analyzere-extras 0.2.1

Description:

analyzereextras 0.2.1

An extension to the analyzere python library that facilitates “extras”
including visualizations of Analyze Re LayerView objects.

Installation
pip install analyzere_extras


Graphing Options
This graphing utility provides some methods of controlling the style and format of the rendered image.

rankdir=’XX’
Option that controls the orientation of the graph. Options include:

‘BT’ bottom to top (default)
‘TB’ top to bottom
‘LR’ left to right
‘RL’ right to left


compact=True|False
Controls if duplicate nodes should be omitted (default=True). This option tends to produce smaller graphs, which should be easier to read.

with_terms=True|False
Specify that a Layer’s terms are included in each node of the graph (default=True).

warnings=True|False
Highlight nodes with suspicious terms by coloring the node red. Warning nodes are generated when any of the following conditions are true:

participation = 0.0
invert = true and filters = []
attachment or aggregate_attachment = unlimited


max_depth=0
The maximum depth of the graph to process. For very deeply nested structures this can reduce the size. (default=0 == all levels).

max_sources=0
The maximum number of Loss sources to graph in detail for a single node. (default=0 == all sources).

colors=[1-12]
The number of colors to be used when coloring nodes and edges. (default=1 == black, max=12).

color_mode=[‘breadth’|’depth’]
The mode to use when applying colors. Options include: [‘breadth’, ‘depth’], default: ‘breadth’.


Sample LayerView Images:


LayerViewDigraph(lv, ...)
render(...)

compact=
with_terms=
warnings=
rankdir='BT'
rankdir='LR'



True
True
True



True
True
False



True
False
True



False
False
False





Colorization:


LayerViewDigraph(lv, ...)
render(...)

compact=
colors=
color_mode=
rankdir='BT'



True
4
breadth


True
4
depth


False
4
breadth


False
4
depth






Usage
In order to make use of the tools in the analyzere_extras
module you will need to import the analyzere module.
You will need to define your connection information:
import analyzere
analyzere.base_url = '<your server url>'
analyzere.username = '<your userid>'
analyzere.password = '<your password>'

Visualization
To make use of the visualization tool, you will need to query a LayerView
that you would like to graph:
from analyzere import LayerView

lv = analyzere.LayerView.retrieve('011785b1-203b-696e-424e-7da9b0ec779a')
Now you can generate a graph of your LayerView:
from analyzere_extras.visualizations import LayerViewDigraph

g = LayerViewDigraph(lv) # defaults: with_terms=True, compact=True, rankdir='TB', warnings=True
g = LayerViewDigraph(lv, with_terms=False) # omit Layer terms from nodes
g = LayerViewDigraph(lv, compact=False) # graph duplicate nodes
g = LayerViewDigraph(lv, rankdir='LR') # render the graph from Left to Right
g = LayerViewDigraph(lv, warnings=False) # disable error node highlighting
Then to render your graph:
g.render() # defaults: filename=None, view=True, format=None, rankdir=None
g.render(filename='mygraph') # write graph to 'mygraph'
g.render(view=True) # attempt to auto display the graph
g.render(format='pdf') # change the output format 'pdf'
g.render(rankdir='LR') # render the graph from Left to Right
Shortcut: generate a graph for a given LayerView Id:
graph = LayerViewDigraph.from_id('011785b1-203b-696e-424e-7da9b0ec779a')


ELT Combination
To make use of the ELT combiner tool, you will need to define the list of
uuids representing the resources with ELTs that you would like to combine:
uuid_list = ['26a8f73b-0fbb-46c7-8dcf-f4de1e222994', 'cd67ba03-302b-45e5-9341-a4267875c1f8']
You will need to indicate which catalog these ELTs correspond to:
catalog_uuid = '61378251-ce85-4b6e-a63c-f5d67c4e4877'
Then to combine the ELTs into a single ELT:
from analyzere_extras.combine_elts import ELTCombiner

elt_combiner = ELTCombiner()

combined_elt = elt_combiner.combine_elts_from_resources(
uuid_list,
catalog_uuid,
uuid_type='all',
description='My Combined ELT'
)
uuid_type specifies which the type of resources in uuid_list. Valid
values for uuid_type are:


'Portfolio'
'PortfolioView'
'Layer'
'LayerView'
'LossSet'
'all'


If uuid_type='all' is set, then the resources in uuid_list can be a mix
of Portfolios, PortfolioViews, Layers, LayerViews, and LossSets. The default
value of uuid_type is 'all'.
description defines the description for the uploaded combined ELT. If not
set, the default is 'analyzere-python-extras: Combined ELT'.



Testing
We currently commit to being compatible with Python 2.7 and Python 3.4 to 3.7.
In order to run tests against against each environment we use
tox and py.test. You’ll
need an interpreter installed for each of the versions of Python we test.
You can find these via your system’s package manager or
on the Python site.
To start, install tox:
pip install tox
Then, run the full test suite:
tox
To run tests for a specific module, test case, or single test, you can pass
arguments to py.test through tox with --. E.g.:
tox -- tests/test_base_resources.py::TestReferences::test_known_resource
See tox --help and py.test --help for more information.


Publishing

Install twine and
wheel:
pip install twine wheel

Increment version number in setup.py according to
PEP 440.
Commit your change to setup.py and create a tag for it with the version
number. e.g.:
git tag 0.1.0
git push origin 0.1.0

Package source and wheel distributions:
python setup.py sdist bdist_wheel

Upload to PyPI with twine:
twine upload dist/*