def sub_test_store(self, readWrite): td = TrainData() x,y,w = self.createSimpleArray('int32'), self.createSimpleArray('float32'), self.createSimpleArray('int32') x_orig=x.copy() x2,y2,_ = self.createSimpleArray('float32'), self.createSimpleArray('float32'), self.createSimpleArray('int32') x2_orig=x2.copy() y_orig=y.copy() td._store([x,x2], [y,y2], [w]) if readWrite: td.writeToFile("testfile.tdjctd") td = TrainData() td.readFromFile("testfile.tdjctd") os.system('rm -f testfile.tdjctd') shapes = td.getNumpyFeatureShapes() self.assertEqual([[3, 5, 6], [1], [3, 5, 6], [1]], shapes,"shapes") self.assertEqual(2, td.nFeatureArrays()) self.assertEqual(2, td.nTruthArrays()) self.assertEqual(1, td.nWeightArrays()) f = td.transferFeatureListToNumpy(False) t = td.transferTruthListToNumpy(False) w = td.transferWeightListToNumpy(False) xnew = SimpleArray(f[0],np.array(f[1],dtype='int64')) self.assertEqual(x_orig, xnew) xnew = SimpleArray(f[2],np.array(f[3],dtype='int64')) self.assertEqual(x2_orig, xnew) ynew = SimpleArray(t[0],np.array(t[1],dtype='int64')) self.assertEqual(y_orig, ynew)
import numpy as np from DeepJetCore.TrainData import TrainData from argparse import ArgumentParser import matplotlib.pyplot as plt import matplotlib.image as mpimg import matplotlib.patches as patches import math from numba import jit from inference import collect_condensates, make_inference_dict td = TrainData() #td.readFromFile("../results_partial/predictions/pred_9.djctd") td.readFromFile("../data/test_data/9.djctd") td.x = td.transferFeatureListToNumpy() td.y = td.transferTruthListToNumpy() td.z = td.transferWeightListToNumpy() x = td.x y = td.y z = td.z print(len(x)) print(x[0].shape) print(x[1].shape) print(x[2].shape) #print(y.shape) #print(z.shape) data = make_inference_dict(td.x[0], td.x[1], td.x[2])