bcompiler¶

A tool for managing DfT BICC data.

Introduction¶

bcompiler is a tool to manage data involved in the BICC reporting process at the UK Department for Transport.

It is developed and maintained by Matthew Lemon and licensed under MIT. Source code is available at Bitbucket.

bcompiler processes data held in Excel files, either compiling similar data from many Excel files into a single master spreadsheet, or populating many Excel files using the data from a master spreadsheet.

“Auxiliary” files (see Auxiliary files) are required to map data in each direction, and to templates. These files are contained in a DfT reporitory on GitHub. bcompiler can be used to obtain/update these files.

Quick Start¶

Ensure Python 3.6.2 or later is installed on your system.
Ensure git is installed on your system.
pip install bcompiler
bcompiler-init
Refer to Check integrity of populated template files.

Concept¶

Template and master¶

The primary function of bcompiler is very simple: it extracts data from one Excel spreadsheet and puts it into another spreadsheet. More specifically, it extracts data from a spreadsheet which has an ad hoc layout, and multiple sheets (which we call a template), and puts it into a simple database-like table on a single sheet (which we call a master). The template is a controlled document which is intended to be completed as a form by some stakeholder and the master is a document store which holds data from multiple templates. It could therefore be said bcompiler is a collection tool that gathers data from a controlled, Excel-based, user interface and “compiles” it into a central point, allowing for storage or further interrogation by other tools, such as Excel or even bcompiler itself, as we shall see.

This process can also be operated in reverse, i.e. data can be transferred from a master to a set of templates.

Datamap¶

A template is intended to be used as a form to collect data. It’s design is free-form and should facilitate data-entry in a user-friendly way, therefore it is likely to contain:

empty cells, for the user to complete
locked cells, containing formulae
locked cells, for spacing or other aesthetic purposes
cells controlled by data verification, such as drop-down lists
styled cells and various formatting
any other plausible design element which facilitates successful data-entry

When seeking to extract data from a template which has been populated by a user, the task is therefore to know which cells in the template contain the data entered and which can be ignored as cells used for aesthetics, user information, spacing, design, etc. This is achieved in bcompiler using a datamap.

The datamap is a simple CSV file which maps keys to values. The key is the arbitary name, or descriptor of some piece of data you want to capture, and the value is the data contained in the cell which represents that piece of data. The job of the datamap is to tell bcompiler which cell in the template contains that piece of data you want to capture.

An extract from a datamap:

First Name,Summary,F10,
Last Name,Summary,F11
Date of Birth,Summary,G10,
Nickname,Summary,G11

Here, “First Name” is a key, whose value can be found in cell F10 of the Summary sheet in the target template. Likewise for “Last Name”, “Date of Birth”, etc.

Note

The datamap (called datamap.csv) is an auxiliary file (see Auxiliary files), created by bcompiler in a special location inside the the Documents folder of your computer. An auxiliary file is simply a file whose contents help bcompiler do its job and can be amended by any user of the program.

Warning

Without a correctly populated datamap, bcompiler has no way of finding or placing data, so it is an essential component of the process and can be the source errors and unexpected values.

Designing or amending a template¶

The process of designing a new template (or amending and existing one) is therefore very straightforward.

The template is laid-out according to whatever design/principles are suitable. Cell-locking and other security measures are inacted within the Excel file to control where data can be entered by the user and to protect formulas, adding or deleted rows/columns, etc.

A datamap.csv file is then created (or amended if changing an existing template), using Excel or Notepad or any other text editing application, and each cell in the template intended to be populated by the user and/or captured by bcompiler is listed on a single line, in CSV (comma-seperated) format:

First Name,Summary,F10,
Last Name,Summary,F11
Date of Birth,Summary,G10,
Nickname,Summary,G11
Data Field 1,Finance,A3
Data Field 2,Finance,A4
...

The datamap.csv file is saved and placed in the bcompiler/source/ directory in the computer’s Documents directory (the name of which differs depending on whether using Windows, Mac OS X or Linux).

Installation¶

Note

This guide refers specifically to installing on a Windows system as that is anticipated to be the primary operating system for typical bcompiler users. However, bcompiler is installable on Linux and Mac using the same pip commands. The only difference is how Python and git are installed on those systems. Please refer to python.org and git-scm.com.

Install Python¶

To install Python, download installer file from http://www.python.org/ftp/python/3.6.2/python-3.6.3.exe. Choose to save it to a location on your harddrive, such as your Desktop or Downloads folder.
Run the installer. On the Install Python Setup screen, ensure “Add Python 3.6 to PATH” and “Install launcher for all users (recommended)” is checked. Click “Install Now”.
Open a new command window (Start -> type “cmd” in Search box and hit enter).

Update pip (if required)¶

In command window, type python -m pip install -U pip.

Install git¶

Go to https://git-scm.com/download/win. The download will begin automatically. Save it to a location on your hardrive, such as your Desktop or Downloads folder.
Run the installer, accepting all default options. If you get a message saying that you cannot run the 64-bit installer, choose the 32-bit installer from the above page.

Install bcompiler¶

If you do not already have bcompiler installed, in the command window, type pip install bcompiler.
If you have bcompiler installed, it is a good idea to update to the latest version. In the command window, type pip install -U bcompiler.

Note

Use the latest version of bcompiler. You can find out what the latest version of bcompiler is by doing pip search bcompiler. If you can see that there is a later version, but pip install -U bcompiler does not install the latest version for some reason, try uninstalling bcompiler pip uninstall bcompiler first, then installing with pip install bcompiler. You can also specify which version of bcompiler you want to download with pip install bcompiler==1.1.0a1 - make sure that version is listed as the latest doing pip search bcompiler.

Initialise bcompiler¶

bcompiler needs auxiliary files to run, including a datamap.csv and config.ini files. These files are stored in a directory called bcompiler in your Documents directory. Before running bcompiler, this directory structure needs to be set up. The auxiliary files also need to be downloaded from a git repository on Github. bcompiler can do the necessary work to set this up.

In the command window, type bcompiler-init.

Changing settings for various things in bcompiler is done using a config.ini file.

Auxiliary files¶

bcompiler requires three files to be present in the auxiliary directory, created during bcompiler-init:

config.ini
datamap.csv
bicc_template.xlsm

config.ini¶

This is a text file in Documents/bcompiler/source that allows allows the user to set basic configuration options.

INI files are an informal standard for configuration files. The basic element contained in an INI file is the key or property. Every key has a name and value, delimted by an equals sign (=). The name appears to the left of the equals sign.

Keys may be grouped into sections (this is the case for bcompiler). The section name appears on a line by itself in square brackets ([ and ]). All keys declared after the section declaration are associated with that section.

Example:

[QuarterData]
CurrentQuarter = Q2 Jul - Oct 2017

The options available to set for bcompiler are:

Purpose	Description
QuarterData	In `Q2 Jul - Oct 2017`. Appears in appropriate field in template.
TemplateSheets	The names of each relevant sheet in the template must be set here
BlankTemplate	Set the name of the template kept in the Documents/bcompiler/source directory
Datamap	Set the name of the datamap kept in the Documents/bcompiler/source directory
Master	Set the name of the master file kept in the Documents/bcompiler/source directory

Note that sensible values are set by default. The option you will most likely need to change is Master as this is most often renamed by the user ourside of bcompiler use.

datamap.csv¶

In order for bcompiler to retrieve data from cells in an Excel spreadsheet, it requires a mapping between the master to the template. This is achieved in a CSV file with the following headers:

cell_key: The name of the value as it appears in Column A of the master
template_sheet: The name of the sheet in the template
cell_reference: The cell reference of the cell where data lives in the template
verification_list: LEGACY Not currently implemented

bicc_template.xlsm¶

The Excel file that is populated by bcompiler and sent to project teams and subsquently queried by bcompiler when populating the master spreadsheet. Contains macros to handle cell verification so must be saved in .xlsm format.

Other options¶

In a command window, run bcompiler --help to see other options. Please note: some of these are legacy options and will be changed or removed in future versions of bcompiler.

Populating templates based on a master spreadsheet¶

Attention

The macros explained in the Handling RAG-colour and Data Validation macros section below have been replaced with a single macro calledd UniversalMacro which will unlock all worksheets in the template, run both formatting macros and re-lock sheets Use this unless you need to debug a particular step, or you’re a maschocist…

Ensure the master spreadsheet is in the Documents/bcompiler directory.
Ensure the filename of the master spreadsheet is included in the [Master] section in config.ini.
In a command window, run bcompiler -a.
The resulting files will be created in Documents/bcompiler/output.
Carry out RAG-colour and Data Validation handling as described.
Ensure each sheet and each workbook is protected using a password (either View, Protect Sheet and View, Protect Workbook, or by running the macro Protect_All_Sheets).
Save the workbook

Warning

Make sure the password is retained by all admin users. You will not be able to amend the worksheet or workbook if the password is forgotten.

Handling RAG-colour and Data Validation macros¶

The BICC data collection process requires that ‘blank’ templates are sent to project teams using a number of data validation rules. For example, certain cells must only be populated by dates or by one a restricted list of options. This is handled by standard Excel data validation which is mostly set within the bicc_template.xlsm form.

However, currently the form contains two macros which must be run following a bcompiler -a operation to populate all templates from a master spreadsheet:

DataVerification
RAG_Conditional

which provide the template with dropdown choices on certain cells and conditional formatting on all cells whose value relates to a RAG rating. These macros are required due to limitations in creating data validation within bcompiler and its underlying libraries.

Unfortunately, the macros have to be run on each individual file.

To apply data validation and RAG conditional formatting, do the following:

Run bcompiler -a, as explained above.

Ensure no other Excel files are open on your machine to prevent additional macros being listed. Then, open each exported populated template in turn, and:

Unprotect each sheet (either Review, Unprotect Sheet, or run the Unprotect_All_Sheets macro)
Run the DataVerification macro (View, Macros, highlight DataVerification, click Run)
Run the RAG_Conditional macro (View, Macros, highlight RAG_Conditional, click Run)

Warning

You must unlock each worksheet before running the macros, otherwise you will encounter a Run-time error '1004' message in Excel.

Creating a master spreadsheet from populated templates¶

Ensure all populated returns are copied to the Documents/bcompiler/source/returns directory. Ensure no other files are present in this directory.
In a command window, run bcompiler (no arguments are required).
The resulting master file will be created in Documents/bcompiler/output directory.
To compare values from a previous master, run bcompiler --compare <PATH-TO-MASTER-TO-COMPARE>

Check integrity of populated template files¶

The template used to collect data should not be changed by the user; allowing the user to add rows or columns will cause a world of problems for bcompiler. To ensure the integrity of the template, sheets in bicc_template.xlsm are locked to prevent rows being added or deleted.

However, bcompiler is able to check the validity of all returned templates if required, by comparing the number of rows in each sheet with what it expects from bicc_template.xlsm.

Ensure all populated returns are copied to Documents/bcompiler/source/returns.
In a command window, run bcompiler -r

This will print the count of rows in each sheet in each template file. Any row count that differs from the equivalent sheet in bicc_template.xlsm will be marked with a *.

To output this data to the Documents/bcompiler/output directory, run bcompiler -r --csv.
To only show differences between the file and bicc_template.xlsm, run bcompiler -r --quiet.

Extending bcompiler¶

The main functionality of bcompiler is obtained via the command line, e.g:

bcompiler -h

is used to obtain the basic help menu.

In addition, bcompiler allows anyone with a knowledge of basic Python to be able to interact with the program and to generate their own output. Some parts of bcompiler are ‘exposed’ to the user via an API (Application Programming Interface) which is designed to be easy to use and useful as a component to building new functionality.

In this version of bcompiler (1.3 series), the API is very limited, however it allows you to interrogate the data held in an master xlsx file and do things with the data. Essentially bcompiler does the hard work of pulling the data out of a master, formatting it in some way, and presenting it to you in a format for doing something else with, e.g. writing it to another file, such as an Excel or a Word document.

The key API objects documented here are:

Master
Quarter
FinancialYear
Row

Examples¶

Filtering project data¶

One of the most simple tasks might be to list the projects contained with a particular master file:

from bcompiler.api import project_data_from_master
m = project_data_from_master('/tmp/master_1_2017.xlsx', 1, 2017)
m.projects

output: ['Project Name 1', 'Project Name 2', ...]

Say you wish to interrogate a master file and output all values from keys which contain the word “Total” in a project whose title is “Project Name 1”. Here’s what you could do:

from bcompiler.api import project_data_from_master
m = project_data_from_master('/tmp/master_1_2017.xlsx', 1, 2017)
p = m['Project Name 1']
totals = p.key_filter("Total")

output: [('Import Total Budget/BL', 10), ('Another Total Budget/BL', 199.1),` …

Checking for duplicate keys in a master¶

A master file containing duplicate keys will not function correctly. bcompiler will test for this during its normal operation, but if you wish to check a master file yourself, you can do this very easily once you have a bcompiler.api.Master object, obtained using the project_data_from_master function as demonstrated above, or by directly creating a bcompiler.api.Master object (see Master):

from bcompiler.api import project_data_from_master
m = project_data_from_master('/tmp/master_1_2017.xlsx', 1, 2017)
m.duplicate_keys()

output: False if there are no duplicate keys, True otherwise.

Computing financial quarter/year dates¶

You’re writing a script that requires computation involved with financial years…:

from bcompiler.api import FinancialYear
fy = FinancialYear(2016)
fy.start_date

output: datetime.date(2016, 4, 1):

fy.end_date

output: datetime.date(2017, 3, 31):

quarter1_2016 = fy.q1

output: Quarter(1, 2016):

quarter1_2016.fy

output: 2016:

quarter1_2016.end_date

output: datetime.date(2016, 6, 30)

Writing data to a new Excel file¶

You are writing a program that exports data from a master file to another workbook, in the same way that bcompiler analysers work.

To write data into a row in your workbook, bcompiler will do the hard work for you - you don’t have to write data into individual cells. Use the bcompiler.api.Row object:

from openpyxl import Workbook

from bcompiler.api import Row

data = [1, 2, 3, 4]

wb = Worbook()
ws = wb.active

r = Row(1, 1, data)
r.bind(ws)

wb.save('/tmp/test.xlsx')

output: an Excel file at /tmp/test.xlsx whose default sheet contains a row of values: 1 2 3 4, starting at cell A1 (or 1, 1).

This could be combined with other elements of the API, for example to write the list of project titles from a master file to a new Excel file:

from openpyxl import Workbook

from bcompiler.api import Row
from bcompiler.api import project_data_from_master

m = project_data_from_master('/tmp/master_1_2017.xlsx', 1, 2017)
projects = m.projects

wb = Worbook()
ws = wb.active

r = Row(1, 1, projects)
r.bind(ws)

wb.save('/tmp/test.xlsx')

You can also use the column letter as the first parameter in the Row() function:

r = Row('A', 1, projects)

and the effect will be the same.

API Reference¶

Master¶

project_data_from_master¶

As well as dealing with Master objects directly, the bcompiler.api.project_data_from_master() function does exactly the same job.

Note

This function is not the same as bcompiler.utils.project_data_from_master(). That function produces a complex data structure containing a dictionary of collections.OrderedDict objects, whilst this one returns a bcompiler.api.Master object, which is more user-friendly to work with.

Quarter¶

class bcompiler.api.Quarter(quarter: int, year: int)¶

A Quarter object enapsulates data about a financial quarter in bcompiler. Because it contains data about dates (start dates and end dates for a particular quarter, for instance), it can be used for calculating differences between dates and ordering objects which are associated with it. A good example is a bcompiler.api.Master object, which is a composition of an Excel file (providing the data) and a bcompiler.api.Quarter object (providing temporal data). This allows bcompiler and anyone using a bcompiler.api.Master object to order data by date.

To create a Quarter object is very easy:

from bcompiler.api import Quarter
q1 = Quarter(2, 2015)

The following attributes of the resulting object are available:

year¶

An integer representing the calendar year.

quarter

An integer representing the quarter (1, 2, 3 or 4)

start_date¶

A datetime.date object

end_date¶

A datetime.date object

fy¶

A bcompiler.api.FinancialYear object

FinancialYear¶

Row¶

See an example of Row in use: Writing data to a new Excel file

Analysers¶

Introduction¶

bcompiler is able to conduct basic analysis on spreadsheets. An analyser will usually process some data in a master spreadsheet and produce another spreadsheet (CSV, Excel), an Excel chart, commandline output, or some other data type.

Built-in analysers can be used in two ways:

from the command line
importing into your own Python programs

Analysers available from the commandline use mostly default options and are relatively limited. More extensive configuration can be gained by writing your own scripts and importing bcompiler analyser code into your project to help you. See Importing analyser code into your own projects for more details.

Running from the commandline¶

Basic command¶

>> bcompiler --analyser ANALYSER OPTIONS

Available options¶

Available to all analysers¶

--master PATH_TO_DIRECTORY_CONTAINING_MASTER

Available to swimlane_milestones analyser¶

The default is chart milestones within a range of 365 days from today. However, the following options are available to give greater control to this band:

--output PATH_TO_OUTPUT_DIRECTORY
--start_date DATE (dd/mm/yyyy)
--end_date DATE (dd/mm/yyyy)

Importing analyser code into your own projects¶

Warning

This functionality is not yet implemented.

from bcompiler.analysers import Swimlane

s = SwimlaneMilestones()
s.output('/home/user/Desktop/swimlane_milestones.xlsx')
s.add_to_worksheet(worksheet)
workbook.save()

Built-in Analysers¶

rcf¶

Perform Reference Class Forecasting on selected master files. Target master files must be named according to this pattern: *_N_YYYY.xlsx where N represents a number between 1 and 4 and YYY represents a year. This file therefore represents the Nth quarter of year Financial Year YYYY.

Default >> bcompiler --analyser rcf

Default options require master files to be referenced in Documents/bcompiler directory A single workbook for each project is output to the Documents/bcompiler directory.

Output files to a different directory >> bcompiler --analyser rcf --output C:\Users\jim\Desktop

This options requires the master files to be present in the Documents/bcompiler directory. The data is output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

Set target master directory manually

>> bcompiler --analyser rcf --master C:\Users\jim\Downloads

This options requires a master files to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The files are output to Documents/bcompiler/output directory.

financial analysis¶

Output a workbook containing a single worksheet which includes a graph mapping change in certain financial data from designated quarters. Must include four quarters, named correctly in config.ini file.

Default >> bcompiler --analyser financial

Default options require master files to be referenced in config.ini file and present in Documents/bcompiler directory. A single workbook for each project is output to the Documents/bcompiler directory.

Output files to a different directory >> bcompiler --analyser financial --output C:\Users\jim\Desktop

This options requires the master files to be present in the Documents/bcompiler directory, and referenced in the config.ini file. The data is output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

keyword¶

Search for a keyword in the master key column (Column A) (e.g. RAG, or SRO). By default, outputs to terminal.

Default

>> bcompiler --analyser keyword "RAG"

Default options require a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file.

Output is sent to your terminal.

Warning

Terminal output will exceed 80 characters. If you are using Windows, you should go to Preferences in cmd application and increase the width of the terminal window to something like 150 characters.

Output to xlsx (Excel) file

>> bcompiler --analyser keyword "RAG" --xlsx C:\Users\jim\Desktop\rag.xlsx

This options requires a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file. The data is output to the file specified after the --xlsx flag, in this case C:\Users\jim\Desktop\rag.xlsx.

Output to xlsx (Excel) and get data from a specific master

>> bcompiler --analyser keyword "RAG" --xlsx C:\Users\jim\Desktop\rag.xlsx --master C:\Users\jim\Downloads\q1_master.xlsx

This options requires a master file to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The data is output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop\rag.xlsx.

annex¶

Creates individual project spreadsheets pulling out pertinent headline and textual data from a master. Intended to be used a Annex to BICC report. The analyser relies on two master files to be present: a master representing current data and one representing historical data. This is to allow for annex to report a “DCA Last Quarter” value.

Default

>> bcompiler --analyser annex

Default options require a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file, and a second master file, perhaps representing the previous quarter, named compare_master.xlsx in the same directory. You can use different filenames but this must be reflected in [MasterForAnalysis] and [AnalyserAnnex] in config.ini.

Set compare master manually (overriding value in config.ini)

>> bcompiler --analyser annex --compare C:\Users\jim\Desktop\q1_master.xlsx

Set output directory manually (overriding default of Documents/bcompiler/output

>> bcompiler --analyser annex --output C:\Users\jim\Desktop

This options requires a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file. The files are output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

Set output directory manually (overriding default output directory of Documents/bcompiler/output and master set in config.ini

>> bcompiler --analyser annex --output C:\Users\jim\Desktop --master C:\Users\jim\Downloads\q1_master.xlsx

This options requires a master file to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The files are output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

Set target master manually (overriding default set in config.ini)

>> bcompiler --analyser annex --master C:\Users\jim\Downloads\q1_master.xlsx

This options requires a master file to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The files are output to Documents/bcompiler/output directory.

swimlane_milestones¶

Specific analyser uses project data from a master file and creates a new Excel scatter chart, showing a timeline of major approval milestones horizontally in swimlane fashion.

Note

By default, the swimlane chart will be produced with multi-coloured markers. If you wish all markers to be grey, ensure the following setting is present in config.ini:

[AnalyserSwimlane]
grey_markers = true

Note

Basic configuration for milestones analysers is done in config.ini. Documentation for these is contained in comments in the file.

Default options

>> bcompiler --analyser swimlane_milestones

Default options require a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file. The chart is output in a file called swimlane_milestones.xlsx in the Documents/bcompiler/output directory.

By default, the analyser will chart only those milestones that fall within 365 days of today. This can be changed in config.ini by changing the range value in the ['AnalyserSwimlane'] section.

Set output directory manually (overriding default of Documents/bcompiler/output

>> bcompiler --analyser swimlane_milestones --output C:\Users\jim\Desktop

This options requires a master file to be present in the Documents/bcompiler directory, named target_master.xlsx as per the config.ini file. The chart is output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

Set output directory manually (overriding default output directory of Documents/bcompiler/output and master set in config.ini

>> bcompiler --analyser swimlane_milestones --output C:\Users\jim\Desktop --master C:\Users\jim\Downloads\q1_master.xlsx

This options requires a master file to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The chart is output to the directory specified after the --output flag, in this case C:\Users\jim\Desktop.

Set target master manually (overriding default set in config.ini)

>> bcompiler --analyser swimlane_milestones --master C:\Users\jim\Downloads\q1_master.xlsx

This options requires a master file to be present in the C:\Users\jim\Downloads directory, named q1_master.xlsx. The chart is output to Documents/bcompiler/output directory.

Set start and end date

>> bcompiler --analyser swimlane_milestones --start_date 20/1/2016 --end_date 20/1/2017

swimlane_assurance_milestones¶

As swimlane_milestones but showing assurance milestones.

Roadmap¶

bcompiler makes use of semantic versioning and therefore follows the MAJOR.MINOR.PATH version pattern.

2.0 - Plugins¶

Allow integration of own analysers written in Python
Simple plugin management interface through commandline

1.0 - Analysers¶

Commandline analysers for simple features
API for analysers to be customised and used outside bcompiler
bcompiler-init wrapper for auxiliary files repository so user doesn’t have to push, pull and merge in git

Commandline analysers¶

Analyser	Product	Status
swimlane_milestones	Excel chart	Implemented
financial_analysis	Excel spreadsheet
report_annex	Excel spreadsheet
project_list	terminal output
sro_list	terminal output
rag_ratings	terminal output

API¶

Analyser	Product	Status
swimlane_milestones	SwimlaneChart()
others…

0.0 - Stability¶

Compile master from populated templates
Populate templates from master
Commandline interface
Test suite
Clean data in both directions
Integrate with auxiliary files repository
bcompiler-init to set up project
Documentation

Changes¶

v1.3.19¶

annex analyser fixes

v1.3.18¶

change to require openpyxl 2.4.9

v1.3.17¶

date fix

v1.3.16¶

introduced the bcompiler.api module
updated docs to cover basic API and give examples of use

v1.3.15¶

fixed encoding bug that was preventing running of –compare function

v1.3.14¶

provisional fix Windows character encoding bug
provisional fix Excel file corruption

v1.3.13¶

small change to financial analyser to allow additional keys to be collected in certain circumstances
improved date handling
improved string cleaning of master keys
various bug fixes

v1.3.12¶

improvements to bcompiler-init bootstrapping functionality

v1.3.11¶

significant speed optimisation when using bcompiler -a option
when doing bcompiler -a will warn if master contains duplicate keys, which aren’t allowed

v1.3.10¶

Fixed bug where rcf analyser wouldn’t run with no arguments
Removed necessity to have to stipulate different keys for Q3 and Q4 in financial analysis
Improved test speed; better test coverage
Feneral improvements and rationalisations in template population code
Bug fixes

v1.3.9¶

ability to output only grey markers on the swimlane analyser charts

v1.3.8¶

new Reference Class Forecasting analyser

v1.3.7¶

new financial analysis analyser

v1.3.6¶

Chart is based on start_date option when using swimlane analysers, rather than today’s date.
swimlane charts use 30 as main x axis unit rather than 50 to approximate months.

v1.3.5¶

Bug fixes

v1.3.4¶

Fixed bug whereby creating an annex from a master containing a project not in the compare master threw an error
Fixes for annex analyser

v1.3.3¶

new swimlane assurance milestones analyser
annex analyser now does comparison with previous master document
fix issues in annex analyser

v1.3.2¶

Partial fix for final project milestone not ending up on swimlane chart.

v1.3.1¶

Fixed bug which prevented setting the title of the output sheet from the keyword analyser with xlsx output option, to a disallowed character.

v1.3.0¶

Added keyword analsyer. Search fields in a master file and return the values for each field, for each project in the terminal or optionally to an xlsx file.

v1.2.2¶

Ability to set --start_date and --end_date parameters for swimlane_milestones analyser.
Fix bug where date differences not being calculated correctly in swimlane_milestones analyser.
Fix bug where wrong milestone type was being charted by swimlane_milestones analyser.
Many more configurations available in config.ini file relating to swimlane_analyser.
Better logging to bcompiler.log during swimlane_milestones analyser.
Better handling of date objects.
Various bug fixes

v1.2.1¶

Added annex analyser, allowing for easy summarise by project from master.
Added ASCII art to bcompiler --help!
Various bug fixes

30 October 2017¶

Fix bug where not all columns in master are being processed during swimlane analyser.

17 October 2017¶

Changed ERROR log message to WARNING to accommodate dates mixed with free text.

16 October 2017¶

Fix bug where cell value in string and datetime value would try to compare arithmetically.