This series of articles would cover Python Gateway for InterSystems Data Platforms. Leverage modern AI/ML tools and execute Python code and more from InterSystems IRIS. This project brings you the power of Python right into your InterSystems IRIS environment:

• Execute arbitrary Python code
• Seamlessly transfer data from InterSystems IRIS into Python
• Build intelligent Interoperability business processes with Python Interoperability Adapter
• Save, examine, modify and restore Python context from InterSystems IRIS

Index

The plan for the series so far (subject to change).

Keywords:   Jupyter Notebook, Tensorflow GPU, Keras, Deep Learning, MLP,  and HealthShare    

1. Purpose and Objectives

In  previous"Part I" we have set up a deep learning demo environment. In this "Part II" we will test what we could do with it.

Many people at my age had started with the classic MLP (Multi-Layer Perceptron) model. It is intuitive hence conceptually easier to start with.

So let's try a Keras "deep learning MLP" with standard demo data that everybody in AI/NN community has been using. It is a kind of so called "supervised learning".

Hi Community!

Please welcome a new video on InterSystems YouTube Channel:

InterSystems and Python QuickStart

Hi all. Today we are going to upload a ML model into IRIS Manager and test it.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, Python 3.6.5.

Introduction

These days many available different tools for Data Mining enable you to develop predictive models and analyze the data you have with unprecedented ease. InterSystems IRIS Data Platform provide a stable foundation for your big data and fast data applications, providing interoperability with modern DataMining tools. 

In this series of articles we explore Data mining capabilities available with InterSystems IRIS.

Hey Community!

The latest webinar, recorded by InterSystems Sales Engineers @Sergey Lukyanchikov and @Eduard Lebedyuk, is already on InterSystems Developers YouTube! Please welcome:

"Machine Learning Toolkit (Python, ObjectScript, Interoperability, Analytics) for InterSystems IRIS" 

The preview release of InterSystems IRIS 2019.2 is now  available - give it a try!

Container images are available via the WRC's preview download site.

The build number for these releases is 2019.2.0.100.0.

InterSystems IRIS Data Platform 2019.2  is the first CD (continuous delivery) release of InterSystems IRIS.  It has many new capabilities including:

• Addition of the IRIS Native API for Python and Node.js and relational access for Node.

Hi

I am working with a Caché system with a Python binding and big datasets in health care. I am using the Python library Pandas and Jupyter notebook for data manipulation and often special modules for different projects, including different versions of Python. To keep the environments separated, I have been testing Docker, but I am at loss on how to install the Python binding. Has anyone any experience with this? Or better yet, a Docker image that has the binding installed?

I am also considering that Docker might not be the suitable for this situation and I am grateful for any thoughts on this.

Hey Developers!

Do you want to reap the benefits of the advances in the fields of artificial intelligence and machine learning? With InterSystems IRIS and the Machine Learning (ML) Toolkit it’s easier than ever.

Join InterSystems Sales Engineers, @Sergey Lukyanchikov and @Eduard Lebedyuk, for the Machine Learning Toolkit for InterSystems IRIS webinar on Tuesday, April 23rd at 11 a.m. EDT to find out how InterSystems IRIS can be used as both a standalone development platform and an orchestration tool for predictive modelling that helps stitch together Python and other external tools.

Hi Community!

We are pleased to invite you to the upcoming webinar "Machine Learning Toolkit (Python, ObjectScript, Interoperability, Analytics) for InterSystems IRIS" on 26th of March at 10:00 (Moscow time)!

Keywords:  Anaconda, Jupyter Notebook, Tensorflow GPU, Deep Learning,  Python 3 and HealthShare    

1. Purpose and Objectives

This "Part I" is a quick record on how to set up a "simple" but popular deep learning demo environment step-by-step with a Python 3 binding to a HealthShare 2017.2.1 instance .  I used a Win10 laptop at hand, but the approach works the same on MacOS and Linux.

Last week it was noticed that Python overtook Java by becoming the most popular language in PYPL Index  Tensorflow is a powerful computation engine, very popular in research and academic worlds too.

Hello,

I'm trying to set Caché-Python Binding on Mac, but there is a problem.

I performed installation and configuration of Caché-Python binding module based on the manual (URL)
including setting of PATH and LD_LIBRARY_PATH in "bash_profile", 
and they seems to be done successfully (there was no error in the process).

However, when I execute test python file (test.py), it failed with error massage as below.
-----------------------Error Massage----------------------
Traceback (most recent call last):
  File "test.py", line 2, in <module>
    import intersys.pythonbind3
  File "/$USER$/.

Headache-free stored objects: a simple example of working with InterSystems Caché objects in ObjectScript and Python

Neuschwanstein Castle

Tabular data storages based on what is formally known as the relational data model will be celebrating their 50th anniversary in June 2020. Here is an official document – that very famous article.  Many thanks for it to Doctor Edgar Frank Codd. By the way, the relational data model is on the list of the most important global innovations of the past 100 years published by Forbes.

On the other hand, oddly enough, Codd viewed relational databases and SQL as a distorted implementation of his theory.  For general guidance, he created 12 rules that any relational database management system must comply with (there are actually 13 rules). Honestly speaking, there is zero DBMS's on the market that observes at least Rule 0. Therefore, no one can call their DBMS 100% relational :) If you know any exceptions, please let me know.

Found a strange documentation block about Python bindings in Chapter 3.6 of the Caché manipulation functions with time and date. It looks like this text accidentally copied one to one of the Perl binging documentation. It's funny that the packages of functions called PTIME_STRUCTPtr, PDATE_STRUCTPtr and PTIMESTAMP_STRUCTPtr is suspicious for Python :)  

On the other hand, nothing is said about the functions parse_time, parse_date, parse_timestamp, which are present in /dev/python/intersys/pythonbind.py ahhh!

Has anyone encountered this problem?

Hi all. We are going to find duplicates in a dataset using Apache Spark Machine Learning algorithms.

Note: I have done the following on Ubuntu 18.04, Python 3.6.5, Zeppelin 0.8.0, Spark 2.1.1

Introduction

In previous articles we have done the following:

• The way to launch Jupyter Notebook + Apache Spark + InterSystems IRIS
• Load a ML model into InterSystems IRIS
• K-Means clustering of the Iris Dataset
• The way to launch Apache Spark + Apache Zeppelin + InterSystems IRIS

In this series of articles, we explore Machine Learning and record linkage.

I'm new to Iris, trying to follow this guide ( https://community.intersystems.com/post/k-means-clustering-iris-dataset ) about using clustering. I've followed all instructions until showing the data. At that point, `dataFrame.show()` fails with `java.sql.SQLException: Access Denied`.

I'm using IRIS 2018.12.609.0 on Windows 10 Pro 64 bits. Python is 3.6.6, PySpark is 2.3.1, installed from Anaconda (Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 11:27:44) [MSC v.1900 64 bit (AMD64)] on win32).

Hey guys,
I need your help.
I am writing a code in Python and I want to create a database and some properties and then to send json files (data) to this database. (I use client-server-model for loading the data into IRIS)
I use curl methods and convert it in Python code with:

curl.trillworks.com/#python

My code so far:
 

url = "http://127.0.0.1:52773/api/docdb/v1/NamespaceName/db/DBName"
url2 = "http://127.0.0.1:52773/api/docdb/v1/NamespaceName/doc/DBName/"


header = {
        'Content-Type': 'application/json',
    }
    
response = requests.get(url, headers=header)
print(response.

Hello everyone,

Im just wondering if there is any possibility to "Listen" to a cache DB? We have our cache DB somewhere else provided by a different company, we are provided the interface to connect to that cache DB so we can extract the cache DB every night.

Im just curious if theres a way to "listen" to the cache DB, so if theres any changes on the table in the cache DB, I could make a trigger to extract the table again.

I know i could just set my ETL every hour or so... but that would extract all the tables in cache DB.

Thanks a lot for any help and information.

Kind regards,

mark

Hi,

this is a public announcement for the first release of Intersystems Cache Object-Relational Mapper in Python 3. Project's main repository is located at Github (healiseu/IntersystemsCacheORM).

About the project

CacheORM module is an enhanced OOP porting of Intersystems Cache-Python binding. There are three classes implemented:

• CacheClient This is the super class of CachePython module. It wraps two functions from intersys.pythonbind module pythonbind3.connection() and pythonbind3.database().

• CacheQuery A subclass of CacheClient that wraps methods and adds extra functionality in intersys.pythonbind.databaseand intersys.pythonbind.query classes

• CacheClass A subclass of CacheClient, that wraps methods and adds extra functionality in intersys.pythonbind.databaseand intersys.pythonbind.object classes

The intersys.pythonbind package is a Python C extension that provides Python application with transparent connectivity to the objects stored in the Caché database.

Hi all. Today we are going to use k-means algorithm on the Iris Dataset.

Note: I have done the following on Ubuntu 18.04, Apache Zeppelin 0.8.0, python 3.6.5.

Introduction

K-Means is one of the simplest unsupervised learning algorithms that solves the clustering problem. It groups all the objects in such a way that objects in the same group (group is a cluster) are more similar (in some sense) to each other than to those in other groups. For example, assume you have an image with a red ball on the green grass. K-Means will split all pixels into two clusters.

Hi - has anyone successfully used the python binding on a mac. I carried out the install instructions per InterSystems documentation and it fails completely. 204 warnings and 9 errors. Obviously this was never tested by InterSystems. Is it even worth pursuing?

Thanks

Connected Data London Conference

TRIADB is an emerging unique and valuable technology in NoSQL database modelling and BI analytics. The following video is from a presentation and demonstration of TRIADB prototype implemented on top of Intersystems Cache database and driven with a CLI in Python (Jupyter-Pandas). In fact this is the second time in the past year that a prototype based on this technology is implemented and demonstrated. The first one was built on top of OrientDB multi-model database and driven by a Mathematica notebook.

Apache Spark has rapidly become one of the most exciting technologies for big data analytics and machine learning. Spark is a general data processing engine created for use in clustered computing environments. Its heart is the Resilient Distributed Dataset (RDD) which represents a distributed, fault tolerant, collection of data that can be operated on in parallel across the nodes of a cluster. Spark is implemented using a combination of Java and Scala and so comes as a library that can run on any JVM.

Hi,

I am experimenting with Cache-Python binding.  In the following piece of Python code

import intersys.pythonbind3

conn = intersys.pythonbind3.connection( )
conn.connect_now('localhost[1972]:SAMPLES', '_SYSTEM', '123', None)
samplesDB = intersys.pythonbind3.database(conn)
p10 = samplesDB.openid("Sample.Person",'10',-1,-1)
p10.run_obj_method("PrintPerson",[])

I am opening the 10th record of Sample.Person class and then I am calling an object method (PrintPerson). 

Method PrintPerson()

  {

    Write !, "Name: ", .

This is the first article of a series diving into visualization tools and analysis of time series data. Obviously we are most interested in looking at performance related data we can gather from the Caché family of products. However, as we'll see down the road, we are absolutely not limited to that. For now we are exploring python and the libraries/tools available within that ecosystem.

The series is closely tying into Murray's excellent series about Caché performance and monitoring (see here) and more specifically this article.

In this short article we talk about how to get Yape running in a docker container to avoid having to setup python on your machine.

It's been a while since the last article in this series, so let's recap quickly.

We talked about using matplotlib to create a basic graph. Afterwards we introduced dynamic graphs using bokeh. In the 3rd part we talked about generating heatmaps using monlbl data.

A common theme in the feedback I got over various channels was the difficulty setting up an environment to run any of these.

This is a translation of the following article. Thanks [@Evgeny Shvarov] for the help in translation.

This post is also available on Habrahabr^ru.

The post was inspired by this Habrahabr article: Interval-associative array^ru→en.

Since the original implementation relies on Python slices, the Caché public may find the following article useful: Everything you wanted to know about slices^ru→en.

Note: Please note that the exact functional equivalent of Python slices has never been implemented in Caché, since this functionality has never been required.

All right, let’s cut to the chase and take a look at some examples.

It is possible to update Cache object property from Python using the following Python code, with import of intersys.pythonbind3:

my_object.set("my_property",["A","B","C"])

However, I am unable to save 2D %List with 2D Python array like the following:

my_object.set("my_property",[["A","B","C"],["1","2","3"]])

I am not sure whether this is Python-Cache bind flaw or design issue. Is there any alternative/ workaround to do the same for above?

I have done Python - Cache binding setup following the guide from http://docs.intersystems.com/latest/csp/docbook/DocBook.UI.Page.cls?KEY…. I have also run test.py from sample3 folder and it able to run and complete successfully.

However, when I try to run the same test.py code via $zf, it gives error with exit code 1.

I've tried running help("intersys.pythonbind3") via $zf and also running from Cache terminal as follows:

1. $zf(-1,"C:\Python36\python <path>/script.py")
2. ! C:\Python36\python <path>/script.py

which gives me the following output:

problem in intersys.

In last week's discussion we created a simple graph based on the data input from one file. Now, as we all know, sometimes we have multiple different datafiles to parse and correlate. So this week we are going to load additional perfmon data and learn how to plot that into the same graph. Since we might want to use our generated graphs in reports or on a webpage, we'll also look into ways to export the generated graphs.

Loading windows perfmon data

The perfmon data extracted from standard pbuttons report is a bit of a peculiar data format. On first glance it is a pretty straightforward csv file.

marking code snippets as python doesn't enable highlighting for python code