Open browniea opened 2 years ago
The Turbulent Intensity Function runs slow when a large data set in input.
d3d_data = netCDF4.Dataset('tanana50_map.nc') data x_centerline = np.linspace(xmin, xmax, num=100) y_centerline = np.mean([ymin,ymax]) z_centerline = np.mean([zmin,zmax]) centerline_points = d3d.create_points(x_centerline, y_centerline, z_centerline)
d3d_data = netCDF4.Dataset('tanana50_map.nc')
x_centerline = np.linspace(xmin, xmax, num=100)
y_centerline = np.mean([ymin,ymax])
z_centerline = np.mean([zmin,zmax])
centerline_points = d3d.create_points(x_centerline, y_centerline, z_centerline)
TI=d3d.turbulent_intensity(d3d_data, points= centerline_points ,intermediate_values = True)
Adding a slicing function to limit the data size processed through the function will speed up function.
Describe the bug:
The Turbulent Intensity Function runs slow when a large data set in input.
To Reproduce:
d3d_data = netCDF4.Dataset('tanana50_map.nc')datax_centerline = np.linspace(xmin, xmax, num=100)y_centerline = np.mean([ymin,ymax])z_centerline = np.mean([zmin,zmax])centerline_points = d3d.create_points(x_centerline, y_centerline, z_centerline)TI=d3d.turbulent_intensity(d3d_data, points= centerline_points ,intermediate_values = True)Expected behavior:
Adding a slicing function to limit the data size processed through the function will speed up function.