Wolfram alpha python

Wolfram alpha python DEFAULT

How to Use the Wolfram Alpha API in Python

Wolfram|Alpha is perhaps the most advanced knowledge base in the world.Wolfram|Alpha is perhaps the most advanced knowledge base in the world.

If you’re a Computer Scientist, Physicist, Mathematician, or just an analytical person that likes fast, accurate computation with a knowledge base that is unmatched anywhere else in the world, then you’ve probably worked with WolframAlpha before. Whether for computations, unit conversions, or word definitions, Wolfram seems to have an answer to pretty much every single important question out there, and being able to harness that ability and apply it to our own programs is perhaps one of the most powerful add-ons we can add to an AI or chatbot to enhance responses quickly and easily. In this Quick Tip, we’ll be going through how to integrate WolframAlpha into your Python program to instantly beef up your data power.

Creating an Account

In the free plan, we get access to 2,000 free queries per month, which so far has been enough for all of my applications (even those with 120,000+ users!). If you’re going to be having a small to medium-sized project, the free plan should suffice.

First, you’ll need to create a WolframAlpha API account.

The WolframAlpha API Dashboard.

When you verify your email address, you’ll be redirected to the dashboard, and you’ll be able to proceed to the next step!

Getting Your API Key

In the dashboard, click the Get an AppID button.

Enter the relevant details for the app.

When you click on create, a new API key will be generated for your records. Store this token - it’s what you’ll need to authenticate with the Wolfram API! Once you write it down, we can move on to the next step - setting up our code to authenticate the key, and writing queries and getting responses!

Keep this API key for your records!

First, you’ll need to install the dependency:

Next, you’ll be able to download or copy the gist I created for interfacing with Wolfram Alpha:

Save this as in the root folder of your Python program. Then, in your main Python file, import the module:

Once you’ve imported all the necessary modules, you’ll be able to very easily create a new Wolfram Client object and interface with it.

First, instantiate the Wolfram Client as a variable:

If no errors are returned, authentication succeeded! Now, you’ll be able to easily query responses like so:

If everything is correct, you should get a detailed, multiline response:

A bit hot, don’t you think?

And that’s it! You can now customize it to your heart’s desire. Feel free to implement any changes to my small API, as well as use this code on your programs of any scale whatsoever!

With WolframAlpha, you’ll be able to create extremely intelligent responses to almost any prompt, from weather, to unit conversions, to Calculus. It’s something that I consider essential to all of my AI applications, and sometimes use it for quick conversions so as not to install loads of different modules. I hope you found my Quick Tip interesting, and happy coding!

Sours: https://blog.cgsphoto.com/2019/06/27/wolfram-python/

Python | Create a simple assistant using Wolfram Alpha API.


The provides a web-based API allowing the computational and presentation capabilities of Wolfram|Alpha to be integrated into web, mobile, desktop, and enterprise applications. is an API which can compute expert-level answers using Wolfram’s algorithms, knowledgebase and AI technology. It is made possible by the Wolfram Language. This article tells how to create a simple assistant application in Python which can answer simple questions like the ones listed below.

Input : What is the capital of India? Output : New Delhi Input : What is sin(30)? Output : 0.5

Prerequisite: Basic understanding of python syntax and functions.

 Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.  

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning - Basic Level Course

Getting API Id



  1. Create a account at Wolfram alpha. The account can be created at the official website.
  2. After signing up, sign in using your Wolfram ID.

  3. Now you will see the homepage of the website. Head to the section in the top right corner where you see your email. In the drop down menu, select the My Apps (API) option.
  4. Click the Get an AppID button to get the id.
  5. In the next dialog box, give the app a suitable name and description.
  6. Note down the APPID that appears in the next dialog box. This app id will be specific to the application.

Implementation

Make sure that python package is installed beforehand. It can be done by running the following command in the terminal or cmd –

pip install wolframalpha

Below is the implementation

 

 

 

 

 

 

 

Output:

Sours: https://www.geeksforgeeks.org/python-create-a-simple-assistant-using-wolfram-alpha-api/
  1. A2 summer festival
  2. Vintage tiny homes
  3. Total gym youtube

API Language Libraries

The Wolfram|Alpha Full Results API can be accessed from any language that supports web requests and XML/JSON. Use these language bindings to get a quick start in many popular languages.

Contributed by developers in the Wolfram|Alpha community.

Full Results API

Wolfram LanguageBy Wolfram Research

Python Binding (v1.1)By Derik Pereira

Java Binding (v1.1)By Todd Gayley

.NET Binding (v1.0)By Mathy Lorenzen

Ruby Binding (v0.1)By Ian White

Perl Binding (v1.1)By Gabriel Weinberg

PHP Binding (v0.1)By Mathy Lorenzen

JavaScriptBy Wolfram Research

Simple APIs

Wolfram LanguageBy Wolfram Research

Python Binding (v1.1)By Derik Pereira

Java Binding (v1.1)By Todd Gayley

JavaScriptBy Wolfram Research

Contribute

Contact us to contribute another language binding

Sours: https://products.wolframalpha.com/api/libraries.html
Make your own assistant in python using wolfram alpha in minutes!!

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Sours: https://www.codegrepper.com/code-examples/python/wolfram+alpha+python+module
Make your own assistant in python using wolfram alpha in minutes!!

Announcing the Wolfram Client Library for Python

Get Full Access to the Wolfram Language from Python

The Wolfram Language gives programmers a unique computational language with an enormous array of sophisticated algorithms and built-in real-world knowledge. For many years, people have asked us how to access all the power of our technology from other software environments and programming languages. And over the years, we have built many such connections, like Wolfram CloudConnector for Excel, WSTP (Wolfram Symbolic Transfer Protocol) for C/C++ programs and, of course, J/Link, which provides access to the Wolfram Language directly from Java.

So today we’re happy to formally announce a new and often-requested connection that allows you to call the Wolfram Language directly and efficiently from Python: the Wolfram Client Library for Python. And, even better, this client library is fully open source as the WolframClientForPython git repository under the MIT License, so you can clone it and use it any way you see fit.

Announcing the Wolfram Client Library for Python

“The Wolfram Client Library for Python is fully open source.”

It’s Easy

The Wolfram Client Library makes it easy to integrate the large collection of Wolfram Language algorithms as well as the Wolfram Knowledgebase directly into any Python code that you already have. This saves you considerable time and effort when developing new code. In this post, we’ll first show you how to set up a connection from Python to the Wolfram Language. Next, we’ll explore a few methods and examples you can use to do a computation in the Wolfram Language and then call it for use in your Python session. For a complete introductory tutorial and full reference documentation, visit the documentation home page for the Wolfram Client Library for Python.

Evaluate Locally…

Let’s start with a simple example, which computes the mean and standard deviation of one million numbers drawn from a normal distribution. This example shows how to call a Wolfram Language function from Python and compares the results from Python and the Wolfram Language to show that they are numerically close to one another.

Statistical Data Analysis

First, to connect to the Wolfram Language, you need to create a new session with the Wolfram Engine:

>>> fromwolframclient.evaluationimportWolframLanguageSession>>> session=WolframLanguageSession()
&#10005from wolframclient.evaluation import WolframLanguageSession session=WolframLanguageSession()

To call Wolfram Language functions, you need to import the `wl` factory:

>>> fromwolframclient.languageimportwl
&#10005from wolframclient.language import wl

Now you can evaluate any Wolfram Language code. Set the Python variable sample to a list of one million random numbers drawn from the normal distribution, with a mean of 0 and a standard deviation of 1:

>>> sample=session.evaluate(wl.RandomVariate( wl.NormalDistribution(0,1), 1e6))
&#10005sample = session.evaluate( wl.RandomVariate(wl.NormalDistribution(0,1), 1e6))

You can take a look at the first five of them:

>>> sample[:5][0.44767075774581, 0.9662810005828261, -1.327910570542906, -0.2383857558557122, 1.1826399551062043]

You can compute the mean value of this sample with the Wolfram Language. As expected, it is close to zero:

>>> session.evaluate (wl.Mean(sample)) 0.0013371607703851515
&#10005session.evaluate(wl.Mean(sample))

You can also directly compute this in Python, to verify that you get a numerically similar result:

>>> fromstatisticsimportmean>>> mean(sample)0.0013371607703851474
&#10005from statistics import mean mean(sample)

Similarly, you can compute the standard deviation of sample with the Wolfram Language:

>>> session.evaluate(wl.StandardDeviation(sample)) 1.0014296230797068
&#10005session.evaluate(wl.StandardDeviation(sample))

Again run the following code in Python to verify that you get a similar result:

>>> stdev(sample)1.0014296230797068
&#10005stdev(sample)

It’s good to see that these results are comparable. Now you know how to call some simple Wolfram Language functions from Python. Let’s continue with a more exciting example.

Using the Wolfram Knowledgebase

Let’s take a look at a built-in Wolfram Language function that’s not readily available in Python, :

>>> moons=session.evaluate(wl.WolframAlpha('moons of Saturn', 'Result'))
&#10005moons = session.evaluate( wl.WolframAlpha('moons of Saturn', 'Result'))

The function is one of the high-level functions in the Wolfram Language that interacts with the Wolfram|Alpha servers via a web API. You can use this API directly from Python, but doing it by calling the function is much more powerful and convenient because you can access all the data framework functions from the Wolfram Language directly. Let’s take a look at what the Python variable moons contains:

>>> moonsEntityClass['PlanetaryMoon', 'SaturnMoon']

The output here is the Python representation of a Wolfram Language expression, which can be reused in any subsequent evaluation. For example, if you want to get the list of Saturn’s first four moons (by proximity) explicitly, you can do this:

>>> session.evaluate(wl.EntityList(moons))[:4][Entity['PlanetaryMoon', 'S2009S1'], Entity['PlanetaryMoon', 'Pan'], Entity['PlanetaryMoon', 'Daphnis'], Entity['PlanetaryMoon', 'Atlas']]
&#10005session.evaluate(wl.EntityList(moons))[:4]

Or you can easily get the four largest moons of Saturn by mass with this small snippet of code:

>>> bigmoons=session.evaluate(wl.EntityList( wl.SortedEntityClass(moons, wl.Rule("Mass","Descending"),4)))>>> bigmoons[Entity['PlanetaryMoon', 'Titan'], Entity['PlanetaryMoon', 'Rhea'], Entity['PlanetaryMoon', 'Iapetus'], Entity['PlanetaryMoon', 'Dione']]
&#10005bigmoons = session.evaluate(wl.EntityList( wl.SortedEntityClass(moons, wl.Rule("Mass","Descending"),4))) bigmoons

And you can get a simple array of strings with the names of these moons like this:

>>> session.evaluate(wl.Map(wl.Function(wl.Slot()("Name")), bigmoons))['Titan', 'Rhea', 'Iapetus', 'Dione']
&#10005session.evaluate(wl.Map(wl.Function( wl.Slot()("Name")), bigmoons))

This is all pretty cool. Let’s take a look at another example, using the Wolfram Language’s built-in image processing and machine learning functions.

Image Processing and Machine Learning

First, let’s switch over to another mode to do evaluations directly in the Wolfram Language. So far you’ve used the `wl` factory to build up Wolfram Language expressions in Python. But you can also evaluate Python strings containing Wolfram Language code, and sometimes this is easier to read:

>>> fromwolframclient.languageimportwlexpr
&#10005from wolframclient.language import wlexpr

For example, you can evaluate 1+1 in the Wolfram Language by sending it as a string:

>>> session.evaluate('1+1')2
&#10005session.evaluate('1+1')

Using this method, you can write a small snippet of Wolfram Language code that takes an image and uses the built-in face-detection algorithm to find the location of a face in an image. Here, the image we’re using is the famous painting titled Girl with a Pearl Earring by the Dutch painter Johannes Vermeer (but it works on almost any image with recognizable faces). Because the Python terminal interface does not support the display of images, we’ll need use a Jupyter notebook instead, together with the Python Image Library (PIL) package, to help with displaying the result:

Displaying image results with PIL
&#10005from PIL import Image import io session.evaluate( wlexpr(''' image = ImageResize[ Import["Girl_with_a_Pearl_Earring.jpg"], 300]; boxes = FindFaces[image]; face = ImageAssemble[{{image,HighlightImage[image, boxes, "Blur"]}}]; ''') ) data = session.evaluate( wlexpr('ExportByteArray[ face, "PNG" ]') ) Image.open(io.BytesIO)

Quite easy and powerful. But what if you don’t have a local installation of the Wolfram Engine, and want to use the Wolfram Client Library for Python? You can still use the Wolfram Language directly in the Wolfram Cloud.

To the Cloud

The Wolfram Cloud provides easy access to the Wolfram Language without needing to install it locally. The Wolfram Cloud provides various services, including a notebook web interface for Wolfram Language programming as well as the capability to deploy arbitrary Wolfram Language web APIs.

Here you’ll make use of the latter, deploying a Wolfram Language web API. This particular API accepts the names of two countries ( and ), finds the capital city for each country and then computes the distance between them (in kilometers):

CloudDeploy
&#10005CloudDeploy[ APIFunction[{"country1"->"String","country2"->"String"}, QuantityMagnitude[ GeoDistance[ EntityValue[Entity["Country", #country1], "CapitalCity"], EntityValue[Entity["Country", #country2], "CapitalCity"] ], "Kilometers" ]&, "WXF" ], CloudObject["api/public/capital_distance"], Permissions->"Public"]

After the deployment of this API, you can start a new Wolfram Language session, but this time you connect to the Wolfram Cloud instead of the local desktop engine:

>>> fromwolframclient.evaluationWolframCloudSession>>> cloud=WolframCloudSession()
&#10005from wolframclient.evaluation WolframCloudSession cloud = WolframCloudSession()

To call the API, you have to provide the username (user1) and the API endpoint (api/public/capital_distance). With that information, you can call the cloud…

>>> api=('user1', 'api/public/capital_distance')>>> result=cloud.call(api, {'country1': 'Netherlands', 'country2': 'Spain'})
&#10005api = ('user1', 'api/public/capital_distance') result = cloud.call(api, {'country1': 'Netherlands', 'country2': 'Spain'})

… and get the result:

>>> result.get()1481.4538329484521

Once again, easy and useful.

If you want to keep your deployed Wolfram Language API private, so that only you can use it, you can deploy the API with . Then, to authenticate yourself to the private API, you can generate (in the Wolfram Language) a secured authentication key:

key = GenerateSecuredAuthenticationKey
&#10005key = GenerateSecuredAuthenticationKey["myapp"]

Copy the outputs from these two inputs:

key[
&#10005key["ConsumerKey"]
key[
&#10005key["ConsumerSecret"]

Then paste them into your Python session:

>>> fromwolframclient.evaluationimportSecuredAuthenticationKey>>> sak=SecuredAuthenticationKey( ... '<<paste-consumer-key-here>>', ... '<<paste-consumer-secret-here>>')
&#10005from wolframclient.evaluation import SecuredAuthenticationKey sak = SecuredAuthenticationKey( ... '<

And finally, start a new authenticated cloud session:

>>> cloud=WolframCloudSession(credentials=sak)>>> cloud.start()>>> cloud.authorized() True
&#10005cloud = WolframCloudSession(credentials=sak) cloud.start() cloud.authorized()

That’s it. At this point you (and only you) can use any Wolfram Language API that you have deployed privately.

A Bit about the Underlying Serialization

To make everything very fast and efficient, the Wolfram Client Library for Python uses the open WXF format to exchange expressions between Python and the Wolfram Language. WXF is a binary format for faithfully serializing Wolfram Language expressions, in a form suitable for interchange with external programs. The library function export can serialize Python objects to string input form and WXF, and natively supports a set of built-in Python classes such as dict, list and strings:

>>> fromwolframclient.serializersimportexport>>> export({ 'list': [1,2,3], 'string': u'abc', 'etc': [0, None, -1.2] }) b'<|"list" -> {1, 2, 3}, "string" -> "abc", "etc" -> {0, None, -1.2}|>'
&#10005from wolframclient.serializers import export export({ 'list': [1,2,3], 'string': u'abc', 'etc': [0, None, -1.2] }) b'<|"list" -> {1, 2, 3}, "string" -> "abc", "etc" -> {0, None, -1.2}|>'

WXF represents numeric arrays with packed data, allowing efficient support for NumPy arrays.

Create a new array of 255 unsigned 8-bit integers:

>>> importnumpy>>> array=numpy.arange(255, dtype='uint8')
&#10005import numpy array=numpy.arange(255, dtype='uint8')

Serialize it to WXF bytes and compute the byte count:

>>> wxf=export(array, target_format='wxf')>>> len(wxf) 262
&#10005wxf=export(array, target_format='wxf') len(wxf)

NumPy arrays back many Python libraries. Therefore, an efficient and compact serialization helps in interfacing the Python ecosystem with the Wolfram Language. A direct consequence of supporting NumPy is that the serialization of PILimages is in general very efficient; most of the pixel data modes map to one of the numeric array types, specified by . It’s also worth mentioning that pandas Series and DataFrame are supported natively. The library also provides an extensible mechanism for serializing arbitrary classes.

Available Now

Install the latest version of the Wolfram Client Library for Python with pip:

$ pip install wolframclient
&#10005$ pip install wolframclient

It requires Python 3.5.3 (or above) and Wolfram Language 11.3 (or above). Check out the full documentation on the Wolfram Client Library for Python. The entire source code is hosted in the WolframClientForPython repository on the Wolfram Research GitHub site. And if you see a way to improve it, you can help us make it better by contributing pull requests to this repository.

We’re very excited about this release and hope you find it useful. Let us know what you think in the comments section or on Wolfram Community, and we’ll do our best to respond to questions.

Sours: https://blog.wolfram.com/2019/05/16/announcing-the-wolfram-client-library-for-python/

You will also like:

Source Code

Install the library:

$ pip install wolframclient

Set up your Wolfram Language session:

>>> fromwolframclient.evaluationimportWolframLanguageSession>>> fromwolframclient.languageimportwl,wlexpr>>> session=WolframLanguageSession()

Evaluate any Wolfram Language code from Python:

>>> session.evaluate(wlexpr('Range[5]'))[1, 2, 3, 4, 5]

Immediately call all 6000+ built-in Wolfram Language functions in Python:

>>> session.evaluate(wl.MinMax([1,-3,0,9,5]))[-3, 9]

Build up Wolfram Language code directly in Python:

>>> func_squared=wlexpr('#^2 &')>>> session.evaluate(wl.Map(func_squared,wl.Range(5)))[1, 4, 9, 16, 25]

Direct support for PIL, Pandas and NumPy libraries:

Create a Pandas DataFrame:

>>> importpandas>>> df=pandas.DataFrame({'A':[1,2],'B':[11,12]},index=['id1','id2'])>>> df A Bid1 1 11id2 2 12

Apply Wolfram Language function directly to the DataFrame:

>>> session.evaluate(wl.Total(df)){'A': 3, 'B': 23}

Define native Python functions:

>>> str_reverse=session.function(wl.StringReverse)>>> str_reverse('abc')'cba'

Represent Wolfram Language expressions as Python objects:

Use object representation:

>>> wl.Quantity(12,"Hours")Quantity[12, 'Hours']

Use string representation:

>>> wlexpr('f[x_] := x^2')(f[x_] := x^2)

Within your Python environment, the Wolfram Client Library for Python lets you:

Access the power of Wolfram algorithms:

Get immediate access to the world's largest integrated algorithmbase:

>>> limit=wlexpr('Limit[x Log[x^2], x -> 0]')>>> session.evaluate(limit)0

Access the Wolfram Knowledgebase:

Get the closest ocean:

>>> ocean=wlexpr('GeoNearest[Entity["Ocean"], Here]')>>> session.evaluate(ocean)[Entity['Ocean', 'AtlanticOcean']]

Use Wolfram's natural language understanding in Python:

Query Wolfram|Alpha directly in Python:

>>> session.evaluate(wl.WolframAlpha("number of moons of Saturn","Result"))62

Terminate the session and release all resources:

>>> session.terminate()
Sours: https://reference.wolfram.com/language/WolframClientForPython/


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