Qualified Analysis Example via Plotly 3D Terrain Modelling

A example about interactive data analysis with Plotly(Created by Ideagora Geomatics)


Qualified 3D Terrain Modelling has to important a role due to some chapters.Thus,example about high qualified elevation/bathymetric data production presents to structure information of isocurves with information of 3 point via cursor movement as interactive into .html domain


Where,you will find to instructions about program;

Firstly,established a "3D_Terrain" folder at Desktop.And next,adding to comma separated "Survey_data" file with program;



import numpy as np
import plotly.offline as go_offline
import plotly.graph_objects as go

#READING AND PARSING THE DATA
file=open('C:/Users/Geo/Desktop/3D_Terrain/survey_data.csv','r')
lines=file.readlines()
n_line=len(lines)
x=[]
y=[]
z=[]
for i in range(1,n_line):
    split_line=lines[i].split(",")
    xyz_t=[]
    x.append(float(split_line[0].rstrip()))
    y.append(float(split_line[1].rstrip()))
    z.append(float(split_line[2].rstrip()))
    
#DISTANCE FUNCTION
def distance(x1,y1,x2,y2):
    d=np.sqrt((x1-x2)**2+(y1-y2)**2)
    return d

#CREATING IDW FUNCTION
def idw_npoint(xz,yz,n_point,p):
    r=10 #block radius iteration distance
    nf=0
    while nf<=n_point: #will stop when np reaching at least n_point
        x_block=[]
        y_block=[]
        z_block=[]
        r +=10 # add 10 unit each iteration
        xr_min=xz-r
        xr_max=xz+r
        yr_min=yz-r
        yr_max=yz+r
        for i in range(len(x)):
            # condition to test if a point is within the block
            if ((x[i]>=xr_min and x[i]<=xr_max) and (y[i]>=yr_min and y[i]<=yr_max)):
                x_block.append(x[i])
                y_block.append(y[i])
                z_block.append(z[i])
        nf=len(x_block) #calculate number of point in the block
    
    #calculate weight based on distance and p value
    w_list=[]
    for j in range(len(x_block)):
        d=distance(xz,yz,x_block[j],y_block[j])
        if d>0:
            w=1/(d**p)
            w_list.append(w)
            z0=0
        else:
            w_list.append(0) #if meet this condition, it means d<=0, weight is set to 0
    
    #check if there is 0 in weight list
    w_check=0 in w_list
    if w_check==True:
        idx=w_list.index(0) # find index for weight=0
        z_idw=z_block[idx] # set the value to the current sample value
    else:
        wt=np.transpose(w_list)
        z_idw=np.dot(z_block,wt)/sum(w_list) # idw calculation using dot product
    return z_idw

# POPULATE INTERPOLATION POINTS
n=100 #number of interpolation point for x and y axis
x_min=min(x)
x_max=max(x)
y_min=min(y)
y_max=max(y)
w=x_max-x_min #width
h=y_max-y_min #length
wn=w/n #x interval
hn=h/n #y interval

#list to store interpolation point and elevation
y_init=y_min
x_init=x_min
x_idw_list=[]
y_idw_list=[]
z_head=[]
for i in range(n):
    xz=x_init+wn*i
    yz=y_init+hn*i
    y_idw_list.append(yz)
    x_idw_list.append(xz)
    z_idw_list=[]
    for j in range(n):
        xz=x_init+wn*j
        z_idw=idw_npoint(xz,yz,5,1.5) #min. point=5, p=1.5
        z_idw_list.append(z_idw)
    z_head.append(z_idw_list)

# CREATING 3D TERRAIN
fig=go.Figure()
fig.add_trace(go.Surface(z=z_head,x=x_idw_list,y=y_idw_list))
fig.update_layout(scene=dict(aspectratio=dict(x=2, y=2, z=0.5),xaxis = dict(range=[x_min,x_max],),yaxis = dict(range=[y_min,y_max])))
go_offline.plot(fig,filename='C:/Users/Geo/Desktop/3D_Terrain/survey_data.html',validate=True, auto_open=False)

And,Produced survey_data.html file into same folder as a conclusion of  program compilation;

Thus,some images of .html file;