diff --git a/__pycache__/controller_db.cpython-38.pyc b/__pycache__/controller_db.cpython-38.pyc index 5a26e8d..5d0f6e5 100644 Binary files a/__pycache__/controller_db.cpython-38.pyc and b/__pycache__/controller_db.cpython-38.pyc differ diff --git a/__pycache__/model.cpython-38.pyc b/__pycache__/model.cpython-38.pyc index 0bc9bb7..b36741a 100644 Binary files a/__pycache__/model.cpython-38.pyc and b/__pycache__/model.cpython-38.pyc differ diff --git a/__pycache__/orm.cpython-38.pyc b/__pycache__/orm.cpython-38.pyc index dcf5f47..a0029cc 100644 Binary files a/__pycache__/orm.cpython-38.pyc and b/__pycache__/orm.cpython-38.pyc differ diff --git a/controller_db.py b/controller_db.py index 76f6928..1af807f 100644 --- a/controller_db.py +++ b/controller_db.py @@ -8,7 +8,8 @@ import random import pandas as pd import numpy as np import platform - +import networkx as nx +import json class ControllerDB: dct_parameter = None @@ -41,6 +42,8 @@ class ControllerDB: Firm = pd.read_csv("Firm_amended.csv") Firm['Code'] = Firm['Code'].astype('string') Firm.fillna(0, inplace=True) + + # fill dct_lst_init_remove_firm_prod list_dct = [] for _, row in Firm.iterrows(): code = row['Code'] @@ -52,19 +55,35 @@ class ControllerDB: list_dct = [{'133': ['1.4.4.1']}] # list_dct = [{'2': ['1.1.3']}] # list_dct = [{'135': ['1.3.2.1']}] + + # fill g_bom + BomNodes = pd.read_csv('BomNodes.csv', index_col=0) + BomNodes.set_index('Code', inplace=True) + BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0) + BomCateNet.fillna(0, inplace=True) + g_bom = nx.from_pandas_adjacency(BomCateNet.T, + create_using=nx.MultiDiGraph()) + bom_labels_dict = {} + for code in g_bom.nodes: + bom_labels_dict[code] = BomNodes.loc[code].to_dict() + nx.set_node_attributes(g_bom, bom_labels_dict) + g_product_js = json.dumps(nx.adjacency_data(g_bom)) + + # insert exp for idx_exp, dct in enumerate(list_dct): self.add_experiment_1(idx_exp, self.dct_parameter['n_max_trial'], - dct) + dct, g_product_js) # same g_bom for all exp print(f'Inserted experiment for exp {idx_exp}!') def add_experiment_1(self, idx_exp, n_max_trial, - dct_lst_init_remove_firm_prod): + dct_lst_init_remove_firm_prod, g_bom): e = Experiment( idx_exp=idx_exp, n_sample=int(self.dct_parameter['n_sample']), n_iter=int(self.dct_parameter['n_iter']), n_max_trial=n_max_trial, - dct_lst_init_remove_firm_prod=dct_lst_init_remove_firm_prod) + dct_lst_init_remove_firm_prod=dct_lst_init_remove_firm_prod, + g_bom=g_bom) db_session.add(e) db_session.commit() diff --git a/model.py b/model.py index 37cde3b..8aeddea 100644 --- a/model.py +++ b/model.py @@ -7,6 +7,7 @@ from firm import FirmAgent from product import ProductAgent from orm import db_session, Result import platform +import json class Model(ap.Model): @@ -20,18 +21,8 @@ class Model(ap.Model): self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod # init graph bom - BomNodes = pd.read_csv('BomNodes.csv', index_col=0) - BomNodes.set_index('Code', inplace=True) - BomCateNet = pd.read_csv('BomCateNet.csv', index_col=0) - BomCateNet.fillna(0, inplace=True) - - G_bom = nx.from_pandas_adjacency(BomCateNet.T, - create_using=nx.MultiDiGraph()) - - bom_labels_dict = {} - for code in G_bom.nodes: - bom_labels_dict[code] = BomNodes.loc[code].to_dict() - nx.set_node_attributes(G_bom, bom_labels_dict) + G_bom = nx.adjacency_graph(json.loads(self.p.g_bom)) + self.product_network = ap.Network(self, G_bom) # init graph firm Firm = pd.read_csv("Firm_amended.csv") @@ -90,13 +81,8 @@ class Model(ap.Model): }) for succ_firm in lst_choose_firm] G_Firm.add_edges_from(lst_add_edge) + self.sample.g_firm = json.dumps(nx.adjacency_data(G_Firm)) self.firm_network = ap.Network(self, G_Firm) - self.product_network = ap.Network(self, G_bom) - - out_file = open("myfile.json", "w") - import json - json.dump(nx.adjacency_data(G_Firm), out_file) - out_file.close() # init product for ag_node, attr in self.product_network.graph.nodes(data=True): @@ -151,6 +137,10 @@ class Model(ap.Model): # draw network # self.draw_network() + out_file = open("myfile.json", "w") + json.dump(nx.adjacency_data(G_Firm), out_file) + out_file.close() + def update(self): self.a_lst_total_firms.clean_before_time_step() # output diff --git a/myfile.json b/myfile.json index c5acb43..67a56c0 100644 --- a/myfile.json +++ b/myfile.json @@ -1 +1 @@ -{"directed": true, "multigraph": true, "graph": [], "nodes": [{"Name": "360\u79d1\u6280", "Type_Region": "Beijing", "Revenue_Log": 23.11111077389128, "Product_Code": ["1.4.4"], "id": "0"}, {"Name": "51WORLD", "Type_Region": "Beijing", "Revenue_Log": 18.74276424312148, "Product_Code": ["2.1.1.5"], "id": "1"}, {"Name": "706\u6240", "Type_Region": "Beijing", "Revenue_Log": 18.91495248366715, "Product_Code": ["1.1.3"], "id": "2"}, {"Name": "\u827e\u514b\u65af\u7279", "Type_Region": "Beijing", "Revenue_Log": 17.407722170008157, "Product_Code": ["1.3.1.4", "1.3.1.5", "1.3.1.6", "1.3.4.1"], "id": "3"}, {"Name": "\u7231\u521b\u79d1\u6280", "Type_Region": "Beijing", "Revenue_Log": 19.6558070150236, "Product_Code": ["1.2.2"], "id": "4"}, {"Name": "\u5b89\u534e\u91d1\u548c", "Type_Region": "Beijing", "Revenue_Log": 18.503133696279523, "Product_Code": ["1.4.5.3", "1.4.5.4", "1.4.5.5", "1.4.5.9"], "id": "5"}, {"Name": "\u5b89\u4e16\u4e9a\u592a", "Type_Region": "Beijing", "Revenue_Log": 18.884414760184505, "Product_Code": ["1.3.1.2", "2.1.2.1", "2.1.2.2", "2.1.2.3", "2.1.2.4"], "id": "6"}, {"Name": "\u767e\u671b", "Type_Region": "Beijing", "Revenue_Log": 20.22851328801836, "Product_Code": ["2.2"], "id": "7"}, {"Name": "\u6886\u6886\u5b89\u5168", "Type_Region": "Beijing", "Revenue_Log": 20.02307783148127, "Product_Code": ["1.4.1.1"], "id": "8"}, {"Name": "\u5317\u4eac\u822a\u5929\u6d4b\u63a7", "Type_Region": "Beijing", "Revenue_Log": 20.12502380787598, "Product_Code": ["1.3.3.6", "1.3.3.7"], "id": "9"}, {"Name": "\u5317\u4eac\u82f1\u8d1d\u601d", 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"key": 0}, {"Product": "2.3.2", "id": "155", "key": 0}], [{"Product": "1.1.1", "id": "126", "key": 0}], []]} \ No newline at end of file diff --git a/network.png b/network.png index b9607cc..a4267dd 100644 Binary files a/network.png and b/network.png differ diff --git a/network1.png b/network1.png new file mode 100644 index 0000000..a4267dd Binary files /dev/null and b/network1.png differ diff --git a/orm.py b/orm.py index de49054..4bc80a8 100644 --- a/orm.py +++ b/orm.py @@ -46,6 +46,7 @@ class Experiment(Base): # variables n_max_trial = Column(Integer, nullable=False) dct_lst_init_remove_firm_prod = Column(PickleType, nullable=False) + g_bom = Column(Text(4294000000), nullable=False) sample = relationship('Sample', back_populates='experiment', lazy='dynamic') @@ -65,7 +66,7 @@ class Sample(Base): ts_done = Column(DateTime(timezone=True), onupdate=func.now()) stop_t = Column(Integer, nullable=True) - firm_network = Column(Text(4294000000), nullable=True) + g_firm = Column(Text(4294000000), nullable=True) experiment = relationship('Experiment', back_populates='sample', uselist=False) result = relationship('Result', back_populates='sample', lazy='dynamic') diff --git a/test.ipynb b/test.ipynb index d3e1e55..0c34af9 100644 --- a/test.ipynb +++ b/test.ipynb @@ -65,13 +65,6 @@ "list_succ_firms = [1, 1]\n", "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/test.py b/test.py new file mode 100644 index 0000000..c603322 --- /dev/null +++ b/test.py @@ -0,0 +1,28 @@ +import networkx as nx +import json + +in_file = open("myfile.json", "r") +js_file = json.load(in_file) +in_file.close() +firm_network = nx.adjacency_graph(js_file) + +import matplotlib.pyplot as plt + +plt.rcParams['font.sans-serif'] = 'SimHei' +pos = nx.nx_agraph.graphviz_layout(firm_network, prog="twopi", args="") +node_label = nx.get_node_attributes(firm_network, 'Name') +# print(node_label) +node_degree = dict(firm_network.out_degree()) +node_label = { + key: f"{node_label[key]} {node_degree[key]}" + for key in node_label.keys() +} +node_size = list(nx.get_node_attributes(firm_network, 'Revenue_Log').values()) +node_size = list(map(lambda x: x**2, node_size)) +edge_label = nx.get_edge_attributes(firm_network, "Product") +# multi(di)graphs, the keys are 3-tuples +edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()} +plt.figure(figsize=(12, 12), dpi=300) +nx.draw(firm_network, pos, node_size=node_size, labels=node_label, font_size=6) +nx.draw_networkx_edge_labels(firm_network, pos, edge_label, font_size=4) +plt.savefig("network1.png")