Upload 53 files

.gitattributes

@@ -53,3 +53,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+PEMS03/PEMS03_data.csv filter=lfs diff=lfs merge=lfs -text
+PEMS07M/V_228.csv filter=lfs diff=lfs merge=lfs -text

CA_District5/CA_District5.npy

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CA_District5/CA_District5.npz

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CA_District5/generate_data.py

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+import numpy as np
+import pandas as pd
+
+year = '2017'  # please specify the year, our experiments use 2019
+
+gba_his = pd.read_hdf('gba_his_' + year +'.h5')
+value = gba_his.values
+value = value[:(31+28)*288, :]
+print(value.shape, value[value==0].shape)
+
+# np.savez('CA_District5.npz', data=value)
+datas = np.load('CA_District5.npz')
+print(datas.files)
+print(datas['data'])
+print(datas['data'].shape)
+
+adj = np.load('gba_rn_adj.npy')
+print(adj)
+print(adj.shape, adj.dtype)

NYC_BIKE/NYC_BIKE.npz

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NYC_BIKE/all_graph.h5

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NYC_BIKE/bike_data.h5

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NYC_BIKE/generate_data.py

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+import numpy as np
+import pandas as pd
+import h5py
+import torch
+
+# class StandardScaler:
+#     """
+#     Standard the input
+#     """
+#
+#     def __init__(self, mean, std):
+#         self.mean = mean
+#         self.std = std
+#
+#     def transform(self, data):
+#         return (data - self.mean) / self.std
+#
+#     def inverse_transform(self, data):
+#         if type(data) == torch.Tensor and type(self.mean) == np.ndarray:
+#             self.std = torch.from_numpy(self.std).to(data.device).type(data.dtype)
+#             self.mean = torch.from_numpy(self.mean).to(data.device).type(data.dtype)
+#         return (data * self.std) + self.mean
+#
+# f_bike = h5py.File('bike_data.h5','r')
+# f_bike.keys()
+# print([key for key in f_bike.keys()])
+#
+# f_graph= h5py.File('all_graph.h5','r')
+# f_graph.keys()
+# print([key for key in f_graph.keys()])
+# bike_graph = f_graph['dis_bb'][:]
+# print(f_graph['pcc_bb'][:].shape, f_graph['pcc_bb'][:])
+# print(f_graph['trans_bb'][:].shape, f_graph['trans_bb'][:])
+# bike_drop = np.expand_dims(f_bike['bike_drop'][:], axis=-1)
+# bike_pick = np.expand_dims(f_bike['bike_pick'][:], axis=-1)
+#
+# print(bike_drop[bike_drop==0].shape, bike_pick[bike_pick==0].shape)
+#
+# bike_demand = np.concatenate([bike_drop, bike_pick], axis=-1)
+# # np.savez('NYC_BIKE.npz', data=bike_demand)
+
+datas = np.load('NYC_BIKE.npz')
+print(datas.files)
+print(datas['data'])
+print(datas['data'].shape)
+
+# np.savetxt("NYC_BIKE.csv", bike_graph, delimiter=",")
+A = pd.read_csv("NYC_BIKE.csv", header=None).values.astype(np.float32)
+print(A.shape)
+print(A)
+
+
+# k = datas['data']
+# print(k.dtype, k[k==0].shape, k[k<0.1].shape)
+# scaler_data = StandardScaler(k.mean(), k.std())
+# k = torch.FloatTensor(k)
+# trans1 = scaler_data.transform(k)
+# trans2 = scaler_data.inverse_transform(trans1)
+#
+# print(trans2.dtype, trans2[trans2==0].shape)

PDFormer/CA_District5/CA_District5_dtw.npy

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PDFormer/CA_District5/CA_District5_pattern.npy

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PDFormer/CA_District5/CA_District5_sh_mx.npy

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PDFormer/NYC_BIKE/NYC_BIKE_dtw.npy

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PDFormer/NYC_BIKE/NYC_BIKE_pattern.npy

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PDFormer/NYC_BIKE/NYC_BIKE_sh_mx.npy

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PDFormer/PEMS04/PEMS04_dtw.npy

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PDFormer/PEMS04/PEMS04_pattern.npy

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PDFormer/PEMS04/PEMS04_sh_mx.npy

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PDFormer/PEMS07/PEMS07_dtw.npy

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PDFormer/PEMS07/PEMS07_pattern.npy

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PDFormer/PEMS07M/PEMS07M_dtw.npy

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PDFormer/PEMS07M/PEMS07M_pattern.npy

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PDFormer/PEMS07M/PEMS07M_sh_mx.npy

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PDFormer/PEMS08/PEMS08_dtw.npy

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PDFormer/PEMS08/PEMS08_pattern.npy

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PDFormer/chengdu_didi/chengdu_didi_dtw.npy

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PDFormer/chengdu_didi/chengdu_didi_pattern.npy

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PEMS07/PEMS07.npz

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PEMS07M/PEMS07M.npz

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PEMS07M/V_228.csv

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+size 72230400

PEMS07M/generate_data.py

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+import numpy as np
+import pandas as pd
+
+
+file_path = 'V_228.csv'
+df=pd.read_csv(file_path)
+traffic_speed = df.values
+traffic_speed = np.concatenate([traffic_speed, traffic_speed[0:1, :]], axis=0)
+np.savez('PEMS07M.npz', data=traffic_speed)
+datas = np.load('PEMS07M.npz')
+print(datas.files)
+print(datas['data'])
+print(datas['data'].shape, traffic_speed.shape)
+

PEMS08/PEMS08.csv

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PEMS08/PEMS08.npz

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