Source code for bokeh.util.sampledata

#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2021, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-----------------------------------------------------------------------------
''' Helper functions for downloading and accessing sample data.

'''

#-----------------------------------------------------------------------------
# Boilerplate
#-----------------------------------------------------------------------------
from __future__ import annotations

import logging # isort:skip
log = logging.getLogger(__name__)

#-----------------------------------------------------------------------------
# Imports
#-----------------------------------------------------------------------------

# NOTE: since downloading sampledata is not a common occurrnce, non-stdlib
# imports are generally deferrered in this module

# Standard library imports
import hashlib
import json
from os import mkdir, remove
from os.path import (
    abspath,
    dirname,
    exists,
    expanduser,
    isdir,
    isfile,
    join,
    splitext,
)
from sys import stdout
from typing import Any, TextIO, cast
from urllib.parse import urljoin
from urllib.request import urlopen

#-----------------------------------------------------------------------------
# Globals and constants
#-----------------------------------------------------------------------------

__all__ = (
    'download',
)

DataFrame = Any

#-----------------------------------------------------------------------------
# General API
#-----------------------------------------------------------------------------

[docs]def download(progress: bool = True) -> None: ''' Download larger data sets for various Bokeh examples. ''' data_dir = external_data_dir(create=True) print("Using data directory: %s" % data_dir) # HTTP requests are cheaper for us, and there is nothing private to protect s3 = 'http://sampledata.bokeh.org' files = json.load(open(join(dirname(__file__), "sampledata.json"))) for filename, md5 in files: real_name, ext = splitext(filename) if ext == '.zip': if not splitext(real_name)[1]: real_name += ".csv" else: real_name += ext real_path = join(data_dir, real_name) if exists(real_path): local_md5 = hashlib.md5(open(real_path,'rb').read()).hexdigest() if local_md5 == md5: print(f"Skipping {filename!r} (checksum match)") continue else: print(f"Re-fetching {filename!r} (checksum mismatch)") _download_file(s3, filename, data_dir, progress=progress)
#----------------------------------------------------------------------------- # Dev API #----------------------------------------------------------------------------- def external_csv(module: str, name: str, **kw: Any) -> DataFrame: ''' ''' from .dependencies import import_required pd = import_required('pandas', '%s sample data requires Pandas (http://pandas.pydata.org) to be installed' % module) return cast(Any, pd).read_csv(external_path(name), **kw) def external_data_dir(create: bool = False) -> str: ''' ''' try: import yaml except ImportError: raise RuntimeError("'yaml' and 'pyyaml' are required to use bokeh.sampledata functions") bokeh_dir = _bokeh_dir(create=create) data_dir = join(bokeh_dir, "data") try: config = yaml.safe_load(open(join(bokeh_dir, 'config'))) data_dir = expanduser(config['sampledata_dir']) except (OSError, TypeError): pass if not exists(data_dir): if not create: raise RuntimeError('bokeh sample data directory does not exist, please execute bokeh.sampledata.download()') print("Creating %s directory" % data_dir) try: mkdir(data_dir) except OSError: raise RuntimeError("could not create bokeh data directory at %s" % data_dir) else: if not isdir(data_dir): raise RuntimeError("%s exists but is not a directory" % data_dir) return data_dir def external_path(filename: str) -> str: data_dir = external_data_dir() fn = join(data_dir, filename) if not exists(fn) and isfile(fn): raise RuntimeError('Could not locate external data file %s. Please execute bokeh.sampledata.download()' % fn) return fn def package_csv(module: str, name: str, **kw: Any) -> DataFrame: ''' ''' from .dependencies import import_required pd = import_required('pandas', '%s sample data requires Pandas (http://pandas.pydata.org) to be installed' % module) return cast(Any, pd).read_csv(package_path(name), **kw) def package_dir() -> str: ''' ''' return abspath(join(dirname(__file__), "..", "sampledata", "_data")) def package_path(filename: str) -> str: ''' ''' return join(package_dir(), filename) def open_csv(filename: str) -> TextIO: ''' ''' return open(filename, 'r', newline='', encoding='utf8') #----------------------------------------------------------------------------- # Private API #----------------------------------------------------------------------------- def _bokeh_dir(create: bool = False) -> str: ''' ''' bokeh_dir = join(expanduser("~"), ".bokeh") if not exists(bokeh_dir): if not create: return bokeh_dir print("Creating %s directory" % bokeh_dir) try: mkdir(bokeh_dir) except OSError: raise RuntimeError("could not create bokeh config directory at %s" % bokeh_dir) else: if not isdir(bokeh_dir): raise RuntimeError("%s exists but is not a directory" % bokeh_dir) return bokeh_dir def _download_file(base_url: str, filename: str, data_dir: str, progress: bool = True) -> None: ''' ''' # These are actually somewhat expensive imports that added ~5% to overall # typical bokeh import times. Since downloading sampledata is not a common # action, we defer them to inside this function. from zipfile import ZipFile file_url = urljoin(base_url, filename) file_path = join(data_dir, filename) url = urlopen(file_url) with open(file_path, 'wb') as file: file_size = int(url.headers["Content-Length"]) print("Downloading: %s (%d bytes)" % (filename, file_size)) fetch_size = 0 block_size = 16384 while True: data = url.read(block_size) if not data: break fetch_size += len(data) file.write(data) if progress: status = "\r%10d [%6.2f%%]" % (fetch_size, fetch_size*100.0/file_size) stdout.write(status) stdout.flush() if progress: print() real_name, ext = splitext(filename) if ext == '.zip': if not splitext(real_name)[1]: real_name += ".csv" print("Unpacking: %s" % real_name) with ZipFile(file_path, 'r') as zip_file: zip_file.extract(real_name, data_dir) remove(file_path) #----------------------------------------------------------------------------- # Code #-----------------------------------------------------------------------------