Python util.get_tmp_filename函数代码示例

    def setUp(self):

        # Temporary input file
        self.tmp_otu_filepath = get_tmp_filename(prefix="R_test_otu_table_", suffix=".txt")
        seq_file = open(self.tmp_otu_filepath, "w")
        seq_file.write(test_otu_table)
        seq_file.close()

        self.tmp_map_filepath = get_tmp_filename(prefix="R_test_map_", suffix=".txt")
        seq_file = open(self.tmp_map_filepath, "w")
        seq_file.write(test_map)
        seq_file.close()

        self.files_to_remove = [self.tmp_otu_filepath, self.tmp_map_filepath]

        # Prep input files in R format
        output_dir = mkdtemp()
        self.dirs_to_remove = [output_dir]

        # get random forests results
        mkdir(join(output_dir, "random_forest"))
        self.results = run_supervised_learning(
            self.tmp_otu_filepath,
            self.tmp_map_filepath,
            "Individual",
            ntree=100,
            errortype="oob",
            output_dir=output_dir,
        )

    def test_get_common_OTUs(self):
        """get_common_OTUs works"""

        # create the temporary OTU tables
        otu_table1 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tConsensus Lineage',
                     '0\t0\t2\t0\tlineage0',
                     '1\t1\t0\t0\tlineage1',
                     '2\t1\t1\t1\tlineage2'])
        otu_table2 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tConsensus Lineage',
                     '0\t0\t2\t0\tlineage0',
                     '1\t1\t0\t0\tlineage1',
                     '2\t0\t1\t1\tlineage2'])
        otu_table3 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tConsensus Lineage',
                     '0\t0\t2\t0\tlineage0',
                     '2\t1\t1\t1\tlineage2'])
        category_info = {'sample1':'0.1',
                        'sample2':'0.2',
                        'sample3':'0.3'}
        OTU_list = ['1', '0', '2'] 

        fp1 = get_tmp_filename()
        fp2 = get_tmp_filename()
        fp3 = get_tmp_filename()
        try:
            f1 = open(fp1,'w')
            f2 = open(fp2,'w')
            f3 = open(fp3,'w')
        except IOError, e:
            raise e,"Could not create temporary files: %s, %s" %(f1,f2, f3)

    def setUp(self):
        
        # Temporary input file
        self.tmp_otu_filepath = get_tmp_filename(
            prefix='R_test_otu_table_',
            suffix='.txt'
            )
        seq_file = open(self.tmp_otu_filepath, 'w')
        seq_file.write(test_otu_table)
        seq_file.close()

        self.tmp_map_filepath = get_tmp_filename(
            prefix='R_test_map_',
            suffix='.txt'
            )
        seq_file = open(self.tmp_map_filepath, 'w')
        seq_file.write(test_map)
        seq_file.close()


        self.files_to_remove = \
         [self.tmp_otu_filepath, self.tmp_map_filepath]
   
        # Prep input files in R format
        output_dir = mkdtemp()
        self.dirs_to_remove = [output_dir]

        # get random forests results
        mkdir(join(output_dir, 'random_forest'))
        self.results = run_supervised_learning(
            self.tmp_otu_filepath, self.tmp_map_filepath,'Individual',
            ntree=100, errortype='oob',
            output_dir=output_dir)

 def setUp(self):
     
     self.files_to_remove = []
     self.dirs_to_remove = []
     
     # Create example output directory
     tmp_dir = get_qiime_temp_dir()
     self.test_out = get_tmp_filename(tmp_dir=tmp_dir,
                                      prefix='qiime_parallel_tests_',
                                      suffix='',
                                      result_constructor=str)
     self.dirs_to_remove.append(self.test_out)
     create_dir(self.test_out)
     
     # Create example input file
     self.inseqs1_fp = get_tmp_filename(tmp_dir=self.test_out,
                                         prefix='qiime_inseqs',
                                         suffix='.fasta')
     inseqs1_f = open(self.inseqs1_fp,'w')
     inseqs1_f.write(inseqs1)
     inseqs1_f.close()
     self.files_to_remove.append(self.inseqs1_fp)
     
     # Define number of seconds a test can run for before timing out 
     # and failing
     initiate_timeout(60)

    def setUp(self):
        self._files_to_remove = []
        
        self.fasta_file_path = get_tmp_filename(prefix='fastq_', \
        suffix='.fastq')
        
        fastq_file = open(self.fasta_file_path, 'w')
        
        fastq_file.write(fastq_test_string)
        fastq_file.close()
        
        #Error testing files
        false_fasta_file = '/'
        false_qual_file = '/'
        self.read_only_output_dir = get_tmp_filename(prefix = 'read_only_', \
        suffix = '/')
        create_dir(self.read_only_output_dir)
        chmod(self.read_only_output_dir, 0577)

        self.output_dir = get_tmp_filename(prefix = 'convert_fastaqual_fastq_',\
         suffix = '/')
        self.output_dir += sep
        
        create_dir(self.output_dir)
        
        self._files_to_remove.append(self.fasta_file_path)

    def test_plot_rank_abundance_graphs_dense(self):
        """plot_rank_abundance_graphs works with any number of samples (DenseOTUTable)"""
 
        self.otu_table = parse_biom_table_str(otu_table_dense)
        self.dir = get_tmp_filename(tmp_dir=self.tmp_dir,
                                   prefix="test_plot_rank_abundance",
                                   suffix="/")
        create_dir(self.dir)
        self._dirs_to_remove.append(self.dir)
        tmp_fname = get_tmp_filename(tmp_dir=self.dir)

        #test empty sample name
        self.assertRaises(ValueError, plot_rank_abundance_graphs, tmp_fname,'',
                          self.otu_table)
        #test invalid sample name
        self.assertRaises(ValueError, plot_rank_abundance_graphs, tmp_fname,
                          'Invalid_sample_name',
                          self.otu_table)

        #test with two samples
        file_type="pdf"
        tmp_file = abspath(self.dir+"rank_abundance_cols_0_2."+file_type)
        plot_rank_abundance_graphs(tmp_file, 'S3,S5', self.otu_table,
                                       file_type=file_type)

        self.assertTrue(exists(tmp_file)) 
        self.files_to_remove.append(tmp_file)
        # test with all samples
        tmp_file = abspath(self.dir+"rank_abundance_cols_0_1_2."+file_type)

        plot_rank_abundance_graphs(tmp_file,'*', self.otu_table,file_type=file_type)
        
        self.files_to_remove.append(tmp_file)
        self.assertTrue(exists(tmp_file)) 

    def setUp(self):
        
        # Temporary input file
        self.tmp_pc_fp = get_tmp_filename(
            prefix='R_test_pcoa',
            suffix='.txt'
            )
        seq_file = open(self.tmp_pc_fp, 'w')
        seq_file.write(test_pc)
        seq_file.close()

        self.tmp_map_fp = get_tmp_filename(
            prefix='R_test_map_',
            suffix='.txt'
            )
        map_file = open(self.tmp_map_fp, 'w')
        map_file.write(test_map)
        map_file.close()


        self.files_to_remove = \
         [self.tmp_pc_fp, self.tmp_map_fp]
   
        # Prep input files in R format
        self.output_dir = mkdtemp()
        self.dirs_to_remove = [self.output_dir]

 def setUp(self):
     """ """
     self.files_to_remove = []
     self.dirs_to_remove = []
     
     tmp_dir = get_qiime_temp_dir()
     self.test_out = get_tmp_filename(tmp_dir=tmp_dir,
                                      prefix='qiime_parallel_tests_',
                                      suffix='',
                                      result_constructor=str)
     self.dirs_to_remove.append(self.test_out)
     create_dir(self.test_out)
     
     self.template_fp = get_tmp_filename(tmp_dir=self.test_out,
                                         prefix='qiime_template',
                                         suffix='.fasta')
     template_f = open(self.template_fp,'w')
     template_f.write(pynast_test1_template_fasta)
     template_f.close()
     self.files_to_remove.append(self.template_fp)
     
     self.inseqs1_fp = get_tmp_filename(tmp_dir=self.test_out,
                                         prefix='qiime_inseqs',
                                         suffix='.fasta')
     inseqs1_f = open(self.inseqs1_fp,'w')
     inseqs1_f.write(inseqs1)
     inseqs1_f.close()
     self.files_to_remove.append(self.inseqs1_fp)
     
     initiate_timeout(60)

    def test_call_log_file(self):
        """GenericRepSetPicker.__call__ writes log when expected
        """

        tmp_log_filepath = get_tmp_filename(
            prefix='GenericRepSetPickerTest.test_call_output_to_file_l_',
            suffix='.txt')
        tmp_result_filepath = get_tmp_filename(
            prefix='GenericRepSetPickerTest.test_call_output_to_file_r_',
            suffix='.txt')

        app = GenericRepSetPicker(params=self.params)
        obs = app(self.tmp_seq_filepath, self.tmp_otu_filepath,
                  result_path=tmp_result_filepath, log_path=tmp_log_filepath)

        log_file = open(tmp_log_filepath)
        log_file_str = log_file.read()
        log_file.close()
        # remove the temp files before running the test, so in
        # case it fails the temp file is still cleaned up
        remove(tmp_log_filepath)
        remove(tmp_result_filepath)

        log_file_exp = ["GenericRepSetPicker parameters:",
                        'Algorithm:first',
                        "Application:None",
                        'ChoiceF:first',
                        'ChoiceFRequiresSeqs:False',
                        "Result path: %s" % tmp_result_filepath, ]
        # compare data in log file to fake expected log file
        for i, j in zip(log_file_str.splitlines(), log_file_exp):
            if not i.startswith('ChoiceF:'):  # can't test, different each time
                self.assertEqual(i, j)

    def test_test_wrapper_multiple(self):
        """test_wrapper_multiple works"""
        # create the temporary OTU tables
        otu_table1 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tsample4\tConsensus Lineage',
                     '0\t1\t2\t0\t1\tlineage0',
                     '1\t1\t0\t0\t1\tlineage1',
                     '2\t1\t1\t1\t1\tlineage2'])
        otu_table2 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tsample4\tConsensus Lineage',
                     '0\t0\t2\t0\t1\tlineage0',
                     '1\t1\t0\t0\t1\tlineage1',
                     '2\t0\t1\t1\t1\tlineage2'])
        otu_table3 = '\n'.join(['#Full OTU Counts',
                     '#OTU ID\tsample1\tsample2\tsample3\tConsensus Lineage',
                     '0\t0\t2\t0\t1\tlineage0',
                     '2\t1\t1\t1\t1\tlineage2'])
        category_mapping = ['#SampleID\tcat1\tcat2',
                                      'sample1\tA\t0',
                                      'sample2\tA\t8.0',
                                      'sample3\tB\t1.0',
                                      'sample4\tB\t1.0']
        OTU_list = ['1', '0'] 

        fp1 = get_tmp_filename()
        fp2 = get_tmp_filename()
        fp3 = get_tmp_filename()
        try:
            f1 = open(fp1,'w')
            f2 = open(fp2,'w')
            f3 = open(fp3,'w')
        except IOError, e:
            raise e,"Could not create temporary files: %s, %s" %(f1,f2, f3)

def model2_table(otu_sums, samples, seq_depth, tpk):
    """Return OTU table drawn from dirichlet distribution with given params.
    Inputs:
     otu_sums - array of floats, weights you want to give to each otu in terms 
     of its probability mass for sampling. basically how large you want the sum
     of that otu to be compared to the others.
     samples - int, number of samples for each otu.
     seq_depth - the sum of each col of the data table, the number of 
     observations. 
     tpk - total prior knowledge. controls how spiky the distribution will be. 
     higher total prior knowledge will allow it to be much spikier which means
     less deviation away from otu_sums. 
    """
    prior_vals = otu_sums
    pvs_str =  'c(%s)' % ','.join(map(str,prior_vals))
    out_fp = get_tmp_filename()
    command_str = COMMAND_STR % (pvs_str, tpk, samples, seq_depth, out_fp)
    command_file = get_tmp_filename()
    o = open(command_file, 'w')
    o.write(command_str)
    o.close()
    os.system('R --slave < ' + command_file)
    o = open(out_fp)
    lines = map(str.rstrip ,o.readlines())
    o.close()
    return array([map(float,line.split(',')) for line in lines])

def null_from_data(data, tpk, Rseed=None):
    """generates null from dirichlet model of data based on row sums
    Inputs:
     tpk - int, total prior knowledge to allow the rdirichlet code. higher 
      tpk will result in the simulated table more closely matching the 
      initial data.
     Rseed - int/None, whether or not to seed R at a given value.
    """
    prior_vals = data.sum(1)
    pvs_str =  'c(%s)' % ','.join(map(str,prior_vals))
    sams_str = data.shape[1] # num cols
    seq_depth = data.sum(0).mean()
    out_fp = get_tmp_filename()
    command_str = COMMAND_STR % (pvs_str, tpk, sams_str, seq_depth, out_fp)
    if Rseed!=None:
        command_str = command_str[:23]+'set.seed('+str(Rseed)+')\n'+\
            command_str[23:]
    command_file = get_tmp_filename()
    
    open(command_file, 'w').write(command_str)
    
    cmd_status, cmd_output = getstatusoutput('R --slave < ' + command_file)
    if cmd_status==32512:
        raise ValueError, 'Most likely you do not have R installed, ' +\
            'which is a dependency for QIIME'
    elif cmd_status==256:	
        raise ValueError, 'Most likely you do not have gtools library ' +\
            'installed in R installed, which is a dependency for QIIME'
                
    lines = map(str.rstrip , open(out_fp).readlines())
    
    return array([map(float,line.split(',')) for line in lines])

    def setUp(self):
        # create the temporary input files
        self.tmp_seq_filepath = get_tmp_filename(prefix="ReferenceRepSetPickerTest_", suffix=".fasta")
        seq_file = open(self.tmp_seq_filepath, "w")
        seq_file.write(dna_seqs)
        seq_file.close()

        self.ref_seq_filepath = get_tmp_filename(prefix="ReferenceRepSetPickerTest_", suffix=".fasta")
        seq_file = open(self.ref_seq_filepath, "w")
        seq_file.write(reference_seqs)
        seq_file.close()

        self.tmp_otu_filepath = get_tmp_filename(prefix="ReferenceRepSetPickerTest_", suffix=".otu")
        otu_file = open(self.tmp_otu_filepath, "w")
        otu_file.write(otus_w_ref)
        otu_file.close()

        self.result_filepath = get_tmp_filename(prefix="ReferenceRepSetPickerTest_", suffix=".fasta")
        otu_file = open(self.result_filepath, "w")
        otu_file.write(otus_w_ref)
        otu_file.close()

        self.files_to_remove = [
            self.tmp_seq_filepath,
            self.tmp_otu_filepath,
            self.ref_seq_filepath,
            self.result_filepath,
        ]

        self.params = {"Algorithm": "first", "ChoiceF": first_id}

    def setUp(self):
        self.infernal_test1_input_fp = get_tmp_filename(
            prefix='InfernalAlignerTests_',suffix='.fasta')
        open(self.infernal_test1_input_fp,'w').write(infernal_test1_input_fasta)

        self.infernal_test1_template_fp = get_tmp_filename(
            prefix='InfernalAlignerTests_',suffix='template.sto')
        open(self.infernal_test1_template_fp,'w').\
         write(infernal_test1_template_stockholm)

        # create temp file names (and touch them so we can reliably 
        # clean them up)
        self.result_fp = get_tmp_filename(
            prefix='InfernalAlignerTests_',suffix='.fasta')
        open(self.result_fp,'w').close()
        
        self.log_fp = get_tmp_filename(
            prefix='InfernalAlignerTests_',suffix='.log')
        open(self.log_fp,'w').close()

        self._paths_to_clean_up = [
            self.infernal_test1_input_fp,
            self.result_fp,
            self.log_fp,
            self.infernal_test1_template_fp,
            ]

        self.infernal_test1_aligner = InfernalAligner({
                'template_filepath': self.infernal_test1_template_fp,
                })
        self.infernal_test1_expected_aln = \
         LoadSeqs(data=infernal_test1_expected_alignment,aligned=Alignment,\
            moltype=DNA)

    def _create_directory_structure(self, correct=True):
        """Creates a directory structure for check_exist_filepaths function

        Returns:
            base_dir: the base directory path
            mapping_fps: a list with the relative paths from base_dir to
                the created mapping files

        If correct=False, it adds one mapping file to the mapping_fps list that
            do not exists
        """
        base_dir = get_tmp_filename(tmp_dir=self.tmp_dir, suffix='')
        mkdir(base_dir)

        self._dirs_to_clean_up.append(base_dir)

        mapping_fps = []
        for i in range(5):
            mapping_fp = get_tmp_filename(tmp_dir='', suffix='.txt')
            mapping_fps.append(mapping_fp)
            path_to_create = join(base_dir, mapping_fp)
            f = open(path_to_create, 'w')
            f.close()
            self._paths_to_clean_up.append(path_to_create)

        if not correct:
            mapping_fps.append(get_tmp_filename(tmp_dir='', suffix='.txt'))

        return base_dir, mapping_fps