Test your AI Agents with the all-new Agent to Agent Testing Platform.Learn More

How to use combine method in avocado

Best Python code snippet using avocado_python

bidirectional_feature_pyramid_generators_tf2_test.py

Source:bidirectional_feature_pyramid_generators_tf2_test.py

1# Copyright 2020 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Tests for bidirectional feature pyramid generators."""16import unittest17from absl.testing import parameterized18import tensorflow.compat.v1 as tf19from google.protobuf import text_format20from object_detection.builders import hyperparams_builder21from object_detection.models import bidirectional_feature_pyramid_generators as bifpn_generators22from object_detection.protos import hyperparams_pb223from object_detection.utils import test_case24from object_detection.utils import test_utils25from object_detection.utils import tf_version26@parameterized.parameters({'bifpn_num_iterations': 2},27                          {'bifpn_num_iterations': 8})28@unittest.skipIf(tf_version.is_tf1(), 'Skipping TF2.X only test.')29class BiFPNFeaturePyramidGeneratorTest(test_case.TestCase):30  def _build_conv_hyperparams(self):31    conv_hyperparams = hyperparams_pb2.Hyperparams()32    conv_hyperparams_text_proto = """33      regularizer {34        l2_regularizer {35        }36      }37      initializer {38        truncated_normal_initializer {39        }40      }41      force_use_bias: true42    """43    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)44    return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)45  def test_get_expected_feature_map_shapes(self, bifpn_num_iterations):46    with test_utils.GraphContextOrNone() as g:47      image_features = [48          ('block3', tf.random_uniform([4, 16, 16, 256], dtype=tf.float32)),49          ('block4', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),50          ('block5', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32))51      ]52      bifpn_generator = bifpn_generators.KerasBiFpnFeatureMaps(53          bifpn_num_iterations=bifpn_num_iterations,54          bifpn_num_filters=128,55          fpn_min_level=3,56          fpn_max_level=7,57          input_max_level=5,58          is_training=True,59          conv_hyperparams=self._build_conv_hyperparams(),60          freeze_batchnorm=False)61    def graph_fn():62      feature_maps = bifpn_generator(image_features)63      return feature_maps64    expected_feature_map_shapes = {65        '{}_dn_lvl_3'.format(bifpn_num_iterations): (4, 16, 16, 128),66        '{}_up_lvl_4'.format(bifpn_num_iterations): (4, 8, 8, 128),67        '{}_up_lvl_5'.format(bifpn_num_iterations): (4, 4, 4, 128),68        '{}_up_lvl_6'.format(bifpn_num_iterations): (4, 2, 2, 128),69        '{}_up_lvl_7'.format(bifpn_num_iterations): (4, 1, 1, 128)}70    out_feature_maps = self.execute(graph_fn, [], g)71    out_feature_map_shapes = dict(72        (key, value.shape) for key, value in out_feature_maps.items())73    self.assertDictEqual(expected_feature_map_shapes, out_feature_map_shapes)74  def test_get_expected_variable_names(self, bifpn_num_iterations):75    with test_utils.GraphContextOrNone() as g:76      image_features = [77          ('block3', tf.random_uniform([4, 16, 16, 256], dtype=tf.float32)),78          ('block4', tf.random_uniform([4, 8, 8, 256], dtype=tf.float32)),79          ('block5', tf.random_uniform([4, 4, 4, 256], dtype=tf.float32))80      ]81      bifpn_generator = bifpn_generators.KerasBiFpnFeatureMaps(82          bifpn_num_iterations=bifpn_num_iterations,83          bifpn_num_filters=128,84          fpn_min_level=3,85          fpn_max_level=7,86          input_max_level=5,87          is_training=True,88          conv_hyperparams=self._build_conv_hyperparams(),89          freeze_batchnorm=False,90          name='bifpn')91    def graph_fn():92      return bifpn_generator(image_features)93    self.execute(graph_fn, [], g)94    expected_variables = [95        'bifpn/node_00/0_up_lvl_6/input_0_up_lvl_5/1x1_pre_sample/conv/bias',96        'bifpn/node_00/0_up_lvl_6/input_0_up_lvl_5/1x1_pre_sample/conv/kernel',97        'bifpn/node_03/1_dn_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/bias',98        'bifpn/node_03/1_dn_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/kernel',99        'bifpn/node_04/1_dn_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/bias',100        'bifpn/node_04/1_dn_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/kernel',101        'bifpn/node_05/1_dn_lvl_3/input_0_up_lvl_3/1x1_pre_sample/conv/bias',102        'bifpn/node_05/1_dn_lvl_3/input_0_up_lvl_3/1x1_pre_sample/conv/kernel',103        'bifpn/node_06/1_up_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/bias',104        'bifpn/node_06/1_up_lvl_4/input_0_up_lvl_4/1x1_pre_sample/conv/kernel',105        'bifpn/node_07/1_up_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/bias',106        'bifpn/node_07/1_up_lvl_5/input_0_up_lvl_5/1x1_pre_sample/conv/kernel']107    expected_node_variable_patterns = [108        ['bifpn/node_{:02}/{}_dn_lvl_6/combine/bifpn_combine_weights',109         'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/bias',110         'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/depthwise_kernel',111         'bifpn/node_{:02}/{}_dn_lvl_6/post_combine/separable_conv/pointwise_kernel'],112        ['bifpn/node_{:02}/{}_dn_lvl_5/combine/bifpn_combine_weights',113         'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/bias',114         'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/depthwise_kernel',115         'bifpn/node_{:02}/{}_dn_lvl_5/post_combine/separable_conv/pointwise_kernel'],116        ['bifpn/node_{:02}/{}_dn_lvl_4/combine/bifpn_combine_weights',117         'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/bias',118         'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/depthwise_kernel',119         'bifpn/node_{:02}/{}_dn_lvl_4/post_combine/separable_conv/pointwise_kernel'],120        ['bifpn/node_{:02}/{}_dn_lvl_3/combine/bifpn_combine_weights',121         'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/bias',122         'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/depthwise_kernel',123         'bifpn/node_{:02}/{}_dn_lvl_3/post_combine/separable_conv/pointwise_kernel'],124        ['bifpn/node_{:02}/{}_up_lvl_4/combine/bifpn_combine_weights',125         'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/bias',126         'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/depthwise_kernel',127         'bifpn/node_{:02}/{}_up_lvl_4/post_combine/separable_conv/pointwise_kernel'],128        ['bifpn/node_{:02}/{}_up_lvl_5/combine/bifpn_combine_weights',129         'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/bias',130         'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/depthwise_kernel',131         'bifpn/node_{:02}/{}_up_lvl_5/post_combine/separable_conv/pointwise_kernel'],132        ['bifpn/node_{:02}/{}_up_lvl_6/combine/bifpn_combine_weights',133         'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/bias',134         'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/depthwise_kernel',135         'bifpn/node_{:02}/{}_up_lvl_6/post_combine/separable_conv/pointwise_kernel'],136        ['bifpn/node_{:02}/{}_up_lvl_7/combine/bifpn_combine_weights',137         'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/bias',138         'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/depthwise_kernel',139         'bifpn/node_{:02}/{}_up_lvl_7/post_combine/separable_conv/pointwise_kernel']]140    node_i = 2141    for iter_i in range(1, bifpn_num_iterations+1):142      for node_variable_patterns in expected_node_variable_patterns:143        for pattern in node_variable_patterns:144          expected_variables.append(pattern.format(node_i, iter_i))145        node_i += 1146    expected_variables = set(expected_variables)147    actual_variable_set = set(148        [var.name.split(':')[0] for var in bifpn_generator.variables])149    self.assertSetEqual(expected_variables, actual_variable_set)150# TODO(aom): Tests for create_bifpn_combine_op.151if __name__ == '__main__':...

AnalysisData.py

Source:AnalysisData.py

1'''2Created on 2015/11/33@author: FZY4'''5import pandas as pd6import numpy as np7from MissingValueByPrediction import TunningParamter,Ridge_param,Lasso_param,skl_lr_param8from MissingValueByPrediction import xgb_regression_param,xgb_tree_param,xgb_regression_param_by_tree9from sklearn.datasets import dump_svmlight_file10def getNullMessage(data,name):11    rows = data.shape[0]12    messages = ""13    message = ""14    rateMessages = ""15    for col in data.columns:16        rate = data[np.isnan(data[col])].shape[0]/float(rows)17        message = str(col) +":" +str(rate)18        if rate > 0.1 :19            rateMessages = rateMessages + ":" +message + "\n"20        messages = messages + message + '\n'21    f = open("../../data/analysis/%s.null.analysi.txt"%(name),"wb")22    f.write(messages)23    f.write("----------------------speical message--------------------\n")24    f.write(rateMessages)25    f.close()26#plot the picture of missing data27def plotDataScatter(data,features,name):  28    for feature in features :29        data = data[ ~ np.isnan(data[feature])]30        p = data.plot(kind = 'scatter',x='Id',y=feature).get_figure()31        p.savefig("../../data/img/%s.%s.png"%(name,feature))        32        33def dumpDatatoSVMFormat(data,X_feature,Y_feature,filename):34    dump_svmlight_file(data[X_feature], data[Y_feature], "../../data/analysis/svm/%s"%(filename))35          36#plot the picture of category data37if __name__ == '__main__':38    #train = pd.read_csv("../../data/train_clean_fill_F10.csv")39    #test = pd.read_csv("../../data/test_clean_fill_F10.csv")40    #analysis the data 41    #get the columns for analysis42    #np.savetxt("../../data/analysis/train_columns.name.txt",train.columns, fmt="%s")43    #np.savetxt("../../data/analysis/test_columns.name.txt",test.columns,fmt="%s")44    45    #1.now we need to count the null46    #getNullMessage(train, "train")47    #getNullMessage(test, "test")48    49    #2.now we need to analysis the scatter of data50    #features = ['Feature_1','Feature_2','Feature_4','Feature_10','Feature_20']51    """52    features = []53    for i in range(1,26):54        feature = "Feature_" + str(i)55        features.append(feature)56    plotDataScatter(train, features, "train")57    """58    #plotDataScatter(train, features, "train")59    #plotDataScatter(test, features,"test")60    61    #3.the feature1 and feature10 feat20 is category feature62    #train[['Feature_1','Feature_10','Feature_20']].hist()63    #test[['Feature_1','Feature_10','Feature_20']].hist()64    65    #4.now we need to analysis66    #1.for the Feature_2 there are 22.8% have na value ,so we use regreesion to predict 67    #the vlaue 68    """69    train_use = train[~ np.isnan(train['Feature_2'])]70    test_use  = test[~ np.isnan(test['Feature_2'])]71    features = []72    for i in range(1,26):73        feature = "Feature_" + str(i)74        features.append(feature)75    #drop the feature 76    nullFeatures = ['Feature_1','Feature_4','Feature_10','Feature_20']77    features = list(set(features) -set(nullFeatures))78    #use Ridge to predict the bestValue79    #we need to combine the train and test validate our model80    pieces = [train_use[features],test_use[features]]81    data_combine = pd.concat(pieces)82    data_combine.to_csv("../../data/data_combine.csv")83    """84    #Feature_2 has 22.8% na values 85    #data_combine = pd.read_csv("../../data/data_combine.csv")86    #TunningParamter(Ridge_param, data_combine , data_combine.columns,'Feature_2')87    #TunningParamter(Lasso_param, data_combine, data_combine.columns, 'Feature_2')88    #we use the regression to predict the model ,but the result do not have good result89    #I will add  the reture value feature to regression90    91    """92    train_use = train[~ np.isnan(train['Feature_2'])]93    test_use  = test[~ np.isnan(test['Feature_2'])]94    features = []95    for i in range(1,26):96        feature = "Feature_" + str(i)97        features.append(feature)98        99    # add reture value100    #drop the feature 101    nullFeatures = ['Feature_1','Feature_4','Feature_10','Feature_20']102    features = list(set(features) -set(nullFeatures))103    features = features + ['Ret_MinusTwo','Ret_MinusOne']104    for i in range(2,121):105        feature= "Ret_" + str(i)106        features.append(feature)107    108    print features109    #use Ridge to predict the bestValue110    #we need to combine the train and test validate our model111    pieces = [train_use[features],test_use[features]]112    data_combine = pd.concat(pieces)113    data_combine.to_csv("../../data/data_combine_1.csv")114    115    data_combine = pd.read_csv("../../data/data_combine_1.csv")              116    TunningParamter(Ridge_param, data_combine , data_combine.columns,'Feature_2','combine_1')117    TunningParamter(Lasso_param, data_combine, data_combine.columns, 'Feature_2','combine_1')118    """119    #now we need to deal with Feature_4120    """121    train_use = train[~ np.isnan(train['Feature_4'])]122    test_use  = test[~ np.isnan(test['Feature_4'])]123    features = []124    for i in range(1,26):125        feature = "Feature_" + str(i)126        features.append(feature)127    128    nullFeatures = ['Feature_1','Feature_2','Feature_10','Feature_20']129    features = list(set(features) -set(nullFeatures))130    pieces = [train_use[features],test_use[features]]131    data_combine = pd.concat(pieces)132    data_combine.to_csv("../../data/data_combine_2.csv")133    """134    #data_combine = pd.read_csv("../../data/data_combine_2.csv") 135    #dumpDatatoSVMFormat(data_combine, X_feature, Y_feature, 'combine_2')             136    #TunningParamter(Ridge_param, data_combine , data_combine.columns,'Feature_4','combine_2')137    #TunningParamter(Lasso_param, data_combine, data_combine.columns, 'Feature_4','combine_2')138   139   140    #for the feature_20 ,50% na 141    """142    train_use = train[~ np.isnan(train['Feature_1'])]143    test_use  = test[~ np.isnan(test['Feature_1'])]144    features = []145    for i in range(1,26):146        feature = "Feature_" + str(i)147        features.append(feature)148    149    nullFeatures = ['Feature_10','Feature_2','Feature_20','Feature_4']150    features = list(set(features) -set(nullFeatures))151    features = features + ['Ret_MinusTwo','Ret_MinusOne']152    pieces = [train_use[features],test_use[features]]153    data_combine = pd.concat(pieces)154    data_combine.to_csv("../../data/data_combine_5.csv")155    156    data_combine = pd.read_csv("../../data/data_combine_5.csv") 157    TunningParamter(skl_lr_param, data_combine , data_combine.columns,'Feature_1','combine_5')158    """159    """160    train_use = train[~ np.isnan(train['Feature_20'])]161    test_use  = test[~ np.isnan(test['Feature_20'])]162    features = []163    for i in range(1,26):164        feature = "Feature_" + str(i)165        features.append(feature)166    nullFeatures = ['Feature_2','Feature_4','Feature_1']167    features = list(set(features) -set(nullFeatures))168    features = features + ['Ret_MinusTwo','Ret_MinusOne']169    pieces = [train_use[features],test_use[features]]170    data_combine = pd.concat(pieces)171    data_combine['Feature_20'] = data_combine['Feature_20'] - 2 172    173    TunningParamter(xgb_tree_param, data_combine, data_combine.columns,'Feature_20','data_combine_20_feature')174    """175    ##now we need to get range of Ret_1 ~ Ret_180176    """177    train = pd.read_csv("../../data/train_weight.csv")178    features = []179    for i in range(2,181):180        feature = "Ret_"+str(i)181        features.append(feature)182    #plot scatter183    plotDataScatter(train,features,'train')184    """185    train = pd.read_csv("../../data/train_clean_fill_all_1.csv")186    getNullMessage(train, 'train_2')187    188    189       190    191    192    ...

test_combinations_test.py

Source:test_combinations_test.py

...20from absl.testing import parameterized21from tensorflow.python.framework import test_combinations as combinations22from tensorflow.python.eager import test23class TestingCombinationsTest(test.TestCase):24  def test_combine(self):25    self.assertEqual([{26        "a": 1,27        "b": 228    }, {29        "a": 1,30        "b": 331    }, {32        "a": 2,33        "b": 234    }, {35        "a": 2,36        "b": 337    }], combinations.combine(a=[1, 2], b=[2, 3]))38  def test_arguments_sorted(self):39    self.assertEqual([40        OrderedDict([("aa", 1), ("ab", 2)]),41        OrderedDict([("aa", 1), ("ab", 3)]),42        OrderedDict([("aa", 2), ("ab", 2)]),43        OrderedDict([("aa", 2), ("ab", 3)])44    ], combinations.combine(ab=[2, 3], aa=[1, 2]))45  def test_combine_single_parameter(self):46    self.assertEqual([{47        "a": 1,48        "b": 249    }, {50        "a": 2,51        "b": 252    }], combinations.combine(a=[1, 2], b=2))53  def test_add(self):54    self.assertEqual(55        [{56            "a": 157        }, {58            "a": 259        }, {60            "b": 261        }, {62            "b": 363        }],64        combinations.combine(a=[1, 2]) + combinations.combine(b=[2, 3]))65  def test_times(self):66    c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"])67    c2 = combinations.combine(mode=["eager"], loss=["callable"])68    c3 = combinations.combine(distribution=["d1", "d2"])69    c4 = combinations.times(c3, c1 + c2)70    self.assertEqual([71        OrderedDict([("distribution", "d1"), ("loss", "callable"),72                     ("mode", "graph")]),73        OrderedDict([("distribution", "d1"), ("loss", "tensor"),74                     ("mode", "graph")]),75        OrderedDict([("distribution", "d1"), ("loss", "callable"),76                     ("mode", "eager")]),77        OrderedDict([("distribution", "d2"), ("loss", "callable"),78                     ("mode", "graph")]),79        OrderedDict([("distribution", "d2"), ("loss", "tensor"),80                     ("mode", "graph")]),81        OrderedDict([("distribution", "d2"), ("loss", "callable"),82                     ("mode", "eager")])83    ], c4)84  def test_times_variable_arguments(self):85    c1 = combinations.combine(mode=["graph", "eager"])86    c2 = combinations.combine(optimizer=["adam", "gd"])87    c3 = combinations.combine(distribution=["d1", "d2"])88    c4 = combinations.times(c3, c1, c2)89    self.assertEqual([90        OrderedDict([("distribution", "d1"), ("mode", "graph"),91                     ("optimizer", "adam")]),92        OrderedDict([("distribution", "d1"), ("mode", "graph"),93                     ("optimizer", "gd")]),94        OrderedDict([("distribution", "d1"), ("mode", "eager"),95                     ("optimizer", "adam")]),96        OrderedDict([("distribution", "d1"), ("mode", "eager"),97                     ("optimizer", "gd")]),98        OrderedDict([("distribution", "d2"), ("mode", "graph"),99                     ("optimizer", "adam")]),100        OrderedDict([("distribution", "d2"), ("mode", "graph"),101                     ("optimizer", "gd")]),102        OrderedDict([("distribution", "d2"), ("mode", "eager"),103                     ("optimizer", "adam")]),104        OrderedDict([("distribution", "d2"), ("mode", "eager"),105                     ("optimizer", "gd")])106    ], c4)107    self.assertEqual(108        combinations.combine(109            mode=["graph", "eager"],110            optimizer=["adam", "gd"],111            distribution=["d1", "d2"]), c4)112  def test_overlapping_keys(self):113    c1 = combinations.combine(mode=["graph"], loss=["callable", "tensor"])114    c2 = combinations.combine(mode=["eager"], loss=["callable"])115    with self.assertRaisesRegexp(ValueError, ".*Keys.+overlap.+"):116      _ = combinations.times(c1, c2)117@combinations.generate(combinations.combine(a=[1, 0], b=[2, 3], c=[1]))118class CombineTheTestSuite(parameterized.TestCase):119  def test_add_things(self, a, b, c):120    self.assertLessEqual(3, a + b + c)121    self.assertLessEqual(a + b + c, 5)122  def test_add_things_one_more(self, a, b, c):123    self.assertLessEqual(3, a + b + c)124    self.assertLessEqual(a + b + c, 5)125  def not_a_test(self, a=0, b=0, c=0):126    del a, b, c127    self.fail()128  def _test_but_private(self, a=0, b=0, c=0):129    del a, b, c130    self.fail()131  # Check that nothing funny happens to a non-callable that starts with "_test"....

transcript_two_genome_MCscan.py

Source:transcript_two_genome_MCscan.py

1#!/usr/bin/python32import sys3import os4import mykit5import re6import subprocess7sysname1=sys.argv[1]8sysname2=sys.argv[2]9transcript_prefix=sys.argv[3]10outdir=sys.argv[4]11threads=sys.argv[5]12def genome_transcripts_MC(sysname1,sysname2):13	pwd=subprocess.getoutput('pwd')14	os.system("cat {sysname1}_{sysname2}.blast.result | awk '$3>98' | awk '$4>5000' > {sysname1}_{sysname2}.98_5000.blast.result".format(sysname1=sysname1,sysname2=sysname2))15	os.system("cat {sysname2}_{sysname1}.blast.result | awk '$3>98' | awk '$4>5000' > {sysname2}_{sysname1}.98_5000.blast.result".format(sysname1=sysname1,sysname2=sysname2))16	os.system('cp MCscanX_{sysname1}_{sysname2}.gff MCscanX_{sysname2}_{sysname1}.gff'.format(sysname1=sysname1,sysname2=sysname2))17	combine_list=[sysname1+'_'+sysname2,sysname2+'_'+sysname1]18	for combine in combine_list:19		if mykit.extfile(combine):20			os.system('mkdir '+combine)21			os.system('mkdir '+combine+'/query')22			os.system('mkdir '+combine+'/db')23		with open('{combine}.98_5000.blast.result'.format(combine=combine)) as f:24			blastmsg_list=f.readlines()25		query_chromosome_list=[]26		query_msg_dict={}27		db_chromosome_list=[]28		db_msg_dict={}29		for blastmsg in blastmsg_list:30			query_chr=blastmsg.split('|')[1].split(':')[0]31			db_chr=blastmsg.split('|')[2].split(':')[0]32			if query_chr not in query_chromosome_list:33				query_chromosome_list.append(query_chr)34				query_msg_dict[query_chr]=[]35			if db_chr not in db_chromosome_list:36				db_chromosome_list.append(db_chr)37				db_msg_dict[db_chr]=[]38			query_msg_dict[query_chr].append(blastmsg)39			db_msg_dict[db_chr].append(blastmsg)40		for query_chr in query_msg_dict:41			output=open('{combine}/query/{query_chr}.blast'.format(combine=combine,query_chr=query_chr),'w')42			circos_ctl_list=[]43			for msg in query_msg_dict[query_chr]:44				output.write(msg)45			output.close()46			os.system('cp MCscanX_{sysname1}_{sysname2}.gff {combine}/query/{query_chr}.gff'.format(sysname1=sysname1,sysname2=sysname2,combine=combine,query_chr=query_chr))47			os.system('MCScanX {combine}/query/{query_chr} &> /dev/null'.format(combine=combine,query_chr=query_chr))48			for msg in open('{combine}/query/{query_chr}.collinearity'.format(combine=combine,query_chr=query_chr)):49				if msg.startswith('#'):50					continue51				chr1=msg.split('|')[1].split(':')[0]52				chr2=msg.split('|')[2].split(':')[0]53				if chr1 not in circos_ctl_list:54					circos_ctl_list.append(chr1)55				if chr2 not in circos_ctl_list:56					circos_ctl_list.append(chr2)57			with open('{combine}/query/{query_chr}.dot.ctl'.format(combine=combine,query_chr=query_chr),'w') as f:58				f.write('2000\n')59				writes=''60				for nchr in circos_ctl_list:61					writes+=nchr+','62				writes=re.sub(',$','',writes)63				f.write(writes)64			os.chdir('/home/a2431559261/soft/MCScanX/downstream_analyses')65			os.system('java circle_plotter -g {pwd}/MCscanX_{combine}.gff -s {pwd}/{combine}/query/{query_chr}.collinearity -c {pwd}/{combine}/query/{query_chr}.dot.ctl -o {pwd}/{combine}/query/{query_chr}.dot.PNG &> /dev/null'.format(pwd=pwd,combine=combine,query_chr=query_chr))66			os.chdir(pwd)67		for db_chr in db_msg_dict:68			output=open('{combine}/db/{db_chr}.blast'.format(combine=combine,db_chr=db_chr),'w')69			circos_ctl_list=[]70			for msg in db_msg_dict[db_chr]:71				output.write(msg)72			output.close()73			os.system('cp MCscanX_{sysname1}_{sysname2}.gff {combine}/db/{db_chr}.gff'.format(sysname1=sysname1,sysname2=sysname2,db_chr=db_chr,combine=combine))74			os.system('MCScanX {combine}/db/{db_chr} &> /dev/null'.format(combine=combine,db_chr=db_chr))75			for msg in open('{combine}/db/{db_chr}.collinearity'.format(combine=combine,db_chr=db_chr)):76				if msg.startswith('#'):77					continue78				chr1=msg.split('|')[1].split(':')[0]79				chr2=msg.split('|')[2].split(':')[0]80				if chr1 not in circos_ctl_list:81					circos_ctl_list.append(chr1)82				if chr2 not in circos_ctl_list:83					circos_ctl_list.append(chr2)84			with open('{combine}/db/{db_chr}.dot.ctl'.format(combine=combine,db_chr=db_chr),'w') as f:85				f.write('2000\n')86				writes=''87				for nchr in circos_ctl_list:88					writes+=nchr+','89				writes=re.sub(',$','',writes)90				f.write(writes)91			os.chdir('/home/a2431559261/OLDISK/soft/MCScanX/downstream_analyses')92			os.system('java circle_plotter -g {pwd}/MCscanX_{combine}.gff -s {pwd}/{combine}/db/{db_chr}.collinearity -c {pwd}/{combine}/db/{db_chr}.dot.ctl -o {pwd}/{combine}/db/{db_chr}.dot.PNG &> /dev/null'.format(pwd=pwd,combine=combine,db_chr=db_chr))93			os.chdir(pwd)94mykit.genome.genome_transcript_circos(sysname1,sysname2,transcript_prefix,outdir,threads)95genome_transcripts_MC(sysname1,sysname2)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.