Python csv.excel_tab方法代码示例

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def get_test_data(fn, col_name, f0_base, sample):
    """Get frame and col_name data from output file named by f0_base and sample.

    That is, given fn, return the data produced from that input file
    (the filenames appear in the first column), taking the data from the output
    file whose name has f0_base (sf0, pf0, shrf0, strf0) and sample (1ms, 9seg)
    in it.  Return a list of tuples consisting of the frame offset (t_ms) and
    the data from the column named by col_name, converted to floats.

    """
    in_name = os.path.splitext(os.path.basename(fn))[0]
    fn = os.path.join(data_path, 'output-' + f0_base + '-' + sample + '.txt')
    res = []
    with open(fn) as f:
        c = csv.DictReader(f, dialect=csv.excel_tab)
        for row in c:
            if row['Filename'].startswith(in_name):
                res.append((float(row['t_ms']), float(row[col_name])))
    return res 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def __init__(self, infile, **kwargs):
        blast_file = open(infile, 'r')
        self.reader = csv.reader(blast_file, dialect=csv.excel_tab) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def __init__(self, infile, **kwargs):

        blast_file = open(infile, 'r')
        self.reader = csv.reader(blast_file, dialect=csv.excel_tab) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def convert(input, out):
    if os.path.exists(out):
        raise ValueError("Output file already exists")

    reader = csv.reader(open(input, 'rU'), dialect=csv.excel_tab)
    writer = csv.writer(open(out, "wb+"), dialect="excel")
    for row in reader:
        writer.writerow(row) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def get_db(cls, filename):
    # 駅データのcsvを読み込んでキャッシュする
    if cls.db == None:
      cls.db = []
      for row in csv.reader(open(filename, 'rU'), delimiter=',', dialect=csv.excel_tab):
        cls.db.append(cls(row))
    return cls.db 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def _read_tsv_file(self, file_like, **kwargs):
        return self._read_csv_file(file_like, dialect=csv.excel_tab, **kwargs) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def tsvb(lis, columns=[], encoding="utf-8"):
    """bytes -> list"""
    return csvb(lis, columns=columns, dialect=csv.excel_tab, encoding=encoding) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def untsvb(bytes, columns=[], encoding="utf-8"):
    """bytes -> list"""
    return uncsvb(bytes, columns=columns, dialect=csv.excel_tab, encoding=encoding) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def dtsvb(lis, encoding="utf-8"):
    """bytes -> list"""
    return dcsvb(lis, dialect=csv.excel_tab, encoding=encoding) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def undtsvb(bytes, encoding="utf-8"):
    """bytes -> list"""
    return undcsvb(bytes, dialect=csv.excel_tab, encoding=encoding) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def bootstrap_query_file(input_file, output_file, nlp, **kwargs):
    """
    Apply predicted annotations to a file of text queries

    Args:
        input_file (str): filename of queries to be processed
        output_file (str or None): filename for processed queries
        nlp (NaturalLanguageProcessor): an application's NLP with built models
        kwargs (dict): A dictionary of additional args
    """
    show_confidence = kwargs.get("confidence")
    with open(output_file, "w") if output_file else sys.stdout as csv_file:
        field_names = ["query"]
        if not kwargs.get("no_domain"):
            field_names.append("domain")
            if show_confidence:
                field_names.append("domain_conf")
        if not kwargs.get("no_intent"):
            field_names.append("intent")
            if show_confidence:
                field_names.append("intent_conf")
        if show_confidence and not kwargs.get("no_entity"):
            field_names.append("entity_conf")
        if show_confidence and not kwargs.get("no_role"):
            field_names.append("role_conf")
        csv_output = csv.DictWriter(csv_file, field_names, dialect=csv.excel_tab)
        csv_output.writeheader()

        for raw_query in mark_down_file(input_file):
            proc_query = nlp.process_query(nlp.create_query(raw_query), verbose=True)
            csv_row = bootstrap_query_row(proc_query, show_confidence, **kwargs)
            csv_output.writerow(csv_row) 

# 需要导入模块: import csv [as 别名]
# 或者: from csv import excel_tab [as 别名]
def question_uploader_2(root_path, file_name="questions.csv"):
    """
    This is the uploader for the second data set collected by Isaac
    The root path contains "question.csv" file.
    headers are:
    image name, text, choice 1, choice 2, ... , choice 5, answer, ... , is multiple choice
    :param root_path:
    :return:
    """
    file_path = os.path.join(root_path, file_name)
    flag = False
    with open(file_path, 'rU') as csvfile:
        reader = csv.reader(csvfile, delimiter=',', dialect=csv.excel_tab)
        reader.next()
        for row in reader:
            image_name = row[0]
            question_text = row[1]
            choices = row[2:7]
            answer = row[7]
            is_problematic = row[9] == '1'
            has_choices = row[10] == '1'
            has_answer = row[11] == '1'

            """
            if image_name == "0510.png":
                flag = True
            if not flag:
                continue
            """

            if is_problematic:
                continue

            if has_choices:
                answer = answer.split(" ")[1]
            else:
                choices = []

            image_path = os.path.join(root_path, image_name)
            geoserver_interface.upload_question(question_text, image_path, choices, answer)
            print(image_name)