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How to use transform_in method in pandera

Best Python code snippet using pandera_python

bresanham_line.py

Source:bresanham_line.py

...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,...

loaders.py

Source:loaders.py

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))...

LinkedTransformTest.py

Source:LinkedTransformTest.py

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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