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How to use source_type method in localstack

Best Python code snippet using localstack_python

data.py

Source:data.py

1import json, os2import math, copy, time3import numpy as np4from collections import defaultdict5import pandas as pd6from utils.utils import *7import math8from tqdm import tqdm9import seaborn as sb10import matplotlib.pyplot as plt11import matplotlib.cm as cm12import dill13from functools import partial14import multiprocessing as mp15class Graph():16    def __init__(self):17        super(Graph, self).__init__()18        '''19            node_forward and node_backward are only used when building the data. 20            Afterwards will be transformed into node_feature by DataFrame21            22            node_forward: name -> node_id23            node_backward: node_id -> feature_dict24            node_feature: a DataFrame containing all features25        '''26        self.node_forward = defaultdict(lambda: {})27        self.node_backward = defaultdict(lambda: [])28        self.node_feature = defaultdict(lambda: [])29        '''30            edge_list: index the adjacancy matrix (time) by 31            <target_type, source_type, relation_type, target_id, source_id>32        '''33        self.edge_list = defaultdict( #target_type34                            lambda: defaultdict(  #source_type35                                lambda: defaultdict(  #relation_type36                                    lambda: defaultdict(  #target_id37                                        lambda: defaultdict( #source_id(38                                            lambda: int # time39                                        )))))40        self.times = {}41    def add_node(self, node):42        nfl = self.node_forward[node['type']]43        if node['id'] not in nfl:44            self.node_backward[node['type']] += [node]45            ser = len(nfl)46            nfl[node['id']] = ser47            return ser48        return nfl[node['id']]49    def add_edge(self, source_node, target_node, time = None, relation_type = None, directed = True):50        edge = [self.add_node(source_node), self.add_node(target_node)]51        '''52            Add bi-directional edges with different relation type53        '''54        self.edge_list[target_node['type']][source_node['type']][relation_type][edge[1]][edge[0]] = time55        if directed:56            self.edge_list[source_node['type']][target_node['type']]['rev_' + relation_type][edge[0]][edge[1]] = time57        else:58            self.edge_list[source_node['type']][target_node['type']][relation_type][edge[0]][edge[1]] = time59        self.times[time] = True60        61    def update_node(self, node):62        nbl = self.node_backward[node['type']]63        ser = self.add_node(node)64        for k in node:65            if k not in nbl[ser]:66                nbl[ser][k] = node[k]67    def get_meta_graph(self):68        types = self.get_types()69        metas = []70        for target_type in self.edge_list:71            for source_type in self.edge_list[target_type]:72                for r_type in self.edge_list[target_type][source_type]:73                    metas += [(target_type, source_type, r_type)]74        return metas75    76    def get_types(self):77        return list(self.node_feature.keys())78def sample_subgraph(graph, time_range, sampled_depth = 2, sampled_number = 8, inp = None, feature_extractor = feature_OAG):79    '''80        Sample Sub-Graph based on the connection of other nodes with currently sampled nodes81        We maintain budgets for each node type, indexed by <node_id, time>.82        Currently sampled nodes are stored in layer_data.83        After nodes are sampled, we construct the sampled adjacancy matrix.84    '''85    layer_data  = defaultdict( #target_type86                        lambda: {} # {target_id: [ser, time]}87                    )88    budget     = defaultdict( #source_type89                                    lambda: defaultdict(  #source_id90                                        lambda: [0., 0] #[sampled_score, time]91                            ))92    new_layer_adj  = defaultdict( #target_type93                                    lambda: defaultdict(  #source_type94                                        lambda: defaultdict(  #relation_type95                                            lambda: [] #[target_id, source_id]96                                )))97    '''98        For each node being sampled, we find out all its neighborhood, 99        adding the degree count of these nodes in the budget.100        Note that there exist some nodes that have many neighborhoods101        (such as fields, venues), for those case, we only consider 102    '''103    def add_budget(te, target_id, target_time, layer_data, budget):104        for source_type in te:105            tes = te[source_type]106            for relation_type in tes:107                if relation_type == 'self' or target_id not in tes[relation_type]:108                    continue109                adl = tes[relation_type][target_id]110                if len(adl) < sampled_number:111                    sampled_ids = list(adl.keys())112                else:113                    sampled_ids = np.random.choice(list(adl.keys()), sampled_number, replace = False)114                for source_id in sampled_ids:115                    source_time = adl[source_id]116                    if source_time == None:117                        source_time = target_time118                    if source_time > np.max(list(time_range.keys())) or source_id in layer_data[source_type]:119                        continue120                    budget[source_type][source_id][0] += 1. / len(sampled_ids)121                    budget[source_type][source_id][1] = source_time122    '''123        First adding the sampled nodes then updating budget.124    '''125    for _type in inp:126        for _id, _time in inp[_type]:127            layer_data[_type][_id] = [len(layer_data[_type]), _time]128    for _type in inp:129        te = graph.edge_list[_type]130        for _id, _time in inp[_type]:131            add_budget(te, _id, _time, layer_data, budget)132    '''133        We recursively expand the sampled graph by sampled_depth.134        Each time we sample a fixed number of nodes for each budget,135        based on the accumulated degree.136    '''137    for layer in range(sampled_depth):138        sts = list(budget.keys())139        for source_type in sts:140            te = graph.edge_list[source_type]141            keys  = np.array(list(budget[source_type].keys()))142            if sampled_number > len(keys):143                '''144                    Directly sample all the nodes145                '''146                sampled_ids = np.arange(len(keys))147            else:148                '''149                    Sample based on accumulated degree150                '''151                score = np.array(list(budget[source_type].values()))[:,0] ** 2152                score = score / np.sum(score)153                sampled_ids = np.random.choice(len(score), sampled_number, p = score, replace = False) 154            sampled_keys = keys[sampled_ids]155            '''156                First adding the sampled nodes then updating budget.157            '''158            for k in sampled_keys:159                layer_data[source_type][k] = [len(layer_data[source_type]), budget[source_type][k][1]]160            for k in sampled_keys:161                add_budget(te, k, budget[source_type][k][1], layer_data, budget)162                budget[source_type].pop(k)   163    '''164        Prepare feature, time and adjacency matrix for the sampled graph165    '''166    feature, times, indxs, texts = feature_extractor(layer_data, graph)167            168    edge_list = defaultdict( #target_type169                        lambda: defaultdict(  #source_type170                            lambda: defaultdict(  #relation_type171                                lambda: [] # [target_id, source_id] 172                                    )))173    for _type in layer_data:174        for _key in layer_data[_type]:175            _ser = layer_data[_type][_key][0]176            edge_list[_type][_type]['self'] += [[_ser, _ser]]177    '''178        Reconstruct sampled adjacancy matrix by checking whether each179        link exist in the original graph180    '''181    for target_type in graph.edge_list:182        te = graph.edge_list[target_type]183        tld = layer_data[target_type]184        for source_type in te:185            tes = te[source_type]186            sld  = layer_data[source_type]187            for relation_type in tes:188                tesr = tes[relation_type]189                for target_key in tld:190                    if target_key not in tesr:191                        continue192                    target_ser = tld[target_key][0]193                    for source_key in tesr[target_key]:194                        '''195                            Check whether each link (target_id, source_id) exist in original adjacancy matrix196                        '''197                        if source_key in sld:198                            source_ser = sld[source_key][0]199                            edge_list[target_type][source_type][relation_type] += [[target_ser, source_ser]]200    return feature, times, edge_list, indxs, texts201def to_torch(feature, time, edge_list, graph):202    '''203        Transform a sampled sub-graph into pytorch Tensor204        node_dict: {node_type: <node_number, node_type_ID>} node_number is used to trace back the nodes in original graph.205        edge_dict: {edge_type: edge_type_ID}206    '''207    node_dict = {}208    node_feature = []209    node_type    = []210    node_time    = []211    edge_index   = []212    edge_type    = []213    edge_time    = []214    215    node_num = 0216    types = graph.get_types()217    for t in types:218        node_dict[t] = [node_num, len(node_dict)]219        node_num     += len(feature[t])220    for t in types:221        node_feature += list(feature[t])222        node_time    += list(time[t])223        node_type    += [node_dict[t][1] for _ in range(len(feature[t]))]224        225    edge_dict = {e[2]: i for i, e in enumerate(graph.get_meta_graph())}226    edge_dict['self'] = len(edge_dict)227    for target_type in edge_list:228        for source_type in edge_list[target_type]:229            for relation_type in edge_list[target_type][source_type]:230                for ii, (ti, si) in enumerate(edge_list[target_type][source_type][relation_type]):231                    tid, sid = ti + node_dict[target_type][0], si + node_dict[source_type][0]232                    edge_index += [[sid, tid]]233                    edge_type  += [edge_dict[relation_type]]   234                    '''235                        Our time ranges from 1900 - 2020, largest span is 120.236                    '''237                    edge_time  += [node_time[tid] - node_time[sid] + 120]238    node_feature = torch.FloatTensor(node_feature)239    node_type    = torch.LongTensor(node_type)240    edge_time    = torch.LongTensor(edge_time)241    edge_index   = torch.LongTensor(edge_index).t()242    edge_type    = torch.LongTensor(edge_type)243    return node_feature, node_type, edge_time, edge_index, edge_type, node_dict, edge_dict244    245    246class RenameUnpickler(dill.Unpickler):247    def find_class(self, module, name):248        renamed_module = module249        if module == "GPT_GNN.data" or module == 'data':250            renamed_module = "pyHGT.data"251        return super(RenameUnpickler, self).find_class(renamed_module, name)252def renamed_load(file_obj):...

ma.py

Source:ma.py

1from typing import Union2import numpy as np3from jesse.helpers import get_candle_source, slice_candles4def ma(candles: np.ndarray, period: int = 30, matype: int = 0,  source_type: str = "close", sequential: bool = False) -> Union[5    float, np.ndarray]:6    """7    MA - (nearly) All Moving Averages of Jesse8    :param candles: np.ndarray9    :param period: int - default: 3010    :param matype: int - default: 011    :param source_type: str - default: "close"12    :param sequential: bool - default: False13    :return: float | np.ndarray14    """15    candles = slice_candles(candles, sequential)16    if matype <= 8:17        from talib import MA18        if len(candles.shape) != 1:19            candles = get_candle_source(candles, source_type=source_type)20        res = MA(candles, timeperiod=period, matype=matype)21    elif matype == 9:22        from . import fwma23        res = fwma(candles, period, source_type=source_type, sequential=True)24    elif matype == 10:25        from . import hma26        res = hma(candles, period, source_type=source_type,  sequential=True)27    elif matype == 11:28        from talib import LINEARREG29        if len(candles.shape) != 1:30            candles = get_candle_source(candles, source_type=source_type)31        res = LINEARREG(candles, period)32    elif matype == 12:33        from . import wilders34        res = wilders(candles, period, source_type=source_type,  sequential=True)35    elif matype == 13:36        from . import sinwma37        res = sinwma(candles, period, source_type=source_type,  sequential=True)38    elif matype == 14:39        from . import supersmoother40        res = supersmoother(candles, period, source_type=source_type,  sequential=True)41    elif matype == 15:42        from . import supersmoother_3_pole43        res = supersmoother_3_pole(candles, period, source_type=source_type,  sequential=True)44    elif matype == 16:45        from . import gauss46        res = gauss(candles, period, source_type=source_type,  sequential=True)47    elif matype == 17:48        from . import high_pass49        res = high_pass(candles, period, source_type=source_type,  sequential=True)50    elif matype == 18:51        from . import high_pass_2_pole52        res = high_pass_2_pole(candles, period, source_type=source_type,  sequential=True)53    elif matype == 19:54        from talib import HT_TRENDLINE55        if len(candles.shape) != 1:56            candles = get_candle_source(candles, source_type=source_type)57        res = HT_TRENDLINE(candles)58    elif matype == 20:59        from . import jma60        res = jma(candles, period, source_type=source_type,  sequential=True)61    elif matype == 21:62        from . import reflex63        res = reflex(candles, period, source_type=source_type,  sequential=True)64    elif matype == 22:65        from . import trendflex66        res = trendflex(candles, period, source_type=source_type,  sequential=True)67    elif matype == 23:68        from . import smma69        res = smma(candles, period, source_type=source_type,  sequential=True)70    elif matype == 24:71        if len(candles.shape) == 1:72          raise ValueError("vwma only works with normal candles.")73        from . import vwma74        res = vwma(candles, period, source_type=source_type,  sequential=True)75    elif matype == 25:76        from . import pwma77        res = pwma(candles, period, source_type=source_type,  sequential=True)78    elif matype == 26:79        from . import swma80        res = swma(candles, period, source_type=source_type,  sequential=True)81    elif matype == 27:82        from . import alma83        res = alma(candles, period, source_type=source_type,  sequential=True)84    elif matype == 28:85        from . import hwma86        res = hwma(candles, source_type=source_type,  sequential=True)87    elif matype == 29:88        from . import vwap89        if len(candles.shape) == 1:90          raise ValueError("vwap only works with normal candles.")91        res = vwap(candles, source_type=source_type,  sequential=True)92    elif matype == 30:93        from . import nma94        res = nma(candles, period, source_type=source_type,  sequential=True)95    elif matype == 31:96        from . import edcf97        res = edcf(candles, period, source_type=source_type,  sequential=True)98    elif matype == 32:99        from . import mwdx100        res = mwdx(candles, source_type=source_type,  sequential=True)101    elif matype == 33:102        from . import maaq103        res = maaq(candles, period, source_type=source_type,  sequential=True)104    elif matype == 34:105        from . import srwma106        res = srwma(candles, period, source_type=source_type,  sequential=True)107    elif matype == 35:108        from . import sqwma109        res = sqwma(candles, period, source_type=source_type,  sequential=True)110    elif matype == 36:111        from . import vpwma112        res = vpwma(candles, period, source_type=source_type,  sequential=True)113    elif matype == 37:114        from . import cwma115        res = cwma(candles, period, source_type=source_type,  sequential=True)116    elif matype == 38:117        from . import jsa118        res = jsa(candles, period, source_type=source_type,  sequential=True)119    elif matype == 39:120        from . import epma121        res = epma(candles, period, source_type=source_type,  sequential=True)...

source_type_test.py

Source:source_type_test.py

1# -*- coding: utf-8 -*-2"""Tests for the source type objects."""3import unittest4from artifacts import errors5from artifacts import source_type6class TestSourceType(source_type.SourceType):7  """Class that implements a test source type."""8  TYPE_INDICATOR = u'test'9  def __init__(self, test=None):10    """Initializes the source type object.11    Args:12      test: optional test string. The default is None.13    Raises:14      FormatError: when test is not set.15    """16    if not test:17      raise errors.FormatError(u'Missing test value.')18    super(TestSourceType, self).__init__()19    self.test = test20  def CopyToDict(self):21    """Copies the source type to a dictionary.22    Returns:23      A dictionary containing the source type attributes.24    """25    return {u'test': self.test}26class SourceTypeTest(unittest.TestCase):27  """Class to test the artifact source type."""28class ArtifactGroupSourceTypeTest(unittest.TestCase):29  """Class to test the artifact group source type."""30  def testInitialize(self):31    """Tests the __init__ function."""32    source_type.ArtifactGroupSourceType(names=[u'test'])33class FileSourceTypeTest(unittest.TestCase):34  """Class to test the files source type."""35  def testInitialize(self):36    """Tests the __init__ function."""37    source_type.FileSourceType(paths=[u'test'])38    source_type.FileSourceType(paths=[u'test'], separator=u'\\')39class PathSourceTypeTest(unittest.TestCase):40  """Class to test the paths source type."""41  def testInitialize(self):42    """Tests the __init__ function."""43    source_type.PathSourceType(paths=[u'test'])44    source_type.PathSourceType(paths=[u'test'], separator=u'\\')45class WindowsRegistryKeySourceTypeTest(unittest.TestCase):46  """Class to test the Windows Registry keys source type."""47  def testInitialize(self):48    """Tests the __init__ function."""49    source_type.WindowsRegistryKeySourceType(keys=[u'HKEY_LOCAL_MACHINE\\test'])50    with self.assertRaises(errors.FormatError):51      source_type.WindowsRegistryKeySourceType(keys=u'HKEY_LOCAL_MACHINE\\test')52class WindowsRegistryValueSourceTypeTest(unittest.TestCase):53  """Class to test the Windows Registry value source type."""54  def testInitialize(self):55    """Tests the __init__ function."""56    key_value_pair = {'key': u'HKEY_LOCAL_MACHINE\\test', 'value': u'test'}57    source_type.WindowsRegistryValueSourceType(key_value_pairs=[key_value_pair])58    key_value_pair = {'bad': u'test', 'value': u'test'}59    with self.assertRaises(errors.FormatError):60      source_type.WindowsRegistryValueSourceType(61          key_value_pairs=[key_value_pair])62    with self.assertRaises(errors.FormatError):63      source_type.WindowsRegistryValueSourceType(key_value_pairs=key_value_pair)64class WMIQuerySourceTypeTest(unittest.TestCase):65  """Class to test the WMI query source type."""66  def testInitialize(self):67    """Tests the __init__ function."""68    source_type.WMIQuerySourceType(query=u'test')69class SourceTypeFactoryTest(unittest.TestCase):70  """Class to test the source type factory."""71  def testCreateSourceType(self):72    """Tests the source type creation."""73    source_type.SourceTypeFactory.RegisterSourceTypes([TestSourceType])74    with self.assertRaises(KeyError):75      source_type.SourceTypeFactory.RegisterSourceTypes([TestSourceType])76    source_object = source_type.SourceTypeFactory.CreateSourceType(77        u'test', {u'test': u'test123'})78    self.assertIsNotNone(source_object)79    self.assertEqual(source_object.test, u'test123')80    with self.assertRaises(errors.FormatError):81      source_object = source_type.SourceTypeFactory.CreateSourceType(u'test',82                                                                     {})83    with self.assertRaises(errors.FormatError):84      source_object = source_type.SourceTypeFactory.CreateSourceType(u'bogus',85                                                                     {})86    source_type.SourceTypeFactory.DeregisterSourceType(TestSourceType)87  def testRegisterSourceType(self):88    """Tests the source type registration functions."""89    expected_number_of_source_types = len(90        source_type.SourceTypeFactory.GetSourceTypes())91    source_type.SourceTypeFactory.RegisterSourceType(TestSourceType)92    number_of_source_types = len(source_type.SourceTypeFactory.GetSourceTypes())93    self.assertEqual(number_of_source_types,94                     expected_number_of_source_types + 1)95    source_type.SourceTypeFactory.DeregisterSourceType(TestSourceType)96    number_of_source_types = len(source_type.SourceTypeFactory.GetSourceTypes())97    self.assertEqual(number_of_source_types, expected_number_of_source_types)98  def testRegisterSourceTypeRaisesWhenAlreadyRegistered(self):99    """Tests the source type registration functions when already registered."""100    source_type.SourceTypeFactory.RegisterSourceType(TestSourceType)101    with self.assertRaises(KeyError):102      source_type.SourceTypeFactory.RegisterSourceType(TestSourceType)103    source_type.SourceTypeFactory.DeregisterSourceType(TestSourceType)104if __name__ == '__main__':...

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