Oise - Non-stationary estimation¶

This tutorial is meatn to prepare the data sets for the estimation of the thickness of sediments along the Oise river. Due to the large computing time, this first phase is used to prepare the data sets.

In [1]:
import matplotlib.pyplot as plt
import gstlearn as gl
import gstlearn.plot as gp
import gstlearn.document as gdoc
import numpy as np
import pandas as pd
import scipy as sp
from scipy import interpolate
import os
import tempfile

gdoc.setNoScroll()
In [2]:
#!pip uninstall -y gstlearn
#!pip install gstlearn==0.1.38
In [3]:
DIRDATA = tempfile.gettempdir()

Creation of the data base for thickness¶

In [4]:
filename = gdoc.loadData("Alluvial", "Oise_Thickness.csv")
In [5]:
csv = gl.CSVformat(True, 0, ";", ",", "9999")
data = gl.Db.createFromCSV(filename, csv, False)
data.setLocator("X",gl.ELoc.X,0)
data.setLocator("Y",gl.ELoc.X,1)
data.setLocator("Epaisseur",gl.ELoc.L,0)
data.setLocator("Epaisseur_1",gl.ELoc.U,0)
sf = gl.DbStringFormat()
sf.setFlags(flag_stats=True) 
thickness = data.getWithinBounds(0)
err = data.addColumns(thickness,"Thickness",gl.ELoc.Z)
err = gl.db_duplicate(data)
data.display()

Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 7
Total number of samples      = 1022
Number of active samples     = 1007

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = X - Locator = x1
Column = 2 - Name = Y - Locator = x2
Column = 3 - Name = Epaisseur - Locator = lower1
Column = 4 - Name = Epaisseur_1 - Locator = upper1
Column = 5 - Name = Thickness - Locator = z1
Column = 6 - Name = Duplicate - Locator = sel
In [6]:
gp.plot(data,nameColor="Thickness")
In [7]:
filename = os.path.join(DIRDATA,"Oise_Data.ascii")
err = data.dumpToNF(filename)

Creation of the grid¶

Creation of the polygon¶

In [8]:
fileAlluvial = gdoc.loadData("Alluvial", "Oise_Alluvial.csv")
In [9]:
poly = gl.Polygons.createFromCSV(fileAlluvial, csv, False)
poly.display()

Polygons
--------
Number of Polygon Sets = 1
In [10]:
ax = poly.plot()
In [11]:
grid = gl.DbGrid.create(nx = [3300,400],dx = [50.,50.], x0 = [630000.,6865000.],angles=[40,0])
In [12]:
fig, ax = gp.initGeographic()
ax.raster(grid,"x1",alpha=0.3)
ax.symbol(data)
Out[12]:
<matplotlib.collections.PathCollection at 0x7efe624a1600>
In [13]:
err = gl.db_polygon(grid,poly)
grid
Out[13]:
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Total number of samples      = 1320000
Number of active samples     = 138248

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = sel

Creation of angles¶

In [14]:
fileCenter = gdoc.loadData("Alluvial", "Oise_Centerline.csv")

## Loading Centerline data from file
df1 = pd.read_csv (fileCenter, sep=';')
xc = list(df1['X'])
yc = list(df1['Y'])
x1=xc[1:499]
y1=yc[1:499]

## Loading alluvial plain contours from file & Separate into two polylines 
df = pd.read_csv (fileAlluvial, sep=';')
xp = list(df['X'])
yp = list(df['Y'])
x2=xp[1:xp.index(max(xp))]
x3=xp[len(xp):xp.index(max(xp))+1:-1]
y2=yp[1:xp.index(max(xp))]
y3=yp[len(xp):xp.index(max(xp))+1:-1]

## Adding supplementary control points at the edges
# coordinates of extremes
XA1=640000
YA1=6875000
XB1=740000
YB1=6955000

XA2=630000
YA2=6890000
XB2=735000
YB2=6980000

# Forming two supplementary vectors at the edges
n=19
x4= np.zeros(n)
y4= np.zeros(n)
x5= np.zeros(n)
y5= np.zeros(n)
for i in range(0,len(x4)):
    x4[i]=(XA1+(XB1-XA1)/(n-1)*i)
    y4[i]=(YA1+(YB1-YA1)/(n-1)*i)
    x5[i]=(XA2+(XB2-XA2)/(n-1)*i)
    y5[i]=(YA2+(YB2-YA2)/(n-1)*i)

# Methode du gradient 
#  https://stackoverflow.com/questions/28269379/curve-curvature-in-numpy
dx_dt1 = np.gradient(x1)
dy_dt1 = np.gradient(y1)
velocity = np.array([ [dx_dt1[i], dy_dt1[i]] for i in range(dx_dt1.size)])
ds_dt1 = np.sqrt(dx_dt1 * dx_dt1 + dy_dt1 * dy_dt1)
tangent1 = np.array([1/ds_dt1] * 2).transpose() * velocity

dx_dt2 = np.gradient(x2)
dy_dt2 = np.gradient(y2)
velocity2 = np.array([ [dx_dt2[i], dy_dt2[i]] for i in range(dx_dt2.size)])
ds_dt2 = np.sqrt(dx_dt2 * dx_dt2 + dy_dt2 * dy_dt2)
tangent2 = np.array([1/ds_dt2] * 2).transpose() * velocity2

dx_dt3 = np.gradient(x3)
dy_dt3 = np.gradient(y3)
velocity3 = np.array([ [dx_dt3[i], dy_dt3[i]] for i in range(dx_dt3.size)])
ds_dt3 = np.sqrt(dx_dt3 * dx_dt3 + dy_dt3 * dy_dt3)
tangent3 = np.array([1/ds_dt3] * 2).transpose() * velocity3

dx_dt4 = np.gradient(x4)
dy_dt4 = np.gradient(y4)
velocity4 = np.array([ [dx_dt4[i], dy_dt4[i]] for i in range(dx_dt4.size)])
ds_dt4 = np.sqrt(dx_dt4 * dx_dt4 + dy_dt4 * dy_dt4)
tangent4 = np.array([1/ds_dt4] * 2).transpose() * velocity4

dx_dt5 = np.gradient(x5)
dy_dt5 = np.gradient(y5)
velocity5 = np.array([ [dx_dt5[i], dy_dt5[i]] for i in range(dx_dt5.size)])
ds_dt5 = np.sqrt(dx_dt5 * dx_dt5 + dy_dt5 * dy_dt5)
tangent5 = np.array([1/ds_dt5] * 2).transpose() * velocity5
    
tangent=np.concatenate((tangent1,tangent2,tangent3,tangent4,tangent5),axis=0) 

x0=np.concatenate((x1,x2,x3,x4,x5),axis=0) 
y0=np.concatenate((y1,y2,y3,y4,y5),axis=0) 
u0 = tangent[:, 0]
v0 = tangent[:, 1]


# Visualize vectors
fig, ax = plt.subplots(figsize = (100,100))
plt.figure(1)
plt.quiver(x0,y0,u0,v0)

plt.plot(x0,y0,'ro')
#plt.savefig('vectors.pdf')
Out[14]:
[<matplotlib.lines.Line2D at 0x7efe623a4790>]
In [15]:
df = pd.read_csv (fileAlluvial, sep=';')
df
Out[15]:
X Y
0 631065 6875650
1 631083 6875930
2 631145 6876080
3 631200 6876700
4 631169 6877010
... ... ...
1432 632202 6878600
1433 632195 6878410
1434 632206 6878200
1435 632205 6877830
1436 632413 6876610

1437 rows × 2 columns

In [16]:
# Interpolating the vectors into vector map -> https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.griddata.html

xx = np.linspace(630000, 742000, 2240)
yy = np.linspace(6875000, 6982200, 2144)
xx, yy = np.meshgrid(xx, yy)

u0.shape=(np.size(u0),)
np.shape(u0)
v0.shape=(np.size(v0),)
np.shape(v0)

points = np.transpose(np.vstack((x0, y0)))
u_interp = interpolate.griddata(points, u0, (xx, yy), method='linear')
v_interp = interpolate.griddata(points, v0, (xx, yy), method='linear')

xx1 = np.concatenate(xx)
yy1 = np.concatenate(yy)
u_interp1 = np.concatenate(u_interp)
v_interp1 = np.concatenate(v_interp)
In [17]:
#Créer une Db point avec xx, yy, u_interp, v_interp
dbv = gl.Db()
dbv.addColumns(xx1,"xx",gl.ELoc.X, 0)
dbv.addColumns(yy1,"yy",gl.ELoc.X, 1)
dbv.addColumns(u_interp1,"u_interp",gl.ELoc.Z, 0)
dbv.addColumns(v_interp1,"v_interp",gl.ELoc.Z, 1)
dbv.display()
grid.setLocator("Pol*")
grid.display()

Data Base Characteristics
=========================

Data Base Summary
-----------------
File is organized as a set of isolated points
Space dimension              = 2
Number of Columns            = 4
Total number of samples      = 4802560

Variables
---------
Column = 0 - Name = xx - Locator = x1
Column = 1 - Name = yy - Locator = x2
Column = 2 - Name = u_interp - Locator = z1
Column = 3 - Name = v_interp - Locator = z2
 
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 4
Total number of samples      = 1320000

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = NA
In [18]:
#Tester la valeur retournée par migrate :
grid.deleteColumn("Migr*")
gl.migrateMulti(dbv, grid, ["u_interp","v_interp"])
grid.setLocator("Migrate*", gl.ELoc.Z)
grid.display()

Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 6
Total number of samples      = 1320000

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = NA
Column = 4 - Name = Migrate.u_interp - Locator = z1
Column = 5 - Name = Migrate.v_interp - Locator = z2
In [19]:
grid.useSel = False
grid.setLocator("Polygon")
uid_selection = grid.addSelectionByLimit("*u_interp*", gl.Limits([-10], [10]), "vec_define")
In [20]:
xplt = grid.getColumnByLocator(gl.ELoc.X,0,useSel=True)
yplt = grid.getColumnByLocator(gl.ELoc.X,1,useSel=True)
uplt = grid.getColumnByLocator(gl.ELoc.Z,0,useSel=True)
vplt = grid.getColumnByLocator(gl.ELoc.Z,1,useSel=True)
In [21]:
plt.figure(figsize=(200,200))
plt.quiver(xplt, yplt, uplt, vplt, headwidth =3, width= 0.0005, scale = 200, headlength= 5,  label='Field vectors')
plt.plot(x0,y0,'ro',markersize=10, label='Alluvial plain')
#plt.savefig('vectorsfield.pdf')
Out[21]:
[<matplotlib.lines.Line2D at 0x7efe6240e530>]
In [22]:
grid
Out[22]:
Data Base Grid Characteristics
==============================

Data Base Summary
-----------------
File is organized as a regular grid
Space dimension              = 2
Number of Columns            = 7
Total number of samples      = 1320000
Number of active samples     = 927470

Grid characteristics:
---------------------
Origin : 630000.0006865000.000
Mesh   :     50.000    50.000
Number :       3300       400
Rotation Angles        =     40.000     0.000
Direct Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766    -0.643
     [  1,]     0.643     0.766
Inverse Rotation Matrix
               [,  0]    [,  1]
     [  0,]     0.766     0.643
     [  1,]    -0.643     0.766

Variables
---------
Column = 0 - Name = rank - Locator = NA
Column = 1 - Name = x1 - Locator = x1
Column = 2 - Name = x2 - Locator = x2
Column = 3 - Name = Polygon - Locator = NA
Column = 4 - Name = Migrate.u_interp - Locator = z1
Column = 5 - Name = Migrate.v_interp - Locator = z2
Column = 6 - Name = vec_define - Locator = sel
In [23]:
fig,ax = gp.initGeographic()
ax.raster(grid,"*.u_interp",alpha=0.3,useSel=False)
ax.polygon(poly)
ax.symbol(data)
Out[23]:
<matplotlib.collections.PathCollection at 0x7efe648587c0>
In [24]:
filename = os.path.join(DIRDATA,"Oise_GridVector.ascii")
grid.dumpToNF(filename)
Out[24]:
True

Creating relevant information¶

In [25]:
fileSave = os.path.join(DIRDATA,"Oise_GridVector.ascii")
grid = gl.DbGrid.createFromNF(fileSave)
In [26]:
u = grid["Migrate.u_interp"]
v = grid["Migrate.v_interp"]
res = 0. * u

for i in range(u.shape[0]):
    res[i] = gl.GH.rotationGetAngles((u[i], v[i]))[0]
    
grid.deleteColumn("angles*")
grid["angles1"]= res
grid.setLocator("angles*",gl.ELoc.NOSTAT)

Creation of a selection (polygon + borders by morphological dilation)¶

In [27]:
grid.setLocator("Poly*", gl.ELoc.SEL)
In [28]:
vmin = 0.5
vmax = 1.5
nxy = grid.getNXs()
image2 = gl.BImage(nxy)

tab = grid.getColumn("Polygon",useSel=False)
image = gl.morpho_double2image(nxy,tab,vmin,vmax)
localVD = gl.VectorDouble(len(tab))
gl.morpho_dilation(0, [3,3], image, image2)
for i in range(10):
    gl.morpho_dilation(0, [1,1], image, image2)
    gl.morpho_dilation(0, [1,1], image2, image)
#gl.morpho_dilation(0, [1,1], image, image2)

gl.morpho_image2double(image2, 0, 1., 0., localVD)
In [29]:
grid["res"]=localVD.getVector()
In [30]:
grid.setLocator("res",gl.ELoc.SEL)
In [31]:
fig,ax = gp.initGeographic()
ax.raster(grid,"res")
Out[31]:
<matplotlib.collections.QuadMesh at 0x7efe7eebbbe0>

Save the result¶

In [32]:
fileFinal = os.path.join(DIRDATA,"Oise_GridVectorFinal.ascii")
grid.dumpToNF(fileFinal)
Out[32]:
True