From 56fe1e00eff033f57dedd14d8bbee4390fc8df8b Mon Sep 17 00:00:00 2001 From: James Whiteside Date: Fri, 21 Jul 2023 13:01:26 +0100 Subject: [PATCH 01/27] Updated project files. --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c036250..24f1dc5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,7 @@ classifiers = [ "Topic :: Database", ] dependencies = [ - "typedb-client~=2.17", + "typedb-client~=2.18", "ipython" ] From 3ecc610d3aaa870e796b1222b95ebbf3de073c87 Mon Sep 17 00:00:00 2001 From: James Whiteside Date: Fri, 21 Jul 2023 13:01:59 +0100 Subject: [PATCH 02/27] Fixed bad parsing of sub-pattern blocks. --- src/typedb_jupyter/magic.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index 94d7427..df708c6 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -38,6 +38,9 @@ def substitute_vars(query, local_ns): return query for var in query_vars: + if var.strip()[-1] == ";": + continue + try: val = local_ns[var] except KeyError: From d821ab3461c13ce6581b9c2646844799a85260b4 Mon Sep 17 00:00:00 2001 From: James Whiteside Date: Tue, 25 Jul 2023 11:34:26 +0100 Subject: [PATCH 03/27] Added TypeQL output format. --- README.md | 2 +- RELEASE_NOTES.md | 8 ++ src/typedb_jupyter/magic.py | 25 ++-- src/typedb_jupyter/query.py | 65 +++-------- src/typedb_jupyter/response.py | 202 +++++++++++++++++++++++++++++++++ 5 files changed, 242 insertions(+), 60 deletions(-) create mode 100644 src/typedb_jupyter/response.py diff --git a/README.md b/README.md index da2cd9d..d78878a 100644 --- a/README.md +++ b/README.md @@ -266,7 +266,7 @@ The following tables list the arguments that can be provided to the `%typedb` an | `%typedb` | `-l` | List currently open connections. | | `%typedb` | `-k ` | Close a connection by name. | | `%typedb` | `-x ` | Close a connection by name and delete its database. | -| `%typeql` | `-r ` | Assign query result to the named variable instead of printing. | +| `%typeql` | `-r ` | Assign read query results to the named variable instead of printing. | | `%typeql` | `-f ` | Read in query from a TypeQL file at the specified path. | | `%typeql` | `-i ` | Enable (`True`) or disable (`False`) rule inference for query. | | `%typeql` | `-s ` | Force a particular session type for query, `schema` or `data`. | diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 4a0383f..510f30d 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,13 @@ # TypeDB Jupyter connector +## Version 0.4 +- A TypeQL output format has been added for `match` queries. The TypeQL returned contains all the necessary information +to reconstruct the original query in a new database. To do so: commit the same schema used for the initial database, +then place the returned TypeQL in an insert query and run it. When the original query is run on the new database, the +same results will be returned as with the initial database. This output format is not available for queries with `group` +or `aggregate` modifiers. +- Fixed bug in parsing of queries containing sub-pattern blocks (disjunctions and negations). + ## Version 0.3 - The `%tql` magic command has been replaced with two new ones: `%typedb` and `%typeql`. `%typedb` is used for opening diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index df708c6..5dec33f 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -145,12 +145,13 @@ class TypeQLMagic(Magics, Configurable): @line_magic("typeql") @cell_magic("typeql") @magic_arguments() - @argument("line", default="", nargs="*", type=str, help="Valid TypeQL string.") - @argument("-r", "--result", type=str, help="Assign query result to the named variable instead of printing.") + @argument("line", nargs="*", type=str, default="", help="Valid TypeQL string.") + @argument("-r", "--result", type=str, help="Assign read query results to the named variable instead of printing.") @argument("-f", "--file", type=str, help="Read in query from a TypeQL file at the specified path.") @argument("-i", "--inference", type=bool, help="Enable (True) or disable (False) rule inference for query.") @argument("-s", "--session", type=str, help="Force a particular session type for query, 'schema' or 'data'.") @argument("-t", "--transaction", type=str, help="Force a particular transaction type for query, 'read' or 'write'.") + @argument("-o", "--output", type=str, default="json", help="Output format for read query results.") def execute(self, line="", cell="", local_ns=None): if local_ns is None: local_ns = {} @@ -172,15 +173,21 @@ def execute(self, line="", cell="", local_ns=None): connection = Connection.get() query = Query(query, args.session, args.transaction, args.inference, self.strict_transactions, self.global_inference) - result = query.run(connection, self.show_info) + response = query.run(connection, args.output, self.show_info) - if args.result: - print("Returning data to local variable: '{}'".format(args.result)) - self.shell.user_ns.update({args.result: result}) - return + if response.message is not None: + print(response.message) + + if response.result is not None: + if args.result: + print("Returning data to local variable: '{}'".format(args.result)) + self.shell.user_ns.update({args.result: response.result}) + return - # Return results into the default ipython _ variable - return result + # Return results into the default ipython _ variable + return response.result + else: + return def __init__(self, shell): Configurable.__init__(self, config=shell.config) diff --git a/src/typedb_jupyter/query.py b/src/typedb_jupyter/query.py index 01aecc1..15aab76 100644 --- a/src/typedb_jupyter/query.py +++ b/src/typedb_jupyter/query.py @@ -19,16 +19,12 @@ # under the License. # -import math from typedb.client import TypeDBOptions from typedb.api.connection.session import SessionType from typedb.api.connection.transaction import TransactionType -from typedb.concept.answer.concept_map import ConceptMap -from typedb.concept.answer.concept_map_group import ConceptMapGroup -from typedb.concept.answer.numeric import Numeric -from typedb.concept.answer.numeric_group import NumericGroup from typedb_jupyter.connection import Connection from typedb_jupyter.exception import ArgumentError, QueryParsingError +from typedb_jupyter.response import Response class Query(object): @@ -229,38 +225,7 @@ def _print_info(self, connection): print(info) - @staticmethod - def _group_key(concept): - if concept.is_type(): - return str(concept.as_type().get_label()) - elif concept.is_entity(): - return concept.as_entity().get_iid() - elif concept.is_relation(): - return concept.as_relation().get_iid() - elif concept.is_attribute(): - return concept.as_attribute().get_value() - else: - raise ValueError("Unknown concept type. Please report this error.") - - @staticmethod - def _parse_answer(answer, answer_type): - if answer_type is ConceptMap: - return [concept_map.to_json() for concept_map in answer] - elif answer_type is ConceptMapGroup: - return {Query._group_key(map_group.owner()): Query._parse_answer(map_group.concept_maps(), ConceptMap) for map_group in answer} - elif answer_type is Numeric: - if answer.is_int(): - return answer.as_int() - elif answer.is_float(): - return answer.as_float() - else: - return math.nan - elif answer_type is NumericGroup: - return {Query._group_key(numeric_group.owner()): Query._parse_answer(numeric_group.numeric(), Numeric) for numeric_group in answer} - else: - raise ValueError("Unknown answer type. Please report this error.") - - def run(self, connection, show_info): + def run(self, connection, output_format, show_info): Connection.set_session(self.session_type) options = self._get_options(connection) @@ -270,29 +235,29 @@ def run(self, connection, show_info): try: with connection.session.transaction(self.transaction_type, options) as transaction: if self.query_type == "match": - results = self._parse_answer(transaction.query().match(self.query), ConceptMap) + answer = transaction.query().match(self.query) elif self.query_type == "match-aggregate": - results = self._parse_answer(transaction.query().match_aggregate(self.query).get(), Numeric) + answer = transaction.query().match_aggregate(self.query).get() elif self.query_type == "match-group": - results = self._parse_answer(transaction.query().match_group(self.query), ConceptMapGroup) + answer = transaction.query().match_group(self.query) elif self.query_type == "match-group-aggregate": - results = self._parse_answer(transaction.query().match_group_aggregate(self.query), NumericGroup) + answer = transaction.query().match_group_aggregate(self.query) elif self.query_type == "define": - transaction.query().define(self.query) + answer = transaction.query().define(self.query) elif self.query_type == "undefine": - transaction.query().undefine(self.query) + answer = transaction.query().undefine(self.query) elif self.query_type == "insert": - transaction.query().insert(self.query) + answer = transaction.query().insert(self.query) elif self.query_type == "delete": - transaction.query().delete(self.query) + answer = transaction.query().delete(self.query) elif self.query_type == "update": - transaction.query().update(self.query) + answer = transaction.query().update(self.query) + + response = Response(self, answer, output_format, transaction) if self.transaction_type == TransactionType.WRITE: transaction.commit() - print('{} query success.'.format(self.query_type.title())) - return - else: - return results + + return response finally: Connection.set_session(SessionType.DATA) diff --git a/src/typedb_jupyter/response.py b/src/typedb_jupyter/response.py new file mode 100644 index 0000000..b1be3d2 --- /dev/null +++ b/src/typedb_jupyter/response.py @@ -0,0 +1,202 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +import math +from typedb.concept.answer.concept_map import ConceptMap +from typedb.concept.answer.concept_map_group import ConceptMapGroup +from typedb.concept.answer.numeric import Numeric +from typedb.concept.answer.numeric_group import NumericGroup +from typedb_jupyter.exception import ArgumentError + + +class Response(object): + def __init__(self, query, answer, output_format, transaction): + self.query = query + self.output_format = output_format + self.answer_type = self._get_answer_type() + self.result, self.message = self._format(self.query, answer, self.answer_type, output_format, transaction) + + def _get_answer_type(self): + if self.query.query_type == "match": + return ConceptMap + elif self.query.query_type == "match-aggregate": + return Numeric + elif self.query.query_type == "match-group": + return ConceptMapGroup + elif self.query.query_type == "match-group-aggregate": + return NumericGroup + elif self.query.query_type == "define": + return None + elif self.query.query_type == "undefine": + return None + elif self.query.query_type == "insert": + return None + elif self.query.query_type == "delete": + return None + elif self.query.query_type == "update": + return None + + @staticmethod + def _group_key(concept): + if concept.is_type(): + return concept.as_type().get_label().name() + elif concept.is_entity(): + return concept.as_entity().get_iid() + elif concept.is_relation(): + return concept.as_relation().get_iid() + elif concept.is_attribute(): + return concept.as_attribute().get_value() + else: + raise ValueError("Unknown concept type. Please report this error.") + + @staticmethod + def _format_json(answer, answer_type): + if answer_type is ConceptMap: + return [concept_map.to_json() for concept_map in answer] + elif answer_type is ConceptMapGroup: + return {Response._group_key(map_group.owner()): Response._format_json(map_group.concept_maps(), ConceptMap) for map_group in answer} + elif answer_type is Numeric: + if answer.is_int(): + return answer.as_int() + elif answer.is_float(): + return answer.as_float() + else: + return math.nan + elif answer_type is NumericGroup: + return {Response._group_key(numeric_group.owner()): Response._format_json(numeric_group.numeric(), Numeric) for numeric_group in answer} + else: + raise ValueError("Unknown answer type. Please report this error.") + + @staticmethod + def _serialise_concepts(results, transaction): + concepts = dict() + binding_counts = dict() + + for result in results: + concept_map = result.map() + + for binding in concept_map.keys(): + if not concept_map[binding].is_thing(): + continue + + thing = concept_map[binding].as_thing() + iid = thing.get_iid() + + if iid not in concepts.keys(): + if binding not in binding_counts.keys(): + binding_counts[binding] = 1 + else: + binding_counts[binding] += 1 + + concept = { + "binding": "{}_{}".format(binding, binding_counts[binding]), + "object": thing, + } + + concepts[iid] = concept + + for concept in concepts.values(): + concept["type"] = concept["object"].get_type().get_label().name() + + if concept["object"].is_attribute(): + concept["root-type"] = transaction.concepts().get_root_attribute_type().get_label().name() + concept["value"] = concept["object"].as_attribute().get_value() + concept["value-type"] = str(concept["object"].get_type().get_value_type()) + + if concept["object"].is_entity(): + concept["root-type"] = transaction.concepts().get_root_entity_type().get_label().name() + ownerships = [attribute.get_iid() for attribute in concept["object"].as_remote(transaction).get_has()] + concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] + + if concept["object"].is_relation(): + concept["root-type"] = transaction.concepts().get_root_relation_type().get_label().name() + ownerships = [attribute.get_iid() for attribute in concept["object"].as_remote(transaction).get_has()] + concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] + roleplayers = concept["object"].as_remote(transaction).get_players_by_role_type() + concept["roleplayers"] = list() + + for role in roleplayers.keys(): + for roleplayer in roleplayers[role]: + iid = roleplayer.get_iid() + + if iid in concepts.keys(): + concept["roleplayers"].append((role.get_label().name(), concepts[iid]["binding"])) + + concept.pop("object") + + serial = {concept["binding"]: concept for concept in concepts.values()} + + for entry in serial.values(): + entry.pop("binding") + + return serial + + @staticmethod + def _format_typeql(answer, answer_type, transaction): + if answer_type is ConceptMap: + concepts = Response._serialise_concepts(answer, transaction) + lines = list() + + for binding, concept in concepts.items(): + lines.append("${} isa {};".format(binding, concept["type"])) + + if "value" in concept.keys(): + if concept["value-type"] == "string": + lines.append("${} \"{}\";".format(binding, concept["value"])) + else: + lines.append("${} {};".format(binding, concept["value"])) + + if "ownerships" in concept.keys(): + for attribute_binding in concept["ownerships"]: + lines.append("${} has ${};".format(binding, attribute_binding)) + + if "roleplayers" in concept.keys(): + if len(concept["roleplayers"]) > 0: + roleplayers = list() + + for roleplayer in concept["roleplayers"]: + roleplayers.append("{}: ${}".format(roleplayer[0], roleplayer[1])) + + lines.append("${} ({});".format(binding, ", ".join(roleplayers))) + + return "\n".join(lines) + elif answer_type in (ConceptMapGroup, Numeric, NumericGroup): + raise ArgumentError("TypeQL output is not possible for group and aggregate queries.") + else: + raise ValueError("Unknown answer type. Please report this error.") + + @staticmethod + def _format(query, answer, answer_type, output_format, transaction): + if answer_type is None: + result = None + message = "{} query success.".format(query.query_type.title()) + return result, message + else: + if output_format == "json": + result = Response._format_json(answer, answer_type) + message = None + elif output_format == "typeql": + result = Response._format_typeql(answer, answer_type, transaction) + message = None + else: + raise ArgumentError("Unknown output format: '{}'".format(output_format)) + + return result, message From c6435bd591587dd59b4e167c2eb42e58cbd88c02 Mon Sep 17 00:00:00 2001 From: James Whiteside Date: Mon, 31 Jul 2023 12:49:30 +0100 Subject: [PATCH 04/27] Fixed bug in TypeQL output of datetimes. --- src/typedb_jupyter/response.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/typedb_jupyter/response.py b/src/typedb_jupyter/response.py index b1be3d2..1d4d51c 100644 --- a/src/typedb_jupyter/response.py +++ b/src/typedb_jupyter/response.py @@ -161,6 +161,8 @@ def _format_typeql(answer, answer_type, transaction): if "value" in concept.keys(): if concept["value-type"] == "string": lines.append("${} \"{}\";".format(binding, concept["value"])) + elif concept["value-type"] == "datetime": + lines.append("${} {};".format(binding, str(concept["value"]).replace(" ", "T"))) else: lines.append("${} {};".format(binding, concept["value"])) From 599820fbeab3679f3c4adee908a9737780d3df7d Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Sun, 26 Jan 2025 16:33:53 +0530 Subject: [PATCH 05/27] Towards an implementation --- RELEASE_NOTES.md | 3 + pyproject.toml | 8 +- src/Sample.ipynb | 417 ++++++++++++++++++++++++++++++ src/typedb_jupyter/connection.py | 159 ++++-------- src/typedb_jupyter/exception.py | 11 + src/typedb_jupyter/magic.py | 137 ++++------ src/typedb_jupyter/query.py | 263 ------------------- src/typedb_jupyter/response.py | 340 ++++++++++++------------ src/typedb_jupyter/subcommands.py | 200 ++++++++++++++ 9 files changed, 904 insertions(+), 634 deletions(-) create mode 100644 src/Sample.ipynb delete mode 100644 src/typedb_jupyter/query.py create mode 100644 src/typedb_jupyter/subcommands.py diff --git a/RELEASE_NOTES.md b/RELEASE_NOTES.md index 510f30d..05ce53c 100644 --- a/RELEASE_NOTES.md +++ b/RELEASE_NOTES.md @@ -1,5 +1,8 @@ # TypeDB Jupyter connector +## Version 0.5 +- Bump TypeDB Driver dependency to 2.28.4. + ## Version 0.4 - A TypeQL output format has been added for `match` queries. The TypeQL returned contains all the necessary information to reconstruct the original query in a new database. To do so: commit the same schema used for the initial database, diff --git a/pyproject.toml b/pyproject.toml index 24f1dc5..4b0db3b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "typedb-jupyter" -version = "0.3" +version = "0.4" description = "Jupyter connector for TypeDB" readme = "README.md" requires-python = ">=3.7" @@ -22,12 +22,12 @@ classifiers = [ "Topic :: Database", ] dependencies = [ - "typedb-client~=2.18", + "typedb-driver~=3.0.2", "ipython" ] [project.urls] "Repository" = "https://github.com/typedb-osi/typedb-jupyter" "Release notes" = "https://github.com/typedb-osi/typedb-jupyter/blob/master/RELEASE_NOTES.md" -"TypeDB" = "https://github.com/vaticle/typedb" -"Vaticle" = "https://vaticle.com/" +"TypeDB" = "https://github.com/typedb/typedb" +"Vaticle" = "https://typedb.com/" diff --git a/src/Sample.ipynb b/src/Sample.ipynb new file mode 100644 index 0000000..706eee6 --- /dev/null +++ b/src/Sample.ipynb @@ -0,0 +1,417 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "adcdc08e-702a-4c23-acfb-d9f8b9612915", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext typedb_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c4b2cf02-baa2-4e71-86c3-824030318ee9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available commands: connect, database, transaction, help\n", + "TODO: Print subcommand help\n" + ] + } + ], + "source": [ + "%typedb help" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "994ca437-cbbc-4ac5-a953-a44e196a9512", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened connection to: 127.0.0.1:1729\n" + ] + } + ], + "source": [ + "%typedb connect open core 127.0.0.1:1729 admin password" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1ef0b8de-4e09-4588-bea7-f67fce0bfe95", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created database test_jupyter\n" + ] + } + ], + "source": [ + "%typedb database create test_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2775578e-cbe6-498d-82c6-18d8d4c4f0c0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" + ] + } + ], + "source": [ + "%typedb database list" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6415f8cf-34e7-42b8-9294-0113c584fc33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deleted database test_jupyter\n" + ] + } + ], + "source": [ + "%typedb database delete test_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "85c80d7e-566b-4b92-9704-e27f68a58919", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Databases: moo, jupyter-test, typedb-iam\n" + ] + } + ], + "source": [ + "%typedb database list" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be17ff6a-8020-43a4-9611-2f5def7bab0d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recreated database test_jupyter\n" + ] + } + ], + "source": [ + "%typedb database recreate test_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0cd600f3-6f6d-4b03-b3ec-fcf9b593e4b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" + ] + } + ], + "source": [ + "%typedb database list" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7b81db96-ae1a-43b3-a920-6e7443e34aeb", + "metadata": {}, + "outputs": [], + "source": [ + "# This is not implemented yet: %typedb database schema test_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c1260950-fae9-4ef7-9940-ff963a2ab53a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recreated database test_jupyter\n" + ] + } + ], + "source": [ + "%typedb database recreate test_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bfeae364-194b-4478-b02d-2b29c1ede228", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened schema transaction on database 'test_jupyter' \n" + ] + } + ], + "source": [ + "%typedb transaction open test_jupyter schema" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d3a65846-4d15-4376-bfb1-c3c921355aab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query completed successfully! (No results to show)\n" + ] + } + ], + "source": [ + "%%typeql \n", + "define\n", + " attribute name, value string;\n", + " entity person, owns name;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], + "source": [ + "%typedb transaction commit" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened read transaction on database 'test_jupyter' \n" + ] + } + ], + "source": [ + "%typedb transaction open test_jupyter read " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1552def1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TODO: Print rows\n" + ] + }, + { + "data": { + "text/plain": [ + "[| $attribute_type: AttributeType(name) | $owner_type: EntityType(person) |]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql \n", + "match $owner_type owns $attribute_type;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8c9ff85b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "UsageError: unrecognized arguments: -o typeql\n" + ] + } + ], + "source": [ + "%%typeql -o typeql \n", + "insert $p isa person, has name \"James\";" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e32715b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connection: test_1 (jupyter-test@127.0.0.1:1729)\n", + "Session: data\n", + "Transaction: read\n", + "Query: match\n", + "Inference: off\n" + ] + }, + { + "data": { + "text/plain": [ + "'$a_1 isa name;\\n$a_1 \"James\";\\n$p_1 isa person;\\n$p_1 has $a_1;\\n$p_2 isa person;\\n$p_2 has $a_1;\\n$p_3 isa person;\\n$p_3 has $a_1;'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql -o typeql\n", + "match $p has $a; get;" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b649db13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connection: test_1 (jupyter-test@127.0.0.1:1729)\n", + "Session: data\n", + "Transaction: read\n", + "Query: match\n", + "Inference: off\n", + "Returning data to local variable: 'myvar'\n" + ] + } + ], + "source": [ + "%%typeql -r myvar\n", + "match $p has $a; get;" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3f64a613", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'ConceptMap { map: {\"p\": Entity(Entity { iid: ID[0x826e80018000000000000000], type_: EntityType { label: \"person\", is_root: false, is_abstract: false }, is_inferred: false }), \"a\": Attribute(Attribute { iid: ID[0x836f80012800054a616d6573], type_: AttributeType { label: \"name\", is_root: false, is_abstract: false, value_type: String }, value: String(\"James\"), is_inferred: false })}, explainables: Explainables { relations: {}, attributes: {}, ownerships: {} } }'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "myvar[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b61bc157", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/typedb_jupyter/connection.py b/src/typedb_jupyter/connection.py index 326dbd8..6e2cb85 100644 --- a/src/typedb_jupyter/connection.py +++ b/src/typedb_jupyter/connection.py @@ -19,131 +19,76 @@ # under the License. # -from typedb.client import TypeDB -from typedb.api.connection.session import SessionType -from typedb_jupyter.exception import ArgumentError - +from typedb.driver import TypeDB, DriverOptions +from typedb_jupyter.exception import ArgumentError, ConnectionError class Connection(object): current = None - connections = dict() - def __init__(self, client, address, database, credential, alias, create_database): + def __init__(self, driver, address, credential): self.address = address - self.database = database - self.name = "{}@{}".format(database, address) - - if alias is None: - self.alias = self.name - self.verbose_name = self.name - else: - self.alias = alias - self.verbose_name = "{} ({})".format(self.alias, self.name) - - if client is TypeDB.core_client: - self.client = TypeDB.core_client(address) - elif client is TypeDB.cluster_client: - self.client = TypeDB.cluster_client(address, credential) + if driver is TypeDB.core_driver: + self.driver = TypeDB.core_driver(address, credential, DriverOptions()) + elif driver is TypeDB.cloud_driver: + self.driver = TypeDB.cloud_driver(address, credential, DriverOptions()) else: raise ValueError("Unknown client type. Please report this error.") - - if not self.client.databases().contains(database): - if create_database: - self.client.databases().create(database) - print("Created database: {}".format(self.database)) - else: - raise ArgumentError("Database with name '{}' does not exist and automatic database creation has been disabled.".format(database)) - - self.session = self.client.session(database, SessionType.DATA) - self.connections[self.name] = self + self.active_transaction = None def __del__(self): - try: - self.session.close() - finally: - self.client.close() + if self.active_transaction is not None: + self.active_transaction.close() + self.active_transaction = None + self.driver.close() @classmethod - def _get_aliases(cls): - return [cls.connections[name].alias for name in cls.connections] - - @classmethod - def _get_current(cls): - if len(cls.connections) == 0: - raise ArgumentError("No database connection exists. Use -a and -d to specify server address and database name.") - elif cls.current is None: - raise ArgumentError("Current connection was closed. Use -l to list connections and -n to select connection.") - - return cls.current - - @classmethod - def _get_by_alias(cls, alias): - try: - return {cls.connections[name].alias: cls.connections[name] for name in cls.connections}[alias] - except KeyError: - raise ArgumentError("Connection name not recognised. Use -l to list connections.") - - @classmethod - def open(cls, client, address, database, credential, alias, create_database): - if "{}@{}".format(database, address) in cls.connections: - raise ArgumentError("Cannot open more than one connection to the same database. Use -c to close opened connection first.") - elif alias in cls._get_aliases(): - raise ArgumentError("Cannot open more than one connection with the same alias. Use -c to close opened connection first.") - else: - cls.current = Connection(client, address, database, credential, alias, create_database) - print("Opened connection: {}".format(cls.current.verbose_name)) - - @classmethod - def select(cls, alias): - cls.current = cls._get_by_alias(alias) - print("Selected connection: {}".format(cls.current.verbose_name)) - - @classmethod - def get(cls, alias=None): - if alias is None: - return cls._get_current() + def open(cls, client, address, credential): + if cls.current is None: + cls.current = Connection(client, address, credential) + print("Opened connection to: {}".format(cls.current.address)) else: - return cls._get_by_alias(alias) - + raise ArgumentError("Cannot open more than one connection. Use `connection close` to close opened connection first.") @classmethod - def display(cls): - print("Current connection: {}".format(cls._get_current().verbose_name)) + def get(cls): + return cls.current @classmethod - def list(cls): - if len(cls.connections) == 0: - print("No open connections.") + def close(cls): + connection = cls.current + cls.current = None + del connection + print("Closed connection") + + def _ensure_transaction_open(self): + if self.active_transaction is None: + raise ArgumentError("There is no open transaction") + elif not self.active_transaction.is_open(): + self.active_transaction = None + raise ConnectionError("The transaction has been closed") + + def get_active_transaction(self): + self._ensure_transaction_open() + return self.active_transaction + + def open_transaction(self, database, transaction_type): + if self.active_transaction is not None: + raise ArgumentError("Cannot open a transaction when there is one active. Please close it first.") else: - print("Open connections:") - for name in sorted(cls.connections): - if cls.connections[name] == cls.current: - prefix = " * " - else: - prefix = " " - - print("{}{}".format(prefix, cls.connections[name].verbose_name)) + self.active_transaction = self.driver.transaction(database, transaction_type) - @classmethod - def set_session(cls, session_type, alias=None): - connection = cls.get(alias) - if connection.session.session_type() != session_type: - connection.session.close() - connection.session = connection.client.session(connection.database, session_type) - - @classmethod - def close(cls, alias=None, delete=False): - connection = cls.get(alias) - verbose_name = connection.verbose_name - if cls.current is not None and cls.current.alias == alias: - cls.current = None + def close_transaction(self): + self._ensure_transaction_open() + self.active_transaction.close() + self.active_transaction = None - connection = cls.connections[connection.name] + def commit_transaction(self): + self._ensure_transaction_open() + self.active_transaction.commit() + self.active_transaction = None - if delete: - connection.session.close() - connection.client.databases().get(connection.database).delete() - print("Deleted database: {}".format(connection.database)) - del cls.connections[connection.name] - print("Closed connection: {}".format(verbose_name)) + def rollback_transaction(self): + self._ensure_transaction_open() + self.active_transaction.rollback() + self.active_transaction = None diff --git a/src/typedb_jupyter/exception.py b/src/typedb_jupyter/exception.py index 4a3e900..7875039 100644 --- a/src/typedb_jupyter/exception.py +++ b/src/typedb_jupyter/exception.py @@ -25,3 +25,14 @@ class ArgumentError(ValueError): class QueryParsingError(ValueError): pass + +class ConnectionError(BaseException): + pass + + +class CommandParsingError(BaseException): + def __init__(self, what, msg): + BaseException.__init__(self) + self.what = what + self.msg = msg + diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index 5dec33f..67fa1e3 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -24,12 +24,10 @@ from traitlets import Bool from IPython.core.magic import Magics, cell_magic, line_magic, magics_class, needs_local_scope from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring -from typedb.api.connection.credential import TypeDBCredential -from typedb.client import TypeDB from typedb_jupyter.connection import Connection -from typedb_jupyter.query import Query from typedb_jupyter.exception import ArgumentError, QueryParsingError +import typedb_jupyter.subcommands as subcommands def substitute_vars(query, local_ns): try: @@ -64,56 +62,25 @@ class TypeDBMagic(Magics, Configurable): help="Create database when opening a connection if it does not already exist." ) + @line_magic("typedb") - @magic_arguments() - @argument("-a", "--address", type=str, help="TypeDB server address for new connection.") - @argument("-d", "--database", type=str, help="Database name for new connection.") - @argument("-u", "--username", type=str, help="Username for new Cloud/Cluster connection.") - @argument("-p", "--password", type=str, help="Password for new Cloud/Cluster connection.") - @argument("-c", "--certificate", type=str, help="TLS certificate path for new Cloud/Cluster connection.") - @argument("-n", "--alias", type=str, help="Custom alias for new connection, or alias of existing connection to select.") - @argument("-l", "--list", action="store_true", help="List currently open connections.") - @argument("-k", "--close", type=str, help="Close a connection by name.") - @argument("-x", "--delete", type=str, help="Close a connection by name and delete its database.") def execute(self, line=""): - args = parse_argstring(self.execute, line) - - if args.list: - return Connection.list() - elif args.delete: - return Connection.close(args.delete, delete=True) - elif args.close: - return Connection.close(args.close) - else: - cluster_args = (args.username, args.password, args.certificate) - - if args.database is None: - if args.address is not None or not all(arg is None for arg in cluster_args): - raise ArgumentError("Cannot open connection without a database name. Use -d to specify database.") - elif args.alias is None: - Connection.display() - else: - Connection.select(args.alias) + args = line.split(" ") + if len(args) > 0: + command_name = args[0].lower() + if command_name in subcommands.AVAILABLE_COMMANDS: + subcommand = subcommands.AVAILABLE_COMMANDS[args[0]] else: - if all(arg is None for arg in cluster_args): - client = TypeDB.core_client - credential = None - elif all(arg is not None for arg in cluster_args): - client = TypeDB.cluster_client - credential = TypeDBCredential(args.username, args.password, args.certificate) - else: - raise ArgumentError("Cannot open cluster connection without a username, password, and certificate path. Use -u, -p, and -c to specify these.") - - if args.alias is not None and not re.fullmatch(r"[a-zA-Z0-9-_]+", args.alias): - raise ArgumentError("Custom aliases can only contains alphanumeric characters, hyphens, and underscores.") - - if args.address is None: - address = TypeDB.DEFAULT_ADDRESS - else: - address = args.address - - Connection.open(client, address, args.database, credential, args.alias, self.create_database) - return + print("Unrecognised command: ", args[0]) + subcommand = subcommands.Help + else: + subcommand = subcommands.Help + + try: + return subcommand.execute(args[1:]) + except subcommands.CommandParsingError as err: + print("Exception with subcommand: ", err.msg) + return err def __init__(self, shell): Configurable.__init__(self, config=shell.config) @@ -133,7 +100,7 @@ class TypeQLMagic(Magics, Configurable): strict_transactions = Bool( False, config=True, - help="Require session and transaction types to be specified for every transaction." + help="Require transaction types to be specified for every transaction." ) global_inference = Bool( False, @@ -142,52 +109,28 @@ class TypeQLMagic(Magics, Configurable): ) @needs_local_scope - @line_magic("typeql") @cell_magic("typeql") @magic_arguments() - @argument("line", nargs="*", type=str, default="", help="Valid TypeQL string.") - @argument("-r", "--result", type=str, help="Assign read query results to the named variable instead of printing.") - @argument("-f", "--file", type=str, help="Read in query from a TypeQL file at the specified path.") - @argument("-i", "--inference", type=bool, help="Enable (True) or disable (False) rule inference for query.") - @argument("-s", "--session", type=str, help="Force a particular session type for query, 'schema' or 'data'.") - @argument("-t", "--transaction", type=str, help="Force a particular transaction type for query, 'read' or 'write'.") - @argument("-o", "--output", type=str, default="json", help="Output format for read query results.") def execute(self, line="", cell="", local_ns=None): if local_ns is None: local_ns = {} args = parse_argstring(self.execute, line) - query = " ".join(args.line) + "\n" + cell + query = cell query = substitute_vars(query, local_ns) # Save globals and locals, so they can be referenced in bind vars user_ns = self.shell.user_ns.copy() user_ns.update(local_ns) - if args.file: - with open(args.file, "r") as infile: - query = infile.read() + "\n" + query - if query.strip() == "": raise ArgumentError("No query string supplied.") connection = Connection.get() - query = Query(query, args.session, args.transaction, args.inference, self.strict_transactions, self.global_inference) - response = query.run(connection, args.output, self.show_info) - - if response.message is not None: - print(response.message) - - if response.result is not None: - if args.result: - print("Returning data to local variable: '{}'".format(args.result)) - self.shell.user_ns.update({args.result: response.result}) - return - - # Return results into the default ipython _ variable - return response.result - else: - return + tx = connection.get_active_transaction() + answer_type, answer = self._run_query(tx, query) + self._print_answer(answer_type, answer) + return answer def __init__(self, shell): Configurable.__init__(self, config=shell.config) @@ -195,3 +138,37 @@ def __init__(self, shell): # Add ourselves to the list of module configurable via %config self.shell.configurables.append(self) + + def _run_query(self, transaction, query): + from typedb.concept.answer.concept_row_iterator import ConceptRowIterator + from typedb.concept.answer.concept_document_iterator import ConceptDocumentIterator + from typedb.concept.answer.ok_query_answer import OkQueryAnswer + answer = transaction.query(query).resolve() + if answer.is_concept_rows(): + return (ConceptRowIterator, list(answer.as_concept_rows())) + elif answer.is_concept_documents(): + return (ConceptDocumentIterator, list(answer.as_concept_documents)) + elif answer.is_ok(): + return (OkQueryAnswer, None) + else: + raise NotImplementedError("Unhandled answer type") + + + def _print_answer(self, answer_type, answer): + from typedb.concept.answer.concept_row_iterator import ConceptRowIterator + from typedb.concept.answer.concept_document_iterator import ConceptDocumentIterator + from typedb.concept.answer.ok_query_answer import OkQueryAnswer + if answer_type == OkQueryAnswer: + print("Query completed successfully! (No results to show)") + elif answer_type == ConceptDocumentIterator: + self._print_documents(answer) + elif answer_type == ConceptRowIterator: + self._print_rows(answer) + else: + raise NotImplementedError("Unhandled answer type") + + def _print_documents(self, documents): + print("TODO: Print documents") + + def _print_rows(self, rows): + print("TODO: Print rows") diff --git a/src/typedb_jupyter/query.py b/src/typedb_jupyter/query.py deleted file mode 100644 index 15aab76..0000000 --- a/src/typedb_jupyter/query.py +++ /dev/null @@ -1,263 +0,0 @@ -# -# Copyright (C) 2023 Vaticle -# -# Licensed to the Apache Software Foundation (ASF) under one -# or more contributor license agreements. See the NOTICE file -# distributed with this work for additional information -# regarding copyright ownership. The ASF licenses this file -# to you under the Apache License, Version 2.0 (the -# "License"); you may not use this file except in compliance -# with the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, -# software distributed under the License is distributed on an -# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -# KIND, either express or implied. See the License for the -# specific language governing permissions and limitations -# under the License. -# - -from typedb.client import TypeDBOptions -from typedb.api.connection.session import SessionType -from typedb.api.connection.transaction import TransactionType -from typedb_jupyter.connection import Connection -from typedb_jupyter.exception import ArgumentError, QueryParsingError -from typedb_jupyter.response import Response - - -class Query(object): - def __init__(self, query, session_arg, transaction_arg, inference_arg, strict_transactions, global_inference): - self.query = query - self.query_type = self._get_query_type(self.query) - self.session_type = self._get_session_type(self.query_type, session_arg, strict_transactions) - self.transaction_type = self._get_transaction_type(self.query_type, transaction_arg, strict_transactions) - - if inference_arg is None: - self.infer = global_inference - else: - self.infer = inference_arg - - @staticmethod - def _get_query_args(query): - # Warning: This method is experimental and not guaranteed to always function correctly. Copy at your own risk. - - in_escape = False - in_literal = False - in_comment = False - literal_delimiter = None - arg_string = "" - - for char in query: - if in_escape: - in_escape = False - arg_string += " " - continue - - if in_literal and char == "\\": - in_escape = True - arg_string += " " - continue - - if not in_comment and char in ("\"", "'"): - if not in_literal: - in_literal = True - literal_delimiter = char - arg_string += " " - continue - if in_literal and char == literal_delimiter: - in_literal = False - arg_string += " " - continue - - if not in_literal: - if char == "#": - in_comment = True - arg_string += " " - continue - if in_comment and char == "\n": - in_comment = False - arg_string += " " - continue - - if not in_literal and not in_comment: - if char in (",", ";"): - arg_string += " " - else: - arg_string += char - - return arg_string.split() - - @staticmethod - def _get_query_type(query): - # Warning: This method is experimental and not guaranteed to always function correctly. Copy at your own risk. - - query_args = Query._get_query_args(query) - - keyword_counts = { - "match": 0, - "get": 0, - "define": 0, - "undefine": 0, - "insert": 0, - "delete": 0, - "group": 0, - "count": 0, - "sum": 0, - "max": 0, - "min": 0, - "mean": 0, - "median": 0, - "std": 0, - } - - for arg in query_args: - if arg in keyword_counts: - keyword_counts[arg] += 1 - - aggregate_count = sum(( - keyword_counts["count"], - keyword_counts["sum"], - keyword_counts["max"], - keyword_counts["min"], - keyword_counts["mean"], - keyword_counts["median"], - keyword_counts["std"], - )) - - candidate_query_types = list() - - if keyword_counts["group"] > 0 and aggregate_count > 0: - candidate_query_types.append("match-group-aggregate") - elif aggregate_count > 0: - candidate_query_types.append("match-aggregate") - elif keyword_counts["group"] > 0: - candidate_query_types.append("match-group") - elif keyword_counts["get"] > 0: - candidate_query_types.append("match") - - if keyword_counts["define"] > 0: - candidate_query_types.append("define") - - if keyword_counts["undefine"] > 0: - candidate_query_types.append("undefine") - - if keyword_counts["insert"] > 0 and keyword_counts["delete"] > 0: - candidate_query_types.append("update") - elif keyword_counts["insert"] > 0: - candidate_query_types.append("insert") - elif keyword_counts["delete"] > 0: - candidate_query_types.append("delete") - - if len(candidate_query_types) > 1: - raise QueryParsingError("Query contains incompatible keywords: '{}'".format("', '".join(candidate_query_types))) - elif len(candidate_query_types) == 1: - return candidate_query_types[0] - elif keyword_counts["match"] > 0: - return "match" - else: - raise QueryParsingError("Query contains no keywords.") - - @staticmethod - def _get_session_type(query_type, session_arg, strict_transactions): - if session_arg is None: - if strict_transactions: - raise ArgumentError("Strict transaction types is enabled and no session type was provided. Use -s to specify session type.") - elif query_type in ("define", "undefine"): - return SessionType.SCHEMA - else: - return SessionType.DATA - else: - if session_arg.lower() == "schema": - return SessionType.SCHEMA - elif session_arg.lower() == "data": - return SessionType.DATA - else: - raise ArgumentError("Incorrect session type provided. Session type must be 'schema' or 'data'.") - - @staticmethod - def _get_transaction_type(query_type, transaction_arg, strict_transactions): - if transaction_arg is None: - if strict_transactions: - raise ArgumentError("Strict transaction types is enabled and no transaction type was provided. Use -t to specify transaction type.") - elif query_type in ("define", "undefine", "insert", "update", "delete"): - return TransactionType.WRITE - else: - return TransactionType.READ - else: - if transaction_arg.lower() == "read": - return TransactionType.READ - elif transaction_arg.lower() == "write": - return TransactionType.WRITE - else: - raise ArgumentError("Incorrect transaction type provided. Transaction type must be 'read' or 'write'.") - - def _get_options(self, connection): - if connection.client.is_cluster(): - return TypeDBOptions().cluster().set_infer(self.infer) - else: - return TypeDBOptions().core().set_infer(self.infer) - - def _print_info(self, connection): - connection_arg = "Connection: {}".format(connection.verbose_name) - - if self.session_type == SessionType.SCHEMA: - session_arg = "Session: schema" - else: - session_arg = "Session: data" - - if self.transaction_type == TransactionType.READ: - transaction_arg = "Transaction: read" - else: - transaction_arg = "Transaction: write" - - query_arg = "Query: {}".format(self.query_type) - - if self.infer: - inference_arg = "Inference: on" - else: - inference_arg = "Inference: off" - - info = "{}\n{}\n{}\n{}\n{}".format( - connection_arg, session_arg, transaction_arg, query_arg, inference_arg - ) - - print(info) - - def run(self, connection, output_format, show_info): - Connection.set_session(self.session_type) - options = self._get_options(connection) - - if show_info: - self._print_info(connection) - - try: - with connection.session.transaction(self.transaction_type, options) as transaction: - if self.query_type == "match": - answer = transaction.query().match(self.query) - elif self.query_type == "match-aggregate": - answer = transaction.query().match_aggregate(self.query).get() - elif self.query_type == "match-group": - answer = transaction.query().match_group(self.query) - elif self.query_type == "match-group-aggregate": - answer = transaction.query().match_group_aggregate(self.query) - elif self.query_type == "define": - answer = transaction.query().define(self.query) - elif self.query_type == "undefine": - answer = transaction.query().undefine(self.query) - elif self.query_type == "insert": - answer = transaction.query().insert(self.query) - elif self.query_type == "delete": - answer = transaction.query().delete(self.query) - elif self.query_type == "update": - answer = transaction.query().update(self.query) - - response = Response(self, answer, output_format, transaction) - - if self.transaction_type == TransactionType.WRITE: - transaction.commit() - - return response - finally: - Connection.set_session(SessionType.DATA) diff --git a/src/typedb_jupyter/response.py b/src/typedb_jupyter/response.py index 1d4d51c..7560c34 100644 --- a/src/typedb_jupyter/response.py +++ b/src/typedb_jupyter/response.py @@ -19,186 +19,166 @@ # under the License. # -import math -from typedb.concept.answer.concept_map import ConceptMap -from typedb.concept.answer.concept_map_group import ConceptMapGroup -from typedb.concept.answer.numeric import Numeric -from typedb.concept.answer.numeric_group import NumericGroup +from typedb.concept.answer.concept_row_iterator import ConceptRowIterator +from typedb.concept.answer.concept_document_iterator import ConceptDocumentIterator +from typedb.concept.answer.ok_query_answer import OkQueryAnswer from typedb_jupyter.exception import ArgumentError +raise NotImplementedError("Do not import me") -class Response(object): - def __init__(self, query, answer, output_format, transaction): - self.query = query - self.output_format = output_format - self.answer_type = self._get_answer_type() - self.result, self.message = self._format(self.query, answer, self.answer_type, output_format, transaction) - - def _get_answer_type(self): - if self.query.query_type == "match": - return ConceptMap - elif self.query.query_type == "match-aggregate": - return Numeric - elif self.query.query_type == "match-group": - return ConceptMapGroup - elif self.query.query_type == "match-group-aggregate": - return NumericGroup - elif self.query.query_type == "define": - return None - elif self.query.query_type == "undefine": - return None - elif self.query.query_type == "insert": - return None - elif self.query.query_type == "delete": - return None - elif self.query.query_type == "update": - return None - - @staticmethod - def _group_key(concept): - if concept.is_type(): - return concept.as_type().get_label().name() - elif concept.is_entity(): - return concept.as_entity().get_iid() - elif concept.is_relation(): - return concept.as_relation().get_iid() - elif concept.is_attribute(): - return concept.as_attribute().get_value() - else: - raise ValueError("Unknown concept type. Please report this error.") - - @staticmethod - def _format_json(answer, answer_type): - if answer_type is ConceptMap: - return [concept_map.to_json() for concept_map in answer] - elif answer_type is ConceptMapGroup: - return {Response._group_key(map_group.owner()): Response._format_json(map_group.concept_maps(), ConceptMap) for map_group in answer} - elif answer_type is Numeric: - if answer.is_int(): - return answer.as_int() - elif answer.is_float(): - return answer.as_float() - else: - return math.nan - elif answer_type is NumericGroup: - return {Response._group_key(numeric_group.owner()): Response._format_json(numeric_group.numeric(), Numeric) for numeric_group in answer} - else: - raise ValueError("Unknown answer type. Please report this error.") - - @staticmethod - def _serialise_concepts(results, transaction): - concepts = dict() - binding_counts = dict() - - for result in results: - concept_map = result.map() - - for binding in concept_map.keys(): - if not concept_map[binding].is_thing(): - continue - - thing = concept_map[binding].as_thing() - iid = thing.get_iid() - - if iid not in concepts.keys(): - if binding not in binding_counts.keys(): - binding_counts[binding] = 1 - else: - binding_counts[binding] += 1 - - concept = { - "binding": "{}_{}".format(binding, binding_counts[binding]), - "object": thing, - } - - concepts[iid] = concept - - for concept in concepts.values(): - concept["type"] = concept["object"].get_type().get_label().name() - - if concept["object"].is_attribute(): - concept["root-type"] = transaction.concepts().get_root_attribute_type().get_label().name() - concept["value"] = concept["object"].as_attribute().get_value() - concept["value-type"] = str(concept["object"].get_type().get_value_type()) - - if concept["object"].is_entity(): - concept["root-type"] = transaction.concepts().get_root_entity_type().get_label().name() - ownerships = [attribute.get_iid() for attribute in concept["object"].as_remote(transaction).get_has()] - concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] - - if concept["object"].is_relation(): - concept["root-type"] = transaction.concepts().get_root_relation_type().get_label().name() - ownerships = [attribute.get_iid() for attribute in concept["object"].as_remote(transaction).get_has()] - concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] - roleplayers = concept["object"].as_remote(transaction).get_players_by_role_type() - concept["roleplayers"] = list() - - for role in roleplayers.keys(): - for roleplayer in roleplayers[role]: - iid = roleplayer.get_iid() - - if iid in concepts.keys(): - concept["roleplayers"].append((role.get_label().name(), concepts[iid]["binding"])) - - concept.pop("object") - - serial = {concept["binding"]: concept for concept in concepts.values()} - - for entry in serial.values(): - entry.pop("binding") - - return serial - - @staticmethod - def _format_typeql(answer, answer_type, transaction): - if answer_type is ConceptMap: - concepts = Response._serialise_concepts(answer, transaction) - lines = list() - - for binding, concept in concepts.items(): - lines.append("${} isa {};".format(binding, concept["type"])) - - if "value" in concept.keys(): - if concept["value-type"] == "string": - lines.append("${} \"{}\";".format(binding, concept["value"])) - elif concept["value-type"] == "datetime": - lines.append("${} {};".format(binding, str(concept["value"]).replace(" ", "T"))) - else: - lines.append("${} {};".format(binding, concept["value"])) - - if "ownerships" in concept.keys(): - for attribute_binding in concept["ownerships"]: - lines.append("${} has ${};".format(binding, attribute_binding)) - - if "roleplayers" in concept.keys(): - if len(concept["roleplayers"]) > 0: - roleplayers = list() - - for roleplayer in concept["roleplayers"]: - roleplayers.append("{}: ${}".format(roleplayer[0], roleplayer[1])) - - lines.append("${} ({});".format(binding, ", ".join(roleplayers))) - - return "\n".join(lines) - elif answer_type in (ConceptMapGroup, Numeric, NumericGroup): - raise ArgumentError("TypeQL output is not possible for group and aggregate queries.") - else: - raise ValueError("Unknown answer type. Please report this error.") - - @staticmethod - def _format(query, answer, answer_type, output_format, transaction): - if answer_type is None: - result = None - message = "{} query success.".format(query.query_type.title()) - return result, message - else: - if output_format == "json": - result = Response._format_json(answer, answer_type) - message = None - elif output_format == "typeql": - result = Response._format_typeql(answer, answer_type, transaction) - message = None - else: - raise ArgumentError("Unknown output format: '{}'".format(output_format)) - - return result, message +# class Response(object): +# def __init__(self, query, answer, output_format, transaction): +# self.query = query +# self.output_format = output_format +# self.answer_type = self._get_answer_type(answer) +# self.result, self.message = self._format(self.query, answer, self.answer_type, output_format, transaction) +# +# @staticmethod +# def _get_answer_type(answer): +# if answer.is_concept_rows(): +# return ConceptRowIterator +# elif answer.is_concept_documents(): +# return ConceptDocumentIterator +# elif answer.is_ok(): +# return OkQueryAnswer +# else: +# raise NotImplementedError("Unhandled answer type") +# +# @staticmethod +# def _group_key(concept): +# if concept.is_type(): +# return concept.as_type().get_label().name +# elif concept.is_entity(): +# return concept.as_entity().get_iid() +# elif concept.is_relation(): +# return concept.as_relation().get_iid() +# elif concept.is_attribute(): +# return concept.as_attribute().get_value() +# else: +# raise ValueError("Unknown concept type. Please report this error.") +# +# @staticmethod +# def _format_json(answer, answer_type): +# if answer_type is ConceptRowIterator: +# return [str(concept_row) for concept_row in answer] +# else: +# raise ValueError("Unknown answer type. Please report this error.") +# +# @staticmethod +# def _serialise_concepts(results, transaction): +# concepts = dict() +# binding_counts = dict() +# +# for result in results: +# concept_map = result.map +# +# for binding in concept_map.keys(): +# if not concept_map[binding].is_thing(): +# continue +# +# thing = concept_map[binding].as_thing() +# iid = thing.get_iid() +# +# if iid not in concepts.keys(): +# if binding not in binding_counts.keys(): +# binding_counts[binding] = 1 +# else: +# binding_counts[binding] += 1 +# +# concept = { +# "binding": "{}_{}".format(binding, binding_counts[binding]), +# "object": thing, +# } +# +# concepts[iid] = concept +# +# for concept in concepts.values(): +# concept["type"] = concept["object"].get_type().get_label().name +# +# if concept["object"].is_attribute(): +# concept["root-type"] = transaction.concepts.get_root_attribute_type().get_label().name +# concept["value"] = concept["object"].as_attribute().get_value() +# concept["value-type"] = str(concept["object"].get_type().get_value_type()) +# +# if concept["object"].is_entity(): +# concept["root-type"] = transaction.concepts.get_root_entity_type().get_label().name +# ownerships = [attribute.get_iid() for attribute in concept["object"].get_has(transaction)] +# concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] +# +# if concept["object"].is_relation(): +# concept["root-type"] = transaction.concepts.get_root_relation_type().get_label().name +# ownerships = [attribute.get_iid() for attribute in concept["object"].get_has(transaction)] +# concept["ownerships"] = [concepts[iid]["binding"] for iid in ownerships if iid in concepts.keys()] +# roleplayers = concept["object"].get_players_by_role_type(transaction) +# concept["roleplayers"] = list() +# +# for role in roleplayers.keys(): +# for roleplayer in roleplayers[role]: +# iid = roleplayer.get_iid() +# +# if iid in concepts.keys(): +# concept["roleplayers"].append((role.get_label().name, concepts[iid]["binding"])) +# +# concept.pop("object") +# +# serial = {concept["binding"]: concept for concept in concepts.values()} +# +# for entry in serial.values(): +# entry.pop("binding") +# +# return serial +# +# @staticmethod +# def _format_typeql(answer, answer_type, transaction): +# if answer_type is ConceptRow: +# concepts = Response._serialise_concepts(answer, transaction) +# lines = list() +# +# for binding, concept in concepts.items(): +# lines.append("${} isa {};".format(binding, concept["type"])) +# +# if "value" in concept.keys(): +# if concept["value-type"] == "string": +# lines.append("${} \"{}\";".format(binding, concept["value"])) +# elif concept["value-type"] == "datetime": +# lines.append("${} {};".format(binding, str(concept["value"]).replace(" ", "T"))) +# else: +# lines.append("${} {};".format(binding, concept["value"])) +# +# if "ownerships" in concept.keys(): +# for attribute_binding in concept["ownerships"]: +# lines.append("${} has ${};".format(binding, attribute_binding)) +# +# if "roleplayers" in concept.keys(): +# if len(concept["roleplayers"]) > 0: +# roleplayers = list() +# +# for roleplayer in concept["roleplayers"]: +# roleplayers.append("{}: ${}".format(roleplayer[0], roleplayer[1])) +# +# lines.append("${} ({});".format(binding, ", ".join(roleplayers))) +# +# return "\n".join(lines) +# elif answer_type in (ConceptDocumentIterator): +# raise NotImplementedError("fetch") +# else: +# raise ValueError("Unknown answer type. Please report this error.") +# +# @staticmethod +# def _format(query, answer, answer_type, output_format, transaction): +# if answer_type is OkQueryAnswer: +# result = None +# message = "Query executed successfully!" +# return result, message +# else: +# if output_format == "json": +# result = Response._format_json(answer, answer_type) +# message = None +# elif output_format == "typeql": +# raise NotImplementedError("typeql output") +# result = Response._format_typeql(answer, answer_type, transaction) +# message = None +# else: +# raise ArgumentError("Unknown output format: '{}'".format(output_format)) +# +# return result, message diff --git a/src/typedb_jupyter/subcommands.py b/src/typedb_jupyter/subcommands.py new file mode 100644 index 0000000..5ee4fb3 --- /dev/null +++ b/src/typedb_jupyter/subcommands.py @@ -0,0 +1,200 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +import abc +import argparse + +from typedb_jupyter.exception import ArgumentError, CommandParsingError +from typedb.api.connection.transaction import TransactionType + +def parser_exit_override(a, b): + raise CommandParsingError(a, b) + +class SubCommandBase(abc.ABC): + + @classmethod + @abc.abstractmethod + def execute(cls, args): + raise NotImplementedError("abstract") + + @classmethod + def get_parser(cls): + raise NotImplementedError("abstract") + + @classmethod + def help(cls): + cls.get_parser().print_help() + + @classmethod + def name(cls): + return str(cls.get_parser().prog) + +class Connect(SubCommandBase): + _PARSER = None + @classmethod + def get_parser(cls): + if cls._PARSER is None: + parser = argparse.ArgumentParser( + prog='connect', + description='Establishes the connection to TypeDB' + ) + parser.exit = parser_exit_override + parser.add_argument("action", choices=["open", "close"]) + parser.add_argument("kind", choices=["core", "cluster"]) + parser.add_argument("address", default="127.0.0.1:1729") + parser.add_argument("username", default = "admin") + parser.add_argument("password", default = "password") + cls._PARSER = parser + return cls._PARSER + + @classmethod + def execute(cls, args): + from typedb.driver import TypeDB + from typedb.api.connection.credentials import Credentials + from typedb_jupyter.connection import Connection + + cmd = cls.get_parser().parse_args(args) + if cmd.action == "open": + driver = TypeDB.cloud_driver if cmd.kind == "cluster" else TypeDB.core_driver + credential = Credentials(cmd.username, cmd.password) + Connection.open(driver, cmd.address, credential) + elif cmd.action == "close": + Connection.close() + else: + raise NotImplementedError("Unimplemented for action: ", cmd.action) + + + +class Database(SubCommandBase): + _PARSER = None + @classmethod + def get_parser(cls): + if cls._PARSER is None: + parser = argparse.ArgumentParser( + prog='database', + description='Database management' + ) + parser.exit = parser_exit_override + parser.add_argument("action", choices=["create", "recreate", "list", "delete", "schema"]) + parser.add_argument("name", nargs='?') + cls._PARSER = parser + return cls._PARSER + + @classmethod + def execute(cls, args): + from typedb_jupyter.connection import Connection + + cmd = cls.get_parser().parse_args(args) + + driver = Connection.get().driver + if cmd.action == "create": + driver.databases.create(cmd.name) + print("Created database ", cmd.name) + elif cmd.action == "recreate": + if driver.databases.contains(cmd.name): + driver.databases.get(cmd.name).delete() + driver.databases.create(cmd.name) + print("Recreated database ", cmd.name) + elif cmd.action == "delete": + driver.databases.get(cmd.name).delete() + print("Deleted database ", cmd.name) + elif cmd.action == "list": + print("Databases: ", ", ".join(map(lambda db: db.name, driver.databases.all()))) + elif cmd.action == "schema": + db = driver.databases.get(cmd.name) + print("Schema for database: ", db.name) + print(db.schema()) + else: + raise NotImplementedError("Unimplemented for action: ", cmd.action) + + +class Transaction(SubCommandBase): + _PARSER = None + @classmethod + def get_parser(cls): + if cls._PARSER is None: + parser = argparse.ArgumentParser( + prog='transaction', + description='Opens or closes a transaction to a database on the active connection' + ) + parser.exit = parser_exit_override + parser.add_argument("action", choices=["open", "close", "commit", "rollback"]) + parser.add_argument("database", nargs='?', help="Only for 'open'") + parser.add_argument("tx_type", nargs='?', choices=["schema", "write", "read"], help="Only for 'open'") + cls._PARSER = parser + return cls._PARSER + + TX_TYPE_MAP = { + "schema": TransactionType.SCHEMA, + "write": TransactionType.WRITE, + "read": TransactionType.READ, + } + + @classmethod + def execute(cls, args): + from typedb_jupyter.connection import Connection + + cmd = cls.get_parser().parse_args(args) + + connection = Connection.get() + if cmd.action == "open": + if cmd.database is None or cmd.tx_type is None: + raise ArgumentError("transaction open database tx_type") + connection.open_transaction(cmd.database, cls.TX_TYPE_MAP[cmd.tx_type]) + print("Opened {} transaction on database '{}' ".format(cmd.tx_type, cmd.database)) + elif cmd.action == "close": + connection.close_transaction() + print("Transaction closed") + elif cmd.action == "commit": + connection.commit_transaction() + print("Transaction committed") + elif cmd.action == "rollback": + connection.rollback_transaction() + print("Transaction rolled back") + else: + raise NotImplementedError("Unimplemented for action: ", cmd.action) + +class Help(SubCommandBase): + _PARSER = None + + @classmethod + def get_parser(cls): + if cls._PARSER is None: + parser = argparse.ArgumentParser( + prog='help', + description='Shows this help description' + ) + parser.exit = parser_exit_override + cls._PARSER = parser + return cls._PARSER + + @classmethod + def execute(cls, args): + print("Available commands:", ", ".join(AVAILABLE_COMMANDS.keys())) + if not (len(args) > 0 and args[0] == "short"): + print("TODO: Print subcommand help") + + +AVAILABLE_COMMANDS = { + Connect.name() : Connect, + Database.name() : Database, + Transaction.name(): Transaction, + Help.name(): Help, +} From 0cd715e2ea4f88a1c195347dbdb8475648501cf3 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Sun, 26 Jan 2025 17:51:03 +0530 Subject: [PATCH 06/27] Implement printing --- src/Sample.ipynb | 184 ++++++++++++++++++++++---------- src/typedb_jupyter/display.py | 44 ++++++++ src/typedb_jupyter/exception.py | 7 ++ src/typedb_jupyter/magic.py | 49 ++------- 4 files changed, 188 insertions(+), 96 deletions(-) create mode 100644 src/typedb_jupyter/display.py diff --git a/src/Sample.ipynb b/src/Sample.ipynb index 706eee6..b8612f4 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "994ca437-cbbc-4ac5-a953-a44e196a9512", "metadata": {}, "outputs": [ @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "1ef0b8de-4e09-4588-bea7-f67fce0bfe95", "metadata": {}, "outputs": [ @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "2775578e-cbe6-498d-82c6-18d8d4c4f0c0", "metadata": {}, "outputs": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "6415f8cf-34e7-42b8-9294-0113c584fc33", "metadata": {}, "outputs": [ @@ -103,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "85c80d7e-566b-4b92-9704-e27f68a58919", "metadata": {}, "outputs": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "be17ff6a-8020-43a4-9611-2f5def7bab0d", "metadata": {}, "outputs": [ @@ -139,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "0cd600f3-6f6d-4b03-b3ec-fcf9b593e4b0", "metadata": {}, "outputs": [ @@ -157,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "7b81db96-ae1a-43b3-a920-6e7443e34aeb", "metadata": {}, "outputs": [], @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "c1260950-fae9-4ef7-9940-ff963a2ab53a", "metadata": {}, "outputs": [ @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "bfeae364-194b-4478-b02d-2b29c1ede228", "metadata": {}, "outputs": [ @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "d3a65846-4d15-4376-bfb1-c3c921355aab", "metadata": {}, "outputs": [ @@ -213,6 +213,16 @@ "text": [ "Query completed successfully! (No results to show)\n" ] + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -224,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", "metadata": {}, "outputs": [ @@ -242,155 +252,213 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", + "execution_count": 15, + "id": "2385b0db-a4b5-4b5e-b734-64ccc473780a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Opened read transaction on database 'test_jupyter' \n" + "Opened write transaction on database 'test_jupyter' \n" ] } ], "source": [ - "%typedb transaction open test_jupyter read " + "%typedb transaction open test_jupyter write" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "1552def1", + "execution_count": 16, + "id": "d3a6084f-0bda-4985-a6a5-be1e93d138be", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "TODO: Print rows\n" + "Query returned 1 rows.\n" ] }, + { + "data": { + "text/html": [ + "
p
Entity(person: 0x1e00000000000000000000)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ - "[| $attribute_type: AttributeType(name) | $owner_type: EntityType(person) |]" + "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "%%typeql \n", - "match $owner_type owns $attribute_type;\n" + "%%typeql\n", + "insert \n", + "$p isa person, has name \"James\";" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ea614f7f-c26f-4147-afb1-e1b0545744a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], + "source": [ + "%typedb transaction commit" ] }, { "cell_type": "code", "execution_count": 18, - "id": "8c9ff85b", + "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "UsageError: unrecognized arguments: -o typeql\n" + "Opened read transaction on database 'test_jupyter' \n" ] } ], "source": [ - "%%typeql -o typeql \n", - "insert $p isa person, has name \"James\";" + "%typedb transaction open test_jupyter read " ] }, { "cell_type": "code", - "execution_count": 7, - "id": "e32715b5", + "execution_count": 19, + "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Connection: test_1 (jupyter-test@127.0.0.1:1729)\n", - "Session: data\n", - "Transaction: read\n", - "Query: match\n", - "Inference: off\n" + "Query returned 2 rows.\n" ] }, + { + "data": { + "text/html": [ + "
ownerowner_type
Entity(person: 0x1e00000000000000000000)EntityType(person)
Attribute(name: \"James\")AttributeType(name)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "text/plain": [ - "'$a_1 isa name;\\n$a_1 \"James\";\\n$p_1 isa person;\\n$p_1 has $a_1;\\n$p_2 isa person;\\n$p_2 has $a_1;\\n$p_3 isa person;\\n$p_3 has $a_1;'" + "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 7, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "%%typeql -o typeql\n", - "match $p has $a; get;" + "%%typeql \n", + "match $owner isa! $owner_type;" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "b649db13", + "execution_count": 20, + "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Connection: test_1 (jupyter-test@127.0.0.1:1729)\n", - "Session: data\n", - "Transaction: read\n", - "Query: match\n", - "Inference: off\n", - "Returning data to local variable: 'myvar'\n" + "[| $owner: Entity(person: 0x1e00000000000000000000) | $owner_type: EntityType(person) |, | $owner: Attribute(name: \"James\") | $owner_type: AttributeType(name) |]\n" ] } ], "source": [ - "%%typeql -r myvar\n", - "match $p has $a; get;" + "print(_typeql_result)" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "3f64a613", + "execution_count": 21, + "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 1 documents.\n", + "{\n", + " \"attributes\": {\n", + " \"name\": \"James\"\n", + " }\n", + "}\n" + ] + }, { "data": { "text/plain": [ - "'ConceptMap { map: {\"p\": Entity(Entity { iid: ID[0x826e80018000000000000000], type_: EntityType { label: \"person\", is_root: false, is_abstract: false }, is_inferred: false }), \"a\": Attribute(Attribute { iid: ID[0x836f80012800054a616d6573], type_: AttributeType { label: \"name\", is_root: false, is_abstract: false, value_type: String }, value: String(\"James\"), is_inferred: false })}, explainables: Explainables { relations: {}, attributes: {}, ownerships: {} } }'" + "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 5, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "myvar[0]" + "%%typeql \n", + "match $owner isa! $owner_type; entity $owner_type;\n", + "fetch {\n", + " \"attributes\": { $owner.* }\n", + "};" ] }, { "cell_type": "code", - "execution_count": null, - "id": "b61bc157", + "execution_count": 22, + "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction closed\n" + ] + } + ], + "source": [ + "%typedb transaction close" + ] } ], "metadata": { diff --git a/src/typedb_jupyter/display.py b/src/typedb_jupyter/display.py new file mode 100644 index 0000000..838c2c4 --- /dev/null +++ b/src/typedb_jupyter/display.py @@ -0,0 +1,44 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +def print_rows(rows): + if len(rows) == 0: + print("Query returned an empty set of rows.") + else: + from IPython.display import HTML, display + print("Query returned {} rows.".format(len(rows))) + headers = list(rows[0].column_names()) + display(HTML( + '{}
{}
'.format( + ''.join(str(_) for _ in headers), + ''.join( + '{}'.format(''.join(str(_) for _ in row.concepts())) for row in rows) + ) + )) + +def print_documents(documents): + if len(documents) == 0: + print("Query returned an empty set of documents.") + else: + from json import dumps + print("Query returned {} documents.".format(len(documents))) + for document in documents: + print(dumps(document, indent=2)) \ No newline at end of file diff --git a/src/typedb_jupyter/exception.py b/src/typedb_jupyter/exception.py index 7875039..0d2c60e 100644 --- a/src/typedb_jupyter/exception.py +++ b/src/typedb_jupyter/exception.py @@ -36,3 +36,10 @@ def __init__(self, what, msg): self.what = what self.msg = msg +def is_typedb_jupyter_exception(err): + return ( + isinstance(err, ArgumentError) or + isinstance(err, ConnectionError) or + isinstance(err, CommandParsingError) or + isinstance(err, QueryParsingError) + ) diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index 67fa1e3..286ad01 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -29,31 +29,6 @@ import typedb_jupyter.subcommands as subcommands -def substitute_vars(query, local_ns): - try: - query_vars = "".join(query.split("\"")[::2]).replace("{", "}").split("}")[1::2] - except IndexError: - return query - - for var in query_vars: - if var.strip()[-1] == ";": - continue - - try: - val = local_ns[var] - except KeyError: - raise QueryParsingError("No variable found in local namespace with name: {}".format(var)) - - if type(val) is str: - val = "\"{}\"".format(val.replace("\"", "'")) - else: - val = str(val) - - query = query.replace("{" + var + "}", val) - - return query - - @magics_class class TypeDBMagic(Magics, Configurable): create_database = Bool( @@ -108,6 +83,8 @@ class TypeQLMagic(Magics, Configurable): help="Enable rule inference for all queries. Can be overridden per query with -i." ) + QUERY_RESULT_VARIABLE = "_typeql_result" + @needs_local_scope @cell_magic("typeql") @magic_arguments() @@ -117,7 +94,6 @@ def execute(self, line="", cell="", local_ns=None): args = parse_argstring(self.execute, line) query = cell - query = substitute_vars(query, local_ns) # Save globals and locals, so they can be referenced in bind vars user_ns = self.shell.user_ns.copy() @@ -129,8 +105,10 @@ def execute(self, line="", cell="", local_ns=None): connection = Connection.get() tx = connection.get_active_transaction() answer_type, answer = self._run_query(tx, query) - self._print_answer(answer_type, answer) - return answer + self._print_answers(answer_type, answer) + + self.shell.user_ns.update({self.QUERY_RESULT_VARIABLE: answer}) + return "Stored result in variable: {}".format(self.QUERY_RESULT_VARIABLE) def __init__(self, shell): Configurable.__init__(self, config=shell.config) @@ -147,28 +125,23 @@ def _run_query(self, transaction, query): if answer.is_concept_rows(): return (ConceptRowIterator, list(answer.as_concept_rows())) elif answer.is_concept_documents(): - return (ConceptDocumentIterator, list(answer.as_concept_documents)) + return (ConceptDocumentIterator, list(answer.as_concept_documents())) elif answer.is_ok(): return (OkQueryAnswer, None) else: raise NotImplementedError("Unhandled answer type") - def _print_answer(self, answer_type, answer): + def _print_answers(self, answer_type, answer): from typedb.concept.answer.concept_row_iterator import ConceptRowIterator from typedb.concept.answer.concept_document_iterator import ConceptDocumentIterator from typedb.concept.answer.ok_query_answer import OkQueryAnswer + from typedb_jupyter.display import print_rows, print_documents if answer_type == OkQueryAnswer: print("Query completed successfully! (No results to show)") elif answer_type == ConceptDocumentIterator: - self._print_documents(answer) + print_documents(answer) elif answer_type == ConceptRowIterator: - self._print_rows(answer) + print_rows(answer) else: raise NotImplementedError("Unhandled answer type") - - def _print_documents(self, documents): - print("TODO: Print documents") - - def _print_rows(self, rows): - print("TODO: Print rows") From 86968fc88a25792cb5f874f3664f0514cb39347c Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Sun, 26 Jan 2025 23:08:45 +0530 Subject: [PATCH 07/27] WIP: IR --- src/Sample.ipynb | 222 +++++----------------- src/__init__.py | 0 src/typedb_jupyter/magic.py | 2 +- src/typedb_jupyter/utils/__init__.py | 0 src/typedb_jupyter/{ => utils}/display.py | 2 +- src/typedb_jupyter/utils/graph.py | 0 src/typedb_jupyter/utils/ir.py | 128 +++++++++++++ src/typedb_jupyter/utils/parser.py | 188 ++++++++++++++++++ 8 files changed, 362 insertions(+), 180 deletions(-) create mode 100644 src/__init__.py create mode 100644 src/typedb_jupyter/utils/__init__.py rename src/typedb_jupyter/{ => utils}/display.py (97%) create mode 100644 src/typedb_jupyter/utils/graph.py create mode 100644 src/typedb_jupyter/utils/ir.py create mode 100644 src/typedb_jupyter/utils/parser.py diff --git a/src/Sample.ipynb b/src/Sample.ipynb index b8612f4..35f9f4f 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -57,12 +57,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Created database test_jupyter\n" + "Created database typedb_jupyter_sample\n" ] } ], "source": [ - "%typedb database create test_jupyter" + "%typedb database create typedb_jupyter_sample" ] }, { @@ -75,7 +75,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" + "Databases: typedb_jupyter_sample, moo, test_jupyter, jupyter-test, typedb-iam\n" ] } ], @@ -93,12 +93,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Deleted database test_jupyter\n" + "Deleted database typedb_jupyter_sample\n" ] } ], "source": [ - "%typedb database delete test_jupyter" + "%typedb database delete typedb_jupyter_sample" ] }, { @@ -111,7 +111,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: moo, jupyter-test, typedb-iam\n" + "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" ] } ], @@ -129,12 +129,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Recreated database test_jupyter\n" + "Recreated database typedb_jupyter_sample\n" ] } ], "source": [ - "%typedb database recreate test_jupyter" + "%typedb database recreate typedb_jupyter_sample" ] }, { @@ -147,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" + "Databases: typedb_jupyter_sample, moo, test_jupyter, jupyter-test, typedb-iam\n" ] } ], @@ -175,12 +175,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Recreated database test_jupyter\n" + "Recreated database typedb_jupyter_sample\n" ] } ], "source": [ - "%typedb database recreate test_jupyter" + "%typedb database recreate typedb_jupyter_sample" ] }, { @@ -193,12 +193,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "Opened schema transaction on database 'test_jupyter' \n" + "Opened schema transaction on database 'typedb_jupyter_sample' \n" ] } ], "source": [ - "%typedb transaction open test_jupyter schema" + "%typedb transaction open typedb_jupyter_sample schema" ] }, { @@ -208,21 +208,18 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query completed successfully! (No results to show)\n" + "ename": "SyntaxError", + "evalue": "expected '(' (display.py, line 47)", + "output_type": "error", + "traceback": [ + "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", + "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3577\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n", + "\u001b[0m Cell \u001b[1;32mIn[13], line 1\u001b[0m\n get_ipython().run_cell_magic('typeql', '', 'define\\n attribute name, value string;\\n entity person, owns name;\\n')\u001b[0m\n", + "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2541\u001b[0m in \u001b[1;35mrun_cell_magic\u001b[0m\n result = fn(*args, **kwargs)\u001b[0m\n", + "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/typedb_jupyter/magic.py:108\u001b[0m in \u001b[1;35mexecute\u001b[0m\n self._print_answers(answer_type, answer)\u001b[0m\n", + "\u001b[0;36m File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/magic.py:139\u001b[0;36m in \u001b[0;35m_print_answers\u001b[0;36m\n\u001b[0;31m from typedb_jupyter.display import print_rows, print_documents\u001b[0;36m\n", + "\u001b[0;36m File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/display.py:47\u001b[0;36m\u001b[0m\n\u001b[0;31m def extract_\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected '('\n" ] - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -234,76 +231,30 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transaction committed\n" - ] - } - ], + "outputs": [], "source": [ "%typedb transaction commit" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "2385b0db-a4b5-4b5e-b734-64ccc473780a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opened write transaction on database 'test_jupyter' \n" - ] - } - ], + "outputs": [], "source": [ - "%typedb transaction open test_jupyter write" + "%typedb transaction open typedb_jupyter_sample write" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "d3a6084f-0bda-4985-a6a5-be1e93d138be", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query returned 1 rows.\n" - ] - }, - { - "data": { - "text/html": [ - "
p
Entity(person: 0x1e00000000000000000000)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%typeql\n", "insert \n", @@ -312,76 +263,30 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "ea614f7f-c26f-4147-afb1-e1b0545744a3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transaction committed\n" - ] - } - ], + "outputs": [], "source": [ "%typedb transaction commit" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Opened read transaction on database 'test_jupyter' \n" - ] - } - ], + "outputs": [], "source": [ - "%typedb transaction open test_jupyter read " + "%typedb transaction open typedb_jupyter_sample read " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query returned 2 rows.\n" - ] - }, - { - "data": { - "text/html": [ - "
ownerowner_type
Entity(person: 0x1e00000000000000000000)EntityType(person)
Attribute(name: \"James\")AttributeType(name)
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%typeql \n", "match $owner isa! $owner_type;" @@ -389,51 +294,20 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[| $owner: Entity(person: 0x1e00000000000000000000) | $owner_type: EntityType(person) |, | $owner: Attribute(name: \"James\") | $owner_type: AttributeType(name) |]\n" - ] - } - ], + "outputs": [], "source": [ "print(_typeql_result)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Query returned 1 documents.\n", - "{\n", - " \"attributes\": {\n", - " \"name\": \"James\"\n", - " }\n", - "}\n" - ] - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "%%typeql \n", "match $owner isa! $owner_type; entity $owner_type;\n", @@ -444,18 +318,10 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Transaction closed\n" - ] - } - ], + "outputs": [], "source": [ "%typedb transaction close" ] diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index 286ad01..2e6405e 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -136,7 +136,7 @@ def _print_answers(self, answer_type, answer): from typedb.concept.answer.concept_row_iterator import ConceptRowIterator from typedb.concept.answer.concept_document_iterator import ConceptDocumentIterator from typedb.concept.answer.ok_query_answer import OkQueryAnswer - from typedb_jupyter.display import print_rows, print_documents + from typedb_jupyter.utils.display import print_rows, print_documents if answer_type == OkQueryAnswer: print("Query completed successfully! (No results to show)") elif answer_type == ConceptDocumentIterator: diff --git a/src/typedb_jupyter/utils/__init__.py b/src/typedb_jupyter/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/typedb_jupyter/display.py b/src/typedb_jupyter/utils/display.py similarity index 97% rename from src/typedb_jupyter/display.py rename to src/typedb_jupyter/utils/display.py index 838c2c4..796afc2 100644 --- a/src/typedb_jupyter/display.py +++ b/src/typedb_jupyter/utils/display.py @@ -41,4 +41,4 @@ def print_documents(documents): from json import dumps print("Query returned {} documents.".format(len(documents))) for document in documents: - print(dumps(document, indent=2)) \ No newline at end of file + print(dumps(document, indent=2)) diff --git a/src/typedb_jupyter/utils/graph.py b/src/typedb_jupyter/utils/graph.py new file mode 100644 index 0000000..e69de29 diff --git a/src/typedb_jupyter/utils/ir.py b/src/typedb_jupyter/utils/ir.py new file mode 100644 index 0000000..56247b6 --- /dev/null +++ b/src/typedb_jupyter/utils/ir.py @@ -0,0 +1,128 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# +class Label: + def __init__(self, name): + self.name = name + + def __str__(self): + return self.name + +class Var: + _INTERNAL = 0 + def __init__(self, name): + self.name = name + + @classmethod + def next_internal(cls): + cls._INTERNAL += 1 + return "$INTERNAL__{}".format(cls._INTERNAL) + + def __str__(self): + return self.name + +class Literal: + def __init__(self, value): + self.value = value + + def __str__(self): + return self.value + +class Comparator: + def __init__(self, symbol): + self.symbol = symbol + + def __str__(self): + return self.symbol + +# Constraints + +class Constraint: + def may_set_lhs(self, lhs: Var): + pass + +class BinaryConstraint(Constraint): + def __init__(self, lhs:Var, rhs:Var): + self.lhs = lhs + self.rhs = rhs + + def may_set_lhs(self, lhs: Var): + assert self.lhs is None + self.lhs = lhs + + def __str__(self): + return "{}({}, {})".format(self.__class__.__name__, self.lhs, self.rhs) + +class Isa(BinaryConstraint): + def __init__(self, lhs: Var, rhs: Var): + super().__init__(lhs, rhs) + +class Has(BinaryConstraint): + def __init__(self, lhs: Var, rhs: Var): + super().__init__(lhs, rhs) + +class Links(BinaryConstraint): + def __init__(self, lhs: Var, rhs: Var, role): # role would ideally be Var, but we don't have a rolename keyword + super().__init__(lhs, rhs) + self.role = role + + +# TODO: Deprecate +class IsaType(Constraint): + def __init__(self, lhs: Var, rhs: Label): + self.lhs = lhs + self.rhs = rhs + + def __str__(self): + return "{}({}, {})".format(self.__class__.__name__, self.lhs, self.rhs) + + def may_set_lhs(self, lhs: Var): + if self.lhs is None: + self.lhs = lhs + +class AttributeLabelValue(Constraint): + def __init__(self, lhs: Var, label: Label, value: Literal): + self.lhs = lhs + self.label = label + self.value = value + + def __str__(self): + return "{}({}, {}, {})".format(self.__class__.__name__, self.lhs, self.label, self.value) + +class Comparison(Constraint): + def __init__(self, lhs: Var, rhs: Label, comparator): + self.lhs = lhs + self.rhs = rhs + self.comparator = comparator + + def __str__(self): + return "{}({}, {}, {})".format(self.__class__.__name__, self.lhs, self.comparator, self.rhs) + + +class Assign(Constraint): # Not sub edge + # Treat RHS as black box. + def __init__(self, lhs: Var, rhs): + self.assigned = lhs + self.expr = rhs + + def __str__(self): + return "{}({}, {})".format(self.__class__.__name__, self.lhs, self.rhs) + +# TODO: Add schema edges diff --git a/src/typedb_jupyter/utils/parser.py b/src/typedb_jupyter/utils/parser.py new file mode 100644 index 0000000..26acab1 --- /dev/null +++ b/src/typedb_jupyter/utils/parser.py @@ -0,0 +1,188 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + + +from parsimonious.grammar import Grammar +from parsimonious.nodes import Node, NodeVisitor + +from ir import Var, Label, Literal, Comparator, \ + Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign + +class Match: + def __init__(self, constraints): + self.constraints = constraints + + + def __str__(self): + return "Match(%s)"%(", ".join(str(c) for c in self.constraints)) + + +def flatten(l): + flat = [] + for sl in l: + if isinstance(sl, list): + flat = flat + flatten(sl) + else: + flat.append(sl) + return flat + + +def non_null(l): + return [e for e in l if e is not None] + +class TypeQLVisitor(NodeVisitor): + GRAMMAR = Grammar(""" + query = ws match_clause ws + + match_clause = "match" ws (pattern ";" ws)+ + + pattern = native / assign / comparison + assign = "TODO" + + comparison = (var/literal) ws comparator ws (var/literal) ws + comparator = "=" / ">" / ">=" / "<" / "<=" / "!=" / "like" / "contains" + + native = var ws constraint ws ( "," ws constraint ws)* + constraint = has_labelled / has / links / isa + + has_labelled = "has" ws label ws (var / literal) + has = "has" ws var + + links = "links" ws "(" ws role_player ws ( "," ws constraint ws)* ")" + isa = "isa" ws (label/var) + + role_player = (var/label) ws ":" ws var + + label = ~"[A-Za-z0-9_\-]+" + identifier = ~"[A-Za-z0-9_\-]+" + var = ~"\$[A-Za-z0-9_\-]+" + literal = (integer_literal / string_literal) + integer_literal = ~"[0-9]+" + string_literal = ~'"[^\"]+"' + ignored = ~"[^']+" + ws = ~"\s*" + """) + + def visit_ws(self, node:Node, visited_children): + return + + def visit_var(self, node:Node, visited_children): + return Var(node.text) + + def visit_label(self, node:Node, visited_children): + return Label(node.text) + + def visit_identifier(self, node:Node, visited_children): + return node.text + + def visit_literal(self, node:Node, visited_children): + return Literal(node.text) + + def visit_query(self, node:Node, visited_children): + parts = tuple(v for v in flatten(visited_children)) + # assert len(parts) == 2 + return parts + + def visit_match_clause(self, node:Node, visited_children): + return Match(non_null(flatten(visited_children))) + + + def visit_pattern(self, node:Node, visited_children): + return flatten(non_null(visited_children)) # TODO: Try removing non_null + + def visit_assign(self, node: Node, visited_children): + return non_null(visited_children)[0] + + def visit_native(self, node:Node, visited_children): + children = non_null(flatten(visited_children)) + edges = [] + u = children[0] + # print("U was ", u) + for constraint in children[1:]: + constraint.may_set_lhs(u) + # print("Child is", child) + edges.append(constraint) + return edges + + def visit_constraint(self, node: Node, visited_children): + assert len(visited_children) == 1 + return visited_children[0] + + def visit_has_labelled(self, node: Node, visited_children): + [label, rhs] = non_null(flatten(visited_children)) + if isinstance(rhs, Var): + attr_var = rhs + return [Has(None, attr_var), IsaType(attr_var, label)] + else: + assert isinstance(rhs, Literal) + attr_var = Var.next_internal() + return [Has(None, attr_var), AttributeLabelValue(attr_var, label, rhs)] + + def visit_links(self, node: Node, visited_children): + return non_null(visited_children) + + def visit_role_player(self, node: Node, visited_children): + [role, player] = non_null(flatten(visited_children)) + print((role, player)) + if isinstance(role, Var): + return [Links(None, player, role)] + else: + assert isinstance(role, Label) + return [Links(None, player, role)] + + def visit_has(self, node: Node, visited_children): + return [Has(None, visited_children[0])] + + def visit_isa(self, node: Node, visited_children): + [label_or_var] = non_null(flatten(visited_children)) + if isinstance(label_or_var, Label): + return [IsaType(None, label_or_var)] + else: + assert isinstance(label_or_var, Var) + return [Isa(None, label_or_var)] + + def visit_comparison(self, node: Node, visited_children): + [lhs, comparator, rhs] = non_null(flatten(visited_children)) + return [Comparison(lhs, rhs, comparator)] + + def visit_comparator(self, node: Node, visited_children): + return [Comparator(node.text)] + + def generic_visit(self, node:Node, visited_children): + """ The generic visit method. """ + # print("Generic visit for ", node) + return visited_children or None + + +input = """ +match +$x isa cow, has name "Spider Georg"; +$y isa cow, has name "Spider Georg"; +$z isa marriage, links (man: $x); +""" + +tree = TypeQLVisitor.GRAMMAR.parse(input) +# print(tree) + +print("=====") +visitor = TypeQLVisitor() +visited = visitor.visit(tree) +print("\n----\n".join((str(v) for v in visited ))) From 9f2a9501b93850d87bf37699b5091cc5fb2e298d Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Mon, 27 Jan 2025 13:11:47 +0530 Subject: [PATCH 08/27] WIP: graphs --- pyproject.toml | 1 + .../{utils/graph.py => graph/__init__.py} | 0 src/typedb_jupyter/graph/answer.py | 117 ++++++++++++++++++ src/typedb_jupyter/graph/query.py | 62 ++++++++++ src/typedb_jupyter/utils/display.py | 4 + src/typedb_jupyter/utils/ir.py | 3 +- src/typedb_jupyter/utils/parser.py | 29 +++-- 7 files changed, 200 insertions(+), 16 deletions(-) rename src/typedb_jupyter/{utils/graph.py => graph/__init__.py} (100%) create mode 100644 src/typedb_jupyter/graph/answer.py create mode 100644 src/typedb_jupyter/graph/query.py diff --git a/pyproject.toml b/pyproject.toml index 4b0db3b..46eea2e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,6 +23,7 @@ classifiers = [ ] dependencies = [ "typedb-driver~=3.0.2", + "netgraph~=4.13.2", "ipython" ] diff --git a/src/typedb_jupyter/utils/graph.py b/src/typedb_jupyter/graph/__init__.py similarity index 100% rename from src/typedb_jupyter/utils/graph.py rename to src/typedb_jupyter/graph/__init__.py diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py new file mode 100644 index 0000000..ed10db4 --- /dev/null +++ b/src/typedb_jupyter/graph/answer.py @@ -0,0 +1,117 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from abc import abstractmethod + +class AnswerVertex: + + @classmethod + @abstractmethod + def shape(cls): + return cls._SHAPE + + @classmethod + @abstractmethod + def color(cls): + return cls._COLOR + + @abstractmethod + def label(self): + raise NotImplementedError("abstract") + +class RelationVertex(AnswerVertex): + _SHAPE = "o" + def __init__(self, relation): + self.relation = relation + + def label(self): + return "TODO_RELATION" + + +class EntityVertex(AnswerVertex): + def __init__(self, entity): + self.entity = entity + + def label(self): + return "TODO_ENTITY" + + +class AttributeVertex(AnswerVertex): + def __init__(self, attribute): + self.attribute = attribute + + def label(self): + return "TODO_ATTRIBUTE" + +class AnswerEdge: + def __init__(self, left: AnswerVertex, right: AnswerVertex): + self.left = left + self.right = right + + @abstractmethod + def label(self): + raise NotImplementedError("abstract") + +class HasEdge(AnswerEdge): + def label(self): + return "has" + + +class LinksEdge(AnswerEdge): + def __init__(self, left: AnswerVertex, right: AnswerVertex, role): + super().__init__(left, right) + self.role = role + def label(self): + return str(self.role) # TODO + +class AnswerGraphBuilder: + def __init__(self, query_graph): + self.edges = [] + self.query_graph = AnswerGraphBuilder._filter_visualisable_edges(query_graph) + self.answer_edges = [] + self.edge_labels = [] + + @classmethod + def _filter_visualisable_edges(cls, query_graph): + query_graph # TODO + + def add_answer_row(self, row): + for query_edge in self.query_graph: + edge = query_edge.get_answer_edges(row) + self.answer_edges.append((edge.left, edge.right)) + self.edge_labels.append(edge.label) + + def draw(self): + from netgraph import InteractiveGraph + # TODO: derive node_shape, node_labels, node_colors from from edge.left & edge.right + plot_instance = InteractiveGraph(self.answer_edges) + plt.show() + + +if __name__ == "__main__": + import matplotlib.pyplot as plt + from netgraph import Graph, InteractiveGraph, EditableGraph + graph_data = [("a", "b"), ("b", "c")] + # Graph(graph_data) + # plt.show() + node_shapes = { "a" : "o", "b" : "s", "c": "o"} + plot_instance = InteractiveGraph(graph_data, node_shape=node_shapes) + plt.show() diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py new file mode 100644 index 0000000..1a65f03 --- /dev/null +++ b/src/typedb_jupyter/graph/query.py @@ -0,0 +1,62 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +from ir import Var, Label, Literal, Comparator, \ + Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign +from abc import abstractmethod + + +class QueryGraphEdge: + def __init__(self, left:Var, right:Var): + self.left = left + self.right = right + + @abstractmethod + def label(self): + raise NotImplementedError("abstract") + + @abstractmethod + def left_shape(self): + raise NotImplementedError("abstract") + + @abstractmethod + def right_shape(self): + raise NotImplementedError("abstract") + + + def get_answer_edges(self, row): + return () + +class QueryHasEdge(QueryGraphEdge): + def label(self): + return "has" + + def shape(self): + +def lazy_query_graph(constraints): + graph = [] + for c in constraints: + if isinstance(c, Has): + graph.append(QueryGraphEdge(c.lhs, "has", c.rhs)) + elif isinstance(c, Links): + graph.append(QueryGraphEdge(c.lhs, c.role, c.rhs)) + return graph + diff --git a/src/typedb_jupyter/utils/display.py b/src/typedb_jupyter/utils/display.py index 796afc2..2763597 100644 --- a/src/typedb_jupyter/utils/display.py +++ b/src/typedb_jupyter/utils/display.py @@ -42,3 +42,7 @@ def print_documents(documents): print("Query returned {} documents.".format(len(documents))) for document in documents: print(dumps(document, indent=2)) + +def display_graph(graph): + for edge in graph: + \ No newline at end of file diff --git a/src/typedb_jupyter/utils/ir.py b/src/typedb_jupyter/utils/ir.py index 56247b6..84d058b 100644 --- a/src/typedb_jupyter/utils/ir.py +++ b/src/typedb_jupyter/utils/ir.py @@ -79,7 +79,8 @@ def __init__(self, lhs: Var, rhs: Var): super().__init__(lhs, rhs) class Links(BinaryConstraint): - def __init__(self, lhs: Var, rhs: Var, role): # role would ideally be Var, but we don't have a rolename keyword + # TODO: role would ideally be Var, but we don't have a rolename keyword + def __init__(self, lhs: Var, rhs: Var, role): super().__init__(lhs, rhs) self.role = role diff --git a/src/typedb_jupyter/utils/parser.py b/src/typedb_jupyter/utils/parser.py index 26acab1..ce64f5e 100644 --- a/src/typedb_jupyter/utils/parser.py +++ b/src/typedb_jupyter/utils/parser.py @@ -171,18 +171,17 @@ def generic_visit(self, node:Node, visited_children): # print("Generic visit for ", node) return visited_children or None - -input = """ -match -$x isa cow, has name "Spider Georg"; -$y isa cow, has name "Spider Georg"; -$z isa marriage, links (man: $x); -""" - -tree = TypeQLVisitor.GRAMMAR.parse(input) -# print(tree) - -print("=====") -visitor = TypeQLVisitor() -visited = visitor.visit(tree) -print("\n----\n".join((str(v) for v in visited ))) +if __name__ == "__main__": + input = """ + match + $x isa cow, has name "Spider Georg"; + $y isa cow, has name "Spider Georg"; + $z isa marriage, links (man: $x); + """ + + tree = TypeQLVisitor.GRAMMAR.parse(input) + # print(tree) + # print("=====") + visitor = TypeQLVisitor() + visited = visitor.visit(tree) + print("\n----\n".join((str(v) for v in visited ))) From afebf41f724b54bd6db877f15f08c403d2aa516c Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 22:22:36 +0530 Subject: [PATCH 09/27] Towards graphs --- src/Sample.ipynb | 214 ++++++++++++++--- src/graphs.ipynb | 361 ++++++++++++++++++++++++++++ src/typedb_jupyter/graph/answer.py | 55 +++-- src/typedb_jupyter/graph/query.py | 56 +++-- src/typedb_jupyter/utils/display.py | 6 +- src/typedb_jupyter/utils/ir.py | 10 +- src/typedb_jupyter/utils/parser.py | 34 +-- 7 files changed, 637 insertions(+), 99 deletions(-) create mode 100644 src/graphs.ipynb diff --git a/src/Sample.ipynb b/src/Sample.ipynb index 35f9f4f..112d256 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -75,7 +75,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: typedb_jupyter_sample, moo, test_jupyter, jupyter-test, typedb-iam\n" + "Databases: test_jupyter, moo, typedb-iam, jupyter-test, typedb_jupyter_sample\n" ] } ], @@ -111,7 +111,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: moo, test_jupyter, jupyter-test, typedb-iam\n" + "Databases: test_jupyter, moo, typedb-iam, jupyter-test\n" ] } ], @@ -147,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: typedb_jupyter_sample, moo, test_jupyter, jupyter-test, typedb-iam\n" + "Databases: test_jupyter, moo, typedb-iam, jupyter-test, typedb_jupyter_sample\n" ] } ], @@ -208,18 +208,21 @@ "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "expected '(' (display.py, line 47)", - "output_type": "error", - "traceback": [ - "Traceback \u001b[0;36m(most recent call last)\u001b[0m:\n", - "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:3577\u001b[0m in \u001b[1;35mrun_code\u001b[0m\n exec(code_obj, self.user_global_ns, self.user_ns)\u001b[0m\n", - "\u001b[0m Cell \u001b[1;32mIn[13], line 1\u001b[0m\n get_ipython().run_cell_magic('typeql', '', 'define\\n attribute name, value string;\\n entity person, owns name;\\n')\u001b[0m\n", - "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2541\u001b[0m in \u001b[1;35mrun_cell_magic\u001b[0m\n result = fn(*args, **kwargs)\u001b[0m\n", - "\u001b[0m File \u001b[1;32m~/code/side/typedb-jupyter/src/typedb_jupyter/magic.py:108\u001b[0m in \u001b[1;35mexecute\u001b[0m\n self._print_answers(answer_type, answer)\u001b[0m\n", - "\u001b[0;36m File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/magic.py:139\u001b[0;36m in \u001b[0;35m_print_answers\u001b[0;36m\n\u001b[0;31m from typedb_jupyter.display import print_rows, print_documents\u001b[0;36m\n", - "\u001b[0;36m File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/display.py:47\u001b[0;36m\u001b[0m\n\u001b[0;31m def extract_\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected '('\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "Query completed successfully! (No results to show)\n" ] + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -231,30 +234,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], "source": [ "%typedb transaction commit" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "2385b0db-a4b5-4b5e-b734-64ccc473780a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened write transaction on database 'typedb_jupyter_sample' \n" + ] + } + ], "source": [ "%typedb transaction open typedb_jupyter_sample write" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "d3a6084f-0bda-4985-a6a5-be1e93d138be", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 1 rows.\n" + ] + }, + { + "data": { + "text/html": [ + "
p
Entity(person: 0x1e00000000000000000000)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%%typeql\n", "insert \n", @@ -263,51 +312,140 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "ea614f7f-c26f-4147-afb1-e1b0545744a3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], "source": [ "%typedb transaction commit" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened read transaction on database 'typedb_jupyter_sample' \n" + ] + } + ], "source": [ "%typedb transaction open typedb_jupyter_sample read " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 2 rows.\n" + ] + }, + { + "data": { + "text/html": [ + "
instanceinstance-type
Entity(person: 0x1e00000000000000000000)EntityType(person)
Attribute(name: \"James\")AttributeType(name)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%%typeql \n", - "match $owner isa! $owner_type;" + "match $instance isa! $instance-type;" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |, | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'_Attribute' object has no attribute 'iid'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(_typeql_result)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43m_typeql_result\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstance\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miid\u001b[49m())\n", + "\u001b[0;31mAttributeError\u001b[0m: '_Attribute' object has no attribute 'iid'" + ] + } + ], "source": [ - "print(_typeql_result)" + "print(_typeql_result)\n", + "print(_typeql_result[1].get(\"instance\"))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 1 documents.\n", + "{\n", + " \"attributes\": {\n", + " \"name\": \"James\"\n", + " }\n", + "}\n" + ] + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%%typeql \n", "match $owner isa! $owner_type; entity $owner_type;\n", @@ -318,10 +456,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction closed\n" + ] + } + ], "source": [ "%typedb transaction close" ] diff --git a/src/graphs.ipynb b/src/graphs.ipynb new file mode 100644 index 0000000..e96cd83 --- /dev/null +++ b/src/graphs.ipynb @@ -0,0 +1,361 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cf791e21-3eed-4b5d-a603-d993fe5c6b79", + "metadata": {}, + "outputs": [], + "source": [ + "%reload_ext typedb_jupyter" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "52cc65c9-5c72-4c96-b7f9-09cdb0fedd9f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened connection to: 127.0.0.1:1729\n" + ] + } + ], + "source": [ + "%typedb connect open core 127.0.0.1:1729 admin password" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "21a1762c-2dbc-42d8-a820-e80e1bfff9e5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Recreated database typedb_jupyter_graphs\n" + ] + } + ], + "source": [ + "%typedb database recreate typedb_jupyter_graphs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9db34e01-ffef-417d-b844-e058becd12e4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened schema transaction on database 'typedb_jupyter_graphs' \n" + ] + } + ], + "source": [ + "%typedb transaction open typedb_jupyter_graphs schema" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "18e296bd-a459-403b-b45e-2138a2a724c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query completed successfully! (No results to show)\n" + ] + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql\n", + "\n", + "define \n", + "attribute name, value string;\n", + "entity person, owns name @card(0..);\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9e62200e-9a4a-4c66-a3f1-84071c9c7317", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], + "source": [ + "%typedb transaction commit" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ddafea5a-6601-498c-9495-3568a0d2d85f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened write transaction on database 'typedb_jupyter_graphs' \n" + ] + } + ], + "source": [ + "%typedb transaction open typedb_jupyter_graphs write" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ef91ea81-f7e6-46f1-99e2-94713edde1c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 1 rows.\n" + ] + }, + { + "data": { + "text/html": [ + "
pp1p2
Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql\n", + "\n", + "insert \n", + "$p isa person, has name \"John\";\n", + "$p1 isa person, has name \"James\";\n", + "$p2 isa person, has name \"James\", has name \"Jimmy\";" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "be72af7a-ff3f-446a-9952-e1f203fc1fc0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction committed\n" + ] + } + ], + "source": [ + "%typedb transaction commit" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1e03723d-b915-4438-a188-9020f9315a33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened read transaction on database 'typedb_jupyter_graphs' \n" + ] + } + ], + "source": [ + "%typedb transaction open typedb_jupyter_graphs read" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0a51a712-56b2-40e6-9ec5-506641d4c1f2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 4 rows.\n" + ] + }, + { + "data": { + "text/html": [ + "
np
Attribute(name: \"John\")Entity(person: 0x1e00000000000000000000)
Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)
Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)
Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000002)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql\n", + "match $p isa person, has name $n;" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fa1e3c89-3901-4390-babc-2ec3ba3c0fe4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction closed\n" + ] + } + ], + "source": [ + "%typedb transaction close" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "12695159-169f-440f-bf85-4396cf0bf825", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Match(IsaType(p, person), Has(p, n), IsaType(n, name))\n" + ] + } + ], + "source": [ + "from typedb_jupyter.utils.parser import TypeQLVisitor\n", + "\n", + "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")\n", + "print(str(parsed))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "51cb2feb-1fa8-4b3c-9d27-94c65706dc2f", + "metadata": {}, + "outputs": [], + "source": [ + "from typedb_jupyter.graph.query import QueryGraph\n", + "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", + "\n", + "query_graph = QueryGraph(parsed)\n", + "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7965c6b7-84b3-4637-ad2b-2e818761dcb2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'HasEdge(Entity(person: 0x1e00000000000000000000)--has-->Attribute(name: \"John\"))\\nHasEdge(Entity(person: 0x1e00000000000000000001)--has-->Attribute(name: \"James\"))\\nHasEdge(Entity(person: 0x1e00000000000000000002)--has-->Attribute(name: \"James\"))\\nHasEdge(Entity(person: 0x1e00000000000000000002)--has-->Attribute(name: \"Jimmy\"))'" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "str(\"\\n\".join(map(str,answer_graph.edges)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b6da7a4-66f5-4233-9d5e-8855789a08e7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index ed10db4..e2c9b0c 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -21,6 +21,17 @@ from abc import abstractmethod +class AnswerGraph: + def __init__(self, edges): + self.edges = edges + + def draw(self): + from netgraph import InteractiveGraph + # TODO: derive edges, node_shape, node_labels, node_colors from from edge.lhs & edge.rhs + edges = [] + plot_instance = InteractiveGraph(edges) + plt.show() + class AnswerVertex: @classmethod @@ -62,49 +73,53 @@ def label(self): return "TODO_ATTRIBUTE" class AnswerEdge: - def __init__(self, left: AnswerVertex, right: AnswerVertex): - self.left = left - self.right = right + def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex): + self.lhs = lhs + self.rhs = rhs @abstractmethod def label(self): raise NotImplementedError("abstract") + def __str__(self): + return "{}--[{}]-->{}".format(self.lhs, self.label(), self.rhs) + class HasEdge(AnswerEdge): def label(self): return "has" class LinksEdge(AnswerEdge): - def __init__(self, left: AnswerVertex, right: AnswerVertex, role): - super().__init__(left, right) + def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex, role: AnswerVertex): + super().__init__(lhs, rhs) self.role = role + def label(self): return str(self.role) # TODO + class AnswerGraphBuilder: + def __init__(self, query_graph): + self.query_graph = query_graph self.edges = [] - self.query_graph = AnswerGraphBuilder._filter_visualisable_edges(query_graph) - self.answer_edges = [] - self.edge_labels = [] + + @classmethod + def build(cls, query_graph, answers): + relevant_edges = cls._filter_visualisable_edges(query_graph) + builder = AnswerGraphBuilder(query_graph) + for row in answers: + builder._add_answer_row(row) + return AnswerGraph(builder.edges) @classmethod def _filter_visualisable_edges(cls, query_graph): query_graph # TODO - def add_answer_row(self, row): - for query_edge in self.query_graph: - edge = query_edge.get_answer_edges(row) - self.answer_edges.append((edge.left, edge.right)) - self.edge_labels.append(edge.label) - - def draw(self): - from netgraph import InteractiveGraph - # TODO: derive node_shape, node_labels, node_colors from from edge.left & edge.right - plot_instance = InteractiveGraph(self.answer_edges) - plt.show() - + def _add_answer_row(self, row): + for query_edge in self.query_graph.edges: + edge = query_edge.get_answer_edge(row) + self.edges.append(edge) if __name__ == "__main__": import matplotlib.pyplot as plt diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py index 1a65f03..5bb1684 100644 --- a/src/typedb_jupyter/graph/query.py +++ b/src/typedb_jupyter/graph/query.py @@ -19,44 +19,60 @@ # under the License. # -from ir import Var, Label, Literal, Comparator, \ - Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign from abc import abstractmethod +from typedb_jupyter.utils.ir import Var, Label, Literal, Comparator, \ + Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign +from typedb_jupyter.graph.answer import HasEdge, LinksEdge class QueryGraphEdge: - def __init__(self, left:Var, right:Var): - self.left = left - self.right = right + def __init__(self, lhs: Var, rhs: Var): + self.lhs = lhs + self.rhs = rhs - @abstractmethod - def label(self): - raise NotImplementedError("abstract") @abstractmethod - def left_shape(self): + def get_answer_edge(self, row): raise NotImplementedError("abstract") @abstractmethod - def right_shape(self): + def __str__(self): raise NotImplementedError("abstract") +class QueryHasEdge(QueryGraphEdge): + def __init__(self, lhs, rhs): + super().__init__(lhs, rhs) - def get_answer_edges(self, row): - return () + def get_answer_edge(self, row): + return HasEdge(row.get(self.lhs.name), row.get(self.rhs.name)) + + def __str__(self): + return "{}({}, {})".format(self.__class__.__name__, self.lhs, self.rhs) + + +class QueryLinksEdge(QueryGraphEdge): + def __init__(self, lhs, rhs, role): + super().__init__(lhs, rhs) + self.role = role + + def get_answer_edge(self, row): + return LinksEdge(row.get(self.lhs), row.get(self.rhs), row.get(self.role)) + + def __str__(self): + return "{}({}, {}, {})".format(self.__class__.__name__, self.lhs, self.rhs, self.role) + +class QueryGraph: + def __init__(self, query: "typedb_jupyter.utils.ir.Match"): + self.edges = lazy_query_graph(query.constraints) -class QueryHasEdge(QueryGraphEdge): - def label(self): - return "has" - def shape(self): def lazy_query_graph(constraints): - graph = [] + edges = [] for c in constraints: if isinstance(c, Has): - graph.append(QueryGraphEdge(c.lhs, "has", c.rhs)) + edges.append(QueryHasEdge(c.lhs, c.rhs)) elif isinstance(c, Links): - graph.append(QueryGraphEdge(c.lhs, c.role, c.rhs)) - return graph + edges.append(QueryLinksEdge(c.lhs, c.rhs, c.role)) + return edges diff --git a/src/typedb_jupyter/utils/display.py b/src/typedb_jupyter/utils/display.py index 2763597..556393c 100644 --- a/src/typedb_jupyter/utils/display.py +++ b/src/typedb_jupyter/utils/display.py @@ -43,6 +43,6 @@ def print_documents(documents): for document in documents: print(dumps(document, indent=2)) -def display_graph(graph): - for edge in graph: - \ No newline at end of file +# def display_graph(graph): +# for edge in graph: +# \ No newline at end of file diff --git a/src/typedb_jupyter/utils/ir.py b/src/typedb_jupyter/utils/ir.py index 84d058b..5358155 100644 --- a/src/typedb_jupyter/utils/ir.py +++ b/src/typedb_jupyter/utils/ir.py @@ -18,6 +18,14 @@ # specific language governing permissions and limitations # under the License. # + +class Match: + def __init__(self, constraints): + self.constraints = constraints + + def __str__(self): + return "Match(%s)"%(", ".join(str(c) for c in self.constraints)) + class Label: def __init__(self, name): self.name = name @@ -28,7 +36,7 @@ def __str__(self): class Var: _INTERNAL = 0 def __init__(self, name): - self.name = name + self.name = name.lstrip("$") @classmethod def next_internal(cls): diff --git a/src/typedb_jupyter/utils/parser.py b/src/typedb_jupyter/utils/parser.py index ce64f5e..266f612 100644 --- a/src/typedb_jupyter/utils/parser.py +++ b/src/typedb_jupyter/utils/parser.py @@ -19,21 +19,13 @@ # under the License. # - from parsimonious.grammar import Grammar from parsimonious.nodes import Node, NodeVisitor -from ir import Var, Label, Literal, Comparator, \ +from typedb_jupyter.utils.ir import Match, \ + Var, Label, Literal, Comparator, \ Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign -class Match: - def __init__(self, constraints): - self.constraints = constraints - - - def __str__(self): - return "Match(%s)"%(", ".join(str(c) for c in self.constraints)) - def flatten(l): flat = [] @@ -81,6 +73,15 @@ class TypeQLVisitor(NodeVisitor): ws = ~"\s*" """) + @classmethod + def parse_and_visit(cls, input: str): + tree = TypeQLVisitor.GRAMMAR.parse(input) + visitor = TypeQLVisitor() + return visitor.visit(tree)[1] + + def visit_query(self, node:Node, visited_children): + return non_null(flatten(visited_children[1])) + def visit_ws(self, node:Node, visited_children): return @@ -115,10 +116,8 @@ def visit_native(self, node:Node, visited_children): children = non_null(flatten(visited_children)) edges = [] u = children[0] - # print("U was ", u) for constraint in children[1:]: constraint.may_set_lhs(u) - # print("Child is", child) edges.append(constraint) return edges @@ -141,7 +140,6 @@ def visit_links(self, node: Node, visited_children): def visit_role_player(self, node: Node, visited_children): [role, player] = non_null(flatten(visited_children)) - print((role, player)) if isinstance(role, Var): return [Links(None, player, role)] else: @@ -168,7 +166,6 @@ def visit_comparator(self, node: Node, visited_children): def generic_visit(self, node:Node, visited_children): """ The generic visit method. """ - # print("Generic visit for ", node) return visited_children or None if __name__ == "__main__": @@ -178,10 +175,5 @@ def generic_visit(self, node:Node, visited_children): $y isa cow, has name "Spider Georg"; $z isa marriage, links (man: $x); """ - - tree = TypeQLVisitor.GRAMMAR.parse(input) - # print(tree) - # print("=====") - visitor = TypeQLVisitor() - visited = visitor.visit(tree) - print("\n----\n".join((str(v) for v in visited ))) + visited = TypeQLVisitor.parse_and_visit(input) + print(visited) From eebbefe9caad4ed7e6f15bf95b38be21ec79828b Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 22:29:30 +0530 Subject: [PATCH 10/27] Update stuff --- README.md | 285 +---------------------------------------------- src/Sample.ipynb | 28 ++--- src/graphs.ipynb | 24 ++-- 3 files changed, 23 insertions(+), 314 deletions(-) diff --git a/README.md b/README.md index d78878a..686d29c 100644 --- a/README.md +++ b/README.md @@ -1,284 +1 @@ -# TypeDB Jupyter connector - -Runs TypeQL statements against a TypeDB database from a Jupyter notebook using the `%typedb` and `%typeql` IPython magic -commands. Includes: -- Full support for TypeDB Core and Cluster. -- Ability to manage multiple concurrent connections. -- Automatic session and transaction handling. -- JSON-style output for all read queries. -- Variable interpolation from the Jupyter namespace. -- Query reading from supplied filepaths. - -## Getting started - -Install this module with: - -``` -pip install typedb-jupyter -``` - -or your environment equivalent. Load the extension in Jupyter with: - -``` -%load_ext typedb_jupyter -``` - -## Connecting to TypeDB - -Establish a connection with: - -``` -%typedb -d [-a ] [-n ] -``` - -for example: - -``` -In [1]: %typedb -a 111.111.111.111:1729 -d database_1 - -Out[1]: Opened connection: database_1@111.111.111.111:1729 -``` - - -``` -In [2]: %typedb -a 222.222.222.222:1729 -d database_2 -n test_connection - -Out[2]: Opened connection: test_connection (database_2@222.222.222.222:1729) -``` - - -``` -In [3]: %typedb -d database_local - -Out[3]: Opened connection: database_local@localhost:1729 -``` - -If no address is provided, the default `localhost:1729` will be used. If no custom alias is provided, the connection -will be assigned a default alias of the format `@`. Custom aliases can only include -alphanumeric characters, hyphens, and underscores. If a connection with the server is established but no database with -the name provided exists, a new database will be created with that name by default. Only one connection can be opened to -each database at a time. - -For connecting to TypeDB Cluster, use: - -``` -%typedb -d -a -u -p -c [-n ] -``` - -List established connections with: - -``` -In [4]: %typedb -l - -Out[4]: Open connections: - ...: database_1@111.111.111.111:1729 - ...: test_connection (database_2@222.222.222.222:1729) - ...: * database_local@localhost:1729 -``` - -An asterisk appears next to the currently selected connection, which is the last one opened by default. To change the -selected connection, use: - -``` -%typedb -n -``` - -for example: - -``` -In [5]: %typedb -n database_1@111.111.111.111:1729 - -Out[5]: Selected connection: database_1@111.111.111.111:1729 -``` - -``` -In [6]: %typedb -n test_connection - -Out[6]: Selected connection: test_connection -``` - -Close a connection with: - -``` -%typedb -k -``` - -for example: - -``` -In [7]: %typedb -c database_2@222.222.222.222:1729 - -Out[7]: Closed connection: database_2@222.222.222.222:1729 -``` - -If the currently selected connection is closed, a new one must be manually selected before queries can be executed. -Using `-x` instead of `-k` will also delete the database. - -## Executing a query - -Run a query against a database using the selected connection with: - -``` -%typeql -``` - -or - -``` -%%typeql -``` - -For example: - -``` -In [8]: %typeql match $p isa person; - -Out[8]: [{'p': {'type': 'person'}}, - ...: {'p': {'type': 'person'}}] -``` - -``` -In [9]: %%typeql - ...: match - ...: $p isa person, - ...: has name $n, - ...: has age $a; - -Out[9]: [{'a': {'type': 'age', 'value_type': 'long', 'value': 30}, - ...: 'p': {'type': 'person'}, - ...: 'n': {'type': 'name', 'value_type': 'string', 'value': 'Kevin'}}, - ...: {'a': {'type': 'age', 'value_type': 'long', 'value': 50}, - ...: 'p': {'type': 'person'}, - ...: 'n': {'type': 'name', 'value_type': 'string', 'value': 'Gavin'}}] -``` - -Results of read queries are returned in a JSON-like native Python object. The shape of the object is dependent on the -type of query, as described in the following table: - -| Query type | Output object type | -|-------------------------|--------------------| -| `match` | `list` | -| `match-group` | `dict>` | -| `match-aggregate` | `intǀfloat` | -| `match-group-aggregate` | `dict` | - -Queries automatically interpolate variables from the notebook's Python namespace, specified using the syntax -`{}`, for example: - -``` -In [10]: age = 30 - -In [11]: %typeql match $p isa person, has name $n, has age {age}; count; - -Out[11]: 1 -``` - -Similarly, results can be saved to a namespace variable by providing the variable name with: - -``` -%typeql -r -``` - -for example: - -``` -In [12]: %typeql -r name_counts match $p isa person, has name $n, has age $a; group $n; count; - -In [13]: name_counts - -Out[13]: {'Gavin': 1, 'Kevin': 1} -``` - -To execute a query in a stored TypeQL file, supply the filepath with: - -``` -%typeql -f -``` - -Rule inference is disabled by default. It can be enabled for a query with: - -``` -%typeql -i True -``` - -In order to enable rule inference globally, see the [Configuring options](#configuring-options) -section below. - -## Information for advanced users - -Queries are syntactically analysed to automatically determine schema and transaction types, but these can be overridden -with: - -``` -%typeql [-s ] [-t ] -``` - -where `` is either `schema` or `data`, and `` is either `read` or `write`. - -When a connection is instantiated, a data session is opened and persisted for the duration of the connection unless a -schema query is issued, at which point the data session is closed and a schema session is opened. After the schema query -has been executed, the schema session is then closed and a new data session opened. Each call of `%typeql` or `%%typeql` -is executed in a new transaction, which is then immediately closed on completion. All clients, sessions, and -transactions are closed automatically when the notebook's kernel is terminated. - -It is important to note that TypeDB sessions and transactions cannot be opened under certain conditions, regardless of -the client: - -- Only one schema session can be opened at any time. -- Data write transactions cannot be opened while a schema session is open. -- Only one schema write transaction can be opened at any time. - -This means that, when a `define` or `undefine` query is executed in a notebook, this will interfere with queries -performed by other users on the same database. - -## Configuring options - -Certain options can be configured using the `%config` magic with: - -``` -%config ` -``` - -After being set, these options persist for the remainder of the notebook unless -changed again. The following table describes the available arguments: - -| Argument | Usage | Default | -|-----------------------------------------------|-------------------------------------------------------------------------------|---------| -| `TypeDBMagic` | List config options and current set values for `%typedb`. | | -| `TypeDBMagic.create_database = ` | Create database when opening a connection if it does not already exist. | `True` | -| `TypeQLMagic` | List config options and current set values for `%typeql`. | | -| `TypeQLMagic.global_inference = ` | Enable rule inference for all queries. Can be overridden per query with `-i`. | `False` | -| `TypeQLMagic.show_info = ` | Always show full connection information when executing a query. | `True` | -| `TypeQLMagic.strict_transactions = ` | Require session and transaction types to be specified for every transaction. | `False` | - -## Command glossary - -The following tables list the arguments that can be provided to the `%typedb` and `%typeql` magic commands: - -| Magic command | Argument | Usage | -|---------------|-------------------------|-----------------------------------------------------------------------------| -| `%typedb` | `-a ` | TypeDB server address for new connection. | -| `%typedb` | `-d ` | Database name for new connection. | -| `%typedb` | `-u ` | Username for new Cloud/Cluster connection. | -| `%typedb` | `-p ` | Password for new Cloud/Cluster connection. | -| `%typedb` | `-c ` | TLS certificate path for new Cloud/Cluster connection. | -| `%typedb` | `-n ` | Custom alias for new connection, or alias of existing connection to select. | -| `%typedb` | `-l` | List currently open connections. | -| `%typedb` | `-k ` | Close a connection by name. | -| `%typedb` | `-x ` | Close a connection by name and delete its database. | -| `%typeql` | `-r ` | Assign read query results to the named variable instead of printing. | -| `%typeql` | `-f ` | Read in query from a TypeQL file at the specified path. | -| `%typeql` | `-i ` | Enable (`True`) or disable (`False`) rule inference for query. | -| `%typeql` | `-s ` | Force a particular session type for query, `schema` or `data`. | -| `%typeql` | `-t ` | Force a particular transaction type for query, `read` or `write`. | - -## Planned features - -- Add option to close all connections. -- Add more output formats. - -## Acknowledgements - -Many thanks to Catherine Devlin and all the contributors to -[ipython-sql](https://github.com/catherinedevlin/ipython-sql), which served as -the basis for this project. \ No newline at end of file +This readme is out of date. Please `cd src; python3 -m jupyter notebook` to spin up the sample notebooks diff --git a/src/Sample.ipynb b/src/Sample.ipynb index 112d256..f295065 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -75,7 +75,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: test_jupyter, moo, typedb-iam, jupyter-test, typedb_jupyter_sample\n" + "Databases: typedb_jupyter_graphs, typedb_jupyter_sample\n" ] } ], @@ -111,7 +111,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: test_jupyter, moo, typedb-iam, jupyter-test\n" + "Databases: typedb_jupyter_graphs\n" ] } ], @@ -147,7 +147,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: test_jupyter, moo, typedb-iam, jupyter-test, typedb_jupyter_sample\n" + "Databases: typedb_jupyter_graphs, typedb_jupyter_sample\n" ] } ], @@ -330,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 18, "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, "outputs": [ @@ -348,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 19, "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, "outputs": [ @@ -377,7 +377,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 27, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -389,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, "outputs": [ @@ -397,18 +397,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |, | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'_Attribute' object has no attribute 'iid'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[30], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(_typeql_result)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43m_typeql_result\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minstance\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miid\u001b[49m())\n", - "\u001b[0;31mAttributeError\u001b[0m: '_Attribute' object has no attribute 'iid'" + "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |, | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]\n", + "Attribute(name: \"James\")\n" ] } ], diff --git a/src/graphs.ipynb b/src/graphs.ipynb index e96cd83..7dd70a5 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -314,27 +314,29 @@ "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'HasEdge(Entity(person: 0x1e00000000000000000000)--has-->Attribute(name: \"John\"))\\nHasEdge(Entity(person: 0x1e00000000000000000001)--has-->Attribute(name: \"James\"))\\nHasEdge(Entity(person: 0x1e00000000000000000002)--has-->Attribute(name: \"James\"))\\nHasEdge(Entity(person: 0x1e00000000000000000002)--has-->Attribute(name: \"Jimmy\"))'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Entity(person: 0x1e00000000000000000000)--[has]-->Attribute(name: \"John\")\n", + "Entity(person: 0x1e00000000000000000001)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"Jimmy\")\n" + ] } ], "source": [ - "str(\"\\n\".join(map(str,answer_graph.edges)))" + "print(\"\\n\".join(map(str,answer_graph.edges)))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "5b6da7a4-66f5-4233-9d5e-8855789a08e7", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# TODO: answer_graph.draw()" + ] } ], "metadata": { From 98afeac5a43e4f1c81dbcb72b63a8ba753cd715d Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 23:27:31 +0530 Subject: [PATCH 11/27] Wow a graph works --- src/graphs.ipynb | 39 +++++++++++++--- src/typedb_jupyter/graph/answer.py | 73 +++++++++++++++++++++++++----- src/typedb_jupyter/graph/query.py | 27 +++++++++-- 3 files changed, 118 insertions(+), 21 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 7dd70a5..368a34a 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -317,10 +317,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Entity(person: 0x1e00000000000000000000)--[has]-->Attribute(name: \"John\")\n", - "Entity(person: 0x1e00000000000000000001)--[has]-->Attribute(name: \"James\")\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"James\")\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"Jimmy\")\n" + "Entity(person: 0x1e00000000000000000000)--[has]-->True\n", + "Entity(person: 0x1e00000000000000000001)--[has]-->True\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->True\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->True\n" ] } ], @@ -333,10 +333,37 @@ "execution_count": 16, "id": "5b6da7a4-66f5-4233-9d5e-8855789a08e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_parser.py:23: UserWarning: Multi-graphs are not properly supported. Duplicate edges are plotted as a single edge; edge weights (if any) are summed.\n", + " warnings.warn(msg)\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# TODO: answer_graph.draw()" + "answer_graph.draw()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88b1e385-ab1c-464c-956d-b0a131789dc7", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index e2c9b0c..cd2f6e8 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -26,13 +26,33 @@ def __init__(self, edges): self.edges = edges def draw(self): - from netgraph import InteractiveGraph + from netgraph import Graph # TODO: derive edges, node_shape, node_labels, node_colors from from edge.lhs & edge.rhs - edges = [] - plot_instance = InteractiveGraph(edges) - plt.show() + plottable = PlottableGraphBuilder() + for edge in self.edges: + plottable.add_edge(edge) + plot_instance = Graph( + plottable.edges, + edge_labels=plottable.edge_labels, + node_shape=plottable.node_shapes, + node_color=plottable.node_colours, + node_labels=plottable.node_labels, + arrows=True, + node_label_offset=0.075 + ) class AnswerVertex: + def __init__(self, vertex): + self.vertex = vertex + + def __str__(self): + return str(self.vertex) + + def __hash__(self): + return self.vertex.__hash__() + + def __eq__(self, other): + return self.vertex.__eq__(other.vertex) @classmethod @abstractmethod @@ -41,8 +61,8 @@ def shape(cls): @classmethod @abstractmethod - def color(cls): - return cls._COLOR + def colour(cls): + return cls._COLOUR @abstractmethod def label(self): @@ -50,27 +70,33 @@ def label(self): class RelationVertex(AnswerVertex): _SHAPE = "o" + _COLOUR = "green" def __init__(self, relation): - self.relation = relation + super().__init__(relation) def label(self): - return "TODO_RELATION" + return str(self) class EntityVertex(AnswerVertex): + _SHAPE = "o" + _COLOUR = "green" def __init__(self, entity): - self.entity = entity + super().__init__(entity) def label(self): - return "TODO_ENTITY" + return str(self) class AttributeVertex(AnswerVertex): + _SHAPE = "s" + _COLOUR = "green" + def __init__(self, attribute): - self.attribute = attribute + super().__init__(attribute) def label(self): - return "TODO_ATTRIBUTE" + return str(self) class AnswerEdge: def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex): @@ -121,6 +147,29 @@ def _add_answer_row(self, row): edge = query_edge.get_answer_edge(row) self.edges.append(edge) + +class PlottableGraphBuilder: + def __init__(self): + self.edges = [] + self.edge_labels = {} + self.node_shapes = {} + self.node_colours = {} + self.node_labels= {} + + def add_edge(self, edge: AnswerEdge): + self.edges.append((edge.lhs, edge.rhs)) + self.edge_labels[(edge.lhs, edge.rhs)] = edge.label() + self.node_shapes[edge.lhs] = edge.lhs.shape() + self.node_shapes[edge.rhs] = edge.rhs.shape() + + self.node_colours[edge.lhs] = edge.lhs.colour() + self.node_colours[edge.rhs] = edge.rhs.colour() + + self.node_labels[edge.lhs] = edge.lhs.label() + self.node_labels[edge.rhs] = edge.rhs.label() + + + if __name__ == "__main__": import matplotlib.pyplot as plt from netgraph import Graph, InteractiveGraph, EditableGraph diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py index 5bb1684..845401e 100644 --- a/src/typedb_jupyter/graph/query.py +++ b/src/typedb_jupyter/graph/query.py @@ -23,7 +23,8 @@ from typedb_jupyter.utils.ir import Var, Label, Literal, Comparator, \ Isa, Has, Links, IsaType, AttributeLabelValue, Comparison, Assign -from typedb_jupyter.graph.answer import HasEdge, LinksEdge +from typedb_jupyter.graph.answer import HasEdge, LinksEdge, \ + EntityVertex, RelationVertex, AttributeVertex class QueryGraphEdge: def __init__(self, lhs: Var, rhs: Var): @@ -44,7 +45,16 @@ def __init__(self, lhs, rhs): super().__init__(lhs, rhs) def get_answer_edge(self, row): - return HasEdge(row.get(self.lhs.name), row.get(self.rhs.name)) + owner = row.get(self.lhs.name) + if owner.is_entity(): + lhs = EntityVertex(owner) + else: + assert owner.is_relation() + lhs = RelationVertex(owner) + + assert row.get(self.rhs.name).is_attribute() + rhs = AttributeVertex(row.get(self.rhs.name).is_attribute()) + return HasEdge(lhs, rhs) def __str__(self): return "{}({}, {})".format(self.__class__.__name__, self.lhs, self.rhs) @@ -56,7 +66,18 @@ def __init__(self, lhs, rhs, role): self.role = role def get_answer_edge(self, row): - return LinksEdge(row.get(self.lhs), row.get(self.rhs), row.get(self.role)) + assert row.get(self.lhs).is_relation() + rhs = RelationVertex(row.get(self.lhs)) + role = str(row.get(self.role)) + + player = row.get(self.rhs.name) + if player.is_entity(): + lhs = EntityVertex(player) + else: + assert player.is_relation() + lhs = RelationVertex(player) + + return LinksEdge(lhs, rhs, role) def __str__(self): return "{}({}, {}, {})".format(self.__class__.__name__, self.lhs, self.rhs, self.role) From 86d0f18dc96a51a7f786e23100e9759ca70675af Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 23:33:01 +0530 Subject: [PATCH 12/27] update readme.md --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 686d29c..89fe944 100644 --- a/README.md +++ b/README.md @@ -1 +1,10 @@ -This readme is out of date. Please `cd src; python3 -m jupyter notebook` to spin up the sample notebooks +# Jupyter magic for TypeDB 3.x +Last updated for 3.0.4. + +### Getting started + Please + ```bash + cd src; + python3 -m jupyter notebook + ``` +See the [sample](src/Sample.ipynb) & [graph](src/graphs.ipynb) notebooks for more. From 2abbc024d4d1fe2e41ff4881986f957a9c0b0c4a Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 23:39:06 +0530 Subject: [PATCH 13/27] Bugfix for attribute vertex --- src/typedb_jupyter/graph/query.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py index 845401e..9aab1c5 100644 --- a/src/typedb_jupyter/graph/query.py +++ b/src/typedb_jupyter/graph/query.py @@ -53,7 +53,7 @@ def get_answer_edge(self, row): lhs = RelationVertex(owner) assert row.get(self.rhs.name).is_attribute() - rhs = AttributeVertex(row.get(self.rhs.name).is_attribute()) + rhs = AttributeVertex(row.get(self.rhs.name)) return HasEdge(lhs, rhs) def __str__(self): From 9a0d282cc5930be0cb65bd0f1f024842c9d86c0a Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 23:48:43 +0530 Subject: [PATCH 14/27] Set of bugfixes for links --- src/graphs.ipynb | 157 ++++++++++++++++++++++++----- src/typedb_jupyter/graph/query.py | 6 +- src/typedb_jupyter/utils/parser.py | 4 +- 3 files changed, 135 insertions(+), 32 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 368a34a..90ad2e2 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -93,7 +93,8 @@ "\n", "define \n", "attribute name, value string;\n", - "entity person, owns name @card(0..);\n" + "entity person, owns name @card(0..), plays friendship:friend;\n", + "relation friendship, relates friend @card(0..);" ] }, { @@ -148,7 +149,7 @@ { "data": { "text/html": [ - "
pp1p2
Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" + "
f12f23p1p2p3
Relation(friendship: 0x1f00000000000000000000)Relation(friendship: 0x1f00000000000000000001)Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" ], "text/plain": [ "" @@ -172,9 +173,11 @@ "%%typeql\n", "\n", "insert \n", - "$p isa person, has name \"John\";\n", - "$p1 isa person, has name \"James\";\n", - "$p2 isa person, has name \"James\", has name \"Jimmy\";" + "$p1 isa person, has name \"John\";\n", + "$p2 isa person, has name \"James\";\n", + "$p3 isa person, has name \"James\", has name \"Jimmy\";\n", + "$f12 isa friendship, links (friend: $p1, friend: $p2);\n", + "$f23 isa friendship, links (friend: $p2, friend: $p3);" ] }, { @@ -216,7 +219,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "0a51a712-56b2-40e6-9ec5-506641d4c1f2", + "id": "2a986872-2a81-4944-99fa-cc1fb3e135ee", "metadata": {}, "outputs": [ { @@ -277,20 +280,11 @@ "execution_count": 13, "id": "12695159-169f-440f-bf85-4396cf0bf825", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Match(IsaType(p, person), Has(p, n), IsaType(n, name))\n" - ] - } - ], + "outputs": [], "source": [ "from typedb_jupyter.utils.parser import TypeQLVisitor\n", "\n", - "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")\n", - "print(str(parsed))" + "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")" ] }, { @@ -317,10 +311,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Entity(person: 0x1e00000000000000000000)--[has]-->True\n", - "Entity(person: 0x1e00000000000000000001)--[has]-->True\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->True\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->True\n" + "Entity(person: 0x1e00000000000000000000)--[has]-->Attribute(name: \"John\")\n", + "Entity(person: 0x1e00000000000000000001)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"Jimmy\")\n" ] } ], @@ -335,32 +329,141 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "answer_graph.draw()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "88b1e385-ab1c-464c-956d-b0a131789dc7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", "output_type": "stream", "text": [ - "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_parser.py:23: UserWarning: Multi-graphs are not properly supported. Duplicate edges are plotted as a single edge; edge weights (if any) are summed.\n", - " warnings.warn(msg)\n" + "Opened read transaction on database 'typedb_jupyter_graphs' \n" + ] + } + ], + "source": [ + "%typedb transaction open typedb_jupyter_graphs read" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "83e7c39b-a24d-4e35-b141-1a9474d50e51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 6 rows.\n" ] }, { "data": { - "image/png": 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Relation(friendship: 0x1f00000000000000000001)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" + ], "text/plain": [ - "
" + "" ] }, "metadata": {}, "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'Stored result in variable: _typeql_result'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ + "%%typeql\n", + "match\n", + "$f isa friendship, links (friend: $p1, friend: $p2);\n", + "$p1 has name $n1;\n", + "$p2 has name $n2;" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "46d56f54-1068-4eae-9b58-4124a7aba5c1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction closed\n" + ] + } + ], + "source": [ + "%typedb transaction close" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c2d6529f-fee4-491b-be1b-6220193657d9", + "metadata": {}, + "outputs": [ + { + "ename": "TypeDBDriverException", + "evalue": "Query Error: The variable 'friend' does not exist.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeDBDriverException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 10\u001b[0m\n\u001b[1;32m 4\u001b[0m parsed \u001b[38;5;241m=\u001b[39m TypeQLVisitor\u001b[38;5;241m.\u001b[39mparse_and_visit(\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mmatch\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m$f isa friendship, links (friend: $p1, friend: $p2);\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m$p1 has name $n1;\u001b[39m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m$p2 has name $n2;\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m)\n\u001b[1;32m 9\u001b[0m query_graph \u001b[38;5;241m=\u001b[39m QueryGraph(parsed)\n\u001b[0;32m---> 10\u001b[0m answer_graph \u001b[38;5;241m=\u001b[39m \u001b[43mAnswerGraphBuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_typeql_result\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m answer_graph\u001b[38;5;241m.\u001b[39mdraw()\n", + "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/answer.py:138\u001b[0m, in \u001b[0;36mAnswerGraphBuilder.build\u001b[0;34m(cls, query_graph, answers)\u001b[0m\n\u001b[1;32m 136\u001b[0m builder \u001b[38;5;241m=\u001b[39m AnswerGraphBuilder(query_graph)\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m answers:\n\u001b[0;32m--> 138\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_answer_row\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m AnswerGraph(builder\u001b[38;5;241m.\u001b[39medges)\n", + "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/answer.py:147\u001b[0m, in \u001b[0;36mAnswerGraphBuilder._add_answer_row\u001b[0;34m(self, row)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_add_answer_row\u001b[39m(\u001b[38;5;28mself\u001b[39m, row):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m query_edge \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery_graph\u001b[38;5;241m.\u001b[39medges:\n\u001b[0;32m--> 147\u001b[0m edge \u001b[38;5;241m=\u001b[39m \u001b[43mquery_edge\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_answer_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39medges\u001b[38;5;241m.\u001b[39mappend(edge)\n", + "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/query.py:71\u001b[0m, in \u001b[0;36mQueryLinksEdge.get_answer_edge\u001b[0;34m(self, row)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlhs\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39mis_relation()\n\u001b[1;32m 70\u001b[0m rhs \u001b[38;5;241m=\u001b[39m RelationVertex(row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlhs\u001b[38;5;241m.\u001b[39mname))\n\u001b[0;32m---> 71\u001b[0m role \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrole\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 73\u001b[0m player \u001b[38;5;241m=\u001b[39m row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrhs\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m player\u001b[38;5;241m.\u001b[39mis_entity():\n", + "File \u001b[0;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/typedb/concept/answer/concept_row.py:69\u001b[0m, in \u001b[0;36m_ConceptRow.get\u001b[0;34m(self, column_name)\u001b[0m\n\u001b[1;32m 67\u001b[0m concept \u001b[38;5;241m=\u001b[39m concept_row_get(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnative_object, _not_blank_var(column_name))\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m concept:\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TypeDBDriverException(VARIABLE_DOES_NOT_EXIST, column_name)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concept_factory\u001b[38;5;241m.\u001b[39mwrap_concept(concept)\n", + "\u001b[0;31mTypeDBDriverException\u001b[0m: Query Error: The variable 'friend' does not exist." + ] + } + ], + "source": [ + "from typedb_jupyter.graph.query import QueryGraph\n", + "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", + "\n", + "# Parser doesn't support roles yet\n", + "parsed = TypeQLVisitor.parse_and_visit(\"\"\"match\n", + "$f isa friendship, links ($p1, $p2);\n", + "$p1 has name $n1;\n", + "$p2 has name $n2;\n", + "\"\"\")\n", + "query_graph = QueryGraph(parsed)\n", + "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)\n", "answer_graph.draw()" ] }, { "cell_type": "code", "execution_count": null, - "id": "88b1e385-ab1c-464c-956d-b0a131789dc7", + "id": "7681fedf-fd9f-4fe9-b430-44490b75a688", "metadata": {}, "outputs": [], "source": [] diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py index 9aab1c5..8a9b842 100644 --- a/src/typedb_jupyter/graph/query.py +++ b/src/typedb_jupyter/graph/query.py @@ -66,9 +66,9 @@ def __init__(self, lhs, rhs, role): self.role = role def get_answer_edge(self, row): - assert row.get(self.lhs).is_relation() - rhs = RelationVertex(row.get(self.lhs)) - role = str(row.get(self.role)) + assert row.get(self.lhs.name).is_relation() + rhs = RelationVertex(row.get(self.lhs.name)) + role = str(row.get(self.role.name)) player = row.get(self.rhs.name) if player.is_entity(): diff --git a/src/typedb_jupyter/utils/parser.py b/src/typedb_jupyter/utils/parser.py index 266f612..0f1bd1f 100644 --- a/src/typedb_jupyter/utils/parser.py +++ b/src/typedb_jupyter/utils/parser.py @@ -58,7 +58,7 @@ class TypeQLVisitor(NodeVisitor): has_labelled = "has" ws label ws (var / literal) has = "has" ws var - links = "links" ws "(" ws role_player ws ( "," ws constraint ws)* ")" + links = "links" ws "(" ws role_player ws ( "," ws role_player ws)* ")" isa = "isa" ws (label/var) role_player = (var/label) ws ":" ws var @@ -173,7 +173,7 @@ def generic_visit(self, node:Node, visited_children): match $x isa cow, has name "Spider Georg"; $y isa cow, has name "Spider Georg"; - $z isa marriage, links (man: $x); + $z isa marriage, links (man: $x, woman: $y); """ visited = TypeQLVisitor.parse_and_visit(input) print(visited) From b9a90fc586cb884bd1a4036ce673957c91003400 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 29 Jan 2025 23:49:53 +0530 Subject: [PATCH 15/27] update graphs.ipynb --- src/graphs.ipynb | 37 ++++++++++++++++++++----------------- 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 90ad2e2..5db81c9 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -330,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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fn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000000)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
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Relation(friendship: 0x1f00000000000000000001)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" + "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" ], "text/plain": [ "" @@ -400,7 +400,7 @@ "source": [ "%%typeql\n", "match\n", - "$f isa friendship, links (friend: $p1, friend: $p2);\n", + "$f isa friendship, links ($friend: $p1, $friend: $p2);\n", "$p1 has name $n1;\n", "$p2 has name $n2;" ] @@ -430,28 +430,31 @@ "metadata": {}, "outputs": [ { - "ename": "TypeDBDriverException", - "evalue": "Query Error: The variable 'friend' does not exist.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeDBDriverException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 10\u001b[0m\n\u001b[1;32m 4\u001b[0m parsed \u001b[38;5;241m=\u001b[39m TypeQLVisitor\u001b[38;5;241m.\u001b[39mparse_and_visit(\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mmatch\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m$f isa friendship, links (friend: $p1, friend: $p2);\u001b[39m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m$p1 has name $n1;\u001b[39m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m$p2 has name $n2;\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m)\n\u001b[1;32m 9\u001b[0m query_graph \u001b[38;5;241m=\u001b[39m QueryGraph(parsed)\n\u001b[0;32m---> 10\u001b[0m answer_graph \u001b[38;5;241m=\u001b[39m \u001b[43mAnswerGraphBuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_typeql_result\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m answer_graph\u001b[38;5;241m.\u001b[39mdraw()\n", - "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/answer.py:138\u001b[0m, in \u001b[0;36mAnswerGraphBuilder.build\u001b[0;34m(cls, query_graph, answers)\u001b[0m\n\u001b[1;32m 136\u001b[0m builder \u001b[38;5;241m=\u001b[39m AnswerGraphBuilder(query_graph)\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m answers:\n\u001b[0;32m--> 138\u001b[0m \u001b[43mbuilder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_answer_row\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m AnswerGraph(builder\u001b[38;5;241m.\u001b[39medges)\n", - "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/answer.py:147\u001b[0m, in \u001b[0;36mAnswerGraphBuilder._add_answer_row\u001b[0;34m(self, row)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_add_answer_row\u001b[39m(\u001b[38;5;28mself\u001b[39m, row):\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m query_edge \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mquery_graph\u001b[38;5;241m.\u001b[39medges:\n\u001b[0;32m--> 147\u001b[0m edge \u001b[38;5;241m=\u001b[39m \u001b[43mquery_edge\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_answer_edge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrow\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39medges\u001b[38;5;241m.\u001b[39mappend(edge)\n", - "File \u001b[0;32m~/code/side/typedb-jupyter/src/typedb_jupyter/graph/query.py:71\u001b[0m, in \u001b[0;36mQueryLinksEdge.get_answer_edge\u001b[0;34m(self, row)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlhs\u001b[38;5;241m.\u001b[39mname)\u001b[38;5;241m.\u001b[39mis_relation()\n\u001b[1;32m 70\u001b[0m rhs \u001b[38;5;241m=\u001b[39m RelationVertex(row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlhs\u001b[38;5;241m.\u001b[39mname))\n\u001b[0;32m---> 71\u001b[0m role \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[43mrow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrole\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 73\u001b[0m player \u001b[38;5;241m=\u001b[39m row\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrhs\u001b[38;5;241m.\u001b[39mname)\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m player\u001b[38;5;241m.\u001b[39mis_entity():\n", - "File \u001b[0;32m~/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/typedb/concept/answer/concept_row.py:69\u001b[0m, in \u001b[0;36m_ConceptRow.get\u001b[0;34m(self, column_name)\u001b[0m\n\u001b[1;32m 67\u001b[0m concept \u001b[38;5;241m=\u001b[39m concept_row_get(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnative_object, _not_blank_var(column_name))\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m concept:\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TypeDBDriverException(VARIABLE_DOES_NOT_EXIST, column_name)\n\u001b[1;32m 70\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m concept_factory\u001b[38;5;241m.\u001b[39mwrap_concept(concept)\n", - "\u001b[0;31mTypeDBDriverException\u001b[0m: Query Error: The variable 'friend' does not exist." + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_parser.py:23: UserWarning: Multi-graphs are not properly supported. Duplicate edges are plotted as a single edge; edge weights (if any) are summed.\n", + " warnings.warn(msg)\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "from typedb_jupyter.graph.query import QueryGraph\n", "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", "\n", - "# Parser doesn't support roles yet\n", + "# Our mini-parser doesn't support roles yet\n", "parsed = TypeQLVisitor.parse_and_visit(\"\"\"match\n", - "$f isa friendship, links ($p1, $p2);\n", + "$f isa friendship, links ($friend: $p1, $friend: $p2);\n", "$p1 has name $n1;\n", "$p2 has name $n2;\n", "\"\"\")\n", From 9dbbdc99060dd70e7002ee52647d88d0aeaafbcc Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Thu, 30 Jan 2025 12:30:36 +0530 Subject: [PATCH 16/27] help command + bring graph closer to studio --- src/Sample.ipynb | 113 ++++++++++++++++++----------- src/graphs.ipynb | 4 +- src/typedb_jupyter/graph/answer.py | 18 ++--- src/typedb_jupyter/graph/query.py | 2 +- src/typedb_jupyter/subcommands.py | 36 ++++++--- 5 files changed, 106 insertions(+), 67 deletions(-) diff --git a/src/Sample.ipynb b/src/Sample.ipynb index f295065..e182f04 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -21,7 +21,62 @@ "output_type": "stream", "text": [ "Available commands: connect, database, transaction, help\n", - "TODO: Print subcommand help\n" + "--------------------------------------------------------------------------------\n", + "Help for command 'connect':\n", + "usage: connect [-h]\n", + " {open,close,help} [{core,cluster}] [address] [username]\n", + " [password]\n", + "\n", + "Establishes the connection to TypeDB\n", + "\n", + "positional arguments:\n", + " {open,close,help}\n", + " {core,cluster}\n", + " address\n", + " username\n", + " password\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'database':\n", + "usage: database [-h] {create,recreate,list,delete,schema,help} [name]\n", + "\n", + "Database management\n", + "\n", + "positional arguments:\n", + " {create,recreate,list,delete,schema,help}\n", + " name\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'transaction':\n", + "usage: transaction [-h]\n", + " {open,close,commit,rollback,help} [database]\n", + " [{schema,write,read}]\n", + "\n", + "Opens or closes a transaction to a database on the active connection\n", + "\n", + "positional arguments:\n", + " {open,close,commit,rollback,help}\n", + " database Only for 'open'\n", + " {schema,write,read} Only for 'open'\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'help':\n", + "usage: help [-h]\n", + "\n", + "Shows this help description\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n" ] } ], @@ -158,34 +213,6 @@ { "cell_type": "code", "execution_count": 10, - "id": "7b81db96-ae1a-43b3-a920-6e7443e34aeb", - "metadata": {}, - "outputs": [], - "source": [ - "# This is not implemented yet: %typedb database schema test_jupyter" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c1260950-fae9-4ef7-9940-ff963a2ab53a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Recreated database typedb_jupyter_sample\n" - ] - } - ], - "source": [ - "%typedb database recreate typedb_jupyter_sample" - ] - }, - { - "cell_type": "code", - "execution_count": 12, "id": "bfeae364-194b-4478-b02d-2b29c1ede228", "metadata": {}, "outputs": [ @@ -203,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "d3a65846-4d15-4376-bfb1-c3c921355aab", "metadata": {}, "outputs": [ @@ -220,7 +247,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -234,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", "metadata": {}, "outputs": [ @@ -252,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "id": "2385b0db-a4b5-4b5e-b734-64ccc473780a", "metadata": {}, "outputs": [ @@ -270,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "id": "d3a6084f-0bda-4985-a6a5-be1e93d138be", "metadata": {}, "outputs": [ @@ -299,7 +326,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -312,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "id": "ea614f7f-c26f-4147-afb1-e1b0545744a3", "metadata": {}, "outputs": [ @@ -330,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, "outputs": [ @@ -348,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, "outputs": [ @@ -377,7 +404,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -389,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, "outputs": [ @@ -409,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, "outputs": [ @@ -431,7 +458,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -446,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, "outputs": [ diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 5db81c9..ba706b9 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -330,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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ERJSyGGCQ0/l4e8szbVv9GyDkSfJxsl8ppOcqyZ83j+w5eFiioqOdWk4iIko5DDAoRWBWVIy+mfN88STVYnhEeUmOy4XF29tLihTML9t275e5i/+U4yfP6HE2iIgodWOAQSmiYFBead+iifjfyy75j1cSSURMgEnPCh2qIV6RvlK1Qjnx9PTU60PvP5A1m7bKgj9WyIXLD7vCEhFR6sS5SCjFRERGyquDPpQ9B4/oQbculd4vsZ4x8T4HzSoILhCYtG/ZRF7s3FG27z0g9x88sNu3UP4gqVujigRmz5aC74KIiJKCAQalKAQG74z4TDbv3COxntESnOeC3A46JxGZTONaKBG/u9kk8HIRyXojSNxi3aXzk61k6Ft9xdPDQ+deHDx6XPYcOCKRUdZja6AZpkyJYlKrakXJ5O//+N8gERE5xACDUhwChB9//V3mLVkql69e1+vC/e/pQbTcxE08I33EO/xhcFC6eFHp0fkp6dCqmd0Mqpg8bdf+Q3L4+D92eRienh66OaVK+TLi5eX1GN8dERE5wgCDHpuYmBjZvHOv/LJkqRz955SeX8Td3V2yZg6QmlUqSpcO7aRyudKPnJr9TshdnfR5+vwFu23+fr5Ss0olKVuymD42ERG5BgMMSrOuXLsuW3btlWs37OcpyZ41q87PKFwg6JEBCxEROR8DDErT8Od76ux5XaNx18F8JRhDo17NqpIrMIdLykdElFExwKB00/xy6Pg/OkcjIiLSbnupYkWkdrXKkjkg+cOXExHRozHAoHTXNXb3gcNy8OgJHXSYeXh4SMWypaR6pfJ6tFEiIko5DDAoXUJzyY49B+TE6bMOZ3utUbmClC9dQgcdRETkfAwwKF27fvOWTgQ1useaofdKnepVpFjhgkwEJSJyMgYYlO7hT/zshUuydfc+3cXVVt5cOXUiaEJmbI2MjJSbN29KUFBQCpWWiCh9YIBBGUZsbKwef2PnvoN60C5bqMmoX7NavImgo0ePlmXLlsmsWbOkZMmSKVxiIqK0iyMRUYaBgbfKly4p3Z9ur3MwMPqn2elzFyT4TojExhFz37p1S5YsWSJNmjSRokWLPqZSExGlTQwwKMPBFPC1qlaS559pL2VLFrfkX2DMjEIFgsQ9jnwM1F7kz59funTpomd4jYqK0rUiRERkj00klOHdvB2s8zPqVq8iObJldTjE+Pnz56VZs2bSqlUrqVGjhtSuXVvKli3rkvISEaUFrMGgDC9njuzSvkUT/TOu+UuGDRsmly9flmvXrsmePXukUqVKMmLECLuxNoiI6CEGGESPsHPnTtm/f798+OGHsmDBApk8ebLMnj1bfv75Z12zYSs6Otol5SQiSk0YYBDFAzkWCCyqVasmL7zwgmU9ajpu3LhhWf7ll19k8eLF+nfkZwBbH4koI2OAQRSP5cuXy507d6Rt27Y6wdOwcuVKadiwoQ4mjh07pms50GSCrqsrVqzQ+3DwLiLKyB5+1SIiOxERETJw4EBp2bKltG7d2rIeXVUPHz4sHTp0kIIFC+p148eP100j48aN08/Jiuni69Z1YemJiFyLNRhE8QQYnTt31sFF5syZ9brw8HCZO3eu5M6dW5599ln57bffZNSoUTJhwgQ9r8mQIUPE399f12iAbTdW5mcQUUbBAIMoDlmyZNFjX7Rr186yDiN4Xrp0SRo1aqSbSZ555hk5dOiQ/Prrr3r48C+++EIKFCgg//zzj+U5V69e1U0tGDfDyM/g+BlElN6xiYQogTAPCWomfHx8pGPHjvLkk0/qGgsEIajZQJAxePBg3Z11165d+jljx47VtRz379/XSaHvvfeevPvuu3F2hyUiSi8YYBAlkLe3t0yfPl1OnjwpxYoV03kWCDbA19dXJ33myZNH6tSpo3udhISE6IRPDC3+zjvvyPbt22XQoEG6RgPNKpkyxT3nCRFRWscAgyiRSpQooX+++OKLMmDAADl16pQOODAuBn7HT0AtRWBgoGzcuFH3MHnqqad08wn2Qb4GEVF6xnpaoiTq16+fnDhxQtda4IG8C/QgKV26tN6OxFAMyoUg4/3335fbt2/rmg30SkGNBxFResa5SIicAMFD165dde1Fzpw55fvvv9fzlqDGYvXq1TpXA4HFmDFjXF1UIqLHgjUYRE6QI0cO3asEwYXRTILkTgy21aJFC92lFdvRA4WIKCNgDQZRCsBYGWguQb4GurkuXbpUHjx4INu2bRMvLy+rfdFl9cr1G3omVz82nRBROsEAgyiFYFCtoUOHysGDB6VmzZrStGlTPX4GAgqjmyr++0VFRcucX5eIEiXVK5aXiuVKiyeTQIkojWOAQfQYRgQ1urPawn+/rbv2yr7DxyzrMgdkklpVK0mpYkU4nwkRpVkMMIhcKDo6Rn5bsVqu37xlty1XYA6pW6OKFMiX1yVlIyJKDiZ5ErnQmDGjZeOqZdK0QR3J5O8vHw4aIGtXLtfbbty6Lb//tVaWrl4vt++EPLYyYXr6V1999bG9HiUdRo594403XF0MIocYYBDFY+vWrXpQLPN8JAZ0P0XiZnK8/vrr8sknn0iZEsWk+zNPio+Pt13+xbmLl+WXJctk/ZYd8iAs7JHHXL9+vW5awTTziYVRRr/88kudO5Je4HwUKVJE//7SSy/pQc8MjRs3lrfeektSO3yeZ8+e1XPhoMwGjAz7ww8/yOnTp11aPiJHGGAQxQNDg+Mb4t9//63nGDHbtGmTw8Ajsd1bjZlavTw9xdvLS6pVKi8VypSyyr9AS+aREyflp4V/yM59B/XEaSkB43fUq1dPChcunCLHJ+dCt2iMtzJ16lRXF4XIDgMMojiEhobKL7/8In379tWBBL49GvD7yJEjLbUFpUqVknPnziVoSnZs/+OPP+L8Bh0RHi5Tv/xCBvXrLR8NGiAb1qy0bLt69YpOAB078SsdcKBHCmoqUAaUBd9yMfcJZM+eXa/Ht3bAvph8rWjRouLn5yeVK1fWE7SZzZs3T9q3b2+1DmV888039URtCIjy5s1rVQsAmK6+YsWKen6VggUL6lFOcf7M5ytbtmzy559/6pFOMaV9586ddQ0QvoGjhgHlxevExMT8dy4iIvS39Pz58+tj165dW7/PlDJnzhypUaOGDvrwPrt37y7Xr1+3bDc+77/++kuqVq2qzyN6B2EfzJhbtmxZPQsvnmeu3XrUuQ8ODpbnn39ecuXKpbeXLFlSZs6cmaAy4/PC50aU2jDAIIrD/PnzpUyZMvqG2KNHD5kxY4auSYAuXbroCczKly8vV65c0TUcuXPn1jeKPXv26H0c5U8bXVRxA4nLuHHj9A1o7969Mnz4R7Jw7o9y5cLD4MUQFh6um0zm/75czl+6YlmPm/vChQv178ePH9e1LpMmTdLLuMHNnj1bvvnmGzl8+LC8/fbb+n1t2LDBMhrpkSNH9A3WFoIA3OAxYdvnn3+uJ2tbtWqVZTveE4ZFx3Gx79q1a3VAYoYbLvbBzRCTwOFm/fTTT8uyZcv0Azf3adOmWd140YSEZio858CBA3rAstatW+th2Q244ZuDv+RAzdDHH38s+/fv1wOlIWAzAjQzBFhff/21bNmyRS5cuCDPPfecPs8YYA1jnmBQta+++sqy/6POPfJecO4RpBw9elTXSKB2IiFq1aolFy9e1GUlSlXQi4SI7NWrV09NmjRJ/x4VFaVy5syp1q1bZ9k+fPhwVblyZbvn4b/V9u3b7dZHRkaq2NhYq3WNGjVSAwYMsCwXLlxYtW7d2mqfLl26qDZt2qgTp8+qCVO+1ccfPHK0mjLzJ/0Y9++635b8rvdHGbEcHBysTp+7oM5fvKzCw8OVv7+/2rJli9Wxe/Xqpbp166Z/37t3r37e+fPn7crYoEEDq3U1a9ZU77//fpznbsGCBSowMNCyPHPmTH3skydPWtb16dNHl+nevXuWda1atdLr4dy5c8rDw0NdunTJ6tjNmjVTQ4YMsSyXLl1aLVq0SCWF7fm3tXPnTl1uo4zGuV29erVln7Fjx+p1p06dsnpveC+QkHPfvn179fLLLyfpPYSEhOjXX79+fZKeT5RSOJsqkQP49r9jxw5ZvHixXvb09NS1FsjJMCfZxcU2XwPL69at09Xgj1K3bl27ZXw7Llm0sLRv1VQGYup4m9FAATUaAdlzSsS/iaCoLdm6e58eV6NYUG5dg4Bhy80iIyN1VT+E/fs8RxOxVapUyWo5X758Vk0HSHjFt/Rjx47J3bt3dTNQeHi4fk00hwB+Fi9e3PIcTBCHppGAgACrdcZxMUAZmkvQ/GSGZhNMIGfAazrL7t27de0EajBQG4VzCOfPn5dy5co5PB8oM94bZtQ1r8PfD5w8efKR5x7NcJ06ddK1X5izpmPHjjoXJiHQpALJTTgmcjYGGEQOIJDATTIoKMiyDpUTGDALVeNZs2ZN1PG+/fZbXeWekAAjPkZg0abpExLr7iUHjp6QmOiHOQsYCfT4qTNy8sTDG+7hY//InZC7+uGtHuaFoPoe+QxmxiBgRpU8bqzIBTCzHd4czRLGzRdV808++aS+SY4ePVrnaSABtlevXvomagQYjo4R33GRw4EePLjp205vbw5KnOX+/fs6YRKPn376SZ8DBBZYxvswM5c7Ie/jUee+TZs2OocHTUVoemrWrJn0799fxo8f/8hyo2kLbD8zIldjgEFkA4EF2su/+OIL/W3SDN8sMc/Ia6+9Jt7e3lYJiQbcbBytTyjMV2K7jORB803k9q1b0rZtWylfppRM/fZ7q/3d3R6mVm3ds08CAh72UIkUN30zww0Tw5U7gtoFJCgiF8C21iA+CABwM8X5MoZAR/5KcuHbPc4jajQaNmwoKQ01Ibdu3ZJPP/1U57LArl27kn1c1Hw86twbn23Pnj31A+/33XffTVCAcejQIf03h3wgotSEAQaRDfR0wLd4fAO3ralANTZqNxBgoHr/zJkzsm/fPj0tO3oe4EaC9WvWrJH69evrZfSOSIzNmzfrREoEM/g2u2DBAv3t16gOr1Onjr4JokcCbr6L58/V23LmePg6OXLm1N+gD+3bK+UrVREvb2+5dPWmvDlggE4uRDDQoEEDCQkJ0a+FoAI3NQQHzZs317UPeO2EwoRuSI5EUiN6NOCYSGZMLgQ5qPF58cUXdfCCgOPGjRv63KKJwugijERcNM8gYTQ5ChUqpINGvA98vrhxI+EzufB3gZ4w8Z37jz76SKpXr66DBDQB4W/QCCofZePGjTogMZpKiFILBhhENhBA4EbrqBkEAQZu/ujRgN8XLVqku4Wiqyi6FaLHAW6GmEn1u+++01Xiic3uR+8UfHNGN1jcgNAFFNX0BvRmQfCDGxJ6uKA8qGmpX7OaFCleUuddtOvYSZb8+ov8OONbqVWvgbzY+zV5+tlukj8oSN+M0QvD08tbsuTIKcXLV5FfVm7Wx75x+4EsXf6NHL4cYhmH4+DRE3L26i058fyrki9PLpk+YbRVedHjBWX87LPPZMiQIfLEE0/o10BgkFw4pxiIDOcEU92jGQcBFppkzPkyuGEnBW74yK8xahDQG+WDDz7QvV2qVaumaxCeeuqpZL8PBCo4Ps4LBsVCl10cH68FCGxw7vC3gkABAUNCu55iP9tuw0SpAeciIUpn1m3eLkf/OWW3HqOEvti5g65Ob/v8q3L+yiWJ9LEeGRSXgwvbdkv2wgUlc1Aeq23eEX5SKF9+WfbTt5JeoPajd+/euoYhLUK3VgRfCHiNQIkoteBfJFE6cvN2sBw7Gfew0ReuXJVihR7mFyC4+KfOOvudiohcvX5ErlY+YrW65LaHA3ilB8bAWKj9QEJlWoXEVNTyMLig1Ih/lUTpyP4jxyRPzkDJmiWzZMkcoH9mzZxZsmYJEN84poy3k+/fRzqGwbqQZ4OmEKOraFqE0VCJUisGGETpSLMG1mNokGPGaKtElHI4VDgRERE5HQMMIiIicjoGGEREROR0DDCIiIjI6RhgEBERkdMxwCAiIiKnY4BBRERETscAg4iIiJyOA20RZVCYWyQxw39jfyKihGKAQZQBYVbUx/k8Isp4OJsqEREROR1zMIiIiMjpGGAQERGR0zHAICIrew8ekdt3QlxdDCJK4xhgEJHFlWvXZdue/bJi3UaJjIxydXGIKA1jgEFEWkRkpKz6e4sg7/tOyF1Zu2mb/p2IKCkYYBCRDiTWb9khofcfWNadPn9B9h066tJyEVHaxQCDiOT4qTNy6ux5u/VoLrl45apLykREaRsDDKIMDs0hG7ftirNmY9WGLVY1G0RECcEAgygDi4mJ0XkXUdHRdtvc3NzE18dH3NxENu/cw3wMIkoUjuRJlIHFxsbq4MLdzU3c3d3ltxWr5dqNW3pbscIFpXWThq4uIhGlUZyLhCgDQ1Dh4+1tWvaw/M7vHkSUHGwiISIL1GQYYmJiXVoWIkrbGGAQkYW7x3+XBKUYYBBR0jHAICLHNRixDDCIKOkYYBCRhYc5ByOWORhElHQMMIjIws2dNRhE5BwMMIjIwsPd3aoLKxFRUjHAICKrbqsGBhhElBwMMIjIYYDBJhIiSg4GGETkMMBgkicRJQcDDCKycGeSJxE5CQMMInJcg8GBtogoGRhgEJGFuxtzMIjIORhgEJGFh2mo8FjORUJEycAAg4gcd1PlbKpElAwMMIjI4VwkHAeDiJKDAQYRxTGbqtIPIqKkYIBBRA6TPIGJnkSUVAwwiMhhDgYoBhhElEQMMIjI4UBbwBoMIkoqBhhEFGcNBhM9iSipGGAQkcPp2iGW85EQURIxwCCiOGsw2ERCREnFAIOILJjkSUTOwgCDiCyY5ElEzsIAg4jirsHgQFtElEQMMIgo7oG2OOEZESURAwwicjhUOMTGxrisLESUtjHAIKK4u6myiYSIkogBBhFZuJlmU4VYNpEQURIxwCAiCw/bJhLWYBBREjHAIKJ4ZlNlDgYRJQ0DDCKKZ6At1mAQUdIwwCAiCw60lXSNGzeWt956y7JcpEgRmTRpkkvL9OGHH8qrr77q0jKkVrNmzZJs2bKl2PFXrFghVapUydATBjLAIKIMUYOxdetW8fDwkHbt2tltGzFihL4ZOEp6/e233xJ0/EWLFsnHH38szrR+/Xpdhjt37iT6uVevXpUvv/xShg4dKukFzgcCN3jppZf05xZXgPe44HURrJw9e9YqSbp169bi5eUlP/30k2RUDDCIyMLD3SPd5mBMnz5d3njjDfn777/l8uXLTjtuZGSk/pkjRw7JnDmzpBbff/+91KtXTwoXLuzqomRYL730kkyePFkyKgYYRGThZtNEkl6qd0NDQ+WXX36Rvn376hoMfOM04PeRI0fK/v379TdQPLDO+Kb89NNP63XGslHbgRt40aJFxdfXN85v0Pfu3ZNu3bpJpkyZJH/+/DJlyhTLNuMb7759+yzrUFOBdfimju1NmjTR67Nnz67X44ZlfC5jx47Vr+/n5yeVK1eWX3/91eq1582bJ+3bt7dahzK++eab8t577+mAKG/evFa1ADBhwgSpWLGiLnPBggWlX79++vzZNi38+eefUrp0afH395fOnTvLgwcP5IcfftDnCeXF68TE/BegRkREyKBBg/R5wLFr166t32dKCQ4OlhdffFGXBWVs06aN/PPPP3b7/fXXX1K2bFkJCAjQtQ5XrlyxbMP57tixo4wfP17y5csngYGB0r9/f4mKikpQGdq3by+7du2SU6dOSUbEAIOI4h5oK500kcyfP1/KlCmjb4g9evSQGTNmWOZZ6dKli7zzzjtSvnx5fXPBA+t27typt8+cOVOvM5bh5MmTsnDhQt0sYg4QbI0bN07f/Pfu3SuDBw+WAQMGyKpVqxJUZtzc8Rpw/PhxXQY0eQCCi9mzZ8s333wjhw8flrffflu/rw0bNujtt2/fliNHjkiNGjXsjosgADf47du3y+effy6jRo2yKhOayfCtG8fFvmvXrtUBiRmCCeyDIAa5BggUEIgtW7ZMP+bMmSPTpk2zCnpef/113UyF5xw4cECeffZZfUM33/SN4M4ZEBzg5v7777/r18Xn3bZtW6vgAO8DwQPKi5qt8+fP6yDIbN26dTpAwE+cD5QvoWUsVKiQ5MmTRzZu3CgZkiIi+ldsbKyaMvMny2PvwSMqPahXr56aNGmS/j0qKkrlzJlTrVu3zrJ9+PDhqnLlynbPwyVy8eLFVuuwr5eXl7p+/brV+kaNGqkBAwZYlgsXLqxat25ttU+XLl1UmzZt9O9nzpzRx9+7d69le3BwsF5nlA0/sYz1hvDwcOXv76+2bNlidexevXqpbt266d9xTDzv/PnzdmVs0KCB1bqaNWuq999/P85zt2DBAhUYGGhZnjlzpj72yZMnLev69Omjy3Tv3j3LulatWun1cO7cOeXh4aEuXbpkdexmzZqpIUOGWJZLly6tFi1apJLCfP5PnDihy7h582bL9ps3byo/Pz81f/78ON/HlClTVJ48eSzLPXv21J9jdHS0Zd2zzz6rP8eEqlq1qhoxYoTKiDxdHeAQUeqBb5D4Bms0jcSqtN9Egm//O3bskMWLF+tlT09PXUOBnAw0GSQF8hpy5cr1yP3q1q1rt5zcniWoPcE37xYtWtjlglStWlX/HhYWpn8azTdmlSpVslpG1f/169cty6tXr9Y1JMeOHZO7d+9KdHS0hIeH69dEUwPgZ/HixS3Pwbd0NI2gmcG8zjjuwYMHdXNJqVKlrF4bzSZodjDgNZ3h6NGj+nNGM4wBr4MaLGwz2L4P23MBqNlCcrB5H7yfhPLz89PnLiNigEFEdl1VjdSL9DCbKgIJ3CSDgoIs61A54ePjI19//bVkzZo10cdEE4OzeuwYTTWQkLZ9Ix9i6dKlOp/BDO8JcubMaclDsA2E0LPBNqg0AkrkfTz55JM6V2X06NE6T2PTpk3Sq1cvHcAYAYajY8R3XJQZN+ndu3db3azBHJQ8bo7KbP484tonMblJt2/fTlAwmh4xwCAiBze+h8l5thfbtAaBBXIVvvjiC2nZsqXVNiTvzZ07V1577TXx9va2Skg031wcrU+obdu22S0joRCMmw5yK4yaB9t8DpQLzGUoV66cDiSQL9CoUSOHr4tv5VmyZNF5GLa1BvFBAICbJ86XEQAhfyW58P7wHlA70LBhQ0lpOMf47JFngp40cOvWLV2bhfP3uISHh+v8DePzzWgYYBCRlWYN6urAAjeYbFmzSFqGng74Fo9v4LY1FZ06ddK1GwgwUL1/5swZfYMvUKCA7m6KmzjWr1mzRurXr6+X0SMhMTZv3qwTKRHMIJFywYIFuubBqDqvU6eOfPrpp7o3CG6+w4YNs2uKwTdmvA8kKOI5KBsSEZHYiWCgQYMGEhISol8LQUXPnj31Z9e8eXNd+4DXTqgSJUroWpSvvvpK94DAMZFImlwIcp5//nndqwPBC264N27c0OcWTTbG2CRIxEXzDBJGk6NkyZLSoUMHeeWVV3SyKc4ZkmxR44P1j8u2bdv0341tU1lGwV4kRGSlcIH8UrRQASlSML9ky5J6xnVICgQQuNE6agZBgIFeBujRgN/RowHdQlGzgJoNwM0QgQF6dCTlWyh6p+A18NxPPvlEdwFt1aqVZTt6s+CbdvXq1XUXV+xjhhsiutDi5oicBvTEAAzohVE6cTPGt3WUHYELAhVD7969dY+NxFTno8cLyvjZZ59JhQoV9CBReA1nQG8cBBg4J8iFQOCDnjnoaWFADQOCpaTA+0Tehfn1cF7R5IMbPIJm9HCxbfJISXPnztWBldG0lNG4IdPT1YUgIiLnwqUdSY6o6cBYHOkdaj8QVNl2M3WVmzdv6kAKAaY58MtIWINBRJQOoWnl22+/1TUk6RmaljA+BWo/mjVrJqnF2bNn5f/+7/8ybHABrMEgIqI0q1q1ajrPZuDAgXooeEo9GGAQERGR07EXCRE5ZHz3MP9EIh1+Gr+jVwMRkSMMMIjICka3RFdFcyCBERexDt390KaP9RjXAHNsmKeoJiIyMMAgIisYmAgBBLr8YdRF/I4hnDFhE8ZGQHdJc3dAIiJHmINBRAmCMRswcBB6JhARPQoDDCJKEAy1jEGRLl68qEe0RNOJMZw0EZEt1nMSkRUEDsivMH4aORc///yznkTLCCoYXBBRfBhgEJEVzGWBeSKQf4HgAoEGZoQ8fPiwjBgxwikziRJR+scAg4is5MuXT3x9ffVMnpi3AQmdqLn47rvv9KBGREQJwRwMIrJz9OhR2bNnj57fARNGGVNPo7uqo4nDiIhssQaDiOxmgOzTp49uHkFQMX78eD0M8/z58/XETWgmyZEjh6uLSU7Ua+BQuXLtRqKfly9PLpk+YXSKlInSPtZgEJGVWrVqSf369WXixIl6EilM3/37779LQECAXr948WI9RTilH22ff1XOX7kkkT5hCX6Od4SfFMqXX5b9xG7L5BhrMIjIyp07d/RgW9C2bVsZNWqUnkyqcOHCcvfuXbl//77ehu8mHMUz/UBw8U+ddQnev+S2JilaHkr72M+MiKy0bNlS9u/fr4MKNIVgiPDIyEg9mid6kCDxk4joUViDQURWunXrJi+//LKcOHFC2rRpI6GhobpZZNOmTbr5pECBAno/1l4QUXwYYBCRlWHDhsnJkyfl2rVrsnTpUj1j6uzZs3XNxtixYyUwMNDVRSSiNIABBhFZ+euvv/RPDLSF0TpZU0FEScEcDCKyYuRYILDAAyN5Yh6SS5cu6XwMIqKEYIBBRFamT5+u8zAw2BasXLlSOnbsKA0bNtRNJQwyiCghGGAQkZWNGzdKkSJFpGTJknp5+PDhuotqr1699JgY2A6o2SAiigsDDCKycvPmTZ3YiblItm7dKv7+/tK7d28ZOnSoHib81KlTri4iEaUBDDCIyAp6iWAMDEBtBZbNXVMxJgZwEGAiig8DDCKy0q5dO9m2bZuutZg2bZqUKlVKSpQoIefPn5fcuXNL3rx59X7sXUJE8WE3VSKy0qVLF53I+eWXX0rr1q2lf//+loDi7bfflnLlyulldGGl9ANziyRm+G/sTxQfTnZGRJTBcTZVSgkMMIiIiMjpWMdJRERETscAg4iIiJyOAQYRWUGraUxMzL+PWHZHzcDw2e/Ye0CiY2JcXRRKg9iLhIisPAgLk39On5NYFSuxsUrKlCgmAZn8XV0scoH9R47Lrv2HJCIyUhrWruHq4lAawwCDiKyE3g+TLbv2Wpbz583NACMDunrjpmz99+/g4NETkj9vHilWuKCri0VpCJtIiMiKu7v1AFqxbCLJcMIjImTl+k1WzWNrN2+TkHuhLi0XpS0MMIjIiu0AWrExnNQsI0FQsXbTNgm9/8BqfWRklA46mI9BCcUAg4jircGI4aypGS7v4uyFSw633bh129JsQvQoDDCIyIq7u4fVMpI9KePlXcQF+Rinz114bGWitItJnkRkxd1mEjM2kWQcOXNkl97dO+vf79y9Jwv+WGHZ1rpJQykY9O9Ed5yHhhKAAQYRWfHwsMnBYJJnhuHp4YE/gIe/e1rXZHl4eIiXl5eLSkZpEcNQIoo/yZM5GESUBAwwiMiKuxsDDCJKPgYYRGTF3baJhAEGESUBAwwiijfJk91UiSgpGGAQkV0OhpspyFCxTPIkosRjgEFEdswBBmswiCgpGGAQkR0PU08S5mAQUVIwwCCieBM9GWAQUVIwwCCieLuqcqAtIkoKBhhEFO9gWxwqnIiSggEGEcU7oyqTPIkoKRhgEFG8NRiKs6kSURIwwCCieHMwYthEQkRJwACDiOKdUVUxyZOIkoABBhHF20TCHAwiSgoGGEQU73wkHAeDiJKCAQYR2eFAW0SUXAwwiCj+gbY42RkRJQEDDCKKf6At1mAQURIwwCAiOxxoi4iSiwEGEcU/0BYDDCJKAgYYRBTvdO2swSCipGCAQUSPGCqcSZ5ElHgMMIgo/oG2OFQ4ESUBAwwiijfJM5aTnRFREjDAICI77KZKRMnFAIOIHjHQFgMMIko8BhhE9IihwpnkSUSJxwCDiB7RTTXGpWUhorSJAQYR2XEzzaaqWINBREnAAIOI7HiYmkg40BYRJQUDDCKKN8mTA20RUVIwwCCiR47kyZ4kRJRYDDCIKN6BtoDNJESUWAwwiCjeGgxgoicRJRYDDCKy4+HuYbXMrqpElFgMMIjIjptNEwkTPYkosRhgEFG8A20BZ1QlosRigEFEj6zBiGUNBhElEgMMInpkDkYsczCIKJEYYBCRHXfTUOHAJhIiSiwGGEQU72yqwCRPIkosz0Q/g4jSvbCwcLkVfEciI6P0vCSXrl6XnDmyW02CRkQUHwYYRGSppdh36KjMW7JM/lq/SWJi/su7WLh0pZQuXlS6dGgr7Zo3En8/P5eWlYhSPzfFuk+iDG/f4WPyycT/k+OnzujlsIAQuZ/tlsR4Rol7rId4h/lLllt5xS3WXTL5+8mLz3aU117sajfiJ6UfwSEhMnfxUstyu+aNpXCBIJeWidIW1mAQZXBrNm2V9z4eJxFRkXInzyW5HXRWwrLcEbFpDfGI9JbsVwtK4OUiMvWHuXLq7HkZ+8E74u3t5aqiE1EqxgCDKAPbvme/vDPiM4lyi5BzlXbK/ey34tw3xjtSbhY6JbeDzknBw9Vk5YbN4uXlJWM/GMjcDCKyw/pNogwq5O49GfDRaIlWUXKm4rZ4gwuzWM9oOV9xl4RmuylLV6+Xeb8tS/GyElHawwCDKIP6bcUauX8/TK4WPSphWe8k6rnKPVYulN8tyiNGfly4RGI5nTsR2WCAQZQBISD45fdlOkAIznchSceI8YqS23kuyPlLV2Tbnv1OLyMRpW0MMIgyoG2798mFS1d0gIAmj6RCPgb8soTNJERkjQEGUQa068Bh/TMk9+VkHSci4J6E+9+VnfsOOqlkRJResBcJUQZ09+49/TPaOyLZx8IxQkMe6GYXjouRfnh5ekqxwgUty/5+vi4tD6U9vBrQY9O4cWN56623nHKs6dOnS8uWLZ1yrIzImH5duSV/nD0cA+P1ccy+9CUgUyZp3aSh5ZErMIekdV27dpUvvvjC1cXIMBhgkJWXXnpJj2lg+2jdunWCj7F+/Xr9nDt3rHsmLFq0SD7++GPLcpEiRWTSpEmJLmN4eLh8+OGHMnz4cMlIbt++Lc8//7xkyZJFsmXLJr169ZLQ0NBEHWP06NFSr149+WTwQDmycYV4RnknrTA7RGSiiHwscnXdEVFRYeLh4WH1GfXv318CAwMlICBAOnXqJNeuXbM6xPnz56Vdu3bi7+8vuXPnlnfffVeio6Pt/paqVasmPj4+UqJECZk1a5ZdUaZMmaL/lnx9faV27dqyYwcK9x+WxXFZnA01WD/++KPLzguuLy1atJBcuXLp/yN169aVv/76y+oYw4YN0/8HQkJCnP7+yQEMFU5k6Nmzp2rdurW6cuWK1eP27dsJPsa6devwVVYFBwfHu1/hwoXVxIkTE13GOXPmqNKlS6uUFhsbq6KiolRqgc+lcuXKatu2bWrjxo2qRIkSqlu3bok6xkcffaQmTJignnm2i3L38FR5XiyjZIQk7tFZlHiIkg6iPHv5qOz5CipvH1917do1y+u89tprqmDBgmrNmjVq165dqk6dOqpevXqW7dHR0apChQqqefPmau/evWrZsmUqZ86casiQIZZ9Tp8+rfz9/dXAgQPVkSNH1FdffaU8PDzUihUrLPvMmzdPeXt7qxkzZqjDhw+rV155RWXLlo1lSUBZUkJMTIwKDAx0yXkZMGCA+uyzz9SOHTvUiRMn9DYvLy+1Z88eqzLWqFFDff311yl6HughBhhkF2B06NAh3n0QPHz33XeqY8eOys/PT9/olixZoredOXNGbzc/cExo1KiRvggYv9vuFxoaqjJnzqwWLFhg9XqLFy/WF9S7d+/q5Xbt2qlBgwY5LPeIESP0hQfH6dOnj4qIiLC6+I0ZM0YVKVJE+fr6qkqVKlm9lhEY4eJVrVo1fXHCun379qnGjRurgIAAfVxs27lzp+V5v/76qypXrpy+iCJoGj9+vFXZsG706NHq5Zdf1sfAhXTatGmJ+lxwI0HZzK+7fPly5ebmpi5duqSXcfyKFSuq8PBwvYz3XqVKFfXCCy/YHe/7779XHl5eqmyLlsrtI3frAKKvKCkhSrxESSZRUkmUvGvanl+U1Hz4OwKU8o3aqcCcOdXYsWP1se/cuaPPnfncHj16VJd/69atehnn2N3dXV29etWyz9SpU1WWLFksn9l7772nypcvb1XuLl26qFatWlmWa9Wqpfr372/1GQcFBbEsjyhLSsKXBlecF0fw/3LkyJFW67DcoEEDJ71big+bSChJRo4cKc8995wcOHBA2rZtq6vuUYVfsGBBWbhwod7n+PHjcuXKFfnyyy/tno/qzAIFCsioUaP0PnhkypRJt5HOnDnTal8sd+7cWTJnzqyXN23aJDVq1LA75po1a+To0aO6+nju3Ln6NVBOw9ixY2X27NnyzTffyOHDh+Xtt9+WHj16yIYNG6yOM3jwYPn000/1sSpVqqTfG8q6c+dO2b17t96OIbIByzgPKPfBgwdlxIgRuvnGtpoY7b4o8969e6Vfv37St29ffX7M+SlonorL1q1bdbOI+X03b95cJ1Vu375dL0+ePFnu37+vywdDhw7VzVRff/213fHQnOHt6SUeUd6S5Xq+/zaEicgPIpJXRF4VkR4iglaYBf9uR400Op4UE3GLcZccVwpJzsDs0qZ1a11G45xERUXp8hnKlCkjhQoVsuyDnxUrVpQ8efJY9mnVqpXcvXtXfzbGPuZjGPsYx4iMjNSvZd4H5wPLLEv8ZUlJaAJxxXlx1GRz7949yZHDOnekVq1auokmIiL5Cc4UP/YiITt//vmnbgc1++CDD/TDgJtht27d9O9jxozRNzf8p0WuhvEfGu2kuCk6gn1wk0PQkDcv7mYP9e7dW+cIIODIly+fXL9+XZYtWyarV6/W23HDRPtpUJD9rI7e3t4yY8YM3UZbvnx5HbygnRZ5H7iAoZw4DtpmoVixYjpYmTZtmjRq1MhyHDwPbbnmtl8cBxc9KFmypGXbhAkTpFmzZjqogFKlSsmRI0dk3LhxVgEDgjAEFvD+++/LxIkTZd26dVK6dGm9DhdTvN+4XL16VZ9PM09PT30esQ3wmaENHO8F5xX5LXgNtEc74uXlKe7ubhJ0qryEZQ2WSL8HD3MrUAzz/avDv/kWN3GS/61vyiQSdKKSDlCea99Gzh7ZJydOnLCUFZ+F7WePm4NRVvw03yyM7ca2+PbBTSUsLEyCg4P1lPKO9jl27BjLEk9ZUhL+3lxxXmyNHz9e5yjhC4AZrh0IfPC8woULO+EdU1wYYJCdJk2ayNSpU63W2X4LwDd7A2oecFFBMJBc+HaB4OCHH37Q38Rxw8RF4IknntDbcdECJIvZqly5sg4uDAgkcIG5cOGC/vngwQOrwAFwoalatarVOtvakYEDB+rAZ86cOfpb1rPPPivFixfX21DL0aED7sD/qV+/vr6548JqJD6azxcSYBFUmc8XalacAe950KBBOqhCINOgQYM4v93FxMZKo7q1ZN3m7VJkfx05W2m7RF67L4IZ20c7eFIwruoPf815oZhkjyogNSpXkN7dn5Vhw/Y5pfyUujRt2jTBvYMQzKYWP//8s669XLJkiV1g7ufnp3/iekApiwEG2UHAgMz0+BhNBOabprPmo8DNHFnnCDDQPPLyyy9bZutEljl+xzejxDB6WyxdulTy589vtQ2Z+Lbv3wzNHt27d9fPXb58ue69Mm/ePHn66acT/PrJPV+2AQkggx7NUuYaIBxz8+bNOrA5efKkw2NFx8TIwWMnJDYmVsqUKCYREZGyZddeKbGngfwTslGiS4aLaumgbJlEAu7kllC36+J/OYdUaFhKJn08VE/Xjox/oxz4icANtU3mb6W2+9j2JDB6DZj3se1JgGUEs7hJ4D3i4WgfliX+siRUlSpVrJZRE4hmUTx69uzpcNwTNEu44rwY8H8T15AFCxbYNSUB/s8AeptQymIOBjkdqjsB3+AftZ+jfZAXce7cOd3sguYGXMjMzylXrpxeb2v//v2WGg7Ytm2bbjZAXgieg0ACzR0InswPbH8UNH0gZ2PlypXyzDPPWPJEypYtq2/oZljG/uZum86omcAFGO3VhrVr1+qAAm3eBjTNoOoZeSUrVqywy2eJjIySpavWy83b/wVo1SqVl3f79Zasvtkkm0eQeF3wk/yXq0j28EKSNTpIskUWkNx3S0mZvc2lyLFa4heQTQIDvGXGxNGSNXOALgPyX4ymp+rVq+uACusMyDfBuTf2wU/krJiDplWrVukbEz4rYx/zMYx9jGPgbwGvZd6HZUlYWRIKTYDmx1dffaX/tlCrh/9P5m0G/P254rwAcq/whQQ/0aXVkUOHDumcqpw5cybqXFASxJsCShlOXN1Ub9y4YdkHfzbo2WGWNWtWNXPmTP37xYsXde+GWbNmqevXr6t79+7Z9SKBFi1aqKeeekrvbz4+dO/eXffKQFlsoXtep06d7MqNHhrotomucEuXLlV58uRRgwcPtuwzdOhQ3YUO5Tp58qTavXu3mjx5sl6Oq3vtgwcPdAY8tp09e1Zt2rRJFS9eXGfyA46B7PZRo0ap48eP62OhZ41xLuLqjovupsOHD7cso6eHuayO4FxUrVpVbd++XZejZMmSVt1U0R0P5+z333/Xy+ipgl4vp06d0sth4eFq6ozZavDI0apdx07Kx9dXfTj6c/XXqjX6M8L27+fMVT6+fipLrnyqWLUGqmTtJqpwpVoqW94Cqk67Z9W4//teff1//6d8fHz0e0XvlldffVV3OzRn+KPbYaFChdTatWt1t8O6devqh223w5YtW+peOujWmCtXLofdMd99913dq2DKlCkOu2OyLEkrS3Lg/0+OHDns1qOHyJtvvumS8/LTTz8pT09PfT7M1y70UrG9Vvzvf/9zynmg+DHAILv/fLbdR/EwjzvxqAADcMPNmzevDjQcdVMFdEFDV1FcfGxjXfSHx7r58+fblREBBG7i5guH0U0V4zwgiECwgf72RpdNY1yLSZMm6feCbnG4QKE73YYNG+IMMNAFrmvXrrprKW7e6F73+uuvq7CwMLtuqjgmLpLjxo2zKm9CAgycG+M8xeXWrVs6oMB7Q/c8dEs1gjeUB2XAxdsMARzGEwi5e1fNXfynql2/ocPPF+/dgDEEmrdoqV/H28dHFShYSD3Xtbu6/+CBZR+MvYD3inOCbogYm8MM5enXr5/Knj27vhk+/fTT+mJvhoCtTZs2+rNE1+J33nnHbtwRlAtdbfE6xYoVs/obY1mSV5bkmD17tv7/bQuBgNG19HGfF0dd383d5I3XwbXKURnJ+dzwT1JqPohSEhIq0SRx+fJlS5OLGRItMZLhkCFD9DJ6bKAJ4bfffnNBaVO3u6Gh8sdfayXk3n+jfmby95f2LZtIjmxZXVo2St1s84xwu0APr127dslHH32U5kbTRfL64sWLdVMnpTwmeVKqgsxuXMAwDkWfPn0cBhdGrsEff/zx2MuX1ty+EyJ/rFwn900Z88ibaN+qqWSx6YpMZCt79uxWy0jqRM4Dunyje3Zag1wP5JHQ48EaDEpV0GMDcwWgWyq6mNmOxxEX1mDYu37zlvy5ar2EmwYUQo0Fai5Qg0GUEOgxgh5JSMbkuBGUGAwwiNKhy1evy7I1GyQyKsqyLnfOQHmyRWPxtemWSxQXjMKLHiP45o+u1ag1RNdP9PBCDzA0YxLFhd1UidKZcxcvyx+r1lkFF/nz5pGnWjZlcEGJgqZIjDprzHaKpktjUDvbLtBEthhgEKUjJ8+c0zUX5vFFihTML+1aNNYDYhElBpod27dvr3/HkNvG8N5FixaV06dPu7h0lNoxwCBKJ46cOCmr/t5iNbRzqWJFpFXjBuLpxEG/KONALhTm6zGmC8AcI4Dgwnb6ACJb7EVClA7sO3RUD/dtVqF0SWlYp4ZlmHWixMJMwhiyHyPrYoh9DE+P2ZIxuZ9Rs0EUFyZ5EqVh+O+7c99B2bX/kNX6ahXLS+1qlRhcULI4Gu4e8wGhueSzzz6zm7eHyIwBBlEahf+6m3bsloNHH06TbqhTvbIOMIiSy2gSMWBcGkczGRM5wgCDKA3ChFGYZv34Kcyt/hBqK56oU0PKly7p0rIREQGTPInSGEy3/tf6TXbBRbOGdRlckFO9+eabVmNdTJ8+XXdRffLJJ/Vsp0TxYYBBlIZERUXJstUb5Mz5i1bt5K2bNNQ9RoicacWKFdKmTRv9+6VLl6Rv377SuXNnnez5+uuvu7p4lMqxFwlRGoEhvxFcXL1x07LOy9NT2jZvpAfSInK2ixcvSqlSpfTvS5culVq1aukeJEeOHJEGDRq4uniUyrEGgygNeBAWJktWrLEKLnx8vOWpVs0YXFCKyZIli9y+fVv/jhlIMUw4+Pv7S2RkpItLR6kdazCI0sJ06yvXScjde5Z1mfz95MkWTSQwezaXlo3SNzSPYIjwJk2ayJ9//mmZnh01GBjNkyg+7EVClIoFhzycbj30/n/TrWOadUy3jmnXiVJScHCwDjAQULzyyiv6d8DonqGhodK6dWtXF5FSMQYYRKnUzdvBOrgICw+3rMue9eF06wGZON06EaVubCIhSoWuXLsuSzHdeuR/M6LmCsyhp1v340BHRJQGMMAgSmUuXLoiy9f9LdHR/82IGpQ3t7Rt2ogzohJRmsEAgygVOXX2vJ4RFSN1GgoXCJKWjRvoLqlERGkFr1hEqcTRf07J+i07rKZbL1G0sDRrUMfhpFNERKkZAwyiVGD/kWOyecceq3XlSpXQc4u4u3O4Gnr8UIsWEfHfWBdonmOgS4nBAIPIhVBbganWMeW6WZUKZaVu9Sqcbp1cJuTePZm7eKlluV3zxrq5jiihGGAQuTC42LJzr669MKtdDdOtl2NwQURpGgMMIhcFF3dD78vhEyet1qNJpEKZh3M/EBGlZWzcJXIB1E5kzuQvbZs9IR4e7pbp1hlcEFF6wRoMIhdB8mZQ3jzSslEDUaKkWKGCri4SEZHTMMAgciF3NzcpUjA/8y2IKN1hEwmRk5nHsQgJCbEaNMsRBhdElB4xwCByIgQTRsDw448/yqBBg2T79u1WQQcRUUbAAIPIiYxBsSZMmCD9+vWTsmXLSt68ea1qKRhsEFFGwBwMIidbuXKljB8/Xn777Tdp2rSpZf2VK1ckX758OthATQdH6CSi9IwBBpGT3bt3T4oWLSq1a9eWy5cv60Bj9uzZEh0dLTVr1pSpU6cyuCCidI9XOaJkcJTAGRUVJefOnZM+ffrIE088IevWrZP69evLc889JytWrJCtW7e6pKxERI8TazCIkigmJsYy+RN6i3h6ekqmTJmka9eucu3aNTl69Ki89957upmkRIkScuLECZk7d65kzZrV1UUnIkpxDDCIkhlc9O3bVw4cOCDe3t5Sq1Yt+eyzz2TAgAESEREhPj4+OqkTzSNjx44VLy8vCQrihFFElP4xwCBKAiO4QBMImkR69eqll1977TUdfCDJE8EFajZmzZqlm0kOHz4sW7ZskWzZsrm49EREKY85GERJ9PHHH4ufn5+sXr1aevfurXuJIKhAF9WBAwfqfdAccvv2bd17BLUcuXLlcnWxiYgeC9ZgECWQbddSjG/xyiuv6LyLYcOGycyZM+X333+XY8eOyRtvvKG3Iwdj5MiRLi03EZErMMAgSoDIyEidY/HgwQMJDg6W/Pnz61oL5FegV8iiRYtkxowZ0qxZM70ONRmDBw+WYsWKSefOnV1dfCKix45NJERxWLBggUyZMkUHDAguDh48qHuEIO+iW7du8tdff+kajZMnT+okzlatWlmei1yMv//+m8EFEWVYrMEgcgBBxdKlS+X48eOSI0cOHVj06NFDGjZsKJUqVdKJm+PGjdM1GuXLl5ezZ8/qnIyKFSvK22+/LS+88II0aNDA1W+DiMhl3BQnRiByCLUSGCwLo3Gi1uLSpUsyceJE3dX0n3/+kffff1/3EunZs6eEhobKiBEjJHv27LomY/Lkya4uPlGyBIeEyNzFSy3L7Zo3lsIF2MWaEo5NJERxwMBZGNYbSZxoKkFTCIILKFmypHz44Ye6F8nChQt1rgVqMZYtW8bggoiIAQZR/JB7gWnXq1SpIqdOndLNJoaqVavq3iOoxfj00091LUbx4sVdWl4iotSCORhEj+Dr6yvz5s2TDh06yPTp0yUgIEAaNWqkt9WpU0cHGWhpzJ07t6uLSkSUajDAIEoABBXz58/XQQaaSxB0YLZUaN68uauLR0SU6rCJhMiB+w/C7NYFBgbqrqtI+sS8Ivv27XNJ2YiI0gIGGEQ2Ll65Kj8v+kMOHftHN32YYchvjNiJ4b85pwgRUdzYREJkcub8RVm5YbOesOzvbTvF29tLShYtLG5ubpZ90INk1apVerROIiJyjDUYRP86cfqsrFi3UQcXhjPnLoqjgWIYXBARxY81GEQicvj4P/L3tl1WTSKlSxSVJvVqi7up9oKIiBKGAQZleHsOHpZtu/dbratYtpQ0qFXdqmmEiIgSjgEGZViordi2Z7/sPXjEan2NyhWkZpWKDC6IiJKBAQZl2OACSZyHj5+0Wl+vZjWpUr6My8pFRJReMMCgDAdJnGs3bZN/zpyzrENtRaO6NaVcqRIuLRsRUXrBAIMylOiYGFm5fpOcvXDJss7d3V2aN6wrJYoWdmnZiIjSEwYYlGFERkbJ8rV/y6Wr1yzrPD09pFXjhpyGmojIyRhgUIYQHhEhf65aL9dv3rKs8/bykrbNGklQXk5SRkTkbAwwKN27/+CB/LFyndy+E2JZ5+vjI+1bNpFcgTlcWjYiovSKAQalayH3QuWPv9bK3dBQy7pM/v46uMiRLatLy0ZElJ4xwKB0CzUWf6xcazUzatbMAdK+VVPJEhDg0rIREaV3DDAoXUKuBXIukHthQI0Fai5Qg0FERCmLAQalO5evXpdlazZIZFSUZV2eXIHSrnljnXtBREQpjwEGpSvnLl62mxE1f9480rbZE+Ll5eXSshERZSQMMCjdwMicq//eYjUjapGC+aVl4wbi6eHh0rIREWU0DDAoXcCcIphbxBxclCpWRJrUry0eDC6IiB47BhiU5u07dFS27Nprta5C6ZLSsE4NzohKROQiDDAozUJtxY69B2T3gcNW66tVLC+1q1VicEFE5EIMMCjNBhcbt++WQ8dOWK2vW72KVK1YzmXlIiKihxhgUKpm5FSYayNiY2Nl3ebtcvzUGcs6bH+iTg0pX7qkS8pJRETW3G2WiVKVW8F3ZNue/VbTrf+1fpNdcNGsYV0GF0REqQhrMCjVD/e99+AR8fH2koplSsnytRvl4pWrlu3oIdKqcQPdHZWIiFIPBhiU6mswYNvu/XL0xCk9eZnBy9NT2jZvpAfSIiKi1IUBBqVqt/8NMMAcXPj4eMuTzZvoIcCJiCj1YYBBaaIGw8zP11eeatVUArNnc0mZiIjo0ZjkSalWRGSkhN5/YLcek5iFhYe7pExERJQwDDAoTTSPmGEis+Vr/parN24+9jIREVHCsImEUnUPEnNXVH8/X8nk7y8BmR4+gu+ESJ6cgRyxk4goFWKAQSmq18ChcuXajUQ/L1+eXDLuo/fkmbYtdFCB4IKTlhERpR0MMChFIbg4f+WSRPqEJfg53hF++meObFlTsGRERJSSGGBQikNw8U+ddQnev+S2JilaHiIiSnlM8iQiIiKnYw0GERFZCQ4JkcXLVul5gKKiovSouVFR0dKlY1vJmyunq4tHaQQDDCIi0jMXHzx6Qn5ZskyWr/tbBxRmew8dlRnzFkqT+rWlS4e2UqdaZfbgongxwCAiyuAiI6Pkw8+/lGVrNujl8Ex35XaRc/Ig8x2J9YwW9xgP8b2fWbJfLixrNm7Vj9pVK8mEUUMkS0CAq4tPqRQDDCKiDCw8IkL6DxklO/YekPtZb8m1osfkQdZgEZvKifDMd+VO3kviey+L5D5XSrbvFen55vsyY+IYyZ6VPb7IHpM8iYgyqNjYWPlgzAQdXNzJfUnOVt4mD7LZBxe2gcb58rvkRsFTcvLMeXlz6Cc6SCGyxQCDKJVr3LixvPXWW0451vTp06Vly5ZOORalfes2b5dVf2+R0Ow35GKZfaLcVcKe6CZyrdhRCc57XvYdPiYL/liR0kUlF+ratat88cUXiX4eAwwiJ3jppZd0wpvto3Xr1gk+xvr16/Vz7tyxnoNl0aJF8vHHH1uWixQpIpMmTUp0GcPDw+XDDz+U4cOHS0Zy+/Ztef755yVLliySLVs26dWrl4SGhibqGKNHj5Z69eqJv7+/PkZSTZkyRX9+vr6+Urt2bdmxY4fdZ9S/f38JDAyUgIAA6dSpk1y7ds1qn/Pnz0u7du10WXLnzi3vvvuuREdH2/0tVatWTXx8fKREiRIya9Ysh2Xp+GQbObxhmZzbtlPksk1wESUiS0XkM5wAEflFRMynzU3kcq7Dcvbgdnmpa6dklyU1nReWZZbV9mHDhun/AyEh/03fkCCKKAW16f6KKtm6iZIRkuAH9sfz0pKePXuq1q1bqytXrlg9bt++neBjrFu3Dld4FRwcHO9+hQsXVhMnTkx0GefMmaNKly6tUlpsbKyKiopSqQU+l8qVK6tt27apjRs3qhIlSqhu3bol6hgfffSRmjBhgho4cKDKmjVrksoxb9485e3trWbMmKEOHz6sXnnlFZUtWzZ17do1yz6vvfaaKliwoFqzZo3atWuXqlOnjqpXr55le3R0tKpQoYJq3ry52rt3r1q2bJnKmTOnGjJkiGWf06dPK39/f13WI0eOqK+++kp5eHioFStW2JUlf+nKKl+zikqqiRJfUTLI9H+xhijJIkpeFCWvipICoqSgaftHoiS3KO88Aap49YZq/KSvklWW1HReWBYPq7JAjRo11Ndff60SgwEGpaiMFGB06NAh3n0QPHz33XeqY8eOys/PT9/olixZoredOXNGbzc/cExo1KiRGjBggOV32/1CQ0NV5syZ1YIFC6xeb/HixfrCcffuXb3crl07NWjQIIflHjFihL7w4Dh9+vRRERERln1iYmLUmDFjVJEiRZSvr6+qVKmS1WsZgREuXtWqVVNeXl563b59+1Tjxo1VQECAPi627dy50/K8X3/9VZUrV05fRBE0jR8/3qpsWDd69Gj18ssv62PgQjpt2rREfS64YKJs5tddvny5cnNzU5cuXdLLOH7FihVVeHi4XsZ7r1KlinrhhRfsjjdz5sw4A4yDBw/qYCZTpkwqd+7cqkePHurGjRuW7bVq1VL9+/e3Oq9BQUFq7NixevnOnTv63JnP7dGjR3X5t27dqpdxjt3d3dXVq1ct+0ydOlVlyZLF8pm99957qnz58lZl69Kli2rVqpVVWeo3aqoqNH5SZX49z8NgIbMoafbv/8PBosRdlDxr+r/Z/9+/uV7/Lj8vStxE+fTNpI/z9vCxSS5LajovLIuyKwuMHDlSNWjQQCUGm0goxWFuEQz/ndCHMRdJejRy5Eh57rnn5MCBA9K2bVtddY8q/IIFC8rChQv1PsePH5crV67Il19+afd8NJcUKFBARo0apffBI1OmTLqNdObMmVb7Yrlz586SOXNmvbxp0yapUaOG3THXrFkjR48e1dWkc+fO1a+BchrGjh0rs2fPlm+++UYOHz4sb7/9tvTo0UM2bHjYpdEwePBg+fTTT/WxKlWqpN8byrpz507ZvXu33u7l5aX3xTLOA8p98OBBGTFihG6+sa2aRbsvyrx3717p16+f9O3bV58fc34KmqfisnXrVt2kYX7fzZs3F3d3d9m+fbtenjx5sty/f1+XD4YOHaqbqb7++mtJKOzftGlTqVq1quzatUtWrFihq6rxHiEyMlK/Z7y2AWXAMsponBMMamXep0yZMlKoUCHLPvhZsWJFyZMnj2WfVq1ayd27d/VnY+xjPoaxj3EMoyzZcufVy/ez3XzYWF5MRC7++4TLyAD9d50hl4hkNe1zQURyi0TkuS8xXlFy4dKVJJclNZ0XlkWsymKoVauWbqKJSERCL7upUorCrKiP83mu9Oeff+p2ULMPPvhAPwy4GXbr1k3/PmbMGH1zw39a5GrkyJFDr0c7aVzt/NgHs8oiaMib9+ENAnr37q1zBBBw5MuXT65fvy7Lli2T1atXW26AaD8NCgqyO6a3t7fMmDFDt9GWL19eBy9op0XeBy5gKCeOU7duXb1/sWLFdLAybdo0adSokeU4eF6LFi2s2n5xHFz0oGTJkpZtEyZMkGbNmumgAkqVKiVHjhyRcePGWQUMCMIQWMD7778vEydOlHXr1knp0qX1OlxM8X7jcvXqVX0+zTw9PfV5xDbAZ/bjjz/q94LzivwWvAZyNhIKwQiCC5wrA84pAscTJ07o14iJibG60AOWjx07ZikrPgvbzx77GGXFT0fHMLbFtw9uKmFhYRIcHKzLIm4eotyUxHrEPNwpk4jc/PcJyLXA5MW2sX4mUx4Gfv775x7tESl3Q0OTXJbUdF5YFrEqi5/fwz8CXDsQ+GD/woULS0IwwKAUNX0CssMyhiZNmsjUqVOt1hlBgwHf7A2oecBNDMFAcuHbBYKDH374QX8Txw0TF4EnnnhCb8eFApAsZqty5co6uDAgkEAS5IULF/TPBw8eWAUOgAsNbqhmtrUjAwcO1IHPnDlz9DekZ599VooXL663oZajQ4cOVvvXr19f39xxYUUQZXu+kACLoMp8vlCz4gx4z4MGDdJBFQKZBg0aJOr5+/fv10GJbYAJp06d0uc4tfH08hQ35SaCh1sCe4/EwV15iI+3t9PKRqmPEWjgepBQDDCInAQBAzKw42M0EZhvmhiLwBlwM0fWOQIMNI+8/PLLlqGckWWO3/HNKDGM3hZLly6V/PnzW21Dxrnt+zdDs0f37t31c5cvX657r8ybN0+efvrpBL9+cs+XbUACyKBHs5S5BgjH3Lx5sw5sTp48KYmF89S+fXv57DN0ubCGGha8DxzbNsMfy0Y58BOBG2qbzN9Kbfex7UlgHNO8j6PXQTCLmwTKgYe7TgsS8Q73k0j/ByL3/6uR0D9RsRFmU4thu88lEfdoD/GM8pasmTMnuSyp6bywLGJVFgP+z0CuXAmvXWYOBlEqgepO0NXXj9jP0T7Iizh37pxudkFzQ8+ePa2eU65cOb3e0bdvo4YDtm3bpr+Jo3ofz0EggeYOBE/mB7Y/Cpo+kLOxcuVKeeaZZyx5ImXLltU3dDMsY3+j9sJZNRO4AKO92rB27VodUKDbnwFNM6h6Rl4J8ids81keBV380KaNLoW25wmBF85/9erVdb6LAWXAstH0hO0IRMz7IN8E597YBz+Rs2IOmlatWqVvBvisjH3MxzD2MY5hlCX6wV29nP1qwYf5FqdFpMC/Twj69+5wxnQQNJ+EmPbBx39dJPO5vOIW6y6N69dKcllS03lhWcSqLIZDhw7pnKqcORMx2V2iUkKJKFHdVM29CPDfDT07zNAjAT0T4OLFi7p3w6xZs9T169fVvXv37HqRQIsWLdRTTz2l9zcfH7p37657ZaAsttANrVOnTnblRg8NdNtEV7ilS5eqPHnyqMGDB1v2GTp0qAoMDNTlOnnypNq9e7eaPHmyXo6re+2DBw90Bjy2nT17Vm3atEkVL15cZ6wDjoHs9lGjRqnjx4/rY6FnjXEu4uqOi+6mw4cPtyyjp4e5rI7gXFStWlVt375dl6NkyZJW3VT37Nmjz9nvv/+ul9FTBb1eTp06Zdnn3LlzupsfMulxvvA7HsZnhB4puXLlUp07d1Y7duzQ5wnd/F566SXdVdDodujj46PfK3q3vPrqq7rboTnDH90OCxUqpNauXau7HdatW1c/bLsdtmzZUvfSwWvgdR11O3z33Xd1r4IpU6Y47AKJspSqVlcVq1sv7m6qWUVJT1M31QL23VT9cmZXpWs3UQt+XZissqSm88KyeNh1U8W14n//+59KDAYYyWR78U/qGAXONGzYMN1nOiNy1fnHfz7b7qN4mMediCvA+OSTT1T+/Pl1d1PccPPmzasDDUfdVAFd0NBVFBcf2+8I6A+PdfPnz7crIwII3MTRvc22myrGeUAQgZsn/naMLpvGuBaTJk3S7wXd4nCBQhe2DRs2xBlgoAtc165ddddS3LzRve71119XYWFhdt1UcUxcJMeNG2dV3oQEGDg3xnmKy61bt3RAgfeG7nnolmoEBigPyoCLtxkCOIwnYAQHcX2+eO+GEydOqKefflrfBHCey5Qpo9566y19/gwYYwDvFecE3RAxNocZytOvXz+VPXt2fdHH8RComiFga9OmjX4NdC1+55137MYdQbnQ1RavU6xYMavAzVyW7DkClZubu/LI5a2kt02X8aGipOa/gYeXKCkjSt6x3sf/pewqIEcu5enlleyypKbzwrLMtHsdXKuM7q8J5YZ/JB1DVxskbCFLH23Btm3Ev/32m+zbt8+unXfx4sXSsWPHRx4f7VKopjK6AqKKFMM6J2doZ3QXRMIg2ssTO2ogMnxRzYxqsYRm+qZ2OB/oWXD27Fn9E+cYn53RTbFKlSqWkS1v3Lihq6TNSYuphbns+Bs7c+aMXgZ0J0UioNGrIqmQUIkmicuXL1uaXMyQaInq/CFDhljKhCYE/D+gjOfajVvy9P/6y90H9+RMJcxD8rCdPSF87gdI8X0NxFv5yNypE6RMCXOfVkpPkLyOeyKaOhMj3edgYO6FN954Q/7++2990XUWJNcYvQSM4CI1+P7773V3xfQSXCQWEpBSY3DxKEjIxH9i2yF8EwqZ3eitgHEo+vTp4zC4MHINHPV0oIwpT65AmfzJMPFy95KiB+tI5pvW3RXj4heSTYrtqy/uUZ4yZshABhfpnJeXl3z11VeJfl66DjCQ2f3LL7/owXkwFrt5EB/8jsGEkOBmzBuBdcY3SmS6Y52xjG+d+KaMG3jRokUt3f0cTUR17949PdYBvkkj8x6Z/QZ8C8dxzbUm+AaJdfimju2ovYDs2bPr9ca4AEjywaBHeH1k9+Ib76+//mr12sjSRza7Gcr45ptvynvvvacDImQNGzUA5nEJMCALyozkPYw9YJ6vAecGtSkY6wFjEOAmjm/duLGhayTOE8qL1zEnIGJQFnT/w3nAsZFYh/eZUmzn6cD5w3gNTz75pC4zkgtRq4WeAjgvKBMCMtycDcZnjXEMMM4Cbsg4H3hfn3/+uT5/GFsBY/Mb/ve//+nXMMMYEtgPQe6joBsoasNsB69KKJQL402gbEbtRFznBwE3kaFG5QoyZexwyeTtL4UP1ZSie+pJ1mtBOnHTihLJfDO3FD5QS4rvbSDesb4yZsjb0qbpw67QlH717t3bMvZMoqh0bPr06Xr8dPjjjz90kpnRHookNLRFYYhUIyEP65Bch9OCNiiswzKg3RdDACNhDElh+/fvjzMHAwliGM4VyWtIhkPCzMqVK62GhEaCmAFt10Z7Ltp8Fy5cqJfxfJTBaDNHWz3adZF8gwQ0lBHt8OvXr7e0NaPt3radDmVE2zOGg0Y78Q8//KD3M8oEaOtGohDKh3Z8tLf37dvXsh2vhbZyJBji/aP9HW32SB567rnndPs+zjHa8JCcZOjdu7duy/7777914hva2VFmlMNgnO+44LzgvBpt4bZt8PHlwODYyG/45Zdf9PnEMN0Y8rpp06b6PCJ5CuP3m5MicXy01yNhD+8LyX94X8g7eOONN9SxY8f0/AA4tnGuN2/erD/ny5cvW46zaNEi/TdjtPeby47n4lyb1a5d2+q9ET1OJ06fVW8O+0RVbNJeD/1dtmVLVaRjHVWocw1V5OnaqnSr5no9Hr0GDlW7DxxydZEplUvXAQZubEhOAyS1IMHFnJSFizmSxmw5SsbDvrjBGgFHfDc42wx+jOuOJJuEBBhxJc0h6Q5JPFu2bLE6dq9evSwZ8Tgmnnf+/Hm7MtqOIV+zZk31/vvvx3nuML49AggDAgAcG0GCAXNWoEzGDRRwE8Z6I/MeN11jzgdDs2bNrLKYEczgZpwUCQkwkPRqQJIS1iH4NMydO1fPsWH+rM1zeBjvC4EJ5gMwl9uYFwCQLPjZZ59Zltu3b697ESQUErQSsz9RSrhy7bqaPH2OavZsT1Wp6cNgA0FHw47Pq0+/+ladOmd9fSGKS7odaAv9gTHACBJTjOGBu3TpoqurUTWeFMhrSMggI7b9h7GclOm1zVCl/6gRFeMbrdE8IiIYw0kbMBQ0ml8wFgCGiEUuAKYCxmsaOQ34aYzEaAwniyp3c5s+1hnHRaIpmhWQdGqGZhMM/GQwhr5NKeb3bgyJi+Yg8zq8V7xvY3hovC9zbg320YMTubs7fK9GNeK3336rm6IwUA0Gl8KYCwmFZq/EjJJHlBLy5s4lb/yvh36gWTY8IkKP0unM8UkoY0i3AQYCCdwkzXMv4AstBg3CvAFZs2LWnsSxHakwKYwblLnzDtrqnTGiojEACnqf2AZC8Y2IiLwP5A8gVwV5BcjTwFwTvXr10gGMEWA4OkZ8x0WZcVHCIEe2F6fHmWhoLqMxsqWjdeYRIhP7XuHFF1/Uo2gix2PLli06V6Zhw4YJLidyMMwBHJGr4XrlbxrNkUgyeoCBwAJzFGAmxpYtW1ptQ9dTzBj52muvxTkiIm4kjxpNMT4YCdF2GcmFYNz4MSmVUfNg203W0YiO5hEVzRNMmeHmhG/gGK3RttYgPggAcKPE+TICoPnz50ty4f3hPeBbfmJutGkVamXw94VRIBFkoGdIYmCkPCTOEhGlB+kywEBPB3yLxzdw25qKTp066doNBBioBsdYBLjBYwhUVInjJo71GDoVky9hGb0jEgNDHiOrHzcbDLm6YMECyxgcqAavU6eO7k6Ib7i4+Q4bNsyuKQbfjvE+MJsknoOyoTcGxjhAMICxPTA7Jl4LQQWGhTam80XtQ0LG8DBgOGPUoqAbEnqg4JiYmju5EORgym58s0fwgoAD41Tg3KLZAj17AL0f0DyTmDkqUis0k6A2CIGVeajuR0Et0qVLl+ymTSYiSqvSZTdVBBC4UDtqBkGAsWvXLjlw4ID+HQNwoVsoahZQswG4GSIwQHdN2xkjE+Kdd97Rr4HnfvLJJ7oLaKtWrSzb0f0RtSwYSx5dXLGPGZpA0IUW1e1o53/99df1esz0iIGYcDNGjYgxeBgCFfMNDl1VEzMhFLq7ooyYqKlChQry008/6ddwBnybR4CBc4JuTgh8du7cqbt/mvNlECwlBd4n8mtSC/zdIb8Fn7ejqdHjgr891LZl1PFLiCj9SfcjeWY0+Dgx1gRqOjAWR3qH2g8EVajdSQ2Qd4IAEYEVJvdKCOS5lCxZUn7++Wdda0ZElB6kyxqMjAxNK+jJkNQRIdMKNC1hgC/UfjRr1szVxdE1KSgTapkwINlTTz2V4Ocir+aDDz5gcEFE6QprMChNwnwayLMZOHBgqhiZEjkUaKpCLg9GPU0NQQ8RkSsxwCAiIiKnYxMJEREROR0DDCIiInI6BhhERETkdAwwiIiIyOkYYBAREZHTMcAgIiIip2OAQURERE7HAIOIiIicjgEGEREROR0DDCIiInI6BhhERETkdAwwiIiIyOkYYBAREZHTMcAgIiIip2OAQURERE7HAIOIiIicjgEGERERibP9P75m6sjCrpbPAAAAAElFTkSuQmCC", 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", 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index cd2f6e8..3e19467 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -69,8 +69,8 @@ def label(self): raise NotImplementedError("abstract") class RelationVertex(AnswerVertex): - _SHAPE = "o" - _COLOUR = "green" + _SHAPE = "d" + _COLOUR = "yellow" def __init__(self, relation): super().__init__(relation) @@ -79,24 +79,24 @@ def label(self): class EntityVertex(AnswerVertex): - _SHAPE = "o" - _COLOUR = "green" + _SHAPE = "s" + _COLOUR = "pink" def __init__(self, entity): super().__init__(entity) def label(self): - return str(self) + return str(self.vertex.get_type().get_label()) class AttributeVertex(AnswerVertex): - _SHAPE = "s" + _SHAPE = "o" _COLOUR = "green" def __init__(self, attribute): super().__init__(attribute) def label(self): - return str(self) + return "{}:{}".format(self.vertex.get_type().get_label(), self.vertex.get_value()) class AnswerEdge: def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex): @@ -116,12 +116,12 @@ def label(self): class LinksEdge(AnswerEdge): - def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex, role: AnswerVertex): + def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex, role): super().__init__(lhs, rhs) self.role = role def label(self): - return str(self.role) # TODO + return self.role.get_label().split(":")[1] class AnswerGraphBuilder: diff --git a/src/typedb_jupyter/graph/query.py b/src/typedb_jupyter/graph/query.py index 8a9b842..50b2070 100644 --- a/src/typedb_jupyter/graph/query.py +++ b/src/typedb_jupyter/graph/query.py @@ -68,7 +68,7 @@ def __init__(self, lhs, rhs, role): def get_answer_edge(self, row): assert row.get(self.lhs.name).is_relation() rhs = RelationVertex(row.get(self.lhs.name)) - role = str(row.get(self.role.name)) + role = row.get(self.role.name) player = row.get(self.rhs.name) if player.is_entity(): diff --git a/src/typedb_jupyter/subcommands.py b/src/typedb_jupyter/subcommands.py index 5ee4fb3..b7710d8 100644 --- a/src/typedb_jupyter/subcommands.py +++ b/src/typedb_jupyter/subcommands.py @@ -46,6 +46,10 @@ def help(cls): def name(cls): return str(cls.get_parser().prog) + @classmethod + def print_help(cls): + print(cls.get_parser().format_help()) + class Connect(SubCommandBase): _PARSER = None @classmethod @@ -56,11 +60,11 @@ def get_parser(cls): description='Establishes the connection to TypeDB' ) parser.exit = parser_exit_override - parser.add_argument("action", choices=["open", "close"]) - parser.add_argument("kind", choices=["core", "cluster"]) - parser.add_argument("address", default="127.0.0.1:1729") - parser.add_argument("username", default = "admin") - parser.add_argument("password", default = "password") + parser.add_argument("action", choices=["open", "close", "help"]) + parser.add_argument("kind", nargs='?', choices=["core", "cluster"]) + parser.add_argument("address", nargs='?', default="127.0.0.1:1729") + parser.add_argument("username", nargs='?', default = "admin") + parser.add_argument("password", nargs='?', default = "password") cls._PARSER = parser return cls._PARSER @@ -71,7 +75,9 @@ def execute(cls, args): from typedb_jupyter.connection import Connection cmd = cls.get_parser().parse_args(args) - if cmd.action == "open": + if cmd.action == "help": + cls.print_help() + elif cmd.action == "open": driver = TypeDB.cloud_driver if cmd.kind == "cluster" else TypeDB.core_driver credential = Credentials(cmd.username, cmd.password) Connection.open(driver, cmd.address, credential) @@ -81,7 +87,6 @@ def execute(cls, args): raise NotImplementedError("Unimplemented for action: ", cmd.action) - class Database(SubCommandBase): _PARSER = None @classmethod @@ -92,7 +97,7 @@ def get_parser(cls): description='Database management' ) parser.exit = parser_exit_override - parser.add_argument("action", choices=["create", "recreate", "list", "delete", "schema"]) + parser.add_argument("action", choices=["create", "recreate", "list", "delete", "schema", "help"]) parser.add_argument("name", nargs='?') cls._PARSER = parser return cls._PARSER @@ -104,7 +109,9 @@ def execute(cls, args): cmd = cls.get_parser().parse_args(args) driver = Connection.get().driver - if cmd.action == "create": + if cmd.action == "help": + cls.print_help() + elif cmd.action == "create": driver.databases.create(cmd.name) print("Created database ", cmd.name) elif cmd.action == "recreate": @@ -135,7 +142,7 @@ def get_parser(cls): description='Opens or closes a transaction to a database on the active connection' ) parser.exit = parser_exit_override - parser.add_argument("action", choices=["open", "close", "commit", "rollback"]) + parser.add_argument("action", choices=["open", "close", "commit", "rollback", "help"]) parser.add_argument("database", nargs='?', help="Only for 'open'") parser.add_argument("tx_type", nargs='?', choices=["schema", "write", "read"], help="Only for 'open'") cls._PARSER = parser @@ -154,7 +161,9 @@ def execute(cls, args): cmd = cls.get_parser().parse_args(args) connection = Connection.get() - if cmd.action == "open": + if cmd.action == "help": + cls.print_help() + elif cmd.action == "open": if cmd.database is None or cmd.tx_type is None: raise ArgumentError("transaction open database tx_type") connection.open_transaction(cmd.database, cls.TX_TYPE_MAP[cmd.tx_type]) @@ -189,7 +198,10 @@ def get_parser(cls): def execute(cls, args): print("Available commands:", ", ".join(AVAILABLE_COMMANDS.keys())) if not (len(args) > 0 and args[0] == "short"): - print("TODO: Print subcommand help") + for subcommand in AVAILABLE_COMMANDS.values(): + print("-"*80) + print("Help for command '%s':"%subcommand.name()) + subcommand.print_help() AVAILABLE_COMMANDS = { From 2c038f951afcbf1bdd110f60b638a0c0e6978cec Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Thu, 30 Jan 2025 12:36:16 +0530 Subject: [PATCH 17/27] Actually, I like some IID in the label --- src/graphs.ipynb | 4 ++-- src/typedb_jupyter/graph/answer.py | 16 ++++++++++++++-- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index ba706b9..4822358 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -330,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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", 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iWLDq1aur2oaPj49qqhKS51i0aJEaICg9saZOnQp39+LBhaXtwUVEVBIGDgvXoEEDDBw4UAUIT09Pte/UqVPIyclRgwI/+ugjtW/ZsmWq5iHH9O/fXwUNBg8iKgsmx63EW2+9hevXr+Orr75SNQ0ZIDh37lz13Lx58/Dhhx+qHMj58+cxffp0FVSIiMqCgcOK7Ny5U014KEFCgoOQIPLyyy+roPL1118jKioKo0aNwpdffolx48aZushEZIE4jsOKBAcHq8WctEFDelNJkrxdu3bYt28fbty4gb59+6raSWRkpHqeiMhYDBxWRsZ0SCVSgsP333+v9sl4Dhk4uGLFCrUt3XVlxUDpkUVEZCwGDiskCe/k5GRERESoZWUbNWqk8hv/+9//1PP169dXgwQ5DQkRlQUDh5WaPXu2GkkugwBlVt3AwEA1/XpiYqJ6nr2piKismBy3YlLr0K7XIZMjDh48WCXFiYjKg4HDyskEiLL4k3zt0aOH2sfxG0RUHgwcFqwsAUBmya1S5VYLpfbPz0BCRKXFHIcF047+NoZ+0JAgclOjwYkz54w+DxHZLk45YuHKOnWICho3NVi3JRKXEq8gMysbndsVz3dFRFQS1jhstOYhR/++cYsKGmL/wVgcPHqikkpIRNaEgcOKGBM87KtUQaP6dQz27d4fg+Onz1ZCyYjImjBwWFGtw9jmqrYtmt3RPBW5Owpnz8dXcOmIyJowcNi4Tm1bqQCiX2vZvH034hMum7RcRGS+GDiskFz8JfktpNeU+vrn9u2kltKjc3s0a9RAt0+O3RCxA4lXku9TiYnIknAchxWSC/+VlKtwcXZGQWEhYk+cgh3s0De4W4mv2bRtF85duKjb5+ToiJGD+sHfz/c+lZyILAEDh5XKzsnB8lXrkZ2Tq9sngUO/ZnG7wqIirN+6HRcTiuezEq4uLhg1uB98vLwqvcxEZBnYVGWl3Fxd0a9XD4OE+fa9+5Gafu2er3Gwt8egPr0QVN1fty8nNxerNkbgemZmpZeZiCwDA4cVq10jEB3btNRtFxYWYVPkLjXp4b04OjhgSFgIqvn66PZlZWdj9catqhZDRMSmKisnuYtVm7bictKtRLc0V5WU7xDSxHX4+Enk5ubhalo6kq+mws/HW+U8JHdCRLaLgcMGSI1h+aoNqtlJq19wdzRtVL/E1+lPgHj81FnsiIpGNR9vDB/QB46OjpVebiIyT2yqsgHubm4I69XdIN+xbW8U0q5llHpQoQSZgaHBuHI1Feu37lCJdCKyTQwcNqJ2zSB0aN3itnzHTtVdt7Sz6tb58xyXEpMQvm33PceGEJF1Y+CwITK9SFBAdd221Dh27z9Q6tdL7aNL+zaoEVAd5+IvImLXPk7HTmSDGDhsiNQa+vfuoUtu+3h7oU2Lpn958devWcixA0KD4erijJNn47AzKprBg8jGMHDYGA93N4T1Lk6Mjxk+CF5VPUqcHDEhIQHDhw/HhQsXdMHHxdkJYb2Ll6GNPX5KTclORLaDgcMG1alZQ/WqkqnV9VcEvN369evxwAMPqHEfZ86c0e2X19QKCtTlTP44dAQHjxy/L2UnItNjd1y6Q2FhId555x1s3boVHTp0wFNPPYXWre9cHVA+OivXhyMxOUVth/boghZNGpmgxER0P7HGQQYuX76MESNG4N1334Wvry9CQ0N1QaPoLl1wB/YJ1uVMtu3ZjzNxxU1aRGS9GDhIZ926dSpo5OfnY+rUqWjfvj3mzJmDTz/9VD1/ey5EtmUSRMmZaGsg4Tv24MIlruVBZM3YVEUqUEjT1Nq1a9G7d2+MHz9eBQ3x888/Y8qUKYiJiUFQUNA9z7En+iAOxB5T39vb22N4/z6oEXir6y8RWQ/WOGyc5DNGjhyJTZs2YcKECZg5c6YuaAiZWkSS4SkpxXmMe+nWoS0C/5xVV5q01m3Zpua3IiLrwxoHITIyUgWGMWPGGOzfvn07XnvtNQQEBOD3338v8RzyMcrLz8ePv61Bbl6e2ie5j1GDw+DrXbyWh0zpLgtMMYFOZNkYOOiu5s+fj2XLlsHT01MFj169eqlaib+/v6qRSK1CmqT0yUdJ5rL6be0mg3myRg8JQ05OHtaER6Bh3Tqq9xURWS4HUxeAzMvFixcxd+5c7Nu3D02aNFH5DQkU8fHxqnvuhg0bsGTJErRs2fKO4CHJ8kD/aujZuQN27Y/Rzcz7+4Ytanp2mRcrMyvbhD8dEVUEBg7Syc7OVoFCgsSwYcPQv39/nDt3DhkZGejYsSM++OAD5ObmYvTo0Th16pQKGlLLuL23lUxjIjUPbdfcG5lZuucys259T0SWiU1VZGDjxo0q39GmTRs8+eSTOHToENq1a4fq1aurkeRCgsjkyZPxt7/9rcT5rZb9vg7pGdcN9js5OeLpxwxzKURkWdirigwMHDgQjz/+OL7//nvcuHFD9br67rvvkJOTo3pdSdddqZm4u7uXeB6phfTofKt3llZ+fsFdl66V+xdZaEpWG5TmLd7PEJkvNlXRXaWmpqpahwSAtm3b4uuvv0afPn3wxBNPqNxGSWM6hLyubq2aaNuyGQ4dPWHwXGZ2Nny8intaxcVfwvJV6/H7xi0GTVqOjg4YEBKMR0YOUecoaSJGIrq/2FRFdyUD/qQn1YIFC9SUI/IxmTRpEv744w989tlnePHFF+/aPHX7pImyb8X6zbiScmtMx/ABfZGalo4vFi7BvphDal+hUx5y3DNw06EQdkX2cMnxgFNOca2macP6eOaJhzEgpGel/9xE9NcYOOiefvzxR+zfvx+//vqrynvI3FXLly9Hz57FF/D33ntPddetVasWRo0adc/zqG66KVfVOI7U9AxcvJyIT778r1qFMMsrFak1z+N6tSSgit5HUQO4X/ODb0I9eKYGwk5jh+effAx/H/8Iax9EJsbAQSWSQYAy0aHkPebNmwcvLy+Vo/j444+xePFi1K5dG8ePH8dbb72laiR3G9+hb8uOPXjlrfdRZF+I+BZ/INO35BHpwjnLA/Viu8Ix11UFj+cmPFrBPyURGYOBg0okH4///Oc/eOGFFwz2y2SI3t7eKnisXLlS5T6io6PV2I+7ddEV8QmJGP23ycgrysXZtruR65lR6nI45Dmj4YFgFTxmz3qdzVZEJsReVVQiCQDaoCGjyQ8cKF6jfPDgwTh//rzqeSXNVPL49ttvda+5m4SkK6pX1cWmB40KGqLQOQ/nW++Dxk6Db/63jL2uiEyIgYNK5ciRI6p2kZ6errZl5HjVqlVx8OBBtf3ss8+qyRL11yfXJxd66cqb75qF6/6JZSpDnnsmrvsl4cSZOBw6drIcPw0RlQcDB5VKq1atVDOULPAkpMeV5DaioqLUdufOnVXS/F5L0UrgOHvhIlJrnAfKkdtOq3lefV32+9qyn4SIyoWBg0pt4cKFOH36NJ5++mnVo0omPNQOBHT+cxXAe8nNy8f/fluFa4GXylWGLO9UVWvZtG0Xm6uITISBg0pNekvJKoEyX9XmzZvh5+eH4cOHl+q1H3yxAJczLqPI8c5R40axA/JcM1WuREaaE9H9x5HjZHST1aJFi9S0I5LjEPfqRaVv+579uGl/55rlZaE9T3ZOLtxcXSvknERUeqxxUJlqHtqgIcnw0gzI83B3g31hxdynVPnzPHJOIrr/GDioXO42xcjdcg8B/tXgUOAEx5zy1RDsbtrBNcsLVT3c1QqDRHT/MXBQhZGgIQs1HT5mOKmhGD6gj0pQ+CbWKdd7VL0aCId8Z4wc1K9c5yGismPgoHLT1jCku+2yVeuwa/8BnL+YYHDMoD69VC3BN7Eu7G6W/WPnl1BPfR07fHA5S01EZcXAQeUmOY7kq6nYvG0XCgoK1b4tO/cYTJMuzUoPDOkP+wIn+F2qX6b38Ujzh3uGH7p1bIf6dWpVWPllLq6XX365ws5HZO0YOKhCVK/mh5ZNG+m28/LyVSCRSQ+1nnhoFKr5eiPwXDN4Xalh1PldrnuhzrFOap2OyRPHlaussrKh/my+v/32G/7973+X65xE+iIjI9UN1bVr12CNGDiowvTs0hHVfH1020kpV7HvwGHddoC/H+Z/MEt1oa19vD2qxTf862YrDeCZHISGh3rAvsgB782YinYtm1VouWW6eG0vMaLS3GzYOgYOqrDmnqmvvIKdWzZg+gvP4PWXnsfalb/i4JHjuHDpMubMmaMWhOrUrg3iD+xE2oXT8D/VGM32hCHgXDPYRzkC7wOQKajmAXgHcPreHY329EbVrdVxetc2xO2PxLoVPxvUYvLy8vDqq6+iZs2aahR7165d1d1eeZqq6tWrh3feeQfjx4+Hh4cH6tati1WrVqk1SWQ+LtknqyPKolZastSuzBa8Zs0aNG3aFG5ubnjooYfUeBcZ9yLn9PHxwZQpU3Tl/9e//qXGxdxO1nh/4403yviXIKp8DBxUYeQC6efrixW/r8bosY9i/aoVOH40Vq3BUVBYiM8//xxHjx7FkiVL4GZ/E95VcuHj4gv/+EYIPNscyLeD2yYf1G3cBfVad0PRpXxciT4BLyc7rPz9d/z0049qCdtffvlF954yc++ePXuwdOlSHD58GGPGjMGgQYPU1Cha0mQgF3ZjzJ07V829JbMBDx06VE0bL4FE1iWR1REbNmyotvW7HkuQkJ9RyrJhwwYVwEaPHq1G28vjhx9+MCj/3/72NzXflyyWpSXvJz/HxIkTYe0kYEsgfe2111StLzAwELNmzdI9r73ZkBsCWffl+eefR2ZmZrmCdUXdbOTl5anzVq9eHS4uLggODjb4O2rJUgOdOnVSZevRowdOnrw1Oaf8rHKTIJ8LKausdfPII4+oGafNnqzHQVReISEhmuDgYN12xK59mrr1G2j6Dxmu+c/CJZrf1m7SFBUV6Z7/+eefNX5+fpqc3FzNyvXhml4DhskVWNOl/0jN4McmaR57bpomNGyAxs3NTXPjxg3d6wYOHKh59tln1fcXLlzQ2NvbaxISEgzK0q9fP82MGTN0202bNtX89ttvuu0JEyZoRo4caVD2l156Sbddt25dzeOPP67bTkxMVGV74403dPv27Nmj9slzYuHChWr7zJkzumOknCWVXwwePFjz3HPP6bZffPFFTWhoqMYWyO/d09NTM2vWLM2pU6c0ixYt0tjZ2Wk2bdqknp87d65m69atmri4OM2WLVvU31H/dyW/c0dHR03//v01MTExmm3btqnP1IABAzRjx47VHD16VLN69WqNk5OTZunSpbrXPf3005oePXpotm/frv5eH3/8scbZ2VmVQUv+lnL+e31mpkyZoqlRo4Zm3bp16n3keR8fH01qaqp6PiIiQp2ja9eumsjISHVMr1691PtqvfXWWxoPDw/NAw88oImNjVXlCQwM1Pzf//2fxtwxcFCFXQSef/553XZ+QYGmY5eumm7BISpwvPjqDE3Hzl3UP5v8s7i4uKh/rKysLHW8/JPKRVbfm2++qWnRooXBvvHjx2tGjx6tvl+zZo06h7u7u8HDwcFBXTjupTSB46OPPtJt37x5U73P8uXLdfvOnTun9h06dKjM5RcS0Ly9vTU5OTmavLw8deFbvHixxhZvNkTnzp01//jHP+56vPZmQ6sswboibjYyMzNVwFqyZInu+fz8fPXZ1n5utIEjPDxcd8zatWvVPvlbawOHlPX69eu6Y6ZPn66CjbnjXFVUYRwdHW997+CAgGp+SL+ehdSrKfjy00/Qq28/zJw5Ey2aNcXOnTvx1FNPqTU6pBp/++u1TUx326dd80OaLWT6E2kOuH25WslDVNTPop1S5W779NcfMbb8QiaJlJmFV6xYAScnJ7UsrzS32ArJFekLCgpCcnKy+j48PBzvv/8+Tpw4gevXr6OwsFBNsClNUdrPjHyVZkOtgIAA1eyj//eXfdpzxsbGqmYrWSLg9qYnmbRTS97zXs6ePav+TtKUqSV/5y5duqimx3v9fPKzCSlLnTrFA2GlrPodM/R/fnPGwEGVRv6ZAqpXQ/z5OGg0N/HAw+OQlpWPGjVr4fLly+U+f/v27dVFQP7RZH0QS+Tg4IAJEyaoKeslcEgbt6sNTdx4r8Aqq0sOGzYMzz33nFoDRnIg5n6zcTdludm412Jo5oSBgyqVV1UPdO3UCd8WFWFb+Ca0btcB/3zrbfy6dEm5zy13jePGjVNJ6tmzZ6tAIj2ftmzZou70JKktmjVrpu5cJVFtjmR9k+bNm6vvd+3aZerimAW5sMsFVP6u2vnQli9fbhY3Gw0bNlRBXv5W0uNOSA1EkuO2MpCUvaqo0k14/BE8MfFpbF6/Gu+88Q9s3rgBTz/7XIWcW+7UJXBMmzZN9ayRvvbyD6xtChDSkyUj49Ya53JBkjt9c9G4cWPV40YCnPTwIaBRo0bqYjxv3jycO3dO9Tz66quvKvRmQwZ+xsXFqVUs5cZi7dpbq0rK30KaD+/G3d1d1YSmT5+ues8dO3YMkyZNUk1oUiOyBebz30MW7W7dGVeuXKn7/tM5nyC4bxgKC4t0VfL4S5dVd0rtACt56JPuivrdM8Xt3Wqlqv/222+rx73cPluv3G3KheleZZdmkr86h7RN6+8ra/m155amO+luSsXatm2ruuN++OGHmDFjBnr37q0u7nLBr4ibDRmnIzcbCQkJqFatGrp166aaxkp7s/HBBx+ofdJNW7rPSpfbjRs3qu6/tsBOMuSmLgTZhpNn4tQcVlquLi4YO2IQ3P9sr65s6enpqnlBks8y1sIcRgJL05qURS6OFy9etJkLj6WRsUFys/HFF1+YuihmgTUOum+aNqqPhKQrOHHmnNqWpV83b9+NEQP63rGuR2WQAXfSjCV3mjIC3BzIADK5412wYAGDhhnS3mxIrfTvf/+7qYtjNljjoPtKRpD/umYj0q7dagbo1LYVurQ37JZJZA6kQ4XcbEjPN2neKs1ql7aAgYPuOwkav6zZYJDvGNa/D2rXCDR10YioFNiriu47X28v9O7WWbct9y5bduxGdk6OSctFRKXDwEEm0axRA5Xz0MrOycXmbbstYvATka1j4CCT6d21E3y8vHTbkjiPPnzUpGUior/GwEEmI2MwBoT2hIPDrakf/jh0BJcSk0xaLiIqGQMHmZSfjzd6de1kkO8I3858B5E5Y+Ags8h3NGlQzyDfEb59D/MdRGaKgYNMTrrjhnTvDG8vT90+aa5ivoPIPDFwkPnkO0J6Gkx1LfkOSZgTVSZpHpUJFan0OACQzMqxU2cQuTtKt+3u5ooxwwfDzdXFpOUi63L05GksXbkO2/ZGIeN6pmoWdXZ2Qs3AADw4dABGDgpTSwLQ3TFwkFnRJsdPx13Q7ZMR5TKynNM9UHltjNyJ75f9hiMnTqvtAudc5DtnQ1PlJuwLHeCS7Qm7m1Xg7OSEIf1C8Pfxj6BGYHVTF9vsMHCQ2cnPL8DPazYg4/oN3b6uHdqiY5uWJi0XWS6pUXz+3x/w3x9/gQYa3PC7grQaF5DpmwLo3Y/Y5zvCJ6k2fC/Xg1OuG3x9vDD//bfQsmljUxbf7DBwkFm6mpaOX9duUqu1CaltjBzYj3d/VCYfz/8vFv+8EvmuWTjfKgr57lklv0AD+CXUR9CZFnBzdcXizz8ymOnA1jE5Tmapmq8Pgrt00G3L/c3m7btUV10iY/y8eoMKGnlumTjbfudfBw1hB6TWisPF5gfUZ+65199Cut7CTraOgYPMVosmjdCofvGaziIrO0ctBMVKMpVWYVERvlr8E27aF+F8630ocjKu91RGwGUk1z2FlNR0/LpmU6WV09IwcJDZkuap0O5dDHq3XExIxIEjx0xaLrIc23ZHIflqGtIDLqLAtWyzEVytfQ4a+yIsW7VO13Rq6xg4yKw5Ocl8VsEGKwTuizmMxCvJJi0XWQa52AtJhJfVTYdCpFe/hKTkq9ixL7oCS2e5GDjI7Pn7+d6R79i0bbdaepboXiQnseePg8jyTEOex60eemWhDTzrtmyroNJZNgYOsgjSHbJhvTq67azsbGzZsZf5DrqntPTiZHaee/mChv450tKvlftc1oCBgywn39HDMN8Rn3AZB48cN2m5yHxpe+AVORSW+1yaKho1SDCLszYrDBxkMWQ07+35jr0xh5CUnGLScpF5kulqhIwILy+7m3ZqRLm7a/E5bR0DB1lcvqNn59vzHbuY76C7rm0vNVWXzFuzLpeV9hzV/HwroGSWj4GDLE6rZo3RoG5t3XZmVja27mS+gwzJNP09u3SA2w0fuNwoX/DwvVw8nmhoWEgFlc6yMXCQxZG7yD49u8LT41a+48Klyzh09IRJy0Xm55GRQwwu/GVhX+AI7+RaqBUUYFDbtWUMHGTB+Y6eBvmOPdEHkZRy1aTlIvMS3KUjggL84XOlNpyy3ct0jmrxDVV+4+GRQww+b7aMvwWyWNWr+aFHp/aG+Y7IncjNyzNpuch8yMJgL0x8XF3468V2hUOes1Gv90msDf+LjdTkmqOHDKi0cloaBg6yaK2bN0GDOsx30L2NGNgXzzzxMJxy3NDwQHDp8h037eB/oRFqnmyLqh7u+PKDWVzYSQ+nVSeLJzUMmQH1RuatWU97dO6Adi2bmbRcZD7kMvfNkuWY99//qe1M76tIq3ke1/2uAFVuXQId8lzge7kOfBPrwiHfWdVqv/robTTWm2yTGDjISlxJScWK9ZvVgj1C2qJHD+6PAH8/UxeNzMj2vX9g0fIViDpwWG0XORYg3ykHmipFsC90hFOuO+w0dmoMyKhBYfjbow+q4EGGGDjIahw6dgK7omJ029LEMGb4ILg4G9euTdbv7Pl4LF+1HpF7ZM3xG8jLz1efl1qBgXhg6AC1bCzXub83Bg6yGvJR3hCxA3Hxl3T76tephUF9enG9crqnaxnXERN7THXx5uekdJgcJ6sb3+Hh7qbbJ0Ek9vgpk5aLzFdBYaGaeeDEmXM4cuK0qYtjMRg4yKpIs5TMZ6V/57j7jwMqB0J0u937Y9T69mLX/hgkX+XnpDQYOMjqBPpXQ/eO7XTbkjDfvG2Xascm0jp17jyOnjxj8DnZFMnPSWkwcJBVatuyGerVrqnbvp6ZiYid+zi+g3R5DVlW9nbqc7KLn5O/wsBBVkmaqvoGdzPId5yLv8h2bNLlNeTr3Zy7wM/JX2HgIOvOd4T0NMh3sB2b9PMa98LPScnYHZes3oHYY2oCRC2ZVXfMiEFqokSybTGxR7E3+pBue9K4MXB0dDRpmSwBaxxk9dq1ao46NWsYtGNH7o5iOzZRGTFwkNWTpqp+vbrB3c3NYOTw0ZNsxyYqCwYOsgmuLi4YENLDIN+xMyoGKalpJi0XkSVi4CCbERRQHV07tLmt3/5O5OcXmLRcRJaGgYNsSvtWLVC7ZpBuO+NGpprojvkOotJj4CDby3cEd1fTZmudibuAY6dujSAmopIxcJDNkemy+/fueUe+46/69hNRMQYOskmyhnSX9rfyHUVFRdjIfAdRqTBwkM3q0LoFatcI1G3Lgj7b9PId8lUmwiMiQwwcZOPjO3oY5DtOq3zHWWTn5GJNeCT2H4w1aRmJzBEDB8HW8x1hvW8f3xGNn1evx8WERJOWjchcMXCQzasZGIDO7Vob5DuysnPU91xKlOhODBxEAJo3bgAnTm5HVCoOpTuMyHpdSkxC+PbdyC9gjyqi0mCNg2xaavo1RO6KUslwY0i3XXkNR5yTLWKNg2yan483xj04XAWQ+ITLuHDxMpJSruoCgh3sdHkPSZov/X2d6mmVl1e8LrWDgz0a1q2DsSMGY1j/ULi53uqhRWStuJAT0W1y8/IQn5CI+EuX1diOzOxs/LhiDS4nJRc/73YDhU650NhpYF/oBNdML9hp7FQPrZGDwvD8hEfh7eVp6h+DSoELOZUNaxxEd1lytkmDempw4OvvzMbWXXuhsS9CetAlpNW4gNyq1w2Od8hzhk9iHfgl1sVPK9aopUm//GCWwWSKRNaEgYPoLgoKC/HKW+9jV1QMMr2v4mLLaBQ53j15Xuich5R6p5FS5wwC4poCF4EJL7+OH+fPRqB/tftedqLKxuQ40V3M/vI7FTRu+CbjQpt99wwaBqpocKXhCSTVP46Uq2mY/PrbKgBVlCeffBKjRo2qsPMRlRUDB9FtZJbcZavWIc81CxdbRENTxbg04NU6Z3Et4JKa5ypi175KKyeRqTBwkE0LDQ3FlClT8Nprr8HX1xeBgYGY+PQkFBYWIbXWOdyMKgLmA3gXwBwAawDk6Z3gAID3AZwEMA/AOwCWA8lBp5GedBEPjhgKHx8f9R7SM0srLy8Pr776KmrWrAl3d3d07doVkZGRRpV9w4YNCA4Ohre3N/z8/DBs2DCcPXtW9/z58+fVyPfly5ejV69ecHV1RefOnXHq1Cns378fnTp1goeHBwYPHoyUlBSDc3/77bdo3rw5XFxc0KxZM8yfL7+EYvn5+XjhhRcQFBSknq9bty7ef19+CWQrGDjI5i1atEhdvPft24f3P/gAa1b8iuvXruBaQIL0xwUGA3gegLQSxQHYfNsJpBVLKhYPAXhcrthA/uospN+4iMCm7fDx7Dn4+uuv8csvv+heIhfePXv2YOnSpTh8+DDGjBmDQYMG4fTp07pj5KL//fff37PcWVlZmDp1Kv744w9s2bIFVapUwejRo9WSuPreeustzJw5EzExMXBwcMBjjz2mAuVnn32GHTt24MyZM3jzzTd1xy9ZskRtv/vuuzh+/Djee+89vPHGG+r3JD7//HOsWrVKBaSTJ0+q4+vVq1cBfwmyFEyOk81r06aNuriKG7n5cK3qhbS887jpUAh01zvQB0DfP2sdw/T23/xz2/fP7RYADgP2TzrC5XRV5FdxRp8+fRAREYGHH34Y8fHxWLhwofpao0YN9RKpfUgNQvbLhVo0bdoUXl5e9yz3gw8+aLD93Xffwd/fH8eOHUOrVq10++XcAwcOVN+/9NJLePTRR1Wg6dmzp9r31FNPGQQo+V3Mnj0bDzzwgNquX7++OqcEvwkTJqhyN27cWNV2JLhJjYNsCwMH2TwJHFqpadfg4OSC/MKs4h3S8rNTEhd/NlFJkJB8t4z/c/rzRY56QUN4APAG8r2y1WZa+jUEBAQgObl4HEhsbKxqtmrSpIlBOaT5SpqctE6cOFFiuaV2IjUDqSldvXpVV9OQC7t+4ND/+aQconXrW5M66pdNajHS3CXBZNKkSbpjCgsLdUFMkvT9+/dXgU1qSdJENmDAgL/6NZMVYeAgm6c/4CsntziBIYP7ICvJ/gig8581DRkUHg9glQwl/4sG3yrATfviHlUyNYncmWsv7JmZmbC3t0d0dLT6qk9yDqU1fPhwdbf/zTffqJqLnF8ChuQg7vXzaWf7vX2fftmEnFPyLvq0Ze3QoQPi4uKwfv16hIeHY+zYsQgLCzNoiiPrxsBBpEe7qJOMBIcsxyEdqgboBYejpT9XlaLify93dzeD/e3bt1c1DrnLl6R1WaSmpqr8glzgtefYuVOqRuUjtQ8JQufOncO4cePueZynp6dqdpPHQw89pGoeaWlpqoMBWT8GDiI9/n7FFz77Asfi5ie5EY8CIK1KFwH8UfpzyVQkxef0wa2UN1QTlVyUx48fr3IJEkikV5PkHaRZaejQoeo46c0kvZUk4X076aklzVoLFixQvZukeer1119HRXj77bdVLzBpmpKAIE1okoBPT09Xyfg5c+ao95RyS0L+559/Vr3RpHcX2Qb2qiLS06pZYzXnlFOuG+x9HQHJKcuNvPRGPQwgrPTn8k2oq5qBhvQLueM5SYJL4Jg2bZrKFcjAPukiW6dOHd0xUqPIyMjQbUtzkvSKEnLBlh5Z0twlzVOvvPIKPv74Y1SEp59+WnXHlTJKLiQkJEQlzyVJLqpWrYqPPvpIdeeV7r3S7XfdunWqTGQbOMkh0W1++OV3fPSfb5HU4Biu1jlXpnO43PBCo+he6N2tE/7zfnGPrfKSu/9GjRrhiy++qJDzESc5LCveIhDdZsTAfnB2coJfQgPY52u7ThlBA1Q/31h9+/DI4man8pAmojVr1qgBgpKEJjI1Bg6i23hV9cBTjz0ExzwX1D3SGVUKjUgFaoDAMy3gmRqIjm1aomfn9uUuz9/+9jf8/e9/V81aI0eOLPf5iMqLyXGi21b2k/U3hvYLwZm4C9i0bRcaHOyB+BZ/IN+teFzGvUiACTrdEj5XaqN+nZr47N//vKO7bVmsWLGi3OcgqkgMHGSTCgoKcPDoCdzIzEJWdjYys3LUV/11x59/8jF4eVbFz6s3oHFUHzVTblrNC8j0TS6eiuRPLplV4ZtQD97JtVClyB6tmjXBF++9oV5LZI0YOMgmSQJUlnmVZWDvpXo1P7zxyvPo1LYVlvy6GoeP28EzLQBFjvkocMxTgwQdCh3hmFc89iOwejWMHT4Yjz80Aq4uLvfxpyG6vxg4yGa1bNoIefl5Br1qtJycHOHh7qbrTiuPoyfPYPkqWXP8CDJu3PhzGo6qaFi/jgoY0oOqIpqmiMwdAwfZNF9vbxUcbu+V7vfn/tsDzdvTp9znEhKZHwYOslmy0NKWHXvuCBrCz4ejoInuhd1xySYdOXHqjqChn5fw82XgILoXBg6yKRIoog8fxfa9fxgEjcb166qkdp2axetj+Hrfex0MIlvHwEE2QwLFnuiD2BdjmAxv1bQxwnr3gKODAwb2CUZQQHX4sqmK6J6Y4yCbIBMESi3j2KkzBvs7tG6Jrh3a3FqnwsEBw/qHqq9EdHf87yCrJ2tfhO/Yg7PnZRWmW7p3bIf2rWWdV0MMGkQl438IWbWCwkJsiNiBiwmyKlMxqV307tZZda8lIuMxcJDVysvPx7rwbUhMTtHtkzUjwnp1R6P6dU1aNiJLxsBBVknW+V6zOQJX02Th8GIODvYY1KeXrucUEZUNAwdZHZm4cNWmrci4fkO3z8nREUPDQlSPKSIqHwYOsirpGRlYvSkCmVnZBgP7pKeUdj1xIiofBg6yGimpaVizORI5ubm6fTJR4fABfeDjxQF9RBWFgYOsQuKVZKwN32awnoashzFiQF9U9XA3admIrA0DB1m8+ITLqsttYWGRbl81Xx8M698Hbq5cF4OoojFwkEWT5V1lcJ+MDNcKqu6PIWEhcHZyMmnZiKwVAwdZLFlYafve/QaTFdauGaS63HL0N1Hl4X8XWaQDscfUhIX6Gtarowb3cRU+osrFwEEWRWoX+2IOIyb2qMH+5o0bIqR7ZzUynKi05PPC2qnx7DR3W/6MyAzJR3XH3j9w5ORpg/3tWjVXExbevtQrEVUOhlqymBlut+7ci9NxFwz2d+3QFh1at2DQILqPGDjIIma43RS5ExcuXdbtk0DRq2tHtGrWxKRlI7JFDBxk1vLzC7Bu6zZcTko2CBr9enVHkwb1TFo2IlvFwEFmS6YOkSlEZCoRLekxNTA0GPVq1zRp2YhsGQMHmSWZpFAmK5RJC/VnuB3crzdqBgaYtGxEto6Bg8yOTIcu06LL9OhaLs7Oaobb6tX8TFo2ImLgIDMjCy/JAkyyEJOWu1vxDLe+3pzhlsgccLQUmY2k5BSs3BBuEDS8qnpg9JAwBg2qcBs2bMDOnTt12//5z3/Qrl07PPbYY0hPv7VyJN2JgYPMwsWERNU8Jb2otPx8vDF6SH94eniYtGxknaZPn47r16+r72NjYzFt2jQMGTIEcXFxmDp1qqmLZ9bYVEUmd/Z8PDZv320ww22gfzU1w63kNogqgwSIFi1aqO9//fVXDBs2DO+99x5iYmJUAKF7Y42DTOr46bPYtG2XQdCoXSNQ5TQYNKgyOTk5ITu7eInh8PBwDBgwQH3v6+urq4nQ3bHGQSZz8OgJ7N4fY7CvQZ3aCAvpAQfOcEuVLDg4WDVJ9ezZE1FRUVi2bJnaf+rUKdSqVcvUxTNrrHGQSSYrjDpw+I6g0axRAwwI7cmgQffFF198AQcHB/zyyy/48ssvUbNm8aDS9evXY9CgQaYunlnj7Lh0X8nHbWdUNGKPnzLY37ZFM/To3J6TFRJZADZV0X0jeYyIXftw8mycwf7O7VqjU9tWDBpkMrm5ucjPzzfY5+npabLymDs2VdF9UVhUhI2RO+8IGsFdOqrAwaBB91tWVhZeeOEFVK9eHe7u7vDx8TF40L0xcFClk7EZazdHIi7+kuEMt8Hd0aZFU5OWjWzXa6+9hq1bt6r8hrOzM7799lu8/fbbqFGjBhYvXmzq4pk15jio0me4XRu+DclXUw2W65QkuPSgIjKVOnXqqAARGhqqmqVk/EajRo3www8/4KeffsK6detMXUSzxRoHVZqs7Gz8vmGLQdCQ9Z2HhoUyaJDJpaWloUGDBup7CRyyre2mu337dhOXzrwxcFClyLiRiRXrwpF27da06M7OThg+sK8a4EdkahI0ZPS4aNasGZYvX66+X716Nby9vU1cOvPGpiqqcKnp19QMt1nZObp97m6uGNa/j5p/isgczJ07Vy0MNmXKFDVyfPjw4aq7eEFBAebMmYOXXnrJ1EU0WwwcVKHk4ySTFSYkXtHtk0kKZQoRL8+qJi0bUUkuXLiA6Oholedo06aNqYtj1hg4qMLHahQUFmLFus2qmcrHywsjBvZRa2oQmZstW7aoR3JyssF8aeK7774zWbnMHQcAUoWSHlOSAB8xsB+27YlCaI8ucHVxMXWxiO4gXW//9a9/oVOnTggKCuJYIiOwxkGVQu7e5B+R/4xkriRYfPTRR3jiiSdMXRSLw15VVGpyj6G9z7hx4waKiopKrHkwaJA5kylGevToYepiWCQGDioVCRjaGsTvv/+OV155BXv37tUFEiJL8/TTT+PHH380dTEsEnMcVCra2sPXX3+tlth8/fXX1Rw/+rUKbXAhMlf6S8JKc+qCBQtUV1zpReXo6GhwrHTJpbtjjoNKTUbTPvLIIyp4SJ93rStXriAgIED3zyjNVETmqE+fPqU6Tm6AZB4rujvWOKjUJK8h8/v06tULqampap1mmdMnMzNT9UyRyeIYNMicRUREmLoIVoH/5XRXt/dp1yYTL126pEbUdu/eHRs3bkSrVq0wZswYtWratm3bTFJWIrq/WOOgO0hvKZmKQWRkZMDJyQmurq4YPXo0Ll68iCNHjmDy5Mlqec2mTZvi/PnzqubB+X2IbAMDB90zaLz44os4cOCAChwdO3bExx9/rOb1kdXSXP4c1CcpMtkvX6VfPBFZPwYOMqANGv3790d6ejqeffZZtS0rpcnkb59++qkKGrJ62vfff68S5nv27FFfpZcVkSU5duoMDh87qdt+cOiAO3pX0Z0YOOgOH3zwgapByBw+Xl5e+PDDD+Hg4IDPP/9c5T7kqyy1mZKSAg8PDxw6dIhLbZJFys3LM5j6n0qHgYPu6EIrXWtlcJQEDZnP56uvvlI9qGT20Oeeew41a9bEP/7xD8yaNYvdb4lsEAOHjdIO1pOeUpLDyM7OViug1apVCxMnTlS5jv3792PZsmX45ptvVCJcek1JM9WMGTNUt9xHH32UQYPIBvG/3gb9/PPPqrlJgoMEDekl1bdvX7X28tixY9Vay5LrOHPmDPLy8jBkyBDdaydNmqT6wkvQICLbxBqHDZIpFg4ePKjyEgMHDsT48ePRrVs31XNK1iD45JNPVA2kRYsWatzGO++8g/bt26v5qWTMRkhIiKl/BCIyIU45YoOkpiHjMGS9ZQkCCQkJahlNqX2cO3cOr732mmq2mjBhggogb775pgoyUiuRfAeRtYiJPYq90Yd025PGjWGvqlJgU5UNkmaoefPmqWAwf/58nDp1SgUN0aBBAxUopNeUJMTr1aunkuLSfMWgQUSCgcNGyV3VokWLVPOU1DxWr16te05mCv3nP/+J69evq665MkeVrMNMRCSY47Bhzs7Oaj2CUaNGqdyGjMnQzh4qOY+ZM2eq7rbamW+JiARrHDbm9pSWNEktX75czXYrzVayOJNWWFgYBgwYYIJSEpE5Y+CwIYWFhbip0aiHPsl1SPBISkrC+++/j5iYGJOVkYjMHwOHjcjLz8fqTRFYF75Nqh131DwCAwOxcOFCNT8Vpw8hopIwcNiA7Jxc/L5hCxKTU3DxciI2b9ul9t8ePCQBvnnzZtSvX99EJSUiS8DAYeVuZGZhxfrNuJqWrtt38XISMq7fuOv64JIwJyIqCXtVWbFrGdexatNWZGZl6/a5urhgWP9QeHt5mrRsRGS5GDisVEpqGtZsjkRObq5un4e7G4YP6AMfLy+Tlo2ILBsDhxVKvJKMteHbkF9QoNvn5VkVIwb0RVUPd5OWjYgsHwOHlYlPuIwNETtQWFik21fN1wfD+veBm2vxcq9EROXBwGFFzsRdQPiOPWq0t1ZQdX8MCQuB859zURERlRcDh5U4evIMtu/db9DFtnbNIAzq0wuODvwzE1HF4RXFChyIPYY90QcN9jWsVwdhvbqrmXCJiCoSA4cFk9rFvpjDak0Bfc0bN0RI985c1pWIKgUDhwUHjR17/8CRk6cN9rdr1RzdO7a76+A+IqKKwMBhoSv4bd25F6fjLhjs79qhLTq0bsGgQUSVioHDwhQUFmJT5E5cuHRZt08CRa+uHdGqWROTlo2IbAMDhwXJzy/Auq3bcDkp2SBo9OvVHU0a1DNp2YjIdjBwWAiZOkSmEJGpRLSkx9TA0GDUq13TpGUjItvCwGEBZJJCWUsjPSNDt8/J0RGD+/VGzUAu60pE9xcDh5mT6c9lhluZHl3LxdlZzXBbvZqfSctGRLaJgcOMyRoaazZHqIWYtNzdime49fXmDLdEZBoMHGYqKTkFa8IjVUJcy6uqB4YP7AtPDw+Tlo2IbBsDhxm6mJCI9RHbDWa49fPxVjUNN1dXk5aNiIiBw8ycPR+Pzdt3G8xwG+hfTc1wK7kNIiJTY+AwI8dPn0Xk7iiDGW5rBQVicN9ecHR0NGnZiIi0GDjMxKFjJ7ArKsZgX4M6tREW0gMOnOGWiMwIA4eJSe1i/8FY/HHoiMH+po3qo0+PrpzhlojMDgOHiYPGzqhoxB4/ZbC/TYum6Nm5AycrJCKzxMBhIpL8jti1DyfPxhns79yuNTq1bcWgQURmi4HDBAqLirB52y7ExV8y2B/cpaOqbRARmTObbUB/8skn1V29PFauXFmh5/7+++9153755ZcNnpMBfWs3RxoEDTXDbXB3Bg0isgg2GzjEoEGDkJiYiMGDB+v2paWlYdy4cfD09IS3tzeeeuopZGZm6p7Pzc1VQad169ZwcHDAqFGj7jjvww8/rM7bvXt3g/25eXlq3qmEpCu6fZL8HtgnWCXDiYgsQRVTr2SnP9DtfnN2dkZgYKD6qiVB4+jRo9i8eTPWrFmD7du345lnnjEos6urK6ZMmYKwsLC7nleel/M6OTnp9mVlZ2Pl+nAkX03V7XN0cMDQsFDV7ZaIyFIYFThCQ0PxwgsvqIeXlxeqVauGN954QzdgLS8vD6+++ipq1qwJd3d3dO3aFZGRkQZNOHIXv2rVKrRo0UJdsOPj49UxXbp0Ua+R53v27IkLF24ti/rll1+iYcOG6kLctGlT/PDDDwblkqaeb7/9FqNHj4abmxsaN26s3sNYx48fx4YNG9S5pOzBwcGYN28eli5disuXi1fckzJKeSZNmqSCQ2lk3MjEinXhSLt2a1p0Z2cnNe9U7RqlOwcRkcXWOBYtWqSaaKKiovDZZ59hzpw56kIrJKDs2bNHXWgPHz6MMWPGqOag06dP616fnZ2NDz/8UL1G7ux9fX1Vc09ISIh6jbxe7vC1vYpWrFiBl156CdOmTcORI0fw7LPPYuLEiYiIiDAo19tvv42xY8eqcwwZMkTVHKTZSatevXqYNWtWiT+bvLcErk6dOun2Sa1CmpP27duHssjNzcPK9ZtxXa+5y93NFaMGhampRIiIrL5XVe3atTF37lx1YZe7/9jYWLU9cOBALFy4UNUgatSooY6V2ofcwcv+9957T+0rKCjA/Pnz0bZtW7UtF/eMjAwMGzZM1SpE8+bNde/3ySefqJzC888/r7anTp2KvXv3qv19+vTRHSfHPProo+p7ea/PP/9cBTcJXELOLTWkkiQlJaF69eqGvyAHBxXc5Dlj5RcU4OyFeLTJztHtk5ltZbJCL8+qRp+PiMgiaxzdunUzGGMgCWCpUUgAkfb/Jk2awMPDQ/fYtm0bzp49qztempvatGmj25aLslz0JfAMHz5c1WIksazffCRNV/pkW/br0z+nNCdJcjs5+dba3Fu2bFE1ovvlUmISUtOuoajoVg7Hx8sLo4eEMWgQkUWrsHEc0vNI1sCOjo5WX/VJANFPHN8+uE1qJJJsltrJsmXLMHPmTJWcliBVWrdPAijvYWziXXIW+sFGFBYWqlpRafMZ4lz8RWzetttgskJZrW9oWAhcXVyMKhMRkcXXOG5v65dmI0lGt2/fXtU45MLbqFEjg0dpLrry+hkzZmD37t1o1aoVfvzxR12z1a5duwyOlW1Jrlc0qT1du3ZNBT+trVu3qgAkyfLSOHkmDhsjdqrfhZasCz5iQF8GDSKyzRqH5DAkzyBJ6piYGNXraPbs2aqJShLS48ePV9sSCFJSUlQTkTQjDR069K7ni4uLw4IFCzBixAiVGzl58qRq+pLziOnTp6ukt5xPEtWrV6/Gb7/9hvDwcKPK3a9fP9XrqqTmKglSkhORHlNfffWVysfI8Y888ogubyOOHTuG/Px8VRO5ceMGDh48qPZXcXJVc0/p86zqgaH9QznDLRHZbuCQC3pOTo7qPitNUtLjSTvOQZqc3nnnHdUDKiEhQSWjpblJEt/3It1nT5w4oXprpaamIigoCJMnT1aBSUiPK8l7SDJc3qt+/frqfaRrsDEkz3L16tW/PG7JkiUqWEigkd5UDz74oEq065NeW/rdhSWoif8sXGJwnKurC+rWqsGgQURWxU6j3xD/F+Ri3a5dO3z66aewdJKQl2ap8k43Ir++3fsPqPU09LVq1gRvvv6q1fy+iKxRTOxR7I0+pNueNG4MF00rBZueckRGhkviXr6WZ4bb24PG1YQLGBIWih07dlRQSYmIzIfNzo770Ucfqd5bQprHyjLDbfi23aoHlb4enTug4YPD8djDY9S2DCgkIrLZwKE/fYilk4F+tw/2Ky1Jmm+I2IGLl5MMuv+G9uiC5o2LBzFWrcqxGkRknWy2xlFWMsPtuvBtSEq5lWiXJHr/3j3QsF4dk5aNiOh+YOAwQnZODlZvikBq+jXdPgcHewzu0xu1axrf3EVEZIkYOEpJJilcvXGrmulWy8nJEcPCQhFY3d+kZSMiup8YOEpBpkOXmoasqaElo8BlssJqvj4mLRsR0f1mk4Fj/Iuv4UrKrQWVSlJ0swg5ObmQ0S7u7q4YPbg/PNzd1BQi3l6elV5WIiJzY5OBQ4LGleQUBHj+9YVfxnx72DvgyvXraluChQQNCR5ERLbIJgOHkKCx8ZWXS338wLmfIktzUy3A5ObKyQqJyHbZ9MhxY0nAYNAgIlvHwGEEOxiuI0JEZIsYOIiIyCgMHEREZBSbTY4TkW25Wzf8/IJ85OUV6LZXbgi/o0k6wN8Pi+d9dN/KaQkYOIjIZrvhO8nDQe8ymJtv+Jo/u+GTIQYOIrIZZemGT3dijoOIyIxXKrWzs1OP8q5WertZs2bpzm3sKqUMHEREZmzQoEFITEzE4MGDdfvS0tIwbtw4eHp6qsXinnrqKWRm3pqAVRw+fBi9evWCi4sLateurRav0/fqq6+q89aqVcvoMjFwEBGVQHPzplom2lScnZ0RGBiovmpJ0Dh69Cg2b96slr7evn07nnnmGd3z169fx4ABA1C3bl1ER0fj448/VjWMBQsW6I6RZbPlvPb2MrGScWw2xyFJL2PaL+X4ABdOn05k7kJDQ9GqVSv1/Q8//ABHR0c899xz0Gg0qr9UXn4+/vntfPy0dROuZd5Aq/oN8eEzLyK0fUf1mu/Xr8bLX8zB4v+bhV1bViM76wbi49/C+fPn8dprr6kLtpyzZcuW+PHHH9XFWXz55Zf45JNPcPHiRdSvX18tTf3EE0/oyiVNQt988w3Wrl2LjRs3ombNmpg9ezZGjBhh1M93/PhxbNiwAfv370enTp3Uvnnz5mHIkCHq/WvUqIElS5YgPz8f3333HZycnFRZDx48iDlz5hgEmLKyyRqHdK8LkDU0XJxL/ZDj5XVEZP4WLVoEBwcHREVF4bPPPlMXzIRzp9RzL3z2EfYci8XSN9/F4f/+hDEh/TDotSk4fSle9/rsvFx8+NNitGzfDT0GPQBfX1+MGjUKISEhqgloz5496gIswUCsWLECL730EqZNm4YjR47g2WefxcSJExEREWFQrrfffhtjx45V55ALvdQcpNlJq169eqpmUBJ5b2me0gYNERYWplYi3bdvn+6Y3r17q6ChNXDgQJw8eRLp6enl/v3aZI2DfbKJrJu06c+dO1dd2Js2bYrY2Fh88eXX8PPxx8ItaxC/fDVqVCtuQXj1kSewIWoPFq5fjfcmTVb7CgoLMf/lf+C11evUjWNhYSEyMjIwbNgwNGzYUB3TvHlz3fvJnb4ksp9//nm1PXXqVOzdu1ft79Onj+44OebRRx9V37/33nv4/PPPVXCTPIaQc1erVq3Eny0pKQnVq1c32CdBUoKbPKc9Rmo9+gICAnTP+fiUbx0hmwwcRGTdunXrpqsNiO7du6vkcOb1a2qNnSaPP2hwfF5BPvy8vHTbTo6OaNOwsW5bLspy0Ze79v79+6s7fKk5BAUF6ZqPbm8C6tmzp6rt6GvTpo3ue3d3d5XcTk5O1u3bsmULLAEDBxHZjKKiAthXsUf0gsXqqz4PV1fd965OzgaBRyxcuBBTpkxR+YVly5apHIYkpyVIlZbkRvTJexibeJeEtn6wEVIjkiYveU57zJUrVwyO0W5rjykPm8xxEJF107b1a0mzkVtVL1T18lU1juRr6WhUq7bBI9Cv5CYi0b59e8yYMQO7d+9WCXhJjmubrXbt2mVwrGy3aNGign+y4trTtWvXVG8pra1bt6oA1LVrV90x0tOqoODWdCoS5KTZrrzNVIKBg4isTnx8vMozSDL4p59+Ur2O6jRuAXcPT4wLG4Tx783Cb9u3Ii4xAVHHj+L9JQuxds/Oe54vLi5OBQxJOl+4cAGbNm3C6dOndXmO6dOn4/vvv1c9q2S/JON/++03NVbCGP369cMXX3xR4jHynpITmTRpksqPSIB64YUX8Mgjj6geVeKxxx5TiXEZ3yG9wKSGJM1m8jupCGyqIiKrM378eOTk5KBLly5qnIL0eNp/JhFXUq7ipocPqnj5YdwH/0JeTg6cnJzh5VsNDZu1xud7/0BC/Flk5eWp7vrabvhubm44ceKE6q2VmpqqchuTJ09WvaeE9LiSC7Mkw+W9JDEtTVvSNdgYZ8+exdWrV//yOOluK8FCAo30pnrwwQdVol3Ly8tLBTcpY8eOHVXC/c0336yQrrjCTiOdm4mIrIRcrNu1a3fHNBp3mx23NEw5O+6TTz6pmqUqeroRfdIF+OWXX1aP0mKNg4hsgqV2w1+zZo0a5b106VLVHbiiSHdgeWRnZxv9WtY4iMgmahyWKDk5WU0fIqR5TLrwVhTphaUdfOjv76+at0qLgYOIiIzCXlVERGQUBg4iIjIKAwcRERmFgYOIiIzCwEFEREZh4CAiIqMwcBARkVEYOIiIyCgMHEREZBQGDiIiMgoDBxERGYWBg4iIjMLAQURERmHgICIiozBwEBGRURg4iIjIKAwcRERkFAYOIiIyCgMHEREZhYGDiIiMwsBBRERGYeAgIiKjMHAQERGM8f8iZk6bLi8LaAAAAABJRU5ErkJggg==", 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", 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gUiVVGBwNWJcQaqNb20jlypVl/PjxGb5v+vTpcufOHfn5559N33vy5Mlq1cNa7ty5s+4rXLiwfPvtt3r9qlWrqnt75cqVur599OhRWbx4sWzbtk0aNWqk5//yyy9SrVo10+fAw4BxdezYUdzd3dVSb9y4scVYunfvruIORo4cqZMCTBpCQ0MzHP/o0aOlU6dO+hqTD0xy5syZo54JgPfey/K+dOmShcAD4zaOEceHljwhLkZ6a/NXrl2X8POOa82nR4MGDTI9vnfvXjl+/LhOEGDB4wGXfVxcnFruRmrUqGGx1gy3vdFSP3TokK5vm38WJgKYKBjp16+fxMbGSoUKFXRiACE2D2wDcLub/34wKTD3BqRHs2bNTK8xbog6xmPk8OHD0rdv30yvQZwfijwhLkhIcGkpFljEYt82J4m0N+Lj45Pp8aioKBVnrHWbP2CdI5rcCKxvcyDC8ABkJzoerne48bFuD4u9devWFq71nH7G/YLJxOXLlmmUxm1zrwhxXCjyhLgg6VnzV6/fkDPnLoirUL9+fV0jR9pYpUqVLB5ZDTCD1Q6rfOfOnaZ9EPRbt25ZnAdxx5r5N998o0sBWAsPC8tZaeEtW7aYXt+8eVMnJ+bLBFkB3gCMw9xrsHz5cvHz85Pq1avnaHzEPqDIE+KilCtTSooXDXRqaz4zBgwYoJH3iKhH4N2pU6dUgBH5fu7cuSxdAy5yBOoNGTJEtm7dqmL/3HPPqagbQfAb1ukR/X7y5EmNZMfxcuXK5Wj8CDZEbACui+A/fBdkAphPQLA0kBmIO4CYP/HEE7p8sXTpUk2VQwaBeaqh0csB78fVq1f1NaLyif1DkSfERUnPmkcw3qmzWRM4Rwc54+vWrdNAuAcffFCt4GeffVbX5GHJZhUE6yEtrk2bNnodVN6Dd8AI1ucnTZokLVq00LX3FStWyPz58zUdLyeMHTtWhg0bpksOCJLDNZFNYO5RiIiIyPQaiDVAASE8w6ofOHCgpsphAmFOvXr19IFJzJ9//qmvESxI7J98BleZthNC0oB//9mLlmkanRFE3vfr1dUhI+1dAXgb2rVrpy568wC/e4HaAciFz82ys8ipR22C9KriEdtAS54QFyZDaz7cNax5VwPBf8giyGk8QGo++eQTvS7SBYl9wTx5Qlwc5McHFSuqJW7N+82jOh6teecBlfiQygewRGFNXnjhBVN+PgrzEPuB7npCiJw9f1HmL19tsa9L25ZSMcS6YkAIyVvorieESJlSQVKyuKUFBmueNgAhjg1FnhDyb017y7X5G7ci5MRprrES4shQ5AkhSpmSJaRkibupX2DH3v205glxYCjyhBCzSPuaaaz547TmCXFYKPKEEBNlSgZJqaBU1vye/XlSR50QYn0o8oQQC1Lnzd+MoDVPiKNCkSeEWFA6qIQ+Uq/N05onxPGgyBNC0tC4nqU17+1VUOLiE2w2HkeFQYvE1rDiHSEkDYiyx/o8rPfG9WtLqRLFaclnk/iEBHl99Bjp3Kal9O7awdbDIS4KRZ4Qki5d27XUrmZGcc+fn46/7Aj8sHc+lo3bdsn6rSm95in0xBbwv5YQki7GtqVZFffo6OhcHpHjCbxbgTvqsn9n/Ncyd8lKWw+NuCAUeUJIjrh165Z2IUNf8zNnzogrYy7wvj6J8us3m+Tp/ico9MRm0F1PCLkvbt++Ld99953Mnj1bChcuLL169ZLExERxVVIL/I/jt0qd6rekdrVbenzy9Ioq9ICue5JXUOQJIdkiMjJSJk2aJD/++KO2Lh08eLD07NlTLXlXJSOBB+jW+58hh/Q1hZ7kNRR5QkiWLfeffvpJ/vrrL/H29pbq1aur5X7hwgVTcF58fLx4enqKK5GZwBuh0BNbQZEnhGQK1pM///xz+fPPP6VIkSLSrVs3fTRv3lxFHi77Bx98UM6ePUuBT0fgjVDoiS2gyBNC7tm4Jjk5WVq3bi39+/eXZs2amY65u7tLjx495KuvvpI9e/ZI3bp1xVXIjsAbodCTvCafgSWZCCH3ICEhQQUdgm8OXPVYkz969KisXr1aypQpI67A/Qi8Objrfjmxmgo9fqYfjhhGoSe5Ai15QkiWc+aNwDUP9/3y5ct1Hf6LL76gwGcDWvQkr6DIE0KyzLlz51TcYbVHRERI1apVdT2+e/fucvDgQQkPD5euXbuKs2INgTdCoSd5AYvhEEKyzIwZMzTCvnTp0jJkyBAZP368ps+hKt6OHTvk008/lb///lvPTUpKEmfj2MnTsmPPfn1dp/pNqREakaPrQej7dA+Xgp7JGuA4Y8ESjX8gxFpwTZ4QkmVu3rwp69evl2rVqsm3334r06ZNk1atWslLL70k7dq1k6+//lpd985c+W7zzj3yylsfSHxConRpe0HGvr1b3Arc3230ZLiPPPufZnLtRkGpVbWKTPz0Aynk62P1MRPXhSJPCMk2L774okyZMkUt9927d8vatWtl165d4uXlJaGhoZpyBwsft5fUwXrOgDWEngJP8gK66wkh2QKNaLZu3Spjx45Vl/2YMWMkKChI3nvvPa1jj8I4CMYDzijwoFmDujLhk3fF08Ndlq4pJf/9qJ4kJWf9u1LgSV5BkSeEZAsfHx+pXLmyrFixQgUddeuHDx+upW7ff/993W7RooU4O/cr9BR4kpfQXU8IyTbIj3/sscekTp06Uq5cObl8+bJ89tlnEhwcrMF4KJqDCYAr9KDPjuueAk/yGoo8IeS+OHHihPTt21f2798vRYsWlREjRqhFbwQpdv7+/vra2QU/K0JPgSe2wHn/6wghuUpAQICcPn1aLXgUxzEK/IYNG7T07ZNPPinjxo3Tfc4s8Flx3VPgia2gJU8IuW+Q012gQAHTNhrWIJ3uxo0bUrFiRZk1a5b85z//0X3Im3dzc+76W+lZ9OHnvSnwxGZQ5AkhOWLnzp0yffp0teSLFy8uHTp0MOXRozrem2++KefPnxdXwVzo2za/JPsPB1Dgic1wbh8aISTXQZ789u3btb49GtmgOE5sbKxa9Z06ddL1+iVLloirYO66X7MpKEOBR4lcQnIbijwhJEf06dNHhR6d6CD0BQsW1KA7uPHhnkdAHiLwnRE4QsPPX5B5S1dJolkZ37tC75GuwON9U2bNk+VrN8qFS1d0m5DcgO56QkiOeeaZZzSt7u2335Z//vlHc+jhxofQR0ZGip+fnzgTxtvmqfBzsn7rDomOiZX+vbtLYOEAi/P27D8kFUPKpnHRw4r/5c+Zpu0iAf5SI7SyVKkYohMDQqwFRZ4QkmNQ6W7QoEGaVgdRHzZsmLz22msWQXnOCFIDcQvde/CIFAnwk5DgrLXbvX7zlkyfuyjNfje3AlK5fIjUCK0kxYsG5sKIiatBkSeEWIWYmBjNmcdz27ZtxZUwin1WJzVnz1+U+ctXZ3oORL5t88ZStEhhK42SuCLOnc9CCMkzvL29pXHjxqZtZ21Okx6oA5Adeyk6NjbT474+3lK/VvU07n9CsgtFnhCSK7iKwN/P942KjsnwWJ3qVaVx/dri7uQ1BUjewOh6QkiucueOQYvmOBrIFli6dKncvn3b6teOicnYkofLnwJPrAVFnhCSa+vUiYlJsnPffpk6e77D5YWPHDlSnnvuOS3bu3nzZomLizMdy2koU1RMTIYegD0HDsnNiIgcXZ8QIxR5QojVgQju3n9Ifp/xj2zfE6bu6X0Hj4gjMXXqVBk8eLD8/vvvWrVvwoQJEhYWppMXozDfr9gj5Q7r+A3r1JTH+vSwsNxx/Q1bdzJ3nlgFijwhxOpABKOioy2s970HDzuMNY/lBQQSvvvuuzJv3jyt4Pfee+/J008/LRMnTpRjx47lKO4AEfMQ98b1akuAv5+KvTlnL1zSHHxCcgpT6AghucLtqGh108MyNQIxg7DZO8bWuNeuXdNCP2iZW7lyZdm0aZNs2bJFGjZsqILfokULCQkJscqkYvq8xXIrItIiwl6tfHf3HF+fuC4UeUJIrrFu83bZfyTF6gUe7u4y8OEHpKCnp03HdS+M6X/PP/+8nDlzRgPwjKxbt05eeOEFLd0Lsf/444+lZk1LS/x+gPU+f9kqi331a9WQpg3q5PjaxHWhu54QkmvUr13Dopd8QmKi7D1wWOwdCHx8fLwKfKVKlXQfmu+gXW7r1q3lyy+/VGv/5s2bUqVKFat8ZnCpIC2BmzoIz9y6JyS7UOQJIbkGXM7Vq6SIpJGwQ0cl1ixS3V7x9PTUtrlr167VKn5ovoOGO6BBgwbSrVs3baWL/daiRaP6aYLw1m9jEB65fyjyhJBcpUHt6hblXlOseceItH/wwQfVgq9WrZrMnTtX96ERz/Tp02X27NlSpkzWatVnZ1LUoE6NNCVwT51lEB65PyjyhJBcxccb1nxFi31hh47YvTUP6xmueqzBt2nTRh599FEJDg6W9u3by9ixY+Xrr7/Olc9FxTtE3Juzcdsui1a2hGQVBt4RQnKd6JgYmTJrvkXlu3q1qmvfdXsBY4PH4dy5cxpBf+PGDSlRooQ88MADukZ/6NAhWbJkiUa7owGPNYLtstPApkHtGtKkPoPwSPagyBNC8oQN23ZaFMTB2vOAhx4Qb6+CYk9AwE+ePKliHhgYKCVLltTKd7169crTcSxZvV5Onjlr2kYAI1Lq/P0K5ek4iGNDdz0hJE9AVzX0SzcC9zOix+0Bo60zc+ZMFfitW7fK8uXLVdwh9iiKM3DgQFm8eHGejQlBeOY/Lw3CYyU8kk0o8oSQPMHby0tqVKlssW//4aMSE2v7tXlj5TpE0T/77LNqvVeoUEHz5LH+PmjQIE2XGzZsmISHh+fJmAr5+kiD2pZLAuHnL8jps+fz5POJc0B3PSEkz4iJjZUps+ZJUtLdtfm6NapK80b1bV74BtHzsNhR3Q7BdqnZsWOHCjwi7vOKJFTCm7tIIiJvW4j/o6nq3ROSEbTkCSF5as3XDE1lzR85puJvaysehW8CAgJk79698vDDD6cRelS3y0uBB24FCkirJg3SlAveHXYwT8dBHBda8oSQPAXu+Smz5lpY80gba9HYdta8kSNHjsg///yjUfTIj2/atKk89dRTUqtWLZuOa8mq9XIyPCUID+v0DevUkno1q913gxziOlDkCSF5zuadeyysUaSuDXyol+bU57WbHqBcLQLbjNXr9uzZo9Xstm/fruVtUd3u7bfftpmownr/658FUrZ0KbXs4RGhwJOsQHc9ISTPwTq8+ZoyctR35bEL2mjfQMyffPJJLXTTrl07mTJlitStW1fGjx8vo0eP1i5zqGxnS1HFOnz/3j2ka7tW4kWBJ9mAIk8IyXO8ChaUWtVCLfYdPHpCoqJj8uTzMalA3vn+/fvljTfeUIFfuHCh1qk3Rs/DskfO/O+//65tZW2Nn6+PPufPgsDTQUuMUOQJITahTo1QbT1rC2veWEt/1KhRWtFu3LhxugZftGhRk6BPmDBBVq9erU1p7EE0s2K9nz2bsm4fGRmpj9u370blE9eEIk8IsaE1b9mm9eDR43lmzUMEsd6OuvQAAXavvvqq5sjDikcZ21mzZukxR3GPI8cfRXuGDBkiNWrU0G3i2lDkCSE2o06NqhbWPMR1174DefLZfn5+Urx4cdm3b5/myCP4DiIPqz0qKkqWLVuma/SOQHR0tGzbtk09FIgxuHr1qvz444+aDoiqfcR1ocgTQmxGQU9PqV091dr8sRMaTW5t0B721q1bFvtQ0W7BggWa/96vXz8VfrSS/fDDD6VgwYLy0EMPib1z8OBB+e677/S7INe/atWqOnnp0aOH/PHHH1K+fHmdPBHXhCl0hBCbEhcfr1XwEhISTftqhFaSNs0aW+0zwsLCpFmzZmrhIjodHeZQ+AbBdxDCX375RS5fviyFChXSdWw8f/HFF9KyZUuxVy5evChr1qzR8cMb0bdvX/n88881DbBixYry999/S4MGDdRDgbgC4prwN08Isbk1j2I42/eEmfYdOnZSW9H6+fpa5TNQzAYiDoGfNGmS9oJHihzc8U888YRav5s3b9Y2s2gv+9hjj0mpUqXEXkETnf/7v//TPH7U2J8zZ440atRIjx09elTKli0rs2fPVpGnwLs2tOQJITYnPiFB/pg518Kar16lkrRtbh1rPjExUbvJGS3gRx55RDvN9ezZU15//XW7ttjTA7dtpPc9+uijMnToUN0HL8W3336rnfSOHTsm3bt3l48++kj73htTBh0lgJBYD4o8IcQugCVvbs1DkAY81Msq1jxEDkFpY8aMUSt3wIAB6upGoN3hw4e1GM6LL74oVapUEV8reQ9yG+N3Asjl/+233zRgEIV7QkND5fr163L8+HENKnSU70SsDwPvCCF2AVz2np4pZWUB7I+de60TaQ8xjIuLU0sXwXUAljDWsiGQ6B3fqlUrDbiDeDoCsMyR5gdvBL5XUFCQDB8+XMX+k08+0Tx/nPPBBx+Y3oMue3D1E9eBIk8IsQs8PNxV6M05fPykRNyOuq/rYZKAdXhjZDnc861bt5Z69erpNnLkAVzeiEp/6623xNPT02Qd2zvwdPj4+EhMTIz0799fhR0ZArDasTyBtfhp06ap9yL23y5/+Hls2bLF1kMneQjd9YQQuwFr8n/Mmivx8QmmfVUrVZD2LZtm+1ooboPbGyxbRNO/9tprsnLlSg26QytZgOMQRGNjGkcEZXixBGEElfuM3+f8+fO6NAEvBsTdGb4vyR605AkhdmXN161RzWLfkROnJCLydrar2S1dutRUCAblaxF1Dlf8O++8o3nlyIeHNQzBw35HzSU3CjzEHSJuFHBE35crV05F/c0339Rn4/clrgNzKwghdkWtqlVk74HDmj8PIFw79u2XDi2bZfkaiDSH23rVqlWaNoeIc/SKh8t+4sSJ8r///U9fo2BMp06dpEiRIuLImIs7Au0QRAjRHzt2rFryKI7jKMsQxLrQXU8IsTt2hR2QLTv3mrZhgT7Wp4cE+KcEzWWFH374Qb766iu5dOmSNG7cWIPrAIrDoPgNisVgPRutZD/99FONSndUcBvHxAZpcwcOHNA4A1jv1atXN7nv4d0wBh0S14HuekKIXVrzKJJjEWmfjZr2OB/5448//rhWsIMVj6A7lLBFQBoauKD5TNeuXXXbkQXeOAm6efOmvp4xY4Z8//33Uq1aNVmyZIm67du3by9NmjTR7AFjdL2jZBGQnEFLnhBil+wOOyibd+6xELJH+3SXwv7+Wb4Gguxwi6tcubL8+uuvWtWubt26ui4P0QMISkOdemcA1jqWKZA6N2XKFPnmm2+0DgDa6Q4ePFjL3iLCHksVxDWgyBNC7BIEik2ZNV9i4+JM+6pUCJGOrZvf1/VOnTqlBXDgpkd+OcQe+eWlS5d2ukpwJ06c0Lz/zp07a/78vHnztJENbveoa//ee+9pASBsO9t3J5bQXU8IsUtQhhb16805duqM3LgVcV/XQze2p59+Wr788kstGoPUM1SFc0aRw/IEliEQYIjgO+T/v/HGG/pd4cFApgFwxu9OLKHIE0LsFnSj8/a660rXSPu9+7P8/vQclWhGY1yTr1OnjjgjWHdH4xoEFgI045k8ebJ6M9CRD9Y8cQ0o8oQQu8XdzS2NNX/idLhcv2nZFz49EhOTTOelzoGHlwCWvbPSoUMH2blzp9auB0gT7NKli4o7IuyNxYCMOGqNAHJvKPKEELsG3eh8vL2ybc3v3n9Q/p63WJasXi9R0TH6PlcJQUJkPUrdjhgxQnbt2mWy5lG6FwGIhQoVUosebXcBAvWIc8LAO0KI3bPv4BHZsG2naRtryY880E0CCweke350TIxMnT1fkpJS0sTy58sn7Vo2ldCKzmu9pwb1APr27Sv+/v7apAbue4A69liq+PPPP9WtP3XqVO07zyA854QV7wghdk/10EpqmUfHpDRagSChLW3Xdq3SPR+WvlHgQb78+aV0UAlxJRB4h3x5VLorVaqU7lu2bJlMnz5d1q1bJxEREZpZsH37do1NwPlw29Oqdy742ySE2D1uBQpI/Vo1LPadPHNWrt1IKQBjzs2ICDl49ITFvtrVQ8XXx1tcjeDgYBX4/fv3y+jRo+Xjjz+W06dPy/PPP69WPNrRLl68WJv5EOeEljwhxK6B1Q7Xc6XyZdV6hyveCErfdmnbwuL8jdt36/lG0KO+VtXKpvarruaSRiW8119/XW7duiXNmzfXojg1aqRMmFq2bKnPqAyIhj1Gi584D1yTJ4TYNRDn+p0ftMq1di2brZH1rgba7Xp7e2u/+dQ/W6QTbtu2TZv5oJENcS5oyRNCiJMzaNCgNPtQ2hbd+RBlD/c9Bd45ocgTQhyGraNGiXs2W6YmJidLkzFjcm1MjsbZs2dl9uzZsmjRIrl27ZpG4BsnAYywdz4o8oQQhwEC7+7Gvuj3y9q1a+WLL76QixcvSqVKlWTMmDFSv359PbZ3714VeBQJQh49I+2dA/4GCSHERUBLXfSdf+GFFzRPHgKP4ji1a9eWxo0bS8+ePbU6HprZQODRoQ8wdMtxocgTQoiLgLK2y5cvl2eeeUa3hw4dKs8995yK/YEDB2TixInaltcYoIcWvLDo0cyHOCZ01xNCiAvh4+OjzxD0adOm6do8WtICuPBh0SPiPioqSlauXCmvvfaa1KpVS9vVEseDIk8IIS7Inj17tOc83POw1uGaR5odXPVoVQtrHkV0cLx3796abueK6YeODkWeEEJckPPnz0vRokU12A4PCHxycrL8+OOP2r0uJCREa95D5LGWTxwTijwhhLgg6EjXrVs3adasmTRs2FCtdog6GtiMHDlSrXfUtDdG2DO9zjGhyBNCiAvStGlTrVs/btw4GTZsmLrjH330UXnyySe1/K1x7d4o7hR4x4QiTwghLkqjRo20UU2LFi3kiSeekI4dO6apX09xd2wo8oQQ4qKgDe0ff/yhQXcQfKOg0zXvPFDkCSEuw7HT4VK9ckVbD8OuQCEcI6ld8xR7x4ciTwhxGFCHPifvWbluk9y+HSWN69WmeKVDanG/ev2GBBYOUIufOCYUeUKIw2CNRjM79x2Qm7cipUOrpsz7Toc7dwwSFx8nG7bulOOnw6VF4/pSp3pVWw+L3Ccsa0sIcTlOhp+V2YuWS2RUlK2HYlfAgj9xOlymzpqvAg+27w6TmNhYWw+N3Cf5DOw8QAixY3CLSkpKytE1IFhrNm1VK9XcTV/Q01O6tmslpYLYS93IjVsRMn3uIoumNKEVy0uHVs1sOi5yf1DkCSEuAdaXF61cJ9ExMRb7UeylddOGUr1KJZuNzd7YtH2X7Dlw2GJfn64dORlyQOiuJ4S4BMUCi0i/Xl2kRLFAi/2o275m0zZZv3WHviYiDevUEh9vL4t9/Pk4JhR5QojL4O3lJb27dlT3c2rCDh2VhSvWSFx8vLg6Hh7u0qxhPYt912/ekv2Hj9lsTOT+oMgTQlwKtwIFpH3LptK8Yb00aXRnL1ySWQuWys2ICHF1Kpcvl8Y9v233PgbhORgUeUKIywFxr1uzmnTv0EY8UqXRRdyOklkLlsmZcxfE1X9GrZo0tJgIJSQmyuade2w6LpI9KPKEEJelXJlS8mCPzuJfyNdiP8Rs0cq1GnzmyrHJKIRTu1qoxb4jx0/JxctXbDYmkj0o8oQQl6ZIgL881LOLlCkZZLEf4o4o81Ubt0jSfVTacxYa1a0l3l4FLfat37qTQXgOAkWeEOIQPPXUU6a66v/8849Vrz12zBjp3bWDvPT0AFm1bHEay3XekpUuuxadXhDetRs35cCR4zYbE8k6FHlCiMPQtWtXuXjxonTr1s2078aNGzJgwADx8/OTgIAAefbZZyXKrJLdmjVrpHfv3lKyZEntkV63bl2ZOnWqxXWHDx+u1y1TpoxUCimbJiDv0tVrMmP+Us21d0WqVAiRkiUsg/C27t4rMbFxNhsTyRoUeUJIlklOTrapm9bT01OCgoL02QgE/sCBA7J8+XJZsGCBrFu3Tp5//nnT8U2bNmmntVmzZsm+ffvk6aeflieffFLPNeLr66vXRSOWoOLFpHeXDloNzxwU0ZmzeLkcP3VGXDMIr4FlEF4CgvB223Rc5N5Q5AlxYtq2bSsvv/yyPvz9/aVo0aLyzjvvmILJ4uPj1YotXbq0WrlNmjRRy9fIr7/+qtbxvHnzpHr16iqu4eHhek7jxo31PTjeokULOXPmrvj98MMPUrFiRfHw8JDQ0FDtWW4OxOLnn3+Wvn37ire3t1SuXFk/I7scOnRIlixZotfC2Fu2bCkTJkyQadOmyYULKdHxb731lnz44YfSvHlzHdOwYcPUIzB79uwMr4vUsYd7ddHAM3OSkpJl2dqNsn1PmMsF5BUtUlhqVauSZinj0pWrNhsTuTcUeUKcnN9++03c3Nxk27Zt8vXXX8sXX3yhoggg/ps3b1ZRhJXbr18/FcBjx+4WPYmJiZFx48bpe2AxFylSRPr06SNt2rTR9+D9sJyNVt6cOXNUSN944w3Zv3+/DBkyRK3n1atXW4zr/fffl0ceeUSv0b17d7XI4Xo3EhISIu+9916m3w2fjUlGw4YNTfs6duyopWq3bt2a4fsiIiL0e2SGn6+v9O3WScqXLZPmGER+6ZoNkpiYKK4ehLduCyvh2TMUeUKcnODgYPnyyy/VooaQvvLKK7oNi3zy5MkyY8YMadWqlVq5sOphDWO/EQjZ999/r5YwroFmMRDJnj176nuqVasmgwYNkrJly+r5n332mQbJvfjii1KlShV5/fXX5cEHH9T95uCcxx57TCpVqiSffPKJrqNjImIE14bnITMuXbokxYtbrhVjQgMBx7H0+Pvvv2X79u068chK0Bka2DSoXSPNsZNnzsqcxStcqpOdp4eHNGuQNgjv4FEG4dkrFHlCnJymTZtarKU2a9ZMLfWwsDBdY4cQY03a+Fi7dq2cOHHCdD5c7ljTNgIBhUB36dJFevXqpd4BBK2Zu9DhvjcH29hvjvk14fZH4NyVK3fzr1euXKmeBmsCbwLEfdKkSVKjRlrhTg/87JrUryOd2rTQNfvUAofCOa6UN16lYoiULF7MYt+WXc4RhPdULmZwYOnLeO3XXntN8gqKPCEuCixniNbOnTtlz549pgfEGMJtxMvLK020OSx9uMph3U+fPl0nClu2bMnW57unqjSHz8iu2xfBcuYTAwBPA9z+OGYOJi+YlMCLgcC7+ynz2rdbR/Hx9rbYHxsXJ3OXrpJDx+5OjJw+CK9pqkp4CYmyxUkq4XW9jwyOuLg4nSDUqlVLPUlYzkpN//799bqYZOclFHlCnJzUa9MQYwS61atXTy15iCRc5uaP1AKZHnj/qFGjNHq9Zs2a8ueff+p+uO83btxocS62EbhnbXDDvHXrlk5UjKxatUonCwjEM4JAwR49emhsgXnkfXYpXjRQHu7ZOd1Odqs3bpUN21yjSAyC8GpWrWyx7/Dxk5pq6IoZHMnJyToZfvXVVzUmJD1wHNeFZywvocgT4uRg7R3r4keOHJG//vpLo88RGAfrGzcvWLWIND916pSuiY8ZM0YWLlyY4fVwHsQdljwi6pctW6buf4g7ePPNN9U1iQh77EegH66P9f7s0KFDB/n2228zPQefCctr8ODBOnZMJuDif/TRR6VUqVImFz0EHjfghx56SNfq8TAP8ssOsOQz6mS37+ARWbhyrcQnJIiz07hebfEqWFC+GvuRTP/jV31UCCnnkhkcPj4+Oh78HWZlgpyXUOQJcXIg4rGxsXrDfOmll1TgjVYI3O44jkh43CjhZkRQmjGILj1wwzx8+LAKJiYKuBauiyh6gGvA3Y9AO6x7T5w4UT8H6XzZAXEB167d2zJEYZuqVavqpABR+rgJ//TTTxbZBcgQwOQFBXGMDwQD5rSTXbMGddN2sjt/UWa6QCc7DcJrWFdfb920Xpd+3nz7fXnzv6OYwWFH5DO4WrInIS4EhBUV3r766itxdLDmCde8tQOizMFNH0FR2QmMOn32vKxYt0mb2qSOzO/SpqUEly4pzgrko1btunL9xjV5+6PxKsKenh5yaOcWWbRokVrAFSpUUMvc6FkxiiQmnciqgCUPgUY8SJ06dfQ4hDgwMFCteQh9amDVYwJpPpmDmEdHR5u8UBjL22+/rTUSAI4hsHTx4sU6yQCYGMLSNwZ4pvc3hjFioghPmDnI6sAkYujQodn6O83r/0la8oQQhwHrobhRm1erswa4keO6EKPsEhJcOv1OdgmJsmDFGtl70Hk72UFI/f0LSfmKlU1Wdnx8ggQULeGyGRz2hputB0AIIVlh/PjxapkBuNutyQsvvKCWIChWzDI9LKud7CD0y9ZslPOXLpv2Q9w3btsl12/cktbNGqmb39lwd3MTb18/i33nLl5Kk8GROv0QYn+vDA7EUcAbgAwO/O4R+IaUUHvN4LBHKPKEODHmAU6ODtyjqQvfWAtYjveqgHcvEITWs1Nb2bh9t+w/fDRN5HlE5G3p0q5VmopxzsDpk8f1+yOdEJw6fkxKlCyp7ndjBgcKLmUXZHAYsziQSYEMDoi8MYMDRZjyMoOjQYMGGWZw2Ct01xNCiJWAtdq6aUNp06xRGsv04pWrMnPBEi2g42ycPXtWVi+ZJxfOn5NtmzbImpXLpGXbDhKfbNCqhgjuRKXBo0ePasrlRx99JHPnztVqipgEALw2Lms4WgYHOHjwoMYVwMJHRUhj3QlbQ0ueEEKsTI3QyhLg7ydLV2+QuPh40/6o6BiZvWiZdGjZTCqGZJzB4GgMHDhQ/p63WG5NmaqTmyKlysn+8Ksy8OURYrhzR6IN7jJw0FOSFB8nBdw9xNuvsBQPqSJvf/Wz3Lx4Vm5HRUv9zg/KrmWz1cVuzOBAwNv169d1eSajDA5E2ZcvXz7XMzhefvllnRQgqh6ZJd98843FOYjeN0/xgwcC2Doeg9H1hBCSS0TcjpLFK9fKjVsR6TZ7aVinZhqL39GAsCLAbXXYqRxfyyjyzpzB0TaPo+tpyRNCSC6BiPsHu3eWFes3aapd6k52EP/2LZrYVNiszdZRo8Q9GwGGicnJ0mTMGLG3DI5p06ZpEyZrAW8APBGoWQGRzyso8oQQkosgX75b+9ayddc+2RV2wOLYidPhGpCH44V8fcQZgMC7uzlmFsH4XMzgeOCBB0yBeiiuk1dQ5AkhJJeBS75pgzpSpLC/1rg3BpsBBOKhQl639q0kKFV3N0fJ4EDQHNbUHZ3iuZjBUahQIX3kNYyuJ4SQPKJKhRDp0xWd7Lws9iP17J8lKzXVjhBrQpEnhJA8BB3sHu7ZRTvamYO861Ubtsim7btcopMdyRso8oQQksekdLLroD3qU7PnwGGX6WRHch+KPCGE2KgcbMfWzXWtPr1OdrMWLpNbEZE2Gx9xDijyhBBiIyDu9WvVkK7tWqnomwOBn7lwqQr+vTAP5CPEHIo8IYTYmPJly2iDGz+zpi3GTnY3IiIyrZqG9XuUjp00aVIejJQ4GhR5QgixAwILB8hDPTtLqaC7KVxVK1WQOtWrZloVD6Vd0SI3gWv4JB0o8oQQYiegk1uvTu2kRmglzZlv27xxplb8nDlz9IFa6miXC1ipnJjDYjiEEGJnnezaNGssSf+us2dkxR85ckRGjBgh7du31xaoxn7tcN+n7t1OXBda8oQQYoe4FSigHc/SA+1MBwwYoB3aLl++rB3Z0AUN2FrgUYs+MSkbDwYN5irsQkcIIQ4E1t7hnkdE/euvvy5t2rRRoUczlW7dusnHH3+s1n9edrezVllbW3ehc0ZoyRNCiIMAm+yNN96QqKgoef7556Vjx44qimXKlJGQkBA5ePCgWv/mAp8X1fOOnQ7P9c8g9wfX5AkhxEH4/PPPZdeuXTJo0CDp06ePaT/2HTt2TJ5++mndPnTokMTExOhaPUQfVn9uufGv37wl67dsl+cHPmLa5+HuLn27d5LC/n5pUgL/mDlXEpOSTPuaNawndaqH6mu3VLUCSM6hJU8IIQ4ARHvt2rXSsmVLeeKJJ0z7z507J2PHjlVrHsdw3l9//SUvvfSSvPrqq3oOBD43LHqU3l2yap0kJ6cE+xkfndq20Nr88DKYP3x8vKV6lUoW5x4+dlLFHcfzconBVeCaPCGEOAhY+759+7YUKVJEt+Pj42XYsGGyf/9+efnll+XRRx/V/bGxsbJv3z556qmnpGbNmjJ9+vQMg/juF0jH4lXr5PTZ8xb7UcEPpXoz4satCJn2z0KLfT06tpVyZUpZdXwkBVryhBDiIMDaNQo8+Oijj9Q136VLF6lataoMHTpU0+rQ471JkyayYMECCQsLk02bNll9LDv3HUgj8MGlgqRxvVqZvq9IgL+UKRlksW/foSNWHx9JgSJPCCEOyOHDh2XevHlSo0YN6dChg1rx69at00h7BOWNHj1aAgMDxcPDQ27evKnvMXfc5sSJG37+gmzfE2axr5CvjzbcyYrHoGa1yhbbqM/PZjy5A6McCCHEAYHlPm3aNClWrJjMnj1bLl26JLdu3VKX/tatWzU4D5OA8+fPS/HiKaVyzde88fp+AvIibkfJ8nWbLCYJuAaa7KBiX1YIKVNafH28JSo6xrRv/5Fj0rJxg2yNhdwbWvKEEOKgVKtWTYoWLaqCXaJECbl48aK69BGA984778jevXvlzTffVNc9rHm8Rvnbt956yyTO2elgh6j4pavXS3y8ZZ38Ns0aSbHAu8sI9wLWfs2qltb84eMndYJCrAsD7wghxMFB5Dxc9qiEBwt+9+7dsnjxYmndurVa+QDR9hs2bJAWLVrIli1bNKJ95cqVUqhQoSx9BqRi1YYtcuTEKYv9qLOPMrzZJTYuTn6fMddikoHJQo1QS/EnOYOWPCGEODiwjFevXq1WOqzzqVOnalQ93PnGaHtE5YeGhsr333+vefVw8z/33HNaWCcr7D98LI3AlygWKC3u08UO136l8mUt9oUdOsoGO1aGIk8IIU4CoutR5tbHx0f+97//adAdyuB6eXlJ//79Zc+ePbpOD8aNGycPPPBAlkT10pWrsmHbzjQi3aVtK62xf7/UqlrFYhtNeaJi7q7Tk5xDdz0hhDgZkZGR4ufnpwF4y5cv17X4uLg4rWt/7do1mThxoq7dY52+cOHCmV4rJjZWZsxfItExsaZ9iAF4oEt7KR1UIsdjnbVwqXh6eEitaqFStnRJgSLlz8+iONaCljwhhDgZEHiwY8cOWbRokYSHh4u/v788/vjj8ueff2r+PLiXwGO9fOmajRYCbyxFaw2BB706t5eendppjj0mDxR460KRJ4QQJwXBdmhcg6C8VatWyYEDB3StPiIiIkvv37xzj1y8fMViX6Xy5Uy15q2B+7/16q1dkY+kQHc9IYQ4OW+//bbMmTNH1+YRTf/3339r4F1mHD15Wlas25SmWt1DPTrbtB3s0aNHNe8/ICDAZmNwJCjyhBDiApw4cUJd9rCYURoXaXeprWc0nMH6+LUbN2X2omWSlJRs0Vnu4Z5dJCBVZ7m8APnzyB747rvvND3w/fffN3XcI5lDkSeEEBcCt/zU3d4g+CfOnJVN23dJ+5ZNZd3m7VrZzpxu7VtL+bJl8nSs169flxkzZsikSZO0RC867TVv3lyXH8qXL5+nY3FUKPKEEOLCQODRE37OouVa0Q5iX9ivkAQWuRuU16B2DWlSP+POctYEkoSuehMmTNDsgKtXr2pmwIsvvihDhgyR4ODgPBmHs0CRJ4QQFwW3//iERJk+d6FG0J+/dFkuXEoJtCtZvJiULllCypYpJT06tMn1wDiMZdasWZreh9Q+WOpNmzZVy/3KlSuaEYC1eKQBIvcfNQDIvWGDGkIIcVHgtncrkF+CihfT1rFGgQcXr1zV5yce7p0nke8YC8rtQsifeeYZLb9btuzdingItOvRo4d88MEHFPhsQJEnhBAXBjXsm9SrLVt27kkjuoUD/GXRyrXSrUMb8S/kmydZACjLm149/VOnTqmr/tixY9qBj2QNJiYSQogLg3X4ZWs3ahe5SiFlTVZ7SHBp8fH2khu3ImTmgiVy7uKlXB8LrPXUAr99+3b5v//7Pxk5cqQ23KHAZw+uyRNCiIuC2//K9Zs1J968jC36vBcvGpjGsm/VpIHUTFVvPjeIjo7Wcrzz58+Xw4cPq3WPmvwQel9fX1P6X3qZAsQSijwhhLgo+w4eSdN4JqhYUencpoUsX785TbU7UDO0srRoXF+FNzeAgD/yyCPqnkfKXN26daVXr17SoEED7aY3ZcoUtfhxTnq5/sQSrskTQogLAgHfuH2XxT5vr4LSpV1L8fH2lgc6t5N1W3bIoWMnLM7Zf+SY3IyIlM5tW2gnOmsD0e7WrZta87Vr19aGOuieh/V4FPFB0N17770nLVu2lFKlStGavwe05AkhxMWIjonRznIxsXGmfRDK3l06SKmg4qZ9kAf0eMdkILVU+Pn6SrcOrSWwcO6Ul0VufPfu3WXz5s3qoq9Ro4Z8/vnnatF37dpVKleurLn0aKKTW14FZ4B+DkIIcSEgisvWbLQQeNC8UT0LgTcKf+3qodKzY1vx8LCsVx8ZFaUFdE6fPZ8r40TO/OnTp3VN/vjx45o3j3K2YMCAAVooB1DgM4ciTwghLsSmHbtNOfBGKpcvJ7WrZdxZLrh0SXm4R9q69QmJibJ41TrZFXYgjaWfU2C9R0ZGSsmSJbXm/n//+19Zs2aNNqhBHfuKFSvqGj3JHIo8IYS4CEdOnFL3uzlwt7dt3vie69oQeHSgg+CbA3HfsnOvRuknJd9taJNTevfurWvwqICHILygoCBdo3/ggQdk48aN8uCDD2pXPZI5XJMnhBAXIN3Och7u0q9nV/H3S1t8JiMQ0b55xx7Ze/BwmmNIu+vWvpUG7lmD9evXa1vcuXPnamtcdKBr3LixDBs2TB577DGrfIazQ5EnhBAnJy4+XmbOX6rr6OZ079BGi97cD4ePn5Q1m7ap6JuDAjpd27WWEsUs8+zvh6SkJA24Q5W7hx9+WF566SXNlzdy7tw5TbMjGUORJ4QQJwa3+IUr1kr4+QsW+xvWqSmN69XO0bUvXbkqi1etl9g4yyA+BMO1a9FEqlQIkZyCvHhUujPWscf3QRW8V155RdPtOnfurEVyvL29mU6XDlyTJ4QQJ2bH3v1pBL5s6VLSqG6tHF8bjW0e7tlFipq1pTVG8K9Yt0nX6nNqRw4cOFAF/uTJkyYR//XXX7WRDdbt//nnHxk7dqzpc4klFHlCCHFSkN62fU9Ymvz2jq2bWc3iLeTrI327dZSKIXc7xhlB1P2S1eslISExR58xffp0ee211zSVDqBQjjHiHq1nEZxnbLZDLKHIE0KIExIReVtWrN9ksc/NrYB0bd9KCnp6WvWz3N3dtRRuet6BU+HnNOAv4rZlPEB2gCs+PDxcI+zRSx4589gXHx8vjRo10jK3SK8jaaHIE0KIk5GYmJiuBd2mWeM0rnVrAc8ARL5L25Y6mTAHnexmLVgq5y9dvq9ro3b9mTNntAAOytoifx7WvOe/k5Xhw4er+56khYF3hBDiROCWjvXwY6fOWOxH97jWTRvmWboe+tCjm13qiUDrpo2kRmilbF8TQr5p0yaNsofV7uPjI3/99Zceu3XrllrzJC205P8FfzT4A8QfCyGEOCoodpNa4EsWLyYtGtXLszHAW/Bwz676uaknIGs3b9PGN9kNknvnnXekffv28ueff2q528GDB5uOUeBd0JJ/6qmnVLAReZlVkW/Xrp3cvHmTfzCEEIfkwqUrMnfpSouIdnSW69erq9UK1GQHVMBbt3m75tSnpnRQCe14l934AFS/Q/c5o6ueZA4teUIIcZLOcsvWbrAQeHgnsUZuC4EHbv/my6P/fOpofqzPY50e6/XZAUF3EPjURXiIFUS+bdu28uqrr8qIESO0pjAiHdHX18gXX3whtWrV0rUS9P598cUXJcqswhJyG2ElL1iwQEJDQzU6EusrMTEx8ttvv0lISIgULlxYP8PclYMISqzHlC5dWq/dpEmTbEdS4hq4LoIzChYsqL2IUVAhNTt37pSGDRvq2Jo3by5HjhwxHcN3rVu3rvzxxx86VqRwPProo3L79u1sjYUQQqwJ7pdLV29I01muRaP6UrKEbQPSIO51qleVHh3bpOlkh4j72QuXyYXLV7J9XRTCSQ80zYmNi7/v8YqrW/IQYwgtohzHjx8vH3zwgSxfvjzlYvnzyzfffCMHDhzQ81atWqUTAnMg6Dhn2rRpsmTJEhXrvn37yqJFi/QBAUXO48yZM03vefnll7WnMN6zb98+6devn/YTRqlDI8YCCRmBcaB1Ica1a9cuqVSpknTp0kVu3Lhhcd7//d//ac/iHTt2aM7lM888Y3H8xIkTugSAiQoea9euNRViIIQQW4B7b4liRS32odpcrWpVxF5AAR40uEldJx+iPHfJSg3Su5/VY1j0eN+tiEhtofu/v2alie53aQzZoE2bNoaWLVta7GvUqJFh5MiR6Z4/Y8YMQ2BgoGl78uTJ+A0ajh8/bto3ZMgQg7e3t+H27dumfV26dNH94MyZM4YCBQoYzp8/b3HtDh06GEaNGmXaDg0NNcyePdu0PWjQIEPv3r31dVRUlMHd3d0wdepU0/GEhARDqVKlDOPHj9ft1atX69hWrFhhOmfhwoW6LzY2VrdHjx6tY42MjDSd8+abbxqaNGmSpZ8fIYTkJkdPnjZM/GO6Ydo/Cw0JiYkGeyQ2Ls4wb+lKw3eTp1o8lqxal+Vr3LlzR5/j4uMNO/ftN/w+4x/TdWYvXJaLo3c8sl0eCK3+zEGv3ytXUlwtK1askDFjxsjhw4c1jxHNBeLi4tR6h/sb4Bl9gI2UKFFCXd/oHWy+z3jNsLAwdUVVqVIljfs9MPBuAwR8ZkbA+kbeaIsWLSyKN6Cb0aFDhzL8fvhuAGMx1k3GWAsVKpTu9yeEEFtSsVywFCtSWAoUyC/udlr9DYF2PTq21b72+w7eXQ49GX5OIm9Hia+Pj+TPn341PmNZ25jYWNm6a59mEaSO0i8VxHx5c7L9VwBxNAc/cLhLkNLQs2dPGTp0qJYZxJr9hg0b5Nlnn9UKRUaRT+/9GV0TYE0fzQ6wVo5nc8wnBtbCfCzGQBHzAI/MxkoIIbnB1KlTpWnTpiYDKaNGLHDbwx1u701aMM6WjRtoL/u1m7ebXO7L122U3l07Sj5D/jTfAcePnjgtR0+elrMXLmZ47VI2jkFw2uh6iDB+UVjPxh8jLO8LFyybItwP9erV05karGWso5s/EPiXFfCPgSpJGzduNO2DZY/Au+rVq+d4jIQQklssXrxYnnzySfnss8/0PgsggBmtX9u7wJtTrXJF6d2lvXgVLKjbl69el0Ur1siV65axUtCWP+fMl89++EWOn7asAZD6uwcVt4xNcHWs5s+B6EI4J0yYoCUIIag//vhjjq+LycKAAQP0jxwTCIj+1atXZeXKlepa79Gjh55XtWpVXSpAEF9qECgID8Obb76pHga43hE0iGUEeBoIIcRe6datmwYdv/XWW3L27FkZMmSIdOrUSbOEnAFE/z/Us7MsWbVeK+Wdu3hZzi1YKp6eHpKYkCj1alVXyz8iMkpqVqsixYoUkYjbt9Od5JQoGpjG2+rqWM2Sr1OnjqbQjRs3TmrWrKnuJYiuNZg8ebKK/BtvvKGpd3369FEr3LhODpDqFhERYTHzM+9IhAj4hx56SJ544gmpX7++djNaunSppuwRQog9gvsYPJnIKILQY1kU91X0WL9+/bo4C+iMh052FcoGm/bFxyfIHYNBdu47YOqkhziDkiWKaXGf9FIDSwWVyNNxOwJOW/EOKXbwLnz77be2HgohhNwXEHjEIu3Zs0cmTZoke/fuVQMFgc2vv/66DBo0SCpXrpzpOr0jge+wY+/+NO1xzUHMwYAHe+m5J06HawCfsUZ+r07tJLh0SsA0ScE+wy9zAMrSYqkA+fcvvPBCtmfNYYePyv7Dx+TwsZNyOzpa8ufLJ8UCi0j1KpWkXq1qmutJCCF5AQQeQo9lSBTeQp2PChUqaHAz1uhRK+Q///mPFghzdIE372RX2N9PVm3cIklJyem20EV0vbeXl1QqX07KBZeW3WEHNVI/wL+QbNy2Sw4ePS7HT4dLXHy8Wv/BpUpK9SoVpUGdmlIkwF9cCacTeRSvgSsfrv3evXtn6T2xcXHy97wl8ve8RRJ+PuOoTdC4Xm15vG9Pad+yqVP8UxFC7Bt0XkO808CBA03R9W+//bYukaJiKCpuPvbYYzoJcJb16PJly0iRAwFy5dr1DGv0Q+ABRLxsmVKyasMW6fr4YE3Dywg3Nzfte/9kvz731QnPEXFad31W2RV2QN4d/42cOZeSCZDkliDRha9JbKEISfKIFzHkE49Yb/G67S++t4pqagdAy8bRb7wsxYvezdUnhBBrgzofKCn++++/axOt2NhYDbqDkdGqVSut4Pn888/Ll19+Kc4AJGnNpm1y6NiJDM8xts3FuXMWLZdPf/jF5LKP94qWGP/rEusbKXfcEiXfnQLiGV1IvCMLi/ftlOZjyMOH0L/09IBsN8hxNFxa5P+et1g++uoH/UNJKBgtV0KOSkSxi2IokH7ee4EEDylyoZwUC68k+e8UUJfSD+Ped5kZISEkbzBfX8drRNMjsh61R4oVu9u+Fa767t27awvW1HVEHJW9Bw+ryz0zkF//UM8uMvrTb2Te0lW6L9r/ut7DowOui2TgZPWM9pWiZytI4UspQdu4d/8w7j0p7O+8LnyXFXk0RRj92QR9fb3UablU8ZAYCmStvzEs+zKH6unMsJCvj/z2zTip/K/riBBCchpoh2qhcC2jGBiKfl26dEkefPBBOXr0qPb+QI2QgwcPyrBhw7RqZ+qKoI4K5OjkmbMSn5AgiUlJkpiYpM9JZq9TnhNl+579Mn/ZKjHkuyMXKx2QG6XOZCjuqfG5Gaj3cPeEglKtcgX535djxNfHNp36chuXFHmUQuw/5DX9Y7kcckSuhtxtdJNV8iXnl3JhjdWFj1KS03/6Sjw9PHJlvIQQ5weBv8bOaqjrsX//fk0ZhpX++OOPy8WLFzUVGCnFSP1Fu9X//ve/aZpouQJw0b/76Tcq8GdqbpeowKvZvoZ7rLdU2N1M3BO8pG+3TvLBiFfFGXE5kcdMecBLw+XAkeMSUeyCnK2+K8uzv9TkT3KTytvbiHu8lzz3eD8ZNvhJaw+XEOJi9O/fX6Pm0Q775MmTKu4dOnSQ999/X7y8vOT8+fNy69YtbduN9tuuBqri9Xn6RV2Dv1A5TG6UzrgC3r0oGOkvFXe3lHyGfOq2R6ldZ8NqxXAchQ3bdqrAI8DuQuX99y3w4I5bkpyvsk9fT5k9TyKjMo7qJISQjDDaWnDHI6hu+vTp6pb/6aefNEsIKcGIrod1D2GvUaOGSwo8mDprngo81uDVRZ8D4vwi5FrwcX098ffp4oy4nMhPm7tIn2+UPi3JHgk5vh7cRDGFbklcXLzM/zcAhBBCsgOC7FC1EwVuIPQoxQ1KlSolI0aM0FK2KMMNoV+yZIm4Klirn7N4ub6+Uu5Yjow0I9eCT8qd/Mmy58AhOXL8lDgbLiXyyIffvH23vr5ZMtxq1zVea9XGrVa7JiHEtUBVO/TlQHvtWbNmmfbDskePjVdffVWreBpbYLsiu8MOya3I25oNhVRna5DsniiRxVLqo6AAj7PhdMVwMuPIidOSfOeOJBSMkcSCcVa7rqZsiMihoyecorQkIcQ2a/FlypSRr7/+Wn7++Wet3ommWliHNzaqQWU7NNlyVQ4cTQmSjva/YRUr3giuF3C5jFbKczZcypI/eTrF4o7zibTqdRO8otXdgzK4lzOo0EQIIekRHR0thw8f1uj6Fi1ayFdffaWBdosWLVLrHcVwjLiywIOTp8/qc5yvde/hcb4RpswrZ8OlRD42Pl6fk90SrXvhfCLJBRJNnZMIIeReWT5gxowZ0rNnT+ncubOUL19e3n33XfH29paPPvpIO89B4NGEZutWLgWa38MR9GxNkv+9njPev11K5FHjGKBanVUx3L2m8TMIISQ9sKSHgjf79u2Tp59+Wnr06KFR8/Xq1ZNff/1V3fQQevTfGDx4sBa+wQSA3L2/5ku27j08/50UKXR3d777t/N9o0wwdpBDHWNr4h5fUAoku2sxnOLFWMueEJIxxpgdFLZBY5nhw4drRD26Z44bN04FPTw8XMvX4jjK1vo7cdnV7FD23zaynjG+Vr2uZ7RfyvVLOV9Qo0tZ8tWqVDT9geRPtN78xiuysD6HViwvbk5SP5oQYj1u3LiRZh8q1jVq1MgUVIeqdqheB1c+esej4QzKufr5pQgQEW35DVBS3Jp4R6Y0rqkeWlmcDZcSef9CvlKzamXJJ/k0ktJaFL6Ucq2mDeta7ZqEEOdgypQpapHHxVlm9Hh4eMjcuXOlT58+2iMeVrzRnY/Ocqhbj/r1zNa5S/3aNdRj6hXlLwVvW2fyky85v/hfTiks1MwJ7+EuJfLgkQe663Pg+fL6y80pnlGFxPdGcW1d2K9nFyuMkBDiTKA87QsvvKD57pcvX9ZoegA3PcrTrly5UrvJ4TjW49955x1do0cZW5LWUOvavpW+Rjc5axBwqYy4JXlISHBpaVKvtjgbLify3dq3kqDiRcUz1keKn8lh56Y7+aT0kTrqGejWvo0EFb/bApIQQuB6DwkJkb59+6qAo+HM559/LtevX5fKlSvLa6+9Jg0bNtSGNB07dpRevXrJtGnTZM6cOeLu7m7r4dslTzzcWwrkzy8BV8qI7/Wc3XPd4wpK0Mlq+nrQI31NDYKcCZdrUAPQq/iFkaPFIAYJr7FTbhe7lP2LGERKHqspgRdCpHCAn/z29TgJ8C8kd+4YTHWokfeKlwbDHVORnBLFitL9RoiLtY7FvQAP5MC/9dZb8uijj2qaXNmyZWX79u2ye/duWbt2rfaNr1Onjkbak4z55pc/ZNKUvyXJPV5O1tskCd4p3pHskD+pgITsa6rr+43r1ZZJn31IkXcmxn/3s/wxc662KkQno5slz2a5ghLc/BD4IpfK6ozy8/f+q0UU8E+cGU3q15EGtWtY5wsQQhwG9IJHtPzEiRO12Qws+4oVK8qECROkWbNmth6ew5GQkCjPvP6W7D1wWBI94iS8xg6J9b+VLQs++GADFfjAwgEy9fvPpHRQCXFGnG/akkWGD31GHurRRfIZ8kvpo3WkXFgj8Yy+R1qGQdQ9VGlHG5PAf/LW69KhVTMpVaJ4pm8tUzJI6teqbt0vQQixO1B7Hu1gjfbTli1btF0sGs1gX9u2bbWFLDx6DzzwgPz111/qyidZx8PDXb4bM1pqVa0i7gkFpcLuFhJ0vJoUSPC4p4FW5Hw5qbS9rUngf/rsQ6cVeJe25AG++l//LJQvf/pVu8iBqIBrElXkqsT6RkiSR8o+rN973fYXv6ul9DUoUypIPhwxTBrWqanb+w4e0Ta26eFVsKD0791NvP+tQU0IcU4g5mgBi0p2Dz30kJw+fVpT4bAfa+0Q9sTERI2sB1iLh3U/ZswYGTlypK2H73DExMbKFz9OlunzFus2PLORRS9pP5FY30i545Yo+e4UkILRvprq7H+llAbZAQTZfTBimJQKytxAc3RcWuSNnD1/USb8b4osX7dJ81Izo7C/nzzcq6s893g/8fYqaNqPXvJTZs5L9z0FPT3V2i9XJqUYDyHEOUGU/PHjx2X+/PnaUQ4laffu3av3lRUrVkitWrX0vISEBJPQQ+Rr165Nt30O2LZ7n/z4+zTZvifsnueGBJeWp/r3lb7dOjnlGnxqKPJmXLtxU5av3ST7jxyVw8dOqnDDJV8sMFCqV6ko9WpWl/Ytm6qrKD2mz10k129mvC5Up3pVadqgjgbiEEKcDwTTYZ198+bNMmDAAGnQoIFWsPvuu+9UyIcNGybt2rXTc2HRM4Leupw4HS6rNmzRbnLHT4dLbFy8lqpFJTsU0kEefKO6tVwq+Jkib0W27torO/cdyPScYoFFpHObFuLvZ93SuoQQ2wMrHtHzy5cv1xaxFy5c0P1//PGHfP/999pFDuKPtrKY7LM1NcltnN9XkYeEBN+toufh7q5lblNz9foNmTF/iRw9eTqPR0cIyW0qVaokNWrUkIiICM22efvtt3X/E088IV988YXJPf/BBx9osB0FnuQ2FHkrUrxoEdM6fZvmjXUdHlY7BN+chMREWbFuk7qV4LIjhDgPxYsXl6lTp8qoUaM02A4R9KhdjzX3yZMna248qty5wnowsT1011uZNZu2qQuuXYsmpn0Rt6NkxbqNcvnq9TTnB/j76USgaBHrNlwghNgWWOqLFi2Sb7/9VmJiYnRdHiVuwalTp9g+luQJFHkrA3d8gF+hNAE1qHy1bU+Y7A47mOY9WJtr3rBeSvMcuu8IcRrwf49Kdj/++KNs3bpVXn75ZXnzzTe5Fk/yDIq8DdL1VqzfLLGpOlKB8mXLqAcAKXeEEOfhwIEDWtIWE3oIPiF5BUXeRgUcVq7fLGcvpK2Z7+vjLf0f6C6enhlXbkJAD0pjtm/fPpdHSgjJKlHRMfr/m5GVfuXKFS1tSwue5CWM/LABqHzXs1M7adagbpp/+CoVQjLMwzeCKN2XXnpJc3EJIbbndlS0/D1vsazeuEWbVKXXxwIBeRR4ktdQ5G0E/tnr1aoufbt3kkK+KaVyg4oVlcb162R6I9i1a5dG7CLXlp2qCLE9ScnJsnTNBomLj5dDx07K7EXLdDmOTlJiD9BdbwfEJyRo3fsm9epoCl5GqTVRUVHSpUsXraKFghtBQUESHx8vnlzDJ8SmGTWosGYOGqe0bNKAljuxObTk7QBPDw9p36Kp+Hh7ZSjwmIsNHz5cDh06pNszZ86U2NhYFXjO0wixDRD31AKPdNimDdMuxRFiCyjydgJuCJndFKZMmSJLly6VFi1aaFWtVatWSYUKFeTIkSO8mRBiA65cuy7rt1p2nkRmTNf2rcTdzc1m4yI556mnnpI+ffqIM0CRdwCOHj2q7SofeeQRmTdvnrz66qsye/ZsqVixolr05qQX8EMIsS4xsXGyZPV6zYM3gsl2p9bNxc/X16ZjI8QcirydgzX3IUOGSLVq1eSZZ54xWe3Yf/LkSVNrXFTXiouLU3c/3PcUe0JyB/xvoSw1UubMaVyvtgSXLmmzcdk7bdu2VQNlxIgR2qgHMUXvvfeeRdYQWvH6+PhIcHCwvPjiixqHZOTXX3+VgIAAWbBggYSGhoq3t7c8/PDDWk3wt99+k5CQEClcuLB+hvnkKz4+Xpc6S5curddu0qSJpiBnhyVLlmi1Qnx+YGCg9OzZU06cOGE6fvr0ab03//3339KqVSttTtSoUSM10LZv3y4NGzYUX19f6datm7YgNufnn3/W+3vBggWlatWq2sjICFoSo4BSyZIl9Xi5cuVkzJgx2Ro7Rd7OeeWVV7R63pNPPql/2EZg2eMfoWnTpir0+MOApT937lz9Y4PYU+gJsT5bd++TcxcvpSlkVb9WdZuNyVGAGENoUf1v/Pjx2qgHHfsA7lnffPONFg7CeViSxITAHAg6zkGGEYQXYt23b18tH4wHuv2hAZC5h/Pll1/WdGO8Z9++fdKvXz/p2rWrHDt2zHQO7pmYRGREdHS0vP7667Jjxw5T3wF8bup77OjRo7UpEbKg3Nzc5PHHH9fv8PXXX8v69eu1S+G7775rOh89DrD98ccfa7zVJ598Iu+8845+f4DvCu8tJg9YmsX5mMxkC0TXE/vk9u3bhkcffdQwduxYi/3z5883NG/e3PDyyy8boqKidN+GDRsMn376qcHPz88wfPhwG42YEOfmxOlww3eTp1o8psyaZ4iPT7D10OyeNm3aGFq2bGmxr1GjRoaRI0eme/6MGTMMgYGBpu3Jkycjwthw/Phx074hQ4YYvL299V5ppEuXLrofnDlzxlCgQAHD+fPnLa7doUMHw6hRo0zboaGhhtmzZ5u2Bw0aZOjdu3eG3+Xq1as6lrCwMN0+deqUbv/888+mc/766y/dt3LlStO+MWPG6GcZqVixouHPP/+0uPaHH35oaNasmb5+5ZVXDO3btzfcuXPHcL8wOsSOgXvnr7/+0ih6I2hsgZkqKmehIM7ly5fl+vXr6uZCUF6bNm3U6u/cubN06tTJpuMnxJm4GRGhlSrNQYBdt/at71nAiqRQu3Zti224oVEJEKxYsUJd0YcPH5bIyEj1UGIJEtY7XPMAz4hFMlKiRAm1bHGvNN9nvGZYWJi67qtUqWLxuXDhw+1uBJ+ZGbD6YXHDA3Ht2jWTBR8eHi41a9ZM9/thHAD35vTGBu8AXP7PPvusDB482HQOvre/v78pABD3cXhx4X3AMgHu7dmBIu8AYH0HYK0df2hYp0J/6oULF2qzi/r16+s/Av5Bevfurf8I586dM73HuI6PP0y2tyQk+yQkJMriVesl8d8YGCPoNVEkIOWGTO5N6sZduDfhvoQ1bQjY0KFD1XWNNfsNGzaoAGJd2ijy6b0/o2uCqKgo7Rewc+dOfTbHfGJwL3r16qXr4ZMmTZJSpUrp9SHuGFtG38943029z3xsANdEnIA5xrHi3g7DbvHixToJwpJsx44d0wRcZwZF3oGARQ/BRwAI/ihQ8e6nn37SgBasTz399NPSvXt3nQUb+9Sbp9cZ1+nvla5HCLkLJsqrNm6RWxGRFvvr1qwmlcqXs9m4nAmIMO5Nn3/+uckQwTp0TqlXr55a8rCeERB3P8BTivVwiLHxGpiA5BRY9ZgwIIAaFUwzws/PT/r3768PBBrCor9x44ZOhLICRd6BwGwWog53DtxGRYsWVcsdrnsElyBCE64dTACef/55fc9HH32kLiW49REUgpkhYKtLQrLGnv2H5OSZsxb7SgeVkKb169hsTM4Gan/AMJkwYYJazRs3brRKt74qVaqogGIJExMIiD6i2xE8B9d6jx499DxEtcMTimC61CBiH6593HuxvID76X//+1+xBu+//75mA8A9D/HGMgKC+5AthUA/ZBzgMzFuTH5mzJihWQmI8s8q9N06IIjaLFOmjBQqVEiee+45OXjwoLru4cKBawgpGQDbv/zyi/5xYMbYunVr/aMBFHhC7g06RW7Ztddin4+3t3Rq04JLX1akTp06em8aN26cusERRZ7dVLGMmDx5sor8G2+8oWvbKHKDtLayZcuazoGlHhERYdqGVwH3WYDfMyLz4W3A2P7zn//Ip59+apWx4f6N+zXGiLV7xFQhyr98+fJ6HPd4ZCEgBQ8peVjWQBZBdv72WLvegcFsD2vzmNUhPePs2bOaSvfCCy/oH+msWbNk5MiRsm7dOp0UTJ8+XVPv8GjWrJmth0+I3XeWmzF/iTaeMaKpU906SYlid4O2iPPRtWtX9S58++234uhQ5J2AixcvamQ9ZoLIkzdy+/ZtbWgDVw/yURFosnbtWnVNmc9iCSFpO8vNWbRcrl6/YbG/TbPGUiO0ks3GRXLfcNq4caOufcN6d4bStvQ3OQFYs5kzZ47+UQIEiKCgBFw9o0aN0sAOFINAM5t27dpR4Am5B+u37Egj8NUqV6TAOznPPPOMekLh2ke8kzNAS97JwJoN1nkwA0UwHkAaCoI54NJHpCYhJGMOHDkuazdvs9hXLLCI9O3eSdxSpWERYu8wut7JQGEIiDryTc+cOSOPPfaYRoci9Q659BR5QjLm8lV0ltuRtrNcu1YUeOKQ0JJ3UlDpCa4npNuhsAIse9RUNsfYyAbBRIy2J64OOssh0C465m7jGfxf9OzUToJLBdl0bITcLxR5JwfpdUifM+ZVps6PX7Ryre5r16KpeHsVtOFICbEdmOzOX7Zazl+6bLG/aYM6Ur9WDZuNi5CcQpF3UfBrDzt0VDZs26nbPt5e0rF1cy3yQYirsWnHbi16Y06FssHSpV1LermIQ8PoehcV+Bu3IvTGZiQ6JlbmLV0l23bvY4ta4lKcOB2eRuAD/P2kfcumFHji8FDkXZCUG5dBCvmkNH0wF/8de/fL3CUrtRAIIc4OJrurNmxJp7NcK3aWI04B3fUu3lkLqULHTp1Jc8zT00M7bMFlSYiz/v3PWLBEIiJvW+zv0ralVAxhLQniHFDkXRz8+o+cOCXrtmyXpKTkNMdrVq0izRvVY/oQcbq/+6WrN8jJcMvGM/VqVZdmDerabFyEWBuKPFFuRkTI8rWb5NqNm2mOFS1SWDq1aS6F/dk3mzgHu8IOyJadlo1nypQMkp6d2rLxDHEqKPLEol735h27Neo+NW5uBaRVk4ZStVIFBiMRh+bs+YuyYMUateaN+Pp4y8M9uzKNlDgdFHmSBrgwV2/cKvHxCWmOVS5fTpt0MCiJOCKRUVEyc/5Si85yKBbVt1tHKV6UneWI80GRJ+mC6PoV6zfLxctX0hzzL+Sr/bR5UyTO0FmubfPGUr0KG88Q54QiTzIE+fJIqdu574CFaxNg3bJpg7pSp3oo3ffErsDfamJikoW3CfvgnTp8/KTFuRB3iDwhzgojTEiGQMgb16stD3RprxXxUk8ANm3fJQtXrNWa34TYCxD0mQuXag68kYNHj6cReHiiWjZpYIMREpJ30JInWSI2Lk6Lhpw5dyHNMUwAUB0sqFjRHH+Om5sbPQMkRyQmJcmkKX9rURv8Xfr4eMs/i1dYVHL0KlhQHu7ZRQr5+th0rITkNhR5kmXwp7Lv0BHZvGNPmtK32J74x/Qcf8auZbPF3Z1BfeT+iU9IkF/+nGna9nB3l4TERNM2JpG9OrfTlDlCnB2660mWwc2xTvWq8lCPzuLvV8jiGOeKxF5ITracgJoLPGhavw4FnrgMbrYeAHE8igUWkX49u8r6rTu0Wl5qto4aJe7ZqJCXmJwsTcaMsfIoiauSWYOl4NIlpW7Nank6HkJsCS15cl8gcrlDq2bSoWUzXfs0BwLv7paNB0vmEiuSnInIn794WQ4ePZGn4yHEllDkSY4IrVRe+vXqqqVvCbEH7txJzuTYHW3KhHQ65M0T4uxQ5EmOQe/tPt062noYxEU4f+myLFu7McPjd+5kHh/i4+0tdwx35PqNW7kwOkLsC67JE6vALnUiZ86ckdGjR8vy5culY8eOMnHiRClY0LIW+unTp+XgwYPSoUMH8fT0tNlYHVngn3ntLbl09arE//c/0qtTu3uuySO6vlRQcQ22Cy4dJAF+fkzTJC4DRZ7YFbvCDoq3l5eu+ePm7OnhkfL632087LFLWFxcnIwdO1Z27doln3zyiRQuXFiFBFkHEJ3k5GTx8PCQnTt3ymeffSb169eXoCBGeN+PwF/4t9Ty22O/1OfUQo+fecnixaRMqSAJLhWkgaKoT0+IK0KRJ3bFtt377nlDRqAfBFNF3+Pua50QuFtuu3u4/bsf56Ycx7a1JwqRkZFy4sQJ6du3rwwaNMjiGL6P8Ts99NBD+sgICFRqKxMTBON4MWHA8YzGj+M4tmDBApk8ebJOOEJDQ8WZBL5S+UhpUPuGTJ8bkq7Qo5Jd3+6dbDhaQuwHijxxyIpmeETn4BponauTAJ0MpHgIUl4bJwQpD/Ptgh4eUjSwSJprHT9+XLp166Yiv3XrVpk7d67UrFlTWrVqJZUqVZLDhw/LxYsXZfDgwZKQkCC3bt2SBg0amITaXNjTcyObT3ruNQEyivypU6ckLCxMYmJi0nyGowv8L19skcL+CeLpkSy/z6iYoUVPCKHIExclKSlZkpJiUbA3WwGGj/ftmWY/hPzrr7+WYcOGSefOndWS/9///ifvv/++VK1aVUJCQuTatWt6Lizr7du3y/r169WlHxUVpRb3hAkT5NKlS3r+Rx99pNcB27Ztk82bN0ujRo30OCYUDRs2lObNm1us9xtFHGWBQWxsrBQtWlR8fOyjbOvJM2elQrlgqwh8kYCUFsjDhx7SZwo9IRljf4ubxKWpUqG8hASX1kAppOX5+fpKQU9Pu7BCYdFnRPHixdUT0KZNG2ncuLFUq1ZNbty4Ie3bt5dJkybJvHnzpFy5chIdHa1CbizdO3LkSPn9999V/LGe36lTJ3n11VdlxYoVevzAgQPy6aefyjvvvCOLFy9WN/yzzz4rf//9t+mzk5KS5IsvvpBSpUppMB8mHCdPnlSBN4q+LX9+mIAsXbNBtu7am63KiJkJPMBXgtA/2e+ERtRD6OcvX51L34IQx4SWPLEr2rdskm7teogD8prh7kaZ0viERElMwHPKdkJC4r/7zbb/PZ6I7cQkfZ1ZNbR7Abd+RsAtHh8fL97e3roNqxtC379/f3Wx4xgE+OrVq1KmTBnx9fXVILwNGzbIBx98IL1799b3ffzxx7Jjxw5ZunSpRugjjgATg7p162rkvp+fn/Tr10+t/9atW6uXAO/BRAIThu7du8s333wjf/zxh77G+9PD2KEtJXbBPVcbA6HtK35/aFl8K/K2No1JXUApuwKfWugBLXpC0kKRJw4BBAjCgEdOHNCmiYJOAFImBhYTB+NEISHtxME3E9c3outhUcMFDyIiIlTMjWlyRgHFenyNGjX0NVLprly5oqKNyQHOwfuRZhcQEKDn3L59W4oVKybPPfecCjyAK/+XX37RYD/w22+/yQsvvKDLBQDLBMuWLdPvkjqFz8iaTVvl0pWUJQTj+MwzGsxjFe4GMhpjFf6NU0gd5Oie/kTBvHb8idPhcjsqWrp3aK1ZFDkR+Ltjp9ATkhEUeeJy+fxuXl4ZCsz9ApFHFLy/v7/JsofAGy17Y5AdhNl4DiYCEPUZM2aoKx+Cj+MQdrjejZOCEiVKWHg38DlGrwEmBNevX5eWLVvqMUxYihQpohMDuOszsuQxgTEHlnZ8fII+bt9nSKNOxCD8qQIYU3Pl2nWZuWCpdO/QJk2lxOwK/N3PptATkh4UeWJ10HAmN8+3RyDycMsbBRwiDJFNbckjIM5o7SOA7vz58yrEwcHB+kgNXPWYIMC9bwQTCFwPIo/PxWcZP9f4OdhfqFAh05r8vUTeGmCiYPSAREWnRPVnBI7PWbRcOrVpoTEYORF4IxR6QtJCkSdWxxU7ysEqR6CdMZod1e9KlixpssCN4ou1erjQERvQtGlTadasmQwZMkSD61AgB5b7zJkz5eGHH5YKFSqogHt5eenDCPZhYoAJBAL+8Iw0vXr16unnwdIPDw/XNfuMRB77U/dZz2uQBrl41Tpp0ai+BBYJyJHAG6HQE2IJRZ4QK/DUU0/JAw88oK5ygAh3WODGNXGjux7FchCQZ9xGTv2bb74pAwcOVFc9JgbIsR8wYIAeh6V/8+ZNixK4SMeDNY9rQ6wfe+wxXYeHi798+fLyxhtv6LUwlozy6h9/MCUVEJMNiG3qOIQ0sQqZBDkaA+vuh8DCAVKiWKAMHv6OCrxXwST56dOt9yXwqYU+/LyPrNkUJG+P/UrKlS4ltas7flEgQrJLPsP9/ncSkjr6PSkpx9fJzShvewZii7V4iDeEGWvq+Dkgpx5r7l27djWd++uvv8qePXvkyy+/1HPg0kdaHdL0AgMDZfjw4ZpyB7FHRH5e/O4h9HcnAXhO2Ubw39nzl+Rk+FmL92CS06B2Dalfq7p+303bd8kr//eRvr9b+/PyyVt7xK3A/d+alq0tKSM+qC/Jd/JJ/we6yf+9NtQl/64IocgT4kQTBaOFf+7cOV06wGTB1uwOOyibd+4xbSPYDml0qYPurCX0FHhC7kKRJ4TkKlt27pVdYQfUem9Ut5bUrVE1w2WEnAo9BZ4QS1jxjhCSq0Cw0QmuX6+u6qLPrP5+80b1ZcLHb2tQ4OJVpeWtT+pKUnLWRJoCT0haaMkTQnKVS1evSfHAItnq/Jddi54CT0j6UOQJIXZJVoWeAk9IxtBdTwixS7LiuqfAE5I5FHlCiEMKPQWekHtDdz0hxOFc9+1bXJL/fkyBJ+ReUOQJIQ4n9EYo8IRkDt31JFdLveLmi8c///xj1Wu/9957pmt/9dVXVr02sX/XPaDAE3JvKPIkV0E51osXL0q3bt1M+9DIBbXZ0R8dfdNRkjUqKsriffv27ZNWrVpp9TZ0Zxs/frzFcZRuxXXRs524ntAPfPgBCjwhWYANapwcdCTDjTA7OcrWBI1VgoKCLPZB4CHQy5cv19rmTz/9tDz//PPy559/6nE0V+ncubN07NhRfvzxRwkLC5NnnnlGJwQ4D6D1Kh6ZFVYhziv0eBBC7g0teTujbdu28vLLL+sDPcKLFi2qzUaMoRNoMwortnTp0lqbvEmTJrJmzRqL5iUQQzQrqV69uoos2o7inMaNG+t7cLxFixbaDtXIDz/8IBUrVtQWpqGhofLHH39YjAsThZ9//lm7qKGPeeXKlfUzssuhQ4dkyZIlei2MvWXLljJhwgSZNm2aXLhwQc+ZOnWqNjn53//+JzVq1JBHH31UXn31Vfniiy9y8JMlhBDXgyJvh/z222/ajW3btm3ashTiBlEEEP/NmzerKMKl3a9fP3WJHzt2zPR+NCkZN26cvufAgQPacrRPnz7Spk0bfQ/eD4vY6OqcM2eODBs2TLuW7d+/X/ubw7pevXq1xbjQzvSRRx7Ra3Tv3l0tcrjejYSEhOhaeWbgszHJaNiwoWkfLHZ4GrZu3Wo6B73QMeEw0qVLFzly5Ii2XSWEEJI16K63Q7AGbWwjCqsa7mpsQ+gmT56sljl6hwNY9bCMsf+TTz7RfXCBf//991KnTh3dhhBHRERIz5491VoH6Glu5LPPPtMguRdffFG3X3/9ddmyZYvub9eunek8nIPe5QCf9c033+hExNgGFdeG5yEzLl26JMWLF7fYhwkNJiI4ZjwHfdHNKVGihOlY4cKW3csIIYSkDy15O6Rp06YWAUXNmjVTSx1ijzX2KlWqmNak8Vi7dq2cOHHCdD4s4Nq1a5u2IaAQaEwSevXqpd4BrImbu9DhvjcH29hvjvk14fZH4NyVK1dM+1auXKmeBkIIIfYBLXkHAhHoCDTbuXNnmoAziL0RLy+vNFHHsPSxrg2rf/r06fL2229r4BsmFFnF/d/UJSP4DPQwzw4IwjOfGICkpCT1NhgD9PB8+fJli3OM26mD+AghhGQMLXk7xLg2bQSucwS61atXTy15iGSlSpUsHlkRP7x/1KhRsmnTJqlZs6Ypmh2u+40bN1qci20E7lkbeCVu3bqlExUjq1at0skCAvGM56xbt06XHYxgQoKlC7rqCSEk61Dk7RCsuWNdHIFmf/31l0afIzAObnoEuz355JMye/ZsOXXqlK6JjxkzRhYuXJjh9XAexB0BbYioX7Zsmbr/jevyb775pkblI8Ie+xHoh+tjvT87dOjQQb799ttMz8FnYg1/8ODBOnZMJuDiRwS9Mc7g8ccf1yUH5M8jcBCeBywx4GdCCCEk69Bdb4dAxGNjYzXlDW55CLwxPxxu948++kgj4c+fP6+BbnC5I6guI5DydvjwYY3av379upQsWVJeeukljaIHiLyHiCLQDp+FoDd8DtL5sgPiAq5du3bP85AiB2HHpABR9Q899JAG8RlB6iAmIhhjgwYN9Du+++67pp8BIYSQLILa9cR+aNOmjWHYsGEGZ2DQoEGG3r175+pnlCtXzvDll1/m6mcQg+HOnTuGwYMHGwoXLoyCDYbdu3enex6OzZkzJ9fHk53f+73OPXXqVKbfKTv/u7iONa6V3v+S8dp58fMlzgPd9SRXWbBggQYF4tmaIIUP18XSBsl9ELCJJR38HpGZgZiO9EhdwthRUlYz+07ZActQqa+Fv9EePXqoRw3po1geQ7CpOShWVb9+fS1ehRgb/KzNSZ0RQ0hWobue5BqoN48ofoAlAmvywgsvaGEeUKxYMatem6S/FIPfYfPmzdM9jgqFiKNwxOwHLIlZa9wQcvNrIVAWAo99CHiFUGM5DpkqxroWiJnBOfibxlIWUlGfe+45/Xkj7dW4hIUHIdnG1q4EQoh9Y+4qxgPub7imX3rpJV1aCgwMNLRt21bPTe1ODg8PN/Tr18/g7++vrv4HHnhA3eOpl3Q+/fRTQ1BQkKFIkSKGF1980ZCQkGA65/Lly4aePXsaChYsaAgJCTFMmTLFwgWPpYTRo0cbgoODDR4eHoaSJUsaXnnlFdP7ce7HH39sePrppw2+vr563sSJEzN0169evVq3FyxYYKhVq5bB09PT0KRJE0NYWFi2l9oWLVpkyJ8/v+HSpUumfT/88IPBz8/PEB8fr9sjRoww1KhRw+J9/fv3N3Tp0iXNZ9BdT7IL3fWEkEyBq/iDDz7Qjn+wRLdv3677EcgJ6x0ZEmgklBqkQMISLVSokKxfv17PwxILsitg+RtB+WR4CvCMa8JVbe6uRiGns2fP6vGZM2dqNUfzWguzZs3SipATJ07U7BC0Na5Vq5bFWD7//HMtpbx7926t7Dh06FDNXskMuNXxPnxfeItQSMo8rRN1IlK71VODjBaMxVixEeBngiZMyBwxnoPSzubgHOwnJKfQXU8IyRS4iSHUqd3aqN2QugWwOUh9RP0D9FAwFmdC1gZ6F2ANGp0GAWofIPUS169ataq6ruGyxvr20aNHZfHixZpu2ahRIz3/l19+sSjLjDVvjAtCCTd42bJlNTPFHPRaMJZtHjlypE4KMGlA7YWMGD16tHTq1ElfY/KBSQ76PBiXifDee7nQUYbZXOBTl2jO7BxMBJBlg+JWhNwvtOQJIfcF0hszY+/evXL8+HGdIBhLMKPEclxcnEUZZnQaNK/giLVoo6WO0srobWD+WZgIYKJgBE2aIIYVKlTQiQGEOHVgm3lJZkw40qu8mBoUZTKCcUPUzUs9Iy0VXRkJsWdoyRNC7gv0L7hXGWaIM4LJUmMeLJnTcsmIjofrfcWKFVoZERb7p59+qj0djNe2Rknm+wGTCXghMivRnFEZZ/SGoBVPcgoteUJIroCUMKyRI20sdRnmrEaKw2qHVW5eBhmCjtLI5kAMsWaOokpYCsB6Nho65QSUkzaCFsdYOjBfJsgK8AZgHOZeA0xEIODGstE4B8sT5uAcc08CIfcLRZ4QkiugBDOqFfbu3VsD75AqBgFGo6Rz585l6RpwkSNQD9UZ0dMBYo/0MnMLF8FvWKffv3+/nDx5UqZMmaLHy5Url6PxI9gQ4ovrIvgP3wXVIc0nIFgayAzEHUDMn3jiCV2+WLp0qaaVopojcuIBUucw7hEjRugSAAIL//77b/nPf/6To/ETAijyhJBcATnjaDSEQLgHH3xQrWD0I8CaPCzZrIJgPfQ1aNOmjV4H5Y3hHTCC9flJkyZpe2SsvcNtP3/+fAkMDMzR+MeOHatlnrHkgOA4XBPZBOYehYiIiEyvgVgDFBDCMyzzgQMHap48JhBGUEYavSdgvdepU0cj+hGsaMyRJyQn5EMeXY6uQAghTgS8De3atVMXvXmA371Ar4e6devKV199lWtjQywBvAfmHgVCMoOWPCGEWAm42pFFkNN4gNTApY/rEpJdaMkTQogVLHl0hUQqH8AShblrP6cgcA9588YUw3tlNhBihCJPCCGEOCl01xNCCCFOCkWeEEIIcVIo8oQQQoiTQpEnhBBCnBSKPCGEEOKkUOQJIYQQJ4UiTwghhDgpFHlCCCFEnJP/BwQQRZ9VPag+AAAAAElFTkSuQmCC", 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" ] diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index 3e19467..5f67fe1 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -68,6 +68,16 @@ def colour(cls): def label(self): raise NotImplementedError("abstract") + @classmethod + def trim_iid(cls, iid): + full_iid = str(iid) + thing_id = full_iid[4:] + trimmed = thing_id.lstrip("0") + if len(trimmed) < 2: + return thing_id[-2:] + else: + return trimmed + class RelationVertex(AnswerVertex): _SHAPE = "d" _COLOUR = "yellow" @@ -75,7 +85,8 @@ def __init__(self, relation): super().__init__(relation) def label(self): - return str(self) + trimmed_iid = self.__class__.trim_iid(self.vertex.get_iid()) + return "{}[{}]".format(self.vertex.get_type().get_label(), trimmed_iid) class EntityVertex(AnswerVertex): @@ -85,7 +96,8 @@ def __init__(self, entity): super().__init__(entity) def label(self): - return str(self.vertex.get_type().get_label()) + trimmed_iid = self.__class__.trim_iid(self.vertex.get_iid()) + return "{}[{}]".format(self.vertex.get_type().get_label(), trimmed_iid) class AttributeVertex(AnswerVertex): From 0b14df420d468e9893787f401f21a0299a34a8d2 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Fri, 14 Mar 2025 20:21:27 +0100 Subject: [PATCH 18/27] Add support for tls enabled cloud connections --- src/typedb_jupyter/connection.py | 10 +++++----- src/typedb_jupyter/subcommands.py | 3 ++- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/src/typedb_jupyter/connection.py b/src/typedb_jupyter/connection.py index 6e2cb85..7173c2c 100644 --- a/src/typedb_jupyter/connection.py +++ b/src/typedb_jupyter/connection.py @@ -25,12 +25,12 @@ class Connection(object): current = None - def __init__(self, driver, address, credential): + def __init__(self, driver, address, credential, tls_enabled): self.address = address if driver is TypeDB.core_driver: - self.driver = TypeDB.core_driver(address, credential, DriverOptions()) + self.driver = TypeDB.core_driver(address, credential, DriverOptions(tls_enabled)) elif driver is TypeDB.cloud_driver: - self.driver = TypeDB.cloud_driver(address, credential, DriverOptions()) + self.driver = TypeDB.cloud_driver(address, credential, DriverOptions(tls_enabled)) else: raise ValueError("Unknown client type. Please report this error.") self.active_transaction = None @@ -42,9 +42,9 @@ def __del__(self): self.driver.close() @classmethod - def open(cls, client, address, credential): + def open(cls, client, address, credential, tls_enabled): if cls.current is None: - cls.current = Connection(client, address, credential) + cls.current = Connection(client, address, credential, tls_enabled) print("Opened connection to: {}".format(cls.current.address)) else: raise ArgumentError("Cannot open more than one connection. Use `connection close` to close opened connection first.") diff --git a/src/typedb_jupyter/subcommands.py b/src/typedb_jupyter/subcommands.py index b7710d8..c6b599d 100644 --- a/src/typedb_jupyter/subcommands.py +++ b/src/typedb_jupyter/subcommands.py @@ -65,6 +65,7 @@ def get_parser(cls): parser.add_argument("address", nargs='?', default="127.0.0.1:1729") parser.add_argument("username", nargs='?', default = "admin") parser.add_argument("password", nargs='?', default = "password") + parser.add_argument("--tls-enabled", action="store_true", help="Use for encrypted servers") cls._PARSER = parser return cls._PARSER @@ -80,7 +81,7 @@ def execute(cls, args): elif cmd.action == "open": driver = TypeDB.cloud_driver if cmd.kind == "cluster" else TypeDB.core_driver credential = Credentials(cmd.username, cmd.password) - Connection.open(driver, cmd.address, credential) + Connection.open(driver, cmd.address, credential, bool(cmd.tls_enabled)) elif cmd.action == "close": Connection.close() else: From 47927809acccefa4c74b183d68718f8c4907831e Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Sat, 15 Mar 2025 13:51:03 +0100 Subject: [PATCH 19/27] Interactive graphs work --- src/Sample.ipynb | 11 +-- src/graphs.ipynb | 139 ++++++++++++++++++++++------- src/typedb_jupyter/graph/answer.py | 6 +- 3 files changed, 116 insertions(+), 40 deletions(-) diff --git a/src/Sample.ipynb b/src/Sample.ipynb index e182f04..3dfbcd0 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -23,7 +23,7 @@ "Available commands: connect, database, transaction, help\n", "--------------------------------------------------------------------------------\n", "Help for command 'connect':\n", - "usage: connect [-h]\n", + "usage: connect [-h] [--tls-enabled]\n", " {open,close,help} [{core,cluster}] [address] [username]\n", " [password]\n", "\n", @@ -38,6 +38,7 @@ "\n", "options:\n", " -h, --help show this help message and exit\n", + " --tls-enabled Use for encrypted servers\n", "\n", "--------------------------------------------------------------------------------\n", "Help for command 'database':\n", @@ -130,7 +131,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: typedb_jupyter_graphs, typedb_jupyter_sample\n" + "Databases: tests, typedb_jupyter_sample, typedb_jupyter_graphs\n" ] } ], @@ -166,7 +167,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: typedb_jupyter_graphs\n" + "Databases: tests, typedb_jupyter_graphs\n" ] } ], @@ -202,7 +203,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: typedb_jupyter_graphs, typedb_jupyter_sample\n" + "Databases: tests, typedb_jupyter_sample, typedb_jupyter_graphs\n" ] } ], @@ -506,7 +507,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 4822358..eda90e6 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "4a203691-58f8-4c75-be61-25f381a0c73a", + "metadata": {}, + "source": [ + "# Visualisation\n", + "We use the [netgraph](https://github.com/paulbrodersen/netgraph) library along with [matplotlib](https://matplotlib.org) to visualise graphs.\n", + "First, we set up some data. If you are unfamiliar with that part, view the [Sample notebook](./Sample.ipynb) first" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -198,9 +208,39 @@ "%typedb transaction commit" ] }, + { + "cell_type": "markdown", + "id": "7c3ef991-5bca-45e9-9417-457a9f8b7b4a", + "metadata": {}, + "source": [ + "# Visualisation\n", + "\n", + "### Intialise matplotlib\n", + "Initialise matplotlib first. The `widget` mode allows interactive graphs inline." + ] + }, { "cell_type": "code", "execution_count": 10, + "id": "4417485b-2d6f-45df-88db-a268a1c6d2a8", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib widget\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "e2b6cbff-d4dc-40a8-9328-1af6ccdeb19f", + "metadata": {}, + "source": [ + "### Read some data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "1e03723d-b915-4438-a188-9020f9315a33", "metadata": {}, "outputs": [ @@ -218,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "2a986872-2a81-4944-99fa-cc1fb3e135ee", "metadata": {}, "outputs": [ @@ -247,7 +287,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -259,7 +299,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "fa1e3c89-3901-4390-babc-2ec3ba3c0fe4", "metadata": {}, "outputs": [ @@ -276,35 +316,32 @@ ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "12695159-169f-440f-bf85-4396cf0bf825", + "cell_type": "markdown", + "id": "030150b5-5364-49a8-a671-194d38cbf618", "metadata": {}, - "outputs": [], "source": [ - "from typedb_jupyter.utils.parser import TypeQLVisitor\n", - "\n", - "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")" + "### Create a graph from this data\n", + "Sadly, the basic TypeDB answers do not hold any information about the query structure. Till it does, we use a simple parser to parse out the structure from the query and reconstruct the graph." ] }, { "cell_type": "code", "execution_count": 14, - "id": "51cb2feb-1fa8-4b3c-9d27-94c65706dc2f", + "id": "12695159-169f-440f-bf85-4396cf0bf825", "metadata": {}, "outputs": [], "source": [ + "from typedb_jupyter.utils.parser import TypeQLVisitor\n", "from typedb_jupyter.graph.query import QueryGraph\n", - "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", "\n", - "query_graph = QueryGraph(parsed)\n", - "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)" + "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")\n", + "query_graph = QueryGraph(parsed)" ] }, { "cell_type": "code", "execution_count": 15, - "id": "7965c6b7-84b3-4637-ad2b-2e818761dcb2", + "id": "51cb2feb-1fa8-4b3c-9d27-94c65706dc2f", "metadata": {}, "outputs": [ { @@ -319,20 +356,39 @@ } ], "source": [ - "print(\"\\n\".join(map(str,answer_graph.edges)))" + "# Combine the data & the parsed query structure into the data-graph\n", + "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", + "\n", + "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)\n", + "print(\"\\n\".join(map(str,answer_graph.edges))) # We now have a list of edges" ] }, { "cell_type": "code", "execution_count": 16, - "id": "5b6da7a4-66f5-4233-9d5e-8855789a08e7", + "id": "ea10b692-7ff7-4d84-a683-4922c9a8d057", "metadata": {}, "outputs": [ { "data": { - "image/png": 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CFYC26BWEv5dfflkGDRokS5YskTNnzji6agCACohZwEAFULBH73333Sfz58+XyZMnS8eOHSUkJOSqu4cBACAAAhXE2rVrZfXq1TJnzhxp2bKlnD9/Xs6ePSuHDh2SmJgYCQgIMC2FBWERAICLIQACTqigNa9wq97JkyfNOMCUlBSZO3euLFiwQObNmydZWVnSuHFjmTVrlnh4eDi66gCACoBJIIATT/YoLCkpSQYOHGha/TQM3nHHHdKiRQtxd3eXcePGyT/+8Q/p06ePQ+oMAKhYaAEEnDT8TZo0SeLj4yUiIkKuu+46adWqlcycOdOUxcbGSnBwsPj5+cnOnTvN11FRUY6uPgCggqAFEHAyOo6vf//+cuTIEWnWrJkZ67dq1SqJi4uTOnXq2M47d+6cHDt2TO666y4JDQ014VCXhgEA4HIYLQ44EQ17I0aMMOP+NPRNnz5dhgwZIidOnJDrr79eUlNTzXmbN2+WRx55RG644QapUaOGGRNI+AMAXCkCIOAg2viurX2FZWdnS5UqVWT8+PESGBgoEyZMkFGjRskrr7xizr3tttvMedoy2Lp1a3nhhRdkxowZDvoJAAAVFV3AQDlLSEgwIc/T09NWVjCOLzIyUk6fPm3W95syZYq888475rlXr14yduxYmThxotx///1mwgcAAMVFCyBQjrZt22Z28fjkk09sZbfffrvceOON0rx5c3n99ddNN692AS9evFgGDx5swp/y8vKSvn37mrGAuhQMn90AAMXFLGCgHOl6fU2aNJHZs2ebfXx1Ru/Bgwfl008/lf/85z+mO1cnf2gXcEZGhqxbt8425k/X/Xv22WfNjGB2/AAAlARdwEA5y8zMNK2A1apVMzN5hw8fLr179zavvfnmm/LNN9+Y9f50jT/t7tWxgMePHzfr/n300UeOrj4AoBIgAAIOkJiYaJZ62b17t/z888/StWtXU66/jo8//rhs2rTJTPi49tprZdmyZWbMoM4CBgCgNBAAAQf57bffzBIvAwYMMLN8deKH0l0+NATq65MnT5bu3bs7uqoAgEqGSSCAg+ikD13mZfXq1TJt2jTbpI7w8HB54oknzOQP7QYGAKC00QIIlBP9VStq8sZzzz0nS5culZEjR8rQoUMdUjcAgLXQAgiUQ/Bbv3mbJKekXrDws3rppZckJiZGPvzwQ/nll18cUkcAgLUQAIEypDt7/Lx4uaze8JvMXbBEsnNyLgiBbm5uJvypsLAwB9UUAGAldAEDZSQ1LU1+WvirnDqTbCurHllVBvXrXWRXsIZFDw+Pcq4lAMCKWAgaKANHE5Nk3uJlkpGZaVfu5uoqOTk5RQY9wh8AoLwQAIFStm1XvPy6au0FW7U1axwrndq0FFdXRl4AAByLAAiUktzcXFmxdqNs3bnbrlwDX/eObaVR/boOqxsAAIURAIFScD4jQ35ZskISEo/blft4e0v/nl2kWkRVh9UNAIA/IgACJaSTPHSyh076KCw8NESu69VNAvz9HFY3AACKQgAESmD/oSOy4NeVZnmXwurWipFeXTqIhzu/YgAA58NfJ6AYdILHhi3bZc3GzRdM9mjXspm0bnZNkUu9AADgDAiAwFXS1r7FK1ZL/P6DduXa2te7W0epExPtsLoBAHAlCIDAVUg7ly4/LfpVTpw6bVeu4/x0vJ+O+wMAwNkRAIErlJh0wizunH4+w65cd/fo16OLmfELAEBFQAAErsDO+H2yZOWaC/bxvSa2nnRp19rs5wsAQEVBAAQuQQNf3LpN8tv2nXblOsGja/vW0qRhA4fVDQCA4iIAAheRmZUlvyxdIYcTjtmVe3t5Sd8enaVGtUiH1Q0AgJIgAAJFOJOSIj8u/FVSUs/alYcGB8l1vbtLUIC/w+oGAEBJEQCBPziUcNS0/GVlZduV146pIb27dBRPTw+H1Q0AgNJAAAT+Sxd0/m37Lolbt/GCxZ11YWdd4JnFnQEAlQEBEBCRnNxcWRq3RnbF77crd3d3k56dO0j92jUdVjcAAEobARCWdy493azvd/zEKbtyP19fGdC7m1QJC3VY3QAAKAsEQFha0slT8tOiZSYEFhZZJVz69+oqvj4+DqsbAABlhQAIy9qz/6AsWr5KcnNz7cob1qsj3Tq2FXcWdwYAVFIEQFiOTvBYvWGzbNiyza5cJ3h0attSmjWKZbIHAKBSIwDCUnRplwXLVsqBwwl25bq0S7/uXSQ6qprD6gYAQHkhAMIydFHnnxb9KqeTU+zKg4MCZUCvbuYZAAArIADCEo4cS5SflyyXzMwsu/KYqOpybfdO4uXp6bC6AQBQ3giAqPTj/bbu3CPL16y/YHHnFk0aSYdWzcXV1dVh9QMAwBEIgKi0dHbvstXrZfvueLtyNzc36dGpncTWre2wugEA4EgEQFRK6eczTJfvseNJduV+vj7Sv2c3iagS5rC6AQDgaARAOLU+tw67YJHmy9Gu3uycHPFw95C7bxlkK68aHib9e3YVfz/fMqgpAAAVBwEQTk3DX9q5dPH38rri97j8d7kXKTTkr0GdWtK9UzvxcOefPAAA/DWE09PwFzf2mat6T8fXXpesvDyzoLNO9NAJHyzuDADA7wiAqNSu69VNakVHOboaAAA4Fda/QKXl4eFO+AMAoAgEQFRadPkCAFA0AiAAAIDFEAABAAAshgCISisnJ1f2Hjhk1gQEAAD/wyxgVFp5eXlmNxBd+69mdJTUrRktMTWqsxYgAMDy+EuISk9bAOP3HzQPwiAAAARAWAxhEAAAAiAqMXd3N6lXu6YcPJxQ5DhAwiAAwKr4K4dKy9XVVfp272yC3uGEYxJ/4BBhEAAAXSs3Pz8/39GVAC6m48ChknYu3ewHfDXSMjPF389X4n742q78SsJgYYRBAEBlxF8zODU/X99ivc/f3bfI92qAq1Mz2jxoGQQAWBUtgAAtgwAAiyEAAqUUBuvVipHoqGqEQQCA0yMAApdAGAQAVEZsBQensWTJEnFxcTGPwYMHl+q1Dxw4YLt2ixYtrvh9BWMGdTbxvbfdJP17djVLy1ws2BWMGZy3eJl8Pn2W/LJ0hew7eJjt6AA4JWe876J8EADhdHbt2iWff/65Xdnf//53qVWrlnh7e0v79u1lzZo1ttdOnz4tjzzyiMTGxoqPj4/ExMTIo48+KikpKbZzoqOj5dixY/Lkk08Wu15/DIP9enQhDAKw5H1XZWRkyF//+lcJCwsTf39/+dOf/iTHjx8v1fsuyg79U7is3Nxc8wlO19UrD1WrVpXg4GDb8ddffy1PPPGETJkyxdyE3n33XenXr5+5Yem5R48eNY8333xTGjduLAcPHpSHHnrIlM2cOdNcw83NTSIjI81NqjRo6KtbK8Y8NNAdOnJU9h48fFWziekmBlBR77vq8ccfl7lz58qMGTMkKChIRo4cKTfddJOsWLGiTO67KF20AFZCPXr0ML+I+tBfyvDwcBk3bpwUDPfMzMyU0aNHS1RUlPj5+Zlfbu0GKKCfAvVGMGfOHBOovLy85NChQ+acdu3amffo6507dzZhq8CHH34odevWFU9PT9Ma98UXX9jVS29mn3zyiQwZMkR8fX2lfv365ntczttvvy0PPPCADBs2zNRHb0j6/s8++8y83qRJE/n222/lhhtuMN+/V69eMmHCBPn+++8lpxxa2wrCIC2DgHVZ7b6rPSyffvqpOU/vua1bt5apU6fKypUrZdWqVaX4XxZlhQBYSU2bNk3c3d1Nk/17771nfkn1JqD0BhUXFyfTp0+XzZs3yy233CL9+/eXPXv22N6fnp4uEydONO/Ztm2bhIaGmvEh3bt3N+/R9z/44IPm5qK+++47eeyxx0xT/9atW2X48OHmxrF48WK7er300kty6623mmsMGDBA7rjjDtOFezFZWVmyfv166dOnj61MPxHrsdbhYvTmFBgYaP4blCfCIGBdVrrv6uvZ2dl25zRs2NAMwbnUvRnOg76nSkrHXrzzzjvmRqGfCrds2WKOtQlfP6XpJ8vq1aubc/VT6bx580z5q6++asr0F/uDDz6Q5s2bm2O9WWioGjhwoPm0qRo1amT7ftr9eu+998qIESPMsXYd6KdALe/Zs6ftPD3n9ttvN1/r95o0aZK5WeqNsCgnT540XSERERF25Xq8c+fOi77nlVdeMTdKR7pYN/GBw0ckJyf3gvPpJgYqNivddxMTE02rY+Fu44Jz9DU4P/6qVFIdOnSwfUpUHTt2lLfeesvckPQXu0GDBnbna/eEDuQtoL/YzZo1sx3rJ1G9ieiN7NprrzWf+vQTZbVq1czrO3bsuCBwaVeFfgourPA1tUtDW+mSkpJK7edOTU2V66+/3nRZvPjii+IsCINA5WfV+y4qJv6SWExaWpoZmKvN9/pcWOGBujqbtvCNTOknVZ1dq59adYDwc889J/Pnzzc3vSvl4eFhd6zfIy8v76Ln6zgarWfhmWVKj3VwcWFnz541n2gDAgJM18gfv5ezIAwClVefW4fJufR08/XpxAQT/EaNG683O6nXpusF99X4U+fNnudJh/dLXn5+hbnv6rN2FScnJ9u1AhZ1b4ZzYgxgJbV69Wq7Y+0W0MG/LVu2NDck/fRXr149u8eV/NLq+8eOHWsG+urki6+++srWLVEw86uAHmtLXEnoJ2IdXLxw4UJbmd649Fg/XRdu+evbt685Xwc467IFFUHhMYPDbvuTbcygu7t9OC/AmEHAue+7Gv7SzqWL5ORK2umT4u3rL35+gSL5+ZJ9/rx4e/naPTzdPc25mZlZekqFue/q6xosC5+jM4S1m7vwvRnOi+aDSkp/CXU8iA4K3rBhg0yePNl0RWgXhA4Avvvuu82x3lhOnDhhfom1m0C7T4uyf/9++fjjj+XGG280Y1j0F10HL+t11JgxY0zXhF5Puyl0Bu6sWbNkwYIFJf5Z9Oe45557pE2bNmY2nC5HcO7cOTPYuXD40wHU//rXv8yxPlSVKlUuaOl0VrQMAhX/vut/PkciomrJo107yANLf5R3RoyS4TfeJHeOd5UVWzfLKwMHSMv6sXIiOVkWblgjzerUl+s7dpG6Ix+qUPddnel8//33m/O0q1q7lXU9Vg1/V9M6Ccfhr0QlpTeI8+fPm19cDUA6U6xgrIh2KYwfP97MHEtISDDN/foLqwONL0an/+vgX53ldurUKTMGRRcA1YCpdKaajjvRwcf6vWrXrm2+jy6NUFJDhw41IfX55583g4t1RXntDikYoKwBt6DFU1sy/3gD1YVMK5qiwqDZju5IAmEQcOL77swffpatqxbJX39bJY/dfJs8eMMQ89rUZ16Q8V98Kk9+8J4knEyS8KBg6dC4iQzs2LVC3neVTnDR2cG6ALSOZ9SxijqJBRUDewFXQvrLr7+s+omtItH1rnTm2pkzZy6YWVZadGLI7NmzZdOmTVIRXUkYLIwwCJTvfXd1/DHTpRs39pmren/H114XcXeTuB++lvLEfde6+GsAp1OjRg2zqPO///3vUu2a0XExOmi5pONjrN4yqGOBdHsnXdAWQOXAfdd6CIBwGroyfsGiqKW9dZCOnyn49Kkr7FcGjgqD69atM0tT6D6hhdcaA1DxcN+1LrqAgUqmrLuJn332WTMYXccd6ZgjbREsr/1KAWenS7pUpC5gWBctgEAlUxotg21bNJXgwIAL1iTTnQoWLVpkBntr+FMa/vRz5B/PBQA4LwIgUIkVNwx2bN2iyEC3fft2ycnJMess6jJCOiNQlxUqfK6GQX3QKggAzosACFjElYbB8NAQCfD3K/IaM2bMMGN6dF/SOnXqmJnmM2fONGuVVa1a1ZyjYbAgEOqi4xoEaR0EAOdCAAQs6FJhsF7tmCLH9Wn3708//SS33HKLfP7552ZQt24w37ZtW9m6dav06tVL5s6dK3FxcWYze10brKIswg0AVkMfDWBxBWFQt6HT7eiaNootsvtWQ567u7tZILZgRp8uVBsdHW3WENPWvs2bN5uvdaJITEzMRReF1XP1AQBwDFoAAdhcagawrg+myzroHqAFli5danaS0TGB2to3evRo28bz2j08ZcoUs71gzZo17a5VuGWQCSQAUP4IgAAuS7t/ly9fbvZc1ha/whvPh4WFmS0HdWzgjz/+aMKc7k+q+4jqmEHtNn7ooYfkyJEjpotY92sOCAgwawnqeYQ/VDZpmZm/L+tyle/xd/ctszoBf0QXMIDL0q7do0ePStOmTW1l8fHxZq9lXeFfX2/VqpXZk1lbBXVcoD50PGD9+vXN+RMmTJBJkyaZEKljBJ955hmzd+rx48cd+JMBpcvP11f8/XzNmn4Fj3w3V8nKy7M9cvTEQq/rQ9+j7wXKCy2AAC6rQYMGZuePNm3a2Mrmz59vNoDXsn/+858m2P3nP/8xXbq6ebwGPN3AvmC3EN0OSkPhuHHjzPGDDz4oGzduNOMKgcpiwTdTLyg7ceq0zPh+nu24SWx96daxbTnXDLDHnRfAZWmXrY7lK+zkyZPi4+MjXbt2la+//tp8rbRLV1v1dLeQHj16mAklOuHjxhtvNLOGdfzfyJEjpWXLlmY/YU9PTwf9VABgXWwFB6DY0tLSzP6h3333nRnnp929vXv3lu+//96MCfzhhx9kwIABtvPXrl0rkydPlrNnz5ouYTaIhxXQAghnxBhAAMVWsHn8kCFDZMOGDTJw4ECzRMygQYNMeZ8+fczzF198YUKfdgGPHz/ejCd84YUXTFlRMjIzZd/Bw2aNQgBA6aMLGECp0O5cHfenUlNTpUWLFqZ7V7eP+8c//mFC33333SdVqlQx4wZ1BnFBt3Fhugj1zj37ZOW6jWZZmlrRUWadwuioapdcpgYAcOW4mwIodYGBgaYVUEeY6LZxw4cPN3sHv/LKK6abWBeQ1mVidALIH3cd0a91VxKlLYB79h80D8IgAJQexgACKDcJCQlmqRhtAdSdQopyLj1dpn0z+5LXsYXB2jESXZ0wCOfGGEA4IwIgAKeiM4b3Hzoiew8eNnsT5+Rcess4wiCcHQEQzog7JQCnosvE1Ktd0zx0B5KDR45eMgwW2U1MGASAS+LuCMBp6b7ChEEAKH3cEQFYKgzGRFUXdzc3h/wMAOAsCIAAKhzCIACUDAEQQIVGGHQu9957ryQnJ8vs2ZeeyQ3AsQiAACoNwiAAXBm2ggNQqcNgvx5dZNjQm6Rv985mEWl396KDXUEYnLdomUz997cyf+kK2XfosOTkXnoZmvLQo0cPefTRR+Wpp56S0NBQiYyMlBdffNH2+ttvvy1NmzYVPz8/iY6OlhEjRph9mgt8/vnnEhwcbPZmjo2NNQtx33zzzZKuay5Omya1atWSkJAQ8z10GZ4CmZmZMnr0aLPLi167ffv2smTJkquq+7x586RLly7m+4eFhZntAvfu3Wt7/cCBA+Li4iLffPONdO3a1ewOo1sG7t692+wdrWtG6paD1113nZw4ccLu2p988ok0atRIvL29pWHDhvLBBx/YXsvKypKRI0dKtWrVzOs1a9aU11577ar/2wOVFS2AACq9ytAyqEHtiSeeMAtpx8XFma7Wzp07y7XXXmt2T5k0aZLUrl1b9u3bZwKghsXCgUjDnp4zffp0swfzTTfdZPZw1mD2448/mvf96U9/MtccOnSoeY8GKN3KT99TvXp1+e6776R///6yZcsWs6OL0vA2depUU5+inDt3ztS7WbNmJpQ+//zz5vtu2rTJbgcY3Rv63XffNQuE65aBf/7znyUgIEDee+89E1hvvfVW894PP/zQnP/ll1+a4/fff19atmwpGzdulAceeMAEVd1lRn/WOXPmmGCp1zx8+LB5APgdC0EDsKyCMKhbzx1KOHrZRac9PTykZo3q5R4GtQVQW+aWLVtmK2vXrp306tVLXn/99QvOnzlzpjz00ENy8uRJWwvgsGHDJD4+3mzNp/T1L774Qo4fP25a2JSGO20NnDJlihw6dEjq1KljnjX8FejTp4/53q+++qo51pY3bVnTUHclYwC1TroftIbIJk2amBZADa7amnf//febczRw3n777bJw4ULzMyr9OfXn2LlzpzmuV6+e2VpQzyswfvx4E2ZXrlxpWjO3bdsmCxYsMCHVkVgIGs6IFkAAllVUy+ClwmBWdratZbC8w6C2oBWmXZtJSUnmaw05GsI0HKWmpkpOTo5kZGSYVj9tPVP6XBD+VEREhAl7BeGvoKzgmhrQNHQ2aNDA7vtqt7B25RYoCGQXs2fPHtNSpy2XGv5072elwVIDYFE/n9ZDabd2UXXTVkXtRtbAqK1+BfTnDgoKsgVRbR3VLm8Nttr13Ldv30vWFbASAiAAVIAwqPUrTFu1NExpC5qGm4cfflgmTJhgxgguX77chCMdB1cQAIt6/8WuqbS7VndlWb9+vXkurHBovJwbbrjBjL/7xz/+YVoS9foa/LRuF/v5Clrs/lhWuG5Kr6njEgsrqGurVq1k//798tNPP5mArF3I2nqpraMACIAAUOHCYGEa0DQYvfXWW7YxdTruraR0XJ22AGqrm07OKI5Tp07Jrl27TFAruIaG05LS1kANkzpu8Y477rjoeYGBgWY8oz500ou2BJ4+fdqEZMDqCIAAUIHDoI6F03pNnjzZtLatWLHCjOErKe361XB19913m3CpgVBn4eq4PO2uvf7664scA1iYzizW7uKPP/7YdFlrt+8zzzwjpeGll14y4/y0y1eDnXZNr1u3Ts6cOWMmnejMaP2eWm8NxjNmzDCzp3XSCwACIABU6DDYvHlzE3YmTpwoY8eOlW7duplApsGtpHR2r06sePLJJyUhIUHCw8OlQ4cOpsu5gLbwpaSk2I61NdL9v/sua/DSCR0a1LTbV8fj6excndRSUn/5y19M9/bf/vY3GTNmjJn9q2MGR40aZV7XGcRvvPGGGYOo3cK6tIxOECk88xiwMmYBA0AFmE2s3bEaKH28va+qbidPn5Htu+Il8cTvEzCCgwKlUf26El09skzCkLbGaaukLs+C3zELGM6IFkAAqAAtg4ePJsrOPfukX88ul13WJP18hnz/yyL5es5PsmffgSLPCQ8NkZuu7yu33nCdRFT536ze4tKuV+1+1oWidYkZAM6NFkAAqAAtgwuXx8mu+P3Su0tHia1X+6LXWL3hN3n+jUly9PjvS6Zk+qRJemCyZPmc0zu+uGd6i8/ZIPE5Gywu4iI+3l7y+IPDZOig60rUIqhjAHXnDl2EWbuNHb32njOhBRDOiAAIAE4eBmtERso/Z86WrKxsUz500AAJ8Pe74D2f/numvPvxNPN1aliinIiJl/OBySJFZDGP8z4SllDbPFzyXaRXlw7yxrgx4uXpWXo/NAwCIJwRARAAnDwMastcwRp4qnpkVRnUr7ddK9u0b2bLmx9+KnluuZLQ4DdJqXq0yOD3R95ngyRmeyvxPO9nQuA7L41lokQpIwDCGfFbDgAOGjPYv2dXGTb0JunbvbPUqRkt7u5Fj/8rHP7U0cQk+W37Ltvxtl3x8vZHUyXfNU8ONF0tKRFXFv5URkCK7Gux0nQRL1q+Sqb/58eS/XAAKgQCIABUoDBYeKzfqTPJJhw+/8Z75vlYne2SHnz6quuQ45UphxpvkHyXfHnno6lmxjCAyo1ZwADgxLOJt+zYLceSThS5LMzCZXGmO3j3vgOSHpAsp6OKnvF7pS2Bp6L2SfiRuvLNnJ/k0fvvKuFPA8CZ0QIIAE4cBgMDit5318vLU6qEhZqwpk7V2H/F3b4XcyrqgORLvnz7w88XdDsDqFxoAQQAJ5WTmyv7Dx+xC311YqKlXq0Y0/KnkzVeeut903WbGn6sxN8v2+e8mTV8OtnFtD7WjqlR4msCcE4EQABwUkeOJpqZvrpzR0Ho023NCiSdPGXGAWb6nZV8t9JpsTsfkCy+qSGyY89eAiBQiREAAcBJVQkLkXtvHWIX+go7k5JqnrO9MkrtexZc60zy//b3BVD5EAABwEn5+fpe8nXbOoCluZrrf6/lwlqAQKXGbzgAVFARVcLNs9f5C3cFKa6Ca0X+99oAKicCIABUUEEB/lKjeqR4ZviJW1bpbOHmkxpinhvH1iuV6wFwTgRAAKjAOrVpaZ6Dj5d8woZPapB4pweYyR8R4WGlUDsAzooACAAV2K03XGeew47UFpfcS+8ecjnhh35v9Rs6aIDdPsMAKh8CIABUYLH1akuPTu3FM9NHIvc1LPZ1ApOqSdDJahJRJUwG9+9dqnUE4HwIgABQwY17/GEJ8PeTsITaEppQ86rf75MSLDV2NTdfvzT60cvOPgZQ8REAAaCCqxoeJm8+/5R4uLtL9T1Npdqea66sOzhfJORojNTe3FFcc91lxL1/ls7tWpVHlQE4GAEQACqBTm1byZsvPCUB/r6mJbD+mu4Sdri2uGV5XHCuS66r6fKts7GTRO1uJu757jLqwXvkobtvc0jdAZQ/FoIGgAoqMytLjiYmyZFjiXLk6HE5k5Ii06e8I3/74FNZsnKNVNt7jXlk+pyTLJ80s2ewR6aPeJ8LEJf83z//160ZLS8/9Zg0axzr6B8HQDkiAAJABZGTmyuJSSck4dhxE/qSTp6W/Hz7bUCqVa0ikyeMM3v5fv2fn2TV+o2SkJhkt1h0cGCAtGzaWG4e2F+6tGslruz6AVgOARAAKgANems3bZGNW7Zf8ryCMNeofl15cfRI83VySqocP3FKcvNyJSQoUCKrVmGZF8DiCIAAUAFoYOvYuoWEBgXJkrg1kpubW+Q5RbXmBQcFmgcAFKDdHwAq2Lp/fbp2LPI1DX+07AG4EgRAAKhAsrOzTVdwUdzcuKUDuDLcLQCgAo0DXLRitZxOTrGVFe7ydXMt2VZwAKyDMYAAUEFs2rpD9h44ZFfWs3N7cXN1lcUrVjObF8AVIwACQAVw+GiirNrwm11Z00YNJLZubfN1SHCQrFy30UG1A1DR8HERAJxcalqazF+6wm7Nv2oRVaVTm5a247CQYBnQq5uDagigoiEAAoATy87JkXmLlklGZqatzM/XV/r16CxubvZj/v54DAAXQwAEACelLX6/xq2Vk6fP2Mp0nF+/nl3E18fHoXUDULERAAHASW3duUd27d1vV9a1fRuJrBLusDoBqBwIgADghI4mJsnyNevtyho3qCfXxNZzWJ0AVB4EQABwMmnn0uWXpcvtJn1EVAmTLu1bO7ReACoPAiAAOJGc3Fz5eckyST+fYSvz8faWfj26ijuTPACUEgIgADiRFWvWy/ETp2zHurdvvx5dxN/P16H1AlC5EAABwEls3x0v23bF25V1bttKqkdWdVidAFROBEAAcALa6vfrqnV2ZQ3q1DK7fQBAaSMAAoCD6Xi/eYuXSV5enq0sPDREundqZ7qAAaC0EQABwIFyc3PNjN9z6em2Mi8vT+nfq6t4uLNdO4CyQQAEAAeKW7/JrPlXQFv8+nbrLIH+/g6tF4DKjQAIAA6ye98B2bx9l11Zh1bNJTqqmsPqBMAaCIAA4AC6v++SlavtyurWipEWTRo5rE4ArIMACADlLCMzU+YtWiY5Obm2stDgIOnVuT2TPgCUCwIgAJQjnek7f+kKSU1Ls5V5enpI/55dxcPDw6F1A2AdBEAAKEdrNm6Rw0cT7cr6dO0kwUGBDqsTAOshAAJAOdl74JBs2LLNrqxti6ZSKzrKYXUCYE0EQAAoB6eTU2TR8lV2ZRr82jRv4rA6AbAuVhkFgDKWmZUlPy36VbJzcmxlQYEB0rtrRyZ9WICfr690adfadhwWEuzQ+gCKAAgAZSg/P18WLouTlNSztjLd4UMnfXh5ejq0bigfPt5eck1sPdsxoR/OgAAIAGVo3W9b5cDhBLuyXl060ApkIRr43NzcHF0NwA5jAAGgjCSeOGkCYGEtmzY2Cz4DgCMRAAGgjLp+I8LDpGWhnT2iq0dK+5bNHFovAFAu+XqXAgCUmX0HD8vqjZtlcP/e4uPt7ejqAAABEADKY/ePvPx8cWccmGX16tXLtApficWLF5d5fQAmgQBAMekf9CuZ0enq6sp4G4tr0aKF3XF2drZs3rzZPO655x7zbwQoT7QAAkAJw9/BgwclIiJCvOnexVV65ZVXJC0tTSZOnOjoqsBiCIAAUIIA+NZbb8mGDRtkxIgR0rFjR1tLzpW2DsLa9u7dK+3atZNTp045uiqwGNqcAaAYY/o03P3www/y4osvyoABA6RJkyZ23Xj6up4HXMrKlSvFkwXB4QCMAQSAq6RBLyUlRcaMGSMTJkyQO+64Q86dOycbN26Ur776Svz8/OSpp54SX19fR1cVTmLIkCF2x9pCfOzYMVm3bp08//zzDqsXrIsACADFkJ6eLiEhIVKzZk0zhkuD4KJFiyQ3N1cyMjJMQHznnXccXU04Cf238scPEY0bN5ZXX31Vevfu7bB6wboYAwgAV0lvmzpma/DgwRIQEGBmcrZs2VL+/Oc/m8cjjzxiAuKnn37q6KrCiei/k/j4eGndurX54AA4Ei2AAHCVdHxfeHi4vP/++zJjxgzp0KGDPPHEE+Lj42NeP336NHu/ws57771n/o14eHiYfz/ff/+99OnTRyZNmmRajR9//HFHVxEWQwsgAFyG/oHWQHfo0CHZvXu3HD16VDp37ix169a1m+2bmJgo06ZNk9dee0127twpkZGRjq46nESNGjXMuNBHH31URo8eLZs2bZIFCxbI0qVLTYuxtg4C5YkWQAC4gvB34sQJM5D/7NmzpvVP/5B/+OGHcvvtt9vWAnzuuefMRJCZM2cS/mAnOTlZbrjhBvP1rbfeKtOnTzdf165dW/bt2+fg2sGKWAYGAC6hoCt34MCB0rBhQ9Nyo2P7dOKHzv596KGHzOs6pmvkyJHy8ccfm649oLBu3brJ8uXLzdehoaGSmppqvtbwp8dAeaMFEAAuQ5d20fF9Gu50aZeHH37YtObceOON8sADD8iRI0fko48+kvbt2zu6qnBS+mHhmWeeMS3FUVFRkpOTI99++62MGzfO1jIIlCfGAALARRSM79OxWuvXr5enn35aXn75ZZk9e7YsXLjQLOCrS3isWbNGevToYZaBAYpS1KSgsLAw0x2s28Dp2pFAeaIFEAAuMu6vIABql65O+lA6xm/o0KG2dd10BvCzzz5rAiBwMWfOnLE71g8P7B0NRyIAAsAfaPjTnT10kd79+/ebAKhBTxfuTUpKMtt3KW0F/Prrr81EkMDAQEdXG06Mfx9wNnQBA4CIfPnll1K1alW59tprzXGbNm3Ey8vL7OerEz7q1asnI0aMMC2C99xzj1noWVtwhg0bZoIicCk6a1w/WBTsDqMTiXQNwOjoaPnggw8kJibG0VWExRAAAVieLtHRqVMnsyTHvffea1prdOFeXapDv/7pp5/Mos8a+gYMGCA9e/Y0OzrouC0G8ONKNGjQwPwb6tu3ryQkJJh/azoBZMWKFaY7eM6cOY6uIiyGAAgA/w2BOrv35MmTEhsba8LeZ599Znt927Zt8vrrr8vhw4elWbNmMn78eLr1cMV09vj27dulVq1aZjb5P//5T7MsjJZ16dLF7B4DlCfWAQQAEQkODpZ//etfpiXwu+++M+P7NPQVuOaaa+Tvf/+7tGrVSjZs2CCZmZkOrS8qFv2wUBDyfvnlF9takRoMs7KyHFw7WBEtgADwB/PmzZNRo0ZJ8+bNZfjw4dKrVy+713VLOMZs4WroWFHdHlCHD7z99tuydu1aadq0qfz4449meaEtW7Y4uoqwGAIgABRhz549ct9995lxfnfeeafcdttt4u7Owgko/jIwf/3rX02Xry4erl8r7QbWSUb9+/d3dBVhMQRAALgI/cOsW71pi59u5TVmzBgJCgpydLUAoMQYAwgA/93144/8/f3NYH3tAtbxgAEBAQ6pGwCUNloAAVie3gbPJKdISHCQWeevKLowNNt1AagsaAEEYHnrftsqX8/5SX7bttMcF/W5mPAHoDIhAAKwtAOHE0wA1NC3ct1G+WXpiiIDIABUJkxpA2BZySmpsmDZSrvAx/p+KG3676vwvzEdZnCxoQZAeSEAArCk7Oxsmbd4mWRlZdvKAvz9pE+3TuLqSucISk9mVpYcTjhmOw4KDJCq4WEOrRNAAARgOdoas2jFajmdnGIrc3Nzk/49u4qPt7dD64bK52zaOZn/60rbcZPY+gRAOBwfcwFYzqatO2TvgUN2ZT06tZMqYaEOqxMAlCcCIABLOXw0UVZt+M2urGmjBhJbt7bD6gQA5Y0ACMAyUtPSZP4fZvlWi6gqndq0dGi9AKC8EQABWEJ2To7MW7RMMgrN8vXz9ZV+PTqb8X8AYCUEQACVnrb4/Rq3Vk6ePmMr05m+/Xp2EV8fH4fWDQAcgQAIoNLbunOP7Nq7366sa/s2Elkl3GF1AgBHIgACqNSOJibJ8jXr7coaN6gn18TWc1idAMDRCIAAKq20c+nyy9LldpM+IqqESZf2rR1aLwBwNAIggEopJzdXfl6yTNLPZ9jKdJHnfj26ijuTPgBYHAEQQKW0Ys16OX7ilO1Y917t16OL+Pv5OrReAOAMCIAAKp3tu+Nl2654u7LObVtJ9ciqDqsTADgTAiCASkVb/X5dtc6urEGdWma3DwDA7wiAACoNHe83b/EyycvLs5WFh4ZI907tTBcwAOB3BEAAlUJubq6Z8XsuPd1W5uXlKf17dRUPd3eH1g0AnA0BEEClELd+k1nzr4C2+PXt1lkC/f0dWi8AcEYEQAAV3u59B2Tz9l12Ze1bNZPoqGoOqxMAODMCIIAKTff3XbJytV1ZnZrR0rJJY4fVCQCcHQEQQIWVkZkp8xYtk5ycXFtZSFCQ9OrcgUkfAHAJBEAApWLJkiUmdOlj8ODBpX79gmsHBwebY53pO3/pCklNS7Od4+npIdf16mqeAQAXRwAEUKp27doln3/+uV3Z3//+d6lVq5Z4e3tL+/btZc2aNXavf/zxx9KjRw8JDAw0IS85OfmC6x47dkzeffdd2/GajVvk8NFEu3P6dO0kwUGBpf4zAUBlQwAELLA8SuF18cpa1apVba106uuvv5YnnnhCXnjhBdmwYYM0b95c+vXrJ0lJ/5uxm56eLv3795dnn332oteNjIyUoKAg8/W+g4dlw5Ztdq+3bdFUakVHlcnPBACVDQEQcDLaEjZy5Ejz0MATHh4u48aNk/z8fPN6ZmamjB49WqKiosTPz8+0qGn3awFtfdMANmfOHGncuLF4eXnJoUOHzDnt2rUz79HXO3fuLAcPHrS978MPP5S6deuKp6enxMbGyhdffGFXL22Z++STT2TIkCHi6+sr9evXN9/jct5++2154IEHZNiwYaY+U6ZMMe//7LPPbOeMGjVKnnnmGenQocNlr6f/HRYui7Mr0+DXpnmTy74XAPA7AiDghKZNmybu7u6mq/S9994zIUrDl9JgGBcXJ9OnT5fNmzfLLbfcYlrP9uzZY9eiNnHiRPOebdu2SWhoqBmX1717d/Meff+DDz5omyjx3XffyWOPPSZPPvmkbN26VYYPH24C2+LFi+3q9dJLL8mtt95qrjFgwAC544475PTp0xf9ObKysmT9+vXSp08fW5mrq6s51jpcreycHDPhQ58LBAUGSO+uHZn0AQBXgeXxAScUHR0t77zzjgk12hq3ZcsWc6xdp1OnTjUtetWrVzfnamvgvHnzTPmrr75qyrKzs+WDDz4w3a1KQ1pKSooMHDjQtPKpRo0a2b7fm2++Kffee6+MGDHCHGuX7apVq0x5z549befpObfffrv5Wr/XpEmTTEjVAFqUkydPmi7oiIgIu3I93rlz51X9N9GWv+27420toUp3+Ojfs6t4eXpe1bUAwOpoAQSckHaFFm7R6tixo2nh0yCogapBgwbi7+9veyxdulT27t1rO1+7cZs1a2Y71hZADW8aIG+44QbTqqiTKgrs2LHDdAkXpsdaXljha2pXsk7aKDyWryyt+22rWfOvsF5dOkhYyP/GGwIArgwtgEAFkpaWJm5ubqZbVZ8L0yBYwMfH54IuUW0hfPTRR01roU7MeO6552T+/PlXNO6ugIeH/fIq+j0uNcFExy9qPY8fP25Xrsc6qeNKHTicYAJgYS2bNpa6tWKu+BoAgP+hBRBwQqtX2+9sod2xOumiZcuWpgVQW93q1atn97iSQKXvHzt2rKxcuVKaNGkiX331la07eMWKFXbn6rFO2igJbYls3bq1LFy40FamgVGPtVXzSiSnpMqCZSvtun6jq0dK+5b/a40EAFwdWgABJ6Rj/HQcnk7G0KVTJk+eLG+99Zbp+tWJF3fffbc51kB34sQJE6i0e/b6668v8nr79+83a+3deOONZuygrtWnXcp6HTVmzBgzuUOvpxM0vv/+e5k1a5YsWLCgxD+L/hz33HOPtGnTxsxC1rX8zp07ZyaZFEhMTDSP+Ph4c6xd3QEBAVKtWjVZHLdOsrKybedqw2afbp3MZBIAQPEQAAEnpMHs/PnzJjBpF6rO0NVZuwVduePHjzczdhMSEkw3q3bj6gSPi9FlV3TShc4uPnXqlAlWf/3rX03AVDpDWMcF6qQP/V61a9c230eXpCmpoUOHmpD6/PPPm5DXokUL0w1deGKILg2jM4wLdOvWzTw/8fRYqdvwf8u7aOjT2dE+3t4lrhcAWJlLfuF+FQAOp6FLQ1LhXS8qAl1nUGcMnzlzxm4h6OLauGW7xK3fZFeWkpQgE1+dUOROIYCzOnHqtMz4fp7tuElsfenWsa1D6wTQAgigVNWoUcPMNP73v/9d7GvoFm+rNvxmVzZ6xF/M+EHdTg4AUDIEQAClQnckKViMuvCM5AJ9bh0m59LTL3sd7ZTIzv7fQs/K29tLNm7caLrD/zj7GQBw9QiAgJMpvK1bRaJLz+hs5IvR8Jd2Ll38vbwueR1dvMaz0ASPtMxM8fBwNwtiAwBKBwEQQLnR8Bc39pmrek/H117/feovAKDUsI4CAACAxRAAAQAALIYACAAAYDEEQAAAAIshAAIAAFgMARAAAMBiCIAAAAAWQwAEAACwGAIgAACAxRAAAQAALIYACAAAYDHsBQyg3KRlZv6+t+9Vvsff3bfM6gQAVkQABFAu/HyLF+I0/BX3vQCAohEAAZSLBd9MdXQVAAD/xRhAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABbjkp+fn+/oSgAAUJmsXLtBDh45Zr7Ozc2V1LQ022uenh7i5+NrvvbwcJcBvbuLr4+3w+oKa6IFEACAUtayaWPJys6WMykpduFPZWX9Xq6Ppo0aEP7gEARAAABKmY+3t/Tt3klcXFwuek7jBvUktm7tcq0XUIAACABAGagWUVXat2pW5GthIcHSuV2rcq8TUIAACABAGWnZpLHERFW3K/Nwd5e+PTqbZ8BRCIAAAJQR7QLu3bWD+Pn+PulDdevYVkKCghxaL4AACABAOY0HZNwfnAUBEACAMnQuPV2OnzglocFB4u7uLkcTk4QV2OBoDEAAAKCUJaekyn9+Xiiz5y2UvQcOXRD4QoICpUen9nLb4AGmVRAobywEDQBAKdE/qTO+nydvTflM0s9nmLIsr/OS4Z8iOR5Z4prvKl7nAsT7XIC45P/eCde3e2f5v1EPmxZCoLwQAAEAKAXnMzLkqVf+JktWrpF8yZcz1Q7L6aj9kuF/9oJzXbPdJeR4tIQfriMemT4SEhwof3/1BbMwNFAeCIAAAJRQdna2/PXZlyVu3SbJ9EmTI402yfnA5Mu+zzXHXarFN5aQxBjx9fWRqe+8SpcwygWTQAAAKKEP/zndhL8Mv1TZ13LlFYU/leeeIwmxmyUpZo+kp5+XJ1+caOs6BsoSARAAgBLYGb9PPv1qpuS55crBJusk1zPr6i7gIpJUe5ekhh2XI8cSZfJnX5RVVQEbAiAAACUwdfosycvLk+O1dkq2T3rxLuIicrTBZslzzZVv5vwkKakXjhsEShMBEACAYjqTkiK/LF0uuW45cqbaoRJdK8crU5IjjkhWVrZZQgYoSwRAAACKaeOWHZKTkyup4cckzz23xNdLjkgwz2s3bSmF2gEXRwAEAKCYtu+ON89XOunjcnS9QF1CpuC6QFkhAAIAUEwnT58xz1nexRz79wfaipjjmSknTp1huziUKQIgAADFVFYhjfCHskYABACgmMJCQ8yzR4ZPqVzPJddV3LO9JCwkWFxcXErlmkBRCIAAABRT4/p1zbPv2eBSuZ53WpC45LuwGwjKHAEQAIBiatGkkbi6ukrgyWqm9a6kgpOqm+c2zZuUQu2AiyMAAgBQTOGhIdK7a0dxy/GQkMToEl3LLcvTXMPD3V0G9e9danUEikIABACgBIYNvcmM14vc30g8MryLfZ3qe5qIa667DBlwrRkDCJQlAiAAACXQtFEDuevmQeKa4y4xW9uY56sVfqiuBJ2oLpFVw+XxB+8tk3oChREAAQAooUfuv1NaNW0sPmnBUntjJ/FKC7ii9+m4wcg910jkvkbi5eUpfxv3lPj7+ZZ5fQGXfBYbAgCgxNLOpctj4ybImo2bJd8lT05F7ZdTUQcl2ye9yOAXlBQlVQ7VE6/zfib0TRr/nLRt0dQhdYf1EAABACglubm58s8Z/5H3P/uXZGVnm23dMn3TJCMgRXI8ssQlz1W8zwWIT1qQGe+nOrdrJS8+OVIiq1ZxdPVhIQRAAABK2fETp+TbuT/L7HkL5NjxExe8rt293Tu0ldsGX2+WfGHRZ5Q3AiAAAGW8X/Cu+P1y9tw5s8RLTI3qUjumhri7uTm6arAwAiAAAIDFMAsYAADAYgiAAAAAFkMABAAAsBgCIAAAgMUQAAEAACyGAAgAAGAxBEAAAACLIQACAABYDAEQAIAKaMmSJWYLueTkZEdXBRUQARAAACdw7733yuDBgx1dDVgEARAAAMBiCIAAgEqrR48e8uijj8pTTz0loaGhEhkZKS+++KLt9bfffluaNm0qfn5+Eh0dLSNGjJC0tDTb659//rkEBwfLDz/8ILGxseLr6ys333yzpKeny7Rp06RWrVoSEhJivkdubq7tfZmZmTJ69GiJiooy127fvr3psr0aeg29btWqVcXb21u6dOkia9euveC89evXS5s2bUzdOnXqJLt27bK9pj9rixYt5IsvvjB1DQoKkttuu03Onj1bjP+aqEwIgACASk2Dmoaw1atXyxtvvCEvv/yyzJ8/37zm6uoqkyZNkm3btpnzFi1aZMJiYRr29Jzp06fLvHnzTJAbMmSI/Pjjj+ah4eqjjz6SmTNn2t4zcuRIiYuLM+/ZvHmz3HLLLdK/f3/Zs2eP7Rwdv6cB82K0Ht9++62p14YNG6RevXrSr18/OX36tN15//d//ydvvfWWrFu3Ttzd3eW+++6ze33v3r0ye/ZsE2L1sXTpUnn99ddL/N8VFVw+AACVVPfu3fO7dOliV9a2bdv8p59+usjzZ8yYkR8WFmY7njp1ar7+qYyPj7eVDR8+PN/X1zf/7NmztrJ+/fqZcnXw4MF8Nze3/ISEBLtr9+7dO3/s2LG249jY2PxZs2bZju+55578QYMGma/T0tLyPTw88r/88kvb61lZWfnVq1fPf+ONN8zx4sWLTd0WLFhgO2fu3Lmm7Pz58+b4hRdeMHVNTU21nTNmzJj89u3bX9F/P1Re7o4OoAAAlKVmzZrZHVerVk2SkpLM1wsWLJDXXntNdu7cKampqZKTkyMZGRmm1U+7VJU+161b1/b+iIgI053q7+9vV1ZwzS1btpju4AYNGlzQpRsWFmY71u95Mdpql52dLZ07d7aVeXh4SLt27WTHjh0X/fn0Z1Nal5iYGPO11jUgIKDInx/WRQAEAFRqGpwK067XvLw8OXDggAwcOFAefvhhmTBhghkjuHz5crn//vslKyvLFgCLev/Frql0DKGbm5sZm6fPhRUOjWXx82k9VEFdLlb/wq/DmgiAAABL0oCmQUj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+ "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], "text/plain": [ - "
" + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, @@ -340,7 +396,17 @@ } ], "source": [ - "answer_graph.draw()" + "# The AnswerGraph plot method will plot onto the matplotlib plot. \n", + "plt.figure() # For cleanliness, we'll tell matplotlib to create a new \"figure\" each time\n", + "plot_instance_1 = answer_graph.plot() # Limitations of netgraph require that you hold on to the returned value" + ] + }, + { + "cell_type": "markdown", + "id": "8d4d479f-3c5e-4c42-9f4e-4024fd182abf", + "metadata": {}, + "source": [ + "## Some more examples" ] }, { @@ -377,7 +443,7 @@ { "data": { "text/html": [ - "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" + "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" ], "text/plain": [ "" @@ -439,9 +505,24 @@ }, { "data": { - "image/png": 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", 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+ "text/html": [ + "\n", + "
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" + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, @@ -449,6 +530,7 @@ } ], "source": [ + "%matplotlib widget\n", "from typedb_jupyter.graph.query import QueryGraph\n", "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", "\n", @@ -460,16 +542,9 @@ "\"\"\")\n", "query_graph = QueryGraph(parsed)\n", "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)\n", - "answer_graph.draw()" + "plt.figure()\n", + "plot_instance_2 = answer_graph.plot() # We use a different name to avoid clobbering the earlier visualisation" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7681fedf-fd9f-4fe9-b430-44490b75a688", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index 5f67fe1..7c7b254 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -25,13 +25,13 @@ class AnswerGraph: def __init__(self, edges): self.edges = edges - def draw(self): - from netgraph import Graph + def plot(self): + from netgraph import InteractiveGraph # TODO: derive edges, node_shape, node_labels, node_colors from from edge.lhs & edge.rhs plottable = PlottableGraphBuilder() for edge in self.edges: plottable.add_edge(edge) - plot_instance = Graph( + return InteractiveGraph( plottable.edges, edge_labels=plottable.edge_labels, node_shape=plottable.node_shapes, From 5302f82e7bb158298d03b47a849c6b28fe7f8997 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Sat, 15 Mar 2025 14:22:30 +0100 Subject: [PATCH 20/27] More text --- src/Sample.ipynb | 260 +++++++++++++++++++++++++++++++++++------------ src/graphs.ipynb | 2 +- 2 files changed, 197 insertions(+), 65 deletions(-) diff --git a/src/Sample.ipynb b/src/Sample.ipynb index 3dfbcd0..5ac5feb 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -1,5 +1,19 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "74a87d1f-52e4-458e-af4d-6db4f9bc3c27", + "metadata": {}, + "source": [ + "# TypeDB Jupyter\n", + "`typedb-jupyter` is a python library that introduces a few useful jupyter commands as well as python functions to enable users to work with TypeDB through jupyter notebooks, without having to pass through too much of the python driver.\n", + "\n", + "The `%typedb` 'line magic' allows administrative server like user management, database management and transactional commands.\n", + "The `%%typeql` 'cell magic' runs a query within the active transaction.\n", + "\n", + "To load the typedb-jupyter extension, we use:" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -10,19 +24,24 @@ "%reload_ext typedb_jupyter" ] }, + { + "cell_type": "markdown", + "id": "7457a903-f29f-47a0-be6b-de06aba502a2", + "metadata": {}, + "source": [ + "#### Open a connection" + ] + }, { "cell_type": "code", "execution_count": 2, - "id": "c4b2cf02-baa2-4e71-86c3-824030318ee9", + "id": "c78d12da-305a-4eca-bdc8-a19441fb521a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Available commands: connect, database, transaction, help\n", - "--------------------------------------------------------------------------------\n", - "Help for command 'connect':\n", "usage: connect [-h] [--tls-enabled]\n", " {open,close,help} [{core,cluster}] [address] [username]\n", " [password]\n", @@ -39,56 +58,18 @@ "options:\n", " -h, --help show this help message and exit\n", " --tls-enabled Use for encrypted servers\n", - "\n", - "--------------------------------------------------------------------------------\n", - "Help for command 'database':\n", - "usage: database [-h] {create,recreate,list,delete,schema,help} [name]\n", - "\n", - "Database management\n", - "\n", - "positional arguments:\n", - " {create,recreate,list,delete,schema,help}\n", - " name\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", - "\n", - "--------------------------------------------------------------------------------\n", - "Help for command 'transaction':\n", - "usage: transaction [-h]\n", - " {open,close,commit,rollback,help} [database]\n", - " [{schema,write,read}]\n", - "\n", - "Opens or closes a transaction to a database on the active connection\n", - "\n", - "positional arguments:\n", - " {open,close,commit,rollback,help}\n", - " database Only for 'open'\n", - " {schema,write,read} Only for 'open'\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", - "\n", - "--------------------------------------------------------------------------------\n", - "Help for command 'help':\n", - "usage: help [-h]\n", - "\n", - "Shows this help description\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", "\n" ] } ], "source": [ - "%typedb help" + "%typedb connect help" ] }, { "cell_type": "code", "execution_count": 3, - "id": "994ca437-cbbc-4ac5-a953-a44e196a9512", + "id": "484f0530-a414-4658-95ce-1ae6367a77cb", "metadata": {}, "outputs": [ { @@ -103,9 +84,45 @@ "%typedb connect open core 127.0.0.1:1729 admin password" ] }, + { + "cell_type": "markdown", + "id": "75c2970b-a4b0-47e5-94bf-0f26ee466487", + "metadata": {}, + "source": [ + "## Database Management\n" + ] + }, { "cell_type": "code", "execution_count": 4, + "id": "d7968c05-f1a8-4318-857b-fa4c349b95eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: database [-h] {create,recreate,list,delete,schema,help} [name]\n", + "\n", + "Database management\n", + "\n", + "positional arguments:\n", + " {create,recreate,list,delete,schema,help}\n", + " name\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n" + ] + } + ], + "source": [ + "%typedb database help" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "1ef0b8de-4e09-4588-bea7-f67fce0bfe95", "metadata": {}, "outputs": [ @@ -123,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "2775578e-cbe6-498d-82c6-18d8d4c4f0c0", "metadata": {}, "outputs": [ @@ -141,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "6415f8cf-34e7-42b8-9294-0113c584fc33", "metadata": {}, "outputs": [ @@ -159,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "85c80d7e-566b-4b92-9704-e27f68a58919", "metadata": {}, "outputs": [ @@ -177,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "be17ff6a-8020-43a4-9611-2f5def7bab0d", "metadata": {}, "outputs": [ @@ -195,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "0cd600f3-6f6d-4b03-b3ec-fcf9b593e4b0", "metadata": {}, "outputs": [ @@ -211,9 +228,20 @@ "%typedb database list" ] }, + { + "cell_type": "markdown", + "id": "17144144-4053-450d-9a57-3f2bfacf4775", + "metadata": {}, + "source": [ + "## Transactions & queries\n", + "To query TypeDB, one needs to use transactions. \n", + "### Defining the schema\n", + "Open a `schema` transaction, define our schema, and commit." + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "bfeae364-194b-4478-b02d-2b29c1ede228", "metadata": {}, "outputs": [ @@ -231,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "d3a65846-4d15-4376-bfb1-c3c921355aab", "metadata": {}, "outputs": [ @@ -248,7 +276,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "76949fbe-c0fc-4973-ad3b-b0a60c3499d4", "metadata": {}, "outputs": [ @@ -278,9 +306,19 @@ "%typedb transaction commit" ] }, + { + "cell_type": "markdown", + "id": "f883438e-6c35-4499-a3f0-4aa4dbda9dad", + "metadata": {}, + "source": [ + "### Writing data\n", + "Open a `write`, insert some data, and commit. \n", + "Notice that the insert query does return the data." + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "2385b0db-a4b5-4b5e-b734-64ccc473780a", "metadata": {}, "outputs": [ @@ -298,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "d3a6084f-0bda-4985-a6a5-be1e93d138be", "metadata": {}, "outputs": [ @@ -327,7 +365,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -340,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "ea614f7f-c26f-4147-afb1-e1b0545744a3", "metadata": {}, "outputs": [ @@ -356,9 +394,18 @@ "%typedb transaction commit" ] }, + { + "cell_type": "markdown", + "id": "8beaad15-5ed0-467a-9a8e-edcf17eb3317", + "metadata": {}, + "source": [ + "#### Reading data\n", + "We can read data through `match` queries, with a `fetch` at the end if desired. The collected result is stored automatically in the `_typeql_result` python variable" + ] + }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "381ab58e-fc12-43cb-a3dc-7a4aeba68da3", "metadata": {}, "outputs": [ @@ -376,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "dbc8419c-ca70-43d2-94d2-48553f3c3a20", "metadata": {}, "outputs": [ @@ -405,7 +452,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -417,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", "metadata": {}, "outputs": [ @@ -431,13 +478,14 @@ } ], "source": [ + "# Access the result through the _typeql_result variable\n", "print(_typeql_result)\n", "print(_typeql_result[1].get(\"instance\"))" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, "outputs": [ @@ -459,7 +507,7 @@ "'Stored result in variable: _typeql_result'" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -474,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, "outputs": [ @@ -489,6 +537,90 @@ "source": [ "%typedb transaction close" ] + }, + { + "cell_type": "markdown", + "id": "4e34041a-d60c-4278-bb31-c82b4cd8a200", + "metadata": {}, + "source": [ + "## Miscellaneous\n", + "One can list all available commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c4b2cf02-baa2-4e71-86c3-824030318ee9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available commands: connect, database, transaction, help\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'connect':\n", + "usage: connect [-h] [--tls-enabled]\n", + " {open,close,help} [{core,cluster}] [address] [username]\n", + " [password]\n", + "\n", + "Establishes the connection to TypeDB\n", + "\n", + "positional arguments:\n", + " {open,close,help}\n", + " {core,cluster}\n", + " address\n", + " username\n", + " password\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + " --tls-enabled Use for encrypted servers\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'database':\n", + "usage: database [-h] {create,recreate,list,delete,schema,help} [name]\n", + "\n", + "Database management\n", + "\n", + "positional arguments:\n", + " {create,recreate,list,delete,schema,help}\n", + " name\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'transaction':\n", + "usage: transaction [-h]\n", + " {open,close,commit,rollback,help} [database]\n", + " [{schema,write,read}]\n", + "\n", + "Opens or closes a transaction to a database on the active connection\n", + "\n", + "positional arguments:\n", + " {open,close,commit,rollback,help}\n", + " database Only for 'open'\n", + " {schema,write,read} Only for 'open'\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n", + "--------------------------------------------------------------------------------\n", + "Help for command 'help':\n", + "usage: help [-h]\n", + "\n", + "Shows this help description\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + "\n" + ] + } + ], + "source": [ + "%typedb help" + ] } ], "metadata": { diff --git a/src/graphs.ipynb b/src/graphs.ipynb index eda90e6..a899fc3 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -563,7 +563,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.11" } }, "nbformat": 4, From 514d13cc18ac99b0e942647f67d7bdff70157286 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Tue, 18 Mar 2025 23:35:49 +0100 Subject: [PATCH 21/27] WIP: Try a IGraphVisualisationBuilder interface --- src/graphs.ipynb | 17 ++-- src/typedb_jupyter/graph/answer.py | 129 ++++++++++++++++++++++------- 2 files changed, 107 insertions(+), 39 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index a899fc3..5f8b62b 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -365,25 +365,25 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "id": "ea10b692-7ff7-4d84-a683-4922c9a8d057", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e04608fc69464c0eb6f3ee15b60b7112", + "model_id": "2cc246e0a4b64b379edba50b2a07bac9", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" + "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
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Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
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", 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", 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\n", - " \n", + " \n", "
\n", " " ], diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index 7c7b254..a2c4281 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -20,27 +20,12 @@ # from abc import abstractmethod +from typing import List, Any -class AnswerGraph: - def __init__(self, edges): - self.edges = edges - - def plot(self): - from netgraph import InteractiveGraph - # TODO: derive edges, node_shape, node_labels, node_colors from from edge.lhs & edge.rhs - plottable = PlottableGraphBuilder() - for edge in self.edges: - plottable.add_edge(edge) - return InteractiveGraph( - plottable.edges, - edge_labels=plottable.edge_labels, - node_shape=plottable.node_shapes, - node_color=plottable.node_colours, - node_labels=plottable.node_labels, - arrows=True, - node_label_offset=0.075 - ) +############ +# Vertices # +############ class AnswerVertex: def __init__(self, vertex): self.vertex = vertex @@ -110,6 +95,9 @@ def __init__(self, attribute): def label(self): return "{}:{}".format(self.vertex.get_type().get_label(), self.vertex.get_value()) +######### +# Edges # +######### class AnswerEdge: def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex): self.lhs = lhs @@ -135,9 +123,67 @@ def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex, role): def label(self): return self.role.get_label().split(":")[1] +########## +# Graphs # +########## +class IGraphVisualisationBuilder: -class AnswerGraphBuilder: + @abstractmethod + def __init__(self): + raise NotImplementedError("abstract") + + def add_entity_vertex(self, vertex: EntityVertex): + raise NotImplementedError("abstract") + + def add_relation_vertex(self, vertex: RelationVertex): + raise NotImplementedError("abstract") + + def add_attribute_vertex(self, vertex: AttributeVertex): + raise NotImplementedError("abstract") + + def add_has_edge(self, edge: HasEdge): + raise NotImplementedError("abstract") + + def add_links_edge(self, edge: LinksEdge): + raise NotImplementedError("abstract") + def plot(self) -> "Any": + raise NotImplementedError("abstract") + +class AnswerGraph: + def __init__(self, edges: List[AnswerEdge]): + self.edges = edges + + def plot(self): + self.plot_with_visualiser(PlottableGraphBuilder()) + + def plot_with_visualiser(self, visualiser: IGraphVisualisationBuilder): + for edge in self.edges: + self._plot_vertex(visualiser, edge.lhs) + self._plot_vertex(visualiser, edge.rhs) + self._plot_edge(visualiser, edge) + return visualiser.plot() + + def _plot_vertex(self, visualiser: IGraphVisualisationBuilder, vertex: AnswerVertex): + if isinstance(vertex, EntityVertex): + visualiser.add_entity_vertex(vertex) + elif isinstance(vertex, RelationVertex): + visualiser.add_relation_vertex(vertex) + elif isinstance(vertex, AttributeVertex): + visualiser.add_attribute_vertex(vertex) + else: + raise ValueError(f"Unknown vertex type: {vertex}") + + def _plot_edge(self, visualiser: IGraphVisualisationBuilder, edge: AnswerEdge): + if isinstance(edge, HasEdge): + visualiser.add_has_edge(edge) + elif isinstance(edge, LinksEdge): + visualiser.add_links_edge(edge) + else: + raise ValueError(f"Unknown edge type: {edge}") + + +class AnswerGraphBuilder: def __init__(self, query_graph): self.query_graph = query_graph self.edges = [] @@ -160,7 +206,7 @@ def _add_answer_row(self, row): self.edges.append(edge) -class PlottableGraphBuilder: +class PlottableGraphBuilder(IGraphVisualisationBuilder): def __init__(self): self.edges = [] self.edge_labels = {} @@ -168,26 +214,47 @@ def __init__(self): self.node_colours = {} self.node_labels= {} - def add_edge(self, edge: AnswerEdge): + def _add_edge_defaults(self, edge: AnswerEdge): self.edges.append((edge.lhs, edge.rhs)) self.edge_labels[(edge.lhs, edge.rhs)] = edge.label() - self.node_shapes[edge.lhs] = edge.lhs.shape() - self.node_shapes[edge.rhs] = edge.rhs.shape() - self.node_colours[edge.lhs] = edge.lhs.colour() - self.node_colours[edge.rhs] = edge.rhs.colour() + def _add_vertex_defaults(self, vertex: AnswerVertex): + self.node_shapes[vertex] = vertex.shape() + self.node_colours[vertex] = vertex.colour() + self.node_labels[vertex] = vertex.label() + + + def add_entity_vertex(self, vertex: EntityVertex): + self._add_vertex_defaults(vertex) + + def add_relation_vertex(self, vertex: RelationVertex): + self._add_vertex_defaults(vertex) + + def add_attribute_vertex(self, vertex: AttributeVertex): + self._add_vertex_defaults(vertex) - self.node_labels[edge.lhs] = edge.lhs.label() - self.node_labels[edge.rhs] = edge.rhs.label() + def add_has_edge(self, edge: HasEdge): + self._add_edge_defaults(edge) + def add_links_edge(self, edge: LinksEdge): + self._add_edge_defaults(edge) + def plot(self): + from netgraph import InteractiveGraph + return InteractiveGraph( + self.edges, + edge_labels=self.edge_labels, + node_shape=self.node_shapes, + node_color=self.node_colours, + node_labels=self.node_labels, + arrows=True, + node_label_offset=0.075 + ) if __name__ == "__main__": import matplotlib.pyplot as plt - from netgraph import Graph, InteractiveGraph, EditableGraph + from netgraph import InteractiveGraph graph_data = [("a", "b"), ("b", "c")] - # Graph(graph_data) - # plt.show() node_shapes = { "a" : "o", "b" : "s", "c": "o"} plot_instance = InteractiveGraph(graph_data, node_shape=node_shapes) plt.show() From a9286eba88c8ca72a7c5857d592c2c0bc9f7c42c Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 19 Mar 2025 00:58:42 +0100 Subject: [PATCH 22/27] UX; Need to expose more info to user --- src/Sample.ipynb | 63 ++++--- src/graphs.ipynb | 264 ++++++++++++++++++++++++----- src/typedb_jupyter/graph/answer.py | 89 +++++----- src/typedb_jupyter/magic.py | 6 +- 4 files changed, 319 insertions(+), 103 deletions(-) diff --git a/src/Sample.ipynb b/src/Sample.ipynb index 5ac5feb..b94d0e2 100644 --- a/src/Sample.ipynb +++ b/src/Sample.ipynb @@ -148,7 +148,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: tests, typedb_jupyter_sample, typedb_jupyter_graphs\n" + "Databases: typedb_jupyter_graphs, typedb_jupyter_sample, tests, elgud\n" ] } ], @@ -184,7 +184,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: tests, typedb_jupyter_graphs\n" + "Databases: typedb_jupyter_graphs, tests, elgud\n" ] } ], @@ -220,7 +220,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Databases: tests, typedb_jupyter_sample, typedb_jupyter_graphs\n" + "Databases: typedb_jupyter_graphs, typedb_jupyter_sample, tests, elgud\n" ] } ], @@ -269,16 +269,6 @@ "text": [ "Query completed successfully! (No results to show)\n" ] - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -362,7 +352,7 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[| $p: Entity(person: 0x1e00000000000000000000) |]" ] }, "execution_count": 15, @@ -449,7 +439,8 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |,\n", + " | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]" ] }, "execution_count": 18, @@ -465,27 +456,49 @@ { "cell_type": "code", "execution_count": 19, - "id": "d987c302-a39c-4b79-9a0e-0259a35e09c6", + "id": "13ee267b-041c-4847-9622-49b8b5d7bd27", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |, | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]\n", - "Attribute(name: \"James\")\n" + "[| $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |, | $instance: Attribute(name: \"James\") | $instance-type: AttributeType(name) |]\n" ] } ], "source": [ - "# Access the result through the _typeql_result variable\n", - "print(_typeql_result)\n", - "print(_typeql_result[1].get(\"instance\"))" + "# As usual, the result can be accessed through the `_` variable.\n", + "print(_)" ] }, { "cell_type": "code", "execution_count": 20, + "id": "b835ea11-45fb-4b60-a171-b6245cf4d073", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query string was:\n", + "\t match $instance isa! $instance-type;\n", + "\n", + "First row of the result:\n", + "\t | $instance: Entity(person: 0x1e00000000000000000000) | $instance-type: EntityType(person) |\n" + ] + } + ], + "source": [ + "# Additionally, the result is stored in the `_typeql_result`, and the query string in `typeql_query_string`\n", + "print(\"Query string was:\\n\\t\", _typeql_query_string)\n", + "print(\"First row of the result:\\n\\t\", _typeql_result[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "id": "ef9f8c8c-6a88-4530-813d-d45844ef3293", "metadata": {}, "outputs": [ @@ -504,10 +517,10 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[{'attributes': {'name': 'James'}}]" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -522,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "9c48180e-84b5-4b0c-b2b6-3611640193d3", "metadata": {}, "outputs": [ @@ -549,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "c4b2cf02-baa2-4e71-86c3-824030318ee9", "metadata": {}, "outputs": [ diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 5f8b62b..bcb3d83 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -86,16 +86,6 @@ "text": [ "Query completed successfully! (No results to show)\n" ] - }, - { - "data": { - "text/plain": [ - "'Stored result in variable: _typeql_result'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -171,7 +161,7 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[| $f12: Relation(friendship: 0x1f00000000000000000000) | $f23: Relation(friendship: 0x1f00000000000000000001) | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) | $p3: Entity(person: 0x1e00000000000000000002) |]" ] }, "execution_count": 8, @@ -284,7 +274,10 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[| $n: Attribute(name: \"John\") | $p: Entity(person: 0x1e00000000000000000000) |,\n", + " | $n: Attribute(name: \"James\") | $p: Entity(person: 0x1e00000000000000000001) |,\n", + " | $n: Attribute(name: \"James\") | $p: Entity(person: 0x1e00000000000000000002) |,\n", + " | $n: Attribute(name: \"Jimmy\") | $p: Entity(person: 0x1e00000000000000000002) |]" ] }, "execution_count": 12, @@ -334,7 +327,7 @@ "from typedb_jupyter.utils.parser import TypeQLVisitor\n", "from typedb_jupyter.graph.query import QueryGraph\n", "\n", - "parsed = TypeQLVisitor.parse_and_visit(\"match $p isa person, has name $n;\")\n", + "parsed = TypeQLVisitor.parse_and_visit(_typeql_query_string)\n", "query_graph = QueryGraph(parsed)" ] }, @@ -348,42 +341,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "Entity(person: 0x1e00000000000000000000)--[has]-->Attribute(name: \"John\")\n", - "Entity(person: 0x1e00000000000000000001)--[has]-->Attribute(name: \"James\")\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"James\")\n", - "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"Jimmy\")\n" + "[]\n", + "[]\n", + "[]\n", + "[]\n" ] } ], "source": [ "# Combine the data & the parsed query structure into the data-graph\n", - "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", + "from typedb_jupyter.graph.answer import AnswerGraph\n", "\n", - "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)\n", + "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", "print(\"\\n\".join(map(str,answer_graph.edges))) # We now have a list of edges" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "id": "ea10b692-7ff7-4d84-a683-4922c9a8d057", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2cc246e0a4b64b379edba50b2a07bac9", + "model_id": "3109154c7bb843f2abd84e55b6a12690", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" + "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" ], "text/plain": [ "" @@ -456,7 +448,12 @@ { "data": { "text/plain": [ - "'Stored result in variable: _typeql_result'" + "[| $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"John\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"John\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000000) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"Jimmy\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |]" ] }, "execution_count": 18, @@ -494,7 +491,9 @@ "cell_type": "code", "execution_count": 20, "id": "c2d6529f-fee4-491b-be1b-6220193657d9", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stderr", @@ -507,18 +506,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f8e2f31b6c64954be18e5088a57e3f9", + "model_id": "b67ea2bae465402aa624f66920da7de6", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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"text/html": [ "\n", "
\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -533,19 +532,208 @@ "source": [ "%matplotlib widget\n", "from typedb_jupyter.graph.query import QueryGraph\n", - "from typedb_jupyter.graph.answer import AnswerGraphBuilder\n", + "from typedb_jupyter.graph.answer import AnswerGraph\n", "\n", - "# Our mini-parser doesn't support roles yet\n", - "parsed = TypeQLVisitor.parse_and_visit(\"\"\"match\n", - "$f isa friendship, links ($friend: $p1, $friend: $p2);\n", - "$p1 has name $n1;\n", - "$p2 has name $n2;\n", - "\"\"\")\n", + "parsed = TypeQLVisitor.parse_and_visit(_typeql_query_string)\n", "query_graph = QueryGraph(parsed)\n", - "answer_graph = AnswerGraphBuilder.build(query_graph, _typeql_result)\n", + "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", "plt.figure()\n", "plot_instance_2 = answer_graph.plot() # We use a different name to avoid clobbering the earlier visualisation" ] + }, + { + "cell_type": "markdown", + "id": "574ad2db-b5e9-46c1-8428-3ee9db97f164", + "metadata": {}, + "source": [ + "### Custom visualisation\n", + "The IGraphVisualisationBuilder provides an interface for easy building" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "de10958b-0a36-4000-85e2-f53e58ff7fff", + "metadata": {}, + "outputs": [], + "source": [ + "# You can also customise this greatly through the \n", + "from typedb_jupyter.graph.answer import IGraphVisualisationBuilder\n", + "from typedb_jupyter.graph.answer import EntityVertex, RelationVertex, AttributeVertex, HasEdge, LinksEdge\n", + "from typing import Any\n", + "\n", + "\n", + "class MyVisualisationBuilder(IGraphVisualisationBuilder):\n", + " \"\"\"\n", + " This class will colour edges belonging to the same query\n", + " \"\"\"\n", + " def __init__(self):\n", + " self.edges = []\n", + " self.edge_labels = dict()\n", + " self.edge_colours = dict()\n", + " self.current_colour = 0x000000000 # RGBA colour\n", + " self.node_labels = dict()\n", + "\n", + " def notify_start_next_answer(self, index: int):\n", + " self.current_colour = (self.current_colour + 0x3377bb00) % 0x100000000\n", + " \n", + " def add_entity_vertex(self, answer_index: int, vertex: EntityVertex):\n", + " pass\n", + "\n", + " def add_relation_vertex(self, answer_index: int, vertex: RelationVertex):\n", + " pass\n", + "\n", + " def add_attribute_vertex(self, answer_index: int, vertex: AttributeVertex):\n", + " pass\n", + "\n", + " def add_has_edge(self, answer_index: int, edge: HasEdge):\n", + " pair = (edge.lhs,edge.rhs)\n", + " self.edges.append(pair)\n", + " self.edge_labels[pair] = \"has\"\n", + " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", + "\n", + " def add_links_edge(self, answer_index: int, edge: LinksEdge):\n", + " pair = (edge.lhs,edge.rhs)\n", + " self.edges.append(pair)\n", + " self.edge_labels[pair] = edge.role\n", + " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", + "\n", + "\n", + " def plot(self) -> Any:\n", + " from netgraph import InteractiveGraph\n", + " return InteractiveGraph(\n", + " self.edges,\n", + " edge_color=self.edge_colours,\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "896949b2-866e-4974-8a37-a91f46566be6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Opened read transaction on database 'typedb_jupyter_graphs' \n" + ] + } + ], + "source": [ + "%typedb transaction open typedb_jupyter_graphs read\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b60dd08f-f717-418b-bac9-1e091a2a6c14", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Query returned 4 rows.\n" + ] + }, + { + "data": { + "text/html": [ + "
np
Attribute(name: \"John\")Entity(person: 0x1e00000000000000000000)
Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)
Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)
Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000002)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "[| $n: Attribute(name: \"John\") | $p: Entity(person: 0x1e00000000000000000000) |,\n", + " | $n: Attribute(name: \"James\") | $p: Entity(person: 0x1e00000000000000000001) |,\n", + " | $n: Attribute(name: \"James\") | $p: Entity(person: 0x1e00000000000000000002) |,\n", + " | $n: Attribute(name: \"Jimmy\") | $p: Entity(person: 0x1e00000000000000000002) |]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%typeql\n", + "match $p has name $n;\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "63adcc82-e76f-4fca-9e6d-8a0fc6f66059", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Transaction closed\n" + ] + } + ], + "source": [ + "%typedb transaction close" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "2684cd61-ae9b-4fea-be4b-394e18c81bc2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_utils.py:360: RuntimeWarning: invalid value encountered in divide\n", + " v = v / np.linalg.norm(v, axis=-1)[:, None] # unit vector\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "278a065a39a44bf3b97882a8fec764f9", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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notify_start_next_answer(self, index: int): + pass + + @abstractmethod + def add_entity_vertex(self, answer_index: int, vertex: EntityVertex): raise NotImplementedError("abstract") - def add_relation_vertex(self, vertex: RelationVertex): + @abstractmethod + def add_relation_vertex(self, answer_index: int, vertex: RelationVertex): raise NotImplementedError("abstract") - def add_attribute_vertex(self, vertex: AttributeVertex): + @abstractmethod + def add_attribute_vertex(self, answer_index: int, vertex: AttributeVertex): raise NotImplementedError("abstract") - def add_has_edge(self, edge: HasEdge): + @abstractmethod + def add_has_edge(self, answer_index: int, edge: HasEdge): raise NotImplementedError("abstract") - def add_links_edge(self, edge: LinksEdge): + @abstractmethod + def add_links_edge(self, answer_index: int, edge: LinksEdge): raise NotImplementedError("abstract") - def plot(self) -> "Any": + @abstractmethod + def plot(self) -> Any: raise NotImplementedError("abstract") + class AnswerGraph: - def __init__(self, edges: List[AnswerEdge]): + def __init__(self, edges: List[List[AnswerEdge]]): self.edges = edges + @classmethod + def build(cls, query_graph, answers): + builder = AnswerGraphBuilder(query_graph) + for row in answers: + builder._add_answer_row(row) + return AnswerGraph(builder.answer_edges) + def plot(self): - self.plot_with_visualiser(PlottableGraphBuilder()) + return self.plot_with_visualiser(PlottableGraphBuilder()) def plot_with_visualiser(self, visualiser: IGraphVisualisationBuilder): - for edge in self.edges: - self._plot_vertex(visualiser, edge.lhs) - self._plot_vertex(visualiser, edge.rhs) - self._plot_edge(visualiser, edge) + for (index, edge_list) in enumerate(self.edges): + visualiser.notify_start_next_answer(index) + for edge in edge_list: + self._plot_vertex(visualiser, index, edge.lhs) + self._plot_vertex(visualiser, index, edge.rhs) + self._plot_edge(visualiser, index, edge) return visualiser.plot() - def _plot_vertex(self, visualiser: IGraphVisualisationBuilder, vertex: AnswerVertex): + def _plot_vertex(self, visualiser: IGraphVisualisationBuilder, index: int, vertex: AnswerVertex): if isinstance(vertex, EntityVertex): - visualiser.add_entity_vertex(vertex) + visualiser.add_entity_vertex(index, vertex) elif isinstance(vertex, RelationVertex): - visualiser.add_relation_vertex(vertex) + visualiser.add_relation_vertex(index, vertex) elif isinstance(vertex, AttributeVertex): - visualiser.add_attribute_vertex(vertex) + visualiser.add_attribute_vertex(index, vertex) else: raise ValueError(f"Unknown vertex type: {vertex}") - def _plot_edge(self, visualiser: IGraphVisualisationBuilder, edge: AnswerEdge): + def _plot_edge(self, visualiser: IGraphVisualisationBuilder, index: int, edge: AnswerEdge): if isinstance(edge, HasEdge): - visualiser.add_has_edge(edge) + visualiser.add_has_edge(index, edge) elif isinstance(edge, LinksEdge): - visualiser.add_links_edge(edge) + visualiser.add_links_edge(index, edge) else: raise ValueError(f"Unknown edge type: {edge}") @@ -186,24 +205,18 @@ def _plot_edge(self, visualiser: IGraphVisualisationBuilder, edge: AnswerEdge): class AnswerGraphBuilder: def __init__(self, query_graph): self.query_graph = query_graph - self.edges = [] + self.answer_edges = [] - @classmethod - def build(cls, query_graph, answers): - relevant_edges = cls._filter_visualisable_edges(query_graph) - builder = AnswerGraphBuilder(query_graph) - for row in answers: - builder._add_answer_row(row) - return AnswerGraph(builder.edges) - - @classmethod - def _filter_visualisable_edges(cls, query_graph): - query_graph # TODO + # + # @classmethod + # def _filter_visualisable_edges(cls, query_graph): + # query_graph # TODO def _add_answer_row(self, row): + this_answer_edges = [] for query_edge in self.query_graph.edges: - edge = query_edge.get_answer_edge(row) - self.edges.append(edge) + this_answer_edges.append(query_edge.get_answer_edge(row)) + self.answer_edges.append(this_answer_edges) class PlottableGraphBuilder(IGraphVisualisationBuilder): @@ -224,19 +237,19 @@ def _add_vertex_defaults(self, vertex: AnswerVertex): self.node_labels[vertex] = vertex.label() - def add_entity_vertex(self, vertex: EntityVertex): + def add_entity_vertex(self, answer_index: int, vertex: EntityVertex): self._add_vertex_defaults(vertex) - def add_relation_vertex(self, vertex: RelationVertex): + def add_relation_vertex(self, answer_index: int, vertex: RelationVertex): self._add_vertex_defaults(vertex) - def add_attribute_vertex(self, vertex: AttributeVertex): + def add_attribute_vertex(self, answer_index: int, vertex: AttributeVertex): self._add_vertex_defaults(vertex) - def add_has_edge(self, edge: HasEdge): + def add_has_edge(self, answer_index: int, edge: HasEdge): self._add_edge_defaults(edge) - def add_links_edge(self, edge: LinksEdge): + def add_links_edge(self, answer_index: int, edge: LinksEdge): self._add_edge_defaults(edge) def plot(self): diff --git a/src/typedb_jupyter/magic.py b/src/typedb_jupyter/magic.py index 2e6405e..d76dbd0 100644 --- a/src/typedb_jupyter/magic.py +++ b/src/typedb_jupyter/magic.py @@ -84,6 +84,7 @@ class TypeQLMagic(Magics, Configurable): ) QUERY_RESULT_VARIABLE = "_typeql_result" + QUERY_STRING_VARIABLE = "_typeql_query_string" @needs_local_scope @cell_magic("typeql") @@ -99,7 +100,7 @@ def execute(self, line="", cell="", local_ns=None): user_ns = self.shell.user_ns.copy() user_ns.update(local_ns) - if query.strip() == "": + if not query.strip(): raise ArgumentError("No query string supplied.") connection = Connection.get() @@ -108,7 +109,8 @@ def execute(self, line="", cell="", local_ns=None): self._print_answers(answer_type, answer) self.shell.user_ns.update({self.QUERY_RESULT_VARIABLE: answer}) - return "Stored result in variable: {}".format(self.QUERY_RESULT_VARIABLE) + self.shell.user_ns.update({self.QUERY_STRING_VARIABLE: query}) + return answer def __init__(self, shell): Configurable.__init__(self, config=shell.config) From 725063378904caac3ad09671e866882d202ac7aa Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 19 Mar 2025 01:29:32 +0100 Subject: [PATCH 23/27] Example of Custom visualiser --- src/graphs.ipynb | 172 +++++++++++++++-------------- src/typedb_jupyter/graph/answer.py | 43 +++++--- 2 files changed, 117 insertions(+), 98 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index bcb3d83..3c36f3c 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -341,10 +341,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n", - "[]\n", - "[]\n", - "[]\n" + "[]\n", + "[]\n", + "[]\n", + "[]\n" ] } ], @@ -365,18 +365,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3109154c7bb843f2abd84e55b6a12690", + "model_id": "e1645508895a444f8c6f5dc172d5227f", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", + "image/png": 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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -547,69 +547,12 @@ "metadata": {}, "source": [ "### Custom visualisation\n", - "The IGraphVisualisationBuilder provides an interface for easy building" + "The IGraphVisualisationBuilder provides an interface for easy building. But first we get an easy query" ] }, { "cell_type": "code", "execution_count": 21, - "id": "de10958b-0a36-4000-85e2-f53e58ff7fff", - "metadata": {}, - "outputs": [], - "source": [ - "# You can also customise this greatly through the \n", - "from typedb_jupyter.graph.answer import IGraphVisualisationBuilder\n", - "from typedb_jupyter.graph.answer import EntityVertex, RelationVertex, AttributeVertex, HasEdge, LinksEdge\n", - "from typing import Any\n", - "\n", - "\n", - "class MyVisualisationBuilder(IGraphVisualisationBuilder):\n", - " \"\"\"\n", - " This class will colour edges belonging to the same query\n", - " \"\"\"\n", - " def __init__(self):\n", - " self.edges = []\n", - " self.edge_labels = dict()\n", - " self.edge_colours = dict()\n", - " self.current_colour = 0x000000000 # RGBA colour\n", - " self.node_labels = dict()\n", - "\n", - " def notify_start_next_answer(self, index: int):\n", - " self.current_colour = (self.current_colour + 0x3377bb00) % 0x100000000\n", - " \n", - " def add_entity_vertex(self, answer_index: int, vertex: EntityVertex):\n", - " pass\n", - "\n", - " def add_relation_vertex(self, answer_index: int, vertex: RelationVertex):\n", - " pass\n", - "\n", - " def add_attribute_vertex(self, answer_index: int, vertex: AttributeVertex):\n", - " pass\n", - "\n", - " def add_has_edge(self, answer_index: int, edge: HasEdge):\n", - " pair = (edge.lhs,edge.rhs)\n", - " self.edges.append(pair)\n", - " self.edge_labels[pair] = \"has\"\n", - " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", - "\n", - " def add_links_edge(self, answer_index: int, edge: LinksEdge):\n", - " pair = (edge.lhs,edge.rhs)\n", - " self.edges.append(pair)\n", - " self.edge_labels[pair] = edge.role\n", - " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", - "\n", - "\n", - " def plot(self) -> Any:\n", - " from netgraph import InteractiveGraph\n", - " return InteractiveGraph(\n", - " self.edges,\n", - " edge_color=self.edge_colours,\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, "id": "896949b2-866e-4974-8a37-a91f46566be6", "metadata": {}, "outputs": [ @@ -627,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "b60dd08f-f717-418b-bac9-1e091a2a6c14", "metadata": {}, "outputs": [ @@ -659,7 +602,7 @@ " | $n: Attribute(name: \"Jimmy\") | $p: Entity(person: 0x1e00000000000000000002) |]" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -671,7 +614,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "63adcc82-e76f-4fca-9e6d-8a0fc6f66059", "metadata": {}, "outputs": [ @@ -689,33 +632,87 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 30, + "id": "de10958b-0a36-4000-85e2-f53e58ff7fff", + "metadata": {}, + "outputs": [], + "source": [ + "# You can also customise this greatly through the \n", + "from typedb_jupyter.graph.answer import IGraphVisualisationBuilder\n", + "from typedb_jupyter.graph.answer import EntityVertex, RelationVertex, AttributeVertex, HasEdge, LinksEdge\n", + "from typing import Any\n", + "\n", + "\n", + "class MyVisualisationBuilder(IGraphVisualisationBuilder):\n", + " \"\"\"\n", + " This class will colour edges belonging to the same query\n", + " \"\"\"\n", + " def __init__(self):\n", + " self.edges = []\n", + " self.edge_labels = dict()\n", + " self.edge_colours = dict()\n", + " self.current_colour = 0x000000000 # RGBA colour\n", + " self.node_labels = dict()\n", + "\n", + " def notify_start_next_answer(self, index: int):\n", + " # Change the colour for every new answer\n", + " self.current_colour = (self.current_colour + 0x3377bb00) % 0x100000000\n", + " \n", + " def add_entity_vertex(self, answer_index: int, vertex: EntityVertex):\n", + " self.node_labels[vertex] = \"ENT[%s:%s]\"%(vertex.type(), vertex.iid()[-4:])\n", + "\n", + " def add_relation_vertex(self, answer_index: int, vertex: RelationVertex):\n", + " self.node_labels[vertex] = \"REL[%s:%s]\"%(vertex.type(), vertex.iid()[-4:])\n", + "\n", + " def add_attribute_vertex(self, answer_index: int, vertex: AttributeVertex):\n", + " self.node_labels[vertex] = \"ATT[%s:%s]\"%(vertex.type(), vertex.iid())\n", + "\n", + " def add_has_edge(self, answer_index: int, edge: HasEdge):\n", + " pair = (edge.lhs,edge.rhs)\n", + " self.edges.append(pair)\n", + " self.edge_labels[pair] = \"has\"\n", + " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", + "\n", + " def add_links_edge(self, answer_index: int, edge: LinksEdge):\n", + " pair = (edge.lhs,edge.rhs)\n", + " self.edges.append(pair)\n", + " self.edge_labels[pair] = edge.role()\n", + " self.edge_colours[pair] = \"#%0.8x\"%self.current_colour\n", + "\n", + "\n", + " def plot(self) -> Any:\n", + " # https://netgraph.readthedocs.io/en/latest/index.html\n", + " from netgraph import BaseGraph # We use InteractiveGraph to allow dragging\n", + " return BaseGraph(\n", + " self.edges,\n", + " node_labels=self.node_labels,\n", + " edge_color=self.edge_colours,\n", + " node_layout='bipartite', # Try others: https://netgraph.readthedocs.io/en/latest/graph_classes.html#netgraph.InteractiveGraph\n", + " node_label_offset=0.075\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, "id": "2684cd61-ae9b-4fea-be4b-394e18c81bc2", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_utils.py:360: RuntimeWarning: invalid value encountered in divide\n", - " v = v / np.linalg.norm(v, axis=-1)[:, None] # unit vector\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "278a065a39a44bf3b97882a8fec764f9", + "model_id": "619f6f3cf24242aaa3bb409f49ece6ed", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", "text/html": [ "\n", "
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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -728,12 +725,21 @@ } ], "source": [ + "%matplotlib widget\n", "plt.figure()\n", "parsed = TypeQLVisitor.parse_and_visit(_typeql_query_string)\n", "query_graph = QueryGraph(parsed)\n", "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", "plot_instance_3 = answer_graph.plot_with_visualiser(MyVisualisationBuilder()) # We use a different name to avoid clobbering the earlier visualisation" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec3be1dc-dd31-4ec5-8fb6-6eb064917de8", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/typedb_jupyter/graph/answer.py b/src/typedb_jupyter/graph/answer.py index 0de1041..a0e1929 100644 --- a/src/typedb_jupyter/graph/answer.py +++ b/src/typedb_jupyter/graph/answer.py @@ -22,14 +22,21 @@ from abc import abstractmethod from typing import List, Any - ############ # Vertices # ############ class AnswerVertex: + _SHAPE = None + _COLOUR = None def __init__(self, vertex): self.vertex = vertex + def iid(self): + return self.vertex.get_iid() + + def type(self): + return self.vertex.get_type().get_label() + def __str__(self): return str(self.vertex) @@ -41,16 +48,16 @@ def __eq__(self, other): @classmethod @abstractmethod - def shape(cls): + def _default_shape(cls): return cls._SHAPE @classmethod @abstractmethod - def colour(cls): + def _default_colour(cls): return cls._COLOUR @abstractmethod - def label(self): + def _default_label(self): raise NotImplementedError("abstract") @classmethod @@ -69,7 +76,7 @@ class RelationVertex(AnswerVertex): def __init__(self, relation): super().__init__(relation) - def label(self): + def _default_label(self): trimmed_iid = self.__class__.trim_iid(self.vertex.get_iid()) return "{}[{}]".format(self.vertex.get_type().get_label(), trimmed_iid) @@ -80,7 +87,7 @@ class EntityVertex(AnswerVertex): def __init__(self, entity): super().__init__(entity) - def label(self): + def _default_label(self): trimmed_iid = self.__class__.trim_iid(self.vertex.get_iid()) return "{}[{}]".format(self.vertex.get_type().get_label(), trimmed_iid) @@ -92,9 +99,12 @@ class AttributeVertex(AnswerVertex): def __init__(self, attribute): super().__init__(attribute) - def label(self): + def _default_label(self): return "{}:{}".format(self.vertex.get_type().get_label(), self.vertex.get_value()) + def iid(self): + return self.vertex.get_value() + ######### # Edges # ######### @@ -104,14 +114,14 @@ def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex): self.rhs = rhs @abstractmethod - def label(self): + def _default_label(self): raise NotImplementedError("abstract") def __str__(self): - return "{}--[{}]-->{}".format(self.lhs, self.label(), self.rhs) + return "{}--[{}]-->{}".format(self.lhs, self._default_label(), self.rhs) class HasEdge(AnswerEdge): - def label(self): + def _default_label(self): return "has" @@ -120,7 +130,10 @@ def __init__(self, lhs: AnswerVertex, rhs: AnswerVertex, role): super().__init__(lhs, rhs) self.role = role - def label(self): + def role(self): + self.role.get_label() + + def _default_label(self): return self.role.get_label().split(":")[1] ########## @@ -229,12 +242,12 @@ def __init__(self): def _add_edge_defaults(self, edge: AnswerEdge): self.edges.append((edge.lhs, edge.rhs)) - self.edge_labels[(edge.lhs, edge.rhs)] = edge.label() + self.edge_labels[(edge.lhs, edge.rhs)] = edge._default_label() def _add_vertex_defaults(self, vertex: AnswerVertex): - self.node_shapes[vertex] = vertex.shape() - self.node_colours[vertex] = vertex.colour() - self.node_labels[vertex] = vertex.label() + self.node_shapes[vertex] = vertex._SHAPE + self.node_colours[vertex] = vertex._COLOUR + self.node_labels[vertex] = vertex._default_label() def add_entity_vertex(self, answer_index: int, vertex: EntityVertex): From da707da57b326cce23da5ad4bd003e61d226a71c Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 19 Mar 2025 01:31:26 +0100 Subject: [PATCH 24/27] Change node layout for something really pretty --- src/graphs.ipynb | 59 ++++++++++++++++++++++++++++-------------------- 1 file changed, 34 insertions(+), 25 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index 3c36f3c..d4ccc92 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -341,10 +341,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n", - "[]\n", - "[]\n", - "[]\n" + "[]\n", + "[]\n", + "[]\n", + "[]\n" ] } ], @@ -365,18 +365,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1645508895a444f8c6f5dc172d5227f", + "model_id": "7a23e471902a46b290cced2efba63e22", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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" + "
ffriendn1n2p1p2
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"John\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000000)
Relation(friendship: 0x1f00000000000000000000)RoleType(friendship:friend)Attribute(name: \"John\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000000)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"James\")Attribute(name: \"Jimmy\")Entity(person: 0x1e00000000000000000001)Entity(person: 0x1e00000000000000000002)
" ], "text/plain": [ "" @@ -448,12 +448,12 @@ { "data": { "text/plain": [ - "[| $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"John\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"John\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000000) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"Jimmy\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", + "[| $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"John\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000000) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"John\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |]" + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"Jimmy\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |]" ] }, "execution_count": 18, @@ -491,9 +491,7 @@ "cell_type": "code", "execution_count": 20, "id": "c2d6529f-fee4-491b-be1b-6220193657d9", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -506,18 +504,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d0e0add651a4a6e8ae59a6ae6ec6efb", + "model_id": "51526b59eff44a789df7e58e51437ba9", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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\n", " " ], @@ -632,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 24, "id": "de10958b-0a36-4000-85e2-f53e58ff7fff", "metadata": {}, "outputs": [], @@ -688,31 +686,42 @@ " node_labels=self.node_labels,\n", " edge_color=self.edge_colours,\n", " node_layout='bipartite', # Try others: https://netgraph.readthedocs.io/en/latest/graph_classes.html#netgraph.InteractiveGraph\n", - " node_label_offset=0.075\n", + " node_label_offset=(0,-0.05)\n", " )\n" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 25, "id": "2684cd61-ae9b-4fea-be4b-394e18c81bc2", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_node_layout.py:1214: UserWarning: The graph consistst of multiple components, and hence the partitioning into two subsets/layers is ambiguous!\n", + "Use the `subsets` argument to explicitly specify the desired partitioning.\n", + " warnings.warn(msg)\n", + "/Users/krishnangovindraj/code/side/typedb-jupyter/src/.venv/lib/python3.11/site-packages/netgraph/_utils.py:360: RuntimeWarning: invalid value encountered in divide\n", + " v = v / np.linalg.norm(v, axis=-1)[:, None] # unit vector\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "619f6f3cf24242aaa3bb409f49ece6ed", + "model_id": "18c23b4b4d694f14b1d670e80157afc2", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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\n", " " ], From a70e0bad40c9fedd2a04124ea01b3b659db9a697 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 19 Mar 2025 01:32:13 +0100 Subject: [PATCH 25/27] Now it appears --- src/graphs.ipynb | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index d4ccc92..51f8eeb 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -341,10 +341,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n", - "[]\n", - "[]\n", - "[]\n" + "[]\n", + "[]\n", + "[]\n", + "[]\n" ] } ], @@ -365,18 +365,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a23e471902a46b290cced2efba63e22", + "model_id": "bf10c257ea82446c80a7adeb69ea50ef", "version_major": 2, "version_minor": 0 }, - "image/png": 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z8004nDNnjukNqPQxAABKQgAEnIz2/rvrrrtMP8Dw8HB57733ZNKkSdK+fXvp2LGjvP/++44eIgCggiMAAk5m+/btkpSUJCtXrpSPP/7YHAH34Ycfmud69+4tcXFxZvbPw8PD0UMFAFRQBEDAyVx11VWSkJBgikCU7vnT699//920g4mJiTHXAACcC1XAgBPSM3+15UvXrl3NqSADBw40S8P69fjjj8t9991HOxgAwDkRAAEndPToUXnooYfMjJ+eCayzgR06dDDHxQ0dOlT8/f0dPUQAQAVGAASckB75Fh0dLX379pWePXtKr169pFq1arbnmf0DAPwVAiDgpLKyssTX19d2nZubK1999ZXMnj3bNIW+5ZZbpHr16lJQUHDW2cEAAGsjAAJOqGiGb+vWrZKeni5t27aV3bt3y6BBg0wRyOHDhyUwMFAWL17MbCAA4CxMCwBOSANdfHy8jB49WjZu3Ggey8zMlE2bNpkzgT/99FNZs2aNOTVEX8vf8wAAxREAAScVFRUlBw8elKpVq5pr/blTp06SnZ1tZgGbNWsmX3zxhXlOl4EBAChCAAScWJMmTcwyr9Lgp18BAQGSk5Mj119/ve11NIUGABTnaXcFwKk88MADMmrUKBk/frxp/pyYmGhmBnXZt0+fPhIZGenoIQIAKiCKQAAnN23aNFP5q+HvxRdflLvvvtvRQwIAVHAEQMAF7Nu3zzSEDgkJcfRQAABOgAAIuBBavgAASoMiEMCFEP4AAKVBAAQAALAYAiDggtjZAQD4KwRAwEXk/9nsOS8/X7Kysh09HABABUYfQMBFZvz+2L1P9h08JIcOx0uj+nXl2vZtHD0sAEAFxQwg4CLFHzv37pPYg3FmBjD24CGWgQEA50QABFxErRrVbT+npWdIYnKKQ8cDAKi4CICAi6hdI9ruet+BQw4bCwCgYiMAAi4iOChQqgQH2651ORgAgJIQAAEXUivm9CxgQmKSWQoGAOBMBEDAhZeBtRgEAIAzEQABFxIRFip+vr62630sAwMASkAABFyIu7u71KxezXYdd+So5OTkOnRMAICKhwAIuJjaMafbwRQUFMjBw0ccOh4AQMVDAARcTPVqUeLh4WG7phoYAHAmAiDgYrw8PaVGtSjbdeyhODMTCABAEQIg4IJqFasGzs7Okfhjxx06HgBAxUIABFw0AOr5wEVoBwMAKI4ACLigSn5+EhEWYncsXGFhoUPHBACoOAiAgIuqVeN0NXDqyTRJTj3h0PEAACoOAiBggX2AimVgAEARAiDgokIqB0twYIDtOvYA7WAAAKcQAAEXpUUgxZeBjx5PlIzMTIeOCQBQMRAAARdWK+b0MrAWgew/dNih4wEAVAwEQMCFRYWHiY+Pt+2aU0EAAIoACLgwPRKuZnQ127WeC5ybl+fQMQEAHI8ACFioGjgvL1/ijhx16HgAAI5HAARcXEx0NXF3d7drCg0AsDYCIODivL29JDoqwna9/1Acp4IAgMURAAELKN4OJiMzy7SEAQBYFwEQsOKpIBWwKfTy5ctN70L96tevX7neOzY21nbvli1bluu9AcAZEQABCwgM8JewkCpOcSzczp07ZdasWXaPvfvuu1KrVi3x9fWVdu3aydq1a23PJSUlyWOPPSYNGzYUPz8/iYmJkZEjR0pqaqrtNTVq1JAjR47I2LFjL+vvAgAVFQEQsIjaMaeXgZNSUiX1xMlSvS8/P18KCgrkcomIiJDKlSvbrj/77DMZM2aMvPjii7JhwwZp0aKFdO/eXY4dO2aeP3z4sPl68803ZcuWLSY8Ll68WIYNG2bXDicqKkoCAk4fjQcAVkYABFxM586d5dFHHzVf3j4+4u3jK3WbtpK/vTJZ/u+/8+U/c+fJyy/9QyIiI8XT00sqh0VI2xt7SY+77jdfzdpfa94zf/58adKkifj4+MiBAwfMEm3btm3F39/fBLSOHTvK/v37bZ87bdo0qVu3rnh7e5vZuI8++shuXLr8+sEHH0j//v2lUqVKUr9+ffMZ5zN58mQZPny4DB061Ixn+vTp5v0ffviheb5p06by3//+V/r06WM+/4YbbpAJEybIggULJI+ehwBQIgIg4IJmz54tnp6e0q5rH6lat4ns37FZDu/aLkGeXnJo20bJTEmSlm06ydU39JLIqBqyfvliSU9IEMnMltTUk5KXlysTJ040gW3r1q0SEhJi9uVdd911smnTJlm9erU88MADJtSpefPmyahRo8wSq87CPfjggyaw/fDDD3bjGj9+vAwYMMDco2fPnjJo0CCzhHsuOTk5sn79eunSpYvtMW1po9c6hnPR5d+goCDzZwAAOBv/dgRckO55mzJlitw0aLg0bniF3Nmymcxf9bPMePY5qTP/Uzkwd4FUCwu3vb7LmBHSNjJEXhn+iDR7fKQcLCiQ9957zyy3Kg1pGqp69+5tZtlU48aNbe/X5dd7771XRowYYa51yfaXX34xj19//fW21+lr7rzzTvPzK6+8Im+//bbZz9ejR48Sf4/jx4+bJejIyEi7x/V6x44d53zPSy+9ZAIqAKBkzAACLqh9+/a22TnV4YrmsuvQAdm8d7fkF+RLg7tvlYAe19q+fvx9g+w5fLowxM3dXZo3b2671hlADW+6906XWqdOnWqKKops377dLAkXp9f6eHHF76lLyTpLV7SXrzycOHFCevXqZZaKx40bV273BQBXwwwgYCFpmRni4e4h62f8x3wvLsDPz/azh4enXYBUM2fONNW1WmChhRnPPfecLFmyxITN0vLy8rK71s/4qwKTsLAwU8Bx9Kj98XV6rUUdxZ08edLMJAYGBpol6TM/CwBwGjOAgAtas2aN3fUv2zZL/eox0qp+QzMDeCwlWepVr2H3FRUadt77tmrVSp599llZtWqVKb745JNPbMvBK1eutHutXutMXFloQcmVV14py5Ytsz2mgVGvO3ToYDfz161bN/N6LSzRdjEAgHNjBhBwQVq1q/vw0k+kyImj8bJiywaZNGK0NKhRUwZ16SGDXxknk0aMMoEwISVFlm1YK83r1JdeHa4p8X779u2TGTNmyM033yzVqlUzvfp27dolgwcPNs8/+eSTprhDA6IWaGgF7pdffilLly4t8++iv8eQIUOkTZs2pgr5rbfekvT0dFNkUjz8ZWRkyP/93/+Za/1S4eHhZgYRAGCPAAi4IA1mmZmZ8sv/tM2Kmzw1cJA80Ke/eW7mMy/Kyx/9W8a+N1Xijh+TsODK0r5JU+ndodM576dtV7ToQquLExMTpWrVqvLII4+Yal+lFcK6L1CLPrQauHbt2mbJWFvSlNXAgQMlISFBXnjhBYmPjzcneegydFFhiPYGLJrxrFev3lnBVRtIAwDsuRVyKjzgUjR0aUjSmTLt66etXRaPHlXq9/d4a6qIn48s/uQDuZy0z6BWDCcnJ9s1gi5PWhjy1VdfycaNGy/J/QHAWbAHEECFUr16dVurmPJcEtdTQLT1DHC+86hTUlIcPRTgkmMJGECFoGf86r5CVd5Htum+xaJZPz3ZBNagrYs0zOmsLwB7BEDABWcxnJGfn99Ze/jKi54IcqnuDQDOiCVgAMAF7THVfpBPPfWUaRCu/RiLN93Ws5ubNWtmGn3riTR6OkxaWprt+VmzZpk9nt988405M1oLjG677TZTxa1FRlq0U6VKFfMZegpMkezsbHniiSckOjra3FtnjC/0Lzt6D71vRESEaRV0zTXXyLp16856nR4/qFXnOrarr77aVL0X0d9V99jqWdc61uDgYLnjjjtMH0rAmTADCLi4+NTUU4UdF/D6KL+ISzomODcNatqeR6uv9UxmXWrVk1+6du1qzmrWI/60Enzv3r0mAGpY1KMFi2jY09fMmTPHBKdbbrlF+vfvb4LhokWLzPtuvfVWc0+tAlePPvqobNu2zbxHl/S12bc2/t68ebPUr1/fvEb372n1uY6nJDqO//73v2b8NWvWlNdff92cbrN7924TZov8/e9/l0mTJpk2Qg899JDcd999dn0u9+zZY5aVNcRq0ZK2QHrttddkwoQJl/BPHShfzAACLiwqPEyioiJMVe+ZXxmFBXIiL9d86c9Fj+vr9X3AueiRfi+++KIJXtpySGfLipp1jx492lRz6+zYDTfcIC+//LLMnTvX7v25ubkybdo00zfy2muvNTOAK1askH//+9+mebieOa33+OGHH2xFPBrsPv/8c+nUqZM5j1pnA3UGTx8vojOKOiNXEu0dqZ/5xhtvyE033WQ+51//+pfZeqCfW5wGueuuu8685plnnjGNz7OysuyaketMpjZD1/Hcc889ds3KAWfADCDgwmZNfe2cz/20ep1s2Xmq6MLb20uG3XnbWce/ASUpfqaz0r6QRWc6a/PvV1991fSN1IbceXl5JjzprJ8uqSr9riGuiPZ01MBYvPhHHyu6p87y6XJwgwYNzlrSDQ0NtV3rZ56Lztpp8Cx+ZrUeF6jNxf/qzGr93ZSOJSYmxvysY9UjB0v6/QFnQQAELCqkyumZkpycXEnPyJQA/1P/gQYu5kzn2NhYM3v38MMPm1k0XVbVmb1hw4ZJTk6OLQCW9P6/Oida9xDqiS66N+/Mk13Ku2L8zPEV/aWo+JnVF3qmNVAREQABiwo5o9lyUkoKARBlogFNg5Dun9O9gOrM5d+LoUvFOgOos2y65HoxdMZRz4rWvXy6/0/pjKAWgeiyNWA17AEELKpK5SC766TkVIeNBa5BW+1oqHrnnXdMIYdWyk6fPr3M99Wl30GDBpn9hnrGtB7xt3btWrPUvHDhQtvrGjVqZIpDSqKVwzozqedW61GCWlAyfPhwszStM5SA1RAAAYvy8/UV/0p+tutETj9AGbVo0cK0gZk4caIpkPj4449NSCsPWuyhAXDs2LGm2EPPn9bZu6J9eUrbtaSmnv6LjM5Gag/IIlqpq9XFWrTRunVrU/373XffmbYzgNVwFjBgYQv+970cPBxvfg4PDZHb+/Rw9JCAcqNtYnRW8p///KejhwJUOMwAAhZWfB9gcmoqG9nhErQ3n/bo00bRXbp0cfRwgAqJIhDAwopXAufl5cvJtHQJDjrd3gJwRtq4WZeHdbm4b9++jh4OUCERAAELO7MSWPcBEgDh7M5VCALgNJaAAQsLoRIYACyJAAhYmDa0DSrWSDcphQAIAFZAAAQsrvg+wKRkWsEAgBUQAAGLs68EPmFOXAAAuDYCIGBxxWcAtS1oyomTDh0PAODSIwACFhdS+XQAVOwDBADXRwAELK5ycJC4ubnZrtkHCACujwAIWJynh4dUDjrdDoYZQDgD3a6Qm5trvgO4cDSCBmD2AepRcIoZQFRUcfFH5fMFi2Xh0uVyPCnZnF7j5ekpYaFVpHeXzuYs66qREY4eJuAU3Ar56xNgees2bjZfSpeD77/rNtMjEKgItuz4Q6b/Z4789MuvZsYv3ytXsvxOSoFHnrjne4pvRqB45HmZ/9+9rkNbeWjwHXJFw3qOHjZQoTEDCEBCq5xuBaP/gdV2MBFhoQ4dE6D+9+NKeWbCm5KbmyfpQUmSFL1fToQfkUL3Attr3PLdJTihqoQcriXLV62R1b9ukInPPyk3XtPBoWMHKjL2AAKgEhgV0rKfV8sT4ydKdmGWxDZfI/tar5LUyDi78KcKPQokJSpO9rZeKbHN1kpWfpY8/sKr8uPqtQ4bO1DREQABSFBggHh4eNiu2QcIR9u5Z5889fIbku+RJ3tbrJK0kIRSvS8t9Jh5fYF7nowdP1F279t/yccKOCMCIABxd3eXKsGnK4ETmQGEg83+bJ7k5OTKwUYbJCvwxAW9NzMoVQ423CjZ2Tnyn8+/vmRjBJwZARDAWfsAk5IJgHAcrUj/9oefJcv/pJwMPXpR99B9gtmV0mXhsuWSejKt3McIODsCIICz9gGmZ2RIVna2Q8cD65q3aKnk5eVJUrVYkdM9yi+Mm0hS1Vgzi/j14qXlPELA+REAARghxWYAVTLLwHCQJT+tNIUeKZFxZbpPctQhKXQrMJXEAOwRAAEYVAKjotAtCLneWVLgmVem+xR45Uqed46kpF7YHkLACgiAAIwA/0riXaz5cyL7AOEgmVlZUuCeXy730mrgjMyscrkX4EoIgAAMPUWh+CxgUgqtYOAYAf7+4pFfPucUeOR7SWCAf7ncC3AlBEAAJe4D1GU4ToqEI1SNDBevHF/xyvIt0328MiuJZ463RIWHldvYAFdBAARgU3wGUKuAdSkOuNz69bhRpNBNqhyJKdN9Qg7r+92k301dym1sgKsgAAKwCaliXwjCPkA4QrfO10hwUICEHKkpbgUX1wdGzwcOia8pVSoHS5dOnAkMnIkACMAmpLJ9Kxj2AcIRfLy95Zae3cUzx0dCD9a5qHuEHawrHrleclvv7uJVrLgJwCnls8sWgEuo5OcrYSFVpJKfn5kNjAwLdfSQYFGDb+8r337/o8i+xpLnky0pUYdK/d4qh2tIZGxDqRYVIXffevMlHSfgrNwK2eUNoAQFBQW2c4IBR9gTe0AGj3xKTpxMl2M1/5DjNfb+ZW9A91xPCT9YT8IP1JPgoED56J3XpXZM9cs6ZsBZEAABABXW3gMH5eGnX5TD8QlS4JEvKRGHJKnafskKOHHqmLhCEd+0IAk5XFOqHKsubvkeUr1alEyfOF5qVq/m6OEDFRYBEABQoaWeOCn/Xfg/+Wz+Ijkcf8z2eKFHvgl8RapXjZSBfXtK/57dJDgwwEGjBZwDARAA4BTy8/Nl5brfZOHS5ZKYlCxpGZnmBBvdt9q7a2e5uk0rtiwApUQABFDqPYHp6ely8OBB2bVrl6xbt07++OMPmTt3rqOHBgs6mZYuC5b8IDd3u8GEQAAXhipgAGdJS0uTo0ePmqC3ceNGWbt2rcTGxsqxY8ekTp06pq1GSEiIBAcHS3Z2tvj4+Dh6yLDYTOCSH1dKSuoJWfLTKunb/QZm/oALRAAEcJa+ffvK8ePHpUqVKlK5cmUJDw8XX19fycjIkFatWskdd9whjRo1koCAAHqs4bJb89smiU84bn4+cvSYrNu4Wdq1buHoYQFOhQAI4CwjR46UvLw8qV27tkRFRZkg6OfnJ1u2bJEJEyZIYmKieYwdJLjcYg/GycYt2+0e27B5m1SLjJAa0VUdNi7A2bAHEMAFue+++yQnJ0f+7//+z4RET0/+HonLt+9v7oJvJTs756zn/Hx9ZcDNPcS/EvsBgdJg0wSAUlu9erWZBaxRo4a59vA43YIDuBz7/koKfyozK8vsByxqYA7gr/FXdwAl/sd21apVtopf/R4fHy87d+6U0NBQuffee83r3Ny0Ey9weZZ+3T3czfFuOTm5cjwp2factoHx9j61FzX2UJzUiTn1FxQA58YSMICz6NJuu3btTHWvFnro/j/dC9i8eXO5++67TfUv4Chx8Ufl68XLbNe39OwqURHhDh0T4GyYAQRwFt3X99RTT0lQUJDk5uZK48aNpV69embG78iRI7Jp0yZp2bKlBAYGOnqoAICLwB5AACUaOHCgWf4dO3astG7dWp555hnT8+/AgQMydepUWbJkiXkde64AwPkQAAGUaPHixTJ58mS555575N1335WffvrJfNel4apVq8oXX3xhXkcABADnQwAEUCLt9aenK7zwwgsyePBg6dOnj3zzzTfmuTZt2sj27ad6sXECAwA4H/7NDaBEuuyrdMlXdejQwRb6Vq5cKdWrVzc/EwABwPlQBAKgRFr48eCDD8rjjz9ujn7Tnn96PNyoUaPMcvDbb7/t6CECAC4SARDAOSUlJcm8efPMl9JzgbUZ9OjRo+WGG25w9PAAABeJAAjgnLT/n1b/durUSRo2bCh16tSxe17biNIMGgCcDwEQwDmNGzfurMf0VJDly5dLrVq1TDAEADgfAiCAc0pLS5Ovv/7anAhy2223mT2A2iB6w4YNZjlYq4MfeeQRZgIBwMlQvgfgnDIzM2XKlCkm4BVVBH/++efywQcfyIABA+S1114zjxP+AMC5EAABnFN4eLjExsZKgwYNzPXWrVulc+fOcs0115gTQnSGUJ8HADgXAiCAv6Tn/e7Zs8f8vHnzZqlbt66cOHFCUlNTJTIy0tYbsGiWEABQ8bEHEMBf6tevnzkS7tChQzJ//nyzFzA4OFj27t0rQ4cOlejoaPM6loEBwHkwAwjgL+lS7/XXX29CoLaB0bOBVVRUlAwfPlyaNm3q6CECAC6QWyHrNgDOIzs7W44cOSI1atQwJ4IAjhQXf1S+XrzMdn1Lz64SFRHu0DEBzoYlYADnpW1gtO8fAMA1sAQMAABgMQRAAAAAiyEAAgAAWAwBEMB5aa1Yfn4+vf4AwEVQBALgvAoKCmTrH7slKTlVklJSpH6tmtKsSUNHDwsAcJEIgADOS1u/bNi0VTIys8x1cFCgo4cEACgDloABlEpI5cq2n3UmEADgvAiAAEolpEqw7efk1FT2AwKAEyMAAiiVkMqnA2BeXr6cOJnm0PEAAC4eARBAqYRUOb0ErBJTUhw2FgBA2RAAAZRKSPDpGUDFPkAAcF4EQACl4u3tJYEB/rbrpBQCIAA4KwIggIvaB5iUzBIwADgrAiCAi9oHmJx6wpwOAgBwPgRAABc1A6htYFJOnHToeAAAF4cACKDUQs+oBGYfIAA4JwIggFKrHBwkbm5utmv2AQKAcyIAAig1Tw8Pu3OAmQEEAOdEAARw8ZXABEAAcEoEQAAXvQ9Qj4PLzctz6HgAABeOAAigbJXAqSccOh4AwIUjAAK4ICFVOBIOAJwdARDABQkODBR399P/6khMoRIYAJwNARDABdHwZ38kHDOAAOBsCIAAylgJzAwgADgbAiCAMu0DTEvPkOycHIeOBwBwYQiAAC5YSGX7I+GS6QcIAE7F09EDAOD8lcB7DxyS5NQTZj+gn5+PtG52hcPGBgA4PwIggFLJz88/FfJSUiXxjDOAN27Zbvv5ll7dHDA6AMCFIAACKBU98eOn1eskPuH4OV9Ts3o1iQoPu6zjAgBcOPYAAigVXx8f6dPteomOijzna9q2an5ZxwQAuDgEQACl5uXlJb26dpZaNaLPeq5OTA0JDw1xyLgAABeGAAjggnh6eEj3ztdI/do1bY+5ublJm5ZNHTouAEDpEQABXDAPDw+5sVMHadKgnrmuWytGwkKqOHpYAIBSoggEwEUfCXddh6ukUAolIjREtv2x2+wTrBwcZHdSCACg4iEAArgoW3b8IXO+XiTffv+T5OTk2j3X8orGMrBvT+l2XUfx9vZy2BgBACUjAAK4IL9t3iYT3/2XbN2521xnV0qT9NAkKfDIFbcCD/HOrCS/bS2UjVu3m9fdO7C/3HfHrWafIACgYnArLCwsdPQgADiH75avkGdfmSS5ebmSGhovSdGxkl45UeSMbOeV6SchR2Ik5EhN8cj1ll5dOstLT400VcRAWel/tgoKCuy2I/AXDODCEAABlMrPa36VR//2kuS558j+K9ZJepXE877HI9dLYra0Ef/UULm52w3y8jOj+Q81AFQAVAEDOK/4YwkyZtxrku+WJ/ua/1Kq8KfyvXIltvkayQhKlvn/+14+mffNJR8rAOD8CIAAzmvugsWSlZUth+tukcwg+3OAz6fQo0D2N10nhR758p/PvzJnCgMAHIsACOAv5ebmyhfffGdm81IiD13UPfK9cyQp8qAcjj8mK9duKPcxAgAuDAEQwF9a+vNqSU5JlaSo/WY272IlVdtvvmvrGACAYxEAAfylFWvXm+8pURc3+1ckO+CkZAakyMp1G0wVZ1nde++90q9fvzLfBwCsiAAI4C+dOJlmvuf6ZJb5Xrk+WaZ9R3pG2e8FALh4BEAANp07d5aRI0fKU089JSEhIRIVFSWrly81zxW6F4isEpH3RGSCiEwWES3qzS52g99E5FUR2Ski74jIyyLymYjkiMhGkbhvN8m2Fd/JmDGP2xWDZGdnyxNPPCHR0dHi7+8v7dq1k+XLl1/Q2BcvXizXXHONVK5cWUJDQ6V3796yZ88e2/OxsbGmBc3cuXOlU6dO4ufnJ1dddZX88ccfsm7dOmnTpo0EBATITTfdJAkJCXb3/uCDD6Rx48bi6+srjRo1kvfe0z+EU3JycuTRRx+VqlWrmudr1qwpr76qfwi4FPR/5xUrVtiu3333XWnZsqXcddddkpyc7NCxAc6EAAjAzuzZs00IW7Nmjbz++uuyfvXPkpaUIO55XqcaPt8kIiNERFdf94nIkjNuoKfCrRGR20Tkbk1ef4bAXSKRHRpK9UYtZfasWfLFF1/Y3qIBavXq1TJnzhzZtGmT3H777dKjRw/ZtWuX7TUa3mbNmnXOcaenp8uYMWPk119/lWXLlpnmwP3797drGKxefPFFee6552TDhg3i6elpgoMG3qlTp8rPP/8su3fvlhdeeMH2+o8//thcT5gwQbZv3y6vvPKKPP/88+bPSb399tsyf/58Eyx37txpXl+rVq1y+F8CJXnyySflxIkT5ufNmzfL2LFjpWfPnrJv3z7zvz+A0qERNAC7GUCdmdMgVKRWnbqSnucmeb2yJCUqzv4NW/+cBXy62Azg1yIyUkRC/nxsgYhsEnF73EMar+8mNcKriiTHmZA0ffp0OXDggNSpU8d8r1atmu3WXbp0kbZt25rApXTmTWfWNNQV7QFMSUmRr776qsTf5fjx4xIeHm5CQtOmTc0MYO3atc1s3rBhw8xrNHDeeeedJjDecMMN5rHXXnvNBM0dO3aY63r16slLL71kXlfk5ZdflkWLFsmqVavMjOnWrVtl6dKlNLm+DHSWdsuWLeb/f8aNG2d+1r9MaKDXIBgfH+/oIQJOgbOAAdhp3ry53XXDBvVl3e/bJORwLUlJjxPR1bfjfy796uRa3p9LvN5/vsGrWPhTASJSWaRKcnVxz/eQ22++SX5avECOHTtmntaApqGzQYMGdp+ry8K6lFukKJCdi84W6kydzlxq+Cua+dNgqQGwpN8vMjLSfG/WrJndY0Vj01lFXUbWwDh8+HDba/Ly8iQ4ONgWRLt27SoNGzY0s5a69NytW7fz/jnj4nh7e0tGRob5WUP34MGDzc+6ZaFoZhDA+REAAdg587xeP19fiYwIk4KjPiILReQqEdHJMj9NVyIyXxv9nWdjibtISFwts+Ta/6au8vN339gCWlpamnh4eMj69evN9zNne0qrT58+Zv/dv/71LzOTqPfX4Kd79M71+xXN2J35WPGxKb2n7kssrmisrVu3NsuP3377rQkkAwYMMLOXxZe4UX50n6cu9Xbs2FHWrl0rn32m+wvE7OWsXr26o4cHOA0CIIDzql0jWrbEHhEpcBPpov/mKDy9BFwKHrne4psRKD26XiMhlU/NnBVp1aqVmQHUWTctzrgYiYmJZv+dBrWiexQvFLhYOhuoYXLv3r0yaNCgc74uKChIBg4caL5uu+02MxOYlJRkZqVQvv75z3/KiBEjTMCeNm2aKRxSGsD1zx1A6RAAAZxXRFiodKvTUP69db1Unl9DUq49JBJXKPLr+d/rkxYkbtki4WEhMmr4kLOe16VfDVe6lDdp0iQTCLUKV/fl6XJtr169StwDWFyVKlXMcvGMGTNMNa4u+z7zzDPl8ruPHz/e7PPTJV8NGLo0rYUmWnGqM1GTJ082n6nj1sKTzz//3FRPazUyyl9MTIx8883ZZ0pPmTLFIeMBnBUBEECpTJv0qmzbulnWrfhR3La4iXsNT8m/MVek5BoM8cryk7CDdSTveI5keCTItNfGSVR4WImvnTlzpims0IrOuLg4CQsLk/bt25v9dEV0hi81NdV2rcu0uqSsNHhpQYcGNV321f14Wp2rRS1ldf/990ulSpXkjTfeMBWoWiGtewZHjx5tng8MDDTV0roHUZeFtbWMFojomHBpZWVlnbXEr7OxAM6PKmAApZablycvTX5P5n17qvdLdqU0SawWKxnBSZLvmSfuBe7inekvVY7UkMDESHETN6kRHSXTJ46XmOjTFb7lQWfjtEJXlwRhHVqY8/TTT5u2O7r0f6bi/SUBnBszgABKzcPdXe69o7+0a91cVq/fKIuW/Sg+u09X2BbX4opGckffXtLtuo7i7W1fWFIWuvS6cuVK0yj6oYceKrf7wjloz8YffvjB7P+75557TCNonTV+//33TQsfAKXDDCCA88rOyZHtf+yRzTv+kJNp6VK3Vox073yNpKSekIXLfpTD8cckLT1dfHx8pHJQoFzfsZ00rl/3koxF9wDqyR1Dhgwxy8b03rPeHsD//Oc/Znlfl3u1/5/OBH/00Ufy6aefmuV3AOdHAARwTsmpqbJ52x+yc88+s/xbvCjktt7dHTo2WJO2Btq2bZsJgtr25csvvzQNw7UVj+7NLGrdA+CvsQQM4Cxp6Rnyw6o1cjDuSInP6ywg4Ah6aoyGPQ2AWhmuewE1AC5YsIDKa+ACUKYG4CwB/pWkdbMmEhZSpcTnM7OyJDdXD/0FLq+hQ4fK77//bn7WVj+6B9DX11cef/xxU6UNoHRYAgZwTtpq5bvlK2TfgUNnPTewb08JrcKMCxxr//795hQZ3Qd45jGGAM6NJWAA55SRmSUHD597GZgACEfQJuH6pafHFB3bV+TDDz902LgAZ0IABHBOazb8Lnl5p/uqtW3VXI4nJsveAwfZBwiH0JNZ/vGPf0ibNm3MCSxUgQMXhwAIoERHExJN9W+R8NAQubL5FeY/uHHxRyUtLcOh44M1TZ8+XWbNmmV6AAK4eARAAGfRrcEr1663e+yatq1tsy3RUZEOGhmsTo9+u/rqqx09DMDpUQUM4Cy7Yw9IfMJx23W92jWlamSEQ8cEFJ3N/Mknnzh6GIDTYwYQgB1t+Lz6199s1x4eHtL+yhYOHROsbcyYMbaftehjxowZsnTpUlP16+Vlf8zg5MmTHTBCwPkQAAHY2bhlu2kEXaTlFY0kKCDAoWOCtf322+m/kKiWLVua71u2bLF7nIIQoPQIgABsNPj9tmWb7dq/kp9pCA040g8//ODoIQAuhz2AAM7Z9qVd6xZnLbEBAJwfARDAOdu+NKxb26FjAgBcGgRAAOdt+wIAcC3sAQRA2xc4lYSkJFm5doPt+tp2bSSEYwmBC0IABCyOti9wNjk5uXI4/tjp69xch44HcEYsAQMWR9sXALAeAiBgYbR9AQBrIgACFubp6SE1o6vZrmn7AgDWwB5AwMK8vb2l+/WdJP5YgmzesYu2LwBgEQRAwMLc/2zzEhEWKl06hdH2BQAsggAIQNzd2Q0CAFbCv/UBAAAshgAIuPgJH0XS09MdOhYAQMXBEjDgwgoKCkxj5w8//NAEwF69ekmdOnXsnmf5FwCsh3/zAy4e/vbv3y+PP/64BAQESETEqePdTp48KZmZmYQ/ALAo/u0PuKiicDdq1Cjp16+fDB061LR9Wb58ufTv31+6dOkiH3300VlLxQAA10cABFxYfHy87Nu3T3r37m2up02bJuPGjZPAwECpWbOmfPrppyb80f4FAKyFAAi4sKioKGnevLnMnTtXHnvsMZkxY4bcdtttMm/ePHn44YflyJEjsnPnTkcPEwBwmREAARdVtKzbt29fsw9w69at8vzzz5vgp9auXSu5ubnSqFEjB48UAHC5UQUMuKiiZV2d8dMl4LS0NAkLC5OcnBz55Zdf5M033zRLwgAA6yEAAi6kqK3L77//Lhs2bJDffvtNunfvbtq/+Pr6mtf89NNP8vrrr5uZQS0OAQBYDwEQcBH5+fmm7cvBgwdl8ODB5rpVq1bSp08fueGGG+SVV16Rtm3bmurf6OhoqVatmqOHDABwEPYAAi7W9uWhhx6SK664QrZs2SL333+/+Pv7mybQ7du3N4UgcXFx0rhxYwkODnb0kAEADkIABFxoz5+Gvt27d8v48ePNY08//bQ8+OCDsnDhQjPz9+6770rLli0lLy/P0cMFADgQS8CAC0lOTpYbb7zRtH/59ttvJSkpSUaPHi0hISGmGESXgHV52NOTf/QBwMr4rwDgQjp16mT292mj58TERKlataptr19KSops27ZNGjRo4OhhAgAcjAAIOLHip3js2LHDLO36+fmZa90H+PPPP8uYMWPMnj9dFv74448dPGIAQEVAAAScmAY+Ly8vee2112T27Nmyd+9eufbaa+Wqq66Su+++2zz2xhtvyHfffWcKQGj7AgBQBEDACa1fv16qV68ukZGRcuzYMXnhhRdk1qxZEhERIYsWLZIff/zR9AHUJeHPPvtM6tWrZ6sSBgDArbDovCgATiEhIUG6desmV155pQwYMEBSU1Nl2bJlMn36dNtrvv/+ezP7t2/fPhP83nrrLVP9C7iCuPij8vXiZbbrW3p2laiIcIeOCXA2TAkATiY8PFwmTpwoe/bsMUu/Gv70Zz3Xt4g2fp45c6YMGzbMVAQ3adLEoWMGAFQsBEDACekM4P/+9z+54447ZOXKlabY429/+5vs2rXL9hqd+RsyZIiZCfT29nboeAEAFQsBEHBSWvzxwAMPmAA4duxYU+jx3HPPmcCnLWCK+Pj4OHScAICKhwAIOLmgoCCZMGGCzJs3TwoKCsxpH3//+9/lq6++cvTQAAAVFAEQcBF169aVuXPnyptvvikrVqyQ1atXO3pIAIAKijYwgAvRptDaB/C3336TzMxMRw8HAFBBEQABJ5Wbq02gPaWgsFDc/zwNpPj+QP0CAKAkLAEDTkjbdy743/ey7OfVkpWVba4BACgtZgABJ7Q79oDEJxw3X3v2H5QbO3WQujVrOHpYAAAnwQwg4GRy8/Jk9a+/2a519i88tIpDxwQAcC4EQMDJbNyyXdLSM2zXLa9oJEEBAQ4dEwDAuRAAASeiwe+3Ldts1/6V/KR1M455AwBcGAIg4ETWbPhd8vLybdftWreg2hcAcMEIgICTOJqQKDv37LNdh4eGSMO6tR06JgCAcyIAAk5ACz1Wrl1v99g1bVubxs8AAFwoAiDgRG1fitSrXVOqRkY4dEwAAOd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Relation(friendship: 0x1f00000000000000000001)RoleType(friendship:friend)Attribute(name: \"Jimmy\")Attribute(name: \"James\")Entity(person: 0x1e00000000000000000002)Entity(person: 0x1e00000000000000000001)
" ], "text/plain": [ "" @@ -448,12 +448,12 @@ { "data": { "text/plain": [ - "[| $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"John\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000000) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"John\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + "[| $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"John\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000000) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000000) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"John\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000000) |,\n", " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", - " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"Jimmy\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |]" + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"Jimmy\") | $p1: Entity(person: 0x1e00000000000000000001) | $p2: Entity(person: 0x1e00000000000000000002) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"James\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |,\n", + " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |]" ] }, "execution_count": 18, @@ -504,18 +504,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "51526b59eff44a789df7e58e51437ba9", + "model_id": "9f173e554a11462db4b77da9a018a16a", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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", 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\n", " " ], @@ -394,6 +394,44 @@ "plot_instance_1 = answer_graph.plot() # Limitations of netgraph require that you hold on to the returned value" ] }, + { + "cell_type": "code", + "execution_count": 17, + "id": "570974ff-86e3-4171-bc86-1522a6892aed", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "346c7546168e4a62a614fd1eb06a4399", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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FOlzftitFDh7KPeNI4N69e2XAgAHy+eefN0BPAQANjQAI1LHEuFiHkzrqsyj02fj5+splQwdK74u62Keme3TpJFERzR2mqqsrKSmR22+/XS666CJp1er4CScAAPdBAATqmI+PjyTGt3TYhKFr8JxFg173zhfImEuHSJuEeOnZ9cLThj/14IMPire3t9x2223Sq1evBtvJDABoOARAoJ6ngXWqdUdKmjhbXEyUOUf4TD799FMz7ZuYmGg/Uk43lhACAcC9EACBeqrLV70w8/ZdKeIKdOTvdKN/GzZskCeeeEJCQ0PNhpG33npLOnXqJDk5OSYEAgDcBwEQqAeenp7SNun4+rmDObnmiDZXdfjwYbnjjjukT58+MmvWLHnjjTdk+vTpEhYWJq+88ooJhLZNI5SRAYDGjwAI1JO2iSccDefEzSBnotO7kyZNkvDwcBMCdfpXBQYGypEjR2Tfvn0m0NpGDvUzU8IA0LgRAIF6EtE8zBwPZ7MjJdUlR8902jc7O1v+8Ic/mNIvNjoCqP0dNmyYaX/11Vfy6quvmtFA1gUCQONGAATqiY6UJScdHwU8ml8gmVnZ4koKCgpk69atMmjQIBk/frz9+tq1a+Xdd9+V9u3bm2nh4uJiMxI4e/Zsadu2rWzatIl1gQDQiHlUueKQBOAm8o4clf98+Z29fUG7NjKoby9xJfpXQH5+vjRpcmy08tChQzJ58mQzKvjUU0+ZUcHS0lLx9fW1l4nRIKi7hTt06ODk3gMAzgcjgEA90ingyBbh9vbO1DSXmzrVkUpb+FMa+jIzM2X06NEmDOoo4KhRo+Suu+4yjz///POmWHRqqmuuaQQAnB0BEKhnyUnHNlWokpJSSUt33TN29+/fL1u2bJGuXbuaaWEtBt2vXz+ZMGGCud6xY0dZtmyZREREmOeeajSRSQUAcH1MAQP1rLCoWD767Ct7MGqdEC8jBh/fbOFqjh49anb9ajkYPRJORwGVFoa+77775O233zblYTZv3iyRkZGyc+dOs5ZQT0DRgKh0lJM1ggDguhgBBOpZYIC/xMVE29upe9OltPTYKRuuSKeDg4KCpFmzZibEffLJJybQaUFoLQytPvjgAxP+fv75Zxk3bpzceuutZrfw008/bR7X1/FvSwBwXcdPrAdQr0fDpaUfmzLVMLU7ba+0b5Mkrmzo0KEyZcoUs+ljwYIFsnjxYrNj+IEHHpCxY8ea72PRokXSunVrs25QRwFvuOEGszbw/fffP+N5wwAA52IEEGgAiXGx4uPt7fJFoU+kGz+WLFligp2Gv8svv1xefPFF+yifTgX/9NNPEhwcLAMHDjRnCevooU4LAwBcF2sAgQYyd9FS2b77WPDT0bGJ4y+XoMBAaQy0+PMtt9wiL7zwgtkAoucGd+7c2Uzz6ikiGgb/8pe/mPC3bds2Ux5G1wQCAFwTI4BAA04D22hw2pGSJo2Fbgr58MMPTfhbtWqVPPnkk5KSkmKC7PXXX29GCefPny9+fn4mGBL+AMC1EQCBBtIyOkoC/P3t7e27UqQxCg0NlYyMDPuGD10r2KZNG5k5c6bZKQwAcH0EQKABR9HaJrWytw/m5ErO4TxpbPQouC+//FJWrlwpvXv3NkfI6dFwzZs3Z+QPABoJ1gACDehA9iH5YsZse/uiCy+QPt27SGP17LPPmnOCtWTM3XfffVIAtP31wo5gAHAtBECgAekft0+++t6cEayaBAfJ768e5zYBSTeL6EhndctWr5MLO7ST4KDGseEFAKyAKWCgAWnQS046vhnkaH6BZGZli7uoHv407K7dsFnWbdoin383S/ZlZDq1bwCA4wiAQAOrHgCVrTSMO9Hwl5GVLSvWbTDtouJi+e7H+bJu46+cEAIALoAACDSwkKZNJLJFuL29MzXNnKrhbiOdQQEB0iykqf2aBr9la9bL7PmLXfooPACwAgIg4ATJSYn2r0tKSiUtPUPcMehePXq4tE08vvNZ6TF402fMlkO5h53WNwCwOgIg4AStE+IdNn644zSw0l3Bwwb2kwG9ujt8v4fzjsiXM36UHSl7nNo/ALAqAiDgBIEB/hIXE21vp+5Nd9tpUQ1+nTu2kytGXiJBgQH262Xl5TJn4RJZvHKN202BA4CrIwACLnA0nAYgnRp1Z9GRETJ+7EjzuboNv26Tb2f/JAWFhU7rGwBYDQEQcJLEuFjx8fa2t7fvcs9p4OoCAwJk3PAh0vWC9g7XdcewlorZn5nltL4BgJUQAAEnro9LjG9pb6dnHrDEKJiXl5f063mRDB/U3yEAFxYVyzez58kvv26lVAwA1DMCIOAi08AaenakpIlVtElsJVePGSGhJ5SKWbJyrcxZtFTKytxzTSQAuAICIOBELaOjJMDf397evitFrCQsNESuGT1CklrFOVzfmbJHpn//o+Tm5TmtbwDgzgiAgJOPTmubdLxO3sGcXMk5bK3Q4+vrIyMGDzDTwtVLxWj4m/7dbNmVap1RUQBoKARAwMnaJp5wNJwFNoOcSIOfbgwZN2KoKZFTvVTM7AWLZenqdVJZWenUPgKAOyEAAk4W0TzMnJphsyMl1bKbIGKjIk2pmKgWzR2ur9+0Rb798ScpLCpyWt8AwJ0QAAEXGP1KTjo+Cng0v0Ays7LFqoICA+XykZfIhR2SHa5riRgtFWPlnw0A1BUCIOACqgdAdz4a7lxKxVzcu4c5Rs7b28t+vaCwSL6eNU82btlu2VFSAKgLBEDABegUcGSLcHt7Z2oax6P9FoyvHj3CYYpc1wL+vGK1zPt5GaViAOA8EQABF5GclGj/uqSkVNLSM5zaH1cR3ixUrhkzwqFotm2U9Msf5sjhvCNO6xsANFYEQMBFtE6IdyiDYvVp4Or8fH1l5JCLpU/3Lg4/o0O5h2X697Pd/hxlAKhrBEDARWj5k7iYaHs7dW+6lJYyxWmjwe+iCy+QMZcOcSieXVpWJrN++lmWr/mFUjEAUEMEQMBFj4bTNYCMbJ0sLibKTAlXXzOp1m7cLN/PWWDOFAYAnBkBEHAhiXGx4uPtbemi0DXRJDhILh85TDq1a+twfV9Gpkz/fpYcyD7ktL4BQGNAAARciI+Pj8Nmh/TMA1JQWOjUPrkqby8vGdi3p1wyoK8pG2OTX1AoX82cI5u37aBUDACcBgEQcOFpYA0wO1I4C/dM2rVJlKtHD5emwcH2a7oWcOGyVfLTkuXmODkAgCMCIOBiWkZHOWxy2L4rxan9aQyahzWTa8aOkFYtYxyub9uZIl/9MEfyjuY7rW8A4IoIgICL8fT0lLZJreztgzm5knM4z6l9agz8/fxk1CWDpFe3zg6lYvTn9/l3M2XPvv1O7R8AuBICIOCC2iaecDQcm0FqRINfjy6dZPSwQeLn52u/ruV0ZsxdICvXbTipVIyeMVzOqSsALIYACLigiOZhDsef7UhJZUPDOYiPjZHxY0dKi/Awh+urf9kkM+YtlKLiYvuGkdkLFktmVraTegoAzkEABFx0JEvPwbUdhablTihyfG50U8iVoy6VjsltHK7vTc8wp4do6Ju94GcTBvfuz3RaPwHAGTyqGFYAXFJxcYkJJ81CQ0z407WBOD+/bt8pP69YY4prn24TybXjLmvwfgGAsxyvOAvApfj7+9nXsRH+akdHATXk6XTv0fyCkx7XM4X1BBE9ju9U9Fi+7EM5UlRULAEB/uZeCXGxDptNAKAxYQQQgGUUl5TIrPk/m40fJ7p0UH9pm3h897WOvs6av1imfTNDNm/bedLz27dJkt9dMUouGzrotMERAFwVARBo5PSP8L59++Sjjz6S2NhYufnmm53dJZdVUloq07+bdcq6gBrohg7oY36e//r8a/n7x9PMaGGVVMnR8CwpDjoild7l4lnuLf4FTaRJTqR4VHlIcFCg3Hr9NXLrhGsYEQTQaDAFDDRSubm5MmvWLHn//fclJydHgoODZeTIkc7ulsvSYDfv52WnLQqtG0HKy8vlmb+9I1/MmC0VPqVyKH6P5MakSZl/0UnP9y72l7CMeCnPSJBX//EvM0381EN3myPqAMDVMQIINCIFBQWyaNEi+ec//ymrV6+W7OxsM+LXvXt36dOnjyQnJzuciwtxKAi9aesOs6FG/9qrqKw0Xx/7qJKqqkr55ddtMu2bH6QoOE/2XLhSyv1Kznpfr1JfabWxlwQeDZXrxl0m/3vfHxkJBODyCIBAI7B48WIzxbt27Vrx9/eXNm3aSEREhJn61ZG/O+64Q3r06OHsbjZqM39aJI/8v79KSeBR2dVtiVT61PwMYZ0WTlrXT/wLmsqzj94v40YMrde+AkBtEQCBRmDSpEmSkZEhgwYNkp49e0rnzp0lLOxYkeMXXnhBPvnkE1m/fr0pc8II4Pm5/o8Pyoat22RHrwVSGnjyTuGz8SkKkOSVQ6V9UpJ8/o9XGQUE4NJYAwg0AlOmTDGBQjd5nKhfv37y+uuvy+HDhyU0NNQp/WvsNm/bIRu3bjebPc4n/KmygCI5Ep4p23Z5yC+bt0rXTh3qvJ8AUFcoLgY0Ai1btjwp/JWVlcnPP/8st99+u3To0IGTQmph2jczzeecmNqduZwTe+z1n37zQ530CwDqCyOAQCPzyy+/yI8//igrVqyQvXv3SlJSkjz77LP2KWGcu6Wr10q5b4nkh9XuTOCC0ENS5lckS1atqbO+AUB9IAACjURaWprceeedkp9/rIxJZGSk/O53v5MRI0ZIx44dZf/+/SYE6iYRnJu8I0elzLdIpLbL9jxEyvyK5cjRArPTmHWAAFwVARBoJOLj4yUwMNCUfGnfvr1cdNFFkpCQIH5+frJs2TL54IMPpG/fvqYsDJtBzk1ZeblU+dXNfrgqj2OlZbTMDDUBAbgq1gACjcjHH38st912m0yfPt3sBtbA9/3335vPWgfwmWeeMc8j/J2bJsFB4lXmUyf38iz3MUfDEf4AuDICINDIPPnkk7Jhwwb5+uuvzfTvQw89ZHYA33LLLVJYWGg2hig2hdRcQsuW4lsUZEq51IZ3ib/4FzaRhLiWddY3AKgPBECgEdHp3uXLl8u9994rQ4cOlT/96U9m3Z+ODKrExETZuHGj+drTkz/eNXXNmBHiIR4SltGqVvcJ2x9vzgfW+wGAK+M3BNDIDBgwwJwMUlJSYur+XX311Wb93+effy6HDh0yxaJxbkYMGSBNmwSbs309Ks/zr8XKYwEyKDBARg8bXNddBIA6RQAEGplHH33UbAbRzR5///vfZc+ePeYUEG1fc8010q5dO2d3sdHx9/OTq0ZdKl5lvhKRcn4/v4g9bcW71E+uGDnMrAEEAFfGUXBAIzRt2jSZMGGC+ToqKsqs/7v11lvNFDDOz+G8I3LDXQ9JWnqGZCb9Kgfjdte4LEzYvgSJ2dlJYqMi5D9vvSThzTiRxeoWLFggQ4YMkdzcXE7ogUtiBBBohNLT02XMmDGyZMkSU/9Pd/9q+NPr3377rRw9etTZXWx0QkOayjvPT5XmYc0kandHidneWXyKzzyS513sL9HbO5nw1yy0qbz9/FTCn5u66aab5IorrnB2N4A6Qx1AoBF64IEHzEd1Og18xx13yMGDB02dwPvuu08uvfRSagKeg7jYaPnkrRflj49OFUkVaZYRJ0fDD0hOzB4pDj4ilV7l4lnhLf4FTaTZ/lbS9FCU2fSREBcrbz//lLSMjnL2twAANcIIINBIpaammpG/devWmbaO/GkZmI8++sicHazlYhSnUZyb6MgI+fSdl+XpR+6VTu3ampCXsLG3tF92qXRcfJn5nLChj4QcjJYOrVvLUw9Nlml//1udhr/BgwebAO/u9Pu855575JFHHjG72XU5w1NPPWV//OWXX5YLL7xQgoKCJC4uzuEkHPXhhx+a6VWthalrX3VtrK6Dtf050ELpzZo1M++h/xCy0Q1UWj5Jz9fWe/fu3dtM2Z4LvYfeNyIiwpy+o5uzVq1addLz1qxZIz169DB969evn2zbts3+mH6vXbt2Nbv4ta8hISHmdB9G8NEQGAEEGqmdO3ea0HfdddeZdnh4uPkFqr+IdARQf6FkZGRIdHQ0x5Kdx6aQKy8bZj42bd0uM+YtlIOHcqWgsMjs8tVp3lGXDJILOyTXyc9Vpxe1lqPWdlRffvml+PjUTWFqV6dBTUez9WxrPdFGfxb9+/c3o9dayui1114zyxt2795tAqCGxbfeesv+eg17+pxPP/3UBKerrrpKrrzyShMMf/jhB/M63Smv97T9WZk8ebL8+uuv5jUxMTHy1VdfyciRI00JpbZt25rn6H9X3V2v/TkV7ccXX3xh+t+qVSt54YUXTF1O/XNZ/Vzu//3f/5WXXnpJWrRoIf/zP/9j1uvq0g2bXbt2mf/uGmJ1veC1114r//d//2fO9wbqlW4CAdD45OfnV4WGhlZt3LjRtF988cWqiRMnVmVnZ1fl5eVVDRw4sOo///mPs7uJGrjxxhurLr/88iqrGTRoUNWAAQMcrvXs2bPqT3/60ymf//nnn1eFh4fb2x988IFuYqzauXOn/dodd9xRFRgYWHX06FH7tREjRpjras+ePVVeXl5V6enpDve+5JJLqh577DF7u127dlVffvnlKf8b6Z89Hx8fhz9fpaWlVTExMVUvvPCCac+fP9/0be7cufbnzJgxw1wrKioy7SlTppi+HjlyxP6chx9+uKp37941+vkBtcEUMNBI6dRV586dzfSRjvDpSMe+ffukefPmZrpLRzn0vGCraszTiydOAet76XT/xIkTJTg42Iw46ehvdna2XH755eaa/r+wevXqWvX/6aeflk6dOp3UH52mfOKJJ6Q+aL+r0xHrrKws8/XcuXPlkksuMT/LJk2ayB/+8AdT61K/Bxv9vlq3bm1vR0ZGmu9NfybVr9nuqaN8+v0mJyeb59g+Fi5caEbjbLZu3WpGEk9Fn1dWVmZGFW10xLZXr16yZcuW035/+r0pW1+U9lW/t1N9/0B9IgACjZhOL82ZM0cuvvhiM4Wm079Kf7GPGzfOTAVbmQYdDWH6s9EpOg04+vNStunFzZs3m+f99NNPJixWV316cdasWSbIaSjQqUX90PCttRj1bGYbDd46lamv0SP7xo8fb6YXd+zYYX+OTi9qQDsXr7zyigkcuuZz9OjRJgxpIPz9738va9euNSFI29Ure51r/3V6UgNM9bVs+n76fWidyfpw4lS3/mz0GENd46o73TVA6VSrrqV78803zXNKS0vP+PrT3VNpyNdNUXo/3Thl+9Dv+9VXX63X78+2XKD6MY1n6itQn1gDCDRiw4cPN6Mj//znP8Xb21v++Mc/OhwbZ3UaHqZMmWK+1rVdb7zxhsybN8+sLzvVCJuu0aq+vkxHed5++237CJOOoGloOnDggBk16tixo6n1Nn/+fLO+LC0tzawb08+6tkzpaKCGL73+3HPPmWs6IqcL/s/FqFGjzC5vpRt8tF89e/Y0AVPpsYB9+/Y1fdPRzvPpv24e0nVs2le9t9Kv9XSZpKQkaUga0DQI6fo527GGn332Wa3v261bNzMCqKNs+g+n86E/T19fX7OWT0djbT9rDc5W2LwD90AABBoxHe254IILzHQmzn168S9/+YuZ6jty5IiUl5dLcXGxGTXTacXaTi9Wp9PCuknHRt+zNt+LvqfSKewTr2lfbAHwXPuvJk2aZEYC9f8pDV6ffPKJGX1saG3atDGh6vXXX5exY8easPXOO+/U+r763+aGG24wo6UaLjUQ6lS6/sNAf8Y6uqp09Fz//zjVNLCOKus/th5++GGzvCA+Pt6MMOv/O1qQHWgMCIBAI1Z9B6pt6o/dvjWfXtRf4rrbUn+J6/nK+stbpxdtAbA204sn1l6sHrpq+73Y/hufz/Ti2aYcNWzp6LHujNVRLg1hOnLY0Lp06WJC6PPPPy+PPfaYDBw40AQyDW61paOaOuL74IMPmuLpum62T58+5v8JGy3XkpeXZ2/rz0hH2W10p65e06l43X2spV5mz55tll8AjQEBEHATBD/3mF50Ng05N954owlJGgC1Ll1AQEC9vNepNsfYSuGo+++/33xUp4HLRku0nFimRTf6VN/so05cb6kheOrUqebjdE48JVX/m+qopI3W/tP1lfpxuo08J95DN9NUv3aqvuoUMtPIaAhsAgHcEEd813x6UXdP67q4up5e1Fp+KSkpsnLlSjNyNWPGDPvzdHpRR9hc1W233WY2xejaRZ0OtjKtzac7qTWsDhs2zNndAeoMARBwE/ZpyIIC2Z2219ndcWnVpxe17Ml//vMfE9Lqgo6caQDU6UXd7KHnx+rmAF0nVtPpRWfTDTN6aoUGVS1jY2UagHVzkP731JI7gLvw0GKAzu4EgNpL3Zsuazdulsysg2b92c3XXSW+vtY4TaKx0zIxOiqpu5Rdgf5a0BCotRFPPHMagHtgBBBwE6VlZSb8KV2Hxiig63PF6UXdEatBNDMzs95q/wFwPteZcwBQK4lxseLj7S1l5eWmvX1XqrRv07C123Du04s6PexK04sRERFmV+y7777LjlbAjTEFDLiRuYuWyvbdqfZdwRPHXy5Bv5U0AQDAhilgwI0kt06wf63/ttuRkubU/gAAXBMBEHAjLaOjJMDf397evivFqf0BALgmAiDgRrSocdukY2eTqoM5uZJz+Hi5EQAAFAEQcDNtE49PA9s2gwAAUB0BEHAzEc3DJKRpE3t7R0oqJ4MAABxQBgZwM7r7NzkpQVat32jaR/MLJDMrW6IjI5zdNcBllFdUyIIlK2TG3AWSnZMrBYVFEhjgL+HNQmXU0EFyycV9zJnBgLuiDAzghvKOHJX/fPmdvX1BuzYyqG8vp/YJcAW6JnbaNz/I9O9nSdbBHHOtyqNKqrzKxaPCWzyqPMy1sGYhcs2YkfK7y0dJi/AwJ/caqHsEQMBNfTFjthzIPmS+9vPzlZuuvdIcEQdY1bZdKfLHR5+S7IM5UulVLrmR+yQnZo+UBB0V0dxXJeJbFCRh+1tJWGa8eJZ7S7PQpvLWX6ZIp/bJzu4+UKcIgICb2rhlu/y8YrW9fdnQgZIY39KpfQKcZcuOXXLzfY+Zqd4DCdvkUMvdUuldcdrne1R4Snh6okSltBc/Xz9576VnpesF7Ru0z0B9YhMI4KZaJ8Sb9YA2thNCAKvJzD4odz46VQoKC2Vv+3WSnbDjjOFPVXlVysH4XbKn4xopLi2RyX+eKnvTMxqsz0B9IwACbkoXtMfFRNvbqXvTpbS0zKl9Apzhn59MNzUxM5K2SF5U+jm99miLTNnfZqPkHcmXd/71ab31EWhoBEDAIkfDVVRUyO60vU7tD9DQdNTv29nzpMyvWA61PL+TcXJj0qQkoEBmzl8kuXkUVod7IAACbiwxLlZ8vI9Xe6IoNKxmxtyFUlhULDnRe0Q8z3PJu4dITkyqlJWVy9cz59V1FwGnIAACbkzrmFXf+JGeecCMiABW8dXMOabMS250Wq3ukxu1T6o8K+TLH36ss74BzkQABCw0Dayb/nek1O4XIdCYpGcckFL/Ain3K6nVfSp9yqQ48KjsP5BVZ30DnIkACLi5ltFREuDvb29v33V+66CAxii/oFAqvOtm81OFd7nZSFVWxmYqNH4EQMDNeXp6StukVva27obU0xAAK9Ai6J6VdVMA3bPS0/x58q62rhZorAiAgAW0TTw+DazYDAKrCG3aVHxKAsSj8nhNzPOip4QUB0pI02CH+ppAY0UABCwgonmYhDRtYm/vSEk16wEBdzekf2/xKveRJgejanWf4JwI8S71l6H9+9RZ3wBnIgACFqAjFslJx0cBj+YXSGZWtlP7BDSEa8ddZj6HpzuOgp+r8PRWDvcDGjsCIGAR1QOg4mg4WEFCXKz07dFVgvLCJeBI6Hndwy+/iTTJiZDOHdtJx+Q2dd5HwBkIgIBF6BRwZItwe3tnapo5HQRwdzddd5X53GpzT/EpPr4jvia8S/wkYVMvUw36lglX11MPgYZHAAQsJDkp0f51SUmppHG4PSygX49u8tAfbzFhLmndADOiVxO+hUGStL6/+BQHyJ03XS+XDOhb730FGgoBELCQ1gnxDjsYmQaGVUwcf4Xce9tE8SnxlzZrL5aWv3aVgLxmZnfviQKOhEjs1i7SdvUg8S0KlEk3jJf/mfg7Z3QbqDcUMwIsJDDAX+JioiUtfb9pp+5NN4VtfX19nN01oF7pP3xuu2G8xMVGyxvv/1tS93pKaFZLKQ46IkXBR6TSq1w8K7zFvyBYAvKPrRWMi4mSP944QcYOH+rs7gN1zqOKWhCApeio39xFS+3toQP6SPs2SU7tE9CQ9NfeynUbZNo3P8i8xculsrLS/pinp4cM7NNLfnf5KLN5RAs/A+6IAAhYjB5j9eG0r6SsvNx+VNy4EYxwwJoKi4rMyTir12+SizpfIOHNQiQoMNDZ3QLqHf+0ASzGx8dHEuNb2tvpmQekoLDQqX0CnCUwIECyD+bI/gNZpjYm4Q9WQQAELCi59fGagDoJsCMlzan9AZxFQ9/ytb+Yr9ds2Cx792c6u0tAgyAAAhak074B/sfroW3fleLU/gDOUFRcLLMXLLEfi6ifdX0sI+KwAgIgYEG6sL1t0rGjrdTBnFyzDgqwCg17Py1eflLY01A4Z9FSh40hgDsiAAIW1TbxhKPhdlETENaxftMW2bPvWDmkE+3PzJLVv2xq8D4BDYkACFhURPMwczyczY6UVPtUGGCVdX+nw3pAuDsKQQMWLoybnJQgq9ZvNO2j+QXmF2N0ZISzuwbUK13/evXo4fblDwuWrrQ/NqBXd4mKaG6+9vWhQDrcFwEQsLDqAdBWJJoACHdXfeT7xLV+oSFNJKJ5uBN6BTQspoABi/8ijGxx/JfdztQ0qaiocGqfAAD1jwAIWFxyUqL965KSUklLz3BqfwAA9Y8ACFhc64R4sx6w+jQwAMC9EQABiwsM8Je4mGh7O3VvupSWljm1TwCA+kUABOBwNJyuAdydttep/QEA1C8CIABJjIsVH+/jRQEoCg0A7o0ACEB8fHwkMb6lvZ2eeYDzUAHAjREAAZw0DawnguxISXNqfwAA9YcACMBoGR1lTkiw2b4rxan9AQDUHwIgAMPT01PaJrWyt/WIrJzDeU7tEwCgfhAAAdi1TTw+DazYDAIA7okACMAuonmYwzmpO1JSzXpAAIB7IQACsNMTQZKTjo8CHs0vkMysbKf2CQBQ9wiAABxUD4CKo+EAwP0QAAE40CngyBbh9vbO1DRzOggAwH0QAAGcJDkp0f51SUmppKVnOLU/AIC6RQAEcJLWCfFmPaAN08AA4F4IgABOEhjgL3Ex0fZ26t50KS0tc2qfAAB1hwAI4KxHw+kawN1pe53aHwBA3SEAAjilxLhY8fH2trcpCg0A7oMACOCUfHx8JDG+pb2dnnlACgoLndonAEDdIAACqNE0sJ4IsiMlzan9AQDUDQIggNNqGR0lAf7+9vb2XSlO7Q8AoG4QAAGclqenp7RNamVvH8zJlZzDeU7tEwCg9giAAM6obeIJR8OxGQQAGj0CIIAzimgeZo6Hs9mRkmrWAwIAGi8CIIAz0hNBkpOOjwIezS+QzKxsp/YJAFA7BEAAZ1U9ACqOhgOAxo0ACOCsdAo4skW4vb0zNc2cDgIAaJwIgABqJDkp0f51SUmppKVnOLU/AIDzRwAEUCOtE+LNekAbpoEBoPEiAAKokcAAf4mLiba3U/emS2lpmVP7BAA4PwRAAOd1NJyuAdydttfePpKfL+WsCwSARsHb2R0A0HgkxsWKj7e3lJWXm/aWHbtNENTi0EcLCuQP11zu7C4CAGqAAAigxnx8fCQxvqV9/V/GgSzzoTq1T3ZYIwgAcF0EQABnVVlZKemZWbJjd6pZ+3e60UEAQONAAARwVvsyMmXOoqWm/Mup+Pr4SExURIP3CwBwftgEAuCs4mNjZMIVoyUpPu6Uj7dqGSNeXl4N3i8AwPkhAAKokcCAABkxZIAMH9Rf/P38HB5LiG/ptH4BAM4dU8AAakw3ebRJbCUxUZHy84rVsis1TTw9PaVVbIyzuwYAOAcEQADnVRR6xOABJgCmpO2TX37dKqt/2WRqAVZWVErTJsGSnJQgg/v3NmVjAACuhb+ZAZyXo/kFsmLdBpn29QzZnbbvlM9pHtZMrhkzQq4ZM1IiW4Q3eB8BAKdGAARwzpauWisPTn1e8gsKpcqjUvIiMiQvIl3KfEtEPKrEq8xXmhyKlMoD5fLOvz6Vd//9mTw6+XaZcOVoZ3cdAEAABHCuZv60SB577iUpryqXrITtkhOTJhW+J5eHKQg7KAeStkrogViJSm0vz732jhzMzZW7b/m9U/oNADiOXcAAamzF2l/kz395Wco8SiWl83LJTth5yvBnU+VVIbkxabKz22IpDSiQdz+eJv/9akad9ummm26SK664ok7vCQDujgAIoEb0/F8T/irKZc8Fq6QwNKfmrw0oNIFRw+L/vfF3yczKrte+AgDOjAAI4CSDBw+We+65Rx555BEJCwuTqKgouXXSHZJ1MEdyovdIwZZDIm+JyLMi8rKIfC8iJdVusE5E/iIi20TkdRF5RqTs2yLJaLlZDu1Pkws6dpRmzZqZ96ioqLC/rKSkRB566CGJjY2VoKAg6d27tyxYsOCc+j5r1iwZMGCAhIaGSnh4uIwZM0Z27dplfzw1NdWUs/nss8/k4osvloCAAOnZs6ds375dVq1aJT169JDg4GC57LLLJDvbMai+99570qFDB/H395f27dvLW2/pD+GY0tJSmTx5skRHR5vHW7VqJX/5i/4QAMD1EAABnNJHH31kQtiKFSvkhRdekI8/fF/yc7IlJzZVxENELhORO0VEZ19TRGTOCTco0zljEblGRHTZX6rI4QXpcjT3gCR27SMffPCB/P3vf5fp06fbX6IBatmyZfLpp5/Khg0bZPz48TJy5EjZsWOH/Tka3j788MPT9rugoEAeeOABWb16tcybN8/UKbzyyivNecbVTZkyRR5//HFZu3ateHt7y/XXX28C76uvvio///yz7Ny5U5588kn78//zn/+Y9rPPPitbtmyR5557Tp544gnzc1KvvfaafPvttyZYbtu2zTw/ISGhDv5LAEDdYxMIgFPq3LmzCUnKxz9QApqESE7RXikJyhfpW+2JzURk6G+jgGOqXa/8rR32W7ujiGwQ8b8qVEoOiPiHhMuQIUNk/vz5ct1110laWpoJhfo5JuZYYWkdDdQRPb2ugUu1a9dOQkJCTtvvq6++2qH9/vvvS4sWLeTXX3+VTp062a/rvUeMGGG+vvfee2XChAkmMPbv399cu/XWWx2Cpv4sXnrpJbnqqqtMOzEx0dxTQ+yNN95o+t22bVsz+qghVUcAAcBVEQABnDYA2mzfnSrevv5SIkeOXdAZ1cUicvC3qV8Ne+U6Dyoivr+9yKda+FPBIhIqUhCVLXKgjWzflSKRkZGSlZVlHt64caOZDk5OTnboh04L61SuzdatW8/Ybx0t1JE6Hbk8ePCgfeRPA1r1AFj9+9N+qAsvvNDhmq1vOqqo08gaCidNmmR/Tnl5uT2M6maUSy+91ARUHbXUqefhw4ef/QcNAE5AAARwSj4+muCOOXI033yu9KgUyRWRT0Sk528jfwGarkTkWxGpOMsCE0+Rcp9S+z11pMwW0PLz88XLy0vWrFljPlena/JqauzYsWb07R//+IcZSdT7a/DTNXqn+/60H6e6Vr1vSu+p6xKrs/X1oosukpSUFJk5c6bMnTtXrr32Whk2bJjDFDcAuAoCIICz8vKsluYytL6LiOjglu3y5prfy6PqWNjyPCHkdevWzYwA6qibbs44H4cOHTLr7zSo2e6xeLEOVdaOjgZqmNy9e7fccMMNp31e06ZNzXS2flxzzTVmJDAnJ8dspAEAV0IABHBWerav8qz0OjatqwNjK0VEZ2v3isjqmt/Lu8zv2D2Dgxyu69SvhquJEyeatXYaCHUXrq7L0+na0aOPnSKiu291d61u7DiR7izW6eJ3333X7MbVad9HH31U6sLUqVPNrmWd8tVgp1PTutEkNzfXbDp5+eWXzXtqv3Xjyeeff252T+tuZABwNewCBnBWndonmylR3+IAEV0up3sndGBNq6BsEJFhNb9XSNaxDR5dO3U46THd7KEB8MEHHzRr6bTAs5ZmiY+Ptz9HR/jy8vLsbZ2m1V28SoOX7iDWaWSd9r3//vvlr3/9q9SF2267zZSB0T7qWsFBgwaZTSK6GUQ1adLE7JbWMjJaVkbLzfzwww+mTwDgajyqqqp0MgcAzuiBKX+ROYuWyq5ui6Uo5PB53cOrzEfaL7tU4qNj5ft/vVMn4UhH49q0aSNvvPFGre8F6ymvqJDCoiJ7O9Df3/4PCsCd8U9TADVy3eWjzOfw9GMjXuejWUaceFR6ynXjRtU6/OnU6/fff28KRetmC+B8eHt5SdPgYPsH4Q9Wwf/pAGqkV7fO0johXnaliuSHZcnhqPRzen3AkVCJ3NNe/P395IqRl9S6P7fccouZHtbp4ssvv7zW9wMAK2EKGECN7UpNk99PfliOFhZIerv1NQ6BgXnNJGFTL/Eq95XXn31cBvXtVe99BQCcHgEQwDlZt/FXufOxqZJfUCiHI/fJodhUKWpy+NjxcCfwLQySsP3xEr4/UbzEW55++B65vA5G/wAAtUMABFBj+tdFzuE8WbV+g7z94X9ld9o+c70oOE8OR6RLuW+J/q0iXmW+0uRQhDTJjTCPNwsNkeceu18G9Oru5O8AAKAIgADOSIszZ2RlS+redElNS5cj+fkSFBggf7jmclmxboNM++YHmb9kuVRWnvxXyUUXdjSbR4Zd3E98fY+fsgEAcC4CIICTlJSWSlp6hqSm7ZM96fultLTM4fEAf3+5+XdX2duZWdmyfvNWc7yb1uXTwtFtkxKkbWIrJ/QeAHA27AIGcBINcTtT9kjKb1O8Jzrx341RES1kZESLBuodAKC2qAMI4CQ6wjdyyMVyce8e4nXCmb2qskrPggMat1mzZjmcFf3mm29K165d5frrrzd1JgF3RgAEcEp69FtSq5bi63Py2r2qU6z3Axqbhx9+WI4cOWK+3rhxo6kpOWrUKElJSTHnOwPujClgAKed5p3383IpKi4++TEhAKLx06DXsWNH8/UXX3whY8aMkeeee07Wrl1rgiDgzhgBBHBKazf+KvsyMu3tZiEhMnRAHzMieKodv0Bj4+vrK4WFhebruXPnyvDhw83XYWFh9pFBwF0xAgjgJLqrd+W6Dfa2rgO8dFA/aR7WTKIjWsi8n5c5tX9AXRgwYICZ6u3fv7+sXLlSpk2bZq5v375dWrZs6ezuAfWKEUAAJ5WAmbNoqcNO3/49u5nwp0KaNuE0D7iFN954Q7y9vWX69Ony9ttvS2xsrLk+c+ZMGTlypLO7B9Qr6gACsNO/DmYvWCy79+y1X0uKj5MRQwaYTSEAAPfAFDAAu1+373QIf8FBgTK4fy/CH9xecXGxlJaWOlxr2rSp0/oD1DemgAEYh3IPy+KVa+1tDX2XDuwn/n5+Tu0XUF8KCgpk8uTJEhERIUFBQdKsWTOHD8CdEQABSFl5ucxZuMSc+2vTs+uFEh0Z4dR+AfXpkUcekZ9++sms//Pz85P33ntPpk6dKjExMfKvf/3L2d0D6hVrAAHIgqUrzfSvTWxUpIwdPkQ8Pfk3ItxXfHy8CXqDBw82071a/69Nmzby8ccfy3//+1/54YcfnN1FoN7wtztgcXrmb/Xwp1O+wwb2JfzB7eXk5EhSUpL5WgOgtm3lYRYtWuTk3gH1i7/hAQs7kp8vC5atdLimxZ6DAgOd1iegoWj409NAVPv27eWzzz4zX3/33XcSGhrq5N4B9YsACFiUrvfTdX+lpWX2a106tpeEuGO10AB3d/PNN8svv/xivn700UflzTffFH9/f7n//vvNOcGAO2MNIGBRy9f8Ims3bra3tdDzVaOHi7eXl1P7BTjLnj17ZM2aNWYdYOfOnZ3dHaBeUQcQsKC9+zNl3aZf7W0fb28ZPqg/4Q+WM2/ePPORlZUllZWVDo+9//77TusXUN8IgIDFFBYVy7yfHY96G9i3p4SGUPQW1qIlX55++mnp0aOHREdHU/AclsIUMGAh+sf9+7kLZG96hv1au9aJcsnFfZ3aL8AZNPS98MIL8oc//MHZXQEaHJtAAAv5ZfNWh/AX0rSJDOzTw6l9ApxFj37r16+fs7sBOAUBELCIA9mHZPnaYzseldb503V/Pj4+Tu0X4Cy33XabfPLJJ87uBuAUrAEELEBLvWjJl+qL3Pv26CotwsOc2i+goT3wwAP2r/XPw7vvvitz5841u35P/MfQyy+/7IQeAg2DAAhYYN3fwmUrTdFnm1YtY6Rzh3ZO7RfgDOvWrXNod+3a1XzetGmTw3U2hMDdsQkEcHNbd+6WnxYvt7eDAgPk2nGXSYC/v1P7BQBwHtYAAm4sNy9PFi1f5TCqMWxgP8IfAFgcARBwU7q+ae6iZVJeXmG/1r3zBRIbFenUfgEAnI8pYMBN6R/tA9kH5ceFSyS/oFCiIyPk8hFDze5fAMccPnJE1vxy/EjErp3aS3izZk7tE9AQ2AQCuCmd7o1oHi6/u3y0LFqxSnp360L4A05QXFwi23al2Nttk1oRAGEJBEDAjWng8/HxkGEXU+wWAHAcwwGAm6OcBQDgRARAoJFi+S4A4HwRAIFGGv5sI3tLly6VtLQ0Z3cJANCIEACBRjz699e//lUeeugh2bZtm5SXl5/yOQAAnIhNIEAjrO+nmztWrVolU6dOlQ8//FAGDhwo3t7ekpeXJ4WFhRIdHc3aPwDAaTECCDQytlIuGv5uuukmueaaa+To0aPyzTffyIABA+SKK66QZ555xtndBAC4MEYAgUYqKirKjAbu3btXnnvuOdmzZ4/07t1bQkJCTBi8++67zdcAAJyIEUCgkbGt7YuPj5eZM2ea6d+NGzfKXXfdJe+9955MmDDBrAfcv3+/s7sKAHBRjAACjWjdn7Kt7XvyySelY8eO5uvLLrtMgoKCzNf//ve/xdfXVzp06ODEHgMAXBkBEGhE4W/OnDlmyreiokImTZpk1v/ZHt+0aZP89NNP8tFHH5nSMAAAnA4BEHBxthE/LfcyY8YMM9Knu33ff/99M+V7wQUXmMc19H333XfyzjvvMPoHADgj1gACLkxH9zQAfv311ybsffzxx7J69Wrp2bOnrFixQi666CIT+NTtt99unnPdddc5u9sAABdHAARcmE7tFhcXyz//+U954oknpEePHvLVV1/JrFmzZO7cuXLLLbfInXfeKePGjTP1/1q1auXsLgMAGgECIODiNNj16dNH+vbta6Z+//znP5s6f0OHDpVRo0aZ3cCbN2+WXbt2OburAIBGgjWAgIuf9RsWFiZ/+tOfzEkfK1euNGsAhw8fbh5LSkoyxZ9ffPFFUxcQAICaIAACLqakpET8/Pxk69atMn36dLMOUAs8X3zxxRIeHi5r166VBQsWSNOmTU0w1Gliwh8A4FwQAAEXsXz5cunWrZsJfzrtq1O8OsKnU7u6+7dXr14yefJkeeGFF8y6P60DqCFQXwcAwLlgDSDgAubNm2c2dOhUblZWltn0oev+Fi9eLCkpKeZ8Xx35e/TRR82U8Lp168zooNYFDA0NdXb3AQCNDCOAgAsYMmSI3HTTTfLZZ5/JgQMH7NeUv7+/PPbYY3LhhRfK3//+d1MKZvfu3fLUU0+ZdYEAAJwrRgABF6Dr+B555BFT02/NmjXmOLcff/zR4TljxoyR1157TVq2bCnZ2dmEPwDAefOosp0sD8Al5Ofny4MPPmgC4LXXXiu33nqrJCcn2x/XP7JFRUUSGBjo1H4C7iAzK1u+/GGOvT3m0sESHxvj1D4BDYERQMDFBAcHm5HAqVOnmjV+Tz/9tMyePdv+uJaHIfwBAGqDAAi4IA15EydOlE8//VTS09PlpZdekueff96M/AEAUFsEQMCFnLgiQ6d+Z86caY54W79+vQQEBDitbwAA98EqcsBFpGcckLBmIeLn62s2hdjoLuB//OMfUlBQ4NT+AQDcBwEQcAFH8vNl5vxF4uXpJcMG9pW4mOiTnqNHwAEAUBeYAgacrKKiQuYsXCKlpWVSVFws3/04X1LT0p3dLQCAGyMAAk62av0mOZB9yN5uER4mLWM52xcAUH8IgIAT7d2fKes2/Wpv+3h7y/BB/cXby8up/QIAuDcCIOAkhUXFMu/npQ47fwf27SkhTZs4tV8AAPdHAAScQEPfvMXLTAi0adcmUdq1TnRqvwAA1kAABJzgl81bZW96hr2to34De/dwap8AANZBAAQamG74WLZmvb2tNf903Z+Pj49T+wUAsA4CINCAtNSLlnypvu6vX49uZucvAAANhQAINBANfQuXrTRFn20S4mLlwg7JTu0XAMB6CIBAA9m6c7fsSNljbwcFBsiQ/r3Fw8PDqf0CAFgPARBoALl5efLzitX2toa+YQP7SYC/v1P7BQCwJgIgUM/KzVFvS6W8vMJ+rXvnCyQ2KtKp/QIAWBcBEKhnS1etk4M5ufZ2dGSE9OjSyal9AgBYGwEQqEe70/bKpq3b7W0/P18ZdnFfU/oFAABn4bcQUE+O5hfI/CUrHK7ppo8mwUFO6xMAAIoACMtZsGCB2YShH1dccUWd399276ioSCkpKbVf79Q+WZLi4+r8/QAAOFcEQFjWtm3b5MMPP3S49uabb0pCQoL4+/tL7969ZeXKlQ6Pv/vuuzJ48GBp2rSpCXmHDx8+6b4ZGRnywMOPSFXl8WLP4c1CpV/PbvX43QAAUHMEQLiMiooKqaysbLD3i4iIkNDQUHt72rRp8sADD8iUKVNk7dq10qVLFxkxYoRkZWXZn1NYWCgjR46UP//5z6e9b4V4SHZOnr3t7e0llw7qL95eXvX43QAAUHMEQJw3HQmbPHmy+QgJCZHmzZvLE088YT/mrKSkRB566CGJjY2VoKAgM6Km0682OvqmAezbb7+Vjh07ip+fn6SlpZnn9OrVy7xGH+/fv7/s2XO8gPLbb78trVu3Fl9fX2nXrp18/PHHDv3Skbn33ntPrrzySgkMDJS2bdua9zibl19+WSZNmiQ333yz6c8777xjXv/+++/bn3PffffJo48+Kn369DnlPYqKi2XuomVSJcdH/y7u3UPCQkPO8acLAED9IQCiVj766CPx9vY2U6WvvvqqCVEavpQGw2XLlsmnn34qGzZskPHjx5vRsx07djiMqD3//PPmNZs3b5awsDCzLm/QoEHmNfr622+/3X5axldffSX33nuvPPjgg7Jp0ya54447TGCbP3++Q7+mTp0q1157rbnHqFGj5IYbbpCcnJzTfh+lpaWyZs0aGTZsmP2a7tTVtvahJjT46qaPgsJC+7U2ia2kfZukc/iJAgBQ/7wb4D3gxuLi4uSVV14xAU1H4zZu3GjaOnX6wQcfmBG9mJgY81wdDZw1a5a5/txzz5lrZWVl8tZbb5npVqUhLS8vT8aMGWNG+VSHDh3s7/fiiy/KTTfdJHfeeadp65Tt8uXLzfUhQ4bYn6fPmTBhgvla3+u1114zIVUD6KkcPHjQTEFHRjoWZ9b21q1ba/Sz2Lhlu6TuTbe39WcyqG9PjnoDALgcRgBRKzoVWj3g9O3b14zwaRDUQJWcnCzBwcH2j4ULF8quXbvsz9dp3M6dO9vbOgKo4U0D5NixY82oom6qsNmyZYuZEq5O23q9uur31Klk3bRRfS1fXcs+lCNLV6+ztz3Ew6z98/P1rbf3BADgfDECiHqRn58vXl5eZlpVP1enQdAmICDgpBEyHSG85557zGihbsx4/PHHZc6cOaddd3cqPj4+Dm19jzNtMNH1i9rPAwcOOFzXdlRU1BnfS0cxf1y4xOH+rRPiGPkDALgsRgBRKytWOBY61ulY3XTRrVs3MwKoo25t2rRx+DhboFL6+scee0yWLl0qnTp1kk8++cQ+HbxkyRKH52pbN23Uho5Edu/eXebNm2e/poFO2zqqeSaLVqyWvCNH7e242GiJjz027Q0AgCtiBBC1omv8dB2ebsbQ0imvv/66vPTSS2bqVzdeTJw40bQ10GVnZ5tApdOzo0ePPuX9UlJSTK29cePGmbWDWqtPp5T1Purhhx82mzv0frpB47vvvpMvv/xS5s6dW+vvRb+PG2+8UXr06GF2If/tb3+TgoICs8nEJjMz03zs3LnTtGfMmi2btu+SsLDmEhQcLIEB/nLJgD7y2bTju5YBAHA1BEDUigazoqIiE5h0ClV36OquXdtU7jPPPGN27Kanp5tpVp3G1Q0ep6NlV3TThe4uPnTokERHR8tdd91lAqbSHcK6LlA3feh7JSYmmvfRkjS1dd1115mQ+uSTT5qQ17VrVzMNXX1jiJaG0R3GNr+f8Ltjn2+9XfoOGCSXDOgrgQEBte4LAAD1yaPKVrQNOEcaujQk6UhZY6J1BnXHcG5urkMh6HOh09tf/jDHbP6w6XZhR+nbvau9xqHWDDzVSSEAXEdmVrb5s2wz5tLBLOGAJTACCMtq2bKl2Wn83//+95xfu2LtLw7hL7JFuPTqeqF9k0t5ebk5Tg4AAFdEAITl6IkktmLU1Xck19Sefftl/ebjtQF9fXxk2MD+9t3O69evN59P3P0MAICrIADivFU/1q0x0dIzuhv5fOgpHz8tXu5wbXC/XhLS5HiQPN97AwDQUAiAgG5mufsROZB96KzPKywqkoqK4/X+WjQPkztvur6eewcAQN0iAAJa8Dn7kBzIypbIpk3P+LxAD08R72PlMw8cOSJFxYEN1EMAAOoOARD4jYa/2fffV+Pnj3hFdz9z2gcAoPHhJBAAAACLIQACAABYDAEQAADAYgiAAAAAFsMmEACAZcs9VVRWSGFhsb39xYwfxfuEIu560s+/Xn+hwfoJNAQCIADAsuWeNOo18a72q7Cs/NiH7TVHjjR0N4EGQQAEAFjG+ZV7AtwPawABAAAshgAIAHCLs8k9PDzMxxVXXFGn9y4qOGq/d9euXev03oCzEAABAG5j27Zt8uGHHzpce/PNNyUhIUHmfv6RLF84S1Zu2ezweHFJidz1t+clfNwwCR45UK5+8hE5kHN8s4h/QJBkZGTIgw8+2GDfB1DfWAMIVFvsfS7rffT5kf4t6rVPQGNXUVFhRs48PRtmvCEiIkJCQ0Pt7WnTpskDDzwg77zzjrz32XeyZ8tGGfHw3bLt4+kS0SzMPOf+N1+RGcsXy+dP/UVCgoJl8qt/lauefESWvPFP87iHp6dERUVJcHBwg3wPQENgBBD4rcxDZEQLEX+/Gn/o8/V1gDsZPHiwTJ482XyEhIRI8+bN5YknnpCqqirzeElJiTz00EMSGxsrQUFB0rt3bzP9aqOjbxrAvv32W+nYsaP4+flJWlqaeU6vXr3Ma/Tx/v37y549e+yve/vtt6V169bi6+sr7dq1k48//tihXxoi33vvPbnyyislMDBQ2rZta97jbF5++WWZNGmS3HzzzRIc0kw6duklgf7+8v4Px16bl58v//zhG3n5zvtl6EU9pXu7DvLBn56UpZs2yPLNG+vwJwu4FkYAARFqfAHVfPTRR3LrrbfKypUrZfXq1XL77bdLfHy8CVIaDH/99Vf59NNPJSYmRr766isZOXKkbNy40YQyVVhYKM8//7wJbOHh4RIWFmbWzunr//vf/0ppaam5t4Y6pfe499575W9/+5sMGzZMvv/+exPYWrZsKUOGDLH3a+rUqfLCCy/IX//6V3n99dflhhtuMCFS738q+j5r1qyRxx57zH5N33NY916y7Ndj4W7N9i1SVl5urtm0b5Ug8ZFR9ucA7ogACABwEBcXJ6+88ooJSzoap+FO2yNGjJAPPvjAjOhp+FM6Gjhr1ixz/bnnnjPXysrK5K233pIuXbqYdk5OjuTl5cmYMWPMKJ/q0KGD/f1efPFFuemmm+TOO+80bZ2yXb58ublePQDqcyZMmGC+1vd67bXXTJDUAHoqBw8eNFPQkZGRDtcjm4XJ1rRU83VmziHx9fGR0CZNTnqOPga4K6aAAQAO+vTpYx+dU3379pUdO3aYIKiBKjk52ayHs30sXLhQdu3aZX++TuN27tzZ3tYROg1vGiDHjh0rr776qtlUYbNlyxYzJVydtvV6ddXvqVPJTZs2laysrDr//gErYAQQAFAj+fn54uXlZaZV9XN11TdIBAQEOARIpSOE99xzjxkt1I0Zjz/+uMyZM8eEzZry8fFxaOt7VFZWnvb5un5R+3ngwAGH6wdycyQq7Nj6Xf1cWlYmh48edRgFtD+nkIAJ98QIIADAwYoVKxzaOh2r6/u6detmRgB11K1NmzYOH7pL9mz09boeb+nSpdKpUyf55JNP7NPBS5YscXiutnUTSW3oSGT37t1l3rx59mu6mWXemlXSt+OFpt09uYP4eHvLvLWr7M/ZlpYqaQcy7c8B3BEjgAAAB7rGT9fh3XHHHbJ27Vqz4eKll14yU7+68WLixImmrYEuOzvbBCydnh09evQp75eSkiLvvvuujBs3zqwd1Fp9OqWs91EPP/ywXHvtteZ+ugnku+++ky+//FLmzp1b6+9Fv48bb7xRevToIflHDsvWX1bLkbzDMistQ+b/VvYpsmWi/P7/psr/zf5RvH18ZMuG1RLSrLlM+XEe5Z7gtgiAAAAHGsyKiopM2RadQtUduroT2DaV+8wzz5iiyOnp6WaaVadxdYPH6WjZlq1bt5rdxYcOHZLo6Gi56667TMBUenKHrgvUTR/6XomJieZ9tCRNbV133XUmpD755JOyb1+6BIc2k4sGDRe/arUC2/XoKx7rV8r61T9LZUWlNI+KlQ7d+x4r9+RPuSe4J48qW3EnAIDlaejSki1akqUx0TqDumM4NzfXoRB0XXrqqafk66+/lvXr19fL/YGGxBpAAIDb0NqBtlIxdTklrptcbGVuAHfAFDAAoNHTE0l0XaGq6yPbdN2ibdRPTzYB3AFTwAAAABbDFDAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIDFEAABAAAshgAIAABgMQRAAAAAiyEAAgAAWAwBEAAAwGIIgAAAABZDAAQAALAYAiAAAIBYy/8H5dm2wQicld4AAAAASUVORK5CYII=", + "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from typedb_jupyter.graph import visualise\n", + "plt.figure()\n", + "plot_instance_2 = visualise(_typeql_query_string, _typeql_result)" + ] + }, { "cell_type": "markdown", "id": "8d4d479f-3c5e-4c42-9f4e-4024fd182abf", @@ -404,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "88b1e385-ab1c-464c-956d-b0a131789dc7", "metadata": {}, "outputs": [ @@ -422,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "83e7c39b-a24d-4e35-b141-1a9474d50e51", "metadata": {}, "outputs": [ @@ -456,7 +494,7 @@ " | $f: Relation(friendship: 0x1f00000000000000000001) | $friend: RoleType(friendship:friend) | $n1: Attribute(name: \"Jimmy\") | $n2: Attribute(name: \"James\") | $p1: Entity(person: 0x1e00000000000000000002) | $p2: Entity(person: 0x1e00000000000000000001) |]" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -471,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "46d56f54-1068-4eae-9b58-4124a7aba5c1", "metadata": {}, "outputs": [ @@ -489,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "c2d6529f-fee4-491b-be1b-6220193657d9", "metadata": {}, "outputs": [ @@ -504,18 +542,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f173e554a11462db4b77da9a018a16a", + "model_id": "3f17c107a8d44cafbcf0f1abe91365ea", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -528,15 +566,9 @@ } ], "source": [ - "%matplotlib widget\n", - "from typedb_jupyter.graph.query import QueryGraph\n", - "from typedb_jupyter.graph.answer import AnswerGraph\n", - "\n", - "parsed = TypeQLVisitor.parse_and_visit(_typeql_query_string)\n", - "query_graph = QueryGraph(parsed)\n", - "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", + "# As before, but we will use the convenience method this time\n", "plt.figure()\n", - "plot_instance_2 = answer_graph.plot() # We use a different name to avoid clobbering the earlier visualisation" + "plot_instance_3 = visualise(_typeql_query_string, _typeql_result)" ] }, { @@ -550,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "896949b2-866e-4974-8a37-a91f46566be6", "metadata": {}, "outputs": [ @@ -563,12 +595,12 @@ } ], "source": [ - "%typedb transaction open typedb_jupyter_graphs read\n" + "%typedb transaction open typedb_jupyter_graphs read" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "b60dd08f-f717-418b-bac9-1e091a2a6c14", "metadata": {}, "outputs": [ @@ -600,7 +632,7 @@ " | $n: Attribute(name: \"Jimmy\") | $p: Entity(person: 0x1e00000000000000000002) |]" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -612,7 +644,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "63adcc82-e76f-4fca-9e6d-8a0fc6f66059", "metadata": {}, "outputs": [ @@ -630,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "de10958b-0a36-4000-85e2-f53e58ff7fff", "metadata": {}, "outputs": [], @@ -692,7 +724,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "id": "2684cd61-ae9b-4fea-be4b-394e18c81bc2", "metadata": {}, "outputs": [ @@ -710,7 +742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "af9f51e885d042d880ed79fc13cfc5ed", + "model_id": "25bbb7a72cc04105b5f0557ed3ad7d7d", "version_major": 2, "version_minor": 0 }, @@ -734,12 +766,12 @@ } ], "source": [ - "%matplotlib widget\n", "plt.figure()\n", "parsed = TypeQLVisitor.parse_and_visit(_typeql_query_string)\n", "query_graph = QueryGraph(parsed)\n", "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", - "plot_instance_3 = answer_graph.plot_with_visualiser(MyVisualisationBuilder()) # We use a different name to avoid clobbering the earlier visualisation" + "plot_instance_4 = answer_graph.plot_with_visualiser(MyVisualisationBuilder())\n", + "# We can also call `visualise(_typeql_query_string, _typeql_result, MyVisualisationBuilder())`" ] }, { diff --git a/src/typedb_jupyter/graph/__init__.py b/src/typedb_jupyter/graph/__init__.py index e69de29..26b0b31 100644 --- a/src/typedb_jupyter/graph/__init__.py +++ b/src/typedb_jupyter/graph/__init__.py @@ -0,0 +1,33 @@ +# +# Copyright (C) 2023 Vaticle +# +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. +# + +def visualise(typeql_query_string, typeql_result, visualiser=None): + from typedb_jupyter.graph.query import QueryGraph + from typedb_jupyter.graph.answer import AnswerGraph + from typedb_jupyter.utils.parser import TypeQLVisitor + + parsed = TypeQLVisitor.parse_and_visit(typeql_query_string) + query_graph = QueryGraph(parsed) + answer_graph = AnswerGraph.build(query_graph, typeql_result) + if visualiser is None: + from .answer import PlottableGraphBuilder + visualiser = PlottableGraphBuilder() + answer_graph.plot_with_visualiser(visualiser) \ No newline at end of file From ececc410ae3c376d046be1abf34c1bbaa41eebf5 Mon Sep 17 00:00:00 2001 From: Krishnan Govindraj Date: Wed, 19 Mar 2025 12:06:04 +0100 Subject: [PATCH 27/27] Fix printing of edges in cell --- src/graphs.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/graphs.ipynb b/src/graphs.ipynb index def8930..2754946 100644 --- a/src/graphs.ipynb +++ b/src/graphs.ipynb @@ -333,7 +333,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "id": "51cb2feb-1fa8-4b3c-9d27-94c65706dc2f", "metadata": {}, "outputs": [ @@ -341,10 +341,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n", - "[]\n", - "[]\n", - "[]\n" + "Entity(person: 0x1e00000000000000000000)--[has]-->Attribute(name: \"John\")\n", + "Entity(person: 0x1e00000000000000000001)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"James\")\n", + "Entity(person: 0x1e00000000000000000002)--[has]-->Attribute(name: \"Jimmy\")\n" ] } ], @@ -353,7 +353,7 @@ "from typedb_jupyter.graph.answer import AnswerGraph\n", "\n", "answer_graph = AnswerGraph.build(query_graph, _typeql_result)\n", - "print(\"\\n\".join(map(str,answer_graph.edges))) # We now have a list of edges" + "print(\"\\n\".join(\",\".join(map(str, edges)) for edges in answer_graph.edges)) # We now have a list of (list of edges) per answer" ] }, {