scientimate.zprofilepath#

z, zmean, distxy = scientimate.zprofilepath(xgrid, ygrid, zgrid, x, y, distCalcMethod='cart', CalcMethod='nearest', dispout='no')

Description#

Calculate z (elevation, …) profile along a path over a given 2d x-y domain (map, image, …)

Inputs#

xgrid: x (longitude, pixel, …) data as a [M*N] array
ygrid: y (latitude, pixel, …) data as a [M*N] array
zgrid: z (elevation, …) data as a [M*N] array
x: x (longitude, pixel, …) of the points along the line as (x,y)
y: y (latitude, pixel, …) of the points along the line as (x,y)

If input data are latitude and longitude, they should be in Degree
distCalcMethod=’cart’: Distance calculation method

‘cart’: Distances are calculated on cartesian coordinate

‘gc’: Distances are calculated on Great Circle based on Vincenty formula, Vincenty (1975)

Earth radius coonsidered as mean earth radius=6371000 m
CalcMethod=’nearest’: Interpolation method

‘linear’: Use default or ‘linear’ method to interpolate

‘nearest’: Use nearest neighbor method to interpolate
dispout=’no’: Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs#

z: z (elevation, …) data along a path at given points (x,y)
zmean: Weighted mean of z (elevation) along a line calculated az zmean=1/(total_distance)*(sum(z(i)*dx(i)))
distxy: Distance at each points of (x,y) from the first point of (x(1),y(1))

If input data are latitude and longitude in Degree, distxy is in m

Examples#

import scientimate as sm
import numpy as np

xgrid,ygrid=np.meshgrid(np.linspace(-5,5,100),np.linspace(-5,5,100))
zgrid=np.sin(xgrid**2+ygrid**2)/(xgrid**2+ygrid**2)
x1=-3
y1=-3
x2=3
y2=3
x=np.linspace(x1,x2,1000)
y=np.linspace(y1,y2,1000)
z,zmean,distxy=sm.zprofilepath(xgrid,ygrid,zgrid,x,y,'cart','nearest','yes')

References#

Vincenty, T. (1975). Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations. Survey review, 23(176), 88-93.

scientimate.zprofilepath

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