?)\"\n cursor = conn.cursor()\n cursor.execute(sql, table_data)\n conn.commit()","def do_insert(self,args):\n if len(args) != 0:\n for w in args.split():\n sl.insertList(int(w.rstrip()))","def insert_students(ids, fname, lname, db_name='./grades.sqlite3'):\n names_tupple = list(zip(ids, fname, lname, [0] * len(ids)))\n with lite.connect(db_name) as con:\n cur = con.cursor()\n cur.executemany('INSERT OR REPLACE INTO STUDENTS \\\n (pipeline_id, first_name, second_name, cheating_ratio)'\n ' VALUES (?, ?, ?, ?)', names_tupple)\n con.commit()","def add_answers(conn, cur, answers):\n \n print 'Adding answers...',\n \n for i, answer in enumerate(answers):\n cur.execute('INSERT INTO answers VALUES (\"{_id}\", \"{task_id}\", \"{text}\")'.format(\n _id = i+1,\n task_id = answer['task_id'],\n text = answer['text']\n )\n )\n \n conn.commit()\n \n print 'done.'","def populate(library):\n # Clears table\n table.delete(*table.get_children())\n\n # Inserts each book into the table\n # where text is the key field\n for book in library:\n table.insert(\"\", int(book[0]), text=book[0], values=(book[1], book[2], book[3], book[4]))","def insert_csl(conn, table, sql_param):\r\n header_list = get_header(conn, table)\r\n header_list.pop(0)\r\n translation = {39 : None,\r\n 91 : 40, \r\n 93 : 41}\r\n place_holders = [\"%s\" for item in header_list] # creates a list with many place holders as items in header_list\r\n place_holders = str(place_holders).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\r\n header_list = str(header_list).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\r\n insert = f\"INSERT INTO {table} {header_list} VALUES {place_holders} RETURNING id\"\r\n cursor = conn.cursor()\r\n cursor.execute(insert, sql_param)\r\n conn.commit()\r\n returned_id = cursor.fetchone()\r\n cursor.close()\r\n return returned_id","def add_db_equipement(self,c):\n insertQuery = \"INSERT INTO Equipement(NumeroEquipement,NumeroInstallation,NatureLibelle,InsNom) VALUES (?,?,?,?)\"\n\n c.execute(insertQuery, (self.equipement_id,self.ins_numero_install,self.nature_libelle,self.ins_nom))","def add_entry_to_bibtex_db(self, ent):\n\n # add additional fields manually to the dict\n ent.consolidate_dict()\n self.bibtex_db.entries.append(ent.raw_dict)\n # the following updates the entries dict\n # self.bibtex_db.get_entry_dict()\n # # make sure it's there\n # if ent.ID not in self.bibtex_db.entries_dict:\n # self.bibtex_db.entries_dict[ent.ID] = ent.raw_dict","def insert(self, data):\r\n pass"],"string":"[\n \"def insert_into_citation_table(citations):\\n cursor = connection.cursor()\\n cursor.execute(\\n 'DROP TABLE IF EXISTS Citations;'\\n 'CREATE TABLE Citations(sourcePaperId INT, targetPaperId INT, citationId INT NOT NULL AUTO_INCREMENT PRIMARY KEY);'\\n )\\n cursor.close()\\n i = 0\\n\\n for citation in citations:\\n print i\\n i+=1\\n cursor = connection.cursor()\\n cursor.execute(\\n 'INSERT INTO Citations (sourcePaperId, targetPaperId) VALUES '\\n '((SELECT PaperId FROM Papers WHERE DOI = %s),(SELECT PaperId FROM Papers WHERE DOI = %s));'\\n ,[citation['source'].encode('UTF-8'),citation['target'].encode('UTF-8')])\\n cursor.close()\",\n \"def process_citations(conn: Connection, path: Path) -> None:\\n sql = \\\"INSERT OR IGNORE INTO Citations (note, reference) VALUES (?, ?)\\\"\\n run_sql_on_csv(conn, path, sql, (int, str))\",\n \"def main():\\n papers_with_references = add_references_to_papers(PAPERS_FILE, TEXT_DIR)\\n citations = match_citations_with_papers(papers_with_references)\\n insert_into_citation_table(citations)\",\n \"def insert_books_data():\\n # Get data from csv file\\n print(\\\"Getting data from csv..\\\")\\n file = open(\\\"books.csv\\\")\\n reader = csv.reader(file)\\n\\n # Insert csv data into table\\n print(\\\"Inserting data into 'books' table..\\\")\\n for isbn, title, author, year in reader:\\n try:\\n db.execute(\\\"INSERT INTO books (isbn, title, author, year)\\\\\\n VALUES (:isbn, :title, :author, :year)\\\", {\\n \\\"isbn\\\": isbn, \\\"title\\\": title, \\\"author\\\": author, \\\"year\\\": year })\\n except exc.DataError as err:\\n print(\\\"Invalid entry in csv file\\\")\\n db.commit()\\n print(\\\"Data inserted\\\")\",\n \"def store_news(list_of_news, connection, url):\\n cursor = connection.cursor()\\n list_of_values = []\\n for item in list_of_news:\\n new_date = item.date.strftime('%Y%m%d')\\n new_article = [item.title, item.link, item.date, new_date, item.source, item.description, item.image]\\n list_of_values.append(new_article)\\n\\n sql = \\\"INSERT OR REPLACE INTO news VALUES (?, ?, ?, ?, ?, ?, ?, ?)\\\"\\n cursor.execute(sql, (new_article[0], new_article[1], new_article[2], new_article[3], new_article[4],\\n new_article[5], new_article[6], url))\\n connection.commit()\",\n \"def insertionGenero (cur, conn, genre_list):\\n for genre in genre_list :\\n idGenero = genre[0]\\n genero=genre[1]\\n # print(generoInsert.format(idTitulo,genero))\\n # REGISTER DATA IN GENERO TABLE\\n cur.execute(generoInsert.format(idGenero,genero))\\n conn.commit()\",\n \"def insert_into_db(self, database):\\n\\n # insert person\\n keys = \\\"\\\"\\n values = \\\"\\\"\\n for key, value in self.person.items():\\n # location\\n if key == \\\"location\\\":\\n # ensure location is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \\n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \\n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \\n VALUES((SELECT location_id \\n FROM p21_cdm.location\\n WHERE city='{self.person['location']['city']}' \\n and zip='{self.person['location']['zip']}'), \\n {values[:-1]})\\\"\\\"\\\")\\n\\n # insert visits\\n for visit in self.visits:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n for key, value in visit.items():\\n if key == \\\"care_site_name\\\":\\n # ensure care site is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \\n FROM p21_cdm.care_site \\n WHERE care_site_name='{value}') \\n THEN INSERT INTO p21_cdm.care_site (care_site_name) \\n VALUES ('{value}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \\n VALUES((SELECT care_site_id\\n FROM p21_cdm.care_site\\n WHERE care_site_name='{visit['care_site_name']}'),\\n {values[:-1]}) \\n RETURNING visit_occurrence_id\\\"\\\"\\\")\\n\\n # insert measurements, observations, conditions & procedures\\n for data, tablename in [(self.measurements, \\\"measurement\\\"),\\n (self.observations, \\\"observation\\\"),\\n (self.conditions, \\\"condition_occurrence\\\"),\\n (self.procedures, \\\"procedure_occurrence\\\")]:\\n for entry in data:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n\\n for key, value in entry.items():\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n entry[\\\"sql_id\\\"] = database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\\n VALUES({values[:-1]}) RETURNING {tablename}_id\\\"\\\"\\\")[0][0]\\n\\n # insert fact_relationships in both directions\\n for table1, entry1, table2, entry2 in self.fact_relations:\\n # 44818890 = Finding associated with (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\\\"\\\"\\\")\\n # 44818792 = Associated with finding (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\\\"\\\"\\\")\\n\\n # make transactions persistent\\n database.commit()\",\n \"def insert(self, db):\\n if not self.content:\\n raise ValueError(\\\"SequenceList content property must be filled in before calling save.\\\")\\n if not self.title:\\n raise ValueError(\\\"SequenceList title property must be filled in before calling save.\\\")\\n seq_item_list = SequenceListItems(self.content)\\n cur = db.cursor()\\n self._insert_data(cur, seq_item_list, self.title)\\n cur.close()\\n db.commit()\",\n \"def main2():\\n\\t\\n\\tcu_locations = cu_locations_data();\\n\\t\\n\\tfor row in cu_locations:\\n\\t\\tprint \\\"INSERT INTO contact ('ref_id') VALUES (%s);\\\" % ( row['location_id'] );\",\n \"def fillTweetInDB(self):\\n sqlInsertTweets = \\\"INSERT INTO tweet content VALUES %s\\\"\\n mycursor.executemany(sqlInsertTweets,self.content)\\n mydb.commit()\",\n \"def insert(self, url, values):\\n pass\",\n \"def fill(self):\\n if self.source == 'citations':\\n url = self._scholarly.URLS(\\\"CITATIONPUB\\\").format(self.id_citations)\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\\n\\n if soup.find('a', class_='gsc_vcd_title_link'):\\n self.bib['url'] = soup.find(\\n 'a', class_='gsc_vcd_title_link')['href']\\n\\n for item in soup.find_all('div', class_='gs_scl'):\\n key = item.find(class_='gsc_vcd_field').text\\n val = item.find(class_='gsc_vcd_value')\\n if key == 'Authors':\\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\\n elif key == 'Journal':\\n self.bib['journal'] = val.text\\n elif key == 'Volume':\\n self.bib['volume'] = val.text\\n elif key == 'Issue':\\n self.bib['number'] = val.text\\n elif key == 'Pages':\\n self.bib['pages'] = val.text\\n elif key == 'Publisher':\\n self.bib['publisher'] = val.text\\n elif key == 'Publication date':\\n self.bib['year'] = arrow.get(val.text).year\\n elif key == 'Description':\\n if val.text[0:8].lower() == 'abstract':\\n val = val.text[9:].strip()\\n self.bib['abstract'] = val\\n elif key == 'Total citations':\\n self.id_scholarcitedby = re.findall(\\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\\n\\n # number of citation per year\\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\\n self.cites_per_year = dict(zip(years, cites))\\n\\n if soup.find('div', class_='gsc_vcd_title_ggi'):\\n self.bib['eprint'] = soup.find(\\n 'div', class_='gsc_vcd_title_ggi').a['href']\\n self._filled = True\\n\\n elif self.source == 'scholar':\\n self.bib['add_to_lib'] = self.url_add_sclib\\n\\n try:\\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\\n bibtex = bibtex.find('pre').string\\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\\n self.bib['author_count'] = str(\\n len(self.bib['author'].split('and')))\\n self.bib['age'] = str(\\n int(date.today().year) - int(self.bib['year']))\\n except:\\n # did not find year\\n pass\\n\\n self._filled = True\\n return self\",\n \"def insert(self, *args):\\n return _ida_frame.xreflist_t_insert(self, *args)\",\n \"def insert_into_solr():\\n solr = pysolr.Solr('http://localhost:8983/solr/mag_journals', always_commit=True)\\n filepath = '/vol1/mag/data/2018-07-19/dumps/Journals.txt'\\n\\n list_for_solr = []\\n with open(filepath, \\\"r\\\") as file:\\n csv_reader = csv.reader(file, delimiter='\\\\t')\\n for journal_id, rank, normalized_name, display_name, issn, publisher, webpage, paper_count, citation_count, created_date in csv_reader:\\n solr_record = {}\\n solr_record['journal_id'] = journal_id\\n solr_record['rank'] = rank\\n solr_record['normalized_name'] = normalized_name\\n solr_record['display_name'] = display_name\\n solr_record['issn'] = issn\\n solr_record['publisher'] = publisher\\n solr_record['webpage'] = webpage\\n solr_record['paper_count'] = paper_count\\n solr_record['citation_count'] = citation_count\\n solr_record['created_date'] = created_date\\n list_for_solr.append(solr_record)\\n # Upload to Solr: 48000-odd rows\\n solr.add(list_for_solr)\",\n \"def insert_into_solr(filepath):\\n #filepath = '/vol1/mag/data/2018-07-19/dumps/PaperCitationContexts.txt'\\n record_number = 0\\n list_for_solr = []\\n with open(filepath, \\\"r\\\") as file:\\n # THERE ARE NULL BYTES WHICH MAKE CSV THROW AN ERROR. Replace them \\n csv_reader = csv.reader((line.replace('\\\\0', '') for line in file), delimiter='\\\\t', quoting=csv.QUOTE_NONE)\\n for paper_id, paper_reference_id, citation_context in csv_reader:\\n #for record in csv_reader:\\n #paper_id, paper_reference_id, citation_context = record\\n record_number += 1\\n solr_record = {}\\n solr_record['paper_id'] = paper_id\\n solr_record['paper_reference_id'] = paper_reference_id\\n solr_record['citation_context'] = citation_context\\n # Chunks of 500000\\n if record_number % 25000 == 0:\\n list_for_solr.append(solr_record)\\n try:\\n solr.add(list_for_solr)\\n except Exception as e:\\n print(e, record_number, filepath)\\n list_for_solr = []\\n print(record_number)\\n else:\\n list_for_solr.append(solr_record)\\n #print(record_number)\\n try:\\n solr.add(list_for_solr)\\n except Exception as e:\\n print(e, record_number, filepath)\",\n \"def insert_db():\\n populate_tables()\",\n \"def add_student_data(connection,fname,lname,class_n,marks):\\r\\n with connection:\\r\\n connection.execute(INSERT_STUDENT,(fname,lname,class_n,marks))\",\n \"def insert_values():\\n pass\",\n \"def insertValues(defined_symbols,mysql,list_of_files,last_name,name,newfile,rowd):\\n\\n\\tcrs = mysql.cursor()\\n\\tfor j in list_of_files:\\n\\t\\tfn = j.replace(os.sep,'=')\\n\\t\\tif newfile and rowd != None:\\n\\t\\t\\tif fn in rowd:\\n\\t\\t\\t\\tcontinue \\n\\t\\ttry:\\t\\t\\t\\t\\n\\t\\t\\tltp = defined_symbols[name][j] # list of tuples (line number reference)\\n\\t\\t\\tsql1 = '\\\"%d\\\", \\\"%s\\\"' % (last_name,fn)\\n\\t\\t\\tsql = 'INSERT INTO files (name,filename) VALUES ('+sql1+');'\\n\\t\\t\\tcrs.execute(sql)\\n\\t\\t\\tlast_file = crs.lastrowid\\n\\t\\t\\tfor k in ltp:\\n\\t\\t\\t\\tif k[1] == DEFINED:\\n\\t\\t\\t\\t\\ttp = \\\"defin\\\"\\n\\t\\t\\t\\telif k[1] == USED:\\n\\t\\t\\t\\t\\ttp = \\\"refer\\\"\\n\\t\\t\\t\\telif k[1] == MACRO:\\n\\t\\t\\t\\t\\ttp = \\\"macro\\\"\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttp = \\\"both \\\"\\n\\t\\t\\ts = str(k[0])\\n\\t\\t\\tsql1 = '\\\"%d\\\", %s, \\\"%s\\\"' % (last_file,s,tp)\\n\\t\\t\\tsql = 'INSERT INTO linesinfile (filename,number,ref) VALUES ('+sql1+');'\\n\\t\\t\\tcrs.execute(sql)\\n\\t\\texcept TypeError as e:\\n\\t\\t\\tprint \\\"ERROR (\\\"+str(e)\\n\\t\\t\\tprint name + \\\" , \\\"+ j \\n\\t\\t\\tprint ltp\\n\\t\\t\\tsys.exit()\\n\\t\\texcept mysql.Error as err:\\n\\t\\t\\tprint 'ERROR DataBase '+str(err)\\n\\t\\t\\tmysql.rollback() \\n\\tmysql.commit()\\n\\tcrs.close()\",\n \"def importCrisis ( c, crisisInstance ): \\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n assert str(type(crisisInstance)) == \\\"<type 'instance'>\\\"\\n crisisID = crisisInstance.attrib[\\\"crisisIdent\\\"]\\n \\n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\\n allLocations = crisisInstance.findall(\\\"Location\\\")\\n if len(allLocations) != 0:\\n for instance in allLocations:\\n if len ( instance ) == 2 :\\n sqlQuery ( c, \\\"insert into CrisisLocations values ( '\\\"+crisisID+\\\"', '\\\"+instance[0].text+\\\"', 'Null', '\\\"+instance[1].text+\\\"');\\\")\\n else :\\n sqlQuery ( c, \\\"insert into CrisisLocations values ( '\\\"+crisisID+\\\"', '\\\"+instance[0].text+\\\"', '\\\"+instance[1].text+\\\"', '\\\"+instance[2].text+\\\"');\\\")\\n\\n #Gets list of all resources needed. Iterates through lists and inserts into ResourceNeeded table\\n resourcesNeeded = crisisInstance.findall(\\\"ResourceNeeded\\\")\\n\\n if len(resourcesNeeded) != 0:\\n for instance in resourcesNeeded:\\n sqlQuery ( c, \\\"insert into ResourceNeeded values ( '\\\"+crisisID+\\\"', '\\\"+instance.text+\\\"');\\\")\\n \\n #Gets all URL's in a list. Indexes list, splices tag to get type, inserts data into table\\n externalResources = crisisInstance.find(\\\"ExternalResources\\\")\\n \\n#Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\\n if len(externalResources) != 0:\\n for instance in externalResources:\\n sqlQuery ( c, \\\"insert into CrisisExternalResources values ( '\\\"+crisisID+\\\"', '\\\"+instance.tag+\\\"', '\\\"+instance.text+\\\"');\\\")\\n \\n\\n #Gets list of all RelatedPeople and inserts into CrisesToPeople table\\n relatedPeople = crisisInstance.findall(\\\".//\\\" + \\\"RelatedPerson\\\")\\n\\n if len(relatedPeople) != 0:\\n for instance in relatedPeople:\\n sqlQuery ( c, \\\"insert into CrisesToPeople values ( '\\\"+crisisID+\\\"', '\\\"+instance.attrib[\\\"personIdent\\\"]+\\\"');\\\")\\n\\n #Gets list of all RelatedOrganizations and inserts into OrganizationsToCrises table\\n relatedOrgs = crisisInstance.findall(\\\".//\\\" + \\\"RelatedOrganization\\\")\\n\\n if len(relatedOrgs) != 0:\\n for instance in relatedOrgs:\\n sqlQuery ( c, \\\"insert into OrganizationsToCrises values ( '\\\"+instance.attrib[\\\"organizationIdent\\\"]+\\\"', '\\\"+crisisID+\\\"');\\\")\\n\\n #Gets all tags titled WaysToHelp, and inserts into corresponding table by indexing the list they're stored in\\n waysToHelp = crisisInstance.findall(\\\"WaysToHelp\\\")\\n \\n if len(waysToHelp) != 0:\\n for instance in waysToHelp:\\n sqlQuery ( c, \\\"insert into WaysToHelp values ( '\\\"+crisisID+\\\"', '\\\"+instance.text+\\\"');\\\")\\n\\n #Gets list of HumanImpact elements. Iterates through list and inserts into HumanImpact table\\n humanImpact = crisisInstance.findall(\\\"HumanImpact\\\")\\n\\n if len(humanImpact) !=0:\\n for instance in humanImpact:\\n sqlQuery ( c, \\\"insert into HumanImpact values ( '\\\"+crisisID+\\\"', '\\\"+instance[0].text+\\\"', '\\\"+instance[1].text+\\\"');\\\")\\n\\n #Finds values of remaining elements and inserts into Crises table\\n name = crisisInstance.find(\\\"Name\\\").text\\n kind = crisisInstance.find(\\\"Kind\\\").attrib[\\\"crisisKindIdent\\\"]\\n startDateTime = crisisInstance.find(\\\"StartDateTime\\\")\\n startDate = startDateTime[0].text\\n if len(startDateTime) > 1:\\n startTime = startDateTime[1].text\\n else:\\n startTime = '00:00:00'\\n endDateTime = crisisInstance.find(\\\"EndDateTime\\\")\\n if endDateTime != None :\\n endDate = endDateTime[0].text\\n if len(endDateTime) > 1:\\n endTime = endDateTime[1].text\\n else:\\n endTime = '00:00:00'\\n else :\\n endDate = startDate\\n endTime = '23:59:59'\\n econImpact = crisisInstance.find(\\\"EconomicImpact\\\").text\\n\\n sqlQuery ( c, \\\"insert into Crises values ( '\\\"+crisisID+\\\"', '\\\"+name+\\\"', '\\\"+kind+\\\"', '\\\"+startDate+\\\"', '\\\"+startTime+\\\"', '\\\"+endDate+\\\"', '\\\"+endTime+\\\"', '\\\"+econImpact+\\\"');\\\")\",\n \"def put_in_db(dbase,defined_symbols,createDB):\\n\\t\\n\\tmysql = DB.connect(host=\\\"127.0.0.1\\\",user=myutils.mysqluser,passwd=myutils.mysqlpwd) \\n\\tif createDB:\\n\\t createDataBase(mysql,dbase)\\n\\ttry:\\n\\t mysql.select_db(dbase)\\n\\texcept : \\n\\t print 'DataBase '+dbase+ ' does not exist'\\n\\t return\\n\\treferenceKeys = sorted(defined_symbols.iterkeys())\\n\\n\\tcr = mysql.cursor()\\n\\ttry:\\n\\t\\tif createDB:\\n\\t\\t\\tfor i in referenceKeys:\\n\\t\\t\\t\\tdict_of_files = defined_symbols[i]\\t\\t\\t\\t\\t\\t# dict of files:list of tuples\\n\\t\\t\\t\\tif len(dict_of_files) > 0:\\n\\t\\t\\t\\t\\tlist_of_files = sorted(dict_of_files.iterkeys()) \\t# list of tuples (file , list of tuples)\\n\\t\\t\\t\\t\\tsql1 = '\\\"%s\\\"' % (i)\\n\\t\\t\\t\\t\\tsql = \\\"INSERT INTO names (name) VALUES (\\\"+sql1+\\\");\\\"\\n\\t\\t\\t\\t\\tcr.execute(sql)\\n\\t\\t\\t\\t\\tmysql.commit()\\n\\t\\t\\t\\t\\tlast_name = cr.lastrowid\\n\\t\\t\\t\\t\\tinsertValues(defined_symbols,mysql,list_of_files,last_name,i,False,0)\\n\\t\\t\\tallfilesd = buildallfiles(defined_symbols)\\n\\t\\t\\tcrs = mysql.cursor()\\n\\t\\t\\tfor j in allfilesd.keys():\\n\\t\\t\\t\\tfn = j.replace(os.sep,'=')\\n\\t\\t\\t\\tsql1 = '\\\"%s\\\"' % (fn)\\n\\t\\t\\t\\tsql = \\\"INSERT INTO allfiles (file) VALUES (\\\"+sql1+\\\");\\\"\\n\\t\\t\\t\\tcr.execute(sql)\\n\\t\\t\\t\\tmysql.commit()\\n\\t\\t\\t\\tlast_file = cr.lastrowid\\n\\t\\t\\t\\tfor n in allfilesd[j]:\\n\\t\\t\\t\\t\\tsql1 = '\\\"%d\\\", \\\"%s\\\"' % (last_file,n)\\n\\t\\t\\t\\t\\tsql = 'INSERT INTO allnames (file,name) VALUES ('+sql1+');'\\n\\t\\t\\t\\t\\tcrs.execute(sql)\\n\\t\\t\\tmysql.commit()\\n\\t\\t\\tcrs.close()\\n\\t\\telse:\\n\\t\\t\\tfor i in referenceKeys:\\n\\t\\t\\t\\tsql2 = ''\\n\\t\\t\\t\\tdict_of_files = defined_symbols[i]\\t\\t\\t\\t\\t# dict of files:list of tuples\\n\\t\\t\\t\\tlist_of_files = sorted(dict_of_files.iterkeys()) \\t# list of tuples (file , list of tuples)\\n\\t\\t\\t\\tsql1= '\\\"%s\\\"' % (i)\\n\\t\\t\\t\\tsql = \\\"SELECT * FROM names WHERE name=\\\"+ sql1+\\\";\\\"\\n\\t\\t\\t\\tcr.execute(sql)\\n\\t\\t\\t\\tmysql.commit()\\n\\t\\t\\t\\trow = cr.fetchone()\\n\\t\\t\\t\\tif row != None:\\n\\t\\t\\t\\t\\tsql2 = '\\\"%d\\\"' % (row[0])\\n\\t\\t\\t\\t\\tsql = 'SELECT * FROM files WHERE name='+ sql2+\\\";\\\"\\n\\t\\t\\t\\t\\tcr.execute(sql)\\n\\t\\t\\t\\t\\trowd = cr.fetchall()\\n\\t\\t\\t\\t\\tinsertValues(defined_symbols,mysql,list_of_files,row[0],i,True,rowd)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tsql = \\\"INSERT INTO names (name) VALUES (\\\"+sql1+\\\");\\\"\\n\\t\\t\\t\\t\\tcr.execute(sql)\\n\\t\\t\\t\\t\\tmysql.commit()\\n\\t\\t\\t\\t\\tlast_name = cr.lastrowid\\n\\t\\t\\t\\t\\tinsertValues(defined_symbols,mysql,list_of_files,last_name,i,False,0)\\n\\n\\texcept mysql.Error as err:\\n\\t\\tprint 'ERROR DataBase '+str(err)\\n\\t\\tmysql.rollback() \\n\\t\\treturn\\n\\n\\tcr.close()\\n\\tmysql.close()\",\n \"def insert(title, author, year, isbn,shelf,raw):\\n\\n conn_obj = mysql.connector.connect(host='localhost',database='mydb',user='root',password='kks')\\n cur_obj = conn_obj.cursor()\\n sql=\\\"INSERT INTO book (title, author, year, isbn,shelf,raw) VALUES(%s, %s, %s, %s, %s, %s)\\\"\\n cur_obj.execute(sql,(title, author, year, isbn,shelf,raw))\\n conn_obj.commit()\\n conn_obj.close()\",\n \"def insert(sql, clue):\\n\\t# clue is [game, airdate, round, category, value, clue, answer]\\n\\t# note that at this point, clue[4] is False if round is 3\\n\\tif \\\"\\\\\\\\\\\\'\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\'\\\", \\\"'\\\")\\n\\tif \\\"\\\\\\\\\\\\\\\"\\\" in clue[6]:\\n\\t\\tclue[6] = clue[6].replace(\\\"\\\\\\\\\\\\\\\"\\\", \\\"\\\\\\\"\\\")\\n\\tif not sql:\\n\\t\\tprint clue\\n\\t\\treturn\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO airdates VALUES(?, ?);\\\", (clue[0], clue[1], ))\\n\\tsql.execute(\\\"INSERT OR IGNORE INTO categories(category) VALUES(?);\\\", (clue[3], ))\\n\\tcategory_id = sql.execute(\\\"SELECT id FROM categories WHERE category = ?;\\\", (clue[3], )).fetchone()[0]\\n\\tclue_id = sql.execute(\\\"INSERT INTO documents(clue, answer) VALUES(?, ?);\\\", (clue[5], clue[6], )).lastrowid\\n\\tsql.execute(\\\"INSERT INTO clues(game, round, value) VALUES(?, ?, ?);\\\", (clue[0], clue[2], clue[4], ))\\n\\tsql.execute(\\\"INSERT INTO classifications VALUES(?, ?)\\\", (clue_id, category_id, ))\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def insertionAno(cur, conn, date_list):\\n for date in date_list:\\n idAno = date[0]\\n ano=date[1][0]\\n fechaEstreno=date[1][1]\\n # print(anoInsert.format(idAno,ano,fechaEstreno))\\n # REGISTER ANO IN ACTOR TABLE\\n cur.execute(anoInsert.format(idAno,ano,fechaEstreno))\\n conn.commit()\",\n \"def on_submit(self):\\n\\n\\t\\tfor accounting_entry in self.get('accounting_entries'):\\n\\t\\t\\tledger_entry_doc = frappe.get_doc({\\n\\t\\t\\t\\t'doctype': 'Ledger Entry',\\n\\t\\t\\t\\t'posting_date': self.posting_date,\\n\\t\\t\\t\\t'account': accounting_entry.account,\\n\\t\\t\\t\\t'debit': accounting_entry.debit,\\n\\t\\t\\t\\t'credit': accounting_entry.credit,\\n\\t\\t\\t\\t'voucher_type': 'Journal Entry',\\n\\t\\t\\t\\t'voucher_number': self.name,\\n\\t\\t\\t\\t'company': self.company\\n\\t\\t\\t})\\n\\t\\t\\tledger_entry_doc.insert()\",\n \"def saveData(self):\\r\\n newPaper = self.getData()\\r\\n\\r\\n cur = self.dbConn.execute(\\\"SELECT PaperID FROM Papers WHERE Title = ?\\\",[self.title.getVal()])\\r\\n res = cur.fetchone()\\r\\n if not res==None:\\r\\n self.dbConn.execute(\\\"DELETE FROM CoAuthors WHERE Paper = ?\\\",[res[\\\"PaperID\\\"]])\\r\\n self.dbConn.execute(\\\"DELETE FROM Papers WHERE Title = ?\\\",[self.title.getVal()])\\r\\n\\r\\n sqlStr = str.format(\\\"INSERT into {} ({}) VALUES ({})\\\",self.dataTable,\\\",\\\".join(self.dataCols),\\\"?\\\"+\\\",?\\\"*(len(self.dataCols)-1))\\r\\n newCur = self.dbConn.execute(sqlStr,[newPaper.symposium, newPaper.title,newPaper.primeAuthor,newPaper.correspond])\\r\\n\\r\\n newRow = newCur.lastrowid\\r\\n if len(newPaper.coauthors)>0:\\r\\n for coauth in newPaper.coauthors:\\r\\n sqlStr = str.format(\\\"INSERT into {} ({}) VALUES ({})\\\",self.coauthorTable,\\\",\\\".join(self.coauthorCols),\\\"?\\\"+\\\",?\\\"*(len(self.coauthorCols)-1))\\r\\n newCur = self.dbConn.execute(sqlStr,[newRow,coauth])\\r\\n\\r\\n self.dbConn.commit()\\r\\n self.clearData()\",\n \"def addRow(conn, title, url, text):\\n with conn.cursor() as cursor:\\n sql = \\\"INSERT INTO comments(course_name, course_url, course_comment) VALUES (%s, %s, %s)\\\"\\n cursor.execute(sql,(title, url, text))\\n conn.commit()\",\n \"def add_complaint():\\n complaint = {\\n 'complaint_timestamp': datetime.datetime.now(),\\n 'complaint_type': 'objectionable',\\n 'process_status': 'complaint',\\n 'display_status': 'good',\\n 'pinner_id': 4,\\n 'content_id': 11\\n } \\n complaints = Table('complaints', metadata, autoload=True)\\n i = complaints.insert()\\n out = i.execute(complaint)\\n complaint['complaint_id'] = out.lastrowid\\n \\n print(\\\"complaint_id {}\\\".format(complaint['complaint_id']))\\n\\n # get the value from the db\\n rs = complaints.select().where(complaints.c.complaint_id == complaint['complaint_id'])\\n rrs = rs.execute()\\n for r in rrs:\\n rcomplaint = dict(r)\\n print(\\\"length {}, value {}\\\".format(len(rcomplaint), rcomplaint))\\n \\n # now update the value\\n rs = complaints.update().where(complaints.c.complaint_id == complaint['complaint_id']).\\\\\\n values(reviewer_id=2, process_status='done')\\n rs.execute()\\n \\n rs = complaints.select()\\n run(rs, text='complaints')\\n \\n # now delete all entries\\n rs = complaints.delete()\\n rs.execute()\\n \\n rs = complaints.select()\\n run(rs, text='complaints')\",\n \"def insert_supplier(conn, cursor, supplier: Supplier) -> None:\\n cursor.execute(\\n \\\"INSERT INTO supplier values(?, ?, ?)\\\",\\n (supplier.name, supplier.pull_url, supplier.rating),\\n )\\n conn.commit()\",\n \"def fill_target_table(new_data, curs, conn, overwrite=False):\\n for i in new_data:\\n connect_database.add_target_to_database(list(i), curs, conn, overwrite_exsiting = overwrite)\\n conn.commit()\",\n \"def populate_tables(self, data_book, data_author, datafile_name, initial_stock=20):\\n\\n print(\\\"\\\\nPopulating book table with input data from\\\", datafile_name, \\\"...\\\", end='')\\n count = 0\\n failed_books = []\\n for book in data_book:\\n try:\\n date = datetime.datetime.strptime(book[7], '%m/%d/%Y').date()\\n t = (book[0], book[1], book[8], book[3], date,\\n int(book[4]), initial_stock, book[9])\\n self.cursor.execute(\\n \\\"\\\"\\\"INSERT INTO book (ISBN, title, publisher, lang, publicationDate, pageCount, stock, price) \\n VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\\\"\\\"\\\", t)\\n except Exception as e:\\n count = count + 1\\n failed_books.append(t[1])\\n if failed_books:\\n print(\\\"\\\\nSome books were not added to the database because they had an invalid format:\\\")\\n for book in failed_books:\\n print(book)\\n print(\\\"\\\\nTotal books not included in database: \\\", count)\\n self.cursor.execute(\\n \\\"\\\"\\\"SELECT COUNT(*)\\n FROM book\\\"\\\"\\\")\\n num_successful = self.cursor.fetchall()\\n print(num_successful[0][0], \\\"books successfully inserted into table \\\\\\\"Book\\\\\\\".\\\")\\n self.db.commit()\\n print(\\\"done\\\")\\n # Now we populate authors. First need to get all ISBNs of books that were added to the book table\\n print(\\\"\\\\nAdding authors to \\\\\\\"Author\\\\\\\" table...\\\", end='')\\n self.cursor.execute(\\\"SELECT ISBN FROM Book\\\")\\n list_books = [book[0] for book in self.cursor.fetchall()]\\n\\n for author in data_author:\\n self.cursor.execute(\\\"INSERT INTO author (name) VALUES (%s)\\\", (author,))\\n self.db.commit()\\n for book in data_author[author]:\\n if book in list_books:\\n self.cursor.execute(\\\"SELECT ID FROM author WHERE name = %s\\\", (author,))\\n auth_id = self.cursor.fetchone()[0]\\n self.cursor.execute(\\\"INSERT IGNORE INTO wrote VALUES (%s,%s)\\\", (auth_id, book))\\n self.db.commit()\\n print(\\\"done\\\")\\n # # Finally, populate HasKeyword table. For now just add words in title and author names\\n # print(\\\"\\\\nGenerating keywords for \\\\\\\"HasKeyword\\\\\\\" table...\\\", end='')\\n # for book in list_books:\\n # self.cursor.execute(\\\"SELECT title from book WHERE ISBN = %s\\\", (book,))\\n # keywords = [i[0].split(' ') for i in self.cursor.fetchall()]\\n # self.cursor.execute(\\\"SELECT name FROM author A, wrote W WHERE A.ID = W.authorID AND W.ISBN = %s\\\", (book,))\\n # authors = [i[0].split(' ') for i in self.cursor.fetchall()]\\n #\\n # keywords.extend(authors)\\n # for word_subset in keywords:\\n # for word in word_subset:\\n # if not word.isspace() and word:\\n # self.cursor.execute(\\\"INSERT IGNORE INTO HasKeyword VALUES(%s,%s)\\\", (book, word))\\n # self.db.commit()\\n # print(\\\"done\\\")\",\n \"def save_data(data_tuple_list: list):\\n con = sqlite3.connect(\\\"thecomradenews.db\\\")\\n cur = con.cursor()\\n try:\\n cur.execute('''CREATE TABLE articles\\n (source text,\\n title text,\\n link text,\\n pubdate date,\\n guid integer,\\n description text,\\n content text,\\n categories text,\\n image_link TEXT,\\n UNIQUE(source, title, link, pubdate, guid, description, content, categories, image_link)\\n )''')\\n except sqlite3.OperationalError as e:\\n print(e)\\n for i in data_tuple_list:\\n try:\\n cur.execute(\\\"INSERT INTO articles values (?, ?, ?, ?, ?, ?, ?, ?, ?)\\\", i)\\n print(f\\\"Article Added: '{i[1]}'\\\")\\n con.commit()\\n except sqlite3.OperationalError as e:\\n print(e)\\n except sqlite3.IntegrityError:\\n print(f\\\"Article Already Exists: '{i[1]}'\\\")\\n con.close()\",\n \"def bulkInsert(self, url, values):\\n pass\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def insert_book(self, title, author, year, isbn):\\n self.cursor.execute(\\\"INSERT INTO Book VALUES(NULL, ?, ?, ?, ?)\\\",\\n (title, author, year, isbn))\\n self.connection.commit()\",\n \"def drupal_db_insert(db_obj, db_cur, key_cv, value_cv):\\n\\n # sanity check\\n if len(value_cv) != 1:\\n nori.core.email_logger.error(\\n'''Internal Error: multiple value_cv entries supplied in call to\\ndrupal_db_insert(); call was (in expanded notation):\\n\\ndrupal_db_insert(\\n db_obj={0},\\n db_cur={1},\\n key_cv={2},\\n value_cv={3}\\n)\\n\\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\\n )\\n sys.exit(nori.core.exitvals['internal']['num'])\\n\\n # get the chain type\\n chain_type = get_drupal_chain_type(key_cv, value_cv)\\n if not chain_type:\\n nori.core.email_logger.error(\\n'''Internal Error: invalid field list supplied in call to\\ndrupal_db_insert(); call was (in expanded notation):\\n\\ndrupal_db_insert(\\n db_obj={0},\\n db_cur={1},\\n key_cv={2},\\n value_cv={3}\\n)\\n\\nExiting.'''.format(*map(nori.pps, [db_obj, db_cur, key_cv, value_cv]))\\n )\\n sys.exit(nori.core.exitvals['internal']['num'])\\n\\n # don't insert NULLs\\n if value_cv[0][2] is None:\\n return True\\n\\n #\\n # node -> field (including term references)\\n #\\n if chain_type == 'n-f':\\n # node details\\n node_cv = key_cv[0]\\n node_ident = node_cv[0]\\n node_value_type = node_cv[1]\\n node_value = node_cv[2]\\n node_type = node_ident[1]\\n node_id_type = node_ident[2]\\n\\n # field details\\n field_cv = value_cv[0]\\n field_ident = field_cv[0]\\n field_value_type = field_cv[1]\\n field_value = field_cv[2]\\n field_name = field_ident[1]\\n\\n # get the node IDs\\n ret = get_drupal_node_ids(db_obj, db_cur, node_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following parent node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [node_type, node_id_type,\\n node_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n nid, vid = ret[0]\\n\\n # insert the field entry\\n return insert_drupal_field(db_obj, db_cur, 'node', node_type, nid,\\n vid, field_cv)\\n\\n #\\n # node -> relation -> node\\n #\\n if chain_type == 'n-r-n':\\n # key-node details\\n k_node_cv = key_cv[0]\\n k_node_ident = k_node_cv[0]\\n k_node_value_type = k_node_cv[1]\\n k_node_value = k_node_cv[2]\\n k_node_type = k_node_ident[1]\\n k_node_id_type = k_node_ident[2]\\n\\n # relation details\\n relation_cv = key_cv[1]\\n relation_ident = relation_cv[0]\\n relation_type = relation_ident[1]\\n if len(relation_ident) > 2:\\n relation_field_name = relation_ident[2]\\n relation_value_type = relation_cv[1]\\n relation_value = relation_cv[2]\\n\\n # value-node details\\n v_node_cv = value_cv[0]\\n v_node_ident = v_node_cv[0]\\n v_node_value_type = v_node_cv[1]\\n v_node_value = v_node_cv[2]\\n v_node_type = v_node_ident[1]\\n v_node_id_type = v_node_ident[2]\\n\\n # get the key-node ID\\n if k_node_id_type == 'id':\\n k_nid = k_node_value\\n elif k_node_id_type == 'title':\\n ret = get_drupal_node_ids(db_obj, db_cur, k_node_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following linked node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [k_node_type, k_node_id_type,\\n k_node_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n k_nid, k_vid = ret[0]\\n\\n # get the value-node ID\\n if v_node_id_type == 'id':\\n v_nid = v_node_value\\n elif v_node_id_type == 'title':\\n ret = get_drupal_node_ids(db_obj, db_cur, v_node_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following linked node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [v_node_type, v_node_id_type,\\n v_node_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n v_nid, v_vid = ret[0]\\n\\n # insert the relation\\n return insert_drupal_relation(db_obj, db_cur, 'node', k_nid,\\n relation_cv, 'node', v_nid)[0]\\n\\n #\\n # node -> relation & node -> relation_field (incl. term refs)\\n #\\n if chain_type == 'n-rn-rf':\\n partial = False\\n\\n # node1 details\\n node1_cv = key_cv[0]\\n node1_ident = node1_cv[0]\\n node1_value_type = node1_cv[1]\\n node1_value = node1_cv[2]\\n node1_type = node1_ident[1]\\n node1_id_type = node1_ident[2]\\n\\n # relation details\\n relation_cv = key_cv[1]\\n relation_ident = relation_cv[0]\\n relation_type = relation_ident[1]\\n if len(relation_ident) > 2:\\n relation_field_name = relation_ident[2]\\n relation_value_type = relation_cv[1]\\n relation_value = relation_cv[2]\\n\\n # node2 details\\n node2_cv = key_cv[2]\\n node2_ident = node2_cv[0]\\n node2_value_type = node2_cv[1]\\n node2_value = node2_cv[2]\\n node2_type = node2_ident[1]\\n node2_id_type = node2_ident[2]\\n\\n # field details\\n field_cv = value_cv[0]\\n field_ident = field_cv[0]\\n field_value_type = field_cv[1]\\n field_value = field_cv[2]\\n field_name = field_ident[1]\\n\\n # get node1's ID\\n if node1_id_type == 'id':\\n node1_nid = node1_value\\n elif node1_id_type == 'title':\\n ret = get_drupal_node_ids(db_obj, db_cur, node1_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following linked node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [node1_type, node1_id_type,\\n node1_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n node1_nid, node1_vid = ret[0]\\n\\n # get node2's ID\\n if node2_id_type == 'id':\\n node2_nid = node2_value\\n elif node2_id_type == 'title':\\n ret = get_drupal_node_ids(db_obj, db_cur, node2_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following linked node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [node2_type, node2_id_type,\\n node2_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n node2_nid, node2_vid = ret[0]\\n\\n # get the relation's IDs\\n ret = get_drupal_relation_ids(db_obj, db_cur, 'node', node1_nid,\\n relation_cv, 'node', node2_nid)\\n if ret is None:\\n if len(relation_ident) > 2:\\n msg = (\\n'''Warning: could not get the IDs of the following relation:\\n type: {0}\\n field_name: {1}\\n field_value: {2}\\nwith the following endpoints:\\n''' .\\n format(relation_type, relation_field_name,\\n relation_value)\\n )\\n else:\\n msg = (\\n'''Warning: could not get the IDs of the {0} relation with\\nthe following endpoints:\\n''' .\\n format(relation_type)\\n )\\n msg += (\\n''' node1_type: {0}\\n node1_id_type: {1}\\n node1_value: {2}\\n node2_type: {3}\\n node2_id_type: {4}\\n node2_value: {5}\\nSkipping insert.''' .\\n format(*map(nori.pps, [node1_type, node1_id_type,\\n node1_value, node2_type,\\n node2_id_type, node2_value]))\\n )\\n nori.core.email_logger.error(msg)\\n return None\\n if not ret:\\n # the relation doesn't exist, so insert it\\n ret = insert_drupal_relation(db_obj, db_cur, 'node', node1_nid,\\n relation_cv, 'node', node2_nid)\\n if ret[0] is None:\\n return None\\n if not ret[0]:\\n # keep going, but return partial success when we're done\\n partial = True\\n rid = ret[1]\\n vid = ret[2]\\n else:\\n # we may eventually want to / be able to handle multiple\\n # rows here, but for now just take the first one\\n rid, vid = ret[0]\\n\\n # insert the field entry\\n ret = insert_drupal_field(db_obj, db_cur, 'relation',\\n relation_type, rid, vid, field_cv)\\n if ret is None:\\n return None\\n if (not ret) or partial:\\n return False\\n return True\\n\\n #\\n # node -> fc -> field (including term references)\\n #\\n if chain_type == 'n-fc-f':\\n partial = False\\n\\n # node details\\n node_cv = key_cv[0]\\n node_ident = node_cv[0]\\n node_value_type = node_cv[1]\\n node_value = node_cv[2]\\n node_type = node_ident[1]\\n node_id_type = node_ident[2]\\n\\n # fc details\\n fc_cv = key_cv[1]\\n fc_ident = fc_cv[0]\\n fc_value_type = fc_cv[1]\\n fc_value = fc_cv[2]\\n fc_type = fc_ident[1]\\n fc_id_type = fc_ident[2]\\n\\n # field details\\n field_cv = value_cv[0]\\n field_ident = field_cv[0]\\n field_value_type = field_cv[1]\\n field_value = field_cv[2]\\n field_name = field_ident[1]\\n\\n # get the node IDs\\n ret = get_drupal_node_ids(db_obj, db_cur, node_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following parent node:\\n node_type: {0}\\n node_id_type: {1}\\n node_value: {2}\\nSkipping insert.''' .\\n format(*map(nori.pps, [node_type, node_id_type,\\n node_value]))\\n )\\n return None\\n if not ret:\\n # eventually, we'll want to actually add the node;\\n # for now, this shouldn't even be reached\\n return None\\n # similarly, we may eventually want to / be able to handle\\n # multiple rows here, but for now just take the first one\\n n_id, n_vid = ret[0]\\n\\n # get the field collection's IDs\\n ret = get_drupal_fc_ids(db_obj, db_cur, 'node', node_type, n_id,\\n n_vid, fc_cv)\\n if ret is None:\\n nori.core.email_logger.error(\\n'''Warning: could not get the IDs of the following Drupal parent field\\ncollection:\\n fc_type: {0}\\n fc_id_type: {1}\\n fc_value: {2}\\n node_type: {3}\\n node_id_type: {4}\\n node_value: {5}\\nSkipping insert.''' .\\n format(*map(nori.pps, [fc_type, fc_id_type, fc_value,\\n node_type, node_id_type,\\n node_value]))\\n )\\n return None\\n if not ret:\\n # the fc doesn't exist, so insert it\\n ret = insert_drupal_fc(db_obj, db_cur, 'node', node_type, n_id,\\n n_vid, fc_cv)\\n if ret[0] is None:\\n return None\\n if not ret[0]:\\n # keep going, but return partial success when we're done\\n partial = True\\n fc_id = ret[1]\\n fc_vid = ret[2]\\n else:\\n # we may eventually want to / be able to handle multiple\\n # rows here, but for now just take the first one\\n fc_id, fc_vid = ret[0]\\n\\n # insert the field entry\\n ret = insert_drupal_field(db_obj, db_cur, 'field_collection_item',\\n 'field_' + fc_type, fc_id, fc_vid,\\n field_cv)\\n if ret is None:\\n return None\\n if (not ret) or partial:\\n return False\\n return True\",\n \"def add_ref_tag(basicSeqs):\\r\\n\\r\\n formattedBasicSeqs=list(basicSeqs) \\r\\n for record in formattedBasicSeqs:\\r\\n record.id=record.id+'_Ref'\\r\\n record.name=record.name+'_Ref'\\r\\n record.description=record.description+'_Ref'\\r\\n return formattedBasicSeqs\",\n \"def insert(self):\\n self.getDbRecord().insert()\\n\\n return\",\n \"def process_bibliography(conn: Connection, path: Path) -> None:\\n sql = \\\"INSERT OR IGNORE INTO Bibliography (key, text) VALUES (?, ?)\\\"\\n run_sql_on_csv(conn, path, sql, (str, str))\",\n \"def addrow_from_list(self, list):\\n database = managers.database_manager.get_database(self.owner_id, self.database_id)\\n con = database.get_connection()\\n cur = con.cursor()\\n arg = \\\"(?\\\"\\n for i in xrange(len(list) - 1):\\n arg += \\\", ?\\\"\\n arg += \\\")\\\"\\n sql = \\\"INSERT INTO \\\\'%s\\\\' VALUES %s\\\" % (self.name, arg)\\n cur.execute(sql, tuple(list))\\n con.commit()\",\n \"def add_registrations(conn, reglist):\\n duplicate_registrations = [] # registrations which could not be inserted due to primary key constraints\\n \\n c = conn.cursor()\\n new_regs_num = 0\\n with conn:\\n sql_insert_statement = \\\"\\\"\\\"INSERT INTO registrations(\\n registration_id,\\n first_name,\\n last_name,\\n gender,\\n dob,\\n club,\\n email,\\n medical_conditions,\\n emergency_name,\\n emergency_contact,\\n created,\\n last_updated\\n ) VALUES (\\n (SELECT IFNULL(MAX(registration_id), 0) + 1 FROM registrations),\\n ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?\\n )\\\"\\\"\\\"\\n for reg in reglist:\\n try:\\n reginfo = (\\n reg['first_name'].strip(),\\n reg['last_name'].strip(),\\n reg['gender'].strip(),\\n reg['dob'],\\n reg['club'].strip(),\\n reg['email'].strip(),\\n reg['medical_conditions'].strip(),\\n reg['emergency_name'].strip(),\\n reg['emergency_contact'].strip(),\\n reg['created'],\\n reg['last_updated']\\n )\\n c.execute(sql_insert_statement, reginfo)\\n new_regs_num += 1\\n \\n except sqlite3.IntegrityError as e:\\n # Likely a duplicate entry in the input file\\n duplicate_registrations.append(reg)\\n\\n except sqlite3.Error as e:\\n print(e)\\n print(\\\"ERROR: Unable to insert row for: {}, {} ({})\\\".format(\\n reg['last_name'].strip(), reg['first_name'].strip(), reg['dob']))\\n \\n print(\\\"Added {} registration(s)\\\".format(new_regs_num))\\n return duplicate_registrations\",\n \"def insertData(self, table, title, rating, authorinfo, pubinfo):\\n\\n\\t\\tsql = \\\"insert into %s (bookname, authorinfo, pubinfo, rating) \\\\\\n\\t\\t\\tvalues('%s', '%s', '%s', '%s')\\\" %(table, title, authorinfo,\\n\\t\\t\\tpubinfo, rating)\\n\\t\\ttry:\\n\\t\\t\\tself.cursor.execute(sql)\\n\\t\\t\\tself.conn.commit()\\n\\t\\texcept Exception, e:\\n\\t\\t\\tsys.exit()\",\n \"def insert_supplier(conn: Connection, cursor: Cursor, supplier: Supplier) -> None:\\n cursor.execute(\\n \\\"INSERT INTO supplier values(?, ?, ?)\\\",\\n (supplier.name, supplier.pull_url, supplier.rating),\\n )\\n conn.commit()\",\n \"def run(self):\\n self.db.table('points').insert({\\n 'name': 'biblioteca',\\n 'rfid': '123456'\\n })\",\n \"def dbinsert(db,table,**assignments):\\n cols = \\\"\\\"\\n vals = \\\"\\\"\\n values = []\\n for k,v in assignments.items():\\n if cols!=\\\"\\\": cols += \\\",\\\"\\n cols += k\\n if vals!=\\\"\\\": vals += \\\",\\\"\\n vals += \\\"?\\\"\\n values.append(v)\\n cmd = \\\"insert or replace into \\\"+table+\\\" ( \\\"+cols+\\\" ) values ( \\\"+vals+\\\" ) \\\"\\n params = list(values)\\n # print cmd,params\\n cur = db.cursor()\\n cur.execute(cmd,params)\\n cur.close()\\n del cur\",\n \"def insert_data_category_into_bdd(self):\\n for category in constant.LIST_CATEGORIES:\\n data = Category(name=category)\\n data.save()\\n print(\\\"the category \\\" + str(category) + \\\" has been created\\\")\",\n \"def write_submission_to_db(cls, file_name, obj_list):\\n conn = sqlite3.connect('database.db')\\n c = conn.cursor()\\n query = \\\"DELETE FROM `submission`;\\\"\\n c.execute(query)\\n\\n for obj in obj_list:\\n params = [obj.send_date, obj.name, obj.grade, obj.github_link, obj.student_id]\\n c.execute(\\\"INSERT INTO submission (send_date, grade, name, github_link, student_id) VALUES (?, ?, ?, ?, ?)\\\", params)\\n conn.commit()\\n conn.close()\",\n \"def insert_boost(connection: DBConnection, documents: Sequence[Document]) -> None:\\n max_ = len(documents)\\n current = 0\\n print() # print an extra line, because we will delete lines with printing \\\\r\\n for chunk in chunks(documents):\\n\\n connection.execute(\\\"BEGIN TRANSACTION\\\")\\n for doc in chunk:\\n connection.execute(\\n \\\"INSERT INTO boost(did, date, page) VALUES (?, ?, ?)\\\", (doc.id, doc.date, doc.page))\\n connection.execute(\\\"COMMIT\\\")\\n current += len(chunk)\\n print(f\\\"\\\\r[{current}/{max_}] boost done\\\", end='')\\n print()\",\n \"def insert_to_db(self) -> None:\\n query = '''INSERT INTO ESLReceipts(Transaction_Number, Date, Description, Memo,\\n Amount_Debit, Amount_Credit, Balance, Check_Number, \\n Fees, Card_Type, Is_Payment, Is_Transaction, User_id)\\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?);'''\\n self.db.commit(query, values=self.to_tuple())\\n\\n if self.is_transaction \\\\\\n and self.transaction is not None \\\\\\n and not self.transaction.exists_in_db():\\n self.transaction.insert_to_db()\",\n \"def submitAdoption2db(cursor, dog_id, adoption_fee, adoption_date, application_num):\\n query = f\\\"INSERT INTO Adoption VALUES ({application_num}, {dog_id}, {adoption_fee}, '{adoption_date}');\\\"\\n cursor.execute(query)\",\n \"def insert_data(settings_filename, query_filename, row_list):\\n conn = connect_to_db(settings_filename)\\n cursor = conn.cursor()\\n\\n query = load_query(query_filename)\\n cursor.execute(query,row_list)\\n conn.commit()\\n \\n cursor.close()\\n conn.close()\\n\\n return True\",\n \"def insert(self,table,values):\\n self.connect.execute(self.insert_disc[table],values)\\n self.connect.commit()\",\n \"def insert_data(self):\\n\\n pass\",\n \"def save(self):\\n db = DBStorage()\\n p = self.createPatient()\\n db.add_prescription(p)\",\n \"def insert_book():\\n \\n\\n if request.method == 'POST':\\n new_author = mongo.db.authors.insert_one({\\n 'author_name': request.form.to_dict()['author_name']\\n })\\n \\n author_id = new_author.inserted_id\\n \\n # Create new book in mongo.db.books\\n new_book = mongo.db.books.insert_one({\\n 'title': request.form.to_dict()['title'],\\n 'genre': request.form.to_dict()['genre'],\\n 'pages': request.form.to_dict()['pages'],\\n 'reviews': [],\\n 'likes': [],\\n 'dislikes': [],\\n 'author_id': str(ObjectId(author_id)),\\n 'isbn_num': request.form.to_dict()['isbn_num']\\n })\\n \\n return redirect(url_for('library'))\\n \\n return render_template('insert_book.html', \\n genres=[genre for genre in mongo.db.genres.find()])\",\n \"def insert_data():\\n\\tBase.metadata.drop_all(engine)\\n\\tBase.metadata.create_all(engine)\\n\\tu1 = insert_user(\\\"233@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tu2 = insert_user(\\\"fy@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tc = insert_catalog(u1.id, \\\"Sichuan Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Fujian Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Guangdong Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Zhejiang Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Beijing Dish\\\")\\n\\tinsert_item(u1.id, \\\"Iphone 6 plus\\\", c, 'Is a phone', None)\\n\\tinsert_item(u1.id, \\\"Hot pot\\\", c, \\\"Hot hot hot\\\", None)\\n\\tinsert_item(u2.id, \\\"Kong Bao Chicken\\\", c, \\\"Classic\\\", None)\",\n \"def insertLogList(listLog):\\r\\n try:\\r\\n if listLog:\\r\\n #print(str(listLog))\\r\\n coll = dataBase.log\\r\\n doc = coll.insert(listLog)\\r\\n #print(str(doc))\\r\\n else: \\r\\n return(\\\"No param\\\") \\r\\n except Exception as ex:\\r\\n #return except_handler(\\\"confInsc\\\", ex)\\r\\n print(\\\"error \\\" + str(doc))\\r\\n x=1\",\n \"def add_entry(db, table, columns, values):\\n mycursor = db.cursor()\\n\\n sql = \\\"INSERT INTO \\\" + table + \\\" (\\\" + parse_sql_param_from_array(columns) + \\\") VALUES (\\\" + parse_sql_param_from_array(values, escape=True) + \\\")\\\"\\n mycursor.execute(sql)\\n\\n db.commit()\",\n \"def add_from_file(writer):\\n with open(\\\"to_test_db\\\", \\\"rb\\\") as reader:\\n lines = reader.readlines()\\n place = \\\"out\\\"\\n codes = []\\n code = []\\n for i, line in enumerate(lines):\\n if \\\"<--NEW CODE-->\\\" in line:\\n if \\\"lang\\\" in place:\\n codes += [(q_id, ans_id, \\\"\\\".join(code), lang)]\\n place = \\\"q_id\\\"\\n elif \\\"<--language\\\" in line:\\n place = \\\"lang\\\"\\n lang = line.split(\\\"<--language=\\\")[1].split(\\\"-->\\\")[0]\\n elif \\\"q_id\\\" in place:\\n q_id = line.split(\\\"<--question_id=\\\")[1].split(\\\"-->\\\")[0]\\n place = \\\"a_id\\\"\\n elif \\\"a_id\\\" in place:\\n ans_id = line.split(\\\"<--ans_id=\\\")[1].split(\\\"-->\\\")[0]\\n place = \\\"code\\\"\\n code = []\\n elif \\\"code\\\" in place:\\n code += [line]\\n codes += [(q_id, ans_id, \\\"\\\".join(code), lang)]\\n for next_id, entry in enumerate(codes):\\n (q_id, ans_id, code, lang) = entry\\n print next_id, entry\\n writer.add_document(question_id=return_unicode(int(q_id)), answer_id=return_unicode(int(ans_id)), code=return_unicode(code), language=return_unicode(lang),code_id=return_unicode(next_id))\\n CR_DOCS_DB.insert({\\\"question_id\\\": return_unicode(int(q_id)), \\\"answer_id\\\": return_unicode(int(ans_id)), \\\"code\\\": return_unicode(code), \\\"language\\\": return_unicode(lang), \\\"code_id\\\": return_unicode(next_id)})\\n return len(codes)\",\n \"def add_reference(self,ref):\\n \\n master = None #for the table that is referenced\\n slave = None #for the table that uses the reference\\n new_root = None\\n \\n for child in ref:\\n local_tag = child.tag.split(\\\"}\\\")[1] #gets the local part of the tag\\n if local_tag == 'connections':\\n new_root = child\\n break\\n \\n #new_root == None means the connection exists but is not properly\\n #connected to any table in the diagram\\n if new_root == None: ###\\n self.err.print_error(\\\"dia:ref_not_closed\\\") ###\\n e_code = self.err.exit_code[\\\"diagram\\\"] ###\\n ###\\n exit(e_code) ###\\n \\n \\n for child in new_root: \\n #master table\\n if child.attrib['handle'] == \\\"0\\\":\\n master_id = child.attrib['to'] #gets the master id\\n master = self.table_dict[master_id] \\n \\n #slave table\\n elif child.attrib['handle'] == \\\"1\\\":\\n slave_id = child.attrib['to'] #gets the slave id\\n slave = self.table_dict[slave_id] \\n \\n \\n #error check if either table not found\\n if master == None or slave == None: ###\\n self.err.print_error(\\\"dia:ref_not_closed\\\") ###\\n e_code = self.err.exit_code[\\\"diagram\\\"] ###\\n ###\\n exit(e_code) ###\\n \\n \\n #updating both tables\\n master.add_slave(slave)\\n slave.add_foreign_key(master)\\n \\n return\",\n \"def insert_data(self):\\n # Make a connexion with a mock database\\n self.generate_data_collection()\",\n \"def run_insert_example():\\n table = \\\"actors\\\"\\n insert_values = {\\n 'id': 3,\\n 'name': \\\"Matt\\\",\\n 'last_name': \\\"Smith\\\",\\n 'country': \\\"England\\\"}\\n print querify.insert_from_dict(table, insert_values)\\n\\n insert_col_list = [\\\"id\\\", \\\"name\\\", \\\"last_name\\\", \\\"country\\\"]\\n insert_val_list = [\\n [1, \\\"Chris\\\", \\\"Eccleston\\\", \\\"England\\\"],\\n [2, \\\"David\\\", \\\"Tennant\\\", \\\"Scotland\\\"],\\n [3, \\\"Matt\\\", \\\"Smith\\\", \\\"England\\\"]]\\n print querify.insert_from_list(table, insert_col_list, insert_val_list)\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def insert_into_request_table(self, request_list):\\n query = \\\"\\\"\\\"insert into\\n requests(created_at, track_list, languages, locations, minimum_tweet_per_sec,\\n time_frame, peak_detection_sensibility, analysis_sample_size, db_tweets_name)\\n values(?, ?, ?, ?, ?, ?, ?, ?, ?);\\\"\\\"\\\"\\n with sql.connect('./{}.db'.format(self.name)) as conn:\\n try:\\n conn.execute(query, request_list)\\n except Exception:\\n logger.exception(\\\"fail to insert peak data: {}\\\".format(request_list))\",\n \"def ingest_sources(db, sources, ras, decs, references, comments=None, epochs=None,\\n equinoxes=None, verbose=False, save_db=False):\\n\\n n_added = 0\\n n_sources = len(sources)\\n\\n if epochs is None:\\n epochs = [None] * n_sources\\n if equinoxes is None:\\n equinoxes = [None] * n_sources\\n if comments is None:\\n comments = [None] * n_sources\\n\\n for i, source in enumerate(sources):\\n\\n # Construct data to be added\\n source_data = [{'source': sources[i],\\n 'ra': ras[i],\\n 'dec': decs[i],\\n 'reference': references[i],\\n 'epoch': epochs[i],\\n 'equinox': equinoxes[i],\\n 'comments': comments[i]}]\\n verboseprint(source_data, verbose=verbose)\\n\\n try:\\n db.Sources.insert().execute(source_data)\\n n_added += 1\\n except sqlalchemy.exc.IntegrityError:\\n # try reference without last letter e.g.Smit04 instead of Smit04a\\n if source_data[0]['reference'][-1] in ('a', 'b'):\\n source_data[0]['reference'] = references[i][:-1]\\n try:\\n db.Sources.insert().execute(source_data)\\n n_added += 1\\n except sqlalchemy.exc.IntegrityError:\\n raise SimpleError(\\\"Discovery reference may not exist in the Publications table. \\\"\\n \\\"Add it with add_publication function. \\\")\\n else:\\n raise SimpleError(\\\"Discovery reference may not exist in the Publications table. \\\"\\n \\\"Add it with add_publication function. \\\")\\n\\n if save_db:\\n db.save_database(directory='data/')\\n print(n_added, \\\"sources added to database and saved\\\")\\n else:\\n print(n_added, \\\"sources added to database\\\")\\n\\n return\",\n \"def insert_into_cit_db(dic, name):\\n ndate = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime())\\n try:\\n s = serialize_via_marshal(dic)\\n write_message(\\\"size of \\\"+name+\\\" \\\"+str(len(s)))\\n #check that this column really exists\\n testres = run_sql(\\\"select object_name from rnkCITATIONDATA where object_name = %s\\\",\\n (name,))\\n if testres:\\n run_sql(\\\"UPDATE rnkCITATIONDATA SET object_value = %s where object_name = %s\\\",\\n (s, name))\\n else:\\n #there was no entry for name, let's force..\\n run_sql(\\\"INSERT INTO rnkCITATIONDATA(object_name,object_value) values (%s,%s)\\\",\\n (name,s))\\n run_sql(\\\"UPDATE rnkCITATIONDATA SET last_updated = %s where object_name = %s\\\",\\n (ndate,name))\\n except:\\n register_exception(prefix=\\\"could not write \\\"+name+\\\" into db\\\", alert_admin=True)\",\n \"def insert_data(stored_data, table_name):\\r\\n for item in stored_data:\\r\\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)\",\n \"def add_phosphosites_to_db(phosphosites, db_cursor):\\n\\n for phosphosite in phosphosites:\\n residue = phosphosite.get_residue()\\n position = phosphosite.get_position()\\n uniprotid = phosphosite.get_uniprotid()\\n fold_change = phosphosite.get_fold_change()\\n db_cursor.execute(\\\\\\n \\\"INSERT INTO phosphositetb (residue,position,uniprotid,foldchange) VALUES(?,?,?,?);\\\"\\\\\\n ,(residue,position,uniprotid,fold_change))\",\n \"def insert_many(cursor, data):\\n query = \\\"\\\"\\\"INSERT INTO hasil(label, arti, query_id)\\n VALUES(?, ?, (\\n SELECT id FROM pencarian\\n WHERE query=?))\\\"\\\"\\\"\\n\\n cursor.executemany(query, data)\",\n \"def enrich_textrefs(self, db):\\n tr_list = db.select_all(db.TextRef,\\n db.TextContent.text_ref_id == db.TextRef.id,\\n db.TextContent.source == self.my_source,\\n db.TextRef.manuscript_id.is_(None))\\n file_list = self.ftp.get_csv_as_dict('filelist.csv', header=0)\\n pmcid_mid_dict = {entry['PMCID']: entry['MID'] for entry in file_list}\\n pmid_mid_dict = {entry['PMID']: entry['MID'] for entry in file_list\\n if entry['PMID'] is not '0'}\\n for tr in tr_list:\\n if tr.pmcid is not None:\\n tr.manuscript_id = pmcid_mid_dict[tr.pmcid]\\n elif tr.pmid is not None and tr.pmid in pmid_mid_dict.keys():\\n tr.manuscript_id = pmid_mid_dict[tr.pmid]\\n db.commit(\\\"Could not update text refs with manuscript ids.\\\")\\n return\",\n \"def add_announcement(self, cid, c_name, content, date):\\n query = \\\"INSERT INTO announcement(cid, c_name, content, date) VALUES (%s, %s, %s, %s)\\\"\\n inputs = (cid, c_name, content, date, )\\n return self.database_manager.execute_query(query, inputs)\",\n \"def importOrg ( c, orgInstance ):\\n assert str(type(c)) == \\\"<type '_mysql.connection'>\\\"\\n assert str(type(orgInstance)) == \\\"<type 'instance'>\\\"\\n orgID = orgInstance.attrib[\\\"organizationIdent\\\"]\\n\\n #Gets location sub elements in list. Inserts into CrisisLocation table by indexing list\\n allLocations = orgInstance.findall(\\\"Location\\\") \\n if len(allLocations) != 0:\\n for instance in allLocations:\\n sqlQuery ( c, \\\"insert into OrganizationLocations values ( '\\\"+orgID+\\\"', '\\\"+instance[0].text+\\\"', '\\\"+instance[1].text+\\\"', '\\\"+instance[2].text+\\\"');\\\")\\n\\n #Get all resources. Checks for Citation because it's the only one not ending in 'URL'. Get index of URL for others to splice off. Add to table\\n externalResources = orgInstance.find(\\\"ExternalResources\\\")\\n if len(externalResources) != 0:\\n for instance in externalResources:\\n sqlQuery ( c, \\\"insert into OrganizationExternalResources values ( '\\\"+orgID+\\\"', '\\\"+instance.tag+\\\"', '\\\"+instance.text+\\\"');\\\")\\n\\n #Get all values for Organizations table and insert to DB\\n postalAddress = orgInstance.find(\\\".//\\\" + \\\"PostalAddress\\\")\\n name = orgInstance.find(\\\"Name\\\").text\\n kind = orgInstance.find(\\\"Kind\\\").attrib[\\\"organizationKindIdent\\\"]\\n history = orgInstance.find(\\\"History\\\").text\\n phone = str(orgInstance.find(\\\".//\\\" + \\\"Telephone\\\").text)\\n fax = str(orgInstance.find(\\\".//\\\" + \\\"Fax\\\").text)\\n email = orgInstance.find(\\\".//\\\" + \\\"Email\\\").text\\n address = postalAddress[0].text\\n locality = postalAddress[1].text\\n region = postalAddress[2].text\\n postalCode = postalAddress[3].text\\n country = postalAddress[4].text\\n sqlQuery (c , \\\"insert into Organizations values ( '\\\"+orgID+\\\"', '\\\"+name+\\\"', '\\\"+kind+\\\"', '\\\"+history+\\\"', \\\"+phone+\\\", \\\"+fax+\\\", '\\\"+email+\\\"', '\\\"+address+\\\"', '\\\"+locality+\\\"', '\\\"+region+\\\"', '\\\"+postalCode+\\\"', '\\\"+country+\\\"');\\\")\",\n \"def insert(self, documents, index=0):\\n\\n # Initialize graph backend\\n self.initialize()\\n\\n for _, document, _ in documents:\\n if isinstance(document, dict):\\n # Require text or object field\\n document = document.get(self.text, document.get(self.object))\\n\\n if document is not None:\\n if isinstance(document, list):\\n # Join tokens as text\\n document = \\\" \\\".join(document)\\n\\n # Create node\\n self.addnode(index, data=document)\\n index += 1\",\n \"def insert_characterlist_mongo(characterList):\\n #MONGO_URI, DB_NAME and COLLECTION_NAME is from creds file\\n client = MongoClient(MONGO_URI)\\n db = client[MONGO_DB_NAME]\\n Names = db[MONGO_COLLECTION_NAME]\\n result = Names.insert_many(characterList)\",\n \"def insert_documents(connection: DBConnection, documents: Sequence[Document]) -> None:\\n max_ = len(documents)\\n current = 0\\n print() # print an extra line, because we will delete lines with printing \\\\r\\n for chunk in chunks(documents):\\n connection.execute(\\\"BEGIN TRANSACTION\\\")\\n for doc in chunk:\\n # python doesn't support prepared statements, but instead has a builtin sql cache\\n connection.execute(\\n \\\"INSERT INTO docs(did, title, url) VALUES (?, ?, ?)\\\", doc.convert_to_tuple())\\n current += 1\\n print(f\\\"\\\\r[{current}/{max_}] doc done\\\", end='')\\n connection.execute(\\\"COMMIT\\\")\",\n \"def insert_into_tweets(self, infos):\\n query = \\\"insert into tweets(tweet_id, insert_date, created_at, hashtag) values(?, ?, ?, ?);\\\"\\n with sql.connect('./{}.db'.format(self.name)) as conn:\\n conn.executemany(query, infos)\",\n \"def commit(self,form_list):\\n raise NotImplementedError\",\n \"def store_all_to_database(self, session):\\n\\n description = 'Established in 1974, JSM is a family-owned provider of quality apartments. We offer a variety of units from studios to five bedrooms with every location benefitting from our award winning amenities, responsive 24 hour maintenance, and friendly property management staff. JSM Development began in Champaign, IL, and manages roughly 1,500 apartments and 450,000 sq/ft of commercial space. JSM has been a major contributor to the development of Campustown in Champaign and the East Campus area in Urbana at the University of Illinois. These popular locations are now home to major national retailers such as Urban Outfitters, Chipotle, Panera, Cold Stone Creamery, and Noodles & Co.'\\n\\n # Insert a JSM company instance into the database\\n current_company = Company(\\n name='JSM',\\n baseurl='https://apartments.jsmliving.com/',\\n description = description\\n )\\n session.add(current_company)\\n\\n # Iterate over the apartments, storing each in the database\\n for apartment in self.apartment_data:\\n logging.info(\\\"Inserting %s to database\\\", apartment['name'])\\n new_apartment = Apartment(\\n company=current_company,\\n url=apartment['url'],\\n name=apartment['name'],\\n bedrooms=apartment['bedrooms'],\\n bathrooms=apartment['bathrooms'],\\n price=apartment['price'],\\n leasing_period=apartment['leasing_period'],\\n description=apartment['description'],\\n address=apartment['address'],\\n lat=apartment['lat'],\\n lng=apartment['lng']\\n )\\n session.add(new_apartment)\\n\\n # Insert images for the given apartment\\n for index, image_url in enumerate(apartment['image_urls']):\\n new_image = Image(\\n url=image_url,\\n apartment_id=new_apartment.id,\\n type=0,\\n image_index=index\\n )\\n session.add(new_image)\\n\\n # Connect images to apartment\\n new_apartment.images.append(new_image)\\n\\n # Insert floorplan image, if it exists\\n if apartment['floorplan_url'] != 0:\\n new_floorplan_image = Image(\\n url=apartment['floorplan_url'],\\n apartment_id=new_apartment.id,\\n type=1,\\n image_index=len(apartment['image_urls'])\\n )\\n session.add(new_floorplan_image)\\n\\n # Connect images to apartment\\n new_apartment.images.append(new_floorplan_image)\\n\\n # Insert amenities for the given apartment\\n for amenity in apartment['amenities']:\\n new_amenity = Amenity(\\n apartment_id=new_apartment.id,\\n amenity=amenity\\n )\\n session.add(new_amenity)\\n\\n # Connect amenity to apartment\\n new_apartment.amenities.append(new_amenity)\\n\\n # Write all queries to the database\\n session.commit()\",\n \"def insert_values(listingid_to_text):\\n sql = \\\"INSERT INTO listingid_to_text_english VALUES (%s, %s)\\\"\\n args = [(key, val) for key, val in listingid_to_text.iteritems()]\\n conn = None\\n try:\\n # read database configuration\\n params = config()\\n # connect to the PostgreSQL database\\n conn = psycopg2.connect(**params)\\n # create a new cursor\\n cur = conn.cursor()\\n \\n print(\\\"here\\\")\\n # execute the INSERT statement\\n cur.executemany(sql, args)\\n # commit the changes to the database\\n conn.commit()\\n # close communication with the database\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def insert_vendor_list(vendor_list):\\n sql = \\\"INSERT INTO vendors(vendor_name) VALUES(%s)\\\"\\n conn = None\\n try:\\n # read database configuration\\n params = config()\\n # connect to the PostgreSQL database\\n conn = psycopg2.connect(**params)\\n # create a new cursor\\n cur = conn.cursor()\\n # execute the INSERT statement\\n cur.executemany(sql, vendor_list)\\n # commit the changes to the database\\n conn.commit()\\n # close communication with the database\\n cur.close()\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if conn is not None:\\n conn.close()\",\n \"def add_teacher_data(connection,name,tsc_no,subjects,type_of_teacher):\\r\\n with connection:\\r\\n connection.execute(INSERT_TEACHER,(name,tsc_no,subjects,type_of_teacher))\",\n \"def addRecord(self):\\n\\n ## Saving recorded entries to the CRM and Mailings Database\\n print(\\\"Saving entries to the CRM and Mailings database...\\\")\\n db_connection.executeQuery(\\\"INSERT INTO dbo.CRM (f_name, l_name, company, address, city, county, state, zip, primary_phone, secondary_phone, email_address) VALUES ('\\\" + self.first_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.last_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.crm_company_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.address.title() + \\\"', '\\\" + self.city.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.county.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.state_code.upper() + \\\"', '\\\" + str(self.zip_code) + \\\"', '\\\" + self.phone_number + \\\"', '\\\" + self.phone_number_2 + \\\"' , '\\\" + self.email_address + \\\"'); COMMIT\\\")\\n db_connection.executeQuery(\\\"INSERT INTO dbo.Mailings (name, company, address) VALUES ('\\\" + self.first_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\" \\\" + self.last_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"', '\\\" + self.company_name.replace(\\\"\\\\'\\\", \\\"\\\\'\\\\'\\\").title() + \\\"','\\\" + self.address + \\\" \\\" + self.city.title() + \\\" \\\" + self.county.title() + \\\" \\\" + self.state_code.upper() + \\\" \\\" + str(self.zip_code) + \\\"'); COMMIT\\\")\",\n \"def add_to_db(car_list: list) -> dict:\\n db = CarsDb() # pylint: disable=invalid-name\\n db.create_table()\\n for _cars in car_list:\\n for car in _cars:\\n db.add_cars(car)\\n db.commit()\\n db.close()\",\n \"def save_in_db(self):\\n self.sql_database.table_name = self.table_db\\n self.sql_database.db_name = self.db\\n if self.sql_database.insert_item(text_path=self.path, word_first=self.word_1.get(),\\n word_second=self.word_2.get(),\\n word_third=self.word_3.get(), word_fourth=self.word_4.get(),\\n word_fifth=self.word_5.get()):\\n msg.showinfo(message=\\\"Done\\\")\",\n \"def insert_section(\\n self, args: Union[List[Any], List[List[Any]]], many=False\\n ) -> None:\\n\\n columns = \\\",\\\".join(column[0] for column in DBConnection.column_names)\\n\\n query = f\\\"\\\"\\\"\\n INSERT INTO {self.table_name} ({columns})\\n VALUES\\n ({','.join('?' for _ in range(len(DBConnection.column_names)))})\\n \\\"\\\"\\\"\\n\\n self.select_db()\\n self._insert_or_update(query, args, many)\",\n \"def create_db_post(entry, keys, like):\\n h = get_hash(entry['link'])\\n collection = pos if like else neg\\n return collection.update(\\n {'hash': h},\\n {\\n 'link': entry['link'],\\n 'title': entry['title'],\\n 'published': '',\\n 'content': \\\" \\\".join(keys),\\n 'hash': h,\\n 'read': False\\n }, upsert=True\\n )\",\n \"def create_publication(conn, publication):\\n\\tsql = ''' INSERT INTO publication(eid, doi, publicationDate,publicationYear,title,venueName)\\n\\t\\t\\t VALUES(?,?,?,?,?,?) '''\\n\\tcur = conn.cursor()\\n\\tcur.execute(sql, publication)\\n\\treturn cur.lastrowid\",\n \"def sync(self):\\n try:\\n result = self.client.insert_rows(\\n table=self.table, rows=map(lambda c: c.as_tuple, self.new_contributors())\\n )\\n if result:\\n logging.error(\\\"failed to add new contributors\\\")\\n logging.error(\\\"%s\\\", \\\"\\\\n\\\".join(result))\\n exit(1)\\n\\n except exceptions.BadRequest as exception:\\n if exception.errors[0].get(\\\"message\\\") != \\\"No rows present in the request.\\\":\\n raise exception\",\n \"def add_ref(doc_name, bib_name, keep_label=False, slides=False):\\n bib_path = bib_name # os.path.join(static_path, \\\"tex\\\", bib_name)\\n if not slides:\\n keep_label = True\\n with open(doc_name, \\\"r\\\") as f:\\n _file = f.read()\\n # find citations\\n match = re.findall(r\\\"\\\\[@.*?\\\\]\\\", _file)\\n references = []\\n # print(match)\\n for cit in match:\\n tmp_cite_html = tempfile.NamedTemporaryFile(suffix=\\\".html\\\", delete=True)\\n tmp = tempfile.NamedTemporaryFile(suffix=\\\".md\\\", delete=True)\\n with open(tmp.name, \\\"w\\\") as md_file:\\n # Open the file for writing.\\n md_file.write(cit)\\n\\n pandoc_command = [\\n \\\"pandoc\\\",\\n \\\"-C\\\",\\n tmp.name,\\n \\\"--bibliography\\\",\\n bib_path,\\n \\\"--csl\\\",\\n \\\"./templates/my-csl.csl\\\",\\n \\\"-o\\\",\\n tmp_cite_html.name,\\n ]\\n if not slides:\\n for tag in [\\\"--metadata\\\", \\\"link-citations=true\\\"]:\\n pandoc_command.append(tag)\\n output = subprocess.check_output(\\n pandoc_command, stderr=subprocess.STDOUT\\n ).decode(\\\"utf-8\\\")\\n print(output.rstrip())\\n tmp.close()\\n with open(tmp_cite_html.name, \\\"r\\\") as cite_html:\\n # Open the file for writing.\\n cit_new = cite_html.read()\\n # print(cit_new)\\n if not keep_label:\\n # remove first line\\n cit_new = \\\"\\\\n\\\".join(cit_new.splitlines()[1:])\\n else:\\n lines = cit_new.splitlines()\\n # remove the <p> tags\\n lines[0] = lines[0][3:-4]\\n cit_new = \\\"\\\\n\\\".join(lines)\\n if slides:\\n _file = _file.replace(cit, cit_new)\\n else:\\n _file = _file.replace(cit, lines[0])\\n # collect the references\\n references.append(\\\"<p>\\\")\\n references.append(\\\"\\\\n\\\".join(lines))\\n references.append(\\\"</p>\\\")\\n # paste_refs_end()\\n tmp_cite_html.close()\\n if slides:\\n with open(doc_name, \\\"w\\\") as f:\\n f.write(_file)\\n if not slides:\\n if references == []:\\n _file=clean_html_refs(_file)\\n else:\\n _file = _file.replace(\\\"<bibliography-placeholder>\\\", \\\"\\\\n\\\".join(references))\\n with open(doc_name, \\\"w\\\") as f:\\n f.write(_file)\\n return\",\n \"def add_to_db(self, data_base):\\n comment_data = self.get_comment(dictionary=True)\\n comment_data.pop('id')\\n cursor = data_base.cursor()\\n cursor.execute(f\\\"INSERT INTO comment ({', '.join(comment_data.keys())}) VALUES {tuple(comment_data.values())}\\\")\\n if self.commit_to_db:\\n data_base.commit()\\n cursor.close()\",\n \"def insert(conn, table_info, table_data):\\n\\n sql = ''' INSERT INTO ''' + table_info \\n + ''' VALUES(''' + \\\"?,\\\" * (len(table_data)-1) + \\\"?)\\\"\\n cursor = conn.cursor()\\n cursor.execute(sql, table_data)\\n conn.commit()\",\n \"def do_insert(self,args):\\n if len(args) != 0:\\n for w in args.split():\\n sl.insertList(int(w.rstrip()))\",\n \"def insert_students(ids, fname, lname, db_name='./grades.sqlite3'):\\n names_tupple = list(zip(ids, fname, lname, [0] * len(ids)))\\n with lite.connect(db_name) as con:\\n cur = con.cursor()\\n cur.executemany('INSERT OR REPLACE INTO STUDENTS \\\\\\n (pipeline_id, first_name, second_name, cheating_ratio)'\\n ' VALUES (?, ?, ?, ?)', names_tupple)\\n con.commit()\",\n \"def add_answers(conn, cur, answers):\\n \\n print 'Adding answers...',\\n \\n for i, answer in enumerate(answers):\\n cur.execute('INSERT INTO answers VALUES (\\\"{_id}\\\", \\\"{task_id}\\\", \\\"{text}\\\")'.format(\\n _id = i+1,\\n task_id = answer['task_id'],\\n text = answer['text']\\n )\\n )\\n \\n conn.commit()\\n \\n print 'done.'\",\n \"def populate(library):\\n # Clears table\\n table.delete(*table.get_children())\\n\\n # Inserts each book into the table\\n # where text is the key field\\n for book in library:\\n table.insert(\\\"\\\", int(book[0]), text=book[0], values=(book[1], book[2], book[3], book[4]))\",\n \"def insert_csl(conn, table, sql_param):\\r\\n header_list = get_header(conn, table)\\r\\n header_list.pop(0)\\r\\n translation = {39 : None,\\r\\n 91 : 40, \\r\\n 93 : 41}\\r\\n place_holders = [\\\"%s\\\" for item in header_list] # creates a list with many place holders as items in header_list\\r\\n place_holders = str(place_holders).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\\r\\n header_list = str(header_list).translate(translation) # list to str and ' -> None, [ -> { and ] -> }\\r\\n insert = f\\\"INSERT INTO {table} {header_list} VALUES {place_holders} RETURNING id\\\"\\r\\n cursor = conn.cursor()\\r\\n cursor.execute(insert, sql_param)\\r\\n conn.commit()\\r\\n returned_id = cursor.fetchone()\\r\\n cursor.close()\\r\\n return returned_id\",\n \"def add_db_equipement(self,c):\\n insertQuery = \\\"INSERT INTO Equipement(NumeroEquipement,NumeroInstallation,NatureLibelle,InsNom) VALUES (?,?,?,?)\\\"\\n\\n c.execute(insertQuery, (self.equipement_id,self.ins_numero_install,self.nature_libelle,self.ins_nom))\",\n \"def add_entry_to_bibtex_db(self, ent):\\n\\n # add additional fields manually to the dict\\n ent.consolidate_dict()\\n self.bibtex_db.entries.append(ent.raw_dict)\\n # the following updates the entries dict\\n # self.bibtex_db.get_entry_dict()\\n # # make sure it's there\\n # if ent.ID not in self.bibtex_db.entries_dict:\\n # self.bibtex_db.entries_dict[ent.ID] = ent.raw_dict\",\n \"def insert(self, data):\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6965884","0.68262","0.6096341","0.5778446","0.5740313","0.5732281","0.5607799","0.5572235","0.55622476","0.5558603","0.5553385","0.5523789","0.5512726","0.5501871","0.54990745","0.54926753","0.5489177","0.54863644","0.547148","0.5466431","0.54623103","0.5461697","0.5432289","0.54235494","0.5422917","0.53951794","0.5392401","0.5392188","0.53814864","0.5381423","0.53715986","0.53666306","0.5363537","0.533811","0.532374","0.53171825","0.5308532","0.53079313","0.5303429","0.5288689","0.5285142","0.5275493","0.52671325","0.5262983","0.52627474","0.5260967","0.52412295","0.5232613","0.52319443","0.52266484","0.52251726","0.52247685","0.5222497","0.5202161","0.5197085","0.5196247","0.5192699","0.5186709","0.51732045","0.5172711","0.51620936","0.51477396","0.5142299","0.51408094","0.5140627","0.51311916","0.51267093","0.5125642","0.5124807","0.5121611","0.5110221","0.5094074","0.5093305","0.5091367","0.50827014","0.5077416","0.5072883","0.50664794","0.5065294","0.5060393","0.50411105","0.50382173","0.5038143","0.5036251","0.5034529","0.50321096","0.50271434","0.50265336","0.50260866","0.50200117","0.5019054","0.5012682","0.5010942","0.5007336","0.5005887","0.50022626","0.500184","0.49938494","0.49924332","0.49850944"],"string":"[\n \"0.6965884\",\n \"0.68262\",\n \"0.6096341\",\n \"0.5778446\",\n \"0.5740313\",\n \"0.5732281\",\n \"0.5607799\",\n \"0.5572235\",\n \"0.55622476\",\n \"0.5558603\",\n \"0.5553385\",\n \"0.5523789\",\n \"0.5512726\",\n \"0.5501871\",\n \"0.54990745\",\n \"0.54926753\",\n \"0.5489177\",\n \"0.54863644\",\n \"0.547148\",\n \"0.5466431\",\n \"0.54623103\",\n \"0.5461697\",\n \"0.5432289\",\n \"0.54235494\",\n \"0.5422917\",\n \"0.53951794\",\n \"0.5392401\",\n \"0.5392188\",\n \"0.53814864\",\n \"0.5381423\",\n \"0.53715986\",\n \"0.53666306\",\n \"0.5363537\",\n \"0.533811\",\n \"0.532374\",\n \"0.53171825\",\n \"0.5308532\",\n \"0.53079313\",\n \"0.5303429\",\n \"0.5288689\",\n \"0.5285142\",\n \"0.5275493\",\n \"0.52671325\",\n \"0.5262983\",\n \"0.52627474\",\n \"0.5260967\",\n \"0.52412295\",\n \"0.5232613\",\n \"0.52319443\",\n \"0.52266484\",\n \"0.52251726\",\n \"0.52247685\",\n \"0.5222497\",\n \"0.5202161\",\n \"0.5197085\",\n \"0.5196247\",\n \"0.5192699\",\n \"0.5186709\",\n \"0.51732045\",\n \"0.5172711\",\n \"0.51620936\",\n \"0.51477396\",\n \"0.5142299\",\n \"0.51408094\",\n \"0.5140627\",\n \"0.51311916\",\n \"0.51267093\",\n \"0.5125642\",\n \"0.5124807\",\n \"0.5121611\",\n \"0.5110221\",\n \"0.5094074\",\n \"0.5093305\",\n \"0.5091367\",\n \"0.50827014\",\n \"0.5077416\",\n \"0.5072883\",\n \"0.50664794\",\n \"0.5065294\",\n \"0.5060393\",\n \"0.50411105\",\n \"0.50382173\",\n \"0.5038143\",\n \"0.5036251\",\n \"0.5034529\",\n \"0.50321096\",\n \"0.50271434\",\n \"0.50265336\",\n \"0.50260866\",\n \"0.50200117\",\n \"0.5019054\",\n \"0.5012682\",\n \"0.5010942\",\n \"0.5007336\",\n \"0.5005887\",\n \"0.50022626\",\n \"0.500184\",\n \"0.49938494\",\n \"0.49924332\",\n \"0.49850944\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7009089"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79591,"cells":{"query":{"kind":"string","value":"an aux thing to avoid repeating code"},"document":{"kind":"string","value":"def insert_into_cit_db(dic, name):\n ndate = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n try:\n s = serialize_via_marshal(dic)\n write_message(\"size of \"+name+\" \"+str(len(s)))\n #check that this column really exists\n testres = run_sql(\"select object_name from rnkCITATIONDATA where object_name = %s\",\n (name,))\n if testres:\n run_sql(\"UPDATE rnkCITATIONDATA SET object_value = %s where object_name = %s\",\n (s, name))\n else:\n #there was no entry for name, let's force..\n run_sql(\"INSERT INTO rnkCITATIONDATA(object_name,object_value) values (%s,%s)\",\n (name,s))\n run_sql(\"UPDATE rnkCITATIONDATA SET last_updated = %s where object_name = %s\",\n (ndate,name))\n except:\n register_exception(prefix=\"could not write \"+name+\" into db\", alert_admin=True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def regular(self):","def exo2():","def _regr_basic():","def __call__():","def __call__():","def __call__():","def __call__():","def __call__():","def sth():","def common(self):","def apply(self):","def g():","def firstFunction(self):","def use(self):","def func():","def call(self):","def __call__(self) -> None:","def apply(self) -> None:","def apply(self) -> None:","def _prepare(self):","def _prepare(self):","def substantiate():","def dummy(self):\n pass","def base():","def __call__(self):\n\t\treturn","def fn():","def cx():","def mezclar_bolsa(self):","def degibber(self):","def _hook(self):","def preprocess(self):","def one(self):","def __call__( self ):\n pass","def post_processor(self):","def two(self):","def process(self):","def process(self):","def process(self):","def CL(self):","def dummy_fn(self):\n\t\tpass","def merge_two_calls(self) -> None:","def fun_a(self):\n pass","def task4_1(self):\n\n pass","def __call__(object):","def __call__(self):\n pass","def __call__(self):\n pass","def falcon():","def map():","def result(self):","def result(self):","def support(self):","def elems(self):","def _optimise(self):\n pass","def __init__():","def pick_up(self):","def c(self):\n pass","def c(self):\n pass","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def __init__(*args):","def moi(self):\n\n pass","def transform(self):","def primary(self):\n ...","def a(self):\n pass","def a(self):\n pass","def _postprocess(self):","def simple():","def simple():","def do_manipulations(self, *args, **kwargs):\n pass","def variable(self):","def think(self):\n pass","def wrapup(self):\n pass","def task4(self):\n\n pass","def __call__(self):","def __call__(self):","def pre_processor(self):","def x(self):\n pass","def code():","def b(self):\n pass","def b(self):\n pass","def items():","def __call__(value):","def iterate(self):","def transact(self):","def transact(self):"],"string":"[\n \"def regular(self):\",\n \"def exo2():\",\n \"def _regr_basic():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def sth():\",\n \"def common(self):\",\n \"def apply(self):\",\n \"def g():\",\n \"def firstFunction(self):\",\n \"def use(self):\",\n \"def func():\",\n \"def call(self):\",\n \"def __call__(self) -> None:\",\n \"def apply(self) -> None:\",\n \"def apply(self) -> None:\",\n \"def _prepare(self):\",\n \"def _prepare(self):\",\n \"def substantiate():\",\n \"def dummy(self):\\n pass\",\n \"def base():\",\n \"def __call__(self):\\n\\t\\treturn\",\n \"def fn():\",\n \"def cx():\",\n \"def mezclar_bolsa(self):\",\n \"def degibber(self):\",\n \"def _hook(self):\",\n \"def preprocess(self):\",\n \"def one(self):\",\n \"def __call__( self ):\\n pass\",\n \"def post_processor(self):\",\n \"def two(self):\",\n \"def process(self):\",\n \"def process(self):\",\n \"def process(self):\",\n \"def CL(self):\",\n \"def dummy_fn(self):\\n\\t\\tpass\",\n \"def merge_two_calls(self) -> None:\",\n \"def fun_a(self):\\n pass\",\n \"def task4_1(self):\\n\\n pass\",\n \"def __call__(object):\",\n \"def __call__(self):\\n pass\",\n \"def __call__(self):\\n pass\",\n \"def falcon():\",\n \"def map():\",\n \"def result(self):\",\n \"def result(self):\",\n \"def support(self):\",\n \"def elems(self):\",\n \"def _optimise(self):\\n pass\",\n \"def __init__():\",\n \"def pick_up(self):\",\n \"def c(self):\\n pass\",\n \"def c(self):\\n pass\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def __init__(*args):\",\n \"def moi(self):\\n\\n pass\",\n \"def transform(self):\",\n \"def primary(self):\\n ...\",\n \"def a(self):\\n pass\",\n \"def a(self):\\n pass\",\n \"def _postprocess(self):\",\n \"def simple():\",\n \"def simple():\",\n \"def do_manipulations(self, *args, **kwargs):\\n pass\",\n \"def variable(self):\",\n \"def think(self):\\n pass\",\n \"def wrapup(self):\\n pass\",\n \"def task4(self):\\n\\n pass\",\n \"def __call__(self):\",\n \"def __call__(self):\",\n \"def pre_processor(self):\",\n \"def x(self):\\n pass\",\n \"def code():\",\n \"def b(self):\\n pass\",\n \"def b(self):\\n pass\",\n \"def items():\",\n \"def __call__(value):\",\n \"def iterate(self):\",\n \"def transact(self):\",\n \"def transact(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.6806635\",\n \"0.6386508\",\n \"0.62237775\",\n \"0.61517763\",\n \"0.61517763\",\n \"0.61517763\",\n \"0.61517763\",\n \"0.61517763\",\n \"0.60746104\",\n \"0.60519785\",\n \"0.6034576\",\n \"0.6013738\",\n \"0.6012824\",\n \"0.5982234\",\n \"0.59326035\",\n \"0.59287214\",\n \"0.59233296\",\n \"0.5911283\",\n \"0.5911283\",\n \"0.58887786\",\n \"0.58887786\",\n \"0.5878358\",\n \"0.58523464\",\n \"0.58431655\",\n \"0.5837656\",\n \"0.5830854\",\n \"0.5823784\",\n \"0.58092475\",\n \"0.579919\",\n \"0.57797855\",\n \"0.57505435\",\n \"0.57270795\",\n \"0.5707593\",\n \"0.56633073\",\n \"0.564397\",\n \"0.56369454\",\n \"0.56369454\",\n \"0.56369454\",\n \"0.5630884\",\n \"0.56070817\",\n \"0.5600069\",\n \"0.55995786\",\n \"0.5587197\",\n \"0.55786765\",\n \"0.5574308\",\n \"0.5574308\",\n \"0.5569979\",\n \"0.556621\",\n \"0.55560744\",\n \"0.55560744\",\n \"0.5553007\",\n \"0.55394965\",\n \"0.5524409\",\n \"0.55242354\",\n \"0.5503309\",\n \"0.5500969\",\n \"0.5500969\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.5494191\",\n \"0.54757303\",\n \"0.5474794\",\n \"0.5467373\",\n \"0.54634136\",\n \"0.54634136\",\n \"0.5456279\",\n \"0.545403\",\n \"0.545403\",\n \"0.5441717\",\n \"0.54393244\",\n \"0.5431707\",\n \"0.54153574\",\n \"0.5403543\",\n \"0.5400197\",\n \"0.5400197\",\n \"0.53983265\",\n \"0.5394513\",\n \"0.5394301\",\n \"0.5393157\",\n \"0.5393157\",\n \"0.53866714\",\n \"0.5379264\",\n \"0.53731203\",\n \"0.5373107\",\n \"0.5373107\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":79592,"cells":{"query":{"kind":"string","value":"get a named citation dict from the db"},"document":{"kind":"string","value":"def get_cit_dict(name):\n cdict = {}\n try:\n cdict = run_sql(\"select object_value from rnkCITATIONDATA where object_name = %s\",\n (name,))\n if cdict and cdict[0] and cdict[0][0]:\n dict_from_db = deserialize_via_marshal(cdict[0][0])\n return dict_from_db\n else:\n return {}\n except:\n register_exception(prefix=\"could not read \"+name+\" from db\", alert_admin=True)\n return dict"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_citation(cit):\n if cit is not None:\n if cit['citation-type'] == \"BIBTEX\":\n return pybtex.database.parse_string(cit['citation-value'], \"bibtex\")\n return None","def get_author_by_name(self, name):\n\n cur = self.conn.cursor()\n query = 'SELECT author_id , name FROM author WHERE name = ? '\n cur.execute(query, (name,))\n return row_to_dict_or_false(cur)","def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp': [] }\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n\n if author['dblp'].__contains__('publications'):\n publications['dblp'] = author['dblp']['publications']\n# for pub in author['dblp']['publications']:\n# print(pub)\n\n if author['cdblp'].__contains__('publications'):\n publications['cdblp'] = author['cdblp']['publications']\n# for pub in author['cdblp']['publications']:\n# print(pub)\n return publications","def getDict(name):\n df = Dictionary.objects.select_related().get(name=name)\n return df","def get_bibtex_from_citation_file(package_name):\n package = importlib.import_module(package_name)\n cff_file = os.path.join(package.__path__[0], 'CITATION.cff')\n citation_file = os.path.join(package.__path__[0], 'CITATION')\n if os.path.exists(cff_file):\n with open(cff_file) as f:\n cffstr = f.read()\n citation = Citation(cffstr=cffstr)\n bibtex = citation.as_bibtex()\n elif os.path.exists(citation_file):\n with open(citation_file) as f:\n citestr = f.read()\n match = re.search(cite_tag_pattr, citestr)\n if match is None:\n raise ValueError('Could not find any BibTeX entries in CITATION file')\n for i_end in range(match.end(), len(citestr)):\n bibtex = citestr[match.start():i_end]\n if bibtex.count('{') == bibtex.count('}'):\n break\n else:\n raise ValueError('Mismatched braces in BibTeX entry in CITATION file')\n else:\n bibtex = None\n return bibtex","def get_coauthors_dict(name_url_list, schl_name):\n\n\tpaper_info_dict = {}\n\n\tfor name_url in name_url_list:\n\t\t# Get a sub papers_info dictfor an individual author\n\t\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\n\t\t# add sub dict to full dict\n\t\tpaper_info_dict.update(papers_info)\n\t\n\tif \"Humanities\" in schl_name:\n\t\tschl_name = \"School of Humanities\"\n\n\treturn paper_info_dict","def get_titles_dict(name_url_list):\n\tbib_dict = {}\n\t\n\tfor name_url in name_url_list:\n\t\ttitles = get_author_titles(name_url[1])\n\t\tbib_dict[name_url] = titles\n\n\treturn bib_dict","def _populate(self):\n\n # Assume the first word is what we want, and we can find well formed years\n # This sucks, but will work for these ones.\n # Roll on bibtex for citations in the CIM.\n\n citation_detail = self.doc.citation_detail\n author = citation_detail.split(',')[0]\n match = '([^\\w])19|20\\d\\d([^\\w])*?'\n m = re.search(match, citation_detail)\n if m:\n year = m.group(0)\n else:\n year = None\n\n # one error in existing es-doc content to be fixed:\n if 'van Vuuren DP' in author:\n author = 'van Vuuren'\n print 'applying vv fix'\n\n self.year = int(year)\n\n # We assume that this table will have entries which ne\n\n # I use the first three letters of a an authors name, and for\n # three or more authors, EA, and then the year for my bibtex citation string\n self.citeguess = author[0:3] + 'EA' + year[2:]\n # This is what will appear in the table:\n self.citestring = '%s et al. (%s)' % (author, year)\n # Keep this for a reference list for checking against the eventual bibtex reference list.\n self.text = citation_detail","def bibtex_to_dict(text):\n def _format_person(person):\n von_last = ' '.join(person._prelast + person._last)\n jr = ' '.join(person._lineage)\n first = ' '.join(person._first + person._middle)\n return ' '.join(part for part in (first, von_last, jr) if part)\n\n parser = pybtex.database.input.bibtex.Parser()\n bib_data = parser.parse_stream(StringIO(text))\n\n plain_style = pybtex.style.formatting.plain.Style()\n plain_render = pybtex.backends.plaintext.Backend()\n formatted_entry = next(plain_style.format_entries(bib_data.entries.values()))\n\n # It is possible to parse out multiple BibTeX entries\n # in one go! But, I'm assuming that the metadata\n # contains information about only one publication.\n\n citekey = bib_data.entries.keys()[0]\n entry = bib_data.entries[citekey]\n meta = {\n 'citekey': citekey,\n 'type': entry.type,\n 'authors': [_format_person(person)\n for person in entry.persons['author']],\n 'cite_info': entry.fields,\n 'cite_bibtex': text,\n 'cite_plain': formatted_entry.text.render(plain_render),\n }\n\n # Get important stuff from cite_info\n extract = ('title', 'abstract', 'pdf', 'url', 'year', 'keywords',\n 'poster', 'presentation')\n for key in extract:\n if key in entry.fields:\n meta[key] = meta['cite_info'].pop(key)\n\n # Add editors to cite_info, if they exist\n if 'editor' in entry.persons:\n meta['cite_info']['editors'] = \", \" .join(\n [_format_person(person) for person in entry.persons['editor']])\n\n return meta","def getCitations (self, doc, verbose = False):\r\n if verbose is True:\r\n print \"getting citations for document \" +str(doc) \r\n return self.citations[int(doc)]","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def getCityCodeDict():\n table = 'airport'\n connection = openConnection()\n curs = connection.cursor()\n sqlcmd = \"SELECT * FROM \" + str(table)\n d = {}\n \n curs.execute(sqlcmd)\n for row in curs.fetchall():\n airport = airlineClasses.Airport()\n airport.cityCode = row[0]\n airport.city = row[1] \n d[airport.cityCode] = airport\n \n curs.close()\n connection.close()\n return d","def make_citation(meta):\n pass","def get_publication(project, publication_id):\n connection = db_engine.connect()\n publications = get_table(\"publication\")\n statement = select([publications]).where(publications.c.id == int_or_none(publication_id))\n rows = connection.execute(statement).fetchall()\n result = dict(rows[0])\n connection.close()\n return jsonify(result)","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def get_bibtex_entry(doi, bibtext_cache={}, shortdoi_cache={}):\r\n bibtext = get_bibtext(doi, cache = bibtext_cache)\r\n if not bibtext:\r\n return None\r\n\r\n short_doi = shorten(doi, cache = shortdoi_cache)\r\n parser = BibTexParser()\r\n parser.ignore_nonstandard_types = False\r\n bibdb = bibtexparser.loads(bibtext, parser)\r\n entry, = bibdb.entries\r\n quoted_doi = urllib.request.quote(doi)\r\n entry['link'] = 'https://doi.org/{}'.format(quoted_doi)\r\n if 'author' in entry:\r\n entry['author'] = ' and '.join(entry['author'].rstrip(';').split('; '))\r\n entry['ID'] = short_doi[3:]\r\n return entry","def fill(self):\n if self.source == 'citations':\n url = self._scholarly.URLS(\"CITATIONPUB\").format(self.id_citations)\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\n\n if soup.find('a', class_='gsc_vcd_title_link'):\n self.bib['url'] = soup.find(\n 'a', class_='gsc_vcd_title_link')['href']\n\n for item in soup.find_all('div', class_='gs_scl'):\n key = item.find(class_='gsc_vcd_field').text\n val = item.find(class_='gsc_vcd_value')\n if key == 'Authors':\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\n elif key == 'Journal':\n self.bib['journal'] = val.text\n elif key == 'Volume':\n self.bib['volume'] = val.text\n elif key == 'Issue':\n self.bib['number'] = val.text\n elif key == 'Pages':\n self.bib['pages'] = val.text\n elif key == 'Publisher':\n self.bib['publisher'] = val.text\n elif key == 'Publication date':\n self.bib['year'] = arrow.get(val.text).year\n elif key == 'Description':\n if val.text[0:8].lower() == 'abstract':\n val = val.text[9:].strip()\n self.bib['abstract'] = val\n elif key == 'Total citations':\n self.id_scholarcitedby = re.findall(\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\n\n # number of citation per year\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\n self.cites_per_year = dict(zip(years, cites))\n\n if soup.find('div', class_='gsc_vcd_title_ggi'):\n self.bib['eprint'] = soup.find(\n 'div', class_='gsc_vcd_title_ggi').a['href']\n self._filled = True\n\n elif self.source == 'scholar':\n self.bib['add_to_lib'] = self.url_add_sclib\n\n try:\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\n bibtex = bibtex.find('pre').string\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\n self.bib['author_count'] = str(\n len(self.bib['author'].split('and')))\n self.bib['age'] = str(\n int(date.today().year) - int(self.bib['year']))\n except:\n # did not find year\n pass\n\n self._filled = True\n return self","def get_author_detail(body):\n user_id = get_user_id(body) # user id\n author_details = body.find('div', {'id': 'gsc_prf_i'})\n\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\n\n affiliation = name_attributes[0].text or ''\n interests = name_attributes[1].text or ''\n email = name_attributes[2].text or ''\n\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\n\n year_since_text = citation_indices.find_all('tr')[0]\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\n\n # citations\n citations = citation_indices.find_all('tr')[1]\n h_index = citation_indices.find_all('tr')[2]\n h_index_all = h_index.find_all('td')[1].text\n h_index_since = h_index.find_all('td')[2].text\n citation_all = citations.find_all('td')[1].text\n citation_since = citations.find_all('td')[2].text\n\n author_dict = {'user_id': user_id,\n 'name': name,\n 'affiliation': affiliation,\n 'interests': interests,\n 'email': email,\n 'h_index_all': h_index_all,\n 'h_index_since': h_index_since,\n 'citation_all': citation_all,\n 'citation_since': citation_since,\n 'year_since': year_since}\n return author_dict","def read_county_from_db(state_name, county_name):\n # Connect to database\n conn = sqlite3.connect('./db/incarceration.db')\n\n # Query the database\n data = pd.read_sql_query(f\"\"\"SELECT *\n FROM incarceration\n WHERE county_name = '{county_name}'\n AND state = '{state_name}';\n \"\"\", conn)\n\n # Close connection\n conn.close()\n\n return data","def find_cited_by(doi):\n if doi is None:\n return None\n\n citations = []\n if doi:\n response = requests.get(f\"https://opencitations.net/index/api/v1/citations/{doi}\").json()\n if response:\n citations = [{\"doi\": r['citing'].replace(\"coci =>\", \"\")} for r in response]\n if citations:\n return citations\n else:\n return None","def get_bibtex_from_package(package_name, update_local=False):\n try:\n package = importlib.import_module(package_name)\n for attr in ['__bibtex__', '__citation__']:\n citation_info = getattr(package, attr, None)\n if citation_info:\n break\n except ImportError:\n warnings.warn(f\"{package_name} is not installed, cannot look for \"\n \"package provided BibTeX.\")\n return None\n\n if citation_info and update_local:\n path = os.path.join(_bib_path, '{0}.bib'.format(package_name))\n with open(path, 'w') as f:\n f.write(citation_info)\n\n return citation_info","def authorSign(g = None):\n if not g:\n return\n authors = g.find(\"Author\")\n ret = dict()\n for author in authors:\n ret[author['name']] = author['citation'].replace('\\n','').lower()\n return ret","def parse_citations(s: str):\n print(\"parse_citations(%s)\" % s)\n if pandas.isnull(s) or not s:\n return []\n terms = [e.strip() for e in s.split(\";\")]\n print(\"parse_citations: \" + str(terms))\n out = []\n for term in terms:\n if term.startswith(\"PMID\"):\n out.append({\"db\": \"PubMed\", \"id\": term})\n else:\n raise RuntimeError(\"Unknown citation format: \" + term)\n return out","def get_bibtex(self):\n url=\"http://www.biomedcentral.com/download/citation\"\n parameters={'citation':'on', \n 'result-selection':'THIS_PAGE',\n 'suplements': 'false',\n 'citationType':'BIBTEX_WITH_ABSTRACT',\n 'articleIdThisPage':self.article_ids\n }\n data=urllib.urlencode(parameters)\n self.bibtex=urllib2.urlopen(url,data)","def to_cff_reference(bib_entry: pybtex.database.Entry) -> dict:\n\n def _cff_transform(cff_field, bib_value):\n if cff_field == \"type\":\n if bib_value == \"inproceedings\":\n return \"article\"\n elif bib_value == \"incollection\":\n return \"article\"\n elif cff_field == \"publisher\":\n return {\"name\": bib_value}\n elif cff_field == \"month\":\n try:\n return int(bib_value)\n except ValueError:\n return {\n \"jan\": 1,\n \"feb\": 2,\n \"mar\": 3,\n \"apr\": 4,\n \"may\": 5,\n \"jun\": 6,\n \"jul\": 7,\n \"aug\": 8,\n \"sep\": 9,\n \"oct\": 10,\n \"nov\": 11,\n \"dec\": 12,\n }[bib_value[:3].lower()]\n return bib_value\n\n cff_reference = {\n \"type\": _cff_transform(cff_field=\"type\", bib_value=bib_entry.type),\n \"authors\": [\n to_cff_person(person) for person in bib_entry.persons[\"author\"]\n ],\n }\n # Map BibTeX to CFF fields. This is just a subset of the most relevant\n # fields.\n fields = {\n \"doi\": \"doi\",\n \"edition\": \"edition\",\n \"isbn\": \"isbn\",\n \"license\": \"license\",\n \"month\": \"month\",\n \"number\": \"number\",\n \"pages\": \"pages\",\n \"publisher\": \"publisher\",\n \"title\": \"title\",\n \"url\": \"url\",\n \"version\": \"version\",\n \"volume\": \"volume\",\n \"year\": \"year\",\n \"booktitle\": \"collection-title\",\n }\n for bibtex_field, value in bib_entry.fields.items():\n bibtex_field = bibtex_field.lower()\n if bibtex_field in fields:\n cff_field = fields[bibtex_field]\n cff_reference[cff_field] = _cff_transform(\n cff_field=cff_field, bib_value=value\n )\n return cff_reference","def _get_critter_db() -> Dict[CritterType, List[CritterImage]]:\n with open(os.path.join('critters', 'names.json')) as fp:\n critter_data = json.load(fp)\n\n critter_db = collections.defaultdict(list)\n for critter_name, icon_name, critter_type_str in critter_data:\n critter_type = CritterType.from_str(critter_type_str)\n critter = CritterImage(critter_name, critter_type, icon_name)\n critter_db[critter_type].append(critter)\n return critter_db","def get_CTD_metadata(db):\n\n if not use_sqlite:\n raise RuntimeError('No sqlite standard library found in this python'\n ' installation. This function (get_CTD_metadata)'\n ' is unavailable.')\n\n def _dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d\n\n try:\n con = sqlite3.connect(db)\n\n con.row_factory = _dict_factory\n\n c = con.cursor()\n\n out = c.execute('SELECT * from Stations')\n\n meta_info = out.fetchall()\n\n except sqlite3.Error as e:\n if con:\n con.close()\n print('Error {}:'.format(e.args[0]))\n meta_info = [False]\n\n finally:\n if con:\n con.close()\n\n return meta_info","def _to_dict(self):\n\t\tauthor = Author.query.get(self.authors_id)\n\t\treturn {'id': self.id,\n\t\t\t\t'name': self.name,\n\t\t\t\t'author_id': self.authors_id,\n\t\t\t\t'author_name': author.name,\n\t\t\t\t'author_surname': author.surname}","def get_keyword(self, collection_id, name):\n sql = \"\"\"SELECT keyword.name, keyword.args, keyword.doc\n FROM keyword_table as keyword\n WHERE keyword.collection_id == ?\n AND keyword.name like ?\n \"\"\"\n cursor = self._execute(sql, (collection_id,name))\n # We're going to assume no library has duplicate keywords\n # While that in theory _could_ happen, it never _should_,\n # and you get what you deserve if it does.\n row = cursor.fetchone()\n if row is not None:\n return {\"name\": row[0],\n \"args\": json.loads(row[1]),\n \"doc\": row[2],\n \"collection_id\": collection_id\n }\n return {}","def get_initial_author_dict():\n adict = {}\n try:\n ah = run_sql(\"select aterm,hitlist from rnkAUTHORDATA\")\n for (a, h) in ah:\n adict[a] = deserialize_via_marshal(h)\n return adict\n except:\n register_exception(prefix=\"could not read rnkAUTHORDATA\", alert_admin=True)\n return {}","def make_bibtex(self):\n\n\t\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, ","def sqlite3_dict_factory(cursor, row):\n dict_row = dict()\n for idx, col in enumerate(cursor.description):\n dict_row[col[0]] = row[idx]\n dict_row[idx] = row[idx]\n return dict_row","def get_book_data(isbn: int):\n try:\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\n except StopIteration:\n pass # actually, I could put the whole rest of the function here\n else:\n data = core.Book.view_str(book.id)\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\n del data['borrowed_by_id'], data['__str__']\n return data\n\n try:\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\n r.raise_for_status()\n except requests.exceptions.RequestException:\n raise core.BuchSchlossError('no_connection', 'no_connection')\n\n person_re = re.compile(r'(\\w*, \\w*) \\((\\w*)\\)')\n results = {'concerned_people': []}\n\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n if table is None:\n # see if we got multiple results\n link_to_first = page.select_one('#recordLink_0')\n if link_to_first is None:\n raise core.BuchSchlossError(\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\n page = bs4.BeautifulSoup(r.text)\n table = page.select_one('#fullRecordTable')\n\n for tr in table.select('tr'):\n td = [x.get_text('\\n').strip() for x in tr.select('td')]\n if len(td) == 2:\n if td[0] == 'Titel':\n results['title'] = td[1].split('/')[0].strip()\n elif td[0] == 'Person(en)':\n for p in td[1].split('\\n'):\n g = person_re.search(p)\n if g is None:\n continue\n g = g.groups()\n if g[1] == 'Verfasser':\n results['author'] = g[0]\n else:\n results['concerned_people'].append(g[1]+': '+g[0])\n elif td[0] == 'Verlag':\n results['publisher'] = td[1].split(':')[1].strip()\n elif td[0] == 'Zeitliche Einordnung':\n results['year'] = td[1].split(':')[1].strip()\n elif td[0] == 'Sprache(n)':\n results['language'] = td[1].split(',')[0].split()[0].strip()\n\n results['concerned_people'] = '; '.join(results['concerned_people'])\n return results","def cursor_data(c):\r\n\r\n # pull column description\r\n d = []\r\n for i in range(len(c.description)):\r\n d.append(c.description[i][0])\r\n\r\n # fetch column entries\r\n c = c.fetchall()\r\n\r\n # compile list\r\n info = []\r\n for i in range(len(c)):\r\n # compile dictionary entry\r\n entry = {}\r\n for j in range(len(d)):\r\n entry[d[j]] = c[i][j]\r\n info.append(entry)\t\r\n\r\n # success\r\n return info","def make_citation_key(res):\n year = str(make_year(res))\n\n try:\n last_names = [x['family'] for x in res['author']]\n except KeyError as e:\n last_names = [\"Unknown\"]\n if len(last_names) >= 3:\n key = last_names[0].capitalize() + \"ETAL\" + year\n else:\n key = \"\".join(last_names) + year\n\n return clean_txt(key.replace(\" \", \"\"))","def get_standard_citatation(citation, cache, override):\n try:\n print('Trying metadata cache for {}...'.format(citation))\n metadata = citation_to_metadata(citation, cache, override)\n return metadata['standard_citation'], metadata['citation_id']\n except Exception as e:\n return None, None","def bibtex(self) -> str:\n a = BibDatabase()\n a.entries = [self.bib]\n return bibtexparser.dumps(a)","def get_credentials():\n sql = \"\"\"\n SELECT credential_name, description full_name\n FROM credential_code\n WHERE can_use = 1\n \"\"\"\n\n # [('CRNA', 'Certified Registered Nurse Anesthetist'), ... ]\n record_set = get_record(sql)\n result = dict(record_set)\n return result","def get_single_book_info(self, isbn):\n self.cursor.execute(\"SELECT * FROM book WHERE ISBN=%s\", (isbn,))\n books = self.cursor.fetchall()\n for book in books:\n authors = []\n self.cursor.execute(\"\"\"SELECT name FROM Author A, Wrote W, Book B WHERE A.ID = W.authorID AND\n W.ISBN = B.ISBN AND B.ISBN = %s\"\"\", (isbn,))\n for auth in self.cursor.fetchall():\n authors.append(auth[0])\n return book, authors","def get_author_data():\n entry = mongo.db.Authors\n output = list()\n look_up_type = None\n if 'name' in request.args:\n look_up_type = 'name'\n print(request.args)\n if len(request.args['name']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['name'].strip('\"')\n name = entry.find({'name': {'$regex': value}})\n if name:\n for author in name:\n output.append({'name': author['name']})\n elif 'booktitle' in request.args:\n look_up_type = 'related_books'\n if len(request.args['booktitle']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['booktitle'].strip('\"')\n related_books = entry.find(\n {'author_books': {'$regex': value}})\n if related_books:\n for related in related_books:\n for title in related['author_books']:\n if value in title:\n output.append(({'related_books': title}))\n if len(output) == 0:\n return render_template('error.html', message=\"No Entries Found\"), 400\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200","def get_license_refs_dict(license_refs_list):\n license_ref_dict = {}\n if license_refs_list:\n for ref_dict in license_refs_list:\n license_ref_dict[ref_dict['licenseId']] = ref_dict['extractedText']\n return license_ref_dict","def get_citation(self):\n etree = self.get_eml()\n try:\n citation_ipt = etree.find('./additionalMetadata/metadata/gbif/citation').text\n except AttributeError:\n citation_ipt = None\n if citation_ipt:\n now = datetime.datetime.now().date()\n pola3r = \"(Available: Polar 'Omics Links to Antarctic, Arctic and Alpine Research. Antarctic Biodiversity Portal. Scientific Committee for Antarctic Research. www.biodiversity.aq/pola3r. Accessed: {})\".format(\n now)\n citation = \"{} {}\".format(citation_ipt, pola3r)\n else:\n citation = None\n return citation","def get_coauthors_by_author(cached_list, cached_set, author_name):\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n coauthors = {}\n if author['dblp'].__contains__('coauthors'):\n for author_key in author['dblp']['coauthors']:\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\n\n if author['cdblp'].__contains__('coauthors'):\n for author_key in author['cdblp']['coauthors']:\n if coauthors.__contains__(author_key['full_name']):\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\n else:\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\n\n return coauthors","def dict_factory(cursor, row):\r\n\td = {}\r\n\tfor idx, col in enumerate(cursor.description):\r\n\t\td[col[0]] = row[idx]\r\n\treturn d","def get_bibtex(package_name):\n full_path = os.path.join(_bib_path, '{0}.bib'.format(package_name.lower()))\n if not os.path.exists(full_path):\n raise ValueError('Bibtex not found for {0}! If you know it has a '\n 'citation, please consider adding it via pull request '\n ' to: https://github.com/adrn/makecite')\n\n with open(full_path, 'r') as f:\n bibtex = f.read()\n\n return bibtex","def get_keyword_dict(url):\n try:\n conn = sqlite3.connect('server/database/web.db') \n cur = conn.cursor()\n cursor = cur.execute('SELECT content from global_data where url=?', (url,))\n \n for row in cursor:\n new_dict = { w.split(':')[0] : int(w.split(':')[1]) for w in row[0].split()}\n return new_dict\n\n except sqlite3.Error as error:\n print(error)\n \n finally:\n if (conn): conn.close()","def list_publications(bib_format=\"dict\"):\n\n def get_bibtex(key, value):\n total_keys = [\n \"title\",\n \"journal\",\n \"volume\",\n \"issue\",\n \"number\",\n \"pages\",\n \"numpages\",\n \"year\",\n \"month\",\n \"publisher\",\n \"url\",\n \"doi\",\n \"issn\",\n ]\n bibtex_str = (\n \"@article{\"\n + key\n + \",\\n\"\n + \" author={\"\n + \" and \".join(value[\"author\"])\n + \"},\\n\"\n )\n for key in total_keys:\n if key in value.keys():\n bibtex_str += \" \" + key + \"={\" + value[key] + \"},\\n\"\n bibtex_str += \"}\\n\"\n return bibtex_str\n\n def get_apa(value):\n apa_str = \" & \".join(value[\"author\"])\n if \"year\" in value.keys():\n apa_str += \" (\" + value[\"year\"] + \"). \"\n if \"title\" in value.keys():\n apa_str += value[\"title\"] + \". \"\n if \"journal\" in value.keys():\n apa_str += value[\"journal\"] + \", \"\n if \"volume\" in value.keys():\n apa_str += value[\"volume\"] + \", \"\n if \"pages\" in value.keys():\n apa_str += value[\"pages\"] + \". \"\n if \"doi\" in value.keys():\n apa_str += \"doi: \" + value[\"doi\"] + \"\\n\"\n return apa_str\n\n publication_dict = s.publication_lst\n if bib_format.lower() == \"dict\":\n return publication_dict\n elif bib_format.lower() == \"bibtex\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_bibtex(key, value)\n return total_str\n elif bib_format.lower() == \"apa\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_apa(value)\n return total_str\n else:\n raise ValueError(\"Supported Bibformats are ['dict', 'bibtex', 'apa']\")","def get_author_info_from_people_collection(info):\n # TODO: probably we will need to extract this somewhere else\n URL = ('https://cds.cern.ch/submit/get_authors?'\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\n if '0' in info or not info.get('a'):\n # There is already enough information or we don't have a name to query\n return info\n author_name = info.get('a')\n if PY2:\n # In Python 3, encoded name will change type to bytes and this will\n # cause query to CDS to fail\n author_name = author_name.encode('utf-8')\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\n if not author_info or len(author_info) > 1:\n # Didn't find anything or find to many matches\n return info\n\n # Prepare author name\n author_info = author_info[0]\n if 'name' not in author_info:\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\n author_info['firstname'])\n return MementoDict([\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])","def build_bibtex_entry(metadata: dict):\n # We truncate the author list in the BibTeX entry after 'Developers',\n # because not all journals are happy with printing an excessively long\n # author list, e.g. Phys. Rev. wants at most 15 authors. By truncating the\n # author list here, the user who copies the BibTeX entry doesn't have to\n # make the decision where to truncate.\n authors = [\n pybtex.database.Person(author[\"Name\"])\n for author in (\n metadata[\"Authors\"][\"Core\"][\"List\"]\n + metadata[\"Authors\"][\"Developers\"][\"List\"]\n )\n ] + [pybtex.database.Person(\"others\")]\n entry = pybtex.database.Entry(\n \"software\",\n persons=dict(author=authors),\n fields=dict(\n title=(\n r\"\\texttt{\"\n + metadata[\"Name\"]\n + \" v\"\n + metadata[\"Version\"]\n + \"}\"\n ),\n # The 'version' field is not used by revtex4-2, so we also put the\n # version in the title\n version=metadata[\"Version\"],\n publisher=\"Zenodo\",\n doi=metadata[\"Doi\"],\n url=metadata[\"Homepage\"],\n howpublished=(\n r\"\\href{https://doi.org/\"\n + metadata[\"Doi\"]\n + \"}{\"\n + metadata[\"Doi\"]\n + \"}\"\n ),\n license=metadata[\"License\"],\n year=str(metadata[\"PublicationDate\"].year),\n month=str(metadata[\"PublicationDate\"].month),\n ),\n )\n entry.key = \"spectrecode\"\n return entry","def extract_metadata(res):\n d = OrderedDict()\n\n d['type'] = res['type']\n d['authors'] = make_author_list(res)\n d['citation-authors'] = make_citation_authors(res)\n d['citation'] = make_citation(res)\n d['source-title'] = res['title'].strip(\" \").capitalize()\n d['title'] = \"Notes on '\" + d['source-title'] + \"', by \" + \", \".join(d['authors'][0:-1])\n if len(d['authors']) > 1:\n d['title'] = d['title'] + \", and \" + d['authors'][-1] \n d['citation-authors']\n d['container'] = make_container(res)\n d['publisher'] = res['publisher'] if 'publisher' in res.keys() else '' \n d['issn'] = res['issn'] if 'issn' in res.keys() else ''\n d['year'] = make_year(res)\n d['issue'] = res['issue'] if \"issue\" in res.keys() else \"\"\n d['volume'] = res['volume'] if \"volume\" in res.keys() else \"\"\n d['pages'] = res['page'] if \"page\" in res.keys() else \"\"\n d['subjects'] = make_subject(res)\n d['doi'] = res['DOI']\n d['link'] = res['URL']\n d['citationkey'] = make_citation_key(res)\n d['fetched'] = date.today().strftime(\"%Y%m%d\")\n return d","def get_cit_data(ticker):\n log = logging.getLogger(__name__)\n # TODO: This is probably not working anymore (need to refresh files)\n file = os.path.join(c.cfg['default']['data'], 'COT', 'Supplemental', 'T{}.PRN'.format(ticker))\n columns = ['Non-Commercial Longs', 'Non-Commercial Shorts',\n 'Commercial Longs', 'Commercial Shorts',\n 'Non-Reportable Longs', 'Non-Reportable Shorts',\n 'Index Trader Longs', 'Index Trader Shorts']\n\n # Read Data from csv file\n cit = pd.read_csv(file, parse_dates=True, index_col=0, header=None, names=columns)\n \n\n return cit","def get_authors(config: Config) -> dict:\n\n file_handle = load_csv_file(config.input)\n\n start = time.time()\n raw_authors = get_raw_authors(config, file_handle)\n end = time.time()\n print(f\"get_raw_authors took {end - start} seconds\")\n\n start = time.time()\n sanitized_authors = sanitize_authors(config, raw_authors)\n end = time.time()\n print(f\"sanitize_authors took {end - start} seconds\")\n\n return sanitized_authors","def get_papers_by_institution(inst_names):\n papers = list(ads.SearchQuery(aff=\"IATE\"))\n for p in papers:\n print(p.author)\n print(f\"Paper from year {p.year} with \"\n f\"{len(p.author)} authors and \"\n f\"{p.citation_count} citations.\")\n input()","def to_citation(self, type):\n acs_authors = \"; \".join(self.format_authors(\"acs\"))\n # Some articles don't come with pages. :-(\n pages_with_endash = (self.pages.replace(\"-\", \"\\u2013\") if self.pages\n else \"\")\n # Actually, not using quote() generally gives results that work fine.\n # The only issue is that when using Markdown URLs with parentheses in\n # Jupyter notebooks, the conversion to HTML gets it wrong, thinking\n # that the URL ends at the first close parentheses in the URL. (In\n # the notebook itself, it is fine, only the conversion to HTML messes\n # up.) So we might as well escape them generally.\n doi_url = f\"https://doi.org/{urllib.parse.quote(self.doi)}\"\n\n # BibLaTeX\n if type in [\"bib\", \"b\"]:\n # Create (hopefully) unique identifier\n author_decoded = unidecode(self.authors[0][\"family\"])\n journal_initials = \"\".join(c for c in self.journal_short\n if c.isupper())\n ref_identifier = f\"{author_decoded}{self.year}{journal_initials}\"\n ref_identifier = \"\".join(ref_identifier.split()) # remove spaces\n # Author names in bib style\n author_names = \" and \".join(self.format_authors(\"bib\"))\n journal = self.journal_short.replace(\". \", \".\\\\ \")\n # Open and close braces\n # Truthfully we don't need this. However, including the doubled\n # curly braces in the f-string makes vim's indentation go crazy.\n open, close = \"{\", \"}\"\n # Make the citation\n s = (f\"@article{open}{ref_identifier},\\n\"\n f\" doi = {{{self.doi}}},\\n\"\n f\" author = {{{author_names}}},\\n\"\n f\" journal = {{{journal}}},\\n\"\n f\" title = {{{self.title}}},\\n\"\n f\" year = {{{self.year}}},\\n\")\n if self.volume is not None:\n s += f\" volume = {{{self.volume}}},\\n\"\n if self.issue is not None:\n s += f\" issue = {{{self.issue}}},\\n\"\n if self.pages is not None:\n s += f\" pages = {{{self.pages.replace('-', '--')}}},\\n\"\n s += close\n # Replace Unicode characters with their LaTeX equivalents\n for char in _g.unicodeLatexDict:\n s = s.replace(char, _g.unicodeLatexDict[char])\n return s\n\n # Just DOI\n if type in [\"doi\", \"d\"]:\n return self.doi\n\n # The rest all have a long vs short type.\n # Discern long vs short type\n long = False\n if type[0].upper() == type[0]:\n long = True\n type = type.lower()\n\n # reStructuredText\n if type in [\"rst\", \"r\"]:\n author_title = f\"{acs_authors} {self.title}. \" if long else \"\"\n vol_issue = (f\"*{self.volume}* ({self.issue}), \" if self.issue\n else f\"*{self.volume},* \")\n return (author_title\n + f\"*{self.journal_short}* **{self.year},** \"\n + vol_issue\n + f\"{pages_with_endash}. \"\n + f\"`DOI: {self.doi} <{doi_url}>`_\")\n\n # Markdown\n if type in [\"markdown\", \"m\"]:\n author_title = f\"{acs_authors} {self.title}. \" if long else \"\"\n vol_issue = (f\"*{self.volume}* ({self.issue}), \" if self.issue\n else f\"*{self.volume},* \")\n return (author_title\n + f\"*{self.journal_short}* **{self.year},** \"\n + vol_issue\n + f\"{pages_with_endash}. \"\n + f\"[DOI: {self.doi}]({doi_url})\")\n\n # Word\n elif type in [\"word\", \"w\"]:\n author_title = f\"{acs_authors} {self.title}. \" if long else \"\"\n vol_issue = (f\"{self.volume} ({self.issue}), \" if self.issue\n else f\"{self.volume}, \")\n return (author_title\n + f\"{self.journal_short} {self.year}, \"\n + vol_issue\n + f\"{pages_with_endash}.\")\n\n else:\n raise ValueError(\"Invalid citation type '{type}' given\")","def dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d","def read_vocabulary_by_name(name):\n res = (\n select([vocabulary_table])\n .where(vocabulary_table.c.name == name)\n .execute().first())\n if res is not None:\n return dict(res)","def catalogue(self, fix_author=True):\n response = requests.get(CATALOGUE_URL)\n\n flags = DOTALL | UNICODE\n tables = findall(r'<table.*?</table>', response.text, flags=flags)\n rows = findall(r'<tr.*?</tr>', tables[1], flags=flags)\n\n books = []\n for row in rows[1:]:\n elements = findall('<td.*?</td>', row, flags=flags)\n book_id, title = findall(r'/(.*?)/\">(.*?)<',\n elements[4], flags=flags)[0]\n try:\n author_id, author = findall(r'/authors/(.*?).html\">(.*?)<',\n elements[6], flags=flags)[0]\n except:\n author_id, author = '', ''\n if fix_author:\n # fix_author name collision. TODO\n author = globals()['fix_author'](author)\n book = {\n 'type': findall(r'alt=\"(.*?)\">', elements[0], flags=flags)[0],\n 'book_id': book_id,\n 'title': title,\n 'author_id': author_id,\n 'author': author,\n 'year': elements[8][15:-5],\n 'language': elements[10][-9:-7]\n }\n books.append(book)\n return DataFrame(books)","def getbyname(self, name, doctype='experiment'):\n\n if doctype not in self.documents:\n self.documents[doctype] = esd.search(self.source, doctype)\n return self.documents[doctype].load_document(name)","def get_genre(self, id):\n for row in self.db.cursor().execute('SELECT genre_id, name FROM genres WHERE genre_id=' + str(id)):\n genre = {\n 'id' : row[0],\n 'name' : row[1]\n }\n\n return genre","def get_author_by_id(self, id_num):\n\n cur = self.conn.cursor()\n query = 'SELECT author_id , name FROM author WHERE author_id = ?'\n cur.execute(query, (id_num,))\n return row_to_dict_or_false(cur)","def construct_dict(cursor):\n rows = cursor.fetchall()\n return [dict((cursor.description[i][0], value) for i, value in enumerate(row))\n for row in rows]","def dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d","def dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d","def dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d","def newBookCitation(self, **attrlinks):\n return BookCitation(self, **attrlinks)","def find_book(code: str) -> Dict:\n pass","def get_autor_with_papers(author_id):\n print(\"getting information from dblp about author {}\".format(author_id))\n data = get(\"https://dblp.org/pid/\" + author_id + \".xml\")[\"dblpperson\"]\n author_id = author_id.replace(\"/\", \"_\")\n author = {\n \"name\": data[\"@name\"],\n \"papers\": [get_paper(paper_container) for paper_container in data[\"r\"]],\n }\n\n with open(local.file_name(\"authors\", author_id), \"w\") as out:\n json.dump(author, out, ensure_ascii=False, indent=4)","def dict_factory(self, cursor, row):\n results = {}\n for index, col_name in enumerate(cursor.description):\n results[col_name[0]] = row[index]\n\n return results","def info(entry: BibItem) -> str:\n return \"{title}{author}{date}\".format(\n title=(\n \"Title: {}\\n\".format(re.sub(r\"[}{]\", \"\", entry[\"title\"]))\n if \"title\" in entry\n else \"\"\n ),\n author=(\n \"Author{plural}: {author}\\n\".format(\n plural=\"s\" if len(entry[\"author\"]) > 1 else \"\",\n author=\"; \".join(entry[\"author\"]),\n )\n if \"author\" in entry\n else \"\"\n ),\n date=(\n \"Year: {}\\n\".format(entry[\"date\"].split(\n \"-\")[0]) if \"date\" in entry else \"\"\n ),\n )","def dict_factory(cursor, row):\n dic = {}\n for idx, col in enumerate(cursor.description):\n if isinstance(row[idx], unicode):\n dic[col[0]] = u.unicode_to_string(row[idx])\n else:\n dic[col[0]] = row[idx]\n return dic","def readDB():\n if not os.path.exists(filenameDB):\n return { }\n \n with open(filenameDB, \"r\") as csvfile:\n rows = csv.reader(csvfile)\n if rows:\n db = { }\n for r in rows:\n if len(r)==2 and isinstance(r[0],str) and isinstance(r[1],str):\n db[r[1]] = r[0]\n return db\n return { }","def dictfetchone(cursor):\n desc = cursor.description\n return dict(zip([col[0] for col in desc], cursor.fetchone()))","def colf(c):\r\n return {\r\n 'name' : c.name,\r\n 'desc' : (c.desc or \"\").replace(\"'\", \"''\"),\r\n }","def search_publication(db, name: str = None, doi: str = None, bibcode: str = None, verbose: bool = False):\n\n # Make sure a search term is provided\n if name is None and doi is None and bibcode is None:\n print(\"Name, Bibcode, or DOI must be provided\")\n return False, 0\n\n not_null_pub_filters = []\n if name:\n # fuzzy_query_name = '%' + name + '%'\n not_null_pub_filters.append(db.Publications.c.name.ilike(name))\n if doi:\n not_null_pub_filters.append(db.Publications.c.doi.ilike(doi))\n if bibcode:\n not_null_pub_filters.append(db.Publications.c.bibcode.ilike(bibcode))\n pub_search_table = Table()\n if len(not_null_pub_filters) > 0:\n pub_search_table = db.query(db.Publications).filter(or_(*not_null_pub_filters)).table()\n\n n_pubs_found = len(pub_search_table)\n\n if n_pubs_found == 1:\n verboseprint(f'Found {n_pubs_found} matching publications for {name} or {doi} or {bibcode}', verbose=verbose)\n if verbose:\n pub_search_table.pprint_all()\n return True, 1\n\n if n_pubs_found > 1:\n verboseprint(f'Found {n_pubs_found} matching publications for {name} or {doi} or {bibcode}', verbose=verbose)\n if verbose:\n pub_search_table.pprint_all()\n return False, n_pubs_found\n\n # If no matches found, search using first four characters of input name\n if n_pubs_found == 0 and name:\n shorter_name = name[:4]\n verboseprint(f'No matching publications for {name}, Trying {shorter_name}', verbose=verbose)\n fuzzy_query_shorter_name = '%' + shorter_name + '%'\n pub_search_table = db.query(db.Publications).filter(\n db.Publications.c.name.ilike(fuzzy_query_shorter_name)).table()\n n_pubs_found_short = len(pub_search_table)\n if n_pubs_found_short == 0:\n verboseprint(f'No matching publications for {shorter_name}', verbose=verbose)\n verboseprint('Use add_publication() to add it to the database.', verbose=verbose)\n return False, 0\n\n if n_pubs_found_short > 0:\n print(f'Found {n_pubs_found_short} matching publications for {shorter_name}')\n if verbose:\n pub_search_table.pprint_all()\n return False, n_pubs_found_short\n else:\n return False, n_pubs_found\n\n return","def portrayalCatalogueCitation(self) -> Sequence[Citation]:\n pass","def find_by_name(cls, name):\n authors = []\n for author in cls.db.newsdb.find({'$text': {'$search': name}}):\n authors.append(AuthorModel(author['name'], str(author['_id'])))\n\n return authors","def from_db(self, if_print=True):\n for fiction in conn_db().find():\n if if_print:\n print(fiction)\n self.fictions.append(QdFictionInfo(fiction))","def cite(silent=False):\n if silent is False:\n print(__cite__)\n else:\n return __bibtex__","def get_references(cls, pmids):\n\n references = cls.query.filter(cls.pmid.in_(pmids)).all()\n citations = {}\n\n for reference in references:\n citation_text = reference.authors + \". (\" + str(\n reference.year) + \"). \" + reference.title + \" \" + reference.journal + \".\"\n citations[reference.pmid] = citation_text\n\n return citations","def get_supplier(self, code):\n dictionary = {}\n with Transaction().start(DBNAME, 1):\n party = self.Party.search([('pan', '=', code),\n ('categories', '=', self.category)])[-1]\n dictionary['name'] = party.name\n dictionary['pan'] = party.pan\n dictionary['street'] = party.addresses[0].street\n dictionary['city'] = party.addresses[0].city\n dictionary['zip'] = party.addresses[0].zip\n return dictionary","def publication_data():\n with open('data/publications.json') as pubs:\n pubs = json.load(pubs)\n return pubs","def find_cpd_metabolite_dict(synonym_list, syn_met_dict, met_db_dict):\n mets = []\n \n for synonym in synonym_list:\n try:\n mets.append(syn_met_dict[synonym])\n except:\n pass\n \n mets_list = list(set(mets))\n \n if len(mets_list) <> 1:\n return None\n else:\n return Metabolite.objects.get(id=mets_list[0])","def bibtex_to_dict(bibtex):\n global logger\n result = dict()\n if os.path.exists(bibtex):\n fd = open(bibtex, \"r\")\n logger.debug(\"Reading in file %s\" % bibtex)\n text = fd.read()\n else:\n text = bibtex\n logger.debug(\"Removing comments...\")\n text = re.sub(r\"%.*\", \"\", text)\n logger.debug(\"Removing empty lines...\")\n text = re.sub(r\"^\\s*$\", \"\", text)\n logger.debug(\"Removing newlines...\")\n text = re.sub(r\"\\n\", \"\", text)\n logger.debug(\"Parsing document type and reference\")\n type_ref_re = re.compile(r\"\\s*@\\s*(\\w+)\\s*{([^,]+),?\")\n match = re.match(type_ref_re, text)\n text = re.sub(type_ref_re, \"\", text)\n if not match:\n logger.error(\n \"Type and reference of the bibtex file could not be parsed\"\n )\n sys.exit(1)\n result[\"type\"] = match.group(1)\n result[\"ref\"] = match.group(2)\n for key in result.keys():\n logger.debug(\" [%s] = %s\" % (key, result[key]))\n key_val_re = re.compile(r\"\\s*(\\w+)\\s*=\\s*{(.*)\\s*,?\")\n for line in re.sub(r\"}\\s*,?\", \"\\n\", text).split(\"\\n\"):\n match = re.match(key_val_re, line)\n if match:\n key = match.group(1)\n val = re.sub(r\"\\s+\", \" \", match.group(2))\n result[key.lower()] = val\n logger.debug(\"[%s] = %s\" % (key, val))\n return result","def get_pubmed(pub_query,conn):\n\n get_pm = ('SELECT DISTINCT dx.accession '\n 'FROM pub p, pub_dbxref pd, dbxref dx, db '\n 'WHERE p.pub_id = pd.pub_id '\n 'AND pd.dbxref_id = dx.dbxref_id AND dx.db_id = db.db_id '\n 'AND db.name = \\'pubmed\\' AND pd.is_current = TRUE AND p.uniquename = %s')\n \n pm = connect(get_pm, pub_query, conn)\n if pm:\n return(pm[0][0])\n else:\n return(None)","def getMetadataDict(self, url=None):\n if url is None:\n return {}\n\n sgdxq = \"\"\"xquery version \"%s\";\n let $doc := document(\"%s\")\n return $doc\n \"\"\" % (self.getXQueryVersion(), url)\n try:\n exist = self.getDA()\n res = exist.query(sgdxq, object_only=1)\n return res.getMergedDict(xmled=False)\n except:\n return {}","def _get_metadata(conn):\n metadata_sql = \"SELECT * FROM metadata\"\n metadata = conn.execute(metadata_sql).fetchall()\n return {\n row[0]: {\n \"name\": row[0],\n \"field_name\": row[1],\n \"field_expr\": row[1],\n \"type\": row[2],\n \"minimum\": row[3],\n \"maximum\": row[4],\n \"average\": row[5],\n \"variance\": row[6],\n \"total\": row[7],\n \"stddev\": row[8],\n \"other\": json.loads(row[9]) if row[9] else None,\n }\n for row in metadata\n }","def dict_factory(cursor, row):\n fields = [column[0] for column in cursor.description]\n return {key: value for key, value in zip(fields, row)}","def findall(self, name):\n if name in self._dict:\n return self._dict[name]\n else:\n raise PoseError(\"%s is not in database\" % name)","def citation_formatter(citation, title=False):\n # first we split the author-list\n citation_string = \"\"\n # the spaces around the and are required!\n for match in citation[\"author\"].split(\" and \"):\n last_name, first_names = match.split(\",\", 1)\n citation_string += last_name.strip() + \",\"\n for name in first_names.strip().split(' '):\n citation_string += \" \" + name.strip()[0]\n\n citation_string += \"; \"\n\n if \"journal\" in citation:\n citation_string += \" \" + citation[\"journal\"]\n\n citation_string += \" \" + citation[\"year\"]\n\n if \"doi\" in citation:\n citation_string += \"; \" + citation[\"doi\"]\n\n return citation_string","def scrapeCountries(conn):\n query = \"SELECT * FROM country\"\n c = conn.cursor()\n c.execute(query)\n results = []\n for row in c:\n result={}\n country_fields = {}\n\n result['pk'] = country_codes[row[0]]\n result['model'] = \"infohub.country\"\n result['fields'] = country_fields\n country_fields['name'] = row[1]\n\n results.append(result)\n return results","def get_courts(self, name=\"\", abbreviation=\"\", jurisdiction=\"\", slugs_only=False):\n url_base = self._get_api_url() + \"courts/\"\n url_queries = []\n\n if name:\n url_queries.append(\"name=%s\" % name)\n\n if abbreviation:\n url_queries.append(\"name_abbreviation=%s\" % abbreviations)\n\n if jurisdiction:\n jurisdiction = jurisdiction.lower()\n valid_jurisdictions = [elem['slug'] for elem in self.get_jurisdictions()[\"results\"]]\n if jurisdiction not in valid_jurisdictions:\n raise Exception(\"Jurisdiction not recognized. Check spelling?\")\n url_queries.append(\"jurisdiction=%s\" % jurisdiction)\n\n uri = self._build_uri(url_base, url_queries)\n courts = self._request(uri)\n\n if slugs_only:\n names = self._extract_from_paginated(courts.json(), \"slug\")\n return names\n\n print(uri)\n return courts.json()","def newThesisCitation(self, **attrlinks):\n return ThesisCitation(self, **attrlinks)","def make_citation_authors(res):\n if \"author\" in res.keys():\n first_author = res['author'][0]['family'] + \", \" + res['author'][0]['given']\n last_author = res['author'][-1]['given'] + \" \" + res['author'][-1]['family']\n middle_authors = \", \".join(\" \".join([x['given'], x['family']]) for x in res['author'][1:-1])\n #assemble authors\n author_string = first_author\n author_string = author_string + \", \" + middle_authors if middle_authors != '' else author_string\n author_string = author_string + \", and \" + last_author if len(res['author']) > 1 else author_string\n \n author_string = author_string + \".\" if author_string[-1] != \".\" else author_string\n else:\n author_string = \"Unknown Authors\"\n\n return clean_txt(author_string.capitalize())","def get_bibtex(self, arg=None):\n getter = self.get_getter()\n if getter is None:\n return\n if not arg:\n arg = self.visual.ask_user(f\"Search what on the web?\", multichar=True)\n if not arg:\n self.visual.error(\"Nothing entered, aborting.\")\n return\n try:\n res = getter.get_web_bibtex(arg)\n except Exception as ex:\n self.visual.error(\"Failed to complete the query: {}.\".format(ex))\n return\n if not res:\n self.visual.error(\"No data retrieved.\")\n return\n\n reader2 = Reader(self.config)\n read_entries_dict = reader2.read_entry_list(res)\n self.visual.log(\"Retrieved {} entry item(s) from query [{}]\".format(len(read_entries_dict), arg))\n\n # select subset\n if len(read_entries_dict) > 1:\n ids, content = list(zip(*[(e.ID, e.get_discovery_view()) for e in read_entries_dict.values()]))\n _, selected_ids = self.visual.user_multifilter(content, header=Entry.discovery_keys, reference=ids)\n selected_entries = [v for (k, v) in read_entries_dict.items() if k in selected_ids]\n else:\n selected_entries = list(read_entries_dict.values())\n if not selected_entries:\n return\n self.visual.print_entries_contents(selected_entries)\n\n res = self.visual.ask_user(\"Store?\", \"*yes no-but-keep quit\")\n if utils.matches(res, \"yes\"):\n selected_ids = []\n for entry in selected_entries:\n created_entry = self.entry_collection.add_new_entry(entry)\n if created_entry is None:\n self.visual.error(f\"Entry {entry.ID} already exists in the collection!\")\n else:\n selected_ids.append(created_entry.ID)\n elif utils.matches(res, \"no-but-keep\"):\n selected_ids = [x for x in selected_entries]\n elif utils.matches(res, \"quit\"):\n return\n if not selected_ids:\n return\n if self.visual.yes_no(\"Select it?\"):\n self.selector.select_by_id(selected_ids)\n\n # pdf\n what = self.visual.ask_user(\"Pdf?\", \"local url web-search *skip\")\n if utils.matches(what, \"skip\"):\n return\n if utils.matches(what, \"url\"):\n self.get_pdf_from_web()\n return\n if utils.matches(what, \"local\"):\n self.set_local_pdf_path()\n return\n if utils.matches(what, \"web-search\"):\n self.search_web_pdf()","def getPostalCodeDic(csvfname = 'CP.csv'):\n cpdic = {}\n reader = csv.DictReader(open(csvfname), delimiter=':')\n for row in reader:\n cpdic[row['CP']] = row['arrondissement'].split('|')[-1]\n return cpdic","def get_champion_info_dict():\n conn = get_connect()\n cursor = conn.execute(\"SELECT * FROM championInfo ORDER BY championId\")\n info_dict = {}\n for row in cursor:\n info_dict[row[0]] = [row[1], row[2], row[3]]\n conn.close()\n return info_dict","def newConferenceCitation(self, **attrlinks):\n return ConferenceCitation(self, **attrlinks)","def dict_factory(cursor, row):\n rowdict = {}\n for idx, col in enumerate(cursor.description):\n rowdict[col[0]] = row[idx]\n return rowdict","def _get_card(self, name: str) -> Dict:","def example_data():\n return [\n {'id': 'cc-by', 'title': {'en': 'Creative Commons Attribution'},\n 'type': 'licenses'},\n {'id': 'cc0', 'title': {'en': 'Creative Commons Zero'},\n 'type': 'licenses'},\n ]"],"string":"[\n \"def create_citation(cit):\\n if cit is not None:\\n if cit['citation-type'] == \\\"BIBTEX\\\":\\n return pybtex.database.parse_string(cit['citation-value'], \\\"bibtex\\\")\\n return None\",\n \"def get_author_by_name(self, name):\\n\\n cur = self.conn.cursor()\\n query = 'SELECT author_id , name FROM author WHERE name = ? '\\n cur.execute(query, (name,))\\n return row_to_dict_or_false(cur)\",\n \"def get_publications_by_author(cached_list, cached_set, author_name):\\n publications = { 'dblp': [], 'cdblp': [] }\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n\\n if author['dblp'].__contains__('publications'):\\n publications['dblp'] = author['dblp']['publications']\\n# for pub in author['dblp']['publications']:\\n# print(pub)\\n\\n if author['cdblp'].__contains__('publications'):\\n publications['cdblp'] = author['cdblp']['publications']\\n# for pub in author['cdblp']['publications']:\\n# print(pub)\\n return publications\",\n \"def getDict(name):\\n df = Dictionary.objects.select_related().get(name=name)\\n return df\",\n \"def get_bibtex_from_citation_file(package_name):\\n package = importlib.import_module(package_name)\\n cff_file = os.path.join(package.__path__[0], 'CITATION.cff')\\n citation_file = os.path.join(package.__path__[0], 'CITATION')\\n if os.path.exists(cff_file):\\n with open(cff_file) as f:\\n cffstr = f.read()\\n citation = Citation(cffstr=cffstr)\\n bibtex = citation.as_bibtex()\\n elif os.path.exists(citation_file):\\n with open(citation_file) as f:\\n citestr = f.read()\\n match = re.search(cite_tag_pattr, citestr)\\n if match is None:\\n raise ValueError('Could not find any BibTeX entries in CITATION file')\\n for i_end in range(match.end(), len(citestr)):\\n bibtex = citestr[match.start():i_end]\\n if bibtex.count('{') == bibtex.count('}'):\\n break\\n else:\\n raise ValueError('Mismatched braces in BibTeX entry in CITATION file')\\n else:\\n bibtex = None\\n return bibtex\",\n \"def get_coauthors_dict(name_url_list, schl_name):\\n\\n\\tpaper_info_dict = {}\\n\\n\\tfor name_url in name_url_list:\\n\\t\\t# Get a sub papers_info dictfor an individual author\\n\\t\\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\\n\\t\\t# add sub dict to full dict\\n\\t\\tpaper_info_dict.update(papers_info)\\n\\t\\n\\tif \\\"Humanities\\\" in schl_name:\\n\\t\\tschl_name = \\\"School of Humanities\\\"\\n\\n\\treturn paper_info_dict\",\n \"def get_titles_dict(name_url_list):\\n\\tbib_dict = {}\\n\\t\\n\\tfor name_url in name_url_list:\\n\\t\\ttitles = get_author_titles(name_url[1])\\n\\t\\tbib_dict[name_url] = titles\\n\\n\\treturn bib_dict\",\n \"def _populate(self):\\n\\n # Assume the first word is what we want, and we can find well formed years\\n # This sucks, but will work for these ones.\\n # Roll on bibtex for citations in the CIM.\\n\\n citation_detail = self.doc.citation_detail\\n author = citation_detail.split(',')[0]\\n match = '([^\\\\w])19|20\\\\d\\\\d([^\\\\w])*?'\\n m = re.search(match, citation_detail)\\n if m:\\n year = m.group(0)\\n else:\\n year = None\\n\\n # one error in existing es-doc content to be fixed:\\n if 'van Vuuren DP' in author:\\n author = 'van Vuuren'\\n print 'applying vv fix'\\n\\n self.year = int(year)\\n\\n # We assume that this table will have entries which ne\\n\\n # I use the first three letters of a an authors name, and for\\n # three or more authors, EA, and then the year for my bibtex citation string\\n self.citeguess = author[0:3] + 'EA' + year[2:]\\n # This is what will appear in the table:\\n self.citestring = '%s et al. (%s)' % (author, year)\\n # Keep this for a reference list for checking against the eventual bibtex reference list.\\n self.text = citation_detail\",\n \"def bibtex_to_dict(text):\\n def _format_person(person):\\n von_last = ' '.join(person._prelast + person._last)\\n jr = ' '.join(person._lineage)\\n first = ' '.join(person._first + person._middle)\\n return ' '.join(part for part in (first, von_last, jr) if part)\\n\\n parser = pybtex.database.input.bibtex.Parser()\\n bib_data = parser.parse_stream(StringIO(text))\\n\\n plain_style = pybtex.style.formatting.plain.Style()\\n plain_render = pybtex.backends.plaintext.Backend()\\n formatted_entry = next(plain_style.format_entries(bib_data.entries.values()))\\n\\n # It is possible to parse out multiple BibTeX entries\\n # in one go! But, I'm assuming that the metadata\\n # contains information about only one publication.\\n\\n citekey = bib_data.entries.keys()[0]\\n entry = bib_data.entries[citekey]\\n meta = {\\n 'citekey': citekey,\\n 'type': entry.type,\\n 'authors': [_format_person(person)\\n for person in entry.persons['author']],\\n 'cite_info': entry.fields,\\n 'cite_bibtex': text,\\n 'cite_plain': formatted_entry.text.render(plain_render),\\n }\\n\\n # Get important stuff from cite_info\\n extract = ('title', 'abstract', 'pdf', 'url', 'year', 'keywords',\\n 'poster', 'presentation')\\n for key in extract:\\n if key in entry.fields:\\n meta[key] = meta['cite_info'].pop(key)\\n\\n # Add editors to cite_info, if they exist\\n if 'editor' in entry.persons:\\n meta['cite_info']['editors'] = \\\", \\\" .join(\\n [_format_person(person) for person in entry.persons['editor']])\\n\\n return meta\",\n \"def getCitations (self, doc, verbose = False):\\r\\n if verbose is True:\\r\\n print \\\"getting citations for document \\\" +str(doc) \\r\\n return self.citations[int(doc)]\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def getCityCodeDict():\\n table = 'airport'\\n connection = openConnection()\\n curs = connection.cursor()\\n sqlcmd = \\\"SELECT * FROM \\\" + str(table)\\n d = {}\\n \\n curs.execute(sqlcmd)\\n for row in curs.fetchall():\\n airport = airlineClasses.Airport()\\n airport.cityCode = row[0]\\n airport.city = row[1] \\n d[airport.cityCode] = airport\\n \\n curs.close()\\n connection.close()\\n return d\",\n \"def make_citation(meta):\\n pass\",\n \"def get_publication(project, publication_id):\\n connection = db_engine.connect()\\n publications = get_table(\\\"publication\\\")\\n statement = select([publications]).where(publications.c.id == int_or_none(publication_id))\\n rows = connection.execute(statement).fetchall()\\n result = dict(rows[0])\\n connection.close()\\n return jsonify(result)\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def get_bibtex_entry(doi, bibtext_cache={}, shortdoi_cache={}):\\r\\n bibtext = get_bibtext(doi, cache = bibtext_cache)\\r\\n if not bibtext:\\r\\n return None\\r\\n\\r\\n short_doi = shorten(doi, cache = shortdoi_cache)\\r\\n parser = BibTexParser()\\r\\n parser.ignore_nonstandard_types = False\\r\\n bibdb = bibtexparser.loads(bibtext, parser)\\r\\n entry, = bibdb.entries\\r\\n quoted_doi = urllib.request.quote(doi)\\r\\n entry['link'] = 'https://doi.org/{}'.format(quoted_doi)\\r\\n if 'author' in entry:\\r\\n entry['author'] = ' and '.join(entry['author'].rstrip(';').split('; '))\\r\\n entry['ID'] = short_doi[3:]\\r\\n return entry\",\n \"def fill(self):\\n if self.source == 'citations':\\n url = self._scholarly.URLS(\\\"CITATIONPUB\\\").format(self.id_citations)\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n self.bib['title'] = soup.find('div', id='gsc_vcd_title').text\\n\\n if soup.find('a', class_='gsc_vcd_title_link'):\\n self.bib['url'] = soup.find(\\n 'a', class_='gsc_vcd_title_link')['href']\\n\\n for item in soup.find_all('div', class_='gs_scl'):\\n key = item.find(class_='gsc_vcd_field').text\\n val = item.find(class_='gsc_vcd_value')\\n if key == 'Authors':\\n self.bib['author'] = ' and '.join(self.get_authorlist(val))\\n elif key == 'Journal':\\n self.bib['journal'] = val.text\\n elif key == 'Volume':\\n self.bib['volume'] = val.text\\n elif key == 'Issue':\\n self.bib['number'] = val.text\\n elif key == 'Pages':\\n self.bib['pages'] = val.text\\n elif key == 'Publisher':\\n self.bib['publisher'] = val.text\\n elif key == 'Publication date':\\n self.bib['year'] = arrow.get(val.text).year\\n elif key == 'Description':\\n if val.text[0:8].lower() == 'abstract':\\n val = val.text[9:].strip()\\n self.bib['abstract'] = val\\n elif key == 'Total citations':\\n self.id_scholarcitedby = re.findall(\\n self._scholarly.URLS('SCHOLARPUBRE'), val.a['href'])[0]\\n\\n # number of citation per year\\n years = [int(y.text) for y in soup.find_all(class_='gsc_vcd_g_t')]\\n cites = [int(c.text) for c in soup.find_all(class_='gsc_vcd_g_al')]\\n self.cites_per_year = dict(zip(years, cites))\\n\\n if soup.find('div', class_='gsc_vcd_title_ggi'):\\n self.bib['eprint'] = soup.find(\\n 'div', class_='gsc_vcd_title_ggi').a['href']\\n self._filled = True\\n\\n elif self.source == 'scholar':\\n self.bib['add_to_lib'] = self.url_add_sclib\\n\\n try:\\n bibtex = self._scholarly._get_soup(self.url_scholarbib)\\n bibtex = bibtex.find('pre').string\\n self.bib.update(bibtexparser.loads(bibtex).entries[0])\\n self.bib['author_count'] = str(\\n len(self.bib['author'].split('and')))\\n self.bib['age'] = str(\\n int(date.today().year) - int(self.bib['year']))\\n except:\\n # did not find year\\n pass\\n\\n self._filled = True\\n return self\",\n \"def get_author_detail(body):\\n user_id = get_user_id(body) # user id\\n author_details = body.find('div', {'id': 'gsc_prf_i'})\\n\\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\\n\\n affiliation = name_attributes[0].text or ''\\n interests = name_attributes[1].text or ''\\n email = name_attributes[2].text or ''\\n\\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\\n\\n year_since_text = citation_indices.find_all('tr')[0]\\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\\n\\n # citations\\n citations = citation_indices.find_all('tr')[1]\\n h_index = citation_indices.find_all('tr')[2]\\n h_index_all = h_index.find_all('td')[1].text\\n h_index_since = h_index.find_all('td')[2].text\\n citation_all = citations.find_all('td')[1].text\\n citation_since = citations.find_all('td')[2].text\\n\\n author_dict = {'user_id': user_id,\\n 'name': name,\\n 'affiliation': affiliation,\\n 'interests': interests,\\n 'email': email,\\n 'h_index_all': h_index_all,\\n 'h_index_since': h_index_since,\\n 'citation_all': citation_all,\\n 'citation_since': citation_since,\\n 'year_since': year_since}\\n return author_dict\",\n \"def read_county_from_db(state_name, county_name):\\n # Connect to database\\n conn = sqlite3.connect('./db/incarceration.db')\\n\\n # Query the database\\n data = pd.read_sql_query(f\\\"\\\"\\\"SELECT *\\n FROM incarceration\\n WHERE county_name = '{county_name}'\\n AND state = '{state_name}';\\n \\\"\\\"\\\", conn)\\n\\n # Close connection\\n conn.close()\\n\\n return data\",\n \"def find_cited_by(doi):\\n if doi is None:\\n return None\\n\\n citations = []\\n if doi:\\n response = requests.get(f\\\"https://opencitations.net/index/api/v1/citations/{doi}\\\").json()\\n if response:\\n citations = [{\\\"doi\\\": r['citing'].replace(\\\"coci =>\\\", \\\"\\\")} for r in response]\\n if citations:\\n return citations\\n else:\\n return None\",\n \"def get_bibtex_from_package(package_name, update_local=False):\\n try:\\n package = importlib.import_module(package_name)\\n for attr in ['__bibtex__', '__citation__']:\\n citation_info = getattr(package, attr, None)\\n if citation_info:\\n break\\n except ImportError:\\n warnings.warn(f\\\"{package_name} is not installed, cannot look for \\\"\\n \\\"package provided BibTeX.\\\")\\n return None\\n\\n if citation_info and update_local:\\n path = os.path.join(_bib_path, '{0}.bib'.format(package_name))\\n with open(path, 'w') as f:\\n f.write(citation_info)\\n\\n return citation_info\",\n \"def authorSign(g = None):\\n if not g:\\n return\\n authors = g.find(\\\"Author\\\")\\n ret = dict()\\n for author in authors:\\n ret[author['name']] = author['citation'].replace('\\\\n','').lower()\\n return ret\",\n \"def parse_citations(s: str):\\n print(\\\"parse_citations(%s)\\\" % s)\\n if pandas.isnull(s) or not s:\\n return []\\n terms = [e.strip() for e in s.split(\\\";\\\")]\\n print(\\\"parse_citations: \\\" + str(terms))\\n out = []\\n for term in terms:\\n if term.startswith(\\\"PMID\\\"):\\n out.append({\\\"db\\\": \\\"PubMed\\\", \\\"id\\\": term})\\n else:\\n raise RuntimeError(\\\"Unknown citation format: \\\" + term)\\n return out\",\n \"def get_bibtex(self):\\n url=\\\"http://www.biomedcentral.com/download/citation\\\"\\n parameters={'citation':'on', \\n 'result-selection':'THIS_PAGE',\\n 'suplements': 'false',\\n 'citationType':'BIBTEX_WITH_ABSTRACT',\\n 'articleIdThisPage':self.article_ids\\n }\\n data=urllib.urlencode(parameters)\\n self.bibtex=urllib2.urlopen(url,data)\",\n \"def to_cff_reference(bib_entry: pybtex.database.Entry) -> dict:\\n\\n def _cff_transform(cff_field, bib_value):\\n if cff_field == \\\"type\\\":\\n if bib_value == \\\"inproceedings\\\":\\n return \\\"article\\\"\\n elif bib_value == \\\"incollection\\\":\\n return \\\"article\\\"\\n elif cff_field == \\\"publisher\\\":\\n return {\\\"name\\\": bib_value}\\n elif cff_field == \\\"month\\\":\\n try:\\n return int(bib_value)\\n except ValueError:\\n return {\\n \\\"jan\\\": 1,\\n \\\"feb\\\": 2,\\n \\\"mar\\\": 3,\\n \\\"apr\\\": 4,\\n \\\"may\\\": 5,\\n \\\"jun\\\": 6,\\n \\\"jul\\\": 7,\\n \\\"aug\\\": 8,\\n \\\"sep\\\": 9,\\n \\\"oct\\\": 10,\\n \\\"nov\\\": 11,\\n \\\"dec\\\": 12,\\n }[bib_value[:3].lower()]\\n return bib_value\\n\\n cff_reference = {\\n \\\"type\\\": _cff_transform(cff_field=\\\"type\\\", bib_value=bib_entry.type),\\n \\\"authors\\\": [\\n to_cff_person(person) for person in bib_entry.persons[\\\"author\\\"]\\n ],\\n }\\n # Map BibTeX to CFF fields. This is just a subset of the most relevant\\n # fields.\\n fields = {\\n \\\"doi\\\": \\\"doi\\\",\\n \\\"edition\\\": \\\"edition\\\",\\n \\\"isbn\\\": \\\"isbn\\\",\\n \\\"license\\\": \\\"license\\\",\\n \\\"month\\\": \\\"month\\\",\\n \\\"number\\\": \\\"number\\\",\\n \\\"pages\\\": \\\"pages\\\",\\n \\\"publisher\\\": \\\"publisher\\\",\\n \\\"title\\\": \\\"title\\\",\\n \\\"url\\\": \\\"url\\\",\\n \\\"version\\\": \\\"version\\\",\\n \\\"volume\\\": \\\"volume\\\",\\n \\\"year\\\": \\\"year\\\",\\n \\\"booktitle\\\": \\\"collection-title\\\",\\n }\\n for bibtex_field, value in bib_entry.fields.items():\\n bibtex_field = bibtex_field.lower()\\n if bibtex_field in fields:\\n cff_field = fields[bibtex_field]\\n cff_reference[cff_field] = _cff_transform(\\n cff_field=cff_field, bib_value=value\\n )\\n return cff_reference\",\n \"def _get_critter_db() -> Dict[CritterType, List[CritterImage]]:\\n with open(os.path.join('critters', 'names.json')) as fp:\\n critter_data = json.load(fp)\\n\\n critter_db = collections.defaultdict(list)\\n for critter_name, icon_name, critter_type_str in critter_data:\\n critter_type = CritterType.from_str(critter_type_str)\\n critter = CritterImage(critter_name, critter_type, icon_name)\\n critter_db[critter_type].append(critter)\\n return critter_db\",\n \"def get_CTD_metadata(db):\\n\\n if not use_sqlite:\\n raise RuntimeError('No sqlite standard library found in this python'\\n ' installation. This function (get_CTD_metadata)'\\n ' is unavailable.')\\n\\n def _dict_factory(cursor, row):\\n d = {}\\n for idx, col in enumerate(cursor.description):\\n d[col[0]] = row[idx]\\n return d\\n\\n try:\\n con = sqlite3.connect(db)\\n\\n con.row_factory = _dict_factory\\n\\n c = con.cursor()\\n\\n out = c.execute('SELECT * from Stations')\\n\\n meta_info = out.fetchall()\\n\\n except sqlite3.Error as e:\\n if con:\\n con.close()\\n print('Error {}:'.format(e.args[0]))\\n meta_info = [False]\\n\\n finally:\\n if con:\\n con.close()\\n\\n return meta_info\",\n \"def _to_dict(self):\\n\\t\\tauthor = Author.query.get(self.authors_id)\\n\\t\\treturn {'id': self.id,\\n\\t\\t\\t\\t'name': self.name,\\n\\t\\t\\t\\t'author_id': self.authors_id,\\n\\t\\t\\t\\t'author_name': author.name,\\n\\t\\t\\t\\t'author_surname': author.surname}\",\n \"def get_keyword(self, collection_id, name):\\n sql = \\\"\\\"\\\"SELECT keyword.name, keyword.args, keyword.doc\\n FROM keyword_table as keyword\\n WHERE keyword.collection_id == ?\\n AND keyword.name like ?\\n \\\"\\\"\\\"\\n cursor = self._execute(sql, (collection_id,name))\\n # We're going to assume no library has duplicate keywords\\n # While that in theory _could_ happen, it never _should_,\\n # and you get what you deserve if it does.\\n row = cursor.fetchone()\\n if row is not None:\\n return {\\\"name\\\": row[0],\\n \\\"args\\\": json.loads(row[1]),\\n \\\"doc\\\": row[2],\\n \\\"collection_id\\\": collection_id\\n }\\n return {}\",\n \"def get_initial_author_dict():\\n adict = {}\\n try:\\n ah = run_sql(\\\"select aterm,hitlist from rnkAUTHORDATA\\\")\\n for (a, h) in ah:\\n adict[a] = deserialize_via_marshal(h)\\n return adict\\n except:\\n register_exception(prefix=\\\"could not read rnkAUTHORDATA\\\", alert_admin=True)\\n return {}\",\n \"def make_bibtex(self):\\n\\n\\t\\t# bib = requests.request('GET', 'http://dx.doi.org/' + self.doi, \",\n \"def sqlite3_dict_factory(cursor, row):\\n dict_row = dict()\\n for idx, col in enumerate(cursor.description):\\n dict_row[col[0]] = row[idx]\\n dict_row[idx] = row[idx]\\n return dict_row\",\n \"def get_book_data(isbn: int):\\n try:\\n book = next(iter(core.Book.search(('isbn', 'eq', isbn))))\\n except StopIteration:\\n pass # actually, I could put the whole rest of the function here\\n else:\\n data = core.Book.view_str(book.id)\\n del data['id'], data['status'], data['return_date'], data['borrowed_by']\\n del data['borrowed_by_id'], data['__str__']\\n return data\\n\\n try:\\n r = requests.get('https://portal.dnb.de/opac.htm?query=isbn%3D'\\n + str(isbn) + '&method=simpleSearch&cqlMode=true')\\n r.raise_for_status()\\n except requests.exceptions.RequestException:\\n raise core.BuchSchlossError('no_connection', 'no_connection')\\n\\n person_re = re.compile(r'(\\\\w*, \\\\w*) \\\\((\\\\w*)\\\\)')\\n results = {'concerned_people': []}\\n\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n if table is None:\\n # see if we got multiple results\\n link_to_first = page.select_one('#recordLink_0')\\n if link_to_first is None:\\n raise core.BuchSchlossError(\\n 'Book_not_found', 'Book_with_ISBN_{}_not_in_DNB', isbn)\\n r = requests.get('https://portal.dnb.de'+link_to_first['href'])\\n page = bs4.BeautifulSoup(r.text)\\n table = page.select_one('#fullRecordTable')\\n\\n for tr in table.select('tr'):\\n td = [x.get_text('\\\\n').strip() for x in tr.select('td')]\\n if len(td) == 2:\\n if td[0] == 'Titel':\\n results['title'] = td[1].split('/')[0].strip()\\n elif td[0] == 'Person(en)':\\n for p in td[1].split('\\\\n'):\\n g = person_re.search(p)\\n if g is None:\\n continue\\n g = g.groups()\\n if g[1] == 'Verfasser':\\n results['author'] = g[0]\\n else:\\n results['concerned_people'].append(g[1]+': '+g[0])\\n elif td[0] == 'Verlag':\\n results['publisher'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Zeitliche Einordnung':\\n results['year'] = td[1].split(':')[1].strip()\\n elif td[0] == 'Sprache(n)':\\n results['language'] = td[1].split(',')[0].split()[0].strip()\\n\\n results['concerned_people'] = '; '.join(results['concerned_people'])\\n return results\",\n \"def cursor_data(c):\\r\\n\\r\\n # pull column description\\r\\n d = []\\r\\n for i in range(len(c.description)):\\r\\n d.append(c.description[i][0])\\r\\n\\r\\n # fetch column entries\\r\\n c = c.fetchall()\\r\\n\\r\\n # compile list\\r\\n info = []\\r\\n for i in range(len(c)):\\r\\n # compile dictionary entry\\r\\n entry = {}\\r\\n for j in range(len(d)):\\r\\n entry[d[j]] = c[i][j]\\r\\n info.append(entry)\\t\\r\\n\\r\\n # success\\r\\n return info\",\n \"def make_citation_key(res):\\n year = str(make_year(res))\\n\\n try:\\n last_names = [x['family'] for x in res['author']]\\n except KeyError as e:\\n last_names = [\\\"Unknown\\\"]\\n if len(last_names) >= 3:\\n key = last_names[0].capitalize() + \\\"ETAL\\\" + year\\n else:\\n key = \\\"\\\".join(last_names) + year\\n\\n return clean_txt(key.replace(\\\" \\\", \\\"\\\"))\",\n \"def get_standard_citatation(citation, cache, override):\\n try:\\n print('Trying metadata cache for {}...'.format(citation))\\n metadata = citation_to_metadata(citation, cache, override)\\n return metadata['standard_citation'], metadata['citation_id']\\n except Exception as e:\\n return None, None\",\n \"def bibtex(self) -> str:\\n a = BibDatabase()\\n a.entries = [self.bib]\\n return bibtexparser.dumps(a)\",\n \"def get_credentials():\\n sql = \\\"\\\"\\\"\\n SELECT credential_name, description full_name\\n FROM credential_code\\n WHERE can_use = 1\\n \\\"\\\"\\\"\\n\\n # [('CRNA', 'Certified Registered Nurse Anesthetist'), ... ]\\n record_set = get_record(sql)\\n result = dict(record_set)\\n return result\",\n \"def get_single_book_info(self, isbn):\\n self.cursor.execute(\\\"SELECT * FROM book WHERE ISBN=%s\\\", (isbn,))\\n books = self.cursor.fetchall()\\n for book in books:\\n authors = []\\n self.cursor.execute(\\\"\\\"\\\"SELECT name FROM Author A, Wrote W, Book B WHERE A.ID = W.authorID AND\\n W.ISBN = B.ISBN AND B.ISBN = %s\\\"\\\"\\\", (isbn,))\\n for auth in self.cursor.fetchall():\\n authors.append(auth[0])\\n return book, authors\",\n \"def get_author_data():\\n entry = mongo.db.Authors\\n output = list()\\n look_up_type = None\\n if 'name' in request.args:\\n look_up_type = 'name'\\n print(request.args)\\n if len(request.args['name']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['name'].strip('\\\"')\\n name = entry.find({'name': {'$regex': value}})\\n if name:\\n for author in name:\\n output.append({'name': author['name']})\\n elif 'booktitle' in request.args:\\n look_up_type = 'related_books'\\n if len(request.args['booktitle']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['booktitle'].strip('\\\"')\\n related_books = entry.find(\\n {'author_books': {'$regex': value}})\\n if related_books:\\n for related in related_books:\\n for title in related['author_books']:\\n if value in title:\\n output.append(({'related_books': title}))\\n if len(output) == 0:\\n return render_template('error.html', message=\\\"No Entries Found\\\"), 400\\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200\",\n \"def get_license_refs_dict(license_refs_list):\\n license_ref_dict = {}\\n if license_refs_list:\\n for ref_dict in license_refs_list:\\n license_ref_dict[ref_dict['licenseId']] = ref_dict['extractedText']\\n return license_ref_dict\",\n \"def get_citation(self):\\n etree = self.get_eml()\\n try:\\n citation_ipt = etree.find('./additionalMetadata/metadata/gbif/citation').text\\n except AttributeError:\\n citation_ipt = None\\n if citation_ipt:\\n now = datetime.datetime.now().date()\\n pola3r = \\\"(Available: Polar 'Omics Links to Antarctic, Arctic and Alpine Research. Antarctic Biodiversity Portal. Scientific Committee for Antarctic Research. www.biodiversity.aq/pola3r. Accessed: {})\\\".format(\\n now)\\n citation = \\\"{} {}\\\".format(citation_ipt, pola3r)\\n else:\\n citation = None\\n return citation\",\n \"def get_coauthors_by_author(cached_list, cached_set, author_name):\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n coauthors = {}\\n if author['dblp'].__contains__('coauthors'):\\n for author_key in author['dblp']['coauthors']:\\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\\n\\n if author['cdblp'].__contains__('coauthors'):\\n for author_key in author['cdblp']['coauthors']:\\n if coauthors.__contains__(author_key['full_name']):\\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\\n else:\\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\\n\\n return coauthors\",\n \"def dict_factory(cursor, row):\\r\\n\\td = {}\\r\\n\\tfor idx, col in enumerate(cursor.description):\\r\\n\\t\\td[col[0]] = row[idx]\\r\\n\\treturn d\",\n \"def get_bibtex(package_name):\\n full_path = os.path.join(_bib_path, '{0}.bib'.format(package_name.lower()))\\n if not os.path.exists(full_path):\\n raise ValueError('Bibtex not found for {0}! If you know it has a '\\n 'citation, please consider adding it via pull request '\\n ' to: https://github.com/adrn/makecite')\\n\\n with open(full_path, 'r') as f:\\n bibtex = f.read()\\n\\n return bibtex\",\n \"def get_keyword_dict(url):\\n try:\\n conn = sqlite3.connect('server/database/web.db') \\n cur = conn.cursor()\\n cursor = cur.execute('SELECT content from global_data where url=?', (url,))\\n \\n for row in cursor:\\n new_dict = { w.split(':')[0] : int(w.split(':')[1]) for w in row[0].split()}\\n return new_dict\\n\\n except sqlite3.Error as error:\\n print(error)\\n \\n finally:\\n if (conn): conn.close()\",\n \"def list_publications(bib_format=\\\"dict\\\"):\\n\\n def get_bibtex(key, value):\\n total_keys = [\\n \\\"title\\\",\\n \\\"journal\\\",\\n \\\"volume\\\",\\n \\\"issue\\\",\\n \\\"number\\\",\\n \\\"pages\\\",\\n \\\"numpages\\\",\\n \\\"year\\\",\\n \\\"month\\\",\\n \\\"publisher\\\",\\n \\\"url\\\",\\n \\\"doi\\\",\\n \\\"issn\\\",\\n ]\\n bibtex_str = (\\n \\\"@article{\\\"\\n + key\\n + \\\",\\\\n\\\"\\n + \\\" author={\\\"\\n + \\\" and \\\".join(value[\\\"author\\\"])\\n + \\\"},\\\\n\\\"\\n )\\n for key in total_keys:\\n if key in value.keys():\\n bibtex_str += \\\" \\\" + key + \\\"={\\\" + value[key] + \\\"},\\\\n\\\"\\n bibtex_str += \\\"}\\\\n\\\"\\n return bibtex_str\\n\\n def get_apa(value):\\n apa_str = \\\" & \\\".join(value[\\\"author\\\"])\\n if \\\"year\\\" in value.keys():\\n apa_str += \\\" (\\\" + value[\\\"year\\\"] + \\\"). \\\"\\n if \\\"title\\\" in value.keys():\\n apa_str += value[\\\"title\\\"] + \\\". \\\"\\n if \\\"journal\\\" in value.keys():\\n apa_str += value[\\\"journal\\\"] + \\\", \\\"\\n if \\\"volume\\\" in value.keys():\\n apa_str += value[\\\"volume\\\"] + \\\", \\\"\\n if \\\"pages\\\" in value.keys():\\n apa_str += value[\\\"pages\\\"] + \\\". \\\"\\n if \\\"doi\\\" in value.keys():\\n apa_str += \\\"doi: \\\" + value[\\\"doi\\\"] + \\\"\\\\n\\\"\\n return apa_str\\n\\n publication_dict = s.publication_lst\\n if bib_format.lower() == \\\"dict\\\":\\n return publication_dict\\n elif bib_format.lower() == \\\"bibtex\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_bibtex(key, value)\\n return total_str\\n elif bib_format.lower() == \\\"apa\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_apa(value)\\n return total_str\\n else:\\n raise ValueError(\\\"Supported Bibformats are ['dict', 'bibtex', 'apa']\\\")\",\n \"def get_author_info_from_people_collection(info):\\n # TODO: probably we will need to extract this somewhere else\\n URL = ('https://cds.cern.ch/submit/get_authors?'\\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\\n if '0' in info or not info.get('a'):\\n # There is already enough information or we don't have a name to query\\n return info\\n author_name = info.get('a')\\n if PY2:\\n # In Python 3, encoded name will change type to bytes and this will\\n # cause query to CDS to fail\\n author_name = author_name.encode('utf-8')\\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\\n if not author_info or len(author_info) > 1:\\n # Didn't find anything or find to many matches\\n return info\\n\\n # Prepare author name\\n author_info = author_info[0]\\n if 'name' not in author_info:\\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\\n author_info['firstname'])\\n return MementoDict([\\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])\",\n \"def build_bibtex_entry(metadata: dict):\\n # We truncate the author list in the BibTeX entry after 'Developers',\\n # because not all journals are happy with printing an excessively long\\n # author list, e.g. Phys. Rev. wants at most 15 authors. By truncating the\\n # author list here, the user who copies the BibTeX entry doesn't have to\\n # make the decision where to truncate.\\n authors = [\\n pybtex.database.Person(author[\\\"Name\\\"])\\n for author in (\\n metadata[\\\"Authors\\\"][\\\"Core\\\"][\\\"List\\\"]\\n + metadata[\\\"Authors\\\"][\\\"Developers\\\"][\\\"List\\\"]\\n )\\n ] + [pybtex.database.Person(\\\"others\\\")]\\n entry = pybtex.database.Entry(\\n \\\"software\\\",\\n persons=dict(author=authors),\\n fields=dict(\\n title=(\\n r\\\"\\\\texttt{\\\"\\n + metadata[\\\"Name\\\"]\\n + \\\" v\\\"\\n + metadata[\\\"Version\\\"]\\n + \\\"}\\\"\\n ),\\n # The 'version' field is not used by revtex4-2, so we also put the\\n # version in the title\\n version=metadata[\\\"Version\\\"],\\n publisher=\\\"Zenodo\\\",\\n doi=metadata[\\\"Doi\\\"],\\n url=metadata[\\\"Homepage\\\"],\\n howpublished=(\\n r\\\"\\\\href{https://doi.org/\\\"\\n + metadata[\\\"Doi\\\"]\\n + \\\"}{\\\"\\n + metadata[\\\"Doi\\\"]\\n + \\\"}\\\"\\n ),\\n license=metadata[\\\"License\\\"],\\n year=str(metadata[\\\"PublicationDate\\\"].year),\\n month=str(metadata[\\\"PublicationDate\\\"].month),\\n ),\\n )\\n entry.key = \\\"spectrecode\\\"\\n return entry\",\n \"def extract_metadata(res):\\n d = OrderedDict()\\n\\n d['type'] = res['type']\\n d['authors'] = make_author_list(res)\\n d['citation-authors'] = make_citation_authors(res)\\n d['citation'] = make_citation(res)\\n d['source-title'] = res['title'].strip(\\\" \\\").capitalize()\\n d['title'] = \\\"Notes on '\\\" + d['source-title'] + \\\"', by \\\" + \\\", \\\".join(d['authors'][0:-1])\\n if len(d['authors']) > 1:\\n d['title'] = d['title'] + \\\", and \\\" + d['authors'][-1] \\n d['citation-authors']\\n d['container'] = make_container(res)\\n d['publisher'] = res['publisher'] if 'publisher' in res.keys() else '' \\n d['issn'] = res['issn'] if 'issn' in res.keys() else ''\\n d['year'] = make_year(res)\\n d['issue'] = res['issue'] if \\\"issue\\\" in res.keys() else \\\"\\\"\\n d['volume'] = res['volume'] if \\\"volume\\\" in res.keys() else \\\"\\\"\\n d['pages'] = res['page'] if \\\"page\\\" in res.keys() else \\\"\\\"\\n d['subjects'] = make_subject(res)\\n d['doi'] = res['DOI']\\n d['link'] = res['URL']\\n d['citationkey'] = make_citation_key(res)\\n d['fetched'] = date.today().strftime(\\\"%Y%m%d\\\")\\n return d\",\n \"def get_cit_data(ticker):\\n log = logging.getLogger(__name__)\\n # TODO: This is probably not working anymore (need to refresh files)\\n file = os.path.join(c.cfg['default']['data'], 'COT', 'Supplemental', 'T{}.PRN'.format(ticker))\\n columns = ['Non-Commercial Longs', 'Non-Commercial Shorts',\\n 'Commercial Longs', 'Commercial Shorts',\\n 'Non-Reportable Longs', 'Non-Reportable Shorts',\\n 'Index Trader Longs', 'Index Trader Shorts']\\n\\n # Read Data from csv file\\n cit = pd.read_csv(file, parse_dates=True, index_col=0, header=None, names=columns)\\n \\n\\n return cit\",\n \"def get_authors(config: Config) -> dict:\\n\\n file_handle = load_csv_file(config.input)\\n\\n start = time.time()\\n raw_authors = get_raw_authors(config, file_handle)\\n end = time.time()\\n print(f\\\"get_raw_authors took {end - start} seconds\\\")\\n\\n start = time.time()\\n sanitized_authors = sanitize_authors(config, raw_authors)\\n end = time.time()\\n print(f\\\"sanitize_authors took {end - start} seconds\\\")\\n\\n return sanitized_authors\",\n \"def get_papers_by_institution(inst_names):\\n papers = list(ads.SearchQuery(aff=\\\"IATE\\\"))\\n for p in papers:\\n print(p.author)\\n print(f\\\"Paper from year {p.year} with \\\"\\n f\\\"{len(p.author)} authors and \\\"\\n f\\\"{p.citation_count} citations.\\\")\\n input()\",\n \"def to_citation(self, type):\\n acs_authors = \\\"; \\\".join(self.format_authors(\\\"acs\\\"))\\n # Some articles don't come with pages. :-(\\n pages_with_endash = (self.pages.replace(\\\"-\\\", \\\"\\\\u2013\\\") if self.pages\\n else \\\"\\\")\\n # Actually, not using quote() generally gives results that work fine.\\n # The only issue is that when using Markdown URLs with parentheses in\\n # Jupyter notebooks, the conversion to HTML gets it wrong, thinking\\n # that the URL ends at the first close parentheses in the URL. (In\\n # the notebook itself, it is fine, only the conversion to HTML messes\\n # up.) So we might as well escape them generally.\\n doi_url = f\\\"https://doi.org/{urllib.parse.quote(self.doi)}\\\"\\n\\n # BibLaTeX\\n if type in [\\\"bib\\\", \\\"b\\\"]:\\n # Create (hopefully) unique identifier\\n author_decoded = unidecode(self.authors[0][\\\"family\\\"])\\n journal_initials = \\\"\\\".join(c for c in self.journal_short\\n if c.isupper())\\n ref_identifier = f\\\"{author_decoded}{self.year}{journal_initials}\\\"\\n ref_identifier = \\\"\\\".join(ref_identifier.split()) # remove spaces\\n # Author names in bib style\\n author_names = \\\" and \\\".join(self.format_authors(\\\"bib\\\"))\\n journal = self.journal_short.replace(\\\". \\\", \\\".\\\\\\\\ \\\")\\n # Open and close braces\\n # Truthfully we don't need this. However, including the doubled\\n # curly braces in the f-string makes vim's indentation go crazy.\\n open, close = \\\"{\\\", \\\"}\\\"\\n # Make the citation\\n s = (f\\\"@article{open}{ref_identifier},\\\\n\\\"\\n f\\\" doi = {{{self.doi}}},\\\\n\\\"\\n f\\\" author = {{{author_names}}},\\\\n\\\"\\n f\\\" journal = {{{journal}}},\\\\n\\\"\\n f\\\" title = {{{self.title}}},\\\\n\\\"\\n f\\\" year = {{{self.year}}},\\\\n\\\")\\n if self.volume is not None:\\n s += f\\\" volume = {{{self.volume}}},\\\\n\\\"\\n if self.issue is not None:\\n s += f\\\" issue = {{{self.issue}}},\\\\n\\\"\\n if self.pages is not None:\\n s += f\\\" pages = {{{self.pages.replace('-', '--')}}},\\\\n\\\"\\n s += close\\n # Replace Unicode characters with their LaTeX equivalents\\n for char in _g.unicodeLatexDict:\\n s = s.replace(char, _g.unicodeLatexDict[char])\\n return s\\n\\n # Just DOI\\n if type in [\\\"doi\\\", \\\"d\\\"]:\\n return self.doi\\n\\n # The rest all have a long vs short type.\\n # Discern long vs short type\\n long = False\\n if type[0].upper() == type[0]:\\n long = True\\n type = type.lower()\\n\\n # reStructuredText\\n if type in [\\\"rst\\\", \\\"r\\\"]:\\n author_title = f\\\"{acs_authors} {self.title}. \\\" if long else \\\"\\\"\\n vol_issue = (f\\\"*{self.volume}* ({self.issue}), \\\" if self.issue\\n else f\\\"*{self.volume},* \\\")\\n return (author_title\\n + f\\\"*{self.journal_short}* **{self.year},** \\\"\\n + vol_issue\\n + f\\\"{pages_with_endash}. \\\"\\n + f\\\"`DOI: {self.doi} <{doi_url}>`_\\\")\\n\\n # Markdown\\n if type in [\\\"markdown\\\", \\\"m\\\"]:\\n author_title = f\\\"{acs_authors} {self.title}. \\\" if long else \\\"\\\"\\n vol_issue = (f\\\"*{self.volume}* ({self.issue}), \\\" if self.issue\\n else f\\\"*{self.volume},* \\\")\\n return (author_title\\n + f\\\"*{self.journal_short}* **{self.year},** \\\"\\n + vol_issue\\n + f\\\"{pages_with_endash}. \\\"\\n + f\\\"[DOI: {self.doi}]({doi_url})\\\")\\n\\n # Word\\n elif type in [\\\"word\\\", \\\"w\\\"]:\\n author_title = f\\\"{acs_authors} {self.title}. \\\" if long else \\\"\\\"\\n vol_issue = (f\\\"{self.volume} ({self.issue}), \\\" if self.issue\\n else f\\\"{self.volume}, \\\")\\n return (author_title\\n + f\\\"{self.journal_short} {self.year}, \\\"\\n + vol_issue\\n + f\\\"{pages_with_endash}.\\\")\\n\\n else:\\n raise ValueError(\\\"Invalid citation type '{type}' given\\\")\",\n \"def dict_factory(cursor, row):\\n d = {}\\n for idx, col in enumerate(cursor.description):\\n d[col[0]] = row[idx]\\n return d\",\n \"def read_vocabulary_by_name(name):\\n res = (\\n select([vocabulary_table])\\n .where(vocabulary_table.c.name == name)\\n .execute().first())\\n if res is not None:\\n return dict(res)\",\n \"def catalogue(self, fix_author=True):\\n response = requests.get(CATALOGUE_URL)\\n\\n flags = DOTALL | UNICODE\\n tables = findall(r'<table.*?</table>', response.text, flags=flags)\\n rows = findall(r'<tr.*?</tr>', tables[1], flags=flags)\\n\\n books = []\\n for row in rows[1:]:\\n elements = findall('<td.*?</td>', row, flags=flags)\\n book_id, title = findall(r'/(.*?)/\\\">(.*?)<',\\n elements[4], flags=flags)[0]\\n try:\\n author_id, author = findall(r'/authors/(.*?).html\\\">(.*?)<',\\n elements[6], flags=flags)[0]\\n except:\\n author_id, author = '', ''\\n if fix_author:\\n # fix_author name collision. TODO\\n author = globals()['fix_author'](author)\\n book = {\\n 'type': findall(r'alt=\\\"(.*?)\\\">', elements[0], flags=flags)[0],\\n 'book_id': book_id,\\n 'title': title,\\n 'author_id': author_id,\\n 'author': author,\\n 'year': elements[8][15:-5],\\n 'language': elements[10][-9:-7]\\n }\\n books.append(book)\\n return DataFrame(books)\",\n \"def getbyname(self, name, doctype='experiment'):\\n\\n if doctype not in self.documents:\\n self.documents[doctype] = esd.search(self.source, doctype)\\n return self.documents[doctype].load_document(name)\",\n \"def get_genre(self, id):\\n for row in self.db.cursor().execute('SELECT genre_id, name FROM genres WHERE genre_id=' + str(id)):\\n genre = {\\n 'id' : row[0],\\n 'name' : row[1]\\n }\\n\\n return genre\",\n \"def get_author_by_id(self, id_num):\\n\\n cur = self.conn.cursor()\\n query = 'SELECT author_id , name FROM author WHERE author_id = ?'\\n cur.execute(query, (id_num,))\\n return row_to_dict_or_false(cur)\",\n \"def construct_dict(cursor):\\n rows = cursor.fetchall()\\n return [dict((cursor.description[i][0], value) for i, value in enumerate(row))\\n for row in rows]\",\n \"def dict_factory(cursor, row):\\n d = {}\\n for idx, col in enumerate(cursor.description):\\n d[col[0]] = row[idx]\\n return d\",\n \"def dict_factory(cursor, row):\\n d = {}\\n for idx, col in enumerate(cursor.description):\\n d[col[0]] = row[idx]\\n return d\",\n \"def dict_factory(cursor, row):\\n d = {}\\n for idx, col in enumerate(cursor.description):\\n d[col[0]] = row[idx]\\n return d\",\n \"def newBookCitation(self, **attrlinks):\\n return BookCitation(self, **attrlinks)\",\n \"def find_book(code: str) -> Dict:\\n pass\",\n \"def get_autor_with_papers(author_id):\\n print(\\\"getting information from dblp about author {}\\\".format(author_id))\\n data = get(\\\"https://dblp.org/pid/\\\" + author_id + \\\".xml\\\")[\\\"dblpperson\\\"]\\n author_id = author_id.replace(\\\"/\\\", \\\"_\\\")\\n author = {\\n \\\"name\\\": data[\\\"@name\\\"],\\n \\\"papers\\\": [get_paper(paper_container) for paper_container in data[\\\"r\\\"]],\\n }\\n\\n with open(local.file_name(\\\"authors\\\", author_id), \\\"w\\\") as out:\\n json.dump(author, out, ensure_ascii=False, indent=4)\",\n \"def dict_factory(self, cursor, row):\\n results = {}\\n for index, col_name in enumerate(cursor.description):\\n results[col_name[0]] = row[index]\\n\\n return results\",\n \"def info(entry: BibItem) -> str:\\n return \\\"{title}{author}{date}\\\".format(\\n title=(\\n \\\"Title: {}\\\\n\\\".format(re.sub(r\\\"[}{]\\\", \\\"\\\", entry[\\\"title\\\"]))\\n if \\\"title\\\" in entry\\n else \\\"\\\"\\n ),\\n author=(\\n \\\"Author{plural}: {author}\\\\n\\\".format(\\n plural=\\\"s\\\" if len(entry[\\\"author\\\"]) > 1 else \\\"\\\",\\n author=\\\"; \\\".join(entry[\\\"author\\\"]),\\n )\\n if \\\"author\\\" in entry\\n else \\\"\\\"\\n ),\\n date=(\\n \\\"Year: {}\\\\n\\\".format(entry[\\\"date\\\"].split(\\n \\\"-\\\")[0]) if \\\"date\\\" in entry else \\\"\\\"\\n ),\\n )\",\n \"def dict_factory(cursor, row):\\n dic = {}\\n for idx, col in enumerate(cursor.description):\\n if isinstance(row[idx], unicode):\\n dic[col[0]] = u.unicode_to_string(row[idx])\\n else:\\n dic[col[0]] = row[idx]\\n return dic\",\n \"def readDB():\\n if not os.path.exists(filenameDB):\\n return { }\\n \\n with open(filenameDB, \\\"r\\\") as csvfile:\\n rows = csv.reader(csvfile)\\n if rows:\\n db = { }\\n for r in rows:\\n if len(r)==2 and isinstance(r[0],str) and isinstance(r[1],str):\\n db[r[1]] = r[0]\\n return db\\n return { }\",\n \"def dictfetchone(cursor):\\n desc = cursor.description\\n return dict(zip([col[0] for col in desc], cursor.fetchone()))\",\n \"def colf(c):\\r\\n return {\\r\\n 'name' : c.name,\\r\\n 'desc' : (c.desc or \\\"\\\").replace(\\\"'\\\", \\\"''\\\"),\\r\\n }\",\n \"def search_publication(db, name: str = None, doi: str = None, bibcode: str = None, verbose: bool = False):\\n\\n # Make sure a search term is provided\\n if name is None and doi is None and bibcode is None:\\n print(\\\"Name, Bibcode, or DOI must be provided\\\")\\n return False, 0\\n\\n not_null_pub_filters = []\\n if name:\\n # fuzzy_query_name = '%' + name + '%'\\n not_null_pub_filters.append(db.Publications.c.name.ilike(name))\\n if doi:\\n not_null_pub_filters.append(db.Publications.c.doi.ilike(doi))\\n if bibcode:\\n not_null_pub_filters.append(db.Publications.c.bibcode.ilike(bibcode))\\n pub_search_table = Table()\\n if len(not_null_pub_filters) > 0:\\n pub_search_table = db.query(db.Publications).filter(or_(*not_null_pub_filters)).table()\\n\\n n_pubs_found = len(pub_search_table)\\n\\n if n_pubs_found == 1:\\n verboseprint(f'Found {n_pubs_found} matching publications for {name} or {doi} or {bibcode}', verbose=verbose)\\n if verbose:\\n pub_search_table.pprint_all()\\n return True, 1\\n\\n if n_pubs_found > 1:\\n verboseprint(f'Found {n_pubs_found} matching publications for {name} or {doi} or {bibcode}', verbose=verbose)\\n if verbose:\\n pub_search_table.pprint_all()\\n return False, n_pubs_found\\n\\n # If no matches found, search using first four characters of input name\\n if n_pubs_found == 0 and name:\\n shorter_name = name[:4]\\n verboseprint(f'No matching publications for {name}, Trying {shorter_name}', verbose=verbose)\\n fuzzy_query_shorter_name = '%' + shorter_name + '%'\\n pub_search_table = db.query(db.Publications).filter(\\n db.Publications.c.name.ilike(fuzzy_query_shorter_name)).table()\\n n_pubs_found_short = len(pub_search_table)\\n if n_pubs_found_short == 0:\\n verboseprint(f'No matching publications for {shorter_name}', verbose=verbose)\\n verboseprint('Use add_publication() to add it to the database.', verbose=verbose)\\n return False, 0\\n\\n if n_pubs_found_short > 0:\\n print(f'Found {n_pubs_found_short} matching publications for {shorter_name}')\\n if verbose:\\n pub_search_table.pprint_all()\\n return False, n_pubs_found_short\\n else:\\n return False, n_pubs_found\\n\\n return\",\n \"def portrayalCatalogueCitation(self) -> Sequence[Citation]:\\n pass\",\n \"def find_by_name(cls, name):\\n authors = []\\n for author in cls.db.newsdb.find({'$text': {'$search': name}}):\\n authors.append(AuthorModel(author['name'], str(author['_id'])))\\n\\n return authors\",\n \"def from_db(self, if_print=True):\\n for fiction in conn_db().find():\\n if if_print:\\n print(fiction)\\n self.fictions.append(QdFictionInfo(fiction))\",\n \"def cite(silent=False):\\n if silent is False:\\n print(__cite__)\\n else:\\n return __bibtex__\",\n \"def get_references(cls, pmids):\\n\\n references = cls.query.filter(cls.pmid.in_(pmids)).all()\\n citations = {}\\n\\n for reference in references:\\n citation_text = reference.authors + \\\". (\\\" + str(\\n reference.year) + \\\"). \\\" + reference.title + \\\" \\\" + reference.journal + \\\".\\\"\\n citations[reference.pmid] = citation_text\\n\\n return citations\",\n \"def get_supplier(self, code):\\n dictionary = {}\\n with Transaction().start(DBNAME, 1):\\n party = self.Party.search([('pan', '=', code),\\n ('categories', '=', self.category)])[-1]\\n dictionary['name'] = party.name\\n dictionary['pan'] = party.pan\\n dictionary['street'] = party.addresses[0].street\\n dictionary['city'] = party.addresses[0].city\\n dictionary['zip'] = party.addresses[0].zip\\n return dictionary\",\n \"def publication_data():\\n with open('data/publications.json') as pubs:\\n pubs = json.load(pubs)\\n return pubs\",\n \"def find_cpd_metabolite_dict(synonym_list, syn_met_dict, met_db_dict):\\n mets = []\\n \\n for synonym in synonym_list:\\n try:\\n mets.append(syn_met_dict[synonym])\\n except:\\n pass\\n \\n mets_list = list(set(mets))\\n \\n if len(mets_list) <> 1:\\n return None\\n else:\\n return Metabolite.objects.get(id=mets_list[0])\",\n \"def bibtex_to_dict(bibtex):\\n global logger\\n result = dict()\\n if os.path.exists(bibtex):\\n fd = open(bibtex, \\\"r\\\")\\n logger.debug(\\\"Reading in file %s\\\" % bibtex)\\n text = fd.read()\\n else:\\n text = bibtex\\n logger.debug(\\\"Removing comments...\\\")\\n text = re.sub(r\\\"%.*\\\", \\\"\\\", text)\\n logger.debug(\\\"Removing empty lines...\\\")\\n text = re.sub(r\\\"^\\\\s*$\\\", \\\"\\\", text)\\n logger.debug(\\\"Removing newlines...\\\")\\n text = re.sub(r\\\"\\\\n\\\", \\\"\\\", text)\\n logger.debug(\\\"Parsing document type and reference\\\")\\n type_ref_re = re.compile(r\\\"\\\\s*@\\\\s*(\\\\w+)\\\\s*{([^,]+),?\\\")\\n match = re.match(type_ref_re, text)\\n text = re.sub(type_ref_re, \\\"\\\", text)\\n if not match:\\n logger.error(\\n \\\"Type and reference of the bibtex file could not be parsed\\\"\\n )\\n sys.exit(1)\\n result[\\\"type\\\"] = match.group(1)\\n result[\\\"ref\\\"] = match.group(2)\\n for key in result.keys():\\n logger.debug(\\\" [%s] = %s\\\" % (key, result[key]))\\n key_val_re = re.compile(r\\\"\\\\s*(\\\\w+)\\\\s*=\\\\s*{(.*)\\\\s*,?\\\")\\n for line in re.sub(r\\\"}\\\\s*,?\\\", \\\"\\\\n\\\", text).split(\\\"\\\\n\\\"):\\n match = re.match(key_val_re, line)\\n if match:\\n key = match.group(1)\\n val = re.sub(r\\\"\\\\s+\\\", \\\" \\\", match.group(2))\\n result[key.lower()] = val\\n logger.debug(\\\"[%s] = %s\\\" % (key, val))\\n return result\",\n \"def get_pubmed(pub_query,conn):\\n\\n get_pm = ('SELECT DISTINCT dx.accession '\\n 'FROM pub p, pub_dbxref pd, dbxref dx, db '\\n 'WHERE p.pub_id = pd.pub_id '\\n 'AND pd.dbxref_id = dx.dbxref_id AND dx.db_id = db.db_id '\\n 'AND db.name = \\\\'pubmed\\\\' AND pd.is_current = TRUE AND p.uniquename = %s')\\n \\n pm = connect(get_pm, pub_query, conn)\\n if pm:\\n return(pm[0][0])\\n else:\\n return(None)\",\n \"def getMetadataDict(self, url=None):\\n if url is None:\\n return {}\\n\\n sgdxq = \\\"\\\"\\\"xquery version \\\"%s\\\";\\n let $doc := document(\\\"%s\\\")\\n return $doc\\n \\\"\\\"\\\" % (self.getXQueryVersion(), url)\\n try:\\n exist = self.getDA()\\n res = exist.query(sgdxq, object_only=1)\\n return res.getMergedDict(xmled=False)\\n except:\\n return {}\",\n \"def _get_metadata(conn):\\n metadata_sql = \\\"SELECT * FROM metadata\\\"\\n metadata = conn.execute(metadata_sql).fetchall()\\n return {\\n row[0]: {\\n \\\"name\\\": row[0],\\n \\\"field_name\\\": row[1],\\n \\\"field_expr\\\": row[1],\\n \\\"type\\\": row[2],\\n \\\"minimum\\\": row[3],\\n \\\"maximum\\\": row[4],\\n \\\"average\\\": row[5],\\n \\\"variance\\\": row[6],\\n \\\"total\\\": row[7],\\n \\\"stddev\\\": row[8],\\n \\\"other\\\": json.loads(row[9]) if row[9] else None,\\n }\\n for row in metadata\\n }\",\n \"def dict_factory(cursor, row):\\n fields = [column[0] for column in cursor.description]\\n return {key: value for key, value in zip(fields, row)}\",\n \"def findall(self, name):\\n if name in self._dict:\\n return self._dict[name]\\n else:\\n raise PoseError(\\\"%s is not in database\\\" % name)\",\n \"def citation_formatter(citation, title=False):\\n # first we split the author-list\\n citation_string = \\\"\\\"\\n # the spaces around the and are required!\\n for match in citation[\\\"author\\\"].split(\\\" and \\\"):\\n last_name, first_names = match.split(\\\",\\\", 1)\\n citation_string += last_name.strip() + \\\",\\\"\\n for name in first_names.strip().split(' '):\\n citation_string += \\\" \\\" + name.strip()[0]\\n\\n citation_string += \\\"; \\\"\\n\\n if \\\"journal\\\" in citation:\\n citation_string += \\\" \\\" + citation[\\\"journal\\\"]\\n\\n citation_string += \\\" \\\" + citation[\\\"year\\\"]\\n\\n if \\\"doi\\\" in citation:\\n citation_string += \\\"; \\\" + citation[\\\"doi\\\"]\\n\\n return citation_string\",\n \"def scrapeCountries(conn):\\n query = \\\"SELECT * FROM country\\\"\\n c = conn.cursor()\\n c.execute(query)\\n results = []\\n for row in c:\\n result={}\\n country_fields = {}\\n\\n result['pk'] = country_codes[row[0]]\\n result['model'] = \\\"infohub.country\\\"\\n result['fields'] = country_fields\\n country_fields['name'] = row[1]\\n\\n results.append(result)\\n return results\",\n \"def get_courts(self, name=\\\"\\\", abbreviation=\\\"\\\", jurisdiction=\\\"\\\", slugs_only=False):\\n url_base = self._get_api_url() + \\\"courts/\\\"\\n url_queries = []\\n\\n if name:\\n url_queries.append(\\\"name=%s\\\" % name)\\n\\n if abbreviation:\\n url_queries.append(\\\"name_abbreviation=%s\\\" % abbreviations)\\n\\n if jurisdiction:\\n jurisdiction = jurisdiction.lower()\\n valid_jurisdictions = [elem['slug'] for elem in self.get_jurisdictions()[\\\"results\\\"]]\\n if jurisdiction not in valid_jurisdictions:\\n raise Exception(\\\"Jurisdiction not recognized. Check spelling?\\\")\\n url_queries.append(\\\"jurisdiction=%s\\\" % jurisdiction)\\n\\n uri = self._build_uri(url_base, url_queries)\\n courts = self._request(uri)\\n\\n if slugs_only:\\n names = self._extract_from_paginated(courts.json(), \\\"slug\\\")\\n return names\\n\\n print(uri)\\n return courts.json()\",\n \"def newThesisCitation(self, **attrlinks):\\n return ThesisCitation(self, **attrlinks)\",\n \"def make_citation_authors(res):\\n if \\\"author\\\" in res.keys():\\n first_author = res['author'][0]['family'] + \\\", \\\" + res['author'][0]['given']\\n last_author = res['author'][-1]['given'] + \\\" \\\" + res['author'][-1]['family']\\n middle_authors = \\\", \\\".join(\\\" \\\".join([x['given'], x['family']]) for x in res['author'][1:-1])\\n #assemble authors\\n author_string = first_author\\n author_string = author_string + \\\", \\\" + middle_authors if middle_authors != '' else author_string\\n author_string = author_string + \\\", and \\\" + last_author if len(res['author']) > 1 else author_string\\n \\n author_string = author_string + \\\".\\\" if author_string[-1] != \\\".\\\" else author_string\\n else:\\n author_string = \\\"Unknown Authors\\\"\\n\\n return clean_txt(author_string.capitalize())\",\n \"def get_bibtex(self, arg=None):\\n getter = self.get_getter()\\n if getter is None:\\n return\\n if not arg:\\n arg = self.visual.ask_user(f\\\"Search what on the web?\\\", multichar=True)\\n if not arg:\\n self.visual.error(\\\"Nothing entered, aborting.\\\")\\n return\\n try:\\n res = getter.get_web_bibtex(arg)\\n except Exception as ex:\\n self.visual.error(\\\"Failed to complete the query: {}.\\\".format(ex))\\n return\\n if not res:\\n self.visual.error(\\\"No data retrieved.\\\")\\n return\\n\\n reader2 = Reader(self.config)\\n read_entries_dict = reader2.read_entry_list(res)\\n self.visual.log(\\\"Retrieved {} entry item(s) from query [{}]\\\".format(len(read_entries_dict), arg))\\n\\n # select subset\\n if len(read_entries_dict) > 1:\\n ids, content = list(zip(*[(e.ID, e.get_discovery_view()) for e in read_entries_dict.values()]))\\n _, selected_ids = self.visual.user_multifilter(content, header=Entry.discovery_keys, reference=ids)\\n selected_entries = [v for (k, v) in read_entries_dict.items() if k in selected_ids]\\n else:\\n selected_entries = list(read_entries_dict.values())\\n if not selected_entries:\\n return\\n self.visual.print_entries_contents(selected_entries)\\n\\n res = self.visual.ask_user(\\\"Store?\\\", \\\"*yes no-but-keep quit\\\")\\n if utils.matches(res, \\\"yes\\\"):\\n selected_ids = []\\n for entry in selected_entries:\\n created_entry = self.entry_collection.add_new_entry(entry)\\n if created_entry is None:\\n self.visual.error(f\\\"Entry {entry.ID} already exists in the collection!\\\")\\n else:\\n selected_ids.append(created_entry.ID)\\n elif utils.matches(res, \\\"no-but-keep\\\"):\\n selected_ids = [x for x in selected_entries]\\n elif utils.matches(res, \\\"quit\\\"):\\n return\\n if not selected_ids:\\n return\\n if self.visual.yes_no(\\\"Select it?\\\"):\\n self.selector.select_by_id(selected_ids)\\n\\n # pdf\\n what = self.visual.ask_user(\\\"Pdf?\\\", \\\"local url web-search *skip\\\")\\n if utils.matches(what, \\\"skip\\\"):\\n return\\n if utils.matches(what, \\\"url\\\"):\\n self.get_pdf_from_web()\\n return\\n if utils.matches(what, \\\"local\\\"):\\n self.set_local_pdf_path()\\n return\\n if utils.matches(what, \\\"web-search\\\"):\\n self.search_web_pdf()\",\n \"def getPostalCodeDic(csvfname = 'CP.csv'):\\n cpdic = {}\\n reader = csv.DictReader(open(csvfname), delimiter=':')\\n for row in reader:\\n cpdic[row['CP']] = row['arrondissement'].split('|')[-1]\\n return cpdic\",\n \"def get_champion_info_dict():\\n conn = get_connect()\\n cursor = conn.execute(\\\"SELECT * FROM championInfo ORDER BY championId\\\")\\n info_dict = {}\\n for row in cursor:\\n info_dict[row[0]] = [row[1], row[2], row[3]]\\n conn.close()\\n return info_dict\",\n \"def newConferenceCitation(self, **attrlinks):\\n return ConferenceCitation(self, **attrlinks)\",\n \"def dict_factory(cursor, row):\\n rowdict = {}\\n for idx, col in enumerate(cursor.description):\\n rowdict[col[0]] = row[idx]\\n return rowdict\",\n \"def _get_card(self, name: str) -> Dict:\",\n \"def example_data():\\n return [\\n {'id': 'cc-by', 'title': {'en': 'Creative Commons Attribution'},\\n 'type': 'licenses'},\\n {'id': 'cc0', 'title': {'en': 'Creative Commons Zero'},\\n 'type': 'licenses'},\\n ]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6046626","0.5950197","0.5847985","0.58434343","0.56926155","0.5684362","0.5674185","0.5558614","0.5495795","0.54895663","0.54638165","0.5446902","0.5430732","0.53450066","0.53339255","0.53142464","0.5296164","0.5265383","0.5264633","0.525944","0.52467376","0.5192072","0.5165983","0.51534957","0.51479423","0.5108243","0.5091113","0.5089148","0.5078358","0.50762385","0.50410247","0.50380516","0.5037879","0.50367755","0.50267386","0.50149703","0.49994233","0.49984494","0.4991165","0.49827772","0.49821362","0.49795184","0.49761194","0.49695134","0.49624163","0.49404535","0.49360278","0.49209288","0.49152824","0.4912352","0.49107146","0.49075037","0.49016118","0.4901507","0.4888655","0.4877996","0.48741594","0.48697293","0.48695427","0.48694214","0.48662925","0.48645306","0.48645306","0.48645306","0.48613912","0.48558363","0.48469743","0.48315483","0.48293492","0.48257837","0.48253864","0.48224473","0.48150387","0.48103702","0.4802301","0.48005515","0.47927603","0.4786132","0.47848973","0.47836527","0.47816753","0.4781512","0.47722536","0.4766393","0.47630432","0.47625062","0.4756906","0.47522703","0.47511953","0.4750325","0.47488868","0.47400555","0.4737495","0.47369075","0.473353","0.47329864","0.4725494","0.47234467","0.47232214","0.47114688"],"string":"[\n \"0.6046626\",\n \"0.5950197\",\n \"0.5847985\",\n \"0.58434343\",\n \"0.56926155\",\n \"0.5684362\",\n \"0.5674185\",\n \"0.5558614\",\n \"0.5495795\",\n \"0.54895663\",\n \"0.54638165\",\n \"0.5446902\",\n \"0.5430732\",\n \"0.53450066\",\n \"0.53339255\",\n \"0.53142464\",\n \"0.5296164\",\n \"0.5265383\",\n \"0.5264633\",\n \"0.525944\",\n \"0.52467376\",\n \"0.5192072\",\n \"0.5165983\",\n \"0.51534957\",\n \"0.51479423\",\n \"0.5108243\",\n \"0.5091113\",\n \"0.5089148\",\n \"0.5078358\",\n \"0.50762385\",\n \"0.50410247\",\n \"0.50380516\",\n \"0.5037879\",\n \"0.50367755\",\n \"0.50267386\",\n \"0.50149703\",\n \"0.49994233\",\n \"0.49984494\",\n \"0.4991165\",\n \"0.49827772\",\n \"0.49821362\",\n \"0.49795184\",\n \"0.49761194\",\n \"0.49695134\",\n \"0.49624163\",\n \"0.49404535\",\n \"0.49360278\",\n \"0.49209288\",\n \"0.49152824\",\n \"0.4912352\",\n \"0.49107146\",\n \"0.49075037\",\n \"0.49016118\",\n \"0.4901507\",\n \"0.4888655\",\n \"0.4877996\",\n \"0.48741594\",\n \"0.48697293\",\n \"0.48695427\",\n \"0.48694214\",\n \"0.48662925\",\n \"0.48645306\",\n \"0.48645306\",\n \"0.48645306\",\n \"0.48613912\",\n \"0.48558363\",\n \"0.48469743\",\n \"0.48315483\",\n \"0.48293492\",\n \"0.48257837\",\n \"0.48253864\",\n \"0.48224473\",\n \"0.48150387\",\n \"0.48103702\",\n \"0.4802301\",\n \"0.48005515\",\n \"0.47927603\",\n \"0.4786132\",\n \"0.47848973\",\n \"0.47836527\",\n \"0.47816753\",\n \"0.4781512\",\n \"0.47722536\",\n \"0.4766393\",\n \"0.47630432\",\n \"0.47625062\",\n \"0.4756906\",\n \"0.47522703\",\n \"0.47511953\",\n \"0.4750325\",\n \"0.47488868\",\n \"0.47400555\",\n \"0.4737495\",\n \"0.47369075\",\n \"0.473353\",\n \"0.47329864\",\n \"0.4725494\",\n \"0.47234467\",\n \"0.47232214\",\n \"0.47114688\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7352711"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79593,"cells":{"query":{"kind":"string","value":"read author>citedinlist dict from the db"},"document":{"kind":"string","value":"def get_initial_author_dict():\n adict = {}\n try:\n ah = run_sql(\"select aterm,hitlist from rnkAUTHORDATA\")\n for (a, h) in ah:\n adict[a] = deserialize_via_marshal(h)\n return adict\n except:\n register_exception(prefix=\"could not read rnkAUTHORDATA\", alert_admin=True)\n return {}"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def popAuthors(self):\r\n# cur = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\")\r\n# res = cur.fetchall()\r\n res = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\").fetchall()\r\n\r\n self.authorList = [formatNameSQL(ln) for ln in res]\r\n self.quickAuthors = [ln[\"Lastname\"].lower() for ln in res]\r\n vals = [ln[\"PersonID\"] for ln in res]\r\n \r\n self.authorLookup = dict(zip(self.authorList,vals))","def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\n\n #sorry bout repeated code to get the tags\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n tagvals = {}\n for t in tags:\n try:\n x = config.get(config.get(\"rank_method\", \"function\"), t)\n tagvals[t] = x\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_author_dict\n\n #parse the tags\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\n if task_get_task_param('verbose') >= 9:\n write_message(\"mainauthortag \"+mainauthortag)\n write_message(\"coauthortag \"+coauthortag)\n write_message(\"extauthortag \"+extauthortag)\n\n author_cited_in = initial_author_dict\n if citedbydict:\n i = 0 #just a counter for debug\n write_message(\"Checking records referred to in new records\")\n for u in updated_redic_list:\n if (i % 1000 == 0):\n mesg = \"Author ref done \"+str(i)+\" of \"+str(len(updated_redic_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n if citedbydict.has_key(u):\n these_cite_k = citedbydict[u]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n authors = get_fieldvalues(u, mainauthortag)\n coauthl = get_fieldvalues(u, coauthortag)\n extauthl = get_fieldvalues(u, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author ref done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n #go through the dictionary again: all keys but search only if new records are cited\n write_message(\"Checking authors in new records\")\n i = 0\n for k in citedbydict.keys():\n if (i % 1000 == 0):\n mesg = \"Author cit done \"+str(i)+\" of \"+str(len(citedbydict.keys()))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i + 1\n\n these_cite_k = citedbydict[k]\n if (these_cite_k is None):\n these_cite_k = [] #verify it is an empty list, not None\n #do things only if these_cite_k contains any new stuff\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\n if intersec_list:\n authors = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authors.extend(coauthl)\n authors.extend(extauthl)\n for a in authors:\n if a and author_cited_in.has_key(a):\n #add all elements in these_cite_k\n #that are not there already\n for citer in these_cite_k:\n tmplist = author_cited_in[a]\n if (tmplist.count(citer) == 0):\n tmplist.append(citer)\n author_cited_in[a] = tmplist\n else:\n author_cited_in[a] = these_cite_k\n\n mesg = \"Author cit done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return author_cited_in","def extra_bibparse(db):\n for key,entry in db.entries.items():\n for auth in entry.persons[\"author\"]:\n if (\"Harrison\" not in auth.first_names or\n \"Chapman\" not in auth.last_names):\n entry.add_person(auth, \"otherauthor\")","def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\n selfdic, authorcitdic):\n insert_into_cit_db(reference_dic,\"reversedict\")\n insert_into_cit_db(citation_dic,\"citationdict\")\n insert_into_cit_db(selfcbdic,\"selfcitedbydict\")\n insert_into_cit_db(selfdic,\"selfcitdict\")\n\n for a in authorcitdic.keys():\n lserarr = (serialize_via_marshal(authorcitdic[a]))\n #author name: replace \" with something else\n a.replace('\"', '\\'')\n a = unicode(a, 'utf-8')\n try:\n ablob = run_sql(\"select hitlist from rnkAUTHORDATA where aterm = %s\", (a,))\n if not (ablob):\n #print \"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\" , (a,lserarr)\n run_sql(\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\",\n (a,lserarr))\n else:\n #print \"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\"\"\" , (lserarr,a)\n run_sql(\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\",\n (lserarr,a))\n except:\n register_exception(prefix=\"could not read/write rnkAUTHORDATA aterm=\"+a+\" hitlist=\"+str(lserarr), alert_admin=True)","def load_authors():\n\n ret = {}\n for token in util.git('log', '--format=%aE:::%aN').split('\\n'):\n email, name = token.split(':::')\n ret[email] = name\n return ret","def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp': [] }\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n\n if author['dblp'].__contains__('publications'):\n publications['dblp'] = author['dblp']['publications']\n# for pub in author['dblp']['publications']:\n# print(pub)\n\n if author['cdblp'].__contains__('publications'):\n publications['cdblp'] = author['cdblp']['publications']\n# for pub in author['cdblp']['publications']:\n# print(pub)\n return publications","def get_author_data():\n entry = mongo.db.Authors\n output = list()\n look_up_type = None\n if 'name' in request.args:\n look_up_type = 'name'\n print(request.args)\n if len(request.args['name']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['name'].strip('\"')\n name = entry.find({'name': {'$regex': value}})\n if name:\n for author in name:\n output.append({'name': author['name']})\n elif 'booktitle' in request.args:\n look_up_type = 'related_books'\n if len(request.args['booktitle']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['booktitle'].strip('\"')\n related_books = entry.find(\n {'author_books': {'$regex': value}})\n if related_books:\n for related in related_books:\n for title in related['author_books']:\n if value in title:\n output.append(({'related_books': title}))\n if len(output) == 0:\n return render_template('error.html', message=\"No Entries Found\"), 400\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200","def getPopualrAuthors():\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(\" select count(*) as views , authors.name from articles \"\n + \" inner join \"\n + \"log on concat('/article/', articles.slug) = log.path \"\n + \" inner join authors on articles.author = authors.id \"\n + \"group by name order by views desc; \")\n authors = c.fetchall()\n db.close()\n return authors","def get_user_and_text(db):\n con = lite.connect(db)\n with con:\n cur = con.cursor()\n cur.execute(\"SELECT author, GROUP_CONCAT(text, ' ') FROM Comments GROUP BY author\")\n user_text_list = cur.fetchall() \n return user_text_list","def get_coauthors_by_author(cached_list, cached_set, author_name):\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n coauthors = {}\n if author['dblp'].__contains__('coauthors'):\n for author_key in author['dblp']['coauthors']:\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\n\n if author['cdblp'].__contains__('coauthors'):\n for author_key in author['cdblp']['coauthors']:\n if coauthors.__contains__(author_key['full_name']):\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\n else:\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\n\n return coauthors","def question_2():\n cursor.execute(mostPopAuthors)\n output = cursor.fetchall()\n return output","def getAuthors(self): #$NON-NLS-1$\r","def get_authors(self, blogid=1):\n return self.execute('wp.getAuthors', blogid, self.username, self.password)","def _get_authors_list():\n\n articles = os.listdir(\"../data/\")\n authors = []\n for article in articles:\n with open(\"../data/\" + article, 'r') as file:\n lines = file.readlines()\n author = tuple(\n line.replace(\"\\n\", \"\").split()[1] for line in lines\n if \"Автор:\" in line\n )[0]\n authors.append(author)\n\n return authors","def get_author_info_from_people_collection(info):\n # TODO: probably we will need to extract this somewhere else\n URL = ('https://cds.cern.ch/submit/get_authors?'\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\n if '0' in info or not info.get('a'):\n # There is already enough information or we don't have a name to query\n return info\n author_name = info.get('a')\n if PY2:\n # In Python 3, encoded name will change type to bytes and this will\n # cause query to CDS to fail\n author_name = author_name.encode('utf-8')\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\n if not author_info or len(author_info) > 1:\n # Didn't find anything or find to many matches\n return info\n\n # Prepare author name\n author_info = author_info[0]\n if 'name' not in author_info:\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\n author_info['firstname'])\n return MementoDict([\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])","def get_authors(config: Config) -> dict:\n\n file_handle = load_csv_file(config.input)\n\n start = time.time()\n raw_authors = get_raw_authors(config, file_handle)\n end = time.time()\n print(f\"get_raw_authors took {end - start} seconds\")\n\n start = time.time()\n sanitized_authors = sanitize_authors(config, raw_authors)\n end = time.time()\n print(f\"sanitize_authors took {end - start} seconds\")\n\n return sanitized_authors","def load_authors(keys: [str]):\n not_in_local_cache = []\n result = []\n for key in keys:\n try:\n result.append(_author_data_cache[key].to_dict())\n del _author_data_cache[key]\n except KeyError:\n not_in_local_cache.append(key)\n \n if len(not_in_local_cache):\n doc_refs = [db.collection(AUTHOR_CACHE_COLLECTION).document(key)\n for key in keys]\n data = db.get_all(doc_refs)\n for datum in data:\n if not datum.exists:\n raise cache_buddy.CacheMiss(datum.id)\n result.append(datum.to_dict())\n return [_decompress_record(r) for r in result]","def get_popular_authors():\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n query_popular_authors = \"\"\"\n SELECT aut.name, COUNT(lg.id) AS views\n FROM articles AS art\n JOIN log AS lg ON art.slug = SUBSTRING(lg.path,10)\n AND lg.status = '200 OK'\n JOIN authors AS aut ON aut.id = art.author\n GROUP BY aut.name\n ORDER BY views desc; \"\"\"\n c.execute(query_popular_authors)\n authors = from_db_cursor(c)\n db.close()\n return authors","def AuthorURLs(entry):\n a_URLs = ''\n for a in entry.getAuthors():\n url = a.get('homepage', ' ')\n a_URLs += \"%s and \" % url\n return a_URLs[:-5]","def read_csv(self, filename: str):\n author_field_index = 0\n authors = []\n author_data = []\n with open(filename, 'r') as csvfile:\n csvreader = csv.reader(csvfile)\n fields = next(csvreader)\n for index, field in enumerate(fields):\n if field.lower() == \"author\":\n author_field_index = index\n break\n for row in csvreader:\n authors.append(row[author_field_index])\n for index, author in enumerate(authors):\n if author:\n data = self.get_author_info(author)\n author_data.append((author, data))\n if index % 10 == 0: # every 10 entries we write the data to json in case we get a crash\n self._reference.update_maps_jsons()\n self._reference.update_maps_jsons()\n return author_data","def make_author_list(res):\n try:\n r = [\", \".join([clean_txt(x['family']).capitalize(), clean_txt(x['given']).capitalize()]) for x in res['author']]\n except KeyError as e:\n print(\"No 'author' key, using 'Unknown Author'. You should edit the markdown file to change the name and citationkey.\")\n r = [\"Unknown Authors\"]\n return r","def get_titles_dict(name_url_list):\n\tbib_dict = {}\n\t\n\tfor name_url in name_url_list:\n\t\ttitles = get_author_titles(name_url[1])\n\t\tbib_dict[name_url] = titles\n\n\treturn bib_dict","def to_internal_value(self, data):\n authors = []\n for author in data:\n path = urlparse(author).path\n resolved_func, __, resolved_kwargs = resolve(path)\n person = resolved_func.cls.queryset.get(pk=resolved_kwargs['pk'])\n authors.append(person)\n\n return {'authors': authors}","def get_author_by_id(self, id_num):\n\n cur = self.conn.cursor()\n query = 'SELECT author_id , name FROM author WHERE author_id = ?'\n cur.execute(query, (id_num,))\n return row_to_dict_or_false(cur)","def get_single_book_info(self, isbn):\n self.cursor.execute(\"SELECT * FROM book WHERE ISBN=%s\", (isbn,))\n books = self.cursor.fetchall()\n for book in books:\n authors = []\n self.cursor.execute(\"\"\"SELECT name FROM Author A, Wrote W, Book B WHERE A.ID = W.authorID AND\n W.ISBN = B.ISBN AND B.ISBN = %s\"\"\", (isbn,))\n for auth in self.cursor.fetchall():\n authors.append(auth[0])\n return book, authors","def authors(self):\n return self.properties.get('Authors', ClientValueCollection(SharedWithMeDocumentUser))","def _authors(fname):\n if not os.path.exists(fname):\n raise RuntimeError(\"File {} not found\".format(fname))\n with open(fname, \"r\") as obj:\n lines = [_tostr(item.strip()) for item in obj.readlines()]\n regexp = re.compile(r\"(?:\\s*\\*\\s+)?(.*)\\s+<(.*)>.*\")\n for line in lines:\n match = regexp.match(line)\n if match:\n name, email = match.groups()\n yield name, email","def test_get_authors_from_data(self):\n responses.add(\n responses.GET,\n \"https://openlibrary.org/authors/OL382982A\",\n json={\n \"name\": \"George Elliott\",\n \"personal_name\": \"George Elliott\",\n \"last_modified\": {\n \"type\": \"/type/datetime\",\n \"value\": \"2008-08-31 10:09:33.413686\",\n },\n \"remote_ids\": {\n \"isni\": \"000111\",\n },\n \"key\": \"/authors/OL453734A\",\n \"type\": {\"key\": \"/type/author\"},\n \"id\": 1259965,\n \"revision\": 2,\n },\n status=200,\n )\n results = self.connector.get_authors_from_data(self.work_data)\n result = list(results)[0]\n self.assertIsInstance(result, models.Author)\n self.assertEqual(result.name, \"George Elliott\")\n self.assertEqual(result.openlibrary_key, \"OL453734A\")\n self.assertEqual(result.isni, \"000111\")","def get_author_detail(body):\n user_id = get_user_id(body) # user id\n author_details = body.find('div', {'id': 'gsc_prf_i'})\n\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\n\n affiliation = name_attributes[0].text or ''\n interests = name_attributes[1].text or ''\n email = name_attributes[2].text or ''\n\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\n\n year_since_text = citation_indices.find_all('tr')[0]\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\n\n # citations\n citations = citation_indices.find_all('tr')[1]\n h_index = citation_indices.find_all('tr')[2]\n h_index_all = h_index.find_all('td')[1].text\n h_index_since = h_index.find_all('td')[2].text\n citation_all = citations.find_all('td')[1].text\n citation_since = citations.find_all('td')[2].text\n\n author_dict = {'user_id': user_id,\n 'name': name,\n 'affiliation': affiliation,\n 'interests': interests,\n 'email': email,\n 'h_index_all': h_index_all,\n 'h_index_since': h_index_since,\n 'citation_all': citation_all,\n 'citation_since': citation_since,\n 'year_since': year_since}\n return author_dict","def Authors(self, default=[{}]):\n tmp = self.data.get('metadata', {}).get('authors', default)\n return [HEP.AuthorObject(i) for i in tmp]","def addAuthor2():\n\n author_list = list()\n\n authors = Author.objects.all()\n\n for author in authors:\n author_dict = dict()\n author_dict['id'] = \"{}/api/author/{}\".format(DOMAIN, author.id)\n author_dict['host'] = \"{}/api/\".format(author.host_url)\n author_dict['displayName'] = author.username\n author_dict['url'] = \"{}/api/author/{}\".format(DOMAIN, author.id)\n\n author_list.append(author_dict)\n\n return author_list","def Authors(self, default=[{}]):\n tmp = self.data.get('authors', default)\n return [HEP.AuthorReducedObject(i) for i in tmp]","def authors(self, key, value):\n _authors = self.get(\"authors\", [])\n item = build_ils_contributor(value)\n if item and item not in _authors:\n _authors.append(item)\n try:\n if \"u\" in value:\n other = [\"et al.\", \"et al\"]\n val_u = list(force_list(value.get(\"u\")))\n if [i for i in other if i in val_u]:\n self[\"other_authors\"] = True\n except UnexpectedValue:\n pass\n return _authors","def parse_bib_from_list(filename):\n\tentry_regex = r\"TITEL: .*\\s*AUTOR: .*\"\n\tparse_func = make_parse_func(r\"AUTOR: (.*)\", r\"TITEL: (.*)\", None)\n\treturn parse_bib(filename, entry_regex, parse_func)","def authorSign(g = None):\n if not g:\n return\n authors = g.find(\"Author\")\n ret = dict()\n for author in authors:\n ret[author['name']] = author['citation'].replace('\\n','').lower()\n return ret","def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\n publications = { 'cdblp': [], 'dblp': [] }\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\n for cdblp_pub in pub1.get('cdblp', []):\n authors = set(cdblp_pub.get('authors', []))\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\n publications['cdblp'].append(cdblp_pub)\n\n for dblp_pub in pub1.get('dblp', []):\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\n publications['dblp'].append(dblp_pub)\n\n return publications","def get_author_data(authors):\n\n try:\n author = authors.author.name.cdata.encode(\"utf8\")\n author_id = int(authors.author.id.cdata.encode(\"utf8\"))\n except: # FIXME: running into errors when book has multiple authors\n author = authors.author[0].cdata.encode(\"utf8\")\n author_id = authors.author[0].cdata.encode(\"utf8\")\n\n return (author, author_id)","def find_authors(code):\n url = baseurl(code)\n page = req(url)\n soup = BeautifulSoup(page, 'lxml')\n addr = [t.attrs.get('content', None) \n for t in soup.find_all(\"meta\", {\"name\": \"citation_author_email\"})]\n \n # corresponding authors will have their email under another tag too\n corr = [t.find('a').attrs.get('href', None)\n for t in soup.find_all(None, {\"class\": \"author-corresp-email-link\"})]\n\n addr = [a for a in addr if a is not None]\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\n\n return dict(corr=list(set(corr)), all=list(set(addr)))","def get_coauthors_dict(name_url_list, schl_name):\n\n\tpaper_info_dict = {}\n\n\tfor name_url in name_url_list:\n\t\t# Get a sub papers_info dictfor an individual author\n\t\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\n\t\t# add sub dict to full dict\n\t\tpaper_info_dict.update(papers_info)\n\t\n\tif \"Humanities\" in schl_name:\n\t\tschl_name = \"School of Humanities\"\n\n\treturn paper_info_dict","def get_authors(self):\n return [aer.author for aer in self.authorentryrank_set.all()]","def find_relations_among_authors():\n for book in books:\n if len(books[book]) > 1:\n for i in range(len(books[book])):\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]","def read_authors(filename):\n with Path(filename).open() as file:\n info = json.load(file)\n authors = []\n for author in info['creators']:\n name = ' '.join(author['name'].split(',')[::-1]).strip()\n authors.append(name)\n return ', '.join(authors)","def get_author_info(self, author: str):\n for writer_word in self._writer_words:\n data = json.loads(requests.get(WIKIDATA_SEARCH + \"&srsearch=\" + author + \" \" + writer_word).text)\n pages = data.get(\"query\").get(\"search\")\n if len(pages) >= 1:\n pageid = pages[0].get(\"title\")\n author_details = self._reference.author_map.get(author)\n if author_details:\n return author_details\n if pageid == -1:\n continue\n\n else:\n response = requests.get(WIKIDATA_PARSE + pageid + \".json\")\n data = json.loads(response.text)\n if author.lower() not in data.get(\"entities\").get(pageid).get(\"labels\").get(\"en\").get(\"value\").lower():\n continue\n else:\n try:\n id = data.get(\"entities\").get(pageid).get(\"claims\").get(\"P31\")[0].get(\"mainsnak\").get(\"datavalue\").get(\"value\").get(\"id\")\n if str(id) != \"Q5\": # the id for human\n continue\n except IndexError:\n continue\n properties = data.get(\"entities\").get(pageid).get(\"claims\")\n author_details = {\"id\": pageid, \"gender\": self.get_gender(properties)}\n country_details = self.get_country(properties)\n author_details[\"country\"] = country_details\n self._reference.author_map[author] = author_details\n return author_details\n return {\"id\": \"Unknown\", \"gender\": \"Unknown\", \"country\": [{\"name\": \"Unknown\", \"region\": \"Unknown\"}]}","def find_all(cls):\n return [AuthorModel(a['name'], str(a['_id']))\n for a in cls.db.newsdb.find()]","def get_author_info(path):\n author_df = pandas.read_table(path)\n\n # Detect missing full name\n missing_full_name = author_df[author_df['full_name'].isnull()].index.tolist()\n if missing_full_name:\n msg = f'Missing full name in authors.tsv for author(s): {missing_full_name}'\n raise ValueError(msg)\n\n # Detect missing author initials\n missing_initials = author_df[author_df['initials'].isnull()].index.tolist()\n if missing_initials:\n msg = f'Missing initials in authors.tsv for author(s): {missing_initials}'\n raise ValueError(msg)\n\n # Detect duplicate author initials\n duplicated_initials = list(\n author_df[author_df.duplicated('initials')].initials)\n if duplicated_initials:\n msg = f'Duplicated initials in authors.tsv: {duplicated_initials}'\n raise ValueError(msg)\n\n # Replace missing values with None\n author_df.where(pandas.notnull(author_df), None, inplace=True)\n\n authors = author_df.to_dict(orient='records')\n return authors","def read_from_db():\n\t# prepare the query for reading from DB\n\tquery = \"SELECT * FROM tasks\"\n\n\t# connection to database\n\tconnection = pymysql.connect(user=\"root\", password=\"sysadmin\", host=\"localhost\", database=\"todolist\")\n\n\t# get a cursor\n\tcursor = connection.cursor()\n\n\t# execute query\n\tcursor.execute(query)\n\n\t# fetch result from query\n\tresults = cursor.fetchall()\n\n\t# close cursor and connection\n\tcursor.close()\n\tconnection.close()\n\n\ttask_list = list()\n\tfor result in results:\n\t\ttmp = {'id': result[0], 'description': result[1], 'urgent': result[2]}\n\t\ttask_list.append(tmp)\n\n\treturn task_list","def read_contacts(contacts_file, uidlist, used_uid_names):\n # Read in out values to a list\n lines = read_contacts_to_list(contacts_file)\n\n # Convert our list to dict\n contactsdict = contacts_from_list_to_dict(lines, uidlist, used_uid_names)\n\n #logging.debug(\"Aliasdict: %s\" % aliasdict)\n return contactsdict","def get_authors(draft):\n authors = []\n for a in draft.authors.all():\n initial = ''\n prefix, first, middle, last, suffix = a.person.name_parts()\n if first:\n initial = first + '. '\n entry = '%s%s <%s>' % (initial,last,a.address)\n authors.append(entry)\n return authors","def main(repo: Repository) -> dict:\n data = {}\n for commit in repo.traverse_commits():\n\n if not author_is_bot(commit.author):\n if commit.author.name in data:\n data[commit.author.name] = data[commit.author.name] + commit.lines\n else:\n data[commit.author.name] = commit.lines\n\n # Sort authors name in the dict\n data = dict(sorted(data.items(), key=lambda x: x[0].lower()))\n return data","def get_author_by_name(self, name):\n\n cur = self.conn.cursor()\n query = 'SELECT author_id , name FROM author WHERE name = ? '\n cur.execute(query, (name,))\n return row_to_dict_or_false(cur)","def lookups(self, request, model_admin):\n # lambda article: article.author # 接受一个article，返回对应作者对象\n # map(func, iterable) # 将可迭代对象的每个元素传入func\n # author_list 所有文章的作者列表，去重\n author_list = list(set(map(lambda article: article.author, Article.objects.all())))\n\n # author_list2 = []\n # for author in author_list:\n # author_list2.append((author.id, author.nickname or author.username))\n # return author_list2\n\n for author in author_list: # verbose value, 在过滤器里显示的名称\n yield (author.id, (author.nickname or author.username))","def citing_authors(self, entities):\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\n FROM \"entity_occurrences\"\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\n return [t[0] for t in result]","def read_relations(db, openfile):\n pass","def get_autor_with_papers(author_id):\n print(\"getting information from dblp about author {}\".format(author_id))\n data = get(\"https://dblp.org/pid/\" + author_id + \".xml\")[\"dblpperson\"]\n author_id = author_id.replace(\"/\", \"_\")\n author = {\n \"name\": data[\"@name\"],\n \"papers\": [get_paper(paper_container) for paper_container in data[\"r\"]],\n }\n\n with open(local.file_name(\"authors\", author_id), \"w\") as out:\n json.dump(author, out, ensure_ascii=False, indent=4)","def get_authors():\n authors = []\n authorfile = os.path.join('doc', 'authors.txt')\n with codecs.open(authorfile, 'r', 'utf-8') as f:\n for line in f:\n line = line.strip()\n if line and not line.startswith(u'#'):\n authors.append(line)\n return u\", \".join(authors)","def extract(self, data):\n\n # Read authors from the cell with NAME and COLLABORATORS variables\n authors = []\n for cell in data['cells']:\n if cell['cell_type'] != 'code':\n continue\n\n src = cell['source']\n if src and src[0].startswith('NAME'):\n authors = [*self._extract_name(src, 'NAME'),\n *self._extract_name(src, 'COLLABORATORS')]\n break\n\n return [x for x in authors if x.name]","def articleAuthorsByYear(g = None, year = None):\n if not g:\n return\n articles = g.find(\"Article\", property_key='year', property_value=str(year))\n ret = dict()\n for article in articles:\n ret[article['title']] = article['authors'].replace('\\n','').lower()\n return ret","def collect_citation_metadata(\n metadata: dict, references: List[pybtex.database.Entry]\n) -> dict:\n # Author list\n citation_authors = []\n for author_tier in [\"Core\", \"Developers\", \"Contributors\"]:\n for author in metadata[\"Authors\"][author_tier][\"List\"]:\n family_names, given_names = author[\"Name\"].split(\", \")\n citation_author = {\n \"family-names\": family_names,\n \"given-names\": given_names,\n }\n if \"Orcid\" in author:\n citation_author[\"orcid\"] = (\n \"https://orcid.org/\" + author[\"Orcid\"]\n )\n if \"Affiliations\" in author and len(author[\"Affiliations\"]) > 0:\n citation_author[\"affiliation\"] = \" and \".join(\n author[\"Affiliations\"]\n )\n citation_authors.append(citation_author)\n # References in CITATION.cff format\n citation_references = [to_cff_reference(entry) for entry in references]\n return {\n \"cff-version\": \"1.2.0\",\n \"message\": (\n \"Please cite SpECTRE in any publications that make use of its code\"\n \" or data. Cite the latest version that you use in your\"\n \" publication. The citation for this version is listed below.\"\n ),\n \"title\": metadata[\"Name\"],\n \"url\": metadata[\"Homepage\"],\n \"repository-code\": \"https://github.com/\" + metadata[\"GitHub\"],\n \"version\": metadata[\"Version\"],\n \"date-released\": metadata[\"PublicationDate\"],\n \"doi\": metadata[\"Doi\"],\n \"authors\": citation_authors,\n \"keywords\": metadata[\"Keywords\"],\n \"license\": metadata[\"License\"],\n \"references\": citation_references,\n }","def top_authors():\n\n cur.execute(\"\"\"\n SELECT author, count(*) AS article_author\n FROM article_summary\n GROUP BY author\n ORDER BY article_author DESC;\n \"\"\")\n result = cur.fetchall()\n return result","def extractionTitlePrincipals(cur, conn):\n fh = open(pathTitlePrincipals)\n reader = csv.reader(fh, delimiter = '\\t')\n firstLine = True\n idActor_list = []\n idJugar = 1\n for row in reader:\n if firstLine : firstLine = False # Read header\n else :\n if (row[3]=='actor' or row[3]=='actress'): #only record actors\n idTitulo = int(row[0][2:])\n idActor = int(row[2][2:])\n idActor_list.append(idActor)\n idJugar +=1\n # print(jugarInsert.format(idJugar, idTitulo, idActor))\n # REGISTER DATA IN JUGAR TABLE\n cur.execute(jugarInsert.format(idJugar, idTitulo, idActor))\n conn.commit()\n return idActor_list","def get_authors_from_papers(papers):\n auth_set = set()\n for p in papers:\n auth_set.update(p['authors'])\n return list(auth_set)","def author_articles(self):\n return ArticlePage.objects.live().filter(author=self).order_by('-date')","def author_dictionary (corpus_token_path, correct_author_path):\n token_files = os.listdir(corpus_token_path)\n correct_author = read_file(correct_author_path).split(\"\\n\")\n author_name_token_dict = {}\n for i in range(0, correct_author.__len__()):\n if correct_author[i] not in author_name_token_dict.keys():\n author_name_token_dict[correct_author[i]] = [token_files[i]]\n #print(author_name_token_dict[french_correct_author[i]])\n else:\n existing_token_files = author_name_token_dict[correct_author[i]]\n if existing_token_files is not None:\n author_name_token_dict[correct_author[i]].append(token_files[i])\n return author_name_token_dict","def alt_authors(self, key, value):\n _authors = self.get(\"authors\", [])\n if _authors:\n for i, v in enumerate(force_list(value)):\n _authors[i].update({\"alternative_names\": clean_val(\"a\", v, str)})\n return _authors","def cursor_data(c):\r\n\r\n # pull column description\r\n d = []\r\n for i in range(len(c.description)):\r\n d.append(c.description[i][0])\r\n\r\n # fetch column entries\r\n c = c.fetchall()\r\n\r\n # compile list\r\n info = []\r\n for i in range(len(c)):\r\n # compile dictionary entry\r\n entry = {}\r\n for j in range(len(d)):\r\n entry[d[j]] = c[i][j]\r\n info.append(entry)\t\r\n\r\n # success\r\n return info","def parse_bibtex(self, data: str) -> Dict:\n\n new_bib = [line for line in data.splitlines() if \"= ,\" not in line]\n new_bib = \"\\n\".join(new_bib)\n bib_db: bibtexparser.bibdatabase.BibDatabase = bibtexparser.loads(new_bib)\n result = dict()\n for entry in bib_db.entries:\n osti_id = entry[\"ID\"].split(\"_\")[1]\n result[osti_id] = entry\n return result","def query_authors(cls):\n authors = from_cache('AuthorsList')\n if not authors:\n authors = SuiAuthor.all().order('name').fetch(400)\n to_cache('AuthorsList', authors)\n return authors","def test_citedby_author(self):\n inv_search = 'citedby:author:doggy'\n spi_search = 'find citedby author doggy'\n self._compare_searches(inv_search, spi_search)","def all_authors( data ) :\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))","def get_venues_by_author(cached_list, cached_set, author_name):\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\n venues = {}\n\n if author['dblp'].__contains__('publications'):\n for pub in author['dblp']['publications']:\n if venues.__contains__(pub['venue']):\n venues[pub['venue']]['count'] += 1\n else:\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\n\n if author['cdblp'].__contains__('publications'):\n for pub in author['cdblp']['publications']:\n if venues.__contains__(pub['venue']):\n venues[pub['venue']]['count'] += 1\n else:\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\n\n return venues","def authors(self):\n authors = [\n n.people for n in self.pymbake_person_relationship.all()\n ]\n\n return authors","def getAuthors(self):\n authors = []\n for each in self.context.getAuthors():\n title = each['title']\n firstname = each['firstname']\n middlename = each['middlename']\n lastname = each['lastname']\n author = Author(title, firstname, middlename, lastname)\n authors.append(author)\n return authors","def getAuthor(self):\n\t\tself.authorList = [submission.author for submission in self.subreddit.top(time_filter = 'day', limit = self.limits)]\n\t\treturn self.authorList","def data_pull(database_file, query):\n \n user_tweets = {}\n conn = sqlite3.connect(database_file)\n conn.row_factory = sqlite3.Row\n \n for row in conn.cursor().execute(query):\n if row['text'] is not None:\n data = tweetclean.cleanup(row['text'], True, True)\n try:\n user_tweets[row['owner']].append(data)\n except KeyError:\n user_tweets[row['owner']] = []\n user_tweets[row['owner']].append(data)\n\n conn.close()\n\n return user_tweets","def get_popular_authors():\n query_command = \"SELECT * from popular authors LIMIT 3\"\n query_data = run_query(query_command)\n return query_data","def make_citation_authors(res):\n if \"author\" in res.keys():\n first_author = res['author'][0]['family'] + \", \" + res['author'][0]['given']\n last_author = res['author'][-1]['given'] + \" \" + res['author'][-1]['family']\n middle_authors = \", \".join(\" \".join([x['given'], x['family']]) for x in res['author'][1:-1])\n #assemble authors\n author_string = first_author\n author_string = author_string + \", \" + middle_authors if middle_authors != '' else author_string\n author_string = author_string + \", and \" + last_author if len(res['author']) > 1 else author_string\n \n author_string = author_string + \".\" if author_string[-1] != \".\" else author_string\n else:\n author_string = \"Unknown Authors\"\n\n return clean_txt(author_string.capitalize())","def fetch(id_list):\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\n print(database)\n for sublist in id_list:\n with Entrez.efetch(db=database, id=','.join(sublist),\n retmode='xml') as handle:\n records = Entrez.read(handle)\n\n write_csv_file(records, output_file)","def get_file_contents(db_cursor):\n\n db_cursor.execute(\"\"\"SELECT * FROM data\"\"\")\n db_rows = db_cursor.fetchall()\n return {row[0]: row[1] for row in db_rows if row != []}","def execute_author_query():\n db = psycopg2.connect(database=\"news\")\n c = db.cursor()\n c.execute(author_query)\n content = c.fetchall()\n db.close()\n print(author_query_question)\n for views, author in content:\n print('%s -- %d views' % (author, views))\n print('\\n')","def read_db():\n f_result = []\n result = execute_query('select sitename, id from {} order by sitename;'.format(TABLES[0]))\n sites = [(x['sitename'], x['id']) for x in result]\n for sitename, site_id in sites:\n sitedict = {'name': sitename}\n querystring = 'select settname, settval from {} order by settname where site_id = %s;'\n result = execute_query(querystring.format(TABLES[1]), (site_id,))\n sitedict['settings'] = {x: y for x, y in cur.fetchall()}\n querystring = 'select dirname, id from {} order by dirname where site_id = %s;'\n result = execute_query(querystring.format(TABLES[2]), (site_id,))\n sitedirs = [(x['dirname'], x['id']) for x in cur.fetchall()]\n sitedict['docs'] = []\n # if we keep the site_id in the docstats table we could restrict this to one db-query\n # and filter the result set inside the loop\n # although this should also be possible with a subselect or something like that\n for dirname, dir_id in sitedirs:\n dirlist = []\n querystring = 'select * from {} order by docname where dir_id = %s;'\n result = execute_query(querystring.format(TABLES[3]), (dir_id,))\n for resultdict in cur:\n resultdict['dirname'] = dirname\n dirlist.append(resultdict)\n sitedict['docs'].append(dirlist)\n f_result.append(sitedict)\n return f_result","def get_paper_authors(tree):\n\tpath = '//table/tr/th[text() = \"Glasgow Author(s) Enlighten ID:\"]/following-sibling::td/a'\n\t# Get list of <a> elements, each an author\n\tauthors = tree.xpath(path)\n\t# Make list of (author name, author url) pairs to return\n\tauthors = [(author.text, author.get(\"href\")) for author in authors]\n\n\treturn authors","def _to_dict(self):\n\t\tauthor = Author.query.get(self.authors_id)\n\t\treturn {'id': self.id,\n\t\t\t\t'name': self.name,\n\t\t\t\t'author_id': self.authors_id,\n\t\t\t\t'author_name': author.name,\n\t\t\t\t'author_surname': author.surname}","def get_or_build_author_index(index_file=INDEX_FILE_AUTHORS):\n return (\n SqliteDict(index_file, flag=\"r\")\n if os.path.exists(INDEX_FILE_AUTHORS)\n else index_authors()\n )","def query_by_author(cls,author):\n bakey = 'BookByAuthor_%s'%author\n bks = from_cache(bakey)\n if not bks:\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).filter('authors =',author).fetch(100))\n to_cache(bakey,bks)\n return bks","def corporate_authors(self, key, value):\n _corporate_authors = self.get(\"authors\", [])\n\n for v in force_list(value):\n if key == \"710__\":\n if \"a\" in v:\n _corporate_authors.append(\n {\n \"full_name\": clean_val(\"a\", v, str),\n \"type\": \"ORGANISATION\",\n }\n )\n else:\n self[\"authors\"] = collaborations(self, key, value)\n raise IgnoreKey(\"corporate_authors\")\n else:\n _corporate_authors.append(\n {\"full_name\": clean_val(\"a\", v, str), \"type\": \"ORGANISATION\"}\n )\n return _corporate_authors","def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\n i = 0 #just for debugging ..\n #get the tags for main author, coauthors, ext authors from config\n tags = ['first_author', 'additional_author', 'alternative_author_name']\n for t in tags:\n try:\n dummy = config.get(config.get(\"rank_method\", \"function\"), t)\n except:\n register_exception(prefix=\"attribute \"+t+\" missing in config\", alert_admin=True)\n return initial_selfcitdict\n\n r_mainauthortag = config.get(config.get(\"rank_method\", \"function\"), \"first_author\")\n r_coauthortag = config.get(config.get(\"rank_method\", \"function\"), \"additional_author\")\n r_extauthortag = config.get(config.get(\"rank_method\", \"function\"), \"alternative_author_name\")\n #parse the tags\n mainauthortag = tagify(parse_tag(r_mainauthortag))\n coauthortag = tagify(parse_tag(r_coauthortag))\n extauthortag = tagify(parse_tag(r_extauthortag))\n\n selfcites = initial_selfcitdict\n for k in new_record_list:\n if (i % 1000 == 0):\n mesg = \"Selfcites done \"+str(i)+\" of \"+str(len(new_record_list))+\" records\"\n write_message(mesg)\n task_update_progress(mesg)\n i = i+1\n #get the author of k\n authorlist = get_fieldvalues(k, mainauthortag)\n coauthl = get_fieldvalues(k, coauthortag)\n extauthl = get_fieldvalues(k, extauthortag)\n authorlist.append(coauthl)\n authorlist.append(extauthl)\n #author tag\n #print \"record \"+str(k)+\" by \"+str(authorlist)\n #print \"is cited by\"\n #get the \"x-cites-this\" list\n if citationdic.has_key(k):\n xct = citationdic[k]\n for c in xct:\n #get authors of c\n cauthorlist = get_fieldvalues(c, mainauthortag)\n coauthl = get_fieldvalues(c, coauthortag)\n extauthl = get_fieldvalues(c, extauthortag)\n cauthorlist.extend(coauthl)\n cauthorlist.extend(extauthl)\n #print str(c)+\" by \"+str(cauthorlist)\n for ca in cauthorlist:\n if (ca in authorlist):\n #found!\n if selfcites.has_key(k):\n val = selfcites[k]\n #add only if not there already\n if val:\n if not c in val:\n val.append(c)\n selfcites[k] = val\n else:\n #new key for selfcites\n selfcites[k] = [c]\n\n mesg = \"Selfcites done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n return selfcites","def _parse_mercurial_author(data, id_gen):\n angled = ur'(?P<author>.+?) <(?P<email>.+?)>'\n paren = ur'(?P<email>.+?) \\((?P<author>.+?)\\)'\n simple = ur'(?P<author>[^,]+)'\n author_list = []\n for regex in (angled, paren, simple):\n # Watch out for commas separating multiple names.\n regex += u'(,\\s*)?'\n for match in re.finditer(regex, data):\n # Watch out for suffixes like 'Jr.' when they are comma-separated\n # from the name and thus cause issues when *all* names are only\n # separated by commas.\n match_dict = match.groupdict()\n author = match_dict['author']\n if not author.partition(' ')[1] and author.endswith('.'):\n prev_author = author_list.pop()\n author = ', '.join([prev_author, author])\n if u'email' not in match_dict:\n email = ''\n else:\n email = match_dict['email']\n author_list.append((author, email))\n else:\n # If authors were found then stop searching as only expect one\n # style of author citation.\n if author_list:\n break\n author = Author(author_list[0])\n user = author.first_last\n email = author.email\n uid = id_gen[user]\n return (uid, user, email)","def topAuthors():\n c = db.cursor()\n c.execute(\"select name, sum(hits) as hits\\\n from authorhits group by name\\\n order by hits desc;\")\n results = c.fetchall()\n c.close()\n return results","async def read(articleid=True, uid=False):\n\n # Load Settings\n settings = await fetch_settings()\n table = settings[\"table\"]\n\n if uid:\n connection = await connect()\n row = await connection.fetch(f\"\"\"\n SELECT * FROM \"{table}\" WHERE \"UID\" = $1;\n \"\"\", str(uid))\n await connection.close()\n return row\n try:\n articleid = int(articleid)\n except ValueError:\n return []\n connection = await connect()\n rows = await connection.fetch(f\"\"\"\n SELECT * FROM \"{table}\" WHERE \"ID\" = $1;\n \"\"\", articleid)\n await connection.close()\n\n result = []\n for row in rows:\n row_dict = dict(row)\n row_dict[\"dateReleased\"] = row[\"dateReleased\"].replace(year=(row[\"dateReleased\"].year + GAME_YEAR_OFFSET))\n\n result.append(row_dict)\n\n return result","def dictfetchall(cursor):\n desc = cursor.description\n for row in cursor.fetchall():\n yield dict(zip([col[0] for col in desc], row))","def get_books_data():\n entry = mongo.db.Books\n output = list()\n look_up_type = None\n if 'title' in request.args:\n look_up_type = 'title'\n if len(request.args['title']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['title'].strip('\"')\n title = entry.find({'title': {'$regex': value}})\n if title:\n for book in title:\n output.append({'title': book['title']})\n elif 'related_books' in request.args:\n look_up_type = 'similar_books'\n if len(request.args['related_books']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['related_books'].strip('\"')\n related_books = entry.find(\n {'similar_books': {'$regex': value}})\n if related_books:\n for related in related_books:\n for link in related['similar_books']:\n if value in link:\n output.append(({'similar_books': link}))\n elif 'author' in request.args:\n look_up_type = 'author'\n if len(request.args['author']) <= 2:\n return render_template('error.html', message=\"Must enter characters\"), 400\n value = request.args['author'].strip('\"')\n authors = entry.find({'author': {'$regex': value}})\n if authors:\n for name in authors:\n output.append({'author': name['author']})\n if len(output) == 0:\n return render_template('error.html', message=\"No Entries Found\"), 400\n return render_template('gottenBooks.html', output=output, look_up_type=look_up_type), 200","def find_author(author):\n case = author_case(author)\n queries = author_queries(case, author)\n author_uri_set = set([])\n for query in queries:\n result = vivo_sparql_query(query.encode('utf-8'))\n count = len(result['results']['bindings'])\n if count == 1:\n author_uri_set = set([result['results']['bindings'][0]\\\n ['uri']['value']])\n break\n elif count > 1 and count < len(author_uri_set):\n author_uri_set = set([])\n for row in result['results']['bindings']:\n author_uri_set.add(row['uri']['value'])\n return author_uri_set","def list_publications(bib_format=\"dict\"):\n\n def get_bibtex(key, value):\n total_keys = [\n \"title\",\n \"journal\",\n \"volume\",\n \"issue\",\n \"number\",\n \"pages\",\n \"numpages\",\n \"year\",\n \"month\",\n \"publisher\",\n \"url\",\n \"doi\",\n \"issn\",\n ]\n bibtex_str = (\n \"@article{\"\n + key\n + \",\\n\"\n + \" author={\"\n + \" and \".join(value[\"author\"])\n + \"},\\n\"\n )\n for key in total_keys:\n if key in value.keys():\n bibtex_str += \" \" + key + \"={\" + value[key] + \"},\\n\"\n bibtex_str += \"}\\n\"\n return bibtex_str\n\n def get_apa(value):\n apa_str = \" & \".join(value[\"author\"])\n if \"year\" in value.keys():\n apa_str += \" (\" + value[\"year\"] + \"). \"\n if \"title\" in value.keys():\n apa_str += value[\"title\"] + \". \"\n if \"journal\" in value.keys():\n apa_str += value[\"journal\"] + \", \"\n if \"volume\" in value.keys():\n apa_str += value[\"volume\"] + \", \"\n if \"pages\" in value.keys():\n apa_str += value[\"pages\"] + \". \"\n if \"doi\" in value.keys():\n apa_str += \"doi: \" + value[\"doi\"] + \"\\n\"\n return apa_str\n\n publication_dict = s.publication_lst\n if bib_format.lower() == \"dict\":\n return publication_dict\n elif bib_format.lower() == \"bibtex\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_bibtex(key, value)\n return total_str\n elif bib_format.lower() == \"apa\":\n total_str = \"\"\n for pub in publication_dict:\n for key, value in pub.items():\n total_str += get_apa(value)\n return total_str\n else:\n raise ValueError(\"Supported Bibformats are ['dict', 'bibtex', 'apa']\")","def __get_citedby(self):\n if not hasattr(self, 'id_scholarcitedby'):\n self.fill()\n if hasattr(self, 'id_scholarcitedby'):\n url = self._scholarly.URLS('SCHOLARPUB').format(\n requests.utils.quote(self.id_scholarcitedby))\n soup = self._scholarly._get_soup(\n self._scholarly.URLS('HOST').format(url))\n return self._scholarly._search_scholar_soup(soup)\n else:\n return []","def get_coauthors(self):\n # Get number of authors to search for\n res = download(url=self.coauthor_link, accept='json')\n data = loads(res.text)['search-results']\n N = int(data.get('opensearch:totalResults', 0))\n # Store information in namedtuples\n fields = 'surname given_name id areas affiliation_id name city country'\n coauth = namedtuple('Coauthor', fields)\n coauthors = []\n # Iterate over search results in chunks of 25 results\n count = 0\n while count < N:\n params = {'start': count, 'count': 25}\n res = download(url=self.coauthor_link, params=params, accept='json')\n data = loads(res.text)['search-results'].get('entry', [])\n # Extract information for each coauthor\n for entry in data:\n aff = entry.get('affiliation-current', {})\n try:\n areas = [a['$'] for a in entry.get('subject-area', [])]\n except TypeError: # Only one subject area given\n areas = [entry['subject-area']['$']]\n new = coauth(surname=entry['preferred-name']['surname'],\n given_name=entry['preferred-name'].get('given-name'),\n id=entry['dc:identifier'].split(':')[-1],\n areas='; '.join(areas),\n affiliation_id=aff.get('affiliation-id'),\n name=aff.get('affiliation-name'),\n city=aff.get('affiliation-city'),\n country=aff.get('affiliation-country'))\n coauthors.append(new)\n count += 25\n return coauthors","def index_authors(data_file=DATA_FILE_AUTHORS, index_file=INDEX_FILE_AUTHORS):\n context = etree.iterparse(\n gzip.open(data_file),\n tag=\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description\",\n )\n\n indexed = 0\n authors_idx = SqliteDict(index_file, autocommit=False)\n\n log.info(\"Indexing GND authors (~13.6 Million)\")\n\n for event, elem in context:\n if not elem.attrib.get(\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\"):\n continue\n if elem.getparent().tag == \"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}RDF\":\n name = extract_creator(elem)\n elif elem.getparent().tag == \"{http://www.w3.org/2002/07/owl#}sameAs\":\n name = extract_creator(elem)\n else:\n continue\n\n if name is not None:\n creator_id = str(\n elem.attrib.get(\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\")\n ).split(\"http://d-nb.info/gnd/\")[1]\n authors_idx[creator_id] = name\n log.debug(\"Indexing GND Author {} -> {}\".format(creator_id, name))\n\n indexed += 1\n if not indexed % 100000:\n log.info(\"Indexed {:,} authors\".format(indexed))\n\n # It's safe to call clear() here because no descendants will be\n # accessed\n elem.clear()\n # Also eliminate now-empty references from the root node to elem\n for ancestor in elem.xpath(\"ancestor-or-self::*\"):\n while ancestor.getprevious() is not None:\n del ancestor.getparent()[0]\n\n authors_idx.commit()\n log.info(\"Indexed {:,} authors\".format(len(authors_idx)))\n\n del context\n return authors_idx","def get_literature_recids_for_orcid(orcid):\n orcid_object = f'[{{\"schema\": \"ORCID\", \"value\": \"{orcid}\"}}]'\n # this first query is written in a way that can use the index on (json -> ids)\n\n try:\n author_rec_uuid = (\n db.session.query(RecordMetadata.id)\n .filter(\n type_coerce(RecordMetadata.json, JSONB)[\"ids\"].contains(orcid_object)\n )\n .one()\n .id\n )\n except NoResultFound:\n LOGGER.warning(\"No profile is associated with this account!\", orcid=orcid)\n return []\n\n author_record = (\n db.session.query(PersistentIdentifier)\n .filter(\n PersistentIdentifier.object_type == \"rec\",\n PersistentIdentifier.object_uuid == author_rec_uuid,\n PersistentIdentifier.pid_type == \"aut\",\n )\n .one()\n )\n\n author_recid = (\n author_record.pid_value\n if not author_record.is_redirected()\n else InspireRedirect.get_redirect(author_record).pid_value\n )\n\n query = Q(\"match\", authors__curated_relation=True) & Q(\n \"match\", **{\"authors.record.$ref\": author_recid}\n )\n search_by_curated_author = (\n LiteratureSearch()\n .query(\"nested\", path=\"authors\", query=query)\n .params(_source=[\"control_number\"], size=9999)\n )\n\n return [el[\"control_number\"] for el in search_by_curated_author]","def snippets_by_author(request, username):\n user = get_object_or_404(User, username__exact=username)\n return list_detail.object_list(request,\n queryset=Snippet.objects.get_by_author(user.username),\n extra_context={ 'object': user },\n template_name='cab/user_detail.html',\n **base_generic_dict)","def __get_lucene_contents(self, doc_id, en_list):\n contents = [{'field_name': Lucene.FIELDNAME_ID, 'field_value': doc_id, 'field_type': Lucene.FIELDTYPE_ID}]\n for en_id in en_list:\n contents.append({'field_name': \"content\", 'field_value': en_id, 'field_type': Lucene.FIELDTYPE_ID_TV})\n return contents","def load_data(db_handler):\n\n from random import seed\n from random import random\n \n seed(1)\n\n new_notes = []\n\n for i in range(1,10):\n\n new_notes.append({\n\n\n 'title': str(i) + str(random()),\n 'content': 'Lorem ipsum' + str(i),\n 'active': True,\n 'created_by':\"Cristhian\" + str(i),\n 'created_at': date.today(),\n 'edited_at':date.today(),\n \n })\n\n new_notes.append(\n {\n \"active\": False,\n \"content\": \"Jesenia\",\n \"edited_at\": \"2019-10-24\",\n \"title\": \"Jesenia La chica de al lado\",\n \"created_by\": \"Cristhian1\",\n \"created_at\": \"2019-10-24\"\n })\n\n new_notes.append(\n {\n \"active\": False,\n \"title\": \"La vida de los numeros\",\n \"content\": \"Lorem ipsum y los numeros de la muerte\",\n \"edited_at\": \"2019-10-25\",\n \"created_by\": \"Jesenia\",\n \"created_at\": \"2019-10-24\"\n })\n\n Note.insert_many(new_notes).execute()\n\n User(name=\"Cristhian\", email=\"test@gmail.com\",\n password=b'$2b$12$U/QjtHt/j0xRT4r8Hx3fOe93EssM6M0iiUaQJOrTd64RXbxvhw6Ii').save()"],"string":"[\n \"def popAuthors(self):\\r\\n# cur = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\")\\r\\n# res = cur.fetchall()\\r\\n res = self.dbConn.execute(\\\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\\\").fetchall()\\r\\n\\r\\n self.authorList = [formatNameSQL(ln) for ln in res]\\r\\n self.quickAuthors = [ln[\\\"Lastname\\\"].lower() for ln in res]\\r\\n vals = [ln[\\\"PersonID\\\"] for ln in res]\\r\\n \\r\\n self.authorLookup = dict(zip(self.authorList,vals))\",\n \"def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):\\n\\n #sorry bout repeated code to get the tags\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n tagvals = {}\\n for t in tags:\\n try:\\n x = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n tagvals[t] = x\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_author_dict\\n\\n #parse the tags\\n mainauthortag = tagify(parse_tag(tagvals['first_author']))\\n coauthortag = tagify(parse_tag(tagvals['additional_author']))\\n extauthortag = tagify(parse_tag(tagvals['alternative_author_name']))\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"mainauthortag \\\"+mainauthortag)\\n write_message(\\\"coauthortag \\\"+coauthortag)\\n write_message(\\\"extauthortag \\\"+extauthortag)\\n\\n author_cited_in = initial_author_dict\\n if citedbydict:\\n i = 0 #just a counter for debug\\n write_message(\\\"Checking records referred to in new records\\\")\\n for u in updated_redic_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Author ref done \\\"+str(i)+\\\" of \\\"+str(len(updated_redic_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n if citedbydict.has_key(u):\\n these_cite_k = citedbydict[u]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n authors = get_fieldvalues(u, mainauthortag)\\n coauthl = get_fieldvalues(u, coauthortag)\\n extauthl = get_fieldvalues(u, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author ref done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n #go through the dictionary again: all keys but search only if new records are cited\\n write_message(\\\"Checking authors in new records\\\")\\n i = 0\\n for k in citedbydict.keys():\\n if (i % 1000 == 0):\\n mesg = \\\"Author cit done \\\"+str(i)+\\\" of \\\"+str(len(citedbydict.keys()))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i + 1\\n\\n these_cite_k = citedbydict[k]\\n if (these_cite_k is None):\\n these_cite_k = [] #verify it is an empty list, not None\\n #do things only if these_cite_k contains any new stuff\\n intersec_list = list(set(these_cite_k)&set(updated_redic_list))\\n if intersec_list:\\n authors = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authors.extend(coauthl)\\n authors.extend(extauthl)\\n for a in authors:\\n if a and author_cited_in.has_key(a):\\n #add all elements in these_cite_k\\n #that are not there already\\n for citer in these_cite_k:\\n tmplist = author_cited_in[a]\\n if (tmplist.count(citer) == 0):\\n tmplist.append(citer)\\n author_cited_in[a] = tmplist\\n else:\\n author_cited_in[a] = these_cite_k\\n\\n mesg = \\\"Author cit done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return author_cited_in\",\n \"def extra_bibparse(db):\\n for key,entry in db.entries.items():\\n for auth in entry.persons[\\\"author\\\"]:\\n if (\\\"Harrison\\\" not in auth.first_names or\\n \\\"Chapman\\\" not in auth.last_names):\\n entry.add_person(auth, \\\"otherauthor\\\")\",\n \"def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,\\n selfdic, authorcitdic):\\n insert_into_cit_db(reference_dic,\\\"reversedict\\\")\\n insert_into_cit_db(citation_dic,\\\"citationdict\\\")\\n insert_into_cit_db(selfcbdic,\\\"selfcitedbydict\\\")\\n insert_into_cit_db(selfdic,\\\"selfcitdict\\\")\\n\\n for a in authorcitdic.keys():\\n lserarr = (serialize_via_marshal(authorcitdic[a]))\\n #author name: replace \\\" with something else\\n a.replace('\\\"', '\\\\'')\\n a = unicode(a, 'utf-8')\\n try:\\n ablob = run_sql(\\\"select hitlist from rnkAUTHORDATA where aterm = %s\\\", (a,))\\n if not (ablob):\\n #print \\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\" , (a,lserarr)\\n run_sql(\\\"insert into rnkAUTHORDATA(aterm,hitlist) values (%s,%s)\\\",\\n (a,lserarr))\\n else:\\n #print \\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\"\\\"\\\" , (lserarr,a)\\n run_sql(\\\"UPDATE rnkAUTHORDATA SET hitlist = %s where aterm=%s\\\",\\n (lserarr,a))\\n except:\\n register_exception(prefix=\\\"could not read/write rnkAUTHORDATA aterm=\\\"+a+\\\" hitlist=\\\"+str(lserarr), alert_admin=True)\",\n \"def load_authors():\\n\\n ret = {}\\n for token in util.git('log', '--format=%aE:::%aN').split('\\\\n'):\\n email, name = token.split(':::')\\n ret[email] = name\\n return ret\",\n \"def get_publications_by_author(cached_list, cached_set, author_name):\\n publications = { 'dblp': [], 'cdblp': [] }\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n\\n if author['dblp'].__contains__('publications'):\\n publications['dblp'] = author['dblp']['publications']\\n# for pub in author['dblp']['publications']:\\n# print(pub)\\n\\n if author['cdblp'].__contains__('publications'):\\n publications['cdblp'] = author['cdblp']['publications']\\n# for pub in author['cdblp']['publications']:\\n# print(pub)\\n return publications\",\n \"def get_author_data():\\n entry = mongo.db.Authors\\n output = list()\\n look_up_type = None\\n if 'name' in request.args:\\n look_up_type = 'name'\\n print(request.args)\\n if len(request.args['name']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['name'].strip('\\\"')\\n name = entry.find({'name': {'$regex': value}})\\n if name:\\n for author in name:\\n output.append({'name': author['name']})\\n elif 'booktitle' in request.args:\\n look_up_type = 'related_books'\\n if len(request.args['booktitle']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['booktitle'].strip('\\\"')\\n related_books = entry.find(\\n {'author_books': {'$regex': value}})\\n if related_books:\\n for related in related_books:\\n for title in related['author_books']:\\n if value in title:\\n output.append(({'related_books': title}))\\n if len(output) == 0:\\n return render_template('error.html', message=\\\"No Entries Found\\\"), 400\\n return render_template('gottenAuthors.html', output=output, look_up_type=look_up_type), 200\",\n \"def getPopualrAuthors():\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(\\\" select count(*) as views , authors.name from articles \\\"\\n + \\\" inner join \\\"\\n + \\\"log on concat('/article/', articles.slug) = log.path \\\"\\n + \\\" inner join authors on articles.author = authors.id \\\"\\n + \\\"group by name order by views desc; \\\")\\n authors = c.fetchall()\\n db.close()\\n return authors\",\n \"def get_user_and_text(db):\\n con = lite.connect(db)\\n with con:\\n cur = con.cursor()\\n cur.execute(\\\"SELECT author, GROUP_CONCAT(text, ' ') FROM Comments GROUP BY author\\\")\\n user_text_list = cur.fetchall() \\n return user_text_list\",\n \"def get_coauthors_by_author(cached_list, cached_set, author_name):\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n coauthors = {}\\n if author['dblp'].__contains__('coauthors'):\\n for author_key in author['dblp']['coauthors']:\\n coauthors[author_key] = { 'en': author_key, 'zh': '' }\\n\\n if author['cdblp'].__contains__('coauthors'):\\n for author_key in author['cdblp']['coauthors']:\\n if coauthors.__contains__(author_key['full_name']):\\n coauthors[author_key['full_name']]['zh'] = author_key['zh']\\n else:\\n coauthors[author_key['full_name']] = { 'en': author_key['full_name'], 'zh': author_key['zh'] }\\n\\n return coauthors\",\n \"def question_2():\\n cursor.execute(mostPopAuthors)\\n output = cursor.fetchall()\\n return output\",\n \"def getAuthors(self): #$NON-NLS-1$\\r\",\n \"def get_authors(self, blogid=1):\\n return self.execute('wp.getAuthors', blogid, self.username, self.password)\",\n \"def _get_authors_list():\\n\\n articles = os.listdir(\\\"../data/\\\")\\n authors = []\\n for article in articles:\\n with open(\\\"../data/\\\" + article, 'r') as file:\\n lines = file.readlines()\\n author = tuple(\\n line.replace(\\\"\\\\n\\\", \\\"\\\").split()[1] for line in lines\\n if \\\"Автор:\\\" in line\\n )[0]\\n authors.append(author)\\n\\n return authors\",\n \"def get_author_info_from_people_collection(info):\\n # TODO: probably we will need to extract this somewhere else\\n URL = ('https://cds.cern.ch/submit/get_authors?'\\n 'query={0}&relative_curdir=cdslabs%2Fvideos')\\n if '0' in info or not info.get('a'):\\n # There is already enough information or we don't have a name to query\\n return info\\n author_name = info.get('a')\\n if PY2:\\n # In Python 3, encoded name will change type to bytes and this will\\n # cause query to CDS to fail\\n author_name = author_name.encode('utf-8')\\n author_info = _get_http_request(url=URL.format(author_name), retry=10)\\n if not author_info or len(author_info) > 1:\\n # Didn't find anything or find to many matches\\n return info\\n\\n # Prepare author name\\n author_info = author_info[0]\\n if 'name' not in author_info:\\n author_info['name'] = '{0}, {1}'.format(author_info['lastname'],\\n author_info['firstname'])\\n return MementoDict([\\n (k, v) for k, v in chain(info.items(), iteritems(author_info))])\",\n \"def get_authors(config: Config) -> dict:\\n\\n file_handle = load_csv_file(config.input)\\n\\n start = time.time()\\n raw_authors = get_raw_authors(config, file_handle)\\n end = time.time()\\n print(f\\\"get_raw_authors took {end - start} seconds\\\")\\n\\n start = time.time()\\n sanitized_authors = sanitize_authors(config, raw_authors)\\n end = time.time()\\n print(f\\\"sanitize_authors took {end - start} seconds\\\")\\n\\n return sanitized_authors\",\n \"def load_authors(keys: [str]):\\n not_in_local_cache = []\\n result = []\\n for key in keys:\\n try:\\n result.append(_author_data_cache[key].to_dict())\\n del _author_data_cache[key]\\n except KeyError:\\n not_in_local_cache.append(key)\\n \\n if len(not_in_local_cache):\\n doc_refs = [db.collection(AUTHOR_CACHE_COLLECTION).document(key)\\n for key in keys]\\n data = db.get_all(doc_refs)\\n for datum in data:\\n if not datum.exists:\\n raise cache_buddy.CacheMiss(datum.id)\\n result.append(datum.to_dict())\\n return [_decompress_record(r) for r in result]\",\n \"def get_popular_authors():\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n query_popular_authors = \\\"\\\"\\\"\\n SELECT aut.name, COUNT(lg.id) AS views\\n FROM articles AS art\\n JOIN log AS lg ON art.slug = SUBSTRING(lg.path,10)\\n AND lg.status = '200 OK'\\n JOIN authors AS aut ON aut.id = art.author\\n GROUP BY aut.name\\n ORDER BY views desc; \\\"\\\"\\\"\\n c.execute(query_popular_authors)\\n authors = from_db_cursor(c)\\n db.close()\\n return authors\",\n \"def AuthorURLs(entry):\\n a_URLs = ''\\n for a in entry.getAuthors():\\n url = a.get('homepage', ' ')\\n a_URLs += \\\"%s and \\\" % url\\n return a_URLs[:-5]\",\n \"def read_csv(self, filename: str):\\n author_field_index = 0\\n authors = []\\n author_data = []\\n with open(filename, 'r') as csvfile:\\n csvreader = csv.reader(csvfile)\\n fields = next(csvreader)\\n for index, field in enumerate(fields):\\n if field.lower() == \\\"author\\\":\\n author_field_index = index\\n break\\n for row in csvreader:\\n authors.append(row[author_field_index])\\n for index, author in enumerate(authors):\\n if author:\\n data = self.get_author_info(author)\\n author_data.append((author, data))\\n if index % 10 == 0: # every 10 entries we write the data to json in case we get a crash\\n self._reference.update_maps_jsons()\\n self._reference.update_maps_jsons()\\n return author_data\",\n \"def make_author_list(res):\\n try:\\n r = [\\\", \\\".join([clean_txt(x['family']).capitalize(), clean_txt(x['given']).capitalize()]) for x in res['author']]\\n except KeyError as e:\\n print(\\\"No 'author' key, using 'Unknown Author'. You should edit the markdown file to change the name and citationkey.\\\")\\n r = [\\\"Unknown Authors\\\"]\\n return r\",\n \"def get_titles_dict(name_url_list):\\n\\tbib_dict = {}\\n\\t\\n\\tfor name_url in name_url_list:\\n\\t\\ttitles = get_author_titles(name_url[1])\\n\\t\\tbib_dict[name_url] = titles\\n\\n\\treturn bib_dict\",\n \"def to_internal_value(self, data):\\n authors = []\\n for author in data:\\n path = urlparse(author).path\\n resolved_func, __, resolved_kwargs = resolve(path)\\n person = resolved_func.cls.queryset.get(pk=resolved_kwargs['pk'])\\n authors.append(person)\\n\\n return {'authors': authors}\",\n \"def get_author_by_id(self, id_num):\\n\\n cur = self.conn.cursor()\\n query = 'SELECT author_id , name FROM author WHERE author_id = ?'\\n cur.execute(query, (id_num,))\\n return row_to_dict_or_false(cur)\",\n \"def get_single_book_info(self, isbn):\\n self.cursor.execute(\\\"SELECT * FROM book WHERE ISBN=%s\\\", (isbn,))\\n books = self.cursor.fetchall()\\n for book in books:\\n authors = []\\n self.cursor.execute(\\\"\\\"\\\"SELECT name FROM Author A, Wrote W, Book B WHERE A.ID = W.authorID AND\\n W.ISBN = B.ISBN AND B.ISBN = %s\\\"\\\"\\\", (isbn,))\\n for auth in self.cursor.fetchall():\\n authors.append(auth[0])\\n return book, authors\",\n \"def authors(self):\\n return self.properties.get('Authors', ClientValueCollection(SharedWithMeDocumentUser))\",\n \"def _authors(fname):\\n if not os.path.exists(fname):\\n raise RuntimeError(\\\"File {} not found\\\".format(fname))\\n with open(fname, \\\"r\\\") as obj:\\n lines = [_tostr(item.strip()) for item in obj.readlines()]\\n regexp = re.compile(r\\\"(?:\\\\s*\\\\*\\\\s+)?(.*)\\\\s+<(.*)>.*\\\")\\n for line in lines:\\n match = regexp.match(line)\\n if match:\\n name, email = match.groups()\\n yield name, email\",\n \"def test_get_authors_from_data(self):\\n responses.add(\\n responses.GET,\\n \\\"https://openlibrary.org/authors/OL382982A\\\",\\n json={\\n \\\"name\\\": \\\"George Elliott\\\",\\n \\\"personal_name\\\": \\\"George Elliott\\\",\\n \\\"last_modified\\\": {\\n \\\"type\\\": \\\"/type/datetime\\\",\\n \\\"value\\\": \\\"2008-08-31 10:09:33.413686\\\",\\n },\\n \\\"remote_ids\\\": {\\n \\\"isni\\\": \\\"000111\\\",\\n },\\n \\\"key\\\": \\\"/authors/OL453734A\\\",\\n \\\"type\\\": {\\\"key\\\": \\\"/type/author\\\"},\\n \\\"id\\\": 1259965,\\n \\\"revision\\\": 2,\\n },\\n status=200,\\n )\\n results = self.connector.get_authors_from_data(self.work_data)\\n result = list(results)[0]\\n self.assertIsInstance(result, models.Author)\\n self.assertEqual(result.name, \\\"George Elliott\\\")\\n self.assertEqual(result.openlibrary_key, \\\"OL453734A\\\")\\n self.assertEqual(result.isni, \\\"000111\\\")\",\n \"def get_author_detail(body):\\n user_id = get_user_id(body) # user id\\n author_details = body.find('div', {'id': 'gsc_prf_i'})\\n\\n name = author_details.find('div', attrs={'id': 'gsc_prf_in'}).text\\n name_attributes = author_details.find_all('div', attrs={'class': 'gsc_prf_il'})\\n\\n affiliation = name_attributes[0].text or ''\\n interests = name_attributes[1].text or ''\\n email = name_attributes[2].text or ''\\n\\n citation_indices = body.find('table', attrs={'id': 'gsc_rsb_st'})\\n\\n year_since_text = citation_indices.find_all('tr')[0]\\n year_since = re.sub('Since ', '', year_since_text.find_all('th')[-1].text)\\n\\n # citations\\n citations = citation_indices.find_all('tr')[1]\\n h_index = citation_indices.find_all('tr')[2]\\n h_index_all = h_index.find_all('td')[1].text\\n h_index_since = h_index.find_all('td')[2].text\\n citation_all = citations.find_all('td')[1].text\\n citation_since = citations.find_all('td')[2].text\\n\\n author_dict = {'user_id': user_id,\\n 'name': name,\\n 'affiliation': affiliation,\\n 'interests': interests,\\n 'email': email,\\n 'h_index_all': h_index_all,\\n 'h_index_since': h_index_since,\\n 'citation_all': citation_all,\\n 'citation_since': citation_since,\\n 'year_since': year_since}\\n return author_dict\",\n \"def Authors(self, default=[{}]):\\n tmp = self.data.get('metadata', {}).get('authors', default)\\n return [HEP.AuthorObject(i) for i in tmp]\",\n \"def addAuthor2():\\n\\n author_list = list()\\n\\n authors = Author.objects.all()\\n\\n for author in authors:\\n author_dict = dict()\\n author_dict['id'] = \\\"{}/api/author/{}\\\".format(DOMAIN, author.id)\\n author_dict['host'] = \\\"{}/api/\\\".format(author.host_url)\\n author_dict['displayName'] = author.username\\n author_dict['url'] = \\\"{}/api/author/{}\\\".format(DOMAIN, author.id)\\n\\n author_list.append(author_dict)\\n\\n return author_list\",\n \"def Authors(self, default=[{}]):\\n tmp = self.data.get('authors', default)\\n return [HEP.AuthorReducedObject(i) for i in tmp]\",\n \"def authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n item = build_ils_contributor(value)\\n if item and item not in _authors:\\n _authors.append(item)\\n try:\\n if \\\"u\\\" in value:\\n other = [\\\"et al.\\\", \\\"et al\\\"]\\n val_u = list(force_list(value.get(\\\"u\\\")))\\n if [i for i in other if i in val_u]:\\n self[\\\"other_authors\\\"] = True\\n except UnexpectedValue:\\n pass\\n return _authors\",\n \"def parse_bib_from_list(filename):\\n\\tentry_regex = r\\\"TITEL: .*\\\\s*AUTOR: .*\\\"\\n\\tparse_func = make_parse_func(r\\\"AUTOR: (.*)\\\", r\\\"TITEL: (.*)\\\", None)\\n\\treturn parse_bib(filename, entry_regex, parse_func)\",\n \"def authorSign(g = None):\\n if not g:\\n return\\n authors = g.find(\\\"Author\\\")\\n ret = dict()\\n for author in authors:\\n ret[author['name']] = author['citation'].replace('\\\\n','').lower()\\n return ret\",\n \"def get_coauthored_publications_by_authors(cached_list, cached_set, author1_name, author2_name):\\n publications = { 'cdblp': [], 'dblp': [] }\\n pub1 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author1_name)\\n author2 = DBLPQuery.author_distinct(cached_list, cached_set, author2_name)\\n #pub2 = DBLPQuery.get_publications_by_author(cached_list, cached_set, author2_name)\\n for cdblp_pub in pub1.get('cdblp', []):\\n authors = set(cdblp_pub.get('authors', []))\\n authors_en = set(map(lambda a: CDBLPAuthor.getEnglishName(a)['full_name'], authors))\\n if author2.get('cdblp', {}).get('author_name', {}).get('zh') in authors or author2.get('dblp', {}).get('author_name') in authors_en:\\n publications['cdblp'].append(cdblp_pub)\\n\\n for dblp_pub in pub1.get('dblp', []):\\n authors = set(map(lambda a: a.get('name'), dblp_pub.get('authors', [])))\\n if author2.get('dblp', {}).get('author_name') in authors or author2.get('cdblp', {}).get('author_name', {}).get('full_name') in authors:\\n publications['dblp'].append(dblp_pub)\\n\\n return publications\",\n \"def get_author_data(authors):\\n\\n try:\\n author = authors.author.name.cdata.encode(\\\"utf8\\\")\\n author_id = int(authors.author.id.cdata.encode(\\\"utf8\\\"))\\n except: # FIXME: running into errors when book has multiple authors\\n author = authors.author[0].cdata.encode(\\\"utf8\\\")\\n author_id = authors.author[0].cdata.encode(\\\"utf8\\\")\\n\\n return (author, author_id)\",\n \"def find_authors(code):\\n url = baseurl(code)\\n page = req(url)\\n soup = BeautifulSoup(page, 'lxml')\\n addr = [t.attrs.get('content', None) \\n for t in soup.find_all(\\\"meta\\\", {\\\"name\\\": \\\"citation_author_email\\\"})]\\n \\n # corresponding authors will have their email under another tag too\\n corr = [t.find('a').attrs.get('href', None)\\n for t in soup.find_all(None, {\\\"class\\\": \\\"author-corresp-email-link\\\"})]\\n\\n addr = [a for a in addr if a is not None]\\n corr = [a.replace('mailto:', '') for a in corr if a is not None]\\n\\n return dict(corr=list(set(corr)), all=list(set(addr)))\",\n \"def get_coauthors_dict(name_url_list, schl_name):\\n\\n\\tpaper_info_dict = {}\\n\\n\\tfor name_url in name_url_list:\\n\\t\\t# Get a sub papers_info dictfor an individual author\\n\\t\\tpapers_info = get_papers_info(name_url[1], paper_info_dict.keys())\\n\\t\\t# add sub dict to full dict\\n\\t\\tpaper_info_dict.update(papers_info)\\n\\t\\n\\tif \\\"Humanities\\\" in schl_name:\\n\\t\\tschl_name = \\\"School of Humanities\\\"\\n\\n\\treturn paper_info_dict\",\n \"def get_authors(self):\\n return [aer.author for aer in self.authorentryrank_set.all()]\",\n \"def find_relations_among_authors():\\n for book in books:\\n if len(books[book]) > 1:\\n for i in range(len(books[book])):\\n known_relations[books[book][i]] = books[book][:i] + books[book][i+1:]\",\n \"def read_authors(filename):\\n with Path(filename).open() as file:\\n info = json.load(file)\\n authors = []\\n for author in info['creators']:\\n name = ' '.join(author['name'].split(',')[::-1]).strip()\\n authors.append(name)\\n return ', '.join(authors)\",\n \"def get_author_info(self, author: str):\\n for writer_word in self._writer_words:\\n data = json.loads(requests.get(WIKIDATA_SEARCH + \\\"&srsearch=\\\" + author + \\\" \\\" + writer_word).text)\\n pages = data.get(\\\"query\\\").get(\\\"search\\\")\\n if len(pages) >= 1:\\n pageid = pages[0].get(\\\"title\\\")\\n author_details = self._reference.author_map.get(author)\\n if author_details:\\n return author_details\\n if pageid == -1:\\n continue\\n\\n else:\\n response = requests.get(WIKIDATA_PARSE + pageid + \\\".json\\\")\\n data = json.loads(response.text)\\n if author.lower() not in data.get(\\\"entities\\\").get(pageid).get(\\\"labels\\\").get(\\\"en\\\").get(\\\"value\\\").lower():\\n continue\\n else:\\n try:\\n id = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\").get(\\\"P31\\\")[0].get(\\\"mainsnak\\\").get(\\\"datavalue\\\").get(\\\"value\\\").get(\\\"id\\\")\\n if str(id) != \\\"Q5\\\": # the id for human\\n continue\\n except IndexError:\\n continue\\n properties = data.get(\\\"entities\\\").get(pageid).get(\\\"claims\\\")\\n author_details = {\\\"id\\\": pageid, \\\"gender\\\": self.get_gender(properties)}\\n country_details = self.get_country(properties)\\n author_details[\\\"country\\\"] = country_details\\n self._reference.author_map[author] = author_details\\n return author_details\\n return {\\\"id\\\": \\\"Unknown\\\", \\\"gender\\\": \\\"Unknown\\\", \\\"country\\\": [{\\\"name\\\": \\\"Unknown\\\", \\\"region\\\": \\\"Unknown\\\"}]}\",\n \"def find_all(cls):\\n return [AuthorModel(a['name'], str(a['_id']))\\n for a in cls.db.newsdb.find()]\",\n \"def get_author_info(path):\\n author_df = pandas.read_table(path)\\n\\n # Detect missing full name\\n missing_full_name = author_df[author_df['full_name'].isnull()].index.tolist()\\n if missing_full_name:\\n msg = f'Missing full name in authors.tsv for author(s): {missing_full_name}'\\n raise ValueError(msg)\\n\\n # Detect missing author initials\\n missing_initials = author_df[author_df['initials'].isnull()].index.tolist()\\n if missing_initials:\\n msg = f'Missing initials in authors.tsv for author(s): {missing_initials}'\\n raise ValueError(msg)\\n\\n # Detect duplicate author initials\\n duplicated_initials = list(\\n author_df[author_df.duplicated('initials')].initials)\\n if duplicated_initials:\\n msg = f'Duplicated initials in authors.tsv: {duplicated_initials}'\\n raise ValueError(msg)\\n\\n # Replace missing values with None\\n author_df.where(pandas.notnull(author_df), None, inplace=True)\\n\\n authors = author_df.to_dict(orient='records')\\n return authors\",\n \"def read_from_db():\\n\\t# prepare the query for reading from DB\\n\\tquery = \\\"SELECT * FROM tasks\\\"\\n\\n\\t# connection to database\\n\\tconnection = pymysql.connect(user=\\\"root\\\", password=\\\"sysadmin\\\", host=\\\"localhost\\\", database=\\\"todolist\\\")\\n\\n\\t# get a cursor\\n\\tcursor = connection.cursor()\\n\\n\\t# execute query\\n\\tcursor.execute(query)\\n\\n\\t# fetch result from query\\n\\tresults = cursor.fetchall()\\n\\n\\t# close cursor and connection\\n\\tcursor.close()\\n\\tconnection.close()\\n\\n\\ttask_list = list()\\n\\tfor result in results:\\n\\t\\ttmp = {'id': result[0], 'description': result[1], 'urgent': result[2]}\\n\\t\\ttask_list.append(tmp)\\n\\n\\treturn task_list\",\n \"def read_contacts(contacts_file, uidlist, used_uid_names):\\n # Read in out values to a list\\n lines = read_contacts_to_list(contacts_file)\\n\\n # Convert our list to dict\\n contactsdict = contacts_from_list_to_dict(lines, uidlist, used_uid_names)\\n\\n #logging.debug(\\\"Aliasdict: %s\\\" % aliasdict)\\n return contactsdict\",\n \"def get_authors(draft):\\n authors = []\\n for a in draft.authors.all():\\n initial = ''\\n prefix, first, middle, last, suffix = a.person.name_parts()\\n if first:\\n initial = first + '. '\\n entry = '%s%s <%s>' % (initial,last,a.address)\\n authors.append(entry)\\n return authors\",\n \"def main(repo: Repository) -> dict:\\n data = {}\\n for commit in repo.traverse_commits():\\n\\n if not author_is_bot(commit.author):\\n if commit.author.name in data:\\n data[commit.author.name] = data[commit.author.name] + commit.lines\\n else:\\n data[commit.author.name] = commit.lines\\n\\n # Sort authors name in the dict\\n data = dict(sorted(data.items(), key=lambda x: x[0].lower()))\\n return data\",\n \"def get_author_by_name(self, name):\\n\\n cur = self.conn.cursor()\\n query = 'SELECT author_id , name FROM author WHERE name = ? '\\n cur.execute(query, (name,))\\n return row_to_dict_or_false(cur)\",\n \"def lookups(self, request, model_admin):\\n # lambda article: article.author # 接受一个article，返回对应作者对象\\n # map(func, iterable) # 将可迭代对象的每个元素传入func\\n # author_list 所有文章的作者列表，去重\\n author_list = list(set(map(lambda article: article.author, Article.objects.all())))\\n\\n # author_list2 = []\\n # for author in author_list:\\n # author_list2.append((author.id, author.nickname or author.username))\\n # return author_list2\\n\\n for author in author_list: # verbose value, 在过滤器里显示的名称\\n yield (author.id, (author.nickname or author.username))\",\n \"def citing_authors(self, entities):\\n result = self.db.execute(u'''SELECT DISTINCT(author_id)\\n FROM \\\"entity_occurrences\\\"\\n WHERE entity IN ({}) AND rho > ?'''.format(join_entities_sql(entities)), (DEFAULT_MIN_SCORE,)).fetchall()\\n return [t[0] for t in result]\",\n \"def read_relations(db, openfile):\\n pass\",\n \"def get_autor_with_papers(author_id):\\n print(\\\"getting information from dblp about author {}\\\".format(author_id))\\n data = get(\\\"https://dblp.org/pid/\\\" + author_id + \\\".xml\\\")[\\\"dblpperson\\\"]\\n author_id = author_id.replace(\\\"/\\\", \\\"_\\\")\\n author = {\\n \\\"name\\\": data[\\\"@name\\\"],\\n \\\"papers\\\": [get_paper(paper_container) for paper_container in data[\\\"r\\\"]],\\n }\\n\\n with open(local.file_name(\\\"authors\\\", author_id), \\\"w\\\") as out:\\n json.dump(author, out, ensure_ascii=False, indent=4)\",\n \"def get_authors():\\n authors = []\\n authorfile = os.path.join('doc', 'authors.txt')\\n with codecs.open(authorfile, 'r', 'utf-8') as f:\\n for line in f:\\n line = line.strip()\\n if line and not line.startswith(u'#'):\\n authors.append(line)\\n return u\\\", \\\".join(authors)\",\n \"def extract(self, data):\\n\\n # Read authors from the cell with NAME and COLLABORATORS variables\\n authors = []\\n for cell in data['cells']:\\n if cell['cell_type'] != 'code':\\n continue\\n\\n src = cell['source']\\n if src and src[0].startswith('NAME'):\\n authors = [*self._extract_name(src, 'NAME'),\\n *self._extract_name(src, 'COLLABORATORS')]\\n break\\n\\n return [x for x in authors if x.name]\",\n \"def articleAuthorsByYear(g = None, year = None):\\n if not g:\\n return\\n articles = g.find(\\\"Article\\\", property_key='year', property_value=str(year))\\n ret = dict()\\n for article in articles:\\n ret[article['title']] = article['authors'].replace('\\\\n','').lower()\\n return ret\",\n \"def collect_citation_metadata(\\n metadata: dict, references: List[pybtex.database.Entry]\\n) -> dict:\\n # Author list\\n citation_authors = []\\n for author_tier in [\\\"Core\\\", \\\"Developers\\\", \\\"Contributors\\\"]:\\n for author in metadata[\\\"Authors\\\"][author_tier][\\\"List\\\"]:\\n family_names, given_names = author[\\\"Name\\\"].split(\\\", \\\")\\n citation_author = {\\n \\\"family-names\\\": family_names,\\n \\\"given-names\\\": given_names,\\n }\\n if \\\"Orcid\\\" in author:\\n citation_author[\\\"orcid\\\"] = (\\n \\\"https://orcid.org/\\\" + author[\\\"Orcid\\\"]\\n )\\n if \\\"Affiliations\\\" in author and len(author[\\\"Affiliations\\\"]) > 0:\\n citation_author[\\\"affiliation\\\"] = \\\" and \\\".join(\\n author[\\\"Affiliations\\\"]\\n )\\n citation_authors.append(citation_author)\\n # References in CITATION.cff format\\n citation_references = [to_cff_reference(entry) for entry in references]\\n return {\\n \\\"cff-version\\\": \\\"1.2.0\\\",\\n \\\"message\\\": (\\n \\\"Please cite SpECTRE in any publications that make use of its code\\\"\\n \\\" or data. Cite the latest version that you use in your\\\"\\n \\\" publication. The citation for this version is listed below.\\\"\\n ),\\n \\\"title\\\": metadata[\\\"Name\\\"],\\n \\\"url\\\": metadata[\\\"Homepage\\\"],\\n \\\"repository-code\\\": \\\"https://github.com/\\\" + metadata[\\\"GitHub\\\"],\\n \\\"version\\\": metadata[\\\"Version\\\"],\\n \\\"date-released\\\": metadata[\\\"PublicationDate\\\"],\\n \\\"doi\\\": metadata[\\\"Doi\\\"],\\n \\\"authors\\\": citation_authors,\\n \\\"keywords\\\": metadata[\\\"Keywords\\\"],\\n \\\"license\\\": metadata[\\\"License\\\"],\\n \\\"references\\\": citation_references,\\n }\",\n \"def top_authors():\\n\\n cur.execute(\\\"\\\"\\\"\\n SELECT author, count(*) AS article_author\\n FROM article_summary\\n GROUP BY author\\n ORDER BY article_author DESC;\\n \\\"\\\"\\\")\\n result = cur.fetchall()\\n return result\",\n \"def extractionTitlePrincipals(cur, conn):\\n fh = open(pathTitlePrincipals)\\n reader = csv.reader(fh, delimiter = '\\\\t')\\n firstLine = True\\n idActor_list = []\\n idJugar = 1\\n for row in reader:\\n if firstLine : firstLine = False # Read header\\n else :\\n if (row[3]=='actor' or row[3]=='actress'): #only record actors\\n idTitulo = int(row[0][2:])\\n idActor = int(row[2][2:])\\n idActor_list.append(idActor)\\n idJugar +=1\\n # print(jugarInsert.format(idJugar, idTitulo, idActor))\\n # REGISTER DATA IN JUGAR TABLE\\n cur.execute(jugarInsert.format(idJugar, idTitulo, idActor))\\n conn.commit()\\n return idActor_list\",\n \"def get_authors_from_papers(papers):\\n auth_set = set()\\n for p in papers:\\n auth_set.update(p['authors'])\\n return list(auth_set)\",\n \"def author_articles(self):\\n return ArticlePage.objects.live().filter(author=self).order_by('-date')\",\n \"def author_dictionary (corpus_token_path, correct_author_path):\\n token_files = os.listdir(corpus_token_path)\\n correct_author = read_file(correct_author_path).split(\\\"\\\\n\\\")\\n author_name_token_dict = {}\\n for i in range(0, correct_author.__len__()):\\n if correct_author[i] not in author_name_token_dict.keys():\\n author_name_token_dict[correct_author[i]] = [token_files[i]]\\n #print(author_name_token_dict[french_correct_author[i]])\\n else:\\n existing_token_files = author_name_token_dict[correct_author[i]]\\n if existing_token_files is not None:\\n author_name_token_dict[correct_author[i]].append(token_files[i])\\n return author_name_token_dict\",\n \"def alt_authors(self, key, value):\\n _authors = self.get(\\\"authors\\\", [])\\n if _authors:\\n for i, v in enumerate(force_list(value)):\\n _authors[i].update({\\\"alternative_names\\\": clean_val(\\\"a\\\", v, str)})\\n return _authors\",\n \"def cursor_data(c):\\r\\n\\r\\n # pull column description\\r\\n d = []\\r\\n for i in range(len(c.description)):\\r\\n d.append(c.description[i][0])\\r\\n\\r\\n # fetch column entries\\r\\n c = c.fetchall()\\r\\n\\r\\n # compile list\\r\\n info = []\\r\\n for i in range(len(c)):\\r\\n # compile dictionary entry\\r\\n entry = {}\\r\\n for j in range(len(d)):\\r\\n entry[d[j]] = c[i][j]\\r\\n info.append(entry)\\t\\r\\n\\r\\n # success\\r\\n return info\",\n \"def parse_bibtex(self, data: str) -> Dict:\\n\\n new_bib = [line for line in data.splitlines() if \\\"= ,\\\" not in line]\\n new_bib = \\\"\\\\n\\\".join(new_bib)\\n bib_db: bibtexparser.bibdatabase.BibDatabase = bibtexparser.loads(new_bib)\\n result = dict()\\n for entry in bib_db.entries:\\n osti_id = entry[\\\"ID\\\"].split(\\\"_\\\")[1]\\n result[osti_id] = entry\\n return result\",\n \"def query_authors(cls):\\n authors = from_cache('AuthorsList')\\n if not authors:\\n authors = SuiAuthor.all().order('name').fetch(400)\\n to_cache('AuthorsList', authors)\\n return authors\",\n \"def test_citedby_author(self):\\n inv_search = 'citedby:author:doggy'\\n spi_search = 'find citedby author doggy'\\n self._compare_searches(inv_search, spi_search)\",\n \"def all_authors( data ) :\\n return list(set( chain.from_iterable( [ authors(x) for x in data ] ) ))\",\n \"def get_venues_by_author(cached_list, cached_set, author_name):\\n author = DBLPQuery.author_distinct(cached_list, cached_set, author_name)\\n venues = {}\\n\\n if author['dblp'].__contains__('publications'):\\n for pub in author['dblp']['publications']:\\n if venues.__contains__(pub['venue']):\\n venues[pub['venue']]['count'] += 1\\n else:\\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\\n\\n if author['cdblp'].__contains__('publications'):\\n for pub in author['cdblp']['publications']:\\n if venues.__contains__(pub['venue']):\\n venues[pub['venue']]['count'] += 1\\n else:\\n venues[pub['venue']] = { 'type': pub['venue-type'], 'count': 1 }\\n\\n return venues\",\n \"def authors(self):\\n authors = [\\n n.people for n in self.pymbake_person_relationship.all()\\n ]\\n\\n return authors\",\n \"def getAuthors(self):\\n authors = []\\n for each in self.context.getAuthors():\\n title = each['title']\\n firstname = each['firstname']\\n middlename = each['middlename']\\n lastname = each['lastname']\\n author = Author(title, firstname, middlename, lastname)\\n authors.append(author)\\n return authors\",\n \"def getAuthor(self):\\n\\t\\tself.authorList = [submission.author for submission in self.subreddit.top(time_filter = 'day', limit = self.limits)]\\n\\t\\treturn self.authorList\",\n \"def data_pull(database_file, query):\\n \\n user_tweets = {}\\n conn = sqlite3.connect(database_file)\\n conn.row_factory = sqlite3.Row\\n \\n for row in conn.cursor().execute(query):\\n if row['text'] is not None:\\n data = tweetclean.cleanup(row['text'], True, True)\\n try:\\n user_tweets[row['owner']].append(data)\\n except KeyError:\\n user_tweets[row['owner']] = []\\n user_tweets[row['owner']].append(data)\\n\\n conn.close()\\n\\n return user_tweets\",\n \"def get_popular_authors():\\n query_command = \\\"SELECT * from popular authors LIMIT 3\\\"\\n query_data = run_query(query_command)\\n return query_data\",\n \"def make_citation_authors(res):\\n if \\\"author\\\" in res.keys():\\n first_author = res['author'][0]['family'] + \\\", \\\" + res['author'][0]['given']\\n last_author = res['author'][-1]['given'] + \\\" \\\" + res['author'][-1]['family']\\n middle_authors = \\\", \\\".join(\\\" \\\".join([x['given'], x['family']]) for x in res['author'][1:-1])\\n #assemble authors\\n author_string = first_author\\n author_string = author_string + \\\", \\\" + middle_authors if middle_authors != '' else author_string\\n author_string = author_string + \\\", and \\\" + last_author if len(res['author']) > 1 else author_string\\n \\n author_string = author_string + \\\".\\\" if author_string[-1] != \\\".\\\" else author_string\\n else:\\n author_string = \\\"Unknown Authors\\\"\\n\\n return clean_txt(author_string.capitalize())\",\n \"def fetch(id_list):\\n database = 'pmc' if 'PMC' in id_list[0][0] else 'pubmed'\\n print(database)\\n for sublist in id_list:\\n with Entrez.efetch(db=database, id=','.join(sublist),\\n retmode='xml') as handle:\\n records = Entrez.read(handle)\\n\\n write_csv_file(records, output_file)\",\n \"def get_file_contents(db_cursor):\\n\\n db_cursor.execute(\\\"\\\"\\\"SELECT * FROM data\\\"\\\"\\\")\\n db_rows = db_cursor.fetchall()\\n return {row[0]: row[1] for row in db_rows if row != []}\",\n \"def execute_author_query():\\n db = psycopg2.connect(database=\\\"news\\\")\\n c = db.cursor()\\n c.execute(author_query)\\n content = c.fetchall()\\n db.close()\\n print(author_query_question)\\n for views, author in content:\\n print('%s -- %d views' % (author, views))\\n print('\\\\n')\",\n \"def read_db():\\n f_result = []\\n result = execute_query('select sitename, id from {} order by sitename;'.format(TABLES[0]))\\n sites = [(x['sitename'], x['id']) for x in result]\\n for sitename, site_id in sites:\\n sitedict = {'name': sitename}\\n querystring = 'select settname, settval from {} order by settname where site_id = %s;'\\n result = execute_query(querystring.format(TABLES[1]), (site_id,))\\n sitedict['settings'] = {x: y for x, y in cur.fetchall()}\\n querystring = 'select dirname, id from {} order by dirname where site_id = %s;'\\n result = execute_query(querystring.format(TABLES[2]), (site_id,))\\n sitedirs = [(x['dirname'], x['id']) for x in cur.fetchall()]\\n sitedict['docs'] = []\\n # if we keep the site_id in the docstats table we could restrict this to one db-query\\n # and filter the result set inside the loop\\n # although this should also be possible with a subselect or something like that\\n for dirname, dir_id in sitedirs:\\n dirlist = []\\n querystring = 'select * from {} order by docname where dir_id = %s;'\\n result = execute_query(querystring.format(TABLES[3]), (dir_id,))\\n for resultdict in cur:\\n resultdict['dirname'] = dirname\\n dirlist.append(resultdict)\\n sitedict['docs'].append(dirlist)\\n f_result.append(sitedict)\\n return f_result\",\n \"def get_paper_authors(tree):\\n\\tpath = '//table/tr/th[text() = \\\"Glasgow Author(s) Enlighten ID:\\\"]/following-sibling::td/a'\\n\\t# Get list of <a> elements, each an author\\n\\tauthors = tree.xpath(path)\\n\\t# Make list of (author name, author url) pairs to return\\n\\tauthors = [(author.text, author.get(\\\"href\\\")) for author in authors]\\n\\n\\treturn authors\",\n \"def _to_dict(self):\\n\\t\\tauthor = Author.query.get(self.authors_id)\\n\\t\\treturn {'id': self.id,\\n\\t\\t\\t\\t'name': self.name,\\n\\t\\t\\t\\t'author_id': self.authors_id,\\n\\t\\t\\t\\t'author_name': author.name,\\n\\t\\t\\t\\t'author_surname': author.surname}\",\n \"def get_or_build_author_index(index_file=INDEX_FILE_AUTHORS):\\n return (\\n SqliteDict(index_file, flag=\\\"r\\\")\\n if os.path.exists(INDEX_FILE_AUTHORS)\\n else index_authors()\\n )\",\n \"def query_by_author(cls,author):\\n bakey = 'BookByAuthor_%s'%author\\n bks = from_cache(bakey)\\n if not bks:\\n bks = map(lambda e:str(e.id()), SuiBook.all(keys_only=True).filter('authors =',author).fetch(100))\\n to_cache(bakey,bks)\\n return bks\",\n \"def corporate_authors(self, key, value):\\n _corporate_authors = self.get(\\\"authors\\\", [])\\n\\n for v in force_list(value):\\n if key == \\\"710__\\\":\\n if \\\"a\\\" in v:\\n _corporate_authors.append(\\n {\\n \\\"full_name\\\": clean_val(\\\"a\\\", v, str),\\n \\\"type\\\": \\\"ORGANISATION\\\",\\n }\\n )\\n else:\\n self[\\\"authors\\\"] = collaborations(self, key, value)\\n raise IgnoreKey(\\\"corporate_authors\\\")\\n else:\\n _corporate_authors.append(\\n {\\\"full_name\\\": clean_val(\\\"a\\\", v, str), \\\"type\\\": \\\"ORGANISATION\\\"}\\n )\\n return _corporate_authors\",\n \"def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):\\n i = 0 #just for debugging ..\\n #get the tags for main author, coauthors, ext authors from config\\n tags = ['first_author', 'additional_author', 'alternative_author_name']\\n for t in tags:\\n try:\\n dummy = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), t)\\n except:\\n register_exception(prefix=\\\"attribute \\\"+t+\\\" missing in config\\\", alert_admin=True)\\n return initial_selfcitdict\\n\\n r_mainauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"first_author\\\")\\n r_coauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"additional_author\\\")\\n r_extauthortag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"), \\\"alternative_author_name\\\")\\n #parse the tags\\n mainauthortag = tagify(parse_tag(r_mainauthortag))\\n coauthortag = tagify(parse_tag(r_coauthortag))\\n extauthortag = tagify(parse_tag(r_extauthortag))\\n\\n selfcites = initial_selfcitdict\\n for k in new_record_list:\\n if (i % 1000 == 0):\\n mesg = \\\"Selfcites done \\\"+str(i)+\\\" of \\\"+str(len(new_record_list))+\\\" records\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n i = i+1\\n #get the author of k\\n authorlist = get_fieldvalues(k, mainauthortag)\\n coauthl = get_fieldvalues(k, coauthortag)\\n extauthl = get_fieldvalues(k, extauthortag)\\n authorlist.append(coauthl)\\n authorlist.append(extauthl)\\n #author tag\\n #print \\\"record \\\"+str(k)+\\\" by \\\"+str(authorlist)\\n #print \\\"is cited by\\\"\\n #get the \\\"x-cites-this\\\" list\\n if citationdic.has_key(k):\\n xct = citationdic[k]\\n for c in xct:\\n #get authors of c\\n cauthorlist = get_fieldvalues(c, mainauthortag)\\n coauthl = get_fieldvalues(c, coauthortag)\\n extauthl = get_fieldvalues(c, extauthortag)\\n cauthorlist.extend(coauthl)\\n cauthorlist.extend(extauthl)\\n #print str(c)+\\\" by \\\"+str(cauthorlist)\\n for ca in cauthorlist:\\n if (ca in authorlist):\\n #found!\\n if selfcites.has_key(k):\\n val = selfcites[k]\\n #add only if not there already\\n if val:\\n if not c in val:\\n val.append(c)\\n selfcites[k] = val\\n else:\\n #new key for selfcites\\n selfcites[k] = [c]\\n\\n mesg = \\\"Selfcites done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n return selfcites\",\n \"def _parse_mercurial_author(data, id_gen):\\n angled = ur'(?P<author>.+?) <(?P<email>.+?)>'\\n paren = ur'(?P<email>.+?) \\\\((?P<author>.+?)\\\\)'\\n simple = ur'(?P<author>[^,]+)'\\n author_list = []\\n for regex in (angled, paren, simple):\\n # Watch out for commas separating multiple names.\\n regex += u'(,\\\\s*)?'\\n for match in re.finditer(regex, data):\\n # Watch out for suffixes like 'Jr.' when they are comma-separated\\n # from the name and thus cause issues when *all* names are only\\n # separated by commas.\\n match_dict = match.groupdict()\\n author = match_dict['author']\\n if not author.partition(' ')[1] and author.endswith('.'):\\n prev_author = author_list.pop()\\n author = ', '.join([prev_author, author])\\n if u'email' not in match_dict:\\n email = ''\\n else:\\n email = match_dict['email']\\n author_list.append((author, email))\\n else:\\n # If authors were found then stop searching as only expect one\\n # style of author citation.\\n if author_list:\\n break\\n author = Author(author_list[0])\\n user = author.first_last\\n email = author.email\\n uid = id_gen[user]\\n return (uid, user, email)\",\n \"def topAuthors():\\n c = db.cursor()\\n c.execute(\\\"select name, sum(hits) as hits\\\\\\n from authorhits group by name\\\\\\n order by hits desc;\\\")\\n results = c.fetchall()\\n c.close()\\n return results\",\n \"async def read(articleid=True, uid=False):\\n\\n # Load Settings\\n settings = await fetch_settings()\\n table = settings[\\\"table\\\"]\\n\\n if uid:\\n connection = await connect()\\n row = await connection.fetch(f\\\"\\\"\\\"\\n SELECT * FROM \\\"{table}\\\" WHERE \\\"UID\\\" = $1;\\n \\\"\\\"\\\", str(uid))\\n await connection.close()\\n return row\\n try:\\n articleid = int(articleid)\\n except ValueError:\\n return []\\n connection = await connect()\\n rows = await connection.fetch(f\\\"\\\"\\\"\\n SELECT * FROM \\\"{table}\\\" WHERE \\\"ID\\\" = $1;\\n \\\"\\\"\\\", articleid)\\n await connection.close()\\n\\n result = []\\n for row in rows:\\n row_dict = dict(row)\\n row_dict[\\\"dateReleased\\\"] = row[\\\"dateReleased\\\"].replace(year=(row[\\\"dateReleased\\\"].year + GAME_YEAR_OFFSET))\\n\\n result.append(row_dict)\\n\\n return result\",\n \"def dictfetchall(cursor):\\n desc = cursor.description\\n for row in cursor.fetchall():\\n yield dict(zip([col[0] for col in desc], row))\",\n \"def get_books_data():\\n entry = mongo.db.Books\\n output = list()\\n look_up_type = None\\n if 'title' in request.args:\\n look_up_type = 'title'\\n if len(request.args['title']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['title'].strip('\\\"')\\n title = entry.find({'title': {'$regex': value}})\\n if title:\\n for book in title:\\n output.append({'title': book['title']})\\n elif 'related_books' in request.args:\\n look_up_type = 'similar_books'\\n if len(request.args['related_books']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['related_books'].strip('\\\"')\\n related_books = entry.find(\\n {'similar_books': {'$regex': value}})\\n if related_books:\\n for related in related_books:\\n for link in related['similar_books']:\\n if value in link:\\n output.append(({'similar_books': link}))\\n elif 'author' in request.args:\\n look_up_type = 'author'\\n if len(request.args['author']) <= 2:\\n return render_template('error.html', message=\\\"Must enter characters\\\"), 400\\n value = request.args['author'].strip('\\\"')\\n authors = entry.find({'author': {'$regex': value}})\\n if authors:\\n for name in authors:\\n output.append({'author': name['author']})\\n if len(output) == 0:\\n return render_template('error.html', message=\\\"No Entries Found\\\"), 400\\n return render_template('gottenBooks.html', output=output, look_up_type=look_up_type), 200\",\n \"def find_author(author):\\n case = author_case(author)\\n queries = author_queries(case, author)\\n author_uri_set = set([])\\n for query in queries:\\n result = vivo_sparql_query(query.encode('utf-8'))\\n count = len(result['results']['bindings'])\\n if count == 1:\\n author_uri_set = set([result['results']['bindings'][0]\\\\\\n ['uri']['value']])\\n break\\n elif count > 1 and count < len(author_uri_set):\\n author_uri_set = set([])\\n for row in result['results']['bindings']:\\n author_uri_set.add(row['uri']['value'])\\n return author_uri_set\",\n \"def list_publications(bib_format=\\\"dict\\\"):\\n\\n def get_bibtex(key, value):\\n total_keys = [\\n \\\"title\\\",\\n \\\"journal\\\",\\n \\\"volume\\\",\\n \\\"issue\\\",\\n \\\"number\\\",\\n \\\"pages\\\",\\n \\\"numpages\\\",\\n \\\"year\\\",\\n \\\"month\\\",\\n \\\"publisher\\\",\\n \\\"url\\\",\\n \\\"doi\\\",\\n \\\"issn\\\",\\n ]\\n bibtex_str = (\\n \\\"@article{\\\"\\n + key\\n + \\\",\\\\n\\\"\\n + \\\" author={\\\"\\n + \\\" and \\\".join(value[\\\"author\\\"])\\n + \\\"},\\\\n\\\"\\n )\\n for key in total_keys:\\n if key in value.keys():\\n bibtex_str += \\\" \\\" + key + \\\"={\\\" + value[key] + \\\"},\\\\n\\\"\\n bibtex_str += \\\"}\\\\n\\\"\\n return bibtex_str\\n\\n def get_apa(value):\\n apa_str = \\\" & \\\".join(value[\\\"author\\\"])\\n if \\\"year\\\" in value.keys():\\n apa_str += \\\" (\\\" + value[\\\"year\\\"] + \\\"). \\\"\\n if \\\"title\\\" in value.keys():\\n apa_str += value[\\\"title\\\"] + \\\". \\\"\\n if \\\"journal\\\" in value.keys():\\n apa_str += value[\\\"journal\\\"] + \\\", \\\"\\n if \\\"volume\\\" in value.keys():\\n apa_str += value[\\\"volume\\\"] + \\\", \\\"\\n if \\\"pages\\\" in value.keys():\\n apa_str += value[\\\"pages\\\"] + \\\". \\\"\\n if \\\"doi\\\" in value.keys():\\n apa_str += \\\"doi: \\\" + value[\\\"doi\\\"] + \\\"\\\\n\\\"\\n return apa_str\\n\\n publication_dict = s.publication_lst\\n if bib_format.lower() == \\\"dict\\\":\\n return publication_dict\\n elif bib_format.lower() == \\\"bibtex\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_bibtex(key, value)\\n return total_str\\n elif bib_format.lower() == \\\"apa\\\":\\n total_str = \\\"\\\"\\n for pub in publication_dict:\\n for key, value in pub.items():\\n total_str += get_apa(value)\\n return total_str\\n else:\\n raise ValueError(\\\"Supported Bibformats are ['dict', 'bibtex', 'apa']\\\")\",\n \"def __get_citedby(self):\\n if not hasattr(self, 'id_scholarcitedby'):\\n self.fill()\\n if hasattr(self, 'id_scholarcitedby'):\\n url = self._scholarly.URLS('SCHOLARPUB').format(\\n requests.utils.quote(self.id_scholarcitedby))\\n soup = self._scholarly._get_soup(\\n self._scholarly.URLS('HOST').format(url))\\n return self._scholarly._search_scholar_soup(soup)\\n else:\\n return []\",\n \"def get_coauthors(self):\\n # Get number of authors to search for\\n res = download(url=self.coauthor_link, accept='json')\\n data = loads(res.text)['search-results']\\n N = int(data.get('opensearch:totalResults', 0))\\n # Store information in namedtuples\\n fields = 'surname given_name id areas affiliation_id name city country'\\n coauth = namedtuple('Coauthor', fields)\\n coauthors = []\\n # Iterate over search results in chunks of 25 results\\n count = 0\\n while count < N:\\n params = {'start': count, 'count': 25}\\n res = download(url=self.coauthor_link, params=params, accept='json')\\n data = loads(res.text)['search-results'].get('entry', [])\\n # Extract information for each coauthor\\n for entry in data:\\n aff = entry.get('affiliation-current', {})\\n try:\\n areas = [a['$'] for a in entry.get('subject-area', [])]\\n except TypeError: # Only one subject area given\\n areas = [entry['subject-area']['$']]\\n new = coauth(surname=entry['preferred-name']['surname'],\\n given_name=entry['preferred-name'].get('given-name'),\\n id=entry['dc:identifier'].split(':')[-1],\\n areas='; '.join(areas),\\n affiliation_id=aff.get('affiliation-id'),\\n name=aff.get('affiliation-name'),\\n city=aff.get('affiliation-city'),\\n country=aff.get('affiliation-country'))\\n coauthors.append(new)\\n count += 25\\n return coauthors\",\n \"def index_authors(data_file=DATA_FILE_AUTHORS, index_file=INDEX_FILE_AUTHORS):\\n context = etree.iterparse(\\n gzip.open(data_file),\\n tag=\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}Description\\\",\\n )\\n\\n indexed = 0\\n authors_idx = SqliteDict(index_file, autocommit=False)\\n\\n log.info(\\\"Indexing GND authors (~13.6 Million)\\\")\\n\\n for event, elem in context:\\n if not elem.attrib.get(\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\\\"):\\n continue\\n if elem.getparent().tag == \\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}RDF\\\":\\n name = extract_creator(elem)\\n elif elem.getparent().tag == \\\"{http://www.w3.org/2002/07/owl#}sameAs\\\":\\n name = extract_creator(elem)\\n else:\\n continue\\n\\n if name is not None:\\n creator_id = str(\\n elem.attrib.get(\\\"{http://www.w3.org/1999/02/22-rdf-syntax-ns#}about\\\")\\n ).split(\\\"http://d-nb.info/gnd/\\\")[1]\\n authors_idx[creator_id] = name\\n log.debug(\\\"Indexing GND Author {} -> {}\\\".format(creator_id, name))\\n\\n indexed += 1\\n if not indexed % 100000:\\n log.info(\\\"Indexed {:,} authors\\\".format(indexed))\\n\\n # It's safe to call clear() here because no descendants will be\\n # accessed\\n elem.clear()\\n # Also eliminate now-empty references from the root node to elem\\n for ancestor in elem.xpath(\\\"ancestor-or-self::*\\\"):\\n while ancestor.getprevious() is not None:\\n del ancestor.getparent()[0]\\n\\n authors_idx.commit()\\n log.info(\\\"Indexed {:,} authors\\\".format(len(authors_idx)))\\n\\n del context\\n return authors_idx\",\n \"def get_literature_recids_for_orcid(orcid):\\n orcid_object = f'[{{\\\"schema\\\": \\\"ORCID\\\", \\\"value\\\": \\\"{orcid}\\\"}}]'\\n # this first query is written in a way that can use the index on (json -> ids)\\n\\n try:\\n author_rec_uuid = (\\n db.session.query(RecordMetadata.id)\\n .filter(\\n type_coerce(RecordMetadata.json, JSONB)[\\\"ids\\\"].contains(orcid_object)\\n )\\n .one()\\n .id\\n )\\n except NoResultFound:\\n LOGGER.warning(\\\"No profile is associated with this account!\\\", orcid=orcid)\\n return []\\n\\n author_record = (\\n db.session.query(PersistentIdentifier)\\n .filter(\\n PersistentIdentifier.object_type == \\\"rec\\\",\\n PersistentIdentifier.object_uuid == author_rec_uuid,\\n PersistentIdentifier.pid_type == \\\"aut\\\",\\n )\\n .one()\\n )\\n\\n author_recid = (\\n author_record.pid_value\\n if not author_record.is_redirected()\\n else InspireRedirect.get_redirect(author_record).pid_value\\n )\\n\\n query = Q(\\\"match\\\", authors__curated_relation=True) & Q(\\n \\\"match\\\", **{\\\"authors.record.$ref\\\": author_recid}\\n )\\n search_by_curated_author = (\\n LiteratureSearch()\\n .query(\\\"nested\\\", path=\\\"authors\\\", query=query)\\n .params(_source=[\\\"control_number\\\"], size=9999)\\n )\\n\\n return [el[\\\"control_number\\\"] for el in search_by_curated_author]\",\n \"def snippets_by_author(request, username):\\n user = get_object_or_404(User, username__exact=username)\\n return list_detail.object_list(request,\\n queryset=Snippet.objects.get_by_author(user.username),\\n extra_context={ 'object': user },\\n template_name='cab/user_detail.html',\\n **base_generic_dict)\",\n \"def __get_lucene_contents(self, doc_id, en_list):\\n contents = [{'field_name': Lucene.FIELDNAME_ID, 'field_value': doc_id, 'field_type': Lucene.FIELDTYPE_ID}]\\n for en_id in en_list:\\n contents.append({'field_name': \\\"content\\\", 'field_value': en_id, 'field_type': Lucene.FIELDTYPE_ID_TV})\\n return contents\",\n \"def load_data(db_handler):\\n\\n from random import seed\\n from random import random\\n \\n seed(1)\\n\\n new_notes = []\\n\\n for i in range(1,10):\\n\\n new_notes.append({\\n\\n\\n 'title': str(i) + str(random()),\\n 'content': 'Lorem ipsum' + str(i),\\n 'active': True,\\n 'created_by':\\\"Cristhian\\\" + str(i),\\n 'created_at': date.today(),\\n 'edited_at':date.today(),\\n \\n })\\n\\n new_notes.append(\\n {\\n \\\"active\\\": False,\\n \\\"content\\\": \\\"Jesenia\\\",\\n \\\"edited_at\\\": \\\"2019-10-24\\\",\\n \\\"title\\\": \\\"Jesenia La chica de al lado\\\",\\n \\\"created_by\\\": \\\"Cristhian1\\\",\\n \\\"created_at\\\": \\\"2019-10-24\\\"\\n })\\n\\n new_notes.append(\\n {\\n \\\"active\\\": False,\\n \\\"title\\\": \\\"La vida de los numeros\\\",\\n \\\"content\\\": \\\"Lorem ipsum y los numeros de la muerte\\\",\\n \\\"edited_at\\\": \\\"2019-10-25\\\",\\n \\\"created_by\\\": \\\"Jesenia\\\",\\n \\\"created_at\\\": \\\"2019-10-24\\\"\\n })\\n\\n Note.insert_many(new_notes).execute()\\n\\n User(name=\\\"Cristhian\\\", email=\\\"test@gmail.com\\\",\\n password=b'$2b$12$U/QjtHt/j0xRT4r8Hx3fOe93EssM6M0iiUaQJOrTd64RXbxvhw6Ii').save()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62463015","0.61490846","0.61244714","0.60036224","0.5967608","0.5937393","0.59214526","0.58278465","0.57877076","0.5739218","0.56823266","0.5649391","0.5615111","0.5582629","0.5582394","0.55806595","0.5542368","0.551917","0.5470549","0.5453344","0.5435176","0.5432365","0.5414574","0.53997076","0.53978777","0.53861994","0.5372701","0.5366946","0.53258675","0.5311399","0.53101903","0.5302941","0.529319","0.52887505","0.5266527","0.52628285","0.52497107","0.5246471","0.52446574","0.52446353","0.52440333","0.5238713","0.52339256","0.5232671","0.5204903","0.52026","0.51914126","0.51908284","0.5185219","0.5167659","0.515703","0.5140143","0.5127338","0.5124398","0.51240766","0.5118328","0.5115767","0.511545","0.5105279","0.51038086","0.5101111","0.50977844","0.5092864","0.5089185","0.508003","0.5070661","0.5053782","0.5049724","0.50467247","0.50365275","0.50363886","0.5035784","0.50209486","0.5013677","0.49991554","0.4980367","0.49722776","0.49720022","0.4966969","0.49443915","0.4932374","0.4932212","0.4925887","0.49208412","0.49193412","0.4912571","0.49047154","0.48935384","0.48926255","0.48911768","0.4879393","0.4877735","0.48644525","0.48425284","0.48364353","0.48348942","0.4832574","0.4826707","0.4824028","0.48147863"],"string":"[\n \"0.62463015\",\n \"0.61490846\",\n \"0.61244714\",\n \"0.60036224\",\n \"0.5967608\",\n \"0.5937393\",\n \"0.59214526\",\n \"0.58278465\",\n \"0.57877076\",\n \"0.5739218\",\n \"0.56823266\",\n \"0.5649391\",\n \"0.5615111\",\n \"0.5582629\",\n \"0.5582394\",\n \"0.55806595\",\n \"0.5542368\",\n \"0.551917\",\n \"0.5470549\",\n \"0.5453344\",\n \"0.5435176\",\n \"0.5432365\",\n \"0.5414574\",\n \"0.53997076\",\n \"0.53978777\",\n \"0.53861994\",\n \"0.5372701\",\n \"0.5366946\",\n \"0.53258675\",\n \"0.5311399\",\n \"0.53101903\",\n \"0.5302941\",\n \"0.529319\",\n \"0.52887505\",\n \"0.5266527\",\n \"0.52628285\",\n \"0.52497107\",\n \"0.5246471\",\n \"0.52446574\",\n \"0.52446353\",\n \"0.52440333\",\n \"0.5238713\",\n \"0.52339256\",\n \"0.5232671\",\n \"0.5204903\",\n \"0.52026\",\n \"0.51914126\",\n \"0.51908284\",\n \"0.5185219\",\n \"0.5167659\",\n \"0.515703\",\n \"0.5140143\",\n \"0.5127338\",\n \"0.5124398\",\n \"0.51240766\",\n \"0.5118328\",\n \"0.5115767\",\n \"0.511545\",\n \"0.5105279\",\n \"0.51038086\",\n \"0.5101111\",\n \"0.50977844\",\n \"0.5092864\",\n \"0.5089185\",\n \"0.508003\",\n \"0.5070661\",\n \"0.5053782\",\n \"0.5049724\",\n \"0.50467247\",\n \"0.50365275\",\n \"0.50363886\",\n \"0.5035784\",\n \"0.50209486\",\n \"0.5013677\",\n \"0.49991554\",\n \"0.4980367\",\n \"0.49722776\",\n \"0.49720022\",\n \"0.4966969\",\n \"0.49443915\",\n \"0.4932374\",\n \"0.4932212\",\n \"0.4925887\",\n \"0.49208412\",\n \"0.49193412\",\n \"0.4912571\",\n \"0.49047154\",\n \"0.48935384\",\n \"0.48926255\",\n \"0.48911768\",\n \"0.4879393\",\n \"0.4877735\",\n \"0.48644525\",\n \"0.48425284\",\n \"0.48364353\",\n \"0.48348942\",\n \"0.4832574\",\n \"0.4826707\",\n \"0.4824028\",\n \"0.48147863\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62780523"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79594,"cells":{"query":{"kind":"string","value":"put the referingrecordnumpublicationstring into the \"we are missing these\" table"},"document":{"kind":"string","value":"def insert_into_missing(recid, report):\n report.replace('\"','\\'')\n try:\n srecid = str(recid)\n wasalready = run_sql(\"select id_bibrec from rnkCITATIONDATAEXT where id_bibrec = %s and extcitepubinfo = %s\",\n (srecid,report))\n if not wasalready:\n run_sql(\"insert into rnkCITATIONDATAEXT(id_bibrec, extcitepubinfo) values (%s,%s)\",\n (srecid, report))\n except:\n #we should complain but it can result to million lines of warnings so just pass..\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _add_NR(self, w2, row):\n row['NR'] = None\n return True","def addMissingData():\n\n conn = sqlite3.connect(\"./transactions.db\")\n\n person = pd.read_sql(\n \"\"\"\n select * from person;\n \"\"\",\n conn,\n )\n\n record = pd.read_sql(\n \"\"\"\n select * from record;\n \"\"\",\n conn,\n )\n\n tracked = set([_id for _id in record[\"doc_id\"]])\n\n untracked = []\n\n for url, _id in zip(person[\"url\"], person[\"doc_id\"]):\n if not _id in tracked:\n untracked.append((url[-17:-13], _id))\n\n untracked = pd.DataFrame(untracked, columns=[\"date\", \"doc_id\"])\n\n res = transaction.extractData(untracked)\n\n res.to_sql(\"record\", conn, index=False, if_exists=\"append\")\n\n conn.close()","def _exceptions_lists_new_tblname(self):\n return 'listgen_temp_{0}_new_exceptions_lists'.format(self._run_id)","def get_mt_usr_invalid_link(self):\n schema = self.district.format_schema()\n\n rdf_nav_link = '%s.rdf_nav_link' % schema\n rdf_link = '%s.rdf_link' % schema\n rdf_nav_strand = '%s.rdf_nav_strand' % schema\n usr_node_link = '%s.usr_node_link' % schema\n adas_node_curvature = '%s.adas_node_curvature' % schema\n adas_node_slope = '%s.adas_node_slope' % schema\n rdf_sign_origin = '%s.rdf_sign_origin' % schema\n rdf_sign_destination = '%s.rdf_sign_destination' % schema\n\n sqls = []\n sqls.append('SELECT link_id FROM %s' % rdf_nav_strand)\n sqls.append('SELECT nav_link_id as link_id FROM %s WHERE nav_link_id IS NOT NULL' % usr_node_link)\n sqls.append('SELECT from_link_id AS link_id FROM %s' % adas_node_curvature)\n sqls.append('SELECT to_link_id AS link_id FROM %s' % adas_node_curvature)\n sqls.append('SELECT to_link_id AS link_id FROM %s' % adas_node_slope)\n sqls.append('SELECT originating_link_id AS link_id FROM %s' % rdf_sign_origin)\n sqls.append('SELECT dest_link_id AS link_id FROM %s' % rdf_sign_destination)\n\n\n table = '%s.usr_invalid_link' % schema\n mt = SqlMeta(table)\n mt.add('CREATE TABLE %s AS (SELECT link_id FROM %s INTERSECT (%s) EXCEPT SELECT link_id FROM %s) ' %(table, rdf_link, ' UNION '.join(sqls), rdf_nav_link))\n mt.add(self._pk_sql(table, 'link_id'))\n\n mt.ref(rdf_nav_link)\n mt.ref(rdf_link)\n mt.ref(rdf_nav_strand)\n mt.ref(usr_node_link)\n mt.ref(adas_node_curvature)\n mt.ref(adas_node_slope)\n mt.ref(rdf_sign_origin)\n mt.ref(rdf_sign_destination)\n\n return mt","def get_uid(new_data, metadata, conn):\n for awe in new_data[\"all_wkn_entry\"]:\n for key in [\"wkn\",\"isin\"]:\n if awe[key] == \"\":continue\n if key == \"wkn\":\n s = select([metadata.tables[\"WKN\"]]).where(metadata.tables[\"WKN\"].c.WKN == awe[key]).order_by(\n metadata.tables[\"WKN\"].c.unternehmenId.desc())\n else:\n s = select([metadata.tables[\"WKN\"]]).where(metadata.tables[\"WKN\"].c.ISIN == awe[key]).order_by(\n metadata.tables[\"WKN\"].c.unternehmenId.desc())\n result = conn.execute(s)\n try:\n someid = result.fetchone()[0]\n except:\n new_data['reference'] = new_data['unternehmenId']\n new_data['id'] = \"\"\n return 0\n s = select([metadata.tables['MainRelation']]).where(\n metadata.tables['MainRelation'].c.referenz == someid)\n result = conn.execute(s)\n fone = result.fetchall()\n if len(fone) > 0:\n for row in fone:\n new_data['reference'] = row[0]\n new_data['id'] = row[4]\n return 0\n\n s = select([metadata.tables['MainRelation']]).where(\n metadata.tables['MainRelation'].c.weiteresAuftreten == someid)\n result = conn.execute(s)\n fone = result.fetchall()\n if len(fone) > 0:\n for row in fone:\n new_data['reference'] = row[0]\n new_data['id'] = row[4]\n return 0\n new_data['reference'] = new_data['unternehmenId']\n new_data['id'] = \"\"\n return 0\n #TODO-HINT: Be aware its order is descendent by year to avoid apply new WKN's to old ones which get used in early years!","def _CreateRecordId(self):\n self._record_count += 1\n return '%s_%s' % (self._unique_id, self._record_count)","def delexicaliseReferenceNumber(sent, turn):\n domains = ['restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'] # , 'police']\n if turn['metadata']:\n for domain in domains:\n if turn['metadata'][domain]['book']['booked']:\n for slot in turn['metadata'][domain]['book']['booked'][0]:\n if slot == 'reference':\n val = '[' + domain + '_' + slot + ']'\n else:\n val = '[' + domain + '_' + slot + ']'\n key = normalize(turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n\n # try reference with hashtag\n key = normalize(\"#\" + turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n\n # try reference with ref#\n key = normalize(\"ref#\" + turn['metadata'][domain]['book']['booked'][0][slot])\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\n return sent","def _exceptions_lists_new_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._exceptions_lists_new_tblname, operator_id)","def _exceptions_lists_old_tblname(self):\n return 'listgen_temp_{0}_old_exceptions_lists'.format(self._run_id)","def _processMissBrokenLinks(self, target):\n\n xrdDocs = self._collsXRDMiss[target].find({'storage.location': 'XRD',\n 'target': target,\n 'issue': 'brokenLink'})\n\n self._collsXRDNoLink[target].insert_many(xrdDocs)\n\n self._collsXRDMiss[target].delete_many({'storage.location': 'XRD',\n 'target': target,\n 'issue': 'brokenLink'})","def _exceptions_lists_old_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._exceptions_lists_old_tblname, operator_id)","def get_cypher_xref_tuned_text():\n return \"\"\"\n MERGE (id:CrossReference:Identifier {primaryKey:row.primaryKey})\n ON CREATE SET id.name = row.id,\n id.globalCrossRefId = row.globalCrossRefId,\n id.localId = row.localId,\n id.crossRefCompleteUrl = row.crossRefCompleteUrl,\n id.prefix = row.prefix,\n id.crossRefType = row.crossRefType,\n id.uuid = row.uuid,\n id.page = row.page,\n id.primaryKey = row.primaryKey,\n id.displayName = row.displayName\"\"\"","def dummy_junction13():\n return 'junction:chr1:176-299:+'","def create_rel_doctitle_dict():\n claim_rel_docno_dict = {} #key is claim text, value is a set of doc_title that are relevant\n clm_sen_doc_title_dict = read_pickle(\"sen_doc_title_dict\")\n claim_sen_true_relevance_dict = read_pickle(\"claim_sen_relevance_dict_\"+curr_source)\n exclude = set(string.punctuation)\n docID_title_mapping_wiki_pickle = read_pickle(\"dicID_title_mapping_wiki_pickle\")\n \n title_docID_mapping_wiki_pickle = {}\n for (docID,doc_title) in docID_title_mapping_wiki_pickle.iteritems():\n non_asci_char = [c for c in doc_title if not 0 < ord(c) < 127]\n new_doc_title = doc_title\n for c in non_asci_char:\n new_doc_title = new_doc_title.replace(c,\"\")\n doc_title_no_punc = ''.join(ch for ch in new_doc_title if ch not in exclude)\n doc_title_no_space = doc_title_no_punc.replace(\" \",\"\")\n title_docID_mapping_wiki_pickle[doc_title_no_space] = docID\n# title_docID_mapping_wiki_pickle = dict((y,x) for x,y in docID_title_mapping_wiki_pickle.iteritems()) \n for (clm) in claim_sen_true_relevance_dict.keys():\n rel_docno_set = set()\n for (sen,rel_score) in claim_sen_true_relevance_dict[clm]:\n try: \n if rel_score == 1:\n sen_no_punc = ''.join(ch for ch in sen if ch not in exclude)\n sen_no_space = sen_no_punc.replace(\" \",\"\")\n curr_rel_doc_title = clm_sen_doc_title_dict[sen_no_space]\n non_asci_char = [c for c in curr_rel_doc_title if not 0 < ord(c) < 127]\n new_curr_doc_title = curr_rel_doc_title\n for c in non_asci_char:\n new_curr_doc_title = new_curr_doc_title.replace(c,\"\")\n curr_doc_title_no_punc = ''.join(ch for ch in new_curr_doc_title if ch not in exclude)\n curr_doc_title_no_space = curr_doc_title_no_punc.replace(\" \",\"\")\n rel_docno_set.add((title_docID_mapping_wiki_pickle[curr_doc_title_no_space],1))\n \n except Exception as err: \n sys.stderr.write('problem in sen:'+sen) \n print err.args\n \n rel_docno_list = [(docid,rel_score) for (docid,rel_score) in rel_docno_set]\n claim_rel_docno_dict[clm] = rel_docno_list\n save_pickle(\"claim_rel_docno_dict\", claim_rel_docno_dict)","def _fix_chainID(self):\n\n from string import ascii_uppercase\n\n # get the current names\n data = self.get('chainID')\n natom = len(data)\n\n #get uniques\n chainID = []\n for c in data:\n if c not in chainID:\n chainID.append(c)\n\n if chainID == ['A','B']:\n return\n\n if len(chainID)>26:\n print(\"Warning more than 26 chains have been detected. This is so far not supported\")\n sys.exit()\n\n # declare the new names\n newID = [''] * natom\n\n # fill in the new names\n for ic,chain in enumerate(chainID):\n index = self.get('rowID',chainID=chain)\n for ind in index:\n newID[ind] = ascii_uppercase[ic]\n\n # update the new name\n self.update_column('chainID',newID)","def fix_duplicate_field_names(self,names):\n used = []\n new_names = []\n for name in names:\n if name not in used:\n new_names.append(name)\n else:\n new_name = \"%s_%d\"%(name,used.count(name))\n new_names.append(new_name)\n used.append(name)\n return new_names","def cross_ref_hint():\n relations_dict = parse_hint(hint_csv_path)\n uniprot_ref_dict = get_uniprots_for_hint()\n\n processed_nodes = set()\n actual_cross_links = 0\n breakpoints = 300\n size = len(relations_dict)\n\n log.info('Starting inserting HINT for %s primary nodes' % size)\n\n for i, (legacyId, linked_legacyIds) in enumerate(relations_dict.items()):\n\n if i % breakpoints:\n # TODO: [progress bar]\n log.info('\\t %.2f %%' % (float(i) / float(size) * 100))\n\n if legacyId in list(uniprot_ref_dict.keys()):\n for linked_legacyId in linked_legacyIds:\n if linked_legacyId in list(uniprot_ref_dict.keys()):\n actual_cross_links += 1\n\n DatabaseGraph.link(uniprot_ref_dict[legacyId], uniprot_ref_dict[linked_legacyId],\n 'is_interacting',\n {'source': 'HINT',\n 'parse_type': 'physical_entity_molecular_interaction'})\n\n\n log.info('HINT Cross-links: %s, HINT processed nodes: %s',\n actual_cross_links, len(processed_nodes))","def _showStringXrefs(self):\n\n # Retrieve some config values\n show_misc.entropy = self.config.calculate_misc.entropy\n show_unique_s = self.config.display_unique_strings\n\n self._console_output(\"Calculating string references...\")\n\n self.ba.calculate_strings_list()\n s_ref_list = self.ba.get_string_references()\n\n # Found any references at all?\n nrows = len(s_ref_list)\n if not nrows:\n self._console_output(\"[!] No string references found\", err = True)\n return\n\n if show_misc.entropy:\n self.table.setColumnCount(3)\n self.table.setHorizontalHeaderLabels((\"Address\", \"String\", \"misc.entropy\"))\n\n else:\n self.table.setColumnCount(2)\n self.table.setHorizontalHeaderLabels((\"Address\", \"String\"))\n\n self.table_label.setText(\"String references in current function\")\n self.table.clearContents()\n self.table.setRowCount(0)\n\n # Fill the table\n displayed_strings = []\n\n idx = 0\n for (addr, s) in s_ref_list:\n if show_unique_s and s in displayed_strings:\n continue\n\n displayed_strings.append(s)\n\n self.table.insertRow(idx)\n addr_item = QTableWidgetItem(\"%08x\" % addr)\n addr_item.setFlags(addr_item.flags() ^ QtCore.Qt.ItemIsEditable)\n string_item = QTableWidgetItem(\"%s\" % s)\n string_item.setFlags(string_item.flags() ^ QtCore.Qt.ItemIsEditable)\n\n self.table.setItem(idx, 0, addr_item)\n self.table.setItem(idx, 1, string_item)\n\n if show_misc.entropy:\n misc.entropy_item = cw.NumQTableWidgetItem(\"%.4f\" % misc.entropy(s))\n self.table.setItem(idx, 2, misc.entropy_item)\n\n idx += 1","def dummy_junction34():\n return 'junction:chr1:351-399:+'","def fixRelations (self):\n\t\tnodes = self.getFieldElements (\"relation\")\n\t\tif not nodes: return\n\t\t\n\t\tprint \"\\n%s\" % self.getId()\n\t\tfor r in nodes:\n\t\t\tvalue = XmlUtils.getText(r)\n\t\t\tif not value: return\n\t\t\tXmlUtils.setText (r,\"\")\n\t\t\tif value.startswith (\"http://\"):\n\t\t\t\tr.setAttribute (\"type\", \"Has part\")\n\t\t\t\tr.setAttribute (\"url\", value)\n\t\t\telse:\n\t\t\t\tr.setAttribute (\"type\", \"Is related\")\n\t\t\t\tr.setAttribute (\"title\", value)\n\t\t\tprint r.toxml()\n\t\tif 0:\n\t\t\tself.write()\n\t\t\tprint \"wrote record\"","def sanitize_record_id(record):\n record.id = READ_INDEX_DIR_RE.sub(\"\", DUP_COLON_RE.sub(r\":\", record.id))","def dummy_junction24():\n return 'junction:chr1:251-399:+'","def append_record_failure():\n\t\tpass","def dummy_junction14():\n return \"junction:chr1:176-324:+\"","def add_wearableName(df_meta):\r\n\r\n recordNames = list(df_meta['recordName'])\r\n df_meta['wearableName'] = [None] * len(recordNames)\r\n\r\n # name the wearable used for each record\r\n for record in recordNames:\r\n\r\n recordSplit = record.split('_')\r\n wearableName = recordSplit[1]\r\n\r\n i = df_meta[ df_meta['recordName']== record].index.values[0]\r\n df_meta.loc[i, 'wearableName' ] = wearableName\r\n\r\n # sort meta file by wearbale name\r\n df_meta = df_meta.sort_values(by = 'wearableName')\r\n\r\n return(df_meta)","def upload_table(tablebogus, n, append, bogus):\n # bucket_name = \"your-bucket-name\"\n # source_file_name = \"local/path/to/file\"\n # destination_blob_name = \"storage-object-name\"\n fake = Faker()\n storage_client = storage.Client()\n bucket = storage_client.bucket(\"brick-layer-testing\")\n outstring = \"\"\n for i in range(0, int(n)):\n populated = {}\n for record in tablebogus:\n # print(ast.literal_eval(record[\"args\"]))\n if record[\"dist\"] == \"fk\":\n record[\"fk_type\"] = getffktype(record[\"from\"].split(\".\")[0], record[\"from\"].split(\".\")[1])\n record[\"bq_type\"] = getfbqtype(record[\"from\"].split(\".\")[0], record[\"from\"].split(\".\")[1])\n record[\"fk_args\"] = getfargs(record[\"from\"].split(\".\")[0], record[\"from\"].split(\".\")[1])\n if (record[\"fk_type\"] == \"past_datetime\"):\n populated[record[\"field_name\"]] = getattr(fake, record[\"fk_type\"])(**ast.literal_eval(json.dumps(record[\"fk_args\"]))).isoformat()\n elif (record[\"fk_type\"] == \"random_element\"):\n populated[record[\"field_name\"]] = random.choice(ast.literal_eval(json.dumps(record[\"fk_args\"][\"elements\"][1:-1].split(\", \"))))\n elif (record[\"fk_type\"] == \"paragraph\"):\n record[\"fk_args\"] = {\n \"nb_sentences\": 3,\n \"variable_nb_sentences\": True\n }\n # print(json.dumps(record[\"fk_args\"]).replace(\"'\",'\"').replace(\"'\",'\"').replace(\"'\",'\"').replace(\"'\",'\"').replace(\"true\", \"True\"))\n populated[record[\"field_name\"]] = getattr(fake, record[\"fk_type\"])(**ast.literal_eval(json.dumps(record[\"fk_args\"]).replace(\"'\",'\"').replace(\"'\",'\"').replace(\"'\",'\"').replace(\"'\",'\"').replace(\"true\", \"True\")))\n # print(ast.literal_eval(record[\"args\"])[\"elements\"][1:-1].split(\", \"))\n # print(populated[record[\"name\"]])\n elif (record[\"fk_type\"] == \"longitude\" or record[\"fk_type\"] == \"latitude\"):\n populated[record[\"field_name\"]] = float(getattr(fake, record[\"fk_type\"])(**ast.literal_eval(json.dumps(record[\"fk_args\"]))))\n else:\n # print(record)\n populated[record[\"field_name\"]] = getattr(fake, record[\"fk_type\"])(**ast.literal_eval(json.dumps(record[\"fk_args\"])))\n outstring += json.dumps(populated) + \"\\n\"\n\n # purg_filename = tablebogus[0]['table_name']+datetime.now().isoformat()+\".txt\"\n # file = open(purg_filename, \"w\")\n # file.write(outstring)\n blob = bucket.blob(tablebogus[0]['table_name'])\n\n blob.upload_from_string(outstring)\n # os.remove(purg_filename)\n\n print(\n ''' \n -> {} uploaded to The Cloud.\n\n '''.format(\n tablebogus[0]['dataset']+\".\"+tablebogus[0]['table_name']\n )\n )\n\n from google.cloud import bigquery\n client = bigquery.Client()\n dataset_ref = tablebogus[0]['dataset']\n\n ids = []\n for i in list(client.list_datasets()):\n ids.append(i.dataset_id)\n if dataset_ref in ids:\n dataset_ref = client.dataset(dataset_ref)\n else:\n dataset_ref = client.create_dataset(dataset_ref) # Make an API request.\n # print(\"Created dataset {}.{}\".format(client.project, dataset.dataset_id))\n print(\" -> This is where I am :: \" + tablebogus[0]['table_name'])\n # dataset_ref = client.dataset(dataset_id)\n job_config = bigquery.LoadJobConfig()\n sch_lst = []\n for field in tablebogus:\n sch_lst.append(bigquery.SchemaField(field['field_name'], field['bq_type']))\n if append:\n job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND\n else:\n job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE\n job_config.schema = sch_lst\n job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON\n uri = \"gs://brick-layer-testing/\"+tablebogus[0]['table_name']\n\n load_job = client.load_table_from_uri(\n uri,\n dataset_ref.table(tablebogus[0]['table_name']),\n location=\"US\", # Location must match that of the destination dataset.\n job_config=job_config,\n ) # API request\n print(\" -> Starting to move shit over to BQ {}\".format(load_job.job_id))\n\n load_job.result() # Waits for table load to complete.\n # print(\"Job finished.\")\n\n destination_table = client.get_table(dataset_ref.table(tablebogus[0]['table_name']))\n print(\" -> There are {} bogus rows.\".format(destination_table.num_rows))\n\n blob = bucket.blob(tablebogus[0]['table_name'])\n blob.delete()\n print(\" -> Tidying up...\")\n # extract schema.json\n # make fakeout.json\n # upload to cloud storage\n # move from cloud storage to bq","def _blacklist_new_tblname(self):\n return 'listgen_temp_{0}_new_blacklist'.format(self._run_id)","def sanitize_record_id_set(record_id_set):\n prev_len = len(record_id_set)\n record_id_set = set(\n READ_INDEX_DIR_RE.sub(\"\", DUP_COLON_RE.sub(r\":\", s)) for s in record_id_set\n )\n\n if prev_len != len(record_id_set):\n ## Uh oh, this hack broke something\n raise Exception(\n \"The list of unpaired reads has name collisions \"\n \"if multiple colons are ignored. Your reads are \"\n \"using a naming convetion that the author of this \"\n \"software (illuminPrep) didn't anticipate. My \"\n \"apologies\"\n )\n\n return record_id_set","def dummy_junction12():\n return \"junction:chr1:176-224:+\"","def xref(self):\n self.appendString(\"\\n\")\n startxref = self.filesize()\n maximumIndexValue = 0\n for i in self.indirectObjects.keys():\n if i > maximumIndexValue:\n maximumIndexValue = i\n self.appendString(\"xref\\n0 %d\\n\" % (maximumIndexValue+1))\n if self.IsWindows():\n eol = '\\n'\n else:\n eol = ' \\n'\n for i in range(0, maximumIndexValue+1):\n if i in self.indirectObjects:\n self.appendString(\"%010d %05d n%s\" % (self.indirectObjects[i], 0, eol))\n else:\n self.appendString(\"0000000000 65535 f%s\" % eol)\n return (startxref, (maximumIndexValue+1))","def replaceRefsById(self,refReplacer):\n log = self.log\n oldIds = set(refReplacer.newIds.keys())\n replCount = {}\n for cell in self.cells:\n objects = cell.getObjects()\n for object in objects.list():\n (iMod,iObj,oldId,objRecords) = object[:4]\n if oldId.lower() in oldIds:\n newId = refReplacer.getNewId(oldId)\n newObject = (iMod,iObj,newId,objRecords)\n objects.replace(object,newObject)\n replCount[oldId] = replCount.get(oldId,0) + 1\n #--Add Records?\n newRecords = refReplacer.getSrcRecords()\n if newRecords:\n selfIds = set([record.getId().lower() for record in self.records if record.getId()])\n log.setHeader(_('Records added:'))\n for newId in sorted(newRecords.keys()):\n if newId not in selfIds:\n self.records.append(newRecords[newId])\n log(newId)\n #--Log\n log.setHeader(_('References replaced:'))\n for oldId in sorted(replCount.keys(),key=lambda a: a.lower()):\n log('%03d %s' % (replCount[oldId], oldId))\n #--Return number of references replaced.\n return sum(replCount.values())","def insert_repnr_fielddesc(inst):\n\telname = \"rn\"\n\telmarccode = \"037__a\"\n\teltype = \"I\"\n\telsize = \"30\"\n\telrows = \"\"\n\telcols = \"\"\n\telmaxlength = \"\"\n\telval = \"%s-<YYYY>-?????\" %(inst.upper())\n\telfidesc = \"\" \n\telmodifytext = \"\"\n\tinsert_element_details(elname, elmarccode, eltype, elsize, elrows, elcols, elmaxlength, elval, elfidesc, elmodifytext) # insert into sbmFIELDDESCR\t","def fix_references(self) -> None:\n\n\t\tself.log_info(\"--- Searching for references to variants ---\")\n\t\tfor publication in Publication.objects.filter(variant_of__isnull=False):\n\t\t\tvariant = publication.variant_of\n\t\t\torigs = PublicationReference.objects.filter(reference=publication)\n\t\t\tfor orig in origs:\n\t\t\t\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\n\t\t\t\t\tcontinue\n\t\t\t\tfixed = PublicationReference(\n\t\t\t\t\treference=variant,\n\t\t\t\t\tpublication=orig.publication,\n\t\t\t\t\tidentifier=('' if orig.identifier is None else orig.identifier) + \"*\",\n\t\t\t\t)\n\t\t\t\ttry:\n\t\t\t\t\tfixed.full_clean()\n\t\t\t\t\tfixed.save()\n\t\t\t\t\tself.log_success(f\"Added reference: {publication} -- {fixed.identifier} -> {variant}\")\n\t\t\t\texcept ValidationError as e:\n\t\t\t\t\traise CommandError(f\"{publication} -- {fixed.identifier} -> {variant}: {e}\")","def dummy_junction23():\n return 'junction:chr1:251-299:+'","def make_link(df, spot):\n\n strings = ['Phase', 'Deleted', 'Client ID', 'Estimating ID', 'Survey ID', 'Block ID',\n 'Profile', 'Sample', 'Sample Req', 'Description', 'Ornament', 'Page', 'Space', 'Form Method']\n end = len(df.columns) - 1\n lbls = ['Model Price', 'Price/Mold',\n 'Price/Unit', 'M/M_Total',\n 'Unit_Total', 'Line_Total']\n additions = zip(df.columns[1:], [alpha[list(df.columns).index(a)] for a in df.columns][1:]) \n\n for i in df.index:\n row = str(i + spot + 3)\n if df.loc[i, 'Deleted'] != 'Total':\n for n,let in additions:\n add = let + row\n if n in strings:\n add += ' & \"\"'\n if n in df.columns:\n df.loc[i, n] = '=Total!' + add\n return df","def _process_pub_dbxref(self, limit):\n\n if self.testMode:\n g = self.testgraph\n else:\n g = self.graph\n model = Model(g)\n raw = '/'.join((self.rawdir, 'pub_dbxref'))\n logger.info(\"processing pub_dbxref\")\n\n line_counter = 0\n\n with open(raw, 'r') as f:\n filereader = csv.reader(f, delimiter='\\t', quotechar='\\\"')\n f.readline() # read the header row; skip\n for line in filereader:\n (pub_dbxref_id, pub_id, dbxref_id, is_current) = line\n # 49648\t43222\t395730\tt\n\n pub_key = pub_id\n pub_id = self.idhash['publication'][pub_key]\n\n if self.testMode and int(pub_key) not in self.test_keys['pub']:\n continue\n\n # get any dbxrefs for pubs, including pmids and dois\n dbxref_key = dbxref_id\n if str(dbxref_key) in self.dbxrefs:\n dbxrefs = self.dbxrefs[str(dbxref_key)]\n # pub_dbs = [75, 51, 76, 95, 126]\n pmid_ids = [50, 77, 275, 286, 347]\n # flybase_ids = [4] # TODO unused\n isbn = [75, 51]\n for d in dbxrefs:\n dbxref_id = None\n if int(d) in pmid_ids:\n if re.match(r'^PMID', dbxrefs[d]):\n dbxref_id = dbxrefs[d].strip()\n else:\n dbxref_id = 'PMID:'+dbxrefs[d].strip()\n model.makeLeader(dbxref_id)\n elif int(d) in isbn:\n dbxref_id = 'ISBN:'+dbxrefs[d].strip()\n elif int(d) == 161:\n dbxref_id = 'DOI:'+dbxrefs[d].strip()\n # elif int(d) == 4:\n # dbxref_id = 'FlyBase:'+dbxrefs[d].strip()\n\n if dbxref_id is not None:\n reference = Reference(\n g, dbxref_id,\n Reference.ref_types['publication'])\n reference.addRefToGraph()\n model.addSameIndividual(pub_id, dbxref_id)\n line_counter += 1\n\n if not self.testMode \\\n and limit is not None and line_counter > limit:\n break\n\n return","def compute_species_from_citation_linking(citelist: list) -> None:\n # for i, cite in enumerate(tqdm(citelist)):\n unrecorded_xrefs = []\n recorded_refs = set()\n for i, cite in enumerate(citelist):\n recorded_refs.add(cite.cite_key)\n if cite.actual == \"=\":\n cname = \"\"\n crossnames = collections.Counter()\n for j in range(i): # only look at entries up to the current one\n tmp = citelist[j]\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\n cname = tmp.name\n crossnames.update([tmp.actual])\n if len(crossnames) == 0:\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\n elif len(crossnames) == 1:\n cite.actual = list(crossnames.keys())[0]\n else:\n # find name(s) with largest count\n mcnt = max(crossnames.values())\n keylist = []\n for key in crossnames:\n if crossnames[key] == mcnt:\n keylist.append(key)\n\n if len(keylist) == 1:\n cite.actual = keylist[0]\n else:\n cite_name = cite.name.lower()\n while cite_name.find(\" \") > -1:\n cite_name = cite_name[cite_name.find(\" \")+1:]\n while cname.find(\" \") > -1:\n cname = cname[cname.find(\" \")+1:]\n\n if cite_name in keylist:\n cite.actual = cite_name\n elif cname in keylist:\n cite.actual = cname\n else:\n cite.actual = keylist[0]\n\n if cite.name_note == \".\":\n cite.name_note = \"in part\"\n else:\n cite.name_note = \"in part; \" + cite.name_note\n\n for x in unrecorded_xrefs:\n if x[1] in recorded_refs:\n report_error(\"Reference {} ({}) does not appear until after citation from {} ({})\".format(x[1], x[3],\n x[0], x[2]))","def add_ref_tag(basicSeqs):\r\n\r\n formattedBasicSeqs=list(basicSeqs) \r\n for record in formattedBasicSeqs:\r\n record.id=record.id+'_Ref'\r\n record.name=record.name+'_Ref'\r\n record.description=record.description+'_Ref'\r\n return formattedBasicSeqs","def new_id(self):\n self._tmp_id_counter += 1\n return '%.' + str(self._tmp_id_counter)","def before_update(mapper, conn, target):\n if not target.id_:\n dataset = ObjectNumber.parse(target.d_id)\n target.id_ = str(PartitionNumber(dataset, target.sequence_id))","def nullify(self):\n self._original_values.clear()\n self._modified_values.clear()\n self._extra_record_data.clear()\n self._references.clear()\n for mtm in self._mtm_referencelist:\n self._mtm_referencelist[mtm].parentobjectid = None\n for chl in self._child_referencelist:\n self._child_referencelist[chl].clear() \n self._ismodified = False\n self._hasdata = False\n self._astxt = \"(null)\"\n if self._table: \n for f in self._table:\n self._original_values[f.name] = None","def _create_key(item, duplicate_sources):\n if item[\"nom\"] not in duplicate_sources:\n return item[\"nom\"]\n dateref = item[\"date_ref\"]\n year = re.search(r\"\\d{4}\", dateref).group(0)\n return f\"{item['nom']}_{year}\"","def rehash(self, new_len):\n print(\"!!!!!!!!!Rehash\")\n oldtable = self.table\n self.table = [set() for x in range(new_len)]\n for numberset in oldtable:\n for number in numberset:\n self.insert(number)\n\n print(\"Rehash-table-print\")\n print(self.table)","def __str__(self):\n pos = self._pos + 1\n if len(self._refr) > len(self._alt):\n dellength = len(self._refr) - len(self._alt)\n return '{:s}:{:d}:{:d}D'.format(self._seqid, pos, dellength)\n else:\n insertion = self._alt[1:]\n return '{:s}:{:d}:I->{:s}'.format(self._seqid, pos, insertion)","def _internal2document_id(value):\n return 2*value + 1","def related_records(self, key, value):\n _migration = self[\"_migration\"]\n _related = _migration[\"related\"]\n relation_type = \"other\"\n try:\n if key == \"775__\" and \"b\" in value:\n relation_type = clean_val(\"b\", value, str)\n if key == \"787__\" and \"i\" in value:\n clean_val(\"i\", value, str, manual=True)\n _related.append(\n {\n \"related_recid\": clean_val(\"w\", value, str, req=True),\n \"relation_type\": relation_type,\n }\n )\n _migration.update({\"related\": _related, \"has_related\": True})\n return _migration\n except ManualImportRequired as e:\n if key == \"775__\":\n e.subfield = \"b or c\"\n else:\n e.subfield = \"i\"\n raise e","def _insert_into_clean(self, entry):\n i = entry.hash\n new_entry = self.table[i]\n while new_entry.key is not None:\n i += self.second_hash(new_entry.key)\n new_entry = self.table[i]\n new_entry.key = entry.key\n new_entry.value = entry.value\n new_entry.hash = entry.hash\n self.used += 1\n self.filled += 1","def _process_dbxref(self):\n\n raw = '/'.join((self.rawdir, 'dbxref'))\n logger.info(\"processing dbxrefs\")\n line_counter = 0\n\n with open(raw, 'r') as f:\n filereader = csv.reader(f, delimiter='\\t', quotechar='\\\"')\n f.readline() # read the header row; skip\n for line in filereader:\n (dbxref_id, db_id, accession, version, description, url) = line\n # dbxref_id\tdb_id\taccession\tversion\tdescription\turl\n # 1\t2\tSO:0000000\t\"\"\n\n db_ids = { # the databases to fetch\n 50: 'PMID', # pubmed\n 68: 'RO', # obo-rel\n 71: 'FBdv', # FBdv\n 74: 'FBbt', # FBbt\n # 28:, # genbank\n 30: 'OMIM', # MIM\n # 38, # ncbi\n 75: 'ISBN', # ISBN\n 46: 'PMID', # PUBMED\n 51: 'ISBN', # isbn\n 52: 'SO', # so\n # 76, # http\n 77: 'PMID', # PMID\n 80: 'FBcv', # FBcv\n # 95, # MEDLINE\n 98: 'REACT', # Reactome\n 103: 'CHEBI', # Chebi\n 102: 'MESH', # MeSH\n 106: 'OMIM', # OMIM\n 105: 'KEGG-path', # KEGG pathway\n 107: 'DOI', # doi\n 108: 'CL', # CL\n 114: 'CHEBI', # CHEBI\n 115: 'KEGG', # KEGG\n 116: 'PubChem', # PubChem\n # 120, # MA???\n 3: 'GO', # GO\n 4: 'FlyBase', # FlyBase\n # 126, # URL\n 128: 'PATO', # PATO\n # 131, # IMG\n 2: 'SO', # SO\n 136: 'MESH', # MESH\n 139: 'CARO', # CARO\n 140: 'NCBITaxon', # NCBITaxon\n # 151, # MP ???\n 161: 'DOI', # doi\n 36: 'BDGP', # BDGP\n # 55, # DGRC\n # 54, # DRSC\n # 169, # Transgenic RNAi project???\n 231: 'RO', # RO ???\n 180: 'NCBIGene', # entrezgene\n # 192, # Bloomington stock center\n 197: 'UBERON', # Uberon\n 212: 'ENSEMBL', # Ensembl\n # 129, # GenomeRNAi\n 275: 'PMID', # PubMed\n 286: 'PMID', # pmid\n 264: 'HGNC',\n # 265: 'OMIM', # OMIM_Gene\n 266: 'OMIM', # OMIM_Phenotype\n 300: 'DOID', # DOID\n 302: 'MESH', # MSH\n 347: 'PMID', # Pubmed\n }\n\n if accession.strip() != '' and int(db_id) in db_ids:\n # scrub some identifiers here\n m = re.match(\n r'(doi|SO|GO|FBcv|FBbt_root|FBdv|FBgn|FBdv_root|FlyBase|FBbt):',\n accession)\n if m:\n accession = re.sub(m.group(1)+r'\\:', '', accession)\n elif re.match(\n r'(FlyBase miscellaneous CV|cell_lineprop|relationship type|FBgn$)',\n accession):\n continue\n elif re.match(r'\\:', accession): # starts with a colon\n accession = re.sub(r'\\:', '', accession)\n elif re.search(r'\\s', accession):\n # skip anything with a space\n # logger.debug(\n # 'dbxref %s accession has a space: %s',\n # dbxref_id, accession)\n continue\n\n if re.match(r'http', accession):\n did = accession.strip()\n else:\n prefix = db_ids.get(int(db_id))\n did = ':'.join((prefix, accession.strip()))\n if re.search(r'\\:', accession) and prefix != 'DOI':\n logger.warning(\n 'id %s may be malformed; skipping', did)\n\n self.dbxrefs[dbxref_id] = {db_id: did}\n\n elif url != '':\n self.dbxrefs[dbxref_id] = {db_id: url.strip()}\n else:\n continue\n\n # the following are some special cases that we scrub\n if int(db_id) == 2 \\\n and accession.strip() == 'transgenic_transposon':\n # transgenic_transposable_element\n self.dbxrefs[dbxref_id] = {db_id: 'SO:0000796'}\n\n line_counter += 1\n\n return","def remove_record_failure():\n\t\tpass","def tmp_table_include_id(self):\n return \"__osc_temp_ids_to_include\"","def record(self, record_meta: Dict[str, Set[str]]):\n if \"ft.onto.base_ontology.Phrase\" not in record_meta.keys():\n record_meta[\"ft.onto.base_ontology.Phrase\"] = set()","def generate_identifier(sender, instance, **kwargs):\n identifier = Concept.create_identifier(instance.query)\n qs = Concept.objects.filter(identifier=identifier, lang=instance.lang)\n if instance.pk:\n qs = qs.exclude(pk=instance.pk)\n if qs.count() > 0:\n raise ValueError(\"Concept identifier conflict\")\n instance.identifier = identifier","def _exceptions_lists_delta_tblname(self):\n return 'listgen_temp_{0}_delta_exceptions_lists'.format(self._run_id)","def makeFootnoteRefId(self, id):\n if self.getConfig(\"UNIQUE_IDS\"):\n return 'fnref%s%d-%s' % (self.sep, self.unique_prefix, id)\n else:\n return 'fnref%s%s' % (self.sep, id)","def _notifications_lists_old_tblname(self):\n return 'listgen_temp_{0}_old_notifications_lists'.format(self._run_id)","def format_relation(relation: list):\n pattern = \"%1s%6s%8i%1i%1s%10.2f%10.2f%1i%5i%5i%1i%10.2f%10.2f%10.2f%1i\"\n return pattern % (\n relation[0],\n relation[1] if relation[1] is not None else 0,\n relation[2],\n relation[3],\n relation[4],\n relation[5],\n relation[6],\n relation[7],\n relation[8],\n relation[9],\n relation[10],\n relation[11],\n relation[12],\n relation[13],\n relation[14]\n )","def _exceptions_lists_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._exceptions_lists_tblname, operator_id)","def update_vluln_table():","def _blacklist_old_tblname(self):\n return 'listgen_temp_{0}_old_blacklist'.format(self._run_id)","def gen_new_table(db_old, db_new, table, col_index, new_col_list, ord_users, ord_subs):\n con = lite.connect(db_old)\n with con:\n cur = con.cursor()\n cur.execute(\"SELECT * FROM \" + table)\n tuple_list = cur.fetchall()\n for i in range(0, len(new_col_list)):\n tuple_list[i] = replace_tuple(tuple_list[i], new_col_list[i], col_index)\n #anonymize username and submission id\n if(table == \"Comments\"):\n anon_users = anonymize(strip_tuple(tuple_list, 1), ord_users)\n anon_subs = anonymize(strip_tuple(tuple_list, 5), ord_subs)\n for i in range(0, len(new_col_list)):\n tuple_list[i] = replace_tuple(tuple_list[i], anon_users[i], 1)\n tuple_list[i] = replace_tuple(tuple_list[i], anon_subs[i], 5)\n elif(table == \"Submissions\"):\n for i in range(0, len(new_col_list)):\n tuple_list[i] = replace_tuple(tuple_list[i], i, 0)\n num_bindings = len(tuple_list[0])\n bindings = ('?,' * num_bindings)[:-1]\n con = lite.connect(db_new)\n with con:\n cur = con.cursor()\n cur.executemany(\"INSERT INTO \" + table + \" VALUES\" + \" (\"+ bindings + \")\", tuple_list)","def _pairings_imei_imsi_new_tblname(self):\n return 'listgen_temp_{0}_new_pairings_imei_imsis'.format(self._run_id)","def _get_record_id(self):\n return \"\".join(\n (self.material_name, self.material_type, str(self.material_count), self.material_usage, self.material_user))","def genQrelStr(queryId, docId, relGrade):\n return f'{queryId} 0 {docId} {relGrade}'","def _notifications_lists_new_tblname(self):\n return 'listgen_temp_{0}_new_notifications_lists'.format(self._run_id)","def update_refseq_info(ensembl_info, word, value):\n if \"refseq\" in word:\n if \"mrna\" in word:\n if \"predicted\" in word:\n ensembl_info[\"refseq_mrna_predicted\"] = value\n else:\n ensembl_info[\"refseq_mrna\"] = value\n\n if \"ncrna\" in word:\n ensembl_info[\"refseq_ncrna\"] = value\n return ensembl_info","def correctfasta(vectint, records):\n\n\n# go through each sequence in genome file\n for record in records:\n if record in vectint:\n # We have the remove keyword. Do not process sequence record\n recordseq = records[record]\n if \"remove\" in vectint[record]:\n continue\n if \"trim3\" in vectint[record]:\n # We cannot work directly on the records hash\n # duplicate the sequence, and modify it\n recordseq = recordseq[:vectint[record][\"trim3\"]]\n if \"trim5\" in vectint[record]:\n # We cannot work directly on the records hash\n # duplicate the sequence, and modify it\n recordseq = recordseq[vectint[record][\"trim5\"]:]\n # print modified sequence\n if len(recordseq.seq) > 0:\n print(\">\"+record)\n print(recordseq.seq)\n else:\n # print unmodified sequence\n print(\">\"+record)\n print(records[record].seq)","def add_synthetic_links(in_file,links_columns_all_details,out_file):\n\n df = pd.read_csv(in_file)\n\n\n links_node_swapped_columns_all_details = links_columns_all_details[math.floor(len(links_columns_all_details)/2):] + links_columns_all_details[0:math.floor(len(links_columns_all_details)/2)]\n\n df_new = df.copy()\n\n for ix1, (Index, row1) in tqdm(enumerate(df.iterrows())):\n temp = []\n for i in range(len(links_columns_all_details)):\n\n if i < math.floor(len(links_columns_all_details)/2):\n temp.append(df.iat[ix1, links_columns_all_details[i]])\n df_new.iat[ix1, links_columns_all_details[i]] = df.iat[ix1, links_node_swapped_columns_all_details[i]]\n else:\n df_new.iat[ix1, links_columns_all_details[i]] = temp[i - math.floor(len(links_columns_all_details)/2)]\n\n print('swapped details of ',ix1)\n\n # import ipdb; ipdb.set_trace()\n\n df_new = df_new.assign(synthetic = np.ones(df_new.shape[0]))\n df = df.assign(synthetic = np.zeros(df.shape[0]))\n\n df = df.append(df_new)\n\n\n df.to_csv(out_file, index=False)\n\n return df","def repair(self) -> None:\n\n rows_without_domain = self._select_generator(\n \"\"\"SELECT rowid, url as 'url [url]'\n FROM bookmarks\n WHERE domain IS NULL\"\"\"\n )\n\n for row in rows_without_domain:\n self._execute(\n \"UPDATE bookmarks SET domain=? WHERE rowid=?\",\n (row[\"url\"].domain, row[\"id\"])\n )","def add_signal_prefix(df_phys, can_id_prefix=False, pgn_prefix=False, bus_prefix=False):\n from J1939_PGN import J1939_PGN\n \n if df_phys.empty:\n return df_phys \n else:\n prefix = \"\"\n if bus_prefix:\n prefix += df_phys[\"BusChannel\"].apply(lambda x: f\"{x}.\")\n if can_id_prefix:\n prefix += df_phys[\"CAN ID\"].apply(lambda x: f\"{hex(int(x))[2:].upper()}.\" )\n if pgn_prefix:\n prefix += df_phys[\"CAN ID\"].apply(lambda x: f\"{J1939_PGN(int(x)).pgn}.\")\n \n df_phys[\"Signal\"] = prefix + df_phys[\"Signal\"]\n \n return df_phys","def fixing_annotation(key, n):\n if key + '.' + n not in self.propbank:\n key = key.replace('-', '_')\n return key + '.' + n","def _exceptions_lists_delta_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._exceptions_lists_delta_tblname, operator_id)","def _fill_reference_loaded_fields(self, head, tail, field_name, value):\n name = \".\".join(head)\n if tail:\n # there is some fields for referenced document\n if name not in self._reference_loaded_fields:\n self._reference_loaded_fields[name] = {}\n self._reference_loaded_fields[name][tail] = value\n # and we should include reference field explicitly\n return (name, QueryFieldList.ONLY)\n else:\n return (field_name, value)","def addRDGfaresinfo(df,lookupdf,postfix):\n #copy of data frame made to avoid SettingWithCopyWarning by making the copy explicit\n df_dt = df.copy()\n\n #datatyping of RDG info to match later match with superfile\n df_dt.loc[:,'Origin Code'] = df.loc[:,'Origin Code'].astype(str)\n df_dt.loc[:,'Destination Code'] = df.loc[:,'Destination Code'].astype(str)\n df_dt.loc[:,'Route Code'] = df.loc[:,'Route Code'].astype(str)\n df_dt.loc[:,'Product Code'] = df.loc[:,'Product Code'].astype(str)\n\n #merge of the RDG info with superfile\n df_dt = pd.merge(left=df_dt,right=lookupdf[['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)','FARE']],\n how='left',\n left_on=['Origin Code','Destination Code','Route Code','Product Code'],\n right_on=['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)'])\n\n #drop unnecessary columns from the merge process\n df_dt.drop(['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)'],axis=1,inplace=True)\n\n #rename columns as needed\n df_dt.rename(columns={'FARE':'RDG_FARES'+postfix,'Fares ticket type description':'RDG_Fares ticket type description'+postfix},inplace=True)\n\n return df_dt","def addRelSample(self, sample_name, filename):\n tax_levels = None\n if len(self.abundance_df.columns) == 0:\n self.abundance_df = pd.read_csv(filename, header=0, sep='\\t') #krona (no header, no index)\n cols = list(self.abundance_df.columns)\n self.abundance_df = self.abundance_df[[cols[0]] + cols[:1:-1]]\n self.tax_levels = self.abundance_df.columns.tolist()[1:]\n #self.abundance_df = self.abundance_df[self.abundance_df.columns[0] + self.tax_levels]\n self.abundance_df.rename(columns={self.abundance_df.columns[0]:sample_name}, inplace=True)\n self.abundance_df.index = self.abundance_df[self.tax_levels[0]]+'_'\n self.abundance_df.index.name = None \n #self.abundance_df = self.abundance_df.loc[:,[self.abundance_df.columns[0]] + self.tax_levels]\n else:\n sample_df = pd.read_csv(filename, header=0, sep='\\t')\n sample_df.rename(columns={sample_df.columns[0]:sample_name}, inplace=True) \n sample_df.index = sample_df[self.tax_levels[0]]+'_'\n sample_df.index.name = None \n self.abundance_df = pd.merge(self.abundance_df, sample_df, how='outer', on=self.tax_levels)\n self.abundance_df.index = self.abundance_df[self.tax_levels[0]]+'_'\n self.abundance_df.index.name = None\n self.abundance_df.fillna(value=0, inplace=True) \n self.abundance_df[sample_name] = self.abundance_df[sample_name].astype(float)\n \n self.sample_names.append(sample_name.strip())\n self.abundance_df = self.abundance_df[self.sample_names + self.tax_levels]\n myindex = list(self.abundance_df.index)\n newlist = sorted(set([i for i in myindex if myindex.count(i)>1]))\n #problems with the ncbi taxonomy (typos?)\n # for i in newlist:\n # self.abundance_df.loc[i,self.sample_names] = self.abundance_df.loc[i].sum(numeric_only=True)\n # self.abundance_df.drop(i, inplace=True)\n self.abundance_raw_df = None\n return self.tax_levels","def _notifications_triplets_new_tblname(self):\n return 'listgen_temp_{0}_new_notifications_triplets'.format(self._run_id)","def _notifications_lists_old_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._notifications_lists_old_tblname, operator_id)","def add_ambig_to_total_assigned(self):\n self.n_assigned_list.append(len(self.ambig))\n self.log_ambig = True","def check_missing_unique_link():\n from mspray.apps.main.utils import queryset_iterator\n\n queryset = SprayDay.objects.filter(spraypoint__isnull=True).only(\n \"pk\", \"location_id\"\n )\n for record in queryset_iterator(queryset):\n add_unique_record(record.pk, record.location_id)\n gc.collect()","def fix_namedict(self, namedict, table):\n experiment_fields = self.experiment_fields()\n for idx, entry in enumerate(namedict):\n for k, v in experiment_fields.iteritems():\n if k == 'experiment_iteration':\n # Initialize iterations at 0\n entry[k] = 0\n elif k not in entry.keys():\n entry[k] = None\n namedict[idx] = entry\n return namedict","def autoname(self):\n ret = \"%(table)s_%(reftable)s_fkey\"%dict(\n table=self.table.name,\n reftable=self.reftable.name,\n )\n return ret","def print_missing(num):\n if not num:\n num = task_get_option(\"print-extcites\")\n\n write_message(\"Listing external papers cited by %i or more internal records:\" % num)\n\n res = run_sql(\"SELECT COUNT(id_bibrec), extcitepubinfo FROM rnkCITATIONDATAEXT \\\n GROUP BY extcitepubinfo HAVING COUNT(id_bibrec) >= %s \\\n ORDER BY COUNT(id_bibrec) DESC\", (num,))\n for (cnt, brec) in res:\n print str(cnt)+\"\\t\"+brec\n\n write_message(\"Listing done.\")","def test_dump_related_records(testdata):\n doc1 = Document.get_record_by_pid(testdata[\"documents\"][0][\"document_pid\"])\n doc2 = Document.get_record_by_pid(testdata[\"documents\"][1][\"document_pid\"])\n doc3 = Document.get_record_by_pid(testdata[\"documents\"][2][\"document_pid\"])\n assert doc1[\"related_records\"] == []\n assert doc2[\"related_records\"] == []\n assert doc3[\"related_records\"] == []\n\n doc1.related.add_language(doc2)\n doc1.commit()\n\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\n assert doc1[\"related_records\"] == [\n record_to_relation_dump(doc2, RelatedRecords.language_relation())\n ]\n assert doc2[\"related_records\"] == [\n record_to_relation_dump(doc1, RelatedRecords.language_relation())\n ]\n\n doc1.related.add_language(doc3)\n doc1.commit()\n\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\n doc3 = Document.get_record_by_pid(doc3[doc3.pid_field])\n assert doc1[\"related_records\"] == [\n record_to_relation_dump(doc2, RelatedRecords.language_relation()),\n record_to_relation_dump(doc3, RelatedRecords.language_relation())\n ]\n assert doc2[\"related_records\"] == [\n record_to_relation_dump(doc1, RelatedRecords.language_relation()),\n record_to_relation_dump(doc3, RelatedRecords.language_relation())\n ]\n assert doc3[\"related_records\"] == [\n record_to_relation_dump(doc1, RelatedRecords.language_relation()),\n record_to_relation_dump(doc2, RelatedRecords.language_relation())\n ]\n\n doc1.related.remove_language(doc2)\n doc1.related.remove_language(doc3)\n doc1.commit()\n\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\n doc3 = Document.get_record_by_pid(doc3[doc3.pid_field])\n assert doc1[\"related_records\"] == []\n assert doc2[\"related_records\"] == []\n assert doc3[\"related_records\"] == []","def get_cypher_xref_text():\n return \"\"\"\n MERGE (id:CrossReference:Identifier {primaryKey:row.primaryKey})\n ON CREATE SET id.name = row.id,\n id.globalCrossRefId = row.globalCrossRefId,\n id.localId = row.localId,\n id.crossRefCompleteUrl = row.crossRefCompleteUrl,\n id.prefix = row.prefix,\n id.crossRefType = row.crossRefType,\n id.uuid = row.uuid,\n id.page = row.page,\n id.primaryKey = row.primaryKey,\n id.displayName = row.displayName\n\n MERGE (o)-[gcr:CROSS_REFERENCE]->(id) \"\"\"","def table_key(self, reindex_dict):\n reindexed_marks = []\n for m in self.component1.marks:\n new_m = reindex_dict.get(m)\n if new_m == None:\n if len(reindex_dict) == 0:\n new_m = 0\n else:\n new_m = max(reindex_dict.values())+1\n reindex_dict[m] = new_m\n reindexed_marks.append(new_m)\n return tuple( [self.component1.genus] + sorted(reindexed_marks) )","def _makeInternalIdentifier(self, prefix, key):\n\n return '_:' + hashlib.sha1(\n ('fb'+prefix+'key'+key).encode('utf-8')).hexdigest()[1:20]","def labelTable(self,table):\n \n for sslice,_, lFields in self._lLabellingInstruction:\n for field in lFields:\n if field is not None:\n try:\n for cell in np.nditer(table.getNPArray()[sslice],['refs_ok'],op_dtypes=np.dtype(object)):\n cell[()].addField(field.cloneMe())\n except: pass","def load_master_table():\n\n # read table\n my_globals['master_table_data'] = pd.read_table(my_globals['master_table_path'])\n\n # also make a new column with a unique subject identifier\n my_globals['master_table_data']['subject'] = my_globals['master_table_data'].expID.apply(lambda x: x.split('e')[0])","def makeBackrefLink(self, info, g_links, i):\n atts, content, infoid, link = '', '', '', ''\n if 'def' in info:\n link = info['def']['link']\n backlink_type = link or g_links\n i_ = self.encode_high(i)\n allow_inc = i not in self.syms\n i_ = int(i_)\n\n if backlink_type == \"!\":\n return ''\n elif backlink_type == '^':\n return \"\"\"<sup><a href=\"#noteref%s\">%s</a></sup>\"\"\" % (\n info['refids'][0], i\n )\n else:\n result = []\n for refid in info['refids']:\n i_entity = self.decode_high(i_)\n sup = \"\"\"<sup><a href=\"#noteref%s\">%s</a></sup>\"\"\" % (\n refid, i_entity\n )\n if allow_inc:\n i_ += 1\n result.append(sup)\n result = ' '.join(result)\n return result","def create_index_row_logical_model_with_supplements(record, directory):\n from bs4 import BeautifulSoup\n soup = BeautifulSoup(\"<b></b>\", 'lxml')\n tr = soup.new_tag(\"tr\")\n td_supplement = soup.new_tag(\"td\")\n\n if \"European Supplement\" in record[\"supplement\"]:\n span_supplement = soup.new_tag(\"spam\")\n span_supplement['class'] = \"badge badge-secondary\"\n span_supplement.string = \"European Supplement\"\n td_supplement.insert(1,span_supplement)\n tr.insert(1,td_supplement)\n\n td_ic_name = soup.new_tag(\"td\")\n td_ic_name[\"data-order\"] = record[\"class name\"]\n if record[\"stereotype\"] != \"missing data\": #The record is a class\n filename = classname_to_filename(str(record['class name']))\n url = directory+filename\n text = record[\"class name\"]\n print(text)\n new_link = soup.new_tag(\"a\")\n new_link['href'] = url\n new_link['target'] = \"_blank\"\n new_link.string = text\n td_ic_name.insert(1,new_link)\n tr.insert(2,td_ic_name)\n else: #The record is a property\n td_ic_name.string = record[\"class name\"]\n tr.insert(2,td_ic_name)\n \n td_dc_name = soup.new_tag(\"td\")\n td_dc_name[\"data-order\"] = str(record[\"property name\"])\n if record[\"stereotype\"] == \"missing data\": #The record is a property\n filename = str(record['class name'])+\".html#\"+str(record['property name'])\n filename = filename.replace(\"/\", \"-\")\n filename = filename.replace(\"*\", \"-\")\n filename = filename.replace(\" \", \"\")\n filename = filename.replace(\"\\t\", \"\")\n filename = filename.replace(\"\\n\", \"\")\n url = directory+filename\n text = str(record[\"property name\"])\n print(text)\n new_link = soup.new_tag(\"a\")\n new_link['href'] = url\n new_link['target'] = \"_blank\"\n new_link.string = text\n td_dc_name.insert(1,new_link)\n tr.insert(3,td_dc_name)\n else: #The record is a class\n td_dc_name.string = \"-\"\n tr.insert(3,td_dc_name)\n\n if record[\"definition\"] != \"\":\n td_def = soup.new_tag(\"td\")\n td_def.string = str(record[\"definition\"])\n tr.insert(4,td_def)\n \n if record[\"type\"] != \"missing data\":\n td_def = soup.new_tag(\"td\")\n td_def.string = str(record[\"type\"])\n tr.insert(5,td_def)\n else:\n td_def = soup.new_tag(\"td\")\n td_def.string = \"-\"\n tr.insert(5,td_def)\n \n return tr","def makeTableNamesList(n, ):","def _notifications_imei_new_tblname(self):\n return 'listgen_temp_{0}_new_notifications_imeis'.format(self._run_id)","def add_blank_record(self, uploader):\n with self.session as session:\n record = IndexRecord()\n base_version = BaseVersion()\n\n did = str(uuid.uuid4())\n baseid = str(uuid.uuid4())\n if self.config.get('PREPEND_PREFIX'):\n did = self.config['DEFAULT_PREFIX'] + did\n\n record.did = did\n base_version.baseid = baseid\n\n record.rev = str(uuid.uuid4())[:8]\n record.baseid = baseid\n record.uploader = uploader\n\n session.add(base_version)\n session.add(record)\n session.commit()\n\n return record.did, record.rev, record.baseid","def reformat(self, seq_name, *, prefix=\"s\"):\n\t\treturn \"%s_%012u\" % (prefix, self.get_sid(seq_name))","def test_data_source_soaps_id_replace_post(self):\n pass","def _notifications_lists_new_part_tblname(self, operator_id):\n return '{0}_{1}'.format(self._notifications_lists_new_tblname, operator_id)","def shorten_url(url: str, next_record: int) -> str:\r\n encoded_record = encode(next_record)\r\n LINKS[next_record] = url\r\n return SITE + f'/{encoded_record}'","def record_prefix(required_type, factory):\n field = record_type(required_type)\n field += factory.get_rule('transaction_sequence_n')\n field += factory.get_rule('record_sequence_n')\n\n # field.leaveWhitespace()\n\n return field","def reset(self):\r\n self.look_up_table = list(map(convert_to_list, self.const_look_up_table))","def _backreference_keys(self, ref, key_in_ref):\n return (self.__class__.REFERENCE_FIELDS +\n self.__class__.DEPENDENT_LINES +\n self.__class__.OTHER_REFERENCES)","def _fix_evidence_refs(db, rid_stmt_trios):\n rid_set = {rid for rid, _, _ in rid_stmt_trios if rid is not None}\n logger.info(\"Getting text refs for %d readings.\" % len(rid_set))\n if rid_set:\n rid_tr_pairs = db.select_all([db.Reading.id, db.TextRef],\n db.Reading.id.in_(rid_set),\n *db.join(db.TextRef, db.Reading))\n rid_tr_dict = {rid: tr for rid, tr in rid_tr_pairs}\n for rid, sid, stmt in rid_stmt_trios:\n if rid is None:\n # This means this statement came from a database, not reading.\n continue\n assert len(stmt.evidence) == 1, \\\n \"Only raw statements can have their refs fixed.\"\n _set_evidence_text_ref(stmt, rid_tr_dict[rid])\n return"],"string":"[\n \"def _add_NR(self, w2, row):\\n row['NR'] = None\\n return True\",\n \"def addMissingData():\\n\\n conn = sqlite3.connect(\\\"./transactions.db\\\")\\n\\n person = pd.read_sql(\\n \\\"\\\"\\\"\\n select * from person;\\n \\\"\\\"\\\",\\n conn,\\n )\\n\\n record = pd.read_sql(\\n \\\"\\\"\\\"\\n select * from record;\\n \\\"\\\"\\\",\\n conn,\\n )\\n\\n tracked = set([_id for _id in record[\\\"doc_id\\\"]])\\n\\n untracked = []\\n\\n for url, _id in zip(person[\\\"url\\\"], person[\\\"doc_id\\\"]):\\n if not _id in tracked:\\n untracked.append((url[-17:-13], _id))\\n\\n untracked = pd.DataFrame(untracked, columns=[\\\"date\\\", \\\"doc_id\\\"])\\n\\n res = transaction.extractData(untracked)\\n\\n res.to_sql(\\\"record\\\", conn, index=False, if_exists=\\\"append\\\")\\n\\n conn.close()\",\n \"def _exceptions_lists_new_tblname(self):\\n return 'listgen_temp_{0}_new_exceptions_lists'.format(self._run_id)\",\n \"def get_mt_usr_invalid_link(self):\\n schema = self.district.format_schema()\\n\\n rdf_nav_link = '%s.rdf_nav_link' % schema\\n rdf_link = '%s.rdf_link' % schema\\n rdf_nav_strand = '%s.rdf_nav_strand' % schema\\n usr_node_link = '%s.usr_node_link' % schema\\n adas_node_curvature = '%s.adas_node_curvature' % schema\\n adas_node_slope = '%s.adas_node_slope' % schema\\n rdf_sign_origin = '%s.rdf_sign_origin' % schema\\n rdf_sign_destination = '%s.rdf_sign_destination' % schema\\n\\n sqls = []\\n sqls.append('SELECT link_id FROM %s' % rdf_nav_strand)\\n sqls.append('SELECT nav_link_id as link_id FROM %s WHERE nav_link_id IS NOT NULL' % usr_node_link)\\n sqls.append('SELECT from_link_id AS link_id FROM %s' % adas_node_curvature)\\n sqls.append('SELECT to_link_id AS link_id FROM %s' % adas_node_curvature)\\n sqls.append('SELECT to_link_id AS link_id FROM %s' % adas_node_slope)\\n sqls.append('SELECT originating_link_id AS link_id FROM %s' % rdf_sign_origin)\\n sqls.append('SELECT dest_link_id AS link_id FROM %s' % rdf_sign_destination)\\n\\n\\n table = '%s.usr_invalid_link' % schema\\n mt = SqlMeta(table)\\n mt.add('CREATE TABLE %s AS (SELECT link_id FROM %s INTERSECT (%s) EXCEPT SELECT link_id FROM %s) ' %(table, rdf_link, ' UNION '.join(sqls), rdf_nav_link))\\n mt.add(self._pk_sql(table, 'link_id'))\\n\\n mt.ref(rdf_nav_link)\\n mt.ref(rdf_link)\\n mt.ref(rdf_nav_strand)\\n mt.ref(usr_node_link)\\n mt.ref(adas_node_curvature)\\n mt.ref(adas_node_slope)\\n mt.ref(rdf_sign_origin)\\n mt.ref(rdf_sign_destination)\\n\\n return mt\",\n \"def get_uid(new_data, metadata, conn):\\n for awe in new_data[\\\"all_wkn_entry\\\"]:\\n for key in [\\\"wkn\\\",\\\"isin\\\"]:\\n if awe[key] == \\\"\\\":continue\\n if key == \\\"wkn\\\":\\n s = select([metadata.tables[\\\"WKN\\\"]]).where(metadata.tables[\\\"WKN\\\"].c.WKN == awe[key]).order_by(\\n metadata.tables[\\\"WKN\\\"].c.unternehmenId.desc())\\n else:\\n s = select([metadata.tables[\\\"WKN\\\"]]).where(metadata.tables[\\\"WKN\\\"].c.ISIN == awe[key]).order_by(\\n metadata.tables[\\\"WKN\\\"].c.unternehmenId.desc())\\n result = conn.execute(s)\\n try:\\n someid = result.fetchone()[0]\\n except:\\n new_data['reference'] = new_data['unternehmenId']\\n new_data['id'] = \\\"\\\"\\n return 0\\n s = select([metadata.tables['MainRelation']]).where(\\n metadata.tables['MainRelation'].c.referenz == someid)\\n result = conn.execute(s)\\n fone = result.fetchall()\\n if len(fone) > 0:\\n for row in fone:\\n new_data['reference'] = row[0]\\n new_data['id'] = row[4]\\n return 0\\n\\n s = select([metadata.tables['MainRelation']]).where(\\n metadata.tables['MainRelation'].c.weiteresAuftreten == someid)\\n result = conn.execute(s)\\n fone = result.fetchall()\\n if len(fone) > 0:\\n for row in fone:\\n new_data['reference'] = row[0]\\n new_data['id'] = row[4]\\n return 0\\n new_data['reference'] = new_data['unternehmenId']\\n new_data['id'] = \\\"\\\"\\n return 0\\n #TODO-HINT: Be aware its order is descendent by year to avoid apply new WKN's to old ones which get used in early years!\",\n \"def _CreateRecordId(self):\\n self._record_count += 1\\n return '%s_%s' % (self._unique_id, self._record_count)\",\n \"def delexicaliseReferenceNumber(sent, turn):\\n domains = ['restaurant', 'hotel', 'attraction', 'train', 'taxi', 'hospital'] # , 'police']\\n if turn['metadata']:\\n for domain in domains:\\n if turn['metadata'][domain]['book']['booked']:\\n for slot in turn['metadata'][domain]['book']['booked'][0]:\\n if slot == 'reference':\\n val = '[' + domain + '_' + slot + ']'\\n else:\\n val = '[' + domain + '_' + slot + ']'\\n key = normalize(turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n\\n # try reference with hashtag\\n key = normalize(\\\"#\\\" + turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n\\n # try reference with ref#\\n key = normalize(\\\"ref#\\\" + turn['metadata'][domain]['book']['booked'][0][slot])\\n sent = (' ' + sent + ' ').replace(' ' + key + ' ', ' ' + val + ' ')\\n return sent\",\n \"def _exceptions_lists_new_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._exceptions_lists_new_tblname, operator_id)\",\n \"def _exceptions_lists_old_tblname(self):\\n return 'listgen_temp_{0}_old_exceptions_lists'.format(self._run_id)\",\n \"def _processMissBrokenLinks(self, target):\\n\\n xrdDocs = self._collsXRDMiss[target].find({'storage.location': 'XRD',\\n 'target': target,\\n 'issue': 'brokenLink'})\\n\\n self._collsXRDNoLink[target].insert_many(xrdDocs)\\n\\n self._collsXRDMiss[target].delete_many({'storage.location': 'XRD',\\n 'target': target,\\n 'issue': 'brokenLink'})\",\n \"def _exceptions_lists_old_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._exceptions_lists_old_tblname, operator_id)\",\n \"def get_cypher_xref_tuned_text():\\n return \\\"\\\"\\\"\\n MERGE (id:CrossReference:Identifier {primaryKey:row.primaryKey})\\n ON CREATE SET id.name = row.id,\\n id.globalCrossRefId = row.globalCrossRefId,\\n id.localId = row.localId,\\n id.crossRefCompleteUrl = row.crossRefCompleteUrl,\\n id.prefix = row.prefix,\\n id.crossRefType = row.crossRefType,\\n id.uuid = row.uuid,\\n id.page = row.page,\\n id.primaryKey = row.primaryKey,\\n id.displayName = row.displayName\\\"\\\"\\\"\",\n \"def dummy_junction13():\\n return 'junction:chr1:176-299:+'\",\n \"def create_rel_doctitle_dict():\\n claim_rel_docno_dict = {} #key is claim text, value is a set of doc_title that are relevant\\n clm_sen_doc_title_dict = read_pickle(\\\"sen_doc_title_dict\\\")\\n claim_sen_true_relevance_dict = read_pickle(\\\"claim_sen_relevance_dict_\\\"+curr_source)\\n exclude = set(string.punctuation)\\n docID_title_mapping_wiki_pickle = read_pickle(\\\"dicID_title_mapping_wiki_pickle\\\")\\n \\n title_docID_mapping_wiki_pickle = {}\\n for (docID,doc_title) in docID_title_mapping_wiki_pickle.iteritems():\\n non_asci_char = [c for c in doc_title if not 0 < ord(c) < 127]\\n new_doc_title = doc_title\\n for c in non_asci_char:\\n new_doc_title = new_doc_title.replace(c,\\\"\\\")\\n doc_title_no_punc = ''.join(ch for ch in new_doc_title if ch not in exclude)\\n doc_title_no_space = doc_title_no_punc.replace(\\\" \\\",\\\"\\\")\\n title_docID_mapping_wiki_pickle[doc_title_no_space] = docID\\n# title_docID_mapping_wiki_pickle = dict((y,x) for x,y in docID_title_mapping_wiki_pickle.iteritems()) \\n for (clm) in claim_sen_true_relevance_dict.keys():\\n rel_docno_set = set()\\n for (sen,rel_score) in claim_sen_true_relevance_dict[clm]:\\n try: \\n if rel_score == 1:\\n sen_no_punc = ''.join(ch for ch in sen if ch not in exclude)\\n sen_no_space = sen_no_punc.replace(\\\" \\\",\\\"\\\")\\n curr_rel_doc_title = clm_sen_doc_title_dict[sen_no_space]\\n non_asci_char = [c for c in curr_rel_doc_title if not 0 < ord(c) < 127]\\n new_curr_doc_title = curr_rel_doc_title\\n for c in non_asci_char:\\n new_curr_doc_title = new_curr_doc_title.replace(c,\\\"\\\")\\n curr_doc_title_no_punc = ''.join(ch for ch in new_curr_doc_title if ch not in exclude)\\n curr_doc_title_no_space = curr_doc_title_no_punc.replace(\\\" \\\",\\\"\\\")\\n rel_docno_set.add((title_docID_mapping_wiki_pickle[curr_doc_title_no_space],1))\\n \\n except Exception as err: \\n sys.stderr.write('problem in sen:'+sen) \\n print err.args\\n \\n rel_docno_list = [(docid,rel_score) for (docid,rel_score) in rel_docno_set]\\n claim_rel_docno_dict[clm] = rel_docno_list\\n save_pickle(\\\"claim_rel_docno_dict\\\", claim_rel_docno_dict)\",\n \"def _fix_chainID(self):\\n\\n from string import ascii_uppercase\\n\\n # get the current names\\n data = self.get('chainID')\\n natom = len(data)\\n\\n #get uniques\\n chainID = []\\n for c in data:\\n if c not in chainID:\\n chainID.append(c)\\n\\n if chainID == ['A','B']:\\n return\\n\\n if len(chainID)>26:\\n print(\\\"Warning more than 26 chains have been detected. This is so far not supported\\\")\\n sys.exit()\\n\\n # declare the new names\\n newID = [''] * natom\\n\\n # fill in the new names\\n for ic,chain in enumerate(chainID):\\n index = self.get('rowID',chainID=chain)\\n for ind in index:\\n newID[ind] = ascii_uppercase[ic]\\n\\n # update the new name\\n self.update_column('chainID',newID)\",\n \"def fix_duplicate_field_names(self,names):\\n used = []\\n new_names = []\\n for name in names:\\n if name not in used:\\n new_names.append(name)\\n else:\\n new_name = \\\"%s_%d\\\"%(name,used.count(name))\\n new_names.append(new_name)\\n used.append(name)\\n return new_names\",\n \"def cross_ref_hint():\\n relations_dict = parse_hint(hint_csv_path)\\n uniprot_ref_dict = get_uniprots_for_hint()\\n\\n processed_nodes = set()\\n actual_cross_links = 0\\n breakpoints = 300\\n size = len(relations_dict)\\n\\n log.info('Starting inserting HINT for %s primary nodes' % size)\\n\\n for i, (legacyId, linked_legacyIds) in enumerate(relations_dict.items()):\\n\\n if i % breakpoints:\\n # TODO: [progress bar]\\n log.info('\\\\t %.2f %%' % (float(i) / float(size) * 100))\\n\\n if legacyId in list(uniprot_ref_dict.keys()):\\n for linked_legacyId in linked_legacyIds:\\n if linked_legacyId in list(uniprot_ref_dict.keys()):\\n actual_cross_links += 1\\n\\n DatabaseGraph.link(uniprot_ref_dict[legacyId], uniprot_ref_dict[linked_legacyId],\\n 'is_interacting',\\n {'source': 'HINT',\\n 'parse_type': 'physical_entity_molecular_interaction'})\\n\\n\\n log.info('HINT Cross-links: %s, HINT processed nodes: %s',\\n actual_cross_links, len(processed_nodes))\",\n \"def _showStringXrefs(self):\\n\\n # Retrieve some config values\\n show_misc.entropy = self.config.calculate_misc.entropy\\n show_unique_s = self.config.display_unique_strings\\n\\n self._console_output(\\\"Calculating string references...\\\")\\n\\n self.ba.calculate_strings_list()\\n s_ref_list = self.ba.get_string_references()\\n\\n # Found any references at all?\\n nrows = len(s_ref_list)\\n if not nrows:\\n self._console_output(\\\"[!] No string references found\\\", err = True)\\n return\\n\\n if show_misc.entropy:\\n self.table.setColumnCount(3)\\n self.table.setHorizontalHeaderLabels((\\\"Address\\\", \\\"String\\\", \\\"misc.entropy\\\"))\\n\\n else:\\n self.table.setColumnCount(2)\\n self.table.setHorizontalHeaderLabels((\\\"Address\\\", \\\"String\\\"))\\n\\n self.table_label.setText(\\\"String references in current function\\\")\\n self.table.clearContents()\\n self.table.setRowCount(0)\\n\\n # Fill the table\\n displayed_strings = []\\n\\n idx = 0\\n for (addr, s) in s_ref_list:\\n if show_unique_s and s in displayed_strings:\\n continue\\n\\n displayed_strings.append(s)\\n\\n self.table.insertRow(idx)\\n addr_item = QTableWidgetItem(\\\"%08x\\\" % addr)\\n addr_item.setFlags(addr_item.flags() ^ QtCore.Qt.ItemIsEditable)\\n string_item = QTableWidgetItem(\\\"%s\\\" % s)\\n string_item.setFlags(string_item.flags() ^ QtCore.Qt.ItemIsEditable)\\n\\n self.table.setItem(idx, 0, addr_item)\\n self.table.setItem(idx, 1, string_item)\\n\\n if show_misc.entropy:\\n misc.entropy_item = cw.NumQTableWidgetItem(\\\"%.4f\\\" % misc.entropy(s))\\n self.table.setItem(idx, 2, misc.entropy_item)\\n\\n idx += 1\",\n \"def dummy_junction34():\\n return 'junction:chr1:351-399:+'\",\n \"def fixRelations (self):\\n\\t\\tnodes = self.getFieldElements (\\\"relation\\\")\\n\\t\\tif not nodes: return\\n\\t\\t\\n\\t\\tprint \\\"\\\\n%s\\\" % self.getId()\\n\\t\\tfor r in nodes:\\n\\t\\t\\tvalue = XmlUtils.getText(r)\\n\\t\\t\\tif not value: return\\n\\t\\t\\tXmlUtils.setText (r,\\\"\\\")\\n\\t\\t\\tif value.startswith (\\\"http://\\\"):\\n\\t\\t\\t\\tr.setAttribute (\\\"type\\\", \\\"Has part\\\")\\n\\t\\t\\t\\tr.setAttribute (\\\"url\\\", value)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tr.setAttribute (\\\"type\\\", \\\"Is related\\\")\\n\\t\\t\\t\\tr.setAttribute (\\\"title\\\", value)\\n\\t\\t\\tprint r.toxml()\\n\\t\\tif 0:\\n\\t\\t\\tself.write()\\n\\t\\t\\tprint \\\"wrote record\\\"\",\n \"def sanitize_record_id(record):\\n record.id = READ_INDEX_DIR_RE.sub(\\\"\\\", DUP_COLON_RE.sub(r\\\":\\\", record.id))\",\n \"def dummy_junction24():\\n return 'junction:chr1:251-399:+'\",\n \"def append_record_failure():\\n\\t\\tpass\",\n \"def dummy_junction14():\\n return \\\"junction:chr1:176-324:+\\\"\",\n \"def add_wearableName(df_meta):\\r\\n\\r\\n recordNames = list(df_meta['recordName'])\\r\\n df_meta['wearableName'] = [None] * len(recordNames)\\r\\n\\r\\n # name the wearable used for each record\\r\\n for record in recordNames:\\r\\n\\r\\n recordSplit = record.split('_')\\r\\n wearableName = recordSplit[1]\\r\\n\\r\\n i = df_meta[ df_meta['recordName']== record].index.values[0]\\r\\n df_meta.loc[i, 'wearableName' ] = wearableName\\r\\n\\r\\n # sort meta file by wearbale name\\r\\n df_meta = df_meta.sort_values(by = 'wearableName')\\r\\n\\r\\n return(df_meta)\",\n \"def upload_table(tablebogus, n, append, bogus):\\n # bucket_name = \\\"your-bucket-name\\\"\\n # source_file_name = \\\"local/path/to/file\\\"\\n # destination_blob_name = \\\"storage-object-name\\\"\\n fake = Faker()\\n storage_client = storage.Client()\\n bucket = storage_client.bucket(\\\"brick-layer-testing\\\")\\n outstring = \\\"\\\"\\n for i in range(0, int(n)):\\n populated = {}\\n for record in tablebogus:\\n # print(ast.literal_eval(record[\\\"args\\\"]))\\n if record[\\\"dist\\\"] == \\\"fk\\\":\\n record[\\\"fk_type\\\"] = getffktype(record[\\\"from\\\"].split(\\\".\\\")[0], record[\\\"from\\\"].split(\\\".\\\")[1])\\n record[\\\"bq_type\\\"] = getfbqtype(record[\\\"from\\\"].split(\\\".\\\")[0], record[\\\"from\\\"].split(\\\".\\\")[1])\\n record[\\\"fk_args\\\"] = getfargs(record[\\\"from\\\"].split(\\\".\\\")[0], record[\\\"from\\\"].split(\\\".\\\")[1])\\n if (record[\\\"fk_type\\\"] == \\\"past_datetime\\\"):\\n populated[record[\\\"field_name\\\"]] = getattr(fake, record[\\\"fk_type\\\"])(**ast.literal_eval(json.dumps(record[\\\"fk_args\\\"]))).isoformat()\\n elif (record[\\\"fk_type\\\"] == \\\"random_element\\\"):\\n populated[record[\\\"field_name\\\"]] = random.choice(ast.literal_eval(json.dumps(record[\\\"fk_args\\\"][\\\"elements\\\"][1:-1].split(\\\", \\\"))))\\n elif (record[\\\"fk_type\\\"] == \\\"paragraph\\\"):\\n record[\\\"fk_args\\\"] = {\\n \\\"nb_sentences\\\": 3,\\n \\\"variable_nb_sentences\\\": True\\n }\\n # print(json.dumps(record[\\\"fk_args\\\"]).replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"true\\\", \\\"True\\\"))\\n populated[record[\\\"field_name\\\"]] = getattr(fake, record[\\\"fk_type\\\"])(**ast.literal_eval(json.dumps(record[\\\"fk_args\\\"]).replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"'\\\",'\\\"').replace(\\\"true\\\", \\\"True\\\")))\\n # print(ast.literal_eval(record[\\\"args\\\"])[\\\"elements\\\"][1:-1].split(\\\", \\\"))\\n # print(populated[record[\\\"name\\\"]])\\n elif (record[\\\"fk_type\\\"] == \\\"longitude\\\" or record[\\\"fk_type\\\"] == \\\"latitude\\\"):\\n populated[record[\\\"field_name\\\"]] = float(getattr(fake, record[\\\"fk_type\\\"])(**ast.literal_eval(json.dumps(record[\\\"fk_args\\\"]))))\\n else:\\n # print(record)\\n populated[record[\\\"field_name\\\"]] = getattr(fake, record[\\\"fk_type\\\"])(**ast.literal_eval(json.dumps(record[\\\"fk_args\\\"])))\\n outstring += json.dumps(populated) + \\\"\\\\n\\\"\\n\\n # purg_filename = tablebogus[0]['table_name']+datetime.now().isoformat()+\\\".txt\\\"\\n # file = open(purg_filename, \\\"w\\\")\\n # file.write(outstring)\\n blob = bucket.blob(tablebogus[0]['table_name'])\\n\\n blob.upload_from_string(outstring)\\n # os.remove(purg_filename)\\n\\n print(\\n ''' \\n -> {} uploaded to The Cloud.\\n\\n '''.format(\\n tablebogus[0]['dataset']+\\\".\\\"+tablebogus[0]['table_name']\\n )\\n )\\n\\n from google.cloud import bigquery\\n client = bigquery.Client()\\n dataset_ref = tablebogus[0]['dataset']\\n\\n ids = []\\n for i in list(client.list_datasets()):\\n ids.append(i.dataset_id)\\n if dataset_ref in ids:\\n dataset_ref = client.dataset(dataset_ref)\\n else:\\n dataset_ref = client.create_dataset(dataset_ref) # Make an API request.\\n # print(\\\"Created dataset {}.{}\\\".format(client.project, dataset.dataset_id))\\n print(\\\" -> This is where I am :: \\\" + tablebogus[0]['table_name'])\\n # dataset_ref = client.dataset(dataset_id)\\n job_config = bigquery.LoadJobConfig()\\n sch_lst = []\\n for field in tablebogus:\\n sch_lst.append(bigquery.SchemaField(field['field_name'], field['bq_type']))\\n if append:\\n job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND\\n else:\\n job_config.write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE\\n job_config.schema = sch_lst\\n job_config.source_format = bigquery.SourceFormat.NEWLINE_DELIMITED_JSON\\n uri = \\\"gs://brick-layer-testing/\\\"+tablebogus[0]['table_name']\\n\\n load_job = client.load_table_from_uri(\\n uri,\\n dataset_ref.table(tablebogus[0]['table_name']),\\n location=\\\"US\\\", # Location must match that of the destination dataset.\\n job_config=job_config,\\n ) # API request\\n print(\\\" -> Starting to move shit over to BQ {}\\\".format(load_job.job_id))\\n\\n load_job.result() # Waits for table load to complete.\\n # print(\\\"Job finished.\\\")\\n\\n destination_table = client.get_table(dataset_ref.table(tablebogus[0]['table_name']))\\n print(\\\" -> There are {} bogus rows.\\\".format(destination_table.num_rows))\\n\\n blob = bucket.blob(tablebogus[0]['table_name'])\\n blob.delete()\\n print(\\\" -> Tidying up...\\\")\\n # extract schema.json\\n # make fakeout.json\\n # upload to cloud storage\\n # move from cloud storage to bq\",\n \"def _blacklist_new_tblname(self):\\n return 'listgen_temp_{0}_new_blacklist'.format(self._run_id)\",\n \"def sanitize_record_id_set(record_id_set):\\n prev_len = len(record_id_set)\\n record_id_set = set(\\n READ_INDEX_DIR_RE.sub(\\\"\\\", DUP_COLON_RE.sub(r\\\":\\\", s)) for s in record_id_set\\n )\\n\\n if prev_len != len(record_id_set):\\n ## Uh oh, this hack broke something\\n raise Exception(\\n \\\"The list of unpaired reads has name collisions \\\"\\n \\\"if multiple colons are ignored. Your reads are \\\"\\n \\\"using a naming convetion that the author of this \\\"\\n \\\"software (illuminPrep) didn't anticipate. My \\\"\\n \\\"apologies\\\"\\n )\\n\\n return record_id_set\",\n \"def dummy_junction12():\\n return \\\"junction:chr1:176-224:+\\\"\",\n \"def xref(self):\\n self.appendString(\\\"\\\\n\\\")\\n startxref = self.filesize()\\n maximumIndexValue = 0\\n for i in self.indirectObjects.keys():\\n if i > maximumIndexValue:\\n maximumIndexValue = i\\n self.appendString(\\\"xref\\\\n0 %d\\\\n\\\" % (maximumIndexValue+1))\\n if self.IsWindows():\\n eol = '\\\\n'\\n else:\\n eol = ' \\\\n'\\n for i in range(0, maximumIndexValue+1):\\n if i in self.indirectObjects:\\n self.appendString(\\\"%010d %05d n%s\\\" % (self.indirectObjects[i], 0, eol))\\n else:\\n self.appendString(\\\"0000000000 65535 f%s\\\" % eol)\\n return (startxref, (maximumIndexValue+1))\",\n \"def replaceRefsById(self,refReplacer):\\n log = self.log\\n oldIds = set(refReplacer.newIds.keys())\\n replCount = {}\\n for cell in self.cells:\\n objects = cell.getObjects()\\n for object in objects.list():\\n (iMod,iObj,oldId,objRecords) = object[:4]\\n if oldId.lower() in oldIds:\\n newId = refReplacer.getNewId(oldId)\\n newObject = (iMod,iObj,newId,objRecords)\\n objects.replace(object,newObject)\\n replCount[oldId] = replCount.get(oldId,0) + 1\\n #--Add Records?\\n newRecords = refReplacer.getSrcRecords()\\n if newRecords:\\n selfIds = set([record.getId().lower() for record in self.records if record.getId()])\\n log.setHeader(_('Records added:'))\\n for newId in sorted(newRecords.keys()):\\n if newId not in selfIds:\\n self.records.append(newRecords[newId])\\n log(newId)\\n #--Log\\n log.setHeader(_('References replaced:'))\\n for oldId in sorted(replCount.keys(),key=lambda a: a.lower()):\\n log('%03d %s' % (replCount[oldId], oldId))\\n #--Return number of references replaced.\\n return sum(replCount.values())\",\n \"def insert_repnr_fielddesc(inst):\\n\\telname = \\\"rn\\\"\\n\\telmarccode = \\\"037__a\\\"\\n\\teltype = \\\"I\\\"\\n\\telsize = \\\"30\\\"\\n\\telrows = \\\"\\\"\\n\\telcols = \\\"\\\"\\n\\telmaxlength = \\\"\\\"\\n\\telval = \\\"%s-<YYYY>-?????\\\" %(inst.upper())\\n\\telfidesc = \\\"\\\" \\n\\telmodifytext = \\\"\\\"\\n\\tinsert_element_details(elname, elmarccode, eltype, elsize, elrows, elcols, elmaxlength, elval, elfidesc, elmodifytext) # insert into sbmFIELDDESCR\\t\",\n \"def fix_references(self) -> None:\\n\\n\\t\\tself.log_info(\\\"--- Searching for references to variants ---\\\")\\n\\t\\tfor publication in Publication.objects.filter(variant_of__isnull=False):\\n\\t\\t\\tvariant = publication.variant_of\\n\\t\\t\\torigs = PublicationReference.objects.filter(reference=publication)\\n\\t\\t\\tfor orig in origs:\\n\\t\\t\\t\\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tfixed = PublicationReference(\\n\\t\\t\\t\\t\\treference=variant,\\n\\t\\t\\t\\t\\tpublication=orig.publication,\\n\\t\\t\\t\\t\\tidentifier=('' if orig.identifier is None else orig.identifier) + \\\"*\\\",\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tfixed.full_clean()\\n\\t\\t\\t\\t\\tfixed.save()\\n\\t\\t\\t\\t\\tself.log_success(f\\\"Added reference: {publication} -- {fixed.identifier} -> {variant}\\\")\\n\\t\\t\\t\\texcept ValidationError as e:\\n\\t\\t\\t\\t\\traise CommandError(f\\\"{publication} -- {fixed.identifier} -> {variant}: {e}\\\")\",\n \"def dummy_junction23():\\n return 'junction:chr1:251-299:+'\",\n \"def make_link(df, spot):\\n\\n strings = ['Phase', 'Deleted', 'Client ID', 'Estimating ID', 'Survey ID', 'Block ID',\\n 'Profile', 'Sample', 'Sample Req', 'Description', 'Ornament', 'Page', 'Space', 'Form Method']\\n end = len(df.columns) - 1\\n lbls = ['Model Price', 'Price/Mold',\\n 'Price/Unit', 'M/M_Total',\\n 'Unit_Total', 'Line_Total']\\n additions = zip(df.columns[1:], [alpha[list(df.columns).index(a)] for a in df.columns][1:]) \\n\\n for i in df.index:\\n row = str(i + spot + 3)\\n if df.loc[i, 'Deleted'] != 'Total':\\n for n,let in additions:\\n add = let + row\\n if n in strings:\\n add += ' & \\\"\\\"'\\n if n in df.columns:\\n df.loc[i, n] = '=Total!' + add\\n return df\",\n \"def _process_pub_dbxref(self, limit):\\n\\n if self.testMode:\\n g = self.testgraph\\n else:\\n g = self.graph\\n model = Model(g)\\n raw = '/'.join((self.rawdir, 'pub_dbxref'))\\n logger.info(\\\"processing pub_dbxref\\\")\\n\\n line_counter = 0\\n\\n with open(raw, 'r') as f:\\n filereader = csv.reader(f, delimiter='\\\\t', quotechar='\\\\\\\"')\\n f.readline() # read the header row; skip\\n for line in filereader:\\n (pub_dbxref_id, pub_id, dbxref_id, is_current) = line\\n # 49648\\t43222\\t395730\\tt\\n\\n pub_key = pub_id\\n pub_id = self.idhash['publication'][pub_key]\\n\\n if self.testMode and int(pub_key) not in self.test_keys['pub']:\\n continue\\n\\n # get any dbxrefs for pubs, including pmids and dois\\n dbxref_key = dbxref_id\\n if str(dbxref_key) in self.dbxrefs:\\n dbxrefs = self.dbxrefs[str(dbxref_key)]\\n # pub_dbs = [75, 51, 76, 95, 126]\\n pmid_ids = [50, 77, 275, 286, 347]\\n # flybase_ids = [4] # TODO unused\\n isbn = [75, 51]\\n for d in dbxrefs:\\n dbxref_id = None\\n if int(d) in pmid_ids:\\n if re.match(r'^PMID', dbxrefs[d]):\\n dbxref_id = dbxrefs[d].strip()\\n else:\\n dbxref_id = 'PMID:'+dbxrefs[d].strip()\\n model.makeLeader(dbxref_id)\\n elif int(d) in isbn:\\n dbxref_id = 'ISBN:'+dbxrefs[d].strip()\\n elif int(d) == 161:\\n dbxref_id = 'DOI:'+dbxrefs[d].strip()\\n # elif int(d) == 4:\\n # dbxref_id = 'FlyBase:'+dbxrefs[d].strip()\\n\\n if dbxref_id is not None:\\n reference = Reference(\\n g, dbxref_id,\\n Reference.ref_types['publication'])\\n reference.addRefToGraph()\\n model.addSameIndividual(pub_id, dbxref_id)\\n line_counter += 1\\n\\n if not self.testMode \\\\\\n and limit is not None and line_counter > limit:\\n break\\n\\n return\",\n \"def compute_species_from_citation_linking(citelist: list) -> None:\\n # for i, cite in enumerate(tqdm(citelist)):\\n unrecorded_xrefs = []\\n recorded_refs = set()\\n for i, cite in enumerate(citelist):\\n recorded_refs.add(cite.cite_key)\\n if cite.actual == \\\"=\\\":\\n cname = \\\"\\\"\\n crossnames = collections.Counter()\\n for j in range(i): # only look at entries up to the current one\\n tmp = citelist[j]\\n if (tmp.cite_key == cite.application) and match_num_ref(tmp.name_key, cite.cite_n):\\n cname = tmp.name\\n crossnames.update([tmp.actual])\\n if len(crossnames) == 0:\\n unrecorded_xrefs.append([cite.cite_key, cite.application, cite.name_key, cite.cite_n])\\n elif len(crossnames) == 1:\\n cite.actual = list(crossnames.keys())[0]\\n else:\\n # find name(s) with largest count\\n mcnt = max(crossnames.values())\\n keylist = []\\n for key in crossnames:\\n if crossnames[key] == mcnt:\\n keylist.append(key)\\n\\n if len(keylist) == 1:\\n cite.actual = keylist[0]\\n else:\\n cite_name = cite.name.lower()\\n while cite_name.find(\\\" \\\") > -1:\\n cite_name = cite_name[cite_name.find(\\\" \\\")+1:]\\n while cname.find(\\\" \\\") > -1:\\n cname = cname[cname.find(\\\" \\\")+1:]\\n\\n if cite_name in keylist:\\n cite.actual = cite_name\\n elif cname in keylist:\\n cite.actual = cname\\n else:\\n cite.actual = keylist[0]\\n\\n if cite.name_note == \\\".\\\":\\n cite.name_note = \\\"in part\\\"\\n else:\\n cite.name_note = \\\"in part; \\\" + cite.name_note\\n\\n for x in unrecorded_xrefs:\\n if x[1] in recorded_refs:\\n report_error(\\\"Reference {} ({}) does not appear until after citation from {} ({})\\\".format(x[1], x[3],\\n x[0], x[2]))\",\n \"def add_ref_tag(basicSeqs):\\r\\n\\r\\n formattedBasicSeqs=list(basicSeqs) \\r\\n for record in formattedBasicSeqs:\\r\\n record.id=record.id+'_Ref'\\r\\n record.name=record.name+'_Ref'\\r\\n record.description=record.description+'_Ref'\\r\\n return formattedBasicSeqs\",\n \"def new_id(self):\\n self._tmp_id_counter += 1\\n return '%.' + str(self._tmp_id_counter)\",\n \"def before_update(mapper, conn, target):\\n if not target.id_:\\n dataset = ObjectNumber.parse(target.d_id)\\n target.id_ = str(PartitionNumber(dataset, target.sequence_id))\",\n \"def nullify(self):\\n self._original_values.clear()\\n self._modified_values.clear()\\n self._extra_record_data.clear()\\n self._references.clear()\\n for mtm in self._mtm_referencelist:\\n self._mtm_referencelist[mtm].parentobjectid = None\\n for chl in self._child_referencelist:\\n self._child_referencelist[chl].clear() \\n self._ismodified = False\\n self._hasdata = False\\n self._astxt = \\\"(null)\\\"\\n if self._table: \\n for f in self._table:\\n self._original_values[f.name] = None\",\n \"def _create_key(item, duplicate_sources):\\n if item[\\\"nom\\\"] not in duplicate_sources:\\n return item[\\\"nom\\\"]\\n dateref = item[\\\"date_ref\\\"]\\n year = re.search(r\\\"\\\\d{4}\\\", dateref).group(0)\\n return f\\\"{item['nom']}_{year}\\\"\",\n \"def rehash(self, new_len):\\n print(\\\"!!!!!!!!!Rehash\\\")\\n oldtable = self.table\\n self.table = [set() for x in range(new_len)]\\n for numberset in oldtable:\\n for number in numberset:\\n self.insert(number)\\n\\n print(\\\"Rehash-table-print\\\")\\n print(self.table)\",\n \"def __str__(self):\\n pos = self._pos + 1\\n if len(self._refr) > len(self._alt):\\n dellength = len(self._refr) - len(self._alt)\\n return '{:s}:{:d}:{:d}D'.format(self._seqid, pos, dellength)\\n else:\\n insertion = self._alt[1:]\\n return '{:s}:{:d}:I->{:s}'.format(self._seqid, pos, insertion)\",\n \"def _internal2document_id(value):\\n return 2*value + 1\",\n \"def related_records(self, key, value):\\n _migration = self[\\\"_migration\\\"]\\n _related = _migration[\\\"related\\\"]\\n relation_type = \\\"other\\\"\\n try:\\n if key == \\\"775__\\\" and \\\"b\\\" in value:\\n relation_type = clean_val(\\\"b\\\", value, str)\\n if key == \\\"787__\\\" and \\\"i\\\" in value:\\n clean_val(\\\"i\\\", value, str, manual=True)\\n _related.append(\\n {\\n \\\"related_recid\\\": clean_val(\\\"w\\\", value, str, req=True),\\n \\\"relation_type\\\": relation_type,\\n }\\n )\\n _migration.update({\\\"related\\\": _related, \\\"has_related\\\": True})\\n return _migration\\n except ManualImportRequired as e:\\n if key == \\\"775__\\\":\\n e.subfield = \\\"b or c\\\"\\n else:\\n e.subfield = \\\"i\\\"\\n raise e\",\n \"def _insert_into_clean(self, entry):\\n i = entry.hash\\n new_entry = self.table[i]\\n while new_entry.key is not None:\\n i += self.second_hash(new_entry.key)\\n new_entry = self.table[i]\\n new_entry.key = entry.key\\n new_entry.value = entry.value\\n new_entry.hash = entry.hash\\n self.used += 1\\n self.filled += 1\",\n \"def _process_dbxref(self):\\n\\n raw = '/'.join((self.rawdir, 'dbxref'))\\n logger.info(\\\"processing dbxrefs\\\")\\n line_counter = 0\\n\\n with open(raw, 'r') as f:\\n filereader = csv.reader(f, delimiter='\\\\t', quotechar='\\\\\\\"')\\n f.readline() # read the header row; skip\\n for line in filereader:\\n (dbxref_id, db_id, accession, version, description, url) = line\\n # dbxref_id\\tdb_id\\taccession\\tversion\\tdescription\\turl\\n # 1\\t2\\tSO:0000000\\t\\\"\\\"\\n\\n db_ids = { # the databases to fetch\\n 50: 'PMID', # pubmed\\n 68: 'RO', # obo-rel\\n 71: 'FBdv', # FBdv\\n 74: 'FBbt', # FBbt\\n # 28:, # genbank\\n 30: 'OMIM', # MIM\\n # 38, # ncbi\\n 75: 'ISBN', # ISBN\\n 46: 'PMID', # PUBMED\\n 51: 'ISBN', # isbn\\n 52: 'SO', # so\\n # 76, # http\\n 77: 'PMID', # PMID\\n 80: 'FBcv', # FBcv\\n # 95, # MEDLINE\\n 98: 'REACT', # Reactome\\n 103: 'CHEBI', # Chebi\\n 102: 'MESH', # MeSH\\n 106: 'OMIM', # OMIM\\n 105: 'KEGG-path', # KEGG pathway\\n 107: 'DOI', # doi\\n 108: 'CL', # CL\\n 114: 'CHEBI', # CHEBI\\n 115: 'KEGG', # KEGG\\n 116: 'PubChem', # PubChem\\n # 120, # MA???\\n 3: 'GO', # GO\\n 4: 'FlyBase', # FlyBase\\n # 126, # URL\\n 128: 'PATO', # PATO\\n # 131, # IMG\\n 2: 'SO', # SO\\n 136: 'MESH', # MESH\\n 139: 'CARO', # CARO\\n 140: 'NCBITaxon', # NCBITaxon\\n # 151, # MP ???\\n 161: 'DOI', # doi\\n 36: 'BDGP', # BDGP\\n # 55, # DGRC\\n # 54, # DRSC\\n # 169, # Transgenic RNAi project???\\n 231: 'RO', # RO ???\\n 180: 'NCBIGene', # entrezgene\\n # 192, # Bloomington stock center\\n 197: 'UBERON', # Uberon\\n 212: 'ENSEMBL', # Ensembl\\n # 129, # GenomeRNAi\\n 275: 'PMID', # PubMed\\n 286: 'PMID', # pmid\\n 264: 'HGNC',\\n # 265: 'OMIM', # OMIM_Gene\\n 266: 'OMIM', # OMIM_Phenotype\\n 300: 'DOID', # DOID\\n 302: 'MESH', # MSH\\n 347: 'PMID', # Pubmed\\n }\\n\\n if accession.strip() != '' and int(db_id) in db_ids:\\n # scrub some identifiers here\\n m = re.match(\\n r'(doi|SO|GO|FBcv|FBbt_root|FBdv|FBgn|FBdv_root|FlyBase|FBbt):',\\n accession)\\n if m:\\n accession = re.sub(m.group(1)+r'\\\\:', '', accession)\\n elif re.match(\\n r'(FlyBase miscellaneous CV|cell_lineprop|relationship type|FBgn$)',\\n accession):\\n continue\\n elif re.match(r'\\\\:', accession): # starts with a colon\\n accession = re.sub(r'\\\\:', '', accession)\\n elif re.search(r'\\\\s', accession):\\n # skip anything with a space\\n # logger.debug(\\n # 'dbxref %s accession has a space: %s',\\n # dbxref_id, accession)\\n continue\\n\\n if re.match(r'http', accession):\\n did = accession.strip()\\n else:\\n prefix = db_ids.get(int(db_id))\\n did = ':'.join((prefix, accession.strip()))\\n if re.search(r'\\\\:', accession) and prefix != 'DOI':\\n logger.warning(\\n 'id %s may be malformed; skipping', did)\\n\\n self.dbxrefs[dbxref_id] = {db_id: did}\\n\\n elif url != '':\\n self.dbxrefs[dbxref_id] = {db_id: url.strip()}\\n else:\\n continue\\n\\n # the following are some special cases that we scrub\\n if int(db_id) == 2 \\\\\\n and accession.strip() == 'transgenic_transposon':\\n # transgenic_transposable_element\\n self.dbxrefs[dbxref_id] = {db_id: 'SO:0000796'}\\n\\n line_counter += 1\\n\\n return\",\n \"def remove_record_failure():\\n\\t\\tpass\",\n \"def tmp_table_include_id(self):\\n return \\\"__osc_temp_ids_to_include\\\"\",\n \"def record(self, record_meta: Dict[str, Set[str]]):\\n if \\\"ft.onto.base_ontology.Phrase\\\" not in record_meta.keys():\\n record_meta[\\\"ft.onto.base_ontology.Phrase\\\"] = set()\",\n \"def generate_identifier(sender, instance, **kwargs):\\n identifier = Concept.create_identifier(instance.query)\\n qs = Concept.objects.filter(identifier=identifier, lang=instance.lang)\\n if instance.pk:\\n qs = qs.exclude(pk=instance.pk)\\n if qs.count() > 0:\\n raise ValueError(\\\"Concept identifier conflict\\\")\\n instance.identifier = identifier\",\n \"def _exceptions_lists_delta_tblname(self):\\n return 'listgen_temp_{0}_delta_exceptions_lists'.format(self._run_id)\",\n \"def makeFootnoteRefId(self, id):\\n if self.getConfig(\\\"UNIQUE_IDS\\\"):\\n return 'fnref%s%d-%s' % (self.sep, self.unique_prefix, id)\\n else:\\n return 'fnref%s%s' % (self.sep, id)\",\n \"def _notifications_lists_old_tblname(self):\\n return 'listgen_temp_{0}_old_notifications_lists'.format(self._run_id)\",\n \"def format_relation(relation: list):\\n pattern = \\\"%1s%6s%8i%1i%1s%10.2f%10.2f%1i%5i%5i%1i%10.2f%10.2f%10.2f%1i\\\"\\n return pattern % (\\n relation[0],\\n relation[1] if relation[1] is not None else 0,\\n relation[2],\\n relation[3],\\n relation[4],\\n relation[5],\\n relation[6],\\n relation[7],\\n relation[8],\\n relation[9],\\n relation[10],\\n relation[11],\\n relation[12],\\n relation[13],\\n relation[14]\\n )\",\n \"def _exceptions_lists_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._exceptions_lists_tblname, operator_id)\",\n \"def update_vluln_table():\",\n \"def _blacklist_old_tblname(self):\\n return 'listgen_temp_{0}_old_blacklist'.format(self._run_id)\",\n \"def gen_new_table(db_old, db_new, table, col_index, new_col_list, ord_users, ord_subs):\\n con = lite.connect(db_old)\\n with con:\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM \\\" + table)\\n tuple_list = cur.fetchall()\\n for i in range(0, len(new_col_list)):\\n tuple_list[i] = replace_tuple(tuple_list[i], new_col_list[i], col_index)\\n #anonymize username and submission id\\n if(table == \\\"Comments\\\"):\\n anon_users = anonymize(strip_tuple(tuple_list, 1), ord_users)\\n anon_subs = anonymize(strip_tuple(tuple_list, 5), ord_subs)\\n for i in range(0, len(new_col_list)):\\n tuple_list[i] = replace_tuple(tuple_list[i], anon_users[i], 1)\\n tuple_list[i] = replace_tuple(tuple_list[i], anon_subs[i], 5)\\n elif(table == \\\"Submissions\\\"):\\n for i in range(0, len(new_col_list)):\\n tuple_list[i] = replace_tuple(tuple_list[i], i, 0)\\n num_bindings = len(tuple_list[0])\\n bindings = ('?,' * num_bindings)[:-1]\\n con = lite.connect(db_new)\\n with con:\\n cur = con.cursor()\\n cur.executemany(\\\"INSERT INTO \\\" + table + \\\" VALUES\\\" + \\\" (\\\"+ bindings + \\\")\\\", tuple_list)\",\n \"def _pairings_imei_imsi_new_tblname(self):\\n return 'listgen_temp_{0}_new_pairings_imei_imsis'.format(self._run_id)\",\n \"def _get_record_id(self):\\n return \\\"\\\".join(\\n (self.material_name, self.material_type, str(self.material_count), self.material_usage, self.material_user))\",\n \"def genQrelStr(queryId, docId, relGrade):\\n return f'{queryId} 0 {docId} {relGrade}'\",\n \"def _notifications_lists_new_tblname(self):\\n return 'listgen_temp_{0}_new_notifications_lists'.format(self._run_id)\",\n \"def update_refseq_info(ensembl_info, word, value):\\n if \\\"refseq\\\" in word:\\n if \\\"mrna\\\" in word:\\n if \\\"predicted\\\" in word:\\n ensembl_info[\\\"refseq_mrna_predicted\\\"] = value\\n else:\\n ensembl_info[\\\"refseq_mrna\\\"] = value\\n\\n if \\\"ncrna\\\" in word:\\n ensembl_info[\\\"refseq_ncrna\\\"] = value\\n return ensembl_info\",\n \"def correctfasta(vectint, records):\\n\\n\\n# go through each sequence in genome file\\n for record in records:\\n if record in vectint:\\n # We have the remove keyword. Do not process sequence record\\n recordseq = records[record]\\n if \\\"remove\\\" in vectint[record]:\\n continue\\n if \\\"trim3\\\" in vectint[record]:\\n # We cannot work directly on the records hash\\n # duplicate the sequence, and modify it\\n recordseq = recordseq[:vectint[record][\\\"trim3\\\"]]\\n if \\\"trim5\\\" in vectint[record]:\\n # We cannot work directly on the records hash\\n # duplicate the sequence, and modify it\\n recordseq = recordseq[vectint[record][\\\"trim5\\\"]:]\\n # print modified sequence\\n if len(recordseq.seq) > 0:\\n print(\\\">\\\"+record)\\n print(recordseq.seq)\\n else:\\n # print unmodified sequence\\n print(\\\">\\\"+record)\\n print(records[record].seq)\",\n \"def add_synthetic_links(in_file,links_columns_all_details,out_file):\\n\\n df = pd.read_csv(in_file)\\n\\n\\n links_node_swapped_columns_all_details = links_columns_all_details[math.floor(len(links_columns_all_details)/2):] + links_columns_all_details[0:math.floor(len(links_columns_all_details)/2)]\\n\\n df_new = df.copy()\\n\\n for ix1, (Index, row1) in tqdm(enumerate(df.iterrows())):\\n temp = []\\n for i in range(len(links_columns_all_details)):\\n\\n if i < math.floor(len(links_columns_all_details)/2):\\n temp.append(df.iat[ix1, links_columns_all_details[i]])\\n df_new.iat[ix1, links_columns_all_details[i]] = df.iat[ix1, links_node_swapped_columns_all_details[i]]\\n else:\\n df_new.iat[ix1, links_columns_all_details[i]] = temp[i - math.floor(len(links_columns_all_details)/2)]\\n\\n print('swapped details of ',ix1)\\n\\n # import ipdb; ipdb.set_trace()\\n\\n df_new = df_new.assign(synthetic = np.ones(df_new.shape[0]))\\n df = df.assign(synthetic = np.zeros(df.shape[0]))\\n\\n df = df.append(df_new)\\n\\n\\n df.to_csv(out_file, index=False)\\n\\n return df\",\n \"def repair(self) -> None:\\n\\n rows_without_domain = self._select_generator(\\n \\\"\\\"\\\"SELECT rowid, url as 'url [url]'\\n FROM bookmarks\\n WHERE domain IS NULL\\\"\\\"\\\"\\n )\\n\\n for row in rows_without_domain:\\n self._execute(\\n \\\"UPDATE bookmarks SET domain=? WHERE rowid=?\\\",\\n (row[\\\"url\\\"].domain, row[\\\"id\\\"])\\n )\",\n \"def add_signal_prefix(df_phys, can_id_prefix=False, pgn_prefix=False, bus_prefix=False):\\n from J1939_PGN import J1939_PGN\\n \\n if df_phys.empty:\\n return df_phys \\n else:\\n prefix = \\\"\\\"\\n if bus_prefix:\\n prefix += df_phys[\\\"BusChannel\\\"].apply(lambda x: f\\\"{x}.\\\")\\n if can_id_prefix:\\n prefix += df_phys[\\\"CAN ID\\\"].apply(lambda x: f\\\"{hex(int(x))[2:].upper()}.\\\" )\\n if pgn_prefix:\\n prefix += df_phys[\\\"CAN ID\\\"].apply(lambda x: f\\\"{J1939_PGN(int(x)).pgn}.\\\")\\n \\n df_phys[\\\"Signal\\\"] = prefix + df_phys[\\\"Signal\\\"]\\n \\n return df_phys\",\n \"def fixing_annotation(key, n):\\n if key + '.' + n not in self.propbank:\\n key = key.replace('-', '_')\\n return key + '.' + n\",\n \"def _exceptions_lists_delta_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._exceptions_lists_delta_tblname, operator_id)\",\n \"def _fill_reference_loaded_fields(self, head, tail, field_name, value):\\n name = \\\".\\\".join(head)\\n if tail:\\n # there is some fields for referenced document\\n if name not in self._reference_loaded_fields:\\n self._reference_loaded_fields[name] = {}\\n self._reference_loaded_fields[name][tail] = value\\n # and we should include reference field explicitly\\n return (name, QueryFieldList.ONLY)\\n else:\\n return (field_name, value)\",\n \"def addRDGfaresinfo(df,lookupdf,postfix):\\n #copy of data frame made to avoid SettingWithCopyWarning by making the copy explicit\\n df_dt = df.copy()\\n\\n #datatyping of RDG info to match later match with superfile\\n df_dt.loc[:,'Origin Code'] = df.loc[:,'Origin Code'].astype(str)\\n df_dt.loc[:,'Destination Code'] = df.loc[:,'Destination Code'].astype(str)\\n df_dt.loc[:,'Route Code'] = df.loc[:,'Route Code'].astype(str)\\n df_dt.loc[:,'Product Code'] = df.loc[:,'Product Code'].astype(str)\\n\\n #merge of the RDG info with superfile\\n df_dt = pd.merge(left=df_dt,right=lookupdf[['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)','FARE']],\\n how='left',\\n left_on=['Origin Code','Destination Code','Route Code','Product Code'],\\n right_on=['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)'])\\n\\n #drop unnecessary columns from the merge process\\n df_dt.drop(['ORIGIN_CODE','DESTINATION_CODE','ROUTE_CODE','Lennon product code (CTOT)'],axis=1,inplace=True)\\n\\n #rename columns as needed\\n df_dt.rename(columns={'FARE':'RDG_FARES'+postfix,'Fares ticket type description':'RDG_Fares ticket type description'+postfix},inplace=True)\\n\\n return df_dt\",\n \"def addRelSample(self, sample_name, filename):\\n tax_levels = None\\n if len(self.abundance_df.columns) == 0:\\n self.abundance_df = pd.read_csv(filename, header=0, sep='\\\\t') #krona (no header, no index)\\n cols = list(self.abundance_df.columns)\\n self.abundance_df = self.abundance_df[[cols[0]] + cols[:1:-1]]\\n self.tax_levels = self.abundance_df.columns.tolist()[1:]\\n #self.abundance_df = self.abundance_df[self.abundance_df.columns[0] + self.tax_levels]\\n self.abundance_df.rename(columns={self.abundance_df.columns[0]:sample_name}, inplace=True)\\n self.abundance_df.index = self.abundance_df[self.tax_levels[0]]+'_'\\n self.abundance_df.index.name = None \\n #self.abundance_df = self.abundance_df.loc[:,[self.abundance_df.columns[0]] + self.tax_levels]\\n else:\\n sample_df = pd.read_csv(filename, header=0, sep='\\\\t')\\n sample_df.rename(columns={sample_df.columns[0]:sample_name}, inplace=True) \\n sample_df.index = sample_df[self.tax_levels[0]]+'_'\\n sample_df.index.name = None \\n self.abundance_df = pd.merge(self.abundance_df, sample_df, how='outer', on=self.tax_levels)\\n self.abundance_df.index = self.abundance_df[self.tax_levels[0]]+'_'\\n self.abundance_df.index.name = None\\n self.abundance_df.fillna(value=0, inplace=True) \\n self.abundance_df[sample_name] = self.abundance_df[sample_name].astype(float)\\n \\n self.sample_names.append(sample_name.strip())\\n self.abundance_df = self.abundance_df[self.sample_names + self.tax_levels]\\n myindex = list(self.abundance_df.index)\\n newlist = sorted(set([i for i in myindex if myindex.count(i)>1]))\\n #problems with the ncbi taxonomy (typos?)\\n # for i in newlist:\\n # self.abundance_df.loc[i,self.sample_names] = self.abundance_df.loc[i].sum(numeric_only=True)\\n # self.abundance_df.drop(i, inplace=True)\\n self.abundance_raw_df = None\\n return self.tax_levels\",\n \"def _notifications_triplets_new_tblname(self):\\n return 'listgen_temp_{0}_new_notifications_triplets'.format(self._run_id)\",\n \"def _notifications_lists_old_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._notifications_lists_old_tblname, operator_id)\",\n \"def add_ambig_to_total_assigned(self):\\n self.n_assigned_list.append(len(self.ambig))\\n self.log_ambig = True\",\n \"def check_missing_unique_link():\\n from mspray.apps.main.utils import queryset_iterator\\n\\n queryset = SprayDay.objects.filter(spraypoint__isnull=True).only(\\n \\\"pk\\\", \\\"location_id\\\"\\n )\\n for record in queryset_iterator(queryset):\\n add_unique_record(record.pk, record.location_id)\\n gc.collect()\",\n \"def fix_namedict(self, namedict, table):\\n experiment_fields = self.experiment_fields()\\n for idx, entry in enumerate(namedict):\\n for k, v in experiment_fields.iteritems():\\n if k == 'experiment_iteration':\\n # Initialize iterations at 0\\n entry[k] = 0\\n elif k not in entry.keys():\\n entry[k] = None\\n namedict[idx] = entry\\n return namedict\",\n \"def autoname(self):\\n ret = \\\"%(table)s_%(reftable)s_fkey\\\"%dict(\\n table=self.table.name,\\n reftable=self.reftable.name,\\n )\\n return ret\",\n \"def print_missing(num):\\n if not num:\\n num = task_get_option(\\\"print-extcites\\\")\\n\\n write_message(\\\"Listing external papers cited by %i or more internal records:\\\" % num)\\n\\n res = run_sql(\\\"SELECT COUNT(id_bibrec), extcitepubinfo FROM rnkCITATIONDATAEXT \\\\\\n GROUP BY extcitepubinfo HAVING COUNT(id_bibrec) >= %s \\\\\\n ORDER BY COUNT(id_bibrec) DESC\\\", (num,))\\n for (cnt, brec) in res:\\n print str(cnt)+\\\"\\\\t\\\"+brec\\n\\n write_message(\\\"Listing done.\\\")\",\n \"def test_dump_related_records(testdata):\\n doc1 = Document.get_record_by_pid(testdata[\\\"documents\\\"][0][\\\"document_pid\\\"])\\n doc2 = Document.get_record_by_pid(testdata[\\\"documents\\\"][1][\\\"document_pid\\\"])\\n doc3 = Document.get_record_by_pid(testdata[\\\"documents\\\"][2][\\\"document_pid\\\"])\\n assert doc1[\\\"related_records\\\"] == []\\n assert doc2[\\\"related_records\\\"] == []\\n assert doc3[\\\"related_records\\\"] == []\\n\\n doc1.related.add_language(doc2)\\n doc1.commit()\\n\\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\\n assert doc1[\\\"related_records\\\"] == [\\n record_to_relation_dump(doc2, RelatedRecords.language_relation())\\n ]\\n assert doc2[\\\"related_records\\\"] == [\\n record_to_relation_dump(doc1, RelatedRecords.language_relation())\\n ]\\n\\n doc1.related.add_language(doc3)\\n doc1.commit()\\n\\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\\n doc3 = Document.get_record_by_pid(doc3[doc3.pid_field])\\n assert doc1[\\\"related_records\\\"] == [\\n record_to_relation_dump(doc2, RelatedRecords.language_relation()),\\n record_to_relation_dump(doc3, RelatedRecords.language_relation())\\n ]\\n assert doc2[\\\"related_records\\\"] == [\\n record_to_relation_dump(doc1, RelatedRecords.language_relation()),\\n record_to_relation_dump(doc3, RelatedRecords.language_relation())\\n ]\\n assert doc3[\\\"related_records\\\"] == [\\n record_to_relation_dump(doc1, RelatedRecords.language_relation()),\\n record_to_relation_dump(doc2, RelatedRecords.language_relation())\\n ]\\n\\n doc1.related.remove_language(doc2)\\n doc1.related.remove_language(doc3)\\n doc1.commit()\\n\\n doc1 = Document.get_record_by_pid(doc1[doc1.pid_field])\\n doc2 = Document.get_record_by_pid(doc2[doc2.pid_field])\\n doc3 = Document.get_record_by_pid(doc3[doc3.pid_field])\\n assert doc1[\\\"related_records\\\"] == []\\n assert doc2[\\\"related_records\\\"] == []\\n assert doc3[\\\"related_records\\\"] == []\",\n \"def get_cypher_xref_text():\\n return \\\"\\\"\\\"\\n MERGE (id:CrossReference:Identifier {primaryKey:row.primaryKey})\\n ON CREATE SET id.name = row.id,\\n id.globalCrossRefId = row.globalCrossRefId,\\n id.localId = row.localId,\\n id.crossRefCompleteUrl = row.crossRefCompleteUrl,\\n id.prefix = row.prefix,\\n id.crossRefType = row.crossRefType,\\n id.uuid = row.uuid,\\n id.page = row.page,\\n id.primaryKey = row.primaryKey,\\n id.displayName = row.displayName\\n\\n MERGE (o)-[gcr:CROSS_REFERENCE]->(id) \\\"\\\"\\\"\",\n \"def table_key(self, reindex_dict):\\n reindexed_marks = []\\n for m in self.component1.marks:\\n new_m = reindex_dict.get(m)\\n if new_m == None:\\n if len(reindex_dict) == 0:\\n new_m = 0\\n else:\\n new_m = max(reindex_dict.values())+1\\n reindex_dict[m] = new_m\\n reindexed_marks.append(new_m)\\n return tuple( [self.component1.genus] + sorted(reindexed_marks) )\",\n \"def _makeInternalIdentifier(self, prefix, key):\\n\\n return '_:' + hashlib.sha1(\\n ('fb'+prefix+'key'+key).encode('utf-8')).hexdigest()[1:20]\",\n \"def labelTable(self,table):\\n \\n for sslice,_, lFields in self._lLabellingInstruction:\\n for field in lFields:\\n if field is not None:\\n try:\\n for cell in np.nditer(table.getNPArray()[sslice],['refs_ok'],op_dtypes=np.dtype(object)):\\n cell[()].addField(field.cloneMe())\\n except: pass\",\n \"def load_master_table():\\n\\n # read table\\n my_globals['master_table_data'] = pd.read_table(my_globals['master_table_path'])\\n\\n # also make a new column with a unique subject identifier\\n my_globals['master_table_data']['subject'] = my_globals['master_table_data'].expID.apply(lambda x: x.split('e')[0])\",\n \"def makeBackrefLink(self, info, g_links, i):\\n atts, content, infoid, link = '', '', '', ''\\n if 'def' in info:\\n link = info['def']['link']\\n backlink_type = link or g_links\\n i_ = self.encode_high(i)\\n allow_inc = i not in self.syms\\n i_ = int(i_)\\n\\n if backlink_type == \\\"!\\\":\\n return ''\\n elif backlink_type == '^':\\n return \\\"\\\"\\\"<sup><a href=\\\"#noteref%s\\\">%s</a></sup>\\\"\\\"\\\" % (\\n info['refids'][0], i\\n )\\n else:\\n result = []\\n for refid in info['refids']:\\n i_entity = self.decode_high(i_)\\n sup = \\\"\\\"\\\"<sup><a href=\\\"#noteref%s\\\">%s</a></sup>\\\"\\\"\\\" % (\\n refid, i_entity\\n )\\n if allow_inc:\\n i_ += 1\\n result.append(sup)\\n result = ' '.join(result)\\n return result\",\n \"def create_index_row_logical_model_with_supplements(record, directory):\\n from bs4 import BeautifulSoup\\n soup = BeautifulSoup(\\\"<b></b>\\\", 'lxml')\\n tr = soup.new_tag(\\\"tr\\\")\\n td_supplement = soup.new_tag(\\\"td\\\")\\n\\n if \\\"European Supplement\\\" in record[\\\"supplement\\\"]:\\n span_supplement = soup.new_tag(\\\"spam\\\")\\n span_supplement['class'] = \\\"badge badge-secondary\\\"\\n span_supplement.string = \\\"European Supplement\\\"\\n td_supplement.insert(1,span_supplement)\\n tr.insert(1,td_supplement)\\n\\n td_ic_name = soup.new_tag(\\\"td\\\")\\n td_ic_name[\\\"data-order\\\"] = record[\\\"class name\\\"]\\n if record[\\\"stereotype\\\"] != \\\"missing data\\\": #The record is a class\\n filename = classname_to_filename(str(record['class name']))\\n url = directory+filename\\n text = record[\\\"class name\\\"]\\n print(text)\\n new_link = soup.new_tag(\\\"a\\\")\\n new_link['href'] = url\\n new_link['target'] = \\\"_blank\\\"\\n new_link.string = text\\n td_ic_name.insert(1,new_link)\\n tr.insert(2,td_ic_name)\\n else: #The record is a property\\n td_ic_name.string = record[\\\"class name\\\"]\\n tr.insert(2,td_ic_name)\\n \\n td_dc_name = soup.new_tag(\\\"td\\\")\\n td_dc_name[\\\"data-order\\\"] = str(record[\\\"property name\\\"])\\n if record[\\\"stereotype\\\"] == \\\"missing data\\\": #The record is a property\\n filename = str(record['class name'])+\\\".html#\\\"+str(record['property name'])\\n filename = filename.replace(\\\"/\\\", \\\"-\\\")\\n filename = filename.replace(\\\"*\\\", \\\"-\\\")\\n filename = filename.replace(\\\" \\\", \\\"\\\")\\n filename = filename.replace(\\\"\\\\t\\\", \\\"\\\")\\n filename = filename.replace(\\\"\\\\n\\\", \\\"\\\")\\n url = directory+filename\\n text = str(record[\\\"property name\\\"])\\n print(text)\\n new_link = soup.new_tag(\\\"a\\\")\\n new_link['href'] = url\\n new_link['target'] = \\\"_blank\\\"\\n new_link.string = text\\n td_dc_name.insert(1,new_link)\\n tr.insert(3,td_dc_name)\\n else: #The record is a class\\n td_dc_name.string = \\\"-\\\"\\n tr.insert(3,td_dc_name)\\n\\n if record[\\\"definition\\\"] != \\\"\\\":\\n td_def = soup.new_tag(\\\"td\\\")\\n td_def.string = str(record[\\\"definition\\\"])\\n tr.insert(4,td_def)\\n \\n if record[\\\"type\\\"] != \\\"missing data\\\":\\n td_def = soup.new_tag(\\\"td\\\")\\n td_def.string = str(record[\\\"type\\\"])\\n tr.insert(5,td_def)\\n else:\\n td_def = soup.new_tag(\\\"td\\\")\\n td_def.string = \\\"-\\\"\\n tr.insert(5,td_def)\\n \\n return tr\",\n \"def makeTableNamesList(n, ):\",\n \"def _notifications_imei_new_tblname(self):\\n return 'listgen_temp_{0}_new_notifications_imeis'.format(self._run_id)\",\n \"def add_blank_record(self, uploader):\\n with self.session as session:\\n record = IndexRecord()\\n base_version = BaseVersion()\\n\\n did = str(uuid.uuid4())\\n baseid = str(uuid.uuid4())\\n if self.config.get('PREPEND_PREFIX'):\\n did = self.config['DEFAULT_PREFIX'] + did\\n\\n record.did = did\\n base_version.baseid = baseid\\n\\n record.rev = str(uuid.uuid4())[:8]\\n record.baseid = baseid\\n record.uploader = uploader\\n\\n session.add(base_version)\\n session.add(record)\\n session.commit()\\n\\n return record.did, record.rev, record.baseid\",\n \"def reformat(self, seq_name, *, prefix=\\\"s\\\"):\\n\\t\\treturn \\\"%s_%012u\\\" % (prefix, self.get_sid(seq_name))\",\n \"def test_data_source_soaps_id_replace_post(self):\\n pass\",\n \"def _notifications_lists_new_part_tblname(self, operator_id):\\n return '{0}_{1}'.format(self._notifications_lists_new_tblname, operator_id)\",\n \"def shorten_url(url: str, next_record: int) -> str:\\r\\n encoded_record = encode(next_record)\\r\\n LINKS[next_record] = url\\r\\n return SITE + f'/{encoded_record}'\",\n \"def record_prefix(required_type, factory):\\n field = record_type(required_type)\\n field += factory.get_rule('transaction_sequence_n')\\n field += factory.get_rule('record_sequence_n')\\n\\n # field.leaveWhitespace()\\n\\n return field\",\n \"def reset(self):\\r\\n self.look_up_table = list(map(convert_to_list, self.const_look_up_table))\",\n \"def _backreference_keys(self, ref, key_in_ref):\\n return (self.__class__.REFERENCE_FIELDS +\\n self.__class__.DEPENDENT_LINES +\\n self.__class__.OTHER_REFERENCES)\",\n \"def _fix_evidence_refs(db, rid_stmt_trios):\\n rid_set = {rid for rid, _, _ in rid_stmt_trios if rid is not None}\\n logger.info(\\\"Getting text refs for %d readings.\\\" % len(rid_set))\\n if rid_set:\\n rid_tr_pairs = db.select_all([db.Reading.id, db.TextRef],\\n db.Reading.id.in_(rid_set),\\n *db.join(db.TextRef, db.Reading))\\n rid_tr_dict = {rid: tr for rid, tr in rid_tr_pairs}\\n for rid, sid, stmt in rid_stmt_trios:\\n if rid is None:\\n # This means this statement came from a database, not reading.\\n continue\\n assert len(stmt.evidence) == 1, \\\\\\n \\\"Only raw statements can have their refs fixed.\\\"\\n _set_evidence_text_ref(stmt, rid_tr_dict[rid])\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.53357","0.5239754","0.50661397","0.5053511","0.50410986","0.5036657","0.49877408","0.49684855","0.4958167","0.4932847","0.49291137","0.49014091","0.48973417","0.4891694","0.48872414","0.48609567","0.48588482","0.48420462","0.4841088","0.4835849","0.48230368","0.4808192","0.48045275","0.48007023","0.47977185","0.4797717","0.47941247","0.47835562","0.4771736","0.47520578","0.4740473","0.47335303","0.47288075","0.47204822","0.47123787","0.47026426","0.46953523","0.46824014","0.46675074","0.46608964","0.46596456","0.46564162","0.46497107","0.46460778","0.46442914","0.46417946","0.46359372","0.46338558","0.46275863","0.46275595","0.4622689","0.46197218","0.46051067","0.46043128","0.46014878","0.46014315","0.4597908","0.45977604","0.4594533","0.4582818","0.45713934","0.4566389","0.45624074","0.45604706","0.45523185","0.45521393","0.4539826","0.45379245","0.4525861","0.4524759","0.45138243","0.45100522","0.45086008","0.45082214","0.4503101","0.4501747","0.44984686","0.44950217","0.4494914","0.44948277","0.44866326","0.44855863","0.44778836","0.44734418","0.44712862","0.44673246","0.4464821","0.44588107","0.44586155","0.4454921","0.44536984","0.44523048","0.44516885","0.44492906","0.44474453","0.44450936","0.44446763","0.44417125","0.44388014","0.4433045"],"string":"[\n \"0.53357\",\n \"0.5239754\",\n \"0.50661397\",\n \"0.5053511\",\n \"0.50410986\",\n \"0.5036657\",\n \"0.49877408\",\n \"0.49684855\",\n \"0.4958167\",\n \"0.4932847\",\n \"0.49291137\",\n \"0.49014091\",\n \"0.48973417\",\n \"0.4891694\",\n \"0.48872414\",\n \"0.48609567\",\n \"0.48588482\",\n \"0.48420462\",\n \"0.4841088\",\n \"0.4835849\",\n \"0.48230368\",\n \"0.4808192\",\n \"0.48045275\",\n \"0.48007023\",\n \"0.47977185\",\n \"0.4797717\",\n \"0.47941247\",\n \"0.47835562\",\n \"0.4771736\",\n \"0.47520578\",\n \"0.4740473\",\n \"0.47335303\",\n \"0.47288075\",\n \"0.47204822\",\n \"0.47123787\",\n \"0.47026426\",\n \"0.46953523\",\n \"0.46824014\",\n \"0.46675074\",\n \"0.46608964\",\n \"0.46596456\",\n \"0.46564162\",\n \"0.46497107\",\n \"0.46460778\",\n \"0.46442914\",\n \"0.46417946\",\n \"0.46359372\",\n \"0.46338558\",\n \"0.46275863\",\n \"0.46275595\",\n \"0.4622689\",\n \"0.46197218\",\n \"0.46051067\",\n \"0.46043128\",\n \"0.46014878\",\n \"0.46014315\",\n \"0.4597908\",\n \"0.45977604\",\n \"0.4594533\",\n \"0.4582818\",\n \"0.45713934\",\n \"0.4566389\",\n \"0.45624074\",\n \"0.45604706\",\n \"0.45523185\",\n \"0.45521393\",\n \"0.4539826\",\n \"0.45379245\",\n \"0.4525861\",\n \"0.4524759\",\n \"0.45138243\",\n \"0.45100522\",\n \"0.45086008\",\n \"0.45082214\",\n \"0.4503101\",\n \"0.4501747\",\n \"0.44984686\",\n \"0.44950217\",\n \"0.4494914\",\n \"0.44948277\",\n \"0.44866326\",\n \"0.44855863\",\n \"0.44778836\",\n \"0.44734418\",\n \"0.44712862\",\n \"0.44673246\",\n \"0.4464821\",\n \"0.44588107\",\n \"0.44586155\",\n \"0.4454921\",\n \"0.44536984\",\n \"0.44523048\",\n \"0.44516885\",\n \"0.44492906\",\n \"0.44474453\",\n \"0.44450936\",\n \"0.44446763\",\n \"0.44417125\",\n \"0.44388014\",\n \"0.4433045\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5318926"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":79595,"cells":{"query":{"kind":"string","value":"temporary simple table + index"},"document":{"kind":"string","value":"def create_analysis_tables():\n sql1 = \"CREATE TABLE IF NOT EXISTS tmpcit (citer mediumint(10), cited mediumint(10)) TYPE=MyISAM\"\n sql2 = \"CREATE UNIQUE INDEX citercited on tmpcit(citer, cited)\"\n sql3 = \"CREATE INDEX citer on tmpcit(citer)\"\n sql4 = \"CREATE INDEX cited on tmpcit(cited)\"\n try:\n run_sql(sql1)\n run_sql(sql2)\n run_sql(sql3)\n run_sql(sql4)\n except:\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_index():","def T1(request):\n T = _get_test_table()\n if request.param:\n T.add_index(\"a\")\n return T","def test_index_alternate(self):\n self.insert()\n self.tbl[::2]","def build_index():\n pass","def index_object(idxs=None):","def create_new_index(self, dict_pg_info):\n # ! Setting if fun can use default setting\n ruler = Rules()\n str_conn = ruler.pg_info_rules(dict_pg_info)\n conn = psycopg2.connect(str_conn)\n\n with conn:\n with conn.cursor() as cur:\n str_create_table = \"CREATE TABLE \" + dict_pg_info['table'] + \" (path varchar PRIMARY KEY);\"\n # ! Check if table already exit\n cur.execute(str_create_table)\n cur.close()\n\n conn.close()","def get_table(base, engine):\n class w1_temp_table(base):\n __tablename__ = 'w1_temp'\n __table_args__ = {\"useexisting\": True}\n\n id = sa.Column(sa.types.Integer, primary_key=True)\n logger_id = sa.Column(sa.types.Integer)\n value = sa.Column(sa.types.String)\n datetime = sa.Column(sa.types.DateTime)\n return w1_temp_table","def index_schema_builder(table):\n conn = table.parent.parent.connection\n\n idx = OrderedDict()\n indexes = conn.execute(\"SHOW INDEXES FROM `%s`.`%s`\" % (table.parent.name, table.name))\n\n if not indexes:\n return idx\n\n for index in indexes:\n n = index['Key_name']\n if n not in idx:\n indexitem = IndexSchema(name=n, parent=table)\n indexitem.non_unique = (bool(index['Non_unique'])) # == not unique\n indexitem.table_name = index['Table']\n\n key_type = index['Index_type'].upper()\n\n if index['Key_name'].upper() == \"PRIMARY\":\n indexitem.kind = \"PRIMARY\"\n elif not indexitem.non_unique:\n indexitem.kind = \"UNIQUE\"\n elif key_type in ('FULLTEXT', 'SPATIAL'):\n indexitem.kind = key_type\n else:\n indexitem.kind = \"INDEX\"\n\n if key_type in ('BTREE', 'HASH', 'RTREE'):\n indexitem.type = key_type\n\n indexitem.collation = index['Collation']\n indexitem.comment = index['Comment']\n\n idx[n] = indexitem\n\n if index['Column_name'] not in idx[n].fields:\n idx[n].fields.insert(index['Seq_in_index'], (index['Column_name'], index['Sub_part'] or 0))\n\n return idx","def generate_inv_index(people):\n pass","def __call__(self, i, j):\n return _table.Table___call__(self, i, j)","def create_index(self):\r\n\r\n #### Begin functionality here\r\n\r\n return()","def create_index(self, table_name, index, timeout):\n _abstract()","def create_index(self, table_name, index, timeout):\n _abstract()","def build_index(self):\n \n \n geoids = self.partitions.find_or_new(table='facilities_geoids')\n addresses = self.partitions.find_or_new(table='facilities_addresses')\n facilities = self.partitions.find(table='facilities')\n \n facilities.attach(addresses,'addresses')\n facilities.attach(geoids,'geoids')\n \n q = \"\"\"\n SELECT year, type, oshpd_id, facility_name, dba_city, dba_zip_code, blockgroup_gvid, tract_gvid, county_gvid\n FROM facilities\n JOIN geoids.facilities_geoids AS geoids ON geoids.facilities_id = facilities.id\n JOIN addresses.facilities_addresses AS addresses ON addresses.facilities_id = facilities.id\n \"\"\"\n \n p = self.partitions.find_or_new(table='facilities_index')\n p.clean()\n lr = self.init_log_rate()\n \n with p.inserter() as ins:\n for row in facilities.query(q):\n ins.insert(row)\n lr(str(p.identity))","def build_index(self):\r\n date_time('Building indexes in citations table')\r\n self.cursor.execute('DROP INDEX IF EXISTS IDX_citations ;')\r\n self.cursor.execute('CREATE INDEX IDX_citations ON citations (citation);')\r\n self.conn.commit()\r\n gc.collect()","def reindex(self):","def reindex(self):","def table(self, i):\n return self.__tables[i]","def table(self, i):\n return self.__tables[i]","def test_get_table_indices(self):\n indices = self.connection.get_table_indices(self.TABLE_NAME)\n print(indices)\n\n self.assertEqual(indices[0].name, 'PRIMARY')\n self.assertEqual(indices[1].name, 'idx_foo')\n\n self.assertListEqual(indices[0].columns, ['id', 'foo'])\n self.assertListEqual(indices[1].columns, ['foo'])\n\n self.assertTrue(indices[0].is_primary)\n self.assertTrue(indices[0].is_unique)\n self.assertFalse(indices[1].is_primary)\n self.assertFalse(indices[1].is_unique)\n\n # assert False","def reindex_table(conn, table_name, verbose=0):\n t1 = time.time()\n # check if index exists, delete if needed\n try:\n q = \"DROP INDEX \" + INDEX_NAMES + table_name + '_1'\n conn.execute(q)\n except sqlite3.OperationalError, e:\n #print 'sqlite error:', e\n pass\n try:\n q = \"DROP INDEX \" + INDEX_NAMES + table_name + '_2'\n conn.execute(q)\n except sqlite3.OperationalError, e:\n #print 'sqlite error:', e\n pass\n # create index on jump codes\n q = \"CREATE INDEX \" + INDEX_NAMES + table_name + '_1'\n q += \" ON \" + table_name + \" (jumpcode ASC, weight ASC)\"\n conn.execute(q)\n conn.commit()\n if verbose > 0:\n strtime = str(datetime.timedelta(seconds=time.time() - t1))\n print 'REINDEXED THE TABLE %s (jumpcode,weight) IN %s' % (table_name, strtime)\n t2 = time.time()\n # create index on tids\n q = \"CREATE INDEX \" + INDEX_NAMES + table_name + '_2'\n q += \" ON \" + table_name + \" (tidid ASC)\"\n conn.execute(q)\n conn.commit()\n # verbose\n if verbose > 0:\n strtime = str(datetime.timedelta(seconds=time.time() - t2))\n print 'REINDEXED THE TABLE %s (tidid) IN %s' % (table_name, strtime)","def simple_index():\n examples = [\n benchmark.Example(\n inputs=[\n [12, 34, 56, 78],\n -2,\n ],\n output=56,\n ),\n ]\n constants = []\n description = 'Index into a tensor'\n target_program = 'in1[in2]'\n source = 'handwritten task'\n return benchmark.Benchmark(examples=examples,\n constants=constants,\n description=description,\n target_program=target_program,\n source=source,\n name='simple_index')","def build_index(self, dict_pg_info, list_insert):\n flag_exit = True\n if flag_exit is False:\n self.create_new_index(dict_pg_info)\n self.insert_index(dict_pg_info, list_insert)","def __call__(self, i, j):\n return _table.STable___call__(self, i, j)","def build_index(self):\n self.rebuild_index()","def convert_to_index(df, table):\n df[USER_IDX] = df[COMP_MID].apply(table.user_to_index)\n df[PROD_IDX] = df[PROD_NO].apply(table.item_to_index)\n index = df[(df[USER_IDX] < 0) | (df[PROD_IDX] < 0)].index\n df.drop(index=index, columns=[COMP_MID, PROD_NO], inplace=True)","def test_creating_index_type(self):","def init_index(self):\n raise NotImplementedError","def generate_reverse_index(self):","def ensure_table_indexes(collection) -> None:\n # Unique ticker event ID\n collection.create_index('data.u', unique=True)\n # The symbol, e.g. BTCUSDT\n collection.create_index('data.s')\n # The time for sorting.\n collection.create_index('time')","def get_tab_index(raw_read):\n # --------------------------------------------------------------------------\n # Get the index\n # --------------------------------------------------------------------------\n indx = raw_read['IndexSheet'].loc[4:,:].copy()\n # --------------------------------------------------------------------------\n # Reset the column so that we remove Table #\n # --------------------------------------------------------------------------\n ref_col = indx.columns.to_list()[0]\n indx[ref_col] = [i[i.find('-')+2:] for i in indx[ref_col].to_list()]\n # --------------------------------------------------------------------------\n # Put the reference tab\n # --------------------------------------------------------------------------\n indx['tab'] = [f'T{i+1}' for i in range(len(indx[ref_col]))]\n # --------------------------------------------------------------------------\n # Set the index\n # --------------------------------------------------------------------------\n indx.set_index(ref_col,inplace=True)\n return indx","def __getitem__(self, key):\n return TableWrapper(self.dbms, key, self.db)","def getTableByIndex(self, index):\n pass","def get_index_on_table(self, table, full=False):\n indexes = self.get_index_list()\n if full:\n return [la for la in indexes if la[1] == table]\n return [la[0] for la in indexes if la[1] == table]","def create_indices():\n conn = connect()\n c = conn.cursor()\n\n # To prevent rematch btw players\n c.execute(\n \"\"\"\n CREATE UNIQUE INDEX matches_uniq_idx ON matches\n (greatest(winner, loser), least(winner, loser));\n \"\"\")\n conn.commit()\n conn.close()","def makeTableForQuery(queriedTable, resultTableDef, fragment, pars, \n\t\tdistinct=False, limits=None, suppressIndex=True,\n\t\tconnection=None):\n\treturn TableForDef(resultTableDef, suppressIndex=suppressIndex,\n\t\tconnection=connection,\n\t\trows=[r for r in queriedTable.iterQuery(resultTableDef, fragment, pars,\n\t\t\tdistinct, limits)])","def indices(self):\n if self._indices is None:\n i = []\n\n # TODO: this is not right for multi-column keys\n # TODO: new style indexes\n\n global_name = '^DD(%s,0,\"IX\",\"0\")' % self.fileid\n prefix = '^DD(%s,0,\"IX\",' % self.fileid\n while 1:\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\"\"))[0]\n if not global_name or not global_name.startswith(prefix):\n break\n suffix = global_name[len(prefix):-1]\n parts = suffix.split(\",\")\n idx_name = parts[0][1:-1]\n idx_table = parts[1]\n idx_columns = parts[2:]\n index = Index(idx_name, idx_table, idx_columns)\n i.append(index)\n\n # A second list, gives indices for a field\n columns = {}\n for idx in i:\n for c in idx.columns:\n columns[c] = 1\n\n # Now trawl the listed columns in the data dictionary, and load their\n # cross references.\n cr_names = {}\n for c in columns.keys():\n idx_root = M.Globals[\"^DD\"][self.fileid][c][1]\n if not idx_root[0].exists():\n continue\n for cr_id, val in idx_root.keys_with_decendants():\n if float(cr_id) > 0:\n cr_header = idx_root[cr_id][0].value\n parts = cr_header.split(\"^\")\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\n f = cr_names.get(parts[1], list())\n f.append(c)\n cr_names[parts[1]] = f\n\n # Now, just delete items from the index list if they are not in cr_names\n self._indices = []\n for index in i:\n cr = cr_names.get(index.name)\n if cr:\n # verify columns - lots of errors in real systems\n if len(cr) == len(index.columns):\n invalid = False\n for c in cr:\n if c not in index.columns:\n invalid = True\n continue\n if not invalid:\n self._indices.append(index)\n\n return self._indices","def get_indices(self):\n\n def query(rel): \n return \"\"\"SELECT pg_class.relname, pg_index.indkey\n FROM pg_class, pg_index\n WHERE (pg_index.indexrelid = pg_class.oid)\n AND (pg_index.indrelid = (SELECT pg_class.oid FROM pg_class WHERE pg_class.relname = \\'{}\\'));\n \"\"\".format(rel)\n\n rels = tpch.schema.keys()\n idxs = dict.fromkeys(rels)\n\n with self.tpch_cxn.cursor() as curs:\n for rel in rels:\n curs.execute(query(rel))\n idxs_ = curs.fetchall()\n idxs_ = dict(idxs_) # index -> index keys \n \n # TODO this can be done cleanly in query\n # pg_index.indkey is a SQL array of attributes indices in their respective tables\n split=lambda attrs: attrs.split() \n cast=lambda attrs: list(map(lambda attr: int(attr)-1, attrs))\n invertindex=lambda attrs: list(np.array(schema[rel])[attrs])\n\n attrs = idxs_.values() \n attrs = list(map(split, attrs))\n attrs = list(map(cast, attrs))\n attrs = list(map(invertindex, attrs))\n\n idxs_ = {key : attrs[i] for i, key in enumerate(idxs_.keys())}\n idxs[rel] = idxs_\n return idxs","def tableIndexer(self, indVar, i):\n assert indVar in self.indVars, \"Input indVar %s is not a valid indVar!\" % indVar\n assert i >= 0, \"Index in tableIndexer, %s, is less than zero. Dummy.\" % i\n assert i < self.tableShapeDict[indVar], \\\n \"Index in tableIndexer, %s, is greater than table extends in indVar direction!\" % i\n\n result = []\n for var in self.indVars:\n if indVar == var:\n result.append(i)\n else:\n result.append(slice(None))\n return tuple(result)","def createTNTTempTable(self):\n\t\t\n\t\tquery = \"\"\"\n\t\tSELECT \n\t\tIDENTITY (INT) as rownumber,\n\t\ta.NameID, a.Term, a.Value, a.Reference\n\t\tINTO [{0}]\n\t\tFROM (\n\t\tSELECT a.AnalysisID, l.NameID, SUBSTRING(ac.DisplayText,18,255) AS Term, a.AnalysisValue AS Value, lr.TaxonNameListRefText AS Reference\n\t\tFROM DiversityTaxonNames_{1}.dbo.TaxonNameList l\n\t\t\tINNER JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListReference lr ON lr.NameID=l.NameID AND lr.ProjectID=l.ProjectID\n\t\t\tLEFT JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListAnalysis a ON a.NameID=lr.NameID AND a.ProjectID=lr.ProjectID\n\t\t\tLEFT JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListAnalysisCategory ac ON ac.AnalysisID=a.AnalysisID\n\t\tWHERE\n\t\t\tac.DisplayText IN (\n\t\t\t -- Vertebrata\n\t\t\t'RoteListe_D_2009_LangfristigerBestandstrend',\n\t\t\t'RoteListe_D_2009_Verantwortlichkeit',\n\t\t\t'RoteListe_D_2009_AktuelleBestandssituation',\n\t\t\t'RoteListe_D_2009_RL-Kategorie',\n\t\t\t'RoteListe_D_2009_KurzfristigerBestandstrend',\n\t\t\t'RoteListe_D_2009_Sonderfälle',\n\t\t\t'RoteListe_D_2009_Neobiota',\n\t\t\t'RoteListe_D_2009_LetzterNachweis',\n\t\t\t'RoteListe_D_2009_Risikofaktoren',\n\t\t\t -- Insecta and Animalia\n\t\t\t'RoteListe_D_2016_RL-Kategorie',\n\t\t\t'RoteListe_D_2016_AktuelleBestandssituation',\n\t\t\t'RoteListe_D_2016_LangfristigerBestandstrend',\n\t\t\t'RoteListe_D_2016_KurzfristigerBestandstrend',\n\t\t\t'RoteListe_D_2016_Risikofaktoren',\n\t\t\t'RoteListe_D_2016_Verantwortlichkeit',\n\t\t\t'RoteListe_D_2016_Sonderfälle',\n\t\t\t'RoteListe_D_2016_Neobiota',\n\t\t\t'RoteListe_D_2016_LetzterNachweis',\n\t\t\t'RoteListe_D_2011_AktuelleBestandssituation',\n\t\t\t'RoteListe_D_2011_KurzfristigerBestandstrend',\n\t\t\t'RoteListe_D_2011_LangfristigerBestandstrend',\n\t\t\t'RoteListe_D_2011_Risikofaktoren',\n\t\t\t'RoteListe_D_2011_RL-Kategorie',\n\t\t\t'RoteListe_D_2011_Verantwortlichkeit',\n\t\t\t'RoteListe_D_2011_Neobiota',\n\t\t\t'RoteListe_D_2011_LetzterNachweis'\n\t\t\t) {2}\n\t\t) AS a\n\t\t;\"\"\".format(self.temptable, self.tnt_dbname, self.projectclause)\n\t\t\n\t\t\n\t\tself.cur.execute(query)\n\t\tself.con.commit()","def myhtable_create_index(files):\n res_buckets = htable(4011)\n for id, file in enumerate(files):\n if file[-4:] == '.txt':\n word_list = words(get_text(file))\n for word in word_list:\n value = htable_get(res_buckets, word)\n if value == None:\n htable_put(res_buckets, word, {id})\n else:\n value.add(id)\n htable_put(res_buckets, word, value)\n return res_buckets","def insert_index(self):\n pass","def id_to_local_index(self, id):\n raise NotImplementedError","def index_stmt(self, idx):\n return Statement(\"index\", self, idx)","def test_index_0(self):\n self.insert()\n data = self.tbl[0]\n assert self.check(self.idata[:0], data)","def bucket_indexof(table, key):","def truthtable(self):\n table = []\n for i in xrange(self.length):\n inputs = []\n binary = bin(i).lstrip('0b')\n for i in xrange(len(binary)):\n inputs.append(int(binary[i]))\n inputs.append(1)\n table.append(self.compute(inputs))\n return table","def load(cls):\n df = Operator_Table.df\n df.operator = df.operator.apply(sp.normalize)\n df.operator_alias = df.operator_alias.apply(sp.normalize)\n df = df.rename(columns={\"operator_alias\": \"alias\"})\n return SQLIndex(data=df).set_index(\"operator\")","def migrate_8(session, **kwargs):\n session.execute(\n \"CREATE INDEX ix_{tb}_baseid ON {tb} ( baseid )\"\n .format(tb=IndexRecord.__tablename__))","def _reset_index():\r\n WIX = create_in(INDEX_NAME, BmarkSchema) # noqa\r","def T1m(request):\n T = _get_test_table()\n add_index, is_quantity = request.param\n if is_quantity:\n T[\"a\"] = T[\"a\"] * u.m\n if add_index:\n T.add_index(\"a\")\n return T","def migrate_9(session, **kwargs):\n session.execute(\n \"CREATE INDEX ix_{tb}_size ON {tb} ( size )\"\n .format(tb=IndexRecord.__tablename__))\n\n session.execute(\n \"CREATE INDEX index_record_hash_type_value_idx ON {tb} ( hash_value, hash_type )\"\n .format(tb=IndexRecordHash.__tablename__))","def test_temp_table(self):\n self.assertEqual([\"test\", [[\"c1\", \"INT\"], [\"c2\", \"VARCHAR(200)\"]]],\n grammar._TEMP_TABLE_EXPR.parseString(\"test(c1 INT, c2 VARCHAR(200));\").asList())","def _get_index(self):\n\n return self.index\n\n # to avoid having differences bigger than 2pi","async def build_secret_index(self):\n pass","def __init__(self):\n self.inverted_index = OrderedDict({})\n self.i=0\n self.unique_doc_ids=set()","def migrate_2(session, **kwargs):\n try:\n session.execute(\n \"ALTER TABLE {} \\\n ADD COLUMN baseid VARCHAR DEFAULT NULL, \\\n ADD COLUMN created_date TIMESTAMP DEFAULT NOW(), \\\n ADD COLUMN updated_date TIMESTAMP DEFAULT NOW()\".format(IndexRecord.__tablename__))\n except ProgrammingError:\n session.rollback()\n session.commit()\n\n count = session.execute(\n \"SELECT COUNT(*) FROM {};\"\n .format(IndexRecord.__tablename__)).fetchone()[0]\n\n # create tmp_index_record table for fast retrival\n try:\n session.execute(\n \"CREATE TABLE tmp_index_record AS SELECT did, ROW_NUMBER() OVER (ORDER BY did) AS RowNumber \\\n FROM {}\".format(IndexRecord.__tablename__))\n except ProgrammingError:\n session.rollback()\n\n for loop in range(count):\n baseid = str(uuid.uuid4())\n session.execute(\n \"UPDATE index_record SET baseid = '{}'\\\n WHERE did = (SELECT did FROM tmp_index_record WHERE RowNumber = {});\".format(baseid, loop + 1))\n session.execute(\n \"INSERT INTO {}(baseid) VALUES('{}');\".format(BaseVersion.__tablename__, baseid))\n\n session.execute(\n \"ALTER TABLE {} \\\n ADD CONSTRAINT baseid_FK FOREIGN KEY (baseid) references base_version(baseid);\"\n .format(IndexRecord.__tablename__))\n\n # drop tmp table\n session.execute(\n \"DROP TABLE IF EXISTS tmp_index_record;\"\n )","def describe_index(self, table_name, timeout):\n\n _abstract()","def describe_index(self, table_name, timeout):\n\n _abstract()","def id_to_index(self, id):\n raise NotImplementedError","def add_table_index(self, table: pd.DataFrame, table_name: str, table_description: str) -> None:\n\n indexes = table.index\n columns = table.columns\n\n columns_lists = []\n for i in range(0, len(columns), self.table_columns_count):\n columns_list = []\n for j in range(i, len(columns)):\n columns_list.append(columns[j])\n if(len(columns_list) == self.table_columns_count):\n break\n columns_lists.append(columns_list)\n\n tag = r''\n for i in range(len(columns_lists)):\n columns_list = columns_lists[i]\n\n if(len(columns_lists) == 1):\n table_num = r''\n else:\n table_num = r' [' + str(i + 1) + '/' + str(len(columns_lists)) + ']'\n\n if(len(table) < 30):\n tag = tag + r'''\n \\begin{table}[H]\n \\center\n \\caption{''' + table_description + table_num + '''}\n \\label{tab:''' + table_name + str(len(self.tables)) + r'''}\n \\begin{tabular}{c''' + ' c' * len(columns_list) + '''}\n '''\n else:\n tag = tag + r'''\n \\begin{longtable}{''' + 'c ' + ' c' * len(columns_list) + '''}\n \\caption{''' + table_description + table_num + '''\\label{tab:''' + table_name + str(len(self.tables)) + r'''}}\\\\\n '''\n\n cell = r'''\n & ''' + str(columns_list[0])\n\n for column in columns_list[1:]:\n cell = cell + r' & ' + str(column)\n tag = tag + cell + r''' \\\\\n\n \\hline\n '''\n\n for index in indexes:\n cell = str(index)\n\n for column in columns_list:\n cell = cell + r' & ' + str(table.loc[index, column])\n\n tag = tag + cell + r''' \\\\\n '''\n\n if(len(table) < 30):\n tag = tag + r'''\n \\hline\n \\end{tabular}\n \\end{table}\n '''\n else:\n tag = tag + r'''\n \\hline\n \\end{longtable}\n '''\n\n self.tables[len(self.tables)] = [table_name, table_description]\n\n tag = tag.replace('%', '\\%').replace('_', '\\_').replace('#', '\\#')\n\n self.doc = self.doc + tag","def return_index(self, idx):\n return (\n self.timeseries[:, idx],\n self.ch_amount,\n self.freq,\n self.ch_name[idx],\n self.units[idx],\n self.start_time,\n )","def get_table(self):\n return copy.deepcopy(self._table)","def init_index(clear=False):\n return _run_indexer_func(\"init_index\", clear)","def get_prod_table_indexed(prod_table,\n prod_l_code,\n prod_l_desc,\n prod_l_plus_desc,\n prod_l_cat):\n \n char_replace = F.udf(replace_chars, F.StringType()) #udf will run row by row on partition dataframe, but normal full will run on dataframe # * will serialise the list , ** will serialise the dictionary, krags,kwargs\n if prod_l_plus_desc in trans.columns:\n prod_columns = [prod_l_code, prod_l_desc, prod_l_plus_desc]\n return prod_table.select(prod_columns).withColumn(\n prod_l_cat,\n F.concat(F.lit(\"_\"),\n char_replace(prod_l_code, F.lit(r\"[^A-Za-z0-9]+\"), F.lit(r\"\")),\n F.lit(\"_\"),\n char_replace(prod_l_desc, F.lit(r\"[^A-Za-z]+\"), F.lit(r\"\")),\n F.lit(\"_\"),\n char_replace(prod_l_plus_desc, F.lit(r\"[^A-Za-z]+\"), F.lit(r\"\"))\n )\n ).withColumn(\n \"prod_index\",\n F.dense_rank().over(Window.orderBy(prod_l_cat)))\n \n else:\n prod_columns = [prod_l_code, prod_l_desc]\n return prod_table.select(prod_columns).withColumn(\n prod_l_cat,\n F.concat(F.lit(\"_\"),\n char_replace(prod_l_code, F.lit(r\"[^A-Za-z0-9]+\"), F.lit(r\"\")),\n F.lit(\"_\"),\n char_replace(prod_l_desc, F.lit(r\"[^A-Za-z]+\"), F.lit(r\"\"))\n )\n ).withColumn(\n \"prod_index\",\n F.dense_rank().over(Window.orderBy(prod_l_cat)))","def test_integer_index():\n\tlib.backup_and_restore(\n\t\tlambda context: create_indexes(lib.create_integer_index),\n\t\tNone,\n\t\tlambda context: check_indexes(lib.check_simple_index, 12345)\n\t)","def create_index_buffer(init):\n return ffi.new('unsigned short[]', init)","def add_mode_index(self) -> None:","def test_index_12(self):\n self.insert()\n data = self.tbl[2:6]\n assert self.check(self.idata[:2], data)","def _initIndexes(self):\n class Record:\n \"\"\" a moron simple object for carrying the 'extra'-payload to index\n constructors\n \"\"\"\n def __init__(self, **kw):\n self.__dict__.update(kw)\n\n addIndex = self.addIndex\n addColumn = self.addColumn\n\n # Content indexes\n self._catalog.indexes.clear()\n for (index_name, index_type, extra) in self.enumerateIndexes():\n if extra is None:\n addIndex( index_name, index_type)\n else:\n if isinstance(extra, StringTypes):\n p = Record(indexed_attrs=extra)\n elif isinstance(extra, DictType):\n p = Record(**extra)\n else:\n p = Record()\n addIndex( index_name, index_type, extra=p )\n\n # Cached metadata\n self._catalog.names = ()\n self._catalog.schema.clear()\n for column_name in self.enumerateColumns():\n addColumn( column_name )","def create_index_tables(self):\n # List of urls that have been indexed\n self.con.execute('create table urllist(url)')\n # List of words\n self.con.execute('create table wordlist(word)')\n # What doc the word is and where it is in the doc\n self.con.execute('create table wordlocation(urlid, wordid, location)')\n # Indicates a link from one url to another\n self.con.execute('create table link(fromid integer, toid integer)')\n # which words are actually in a link\n self.con.execute('create table linkwords(wordid, linkid)')\n self.con.execute('create index wordidx on wordlist(word)')\n self.con.execute('create index urlidx on urllist(url)')\n self.con.execute('create index wordurlidx on wordlocation(wordid)')\n self.con.execute('create index urltoidx on link(toid)')\n self.con.execute('create index urlfromidx on link(fromid)')\n self.dbcommit()","def return_index(self, idx):\n return (\n self.timeseries[idx],\n self.ch_amount,\n self.freq[idx],\n self.ch_name[idx],\n self.units[idx],\n )","def test_index_1(self):\n self.insert()\n data = self.tbl[2]\n assert self.check(self.idata[:1], data)","def instantiate_indexor(prefix, width):\n stdlib = py_ast.Stdlib()\n name = py_ast.CompVar(NAME_SCHEME[\"index name\"].format(prefix=prefix))\n add_name = py_ast.CompVar(f\"{prefix}_add\")\n cells = [\n py_ast.Cell(name, stdlib.register(width)),\n py_ast.Cell(add_name, stdlib.op(\"add\", width, signed=False)),\n ]\n\n init_name = py_ast.CompVar(NAME_SCHEME[\"index init\"].format(prefix=prefix))\n init_group = py_ast.Group(\n init_name,\n connections=[\n py_ast.Connect(\n py_ast.ConstantPort(width, 2 ** width - 1), py_ast.CompPort(name, \"in\")\n ),\n py_ast.Connect(\n py_ast.ConstantPort(1, 1), py_ast.CompPort(name, \"write_en\")\n ),\n py_ast.Connect(\n py_ast.CompPort(name, \"done\"), py_ast.HolePort(init_name, \"done\")\n ),\n ],\n )\n\n upd_name = py_ast.CompVar(NAME_SCHEME[\"index update\"].format(prefix=prefix))\n upd_group = py_ast.Group(\n upd_name,\n connections=[\n py_ast.Connect(\n py_ast.ConstantPort(width, 1), py_ast.CompPort(add_name, \"left\")\n ),\n py_ast.Connect(\n py_ast.CompPort(name, \"out\"), py_ast.CompPort(add_name, \"right\")\n ),\n py_ast.Connect(\n py_ast.CompPort(add_name, \"out\"), py_ast.CompPort(name, \"in\")\n ),\n py_ast.Connect(\n py_ast.ConstantPort(1, 1), py_ast.CompPort(name, \"write_en\")\n ),\n py_ast.Connect(\n py_ast.CompPort(name, \"done\"), py_ast.HolePort(upd_name, \"done\")\n ),\n ],\n )\n\n return (cells, [init_group, upd_group])","def create_index(cls, engine):\n\n reg_imei = db.Index('reg_imei_index', cls.imei, postgresql_concurrently=True)\n reg_imei.create(bind=engine)\n\n reg_normalized_imei = db.Index('reg_normalized_imei_index', cls.normalized_imei, postgresql_concurrently=True)\n reg_normalized_imei.create(bind=engine)","def _mk_index(self):\n index = defaultdict(list)\n for line_no, line in enumerate(self._stripped_lines):\n if line:\n index[line].append(line_no)\n return index","def __getitem__(self, key: str) -> ir.TableExpr:\n return self.table(key)","def __init__(self, index=None):\n self.index = index or {}","def exercise_indexes():\n print(exercise_indexes.__doc__)\n print(\"The indexes of 'data' are:\", data.index)\n print(data, \"\\n\")\n print(\"Changing the indexes of 'data'\")\n print(data.reindex([2, 0, 1]), \"\\n\")\n print(\"Changing the indexes of 'data' randomly\")\n print(data.reindex(np.random.permutation(data.index)))","def createindexes():\n index = [{}, {}, {}, {}]\n readcorpus(index)\n buildindex4(index[2], index[3])\n writeindextofile(index)\n return index","def index(self):\n path = self.path.format('index')\n \n with open(path, 'r', newline='') as file:\n l = list(csv.reader(file))\n \n index = [v for _ in l for v in _]\n index = dict((v, i) for (i, v) in enumerate(index))\n \n return index","def _create_idx(self):\n self._idx = {}\n for idx, (L, M, N) in enumerate(self.modes):\n if L not in self._idx:\n self._idx[L] = {}\n if M not in self._idx[L]:\n self._idx[L][M] = {}\n self._idx[L][M][N] = idx","def find_coverage_index(self):\n idx_on_new_table = [self._new_table.primary_key] + self._new_table.indexes\n old_pk_len = len(self._pk_for_filter)\n for idx in idx_on_new_table:\n # list[:idx] where idx > len(list) yields full list\n idx_prefix = idx.column_list[:old_pk_len]\n idx_name_list = [col.name for col in idx_prefix]\n if self._pk_for_filter == idx_name_list:\n if idx.is_unique:\n return idx.name\n return None","def visit_table(self, sytable):\n def index(sytable):\n try:\n return sytable.get_column(self.input_index)\n except:\n return np.arange(sytable.number_of_rows())\n\n def slices_using_group_array(group_array):\n \"\"\"Return the slices to split by.\n A group array is made of strictly increasing group identifiers.\n\n >>> slices_using_group_array(np.array([0, 0, 0, 1, 1, 2, 3, 3, 3]))\n [(0, 3), (3, 5), (5, 6), (6, 9)]\n \"\"\"\n unique_elements = np.unique(group_array)\n slices = []\n for unique_element in unique_elements:\n indexes = np.flatnonzero(group_array == unique_element)\n low, high = (indexes[0], indexes[-1] + 1)\n slices.append((unique_element, slice(low, high)))\n return slices\n\n def indices_using_group_array(group_array):\n \"\"\"\n Return list of index lists, ordered by first occurance of value.\n \"\"\"\n unique_elements = np.unique(group_array)\n indices = []\n for unique_element in unique_elements:\n indices.append((unique_element,\n np.flatnonzero(group_array == unique_element)))\n return indices\n\n columns = sytable.columns()\n # Perform the split and append the new tables to output.\n slice_indices = indices_using_group_array(index(sytable))\n column_attrs = {}\n\n for unique_element, slice_index in slice_indices:\n # Sets of all columns except for the INDEX columns.\n result = type(sytable)(sytable.container_type)\n self.output_list.append((unique_element, result))\n\n for column in columns:\n array = sytable.get_column(column)[slice_index]\n if self.remove_fill and len(array):\n kind = array.dtype.kind\n if kind in ['S', 'U']:\n if np.all(array == ''):\n continue\n else:\n if not len(array) or np.isnan(np.min(array)):\n continue\n\n result.set_column(column, array)\n if column in column_attrs:\n attrs = column_attrs[column]\n else:\n attrs = dict(\n sytable.get_column_attributes(column).get())\n column_attrs[column] = attrs\n result.get_column_attributes(column).set(attrs)","def start_table(self):\n raise NotImplementedError","def create_reference_ids_table(self, vals, table_name='_ref'):\n # remove existing\n self._drop_tables([table_name])\n cursor = self.conn.cursor()\n cursor.execute('CREATE TEMP TABLE %s (id INTEGER)' % table_name)\n for i, v in enumerate(vals):\n cursor.execute('INSERT INTO %s VALUES(%d)' % (table_name, v))","def _create_indices(cls):\r\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\r\n \r\n if not _hosts: return\r\n for column in cls._columns.values():\r\n if column.index or _index_all_fields:\r\n create_key_index(column.db_field_name)","def test_compound_index(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dt = np.dtype([('a', 'i4'), ('b', 'f8')])\n v = np.ones((4,), dtype=dt)\n dset = f.create_dataset('foo', (4,), data=v)\n assert dset[0] == v[0]\n assert isinstance(dset[0], np.void)","def __init__(self,index):\n self.index=index","def test_atom_index_cache(self):\n # First make OH-\n mol = Molecule()\n mol.add_atom(8, -1, False, None)\n mol.add_atom(1, 0, False, None)\n mol.add_bond(0, 1, 1, False)\n assert mol.atom(0).molecule_atom_index == 0\n assert mol.atom(1).molecule_atom_index == 1\n\n # Now convert it to H2O and ask for atom indices again\n mol.add_atom(1, 0, False, None)\n mol.add_bond(1, 2, 1, False)\n mol.atom(0).formal_charge = 0\n assert mol.atom(0).molecule_atom_index == 0\n assert mol.atom(1).molecule_atom_index == 1\n assert mol.atom(2).molecule_atom_index == 2","def make_temp_tbl(self, type: str = \"user_details\"):\n uid = uuid.uuid4()\n temp_tbl_name = \"temp_\" + str(uid).replace('-', '_')\n\n if self.config.dbtype.lower() == \"mysql\":\n create_temp_tbl_sql = f\"CREATE TABLE {temp_tbl_name} LIKE {type};\"\n elif self.config.dbtype.lower() == \"sqlite\":\n create_temp_tbl_sql = f\"CREATE TABLE {temp_tbl_name} AS SELECT * FROM {type} WHERE 0\"\n self.engine.execute(create_temp_tbl_sql)\n return temp_tbl_name","def _extract_subtable_by_condition(self, condition):\n indices = [i for i in range(len(self)) if condition(i)]\n return ([self.objects[i] for i in indices],\n [self.table[i] for i in indices])","def generate_table(self, rows):\n ...","def test_index_table(self, mapping_cls, bulk_mock):\n indices = self.elasticsearch_cls().indices\n mapping = mapping_cls()\n rows = [\n {'id': 1, 'number': '123456789'},\n {'id': 2, 'number': '234567890'},\n {'id': 3, 'number': '345678901'},\n ]\n bulk_mock.return_value = (len(rows), [])\n\n db_path = 'some path'\n table_name = 'calls'\n table_reader = Mock()\n table_reader.rows.return_value = rows\n table_reader.database.db_filename = db_path\n table_reader.table.name = table_name\n documents_indexed = self.client._index_table(table_reader)\n indices.put_mapping.assert_called_once_with(\n index=self.client.INDEX_NAME,\n doc_type=hashlib.md5(\n '{}:{}'.format(db_path, table_name)).hexdigest(),\n body=mapping.mapping)\n self.assertEqual(documents_indexed, len(rows))","def prepare_table(table):\n n = len(table)\n for i, row in enumerate(table):\n assert len(row) == n, f\"len(row) = {len(row)} != {n} = n\"\n for j, _ in enumerate(row):\n if i == j:\n table[i][i] = 0.0\n elif i > j:\n table[i][j] = 1 - table[j][i]\n return table","def make_stat_table(N, indices, stat_pairs):\n table = [[None] * N for i in xrange(N)]\n for (i, j), stat_pair in zip(indices, stat_pairs):\n if i == j:\n table[i][j] = stat_pair\n else:\n table[i][j] = stat_pair\n table[j][i] = stat_pair.swapped\n return table","def indexed_dataset(self) -> Dict[int, List]:\n if self.__indexed_dataset is None:\n dataset = self.dataset()\n truncated_dataset = dataset[:1000]\n self.__indexed_dataset = {\n i: dataset[i] for i in range(len(dataset))\n }\n return self.__indexed_dataset","def __init__(self):\n self.buckets = 1009\n self.table = [[] for _ in range(self.buckets)]","def reset_indexes(self) -> None:\n raise NotImplementedError","def index(self):\n if hasattr(self, '_m_index'):\n return self._m_index if hasattr(self, '_m_index') else None\n\n self._m_index = (self.index_separate if self.is_index_separate else self.index_in_tag)\n return self._m_index if hasattr(self, '_m_index') else None"],"string":"[\n \"def create_index():\",\n \"def T1(request):\\n T = _get_test_table()\\n if request.param:\\n T.add_index(\\\"a\\\")\\n return T\",\n \"def test_index_alternate(self):\\n self.insert()\\n self.tbl[::2]\",\n \"def build_index():\\n pass\",\n \"def index_object(idxs=None):\",\n \"def create_new_index(self, dict_pg_info):\\n # ! Setting if fun can use default setting\\n ruler = Rules()\\n str_conn = ruler.pg_info_rules(dict_pg_info)\\n conn = psycopg2.connect(str_conn)\\n\\n with conn:\\n with conn.cursor() as cur:\\n str_create_table = \\\"CREATE TABLE \\\" + dict_pg_info['table'] + \\\" (path varchar PRIMARY KEY);\\\"\\n # ! Check if table already exit\\n cur.execute(str_create_table)\\n cur.close()\\n\\n conn.close()\",\n \"def get_table(base, engine):\\n class w1_temp_table(base):\\n __tablename__ = 'w1_temp'\\n __table_args__ = {\\\"useexisting\\\": True}\\n\\n id = sa.Column(sa.types.Integer, primary_key=True)\\n logger_id = sa.Column(sa.types.Integer)\\n value = sa.Column(sa.types.String)\\n datetime = sa.Column(sa.types.DateTime)\\n return w1_temp_table\",\n \"def index_schema_builder(table):\\n conn = table.parent.parent.connection\\n\\n idx = OrderedDict()\\n indexes = conn.execute(\\\"SHOW INDEXES FROM `%s`.`%s`\\\" % (table.parent.name, table.name))\\n\\n if not indexes:\\n return idx\\n\\n for index in indexes:\\n n = index['Key_name']\\n if n not in idx:\\n indexitem = IndexSchema(name=n, parent=table)\\n indexitem.non_unique = (bool(index['Non_unique'])) # == not unique\\n indexitem.table_name = index['Table']\\n\\n key_type = index['Index_type'].upper()\\n\\n if index['Key_name'].upper() == \\\"PRIMARY\\\":\\n indexitem.kind = \\\"PRIMARY\\\"\\n elif not indexitem.non_unique:\\n indexitem.kind = \\\"UNIQUE\\\"\\n elif key_type in ('FULLTEXT', 'SPATIAL'):\\n indexitem.kind = key_type\\n else:\\n indexitem.kind = \\\"INDEX\\\"\\n\\n if key_type in ('BTREE', 'HASH', 'RTREE'):\\n indexitem.type = key_type\\n\\n indexitem.collation = index['Collation']\\n indexitem.comment = index['Comment']\\n\\n idx[n] = indexitem\\n\\n if index['Column_name'] not in idx[n].fields:\\n idx[n].fields.insert(index['Seq_in_index'], (index['Column_name'], index['Sub_part'] or 0))\\n\\n return idx\",\n \"def generate_inv_index(people):\\n pass\",\n \"def __call__(self, i, j):\\n return _table.Table___call__(self, i, j)\",\n \"def create_index(self):\\r\\n\\r\\n #### Begin functionality here\\r\\n\\r\\n return()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def create_index(self, table_name, index, timeout):\\n _abstract()\",\n \"def build_index(self):\\n \\n \\n geoids = self.partitions.find_or_new(table='facilities_geoids')\\n addresses = self.partitions.find_or_new(table='facilities_addresses')\\n facilities = self.partitions.find(table='facilities')\\n \\n facilities.attach(addresses,'addresses')\\n facilities.attach(geoids,'geoids')\\n \\n q = \\\"\\\"\\\"\\n SELECT year, type, oshpd_id, facility_name, dba_city, dba_zip_code, blockgroup_gvid, tract_gvid, county_gvid\\n FROM facilities\\n JOIN geoids.facilities_geoids AS geoids ON geoids.facilities_id = facilities.id\\n JOIN addresses.facilities_addresses AS addresses ON addresses.facilities_id = facilities.id\\n \\\"\\\"\\\"\\n \\n p = self.partitions.find_or_new(table='facilities_index')\\n p.clean()\\n lr = self.init_log_rate()\\n \\n with p.inserter() as ins:\\n for row in facilities.query(q):\\n ins.insert(row)\\n lr(str(p.identity))\",\n \"def build_index(self):\\r\\n date_time('Building indexes in citations table')\\r\\n self.cursor.execute('DROP INDEX IF EXISTS IDX_citations ;')\\r\\n self.cursor.execute('CREATE INDEX IDX_citations ON citations (citation);')\\r\\n self.conn.commit()\\r\\n gc.collect()\",\n \"def reindex(self):\",\n \"def reindex(self):\",\n \"def table(self, i):\\n return self.__tables[i]\",\n \"def table(self, i):\\n return self.__tables[i]\",\n \"def test_get_table_indices(self):\\n indices = self.connection.get_table_indices(self.TABLE_NAME)\\n print(indices)\\n\\n self.assertEqual(indices[0].name, 'PRIMARY')\\n self.assertEqual(indices[1].name, 'idx_foo')\\n\\n self.assertListEqual(indices[0].columns, ['id', 'foo'])\\n self.assertListEqual(indices[1].columns, ['foo'])\\n\\n self.assertTrue(indices[0].is_primary)\\n self.assertTrue(indices[0].is_unique)\\n self.assertFalse(indices[1].is_primary)\\n self.assertFalse(indices[1].is_unique)\\n\\n # assert False\",\n \"def reindex_table(conn, table_name, verbose=0):\\n t1 = time.time()\\n # check if index exists, delete if needed\\n try:\\n q = \\\"DROP INDEX \\\" + INDEX_NAMES + table_name + '_1'\\n conn.execute(q)\\n except sqlite3.OperationalError, e:\\n #print 'sqlite error:', e\\n pass\\n try:\\n q = \\\"DROP INDEX \\\" + INDEX_NAMES + table_name + '_2'\\n conn.execute(q)\\n except sqlite3.OperationalError, e:\\n #print 'sqlite error:', e\\n pass\\n # create index on jump codes\\n q = \\\"CREATE INDEX \\\" + INDEX_NAMES + table_name + '_1'\\n q += \\\" ON \\\" + table_name + \\\" (jumpcode ASC, weight ASC)\\\"\\n conn.execute(q)\\n conn.commit()\\n if verbose > 0:\\n strtime = str(datetime.timedelta(seconds=time.time() - t1))\\n print 'REINDEXED THE TABLE %s (jumpcode,weight) IN %s' % (table_name, strtime)\\n t2 = time.time()\\n # create index on tids\\n q = \\\"CREATE INDEX \\\" + INDEX_NAMES + table_name + '_2'\\n q += \\\" ON \\\" + table_name + \\\" (tidid ASC)\\\"\\n conn.execute(q)\\n conn.commit()\\n # verbose\\n if verbose > 0:\\n strtime = str(datetime.timedelta(seconds=time.time() - t2))\\n print 'REINDEXED THE TABLE %s (tidid) IN %s' % (table_name, strtime)\",\n \"def simple_index():\\n examples = [\\n benchmark.Example(\\n inputs=[\\n [12, 34, 56, 78],\\n -2,\\n ],\\n output=56,\\n ),\\n ]\\n constants = []\\n description = 'Index into a tensor'\\n target_program = 'in1[in2]'\\n source = 'handwritten task'\\n return benchmark.Benchmark(examples=examples,\\n constants=constants,\\n description=description,\\n target_program=target_program,\\n source=source,\\n name='simple_index')\",\n \"def build_index(self, dict_pg_info, list_insert):\\n flag_exit = True\\n if flag_exit is False:\\n self.create_new_index(dict_pg_info)\\n self.insert_index(dict_pg_info, list_insert)\",\n \"def __call__(self, i, j):\\n return _table.STable___call__(self, i, j)\",\n \"def build_index(self):\\n self.rebuild_index()\",\n \"def convert_to_index(df, table):\\n df[USER_IDX] = df[COMP_MID].apply(table.user_to_index)\\n df[PROD_IDX] = df[PROD_NO].apply(table.item_to_index)\\n index = df[(df[USER_IDX] < 0) | (df[PROD_IDX] < 0)].index\\n df.drop(index=index, columns=[COMP_MID, PROD_NO], inplace=True)\",\n \"def test_creating_index_type(self):\",\n \"def init_index(self):\\n raise NotImplementedError\",\n \"def generate_reverse_index(self):\",\n \"def ensure_table_indexes(collection) -> None:\\n # Unique ticker event ID\\n collection.create_index('data.u', unique=True)\\n # The symbol, e.g. BTCUSDT\\n collection.create_index('data.s')\\n # The time for sorting.\\n collection.create_index('time')\",\n \"def get_tab_index(raw_read):\\n # --------------------------------------------------------------------------\\n # Get the index\\n # --------------------------------------------------------------------------\\n indx = raw_read['IndexSheet'].loc[4:,:].copy()\\n # --------------------------------------------------------------------------\\n # Reset the column so that we remove Table #\\n # --------------------------------------------------------------------------\\n ref_col = indx.columns.to_list()[0]\\n indx[ref_col] = [i[i.find('-')+2:] for i in indx[ref_col].to_list()]\\n # --------------------------------------------------------------------------\\n # Put the reference tab\\n # --------------------------------------------------------------------------\\n indx['tab'] = [f'T{i+1}' for i in range(len(indx[ref_col]))]\\n # --------------------------------------------------------------------------\\n # Set the index\\n # --------------------------------------------------------------------------\\n indx.set_index(ref_col,inplace=True)\\n return indx\",\n \"def __getitem__(self, key):\\n return TableWrapper(self.dbms, key, self.db)\",\n \"def getTableByIndex(self, index):\\n pass\",\n \"def get_index_on_table(self, table, full=False):\\n indexes = self.get_index_list()\\n if full:\\n return [la for la in indexes if la[1] == table]\\n return [la[0] for la in indexes if la[1] == table]\",\n \"def create_indices():\\n conn = connect()\\n c = conn.cursor()\\n\\n # To prevent rematch btw players\\n c.execute(\\n \\\"\\\"\\\"\\n CREATE UNIQUE INDEX matches_uniq_idx ON matches\\n (greatest(winner, loser), least(winner, loser));\\n \\\"\\\"\\\")\\n conn.commit()\\n conn.close()\",\n \"def makeTableForQuery(queriedTable, resultTableDef, fragment, pars, \\n\\t\\tdistinct=False, limits=None, suppressIndex=True,\\n\\t\\tconnection=None):\\n\\treturn TableForDef(resultTableDef, suppressIndex=suppressIndex,\\n\\t\\tconnection=connection,\\n\\t\\trows=[r for r in queriedTable.iterQuery(resultTableDef, fragment, pars,\\n\\t\\t\\tdistinct, limits)])\",\n \"def indices(self):\\n if self._indices is None:\\n i = []\\n\\n # TODO: this is not right for multi-column keys\\n # TODO: new style indexes\\n\\n global_name = '^DD(%s,0,\\\"IX\\\",\\\"0\\\")' % self.fileid\\n prefix = '^DD(%s,0,\\\"IX\\\",' % self.fileid\\n while 1:\\n global_name = M.mexec('set s0=$query(%s)' % global_name, M.INOUT(\\\"\\\"))[0]\\n if not global_name or not global_name.startswith(prefix):\\n break\\n suffix = global_name[len(prefix):-1]\\n parts = suffix.split(\\\",\\\")\\n idx_name = parts[0][1:-1]\\n idx_table = parts[1]\\n idx_columns = parts[2:]\\n index = Index(idx_name, idx_table, idx_columns)\\n i.append(index)\\n\\n # A second list, gives indices for a field\\n columns = {}\\n for idx in i:\\n for c in idx.columns:\\n columns[c] = 1\\n\\n # Now trawl the listed columns in the data dictionary, and load their\\n # cross references.\\n cr_names = {}\\n for c in columns.keys():\\n idx_root = M.Globals[\\\"^DD\\\"][self.fileid][c][1]\\n if not idx_root[0].exists():\\n continue\\n for cr_id, val in idx_root.keys_with_decendants():\\n if float(cr_id) > 0:\\n cr_header = idx_root[cr_id][0].value\\n parts = cr_header.split(\\\"^\\\")\\n if len(parts) == 2 and parts[1]: # if more than 2 parts, assume MUMPs trigger\\n f = cr_names.get(parts[1], list())\\n f.append(c)\\n cr_names[parts[1]] = f\\n\\n # Now, just delete items from the index list if they are not in cr_names\\n self._indices = []\\n for index in i:\\n cr = cr_names.get(index.name)\\n if cr:\\n # verify columns - lots of errors in real systems\\n if len(cr) == len(index.columns):\\n invalid = False\\n for c in cr:\\n if c not in index.columns:\\n invalid = True\\n continue\\n if not invalid:\\n self._indices.append(index)\\n\\n return self._indices\",\n \"def get_indices(self):\\n\\n def query(rel): \\n return \\\"\\\"\\\"SELECT pg_class.relname, pg_index.indkey\\n FROM pg_class, pg_index\\n WHERE (pg_index.indexrelid = pg_class.oid)\\n AND (pg_index.indrelid = (SELECT pg_class.oid FROM pg_class WHERE pg_class.relname = \\\\'{}\\\\'));\\n \\\"\\\"\\\".format(rel)\\n\\n rels = tpch.schema.keys()\\n idxs = dict.fromkeys(rels)\\n\\n with self.tpch_cxn.cursor() as curs:\\n for rel in rels:\\n curs.execute(query(rel))\\n idxs_ = curs.fetchall()\\n idxs_ = dict(idxs_) # index -> index keys \\n \\n # TODO this can be done cleanly in query\\n # pg_index.indkey is a SQL array of attributes indices in their respective tables\\n split=lambda attrs: attrs.split() \\n cast=lambda attrs: list(map(lambda attr: int(attr)-1, attrs))\\n invertindex=lambda attrs: list(np.array(schema[rel])[attrs])\\n\\n attrs = idxs_.values() \\n attrs = list(map(split, attrs))\\n attrs = list(map(cast, attrs))\\n attrs = list(map(invertindex, attrs))\\n\\n idxs_ = {key : attrs[i] for i, key in enumerate(idxs_.keys())}\\n idxs[rel] = idxs_\\n return idxs\",\n \"def tableIndexer(self, indVar, i):\\n assert indVar in self.indVars, \\\"Input indVar %s is not a valid indVar!\\\" % indVar\\n assert i >= 0, \\\"Index in tableIndexer, %s, is less than zero. Dummy.\\\" % i\\n assert i < self.tableShapeDict[indVar], \\\\\\n \\\"Index in tableIndexer, %s, is greater than table extends in indVar direction!\\\" % i\\n\\n result = []\\n for var in self.indVars:\\n if indVar == var:\\n result.append(i)\\n else:\\n result.append(slice(None))\\n return tuple(result)\",\n \"def createTNTTempTable(self):\\n\\t\\t\\n\\t\\tquery = \\\"\\\"\\\"\\n\\t\\tSELECT \\n\\t\\tIDENTITY (INT) as rownumber,\\n\\t\\ta.NameID, a.Term, a.Value, a.Reference\\n\\t\\tINTO [{0}]\\n\\t\\tFROM (\\n\\t\\tSELECT a.AnalysisID, l.NameID, SUBSTRING(ac.DisplayText,18,255) AS Term, a.AnalysisValue AS Value, lr.TaxonNameListRefText AS Reference\\n\\t\\tFROM DiversityTaxonNames_{1}.dbo.TaxonNameList l\\n\\t\\t\\tINNER JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListReference lr ON lr.NameID=l.NameID AND lr.ProjectID=l.ProjectID\\n\\t\\t\\tLEFT JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListAnalysis a ON a.NameID=lr.NameID AND a.ProjectID=lr.ProjectID\\n\\t\\t\\tLEFT JOIN DiversityTaxonNames_{1}.dbo.TaxonNameListAnalysisCategory ac ON ac.AnalysisID=a.AnalysisID\\n\\t\\tWHERE\\n\\t\\t\\tac.DisplayText IN (\\n\\t\\t\\t -- Vertebrata\\n\\t\\t\\t'RoteListe_D_2009_LangfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2009_Verantwortlichkeit',\\n\\t\\t\\t'RoteListe_D_2009_AktuelleBestandssituation',\\n\\t\\t\\t'RoteListe_D_2009_RL-Kategorie',\\n\\t\\t\\t'RoteListe_D_2009_KurzfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2009_Sonderfälle',\\n\\t\\t\\t'RoteListe_D_2009_Neobiota',\\n\\t\\t\\t'RoteListe_D_2009_LetzterNachweis',\\n\\t\\t\\t'RoteListe_D_2009_Risikofaktoren',\\n\\t\\t\\t -- Insecta and Animalia\\n\\t\\t\\t'RoteListe_D_2016_RL-Kategorie',\\n\\t\\t\\t'RoteListe_D_2016_AktuelleBestandssituation',\\n\\t\\t\\t'RoteListe_D_2016_LangfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2016_KurzfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2016_Risikofaktoren',\\n\\t\\t\\t'RoteListe_D_2016_Verantwortlichkeit',\\n\\t\\t\\t'RoteListe_D_2016_Sonderfälle',\\n\\t\\t\\t'RoteListe_D_2016_Neobiota',\\n\\t\\t\\t'RoteListe_D_2016_LetzterNachweis',\\n\\t\\t\\t'RoteListe_D_2011_AktuelleBestandssituation',\\n\\t\\t\\t'RoteListe_D_2011_KurzfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2011_LangfristigerBestandstrend',\\n\\t\\t\\t'RoteListe_D_2011_Risikofaktoren',\\n\\t\\t\\t'RoteListe_D_2011_RL-Kategorie',\\n\\t\\t\\t'RoteListe_D_2011_Verantwortlichkeit',\\n\\t\\t\\t'RoteListe_D_2011_Neobiota',\\n\\t\\t\\t'RoteListe_D_2011_LetzterNachweis'\\n\\t\\t\\t) {2}\\n\\t\\t) AS a\\n\\t\\t;\\\"\\\"\\\".format(self.temptable, self.tnt_dbname, self.projectclause)\\n\\t\\t\\n\\t\\t\\n\\t\\tself.cur.execute(query)\\n\\t\\tself.con.commit()\",\n \"def myhtable_create_index(files):\\n res_buckets = htable(4011)\\n for id, file in enumerate(files):\\n if file[-4:] == '.txt':\\n word_list = words(get_text(file))\\n for word in word_list:\\n value = htable_get(res_buckets, word)\\n if value == None:\\n htable_put(res_buckets, word, {id})\\n else:\\n value.add(id)\\n htable_put(res_buckets, word, value)\\n return res_buckets\",\n \"def insert_index(self):\\n pass\",\n \"def id_to_local_index(self, id):\\n raise NotImplementedError\",\n \"def index_stmt(self, idx):\\n return Statement(\\\"index\\\", self, idx)\",\n \"def test_index_0(self):\\n self.insert()\\n data = self.tbl[0]\\n assert self.check(self.idata[:0], data)\",\n \"def bucket_indexof(table, key):\",\n \"def truthtable(self):\\n table = []\\n for i in xrange(self.length):\\n inputs = []\\n binary = bin(i).lstrip('0b')\\n for i in xrange(len(binary)):\\n inputs.append(int(binary[i]))\\n inputs.append(1)\\n table.append(self.compute(inputs))\\n return table\",\n \"def load(cls):\\n df = Operator_Table.df\\n df.operator = df.operator.apply(sp.normalize)\\n df.operator_alias = df.operator_alias.apply(sp.normalize)\\n df = df.rename(columns={\\\"operator_alias\\\": \\\"alias\\\"})\\n return SQLIndex(data=df).set_index(\\\"operator\\\")\",\n \"def migrate_8(session, **kwargs):\\n session.execute(\\n \\\"CREATE INDEX ix_{tb}_baseid ON {tb} ( baseid )\\\"\\n .format(tb=IndexRecord.__tablename__))\",\n \"def _reset_index():\\r\\n WIX = create_in(INDEX_NAME, BmarkSchema) # noqa\\r\",\n \"def T1m(request):\\n T = _get_test_table()\\n add_index, is_quantity = request.param\\n if is_quantity:\\n T[\\\"a\\\"] = T[\\\"a\\\"] * u.m\\n if add_index:\\n T.add_index(\\\"a\\\")\\n return T\",\n \"def migrate_9(session, **kwargs):\\n session.execute(\\n \\\"CREATE INDEX ix_{tb}_size ON {tb} ( size )\\\"\\n .format(tb=IndexRecord.__tablename__))\\n\\n session.execute(\\n \\\"CREATE INDEX index_record_hash_type_value_idx ON {tb} ( hash_value, hash_type )\\\"\\n .format(tb=IndexRecordHash.__tablename__))\",\n \"def test_temp_table(self):\\n self.assertEqual([\\\"test\\\", [[\\\"c1\\\", \\\"INT\\\"], [\\\"c2\\\", \\\"VARCHAR(200)\\\"]]],\\n grammar._TEMP_TABLE_EXPR.parseString(\\\"test(c1 INT, c2 VARCHAR(200));\\\").asList())\",\n \"def _get_index(self):\\n\\n return self.index\\n\\n # to avoid having differences bigger than 2pi\",\n \"async def build_secret_index(self):\\n pass\",\n \"def __init__(self):\\n self.inverted_index = OrderedDict({})\\n self.i=0\\n self.unique_doc_ids=set()\",\n \"def migrate_2(session, **kwargs):\\n try:\\n session.execute(\\n \\\"ALTER TABLE {} \\\\\\n ADD COLUMN baseid VARCHAR DEFAULT NULL, \\\\\\n ADD COLUMN created_date TIMESTAMP DEFAULT NOW(), \\\\\\n ADD COLUMN updated_date TIMESTAMP DEFAULT NOW()\\\".format(IndexRecord.__tablename__))\\n except ProgrammingError:\\n session.rollback()\\n session.commit()\\n\\n count = session.execute(\\n \\\"SELECT COUNT(*) FROM {};\\\"\\n .format(IndexRecord.__tablename__)).fetchone()[0]\\n\\n # create tmp_index_record table for fast retrival\\n try:\\n session.execute(\\n \\\"CREATE TABLE tmp_index_record AS SELECT did, ROW_NUMBER() OVER (ORDER BY did) AS RowNumber \\\\\\n FROM {}\\\".format(IndexRecord.__tablename__))\\n except ProgrammingError:\\n session.rollback()\\n\\n for loop in range(count):\\n baseid = str(uuid.uuid4())\\n session.execute(\\n \\\"UPDATE index_record SET baseid = '{}'\\\\\\n WHERE did = (SELECT did FROM tmp_index_record WHERE RowNumber = {});\\\".format(baseid, loop + 1))\\n session.execute(\\n \\\"INSERT INTO {}(baseid) VALUES('{}');\\\".format(BaseVersion.__tablename__, baseid))\\n\\n session.execute(\\n \\\"ALTER TABLE {} \\\\\\n ADD CONSTRAINT baseid_FK FOREIGN KEY (baseid) references base_version(baseid);\\\"\\n .format(IndexRecord.__tablename__))\\n\\n # drop tmp table\\n session.execute(\\n \\\"DROP TABLE IF EXISTS tmp_index_record;\\\"\\n )\",\n \"def describe_index(self, table_name, timeout):\\n\\n _abstract()\",\n \"def describe_index(self, table_name, timeout):\\n\\n _abstract()\",\n \"def id_to_index(self, id):\\n raise NotImplementedError\",\n \"def add_table_index(self, table: pd.DataFrame, table_name: str, table_description: str) -> None:\\n\\n indexes = table.index\\n columns = table.columns\\n\\n columns_lists = []\\n for i in range(0, len(columns), self.table_columns_count):\\n columns_list = []\\n for j in range(i, len(columns)):\\n columns_list.append(columns[j])\\n if(len(columns_list) == self.table_columns_count):\\n break\\n columns_lists.append(columns_list)\\n\\n tag = r''\\n for i in range(len(columns_lists)):\\n columns_list = columns_lists[i]\\n\\n if(len(columns_lists) == 1):\\n table_num = r''\\n else:\\n table_num = r' [' + str(i + 1) + '/' + str(len(columns_lists)) + ']'\\n\\n if(len(table) < 30):\\n tag = tag + r'''\\n \\\\begin{table}[H]\\n \\\\center\\n \\\\caption{''' + table_description + table_num + '''}\\n \\\\label{tab:''' + table_name + str(len(self.tables)) + r'''}\\n \\\\begin{tabular}{c''' + ' c' * len(columns_list) + '''}\\n '''\\n else:\\n tag = tag + r'''\\n \\\\begin{longtable}{''' + 'c ' + ' c' * len(columns_list) + '''}\\n \\\\caption{''' + table_description + table_num + '''\\\\label{tab:''' + table_name + str(len(self.tables)) + r'''}}\\\\\\\\\\n '''\\n\\n cell = r'''\\n & ''' + str(columns_list[0])\\n\\n for column in columns_list[1:]:\\n cell = cell + r' & ' + str(column)\\n tag = tag + cell + r''' \\\\\\\\\\n\\n \\\\hline\\n '''\\n\\n for index in indexes:\\n cell = str(index)\\n\\n for column in columns_list:\\n cell = cell + r' & ' + str(table.loc[index, column])\\n\\n tag = tag + cell + r''' \\\\\\\\\\n '''\\n\\n if(len(table) < 30):\\n tag = tag + r'''\\n \\\\hline\\n \\\\end{tabular}\\n \\\\end{table}\\n '''\\n else:\\n tag = tag + r'''\\n \\\\hline\\n \\\\end{longtable}\\n '''\\n\\n self.tables[len(self.tables)] = [table_name, table_description]\\n\\n tag = tag.replace('%', '\\\\%').replace('_', '\\\\_').replace('#', '\\\\#')\\n\\n self.doc = self.doc + tag\",\n \"def return_index(self, idx):\\n return (\\n self.timeseries[:, idx],\\n self.ch_amount,\\n self.freq,\\n self.ch_name[idx],\\n self.units[idx],\\n self.start_time,\\n )\",\n \"def get_table(self):\\n return copy.deepcopy(self._table)\",\n \"def init_index(clear=False):\\n return _run_indexer_func(\\\"init_index\\\", clear)\",\n \"def get_prod_table_indexed(prod_table,\\n prod_l_code,\\n prod_l_desc,\\n prod_l_plus_desc,\\n prod_l_cat):\\n \\n char_replace = F.udf(replace_chars, F.StringType()) #udf will run row by row on partition dataframe, but normal full will run on dataframe # * will serialise the list , ** will serialise the dictionary, krags,kwargs\\n if prod_l_plus_desc in trans.columns:\\n prod_columns = [prod_l_code, prod_l_desc, prod_l_plus_desc]\\n return prod_table.select(prod_columns).withColumn(\\n prod_l_cat,\\n F.concat(F.lit(\\\"_\\\"),\\n char_replace(prod_l_code, F.lit(r\\\"[^A-Za-z0-9]+\\\"), F.lit(r\\\"\\\")),\\n F.lit(\\\"_\\\"),\\n char_replace(prod_l_desc, F.lit(r\\\"[^A-Za-z]+\\\"), F.lit(r\\\"\\\")),\\n F.lit(\\\"_\\\"),\\n char_replace(prod_l_plus_desc, F.lit(r\\\"[^A-Za-z]+\\\"), F.lit(r\\\"\\\"))\\n )\\n ).withColumn(\\n \\\"prod_index\\\",\\n F.dense_rank().over(Window.orderBy(prod_l_cat)))\\n \\n else:\\n prod_columns = [prod_l_code, prod_l_desc]\\n return prod_table.select(prod_columns).withColumn(\\n prod_l_cat,\\n F.concat(F.lit(\\\"_\\\"),\\n char_replace(prod_l_code, F.lit(r\\\"[^A-Za-z0-9]+\\\"), F.lit(r\\\"\\\")),\\n F.lit(\\\"_\\\"),\\n char_replace(prod_l_desc, F.lit(r\\\"[^A-Za-z]+\\\"), F.lit(r\\\"\\\"))\\n )\\n ).withColumn(\\n \\\"prod_index\\\",\\n F.dense_rank().over(Window.orderBy(prod_l_cat)))\",\n \"def test_integer_index():\\n\\tlib.backup_and_restore(\\n\\t\\tlambda context: create_indexes(lib.create_integer_index),\\n\\t\\tNone,\\n\\t\\tlambda context: check_indexes(lib.check_simple_index, 12345)\\n\\t)\",\n \"def create_index_buffer(init):\\n return ffi.new('unsigned short[]', init)\",\n \"def add_mode_index(self) -> None:\",\n \"def test_index_12(self):\\n self.insert()\\n data = self.tbl[2:6]\\n assert self.check(self.idata[:2], data)\",\n \"def _initIndexes(self):\\n class Record:\\n \\\"\\\"\\\" a moron simple object for carrying the 'extra'-payload to index\\n constructors\\n \\\"\\\"\\\"\\n def __init__(self, **kw):\\n self.__dict__.update(kw)\\n\\n addIndex = self.addIndex\\n addColumn = self.addColumn\\n\\n # Content indexes\\n self._catalog.indexes.clear()\\n for (index_name, index_type, extra) in self.enumerateIndexes():\\n if extra is None:\\n addIndex( index_name, index_type)\\n else:\\n if isinstance(extra, StringTypes):\\n p = Record(indexed_attrs=extra)\\n elif isinstance(extra, DictType):\\n p = Record(**extra)\\n else:\\n p = Record()\\n addIndex( index_name, index_type, extra=p )\\n\\n # Cached metadata\\n self._catalog.names = ()\\n self._catalog.schema.clear()\\n for column_name in self.enumerateColumns():\\n addColumn( column_name )\",\n \"def create_index_tables(self):\\n # List of urls that have been indexed\\n self.con.execute('create table urllist(url)')\\n # List of words\\n self.con.execute('create table wordlist(word)')\\n # What doc the word is and where it is in the doc\\n self.con.execute('create table wordlocation(urlid, wordid, location)')\\n # Indicates a link from one url to another\\n self.con.execute('create table link(fromid integer, toid integer)')\\n # which words are actually in a link\\n self.con.execute('create table linkwords(wordid, linkid)')\\n self.con.execute('create index wordidx on wordlist(word)')\\n self.con.execute('create index urlidx on urllist(url)')\\n self.con.execute('create index wordurlidx on wordlocation(wordid)')\\n self.con.execute('create index urltoidx on link(toid)')\\n self.con.execute('create index urlfromidx on link(fromid)')\\n self.dbcommit()\",\n \"def return_index(self, idx):\\n return (\\n self.timeseries[idx],\\n self.ch_amount,\\n self.freq[idx],\\n self.ch_name[idx],\\n self.units[idx],\\n )\",\n \"def test_index_1(self):\\n self.insert()\\n data = self.tbl[2]\\n assert self.check(self.idata[:1], data)\",\n \"def instantiate_indexor(prefix, width):\\n stdlib = py_ast.Stdlib()\\n name = py_ast.CompVar(NAME_SCHEME[\\\"index name\\\"].format(prefix=prefix))\\n add_name = py_ast.CompVar(f\\\"{prefix}_add\\\")\\n cells = [\\n py_ast.Cell(name, stdlib.register(width)),\\n py_ast.Cell(add_name, stdlib.op(\\\"add\\\", width, signed=False)),\\n ]\\n\\n init_name = py_ast.CompVar(NAME_SCHEME[\\\"index init\\\"].format(prefix=prefix))\\n init_group = py_ast.Group(\\n init_name,\\n connections=[\\n py_ast.Connect(\\n py_ast.ConstantPort(width, 2 ** width - 1), py_ast.CompPort(name, \\\"in\\\")\\n ),\\n py_ast.Connect(\\n py_ast.ConstantPort(1, 1), py_ast.CompPort(name, \\\"write_en\\\")\\n ),\\n py_ast.Connect(\\n py_ast.CompPort(name, \\\"done\\\"), py_ast.HolePort(init_name, \\\"done\\\")\\n ),\\n ],\\n )\\n\\n upd_name = py_ast.CompVar(NAME_SCHEME[\\\"index update\\\"].format(prefix=prefix))\\n upd_group = py_ast.Group(\\n upd_name,\\n connections=[\\n py_ast.Connect(\\n py_ast.ConstantPort(width, 1), py_ast.CompPort(add_name, \\\"left\\\")\\n ),\\n py_ast.Connect(\\n py_ast.CompPort(name, \\\"out\\\"), py_ast.CompPort(add_name, \\\"right\\\")\\n ),\\n py_ast.Connect(\\n py_ast.CompPort(add_name, \\\"out\\\"), py_ast.CompPort(name, \\\"in\\\")\\n ),\\n py_ast.Connect(\\n py_ast.ConstantPort(1, 1), py_ast.CompPort(name, \\\"write_en\\\")\\n ),\\n py_ast.Connect(\\n py_ast.CompPort(name, \\\"done\\\"), py_ast.HolePort(upd_name, \\\"done\\\")\\n ),\\n ],\\n )\\n\\n return (cells, [init_group, upd_group])\",\n \"def create_index(cls, engine):\\n\\n reg_imei = db.Index('reg_imei_index', cls.imei, postgresql_concurrently=True)\\n reg_imei.create(bind=engine)\\n\\n reg_normalized_imei = db.Index('reg_normalized_imei_index', cls.normalized_imei, postgresql_concurrently=True)\\n reg_normalized_imei.create(bind=engine)\",\n \"def _mk_index(self):\\n index = defaultdict(list)\\n for line_no, line in enumerate(self._stripped_lines):\\n if line:\\n index[line].append(line_no)\\n return index\",\n \"def __getitem__(self, key: str) -> ir.TableExpr:\\n return self.table(key)\",\n \"def __init__(self, index=None):\\n self.index = index or {}\",\n \"def exercise_indexes():\\n print(exercise_indexes.__doc__)\\n print(\\\"The indexes of 'data' are:\\\", data.index)\\n print(data, \\\"\\\\n\\\")\\n print(\\\"Changing the indexes of 'data'\\\")\\n print(data.reindex([2, 0, 1]), \\\"\\\\n\\\")\\n print(\\\"Changing the indexes of 'data' randomly\\\")\\n print(data.reindex(np.random.permutation(data.index)))\",\n \"def createindexes():\\n index = [{}, {}, {}, {}]\\n readcorpus(index)\\n buildindex4(index[2], index[3])\\n writeindextofile(index)\\n return index\",\n \"def index(self):\\n path = self.path.format('index')\\n \\n with open(path, 'r', newline='') as file:\\n l = list(csv.reader(file))\\n \\n index = [v for _ in l for v in _]\\n index = dict((v, i) for (i, v) in enumerate(index))\\n \\n return index\",\n \"def _create_idx(self):\\n self._idx = {}\\n for idx, (L, M, N) in enumerate(self.modes):\\n if L not in self._idx:\\n self._idx[L] = {}\\n if M not in self._idx[L]:\\n self._idx[L][M] = {}\\n self._idx[L][M][N] = idx\",\n \"def find_coverage_index(self):\\n idx_on_new_table = [self._new_table.primary_key] + self._new_table.indexes\\n old_pk_len = len(self._pk_for_filter)\\n for idx in idx_on_new_table:\\n # list[:idx] where idx > len(list) yields full list\\n idx_prefix = idx.column_list[:old_pk_len]\\n idx_name_list = [col.name for col in idx_prefix]\\n if self._pk_for_filter == idx_name_list:\\n if idx.is_unique:\\n return idx.name\\n return None\",\n \"def visit_table(self, sytable):\\n def index(sytable):\\n try:\\n return sytable.get_column(self.input_index)\\n except:\\n return np.arange(sytable.number_of_rows())\\n\\n def slices_using_group_array(group_array):\\n \\\"\\\"\\\"Return the slices to split by.\\n A group array is made of strictly increasing group identifiers.\\n\\n >>> slices_using_group_array(np.array([0, 0, 0, 1, 1, 2, 3, 3, 3]))\\n [(0, 3), (3, 5), (5, 6), (6, 9)]\\n \\\"\\\"\\\"\\n unique_elements = np.unique(group_array)\\n slices = []\\n for unique_element in unique_elements:\\n indexes = np.flatnonzero(group_array == unique_element)\\n low, high = (indexes[0], indexes[-1] + 1)\\n slices.append((unique_element, slice(low, high)))\\n return slices\\n\\n def indices_using_group_array(group_array):\\n \\\"\\\"\\\"\\n Return list of index lists, ordered by first occurance of value.\\n \\\"\\\"\\\"\\n unique_elements = np.unique(group_array)\\n indices = []\\n for unique_element in unique_elements:\\n indices.append((unique_element,\\n np.flatnonzero(group_array == unique_element)))\\n return indices\\n\\n columns = sytable.columns()\\n # Perform the split and append the new tables to output.\\n slice_indices = indices_using_group_array(index(sytable))\\n column_attrs = {}\\n\\n for unique_element, slice_index in slice_indices:\\n # Sets of all columns except for the INDEX columns.\\n result = type(sytable)(sytable.container_type)\\n self.output_list.append((unique_element, result))\\n\\n for column in columns:\\n array = sytable.get_column(column)[slice_index]\\n if self.remove_fill and len(array):\\n kind = array.dtype.kind\\n if kind in ['S', 'U']:\\n if np.all(array == ''):\\n continue\\n else:\\n if not len(array) or np.isnan(np.min(array)):\\n continue\\n\\n result.set_column(column, array)\\n if column in column_attrs:\\n attrs = column_attrs[column]\\n else:\\n attrs = dict(\\n sytable.get_column_attributes(column).get())\\n column_attrs[column] = attrs\\n result.get_column_attributes(column).set(attrs)\",\n \"def start_table(self):\\n raise NotImplementedError\",\n \"def create_reference_ids_table(self, vals, table_name='_ref'):\\n # remove existing\\n self._drop_tables([table_name])\\n cursor = self.conn.cursor()\\n cursor.execute('CREATE TEMP TABLE %s (id INTEGER)' % table_name)\\n for i, v in enumerate(vals):\\n cursor.execute('INSERT INTO %s VALUES(%d)' % (table_name, v))\",\n \"def _create_indices(cls):\\r\\n from thunderdome.connection import _hosts, _index_all_fields, create_key_index\\r\\n \\r\\n if not _hosts: return\\r\\n for column in cls._columns.values():\\r\\n if column.index or _index_all_fields:\\r\\n create_key_index(column.db_field_name)\",\n \"def test_compound_index(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dt = np.dtype([('a', 'i4'), ('b', 'f8')])\\n v = np.ones((4,), dtype=dt)\\n dset = f.create_dataset('foo', (4,), data=v)\\n assert dset[0] == v[0]\\n assert isinstance(dset[0], np.void)\",\n \"def __init__(self,index):\\n self.index=index\",\n \"def test_atom_index_cache(self):\\n # First make OH-\\n mol = Molecule()\\n mol.add_atom(8, -1, False, None)\\n mol.add_atom(1, 0, False, None)\\n mol.add_bond(0, 1, 1, False)\\n assert mol.atom(0).molecule_atom_index == 0\\n assert mol.atom(1).molecule_atom_index == 1\\n\\n # Now convert it to H2O and ask for atom indices again\\n mol.add_atom(1, 0, False, None)\\n mol.add_bond(1, 2, 1, False)\\n mol.atom(0).formal_charge = 0\\n assert mol.atom(0).molecule_atom_index == 0\\n assert mol.atom(1).molecule_atom_index == 1\\n assert mol.atom(2).molecule_atom_index == 2\",\n \"def make_temp_tbl(self, type: str = \\\"user_details\\\"):\\n uid = uuid.uuid4()\\n temp_tbl_name = \\\"temp_\\\" + str(uid).replace('-', '_')\\n\\n if self.config.dbtype.lower() == \\\"mysql\\\":\\n create_temp_tbl_sql = f\\\"CREATE TABLE {temp_tbl_name} LIKE {type};\\\"\\n elif self.config.dbtype.lower() == \\\"sqlite\\\":\\n create_temp_tbl_sql = f\\\"CREATE TABLE {temp_tbl_name} AS SELECT * FROM {type} WHERE 0\\\"\\n self.engine.execute(create_temp_tbl_sql)\\n return temp_tbl_name\",\n \"def _extract_subtable_by_condition(self, condition):\\n indices = [i for i in range(len(self)) if condition(i)]\\n return ([self.objects[i] for i in indices],\\n [self.table[i] for i in indices])\",\n \"def generate_table(self, rows):\\n ...\",\n \"def test_index_table(self, mapping_cls, bulk_mock):\\n indices = self.elasticsearch_cls().indices\\n mapping = mapping_cls()\\n rows = [\\n {'id': 1, 'number': '123456789'},\\n {'id': 2, 'number': '234567890'},\\n {'id': 3, 'number': '345678901'},\\n ]\\n bulk_mock.return_value = (len(rows), [])\\n\\n db_path = 'some path'\\n table_name = 'calls'\\n table_reader = Mock()\\n table_reader.rows.return_value = rows\\n table_reader.database.db_filename = db_path\\n table_reader.table.name = table_name\\n documents_indexed = self.client._index_table(table_reader)\\n indices.put_mapping.assert_called_once_with(\\n index=self.client.INDEX_NAME,\\n doc_type=hashlib.md5(\\n '{}:{}'.format(db_path, table_name)).hexdigest(),\\n body=mapping.mapping)\\n self.assertEqual(documents_indexed, len(rows))\",\n \"def prepare_table(table):\\n n = len(table)\\n for i, row in enumerate(table):\\n assert len(row) == n, f\\\"len(row) = {len(row)} != {n} = n\\\"\\n for j, _ in enumerate(row):\\n if i == j:\\n table[i][i] = 0.0\\n elif i > j:\\n table[i][j] = 1 - table[j][i]\\n return table\",\n \"def make_stat_table(N, indices, stat_pairs):\\n table = [[None] * N for i in xrange(N)]\\n for (i, j), stat_pair in zip(indices, stat_pairs):\\n if i == j:\\n table[i][j] = stat_pair\\n else:\\n table[i][j] = stat_pair\\n table[j][i] = stat_pair.swapped\\n return table\",\n \"def indexed_dataset(self) -> Dict[int, List]:\\n if self.__indexed_dataset is None:\\n dataset = self.dataset()\\n truncated_dataset = dataset[:1000]\\n self.__indexed_dataset = {\\n i: dataset[i] for i in range(len(dataset))\\n }\\n return self.__indexed_dataset\",\n \"def __init__(self):\\n self.buckets = 1009\\n self.table = [[] for _ in range(self.buckets)]\",\n \"def reset_indexes(self) -> None:\\n raise NotImplementedError\",\n \"def index(self):\\n if hasattr(self, '_m_index'):\\n return self._m_index if hasattr(self, '_m_index') else None\\n\\n self._m_index = (self.index_separate if self.is_index_separate else self.index_in_tag)\\n return self._m_index if hasattr(self, '_m_index') else None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6378675","0.6244597","0.61699903","0.61214757","0.6035877","0.5879814","0.5815851","0.5764521","0.57210124","0.56768274","0.5626417","0.56132317","0.56132317","0.55983233","0.559672","0.55530435","0.55530435","0.554623","0.554623","0.5543864","0.55301994","0.5499681","0.54946655","0.54904217","0.547362","0.54599166","0.5446774","0.54436535","0.54425985","0.54382515","0.5438152","0.54363805","0.54343385","0.5429189","0.54273087","0.5405663","0.5399916","0.53804576","0.53747386","0.5367114","0.536268","0.53461045","0.5334319","0.5320028","0.5294907","0.52924806","0.52889115","0.52782583","0.5277117","0.527124","0.5265007","0.52494615","0.5248211","0.5216628","0.52148","0.5213774","0.5211344","0.52066374","0.52066374","0.52049565","0.5203803","0.5199609","0.51994866","0.5190363","0.51807547","0.5179518","0.51703846","0.5157267","0.51556635","0.51527977","0.5145756","0.5143137","0.51404643","0.51394403","0.51373273","0.51291025","0.5126009","0.51170707","0.5114571","0.51117355","0.51039726","0.5097136","0.5089564","0.5088784","0.50839245","0.50833756","0.50808686","0.50794995","0.5064964","0.5058138","0.5055031","0.5052301","0.5045796","0.5043806","0.50421584","0.5035219","0.50300974","0.50223595","0.5017834","0.5012855"],"string":"[\n \"0.6378675\",\n \"0.6244597\",\n \"0.61699903\",\n \"0.61214757\",\n \"0.6035877\",\n \"0.5879814\",\n \"0.5815851\",\n \"0.5764521\",\n \"0.57210124\",\n \"0.56768274\",\n \"0.5626417\",\n \"0.56132317\",\n \"0.56132317\",\n \"0.55983233\",\n \"0.559672\",\n \"0.55530435\",\n \"0.55530435\",\n \"0.554623\",\n \"0.554623\",\n \"0.5543864\",\n \"0.55301994\",\n \"0.5499681\",\n \"0.54946655\",\n \"0.54904217\",\n \"0.547362\",\n \"0.54599166\",\n \"0.5446774\",\n \"0.54436535\",\n \"0.54425985\",\n \"0.54382515\",\n \"0.5438152\",\n \"0.54363805\",\n \"0.54343385\",\n \"0.5429189\",\n \"0.54273087\",\n \"0.5405663\",\n \"0.5399916\",\n \"0.53804576\",\n \"0.53747386\",\n \"0.5367114\",\n \"0.536268\",\n \"0.53461045\",\n \"0.5334319\",\n \"0.5320028\",\n \"0.5294907\",\n \"0.52924806\",\n \"0.52889115\",\n \"0.52782583\",\n \"0.5277117\",\n \"0.527124\",\n \"0.5265007\",\n \"0.52494615\",\n \"0.5248211\",\n \"0.5216628\",\n \"0.52148\",\n \"0.5213774\",\n \"0.5211344\",\n \"0.52066374\",\n \"0.52066374\",\n \"0.52049565\",\n \"0.5203803\",\n \"0.5199609\",\n \"0.51994866\",\n \"0.5190363\",\n \"0.51807547\",\n \"0.5179518\",\n \"0.51703846\",\n \"0.5157267\",\n \"0.51556635\",\n \"0.51527977\",\n \"0.5145756\",\n \"0.5143137\",\n \"0.51404643\",\n \"0.51394403\",\n \"0.51373273\",\n \"0.51291025\",\n \"0.5126009\",\n \"0.51170707\",\n \"0.5114571\",\n \"0.51117355\",\n \"0.51039726\",\n \"0.5097136\",\n \"0.5089564\",\n \"0.5088784\",\n \"0.50839245\",\n \"0.50833756\",\n \"0.50808686\",\n \"0.50794995\",\n \"0.5064964\",\n \"0.5058138\",\n \"0.5055031\",\n \"0.5052301\",\n \"0.5045796\",\n \"0.5043806\",\n \"0.50421584\",\n \"0.5035219\",\n \"0.50300974\",\n \"0.50223595\",\n \"0.5017834\",\n \"0.5012855\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5733679"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":79596,"cells":{"query":{"kind":"string","value":"write an entry to tmp table"},"document":{"kind":"string","value":"def write_citer_cited(citer, cited):\n sciter = str(citer)\n scited = str(cited)\n try:\n run_sql(\"insert into tmpcit(citer, cited) values (%s,%s)\", (sciter, scited))\n except:\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def htable_put(table, key, value):","def _store_entry_in_table(conn, table_name, entry):\n # Create entry insertion template.\n template = ('?, ' * len(entry)).rstrip(', ') # \"?\" for each value\n template = '(%s)' % template # enclose in parentheses\n # Try to insert a new row into the table.\n with conn:\n cur = conn.cursor()\n cur.execute('INSERT INTO %s VALUES%s' % (table_name, template), entry)","def persist_record(conn,data,tb_name):\n\tquery_param\t\t= tuple(list(map(lambda k : data[k],col_list[tb_name])))\n\texecute_query(conn,query_strings[tb_name],query_param)\n\treturn","def Write(self):\n table_data = self._TABLE.build(self._timestamps)\n self._zip_file.writestr(self._stream_name, table_data)","def update(table, id_, record):\n\n index_id = 0\n record.insert(index_id, common.generate_random(table))\n table.append(record)\n data_manager.write_table_to_file(\"model/sales/sales.csv\", table)\n\n entry_index = 0\n for entry in table:\n entry_id_ = entry[0]\n if entry_id_ == id_:\n del table[entry_index]\n entry_index += 1\n\n return table","def put_data(data):\n at_write = airtable.Airtable(app.config['AIRTABLE_BASE'],\n app.config['AIRTABLE_WRITE_KEY'])\n return at_write.create(app.config['AIRTABLE_TABLE'] , data)","def putRow(self, idx, row):\n if idx >= self.N:\n raise IndexError(\"Attempting to write index %s, raw store only has %d rows\"%(\n idx, self.N))\n if len(row) != len(self.dtypes):\n raise ValueError(\n \"data value only has %s entries, \"\n \"raw store has %s columns\"%(\n len(row), len(self.dtypes)))\n\n # checks row datatypes\n try:\n v = [dtype(v) for dtype,v in zip(self.dtypes, row)]\n except TypeError as e:\n # we might have None's here\n message = [str(e)]\n for i,(dtype,v) in enumerate(zip(self.dtypes, row)):\n try: dtype(v)\n except:\n message.append(\"\\tFor column %r can't convert %r to %s\"%(\n self.colnames[i],\n v,\n dtype))\n raise TypeError(\"\\n\".join(message))\n \n offset = idx * self.rowbytes\n self.f.seek(offset,0)\n try:\n bytes = struct.pack(self.pack_format, *[convert_string(x) for x in row])\n except struct.error:\n logging.exception(\"Can't write row %r\\ntypes: %r\\nformat: %r\",\n row,\n self.dtypes,\n self.pack_format)\n raise\n try:\n self.f.write(bytes)\n except Exception as e:\n logging.error(\"Attempting to write to offset: %s\", offset)\n logging.error(\"Rowsize: %s\", self.rowbytes)\n logging.error(\"Row: %s\", idx)\n logging.error(\"Max Row: %s\", self.N)\n logging.error(\"Filesize is %s\",\n os.path.getsize(self.fname))\n raise","def writeFile( self, file_handle=None, table_name=None, data=None ):\n\n # Record the next primary key id.\n nextId = self.nextPrimaryKey( table_name )\n\n # Generate the string list of data to be written in the file.\n values = '\\t'.join( data )\n\n # Actual put together the primary key id, the string values and the new line character to be writen in the file.\n insert = str(nextId) + '\\t' + str(values) + \"\\n\"\n\n # Write the stuff in the file.\n file_handle.write( insert )\n\n # DON'T MESS WITH THAT!!!!! YOU'RE WARNED!!!\n # Messing with this cute id will kill your importer because the table relationships files relies on that!!!\n # Take a look on the lines like 'taxonomiesInserted' or 'proteinsInserted'.\n return nextId","def write_table(table, file_path):\n\n\twith open(file_path, 'w') as file:\n\t\tfile.write(table)","def write_data():","def Write(self):\n table_data = self._TABLE.build(self._offsets)\n self._zip_file.writestr(self._stream_name, table_data)","def test_write(drop_tracker_test_table, db_string, tracker_test_table_name):\n\n tracker = pawprint.Tracker(db=db_string, table=tracker_test_table_name, schema={\"id\": \"INT\"})\n tracker.create_table()\n\n # Check the table's empty\n assert pd.io.sql.execute(\n \"SELECT COUNT(*) FROM {}\".format(tracker.table), tracker.db\n ).fetchall() == [(0,)]\n\n # Add some data and check if the row count increases by one\n tracker.write(id=1337)\n assert pd.io.sql.execute(\n \"SELECT COUNT(*) FROM {}\".format(tracker.table), tracker.db\n ).fetchall() == [(1,)]\n\n # Pull the data and ensure it's correct\n data = pd.read_sql(\"SELECT * FROM {}\".format(tracker.table), tracker.db)\n assert isinstance(data, pd.DataFrame)\n assert len(data.columns) == 1\n assert data.columns[0] == \"id\"\n assert data.id[0] == 1337","def save(file, table):\n pq.write_table(pa.Table.from_pandas(table), file)","def export_to_mysql(entry, mysql_cnx):\n mysql_cursor = mysql_cnx.cursor()\n\n entry = defaultdict(lambda: None, entry)\n\n query = \"INSERT INTO crashes (job_id, type, is_security_issue, is_crash, sample_path, backtrace, backtrace_hash, return_code, create_time) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, NOW())\"\n try:\n mysql_cursor.execute(\n query,\n (\n jobid,\n entry[\"type\"],\n entry[\"is_security_issue\"],\n entry[\"is_crash\"],\n entry[\"sample\"],\n entry[\"backtrace\"],\n entry[\"hash\"],\n entry[\"return_code\"],\n ),\n )\n except mysql.connector.errors.IntegrityError as err:\n print(\"Integrity: {}\".format(err))\n\n mysql_cnx.commit()\n mysql_cursor.close()","def _writeRecord(self, path, name, data):\n file_path = os.path.join(path, name)\n with open(file_path, 'w') as f:\n for item in data:\n f.write(str(item)+'\\t')\n f.write('\\n')","def save_table_scraperwiki(uniques,table,name):\n for row in table:\n scraperwiki.sqlite.save(\n unique_keys=uniques\n , data=row\n , table_name=name\n )","def _insert_into_clean(self, entry):\n i = entry.hash\n new_entry = self.table[i]\n while new_entry.key is not None:\n i += self.second_hash(new_entry.key)\n new_entry = self.table[i]\n new_entry.key = entry.key\n new_entry.value = entry.value\n new_entry.hash = entry.hash\n self.used += 1\n self.filled += 1","def add(table, record):\n index_id = 0\n record.insert(index_id, common.generate_random(table))\n table.append(record)\n data_manager.write_table_to_file(\"model/sales/sales.csv\", table)\n\n return table","def store_hive_table(data, directory, file_name):\n table_name = directory + \".\" + file_name\n data.write.saveAsTable(table_name)","def write(self):\n if not self._table: raise ValueError ( \"_table is Null\" )\n if self._isnew:\n for m in self._modified_values:\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\n \n try:\n rec = CFG.CX.insert ( CFG.DB.SCHEMA + \".\" + self._table.name,\n self._modified_values )\n #this will automatically re-read the data from the db, to take all changes\n #done by triggers and default values into account.\n self._objectid = rec['objectid']\n\n #print \"Record # {0} inserted into {1}.\".format(self._objectid, self._table.name)\n self.raiseEvent ( \"record_added\", self )\n \n except pg.DatabaseError, e:\n print \"Error inserting record.\"\n raise Record.DataManipulationError ( \"Inserting a new record into '{0}'\".format(self._table.name),\n str(self._modified_values),\n e)\n elif self._ismodified:\n \n for m in self._modified_values: \n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\n \n self._modified_values['objectid'] = self._objectid\n del self.TextCache[self._objectid]\n try:\n rec = CFG.CX.update ( CFG.DB.SCHEMA + \".\" + self._table.name,\n self._modified_values )\n self.read() \n self.raiseEvent ( \"record_saved\", self )\n except pg.DatabaseError, e:\n print \"Error updating record\"\n raise Record.DataManipulationError ( \"Updating record {1} of '{0}'\".format(self._table.name, self._objectid),\n str(self._modified_values),\n e)","def store_in_sql(cache, db_handler, TABLE_NAME):\r\n if len(cache) == 0 and len(cache[0]) == 0:\r\n return\r\n\r\n # create new table for new streaming event\r\n if TABLE_NAME[0] == '':\r\n TABLE_NAME[0] = db_handler.create_table(cache[0])\r\n \r\n # write message to database\r\n for message in cache:\r\n db_handler.insert_entry(message)","def write(engine, table_name, src, return_id=True, do_update=False):\n query = build_upsert_query(engine, table_name, src, do_update=do_update)\n LOGGER.debug(query)\n engine.execute(query)\n if return_id:\n return fetch_id(engine, table_name, src)","def save_table(date, table):\n if os.path.isfile(date+\".table\"):\n file_using = open(date+\".table\", \"w\")\n else:\n return False\n file_using.seek(0)\n file_using.truncate()\n for line in table:\n file_using.write(\"{},{},{},{},{}\\n\".format(line[0], line[1], line[2], line[3], line[4]))\n file_using.close()","def write(self, row):\n self.append_rows.append(tuple(row))","def write(data):","def _storeData(self, data, table, query=None):\n print ('Storing data')\n conn = dbo.getConnection()\n\n if query == None:\n num_cols = len(data[0])\n cols = ','.join(['%s ' for i in range(0, num_cols)])\n query = \"INSERT INTO \" + table + \" VALUES (\" + cols + \")\"\n\n dbo.execute_query(conn, query, data, multiple=True)\n dbo.closeConnection(conn)\n return","def set_temp_entry(self, val):\n self.temp = val","def __setitem__(self, index, value):\n\t\t# create value and key storage\n\t\tself.filep.write(pack('<LL',len(index), len(value)))\n\t\tself.filep.write(index)\n\t\tself.filep.write(value)\n\n\t\t# grab a hash for the key\n\t\thash = calc_hash(index)\n\n\t\t# dump a new hash into our bucket\n\t\tself.hashbucket[hash % 256].fromlist([hash, self.position_hash])\n\t\tself.position_hash += 8 + (len(index) + len(value))","def write_to_file(entry, file):\n with open(file, \"a\") as f:\n f.write(entry)","def write_trace_entry(self, entry):\n self.gpr[entry.rd] = entry.rd_val\n self.csv_writer.writerow({'str' : entry.instr_str,\n 'rd' : entry.rd,\n 'rd_val' : entry.rd_val,\n 'rs1' : entry.rs1,\n 'rs1_val' : entry.rs1_val,\n 'rs2' : entry.rs2,\n 'rs2_val' : entry.rs2_val,\n 'addr' : entry.addr,\n 'instr' : entry.instr,\n 'imm' : entry.imm,\n 'csr' : entry.csr,\n 'binary' : entry.binary})","def makeFilePointer(self, key, tmppath):\n fp = open(tmppath, 'w')\n self.getFile(key, fp)\n fp.close()","def write_trade() -> None:\n try:\n ddb_table.put_item(Item = data_to_insert)\n except Exception as e:\n _LOGGER.error('Problem updating dynamodb trade data. {0}'.format(e))\n raise Exception('Problem updating dynamodb trade data. {0}'.format(e))","def put(data):","def journal_write(session, k, v):\n entry = models.VppEtcdJournal(k=k, v=v)\n session.add(entry)\n session.flush()","def add_entry(self, entry):\n if self.get_entry(entry):\n return entry\n\n keys, values = [], []\n for i in entry:\n keys.append(\"'{}'\".format(i))\n if not isinstance(entry[i], str):\n values.append(\"'{}'\".format(str(entry[i])))\n else:\n values.append(\"'{}'\".format(entry[i]))\n\n keys.append(\"'hash'\")\n values.append(\"'{}'\".format(self._calculate_hash(entry)))\n sql = 'INSERT INTO {t_id} ({keys}) VALUES ({values})'.format(\n t_id=self.table_id, keys=','.join(keys), values=','.join(values))\n self.fusiontables.query().sql(sql=sql).execute()","def write_data_to_result_table(self, id_column, path_src,\n path_result, size_of_result_file):\n self.cursor.execute(\"\"\"INSERT INTO result_files\n VALUES('1','2','3','4');\"\"\")","def write (self, path):\n\t\ts=[]; add=s.append\n\t\tadd ('\\t'.join (self.schema))\n\t\tfor record in self.data:\n\t\t\tadd (record.asTabDelimitedRecord())\n\t\t\n\t\t# f = open (path, 'w')\n\t\tf = codecs.open(path, 'w', 'utf-8')\n\t\tf.write (self.linesep.join (s))\n\t\tf.close()\n\t\tprint (\"data written to \" + path)","def commit_to_database(self, table, output):\n try:\n # TODO: Is this the correct method for this?\n with SESSION.begin_nested():\n i = insert(getattr(db, table))\n vals = i.values(output)\n SESSION.execute(vals) # TODO: What is this?\n SESSION.flush()\n except Exception as error:\n log.debug(error, exc_info=True)\n SESSION.rollback()\n\n SESSION.commit() # TODO: Should this be here?","def save_entries(self):\n with open(self.file_name, \"w\") as file:\n file.write('date,name,minutes,note\\n')\n for entry in self.entries:\n writer = csv.writer(file)\n writer.writerow([entry.date, entry.name, entry.minutes, entry.note])","def save_table(data, out_file):\n logging.info(\"Saving table\")\n #header, data = data\n #out = pd.DataFrame(data=data, columns = header.keys())\n joblib.dump(data, out_file)","def _save (self, expires):\n\n pickled_data = pickle.dumps (self._data, self.pickle_protocol)\n\n self._delete ()\n self._exec (\n \"\"\"\\\n insert into table_name (id, expires, data)\n values (%(id)s, %(expires)s, %(data)s)\n \"\"\",\n data = pickled_data,\n expires = expires\n )","def write(self, batch):\n cursor = connection.cursor()\n while batch:\n values = [\n \"(%s, %s)\" % (\n psycopg2.Binary(k),\n psycopg2.Binary(v)\n ) for k, v in batch[:self.BATCH_SIZE]]\n sql = \"INSERT INTO %s(k,v) VALUES %s\" % (self.table, \",\".join(values))\n batch = batch[self.BATCH_SIZE:]\n cursor.execute(sql)\n cursor.execute(\"COMMIT\")","def taskdetail_save(td):\n return IMPL.taskdetail_save(td)","def write( data ):","def save_record(self, data, rownum=None):\n\n if rownum is not None:\n # This is an update\n records = self.get_all_records()\n records[rownum] = data\n with open(self.filename, 'w', encoding='utf-8') as fh:\n csvwriter = csv.DictWriter(fh, fieldnames=self.fields.keys())\n csvwriter.writeheader()\n csvwriter.writerows(records)\n else:\n # This is a new record\n newfile = not os.path.exists(self.filename)\n\n with open(self.filename, 'a', encoding='utf-8') as fh:\n csvwriter = csv.DictWriter(fh, fieldnames=self.fields.keys())\n if newfile:\n csvwriter.writeheader()\n csvwriter.writerow(data)","def write_row(self, data):\n raise NotImplementedError()","def exposed_write_data(self, chunk_id, data):\n local_filename = self.chunk_filename(chunk_id)\n with open(local_filename, \"w\") as file:\n file.write(data)\n # self.handle_table[chunk_id] = local_filename","def store(self, key, headers, value):","def process(self, row):\n #print (row)\n \n key = conf[\"equipment_id\"]\n \n result = \"test start\" \n result = \"test end\" \n \n data = 1 ## 1\n \n self.db.save(key, result, data)","def write_to_database(info,timer):\n\n inserts = create_sql_write(info,timer)\n\n connection = engine.connect()\n for insert in inserts:\n connection.execute(insert)\n connection.close()","def abstract_write(self, *params): \n section = self.table_section_from_parameter(*params)\n row_args = self.table_row_args_from_parameter(*params)\n new_row = self.table_row_class(*row_args)\n # IMPORTANTE! ASUMO QUE UN BLOQUE INVALIDO ES IGUAL A UNO VACIO!\n # TO DO: PREGUNTAR EN ISSUE\n found_index = False\n for index, table_row in enumerate(section.copy()): \n if table_row is None or not table_row.valid:\n found_index = True\n overwrite_index = index\n break\n\n if not found_index:\n overwrite_index = self.next_replace(section)\n\n replace_index = self.index_mapping_from_parameter(overwrite_index, *params)\n\n\n old_line = self.table[replace_index]\n #print(f\"{self.__class__.__name__} Replace -> Index: {replace_index}\")\n\n # Perfom the actual write\n self.table[replace_index] = new_row","def save_db():\n # establish connection to db\n conn = sqlite3.connect('journal_entries.db')\n\n # create cursor to execute commands\n c = conn.cursor()\n\n # create table if specified table does not exist\n c.execute(\"\"\"SELECT count(name) \n FROM sqlite_master \n WHERE type='table' AND name='entries' \n \"\"\")\n table_check = c.fetchone()[0]\n if table_check == 0: # does not exist\n c.execute(\"\"\"CREATE TABLE entries (\n date_time text,\n q_1 text,\n q_2 text,\n q_3 text,\n q_4 text,\n q_5 text,\n q_6 text\n )\"\"\")\n\n # insert new row (entry)\n # TODO: refactor code/make flexible. Build dictionary of column, inputs then loop through?\n c.execute(\"INSERT INTO entries VALUES (:date_time, :q_1, :q_2, :q_3, :q_4, :q_5, :q_6)\",\n {\n 'date_time': get_date_time(),\n 'q_1': entries[0].get('1.0', 'end-1c'),\n 'q_2': entries[1].get('1.0', 'end-1c'),\n 'q_3': entries[2].get('1.0', 'end-1c'),\n 'q_4': entries[3].get('1.0', 'end-1c'),\n 'q_5': entries[4].get('1.0', 'end-1c'),\n 'q_6': entries[5].get('1.0', 'end-1c')\n }\n )\n\n # commit changes\n conn.commit()\n\n # c.execute(\"SELECT * from entries\")\n # print(c.fetchall())\n\n # close connection\n conn.close()","def store_rows(self, course_id, filename, rows):\r\n output_buffer = StringIO()\r\n gzip_file = GzipFile(fileobj=output_buffer, mode=\"wb\")\r\n csv.writer(gzip_file).writerows(rows)\r\n gzip_file.close()\r\n\r\n self.store(course_id, filename, output_buffer)","def put(self, record, _id=None, __force=False):\n # check read only\n if self.__read_only and not __force:\n raise IOError(\"Table is for read only.\")\n\n # make record\n record = Record.from_dict(record, _id)\n\n # add record to data\n _id = record._id\n self.__data[_id] = record\n return _id","def store_rows(self, course_id, filename, rows):\r\n output_buffer = StringIO()\r\n csv.writer(output_buffer).writerows(rows)\r\n self.store(course_id, filename, output_buffer)","def _write(self, tkt_id, repo_id, changesets):\n @self.env.with_transaction()\n def do_update(db):\n cursor = db.cursor()\n value = str(changesets)\n if changesets.exists:\n if value:\n cursor.execute('UPDATE ticket_changesets SET value=%s '\n 'WHERE ticket=%s AND repository=%s',\n [value, tkt_id, repo_id])\n else:\n cursor.execute('DELETE FROM ticket_changesets '\n 'WHERE ticket=%s AND repository=%s',\n [tkt_id, repo_id])\n elif value:\n cursor.execute('INSERT INTO ticket_changesets '\n '(ticket,repository,value) VALUES(%s,%s,%s)',\n [tkt_id, repo_id, value])","def add_entry_to_db(entry):\n db.session.add(entry)\n db.session.commit()","def _batch_write(self):\n if self.to_put:\n db.put(self.to_put)\n self.to_put = []\n if self.to_delete:\n db.delete(self.to_delete)\n self.to_delete = []","def write_to_destination(DbClass, src_vals, dest_timestamps, set_cols):\n session = get_db_session(\"dest\")\n write_count = 0\n for sv in src_vals:\n if sv[\"timestamp\"] in dest_timestamps:\n print(f\"{sv['timestamp']} already in destination database\")\n continue\n new_row = DbClass()\n for k, v in sv.items():\n setattr(new_row, k, v)\n for k, v in set_cols.items():\n setattr(new_row, k, v)\n print(f\"adding data for {sv['timestamp']}\")\n session.add(new_row)\n write_count += 1\n session.commit()\n session_close(session)\n print(f\"Wrote {write_count} rows to destination database\")\n return True","def populate_stat(self, table):\n myrow = table.row\n # HDF5 doesn't handle unicode strings, so we need to convert to \n # *byte* strings, which we can put in the HDF5 file \n addy = numpy.zeros(len(self.address), \n dtype=(numpy.str, glob.nchar_address))\n for i in range(len(addy)):\n addy[i] = (self.address[i]).encode('utf8')\n\n myrow[\"address\"] = addy\n myrow[\"bike_stands\"] = self.bike_stands\n myrow[\"number\"] = self.number\n myrow[\"position\"] = self.position\n myrow.append()\n table.flush()","def write(self, data):\n self.mycol.insert_one({\"query\":data})","def __write_event(self, handle, nbr):\n try:\n event = self.database.get_event_from_handle(handle)\n self.__write_row(nbr, handle, event)\n except:\n event = \"NOT FOUND\"\n self.__write_row(nbr, handle, event)","def write(self, row):\n bytes = struct.pack(self.pack_format, *row)\n self.f.write(bytes)","def save_tmp_file(self, data):\n with open(self.tmp_file, 'wb') as f:\n f.write(data)","def insert(conn, table_info, table_data):\n\n sql = ''' INSERT INTO ''' + table_info \n + ''' VALUES(''' + \"?,\" * (len(table_data)-1) + \"?)\"\n cursor = conn.cursor()\n cursor.execute(sql, table_data)\n conn.commit()","def write_main_mk_table(file_name, repository, last_commit, deploy_time):\n row = \"|\" + repository + \\\n \"|\" + last_commit + \\\n \"|\" + deploy_time + \\\n \"|\" + '\\n'\n write_file = open(file_name, \"a\")\n write_file.write(row)","def save_data(self, record):\n self.dbm.addRecord(record)","def write_table(self, tab):\n self.save_text()\n\n table = list()\n row = list()\n headers = tab['c'][3]\n if headers:\n has_content = False\n for col in headers:\n self.list_parse(col, cell_content=True)\n cell_content = self.get_content()\n row.append(cell_content)\n if cell_content != '':\n has_content = True\n if has_content:\n row = tuple(row)\n table.append(row)\n t_content = tab['c'][4]\n for line in t_content:\n row = list()\n for col in line:\n self.list_parse(col, cell_content=True)\n cell_content = self.get_content()\n row.append(cell_content)\n row = tuple(row)\n table.append(row)\n table = tuple(table)\n self.tables.append((table, (self.context, self.ancestor)))","def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\n pass","def write_to(self, fname, **kwargs):\n data = self.to_Table()\n data.write(fname, **kwargs)","def put_record(self, record):\r\n row = [record.get(field) for field in self.fields.names()]\r\n\r\n self.put(row)","def tabser(filename, body, data):\n # XXX checksums ignored\n head = Struct(\"!BiHBxxxB\")\n body = Struct(body)\n # foot = Struct(\"!4s\")\n\n buffer = bytearray([0] * (2 ** 16))\n head.pack_into(buffer, 0, 0, int(time()), len(data), body.size, 0),\n offset = head.size\n for row in data:\n body.pack_into(buffer, offset, *row, 0)\n offset += body.size\n else:\n print(\"write %d rows\" % len(data))\n # offset = 2 ** 16 - foot.size\n # foot.pack_into(buffer, offset, bytes([0, 0, 0, 0]))\n with open(filename, \"wb\") as f:\n f.write(buffer)","def add_to_db_single(self, element):\r\n def quot(string):\r\n \"\"\" Replace \" with ' in text strings that goes into the\r\n db, right now it is only done on the name, but it should\r\n be done on all fields that might contain such characters\r\n \"\"\"\r\n return string.replace('\"', \"'\")\r\n\r\n # Make entry i measurements table\r\n query = ('INSERT INTO {table} SET '\r\n 'time=FROM_UNIXTIME({time}), '\r\n 'type=2, '\r\n 'timestep={timestep}, '\r\n 'comment=\"{comment}\", '\r\n 'pass_energy={pass_energy}, '\r\n 'excitation_energy={excitation_energy}, '\r\n 'number_of_scans={number_of_scans}, '\r\n 'project=\"{project}\", '\r\n 'file_name=\"{file_name}\", '\r\n 'name=\"{name}\";').format(\r\n table=self.tables['measurements'],\r\n time=element[0]['date'],\r\n timestep=element[0]['dwell_time'],\r\n comment=element[0]['unique_name'],\r\n pass_energy=element[0]['pass_energy'],\r\n excitation_energy=element[0]['excitation_energy'],\r\n number_of_scans=element[0]['num_scans'],\r\n project=element[0]['project'],\r\n file_name=element[0]['unique_name'].replace('\\\\', '\\\\\\\\'),\r\n name=quot(element[0]['name']))\r\n\r\n # Christian, comment this in to see a list of metadata\r\n #print element[0]\r\n self.cursor.execute(query) # COMMENT\r\n\r\n # Get the id of it\r\n query = ('select id from {table} where type=2 '\r\n 'order by id desc limit 1;').\\\r\n format(table=self.tables['measurements'])\r\n self.cursor.execute(query)\r\n id_ = self.cursor.fetchall()[0][0]\r\n\r\n # Add the data to xy_values table in chunks of 100 data points\r\n counter = 0\r\n query_reset = 'INSERT INTO {table} (measurement, x, y) VALUES'.format(\r\n table=self.tables['xy'])\r\n query = query_reset\r\n # element[1] is tuple of data: (Array(x0, x1, x2), Array(y0, y1, y2)).\r\n # The zip statement (where * pulls out both value) turns it into:\r\n # [(x0, y0), (x1, y1), (x2, y2)]\r\n for x_value, y_value in zip(*element[1]):\r\n counter += 1\r\n query += '({0},{1},{2})'.format(id_, x_value, y_value)\r\n if counter < 100:\r\n query += ','\r\n else:\r\n query += ';'\r\n self.cursor.execute(query)\r\n counter = 0\r\n query = query_reset\r\n # Remember to write the last less than 100 points\r\n if query != query_reset:\r\n # Remove the last , and add a ;\r\n query = query[0: -1] + ';'\r\n self.cursor.execute(query)","def store_mrs_data(conn, file_id, file_name, file_contents, group_label):\n # Create the table if it does not exist.\n _create_table(conn, TABLE_NAME_BRAINSCANS, TABLE_COLS_BRAINSCANS)\n # Try to insert a new row into the table.\n table_entry = (file_id, file_name, file_contents, group_label)\n _store_entry_in_table(conn, TABLE_NAME_BRAINSCANS, table_entry)","def write_to_db(batch_num,pH,cursor):\n\n\t#Batch Number\n\tbatch = 1\n\t\t\n\n\tcur_time = datetime.now()\n\theater_state = 1\n\ttemp = 30.1\n\tamb_hum, amb_temp = 25.1,30.2\n\n\tcursor.execute('''INSERT INTO Kombucha_Data('Time', 'Batch', 'Heater State', 'Temperature', 'Ambient Temperature', 'Ambient Humidity','pH')\n\t\t\t\t VALUES(?,?,?,?,?,?,NULL)''', (cur_time, batch_num, heater_state, temp, amb_temp, amb_hum))\n\tcursor.commit()\n\treturn","def __setitem__(self,key,value):\n self.table.setItem(key,self.column,value)","def save(self):\n args = list(map(self._get_value_or_default, self.COLUMN_TO_FILED))\n columns = list(map(lambda k: k, self.COLUMN_TO_FILED))\n sql = 'INSERT INTO {} ({}) VALUES({});'.format(\n self.TABLE_NAME,\n ', '.join(columns),\n '%s,'.join(' '*len(columns)) + '%s'\n )\n cursor = yield self._pool.execute(sql, args)\n app_log.info('save arg %s', args)\n count = cursor.rowcount\n result = True if count == 1 else False\n return result","def save_event(self, data):\n rdb.table(self.rdb_table).insert(data)","def update(table, id_):\n # 4\n for row in table:\n if row[0] == id_:\n addnew = ui.get_inputs(\n ['month: ',\n 'day: ',\n 'year: ',\n 'type (in=income, out= outflow): ',\n 'amount (of transaction in USD): '],\n 'Updating item in Accounting table')\n addnew.insert(0, id_)\n row = addnew\n data_manager.write_table_to_file('accounting/items.csv', table)\n\n return table","def update(table, id_):\n\n # your code\n key = common.check_for_key(id_,table)\n if key == None:\n ui.print_error_message('Key does not exist')\n else:\n return_inputs = ui.get_inputs(['Name', 'Age'], 'Enter New Values')\n modif_index = key\n\n table[modif_index][NAME] = return_inputs[FIRST_PROP]\n table[modif_index][AGE] = return_inputs[SECOND_PROP]\n data_manager.write_table_to_file('hr/persons.csv', table) \n\n return table","def write_table_to_file(table):\n with open(\"story.csv\", \"w\") as file:\n for record in table:\n row = ';'.join(record)\n file.write(row + \"\\n\")","def write_file(file_name, table):\r\n \r\n savectr=len(table)\r\n try:\r\n with open (file_name, 'wb') as objFile:\r\n pickle.dump(table,objFile) #pickle my 2D list\r\n print ('{} CD(s) saved into {}.\\n'.format(savectr,file_name))\r\n except PermissionError as e:\r\n print('Not enough rights to create/modify ' + file_name + '.') #if unable pickle data due to permission issues\r\n print ()\r\n print (e, e.__doc__, sep='\\n')\r\n print ()\r\n except IOError as e:\r\n print ('I/O error({0}): {1}'.format(e.errno,e.strerror))#if unable to pickle data due to IO errors such as disk space issues\r\n print ()\r\n print (e, e.__doc__, sep='\\n')\r\n print ()\r\n except pickle.PickleError as e:\r\n print ('Unable to write data into ' + file_name + '.') #if unable to pickle 2D list, exception handling for pickling errors\r\n print ()\r\n print (e, e.__doc__, sep='\\n')\r\n print ()","def test_write_to_db(self):\n\n measurement, sensor_name = [\"testItem\", \"sensorA\"]\n query = f\"Select * from {measurement}\"\n\n def get_points(_query):\n \"\"\"Get measurement points from db given a query\"\"\"\n\n try:\n return self.test_client.query(\n _query).raw[\"series\"][0][\"values\"]\n except KeyError:\n return []\n\n test_point = main.generate_a_measurement_point(\n measurement,\n sensor_name=sensor_name,\n sensor_output_file_dir=\"tests/datafiles/\",\n output_filename=\"sensor_out_valid.txt\")\n\n original_points_in_db = get_points(query)\n main.write_data_to_db(self.test_client, [test_point])\n new_points_in_db = get_points(query)\n\n assert len(new_points_in_db) > len(original_points_in_db)\n assert sensor_name in str(new_points_in_db[0])\n assert str(self.expected_temp_val) in str(new_points_in_db[0])","def insert_record(record, table):\n\n\ttups = [(key, val) for key, val in record.iteritems()]\n\tkeys = [key for key, val in tups]\n\tvals = [val for key, val in tups]\n\n\tconn = get_database_connection(port = 2001)\n\tcursor = conn.cursor()\n\n\tnum_cols = len(keys)\n\tkey_str = ','.join(keys)\n\tval_str = ','.join(['%s'] * num_cols)\n\n\tqry = \"REPLACE INTO %s (%s) VALUES (%s)\" % (table, key_str, val_str)\n\tcursor.execute(qry, vals)\n\n\tconn.commit()\n\tcursor.close()\n\tconn.close()","def store_data(self, data):\n self.data = data\n # HERE\n the_main_dict = {**self.user_data(), **self.entities_data(), **self.extract_relevant(), **self.locate(),\n **self.calculate_days(), **self.clean_user_desc()}\n # The below is the reason that the table creation must be written in alphabetical order. This is simpler than\n # writing the complex joins that would otherwise be needed.\n my_keys_list = sorted(the_main_dict.keys())\n my_items = list(map(lambda x: str(the_main_dict[x]).replace(\"'\", ''), my_keys_list))\n try:\n # Unpacks the items into an insert statement for the SQLite table\n self.conn.execute(\"INSERT INTO {0} VALUES('{1}','{2}','{3}','{4}','{5}','{6}','{7}','{8}','{9}',\"\n \"'{10}','{11}','{12}','{13}','{14}','{15}','{16}','{17}','{18}','{19}','{20}',\"\n \"'{21}','{22}','{23}','{24}','{25}','{26}','{27}','{28}')\".format(self.table, *my_items))\n self.limiting += 1\n return 0\n except sqlite3.IntegrityError:\n return 1","def store_datapoint(sql, parts):\n t = datetime.fromtimestamp(parts[0])\n humid = parts[1]\n temp_c = parts[2]\n temp_f = parts[3]\n heat_c = parts[4]\n heat_f = parts[5]\n c = sql.cursor()\n c.execute(\"INSERT INTO points VALUES (?,?,?,?,?,?)\",\n (t, humid, temp_c, temp_f, heat_c, heat_f))\n sql.commit()","def db_commit(table, field_list, value_list):\r\n logger = logging.getLogger(__name__)\r\n db_conn = MySQLConnector()\r\n\r\n logger.info(\"Calling the WriteToDB function...\")\r\n db_conn.writeToDB(table=table, fieldList=field_list, valueList=value_list)","def test_write(self):\n writer = BaseTSVWriter([\n ('Prop1', 'prop1'),\n ('Prop2', 'prop2'),\n ])\n\n row = NonCallableMock(\n prop1=145,\n prop2=None,\n )\n\n valid, invalid = writer.write(self.tsv_file, [row])\n\n assert valid == [row]\n assert invalid == []\n assert self.tsv_value == (\n \"Prop1\\tProp2\\r\\n\"\n \"145\\t\\r\\n\" # None should convert to an empty string\n )","def commitToDatabase(self, tiltseriesdata):\n\t\treturn","def write_to_db(self, table, db, if_exists=\"append\", index=False):\n\n # Cast all sessions to dataframe\n sessions_df = pd.DataFrame.from_records(self.closed_sessions)\n\n # If we have sessions to write, sort them and write to the database\n if sessions_df.shape[0] > 0:\n sessions_df = sessions_df.sort_values(\"last_timestamp\", ascending=True)\n sessions_df.to_sql(table, db, if_exists=if_exists, index=index)\n\n # Don't double-write closed events\n self.closed_sessions = []","def write_entries(self, entries):\n for entry in entries:\n self.write(entry)","def __write_place(self, handle, nbr):\n try:\n place = self.database.get_place_from_handle(handle)\n self.__write_row(nbr, handle, place)\n except:\n place = \"NOT FOUND\"\n self.__write_row(nbr, handle, place)","def writerow(self, row):\n data = []\n basestring_type = six.string_types[0]\n for value in row:\n if not isinstance(value, basestring_type):\n value = '%s' % (value,)\n data.append(value.encode('utf-8'))\n self.writer.writerow(data)\n data = self.queue.getvalue()\n data = data.decode('utf-8')\n self.stream.write(data)\n self.queue.truncate(0)","def WriteEntry(self, data):\n if not self._file_object:\n raise IOError('Unable to write to closed serialized data stream.')\n\n data_size = len(data)\n # TODO: Fix maximum size limit handling to create new stream.\n # data_end_offset = (\n # self._file_object.tell() + self._DATA_ENTRY_SIZE + data_size)\n # if data_end_offset > self._maximum_data_size:\n # raise IOError('Unable to write data entry size value out of bounds.')\n\n data_size = construct.ULInt32('size').build(data_size)\n self._file_object.write(data_size)\n self._file_object.write(data)\n\n return self._file_object.tell()","def save(self, ip='', result='', dt=datetime.datetime.now()):\n self.ping_table.insert({\"host\": ip, \"result\": result, \"datetime\": str(dt)})\n return","def put(self, item):\n\n tableData = {}\n tableData['itemId'] = item.id\n for tagName in item.tags:\n tableData[tagName] = item.tags[tagName];\n\n table = self.__getTable();\n table.put_item(data = tableData, overwrite = True)","def SaveData(self, conn):\n t = time.process_time()\n log = Logger()\n cursor = conn.cursor()\n sqlQuery = \"insert into old_data \" \\\n \"select * from new_data\"\n cursor.execute(sqlQuery)\n conn.commit()\n conn.close()\n elapsed_t = time.process_time() - t\n log.log('Datos de cambios copiados a tabla', elapsed_t)","def write_tab(gdf, tab_name, crs_wkt=WKT_SWISS, index=None):\n\n\t\tgdf.crs = WKT_SWISS\n\t\t\n\t\t# fiona can't write integer columns, convert columns to float\n\t\tfor col in gdf.columns:\n\t\t\tstype = str(gdf[col].dtype)\n\t\t\tif stype.startswith('int'):\n\t\t\t\tgdf[col] = gdf[col].astype(float)\n\t\t\t\t\n\t\t# fiona can't write integer columns, convert index to float\n\t\tstype = str(gdf.index.dtype)\n\t\tif stype.startswith('int'):\n\t\t\tgdf.index = gdf.index.astype(float)\n\n\t\tgdf.to_file(tab_name,driver='MapInfo File', index=index) \n\t\treturn print(len(gdf), 'row(s) written to mapinfo file.')","def create_transaction(conn,task):\r\n sql = ''' INSERT OR IGNORE INTO emp1(emp_id,location,login_time)\r\n VALUES(?,?,?) '''\r\n c = conn.cursor()\r\n c.execute(sql,task)\r\n conn.commit()","def func():\n try:\n session.add(point_table(id=4, ptest=(1, None)))\n session.commit()\n session.close()\n except Exception as e:\n session.close()\n raise e"],"string":"[\n \"def htable_put(table, key, value):\",\n \"def _store_entry_in_table(conn, table_name, entry):\\n # Create entry insertion template.\\n template = ('?, ' * len(entry)).rstrip(', ') # \\\"?\\\" for each value\\n template = '(%s)' % template # enclose in parentheses\\n # Try to insert a new row into the table.\\n with conn:\\n cur = conn.cursor()\\n cur.execute('INSERT INTO %s VALUES%s' % (table_name, template), entry)\",\n \"def persist_record(conn,data,tb_name):\\n\\tquery_param\\t\\t= tuple(list(map(lambda k : data[k],col_list[tb_name])))\\n\\texecute_query(conn,query_strings[tb_name],query_param)\\n\\treturn\",\n \"def Write(self):\\n table_data = self._TABLE.build(self._timestamps)\\n self._zip_file.writestr(self._stream_name, table_data)\",\n \"def update(table, id_, record):\\n\\n index_id = 0\\n record.insert(index_id, common.generate_random(table))\\n table.append(record)\\n data_manager.write_table_to_file(\\\"model/sales/sales.csv\\\", table)\\n\\n entry_index = 0\\n for entry in table:\\n entry_id_ = entry[0]\\n if entry_id_ == id_:\\n del table[entry_index]\\n entry_index += 1\\n\\n return table\",\n \"def put_data(data):\\n at_write = airtable.Airtable(app.config['AIRTABLE_BASE'],\\n app.config['AIRTABLE_WRITE_KEY'])\\n return at_write.create(app.config['AIRTABLE_TABLE'] , data)\",\n \"def putRow(self, idx, row):\\n if idx >= self.N:\\n raise IndexError(\\\"Attempting to write index %s, raw store only has %d rows\\\"%(\\n idx, self.N))\\n if len(row) != len(self.dtypes):\\n raise ValueError(\\n \\\"data value only has %s entries, \\\"\\n \\\"raw store has %s columns\\\"%(\\n len(row), len(self.dtypes)))\\n\\n # checks row datatypes\\n try:\\n v = [dtype(v) for dtype,v in zip(self.dtypes, row)]\\n except TypeError as e:\\n # we might have None's here\\n message = [str(e)]\\n for i,(dtype,v) in enumerate(zip(self.dtypes, row)):\\n try: dtype(v)\\n except:\\n message.append(\\\"\\\\tFor column %r can't convert %r to %s\\\"%(\\n self.colnames[i],\\n v,\\n dtype))\\n raise TypeError(\\\"\\\\n\\\".join(message))\\n \\n offset = idx * self.rowbytes\\n self.f.seek(offset,0)\\n try:\\n bytes = struct.pack(self.pack_format, *[convert_string(x) for x in row])\\n except struct.error:\\n logging.exception(\\\"Can't write row %r\\\\ntypes: %r\\\\nformat: %r\\\",\\n row,\\n self.dtypes,\\n self.pack_format)\\n raise\\n try:\\n self.f.write(bytes)\\n except Exception as e:\\n logging.error(\\\"Attempting to write to offset: %s\\\", offset)\\n logging.error(\\\"Rowsize: %s\\\", self.rowbytes)\\n logging.error(\\\"Row: %s\\\", idx)\\n logging.error(\\\"Max Row: %s\\\", self.N)\\n logging.error(\\\"Filesize is %s\\\",\\n os.path.getsize(self.fname))\\n raise\",\n \"def writeFile( self, file_handle=None, table_name=None, data=None ):\\n\\n # Record the next primary key id.\\n nextId = self.nextPrimaryKey( table_name )\\n\\n # Generate the string list of data to be written in the file.\\n values = '\\\\t'.join( data )\\n\\n # Actual put together the primary key id, the string values and the new line character to be writen in the file.\\n insert = str(nextId) + '\\\\t' + str(values) + \\\"\\\\n\\\"\\n\\n # Write the stuff in the file.\\n file_handle.write( insert )\\n\\n # DON'T MESS WITH THAT!!!!! YOU'RE WARNED!!!\\n # Messing with this cute id will kill your importer because the table relationships files relies on that!!!\\n # Take a look on the lines like 'taxonomiesInserted' or 'proteinsInserted'.\\n return nextId\",\n \"def write_table(table, file_path):\\n\\n\\twith open(file_path, 'w') as file:\\n\\t\\tfile.write(table)\",\n \"def write_data():\",\n \"def Write(self):\\n table_data = self._TABLE.build(self._offsets)\\n self._zip_file.writestr(self._stream_name, table_data)\",\n \"def test_write(drop_tracker_test_table, db_string, tracker_test_table_name):\\n\\n tracker = pawprint.Tracker(db=db_string, table=tracker_test_table_name, schema={\\\"id\\\": \\\"INT\\\"})\\n tracker.create_table()\\n\\n # Check the table's empty\\n assert pd.io.sql.execute(\\n \\\"SELECT COUNT(*) FROM {}\\\".format(tracker.table), tracker.db\\n ).fetchall() == [(0,)]\\n\\n # Add some data and check if the row count increases by one\\n tracker.write(id=1337)\\n assert pd.io.sql.execute(\\n \\\"SELECT COUNT(*) FROM {}\\\".format(tracker.table), tracker.db\\n ).fetchall() == [(1,)]\\n\\n # Pull the data and ensure it's correct\\n data = pd.read_sql(\\\"SELECT * FROM {}\\\".format(tracker.table), tracker.db)\\n assert isinstance(data, pd.DataFrame)\\n assert len(data.columns) == 1\\n assert data.columns[0] == \\\"id\\\"\\n assert data.id[0] == 1337\",\n \"def save(file, table):\\n pq.write_table(pa.Table.from_pandas(table), file)\",\n \"def export_to_mysql(entry, mysql_cnx):\\n mysql_cursor = mysql_cnx.cursor()\\n\\n entry = defaultdict(lambda: None, entry)\\n\\n query = \\\"INSERT INTO crashes (job_id, type, is_security_issue, is_crash, sample_path, backtrace, backtrace_hash, return_code, create_time) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, NOW())\\\"\\n try:\\n mysql_cursor.execute(\\n query,\\n (\\n jobid,\\n entry[\\\"type\\\"],\\n entry[\\\"is_security_issue\\\"],\\n entry[\\\"is_crash\\\"],\\n entry[\\\"sample\\\"],\\n entry[\\\"backtrace\\\"],\\n entry[\\\"hash\\\"],\\n entry[\\\"return_code\\\"],\\n ),\\n )\\n except mysql.connector.errors.IntegrityError as err:\\n print(\\\"Integrity: {}\\\".format(err))\\n\\n mysql_cnx.commit()\\n mysql_cursor.close()\",\n \"def _writeRecord(self, path, name, data):\\n file_path = os.path.join(path, name)\\n with open(file_path, 'w') as f:\\n for item in data:\\n f.write(str(item)+'\\\\t')\\n f.write('\\\\n')\",\n \"def save_table_scraperwiki(uniques,table,name):\\n for row in table:\\n scraperwiki.sqlite.save(\\n unique_keys=uniques\\n , data=row\\n , table_name=name\\n )\",\n \"def _insert_into_clean(self, entry):\\n i = entry.hash\\n new_entry = self.table[i]\\n while new_entry.key is not None:\\n i += self.second_hash(new_entry.key)\\n new_entry = self.table[i]\\n new_entry.key = entry.key\\n new_entry.value = entry.value\\n new_entry.hash = entry.hash\\n self.used += 1\\n self.filled += 1\",\n \"def add(table, record):\\n index_id = 0\\n record.insert(index_id, common.generate_random(table))\\n table.append(record)\\n data_manager.write_table_to_file(\\\"model/sales/sales.csv\\\", table)\\n\\n return table\",\n \"def store_hive_table(data, directory, file_name):\\n table_name = directory + \\\".\\\" + file_name\\n data.write.saveAsTable(table_name)\",\n \"def write(self):\\n if not self._table: raise ValueError ( \\\"_table is Null\\\" )\\n if self._isnew:\\n for m in self._modified_values:\\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\\n \\n try:\\n rec = CFG.CX.insert ( CFG.DB.SCHEMA + \\\".\\\" + self._table.name,\\n self._modified_values )\\n #this will automatically re-read the data from the db, to take all changes\\n #done by triggers and default values into account.\\n self._objectid = rec['objectid']\\n\\n #print \\\"Record # {0} inserted into {1}.\\\".format(self._objectid, self._table.name)\\n self.raiseEvent ( \\\"record_added\\\", self )\\n \\n except pg.DatabaseError, e:\\n print \\\"Error inserting record.\\\"\\n raise Record.DataManipulationError ( \\\"Inserting a new record into '{0}'\\\".format(self._table.name),\\n str(self._modified_values),\\n e)\\n elif self._ismodified:\\n \\n for m in self._modified_values: \\n self._modified_values[m] = self._table[m].val_py2sql(self._modified_values[m])\\n \\n self._modified_values['objectid'] = self._objectid\\n del self.TextCache[self._objectid]\\n try:\\n rec = CFG.CX.update ( CFG.DB.SCHEMA + \\\".\\\" + self._table.name,\\n self._modified_values )\\n self.read() \\n self.raiseEvent ( \\\"record_saved\\\", self )\\n except pg.DatabaseError, e:\\n print \\\"Error updating record\\\"\\n raise Record.DataManipulationError ( \\\"Updating record {1} of '{0}'\\\".format(self._table.name, self._objectid),\\n str(self._modified_values),\\n e)\",\n \"def store_in_sql(cache, db_handler, TABLE_NAME):\\r\\n if len(cache) == 0 and len(cache[0]) == 0:\\r\\n return\\r\\n\\r\\n # create new table for new streaming event\\r\\n if TABLE_NAME[0] == '':\\r\\n TABLE_NAME[0] = db_handler.create_table(cache[0])\\r\\n \\r\\n # write message to database\\r\\n for message in cache:\\r\\n db_handler.insert_entry(message)\",\n \"def write(engine, table_name, src, return_id=True, do_update=False):\\n query = build_upsert_query(engine, table_name, src, do_update=do_update)\\n LOGGER.debug(query)\\n engine.execute(query)\\n if return_id:\\n return fetch_id(engine, table_name, src)\",\n \"def save_table(date, table):\\n if os.path.isfile(date+\\\".table\\\"):\\n file_using = open(date+\\\".table\\\", \\\"w\\\")\\n else:\\n return False\\n file_using.seek(0)\\n file_using.truncate()\\n for line in table:\\n file_using.write(\\\"{},{},{},{},{}\\\\n\\\".format(line[0], line[1], line[2], line[3], line[4]))\\n file_using.close()\",\n \"def write(self, row):\\n self.append_rows.append(tuple(row))\",\n \"def write(data):\",\n \"def _storeData(self, data, table, query=None):\\n print ('Storing data')\\n conn = dbo.getConnection()\\n\\n if query == None:\\n num_cols = len(data[0])\\n cols = ','.join(['%s ' for i in range(0, num_cols)])\\n query = \\\"INSERT INTO \\\" + table + \\\" VALUES (\\\" + cols + \\\")\\\"\\n\\n dbo.execute_query(conn, query, data, multiple=True)\\n dbo.closeConnection(conn)\\n return\",\n \"def set_temp_entry(self, val):\\n self.temp = val\",\n \"def __setitem__(self, index, value):\\n\\t\\t# create value and key storage\\n\\t\\tself.filep.write(pack('<LL',len(index), len(value)))\\n\\t\\tself.filep.write(index)\\n\\t\\tself.filep.write(value)\\n\\n\\t\\t# grab a hash for the key\\n\\t\\thash = calc_hash(index)\\n\\n\\t\\t# dump a new hash into our bucket\\n\\t\\tself.hashbucket[hash % 256].fromlist([hash, self.position_hash])\\n\\t\\tself.position_hash += 8 + (len(index) + len(value))\",\n \"def write_to_file(entry, file):\\n with open(file, \\\"a\\\") as f:\\n f.write(entry)\",\n \"def write_trace_entry(self, entry):\\n self.gpr[entry.rd] = entry.rd_val\\n self.csv_writer.writerow({'str' : entry.instr_str,\\n 'rd' : entry.rd,\\n 'rd_val' : entry.rd_val,\\n 'rs1' : entry.rs1,\\n 'rs1_val' : entry.rs1_val,\\n 'rs2' : entry.rs2,\\n 'rs2_val' : entry.rs2_val,\\n 'addr' : entry.addr,\\n 'instr' : entry.instr,\\n 'imm' : entry.imm,\\n 'csr' : entry.csr,\\n 'binary' : entry.binary})\",\n \"def makeFilePointer(self, key, tmppath):\\n fp = open(tmppath, 'w')\\n self.getFile(key, fp)\\n fp.close()\",\n \"def write_trade() -> None:\\n try:\\n ddb_table.put_item(Item = data_to_insert)\\n except Exception as e:\\n _LOGGER.error('Problem updating dynamodb trade data. {0}'.format(e))\\n raise Exception('Problem updating dynamodb trade data. {0}'.format(e))\",\n \"def put(data):\",\n \"def journal_write(session, k, v):\\n entry = models.VppEtcdJournal(k=k, v=v)\\n session.add(entry)\\n session.flush()\",\n \"def add_entry(self, entry):\\n if self.get_entry(entry):\\n return entry\\n\\n keys, values = [], []\\n for i in entry:\\n keys.append(\\\"'{}'\\\".format(i))\\n if not isinstance(entry[i], str):\\n values.append(\\\"'{}'\\\".format(str(entry[i])))\\n else:\\n values.append(\\\"'{}'\\\".format(entry[i]))\\n\\n keys.append(\\\"'hash'\\\")\\n values.append(\\\"'{}'\\\".format(self._calculate_hash(entry)))\\n sql = 'INSERT INTO {t_id} ({keys}) VALUES ({values})'.format(\\n t_id=self.table_id, keys=','.join(keys), values=','.join(values))\\n self.fusiontables.query().sql(sql=sql).execute()\",\n \"def write_data_to_result_table(self, id_column, path_src,\\n path_result, size_of_result_file):\\n self.cursor.execute(\\\"\\\"\\\"INSERT INTO result_files\\n VALUES('1','2','3','4');\\\"\\\"\\\")\",\n \"def write (self, path):\\n\\t\\ts=[]; add=s.append\\n\\t\\tadd ('\\\\t'.join (self.schema))\\n\\t\\tfor record in self.data:\\n\\t\\t\\tadd (record.asTabDelimitedRecord())\\n\\t\\t\\n\\t\\t# f = open (path, 'w')\\n\\t\\tf = codecs.open(path, 'w', 'utf-8')\\n\\t\\tf.write (self.linesep.join (s))\\n\\t\\tf.close()\\n\\t\\tprint (\\\"data written to \\\" + path)\",\n \"def commit_to_database(self, table, output):\\n try:\\n # TODO: Is this the correct method for this?\\n with SESSION.begin_nested():\\n i = insert(getattr(db, table))\\n vals = i.values(output)\\n SESSION.execute(vals) # TODO: What is this?\\n SESSION.flush()\\n except Exception as error:\\n log.debug(error, exc_info=True)\\n SESSION.rollback()\\n\\n SESSION.commit() # TODO: Should this be here?\",\n \"def save_entries(self):\\n with open(self.file_name, \\\"w\\\") as file:\\n file.write('date,name,minutes,note\\\\n')\\n for entry in self.entries:\\n writer = csv.writer(file)\\n writer.writerow([entry.date, entry.name, entry.minutes, entry.note])\",\n \"def save_table(data, out_file):\\n logging.info(\\\"Saving table\\\")\\n #header, data = data\\n #out = pd.DataFrame(data=data, columns = header.keys())\\n joblib.dump(data, out_file)\",\n \"def _save (self, expires):\\n\\n pickled_data = pickle.dumps (self._data, self.pickle_protocol)\\n\\n self._delete ()\\n self._exec (\\n \\\"\\\"\\\"\\\\\\n insert into table_name (id, expires, data)\\n values (%(id)s, %(expires)s, %(data)s)\\n \\\"\\\"\\\",\\n data = pickled_data,\\n expires = expires\\n )\",\n \"def write(self, batch):\\n cursor = connection.cursor()\\n while batch:\\n values = [\\n \\\"(%s, %s)\\\" % (\\n psycopg2.Binary(k),\\n psycopg2.Binary(v)\\n ) for k, v in batch[:self.BATCH_SIZE]]\\n sql = \\\"INSERT INTO %s(k,v) VALUES %s\\\" % (self.table, \\\",\\\".join(values))\\n batch = batch[self.BATCH_SIZE:]\\n cursor.execute(sql)\\n cursor.execute(\\\"COMMIT\\\")\",\n \"def taskdetail_save(td):\\n return IMPL.taskdetail_save(td)\",\n \"def write( data ):\",\n \"def save_record(self, data, rownum=None):\\n\\n if rownum is not None:\\n # This is an update\\n records = self.get_all_records()\\n records[rownum] = data\\n with open(self.filename, 'w', encoding='utf-8') as fh:\\n csvwriter = csv.DictWriter(fh, fieldnames=self.fields.keys())\\n csvwriter.writeheader()\\n csvwriter.writerows(records)\\n else:\\n # This is a new record\\n newfile = not os.path.exists(self.filename)\\n\\n with open(self.filename, 'a', encoding='utf-8') as fh:\\n csvwriter = csv.DictWriter(fh, fieldnames=self.fields.keys())\\n if newfile:\\n csvwriter.writeheader()\\n csvwriter.writerow(data)\",\n \"def write_row(self, data):\\n raise NotImplementedError()\",\n \"def exposed_write_data(self, chunk_id, data):\\n local_filename = self.chunk_filename(chunk_id)\\n with open(local_filename, \\\"w\\\") as file:\\n file.write(data)\\n # self.handle_table[chunk_id] = local_filename\",\n \"def store(self, key, headers, value):\",\n \"def process(self, row):\\n #print (row)\\n \\n key = conf[\\\"equipment_id\\\"]\\n \\n result = \\\"test start\\\" \\n result = \\\"test end\\\" \\n \\n data = 1 ## 1\\n \\n self.db.save(key, result, data)\",\n \"def write_to_database(info,timer):\\n\\n inserts = create_sql_write(info,timer)\\n\\n connection = engine.connect()\\n for insert in inserts:\\n connection.execute(insert)\\n connection.close()\",\n \"def abstract_write(self, *params): \\n section = self.table_section_from_parameter(*params)\\n row_args = self.table_row_args_from_parameter(*params)\\n new_row = self.table_row_class(*row_args)\\n # IMPORTANTE! ASUMO QUE UN BLOQUE INVALIDO ES IGUAL A UNO VACIO!\\n # TO DO: PREGUNTAR EN ISSUE\\n found_index = False\\n for index, table_row in enumerate(section.copy()): \\n if table_row is None or not table_row.valid:\\n found_index = True\\n overwrite_index = index\\n break\\n\\n if not found_index:\\n overwrite_index = self.next_replace(section)\\n\\n replace_index = self.index_mapping_from_parameter(overwrite_index, *params)\\n\\n\\n old_line = self.table[replace_index]\\n #print(f\\\"{self.__class__.__name__} Replace -> Index: {replace_index}\\\")\\n\\n # Perfom the actual write\\n self.table[replace_index] = new_row\",\n \"def save_db():\\n # establish connection to db\\n conn = sqlite3.connect('journal_entries.db')\\n\\n # create cursor to execute commands\\n c = conn.cursor()\\n\\n # create table if specified table does not exist\\n c.execute(\\\"\\\"\\\"SELECT count(name) \\n FROM sqlite_master \\n WHERE type='table' AND name='entries' \\n \\\"\\\"\\\")\\n table_check = c.fetchone()[0]\\n if table_check == 0: # does not exist\\n c.execute(\\\"\\\"\\\"CREATE TABLE entries (\\n date_time text,\\n q_1 text,\\n q_2 text,\\n q_3 text,\\n q_4 text,\\n q_5 text,\\n q_6 text\\n )\\\"\\\"\\\")\\n\\n # insert new row (entry)\\n # TODO: refactor code/make flexible. Build dictionary of column, inputs then loop through?\\n c.execute(\\\"INSERT INTO entries VALUES (:date_time, :q_1, :q_2, :q_3, :q_4, :q_5, :q_6)\\\",\\n {\\n 'date_time': get_date_time(),\\n 'q_1': entries[0].get('1.0', 'end-1c'),\\n 'q_2': entries[1].get('1.0', 'end-1c'),\\n 'q_3': entries[2].get('1.0', 'end-1c'),\\n 'q_4': entries[3].get('1.0', 'end-1c'),\\n 'q_5': entries[4].get('1.0', 'end-1c'),\\n 'q_6': entries[5].get('1.0', 'end-1c')\\n }\\n )\\n\\n # commit changes\\n conn.commit()\\n\\n # c.execute(\\\"SELECT * from entries\\\")\\n # print(c.fetchall())\\n\\n # close connection\\n conn.close()\",\n \"def store_rows(self, course_id, filename, rows):\\r\\n output_buffer = StringIO()\\r\\n gzip_file = GzipFile(fileobj=output_buffer, mode=\\\"wb\\\")\\r\\n csv.writer(gzip_file).writerows(rows)\\r\\n gzip_file.close()\\r\\n\\r\\n self.store(course_id, filename, output_buffer)\",\n \"def put(self, record, _id=None, __force=False):\\n # check read only\\n if self.__read_only and not __force:\\n raise IOError(\\\"Table is for read only.\\\")\\n\\n # make record\\n record = Record.from_dict(record, _id)\\n\\n # add record to data\\n _id = record._id\\n self.__data[_id] = record\\n return _id\",\n \"def store_rows(self, course_id, filename, rows):\\r\\n output_buffer = StringIO()\\r\\n csv.writer(output_buffer).writerows(rows)\\r\\n self.store(course_id, filename, output_buffer)\",\n \"def _write(self, tkt_id, repo_id, changesets):\\n @self.env.with_transaction()\\n def do_update(db):\\n cursor = db.cursor()\\n value = str(changesets)\\n if changesets.exists:\\n if value:\\n cursor.execute('UPDATE ticket_changesets SET value=%s '\\n 'WHERE ticket=%s AND repository=%s',\\n [value, tkt_id, repo_id])\\n else:\\n cursor.execute('DELETE FROM ticket_changesets '\\n 'WHERE ticket=%s AND repository=%s',\\n [tkt_id, repo_id])\\n elif value:\\n cursor.execute('INSERT INTO ticket_changesets '\\n '(ticket,repository,value) VALUES(%s,%s,%s)',\\n [tkt_id, repo_id, value])\",\n \"def add_entry_to_db(entry):\\n db.session.add(entry)\\n db.session.commit()\",\n \"def _batch_write(self):\\n if self.to_put:\\n db.put(self.to_put)\\n self.to_put = []\\n if self.to_delete:\\n db.delete(self.to_delete)\\n self.to_delete = []\",\n \"def write_to_destination(DbClass, src_vals, dest_timestamps, set_cols):\\n session = get_db_session(\\\"dest\\\")\\n write_count = 0\\n for sv in src_vals:\\n if sv[\\\"timestamp\\\"] in dest_timestamps:\\n print(f\\\"{sv['timestamp']} already in destination database\\\")\\n continue\\n new_row = DbClass()\\n for k, v in sv.items():\\n setattr(new_row, k, v)\\n for k, v in set_cols.items():\\n setattr(new_row, k, v)\\n print(f\\\"adding data for {sv['timestamp']}\\\")\\n session.add(new_row)\\n write_count += 1\\n session.commit()\\n session_close(session)\\n print(f\\\"Wrote {write_count} rows to destination database\\\")\\n return True\",\n \"def populate_stat(self, table):\\n myrow = table.row\\n # HDF5 doesn't handle unicode strings, so we need to convert to \\n # *byte* strings, which we can put in the HDF5 file \\n addy = numpy.zeros(len(self.address), \\n dtype=(numpy.str, glob.nchar_address))\\n for i in range(len(addy)):\\n addy[i] = (self.address[i]).encode('utf8')\\n\\n myrow[\\\"address\\\"] = addy\\n myrow[\\\"bike_stands\\\"] = self.bike_stands\\n myrow[\\\"number\\\"] = self.number\\n myrow[\\\"position\\\"] = self.position\\n myrow.append()\\n table.flush()\",\n \"def write(self, data):\\n self.mycol.insert_one({\\\"query\\\":data})\",\n \"def __write_event(self, handle, nbr):\\n try:\\n event = self.database.get_event_from_handle(handle)\\n self.__write_row(nbr, handle, event)\\n except:\\n event = \\\"NOT FOUND\\\"\\n self.__write_row(nbr, handle, event)\",\n \"def write(self, row):\\n bytes = struct.pack(self.pack_format, *row)\\n self.f.write(bytes)\",\n \"def save_tmp_file(self, data):\\n with open(self.tmp_file, 'wb') as f:\\n f.write(data)\",\n \"def insert(conn, table_info, table_data):\\n\\n sql = ''' INSERT INTO ''' + table_info \\n + ''' VALUES(''' + \\\"?,\\\" * (len(table_data)-1) + \\\"?)\\\"\\n cursor = conn.cursor()\\n cursor.execute(sql, table_data)\\n conn.commit()\",\n \"def write_main_mk_table(file_name, repository, last_commit, deploy_time):\\n row = \\\"|\\\" + repository + \\\\\\n \\\"|\\\" + last_commit + \\\\\\n \\\"|\\\" + deploy_time + \\\\\\n \\\"|\\\" + '\\\\n'\\n write_file = open(file_name, \\\"a\\\")\\n write_file.write(row)\",\n \"def save_data(self, record):\\n self.dbm.addRecord(record)\",\n \"def write_table(self, tab):\\n self.save_text()\\n\\n table = list()\\n row = list()\\n headers = tab['c'][3]\\n if headers:\\n has_content = False\\n for col in headers:\\n self.list_parse(col, cell_content=True)\\n cell_content = self.get_content()\\n row.append(cell_content)\\n if cell_content != '':\\n has_content = True\\n if has_content:\\n row = tuple(row)\\n table.append(row)\\n t_content = tab['c'][4]\\n for line in t_content:\\n row = list()\\n for col in line:\\n self.list_parse(col, cell_content=True)\\n cell_content = self.get_content()\\n row.append(cell_content)\\n row = tuple(row)\\n table.append(row)\\n table = tuple(table)\\n self.tables.append((table, (self.context, self.ancestor)))\",\n \"def _insert_table_row(self, db: str, table: str, row: Dict[str, Any]):\\n pass\",\n \"def write_to(self, fname, **kwargs):\\n data = self.to_Table()\\n data.write(fname, **kwargs)\",\n \"def put_record(self, record):\\r\\n row = [record.get(field) for field in self.fields.names()]\\r\\n\\r\\n self.put(row)\",\n \"def tabser(filename, body, data):\\n # XXX checksums ignored\\n head = Struct(\\\"!BiHBxxxB\\\")\\n body = Struct(body)\\n # foot = Struct(\\\"!4s\\\")\\n\\n buffer = bytearray([0] * (2 ** 16))\\n head.pack_into(buffer, 0, 0, int(time()), len(data), body.size, 0),\\n offset = head.size\\n for row in data:\\n body.pack_into(buffer, offset, *row, 0)\\n offset += body.size\\n else:\\n print(\\\"write %d rows\\\" % len(data))\\n # offset = 2 ** 16 - foot.size\\n # foot.pack_into(buffer, offset, bytes([0, 0, 0, 0]))\\n with open(filename, \\\"wb\\\") as f:\\n f.write(buffer)\",\n \"def add_to_db_single(self, element):\\r\\n def quot(string):\\r\\n \\\"\\\"\\\" Replace \\\" with ' in text strings that goes into the\\r\\n db, right now it is only done on the name, but it should\\r\\n be done on all fields that might contain such characters\\r\\n \\\"\\\"\\\"\\r\\n return string.replace('\\\"', \\\"'\\\")\\r\\n\\r\\n # Make entry i measurements table\\r\\n query = ('INSERT INTO {table} SET '\\r\\n 'time=FROM_UNIXTIME({time}), '\\r\\n 'type=2, '\\r\\n 'timestep={timestep}, '\\r\\n 'comment=\\\"{comment}\\\", '\\r\\n 'pass_energy={pass_energy}, '\\r\\n 'excitation_energy={excitation_energy}, '\\r\\n 'number_of_scans={number_of_scans}, '\\r\\n 'project=\\\"{project}\\\", '\\r\\n 'file_name=\\\"{file_name}\\\", '\\r\\n 'name=\\\"{name}\\\";').format(\\r\\n table=self.tables['measurements'],\\r\\n time=element[0]['date'],\\r\\n timestep=element[0]['dwell_time'],\\r\\n comment=element[0]['unique_name'],\\r\\n pass_energy=element[0]['pass_energy'],\\r\\n excitation_energy=element[0]['excitation_energy'],\\r\\n number_of_scans=element[0]['num_scans'],\\r\\n project=element[0]['project'],\\r\\n file_name=element[0]['unique_name'].replace('\\\\\\\\', '\\\\\\\\\\\\\\\\'),\\r\\n name=quot(element[0]['name']))\\r\\n\\r\\n # Christian, comment this in to see a list of metadata\\r\\n #print element[0]\\r\\n self.cursor.execute(query) # COMMENT\\r\\n\\r\\n # Get the id of it\\r\\n query = ('select id from {table} where type=2 '\\r\\n 'order by id desc limit 1;').\\\\\\r\\n format(table=self.tables['measurements'])\\r\\n self.cursor.execute(query)\\r\\n id_ = self.cursor.fetchall()[0][0]\\r\\n\\r\\n # Add the data to xy_values table in chunks of 100 data points\\r\\n counter = 0\\r\\n query_reset = 'INSERT INTO {table} (measurement, x, y) VALUES'.format(\\r\\n table=self.tables['xy'])\\r\\n query = query_reset\\r\\n # element[1] is tuple of data: (Array(x0, x1, x2), Array(y0, y1, y2)).\\r\\n # The zip statement (where * pulls out both value) turns it into:\\r\\n # [(x0, y0), (x1, y1), (x2, y2)]\\r\\n for x_value, y_value in zip(*element[1]):\\r\\n counter += 1\\r\\n query += '({0},{1},{2})'.format(id_, x_value, y_value)\\r\\n if counter < 100:\\r\\n query += ','\\r\\n else:\\r\\n query += ';'\\r\\n self.cursor.execute(query)\\r\\n counter = 0\\r\\n query = query_reset\\r\\n # Remember to write the last less than 100 points\\r\\n if query != query_reset:\\r\\n # Remove the last , and add a ;\\r\\n query = query[0: -1] + ';'\\r\\n self.cursor.execute(query)\",\n \"def store_mrs_data(conn, file_id, file_name, file_contents, group_label):\\n # Create the table if it does not exist.\\n _create_table(conn, TABLE_NAME_BRAINSCANS, TABLE_COLS_BRAINSCANS)\\n # Try to insert a new row into the table.\\n table_entry = (file_id, file_name, file_contents, group_label)\\n _store_entry_in_table(conn, TABLE_NAME_BRAINSCANS, table_entry)\",\n \"def write_to_db(batch_num,pH,cursor):\\n\\n\\t#Batch Number\\n\\tbatch = 1\\n\\t\\t\\n\\n\\tcur_time = datetime.now()\\n\\theater_state = 1\\n\\ttemp = 30.1\\n\\tamb_hum, amb_temp = 25.1,30.2\\n\\n\\tcursor.execute('''INSERT INTO Kombucha_Data('Time', 'Batch', 'Heater State', 'Temperature', 'Ambient Temperature', 'Ambient Humidity','pH')\\n\\t\\t\\t\\t VALUES(?,?,?,?,?,?,NULL)''', (cur_time, batch_num, heater_state, temp, amb_temp, amb_hum))\\n\\tcursor.commit()\\n\\treturn\",\n \"def __setitem__(self,key,value):\\n self.table.setItem(key,self.column,value)\",\n \"def save(self):\\n args = list(map(self._get_value_or_default, self.COLUMN_TO_FILED))\\n columns = list(map(lambda k: k, self.COLUMN_TO_FILED))\\n sql = 'INSERT INTO {} ({}) VALUES({});'.format(\\n self.TABLE_NAME,\\n ', '.join(columns),\\n '%s,'.join(' '*len(columns)) + '%s'\\n )\\n cursor = yield self._pool.execute(sql, args)\\n app_log.info('save arg %s', args)\\n count = cursor.rowcount\\n result = True if count == 1 else False\\n return result\",\n \"def save_event(self, data):\\n rdb.table(self.rdb_table).insert(data)\",\n \"def update(table, id_):\\n # 4\\n for row in table:\\n if row[0] == id_:\\n addnew = ui.get_inputs(\\n ['month: ',\\n 'day: ',\\n 'year: ',\\n 'type (in=income, out= outflow): ',\\n 'amount (of transaction in USD): '],\\n 'Updating item in Accounting table')\\n addnew.insert(0, id_)\\n row = addnew\\n data_manager.write_table_to_file('accounting/items.csv', table)\\n\\n return table\",\n \"def update(table, id_):\\n\\n # your code\\n key = common.check_for_key(id_,table)\\n if key == None:\\n ui.print_error_message('Key does not exist')\\n else:\\n return_inputs = ui.get_inputs(['Name', 'Age'], 'Enter New Values')\\n modif_index = key\\n\\n table[modif_index][NAME] = return_inputs[FIRST_PROP]\\n table[modif_index][AGE] = return_inputs[SECOND_PROP]\\n data_manager.write_table_to_file('hr/persons.csv', table) \\n\\n return table\",\n \"def write_table_to_file(table):\\n with open(\\\"story.csv\\\", \\\"w\\\") as file:\\n for record in table:\\n row = ';'.join(record)\\n file.write(row + \\\"\\\\n\\\")\",\n \"def write_file(file_name, table):\\r\\n \\r\\n savectr=len(table)\\r\\n try:\\r\\n with open (file_name, 'wb') as objFile:\\r\\n pickle.dump(table,objFile) #pickle my 2D list\\r\\n print ('{} CD(s) saved into {}.\\\\n'.format(savectr,file_name))\\r\\n except PermissionError as e:\\r\\n print('Not enough rights to create/modify ' + file_name + '.') #if unable pickle data due to permission issues\\r\\n print ()\\r\\n print (e, e.__doc__, sep='\\\\n')\\r\\n print ()\\r\\n except IOError as e:\\r\\n print ('I/O error({0}): {1}'.format(e.errno,e.strerror))#if unable to pickle data due to IO errors such as disk space issues\\r\\n print ()\\r\\n print (e, e.__doc__, sep='\\\\n')\\r\\n print ()\\r\\n except pickle.PickleError as e:\\r\\n print ('Unable to write data into ' + file_name + '.') #if unable to pickle 2D list, exception handling for pickling errors\\r\\n print ()\\r\\n print (e, e.__doc__, sep='\\\\n')\\r\\n print ()\",\n \"def test_write_to_db(self):\\n\\n measurement, sensor_name = [\\\"testItem\\\", \\\"sensorA\\\"]\\n query = f\\\"Select * from {measurement}\\\"\\n\\n def get_points(_query):\\n \\\"\\\"\\\"Get measurement points from db given a query\\\"\\\"\\\"\\n\\n try:\\n return self.test_client.query(\\n _query).raw[\\\"series\\\"][0][\\\"values\\\"]\\n except KeyError:\\n return []\\n\\n test_point = main.generate_a_measurement_point(\\n measurement,\\n sensor_name=sensor_name,\\n sensor_output_file_dir=\\\"tests/datafiles/\\\",\\n output_filename=\\\"sensor_out_valid.txt\\\")\\n\\n original_points_in_db = get_points(query)\\n main.write_data_to_db(self.test_client, [test_point])\\n new_points_in_db = get_points(query)\\n\\n assert len(new_points_in_db) > len(original_points_in_db)\\n assert sensor_name in str(new_points_in_db[0])\\n assert str(self.expected_temp_val) in str(new_points_in_db[0])\",\n \"def insert_record(record, table):\\n\\n\\ttups = [(key, val) for key, val in record.iteritems()]\\n\\tkeys = [key for key, val in tups]\\n\\tvals = [val for key, val in tups]\\n\\n\\tconn = get_database_connection(port = 2001)\\n\\tcursor = conn.cursor()\\n\\n\\tnum_cols = len(keys)\\n\\tkey_str = ','.join(keys)\\n\\tval_str = ','.join(['%s'] * num_cols)\\n\\n\\tqry = \\\"REPLACE INTO %s (%s) VALUES (%s)\\\" % (table, key_str, val_str)\\n\\tcursor.execute(qry, vals)\\n\\n\\tconn.commit()\\n\\tcursor.close()\\n\\tconn.close()\",\n \"def store_data(self, data):\\n self.data = data\\n # HERE\\n the_main_dict = {**self.user_data(), **self.entities_data(), **self.extract_relevant(), **self.locate(),\\n **self.calculate_days(), **self.clean_user_desc()}\\n # The below is the reason that the table creation must be written in alphabetical order. This is simpler than\\n # writing the complex joins that would otherwise be needed.\\n my_keys_list = sorted(the_main_dict.keys())\\n my_items = list(map(lambda x: str(the_main_dict[x]).replace(\\\"'\\\", ''), my_keys_list))\\n try:\\n # Unpacks the items into an insert statement for the SQLite table\\n self.conn.execute(\\\"INSERT INTO {0} VALUES('{1}','{2}','{3}','{4}','{5}','{6}','{7}','{8}','{9}',\\\"\\n \\\"'{10}','{11}','{12}','{13}','{14}','{15}','{16}','{17}','{18}','{19}','{20}',\\\"\\n \\\"'{21}','{22}','{23}','{24}','{25}','{26}','{27}','{28}')\\\".format(self.table, *my_items))\\n self.limiting += 1\\n return 0\\n except sqlite3.IntegrityError:\\n return 1\",\n \"def store_datapoint(sql, parts):\\n t = datetime.fromtimestamp(parts[0])\\n humid = parts[1]\\n temp_c = parts[2]\\n temp_f = parts[3]\\n heat_c = parts[4]\\n heat_f = parts[5]\\n c = sql.cursor()\\n c.execute(\\\"INSERT INTO points VALUES (?,?,?,?,?,?)\\\",\\n (t, humid, temp_c, temp_f, heat_c, heat_f))\\n sql.commit()\",\n \"def db_commit(table, field_list, value_list):\\r\\n logger = logging.getLogger(__name__)\\r\\n db_conn = MySQLConnector()\\r\\n\\r\\n logger.info(\\\"Calling the WriteToDB function...\\\")\\r\\n db_conn.writeToDB(table=table, fieldList=field_list, valueList=value_list)\",\n \"def test_write(self):\\n writer = BaseTSVWriter([\\n ('Prop1', 'prop1'),\\n ('Prop2', 'prop2'),\\n ])\\n\\n row = NonCallableMock(\\n prop1=145,\\n prop2=None,\\n )\\n\\n valid, invalid = writer.write(self.tsv_file, [row])\\n\\n assert valid == [row]\\n assert invalid == []\\n assert self.tsv_value == (\\n \\\"Prop1\\\\tProp2\\\\r\\\\n\\\"\\n \\\"145\\\\t\\\\r\\\\n\\\" # None should convert to an empty string\\n )\",\n \"def commitToDatabase(self, tiltseriesdata):\\n\\t\\treturn\",\n \"def write_to_db(self, table, db, if_exists=\\\"append\\\", index=False):\\n\\n # Cast all sessions to dataframe\\n sessions_df = pd.DataFrame.from_records(self.closed_sessions)\\n\\n # If we have sessions to write, sort them and write to the database\\n if sessions_df.shape[0] > 0:\\n sessions_df = sessions_df.sort_values(\\\"last_timestamp\\\", ascending=True)\\n sessions_df.to_sql(table, db, if_exists=if_exists, index=index)\\n\\n # Don't double-write closed events\\n self.closed_sessions = []\",\n \"def write_entries(self, entries):\\n for entry in entries:\\n self.write(entry)\",\n \"def __write_place(self, handle, nbr):\\n try:\\n place = self.database.get_place_from_handle(handle)\\n self.__write_row(nbr, handle, place)\\n except:\\n place = \\\"NOT FOUND\\\"\\n self.__write_row(nbr, handle, place)\",\n \"def writerow(self, row):\\n data = []\\n basestring_type = six.string_types[0]\\n for value in row:\\n if not isinstance(value, basestring_type):\\n value = '%s' % (value,)\\n data.append(value.encode('utf-8'))\\n self.writer.writerow(data)\\n data = self.queue.getvalue()\\n data = data.decode('utf-8')\\n self.stream.write(data)\\n self.queue.truncate(0)\",\n \"def WriteEntry(self, data):\\n if not self._file_object:\\n raise IOError('Unable to write to closed serialized data stream.')\\n\\n data_size = len(data)\\n # TODO: Fix maximum size limit handling to create new stream.\\n # data_end_offset = (\\n # self._file_object.tell() + self._DATA_ENTRY_SIZE + data_size)\\n # if data_end_offset > self._maximum_data_size:\\n # raise IOError('Unable to write data entry size value out of bounds.')\\n\\n data_size = construct.ULInt32('size').build(data_size)\\n self._file_object.write(data_size)\\n self._file_object.write(data)\\n\\n return self._file_object.tell()\",\n \"def save(self, ip='', result='', dt=datetime.datetime.now()):\\n self.ping_table.insert({\\\"host\\\": ip, \\\"result\\\": result, \\\"datetime\\\": str(dt)})\\n return\",\n \"def put(self, item):\\n\\n tableData = {}\\n tableData['itemId'] = item.id\\n for tagName in item.tags:\\n tableData[tagName] = item.tags[tagName];\\n\\n table = self.__getTable();\\n table.put_item(data = tableData, overwrite = True)\",\n \"def SaveData(self, conn):\\n t = time.process_time()\\n log = Logger()\\n cursor = conn.cursor()\\n sqlQuery = \\\"insert into old_data \\\" \\\\\\n \\\"select * from new_data\\\"\\n cursor.execute(sqlQuery)\\n conn.commit()\\n conn.close()\\n elapsed_t = time.process_time() - t\\n log.log('Datos de cambios copiados a tabla', elapsed_t)\",\n \"def write_tab(gdf, tab_name, crs_wkt=WKT_SWISS, index=None):\\n\\n\\t\\tgdf.crs = WKT_SWISS\\n\\t\\t\\n\\t\\t# fiona can't write integer columns, convert columns to float\\n\\t\\tfor col in gdf.columns:\\n\\t\\t\\tstype = str(gdf[col].dtype)\\n\\t\\t\\tif stype.startswith('int'):\\n\\t\\t\\t\\tgdf[col] = gdf[col].astype(float)\\n\\t\\t\\t\\t\\n\\t\\t# fiona can't write integer columns, convert index to float\\n\\t\\tstype = str(gdf.index.dtype)\\n\\t\\tif stype.startswith('int'):\\n\\t\\t\\tgdf.index = gdf.index.astype(float)\\n\\n\\t\\tgdf.to_file(tab_name,driver='MapInfo File', index=index) \\n\\t\\treturn print(len(gdf), 'row(s) written to mapinfo file.')\",\n \"def create_transaction(conn,task):\\r\\n sql = ''' INSERT OR IGNORE INTO emp1(emp_id,location,login_time)\\r\\n VALUES(?,?,?) '''\\r\\n c = conn.cursor()\\r\\n c.execute(sql,task)\\r\\n conn.commit()\",\n \"def func():\\n try:\\n session.add(point_table(id=4, ptest=(1, None)))\\n session.commit()\\n session.close()\\n except Exception as e:\\n session.close()\\n raise e\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68487287","0.64390826","0.60907364","0.5969685","0.59630823","0.58389467","0.56817126","0.56734014","0.56520325","0.56517667","0.56272185","0.5623497","0.56175345","0.56050855","0.55885863","0.5566574","0.5550937","0.552071","0.5517991","0.5485367","0.5485249","0.5482685","0.5469373","0.5401409","0.5388986","0.5385193","0.5368341","0.53612685","0.5355809","0.53267324","0.53235745","0.53213274","0.53099453","0.5300878","0.52768475","0.527126","0.5270491","0.525285","0.5243045","0.52424306","0.52394944","0.5234875","0.52335227","0.5233453","0.52258474","0.52133906","0.52096856","0.5196085","0.519485","0.5194113","0.51939046","0.5184745","0.5181999","0.51786983","0.5156709","0.51528466","0.5144143","0.5144097","0.5141902","0.5138752","0.51340955","0.5131972","0.5131291","0.5103348","0.5097869","0.5083323","0.50801396","0.50731283","0.5061068","0.5054284","0.50530994","0.50522333","0.50481224","0.5044879","0.50429565","0.50397915","0.5036791","0.5029256","0.50268793","0.50257033","0.50165874","0.5013512","0.5010084","0.5004866","0.5002759","0.5002202","0.49964848","0.49903047","0.49855122","0.49846467","0.498331","0.49813366","0.49810523","0.49635997","0.49633455","0.49624652","0.49555892","0.49502486","0.49445957","0.49392766"],"string":"[\n \"0.68487287\",\n \"0.64390826\",\n \"0.60907364\",\n \"0.5969685\",\n \"0.59630823\",\n \"0.58389467\",\n \"0.56817126\",\n \"0.56734014\",\n \"0.56520325\",\n \"0.56517667\",\n \"0.56272185\",\n \"0.5623497\",\n \"0.56175345\",\n \"0.56050855\",\n \"0.55885863\",\n \"0.5566574\",\n \"0.5550937\",\n \"0.552071\",\n \"0.5517991\",\n \"0.5485367\",\n \"0.5485249\",\n \"0.5482685\",\n \"0.5469373\",\n \"0.5401409\",\n \"0.5388986\",\n \"0.5385193\",\n \"0.5368341\",\n \"0.53612685\",\n \"0.5355809\",\n \"0.53267324\",\n \"0.53235745\",\n \"0.53213274\",\n \"0.53099453\",\n \"0.5300878\",\n \"0.52768475\",\n \"0.527126\",\n \"0.5270491\",\n \"0.525285\",\n \"0.5243045\",\n \"0.52424306\",\n \"0.52394944\",\n \"0.5234875\",\n \"0.52335227\",\n \"0.5233453\",\n \"0.52258474\",\n \"0.52133906\",\n \"0.52096856\",\n \"0.5196085\",\n \"0.519485\",\n \"0.5194113\",\n \"0.51939046\",\n \"0.5184745\",\n \"0.5181999\",\n \"0.51786983\",\n \"0.5156709\",\n \"0.51528466\",\n \"0.5144143\",\n \"0.5144097\",\n \"0.5141902\",\n \"0.5138752\",\n \"0.51340955\",\n \"0.5131972\",\n \"0.5131291\",\n \"0.5103348\",\n \"0.5097869\",\n \"0.5083323\",\n \"0.50801396\",\n \"0.50731283\",\n \"0.5061068\",\n \"0.5054284\",\n \"0.50530994\",\n \"0.50522333\",\n \"0.50481224\",\n \"0.5044879\",\n \"0.50429565\",\n \"0.50397915\",\n \"0.5036791\",\n \"0.5029256\",\n \"0.50268793\",\n \"0.50257033\",\n \"0.50165874\",\n \"0.5013512\",\n \"0.5010084\",\n \"0.5004866\",\n \"0.5002759\",\n \"0.5002202\",\n \"0.49964848\",\n \"0.49903047\",\n \"0.49855122\",\n \"0.49846467\",\n \"0.498331\",\n \"0.49813366\",\n \"0.49810523\",\n \"0.49635997\",\n \"0.49633455\",\n \"0.49624652\",\n \"0.49555892\",\n \"0.49502486\",\n \"0.49445957\",\n \"0.49392766\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51368165"},"document_rank":{"kind":"string","value":"60"}}},{"rowIdx":79597,"cells":{"query":{"kind":"string","value":"Print the contents of rnkCITATIONDATAEXT table containing external records that were cited by NUM or more internal records. NUM is by default taken from the E command line option."},"document":{"kind":"string","value":"def print_missing(num):\n if not num:\n num = task_get_option(\"print-extcites\")\n\n write_message(\"Listing external papers cited by %i or more internal records:\" % num)\n\n res = run_sql(\"SELECT COUNT(id_bibrec), extcitepubinfo FROM rnkCITATIONDATAEXT \\\n GROUP BY extcitepubinfo HAVING COUNT(id_bibrec) >= %s \\\n ORDER BY COUNT(id_bibrec) DESC\", (num,))\n for (cnt, brec) in res:\n print str(cnt)+\"\\t\"+brec\n\n write_message(\"Listing done.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_icd(self):\n wiki = wikipediaapi.Wikipedia('en') # may as well declare this here so I don't need to call it every query\n supplemental_articles = []\n with open(ICD10_DESC_PATH, 'r') as f:\n current_family = [] # list of lists of descriptions within the current family (3 letter code = family)\n current_parent = None # Most recent 3 letter code seen\n for line in tqdm(f.readlines(), desc=\"ICD10 Lines Processed\"):\n\n code = line[6:14].strip().lower()\n description = simple_clean(line[77:])\n self.code2desc[code] = description.split()\n\n if len(code) == PARENT_CODE_LENGTH: # found a parent\n # query web if set params to True\n wiki_result = self.query_wikipedia(wiki, description) if self.query else []\n pubmed_result = self.query_pubmed(description) if self.query else []\n\n # store results\n if wiki_result:\n supplemental_articles.extend(wiki_result)\n if pubmed_result:\n supplemental_articles.extend(pubmed_result)\n\n # update metrics using current family\n self.process_family_frequencies(current_parent, current_family)\n current_family = []\n current_parent = code\n current_family.append(description.split())\n self.n_desc += 1\n\n # process the last family\n self.process_family_frequencies(current_parent, current_family)\n # go through all the articles we found, preprocess, and add to self.data\n self.data.extend(self.process_articles(supplemental_articles))\n\n # lastly calculate tf and idf over all descriptions (not including articles here) for use in weighting later\n self.n_words = log10(self.n_words)\n self.n_desc = log10(self.n_words)\n self.word2tf = {word: log10(count) - self.n_words for word, count in self.word2tf.items()}\n self.word2df = {word: count - self.n_desc for word, count in self.word2df.items()}\n self.dump()","def identifier_entry(self):\n\t self.result_code = open(\"result.c\", \"r\")\n\t self.line_array = self.result_code.readlines()\n\t self.result_code.close()\n\n\t #self.symbol_table = open(\"symbol.csv\", \"w\")\n\t datatypes = [\"int\",\"float\",\"char\",\"double\"] #the datatypes\n\t '''General Pattern for search and sub function for all datatype '''\n\t struct = \"&& .*\"\n\t struct1 = \"&& \"\n\n\t # Looping over all the lines in the intermediate program.\n\t for line in self.line_array:\n\t # Checking for integer declarations.\n\t for datatype in datatypes:\n\t pat = re.sub(\"&&\",datatype,struct)\n\t pat1 = re.sub(\"&&\",datatype,struct1)\n\t if re.search(pat,line):\n\t dec1 = re.split(pat1,line)\n \n\t self.process_declaration(dec1[1],datatype)\n\t print(self.identifiers)\n\t print(self.array_structure)\n\n\t #self.symbol_table.close()","def cite_report(self):\n\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\n print(\"number of citations = \", num_cites)\n\n for n, p in enumerate(self.mypapers):\n clean_tags = re.compile(\"<.*?>\")\n title = re.sub(clean_tags, \"\", p.title[0])\n tt = textwrap.wrap(title, 60)\n print(f\"{n+1:3} | {p.citation_count:4} {tt[0]}\")\n if len(tt) > 1:\n for line in tt[1:]:\n print(\"{:3} | {:4} {}\".format(\"\", \"\", line))\n print(\"{:3} | {:4} {}\".format(\"\", \"\", \"\"))","def display_digits():\n digits = load_digits()\n print(digits.DESCR)\n fig = plt.figure()\n for i in range(10):\n subplot = fig.add_subplot(5, 2, i+1)\n subplot.matshow(numpy.reshape(digits.data[i], (8, 8)), cmap='gray')\n\n plt.show()","def print_entries(self):\n self.print_selected_entries(self.entries)","def extensions():\r\n document.add_page_break()\r\n document.add_heading('Extensions', level=1)\r\n extensions = get_qlik_sense.get_extensions()\r\n num_of_extensions = len(extensions)\r\n table = document.add_table(rows=num_of_extensions+1, cols=1)\r\n table.style = 'Grid Table 1 Light Accent 1'\r\n row = table.rows[0]\r\n row.cells[0].text = 'name'\r\n\r\n for extension in range(num_of_extensions):\r\n row = table.rows[extension+1]\r\n row.cells[0].text = str(extensions[extension])","def show_entries(self, inp=None):\n nums = self.selector.select_by_index(inp)\n if not nums:\n # self.visual.error(\"No selection parsed to show.\")\n return\n ids = [self.reference_entry_id_list[n] for n in nums]\n # self.visual.print(\"Entry #[{}]\".format(ones_idx))\n self.visual.print_entries_contents([self.entry_collection.entries[ID] for ID in ids])","def load_icd10to9():\n icd10to9 = pd.read_csv(pkg_resources.resource_filename(__name__,'2018_I10gem.txt'),delim_whitespace=True,header=None,dtype=str)\n icd10to9.columns=['icd10','icd9','flag']\n return icd10to9","def load_icd9to10():\n icd9to10 = pd.read_csv(pkg_resources.resource_filename(__name__,'2018_I9gem.txt'),delim_whitespace=True,header=None,dtype=str)\n icd9to10.columns=['icd9','icd10','flag']\n return icd9to10","def get_inf_data(self, driver):\r\n if 'Found 0 Title(s)' != driver.find_element_by_xpath('/html/body/h3').text:\r\n # select custom information\r\n driver.find_element_by_xpath('//*[@id=\"display_format_custom\"]').click()\r\n # select all useful information\r\n for i in range(1, 9):\r\n for j in range(1, 5):\r\n if (i == 1 and j < 4) or (i == 2 and j == 1) or (i == 2 and j == 4) or (i == 3 and j == 4) or (\r\n i == 5 and j > 1) or i == 6 or (i == 7 and j < 3) or (i == 8 and j == 3):\r\n driver.find_element_by_xpath(\r\n '/html/body/form/ul[2]/table/tbody/tr[' + str(i) + ']/td[' + str(j) + ']/input[2]').click()\r\n # display\r\n driver.find_element_by_xpath('//*[@id=\"btn_find_display\"]').click()\r\n\r\n # the number of records\r\n number = len(driver.find_elements_by_xpath('//*[@id=\"list_form\"]/ul'))\r\n # all useful information\r\n for i in range(1, number + 1):\r\n self.doi_l.append('')\r\n information_l = driver.find_element_by_xpath('//*[@id=\"list_form\"]/ul[' + str(i) + ']').text.split('\\n')\r\n self.record_number_l.append(re.findall(r'(?<== ).*$', information_l[0])[0])\r\n reaction_method = re.findall(r'(?<== ).*$', information_l[1])[0]\r\n self.reaction_method_l.append(reaction_method)\r\n self.data_type_l.append(re.findall(r'(?<== ).*$', information_l[2])[0])\r\n self.qdb_code(reaction_method)\r\n self.qdb_process_l.append(self.qdb_process)\r\n prod = re.findall(r'(?<== ).*$', information_l[5])[0]\r\n self.x_unit_l.append(re.findall(r'(?<== ).*$', information_l[6])[0])\r\n self.y_unit_l.append(re.findall(r'(?<== ).*$', information_l[7])[0])\r\n self.ref_number_l.append(re.findall(r'(?<== ).*$', information_l[8])[0])\r\n author = re.findall(r'(?<== ).*$', information_l[9])[0]\r\n rewrite_author = re.sub('[$]', ' ', author)\r\n self.author_l.append(rewrite_author)\r\n self.title_l.append(re.findall(r'(?<== ).*$', information_l[10])[0])\r\n self.journal_l.append(re.findall(r'(?<== ).*$', information_l[11])[0])\r\n self.volume_l.append(re.findall(r'(?<== ).*$', information_l[12])[0])\r\n self.page_l.append(re.findall(r'(?<== ).*$', information_l[13])[0])\r\n self.publish_year_l.append(re.findall(r'(?<== ).*$', information_l[14])[0])\r\n reaction_formula = re.findall(r'(?<== ).*$', information_l[15])[0]\r\n self.reaction_formula_l.append(reaction_formula)\r\n self.decompose_nifs(reaction_formula, prod)\r\n self.reactants_l.append(self.reactants)\r\n self.products_l.append(self.products)\r\n df_information = pd.DataFrame([self.record_number_l, self.reaction_formula_l, self.reaction_method_l,\r\n self.qdb_process_l, self.reactants_l, self.products_l, self.data_type_l,\r\n self.x_unit_l, self.y_unit_l, self.ref_number_l, self.doi_l,\r\n self.title_l, self.author_l, self.journal_l, self.volume_l, self.page_l,\r\n self.publish_year_l])\r\n df_information = df_information.T\r\n df_information.rename(columns={0: 'record_number', 1: 'reaction_formula', 2: 'reaction_method',\r\n 3: 'QDB_process', 4: 'reactants', 5: 'products',\r\n 6: 'Data_type', 7: 'temperature_unit', 8: 'rate_coefficient_unit',\r\n 9: 'reference_number', 10: 'DOI',\r\n 11: 'title', 12: 'author', 13: 'journal', 14: 'volume', 15: 'page',\r\n 16: 'publish_year'},\r\n inplace=True)\r\n else:\r\n df_information = pd.DataFrame()\r\n return df_information","def show_research(self):\n self.list_research = orm_imp.read_research()\n print(\"========================================================\")\n for row in self.list_research:\n print(\n row[\"date\"], \"|| Produit :\", row['subcat'],\n \"|| Meilleure proposition :\", row['product'], \"| Score :\",\n row['nutriscore'], \"| Lien :\", row['url'],\n \"| Ingrédients :\", row['ingredient'])\n print(\"========================================================\")","def PrintInfo(self):\n infoname = list(zip(*self.basicinfo.cursor.execute(\n \"SELECT Name FROM Infoname\").fetchall()))[0]\n for obj in infoname:\n s = (\"SELECT Col, Row, TableNum FROM %s WHERE \"\n \"TestItems=%s\" % (self.position, \"'\" + obj + \"'\"))\n [col, row, tablenum] = self.basicinfo.cursor.execute(s).fetchone()\n s = \"SELECT %s FROM TestInfo\" % obj\n content = self.db.cursor.execute(s).fetchone()[0]\n if tablenum is not None:\n self.doc.TableContent(\n tablenum=tablenum, cellrow=row, cellcolum=col,\n insertcontent=content)","def get_text_lines(instText):\n\n # Find out which part this is\n part = instText.part\n # Get the necessary parameters: lng, ext, dir\n sLng = part.corpus.get_lng_display()\n sDir = part.dir\n sName = instText.fileName\n sFormat = instText.get_format_display()\n # Now try to get the information\n oBack = get_crpp_text(sLng, sDir, sFormat, sName)\n # Prepare what we return\n if oBack == None or oBack['status'] == 'error':\n return None\n else:\n return oBack","def print_cat(self):\n for ion in self.cations:\n data_str = (\"HETATM\", int(self.nmb) + ion, self.cations[ion][0], \"\",\n self.cations[ion][0],\"\", ion, \"\",\n Vector.x(self.cations[ion][1]), Vector.y(self.cations[ion][1]),\n Vector.z(self.cations[ion][1]), 1.00, 0.00,\n self.cations[ion][0], \"\",\"\\n\")\n out_str = self.pdb_format % data_str\n self.output.write(out_str)\n self.output.close()\n return 1","def print_the_contents_of_all_entries(self):\n\n if len(self.student_list):\n self.print_dataframe(self.student_list)\n else:\n print('There is no contents to show')","def document_number(self, **kw):\r\n for docnum in self.document_numbers(**kw):\r\n return docnum","def load_cci10():\n \n cci10 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci_icd10cm_2019_1.csv'))\n \n cci10.columns = [i.strip('\\'') for i in cci10.columns]\n \n for col in cci10.columns:\n cci10.loc[:,col] = cci10[col].str.strip('\\'')\n cci10 = cci10.replace(r'^\\s*$', np.nan, regex=True)\n cci10.columns = [i.replace('CHRONIC INDICATOR','CHRONIC') for i in cci10.columns]\n \n dict_bodysystem = [\n ('1','Infectious and parasitic disease'),\n ('2','Neoplasms'),\n ('3','Endocrine, nutritional, and metabolic diseases and immunity disorders'),\n ('4','Diseases of blood and blood-forming organs'),\n ('5','Mental disorders'),\n ('6','Diseases of the nervous system and sense organs'),\n ('7','Diseases of the circulatory system'),\n ('8','Diseases of the respiratory system'),\n ('9','Diseases of the digestive system'),\n ('10','Diseases of the genitourinary system'),\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\n ('12','Diseases of the skin and subcutaneous tissue'),\n ('13','Diseases of the musculoskeletal system'),\n ('14','Congenital anomalies'),\n ('15','Certain conditions originating in the perinatal period'),\n ('16','Symptoms, signs, and ill-defined conditions'),\n ('17','Injury and poisoning'),\n ('18','Factors influencing health status and contact with health services'),\n ]\n \n cci10 = cci10.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\n \n cci10.loc[:,'ICD-10-CM CODE'] = cci10['ICD-10-CM CODE'].str.replace(' ','')\n \n return cci10","def print_num(self, num):\r\n if num == \"\":\r\n print(str(self.text_lines))\r\n else:\r\n num = int(num)\r\n if num == 0:\r\n raise ValueError(\"Zero is not a valid line number\")\r\n elif num > 0:\r\n num -= 1\r\n print(self.text_lines[num])","def fetch_gene_descriptions(self, metrics, coeff='cohen', nih_fetch_num=20, alpha=.05, **kwargs):\n if 'verbose' not in kwargs:\n kwargs['verbose'] = True\n if 'nih_dl' not in kwargs:\n kwargs['nih_dl'] = False\n\n if kwargs['nih_dl'] == False:\n if 'csv_path' not in kwargs:\n raise ValueError(\"'csv_path' argument in **kwargs missing; provide argument or use 'nih_dl':True .\")\n\n top_genes = []\n if coeff == 'cohen':\n # Checking if users mixed up coeff/metric parameters\n if isinstance(metrics, list):\n raise ValueError(\"list passed with coeff='cohen'; if you want to use Spearman's Rho use coeff='spearman'.\")\n\n for rec in metrics['results'][:nih_fetch_num]:\n try:\n if kwargs['nih_dl']:\n gene_name, gene_description = gene_info(str(rec.entrez))\n else:\n gene_dat = get_local_gene_info(kwargs['csv_path'], [rec.entrez])\n gene_name = gene_dat[0].name\n gene_description = gene_dat[0].description\n top_genes.append((rec.entrez, rec.cohen_d, rec.p_value, gene_name, gene_description))\n except IndexError:\n continue\n\n if kwargs['verbose']:\n print \"\\nCorrected Bonferroni Alpha: %.3E\\n\\n\" % (alpha / float(metrics['gene_sample_size']))\n for eid, coh_d, p_val, gene_i, descr in top_genes:\n if len(descr) == 1:\n print \"%d (p = %.3E; d = %.3f): < No description found >\\n\\n\" % (eid, p_val, coh_d)\n else:\n print \"%d (p = %.3E; d = %.3f): %s\\n\\n\" % (eid, p_val, coh_d, descr)\n elif coeff == 'spearman':\n # Checking if users mixed up coeff/metric parameters\n if isinstance(metrics, dict):\n raise ValueError(\"dict passed with coeff='spearman'; if you want to use Cohen's d use coeff='cohen'.\")\n\n top_ids = np.argsort(metrics)[:nih_fetch_num]\n\n top_rs = [r for r in reversed(np.sort(metrics))][:nih_fetch_num]\n top_genes = []\n for x in xrange(len(top_ids)):\n try:\n if kwargs['nih_dl']:\n gene_name, gene_description = gene_info(str(top_ids[x]))\n else:\n gene_dat = get_gene_info(kwargs['csv_path'], [top_ids[x]])\n gene_name = gene_dat[0].name\n gene_description = gene_dat[0].description\n top_genes.append((int(top_ids[x]), top_rs[x], gene_name, gene_description))\n except IndexError:\n continue\n\n if kwargs['verbose']:\n print \"\\nCorrected Bonferroni Alpha: %.3E\\n\\n\" % (alpha/float(len(self.no.ge.keys())))\n for eid, rho, gene_i, descr in top_genes:\n if len(descr) == 1:\n print \"%d (r = %.3f): < No description found >\\n\\n\" % (eid, rho)\n else:\n print \"%d (r = %.3f): %s\\n %s\\n\\n\" % (eid, rho, gene_i, descr)\n\n else:\n raise ValueError(\"Invalid parameter value for 'coeff'; use 'spearman' or 'cohen'.\")\n\n return top_genes","def get_examples():\n\n base_query = \"[[Medium::TCG]] [[Database ID::+]]\"\n base_printouts = [\n \"Password\",\n \"English name\",\n \"Card type\",\n ]\n all_res = {}\n\n n = len(ALL_DISPLAYS)\n for i, x in enumerate(ALL_DISPLAYS):\n query = f\"{base_query} [[{x}::+]]\"\n print(i, n, query)\n try:\n data = ask_yugipedia(query, base_printouts + [x])\n except:\n print(\"ERROR\", i, x)\n continue\n all_res[x] = data\n return all_res","def do_ascii(catalog):\n task_str = catalog.get_current_task_str()\n\n\n # Howerton Catalog\n datafile = os.path.join(catalog.get_current_task_repo(), 'ASCII',\n 'vizier_J_MNRAS_441_1186_table1 J_MNRAS_441_1186_table3_20200403.csv')\n data = read(datafile, format='csv')\n for rrow in pbar(data, task_str):\n row = dict((x, str(rrow[x])) for x in rrow.columns)\n# if any(x in row['Notes'].lower() for x in ['artifact']):\n# continue\n# ctypes = row['Type'].split('/')\n# nonsne = False\n# for ct in ctypes:\n# if ct.replace('?', '') in catalog.nonsnetypes:\n# nonsne = True\n# else:\n# nonsne = False\n# break\n# if nonsne:\n# continue\n name, source = catalog.new_entry(\n row['CRTS'],\n srcname='CRTS',\n bibcode='2014MNRAS.441.1186D')\n# if row['IAU des.'] != '--':\n# catalog.entries[name].add_quantity(SUPERNOVA.ALIAS,\n# row['IAU des.'], source)\n# for ct in ctypes:\n# catalog.entries[name].add_quantity(SUPERNOVA.CLAIMED_TYPE, ct,\n# source)\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVERER,\n# row['Discoverer'], source)\n# date = row['Discovery'].split('/')\n# date = '/'.join([date[-1].zfill(2), date[0].zfill(2), date[1]])\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVER_DATE, date,\n# source)\n catalog.entries[name].add_quantity(CATACLYSMIC.VISUAL_MAG, row['Vmax'],\n source)\n catalog.entries[name].add_quantity(CATACLYSMIC.RA, row['RAJ2000'], source)\n catalog.entries[name].add_quantity(CATACLYSMIC.DEC, row['DEJ2000'], source)\n catalog.journal_entries()\n\n # Howerton Catalog","def uglyprint(self):\n\n ctmp = self.conn.cursor()\n ctmp.execute(\"SELECT * FROM ATOM\")\n print(ctmp.fetchall())","def output_file(data, k):\n if k:\n filename = 'digitsOutput' + str(k) + '.csv'\n else:\n filename = 'digitsOutput.csv'\n f = open(filename, 'w')\n for line in data:\n f.write(str(line) + '\\n')\n f.close()","def print_output(data,alignments,file):\n print(\"######################################################################\")\n print(\"Task 1 : IBM model 1 and EM algorithm implementation ,with corpus @\",file)\n print(\"######################################################################\")\n\n for i in range(len(data)):\n print(\"English Sentence : \",data[i][\"en\"])\n print(\"Foreign Sentence : \",data[i][\"fr\"])\n print(\"Alignment : \",alignments[i])\n print(\"----------------------------------------------------------------------\")","def show_recs(self):\n if len(self.storage.records) == 0:\n return \"Records not found!\"\n else:\n string_of_records = \"\"\n for record in self.storage.records:\n string_of_records += record.to_string()\n return string_of_records","def getExpAccessions(cf):\n\tplatform = cf.get_parameter('platform')\n\tsrafetchxml = cf.get_input('srafetchxml')\n\tsraexplist = cf.get_output('sraexplist')\n\tsraxmlparser = SRAXMLParser()\n\truns = sraxmlparser.parse(srafetchxml)\n\twriter = csv.writer(open(sraexplist, 'wb'), quoting=csv.QUOTE_NONE)\n\twriter.writerow(['NCBISRAExpID'])\n\taccessions = []\n\tfor run in runs:\n\t\tif platform and \\\n\t\t\tnot run.platform == platform:\n\t\t\tcontinue\n\t\telif not run.exp_accession in accessions:\n\t\t\twriter.writerow([run.exp_accession])\n\t\t\taccessions.append(run.exp_accession)\n\tcf.write_log(\"GetExpAccessions: wrote %s experiment accessions\" % len(accessions))\n\treturn constants.OK","def getArticleRows(baseName, extId):\n pr = pubStore.PubReaderFile(baseName)\n for artRow, fileRows in pr.iterArticlesFileList():\n if artRow.externalId==extId:\n return artRow, fileRows","def numbers():\n return '<pre>' + '\\n'.join(phone_numbers) + '</pre>'","def file_line(ftype, c):\n if ftype in column_names:\n if ftype == 'mwdata':\n # this fix should not be necessary\n if c['rank'] is None and c['analytic_rank'] is None:\n c['reg'] = None\n # for k in column_names[ftype]:\n # print(\"{}: {} --> {} ({})\".format(k, c[k], get_encoder(k)(c[k]), type(get_encoder(k)(c[k]))))\n return \" \".join([get_encoder(k)(c[k]) for k in column_names[ftype]])\n else:\n raise ValueError(\"{} is not a valid file type\".format(ftype))","def test_nwb_extractor_channel_ids_retrieval(path_to_nwbfile, nwbfile_with_ecephys_content):\n electrical_series_name_list = [\"ElectricalSeries1\", \"ElectricalSeries2\"]\n for electrical_series_name in electrical_series_name_list:\n recording_extractor = NwbRecordingExtractor(path_to_nwbfile, electrical_series_name=electrical_series_name)\n\n nwbfile = nwbfile_with_ecephys_content\n electrical_series = nwbfile.acquisition[electrical_series_name]\n electrical_series_electrode_indices = electrical_series.electrodes.data[:]\n electrodes_table = nwbfile.electrodes.to_dataframe()\n sub_electrodes_table = electrodes_table.iloc[electrical_series_electrode_indices]\n\n expected_channel_ids = sub_electrodes_table[\"channel_name\"].values\n extracted_channel_ids = recording_extractor.channel_ids\n assert np.array_equal(extracted_channel_ids, expected_channel_ids)","def test_fetchSpecificNumberedText(self):\n d = self.client.fetchSpecific('4', headerType='TEXT', headerNumber=7)\n self.assertEqual(\n self.transport.value(), b'0001 FETCH 4 BODY[7.TEXT]\\r\\n')\n self.client.lineReceived(b'* 4 FETCH (BODY[7.TEXT] \"Some body\")')\n self.client.lineReceived(b'0001 OK FETCH completed')\n self.assertEqual(\n self.successResultOf(d),\n {4: [['BODY', ['7.TEXT'], \"Some body\"]]})","def get_citation_informations(recid_list, config):\n begin_time = os.times()[4]\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\n d_records_s = {} #dict of recid -> this_records_publication_info\n citation_informations = []\n\n write_message(\"config function \"+config.get(\"rank_method\", \"function\"), verbose=9)\n function = \"\"\n try:\n function = config.get(\"rank_method\", \"function\")\n except:\n register_exception(prefix=\"cfg section [rank_method] has no attribute called function\", alert_admin=True)\n #we cannot continue\n return [ {}, {}, {}, {} ]\n record_pri_number_tag = \"\"\n try:\n record_pri_number_tag = config.get(function, \"primary_report_number\")\n except:\n register_exception(prefix=\"cfg section \"+function+\" has no attribute primary_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n record_add_number_tag = \"\"\n try:\n record_add_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"additional_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute additional_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_number_tag = \"\"\n try:\n reference_number_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_report_number\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_report_number\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n reference_tag = \"\"\n try:\n reference_tag = config.get(config.get(\"rank_method\", \"function\"),\n \"reference_via_pubinfo\")\n except:\n register_exception(prefix=\"config error. cfg section \"+function+\" has no attribute reference_via_pubinfo\", alert_admin=True)\n return [ {}, {}, {}, {} ]\n\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\n #037a: contains (often) the \"hep-ph/0501084\" tag of THIS record\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\n #088a: additional short identifier for the record\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\n p_reference_tag = tagify(parse_tag(reference_tag))\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\n #fields needed to construct the pubinfo for this record\n publication_pages_tag = \"\"\n publication_year_tag = \"\"\n publication_journal_tag = \"\"\n publication_volume_tag = \"\"\n publication_format_string = \"p v (y) c\"\n try:\n tag = config.get(function, \"pubinfo_journal_page\")\n publication_pages_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_year\")\n publication_year_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_title\")\n publication_journal_tag = tagify(parse_tag(tag))\n tag = config.get(function, \"pubinfo_journal_volume\")\n publication_volume_tag = tagify(parse_tag(tag))\n publication_format_string = config.get(function, \"pubinfo_journal_format\")\n except:\n pass\n\n #print values for tags for debugging\n if task_get_task_param('verbose') >= 9:\n write_message(\"tag values\")\n write_message(\"p_record_pri_number_tag \"+str(p_record_pri_number_tag))\n write_message(\"p_reference_tag \"+str(p_reference_tag))\n write_message(\"publication_journal_tag \"+str(publication_journal_tag))\n write_message(\"publication_format_string is \"+publication_format_string)\n done = 0 #for status reporting\n numrecs = len(recid_list)\n\n # perform quick check to see if there are some records with\n # reference tags, because otherwise get.cit.inf would be slow even\n # if there is nothing to index:\n if run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_tag[0:2],\n (p_reference_tag,)) or \\\n run_sql(\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\" % p_reference_number_tag[0:2],\n (p_reference_number_tag,)):\n for recid in recid_list:\n if (done % 10 == 0):\n task_sleep_now_if_required()\n #in fact we can sleep any time here\n\n if (done % 1000 == 0):\n mesg = \"get cit.inf done \"+str(done)+\" of \"+str(numrecs)\n write_message(mesg)\n task_update_progress(mesg)\n done = done+1\n\n if recid in INTBITSET_OF_DELETED_RECORDS:\n # do not treat this record since it was deleted; we\n # skip it like this in case it was only soft-deleted\n # e.g. via bibedit (i.e. when collection tag 980 is\n # DELETED but other tags like report number or journal\n # publication info remained the same, so the calls to\n # get_fieldvalues() below would return old values)\n continue\n\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\n references_s = get_fieldvalues(recid, p_reference_tag)\n\n l_report_numbers = pri_report_numbers\n l_report_numbers.extend(add_report_numbers)\n d_reports_numbers[recid] = l_report_numbers\n\n if reference_report_numbers:\n write_message(str(recid)+\"'s report number values \"+str(l_report_numbers), verbose=9)\n d_references_report_numbers[recid] = reference_report_numbers\n\n references_s = get_fieldvalues(recid, p_reference_tag)\n if references_s:\n write_message(str(recid)+\"'s \"+str(p_reference_tag)+\" values \"+str(references_s), verbose=9)\n d_references_s[recid] = references_s\n\n #get a combination of\n #journal vol (year) pages\n if publication_pages_tag and publication_journal_tag and \\\n publication_volume_tag and publication_year_tag and publication_format_string:\n tagsvalues = {} #we store the tags and their values here\n #like c->444 y->1999 p->\"journal of foo\",v->20\n tagsvalues[\"p\"] = \"\"\n tagsvalues[\"y\"] = \"\"\n tagsvalues[\"c\"] = \"\"\n tagsvalues[\"v\"] = \"\"\n tmp = get_fieldvalues(recid, publication_journal_tag)\n if tmp:\n tagsvalues[\"p\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_volume_tag)\n if tmp:\n tagsvalues[\"v\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_year_tag)\n if tmp:\n tagsvalues[\"y\"] = tmp[0]\n tmp = get_fieldvalues(recid, publication_pages_tag)\n if tmp:\n #if the page numbers have \"x-y\" take just x\n pages = tmp[0]\n hpos = pages.find(\"-\")\n if hpos > 0:\n pages = pages[:hpos]\n tagsvalues[\"c\"] = pages\n #format the publ infostring according to the format\n publ = \"\"\n ok = 1\n for i in range (0, len(publication_format_string)):\n current = publication_format_string[i]\n #these are supported\n if current == \"p\" or current == \"c\" or current == \"v\" \\\n or current == \"y\":\n if tagsvalues[current]:\n #add the value in the string\n publ += tagsvalues[current]\n else:\n ok = 0\n break #it was needed and not found\n else:\n publ += current #just add the character in the format string\n if ok:\n write_message(\"d_records_s (publication info) for \"+str(recid)+\" is \"+publ, verbose=9)\n d_records_s[recid] = publ\n else:\n mesg = \"Warning: there are no records with tag values for \"\n mesg += p_reference_number_tag+\" or \"+p_reference_tag+\". Nothing to do.\"\n write_message(mesg)\n\n mesg = \"get cit.inf done fully\"\n write_message(mesg)\n task_update_progress(mesg)\n\n citation_informations.append(d_reports_numbers)\n citation_informations.append(d_references_report_numbers)\n citation_informations.append(d_references_s)\n citation_informations.append(d_records_s)\n end_time = os.times()[4]\n write_message(\"Execution time for generating citation info from record: %.2f sec\" % \\\n (end_time - begin_time))\n return citation_informations","def as_text(self):\n buf=BIO.MemoryBuffer()\n m2.x509_crl_print(buf.bio_ptr(), self.crl)\n return buf.read_all()","def read_ext_data(self, ext_filename):\n\n # read in the FITS file\n hdulist = fits.open(ext_filename)\n\n # get the list of extension names\n extnames = [hdulist[i].name for i in range(len(hdulist))]\n\n # the extinction curve itself\n for curname in _poss_datasources:\n curext = \"%sEXT\" % curname\n if curext in extnames:\n self.waves[curname] = hdulist[curext].data['WAVELENGTH']\n self.exts[curname] = hdulist[curext].data['EXT']\n if 'UNC' in hdulist[curext].data.columns.names:\n self.uncs[curname] = hdulist[curext].data['UNC']\n else:\n self.uncs[curname] = hdulist[curext].data['EXT_UNC']\n if 'NPTS' in hdulist[curext].data.columns.names:\n self.npts[curname] = hdulist[curext].data['NPTS']\n else:\n self.npts[curname] = np.full(len(self.waves[curname]), 1)\n\n # get the parameters of the extinction curve\n pheader = hdulist[0].header\n self.type = pheader.get('EXTTYPE')\n self.red_file = pheader.get('R_FILE')\n self.comp_file = pheader.get('C_FILE')\n\n column_keys = ['AV', 'EBV', 'RV', 'LOGHI', 'LOGHIMW', 'NHIAV']\n for curkey in column_keys:\n if pheader.get(curkey):\n if pheader.get('%s_UNC' % curkey):\n self.columns[curkey] = (pheader.get(curkey),\n pheader.get('%s_UNC' % curkey))\n else:\n self.columns[curkey] = (pheader.get(curkey),\n 0.0)\n\n # get FM90 parameters if they exist\n if pheader.get('FMC2'):\n FM90_keys = ['C1', 'C2', 'C3', 'C4', 'x0', 'gam']\n self.fm90 = {}\n for curkey in FM90_keys:\n if pheader.get(curkey):\n self.fm90[curkey] = (float(pheader.get('FM%s' % curkey)),\n float(pheader.get('FM%sU' % curkey)))\n # for completeness, populate C1 using from the FM07 relationship\n # if not already present\n if 'C1' not in self.fm90.keys():\n self.fm90['C1'] = (2.09 - 2.84*self.fm90['C2'][0],\n 2.84*self.fm90['C2'][1])\n\n # get P92 parameters if they exist\n if pheader.get('BKG_amp'):\n P92_mkeys = ['BKG', 'FUV', 'NUV',\n 'SIL1', 'SIL2', 'FIR']\n P92_types = ['AMP', 'LAMBDA', 'B', 'N']\n self.p92_best_fit = {}\n for curmkey in P92_mkeys:\n for curtype in P92_types:\n curkey = '%s_%s' % (curmkey, curtype)\n if pheader.get(curkey):\n self.p92_best_fit[curkey] = \\\n float(pheader.get('%s' % curkey))","def get_ct_rawdata(nctid, data_path):\n url = 'http://clinicaltrials.gov/show/%s?displayxml=true' % nctid\n raw_data = download_web_data(url)\n out_path = data_path + '/' + nctid + '.xml'\n try:\n fid = open(out_path, 'w')\n fid.write(raw_data)\n fid.close()\n return True\n except Exception as e:\n log.error(e)\n return False","def info(out: Export = Export(\"cwb.encoded/data/.info\"),\n sentences: AnnotationCommonData = AnnotationCommonData(\"misc.<sentence>_count\"),\n firstdate: AnnotationCommonData = AnnotationCommonData(\"cwb.datefirst\"),\n lastdate: AnnotationCommonData = AnnotationCommonData(\"cwb.datelast\"),\n resolution: AnnotationCommonData = AnnotationCommonData(\"dateformat.resolution\"),\n protected: bool = Config(\"korp.protected\")):\n create_info_file(sentences, firstdate, lastdate, resolution, protected, out)","def toc(self):\n return [\n f\"Pnum: {key}, Edition: {self.catalog[key]['edition']}, \"\n f\"length: {len(self.catalog[key]['transliteration'])} line(s)\"\n for key in sorted(self.catalog.keys())]","def print_file(chr_list,filename):\n infile = open(filename)\n for line in infile:\n if line.startswith('SL2.40'):\n chr = int(line.strip().split()[0][-2:])\n loci = int(line.strip().split()[1])\n for chr_i,chr_l in enumerate(chr_list):\n for loc in chr_l:\n if chr==chr_i and loci==loc:\n print line\n return","def print_file(list):\n chr_name_list = ['SL2.40ch00','SL2.40ch01','SL2.40ch02','SL2.40ch03','SL2.40ch04','SL2.40ch05','SL2.40ch06','SL2.40ch07','SL2.40ch08','SL2.40ch09','SL2.40ch10','SL2.40ch11','SL2.40ch12']\n for index,chr_list in enumerate(list):\n if chr_list:\n chr = chr_name_list[index]\n for loci in chr_list:\n print \"%s\\t%d\\t%d\\t%s\\t%d\" % (chr,loci[0],loci[1],'\\t'.join(loci[2]),len(loci[2])-loci[2].count('0'))","def printing_resident_sheets(entry, file):\n nhi = entry.get()\n if re.match(\"^[A-Za-z]{3}[0-9]{4}$\", nhi):\n pass\n else:\n popup_error(\"Incorrect NHI format entered, please try again\")\n\n if file_available(file):\n ecase_driver = ecase_downloader.ecase_login()\n ecase_downloader.resident_contacts(ecase_driver, nhi)\n ecase_downloader.preferred_name_and_image(ecase_driver, nhi)\n ecase_driver.quit()","def print_all(self):\n with open(self.file, 'r', encoding='utf-8') as self.contacts_file:\n for i in self.contacts_file.readlines():\n print(i)","def get_cit_data(ticker):\n log = logging.getLogger(__name__)\n # TODO: This is probably not working anymore (need to refresh files)\n file = os.path.join(c.cfg['default']['data'], 'COT', 'Supplemental', 'T{}.PRN'.format(ticker))\n columns = ['Non-Commercial Longs', 'Non-Commercial Shorts',\n 'Commercial Longs', 'Commercial Shorts',\n 'Non-Reportable Longs', 'Non-Reportable Shorts',\n 'Index Trader Longs', 'Index Trader Shorts']\n\n # Read Data from csv file\n cit = pd.read_csv(file, parse_dates=True, index_col=0, header=None, names=columns)\n \n\n return cit","def print_record(self, key):\n percentile_amount = str(int(round(self.running_percentile[key].get_percentile())))\n total_amount = str(int(round(self.running_percentile[key].total_amount)))\n count = str(len(self.running_percentile[key]))\n record = [key.recipient, key.zip_code, str(key.year), percentile_amount, total_amount, count]\n return '|'.join(record)","def InfoPrint(currentFile):\n Chains = ReturnChain(currentFile)\n chainlist = ReturnChain(currentFile)\n if len(chainlist) > 1:\n chainlist.insert(len(chainlist)-1,'and')\n print('CHAINS:',' '.join(chainlist))\n else:\n print('CHAIN: %s'%''.join(chainlist))\n x= ' '\n for chain in Chains:\n with open(currentFile) as fileIn:\n sequence=[]\n helix_count = 0\n sheet_count = 0\n line = fileIn.readline()\n while line:\n if line.startswith('SEQRES'):\n words = line.split()\n chaintype = words[2]\n if chaintype == chain:\n for word in words:\n if word in aminoacidsCode.values():\n sequence.append(word)\n\n else:\n pass\n\n\n if line.startswith('HELIX'):\n helixInfo = line.split()\n chaintype = helixInfo[4]\n if chaintype == chain:\n helix_count = helix_count + 1\n\n else:\n pass\n\n if line.startswith('SHEET'):\n sheetInfo = line.split()\n chaintype = sheetInfo[5]\n if chaintype == chain:\n sheet_count = sheet_count + 1\n\n else:\n pass\n\n line = fileIn.readline()\n\n print('- Chain %s'%chain)\n print('Number of amino acids: %s'%len(sequence))\n print('Number of helix:%s %s'%(x*7,helix_count))\n print('Number of sheet:%s %s'%(x*7,sheet_count))\n print('Sequence:'),seqprint(sequence)","def display(digit_logits: np.array, fname: str):\n digit_probs = softmax(np.reshape(digit_logits[:, 1:], (9, 9, 9)), axis=2).reshape((9, 9, 3, 3))\n nrows = 1\n ncols = 9\n\n fig = plt.figure(figsize=(ncols, nrows))\n outer = gridspec.GridSpec(nrows, ncols, wspace=0.0, hspace=0.0)\n\n digits = np.arange(1, 10).reshape(3, 3)\n for i in range(nrows):\n for j in range(ncols):\n rlw, clw = 1, 1\n\n if i % 3 == 0:\n rlw = 2\n if j % 3 == 0:\n clw = 2\n print(i, j)\n\n inner = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=outer[i, j], wspace=0.0, hspace=0.0)\n for c in range(3):\n for r in range(3):\n plt.subplot(inner[c, r])\n plt.text(0.5, 0.5, str(digits[c, r]), horizontalalignment='center', verticalalignment='center', fontsize=10 * digit_probs[i, j, c, r] ** (1 / 4))\n plt.xticks([])\n plt.yticks([])\n\n ax = fig.gca()\n for sp in ax.spines.values():\n sp.set_visible(False)\n if ax.is_first_row():\n top = ax.spines['top']\n top.set_visible(True)\n top.set_linewidth(rlw)\n if ax.is_last_row():\n bottom = ax.spines['bottom']\n bottom.set_visible(True)\n if i == nrows - 1 and (i + 1) % 3 == 0:\n bottom.set_linewidth(2)\n if ax.is_first_col():\n left = ax.spines['left']\n left.set_visible(True)\n left.set_linewidth(clw)\n if ax.is_last_col():\n right = ax.spines['right']\n right.set_visible(True)\n if j == ncols - 1 and (j + 1) % 3 == 0:\n right.set_linewidth(2)\n\n plt.savefig(fname, bbox_inches='tight', pad_inches=0)\n plt.close()","def data_format(self):\n data_format = self.query('FORMAT:DATA?')\n if '32' in data_format:\n return 'REAL,+32'\n elif '64' in data_format:\n return 'REAL,+64'\n elif '0' in data_format:\n return 'ASC,+0'\n else:\n raise InstrIOError(cleandoc('''Agilent PNA did not return the\n data format'''))","def fetchFullText(self, arnumbers):\n if type(arnumbers) != list:\n arnumbers = [arnumbers]\n browser = webdriver.Chrome()\n articles = []\n for num in arnumbers:\n browser.get(\"http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=\" + str(num))\n # TODO: Ensure the page load is ready\n page = pq(browser.page_source)\n articles.append(page('div#article'))\n browser.quit()\n return articles","def wmo_code_table():\n import pandas as pd\n from ..fun import get_data\n\n return pd.read_csv(get_data('Common_C02_20181107_en.txt'))","def coeff_display_file(filename,zernike_max_order=20):\n hdu = pf.open(filename)\n nn = len(hdu)\n data = []\n colnames = ['x','y','M20','M22','M31','M33']\n for hdui in hdu[1:]:\n Nobj = hdui.data.shape[0]\n M20=np.zeros(Nobj)\n M22=np.zeros(Nobj).astype(complex)\n M31=np.zeros(Nobj).astype(complex)\n M33=np.zeros(Nobj).astype(complex)\n #sigma = 1.1/0.27\n sigma = 1.08/scale\n for i in range(Nobj):\n M20[i],M22[i],M31[i],M33[i]=complexMoments(data=hdui.data[i][4:].reshape(npix,npix),sigma=sigma)\n x=hdui.header['ccdXcen']\n y=hdui.header['ccdYcen']\n data.append([x,y,np.median(M20), np.median(M22), np.median(M31), np.median(M33)])\n data=np.array(data)\n betaAll=[]\n betaErrAll=[]\n R2adjAll=[]\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,2].real,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n for i in range(3,6):\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].real,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].imag,max_order=zernike_max_order)\n betaAll.append(beta)\n betaErrAll.append(betaErr)\n R2adjAll.append(R2_adj)\n betaAll = np.array(betaAll)\n betaErrAll = np.array(betaErrAll)\n R2adjAll = np.array(R2adjAll)\n ind = np.arange(len(betaAll[0]))\n momname = ('M20','M22.Real','M22.imag','M31.real','M31.imag','M33.real','M33.imag')\n fmtarr = ['bo-','ro-','go-','co-','mo-','yo-','ko-']\n pl.figure(figsize=(17,13))\n for i in range(7):\n pl.subplot(7,1,i+1)\n pl.errorbar(ind,betaAll[i],yerr = betaErrAll[i],fmt=fmtarr[i])\n if i == 0:\n pl.title('x: '+str(hdu[0].header['x'])+' y: '+str(hdu[0].header['y'])+' z: '+str(hdu[0].header['z'])+' tilt: '+str(hdu[0].header['theta'])+' fwhm: '+str(hdu[0].header['s_fwhm'])+' e1: '+str(hdu[0].header['e1'])+' e2: '+str(hdu[0].header['e2']))\n pl.grid()\n pl.xlim(-1,21)\n if i ==0:\n pl.ylim(-50,100)\n elif i ==1:\n pl.ylim(-10,10)\n elif i ==2:\n pl.ylim(-10,10)\n elif i == 3:\n pl.ylim(-0.01,0.01)\n elif i == 4:\n pl.ylim(-0.01,0.01)\n elif i ==5:\n pl.ylim(-20,20)\n elif i == 6:\n pl.ylim(-20,20)\n pl.xticks(ind,('','','','','','','','','','','','','','','','','','','',''))\n pl.ylabel(momname[i])\n pl.xticks(ind,('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20'))\n pl.xlabel('Zernike Coefficients')\n return betaAll,betaErrAll","def display_accounts_details():\n return Records.display_records()","def __prcTyp_findOidSortedCandidateExpPrcInsOidList(self, prcTypInfo,\n lastTradeIsoDate):\n assert isinstance(prcTypInfo, FDelPriceUtil.PRICE_TYPE_INFO)\n # All instrument oid in the trade table.\n tradeFilter = ''\n if lastTradeIsoDate:\n tradeFilter += (\n ' OR (i.insaddr IN ( ' # (a.2)\n 'SELECT t.insaddr '\n 'FROM trade t '\n 'WHERE t.time < \\'{0}\\' '\n 'GROUP BY t.insaddr) '\n ')'.format(lastTradeIsoDate)\n )\n\n candExpUnTrdInsOidQry = (\n 'SELECT i.insaddr '\n 'FROM instrument i '\n 'WHERE ( '\n '(i.insaddr NOT IN ( ' # (a.1)\n 'SELECT t.insaddr '\n 'FROM trade t '\n 'GROUP BY t.insaddr) '\n '){5} ) '\n 'AND i.generic = 0 ' # (b)\n 'AND i.instype <> {0} ' # (c)\n 'AND (i.instype = {1} ' # (d)\n 'OR (i.exp_day > \\'1900-01-01\\' '\n 'AND i.exp_day < \\'{2}\\' '\n 'AND i.open_end <> {3})) '\n 'AND i.insaddr IN (SELECT p.insaddr ' # (e)\n 'FROM {4} p '\n 'GROUP BY p.insaddr) '\n 'ORDER BY i.insaddr'.format(\n FDelPriceUtil.INS_TYPE_ENUM_CURR,\n FDelPriceUtil.INS_TYPE_ENUM_COMBINATION,\n self.__strIsoDateToday,\n FDelPriceUtil.OPEN_END_STATUS_ENUM_OPEN_END,\n prcTypInfo.tblName,\n tradeFilter))\n result, msDuration = FDelPriceUtil.timedAelDbSql(\n candExpUnTrdInsOidQry)\n candExpUnTrdInsOidList = [row[0] for row in result]\n\n self._logDebug(' Found {0} instruments referred by '\n 'the {1}s. [QueryTime = {2} ms]'.format(\n len(candExpUnTrdInsOidQry), prcTypInfo.desc, msDuration))\n return candExpUnTrdInsOidList","def load_cci9():\n cci9 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci2015.csv'),skiprows=1)\n cci9.columns = [i.strip('\\'') for i in cci9.columns]\n \n for col in cci9.columns:\n cci9.loc[:,col] = cci9[col].str.strip('\\'')\n cci9 = cci9.replace(r'^\\s*$', np.nan, regex=True)\n cci9.columns=[i.replace('CATEGORY DESCRIPTION','CHRONIC') for i in cci9.columns]\n\n dict_bodysystem=[\n ('1' ,'Infectious and parasitic disease'),\n ('2' ,'Neoplasms'),\n ('3' ,'Endocrine, nutritional, and metabolic diseases and immunity disorders'),\n ('4' ,'Diseases of blood and blood-forming organs'),\n ('5' ,'Mental disorders'),\n ('6' ,'Diseases of the nervous system and sense organs'),\n ('7' ,'Diseases of the circulatory system'),\n ('8' ,'Diseases of the respiratory system'),\n ('9' ,'Diseases of the digestive system'),\n ('10','Diseases of the genitourinary system'),\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\n ('12','Diseases of the skin and subcutaneous tissue'),\n ('13','Diseases of the musculoskeletal system'),\n ('14','Congenital anomalies'),\n ('15','Certain conditions originating in the perinatal period'),\n ('16','Symptoms, signs, and ill-defined conditions'),\n ('17','Injury and poisoning'),\n ('18','Factors influencing health status and contact with health service'),\n ]\n \n cci9 = cci9.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\n \n cci9.loc[:,'ICD-9-CM CODE'] = cci9['ICD-9-CM CODE'].str.replace(' ','')\n \n return cci9","def print_row(n, version, sz, last_modified):\n print (get_pad1(n) + str(n) + \". \" + sz + \" \" * 13 + last_modified)","def citations(request, format='csv'):\n \n filtered = False # make sure we're filtering by something\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\n \n \n # accession number\n if request.GET.get('gabi_acc_number'):\n filtered = True\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\n \n # species AND bentity\n if request.GET.get('species'):\n filtered = True\n if request.GET.get('species'): \n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\n if request.GET.get('bentity_id'):\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\n \n # lat and lon\n if request.GET.get('lat') and request.GET.get('lon'):\n filtered = True\n if request.GET.get('lat'):\n records = records.filter(lat=request.GET.get('lat'))\n if request.GET.get('lon'):\n records = records.filter(lon=request.GET.get('lon'))\n \n # status\n if request.GET.get('status'):\n records = records.filter(status=request.GET.get('status')[0].upper())\n \n \n # error message if the user didn't supply an argument to filter the records\n if not filtered: \n return errorResponse(\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\", format, {'records': []})\n \n \n # fetch all the bentitites at once, so we don't have to hit the database once for each record\n records = records.prefetch_related('bentity') \n \n output_objects = [{\n 'gabi_acc_number': r.gabi_acc_number,\n 'species': r.valid_species_name_id,\n 'bentity_id': r.bentity_id,\n 'bentity_name': r.bentity.bentity,\n 'status': r.status,\n 'type_of_data': r.type_of_data,\n 'lat': r.lat,\n 'lon': r.lon, \n 'citation': r.citation,\n } for r in records]\n \n \n \n if format == 'csv':\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\n \n else:\n return JSONResponse({'records': output_objects})","def print_gene_list(gene_entries):\n\n for g in gene_entries:\n if g:\n print(g.contig, g.start, g.end,\n g.strand, g.gene_id, g.name, sep='\\t')","def print_records_like(self, exploitability):\n if not exploitability:\n query = \"SELECT * FROM Crashes\"\n\n else:\n query = \"SELECT * FROM Crashes WHERE Exploitable = '%s';\" % exploitability\n\n display_fmt = \"%d %s %s %s %s 0x%x %s %s %s %s\"\n\n try:\n self.cur.execute(query)\n rows = self.cur.fetchall()\n\n if not rows:\n if not exploitability:\n print 'Could not find records with any exploitability level'\n\n else:\n print 'Could not find records with exploitability:', exploitability\n\n return\n\n for row in rows:\n (idx, n, v, c, e, i, s, cl, ex, f) = row\n values = row[:5] + (int(i),) + row[6:]\n print display_fmt %values\n\n except sqlite.Error, e:\n print 'Unable to display records.'\n print 'Exception follows:'\n print e","def registry_to_text(registry):\n output = [CREDITS.format(dt=datetime.utcnow().isoformat())]\n for collector, samples in registry.get_samples():\n output.append(collector.text_export_header)\n for sample in samples:\n output.append(sample.export_str)\n output.append(\"\")\n return \"\\n\".join(output)","def txt_of_sources_in_run (ins, exp, run) :\n list_of_detectors = detectors(ins, exp, run)\n txt = '\\nList of detectors for inst: %s exp: %s run %d' % (ins, exp, run)\n for det in list_of_detectors :\n txt += '\\n'+det\n return txt","def exon_line_print(temp_line, trx_exons, parent, ftype):\n for ex in trx_exons:\n temp_line[2] = ftype\n temp_line[3] = str(ex[0])\n temp_line[4] = str(ex[1])\n temp_line[8] = 'Parent='+parent\n print '\\t'.join(temp_line)","def demo1():\n \n print \"loading the NIPS publications dataset...\"\n pubs = loadPubs('pubs_nips')\n \n # get all papers that mention mnist\n p = [x for x in pubs if 'mnist' in x.get('pdf_text',{})]\n print \"titles of papers that mention MNIST dataset:\"\n for x in p:\n print x['title']\n print \"total of %d publications mention MNIST.\" %(len(p),)\n \n # sort by number of occurences\n occ = [(x['year'], x['pdf']) for i,x in enumerate(p) if x['pdf_text']['mnist']>1]\n occ.sort(reverse = True)\n \n # open the top 3 latest in browser\n print \"opening the top 3...\"\n openPDFs([x for year,x in occ[:3]])","def test_topcit(self):\n inv_search = \"cited:50->999999999\"\n spi_search = \"find topcit 50+\"\n self._compare_searches(inv_search, spi_search)","def show_data():\n with open(\"ScansforStudents.csv\", \"rU\") as csvfile:\n reader = csv.reader(csvfile, delimiter = ',', quotechar = '|')\n k = 0\n for row in reader:\n print(row)\n if k == 100:\n break\n k += 1","def test_load_edition_data(self):\n key = \"OL1234W\"\n responses.add(\n responses.GET,\n \"https://openlibrary.org/works/OL1234W/editions\",\n json={\"hi\": \"there\"},\n )\n result = self.connector.load_edition_data(key)\n self.assertEqual(result, {\"hi\": \"there\"})","def insert_repnr_fielddesc(inst):\n\telname = \"rn\"\n\telmarccode = \"037__a\"\n\teltype = \"I\"\n\telsize = \"30\"\n\telrows = \"\"\n\telcols = \"\"\n\telmaxlength = \"\"\n\telval = \"%s-<YYYY>-?????\" %(inst.upper())\n\telfidesc = \"\" \n\telmodifytext = \"\"\n\tinsert_element_details(elname, elmarccode, eltype, elsize, elrows, elcols, elmaxlength, elval, elfidesc, elmodifytext) # insert into sbmFIELDDESCR\t","def additional(self):\r\n data = self.extract_data()\r\n st = \"\"\r\n for i in data[8:-5]:\r\n st += i + \"\\n\"\r\n return st","def comment_for_run (ins, exp, runnum) :\n return dict_of_recs_for_run(ins, exp, runnum)['comment']","def print_data(self):\n for chain, gen in self.generations.items():\n print('Generations for chain %s: %d' % (chain, gen))\n print('Log likelihood effective size: %d' % self.loglik_effsize)\n print('Log likelihood relative difference: %f' % self.loglik_rel_diff)\n print('Max diff: %f' % self.max_diff)","def displayRecord(self):\n counter = 0\n selectedData = self.controller.chooseRecord(\"Enter how many records you want to display: \")\n if selectedData != 0 and len(self.dto.getRecord()) >= selectedData >= 0:\n for line in self.dto.getRecord():\n print(line.__dict__) # cast the object to dictionary\n counter = counter + 1\n if counter == selectedData:\n break\n else:\n print(\"Please choose number within the number of records.\")","def _info(self, N):\n IDs = self.chemicals.IDs\n data = self.data\n IDs, data = nonzeros(IDs, data)\n N_IDs = len(IDs)\n if N_IDs == 0:\n return f\"{type(self).__name__}: (all zeros)\"\n else:\n basic_info = f\"{type(self).__name__}:\\n\"\n new_line = '\\n'\n data_info = ''\n lengths = [len(i) for i in IDs]\n maxlen = max(lengths) + 1\n N_max = N or tmo.Stream.display_units.N\n too_many_chemicals = N_IDs > N_max\n N = N_max if too_many_chemicals else N_IDs\n for i in range(N):\n spaces = ' ' * (maxlen - lengths[i])\n if i != 0:\n data_info += new_line\n data_info += IDs[i] + spaces + f' {data[i]:.3g}'\n if too_many_chemicals:\n data_info += new_line + '...'\n return (basic_info\n + data_info)","def getexperimentinfo(expid):\n rdata = {}\n rdata['expId'] = expid\n res = requests.get(scbd_server_address + '/experiments/get_details', json=rdata)\n if res.status_code == 200:\n outstr = ''\n for cres in res.json()['details']:\n outstr += cres[0] + ':' + cres[1] + '<br>'\n # details=res.json()['details']\n return outstr\n return []","def identify(self):\n print(self.query(\"*IDN?\\n\"))","def contents(self, n, m):\n str = \"\"\n subset = self.getlines(n, m)\n for line in subset:\n str = str + line + \"\\n\"\n return str.rstrip(\"\\n\")","def display_finds(r, width = 500):\n base = \"https://www.nb.no/items/\"\n rows = []\n pages = []\n urns = []\n urnstore = []\n for row in r:\n \n urnstring = re.findall(\"URN[^/]*\", row)[0]\n prefix, doctyp, urn, page = urnstring.split('_')\n title = f\"{prefix}_{doctyp}_{urn}\" #urnstring #iiif_manifest(f\"{prefix}_{doctyp}_{urn}\")['label']\n urnpage = f\"{urn}_{page}\"\n if not urn in urnstore:\n urns.append((urn, title))\n urnstore.append(urn)\n if not urnpage in pages:\n rows.append((urn, int(page), f\"[]({base}{prefix}_{doctyp}_{urn}?page={int(page) + 1})\"))\n pages.append(f\"{urn}_{page}\")\n \n \n result = pd.DataFrame(rows, columns=['urn', 'page', 'link'])\n return result, urns","def listing(self, n=5):\n # TODO: Currently, just as with ipdb on the command line,\n # there is no support for displaying code in Cython files.\n # This shouldn't be too hard to add.\n curframe = self.curframe()\n filename = curframe.f_code.co_filename\n lineno = curframe.f_lineno\n import linecache\n w = []\n for i in range(lineno-n, lineno+n+1):\n z = linecache.getline(filename, i, curframe.f_globals)\n if z: w.append(('--> ' if i ==lineno else ' ') + '%-5s'%i + z)\n code = ''.join(w)\n if not code.strip():\n code = '(code not available)'\n\n # This is a hideous hack to get around how the notebook \"works\".\n # If the output of anything contains the string TRACEBACK then\n # it will get mangled. So we replace TRACEBACK in our code block\n # by the harmless version with the colon missing. This sucks.\n from sagenb.notebook.cell import TRACEBACK\n code = code.replace(TRACEBACK, TRACEBACK[:-1])\n\n # Create a hyperlink to the file, if possible.\n i = filename.rfind('site-packages/sage')\n if i != -1:\n fname = filename[i+len('site-packages/sage')+1:].rstrip('/')\n file = '<a href=\"/src/%s\" target=\"_new\">src/sage/%s</a>'%(fname,fname)\n else:\n file = filename\n\n import cgi\n t = \"\"\"%s<hr>> %s\"\"\"%(cgi.escape(code), file)\n return t","def get_curation_row(self, external_id, external_entry) -> Sequence[str]: # noqa:DAR202\n rv = []\n for k in self.curation_header:\n value = external_entry.get(k)\n if value is None:\n rv.append(\"\")\n elif isinstance(value, str):\n rv.append(value.strip())\n elif isinstance(value, bool):\n rv.append(\"true\" if value else \"false\")\n elif isinstance(value, (list, tuple, set)):\n rv.append(\"|\".join(sorted(v.strip() for v in value)))\n else:\n raise TypeError(f\"unexpected type in curation header: {value}\")\n return rv","def get_c_detailname(self, teacher_id, course_id):\n seq_list = self.get_seqnumlist(course_id, teacher_id)\n print seq_list\n if seq_list:\n seq_id = max(seq_list)\n file_name = 'OutData/' + str(teacher_id) + '_' + str(course_id) + '_' + \\\n str(seq_id) + '_' + 'checkinDetail.csv'\n return file_name\n else:\n print \"查找考勤次序号错误，可能该教师还未生成考勤记录\"","def PrintEnergy(self, n_iter):\n e = self.efile\n t = self.traj\n grad = t.grad[n_iter]\n disp = t.coords[n_iter] - t.coords[max(0, n_iter-1)]\n delta_e = t.energy[n_iter] - t.energy[max(0, n_iter-1)]\n gmax = numpy.amax(grad)\n dmax = numpy.amax(disp)\n grms = math.sqrt(numpy.mean(grad**2))\n drms = math.sqrt(numpy.mean(disp**2))\n conv_str = [' ' for i in range(5)]\n if n_iter > 0:\n conv_str[0] = '*' if abs(delta_e) < self.conv_delta_e else ' '\n conv_str[1] = '*' if gmax < self.conv_grad_max else ' '\n conv_str[2] = '*' if grms < self.conv_grad_rms else ' '\n conv_str[3] = '*' if dmax < self.conv_disp_max else ' '\n conv_str[4] = '*' if drms < self.conv_disp_rms else ' '\n pstr = '%3i' % (n_iter)\n pstr += ' %18.12f' % (t.energy[n_iter])\n pstr += ' %10.3e%s' % (delta_e, conv_str[0])\n pstr += ' %9.3e%s' % (gmax, conv_str[1])\n pstr += ' %9.3e%s' % (grms, conv_str[2])\n pstr += ' %9.3e%s' % (dmax, conv_str[3])\n pstr += ' %9.3e%s' % (drms, conv_str[4])\n e.write('%s\\n' % (pstr))","def print_data_list(self):\n print('\\n{0}'.format(self.webDataFrame))","def get_kit_descr_collapse(self):\n self.ensure_one()\n if not self.is_kit_invoice_line:\n return \"\"\n comp_obj = self.env['account.invoice.line.comp']\n components = comp_obj.search([('invoice_line_id','=',self.id),('is_kit_invoice_comp','=',False)]) # get all comps that are not kits\n ir_model_data = self.env['ir.model.data']\n units_id = ir_model_data.get_object_reference('product','product_uom_unit')[1]\n res = []\n for comp in components:\n qty_int_val = int(comp.qty_total)\n if comp.uom_id.id == units_id: # uom is units, no need to print it\n qty = str(qty_int_val) # qty is an int because it's in units\n comp_str = comp.name + \": \" + qty\n else:\n if qty_int_val == comp.qty_total:\n qty = str(qty_int_val)\n else:\n qty = str(comp.qty_total)\n comp_str = comp.name + \": \" + qty + \" \" + comp.uom_id.name\n res.append(comp_str)\n res = \" (\" + \", \".join(res) + \")\"\n return res","def print_quantities(shared):\n from .interactive import window_width\n field_width = (window_width / 2)\n titles = []\n\n print()\n print('Defined data constants:')\n if shared.data_constants:\n for name, value in shared.data_constants.items():\n print(' {} = {}'.format(name, value))\n else:\n print(' None')\n print()\n\n print('Extra quantities:')\n\n are_any = False\n for i, field_mapping in enumerate(shared.field_mappings):\n if field_mapping.extra is not None:\n are_any = True\n title = field_mapping.title\n expression = field_mapping.extra\n print(' {}) {} = {}'.format(i+1, title, expression))\n if not are_any:\n print(' None')\n print()","def print_all(self):\n if not request:\n raise exceptions.Warning(_(''), _(''))\n session_id = request.session.sid\n config = self.env['ir.config_parameter']\n addons_url = config.get_param('addons_path')\n phantomjs_path = config.get_param('phantomjs_path')\n phantomjs_path = 'phantomjs' if not phantomjs_path else phantomjs_path\n print_url = self.env.context.get('protocol_url', False)\n if print_url:\n print_urls = [print_url]\n else:\n print_urls = self._get_print_urls()\n if not print_urls:\n return\n phantom = [\n phantomjs_path,\n addons_url +\n '/quality_protocol_report/static/src/js/phantom_url_to_pdf.js',\n session_id, \"/tmp\"] + print_urls\n process = subprocess.Popen(phantom)\n process.communicate()\n filenames = []\n for url in print_urls:\n fname = url.replace('/', '').replace(':', '')\n weight_pos = fname.find('?weight=')\n if weight_pos > -1:\n fname = fname[weight_pos+8:weight_pos+10] + '-' + fname[:weight_pos]\n filenames.append('/tmp/' + fname + '.pdf')\n filepath = self._merge_pdf(sorted(filenames))\n fildecode = open(filepath, 'r')\n encode_data = fildecode.read()\n fildecode.close()\n active_model = self.env.context.get('active_model', False)\n active_id = self.env.context.get('active_id', False)\n ungrouped_also = self.env.context.get('print_ungrouped_also', False)\n if active_model and active_id and not ungrouped_also:\n active_name = self.env[active_model].browse([active_id]).name\n else:\n dt = fields.Datetime.context_timestamp(self, datetime.now())\n active_name = dt.strftime('%d-%m-%Y_%Hh%M')\n filename = 'protocolo.pdf' if print_url else \\\n 'protocolos_' + str(active_name).lower() + '.pdf'\n attachment_data = {\n 'name': filename,\n 'datas_fname': filename,\n 'datas': base64.b64encode(encode_data),\n 'res_model': active_model,\n 'res_id': 0 if print_url else self.env.context.get('active_id', False),\n }\n self.env['ir.attachment'].search(\n [('name', '=', attachment_data['name']),\n ('res_id', '=', attachment_data['res_id']),\n ('res_model', '=', attachment_data['res_model'])]).unlink()\n attachment = self.env['ir.attachment'].create(attachment_data)\n\n filenames.append(filepath)\n for my_file in filenames:\n os.remove(my_file)\n\n if print_url:\n return {\n 'type': 'ir.actions.act_url',\n 'url': '/web/binary/saveas?model=ir.attachment&field=datas' +\n '&filename_field=name&id=%s' % (attachment.id),\n 'target': 'self',\n }\n else:\n return {'type': 'ir.actions.act_window_close'}","def test_pds_txt(self):\n lcurve_txt = os.path.join(self.datadir,\n 'lcurve_txt_lc' +\n HEN_FILE_EXTENSION)\n command = '{0} -f 128'.format(lcurve_txt)\n hen.fspec.main(command.split())","def DicResNameNum(group, type, typeList, eraseList, typeDictionary, pick):\n typeList = sorted(list(set(typeList).difference(eraseList)))\n globals().update({type+str(\"List\") : typeList})\n pdbARMTemp = pdbName[:-3]+chainName+'.temp'\n\n with open(pdbARMTemp) as pdbTemp:\n for line in pdbTemp:\n for i in range(0, len(typeList)):\n if \"ATOM\" in line and typeList[i] in line:\n typeDictionary.update({ typeList[i] : line.split()[5]} )\n\n globals().update({type+str(\"Dic\") : typeDictionary})\n\n ChooseNumOption(typeList,type, type, '\\n The following are identified as possible', 'in the '+str(pdbName)+ ' \\\nfile:', 'is selected as the '+type, pick, typeDictionary)","def tcvs(self): \n return self._link_reg.tcvs","def disp_found(num):\n from x84.bbs import getterminal, echo\n term = getterminal()\n echo(u''.join((u'\\r',\n term.bold_white(u'%d' % (num,)),\n term.yellow(u' lOCAtiON%s diSCOVEREd ' %\n (u's' if num > 1 else u'')),\n term.bold_black(u'...'),)))","def whatareks():\n\n print(\"loading data\")\n dataset = datagen.DataSet(datapath)\n dataset.load()\n\n for k, ens in enumerate(dataset.ensembles):\n\n print(\n f\"k: {k} --> b={ens.b}, N={ens.grid_shape[0]}, iternum={ens.iternum}\")","def test_text_classifier_get_details_all(self):\n pass","def display_nbr(ctx, as_port=False):\n ctl = ctx.ctl\n\n jobs = ctl('list-avail', '--partition', 'main', flatten=False)\n\n if len(jobs) == 0:\n click.echo('No jobs running', err=True)\n sys.exit(1)\n\n for job in jobs:\n info = collect_vnc_info(ctl, job['id'], ctx.ssh_cfg)\n\n if as_port:\n click.echo('%d' % info['port'])\n else:\n click.echo(':%d' % info['display'])","def print_detailed_RISCC_data(self, OUTPUT=sys.stdout, sort_data_by=None, max_distance=MAX_POSITION_DISTANCE):\n # TODO docstring!\n\n # TODO should probably add header\n # TODO add annotation!\n # TODO change this to be a proper tab-separated file?\n\n ### sort all mutants by position or readcount (for single datasets only for now), or don't sort at all\n sorted_mutants = self._sort_data(sort_data_by)\n\n ### Quick summary\n N_total = len(self)\n # using sum because you can't do len on generators\n N_single_genomic = sum(1 for m in self if m.RISCC_N_distinct_regions(max_distance)[0]==1)\n N_single_chrom = sum(1 for m in self if m.RISCC_N_genomic_chromosomes==1)\n N_cassette = sum(1 for m in self if m.RISCC_N_distinct_regions(max_distance)[1]>0)\n OUTPUT.write(\"# %s mutants total; %s have one genomic location; \"%(N_total, \n value_and_percentages(N_single_genomic, [N_total]))\n +\"%s have locations on only one genomic chromosome; \"%(value_and_percentages(N_single_chrom, [N_total]))\n +\"%s also have one or more cassette locations.\\n\"%(value_and_percentages(N_cassette, [N_total])) )\n\n # TODO add info on how many actually have ANY genomic-side reads! And out of those, how many are confirmed vs not.\n\n ### Print data for all mutants\n for mutant in sorted_mutants:\n mutant.RISCC_print_detail(OUTPUT, max_distance)","def getCitations (self, doc, verbose = False):\r\n if verbose is True:\r\n print \"getting citations for document \" +str(doc) \r\n return self.citations[int(doc)]","def uci_pen_digits(redownload: bool = False) -> Dataset:\n return Dataset.get(\"uci_pen_digits\", redownload=redownload)","def reporter(self, lnum, col, text, check):\n self.output.append([lnum, col, text])","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def tblrowsscans(self):\n sql = '''select to_char(value, 'FM99999999999999990') retvalue from \n v$sysstat where name = 'table scan rows gotten' '''\n self.cur.execute(sql)\n res = self.cur.fetchall()\n for i in res:\n print(i[0])","def display_cord_seq():\n \n import os\n choice = input('Enter the name of the file: ')\n filepath = os.path.join('/home/njesh/python-mini-project-JaneNjeri/Data', choice)\n with open(filepath, 'r') as file:\n for line in file:\n if line[:4] == 'ATOM':\n line_split = line.split()[3:4]\n print(line_split)\n return file","def _compute_credit_control_lines_html(self):\n for comm in self:\n comm.credit_control_lines_html = (\n self.env[\"ir.qweb\"]\n .render(\n \"altinkaya_credit_control.report_credit_control_lines\",\n values={\"doc\": self},\n )\n .decode(\"utf8\")\n )\n return True","def print_splitted_fp_in_csv(use_atg = True):\n delimiter = \" \"\n cids = load_cids(\"data/fp_370_atb.txt\")\n print(\"cid, first 110, second 110, last 150\")\n for cid in sorted(cids):\n if use_atg:\n print(\"%s,%s,%s,%s\" %(cid,\n delimiter.join(map(lambda d: \"%.2f\" %d, first_110_atg_dataloader(cid))),\n delimiter.join(map(lambda d: \"%.2f\" %d, second_110_atg_dataloader(cid))),\n delimiter.join(map(lambda d: \"%.2f\" %d, last_150_atg_dataloader(cid))),\n ))\n else:\n print(\"%s,%s,%s,%s\" %(cid,\n delimiter.join(map(lambda d: \"%.2f\" %d, first_110_atb_dataloader(cid))),\n delimiter.join(map(lambda d: \"%.2f\" %d, second_110_atb_dataloader(cid))),\n delimiter.join(map(lambda d: \"%.2f\" %d, last_150_atb_dataloader(cid))),\n ))","def help_description():\n # for ain\n print(\"--------TABLE FOR AIN(AIN4=GND)-------\")\n print(\"--------------------------------------\")\n print(\"| CODE (10) | CODE (2) | AINP | AINN |\")\n for i in range(8):\n print(\"| {} | {} | AIN{} | AIN{} |\".format(str(i), bin(i)[2:].zfill(3), DICT_AIN[i][0],\n DICT_AIN[i][1]))\n print(\"--------------------------------------\")\n print(\"------------TABLE FOR FSR------------\")\n print(\"--------------------------------------\")\n print(\"| CODE (10) | CODE (2) | FSR |\")\n for i in range(6):\n print(\"| {} | {} | {} |\".format(str(i), bin(i)[2:].zfill(3), DICT_FSR[i]))\n print(\"--------------------------------------\")\n print(\"------------TABLE FOR RATE------------\")\n print(\"--------------------------------------\")\n print(\"| CODE (10) | CODE (2) | RATE |\")\n for i in range(8):\n print(\"| {} | {} | {} |\".format(str(i), bin(i)[2:].zfill(3), DICT_RATE[i].rjust(7, ' ')))\n print(\"--------------------------------------\")","def view(args):\n print(\"List of all available phonebooks:\")\n for file in glob.glob(\"*.ph\"):\n print(file)","def python_data_printer(cur):\n # Print a header.\n for fieldDesc in cur.description:\n print (fieldDesc[fdb.DESCRIPTION_NAME].ljust(fieldDesc[fdb.DESCRIPTION_DISPLAY_SIZE]),end=' ')\n print('')\n for fieldDesc in cur.description:\n print (\"-\" * max((len(fieldDesc[fdb.DESCRIPTION_NAME]),fieldDesc[fdb.DESCRIPTION_DISPLAY_SIZE])),end=' ')\n print('')\n # For each row, print the value of each field left-justified within\n # the maximum possible width of that field.\n fieldIndices = range(len(cur.description))\n for row in cur:\n for fieldIndex in fieldIndices:\n fieldValue = row[fieldIndex]\n if not isinstance(fieldValue,types.StringTypes):\n fieldValue = str(fieldValue)\n if isinstance(fieldValue,types.UnicodeType):\n fieldValue = fieldValue.encode('utf8')\n fieldMaxWidth = max((len(cur.description[fieldIndex][fdb.DESCRIPTION_NAME]),cur.description[fieldIndex][fdb.DESCRIPTION_DISPLAY_SIZE]))\n print (fieldValue.ljust(fieldMaxWidth),end=' ')\n print('')"],"string":"[\n \"def read_icd(self):\\n wiki = wikipediaapi.Wikipedia('en') # may as well declare this here so I don't need to call it every query\\n supplemental_articles = []\\n with open(ICD10_DESC_PATH, 'r') as f:\\n current_family = [] # list of lists of descriptions within the current family (3 letter code = family)\\n current_parent = None # Most recent 3 letter code seen\\n for line in tqdm(f.readlines(), desc=\\\"ICD10 Lines Processed\\\"):\\n\\n code = line[6:14].strip().lower()\\n description = simple_clean(line[77:])\\n self.code2desc[code] = description.split()\\n\\n if len(code) == PARENT_CODE_LENGTH: # found a parent\\n # query web if set params to True\\n wiki_result = self.query_wikipedia(wiki, description) if self.query else []\\n pubmed_result = self.query_pubmed(description) if self.query else []\\n\\n # store results\\n if wiki_result:\\n supplemental_articles.extend(wiki_result)\\n if pubmed_result:\\n supplemental_articles.extend(pubmed_result)\\n\\n # update metrics using current family\\n self.process_family_frequencies(current_parent, current_family)\\n current_family = []\\n current_parent = code\\n current_family.append(description.split())\\n self.n_desc += 1\\n\\n # process the last family\\n self.process_family_frequencies(current_parent, current_family)\\n # go through all the articles we found, preprocess, and add to self.data\\n self.data.extend(self.process_articles(supplemental_articles))\\n\\n # lastly calculate tf and idf over all descriptions (not including articles here) for use in weighting later\\n self.n_words = log10(self.n_words)\\n self.n_desc = log10(self.n_words)\\n self.word2tf = {word: log10(count) - self.n_words for word, count in self.word2tf.items()}\\n self.word2df = {word: count - self.n_desc for word, count in self.word2df.items()}\\n self.dump()\",\n \"def identifier_entry(self):\\n\\t self.result_code = open(\\\"result.c\\\", \\\"r\\\")\\n\\t self.line_array = self.result_code.readlines()\\n\\t self.result_code.close()\\n\\n\\t #self.symbol_table = open(\\\"symbol.csv\\\", \\\"w\\\")\\n\\t datatypes = [\\\"int\\\",\\\"float\\\",\\\"char\\\",\\\"double\\\"] #the datatypes\\n\\t '''General Pattern for search and sub function for all datatype '''\\n\\t struct = \\\"&& .*\\\"\\n\\t struct1 = \\\"&& \\\"\\n\\n\\t # Looping over all the lines in the intermediate program.\\n\\t for line in self.line_array:\\n\\t # Checking for integer declarations.\\n\\t for datatype in datatypes:\\n\\t pat = re.sub(\\\"&&\\\",datatype,struct)\\n\\t pat1 = re.sub(\\\"&&\\\",datatype,struct1)\\n\\t if re.search(pat,line):\\n\\t dec1 = re.split(pat1,line)\\n \\n\\t self.process_declaration(dec1[1],datatype)\\n\\t print(self.identifiers)\\n\\t print(self.array_structure)\\n\\n\\t #self.symbol_table.close()\",\n \"def cite_report(self):\\n\\n num_cites = np.sum(np.array([q.citation_count for q in self.mypapers]))\\n print(\\\"number of citations = \\\", num_cites)\\n\\n for n, p in enumerate(self.mypapers):\\n clean_tags = re.compile(\\\"<.*?>\\\")\\n title = re.sub(clean_tags, \\\"\\\", p.title[0])\\n tt = textwrap.wrap(title, 60)\\n print(f\\\"{n+1:3} | {p.citation_count:4} {tt[0]}\\\")\\n if len(tt) > 1:\\n for line in tt[1:]:\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", line))\\n print(\\\"{:3} | {:4} {}\\\".format(\\\"\\\", \\\"\\\", \\\"\\\"))\",\n \"def display_digits():\\n digits = load_digits()\\n print(digits.DESCR)\\n fig = plt.figure()\\n for i in range(10):\\n subplot = fig.add_subplot(5, 2, i+1)\\n subplot.matshow(numpy.reshape(digits.data[i], (8, 8)), cmap='gray')\\n\\n plt.show()\",\n \"def print_entries(self):\\n self.print_selected_entries(self.entries)\",\n \"def extensions():\\r\\n document.add_page_break()\\r\\n document.add_heading('Extensions', level=1)\\r\\n extensions = get_qlik_sense.get_extensions()\\r\\n num_of_extensions = len(extensions)\\r\\n table = document.add_table(rows=num_of_extensions+1, cols=1)\\r\\n table.style = 'Grid Table 1 Light Accent 1'\\r\\n row = table.rows[0]\\r\\n row.cells[0].text = 'name'\\r\\n\\r\\n for extension in range(num_of_extensions):\\r\\n row = table.rows[extension+1]\\r\\n row.cells[0].text = str(extensions[extension])\",\n \"def show_entries(self, inp=None):\\n nums = self.selector.select_by_index(inp)\\n if not nums:\\n # self.visual.error(\\\"No selection parsed to show.\\\")\\n return\\n ids = [self.reference_entry_id_list[n] for n in nums]\\n # self.visual.print(\\\"Entry #[{}]\\\".format(ones_idx))\\n self.visual.print_entries_contents([self.entry_collection.entries[ID] for ID in ids])\",\n \"def load_icd10to9():\\n icd10to9 = pd.read_csv(pkg_resources.resource_filename(__name__,'2018_I10gem.txt'),delim_whitespace=True,header=None,dtype=str)\\n icd10to9.columns=['icd10','icd9','flag']\\n return icd10to9\",\n \"def load_icd9to10():\\n icd9to10 = pd.read_csv(pkg_resources.resource_filename(__name__,'2018_I9gem.txt'),delim_whitespace=True,header=None,dtype=str)\\n icd9to10.columns=['icd9','icd10','flag']\\n return icd9to10\",\n \"def get_inf_data(self, driver):\\r\\n if 'Found 0 Title(s)' != driver.find_element_by_xpath('/html/body/h3').text:\\r\\n # select custom information\\r\\n driver.find_element_by_xpath('//*[@id=\\\"display_format_custom\\\"]').click()\\r\\n # select all useful information\\r\\n for i in range(1, 9):\\r\\n for j in range(1, 5):\\r\\n if (i == 1 and j < 4) or (i == 2 and j == 1) or (i == 2 and j == 4) or (i == 3 and j == 4) or (\\r\\n i == 5 and j > 1) or i == 6 or (i == 7 and j < 3) or (i == 8 and j == 3):\\r\\n driver.find_element_by_xpath(\\r\\n '/html/body/form/ul[2]/table/tbody/tr[' + str(i) + ']/td[' + str(j) + ']/input[2]').click()\\r\\n # display\\r\\n driver.find_element_by_xpath('//*[@id=\\\"btn_find_display\\\"]').click()\\r\\n\\r\\n # the number of records\\r\\n number = len(driver.find_elements_by_xpath('//*[@id=\\\"list_form\\\"]/ul'))\\r\\n # all useful information\\r\\n for i in range(1, number + 1):\\r\\n self.doi_l.append('')\\r\\n information_l = driver.find_element_by_xpath('//*[@id=\\\"list_form\\\"]/ul[' + str(i) + ']').text.split('\\\\n')\\r\\n self.record_number_l.append(re.findall(r'(?<== ).*$', information_l[0])[0])\\r\\n reaction_method = re.findall(r'(?<== ).*$', information_l[1])[0]\\r\\n self.reaction_method_l.append(reaction_method)\\r\\n self.data_type_l.append(re.findall(r'(?<== ).*$', information_l[2])[0])\\r\\n self.qdb_code(reaction_method)\\r\\n self.qdb_process_l.append(self.qdb_process)\\r\\n prod = re.findall(r'(?<== ).*$', information_l[5])[0]\\r\\n self.x_unit_l.append(re.findall(r'(?<== ).*$', information_l[6])[0])\\r\\n self.y_unit_l.append(re.findall(r'(?<== ).*$', information_l[7])[0])\\r\\n self.ref_number_l.append(re.findall(r'(?<== ).*$', information_l[8])[0])\\r\\n author = re.findall(r'(?<== ).*$', information_l[9])[0]\\r\\n rewrite_author = re.sub('[$]', ' ', author)\\r\\n self.author_l.append(rewrite_author)\\r\\n self.title_l.append(re.findall(r'(?<== ).*$', information_l[10])[0])\\r\\n self.journal_l.append(re.findall(r'(?<== ).*$', information_l[11])[0])\\r\\n self.volume_l.append(re.findall(r'(?<== ).*$', information_l[12])[0])\\r\\n self.page_l.append(re.findall(r'(?<== ).*$', information_l[13])[0])\\r\\n self.publish_year_l.append(re.findall(r'(?<== ).*$', information_l[14])[0])\\r\\n reaction_formula = re.findall(r'(?<== ).*$', information_l[15])[0]\\r\\n self.reaction_formula_l.append(reaction_formula)\\r\\n self.decompose_nifs(reaction_formula, prod)\\r\\n self.reactants_l.append(self.reactants)\\r\\n self.products_l.append(self.products)\\r\\n df_information = pd.DataFrame([self.record_number_l, self.reaction_formula_l, self.reaction_method_l,\\r\\n self.qdb_process_l, self.reactants_l, self.products_l, self.data_type_l,\\r\\n self.x_unit_l, self.y_unit_l, self.ref_number_l, self.doi_l,\\r\\n self.title_l, self.author_l, self.journal_l, self.volume_l, self.page_l,\\r\\n self.publish_year_l])\\r\\n df_information = df_information.T\\r\\n df_information.rename(columns={0: 'record_number', 1: 'reaction_formula', 2: 'reaction_method',\\r\\n 3: 'QDB_process', 4: 'reactants', 5: 'products',\\r\\n 6: 'Data_type', 7: 'temperature_unit', 8: 'rate_coefficient_unit',\\r\\n 9: 'reference_number', 10: 'DOI',\\r\\n 11: 'title', 12: 'author', 13: 'journal', 14: 'volume', 15: 'page',\\r\\n 16: 'publish_year'},\\r\\n inplace=True)\\r\\n else:\\r\\n df_information = pd.DataFrame()\\r\\n return df_information\",\n \"def show_research(self):\\n self.list_research = orm_imp.read_research()\\n print(\\\"========================================================\\\")\\n for row in self.list_research:\\n print(\\n row[\\\"date\\\"], \\\"|| Produit :\\\", row['subcat'],\\n \\\"|| Meilleure proposition :\\\", row['product'], \\\"| Score :\\\",\\n row['nutriscore'], \\\"| Lien :\\\", row['url'],\\n \\\"| Ingrédients :\\\", row['ingredient'])\\n print(\\\"========================================================\\\")\",\n \"def PrintInfo(self):\\n infoname = list(zip(*self.basicinfo.cursor.execute(\\n \\\"SELECT Name FROM Infoname\\\").fetchall()))[0]\\n for obj in infoname:\\n s = (\\\"SELECT Col, Row, TableNum FROM %s WHERE \\\"\\n \\\"TestItems=%s\\\" % (self.position, \\\"'\\\" + obj + \\\"'\\\"))\\n [col, row, tablenum] = self.basicinfo.cursor.execute(s).fetchone()\\n s = \\\"SELECT %s FROM TestInfo\\\" % obj\\n content = self.db.cursor.execute(s).fetchone()[0]\\n if tablenum is not None:\\n self.doc.TableContent(\\n tablenum=tablenum, cellrow=row, cellcolum=col,\\n insertcontent=content)\",\n \"def get_text_lines(instText):\\n\\n # Find out which part this is\\n part = instText.part\\n # Get the necessary parameters: lng, ext, dir\\n sLng = part.corpus.get_lng_display()\\n sDir = part.dir\\n sName = instText.fileName\\n sFormat = instText.get_format_display()\\n # Now try to get the information\\n oBack = get_crpp_text(sLng, sDir, sFormat, sName)\\n # Prepare what we return\\n if oBack == None or oBack['status'] == 'error':\\n return None\\n else:\\n return oBack\",\n \"def print_cat(self):\\n for ion in self.cations:\\n data_str = (\\\"HETATM\\\", int(self.nmb) + ion, self.cations[ion][0], \\\"\\\",\\n self.cations[ion][0],\\\"\\\", ion, \\\"\\\",\\n Vector.x(self.cations[ion][1]), Vector.y(self.cations[ion][1]),\\n Vector.z(self.cations[ion][1]), 1.00, 0.00,\\n self.cations[ion][0], \\\"\\\",\\\"\\\\n\\\")\\n out_str = self.pdb_format % data_str\\n self.output.write(out_str)\\n self.output.close()\\n return 1\",\n \"def print_the_contents_of_all_entries(self):\\n\\n if len(self.student_list):\\n self.print_dataframe(self.student_list)\\n else:\\n print('There is no contents to show')\",\n \"def document_number(self, **kw):\\r\\n for docnum in self.document_numbers(**kw):\\r\\n return docnum\",\n \"def load_cci10():\\n \\n cci10 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci_icd10cm_2019_1.csv'))\\n \\n cci10.columns = [i.strip('\\\\'') for i in cci10.columns]\\n \\n for col in cci10.columns:\\n cci10.loc[:,col] = cci10[col].str.strip('\\\\'')\\n cci10 = cci10.replace(r'^\\\\s*$', np.nan, regex=True)\\n cci10.columns = [i.replace('CHRONIC INDICATOR','CHRONIC') for i in cci10.columns]\\n \\n dict_bodysystem = [\\n ('1','Infectious and parasitic disease'),\\n ('2','Neoplasms'),\\n ('3','Endocrine, nutritional, and metabolic diseases and immunity disorders'),\\n ('4','Diseases of blood and blood-forming organs'),\\n ('5','Mental disorders'),\\n ('6','Diseases of the nervous system and sense organs'),\\n ('7','Diseases of the circulatory system'),\\n ('8','Diseases of the respiratory system'),\\n ('9','Diseases of the digestive system'),\\n ('10','Diseases of the genitourinary system'),\\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\\n ('12','Diseases of the skin and subcutaneous tissue'),\\n ('13','Diseases of the musculoskeletal system'),\\n ('14','Congenital anomalies'),\\n ('15','Certain conditions originating in the perinatal period'),\\n ('16','Symptoms, signs, and ill-defined conditions'),\\n ('17','Injury and poisoning'),\\n ('18','Factors influencing health status and contact with health services'),\\n ]\\n \\n cci10 = cci10.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\\n \\n cci10.loc[:,'ICD-10-CM CODE'] = cci10['ICD-10-CM CODE'].str.replace(' ','')\\n \\n return cci10\",\n \"def print_num(self, num):\\r\\n if num == \\\"\\\":\\r\\n print(str(self.text_lines))\\r\\n else:\\r\\n num = int(num)\\r\\n if num == 0:\\r\\n raise ValueError(\\\"Zero is not a valid line number\\\")\\r\\n elif num > 0:\\r\\n num -= 1\\r\\n print(self.text_lines[num])\",\n \"def fetch_gene_descriptions(self, metrics, coeff='cohen', nih_fetch_num=20, alpha=.05, **kwargs):\\n if 'verbose' not in kwargs:\\n kwargs['verbose'] = True\\n if 'nih_dl' not in kwargs:\\n kwargs['nih_dl'] = False\\n\\n if kwargs['nih_dl'] == False:\\n if 'csv_path' not in kwargs:\\n raise ValueError(\\\"'csv_path' argument in **kwargs missing; provide argument or use 'nih_dl':True .\\\")\\n\\n top_genes = []\\n if coeff == 'cohen':\\n # Checking if users mixed up coeff/metric parameters\\n if isinstance(metrics, list):\\n raise ValueError(\\\"list passed with coeff='cohen'; if you want to use Spearman's Rho use coeff='spearman'.\\\")\\n\\n for rec in metrics['results'][:nih_fetch_num]:\\n try:\\n if kwargs['nih_dl']:\\n gene_name, gene_description = gene_info(str(rec.entrez))\\n else:\\n gene_dat = get_local_gene_info(kwargs['csv_path'], [rec.entrez])\\n gene_name = gene_dat[0].name\\n gene_description = gene_dat[0].description\\n top_genes.append((rec.entrez, rec.cohen_d, rec.p_value, gene_name, gene_description))\\n except IndexError:\\n continue\\n\\n if kwargs['verbose']:\\n print \\\"\\\\nCorrected Bonferroni Alpha: %.3E\\\\n\\\\n\\\" % (alpha / float(metrics['gene_sample_size']))\\n for eid, coh_d, p_val, gene_i, descr in top_genes:\\n if len(descr) == 1:\\n print \\\"%d (p = %.3E; d = %.3f): < No description found >\\\\n\\\\n\\\" % (eid, p_val, coh_d)\\n else:\\n print \\\"%d (p = %.3E; d = %.3f): %s\\\\n\\\\n\\\" % (eid, p_val, coh_d, descr)\\n elif coeff == 'spearman':\\n # Checking if users mixed up coeff/metric parameters\\n if isinstance(metrics, dict):\\n raise ValueError(\\\"dict passed with coeff='spearman'; if you want to use Cohen's d use coeff='cohen'.\\\")\\n\\n top_ids = np.argsort(metrics)[:nih_fetch_num]\\n\\n top_rs = [r for r in reversed(np.sort(metrics))][:nih_fetch_num]\\n top_genes = []\\n for x in xrange(len(top_ids)):\\n try:\\n if kwargs['nih_dl']:\\n gene_name, gene_description = gene_info(str(top_ids[x]))\\n else:\\n gene_dat = get_gene_info(kwargs['csv_path'], [top_ids[x]])\\n gene_name = gene_dat[0].name\\n gene_description = gene_dat[0].description\\n top_genes.append((int(top_ids[x]), top_rs[x], gene_name, gene_description))\\n except IndexError:\\n continue\\n\\n if kwargs['verbose']:\\n print \\\"\\\\nCorrected Bonferroni Alpha: %.3E\\\\n\\\\n\\\" % (alpha/float(len(self.no.ge.keys())))\\n for eid, rho, gene_i, descr in top_genes:\\n if len(descr) == 1:\\n print \\\"%d (r = %.3f): < No description found >\\\\n\\\\n\\\" % (eid, rho)\\n else:\\n print \\\"%d (r = %.3f): %s\\\\n %s\\\\n\\\\n\\\" % (eid, rho, gene_i, descr)\\n\\n else:\\n raise ValueError(\\\"Invalid parameter value for 'coeff'; use 'spearman' or 'cohen'.\\\")\\n\\n return top_genes\",\n \"def get_examples():\\n\\n base_query = \\\"[[Medium::TCG]] [[Database ID::+]]\\\"\\n base_printouts = [\\n \\\"Password\\\",\\n \\\"English name\\\",\\n \\\"Card type\\\",\\n ]\\n all_res = {}\\n\\n n = len(ALL_DISPLAYS)\\n for i, x in enumerate(ALL_DISPLAYS):\\n query = f\\\"{base_query} [[{x}::+]]\\\"\\n print(i, n, query)\\n try:\\n data = ask_yugipedia(query, base_printouts + [x])\\n except:\\n print(\\\"ERROR\\\", i, x)\\n continue\\n all_res[x] = data\\n return all_res\",\n \"def do_ascii(catalog):\\n task_str = catalog.get_current_task_str()\\n\\n\\n # Howerton Catalog\\n datafile = os.path.join(catalog.get_current_task_repo(), 'ASCII',\\n 'vizier_J_MNRAS_441_1186_table1 J_MNRAS_441_1186_table3_20200403.csv')\\n data = read(datafile, format='csv')\\n for rrow in pbar(data, task_str):\\n row = dict((x, str(rrow[x])) for x in rrow.columns)\\n# if any(x in row['Notes'].lower() for x in ['artifact']):\\n# continue\\n# ctypes = row['Type'].split('/')\\n# nonsne = False\\n# for ct in ctypes:\\n# if ct.replace('?', '') in catalog.nonsnetypes:\\n# nonsne = True\\n# else:\\n# nonsne = False\\n# break\\n# if nonsne:\\n# continue\\n name, source = catalog.new_entry(\\n row['CRTS'],\\n srcname='CRTS',\\n bibcode='2014MNRAS.441.1186D')\\n# if row['IAU des.'] != '--':\\n# catalog.entries[name].add_quantity(SUPERNOVA.ALIAS,\\n# row['IAU des.'], source)\\n# for ct in ctypes:\\n# catalog.entries[name].add_quantity(SUPERNOVA.CLAIMED_TYPE, ct,\\n# source)\\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVERER,\\n# row['Discoverer'], source)\\n# date = row['Discovery'].split('/')\\n# date = '/'.join([date[-1].zfill(2), date[0].zfill(2), date[1]])\\n# catalog.entries[name].add_quantity(SUPERNOVA.DISCOVER_DATE, date,\\n# source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.VISUAL_MAG, row['Vmax'],\\n source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.RA, row['RAJ2000'], source)\\n catalog.entries[name].add_quantity(CATACLYSMIC.DEC, row['DEJ2000'], source)\\n catalog.journal_entries()\\n\\n # Howerton Catalog\",\n \"def uglyprint(self):\\n\\n ctmp = self.conn.cursor()\\n ctmp.execute(\\\"SELECT * FROM ATOM\\\")\\n print(ctmp.fetchall())\",\n \"def output_file(data, k):\\n if k:\\n filename = 'digitsOutput' + str(k) + '.csv'\\n else:\\n filename = 'digitsOutput.csv'\\n f = open(filename, 'w')\\n for line in data:\\n f.write(str(line) + '\\\\n')\\n f.close()\",\n \"def print_output(data,alignments,file):\\n print(\\\"######################################################################\\\")\\n print(\\\"Task 1 : IBM model 1 and EM algorithm implementation ,with corpus @\\\",file)\\n print(\\\"######################################################################\\\")\\n\\n for i in range(len(data)):\\n print(\\\"English Sentence : \\\",data[i][\\\"en\\\"])\\n print(\\\"Foreign Sentence : \\\",data[i][\\\"fr\\\"])\\n print(\\\"Alignment : \\\",alignments[i])\\n print(\\\"----------------------------------------------------------------------\\\")\",\n \"def show_recs(self):\\n if len(self.storage.records) == 0:\\n return \\\"Records not found!\\\"\\n else:\\n string_of_records = \\\"\\\"\\n for record in self.storage.records:\\n string_of_records += record.to_string()\\n return string_of_records\",\n \"def getExpAccessions(cf):\\n\\tplatform = cf.get_parameter('platform')\\n\\tsrafetchxml = cf.get_input('srafetchxml')\\n\\tsraexplist = cf.get_output('sraexplist')\\n\\tsraxmlparser = SRAXMLParser()\\n\\truns = sraxmlparser.parse(srafetchxml)\\n\\twriter = csv.writer(open(sraexplist, 'wb'), quoting=csv.QUOTE_NONE)\\n\\twriter.writerow(['NCBISRAExpID'])\\n\\taccessions = []\\n\\tfor run in runs:\\n\\t\\tif platform and \\\\\\n\\t\\t\\tnot run.platform == platform:\\n\\t\\t\\tcontinue\\n\\t\\telif not run.exp_accession in accessions:\\n\\t\\t\\twriter.writerow([run.exp_accession])\\n\\t\\t\\taccessions.append(run.exp_accession)\\n\\tcf.write_log(\\\"GetExpAccessions: wrote %s experiment accessions\\\" % len(accessions))\\n\\treturn constants.OK\",\n \"def getArticleRows(baseName, extId):\\n pr = pubStore.PubReaderFile(baseName)\\n for artRow, fileRows in pr.iterArticlesFileList():\\n if artRow.externalId==extId:\\n return artRow, fileRows\",\n \"def numbers():\\n return '<pre>' + '\\\\n'.join(phone_numbers) + '</pre>'\",\n \"def file_line(ftype, c):\\n if ftype in column_names:\\n if ftype == 'mwdata':\\n # this fix should not be necessary\\n if c['rank'] is None and c['analytic_rank'] is None:\\n c['reg'] = None\\n # for k in column_names[ftype]:\\n # print(\\\"{}: {} --> {} ({})\\\".format(k, c[k], get_encoder(k)(c[k]), type(get_encoder(k)(c[k]))))\\n return \\\" \\\".join([get_encoder(k)(c[k]) for k in column_names[ftype]])\\n else:\\n raise ValueError(\\\"{} is not a valid file type\\\".format(ftype))\",\n \"def test_nwb_extractor_channel_ids_retrieval(path_to_nwbfile, nwbfile_with_ecephys_content):\\n electrical_series_name_list = [\\\"ElectricalSeries1\\\", \\\"ElectricalSeries2\\\"]\\n for electrical_series_name in electrical_series_name_list:\\n recording_extractor = NwbRecordingExtractor(path_to_nwbfile, electrical_series_name=electrical_series_name)\\n\\n nwbfile = nwbfile_with_ecephys_content\\n electrical_series = nwbfile.acquisition[electrical_series_name]\\n electrical_series_electrode_indices = electrical_series.electrodes.data[:]\\n electrodes_table = nwbfile.electrodes.to_dataframe()\\n sub_electrodes_table = electrodes_table.iloc[electrical_series_electrode_indices]\\n\\n expected_channel_ids = sub_electrodes_table[\\\"channel_name\\\"].values\\n extracted_channel_ids = recording_extractor.channel_ids\\n assert np.array_equal(extracted_channel_ids, expected_channel_ids)\",\n \"def test_fetchSpecificNumberedText(self):\\n d = self.client.fetchSpecific('4', headerType='TEXT', headerNumber=7)\\n self.assertEqual(\\n self.transport.value(), b'0001 FETCH 4 BODY[7.TEXT]\\\\r\\\\n')\\n self.client.lineReceived(b'* 4 FETCH (BODY[7.TEXT] \\\"Some body\\\")')\\n self.client.lineReceived(b'0001 OK FETCH completed')\\n self.assertEqual(\\n self.successResultOf(d),\\n {4: [['BODY', ['7.TEXT'], \\\"Some body\\\"]]})\",\n \"def get_citation_informations(recid_list, config):\\n begin_time = os.times()[4]\\n d_reports_numbers = {} #dict of recid -> institute-given-report-code\\n d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']\\n d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography\\n d_records_s = {} #dict of recid -> this_records_publication_info\\n citation_informations = []\\n\\n write_message(\\\"config function \\\"+config.get(\\\"rank_method\\\", \\\"function\\\"), verbose=9)\\n function = \\\"\\\"\\n try:\\n function = config.get(\\\"rank_method\\\", \\\"function\\\")\\n except:\\n register_exception(prefix=\\\"cfg section [rank_method] has no attribute called function\\\", alert_admin=True)\\n #we cannot continue\\n return [ {}, {}, {}, {} ]\\n record_pri_number_tag = \\\"\\\"\\n try:\\n record_pri_number_tag = config.get(function, \\\"primary_report_number\\\")\\n except:\\n register_exception(prefix=\\\"cfg section \\\"+function+\\\" has no attribute primary_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n record_add_number_tag = \\\"\\\"\\n try:\\n record_add_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"additional_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute additional_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_number_tag = \\\"\\\"\\n try:\\n reference_number_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_report_number\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_report_number\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n reference_tag = \\\"\\\"\\n try:\\n reference_tag = config.get(config.get(\\\"rank_method\\\", \\\"function\\\"),\\n \\\"reference_via_pubinfo\\\")\\n except:\\n register_exception(prefix=\\\"config error. cfg section \\\"+function+\\\" has no attribute reference_via_pubinfo\\\", alert_admin=True)\\n return [ {}, {}, {}, {} ]\\n\\n p_record_pri_number_tag = tagify(parse_tag(record_pri_number_tag))\\n #037a: contains (often) the \\\"hep-ph/0501084\\\" tag of THIS record\\n p_record_add_number_tag = tagify(parse_tag(record_add_number_tag))\\n #088a: additional short identifier for the record\\n p_reference_number_tag = tagify(parse_tag(reference_number_tag))\\n #999C5r. this is in the reference list, refers to other records. Looks like: hep-ph/0408002\\n p_reference_tag = tagify(parse_tag(reference_tag))\\n #999C5s. A standardized way of writing a reference in the reference list. Like: Nucl. Phys. B 710 (2000) 371\\n #fields needed to construct the pubinfo for this record\\n publication_pages_tag = \\\"\\\"\\n publication_year_tag = \\\"\\\"\\n publication_journal_tag = \\\"\\\"\\n publication_volume_tag = \\\"\\\"\\n publication_format_string = \\\"p v (y) c\\\"\\n try:\\n tag = config.get(function, \\\"pubinfo_journal_page\\\")\\n publication_pages_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_year\\\")\\n publication_year_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_title\\\")\\n publication_journal_tag = tagify(parse_tag(tag))\\n tag = config.get(function, \\\"pubinfo_journal_volume\\\")\\n publication_volume_tag = tagify(parse_tag(tag))\\n publication_format_string = config.get(function, \\\"pubinfo_journal_format\\\")\\n except:\\n pass\\n\\n #print values for tags for debugging\\n if task_get_task_param('verbose') >= 9:\\n write_message(\\\"tag values\\\")\\n write_message(\\\"p_record_pri_number_tag \\\"+str(p_record_pri_number_tag))\\n write_message(\\\"p_reference_tag \\\"+str(p_reference_tag))\\n write_message(\\\"publication_journal_tag \\\"+str(publication_journal_tag))\\n write_message(\\\"publication_format_string is \\\"+publication_format_string)\\n done = 0 #for status reporting\\n numrecs = len(recid_list)\\n\\n # perform quick check to see if there are some records with\\n # reference tags, because otherwise get.cit.inf would be slow even\\n # if there is nothing to index:\\n if run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_tag[0:2],\\n (p_reference_tag,)) or \\\\\\n run_sql(\\\"SELECT value FROM bib%sx WHERE tag=%%s LIMIT 1\\\" % p_reference_number_tag[0:2],\\n (p_reference_number_tag,)):\\n for recid in recid_list:\\n if (done % 10 == 0):\\n task_sleep_now_if_required()\\n #in fact we can sleep any time here\\n\\n if (done % 1000 == 0):\\n mesg = \\\"get cit.inf done \\\"+str(done)+\\\" of \\\"+str(numrecs)\\n write_message(mesg)\\n task_update_progress(mesg)\\n done = done+1\\n\\n if recid in INTBITSET_OF_DELETED_RECORDS:\\n # do not treat this record since it was deleted; we\\n # skip it like this in case it was only soft-deleted\\n # e.g. via bibedit (i.e. when collection tag 980 is\\n # DELETED but other tags like report number or journal\\n # publication info remained the same, so the calls to\\n # get_fieldvalues() below would return old values)\\n continue\\n\\n pri_report_numbers = get_fieldvalues(recid, p_record_pri_number_tag)\\n add_report_numbers = get_fieldvalues(recid, p_record_add_number_tag)\\n reference_report_numbers = get_fieldvalues(recid, p_reference_number_tag)\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n\\n l_report_numbers = pri_report_numbers\\n l_report_numbers.extend(add_report_numbers)\\n d_reports_numbers[recid] = l_report_numbers\\n\\n if reference_report_numbers:\\n write_message(str(recid)+\\\"'s report number values \\\"+str(l_report_numbers), verbose=9)\\n d_references_report_numbers[recid] = reference_report_numbers\\n\\n references_s = get_fieldvalues(recid, p_reference_tag)\\n if references_s:\\n write_message(str(recid)+\\\"'s \\\"+str(p_reference_tag)+\\\" values \\\"+str(references_s), verbose=9)\\n d_references_s[recid] = references_s\\n\\n #get a combination of\\n #journal vol (year) pages\\n if publication_pages_tag and publication_journal_tag and \\\\\\n publication_volume_tag and publication_year_tag and publication_format_string:\\n tagsvalues = {} #we store the tags and their values here\\n #like c->444 y->1999 p->\\\"journal of foo\\\",v->20\\n tagsvalues[\\\"p\\\"] = \\\"\\\"\\n tagsvalues[\\\"y\\\"] = \\\"\\\"\\n tagsvalues[\\\"c\\\"] = \\\"\\\"\\n tagsvalues[\\\"v\\\"] = \\\"\\\"\\n tmp = get_fieldvalues(recid, publication_journal_tag)\\n if tmp:\\n tagsvalues[\\\"p\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_volume_tag)\\n if tmp:\\n tagsvalues[\\\"v\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_year_tag)\\n if tmp:\\n tagsvalues[\\\"y\\\"] = tmp[0]\\n tmp = get_fieldvalues(recid, publication_pages_tag)\\n if tmp:\\n #if the page numbers have \\\"x-y\\\" take just x\\n pages = tmp[0]\\n hpos = pages.find(\\\"-\\\")\\n if hpos > 0:\\n pages = pages[:hpos]\\n tagsvalues[\\\"c\\\"] = pages\\n #format the publ infostring according to the format\\n publ = \\\"\\\"\\n ok = 1\\n for i in range (0, len(publication_format_string)):\\n current = publication_format_string[i]\\n #these are supported\\n if current == \\\"p\\\" or current == \\\"c\\\" or current == \\\"v\\\" \\\\\\n or current == \\\"y\\\":\\n if tagsvalues[current]:\\n #add the value in the string\\n publ += tagsvalues[current]\\n else:\\n ok = 0\\n break #it was needed and not found\\n else:\\n publ += current #just add the character in the format string\\n if ok:\\n write_message(\\\"d_records_s (publication info) for \\\"+str(recid)+\\\" is \\\"+publ, verbose=9)\\n d_records_s[recid] = publ\\n else:\\n mesg = \\\"Warning: there are no records with tag values for \\\"\\n mesg += p_reference_number_tag+\\\" or \\\"+p_reference_tag+\\\". Nothing to do.\\\"\\n write_message(mesg)\\n\\n mesg = \\\"get cit.inf done fully\\\"\\n write_message(mesg)\\n task_update_progress(mesg)\\n\\n citation_informations.append(d_reports_numbers)\\n citation_informations.append(d_references_report_numbers)\\n citation_informations.append(d_references_s)\\n citation_informations.append(d_records_s)\\n end_time = os.times()[4]\\n write_message(\\\"Execution time for generating citation info from record: %.2f sec\\\" % \\\\\\n (end_time - begin_time))\\n return citation_informations\",\n \"def as_text(self):\\n buf=BIO.MemoryBuffer()\\n m2.x509_crl_print(buf.bio_ptr(), self.crl)\\n return buf.read_all()\",\n \"def read_ext_data(self, ext_filename):\\n\\n # read in the FITS file\\n hdulist = fits.open(ext_filename)\\n\\n # get the list of extension names\\n extnames = [hdulist[i].name for i in range(len(hdulist))]\\n\\n # the extinction curve itself\\n for curname in _poss_datasources:\\n curext = \\\"%sEXT\\\" % curname\\n if curext in extnames:\\n self.waves[curname] = hdulist[curext].data['WAVELENGTH']\\n self.exts[curname] = hdulist[curext].data['EXT']\\n if 'UNC' in hdulist[curext].data.columns.names:\\n self.uncs[curname] = hdulist[curext].data['UNC']\\n else:\\n self.uncs[curname] = hdulist[curext].data['EXT_UNC']\\n if 'NPTS' in hdulist[curext].data.columns.names:\\n self.npts[curname] = hdulist[curext].data['NPTS']\\n else:\\n self.npts[curname] = np.full(len(self.waves[curname]), 1)\\n\\n # get the parameters of the extinction curve\\n pheader = hdulist[0].header\\n self.type = pheader.get('EXTTYPE')\\n self.red_file = pheader.get('R_FILE')\\n self.comp_file = pheader.get('C_FILE')\\n\\n column_keys = ['AV', 'EBV', 'RV', 'LOGHI', 'LOGHIMW', 'NHIAV']\\n for curkey in column_keys:\\n if pheader.get(curkey):\\n if pheader.get('%s_UNC' % curkey):\\n self.columns[curkey] = (pheader.get(curkey),\\n pheader.get('%s_UNC' % curkey))\\n else:\\n self.columns[curkey] = (pheader.get(curkey),\\n 0.0)\\n\\n # get FM90 parameters if they exist\\n if pheader.get('FMC2'):\\n FM90_keys = ['C1', 'C2', 'C3', 'C4', 'x0', 'gam']\\n self.fm90 = {}\\n for curkey in FM90_keys:\\n if pheader.get(curkey):\\n self.fm90[curkey] = (float(pheader.get('FM%s' % curkey)),\\n float(pheader.get('FM%sU' % curkey)))\\n # for completeness, populate C1 using from the FM07 relationship\\n # if not already present\\n if 'C1' not in self.fm90.keys():\\n self.fm90['C1'] = (2.09 - 2.84*self.fm90['C2'][0],\\n 2.84*self.fm90['C2'][1])\\n\\n # get P92 parameters if they exist\\n if pheader.get('BKG_amp'):\\n P92_mkeys = ['BKG', 'FUV', 'NUV',\\n 'SIL1', 'SIL2', 'FIR']\\n P92_types = ['AMP', 'LAMBDA', 'B', 'N']\\n self.p92_best_fit = {}\\n for curmkey in P92_mkeys:\\n for curtype in P92_types:\\n curkey = '%s_%s' % (curmkey, curtype)\\n if pheader.get(curkey):\\n self.p92_best_fit[curkey] = \\\\\\n float(pheader.get('%s' % curkey))\",\n \"def get_ct_rawdata(nctid, data_path):\\n url = 'http://clinicaltrials.gov/show/%s?displayxml=true' % nctid\\n raw_data = download_web_data(url)\\n out_path = data_path + '/' + nctid + '.xml'\\n try:\\n fid = open(out_path, 'w')\\n fid.write(raw_data)\\n fid.close()\\n return True\\n except Exception as e:\\n log.error(e)\\n return False\",\n \"def info(out: Export = Export(\\\"cwb.encoded/data/.info\\\"),\\n sentences: AnnotationCommonData = AnnotationCommonData(\\\"misc.<sentence>_count\\\"),\\n firstdate: AnnotationCommonData = AnnotationCommonData(\\\"cwb.datefirst\\\"),\\n lastdate: AnnotationCommonData = AnnotationCommonData(\\\"cwb.datelast\\\"),\\n resolution: AnnotationCommonData = AnnotationCommonData(\\\"dateformat.resolution\\\"),\\n protected: bool = Config(\\\"korp.protected\\\")):\\n create_info_file(sentences, firstdate, lastdate, resolution, protected, out)\",\n \"def toc(self):\\n return [\\n f\\\"Pnum: {key}, Edition: {self.catalog[key]['edition']}, \\\"\\n f\\\"length: {len(self.catalog[key]['transliteration'])} line(s)\\\"\\n for key in sorted(self.catalog.keys())]\",\n \"def print_file(chr_list,filename):\\n infile = open(filename)\\n for line in infile:\\n if line.startswith('SL2.40'):\\n chr = int(line.strip().split()[0][-2:])\\n loci = int(line.strip().split()[1])\\n for chr_i,chr_l in enumerate(chr_list):\\n for loc in chr_l:\\n if chr==chr_i and loci==loc:\\n print line\\n return\",\n \"def print_file(list):\\n chr_name_list = ['SL2.40ch00','SL2.40ch01','SL2.40ch02','SL2.40ch03','SL2.40ch04','SL2.40ch05','SL2.40ch06','SL2.40ch07','SL2.40ch08','SL2.40ch09','SL2.40ch10','SL2.40ch11','SL2.40ch12']\\n for index,chr_list in enumerate(list):\\n if chr_list:\\n chr = chr_name_list[index]\\n for loci in chr_list:\\n print \\\"%s\\\\t%d\\\\t%d\\\\t%s\\\\t%d\\\" % (chr,loci[0],loci[1],'\\\\t'.join(loci[2]),len(loci[2])-loci[2].count('0'))\",\n \"def printing_resident_sheets(entry, file):\\n nhi = entry.get()\\n if re.match(\\\"^[A-Za-z]{3}[0-9]{4}$\\\", nhi):\\n pass\\n else:\\n popup_error(\\\"Incorrect NHI format entered, please try again\\\")\\n\\n if file_available(file):\\n ecase_driver = ecase_downloader.ecase_login()\\n ecase_downloader.resident_contacts(ecase_driver, nhi)\\n ecase_downloader.preferred_name_and_image(ecase_driver, nhi)\\n ecase_driver.quit()\",\n \"def print_all(self):\\n with open(self.file, 'r', encoding='utf-8') as self.contacts_file:\\n for i in self.contacts_file.readlines():\\n print(i)\",\n \"def get_cit_data(ticker):\\n log = logging.getLogger(__name__)\\n # TODO: This is probably not working anymore (need to refresh files)\\n file = os.path.join(c.cfg['default']['data'], 'COT', 'Supplemental', 'T{}.PRN'.format(ticker))\\n columns = ['Non-Commercial Longs', 'Non-Commercial Shorts',\\n 'Commercial Longs', 'Commercial Shorts',\\n 'Non-Reportable Longs', 'Non-Reportable Shorts',\\n 'Index Trader Longs', 'Index Trader Shorts']\\n\\n # Read Data from csv file\\n cit = pd.read_csv(file, parse_dates=True, index_col=0, header=None, names=columns)\\n \\n\\n return cit\",\n \"def print_record(self, key):\\n percentile_amount = str(int(round(self.running_percentile[key].get_percentile())))\\n total_amount = str(int(round(self.running_percentile[key].total_amount)))\\n count = str(len(self.running_percentile[key]))\\n record = [key.recipient, key.zip_code, str(key.year), percentile_amount, total_amount, count]\\n return '|'.join(record)\",\n \"def InfoPrint(currentFile):\\n Chains = ReturnChain(currentFile)\\n chainlist = ReturnChain(currentFile)\\n if len(chainlist) > 1:\\n chainlist.insert(len(chainlist)-1,'and')\\n print('CHAINS:',' '.join(chainlist))\\n else:\\n print('CHAIN: %s'%''.join(chainlist))\\n x= ' '\\n for chain in Chains:\\n with open(currentFile) as fileIn:\\n sequence=[]\\n helix_count = 0\\n sheet_count = 0\\n line = fileIn.readline()\\n while line:\\n if line.startswith('SEQRES'):\\n words = line.split()\\n chaintype = words[2]\\n if chaintype == chain:\\n for word in words:\\n if word in aminoacidsCode.values():\\n sequence.append(word)\\n\\n else:\\n pass\\n\\n\\n if line.startswith('HELIX'):\\n helixInfo = line.split()\\n chaintype = helixInfo[4]\\n if chaintype == chain:\\n helix_count = helix_count + 1\\n\\n else:\\n pass\\n\\n if line.startswith('SHEET'):\\n sheetInfo = line.split()\\n chaintype = sheetInfo[5]\\n if chaintype == chain:\\n sheet_count = sheet_count + 1\\n\\n else:\\n pass\\n\\n line = fileIn.readline()\\n\\n print('- Chain %s'%chain)\\n print('Number of amino acids: %s'%len(sequence))\\n print('Number of helix:%s %s'%(x*7,helix_count))\\n print('Number of sheet:%s %s'%(x*7,sheet_count))\\n print('Sequence:'),seqprint(sequence)\",\n \"def display(digit_logits: np.array, fname: str):\\n digit_probs = softmax(np.reshape(digit_logits[:, 1:], (9, 9, 9)), axis=2).reshape((9, 9, 3, 3))\\n nrows = 1\\n ncols = 9\\n\\n fig = plt.figure(figsize=(ncols, nrows))\\n outer = gridspec.GridSpec(nrows, ncols, wspace=0.0, hspace=0.0)\\n\\n digits = np.arange(1, 10).reshape(3, 3)\\n for i in range(nrows):\\n for j in range(ncols):\\n rlw, clw = 1, 1\\n\\n if i % 3 == 0:\\n rlw = 2\\n if j % 3 == 0:\\n clw = 2\\n print(i, j)\\n\\n inner = gridspec.GridSpecFromSubplotSpec(3, 3, subplot_spec=outer[i, j], wspace=0.0, hspace=0.0)\\n for c in range(3):\\n for r in range(3):\\n plt.subplot(inner[c, r])\\n plt.text(0.5, 0.5, str(digits[c, r]), horizontalalignment='center', verticalalignment='center', fontsize=10 * digit_probs[i, j, c, r] ** (1 / 4))\\n plt.xticks([])\\n plt.yticks([])\\n\\n ax = fig.gca()\\n for sp in ax.spines.values():\\n sp.set_visible(False)\\n if ax.is_first_row():\\n top = ax.spines['top']\\n top.set_visible(True)\\n top.set_linewidth(rlw)\\n if ax.is_last_row():\\n bottom = ax.spines['bottom']\\n bottom.set_visible(True)\\n if i == nrows - 1 and (i + 1) % 3 == 0:\\n bottom.set_linewidth(2)\\n if ax.is_first_col():\\n left = ax.spines['left']\\n left.set_visible(True)\\n left.set_linewidth(clw)\\n if ax.is_last_col():\\n right = ax.spines['right']\\n right.set_visible(True)\\n if j == ncols - 1 and (j + 1) % 3 == 0:\\n right.set_linewidth(2)\\n\\n plt.savefig(fname, bbox_inches='tight', pad_inches=0)\\n plt.close()\",\n \"def data_format(self):\\n data_format = self.query('FORMAT:DATA?')\\n if '32' in data_format:\\n return 'REAL,+32'\\n elif '64' in data_format:\\n return 'REAL,+64'\\n elif '0' in data_format:\\n return 'ASC,+0'\\n else:\\n raise InstrIOError(cleandoc('''Agilent PNA did not return the\\n data format'''))\",\n \"def fetchFullText(self, arnumbers):\\n if type(arnumbers) != list:\\n arnumbers = [arnumbers]\\n browser = webdriver.Chrome()\\n articles = []\\n for num in arnumbers:\\n browser.get(\\\"http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=\\\" + str(num))\\n # TODO: Ensure the page load is ready\\n page = pq(browser.page_source)\\n articles.append(page('div#article'))\\n browser.quit()\\n return articles\",\n \"def wmo_code_table():\\n import pandas as pd\\n from ..fun import get_data\\n\\n return pd.read_csv(get_data('Common_C02_20181107_en.txt'))\",\n \"def coeff_display_file(filename,zernike_max_order=20):\\n hdu = pf.open(filename)\\n nn = len(hdu)\\n data = []\\n colnames = ['x','y','M20','M22','M31','M33']\\n for hdui in hdu[1:]:\\n Nobj = hdui.data.shape[0]\\n M20=np.zeros(Nobj)\\n M22=np.zeros(Nobj).astype(complex)\\n M31=np.zeros(Nobj).astype(complex)\\n M33=np.zeros(Nobj).astype(complex)\\n #sigma = 1.1/0.27\\n sigma = 1.08/scale\\n for i in range(Nobj):\\n M20[i],M22[i],M31[i],M33[i]=complexMoments(data=hdui.data[i][4:].reshape(npix,npix),sigma=sigma)\\n x=hdui.header['ccdXcen']\\n y=hdui.header['ccdYcen']\\n data.append([x,y,np.median(M20), np.median(M22), np.median(M31), np.median(M33)])\\n data=np.array(data)\\n betaAll=[]\\n betaErrAll=[]\\n R2adjAll=[]\\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,2].real,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n for i in range(3,6):\\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].real,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n beta,betaErr,R2_adj = zernikeFit(data[:,0].real,data[:,1].real,data[:,i].imag,max_order=zernike_max_order)\\n betaAll.append(beta)\\n betaErrAll.append(betaErr)\\n R2adjAll.append(R2_adj)\\n betaAll = np.array(betaAll)\\n betaErrAll = np.array(betaErrAll)\\n R2adjAll = np.array(R2adjAll)\\n ind = np.arange(len(betaAll[0]))\\n momname = ('M20','M22.Real','M22.imag','M31.real','M31.imag','M33.real','M33.imag')\\n fmtarr = ['bo-','ro-','go-','co-','mo-','yo-','ko-']\\n pl.figure(figsize=(17,13))\\n for i in range(7):\\n pl.subplot(7,1,i+1)\\n pl.errorbar(ind,betaAll[i],yerr = betaErrAll[i],fmt=fmtarr[i])\\n if i == 0:\\n pl.title('x: '+str(hdu[0].header['x'])+' y: '+str(hdu[0].header['y'])+' z: '+str(hdu[0].header['z'])+' tilt: '+str(hdu[0].header['theta'])+' fwhm: '+str(hdu[0].header['s_fwhm'])+' e1: '+str(hdu[0].header['e1'])+' e2: '+str(hdu[0].header['e2']))\\n pl.grid()\\n pl.xlim(-1,21)\\n if i ==0:\\n pl.ylim(-50,100)\\n elif i ==1:\\n pl.ylim(-10,10)\\n elif i ==2:\\n pl.ylim(-10,10)\\n elif i == 3:\\n pl.ylim(-0.01,0.01)\\n elif i == 4:\\n pl.ylim(-0.01,0.01)\\n elif i ==5:\\n pl.ylim(-20,20)\\n elif i == 6:\\n pl.ylim(-20,20)\\n pl.xticks(ind,('','','','','','','','','','','','','','','','','','','',''))\\n pl.ylabel(momname[i])\\n pl.xticks(ind,('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20'))\\n pl.xlabel('Zernike Coefficients')\\n return betaAll,betaErrAll\",\n \"def display_accounts_details():\\n return Records.display_records()\",\n \"def __prcTyp_findOidSortedCandidateExpPrcInsOidList(self, prcTypInfo,\\n lastTradeIsoDate):\\n assert isinstance(prcTypInfo, FDelPriceUtil.PRICE_TYPE_INFO)\\n # All instrument oid in the trade table.\\n tradeFilter = ''\\n if lastTradeIsoDate:\\n tradeFilter += (\\n ' OR (i.insaddr IN ( ' # (a.2)\\n 'SELECT t.insaddr '\\n 'FROM trade t '\\n 'WHERE t.time < \\\\'{0}\\\\' '\\n 'GROUP BY t.insaddr) '\\n ')'.format(lastTradeIsoDate)\\n )\\n\\n candExpUnTrdInsOidQry = (\\n 'SELECT i.insaddr '\\n 'FROM instrument i '\\n 'WHERE ( '\\n '(i.insaddr NOT IN ( ' # (a.1)\\n 'SELECT t.insaddr '\\n 'FROM trade t '\\n 'GROUP BY t.insaddr) '\\n '){5} ) '\\n 'AND i.generic = 0 ' # (b)\\n 'AND i.instype <> {0} ' # (c)\\n 'AND (i.instype = {1} ' # (d)\\n 'OR (i.exp_day > \\\\'1900-01-01\\\\' '\\n 'AND i.exp_day < \\\\'{2}\\\\' '\\n 'AND i.open_end <> {3})) '\\n 'AND i.insaddr IN (SELECT p.insaddr ' # (e)\\n 'FROM {4} p '\\n 'GROUP BY p.insaddr) '\\n 'ORDER BY i.insaddr'.format(\\n FDelPriceUtil.INS_TYPE_ENUM_CURR,\\n FDelPriceUtil.INS_TYPE_ENUM_COMBINATION,\\n self.__strIsoDateToday,\\n FDelPriceUtil.OPEN_END_STATUS_ENUM_OPEN_END,\\n prcTypInfo.tblName,\\n tradeFilter))\\n result, msDuration = FDelPriceUtil.timedAelDbSql(\\n candExpUnTrdInsOidQry)\\n candExpUnTrdInsOidList = [row[0] for row in result]\\n\\n self._logDebug(' Found {0} instruments referred by '\\n 'the {1}s. [QueryTime = {2} ms]'.format(\\n len(candExpUnTrdInsOidQry), prcTypInfo.desc, msDuration))\\n return candExpUnTrdInsOidList\",\n \"def load_cci9():\\n cci9 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci2015.csv'),skiprows=1)\\n cci9.columns = [i.strip('\\\\'') for i in cci9.columns]\\n \\n for col in cci9.columns:\\n cci9.loc[:,col] = cci9[col].str.strip('\\\\'')\\n cci9 = cci9.replace(r'^\\\\s*$', np.nan, regex=True)\\n cci9.columns=[i.replace('CATEGORY DESCRIPTION','CHRONIC') for i in cci9.columns]\\n\\n dict_bodysystem=[\\n ('1' ,'Infectious and parasitic disease'),\\n ('2' ,'Neoplasms'),\\n ('3' ,'Endocrine, nutritional, and metabolic diseases and immunity disorders'),\\n ('4' ,'Diseases of blood and blood-forming organs'),\\n ('5' ,'Mental disorders'),\\n ('6' ,'Diseases of the nervous system and sense organs'),\\n ('7' ,'Diseases of the circulatory system'),\\n ('8' ,'Diseases of the respiratory system'),\\n ('9' ,'Diseases of the digestive system'),\\n ('10','Diseases of the genitourinary system'),\\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\\n ('12','Diseases of the skin and subcutaneous tissue'),\\n ('13','Diseases of the musculoskeletal system'),\\n ('14','Congenital anomalies'),\\n ('15','Certain conditions originating in the perinatal period'),\\n ('16','Symptoms, signs, and ill-defined conditions'),\\n ('17','Injury and poisoning'),\\n ('18','Factors influencing health status and contact with health service'),\\n ]\\n \\n cci9 = cci9.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\\n \\n cci9.loc[:,'ICD-9-CM CODE'] = cci9['ICD-9-CM CODE'].str.replace(' ','')\\n \\n return cci9\",\n \"def print_row(n, version, sz, last_modified):\\n print (get_pad1(n) + str(n) + \\\". \\\" + sz + \\\" \\\" * 13 + last_modified)\",\n \"def citations(request, format='csv'):\\n \\n filtered = False # make sure we're filtering by something\\n records = Record.objects.distinct() #.order_by('gabi_acc_number')\\n \\n \\n # accession number\\n if request.GET.get('gabi_acc_number'):\\n filtered = True\\n records = records.filter(gabi_acc_number=request.GET.get('gabi_acc_number').upper())\\n \\n # species AND bentity\\n if request.GET.get('species'):\\n filtered = True\\n if request.GET.get('species'): \\n records = records.filter(valid_species_name_id=request.GET.get('species').capitalize())\\n if request.GET.get('bentity_id'):\\n records = records.filter(bentity_id=request.GET.get('bentity_id').upper())\\n \\n # lat and lon\\n if request.GET.get('lat') and request.GET.get('lon'):\\n filtered = True\\n if request.GET.get('lat'):\\n records = records.filter(lat=request.GET.get('lat'))\\n if request.GET.get('lon'):\\n records = records.filter(lon=request.GET.get('lon'))\\n \\n # status\\n if request.GET.get('status'):\\n records = records.filter(status=request.GET.get('status')[0].upper())\\n \\n \\n # error message if the user didn't supply an argument to filter the records\\n if not filtered: \\n return errorResponse(\\\"Please supply at least one these argument-combinations: 'gabi_acc_number', ('species' and 'bentity_id'), or ('lat' and 'lon').\\\", format, {'records': []})\\n \\n \\n # fetch all the bentitites at once, so we don't have to hit the database once for each record\\n records = records.prefetch_related('bentity') \\n \\n output_objects = [{\\n 'gabi_acc_number': r.gabi_acc_number,\\n 'species': r.valid_species_name_id,\\n 'bentity_id': r.bentity_id,\\n 'bentity_name': r.bentity.bentity,\\n 'status': r.status,\\n 'type_of_data': r.type_of_data,\\n 'lat': r.lat,\\n 'lon': r.lon, \\n 'citation': r.citation,\\n } for r in records]\\n \\n \\n \\n if format == 'csv':\\n return CSVResponse(output_objects, ('gabi_acc_number', 'species', 'bentity_id', 'bentity_name', 'lat', 'lon', 'status', 'type_of_data', 'citation'))\\n \\n else:\\n return JSONResponse({'records': output_objects})\",\n \"def print_gene_list(gene_entries):\\n\\n for g in gene_entries:\\n if g:\\n print(g.contig, g.start, g.end,\\n g.strand, g.gene_id, g.name, sep='\\\\t')\",\n \"def print_records_like(self, exploitability):\\n if not exploitability:\\n query = \\\"SELECT * FROM Crashes\\\"\\n\\n else:\\n query = \\\"SELECT * FROM Crashes WHERE Exploitable = '%s';\\\" % exploitability\\n\\n display_fmt = \\\"%d %s %s %s %s 0x%x %s %s %s %s\\\"\\n\\n try:\\n self.cur.execute(query)\\n rows = self.cur.fetchall()\\n\\n if not rows:\\n if not exploitability:\\n print 'Could not find records with any exploitability level'\\n\\n else:\\n print 'Could not find records with exploitability:', exploitability\\n\\n return\\n\\n for row in rows:\\n (idx, n, v, c, e, i, s, cl, ex, f) = row\\n values = row[:5] + (int(i),) + row[6:]\\n print display_fmt %values\\n\\n except sqlite.Error, e:\\n print 'Unable to display records.'\\n print 'Exception follows:'\\n print e\",\n \"def registry_to_text(registry):\\n output = [CREDITS.format(dt=datetime.utcnow().isoformat())]\\n for collector, samples in registry.get_samples():\\n output.append(collector.text_export_header)\\n for sample in samples:\\n output.append(sample.export_str)\\n output.append(\\\"\\\")\\n return \\\"\\\\n\\\".join(output)\",\n \"def txt_of_sources_in_run (ins, exp, run) :\\n list_of_detectors = detectors(ins, exp, run)\\n txt = '\\\\nList of detectors for inst: %s exp: %s run %d' % (ins, exp, run)\\n for det in list_of_detectors :\\n txt += '\\\\n'+det\\n return txt\",\n \"def exon_line_print(temp_line, trx_exons, parent, ftype):\\n for ex in trx_exons:\\n temp_line[2] = ftype\\n temp_line[3] = str(ex[0])\\n temp_line[4] = str(ex[1])\\n temp_line[8] = 'Parent='+parent\\n print '\\\\t'.join(temp_line)\",\n \"def demo1():\\n \\n print \\\"loading the NIPS publications dataset...\\\"\\n pubs = loadPubs('pubs_nips')\\n \\n # get all papers that mention mnist\\n p = [x for x in pubs if 'mnist' in x.get('pdf_text',{})]\\n print \\\"titles of papers that mention MNIST dataset:\\\"\\n for x in p:\\n print x['title']\\n print \\\"total of %d publications mention MNIST.\\\" %(len(p),)\\n \\n # sort by number of occurences\\n occ = [(x['year'], x['pdf']) for i,x in enumerate(p) if x['pdf_text']['mnist']>1]\\n occ.sort(reverse = True)\\n \\n # open the top 3 latest in browser\\n print \\\"opening the top 3...\\\"\\n openPDFs([x for year,x in occ[:3]])\",\n \"def test_topcit(self):\\n inv_search = \\\"cited:50->999999999\\\"\\n spi_search = \\\"find topcit 50+\\\"\\n self._compare_searches(inv_search, spi_search)\",\n \"def show_data():\\n with open(\\\"ScansforStudents.csv\\\", \\\"rU\\\") as csvfile:\\n reader = csv.reader(csvfile, delimiter = ',', quotechar = '|')\\n k = 0\\n for row in reader:\\n print(row)\\n if k == 100:\\n break\\n k += 1\",\n \"def test_load_edition_data(self):\\n key = \\\"OL1234W\\\"\\n responses.add(\\n responses.GET,\\n \\\"https://openlibrary.org/works/OL1234W/editions\\\",\\n json={\\\"hi\\\": \\\"there\\\"},\\n )\\n result = self.connector.load_edition_data(key)\\n self.assertEqual(result, {\\\"hi\\\": \\\"there\\\"})\",\n \"def insert_repnr_fielddesc(inst):\\n\\telname = \\\"rn\\\"\\n\\telmarccode = \\\"037__a\\\"\\n\\teltype = \\\"I\\\"\\n\\telsize = \\\"30\\\"\\n\\telrows = \\\"\\\"\\n\\telcols = \\\"\\\"\\n\\telmaxlength = \\\"\\\"\\n\\telval = \\\"%s-<YYYY>-?????\\\" %(inst.upper())\\n\\telfidesc = \\\"\\\" \\n\\telmodifytext = \\\"\\\"\\n\\tinsert_element_details(elname, elmarccode, eltype, elsize, elrows, elcols, elmaxlength, elval, elfidesc, elmodifytext) # insert into sbmFIELDDESCR\\t\",\n \"def additional(self):\\r\\n data = self.extract_data()\\r\\n st = \\\"\\\"\\r\\n for i in data[8:-5]:\\r\\n st += i + \\\"\\\\n\\\"\\r\\n return st\",\n \"def comment_for_run (ins, exp, runnum) :\\n return dict_of_recs_for_run(ins, exp, runnum)['comment']\",\n \"def print_data(self):\\n for chain, gen in self.generations.items():\\n print('Generations for chain %s: %d' % (chain, gen))\\n print('Log likelihood effective size: %d' % self.loglik_effsize)\\n print('Log likelihood relative difference: %f' % self.loglik_rel_diff)\\n print('Max diff: %f' % self.max_diff)\",\n \"def displayRecord(self):\\n counter = 0\\n selectedData = self.controller.chooseRecord(\\\"Enter how many records you want to display: \\\")\\n if selectedData != 0 and len(self.dto.getRecord()) >= selectedData >= 0:\\n for line in self.dto.getRecord():\\n print(line.__dict__) # cast the object to dictionary\\n counter = counter + 1\\n if counter == selectedData:\\n break\\n else:\\n print(\\\"Please choose number within the number of records.\\\")\",\n \"def _info(self, N):\\n IDs = self.chemicals.IDs\\n data = self.data\\n IDs, data = nonzeros(IDs, data)\\n N_IDs = len(IDs)\\n if N_IDs == 0:\\n return f\\\"{type(self).__name__}: (all zeros)\\\"\\n else:\\n basic_info = f\\\"{type(self).__name__}:\\\\n\\\"\\n new_line = '\\\\n'\\n data_info = ''\\n lengths = [len(i) for i in IDs]\\n maxlen = max(lengths) + 1\\n N_max = N or tmo.Stream.display_units.N\\n too_many_chemicals = N_IDs > N_max\\n N = N_max if too_many_chemicals else N_IDs\\n for i in range(N):\\n spaces = ' ' * (maxlen - lengths[i])\\n if i != 0:\\n data_info += new_line\\n data_info += IDs[i] + spaces + f' {data[i]:.3g}'\\n if too_many_chemicals:\\n data_info += new_line + '...'\\n return (basic_info\\n + data_info)\",\n \"def getexperimentinfo(expid):\\n rdata = {}\\n rdata['expId'] = expid\\n res = requests.get(scbd_server_address + '/experiments/get_details', json=rdata)\\n if res.status_code == 200:\\n outstr = ''\\n for cres in res.json()['details']:\\n outstr += cres[0] + ':' + cres[1] + '<br>'\\n # details=res.json()['details']\\n return outstr\\n return []\",\n \"def identify(self):\\n print(self.query(\\\"*IDN?\\\\n\\\"))\",\n \"def contents(self, n, m):\\n str = \\\"\\\"\\n subset = self.getlines(n, m)\\n for line in subset:\\n str = str + line + \\\"\\\\n\\\"\\n return str.rstrip(\\\"\\\\n\\\")\",\n \"def display_finds(r, width = 500):\\n base = \\\"https://www.nb.no/items/\\\"\\n rows = []\\n pages = []\\n urns = []\\n urnstore = []\\n for row in r:\\n \\n urnstring = re.findall(\\\"URN[^/]*\\\", row)[0]\\n prefix, doctyp, urn, page = urnstring.split('_')\\n title = f\\\"{prefix}_{doctyp}_{urn}\\\" #urnstring #iiif_manifest(f\\\"{prefix}_{doctyp}_{urn}\\\")['label']\\n urnpage = f\\\"{urn}_{page}\\\"\\n if not urn in urnstore:\\n urns.append((urn, title))\\n urnstore.append(urn)\\n if not urnpage in pages:\\n rows.append((urn, int(page), f\\\"[]({base}{prefix}_{doctyp}_{urn}?page={int(page) + 1})\\\"))\\n pages.append(f\\\"{urn}_{page}\\\")\\n \\n \\n result = pd.DataFrame(rows, columns=['urn', 'page', 'link'])\\n return result, urns\",\n \"def listing(self, n=5):\\n # TODO: Currently, just as with ipdb on the command line,\\n # there is no support for displaying code in Cython files.\\n # This shouldn't be too hard to add.\\n curframe = self.curframe()\\n filename = curframe.f_code.co_filename\\n lineno = curframe.f_lineno\\n import linecache\\n w = []\\n for i in range(lineno-n, lineno+n+1):\\n z = linecache.getline(filename, i, curframe.f_globals)\\n if z: w.append(('--> ' if i ==lineno else ' ') + '%-5s'%i + z)\\n code = ''.join(w)\\n if not code.strip():\\n code = '(code not available)'\\n\\n # This is a hideous hack to get around how the notebook \\\"works\\\".\\n # If the output of anything contains the string TRACEBACK then\\n # it will get mangled. So we replace TRACEBACK in our code block\\n # by the harmless version with the colon missing. This sucks.\\n from sagenb.notebook.cell import TRACEBACK\\n code = code.replace(TRACEBACK, TRACEBACK[:-1])\\n\\n # Create a hyperlink to the file, if possible.\\n i = filename.rfind('site-packages/sage')\\n if i != -1:\\n fname = filename[i+len('site-packages/sage')+1:].rstrip('/')\\n file = '<a href=\\\"/src/%s\\\" target=\\\"_new\\\">src/sage/%s</a>'%(fname,fname)\\n else:\\n file = filename\\n\\n import cgi\\n t = \\\"\\\"\\\"%s<hr>> %s\\\"\\\"\\\"%(cgi.escape(code), file)\\n return t\",\n \"def get_curation_row(self, external_id, external_entry) -> Sequence[str]: # noqa:DAR202\\n rv = []\\n for k in self.curation_header:\\n value = external_entry.get(k)\\n if value is None:\\n rv.append(\\\"\\\")\\n elif isinstance(value, str):\\n rv.append(value.strip())\\n elif isinstance(value, bool):\\n rv.append(\\\"true\\\" if value else \\\"false\\\")\\n elif isinstance(value, (list, tuple, set)):\\n rv.append(\\\"|\\\".join(sorted(v.strip() for v in value)))\\n else:\\n raise TypeError(f\\\"unexpected type in curation header: {value}\\\")\\n return rv\",\n \"def get_c_detailname(self, teacher_id, course_id):\\n seq_list = self.get_seqnumlist(course_id, teacher_id)\\n print seq_list\\n if seq_list:\\n seq_id = max(seq_list)\\n file_name = 'OutData/' + str(teacher_id) + '_' + str(course_id) + '_' + \\\\\\n str(seq_id) + '_' + 'checkinDetail.csv'\\n return file_name\\n else:\\n print \\\"查找考勤次序号错误，可能该教师还未生成考勤记录\\\"\",\n \"def PrintEnergy(self, n_iter):\\n e = self.efile\\n t = self.traj\\n grad = t.grad[n_iter]\\n disp = t.coords[n_iter] - t.coords[max(0, n_iter-1)]\\n delta_e = t.energy[n_iter] - t.energy[max(0, n_iter-1)]\\n gmax = numpy.amax(grad)\\n dmax = numpy.amax(disp)\\n grms = math.sqrt(numpy.mean(grad**2))\\n drms = math.sqrt(numpy.mean(disp**2))\\n conv_str = [' ' for i in range(5)]\\n if n_iter > 0:\\n conv_str[0] = '*' if abs(delta_e) < self.conv_delta_e else ' '\\n conv_str[1] = '*' if gmax < self.conv_grad_max else ' '\\n conv_str[2] = '*' if grms < self.conv_grad_rms else ' '\\n conv_str[3] = '*' if dmax < self.conv_disp_max else ' '\\n conv_str[4] = '*' if drms < self.conv_disp_rms else ' '\\n pstr = '%3i' % (n_iter)\\n pstr += ' %18.12f' % (t.energy[n_iter])\\n pstr += ' %10.3e%s' % (delta_e, conv_str[0])\\n pstr += ' %9.3e%s' % (gmax, conv_str[1])\\n pstr += ' %9.3e%s' % (grms, conv_str[2])\\n pstr += ' %9.3e%s' % (dmax, conv_str[3])\\n pstr += ' %9.3e%s' % (drms, conv_str[4])\\n e.write('%s\\\\n' % (pstr))\",\n \"def print_data_list(self):\\n print('\\\\n{0}'.format(self.webDataFrame))\",\n \"def get_kit_descr_collapse(self):\\n self.ensure_one()\\n if not self.is_kit_invoice_line:\\n return \\\"\\\"\\n comp_obj = self.env['account.invoice.line.comp']\\n components = comp_obj.search([('invoice_line_id','=',self.id),('is_kit_invoice_comp','=',False)]) # get all comps that are not kits\\n ir_model_data = self.env['ir.model.data']\\n units_id = ir_model_data.get_object_reference('product','product_uom_unit')[1]\\n res = []\\n for comp in components:\\n qty_int_val = int(comp.qty_total)\\n if comp.uom_id.id == units_id: # uom is units, no need to print it\\n qty = str(qty_int_val) # qty is an int because it's in units\\n comp_str = comp.name + \\\": \\\" + qty\\n else:\\n if qty_int_val == comp.qty_total:\\n qty = str(qty_int_val)\\n else:\\n qty = str(comp.qty_total)\\n comp_str = comp.name + \\\": \\\" + qty + \\\" \\\" + comp.uom_id.name\\n res.append(comp_str)\\n res = \\\" (\\\" + \\\", \\\".join(res) + \\\")\\\"\\n return res\",\n \"def print_quantities(shared):\\n from .interactive import window_width\\n field_width = (window_width / 2)\\n titles = []\\n\\n print()\\n print('Defined data constants:')\\n if shared.data_constants:\\n for name, value in shared.data_constants.items():\\n print(' {} = {}'.format(name, value))\\n else:\\n print(' None')\\n print()\\n\\n print('Extra quantities:')\\n\\n are_any = False\\n for i, field_mapping in enumerate(shared.field_mappings):\\n if field_mapping.extra is not None:\\n are_any = True\\n title = field_mapping.title\\n expression = field_mapping.extra\\n print(' {}) {} = {}'.format(i+1, title, expression))\\n if not are_any:\\n print(' None')\\n print()\",\n \"def print_all(self):\\n if not request:\\n raise exceptions.Warning(_(''), _(''))\\n session_id = request.session.sid\\n config = self.env['ir.config_parameter']\\n addons_url = config.get_param('addons_path')\\n phantomjs_path = config.get_param('phantomjs_path')\\n phantomjs_path = 'phantomjs' if not phantomjs_path else phantomjs_path\\n print_url = self.env.context.get('protocol_url', False)\\n if print_url:\\n print_urls = [print_url]\\n else:\\n print_urls = self._get_print_urls()\\n if not print_urls:\\n return\\n phantom = [\\n phantomjs_path,\\n addons_url +\\n '/quality_protocol_report/static/src/js/phantom_url_to_pdf.js',\\n session_id, \\\"/tmp\\\"] + print_urls\\n process = subprocess.Popen(phantom)\\n process.communicate()\\n filenames = []\\n for url in print_urls:\\n fname = url.replace('/', '').replace(':', '')\\n weight_pos = fname.find('?weight=')\\n if weight_pos > -1:\\n fname = fname[weight_pos+8:weight_pos+10] + '-' + fname[:weight_pos]\\n filenames.append('/tmp/' + fname + '.pdf')\\n filepath = self._merge_pdf(sorted(filenames))\\n fildecode = open(filepath, 'r')\\n encode_data = fildecode.read()\\n fildecode.close()\\n active_model = self.env.context.get('active_model', False)\\n active_id = self.env.context.get('active_id', False)\\n ungrouped_also = self.env.context.get('print_ungrouped_also', False)\\n if active_model and active_id and not ungrouped_also:\\n active_name = self.env[active_model].browse([active_id]).name\\n else:\\n dt = fields.Datetime.context_timestamp(self, datetime.now())\\n active_name = dt.strftime('%d-%m-%Y_%Hh%M')\\n filename = 'protocolo.pdf' if print_url else \\\\\\n 'protocolos_' + str(active_name).lower() + '.pdf'\\n attachment_data = {\\n 'name': filename,\\n 'datas_fname': filename,\\n 'datas': base64.b64encode(encode_data),\\n 'res_model': active_model,\\n 'res_id': 0 if print_url else self.env.context.get('active_id', False),\\n }\\n self.env['ir.attachment'].search(\\n [('name', '=', attachment_data['name']),\\n ('res_id', '=', attachment_data['res_id']),\\n ('res_model', '=', attachment_data['res_model'])]).unlink()\\n attachment = self.env['ir.attachment'].create(attachment_data)\\n\\n filenames.append(filepath)\\n for my_file in filenames:\\n os.remove(my_file)\\n\\n if print_url:\\n return {\\n 'type': 'ir.actions.act_url',\\n 'url': '/web/binary/saveas?model=ir.attachment&field=datas' +\\n '&filename_field=name&id=%s' % (attachment.id),\\n 'target': 'self',\\n }\\n else:\\n return {'type': 'ir.actions.act_window_close'}\",\n \"def test_pds_txt(self):\\n lcurve_txt = os.path.join(self.datadir,\\n 'lcurve_txt_lc' +\\n HEN_FILE_EXTENSION)\\n command = '{0} -f 128'.format(lcurve_txt)\\n hen.fspec.main(command.split())\",\n \"def DicResNameNum(group, type, typeList, eraseList, typeDictionary, pick):\\n typeList = sorted(list(set(typeList).difference(eraseList)))\\n globals().update({type+str(\\\"List\\\") : typeList})\\n pdbARMTemp = pdbName[:-3]+chainName+'.temp'\\n\\n with open(pdbARMTemp) as pdbTemp:\\n for line in pdbTemp:\\n for i in range(0, len(typeList)):\\n if \\\"ATOM\\\" in line and typeList[i] in line:\\n typeDictionary.update({ typeList[i] : line.split()[5]} )\\n\\n globals().update({type+str(\\\"Dic\\\") : typeDictionary})\\n\\n ChooseNumOption(typeList,type, type, '\\\\n The following are identified as possible', 'in the '+str(pdbName)+ ' \\\\\\nfile:', 'is selected as the '+type, pick, typeDictionary)\",\n \"def tcvs(self): \\n return self._link_reg.tcvs\",\n \"def disp_found(num):\\n from x84.bbs import getterminal, echo\\n term = getterminal()\\n echo(u''.join((u'\\\\r',\\n term.bold_white(u'%d' % (num,)),\\n term.yellow(u' lOCAtiON%s diSCOVEREd ' %\\n (u's' if num > 1 else u'')),\\n term.bold_black(u'...'),)))\",\n \"def whatareks():\\n\\n print(\\\"loading data\\\")\\n dataset = datagen.DataSet(datapath)\\n dataset.load()\\n\\n for k, ens in enumerate(dataset.ensembles):\\n\\n print(\\n f\\\"k: {k} --> b={ens.b}, N={ens.grid_shape[0]}, iternum={ens.iternum}\\\")\",\n \"def test_text_classifier_get_details_all(self):\\n pass\",\n \"def display_nbr(ctx, as_port=False):\\n ctl = ctx.ctl\\n\\n jobs = ctl('list-avail', '--partition', 'main', flatten=False)\\n\\n if len(jobs) == 0:\\n click.echo('No jobs running', err=True)\\n sys.exit(1)\\n\\n for job in jobs:\\n info = collect_vnc_info(ctl, job['id'], ctx.ssh_cfg)\\n\\n if as_port:\\n click.echo('%d' % info['port'])\\n else:\\n click.echo(':%d' % info['display'])\",\n \"def print_detailed_RISCC_data(self, OUTPUT=sys.stdout, sort_data_by=None, max_distance=MAX_POSITION_DISTANCE):\\n # TODO docstring!\\n\\n # TODO should probably add header\\n # TODO add annotation!\\n # TODO change this to be a proper tab-separated file?\\n\\n ### sort all mutants by position or readcount (for single datasets only for now), or don't sort at all\\n sorted_mutants = self._sort_data(sort_data_by)\\n\\n ### Quick summary\\n N_total = len(self)\\n # using sum because you can't do len on generators\\n N_single_genomic = sum(1 for m in self if m.RISCC_N_distinct_regions(max_distance)[0]==1)\\n N_single_chrom = sum(1 for m in self if m.RISCC_N_genomic_chromosomes==1)\\n N_cassette = sum(1 for m in self if m.RISCC_N_distinct_regions(max_distance)[1]>0)\\n OUTPUT.write(\\\"# %s mutants total; %s have one genomic location; \\\"%(N_total, \\n value_and_percentages(N_single_genomic, [N_total]))\\n +\\\"%s have locations on only one genomic chromosome; \\\"%(value_and_percentages(N_single_chrom, [N_total]))\\n +\\\"%s also have one or more cassette locations.\\\\n\\\"%(value_and_percentages(N_cassette, [N_total])) )\\n\\n # TODO add info on how many actually have ANY genomic-side reads! And out of those, how many are confirmed vs not.\\n\\n ### Print data for all mutants\\n for mutant in sorted_mutants:\\n mutant.RISCC_print_detail(OUTPUT, max_distance)\",\n \"def getCitations (self, doc, verbose = False):\\r\\n if verbose is True:\\r\\n print \\\"getting citations for document \\\" +str(doc) \\r\\n return self.citations[int(doc)]\",\n \"def uci_pen_digits(redownload: bool = False) -> Dataset:\\n return Dataset.get(\\\"uci_pen_digits\\\", redownload=redownload)\",\n \"def reporter(self, lnum, col, text, check):\\n self.output.append([lnum, col, text])\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def tblrowsscans(self):\\n sql = '''select to_char(value, 'FM99999999999999990') retvalue from \\n v$sysstat where name = 'table scan rows gotten' '''\\n self.cur.execute(sql)\\n res = self.cur.fetchall()\\n for i in res:\\n print(i[0])\",\n \"def display_cord_seq():\\n \\n import os\\n choice = input('Enter the name of the file: ')\\n filepath = os.path.join('/home/njesh/python-mini-project-JaneNjeri/Data', choice)\\n with open(filepath, 'r') as file:\\n for line in file:\\n if line[:4] == 'ATOM':\\n line_split = line.split()[3:4]\\n print(line_split)\\n return file\",\n \"def _compute_credit_control_lines_html(self):\\n for comm in self:\\n comm.credit_control_lines_html = (\\n self.env[\\\"ir.qweb\\\"]\\n .render(\\n \\\"altinkaya_credit_control.report_credit_control_lines\\\",\\n values={\\\"doc\\\": self},\\n )\\n .decode(\\\"utf8\\\")\\n )\\n return True\",\n \"def print_splitted_fp_in_csv(use_atg = True):\\n delimiter = \\\" \\\"\\n cids = load_cids(\\\"data/fp_370_atb.txt\\\")\\n print(\\\"cid, first 110, second 110, last 150\\\")\\n for cid in sorted(cids):\\n if use_atg:\\n print(\\\"%s,%s,%s,%s\\\" %(cid,\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, first_110_atg_dataloader(cid))),\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, second_110_atg_dataloader(cid))),\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, last_150_atg_dataloader(cid))),\\n ))\\n else:\\n print(\\\"%s,%s,%s,%s\\\" %(cid,\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, first_110_atb_dataloader(cid))),\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, second_110_atb_dataloader(cid))),\\n delimiter.join(map(lambda d: \\\"%.2f\\\" %d, last_150_atb_dataloader(cid))),\\n ))\",\n \"def help_description():\\n # for ain\\n print(\\\"--------TABLE FOR AIN(AIN4=GND)-------\\\")\\n print(\\\"--------------------------------------\\\")\\n print(\\\"| CODE (10) | CODE (2) | AINP | AINN |\\\")\\n for i in range(8):\\n print(\\\"| {} | {} | AIN{} | AIN{} |\\\".format(str(i), bin(i)[2:].zfill(3), DICT_AIN[i][0],\\n DICT_AIN[i][1]))\\n print(\\\"--------------------------------------\\\")\\n print(\\\"------------TABLE FOR FSR------------\\\")\\n print(\\\"--------------------------------------\\\")\\n print(\\\"| CODE (10) | CODE (2) | FSR |\\\")\\n for i in range(6):\\n print(\\\"| {} | {} | {} |\\\".format(str(i), bin(i)[2:].zfill(3), DICT_FSR[i]))\\n print(\\\"--------------------------------------\\\")\\n print(\\\"------------TABLE FOR RATE------------\\\")\\n print(\\\"--------------------------------------\\\")\\n print(\\\"| CODE (10) | CODE (2) | RATE |\\\")\\n for i in range(8):\\n print(\\\"| {} | {} | {} |\\\".format(str(i), bin(i)[2:].zfill(3), DICT_RATE[i].rjust(7, ' ')))\\n print(\\\"--------------------------------------\\\")\",\n \"def view(args):\\n print(\\\"List of all available phonebooks:\\\")\\n for file in glob.glob(\\\"*.ph\\\"):\\n print(file)\",\n \"def python_data_printer(cur):\\n # Print a header.\\n for fieldDesc in cur.description:\\n print (fieldDesc[fdb.DESCRIPTION_NAME].ljust(fieldDesc[fdb.DESCRIPTION_DISPLAY_SIZE]),end=' ')\\n print('')\\n for fieldDesc in cur.description:\\n print (\\\"-\\\" * max((len(fieldDesc[fdb.DESCRIPTION_NAME]),fieldDesc[fdb.DESCRIPTION_DISPLAY_SIZE])),end=' ')\\n print('')\\n # For each row, print the value of each field left-justified within\\n # the maximum possible width of that field.\\n fieldIndices = range(len(cur.description))\\n for row in cur:\\n for fieldIndex in fieldIndices:\\n fieldValue = row[fieldIndex]\\n if not isinstance(fieldValue,types.StringTypes):\\n fieldValue = str(fieldValue)\\n if isinstance(fieldValue,types.UnicodeType):\\n fieldValue = fieldValue.encode('utf8')\\n fieldMaxWidth = max((len(cur.description[fieldIndex][fdb.DESCRIPTION_NAME]),cur.description[fieldIndex][fdb.DESCRIPTION_DISPLAY_SIZE]))\\n print (fieldValue.ljust(fieldMaxWidth),end=' ')\\n print('')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5283312","0.47920322","0.47803637","0.46489966","0.46426672","0.46010867","0.45646727","0.4531334","0.45046958","0.4470535","0.44359282","0.44248855","0.4385767","0.43826196","0.43532607","0.43172917","0.43149775","0.43144825","0.43024385","0.4301603","0.42922202","0.42842722","0.4282375","0.42764685","0.42700464","0.42638078","0.4261497","0.42581743","0.42551786","0.42501983","0.42482698","0.42469406","0.42408553","0.42359713","0.42348632","0.42313218","0.42268392","0.42262298","0.42191252","0.42060125","0.42045453","0.42044494","0.41836995","0.4172519","0.41623205","0.41610566","0.41481245","0.41415814","0.41397533","0.41307396","0.4128261","0.41278008","0.4121033","0.41194984","0.41158807","0.41151142","0.41036677","0.41017333","0.410051","0.40958482","0.4094175","0.40910187","0.4083987","0.40829158","0.40803778","0.4079647","0.4077138","0.407229","0.40689912","0.40672117","0.40584406","0.40558642","0.405358","0.40467817","0.40412724","0.4038725","0.4033926","0.40332705","0.40311953","0.40245226","0.4022146","0.4016694","0.40164414","0.4009582","0.40089133","0.40062642","0.40058678","0.4002617","0.4002562","0.39994156","0.39984825","0.3994285","0.3991805","0.39911175","0.39877775","0.39864373","0.39863876","0.39853168","0.3981765","0.39736915"],"string":"[\n \"0.5283312\",\n \"0.47920322\",\n \"0.47803637\",\n \"0.46489966\",\n \"0.46426672\",\n \"0.46010867\",\n \"0.45646727\",\n \"0.4531334\",\n \"0.45046958\",\n \"0.4470535\",\n \"0.44359282\",\n \"0.44248855\",\n \"0.4385767\",\n \"0.43826196\",\n \"0.43532607\",\n \"0.43172917\",\n \"0.43149775\",\n \"0.43144825\",\n \"0.43024385\",\n \"0.4301603\",\n \"0.42922202\",\n \"0.42842722\",\n \"0.4282375\",\n \"0.42764685\",\n \"0.42700464\",\n \"0.42638078\",\n \"0.4261497\",\n \"0.42581743\",\n \"0.42551786\",\n \"0.42501983\",\n \"0.42482698\",\n \"0.42469406\",\n \"0.42408553\",\n \"0.42359713\",\n \"0.42348632\",\n \"0.42313218\",\n \"0.42268392\",\n \"0.42262298\",\n \"0.42191252\",\n \"0.42060125\",\n \"0.42045453\",\n \"0.42044494\",\n \"0.41836995\",\n \"0.4172519\",\n \"0.41623205\",\n \"0.41610566\",\n \"0.41481245\",\n \"0.41415814\",\n \"0.41397533\",\n \"0.41307396\",\n \"0.4128261\",\n \"0.41278008\",\n \"0.4121033\",\n \"0.41194984\",\n \"0.41158807\",\n \"0.41151142\",\n \"0.41036677\",\n \"0.41017333\",\n \"0.410051\",\n \"0.40958482\",\n \"0.4094175\",\n \"0.40910187\",\n \"0.4083987\",\n \"0.40829158\",\n \"0.40803778\",\n \"0.4079647\",\n \"0.4077138\",\n \"0.407229\",\n \"0.40689912\",\n \"0.40672117\",\n \"0.40584406\",\n \"0.40558642\",\n \"0.405358\",\n \"0.40467817\",\n \"0.40412724\",\n \"0.4038725\",\n \"0.4033926\",\n \"0.40332705\",\n \"0.40311953\",\n \"0.40245226\",\n \"0.4022146\",\n \"0.4016694\",\n \"0.40164414\",\n \"0.4009582\",\n \"0.40089133\",\n \"0.40062642\",\n \"0.40058678\",\n \"0.4002617\",\n \"0.4002562\",\n \"0.39994156\",\n \"0.39984825\",\n \"0.3994285\",\n \"0.3991805\",\n \"0.39911175\",\n \"0.39877775\",\n \"0.39864373\",\n \"0.39863876\",\n \"0.39853168\",\n \"0.3981765\",\n \"0.39736915\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6221545"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":79598,"cells":{"query":{"kind":"string","value":"aux auf to make '100__a' out of ['100','','','a']"},"document":{"kind":"string","value":"def tagify(parsedtag):\n tag = \"\"\n for t in parsedtag:\n if t == '':\n t = '_'\n tag = tag+t\n return tag"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _alphanum_list(x):\n return [_alphanum(y) for y in x]","def clean(a):\n b = a.lower()\n # en c se encuentra la lista de todos los documentos juntos y limpios\n c = re.sub('[^A-Za-z0-9]+', ' ', b).split()\n return c","def generate_a_values() -> List[str]:\n return [\"A_1\", \"A_2\", \"A_3\"]","def short(products, field):\n\n li = []\n for p in products:\n if not p[field].replace(\" \", \"_\") in li:\n li.append(p[field].replace(\" \", \"_\"))\n return li","def str2nopunc(x):\n if isinstance(x,str):\n y = re.sub(r'[^\\w\\s]','',x.lower().strip()).replace('_','')\n else:\n y = x\n return y","def fn(s):\n if len(s) == 1: return [s] # edge case \n if s.startswith(\"0\") and s.endswith(\"0\"): return []\n if s.startswith(\"0\"): return [s[:1] + \".\" + s[1:]]\n if s.endswith(\"0\"): return [s]\n return [s] + [s[:i] + \".\" + s[i:] for i in range(1, len(s))]","def fix_extra(in_str):\n spaced = camel_re.sub(\"_\", in_str)\n return spaced.split(\"_\")[0]","def clean_ean(ean):\n return [int(x) for x in ean.translate(None, \" -\")]","def _clean(cls, value, invalid):\r\n return re.sub('_+', '_', invalid.sub('_', value))","def underscore(text):\n # return '__'+text.replace(' ','__') + '__'\n return text.replace(' ', '__')","def prepseq(self, seq):\n\n wtf = re.sub(r'\\*$', '', seq)\n return wtf","def removePunc(s,temp):\n for x in range(len(s)):\n if s[x] not in \" ,?.''\":\n temp.append(s[x].lower())","def _repair_names_unique(\n names: Sequence[str],\n quiet: bool = False,\n sanitizer: Callable = None,\n base0_: bool = None,\n) -> List[str]:\n base = int(not base0_)\n min_names = _repair_names_minimal(names)\n neat_names = [\n re.sub(r\"(?:(?<!_)_{1,2}\\d+|(?<!_)__)+$\", \"\", name)\n for name in min_names\n ]\n if callable(sanitizer):\n neat_names = [sanitizer(name) for name in neat_names]\n\n new_names = []\n changed_names = []\n for i, name in enumerate(neat_names):\n if neat_names.count(name) > 1 or name == \"\":\n name = f\"{name}__{i + base}\"\n if name != names[i]:\n changed_names.append((names[i], name))\n new_names.append(name)\n if not quiet:\n _log_changed_names(changed_names)\n return new_names","def clean_isbn(isbn):\n digits = set(\"0123456789\")\n return [int(x if x in digits else 10) for x in isbn.translate(None, \" -\")]","def __alphanum_key(self, s):\n return [self.__tryint(c) for c in re.split(\"([0-9]+)\", s)]","def alphanum_key(s):\r\n\treturn [tryint(c) for c in re.split('([0-9]+)', s)]","def normalize(item):\n item = item.lower().strip().rstrip('_')\n return item","def normalize(val):\n \n if val.find('-') != -1:\n val = val.replace('-','_')\n\n return val","def unscorize(s):\n return s.replace(\" \", \"_\")","def original_names(names):\n if len(names) > 0 and isinstance(names[0], (list, tuple)):\n names = [x[0] for x in names]\n\n return [re.sub('_+$', '', x) for x in names]","def alphanum_key(s):\n return [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]","def alphanum_key(s):\n return [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]","def alphanum_key(s):\n return [ tryint(c) for c in re.split('(\\-?[0-9]+)', s) ]","def alphanum_key(s):\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]","def alphanum_key(s):\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]","def alphanum_key(s):\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]","def alphanum_key(s):\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]","def get_naughty_string_list():\n return naughty_string_list","def clean_keeping_underscores(cls, value):\r\n return INVALID_CHARS.sub('_', value)","def __prepare_value(self, val, none_symbol=\"-\"):\n\t\t# If type is a tuple, then it is a \n\t\t# (func_unit_label, replica_id) pair. \n\t\t# Concatenate then using \"_\" symbol.\n\t\tif type(val) == type(()):\n\t\t\tval = \"_\".join(map(str, val))\n\n\t\t# Cast value to string/character type,\n\t\t# if it is not a None value\n\t\tif val is not None:\n\t\t\tval = str(val)\n\t\telse:\n\t\t\tval = none_symbol\n\n\t\t# Value processing finished\n\t\treturn val","def cast_name(key):\n special_symbols = set('{}{}'.format(punctuation, ' '))\n special_symbols.remove('_')\n new_key = ['_' if x in special_symbols else x for x in key]\n casted_key = ''.join(new_key)\n return casted_key","def _arg_to_empty(name: str) -> str:\n arg = \"{\" + name + \"}\"\n return arg","def alphanum_key(s):\n return [tryint(c) for c in re.split('([0-9]+)', s)]","def alphanum_key(element):\n return [int(c) if c.isdigit() else c for c in prog.split(element[0])]","def clean(value):\r\n return re.sub('_+', '_', INVALID_CHARS.sub('_', value))","def underscore_strings(entry):\n\tchars = \"\"\n\tin_string = False\n\tfor ch in entry:\n\t\tif ch == '\"':\n\t\t\tin_string = not in_string\n\t\telif ch == ' ' and in_string:\n\t\t\tch = '_'\n\t\tchars += ch\n\tif in_string:\n\t\traise Exception(\"unfinished string\")\n\treturn chars","def _parse_mut(subs):\n if subs!=\"0\":\n subs = [[subs.replace(subs[-2:], \"\"),subs[-2], subs[-1]]]\n return subs","def alphanum_key(s):\n return [tryint(c) for c in re.split('([0-9]+)', s)]","def get_non_digit_prefix(characters):\n prefix = []\n while characters and not characters[0].isdigit():\n prefix.append(characters.pop(0))\n return prefix","def formatlist(input_list):\n\n output_list = []\n for item in input_list:\n item = str(item)\n item = item.replace(\" \", \"_\")\n output_list.append(item)\n return output_list","def _repair_names_universal(\n names: Iterable[str], quiet: bool = False, base0_: bool = None\n) -> List[str]:\n min_names = _repair_names_minimal(names)\n neat_names = [re.sub(r\"[^\\w]\", \"_\", name) for name in min_names]\n new_names = _repair_names_unique(\n neat_names,\n quiet=True,\n sanitizer=lambda name: (\n f\"_{name}\"\n if keyword.iskeyword(name) or (name and name[0].isdigit())\n else name\n ),\n base0_=base0_,\n )\n if not quiet:\n changed_names = [\n (orig_name, new_name)\n for orig_name, new_name in zip(names, new_names)\n if orig_name != new_name\n ]\n _log_changed_names(changed_names)\n return new_names","def convertToStrings(A):\n\n for candidate in A:\n if candidate[2] >= 0:\n candidate[2] = '+' + str(candidate[2])\n else:\n candidate[2] = str(candidate[2])\n return A","def disperse_string(solid_string):\r\n normal_list = list(solid_string)\r\n return list(itertools.chain.from_iterable(zip(normal_list, [0] * len(normal_list))))","def func2(str):\n numlist=[]\n if type(str) == type(\"\"):\n for i in range(1,len(str)-1):\n numlist.append(str[i])\n return (''.join(numlist))\n else:\n return (str)","def split_alnum(s):\n def convert(x):\n try:\n return int(x)\n except ValueError:\n return x\n r = []\n digit = None\n for c in s:\n d = c.isdigit()\n if d != digit:\n digit = d\n r += [c]\n else:\n r[-1] += c\n return [convert(x) for x in r]","def sanitize(name):\n return re.sub(\"\\\\W|^(?=\\\\d)\", \"_\", name)","def natural_order(key):\n return [number_else_string(c) for c in re.split(r'(\\d+)', key)]","def _replace_none(lst, repl=\"\"):\n return ['' if v is None else v for v in lst]","def sanitize_txt(x):\n return '_'.join(smart_split(x.lower()))","def clean_identifier(self, identifier):\n if isinstance(identifier, list):\n return '%2F'.join(identifier)\n else:\n return identifier","def preprocess_data(extracted_data: List[Tuple[str, str]]) -> List[str]:\n return [f'__label__{data[0]} {clean_formatting(data[1])}' for data in extracted_data]","def fillBlanks(w_cards, b_card):\n b_card = b_card.replace('_', w_cards.replace('.', ''), 1)\n\n return b_card","def sanitize(name):\n return re.sub('\\\\W|^(?=\\\\d)', '_', name)","def sanitize_key(key):\n return re.sub('\\W|^(?=\\d)','_', key)","def gen_all_strings(word):\r\n if len(word) == 0:\r\n return ['']\r\n else:\r\n first = word[0]\r\n rest = gen_all_strings(word[1:])\r\n new = []\r\n for item in rest:\r\n if len(item) > 0:\r\n for pos in range(len(item)):\r\n new.append(item[:pos] + first + item[pos:])\r\n new.append(item + first)\r\n new.append(first)\r\n new.extend(rest)\r\n return new","def _getLilyAccidental(self):\n return \"\"","def trimname(name):\n while name[-1].isdigit():\n name = name[:-1]\n if name[-1] == '_':\n name = name[:-1]\n return name","def descorize(s):\n return s.replace(\"_\", \" \")","def kludge_gvars(mangled):\n # Loop in reverse looking for '+-', but don't run off the end\n for idx in range(len(mangled) - 1)[::-1]:\n if mangled[idx + 1] == '+-':\n reunited = ' '.join(mangled[idx:idx + 3])\n # Throw away the used elements...\n for _ in range(3):\n mangled.pop(idx)\n # Repair the list with reunited gvar string\n mangled.insert(idx, reunited)\n return mangled","def getStrs(pre,num):\n result = []\n for i in range(num):\n result.append(pre+str(i))\n return result","def encode_int_list(L):\n return str(L).replace(\" \", \"\")","def buildstrings(self):\n slist = []\n if 0 == len(self._pctnumbers):\n slist.append(self.buildonestring('NNNN'))\n else:\n for pctnumber in sorted(self._pctnumbers):\n slist.append(self.buildonestring(pctnumber))\n\n return slist","def alphanum(v):\n return re.sub('\\\\W+', '', v)","def allstrings2(alphabet, length):\n\n c = []\n for i in range(length):\n c = [[x]+y for x in alphabet for y in c or [[]]]\n\n return c","def _make_unique_kwarg_list(\n seq: Sequence[Tuple[Any, Any]]\n) -> Sequence[Tuple[Any, Any]]:\n return [\n (pair[0], \"_\".join([pair[1], str(seq[:i].count(pair))]))\n if seq.count(pair) > 1\n else pair\n for i, pair in enumerate(seq)\n ]","def remove_one_letter(word):\n for i in range(len(word)):\n yield word[:i] + word[i + 1:]","def prep_json_for_BQ_callback(self, key):\n # add _ if first character in key is numeric\n if key[0].isnumeric():\n key = \"_\" + key\n # replace \".\" or \" \" with \"_\"\n key = re.sub(r\"[. ]\",\"_\",key)\n # replace non-alphanumeric, non-\"_\" characters with nothing\n key = re.sub(r\"[^a-zA-Z0-9_]\",\"\",key)\n return key","def NFelt(a):\n return \",\".join([str(c) for c in list(a)])","def make_safe(value, delimiter):\n if isinstance(value,list):\n return delimiter.join(map(lambda x: make_safe(x, delimiter),value))\n return str(value)","def createtext(lst):\n newlst = []\n for item in lst:\n item = item.replace(\"_!\",\"\")\n newlst.append(item)\n text = ' '.join(newlst)\n # Lower-casing\n return text.lower()","def sanitize(wl):\n s = []\n for word in wl:\n for symbol in ['.', '!', ',', '\\n', '\\r', '?']:\n if symbol in word:\n s.append(symbol)\n word = word.replace(symbol, '')\n \n s.append(word)\n return s","def getUniStr(self):\n return('-'.join([\"%s-%s\"%(x.id,(x.id in self.special))\n for x in self.getNested()]))","def remove_empty(data):\n out = []\n for item in data:\n if item == '':\n continue\n out.append(item)\n return out","def flatten_param(param):\n\n param = param.replace(']', '').replace('[', '_').replace('<','').replace('>','')\n if param.startswith('_'):\n param = param.replace('_', '', 1)\n\n return param","def normalize_const(var_name):\n return var_name.lower().split('_')","def daqStringMod(self, arg):\n\t\tself.stuff = []\n\t\tfor i in arg:\n\t\t\tself.stuff.append(\"\\'\" + i + \"\\'\")\n\t\treturn self.stuff","def _clean_list(self, items):\n itemlist = list(filter(None, items))\n if len(itemlist) < 3:\n itemlist.append(\"\")\n return itemlist\n\n return itemlist","def clean_units(units):\n return [clean_unit(unit) for unit in units]","def _init_as_list(arg):\n if arg is None:\n return []\n elif isinstance(arg, str):\n return [arg]\n else:\n return arg","def __lettersToString(self, words):\r\n \r\n li = []\r\n \r\n for word in words:\r\n li.append(\"\".join(word))\r\n \r\n return li","def createWordKnown(self):\n return ''.join(['_ ' for m in range(self.wordLen)])","def generatemoves(self, x):\n returnvalue = [x[0], \"_\"] if x[0] != '_' else [x[0]]\n\n for u in range(1, len(x)):\n if x[u] == '_':\n returnvalue = [y + \"_\" for y in returnvalue]\n else:\n returnvalue = [y + x[u] for y in returnvalue] + [y + \"_\" for y in returnvalue]\n\n returnvalue.remove((\"_\" * len(x)))\n return returnvalue","def _expanded(var):\r\n if var == '*':\r\n return []\r\n n = var.split(',')\r\n n2 = [i.split('-') for i in n if i.find('-') > -1]\r\n n3 = set([int(i) for i in n if i.find('-') == -1])\r\n for i in n2:\r\n for x in range(int(i[0]), int(i[1]) + 1):\r\n n3.add(x)\r\n return n3","def repair_size_list(self, str_val):\n return [word for word in str_val[2:-2].split('\\', \\'')]","def name_supply(stems=string.ascii_lowercase, drop_zero=True):\n k = 0\n while 1:\n for a in stems:\n yield a+str(k) if (k or not drop_zero) else a\n k = k+1","def sort_nicely(l):\n l.sort(key=alphanum_key)\n return l","def lower_replace(value):\n return value.lower().replace(\" \",\"_\")","def make_unique(name, reserved_names):\n while name in reserved_names:\n name += '_'\n\n return name","def safe(s):\n return (\"\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())","def _getFieldNameList(name):\n\n\t# check symbol set\n\tif not re.search(r\"^[\\w\\d_\\-\\.]+$\", name):\n\t\treturn None\n\n\telements = name.split('.')\n\n\tname_list = []\n\tfor element in elements:\n\n\t\tif len(element) == 0:\n\t\t\tname_list.append(None)\n\t\telif element[0].isdigit():\n\t\t\ttry:\n\t\t\t\tname_list.append(int(element))\n\t\t\texcept ValueError:\n\t\t\t\treturn None\n\t\telif element[0].isalpha() or element[0] == '_':\n\t\t\tname_list.append(element)\n\t\telse:\n\t\t\treturn None\n\n\treturn name_list","def human_sort(l):\n l.sort(key=alphanum_key)\n return l","def sort_nicely(l):\r\n\tl.sort(key=alphanum_key)","def _sanitize_value(self, value):\n if isinstance(value, collections.Iterable):\n newcollection = []\n for i in value:\n if len(i) == 1:\n newcollection.append((i[0], None))\n elif len(i) >= 2:\n newcollection.append((i[0], i[1]))\n return newcollection\n else:\n return value","def prepare_value(self, value):\n if value is None:\n return value\n value = value.replace(\" \", \"\").replace(\".\", \"\")\n if value:\n return \"%s.%s.%s.%s\" % (value[0:3], value[3:7], value[7:11], value[11:])\n return value","def prepare_numbers(numbers):\n \n numb = []\n for item in numbers:\n numb.append(int(item))\n return numb","def sanitize_chars(item):\n new_item = []\n for char in item:\n if char.isalnum() or char in \" ,./-\":\n new_item.append(char)\n if new_item[0].isnumeric():\n # look for duplicated \",\", and replace the last for a dot if no dots are found\n if new_item.count(\",\") > 1 and new_item.count(\".\") == 0:\n new_item[len(new_item) - 1 - new_item[::-1].index(\",\")] = \".\"\n return \"\".join(new_item)","def prepare(x):\n try:\n ind = x.index('\"')\n except:\n ind = -1\n if ind != -1:\n d = x[ind:]\n x = x[:ind]\n x = x.split(' ')\n tmp = []\n final = []\n for i in x:\n if i:\n if i[0] != ':':\n tmp.append(i)\n for i in tmp[:4]:\n final.append(i)\n if not d:\n d = \"\".join(tmp[4:])\n final.append(d.strip())\n return final","def normalize_tokens(g):\n tokens = g.get('tokens', [])\n g['tokens'] = [re.sub(r\"\\d+\", \"0\", t.lower()) for t in tokens]\n return g","def gen_all_strings(word):\n if len(word) == 0:\n return [\"\"]\n elif len(word) == 1:\n return [\"\",word]\n else:\n result_strings = []\n first = word[0]\n rest = word[1:]\n rest_strings = gen_all_strings(rest)\n new_strings = []\n for rest_string in rest_strings:\n for dummy_index in range(len(rest_string)):\n #在首位插入\n if dummy_index == 0:\n new_string = first + rest_string\n new_strings.append(new_string)\n #在中间插入 \n else:\n new_string = rest_string[0:dummy_index] + first + rest_string[dummy_index:]\n new_strings.append(new_string)\n #在末尾插入\n new_strings.append(rest_string + first)\n \n result_strings.extend(rest_strings)\n result_strings.extend(new_strings)\n \n return result_strings","def clean(key):\n\treturn key.strip().replace('%','').replace(' ', '-')"],"string":"[\n \"def _alphanum_list(x):\\n return [_alphanum(y) for y in x]\",\n \"def clean(a):\\n b = a.lower()\\n # en c se encuentra la lista de todos los documentos juntos y limpios\\n c = re.sub('[^A-Za-z0-9]+', ' ', b).split()\\n return c\",\n \"def generate_a_values() -> List[str]:\\n return [\\\"A_1\\\", \\\"A_2\\\", \\\"A_3\\\"]\",\n \"def short(products, field):\\n\\n li = []\\n for p in products:\\n if not p[field].replace(\\\" \\\", \\\"_\\\") in li:\\n li.append(p[field].replace(\\\" \\\", \\\"_\\\"))\\n return li\",\n \"def str2nopunc(x):\\n if isinstance(x,str):\\n y = re.sub(r'[^\\\\w\\\\s]','',x.lower().strip()).replace('_','')\\n else:\\n y = x\\n return y\",\n \"def fn(s):\\n if len(s) == 1: return [s] # edge case \\n if s.startswith(\\\"0\\\") and s.endswith(\\\"0\\\"): return []\\n if s.startswith(\\\"0\\\"): return [s[:1] + \\\".\\\" + s[1:]]\\n if s.endswith(\\\"0\\\"): return [s]\\n return [s] + [s[:i] + \\\".\\\" + s[i:] for i in range(1, len(s))]\",\n \"def fix_extra(in_str):\\n spaced = camel_re.sub(\\\"_\\\", in_str)\\n return spaced.split(\\\"_\\\")[0]\",\n \"def clean_ean(ean):\\n return [int(x) for x in ean.translate(None, \\\" -\\\")]\",\n \"def _clean(cls, value, invalid):\\r\\n return re.sub('_+', '_', invalid.sub('_', value))\",\n \"def underscore(text):\\n # return '__'+text.replace(' ','__') + '__'\\n return text.replace(' ', '__')\",\n \"def prepseq(self, seq):\\n\\n wtf = re.sub(r'\\\\*$', '', seq)\\n return wtf\",\n \"def removePunc(s,temp):\\n for x in range(len(s)):\\n if s[x] not in \\\" ,?.''\\\":\\n temp.append(s[x].lower())\",\n \"def _repair_names_unique(\\n names: Sequence[str],\\n quiet: bool = False,\\n sanitizer: Callable = None,\\n base0_: bool = None,\\n) -> List[str]:\\n base = int(not base0_)\\n min_names = _repair_names_minimal(names)\\n neat_names = [\\n re.sub(r\\\"(?:(?<!_)_{1,2}\\\\d+|(?<!_)__)+$\\\", \\\"\\\", name)\\n for name in min_names\\n ]\\n if callable(sanitizer):\\n neat_names = [sanitizer(name) for name in neat_names]\\n\\n new_names = []\\n changed_names = []\\n for i, name in enumerate(neat_names):\\n if neat_names.count(name) > 1 or name == \\\"\\\":\\n name = f\\\"{name}__{i + base}\\\"\\n if name != names[i]:\\n changed_names.append((names[i], name))\\n new_names.append(name)\\n if not quiet:\\n _log_changed_names(changed_names)\\n return new_names\",\n \"def clean_isbn(isbn):\\n digits = set(\\\"0123456789\\\")\\n return [int(x if x in digits else 10) for x in isbn.translate(None, \\\" -\\\")]\",\n \"def __alphanum_key(self, s):\\n return [self.__tryint(c) for c in re.split(\\\"([0-9]+)\\\", s)]\",\n \"def alphanum_key(s):\\r\\n\\treturn [tryint(c) for c in re.split('([0-9]+)', s)]\",\n \"def normalize(item):\\n item = item.lower().strip().rstrip('_')\\n return item\",\n \"def normalize(val):\\n \\n if val.find('-') != -1:\\n val = val.replace('-','_')\\n\\n return val\",\n \"def unscorize(s):\\n return s.replace(\\\" \\\", \\\"_\\\")\",\n \"def original_names(names):\\n if len(names) > 0 and isinstance(names[0], (list, tuple)):\\n names = [x[0] for x in names]\\n\\n return [re.sub('_+$', '', x) for x in names]\",\n \"def alphanum_key(s):\\n return [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]\",\n \"def alphanum_key(s):\\n return [int(c) if c.isdigit() else c for c in re.split('([0-9]+)', s)]\",\n \"def alphanum_key(s):\\n return [ tryint(c) for c in re.split('(\\\\-?[0-9]+)', s) ]\",\n \"def alphanum_key(s):\\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]\",\n \"def alphanum_key(s):\\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]\",\n \"def alphanum_key(s):\\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]\",\n \"def alphanum_key(s):\\n return [ tryint(c) for c in re.split('([0-9]+)', s) ]\",\n \"def get_naughty_string_list():\\n return naughty_string_list\",\n \"def clean_keeping_underscores(cls, value):\\r\\n return INVALID_CHARS.sub('_', value)\",\n \"def __prepare_value(self, val, none_symbol=\\\"-\\\"):\\n\\t\\t# If type is a tuple, then it is a \\n\\t\\t# (func_unit_label, replica_id) pair. \\n\\t\\t# Concatenate then using \\\"_\\\" symbol.\\n\\t\\tif type(val) == type(()):\\n\\t\\t\\tval = \\\"_\\\".join(map(str, val))\\n\\n\\t\\t# Cast value to string/character type,\\n\\t\\t# if it is not a None value\\n\\t\\tif val is not None:\\n\\t\\t\\tval = str(val)\\n\\t\\telse:\\n\\t\\t\\tval = none_symbol\\n\\n\\t\\t# Value processing finished\\n\\t\\treturn val\",\n \"def cast_name(key):\\n special_symbols = set('{}{}'.format(punctuation, ' '))\\n special_symbols.remove('_')\\n new_key = ['_' if x in special_symbols else x for x in key]\\n casted_key = ''.join(new_key)\\n return casted_key\",\n \"def _arg_to_empty(name: str) -> str:\\n arg = \\\"{\\\" + name + \\\"}\\\"\\n return arg\",\n \"def alphanum_key(s):\\n return [tryint(c) for c in re.split('([0-9]+)', s)]\",\n \"def alphanum_key(element):\\n return [int(c) if c.isdigit() else c for c in prog.split(element[0])]\",\n \"def clean(value):\\r\\n return re.sub('_+', '_', INVALID_CHARS.sub('_', value))\",\n \"def underscore_strings(entry):\\n\\tchars = \\\"\\\"\\n\\tin_string = False\\n\\tfor ch in entry:\\n\\t\\tif ch == '\\\"':\\n\\t\\t\\tin_string = not in_string\\n\\t\\telif ch == ' ' and in_string:\\n\\t\\t\\tch = '_'\\n\\t\\tchars += ch\\n\\tif in_string:\\n\\t\\traise Exception(\\\"unfinished string\\\")\\n\\treturn chars\",\n \"def _parse_mut(subs):\\n if subs!=\\\"0\\\":\\n subs = [[subs.replace(subs[-2:], \\\"\\\"),subs[-2], subs[-1]]]\\n return subs\",\n \"def alphanum_key(s):\\n return [tryint(c) for c in re.split('([0-9]+)', s)]\",\n \"def get_non_digit_prefix(characters):\\n prefix = []\\n while characters and not characters[0].isdigit():\\n prefix.append(characters.pop(0))\\n return prefix\",\n \"def formatlist(input_list):\\n\\n output_list = []\\n for item in input_list:\\n item = str(item)\\n item = item.replace(\\\" \\\", \\\"_\\\")\\n output_list.append(item)\\n return output_list\",\n \"def _repair_names_universal(\\n names: Iterable[str], quiet: bool = False, base0_: bool = None\\n) -> List[str]:\\n min_names = _repair_names_minimal(names)\\n neat_names = [re.sub(r\\\"[^\\\\w]\\\", \\\"_\\\", name) for name in min_names]\\n new_names = _repair_names_unique(\\n neat_names,\\n quiet=True,\\n sanitizer=lambda name: (\\n f\\\"_{name}\\\"\\n if keyword.iskeyword(name) or (name and name[0].isdigit())\\n else name\\n ),\\n base0_=base0_,\\n )\\n if not quiet:\\n changed_names = [\\n (orig_name, new_name)\\n for orig_name, new_name in zip(names, new_names)\\n if orig_name != new_name\\n ]\\n _log_changed_names(changed_names)\\n return new_names\",\n \"def convertToStrings(A):\\n\\n for candidate in A:\\n if candidate[2] >= 0:\\n candidate[2] = '+' + str(candidate[2])\\n else:\\n candidate[2] = str(candidate[2])\\n return A\",\n \"def disperse_string(solid_string):\\r\\n normal_list = list(solid_string)\\r\\n return list(itertools.chain.from_iterable(zip(normal_list, [0] * len(normal_list))))\",\n \"def func2(str):\\n numlist=[]\\n if type(str) == type(\\\"\\\"):\\n for i in range(1,len(str)-1):\\n numlist.append(str[i])\\n return (''.join(numlist))\\n else:\\n return (str)\",\n \"def split_alnum(s):\\n def convert(x):\\n try:\\n return int(x)\\n except ValueError:\\n return x\\n r = []\\n digit = None\\n for c in s:\\n d = c.isdigit()\\n if d != digit:\\n digit = d\\n r += [c]\\n else:\\n r[-1] += c\\n return [convert(x) for x in r]\",\n \"def sanitize(name):\\n return re.sub(\\\"\\\\\\\\W|^(?=\\\\\\\\d)\\\", \\\"_\\\", name)\",\n \"def natural_order(key):\\n return [number_else_string(c) for c in re.split(r'(\\\\d+)', key)]\",\n \"def _replace_none(lst, repl=\\\"\\\"):\\n return ['' if v is None else v for v in lst]\",\n \"def sanitize_txt(x):\\n return '_'.join(smart_split(x.lower()))\",\n \"def clean_identifier(self, identifier):\\n if isinstance(identifier, list):\\n return '%2F'.join(identifier)\\n else:\\n return identifier\",\n \"def preprocess_data(extracted_data: List[Tuple[str, str]]) -> List[str]:\\n return [f'__label__{data[0]} {clean_formatting(data[1])}' for data in extracted_data]\",\n \"def fillBlanks(w_cards, b_card):\\n b_card = b_card.replace('_', w_cards.replace('.', ''), 1)\\n\\n return b_card\",\n \"def sanitize(name):\\n return re.sub('\\\\\\\\W|^(?=\\\\\\\\d)', '_', name)\",\n \"def sanitize_key(key):\\n return re.sub('\\\\W|^(?=\\\\d)','_', key)\",\n \"def gen_all_strings(word):\\r\\n if len(word) == 0:\\r\\n return ['']\\r\\n else:\\r\\n first = word[0]\\r\\n rest = gen_all_strings(word[1:])\\r\\n new = []\\r\\n for item in rest:\\r\\n if len(item) > 0:\\r\\n for pos in range(len(item)):\\r\\n new.append(item[:pos] + first + item[pos:])\\r\\n new.append(item + first)\\r\\n new.append(first)\\r\\n new.extend(rest)\\r\\n return new\",\n \"def _getLilyAccidental(self):\\n return \\\"\\\"\",\n \"def trimname(name):\\n while name[-1].isdigit():\\n name = name[:-1]\\n if name[-1] == '_':\\n name = name[:-1]\\n return name\",\n \"def descorize(s):\\n return s.replace(\\\"_\\\", \\\" \\\")\",\n \"def kludge_gvars(mangled):\\n # Loop in reverse looking for '+-', but don't run off the end\\n for idx in range(len(mangled) - 1)[::-1]:\\n if mangled[idx + 1] == '+-':\\n reunited = ' '.join(mangled[idx:idx + 3])\\n # Throw away the used elements...\\n for _ in range(3):\\n mangled.pop(idx)\\n # Repair the list with reunited gvar string\\n mangled.insert(idx, reunited)\\n return mangled\",\n \"def getStrs(pre,num):\\n result = []\\n for i in range(num):\\n result.append(pre+str(i))\\n return result\",\n \"def encode_int_list(L):\\n return str(L).replace(\\\" \\\", \\\"\\\")\",\n \"def buildstrings(self):\\n slist = []\\n if 0 == len(self._pctnumbers):\\n slist.append(self.buildonestring('NNNN'))\\n else:\\n for pctnumber in sorted(self._pctnumbers):\\n slist.append(self.buildonestring(pctnumber))\\n\\n return slist\",\n \"def alphanum(v):\\n return re.sub('\\\\\\\\W+', '', v)\",\n \"def allstrings2(alphabet, length):\\n\\n c = []\\n for i in range(length):\\n c = [[x]+y for x in alphabet for y in c or [[]]]\\n\\n return c\",\n \"def _make_unique_kwarg_list(\\n seq: Sequence[Tuple[Any, Any]]\\n) -> Sequence[Tuple[Any, Any]]:\\n return [\\n (pair[0], \\\"_\\\".join([pair[1], str(seq[:i].count(pair))]))\\n if seq.count(pair) > 1\\n else pair\\n for i, pair in enumerate(seq)\\n ]\",\n \"def remove_one_letter(word):\\n for i in range(len(word)):\\n yield word[:i] + word[i + 1:]\",\n \"def prep_json_for_BQ_callback(self, key):\\n # add _ if first character in key is numeric\\n if key[0].isnumeric():\\n key = \\\"_\\\" + key\\n # replace \\\".\\\" or \\\" \\\" with \\\"_\\\"\\n key = re.sub(r\\\"[. ]\\\",\\\"_\\\",key)\\n # replace non-alphanumeric, non-\\\"_\\\" characters with nothing\\n key = re.sub(r\\\"[^a-zA-Z0-9_]\\\",\\\"\\\",key)\\n return key\",\n \"def NFelt(a):\\n return \\\",\\\".join([str(c) for c in list(a)])\",\n \"def make_safe(value, delimiter):\\n if isinstance(value,list):\\n return delimiter.join(map(lambda x: make_safe(x, delimiter),value))\\n return str(value)\",\n \"def createtext(lst):\\n newlst = []\\n for item in lst:\\n item = item.replace(\\\"_!\\\",\\\"\\\")\\n newlst.append(item)\\n text = ' '.join(newlst)\\n # Lower-casing\\n return text.lower()\",\n \"def sanitize(wl):\\n s = []\\n for word in wl:\\n for symbol in ['.', '!', ',', '\\\\n', '\\\\r', '?']:\\n if symbol in word:\\n s.append(symbol)\\n word = word.replace(symbol, '')\\n \\n s.append(word)\\n return s\",\n \"def getUniStr(self):\\n return('-'.join([\\\"%s-%s\\\"%(x.id,(x.id in self.special))\\n for x in self.getNested()]))\",\n \"def remove_empty(data):\\n out = []\\n for item in data:\\n if item == '':\\n continue\\n out.append(item)\\n return out\",\n \"def flatten_param(param):\\n\\n param = param.replace(']', '').replace('[', '_').replace('<','').replace('>','')\\n if param.startswith('_'):\\n param = param.replace('_', '', 1)\\n\\n return param\",\n \"def normalize_const(var_name):\\n return var_name.lower().split('_')\",\n \"def daqStringMod(self, arg):\\n\\t\\tself.stuff = []\\n\\t\\tfor i in arg:\\n\\t\\t\\tself.stuff.append(\\\"\\\\'\\\" + i + \\\"\\\\'\\\")\\n\\t\\treturn self.stuff\",\n \"def _clean_list(self, items):\\n itemlist = list(filter(None, items))\\n if len(itemlist) < 3:\\n itemlist.append(\\\"\\\")\\n return itemlist\\n\\n return itemlist\",\n \"def clean_units(units):\\n return [clean_unit(unit) for unit in units]\",\n \"def _init_as_list(arg):\\n if arg is None:\\n return []\\n elif isinstance(arg, str):\\n return [arg]\\n else:\\n return arg\",\n \"def __lettersToString(self, words):\\r\\n \\r\\n li = []\\r\\n \\r\\n for word in words:\\r\\n li.append(\\\"\\\".join(word))\\r\\n \\r\\n return li\",\n \"def createWordKnown(self):\\n return ''.join(['_ ' for m in range(self.wordLen)])\",\n \"def generatemoves(self, x):\\n returnvalue = [x[0], \\\"_\\\"] if x[0] != '_' else [x[0]]\\n\\n for u in range(1, len(x)):\\n if x[u] == '_':\\n returnvalue = [y + \\\"_\\\" for y in returnvalue]\\n else:\\n returnvalue = [y + x[u] for y in returnvalue] + [y + \\\"_\\\" for y in returnvalue]\\n\\n returnvalue.remove((\\\"_\\\" * len(x)))\\n return returnvalue\",\n \"def _expanded(var):\\r\\n if var == '*':\\r\\n return []\\r\\n n = var.split(',')\\r\\n n2 = [i.split('-') for i in n if i.find('-') > -1]\\r\\n n3 = set([int(i) for i in n if i.find('-') == -1])\\r\\n for i in n2:\\r\\n for x in range(int(i[0]), int(i[1]) + 1):\\r\\n n3.add(x)\\r\\n return n3\",\n \"def repair_size_list(self, str_val):\\n return [word for word in str_val[2:-2].split('\\\\', \\\\'')]\",\n \"def name_supply(stems=string.ascii_lowercase, drop_zero=True):\\n k = 0\\n while 1:\\n for a in stems:\\n yield a+str(k) if (k or not drop_zero) else a\\n k = k+1\",\n \"def sort_nicely(l):\\n l.sort(key=alphanum_key)\\n return l\",\n \"def lower_replace(value):\\n return value.lower().replace(\\\" \\\",\\\"_\\\")\",\n \"def make_unique(name, reserved_names):\\n while name in reserved_names:\\n name += '_'\\n\\n return name\",\n \"def safe(s):\\n return (\\\"\\\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())\",\n \"def _getFieldNameList(name):\\n\\n\\t# check symbol set\\n\\tif not re.search(r\\\"^[\\\\w\\\\d_\\\\-\\\\.]+$\\\", name):\\n\\t\\treturn None\\n\\n\\telements = name.split('.')\\n\\n\\tname_list = []\\n\\tfor element in elements:\\n\\n\\t\\tif len(element) == 0:\\n\\t\\t\\tname_list.append(None)\\n\\t\\telif element[0].isdigit():\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tname_list.append(int(element))\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\treturn None\\n\\t\\telif element[0].isalpha() or element[0] == '_':\\n\\t\\t\\tname_list.append(element)\\n\\t\\telse:\\n\\t\\t\\treturn None\\n\\n\\treturn name_list\",\n \"def human_sort(l):\\n l.sort(key=alphanum_key)\\n return l\",\n \"def sort_nicely(l):\\r\\n\\tl.sort(key=alphanum_key)\",\n \"def _sanitize_value(self, value):\\n if isinstance(value, collections.Iterable):\\n newcollection = []\\n for i in value:\\n if len(i) == 1:\\n newcollection.append((i[0], None))\\n elif len(i) >= 2:\\n newcollection.append((i[0], i[1]))\\n return newcollection\\n else:\\n return value\",\n \"def prepare_value(self, value):\\n if value is None:\\n return value\\n value = value.replace(\\\" \\\", \\\"\\\").replace(\\\".\\\", \\\"\\\")\\n if value:\\n return \\\"%s.%s.%s.%s\\\" % (value[0:3], value[3:7], value[7:11], value[11:])\\n return value\",\n \"def prepare_numbers(numbers):\\n \\n numb = []\\n for item in numbers:\\n numb.append(int(item))\\n return numb\",\n \"def sanitize_chars(item):\\n new_item = []\\n for char in item:\\n if char.isalnum() or char in \\\" ,./-\\\":\\n new_item.append(char)\\n if new_item[0].isnumeric():\\n # look for duplicated \\\",\\\", and replace the last for a dot if no dots are found\\n if new_item.count(\\\",\\\") > 1 and new_item.count(\\\".\\\") == 0:\\n new_item[len(new_item) - 1 - new_item[::-1].index(\\\",\\\")] = \\\".\\\"\\n return \\\"\\\".join(new_item)\",\n \"def prepare(x):\\n try:\\n ind = x.index('\\\"')\\n except:\\n ind = -1\\n if ind != -1:\\n d = x[ind:]\\n x = x[:ind]\\n x = x.split(' ')\\n tmp = []\\n final = []\\n for i in x:\\n if i:\\n if i[0] != ':':\\n tmp.append(i)\\n for i in tmp[:4]:\\n final.append(i)\\n if not d:\\n d = \\\"\\\".join(tmp[4:])\\n final.append(d.strip())\\n return final\",\n \"def normalize_tokens(g):\\n tokens = g.get('tokens', [])\\n g['tokens'] = [re.sub(r\\\"\\\\d+\\\", \\\"0\\\", t.lower()) for t in tokens]\\n return g\",\n \"def gen_all_strings(word):\\n if len(word) == 0:\\n return [\\\"\\\"]\\n elif len(word) == 1:\\n return [\\\"\\\",word]\\n else:\\n result_strings = []\\n first = word[0]\\n rest = word[1:]\\n rest_strings = gen_all_strings(rest)\\n new_strings = []\\n for rest_string in rest_strings:\\n for dummy_index in range(len(rest_string)):\\n #在首位插入\\n if dummy_index == 0:\\n new_string = first + rest_string\\n new_strings.append(new_string)\\n #在中间插入 \\n else:\\n new_string = rest_string[0:dummy_index] + first + rest_string[dummy_index:]\\n new_strings.append(new_string)\\n #在末尾插入\\n new_strings.append(rest_string + first)\\n \\n result_strings.extend(rest_strings)\\n result_strings.extend(new_strings)\\n \\n return result_strings\",\n \"def clean(key):\\n\\treturn key.strip().replace('%','').replace(' ', '-')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.626513","0.5933304","0.5747301","0.5679641","0.55706316","0.54705375","0.5390125","0.53830534","0.536347","0.53619814","0.5348391","0.53348833","0.53115875","0.5304542","0.5290489","0.5288956","0.52829987","0.5279524","0.5274321","0.52679646","0.5253277","0.5253277","0.5228514","0.52121115","0.52121115","0.52121115","0.52121115","0.52084786","0.5204537","0.51950455","0.5183242","0.51730233","0.5172404","0.51626813","0.51587933","0.51421386","0.51402295","0.513679","0.5133817","0.5125973","0.5109446","0.5106423","0.50879425","0.5065218","0.50649256","0.50482816","0.5047728","0.5046066","0.50258607","0.50152045","0.5004803","0.50035655","0.4983945","0.49836504","0.49769446","0.49627945","0.49607563","0.49449596","0.49277297","0.49261248","0.4914645","0.4913866","0.49125016","0.49079466","0.4906777","0.4905782","0.48932302","0.4885909","0.48830348","0.48747644","0.4874191","0.48711082","0.48659393","0.48580974","0.48512903","0.4838245","0.4838244","0.4836449","0.4831557","0.4818254","0.48172292","0.48168942","0.48135206","0.48064888","0.4801407","0.4801239","0.47988105","0.47972265","0.47952944","0.47916892","0.47899422","0.4788151","0.478787","0.47869056","0.4786742","0.4784495","0.47843653","0.47829226","0.47792065","0.47784486"],"string":"[\n \"0.626513\",\n \"0.5933304\",\n \"0.5747301\",\n \"0.5679641\",\n \"0.55706316\",\n \"0.54705375\",\n \"0.5390125\",\n \"0.53830534\",\n \"0.536347\",\n \"0.53619814\",\n \"0.5348391\",\n \"0.53348833\",\n \"0.53115875\",\n \"0.5304542\",\n \"0.5290489\",\n \"0.5288956\",\n \"0.52829987\",\n \"0.5279524\",\n \"0.5274321\",\n \"0.52679646\",\n \"0.5253277\",\n \"0.5253277\",\n \"0.5228514\",\n \"0.52121115\",\n \"0.52121115\",\n \"0.52121115\",\n \"0.52121115\",\n \"0.52084786\",\n \"0.5204537\",\n \"0.51950455\",\n \"0.5183242\",\n \"0.51730233\",\n \"0.5172404\",\n \"0.51626813\",\n \"0.51587933\",\n \"0.51421386\",\n \"0.51402295\",\n \"0.513679\",\n \"0.5133817\",\n \"0.5125973\",\n \"0.5109446\",\n \"0.5106423\",\n \"0.50879425\",\n \"0.5065218\",\n \"0.50649256\",\n \"0.50482816\",\n \"0.5047728\",\n \"0.5046066\",\n \"0.50258607\",\n \"0.50152045\",\n \"0.5004803\",\n \"0.50035655\",\n \"0.4983945\",\n \"0.49836504\",\n \"0.49769446\",\n \"0.49627945\",\n \"0.49607563\",\n \"0.49449596\",\n \"0.49277297\",\n \"0.49261248\",\n \"0.4914645\",\n \"0.4913866\",\n \"0.49125016\",\n \"0.49079466\",\n \"0.4906777\",\n \"0.4905782\",\n \"0.48932302\",\n \"0.4885909\",\n \"0.48830348\",\n \"0.48747644\",\n \"0.4874191\",\n \"0.48711082\",\n \"0.48659393\",\n \"0.48580974\",\n \"0.48512903\",\n \"0.4838245\",\n \"0.4838244\",\n \"0.4836449\",\n \"0.4831557\",\n \"0.4818254\",\n \"0.48172292\",\n \"0.48168942\",\n \"0.48135206\",\n \"0.48064888\",\n \"0.4801407\",\n \"0.4801239\",\n \"0.47988105\",\n \"0.47972265\",\n \"0.47952944\",\n \"0.47916892\",\n \"0.47899422\",\n \"0.4788151\",\n \"0.478787\",\n \"0.47869056\",\n \"0.4786742\",\n \"0.4784495\",\n \"0.47843653\",\n \"0.47829226\",\n \"0.47792065\",\n \"0.47784486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.48427668"},"document_rank":{"kind":"string","value":"75"}}},{"rowIdx":79599,"cells":{"query":{"kind":"string","value":"Change the default mute time for the first warning"},"document":{"kind":"string","value":"async def muterole(self, ctx, rolename: str):\n self.data_check(ctx)\n server = ctx.message.server\n \n self.riceCog2[server.id][\"muterole\"] = rolename\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n await self.bot.say(\"Muted role name is now: **{}**\".format(rolename))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def silly(self):\n print(\"you are getting silly\")\n # 设置时效\n return self._silly if time.localtime(time.time() - self._old_time ).tm_sec < 3 else \"\"","def mute(self, msg, args):\n if self.mute:\n self.mute=False\n return \"Yay, I can make noise again!\"\n else:\n self.mute=True\n return \"OK, I'll shut up now!\"","async def mutetime(self, ctx):\n self.data_check(ctx)\n server = ctx.message.server\n \n await self.bot.say(\"Please make sure to set the time with the correct time prefix at the end. (*For minutes 'm', for hours 'h'*)\\n\\nPlease type your timeframe now.\")\n muteroletime = await self.bot.wait_for_message(channel = ctx.message.channel, author = ctx.message.author)\n\n if \"m\" in muteroletime.content or \"s\" in muteroletime.content or \"h\" in muteroletime.content:\n self.riceCog2[server.id][\"mutetime\"] = muteroletime.content\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n await self.bot.say(\"Default mute time is now: **{}**\".format(muteroletime.content))\n else:\n await self.bot.say(\"You've done something wrong! Please make sure that the format is correct!\")\n return","async def mute(self, *args, **kwargs):\n self.muted = not self.muted # toogle\n if self.muted:\n self.just_muted = True\n return \"I've been muted :(\"\n return \"I'm back! :D\"","async def mute(self, ctx, user: discord.Member, time_and_unit=None, *, reason: str = None):\r\n server = ctx.message.guild\r\n channel = ctx.message.channel\r\n author = ctx.message.author\r\n if channel.permissions_for(user).administrator:\r\n await ctx.send(\"That user has administrator perms, why would I even try :no_entry:\")\r\n return\r\n if user.top_role.position >= author.top_role.position:\r\n if author == server.owner:\r\n pass\r\n else:\r\n await ctx.send(\"You can not mute someone higher than your own role :no_entry:\")\r\n return\r\n if not time_and_unit:\r\n time2 = 600\r\n time = \"10\"\r\n unit = \"minutes\"\r\n else:\r\n try:\r\n unit = time_and_unit[len(time_and_unit) - 1:len(time_and_unit)]\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n try:\r\n time = time_and_unit[0:len(time_and_unit) - 1]\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if unit == \"s\":\r\n try:\r\n time2 = int(time)\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"second\"\r\n else:\r\n unit = \"seconds\"\r\n elif unit == \"m\":\r\n try:\r\n time2 = int(time) * 60\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"minute\"\r\n else:\r\n unit = \"minutes\"\r\n elif unit == \"h\":\r\n try:\r\n time2 = int(time) * 3600\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"hour\"\r\n else:\r\n unit = \"hours\"\r\n elif unit == \"d\":\r\n try:\r\n time2 = int(time) * 86400\r\n except ValueError:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n if time == \"1\":\r\n unit = \"day\"\r\n else:\r\n unit = \"days\"\r\n else:\r\n await ctx.send(\"Invalid time unit :no_entry:\")\r\n return\r\n action = \"Mute ({} {})\".format(time, unit)\r\n if str(server.id) not in self.d:\r\n self.d[str(server.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if str(user.id) not in self.d[str(server.id)]:\r\n self.d[str(server.id)][str(user.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if \"toggle\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n dataIO.save_json(self.file, self.d)\r\n if \"time\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"time\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n if \"amount\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n role = discord.utils.get(server.roles, name=\"Muted - Sensei\")\r\n overwrite = discord.PermissionOverwrite()\r\n overwrite.send_messages = False\r\n perms = discord.PermissionOverwrite()\r\n perms.speak = False\r\n if not role:\r\n role = await server.create_role(name=\"Muted - Sensei\")\r\n for channels in ctx.guild.text_channels:\r\n await channels.set_permissions(role, overwrite=overwrite)\r\n for channels in ctx.guild.voice_channels:\r\n await channels.set_permissions(role, overwrite=perms)\r\n if role in user.roles:\r\n await ctx.send(\"**{}** is already muted :no_entry:\".format(user))\r\n return\r\n try:\r\n await user.add_roles(role)\r\n except:\r\n await ctx.send(\"I cannot add the mute role to the user :no_entry:\")\r\n return\r\n await ctx.send(f\"**{user}** has been muted for {time} {unit} {self.bot.get_emoji(470063310386233344)}\")\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = True\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = time2\r\n self.d[str(server.id)][str(user.id)][\"time\"] = ctx.message.created_at.timestamp()\r\n dataIO.save_json(self.file, self.d)\r\n try:\r\n s = discord.Embed(title=\"You have been muted in {} :speak_no_evil:\".format(server.name), colour=0xfff90d,\r\n timestamp=__import__('datetime').datetime.utcnow())\r\n s.add_field(name=\"Moderator\", value=\"{} ({})\".format(author, str(author.id)), inline=False)\r\n s.add_field(name=\"Time\", value=\"{} {}\".format(time, unit), inline=False)\r\n if reason:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n await user.send(embed=s)\r\n except:\r\n pass","async def _mute(ctx, user:discord.Member, time: str, self: bool):\n if user.id in PI_BOT_IDS:\n return await ctx.send(\"Hey! You can't mute me!!\")\n if time == None:\n return await ctx.send(\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\")\n role = None\n if self:\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\n else:\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\n parsed = \"indef\"\n if time != \"indef\":\n parsed = dateparser.parse(time, settings={\"PREFER_DATES_FROM\": \"future\"})\n if parsed == None:\n return await ctx.send(\"Sorry, but I don't understand that length of time.\")\n CRON_LIST.append({\"date\": parsed, \"do\": f\"unmute {user.id}\"})\n await user.add_roles(role)\n eastern = pytz.timezone(\"US/Eastern\")\n await ctx.send(f\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\")","def mute():\n request_command(tv_command=TVCommand.mute)","async def moderation(self, ctx):\n\n new_value = await self.toggle_dm_setting(ctx.author.id, \"ban_kick_mute\")\n\n if new_value:\n message = \":white_check_mark: You will now receive DMs when you get muted, kicked or banned by me.\"\n else:\n message = \":white_check_mark: You will no longer receive DMs when you get muted, kicked or banned.\"\n\n await ctx.send(message)","async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\r\n\r\n if member.top_role >= ctx.author.top_role:\r\n return await ctx.send(\"you can't mute that person\")\r\n\r\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\r\n await member.add_roles(role)\r\n await ctx.reply((f\"Muted {member} for {time}s\" if time else f\"Muted {member}\"))\r\n\r\n if time:\r\n await asyncio.sleep(time)\r\n await member.remove_roles(role)","def mute_track(self, track, muted):\n pass","def set_time(self, value: float):\n super().set_time(value)\n self.music.set_time(value)","def _mute_update_handler(self, new_mute, dev_path):\n # Only do something if it's one of the default/fallback devices\n if dev_path == self.pulse_core.Get(\"org.PulseAudio.Core1\", \"FallbackSink\"):\n self.output_muted = bool(new_mute)\n # self.set_mute_output(bool(new_mute))\n elif dev_path == self.pulse_core.Get(\"org.PulseAudio.Core1\", \"FallbackSource\"):\n self.input_muted = bool(new_mute)\n # self.set_mute_input(bool(new_mute))\n else:\n print(\"Don't care about this device\", dev_path, new_mute)","def disable_mute(self):\n self.mute = False","async def mutedlist(self, ctx):\r\n server = ctx.message.guild\r\n msg = \"\"\r\n i = 0\r\n try:\r\n for userid in self.d[str(server.id)]:\r\n if self.d[str(server.id)][userid][\"toggle\"] == True:\r\n i = i + 1\r\n except:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n if i == 0:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n for userid in self.d[str(server.id)]:\r\n if self.d[str(server.id)][userid][\"time\"] == None or self.d[str(server.id)][userid][\"time\"] - ctx.message.created_at.timestamp() + self.d[str(server.id)][userid][\"amount\"] <= 0:\r\n time = \"Infinite\"\r\n else:\r\n m, s = divmod(self.d[str(server.id)][userid][\"time\"] - ctx.message.created_at.timestamp() +\r\n self.d[str(server.id)][userid][\"amount\"], 60)\r\n h, m = divmod(m, 60)\r\n d, h = divmod(h, 24)\r\n if d == 0:\r\n time = \"%d hours %d minutes %d seconds\" % (h, m, s)\r\n if h == 0 and d == 0:\r\n time = \"%d minutes %d seconds\" % (m, s)\r\n elif h == 0 and m == 0:\r\n time = \"%d seconds\" % (s)\r\n else:\r\n time = \"%d days %d hours %d minutes %d seconds\" % (d, h, m, s)\r\n if self.d[str(server.id)][userid][\"toggle\"] == True:\r\n user = discord.utils.get(server.members, id=int(userid))\r\n if user:\r\n msg += \"{} - {} (Till mute ends)\\n\".format(user, time)\r\n if not msg:\r\n await ctx.send(\"No one is muted in this server :no_entry:\")\r\n return\r\n s = discord.Embed(description=msg, colour=0xfff90d, timestamp=datetime.datetime.utcnow())\r\n s.set_author(name=\"Mute List for {}\".format(server), icon_url=server.icon_url)\r\n await ctx.send(embed=s)","async def selfmute(ctx, *args):\n user = ctx.message.author\n if await is_staff(ctx):\n return await ctx.send(\"Staff members can't self mute.\")\n time = \" \".join(args)\n await _mute(ctx, user, time, self=True)","async def mute(self, ctx, user: discord.Member, amount: int, time_unit: str, *, reason: typing.Optional[str]):\n length, error_msg = self.convert_mute_length(amount, time_unit)\n if not length:\n await ctx.send(error_msg)\n return\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\n mute_message = f\"user {user.mention} was muted\"\n await user.add_roles(self.mute_role)\n await ctx.send(mute_message)\n if reason:\n mute_message = f\"{mute_message} for the following reason:\\n{reason}\"\n await self.add_mute_to_mute_list(user.id, unmute_ts)\n await self.check_channel.send(mute_message)","def _change_time(self):\r\n msg = \"Notice! if you don't write hours the time\\nwill be calculated as seconds.\\nEnter new time:\"\r\n new_time = simpledialog.askstring(title=\"Change recording time\", prompt=msg)\r\n\r\n # new_time has to be a digit bigger than 0\r\n while not new_time:\r\n msg = \"Time must have a value. For example: 1 hours/ 1.5 hours/ 25 seconds\"\r\n messagebox.showerror(title=\"ERROR\", message=msg)\r\n new_time = simpledialog.askstring(title=\"Change recording time\", prompt=\"Enter new time:\")\r\n if new_time:\r\n self.time.set(\"time: \" + new_time + ''.join(' ' for _ in range(42 - len(new_time))))","def mute(self) -> None:\n self.system.notify(\"Jarvis::Muted\")\n self.media.mute(True)","async def selfmute(self, ctx, amount:int, time_unit:str):\n\n length, error_msg = self.convert_mute_length(amount, time_unit)\n if not length:\n await ctx.send(error_msg)\n return\n if length > 7 * self.units[\"days\"]:\n question = await ctx.send(f\"Are you sure you want to be muted for {(length/self.units['days']):.2f} days?\\n\"\n f\"answer with Y[es] or N[o]\")\n\n def msg_check(message):\n return message.author.id == ctx.author.id and message.channel.id == ctx.message.channel.id\n try:\n message = await self.bot.wait_for(\"message\", check=msg_check, timeout=20.0)\n if re.match(r\"y(es)?\", message.content.lower()):\n pass\n else:\n await question.edit(content=\"self mute aborted\")\n return\n except asyncio.TimeoutError:\n await question.edit(content=\"Timeout: mute aborted\")\n return\n\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\n await ctx.author.add_roles(self.mute_role)\n await ctx.send(\"You have been muted\")\n await self.add_mute_to_mute_list(ctx.author.id, unmute_ts)","def alarm_in_setup_change():\n setup_write(\"!M1 meas interval\", \"00:01:00\")\n setup_write(\"!M2 meas interval\", \"00:01:00\")\n setup_write(\"!TX3 scheduled interval\", \"00:05:00\")","async def tempmute(self, ctx,\n\t\ttarget: discord.Member,\n\t\tduration: DurationConverter,\n\t\t*, reason: str = \"No reason given.\"\n\t):\n\n\t\tself.check_perms(ctx.author, target)\n\n\t\thandler = await Handler.new(self.bot, ctx.guild)\n\t\tawait handler.mute(ctx.author, target, reason, duration)\n\n\t\tawait ctx.success(f\"{target} (`{target.id}`) has been muted for {time_since(seconds=duration)} for:\\n{reason}\")","async def mute(self, ctx, member: discord.Member, time='15m'):\n guild_permissions = member.guild_permissions\n wait_time = parse_time(time).total_seconds()\n # Because sometimes members have nicknames with markdown\n escaped_name = escape_markdown(member.display_name)\n\n if guild_permissions.kick_members:\n # do not mute someone who has permissions to kick members\n await ctx.send(f'Cannot mute {escaped_name} due to roles.')\n\n elif member.bot:\n # do not mute bots\n await ctx.send(f'Cannot mute {escaped_name} (is a bot).')\n\n else:\n overwrite = discord.PermissionOverwrite(\n add_reactions=False,\n send_messages=False,\n )\n\n log_str = (f'{ctx.author.display_name} has muted '\n f'member {member} (<@{member.id}>) for {time}.')\n logger.info(log_str)\n\n for channel in ctx.guild.text_channels:\n permissions = channel.permissions_for(member)\n\n if permissions.read_messages:\n await channel.set_permissions(member, overwrite=overwrite)\n\n await asyncio.sleep(wait_time)\n await ctx.invoke(self.unmute, member)","def mute(self, value: bool) -> None:\n self.audio_mixer.setmute(value)\n self.update_status()","def play_warning_song(self):\n if self._warning_song_num is None:\n self.set_warning_song(0)\n\n self._serial_conn.send_command(\"141 \" + str(self._warning_song_num))","async def resettimer(self, ctx:commands.Context, member: Member = None):\r\n\r\n await self.config.member(member if not member == None else ctx.message.author).currently_fishing.set(False)\r\n await ctx.send('Fishing cooldown reset')","async def warn(self, ctx, user: discord.Member, *, reason: str = None):\r\n author = ctx.message.author\r\n server = ctx.message.guild\r\n channel = ctx.message.channel\r\n if user == author:\r\n await ctx.send(\"You can not warn yourself :no_entry:\")\r\n return\r\n if user.top_role.position >= author.top_role.position:\r\n if author == server.owner:\r\n pass\r\n else:\r\n await ctx.send(\"You can not warn someone higher than your own role :no_entry:\")\r\n return\r\n if str(server.id) not in self.d:\r\n self.d[str(server.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if str(user.id) not in self.d[str(server.id)]:\r\n self.d[str(server.id)][str(user.id)] = {}\r\n dataIO.save_json(self.file, self.d)\r\n if \"muted\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n dataIO.save_json(self.file, self.d)\r\n if \"time\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"time\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n if \"amount\" not in self.d[str(server.id)][str(user.id)]:\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n role = discord.utils.get(server.roles, name=\"Muted - Sensei\")\r\n overwrite = discord.PermissionOverwrite()\r\n overwrite.send_messages = False\r\n perms = discord.PermissionOverwrite()\r\n perms.speak = False\r\n if not role:\r\n role = await server.create_role(name=\"Muted - Sensei\")\r\n for channels in server.text_channels:\r\n await channels.set_permissions(role, overwrite=overwrite)\r\n for channels in server.voice_channels:\r\n await channels.set_permissions(role, overwrite=perms)\r\n await self._create_warn(server, user)\r\n if reason:\r\n if reason not in self.data[str(server.id)][\"user\"][str(user.id)][\"reasons\"]:\r\n self.data[str(server.id)][\"user\"][str(user.id)][\"reasons\"][reason] = {}\r\n self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] = self.data[str(server.id)][\"user\"][str(user.id)][\r\n \"warnings\"] + 1\r\n dataIO.save_json(self.JSON, self.data)\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 1:\r\n await ctx.send(\"**{}** has been warned :warning:\".format(user))\r\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\r\n s.set_author(name=\"You have been warned in {}\".format(server.name), icon_url=server.icon_url)\r\n try:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n except:\r\n s.add_field(name=\"Reason\", value=\"None Given\", inline=False)\r\n s.add_field(name=\"Moderator\", value=author)\r\n s.add_field(name=\"Next Action\", value=\"Mute\")\r\n action = \"Warn\"\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 2:\r\n try:\r\n await user.add_roles(role)\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = True\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = 600\r\n self.d[str(server.id)][str(user.id)][\"time\"] = ctx.message.created_at.timestamp()\r\n dataIO.save_json(self.file, self.d)\r\n except:\r\n await ctx.send(\"I cannot add the mute role to the user :no_entry:\")\r\n return\r\n await ctx.send(\"**{}** has been muted due to their second warning :white_check_mark:\".format(user))\r\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\r\n s.set_author(name=\"You have been muted in {}\".format(server.name), icon_url=server.icon_url)\r\n try:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n except:\r\n s.add_field(name=\"Reason\", value=\"None Given\", inline=False)\r\n s.add_field(name=\"Moderator\", value=author)\r\n s.add_field(name=\"Next Action\", value=\"Kick\")\r\n action = \"Mute\"\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n await asyncio.sleep(600)\r\n if role in user.roles:\r\n try:\r\n await user.remove_roles(role)\r\n except:\r\n pass\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n dataIO.save_json(self.file, self.d)\r\n action = \"Unmute\"\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 3:\r\n try:\r\n await server.kick(user, reason=\"Kick made by {}\".format(author))\r\n except:\r\n await ctx.send(\"I'm not able to kick that user :no_entry:\")\r\n return\r\n await ctx.send(\"**{}** has been kicked due to their third warning :white_check_mark:\".format(user))\r\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\r\n s.set_author(name=\"You have been kicked from {}\".format(server.name), icon_url=server.icon_url)\r\n try:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n except:\r\n s.add_field(name=\"Reason\", value=\"None Given\", inline=False)\r\n s.add_field(name=\"Moderator\", value=author)\r\n s.add_field(name=\"Next Action\", value=\"Ban\")\r\n action = \"Kick\"\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] >= 4:\r\n try:\r\n await server.ban(user, reason=\"Ban made by {}\".format(author))\r\n except:\r\n await ctx.send(\"I'm not able to ban that user :no_entry:\")\r\n del self.data[str(server.id)][\"user\"][str(user.id)]\r\n dataIO.save_json(self.JSON, self.data)\r\n return\r\n await ctx.send(\"**{}** has been banned due to their fourth warning :white_check_mark:\".format(user))\r\n await server.ban(user, reason=\"Ban made by {}\".format(author))\r\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\r\n s.set_author(name=\"You have been banned from {}\".format(server.name), icon_url=server.icon_url)\r\n try:\r\n s.add_field(name=\"Reason\", value=reason, inline=False)\r\n except:\r\n s.add_field(name=\"Reason\", value=\"None Given\", inline=False)\r\n s.add_field(name=\"Moderator\", value=author)\r\n s.add_field(name=\"Next Action\", value=\"None\")\r\n del self.data[str(server.id)][\"user\"][str(user.id)]\r\n dataIO.save_json(self.JSON, self.data)\r\n action = \"Ban\"\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n try:\r\n await user.send(embed=s)\r\n except:\r\n pass","def handle_mic_mute(_):\n loop.mute()","def med_timer(self):\n self.start_button.config(text='Sit', state='disabled')\n self.start_button.update()\n if self.mins.get() == \"\":\n num_mins = 0\n else:\n num_mins = float(self.mins.get())\n time_in_seconds = num_mins * 60\n self.t = Timer(time_in_seconds, self.play_wav)\n self.t.start()","def mute_members(_) -> int:\n return 1 << 22","def mute_members(_) -> int:\n return 1 << 22","def set_fan_timer_timeout(self, time_: str = None):\r\n if time_ is not None:\r\n self._fan_timer_timeout = datetime.fromisoformat(time_)\r\n else:\r\n self._fan_timer_timeout = datetime.now() + self._fan_timer_duration\r\n\r\n self._logger.info(log_message_formatter(\r\n \"set\", f\"{self}\", \"fan_timer-timeout\", self.fan_timer_timeout))","def set_sleep_timer(self, option, time):\n params = [\n ('option', option),\n ('sleeptime', int(time)),\n ]\n\n self.get(COMMAND_UIC, 'SetSleepTimer', params)","def set_mute(self, track, xclip, ident, value = None):\n if track in self.song().tracks + self.song().return_tracks:\n if value in KEYWORDS:\n track.mute = KEYWORDS[value]\n else:\n track.mute = not(track.mute)","async def reset(self, ctx):\n self.data_check(ctx)\n server = ctx.message.server\n author = ctx.message.author\n channel = ctx.message.channel\n await self.bot.say(\"Are you sure you want to reset all warn settings\"\n \"for this server?\\n\"\n \"Type **yes** within the next 15 seconds.\")\n msg = await self.bot.wait_for_message(author=author,\n channel=channel,\n timeout=15.0)\n if msg.content.lower().strip() == \"yes\":\n self.riceCog2[server.id][\"warn_message\"] = default_warn\n self.riceCog2[server.id][\"ban_message\"] = default_ban\n self.riceCog2[server.id][\"max\"] = default_max\n self.riceCog2[server.id][\"defchannel\"] = default_channel\n self.riceCog2[server.id][\"mutetime\"] = defmutetime\n else:\n await self.bot.say(\"Nevermind then.\")\n return","def lightleep(time_ms: int = None) -> None:","async def _authorities_setheist(self, ctx, seconds: int):\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n theme = await self.thief.config.guild(guild).Theme()\r\n t_police = theme[\"Police\"]\r\n\r\n if seconds > 0:\r\n config[\"Police\"] = seconds\r\n await self.thief.config.guild(guild).Config.set(config)\r\n time_fmt = self.thief.time_format(seconds)\r\n msg = \"Setting {} alert time to {}.\".format(t_police, time_fmt)\r\n else:\r\n msg = \"Need a number higher than 0.\"\r\n await ctx.send(msg)","def set_time_now(self, *args, **kwargs):\n return _uhd_swig.usrp_sink_set_time_now(self, *args, **kwargs)","def silence(self):\n return 'Fine. Be that way!'","async def async_set_muted(self, mute):\n self._muted = mute","def mute(self, nick, chan, arg):\n if not arg:\n \treturn bot.msg(chan, get_doc())\n self.state.mute(arg)\n self.msg(chan, \"%s: You are temporarily prohibited from using this bot\" % (arg))","async def mute(\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\n ):\n\n if target.guild_permissions.kick_members:\n return await ctx.send(\"You can't punish that person!\")\n\n if isinstance(reason, time.UserFriendlyTime):\n expires_at = reason.dt\n reason = reason.arg\n else:\n expires_at = None\n\n action = Mute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n created_at=ctx.message.created_at,\n expires_at=expires_at,\n )\n await action.execute(ctx)\n await action.notify()\n if action.duration is None:\n await ctx.send(f\"Muted **{target}**.\")\n else:\n await ctx.send(f\"Muted **{target}** for **{time.human_timedelta(action.duration)}**.\")","def bot_settings_set_nul(chat_id):\n\n bot_collection[chat_id].timers.extended = 0\n bot_collection[chat_id].timers.additional_time = False\n bot_collection[chat_id].timers.current_time = 0\n bot_collection[chat_id].paused = False\n\n save_timers(chat_id)","def set_sleep_time(self, time):\n self.sleep_time = time","def alarmoff() :\n s.alarm(False, \"\")","def muteTrack(*args, **kwargs):\n pass","def mute_volume(self, mute):\n mute_numeric = '1' if mute else '0'\n self._lms.query(self._id, 'mixer', 'muting', mute_numeric)\n self.update_ha_state()","async def set_timer(self, ctx: commands.Context, seconds: int = None):\n if seconds is not None:\n await ctx.cfg_guild.autopostseconds.set(seconds)\n await ctx.send(\"Auto-post timer has been set to {}\".format(seconds))\n else:\n seconds = await ctx.cfg_guild.autopostseconds()\n await ctx.send(f\"Currently posting every {seconds} seconds.\")","def set_current_time(self):\n now = datetime.datetime.now()\n answer = self.UsbHost.send_command(self.state.ser, \"SetCurrentTime\", str(self.state.device_id),\n now.year, now.month, now.day,\n now.hour, now.minute, now.second, int(now.microsecond / 1000))\n if answer in wrong_answers:\n error_message(\"Не удалось задать время\")\n self.statusbar.showMessage(answer_translate[answer])\n else:\n self.state.syncro = True\n self.create_message()","def mute_volume(self, mute: bool) -> None:\n self._monoprice.set_mute(self._zone_id, mute)","def mute2(individual):\n mutatePt=random.randint(0,len(individual)-1)\n if mutatePt==0:\n individual[mutatePt]=random.uniform(0.0, 0.02)\n elif mutatePt>=2 and mutatePt<=4:\n individual[mutatePt]=random.uniform(0.0, 0.005)\n elif mutatePt==5:\n individual[mutatePt]=random.uniform(0.0, 0.07)\n \n return individual,","def set_display_time(log_mes,displaytime = 1800000):\n kill_adb_uiautomator_block_old()\n if int(get_screen_off_time(log_mes)) == displaytime:\n if int(displaytime) >= 60000:\n log_mes.info( 'screen off time is already %s mins'%(displaytime/60000))\n else:\n log_mes.info('screen off time is already %s secs'%(displaytime/1000))\n else:\n os.system('adb shell am start -a android.settings.DISPLAY_SETTINGS')\n device(text=\"Sleep\").click()\n kill_adb_uiautomator_block_old()\n if int(displaytime) >= 60000:\n device(text=\"%s minutes\"%(displaytime/60000)).click()\n else:\n device(text=\"%s seconds\"%(displaytime/1000)).click()\n time.sleep(1)\n os.system(\"adb shell am force-stop com.android.settings\")","def get_mute(self):\n return on_off_bool(self.get(COMMAND_UIC, 'GetMute')['mute'])","async def warnings(self, ctx, user: discord.Member):\r\n server = ctx.message.guild\r\n try:\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 1:\r\n action = \"Mute\"\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 2:\r\n action = \"Kick\"\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] >= 3:\r\n action = \"Ban\"\r\n if not self.data[str(server.id)][\"user\"][str(user.id)][\"reasons\"]:\r\n reasons = \"None\"\r\n else:\r\n reasons = \", \".join([x for x in self.data[str(server.id)][\"user\"][str(user.id)][\"reasons\"]])\r\n if self.data[str(server.id)][\"user\"][str(user.id)][\"warnings\"] == 1:\r\n s = discord.Embed(description=\"{} is on 1 warning\".format(user), colour=user.colour)\r\n s.set_author(name=str(user), icon_url=user.avatar_url)\r\n s.add_field(name=\"Next Action\", value=action, inline=False)\r\n s.add_field(name=\"Reasons\", value=reasons, inline=False)\r\n await ctx.send(embed=s)\r\n else:\r\n try:\r\n s = discord.Embed(description=\"{} is on {} warnings\".format(user, self.data[str(server.id)][\"user\"][\r\n str(user.id)][\"warnings\"]), colour=user.colour)\r\n s.set_author(name=str(user), icon_url=user.avatar_url)\r\n s.add_field(name=\"Next Action\", value=action, inline=False)\r\n s.add_field(name=\"Reasons\", value=reasons, inline=False)\r\n await ctx.send(embed=s)\r\n except:\r\n await ctx.send(\"That user has no warnings :no_entry:\")\r\n except:\r\n await ctx.send(\"That user has no warnings :no_entry:\")","def time(self):\r\n time = datetime.datetime.now().strftime(\"%I:%M:%S\")\r\n self.speak(\"the current time is\")\r\n self.speak(time)","async def mute(self, ctx, member : discord.Member, *, reason : str):\r\n mutedRole = discord.utils.get(ctx.guild.roles, name = \"Muted\")\r\n if not mutedRole:\r\n channels = 0\r\n mutedRole = await ctx.guild.create_role(name=\"Muted\")\r\n for channel in ctx.guild.text_channels:\r\n await channel.set_permissions(mutedRole, send_messages=False)\r\n channels += 1 \r\n await ctx.send(f\"Successfully applied overwrites for {channels} channels\")\r\n await member.add_roles(mutedRole)\r\n embed = discord.Embed(title=\"Muted\", description = f\"You have been muted in **{ctx.guild.name}** by **{ctx.author}** **indefinetly** for reason **{reason}**\", colour = ctx.author.color, timestamp = datetime.datetime.now())\r\n await member.send(embed=embed)","async def _wait_setheist(self, ctx, seconds: int):\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n theme = await self.thief.config.guild(guild).Theme()\r\n t_crew = theme[\"Crew\"]\r\n\r\n if seconds > 0:\r\n config[\"Wait\"] = seconds\r\n await self.thief.config.guild(guild).Config.set(config)\r\n time_fmt = self.thief.time_format(seconds)\r\n msg = \"Setting {} gather time to {}.\".format(t_crew, time_fmt)\r\n else:\r\n msg = \"Need a number higher than 0.\"\r\n await ctx.send(msg)","def is_volume_muted(self) -> str | None:\n return self.zone.Mute","def set_max_time(self, time):\n self.widget().setMaximumTime(time)","def mute(self) -> bool:\n return bool(self.audio_mixer.getmute()[0])","def alarm_out_setup_change():\n setup_write(\"!M1 meas interval\", \"00:10:00\")\n setup_write(\"!M2 meas interval\", \"00:10:00\")\n setup_write(\"!TX3 scheduled interval\", \"01:00:00\")","async def mutechannel(self, ctx, channel: str):\n self.data_check(ctx)\n server = ctx.message.server\n\n self.riceCog2[server.id][\"mutechannel\"] = channel\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n await self.bot.say(\"Mute channel is now: **{}**\".format(channel))","def set_warning_message(msg):\n set_message(msg, TYPE_WARNING)","def set_mute(cls, mute: bool):\n raise NotImplementedError","def update_time_default(self, id_user:int, time_new:int) -> bool:\n try:\n self.cursor.execute(f\"UPDATE {table_users_settings} SET text_minutes = ? WHERE id_user = ?;\", (time_new, id_user))\n self.connection.commit()\n return True\n except Exception as e:\n msg = f\"We face problems with updating the default name; Mistake: {e}\"\n self.proceed_error(msg)\n return False","def set_time_now(self, *args, **kwargs):\n return _uhd_swig.usrp_sink_sptr_set_time_now(self, *args, **kwargs)","async def _death_setheist(self, ctx, seconds: int):\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n\r\n if seconds > 0:\r\n config[\"Death\"] = seconds\r\n await self.thief.config.guild(guild).Config.set(config)\r\n time_fmt = self.thief.time_format(seconds)\r\n msg = \"Setting death timer to {}.\".format(time_fmt)\r\n else:\r\n msg = \"Need a number higher than 0.\"\r\n await ctx.send(msg)","def set_time(self, sec):\n self.set_timed(round(sec * 10.0))","async def remove_mute(id: int) -> None:\n\n guild = BOT_GLOBAL.get_guild(BOT_GLOBAL.settings.guild_id)\n if guild is not None:\n mute_role = BOT_GLOBAL.settings.guild().role_mute\n mute_role = guild.get_role(mute_role)\n if mute_role is not None:\n user = guild.get_member(id)\n if user is not None:\n await user.remove_roles(mute_role)\n case = Case(\n _id=BOT_GLOBAL.settings.guild().case_id,\n _type=\"UNMUTE\",\n mod_id=BOT_GLOBAL.user.id,\n mod_tag=str(BOT_GLOBAL.user),\n reason=\"Temporary mute expired.\",\n )\n await BOT_GLOBAL.settings.inc_caseid()\n await BOT_GLOBAL.settings.add_case(user.id, case)\n\n u = await BOT_GLOBAL.settings.user(id=user.id)\n u.is_muted = False\n u.save()\n\n log = await prepare_unmute_log(BOT_GLOBAL.user, user, case)\n\n log.remove_author()\n log.set_thumbnail(url=user.avatar_url)\n\n public_chan = guild.get_channel(\n BOT_GLOBAL.settings.guild().channel_public)\n \n dmed = True\n try:\n await user.send(embed=log)\n except Exception:\n dmed = False\n \n await public_chan.send(user.mention if not dmed else \"\", embed=log)\n\n else:\n case = Case(\n _id=BOT_GLOBAL.settings.guild().case_id,\n _type=\"UNMUTE\",\n mod_id=BOT_GLOBAL.user.id,\n mod_tag=str(BOT_GLOBAL.user),\n reason=\"Temporary mute expired.\",\n )\n await BOT_GLOBAL.settings.inc_caseid()\n await BOT_GLOBAL.settings.add_case(id, case)\n\n u = await BOT_GLOBAL.settings.user(id=id)\n u.is_muted = False\n u.save()","def set_off_alarm(announcement:str):\r\n engine = pyttsx3.init()\r\n try:\r\n engine.endLoop()\r\n except:\r\n pass\r\n engine.say(announcement)\r\n engine.runAndWait()\r\n engine.stop()\r\n logging.info(\"Alarm announced in set_off_alarm(): \" + announcement)\r\n return redirect('/index')","def mute_health_alert(\n alert: str, node: CephAdmin, duration: str = None, sticky: bool = False\n) -> bool:\n all_alerts = get_alerts(node)\n if alert not in all_alerts[\"active_alerts\"] + all_alerts[\"muted_alerts\"]:\n log.info(f\"the alert: {alert} not generated on the cluster, Cannot mute\")\n return True\n if alert in all_alerts[\"muted_alerts\"]:\n log.info(f\"the alert: {alert} is already muted\")\n return True\n\n # Muting the given alert along with specified duration\n cmd = f\"ceph health mute {alert}\"\n if duration:\n cmd += f\" {duration}\"\n if sticky:\n cmd += \" --sticky\"\n node.shell([cmd])\n\n # Sleeping for 5 sec for the alert to be logged\n time.sleep(5)\n all_alerts = get_alerts(node)\n log.info(\n f\"Muted the alert : {alert}. All the muted alerts : {all_alerts['muted_alerts']}\"\n )\n return True if alert in all_alerts[\"muted_alerts\"] else False","def valkkafsmanager_set_time_cb(self, t):\n self.signals.set_time.emit(t)","def Init():\n global MySet\n global lastMessage\n\n MySet = Settings(settingsFile)\n lastMessage = time.time() - MySet.Cooldown*60","def auto_unmute():\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\"ids\"])\n\n # put user IDs of people you want to remain muted here\n users_keep_muted = set([])\n \n # mute all \n for user_id in muted:\n if user_id not in users_keep_muted:\n t.mutes.users.destroy(user_id=user_id)\n print(\"unmuted %d\" % (user_id))","def on_update_playback(self, event):\n try:\n offset = self.mplayer.GetTimePos()\n except:\n return\n print offset\n mod_off = str(offset)[-1]\n if mod_off == '0':\n print \"mod_off\"\n offset = int(offset)\n self.playbackSlider.SetValue(offset)\n secsPlayed = time.strftime('%M:%S', time.gmtime(offset))\n self.trackCounter.SetLabel(secsPlayed)","async def _sentence_setheist(self, ctx, seconds: int):\r\n guild = ctx.guild\r\n config = await self.thief.get_guild_settings(guild)\r\n theme = await self.thief.config.guild(guild).Theme()\r\n t_jail = theme[\"Jail\"]\r\n t_sentence =theme[\"Sentence\"]\r\n\r\n if seconds > 0:\r\n config[\"Sentence\"] = seconds\r\n await self.thief.config.guild(guild).Config.set(config)\r\n time_fmt = self.thief.time_format(seconds)\r\n msg = \"Setting base {} {} to {}.\".format(t_jail, t_sentence, time_fmt)\r\n else:\r\n msg = \"Need a number higher than 0.\"\r\n await ctx.send(msg)","async def before_ad_reminder(self):\r\n await self.bot.wait_until_ready()\r\n await sleep(5) # To make sure bot reads settings\r\n now = datetime.now()\r\n next_hour = now.replace(minute=0, second=0, microsecond=0) + timedelta(hours=1)\r\n await sleep((next_hour - now).total_seconds())","def mute_output_toggle(self):\n self._mute_toggle(self.pulse_core.Get(\"org.PulseAudio.Core1\", \"FallbackSink\"))","def wishMe():\r\n hour = int(datetime.datetime.now().hour)\r\n\r\n if hour >= 0 and hour < 12:\r\n speak(\"Good morning\" + MASTER)\r\n\r\n elif hour >= 12 and hour < 18:\r\n speak(\"Good afternoon\" + MASTER)\r\n else:\r\n speak(\"Good Evening\" + MASTER)\r\n # speak(\"I am VA. How may I help you?\")\r","async def voice_mute(self, ctx, member: discord.Member, *,reason: typing.Optional[str]):\n await member.edit(mute=True, reason=reason[:512])\n await ctx.send(f\"User {member.mention} successfully muted from voice\")\n if reason:\n await self.check_channel.send(f\"user {member.mention} muted from voice for the following reason:\\n\"\n f\"{reason}\")","async def tradingmute(\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\n ):\n\n if target.guild_permissions.kick_members:\n return await ctx.send(\"You can't punish that person!\")\n\n if isinstance(reason, time.UserFriendlyTime):\n expires_at = reason.dt\n reason = reason.arg\n else:\n expires_at = None\n\n action = TradingMute(\n target=target,\n user=ctx.author,\n reason=reason,\n guild_id=ctx.guild.id,\n created_at=ctx.message.created_at,\n expires_at=expires_at,\n )\n await action.execute(ctx)\n await action.notify()\n if action.duration is None:\n await ctx.send(f\"Muted **{target}** in trading channels.\")\n else:\n await ctx.send(\n f\"Muted **{target}** in trading channels for **{time.human_timedelta(action.duration)}**.\"\n )","def getSafetyMute(self, unitCode=0):\n resp = self.XAPCommand('SFTYMUTE', unitCode=unitCode)\n return bool(int(resp))","async def unmute(self, ctx, user: discord.Member, *, reason: str = None):\r\n server = ctx.message.guild\r\n channel = ctx.message.channel\r\n author = ctx.message.author\r\n action = \"Unmute\"\r\n role = discord.utils.get(server.roles, name=\"Muted - Sensei\")\r\n if not role:\r\n await ctx.send(\"No-one is muted in this server :no_entry:\")\r\n return\r\n if role not in user.roles:\r\n await ctx.send(\"**{}** is not muted :no_entry:\".format(user))\r\n return\r\n try:\r\n await user.remove_roles(role)\r\n except:\r\n await ctx.send(\"I cannot remove the mute role from the user :no_entry:\")\r\n return\r\n await ctx.send(f\"**{user}** has been unmuted {self.bot.get_emoji(470063310386233344)}\")\r\n try:\r\n await self._log(author, server, action, reason, user)\r\n except:\r\n pass\r\n self.d[str(server.id)][str(user.id)][\"toggle\"] = False\r\n self.d[str(server.id)][str(user.id)][\"time\"] = None\r\n self.d[str(server.id)][str(user.id)][\"amount\"] = None\r\n dataIO.save_json(self.file, self.d)\r\n try:\r\n s = discord.Embed(title=\"You have been unmuted early in {}\".format(server.name), colour=000000,\r\n timestamp=datetime.datetime.utcnow())\r\n s.add_field(name=\"Moderator\", value=\"{} ({})\".format(author, str(author.id)))\r\n await user.send(embed=s)\r\n except:\r\n pass","def LingerTime(self) -> int:","def wishMe():\n hour = int(datetime.datetime.now().hour)\n if 0 <= hour < 12:\n speak(\"Good Morning Boss\")\n\n elif 12 <= hour < 18:\n speak(\"Good Afternoon Boss\")\n\n else:\n speak(\"Good Evening Boss!\")\n\n speak(\"This is mayaa appointed as your Assistant\")","async def set_mute(self, value: bool):\n await self._pytheos.api.player.set_mute(self.id, value)","def set_sleep_time(self, milliseconds:int):\n self.send_command(f\"configure mainLoopSleepTime {milliseconds}\")","def silence(self):\n return self._replace(velocity=0.)","def set_time_unknown_pps(self, *args, **kwargs):\n return _uhd_swig.usrp_sink_set_time_unknown_pps(self, *args, **kwargs)","def reset_timer(self, *_) -> \"ALL\":\n self.last = time.time()\n delta = time.time() - self.last\n if delta > 180:\n print(\n \"!!! Warning: Watchdog failure detected, spawning a fallback \"\n \"thread.\"\n )\n self.watchdog = FallbackWatchdog(self)\n self.watchdog.start()","def catchall_handler(*args, **kwargs):\n if len(args) < 2 or args[0] != 'org.mpris.MediaPlayer2.Player':\n return\n\n song_id = args[1]['Metadata']['mpris:trackid']\n\n if not muted and song_id.startswith(AD_ID):\n mute()\n elif muted and not song_id.startswith(AD_ID):\n time.sleep(1.5) # Adding a delay since the signal is released early\n unmute()","def _set_default_rtimes(channelFormats):\n for channelFormat in channelFormats:\n for bf in channelFormat.audioBlockFormats:\n if bf.rtime is None and bf.duration is not None:\n bf.rtime = Fraction(0)\n warnings.warn(\n f\"added missing rtime to {bf.id}; BS.2127-0 states that \"\n \"rtime and duration should both be present or absent\"\n )","def _handle_timeme_action(bot, event, command):\n if event.text.startswith('/timer ') or event.text.startswith('/timeme '):\n full_tokens = re.split('\\s+', event.text)\n if len(full_tokens) > 1:\n full_tokens[0] = \"timeme\"\n yield from asyncio.sleep(0.2)\n yield from command.run(bot, event, *full_tokens)","def setInitialTime(self, T0):\n raise \"use method setInitialTime of class ReactorNet\"\n #_cantera.reactor_setInitialTime(self.__reactor_id, T0)","async def warn(self, ctx, user: discord.Member, *, reason: str=None):\n self.data_check(ctx)\n server = ctx.message.server\n author = ctx.message.author\n channel = ctx.message.channel\n\n can_ban = channel.permissions_for(server.me).ban_members\n can_role = channel.permissions_for(server.me).manage_roles\n\n if reason is None:\n msg = await self.bot.say(\"Please enter a reason for the warning!\")\n await asyncio.sleep(5)\n await self.bot.delete_message(msg)\n await self.bot.delete_message(ctx.message)\n return\n\n if can_ban:\n pass\n await self.bot.delete_message(ctx.message)\n else:\n await self.bot.say(\"Sorry, I can't warn this user.\\n\"\n \"I am missing the `ban_members` permission\")\n return\n\n if server.id not in self.riceCog2:\n msg = default_warn\n ban = default_ban\n _max = default_max\n defchannel = default_channel\n\n if server.id not in self.riceCog:\n self.riceCog[server.id] = {}\n \n if server.id not in self.warnlist2:\n self.warnlist2[server.id] = {} \n\n if 'pm_warn' not in self.riceCog[server.id]:\n self.riceCog[server.id]['pm_warn'] = False\n\n p = self.riceCog[server.id]['pm_warn']\n\n try:\n msg = self.riceCog2[server.id][\"warn_message\"]\n except:\n msg = default_warn\n try:\n mutetime = self.riceCog2[server.id][\"mutetime\"]\n except:\n mutetime = defmutetime\n try:\n ban = self.riceCog2[server.id][\"ban_message\"]\n except:\n ban = default_ban\n try:\n _max = self.riceCog2[server.id][\"max\"]\n except:\n _max = default_max\n try:\n defchannel = self.riceCog2[server.id][\"defchannel\"]\n except:\n defchannel = default_channel\n colour = 0x9e0101\n\n # checks if the user is in the file\n if server.id not in self.riceCog2:\n self.riceCog2[server.id] = {}\n dataIO.save_json(self.warning_settings,\n self.riceCog2)\n if server.id not in self.riceCog:\n self.riceCog[server.id] = {}\n dataIO.save_json(self.profile,\n self.riceCog)\n if user.id not in self.riceCog[server.id]:\n self.riceCog[server.id][user.id] = {}\n dataIO.save_json(self.profile,\n self.riceCog)\n else:\n pass\n else:\n if user.id not in self.riceCog[server.id]:\n self.riceCog[server.id][user.id] = {}\n dataIO.save_json(self.profile,\n self.riceCog)\n else:\n pass\n\n if \"Count\" in self.riceCog[server.id][user.id]:\n count = self.riceCog[server.id][user.id][\"Count\"]\n else:\n count = 0 \n try:\n defchannel = self.riceCog2[server.id][\"defchannel\"]\n except:\n defchannel = default_channel\n channel = discord.utils.get(server.channels, name = defchannel)\n if channel is None:\n msg = await self.bot.say (\"I was unable to write to your log channel. Please make sure there is a channel called {} on the server!\".format(defchannel))\n return\n else:\n pass\n \n\n # checks how many warnings the user has\n if count == 0:\n count += 1\n msg = await self.filter_message(msg=msg,\n user=user,\n count=count,\n _max=_max)\n data = discord.Embed(colour=colour)\n data.add_field(name=\"Warning:\",\n value=msg)\n if reason:\n data.add_field(name=\"Reason:\",\n value=reason,\n inline=False)\n data.add_field(name=\"Additional Actions:\", value=\"*In addition to this you have been muted for {} as a result of your actions.*\".format(mutetime), inline=False)\n data.set_footer(text=server.name)\n if p:\n #if dm is on\n await self.bot.send_message(user, embed=data) \n elif not p:\n #if dm is not on\n await self.bot.say(embed=data)\n \n #run and log\n await self._punish_cmd_common(ctx, user, reason=reason, duration=mutetime)\n mod=author\n user=user\n reason=reason\n countnum = \"{}/3\".format(count)\n ID = uuid.uuid4()\n jsonid = \"{}\".format(ID)\n embed=discord.Embed(title=\"User Warned:\", color=colour)\n embed.add_field(name=\"Case ID:\", value=ID, inline=False)\n embed.add_field(name=\"Moderator:\", value=mod, inline=False)\n embed.add_field(name=\"User:\", value=\"{0} ({0.id})\".format(user), inline=False)\n embed.add_field(name=\"Reason:\", value=reason, inline=False)\n embed.add_field(name=\"Warning Number:\", value=countnum, inline=False)\n react = await self.bot.send_message(channel, embed=embed)\n await self.bot.add_reaction(react, \"\\U0001f44d\")\n await self.bot.add_reaction(react, \"\\U0001f44e\")\n await self.bot.add_reaction(react, \"\\U0001f937\")\n\n self.riceCog[server.id][user.id].update({\"Count\": count})\n dataIO.save_json(self.profile,\n self.riceCog)\n \n self.warnlist2[server.id][jsonid] = {\n 'User': user.id,\n 'Mod': mod.id,\n 'Reason': reason,\n 'Warning Number': countnum\n }\n dataIO.save_json(self.warnlist,\n self.warnlist2) \n \n elif count > 0 and count < _max -1:\n count += 1\n msg = await self.filter_message(msg=msg,\n user=user,\n count=count,\n _max=_max)\n data = discord.Embed(colour=colour)\n data.add_field(name=\"Warning:\",\n value=msg)\n if reason:\n data.add_field(name=\"Reason:\",\n value=reason,\n inline=False)\n data.set_footer(text=server.name)\n if p:\n #if pm on\n await self.bot.send_message(user, embed=data)\n elif not p:\n #if pm off\n await self.bot.say(embed=data)\n \n #run and log\n mod=author\n user=user\n reason=reason\n ID = uuid.uuid4()\n jsonid = \"{}\".format(ID)\n countnum = \"{}/3\".format(count)\n embed=discord.Embed(title=\"User Warned:\", color=colour)\n embed.add_field(name=\"Case ID:\", value=ID, inline=False)\n embed.add_field(name=\"Moderator:\", value=mod, inline=False)\n embed.add_field(name=\"User:\", value=\"{0} ({0.id})\".format(user), inline=False)\n embed.add_field(name=\"Reason:\", value=reason, inline=False)\n embed.add_field(name=\"Warning Number:\", value=countnum, inline=False)\n react = await self.bot.send_message(channel, embed=embed)\n await self.bot.add_reaction(react, \"\\U0001f44d\")\n await self.bot.add_reaction(react, \"\\U0001f44e\")\n await self.bot.add_reaction(react, \"\\U0001f937\")\n self.riceCog[server.id][user.id].update({\"Count\": count})\n dataIO.save_json(self.profile,\n self.riceCog)\n self.warnlist2[server.id][jsonid]= {\n 'User': user.id,\n 'Mod': mod.id,\n 'Reason': reason,\n 'Warning Number': countnum\n }\n dataIO.save_json(self.warnlist,\n self.warnlist2) \n\n else:\n msg = ban\n msg = await self.filter_message(msg=msg,\n user=user,\n count=count,\n _max=_max)\n data = discord.Embed(colour=colour)\n data.add_field(name=\"Warning\",\n value=msg)\n if reason:\n data.add_field(name=\"Reason\",\n value=reason,\n inline=False)\n data.set_footer(text=server.name)\n if p:\n await self.bot.send_message(user, embed=data)\n elif not p:\n await self.bot.say(embed=data)\n \n #run and log\n mod=author\n user=user\n reason=reason\n ID = uuid.uuid4()\n jsonid = \"{}\".format(ID)\n bantext = \"Max Warnings reached.\"\n embed=discord.Embed(title=\"User Banned:\", color=colour)\n embed.add_field(name=\"Case ID:\", value=ID, inline=False)\n embed.add_field(name=\"Moderator:\", value=mod, inline=False)\n embed.add_field(name=\"User:\", value=\"{0} ({0.id})\".format(user), inline=False)\n embed.add_field(name=\"Reason:\", value=reason, inline=False)\n embed.add_field(name=\"Additional Actions:\", value=\"*As the user has reached 3 warnings they have been banned from the server.*\", inline=False)\n react = await self.bot.send_message(channel, embed=embed)\n await self.bot.add_reaction(react, \"\\U0001f44d\")\n await self.bot.add_reaction(react, \"\\U0001f44e\")\n await self.bot.add_reaction(react, \"\\U0001f937\")\n count = 0\n self.riceCog[server.id][user.id].update({\"Count\": count})\n dataIO.save_json(self.profile,\n self.riceCog)\n self.warnlist2[server.id][jsonid] = {\n 'User': user.id,\n 'Mod': mod.id,\n 'Reason': reason,\n 'Warning Number': bantext\n }\n dataIO.save_json(self.warnlist,\n self.warnlist2) \n try:\n await self.bot.ban(user, delete_message_days=0)\n except discord.errors.Forbidden:\n await self.bot.say(\"I don't have permissions to ban that user.\")\n\n if 'poop' in self.riceCog2[server.id] and can_role:\n if self.riceCog2[server.id]['poop'] == True:\n poops = count * \"\\U0001f528\"\n role_name = \"Warning {}\".format(poops)\n is_there = False\n colour = 0xbc7642\n for role in server.roles:\n if role.name == role_name:\n poop_role = role\n is_there = True\n if not is_there:\n poop_role = await self.bot.create_role(server)\n await self.bot.edit_role(role=poop_role,\n name=role_name,\n server=server)\n try:\n await self.bot.add_roles(user,\n poop_role)\n except discord.errors.Forbidden:\n await self.bot.say(\"No permission to add roles\")","async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\"no reason\"):\n\n role = discord.utils.get(ctx.guild.roles, name=\"Muted\")\n member_display = []\n\n for i, member in enumerate(members):\n if role in member.roles:\n await ctx.send(f\"guild member `{member.display_name}` is already muted\", delete_after=8)\n del members[i]\n\n if role is None:\n permissions = discord.Permissions()\n permissions.change_nickname = True\n permissions.send_messages = False\n permissions.read_message_history = True\n role = await ctx.guild.create_role(name=\"Muted\", permissions=permissions)\n\n await self.set_perms(ctx.guild, role)\n\n for member in members:\n\n if await self.hiearchy_check(ctx, member):\n continue\n\n member_display.append(str(member))\n await member.add_roles(role, reason=reason)\n\n member_display = \", \".join(member_display)\n\n if not member_display:\n member_display = \"no one\"\n\n await ctx.send(f\"> {ctx.author.name} muted {member_display}\")","def default_delay(self):\n return DEFAULT_DELAY","def set_timer(bot, update, args, job_queue, chat_data):\n chat_id = update.message.chat_id\n try:\n # args[0] should contain the time for the timer in seconds\n due = int(args[0])\n if due < 0:\n update.message.reply_text('Sinto muito, mas ainda nao podemos viajar no tempo')\n return\n\n # Add job to queue\n job = job_queue.run_repeating(alarm, due, context=chat_id)\n chat_data['job'] = job\n check = emojize(\":white_check_mark:\", use_aliases=True)\n update.message.reply_text('Intervalo das notificacoes setado com sucesso '+check+'')\n\n except (IndexError, ValueError):\n update.message.reply_text('Exemplo: /set <seconds>')","def _send_time(self):\n if 'time' not in self.loopback_guard:\n content = {'time': self.time.isoformat()}\n self.send_action('set_time', content)","async def warnCMD(\n self,\n ctx: SlashContext,\n user: typing.Union[discord.User, discord.Member],\n reason: str = None,\n ):\n await ctx.defer()\n user_data = await self.bot.get_member_data(ctx.guild_id, user.id)\n warning_num = int(user_data.warnings) + 1\n\n embed = discord.Embed(title=f\"Warning for {user.name} #{user.discriminator}\", color=0xE7C30D)\n embed.add_field(\n name=\"Warning LVL.1\",\n value=\":white_check_mark:\" if warning_num >= 1 else \":x:\",\n inline=False,\n )\n embed.add_field(\n name=\"Warning LVL.2\",\n value=\":white_check_mark:\" if warning_num >= 2 else \":x:\",\n inline=False,\n )\n embed.add_field(\n name=\"Warning LVL.3\",\n value=\":white_check_mark:\" if warning_num >= 3 else \":x:\",\n inline=False,\n )\n embed.set_footer(\n text=f\"Warned by {ctx.author.name}#{ctx.author.discriminator}\",\n icon_url=ctx.author.avatar_url,\n )\n if warning_num >= 3:\n embed.colour = discord.Colour.red()\n action = None\n autoReason = None\n if warning_num == 4:\n action = ModActions.kick\n autoReason = \"Auto: 4th warning\"\n embed.description = \"User has 4 warnings, they have been kicked\"\n await user.kick(reason=autoReason)\n elif warning_num >= 5:\n action = ModActions.ban\n autoReason = f\"Auto: {warning_num}th warning\"\n embed.description = \"User has 5 warnings, they have been banned\"\n await ctx.guild.ban(user, reason=autoReason, delete_message_days=0)\n if action and autoReason:\n await self.bot.paladinEvents.add_item(\n Action(\n actionType=action,\n moderator=ctx.author,\n guild=ctx.guild,\n user=user,\n reason=autoReason,\n )\n )\n await ctx.send(embed=embed)\n\n await self.bot.paladinEvents.add_item(\n Action(\n actionType=ModActions.warn,\n moderator=ctx.author,\n guild=ctx.guild,\n user=user,\n extra=warning_num,\n reason=reason,\n )\n )\n\n user_data.warnings = warning_num\n await self.bot.redis.set(user_data.key, user_data.to_json())","def remaining_ms():"],"string":"[\n \"def silly(self):\\n print(\\\"you are getting silly\\\")\\n # 设置时效\\n return self._silly if time.localtime(time.time() - self._old_time ).tm_sec < 3 else \\\"\\\"\",\n \"def mute(self, msg, args):\\n if self.mute:\\n self.mute=False\\n return \\\"Yay, I can make noise again!\\\"\\n else:\\n self.mute=True\\n return \\\"OK, I'll shut up now!\\\"\",\n \"async def mutetime(self, ctx):\\n self.data_check(ctx)\\n server = ctx.message.server\\n \\n await self.bot.say(\\\"Please make sure to set the time with the correct time prefix at the end. (*For minutes 'm', for hours 'h'*)\\\\n\\\\nPlease type your timeframe now.\\\")\\n muteroletime = await self.bot.wait_for_message(channel = ctx.message.channel, author = ctx.message.author)\\n\\n if \\\"m\\\" in muteroletime.content or \\\"s\\\" in muteroletime.content or \\\"h\\\" in muteroletime.content:\\n self.riceCog2[server.id][\\\"mutetime\\\"] = muteroletime.content\\n dataIO.save_json(self.warning_settings,\\n self.riceCog2)\\n await self.bot.say(\\\"Default mute time is now: **{}**\\\".format(muteroletime.content))\\n else:\\n await self.bot.say(\\\"You've done something wrong! Please make sure that the format is correct!\\\")\\n return\",\n \"async def mute(self, *args, **kwargs):\\n self.muted = not self.muted # toogle\\n if self.muted:\\n self.just_muted = True\\n return \\\"I've been muted :(\\\"\\n return \\\"I'm back! :D\\\"\",\n \"async def mute(self, ctx, user: discord.Member, time_and_unit=None, *, reason: str = None):\\r\\n server = ctx.message.guild\\r\\n channel = ctx.message.channel\\r\\n author = ctx.message.author\\r\\n if channel.permissions_for(user).administrator:\\r\\n await ctx.send(\\\"That user has administrator perms, why would I even try :no_entry:\\\")\\r\\n return\\r\\n if user.top_role.position >= author.top_role.position:\\r\\n if author == server.owner:\\r\\n pass\\r\\n else:\\r\\n await ctx.send(\\\"You can not mute someone higher than your own role :no_entry:\\\")\\r\\n return\\r\\n if not time_and_unit:\\r\\n time2 = 600\\r\\n time = \\\"10\\\"\\r\\n unit = \\\"minutes\\\"\\r\\n else:\\r\\n try:\\r\\n unit = time_and_unit[len(time_and_unit) - 1:len(time_and_unit)]\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n try:\\r\\n time = time_and_unit[0:len(time_and_unit) - 1]\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if unit == \\\"s\\\":\\r\\n try:\\r\\n time2 = int(time)\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"second\\\"\\r\\n else:\\r\\n unit = \\\"seconds\\\"\\r\\n elif unit == \\\"m\\\":\\r\\n try:\\r\\n time2 = int(time) * 60\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"minute\\\"\\r\\n else:\\r\\n unit = \\\"minutes\\\"\\r\\n elif unit == \\\"h\\\":\\r\\n try:\\r\\n time2 = int(time) * 3600\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"hour\\\"\\r\\n else:\\r\\n unit = \\\"hours\\\"\\r\\n elif unit == \\\"d\\\":\\r\\n try:\\r\\n time2 = int(time) * 86400\\r\\n except ValueError:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n if time == \\\"1\\\":\\r\\n unit = \\\"day\\\"\\r\\n else:\\r\\n unit = \\\"days\\\"\\r\\n else:\\r\\n await ctx.send(\\\"Invalid time unit :no_entry:\\\")\\r\\n return\\r\\n action = \\\"Mute ({} {})\\\".format(time, unit)\\r\\n if str(server.id) not in self.d:\\r\\n self.d[str(server.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if str(user.id) not in self.d[str(server.id)]:\\r\\n self.d[str(server.id)][str(user.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"toggle\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"time\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"amount\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n role = discord.utils.get(server.roles, name=\\\"Muted - Sensei\\\")\\r\\n overwrite = discord.PermissionOverwrite()\\r\\n overwrite.send_messages = False\\r\\n perms = discord.PermissionOverwrite()\\r\\n perms.speak = False\\r\\n if not role:\\r\\n role = await server.create_role(name=\\\"Muted - Sensei\\\")\\r\\n for channels in ctx.guild.text_channels:\\r\\n await channels.set_permissions(role, overwrite=overwrite)\\r\\n for channels in ctx.guild.voice_channels:\\r\\n await channels.set_permissions(role, overwrite=perms)\\r\\n if role in user.roles:\\r\\n await ctx.send(\\\"**{}** is already muted :no_entry:\\\".format(user))\\r\\n return\\r\\n try:\\r\\n await user.add_roles(role)\\r\\n except:\\r\\n await ctx.send(\\\"I cannot add the mute role to the user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(f\\\"**{user}** has been muted for {time} {unit} {self.bot.get_emoji(470063310386233344)}\\\")\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = True\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = time2\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = ctx.message.created_at.timestamp()\\r\\n dataIO.save_json(self.file, self.d)\\r\\n try:\\r\\n s = discord.Embed(title=\\\"You have been muted in {} :speak_no_evil:\\\".format(server.name), colour=0xfff90d,\\r\\n timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.add_field(name=\\\"Moderator\\\", value=\\\"{} ({})\\\".format(author, str(author.id)), inline=False)\\r\\n s.add_field(name=\\\"Time\\\", value=\\\"{} {}\\\".format(time, unit), inline=False)\\r\\n if reason:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n await user.send(embed=s)\\r\\n except:\\r\\n pass\",\n \"async def _mute(ctx, user:discord.Member, time: str, self: bool):\\n if user.id in PI_BOT_IDS:\\n return await ctx.send(\\\"Hey! You can't mute me!!\\\")\\n if time == None:\\n return await ctx.send(\\\"You need to specify a length that this used will be muted. Examples are: `1 day`, `2 months, 1 day`, or `indef` (aka, forever).\\\")\\n role = None\\n if self:\\n role = discord.utils.get(user.guild.roles, name=ROLE_SELFMUTE)\\n else:\\n role = discord.utils.get(user.guild.roles, name=ROLE_MUTED)\\n parsed = \\\"indef\\\"\\n if time != \\\"indef\\\":\\n parsed = dateparser.parse(time, settings={\\\"PREFER_DATES_FROM\\\": \\\"future\\\"})\\n if parsed == None:\\n return await ctx.send(\\\"Sorry, but I don't understand that length of time.\\\")\\n CRON_LIST.append({\\\"date\\\": parsed, \\\"do\\\": f\\\"unmute {user.id}\\\"})\\n await user.add_roles(role)\\n eastern = pytz.timezone(\\\"US/Eastern\\\")\\n await ctx.send(f\\\"Successfully muted {user.mention} until `{str(eastern.localize(parsed))} EST`.\\\")\",\n \"def mute():\\n request_command(tv_command=TVCommand.mute)\",\n \"async def moderation(self, ctx):\\n\\n new_value = await self.toggle_dm_setting(ctx.author.id, \\\"ban_kick_mute\\\")\\n\\n if new_value:\\n message = \\\":white_check_mark: You will now receive DMs when you get muted, kicked or banned by me.\\\"\\n else:\\n message = \\\":white_check_mark: You will no longer receive DMs when you get muted, kicked or banned.\\\"\\n\\n await ctx.send(message)\",\n \"async def mute(self, ctx, member: discord.Member, *, time:TimeConverter = None):\\r\\n\\r\\n if member.top_role >= ctx.author.top_role:\\r\\n return await ctx.send(\\\"you can't mute that person\\\")\\r\\n\\r\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\r\\n await member.add_roles(role)\\r\\n await ctx.reply((f\\\"Muted {member} for {time}s\\\" if time else f\\\"Muted {member}\\\"))\\r\\n\\r\\n if time:\\r\\n await asyncio.sleep(time)\\r\\n await member.remove_roles(role)\",\n \"def mute_track(self, track, muted):\\n pass\",\n \"def set_time(self, value: float):\\n super().set_time(value)\\n self.music.set_time(value)\",\n \"def _mute_update_handler(self, new_mute, dev_path):\\n # Only do something if it's one of the default/fallback devices\\n if dev_path == self.pulse_core.Get(\\\"org.PulseAudio.Core1\\\", \\\"FallbackSink\\\"):\\n self.output_muted = bool(new_mute)\\n # self.set_mute_output(bool(new_mute))\\n elif dev_path == self.pulse_core.Get(\\\"org.PulseAudio.Core1\\\", \\\"FallbackSource\\\"):\\n self.input_muted = bool(new_mute)\\n # self.set_mute_input(bool(new_mute))\\n else:\\n print(\\\"Don't care about this device\\\", dev_path, new_mute)\",\n \"def disable_mute(self):\\n self.mute = False\",\n \"async def mutedlist(self, ctx):\\r\\n server = ctx.message.guild\\r\\n msg = \\\"\\\"\\r\\n i = 0\\r\\n try:\\r\\n for userid in self.d[str(server.id)]:\\r\\n if self.d[str(server.id)][userid][\\\"toggle\\\"] == True:\\r\\n i = i + 1\\r\\n except:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n if i == 0:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n for userid in self.d[str(server.id)]:\\r\\n if self.d[str(server.id)][userid][\\\"time\\\"] == None or self.d[str(server.id)][userid][\\\"time\\\"] - ctx.message.created_at.timestamp() + self.d[str(server.id)][userid][\\\"amount\\\"] <= 0:\\r\\n time = \\\"Infinite\\\"\\r\\n else:\\r\\n m, s = divmod(self.d[str(server.id)][userid][\\\"time\\\"] - ctx.message.created_at.timestamp() +\\r\\n self.d[str(server.id)][userid][\\\"amount\\\"], 60)\\r\\n h, m = divmod(m, 60)\\r\\n d, h = divmod(h, 24)\\r\\n if d == 0:\\r\\n time = \\\"%d hours %d minutes %d seconds\\\" % (h, m, s)\\r\\n if h == 0 and d == 0:\\r\\n time = \\\"%d minutes %d seconds\\\" % (m, s)\\r\\n elif h == 0 and m == 0:\\r\\n time = \\\"%d seconds\\\" % (s)\\r\\n else:\\r\\n time = \\\"%d days %d hours %d minutes %d seconds\\\" % (d, h, m, s)\\r\\n if self.d[str(server.id)][userid][\\\"toggle\\\"] == True:\\r\\n user = discord.utils.get(server.members, id=int(userid))\\r\\n if user:\\r\\n msg += \\\"{} - {} (Till mute ends)\\\\n\\\".format(user, time)\\r\\n if not msg:\\r\\n await ctx.send(\\\"No one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n s = discord.Embed(description=msg, colour=0xfff90d, timestamp=datetime.datetime.utcnow())\\r\\n s.set_author(name=\\\"Mute List for {}\\\".format(server), icon_url=server.icon_url)\\r\\n await ctx.send(embed=s)\",\n \"async def selfmute(ctx, *args):\\n user = ctx.message.author\\n if await is_staff(ctx):\\n return await ctx.send(\\\"Staff members can't self mute.\\\")\\n time = \\\" \\\".join(args)\\n await _mute(ctx, user, time, self=True)\",\n \"async def mute(self, ctx, user: discord.Member, amount: int, time_unit: str, *, reason: typing.Optional[str]):\\n length, error_msg = self.convert_mute_length(amount, time_unit)\\n if not length:\\n await ctx.send(error_msg)\\n return\\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\\n mute_message = f\\\"user {user.mention} was muted\\\"\\n await user.add_roles(self.mute_role)\\n await ctx.send(mute_message)\\n if reason:\\n mute_message = f\\\"{mute_message} for the following reason:\\\\n{reason}\\\"\\n await self.add_mute_to_mute_list(user.id, unmute_ts)\\n await self.check_channel.send(mute_message)\",\n \"def _change_time(self):\\r\\n msg = \\\"Notice! if you don't write hours the time\\\\nwill be calculated as seconds.\\\\nEnter new time:\\\"\\r\\n new_time = simpledialog.askstring(title=\\\"Change recording time\\\", prompt=msg)\\r\\n\\r\\n # new_time has to be a digit bigger than 0\\r\\n while not new_time:\\r\\n msg = \\\"Time must have a value. For example: 1 hours/ 1.5 hours/ 25 seconds\\\"\\r\\n messagebox.showerror(title=\\\"ERROR\\\", message=msg)\\r\\n new_time = simpledialog.askstring(title=\\\"Change recording time\\\", prompt=\\\"Enter new time:\\\")\\r\\n if new_time:\\r\\n self.time.set(\\\"time: \\\" + new_time + ''.join(' ' for _ in range(42 - len(new_time))))\",\n \"def mute(self) -> None:\\n self.system.notify(\\\"Jarvis::Muted\\\")\\n self.media.mute(True)\",\n \"async def selfmute(self, ctx, amount:int, time_unit:str):\\n\\n length, error_msg = self.convert_mute_length(amount, time_unit)\\n if not length:\\n await ctx.send(error_msg)\\n return\\n if length > 7 * self.units[\\\"days\\\"]:\\n question = await ctx.send(f\\\"Are you sure you want to be muted for {(length/self.units['days']):.2f} days?\\\\n\\\"\\n f\\\"answer with Y[es] or N[o]\\\")\\n\\n def msg_check(message):\\n return message.author.id == ctx.author.id and message.channel.id == ctx.message.channel.id\\n try:\\n message = await self.bot.wait_for(\\\"message\\\", check=msg_check, timeout=20.0)\\n if re.match(r\\\"y(es)?\\\", message.content.lower()):\\n pass\\n else:\\n await question.edit(content=\\\"self mute aborted\\\")\\n return\\n except asyncio.TimeoutError:\\n await question.edit(content=\\\"Timeout: mute aborted\\\")\\n return\\n\\n unmute_ts = datetime.datetime.utcnow() + datetime.timedelta(seconds=length)\\n await ctx.author.add_roles(self.mute_role)\\n await ctx.send(\\\"You have been muted\\\")\\n await self.add_mute_to_mute_list(ctx.author.id, unmute_ts)\",\n \"def alarm_in_setup_change():\\n setup_write(\\\"!M1 meas interval\\\", \\\"00:01:00\\\")\\n setup_write(\\\"!M2 meas interval\\\", \\\"00:01:00\\\")\\n setup_write(\\\"!TX3 scheduled interval\\\", \\\"00:05:00\\\")\",\n \"async def tempmute(self, ctx,\\n\\t\\ttarget: discord.Member,\\n\\t\\tduration: DurationConverter,\\n\\t\\t*, reason: str = \\\"No reason given.\\\"\\n\\t):\\n\\n\\t\\tself.check_perms(ctx.author, target)\\n\\n\\t\\thandler = await Handler.new(self.bot, ctx.guild)\\n\\t\\tawait handler.mute(ctx.author, target, reason, duration)\\n\\n\\t\\tawait ctx.success(f\\\"{target} (`{target.id}`) has been muted for {time_since(seconds=duration)} for:\\\\n{reason}\\\")\",\n \"async def mute(self, ctx, member: discord.Member, time='15m'):\\n guild_permissions = member.guild_permissions\\n wait_time = parse_time(time).total_seconds()\\n # Because sometimes members have nicknames with markdown\\n escaped_name = escape_markdown(member.display_name)\\n\\n if guild_permissions.kick_members:\\n # do not mute someone who has permissions to kick members\\n await ctx.send(f'Cannot mute {escaped_name} due to roles.')\\n\\n elif member.bot:\\n # do not mute bots\\n await ctx.send(f'Cannot mute {escaped_name} (is a bot).')\\n\\n else:\\n overwrite = discord.PermissionOverwrite(\\n add_reactions=False,\\n send_messages=False,\\n )\\n\\n log_str = (f'{ctx.author.display_name} has muted '\\n f'member {member} (<@{member.id}>) for {time}.')\\n logger.info(log_str)\\n\\n for channel in ctx.guild.text_channels:\\n permissions = channel.permissions_for(member)\\n\\n if permissions.read_messages:\\n await channel.set_permissions(member, overwrite=overwrite)\\n\\n await asyncio.sleep(wait_time)\\n await ctx.invoke(self.unmute, member)\",\n \"def mute(self, value: bool) -> None:\\n self.audio_mixer.setmute(value)\\n self.update_status()\",\n \"def play_warning_song(self):\\n if self._warning_song_num is None:\\n self.set_warning_song(0)\\n\\n self._serial_conn.send_command(\\\"141 \\\" + str(self._warning_song_num))\",\n \"async def resettimer(self, ctx:commands.Context, member: Member = None):\\r\\n\\r\\n await self.config.member(member if not member == None else ctx.message.author).currently_fishing.set(False)\\r\\n await ctx.send('Fishing cooldown reset')\",\n \"async def warn(self, ctx, user: discord.Member, *, reason: str = None):\\r\\n author = ctx.message.author\\r\\n server = ctx.message.guild\\r\\n channel = ctx.message.channel\\r\\n if user == author:\\r\\n await ctx.send(\\\"You can not warn yourself :no_entry:\\\")\\r\\n return\\r\\n if user.top_role.position >= author.top_role.position:\\r\\n if author == server.owner:\\r\\n pass\\r\\n else:\\r\\n await ctx.send(\\\"You can not warn someone higher than your own role :no_entry:\\\")\\r\\n return\\r\\n if str(server.id) not in self.d:\\r\\n self.d[str(server.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if str(user.id) not in self.d[str(server.id)]:\\r\\n self.d[str(server.id)][str(user.id)] = {}\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"muted\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"time\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n if \\\"amount\\\" not in self.d[str(server.id)][str(user.id)]:\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n role = discord.utils.get(server.roles, name=\\\"Muted - Sensei\\\")\\r\\n overwrite = discord.PermissionOverwrite()\\r\\n overwrite.send_messages = False\\r\\n perms = discord.PermissionOverwrite()\\r\\n perms.speak = False\\r\\n if not role:\\r\\n role = await server.create_role(name=\\\"Muted - Sensei\\\")\\r\\n for channels in server.text_channels:\\r\\n await channels.set_permissions(role, overwrite=overwrite)\\r\\n for channels in server.voice_channels:\\r\\n await channels.set_permissions(role, overwrite=perms)\\r\\n await self._create_warn(server, user)\\r\\n if reason:\\r\\n if reason not in self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"reasons\\\"]:\\r\\n self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"reasons\\\"][reason] = {}\\r\\n self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] = self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\r\\n \\\"warnings\\\"] + 1\\r\\n dataIO.save_json(self.JSON, self.data)\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 1:\\r\\n await ctx.send(\\\"**{}** has been warned :warning:\\\".format(user))\\r\\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.set_author(name=\\\"You have been warned in {}\\\".format(server.name), icon_url=server.icon_url)\\r\\n try:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n except:\\r\\n s.add_field(name=\\\"Reason\\\", value=\\\"None Given\\\", inline=False)\\r\\n s.add_field(name=\\\"Moderator\\\", value=author)\\r\\n s.add_field(name=\\\"Next Action\\\", value=\\\"Mute\\\")\\r\\n action = \\\"Warn\\\"\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 2:\\r\\n try:\\r\\n await user.add_roles(role)\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = True\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = 600\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = ctx.message.created_at.timestamp()\\r\\n dataIO.save_json(self.file, self.d)\\r\\n except:\\r\\n await ctx.send(\\\"I cannot add the mute role to the user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(\\\"**{}** has been muted due to their second warning :white_check_mark:\\\".format(user))\\r\\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.set_author(name=\\\"You have been muted in {}\\\".format(server.name), icon_url=server.icon_url)\\r\\n try:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n except:\\r\\n s.add_field(name=\\\"Reason\\\", value=\\\"None Given\\\", inline=False)\\r\\n s.add_field(name=\\\"Moderator\\\", value=author)\\r\\n s.add_field(name=\\\"Next Action\\\", value=\\\"Kick\\\")\\r\\n action = \\\"Mute\\\"\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n await asyncio.sleep(600)\\r\\n if role in user.roles:\\r\\n try:\\r\\n await user.remove_roles(role)\\r\\n except:\\r\\n pass\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n dataIO.save_json(self.file, self.d)\\r\\n action = \\\"Unmute\\\"\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 3:\\r\\n try:\\r\\n await server.kick(user, reason=\\\"Kick made by {}\\\".format(author))\\r\\n except:\\r\\n await ctx.send(\\\"I'm not able to kick that user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(\\\"**{}** has been kicked due to their third warning :white_check_mark:\\\".format(user))\\r\\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.set_author(name=\\\"You have been kicked from {}\\\".format(server.name), icon_url=server.icon_url)\\r\\n try:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n except:\\r\\n s.add_field(name=\\\"Reason\\\", value=\\\"None Given\\\", inline=False)\\r\\n s.add_field(name=\\\"Moderator\\\", value=author)\\r\\n s.add_field(name=\\\"Next Action\\\", value=\\\"Ban\\\")\\r\\n action = \\\"Kick\\\"\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] >= 4:\\r\\n try:\\r\\n await server.ban(user, reason=\\\"Ban made by {}\\\".format(author))\\r\\n except:\\r\\n await ctx.send(\\\"I'm not able to ban that user :no_entry:\\\")\\r\\n del self.data[str(server.id)][\\\"user\\\"][str(user.id)]\\r\\n dataIO.save_json(self.JSON, self.data)\\r\\n return\\r\\n await ctx.send(\\\"**{}** has been banned due to their fourth warning :white_check_mark:\\\".format(user))\\r\\n await server.ban(user, reason=\\\"Ban made by {}\\\".format(author))\\r\\n s = discord.Embed(colour=000000, timestamp=__import__('datetime').datetime.utcnow())\\r\\n s.set_author(name=\\\"You have been banned from {}\\\".format(server.name), icon_url=server.icon_url)\\r\\n try:\\r\\n s.add_field(name=\\\"Reason\\\", value=reason, inline=False)\\r\\n except:\\r\\n s.add_field(name=\\\"Reason\\\", value=\\\"None Given\\\", inline=False)\\r\\n s.add_field(name=\\\"Moderator\\\", value=author)\\r\\n s.add_field(name=\\\"Next Action\\\", value=\\\"None\\\")\\r\\n del self.data[str(server.id)][\\\"user\\\"][str(user.id)]\\r\\n dataIO.save_json(self.JSON, self.data)\\r\\n action = \\\"Ban\\\"\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n try:\\r\\n await user.send(embed=s)\\r\\n except:\\r\\n pass\",\n \"def handle_mic_mute(_):\\n loop.mute()\",\n \"def med_timer(self):\\n self.start_button.config(text='Sit', state='disabled')\\n self.start_button.update()\\n if self.mins.get() == \\\"\\\":\\n num_mins = 0\\n else:\\n num_mins = float(self.mins.get())\\n time_in_seconds = num_mins * 60\\n self.t = Timer(time_in_seconds, self.play_wav)\\n self.t.start()\",\n \"def mute_members(_) -> int:\\n return 1 << 22\",\n \"def mute_members(_) -> int:\\n return 1 << 22\",\n \"def set_fan_timer_timeout(self, time_: str = None):\\r\\n if time_ is not None:\\r\\n self._fan_timer_timeout = datetime.fromisoformat(time_)\\r\\n else:\\r\\n self._fan_timer_timeout = datetime.now() + self._fan_timer_duration\\r\\n\\r\\n self._logger.info(log_message_formatter(\\r\\n \\\"set\\\", f\\\"{self}\\\", \\\"fan_timer-timeout\\\", self.fan_timer_timeout))\",\n \"def set_sleep_timer(self, option, time):\\n params = [\\n ('option', option),\\n ('sleeptime', int(time)),\\n ]\\n\\n self.get(COMMAND_UIC, 'SetSleepTimer', params)\",\n \"def set_mute(self, track, xclip, ident, value = None):\\n if track in self.song().tracks + self.song().return_tracks:\\n if value in KEYWORDS:\\n track.mute = KEYWORDS[value]\\n else:\\n track.mute = not(track.mute)\",\n \"async def reset(self, ctx):\\n self.data_check(ctx)\\n server = ctx.message.server\\n author = ctx.message.author\\n channel = ctx.message.channel\\n await self.bot.say(\\\"Are you sure you want to reset all warn settings\\\"\\n \\\"for this server?\\\\n\\\"\\n \\\"Type **yes** within the next 15 seconds.\\\")\\n msg = await self.bot.wait_for_message(author=author,\\n channel=channel,\\n timeout=15.0)\\n if msg.content.lower().strip() == \\\"yes\\\":\\n self.riceCog2[server.id][\\\"warn_message\\\"] = default_warn\\n self.riceCog2[server.id][\\\"ban_message\\\"] = default_ban\\n self.riceCog2[server.id][\\\"max\\\"] = default_max\\n self.riceCog2[server.id][\\\"defchannel\\\"] = default_channel\\n self.riceCog2[server.id][\\\"mutetime\\\"] = defmutetime\\n else:\\n await self.bot.say(\\\"Nevermind then.\\\")\\n return\",\n \"def lightleep(time_ms: int = None) -> None:\",\n \"async def _authorities_setheist(self, ctx, seconds: int):\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n theme = await self.thief.config.guild(guild).Theme()\\r\\n t_police = theme[\\\"Police\\\"]\\r\\n\\r\\n if seconds > 0:\\r\\n config[\\\"Police\\\"] = seconds\\r\\n await self.thief.config.guild(guild).Config.set(config)\\r\\n time_fmt = self.thief.time_format(seconds)\\r\\n msg = \\\"Setting {} alert time to {}.\\\".format(t_police, time_fmt)\\r\\n else:\\r\\n msg = \\\"Need a number higher than 0.\\\"\\r\\n await ctx.send(msg)\",\n \"def set_time_now(self, *args, **kwargs):\\n return _uhd_swig.usrp_sink_set_time_now(self, *args, **kwargs)\",\n \"def silence(self):\\n return 'Fine. Be that way!'\",\n \"async def async_set_muted(self, mute):\\n self._muted = mute\",\n \"def mute(self, nick, chan, arg):\\n if not arg:\\n \\treturn bot.msg(chan, get_doc())\\n self.state.mute(arg)\\n self.msg(chan, \\\"%s: You are temporarily prohibited from using this bot\\\" % (arg))\",\n \"async def mute(\\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\\n ):\\n\\n if target.guild_permissions.kick_members:\\n return await ctx.send(\\\"You can't punish that person!\\\")\\n\\n if isinstance(reason, time.UserFriendlyTime):\\n expires_at = reason.dt\\n reason = reason.arg\\n else:\\n expires_at = None\\n\\n action = Mute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n created_at=ctx.message.created_at,\\n expires_at=expires_at,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n if action.duration is None:\\n await ctx.send(f\\\"Muted **{target}**.\\\")\\n else:\\n await ctx.send(f\\\"Muted **{target}** for **{time.human_timedelta(action.duration)}**.\\\")\",\n \"def bot_settings_set_nul(chat_id):\\n\\n bot_collection[chat_id].timers.extended = 0\\n bot_collection[chat_id].timers.additional_time = False\\n bot_collection[chat_id].timers.current_time = 0\\n bot_collection[chat_id].paused = False\\n\\n save_timers(chat_id)\",\n \"def set_sleep_time(self, time):\\n self.sleep_time = time\",\n \"def alarmoff() :\\n s.alarm(False, \\\"\\\")\",\n \"def muteTrack(*args, **kwargs):\\n pass\",\n \"def mute_volume(self, mute):\\n mute_numeric = '1' if mute else '0'\\n self._lms.query(self._id, 'mixer', 'muting', mute_numeric)\\n self.update_ha_state()\",\n \"async def set_timer(self, ctx: commands.Context, seconds: int = None):\\n if seconds is not None:\\n await ctx.cfg_guild.autopostseconds.set(seconds)\\n await ctx.send(\\\"Auto-post timer has been set to {}\\\".format(seconds))\\n else:\\n seconds = await ctx.cfg_guild.autopostseconds()\\n await ctx.send(f\\\"Currently posting every {seconds} seconds.\\\")\",\n \"def set_current_time(self):\\n now = datetime.datetime.now()\\n answer = self.UsbHost.send_command(self.state.ser, \\\"SetCurrentTime\\\", str(self.state.device_id),\\n now.year, now.month, now.day,\\n now.hour, now.minute, now.second, int(now.microsecond / 1000))\\n if answer in wrong_answers:\\n error_message(\\\"Не удалось задать время\\\")\\n self.statusbar.showMessage(answer_translate[answer])\\n else:\\n self.state.syncro = True\\n self.create_message()\",\n \"def mute_volume(self, mute: bool) -> None:\\n self._monoprice.set_mute(self._zone_id, mute)\",\n \"def mute2(individual):\\n mutatePt=random.randint(0,len(individual)-1)\\n if mutatePt==0:\\n individual[mutatePt]=random.uniform(0.0, 0.02)\\n elif mutatePt>=2 and mutatePt<=4:\\n individual[mutatePt]=random.uniform(0.0, 0.005)\\n elif mutatePt==5:\\n individual[mutatePt]=random.uniform(0.0, 0.07)\\n \\n return individual,\",\n \"def set_display_time(log_mes,displaytime = 1800000):\\n kill_adb_uiautomator_block_old()\\n if int(get_screen_off_time(log_mes)) == displaytime:\\n if int(displaytime) >= 60000:\\n log_mes.info( 'screen off time is already %s mins'%(displaytime/60000))\\n else:\\n log_mes.info('screen off time is already %s secs'%(displaytime/1000))\\n else:\\n os.system('adb shell am start -a android.settings.DISPLAY_SETTINGS')\\n device(text=\\\"Sleep\\\").click()\\n kill_adb_uiautomator_block_old()\\n if int(displaytime) >= 60000:\\n device(text=\\\"%s minutes\\\"%(displaytime/60000)).click()\\n else:\\n device(text=\\\"%s seconds\\\"%(displaytime/1000)).click()\\n time.sleep(1)\\n os.system(\\\"adb shell am force-stop com.android.settings\\\")\",\n \"def get_mute(self):\\n return on_off_bool(self.get(COMMAND_UIC, 'GetMute')['mute'])\",\n \"async def warnings(self, ctx, user: discord.Member):\\r\\n server = ctx.message.guild\\r\\n try:\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 1:\\r\\n action = \\\"Mute\\\"\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 2:\\r\\n action = \\\"Kick\\\"\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] >= 3:\\r\\n action = \\\"Ban\\\"\\r\\n if not self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"reasons\\\"]:\\r\\n reasons = \\\"None\\\"\\r\\n else:\\r\\n reasons = \\\", \\\".join([x for x in self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"reasons\\\"]])\\r\\n if self.data[str(server.id)][\\\"user\\\"][str(user.id)][\\\"warnings\\\"] == 1:\\r\\n s = discord.Embed(description=\\\"{} is on 1 warning\\\".format(user), colour=user.colour)\\r\\n s.set_author(name=str(user), icon_url=user.avatar_url)\\r\\n s.add_field(name=\\\"Next Action\\\", value=action, inline=False)\\r\\n s.add_field(name=\\\"Reasons\\\", value=reasons, inline=False)\\r\\n await ctx.send(embed=s)\\r\\n else:\\r\\n try:\\r\\n s = discord.Embed(description=\\\"{} is on {} warnings\\\".format(user, self.data[str(server.id)][\\\"user\\\"][\\r\\n str(user.id)][\\\"warnings\\\"]), colour=user.colour)\\r\\n s.set_author(name=str(user), icon_url=user.avatar_url)\\r\\n s.add_field(name=\\\"Next Action\\\", value=action, inline=False)\\r\\n s.add_field(name=\\\"Reasons\\\", value=reasons, inline=False)\\r\\n await ctx.send(embed=s)\\r\\n except:\\r\\n await ctx.send(\\\"That user has no warnings :no_entry:\\\")\\r\\n except:\\r\\n await ctx.send(\\\"That user has no warnings :no_entry:\\\")\",\n \"def time(self):\\r\\n time = datetime.datetime.now().strftime(\\\"%I:%M:%S\\\")\\r\\n self.speak(\\\"the current time is\\\")\\r\\n self.speak(time)\",\n \"async def mute(self, ctx, member : discord.Member, *, reason : str):\\r\\n mutedRole = discord.utils.get(ctx.guild.roles, name = \\\"Muted\\\")\\r\\n if not mutedRole:\\r\\n channels = 0\\r\\n mutedRole = await ctx.guild.create_role(name=\\\"Muted\\\")\\r\\n for channel in ctx.guild.text_channels:\\r\\n await channel.set_permissions(mutedRole, send_messages=False)\\r\\n channels += 1 \\r\\n await ctx.send(f\\\"Successfully applied overwrites for {channels} channels\\\")\\r\\n await member.add_roles(mutedRole)\\r\\n embed = discord.Embed(title=\\\"Muted\\\", description = f\\\"You have been muted in **{ctx.guild.name}** by **{ctx.author}** **indefinetly** for reason **{reason}**\\\", colour = ctx.author.color, timestamp = datetime.datetime.now())\\r\\n await member.send(embed=embed)\",\n \"async def _wait_setheist(self, ctx, seconds: int):\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n theme = await self.thief.config.guild(guild).Theme()\\r\\n t_crew = theme[\\\"Crew\\\"]\\r\\n\\r\\n if seconds > 0:\\r\\n config[\\\"Wait\\\"] = seconds\\r\\n await self.thief.config.guild(guild).Config.set(config)\\r\\n time_fmt = self.thief.time_format(seconds)\\r\\n msg = \\\"Setting {} gather time to {}.\\\".format(t_crew, time_fmt)\\r\\n else:\\r\\n msg = \\\"Need a number higher than 0.\\\"\\r\\n await ctx.send(msg)\",\n \"def is_volume_muted(self) -> str | None:\\n return self.zone.Mute\",\n \"def set_max_time(self, time):\\n self.widget().setMaximumTime(time)\",\n \"def mute(self) -> bool:\\n return bool(self.audio_mixer.getmute()[0])\",\n \"def alarm_out_setup_change():\\n setup_write(\\\"!M1 meas interval\\\", \\\"00:10:00\\\")\\n setup_write(\\\"!M2 meas interval\\\", \\\"00:10:00\\\")\\n setup_write(\\\"!TX3 scheduled interval\\\", \\\"01:00:00\\\")\",\n \"async def mutechannel(self, ctx, channel: str):\\n self.data_check(ctx)\\n server = ctx.message.server\\n\\n self.riceCog2[server.id][\\\"mutechannel\\\"] = channel\\n dataIO.save_json(self.warning_settings,\\n self.riceCog2)\\n await self.bot.say(\\\"Mute channel is now: **{}**\\\".format(channel))\",\n \"def set_warning_message(msg):\\n set_message(msg, TYPE_WARNING)\",\n \"def set_mute(cls, mute: bool):\\n raise NotImplementedError\",\n \"def update_time_default(self, id_user:int, time_new:int) -> bool:\\n try:\\n self.cursor.execute(f\\\"UPDATE {table_users_settings} SET text_minutes = ? WHERE id_user = ?;\\\", (time_new, id_user))\\n self.connection.commit()\\n return True\\n except Exception as e:\\n msg = f\\\"We face problems with updating the default name; Mistake: {e}\\\"\\n self.proceed_error(msg)\\n return False\",\n \"def set_time_now(self, *args, **kwargs):\\n return _uhd_swig.usrp_sink_sptr_set_time_now(self, *args, **kwargs)\",\n \"async def _death_setheist(self, ctx, seconds: int):\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n\\r\\n if seconds > 0:\\r\\n config[\\\"Death\\\"] = seconds\\r\\n await self.thief.config.guild(guild).Config.set(config)\\r\\n time_fmt = self.thief.time_format(seconds)\\r\\n msg = \\\"Setting death timer to {}.\\\".format(time_fmt)\\r\\n else:\\r\\n msg = \\\"Need a number higher than 0.\\\"\\r\\n await ctx.send(msg)\",\n \"def set_time(self, sec):\\n self.set_timed(round(sec * 10.0))\",\n \"async def remove_mute(id: int) -> None:\\n\\n guild = BOT_GLOBAL.get_guild(BOT_GLOBAL.settings.guild_id)\\n if guild is not None:\\n mute_role = BOT_GLOBAL.settings.guild().role_mute\\n mute_role = guild.get_role(mute_role)\\n if mute_role is not None:\\n user = guild.get_member(id)\\n if user is not None:\\n await user.remove_roles(mute_role)\\n case = Case(\\n _id=BOT_GLOBAL.settings.guild().case_id,\\n _type=\\\"UNMUTE\\\",\\n mod_id=BOT_GLOBAL.user.id,\\n mod_tag=str(BOT_GLOBAL.user),\\n reason=\\\"Temporary mute expired.\\\",\\n )\\n await BOT_GLOBAL.settings.inc_caseid()\\n await BOT_GLOBAL.settings.add_case(user.id, case)\\n\\n u = await BOT_GLOBAL.settings.user(id=user.id)\\n u.is_muted = False\\n u.save()\\n\\n log = await prepare_unmute_log(BOT_GLOBAL.user, user, case)\\n\\n log.remove_author()\\n log.set_thumbnail(url=user.avatar_url)\\n\\n public_chan = guild.get_channel(\\n BOT_GLOBAL.settings.guild().channel_public)\\n \\n dmed = True\\n try:\\n await user.send(embed=log)\\n except Exception:\\n dmed = False\\n \\n await public_chan.send(user.mention if not dmed else \\\"\\\", embed=log)\\n\\n else:\\n case = Case(\\n _id=BOT_GLOBAL.settings.guild().case_id,\\n _type=\\\"UNMUTE\\\",\\n mod_id=BOT_GLOBAL.user.id,\\n mod_tag=str(BOT_GLOBAL.user),\\n reason=\\\"Temporary mute expired.\\\",\\n )\\n await BOT_GLOBAL.settings.inc_caseid()\\n await BOT_GLOBAL.settings.add_case(id, case)\\n\\n u = await BOT_GLOBAL.settings.user(id=id)\\n u.is_muted = False\\n u.save()\",\n \"def set_off_alarm(announcement:str):\\r\\n engine = pyttsx3.init()\\r\\n try:\\r\\n engine.endLoop()\\r\\n except:\\r\\n pass\\r\\n engine.say(announcement)\\r\\n engine.runAndWait()\\r\\n engine.stop()\\r\\n logging.info(\\\"Alarm announced in set_off_alarm(): \\\" + announcement)\\r\\n return redirect('/index')\",\n \"def mute_health_alert(\\n alert: str, node: CephAdmin, duration: str = None, sticky: bool = False\\n) -> bool:\\n all_alerts = get_alerts(node)\\n if alert not in all_alerts[\\\"active_alerts\\\"] + all_alerts[\\\"muted_alerts\\\"]:\\n log.info(f\\\"the alert: {alert} not generated on the cluster, Cannot mute\\\")\\n return True\\n if alert in all_alerts[\\\"muted_alerts\\\"]:\\n log.info(f\\\"the alert: {alert} is already muted\\\")\\n return True\\n\\n # Muting the given alert along with specified duration\\n cmd = f\\\"ceph health mute {alert}\\\"\\n if duration:\\n cmd += f\\\" {duration}\\\"\\n if sticky:\\n cmd += \\\" --sticky\\\"\\n node.shell([cmd])\\n\\n # Sleeping for 5 sec for the alert to be logged\\n time.sleep(5)\\n all_alerts = get_alerts(node)\\n log.info(\\n f\\\"Muted the alert : {alert}. All the muted alerts : {all_alerts['muted_alerts']}\\\"\\n )\\n return True if alert in all_alerts[\\\"muted_alerts\\\"] else False\",\n \"def valkkafsmanager_set_time_cb(self, t):\\n self.signals.set_time.emit(t)\",\n \"def Init():\\n global MySet\\n global lastMessage\\n\\n MySet = Settings(settingsFile)\\n lastMessage = time.time() - MySet.Cooldown*60\",\n \"def auto_unmute():\\n muted = set(t.mutes.users.ids(screen_name=TWITTER_HANDLE)[\\\"ids\\\"])\\n\\n # put user IDs of people you want to remain muted here\\n users_keep_muted = set([])\\n \\n # mute all \\n for user_id in muted:\\n if user_id not in users_keep_muted:\\n t.mutes.users.destroy(user_id=user_id)\\n print(\\\"unmuted %d\\\" % (user_id))\",\n \"def on_update_playback(self, event):\\n try:\\n offset = self.mplayer.GetTimePos()\\n except:\\n return\\n print offset\\n mod_off = str(offset)[-1]\\n if mod_off == '0':\\n print \\\"mod_off\\\"\\n offset = int(offset)\\n self.playbackSlider.SetValue(offset)\\n secsPlayed = time.strftime('%M:%S', time.gmtime(offset))\\n self.trackCounter.SetLabel(secsPlayed)\",\n \"async def _sentence_setheist(self, ctx, seconds: int):\\r\\n guild = ctx.guild\\r\\n config = await self.thief.get_guild_settings(guild)\\r\\n theme = await self.thief.config.guild(guild).Theme()\\r\\n t_jail = theme[\\\"Jail\\\"]\\r\\n t_sentence =theme[\\\"Sentence\\\"]\\r\\n\\r\\n if seconds > 0:\\r\\n config[\\\"Sentence\\\"] = seconds\\r\\n await self.thief.config.guild(guild).Config.set(config)\\r\\n time_fmt = self.thief.time_format(seconds)\\r\\n msg = \\\"Setting base {} {} to {}.\\\".format(t_jail, t_sentence, time_fmt)\\r\\n else:\\r\\n msg = \\\"Need a number higher than 0.\\\"\\r\\n await ctx.send(msg)\",\n \"async def before_ad_reminder(self):\\r\\n await self.bot.wait_until_ready()\\r\\n await sleep(5) # To make sure bot reads settings\\r\\n now = datetime.now()\\r\\n next_hour = now.replace(minute=0, second=0, microsecond=0) + timedelta(hours=1)\\r\\n await sleep((next_hour - now).total_seconds())\",\n \"def mute_output_toggle(self):\\n self._mute_toggle(self.pulse_core.Get(\\\"org.PulseAudio.Core1\\\", \\\"FallbackSink\\\"))\",\n \"def wishMe():\\r\\n hour = int(datetime.datetime.now().hour)\\r\\n\\r\\n if hour >= 0 and hour < 12:\\r\\n speak(\\\"Good morning\\\" + MASTER)\\r\\n\\r\\n elif hour >= 12 and hour < 18:\\r\\n speak(\\\"Good afternoon\\\" + MASTER)\\r\\n else:\\r\\n speak(\\\"Good Evening\\\" + MASTER)\\r\\n # speak(\\\"I am VA. How may I help you?\\\")\\r\",\n \"async def voice_mute(self, ctx, member: discord.Member, *,reason: typing.Optional[str]):\\n await member.edit(mute=True, reason=reason[:512])\\n await ctx.send(f\\\"User {member.mention} successfully muted from voice\\\")\\n if reason:\\n await self.check_channel.send(f\\\"user {member.mention} muted from voice for the following reason:\\\\n\\\"\\n f\\\"{reason}\\\")\",\n \"async def tradingmute(\\n self, ctx, target: discord.Member, *, reason: Union[ModerationUserFriendlyTime, str]\\n ):\\n\\n if target.guild_permissions.kick_members:\\n return await ctx.send(\\\"You can't punish that person!\\\")\\n\\n if isinstance(reason, time.UserFriendlyTime):\\n expires_at = reason.dt\\n reason = reason.arg\\n else:\\n expires_at = None\\n\\n action = TradingMute(\\n target=target,\\n user=ctx.author,\\n reason=reason,\\n guild_id=ctx.guild.id,\\n created_at=ctx.message.created_at,\\n expires_at=expires_at,\\n )\\n await action.execute(ctx)\\n await action.notify()\\n if action.duration is None:\\n await ctx.send(f\\\"Muted **{target}** in trading channels.\\\")\\n else:\\n await ctx.send(\\n f\\\"Muted **{target}** in trading channels for **{time.human_timedelta(action.duration)}**.\\\"\\n )\",\n \"def getSafetyMute(self, unitCode=0):\\n resp = self.XAPCommand('SFTYMUTE', unitCode=unitCode)\\n return bool(int(resp))\",\n \"async def unmute(self, ctx, user: discord.Member, *, reason: str = None):\\r\\n server = ctx.message.guild\\r\\n channel = ctx.message.channel\\r\\n author = ctx.message.author\\r\\n action = \\\"Unmute\\\"\\r\\n role = discord.utils.get(server.roles, name=\\\"Muted - Sensei\\\")\\r\\n if not role:\\r\\n await ctx.send(\\\"No-one is muted in this server :no_entry:\\\")\\r\\n return\\r\\n if role not in user.roles:\\r\\n await ctx.send(\\\"**{}** is not muted :no_entry:\\\".format(user))\\r\\n return\\r\\n try:\\r\\n await user.remove_roles(role)\\r\\n except:\\r\\n await ctx.send(\\\"I cannot remove the mute role from the user :no_entry:\\\")\\r\\n return\\r\\n await ctx.send(f\\\"**{user}** has been unmuted {self.bot.get_emoji(470063310386233344)}\\\")\\r\\n try:\\r\\n await self._log(author, server, action, reason, user)\\r\\n except:\\r\\n pass\\r\\n self.d[str(server.id)][str(user.id)][\\\"toggle\\\"] = False\\r\\n self.d[str(server.id)][str(user.id)][\\\"time\\\"] = None\\r\\n self.d[str(server.id)][str(user.id)][\\\"amount\\\"] = None\\r\\n dataIO.save_json(self.file, self.d)\\r\\n try:\\r\\n s = discord.Embed(title=\\\"You have been unmuted early in {}\\\".format(server.name), colour=000000,\\r\\n timestamp=datetime.datetime.utcnow())\\r\\n s.add_field(name=\\\"Moderator\\\", value=\\\"{} ({})\\\".format(author, str(author.id)))\\r\\n await user.send(embed=s)\\r\\n except:\\r\\n pass\",\n \"def LingerTime(self) -> int:\",\n \"def wishMe():\\n hour = int(datetime.datetime.now().hour)\\n if 0 <= hour < 12:\\n speak(\\\"Good Morning Boss\\\")\\n\\n elif 12 <= hour < 18:\\n speak(\\\"Good Afternoon Boss\\\")\\n\\n else:\\n speak(\\\"Good Evening Boss!\\\")\\n\\n speak(\\\"This is mayaa appointed as your Assistant\\\")\",\n \"async def set_mute(self, value: bool):\\n await self._pytheos.api.player.set_mute(self.id, value)\",\n \"def set_sleep_time(self, milliseconds:int):\\n self.send_command(f\\\"configure mainLoopSleepTime {milliseconds}\\\")\",\n \"def silence(self):\\n return self._replace(velocity=0.)\",\n \"def set_time_unknown_pps(self, *args, **kwargs):\\n return _uhd_swig.usrp_sink_set_time_unknown_pps(self, *args, **kwargs)\",\n \"def reset_timer(self, *_) -> \\\"ALL\\\":\\n self.last = time.time()\\n delta = time.time() - self.last\\n if delta > 180:\\n print(\\n \\\"!!! Warning: Watchdog failure detected, spawning a fallback \\\"\\n \\\"thread.\\\"\\n )\\n self.watchdog = FallbackWatchdog(self)\\n self.watchdog.start()\",\n \"def catchall_handler(*args, **kwargs):\\n if len(args) < 2 or args[0] != 'org.mpris.MediaPlayer2.Player':\\n return\\n\\n song_id = args[1]['Metadata']['mpris:trackid']\\n\\n if not muted and song_id.startswith(AD_ID):\\n mute()\\n elif muted and not song_id.startswith(AD_ID):\\n time.sleep(1.5) # Adding a delay since the signal is released early\\n unmute()\",\n \"def _set_default_rtimes(channelFormats):\\n for channelFormat in channelFormats:\\n for bf in channelFormat.audioBlockFormats:\\n if bf.rtime is None and bf.duration is not None:\\n bf.rtime = Fraction(0)\\n warnings.warn(\\n f\\\"added missing rtime to {bf.id}; BS.2127-0 states that \\\"\\n \\\"rtime and duration should both be present or absent\\\"\\n )\",\n \"def _handle_timeme_action(bot, event, command):\\n if event.text.startswith('/timer ') or event.text.startswith('/timeme '):\\n full_tokens = re.split('\\\\s+', event.text)\\n if len(full_tokens) > 1:\\n full_tokens[0] = \\\"timeme\\\"\\n yield from asyncio.sleep(0.2)\\n yield from command.run(bot, event, *full_tokens)\",\n \"def setInitialTime(self, T0):\\n raise \\\"use method setInitialTime of class ReactorNet\\\"\\n #_cantera.reactor_setInitialTime(self.__reactor_id, T0)\",\n \"async def warn(self, ctx, user: discord.Member, *, reason: str=None):\\n self.data_check(ctx)\\n server = ctx.message.server\\n author = ctx.message.author\\n channel = ctx.message.channel\\n\\n can_ban = channel.permissions_for(server.me).ban_members\\n can_role = channel.permissions_for(server.me).manage_roles\\n\\n if reason is None:\\n msg = await self.bot.say(\\\"Please enter a reason for the warning!\\\")\\n await asyncio.sleep(5)\\n await self.bot.delete_message(msg)\\n await self.bot.delete_message(ctx.message)\\n return\\n\\n if can_ban:\\n pass\\n await self.bot.delete_message(ctx.message)\\n else:\\n await self.bot.say(\\\"Sorry, I can't warn this user.\\\\n\\\"\\n \\\"I am missing the `ban_members` permission\\\")\\n return\\n\\n if server.id not in self.riceCog2:\\n msg = default_warn\\n ban = default_ban\\n _max = default_max\\n defchannel = default_channel\\n\\n if server.id not in self.riceCog:\\n self.riceCog[server.id] = {}\\n \\n if server.id not in self.warnlist2:\\n self.warnlist2[server.id] = {} \\n\\n if 'pm_warn' not in self.riceCog[server.id]:\\n self.riceCog[server.id]['pm_warn'] = False\\n\\n p = self.riceCog[server.id]['pm_warn']\\n\\n try:\\n msg = self.riceCog2[server.id][\\\"warn_message\\\"]\\n except:\\n msg = default_warn\\n try:\\n mutetime = self.riceCog2[server.id][\\\"mutetime\\\"]\\n except:\\n mutetime = defmutetime\\n try:\\n ban = self.riceCog2[server.id][\\\"ban_message\\\"]\\n except:\\n ban = default_ban\\n try:\\n _max = self.riceCog2[server.id][\\\"max\\\"]\\n except:\\n _max = default_max\\n try:\\n defchannel = self.riceCog2[server.id][\\\"defchannel\\\"]\\n except:\\n defchannel = default_channel\\n colour = 0x9e0101\\n\\n # checks if the user is in the file\\n if server.id not in self.riceCog2:\\n self.riceCog2[server.id] = {}\\n dataIO.save_json(self.warning_settings,\\n self.riceCog2)\\n if server.id not in self.riceCog:\\n self.riceCog[server.id] = {}\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n if user.id not in self.riceCog[server.id]:\\n self.riceCog[server.id][user.id] = {}\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n else:\\n pass\\n else:\\n if user.id not in self.riceCog[server.id]:\\n self.riceCog[server.id][user.id] = {}\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n else:\\n pass\\n\\n if \\\"Count\\\" in self.riceCog[server.id][user.id]:\\n count = self.riceCog[server.id][user.id][\\\"Count\\\"]\\n else:\\n count = 0 \\n try:\\n defchannel = self.riceCog2[server.id][\\\"defchannel\\\"]\\n except:\\n defchannel = default_channel\\n channel = discord.utils.get(server.channels, name = defchannel)\\n if channel is None:\\n msg = await self.bot.say (\\\"I was unable to write to your log channel. Please make sure there is a channel called {} on the server!\\\".format(defchannel))\\n return\\n else:\\n pass\\n \\n\\n # checks how many warnings the user has\\n if count == 0:\\n count += 1\\n msg = await self.filter_message(msg=msg,\\n user=user,\\n count=count,\\n _max=_max)\\n data = discord.Embed(colour=colour)\\n data.add_field(name=\\\"Warning:\\\",\\n value=msg)\\n if reason:\\n data.add_field(name=\\\"Reason:\\\",\\n value=reason,\\n inline=False)\\n data.add_field(name=\\\"Additional Actions:\\\", value=\\\"*In addition to this you have been muted for {} as a result of your actions.*\\\".format(mutetime), inline=False)\\n data.set_footer(text=server.name)\\n if p:\\n #if dm is on\\n await self.bot.send_message(user, embed=data) \\n elif not p:\\n #if dm is not on\\n await self.bot.say(embed=data)\\n \\n #run and log\\n await self._punish_cmd_common(ctx, user, reason=reason, duration=mutetime)\\n mod=author\\n user=user\\n reason=reason\\n countnum = \\\"{}/3\\\".format(count)\\n ID = uuid.uuid4()\\n jsonid = \\\"{}\\\".format(ID)\\n embed=discord.Embed(title=\\\"User Warned:\\\", color=colour)\\n embed.add_field(name=\\\"Case ID:\\\", value=ID, inline=False)\\n embed.add_field(name=\\\"Moderator:\\\", value=mod, inline=False)\\n embed.add_field(name=\\\"User:\\\", value=\\\"{0} ({0.id})\\\".format(user), inline=False)\\n embed.add_field(name=\\\"Reason:\\\", value=reason, inline=False)\\n embed.add_field(name=\\\"Warning Number:\\\", value=countnum, inline=False)\\n react = await self.bot.send_message(channel, embed=embed)\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44d\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44e\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f937\\\")\\n\\n self.riceCog[server.id][user.id].update({\\\"Count\\\": count})\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n \\n self.warnlist2[server.id][jsonid] = {\\n 'User': user.id,\\n 'Mod': mod.id,\\n 'Reason': reason,\\n 'Warning Number': countnum\\n }\\n dataIO.save_json(self.warnlist,\\n self.warnlist2) \\n \\n elif count > 0 and count < _max -1:\\n count += 1\\n msg = await self.filter_message(msg=msg,\\n user=user,\\n count=count,\\n _max=_max)\\n data = discord.Embed(colour=colour)\\n data.add_field(name=\\\"Warning:\\\",\\n value=msg)\\n if reason:\\n data.add_field(name=\\\"Reason:\\\",\\n value=reason,\\n inline=False)\\n data.set_footer(text=server.name)\\n if p:\\n #if pm on\\n await self.bot.send_message(user, embed=data)\\n elif not p:\\n #if pm off\\n await self.bot.say(embed=data)\\n \\n #run and log\\n mod=author\\n user=user\\n reason=reason\\n ID = uuid.uuid4()\\n jsonid = \\\"{}\\\".format(ID)\\n countnum = \\\"{}/3\\\".format(count)\\n embed=discord.Embed(title=\\\"User Warned:\\\", color=colour)\\n embed.add_field(name=\\\"Case ID:\\\", value=ID, inline=False)\\n embed.add_field(name=\\\"Moderator:\\\", value=mod, inline=False)\\n embed.add_field(name=\\\"User:\\\", value=\\\"{0} ({0.id})\\\".format(user), inline=False)\\n embed.add_field(name=\\\"Reason:\\\", value=reason, inline=False)\\n embed.add_field(name=\\\"Warning Number:\\\", value=countnum, inline=False)\\n react = await self.bot.send_message(channel, embed=embed)\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44d\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44e\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f937\\\")\\n self.riceCog[server.id][user.id].update({\\\"Count\\\": count})\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n self.warnlist2[server.id][jsonid]= {\\n 'User': user.id,\\n 'Mod': mod.id,\\n 'Reason': reason,\\n 'Warning Number': countnum\\n }\\n dataIO.save_json(self.warnlist,\\n self.warnlist2) \\n\\n else:\\n msg = ban\\n msg = await self.filter_message(msg=msg,\\n user=user,\\n count=count,\\n _max=_max)\\n data = discord.Embed(colour=colour)\\n data.add_field(name=\\\"Warning\\\",\\n value=msg)\\n if reason:\\n data.add_field(name=\\\"Reason\\\",\\n value=reason,\\n inline=False)\\n data.set_footer(text=server.name)\\n if p:\\n await self.bot.send_message(user, embed=data)\\n elif not p:\\n await self.bot.say(embed=data)\\n \\n #run and log\\n mod=author\\n user=user\\n reason=reason\\n ID = uuid.uuid4()\\n jsonid = \\\"{}\\\".format(ID)\\n bantext = \\\"Max Warnings reached.\\\"\\n embed=discord.Embed(title=\\\"User Banned:\\\", color=colour)\\n embed.add_field(name=\\\"Case ID:\\\", value=ID, inline=False)\\n embed.add_field(name=\\\"Moderator:\\\", value=mod, inline=False)\\n embed.add_field(name=\\\"User:\\\", value=\\\"{0} ({0.id})\\\".format(user), inline=False)\\n embed.add_field(name=\\\"Reason:\\\", value=reason, inline=False)\\n embed.add_field(name=\\\"Additional Actions:\\\", value=\\\"*As the user has reached 3 warnings they have been banned from the server.*\\\", inline=False)\\n react = await self.bot.send_message(channel, embed=embed)\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44d\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f44e\\\")\\n await self.bot.add_reaction(react, \\\"\\\\U0001f937\\\")\\n count = 0\\n self.riceCog[server.id][user.id].update({\\\"Count\\\": count})\\n dataIO.save_json(self.profile,\\n self.riceCog)\\n self.warnlist2[server.id][jsonid] = {\\n 'User': user.id,\\n 'Mod': mod.id,\\n 'Reason': reason,\\n 'Warning Number': bantext\\n }\\n dataIO.save_json(self.warnlist,\\n self.warnlist2) \\n try:\\n await self.bot.ban(user, delete_message_days=0)\\n except discord.errors.Forbidden:\\n await self.bot.say(\\\"I don't have permissions to ban that user.\\\")\\n\\n if 'poop' in self.riceCog2[server.id] and can_role:\\n if self.riceCog2[server.id]['poop'] == True:\\n poops = count * \\\"\\\\U0001f528\\\"\\n role_name = \\\"Warning {}\\\".format(poops)\\n is_there = False\\n colour = 0xbc7642\\n for role in server.roles:\\n if role.name == role_name:\\n poop_role = role\\n is_there = True\\n if not is_there:\\n poop_role = await self.bot.create_role(server)\\n await self.bot.edit_role(role=poop_role,\\n name=role_name,\\n server=server)\\n try:\\n await self.bot.add_roles(user,\\n poop_role)\\n except discord.errors.Forbidden:\\n await self.bot.say(\\\"No permission to add roles\\\")\",\n \"async def mute(self, ctx: Context, members: commands.Greedy[discord.Member], reason=\\\"no reason\\\"):\\n\\n role = discord.utils.get(ctx.guild.roles, name=\\\"Muted\\\")\\n member_display = []\\n\\n for i, member in enumerate(members):\\n if role in member.roles:\\n await ctx.send(f\\\"guild member `{member.display_name}` is already muted\\\", delete_after=8)\\n del members[i]\\n\\n if role is None:\\n permissions = discord.Permissions()\\n permissions.change_nickname = True\\n permissions.send_messages = False\\n permissions.read_message_history = True\\n role = await ctx.guild.create_role(name=\\\"Muted\\\", permissions=permissions)\\n\\n await self.set_perms(ctx.guild, role)\\n\\n for member in members:\\n\\n if await self.hiearchy_check(ctx, member):\\n continue\\n\\n member_display.append(str(member))\\n await member.add_roles(role, reason=reason)\\n\\n member_display = \\\", \\\".join(member_display)\\n\\n if not member_display:\\n member_display = \\\"no one\\\"\\n\\n await ctx.send(f\\\"> {ctx.author.name} muted {member_display}\\\")\",\n \"def default_delay(self):\\n return DEFAULT_DELAY\",\n \"def set_timer(bot, update, args, job_queue, chat_data):\\n chat_id = update.message.chat_id\\n try:\\n # args[0] should contain the time for the timer in seconds\\n due = int(args[0])\\n if due < 0:\\n update.message.reply_text('Sinto muito, mas ainda nao podemos viajar no tempo')\\n return\\n\\n # Add job to queue\\n job = job_queue.run_repeating(alarm, due, context=chat_id)\\n chat_data['job'] = job\\n check = emojize(\\\":white_check_mark:\\\", use_aliases=True)\\n update.message.reply_text('Intervalo das notificacoes setado com sucesso '+check+'')\\n\\n except (IndexError, ValueError):\\n update.message.reply_text('Exemplo: /set <seconds>')\",\n \"def _send_time(self):\\n if 'time' not in self.loopback_guard:\\n content = {'time': self.time.isoformat()}\\n self.send_action('set_time', content)\",\n \"async def warnCMD(\\n self,\\n ctx: SlashContext,\\n user: typing.Union[discord.User, discord.Member],\\n reason: str = None,\\n ):\\n await ctx.defer()\\n user_data = await self.bot.get_member_data(ctx.guild_id, user.id)\\n warning_num = int(user_data.warnings) + 1\\n\\n embed = discord.Embed(title=f\\\"Warning for {user.name} #{user.discriminator}\\\", color=0xE7C30D)\\n embed.add_field(\\n name=\\\"Warning LVL.1\\\",\\n value=\\\":white_check_mark:\\\" if warning_num >= 1 else \\\":x:\\\",\\n inline=False,\\n )\\n embed.add_field(\\n name=\\\"Warning LVL.2\\\",\\n value=\\\":white_check_mark:\\\" if warning_num >= 2 else \\\":x:\\\",\\n inline=False,\\n )\\n embed.add_field(\\n name=\\\"Warning LVL.3\\\",\\n value=\\\":white_check_mark:\\\" if warning_num >= 3 else \\\":x:\\\",\\n inline=False,\\n )\\n embed.set_footer(\\n text=f\\\"Warned by {ctx.author.name}#{ctx.author.discriminator}\\\",\\n icon_url=ctx.author.avatar_url,\\n )\\n if warning_num >= 3:\\n embed.colour = discord.Colour.red()\\n action = None\\n autoReason = None\\n if warning_num == 4:\\n action = ModActions.kick\\n autoReason = \\\"Auto: 4th warning\\\"\\n embed.description = \\\"User has 4 warnings, they have been kicked\\\"\\n await user.kick(reason=autoReason)\\n elif warning_num >= 5:\\n action = ModActions.ban\\n autoReason = f\\\"Auto: {warning_num}th warning\\\"\\n embed.description = \\\"User has 5 warnings, they have been banned\\\"\\n await ctx.guild.ban(user, reason=autoReason, delete_message_days=0)\\n if action and autoReason:\\n await self.bot.paladinEvents.add_item(\\n Action(\\n actionType=action,\\n moderator=ctx.author,\\n guild=ctx.guild,\\n user=user,\\n reason=autoReason,\\n )\\n )\\n await ctx.send(embed=embed)\\n\\n await self.bot.paladinEvents.add_item(\\n Action(\\n actionType=ModActions.warn,\\n moderator=ctx.author,\\n guild=ctx.guild,\\n user=user,\\n extra=warning_num,\\n reason=reason,\\n )\\n )\\n\\n user_data.warnings = warning_num\\n await self.bot.redis.set(user_data.key, user_data.to_json())\",\n \"def remaining_ms():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6638432","0.6581909","0.65700525","0.6485997","0.63749397","0.62527746","0.60601157","0.6041151","0.60398245","0.60359174","0.60047853","0.59576213","0.5875881","0.5872893","0.58570814","0.5830865","0.5806074","0.57983273","0.57898366","0.57819015","0.57704246","0.57191485","0.5686018","0.5681742","0.56801695","0.56788456","0.56645495","0.5654289","0.5648164","0.5648164","0.5647252","0.56250006","0.5613976","0.5607014","0.5605431","0.5587929","0.5582516","0.5576323","0.556789","0.555423","0.5553564","0.5531373","0.55190384","0.55169183","0.5477676","0.5477284","0.5457958","0.5452134","0.543865","0.543812","0.5433922","0.5424412","0.5407406","0.54043555","0.5400683","0.53992933","0.5386674","0.53772944","0.5376655","0.5376294","0.53543997","0.534472","0.53405267","0.53295296","0.53273237","0.53151876","0.5299451","0.52940816","0.5291734","0.5288316","0.5278977","0.527134","0.526064","0.5257644","0.5255547","0.52359605","0.5235731","0.52315265","0.5227224","0.5221796","0.5218591","0.5216533","0.52137303","0.52124995","0.5205875","0.52036124","0.5199951","0.5196379","0.5196081","0.51914245","0.5180783","0.51801425","0.51788896","0.5173672","0.5171434","0.5170063","0.51637805","0.5160247","0.51565045","0.5156393"],"string":"[\n \"0.6638432\",\n \"0.6581909\",\n \"0.65700525\",\n \"0.6485997\",\n \"0.63749397\",\n \"0.62527746\",\n \"0.60601157\",\n \"0.6041151\",\n \"0.60398245\",\n \"0.60359174\",\n \"0.60047853\",\n \"0.59576213\",\n \"0.5875881\",\n \"0.5872893\",\n \"0.58570814\",\n \"0.5830865\",\n \"0.5806074\",\n \"0.57983273\",\n \"0.57898366\",\n \"0.57819015\",\n \"0.57704246\",\n \"0.57191485\",\n \"0.5686018\",\n \"0.5681742\",\n \"0.56801695\",\n \"0.56788456\",\n \"0.56645495\",\n \"0.5654289\",\n \"0.5648164\",\n \"0.5648164\",\n \"0.5647252\",\n \"0.56250006\",\n \"0.5613976\",\n \"0.5607014\",\n \"0.5605431\",\n \"0.5587929\",\n \"0.5582516\",\n \"0.5576323\",\n \"0.556789\",\n \"0.555423\",\n \"0.5553564\",\n \"0.5531373\",\n \"0.55190384\",\n \"0.55169183\",\n \"0.5477676\",\n \"0.5477284\",\n \"0.5457958\",\n \"0.5452134\",\n \"0.543865\",\n \"0.543812\",\n \"0.5433922\",\n \"0.5424412\",\n \"0.5407406\",\n \"0.54043555\",\n \"0.5400683\",\n \"0.53992933\",\n \"0.5386674\",\n \"0.53772944\",\n \"0.5376655\",\n \"0.5376294\",\n \"0.53543997\",\n \"0.534472\",\n \"0.53405267\",\n \"0.53295296\",\n \"0.53273237\",\n \"0.53151876\",\n \"0.5299451\",\n \"0.52940816\",\n \"0.5291734\",\n \"0.5288316\",\n \"0.5278977\",\n \"0.527134\",\n \"0.526064\",\n \"0.5257644\",\n \"0.5255547\",\n \"0.52359605\",\n \"0.5235731\",\n \"0.52315265\",\n \"0.5227224\",\n \"0.5221796\",\n \"0.5218591\",\n \"0.5216533\",\n \"0.52137303\",\n \"0.52124995\",\n \"0.5205875\",\n \"0.52036124\",\n \"0.5199951\",\n \"0.5196379\",\n \"0.5196081\",\n \"0.51914245\",\n \"0.5180783\",\n \"0.51801425\",\n \"0.51788896\",\n \"0.5173672\",\n \"0.5171434\",\n \"0.5170063\",\n \"0.51637805\",\n \"0.5160247\",\n \"0.51565045\",\n \"0.5156393\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5353241"},"document_rank":{"kind":"string","value":"61"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":795,"numItemsPerPage":100,"numTotalItems":95772,"offset":79500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjI2NTE4MiwianRpIjoiOGJiMzYyNjgtMGI5OC00YThmLThhZWUtY2ViNzlkNzc5ZjMyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyNjg3ODIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.keWDPwgJ7hOs6nAUxSdv7ggZSaIkLto6Vl4zN9Z5BORAWSnqnsIl63dtNbowgzvP30QtdCtpPFGe68XgANdJAQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Waits for messages from other components of the app and dispatches commands as necessary | def run(self):
# waits for new messages and reacts based on message
try:
self.__email_handler.run()
except KeyboardInterrupt:
print('\nClosing...\n')
except Exception as e:
print(f'{self.__source}: and error has occured: %s' % e) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _execute(self):\n LOG.info(\"Waiting for a message...\")",
"def main(self):\n\n updater = Updater(self.token)\n dp = updater.dispatcher\n\n dp.add_handler(MessageHandler(Filters.text, self.__msg_handler))\n dp.add_handler(MessageHandler(Filters.command, self.__msg_handler))... | [
"0.722827",
"0.6583136",
"0.6575468",
"0.63810545",
"0.6380672",
"0.6361295",
"0.6314942",
"0.62583387",
"0.6249404",
"0.6244898",
"0.616663",
"0.60918003",
"0.6082881",
"0.6080931",
"0.60132414",
"0.60022396",
"0.59993714",
"0.5983036",
"0.59463596",
"0.59252965",
"0.5925011... | 0.5945537 | 19 |
Try finding good candidates for compaction | def _compact_pass1(self, min_cardinality, child_ratio):
def grandchildren(tree):
for c in tree.children.values():
for gc in c.children.keys():
yield gc
def recursive(tree):
child_count = len(tree.children)
if child_count > min_card... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, full_table, bad_tables, good_tables, **kwargs):\n self.full_table = full_table\n self.bad_tables = bad_tables\n self.good_tables = good_tables\n self.bad_err_funcs = [self.err_func.clone() for t in bad_tables]\n self.good_err_funcs = [self.err_func.clone() for ... | [
"0.5891554",
"0.58388346",
"0.55771506",
"0.55318457",
"0.54864943",
"0.5392519",
"0.5326511",
"0.5310141",
"0.5301276",
"0.53007215",
"0.5286802",
"0.52391326",
"0.522699",
"0.519896",
"0.51907796",
"0.5184375",
"0.5182485",
"0.5163144",
"0.515931",
"0.51409996",
"0.5140504"... | 0.0 | -1 |
! Clamp angles between (pi, pi] angle The angle Clamped angle | def clamp(angle):
while angle > np.pi:
angle -= 2 * np.pi
while angle <= -np.pi:
angle += 2 * np.pi
return angle | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wrap_angle(angle):\n\n angle = (angle + np.pi) % (2 * np.pi) - np.pi\n\n return angle",
"def simplify_angle(angle):\n if angle > math.pi:\n return math.pi - angle\n return angle",
"def wrap_angles(angle):\n return (angle + pi) % (2 * pi) - pi",
"def _confined_angle_pi(a):\n while... | [
"0.7253713",
"0.7211587",
"0.71298516",
"0.712739",
"0.7106415",
"0.70632184",
"0.7024957",
"0.7020985",
"0.7001495",
"0.6973188",
"0.6972748",
"0.696525",
"0.69315684",
"0.6906103",
"0.6902425",
"0.6859615",
"0.68486387",
"0.6762119",
"0.6724876",
"0.6715224",
"0.6715224",
... | 0.7882026 | 0 |
! Get the 4x4 transformation matrix from link to world | def FK_dh(dh_params, joint_angles, link):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMatrix(self) -> CMatrix4:\n ...",
"def matrix(self):\n m = Matrix.identity(4, 4)\n\n m[:3, :3] = self.rotation.matrix.data\n m[:3, 3:4] = self.translation.matrix.data\n\n return m",
"def getTransposeMatrix(self) -> CMatrix4:\n ...",
"def get_direction_matrix(s... | [
"0.67277634",
"0.65599537",
"0.6540112",
"0.64462286",
"0.6384859",
"0.63385653",
"0.6266697",
"0.6194439",
"0.6133355",
"0.6114672",
"0.6102785",
"0.60988957",
"0.60400885",
"0.6003334",
"0.6003334",
"0.59956646",
"0.5993969",
"0.5979662",
"0.59764636",
"0.5951162",
"0.59470... | 0.0 | -1 |
! Gets the transformation matrix from dh parameters. | def get_transform_from_dh(a, alpha, d, theta):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dhMatrix(self):\n row1 = np.array([np.cos(self.theta), -np.sin(self.theta)*np.cos(self.alpha), np.sin(self.theta)*np.sin(self.alpha), self.a*np.cos(self.theta)])\n row2 = np.array([np.sin(self.theta), np.cos(self.theta)*np.cos(self.alpha), -np.cos(self.theta)*np.sin(self.alpha), self.a*np.sin(s... | [
"0.7223759",
"0.7196637",
"0.6873913",
"0.673187",
"0.6503569",
"0.6426385",
"0.6411647",
"0.6405354",
"0.6387804",
"0.6387804",
"0.63843906",
"0.6356444",
"0.63466644",
"0.6344464",
"0.6341292",
"0.63363296",
"0.6311011",
"0.6265211",
"0.6264327",
"0.62036526",
"0.6183622",
... | 0.6528768 | 4 |
! Gets the euler angles from a transformation matrix. | def get_euler_angles_from_T(T):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rotation_matrix_to_euler_angles(self, R):\n assert (self._is_rotation_matrix(R))\n\n sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])\n singular = sy < 1e-6\n\n if not singular:\n x = math.atan2(R[2, 1], R[2, 2])\n y = math.atan2(-R[2, 0], sy)\n ... | [
"0.75182706",
"0.7428497",
"0.7422957",
"0.71761715",
"0.7145424",
"0.71223485",
"0.69868743",
"0.6967",
"0.69654804",
"0.66869587",
"0.668514",
"0.6603598",
"0.6544097",
"0.6526637",
"0.6484461",
"0.6440097",
"0.6358438",
"0.63052535",
"0.6285869",
"0.6279883",
"0.62661356",... | 0.6803455 | 9 |
! Gets the pose from T. | def get_pose_from_T(T):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPose(self):\n\t\treturn self.__subs['pose'].getData()",
"def get_pose(self, joint_angles: dict, query_node: str) -> SE3:\n kinematic_map = self.kinematic_map\n parents = self.parents\n T_ref = self.T_zero\n T = T_ref[\"p0\"]\n for node in kinematic_map[\"p0\"][query_node... | [
"0.7187253",
"0.6933531",
"0.672665",
"0.65035844",
"0.6450835",
"0.62265545",
"0.6220612",
"0.6193213",
"0.6180652",
"0.61510414",
"0.61258614",
"0.61116815",
"0.6047407",
"0.6039513",
"0.5990924",
"0.59187305",
"0.5886028",
"0.58447236",
"0.5842025",
"0.58301324",
"0.580211... | 0.8583743 | 0 |
! Get a 4tuple (x, y, z, phi) representing the pose of the desired link | def FK_pox(joint_angles, m_mat, s_lst):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_goal_ee_pose(self):\n #self.target_endpoint = #magic tf call that I can add ie the pose of the palm from camera aruco detection\n while True:\n try:\n translation, rotation = self.listener.lookupTransform('world_frame', 'palm_frame_camera', rospy.Time()) # ee_frame_c... | [
"0.6071441",
"0.6025158",
"0.6019991",
"0.5937238",
"0.59027195",
"0.58826804",
"0.5766654",
"0.5766654",
"0.5766654",
"0.57409614",
"0.57398325",
"0.57385",
"0.5692674",
"0.5679011",
"0.5676367",
"0.5675034",
"0.5660762",
"0.5649292",
"0.5632161",
"0.5619964",
"0.56163067",
... | 0.0 | -1 |
! Convert to s matrix. | def to_s_matrix(w,v):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def as_matrix(self) -> types.Matrix:",
"def get_stain_matrix(I):",
"def convert(self, s, numRows):\n \n if numRows == 1:\n return s\n \n matrix = []\n for i in range(numRows):\n\n # Change from storing matrices of letter to strings\n # matrix.... | [
"0.68945247",
"0.6642457",
"0.6603314",
"0.6522115",
"0.6472364",
"0.64635885",
"0.6433777",
"0.6428377",
"0.63919264",
"0.63550717",
"0.6309325",
"0.6300631",
"0.62582445",
"0.6221067",
"0.6177143",
"0.61561906",
"0.61437947",
"0.6129541",
"0.6115956",
"0.61145544",
"0.60799... | 0.81369454 | 0 |
! Get all possible joint configs that produce the pose. | def IK_geometric(dh_params, pose):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameters(self):\n return [i.parameter for i in self.joints.values()]",
"def chs_config(self):\n conf = set()\n for j in self.get_fd_j(self.id):\n if self.get_config_j(j) != constants.NOT_PARTICIPANT:\n conf |= set(self.get_config_j(j))\n if conf == set(... | [
"0.61084604",
"0.5877382",
"0.5827816",
"0.57932264",
"0.57727504",
"0.576476",
"0.5750087",
"0.5727622",
"0.5699759",
"0.56863415",
"0.5679535",
"0.5605332",
"0.55895245",
"0.5579664",
"0.5503426",
"0.55003947",
"0.5475498",
"0.5474627",
"0.5438028",
"0.5417057",
"0.54132235... | 0.0 | -1 |
Computes the truncated SVD of A. If r is None or equals the number of nonzero singular values, it is the compact SVD. | def truncated_svd(A,k=None):
AHA=np.conj(A).T.dot(A)
evals,evecs=la.eig(AHA)
order=np.argsort(evals)
evals=evals[order][::-1].copy()
evecs=evecs.T[order][::-1].copy()
m,n=AHA.shape
tol=1e-12
Vh=[]
for i in xrange(0,m):
if np.abs(evals[i])>=tol:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def truncated_svd(A,k=None):",
"def svd0(A):\n M,N = A.shape\n if M>N: return sla.svd(A, full_matrices=True)\n else: return sla.svd(A, full_matrices=False)",
"def pinv_damped(a, l, rcond=1e-15 ):\n # a, wrap = np.linalg._makearray(a)\n # np.linalg._assertNoEmpty2d(a)\n a = a.conjugate()\n u, s... | [
"0.7408433",
"0.6813271",
"0.6227772",
"0.6188896",
"0.6054052",
"0.59679675",
"0.59457225",
"0.58844644",
"0.5869929",
"0.58507484",
"0.5739411",
"0.5691503",
"0.5602991",
"0.5565803",
"0.5549047",
"0.5537558",
"0.5508911",
"0.5458776",
"0.5437982",
"0.5437848",
"0.5436468",... | 0.6630652 | 2 |
Plot each transformation associated with the SVD of A. | def visualize_svd():
A=np.array([[3,1],[1,3]])
U,s,Vh=truncated_svd(A)
twopi=np.linspace(0,2.*np.pi,360)
one=np.reshape(np.linspace(0,1,100),(1,100))
zeros=np.zeros((1,100))
S=np.vstack((np.reshape(np.cos(twopi),(1,360)),np.reshape(np.sin(twopi),(1,360))))
e1=np.vstack((zeros,one))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visualize_svd(A):\r\n theta = np.linspace(0,2*np.pi,200)\r\n #Set S as unit circle\r\n S = np.array([np.cos(theta), np.sin(theta)])\r\n #Set E as orthogonal basis\r\n E = np.array([[1,0,0],[0,0,1]])\r\n U,Si,Vh = la.svd(A)\r\n Si = np.diag(Si)\r\n\r\n #plot original S and E\r\n first... | [
"0.7446054",
"0.6830006",
"0.66623765",
"0.60932344",
"0.60709465",
"0.60032773",
"0.5920726",
"0.5840712",
"0.56891584",
"0.5674648",
"0.56161666",
"0.55661666",
"0.55647403",
"0.5467181",
"0.5462486",
"0.54301476",
"0.5419256",
"0.54070497",
"0.53769505",
"0.53765595",
"0.5... | 0.663423 | 3 |
Returns best rank k approximation to A with respect to the induced 2norm. | def svd_approx(A, k):
U,s,Vh=la.svd(A,full_matrices=False)
return U[:,:k].dot(np.diag(s[:k])).dot(Vh[:k,:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def k_rank_approximate(doc_matrix, k):\n return []",
"def computeBound(A, omega, Q, omega2, Q2, k):\n A = A.todense()\n M = Q2.T.dot(A).dot(Q2)\n R = A.dot(Q2) - Q2.dot(M)\n \n normR = numpy.linalg.norm(R) \n lmbda, U = numpy.linalg.eigh(M)\n L2 = omega[k:]\n\n delta = float(\"inf\"... | [
"0.62445366",
"0.62090725",
"0.61899227",
"0.59206605",
"0.590111",
"0.588553",
"0.58643836",
"0.5837029",
"0.58113295",
"0.5809164",
"0.5769161",
"0.5729828",
"0.57291305",
"0.5697681",
"0.5696698",
"0.5694108",
"0.5687137",
"0.5647675",
"0.5642368",
"0.56338096",
"0.5629287... | 0.0 | -1 |
Returns the lowest rank approximation of A with error less than e with respect to the induced 2norm. | def lowest_rank_approx(A,e):
U,s,Vh=la.svd(A,full_matrices=False)
t=s.copy()
t[t>e]=0
i=t.nonzero()[0][0]
return U[:,:i].dot(np.diag(s[:i])).dot(Vh[:i,:]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lowest_rank_approx(A,e):",
"def lowest_rank_approx(A, err):\n \n #Is this not just problem 1 repeated, just with different returns?\n \n u, d, v = compact_svd(A)\n \n s = len(d)\n for i in range(0, len(d)):\n if d[i] < err:\n s = i\n break\n \n v = v.co... | [
"0.7423881",
"0.6019328",
"0.6006175",
"0.5983821",
"0.58116776",
"0.58109677",
"0.57976466",
"0.5749795",
"0.5728308",
"0.56952167",
"0.56908226",
"0.56268173",
"0.5626445",
"0.56208396",
"0.5615378",
"0.5576524",
"0.55443656",
"0.5530991",
"0.5524992",
"0.55179006",
"0.5516... | 0.68139 | 1 |
Plot the original image found at 'filename' and the rank k approximation of the image found at 'filename.' filename jpg image file path k rank | def compress_image(filename,k):
img_color=plt.imread(filename)
orig=img_color.copy()
R=img_color[:,:,0]
G=img_color[:,:,1]
B=img_color[:,:,2]
m,n=(R.shape[0],R.shape[1])
u1,s1,vh1=la.svd(R,full_matrices=False)
u2,s2,vh2=la.svd(G,full_matrices=False)
u3,s3,vh3=la.svd(B,full_matri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_comparison(filename, k_values):\n basename = os.path.basename(filename).split(\".\")[0]\n\n K_nums = [str(val[\"K\"]) for val in k_values]\n print(f\"\\nstart K-means on {basename} for K=[{', '.join(K_nums)}]\")\n\n if not os.path.exists(basename):\n os.mkdir(basename)\n\n img = i... | [
"0.61253667",
"0.5967198",
"0.5783416",
"0.5655451",
"0.5476648",
"0.5382434",
"0.5345423",
"0.53212297",
"0.53069985",
"0.5282727",
"0.5280036",
"0.52722347",
"0.5271273",
"0.5256575",
"0.5243671",
"0.5242266",
"0.52320015",
"0.5218855",
"0.52187943",
"0.5211906",
"0.5202215... | 0.5404266 | 5 |
Export entity specific data as DXF tags. | def export_entity(self, tagwriter: TagWriter) -> None:
super().export_entity(tagwriter)
# AcDbEntity export is done by parent class
# AcDbCircle export is done by parent class
if tagwriter.dxfversion > DXF12:
tagwriter.write_tag2(SUBCLASS_MARKER, acdb_arc.name)
self.d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_entity(self, tagwriter: 'TagWriter') -> None:\n # base class export is done by parent class\n super().export_entity(tagwriter)\n # AcDbEntity export is done by parent class\n tagwriter.write_tag2(SUBCLASS_MARKER, acdb_tolerance.name)\n self.dxf.export_dxf_attribs(tagwr... | [
"0.63105893",
"0.6185762",
"0.61040395",
"0.6035611",
"0.58427787",
"0.5701736",
"0.5509082",
"0.54831487",
"0.54149663",
"0.54122865",
"0.5400104",
"0.5375761",
"0.5325041",
"0.52323043",
"0.519035",
"0.5183843",
"0.5177302",
"0.5173674",
"0.5170246",
"0.51581097",
"0.515787... | 0.61629796 | 2 |
Returns the start point of the arc in WCS, takes OCS into account. | def start_point(self) -> Vec3:
v = list(self.vertices([self.dxf.start_angle]))
return v[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_arc_center(self):\n # First two anchors and handles\n a1, h1, h2, a2 = self.points[:4]\n # Tangent vectors\n t1 = h1 - a1\n t2 = h2 - a2\n # Normals\n n1 = rotate_vector(t1, TAU / 4)\n n2 = rotate_vector(t2, TAU / 4)\n try:\n return ... | [
"0.6179543",
"0.6066767",
"0.60599524",
"0.56948525",
"0.5665913",
"0.56469965",
"0.5609979",
"0.5566748",
"0.55583507",
"0.5535",
"0.5533847",
"0.55278826",
"0.55274695",
"0.55264086",
"0.548918",
"0.5485597",
"0.5480605",
"0.54784",
"0.54637015",
"0.54637015",
"0.54563135",... | 0.6059262 | 3 |
Returns the end point of the arc in WCS, takes OCS into account. | def end_point(self) -> Vec3:
v = list(self.vertices([self.dxf.end_angle]))
return v[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_end_point(arc: FpArc)->Coords:\n x: float = float(arc.start[0])\n y: float = float(arc.start[1])\n ang: float = float(arc.angle)\n x_c: float = float(arc.end[0])\n y_c: float = float(arc.end[1])\n r: float = math.sqrt((x - x_c) * (x - x_c) + (y - y_c) * (y - y_c))\n start_angle: float ... | [
"0.6821191",
"0.62864006",
"0.5807849",
"0.5759162",
"0.5675946",
"0.5645561",
"0.55972826",
"0.55416834",
"0.5516155",
"0.5422003",
"0.54003006",
"0.53776693",
"0.53365004",
"0.5305714",
"0.52976304",
"0.5211127",
"0.5184522",
"0.5172099",
"0.5158973",
"0.515795",
"0.5144223... | 0.627563 | 2 |
Returns `num` angles from start to end angle in degrees in counter clockwise order. All angles are normalized in the range from [0, 360). | def angles(self, num: int) -> Iterable[float]:
if num < 2:
raise ValueError("num >= 2")
start = self.dxf.start_angle % 360
stop = self.dxf.end_angle % 360
if stop <= start:
stop += 360
for angle in linspace(start, stop, num=num, endpoint=True):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_angle(n):\n return n % 360 if n > 360 else (n * 180) / PI",
"def calculate_angle(start: tuple, end: tuple):\n radians = -math.atan2(end[0] - start[0], end[1] - start[1])\n return math.degrees(radians) % 360",
"def degree_range(n, start=0, end=180):\n start_ = np.linspace(start, end, n+1, en... | [
"0.69789016",
"0.6480122",
"0.64355767",
"0.63461417",
"0.61619854",
"0.6121664",
"0.6098144",
"0.60696733",
"0.60160345",
"0.58649814",
"0.58536875",
"0.5849952",
"0.57118773",
"0.56563556",
"0.5630533",
"0.5594795",
"0.5569927",
"0.5524123",
"0.55092126",
"0.55007035",
"0.5... | 0.7683883 | 0 |
Approximate the arc by vertices in WCS, argument `segment` is the max. distance from the center of an arc segment to the center of its chord. | def flattening(self, sagitta: float) -> Iterator[Vec3]:
arc = self.construction_tool()
ocs = self.ocs()
elevation = Vec3(self.dxf.center).z
if ocs.transform:
to_wcs = ocs.points_to_wcs
else:
to_wcs = Vec3.generate
yield from to_wcs(
Ve... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def segmentarc(c,u1,u2):\n\n pol1=samplearc(c,u1,polar=True)\n pol2=samplearc(c,u2,polar=True)\n sr= (c[1][3] == -2)\n if sr:\n return arc(pol1[0],pol1[1],pol2[2],pol1[2],samplereverse=True)\n else:\n return arc(pol1[0],pol1[1],pol1[2],pol2[2])",
"def intersection_segment_plane(segme... | [
"0.56789714",
"0.56275415",
"0.5404232",
"0.526627",
"0.50678796",
"0.5067105",
"0.50314313",
"0.5003801",
"0.49465248",
"0.49374533",
"0.49315086",
"0.4920969",
"0.4898533",
"0.48621497",
"0.48603126",
"0.48142242",
"0.48120928",
"0.47588274",
"0.47166684",
"0.4711173",
"0.4... | 0.0 | -1 |
Transform ARC entity by transformation matrix `m` inplace. Raises ``NonUniformScalingError()`` for non uniform scaling. | def transform(self, m: Matrix44) -> Arc:
ocs = OCSTransform(self.dxf.extrusion, m)
super()._transform(ocs)
s: float = self.dxf.start_angle
e: float = self.dxf.end_angle
if not math.isclose(arc_angle_span_deg(s, e), 360.0):
(
self.dxf.start_angle,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_M(pca, M):\n return pca.transform(M)",
"def apply_affine_transform(x, M):\n is1d = len(x.shape) == 1\n if is1d:\n x = np.expand_dims(x, axis=0)\n\n x_hom = np.concatenate(\n [x, np.ones((x.shape[0], 1), dtype=x.dtype)], axis=-1\n )\n x_out = x_hom @ M.T\n if is1d:... | [
"0.6531993",
"0.6270979",
"0.61930734",
"0.6004775",
"0.5982845",
"0.5892362",
"0.58800626",
"0.5865425",
"0.5843489",
"0.5809627",
"0.58091384",
"0.58003235",
"0.58003235",
"0.5765189",
"0.5754191",
"0.5752435",
"0.5734377",
"0.57039636",
"0.56810164",
"0.56775236",
"0.56762... | 0.5440676 | 29 |
Creates a YML file to be used as a custom exclusions template, so users can fill out the fields without needing to look up the required format. | def create_exclusions_file(output_file: str, verbosity: int) -> None:
set_log_level(verbosity)
with open(output_file, "a") as file_obj:
for line in EXCLUSIONS_TEMPLATE:
file_obj.write(line)
utils.print_green(f"Success! Exclusions template file written to: {output_file}")
print(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_exclusions(proteins):\n pass",
"def test__create_excl_file_2(self):\n rsync = RsyncMethod(self.settings, self.meta, self.log, self.comms, False)\n rsync.settings.set('debug-level', 1)\n rsync.exclude_file = os.path.join(os.environ['HOME'],\"temp/myocp_excl\")\n rsync._... | [
"0.58035815",
"0.53256804",
"0.53069764",
"0.5283049",
"0.52198035",
"0.51952946",
"0.51761174",
"0.5160965",
"0.5155543",
"0.51421845",
"0.5109539",
"0.51084596",
"0.50987554",
"0.50949365",
"0.5078183",
"0.50466263",
"0.50214356",
"0.4995813",
"0.49698544",
"0.4954272",
"0.... | 0.61798495 | 0 |
in case of a non file you wan to use nocheck=True (like /dev/null) | async def getfile(url: str, filepath, chunk_size=150000, bytes_range=None,
retries=5, nocheck=False, **kwargs) -> None:
callback = kwargs.pop('callback', None)
userdata = kwargs.pop('userdata', None)
kwargs.setdefault('raise_for_status', True)
def get_headers(file_size=None) -> dict:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def noCheck():\n dislin.nochek()",
"def test_check(self):\n\n self.assertTrue(Naive().check(self.file_gitignore))\n self.assertTrue(Naive().check(self.file_tests))\n self.assertTrue(Naive().check(self.file_bin))\n self.assertTrue(Naive().check(self.file_py))\n self.assertTru... | [
"0.6305159",
"0.62550896",
"0.6145006",
"0.61087954",
"0.59869045",
"0.59646285",
"0.59346807",
"0.592191",
"0.5865518",
"0.5859718",
"0.5827415",
"0.5811571",
"0.57681894",
"0.57546294",
"0.57245594",
"0.57193375",
"0.56955117",
"0.56896937",
"0.56348944",
"0.56010413",
"0.5... | 0.0 | -1 |
Function construct complex number | def __init__(self, a=0, b=0):
self._a = a
self._b = b | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex(real, imag):",
"def _complex(real, imag):\n real = np.asarray(real)\n imag = np.asarray(imag)\n cplx = 1j * imag \n return cplx + real",
"def __complex__(self):\n return complex(self._reNum, self._imNum)",
"def __complex__(self):\n return complex(self.q[0], self.q[1])... | [
"0.8198033",
"0.77351445",
"0.76188505",
"0.73935705",
"0.7340337",
"0.7271693",
"0.71695983",
"0.7168124",
"0.71572167",
"0.70651966",
"0.7025084",
"0.68867147",
"0.68855846",
"0.68855846",
"0.68855846",
"0.68855846",
"0.68855846",
"0.68855846",
"0.68855846",
"0.68855846",
"... | 0.0 | -1 |
Function return real num | def get_a(self):
return self._a | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def real(z):",
"def get_real(val, precision):\n if precision == \"single\":\n return numpy.float32(val)\n elif precision == \"double\":\n return numpy.float64(val)\n else:\n raise ValueError (\"precision %s not supported!\"%(precision))",
"def getNumber():",
"def Sqr(num):\n ... | [
"0.70360833",
"0.66534823",
"0.6416397",
"0.6371853",
"0.6340088",
"0.6171573",
"0.6140519",
"0.6118412",
"0.61131155",
"0.61131155",
"0.6103398",
"0.60876864",
"0.6082805",
"0.60586",
"0.6041225",
"0.6012087",
"0.6000757",
"0.59871763",
"0.59682447",
"0.5883435",
"0.5818909"... | 0.0 | -1 |
Function return real num | def get_b(self):
return self._b | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def real(z):",
"def get_real(val, precision):\n if precision == \"single\":\n return numpy.float32(val)\n elif precision == \"double\":\n return numpy.float64(val)\n else:\n raise ValueError (\"precision %s not supported!\"%(precision))",
"def getNumber():",
"def Sqr(num):\n ... | [
"0.70360833",
"0.66534823",
"0.6416397",
"0.6371853",
"0.6340088",
"0.6171573",
"0.6140519",
"0.6118412",
"0.61131155",
"0.61131155",
"0.6103398",
"0.60876864",
"0.6082805",
"0.60586",
"0.6041225",
"0.6012087",
"0.6000757",
"0.59871763",
"0.59682447",
"0.5883435",
"0.5818909"... | 0.0 | -1 |
Function prints complex number in correct form | def __str__(self):
sign = ''
if self.get_b() > 0:
sign = '+'
elif self.get_a() == self.get_b() or self.get_b() == 0:
return f'z = {self.get_a()}i'
elif self.get_a() == 0:
return f'z = {self.get_b()}i'
return f'z = {self.get_a()}{sign}{self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complexinfo(a, str=None):\n\n if str:\n print \n print \"\\t\", str\n re = a.real.copy()\n im = a.imag.copy()\n _log.debug(\"\\t%.2e %.2g = re.sum im.sum\" % (re.sum(), im.sum()))\n _log.debug(\"\\t%.2e %.2g = abs(re).sum abs(im).sum\" % (abs(re).sum(), abs(im).sum()))",
"d... | [
"0.7244853",
"0.7220926",
"0.72013825",
"0.6815524",
"0.6771268",
"0.6730379",
"0.670886",
"0.66197026",
"0.6509665",
"0.6499921",
"0.6454943",
"0.6434747",
"0.64002264",
"0.6315071",
"0.628439",
"0.62117547",
"0.6205477",
"0.6205477",
"0.6205477",
"0.6205477",
"0.6205477",
... | 0.0 | -1 |
out_data2 = imutil.imread(out_path) out_data == out_data2 image = out_data2 | def _imshow_dtm(image):
import copy
import matplotlib as mpl
from matplotlib.colors import Normalize
import plottool as pt
UNKNOWN = -32767
vmin = image[image != UNKNOWN].min()
vmax = image.max()
norm = Normalize(vmin=vmin, vmax=vmax)
cmap = copy.copy(mpl.cm.get_cmap('viridis'))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_RawImage_write_out():\n i.write_out()\n # now compare the output with reference\n print i.outpath\n print t.processed_path\n assert_image_equal(i.outpath, t.processed_path)",
"def assert_image_equal(path1, path2):\n test_im = np.asarray(Image.open(path1))\n ref_im = np.asarray(Image... | [
"0.6522312",
"0.63933545",
"0.6368202",
"0.636577",
"0.6283282",
"0.61069536",
"0.60416305",
"0.59845793",
"0.5942118",
"0.59081787",
"0.5860526",
"0.5857463",
"0.5819259",
"0.58096236",
"0.57952625",
"0.578652",
"0.5738333",
"0.5736712",
"0.57099766",
"0.5691949",
"0.5684920... | 0.0 | -1 |
Loads the source data into the Inputs format for further processing. | def load_fullres_inputs(task, subdir='training'):
tagged_paths = {
'gt': glob.glob(join(task.root, subdir, '*_GTL.tif')),
'im': glob.glob(join(task.root, subdir, '*_RGB.tif')),
'gti': glob.glob(join(task.root, subdir, '*_GTI.tif')),
# digital terrain model
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self, source):\n try:\n inputdata = self.__inputmanager.read(source)\n self.__suitables = self.__inputmanager.map(inputdata)\n self.__data = inputdata\n except ValueError as e:\n print (\"Failed to load the dataset: %s\" % e)\n raise\n\n... | [
"0.72463214",
"0.69247866",
"0.6851725",
"0.6774142",
"0.67552024",
"0.66952884",
"0.6576251",
"0.6491617",
"0.6390504",
"0.6388523",
"0.63387704",
"0.6287613",
"0.6287613",
"0.6264103",
"0.6261214",
"0.62299746",
"0.621701",
"0.6204398",
"0.61860484",
"0.6114994",
"0.6112335... | 0.0 | -1 |
Inplace / lazy modification of groundtruth labels hacky. | def rebase_groundtruth(task, fullres, force=False):
# Remap the original three labels to [0, 1, 2]
orig_labels = [2, 6, 65]
mapping = np.full(max(orig_labels) + 1, fill_value=-1)
mapping[orig_labels] = np.arange(len(orig_labels))
datadir = ub.ensuredir((task.workdir, 'data'))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def UpdateLabel(self) -> _n_6_t_0:",
"def test_issue_replace_labels(self):\n pass",
"def _relabel(labels, minval=0, bgval=None):\n\n labels = np.unique(labels, return_inverse=True)[-1] + minval\n if bgval is not None:\n labels[labels == minval] = bgval\n return labels",
"def forget_lab... | [
"0.6552775",
"0.64296865",
"0.6331347",
"0.62288725",
"0.6124574",
"0.60941124",
"0.60580987",
"0.6044636",
"0.599782",
"0.59934396",
"0.5971612",
"0.59426975",
"0.59148395",
"0.58575964",
"0.5838784",
"0.5820563",
"0.5820563",
"0.58087516",
"0.57766753",
"0.5771515",
"0.5760... | 0.56884044 | 30 |
gti_data = np.array([ [0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 5, 5, 5, 5, 5, 5, 2, 2, 2], [0, 0, 5, 5, 5, 5, 5, 5, 2, 2, 2], [0, 5, 5, 5, 5, 5, 5, 5, 2, 2, 2], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 5, 5, 5, 5, 5, 5, 5, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 3,... | def instance_boundary(gti_data, gtl_data):
out_data = np.full(gti_data.shape, dtype=np.uint8,
fill_value=NON_BUILDING)
# 5x5 erosion is equivalent to two iterations of a 3x3 erosion
kernel = np.ones((3, 3))
for label, submask, rc_off, rc_sl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def erode(img, kernel = (5,5), iterations = 1):\n\ttmp = grayscale(img)\n\tk = np.ones(kernel, np.uint8)\n\terosion = cv2.erode(tmp, k, iterations= iterations)\n\treturn erosion",
"def erode(image, kernel_size=(5, 5)):\n kernel = np.ones(kernel_size, np.uint8)\n image = cv2.erode(image, kernel)\n return... | [
"0.6813081",
"0.6655335",
"0.6655335",
"0.6326385",
"0.62629443",
"0.6173644",
"0.616028",
"0.60584724",
"0.6048696",
"0.60176474",
"0.6009464",
"0.5972192",
"0.59574443",
"0.5947122",
"0.59254193",
"0.5881773",
"0.58735096",
"0.58453214",
"0.5690558",
"0.5689311",
"0.5658610... | 0.55255014 | 35 |
Recombine parts back into an entire image | def stitch_tiles(rc_locs, tiles):
shapes = [t.shape[0:2] for t in tiles]
n_channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
bboxes = np.array([
(r, c, r + h, c + w)
for ((r, c), (h, w)) in zip(rc_locs, shapes)
])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reconstruct_image(patch_list, patch_nb=2):\n line_list = []\n for i in range(0, patch_nb ** 2 - 1, patch_nb):\n line_list.append(cv2.hconcat(patch_list[i : i + patch_nb]))\n final_img = cv2.vconcat(line_list)\n return final_img",
"def transform_images(img1,img2):",
"def transform(self, p... | [
"0.67004097",
"0.66201097",
"0.6563718",
"0.64026254",
"0.631528",
"0.62526196",
"0.6201229",
"0.6071955",
"0.60583776",
"0.60260767",
"0.5984834",
"0.5976638",
"0.59046507",
"0.5903756",
"0.58812374",
"0.5855542",
"0.5846808",
"0.5841477",
"0.581932",
"0.58078796",
"0.579899... | 0.0 | -1 |
Recombine parts back into an entire image | def stitch_tiles_ave(rc_locs, tiles, weighted=False):
shapes = [t.shape[0:2] for t in tiles]
n_channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
bboxes = np.array([
(r, c, r + h, c + w)
for ((r, c), (h, w)) in zip(rc_locs, shapes)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reconstruct_image(patch_list, patch_nb=2):\n line_list = []\n for i in range(0, patch_nb ** 2 - 1, patch_nb):\n line_list.append(cv2.hconcat(patch_list[i : i + patch_nb]))\n final_img = cv2.vconcat(line_list)\n return final_img",
"def transform_images(img1,img2):",
"def transform(self, p... | [
"0.6700326",
"0.6619646",
"0.65631014",
"0.64027864",
"0.63155764",
"0.6252576",
"0.6201516",
"0.6072268",
"0.6058612",
"0.6026108",
"0.59840626",
"0.5976472",
"0.59055954",
"0.5903025",
"0.58820647",
"0.58544",
"0.58464336",
"0.58426243",
"0.5818387",
"0.58068055",
"0.579777... | 0.0 | -1 |
Recombine parts back into an entire image | def stitch_tiles_vote(rc_locs, tiles):
shapes = [t.shape[0:2] for t in tiles]
n_channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
assert n_channels == 1
bboxes = np.array([
(r, c, r + h, c + w)
for ((r, c), (h, w)) in zip(rc_l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reconstruct_image(patch_list, patch_nb=2):\n line_list = []\n for i in range(0, patch_nb ** 2 - 1, patch_nb):\n line_list.append(cv2.hconcat(patch_list[i : i + patch_nb]))\n final_img = cv2.vconcat(line_list)\n return final_img",
"def transform_images(img1,img2):",
"def transform(self, p... | [
"0.6700326",
"0.6619646",
"0.65631014",
"0.64027864",
"0.63155764",
"0.6252576",
"0.6201516",
"0.6072268",
"0.6058612",
"0.6026108",
"0.59840626",
"0.5976472",
"0.59055954",
"0.5903025",
"0.58820647",
"0.58544",
"0.58464336",
"0.58426243",
"0.5818387",
"0.58068055",
"0.579777... | 0.0 | -1 |
Do some postprocessing to label instances instead of classes | def instance_label(task, pred, k=15, n_iters=1, dist_thresh=5, watershed=False):
mask = pred
# noise removal
if k > 1 and n_iters > 0:
kernel = np.ones((k, k), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,
iterations=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def multi_label_cls_head__post_process(ctx, self, pred, **kwargs):\n return pred",
"def _post_transform(self):\n # Reclassify strategy post __init__, if needed.\n for (reclassifier, args, kwargs) in self._reclassifiers:\n self.classifier = reclassifier(self.classifier, *args, **kwargs... | [
"0.6998397",
"0.6416584",
"0.6398392",
"0.6248536",
"0.623932",
"0.6210227",
"0.6058601",
"0.59982306",
"0.59315073",
"0.59097195",
"0.59043396",
"0.5815752",
"0.5814318",
"0.5801434",
"0.57983583",
"0.5782739",
"0.57623565",
"0.5757331",
"0.57066524",
"0.5687625",
"0.5673649... | 0.5417718 | 51 |
Iterate over a cropped mask for each instance in an instance segmentation | def instance_submasks(gti):
rc_locs = np.where(gti > 0)
grouped_cc_rcs = util.group_items(
np.ascontiguousarray(np.vstack(rc_locs).T),
gti[rc_locs], axis=0
)
def bounding_box(rcs):
rc1 = rcs.min(axis=0)
rc2 = rcs.max(axis=0)
return rc1, rc2
for label, rcs in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bg_mask(query_imgs, method):\n print(\"Obtaining masks\")\n segmentation_method = get_method(method)\n return [segmentation_method(img) for img in query_imgs]",
"def img_process(fgMask):\n backSub = cv.createBackgroundSubtractorKNN()\n kernel1 = cv.getStructuringElement(shape=cv.MORPH_ELLIPSE,... | [
"0.63669485",
"0.62147236",
"0.61084807",
"0.6079566",
"0.60006905",
"0.5989203",
"0.5969494",
"0.5966395",
"0.591953",
"0.5907332",
"0.58997315",
"0.5882974",
"0.58462447",
"0.5816596",
"0.58141476",
"0.58063954",
"0.5799162",
"0.57579124",
"0.57267785",
"0.5714815",
"0.5706... | 0.64390075 | 0 |
Extracts a contour for each (nonoverlapping) instance label in a mask | def instance_contours(gti):
# TODO: move to somewhere better
import cv2
rc_locs = np.where(gti > 0)
grouped_cc_rcs = util.group_items(
np.ascontiguousarray(np.vstack(rc_locs).T),
gti[rc_locs], axis=0
)
def bounding_box(rcs):
rc1 = rcs.min(axis=0)
rc2 = rcs.max(a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mask_label_contour(image, seg):\n return sitk.Mask(image, sitk.LabelContour(seg+1)==0)",
"def get_contour_features(mask,selectcell=\"centered\"):\r\n \r\n #binarize image (everything above 0 becomes 1)\r\n mask = np.clip(mask,a_min=0,a_max=1)\r\n\r\n #for contours, dont use RETR_TREE, but RETR_... | [
"0.7284189",
"0.7216781",
"0.6806945",
"0.67010385",
"0.6601212",
"0.6331396",
"0.63178694",
"0.6256933",
"0.61907893",
"0.6157728",
"0.6108738",
"0.60891694",
"0.6067135",
"0.6033934",
"0.60224605",
"0.6009662",
"0.59973556",
"0.5993736",
"0.5985048",
"0.5974666",
"0.5945327... | 0.66997796 | 4 |
img = util.imread('/home/joncrall/remote/aretha/data/UrbanMapper3D/training/TAM_Tile_003_RGB.tif') gti = util.imread(ub.truepath('~/remote/aretha/data/UrbanMapper3D/training/TAM_Tile_003_GTI.tif')) gtl = util.imread('/home/joncrall/remote/aretha/data/UrbanMapper3D/training/TAM_Tile_003_GTL.tif') thickness = 2 alpha = 1 | def draw_instance_contours(img, gti, gtl=None, thickness=2, alpha=1, color=None):
import cv2
grouped_contours = instance_contours(gti)
if gtl is not None:
unknown_labels = set(np.unique(gti[gtl == 65]))
else:
unknown_labels = set()
known_labels = set(grouped_contours.keys()) - unk... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_load_tif():\n parameters = {'path': 'green-dot.tif'}\n\n img = images.load(parameters)\n\n numpy.testing.assert_array_equal(img[10, 5], [0, 255, 0])",
"def test_save_tif():\n img = Image.new('RGB', (10, 20))\n\n parameters = {'path': 'green-dot.tif', 'data': [img]}\n\n assert images.sa... | [
"0.7076391",
"0.6545913",
"0.64205337",
"0.6392171",
"0.63680726",
"0.6344497",
"0.6018894",
"0.6004421",
"0.5993268",
"0.5986748",
"0.5969459",
"0.59548044",
"0.5954693",
"0.59082866",
"0.58946025",
"0.58738846",
"0.5857484",
"0.5810171",
"0.5803431",
"0.5800404",
"0.5785079... | 0.0 | -1 |
compute ROUGEN for a single pair of summary and reference | def compute_rouge_n(output, reference, n=1, mode='f'):
assert mode in list('fpr') # F-1, precision, recall
match = _n_gram_match(reference, output, n)
if match == 0:
score = 0.0
else:
precision = match / len(list(make_n_grams(output, n)))
recall = match / len(list(make_n_grams(r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rouge(ref_file, summarization_file, subword_option=None):\n\n references = []\n with codecs.getreader(\"utf-8\")(tf.gfile.GFile(ref_file, \"rb\")) as fh:\n for line in fh:\n references.append(_clean(line, subword_option))\n\n hypotheses = []\n with codecs.getreader(\"utf-8\")(\n tf.gfile.GFil... | [
"0.59994656",
"0.580792",
"0.5503322",
"0.5481186",
"0.5475576",
"0.547244",
"0.5428628",
"0.54162747",
"0.5406109",
"0.53631014",
"0.5319566",
"0.5271652",
"0.526884",
"0.5182089",
"0.51223683",
"0.5116938",
"0.51069325",
"0.51056755",
"0.50990134",
"0.50808424",
"0.5057335"... | 0.48229542 | 61 |
compute the len dp of lcs | def _lcs_dp(a, b):
dp = [[0 for _ in range(0, len(b) + 1)]
for _ in range(0, len(a) + 1)]
# dp[i][j]: lcs_len(a[:i], b[:j])
for i in range(1, len(a) + 1):
for j in range(1, len(b) + 1):
if a[i - 1] == b[j - 1]:
dp[i][j] = dp[i - 1][j - 1] + 1
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _lcs_len(a, b):\n dp = _lcs_dp(a, b)\n return dp[-1][-1]",
"def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m = len(x), len(y)\n return table[n, m]",
"def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m = len(x), len(y)\n return table[n, m]",
"def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m =... | [
"0.7369952",
"0.72339016",
"0.72339016",
"0.7178741",
"0.66930044",
"0.65075463",
"0.6326829",
"0.6310558",
"0.6306825",
"0.6279071",
"0.6263366",
"0.62361485",
"0.62065816",
"0.61543864",
"0.61426204",
"0.61426204",
"0.6130789",
"0.6125237",
"0.610565",
"0.6093229",
"0.60821... | 0.7251978 | 1 |
compute the length of longest common subsequence between a and b | def _lcs_len(a, b):
dp = _lcs_dp(a, b)
return dp[-1][-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def longestCommonSubsequence(self, text1: str, text2: str) -> int:\n if len(text1) == 0 or len(text2) == 0:\n return 0\n if text1[0] == text2[0]:\n return 1 + self.longestCommonSubsequence(text1[1:], text2[1:])\n else:\n return max(self.longestCommonSubsequence... | [
"0.77036387",
"0.7551531",
"0.7479743",
"0.74269044",
"0.74152994",
"0.739756",
"0.7195203",
"0.71844494",
"0.70629114",
"0.7058059",
"0.7053145",
"0.7036421",
"0.69795805",
"0.69477475",
"0.69437826",
"0.69384384",
"0.6936886",
"0.69272065",
"0.6915973",
"0.68329513",
"0.680... | 0.7914411 | 0 |
compute ROUGEL for a single pair of summary and reference output, reference are list of words | def compute_rouge_l(output, reference, mode='f'):
assert mode in list('fpr') # F-1, precision, recall
lcs = _lcs_len(output, reference)
if lcs == 0:
score = 0.0
else:
precision = lcs / len(output)
recall = lcs / len(reference)
f_score = 2 * (precision * recall) / (precis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _rouge(ref_file, summarization_file, subword_option=None):\n\n references = []\n with codecs.getreader(\"utf-8\")(tf.gfile.GFile(ref_file, \"rb\")) as fh:\n for line in fh:\n references.append(_clean(line, subword_option))\n\n hypotheses = []\n with codecs.getreader(\"utf-8\")(\n tf.gfile.GFil... | [
"0.68512774",
"0.6794853",
"0.6781679",
"0.6636516",
"0.6634663",
"0.6033136",
"0.60246843",
"0.5999753",
"0.591893",
"0.5897353",
"0.5897353",
"0.58255905",
"0.578991",
"0.5759897",
"0.57576495",
"0.566979",
"0.5570331",
"0.5539233",
"0.55027527",
"0.5498804",
"0.54661024",
... | 0.62309587 | 5 |
Takes self.url (for a general MyLife search), scrapes the site data, and adds it to the self.data_from_website DataFrame. MyLife keeps its full data set on the page for the specific record, so self._gather_deep_data() can be used to pull that deeper data. | def get_data(self):
def _clean_search_hit(search_hit):
"""
Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.
:param search_hit:
:return Dictionary: A dictionary with the cleaned data
"""
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _gather_deep_data(self):\n\n cleaned_data_from_website = list()\n\n for i, search_result in self.data_from_website.iterrows():\n cleaned_data_from_website.append(self._deep_data(search_result.url))\n\n cleaned_data_from_website = pd.DataFrame(cleaned_data_from_website)\n ... | [
"0.734057",
"0.6615082",
"0.649611",
"0.6229477",
"0.62144905",
"0.61352277",
"0.60608697",
"0.59864897",
"0.58287615",
"0.5696052",
"0.56581056",
"0.56528085",
"0.56447047",
"0.5644577",
"0.5631243",
"0.5629284",
"0.55869025",
"0.5567835",
"0.5558714",
"0.55327916",
"0.55251... | 0.5811679 | 9 |
Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema. | def _clean_search_hit(search_hit):
hit_name = search_hit.find(class_='hit-name')
hit_url = hit_name.get('href')
hit_id = hit_url.split('/')[-1]
name = hit_name.get_text().split(',')[0].title().split()
current_city = search_hit.find(class_='hit-location').get... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(self):\n def _clean_search_hit(search_hit):\n \"\"\"\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\n\n :param search_hit:\n :return Dictionary: A dictionary with the cleaned data\n ... | [
"0.7068637",
"0.6284706",
"0.6284706",
"0.6060897",
"0.60550624",
"0.59880733",
"0.580835",
"0.57218945",
"0.5721135",
"0.5691528",
"0.5633391",
"0.5621051",
"0.56149995",
"0.557798",
"0.5557854",
"0.5551034",
"0.5529365",
"0.55274594",
"0.5495556",
"0.5424936",
"0.5424492",
... | 0.5892267 | 6 |
Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and press the option if found. Returns Boolean for found status | def _refine_search(search_str, options):
search_str = search_str.upper()
logging.info(f'Looking for \'{search_str}\'')
try:
for option in options:
option_text = option.text.upper()
logging.info(f'Option Checked: {option_text}')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enable_search(self):\n html_element = self.find_element_by_xpath(\n '/html/body').get_attribute('outerHTML')\n soup = Scraper(html_element)\n\n elms_obj = soup.find_search_enable_btn()\n\n for tag, target in elms_obj.items():\n if len(target) > 0:\n ... | [
"0.6565995",
"0.6007825",
"0.5899171",
"0.588052",
"0.58556384",
"0.5773957",
"0.5702507",
"0.5661538",
"0.56134385",
"0.5523983",
"0.5489346",
"0.5425644",
"0.5402936",
"0.53933203",
"0.5385448",
"0.5371235",
"0.53526896",
"0.53496426",
"0.53259146",
"0.53237027",
"0.5322532... | 0.74813646 | 0 |
Takes a URL for a specific MyLife record, scrapes the JSON data and returns a dictionary. | def _deep_data(self, url):
def _nested_persons(persons):
_persons = list()
for person_ in persons:
person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]
person = {'name': person_[0][0].title()}
if len(person_[0]) =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetchJson(url):",
"def Access_URL(url): \n r = requests.get(url) \n json = r.json() \n return json",
"async def fetch_data(self, url: str) -> dict:\n async with self.bot.http_session.get(url) as r:\n return await r.json()",
"def find_details_json(self, url):\n respons... | [
"0.6364494",
"0.62634754",
"0.5954379",
"0.58847296",
"0.58285487",
"0.57935375",
"0.5754346",
"0.57019734",
"0.56669426",
"0.56618446",
"0.5656705",
"0.565219",
"0.5648498",
"0.5584223",
"0.5552247",
"0.5552037",
"0.5542149",
"0.5536617",
"0.5501606",
"0.54985535",
"0.549855... | 0.53848726 | 36 |
Gathers the data that is deeper within the website by calling self._deep_data(url) for each record found during the general search in self.get_data() | def _gather_deep_data(self):
cleaned_data_from_website = list()
for i, search_result in self.data_from_website.iterrows():
cleaned_data_from_website.append(self._deep_data(search_result.url))
cleaned_data_from_website = pd.DataFrame(cleaned_data_from_website)
if len(cleane... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _deep_data(self, url):\n def _nested_persons(persons):\n _persons = list()\n for person_ in persons:\n person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]\n person = {'name': person_[0][0].title()}\n if len(pe... | [
"0.6764181",
"0.6504204",
"0.6407108",
"0.6405198",
"0.6028385",
"0.6017134",
"0.60075647",
"0.5994258",
"0.5978083",
"0.59313196",
"0.588016",
"0.5875708",
"0.58599573",
"0.58480936",
"0.5798911",
"0.57943964",
"0.5777602",
"0.57580256",
"0.5734268",
"0.5704545",
"0.5704199"... | 0.7611993 | 0 |
Attempts to find the github config file. | def _github_config(self, config_file_name):
home = os.path.abspath(os.environ.get('HOME', ''))
config_file_path = os.path.join(home, config_file_name)
return config_file_path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _findconfigfile():\n\n # A ordered list of possible config files\n configfiles = [\"~/.githubhooksrc\",\n \"/etc/githubhooks\"]\n\n for configfile in configfiles:\n if os.path.isfile(os.path.expanduser(configfile)):\n return os.path.expanduser(configfile)\n\n # N... | [
"0.78150916",
"0.7267893",
"0.6513972",
"0.64564544",
"0.64560837",
"0.640976",
"0.63672173",
"0.62919074",
"0.6254891",
"0.6234435",
"0.62150365",
"0.6196737",
"0.6164332",
"0.614716",
"0.61321723",
"0.61281306",
"0.60862803",
"0.606665",
"0.60641634",
"0.6059604",
"0.605909... | 0.7761752 | 1 |
Callback if two factor authentication is requested. | def _two_factor_code(self):
code = ''
while not code:
code = input('Enter 2FA code: ')
return code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def require_two_factor_authentication(self) -> pulumi.Output[bool]:\n return pulumi.get(self, \"require_two_factor_authentication\")",
"def require_two_factor_authentication(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"require_two_factor_authentication\")",
"def require_two_... | [
"0.6953113",
"0.67109174",
"0.67109174",
"0.647221",
"0.62050366",
"0.61577046",
"0.60967463",
"0.6010912",
"0.5951027",
"0.58988005",
"0.5897524",
"0.58657104",
"0.58193564",
"0.5797635",
"0.57952434",
"0.5794757",
"0.57754326",
"0.57652265",
"0.5760432",
"0.574998",
"0.5741... | 0.0 | -1 |
Take a URL, generate a unique filename, save the image to said file and return the filename. | def save_image(url):
ext = url.split('.')[-1]
filename = IMAGEDIR+os.sep+hashlib.md5(url.encode('utf-8')).hexdigest()+'.'+ext
if os.path.exists(filename):
return filename
try:
content = urlopen(url).read()
f = open(filename,'wb')
f.write(content)
f.close()
ex... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_image(filename: str, img_url: str) -> None:\n\n if not (os.path.isfile(filename)): # Check if the file already exists\n print('Downloading image {}...'.format(img_url))\n res = requests.get(img_url) # Download the image.\n res.ra... | [
"0.7339656",
"0.72139686",
"0.7092204",
"0.7006235",
"0.69701207",
"0.68517417",
"0.6776711",
"0.67724454",
"0.6753113",
"0.6721219",
"0.6714699",
"0.67063594",
"0.66669434",
"0.6656108",
"0.6643168",
"0.66106606",
"0.65638",
"0.6506329",
"0.6501225",
"0.6394256",
"0.63756764... | 0.8284375 | 0 |
Scrape an input scamdiggers page for the profile content of the scammer. | def scrape_profile(inhandle, outfile, year, month):
#Read file
html = inhandle.read()
soup = BeautifulSoup(html, 'html.parser')
#Find main page content
content = soup.find('div', {'class':'entry-content'})
profile = {}
#Fill in known info from URL
profile['year_reported'] = year
profile['month_repo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape(self):\n pass",
"def _scrape(self):",
"def scrape(self, page_src):\n soup = bs(page_src, 'html.parser')\n self._data = soup.text.split()",
"def page_data():\n return scrape()",
"def profiles_search(pages: list, session: requests.Session):\n result = []\n for page in... | [
"0.636905",
"0.60857433",
"0.5832773",
"0.5770994",
"0.56837744",
"0.55691594",
"0.5531926",
"0.54199207",
"0.53520995",
"0.5347733",
"0.52559036",
"0.52416384",
"0.5236958",
"0.52313113",
"0.5225",
"0.52243483",
"0.5221377",
"0.5184076",
"0.515028",
"0.51449376",
"0.51292944... | 0.502299 | 28 |
Extract all the profile page links from this index page. | def enumerate_profiles(inhandle, page):
html = inhandle.read()
soup = BeautifulSoup(html, 'html.parser')
urls = [ node.find('a')['href'] for node in soup.findAll('h1', {'class':'entry-title'})]
return urls | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def profiles_search(pages: list, session: requests.Session):\n result = []\n for page in pages:\n sleep(1)\n soup = BeautifulSoup(session.get(page).text, \"lxml\")\n table = soup.find(\"table\", class_=\"seaman-list-table va-top seaman-list-table-2\")\n for rows in table.find_all(... | [
"0.5955885",
"0.5873089",
"0.58373713",
"0.5822768",
"0.5612937",
"0.5595032",
"0.55749434",
"0.55528224",
"0.55278695",
"0.55123425",
"0.5501893",
"0.5496583",
"0.5495312",
"0.5468743",
"0.54336965",
"0.54304",
"0.54241014",
"0.54207915",
"0.5416719",
"0.539653",
"0.5382196"... | 0.68228364 | 0 |
Walk the index pages, harvesting the profile URLs, and then download and process all the profiles stored under this year and month. | def gather_all_profiles(year, month):
page = 1
urls = []
print("{}-{} : Begin indexing.".format(year, month))
while (page > 0):
urlstring = "http://scamdigger.com/{}/{}/page/{}".format(year,month,page)
jitter = random.choice([0,1])
try:
urlhandle = urlopen(urlstring)
urls += enumer... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape(startyear, startmonth, endyear, endmonth):\n year = startyear\n month = startmonth\n while (not (year == endyear and month == endmonth)):\n ys = \"{}\".format(year)\n ms = \"{:02d}\".format(month)\n gather_all_profiles(ys,ms) \n if month == 12:\n year += 1\n month = 0\n month... | [
"0.64442813",
"0.62138397",
"0.58489907",
"0.56789356",
"0.5665456",
"0.56577927",
"0.5597599",
"0.55777854",
"0.5560226",
"0.5555722",
"0.55447924",
"0.55403745",
"0.552134",
"0.5456203",
"0.5386207",
"0.53852963",
"0.5349402",
"0.53458893",
"0.5320046",
"0.52948135",
"0.529... | 0.795587 | 0 |
Walk the database through the defined ranges, downloading everything. | def scrape(startyear, startmonth, endyear, endmonth):
year = startyear
month = startmonth
while (not (year == endyear and month == endmonth)):
ys = "{}".format(year)
ms = "{:02d}".format(month)
gather_all_profiles(ys,ms)
if month == 12:
year += 1
month = 0
month += 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ooni_data(range):\n\n last_ooni_report_generated = get_sys_info(request='last_ooni_report_generated', update=True)\n\n configs = get_configs()\n bucket = 'ooni-data-eu-fra'\n \n session = boto3.Session(profile_name=configs['profile'])\n client = session.client('s3')\n \n #get date r... | [
"0.6517533",
"0.61506337",
"0.611892",
"0.6087091",
"0.5753793",
"0.57496923",
"0.57288957",
"0.5696095",
"0.56846505",
"0.55612725",
"0.553414",
"0.55322355",
"0.5489378",
"0.545357",
"0.5439448",
"0.5427855",
"0.54234684",
"0.54192215",
"0.54063314",
"0.5394056",
"0.5371635... | 0.0 | -1 |
Tests if the constructor handles JSON correctly. That is, set the content of each language to the value given in the serialized JSON. | def test_constructor_serialized_json(self):
content = json.dumps({
"nb": "test-nb",
"en": "test-en",
})
structure = MultiLingualTextStructure(content, use_default_for_empty=True)
self.assertEqual(structure["nb"], "test-nb")
self.assertEqual(structure["en"]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_json(self, inputfile):\n transtransfile = json.load(inputfile)\n self.language = transfile['lang']\n self.translations = transfile['strings']",
"def __init__(self, langConf: str) -> None:\n with open(r\"localization\\localization.json\", \"rt\", encoding=\"utf-8\") as lang:\n ... | [
"0.6162473",
"0.60640824",
"0.59679496",
"0.5962652",
"0.59325093",
"0.5919701",
"0.5907977",
"0.5876794",
"0.5847541",
"0.5780988",
"0.5777279",
"0.57638365",
"0.57590663",
"0.57392234",
"0.56996155",
"0.5672908",
"0.5637319",
"0.5626917",
"0.56231356",
"0.5613871",
"0.55942... | 0.73586935 | 0 |
Tests if the constructor handles corrupt data (i.e. a string) correctly. That is, set the content of the default language to this string. | def test_constructor_string(self):
structure = MultiLingualTextStructure("test-nb", use_default_for_empty=True)
self.assertEqual(structure["nb"], "test-nb")
self.assertEqual(structure["en"], "test-nb") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lang_init():\n _locale, _encoding = locale.getdefaultlocale() # Default system values\n path = os.path.join(os.path.dirname(sys.argv[0]), 'localization/lang')\n if os.path.exists(path):\n lang = gettext.translation('UnrulyPuzzlePython', path, [_locale],\n fall... | [
"0.5990697",
"0.59643537",
"0.58305866",
"0.58138573",
"0.57625884",
"0.5755355",
"0.5742913",
"0.56937104",
"0.5680758",
"0.5666587",
"0.5589061",
"0.5564959",
"0.5558148",
"0.54922247",
"0.54866093",
"0.54540205",
"0.5452537",
"0.5439756",
"0.5436664",
"0.54336536",
"0.5420... | 0.55438876 | 13 |
Tests if the constructor handles the ``None`` value correctly. That is, the same as if the structure is empty. | def test_constructor_None(self):
structure = MultiLingualTextStructure(None, use_default_for_empty=True)
self.assertEqual(structure["nb"], "")
self.assertEqual(structure["en"], "") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_none(self) -> PossibleResult[T]:\n if self.constructor == type(None):\n if not self.obj is None:\n raise DeserializeError(\n type(None), self.obj, self.new_depth, self.key\n )\n return self.obj # type: ignore\n return ... | [
"0.7914818",
"0.72811294",
"0.7015134",
"0.68750614",
"0.68750614",
"0.68750614",
"0.68750614",
"0.68750614",
"0.6769645",
"0.67520285",
"0.67520285",
"0.67520285",
"0.67520285",
"0.67520285",
"0.6728802",
"0.671539",
"0.6699236",
"0.6644656",
"0.6633402",
"0.6633402",
"0.659... | 0.70171434 | 2 |
Tests the ``__str__()`` method. It should return the value of the current language of the thread. | def test_str(self):
previous_language = translation.get_language()
content = json.dumps({
"nb": "test-nb",
"en": "test-en",
})
structure = MultiLingualTextStructure(content, use_default_for_empty=True)
translation.activate("nb")
self.assertEqual(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_unicode(self):\n thread = mommy.prepare('connectmessages.Thread')\n self.assertEqual(str(thread), \"Thread %s\" % thread.subject)",
"def test_i18n11(self):\n output = self.engine.render_to_string('i18n11', {'bool': True})\n self.assertEqual(output, 'ja')",
"def language(sel... | [
"0.63634586",
"0.59879065",
"0.5887455",
"0.5887455",
"0.5860573",
"0.5837954",
"0.58233184",
"0.5784177",
"0.5774993",
"0.57586485",
"0.5745439",
"0.5724829",
"0.5685965",
"0.56742054",
"0.56568784",
"0.56481004",
"0.56409115",
"0.5639232",
"0.5605423",
"0.56049037",
"0.5604... | 0.6393691 | 0 |
Tests if the builtin set item function is correctly overwritten, so that we can set the value of a language in the array syntax way. | def test_set_item(self):
content = json.dumps({
"nb": "test-nb",
"en": "test-en",
})
structure = MultiLingualTextStructure(content, use_default_for_empty=True)
self.assertEqual(structure["nb"], "test-nb")
self.assertEqual(structure["en"], "test-en")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __setitem__(self, *args, **kwargs): # real signature unknown\n pass",
"def __setitem__(self, *args, **kwargs): # real signature unknown\n pass",
"def __setitem__(self, *args, **kwargs): # real signature unknown\n pass",
"def __setitem__(self, *args, **kwargs): # real signature unknow... | [
"0.5940166",
"0.5940166",
"0.5940166",
"0.5940166",
"0.5866111",
"0.58568",
"0.57745904",
"0.5762412",
"0.5742872",
"0.5731247",
"0.5723469",
"0.5625247",
"0.55334586",
"0.55304945",
"0.55180305",
"0.5504036",
"0.5472391",
"0.5472391",
"0.5472391",
"0.5463017",
"0.5438905",
... | 0.53934044 | 21 |
Tests the ``to_python()`` method. It should return ``None`` (if ``None`` given), the object if ``MultiLingualTextStructure``, or the object converted to ``MultiLingualTextStructure`` otherwise. | def test_to_python(self):
field = MultiLingualTextField()
self.assertEqual(None, field.to_python(None), "to_python of None should always return None.")
content = json.dumps({
"nb": "test-nb",
"en": "test-en",
})
structure = MultiLingualTextStructure(cont... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_from_db_value(self):\n field = MultiLingualTextField()\n\n result_none = field.from_db_value(None, None, None)\n self.assertEqual(MultiLingualTextStructure, type(result_none),\n \"from_db_value should always be of type MultiLingualTextStructure\")\n self... | [
"0.57009166",
"0.56299824",
"0.55680037",
"0.5488348",
"0.5338049",
"0.53207386",
"0.5283834",
"0.51322764",
"0.5092174",
"0.50363445",
"0.5033038",
"0.502039",
"0.4997337",
"0.49939436",
"0.49646625",
"0.4963914",
"0.4873973",
"0.4870973",
"0.48417282",
"0.48417282",
"0.4840... | 0.8195778 | 0 |
Tests the ``get_prep_value()`` method. This should return ``None`` (if ``None`` given), serialized JSON of its content if ``MultiLingualTextStructure`` is given, or just the value otherwise. | def test_get_prep_value(self):
field = MultiLingualTextField()
self.assertEqual(None, field.get_prep_value(None), "get_prep_value of None should always return None.")
content = {
"nb": "test-nb",
"en": "test-en",
}
structure = MultiLingualTextStructure(js... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_from_db_value(self):\n field = MultiLingualTextField()\n\n result_none = field.from_db_value(None, None, None)\n self.assertEqual(MultiLingualTextStructure, type(result_none),\n \"from_db_value should always be of type MultiLingualTextStructure\")\n self... | [
"0.66328865",
"0.6586675",
"0.64637977",
"0.6134948",
"0.610983",
"0.606463",
"0.6057703",
"0.60523206",
"0.60322195",
"0.60241705",
"0.59964967",
"0.58020043",
"0.5756462",
"0.575159",
"0.5704311",
"0.566441",
"0.56615865",
"0.5536008",
"0.5517607",
"0.5486199",
"0.54026115"... | 0.835836 | 0 |
Tests the ``from_db_value()`` method. Which should always return a ``MultiLingualTextStructure``. | def test_from_db_value(self):
field = MultiLingualTextField()
result_none = field.from_db_value(None, None, None)
self.assertEqual(MultiLingualTextStructure, type(result_none),
"from_db_value should always be of type MultiLingualTextStructure")
self.assertEqual(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_to_python(self):\n field = MultiLingualTextField()\n self.assertEqual(None, field.to_python(None), \"to_python of None should always return None.\")\n\n content = json.dumps({\n \"nb\": \"test-nb\",\n \"en\": \"test-en\",\n })\n\n structure = MultiL... | [
"0.66070205",
"0.65070033",
"0.5828789",
"0.5672591",
"0.55029804",
"0.5491402",
"0.5434026",
"0.53811914",
"0.5304458",
"0.5277097",
"0.5271622",
"0.52714705",
"0.526597",
"0.52633446",
"0.52534556",
"0.5252944",
"0.5239474",
"0.52332056",
"0.52197295",
"0.5208889",
"0.51865... | 0.8323718 | 0 |
Tests the ``compress()`` method. We can assume that the data passed is valid data, as the data is cleaned for each individual field before being passed to the method. | def test_compress(self):
form_field = MultiLingualFormField()
compressed_data = form_field.compress(["test-nb", "test-en"])
self.assertEqual(MultiLingualTextStructure, type(compressed_data))
self.assertEqual(compressed_data['nb'], "test-nb")
self.assertEqual(compressed_data['en']... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_compress():\n print('Testing compress')\n\n # Cases given to test this problem\n assert_equals('c1o17l1k1a1n1g1a1r1o2',\n hw1.compress('cooooooooooooooooolkangaroo'))\n assert_equals('a3', hw1.compress('aaa'))\n assert_equals('', hw1.compress(''))\n\n # Additional cases ... | [
"0.7139627",
"0.66479385",
"0.6284567",
"0.62130433",
"0.60791487",
"0.5939703",
"0.5841159",
"0.5840814",
"0.5834568",
"0.5802215",
"0.57965",
"0.57555175",
"0.5736981",
"0.5707062",
"0.56178164",
"0.56031865",
"0.55972594",
"0.5589935",
"0.555559",
"0.5528295",
"0.54627",
... | 0.7089348 | 1 |
Recursively takes a selfnested list and returns an HTML unordered list WITHOUT opening and closing tags. The list is assumed to be in the proper format. For example, if ``var`` | def format_threaded_comments(value, user_voting_data, autoescape=None):
if autoescape:
escaper = conditional_escape
else:
escaper = lambda x: x
def convert_old_style_list(list_):
"""
Converts old style lists to the new easier to understand format.
The old list forma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def html_unordered_list(items):\n if not items:\n return \"\"\n\n inner = \"\".join(map(html_list_item, items))\n if inner == \"\":\n return \"\"\n\n return \"<ul>\\n\" + inner + \"</ul>\\n\"",
"def _parse_list(tag):\r\n\r\n if tag.name == 'ul':\r\n return [_parse_list(item)\r... | [
"0.6655576",
"0.63954276",
"0.6111492",
"0.59855485",
"0.59621733",
"0.5900856",
"0.5793081",
"0.5792448",
"0.5735775",
"0.5710147",
"0.5704236",
"0.55589944",
"0.5539719",
"0.5525168",
"0.54968905",
"0.5493789",
"0.545797",
"0.54529655",
"0.54428005",
"0.54400045",
"0.543891... | 0.50926214 | 52 |
Converts old style lists to the new easier to understand format. | def convert_old_style_list(list_):
if not isinstance(list_, (tuple, list)) or len(list_) != 2:
return list_, False
first_item, second_item = list_
if second_item == []:
return [first_item], True
try:
# see if second item is iterable
iter(se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _list_parser(self, old_list):\n for i, item in enumerate(old_list):\n if isinstance(item, dict):\n old_list[i] = Yaco(item)\n elif isinstance(item, list):\n old_list[i] = self._list_parser(item)\n else:\n pass\n return ... | [
"0.6803961",
"0.67613524",
"0.6736399",
"0.65787864",
"0.6547683",
"0.63027596",
"0.6184645",
"0.61626476",
"0.6118478",
"0.5990201",
"0.5983553",
"0.5979835",
"0.58792496",
"0.58595496",
"0.5827031",
"0.5819523",
"0.5810015",
"0.58020484",
"0.58003056",
"0.57974803",
"0.5787... | 0.6782985 | 1 |
Logs into an OpenShift server and returns the user's token. | def login(host, port, username, password):
headers = {'X-Csrf-Token': '1'}
host = "https://{}:{}".format(host, port)
url = urlparse.urljoin(host, AUTH_PATH)
auth = requests.auth.HTTPBasicAuth(username, password)
response = requests.get(url, verify=False, headers=headers, auth=auth)
parsed_url... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login_user(self):\n response = self.client.post(self.login_url, self.login_data, format='json')\n return response.data['token']",
"def login(self):\n r = self._login_token()",
"def login(self):\n # create auth payload\n payload = '{{\"grant_type\": \"password\", \"usernam... | [
"0.69959384",
"0.6738701",
"0.6573574",
"0.65415555",
"0.6456356",
"0.64514875",
"0.6373826",
"0.635158",
"0.63260525",
"0.63119733",
"0.630883",
"0.627969",
"0.6261839",
"0.6261262",
"0.6233389",
"0.62330043",
"0.62210727",
"0.6188914",
"0.6188914",
"0.6164583",
"0.6160506",... | 0.0 | -1 |
initializes the neural net | def __init__(self, layer_neuron):
self.num_layers = len(layer_neuron)
self.layer_neuron = layer_neuron
#a list of numpy ndarrays
self.weights = []
self.input_len = 0
self.target_vals = []
self.current_guess = 0
self.layer_inputs = [[]]*(len(layer_neuron)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init():\n global neural_network\n global labels\n\n # load objects required by run() for inferencing\n model_dir = Model.get_model_path(\"mnist-fashion\")\n # neural model\n neural_network = keras.models.load_model(f\"{model_dir}/neural-network.h5\")\n # labels\n with open(f\"{model_dir... | [
"0.7754835",
"0.7486194",
"0.73951536",
"0.7314419",
"0.7302831",
"0.7238307",
"0.72338986",
"0.7233569",
"0.72288126",
"0.7175292",
"0.716469",
"0.71524554",
"0.7140224",
"0.71354145",
"0.7134994",
"0.71149826",
"0.7084607",
"0.70751315",
"0.7072266",
"0.7027148",
"0.7013910... | 0.65654284 | 100 |
connection to server and whit to the client | def Connection(self):
try:
system(
f'netsh advfirewall firewall add rule name="Open Port {self.PORT}" dir=in action=allow protocol=TCP localport={self.PORT} remoteip={self.HOST}')
with socket() as s: # Create a socket object
print('Server started!')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connectToServer(self):\n self.client = Client(base_url = self.server)\n self.ping()",
"def client():",
"def server(conn, address):\n print(\"Client Connection Open\")\n while True:\n request = server_read(conn)\n if request:\n print(request)\n manage_... | [
"0.7488699",
"0.724132",
"0.7116576",
"0.70362324",
"0.70091325",
"0.69784814",
"0.69784814",
"0.6926734",
"0.6908382",
"0.6864912",
"0.68283075",
"0.68283075",
"0.6800894",
"0.6781671",
"0.6744749",
"0.67159367",
"0.67054135",
"0.66798097",
"0.6676666",
"0.6641176",
"0.66130... | 0.6269943 | 66 |
sending a message to the client | def Send(self, stringSend):
self.c.send(stringSend.encode()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, msg: str):\n\t\tself.client.send(msg.encode())",
"def send(self, message):\n pass",
"def send(msg): # event is passed by binders.\n # print(\"i sended: \" + msg)\n msg = msg + \";\"\n client_socket.send(bytes(msg, \"utf8\"))",
"def send(self, msg):\n pass",
"def send(... | [
"0.82234496",
"0.8153046",
"0.8039892",
"0.8027347",
"0.8027347",
"0.8027347",
"0.80146086",
"0.8005253",
"0.79704624",
"0.79648054",
"0.7961565",
"0.7955438",
"0.7953542",
"0.78614396",
"0.78362805",
"0.78337246",
"0.78175944",
"0.7806808",
"0.77658594",
"0.77483255",
"0.774... | 0.0 | -1 |
getting message from the client | def Recv(self):
return self.c.recv(RECV_SIZE).decode('UTF-8') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_message():\n\tincoming_message = conn.recv(1024)\n\tincoming_message = incoming_message.decode()\n\treturn incoming_message",
"def get_message(self):\n client_message = self.connection_with_client.recv(1024)\n client_message_decoded = client_message.decode()\n\n if \"quit\" in client... | [
"0.77370644",
"0.77175355",
"0.7423818",
"0.7406984",
"0.72665787",
"0.7243788",
"0.72275466",
"0.7224105",
"0.722228",
"0.7163119",
"0.7163119",
"0.7122896",
"0.711274",
"0.7047482",
"0.70223814",
"0.7017876",
"0.69941616",
"0.69934946",
"0.69835466",
"0.6960825",
"0.6960825... | 0.0 | -1 |
DestinyDefinitionsDestinyItemPreviewBlockDefinition a model defined in Swagger | def __init__(self, preview_vendor_hash=None, preview_action_string=None, derived_item_categories=None): # noqa: E501 # noqa: E501
self._preview_vendor_hash = None
self._preview_action_string = None
self._derived_item_categories = None
self.discriminator = None
if preview_vend... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_definition(self, block_type, slug=None):\n raise NotImplementedError()",
"def mock_deposit(obj, overwrite, **kwargs):\n return Preview(source_id=obj.source_id,\n checksum=obj.checksum,\n metadata=Metadata(added=added,\n ... | [
"0.5170593",
"0.500709",
"0.49913558",
"0.49265093",
"0.4903068",
"0.48698387",
"0.48586363",
"0.48017275",
"0.47947973",
"0.47871843",
"0.4782398",
"0.47822326",
"0.47781777",
"0.47469652",
"0.4729438",
"0.4692864",
"0.46779555",
"0.46296787",
"0.46135998",
"0.46099576",
"0.... | 0.0 | -1 |
Sets the preview_vendor_hash of this DestinyDefinitionsDestinyItemPreviewBlockDefinition. | def preview_vendor_hash(self, preview_vendor_hash):
self._preview_vendor_hash = preview_vendor_hash | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vendor_reference(self, vendor_reference):\n\n self._vendor_reference = vendor_reference",
"def vendor(self, vendor):\n\n self._vendor = vendor",
"def __init__(self, preview_vendor_hash=None, preview_action_string=None, derived_item_categories=None): # noqa: E501 # noqa: E501\n\n self... | [
"0.54515517",
"0.49631733",
"0.4793878",
"0.469866",
"0.45657286",
"0.43995756",
"0.43969625",
"0.43132776",
"0.4206586",
"0.41582528",
"0.41371256",
"0.41370988",
"0.41018575",
"0.40580624",
"0.40572527",
"0.40495327",
"0.40088585",
"0.40035468",
"0.399779",
"0.39702928",
"0... | 0.8130399 | 0 |
Sets the preview_action_string of this DestinyDefinitionsDestinyItemPreviewBlockDefinition. | def preview_action_string(self, preview_action_string):
self._preview_action_string = preview_action_string | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_previewable(self, previewable):\n self._is_previewable = bool(previewable)",
"def __previewEditor(self, checked):\n Preferences.setUI(\"ShowFilePreview\", checked)\n self.previewStateChanged.emit(checked)",
"def __init__(self, preview_vendor_hash=None, preview_action_string=None, d... | [
"0.49170393",
"0.48936027",
"0.48737937",
"0.46655303",
"0.45847452",
"0.45847452",
"0.44724616",
"0.44220495",
"0.44209763",
"0.44209763",
"0.44209763",
"0.44209763",
"0.44209763",
"0.44209763",
"0.4420819",
"0.43746156",
"0.43392828",
"0.42996767",
"0.4286542",
"0.4258411",
... | 0.8190341 | 0 |
Sets the derived_item_categories of this DestinyDefinitionsDestinyItemPreviewBlockDefinition. | def derived_item_categories(self, derived_item_categories):
self._derived_item_categories = derived_item_categories | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def categories(self, categories):\n self._categories = categories",
"def fill_tab_categories(self):\n self.category.fill_tab_categories(self.list_categories, self.mycursor, self.my_database)",
"def on_category(self):\n super(ToolSettings, self).on_category()\n selItems = self.tw_cat... | [
"0.42504486",
"0.41763225",
"0.41738588",
"0.4147597",
"0.4145194",
"0.41436017",
"0.41436017",
"0.41436017",
"0.41436017",
"0.41305125",
"0.41266572",
"0.41144997",
"0.40753424",
"0.40561587",
"0.38878268",
"0.38473186",
"0.3840825",
"0.38260534",
"0.38147235",
"0.37995836",
... | 0.7910564 | 0 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.8585799",
"0.7814791",
"0.77903897",
"0.7750947",
"0.7750947",
"0.7713712",
"0.7699211",
"0.76708376",
"0.76511395",
"0.7601015",
"0.75830185",
"0.7570755",
"0.7540716",
"0.7523477",
"0.75169474",
"0.7501407",
"0.7487798",
"0.7487798",
"0.7470098",
"0.74518627",
"0.7446157... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.75577617",
"0.73375154",
"0.6986672",
"0.698475",
"0.6944995",
"0.692333",
"0.6899106",
"0.6898902",
"0.68146646",
"0.6806209",
"0.6753795",
"0.67497987",
"0.6744008",
"0.6700308",
"0.6691256",
"0.6674591",
"0.6658083",
"0.66091245",
"0.6606931",
"0.6601862",
"0.6563738",
... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, DestinyDefinitionsDestinyItemPreviewBlockDefinition):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Return list of recIDs matching query for PVALUE and FVALUE. | def get_recids_matching_query(pvalue, fvalue):
rec_id = list(search_pattern(p=pvalue, f=fvalue, m='e') - INTBITSET_OF_DELETED_RECORDS)
return rec_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_values(self, value):\n return [\n obj for obj in self if obj == value\n ]",
"def values(self):\n if '%' in self.starid:\n query = \"\"\"SELECT * from ngc2236 where starid like '%s'\"\"\" % self.starid\n else:\n query = \"\"\"SELECT * from ngc22... | [
"0.5373899",
"0.51960784",
"0.51567584",
"0.5128959",
"0.5087617",
"0.50727177",
"0.49959168",
"0.49717903",
"0.49696368",
"0.49286002",
"0.49211693",
"0.4917529",
"0.48955002",
"0.48827276",
"0.48814398",
"0.48741",
"0.48672736",
"0.4864506",
"0.48131928",
"0.48071274",
"0.4... | 0.7764384 | 0 |
return a dictionary which is used by bibrank daemon for generating the index of sorted research results by citation information | def get_citation_weight(rank_method_code, config):
begin_time = time.time()
last_update_time = get_bibrankmethod_lastupdate(rank_method_code)
if task_get_option("quick") == "no":
last_update_time = "0000-00-00 00:00:00"
write_message("running thorough indexing since quick option not used", ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_dict(results, chunk):\n from math import inf\n from collections import defaultdict\n chunk = [int(au) for au in chunk]\n d = defaultdict(\n lambda: {\"first_year\": inf, \"pubs\": set(), \"coauth\": set(),\n \"n_coauth\": inf, \"n_pubs\": inf})\n for pub in results:\... | [
"0.615179",
"0.5961631",
"0.59024155",
"0.58764994",
"0.5853894",
"0.5837746",
"0.5777888",
"0.573996",
"0.573696",
"0.5724125",
"0.5716872",
"0.56960845",
"0.5678323",
"0.5668764",
"0.56393546",
"0.5624318",
"0.5615255",
"0.5601827",
"0.5591958",
"0.55661815",
"0.55574524",
... | 0.55858314 | 19 |
return the last excution date of bibrank method | def get_bibrankmethod_lastupdate(rank_method_code):
query = """select last_updated from rnkMETHOD where name ='%s'""" % rank_method_code
last_update_time = run_sql(query)
r = last_update_time[0][0]
if r is None:
return "0000-00-00 00:00:00"
return r | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lastdate(self):\n if hasattr(self, \"_lastdate\"):\n return self._lastdate\n else:\n return None",
"def _format_last_br_date(self, data):\n raise NotImplementedError",
"def _get_eur_gbp_last_daily(self) -> None:\n data = _get_ecb_data(FREQUENCY_DAILY, _ten_... | [
"0.62563515",
"0.6135235",
"0.5993334",
"0.59568703",
"0.58971405",
"0.5813312",
"0.58071977",
"0.57964516",
"0.577513",
"0.576336",
"0.5753354",
"0.57206935",
"0.5710407",
"0.5695503",
"0.56912684",
"0.56911653",
"0.56623274",
"0.5656828",
"0.56515944",
"0.5618803",
"0.56027... | 0.6489557 | 0 |
return the list of recods which have been modified after the last exec of bibrank method. The result is expected to have ascending num order. | def get_last_modified_rec(bibrank_method_lastupdate):
query = """SELECT id FROM bibrec
WHERE modification_date >= '%s' """ % bibrank_method_lastupdate
query += "order by id ASC"
ilist = run_sql(query)
return ilist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateResorts(self):\n self.resorts = []\n for x in self._dfs_non_recursive(self.V):\n pass\n #print(\"visited\", x)",
"def getChanges():",
"def reset(self) -> List[int]:",
"def get_blists(self):\n return self.blists[:]",
"def getchangableobslist(self):\n ... | [
"0.5999729",
"0.5646804",
"0.55687803",
"0.55413765",
"0.5502476",
"0.54501814",
"0.54318994",
"0.5424124",
"0.5380979",
"0.53601855",
"0.53181535",
"0.5303631",
"0.52810645",
"0.52623254",
"0.52623254",
"0.52623254",
"0.52623254",
"0.52623254",
"0.52623254",
"0.52623254",
"0... | 0.54004663 | 8 |
Create a list of record ids out of RECIDS. The result is expected to have ascending numerical order. | def create_recordid_list(rec_ids):
rec_list = []
for row in rec_ids:
rec_list.append(row[0])
return rec_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_recordIds(self):\n record_ids = []\n for item in self.order_items:\n record_ids.append(item.get_recordId())\n \n return record_ids",
"def get_rsids(input_file: str) -> list:\n\n column_names: list = [\"rsid\", \"chr\"]\n # catching the error if the file do... | [
"0.71660554",
"0.6755276",
"0.6567358",
"0.6502151",
"0.6431556",
"0.6394535",
"0.6390275",
"0.6199647",
"0.61650753",
"0.603711",
"0.5996257",
"0.5975088",
"0.5970256",
"0.5936337",
"0.5927022",
"0.58693117",
"0.5824793",
"0.58174443",
"0.5816194",
"0.5816194",
"0.58141625",... | 0.7744344 | 0 |
Creates a tuple of record id from a list of id. The result is expected to have ascending numerical order. | def create_record_tuple(ilist):
list_length = len(ilist)
if list_length:
rec_tuple = '('
for row in list[0:list_length-1]:
rec_tuple += str(row)
rec_tuple += ','
rec_tuple += str(list[list_length-1])
rec_tuple += ')'
else: rec_tuple = '()'
return r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_featseltuple(ids):\n newlist = []\n for part_id in ids:\n newlist.extend([part_id, part_id + 91])\n return tuple(sorted(newlist))",
"def create_recordid_list(rec_ids):\n rec_list = []\n for row in rec_ids:\n rec_list.append(row[0])\n return rec_list",
"def create_id(e... | [
"0.70673424",
"0.67906827",
"0.6282754",
"0.60380954",
"0.5777089",
"0.577077",
"0.56931186",
"0.56780326",
"0.5641889",
"0.5606788",
"0.55963814",
"0.5531544",
"0.55299294",
"0.5516861",
"0.55163807",
"0.5484265",
"0.5466873",
"0.5397534",
"0.53723544",
"0.5363456",
"0.53095... | 0.5790029 | 4 |
return the last value of dictionary in rnkMETHODDATA table if it exists and initialize the value of last updated records by zero, otherwise an initial dictionary with zero as value for all recids | def last_updated_result(rank_method_code):
result = [{}, {}, {}]
query = """select relevance_data from rnkMETHOD, rnkMETHODDATA where
rnkMETHOD.id = rnkMETHODDATA.id_rnkMETHOD
and rnkMETHOD.Name = '%s'"""% rank_method_code
rdict = run_sql(query)
if rdict and rdict[0] and rd... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_rec(self):\n return {'cal': 0}",
"def _update(d):\n newd = copy.deepcopy(default)\n\n if 'lastdir' in d:\n newd['lastdir'] = d['lastdir']\n\n return newd",
"def last_dict():\n\n # newest_d = {}\n # my_dic = pd.read_excel('grad_gen.xlsx', index_col=0).to_dict()\n # # new_... | [
"0.58107096",
"0.5758537",
"0.5744504",
"0.5732967",
"0.53722936",
"0.5282241",
"0.5257113",
"0.5206331",
"0.520421",
"0.5142212",
"0.5122019",
"0.5088636",
"0.50742084",
"0.5067339",
"0.5063233",
"0.50564384",
"0.50432706",
"0.50253505",
"0.5007381",
"0.50047207",
"0.4986021... | 0.5143988 | 9 |
scans the collections searching references (999C5x fields) and citations for items in the recid_list returns a 4 list of dictionaries that contains the citation information of cds records | def get_citation_informations(recid_list, config):
begin_time = os.times()[4]
d_reports_numbers = {} #dict of recid -> institute-given-report-code
d_references_report_numbers = {} #dict of recid -> ['astro-ph/xyz']
d_references_s = {} #dict of recid -> list_of_the_entries_of_this_recs_bibliography
d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_references(cls, pmids):\n\n references = cls.query.filter(cls.pmid.in_(pmids)).all()\n citations = {}\n\n for reference in references:\n citation_text = reference.authors + \". (\" + str(\n reference.year) + \"). \" + reference.title + \" \" + reference.journa... | [
"0.64813673",
"0.6178384",
"0.60846525",
"0.6077381",
"0.6043557",
"0.59674054",
"0.59034324",
"0.58734137",
"0.58487546",
"0.58361",
"0.5798192",
"0.5778596",
"0.57374436",
"0.563813",
"0.5604259",
"0.5579555",
"0.556315",
"0.5559273",
"0.5546832",
"0.5529941",
"0.5516851",
... | 0.690522 | 0 |
Check which items have been cited by one of the authors of the | def get_self_citations(new_record_list, citationdic, initial_selfcitdict, config):
i = 0 #just for debugging ..
#get the tags for main author, coauthors, ext authors from config
tags = ['first_author', 'additional_author', 'alternative_author_name']
for t in tags:
try:
dummy = config... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cross_check(context, authors, poscom):\n displaynames = [x['author']['displayname'] for x in poscom]\n\n for author in authors:\n if author.user.username not in displaynames:\n context.assertFalse(True, \"%s not in list\" %author.user.username)",
"def test_citedby_author(self):\n ... | [
"0.71577066",
"0.69033414",
"0.64529437",
"0.6411667",
"0.62453896",
"0.62002295",
"0.61882716",
"0.6115645",
"0.605986",
"0.59958255",
"0.59832895",
"0.591335",
"0.5908707",
"0.59039694",
"0.58876854",
"0.5848254",
"0.5828852",
"0.5805382",
"0.58008856",
"0.5780458",
"0.5744... | 0.0 | -1 |
Traverses citedbydict in order to build "which author is quoted where" dict. The keys of this are author names. An entry like "Apollinaire">[1,2,3] means Apollinaire is cited in records 1,2 and 3. | def get_author_citations(updated_redic_list, citedbydict, initial_author_dict, config):
#sorry bout repeated code to get the tags
tags = ['first_author', 'additional_author', 'alternative_author_name']
tagvals = {}
for t in tags:
try:
x = config.get(config.get("rank_method", "functi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_citedby_author(self):\n inv_search = 'citedby:author:doggy'\n spi_search = 'find citedby author doggy'\n self._compare_searches(inv_search, spi_search)",
"def get_publications_by_author(cached_list, cached_set, author_name):\n publications = { 'dblp': [], 'cdblp':... | [
"0.5992074",
"0.59668666",
"0.5799877",
"0.57464516",
"0.5611583",
"0.5566841",
"0.5525199",
"0.55247015",
"0.548933",
"0.5444908",
"0.54439306",
"0.53777725",
"0.533273",
"0.53153",
"0.5309737",
"0.5296748",
"0.5278362",
"0.5263696",
"0.5235916",
"0.5218292",
"0.520415",
"... | 0.63774025 | 0 |
Analyze the citation informations and calculate the citation weight and cited by list dictionary. | def ref_analyzer(citation_informations, initialresult, initial_citationlist,
initial_referencelist,config, updated_rec_list ):
function = ""
try:
function = config.get("rank_method", "function")
except:
register_exception(prefix="cfg section [rank_method] has no attr functio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_citation(style, reference_list, citation):\n processed_citation = Element(\"span\", attrib={\"class\":\"citation\"})\n\n for reference in reference_list:\n citeref = SubElement(processed_citation, \"span\")\n for style_node in style.citation.layout:\n process_node(citeref... | [
"0.60321796",
"0.5845413",
"0.5744994",
"0.56930727",
"0.5662978",
"0.56200105",
"0.55414337",
"0.5474391",
"0.5392177",
"0.5307543",
"0.5281071",
"0.5271394",
"0.52353287",
"0.52269924",
"0.5194141",
"0.5171498",
"0.51695114",
"0.5145369",
"0.5134784",
"0.5106476",
"0.510390... | 0.5646937 | 5 |
Insert the reference and citation list into the database | def insert_cit_ref_list_intodb(citation_dic, reference_dic, selfcbdic,
selfdic, authorcitdic):
insert_into_cit_db(reference_dic,"reversedict")
insert_into_cit_db(citation_dic,"citationdict")
insert_into_cit_db(selfcbdic,"selfcitedbydict")
insert_into_cit_db(selfdic,"selfci... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert_into_citation_table(citations):\n cursor = connection.cursor()\n cursor.execute(\n 'DROP TABLE IF EXISTS Citations;'\n 'CREATE TABLE Citations(sourcePaperId INT, targetPaperId INT, citationId INT NOT NULL AUTO_INCREMENT PRIMARY KEY);'\n )\n cursor.close()\n i = 0\n\n for ... | [
"0.6965884",
"0.68262",
"0.6096341",
"0.5778446",
"0.5740313",
"0.5732281",
"0.5607799",
"0.5572235",
"0.55622476",
"0.5558603",
"0.5553385",
"0.5523789",
"0.5512726",
"0.5501871",
"0.54990745",
"0.54926753",
"0.5489177",
"0.54863644",
"0.547148",
"0.5466431",
"0.54623103",
... | 0.7009089 | 0 |
an aux thing to avoid repeating code | def insert_into_cit_db(dic, name):
ndate = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
try:
s = serialize_via_marshal(dic)
write_message("size of "+name+" "+str(len(s)))
#check that this column really exists
testres = run_sql("select object_name from rnkCITATIONDATA wher... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def regular(self):",
"def exo2():",
"def _regr_basic():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def __call__():",
"def sth():",
"def common(self):",
"def apply(self):",
"def g():",
"def firstFunction(self):",
"def use(self):",
"def func():",
"def... | [
"0.6806635",
"0.6386508",
"0.62237775",
"0.61517763",
"0.61517763",
"0.61517763",
"0.61517763",
"0.61517763",
"0.60746104",
"0.60519785",
"0.6034576",
"0.6013738",
"0.6012824",
"0.5982234",
"0.59326035",
"0.59287214",
"0.59233296",
"0.5911283",
"0.5911283",
"0.58887786",
"0.5... | 0.0 | -1 |
get a named citation dict from the db | def get_cit_dict(name):
cdict = {}
try:
cdict = run_sql("select object_value from rnkCITATIONDATA where object_name = %s",
(name,))
if cdict and cdict[0] and cdict[0][0]:
dict_from_db = deserialize_via_marshal(cdict[0][0])
return dict_from_db
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_citation(cit):\n if cit is not None:\n if cit['citation-type'] == \"BIBTEX\":\n return pybtex.database.parse_string(cit['citation-value'], \"bibtex\")\n return None",
"def get_author_by_name(self, name):\n\n cur = self.conn.cursor()\n query = 'SELECT author_id , n... | [
"0.6046626",
"0.5950197",
"0.5847985",
"0.58434343",
"0.56926155",
"0.5684362",
"0.5674185",
"0.5558614",
"0.5495795",
"0.54895663",
"0.54638165",
"0.5446902",
"0.5430732",
"0.53450066",
"0.53339255",
"0.53142464",
"0.5296164",
"0.5265383",
"0.5264633",
"0.525944",
"0.5246737... | 0.7352711 | 0 |
read author>citedinlist dict from the db | def get_initial_author_dict():
adict = {}
try:
ah = run_sql("select aterm,hitlist from rnkAUTHORDATA")
for (a, h) in ah:
adict[a] = deserialize_via_marshal(h)
return adict
except:
register_exception(prefix="could not read rnkAUTHORDATA", alert_admin=True)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def popAuthors(self):\r\n# cur = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\")\r\n# res = cur.fetchall()\r\n res = self.dbConn.execute(\"SELECT * FROM People WHERE PersonID>0 ORDER BY Lastname\").fetchall()\r\n\r\n self.authorList = [formatNameSQL(ln) ... | [
"0.62463015",
"0.61490846",
"0.61244714",
"0.60036224",
"0.5967608",
"0.5937393",
"0.59214526",
"0.58278465",
"0.57877076",
"0.5739218",
"0.56823266",
"0.5649391",
"0.5615111",
"0.5582629",
"0.5582394",
"0.55806595",
"0.5542368",
"0.551917",
"0.5470549",
"0.5453344",
"0.54351... | 0.62780523 | 0 |
put the referingrecordnumpublicationstring into the "we are missing these" table | def insert_into_missing(recid, report):
report.replace('"','\'')
try:
srecid = str(recid)
wasalready = run_sql("select id_bibrec from rnkCITATIONDATAEXT where id_bibrec = %s and extcitepubinfo = %s",
(srecid,report))
if not wasalready:
run_sql("i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_NR(self, w2, row):\n row['NR'] = None\n return True",
"def addMissingData():\n\n conn = sqlite3.connect(\"./transactions.db\")\n\n person = pd.read_sql(\n \"\"\"\n select * from person;\n \"\"\",\n conn,\n )\n\n record = pd.read_sql(\n \"\"\"\n sel... | [
"0.53357",
"0.5239754",
"0.50661397",
"0.5053511",
"0.50410986",
"0.5036657",
"0.49877408",
"0.49684855",
"0.4958167",
"0.4932847",
"0.49291137",
"0.49014091",
"0.48973417",
"0.4891694",
"0.48872414",
"0.48609567",
"0.48588482",
"0.48420462",
"0.4841088",
"0.4835849",
"0.4823... | 0.5318926 | 1 |
temporary simple table + index | def create_analysis_tables():
sql1 = "CREATE TABLE IF NOT EXISTS tmpcit (citer mediumint(10), cited mediumint(10)) TYPE=MyISAM"
sql2 = "CREATE UNIQUE INDEX citercited on tmpcit(citer, cited)"
sql3 = "CREATE INDEX citer on tmpcit(citer)"
sql4 = "CREATE INDEX cited on tmpcit(cited)"
try:
run_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_index():",
"def T1(request):\n T = _get_test_table()\n if request.param:\n T.add_index(\"a\")\n return T",
"def test_index_alternate(self):\n self.insert()\n self.tbl[::2]",
"def build_index():\n pass",
"def index_object(idxs=None):",
"def create_new_index(self... | [
"0.6378675",
"0.6244597",
"0.61699903",
"0.61214757",
"0.6035877",
"0.5879814",
"0.5815851",
"0.5764521",
"0.57210124",
"0.56768274",
"0.5626417",
"0.56132317",
"0.56132317",
"0.55983233",
"0.559672",
"0.55530435",
"0.55530435",
"0.554623",
"0.554623",
"0.5543864",
"0.5530199... | 0.5733679 | 8 |
write an entry to tmp table | def write_citer_cited(citer, cited):
sciter = str(citer)
scited = str(cited)
try:
run_sql("insert into tmpcit(citer, cited) values (%s,%s)", (sciter, scited))
except:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def htable_put(table, key, value):",
"def _store_entry_in_table(conn, table_name, entry):\n # Create entry insertion template.\n template = ('?, ' * len(entry)).rstrip(', ') # \"?\" for each value\n template = '(%s)' % template # enclose in parentheses\n # Try to insert a new row into the table.\n ... | [
"0.68487287",
"0.64390826",
"0.60907364",
"0.5969685",
"0.59630823",
"0.58389467",
"0.56817126",
"0.56734014",
"0.56520325",
"0.56517667",
"0.56272185",
"0.5623497",
"0.56175345",
"0.56050855",
"0.55885863",
"0.5566574",
"0.5550937",
"0.552071",
"0.5517991",
"0.5485367",
"0.5... | 0.51368165 | 60 |
Print the contents of rnkCITATIONDATAEXT table containing external records that were cited by NUM or more internal records. NUM is by default taken from the E command line option. | def print_missing(num):
if not num:
num = task_get_option("print-extcites")
write_message("Listing external papers cited by %i or more internal records:" % num)
res = run_sql("SELECT COUNT(id_bibrec), extcitepubinfo FROM rnkCITATIONDATAEXT \
GROUP BY extcitepubinfo HAVING COUNT(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_icd(self):\n wiki = wikipediaapi.Wikipedia('en') # may as well declare this here so I don't need to call it every query\n supplemental_articles = []\n with open(ICD10_DESC_PATH, 'r') as f:\n current_family = [] # list of lists of descriptions within the current family ... | [
"0.5283312",
"0.47920322",
"0.47803637",
"0.46489966",
"0.46426672",
"0.46010867",
"0.45646727",
"0.4531334",
"0.45046958",
"0.4470535",
"0.44359282",
"0.44248855",
"0.4385767",
"0.43826196",
"0.43532607",
"0.43172917",
"0.43149775",
"0.43144825",
"0.43024385",
"0.4301603",
"... | 0.6221545 | 0 |
aux auf to make '100__a' out of ['100','','','a'] | def tagify(parsedtag):
tag = ""
for t in parsedtag:
if t == '':
t = '_'
tag = tag+t
return tag | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _alphanum_list(x):\n return [_alphanum(y) for y in x]",
"def clean(a):\n b = a.lower()\n # en c se encuentra la lista de todos los documentos juntos y limpios\n c = re.sub('[^A-Za-z0-9]+', ' ', b).split()\n return c",
"def generate_a_values() -> List[str]:\n return [\"A_1\", \"A_2\", \"A_... | [
"0.626513",
"0.5933304",
"0.5747301",
"0.5679641",
"0.55706316",
"0.54705375",
"0.5390125",
"0.53830534",
"0.536347",
"0.53619814",
"0.5348391",
"0.53348833",
"0.53115875",
"0.5304542",
"0.5290489",
"0.5288956",
"0.52829987",
"0.5279524",
"0.5274321",
"0.52679646",
"0.5253277... | 0.48427668 | 75 |
Change the default mute time for the first warning | async def muterole(self, ctx, rolename: str):
self.data_check(ctx)
server = ctx.message.server
self.riceCog2[server.id]["muterole"] = rolename
dataIO.save_json(self.warning_settings,
self.riceCog2)
await self.bot.say("Muted role name is now: **{}... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def silly(self):\n print(\"you are getting silly\")\n # 设置时效\n return self._silly if time.localtime(time.time() - self._old_time ).tm_sec < 3 else \"\"",
"def mute(self, msg, args):\n if self.mute:\n self.mute=False\n return \"Yay, I can make noise again!\"\n ... | [
"0.6638432",
"0.6581909",
"0.65700525",
"0.6485997",
"0.63749397",
"0.62527746",
"0.60601157",
"0.6041151",
"0.60398245",
"0.60359174",
"0.60047853",
"0.59576213",
"0.5875881",
"0.5872893",
"0.58570814",
"0.5830865",
"0.5806074",
"0.57983273",
"0.57898366",
"0.57819015",
"0.5... | 0.5353241 | 61 |
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