How to visualize a dataset , generated in curvilinear grid, in opengl with texture maping?

I have a dataset obtained from astronomical simulation. This data was created in a curvilinear grid with polar coordinates.

The form of this dataset is a simple array (1d) of intensities, i can visualize this data in python whit the next code.

densINcgs = 8888035.76
    Munit = 1.0   #solar mass
    Runit = 1.0   #au
    DensUnit = Munit / Runit**2.0 * densINcgs # grams / (cm^2)
    # grid specification
    nrad = 128 #128 #500
    nsec = 384 # 256 #1500
    Rmin = 0.4
    Rmax = 1.8
 
    r = np.linspace(Rmin, Rmax, nrad)
    print("rx: ",np.linspace(179517444792000.0,897587223960000.2,384))
 
    rr = []
    for i in range(0,nrad):
        rr.append(Rmin*exp(i*log(Rmax/Rmin)/nrad))
    print("rr",rr)
    print("lin",np.linspace(0.,2.*np.pi, nsec))
    theta, rad   = np.meshgrid(np.linspace(0., 2.*np.pi, nsec), rr)
    xi = rad * np.cos(theta)
    yi = rad * np.sin(theta)
 
    #READ DATA
    rho =   fromfile("dens10.dat",dtype='float32')        
    Rho =          rho.reshape(nrad,nsec)

#FIRST IMAGE
    figure(100)
    imshow(log10(Rho*DensUnit),origin='lower',
    cmap=cm.Oranges_r,aspect='auto')
    xlabel('x [AU]', fontsize=16)
    ylabel('y [AU]', fontsize=16)   
    cb = plt.colorbar()

#SECON iMAGE
    cb.set_label('log Density [$\rm g$ $\rm cm^{-2}$]')
    zc = np.linspace(0, 255, 49152).reshape(128, 384)
    zc=np.random.rand(49152)*10
    zc= zc.reshape(128, 384)
    figure(17)How visualize a data generate in curvilinear grid in opengl
    pcolormesh(xi,yi,log10(Rho*DensUnit))

show()

First:
https://i.imgur.com/scC2yhX.png

Second:

https://i.imgur.com/ieVg7vw.png

Ther first image is the direct visualization of the data, and the second image is ta correct visualization of the data on a curvilinear grid. I generated this visualization with pcolormesh.

But i want generate the second visualization with openGL and glsl, using texture mapping. i tried this with the next code ( a part).

Load textures

I define a quad with this vertices.

float vertices[] = {
    -0.5f, -0.5f, -0.5f,  0.0f, 0.0f,
    0.5f, -0.5f, -0.5f,  1.0f, 0.0f,
    0.5f,  0.5f, -0.5f,  1.0f, 1.0f,
    0.5f,  0.5f, -0.5f,  1.0f, 1.0f,
    -0.5f,  0.5f, -0.5f,  0.0f, 1.0f,
   -0.5f, -0.5f, -0.5f,  0.0f, 0.0f

    }

I load the data set ( x=128 , y=384 )

float *pData = new   float[XDIM * YDIM];
    FILE *archivo;
    archivo = fopen("dens10.dat", "rb");
    //  archivo=fopen("avance/media/data5.out","rb");
    std::cout << "A3" << std::endl;
    if (archivo != NULL){
     std::cout << "A3.1" << std::endl;
    }
    for (int i = 0; i < XDIM * YDIM ; i++)
    {   //std::cout << "A3.5" << std::endl;
    float v;
    fread((void*)(&v), sizeof(v), 1, archivo);
    pData[i]=v;

   }

and i define te textures

glGenTextures(1, &texture1);
    glBindTexture(GL_TEXTURE_2D, texture1);
    // set the texture wrapping parameters
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_BORDER);
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_BORDER);
    // set texture filtering parameters
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER,        GL_LINEAR_ATTENUATION);
    glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER,    GL_LINEAR_ATTENUATION);

vertex shader

#version 330 core
    layout (location = 0) in vec3 aPos;
    layout (location = 1) in vec2 aTexCoord;

    out vec2 TexCoord;

    uniform mat4 model;
    uniform mat4 view;
    uniform mat4 projection;

    void main()
    {	
	
	vec4 cord = projection * view * model * vec4(aPos, 1.0f);
	
	gl_Position = vec4(cord);

	TexCoord=aPos.xy;
    }

fragment shader

#version 330 core
    out vec4 FragColor;

    in vec2 TexCoord;

    // texture samplers
    uniform sampler2D texture1;
    // uniform sampler2D texture2;

    #define PI 3.14159265358979323844

void main()
    {
	
	float c=texture(texture1,TexCoord ).r;
	// vec4 col=vec4(10000*c,30*c,20*c,1.0);
	vec4 col=vec4(c*1000,300*c,100*c,1.0);

	FragColor = col;
    }

The result is the next.

https://i.imgur.com/ByAHaXp.png

As can be seen this image is very similar to firt image obtained in python but my intention is get a image similary to the second.

This is the dataset
https://drive.google.com/file/d/14C02cVNkwWMbrG9DAjC05y8YAfCUUAOJ/view?usp=sharing

Thank you very much in advance.