diff --git a/AmendFirm_20230216.ipynb b/AmendFirm_20230216.ipynb index 500a2fc..41ab6d9 100644 --- a/AmendFirm_20230216.ipynb +++ b/AmendFirm_20230216.ipynb @@ -5,15 +5,57 @@ "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Revenue_Log R-squared: 0.586\n", + "Model: OLS Adj. R-squared: 0.580\n", + "Method: Least Squares F-statistic: 94.86\n", + "Date: Sat, 01 Apr 2023 Prob (F-statistic): 1.85e-14\n", + "Time: 18:07:54 Log-Likelihood: -128.58\n", + "No. Observations: 69 AIC: 261.2\n", + "Df Residuals: 67 BIC: 265.6\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "==================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "----------------------------------------------------------------------------------\n", + "Intercept 14.3160 0.806 17.757 0.000 12.707 15.925\n", + "Num_Employ_Log 0.9728 0.100 9.740 0.000 0.773 1.172\n", + "==============================================================================\n", + "Omnibus: 55.594 Durbin-Watson: 1.958\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 244.671\n", + "Skew: 2.451 Prob(JB): 7.42e-54\n", + "Kurtosis: 10.815 Cond. No. 34.6\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "['1', '1.1', '1.2', '1.3', '1.4', '2', '1.1.1', '1.1.2', '1.1.3', '1.2.1', '1.2.2', '1.2.3', '1.3.1', '1.3.2', '1.3.3', '1.3.4', '1.3.5', '1.4.1', '1.4.2', '1.4.3', '1.4.4', '1.4.5', '2.1', '2.2', '2.3', '1.3.1.1', '1.3.1.2', '1.3.1.3', '1.3.1.4', '1.3.1.5', '1.3.1.6', '1.3.1.7', '1.3.2.1', '1.3.3.1', '1.3.3.2', '1.3.3.3', '1.3.3.4', '1.3.3.5', '1.3.3.6', '1.3.3.7', '1.3.4.1', '1.3.4.2', '1.3.4.3', '1.3.5.1', '1.4.1.1', '1.4.1.2', '1.4.1.3', '1.4.1.4', '1.4.1.5', '1.4.2.1', '1.4.2.2', '1.4.2.3', '1.4.2.4', '1.4.2.5', '1.4.2.6', '1.4.2.7', '1.4.3.1', '1.4.3.2', '1.4.3.3', '1.4.3.4', '1.4.3.5', '1.4.3.6', '1.4.4.1', '1.4.4.2', '1.4.4.3', '1.4.4.4', '1.4.4.5', '1.4.5.1', '1.4.5.2', '1.4.5.3', '1.4.5.4', '1.4.5.5', '1.4.5.6', '1.4.5.7', '1.4.5.8', '1.4.5.9', '2.1.1', '2.1.2', '2.1.3', '2.1.4', '2.3.1', '2.3.2', '2.3.3', '2.1.1.1', '2.1.1.2', '2.1.1.3', '2.1.1.4', '2.1.1.5', '2.1.2.1', '2.1.2.2', '2.1.2.3', '2.1.2.4', '2.1.3.1', '2.1.3.2', '2.1.3.3', '2.1.3.4', '2.1.3.5', '2.1.3.6', '2.1.3.7', '2.1.4.1', '2.1.4.2', '2.1.4.1.1', '2.1.4.1.2', '2.1.4.1.3', '2.1.4.1.4', '2.1.4.2.1', '2.1.4.2.2']\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_1080\\1275047540.py:48: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_17316\\1464358194.py:49: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " Firm_copy.insert(Firm_copy.columns.get_loc('Revenue'),'Revenue_Log', series_Revenue_Log)\n", - "C:\\Users\\25759\\AppData\\Local\\Temp\\ipykernel_1080\\1275047540.py:50: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", + "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_17316\\1464358194.py:51: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n", " Firm_copy.insert(Firm_copy.columns.get_loc('Num_Employ'),'Num_Employ_Log', series_Num_Employ_Log)\n" ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -60,7 +102,8 @@ "ols_results = ols_model.fit()\n", "b = ols_results.params.Intercept\n", "a = ols_results.params.Num_Employ_Log\n", - "# sns.regplot(x='Num_Employ_Log',y='Revenue_Log',data=data_ols[data_ols.notnull().all(axis=1)])\n", + "print(ols_results.summary())\n", + "sns.regplot(x='Num_Employ_Log',y='Revenue_Log',data=data_ols[data_ols.notnull().all(axis=1)])\n", "\n", "Firm_copy.loc[Firm_copy['Revenue_Log'].isnull(), 'Revenue_Log'] = Firm_copy[Firm_copy['Revenue_Log'].isnull()]['Num_Employ_Log'].map(lambda x: a*x + b)\n", "series_Revenue_Log = Firm_copy.pop('Revenue_Log')\n", @@ -115,7 +158,7 @@ " print(f\"Warning: node, {node}, in tier 0 does not contain firm\")\n", "\n", "# output\n", - "Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')" + "# Firm_copy.to_csv('Firm_amended.csv', index=False, encoding='utf-8-sig')" ] }, { @@ -276,12 +319,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "bcdafc093860683ffb58d6956591562b7f8ed5d58147d17d71a5d4d6605a08df" + "hash": "a90aeebcf29d64a654773811cc170cb25061cb2498f10ac689db374c7bf325de" } } }, diff --git a/analysis/count.csv b/analysis/count.csv new file mode 100644 index 0000000..9b72392 --- /dev/null +++ b/analysis/count.csv @@ -0,0 +1,3539 @@ +s_id,id_firm,id_product,ts,is_disrupted,is_removed +8257,49,1.3.1.4,0,1,1.0 +8257,100,1.3.1,1,1, +1369,13,2.1.3.3,0,1,1.0 +1369,106,2.1.3,1,1,1.0 +21519,149,2.1.2.4,0,1,1.0 +21519,58,2.1.2,1,1, +15317,99,2.1,0,1,1.0 +15317,102,2,1,1, +3165,22,2.1.3.3,0,1,1.0 +3165,148,2.1.3,1,1, +5733,36,1.1.1,0,1,1.0 +5733,94,1.1,1,1, +19407,135,2.1.3.5,0,1,1.0 +19407,148,2.1.3,1,1, +13052,79,2.1.3.7,0,1,1.0 +13052,106,2.1.3,1,1, +5599,33,2.1.2.4,0,1,1.0 +5599,79,2.1.2,1,1,1.0 +11157,62,2.1.2.4,0,1,1.0 +11157,159,2.1.2,1,1, 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/dev/null +++ b/analysis/count_dcp_prod.csv @@ -0,0 +1,98 @@ +up_id_product,up_name_product,down_id_product,down_name_product,count +1.4,工业互联网安全,1,供给,118 +1.4.3,网络安全,1.4,工业互联网安全,96 +1.4.5,数据安全,1.4,工业互联网安全,92 +1.4.2,控制安全,1.4,工业互联网安全,92 +2.1,PaaS,2,工业互联网平台,77 +1.4.4.5,安全态势感知,1.4.4,平台安全,76 +1.3.2.1,供应链管理SCM,1.3.2,采购供应,76 +1.3.2,采购供应,1.3,工业软件,74 +1.3.5,仓储物流,1.3,工业软件,72 +1.1.1,工业计算芯片,1.1,工业自动化,67 +1.4.5.8,数据加密,1.4.5,数据安全,50 +1.4.5.1,恶意代码检测系统,1.4.5,数据安全,50 +1.4.3.6,沙箱类设备,1.4.3,网络安全,50 +1.4.2.7,工控原生安全,1.4.2,控制安全,50 +1.4.2.3,工控漏洞扫描,1.4.2,控制安全,50 +1.4.1,设备安全,1.4,工业互联网安全,50 +1.4.3.2,流量检测,1.4.3,网络安全,50 +2.3.3,协议转换,2.3,边缘层,37 +1.3.2.1,供应链管理SCM,1.3,工业软件,37 +2.3.1,工业数据接入,2.3,边缘层,33 +2.1.3.6,微服务,2.1.3,工业物联网,33 +2.3.2,边缘数据处理,2.3,边缘层,30 +2.1.3.4,应用管理服务,2.1.3,工业物联网,30 +2.1.2.4,行业机理模型,2.1.2,工业模型库,30 +2.1.2.2,业务流程模型,2.1.2,工业模型库,28 +2.1.3.7,制造类API,2.1.3,工业物联网,28 +1.3.1.1,计算机辅助设计CAD,1.3.1,设计研发,28 +2.1.2.1,数据算法模型,2.1.2,工业模型库,27 +1.3.1.2,计算机辅助工程CAE,1.3.1,设计研发,26 +2.1.3.1,物联网服务,2.1.3,工业物联网,25 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b/analysis/count_dcp_prod_network20230407.png new file mode 100644 index 0000000..03b40d2 Binary files /dev/null and b/analysis/count_dcp_prod_network20230407.png differ diff --git a/analysis/count_prod.xlsx b/analysis/count_prod.xlsx new file mode 100644 index 0000000..d6dba8b Binary files /dev/null and b/analysis/count_prod.xlsx differ diff --git a/analysis/count_prod_network20230406.png b/analysis/count_prod_network20230406.png new file mode 100644 index 0000000..e071a3f Binary files /dev/null and b/analysis/count_prod_network20230406.png differ diff --git a/analysis/count_prod_pie.png b/analysis/count_prod_pie.png new file mode 100644 index 0000000..b7e8737 Binary files /dev/null and b/analysis/count_prod_pie.png differ diff --git a/analysis/g_bom_exp_id_1.png b/analysis/g_bom_exp_id_1.png index 2763e2b..7ff2bf1 100644 Binary files a/analysis/g_bom_exp_id_1.png and b/analysis/g_bom_exp_id_1.png differ diff --git a/analysis_count.py b/analysis_count.py new file mode 100644 index 0000000..eb7e27c --- /dev/null +++ b/analysis_count.py @@ -0,0 +1,8 @@ +import pandas as pd + +count = pd.read_csv("analysis\\count.csv", dtype={'s_id': str, 'id_firm': str}) +print(count) +print(len(count['s_id'].unique())) +count_max_ts = count.groupby('s_id')['ts'].max() +print(count_max_ts.value_counts()) +print(count_max_ts.value_counts()/1593) \ No newline at end of file diff --git a/analysis_firm_network.py b/analysis_firm_network.py new file mode 100644 index 0000000..53e4d97 --- /dev/null +++ b/analysis_firm_network.py @@ -0,0 +1,101 @@ +import pandas as pd +import matplotlib.pyplot as plt +import networkx as nx +import math + +plt.rcParams['font.sans-serif'] = 'SimHei' + +# count firm category +count_firm = pd.read_csv("analysis\\count_firm.csv") +count_firm = count_firm[count_firm['count'] > 4] +print(count_firm.describe()) + +count_dcp = pd.read_csv("analysis\\count_dcp.csv", + dtype={ + 'up_id_firm': str, + 'down_id_firm': str + }) +# print(count_dcp) +count_dcp = count_dcp[count_dcp['count'] > 2] + +list_firm = count_dcp['up_id_firm'].tolist( +) + count_dcp['down_id_firm'].tolist() +list_firm = list(set(list_firm)) + +# init graph firm +Firm = pd.read_csv("Firm_amended.csv") +Firm['Code'] = Firm['Code'].astype('string') +Firm.fillna(0, inplace=True) +Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]] +firm_product = [] +for _, row in Firm.loc[:, '1':].iterrows(): + firm_product.append(row[row == 1].index.to_list()) +Firm_attr.loc[:, 'Product_Code'] = firm_product +Firm_attr.set_index('Code', inplace=True) + +G_firm = nx.MultiDiGraph() +G_firm.add_nodes_from(list_firm) + +firm_labels_dict = {} +for code in G_firm.nodes: + firm_labels_dict[code] = Firm_attr.loc[code].to_dict() +nx.set_node_attributes(G_firm, firm_labels_dict) + +count_max = count_dcp['count'].max() +count_min = count_dcp['count'].min() +k = 5 / (count_max - count_min) +for _, row in count_dcp.iterrows(): + # print(row) + lst_add_edge = [( + row['up_id_firm'], + row['down_id_firm'], + { + 'up_id_product': row['up_id_product'], + 'up_name_product': row['up_name_product'], + 'down_id_product': row['down_id_product'], + 'down_name_product': row['down_name_product'], + # 'edge_label': f"{row['up_id_product']} {row['up_name_product']} - {row['down_id_product']} {row['down_name_product']}", + 'edge_label': f"{row['up_id_product']} - {row['down_id_product']}", + 'edge_width': k * (row['count'] - count_min), + 'count': row['count'] + })] + G_firm.add_edges_from(lst_add_edge) + +# dcp_networkx +pos = nx.nx_agraph.graphviz_layout(G_firm, prog="dot", args="") +node_label = nx.get_node_attributes(G_firm, 'Name') +# node_degree = dict(G_firm.out_degree()) +node_label = { + # key: f"{node_label[key]} {node_degree[key]}" + key: f"{node_label[key]}" + for key in node_label.keys() +} +node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values()) +node_size = list(map(lambda x: x**2, node_size)) +edge_label = nx.get_edge_attributes(G_firm, "edge_label") +edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()} +edge_width = nx.get_edge_attributes(G_firm, "edge_width") +edge_width = [w for (n1, n2, _), w in edge_width.items()] +colors = nx.get_edge_attributes(G_firm, "count") +colors = [w for (n1, n2, _), w in colors.items()] +vmin = min(colors) +vmax = max(colors) +cmap = plt.cm.Blues +fig = plt.figure(figsize=(10, 8), dpi=300) +nx.draw(G_firm, + pos, + node_size=node_size, + labels=node_label, + font_size=6, + width = 3, + edge_color=colors, + edge_cmap=cmap, + edge_vmin=vmin, + edge_vmax=vmax) +nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=6) +sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) +sm._A = [] +position=fig.add_axes([0.9, 0.05, 0.01, 0.3]) +plt.colorbar(sm, fraction=0.01, cax=position) +# plt.savefig("analysis\\count_dcp_network") +plt.close() diff --git a/analysis_prod.py b/analysis_prod.py new file mode 100644 index 0000000..b191d22 --- /dev/null +++ b/analysis_prod.py @@ -0,0 +1,22 @@ +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import networkx as nx +import math + +plt.rcParams['font.sans-serif'] = 'SimHei' + +Firm = pd.read_csv("Firm_amended.csv") +Firm['Code'] = Firm['Code'].astype('string') +Firm.fillna(0, inplace=True) +count_prod = pd.read_csv("analysis\\count_prod.csv") +for index, row in count_prod.iterrows(): + count_prod.loc[index, 'num_firm'] = sum(Firm[row['id_product']]==1) + count_prod.loc[index, 'avg_size'] = Firm.loc[Firm[row['id_product']]==1, 'Revenue_Log'].median() + + +print(count_prod) +# sns.scatterplot(x='count', y='avg_size',data=count_prod) +ax = plt.subplot(projection = '3d') # 创建一个三维的绘图工程 +ax.scatter(count_prod['avg_size'], count_prod['num_firm'], count_prod['count'], c = 'r') +plt.show() diff --git a/analysis_prod_network.py b/analysis_prod_network.py new file mode 100644 index 0000000..6742eaf --- /dev/null +++ b/analysis_prod_network.py @@ -0,0 +1,176 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math + +plt.rcParams['font.sans-serif'] = 'SimHei' + +count_prod = pd.read_csv("analysis\\count_prod.csv") +print(count_prod) + +# category +print(count_prod.describe()) + +# pie +count_prod_trim = count_prod[count_prod['count'] > 50] +plt.pie(count_prod_trim['count'], labels=count_prod_trim['Name']) +plt.savefig("analysis\\count_prod_pie") +plt.close() + +# prod_networkx +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 = nx.from_pandas_adjacency(BomCateNet.T, create_using=nx.MultiDiGraph()) + +labels_dict = {} +for code in G.nodes: + node_attr = BomNodes.loc[code].to_dict() + index_list = count_prod[count_prod['id_product'] == code].index.tolist() + index = index_list[0] if len(index_list) == 1 else -1 + node_attr['count'] = count_prod['count'].get(index, 0) + node_attr['node_size'] = 5 * count_prod['count'].get(index, 0) + node_attr['node_color'] = count_prod['count'].get(index, 0) + labels_dict[code] = node_attr +nx.set_node_attributes(G, labels_dict) +# print(labels_dict) + +pos = nx.nx_agraph.graphviz_layout(G, prog="twopi", args="") +dict_node_name = nx.get_node_attributes(G, 'Name') +node_labels = {} +for node in nx.nodes(G): + node_labels[node] = f"{node} {str(dict_node_name[node])}" + # node_labels[node] = f"{str(dict_node_name[node])}" +colors = list(nx.get_node_attributes(G, 'node_color').values()) +vmin = min(colors) +vmax = max(colors) +cmap = plt.cm.Blues +fig = plt.figure(figsize=(10, 10), dpi=300) +nx.draw(G, + pos, + node_size=list(nx.get_node_attributes(G, 'node_size').values()), + labels=node_labels, + font_size=6, + node_color=colors, + cmap=cmap, + vmin=vmin, + vmax=vmax, + edge_color='grey') +sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) +sm._A = [] +position = fig.add_axes([0.01, 0.05, 0.01, 0.3]) +plt.colorbar(sm, fraction=0.01, cax=position) +# plt.savefig("analysis\\count_prod_network") +plt.close() + +# dcp_prod +count_dcp = pd.read_csv("analysis\\count_dcp.csv", + dtype={ + 'up_id_firm': str, + 'down_id_firm': str + }) +count_dcp_prod = count_dcp.groupby(['up_id_product','up_name_product', 'down_id_product', 'down_name_product'])['count'].sum() +count_dcp_prod = count_dcp_prod.reset_index() +count_dcp_prod.sort_values('count', inplace=True, ascending=False) +count_dcp_prod.to_csv('analysis\\count_dcp_prod.csv', + index=False, + encoding='utf-8-sig') +count_dcp_prod = count_dcp_prod[count_dcp_prod['count'] > 2] +# print(count_dcp_prod) + +list_prod = count_dcp_prod['up_id_product'].tolist( +) + count_dcp['down_id_product'].tolist() +list_prod = list(set(list_prod)) + +# init graph bom + +BomNodes = pd.read_csv('BomNodes.csv', index_col=0) +BomNodes.set_index('Code', inplace=True) + +g_bom = nx.MultiDiGraph() +g_bom.add_nodes_from(list_prod) + +bom_labels_dict = {} +for code in list_prod: + dct_attr = BomNodes.loc[code].to_dict() + bom_labels_dict[code] = dct_attr +nx.set_node_attributes(g_bom, bom_labels_dict) + + +count_max = count_dcp_prod['count'].max() +count_min = count_dcp_prod['count'].min() +k = 5 / (count_max - count_min) +for _, row in count_dcp_prod.iterrows(): + # print(row) + lst_add_edge = [( + row['up_id_product'], + row['down_id_product'], + { + 'count': row['count'] + })] + g_bom.add_edges_from(lst_add_edge) + +# dcp_networkx +pos = nx.nx_agraph.graphviz_layout(g_bom, prog="dot", args="") +node_labels = nx.get_node_attributes(g_bom, 'Name') +temp = {} +for key, value in node_labels.items(): + temp[key] = key + " " + value +node_labels = temp +colors = nx.get_edge_attributes(g_bom, "count") +colors = [w for (n1, n2, _), w in colors.items()] +vmin = min(colors) +vmax = max(colors) +cmap = plt.cm.Blues + +# dct_row = {} +# for node, p in pos.items(): +# if p[1] not in dct_row.keys(): +# dct_row[p[1]] = {node: p} +# else: +# dct_row[p[1]][node] = p +# dct_row = dict(sorted(dct_row.items(), key=lambda d: d[0], reverse=True)) +# dct_up = dct_row[max(dct_row.keys())] +# dct_up = dict(sorted(dct_up.items(), key=lambda d: d[1][0], reverse=True)) +# h = list(dct_row.keys())[0] - list(dct_row.keys())[1] +# n = len(dct_up.items()) +# arr_h = np.linspace(list(dct_row.keys())[0]-h/2, list(dct_row.keys())[0]+2*h, num=n) +# dct_up_new = {} +# for index, (node, p) in enumerate(dct_up.items()): +# dct_up_new[node] = (p[0], arr_h[index]) +# pos_new = {} +# for row, dct in dct_row.items(): +# if row == list(dct_row.keys())[0]: +# pos_new.update(dct_up_new) +# else: +# pos_new.update(dct) +pos_new ={} +for node, p in pos.items(): + pos_new[node] = (p[1], p[0]) + +fig = plt.figure(figsize=(6, 10), dpi=300) +# plt.subplots_adjust(right=0.7) +nx.draw(g_bom, + pos_new, + node_size=50, + labels=node_labels, + font_size=6, + width = 1.5, + edge_color=colors, + edge_cmap=cmap, + edge_vmin=vmin, + edge_vmax=vmax) +plt.axis('off') +axis = plt.gca() +axis.set_xlim([1.2*x for x in axis.get_xlim()]) +axis.set_ylim([1.2*y for y in axis.get_ylim()]) + +sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax)) +sm._A = [] +position=fig.add_axes([0.1, 0.4, 0.01, 0.2]) +plt.colorbar(sm, fraction=0.01, cax=position) +# plt.savefig("analysis\\count_dcp_prod_network") +plt.close() \ No newline at end of file diff --git a/size_stats.py b/size_stats.py new file mode 100644 index 0000000..520fae3 --- /dev/null +++ b/size_stats.py @@ -0,0 +1,8 @@ +import pandas as pd + +Firm = pd.read_csv("Firm.csv") +data = Firm[['Revenue', 'Num_Employ','Size']] +print(data.describe(include='all')) +print(Firm[['Size']].value_counts()) + +print(Firm[['Source']].value_counts()) \ No newline at end of file diff --git a/analysis.py b/sum_result.py similarity index 98% rename from analysis.py rename to sum_result.py index bca4997..22a465d 100644 --- a/analysis.py +++ b/sum_result.py @@ -22,6 +22,10 @@ for s_id in lst_s_id: result.set_index('id', inplace=True) result.sort_index(inplace=True) result['id_firm'] = result['id_firm'].astype('string') +result.to_csv('analysis\\count.csv', + index=False, + encoding='utf-8-sig') +print(result) # G bom plt.rcParams['font.sans-serif'] = 'SimHei' diff --git a/test.ipynb b/test.ipynb index 0c34af9..b3c97e9 100644 --- a/test.ipynb +++ b/test.ipynb @@ -65,6 +65,77 @@ "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": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.17307692307692307, 0.19230769230769232, 0.20192307692307693, 0.21153846153846154, 0.22115384615384615]\n", + "[0.14899116146026878, 0.1819782155490595, 0.20111703154812216, 0.22226869439668717, 0.24564489704586234]\n", + "[0.10801741721030356, 0.16114305076975205, 0.19682056666851946, 0.2403971829915773, 0.29362178235984765]\n", + "[0.07643198434626533, 0.13926815562848321, 0.18799234648997357, 0.25376312466637047, 0.34254438886890737]\n" + ] + } + ], + "source": [ + "import math\n", + "size = [18,20,21,22,23]\n", + "p = [s / sum(size) for s in size]\n", + "print(p)\n", + "for beta in [0.1, 0.2, 0.3]:\n", + " damp_size = [math.exp(beta*s) for s in size]\n", + " print([s / sum(damp_size) for s in damp_size])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.16666666666666666, 0.5, 0.6666666666666666, 0.8333333333333334, 1.0]\n", + "[0.8359588020779368, 0.9330329915368074, 0.960264500792218, 0.9819330445619127, 1.0]\n", + "[0.408248290463863, 0.7071067811865476, 0.816496580927726, 0.9128709291752769, 1.0]\n", + "[0.23849484685087588, 0.5743491774985174, 0.7229811807984657, 0.8642810744472068, 1.0]\n" + ] + } + ], + "source": [ + "import math\n", + "size = [18,20,21,22,23]\n", + "p = [(s - min(size) + 1)/(max(size)-min(size)+1) for s in size]\n", + "print(p)\n", + "for beta in [0.1, 0.5, 0.8]:\n", + " p = [((s - min(size) + 1)/(max(size)-min(size)+1))**beta for s in size]\n", + " print(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "32\n" + ] + } + ], + "source": [ + "import multiprocess as mp\n", + "\n", + "print(mp.cpu_count())" + ] } ], "metadata": { @@ -83,7 +154,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.8.8" }, "orig_nbformat": 4, "vscode": {