How to use seq method in fMBT

Best Python code snippet using fMBT_python

DatasetTest.py

Source:DatasetTest.py

1from torch.utils.data import Dataset2import numpy as np3import os4class DatasetTest(Dataset):5    def __init__(self, dataset_path, action_type, postex, global_max, global_min):6        super(DatasetTest, self).__init__()7        self.dataset_path = dataset_path + action_type + postex8        self.seq_45 = np.load(self.dataset_path)9        self.seq_45 = np.transpose(self.seq_45, (0, 2, 1))10        11        self.global_max = global_max12        self.global_min = global_min13        # Normalize14        self.seq_45 = (self.seq_45 - self.global_min) / (self.global_max - self.global_min)15        self.seq_45 = self.seq_45 * 2 - 116        # self.seq_pre_10 = self.seq_45[:, :10, :]17        # self.seq_mid_25 = self.seq_45[:, 10:35, :]18        # self.seq_post_10 = self.seq_45[:, 35:, :]19        # self.seq_pre_10 = self.seq_45[:, 9:10, :]20        # self.seq_mid_25 = self.seq_45[:, 10:35, :]21        # self.seq_post_10 = self.seq_45[:, 35:36, :]22        # self.seq_pre_10 = self.seq_45[:, 5:10, :]23        # self.seq_mid_25 = self.seq_45[:, 10:35, :]24        # self.seq_post_10 = self.seq_45[:, 35:40, :]25        # last_x = self.seq_45[:, 9, :]26        # first_z = self.seq_45[:, 35, :]27        #5_25_528        # self.seq_pre_10 = self.seq_45[:, :5, :]29        # self.seq_mid_25 = self.seq_45[:, 5:30, :]30        # self.seq_post_10 = self.seq_45[:, 30:, :]31        # last_x = self.seq_45[:, 4, :]32        # first_z = self.seq_45[:, 30, :]33        #10_25_1034        # self.seq_pre_10 = self.seq_45[:, :10, :]35        # self.seq_mid_25 = self.seq_45[:, 10:35, :]36        # self.seq_post_10 = self.seq_45[:, 35:, :]37        # last_x = self.seq_45[:, 9, :]38        # first_z = self.seq_45[:, 35, :]39        #15_25_1040        # self.seq_pre_10 = self.seq_45[:, :15, :]41        # self.seq_mid_25 = self.seq_45[:, 15:40, :]42        # self.seq_post_10 = self.seq_45[:, 40:, :]43        # last_x = self.seq_45[:, 14, :]44        # first_z = self.seq_45[:, 40, :]45        #20_25_2046        # self.seq_pre_10 = self.seq_45[:, :20, :]47        # self.seq_mid_25 = self.seq_45[:, 20:45, :]48        # self.seq_post_10 = self.seq_45[:, 45:, :]49        # last_x = self.seq_45[:, 19, :]50        # first_z = self.seq_45[:, 45, :]51        # print(self.seq_45.shape)52        self.seq_pre_10 = self.seq_45[:, 10:20, :]53        self.seq_mid_25 = self.seq_45[:, 20:45, :]54        self.seq_post_10 = self.seq_45[:, 45:55, :]55        last_x = self.seq_45[:, 19, :]56        first_z = self.seq_45[:, 45, :]57        self.mid_resid = np.linspace(last_x, first_z, 25).transpose((1, 0, 2))58        # self.mid_repx = np.repeat(self.seq_45[:, 9:10, :], (1, 25, 1))59        # self.mid_repz = np.repeat(self.seq_45[:, 35:36, :], (1, 25, 1))60        # self.seq_pre_10 = self.seq_pre_10[:48]61        # self.seq_mid_25 = self.seq_mid_25[:48]62        # self.mid_resid = self.mid_resid[:48]63        # self.seq_post_10 = self.seq_post_10[:48]64        self.data_num = len(self.seq_pre_10)65    def __len__(self):66        return self.data_num67    def __getitem__(self, item):68        output = {69            'pre_10': self.seq_pre_10[item],70            'mid_25': self.seq_mid_25[item],71            'mid_resid': self.mid_resid[item],72            'post_10': self.seq_post_10[item],73            # 'mid_repx':self.mid_repx[item],74            # 'mid_repz': self.mid_repz[item]75        }...

DatasetTrain.py

Source:DatasetTrain.py

1from torch.utils.data import Dataset2import numpy as np3import os4class DatasetTrain(Dataset):5    def __init__(self, dataset_path, mode_name, global_max=1e20, global_min=-1e20):6        super(DatasetTrain, self).__init__()7        self.dataset_path = dataset_path8        self.seq_45 = np.load(dataset_path)9        10        if mode_name == 'train':11            self.global_max = np.max(self.seq_45)12            self.global_min = np.min(self.seq_45)13        else:14            self.global_max = global_max15            self.global_min = global_min16        # Normalize17        self.seq_45 = (self.seq_45 - self.global_min) / (self.global_max - self.global_min)18        self.seq_45 = self.seq_45 * 2 - 119        ma = np.max(self.seq_45)20        mb = np.min(self.seq_45)21        # self.seq_pre_10 = self.seq_45[:, :10, :]22        # self.seq_mid_25 = self.seq_45[:, 10:35, :]23        # self.seq_post_10 = self.seq_45[:, 35:, :]24        # self.seq_pre_10 = self.seq_45[:, 9:10, :]25        # self.seq_mid_25 = self.seq_45[:, 10:35, :]26        # self.seq_post_10 = self.seq_45[:, 35:36, :]27        # self.seq_pre_10 = self.seq_45[:, 5:10, :]28        # self.seq_mid_25 = self.seq_45[:, 10:35, :]29        # self.seq_post_10 = self.seq_45[:, 35:40, :]30        # last_x = self.seq_45[:, 9, :]31        # first_z = self.seq_45[:, 35, :]32        #5_25_533        # self.seq_pre_10 = self.seq_45[:, :5, :]34        # self.seq_mid_25 = self.seq_45[:, 5:30, :]35        # self.seq_post_10 = self.seq_45[:, 30:, :]36        # last_x = self.seq_45[:, 4, :]37        # first_z = self.seq_45[:, 30, :]38        #10_25_1039        # self.seq_pre_10 = self.seq_45[:, :10, :]40        # self.seq_mid_25 = self.seq_45[:, 10:35, :]41        # self.seq_post_10 = self.seq_45[:, 35:, :]42        # last_x = self.seq_45[:, 9, :]43        # first_z = self.seq_45[:, 35, :]44        #15_25_1545        # self.seq_pre_10 = self.seq_45[:, :15, :]46        # self.seq_mid_25 = self.seq_45[:, 15:40, :]47        # self.seq_post_10 = self.seq_45[:, 40:, :]48        # last_x = self.seq_45[:, 14, :]49        # first_z = self.seq_45[:, 40, :]50        #20_25_2051        # self.seq_pre_10 = self.seq_45[:, :20, :]52        # self.seq_mid_25 = self.seq_45[:, 20:45, :]53        # self.seq_post_10 = self.seq_45[:, 45:, :]54        # last_x = self.seq_45[:, 19, :]55        # first_z = self.seq_45[:, 45, :]56        self.seq_pre_10 = self.seq_45[:, 10:20, :]57        self.seq_mid_25 = self.seq_45[:, 20:45, :]58        self.seq_post_10 = self.seq_45[:, 45:55, :]59        last_x = self.seq_45[:, 19, :]60        first_z = self.seq_45[:, 45, :]61        self.mid_resid = np.linspace(last_x, first_z, 25).transpose((1, 0, 2))62        # self.mid_resid = np.repeat(self.seq_45[:, 9:10, :], (1, 25, 1))63        self.data_num = len(self.seq_post_10)64    def __len__(self):65        return self.data_num66    def __getitem__(self, item):67        output = {68            'pre_10': self.seq_pre_10[item],69            'mid_25': self.seq_mid_25[item],70            'mid_resid': self.mid_resid[item],71            'post_10': self.seq_post_10[item]72        }...

strand-guess.py

Source:strand-guess.py

1#! /usr/bin/env python32def strand_guess(aligner, q, t, minpct=50):3    """Guess the strand of t (target) that q (query) lies on.4    Given a Bio.Align aligner and two Bio.SeqRecords q and t, guess which strand5    of t that q lies on. The approach is to align both q and q.reverse_complement()6    to t, seeing which scores higher. The score has to be at least minpct of the7    maximum possible score, default 50 percent.8    ARGUMENTS9    aligner:   A Bio.Align aligner.10    q:         Query sequence as a Bio.SeqRecord.11    t:         Target sequence as a Bio.SeqRecord.12    minpct:    Score of best alignment between q and t must be at least minpct13               percent of the maximum possible score.14    RETURNS15    > 0:       q appears to lie on the forward strand of t.16    < 0:       q appears to lie on the reverse strand of t.17    otherwise: unable to determine which strand of t.18    """19    score_max = min(aligner.score(t.seq, t.seq), aligner.score(q.seq, q.seq))20    score_f = aligner.score(q.seq, t.seq)21    score_r = aligner.score(q.reverse_complement().seq, t.seq)22    if score_f > score_r and score_f >= minpct * score_max / 100:23        return 124    elif score_r > score_f and score_r >= minpct * score_max / 100:25        return -126    else:27        return 028if __name__ == "__main__":29    # Do a few checks30    from Bio import Align31    from Bio.Seq import Seq32    from Bio.SeqRecord import SeqRecord33    aligner = Align.PairwiseAligner()34    aligner.mode = "global"35    # x aligns with the forward strand of y with a small insertion36    x = SeqRecord(37        Seq("CATCAAGCCCTGTGAGCTGAAAAACTTAGCCGGGGACAATGGAGGTGCTGGGTTTGGTGGATATTCCGACATG")38    )39    y = SeqRecord(Seq("GCTGAACTTAGCCGGGGACAATG"))40    print(f"Test 1: Got {strand_guess(aligner, x, y)}, expecting 1")41    # x aligns with the forward strand of y with a small insertion42    x = SeqRecord(Seq("GCTGAACTTAGCCGGGGACAATG"))43    y = SeqRecord(44        Seq("CATCAAGCCCTGTGAGCTGAAAAACTTAGCCGGGGACAATGGAGGTGCTGGGTTTGGTGGATATTCCGACATG")45    )46    print(f"Test 2: Got {strand_guess(aligner, x, y)}, expecting 1")47    # x aligns with the reverse strand of y with a small insertion48    x = SeqRecord(49        Seq("CATGTCGGAATATCCACCAAACCCAGCACCTCCATTGTCCCCGGCTAAGTTTTTCAGCTCACAGGGCTTGATG")50    )51    y = SeqRecord(Seq("GCTGAACTTAGCCGGGGACAATG"))52    print(f"Test 3: Got {strand_guess(aligner, x, y)}, expecting -1")53    # x aligns with the reverse strand of y with a small insertion54    x = SeqRecord(Seq("GCTGAACTTAGCCGGGGACAATG"))55    y = SeqRecord(56        Seq("CATGTCGGAATATCCACCAAACCCAGCACCTCCATTGTCCCCGGCTAAGTTTTTCAGCTCACAGGGCTTGATG")57    )...

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