binning 2nd order derivative d2/dx2(c) and pycompact complete interface/bugfix
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2 changed files with 273 additions and 17 deletions
165
binning_ddxc_d2dxc_given_c_from_raw.py
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165
binning_ddxc_d2dxc_given_c_from_raw.py
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@ -0,0 +1,165 @@
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import argparse
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import datetime
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import sys
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import os
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import numpy as np
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import dnstool
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from pycompact import CompactScheme
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program_description = '''\
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Read all DNS data files and collect c and ddx(c) at selected x coordinates.
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Compute conditional mean of ddx(c) given condition c by data binning
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'''
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# Commandline argument parser
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parser = argparse.ArgumentParser(description=program_description)
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parser.add_argument("-c", "--case", help="target case name, required")
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parser.add_argument("-l", "--list", help="list all case names", action='store_true')
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parser.add_argument("-t", "--test", help="test run, use only 10 timesteps", action='store_true')
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args = parser.parse_args()
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params = vars(args)
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cases = dnstool.case_library()
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if params["list"] :
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for c in cases:
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print(c, cases[c].case_root)
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sys.exit(0)
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else:
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if params["case"] is None :
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sys.exit("--case CASE not given")
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casename = params["case"]
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case = cases[casename]
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nx, ny, nz = case.shape
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n_timestep = len(case.data_files)
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if params["test"] :
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n_timestep = 10
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u_idx = 0
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y_idx = 4
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x_indices_all = {
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'IC1': np.array([272, 285, 294, 302, 309, 315, 321, 329, 339]),
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'IC2': np.array([251, 273, 286, 298, 309, 320, 330, 342, 359]),
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'IC3': np.array([246, 270, 285, 299, 311, 322, 333, 346, 363]),
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'IC4': np.array([262, 279, 291, 300, 309, 317, 326, 337, 349]),
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'IC8': np.array([254, 277, 291, 302, 312, 321, 330, 340, 354]),
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'IC10': np.array([285, 294, 300, 304, 308, 312, 316, 320, 326]),
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'IC11': np.array([264, 281, 292, 301, 309, 317, 325, 334, 347]),
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'IC12': np.array([253, 276, 290, 301, 311, 320, 330, 340, 355]),
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'IC13': np.array([243, 269, 287, 300, 312, 323, 334, 347, 365]),
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'VIC1': np.array([286, 294, 299, 304, 308, 312, 315, 320, 326]),
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'VIC2': np.array([264, 280, 291, 300, 309, 317, 325, 335, 348]),
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'VIC4': np.array([273, 287, 295, 302, 309, 315, 321, 328, 338]),
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'VIC10': np.array([292, 298, 302, 305, 308, 310, 313, 316, 320]),
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'VIC13': np.array([253, 276, 291, 302, 312, 321, 331, 341, 355]),}
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x_indices = x_indices_all[casename]
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print("Calculating Conditional Mean of ddx(c) given c")
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print(" * Case:", casename)
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print(" * x index:", x_indices)
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class ConditionalMeanBinning:
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def __init__ (self, name='', nbins=100, boundary=0.1):
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self.eps = np.finfo(np.float).eps
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self.name = name
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# Point grid containing interval boundaries
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self.grid = np.hstack(
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(np.linspace(0, boundary, nbins),
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np.linspace(boundary, 1-boundary, nbins)[1:],
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np.linspace(1-boundary, 1, nbins)[1:],
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)
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)
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self.grid[0] = -self.eps # to include c = 0 samples to the first bin
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# interval center points
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self.cstar = np.zeros(self.grid.size + 1)
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self.cstar[1:-1] = (self.grid[1:]+self.grid[:-1])/2.
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self.cstar[-1] = 1.0
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self.v_sums = np.zeros(self.grid.size)
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self.v_counts = np.zeros(self.grid.size, dtype=int)
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def feed_samples (self, c, v):
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''' Feed arrays c, v of samples and conditions '''
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bin_indices = np.digitize(c, self.grid, right=True)
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# assign bin indices. bin intervals are right inclusive
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# i-th bin: c_bin[i-1] < c <= c_bin[i]
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for i in range(1, self.grid.size):
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binned = v[bin_indices == i]
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self.v_sums[i] += binned.sum()
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self.v_counts[i] += binned.size
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def average (self):
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v_avg = np.zeros(self.v_sums.size+1)
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v_avg[:-1] = self.v_sums / (self.v_counts + self.eps)
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# add eps to prevent nan values from divide by zero
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d = self.sum_count()
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d["bin_cstar"] = self.cstar
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d["bin_avg"] = v_avg
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return d
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def sum_count (self):
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return {
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"bin_grid" : self.grid,
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"bin_sum" : self.v_sums,
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"bin_count" : self.v_counts
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}
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cs = CompactScheme(nx, ny, nz, False, True, True, 4, 2, 2)
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# c mean at sampling x coordinates (verification purpose)
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cmean_verify = np.zeros(len(x_indices))
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avg_objs_u = [ConditionalMeanBinning(name=str(xi)) for xi in x_indices]
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avg_objs_ddxc = [ConditionalMeanBinning(name=str(xi)) for xi in x_indices]
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avg_objs_uddxc = [ConditionalMeanBinning(name=str(xi)) for xi in x_indices]
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for i, fname in enumerate(case.data_files[:n_timestep]):
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print(datetime.datetime.now(), '{:5d}/{}'.format(i+1, n_timestep), fname)
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fpath = os.path.join(case.case_root, fname)
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time, u = case.read_single_field(fpath, u_idx)
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time, y = case.read_single_field(fpath, y_idx)
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c = (1. - y)
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ddxc = cs.ddx(c)
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for j, xi in enumerate(x_indices):
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avg_objs_u[j].feed_samples(c[:,:,xi], u[:,:,xi])
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avg_objs_ddxc[j].feed_samples(c[:,:,xi], ddxc[:,:,xi])
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avg_objs_uddxc[j].feed_samples(c[:,:,xi], u[:,:,xi]*ddxc[:,:,xi])
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cmean_verify[j] += np.sum(c[:,:,xi])
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print(datetime.datetime.now(), 'Finished')
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print()
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print("Verify mean(c) at sampling x coordinates")
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cmean_verify /= (n_timestep * ny * nz)
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for xi_cmean_pair in zip(x_indices, cmean_verify):
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print (xi_cmean_pair)
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print()
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print("Save the result")
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for j, xi in enumerate(x_indices):
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filename = "cavg_u_c_{}_{}".format(casename, avg_objs_u[j].name)
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np.savez(filename, **avg_objs_u[j].average())
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print("saved ", filename)
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for j, xi in enumerate(x_indices):
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filename = "cavg_ddxc_c_{}_{}".format(casename, avg_objs_ddxc[j].name)
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np.savez(filename, **avg_objs_ddxc[j].average())
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print("saved ", filename)
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for j, xi in enumerate(x_indices):
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filename = "cavg_uddxc_c_{}_{}".format(casename, avg_objs_uddxc[j].name)
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np.savez(filename, **avg_objs_uddxc[j].average())
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print("saved ", filename)
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125
pycompact.py
125
pycompact.py
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@ -202,9 +202,6 @@ class CompactScheme:
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nz, ny, nx = self.shape
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xsrc = np.zeros((ny, nx,), dtype=np.float64, order="F")
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# dst = np.zeros((nx, ny, nz,), order="F")
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.px: # Periodic BC
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@ -225,9 +222,6 @@ class CompactScheme:
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nz, ny, nx = self.shape
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#xsrc = np.zeros((ny, nx,), dtype=np.float64, order="F")
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# dst = np.zeros((nx, ny, nz,), order="F")
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.py: # Periodic BC
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@ -248,21 +242,71 @@ class CompactScheme:
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nz, ny, nx = self.shape
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# dst = np.zeros((nx, ny, nz,), order="F")
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.pz: # Periodic BC
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for i in range(ny):
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dst[:,i,:] = compact.dfp(self.hx, src[:,i,:], 3)
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dst[:,i,:] = compact.dfp(self.hx, src[:,i,:].T, 3).T
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else:
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for i in range(ny):
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dst[:,i,:] = compact.dfnonp(self.hx, src[:,i,:], 3)
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dst[:,i,:] = compact.dfnonp(self.hx, src[:,i,:].T, 3).T
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# return np.swapaxes(dst, 1, 2)
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return dst
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def d2dx (self, src):
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if src.shape != self.shape: print ("error")
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nz, ny, nx = self.shape
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.px: # Periodic BC
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for i in range(nz):
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dst[i] = compact.d2fp(self.hx, src[i], 1)
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else:
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for i in range(nz):
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dst[i] = compact.d2fnonp(self.hx, src[i], 1)
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return dst
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def d2dy (self, src):
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if src.shape != self.shape: print ("error")
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nz, ny, nx = self.shape
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.py: # Periodic BC
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for i in range(nz):
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dst[i] = compact.d2fp(self.hx, src[i].T, 2).T
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else:
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for i in range(nz):
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dst[i] = compact.d2fnonp(self.hx, src[i].T, 2).T
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return dst
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def d2dz (self, src):
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if src.shape != self.shape: print ("error")
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nz, ny, nx = self.shape
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dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
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if self.pz: # Periodic BC
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for i in range(ny):
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dst[:,i,:] = compact.d2fp(self.hx, src[:,i,:].T, 3).T
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else:
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for i in range(ny):
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dst[:,i,:] = compact.d2fnonp(self.hx, src[:,i,:].T, 3).T
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return dst
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def port_nonp_coef (self):
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# SUBROUTINE nonp_lud(xyz,xx)
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@ -437,11 +481,23 @@ def validate_trigonometric():
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print ("1-D sine test 2")
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cos_fortran = compact.d2fnonp(hxp, np.sin(1.1*XX).reshape((1,-1)), 1)
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cos_exact = - 1.1 * 1.1 * np.sin(1.1*XX).reshape((1,-1))
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print ("Compact Scheme: ", cos_fortran.min(), cos_fortran.max())
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print ("Exact : ",cos_exact.min(), cos_exact.max())
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print ("Norm of relative errors: ", np.linalg.norm((cos_fortran - cos_exact)/cos_exact))
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# print (((cos_fortran - cos_exact)/cos_exact))
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print ("3-D trigonometric test")
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zz, yy, xx = np.meshgrid(ZZ, YY, XX)
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zz, yy, xx = np.meshgrid(ZZ, YY, XX, indexing='ij')
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Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
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def compare_3d_result (true1, dY1):
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@ -455,15 +511,24 @@ def validate_trigonometric():
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print ("Relative Error", np.nanmin(relerr), np.nanmax(relerr))
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print(" DDX Test ")
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Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
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true[:] = (1.1 * np.cos(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz))[:]
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dY1 = cs.ddx(Y1)[:]
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compare_3d_result(true, dY1)
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print("D2DX Test ")
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Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
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true[:] = (- 1.1 * 1.1 * np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz))[:]
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dY1 = cs.d2dx(Y1)[:]
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compare_3d_result(true, dY1)
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print(" DDY Test ")
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Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
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true[:] = (-3.0 * np.cos(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz))[:]
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true[:] = (3.0 * np.sin(1.1 * xx) * np.cos(3.0 * yy) * np.sin(2.0 * zz))[:]
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dY1 = cs.ddy(Y1)[:]
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compare_3d_result(true, dY1)
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@ -471,7 +536,7 @@ def validate_trigonometric():
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print(" DDZ Test ")
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Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
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true[:] = (-2.0 * np.cos(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz))[:]
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true[:] = (2.0 * np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.cos(2.0 * zz))[:]
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dY1 = cs.ddz(Y1)[:]
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compare_3d_result(true, dY1)
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@ -519,10 +584,36 @@ def test_dns_data():
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if __name__ == "__main__":
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import argparse
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# validate_trigonometric()
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program_description = '''\
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Test Python interface to Fortran Compact Scheme Module"
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'''
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# argparse enumerated values
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# Commandline argument parser
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parser = argparse.ArgumentParser(description=program_description)
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parser.add_argument("-t", "--test", help="test to be run")
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parser.add_argument("-l", "--list", help="list all tests available", action="store_true")
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args = parser.parse_args()
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params = vars(args)
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print("DNS Field Test")
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test_dns_data()
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# Parameters
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testname = params["test"]
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list_tests = params["list"]
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if not list_tests and testname is None:
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print ("test not selected!")
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exit(-1)
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if testname == "tri":
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print("Trigonometric Function Test")
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validate_trigonometric()
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if testname == "dns":
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print("DNS Field Test")
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test_dns_data()
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