How to use combine method in avocado

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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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