How to use transform_in method in pandera

Best Python code snippet using pandera_python

bresanham_line.py

Source:bresanham_line.py Github

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...122 transform_out = transform_in123 transform_final = lambda point: Point(transform_out(point).x + start.x,124 transform_out(point).y + start.y)125 return (transform_final(point) for point in126 bresanham_quad0(Point(0, 0), transform_in(end)))127if __name__ == '__main__':128 parser = argparse.ArgumentParser(129 formatter_class=argparse.ArgumentDefaultsHelpFormatter)130 parser.add_argument("X_START",131 type=int,132 help="X coordinate of starting point")133 parser.add_argument("Y_START",134 type=int,135 help="Y coordinate of starting point")136 parser.add_argument("X_END",137 type=int,138 help="X coordinate of ending point")139 parser.add_argument("Y_END",140 type=int,...

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loaders.py

Source:loaders.py Github

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1from torchvision import datasets2from torch.utils.data import DataLoader3class LoadData:4 def __init__(self, dataset, transform_in, args):5 dataset = dataset.upper()6 if dataset == 'MNIST':7 self.train_loader, self.test_loader, self.train_set, self.test_set = self.mnist(transform_in, args)8 elif dataset == 'FMNIST':9 self.train_loader, self.test_loader, self.train_set, self.test_set = self.fmnist(transform_in, args)10 elif dataset == 'CIFAR10':11 self.train_loader, self.test_loader, self.train_set, self.test_set = self.cifar10(transform_in, args)12 elif dataset == 'CIFAR100':13 self.train_loader, self.test_loader, self.train_set, self.test_set = self.cifar100(transform_in, args)14 else:15 print('Must choose a dataset')16 #print(f"Training Input Shape: {self.train_set.data[0].shape}")17 #print(f"Test Input Shape: {self.test_set.data[0].shape}")18 def get_datasets(self):19 return self.train_set, self.test_set20 def get_loaders(self):21 return self.train_loader, self.test_loader22 @staticmethod23 def mnist(transform_in, args):24 train_set = datasets.MNIST(args.data_path, train=True, download=True,25 transform=transform_in)26 train_loader = DataLoader(train_set, batch_size=args.batch_size,27 shuffle=True, num_workers=args.num_workers,28 drop_last=True)29 test_set = datasets.MNIST(args.data_path, train=False, download=True,30 transform=transform_in)31 test_loader = DataLoader(test_set, batch_size=args.batch_size,32 shuffle=False, num_workers=round(args.num_workers / 2),33 drop_last=True)34 return train_loader, test_loader, train_set, test_set35 @staticmethod36 def fmnist(transform_in, args):37 train_set = datasets.FashionMNIST(args.data_path, train=True, download=True,38 transform=transform_in)39 train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)40 test_set = datasets.FashionMNIST(args.data_path, train=False, download=True,41 transform=transform_in)42 test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=round(args.num_workers / 2))43 return train_loader, test_loader, train_set, test_set44 @staticmethod45 def cifar10(transform_in, args):46 train_set = datasets.CIFAR10(args.data_path, train=True, download=True,47 transform=transform_in)48 train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)49 test_set = datasets.CIFAR10(args.data_path, train=False, download=True,50 transform=transform_in)51 test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=round(args.num_workers / 2))52 return train_loader, test_loader, train_set, test_set53 @staticmethod54 def cifar100(transform_in, args):55 train_set = datasets.CIFAR100(args.data_path, train=True, download=True,56 transform=transform_in)57 train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)58 test_set = datasets.CIFAR100(args.data_path, train=False, download=True,59 transform=transform_in)60 test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=round(args.num_workers / 2))...

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LinkedTransformTest.py

Source:LinkedTransformTest.py Github

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1import unittest2from kivy.event import EventDispatcher3from .LinkedTransform import LinkedTransform4import numpy as np5import cv26from pathlib import Path7class LinkedTransformMock(LinkedTransform):8 def __init__(self, *args, **kwargs):9 self.received = []10 super().__init__(*args, **kwargs)11 def receive_frame(self, frame: np.ndarray):12 self.received.append(frame)13class SimpleObserver(EventDispatcher):14 pass15class LinkedTransformTest(unittest.TestCase):16 @classmethod17 def get_test_image(cls) -> np.ndarray:18 return cv2.imread(str((Path(__file__).parent.parent / Path('resource/test/Archaeologist-Tux-icon.png'))))19 def test_frames_are_passed_through(self):20 transform_in = LinkedTransform()21 transform_out = LinkedTransformMock()22 transform_in.attach_sink(transform_out)23 frame = LinkedTransformTest.get_test_image()24 transform_in.receive_frame(frame)25 self.assertIn(frame, transform_out.received, 'Transform didn\'t passthrough the frame')26 def test_frame_is_being_transformed(self):27 transform_in = LinkedTransform()28 transform_out = LinkedTransformMock()29 transform_in.attach_sink(transform_out)30 frame = LinkedTransformTest.get_test_image()31 transform_in.transform_fn = np.transpose32 transform_in.receive_frame(frame)33 self.assertTrue((frame.T == transform_out.received).all())34 def test_observer_is_being_notified(self):35 transform_in = LinkedTransform()36 transform_out = LinkedTransformMock()37 transform_in.attach_sink(transform_out)38 results = {'received': False, 'processed': False}39 def register_frame_received(*args):40 results['received'] = True41 def register_frame_processed(*args):42 results['processed'] = True43 transform_in.bind(on_frame_received=register_frame_received, on_frame_processed=register_frame_processed)44 frame = LinkedTransformTest.get_test_image()45 transform_in.receive_frame(frame)46 self.assertTrue(results['received'], 'Observer did not receive "on_frame_received" event')...

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