v1.0.24

Version 1.0.24, Apr 2021

• Categorize histogram now handles nones and nans in friendlier way, they are converted to "NaN".
• make_histogram() now casts spark nulls to nan in case of numeric columns. scala interprets null as 0.
• SparselyBin histograms did not add up nanflow when added. Now fixed.
• Added unit test for doing checks on null conversion to nans
• Use new histogrammar-scala jar files, v1.0.20
• Added new histogrammar-scala v1.0.20 jar files to tests/jars/

v1.0.20

Added make_histograms functionality from popmon to dataframes

- Add n_dim and datatype properties to all histogram types
- Added unit test of new datetype property of histograms
- Moved sparksql.py to new dfinterface directory
- Added addmethods functions for spark and pandas
- Set default identity function for quantity for each histogram type
- Added make_histograms functionality from popmon to spark and pandas dataframes
- force sparselybin origin and binwidth to be float always
- Fix spark filling of multiple histograms
- make_histograms now includes all available histograms
- Updated docs strings for histogram makers
- Accept list and tuple as numpy array
- 1d plotting for irregularlybin and centrallybin histrograms
- Added default values for time_width and time_offset
- working unit tests for pandas_histogrammar and spark_histogrammar
- handy monkey patch functions for pandas and spark dataframes
- plot function for 2d irregularlybin
- in categorize plot sort labels alphabetically
- implemented matplotlib function for most 2d count histograms
- autopep8 of primitives
- fix flake8 errors of all code
- Bump version to 1.0.20
- added json dumper util function
- added bin_centers function for categorize histograms
- popmon reference in histogram_filler_base.py transform function
- fix small assignment bug in Sum histogram
- make convenience definitions work
- add basic tutorial notebook for histogrammar
- added advanced histogrammar notebook
- unit tests for notebooks
- added manifest and notice file
- update of readme.rst file
- remove python 2.7 and 3.4, 3.5 from travis config

1.0.9

Fixes two bugs in Categorize and SparselyBin when used with SparkSQL.

1.0.8

Fixes a bug in SparselyBin and adds an ASCII method to Bin.

1.0.7

Max Baak (mbaak) added visualization methods for Categorize in Matplotlib and PyROOT, as well as fixing some Python 3 bugs.

1.0.6

Until this version, the Categorize aggregator didn't have a correct Numpy implementation (one that permits sub-aggregators other than Count). This version allows nested sub-aggregators in a Categorize with Numpy and is also 10X faster.

1.0.5

Recognizes PySpark Columns as a kind of user function. Wraps PySpark DataFrames with Histogrammar methods, and the filling is dispatched to Histogrammar-Scala on the JVM.

1.0.4

Introduces the h.plot.root(), h.plot.matplotlib(), h.plot.bokeh() syntax for plotting front-ends, symmetric with h.fill.root(), h.fill.numpy(), h.fill.pycuda(), etc.

Also, the tutorial data reader is now included in the codebase.

1.0.3

Adds scalar multiplication to reweight filled aggregators. (Multiplying an aggregation tree by a scalar factor has the same effect as filling with weight * factor would have. In particular, multiplying by a non-positive number is equivalent to zero.)

For ROOT filling, now only reading TBranches from the file that will be used (big I/O streamlining).

Tests ROOT reading from TChains (no changes were required).

Fixed issue with Python 3 not running specification tests.

1.0.2

This version only changes the setup script so that the version on PyPI exactly matches the version in GitHub.