CARTOframes¶
A Python package for integrating CARTO maps, analysis, and data services into data science workflows.
Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO’s services like hosted, dynamic or static maps and Data Observatory augmentation.
Features
- Write pandas DataFrames to CARTO tables
- Read CARTO tables and queries into pandas DataFrames
- Create customizable, interactive CARTO maps in a Jupyter notebook
- Interact with CARTO’s Data Observatory
- Use CARTO’s spatially-enabled database for analysis
More info
- Complete documentation: https://cartodb.github.io/cartoframes
- Source code: https://github.com/CartoDB/cartoframes
- bug tracker / feature requests: https://github.com/CartoDB/cartoframes/issues
Note
cartoframes users must have a CARTO API key for most cartoframes functionality. For example, writing DataFrames to an account, reading from private tables, and visualizing data on maps all require an API key. CARTO provides API keys for education and nonprofit uses, among others. Request access at<EMAIL_ADDRESS>API key access is also given through GitHub’s Student Developer Pack.
Install Instructions¶
To install cartoframes (currently in beta) on your machine, do the following to install the latest pre-release version:
$ pip install cartoframes
It is recommended to use cartoframes in Jupyter Notebooks (pip install jupyter). See the example usage section below or notebooks in the examples directory for using cartoframes in that environment.
Virtual Environment¶
To setup cartoframes and Jupyter in a virtual environment:
$ virtualenv venv
$ source venv/bin/activate
(venv) $ pip install cartoframes
(venv) $ pip install jupyter
(venv) $ jupyter notebook
Then create a new notebook and try the example code snippets below with tables that are in your CARTO account.
Example usage¶
Data workflow¶
Get table from CARTO, make changes in pandas, sync updates with CARTO:
import cartoframes
# `base_url`s are of the form `http://{username}.carto.com/` for most users
cc = cartoframes.CartoContext(base_url='https://eschbacher.carto.com/',
api_key=APIKEY)
# read a table from your CARTO account to a DataFrame
df = cc.read('brooklyn_poverty_census_tracts')
# do fancy pandas operations (add/drop columns, change values, etc.)
df['poverty_per_pop'] = df['poverty_count'] / df['total_population']
# updates CARTO table with all changes from this session
cc.write(df, 'brooklyn_poverty_census_tracts', overwrite=True)
Write an existing pandas DataFrame to CARTO.
import pandas as pd
import cartoframes
df = pd.read_csv('acadia_biodiversity.csv')
cc = cartoframes.CartoContext(base_url=BASEURL,
api_key=APIKEY)
cc.write(df, 'acadia_biodiversity')
Map workflow¶
The following will embed a CARTO map in a Jupyter notebook, allowing for custom styling of the maps driven by TurboCARTO and CARTOColors. See the CARTOColors wiki for a full list of available color schemes.
from cartoframes import Layer, BaseMap, styling
cc = cartoframes.CartoContext(base_url=BASEURL,
api_key=APIKEY)
cc.map(layers=[BaseMap('light'),
Layer('acadia_biodiversity',
color={'column': 'simpson_index',
'scheme': styling.tealRose(5)}),
Layer('peregrine_falcon_nest_sites',
size='num_eggs',
color={'column': 'bird_id',
'scheme': styling.vivid(10)})],
interactive=True)
Augment from Data Observatory¶
Interact with CARTO’s Data Observatory:
import cartoframes
cc = cartoframes.CartoContext(BASEURL, APIKEY)
# total pop, high school diploma (normalized), median income, poverty status (normalized)
# See Data Observatory catalog for codes: https://cartodb.github.io/bigmetadata/index.html
data_obs_measures = [{'numer_id': 'us.census.acs.B01003001'},
{'numer_id': 'us.census.acs.B15003017',
'normalization': 'predenominated'},
{'numer_id': 'us.census.acs.B19013001'},
{'numer_id': 'us.census.acs.B17001002',
'normalization': 'predenominated'},]
df = cc.data('transactions', data_obs_measures)
CARTO Credential Management¶
Save and update your CARTO credentials for later use.
from cartoframes import Credentials, CartoContext
creds = Credentials(username='eschbacher', key='abcdefg')
creds.save() # save credentials for later use (not dependent on Python session)
Once you save your credentials, you can get started in future sessions more quickly:
from cartoframes import CartoContext
cc = CartoContext() # automatically loads credentials if previously saved
CARTOframes Functionality¶
CartoContext¶
-
class
context.
CartoContext
(base_url=None, api_key=None, creds=None, session=None, verbose=0)¶ CartoContext class for authentication with CARTO and high-level operations such as reading tables from CARTO into dataframes, writing dataframes to CARTO tables, creating custom maps from dataframes and CARTO tables, and augmenting data using CARTO’s Data Observatory. Future methods will interact with CARTO’s services like routing, geocoding, and isolines, PostGIS backend for spatial processing, and much more.
Manages connections with CARTO for data and map operations. Modeled after SparkContext.
-
creds
¶ cartoframes.Credentials –
Credentials
instance
Parameters: - base_url (str) – Base URL of CARTO user account. Cloud-based accounts
should use the form
https://{username}.carto.com
(e.g., https://eschbacher.carto.com for usereschbacher
) whether on a personal or multi-user account. On-premises installation users should ask their admin. - api_key (str) – CARTO API key.
- session (requests.Session, optional) – requests session. See requests documentation for more information.
- verbose (bool, optional) – Output underlying process states (True), or suppress (False, default)
Returns: A CartoContext object that is authenticated against the user’s CARTO account.
Return type: Example
Create a CartoContext object:
import cartoframes cc = cartoframes.CartoContext(BASEURL, APIKEY)
-
write
(df, table_name, temp_dir=SYSTEM_TMP_PATH, overwrite=False, lnglat=None, encode_geom=False, geom_col=None, **kwargs)¶ Write a DataFrame to a CARTO table.
Example
Write a pandas DataFrame to CARTO.
cc.write(df, 'brooklyn_poverty', overwrite=True)
Scrape an HTML table from Wikipedia and send to CARTO with content guessing to create a geometry from the country column. This uses a CARTO Import API param content_guessing parameter.
url = 'https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy' # retrieve first HTML table from that page df = pd.read_html(url, header=0)[0] # send to carto, let it guess polygons based on the 'country' # column. Also set privacy to 'public' cc.write(df, 'life_expectancy', content_guessing=True, privacy='public') cc.map(layers=Layer('life_expectancy', color='both_sexes_life_expectancy'))
Parameters: - df (pandas.DataFrame) – DataFrame to write to
table_name
in user CARTO account - table_name (str) – Table to write
df
to in CARTO. - temp_dir (str, optional) – Directory for temporary storage of data that is sent to CARTO. Defaults are defined by appdirs.
- overwrite (bool, optional) – Behavior for overwriting
table_name
if it exits on CARTO. Defaults toFalse
. - lnglat (tuple, optional) – lng/lat pair that can be used for
creating a geometry on CARTO. Defaults to
None
. In some cases, geometry will be created without specifying this. See CARTO’s Import API for more information. - encode_geom (bool, optional) – Whether to write geom_col to CARTO as the_geom.
- geom_col (str, optional) – The name of the column where geometry information is stored. Used in conjunction with encode_geom.
- kwargs – Keyword arguments from CARTO’s Import API. See the params listed in the documentation for more information. For example, when using content_guessing=’true’, a column named ‘countries’ with country names will be used to generate polygons for each country. To avoid unintended consequences, avoid file, url, and other similar arguments. Note: Combining privacy with overwrite (defined above) does not currently update the privacy of a dataset: https://github.com/CartoDB/cartoframes/issues/252.
Returns: If lnglat flag is set and the DataFrame has more than 100,000 rows, a
BatchJobStatus
instance is returned. Otherwise, None.Return type: BatchJobStatus
or NoneNote
DataFrame indexes are changed to ordinary columns. CARTO creates an index called cartodb_id for every table that runs from 1 to the length of the DataFrame.
- df (pandas.DataFrame) – DataFrame to write to
-
read
(table_name, limit=None, index='cartodb_id', decode_geom=False)¶ Read a table from CARTO into a pandas DataFrames.
Parameters: - table_name (str) – Name of table in user’s CARTO account.
- limit (int, optional) – Read only limit lines from
table_name. Defaults to
None
, which reads the full table. - index (str, optional) – Not currently in use.
- decode_geom (bool, optional) – Decodes CARTO’s geometries into a Shapely object that can be used, for example, in GeoPandas.
Returns: DataFrame representation of table_name from CARTO.
Return type: pandas.DataFrame
Example
import cartoframes cc = cartoframes.CartoContext(BASEURL, APIKEY) df = cc.read('acadia_biodiversity')
-
delete
(table_name)¶ Delete a table in user’s CARTO account.
Parameters: table_name (str) – Name of table to delete Returns: None
-
query
(query, table_name=None, decode_geom=False)¶ Pull the result from an arbitrary SQL query from a CARTO account into a pandas DataFrame. Can also be used to perform database operations (creating/dropping tables, adding columns, updates, etc.).
Parameters: - query (str) – Query to run against CARTO user database. This data will then be converted into a pandas DataFrame.
- table_name (str, optional) – If set, this will create a new table in the user’s CARTO account that is the result of the query. Defaults to None (no table created).
- decode_geom (bool, optional) – Decodes CARTO’s geometries into a Shapely object that can be used, for example, in GeoPandas.
Returns: DataFrame representation of query supplied. Pandas data types are inferred from PostgreSQL data types. In the case of PostgreSQL date types, dates are attempted to be converted, but on failure a data type ‘object’ is used.
Return type: pandas.DataFrame
-
map
(layers=None, interactive=True, zoom=None, lat=None, lng=None, size=(800, 400), ax=None)¶ Produce a CARTO map visualizing data layers.
Examples
Create a map with two data layers, and one BaseMap layer:
import cartoframes from cartoframes import Layer, BaseMap, styling cc = cartoframes.CartoContext(BASEURL, APIKEY) cc.map(layers=[BaseMap(), Layer('acadia_biodiversity', color={'column': 'simpson_index', 'scheme': styling.tealRose(7)}), Layer('peregrine_falcon_nest_sites', size='num_eggs', color={'column': 'bird_id', 'scheme': styling.vivid(10))], interactive=True)
Create a snapshot of a map at a specific zoom and center:
cc.map(layers=Layer('acadia_biodiversity', color='simpson_index'), interactive=False, zoom=14, lng=-68.3823549, lat=44.3036906)
Parameters: - layers (list, optional) –
List of one or more of the following:
- Layer: cartoframes Layer object for visualizing data from a CARTO table. See layer.Layer for all styling options.
- BaseMap: Basemap for contextualizng data layers. See layer.BaseMap for all styling options.
- QueryLayer: Layer from an arbitrary query. See layer.QueryLayer for all styling options.
- interactive (bool, optional) – Defaults to
True
to show an interactive slippy map. Setting toFalse
creates a static map. - zoom (int, optional) – Zoom level of map. Acceptable values are
usually in the range 0 to 19. 0 has the entire earth on a
single tile (256px square). Zoom 19 is the size of a city
block. Must be used in conjunction with
lng
andlat
. Defaults to a view to have all data layers in view. - lat (float, optional) – Latitude value for the center of the map.
Must be used in conjunction with
zoom
andlng
. Defaults to a view to have all data layers in view. - lng (float, optional) – Longitude value for the center of the map.
Must be used in conjunction with
zoom
andlat
. Defaults to a view to have all data layers in view. - size (tuple, optional) – List of pixel dimensions for the map.
Format is
(width, height)
. Defaults to(800, 400)
. - ax – matplotlib axis on which to draw the image. Only used when
interactive
isFalse
.
Returns: Interactive maps are rendered as HTML in an iframe, while static maps are returned as matplotlib Axes objects or IPython Image.
Return type: IPython.display.HTML or matplotlib Axes
- layers (list, optional) –
-
data_discovery
(region, keywords=None, regex=None, time=None, boundaries=None)¶ Discover Data Observatory measures. This method returns the full Data Observatory metadata model for each measure or measures that match the conditions from the inputs. The full metadata in each row uniquely defines a measure based on the timespan, geographic resolution, and normalization (if any). Read more about the metadata response in Data Observatory documentation.
Internally, this method finds all measures in region that match the conditions set in keywords, regex, time, and boundaries (if any of them are specified). Then, if boundaries is not specified, a geographical resolution for that measure will be chosen subject to the type of region specified:
- If region is a table name, then a geographical resolution that is roughly equal to region size / number of subunits.
- If region is a country name or bounding box, then a geographical resolution will be chosen roughly equal to region size / 500.
Since different measures are in some geographic resolutions and not others, different geographical resolutions for different measures are oftentimes returned.
Tip
To remove the guesswork in how geographical resolutions are selected, specify one or more boundaries in boundaries. See the boundaries section for each region in the Data Observatory catalog.
The metadata returned from this method can then be used to create raw tables or for augmenting an existing table from these measures using CartoContext.data. For the full Data Observatory catalog, visit https://cartodb.github.io/bigmetadata/. When working with the metadata DataFrame returned from this method, be careful to only remove rows not columns as CartoContext.data generally needs the full metadata.
Note
Narrowing down a discovery query using the keywords, regex, and time filters is important for getting a manageable metadata set. Besides there being a large number of measures in the DO, a metadata response has acceptable combinations of measures with demonimators (normalization and density), the same measure from other years, and quantiles measurements.
For example, setting the region to be United States counties with no filter values set will result in many thousands of measures.
Examples
Get all European Union measures that mention
freight
.meta = cc.data_discovery('European Union', keywords='freight', time='2010') print(meta['numer_name'].values)
Parameters: - region (str or list of float) –
Information about the region of interest. region can be one of three types:
- region name (str): Name of region of interest. Acceptable values are limited to: ‘Australia’, ‘Brazil’, ‘Canada’, ‘European Union’, ‘France’, ‘Mexico’, ‘Spain’, ‘United Kingdom’, ‘United States’.
- table name (str): Name of a table in user’s CARTO account
with geometries. The region will be the bounding box of
the table.
Note
If a table name is also a valid Data Observatory region name, the Data Observatory name will be chosen over the table.
- bounding box (list of float): List of four values (two
lng/lat pairs) in the following order: western longitude,
southern latitude, eastern longitude, and northern latitude.
For example, Switzerland fits in
[5.9559111595,45.8179931641,10.4920501709,47.808380127]
Note
Geometry levels are generally chosen by subdividing the region into the next smallest administrative unit. To override this behavior, specify the boundaries flag. For example, set boundaries to
'us.census.tiger.census_tract'
to choose US census tracts. - keywords (str or list of str, optional) – Keyword or list of keywords in measure description or name. Response will be matched on all keywords listed (boolean or).
- regex (str, optional) – A regular expression to search the measure
descriptions and names. Note that this relies on PostgreSQL’s
case insensitive operator
~*
. See PostgreSQL docs for more information. - boundaries (str or list of str, optional) – Boundary or list of boundaries that specify the measure resolution. See the boundaries section for each region in the Data Observatory catalog.
Returns: A dataframe of the complete metadata model for specific measures based on the search parameters.
Return type: pandas.DataFrame
Raises: ValueError
– If region is alist
and does not consist of four elements, or if region is neither an acceptable region nor a table in user account.
-
data
(table_name, metadata, persist_as=None, how='the_geom')¶ Get an augmented CARTO dataset with Data Observatory measures. Use CartoContext.data_discovery to search for available measures, or see the full Data Observatory catalog. Optionally persist the data as a new table.
Example
Get a DataFrame with Data Observatory measures based on the geometries in a CARTO table.
cc = cartoframes.CartoContext(BASEURL, APIKEY) median_income = cc.data_discovery('transaction_events', regex='.*median income.*', time='2011 - 2015') df = cc.data(median_income, 'transaction_event')
Pass in cherry-picked measures from the Data Observatory catalog. The rest of the metadata will be filled in, but it’s important to specify the geographic level as this will not show up in the column name.
median_income = [{'numer_id': 'us.census.acs.B19013001', 'geom_id': 'us.census.tiger.block_group', 'numer_timespan': '2011 - 2015'}] df = cc.data('transaction_events', median_income)
Parameters: - table_name (str) – Name of table on CARTO account that Data Observatory measures are to be added to.
- metadata (pandas.DataFrame) – List of all measures to add to table_name. See CartoContext.data_discovery outputs for a full list of metadata columns.
- persist_as (str, optional) – Output the results of augmenting
table_name to persist_as as a persistent table on CARTO.
Defaults to
None
, which will not create a table. - how (str, optional) – Not fully implemented. Column name for identifying the geometry from which to fetch the data. Defaults to the_geom, which results in measures that are spatially interpolated (e.g., a neighborhood boundary’s population will be calculated from underlying census tracts). Specifying a column that has the geometry identifier (for example, GEOID for US Census boundaries), results in measures directly from the Census for that GEOID but normalized how it is specified in the metadata.
Returns: A DataFrame representation of table_name which has new columns for each measure in metadata.
Return type: pandas.DataFrame
Raises: NameError
– If the columns in table_name are in thesuggested_name
column of metadata.ValueError
– If metadata object is invalid or empty, or if the number of requested measures exceeds 50.
-
Map Layer Classes¶
Layer classes for map creation. See examples in layer.Layer and layer.QueryLayer for example usage for data layers. See layer.BaseMap for basemap layers.
-
class
layer.
BaseMap
(source='voyager', labels='back', only_labels=False)¶ Layer object for adding basemaps to a cartoframes map.
Example
Add a custom basemap to a cartoframes map.
import cartoframes from cartoframes import BaseMap, Layer cc = cartoframes.CartoContext(BASEURL, APIKEY) cc.map(layers=[BaseMap(source='light', labels='front'), Layer('acadia_biodiversity')])
Parameters: - source (str, optional) – One of
light
ordark
. Defaults tovoyager
. Basemaps come from https://carto.com/location-data-services/basemaps/ - labels (str, optional) – One of
back
,front
, or None. Labels on the front will be above the data layers. Labels on back will be underneath the data layers but on top of the basemap. Setting labels toNone
will only show the basemap. - only_labels (bool, optional) – Whether to show labels or not.
-
is_basic
()¶ Does BaseMap pull from CARTO default basemaps?
Returns: True if using a CARTO basemap (Dark Matter, Positron or Voyager), False otherwise. Return type: bool
- source (str, optional) – One of
-
class
layer.
Layer
(table_name, source=None, overwrite=False, time=None, color=None, size=None, tooltip=None, legend=None)¶ A cartoframes Data Layer based on a specific table in user’s CARTO database. This layer class is used for visualizing individual datasets with CartoContext.map()’s
layers
keyword argument.Example
import cartoframes from cartoframes import QueryLayer, styling cc = cartoframes.CartoContext(BASEURL, APIKEY) cc.map(layers=[Layer('fantastic_sql_table', size=7, color={'column': 'mr_fox_sightings', 'scheme': styling.prism(10)})])
Parameters: - table_name (str) – Name of table in CARTO account
- Styling – See
QueryLayer
for a full list of all arguments arguments for styling this map data layer. - source (pandas.DataFrame, optional) – Not currently implemented
- overwrite (bool, optional) – Not currently implemented
-
class
layer.
QueryLayer
(query, time=None, color=None, size=None, tooltip=None, legend=None)¶ cartoframes data layer based on an arbitrary query to the user’s CARTO database. This layer class is useful for offloading processing to the cloud to do some of the following:
- Visualizing spatial operations using PostGIS and PostgreSQL, which is the database underlying CARTO
- Performing arbitrary relational database queries (e.g., complex JOINs in SQL instead of in pandas)
- Visualizing a subset of the data (e.g.,
SELECT * FROM table LIMIT 1000
)
Used in the layers keyword in CartoContext.map().
Example
Underlay a QueryLayer with a complex query below a layer from a table. The QueryLayer is colored by the calculated column
abs_diff
, and points are sized by the columni_measure
.import cartoframes from cartoframes import QueryLayer, styling cc = cartoframes.CartoContext(BASEURL, APIKEY) cc.map(layers=[QueryLayer(''' WITH i_cte As ( SELECT ST_Buffer(the_geom::geography, 500)::geometry As the_geom, cartodb_id, measure, date FROM interesting_data WHERE date > '2017-04-19' ) SELECT i.cartodb_id, i.the_geom, ST_Transform(i.the_geom, 3857) AS the_geom_webmercator, abs(i.measure - j.measure) AS abs_diff, i.measure AS i_measure FROM i_cte AS i JOIN awesome_data AS j ON i.event_id = j.event_id WHERE j.measure IS NOT NULL AND j.date < '2017-04-29' ''', color={'column': 'abs_diff', 'scheme': styling.sunsetDark(7)}, size='i_measure'), Layer('fantastic_sql_table')])
Parameters: - query (str) – Query to expose data on a map layer. At a minimum, a query needs to have the columns cartodb_id, the_geom, and the_geom_webmercator for the map to display. Read more about queries in CARTO’s docs.
- time (dict or str, optional) –
Time-based style to apply to layer.
If time is a
str
, it must be the name of a column which has a data type of datetime or float.If time is a
dict
, the following keys are options:- column (str, required): Column for animating map, which must be of type datetime or float.
- method (str, optional): Type of aggregation method for
operating on Torque TileCubes. Must be one of
avg
,sum
, or another PostgreSQL aggregate functions with a numeric output. Defaults tocount
. - cumulative (bool, optional): Whether to accumulate points over
time (
True
) or not (False
, default) - frames (int, optional): Number of frames in the animation. Defaults to 256.
- duration (int, optional): Number of seconds in the animation. Defaults to 30.
- trails (int, optional): Number of trails after the incidence of a point. Defaults to 2.
- color (dict or str, optional) –
Color style to apply to map. For example, this can be used to change the color of all geometries in this layer, or to create a graduated color or choropleth map.
If color is a
str
, there are two options:- A column name to style by to create, for example, a choropleth map if working with polygons. The default classification is quantiles for quantitative data and category for qualitative data.
- A hex value or web color name.
If color is a
dict
, the following keys are options, with values described:- column (str): Column used for the basis of styling
- scheme (dict, optional): Scheme such as styling.sunset(7)
from the styling module of cartoframes that
exposes CARTOColors.
Defaults to mint scheme for quantitative
data and bold for qualitative data. More control is given by
using styling.scheme.
If you wish to define a custom scheme outside of CARTOColors, it is recommended to use the styling.custom utility function.
- size (dict or int, optional) –
Size style to apply to point data.
If size is an
int
, all points are sized by this value.If size is a
str
, this value is interpreted as a column, and the points are sized by the value in this column. The classification method defaults to quantiles, with a min size of 5, and a max size of 5. Use thedict
input to override these values.If size is a
dict
, the follow keys are options, with values described as:- column (str): Column to base sizing of points on
- bin_method (str, optional): Quantification method for dividing
data range into bins. Must be one of the methods in
BinMethod
(excluding category). - bins (int, optional): Number of bins to break data into. Defaults to 5.
- max (int, optional): Maximum point width (in pixels). Defaults to 25.
- min (int, optional): Minimum point width (in pixels). Defaults to 5.
- tooltip (tuple, optional) – Not yet implemented.
- legend – Not yet implemented.
Map Styling Functions¶
Styling module that exposes CARTOColor schemes. Read more about CARTOColor in its GitHub repository.
-
class
styling.
BinMethod
¶ Data classification methods used for the styling of data on maps.
-
quantiles
¶ str – Quantiles classification for quantitative data
-
jenks
¶ str – Jenks classification for quantitative data
-
headtails
¶ str – Head/Tails classification for quantitative data
-
equal
¶ str – Equal Interval classification for quantitative data
-
category
¶ str – Category classification for qualitative data
-
-
styling.
antique
(bins, bin_method='category')¶ CARTOColor Antique qualitative scheme
-
styling.
armyRose
(bins, bin_method='quantiles')¶ CARTOColor ArmyRose divergent quantitative scheme
-
styling.
bluGrn
(bins, bin_method='quantiles')¶ CARTOColor BluGrn quantitative scheme
-
styling.
bluYl
(bins, bin_method='quantiles')¶ CARTOColor BluYl quantitative scheme
-
styling.
bold
(bins, bin_method='category')¶ CARTOColor Bold qualitative scheme
-
styling.
brwnYl
(bins, bin_method='quantiles')¶ CARTOColor BrwnYl quantitative scheme
-
styling.
burg
(bins, bin_method='quantiles')¶ CARTOColor Burg quantitative scheme
-
styling.
burgYl
(bins, bin_method='quantiles')¶ CARTOColor BurgYl quantitative scheme
-
styling.
custom
(colors, bins=None, bin_method='quantiles')¶ Create a custom scheme.
Parameters: - colors (list of str) – List of hex values for styling data
- bins (int, optional) – Number of bins to style by. If not given, the number of colors will be used.
- bin_method (str, optional) – Classification method. One of the values
in
BinMethod
. Defaults to quantiles, which only works with quantitative data.
-
styling.
darkMint
(bins, bin_method='quantiles')¶ CARTOColor DarkMint quantitative scheme
-
styling.
earth
(bins, bin_method='quantiles')¶ CARTOColor Earth divergent quantitative scheme
-
styling.
emrld
(bins, bin_method='quantiles')¶ CARTOColor Emrld quantitative scheme
-
styling.
fall
(bins, bin_method='quantiles')¶ CARTOColor Fall divergent quantitative scheme
-
styling.
get_scheme_cartocss
(column, scheme_info)¶ Get TurboCARTO CartoCSS based on input parameters
-
styling.
geyser
(bins, bin_method='quantiles')¶ CARTOColor Geyser divergent quantitative scheme
-
styling.
magenta
(bins, bin_method='quantiles')¶ CARTOColor Magenta quantitative scheme
-
styling.
mint
(bins, bin_method='quantiles')¶ CARTOColor Mint quantitative scheme
-
styling.
orYel
(bins, bin_method='quantiles')¶ CARTOColor OrYel quantitative scheme
-
styling.
pastel
(bins, bin_method='category')¶ CARTOColor Pastel qualitative scheme
-
styling.
peach
(bins, bin_method='quantiles')¶ CARTOColor Peach quantitative scheme
-
styling.
pinkYl
(bins, bin_method='quantiles')¶ CARTOColor PinkYl quantitative scheme
-
styling.
prism
(bins, bin_method='category')¶ CARTOColor Prism qualitative scheme
-
styling.
purp
(bins, bin_method='quantiles')¶ CARTOColor Purp quantitative scheme
-
styling.
purpOr
(bins, bin_method='quantiles')¶ CARTOColor PurpOr quantitative scheme
-
styling.
redOr
(bins, bin_method='quantiles')¶ CARTOColor RedOr quantitative scheme
-
styling.
safe
(bins, bin_method='category')¶ CARTOColor Safe qualitative scheme
-
styling.
scheme
(name, bins, bin_method)¶ Return a custom scheme based on CARTOColors.
Parameters: - name (str) – Name of a CARTOColor.
- bins (int) – Number of bins for classifying data. CARTOColors have 7 bins max for quantitative data, and 11 max for qualitative data.
- bin_method (str) – One of methods in
BinMethod
.
Warning
Input types are particularly sensitive in this function, and little feedback is given for errors.
name
andbin_method
arguments are case-sensitive.
-
styling.
sunset
(bins, bin_method='quantiles')¶ CARTOColor Sunset quantitative scheme
-
styling.
sunsetDark
(bins, bin_method='quantiles')¶ CARTOColor SunsetDark quantitative scheme
-
styling.
teal
(bins, bin_method='quantiles')¶ CARTOColor Teal quantitative scheme
-
styling.
tealGrn
(bins, bin_method='quantiles')¶ CARTOColor TealGrn quantitative scheme
-
styling.
tealRose
(bins, bin_method='quantiles')¶ CARTOColor TealRose divergent quantitative scheme
-
styling.
temps
(bins, bin_method='quantiles')¶ CARTOColor Temps divergent quantitative scheme
-
styling.
tropic
(bins, bin_method='quantiles')¶ CARTOColor Tropic divergent quantitative scheme
-
styling.
vivid
(bins, bin_method='category')¶ CARTOColor Vivid qualitative scheme
BatchJobStatus¶
-
class
context.
BatchJobStatus
(carto_context, job)¶ Status of a write or query operation. Read more at Batch SQL API docs about responses and how to interpret them.
Example
Poll for a job’s status if you’ve caught the
BatchJobStatus
instance.import time job = cc.write(df, 'new_table', lnglat=('lng_col', 'lat_col')) while True: curr_status = job.status()['status'] if curr_status in ('done', 'failed', 'canceled', 'unknown', ): print(curr_status) break time.sleep(5)
Create a
BatchJobStatus
instance if you have a job_id output from a cc.write operation.>>> from cartoframes import CartoContext, BatchJobStatus >>> cc = CartoContext(username='...', api_key='...') >>> cc.write(df, 'new_table', lnglat=('lng', 'lat')) 'BatchJobStatus(job_id='job-id-string', ...)' >>> batch_job = BatchJobStatus(cc, 'job-id-string')
- Attrs:
- job_id (str): Job ID of the Batch SQL API job
last_status (str): Status of
job_id
job when last polled created_at (str): Time and date when job was created
Parameters: - carto_context (carto.CartoContext) – CartoContext instance
- job (dict or str) – If a dict, job status dict returned after sending a Batch SQL API request. If str, a Batch SQL API job id.
-
get_status
()¶ return current status of job
-
status
()¶ Checks the current status of job
job_id
Returns: Status and time it was updated Return type: dict Warns: UserWarning – If the job failed, a warning is raised with information about the failure
Credentials Management¶
Credentials management for cartoframes usage.
-
class
credentials.
Credentials
(creds=None, key=None, username=None, base_url=None, cred_file=None)¶ Credentials class for managing and storing user CARTO credentials. The arguments are listed in order of precedence:
Credentials
instances are first, key and base_url/username are taken next, and config_file (if given) is taken last. If no arguments are passed, then there will be an attempt to retrieve credentials from a previously saved session. One of the above scenarios needs to be met to successfully instantiate aCredentials
object.Parameters: - creds (
cartoframes.Credentials
, optional) – Credentials instance - key (str, optional) – API key of user’s CARTO account
- username (str, optional) – Username of CARTO account
- base_url (str, optional) – Base URL used for API calls. This is usually of the form https://eschbacher.carto.com/ for user eschbacher. On premises installations (and others) have a different URL pattern.
- cred_file (str, optional) – Pull credentials from a stored file. If this and all other args are not entered, Credentials will attempt to load a user config credentials file that was previously set with Credentials(…).save().
Raises: RuntimeError
– If not enough credential information is passed and no stored credentials file is found, this error will be raised.Example
from cartoframes import Credentials, CartoContext creds = Credentials(key='abcdefg', username='eschbacher') cc = CartoContext(creds=creds)
-
base_url
(base_url=None)¶ Return or set base_url.
Parameters: base_url (str, optional) – If set, updates the base_url. Otherwise returns current base_url. Note
This does not update the username attribute. Separately update the username with
Credentials.username
or update base_url and username at the same time withCredentials.set
.Example
>>> from cartoframes import Credentials # load credentials saved in previous session >>> creds = Credentials() # returns current base_url >>> creds.base_url() 'https://eschbacher.carto.com/' # updates base_url with new value >>> creds.base_url('new_base_url')
-
delete
(config_file=None)¶ Deletes the credentials file specified in config_file. If no file is specified, it deletes the default user credential file.
Parameters: config_file (str) – Path to configuration file. Defaults to delete the user default location if None. Tip
To see if there is a default user credential file stored, do the following:
>>> creds = Credentials() >>> print(creds) Credentials(username=eschbacher, key=abcdefg, base_url=https://eschbacher.carto.com/)
-
key
(key=None)¶ Return or set API key.
Parameters: key (str, optional) – If set, updates the API key, otherwise returns current API key. Example
>>> from cartoframes import Credentials # load credentials saved in previous session >>> creds = Credentials() # returns current API key >>> creds.key() 'abcdefg' # updates API key with new value >>> creds.key('new_api_key')
-
save
(config_loc=None)¶ Saves current user credentials to user directory.
Parameters: config_loc (str, optional) – Location where credentials are to be stored. If no argument is provided, it will be send to the default location. Example
from cartoframes import Credentials creds = Credentials(username='eschbacher', key='abcdefg') creds.save() # save to default location
-
set
(key=None, username=None, base_url=None)¶ Update the credentials of a Credentials instance instead with new values.
Parameters: - key (str) – API key of user account. Defaults to previous value if not specified.
- username (str) – User name of account. This parameter is optional if base_url is not specified, but defaults to the previous value if not set.
- base_url (str) – Base URL of user account. This parameter is
optional if username is specified and on CARTO’s
cloud-based account. Generally of the form
https://your_user_name.carto.com/
for cloud-based accounts. If on-prem or otherwise, contact your admin.
Example
from cartoframes import Credentials # load credentials saved in previous session creds = Credentials() # set new API key creds.set(key='new_api_key') # save new creds to default user config directory creds.save()
Note
If the username is specified but the base_url is not, the base_url will be updated to
https://<username>.carto.com/
.
-
username
(username=None)¶ Return or set username.
Parameters: username (str, optional) – If set, updates the username. Otherwise returns current username. Note
This does not update the base_url attribute. Use Credentials.set to have that updated with username.
Example
>>> from cartoframes import Credentials # load credentials saved in previous session >>> creds = Credentials() # returns current username >>> creds.username() 'eschbacher' # updates username with new value >>> creds.username('new_username')
- creds (


Image Title
Depart do be so he enough talent.
Depart do be so he enough talent.
Impossible considered invitation him men instrument saw celebrated unpleasant. Put rest and must set kind next many near nay. He exquisite continued explained middleton am. Voice hours young woody has she think equal. Estate moment he at on wonder at season little. Six garden result summer set family esteem nay estate. End admiration mrs unreserved discovered comparison especially invitation.
Impossible considered invitation him men instrument saw celebrated unpleasant. Put rest and must set kind next many near nay. He exquisite continued explained middleton am. Voice hours young woody has she think equal. Estate moment he at on wonder at season little. Six garden result summer set family esteem nay estate. End admiration mrs unreserved discovered comparison especially invitation.
Impossible considered invitation him men instrument saw celebrated unpleasant. Put rest and must set kind next many near nay. He exquisite continued explained middleton am. Voice hours young woody has she think equal. Estate moment he at on wonder at season little. Six garden result summer set family esteem nay estate. End admiration mrs unreserved discovered comparison especially invitation.


This article assumes that you know to compile a cross-compiler from scratch. If not, I recommend this succinct guide, or this longer guide if you want to have some context.
On x86_64 architectures, building gcc such that it can generate both 32- and
64-bit code is easy: just use the --enable-multilib
flag at configuration
time, and everything will follow smoothly (see Arch Linux’s PKGBUILD for
gcc, the packaging is a bit more complicated, but the build itself is
simple).
It is not as easy when targeting the MIPS architecture, specifically
mips64-elf
. By default, the 32-bit ABIs are not part of the multilib setup.
gcc exposes the multilib setup that was used to compile a toolchain via the
-print-multi-lib
option. On your own x86_64 system, you should see something
similar to this (assuming you have gcc-multilib
on your system)
valandil ~ $ gcc -print-multi-lib
.;
32;@m32
where the second line shows the use of -m32
in the build options for the gcc
toolchain itself, and the libraries are stored in the 32/
directory. This
toolchain is thus comprised of two sets of GCC libraries, one compiled with
default options, and one with -m32
.
If you had installed a mips64-elf toolchain with --enable-multilib
, by default
you would get the following output
valandil ~ $ mips64-elf-gcc -print-multi-lib
.;
soft-float;@msoft-float
el;@EL
soft-float/el;@msoft-float@EL
In order to enable other ABIs, specify other build options for the GCC libraries in general, you need to know about Target makefile fragments.
In short, target Makefile fragments are the gcc/config/<target>/t*
files in
gcc’s source tree. When you specify a target at the configure
step, it looks
for those in order to generate the Makefile that will actually build the gcc
libraries 1. For a mips64-elf
target, the file that is of interest to us is
the gcc/config/mips/t-elf
file. By default, it sets three Makefile variables
MULTILIB_OPTIONS = msoft-float EL/EB
MULTILIB_DIRNAMES = soft-float el eb
MULTILIB_MATCHES = EL=mel EB=meb msingle-float=m4650
which instructs the gcc build system to generate libraries for combinations of
these three build flags. Flags that are separated by a space are combined, while
incompatible flag are separated with a slash. It is this fragment that generated
the mips64-elf-gcc -print-multi-lib
output that we saw above.
So, if you want to generate libraries for other ABIs, you simply edit (or create
a new one!) that file to what you need. In my case, I wanted to have libraries
compiled with -mabi=32
, at it is the ABI used by most N64 games. So, I edited
my t-elf
fragment to be
MULTILIB_OPTIONS = mabi=32 msoft-float EL/EB
MULTILIB_DIRNAMES = 32 soft-float el eb
MULTILIB_MATCHES = EL=mel EB=meb msingle-float=m4650
If you were to compile this toolchain, you would get the following
-print-multib-lib
output:
.;
32;@mabi=32
soft-float;@msoft-float
el;@EL
soft-float/el;@msoft-float@EL
32/soft-float;@mabi=32@msoft-float
32/el;@mabi=32@EL
32/soft-float/el;@mabi=32@msoft-float@EL
MULTILIB_DIRNAMES
controls the name of the folder in which the libraries
reside. Note that this corresponds to the information before the semi-colon in
the -print-multilib-output
, while the string after describes the build
options, prepended with @
. and MULTILIB_MATCHES
provides the synonyms of
some build options.
There are other options than those discussed here. Feel free to read the Makefile Fragment of the gcc docs to have the whole story, or look at an example of custom target in glank’s n64 repo, under config (as of July 9th, this is only in the n64-ultra branch).
-
At least, that is how I think it works. ↩


A Hole in the Head * * (out of * * * * )
Directed by Eli Kabillio. 1999 - 54 minutes Not Rated (not for the squeamish). Reviewed July 22, 1999.
"A Hole in the Head," a documentary which premiered in February on cable's The Learning Channel and is now available on video for $19.95 through Mad Dog Films, Inc. (1-800-475-2233), is the first in a planned series of films from Eli Kabillio on unconventional topics in medical, biological, or other scientific realms. The title, "A Hole in a Head," says it all, as this fascinating, but ultimately empty, documentary focuses on the development of "modern" trepanation, a controversial procedure that some people have been known to perform on themselves, in which a sizable hole is drilled out of the skull, allowing greater brain blood volume and increasing the person's metabolism and their energy. Trepanation is a hotly-debated surgery that I knew nothing about going into the film, and although informative and educational, as a documentary film it is decidedly lackluster, with the majority of it merely being the "talking heads" of professionals and patients discussing their opinions on the subject. While we learn a lot about trepanation, we learn virtually nothing about the people on view, thus giving us no emotional center or added interest.
- Copyright 1999 by Dustin Putman Http://hometown.aol.com/FilmFan16/index.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


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Cirque du Soleil co-founder detained for allegedly growing cannabis on private South Pacific island
by Bradford BetzThe co-founder of the global circus company Cirque du Soleil has been detained in French Polynesia for allegedly growing marijuana on his private island.
A French police official told The Associated Press that Guy Laliberte, 60, was expected to appear before a judge on Wednesday in Papeete, the territory's capital on the island of Tahiti.
Lune Rouge, a Montreal-based company headed by Laliberte, confirmed his detention. It said Laliberte is being questioned about cannabis reportedly grown for personal use on his private island of Nukutepipi.
The company said Laliberte is a medical marijuana user, but he “categorically denies” involvement in the sale or trafficking of controlled substances.
POT OR HEMP? NYPD BUSTS HIGHLIGHTS GROWING DRUG CONFUSION
“Guy Laliberte completely dissociates himself from any rumor implicating him…in the sale or traffic of drugs,” the company said in a statement.
CLICK HERE TO GET THE FOX NEWS APP
Local broadcaster Polynesie 1 said police recently detained a person close to Laliberte for drug possession and found photos of cannabis plantations in the person’s cellphone.
The Associated Press contributed to this report.


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I Alone Level-Up (Solo Leveling)
Solo Leveling
I Alone Level-Up known as Solo Leveling is a popular light novel covering Action, Adventure, and Fantasy genres. Written by Chu-Gong (추공) in February 14, 2014. It was then serialized as a novel in Papyrus that ended with 14 volumes and 270 chapters.
Featured Article
The Ant King (왕) was an immensely powerful Magic Beast born from the Ant Queen on Jeju Island where an S-Rank Gate had opened. The Ant Queen, who was monitored 24 hours a day by a Japanese satellite, showed a massive decline in its magical energy. It was later learned that this was due to the Ant Queen birthing her strongest child, the Ant King, for the sole purpose of beating strong Hunters. The King was one of the few magic beasts capable of communicating with Sung Jin-Woo similar to Baruka of the Red Gate and Kargalgan. Through his ability, Gluttony, the Ant King was able to learn human languages including Korean after devouring powerful S-Rank hunters. It is one of the most powerful magic beasts introduced within the series, with no hunters present during the Jeju Island Raid being able to fight against it, excluding Sung Jin-Woo. The Ant King is the primary antagonist of the Jeju Island Arc. To view the entire article, click Ant King.
Summary
10 years ago, after “the Gate” that connected the real world with the monster world opened, some of the ordinary, everyday people received the power to hunt monsters within the Gate. They are known as “Hunters”. However, not all Hunters are powerful. My name is Sung Jin-Woo, an E-rank Hunter. I’m someone who has to risk his life in the lowliest of dungeons, the “World’s Weakest”. Having no skills whatsoever to display, I barely earned the required money by fighting in low-leveled dungeons… at least until I found a hidden dungeon with the hardest difficulty within the D-rank dungeons! In the end, as I was accepting death, I suddenly received a strange power, a quest log that only I could see, a secret to leveling up that only I know about! If I trained in accordance with my quests and hunted monsters, my level would rise. Changing from the weakest Hunter to the strongest S-rank Hunter!
- 1 Prologue
- 2 Chapter 2
- 3 Chapter 3
- 4 Chapter 4
- 5 Chapter 5
- 6 Chapter 6
- 7 Chapter 7
- 8 Chapter 8
- 9 Chapter 9
- 10 Chapter 10
- 11 Chapter 11
- 12 Chapter 12
- 13 Chapter 13
- 14 Chapter 14
- 15 Chapter 15
- 16 Chapter 16
- 17 Chapter 17
- 18 Chapter 18
- 19 Chapter 19
- 20 Chapter 20
- 21 Chapter 21
- 22 Chapter 22
- 23 Chapter 23
- 24 Chapter 24
- 25 Chapter 25
- 26 Chapter 26
- 27 Chapter 27
- 28 Chapter 28
- 29 Chapter 29
- 30 Chapter 30
- 31 Chapter 31
- 32 Chapter 32
- 33 Chapter 33
- 34 Chapter 34
- 35 Chapter 35
- 36 Chapter 36
- 37 Chapter 37
- 38 Chapter 38
- 39 Chapter 39
- 40 Chapter 40
- 41 Chapter 41
- 42 Chapter 42
- 43 Chapter 43
- 44 Chapter 44
- 45 Chapter 45
- 46 Chapter 46
- 47 Chapter 47
- 48 Chapter 48
- 49 Chapter 49
- 50 Chapter 50
- 51 Chapter 51
- 52 Chapter 52
- 53 Chapter 53
- 54 Chapter 54
- 55 Chapter 55
- 56 Chapter 56
- 57 Chapter 57
- 58 Chapter 58
- 59 Chapter 59
- 60 Chapter 60
- 61 Chapter 61
- 62 Chapter 62
- 63 Chapter 63
- 64 Chapter 64
- 65 Chapter 65
- 66 Chapter 66
- 67 Chapter 67
- 68 Chapter 68
- 69 Chapter 69
- 70 Chapter 70
- 71 Chapter 71
- 72 Chapter 72
- 73 Chapter 73
- 74 Chapter 74
- 75 Chapter 75
- 76 Chapter 76
- 77 Chapter 77
- 78 Chapter 78
- 79 Chapter 79
- 80 Chapter 80
- 81 Chapter 81
- 82 Chapter 82
- 83 Chapter 83
- 84 Chapter 84
- 85 Chapter 85
- 86 Chapter 86
- 87 Chapter 87
- 88 Chapter 88
- 89 Chapter 89
- 90 Chapter 90
- 91 Chapter 91
- 92 Chapter 92
- 93 Chapter 93
- 94 Chapter 94
- 95 Chapter 95
- 96 Chapter 96
- 97 Chapter 97
- 98 Chapter 98
- 99 Chapter 99
- 100 Chapter 100
99 Reviews
Write a reviewPlease share your thoughts to rate and score...
For those looking for other necromancer mc novels, I recommend Seoul Station’s Necromancer. Found that after looking for a novel like Solo Leveling. Solo Leveling is great, but I found SSN more enjoyable due to the MC’s personality since I found Solo Leveling’s mc’s personality honestly lacklustre. Hope this helped.
i have read many novels but this one amazing... and my first recommendation...
So amazing and entertaining, MUST read also the manwa, both are just perfect. The narrative and translation quality is great, I really recommend it
Thank you so much, Chu-Gong (추공). For blessing your readers with such a great story. Truly one of the best in my books. It was a heartfelt ending indeed.
This is one of my favorite novels. the protagonist is fantastic, his growth is magnificent and the ending leaves a lot open. I read it all in one go.
the story is very very good, interesting, and well made. it has a unique concept compared to the other novels i've read so far but the ending somehow feels like sad and tragic for me,, STILL ONE OF THE BEST NOVELS OUT THERE!!
absolutely love this story. it was very well done and i comend the author this is one of my favorite light novels. the character growth is just 👌 and he doesn't feel forced or end up a selfish ass when he gets stronger, which is a common grief with this kind of thing
Came to read this since I got impatient waiting for the webtoon chapters. I loved this story, especially the mc, and I wish it was longer. The romance plot could've been explored more, but it's a minor issue for me.
I ABSOLUTELY LOVED IT!!!!!! And the fact that Jin Wo married only one and NOT 10 women is the COMPLETLY BESTTTT!!!!!! YYEEEAAAHHHH for not cheating!!
simply good and unique. I was hesitant to start it but the way it draws u is quite amazing. The whole world and a different concept of God has been visualized. One should read it.
The best gyud! This story have become one of my favorites. I felt really sad when it ended and I miss the characters a lot. Salamat kaayo sa author ani nga story. I look forward to reading another one of your wonderful novels in the future!
Solo leveling has a extremely good story, I never expect I would be reading a novel of any kind but this is an exception. Definitely worth reading
A very good story was a very fun and exciting journey reading through this novel.  The story starts off a the classic revival of the weakest to the strongest, but Jin-Woos character design and choice of power is just sick. The story is honestly for me is one of the best out there. Jin-Woo just gets more OP without making it to much by balancing it with an even better rival or opponent. I definitely recommend this if you got nothing else to do.
User Comments
It is used only as a discussion area. If you want to evaluate and score, use the "Write Review" field.You must be logged in to post a comment. If you don't have an account, sign up now.
I liked the light novel very much I got hooked immediately and finally I read the whole novel I really liked the fact that the mc did not become selfish after becoming very strong and the fact that from the beginning of the story to the end it has matured a lot honestly, the ending for me was very sad especially for the fact that after reading the novel for a long time I was very fond of the series I would have preferred that the series last much longer and in my opinion the romantic plot should have been deepened despite all this is my favorite novel ps: I advise you to read it all because the novel is not at all obvious and is beautiful
GOOOD ONE HERE,BUT IT WOULD BE BETTTER IF THE SIDE CHARACTERS WERE ATLEAST..INCLUDED
i don't know why people always rumbling about how the story is made or there's nothing special in it, seriously? you can't even create a novel like this and you go there complaining..actually it's on e of the best novel out there for me.
Like this novel very very much. You guys should read the manhaw too.
I love this
How can i change language of the book? I can do It on my pc but cant find It on mobile
why is it only the text like were are the drawings
For some reason im not satisfied with the ending in chapter 243 it seems like the chapters i've read so far from the beginning till chapter "242" was just a joke, id tried to read the first chapter of side story but i stopped halfway then. Because i knew already of what will happen.. is there anything i have to look forward with the remaining chapters after 243...? Or should i look for another novel..?
one of the best novel so far! excellent plot.. MC is just .... love it!!
if you ask me manhua or novel? i would say manhua is way better... i didnt like the ending was rushed and open and general dont like ending like this with lose ends.
This is in no way the best novel as few ppl are claiming. It is one of the worse on that I have read.  Just don't expect too much. It nowhere near lives up to the hype.
Dont ask. just read and you will love it. no doubt. ><< /p>
Where will I start reading manhwa chapter 130 here in light novel? Can someone tell me please? Thank you
What a great ending! It is satisfying. Thanks for the journey!
And another great but beautiful novel finishes, Author thank you for making it!
hey can someone tell me from where i need to start when i read the manhua 129?
After the latest manhwa chapter I could contain my curiosity and started from the first chapter of the web novel.
 I have high expectations so Author...... don’t let me down man!


Creating Tag Cloud Using R and Flash / JavaScript (SWFObject)
Tag cloud is a bunch of words drawn in a graph with their sizes proportional to their frequency; it’s widely used in blogs to visualize tags. We can observe important words quickly from a tag cloud, as they often appear in large fontsize. Tony N. Brown asked how to graphically represent frequency of words in a speech the other day in R-help list, which is actually a problem about the tag cloud:
I recently saw a graph on television that displayed selected words/phrases in a speech scaled in size according to their frequency. So words/phrases that were often used appeared large and words that were rarely used appeared small. […]
Marc Schwartz mentioned that Gorjanc Gregor has done some work years ago using R (in grid graphics). The obstacle of creating tag cloud in R, as Gorjanc wrote, lies in deciding the placement of words, and it would be much easier for other applications such as browsers to arrange the texts. That’s true – there have already been a lot of mature programs to deal with tag cloud. One of them is the wp-cumulus
plugin for WordPress, which makes use of a Flash object to generate the tag cloud, and it has fantastic 3D rotation effect of the cloud.
1. Arranging text labels with pointLabel()
Before introducing how to port the plugin into R, I’d like to introduce an R function pointLabel()
in maptools package and it can partially solve the problem of arranging text labels in a plot (using simulated annealing or genetic algorithm). Here is a simulated example:
library(maptools)
set.seed(123)
x = runif(19)
y = runif(19)
w = c("R", "is", "free", "software", "and", "comes",
"with", "ABSOLUTELY", "NO", "WARRANTY", "You", "are", "welcome",
"to", "redistribute", "it", "under", "certain", "conditions")
par(ann = FALSE, xpd = NA, mar = rep(2, 4))
plot(x, y, type = "n", axes = FALSE)
pointLabel(x, y, w, cex = runif(19, 1, 5))
I was fortunate to get a very neat graph with no labels overlapping, but I don’t think this is a good solution, as it doesn’t take care of the initial locations of the words. My rough idea about deciding the initial locations is to sample on circles with radii proportional to the frequency, i.e. let x = freq * sin(θ) and y = freq * cos(θ) where θ ~ U(0, 2π). In this case, important words will be placed near the center of the plot.
2. Creating tag cloud in a Flash movie using R
The problem becomes quite easy with a Flash movie tagcloud.swf and a JavaScript program swfobject.js. The mechanism, briefly speaking, is that the tag information is passed to the Flash object by JavaScript, and the Flash object will read the variable tagcloud
where the sizes, colors and hyperlinks of tags are stored. Finally the tags are visualized like rotating cloud.
It’s not difficult to pass the tag information to JavaScript in pure text. The function tag_cloud()
in the fun package can create an HTML page with a tag cloud Flash movie inside it.
The main argument is tagData
which is a data.frame containing at least three columns (tag
, link
and count
) and looks like:
> head(tagData)
tag link count
1 2D Kernel Density http://yihui.name/en/tag/2d-kernel-density/ 1
2 algorithm http://yihui.name/en/tag/algorithm/ 1
3 Animation http://yihui.name/en/tag/animation/ 11
4 AniWiki http://yihui.name/en/tag/aniwiki/ 2
5 Arcing http://yihui.name/en/tag/arcing/ 1
6 arrows() http://yihui.name/en/tag/arrows/ 1
Additional columns color
and hicolor
will be used if they exist (hexadecimal numbers specifying RGB), e.g.
> head(tagData)
tag link count color hicolor
1 2D Kernel Density http://yihui.name/en/tag/2d-kernel-density/ 1 2163bb f0763d
2 algorithm http://yihui.name/en/tag/algorithm/ 1 9f0f38 d825b1
3 Animation http://yihui.name/en/tag/animation/ 11 800130 5b8d6a
4 AniWiki http://yihui.name/en/tag/aniwiki/ 2 7ce1df 6607b0
5 Arcing http://yihui.name/en/tag/arcing/ 1 df4e4a f5cdf2
6 arrows() http://yihui.name/en/tag/arrows/ 1 31f5fb 19d50d
3. Example
Here is an example on visualizing my blog tags.
library(fun)
tagCloud(tagData)
The above code will generate an HTML page. You can adjust the parameters as you wish.
4. Other issues
There is still one more step to answer Tony’s original question, namely splitting the speech into single words and computing the frequency. This can be (roughly) done by strsplit(..., split = " ")
and table()
.
Encoding problems may exist in the above code, but URLencode(tagXML)
could be of help.
Only Latin characters are supported, but there’s possibility to modify the Flash source file to support other languages. See Roy Tanck’s post for more information.
Other R resources I know so far:
- The R package
R4X
by Romain Fran?ois: you can generate an HTML page containing the tags with dynamic classes attached to the<span>
tags (install the package and read its vignette:install.packages('R4X', repos='http://r-forge.r-project.org'); vignette('r4xslides', package='R4X')
) - The R package
snippets
by Simon Urbanek: there is a functioncloud()
to create word cloud; words are arranged from top to bottom and left to right. See the 23rd reply below for an example (thanks, Emilio).
[Comments]:


@article{Zheng2017sift, author = {Liang Zheng and Yi Yang and Qi Tian}, title = {{SIFT} Meets {CNN}: A Decade Survey of Instance Retrieval}, journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence}, doi = {10.1109/tpami.2017.2709749}, url = {https://doi.org/10.1109/tpami.2017.2709749}, year = {2017}, publisher = {{IEEE}} }


Hover mouse over image to trigger animation
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Integeras nec odio.
Scenario: ???
Actions:
Known browser issues: ???
Result images:


Select your stress-related warning signs:
Select your stress-related symptoms:
Ways in which you deal with stress:


Created by David Gold
Over time, I've noticed that my parents passwords are simple, predictable and guessable. In turn, this makes them hackable.
I created this presentation to introduce them to my favorite password manager.
I can recall some of the methods of creating passwords:
The new prevailing philosophy on password security is to use something called a password manager where you don't know any of your passwords
Why do we care?
By using weak passwords, we open ourselves up to hacking
...but what is hacking?
The majority of hacking to folks like you and me comes from something called
Simply put, password reuse is reusing the same password across different services
You know... passwords like "password" or the name of your first dog
Bad actors will get access to a list of login credentials and try those combinations across banks, instagram, etc
No, not THAT type of bad actor
In 2014, Yahoo was reportedly hacked to the tune of almost nearly 500 million users
This figure was changed to cover the entirety of their 3 billion users
In 2014, Yahoo was reportedly hacked to the tune of almost nearly 500 million users
This figure was changed to cover the entirety of their 3 billion users
Mr. Bad Actor will then take those credentials and then try them across a bunch a sites.
Even random websites that are unimportant might give Mr. Bad Actor additional information to use for other websites
“About 80% of data breaches in 2019 were caused by password compromise.”
“In 2019, 42% of companies were breached by a bad password.”
“The average person reuses each password 14 times.”
It should! We humans are fallable creatures.
Remembering our passwords is an outdated practice.
Information moves too quickly and too often for it to be sustainable.
The industry leader in something called a password manager.
With OnePassword, you can now have an app create your passwords for you
OnePassword can generate a password for you once you sign up for something
OnePassword then saves these passwords into their app so that you don't have to try to remember each one
You're then given a shortcut so that when you need to login, you just press the keys on your keyboard. Your login and password are automatically filled into their appropriate fields
| Computer | Controls |
|---|---|
| PC | ⌘ + / |
| Mac | CTRL + ALT + / |
We get hacked because we're human. Let technology handle secure information for you. They're better at it
Bad actors can spoof their emails to make it look like it's coming from a legitimate source. Right click on senders contact names to verify that it's coming from a proper source.
As I was making this, my sister sent me a text asking if this email was legitimately from Netflix
It wasn't.
Websites like Facebook and LinkedIn will prevent people who you aren't connected with from seeing your entire profile
Hackers might create fake profiles and try to add you in attempt to gain access to more information
If sites have pop ups asking you to download anything, don't do it
Many nefarious sites have fake pop ups in hopes of downloading malware (bad!) onto your computer. You can learn more about how to spot those here
Updates can happen for many reasons, and it's not always security related. But it's best to get updates as often and early as they're available
It's unwise to give personal information out over the phone. Generally speaking, I'd suggest against it.
I never answer any unknown numbers. If it's important they'll leave a message. If I suspect it might require secure information, I can atleast vet their callback number.
I could make another whole presentation on 2-factor authentication (2FA), but I won't. Click here to learn more about what it is
If you'd like to learn more about the project, you can see the repo
Any questions or concerns? Email me


goto CPH is closing in rapidly.
As mentioned previously I am especially looking forward to David Nolen’s talk Putting the Dream Machine to Work.
Om is getting a lot of coverage and even if it is still very new, it is seeing some heavy duty prodcution usage, for instance at Prismatic. But Prismatic has gone even further that just using Om and released om-tools, which makes it even more straight forward to use Om.
Another talk I am looking very much forward to is Greg Young’s talk on Event Sourcing. Event Sourcing is a very interesting pattern which has a very natural fit to many common business scenarios, and I am looking forward to Greg Young giving us some examples of that. The benefits of the event sourcing pattern appears to me to overlap somewhat with the benefits of the Clojure based database Datomic which also emphazises the need for a solid model of time.
If you are interested in dabbling with Event Sourcing and have been inspired by David’s talk on Om/ClojureScript you should check out the JVM Soup post on Event Sourcing in Clojure. This post demonstrates how to use core.async Clojure/ClojureScripts CSP implentation, which David also recommends to do inter component communication when using Om, to do event sourcing in Clojure/ClojureScript.


The Coordel journey is just beginning. At its core, Coordel works when a network of people are coordinating their efforts. There are three ways you can participate in the Coordel Network.
Scroll over and click the binoculars icon to follow a Coordel Opportunity. Non-members will be invited to start a free 30-day trial membership. Opportunities you follow will appear in your Coord list within the application.


Oterre is a design duo comprising of Charis Alexander and Jennifer Choi, Parsons The New School for Design alumni based in NYC. Created from a shared love for design, fashion and technology, the first collection centers around a concept of play and introducing it into the mundane.
Accessories are a daily part of modern human life – the simple habit of subtly changing one's appearance influences both personal and external impressions. This act of customization is celebrated through the use of 3D printing as a method for rapidly creating lightweight, wearable designs inspired by elemental shapes and crisp outlines. When juxtaposed with rich materials, delicate pendants lend an air of sophistication.
Would love to feature us or collaborate?
Feel free to email us at<EMAIL_ADDRESS>or simply use this form.


Dear P
HAPPY BIRTHDAY!
So much that I want to tell you, yet I dont know where to start.
Thanks for loving me, and for showing your love for me in so many different non-verbal ways. I really really appreciate every small (or big) things you do for me, like buying chocolates for me.
Thanks for being the responsible one, feeling like you need to be the one thinking for us, thinking for our future.
This period of time have been and will be very difficult for us. I know it is especially hard for you, that you feel like you need to be the one making the important decisions.
I'm sorry for being so hopeful, sorry for doing things that makes it so much more difficult for you.
I will try not to make requests that will disrupt your thoughts.
I'm sorry for sometimes wanting to run away from all these problems. haha sometimes I do wish God will just solve my problems immediately cause I'm too lazy to face them.
Anyway, I'm not sure how our future will be like but I will support you always :D haha and all the best to your journey in discovering the truth with regards to religion. I will pray for you regardless.
I love you.
Love, C


|
Bomberman
1.0
Re-make of Bomberman game
|
Files | |
| file | Animation.h [code] |
| file | CLEngine.hpp [code] |
| file | FfmpegPlayer.hpp [code] |
| file | Mesh.hpp [code] |
| file | Model.hpp [code] |
| file | MusicPlayer.hpp [code] |
| file | ParticleManager.hpp [code] |
| file | ParticleSystem.hpp [code] |
| file | ParticleSystemBrick.hpp [code] |
| file | ParticleSystemCreator.hpp [code] |
| file | ParticleSystemPoint.hpp [code] |
| file | ParticleSystemQuad.hpp [code] |
| file | ResourceManager.hpp [code] |
| Header file of the ResourceManager class. | |
| file | Shader.hpp [code] |
| file | Skybox.hpp [code] |
| file | Texture.hpp [code] |


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Partial Span Flap (54 kB) |
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Fan Blade Acoustic Pressures (120 kB) |
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2D Rotor-Stator (45 kB) |
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Submarine (18 kB) |
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Vortex Burst (CFL3D/FUN3D) (30 kB) |
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Flap-Edge Flow (39 kB) |
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Blended Wing Body (15 kB) |
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Forced Pitching of Wing 445 With Mesh Deformation
(1.9 MB animation) |


From the underground punk houses to the mega concert theaters of pop music there are endless possibilities of the evolution of music. Our team is committed in providing emerging artists the platform to share their music through our network platforms of over 150 music houses and studios worldwide. We aim to link artist with record labels and other aspiring artists to encourage collaboration and idea-sharing discussions.