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"
"http://127.0.0.1:52773/api/docdb/v1/NamespaceName/doc/DBName/"
else

So I create a Database in the Namespace but all the data is in one column:

I need some properties now, so the data could be parsed.

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

To install the Python binding on Windows one must follow the instructions in the following: 

https://blogs.msdn.microsoft.com/pythonengineering/2016/04/11/unable-to…

The reason is that ActiveState Python version 2.7.X is built with Visual Studio 2008 and Microsoft provides Visual Studio 2008, which one must install, so that Python C extensions can be built. 

Also, if one still sees the "error "error: Unable to find vcvarsall.bat" message at the Visual Studio 2008 command prompt, then one needs to do the following: 

pip install --upgrade​ setuptools

It would be convenient to be able to fetch the whole query result in one operation instead of looping through every row. A fetch_all() method or something similar would provide this functionality I guess. Is there a reason why this is not implemented?