Python PrepTemplate.to_dataframe方法代码示例

# 需要导入模块: from qiita_db.metadata_template.prep_template import PrepTemplate [as 别名]
# 或者: from qiita_db.metadata_template.prep_template.PrepTemplate import to_dataframe [as 别名]
    def test_dataframe_from_template(self):
        template = PrepTemplate(1)
        obs = template.to_dataframe()

        # 27 samples
        self.assertEqual(len(obs), 27)
        self.assertTrue(set(obs.index), {
            u'SKB1.640202', u'SKB2.640194', u'SKB3.640195', u'SKB4.640189',
            u'SKB5.640181', u'SKB6.640176', u'SKB7.640196', u'SKB8.640193',
            u'SKB9.640200', u'SKD1.640179', u'SKD2.640178', u'SKD3.640198',
            u'SKD4.640185', u'SKD5.640186', u'SKD6.640190', u'SKD7.640191',
            u'SKD8.640184', u'SKD9.640182', u'SKM1.640183', u'SKM2.640199',
            u'SKM3.640197', u'SKM4.640180', u'SKM5.640177', u'SKM6.640187',
            u'SKM7.640188', u'SKM8.640201', u'SKM9.640192'})

        self.assertTrue(set(obs.columns), {
            u'tot_org_carb', u'common_name', u'has_extracted_data',
            u'required_sample_info_status', u'water_content_soil',
            u'env_feature', u'assigned_from_geo', u'altitude', u'env_biome',
            u'texture', u'has_physical_specimen', u'description_duplicate',
            u'physical_location', u'latitude', u'ph', u'host_taxid',
            u'elevation', u'description', u'collection_timestamp',
            u'taxon_id', u'samp_salinity', u'host_subject_id', u'sample_type',
            u'season_environment', u'temp', u'country', u'longitude',
            u'tot_nitro', u'depth', u'anonymized_name', u'target_subfragment',
            u'sample_center', u'samp_size', u'run_date', u'experiment_center',
            u'pcr_primers', u'center_name', u'barcodesequence', u'run_center',
            u'run_prefix', u'library_construction_protocol', u'emp_status',
            u'linkerprimersequence', u'experiment_design_description',
            u'target_gene', u'center_project_name', u'illumina_technology',
            u'sequencing_meth', u'platform', u'experiment_title',
            u'study_center'})

# 需要导入模块: from qiita_db.metadata_template.prep_template import PrepTemplate [as 别名]
# 或者: from qiita_db.metadata_template.prep_template.PrepTemplate import to_dataframe [as 别名]
def prep_template_get_req(prep_id, user_id):
    """Gets the json of the full prep template

    Parameters
    ----------
    prep_id : int
        PrepTemplate id to get info for
    user_id : str
        User requesting the sample template info

    Returns
    -------
    dict of objects
    {'status': status,
     'message': message,
     'template': {sample: {column: value, ...}, ...}
    """
    exists = _check_prep_template_exists(int(prep_id))
    if exists['status'] != 'success':
        return exists

    prep = PrepTemplate(int(prep_id))
    access_error = check_access(prep.study_id, user_id)
    if access_error:
        return access_error
    df = prep.to_dataframe()
    return {'status': 'success',
            'message': '',
            'template': df.to_dict(orient='index')}

# 需要导入模块: from qiita_db.metadata_template.prep_template import PrepTemplate [as 别名]
# 或者: from qiita_db.metadata_template.prep_template.PrepTemplate import to_dataframe [as 别名]
def prep_template_summary_get_req(prep_id, user_id):
    """Get the summarized prep template data for each metadata column

    Parameters
    ----------
    prep_id : int
        PrepTemplate id to get info for
    user_id : str
        User requesting the sample template info

    Returns
    -------
    dict of objects
        Dictionary object where the keys are the metadata categories
        and the values are list of tuples. Each tuple is an observed value in
        the category and the number of times its seen.
        Format {'status': status,
                'message': message,
                'num_samples': value,
                'category': [(val1, count1), (val2, count2), ...],
                'editable': bool}
    """
    exists = _check_prep_template_exists(int(prep_id))
    if exists['status'] != 'success':
        return exists

    prep = PrepTemplate(int(prep_id))
    access_error = check_access(prep.study_id, user_id)
    if access_error:
        return access_error

    editable = Study(prep.study_id).can_edit(User(user_id))
    df = prep.to_dataframe()
    out = {'num_samples': df.shape[0],
           'summary': [],
           'status': 'success',
           'message': '',
           'editable': editable}

    cols = sorted(list(df.columns))
    for column in cols:
        counts = df[column].value_counts()
        out['summary'].append(
            (str(column), [(str(key), counts[key])
                           for key in natsorted(counts.index)]))
    return out

# 需要导入模块: from qiita_db.metadata_template.prep_template import PrepTemplate [as 别名]
# 或者: from qiita_db.metadata_template.prep_template.PrepTemplate import to_dataframe [as 别名]
def study_files_get_req(user_id, study_id, prep_template_id, artifact_type):
    """Returns the uploaded files for the study id categorized by artifact_type

    It retrieves the files uploaded for the given study and tries to
    guess on how those files should be added to the artifact of the given
    type. Uses information on the prep template to try to do a better guess.

    Parameters
    ----------
    user_id : str
        The id of the user making the request
    study_id : int
        The study id
    prep_template_id : int
        The prep template id
    artifact_type : str
        The artifact type

    Returns
    -------
    dict of {str: object}
        A dict of the form {'status': str,
                            'message': str,
                            'remaining': list of str,
                            'file_types': list of (str, bool, list of str),
                            'num_prefixes': int}
        where 'status' is a string specifying whether the query is successfull,
        'message' is a human-readable description of the error (optional),
        'remaining' is the list of files that could not be categorized,
        'file_types' is a list of the available filetypes, if it is required
        or not and the list of categorized files for the given artifact type
        and 'num_prefixes' is the number of different run prefix values in
        the given prep template.
    """
    supp_file_types = supported_filepath_types(artifact_type)
    selected = []
    remaining = []
    message = []

    pt = PrepTemplate(prep_template_id)
    if pt.study_id != study_id:
        raise IncompetentQiitaDeveloperError(
            "The requested prep id (%d) doesn't belong to the study "
            "(%d)" % (pt.study_id, study_id))

    uploaded = get_files_from_uploads_folders(study_id)
    pt = pt.to_dataframe()
    ftypes_if = (ft.startswith('raw_') for ft, _ in supp_file_types
                 if ft != 'raw_sff')
    if any(ftypes_if) and 'run_prefix' in pt.columns:
        prep_prefixes = tuple(set(pt['run_prefix']))
        num_prefixes = len(prep_prefixes)
        # sorting prefixes by length to avoid collisions like: 100 1002
        # 10003
        prep_prefixes = sorted(prep_prefixes, key=len, reverse=True)
        # group files by prefix
        sfiles = defaultdict(list)
        for p in prep_prefixes:
            to_remove = []
            for fid, f in uploaded:
                if f.startswith(p):
                    sfiles[p].append(f)
                    to_remove.append((fid, f))
            uploaded = [x for x in uploaded if x not in to_remove]
        inuse = [y for x in sfiles.values() for y in x]
        remaining.extend([f for _, f in uploaded if f not in inuse])
        supp_file_types_len = len(supp_file_types)

        for k, v in viewitems(sfiles):
            len_files = len(v)
            # if the number of files in the k group is larger than the
            # available columns add to the remaining group, if not put them in
            # the selected group
            if len_files > supp_file_types_len:
                remaining.extend(v)
                message.append("'%s' has %d matches." % (k, len_files))
            else:
                v.sort()
                selected.append(v)
    else:
        num_prefixes = 0
        remaining = [f for _, f in uploaded]

    # get file_types, format: filetype, required, list of files
    file_types = [(t, req, [x[i] for x in selected if i+1 <= len(x)])
                  for i, (t, req) in enumerate(supp_file_types)]

    # Create a list of artifacts that the user has access to, in case that
    # he wants to import the files from another artifact
    user = User(user_id)
    artifact_options = []
    user_artifacts = user.user_artifacts(artifact_type=artifact_type)
    study = Study(study_id)
    if study not in user_artifacts:
        user_artifacts[study] = study.artifacts(artifact_type=artifact_type)
    for study, artifacts in viewitems(user_artifacts):
        study_label = "%s (%d)" % (study.title, study.id)
        for a in artifacts:
            artifact_options.append(
                (a.id, "%s - %s (%d)" % (study_label, a.name, a.id)))
#.........这里部分代码省略.........

# 需要导入模块: from qiita_db.metadata_template.prep_template import PrepTemplate [as 别名]
# 或者: from qiita_db.metadata_template.prep_template.PrepTemplate import to_dataframe [as 别名]
            st_df = st.to_dataframe()[columns]
            # converting to datetime
            for col in columns:
                st_df[col] = st_df[col].apply(transform_date)
            st.update(st_df)

if cols_prep:
    with TRN:
        # a few notes: just getting the preps with duplicated values; ignoring
        # column 'sample_id' and tables 'study_sample', 'prep_template',
        # 'prep_template_sample'
        sql = """SELECT table_name, array_agg(column_name::text)
                    FROM information_schema.columns
                    WHERE column_name IN %s
                        AND table_name LIKE 'prep_%%'
                        AND table_name NOT IN (
                            'prep_template', 'prep_template_sample')
                    GROUP BY table_name"""
        # note that we are looking for those columns with duplicated names in
        # the headers
        TRN.add(sql, [tuple(set(cols_prep))])
        for table, columns in viewitems(dict(TRN.execute_fetchindex())):
            # [1] the format is table_# so taking the #
            pt = PrepTemplate(int(table.split('_')[1]))
            # getting just the columns of interest
            pt_df = pt.to_dataframe()[columns]
            # converting to datetime
            for col in columns:
                pt_df[col] = pt_df[col].apply(transform_date)
            pt.update(pt_df)