# Scatter and H-scatter plots¶

This script is meant to demonstrate the various possibilities offered by *gstlearn* and *gstlearn.plot* for calculating and representing scatter plots or h-scatter plots.

```
import gstlearn as gl
import gstlearn.plot as gp
```

We first define a data base containing (*nech*) isolated points randomly located. The samples belong to a square of mesh equal to 1.

```
nech = 100
db = gl.Db.createFillRandom(nech, 2, 0)
db
```

Data Base Characteristics ========================= Data Base Summary ----------------- File is organized as a set of isolated points Space dimension = 2 Number of Columns = 3 Total number of samples = 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x-1 - Locator = x1 Column = 2 - Name = x-2 - Locator = x2

Representing the contents of the data base

```
gp.plot(db)
```

We simulate two random variables linked by a joint model

```
model = gl.Model(2,2)
model.addCovFromParam(gl.ECov.EXPONENTIAL,range=0.8,sills=[2,1,1,2])
model.addCovFromParam(gl.ECov.EXPONENTIAL,range=0.2,sills=[1.1,-1,-1,1.1])
ax = gp.model(model, ivar=0, jvar=1, hmax=1)
```

```
err = gl.simtub(None,db, model)
db
```

Data Base Characteristics ========================= Data Base Summary ----------------- File is organized as a set of isolated points Space dimension = 2 Number of Columns = 5 Total number of samples = 100 Variables --------- Column = 0 - Name = rank - Locator = NA Column = 1 - Name = x-1 - Locator = x1 Column = 2 - Name = x-2 - Locator = x2 Column = 3 - Name = Simu.1 - Locator = z1 Column = 4 - Name = Simu.2 - Locator = z2

## Scatter plot¶

In this section, we present the scatter plot, represented in two different manners. On each figure, we represent the first bissector (in red) and the regression line (in blue).

- as a set of isolated points

```
ax = gp.correlation(db, "Simu.1", "Simu.2", asPoint=True,
bissLine=True, flagSameAxes=True, regrLine=True)
```

- as cells (of a fictitious grid) painted with color representing point density

```
ax = gp.correlation(db, "Simu.1", "Simu.2", asPoint=False,
bissLine=True, flagSameAxes=True, regrLine=True, bins=20, cmin=1)
```

## H-Scatter plot¶

In this section, we represent samples distant by a given distance. This distance is defined using the *VarioParam* description and selecting the *lag* of interest.

We first define the *VarioParam* set of calculation parameters: essentially, we define the lag and the number of lags.

```
varioparam = gl.VarioParam.createOmniDirection(npas=10, dpas=0.1)
```

We represent the H-Scatter plot:

- as a set of isolated symbols

```
ax = gp.hscatter(db, "Simu.1", "Simu.2", varioparam, ipas=8, asPoint=True,
bissLine=True, flagSameAxes=True)
```

- as cells (of a fictitious grid) painted with color representing point density

```
ax = gp.hscatter(db, "Simu.1", "Simu.2", varioparam, ipas=1, asPoint=False,
bissLine=True, flagSameAxes=True, bins=20, cmin=1)
```