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Python data visualisation

Project Description

datavision

Python data visualisation

setup

sudo pip install datavision

qunti and zus lists

Qunti (群体, groups) are lists that act

  • as dictionaries that can contain duplicate keys and
  • as sets for the purposes of enabling set-like operations for qunti objects, such as symmetric difference, intersection and update operations.

Qunti are composed of multiple zu (组, group) objects.

qunti operations

In an update operation, one qunti is used to update another. Any zus in the updating qunti that are not in the updated qunti are appended to the updated qunti. Any zus that are in the updating qunti and the updated qunti replace the corresponding zus in the updated qunti.

The following example illustrates a qunti update operation in which an alpha zus is replaced and a delta zus is appended:

# example qunti update:
a = [['alpha', '10'], ['beta', '20'], ['gamma', '30'], ['gamma', '15']]
b = [['delta', '40'], ['alpha', '50']]
# update of a with b:
a = [['beta', '20'], ['gamma', '30'], ['gamma', '15'], ['delta', '40'], ['alpha', '50']]

The following example illustrates qunti symmetric difference, intersection and update operations. In the update operation, two old gamma zus are replaced by a single new gamma zu:

# example qunti symmetric difference, intersection and update:
a = [['alpha', '10'], ['beta', '20'], ['gamma', '30'], ['gamma', '15']]
b = [['delta', '40'], ['alpha', '50'], ['gamma', '25']]
# symmetric difference of a and b:
[['beta', '20'], ['delta', '40']]
# intersection of a and b:
[['alpha', '10'], ['gamma', '30'], ['gamma', '15'], ['alpha', '50'], ['gamma', '25']]
# update of a with b:
a = [['beta', '20'], ['delta', '40'], ['alpha', '50'], ['gamma', '25']]

data visualisation

Datavision provides utilities for data visualisation. It can visualise matrices as colormaps:

It can visualise histograms:

It can visualise graphs and histograms in a terminal:

   │
   ┼+79.548                                                                 ○
   │
   │                                                                ○
   │
   │                                                        ○
   │
   │                                                ○
   ◽       ◽       ◽                       ○
   │                       ◽       ○
   │                       ○       ◽
───○┼──────○───────○───────────────────────◽────────────────────────────────┼───
   │ +0.046                                         ◽               +8.97638
   │
   │                                                        ◽
   │
   │                                                                ◽
   ┼-48.228
   │                                                                        ◽
   │
echo "0,  1,  4,  9, 16, 25, 36, 49, 64, 81" | datavision_TTY_plot.py
                         │
                         ┼+75503.2
                       ∘∘|∘
                      ∘||||∘
                      ||||||∘
                     ∘|||||||
                     ||||||||∘
                    ∘|||||||||
                    ||||||||||∘
                   ∘|||||||||||
                   |||||||||||∘
                  ∘||||||||||||
                  |||||||||||||∘
                  ||||||||||||||∘
                 ∘|||||||||||||||∘
                ∘|||||||||||||||||∘
               ∘|||||||||||||||||||∘
            ∘∘∘||||||||||┼+1603.2|||∘∘∘
──┼--------------------------------------------┼──
   -4.69099              │              +4.6147

It can plot all combinations of variables:

It can plot all parallel coordinates:

It can perform FFT:

It can graph time:

graphs

databases

Datavision features some scripts for interacting with databases:

  • change_field_name_database_SQLite.py
  • duplicates_database_SQLite.py
  • view_database_SQLite.py
Release History

Release History

This version
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2017.5.26.1740

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2017.4.28.1439

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2017.4.28.538

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2017.4.24.1630

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2017.4.19.1319

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2017.4.11.1548

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2017.3.13.1748

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2017.2.3.1625

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2017.2.3.1617

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2017.1.30.39

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2017.1.19.1921

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2017.1.19.1831

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2017.1.16.1555

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2016.8.9.2319

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2016.8.4.2253

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2016.8.4.2219

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2016.5.13.409

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2016.4.25.2302

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2016.4.25.1027

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2016.4.20.1528

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2016.4.20.1316

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2016.4.15.1420

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2016.4.14.1500

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2016.4.14.611

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2016.4.13.1555

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2016.3.29.1817

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2016.3.29.1600

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2016.3.2.1059

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2016.2.11.1235

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2016.02.03.1402

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2016.02.03.0103

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2016.1.26.1714

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2016.01.23.0045

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2016.1.22.1650

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2016.1.22.1600

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2016.1.21.1358

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2016.01.19.1725

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2016.1.12.2359

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2016.01.06.1910

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2016.01.06.1657

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2015.9.29.1235

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