Commit 952d500b by Martin Reinecke

### remove obsolete files

parent 81910c01
Pipeline #11569 canceled with stage
in 7 minutes and 14 seconds
 #!/usr/bin/env python import itertools import numpy as np import libsharp_wrapper_gl as gl # deterministic np.random.seed(42) def distance_array(nlat, nlon, latitude, longitude): lat = latitude * (np.pi / (nlat - 1)) lon = longitude * (2 * np.pi / (nlon - 1)) # Vincenty formula: https://en.wikipedia.org/wiki/Great-circle_distance # phi_1, lambda_1 = lat, lon # phi_2, lambda_2 = 0 numerator = np.sqrt((np.cos(0) * np.sin(lon - 0))**2 + ((np.cos(lat) * np.sin(0)) - (np.sin(lat) * np.cos(0) * np.cos(lon - 0)))**2) denominator = ( np.sin(lat) * np.sin(0)) + (np.cos(lat) * np.cos(0) * np.cos(lon - 0)) return np.arctan(numerator/denominator) # for GLSpace(nlat=2, nlon=3) da_0 = np.array( [distance_array(2, 3, *divmod(idx, 3)) for idx in np.arange(6)]) # for GLSpace(nlat=2, nlon=3) weight_0 = np.array(list(itertools.chain.from_iterable( itertools.repeat(x, 3) for x in gl.vol(2)))) w_0_x = np.random.rand(6) w_0_res = w_0_x * weight_0 weight_1 = np.array(list(itertools.chain.from_iterable( itertools.repeat(x, 3) for x in gl.vol(2)))) weight_1 = weight_1.reshape([1, 1, 6]) w_1_x = np.random.rand(32, 16, 6) w_1_res = w_1_x * weight_1 # write everything to disk np.savez( 'gl_space', da_0=da_0, w_0_x=w_0_x, w_0_res=w_0_res, w_1_x=w_1_x, w_1_res=w_1_res)
 #!/usr/bin/env python import numpy as np import healpy as hp # deterministic np.random.seed(42) # for HPSpace(nside=2) da_0 = np.array([np.arccos(hp.pix2vec(2, idx)[0]) for idx in np.arange(48)]) # for HPSpace(nside=2) w_0_x = np.random.rand(48) w_0_res = w_0_x * ((4 * np.pi) / 48) w_1_res = w_0_x * (((4 * np.pi) / 48)**2) # write everything to disk np.savez('hp_space', da_0=da_0, w_0_x=w_0_x, w_0_res=w_0_res, w_1_x=w_0_x, w_1_res=w_1_res)
 #!/usr/bin/env python from __future__ import division import numpy as np # deterministic np.random.seed(42) def distance_array_helper(index_arr, lmax): if index_arr <= lmax: index_half = index_arr else: if (index_arr - lmax) % 2 == 0: index_half = (index_arr + lmax) / 2 else: index_half = (index_arr + lmax + 1) / 2 m = ( np.ceil(((2 * lmax + 1) - np.sqrt((2 * lmax + 1)**2 - 8 * (index_half - lmax))) / 2) ).astype(int) return index_half - m * (2 * lmax + 1 - m) // 2 # for LMSpace(5) da_0 = [distance_array_helper(idx, 5) for idx in np.arange(36)] # random input for weight w_0_x = np.random.rand(32, 16, 6) # random input for hermitian h_0_res_real = np.random.rand(32, 16, 6).astype(np.complex128) h_0_res_imag = np.random.rand(32, 16, 6).astype(np.complex128) h_0_x = h_0_res_real + h_0_res_imag * 1j # write everything to disk np.savez('lm_space', da_0=da_0, w_0_x=w_0_x, w_0_res=w_0_x, h_0_x=h_0_x, h_0_res_real=h_0_res_real, h_0_res_imag=h_0_res_imag)
 #!/usr/bin/env python from __future__ import division import numpy as np # deterministic np.random.seed(42) # for now directly the kindex # RGSpace((4, 4), harmonic=True) da_0 = np.array([0, 1.0, 1.41421356, 2., 2.23606798, 2.82842712]) # power 1 w_0_x = np.random.rand(32, 16, 6) # RGSpace((4, 4), harmonic=True) # using rho directly weight_0 = np.array([1, 4, 4, 2, 4, 1]) weight_0 = weight_0.reshape([1, 1, 6]) w_0_res = w_0_x * weight_0 # write everything to disk np.savez('power_space', da_0=da_0, w_0_x=w_0_x, w_0_res=w_0_res)
 #!/usr/bin/env python from __future__ import division import numpy as np # deterministic np.random.seed(42) # for RGSpace(shape=(4, 4), distances=None, zerocenter=[False, False]) cords_0 = np.ogrid[0:4, 0:4] da_0 = ((cords_0[0] - 4 // 2) * 0.25)**2 da_0 = np.fft.fftshift(da_0) temp = ((cords_0[1] - 4 // 2) * 0.25)**2 temp = np.fft.fftshift(temp) da_0 = da_0 + temp da_0 = np.sqrt(da_0) # for RGSpace(shape=(4, 4), distances=None, zerocenter=[True, True]) da_1 = ((cords_0[0] - 4 // 2) * 0.25)**2 temp = ((cords_0[1] - 4 // 2) * 0.25)**2 da_1 = da_1 + temp da_1 = np.sqrt(da_1) # for RGSpace(shape=(4, 4), distances=(12, 12), zerocenter=[True, True]) da_2 = ((cords_0[0] - 4 // 2) * 12)**2 temp = ((cords_0[1] - 4 // 2) * 12)**2 da_2 = da_2 + temp da_2 = np.sqrt(da_2) # power 1 w_0_x = np.random.rand(32, 12, 6) # for RGSpace(shape=(11,11), distances=None, harmonic=False) w_0_res = w_0_x * (1/11 * 1/11) # for RGSpace(shape=(11, 11), distances=(1.3,1.3), harmonic=False) w_1_res = w_0_x * (1.3 * 1.3) # for RGSpace(shape=(11,11), distances=None, harmonic=True) w_2_res = w_0_x * (1.0 * 1.0) # for RGSpace(shape=(11,11), distances=(1.3, 1,3), harmonic=True) w_3_res = w_0_x * (1.3 * 1.3) # hermitianization h_0_x = np.array([ [0.88250339+0.12102381j, 0.54293435+0.7345584j, 0.87057998+0.20515315j, 0.16602950+0.09396132j], [0.83853902+0.17974696j, 0.79735933+0.37104425j, 0.22057732+0.9498977j, 0.14329183+0.47899678j], [0.96934284+0.3792878j, 0.13118669+0.45643055j, 0.16372149+0.48235714j, 0.66141537+0.20383357j], [0.49168197+0.77572178j, 0.09570420+0.14219071j, 0.69735595+0.33017333j, 0.83692452+0.18544449j]]) h_0_res_real = np.array([ [0.88250339+0.j, 0.35448193+0.32029854j, 0.87057998+0.j, 0.35448193-0.32029854j], [0.66511049-0.29798741j, 0.81714193+0.09279988j, 0.45896664+0.30986218j, 0.11949801+0.16840303j], [0.96934284+0.j, 0.39630103+0.12629849j, 0.16372149+0.j, 0.39630103-0.12629849j], [0.66511049+0.29798741j, 0.11949801-0.16840303j, 0.45896664-0.30986218j, 0.81714193-0.09279988j]]) h_0_res_imag = np.array([ [0.12102381+0.j, 0.41425986-0.18845242j, 0.20515315+0.j, 0.41425986+0.18845242j], [0.47773437-0.17342852j, 0.27824437+0.0197826j, 0.64003551+0.23838932j, 0.31059374-0.02379381j], [0.37928780+0.j, 0.33013206+0.26511434j, 0.48235714+0.j, 0.33013206-0.26511434j], [0.47773437+0.17342852j, 0.31059374+0.02379381j, 0.64003551-0.23838932j, 0.27824437-0.0197826j]]) h_1_x = np.array([ [[0.23987021+0.41617749j, 0.34605012+0.55462234j, 0.07947035+0.73360723j, 0.22853748+0.39275304j], [0.90254910+0.02107809j, 0.28195470+0.56031588j, 0.23004043+0.33873536j, 0.56398377+0.68913034j], [0.81897406+0.2050369j, 0.88724852+0.8137488j, 0.84645004+0.0059284j, 0.14950377+0.50013099j]], [[0.93491597+0.73251066j, 0.74764790+0.11539037j, 0.48090736+0.04352568j, 0.49363732+0.97233093j], [0.72761881+0.74636216j, 0.46390134+0.4343401j, 0.88436859+0.79415269j, 0.67027606+0.85498234j], [0.86318727+0.19076379j, 0.36859448+0.89842333j, 0.73407193+0.85091112j, 0.44187657+0.08936409j]] ]) h_1_res_real = np.array([ [[0.23987021+0.j, 0.28729380+0.08093465j, 0.07947035+0.j, 0.28729380-0.08093465j], [0.90254910+0.j, 0.42296924-0.06440723j, 0.23004043+0.j, 0.42296924+0.06440723j], [0.81897406+0.j, 0.51837614+0.1568089j, 0.84645004+0.j, 0.51837614-0.1568089j]], [[0.93491597+0.j, 0.62064261-0.42847028j, 0.48090736+0.j, 0.62064261+0.42847028j], [0.72761881+0.j, 0.56708870-0.21032112j, 0.88436859+0.j, 0.56708870+0.21032112j], [0.86318727+0.j, 0.40523552+0.40452962j, 0.73407193+0.j, 0.40523552-0.40452962j]] ]) h_1_res_imag = np.array([ [[0.41617749+0.j, 0.47368769-0.05875632j, 0.73360723+0.j, 0.47368769+0.05875632j], [0.02107809+0.j, 0.62472311+0.14101454j, 0.33873536+0.j, 0.62472311-0.14101454j], [0.20503690+0.j, 0.65693990-0.36887238j, 0.00592840+0.j, 0.65693990+0.36887238j]], [[0.73251066+0.j, 0.54386065-0.12700529j, 0.04352568+0.j, 0.54386065+0.12700529j], [0.74636216+0.j, 0.64466122+0.10318736j, 0.79415269+0.j, 0.64466122-0.10318736j], [0.19076379+0.j, 0.49389371+0.03664104j, 0.85091112+0.j, 0.49389371-0.03664104j]] ]) # write everything to disk np.savez('rg_space', da_0=da_0, da_1=da_1, da_2=da_2, w_0_x=w_0_x, w_0_res=w_0_res, w_1_res=w_1_res, w_2_res=w_2_res, w_3_res=w_3_res, h_0_x=h_0_x, h_0_res_real=h_0_res_real, h_0_res_imag=h_0_res_imag, h_1_x=h_1_x, h_1_res_real=h_1_res_real, h_1_res_imag=h_1_res_imag)
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!