Migrate Facility¶
In [1]:
%%javascript
IPython.OutputArea.prototype._should_scroll = function(lines) {
return false;
}
This case study is meant to demonstrate how to use gstlearn for migrating information from Db to DbGrid and vice-versa. Note that, in order to test the whole possibilities, we add a selection to both files.
In [2]:
import gstlearn as gl
import gstlearn.plot as gp
import matplotlib.pyplot as plt
import numpy as np
Global parameters.
In [3]:
ndim = 2
gl.defineDefaultSpace(gl.ESpaceType.RN,ndim)
Generate initial data set
In [4]:
data = gl.Db.createFillRandom(ndat=20, ndim=ndim, nvar=1)
data.addSelectionByRanks(np.arange(2,18))
data.display()
Data Base Characteristics ========================= Data Base Summary ----------------- File is organized as a set of isolated points Space dimension = 2 Number of Columns = 4 Maximum Number of UIDs = 4 Total number of samples = 20 Number of active samples = 16 Variables --------- Column = 0 - Name = x-1 - Locator = x1 Column = 1 - Name = x-2 - Locator = x2 Column = 2 - Name = z - Locator = z1 Column = 3 - Name = NewSel - Locator = sel
In [5]:
gp.plot(data, name_size="z")
Create a grid, over the [0,1] x [0,1]
In [6]:
grid = gl.DbGrid.create([50,50],dx=[0.02,0.02])
grid.addSelectionFromDbByConvexHull(data,0.05)
grid.display()
Data Base Grid Characteristics ============================== Data Base Summary ----------------- File is organized as a regular grid Space dimension = 2 Number of Columns = 4 Maximum Number of UIDs = 4 Total number of samples = 2500 Number of active samples = 1738 Grid characteristics: --------------------- Origin : 0.000 0.000 Mesh : 0.020 0.020 Number : 50 50 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x1 - Locator = x1 Column = 2 - Name = x2 - Locator = x2 Column = 3 - Name = Hull - Locator = sel
Migrate the information from data to grid. The migration is limited to te cell to which each sample belongs.
In [7]:
err = gl.migrate(data, grid, "z", namconv=gl.NamingConvention("Migrate-NoFill",False))
ax = gp.grid(grid)
ax = gp.plot(data)
In this second attempt, we use the flag_fill option to fill the whole grid. However we compensate by specifying a maximum filling distance. Moreover we make this maximum distance anisotropic for ckecking.
In [8]:
err = gl.migrate(data, grid, "z", flag_fill=True, dmax=[0.1,0.2],
namconv=gl.NamingConvention("Migrate-Fill",False))
ax = gp.grid(grid)
ax = gp.plot(data)
