arcgis.features.use_proximity module¶
These functions help you answer one of the most common questions posed in spatial analysis: “What is near what?”
connect_origins_to_destinations measures the travel time or distance between pairs of points. create_buffers create areas of equal distance from features. create_drive_time_areas finds areas around locations that can be reached within a time period. find_nearest identifies those places that are the closest to known locations. plan_routes determines the best way to route a fleet of vehicles to visit many stops.
connect_origins_to_destinations¶
-
use_proximity.
connect_origins_to_destinations
(origins_layer, destinations_layer, measurement_type='DrivingTime', origins_layer_route_id_field=None, destinations_layer_route_id_field=None, time_of_day=None, time_zone_for_time_of_day='GeoLocal', output_name=None, context=None, gis=None, estimate=False, point_barrier_layer=None, line_barrier_layer=None, polygon_barrier_layer=None)¶ The Connect Origins to Destinations task measures the travel time or distance between pairs of points. Using this tool, you can
- Calculate the total distance or time commuters travel on their home-to-work trips.
- Measure how far customers are traveling to shop at your stores. Use this information to define your market reach, especially when targeting advertising campaigns or choosing new store locations.
- Calculate the expected trip mileage for your fleet of vehicles. Afterward, run the Summarize Within tool to report mileage by state or other region.
You provide starting and ending points, and the tool returns a layer containing route lines, including measurements, between the paired origins and destinations.
Argument Description origins_layer Required layer. The starting point or points of the routes to be generated. See Feature Input. destinations_layer Required layer. The routes end at points in the destinations layer. See Feature Input. measurement_type Required string. The origins and destinations can be connected by measuring straight-line distance, or by measuring travel time or travel distance along a street network using various modes of transportation known as travel modes.
Valid values are a string, StraightLine, which indicates Euclidean distance to be used as distance measure or a Python dictionary representing settings for a travel mode.
When using a travel mode for the measurement_type, you need to specify a dictionary containing the settings for a travel mode supported by your organization. The code in the example section below generates a valid Python dictionary and then passes it as the value for the measurement_type parameter.
Supported travel modes: [‘Driving Distance’, ‘Driving Time’, ‘Rural Driving Distance’, ‘Rural Driving Time’, ‘Trucking Distance’, ‘Trucking Time’, ‘Walking Distance’, ‘Walking Time’]
origins_layer_route_id_field Optional string. Specify the field in the origins layer containing the IDs that pair origins with destinations.
- The ID values must uniquely identify points in the origins layer.
- Each ID value must also correspond with exactly one route ID value in the destinations layer. Route IDs that match across the layers create origin-destination pairs, which the tool connects together.
- Specifying origins_layer_route_id_field is optional when there is exactly one point feature in the origins or destinations layer. The tool will connect all origins to the one destination or the one origin to all destinations, depending on which layer contains one point.
destinations_layer_route_id_field Optional string. Specify the field in the destinations layer containing the IDs that pair origins with destinations.
- The ID values must uniquely identify points in the destinations layer.
- Each ID value must also correspond with exactly one route ID value in the origins layer. Route IDs that match across the layers create origin-destination pairs, which the tool connects together.
- Specifying destinations_layer_route_id_field is optional when there is exactly one point feature in the origins or destinations layer. The tool will connect all origins to the one destination or the one origin to all destinations, depending on which layer contains one point.
time_of_day Optional datetime.datetime. Specify whether travel times should consider traffic conditions. To use traffic in the analysis, set measurement_type to a travel mode object whose impedance_attribute_name property is set to travel_time and assign a value to time_of_day. (A travel mode with other impedance_attribute_name values don’t support traffic.) The time_of_day value represents the time at which travel begins, or departs, from the origin points. The time is specified as datetime.datetime.
The service supports two kinds of traffic: typical and live. Typical traffic references travel speeds that are made up of historical averages for each five-minute interval spanning a week. Live traffic retrieves speeds from a traffic feed that processes phone probe records, sensors, and other data sources to record actual travel speeds and predict speeds for the near future.
The data coverage page shows the countries Esri currently provides traffic data for.
Typical Traffic:
To ensure the task uses typical traffic in locations where it is available, choose a time and day of the week, and then convert the day of the week to one of the following dates from 1990:
- Monday—1/1/1990
- Tuesday—1/2/1990
- Wednesday—1/3/1990
- Thursday—1/4/1990
- Friday—1/5/1990
- Saturday—1/6/1990
- Sunday—1/7/1990
Set the time and date as datetime.datetime.
For example, to solve for 1:03 p.m. on Thursdays, set the time and date to 1:03 p.m., 4 January 1990; and convert to datetime eg. datetime.datetime(1990, 1, 4, 1, 3).
Live Traffic:
To use live traffic when and where it is available, choose a time and date and convert to datetime.
Esri saves live traffic data for 12 hours and references predictive data extending 12 hours into the future. If the time and date you specify for this parameter is outside the 24-hour time window, or the travel time in the analysis continues past the predictive data window, the task falls back to typical traffic speeds.
Examples: from datetime import datetime
- “time_of_day”: datetime(1990, 1, 4, 1, 3) # 13:03, 4 January 1990. Typical traffic on Thursdays at 1:03 p.m.
- “time_of_day”: datetime(1990, 1, 7, 17, 0) # 17:00, 7 January 1990. Typical traffic on Sundays at 5:00 p.m.
- “time_of_day”: datetime(2014, 10, 22, 8, 0) # 8:00, 22 October 2014. If the current time is between 8:00 p.m., 21 Oct. 2014 and 8:00 p.m., 22 Oct. 2014, live traffic speeds are referenced in the analysis; otherwise, typical traffic speeds are referenced.
- “time_of_day”: datetime(2015, 3, 18, 10, 20) # 10:20, 18 March 2015. If the current time is between 10:20 p.m., 17 Mar. 2015 and 10:20 p.m., 18 Mar. 2015, live traffic speeds are referenced in the analysis; otherwise, typical traffic speeds are referenced.
time_zone_for_time_of_day Optional string. Specify the time zone or zones of the timeOfDay parameter. Choice list: [‘GeoLocal’, ‘UTC’]
GeoLocal-refers to the time zone in which the originsLayer points are located.
UTC-refers to Coordinated Universal Time.
include_route_layers Optional Boolean. When include_route_layers is set to true, each route from the result is also saved as a route layer item. A route layer includes all the information for a particular route such as the stops assigned to the route as well as the travel directions. Creating route layers is useful if you want to share individual routes with other members in your organization. The route layers use the output feature service name provided in the outputName parameter as a prefix and the route name generated as part of the analysis is added to create a unique name for each route layer.
Caution: Route layers cannot be created when the output is a feature collection. The task will raise an error if output_name is not specified (which indicates feature collection output) and include_route_layers is true.
The maximum number of route layers that can be created is 1,000. If the result contains more than 1,000 routes and include_route_layers is true, the task will only create the output feature service.
output_name Optional string. If provided, the task will create a feature layer of the results. You define the name of the layer. If output_name is not supplied, the task will return a feature collection. context Optional string. Additional settings such as processing extent and output spatial reference. For calculate_density, there are two settings.
- Extent (extent)-a bounding box that defines the analysis area. Only those points in the origins_layer and destinations_layer that intersect the bounding box will be analyzed.
- Output Spatial Reference (outSR)
- If the output is a feature service, the spatial reference will be the same as originsLayer. Setting outSR for feature services has no effect.
- If the output is a feature collection, the features will be in the spatial reference of the outSRvalue or the spatial reference of originsLayer when outSR is not specified.
gis Optional, the GIS on which this tool runs. If not specified, the active GIS is used. estimate Optional Boolean. Is true, the number of credits needed to run the operation will be returned as a float. point_barrier_layer Optional layer. Specify one or more point features that act as temporary restrictions (in other words, barriers) when traveling on the underlying streets.
A point barrier can model a fallen tree, an accident, a downed electrical line, or anything that completely blocks traffic at a specific position along the street. Travel is permitted on the street but not through the barrier. See Feature Input.
line_barrier_layer Optional layer. Specify one or more line features that prohibit travel anywhere the lines intersect the streets.
A line barrier prohibits travel anywhere the barrier intersects the streets. For example, a parade or protest that blocks traffic across several street segments can be modeled with a line barrier. See Feature Input.
polygon_barrier_layer Optional string. Specify one or more polygon features that completely restrict travel on the streets intersected by the polygons.
One use of this type of barrier is to model floods covering areas of the street network and making road travel there impossible. See Feature Input.
Returns: dict with the following keys: ”routes_layer” : layer (FeatureCollection)
”unassigned_origins_layer” : layer (FeatureCollection)
”unassigned_destinations_layer” : layer (FeatureCollection)
USAGE EXAMPLE: To retrieve trvel modes and run connect_origins_to_destinations tool. This example creates route between esri regional offices to esri headquarter. import arcgis.network as network route_service = network.RouteLayer(gis.properties.helperServices.route.url, gis=gis) travel_mode = [i for i in route_service.retrieve_travel_modes()['supportedTravelModes'] if i['name'] == 'Rural Driving Distance'][0] routes = connect_origins_to_destinations(origins_layer=esri_regional, destinations_layer=dest_layer, measurement_type=travel_mode, time_of_day=datetime(1990, 1, 4, 1, 3), output_name="routes_from_offices_to_hq")
create_buffers¶
-
use_proximity.
create_buffers
(input_layer, distances=[], field=None, units='Meters', dissolve_type='None', ring_type='Disks', side_type='Full', end_type='Round', output_name=None, context=None, gis=None, estimate=False)¶ Creates buffer polygon(s) around input features.
- input_layer : Required layer (see Feature Input in documentation)
- The input to be buffered.
- distances : Optional list of floats
- The distance(s) that will be buffered.
- field : Optional string
- Buffers will be created using field values.
- units : Optional string
- The linear unit to be used with the distance value(s).
- dissolve_type : Optional string
- Specifies the dissolve to be performed to remove buffer overlap.
- ring_type : Optional string
- The ring type.
- side_type : Optional string
- The side(s) of the input that will be buffered.
- end_type : Optional string
- The shape of the buffer at the end of buffered line features.
- output_name : Optional string
- Additional properties such as output feature service name.
- context : Optional string
- Additional settings such as processing extent and output spatial reference.
- gis :
- Optional, the GIS on which this tool runs. If not specified, the active GIS is used.
- estimate :
- Optional Boolean. If True, the number of credits to run the operation will be returned.
buffer_layer : layer (FeatureCollection)
create_drive_time_areas¶
-
use_proximity.
create_drive_time_areas
(input_layer, break_values=[5, 10, 15], break_units='Minutes', travel_mode='Driving', overlap_policy='Overlap', time_of_day=None, time_zone_for_time_of_day='GeoLocal', output_name=None, context=None, gis=None, estimate=False)¶ input_layer : Required layer (see Feature Input in documentation)
break_values : Optional list of floats
break_units : Optional string
travel_mode : Optional string
overlap_policy : Optional string
time_of_day : Optional datetime.datetime
time_zone_for_time_of_day : Optional string
- output_name : Optional string
- Additional properties such as output feature service name.
- context : Optional string
- Additional settings such as processing extent and output spatial reference.
- gis :
- Optional, the GIS on which this tool runs. If not specified, the active GIS is used.
- estimate :
- Optional Boolean. If True, the number of credits to run the operation will be returned.
drive_time_areas_layer : layer (FeatureCollection)
find_nearest¶
-
use_proximity.
find_nearest
(analysis_layer, near_layer, measurement_type='StraightLine', max_count=100, search_cutoff=2147483647, search_cutoff_units=None, time_of_day=None, time_zone_for_time_of_day='GeoLocal', output_name=None, context=None, gis=None, estimate=False)¶ Measures the straight-line distance, driving distance, or driving time from features in the analysis layer to features in the near layer, and copies the nearest features in the near layer to a new layer. Returns a layer containing the nearest features and a line layer that links the start locations to their nearest locations.
- analysis_layer : Required layer (see Feature Input in documentation)
- For each feature in this layer, the task finds the nearest features from the nearLayer.
- near_layer : Required layer (see Feature Input in documentation)
- The features from which the nearest locations are found.
- measurement_type : Required string
- The nearest locations can be determined by measuring straight-line distance, driving distance, or driving time
- max_count : Optional int
- The maximum number of near locations to find for each feature in analysisLayer.
- search_cutoff : Optional float
- Limits the search range to this value
- search_cutoff_units : Optional string
- The units for the value specified as searchCutoff
- time_of_day : Optional datetime.datetime
- When measurementType is DrivingTime, this value specifies the time of day to be used for driving time calculations based on traffic.
time_zone_for_time_of_day : Optional string
- output_name : Optional string
- Additional properties such as output feature service name
- context : Optional string
- Additional settings such as processing extent and output spatial reference
- gis :
- Optional, the GIS on which this tool runs. If not specified, the active GIS is used.
- estimate :
- Optional Boolean. If True, the number of credits to run the operation will be returned.
- dict with the following keys:
- “nearest_layer” : layer (FeatureCollection) “connecting_lines_layer” : layer (FeatureCollection)
plan_routes¶
-
use_proximity.
plan_routes
(stops_layer, route_count, max_stops_per_route, route_start_time, start_layer, start_layer_route_id_field=None, return_to_start=True, end_layer=None, end_layer_route_id_field=None, travel_mode='Driving', stop_service_time=0, max_route_time=525600, include_route_layers=False, output_name=None, context=None, gis=None, estimate=False)¶ You provide a set of stops and the number of vehicles available to visit the stops, and Plan Routes determines how to efficiently assign the stops to the vehicles and route the vehicles to the stops.
Use this tool to plan work for a mobile team of inspectors, appraisers, in-home support service providers, and others; deliver or pick up items from remote locations; or offer transportation services to people.
stops_layer : Required layer (see Feature Input in documentation)
route_count : Required int
max_stops_per_route : Required int
route_start_time : Required datetime.datetime
start_layer : Required layer (see Feature Input in documentation)
start_layer_route_id_field : Optional string
return_to_start : Optional bool
end_layer : Optional layer (see Feature Input in documentation)
end_layer_route_id_field : Optional string
travel_mode : Optional string
stop_service_time : Optional float
max_route_time : Optional float
include_route_layers : Optional bool
output_name : Optional string
context : Optional string
- gis :
- Optional, the GIS on which this tool runs. If not specified, the active GIS is used.
- estimate :
- Optional Boolean. If True, the number of credits to run the operation will be returned.
- dict with the following keys:
- “routes_layer” : layer (FeatureCollection) “assigned_stops_layer” : layer (FeatureCollection) “unassigned_stops_layer” : layer (FeatureCollection)


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@Target([AnnotationTarget.CLASS, AnnotationTarget.FUNCTION, AnnotationTarget.VALUE_PARAMETER, AnnotationTarget.PROPERTY]) annotation class Status
(source)
Indicates the status of the implementation of the respective target.
Any annotation declared for a class or property also holds for their descendants or overriding members.
Api |
Official and minimal API, should be safe to rely on. annotation class Api |
Beta |
Might change in the future, but I will try my best to keep changes compatible. annotation class Beta |
Impl |
An exposed implementation detail with side effects; use at your own risk. annotation class Impl |
Util |
A utility function without side effects which is not intended for public use, but you might still want to reuse it for custom purposes, at your own risk. annotation class Util |
<init> |
Indicates the status of the implementation of the respective target. Status() |


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Tone.js Simple pitch sequence
Tone.js Simple pitch and duration sequence
Tone.js Simple pitch sequence with randomization
Tone.js Simple Order-1 Markov Chain
Stop all loops


- sendAsyncMsgToSlave(Request) - Method in class de.tub.ise.ec.clients.Client
-
Method which sends the request to slave server asynchronously.
- sendMsgToMaster(Request) - Method in class de.tub.ise.ec.clients.Client
-
Method which sends the request to master server
- sendMsgToSlave(Request) - Method in class de.tub.ise.ec.clients.Client
-
Method which sends the request to slave server
- SlaveMessageFactory - Class in de.tub.ise.ec.messagehandlers.factories
-
- SlaveServer - Class in de.tub.ise.ec.servers
-
From this class client can only read the value from the keystore
This class can propagate the values to other slaves in asynchronous replication
- SlaveServer() - Constructor for class de.tub.ise.ec.servers.SlaveServer
-
- SlaveServer(int, String) - Constructor for class de.tub.ise.ec.servers.SlaveServer
-
- SlaveStorageMessageHandler - Class in de.tub.ise.ec.messagehandlers
-
Class handles the messages sent by slave server
Created by tomasztkaczyk on 06.12.18.
- SlaveStorageMessageHandler(String) - Constructor for class de.tub.ise.ec.messagehandlers.SlaveStorageMessageHandler
-
- StalenessCalculator - Class in de.tub.ise.ec.calculators
-
Class responsible for staleness calculation.
- StalenessCalculator() - Constructor for class de.tub.ise.ec.calculators.StalenessCalculator
-
- store(String, Serializable) - Method in class de.tub.ise.ec.kv.FileSystemKVStore
-
stores a key value pair on the file system, whereby the key maps to the file
name and the value to the file's content
- store(String, Serializable) - Method in interface de.tub.ise.ec.kv.KeyValueInterface
-
stores a key value pair
- SyncAddValue - Class in de.tub.ise.ec.messagehandlers.messages
-
Class that represents add value message
It is sent from client to master, and triggers synchronous replication.
- SyncAddValue(Request, KeyValueInterface) - Constructor for class de.tub.ise.ec.messagehandlers.messages.SyncAddValue
-
- syncDelete(String) - Method in class de.tub.ise.ec.clients.Client
-
- SyncDeleteKey - Class in de.tub.ise.ec.messagehandlers.messages
-
Class that represents delete key-value pairs message.
- SyncDeleteKey(Request, KeyValueInterface) - Constructor for class de.tub.ise.ec.messagehandlers.messages.SyncDeleteKey
-
- syncUpdate(String, String) - Method in class de.tub.ise.ec.clients.Client
-
- SyncUpdateKey - Class in de.tub.ise.ec.messagehandlers.messages
-
Class that represents update keys message
It is sent by client to master, and trigger synchronous replication .
- SyncUpdateKey(Request, KeyValueInterface) - Constructor for class de.tub.ise.ec.messagehandlers.messages.SyncUpdateKey
-
- syncWrite(String, Serializable) - Method in class de.tub.ise.ec.clients.Client
-
This method triggers synchronous replication on a write request.
- syncWrite(String, Serializable) - Method in interface de.tub.ise.ec.clients.ICrud
-
This method triggers synchronous replication on a write request.


Troubleshooting
This page lists issues people have run into when using Storm along with their solutions.
Worker processes are crashing on startup with no stack trace
Possible symptoms:
- Topologies work with one node, but workers crash with multiple nodes
Solutions:
- You may have a misconfigured subnet, where nodes can’t locate other nodes based on their hostname. ZeroMQ sometimes crashes the process when it can’t resolve a host. There are two solutions:
- Make a mapping from hostname to IP address in /etc/hosts
- Set up an internal DNS so that nodes can locate each other based on hostname.
Nodes are unable to communicate with each other
Possible symptoms:
- Every spout tuple is failing
- Processing is not working
Solutions:
- Storm doesn’t work with ipv6. You can force ipv4 by adding
-Djava.net.preferIPv4Stack=true
to the supervisor child options and restarting the supervisor. - You may have a misconfigured subnet. See the solutions for
Worker processes are crashing on startup with no stack trace
Topology stops processing tuples after awhile
Symptoms:
- Processing works fine for awhile, and then suddenly stops and spout tuples start failing en masse.
Solutions:
- This is a known issue with ZeroMQ 2.1.10. Downgrade to ZeroMQ 2.1.7.
Not all supervisors appear in Storm UI
Symptoms:
- Some supervisor processes are missing from the Storm UI
- List of supervisors in Storm UI changes on refreshes
Solutions:
- Make sure the supervisor local dirs are independent (e.g., not sharing a local dir over NFS)
- Try deleting the local dirs for the supervisors and restarting the daemons. Supervisors create a unique id for themselves and store it locally. When that id is copied to other nodes, Storm gets confused.
“Multiple defaults.yaml found” error
Symptoms:
- When deploying a topology with “storm jar”, you get this error
Solution:
- You’re most likely including the Storm jars inside your topology jar. When packaging your topology jar, don’t include the Storm jars as Storm will put those on the classpath for you.
“NoSuchMethodError” when running storm jar
Symptoms:
- When running storm jar, you get a cryptic “NoSuchMethodError”
Solution:
- You’re deploying your topology with a different version of Storm than you built your topology against. Make sure the storm client you use comes from the same version as the version you compiled your topology against.
Kryo ConcurrentModificationException
Symptoms:
- At runtime, you get a stack trace like the following:
java.lang.RuntimeException: java.util.ConcurrentModificationException
at backtype.storm.utils.DisruptorQueue.consumeBatchToCursor(DisruptorQueue.java:84)
at backtype.storm.utils.DisruptorQueue.consumeBatchWhenAvailable(DisruptorQueue.java:55)
at backtype.storm.disruptor$consume_batch_when_available.invoke(disruptor.clj:56)
at backtype.storm.disruptor$consume_loop_STAR_$fn__1597.invoke(disruptor.clj:67)
at backtype.storm.util$async_loop$fn__465.invoke(util.clj:377)
at clojure.lang.AFn.run(AFn.java:24)
at java.lang.Thread.run(Thread.java:679)
Caused by: java.util.ConcurrentModificationException
at java.util.LinkedHashMap$LinkedHashIterator.nextEntry(LinkedHashMap.java:390)
at java.util.LinkedHashMap$EntryIterator.next(LinkedHashMap.java:409)
at java.util.LinkedHashMap$EntryIterator.next(LinkedHashMap.java:408)
at java.util.HashMap.writeObject(HashMap.java:1016)
at sun.reflect.GeneratedMethodAccessor17.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:616)
at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:959)
at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1480)
at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1416)
at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1174)
at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:346)
at backtype.storm.serialization.SerializableSerializer.write(SerializableSerializer.java:21)
at com.esotericsoftware.kryo.Kryo.writeClassAndObject(Kryo.java:554)
at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:77)
at com.esotericsoftware.kryo.serializers.CollectionSerializer.write(CollectionSerializer.java:18)
at com.esotericsoftware.kryo.Kryo.writeObject(Kryo.java:472)
at backtype.storm.serialization.KryoValuesSerializer.serializeInto(KryoValuesSerializer.java:27)
Solution:
- This means that you’re emitting a mutable object as an output tuple. Everything you emit into the output collector must be immutable. What’s happening is that your bolt is modifying the object while it is being serialized to be sent over the network.
NullPointerException from deep inside Storm
Symptoms:
- You get a NullPointerException that looks something like:
java.lang.RuntimeException: java.lang.NullPointerException
at backtype.storm.utils.DisruptorQueue.consumeBatchToCursor(DisruptorQueue.java:84)
at backtype.storm.utils.DisruptorQueue.consumeBatchWhenAvailable(DisruptorQueue.java:55)
at backtype.storm.disruptor$consume_batch_when_available.invoke(disruptor.clj:56)
at backtype.storm.disruptor$consume_loop_STAR_$fn__1596.invoke(disruptor.clj:67)
at backtype.storm.util$async_loop$fn__465.invoke(util.clj:377)
at clojure.lang.AFn.run(AFn.java:24)
at java.lang.Thread.run(Thread.java:662)
Caused by: java.lang.NullPointerException
at backtype.storm.serialization.KryoTupleSerializer.serialize(KryoTupleSerializer.java:24)
at backtype.storm.daemon.worker$mk_transfer_fn$fn__4126$fn__4130.invoke(worker.clj:99)
at backtype.storm.util$fast_list_map.invoke(util.clj:771)
at backtype.storm.daemon.worker$mk_transfer_fn$fn__4126.invoke(worker.clj:99)
at backtype.storm.daemon.executor$start_batch_transfer__GT_worker_handler_BANG_$fn__3904.invoke(executor.clj:205)
at backtype.storm.disruptor$clojure_handler$reify__1584.onEvent(disruptor.clj:43)
at backtype.storm.utils.DisruptorQueue.consumeBatchToCursor(DisruptorQueue.java:81)
... 6 more
Solution:
- This is caused by having multiple threads issue methods on the
OutputCollector
. All emits, acks, and fails must happen on the same thread. One subtle way this can happen is if you make aIBasicBolt
that emits on a separate thread.IBasicBolt
’s automatically ack after execute is called, so this would cause multiple threads to use theOutputCollector
leading to this exception. When using a basic bolt, all emits must happen in the same thread that runsexecute
.


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Follow these steps to add custom libraries to your C# project:
lean library add "<projectName>"
<packageName>
to add the <packageName>
NuGet package to the project in . / <projectName>.$ lean library add "My Project" Microsoft.ML Retrieving latest available version from NuGet Adding Microsoft.ML 1.5.5 to 'My Project/My Project.csproj' Restoring packages in 'My Project' to provide local autocomplete
Microsoft.ML
package.
If you want to use a different version you can use the --version <value>
option.
Additionally, you can pass the --no-local
flag to skip restoring the packages locally.
Follow these steps to remove custom libraries from your C# project:
lean library remove "<projectName>" <packageName>
to remove the <packageName>
NuGet package from the project in . / <projectName>.$ lean library remove "My Project" Microsoft.ML Removing Microsoft.ML from 'My Project/My Project.csproj' Restoring packages in 'My Project'
--no-local
flag to skip restoring the packages locally.
Additionally, you can also add or remove custom C# libraries by modifying the C# project file (the file ending with .csproj). This can be done manually or through your editor's built-in NuGet tooling if your editor has such a feature.


Simply put, an object is a variable that has values. These values are properties of the object.
This assigned the value "Mike" to person. If we wanted to add several values, or properties, we can do that in a couple of different ways.
Person now has several properties as key/value pairs. If we needed to call on one of those properties, we could simply return person.name or person.age to use those properties. If we wanted to add a property, you would just code person.newProperty = "Foo".
You can also format an object like this:
In the example above, we defined the properties by literally assigning the values. There is another way to create objects called the constructor method.
In this example, we created the same object as we did in the first example. By adding a function, we can create multiple people with the same properties without having repeat code.
JavaScript objects, while themselves simple data structures, can also be used to model object orientation for the JavaScript language.
To demonstrate, here are some examples on how you might use the Object data type to model silverware in an OOP fashion.
Creating a "Silverware" object
Using the object
Adapting the object


Referenties
Agentschap voor Natuur en Bos. 2015. Soortenbeschermingsprogramma voor de Europese bever (Castor fiber) in Vlaanderen. Brussel, België: Agentschap voor Natuur en Bos.
Arjo, WM, RE Joos, CO Kochanny, JL Harper, DL Nolte, and DL Bergman. 2008. “Assessment of transmitter models to monitor beaver Castor canadensis and C. fiber populations.” Wildlife Biology 14 (3): 309–17.
Burgman, MA, and JC Fox. 2003. “Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning.” Animal Conservation 6: 19–28.
Burt, HW. 1943. “Territoriality and Home Range Concepts as Applied to Mammals.” Journal of Mammalogy 24 (3): 346–52.
Calenge, C. 2006. “The package Adehabitat for the R software: tool for theanalysis of space and habitat use by animals.” Ecological Modelling 197: 516–19.
Campbell, RD, F Rosell, BA Nolet, and VAA Dijkstra. 2005. “Territory and group sizes in Eurasian beavers (Castor fiber): echoes of settlement and reproduction?” Behavioral Ecology and Sociobiology 58 (6): 597–607.
Campbell-Palmer, R, D Gow, R Campbell, H Dickinson, S Girling, J Gurnell, D Halley, et al. 2016. The Eurasian Beaver Handbook: Ecology and Management of Castor fiber. Exeter, UK: Pelagic Publishing Ltd.
Fryxell, JM. 2001. “Habitat suitability and source–sink dynamics of beavers.” Journal of Animal Ecology 70: 310–16.
Fryxell, JM, REA Sinclair, and G Caughly. 2014. Wildlife Ecology, Conservation, and Management. 3rd ed. Chichester, UK: Wiley-Blackwell.
Fustec, J, T Lode, D Le Jacques, and JP Cormier. 2001. “Colonization, riparian habitat selection and home range size in a reintroduced population of European beavers in the Loire.” Freshwater Biology 46 (10): 1361–71.
Graf, PM, Mayer M, Zedrosser A., Hackländer K, and Rosell F. 2016. “Territory size and age explain movement patterns in the Eurasian beaver.” Mammalian Biology 81: 587–94.
Halley, D, Rosell F, and Saveljev A. 2012. “Population and distribution of Eurasian beaver (Castor fiber).” Baltic Forestry 18 (1): 168–75.
Harris, S., WJ Cresswell, PG Forde, WJ Trewhella, T Woollard, and S Wray. 1990. “Home-range analysis using radio-tracking data - a review of problems and techniques particularly as applied to the study of mammals.” Mammal Review 20 (2/3): 97–123.
Heidecke, D. 1986. “Bestandssituation und Schutz von Castor fiber albicus (Mammalia, Rodentia, Castoridae).” Zoologische Abhandlungen 9: 111–19.
Herr, J, and F Rosell. 2004. “Use of space and movement patterns in monogamous adult Eurasian beavers (Castor fiber).” Journal of Zoology 262 (3): 257–64.
Huysentruyt, F, T Van Daele, P Verschelde, N Boone, S Devisscher, and J Vernaillen. 2019. Evaluatie van de monitoring van bever (Castor fiber) in Vlaanderen: Kwaliteitscontrole in het kader van het soortbeschermingsprogramma. Brussel, België: Rapporten van het Instituut voor Natuur- en Bosonderzoek.
John, F, and V Kostkan. 2009. “Compositional analysis and GPS/GIS for study of habitat selection by the European beaver, Castor fiber in the middle reaches of the Morava River.” Folia Zoologica 58 (1): 76–86.
Korbelová, J, L Hamšíková, J Maloň, L Válková, and A Vorel. 2016. “Seasonal variation in the home range size of the Eurasian beaver: do patterns vary across habitats?” Mammal Research 61 (3): 243–53.
Müller-Schwarze, D, and L Sun. 2003. The Beaver: Natural History of a Wetlands Engineer. New York, USA: Cornell University Press.
Nolet, BA, and F Rosell. 1998. “Comeback of the beaver Castor fiber: An overview of old and new conservation problems.” Biological Conservation 83 (2): 165–73.
R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Rosell, F, and JK Kvinlaug. 1998. “Methods for live-trapping beaver (Castor spp.).” Fauna Norvegica. Serie A. 19: 1–28.
Rosell, F, and RL Thomsen. 2006. “Sexual Dimorphism in Territorial Scent Marking by Adult Eurasian Beavers (Castor fiber).” Journal of Chemical Ecology 32 (6): 1301–15.
Rosell, F, F Bergan, and H Parker. 1998. “Scent-marking in the Eurasian beaver (Castor fiber) as a means of territory defense.” Journal of Chemical Ecology 24 (2): 207–19.
Van Wijngaarden, A. 1966. “De Bever Castor fiber in Nederland.” Lutra 8 (3): 123–40.
Verkem, S, J De Maeseneer, B Vandendriessche, G Verbeylen, and S Yskout. 2003. Zoogdieren in Vlaanderen. Ecologie en verspreiding van 1987 tot 2002. Mechelen en Gent, België: Natuurpunt Sutdie en JNM-Zoogdierenwerkgroep.
Windels, SK, and JL Belant. 2016. “Performance of tail-mounted transmitters on American beavers Castor canadensis in a northern climate.” Wildlife Biology 22 (3): 124–29.
Worton, BJ. 1989. “Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies.” Ecology 70 (1): 164–68.


|
BitShares-Core
6.1.0
BitShares blockchain implementation and command-line interface software
|
a 160 bit hash of a public key More...
#include <address.hpp>
Public Member Functions | |
| address () | |
| constructs empty / null address More... | |
| address (const std::string &base58str) | |
| converts to binary, validates checksum More... | |
| address (const fc::ecc::public_key &pub) | |
| converts to address More... | |
| address (const fc::ecc::public_key_data &pub) | |
| converts to address More... | |
| address (const pts_address &pub) | |
| converts to address More... | |
| address (const public_key_type &pubkey) | |
| converts to address More... | |
| operator std::string () const | |
| converts to base58 + checksum More... | |
Static Public Member Functions | |
| static bool | is_valid (const std::string &base58str, const std::string &prefix=GRAPHENE_ADDRESS_PREFIX) |
Public Attributes | |
| fc::ripemd160 | addr |
a 160 bit hash of a public key
An address can be converted to or from a base58 string with 32 bit checksum.
An address is calculated as ripemd160( sha512( compressed_ecc_public_key ) )
When converted to a string, checksum calculated as the first 4 bytes ripemd160( address ) is appended to the binary address before converting to base58.
Definition at line 44 of file address.hpp.
|
inline |
constructs empty / null address
Definition at line 47 of file address.hpp.
|
explicit |
converts to binary, validates checksum
Definition at line 33 of file address.cpp.
|
explicit |
converts to address
Definition at line 69 of file address.cpp.
|
explicit |
converts to address
Definition at line 80 of file address.cpp.
|
explicit |
converts to address
Definition at line 75 of file address.cpp.
|
explicit |
converts to address
Definition at line 85 of file address.cpp.
|
static |
Definition at line 42 of file address.cpp.
|
explicit |
converts to base58 + checksum
Definition at line 90 of file address.cpp.
| fc::ripemd160 graphene::protocol::address::addr |
Definition at line 58 of file address.hpp.


A very light implementation yet secure for remote procedure calls with unified interface via ssh (OpenSSH) or plink/plink.exe (PuTTY).
| Version: | 0.2-1 |
| Depends: | R (≥ 3.2.0), tools |
| Enhances: | pbdZMQ, remoter, pbdMPI |
| Published: | 2018-05-05 |
| Author: | Wei-Chen Chen [aut, cre], Drew Schmidt [aut] |
| Maintainer: | Wei-Chen Chen <wccsnow at gmail.com> |
| BugReports: | https://github.com/snoweye/pbdRPC/issues |
| License: | Mozilla Public License 2.0 |
| Copyright: | See pbdRPC/inst/putty_LICENCE for files in src/putty/. |
| URL: | http://r-pbd.org/ |
| NeedsCompilation: | yes |
| SystemRequirements: | ssh (OpenSSH) or plink (PuTTY) on Solaris, Linux, and Mac. |
| Citation: | pbdRPC citation info |
| Materials: | README ChangeLog INSTALL |
| CRAN checks: | pbdRPC results |
| Reference manual: | pbdRPC.pdf |
| Vignettes: |
pbdRPC-guide |
| Package source: | pbdRPC_0.2-1.tar.gz |
| Windows binaries: | r-devel: pbdRPC_0.2-1.zip, r-release: pbdRPC_0.2-1.zip, r-oldrel: pbdRPC_0.2-1.zip |
| OS X binaries: | r-release: pbdRPC_0.2-1.tgz, r-oldrel: pbdRPC_0.2-1.tgz |
| Old sources: | pbdRPC archive |
| Reverse suggests: | remoter |
Please use the canonical form https://CRAN.R-project.org/package=pbdRPC to link to this page.


Check the tooltips for each file to view its file size. Large files will freeze your browser.
Click the menu title link to clear the url after the '#'.


WEEK 7 SATURDAY
Year 1 (Year 2)
FIRST READING Sirach 17:1-13
A reading from the Book of Sirach
The Lord created man out of earth, and made him
into his own image; he turned him back into earth again, but clothed him in strength
like his own. He gave to men few days, a limited time, but granted them
authority over the things upon the earth. He placed the fear of them in all
flesh, and granted them dominion over beasts and birds. He made for them
discretion, with a tongue and eyes and ears; he gave them a mind for thinking,
and filled them with the discipline of discernment. He created in them the
knowledge of the spirit; he filled their hearts with understanding, and showed
them good and evil. He placed the fear of him into their hearts, showing them
the majesty of his works. He made them glory in his wondrous deeds, that they
might praise his holy name, to proclaim the grandeur of his works. He bestowed
knowledge upon them, and allotted to them the law of life. He established with
them an eternal covenant, and showed them his justice and his judgments. Their
eyes saw his glorious majesty, and their ears heard the glory of his voice. And
he said to them, “Beware of all unrighteousness.” And he gave commandment to
each of them concerning his neighbour. Their ways are always before him, they
will not be hid from his eyes.
The word of the Lord
RESPONSORIAL PSALM Psalm 103:13-14.15-16.17-18a (R. see 17ab)
R. The mercy of the Lord is everlasting upon those who hold him in fear.
As a father has compassion on his children, the
LORD’s compassion is on those who fear him. For he knows of what we are made;
he remembers that we are dust.
R. The mercy of the Lord is everlasting upon those who
hold him in fear.
Man, his days are like grass; he flowers like the
flower of the field. The wind blows, and it is no more, and its place never
sees it again.
R. The mercy of the Lord is everlasting upon those who
hold him in fear.
But the mercy of the LORD is everlasting upon those
who hold him in fear, upon children's children his justice, for those who keep
his covenant.
R. The mercy of the Lord is everlasting upon those who
hold him in fear.
FIRST READING James 5:13-20
A reading from the Letter of Saint James
Beloved: Is any one among you suffering? Let him pray. Is any cheerful? Let him sing praise. Is any among you sick? Let him call for the elders of the church, and let them pray over him, anointing him with oil in the name of the Lord; and the prayer of faith will save the sick man, and the Lord will raise him up; and if he has committed sins, he will be forgiven. Therefore confess your sins to one another, and pray for one another, that you may be healed. The prayer of a righteous man has great power in its effects. Elijah was a man of like nature with ourselves and he prayed fervently that it might not rain, and for three years and six months it did not rain on the earth. Then he prayed again and the heaven gave rain, and the earth brought forth its fruit. My brethren, if any one among you wanders from the truth and some one brings him back, let him know that whoever brings back a sinner from the error of his way will save his soul from death and will cover a multitude of sins.
The word of the Lord
RESPONSORIAL PSALM Psalm 141:1-2.3 and 8 (R. 2a)
R. O Lord, let my prayer be accepted as incense before you.
I have called to you, LORD; hasten to help me! Hear
my voice when I cry to you. Let my prayer be accepted as incense before you,
the raising of my hands like an evening oblation.
R. O Lord, let my prayer be accepted as incense before
you.
Set, O LORD, a guard on my mouth; keep watch at the
door of my lips!  In you I take
refuge; spare my soul!
R. O Lord, let my prayer be accepted as incense before
you.
R. Alleluia. Blessed are you, Father, Lord of heaven and earth, that you have revealed to little ones the mysteries of the kingdom. R. Alleluia.
GOSPEL Mark 10:13-16
A reading from the holy Gospel according to Mark
At that time: They were bringing children to Jesus, that he might touch them; and the disciples rebuked
them. But when Jesus saw it he was indignant, and said to them, "Let the
children come to me, do not hinder them; for to such belongs the
The Gospel of the Lord.


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General Quiz Competition
Round 1: Pen & Paper Round(GENERAL TECHNICAL QUESTIONS)
Round 2: Buzzer Round
Round 3: Final Round
Time Duration: 90 Minutes (minimum)
Rules: It is on the spot event Rule will be decided at the time of event.Participants are not allowed to use any study material.
3 members only.
7th March 2017
Round 1 : 12:15 to 12:45 PM Round 2 : 1:15 to 1:45 PM Round 3 : 2:15 to 2:45 PM
Narayan Bhavan, Civil Engg. Drawing Hall.
Rs. 60
Akshar Barot
9033456709
barotakshar24@gmail.com


nips nips2002 nips2002-34 nips2002-34-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Nicholas P. Hughes, David Lowe
Abstract: We consider the problem of illusory or artefactual structure from the visualisation of high-dimensional structureless data. In particular we examine the role of the distance metric in the use of topographic mappings based on the statistical ﬁeld of multidimensional scaling. We show that the use of a squared Euclidean metric (i.e. the SS TRESS measure) gives rise to an annular structure when the input data is drawn from a highdimensional isotropic distribution, and we provide a theoretical justiﬁcation for this observation.
[1] T. F. Cox and M. A. A. Cox. Multidimensional scaling. Chapman and Hall, London, 1994.
[2] J. deLeeuw and B. Bettonvil. An upper bound for sstress. Psychometrika, 51:149 – 153, 1986.
[3] G. J. Goodhill, M. W. Simmen, and D. J. Willshaw. An evaluation of the use of multidimensional scaling for understanding brain connectivity. Philosophical Transactions of the Royal Society, Series B, 348:256 – 280, 1995.
[4] H. Klock and J. M. Buhmann. Multidimensional scaling by deterministic annealing. In M. Pelillo and E. R. Hancock, editors, Energy Minimization Methods in Computer Vision and Pattern Recognition, Proc. Int. Workshop EMMCVPR ’97, Venice, Italy, pages 246–260. Springer Lecture Notes in Computer Science, 1997.
[5] D. Lowe and M. E. Tipping. Neuroscale: Novel topographic feature extraction with radial basis function networks. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. Cambridge, MA: MIT Press, 1997.
[6] K. V. Mardia, J. T. Kent, and J. M. Bibby. Multivariate analysis. Academic Press, 1997.
[7] S. T. Roweis, L. K. Saul, and G. E. Hinton. Global coordination of local linear models. In T. G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14. Cambridge, MA: MIT Press, 2002.
[8] J. W. Sammon. A nonlinear mapping for data structure analysis. IEEE Transactions On Computers, C-18(5):401 – 409, 1969.
[9] M. W. Simmen, G. J. Goodhill, and D. J. Willshaw. Scaling and brain connectivity. Nature, 369:448–450, 1994.
[10] J. B. Tenenbaum. Mapping a manifold of perceptual observations. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems 10. Cambridge, MA: MIT Press, 1998.
[11] C. K. Williams. On a connection between kernel PCA and metric multidimensional scaling. In T. K. Leen, T. G. Diettrich, and V. Tresp, editors, Advances in Neural Information Processing Systems 13. Cambridge, MA: MIT Press, 2001.
[12] M. P. Young. Objective analysis of the topological organization of the primate cortical visual system. Nature, 358:152–155, 1992.
[13] M. P. Young, J. W. Scannell, M. A. O’Neill, C. C. Hilgetag, G. Burns, and C. Blakemore. Non-metric multidimensional scaling in the analysis of neuroanatomical connection data and the organization of the primate cortical visual system. Philosophical Transactions of the Royal Society, Series B, 348:281 – 308, 1995.


My name is Matthew Czubakowski, and welcome to my personal website.
I'm an electrical engineer living in Southern California. I graduated from Caltech in 2009.
By day, I work in the Aerospace industry. I have a broad skillset with experience in mixed-signal ASIC design, systems engineering, software development, and circuit design and test.
By night, I experiment with responsive web design, software development, music, and video game livestreaming.
Feel free to connect with me through social media. I'm easiest to get a hold of through e-mail, Twitter, and Facebook.
Thanks for visiting!
For a more detailed resume or specific job-related inquiries, please contact me via e-mail.
California Institute of Technology, BS, Electrical Engineering, Class of 2009
Courses included Electromagnetic Engineering, Solid-State Electronics for Integrated Circuits, Digital Signal Processing, Demonstration Lectures in Optics, Microprocessor Systems Laboratory, and Waves, Quantum Physics, and Statistical Mechanics
Raytheon Space and Airborne Systems, Electrical Engineer II, September 2009-Present
- Designed, tested, and validated electronic systems at module, PCB, and circuit levels
- Performed test systems design, including both hardware and software
- Completed schematic capture, physical design, integration, power analysis, timing closure, functional verification, and tape-out from transistor to chip level
- Designed and implemented digital and analog blocks in multiple mixed-signal ASICs with first-pass success in submicron processes using Cadence tools
- Provided production support, including documenting acceptance requirements, updating test procedures, and coordinating documentation releases
Caltech SURF, Aerospace Corporation SURF Fellow, Summer 2007-Summer 2008
- Conducted research on the subject of Silver-Bonded Lithium Niobate Thin Films under Dr. Harry Atwater for use in electro-optic waveguides and displays
Bionic Harvest Inc., Engineering Intern, Summer 2006
- Constructed and tested MEMS micro-power generator devices
K. Diest, M. J. Archer, J. Dionne, Y. J. Park, M. Czubakowski, H. A. Atwater, Silver diffusion bonding and layer transfer of lithium niobate to silicon, Applied Physics Letters 93, 092906 (2008) [PDF]
K. Diest, J. Dionne, M. Czubakowski, M. J. Archer, Y. J. Park, H. A. Atwater, Full-Color Electro-Optic Resonator-Based Plasmonic Display, MRS Spring Conference, 2008, (L5.5)
- Circuit design, layout, and schematic tools, including Cadence Virtuoso, Allegro, Encounter, Analog Design Environment, TCL scripting, NCSim, and SPICE tools
- Fabrication and testing of optical, solid-state, and RF devices in a clean room environment
- Lab equipment including oscilloscopes, network analyzers, function generators, lock-in amplifiers, wafer probe, and surface-mount soldering
- C++, Verilog, x86 assembly, Python, and Javascript programming
- Some experience with C, Matlab, Labview, Standard ML, PHP, CSS, Git, and Subversion
- Solid-state and thin-film fabrication and testing methods, including wafer bonding, photolithography, sputter deposition, and SEM (scanning electron microscopy)
More to be added soon!
I'm a classically trained pianist and violinist with experience playing both solo and in orchestras and ensembles. I don't play as much anymore, but every once in a while I'll record a cover and post it either on my YouTube or SoundCloud pages. For performance or collaboration requests, feel free to contact me via e-mail.


../ collect-article-stats/ create-article/ delete-article/ expire-article/ lock-article/ mark-article-as-draft/ mark-article-as-pending/ publish-article/ remove-article-slotting/ rename-article/ unlock-article/ unpublish-article/ update-article/


| Class | Loader |
| In: |
lib/sirb/loader.rb
|
| Parent: | Object |
# File lib/sirb/loader.rb, line 7 7: def add_lib(name, &block) 8: name = name.to_s 9: block ||= lambda{require name} 10: self.libs[name] = self.safe_require(&block) 11: end
# File lib/sirb/loader.rb, line 21 21: def failed_libs 22: self.libs.map {|k, v| k unless v }.compact 23: end
# File lib/sirb/loader.rb, line 17 17: def libs_loaded 18: self.libs.map {|k, v| k if v }.compact 19: end
# File lib/sirb/loader.rb, line 25 25: def safe_require(&block) 26: begin 27: block.call 28: # If the lib is already loaded, it may return false, but it's available. 29: # If there's a problem with this, you should require the lib in a block 30: # and raise an exception if it fails. 31: true 32: # If the laod returns false, then 33: rescue Exception => e # Very important 34: false 35: end 36: end


Software Engineer
FULL STACK | DATA
Hello! This is Fei-Fan (Phil). Currently looking for a full time Software Engineer job opportunity.
Want to know more about me? Just take a look at my resume
and projects.
Please feel free to contact me.


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Our founder pat used a cookie cutter like this for his first ever salmon cookie! This cookie cutter is a cut above the rest!
These shirts are marked as Size XL, but they are actually a medium! Impress all your friends with your ability to fill your sleeves.


Adding Non-Input Features To Perform Subgroup Analysis
When loading a pre-trained model and accompanying dataset into the What-If Tool, typically one would think that every feature included in the dataset has to be used by the pre-trained model to generate predictions. However, not all features included in a dataset must behave as input to the model. Sometimes these non-input features (features that are present in a dataset, but are not used in model prediction) are utilized for evaluation purposes, behaving instead as a means of slicing the dataset into subgroups to conduct deeper analysis.
The What-If Tool is actually capable of displaying additional features in datapoints that are not used to query the trained model. Since the What-If Tool requires either a protocol message (tf.Example or SequenceExample), or data expressed as JSON lists or dicts to read files efficiently, there should be no issues adding non-input features—so long as the dataset has the required input feature(s) for the pretrained model to recognize when the What-If Tool queries for predictions.
- For example, if the pre-trained model expects an image file as input, but the dataset included for evaluation purposes also contains non-image features (such as categories and other attributes that describe aspects about the image), then just make sure all the input features are formatted correctly. That way, the pre-trained model will not ingest the wrong input feature or return an error because it was not expecting additional non-input features.
- Also, for each non-input feature column added to the dataset, ensure that it is the same length as the number of datapoints available in the dataset. Mismatch in dimensions could result in loading errors.
Once added, as with any other feature, the non-input features are available to analyze across all workspaces: Datapoint Editor, Performance & Fairness, and Features Overview.
The example in this tutorial borrows from the Smile Detector Demo. There, the task was to predict whether a person in a photo is smiling or not. Trained using only image data, the dataset also contains many feature attributes that describe certain aspects about the person in the photo. For example, is the person in the photo wearing a hat? Did they have make-up on? Did they have a beard?
It's worth acknowledging that all of the attribute annotations (which is captured as our non-input features in this tutorial) in CelebA come operationalized as binary categories.
For example, the Young attribute, which loosely and presumably represents perceived behavior or appearance of young people, is denoted as either present or absent in the image. In other words, the absence of Young in an image does not always imply that the image features an Old celebrity.
Warning: CelebA's categorizations do not reflect real human diversity of attributes. In this demo, we’re dubbing the Young attribute as age group, where the presence of the Young attribute in an image is labeled as a member of the young age group and the absence of the Young attribute is labeled as a member of the old age group. We’re also treating the Male attribute as gender, where the presence of the Male attribute in an image is regarded the same, but the absence of the Male attribute (Not Male in CelebA) is regarded as Female in our demo.
Basically, we’re making assumptions about the information in the dataset that is not mentioned in its original paper. As such, results in this demo is tied to the ways the attributes have been operationalized and annotated by the authors of CelebA.
This model should not be used for commercial purposes as that would violate CelebA's non-commercial research agreement.
As mentioned earlier, one of the advantages to including non-input features is the ability to evaluate model performance with respect to subgroups. This can be done by selecting the feature using Slice By in the Configure module located in the Performance & Fairness workspace as shown below:
Among the list of feature attributes were categories associated with age group and perceived gender—feature attributes that may be regarded as sensitive subgroups to evaluate performance across to ensure fairer, more desirable outcomes. As shown in the figure above, both of those features (age and gender) can be selected for analysis in the Configure module by selecting one of those features (say, age) using the Slice By drop down and selecting the second feature (say, gender) using the Slice By (secondary) drop down.
Using a combination of features that represent individual characteristics to explore the impact of model results with respect to these combinations is known as intersectional model analysis. Intersectionality explores how an individual’s identity and experiences (as captured in input features in the dataset) are shaped not just by unitary characteristics, but instead by a complex combination of many factors. The following is an example of what intersectional model analysis looks like in the What-If Tool:
The What-If Tool was created to help you mitigate, or leverage, the learned signal of a characteristic feature pertaining to a subgroup. As you work with different variables and define tasks for them, it can be useful to think about what comes next.
For example, where are the places or contexts where the interaction of the characteristic feature and the task could be a concern?
With the What-If Tool, anyone can transform their analysis to evaluate model performance across subgroups rather than in aggregate. The ability to use non-input features as a way to perform such analysis provides flexibility for practitioners that wish to omit such features in training, but utilize later for evaluation.


{% extends "skip_logic/base.html" %} {% load i18n %} {% load account socialaccount %} {% block head_title %}Confirm Question Removal{% endblock %} {% block breadcrumbs %} My Surveys > {{ question.page.survey.title }} > {{ question.page.title }} > Confirm Question Removal {% endblock %} {% block page %}
This question will be removed from the page.
If you continue, choices created for this question will also be removed, along with any paths leading from its choices.


This page demonstrates the timeline visualization.
Click and drag to move the timeline, scroll to zoom the timeline.
Starttime: Endtime:
Icons by DryIcons and TpdkDesign.net


We suggest the following mirror site for your download:
http://mirrors.shu.edu.cn/apache/tika/tika-app-1.17.jar
Other mirror sites are suggested below.
It is essential that you verify the integrity of the downloaded file using
the PGP signature (.asc
file) or a hash (.md5
or .sha*
file).
Please only use the backup mirrors to download KEYS, PGP and MD5 sigs/hashes or if no other mirrors are working.
http://mirror.bit.edu.cn/apache/tika/tika-app-1.17.jar
http://mirrors.hust.edu.cn/apache/tika/tika-app-1.17.jar
http://mirrors.shu.edu.cn/apache/tika/tika-app-1.17.jar
http://mirrors.tuna.tsinghua.edu.cn/apache/tika/tika-app-1.17.jar
Please only use the backup mirrors to download KEYS, PGP and MD5 sigs/hashes or if no other mirrors are working.
http://www-eu.apache.org/dist/tika/tika-app-1.17.jar
http://www-us.apache.org/dist/tika/tika-app-1.17.jar
The full listing of mirror sites is also available.
The procedure for setting up new mirrors is described in How to become a mirror.
It is essential that you verify the integrity of the downloaded file using
the PGP signature (.asc
file) or a hash (.md5
or .sha*
file). Please read Verifying Apache Software
Foundation Releases for more information on why
you should verify our releases.
The PGP signature can be verified using PGP or GPG. First download the
KEYS
as well as the asc
signature file for the relevant distribution.
Make sure you get these files from the main distribution site, rather than
from a mirror. Then verify the signatures using
% gpg --import KEYS % gpg --verify downloaded_file.asc downloaded_file
or
% pgpk -a KEYS % pgpv downloaded_file.asc
or
% pgp -ka KEYS % pgp downloaded_file.asc
Alternatively, you can verify the MD5 hash on the file. A unix
program called md5
or md5sum
is included in many unix distributions. It
is also available as part of
GNU Textutils.
Windows users can get binary md5 programs from
here,
here , or
here.


Package org.drip.sequence.metrics
Class SingleSequenceAgnosticMetrics
- java.lang.Object
-
- org.drip.sequence.metrics.SingleSequenceAgnosticMetrics
-
- Direct Known Subclasses:
BoundedSequenceAgnosticMetrics
,IntegerSequenceAgnosticMetrics
,PoissonSequenceAgnosticMetrics
public class SingleSequenceAgnosticMetrics extends java.lang.Object
SingleSequenceAgnosticMetrics contains the Sample Distribution Metrics and Agnostic Bounds related to the specified Sequence.
- Module = Numerical Core Module
- Library = Statistical Learning Library
- Project = Sequence
- Package = Metrics
- Author:
- Lakshmi Krishnamurthy
-
-
Constructor Summary
Constructors Constructor Description SingleSequenceAgnosticMetrics(double[] adblSequence, R1Univariate distPopulation)
Build out the Sequence and their Metrics
-
Method Summary
Modifier and Type Method Description PivotedDepartureBounds
centralMomentBound(double dblLevel, int iMoment)
Retrieve the Mean Departure Bounds Using the Central Moment Bounding InequalityPivotedDepartureBounds
chebyshevAssociationBound(R1ToR1 au1, boolean bNonDecreasing1, R1ToR1 au2, boolean bNonDecreasing2)
Retrieve the Chebyshev's Association Joint Expectation BoundPivotedDepartureBounds
chebyshevBound(double dblLevel)
Retrieve the Mean Departure Bounds Using the Chebyshev's InequalityPivotedDepartureBounds
chebyshevCantelliBound(double dblLevel)
Retrieve the Mean Departure Bounds Using the Chebyshev-Cantelli Inequalitydouble
empiricalAnchorMoment(int iMoment, double dblAnchor, boolean bAbsolute)
Compute the Specified Anchor Moment of the Sample Sequencedouble
empiricalCentralMoment(int iMoment, boolean bAbsolute)
Compute the Specified Central Moment of the Sample Sequencedouble
empiricalExpectation()
Retrieve the Sample Expectationdouble
empiricalRawMoment(int iMoment, boolean bAbsolute)
Compute the Specified Raw Moment of the Sample Sequencedouble
empiricalVariance()
Retrieve the Sample VarianceSingleSequenceAgnosticMetrics
functionSequenceMetrics(R1ToR1 au)
Generate the Metrics for the Univariate Function Sequenceboolean
isPositive()
Retrieve the Sequence Positiveness Flagdouble
markovUpperProbabilityBound(double dblLevel, R1ToR1 auNonDecreasing)
Retrieve the Markov Upper Limiting Probability Bound for the Specified Level: - P (X gte t) lte E[f(X)] / f(t)R1Univariate
populationDistribution()
Retrieve the Population Distributiondouble
populationMean()
Retrieve the Population Meandouble
populationVariance()
Retrieve the Population Variancedouble[]
sequence()
Retrieve the Input SequencePivotedDepartureBounds
weakLawAverageBounds(double dblLevel)
Estimate Mean Departure Bounds of the Average using the Weak Law of Large Numbers
-
-
-
Constructor Detail
-
SingleSequenceAgnosticMetrics
public SingleSequenceAgnosticMetrics(double[] adblSequence, R1Univariate distPopulation) throws java.lang.Exception
Build out the Sequence and their Metrics- Parameters:
adblSequence
- Array of Sequence EntriesdistPopulation
- The True Underlying Generator Distribution of the Population- Throws:
java.lang.Exception
- Thrown if the Inputs are Invalid
-
-
Method Detail
-
empiricalCentralMoment
public double empiricalCentralMoment(int iMoment, boolean bAbsolute) throws java.lang.Exception
Compute the Specified Central Moment of the Sample Sequence- Parameters:
iMoment
- The MomentbAbsolute
- TRUE - The Moment sought is on the Absolute Value- Returns:
- The Specified Central Moment of the Sample Sequence
- Throws:
java.lang.Exception
- Thrown if the Inputs are invalid
-
empiricalRawMoment
public double empiricalRawMoment(int iMoment, boolean bAbsolute) throws java.lang.Exception
Compute the Specified Raw Moment of the Sample Sequence- Parameters:
iMoment
- The MomentbAbsolute
- TRUE - The Moment sought is on the Absolute Value- Returns:
- The Specified Raw Moment of the Sample Sequence
- Throws:
java.lang.Exception
- Thrown if the Inputs are invalid
-
empiricalAnchorMoment
public double empiricalAnchorMoment(int iMoment, double dblAnchor, boolean bAbsolute) throws java.lang.Exception
Compute the Specified Anchor Moment of the Sample Sequence- Parameters:
iMoment
- The MomentdblAnchor
- The Anchor Pivot off of which the Moment is calculatedbAbsolute
- TRUE - The Moment sought is on the Absolute Value- Returns:
- The Specified Anchor Moment of the Sample Sequence
- Throws:
java.lang.Exception
- Thrown if the Inputs are invalid
-
functionSequenceMetrics
public SingleSequenceAgnosticMetrics functionSequenceMetrics(R1ToR1 au)
Generate the Metrics for the Univariate Function Sequence- Parameters:
au
- The Univariate Function- Returns:
- Metrics for the Univariate Function Sequence
-
populationDistribution
public R1Univariate populationDistribution()
Retrieve the Population Distribution- Returns:
- The Population Distribution
-
empiricalExpectation
public double empiricalExpectation()
Retrieve the Sample Expectation- Returns:
- The Sample Expectation
-
populationMean
public double populationMean() throws java.lang.Exception
Retrieve the Population Mean- Returns:
- The Population Mean
- Throws:
java.lang.Exception
- Thrown if the Inputs are Invalid
-
empiricalVariance
public double empiricalVariance()
Retrieve the Sample Variance- Returns:
- The Sample Variance
-
populationVariance
public double populationVariance() throws java.lang.Exception
Retrieve the Population Variance- Returns:
- The Population Variance
- Throws:
java.lang.Exception
- Thrown if the Inputs are Invalid
-
isPositive
public boolean isPositive()
Retrieve the Sequence Positiveness Flag- Returns:
- TRUE - The Sequence is Positiveness
-
sequence
public double[] sequence()
Retrieve the Input Sequence- Returns:
- The Input Sequence
-
markovUpperProbabilityBound
public double markovUpperProbabilityBound(double dblLevel, R1ToR1 auNonDecreasing) throws java.lang.Exception
Retrieve the Markov Upper Limiting Probability Bound for the Specified Level: - P (X gte t) lte E[f(X)] / f(t)- Parameters:
dblLevel
- The Specified LevelauNonDecreasing
- The Non-decreasing Bounding Transformer Function- Returns:
- The Markov Upper Limiting Probability Bound for the Specified Level
- Throws:
java.lang.Exception
- Thrown if the Inputs are invalid
-
chebyshevBound
public PivotedDepartureBounds chebyshevBound(double dblLevel)
Retrieve the Mean Departure Bounds Using the Chebyshev's Inequality- Parameters:
dblLevel
- The Level at which the Departure is sought- Returns:
- The Mean Departure Bounds Instance
-
centralMomentBound
public PivotedDepartureBounds centralMomentBound(double dblLevel, int iMoment)
Retrieve the Mean Departure Bounds Using the Central Moment Bounding Inequality- Parameters:
dblLevel
- The Level at which the Departure is soughtiMoment
- The Moment Bound sought- Returns:
- The Mean Departure Bounds Instance
-
chebyshevCantelliBound
public PivotedDepartureBounds chebyshevCantelliBound(double dblLevel)
Retrieve the Mean Departure Bounds Using the Chebyshev-Cantelli Inequality- Parameters:
dblLevel
- The Level at which the Departure is sought- Returns:
- The Mean Departure Bounds
-
chebyshevAssociationBound
public PivotedDepartureBounds chebyshevAssociationBound(R1ToR1 au1, boolean bNonDecreasing1, R1ToR1 au2, boolean bNonDecreasing2)
Retrieve the Chebyshev's Association Joint Expectation Bound- Parameters:
au1
- Function 1 Operating On Sequence 1bNonDecreasing1
- TRUE - Function 1 is non-decreasingau2
- Function 2 Operating On Sequence 2bNonDecreasing2
- TRUE - Function 2 is non-decreasing- Returns:
- The Chebyshev's Association Joint Expectation Bound
-
weakLawAverageBounds
public PivotedDepartureBounds weakLawAverageBounds(double dblLevel)
Estimate Mean Departure Bounds of the Average using the Weak Law of Large Numbers- Parameters:
dblLevel
- The Level at which the Departure is sought- Returns:
- The Mean Departure Bounds
-
-


>inb4 only certain people had access to previous tests/notes
>hates this inequality
>becomes friend with someone with all teh files
>posts on google drive
>too many people-i should make a website
>i'm an html n00b
>posts everything on a free website with easy website builder
>so many people use it that i make facebook page
>mfw other schools want in
>mfw too many people contribute files
>i need to automate admin
>buys domain
>makes weird index file and puts it on github until I buy hosting
>decides to tweet updates
Testing out sharing/like/follow buttons here.
Google Bookmark
Google Plus Share: +1
Facebook Share: Share
Facebook Like: Like
Twitter follow: Follow @Communotey
Twitter Tweet:Tweet
Note: we were considering putting sharing buttons on our site, but they have trackers and slow down your loading time. We're going to get images soon.
Optimize your web traffic analytics
testing image duality:


public interface UnitConverter
Sizes
class
delegates all size conversions to an implementation of this interface.Sizes
,
ConstantSize
,
AbstractUnitConverter
,
DefaultUnitConverter
| Modifier and Type | Method and Description |
|---|---|
int |
centimeterAsPixel(double cm,
Component component)
Converts Centimeters and returns pixels using the resolution of the
given component's graphics object.
|
int |
dialogUnitXAsPixel(int dluX,
Component component)
Converts horizontal dialog units and returns pixels.
|
int |
dialogUnitYAsPixel(int dluY,
Component component)
Converts vertical dialog units and returns pixels.
|
int |
inchAsPixel(double in,
Component component)
Converts Inches and returns pixels using the specified resolution.
|
int |
millimeterAsPixel(double mm,
Component component)
Converts Millimeters and returns pixels using the resolution of the
given component's graphics object.
|
int |
pointAsPixel(int pt,
Component component)
Converts DTP Points and returns pixels using the resolution of the
given component's graphics object.
|
int inchAsPixel(double in, Component component)
in
- the Inchescomponent
- the component that provides the graphics objectint millimeterAsPixel(double mm, Component component)
mm
- Millimeterscomponent
- the component that provides the graphics objectint centimeterAsPixel(double cm, Component component)
cm
- Centimeterscomponent
- the component that provides the graphics objectint pointAsPixel(int pt, Component component)
pt
- DTP Pointscomponent
- the component that provides the graphics objectint dialogUnitXAsPixel(int dluX, Component component)
dluX
- the horizontal dialog unitscomponent
- a component that provides the font and graphicsint dialogUnitYAsPixel(int dluY, Component component)
dluY
- the vertical dialog unitscomponent
- a component that provides the font and graphicsCopyright © 2002-2015 JGoodies Software GmbH. All Rights Reserved.


| 程序包 | 说明 |
|---|---|
| org.apache.giraph.bsp |
Package of generic bulk synchronous processing objects.
|
| org.apache.giraph.comm |
Package of communication related objects, IPC service.
|
| org.apache.giraph.comm.netty |
Package for netty implementations.
|
| org.apache.giraph.comm.requests |
Package for requests.
|
| org.apache.giraph.conf |
Package of Giraph configuration related things.
|
| org.apache.giraph.examples |
Package of Giraph examples.
|
| org.apache.giraph.graph |
Package of all the graph related objects, built on the
org.apache.bsp package.
|
| org.apache.giraph.integration |
Package of all helper integration test objects.
|
| org.apache.giraph.partition |
Package of partitioning related objects.
|
| org.apache.giraph.worker |
Package of all the worker related things
|
| 类和说明 |
|---|
| GraphPartitionerFactory
Defines the partitioning framework for this application.
|
| PartitionOwner
Metadata about ownership of a partition.
|
| PartitionStats
Used to keep track of statistics of every
Partition . |
| PartitionStore
Structure that stores partitions for a worker.
|
| 类和说明 |
|---|
| Partition
A generic container that stores vertices.
|
| PartitionOwner
Metadata about ownership of a partition.
|
| PartitionStore
Structure that stores partitions for a worker.
|
| 类和说明 |
|---|
| Partition
A generic container that stores vertices.
|
| PartitionOwner
Metadata about ownership of a partition.
|
| 类和说明 |
|---|
| Partition
A generic container that stores vertices.
|
| 类和说明 |
|---|
| GraphPartitionerFactory
Defines the partitioning framework for this application.
|
| Partition
A generic container that stores vertices.
|
| PartitionContext
PartitionContext allows for the execution of user code
on a per-partition basis.
|
| 类和说明 |
|---|
| DefaultPartitionContext
Empty implementation of
PartitionContext |
| PartitionContext
PartitionContext allows for the execution of user code
on a per-partition basis.
|
| 类和说明 |
|---|
| PartitionContext
PartitionContext allows for the execution of user code
on a per-partition basis.
|
| PartitionOwner
Metadata about ownership of a partition.
|
| PartitionStats
Used to keep track of statistics of every
Partition . |
| 类和说明 |
|---|
| GraphPartitionerFactory
Defines the partitioning framework for this application.
|
| HashPartitionerFactory
Divides the vertices into partitions by their hash code using a simple
round-robin hash for great balancing if given a random hash code.
|
| MasterGraphPartitioner
Determines how to divide the graph into partitions, how to manipulate
partitions and then how to assign those partitions to workers.
|
| 类和说明 |
|---|
| BasicPartition
Basic partition class for other partitions to extend.
|
| BasicPartitionOwner
Basic partition owner, can be subclassed for more complicated partition
owner implementations.
|
| GraphPartitionerFactory
Defines the partitioning framework for this application.
|
| HashWorkerPartitioner
Implements hash-based partitioning from the id hash code.
|
| MasterGraphPartitioner
Determines how to divide the graph into partitions, how to manipulate
partitions and then how to assign those partitions to workers.
|
| Partition
A generic container that stores vertices.
|
| PartitionContext
PartitionContext allows for the execution of user code
on a per-partition basis.
|
| PartitionExchange
Describes what is required to send and wait for in a potential partition
exchange between workers.
|
| PartitionOwner
Metadata about ownership of a partition.
|
| PartitionStats
Used to keep track of statistics of every
Partition . |
| PartitionStore
Structure that stores partitions for a worker.
|
| RangePartitionOwner
Added the max key index in to the
PartitionOwner . |
| RangeSplitHint
Hint to the
RangeMasterPartitioner about how a
RangePartitionOwner can be split. |
| ReusesObjectsPartition
Empty interface to characterize
Partition implementations that
don't keep references to the Vertex objects they are passed. |
| WorkerGraphPartitioner
Stores the
PartitionOwner objects from the master and provides the
mapping of vertex to PartitionOwner . |
| 类和说明 |
|---|
| PartitionOwner
Metadata about ownership of a partition.
|
| PartitionStats
Used to keep track of statistics of every
Partition . |
| PartitionStore
Structure that stores partitions for a worker.
|
Copyright © 2011-2014 The Apache Software Foundation. All Rights Reserved.


--- title: "Introduction" author: "DJ Forbes" description: "Introducing Practical R" date: 2019-12-06T21:13:14-05:00 publishdate: 2019-12-06T21:13:14-05:00 categories: ["Introduction"] tags: ["R Markdown", "plot", "scatterplot"] slug: "intro-practical-r" output: blogdown::html_page: toc: true ---
Are you struggling to learn R so you can create beautiful and effect data visualizations and produce data science projects that add value?
You are not alone. Analyzing data is now common to a lot of professions that may not have any background in programming or computer science.
Are you full of excitement for your new R project but finding it difficult to quickly achieve the results you want and need?
Coming from a background in other programming languages may be problematic because R does some things a little bit differently.

Or are you just interested in learning more about R and what R can do?
I would like to help you do more with R. I want to inspire you to create your own data visualizations, master data science, or develop your own data stories. I want to share practical R tips, tricks, and complete projects with you.
Topics covered include anything having to do with R. This blog will focus especially on
On occasion there may even be posts focused on NBA basketball statistics (#GoSpursGo!).
Using both example data and “real” data, I’ll be sharing code like this:
data(iris)
str(iris)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
I’ll be sharing data visualizations as well, including mapping and GIS with R.
hist(iris$Sepal.Width,
main = "Histogram of Sepal Width",
xlab = "Sepal Width",
xlim = c(2,5),
col = rcartocolor::carto_pal(2,"Fall"),
freq = TRUE)
And together we will explore “doing” data science with R.
pairs(iris[1:4],
main = "Scatterplot Matrix (Iris Data)",
pch = 21,
bg = c("red", "green3", "blue")[unclass(iris$Species)])


Field trip on Saturday
September 29, 2008
Last Saturday I rolled up on the field trip from the Geography club to the Arconsanti urban project and the Montezuma Well. It was a nice people and a nice way to get out of Tempe for a few hours. Here you have a few shots...
| Arcosanti&Montezume Well field trip |
I´m sorry for being this absent, life is speeding up like hell at the university and I wanna keep up, there´re so many nice things happening to me on this side (work) of my life that I´m giving it some extra-time I´d usually devote to other tasks. This´what economists usually call investment :)


{% extends 'dashboard/base.html' %}
{% block dashboard_body %}
{{ _('Delete Site') }}
{{ _('If you\'re sure you wish to delete this site, enter the slug of the podcast in the box below.') }}
{{ _('Note that this will only delete the site (http://%s.pinecast.co) and not the podcast itself.') % site.podcast.slug }}


Help
Controls
Levels
Controls
ESC
W
A
S
D
SHIFT
Press this to pause the game and get information about your progress.
Use these keys to move your character around the ocean!
Hold shift to swim quickly while pressing the A key.
Move your mouse and click to get certain information.


As of 3.0.0, the ReleaseNotes.md file has been deprecated. Please refer to the release notes available on Github. Or the release notes on the chaijs.com website.
Added ownPropertyDescriptor
assertion:
expect('test').to.have.ownPropertyDescriptor('length');
expect('test').to.have.ownPropertyDescriptor('length', { enumerable: false, configurable: false, writable: false, value: 4 });
expect('test').not.to.have.ownPropertyDescriptor('length', { enumerable: false, configurable: false, writable: false, value: 3 });
expect('test').ownPropertyDescriptor('length').to.have.property('enumerable', false);
expect('test').ownPropertyDescriptor('length').to.have.keys('value');
ownPropertyDescriptor
assertion.
By @ljharbDeep property strings can now be escaped using \\
- for example:
var deepCss = { '.link': { '[target]': 42 }};
expect(deepCss).to.have.deep.property('\\.link.\\[target\\]', 42)
A minor bug fix. No new features.
Two minor bugfixes. No new features.
deep.property
would not work with single
key names. By @eldritch-fossickerSmall release; fixes an issue where the Chai lib was incorrectly reporting the version number.
Adds new should.fail()
and expect.fail()
methods, which are convinience
methods to throw Assertion Errors.
Unfortunately with 1.10.0 - compatibility broke with older versions because of
the addChainableNoop
. This change has been reverted.
Any plugins using addChainableNoop
should cease to do so.
Any developers wishing for this behaviour can use dirty-chai by @joshperry
.keys()
now accepts Objects, extracting keys from them. By @gregglind.keys()
no longer mutates passed arrays. By @gregglind.which
. By @toastynerd.change
, .increase
and .decrease
assertions. By @cmpolischai.util
is now exposed @DingoEatingFuzz.includes
and .contains
aliases (for .include
and .contain
). By @lo1tuma.any.keys()
and .all.keys()
qualifiers. By @cjqedassert.sameDeepMembers()
. By @cjqedassert.isAbove()
and assert.isBelow()
. By @cjqedproperty
and deep.property
now pass if a value is set to undefined
. By @prodatakeyThe following changes are required if you are upgrading from the previous version:
addChainableNoop
notes below.node_modules
folder for updated dependencies.The following assertions can now also be used in the function-call form:
The above list of assertions are property getters that assert immediately on access. Because of that, they were written to be used by terminating the assertion chain with a property access.
expect(true).to.be.true;
foo.should.be.ok;
This syntax is definitely aesthetically pleasing but, if you are linting your test code, your linter will complain with an error something like "Expected an assignment or function call and instead saw an expression." Since the linter doesn't know about the property getter it assumes this line has no side-effects, and throws a warning in case you made a mistake.
Squelching these errors is not a good solution as test code is getting to be just as important as, if not more than, production code. Catching syntactical errors in tests using static analysis is a great tool to help make sure that your tests are well-defined and free of typos.
A better option was to provide a function-call form for these assertions so that the code's intent is more clear and the linters stop complaining about something looking off. This form is added in addition to the existing property access form and does not impact existing test code.
expect(true).to.be.true();
foo.should.be.ok();
These forms can also be mixed in any way, these are all functionally identical:
expect(true).to.be.true.and.not.false();
expect(true).to.be.true().and.not.false;
expect(true).to.be.true.and.not.false;
If you would like to provide this function-call form for your terminating assertion
properties, there is a new function to register these types of asserts. Instead
of using addProperty
to register terminating assertions, simply use addChainableNoop
instead; the arguments to both are identical. The latter will make the assertion
available in both the attribute and function-call forms and should have no impact
on existing users of your plugin.
-0
. @dasilvacontinThank you to all who took time to contribute!
The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.Thank you to all who took time to contribute!
The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.There have been requests for changes and additions to the configuration mechanisms
and their impact in the Chai architecture. As such, we have decoupled the
configuration from the Assertion
constructor. This not only allows for centralized
configuration, but will allow us to shift the responsibility from the Assertion
constructor to the assert
interface in future releases.
These changes have been implemented in a non-breaking way, but a depretiation
warning will be presented to users until they migrate. The old config method will
be removed in either v1.11.0
or v2.0.0
, whichever comes first.
// change this:
chai.Assertion.includeStack = true;
chai.Assertion.showDiff = false;
// ... to this:
chai.config.includeStack = true;
chai.config.showDiff = false;
false
User configurable property, influences whether stack trace is included in
Assertion error message. Default of false
suppresses stack trace in the error
message.
true
User configurable property, influences whether or not the showDiff
flag
should be included in the thrown AssertionErrors. false
will always be false
;
true
will be true when the assertion has requested a diff be shown.
40
User configurable property, sets length threshold for actual and expected values in assertion errors. If this threshold is exceeded, the value is truncated.
Set it to zero if you want to disable truncating altogether.
chai.config.truncateThreshold = 0; // disable truncating
contain
/include
1.9.0 regression. @leiderssfi
for assert
interface. @refackThank you to all who took time to contribute!
undefined
for actual. (internal api)The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.--watch
. @demandshas
to language chains. @duncanbeevers{a:1,b:2}.should.include({a:1})
@jkrosoThank you to all who took time to contribute!
component(1)
.assert.throws()
returns thrown error for further assertions.The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.node_modules
folder for updated dependencies.This is a small patch that updates the dependency tree so browserify users can install chai. (Remove conditional requires)
The following changes are required if you are upgrading from the previous version:
deep.equal
notes.node_modules
folder for updated dependencies.This version of Chai focused on a overhaul to the deep equal utility. The code for this tool has been removed from the core lib and can now be found at: chai / deep-eql. As stated in previous releases, this is part of a larger initiative to provide transparency, independent testing, and coverage for some of the more complicated internal tools.
For the most part .deep.equal
will behave the same as it has. However, in order to provide a
consistent ruleset across all types being tested, the following changes have been made and might
require changes to your tests.
1. Strict equality for non-traversable nodes according to egal.
Previously: Non-traversable equal via ===
.
expect(NaN).to.deep.equal(NaN);
expect(-0).to.not.deep.equal(+0);
2. Arguments are not Arrays (and all types must be equal):
Previously: Some crazy nonsense that led to empty arrays deep equaling empty objects deep equaling dates.
expect(arguments).to.not.deep.equal([]);
expect(Array.prototype.slice.call(arguments)).to.deep.equal([]);
Chai now runs the browser CI suite using Karma directed at SauceLabs. This means we get to know where our browser support stands... and we get a cool badge:
Look for the list of browsers/versions to expand over the coming releases.
The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.Coverage reporting has always been available for core-developers but the data has never been published for our end users. In our ongoing effort to improve accountability this data will now be published via the coveralls.io service. A badge has been added to the README and the full report can be viewed online at the chai coveralls project. Furthermore, PRs will receive automated messages indicating how their PR impacts test coverage. This service is tied to TravisCI.
bower.json
. (Fix ignore all)The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.Support has been added for the Bower Package Manager ([bower.io])(http://bower.io/). Though
Chai could be installed via Bower in the past, this update adds official support via the bower.json
specification file.
bower.json
.The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated dependencies.Chai now uses chaijs/assertion-error instead an internal constructor. This will allow for further iteration/experimentation of the AssertionError constructor independant of Chai. Future plans include stack parsing for callsite support.
This update constructor has a different constructor param signature that conforms more with the standard
Error
object. If your plugin throws and AssertionError
directly you will need to update your plugin
with the new signature.
var AssertionError = require('chai').AssertionError;
/**
* previous
*
* @param {Object} options
*/
throw new AssertionError({
message: 'An assertion error occurred'
, actual: actual
, expect: expect
, startStackFunction: arguments.callee
, showStack: true
});
/**
* new
*
* @param {String} message
* @param {Object} options
* @param {Function} start stack function
*/
throw new AssertionError('An assertion error occurred', {
actual: actual
, expect: expect
, showStack: true
}, arguments.callee);
// other signatures
throw new AssertionError('An assertion error occurred');
throw new AssertionError('An assertion error occurred', null, arguments.callee);
This is the first non-developement dependency for Chai. As Chai continues to evolve we will begin adding more; the next will likely be improved type detection and deep equality. With Chai's userbase continually growing there is an higher need for accountability and documentation. External dependencies will allow us to iterate and test on features independent from our interfaces.
Note: The browser packaged version chai.js
will ALWAYS contain all dependencies needed to run Chai.
assert.notOk()
. @Bartvdsinspect
utility. @domenicThank you to all who took the time to contribute!
The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated developement dependencies.Regular Expressions are now tested as part of all deep equality assertions. In previous versions they silently passed for all scenarios. Thanks to @katsgeorgeek for the contribution.
Thank you to all who took the time to contribute!
1.8.x
to ensure mocha-phantomjs
compatibility.The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated developement dependencies.Asserts that the target is a superset of set
, or that the target and set
have the same members.
Order is not taken into account. Thanks to @NickHeiner for the contribution.
// (expect/should) full set
expect([4, 2]).to.have.members([2, 4]);
expect([5, 2]).to.not.have.members([5, 2, 1]);
// (expect/should) inclusion
expect([1, 2, 3]).to.include.members([3, 2]);
expect([1, 2, 3]).to.not.include.members([3, 2, 8]);
// (assert) full set
assert.sameMembers([ 1, 2, 3 ], [ 2, 1, 3 ], 'same members');
// (assert) inclusion
assert.includeMembers([ 1, 2, 3 ], [ 2, 1 ], 'include members');
Most assert
functions have a negative version, like instanceOf()
has a corresponding notInstaceOf()
.
However include()
did not have a corresponding notInclude()
. This has been added.
assert.notInclude([ 1, 2, 3 ], 8);
assert.notInclude('foobar', 'baz');
call
/apply
methods for plugin interface. @RubenVerborghmembers
and include.members
assertions. #NickHeinerThank you to all who took time to contribute!
assert#include
will no longer silently pass on wrong-type haystack.assert#notInclude
has been added.v0.10.x
. Support for v0.6.x
has been removed. v0.8.x
is still tested as before.The following changes are required if you are upgrading from the previous version:
node_modules
folder for updated developement dependencies.eqls
alias for eql
. @RubenVerborgh.throw
support for primitives. @RubenVerborgh.throw
support for empty messages. @timnew.above()
and .below()
. @whatthejeffThank you to all who took time to contribute!
.a()
/.an()
to work in cross-browser scenarios..throw()
has cleaner display of errors when WebKit browsers..eql()
now works to compare dom nodes in browsers.1. Component Support: Chai now included the proper configuration to be installed as a component. Component users are encouraged to consult chaijs.com for the latest version number as using the master branch does not gaurantee stability.
// relevant component.json
devDependencies: {
"chaijs/chai": "1.5.0"
}
Alternatively, bleeding-edge is available:
$ component install chaijs/chai
2. Configurable showDiff: Some test runners (such as mocha) include support for showing the diff of strings and objects when an equality error occurs. Chai has already included support for this, however some users may not prefer this display behavior. To revert to no diff display, the following configuration is available:
chai.Assertion.showDiff = false; // diff output disabled
chai.Assertion.showDiff = true; // default, diff output enabled
1. New Utility - type: The new utility .type()
is available as a better implementation of typeof
that can be used cross-browser. It handles the inconsistencies of Array, null
, and undefined
detection.
chai.use(function (c, utils) {
// some examples
utils.type({}); // 'object'
utils.type(null); // `null'
utils.type(undefined); // `undefined`
utils.type([]); // `array`
});
1. Browser Testing: Browser testing of the ./chai.js
file is now available in the command line
via PhantomJS. make test
and Travis-CI will now also rebuild and test ./chai.js
. Consequently, all
pull requests will now be browser tested in this way.
Note: Contributors opening pull requests should still NOT include the browser build.
2. SauceLabs Testing: Early SauceLab support has been enabled with the file ./support/mocha-cloud.js
.
Those interested in trying it out should create a free Open Sauce account
and include their credentials in ./test/auth/sauce.json
.


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| bs4dash_yiq {fresh} | R Documentation |
These variables allow to customize color used if
contrast between a color and its background is under threshold.
For example, it's used to choose text color written in bs4ValueBox
with background defined by a status.
bs4dash_yiq(contrasted_threshold = NULL, text_dark = NULL, text_light = NULL)
contrasted_threshold |
The yiq lightness value that determines when the lightness of color changes from "dark" to "light". Acceptable values are between 0 and 255. |
text_dark |
Dark text color. |
text_light |
Light text color. |
a list
that can be used in create_theme
.
# Contrast colors bs4dash_yiq( contrasted_threshold = 150, text_dark = "#007bff", # blue text_light = "#dc3545" # red ) if (interactive()) { library(shiny) library(bs4Dash) ui <- bs4DashPage( title = "bs4Dash Contrast", navbar = bs4DashNavbar(), sidebar = bs4DashSidebar(), body = bs4DashBody( use_theme(create_theme( bs4dash_yiq( contrasted_threshold = 180, text_dark = "#000", text_light = "#dc3545" ) )), fluidRow( bs4ValueBox( value = 120, subtitle = "ValueBox with primary status", status = "primary", icon = "shopping-cart", href = "#", width = 4 ), bs4ValueBox( value = 150, subtitle = "ValueBox with danger status", status = "danger", icon = "shopping-cart", href = "#", width = 4 ) ) ) ) server <- function(input, output) { } shinyApp(ui, server) }


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