Earth Modelling for Geophysicists
I use Python 3.4-64 Bit version.But,I predict not any problem for 32-Bit.
from scipy.interpolate import RectSphereBivariateSpline
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib.colorbar import ColorbarBase, make_axes_gridspec
r = 0.1648
theta = np.array([0.503352956, 1.006705913, 1.510058869,1.631533785, 2.134886741, 2.638239697 ])
phi = np.array([np.pi/4, np.pi/2, 3*np.pi/4, np.pi, 5*np.pi/4, 3*np.pi/2, 7*np.pi/4, 2*np.pi])
v = np.array([2.29,4.29,6.08,5.52,4.43,2.91,2.22,4.75,6.29,6.11,4.70,2.49,2.26,4.14,5.55,6.25,4.15,3.10,2.37,3.71,6.06,5.67,4.41,2.86,2.49,2.95,5.54,5.65,4.27,2.21,2.57,4.66,5.70,6.55,4.28,2.21,2.47,3.88,6.33,4.62,4.98,2.01,2.19,4.5,6.03,6.61,4.20,2.19])
voltage= np.reshape(v, (6, 8))
spl = RectSphereBivariateSpline(theta, phi, voltage)
theta_itp = np.linspace(0, np.pi, 100)
phi_itp = np.linspace(0, 2 * np.pi, 100)
d_itp = spl(theta_itp, phi_itp)
x_itp = r * np.outer(np.sin(theta_itp), np.cos(phi_itp))
y_itp = r * np.outer(np.sin(theta_itp), np.sin(phi_itp))
z_itp = r * np.outer(np.cos(theta_itp), np.ones_like(phi_itp))
norm = plt.Normalize()
facecolors = plt.cm.jet(norm(d_itp))
fig, ax = plt.subplots(1, 1, subplot_kw={'projection':'3d', 'aspect':'equal'})
ax.hold(True)
ax.plot_surface(x_itp, y_itp, z_itp, rstride=1, cstride=1, facecolors=facecolors)
cax, kw = make_axes_gridspec(ax, shrink=0.6, aspect=15)
cb = ColorbarBase(cax, cmap=plt.cm.jet, norm=norm)
cb.set_label('KWh Distribution on Solar Radiation', fontsize='x-large')
plt.show()
Program uses Spherical Coordinate Concept as parameter input
Format for theta and phi value inputs quite import at program.I have realised data input structure on Google Earth
48 point as Total.
Useful on analysis for especially Geophysicists as application areas of program.For example,visualising to data with a Solar Radiation APK program.Or,for a Gravity distribution,modelling to pressure distribution via voltage changing on piezoelectric material.
Note:dataframe for Matplotlib visualisation is quite slow.Thus,any suggestion as practic will be good.
For example,OpenGL GUI performance is wonderful example for analysis as comparison
There ares some questions that I would like to exressing as cause;
Because,a problem as time-consuming for snapshot processes is possible.Dataframe velocity is requiring with this cause.
Snapshoted images that establishing to base analysis with cover capability of Google Earth.Thus,presenting to examples on above plots;
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| Solar Radiation Distribution Map Example |
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| Solar Radiation Distribution Map Example
I will give answer to questions if I found extra times...
For questions my mail: erkuserk4
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Again,Hello
I am explaining to other steps for Globe Modelling.
I predict that you understanding about how evaluating to chart...(If,you would like to some questions my contact mail:erkuserk4@gmail.com)
Some screenshots as a conclusion of adding and some direction capabilities on image overlap process of Google Earth to spectral distribution patern are presenting;
Here,you see a summary as Magnitude type of effects to Earth of caused events to amazing Mechanic Hazards on last century.
My methodology maybe will be important.Purple line a base point(Thus,a frame on West boundary of half-globe is producing).Globe as a conclusion of horizontal and vertical extensions as other step is completing...especially big selection of kriging point that for aiming to check.
Optionally,.cm alternatives or colour establishing on individual range for program can use.
I will share on my gmail account to .kml file...
Good days
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6*8 point on traditional Theta and Phi Coordinate Concept that is prensenting as mimimum a mesh definition.(You a design as more dense thus can establish)
A useful explanation for 6*8 grid via Google Earth due to some questions:
0,0 as input on Google Earth Search Bar and expressing as (0,0)* of location.Next,establishing to Greenwich.
90,0 as input on Google Earth Search Bar and expressing as (90,0) of location.Next,drawing to orange line from three point.
-90,0 as input on Google Earth Search Bar and expressing as (-90,0) of location.Next,drawing to orange line for South part that to (0,0)*
0,90 as input on Google Earth Search Bar and expressing as (0,90) of location.Next,drawing to Green line for either North or South Hemisphere
0,180 as input on Google Earth Search Bar and expressing as (0,180) of location.Next,drawing to Orange line for Hemisphere.(Thus, Orange circle as a whole is OK)
0,270 as input on Google Earth Search Bar and expressing as (0,270) of location.Next,drawing to Green line for Hemisphere.(Thus, Green circle as a whole is OK)
You know that 8 part as min...So,10000/2 between(0,0)*-(0,90) thus,expressing 45 as location.Next,White line for N and S Hemisphere is drawing.
In the other hand,10000/2 between(0,90)-(0,180) thus,expressing 135 as location.Next,White line for N and S Hemisphere is drawing.
10000/2 between(0,180)-(0,270) thus,expressing 225 as location.Next,White line for N and S Hemisphere is drawing.
And finally,10000/2 between(0,270)-(0,0)* thus,expressing 315 as location.Next,White line for N and S Hemisphere is drawing.
for vertical direction,from N to S You know that 10000/3 between (90,0)-(0,0)*.So,3333 from North Pole Point presents 30 point as red.Next,producing as purple to circle as origin from (90,0)
3333 to S from 30 point that this time will present 60.A circle for 6666 via N Pole Point that establishing as purple
Same procedure for S hemisphere as similar applications for 30 and 60 is applying...
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