Skip to main content

Building TOUGH2/Waiwera models from layers of conceptual models

Project description


pip install -U cmflow

Install Dependency

On Windows, Shapely and Rtree are easier to be installed by using Christoph Gohlke's non-official build:

descartes can be installed on all platform by:

pip install descartes

On Linux (Ubuntu shown here) these can be installed via apt-get:

sudo apt-get install -y python-shapely
sudo apt-get install -y python-rtree
sudo apt-get install -y python-descartes


Creates BMStats that can be used later, from Leapfrog Geology:

# (ONLY ONCE) geo used to get geology from Leapfrog geological model
cmgeo = mulgrid('g_very_fine.dat')

# CSV file created by Leapfrog using cmgeo above
leapfrog = LeapfrogGM()

cm_geology = CM_Blocky(cmgeo, leapfrog)

# whatever active model we are working on
bmgeo = mulgrid('gwaixx_yy.dat')

bms_geology = cm_geology.calc_bmstats(bm_geo)'a.json')

A BMStats object can be reused (very fast) to eg.

bms_geology = BMStats('a.json')

# get a cell's stats
cs = bms_geology.cellstats['abc12']

# rock that occupies most in cell 'abc12'
rock_name = bms_geology.zones[np.argmax(cs)]

# how many rock in cell 'abc12'
n_rock = len(np.nonzero(cs))

# list all rocks in cell 'abc12'
rocks = [bm_geology.zones[i] for i in np.nonzero(cs)]

# find all blocks intersect with the zone
blocks, ratios = bm_geology.blocks_in_zone('BASE1')
block_idx, ratios = bm_geology.blocks_in_zone('BASE1', indices=True)


This is the object that we keep for later use. It is associated to a certain "geometry" file. So each cell has information on zones. Usually this is generated by cm.populate_model(), which can be expensive.

  • ? should I call it CMStats?
  • ? TODO, .cellstats access by cell index
  • ? TODO, .

Base Model Stats, mainly numpy arrays with rows corresponding to mulgrid blocks, and columns corresponding to zones. Each is a value, usually between 0.0 and 1.0. Often 1.0 is indicating that particular block is fully within the zone.

.stats numpy array (n,m), n = num of model blocks, m = num of zones .zones list of zone names (str) .zonestats dict of stats column by zone names .cellstats dict of stats row by block name

6 elements, 3 zones
 A    B    C
0.0, 0.7, 0.3,  -> row sum to 1.0, element 0, 0.7 rock B, 0.3 rock C 
1.0, 0.0, 0.0, 
1.0, 0.0, 0.0, 
0.0, 0.5, 0.5, 
0.1, 0.2, 0.7, 
0.0, 1.0, 0.0, 
(this is only one way of using it, such as a rocktype)

.stats, numpy array (n * m), n number of geometry cells, m number of zones .zones, a list of zone name, eg. geology rock names, fault names etc .zonestats, a dict keyed by zone name, an array of size number of cells, each cell is between .cellstats, a dict of stats by cell name

.save() .load() .add_stats() add another bmstat, merge stats .add_cm() calls cm.populate_model, and merge stats





These are the objects that can be created in order to create the final BMStats objects. The common method .populate_model(bm_geo) is called to create BMStats objects. It means the conceptual model is "applied" onto the bm_geo.

  • TODO, .populate_model() should return BMStats instead
  • ? TODO, .populate_model() should be called something else?

.populate_model(bm_geo) takes a target geometry, and return/creates BMStats


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cmflow, version 0.1.1.dev57
Filename, size File type Python version Upload date Hashes
Filename, size cmflow-0.1.1.dev57-py2-none-any.whl (501.1 kB) File type Wheel Python version py2 Upload date Hashes View
Filename, size cmflow-0.1.1.dev57.tar.gz (502.4 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page