DELTAGRAPH TERNARY PLOTS INSTALL
DELTAGRAPH TERNARY PLOTS CODE

DELTAGRAPH TERNARY PLOTS CODE

ДЇor the original post, you can follow the example examples/flatdata.py from the source code fairly closely, i.e.

DELTAGRAPH TERNARY PLOTS INSTALL

You can pip install using pip install samternary. Just to add in another option (though probably too late to help the OP, but maybe someone else). # 4) plot of the unvalidated triangles from naive Delaunay Triangulation:ДЄx4.annotate('Oil', xy=(0.0, -0.15), xytext=(1, -0.15),ДЄrrowprops=dict(facecolor='green', shrink=0.05), # 3) plot of the fine mesh on which interpolation was done: Plt.tricontour(tri_refi, z_expected, levels=levels, cmap=cmap, # 2) plot of the expected (analytical) data contours (dashed): # 1) plot of the refined (computed) data contours:ДЄx4.icontour(tri_refi, z_test_refi, levels=levels,Д¬olors=,ДЄx4.icontourf(tri_refi, z_test_refi, levels=levels, cmap=cmap) '(application to high-resolution tricontouring)') Plt.title('Filtering a Delaunay mesh ' + Minor_ticks2 = LineTicks(traj2, range(0,n), 4, lw=1) # label the major ticks with the corresponding time in secs. # Add major ticks every 10th time point and minor ticks every 4th #cmap = cm.get_cmap(name='Blues', lut=None)Д¬map = cm.get_cmap(name='terrain', lut=None) Plot_expected = False # plot of analytical function values for comparison Plot_refi_tri = False # plot of refined triangulation Plot_masked_tri = True # plot of excessively flat excluded triangles Plot_tri = True # plot of base triangulation # for the demo: loading the 'flat' triangles for plot Z_expected = experiment_res(tri_refi.x, tri_refi.y) Tri_refi, z_test_refi = refiner.refine_field(z_test, subdiv=subdiv) Mask = TriAnalyzer(tri).get_flat_tri_mask(min_circle_ratio) # masking badly shaped triangles at the border of the triangular mesh. # Improving the triangulation before high-res plots: removing flat triangles

Masked_tri = random_gen.randint(0, ntri, int(ntri*init_mask_frac)) Mask_init = np.zeros(ntri, dtype=np.bool) #y_test = random_gen.uniform(-1., 1., size=n_test) #x_test = random_gen.uniform(-1., 1., size=n_test) # ratio below this will be masked if they touch a 01 # Minimum circle ratio - border triangles with circle # (invalid) initial triangles which will be masked # for the refine mesh: new triangles numbering = (4**subdiv)*ntri Values >3 might result in a very high number of triangles Subdiv = 3 # Number of recursive subdivisions of the initial mesh for smooth N_test = 200 # Number of test data points, tested from 3 to 5000 for subdiv=3 # Generating the initial data test points and triangulation for the demo ''' An analytic function representing experiment results ''' from i import Triangulation, TriAnalyzer, UniformTriRefiner However, for the triangular axis + labels and ticks, I don't know yet, but if anyone has a solution, I'll take it ) Note that, you can remove the x,y axes by doing: plt.axis('off') Trimesh = refiner.refine_triangulation(subdiv=4) Refiner = tri.UniformTriRefiner(triangle) Triangle = tri.Triangulation(corners, corners) # create a triangulation out of these pointsД¬orners = np.array(,, ]) # first load some data: format x1,x2,x3,value You can try something like that: import numpy as np

Deltagraph ternary plots