def __init__(self, *args, **kwargs):
'''
Keywords:
filename (str) : a path to a Jupyter notebook (".ipynb") file
'''
nbformat = import_required(
'nbformat',
'The Bokeh notebook application handler requires Jupyter Notebook to be installed.'
)
nbconvert = import_required(
'nbconvert',
'The Bokeh notebook application handler requires Jupyter Notebook to be installed.'
)
if 'filename' not in kwargs:
raise ValueError('Must pass a filename to NotebookHandler')
filename = kwargs['filename']
with open(filename) as f:
nb = nbformat.read(f, nbformat.NO_CONVERT)
exporter = nbconvert.PythonExporter()
source, meta = exporter.from_notebook_node(nb)
kwargs['source'] = source
super(NotebookHandler, self).__init__(*args, **kwargs)
def __init__(self, *args, **kwargs):
nbformat = import_required('nbformat', 'The Bokeh notebook application handler requires Jupyter Notebook to be installed.')
nbconvert = import_required('nbconvert', 'The Bokeh notebook application handler requires Jupyter Notebook to be installed.')
if 'filename' not in kwargs:
raise ValueError('Must pass a filename to NotebookHandler')
with open(kwargs['filename']) as f:
nb = nbformat.read(f, nbformat.NO_CONVERT)
exporter = nbconvert.PythonExporter()
source, meta = exporter.from_notebook_node(nb)
kwargs['source'] = source
super(NotebookHandler, self).__init__(*args, **kwargs)
def _make_prop_dict(self) -> pd.DataFrame:
""" Returns a dataframe containing all the properties of all the submodels of the model being
analyzed. Used as datasource to show attributes.
"""
pd = import_required(
"pandas",
"Structure graphs require Pandas (http://pandas.pydata.org) to be installed"
)
df = pd.DataFrame()
for x in self._graph.nodes(data=True):
M = self._model.select_one(dict(id=x[0]))
Z = pd.DataFrame(self._obj_props_to_df2(M))
Z["id"] = x[0]
Z["model"] = str(M)
Z["values"] = Z["values"].map(lambda x: str(x))
Z["types"] = Z["types"].map(lambda x: str(x))
df = df.append(Z)
return df
''' The data in world_cities.csv was taken from GeoNames ``cities5000.zip``
downloaded from
.. code-block:: none
http://www.geonames.org/export/
on Tuesday September 15, 2015.
Under ``CC-BY`` license (creative commons attributions license).
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'world_cities sample data requires Pandas (http://pandas.pydata.org) to be installed')
from . import _data_dir
try:
data = pd.read_csv(_data_dir("world_cities.csv"))
except (IOError, OSError):
raise RuntimeError('Could not load file "world_cities.csv". Please execute bokeh.sampledata.download()')
'''
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'population sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from . import _data_dir
def load_population():
csv_file = _data_dir("WPP2012_SA_DB03_POPULATION_QUINQUENNIAL.csv")
df = pd.read_csv(csv_file, encoding="CP1250")
df = df[df.Sex != "Both"]
df = df.drop(["VarID", "Variant", "MidPeriod", "SexID", "AgeGrpSpan"],
axis=1)
df = df.rename(columns={"Time": "Year"})
df.Value *= 1000
return df
client_time datetime64[ns]
device object
event_name object
gender object
city object
latitude float64
longitude float64
state object
zip_code int64
marital_status object
session_id object
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'project_funding sample data requires Pandas (http://pandas.pydata.org) to be installed')
from collections import defaultdict
import json
import os
from pandas.io.json import json_normalize
from six import iteritems
from six.moves.urllib.request import URLopener
def denormalize_column_names(parsed_data):
"""Attempts to remove the column hierarchy if possible when parsing from json.
Args:
parsed_data (:class:`pandas.DataFrame`): df parsed from json data using
:func:`pandas.io.json.json_normalize`.
def test_required_success():
assert dep.import_required('sys', 'yep') is not None
""" Provide U.S. marriage and divorce statistics between 1867 and 2014
Data from the CDC's National Center for Health Statistics (NHCS) database
(http://www.cdc.gov/nchs/).
Data organized by Randal S. Olson (http://www.randalolson.com)
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
"pandas", "us_marriages_divorces sample data requires Pandas (http://pandas.pydata.org) to be installed"
)
from os.path import dirname, join
data = pd.read_csv(join(dirname(__file__), "us_marriages_divorces.csv"))
# Fill in missing data with a simple linear interpolation
data = data.interpolate(method="linear", axis=0).ffill().bfill()
"""
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'sea surface temperature sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import dirname, join
sea_surface_temperature = pd.read_csv(join(dirname(__file__), 'sea_surface_temperature.csv.gz'),
parse_dates=True, index_col=0)
sea_surface_temperature = sea_surface_temperature.rename(columns={'temperature (celsius)': 'temperature'})
sea_surface_temperature.index.name = 'time'
'''
This module provides provides access to probly.csv and numberly.csv
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'perceptioms sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import dirname, join
probly = pd.read_csv(join(dirname(__file__), 'probly.csv'))
numberly = pd.read_csv(join(dirname(__file__), 'numberly.csv'))
''' Provide a pandas DataFrame instance of four of the datasets from gapminder.org.
These are read in from csv filess that have been downloaded from Bokeh's
sample data on S3. But the original code that generated the csvs from the
raw gapminder data is available at the bottom of this file.
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'gapminder sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import join
import sys
from . import _data_dir
data_dir = _data_dir()
datasets = [
'fertility',
'life_expectancy',
'population',
'regions',
]
for dataset in datasets:
filename = join(data_dir, 'gapminder_%s.csv' % dataset)
try:
"""
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'autompg2 sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import dirname, join
autompg2 = pd.read_csv(join(dirname(__file__), 'auto-mpg2.csv'))
def capitalize_words(string):
return " ".join([word.capitalize() for word in string.split(" ")])
autompg2["manufacturer"] = autompg2["manufacturer"].map(capitalize_words)
autompg2["model"] = autompg2["model"].map(capitalize_words)
autompg2["drv"] = autompg2["drv"].replace({
"f": "front",
"r": "rear",
"4": "4x4"
})
''' Provide U.S. marriage and divorce statistics between 1867 and 2014
Data from the CDC's National Center for Health Statistics (NHCS) database
(http://www.cdc.gov/nchs/).
Data organized by Randal S. Olson (http://www.randalolson.com)
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'us_marriages_divorces sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import dirname, join
data = pd.read_csv(
join(dirname(__file__), 'us_marriages_divorces.csv'))
# Fill in missing data with a simple linear interpolation
data = data.interpolate(method='linear', axis=0).ffill().bfill()
'''
This module provides provides access to probly.csv and numberly.csv
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'perceptioms sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import dirname, join
probly = pd.read_csv(join(dirname(__file__), 'probly.csv'))
numberly = pd.read_csv(join(dirname(__file__), 'numberly.csv'))
'''
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'population sample data requires Pandas (http://pandas.pydata.org) to be installed')
from . import _data_dir
def load_population():
csv_file = _data_dir("WPP2012_SA_DB03_POPULATION_QUINQUENNIAL.csv")
df = pd.read_csv(csv_file, encoding="CP1250")
df = df[df.Sex != "Both"]
df = df.drop(["VarID", "Variant", "MidPeriod", "SexID", "AgeGrpSpan"], axis=1)
df = df.rename(columns={"Time": "Year"})
df.Value *= 1000
return df
from os.path import dirname, join
from bokeh.models import Range1d
from bokeh.plotting import figure, show, output_file
from bokeh.util.dependencies import import_required
pandas = import_required('pandas', 'get it suckah')
colormap = {
'chemical explosion': 'orange',
'quarry blast': 'green',
'earthquake': 'violet',
'sonic boom': 'magenta',
'explosion': 'blue',
'landslide': 'brown',
'mining explosion': 'black'
}
title = "QUAKIN' IN MAH BOOTS"
graph = figure(title=title)
graph.xaxis.axis_label = 'Magnitude'
graph.yaxis.axis_label = 'Depth'
quakes = pandas.read_csv(join(dirname(__file__), 'resource/earthquakes.csv'))
graph.circle(quakes['mag'],
quakes['depth'],
color=[colormap[x] for x in quakes['type']],
fill_alpha=0.2,
size=10)
graph.y_range = Range1d(700, -10)
# Bokeh imports
from bokeh.util.dependencies import import_required
# Bokeh imports
from .apps import DjangoBokehConfig
from .routing import autoload, directory, document
from .static import static_extensions
import_required("django", "django is required by bokeh.server.django")
import_required("channels", "The package channels is required by bokeh.server.django and must be installed")
default_app_config = "bokeh.server.django.DjangoBokehConfig"
elements['CPK'] (convention for molecular modeling color)
elements['electronic configuration']
elements['electronegativity'] (units: Pauling)
elements['atomic radius'] (units: pm)
elements['ionic radius'] (units: pm)
elements['van der waals radius'] (units: pm)
elements['ionization enerygy'] (units: kJ/mol)
elements['electron affinity'] (units: kJ/mol)
elements['phase'] (standard state: solid, liquid, gas)
elements['bonding type']
elements['melting point'] (units: K)
elements['boiling point'] (units: K)
elements['density'] (units: g/cm^3)
elements['type'] (see below)
elements['year discovered']
elements['group']
elements['period']
element types: actinoid, alkali metal, alkaline earth metal, halogen, lanthanoid, metal, metalloid, noble gas, nonmetal, transition metalloid
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required("pandas", "periodic_table sample data requires Pandas (http://pandas.pydata.org) to be installed")
from os.path import dirname, join
elements = pd.read_csv(join(dirname(__file__), "elements.csv"))
"""
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'autompg sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import dirname, join
autompg = pd.read_csv(join(dirname(__file__), 'auto-mpg.csv'))
def test_success(self):
assert dep.import_required('sys', 'yep') is not None
def test_success(self) -> None:
assert dep.import_required('sys', 'yep') is not None
def test_fail(self):
with pytest.raises(RuntimeError) as excinfo:
dep.import_required('bleepbloop', 'nope')
assert 'nope' in str(excinfo.value)
import six
if six.PY2:
raise ImportError("bokeh.server.django requires Python 3.x")
from bokeh.util.dependencies import import_required
import_required("django", "django is required by bokeh.server.django")
import_required("channels", "channels is required by bokeh.server.django")
from .apps import DjangoBokehConfig
default_app_config = "bokeh.server.django.DjangoBokehConfig"
from .routing import document, autoload, directory
elements['electronegativity'] (units: Pauling)
elements['atomic radius'] (units: pm)
elements['ionic radius'] (units: pm)
elements['van der waals radius'] (units: pm)
elements['ionization enerygy'] (units: kJ/mol)
elements['electron affinity'] (units: kJ/mol)
elements['phase'] (standard state: solid, liquid, gas)
elements['bonding type']
elements['melting point'] (units: K)
elements['boiling point'] (units: K)
elements['density'] (units: g/cm^3)
elements['type'] (see below)
elements['year discovered']
elements['group']
elements['period']
element types: actinoid, alkali metal, alkaline earth metal, halogen, lanthanoid, metal, metalloid, noble gas, nonmetal, transition metalloid
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'periodic_table sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import dirname, join
elements = pd.read_csv(join(dirname(__file__), 'elements.csv'))
def test_required_fail():
with pytest.raises(RuntimeError) as excinfo:
dep.import_required('bleepbloop', 'nope')
assert 'nope' in str(excinfo.value)
""" The data in airports.json is a subset of US airports with field
elevations > 1500 meters. The query result was taken from
.. code-block:: none
http://services.nationalmap.gov/arcgis/rest/services/GlobalMap/GlobalMapWFS/MapServer/10/query
on October 15, 2015.
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required("pandas", "airports sample data requires Pandas (http://pandas.pydata.org) to be installed")
import json
import os
from . import _data_dir
with open(os.path.join(_data_dir(), "airports.json"), "r") as data_file:
content = data_file.read()
airports = json.loads(content)
schema = [["attributes", "nam"], ["attributes", "zv3"], ["geometry", "x"], ["geometry", "y"]]
data = pd.io.json.json_normalize(airports["features"], meta=schema)
data.rename(columns={"attributes.nam": "name", "attributes.zv3": "elevation"}, inplace=True)
data.rename(columns={"geometry.x": "x", "geometry.y": "y"}, inplace=True)
"""
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'browsers sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import join, dirname
_data_dir = dirname(__file__)
# http://gs.statcounter.com/#browser_version-ww-monthly-201311-201311-bar
_csv_path = join(_data_dir, "browsers_nov_2013.csv")
browsers_nov_2013 = pd.read_csv(_csv_path, names=["Version", "Share"], skiprows=1)
_versions = browsers_nov_2013.Version.map(lambda x: x.rsplit(" ", 1))
browsers_nov_2013["Browser"] = _versions.map(lambda x: x[0])
browsers_nov_2013["VersionNumber"] = _versions.map(lambda x: x[1] if len(x) == 2 else "0")
# https://github.com/alrra/browser-logos
_browsers = ["Chrome", "Firefox", "Safari", "Opera", "IE"]
icons = {}
for browser in _browsers:
icon_path = join(_data_dir, "icons", browser.lower() + "_32x32.png")
with open(icon_path, "rb") as icon:
icons[browser] = icon.read()
''' Provide US Unemployment rate data by year, from 1948 to 20013
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'unemployment1948 sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import dirname, join
data = pd.read_csv(join(dirname(__file__), 'unemployment1948.csv'))
device object
event_name object
gender object
city object
latitude float64
longitude float64
state object
zip_code int64
marital_status object
session_id object
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'project_funding sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
import os
from six.moves.urllib.request import URLopener
from bokeh.charts.utils import df_from_json
DATA_URL = "https://raw.githubusercontent.com/localytics/data-viz-challenge/master/data.json"
DOWNLOAD_NAME = 'project_funding.json'
CSV_NAME = 'project_funding.csv'
# Get absolute path relative to script
data_dir = os.path.dirname(os.path.realpath(__file__))
json_file_path = os.path.join(data_dir, DOWNLOAD_NAME)
""" Daylight hours from http://www.sunrisesunset.com
"""
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'daylight sample data requires Pandas (http://pandas.pydata.org) to be installed')
import re
import datetime
import requests
from six.moves import xrange
from os.path import join, abspath, dirname
url = "http://sunrisesunset.com/calendar.asp"
r0 = re.compile("<[^>]+>| |[\r\n\t]")
r1 = re.compile(r"(\d+)(DST Begins|DST Ends)?Sunrise: (\d+):(\d\d)Sunset: (\d+):(\d\d)")
def fetch_daylight_hours(lat, lon, tz, dst, year):
"""Fetch daylight hours from sunrisesunset.com for a given location.
Parameters
----------
lat : float
Location's latitude.
lon : float
Location's longitude.
tz : int or float
Agriculture
Architecture
Art and Performance
Biology
Business
Communications and Journalism
Computer Science,Education
Engineering
English
Foreign Languages
Health Professions
Math and Statistics
Physical Sciences
Psychology
Public Administration
Social Sciences and History
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required(
'pandas',
'degrees sample data requires Pandas (http://pandas.pydata.org) to be installed'
)
from os.path import dirname, join
xyvalues = pd.read_csv(
join(dirname(__file__), "percent-bachelors-degrees-women-usa.csv"))
''' Provide US Unemployment rate data by year, from 1948 to 20013
'''
from __future__ import absolute_import
from bokeh.util.dependencies import import_required
pd = import_required('pandas',
'unemployment1948 sample data requires Pandas (http://pandas.pydata.org) to be installed')
from os.path import dirname, join
data = pd.read_csv(join(dirname(__file__), 'unemployment1948.csv'))
