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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)...
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