diff --git a/.binder/environment.yml b/.binder/environment.yml old mode 100644 new mode 100755 diff --git a/.dockerignore b/.dockerignore old mode 100644 new mode 100755 diff --git a/.gitattributes b/.gitattributes old mode 100644 new mode 100755 diff --git a/.github/workflows/auto-update-files.yml b/.github/workflows/auto-update-files.yml old mode 100644 new mode 100755 diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml old mode 100644 new mode 100755 diff --git a/.github/workflows/python-request.yml b/.github/workflows/python-request.yml old mode 100644 new mode 100755 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index b75bc92a..74cbae5f --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,9 @@ None*.png ####################### .ipynb_checkpoints Untitled.ipynb +# Personal notebooks # +######################## +/notebooks/ # Large data files # #################### *-complete.dat diff --git a/CONTRIBUTORS.rst b/CONTRIBUTORS.rst old mode 100644 new mode 100755 diff --git a/Dockerfile b/Dockerfile old mode 100644 new mode 100755 diff --git a/LICENSE b/LICENSE old mode 100644 new mode 100755 diff --git a/MANIFEST.in b/MANIFEST.in old mode 100644 new mode 100755 diff --git a/README.rst b/README.rst old mode 100644 new mode 100755 index 5b4185a8..a3105f04 --- a/README.rst +++ b/README.rst @@ -1,6 +1,6 @@ -=============== -gravity-toolkit -=============== +==================== +read-GRACE-harmonics +==================== |Language| |License| @@ -25,6 +25,9 @@ gravity-toolkit Python tools for obtaining and working with Level-2 spherical harmonic coefficients from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and the NASA/GFZ Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions +This repository is **forked** from the original one created by Tyler Sutterley. It contains additions made by Hugo Lecomte, especially for plotting purpose on harmonics and spatial objects. The additions have been developed as part of my PhD work at ITES. +I specially thank Tyler for this tool and I am glad to have been able to contribute to it. + Resources ######### @@ -90,10 +93,8 @@ Data Repositories Download ######## -| The program homepage is: -| https://github.com/tsutterley/gravity-toolkit -| A zip archive of the latest version is available directly at: -| https://github.com/tsutterley/gravity-toolkit/archive/main.zip +| The original program homepage is: +| https://github.com/tsutterley/read-GRACE-harmonics Disclaimer ########## diff --git a/doc/Makefile b/doc/Makefile old mode 100644 new mode 100755 diff --git a/doc/environment.yml b/doc/environment.yml old mode 100644 new mode 100755 diff --git a/doc/make.bat b/doc/make.bat old mode 100644 new mode 100755 diff --git a/doc/source/_assets/geoid_height.svg b/doc/source/_assets/geoid_height.svg old mode 100644 new mode 100755 diff --git a/doc/source/_static/style.css b/doc/source/_static/style.css old mode 100644 new mode 100755 diff --git a/doc/source/_templates/layout.html b/doc/source/_templates/layout.html old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/SLR/C20.rst b/doc/source/api_reference/SLR/C20.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/SLR/C30.rst b/doc/source/api_reference/SLR/C30.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/SLR/C40.rst b/doc/source/api_reference/SLR/C40.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/SLR/C50.rst b/doc/source/api_reference/SLR/C50.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/SLR/CS2.rst b/doc/source/api_reference/SLR/CS2.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/aod1b_geocenter.rst b/doc/source/api_reference/aod1b_geocenter.rst old mode 100644 new mode 100755 index 0d2524d6..d100ae3b --- a/doc/source/api_reference/aod1b_geocenter.rst +++ b/doc/source/api_reference/aod1b_geocenter.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: aod1b_geocenter.py + :filename: ../scripts/aod1b_geocenter.py :func: arguments :prog: aod1b_geocenter.py :nodescription: diff --git a/doc/source/api_reference/aod1b_oblateness.rst b/doc/source/api_reference/aod1b_oblateness.rst old mode 100644 new mode 100755 index e6a7b87d..3e0f40a6 --- a/doc/source/api_reference/aod1b_oblateness.rst +++ b/doc/source/api_reference/aod1b_oblateness.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: aod1b_oblateness.py + :filename: ../scripts/aod1b_oblateness.py :func: arguments :prog: aod1b_oblateness.py :nodescription: diff --git a/doc/source/api_reference/associated_legendre.rst b/doc/source/api_reference/associated_legendre.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/calc_degree_one.rst b/doc/source/api_reference/calc_degree_one.rst old mode 100644 new mode 100755 index 2bd076a3..b06f3c4b --- a/doc/source/api_reference/calc_degree_one.rst +++ b/doc/source/api_reference/calc_degree_one.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_degree_one.py + :filename: ../scripts/calc_degree_one.py :func: arguments :prog: calc_degree_one.py :nodescription: diff --git a/doc/source/api_reference/calc_harmonic_resolution.rst b/doc/source/api_reference/calc_harmonic_resolution.rst old mode 100644 new mode 100755 index cabc22b6..b6f0f882 --- a/doc/source/api_reference/calc_harmonic_resolution.rst +++ b/doc/source/api_reference/calc_harmonic_resolution.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_harmonic_resolution.py + :filename: ../scripts/calc_harmonic_resolution.py :func: arguments :prog: calc_harmonic_resolution.py :nodescription: diff --git a/doc/source/api_reference/calc_mascon.rst b/doc/source/api_reference/calc_mascon.rst old mode 100644 new mode 100755 index ab25b5c3..841b4555 --- a/doc/source/api_reference/calc_mascon.rst +++ b/doc/source/api_reference/calc_mascon.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_mascon.py + :filename: ../scripts/calc_mascon.py :func: arguments :prog: calc_mascon.py :nodescription: diff --git a/doc/source/api_reference/calc_sensitivity_kernel.rst b/doc/source/api_reference/calc_sensitivity_kernel.rst old mode 100644 new mode 100755 index eeb1c800..224eaf8a --- a/doc/source/api_reference/calc_sensitivity_kernel.rst +++ b/doc/source/api_reference/calc_sensitivity_kernel.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_sensitivity_kernel.py + :filename: ../scripts/calc_sensitivity_kernel.py :func: arguments :prog: calc_sensitivity_kernel.py :nodescription: diff --git a/doc/source/api_reference/clenshaw_summation.rst b/doc/source/api_reference/clenshaw_summation.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/cnes_grace_sync.rst b/doc/source/api_reference/cnes_grace_sync.rst old mode 100644 new mode 100755 index 15e2daf7..979244c0 --- a/doc/source/api_reference/cnes_grace_sync.rst +++ b/doc/source/api_reference/cnes_grace_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: cnes_grace_sync.py + :filename: ../scripts/cnes_grace_sync.py :func: arguments :prog: cnes_grace_sync.py :nodescription: diff --git a/doc/source/api_reference/combine_harmonics.rst b/doc/source/api_reference/combine_harmonics.rst old mode 100644 new mode 100755 index 6a670c84..89b51d0c --- a/doc/source/api_reference/combine_harmonics.rst +++ b/doc/source/api_reference/combine_harmonics.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: combine_harmonics.py + :filename: ../scripts/combine_harmonics.py :func: arguments :prog: combine_harmonics.py :nodescription: diff --git a/doc/source/api_reference/convert_harmonics.rst b/doc/source/api_reference/convert_harmonics.rst old mode 100644 new mode 100755 index c29c9eec..f79dd91c --- a/doc/source/api_reference/convert_harmonics.rst +++ b/doc/source/api_reference/convert_harmonics.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: convert_harmonics.py + :filename: ../scripts/convert_harmonics.py :func: arguments :prog: convert_harmonics.py :nodescription: diff --git a/doc/source/api_reference/dealiasing_global_uplift.rst b/doc/source/api_reference/dealiasing_global_uplift.rst old mode 100644 new mode 100755 index 6066ada4..581cc973 --- a/doc/source/api_reference/dealiasing_global_uplift.rst +++ b/doc/source/api_reference/dealiasing_global_uplift.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: dealiasing_global_uplift.py + :filename: ../scripts/dealiasing_global_uplift.py :func: arguments :prog: dealiasing_global_uplift.py :nodescription: diff --git a/doc/source/api_reference/dealiasing_monthly_mean.rst b/doc/source/api_reference/dealiasing_monthly_mean.rst old mode 100644 new mode 100755 index 30f76257..4616976e --- a/doc/source/api_reference/dealiasing_monthly_mean.rst +++ b/doc/source/api_reference/dealiasing_monthly_mean.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: dealiasing_monthly_mean.py + :filename: ../scripts/dealiasing_monthly_mean.py :func: arguments :prog: dealiasing_monthly_mean.py :nodescription: diff --git a/doc/source/api_reference/degree_amplitude.rst b/doc/source/api_reference/degree_amplitude.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/destripe_harmonics.rst b/doc/source/api_reference/destripe_harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/esa_costg_swarm_sync.rst b/doc/source/api_reference/esa_costg_swarm_sync.rst old mode 100644 new mode 100755 index 2e63e482..250e16ce --- a/doc/source/api_reference/esa_costg_swarm_sync.rst +++ b/doc/source/api_reference/esa_costg_swarm_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: esa_costg_swarm_sync.py + :filename: ../scripts/esa_costg_swarm_sync.py :func: arguments :prog: esa_costg_swarm_sync.py :nodescription: diff --git a/doc/source/api_reference/fourier_legendre.rst b/doc/source/api_reference/fourier_legendre.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gauss_weights.rst b/doc/source/api_reference/gauss_weights.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_averaging_kernel.rst b/doc/source/api_reference/gen_averaging_kernel.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_disc_load.rst b/doc/source/api_reference/gen_disc_load.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_harmonics.rst b/doc/source/api_reference/gen_harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_point_load.rst b/doc/source/api_reference/gen_point_load.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_spherical_cap.rst b/doc/source/api_reference/gen_spherical_cap.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gen_stokes.rst b/doc/source/api_reference/gen_stokes.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/geocenter.rst b/doc/source/api_reference/geocenter.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/gfz_icgem_costg_ftp.rst b/doc/source/api_reference/gfz_icgem_costg_ftp.rst old mode 100644 new mode 100755 index df9f4e47..23e97852 --- a/doc/source/api_reference/gfz_icgem_costg_ftp.rst +++ b/doc/source/api_reference/gfz_icgem_costg_ftp.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_icgem_costg_ftp.py + :filename: ../scripts/gfz_icgem_costg_ftp.py :func: arguments :prog: gfz_icgem_costg_ftp.py :nodescription: diff --git a/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst b/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst old mode 100644 new mode 100755 index 923466a8..487a45b6 --- a/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst +++ b/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_isdc_dealiasing_ftp.py + :filename: ../scripts/gfz_isdc_dealiasing_ftp.py :func: arguments :prog: gfz_isdc_dealiasing_ftp.py :nodescription: diff --git a/doc/source/api_reference/gfz_isdc_grace_ftp.rst b/doc/source/api_reference/gfz_isdc_grace_ftp.rst old mode 100644 new mode 100755 index f6f14ccd..c0c868ea --- a/doc/source/api_reference/gfz_isdc_grace_ftp.rst +++ b/doc/source/api_reference/gfz_isdc_grace_ftp.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_isdc_grace_ftp.py + :filename: ../scripts/gfz_isdc_grace_ftp.py :func: arguments :prog: gfz_isdc_grace_ftp.py :nodescription: diff --git a/doc/source/api_reference/grace_date.rst b/doc/source/api_reference/grace_date.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/grace_find_months.rst b/doc/source/api_reference/grace_find_months.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/grace_input_months.rst b/doc/source/api_reference/grace_input_months.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/grace_mean_harmonics.rst b/doc/source/api_reference/grace_mean_harmonics.rst old mode 100644 new mode 100755 index 91ab32c3..abd8303f --- a/doc/source/api_reference/grace_mean_harmonics.rst +++ b/doc/source/api_reference/grace_mean_harmonics.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_mean_harmonics.py + :filename: ../scripts/grace_mean_harmonics.py :func: arguments :prog: grace_mean_harmonics.py :nodescription: diff --git a/doc/source/api_reference/grace_months_index.rst b/doc/source/api_reference/grace_months_index.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/grace_spatial_error.rst b/doc/source/api_reference/grace_spatial_error.rst old mode 100644 new mode 100755 index 9e179790..49c02a3f --- a/doc/source/api_reference/grace_spatial_error.rst +++ b/doc/source/api_reference/grace_spatial_error.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_spatial_error.py + :filename: ../scripts/grace_spatial_error.py :func: arguments :prog: grace_spatial_error.py :nodescription: diff --git a/doc/source/api_reference/grace_spatial_maps.rst b/doc/source/api_reference/grace_spatial_maps.rst old mode 100644 new mode 100755 index 04c5d1c0..be08c9e5 --- a/doc/source/api_reference/grace_spatial_maps.rst +++ b/doc/source/api_reference/grace_spatial_maps.rst @@ -15,7 +15,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_spatial_maps.py + :filename: ../scripts/grace_spatial_maps.py :func: arguments :prog: grace_spatial_maps.py :nodescription: diff --git a/doc/source/api_reference/harmonic_gradients.rst b/doc/source/api_reference/harmonic_gradients.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/harmonic_summation.rst b/doc/source/api_reference/harmonic_summation.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/harmonics.rst b/doc/source/api_reference/harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/itsg_graz_grace_sync.rst b/doc/source/api_reference/itsg_graz_grace_sync.rst old mode 100644 new mode 100755 index 4862ffe0..3963b691 --- a/doc/source/api_reference/itsg_graz_grace_sync.rst +++ b/doc/source/api_reference/itsg_graz_grace_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: itsg_graz_grace_sync.py + :filename: ../scripts/itsg_graz_grace_sync.py :func: arguments :prog: itsg_graz_grace_sync.py :nodescription: diff --git a/doc/source/api_reference/legendre.rst b/doc/source/api_reference/legendre.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/legendre_polynomials.rst b/doc/source/api_reference/legendre_polynomials.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/make_grace_index.rst b/doc/source/api_reference/make_grace_index.rst old mode 100644 new mode 100755 index a3b77b82..061bab4c --- a/doc/source/api_reference/make_grace_index.rst +++ b/doc/source/api_reference/make_grace_index.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: make_grace_index.py + :filename: ../scripts/make_grace_index.py :func: arguments :prog: make_grace_index.py :nodescription: diff --git a/doc/source/api_reference/mascon_reconstruct.rst b/doc/source/api_reference/mascon_reconstruct.rst old mode 100644 new mode 100755 index d87c2fbf..1f300047 --- a/doc/source/api_reference/mascon_reconstruct.rst +++ b/doc/source/api_reference/mascon_reconstruct.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: mascon_reconstruct.py + :filename: ../scripts/mascon_reconstruct.py :func: arguments :prog: mascon_reconstruct.py :nodescription: diff --git a/doc/source/api_reference/mascons.rst b/doc/source/api_reference/mascons.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/monte_carlo_degree_one.rst b/doc/source/api_reference/monte_carlo_degree_one.rst old mode 100644 new mode 100755 index 861c4d1f..6305cc30 --- a/doc/source/api_reference/monte_carlo_degree_one.rst +++ b/doc/source/api_reference/monte_carlo_degree_one.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: monte_carlo_degree_one.py + :filename: ../scripts/monte_carlo_degree_one.py :func: arguments :prog: monte_carlo_degree_one.py :nodescription: diff --git a/doc/source/api_reference/ocean_stokes.rst b/doc/source/api_reference/ocean_stokes.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/piecewise_grace_maps.rst b/doc/source/api_reference/piecewise_grace_maps.rst old mode 100644 new mode 100755 index 611aef51..437e0a1f --- a/doc/source/api_reference/piecewise_grace_maps.rst +++ b/doc/source/api_reference/piecewise_grace_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: piecewise_grace_maps.py + :filename: ../scripts/piecewise_grace_maps.py :func: arguments :prog: piecewise_grace_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_GrIS_maps.rst b/doc/source/api_reference/plot_AIS_GrIS_maps.rst old mode 100644 new mode 100755 index 9d310fac..b1a62b89 --- a/doc/source/api_reference/plot_AIS_GrIS_maps.rst +++ b/doc/source/api_reference/plot_AIS_GrIS_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_GrIS_maps.py + :filename: ../scripts/plot_AIS_GrIS_maps.py :func: arguments :prog: plot_AIS_GrIS_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_3maps.rst b/doc/source/api_reference/plot_AIS_grid_3maps.rst old mode 100644 new mode 100755 index 4e072445..28d7fa58 --- a/doc/source/api_reference/plot_AIS_grid_3maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_3maps.py + :filename: ../scripts/plot_AIS_grid_3maps.py :func: arguments :prog: plot_AIS_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_4maps.rst b/doc/source/api_reference/plot_AIS_grid_4maps.rst old mode 100644 new mode 100755 index 4ecf1ee8..85d5029d --- a/doc/source/api_reference/plot_AIS_grid_4maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_4maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_4maps.py + :filename: ../scripts/plot_AIS_grid_4maps.py :func: arguments :prog: plot_AIS_grid_4maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_maps.rst b/doc/source/api_reference/plot_AIS_grid_maps.rst old mode 100644 new mode 100755 index 1400daee..5fe07791 --- a/doc/source/api_reference/plot_AIS_grid_maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_maps.py + :filename: ../scripts/plot_AIS_grid_maps.py :func: arguments :prog: plot_AIS_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_movie.rst b/doc/source/api_reference/plot_AIS_grid_movie.rst old mode 100644 new mode 100755 index 43fecb5c..8a1d1dca --- a/doc/source/api_reference/plot_AIS_grid_movie.rst +++ b/doc/source/api_reference/plot_AIS_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_movie.py + :filename: ../scripts/plot_AIS_grid_movie.py :func: arguments :prog: plot_AIS_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_regional_maps.rst b/doc/source/api_reference/plot_AIS_regional_maps.rst old mode 100644 new mode 100755 index 4bcd8b8f..d7071303 --- a/doc/source/api_reference/plot_AIS_regional_maps.rst +++ b/doc/source/api_reference/plot_AIS_regional_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_regional_maps.py + :filename: ../scripts/plot_AIS_regional_maps.py :func: arguments :prog: plot_AIS_regional_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_regional_movie.rst b/doc/source/api_reference/plot_AIS_regional_movie.rst old mode 100644 new mode 100755 index fcd604bd..7439fa25 --- a/doc/source/api_reference/plot_AIS_regional_movie.rst +++ b/doc/source/api_reference/plot_AIS_regional_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_regional_movie.py + :filename: ../scripts/plot_AIS_regional_movie.py :func: arguments :prog: plot_AIS_regional_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_3maps.rst b/doc/source/api_reference/plot_GrIS_grid_3maps.rst old mode 100644 new mode 100755 index 122c7fb8..a3181be4 --- a/doc/source/api_reference/plot_GrIS_grid_3maps.rst +++ b/doc/source/api_reference/plot_GrIS_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_3maps.py + :filename: ../scripts/plot_GrIS_grid_3maps.py :func: arguments :prog: plot_GrIS_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_maps.rst b/doc/source/api_reference/plot_GrIS_grid_maps.rst old mode 100644 new mode 100755 index eaca7b98..e3ac1296 --- a/doc/source/api_reference/plot_GrIS_grid_maps.rst +++ b/doc/source/api_reference/plot_GrIS_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_maps.py + :filename: ../scripts/plot_GrIS_grid_maps.py :func: arguments :prog: plot_GrIS_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_movie.rst b/doc/source/api_reference/plot_GrIS_grid_movie.rst old mode 100644 new mode 100755 index 89ed454f..8253904a --- a/doc/source/api_reference/plot_GrIS_grid_movie.rst +++ b/doc/source/api_reference/plot_GrIS_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_movie.py + :filename: ../scripts/plot_GrIS_grid_movie.py :func: arguments :prog: plot_GrIS_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_3maps.rst b/doc/source/api_reference/plot_global_grid_3maps.rst old mode 100644 new mode 100755 index 6c0ba94c..16b3037c --- a/doc/source/api_reference/plot_global_grid_3maps.rst +++ b/doc/source/api_reference/plot_global_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_3maps.py + :filename: ../scripts/plot_global_grid_3maps.py :func: arguments :prog: plot_global_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_4maps.rst b/doc/source/api_reference/plot_global_grid_4maps.rst old mode 100644 new mode 100755 index 80dbf72d..2a63dbdb --- a/doc/source/api_reference/plot_global_grid_4maps.rst +++ b/doc/source/api_reference/plot_global_grid_4maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_4maps.py + :filename: ../scripts/plot_global_grid_4maps.py :func: arguments :prog: plot_global_grid_4maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_5maps.rst b/doc/source/api_reference/plot_global_grid_5maps.rst old mode 100644 new mode 100755 index 8b217506..20bb8396 --- a/doc/source/api_reference/plot_global_grid_5maps.rst +++ b/doc/source/api_reference/plot_global_grid_5maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_5maps.py + :filename: ../scripts/plot_global_grid_5maps.py :func: arguments :prog: plot_global_grid_5maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_9maps.rst b/doc/source/api_reference/plot_global_grid_9maps.rst old mode 100644 new mode 100755 index de3c0543..82a07bda --- a/doc/source/api_reference/plot_global_grid_9maps.rst +++ b/doc/source/api_reference/plot_global_grid_9maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_9maps.py + :filename: ../scripts/plot_global_grid_9maps.py :func: arguments :prog: plot_global_grid_9maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_maps.rst b/doc/source/api_reference/plot_global_grid_maps.rst old mode 100644 new mode 100755 index af5419ac..f7fc602c --- a/doc/source/api_reference/plot_global_grid_maps.rst +++ b/doc/source/api_reference/plot_global_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_maps.py + :filename: ../scripts/plot_global_grid_maps.py :func: arguments :prog: plot_global_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_movie.rst b/doc/source/api_reference/plot_global_grid_movie.rst old mode 100644 new mode 100755 index 0461c659..48479a07 --- a/doc/source/api_reference/plot_global_grid_movie.rst +++ b/doc/source/api_reference/plot_global_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_movie.py + :filename: ../scripts/plot_global_grid_movie.py :func: arguments :prog: plot_global_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/podaac_cumulus.rst b/doc/source/api_reference/podaac_cumulus.rst old mode 100644 new mode 100755 index 2d680b18..42fe9779 --- a/doc/source/api_reference/podaac_cumulus.rst +++ b/doc/source/api_reference/podaac_cumulus.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: podaac_cumulus.py + :filename: ../scripts/podaac_cumulus.py :func: arguments :prog: podaac_cumulus.py :nodescription: diff --git a/doc/source/api_reference/quick_mascon_plot.rst b/doc/source/api_reference/quick_mascon_plot.rst old mode 100644 new mode 100755 index 557f634c..f786eca8 --- a/doc/source/api_reference/quick_mascon_plot.rst +++ b/doc/source/api_reference/quick_mascon_plot.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: quick_mascon_plot.py + :filename: ../scripts/quick_mascon_plot.py :func: arguments :prog: quick_mascon_plot.py :nodescription: diff --git a/doc/source/api_reference/quick_mascon_regress.rst b/doc/source/api_reference/quick_mascon_regress.rst old mode 100644 new mode 100755 index 75082f3e..a51fe1cf --- a/doc/source/api_reference/quick_mascon_regress.rst +++ b/doc/source/api_reference/quick_mascon_regress.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: quick_mascon_regress.py + :filename: ../scripts/quick_mascon_regress.py :func: arguments :prog: quick_mascon_regress.py :nodescription: diff --git a/doc/source/api_reference/read_GRACE_harmonics.rst b/doc/source/api_reference/read_GRACE_harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/read_SLR_harmonics.rst b/doc/source/api_reference/read_SLR_harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/read_gfc_harmonics.rst b/doc/source/api_reference/read_gfc_harmonics.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/read_love_numbers.rst b/doc/source/api_reference/read_love_numbers.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/regress_grace_maps.rst b/doc/source/api_reference/regress_grace_maps.rst old mode 100644 new mode 100755 index 309c3480..53de95be --- a/doc/source/api_reference/regress_grace_maps.rst +++ b/doc/source/api_reference/regress_grace_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: regress_grace_maps.py + :filename: ../scripts/regress_grace_maps.py :func: arguments :prog: regress_grace_maps.py :nodescription: diff --git a/doc/source/api_reference/run_grace_date.rst b/doc/source/api_reference/run_grace_date.rst old mode 100644 new mode 100755 index 6f34775c..17ae4387 --- a/doc/source/api_reference/run_grace_date.rst +++ b/doc/source/api_reference/run_grace_date.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: run_grace_date.py + :filename: ../scripts/run_grace_date.py :func: arguments :prog: run_grace_date.py :nodescription: diff --git a/doc/source/api_reference/run_sea_level_equation.rst b/doc/source/api_reference/run_sea_level_equation.rst old mode 100644 new mode 100755 index ee456d99..37dd55f5 --- a/doc/source/api_reference/run_sea_level_equation.rst +++ b/doc/source/api_reference/run_sea_level_equation.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: run_sea_level_equation.py + :filename: ../scripts/run_sea_level_equation.py :func: arguments :prog: run_sea_level_equation.py :nodescription: diff --git a/doc/source/api_reference/scale_grace_maps.rst b/doc/source/api_reference/scale_grace_maps.rst old mode 100644 new mode 100755 index 22da42b1..625c2612 --- a/doc/source/api_reference/scale_grace_maps.rst +++ b/doc/source/api_reference/scale_grace_maps.rst @@ -17,7 +17,7 @@ Calling Sequence ################ .. argparse:: - :filename: scale_grace_maps.py + :filename: ../scripts/scale_grace_maps.py :func: arguments :prog: scale_grace_maps.py :nodescription: diff --git a/doc/source/api_reference/sea_level_equation.rst b/doc/source/api_reference/sea_level_equation.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/spatial.rst b/doc/source/api_reference/spatial.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time.rst b/doc/source/api_reference/time.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/amplitude.rst b/doc/source/api_reference/time_series/amplitude.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/fit.rst b/doc/source/api_reference/time_series/fit.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/piecewise.rst b/doc/source/api_reference/time_series/piecewise.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/regress.rst b/doc/source/api_reference/time_series/regress.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/savitzky_golay.rst b/doc/source/api_reference/time_series/savitzky_golay.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/time_series/smooth.rst b/doc/source/api_reference/time_series/smooth.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/tools.rst b/doc/source/api_reference/tools.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/units.rst b/doc/source/api_reference/units.rst old mode 100644 new mode 100755 diff --git a/doc/source/api_reference/utilities.rst b/doc/source/api_reference/utilities.rst old mode 100644 new mode 100755 diff --git a/doc/source/conf.py b/doc/source/conf.py old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Background.rst b/doc/source/getting_started/Background.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Citations.rst b/doc/source/getting_started/Citations.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Contributing.rst b/doc/source/getting_started/Contributing.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/GRACE-Data-File-Formats.rst b/doc/source/getting_started/GRACE-Data-File-Formats.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Geocenter-Variations.rst b/doc/source/getting_started/Geocenter-Variations.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Getting-Started.rst b/doc/source/getting_started/Getting-Started.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Install.rst b/doc/source/getting_started/Install.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/NASA-Earthdata.rst b/doc/source/getting_started/NASA-Earthdata.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Resources.rst b/doc/source/getting_started/Resources.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Spatial-Maps.rst b/doc/source/getting_started/Spatial-Maps.rst old mode 100644 new mode 100755 diff --git a/doc/source/getting_started/Time-Series-Analysis.rst b/doc/source/getting_started/Time-Series-Analysis.rst old mode 100644 new mode 100755 diff --git a/doc/source/index.rst b/doc/source/index.rst old mode 100644 new mode 100755 diff --git a/doc/source/user_guide/Examples.rst b/doc/source/user_guide/Examples.rst old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/C20.py b/gravity_toolkit/SLR/C20.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/C30.py b/gravity_toolkit/SLR/C30.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/C40.py b/gravity_toolkit/SLR/C40.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/C50.py b/gravity_toolkit/SLR/C50.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/CS2.py b/gravity_toolkit/SLR/CS2.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/SLR/__init__.py b/gravity_toolkit/SLR/__init__.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/__init__.py b/gravity_toolkit/__init__.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/associated_legendre.py b/gravity_toolkit/associated_legendre.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/clenshaw_summation.py b/gravity_toolkit/clenshaw_summation.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/Load_Love2_CE.dat b/gravity_toolkit/data/Load_Love2_CE.dat old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/PREM-LLNs-truncated.dat b/gravity_toolkit/data/PREM-LLNs-truncated.dat old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/PREMhard-LLNs-truncated.dat b/gravity_toolkit/data/PREMhard-LLNs-truncated.dat old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/PREMsoft-LLNs-truncated.dat b/gravity_toolkit/data/PREMsoft-LLNs-truncated.dat old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/land_fcn_300km.nc b/gravity_toolkit/data/land_fcn_300km.nc old mode 100644 new mode 100755 diff --git a/gravity_toolkit/data/love_numbers b/gravity_toolkit/data/love_numbers old mode 100644 new mode 100755 diff --git a/gravity_toolkit/destripe_harmonics.py b/gravity_toolkit/destripe_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/gen_disc_load.py b/gravity_toolkit/gen_disc_load.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/gen_harmonics.py b/gravity_toolkit/gen_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/gen_point_load.py b/gravity_toolkit/gen_point_load.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index c2029652..6c937b94 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -167,19 +167,33 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, # custom units dfactor = np.copy(UNITS) int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 1): + elif UNITS == 1: # Default Parameter: Input in cm w.e. (g/cm^2) dfactor = factors.spatial(*LOVE).cmwe int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 2): + elif UNITS == 2: # Input in gigatonnes (Gt) dfactor = factors.spatial(*LOVE).cmwe # rad_e: Average Radius of the Earth [cm] int_fact[:] = 1e15/(factors.rad_e**2) - elif (UNITS == 3): + elif UNITS == 3: # Input in kg/m^2 (mm w.e.) dfactor = factors.spatial(*LOVE).mmwe int_fact[:] = np.sin(th)*dphi*dth + elif UNITS == 4: + #-- Inputs in mmGH + dfactor = factors.spatial(*LOVE).mmGH + int_fact[:] = np.sin(th) * dphi * dth + elif UNITS == 5: + dfactor = factors.spatial(*LOVE).microGal + int_fact[:] = np.sin(th) * dphi * dth + elif UNITS == 6: + dfactor = factors.spatial(*LOVE).cmwe_ne + int_fact[:] = np.sin(th) * dphi * dth + elif UNITS == 7: + #-- Inputs in units with no dfactor + dfactor = factors.spatial(*LOVE).norm + int_fact[:] = np.sin(th) * dphi * dth else: raise ValueError(f'Unknown units {UNITS}') @@ -203,12 +217,12 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, plm[:,m,j] = PLM[:,m,j]*int_fact[j] # Initializing preliminary spherical harmonic matrices - yclm = np.zeros((LMAX+1, MMAX+1)) - yslm = np.zeros((LMAX+1, MMAX+1)) + yclm = np.zeros((LMAX + 1, MMAX + 1)) + yslm = np.zeros((LMAX + 1, MMAX + 1)) # Initializing output spherical harmonic matrices Ylms = gravity_toolkit.harmonics(lmax=LMAX, mmax=MMAX) - Ylms.clm = np.zeros((LMAX+1, MMAX+1)) - Ylms.slm = np.zeros((LMAX+1, MMAX+1)) + Ylms.clm = np.zeros((LMAX + 1, MMAX + 1)) + Ylms.slm = np.zeros((LMAX + 1, MMAX + 1)) # Multiplying gridded data with sin/cos of m#phis # This will sum through all phis in the dot product # output [m,theta] diff --git a/gravity_toolkit/geocenter.py b/gravity_toolkit/geocenter.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py old mode 100644 new mode 100755 index 6201292f..4fb9f359 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -302,6 +302,7 @@ def arguments(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES'], help='GRACE/GRACE-FO Processing Center') # GRACE/GRACE-FO data release parser.add_argument('--release','-r', diff --git a/gravity_toolkit/grace_find_months.py b/gravity_toolkit/grace_find_months.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py old mode 100644 new mode 100755 index 5c5675aa..2fb0f2ab --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -415,7 +415,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # read GRACE/GRACE-FO/Swarm file if PROC in ('GRAZ','Swarm'): # Degree 2 zonals will be converted to a tide free state - Ylms = read_gfc_harmonics(infile, TIDE='tide_free') + flag = pathlib.Path(infile).suffix + Ylms = read_gfc_harmonics(infile, TIDE='tide_free', FLAG=flag) else: # Effects of Pole tide drift will be compensated if specified Ylms = read_GRACE_harmonics(infile, LMAX, MMAX=MMAX, @@ -439,12 +440,12 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, FLAGS = [] # Replacing C2,0 with SLR values - if (SLR_C20 == 'CSR'): - if (DREL == 'RL04'): + if SLR_C20 == 'CSR': + if DREL == 'RL04': SLR_file = base_dir.joinpath('TN-05_C20_SLR.txt') - elif (DREL == 'RL05'): + elif DREL == 'RL05': SLR_file = base_dir.joinpath('TN-07_C20_SLR.txt') - elif (DREL == 'RL06'): + elif DREL == 'RL06': # SLR_file = base_dir.joinpath('TN-11_C20_SLR.txt') SLR_file = base_dir.joinpath('C20_RL06.txt') # log SLR file if debugging @@ -453,7 +454,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C20_input = gravity_toolkit.SLR.C20(SLR_file) FLAGS.append('_wCSR_C20') attributes['SLR C20'] = ('CSR', SLR_file.name) - elif (SLR_C20 == 'GFZ'): + elif SLR_C20 == 'GFZ': SLR_file = base_dir.joinpath(f'GFZ_{DREL}_C20_SLR.dat') # log SLR file if debugging logging.debug(f'Reading SLR C20 file: {str(SLR_file)}') @@ -461,7 +462,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C20_input = gravity_toolkit.SLR.C20(SLR_file) FLAGS.append('_wGFZ_C20') attributes['SLR C20'] = ('GFZ', SLR_file.name) - elif (SLR_C20 == 'GSFC'): + elif SLR_C20 == 'GSFC': SLR_file = base_dir.joinpath('TN-14_C30_C20_GSFC_SLR.txt') # log SLR file if debugging logging.debug(f'Reading SLR C20 file: {str(SLR_file)}') @@ -471,7 +472,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C20'] = ('GSFC', SLR_file.name) # Replacing C2,1/S2,1 with SLR values - if (kwargs['SLR_21'] == 'CSR'): + if kwargs['SLR_21'] == 'CSR': SLR_file = base_dir.joinpath(f'C21_S21_{DREL}.txt') # log SLR file if debugging logging.debug(f'Reading SLR C21/S21 file: {str(SLR_file)}') @@ -479,7 +480,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C21_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wCSR_21') attributes['SLR 21'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_21'] == 'GFZ'): + elif kwargs['SLR_21'] == 'GFZ': GravIS_file = 'GRAVIS-2B_GFZOP_GRACE+SLR_LOW_DEGREES_0003.dat' SLR_file = base_dir.joinpath(GravIS_file) # log SLR file if debugging @@ -488,7 +489,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C21_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wGFZ_21') attributes['SLR 21'] = ('GFZ GravIS', SLR_file.name) - elif (kwargs['SLR_21'] == 'GSFC'): + elif kwargs['SLR_21'] == 'GSFC': # calculate monthly averages from 7-day arcs SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging @@ -500,7 +501,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR 21'] = ('GSFC', SLR_file.name) # Replacing C2,2/S2,2 with SLR values - if (kwargs['SLR_22'] == 'CSR'): + if kwargs['SLR_22'] == 'CSR': SLR_file = base_dir.joinpath(f'C22_S22_{DREL}.txt') # log SLR file if debugging logging.debug(f'Reading SLR C22/S22 file: {str(SLR_file)}') @@ -508,7 +509,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C22_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wCSR_22') attributes['SLR 22'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_22'] == 'GSFC'): + elif kwargs['SLR_22'] == 'GSFC': SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging logging.debug(f'Reading SLR C22/S22 file: {str(SLR_file)}') @@ -519,7 +520,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR 22'] = ('GSFC', SLR_file.name) # Replacing C3,0 with SLR values - if (kwargs['SLR_C30'] == 'CSR'): + if kwargs['SLR_C30'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -527,7 +528,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wCSR_C30') attributes['SLR C30'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C30'] == 'LARES'): + elif kwargs['SLR_C30'] == 'LARES': SLR_file = base_dir.joinpath('C30_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -535,7 +536,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wLARES_C30') attributes['SLR_C30'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C30'] == 'GFZ'): + elif kwargs['SLR_C30'] == 'GFZ': GravIS_file = 'GRAVIS-2B_GFZOP_GRACE+SLR_LOW_DEGREES_0003.dat' SLR_file = base_dir.joinpath(GravIS_file) # log SLR file if debugging @@ -544,7 +545,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wGFZ_C30') attributes['SLR C30'] = ('GFZ GravIS', SLR_file.name) - elif (kwargs['SLR_C30'] == 'GSFC'): + elif kwargs['SLR_C30'] == 'GSFC': SLR_file = base_dir.joinpath('TN-14_C30_C20_GSFC_SLR.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -554,7 +555,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C30'] = ('GSFC', SLR_file.name) # Replacing C4,0 with SLR values - if (kwargs['SLR_C40'] == 'CSR'): + if kwargs['SLR_C40'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -562,7 +563,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C40_input = gravity_toolkit.SLR.C40(SLR_file) FLAGS.append('_wCSR_C40') attributes['SLR C40'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C40'] == 'LARES'): + elif kwargs['SLR_C40'] == 'LARES': SLR_file = base_dir.joinpath('C40_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -570,7 +571,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C40_input = gravity_toolkit.SLR.C40(SLR_file) FLAGS.append('_wLARES_C40') attributes['SLR C40'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C40'] == 'GSFC'): + elif kwargs['SLR_C40'] == 'GSFC': SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -581,7 +582,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C40'] = ('GSFC', SLR_file.name) # Replacing C5,0 with SLR values - if (kwargs['SLR_C50'] == 'CSR'): + if kwargs['SLR_C50'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C50 file: {str(SLR_file)}') @@ -589,7 +590,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C50_input = gravity_toolkit.SLR.C50(SLR_file) FLAGS.append('_wCSR_C50') attributes['SLR C50'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C50'] == 'LARES'): + elif kwargs['SLR_C50'] == 'LARES': SLR_file = base_dir.joinpath('C50_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C50 file: {str(SLR_file)}') @@ -597,7 +598,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C50_input = gravity_toolkit.SLR.C50(SLR_file) FLAGS.append('_wLARES_C50') attributes['SLR C50'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C50'] == 'GSFC'): + elif kwargs['SLR_C50'] == 'GSFC': # SLR_file = base_dir.joinpath('GSFC_SLR_C20_C30_C50_GSM_replacement.txt') SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging @@ -610,7 +611,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # Correcting for Degree 1 (geocenter variations) # reading degree 1 file for given release if specified - if (DEG1 == 'Tellus'): + if DEG1 == 'Tellus': # Tellus (PO.DAAC) degree 1 if DREL in ('RL04','RL05'): # old degree one files @@ -629,7 +630,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_tellus(DEG1_file,JPL=JPL) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('JPL Tellus', DEG1_file.name) - elif (DEG1 == 'SLR'): + elif DEG1 == 'SLR': # CSR Satellite Laser Ranging (SLR) degree 1 # # SLR-derived degree-1 mass variations # # ftp://ftp.csr.utexas.edu/pub/slr/geocenter/ @@ -672,7 +673,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_UCI(DEG1_file) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('UCI', DEG1_file.name) - elif (DEG1 == 'Swenson'): + elif DEG1 == 'Swenson': # degree 1 coefficients provided by Sean Swenson in mm w.e. default_geocenter = base_dir.joinpath('geocenter', f'gad_gsm.{DREL}.txt') @@ -683,7 +684,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_swenson(DEG1_file) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('Swenson', DEG1_file.name) - elif (DEG1 == 'GFZ'): + elif DEG1 == 'GFZ': # degree 1 coefficients provided by GFZ GravIS # http://gravis.gfz-potsdam.de/corrections default_geocenter = base_dir.joinpath('geocenter', @@ -717,7 +718,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # replace C20 with SLR coefficients for i,grace_month in enumerate(months): count = np.count_nonzero(C20_input['month'] == grace_month) - if (count != 0): + if count != 0: k, = np.nonzero(C20_input['month'] == grace_month) grace_Ylms['clm'][2,0,i] = np.copy(C20_input['data'][k]) grace_Ylms['eclm'][2,0,i] = np.copy(C20_input['error'][k]) @@ -846,7 +847,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # add files to lineage attribute attributes['lineage'].extend(atm_corr['files']) # Removing GAE/GAF/GAG from RL05 GSM Products - if (DSET == 'GSM'): + if DSET == 'GSM': for m in range(0,MMAX+1):# MMAX+1 to include l for l in range(m,LMAX+1):# LMAX+1 to include LMAX grace_Ylms['clm'][l,m,:] -= atm_corr['clm'][l,m,:] @@ -900,6 +901,9 @@ def read_ecmwf_corrections(base_dir, LMAX, months, MMAX=None): `doi: 10.1093/gji/ggv276 `_ """ + # directory of exact GRACE/GRACE-FO product + base_dir = pathlib.Path(base_dir).expanduser().absolute() + # correction files corr_file = {} corr_file['GAE'] = 'TN-08_GAE-2_2006032-2010031_0000_EIGEN_G---_0005.gz' @@ -951,7 +955,7 @@ def read_ecmwf_corrections(base_dir, LMAX, months, MMAX=None): elif (grace_month >= 98) & (grace_month <= 161): atm_corr['clm'][:,:,i] = atm_corr_clm['GAF'][:,:] atm_corr['slm'][:,:,i] = atm_corr_slm['GAF'][:,:] - elif (grace_month > 161): + elif grace_month > 161: atm_corr['clm'][:,:,i] = atm_corr_clm['GAG'][:,:] atm_corr['slm'][:,:,i] = atm_corr_slm['GAG'][:,:] diff --git a/gravity_toolkit/grace_months_index.py b/gravity_toolkit/grace_months_index.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/harmonic_gradients.py b/gravity_toolkit/harmonic_gradients.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py old mode 100644 new mode 100755 index 83b24f12..2c02afb1 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -73,6 +73,7 @@ can calculate spherical harmonic mean over a range of time indices will also calculate the mean time and month of a harmonics object can create a harmonics object from an open file-like object + Updated 11/2020: added plotting functions for visualization Updated 08/2020: added compression options for ascii, netCDF4 and HDF5 files Updated 07/2020: added class docstring and using kwargs for output to file added case_insensitive_filename function to search directories @@ -98,9 +99,13 @@ import pathlib import zipfile import warnings +import matplotlib import numpy as np import gravity_toolkit.version from gravity_toolkit.time import adjust_months,calendar_to_grace +import scipy as sc +import matplotlib.pyplot as plt +import gravity_toolkit.wavelets as wv from gravity_toolkit.destripe_harmonics import destripe_harmonics from gravity_toolkit.read_gfc_harmonics import read_gfc_harmonics from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics @@ -244,21 +249,21 @@ def from_ascii(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # open the ascii file and extract contents logging.info(self.filename) - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read input ascii data from gzip compressed file and split lines with gzip.open(self.filename, mode='r') as f: file_contents = f.read().decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read input ascii data from zipped file and split lines stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: file_contents = z.read(stem).decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read input file object and split lines file_contents = self.filename.read().splitlines() else: @@ -274,11 +279,12 @@ def from_ascii(self, filename, **kwargs): self.mmax = 0 # for each line in the file for line in file_contents: - l1,m1,clm1,slm1,*aux = rx.findall(line) - # convert line degree and order to integers - l1,m1 = np.array([l1,m1],dtype=np.int64) - self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax - self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax + if not '#' in line[:2]: + l1,m1,clm1,slm1,*aux = rx.findall(line) + # convert line degree and order to integers + l1,m1 = np.array([l1,m1],dtype=np.int64) + self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax + self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax # output spherical harmonics data self.clm = np.zeros((self.lmax+1,self.mmax+1)) self.slm = np.zeros((self.lmax+1,self.mmax+1)) @@ -291,12 +297,13 @@ def from_ascii(self, filename, **kwargs): # extract harmonics and convert to matrix # for each line in the file for line in file_contents: - l1,m1,clm1,slm1,*aux = rx.findall(line) - # convert line degree and order to integers - ll,mm = np.array([l1,m1],dtype=np.int64) - # convert fortran exponentials if applicable - self.clm[ll,mm] = np.float64(clm1.replace('D','E')) - self.slm[ll,mm] = np.float64(slm1.replace('D','E')) + if not '#' in line[:2]: + l1,m1,clm1,slm1,*aux = rx.findall(line) + # convert line degree and order to integers + ll,mm = np.array([l1,m1],dtype=np.int64) + # convert fortran exponentials if applicable + self.clm[ll,mm] = np.float64(clm1.replace('D','E')) + self.slm[ll,mm] = np.float64(slm1.replace('D','E')) # assign degree and order fields self.update_dimensions() return self @@ -323,15 +330,15 @@ def from_netCDF4(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # Open the NetCDF4 file for reading - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read as in-memory (diskless) netCDF4 dataset with gzip.open(self.filename, mode='r') as f: fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=f.read()) - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read zipped file and extract file into in-memory file object stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: @@ -343,7 +350,7 @@ def from_netCDF4(self, filename, **kwargs): f,=[f for f in z.namelist() if re.search(r'\.nc(4)?$',f)] # read bytes from zipfile as in-memory (diskless) netCDF4 dataset fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=z.read(f)) - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read as in-memory (diskless) netCDF4 dataset fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=filename.read()) else: @@ -413,11 +420,11 @@ def from_HDF5(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # Open the HDF5 file for reading - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read gzip compressed file and extract into in-memory file object with gzip.open(self.filename, mode='r') as f: fid = io.BytesIO(f.read()) @@ -427,7 +434,7 @@ def from_HDF5(self, filename, **kwargs): fid.seek(0) # read as in-memory (diskless) HDF5 dataset from BytesIO object fileID = h5py.File(fid, mode='r') - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read zipped file and extract file into in-memory file object stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: @@ -445,7 +452,7 @@ def from_HDF5(self, filename, **kwargs): fid.seek(0) # read as in-memory (diskless) HDF5 dataset from BytesIO object fileID = h5py.File(fid, mode='r') - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read as in-memory (diskless) HDF5 dataset fileID = h5py.File(self.filename, mode='r') else: @@ -617,13 +624,13 @@ def from_index(self, filename, **kwargs): h = [] # for each file in the index for i,f in enumerate(file_list): - if (kwargs['format'] == 'ascii'): + if kwargs['format'] == 'ascii': # ascii (.txt) h.append(harmonics().from_ascii(f, date=kwargs['date'])) - elif (kwargs['format'] == 'netCDF4'): + elif kwargs['format'] == 'netCDF4': # netcdf (.nc) h.append(harmonics().from_netCDF4(f, date=kwargs['date'])) - elif (kwargs['format'] == 'HDF5'): + elif kwargs['format'] == 'HDF5': # HDF5 (.H5) h.append(harmonics().from_HDF5(f, date=kwargs['date'])) # create a single harmonic object from the list @@ -715,21 +722,21 @@ def from_file(self, filename, format=None, date=True, **kwargs): # set filename self.case_insensitive_filename(filename) # set default verbosity - kwargs.setdefault('verbose',False) + kwargs.setdefault('verbose', False) # read from file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) return harmonics().from_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) return harmonics().from_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) return harmonics().from_HDF5(filename, date=date, **kwargs) - elif (format == 'gfc'): + elif format == 'gfc': # ICGEM gravity model (.gfc) return harmonics().from_gfc(filename, **kwargs) - elif (format == 'SHM'): + elif format == 'SHM': # spherical harmonic model return harmonics().from_SHM(filename, self.lmax, **kwargs) @@ -743,10 +750,12 @@ def from_dict(self, d, **kwargs): dictionary object to be converted """ # assign dictionary variables to self - for key in ['l','m','clm','slm','time','month']: + for key in ['l', 'm', 'clm', 'slm', 'time', 'month']: try: setattr(self, key, d[key].copy()) - except (AttributeError, KeyError): + except AttributeError: + setattr(self, key, d[key]) + except KeyError: pass # maximum degree and order self.lmax = np.max(d['l']) @@ -1099,13 +1108,13 @@ def to_index(self, filename, file_list, format=None, date=True, **kwargs): # index harmonics object at i h = self.index(i, date=date) # write to file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) h.to_ascii(f, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) h.to_netCDF4(f, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) h.to_HDF5(f, date=date, **kwargs) # close the index file @@ -1135,13 +1144,13 @@ def to_file(self, filename, format=None, date=True, **kwargs): # set default verbosity kwargs.setdefault('verbose',False) # write to file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) self.to_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) self.to_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) self.to_HDF5(filename, date=date, **kwargs) @@ -1173,21 +1182,21 @@ def to_masked_array(self): # verify dimensions and get shape ndim_prev = np.copy(self.ndim) self.expand_dims() - l1,m1,nt = self.shape + l1, m1, nt = self.shape # create single triangular matrices with harmonics - Ylms = np.ma.zeros((self.lmax+1,2*self.lmax+1,nt)) - Ylms.mask = np.ones((self.lmax+1,2*self.lmax+1,nt),dtype=bool) - for m in range(-self.mmax,self.mmax+1): + Ylms = np.ma.zeros((self.lmax + 1, 2*self.lmax + 1, nt)) + Ylms.mask = np.ones((self.lmax + 1, 2*self.lmax + 1, nt),dtype=bool) + for m in range(-self.mmax, self.mmax + 1): mm = np.abs(m) - for l in range(mm,self.lmax+1): - if (m < 0): - Ylms.data[l,self.lmax+m,:] = self.slm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + for l in range(mm, self.lmax + 1): + if m < 0: + Ylms.data[l, self.lmax+m, :] = self.slm[l, mm, :] + Ylms.mask[l, self.lmax+m, :] = False else: - Ylms.data[l,self.lmax+m,:] = self.clm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + Ylms.data[l, self.lmax+m, :] = self.clm[l,mm,:] + Ylms.mask[l, self.lmax+m, :] = False # reshape to previous - if (self.ndim != ndim_prev): + if self.ndim != ndim_prev: self.squeeze() # return the triangular matrix return Ylms @@ -1197,8 +1206,8 @@ def update_dimensions(self): Update the dimension variables of the ``harmonics`` object """ # calculate spherical harmonic degree and order (0 is falsy) - self.l=np.arange(self.lmax+1) if (self.lmax is not None) else None - self.m=np.arange(self.mmax+1) if (self.mmax is not None) else None + self.l = np.arange(self.lmax + 1) if (self.lmax is not None) else None + self.m = np.arange(self.mmax + 1) if (self.mmax is not None) else None return self def add(self, temp): @@ -1215,16 +1224,32 @@ def add(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] += temp.clm[:l1,:m1] - self.slm[:l1,:m1] += temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] += temp.clm[:l1, :m1] + self.slm[:l1, :m1] += temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] += temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] += temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1] else: - self.clm[:l1,:m1,:] += temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] += temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def subtract(self, temp): @@ -1241,16 +1266,32 @@ def subtract(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1] -= temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1] -= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1] else: - self.clm[:l1,:m1,:] -= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] -= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def multiply(self, temp): @@ -1265,18 +1306,34 @@ def multiply(self, temp): # assign degree and order fields self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1] *= temp.slm[:l1,:m1] + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 + if self.ndim == 2: + self.clm[:l1, :m1] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1] *= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] *= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1] else: - self.clm[:l1,:m1,:] *= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] *= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def divide(self, temp): @@ -1291,23 +1348,39 @@ def divide(self, temp): # assign degree and order fields self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 # indices for cosine spherical harmonics (including zonals) lc,mc = np.tril_indices(l1, m=m1) # indices for sine spherical harmonics (excluding zonals) m0 = np.nonzero(mc != 0) - ls,ms = (lc[m0],mc[m0]) - if (self.ndim == 2): - self.clm[lc,mc] /= temp.clm[lc,mc] - self.slm[ls,ms] /= temp.slm[ls,ms] + ls,ms = (lc[m0], mc[m0]) + if self.ndim == 2: + self.clm[lc, mc] /= temp.clm[lc, mc] + self.slm[ls, ms] /= temp.slm[ls, ms] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[lc,mc,i] /= temp.clm[lc,mc] - self.slm[ls,ms,i] /= temp.slm[ls,ms] + self.clm[lc, mc, i] /= temp.clm[lc, mc] + self.slm[ls, ms, i] /= temp.slm[ls, ms] else: - self.clm[lc,mc,:] /= temp.clm[lc,mc,:] - self.slm[ls,ms,:] /= temp.slm[ls,ms,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] /= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] /= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def copy(self): @@ -1383,11 +1456,11 @@ def expand_dims(self, update_dimensions=True): self.month = np.atleast_1d(self.month) # output harmonics with a third dimension if (self.ndim == 2) and not self.flattened: - self.clm = self.clm[:,:,None] - self.slm = self.slm[:,:,None] + self.clm = self.clm[:, :, None] + self.slm = self.slm[:, :, None] elif (self.ndim == 1) and self.flattened: - self.clm = self.clm[:,None] - self.slm = self.slm[:,None] + self.clm = self.clm[:, None] + self.slm = self.slm[:, None] # assign degree and order fields if update_dimensions: self.update_dimensions() @@ -1429,7 +1502,7 @@ def flatten(self, date=True): ``harmonics`` objects contain date information """ n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax-self.mmax)**2 - - (self.lmax-self.mmax))//2 + 1 + (self.lmax - self.mmax))//2 + 1 # restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) temp.l = np.zeros((n_harm,), dtype=np.int64) @@ -1447,20 +1520,20 @@ def flatten(self, date=True): temp.slm = np.zeros((n_harm)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((n_harm,n)) - temp.slm = np.zeros((n_harm,n)) + temp.clm = np.zeros((n_harm, n)) + temp.slm = np.zeros((n_harm, n)) # create counter variable lm lm = 0 - for m in range(0,self.mmax+1):# MMAX+1 to include MMAX - for l in range(m,self.lmax+1):# LMAX+1 to include LMAX + for m in range(0,self.mmax + 1):# MMAX+1 to include MMAX + for l in range(m,self.lmax + 1):# LMAX+1 to include LMAX temp.l[lm] = np.int64(l) temp.m[lm] = np.int64(m) - if (self.clm.ndim == 2): - temp.clm[lm] = self.clm[l,m] - temp.slm[lm] = self.slm[l,m] + if self.clm.ndim == 2: + temp.clm[lm] = self.clm[l, m] + temp.slm[lm] = self.slm[l, m] else: - temp.clm[lm,:] = self.clm[l,m,:] - temp.slm[lm,:] = self.slm[l,m,:] + temp.clm[lm, :] = self.clm[l, m, :] + temp.slm[lm, :] = self.slm[l, m, :] # add 1 to lm counter variable lm += 1 # update flattened attribute @@ -1480,6 +1553,9 @@ def expand(self, date=True): # number of harmonics n_harm = len(self.l) # restructured degree and order + #n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax - self.mmax)**2 - + # (self.lmax - self.mmax))//2 + 1 + # restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) # get filenames if applicable if getattr(self, 'filename'): @@ -1489,23 +1565,23 @@ def expand(self, date=True): temp.time = np.copy(self.time) temp.month = np.copy(self.month) # restructured spherical harmonic matrices - if (self.clm.ndim == 1): - temp.clm = np.zeros((self.lmax+1,self.mmax+1)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1)) + if self.clm.ndim == 1: + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax + 1,self.mmax + 1, n)) + temp.slm = np.zeros((self.lmax + 1,self.mmax + 1, n)) # create counter variable lm for lm in range(n_harm): l = self.l[lm] m = self.m[lm] - if (self.clm.ndim == 1): - temp.clm[l,m] = self.clm[lm] - temp.slm[l,m] = self.slm[lm] + if self.clm.ndim == 1: + temp.clm[l, m] = self.clm[lm] + temp.slm[l, m] = self.slm[lm] else: - temp.clm[l,m,:] = self.clm[lm,:] - temp.slm[l,m,:] = self.slm[lm,:] + temp.clm[l, m, :] = self.clm[lm, :] + temp.slm[l, m, :] = self.slm[lm, :] # update flattened attribute temp.flattened = False # assign degree and order fields @@ -1527,8 +1603,8 @@ def index(self, indice, date=True): # output harmonics object temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # subset output harmonics - temp.clm = self.clm[:,:,indice].copy() - temp.slm = self.slm[:,:,indice].copy() + temp.clm = self.clm[:, :, indice].copy() + temp.slm = self.slm[:, :, indice].copy() # subset output dates if date: temp.time = self.time[indice].copy() @@ -1563,19 +1639,19 @@ def subset(self, months): m = ','.join([f'{m:03d}' for m in months_check]) raise IOError(f'GRACE/GRACE-FO months {m} not Found') # indices to sort data objects - months_list = [i for i,m in enumerate(self.month) if m in months] + months_list = [i for i, m in enumerate(self.month) if m in months] # output harmonics object temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # create output harmonics - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,n)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,n)) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) temp.time = np.zeros((n)) - temp.month = np.zeros((n),dtype=np.int64) + temp.month = np.zeros((n), dtype=np.int64) temp.filename = [] # for each indice for t,i in enumerate(months_list): - temp.clm[:,:,t] = self.clm[:,:,i].copy() - temp.slm[:,:,t] = self.slm[:,:,i].copy() + temp.clm[:,:, t] = self.clm[:,:, i].copy() + temp.slm[:,:, t] = self.slm[:,:, i].copy() temp.time[t] = self.time[i].copy() temp.month[t] = self.month[i].copy() # subset filenames if applicable @@ -1614,18 +1690,18 @@ def truncate(self, lmax, lmin=0, mmax=None): l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 # create output harmonics - if (temp.ndim == 3): + if temp.ndim == 3: # number of months n = temp.clm.shape[-1] - self.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.slm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.clm[lmin:l1,:m1,:] = temp.clm[lmin:l1,:m1,:].copy() - self.slm[lmin:l1,:m1,:] = temp.slm[lmin:l1,:m1,:].copy() + self.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.slm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.clm[lmin:l1, :m1,:] = temp.clm[lmin:l1, :m1,:].copy() + self.slm[lmin:l1, :m1,:] = temp.slm[lmin:l1, :m1,:].copy() else: - self.clm = np.zeros((self.lmax+1,self.mmax+1)) - self.slm = np.zeros((self.lmax+1,self.mmax+1)) - self.clm[lmin:l1,:m1] = temp.clm[lmin:l1,:m1].copy() - self.slm[lmin:l1,:m1] = temp.slm[lmin:l1,:m1].copy() + self.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.slm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.clm[lmin:l1, :m1] = temp.clm[lmin:l1, :m1].copy() + self.slm[lmin:l1, :m1] = temp.slm[lmin:l1, :m1].copy() # assign degree and order fields self.update_dimensions() # return the truncated or expanded harmonics object @@ -1644,19 +1720,19 @@ def mean(self, apply=False, indices=Ellipsis): """ temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # allocate for mean field - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1)) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1)) # Computes the mean for each spherical harmonic degree and order - for m in range(0,temp.mmax+1):# MMAX+1 to include l - for l in range(m,temp.lmax+1):# LMAX+1 to include LMAX + for m in range(0, temp.mmax + 1):# MMAX+1 to include l + for l in range(m, temp.lmax + 1):# LMAX+1 to include LMAX # calculate mean static field - temp.clm[l,m] = np.mean(self.clm[l,m,indices]) - temp.slm[l,m] = np.mean(self.slm[l,m,indices]) + temp.clm[l, m] = np.mean(self.clm[l, m, indices]) + temp.slm[l, m] = np.mean(self.slm[l, m, indices]) # calculating the time-variable gravity field by removing # the static component of the gravitational field if apply: - self.clm[l,m,:] -= temp.clm[l,m] - self.slm[l,m,:] -= temp.slm[l,m] + self.clm[l, m, :] -= temp.clm[l, m] + self.slm[l, m, :] -= temp.slm[l, m] # calculate mean of temporal variables for key in ['time','month']: try: @@ -1687,19 +1763,19 @@ def scale(self, var): if getattr(self, 'filename'): temp.filename = copy.copy(self.filename) # multiply by a single constant or a time-variable scalar - if (np.ndim(var) == 0): + if np.ndim(var) == 0: temp.clm = var*self.clm temp.slm = var*self.slm elif (np.ndim(var) == 1) and (self.ndim == 2): - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm - temp.slm[:,:,i] = v*self.slm + temp.clm[:, :, i] = v*self.clm + temp.slm[:, :, i] = v*self.slm elif (np.ndim(var) == 1) and (self.ndim == 3): for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm[:,:,i] - temp.slm[:,:,i] = v*self.slm[:,:,i] + temp.clm[:, :, i] = v*self.clm[:, :, i] + temp.slm[:, :, i] = v*self.slm[:, :, i] # assign degree and order fields temp.update_dimensions() return temp @@ -1773,15 +1849,15 @@ def convolve(self, var): # assign degree and order fields self.update_dimensions() # check if a single field or a temporal field - if (self.ndim == 2): - for l in range(0,self.lmax+1):# LMAX+1 to include LMAX - self.clm[l,:] *= var[l] - self.slm[l,:] *= var[l] + if self.ndim == 2: + for l in range(0, self.lmax + 1):# LMAX+1 to include LMAX + self.clm[l, :] *= var[l] + self.slm[l, :] *= var[l] else: for i,t in enumerate(self.time): - for l in range(0,self.lmax+1):# LMAX+1 to include LMAX - self.clm[l,:,i] *= var[l] - self.slm[l,:,i] *= var[l] + for l in range(0, self.lmax + 1):# LMAX+1 to include LMAX + self.clm[l, :, i] *= var[l] + self.slm[l, :, i] *= var[l] # return the convolved field return self @@ -1811,20 +1887,20 @@ def destripe(self, **kwargs): if getattr(self, 'filename'): temp.filename = copy.copy(self.filename) # check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: Ylms = destripe_harmonics(self.clm, self.slm, LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) temp.clm = Ylms['clm'].copy() temp.slm = Ylms['slm'].copy() else: n = self.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + temp.slm = np.zeros((self.lmax+1, self.mmax+1, n)) for i in range(n): - Ylms = destripe_harmonics(self.clm[:,:,i], self.slm[:,:,i], + Ylms = destripe_harmonics(self.clm[:, :, i], self.slm[:, :, i], LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) - temp.clm[:,:,i] = Ylms['clm'].copy() - temp.slm[:,:,i] = Ylms['slm'].copy() + temp.clm[:, :, i] = Ylms['clm'].copy() + temp.slm[:, :, i] = Ylms['slm'].copy() # assign degree and order fields temp.update_dimensions() # return the destriped field @@ -1838,24 +1914,24 @@ def amplitude(self): # temporary matrix for squared harmonics temp = self.power(2) # check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: # allocate for degree amplitudes - amp = np.zeros((self.lmax+1)) - for l in range(self.lmax+1): + amp = np.zeros((self.lmax + 1)) + for l in range(self.lmax + 1): # truncate at mmax - m = np.arange(0,temp.mmax+1) + m = np.arange(0, temp.mmax + 1) # degree amplitude of spherical harmonic degree - amp[l] = np.sqrt(np.sum(temp.clm[l,m] + temp.slm[l,m])) + amp[l] = np.sqrt(np.sum(temp.clm[l, m] + temp.slm[l, m])) else: # allocate for degree amplitudes n = self.shape[-1] - amp = np.zeros((self.lmax+1,n)) - for l in range(self.lmax+1): + amp = np.zeros((self.lmax + 1, n)) + for l in range(self.lmax + 1): # truncate at mmax - m = np.arange(0,temp.mmax+1) + m = np.arange(0, temp.mmax + 1) # degree amplitude of spherical harmonic degree - var = temp.clm[l,m,:] + temp.slm[l,m,:] - amp[l,:] = np.sqrt(np.sum(var, axis=0)) + var = temp.clm[l, m, :] + temp.slm[l, m, :] + amp[l, :] = np.sqrt(np.sum(var, axis=0)) # return the degree amplitudes return amp @@ -1894,6 +1970,7 @@ def __iter__(self): self.__index__ = 0 return self + def __next__(self): """Get the next month of data """ @@ -1914,3 +1991,406 @@ def __next__(self): # add to index self.__index__ += 1 return temp + + def gap_fill(self, apply=False, interpolate=1): + """ + Fill the missing months with a linear interpolation, the interpolation is made on month number, it's imprecise + Options: + apply: apply to the object if True, else return a new instance + interpolate: 0 = fill gap with 0, 1 = linear interpolation + """ + temp = self.copy() + missing_month = self.month[-1] - self.month[0] - len(self.month) + 1 + + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.time = np.zeros(len(self.time) + missing_month) + temp.month = np.arange(self.month[0], self.month[-1] + 1) + + # initialize index and count variables + index = 0 + cmp = 0 + for i in range(int(self.month[0]), int(self.month[-1]) + 1): + if i in self.month: # if month in original object, copy time and data + cmp_miss_mon = 0 # variable for following missing months + temp.time[index] = self.time[index - cmp] + temp.clm[:, :, index] = self.clm[:, :, index - cmp] + temp.slm[:, :, index] = self.slm[:, :, index - cmp] + else: # fill values with a linear interpolation + cmp += 1 + cmp_miss_mon += 1 + # y(t) = (y2 - y1)/(x2 - x1)*t + y1 + temp.time[index] = (self.time[index - cmp + 1] - self.time[index - cmp]) / ( + self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon + self.time[index - cmp] + + if interpolate == 1: + temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.clm[:, :, index - cmp] + temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.slm[:, :, index - cmp] + + elif interpolate == 0: + temp.clm[:, :, index] = 0 + temp.clm[:, :, index] = 0 + + index += 1 + + # -- assign ndim and shape attributes + temp.update_dimensions() + + if apply: + self.clm = temp.clm + self.slm = temp.slm + self.time = temp.time + self.month = temp.month + + self.update_dimensions() + + return temp + + def plot_correlation(self, l, m, save_path=False): + """ + Plot correlation between spherical harmonic coefficients of the object + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + + TODO: Refaire ça avec une matrice carrée sur tous les coeffs test: C20, C21, C22, S21, S22, C30, ... + ou C20, C21, S21, C22, S22, C30, ... + """ + mat_c = np.zeros((self.lmax, self.lmax)) + if m: + mat_s = np.zeros((self.lmax, self.lmax)) + for i in range(self.lmax): + for j in range(i+1): + mat_c[i, i - j] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.clm[i, j]-np.mean(self.clm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.clm[i, j]-np.mean(self.clm[i, j]))**2))) + + if j: + mat_c[i - j, i] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.slm[i, j]-np.mean(self.slm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.slm[i, j]-np.mean(self.slm[i, j]))**2))) + + if m: + mat_s[i, i - j] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.clm[i, j] - np.mean(self.clm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.clm[i, j] - np.mean(self.clm[i, j]))**2))) + + if j: + mat_s[i - j, i] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.slm[i, j] - np.mean(self.slm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.slm[i, j] - np.mean(self.slm[i, j]))**2))) + + plt.figure() + plt.matshow(mat_c) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $C_{' + str(l) + ',' + str(m)+ '}$') + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + if m: + plt.figure() + plt.matshow(mat_s) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $S_{' + str(l) + ',' + str(m) + '}$') + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + plt.show() + + + def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_path=False): + """ + Plot Cl,m and Sl,m harmonic coefficients + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + Options: + dates: list with limits of the xaxis in year + ylms: list of Harmonics objects to plot with the instance + label: list of label for each Harmonics objects with element 0 representing the current Harmonics object + save_path : if not False, give a path to save the figure + """ + #-- figure for Cl,m + plt.figure() + ax = plt.gca() + plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + if len(color): + plt.plot(self.time, self.clm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.clm[l, m, :], label=label[0]) + else: + plt.plot(self.time, self.clm[l, m, :], label="$C_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + if len(color): + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) + + if m: + #-- figure for Sl,m + plt.figure() + ax = plt.gca() + plt.title("Normalized spherical harmonic coefficient $S_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + if len(color): + plt.plot(self.time, self.slm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.slm[l, m, :], label=label[0]) + else: + plt.plot(self.time, self.slm[l, m, :], label="$S_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + if len(color): + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) + + plt.show() + + def plot_fft(self, l, m, save_path=False, fmax=6): + """ + Plot Cl,m and Sl,m harmonic coefficients fast fourrier transform + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + fmax : maximal frequency (default to 6 for period > 2 months) + """ + #-- compute fft and create x monthly frequency + N = len(self.time) + cf = sc.fft.fft(self.clm[l, m, :])[0:N // 2] + sf = sc.fft.fft(self.slm[l, m, :])[0:N // 2] + xf = np.linspace(0.0, 12/2, N // 2) + + # -- figure for Cl,m and Sl,m + plt.figure() + plt.title("Fourier transform of the normalized spherical harmonic coefficients $C_{" + str(l) + "," + str( + m) + "}$ et $S_{" + str( + l) + "," + str(m) + "}$") + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(cf[xf <= fmax]), label="$C_{" + str(l) + "," + str(m) + "}$") + if m: + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(sf[xf <= fmax]), label="$S_{" + str(l) + "," + str(m) + "}$") + + + plt.xlabel("Frequency ($year^{-1}$)") + plt.ylabel("Power") + plt.grid() + plt.legend() + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('CS' + str(l) + str(m) + '_fft.png')) + else: + plt.savefig(save_path) + + plt.show() + + def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): + """ + Plot Cl,m and Sl,m wavelet analysis based on (Torrence and Compo, 1998) + + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + s0 : minimal period of the wavelets, should be higher than 2*dt + pad : boolean for the zero padding of the series + lag1 : caracteristic of the noise: 0 for a white noise (default), 0.72 for a red noise + plot_coi : boolean to display the cone of interest in the figure + mother : name of the wavelet, can be MORLET, DOG or PAUL + param : param of the wavelet, -1 is the default value for each wavelet + func_plot : funtion for reducing the wave, can be np.abs, np.angle, np.real or np.imag + save_path : if not False, give a path to save the figure + """ + # len of the data + ndata = self.time.shape[0] + # compute the mean time delta of the object + dt = np.mean((self.time[1:] - self.time[:-1])) + + # resolution of the wavelet + dj = 0.005 + + if not s0: + s0 = 4 * dt # min scale of the wavelets + + # max resolution of the wavelet, fixed for GRACE + if j1 is None: + j1 = np.log2(11/s0)/dj + + siglvl = 0.95 + + # compute wavelets analysis of Cl,m and Sl,m + wavec = wv.wavelet(self.clm[l,m], dt, pad, dj, s0, j1, mother, param)[0] + waves, period, scale, coi = wv.wavelet(self.slm[l,m], dt, pad, dj, s0, j1, mother, param) + + # compute significativity of the wavelets + signifc = wv.wave_signif(self.clm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + signifs = wv.wave_signif(self.slm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + + # compute wavelet significance test at a level of confidence siglvl% + sig95c = np.abs(wavec**2) / [s * np.ones(ndata) for s in signifc] + sig95s = np.abs(waves**2) / [s * np.ones(ndata) for s in signifs] + + # Wavelet spectrum for fft plot + global_wsc = (np.sum(np.abs(wavec ** 2).conj().transpose(), axis=0) / ndata) + global_wss = (np.sum(np.abs(waves ** 2).conj().transpose(), axis=0) / ndata) + + # compute fft of the signal + fft_sigc = np.fft.fft(self.clm[l,m]) + sxxc = np.abs((fft_sigc * np.conj(fft_sigc)) / ndata)[int(np.ceil(ndata / 2)):] + fft_sigs = np.fft.fft(self.slm[l, m]) + sxxs = np.abs((fft_sigs * np.conj(fft_sigs)) / ndata)[int(np.ceil(ndata / 2)):] + + # compute frequency + f = -np.fft.fftfreq(ndata)[int(np.ceil(ndata / 2)):] + + # prepare yticks + yticks = [] + for i in [0.5, 1, 2, 4, 6, 10, 15]: + if np.min(period) <= i <= np.max(period): + yticks.append(i) + + # create figure Cl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, func_plot(wavec), 100) + axs[0].contour(self.time, period, sig95c, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([np.min(period)], coi, [np.min(period)], period[-1:], period[-1:], + [np.min(period)])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_yticks(yticks) + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxc, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wsc, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifc), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend(loc='upper right') + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) + + if m: + # create figure Sl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, np.abs(waves), 100) + axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_yticks(yticks) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend(loc='upper right') + + if save_path: + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) + + plt.show() diff --git a/gravity_toolkit/legendre.py b/gravity_toolkit/legendre.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/mascons.py b/gravity_toolkit/mascons.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/ocean_stokes.py b/gravity_toolkit/ocean_stokes.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_CSR_monthly_6x1.py b/gravity_toolkit/read_CSR_monthly_6x1.py new file mode 100755 index 00000000..9d3741e2 --- /dev/null +++ b/gravity_toolkit/read_CSR_monthly_6x1.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python +u""" +read_CSR_monthly_6x1.py +Written by Tyler Sutterley (07/2020) + +Reads in monthly 5x5 spherical harmonic coefficients with 1 + coefficient from degree 6 all calculated from SLR measurements + +Dataset distributed by UTCSR + ftp://ftp.csr.utexas.edu/outgoing/cheng/slrgeo.5d561_187_naod + +OPTIONS: + HEADER: file contains header text to be skipped (default: True) + +OUTPUTS: + clm: Cosine spherical harmonic coefficients + slm: Sine spherical harmonic coefficients + error/clm: Cosine spherical harmonic coefficient uncertainty + error/slm: Sine spherical harmonic coefficients uncertainty + MJD: output date as Modified Julian Day + time: output date in year-decimal + +REFERENCE: + Cheng, M., J. C. Ries, and B. D. Tapley, 'Variations of the Earth's Figure + Axis from Satellite Laser Ranging and GRACE', J. Geophys. Res., 116, B01409, + 2011, DOI:10.1029/2010JB000850. + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +PROGRAM DEPENDENCIES: + convert_calendar_decimal.py: converts from calendar dates to decimal years + +UPDATE HISTORY: + Updated 11/2020: following new format without geocenter coefficient + Updated 07/2020: added function docstrings + Updated 07/2019: following new format with mean field in header and no C6,0 + Updated 10/2018: using future division for python3 Compatibility + Updated 10/2017: include the 6,0 and 6,1 coefficients in output Ylms + Written 10/2017 +""" +from __future__ import print_function, division + +import os +import re +import numpy as np +from gravity_toolkit.time import convert_calendar_decimal + +#-- PURPOSE: read low degree harmonic data from Satellite Laser Ranging (SLR) +def read_CSR_monthly_6x1(input_file, HEADER=True): + """ + Reads in monthly low degree and order spherical harmonic coefficients + from Satellite Laser Ranging (SLR) measurements + + Arguments + --------- + input_file: input satellite laser ranging file from CSR + + Keyword arguments + ----------------- + HEADER: file contains header text to be skipped + + Returns + ------- + clm: Cosine spherical harmonic coefficients + slm: Sine spherical harmonic coefficients + error/clm: Cosine spherical harmonic coefficient uncertainty + error/slm: Sine spherical harmonic coefficients uncertainty + MJD: output date as Modified Julian Day + time: output date in year-decimal + """ + + #-- read the file and get contents + with open(os.path.expanduser(input_file),'r') as f: + file_contents = f.read().splitlines() + file_lines = len(file_contents) + + #-- spherical harmonic degree range (full 5x5 with 6,1) + LMIN = 2 + LMAX = 6 + n_harm = (LMAX**2 + LMAX - LMIN**2 - LMIN)//2 + 1 + + #-- counts the number of lines in the header + count = 0 + indice = 0 + #-- Reading over header text + while HEADER: + #-- file line at count + line = file_contents[count] + #-- find end within line to set HEADER flag to False when found + HEADER = not bool(re.match(r'end\sof\sheader',line)) + if bool(re.match(80*r'=',line)): + indice = count + 1 + #-- add 1 to counter + count += 1 + + #-- number of dates within the file + n_dates = (file_lines - count)//(n_harm + 1) + + #-- read mean fields from the header + mean_Ylms = {} + mean_Ylm_error = {} + mean_Ylms['clm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylms['slm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylm_error['clm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylm_error['slm'] = np.zeros((LMAX+1,LMAX+1)) + for i in range(n_harm): + #-- split the line into individual components + line = file_contents[indice+i].split() + #-- degree and order for the line + l1 = np.int(line[0]) + m1 = np.int(line[1]) + #-- fill mean field Ylms + mean_Ylms['clm'][l1,m1] = np.float(line[2].replace('D','E')) + mean_Ylms['slm'][l1,m1] = np.float(line[3].replace('D','E')) + mean_Ylm_error['clm'][l1,m1] = np.float(line[4].replace('D','E')) + mean_Ylm_error['slm'][l1,m1] = np.float(line[5].replace('D','E')) + + #-- output spherical harmonic fields + Ylms = {} + Ylms['error'] = {} + Ylms['MJD'] = np.zeros((n_dates)) + Ylms['time'] = np.zeros((n_dates)) + Ylms['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['error']['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['error']['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + #-- input spherical harmonic anomalies and errors + Ylm_anomalies = {} + Ylm_anomaly_error = {} + Ylm_anomalies['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomalies['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomaly_error['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomaly_error['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + #-- for each date + for d in range(n_dates): + #-- split the date line into individual components + line_contents = file_contents[count].split() + #-- modified Julian date of the middle of the month + Ylms['MJD'][d] = np.mean(np.array(line_contents[5:7],dtype=np.float)) + #-- date of the mid-point of the arc given in years + YY,MM = np.array(line_contents[3:5]) + Ylms['time'][d] = convert_calendar_decimal(YY,MM) + #-- add 1 to counter + count += 1 + + #-- read the anomaly field + for i in range(n_harm): + #-- split the line into individual components + line = file_contents[count].split() + #-- degree and order for the line + l1 = np.int(line[0]) + m1 = np.int(line[1]) + #-- fill anomaly field Ylms (variations and sigmas scaled by 1.0e10) + Ylm_anomalies['clm'][l1,m1,d] = np.float(line[2])*1e-10 + Ylm_anomalies['slm'][l1,m1,d] = np.float(line[3])*1e-10 + Ylm_anomaly_error['clm'][l1,m1,d] = np.float(line[6])*1e-10 + Ylm_anomaly_error['slm'][l1,m1,d] = np.float(line[7])*1e-10 + #-- add 1 to counter + count += 1 + + #-- calculate full coefficients and full errors + Ylms['clm'][:,:,d] = Ylm_anomalies['clm'][:,:,d] + mean_Ylms['clm'][:,:] + Ylms['slm'][:,:,d] = Ylm_anomalies['slm'][:,:,d] + mean_Ylms['slm'][:,:] + Ylms['error']['clm'][:,:,d]=np.sqrt(Ylm_anomaly_error['clm'][:,:,d]**2 + + mean_Ylm_error['clm'][:,:]**2) + Ylms['error']['slm'][:,:,d]=np.sqrt(Ylm_anomaly_error['slm'][:,:,d]**2 + + mean_Ylm_error['slm'][:,:]**2) + + #-- return spherical harmonic fields and date information + return Ylms diff --git a/gravity_toolkit/read_GRACE_harmonics.py b/gravity_toolkit/read_GRACE_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py new file mode 100755 index 00000000..ff8c8152 --- /dev/null +++ b/gravity_toolkit/read_SLR_CS2.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python +u""" +read_SLR_CS2.py +Written by Hugo Lecomte (11/2020) + +Reads monthly degree 2,x spherical harmonic data files from SLR + +Dataset distributed by CSR + http://download.csr.utexas.edu/pub/slr/degree_2/ + C21_S21_RL06.txt or C22_S22_RL06.txt + +REFERENCE: + Dahle, C., Murböck, M., Flechtner, F. , Dobslaw, H., Michalak, G., + Neumayer, K. H., Abrykosov, O., Reinhold, A., König, R., Sulzbach, R. + and Förste C., "The GFZ GRACE RL06 Monthly Gravity Field Time Series: + Processing Details,and Quality Assessment", Remote Sensing, 11(18), 2116, 2019. + https://doi.org/10.3390/rs11182116 + +CALLING SEQUENCE: + SLR_2m = read_SLR_CS2(SLR_file) + +INPUTS: + SLR_file: + CSR 2,1: C21_S21_RL06.txt + CSR 2,2: C22_S22_RL06.txt + +OUTPUTS: + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement (Apr. 2002 = 004) + time: date of SLR measurement + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 11/2020 +""" +import os +import re +import numpy as np + +#-- PURPOSE: read Degree 2,x data from Satellite Laser Ranging (SLR) +def read_SLR_CS2(SLR_file): + """ + Reads CS2,x spherical harmonic coefficients from SLR measurements + + Arguments + --------- + SLR_file: Satellite Laser Ranging file + + Returns + ------- + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement + time: date of SLR measurement + """ + + #-- check that SLR file exists + if not os.access(os.path.expanduser(SLR_file), os.F_OK): + raise IOError('SLR file not found in file system') + #-- output dictionary with input data + dinput = {} + + if bool(re.search('C2\d_S2\d_RL',SLR_file)): + + #-- SLR 2x RL06 file from CSR + #-- automatically skip the header denoted with '#' + content = np.genfromtxt(os.path.expanduser(SLR_file)) + + #-- number of months within the file + n_mon = content.shape[0] + date_conv = content[:,0] + #-- remove the monthly mean of the AOD model + C2x_input = content[:,1] - content[:,5]*10**-10 + eC2x_input = content[:,3]*10**-10 + # -- remove the monthly mean of the AOD model + S2x_input = content[:,2] - content[:,6]*10**-10 + eS2x_input = content[:,4]*10**-10 + mon = np.zeros((n_mon),dtype=np.int) + + #-- for every line convert the date into month number: + for t in range(content.shape[0]): + # -- GRACE/GRACE-FO month of SLR solutions + mon[t] = 1 + t + + #-- convert to output variables and truncate if necessary + dinput['time'] = date_conv + dinput['datac'] = C2x_input + dinput['errorc'] = eC2x_input + dinput['datas'] = S2x_input + dinput['errors'] = eS2x_input + dinput['month'] = mon + + else: + raise FileNotFoundError("Invalid file given to read_SLR_2x:", SLR_file) + + #-- return the input CS2x data, year-decimal date, and GRACE/GRACE-FO month + return dinput diff --git a/gravity_toolkit/read_SLR_harmonics.py b/gravity_toolkit/read_SLR_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_gfc_harmonics.py b/gravity_toolkit/read_gfc_harmonics.py old mode 100644 new mode 100755 index f6eda4d5..d328f32f --- a/gravity_toolkit/read_gfc_harmonics.py +++ b/gravity_toolkit/read_gfc_harmonics.py @@ -164,8 +164,8 @@ def read_gfc_harmonics(input_file, TIDE=None, FLAG='gfc'): itsg_pattern = (r'(AOD1B_RL\d+|model|ITSG)[-_]({0})(_n\d+)?_' r'(\d+)-(\d+)(\.gfc)').format(r'|'.join(itsg_products)) # regular expression operators for Swarm data and models - swarm_data = r'(SW)_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)(\.gfc|\.ZIP)' - swarm_model = r'(GAA|GAB|GAC|GAD)_Swarm_(\d+)_(\d{2})_(\d{4})(\.gfc|\.ZIP)' + swarm_data = r'(SW)_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)(\.gfc|\.ZIP|\.zip)' + swarm_model = r'(GAA|GAB|GAC|GAD)_Swarm_(\d+)_(\d{2})_(\d{4})(\.gfc|\.ZIP|\.zip)' # extract parameters for each data center and product if re.match(itsg_pattern, input_file.name): # compile numerical expression operator for parameters from files @@ -201,9 +201,8 @@ def read_gfc_harmonics(input_file, TIDE=None, FLAG='gfc'): end_date = [int(year),int(month),dpm[int(month)-1],23,59,59] # python dictionary with model input and headers - ZIP = bool(re.search('ZIP', SFX, re.IGNORECASE)) model_input = read_ICGEM_harmonics(input_file, TIDE=TIDE, - FLAG=FLAG, ZIP=ZIP) + FLAG=FLAG) # start and end day of the year start_day = np.sum(dpm[:start_date[1]-1]) + start_date[2] + \ diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py new file mode 100755 index 00000000..8ff07fb2 --- /dev/null +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -0,0 +1,221 @@ +#!/usr/bin/env python +u""" +read_grid_to_harmonics.py +Written by Hugo Lecomte (12/2020) + +Reads netCDF file with grid data and extracts spherical harmonic from those data +Correct data for drift in pole tide following Wahr et al. (2015) +Parses date of GRACE/GRACE-FO data from filename + +Design for JPL MASCON netCDF data available on +https://podaac-tools.jpl.nasa.gov/drive/files +In the folder /allData/tellus/L3/mascon/RL06/JPL/v02 + +INPUTS: + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + +OPTIONS: + MMAX: Maximum order of spherical harmonics (order of truncation) + default is the maximum spherical harmonic degree + POLE_TIDE: correct GSM data for pole tide drift following Wahr et al. (2015) + +OUTPUTS: + time: mid-month date in year-decimal + start: start date of range as Julian day + end: end date of range as Julian day + clm: cosine spherical harmonics of input data (LMAX,MMAX) + slm: sine spherical harmonics of input data (LMAX,MMAX) + eclm: cosine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + eslm: sine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 12/2020 +""" +import os +import re +import io +import numpy as np +from gravity_toolkit.ncdf_read import ncdf_read +from gravity_toolkit.hdf5_read import hdf5_read +from gravity_toolkit.utilities import get_data_path +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.gen_stokes import gen_stokes + +#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF or hdf5 files +def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', + LATNAME='lat', TIMENAME='time', UNITS=1, POLE_TIDE=False): + """ + Reads netCDF or HDF5 file with grid data and extracts spherical harmonic from those data + Correct data prior to Release 6 for pole tide drift + Parses date of GRACE/GRACE-FO data from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + VARNAME: z variable name in the file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + + Keyword arguments + ----------------- + MMAX: Maximum order of spherical harmonics + LONNAME: longitude variable name in the file + LATNAME: latitude variable name in the file + TIMENAME: time variable name in the file + UNITS: input data units + 1: cm of water thickness + 2: Gtons of mass + 3: kg/m^2 + POLE_TIDE: correct for pole tide drift following Wahr et al. (2015) + + Returns + ------- + clm: GRACE/GRACE-FO cosine spherical harmonics + slm: GRACE/GRACE-FO sine spherical harmonics + time: time of each GRACE/GRACE-FO measurement (mid-month) + month: GRACE/GRACE-FO months of input datasets + l: spherical harmonic degree to LMAX + m: spherical harmonic order to MMAX + title: string denoting low degree zonals replacement, geocenter usage and corrections + directory: directory of exact GRACE/GRACE-FO product + """ + # -- parse filename to extract begin date of the file + pfx, center, time, realm, release, v_id, sfx = parse_file(input_file) + + #-- read file content + if input_file[-3:] == '.nc': + file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME) + elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': + file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME) + + #-- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + #-- set maximum spherical harmonic order + MMAX = np.copy(LMAX) if (MMAX is None) else MMAX + + #-- number of dates in data + n_time = file_contents['time'].shape[0] + #-- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + grace_slm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + #-- Time matrix to fill + tdec = np.zeros(n_time) + month = np.zeros(n_time) + #-- output dimensions + lout = np.arange(LMAX + 1) + mout = np.arange(MMAX + 1) + + #-- for each date, conversion to spherical harmonics + for i in range(n_time): + harmo = gen_stokes(file_contents['data'][i, :, :], + file_contents['lon'][:], file_contents['lat'][:], + LMAX=LMAX, MMAX=MMAX, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm + + #-- extract GRACE date information from input file name + start_yr = np.float(time[:4]) + + #-- variables initialization for date conversion + current_year = start_yr + current_month = 1 + cmp_past_dpm = 0 + cmp_past_dpy = 0 + if (start_yr % 4) == 0:#-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else:#-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + #-- for each date, conversion to month and decimal year + for i in range(n_time): + #-- Month iteration + while file_contents['time'][i] - cmp_past_dpm > dpm[(current_month - 1)%12]: + current_month += 1 + cmp_past_dpm += dpm[(current_month - 1)%12] + + #-- Year iteration + while file_contents['time'][i] - cmp_past_dpy > dpy: + current_year += 1 + cmp_past_dpy += dpy + if (current_year % 4) == 0: #-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: #-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + tdec[i] = current_year + (file_contents['time'][i] - cmp_past_dpy)/dpy + month[i] = current_month + + #-- The 'Special Months' (Nov 2011, Dec 2011 and April 2012) with + #-- Accelerometer shutoffs make this relation between month number + #-- and date more complicated as days from other months are used + #-- May15 (month 161) is centered in Apr15 (160) + if (month[i] == 160) and (month[i] == month[i - 1]): + month[i] = month[i - 1] + 1 + + #-- extract GRACE and GRACE-FO file informations + title = file_contents['attributes'] + + #-- Correct Pole Tide following Wahr et al. (2015) 10.1002/2015JB011986 + if POLE_TIDE: + for i in range(n_time): + #-- time since 2000.0 + dt = tdec[i] - 2000.0 + + #-- JPL Pole Tide Correction + #-- values for IERS mean pole [2010] + if tdec[i] < 2010.0: + a = np.array([0.055974,1.8243e-3,1.8413e-4,7.024e-6]) + b = np.array([-0.346346,-1.7896e-3,1.0729e-4,0.908e-6]) + elif tdec[i] >= 2010.0: + a = np.array([0.023513,7.6141e-3,0.0,0.0]) + b = np.array([-0.358891,0.6287e-3,0.0,0.0]) + #-- calculate m1 and m2 values + m1 = np.copy(a[0]) + m2 = np.copy(b[0]) + for x in range(1,4): + m1 += a[x]*dt**x + m2 += b[x]*dt**x + #-- pole tide values for JPL + #-- JPL remove the IERS mean pole from m1 and m2 + #-- before computing their harmonic solutions + C21_PT = -1.551e-9*(m1 - 0.62e-3*dt) - 0.012e-9*(m2 + 3.48e-3*dt) + S21_PT = 0.021e-9*(m1 - 0.62e-3*dt) - 1.505e-9*(m2 + 3.48e-3*dt) + #-- correct GRACE spherical harmonics for pole tide + #-- note: -= means grace_xlm = grace_xlm - PT + grace_clm[2, 1, i] -= C21_PT + grace_clm[2, 1, i] -= S21_PT + + #-- return the GRACE data, GRACE date (mid-month in decimal), and the + #-- start and end days as Julian dates + return {'clm': grace_clm, 'slm': grace_slm, 'time': tdec, 'month': month, + 'l': lout, 'm': mout, 'title': title, 'directory': os.path.split(input_file)[0]} + +#-- PURPOSE: extract parameters from filename +def parse_file(input_file): + """ + Extract parameters from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-2 spherical harmonic data file + """ + #-- compile numerical expression operator for parameters from files + #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + regex_pattern = r'(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(\w{2,})' + rx = re.compile(regex_pattern, re.VERBOSE) + #-- extract parameters from input filename + if isinstance(input_file, io.IOBase): + return rx.findall(input_file.filename).pop() + else: + return rx.findall(os.path.basename(input_file)).pop() diff --git a/gravity_toolkit/sea_level_equation.py b/gravity_toolkit/sea_level_equation.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py old mode 100644 new mode 100755 index 1c90df3c..1e3d1918 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -17,6 +17,10 @@ https://www.h5py.org/ PROGRAM DEPENDENCIES: + ncdf_write.py: writes output spatial data to COARDS-compliant netCDF4 + hdf5_write.py: writes output spatial data to HDF5 + ncdf_read.py: reads spatial data from COARDS-compliant netCDF4 + hdf5_read.py: reads spatial data from HDF5 time.py: utilities for calculating time operations UPDATE HISTORY: @@ -95,6 +99,11 @@ warnings.warn("netCDF4 not available", ImportWarning) # ignore warnings warnings.filterwarnings("ignore") +import scipy as sc +import matplotlib.pyplot as plt +import matplotlib +import cartopy.crs as ccrs + class spatial(object): """ @@ -1485,7 +1494,7 @@ def mean(self, apply=False, indices=Ellipsis): indices of input ``spatial`` object to compute mean """ # output spatial object - temp = spatial(nlon=self.shape[0],nlat=self.shape[1], + temp = spatial(nlon=self.data.shape[0],nlat=self.data.shape[1], fill_value=self.fill_value) # copy dimensions temp.lon = self.lon.copy() @@ -1743,6 +1752,134 @@ def __next__(self): self.__index__ += 1 return temp + def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe', mask=None, normalize=False, weight=False): + import gravity_toolkit.toolbox as tb + mat_svd = np.copy(self.data) + if mask is None: + mat_svd = np.reshape(mat_svd, (self.lat.shape[0] * self.lon.shape[0], self.time.shape[0])) + lat = self.lat.repeat(self.lon.shape[0]) + else: + mat_svd = np.reshape(mat_svd[mask], (np.sum(mask), self.time.shape[0])) + lat = self.lat.repeat(np.sum(mask, axis=0)) + + mat_svd_original = np.copy(mat_svd) + if normalize: + mat_svd = (mat_svd - np.mean(mat_svd, axis = 1).repeat(self.time.shape[0]).reshape(mat_svd.shape)) / np.std(mat_svd, axis=1).repeat(self.time.shape[0]).reshape(mat_svd.shape) + if weight: + mat_svd = mat_svd*np.cos(np.radians(lat).repeat(self.time.shape[0]).reshape(mat_svd.shape)) + + + c_svd = mat_svd.T@mat_svd/(mat_svd.shape[0] - 1) + w, v = sc.linalg.eigh(c_svd) + + v = v[:, ::-1] + w = w[::-1] + s = np.sqrt(w*(mat_svd.shape[0] - 1)) + us = mat_svd_original@v + + eof_grid = spatial() + eof_grid.lat, eof_grid.lon = self.lat, self.lon + eof_grid.time = np.array([0]) + + if not pathlib.Path(path_folder).exists(): + pathlib.Path(path_folder).mkdir(exist_ok=True) + + if mode == 'ts': + plt.figure() + plt.xlabel('Time (year)') + + for k in number: + power = s[k]**2/np.nansum(s**2) + eof = us[:, k]/np.sqrt(mat_svd.shape[1] - 1) + sort_eof = np.sort(eof) + scale_eof = 2*eof/(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + if mask is None: + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0])) + else: + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0])) + eof_grid.data[mask] = scale_eof + eof_grid.data[np.logical_not(mask)] = None + + if mode == 'map': + tb.plot_rms_map(eof_grid, path=pathlib.Path(path_folder) / 'map_eof_'+str(k)+'.png', unit=unit, mask=mask) + + elif mode == 'full': + npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() + f = np.fft.fft(pc, npow2) + xf = np.fft.fftfreq(npow2, d=np.mean(self.time[1:] - self.time[:-1])) + + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) + axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) + + cmap = matplotlib.colormaps.get_cmap(cmap) + immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, + origin='upper', vmin=-1.15, vmax=1.15) + axmap.coastlines('50m') + # stronger linewidth on frame + axmap.spines['geo'].set_linewidth(2.0) + axmap.spines['geo'].set_capstyle('projecting') + + cbar = plt.colorbar(immap, ax=axmap, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=24, labelsize=18, + direction='in') + + power_str = '\nPower: '+str("%1.2f"%power) + cbar.ax.set_xlabel(power_str, labelpad=10, fontsize=18) + + axplot = fig.add_subplot(spec[1:5, 1:], box_aspect=0.5) + axplot.plot(self.time, pc) + axplot.yaxis.tick_right() + axplot.yaxis.set_label_position("right") + axplot.set_xlabel('Time (year)') + + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1 / xf[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10], + 2.0 / len(self.time) * np.abs(f[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0, ) + axfft.set_xlabel('Period (year)') + axfft.yaxis.set_label_position("right") + + if unit == "cmwe": + axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "cmwe_ne": + axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "mmwe": + axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "geoid": + axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "microGal": + axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$\mu Gal^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "secacc": + axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') + + plt.savefig(pathlib.Path(path_folder) / ('eof_pc_'+str(k)+'.png'), bbox_inches='tight') + plt.close() + + elif mode == 'ts': + plt.plot(self.time, pc, label=str(k)) + + if mode == 'ts': + + plt.savefig(pathlib.Path(path_folder) / ('pc_'+'-'.join([str(i) for i in number])+'.png')) + plt.legend() + + # PURPOSE: additional routines for the spatial module # for outputting scaling factor data class scaling_factors(spatial): @@ -2003,4 +2140,4 @@ def update_mask(self): # replace fill values within scaling factor magnitudes if getattr(self, 'magnitude') is not None: self.magnitude[self.mask] = self.fill_value - return self + return self \ No newline at end of file diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py old mode 100644 new mode 100755 index 137eddeb..ea0a8d0d --- a/gravity_toolkit/time.py +++ b/gravity_toolkit/time.py @@ -1155,3 +1155,25 @@ def update_leap_seconds(timeout=20, verbose=False, mode=0o775): pass else: return + +def dpm_count(input_year): + """ + Return the number of days per months on the current year + + Arguments + --------- + input_year: year of interest + + Returns + ------- + dpm: list of the day per month + """ + # -- Calculation of total days since start of campaign + if (input_year % 4) == 0: + # -- Leap Year + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: + # -- Standard Year + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + return dpm diff --git a/gravity_toolkit/time_series/__init__.py b/gravity_toolkit/time_series/__init__.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time_series/fit.py b/gravity_toolkit/time_series/fit.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time_series/savitzky_golay.py b/gravity_toolkit/time_series/savitzky_golay.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py new file mode 100755 index 00000000..7e23b430 --- /dev/null +++ b/gravity_toolkit/toolbox.py @@ -0,0 +1,674 @@ +import os.path + +from gravity_toolkit.gauss_weights import gauss_weights +from gravity_toolkit.gen_stokes import gen_stokes +from gravity_toolkit.harmonics import harmonics +from gravity_toolkit.harmonic_summation import harmonic_summation +from gravity_toolkit.associated_legendre import plm_holmes +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.spatial import spatial +from gravity_toolkit.units import units +from gravity_toolkit.utilities import get_data_path + +import numpy as np +import scipy.signal as sg +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.colors as colors +import matplotlib.animation as animation +import cartopy.crs as ccrs +from IPython.display import HTML + + +def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, dlat=0.5, bounds=None): + """ + Function to convert a harmonic object to grid format + + Parameters + ---------- + Ylms : harmonics object to convert to grid format + lmax : maximum degree of spherical harmonics used + rad : radius of the gaussian filter. If set to 0, no gaussian filter is apply + destripe : boolean to apply or not the destripe method of harmonics + unit : unit of the grid in ['cmwe', 'cmweEl', 'geoid', 'cmwe_ne', 'microGal'] + dlon : output longitude spacing + dlat : output latitude spacing + bounds : list with [lon_max, lon_min, lat_max, lat_min] + + Returns + ------- + grid : spatial object with the grid converted from the original harmonics object + """ + # Output spatial data + grid = spatial() + grid.time = np.copy(Ylms.time) + grid.month = np.copy(Ylms.month) + + # Output Degree Interval + if bounds is None: + grid.lon = np.arange(-180 + dlon / 2.0, 180 + dlon / 2.0, dlon) + grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat) + else: + grid.lon = np.arange(-bounds[1] + dlon / 2.0, bounds[0] + dlon / 2.0, dlon) + grid.lat = np.arange(bounds[2] - dlat / 2.0, -bounds[3] - dlat / 2.0, -dlat) + + if lmax is None: + lmax = Ylms.lmax + else: + Ylms.lmax = lmax + + nlon = len(grid.lon) + nlat = len(grid.lat) + + # Computing plms for converting to spatial domain + theta = (90.0 - grid.lat) * np.pi / 180.0 + PLM, dPLM = plm_holmes(lmax, np.cos(theta)) + + # read load love numbers file + love_numbers_file = get_data_path(['data', 'love_numbers']) + # LMAX of load love numbers from Han and Wahr (1995) is 696. + # from Wahr (2007) linearly interpolating kl worksand ll Love Numbers + hl, kl, ll = read_love_numbers(love_numbers_file, REFERENCE='CF') + + if unit == 'cmwe': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe + elif unit == 'cmweEl': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmweEl + elif unit == 'geoid': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).mmGH + elif unit == 'cmwe_ne': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe_ne + elif unit == 'microGal': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).microGal + elif unit == 'none': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).norm + else: + raise ValueError("Unit not accepted, should be either 'cmwe' pr 'cmweEl' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # converting harmonics to truncated, smoothed coefficients in units + # combining harmonics to calculate output spatial fields + # output spatial grid + if type(Ylms.time) in [list, np.array, np.ndarray] and len(Ylms.time) != 1: + grid.data = np.zeros((nlat, nlon, len(Ylms.month))) + for i, grace_month in enumerate(Ylms.month): + # GRACE/GRACE-FO harmonics for time t + # convert to output units + if destripe: + tmp = Ylms.index(i).destripe() + else: + tmp = Ylms.index(i) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((lmax + 1))) + # convert spherical harmonics to output spatial grid + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + else: + grid.data = np.zeros((nlat, nlon)) + + if destripe: + tmp = Ylms.copy().destripe() + else: + tmp = Ylms.copy() + if len(tmp.clm.shape) == 3: + tmp.clm = tmp.clm.reshape(tmp.clm.shape[:-1]) + tmp.slm = tmp.slm.reshape(tmp.slm.shape[:-1]) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((lmax + 1))) + # convert spherical harmonics to output spatial grid + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + + grid.mask = np.zeros(grid.data.shape) + return grid + + +def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): + """ + Function to convert spatial object (grid) to harmonics object (spherical harmonics) + + Parameters + ---------- + grid : spatial object to convert to harmonics + lmax : maximal degree of the harmonics object to create + mmax : maximal order of the harmonics object to create + unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + + Returns + ------- + harmonics : harmonics object + """ + # -- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + # -- set maximum spherical harmonic order + mmax = np.copy(lmax) if (mmax is None) else mmax + + # -- number of dates in data + if type(grid.time) in [list, np.array, np.ndarray] and len(grid.time) != 1: + n_time = len(grid.time) + else: + n_time = 1 + # -- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((lmax + 1, mmax + 1, n_time)) + grace_slm = np.zeros((lmax + 1, mmax + 1, n_time)) + # -- output dimensions + lout = np.arange(lmax + 1) + mout = np.arange(mmax + 1) + + # -- Test to attribute UNITS number + if unit == 'cmwe': + UNITS = 1 + elif unit == 'geoid': + UNITS = 4 + elif unit == 'cmwe_ne': + UNITS = 6 + elif unit == 'microGal': + UNITS = 5 + elif unit == 'norm': + UNITS = 7 + else: + raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # -- for each date, conversion to spherical harmonics + if n_time != 1: + for i in range(n_time): + harmo = gen_stokes(grid.data[:, :, i], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm + + else: + print('mono grid_to_hs') + harmo = gen_stokes(grid.data[:, :], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, 0] = harmo.clm + grace_slm[:, :, 0] = harmo.slm + + # -- return the GRACE data, GRACE date (mid-month in decimal), and the + # -- start and end days as Julian dates + result_dict = {'clm': grace_clm, 'slm': grace_slm, 'time': grid.time, 'month': grid.month, + 'l': lout, 'm': mout, 'title': '', 'directory': ''} + + return harmonics().from_dict(result_dict) + + +def diff_grid(grid1, grid2): + """ + Create a grid resulting from the difference between the two given grids + + Parameters + ---------- + grid1 : spatial object + grid2 : spatial object to subtract to the first + + Returns + ------- + grid : spatial object with the difference between both grid + """ + exclude1 = set(grid1.month) - set(grid2.month) + + # Output spatial data + grid = spatial() + grid.month = np.array(list(sorted(set(grid1.month) - exclude1))) + grid.time = np.array([grid1.time[i] for i in range(len(grid1.time)) if not (grid1.month[i] in exclude1)]) + + # Output Degree Interval + grid.lon = grid1.lon + grid.lat = grid1.lat + + grid.data = np.zeros((grid.lat.shape[0], grid.lon.shape[0], len(grid.month))) + cmp = 0 + for i in range(len(grid1.month)): + for j in range(len(grid2.month)): + if grid1.month[i] == grid2.month[j]: + grid.data[:, :, cmp] = grid1.data[:, :, i] - grid2.data[:, :, j] + cmp += 1 + + return grid + + +def filt_Ylms(ylms, filt='low', filt_param=None): + """ + Apply a temporal filter on harmonics object + + Parameters + ---------- + ylms : harmonics object to filter + filt : choice of the filter in ['low', 'band', 'fft'] + filt_param : cut frequency of the filter. For band filter, a list with (f_max, f_min) + + Returns + ------- + filtered_ylms : temporally filtered harmonics object + + """ + filtered_ylms = ylms.copy() + + # len of the data + ndata = filtered_ylms.time.shape[0] + # compute the mean time delta of the object + dt = float(np.mean((filtered_ylms.time[1:] - filtered_ylms.time[:-1]))) + + if filt_param is not None and type(filt_param) != list: + filt_param = [filt_param] + + if filt == 'low': + if filt_param is None: + b, a = sg.butter(10, 0.5, analog=False, fs=1 / dt) + else: + b, a = sg.butter(10, filt_param[0], analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + + elif filt == 'band': + if filt_param is None: + b, a = sg.butter(6, 0.3, analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, 0.04, btype='highpass', analog=False, fs=1 / dt) + else: + b, a = sg.butter(6, filt_param[0], analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, filt_param[1], btype='highpass', analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.clm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.slm[i, j]) + + elif filt == 'fft': + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + fc = np.fft.fft(filtered_ylms.clm, n=2 ** n2, axis=2) + fs = np.fft.fft(filtered_ylms.slm, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + if filt_param is None: + to_zero = np.logical_or(freq > 0.5, freq < -0.5) + else: + to_zero = np.logical_or(freq > filt_param[0], freq < -filt_param[0]) + fc[:, :, to_zero] = 0 + fs[:, :, to_zero] = 0 + filtered_ylms.clm = np.real(np.fft.ifft(fc, axis=2))[:, :, :ndata] + filtered_ylms.slm = np.real(np.fft.ifft(fs, axis=2))[:, :, :ndata] + + return filtered_ylms + + +def filt_grid(grid, f_cut=0.5): + """ + Temporally filter a grid with a truncation in fft at 2 years + + Parameters + ---------- + grid : spatial object to filter + f_cut : cutting frequency + + Returns + ------- + filtered_grid : spatial object filtered + + """ + filtered_grid = grid.copy() + time = grid.time + ndata = grid.time.shape[0] + + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + # compute the mean time delta of the object + dt = float(np.mean((time[1:] - time[:-1]))) + + f = np.fft.fft(grid.data, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + + to_zero = np.logical_or(freq > f_cut, freq < -f_cut) + f[:, :, to_zero] = 0 + filtered_grid.data = np.real(np.fft.ifft(f, axis=2))[:, :, :ndata] + + return filtered_grid + + +def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a gif of the spatial object + + Parameters + ---------- + grid : spatial object to convert to gif + path : path of the future gif (mandatory to end in .gif) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r) + """ + matplotlib.rcParams['animation.embed_limit'] = 2**128 + + if mask is None: + data_to_set = grid.data + else: + data_to_set = grid.data*mask + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375,6.625), + subplot_kw=dict(projection=ccrs.PlateCarree())) + + # levels and normalization for plot range + print(np.min(data_to_set), np.max(data_to_set)) + if bound is None: + vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + else: + vmin, vmax = bound + + norm = colors.Normalize(vmin=vmin,vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(np.zeros((np.int(180.0 + 1.0),np.int(360.0 + 1.0))), interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper', animated=True) + ax1.coastlines('50m') + + # add date label + time_text = ax1.text(0.025, 0.025, '', transform=fig.transFigure, + color='k', size=24, ha='left', va='baseline') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + + cbar.ax.yaxis.set_label_coords(1.045, 0.1) + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=20, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98) + + # animate frames + def animate_frames(i): + # set image + im.set_data(data_to_set[:,:,i]) + # add date label + time_text.set_text('{:.2f}'.format(grid.time[i])) + + # set animation + anim = animation.FuncAnimation(fig, animate_frames, frames=len(grid.month)) + #HTML(anim.to_jshtml()) + + anim.save(path, writer='imagemagick', fps=10) + plt.clf() + + +def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a rms map of the spatial object + + Parameters + ---------- + grid : spatial object to convert into a rms map + path : path to save the figure if needed + proj : projection of the map (Recommended: ccrs.PlateCarree(), ccrs.Mollweide()) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r, OrRd, Blues) + """ + data_to_set = np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + + if mask is not None: + data_to_set *= mask + + plt.figure() + matplotlib.rcParams['animation.embed_limit'] = 2 ** 128 + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375, 6.625), + subplot_kw=dict(projection=proj)) + + if bound is None: + vmin, vmax = np.floor(np.min(data_to_set)), np.ceil(np.max(data_to_set)) + else: + vmin, vmax = bound + + norm = colors.Normalize(vmin=vmin, vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(data_to_set, interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper') + ax1.coastlines('50m') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe" or unit == "cmweEl": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0, labelpad=10) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0, labelpad=10) + + cbar.ax.yaxis.set_label_coords(1.1, -0.4) + # Set the tick levels for the colorbar + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.98) + + if path and os.path.isdir(os.path.dirname(str(path))): + plt.savefig(path, bbox_inches='tight') + plt.close() + else: + plt.show() + + return np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + + +def calc_rms_grid(grid, mask=None): + """ + Compute Root Mean Square (RMS) value of a spatial object + + Parameters + ---------- + grid : spatial object + mask : mask to applied before rms computation + + Returns + ------- + rms : rms of the grid + + """ + if mask is None: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in zip(grid.lat, grid.data)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in zip(grid.lat, grid.data)])) + + else: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * np.swapaxes(np.tile(line_mask, (line.shape[1], 1)), 0, 1)) ** 2) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * np.tile(line_mask, (line.shape[1], 1))) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)])) + # attention au cut dans les deux listes + return rms + + +def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): + """ + Create a figure with rms of the grid spatial object in function of time + + Parameters + ---------- + grid : spatial object or list of spatial object + path : path to save the figure if needed + mask : mask to apply on data if needed + unit : unit of the grid + """ + if type(grid) != list: + grid = [grid] + + plot_rms = [] + for g in grid: + l_rms = [] + for i in range(len(g.time)): + if mask is None: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in + zip(g.lat, g.data[:, :, i])]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in + zip(g.lat, g.data[:, :, i])])) + else: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) + for lat, line, line_mask in zip(g.lat, g.data[:, :, i], mask)]) + / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) + for lat, line_mask in zip(g.lat, mask)])) + + l_rms.append(rms) + plot_rms.append(l_rms) + + plt.figure() + if not(type(labels) == list): + for g, rms in zip(grid, plot_rms): + plt.plot(g.time, rms) + else: + for g, rms, l in zip(grid, plot_rms, labels): + plt.plot(g.time, rms, label=l) + + plt.xlabel('Time (y)') + if unit == "cmwe" or unit == "cmweEl": + plt.ylabel('cm EWH') + elif unit == "cmwe_ne": + plt.ylabel('Non elastic cm EWH') + elif unit == "mmwe": + plt.ylabel('mm EWH') + elif unit == "geoid": + plt.ylabel('mm Geoid Height') + elif unit == "microGal": + plt.ylabel('$\mu Gal$') + elif unit == "secacc": + plt.ylabel('$nT.y^{-2}$') + plt.ylabel('Power (cm EWH)') + + if type(labels) == list: + plt.legend() + + if path: + plt.savefig(path, bbox_inches='tight') + else: + plt.show() + plt.close() + + +def hs_to_grid_amp(amplitude, l, m, unit='cmwe', map=False): + """ + Create a grid corresponding to a particular spherical harmonic coefficient in a unit. + Return the amplitude of the grid create by this coefficient in the given unit and the map signal + + Parameters + ---------- + amplitude : amplitude of the Spherical harmonic coefficient + l : degree + m : order + unit : unit of the grid + map : Default to False, True to print a map of the coefficent and give a path to save the map + + Returns + ------- + max, min : bound value of the grid create with the given amplitude in the asked unit + """ + ylms = harmonics(lmax=np.max(l), mmax=np.max(l)) + ylms.time = np.array([0]) + ylms.month = np.array([0]) + + ylms.clm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + ylms.slm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + try: + for amp, i, j in zip(amplitude, l, m): + if j >= 0: + ylms.clm[i, np.abs(j)] = amp + else: + ylms.slm[i, np.abs(j)] = amp + except TypeError: + if m >= 0: + ylms.clm[l, np.abs(m)] = amplitude + else: + ylms.slm[l, np.abs(m)] = amplitude + + grid = create_grid(ylms, l, unit=unit) + + if map: + grid.data = grid.data[:, :, np.newaxis] + plot_rms_map(grid, path=map, unit=unit) + + return np.max(grid.data), np.min(grid.data) \ No newline at end of file diff --git a/gravity_toolkit/tools.py b/gravity_toolkit/tools.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py old mode 100644 new mode 100755 index 3fd42bff..bf068e1f --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -87,6 +87,7 @@ def __init__(self, self.microGal = None self.mbar = None self.Pa = None + self.cmweEl = None self.lmax = lmax # calculate spherical harmonic degree (0 is falsy) self.l = np.arange(self.lmax+1) if (self.lmax is not None) else None @@ -169,6 +170,8 @@ def harmonic(self, hl, kl, ll, **kwargs): self.cmwe = self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3.0 # mmwe, millimeters water equivalent [kg/m^2] self.mmwe = 10.0*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3.0 + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = self.rho_e * self.rad_e * (2.0 * self.l + 1.0) / 3.0 # mmGH, millimeters geoid height self.mmGH = np.ones((self.lmax+1))*(10.0*self.rad_e) # mmCU, millimeters elastic crustal deformation (uplift) @@ -185,6 +188,8 @@ def harmonic(self, hl, kl, ll, **kwargs): self.mbar = self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3e3 # Pa, pascals equivalent surface pressure self.Pa = self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/30.0 + # cmwe, centimeters water equivalent [g/cm^2] considering Earth oblateness + self.cmweEl = self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3.0 *(1 - self.flat) # return the degree dependent unit conversions return self @@ -220,21 +225,27 @@ def spatial(self, hl, kl, ll, **kwargs): """ # set default keyword arguments kwargs.setdefault('include_elastic', True) + kwargs.setdefault('include_ellipsoidal', False) fraction = np.ones((self.lmax+1)) # compensate for elastic deformation within the solid earth if kwargs['include_elastic']: fraction += kl[self.l] + if kwargs['include_ellipsoidal']: + fraction /= (1.0 - self.flat) # degree dependent coefficients # norm, fully normalized spherical harmonics - self.norm = np.ones((self.lmax+1)) + self.norm = np.ones((self.lmax + 1))/(4.0 * np.pi) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0*fraction/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = 3.0 / (1.0 + 2.0*self.l) / (4.0*np.pi*self.rad_e*self.rho_e) # mmwe, millimeters water equivalent [kg/m^2] self.mmwe = 3.0*fraction/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) # mmGH, millimeters geoid height - self.mmGH = np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) + self.mmGH = np.ones((self.lmax+1))/(4.0*np.pi*10*self.rad_e) # microGal, microGal gravity perturbations self.microGal = (self.rad_e**2.0)/(4.0*np.pi*1.e6*self.GM)/(self.l+1.0) + # return the degree dependent unit conversions return self diff --git a/gravity_toolkit/utilities.py b/gravity_toolkit/utilities.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/version.py b/gravity_toolkit/version.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py new file mode 100755 index 00000000..6fb13e0d --- /dev/null +++ b/gravity_toolkit/wavelets.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python +u""" +wavelets.py +Written by Hugo Lecomte (02/2021) + +Function to apply a wavelets analysis, code based on (Torrence and Compo, 1998) +""" +import numpy as np +import scipy.special + +def wave_bases(mother, k, scale, param=-1): + """Computes the wavelet function as a function of Fourier frequency + used for the CWT in Fourier space (Torrence and Compo, 1998) + + Arguments + --------- + mother: str equal to 'MORLET' or 'DOG' to choose the wavelet type + k: vector of the Fourier frequencies + scale: wavelet scales + param: nondimensional parameter for the wavelet function + + Returns + ------- + daughter: the wavelet function + fourier_factor: the ratio of Fourier period to scale + coi: cone-of-influence size at the scale + dofmin: degrees of freedom for each point in the wavelet power (Morlet = 2) + """ + mother = mother.upper() + n = len(k) # length of Fourier frequencies + k = np.array(k) # turn k to array + + if mother == 'MORLET': # choose the wavelet function, in this case Morlet + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + + expnt = -(scale*k - param)**2/2*(k > 0) # table 1 Torrence and Compo (1998) + norm = np.sqrt(scale*k[1])*(np.pi** -0.25)*np.sqrt(len(k)) + + daughter = [] # define daughter as a list + for ex in expnt: # for each value scale (equal to next pow of 2) + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) # transform in array + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = (4*np.pi)/(param + np.sqrt(2 + param * param)) # scale --> Fourier period + coi = fourier_factor/np.sqrt(2) # cone-of-influence + dofmin = 2 # degrees of freedom + + elif mother == 'DOG': # DOG Wavelet + if param == -1: + param = 2 # For DOG this is m (wavenumber), default is 2 + m = param + + expnt = -(scale*k)**2/2 + pws = np.array((scale*k)**m) + # gamma(m+0.5) = 1.3293 + norm = np.sqrt(scale*k[1]/1.3293*np.sqrt(n)) + + daughter = [] + for ex in expnt: + daughter.append(-norm* 1j**m * np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + fourier_factor = 2*np.pi/np.sqrt(m + .5) + coi = fourier_factor/np.sqrt(2) + dofmin = 1 + + elif mother == 'PAUL': # Paul Wavelet + if param == -1: + param = 4 + m = param + + expnt = -(scale*k)*(k > 0) + norm = np.sqrt(scale*k[1]) *(2**m /np.sqrt(m*(np.math.factorial(2*m - 1))))*np.sqrt(n) + pws = np.array((scale*k)**m) + + daughter = [] + for ex in expnt: + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = 4*np.pi/(2*m + 1) + coi = fourier_factor*np.sqrt(2) + dofmin = 2 + + return daughter, fourier_factor, coi, dofmin + + +def wavelet(Y, dt, pad=1, dj=.25, s0=-1, J1=-1, mother='MORLET', param=-1): + """Computes the wavelet continuous transform of the vector Y, + by definition: + W(a,b) = sum(f(t)*psi[a,b](t) dt) a dilate/contract + psi[a,b](t) = 1/sqrt(a) psi(t-b/a) b displace + The wavelet basis is normalized to have total energy = 1 at all scales + + Arguments + --------- + Y: time series + dt: sampling rate + pad: bool for zero padding or not + dj: spacing between discrete scales + s0: smallest scale of the wavelet + J1: total number of scales + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + n1 = len(Y) # time series length + + if s0 == -1: # define s0 as 2 times dt (Shannon criteria) if s0 is not given + s0 = 2 * dt + if J1 == -1: # define J1 if not provide + J1 = int((np.log(n1*dt/s0) / np.log(2))/dj) + + x = Y - np.mean(Y) # remove mean of the time serie + + if pad: # if zero padding, add zeros to x + base2 = int(np.log(n1)/np.log(2) + 0.4999) + x = np.concatenate((x, np.zeros(2**(base2 + 1) - n1))) + + n = len(x) #update length of x + + k = np.arange(0, int(n/2)) + k = k*(2*np.pi) / (n*dt) + k = np.concatenate((k, -k[int((n - 1)/2)::-1])) # be careful for parity + + f = np.fft.fft(x) # fft on the padded time series + + scale = s0 * 2**(np.arange(0, J1, 1)*dj) + # define wavelet array + wave = np.zeros((int(J1 + 1), n)) + wave = wave + 1j * wave # make it complex + + for a1 in range(0, int(J1 + 1)): + daughter, fourier_factor, coi, dofmin = wave_bases(mother, k, scale[a1], param) + wave[a1, :] = np.fft.ifft(f * daughter) + + period = fourier_factor * scale + + # cone-of-influence, differ for uneven len of timeseries: + if n1%2: # uneven + coi = coi * dt * np.concatenate((np.arange(0, n1/2 - 1), np.arange(0, n1/2)[::-1])) + else: # even + coi = coi * dt * np.concatenate((np.arange(0, n1/2), np.arange(0, n1/2)[::-1])) + + # cut zero padding + wave = wave[:, :n1] + + return wave, period, scale, coi + +def wave_signif(Y, dt, scale, dof=-1, lag1=0, siglvl=0.95, mother='MORLET', param=-1): + """Computes the wavelet significance test at a level of confidence siglvl% + + Arguments + --------- + Y: time series + dt: sampling rate + scale: scales of the wavelet decomposition + dof: degrees of freedom + lag1: assuming lag-1 autocorrelation of the serie (0 for white noise RECOMMENDED, 0.72 for red noise) + siglvl: percentage of the confidence level + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + mother = mother.upper() + variance = np.var(Y) + + # define default param and fourier factor for the wavelet + if mother == 'MORLET': + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi) / (param + np.sqrt(2 + param**2)) + + if mother == 'DOG': + if param == -1: + param = 2 # For DOG, default param is 2 + if dof == -1: + dof = 1 + + fourier_factor = float(2 * np.pi / (np.sqrt(param + 0.5))) + + if mother == 'PAUL': + if param == -1: + param = 4 # For PAUL, default param is 4 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi / (2 * param + 1)) + + # compute period from scale + period = [e * fourier_factor for e in scale] + + # compute theoretical fft associated to the theoretical noise of the data given by lag1 + freq = [dt / p for p in period] + fft_theor = [variance*((1 - lag1**2) / (1 - 2*lag1*np.cos(f * 2 * np.pi) + lag1**2)) for f in freq] + + chisquare = scipy.special.gammaincinv(dof/2.0, siglvl)*2.0/dof + signif = [ft * chisquare for ft in fft_theor] + return signif \ No newline at end of file diff --git a/notebooks/GRACE-Geostrophic-Maps.ipynb b/notebooks/GRACE-Geostrophic-Maps.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Harmonic-Plots.ipynb b/notebooks/GRACE-Harmonic-Plots.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Spatial-Error.ipynb b/notebooks/GRACE-Spatial-Error.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Spatial-Maps.ipynb b/notebooks/GRACE-Spatial-Maps.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb new file mode 100755 index 00000000..594d7fe0 --- /dev/null +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -0,0 +1,915 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ec4ce1ef", + "metadata": {}, + "source": [ + "# Gravitational constraints on the Earth's inner core differential rotation\n", + "Python Jupyter notebook to reproduce figures from the article \"Gravitational constraints on the Earth's inner core differential rotation\"\n", + "\n", + "## Installation\n", + "You need to install the module gravity_toolkit from [https://github.com/hulecom/read-GRACE-harmonics](https://github.com/hulecom/read-GRACE-harmonics).\n", + "\n", + "Then follow installation instructions from the documentation [https://gravity-toolkit.readthedocs.io/en/latest/](https://gravity-toolkit.readthedocs.io/en/latest/).\n", + "\n", + "## Download data\n", + "With the \"Getting Started\" instruction, you need to download CSR, GRAZ and COST products.\n", + "\n", + "For this, download manually the data or use the scripts: podaac_cumulus.py, itsg_graz_grace_sync.py and esa_costg_swarm_sync.py\n", + "\n", + "You will also need IGG-SLR data from the link [http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID](http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID) ==> EnsMean dataset\n", + "\n", + "ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc has been provided by Bertrand Descharmes\n", + "\n", + "LOD files are in-house solutions based on C01 and C04 data from [https://hpiers.obspm.fr/](https://hpiers.obspm.fr/). We correct the LOD time-series with zonal tides and for atmospheric, oceanic, hydrologic and sea level angular momentum obtained from the operational products of the Earth-System-Modelling group at GFZ\n", + "\n", + "## Folder organisation\n", + "These datasets will be organized in subfolders:\n", + "```python\n", + "base_dir/\n", + " CSR/RL06/GSM/...\n", + " GRAZ/RL18/GSM/...\n", + " COSTG/RL06/GSM/...\n", + " IGG/IGG_SLR_HYBRID/...\n", + " HYDRO/ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc\n", + " LOD/lod_AAMncep1948-2023.dat\n", + " lod_AOHSl.txt\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "69ec460e", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:42:32.547480Z", + "start_time": "2023-08-23T06:42:30.197057Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import scipy as sc\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as sg\n", + "\n", + "from gravity_toolkit.harmonics import harmonics\n", + "from gravity_toolkit.grace_find_months import grace_find_months\n", + "from gravity_toolkit.grace_input_months import grace_input_months\n", + "from gravity_toolkit.spatial import spatial\n", + "\n", + "from gravity_toolkit.toolbox import create_grid, grid_to_hs, filt_Ylms\n", + "\n", + "# maximal degree to load for the Stokes coefficients\n", + "n_harmo = 4\n", + "\n", + "# Base directory with all the dataset (see read-GRACE-harmonics installation)\n", + "base_dir = '/home/hugo/Documents/GRACE_DATA'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e637b560", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:42:50.644999Z", + "start_time": "2023-08-23T06:42:36.326441Z" + } + }, + "outputs": [], + "source": [ + "# Read CSR, GRAZ and COST-G data from the GRACE mission from the april 2002 to end of 2022\n", + "\n", + "total_months = grace_find_months(base_dir, 'CSR', 'RL06', DSET='GSM')\n", + "start_mon = np.min(total_months['months'])\n", + "end_mon = 251 # end of 2022\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'CSR', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", + "# create harmonics object\n", + "GRACE_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'GRAZ', 'RL18', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'GRAZ', 'RL18', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", + "# create harmonics object\n", + "GRAZ_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'COSTG', 'RL06', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'COSTG', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", + "# create harmonics object\n", + "COSTG_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "# remove mean to consider in gravity anomalies\n", + "GRACE_Ylms.mean(apply=True)\n", + "GRAZ_Ylms.mean(apply=True)\n", + "COSTG_Ylms.mean(apply=True)\n", + "\n", + "# Temporal filtering with a 3 year low pass filter\n", + "GRACE_filt_Ylms = filt_Ylms(GRACE_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "GRAZ_filt_Ylms = filt_Ylms(GRAZ_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "COSTG_filt_Ylms = filt_Ylms(COSTG_Ylms.copy(), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5da6ed08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:43:07.678119Z", + "start_time": "2023-08-23T06:42:51.320617Z" + } + }, + "outputs": [], + "source": [ + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "detrend=True\n", + "\n", + "# list of IGG-SLR file\n", + "files = os.listdir(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID'))\n", + "files.sort()\n", + "\n", + "# create a harmonics object to fill with IGG-SLR data\n", + "ylms_slr = harmonics(lmax=n_harmo, mmax=n_harmo)\n", + "ylms_slr.time = np.zeros(len(files))\n", + "ylms_slr.month = np.zeros(len(files))\n", + "ylms_slr.clm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "ylms_slr.slm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "\n", + "ylms_slr.update_dimensions()\n", + "\n", + "# fill the harmonics object\n", + "for i, f in enumerate(files):\n", + " ylms_tmp = harmonics().from_gfc(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID', f))\n", + " ylms_slr.time[i] = int(f[23:27]) + int(f[28:30])/12 - 1/24\n", + " ylms_slr.clm[:,:, i] = ylms_tmp.clm[:n_harmo+1, :n_harmo+1]\n", + " ylms_slr.slm[:,:, i] = ylms_tmp.slm[:n_harmo+1, :n_harmo+1]\n", + "\n", + "# convert decimal year to GRACE month equivalent\n", + "ylms_slr.month = np.floor((ylms_slr.time - 2002)*12)\n", + "# remove mean to talk in gravity anomalies\n", + "ylms_slr.mean(apply=True)\n", + "\n", + "\n", + "# Read ISBA data in m EWH after index 170 (= start of IGG-SLR product)\n", + "grid_isba_slr = spatial().from_HDF5(os.path.join(base_dir, 'HYDRO/ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc'), date=True, timename='time_counter', lonname='lon', latname='lat', varname='tws')\n", + "grid_isba_slr.data[grid_isba_slr.mask] = 0 # Set masked data to 0\n", + "# swap axes to get (lon, lat, time)\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data[170:, :, :], 0,2)/1000 #divide by rho_water\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data, 0,1)*100 #go to cm EWH\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask[170:, :, :], 0,2)\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask, 0,1)\n", + "\n", + "# concert time from day to decimal year\n", + "grid_isba_slr.time = 1979 + grid_isba_slr.time[170:]/365.25\n", + "# convert decimal year to GRACE month equivalent\n", + "grid_isba_slr.month = np.floor((grid_isba_slr.time - 2002)*12)\n", + "\n", + "# remove mean to talk in gravity anomalies\n", + "grid_isba_slr.mean(apply=True)\n", + "\n", + "# from grid to harmonics\n", + "isba_Ylms_long = grid_to_hs(grid_isba_slr, n_harmo)\n", + "\n", + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "if detrend:\n", + " isba_Ylms_long.slm[2,2] = sg.detrend(isba_Ylms_long.slm[2,2])\n", + " ylms_slr.slm[2,2] = sg.detrend(ylms_slr.slm[2,2])\n", + "\n", + "# Temporal filtering with a 3 year low pass filter \n", + "isba_filt_Ylms_long = filt_Ylms(isba_Ylms_long.copy(), filt='fft', filt_param=[1/3])\n", + "SLR_filt_Ylms = filt_Ylms(ylms_slr.copy(), filt='fft', filt_param=[1/3])\n", + "\n", + "# create IGG-SLR - ISBA + temporal filtering\n", + "SLR_filt_isba_Ylms = filt_Ylms(ylms_slr.copy().subtract(isba_filt_Ylms_long), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "384d9ab9", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:44:03.287631Z", + "start_time": "2023-08-23T06:44:02.223259Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time length of IGG-SLR : 28.083333333333258 yr\n", + "Time length of IGG-SLR - ISBA : 25.75 yr\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Time length of IGG-SLR :\", SLR_filt_Ylms.time[-1] - SLR_filt_Ylms.time[0], \" yr\")\n", + "print(\"Time length of IGG-SLR - ISBA :\", SLR_filt_isba_Ylms.time[-1] - SLR_filt_isba_Ylms.time[0], \" yr\")\n", + "\n", + "# Figure 2a of the paper\n", + "# Fig 2a. Time-series of S2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "plt.figure()\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2], label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(isba_filt_Ylms_long.time, isba_filt_Ylms_long.slm[2,2], label='ISBA', color='C0', linestyle='dashdot')\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure S4a of the paper\n", + "# Fig S4a. Time-series of C2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "plt.figure()\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.clm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.clm[2,2], label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(isba_filt_Ylms_long.time, isba_filt_Ylms_long.clm[2,2], label='ISBA', color='C0', linestyle='dashdot')\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure Supplementary Information S3a of the paper\n", + "# Fig S3a. Time-series of S2,2 coefficient for GRACE CSR (brown), GRAZ (light blue) and COSTG (lime) products and for IGG-SLR (red) product\n", + "\n", + "plt.figure()\n", + "plt.plot(GRACE_filt_Ylms.time, GRACE_filt_Ylms.slm[2,2], label='CSR', color='C5')\n", + "plt.plot(GRAZ_filt_Ylms.time, GRAZ_filt_Ylms.slm[2,2], label='GRAZ', color='C9')\n", + "plt.plot(COSTG_filt_Ylms.time, COSTG_filt_Ylms.slm[2,2], label='COST-G', color='C8')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure 3 of the paper\n", + "# Fig 3. α time-series reconstructed from Eq. (7b) based on the corrected S2,2 variations (green) for different choices of δh (49 m, 90 m, 126 m) and based on the S2,2 time-series uncorrected for hydrological loading with δh = 90 m (brown).\n", + "plt.figure()\n", + "# plot S22/(2*Kappa*delta h)*180/pi (Equation 7b + conversion from radians to degree)\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/49/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 49m)', color='#4bce4b', linestyle='dashed')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 90m)', color='C2')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/126/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 126m)', color='#1c641c', linestyle='dashdot')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from uncorrected $S_{2,2}$ ($\\delta h$ = 90m)', color='#5a3730')\n", + "\n", + "plt.grid()\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=13)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5789a5dc", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:38:46.280642Z", + "start_time": "2023-08-16T07:38:45.875047Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 2b of the paper\n", + "# Fig 2b. Lomb-Scargle periodogram of S2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "slrisba_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_isba_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.449, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_slr_isba = sg.lombscargle(SLR_filt_isba_Ylms.time.copy(), SLR_filt_isba_Ylms.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "pgram_isba = sg.lombscargle(isba_filt_Ylms_long.time.copy(), isba_filt_Ylms_long.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(2*np.pi/w, pgram_slr_isba, label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(2*np.pi/w, pgram_isba, label='ISBA', color='C0', linestyle='dashdot')\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "98f5283b", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:45:55.960549Z", + "start_time": "2023-08-23T06:45:55.576991Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure S4b of the paper\n", + "# Fig S4b. Lomb-Scargle periodogram of C2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "slrisba_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_isba_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.449, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.clm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_slr_isba = sg.lombscargle(SLR_filt_isba_Ylms.time.copy(), SLR_filt_isba_Ylms.clm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "pgram_isba = sg.lombscargle(isba_filt_Ylms_long.time.copy(), isba_filt_Ylms_long.clm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(2*np.pi/w, pgram_slr_isba, label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(2*np.pi/w, pgram_isba, label='ISBA', color='C0', linestyle='dashdot')\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7cf82451", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:38:48.361525Z", + "start_time": "2023-08-16T07:38:47.945254Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S3b of the paper\n", + "# Fig S3b. Lomb-Scargle periodogram of S2,2 coefficient for GRACE CSR (brown), GRAZ (light blue) and COSTG (lime) products and for IGG-SLR (red) product\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "grace_hann = np.concatenate((global_hann, np.ones(len(GRACE_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "graz_hann = np.concatenate((global_hann, np.ones(len(GRAZ_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.63, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_grace = sg.lombscargle(GRACE_filt_Ylms.time.copy(), GRACE_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "pgram_graz = sg.lombscargle(GRAZ_filt_Ylms.time.copy(), GRAZ_filt_Ylms.slm[2,2]*graz_hann, w.copy(), normalize=False)\n", + "pgram_costg = sg.lombscargle(COSTG_filt_Ylms.time.copy(), COSTG_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram_grace, label='CSR', color='C5')\n", + "plt.plot(2*np.pi/w, pgram_graz, label='GRAZ', color='C9')\n", + "plt.plot(2*np.pi/w, pgram_costg, label='COST-G', color='C8')\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "18a5be29", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:38:50.128420Z", + "start_time": "2023-08-16T07:38:49.833504Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 4 of the paper\n", + "# Fig 4. Upper bounds on the combination of α and δh for periods of 4 (orange curve), 5-6 (blue curve) and 8-12 (purple curve) years, based on the amplitudes of the corrected S2,2 signal \n", + "\n", + "plt.figure()\n", + "# create alpha and delta h range\n", + "alpha = np.arange(0.08, 1.2, 0.005)\n", + "h = np.arange(45, 160, 0.01)\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "h2 = np.arange(94.5, 158.2, 0.01) \n", + "\n", + "# three line for three alpha maximal value at delta h = 90m\n", + "plt.plot(h, 90*0.4/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.4} \\frac{\\pi}{180} \\approx 9 \\times 10^{-12}$', lw=2, color='C4')\n", + "plt.plot(h, 90*0.3/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.3} \\frac{\\pi}{180} \\approx 7 \\times 10^{-12}$', lw=2, color='C9', linestyle=(0,(3,1,1,1,1,1)))\n", + "plt.plot(h, 90*0.1/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.1} \\frac{\\pi}{180} \\approx 2 \\times 10^{-12}$', lw=2, color='C1')\n", + "\n", + "# hatch couple values of alpha, delta h that cannot be reach\n", + "plt.fill_between(h, 90*0.4/h, 2*np.ones(h.shape), hatch='//', fc='w', alpha=0.8)\n", + "\n", + "# dashed line for delta h = 90m\n", + "plt.plot([90,90], [1.2,0], '--', lw=2, color='C5')\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "plt.fill_between(h2, 0.455*np.ones(h2.shape), 0.51*np.ones(h2.shape), fc='w') \n", + "plt.text(95, 0.47, 'Above $S_{2,2}$ upper bound constraint', weight=\"bold\")\n", + "\n", + "plt.xlabel('$\\delta h$ (m)', fontsize=12)\n", + "plt.ylabel(r'$\\alpha~(°)$', fontsize=12)\n", + "plt.xticks([50, 70, 90, 110, 130, 150])\n", + "plt.xlim(45, 160)\n", + "plt.ylim(0, 0.95)\n", + "plt.legend(framealpha=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "80fb78ed", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:38:52.214642Z", + "start_time": "2023-08-16T07:38:52.208644Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Low Γ value : 0.18169617014874107\n", + "Large Γ value : 0.027254425522311162\n" + ] + } + ], + "source": [ + "# calculation of maximal value for alpha based on LOD change with an amplitude ms and a period y\n", + "ms = 1e-3\n", + "y = 20\n", + "\n", + "# equation 8\n", + "print(\"Low Γ value :\", 360/86400**2*7.129e37/3e19*ms/(y*31536000))\n", + "print(\"Large Γ value :\", 360/86400**2*7.129e37/2e20*ms/(y*31536000))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "eb22604d", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:38:54.711705Z", + "start_time": "2023-08-16T07:38:53.615328Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 30 yr, spectral resolution on C04 time series is between : 18.014885607955826 and 89.62969719522997\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 21.323655559433195 and 50.58068555210291\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1a and S2a\n", + "# Fig S1a. IERS EOP C01 LOD time-series with (green) and without (orange) removal of the Atmopheric Angular Momentum (AAM) and C04 LOD time-series\n", + "# Fig S2b. α estimated from C01 LOD - AAM time-series (orange and lime) and C04 LOD- AAM time-series (blue and light blue) for the range values of Γ\n", + "# The band-pass filters are between 21 & 50 years\n", + "p = 30 #looking for a signal with a period of 30 years\n", + "\n", + "# read C04 file\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AOHSl.txt\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, Sea level AM\n", + "lod = np.zeros((len(lines) - 7, 7))\n", + "for i, l in enumerate(lines[7:]):\n", + " lod[i, :-1] = np.array(l.split())\n", + " \n", + "l = lod[-1,0] - lod[0,0] #length of the LOD time series for C04\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + " \n", + "lod[:,6] = lod[:,1] - lod[:,2]\n", + "\n", + "for i in range(1,7):\n", + " lod[:,i] = sg.detrend(lod[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "# read C01\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AAMncep1948-2023.dat\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM (OAM and other are not available for C01)\n", + "lod2 = np.zeros((len(lines) - 1, 4))\n", + "for i, l in enumerate(lines[1:]):\n", + " lod2[i, :-1] = np.array(l.split())\n", + "\n", + "l = lod2[-1, 0] - lod2[0, 0] #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "lod2[:,3] = lod2[:,1] - lod2[:,2]\n", + "\n", + "for i in range(1,4):\n", + " lod2[:,i] = sg.detrend(lod2[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + " \n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "9ada98ea", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-16T07:39:01.807707Z", + "start_time": "2023-08-16T07:39:01.023957Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 6 yr, spectral resolution on C04 time series is between : 5.295404578162772 and 6.920877428589756\n", + "0.2851279081703251\n", + "0.07344308913742192\n", + "For a period of 6 yr, spectral resolution on C01 time series is between : 5.548477928692466 and 6.5315196960005\n" + ] + }, + { + "data": { + "image/png": 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egCwDkM6nyeQzFVuPHo5MqzP2aqeCnIfunQRyYh2Vduf0geK9EvBHu2+ZhYXFgclB54SNJj/72c+K/n/sscfyt7/9jRNPPJHVq1fzj3/8gzPOOKPf17vdbtxut9nLtPgIse/dPwHQoNjhoSWw+3XGzF0MFByXSqK3qOgdBg2NI7CrcIMRzUQrVgGoO2G5LEg2qJoInVth2dEE68dAwFNxEaa5Xb3HSmlhSS1XzMLCwgIOQidMc8D6c7sikUi/LpkZ2Gw2vvrVrwLw0kujN3jZ4iNILlNwVfxjILYPYvuoVuc2VvqCnsqlyMjCXerdnR5/PXbAL4l7ukqG/4qaxwYawe6E0HgAAmqIstLhSE2EaSFIKAwXt/qEWZRDS0sLV1xxBVOmTMHtdtPU1MRZZ53Fc889p2+TTqe54oorqKurw+/3c/bZZ7N79+6i/dx0000cf/zx+Hw+qqqqyjr2SSedVLLNkkZnZydXXnklkyZNwuVyMXbsWL761a+yc2dx78KLL75Y75PpdDppaGjg1FNP5e6770ZWnfNyOPTQQ3G5XOzZ039bnmQySXV1NTU1NSSTyT7Paz08H3rooT7PzZo1C0mSRm2800EnwrRcsFJ5X11dXbS3tw/ansJotFywcvqMWVjodG6l2yZaUNRUTYG6GeLfaXGSqfQFXRMzkgL+ve8UnvALsRFQKzcrmQivhyNlGYIi+R2XD3y1BNUT/Wg5YbWeghtoiTCLctm+fTvz58/n+eef55ZbbmH9+vWsWLGCRYsWcfnll+vbXXnllTz22GM89NBDrF69mlgsxplnnkleK1IBMpkM55xzDt/61rcMWVtnZyef+MQnePbZZ7nzzjvZvHkzDz/8MFu2bOGYY45h69atRdufdtppNDc3s337dp588kkWLVrEd7/7Xc4880xyudygx1u9ejWpVIpzzjlnQJH0l7/8hdmzZ3P44Yfz6KOPltymqampT2eEV199lZaWFvx+/+A/vEkcdOHIE088kZtvvpmnn36a888/v+i5p59+Wt+mkqxZswYQatzComy6ttFlF/dJVd5qke8EVCeF8BiN7vQAfkVGcrgKT/jFTUYQ2EdlO9RHs0JgBWQZgmMLT4QnEIyKweeVdsK0kG1VrwpSLRyZzCVJ5pJ4Hd6Krkkjl4vR3f0a1dXHYbePzhpGC0VRSOb6uiSVwOvwlt3T79vf/jaSJPHaa68ViYNZs2ZxySWXACLSs3z5cu6//35OOeUUAB544AGampp49tlnWbxYpCxohV5GuTw/+clP2Lt3L5s3b6axUdz0TJw4kaeeeorp06dz+eWX8+STT+rbu91ufbvx48dz1FFH8YlPfIKTTz6Ze++9l0svvXTA4y1fvpwLLriAE088kcsvv1xvoF5quwsvvBBFUVi+fDlLlizps82SJUu49dZb2bVrF01NTQDcfffdLFmyhD/84Q/Dfk9GykEnwk4++WSmTJnCgw8+yHe+8x3mzp0LQDQa5cYbb8ThcBRVL7a3t9Pe3k5dXd2g1YsDsWHDBsaNG9fH8l29ejW//vWvcbvdfOELXxj2/i0+fijdu+iyC3ep2l0NYXHiqEr2gFT5cKQmZvzeWqg/tPCEJsLyMtgqK3oSWeEuB3o7YQChCQR6PgAq68xB6by5gDOA0+bUe4h5A5UXQLKc4421XyIe/5Ca6k8yb97oXXhGg2QuybEPHjsqx15zwRp8Tt+g23V2drJixQpuuummku6Mdn1Zu3Yt2Wy2qBXTuHHjmD17Ni+//LIuwoxElmUeeughlixZogsrDa/Xy7e//W2uvfZaOjs7qanpf0LFpz/9aebMmcOjjz46oAiLRqP86U9/Ys2aNRx22GHE43FWrlzJokWLirbbsmULr7zyCo8++iiKonDllVeydetWpkyZUrRdQ0MDixcv5r777uPaa68lkUjw8MMPs2rVqlEVYQddONLhcPD73/8eWZY54YQT+MY3vsHVV1/NnDlzeO+991i6dCkzZszQt1+2bBkzZ85k2bJlffZ18cUX63+am5v7PNbeXig3f+SRRxg3bhxnnXUWV1xxBVdffTWnnXYaCxcuJJvNsmzZMiZOnGj+G2Bx0JDo2UFWveur9lTruU41MfG5q3RoK54RTljAUwPuXk2MtXBkXoxUqqTo0d05yQ6hXk5YoJ6ALIoFNLesUpQSYZIkjXpyflfXS8TjIk2js+slYrHBJ4BYVJbNmzejKAqHHXbYgNu1tLTgcrmorq4ueryhoYGWFnN60bW1tdHd3c3MmTNLPj9z5kwURWHz5s2D7uuwww5j+/btA27z0EMPMX36dGbNmoXdbuf8889n+fLlfba7++67Of300/WcsNNOO42777675D4vueQS7r33XhRF4c9//jNTp07VjZrR4qBzwgAWLVrE6tWrue6663jkkUfIZDLMmjWLG2+8saRN2R/33XffgI8tXbpUd88WLVrExo0befPNN1m1ahWpVIqGhgbOO+88vve977FgwYKR/2AWHyu6enYA4JHsInwVngBAdWQfBOvpTndXdHSR7oS59rtD94nvQCCbAaerojlY2pp8X/k71PQaB+QfI/LEqHxOmDYmaf9GujWeGloTrXSkOiq6Ho32jheK/t/RsZJA4NB+tj748Dq8rLlgzagduxy0NkbD/U6Pxiiz3seG8tbee52XXXYZDzzwgP5cLCa+01qIUePCCy9k4cKFdHd3645gPp/nvvvu4ze/+U3Rdt/73ve4/vrrsauRBI0zzjiDb37zm7z44ovcfffdenh3NDkoRRjAggULimLT/bF06dJ+Zz0O1terNyeeeGLFc80sDm66o81ghyqHKnpC4wCJ6mwKED25krlkWWEOI4hHROWVP7lfzpcajgzl0oBrdMKRzgAU5anV673LRi0c6SoWYVqOmPZ8pYlE1gMQCh5JJPoOPZF1o7KO0UKSpIp9V4bL9OnTkSSJjRs38rnPfa7f7RobG8lkMnR1dRW5Ya2trRx//PGmrK2+vp6qqio2bNhQ8vn3338fSZKYOnVqyed7s3HjRiZPngzADTfcwNVXX130/IYNG1izZg2vv/46P/rRj/TH8/k8f/zjH/VCg6eeeoo9e/Zw3nnnFb0+n8/z9NNPc/rppxc97nA4+PKXv8x1113HmjVreOyxxwb/wU3moAtHWlgcLHSqzVirtQRvuxOCjXgVBbfNKbapYEgy3r0dgED3fk2EPWGwOXXRU0nnSQ9HOvdz5wIFERbPxpGV8kviR0Jezus/f8gdKnpOa+sxGjM/ZTlDLCYunuPGiQtWLPp+xddhMTA1NTUsXryYO+64g3g83uf57u5uAObPn4/T6eSZZ57Rn2tububdd981TYTZbDbOPfdcHnzwwT4hz2QyyZ133snixYsHzAcDMd95/fr1fPGLXwRgzJgxTJs2Tf8DwgVbuHAhb7/9NuvWrdP//PCHPywKSS5fvpzzzz+/aJt169axZMmSkqFLECHJVatW8S//8i99wrmjwUHrhFlYfKSRZbrTXRCsptrXq2Ak2IgUbaba4aMl00NXqosJwQkVWVJMzWXy2z3FT0gS+OsIyOKiUSkRls1n9b5lvsevhC/8jxCEAP4xBFXhpaAQz8b1gd5mEsvGUBAO+v5OmCbKRkOEgcKsWf9FKrmHMWMW09q2gmBwFooiI0nWvfiBxJ133snxxx/PggULuOGGGzjyyCPJ5XI888wz3HXXXWzcuJFwOMzXvvY1rrrqKmpra6mpqeHqq6/miCOO0KslAXbu3ElnZyc7d+4kn8+zbt06AKZNm0YgEOh3DW1tbfq2Go2Njdx0000899xznHrqqdxyyy3Mnj2bbdu2ce2115LNZrnjjjuKXpNOp2lpaSGfz7Nv3z5WrFjBzTffzJlnnslFF11U8tjZbJb777+fG264gdmzZxc9d+mll3LLLbfw9ttvM27cOJ544gkef/zxPtt95Stf4YwzzqCtrY36+vqi52bOnEl7ezs+34HhiloizMLiQCTZSZeaWlHVa2QR/jEAhG0uWqjsBT2e6gYgUCq/pelYgskdQEfFwpGaCwbg/2AFOHqJQ389bgVcikJGkohlYhURYVqo0efw4bQ7IZOAd/8C9YcWnLB05UWYzeZmTH2hYm7e3HsrvgaL8pg8eTJvvvkmN910E1dddRXNzc3U19czf/587rrrLn27W2+9FYfDwbnnnksymdTbPvTOg/rZz35WlMc8b948AF544QVOOumkftfw4IMP8uCDDxY9dt1117F06VJeffVVbrjhBr75zW/S3NxMbW0tp512Gg888ECf4rMVK1YwduxYHA4H1dXVzJkzh9tuu42vfOUr2Gylxf/jjz9OR0cHn//85/s8N336dI444giWL1/OIYccgt/v5+STT+6z3aJFiwgGg9x///18//vf7/N8bW1lJnqUgyXCLCwOROLteo+wam8ve1+tRAypmQSVFGEx1eHqE/oDOPc+/Fv/Bv/8cZE4MpN4ThzHY3PiOOs2cPQa+RUQ75NPlsnY7SRylWmUvH9lZP7v3yX54V8IhA4n9AmRZDw6Tpg47g9f/CF5Oc8vFv5CbyBrceAxduxYli1bVrJqX8Pj8XD77bdz++2397vNvffeO+QeYStXrhzw+bq6Om677TZuu+22AbcbzrEBvvjFLxY1nN2fd94pNIq+6qqrSm7jcDjo6CgUwAxWiamFeUcDy4e2sDgQibfRrd7R9m76qYmLoJriVNn8K606srSj5FcLCColeLSEe58rCPO/UvykpwrsLvxqm4pKCcPelZHp7k287H2WNUdXs/Pkcwj5xN33aImw5euX89Kel3i1+VX+++3/HpU1WFhYFGOJMAuL3rz/dzrX3sNlz3yTy569TJ/dWHGqmog0zgL2a3Uw43Q4/ZeEasXorYqGI9VKRL+nquTzWuWZVrFoNprYK+nMSRJ8+1W86vtUcSfMFWbPpt+QcYlT7LbmPxBS1zka4cidu+6hp+WP1DmEel+5/R90dq2hp+etiq/FwsKigCXCLCw01v8ZHrqAZWv+g5f2vsxLe17iznV3js5aqicRCYmu1EW5TBOPhWO/QbBalHdXVITlRWuMgLtEGOvFX+L747+K7SoVjlSPE8jnoPmdvhvUTsWvJsNXzAlTRVjIHaI9WuhJlct148uJirLRcMJ27H6AE7wd1NiFMzjR3sFbb13Atm0Dh5QsLCzMxRJhFhYar95FFng6VHCentz+JFk5OyrL0cJtWkJ3b7THKtoYVa1EDPSu1tRQFPxJIUAqFo7UGrV2bIOXS4sJn6Oy7pwWjqx2+YhJ3QCEHZMAcL12EzA6Iiyd3gfAmOAMFjQuoCcvqj7SmdaKr8XCwqKAJcIsLACi+2DPG2xwu+jJJwm5QvidfqKZKB90fVD59bRtIpoU44mKnLBcGna8QrBL9OqqZGgrrohkWb+vRGXRURfhu/CvYrsKuU6asPIrCnhLuHPr/4y/Y2vRtmaj9whzBZi8V6FxX4r6arWJsytTtE2lyOdTSLIYXD02fCgzqmcURFjaEmEWFqOJJcIsLAB2idDRe3WTAJhTP4d5Y0Q597rWdZVfz/M3Eo3uBdRu8BqpCNxzGqE37gEq7IQh8on83hJOWLAR39gjAcjJObJ5891DzSn0yzKUEoZbX8DXsQWofLGAX/IxeUsHszbFCDZ8EoBsg5j9mcwlK/L+aGQybeL4CjSFD2VK1RQiqgjLZjuRVYfTwsKi8lgizMICoEXkFG30i1Dk4VtfYXb7ToBRccIUZ4Co2kenyAnzVkPtNELVYjRIxUSYohBX+5b51V5l+6OF/qAybpjWokKIsBJO2PTF+BrnVmw90Gu+Zlr9vYQnEqiaS13dydQ3nglqI9dKhiS1kGMkLzGlairTqqaRkCGvTmXLZNorthYLC4tiLBFmYQF6Yvcmm3B7ZnY3MyUqLk5bu7dWfDmps28lpw64LcoJszvgirUEzxQDayt1Mc+nYyRVURgINPTdINaK48Vf4ZZEW41KOE9F4chSIuzws/FNXlix9UCvYoGkKsLqpuFy1TLnyP/h0BnXEnRWvmt+Oi2csEheYnJ4Mk3BJhQk3Q0bzZBkKrWX5uZHyeUqG6K1sDhQsESYhQXAvvdQgB05cXGcvPhXTFn8KwC29mwd0jB3I9AcLrtkx1uiQ32lE/PjvWZU+n0lnLBUD6y8GV9OhNkq4TzprpMsl84Jo9C+olI5YXqxQG4j7dVO8uGx4olcBl65k1A+B1RWhPXEd4hj5iXG+sdS46nBZXPpeWGZUUrOz2Z7eO31z7Fh4w9Yv/7yUVmDhcVoY4kwC4tsCiK76bTZSOTTSEiMn30Oh4w7BgmJSCZS0UHZUBBXQVcQSXXEeqOFKCOZSEUEYtzhAsBlc+Hq3ZlewysG4frUodmVcJ704d2yAt6qvhtkU/jSYh2VEmHxTBwbCjje5e0jwuSCaq6a3QnPXU9ArSCNZyoTHgXoTggRlpG8OO1ObJKNsYGxo+6E7dv3BNms6MPX2fUSsdimUVnHgUhLSwtXXHEFU6ZMwe1209TUxFlnncVzzz2nb5NOp7niiiuoq6vD7/dz9tlns3v37pL7S6fTzJ07F0mS+syE3J+TTjqJK6+8st/nOzs7ufLKK5k0aRIul4uxY8fy1a9+lZ07dxZtd/HFFyNJEpIk4XQ6aWho4NRTT+Xuu+9GVs8T5XDooYficrnYs2dPv9skk0mqq6upqakhmUz2eX7SpElIksRDDz3U57lZs2YhSdKwuvsbgSXCLCx6RKXhbq8QNmN8Y3Db3XgcHup9okN9c7y5cutJdhN9SPTcKkrK13jiSkK//TQAeSVPMtf3pGM0uutUqjEq6IOzferQ7Eo4YXo4UpYLg7t7s+tVfKtuEevJVS4nLGRXQAJJVnCFpgGQyXbRPXYsE5x5fbtKEUuqFy97Iaw91j+WmKw6YdnK3mBotHc8X/z/9uf72fLjxfbt25k/fz7PP/88t9xyC+vXr2fFihUsWrSIyy8vOIZXXnkljz32GA899BCrV68mFotx5plnlhz588Mf/pBx48aNeG2dnZ184hOf4Nlnn+XOO+9k8+bNPPzww2zZsoVjjjmGrVuLUzdOO+00mpub2b59O08++SSLFi3iu9/9LmeeeSa5XG7Q461evZpUKsU555wzoEj6y1/+wuzZszn88MN59NFHS27T1NTEPffcU/TYq6++SktLC35/P+e1CmCJMAuLru0A7AqJXKemYBNseQFe+HfGOoQIqqwI6yTaI9yLkkOnUz14IrtxVHB+pOZsaV3x+2B3gtOvjwlKZisgDLVZlooC7hIizFuNT3UJK+aEZeNUqQ1R3XkHUp3o2N+y76+snRxlXnVlxygBJPIynTkJu6tef2xcYBwx1YzIjoIIUxSFnp63AWhoOBuAnsi6iq/jQOTb3/42kiTx2muv8aUvfYkZM2Ywa9Ysvv/97/Pqq68C0NPTw/Lly/nP//xPTjnlFObNm8cDDzzA+vXrefbZZ4v29+STT/L000/zq1/9asRr+8lPfsLevXt59tln+exnP8vEiRNZuHAhTz31FE6ns0gkArjdbhobGxk/fjxHHXUU11xzDX/961958skny3Keli9fzgUXXMCXv/xl7r777n5d/+XLl3PhhRdy4YUXsnz58pLbLFmyhFWrVrFr1y79sbvvvpslS5bgcIzeGG1LhFlYaCLML5yCpmATbHwcVv2CsVmR47Q3trdy60l265WRpRq14q1CAkI2J1AZEZbcukocOtnd/0buoB6OrITzlNBEmCyDp9T7VF1YTwVEj6zIxLNxwpoIq5sLhxwv/u0WAt/vEM9V0gnb6jyGG5q9ZP1H6481+ht5MerkNdfnmTb1RxVbi4YsJ6mvP5VgYBbjxp0LQCTydkWOnc8nyvoj79ekWZazZb92uHR2drJixQouv/zyku5MVVUVAGvXriWbzfKZz3xGf27cuHHMnj2bl19+WX9s3759fP3rX+f+++/H5+vnBqpMZFnmoYceYsmSJTQ2NhY95/V6+fa3v81TTz1FZ+fAov7Tn/40c+bM6dex0ohGo/zpT3/iwgsv5NRTTyUej5ccLr5lyxZeeeUVzj33XM4991xefvnlPo4cQENDA4sXL+a+++4DIJFI8PDDD3PJJZcM8pOby+jJPwuLAwVVhO11uiEP4wPjQREn0rGqZd4Sb6ncelLdpdtTaKj5V37sdFIZlycZF3lDvoFyOdxB/EoXVGpN6jG8Npdw4vbHW93LmTNfhCVzSRSUghPmLlyo3C5RzOBVz7iVdMK0z26Dr1DVWu+tJyZL7E2nsdv7Fn6Yjd3u4/CZ/wGgi5ZMpo1stgens4SraSArVx1R1nYzZiylacKX9f/v2fsQH3ywtKzXnvzpLcNZGps3b0ZRFA477LABt2tpacHlclFdXV30eENDAy0t4vetKAoXX3wxl112GUcffTTbt28f1po02tra6O7uZubMmSWfnzlzJoqisHnzZhYsWDDgvg477DDeeafEqLFePPTQQ0yfPp1Zs8QM3fPPP5/ly5ezaNGiou3uvvtuTj/9dP29OO2007j77rv5+c9/3mefl1xyCVdddRU/+clP+POf/8zUqVOZO3fugOswG8sJs7BomA0zz6bNJRLOx/jGQNUhAIxNiQvEaDlhA4swQUXyrxpnq4ee3P9G7iDeCjpPWojU21+emiuAdu8fr4DzpBVTVDtErpWntwhT/+1yACgVFWH7EurIol5VrXVqw9325Oj3CLPbfRy74B+cuPAd0wXYgY4WbitVjFPu67XX3n777UQiEX784x8btr7Bjg3lrb33Oi+77DICgYD+R0MLMWpceOGFPProo3R3d+uP5fN57rvvvj7b3XfffSVz48444wxisRgvvvgid99996i7YGA5YRYWMPdfYe6/0vr4FwD1YuUW/a4akz0QgJZEZZ2wiNaTy1UiMV+vRKxcaCup5kx4S7Wn0HAH8afUHKwKVEcm82mxplLvEYAk4ddmR1ageEETVrWqCHO/8wRMv0b82y3ysWw2CZ+tcuFIRVHoSoo+YXW9Jh3UekTVZntq9EUYQCBwaMWOddKJ68vaTpKK3dXx485n3NgvmrEknenTpyNJEhs3buRzn/tcv9s1NjaSyWTo6uoqcsNaW1s5/ngRAn/++ed59dVXcbuLq5mPPvpolixZooflyqW+vp6qqio2bNhQ8vn3338fSZKYOnXqoPvauHEjkyeLG7obbriBq6++uuj5DRs2sGbNGl5//XV+9KNCuDyfz/PHP/6Rb33rWwA89dRT7Nmzh/POO6/o9fl8nqeffprTTz+96HGHw8GXv/xlrrvuOtasWcNjjz02+A9uMpYTZmGh0p4QF6R6Xz2oDUnr4yK/oSPZUbmFJLuJ2cSFfCAnLCCLO72KhP5UEVOqZ5mOO6hXR1ZkTeq4Hd+F/Z9IfU7x/iVyKdNbeWjCqloLR2YLp1ebzY3TJsRiyK5UrEVFLtfN5aH3+I/xCap7FS/Ueevw2RTO8u5k7Zv/WvE+eN09a4nHt/TJu6oEdruvrD82W7EIs9mcZb92uNTU1LB48WLuuOMO4vG+nxHNBZo/fz5Op5NnnnlGf665uZl3331XF2G33XYbb7/9NuvWrWPdunX84x//AODhhx/mpptuGvLabDYb5557Lg8++KAe8tRIJpPceeedLF68mJqa0j37NJ5//nnWr1/PF78oBO2YMWOYNm2a/geEC7Zw4cKi9a9bt44f/vCHRYn3y5cv5/zzzy/aZt26dSxZsqTfBP1LLrmEVatW8S//8i99wrmjgeWEWXy8URRIdJB1BelKi3ymem892D0A1KaF+OhIdRRZ6KaSGiwxXw1H5rLgqpAT1rwOAF9ugDmD7pCeg2W2E5aVs+Rkka/ndZd4j1R87iAQRUEhmUv2X91pAJqwCqpnVffxxQnvblc92VSMKrtSMScsnRE3EQpQ6y1UR9Z6a8krMMubo7v7NWQ5OSLxMFTeffc7pNMtHD3/L4TDcyt23I8Cd955J8cffzwLFizghhtu4MgjjySXy/HMM89w1113sXHjRsLhMF/72te46qqrqK2tpaamhquvvpojjjiCU045BYCJEycW7VcL9U2dOpUJEyYMuIa2trY+/cQaGxu56aabeO655zj11FO55ZZbmD17Ntu2bePaa68lm81yxx13FL0mnU7T0tJCPp9n3759rFixgptvvpkzzzyTiy66qOSxs9ks999/PzfccAOzZ88ueu7SSy/llltu4e2332bcuHE88cQTPP744322+8pXvsIZZ5xBW1sb9fX1Rc/NnDmT9vb2ERcqGIXlhFl8vEl0wi+n0v5LccJy2BxUuavA6QV3mFo1ryAn5yrX5bzcnLCcCMdVJP9q75vi0Mmu/jfq5YSZvabevdEGcue87hCSUhlhqAkrjyTeA1ddcQK4yyPc1YBNIV6hz1JXQjTvTMgSVZ6qwlrsLtzOEFl9fuQAv1eDkeUM6bRwUrzeJgB6Im+z9s1/Zf27V1RsHQcqkydP5s0332TRokVcddVVzJ49m1NPPZXnnnuOu+66S9/u1ltv5XOf+xznnnsun/zkJ/H5fDzxxBPY7fYRr+HBBx9k3rx5RX/++7//m7q6Ol599VUWLVrEN7/5TaZMmcK5557LlClTeP3115kyZUrRflasWMHYsWOZNGkSp512Gi+88AK33XYbf/3rX/td5+OPP05HRwef//zn+zw3ffp0jjjiCJYvX84f/vAH/H4/J598cp/tFi1aRDAY5P777y95jNraWrzeyheklMJywiw+3sRFvkyr2qi13ltfcLuCjbjSPQQdXqK5JB3JDsKl+lEZTaqbmCrCSjZHVRuT+rNpwFUREZbMp0ACb6nmsRruoJ6nZnY4UutDZgecbz8MR3255HaSO4QvoxCXJLEmE8+72u/h75zMbxb+oo+z5FTzwgI2heZ0ZURYT0I0ak0pdpz7hdfqvPXE861UORSy2U683vEVWZM2MFySnDidauhKkenufg23e2xF1nCgM3bsWJYtW8ayZcv63cbj8XD77bdz++23l7XPSZMmlRV2LtUGojd1dXXcdttt3HbbbQNud++99w6rC/0Xv/jFkkn1Gr2rKq+66qqS2zgcDjo6Cikkg1WG9k72rzSWE2bx8WbMYXBtG+2n/wJA75APQFA4F3XqxbRilWTJbuJqTlhJEaaG3/xqTlhlRJiWBF/CmdOoPgRf+JCKrCmVTwHgkWWk9g/639ATqlivMH2qQGQfjhd+hdTybtHzTqeay2dXiGUqE46MJEWT4Sx9R03VeeuIj0LD1nRaVGu63WP0Gx63R4ivTKYVRen/AmxhcbBhiTALC4eLVkSu0xhvr+q/gGgrUKtWSXWkKpScn+omIalOmKOECHP5QbLp+VeVyC9K5EUCta/UeCCNoy7Cq4pZs0cp6YUCriDMPKv/Dd0hvOrdv9lr0n4PgZ698NJ/QfuHRc9POuQyxrfP5qmIk3je/GpNgLg6F1K29bUAq9xVxNXRRdlsT0XWA71FWKFvmdtVjyQ5UJQ86UxbxdZiYTHaWCLMwgJoS/Qt49edMEV8TUbDCSuZSC5Joh2EXMFKREWIMO8g4VgtP0tzqkxbjybCvDXQNEBjSE8IrypWUzlz16Ql5gey6nH8dUXPu90NVJ3xW7KKRMzk90cjlVY/s/YS3dfdVSRVEZbLRSuyHuglwlwFESZJdr2hbTpVwRFhFhajjCXCLCpP1w6457Pw0BJIVe4OvCSv/Q7+8nU6W0XvoFpvbeE5v7go1Kpip2JtKo69jLhdpGv2OzD70z/FP3cJUIFwpKKQVENEXs/AJd2aCDPdCcuW0TID4NjL8DQcUZE1xbIxjvTmmDkhwgdT/OCv77ONNpA9J+fI5AeoNDWIbFYk3NvtfcVz2B0mpYYjc7kKFZ1QEGEud3HPOS0kmUpbIszi44MlwiwqT9VEmP1FdUj2zaO7lu2rYf0j9KhjeardvURG9SEwdi61qjtWqXBkZsGl5BDuTb8tFRZ8Hf9hYvCx6SIsmySpOXMDibC96/D+8QLAfNdJd8JyGRioYjPYiFetJq1EdWS1XcHlhrTLBr66Ptv0/n1WIoysiSunq+/vrcpdRVKRirarBOmMEGGeXuFIKIx2yqSNDUdWugeaxcGF2Z8fS4RZVB5JgjGHQzYBbz0AA/WeMpuEEFZdCKendxk/h/8LfHMV4cNFqXR3ursiS+otqnyO/nvZaN30TRdhmZieo+bt/f7sj82Ot0e0RDDbddJHFrVuhH2lO3hrVCpEmsgm8KuNWl1ZGXy1Rc+n062s/+cZfLtOrKMSDVuVvDiGx9m3gWaVp4qElhOWq3xOmGs/EeZyCdGayRpzs6O1QMhkRvH8YvGRR/v8GNH6oxRWiwqL0aHpWJEzE2+DHS/B1EWDv8YMVBelWxbVf1Xuqj6baI9FKtFWIJsirvbk8tg9OGz9fEU7t+Fr3QRUwFHJxHQnrN85jQA1U/H8659g9ZWk82lkRcYmmXOfpzthsgKe/pu10rkNT4cYpqyFMM0ikUsw1iZEmFNxgb34dydJdtpzHzDDCzYq07D1vfxY2iJdnDHu8D7PVbmraM5KbM1VcWhwdolXm0Nadbo050vD6RKiVWthMVIcDgc+n4+2tjacTic2m+U5fNSoWIPsfpBlmba2Nnw+Hw6HOXLpoBVhr7/+Otdddx2vvPIKmUyGWbNmceWVV3LBBReU9frW1laWL1/O2rVrWbt2rd5nZDBrcqTHPeh5/ufg9MGc82HqyfDOQ7Dj5dETYZoTpjorA4mwijhhnVtI/PE8mDB24O7uL9xEYOOj0DTe/MT8dIykeiIcMAfL5cN7yPGwWvw3lUuZ1qFeC3d6FUXvm1aSyF58+zZAMFCRYgG/ep13SX1/bqezCpAApWLzIz9Iu3gv5uR8f995fmF3mPVJBx22Or4+vnLnp3lz7yWTacfnKx4G79JFmDFOmCRJjB07lm3btrFjxw5D9mlROaKZKLFsDJ/DV5n+jP1gs9mYOHGiaWLwoBRhK1euZPHixbhcLs4//3zC4TCPPvooS5YsYfv27VxzzTWD7mPDhg1cc801SJLE9OnT8fl8JBIDX+yMOO5BTT4Hr94FmRjMWAwTjhYiTHV+Ko46skgGerS5f71znjJxuOuThDPdMCZYGRGWz5DwidBRv0n5AOEm/FWTgQypfIqcnOvfNRspmbguwgYTVW57oR9VIpcwTYQlM6Kazysr4C70LsvnU0iShM2mrqNqIp76wyG10/wQaTZBwKM6Yba+vztJsuN0VpHNdhGwKxXp76ZNeQiVGO1U0ZuLXng8Y/F4+jZlDQYOY9zYcwmGjijxquHhcrmYPn26FZL8iLG9ZzvXPn+t/v9fLPwFM2tnjspaXC6XqS7qQSfCcrkcl156KZIk8eKLLzJv3jwArrvuOo477jiuu+46zjnnHKZPnz7gfmbOnMmqVauYN28ewWCQww47jE2bNpl+3IMaOQuLroG9b0H9YaAlb+95UwiiStvOmRjkM0RtEnl13E6RE+b0Qc8uqiQFCBJJR8y3x8fNI37uPfDsZQOLsFOuw7/oGnjgKEDkhZl1t5hPR0irJ6HBqhFtry/HK9lJKnlTk/N1EabIoDaQ7ehYxfp3/w273cdR8x7E758KVU14p38G1v++IsUCATUc6XKUDpE6HCGy2S58kmK6KAThJgAEnX2b7Gqf9UQuQTafxWl39tmmkoTDRxEOH2X4fm02Gx6Px/D9WpjHk+89SXOmUCX7911/Z974eaO4IvM46ILkzz//PFu2bOGCCy7QhRBAMBjkpz/9KblcjnvuuWfQ/TQ0NLBw4UKCwQE6hJtw3IMapxeOuxy++Huw2WHMLJBskOyEWGvl16OGIrtdwq3xO/247K7C85IElzxF+BsvApBTchUJIWkOyUBJ+QBOuxOXzVX0GjNIyln934O2hFh1Cx610MJMkZFU8/O8kh3sosnnpg9uIJ9PkMm0s2Xrr/RtPeow9koUC/i1nDBH6SpSp12cT7w2hYTJXfPz+TRHu9s53p8l2NuRbP8Qtv2ToCuIQ5IY45DZ2/GKqWspxe7obtbuW4us3gBZWGi82vwqAKdNOg2Al/e+PJrLMZWDToRpc68+85nP9HlOe2zVqlUHzXE/0jg9ol0FQMeHA29rBgkxqqXLVwWUzgdjwtF4GmbrF/JKhG50EVZGKE9zy0wVYROPBUBCKgo3lsTlr0hz1KQqYLzqNINIZD3J5Hb9+fb2F/QGpF51vFPC9KHiCbzqGdXh7luNCOBwCrfSZ4NEytxRQZHUPs4MZzi3JktQC0d2boPfngi7X8Mm2RjnCXLN2BSb3720Iq0cotGNrHv7Ul5/98d84fEvcPGKi1n2Vv/zES0+fsQyMTZ2bATgW3O+BcCu6C66UpUbMl9JDjoR9uGH4mJeKuxXXV1NXV2dvs2Bdtx0Ok0kEin6c1CxZy20bYJ8wVmhVn2/2kdPhHV7hDtR1CNsP7RQn+kVkm8/RGL1fwKD5IRteR7uPB5/RuQpmirCVAfJ5/QNHop1BUSIEJOdMNWR9KrOZUencCvH1J+OzzcZRcnS1SXupr2rfglAaqB+YiMkm89iV3LY1bdHG9a9Pw5nlViTTSGZ7DZtPQA9CRHOScng1cZfvbEcsnFQ25u41fVAHlk2PzyaSu2io+MFdrU9p38+/rDhD3rYNBJ9l/b25yvawd/iwGJz92YUFMb4xjClagpTwlMAWN++fpRXZg4HnQjr6RH9bsLh0vkxoVBI3+ZAO+7NN99MOBzW/zQ1NRm+zlHl8e/CHQtg87OFx+pmiL87Nld+PVo40i0uUOFSVXYfPgPP30SV6riY7oR1biXeLSq5BhRhuQy0vocvnwPMHV2k9+QaLBQJ4PLjUR0VM6sRk+rP61WdOU1w1dR8ivHjlzBp0r/p1XcedZukiUJVe4+eizgYGwlhq5lRcjun7oQpJEz+LEXVuZFpxV4Qz6dcDxf/AyYvBMDvriavGmDZCjRszagd/PcmCyIrnU/z4m4hotev/zZvv/N14vFRuCmzOCD4oOsDAKZXixv0Q6sPBYQ4Oxg56ETYR5kf//jH9PT06H927do12ksyDlkuhBzrChcopXYq++x2lLYPKr8mTYQ5xUW6pBO26R/w4i1U5YR7Z7oIS0cLcyMHyglTe2P55Aq4Tuv+FwBvLjvIlhSFI83sUK/3CXN4URSFWEyEL0KhI5nY9FWmTvkefv80AHxqKDll5nuUS5JWJJ6M+jj8c28hzf9Kye0casK+1wYJk13VWEr048r2rr+y2WHSJyHcBOv/TDDWQVIbXVSBId7ZjHCfI7k8QWeQC2deCMAb+94AwOkU38FKDhS3OLD4sEtcJ2ZUievE5CpxM7WtZ9uorclMDjoRpjlR/blOkUikX7dqtI/rdrsJhUJFfw4a4m2iGlKy6XlgsiLz/X3Pc8rE8Vya+ZCcnKvsmpJqTphDuFwlc8LUkTchdbxLZUSY+FoO6ISpbRm86ntmquCJiC74PsqoCu3thJmYE6a5bF6Hl3w+QXX1J/B6J4mKyP3wOMxPzNfe/8GKKYKBmcQTbvZmbSQy5obcEmq/rTyuvk9m4vCXrxFs21TRId7aLMuYLHFE/REc03gMAG/tewsAp6OqaDuLjx+a46U5YVo4cmvP1lFbk5kcdCJMy8kqlX/V1dVFe3u7KW0iRuu4HxnUcTYEx4JaCv/Czhd4tuMdAF6z53liyxOVXVP9YTDrC3R7hditLjUXUQ1RVqnCoidt8h16Oqr35BpYhKlOmOpOmSowpp4EgNffMPCGoOaEKeavKS8mHHgdPhwOP0cecRfHH/dcoT9YL7Qu/0lTw6PlDRRvaDiTePcE1sQdpoZHAVIZIWRk7T1Z/V/wxHdh9xsQbIBwE0FZruj8yExW3PjEZYkj64/k8FrRyX97ZDuZfEZtaFvZMUoWBxa7Y+JaMTEkbtYnhy0n7CPFiSeeCMDTTz/d5zntMW2bg+G4Hxl6doq/wxP0hx7b/Bggqu4A/m/z/1V2TUd8Cc65hy6/qGQr6YSpsxKr8qLCrhIiTHPCyglHFir/THTC1OaxXm/pir8iXH68lQiRqiLMoyaY70939xvs3HUP8fhWvGqVaSpvXsPORC5BrV3m8NxeYstmDDjP0ucM6K8xk7TmJtlUYbjxCVh7L6g5h4ybK0SYGo6sRE6Y5nDFZYlpVdNo8DUQdAXJK3m29WzTCxey2W7T12Jx4JHNZ2lNiFzG8YHxAEwIiGtGNBPVmw8fTBx0Iuzkk09mypQpPPjgg6xbt05/PBqNcuONN+JwOLj44ov1x9vb23n//fdpbx/ZvLKhHvdjh+aEhUWxQTqfZk3zGgBu//TtAKxrW6dXSVUS7ZghV4nwr+qEBdXeV6b3CeudEzZQiwqXFo6sgODJlefyiHX5dSfM1BYVDhFi855wdcnnt++4kw8//Dld3a/iUUVPUjZPhCVzSY7w5vmXhjTb6+Ki/Uo/eNVQcsLkvmXZrLhgSTb1c6QVv9Qfpv8dlGVSajgynzO/g39WdedieZgUmiQmklSJCMEHXR8UnDBLhH0saenZgazIuO1uat01oCj4nD49X7c51jzIHj56HHQizOFw8Pvf/x5ZljnhhBP4xje+wdVXX82cOXN47733WLp0KTNmFBLDly1bxsyZM1m2rG+vmosvvlj/09zc3Oex3sJtqMf92NGtFhmoTti61nWk8inG+Maw0N3IIZ46ZEXmjZY3KremVATk/MAizFsFQCAjBIXpd2LpKAmpjJwwuwMcHnxKBZLg94jfSXk5YQE9Mb8SwtDnrSUW20Q63VrU58rrPQSAVHI3XpcmwszLOUxkE3jVRq2OGZ+D0ISS28lyFo/HwQSnrLt5ZpHLixsGuyMg2rGkusUT1erMxroZBGWZtFK8vZmk1OHccVnikJD4HU2pEjk/23q26SIsN0oiTLEax44eyW523yt6ao6z+5CWHQ3bRG/NsQEx5qo5fvCJsINubBHAokWLWL16Nddddx2PPPKIPkj7xhtvZMmSJWXv57777hvwsaVLl1JXV2f4cQ9KNCesSjhh73W8B8Dc+rlIz9/A3Pbt7AgGeK/jPRZNrNAw77uOh55dRGeKUSlBV4npCJoTlkmCx07M5C7npKPEXWXkhIEqeCqQE7blOXCBN1uGs9UrMd+sNSmKUhjg7fSy7s3zSadbOHr+n/SxNx6PCGWkUnuoV/Pncshk5SxOm/HjeZK5pN6o1RmeDI4SyfBAOt1M1rWSK8bAXXvLcBZHQDafJqeAwxEsuGCh8aBOiBAiTOnlhFUgMT/XDYDfPUYvmGgKinPCntgenGNFY+DRcMK2b7+Trdtuo672JI444g4kyV7xNXys2fYie+UU4Ge8DHRuEe2MppzEOP84NnRsYG9s72iv0nAOShEGsGDBAp588slBt1u6dClLly4t+dxwOkiXe9yPHT2aEyZOuFpHZJGYO4aZ8S38lTibOvufz2k4KZHfFVVdpNIirEo8l44BYfPDpeko8bAQX4OLMD8+RSQ6m+o6qWE872DrUddkdrPWVD6Fgvhuune9Rjq9T6zPO1HfxusRTlQytQevuzAQOplL4nQZL8ISuQQ+zQlz9l8F7XCI59w2SClltPwYAWvz01m5ew8/+8RnoGOLeLBmSmGDuukEZZkXYw7abYfw6wlfNnU9ABnXVLZ1vUtd4BD9MS3nZ09sT6FFhSrWKkUisYMtW38NKLS1P0NLy+OMHfv5iq7hY8+MxexpuQB2PcX4qqnA27BN9I9r9DcCB6cTdtCFIy0OUPYTYRs6ROLyzNqZcNKPOOzMOwDY2Lmxcmv6wRbyV20aWISp4cigWv1mqghTFEhHiKvhyEHHFvUK/ZnarFV127yl3p/98dfh9YgEfrNywnqLO6XnfUDBbvfjdNbqj3s848QaUntwukLYTc5TE06YOjdy1zv9budwBEEN6yqSuaEvLXQeclcJVwGgdlphA5efoCtMe87G5kQWt7uM6tcRss33Ge5s81AXmKw/Nj4oXMvd0d16M9tK9wlrbvkLULjpbm7+c0WPbwE43Ox1Cl9oXMOR4rF970EuzbiA+D4fjE6YJcIszCebAm1kTGgsiWyCnVFRLXl4jShRn1Et8uX2JfaZH/LTcLiIuQohoZI5YWooK6AmwEezJoqwTBwFhYSamO93DOI8uSvTDiKl5lL1V4lYxGFn4DnlelPXpAkpt2QnUyPSAbzepqKRSlo4MpNpRXZ5TA+Ripww8W/Htv6HDUuSDbtWrWiXTZ3XqImwoCsIXWpFZPWkom2Caq5NtAKD6QH2xYVr2dCr3YnmhHWkOrC5J/GJY5/hmKMfrch6NLo6xe9s0iHfBqC7Zy35vLnVqxZ9aU+InMH66mmiR6Ocg9aNuhO2L7FvNJdnCpYIszCfuCg5xu4GTxU7IuKCUOOpoUoL97mC1HqEk7EjuqNiS9OcLa/Di9NeIkxls4M7TEgVYbFMDNms5N10lLQkkZfKqI4EEY5U12VaYn4+SwpxDK+7vObBXqcQGWaNLdKrNV1BUh5x56yJLg2nswabKnbSzrzpQ8WTuWQhHGkf2DHU2jB4bAoZEys2tUreoDMIEdVBCBcXDATV/8fyKfM+171oSbQA0Ohr1B8Lu8NijUBLsgO/fwouVxntUAwil4sTiYq5hOPGncfYxi8wbdqPPpZJ+q/sfYWfrP4JL+/t/0bCFDq3wdPX0tYtGrLW++qhUXXDWtZT7xWzWNuTI+ticCBiiTAL84mJ8SkEGkCSdJE1Majm8HRsgd/M4ZCo6PC9o6cCIqxjC/zl60RfuhVAvwiUxBMmoF7EFRTzQn8ON4kFX9f/O1j3dY48H++R5wMmOmGZGGlVFHrc5U2a8NqF+NEamBqN9rN6HB49H2z/UJokSXg8wuVJNU7DE2wseq3RJHIFJ8zpGFisaqOLPJK5YeRLQjv4SWMSl9wNUVWEhcYVbROsmkSjQ+bc6jTvf/gfpq0FIJeLkU7sxG9TipwwKFS/jYbTEY1tQFFyuN2NeL0TOPzwXzKx6as4HGU4vwcRe2N7+c7z3+HxLY9zxXNXVDb0t/t1ePl22lSRVe+th4bZ4rnWDdR5hePdkeww1T0eDSwRZmE+E+bDT1rg0mcA2BkRoUitIzLuEHRtZ2JK3LlXxAnr2QXrHyG6XSR+lswH02icjXvsHJyScF1Mywvz1RD/1HcA4czZbYNUZ805D9+8iwATRVg6RlJtHut2lZGY370T75M/MnVNuhOmSKSTe9W19c1ncrnE3XPGlsGrOq6mrSmbxCsNnpgP4HSIz5rHppjmYCqKQrU9T71Twe8MFZyw/USYu2oSVZLMcYE8He3PmrIWja6uV7gwsImv16WLnDCAMb4xALQl2kxdQyny+Thez0QCgZkVP/aBxJ8/+LPuXmfkDA9verhyB2/bREqSiKrfoTpvHdSqI8g6t1LrFVGSVD5lfq/GCmOJMIvK4PSC6kZo4UitTxC+WpDsHJIVuUeaSDMVtTIyopbrDyjC/vWPSN9YRVANx5mZF6Y5I2U1Ru21nWmOSiauO2GawzUgkh2PWolnVjhSCyl6unaQSYiCj1JJ5W73GOx2H3k5qb9P5uWpRXFpOWFquLE/7KrD4rFBotucG45ENopTTZELKk4xtxXE2LDe1E3HroiF503uExZPC4GVlOnjhGkibF9iH5s+uJ4331xCNNr/1AEjqas9ieOPf4E5R/62Isc7UHlmh7hJXtQkWgQ9v/P5yh28axvtdvE5dNlcIj9Xq+Tt3IrX4SWgNl0+2EKSlgizqDiaCNOdMJsNAmOYmBVVeLuiu8xfhNaewiXm6g0owlS0bUxzwlIRkt3bgTJCkQCJTnxqE9xKhCPdjr5zGfvgr8N7+q/VNZkjDNNaOFKRSedEwUcpEXb4zF9y0onrGV/9GbyxdnVN5rxP8Vyan+zxknk3icM9cD6TFubySArJhDkXlFi6Q/+3P63+Hnx1sP/vcMpJONXxMGYnonclhBuXxdmn/UpvJ6yn5y26ul8lla5sOwKtL5ii5NnX+iTbt/83sok5ewcS7cl2tke2IyFxzbHXICGxPbK9cs5k5zba7eL9r/PWiSIbzQnr2g5yXg9JWiLMwmKorPktPPYt2PICUHC6DgkWegURGMPYnJiD2BJvMX9NmhOmNtUMlZF0ruWNmSbC3v8byUdEryatkeWAvHE33gdFTlgilzAnVyIT0weKe+xlrMnhxjtV3EknTUqCT6m/O7eikMqKi0QpEWbTmrKmY3jbRbNSsxLzE9kkcVnCm5SRBhH0DnsvJ0xtgWI0mgjLKeBwV8PRl4hZqSVwqr3LkJOmJqNHUuJ7Ldn7hrW1xOvWRKueU5erQPPY0tjYsOEHbNn6S1KpPaO0hsryTptoqzK1aiqN/kamVYtWJu+0999uxVC6eokwn9oAPTQe7C7IZ6BntyXCLCyGzdZV8PaD0LmVRDZBV1q4FxOCvSq1Ag005kU4si3ZRs7EETNAwQmzizyvARPzX14G/3Ukgbj48psmwvIZkqpTUZYT5qvBq56wZEU2p9IuHSOttswoSxj22i6VS5kiDFNp0XrBo8DRRz3MvLl/0McUlcRbhUftT5c0KWyrj1FSZL2tSX80NV3C39KzeCXmIKGY8znXRFhWsQlH4cxb4fRflNzW4yrksJnphiXTorGw3dH3u6Y5Ya3JVr1woRIiTJZz+oxNDUmS8PnE5ymR2G76Gg4ENBE2p34OAEfWicrE9W3rzT94shuSXbSpIkwT5NjshZYqnVsP2gpJS4RZmM/8r8DJ10HTsXqJesAZKA4BBsZQm5dxYENWZPNtcE2EqUnnA4Yjs0no3kFQFYmmibD5F5P6gshLKSsn7OhL8H6vkDdjSjViJk5KC0faywhHAr73nwJEJWnahPmIafX9d0t2/IHp1NR8EvtALp07iHfSQgBSsjnzGrUEe6+sgHtgJ8zvn0LM3kBElkxLzE9mugHIlXGK9+19h3wF5kdm1FFEjoFEWKJVfz6XM3lOK5BIbOXFf87jpZdOKHrc650knk9uN30NBwKbusSkEjHBBA6tORSALd1bzD941zYA2rxCfGuOF6A39yayR0/Ob0tWvnjDTCwRZmE+MxbDCd+Hxtl6qFFrvqfjq8MGNNjEhV4Ta6aR7AYgqiYvl2zUqjHnfPjaMwTHirmEZlbn9G6/UA4OmwOXTYRUTbmgKzIptYN/uWtyryy0OjAjByuli7CBp64lk7t56eWTWL36eNMLGBqkbr5dnyLV5BpUhAH41ErgZNv7pqwnkRFucx4HxNvFTUc/rmRAlkipUch8zrzPtuZsuUoULmgirCPZgU0N11ZChGnhRoez+Pvv800CPj5O2LYeIYSmVok8rClhkRS/tWer+QfvFMfu8Irvje6EARz9VTjj19D0iaI2FQcTlgizqCilOmYDokISaFDHmZqeF6blhKmNSAd0wqqaoGlBobu4iaOL9PYLZVZHQqGpqxmCR5lzPinVLSwrJwywu/y4TGyOmlYnKngGGcTtcARJpXaRzuzDq4rtZDZu+HoAvCSZ4ZHJ++zlibCo+HwnTBJhKXXsT15ywuNXwH9MhLX3ltw2MPVk0op4g3J5c94fAFl12Xyu2j7P1XhqcEgOFBQyiLBUJcKRqZQoFvB4ipvY+tTwdipZgUrtUSaVS+k9wSaHxTgpTYTtju02xc0uQh1p1+4UN+BFTtjMs+CYr0HdNGrUcWhdqS5z11NhLBFmYS7ZFGx+FlpEboHuhO3XJwi/+OI1qnfklRJhUTUnp5zE/IA6tsc0EfbSb0iuvQcoU4S1vg93n4Y3LS6cZrg8WTmrD8su1wnD5cetzWo0oU1FSv05GwMK7777XZqb/1JyO4cjhE11Cf3vPABAWmtaaiA5OYcdUdnrzA8ejozFNjG7qofTQxkSJonCjNpGRZGckFY/r/66ktv6Q00VccIkWdwk+N31fZ6zSTZ9ekZKFpelyoiw3UBh1qiG2602+q1wheZosCOyAwWFkCtEtVsMUK/z1hF0BZEVme09281dQES8x91q7mm1p7rkZlXuKrFdutvc9VQYS4RZmEvPLnjgi3DPZ4FCmLE/J2xsTlzMmuMmn/w0J0xNZh/QCYs0wyt3EGx+FzBRhO1ZS7LzQ6BMESbnYOcr+HLiTtWU0F8vEVWuE4bLj1etsjMlJ0wbuO5V2Nf6NyLR90puJ0kSTqe4e/a5hMOaNkGoJnNJPKrT5nKExMy7Achk2mn09nCEN29aG48ofp6JOGi3T4GL/yaaJU9fXHJbv9PPnqyNLmqw2crL+xsOdkV8FoLe0oPCNacjqbqouaz54cikGo58YscrrGtdpz+uT1tIHfwibFtEhAMnhyfr81clSWJySLhiprcMUm+MuhDV8UUiLJOA7S/Bpif1xy0nzMJiKKgVhZrI6tcJ08KRaXHRb020mrsuzQlTRcKAIizaDE9dQ3CzaLFhWk5YJl5oB1GO66R2sPfK4uRlRk5Yas1/A2BHwmEbOAersK5AwQkzIRypOWFuu3ivXM7++3K5nOJz5XY61PUYL1STuSQudW6ka/Z5UDN5wO2LmrWa1LesmwB/73HR7Vbn7zm9oLZj2R9/Osb/drpZ2TmGqqqjTVkPwGPRMfy2zU11sHRnek2ExfJCwOfy5jthe7uFQ/9W1w6ufOFK3U3WnLB8PjaKrTIqgzYmblJoUtHj4wLCHTR9fJHmhKk3xJrjBYib+Hs/C49+w3LCLCyGRUJNotxfhPVJzBcn4NqUOOGZnnypiTD1IhhyDhCOVEOVfrX6MG5SCIlMgqSaBF+WE6aGvbx5IcLMcMLSrSJnyS3Z9bvkQTE7HKnu06lOdXKVyDHScDrF3bPbITY2Q4SlcindCXOU6IG1P9qAbzG2yJy+ZdpnVAuhD4Q/I96TWGS3KWsBMUZpQzzBxpSdOn9TyW00p6M7b6Ou9tNUVS0wbT0aWk5Yd16iI9XBiu0rAHA4/DjU/mkHuxumRR2KWgbRS4TFTRZhDbPIjptHVP1eayFRQPQKq54MY+dQrbbtiWVjZPNZc9dUQSwRZmEuSdEbSBNh2oDePiIs0Ain30LdcWJ2YkfKRBEmyzBjMbkpi0jkxQVoQCfMI0SYT829Mk2EZeOk1LyIskSY6oT5VMFjhghLzjobAI+zjL5lvdblVUNK6ZwZ4UixT4ddOCZOV/9OmNMlTugup019rfGiJ5lL4ladMHsZQ58LHfMhJZsrwvyyDP97Djzx3X639QfEdzEhm3dhS+aSunjWHK/9qfWIc0Rr3s2cOb9j+rT/Z9p6AHb27MAriZzQ06aJpse9R/V4PONwOmsqUqU5mmhO11h/8UircX4hwvbETG5Ye9Z/0fPlPwMiN7DoXOwOwHfXwcV/I+irw6bepB5MbpglwizMpZcTFs1E9YtDHxHm8sGx36RulujqbWpDPpsNzrmH6Ln36A8N6BiojpNfzXMyzwkrhCPLEmEOD0h2vLJYlxmJ+Wl1dIjHObi40OnlhCXzJjhPatjCbhMOoBZyLIXmhDkctqLXGrqefAq35oS9+vtBt9dEmE2CjEnCx57Zw2xPjlCmHT58Gj58pt9tA4FxSChkpDw5ky5u2k2V1+HVq3n3RxNnnalOU9awPy/vfgY1os3Jkz4HwOstr+suyzFH/x8LT3jd1BCtxh/e+wOn/+V0fr321+ZMvhgAzQnrI8IqFY4EulPdAIRdYew2e8ltbJJND0lqDb8PBiwRZmEuugir0RuwBl3BfkWG1pAvmUuaN5RaJaa2OvA6vAPnOzk8YHMQkM0WYQmSQ3HCJAlcAVOdMM29KGtupIbTp4swU5ywsJh1qLUJGygcqeWL2dWrbSpvggjLpfBIqhOWHDx/yGbzoiDWk5fMEWHj8x9yaX2GYFYUevRXGQngD45lcSjLFZNzbP7wJlPW0xbZxDnVGc4I9y8wtHBkpUTY1o63AMhJHg6rm03YHSaRS/BB9wcA2MrNgRwh69vW88s3fsnu2G7uefeeig7OVhRFTxHZX4SNV2eKmirC5Dwoii6qwu7wgJvreWGqaDsYsESYhbkktHBkje5uFTXj603zO/g2v4BX7cxuWl6YnAc5ryfYDziyCITYcQfxqSG2RNasOY3xoeWEAbgDeujPlMT8Xa8C4BmkMWoRTi9eTYSZUR0pZ3GgIKnVds4BnTAhwmxq6DJtgvOUzqdxq2dSxyf6D/tpSJKEJInPuCzlDV8PAOp745JVq8c3gAhzB/U+Yfm0Od+57thWPhnIcYSn/xuY3n2gksndRKMbkE2acADwdtculre7cDZchE2yMbNGFAxs7Nho2jFL8cgHjxT9/6FND1Xs2N3pbv1Ga/+K9bFqX8RYNkZPusecBXzwFPy8ge7nlgLFlZHR6Abef/9aWp79MvxqBqz5H8sJs7AYMolCTpgmwjS3qw9PXYP08BJq7EKAtKdMCkluWwU31BD7v8sA8LsGT6bGHRT5NUBOyRkvLhRF5IQNJRwJRe0gTHHC3vwDAB55CGKhdzjSJHcuYBf7lyRnyTE4GqHwHCZP+g5+RMVi0oRZjclcki1pG7vkGtzjTxj8BYBkU3+/NnMGZkuKcPxcaqUh/n5ufBAjxNLqZtmMORdbbZalIpWu0ISCCOtIdfDa62fy2utn6YnzRpPMJXm/Zwfrkw5mT/oqADNrS4swM8ODWTnLczueA+Dnn/w5AK+1vGae6NkPLRRZ563DZS/+3XgdXt2ZMm2MXHQv5NN0qd9LTWRlMu28te4i9uz9I+/ZXqbN3QXR5kLxhuWEWViUSa+cMC0vpM7Tz115wywYfzR16ggh0/LC1OaVcdUkCJST7+QO62E/MCEkmc+AnNPDkeU3Ri04YeYIHiE2y+4RBuD04jHRCUvF9hFQz1wuZ82AVZuh4GymTPkuVU4xCy+tGO88pXIp/tbj4lXmEwrOLus1NrWKUrEBJlR62RSxT29W/XkHCEf6nD7dCcuY1JtLG6PEAH3IejthWgVp1qSk+M1dm5EVmRpPje7MH14j5ia+3ykqgru6X+efq4/jjbVfNGUNAB90fkA0GyXoCnLW1LOYFJqErMis3bfWtGP2RhNhWhL+/mjvjWnzGuddBN99h+5pnwYKTlg63YbPN1XfbNtEL8RaLSfMwmLIaCLMWzO4E3b6L+Drz1FXMx0wMRx56Gfh6s3EP/FNgH4ThYtwB7EDXm1Oo9H5ahkh6oaUmA8iHGmi65RWc6jcQxijhDuERw0pmzK2KBsnD7jth1BVdUx5S1ILL8wQYZrQ9HTtEtMhysDjn872tI0EUqGjvYHYEc6CJ6PmwA0gwpw2J7Js7tgibYyS7gCWQBNhiVwCm0ObH2lOj67tke2AmJWoifgpVWJUz7bINhRFwW7zkMm0kk6ZN73jrVaRlza3fi42ycYxjeLz/Ma+N0w7Zm/6bRmkYroIc7ig+hC6VCWiiaxgcCZHz3+YTx7/TyRsRINO4ukdBSfMqo60sCiTXk7YoCJMRStVN80JszshUE/cLvKcynPC1ApJdV5hPGfwxUoTYbYh5oS5AnjUcKQpjVHVHCqPcwgibNbncC8QAteUPmF2J81ZG+Mm/D9mz/5NWa/xqO5qEsXw8JI+dL11I7z3f2W9ZsK0pfxXq4cPsk7hghqIoig41Jmo3pQqzAfICQOwqTNbcyZUswJkckKE2ez9f478Tr8+jB6buDEyqz3EzuhOjvXn+JS3i2hMOF8TgxORkIhmonSlu3Cp45Uy2XYUxZyw8bq2dQDMHTMXgDn1cwDY0LHBlOPtz76uLQA0NL8Hsb4Nsut94j0wu3m2JqqKeoQh2oRUu4WL3cHeghN2EHXNt0SYhXnkc3pT1KJwpHfgC4L2vKltKih0vvc7y8sJAwioCepaZaVhqM7akJ0wpxePWcOyFYWUGtbyOMp4j3qhhS9NqY5UZ1m6G2aVtX1r61P0KBtxqRWMGYPbVKSyCcY7ZaocCkoZw7uhEG5OSqAESo/xGfZ68inc6s8aSGsibOAbH5uaq5XH+OpRgKw6k9Jh7/+GR5IkfX5kXl2PWU7YjsgO5vtyHJJ7h3hMVEN6HB69QnB7z3a99Ymi5Mlmzbnob+rcBMDsOhHGPrRGCI4POj8wv1WFLNP+/hMA1Le8C3/6qshN7YXmhJl2Ln7xl/DsUrrU0UXa7783NcH5AHS5Igdl13xLhFmYR6ob1Asm3mo9vKg5XX3Y9CTcOpva9/4KmNiw9d1H4e9XE1OHig/FCfNJooeN4ZWILj/ZeReSG6oIm3Ea3hmnAya4TvkM2h7driE0a6UgMoxek6IohfBfmXlqG9//Edtjf6Xabo5YzeZ6+EFjimOm26CMDvUAPkfh/TQ6by6ejeuC06e1zBhknqVTEu+lrJjTMiOvhjmdgzSz1S6yOYTjbJoTFtlJSP08uHoNFD8kdAggRJrN5tSrazMZ40VIJp9hZ3QnANOqpgEwJTwFh+Qgmo3qoULT+PAp2tRcvbp8Hnashu2rizYx3Ql76wFYfSvd6rm+2l1NZ+dL9ETeJq9+L6rqPgVAjy9PUP38RNIHTwNdS4RZmIe/Dn6yD77/Ptgduggb0Anr2UV1UnzBTKsQ2v5PeP13xHvECbAsJyw0Hmqm4Fcv/IYn5ocnkDz9Zv2/ZYuwI8/Fc/QlgAlOWCZOWhOFQ2nW2rUD9+v3mLImTbBU2WVsmQ7kMlpOOB1CgAS0XmGGizAhdGRZQfIM3OdIw2Vz4pYU3JJieC5fPBvXW2Y4E+WJMIfqdCqSOWE3WQ1zupwDvz9aNV4GcbNjxhBvRVHYGdlJUJv36SqcjyaFJwGFodbac2aIsG0925AVmaArqDtOLruLyVWikndT1ybDj1nEOw/Tbhfvc+3kk8Vj6/9UtInpTpg6W7hb/Q5UearY9MH1vPHGF+juFu1xAnXHIskKWaeNYL4bgEjGEmEWFuXh9EBoLHk5rzdh7FeEqVZ0lToeyLS4/37VkWWJsBN/AN95C3/dDMCcId6aOLBLdpxq7lk5aI6Q4Yn52aRerekeytgiJY+nYzNgvMuj7e+c6gxvrj2d5pZHB32NQ50LGnY6TVmTlswu59Ed08HYueMOfjEhyZnhLKn3/2boemLpbhzqZ9seV29kBhFhblcVAJJNQTGheAF1PJN3EBGmOWEpWVyazAhHdqe7iWcj+LXZo736zE0IiPmJWoNSt0uIkHTG+MT0rT1bAZganlpU4XtkqI4Fvhw7u00UYdkkbFpBhyrC6mZ8Vjz+/t+LQpKmOmGZBKhpHV1Z8XsOO7wkEkIABwKiWtXuCuFXT22uhDivRDMHz1B1S4RZVITudDd5JY+EVNSQrwhvFQBVqZj+GlNIi/3HtbyZIbg8mmAzvDoymyIZFyc6j8NT/rDsTBxvshswIRyZTehOWNktMwACjXhMSsxPqu+77mI4+58bqeFUBzEHMaefWl7Nd1JkRcy6KwO7XYhat00hGTW2F1Y8l+aaPV7u7pmKI6c6W+p3qz9cgYn8b4eLjvA5puQidWZzNGcl/N4JpTfIi2rOkFpAkVDzHHN542929sb34tOvfBLOXsJQG9XTHBOtG1yqCMuYIMK2dIuk+KlVhVYM0egGPimv5IRglt3RXYYfUyebJHvURXRpImzyyWIySKId2j/QN+vthBn+uUgIFyxrd5FQv5POfBsg43TW4u4VJp7RGuSYN7sY4xA3wZFMpOLjnczCEmEW5rH5WXjsMnjzD3p+V7Wnuv8RQaoTVq2Kip50D/mhNAktF9UJi6l3/GU1a1Xxq2Ebw8OR6x8h+buTgCGEIgHW3ofnwfMAE8KRvUXYUPqEuXy4J33KlDWl1Ttn7SKqzYYcCId6kQ2pITGjh3grsirq8sBAg+B7YVf7hLklSB5yvKHrSeQSJGQJ2RFGmv9VOOJcGGTslM8V5PWEgw7HIYaP68nKWR7rkvhFi5cJ475U/GSsFf7nJLipAZ67UXfC4nnx3dQErpHsi+/Dr4p4hyOMJBVmFWpd4vfGhTB2udVwZNp4EbatRzg+k8OT9ce2bP1PbOR5JuJkW2yf4cfU8dXQeeJVgHDeq/z1MEFt99IrL0xzwtL5tPEhwJh4T6OBgtjKp7YDEAweXrRpNY2EYnmqckKs55W8KRNCRgNLhFmYR/M78PYfYeerek6B1guoJOrdelj9oiko5tjO6j7japfmspywHS/Df5+Af+sqwIRwZCYx9MpIAJcfj1rWb3xOWGFNQ5odSa/qSINDfym1U7ZPv4iGBn2Nto1fC0ca7ITJeXExkPIKlCnoezthKffQih4GQ3Npfa4QnPVf8MXfDfoa7TsQz8T7VMiNlN7f4eD+IvXv34e9b4Gcg5dvJ5wVn5fd+WqOmvcg06b9yNC1gOiNpYkwp7Oq6DmtOrI92U4mn9GdMDPCkVrIc0JQuIOpVDMdHeL8sjdrY2dkp+HH7I02kaTWU4tNsoF2M7D7dX0bt92tu/+Gp4fEVRHmF9cEv9NPMiFmnQYChxZv6xPbeFIRPVXjYEnOt0SYhXlMPhFOuR5mnl1eUr7TCw4PTiCoOk6mdEbWnDC1VUFZOWH5DLS8gz8u8toMD0ce+02SS/4MDFGEHXURnivfBUToTzayn1E2Qdo2DCcMcO9+U6zJ4PcpnY4goeDRnbCqQV+jbeP3qb3CDG5RoTlhtjxlizAtEd4tGR8e1fY3lM+R1rA49tpvodXY2YlagU3QGSx2wTu3wkY1H27RtfC99wjXikbNrdks1dXH4vNN3n93I2ZfYh9+uybCip3Uane1/llvibdQXXUsU6f8gLGNnzN8HZrbpg3K7uz8J6DgC8ymPWejOd5MxoSB8ygK7HqdjshuoFffxnHzoGYq+OtJJvfoxQha7y7Dz8WaCPOq6QKuIMnEDgB83v1+76oIk5Kdesj6YEnOP2hF2Ouvv85nP/tZqqur8fv9LFiwgAcffHBI+5BlmWXLlnHkkUfi9Xqpr6/n3HPP5cMPPyy5/aRJk9ThvH3/XHbZZUb8WB8tJsyHT10Jh55WngiDQnK+KoxMyQvTEvPzQxBhjUfCkr/gP/Jc8Vqjw5GSRFLrcj6U/CtJKrrYGuo85dL6LMshrQnwrv41YIIIy0TwSKBqQxyOwasRnaoTpoUwDR+lpA6ZtuflsltUFMKRCqmdrxq6nGxyM/9Wn+JoaSMku0AeXJgHnAGO9ecYW5cnETFehDklhdD+RQvr/wIoMPVkUfgSqNerI82cnSicMPFv134iTJIkPSTZHG8mFDqCSZMuo7b2REPXkMgm9EIlLQ+ts+tlAMbUnoTP4WOyO8fbG/4fbe3PGXpsunfC8lNof+zrQK9z8qGnw3feZMehE3j5lYW89PJCOjpe1KMX2noNQxVhEY/4XARdQRJJVYT5JhVve+R5fHDKZ3nT9RLjPeJcdLCIMGOD/wcIK1euZPHixbhcLs4//3zC4TCPPvooS5YsYfv27VxzzTVl7eeyyy7jd7/7HYcffjhXXHEF+/bt4+GHH+bpp5/m5Zdf5vDDD+/zmnA4zJVXXtnn8aOPPnqkP5Zh5OU8e2J7CLgCA4cHDUTLCeu3R5iGtwpiLVQ7fOzCpApJLTFfzREqKxzpq4Hpp+BHFXBGizCG52CACBlopHKpIb++Xw77LKkP74XOjUN3wswKR6ajeijSZvNitw8eJtWEmkdtv5DKGus8SYomwhQos59aIRwJyW0r4birDVtPLtvJNI9MNN8Kv5gEM8+C8x4Y8DV+p59PBnJMdEkkwkGMDJBGMhF+NjZJwLaZWGxTIdSkjXiaeZa+bdhlvgjbl9iHXQHFUYvbM7bP82P9Y9nWs00PF5qBtu+gK6g7O11dawCoqTmecYEXOUzZQKTtr7TZHdTXnWzcwWP7INBIWzAIpItujGU5R2fny+q/02x8/xpqPaKLv/HhSOG0RV0+SEDI6SeVEq6+13tI8baTF9LZ+u/E4x8y3n0Y73DwhCMPOhGWy+W49NJLkSSJF198kXnz5gFw3XXXcdxxx3HddddxzjnnMH369AH388ILL/C73/2OE044gWeeeQa3W5zsL7roIk499VS+9a1vsWrVqj6vq6qqYunSpYb/XEbygxd/wDM7nuFHx/yICw+/0LwD7XkTJAlqp+tf4FIdkYtQnw+reU6GO2H5HOSSyEBcFT1lOWEq2raGi7DXfkdqqwjNDElEdWzBvuLHuJFII3pOVTN4snq5aNWNQ3XCxKzJLCmDwympTASvHoosryeXlpjvTncCHlKdmw1dk00RP6NjSDlhBSfM6HCk1p1eTAdn0PYUID7Xraphlje4YWtPuge3JE4Fmvgk2Q27XxP/nqYKjD1vUrXiB+CAdKaL9zddhyynOXzmfxi6npZ4C3sSDv7tsN9y2Jh5fZ7X8sK04dZmsH8oMp1uI5NpBWyEQkfS6G9kW4cYpxSNvmvswZsWwNWbaH/5evjwz0UizGZzMG/uvUR73mXdu5eSTjcz1TmdFzDDCRPV4FGnOLc0upwoSh6bzYPb3XeKhM83mXj8Qxoc4ibsYHHCDrpw5PPPP8+WLVu44IILdAEGEAwG+elPf0oul+Oee+4ZdD+/+51IZv35z3+uCzCAk08+mcWLF/Piiy/ywQcf9PfyA5pJoUkAbOnZYu6B/u/bovJpzxv9zgbrg3rBqFbHAxl+96UmCSd6tYAIlBNCkvPw5v34t7wAmCDCdrxMcu9aALwDzNfrQy4FHz6FRzZnfqQ2dmioTphX65gvZwwtJU9l4roT5iwjFAlQX/dpTlz4DmubxfuaNnjk1KtxD7e3uglHglCmoNdEmEsy/neW0+aaeurg2lZYfPPAL0C4wWn115TPGxtCjqS7cKlXGu3nZs9aUGSongxVE8Vjko3w3nUAxLIR9ux5gObmPxnat0xWZPYlRNVhg6/0uKgGv3i8NdGKosi0tDzOrl33kjew3cqe2B4AxvlFKDIWE7Mifb7J2O1eGv2N7M6KNy2e2GzosTU6MsJtLEoRefGX8B8TCb75VxobPw/ABEmIUbMS82NOccNd65AACa93IpK0nzRJ9eBVTfUquzjXWSLsAGXlypUAfOYzn+nznPZYKQer1H78fj+f/OQn+zy3ePHifveTTqe57777+Pd//3fuuusu3n777bLXnk6niUQiRX/MQOtLs7V7qyn711Er2fBU6UmdgzphWq8w9S7e8LCElpSv3n05bI7C0OABkeDxK/C/9nvABBGWiReqI4cyLFtNqNZEWNLIAczv/oWUerc61OpItypGFBSyZXS1L5d0Nl5oL1BGUj6AzebG4fDjlkRVVcrg311bVmZL2k7ggifBXl5wwaGOLXLZIGnwBVav1rR7RWuKMnqX+Zw+0or4/OX3rDF0PdFe48d0Ebb3LfH3+PmFDRuPIHzGbQAk8oU8NiNFYWeqk5ycQ0LS2y/sj9Ybqy3ZBkhsfP//8cGHNxraK0wLR2r5YNGoyMMLBmYC0OhrpCcvkcGDouSJqUPGjUSrWNcT80H0CktHoGMzdWoINJjbg4RighMmjh+xqY15PdNYdNJ7zJ1bwiRp3YjnTdGYOWgT55ODRYQddOFILWm+VLixurqaurq6fhPrNeLxOM3NzcyePRu73d7neW3fpfbT0tLCxRdfXPTYaaedxv33309d3cBJ6TfffDPXX3/9gNsYgSbCNndvRlGU8huDDhW13xfeKl1MDeqEab3CVPfE8IocLSlfvTD5nf7yfn6bDVx+/GoStuEiLJvQu9MPtUUFgFeWAbuxrsq+90jJGbDZhubOAZ5esxFT+RQuezlCd3DS2QQfpu281hrmW0d9f2hrsjmBjOHFAprwHUrI1m4Psk2Zy8qODRxjcN5cXqvWtJX/Ows4A6S1cGT7e4auJ54RIkzBhk274dFE2Lhe4UCbHddRX8a78XYRopUcoOTI5aI4HOX1XxsMzQWr89b0O5VijG8MAG2JNiRJwumsJp1uIZvtwuttMmQdmhOmtafI5WPYbC4Can+sRn8jINEuexlnSxGNbSAcnjvyA+cysOxoqJ5EV1A4jFpecDy+GWXafPxTX0KqmUbYbheiOR9nnNNjvAgbNxdcfqKqCAu6gthsbjzuxr7bBsbgcY8DIvgQ39+DJSfsoHPCenrExT4cLh2qCIVC+jYj2Ufv7TQuueQSVq5cSVtbG5FIhFdffZXTTz+dFStWcPbZZw8alvnxj39MT0+P/mfXLnM6Jk8KTcIm2YhkIuYNyc6lQct18YQLOWFqM8Z+mXkWnP5LqsaJxoHdmptmFHYXTFlEfNyRwNC65eMK4FdbQBjvhMX0SsQhiTDNCVOMH06tzDidtE3chAzVCXO4/NhNWFMqGycmS8Szfqqqhlbs4rEZ3ycsK2fJyaKqdSi/N5vNQcI1i7eTDlLq641CUUcE2bub4dFvgjqofiD8Tj8Z1QnLGdwgNaEOiVYkV+GGRw07Mm5un+21CklJFZFGrker0r68ppWVq46gp2ddn2208JzmFGljjTJZ40TI/iHRaVOv5sSF62macBHQKy9NNZETCYOiFt07xJ/db9CtOkna+719+12seffLbE+sBKcHm81JODQXgIku2fhw5L/cAV97mqhDeEF9esj1pmYKni/+EQCXLG6kDxYn7KATYaPJz372M0488UTq6uoIBoMce+yx/O1vf+NTn/oUr7zyCv/4xz8GfL3b7SYUChX9MQOPw6PPSDMtJJnSBKpEzunXvzCDhiMnfRKO/QbVjXMBE5ywuulw0f8RO1FUow0lKR93AL86TiUjZ8jmDUxgziRIqneEQ8q/MlGE5cbNJY/Yr7uMKsTeSC4fbnVNWl6ZEWjd7j1lhZAFiqLwxtpzmDm2jYBNIWlgp+10Ls1poQxfqs4g//0bQ3qtR81FTCrGijBtTqMj3gnvPCTaVAyCz+nTnbBs3tgbjFSmW/zDpn6uE52g9qii8cjijWNthLOZou3zBo4u0kSYV5LJ5xPYHX2//1o4siPVQV7O673EshnjRFhbQoQ2NdcNhDC3q46zcMJgR1J8dxJxg/J3O8R+5JrJhXOyemMciYrUmWBotr55MCR+PxNdedNm+WrNfLUq0f7wekQRg11J4pIUS4QdqGjuVX9uVyQS6dfhGso+em83EDabja9+9asAvPTSS4NuXymmVE0BTEzO10KRnhA96nBWCWnQL5qGJtbMmh+pOVlDc8L8+Hr1XDLUDcvE9WKBITlhNhs4vHgU43PCes99HHLbC6evIAwNzHlKjRGz49yH9M3V7A9JkojHt+B2ZPHbFENFYSqfYp4vz6cCOZT40Jxrj1ttHousz040Akmt1nRm1ZuEwW58KM4JS8vG5qhl1CHcmrNFqhuaPgGNR4Bnv/NBJkZVj8iXyiOcS73QwAA6Uh3YUHBJ4r3Zv08YiPCcTbIhKzKdqU6cLhGuy2aNESGyIqv5ZsUirDdaccDutPhcxI1ywjrF+T5ac4je2LnKXUU2G9EHZ4e2boC/Xg7N7xAKHYFk8yABnelO44pseu1HE2HBfbfy+htfIJncU/IlDkdQn35RY7dE2AHLQPlaXV1dtLe3D9qewu/3M3bsWLZt20Y+37cyZ6C8s1JouWCJxAEy66ptE5My4kRt2miMXkn5esdsV7D/uZH66yKw/SWq2sTJwiwRFlMr5IbkhLmCOAG3WrkZN/DiQDZeCEcOJTEfwOXDI5sQ+tsjqjUlpH7zZ/rFadKaUDghkGWitIFIZPAwm74ctU2Fz6YY2rsslUvh1hrHHvudIb02nN/K4lAWm0vSq3aNQFJbTDjT6s9ZRosKp81JHhF6zirGthXRwol6e4qaKfC1p+Cy1X03rp5ESL0s5fLGD/HuSHbgtYGWBepwVPXZxm6z6/0MW5OtuhNmVDiyO92th7BrvbXIcraPuHHb3dR4atiXE+9FKrWHvBE3WKoT1h0WBQE+hw+X3aW3wfB4JuDasALeegD2vUtd7ac57pNreKjLTU7OEc0a9Dnd+xb8vAH+5ySimShOSUHK7CESeVufJtGHB8/Doxog1Q7Fygk7UDnxRNHZ+Omnn+7znPaYts1g+4nH4yXdq6eeeqrs/QCsWSOqjSZNmlTW9qaz7n9pek/0pNod3W3OMXol5WshxWpPGf2r2t6Hez9L6LkbAXGXZOgonlf/G25uIv72/wJDFWFiW58qSJJGNv3MxPXE/KG2g8Dpx2tG/tWLvxTrsTmGXrzRKxxppBOWzqeZ481Tl36LZLL8GwitYavXBkmDRZhHq9accOyQXutNvs7p4SxuD3oDYSPYlbXzdsKON66KqTJEGIAsuYnnIZ/LGDo/UlYLBfq9uPZGkqhSzxOZnBCTRg7x7kh29BreHep3WLmeF5Zox+VUnTCDwpFaKLLGI4oDmlseZeWq2by/6ad91hCXweaZTn39YnJGhIlVJ6xbHZqtD0xPiN55gcChUKUWH/TsxmZz4nUG8KmFNoaFJJNdor1OPkskEyGsjpGy2339T8FIdlHbnsDrmElCtnLCDlhOPvlkpkyZwoMPPsi6dev0x6PRKDfeeCMOh6OoerG9vZ3333+f9vb2ov184xsiv+Paa68lkyncGT733HM89dRTLFy4kBkzZuiPb9iwge7u7j7rWb16Nb/+9a9xu9184QtfMOaHHCm+WiaoQ7J3Rc1J/u/thGnJ9YMm5QP4aqF2GqEqMTtMVmRjh2WneiAdIT6UuZEaakWlV3XCEkblFuWzkM+QVHvj+HpVFpaFy5zQX1r9+TxDdcEAnF59TYY6Tx2bC33CymxRAYWeYj6bQtrA2ZGpXAqXqk/t9iGEtik4Q4pdAgN7l70Yc3NPh5vq7izYnGU3kN2cr+Une334tyQhY5zLuzmZ54+dLmoazhEPyAP3/QqrrSPSqggz1AlLdfQ7vLs3eoVkss3wcGRrorXoGMnEDmQ5hSQVV+ILISgRGfMtjjziDtyuQUa+lUOHCGv2+KqAQlJ+Ii4e9/umQljt29ZduMnRbqANE2GTToAr18O5fyCaiVKtijC3e2z/N3y+WqZtTzDF+Rl2ZOxEMhFDexCOFgedCHM4HPz+979HlmVOOOEEvvGNb3D11VczZ84c3nvvPZYuXVoknpYtW8bMmTNZtmxZ0X4WLVrEpZdeyj//+U/mzZvHD3/4Q77yla9wxhlnEAqFuOuuu4q2f+SRRxg3bhxnnXUWV1xxBVdffTWnnXYaCxcuJJvNsmzZMiZOnFiR92BQfLU0ZYUI2x3bbc4HWc8JC+shxbJEWO1UuGIt7q88rjtChtrOn7gM/m0tcbU/0VBzwgB86snSMBGmXvCSw8kJg6LQn5Hd11PqvjxDTMoXa/LjUbT9GCgMe3bpMyC1/JBycDi1+ZEKKQP7liWz3YU5li1D6+VkU5OwFZtkqBOmDZf3yorou1emi+lTvwsJmwRJY1wfWZHZlUqxJu6gYYzau/Gu4+E3c6G5dA/FoFoZGEnJBIOzy27KWw4dyY5ezX6r+t1Oc8LaEm26E2ZUOFLLB9MKALR5ifuP6tm/SnPEZFPQI266u93iXLa/E+bzT4WwKNqipxAlqXaH8UqKcekhDhdUTSRbPVFM+VBFmMfdd4yUjjrEO6h+V3JyztCbztHioOsTBkJArV69muuuu45HHnmETCbDrFmzuPHGG1myZEnZ+/ntb3/LkUceyW9/+1tuu+02AoEAZ511FjfddFORkNOOuXHjRt58801WrVpFKpWioaGB8847j+9973ssWLDA6B9z+PhqaczlsCvCpWhLtvWbIDpstOrIXuHIskRYL0KuEKlkyljb2RMGT5jYh+LC5C/TJQBALaH2qRklhoUj1YtmSq2OHHpOmB9P0oRwpLov9xAqEXU+8S3c0beg+VVjE/N91fhsInF3OE6Y1wZtBraESGW7AZAVsG38BxxyQtmvtdv9yAAOm+gebwBZOas3x/UpCrjLF6o+tdI2brOJcFHVyG8ak7kkilphG3AGhOvbsRnknHC9SxAKN0H7K7zdAZd+7q8jXkNvOlOdTFMNJ8cAY6+KnDDnUYCYpWgEfZwwNazu804q2s5wEda9A1DAFaSbQlI+QFytvvT7pkGVer5VBduevQ9ziXct66pthjfP1nJzq9RRRB7PuP43VgtMfKmYXjgRy8SMm5U7ShyUIgxgwYIFPPnkk4Nut3Tp0n5nPdpsNq644gquuOKKQfdz4oknlp0jNur4anECjTLssYuQpPEirFv83SscWVZOWC9C7hCtyVZTYv9aiNNfTp6KhtYYVRVhhjthw2lRAeD04VWMH1sk5j7ahzw3EgBJ0n8OI6sRs74qfQTOUESY5pp5bQppA8fgpDM9SEBOBqmc8Ve9cDoCZAHJE4CJQ8sn649kLslYp0xeAbcii5uOMtHC4AlJEm0kDECrfHNIjkKbk+9tgPYPIFj6ghusFs2kI7mEqBotcwrBYOTkHN3pbrwBLSes/75UuhOWbCMcns+ikzYWGs2OEC0nTOvYn0qJatD9BUghL62NZHIP+XysMPx8OGjhxepDdEcr7A6Ty0XVuZXg90+FvCr6enaDouBy1WGTFBqcsnEibP2foeUdohNEs956px3IlhyoruOtRpYgkd3DYT4nG+Jpotko9ZSefPBR4aALR1qUgXoH2qSWsJuSnN8rMb/3F74s7j0Tbp1NSM29MjQc+cbd8NyNxGMtQJlzIzXUnDCfGmYzTIQ53HD4v5C0i9yrId/ZTTsZ7zgRXjUyHJke5vBuDa3Bq5FOmKInaUuFEThloF1wPZJCymNM93WAdE58NnMyZedeFdYkPk82A6sRk9kk/1af4pqxKXIee98WEANQ55C4flwC9xFew8KR8Wycwz15TglDNPaeCI0GG2DyCaK9SgmCYRGWi9ok6NpuyDpA5DMpKHjV+PFA4WytOrIz1YnN5jBMgIGouAQRjsznk+Ry3YDIh+qNFq60pz/k5VcWsv7doVXf9kETYeGmQoqIp0pvTeF2NYjvSWg8IInE+Xg7fp9oZ1TnUOgxKhz5wQp46TdEm8XkBDE3cpBwpLeKhNfOGv9qLqoSYjBm8BzY0cASYR9H1GqpCRlxcTQlOf+M/4SrNsHRl5Q/vFsjshd6dhFST3w9GQMt8PV/hn/+iph6kRlSYr6nCny1+NR1JYwaf1M1Ec79w/A65gMc+008R54PGCt4kmpYyz0cu3/fe3h2vibWZKA7J+VVQW7z9x3yOwCaCPPaRJsLo8hmxXrywxBhblUE2A1s1prIJXBrOXPy0MKRboefsB0kJygGOmFH+XKcFozQ3VXeTMqwGnaK2CTybe+RyxnTFkGbDrJbrmbGjKU0jPlsv9vWeEX+UadBYrQ3vRu1plJiOLbd7u/jzGkzHXenxPcnlRph/q4aXiQ8oShPNxQ6koUnvMncefeJ5x0uCDbqr/F4xqEAbhvE0/uGf/zeqA2EI+qNWlgdyj2gE+apwq12FPbYZJySYokwi48oniqQbHqFpDZM1lCcHvFF9laXP7xbX5+4cITUgcuGOmHqvhLqhW9IifnzvwI/3Ipvksj7McwJA7L5LDl1TUPOCaPgVhnmhOVzetjOO9RqTYB0FI96521kdaRNnblnY2jORCBwGKH6s9iYshu6nrScZ2fGRjytDFuEOaQ8+bX3GrKeRCaCQ6vWzCtDcsJcavGCJEnInqGFVvsjno3jUa8ydrsf3nsMnvkZbC/RI0xFG19zSABWNn+ft9/+uiFr0brlZ51jaZrwZWpq+m/4q81TNHxeIsXhyHRaiLBSVYGaE7Yt3gPYkOUUmcwI8sO0RPuqJj2sqOWEOZ1hAv5efS/15Pxd2GxuZJuIYuTSLcM/fm9Uka+PLLKJc99gTpgjr2CTxfsUtivG9S0bRSwR9nHEZgNvDY05cZFtSRj0xeoHPSesXCfMrYkwkUFraE6YOsA7lh9GiwoVzakyLDFfzhe5akMdlk0+h0c2OCcsmyClhm3czmGIsJqpuCefaOyaALtN6yc0NJFQVXU0E8f/G6/GHYYO8I7ax/DrfR7e3z0MEaYmhnskSHUaM7kioRYKgCbCqsp+rcdZEGz5wz5jyHqi2SguqdADig+egpd+A7te6/c12lSNiN601Ri3Q3PCtFDjQGgiLJFLkMwlefudb7L6pU/R0/PWiNaQl/O0p4SQGuMtOGGeEg6QlhMWycZxuUUH/VRqBFGL+RfDZ26CyScOXrGuibCIeoPuVNtjZNuGf/zeqA5j1GZDQuFDpjBhwpdxqz9nSTzVSIBbLW4O2S0nzOKjjK+WsaoIa441G7//FT+GJ38EkeahtagAPZk4pI5RMVaEiS9tXA3bDUeEaVVkhjlhbz9E8pci78IhOXDah9iXa/WteB/7FmCwCFPvzD3DeI8I1OMZLwZsG1odicLejITbPUAVVT94UmroEEWvIBzxerRZlsrQRZjHVUM8D1kFkjPPNmQ9SdV1zitgO/zzMHZO2a/1Of36/Mh83pjPdjxTcMIcjkAhL2mAykstTzOlrcWgyRSaE6aF+QYi4AzoUyK6Ul1kMm2k081kMh0jWkNXugtZkbFJNmo8NaRUJ6yUA+R3+vXiFrtLq6QcQf7upE/B8f8G4+YOfk4OqOHIqFifQ12fPW9Qn7CE2E9UAgWJZtc8Dp2xVM+TLIlXrNWdFtetKrtibA/JUcISYR9XfLU0qvPq9iX2GduVHuDNP8Ca/yaXjpY/vFtDC0eqva+MDUdqTphwsYYUjmzfDPeeiW/9Y4CBOWHZBCk1EjGscmszmrVmE6TVnKvhJubr1ZEGhv/eTTm4ZZ+XpqbvD309/kIFsFFiVduPV1ZgiGK1sfFMft5ayx863STDjcasJyvCTDkccM69cOS5Zb/W5/CRVlOOjBJhsWwMd28nrFvLS2rq9zUOmwOf3UNKvQkzzAlTRdh0exu79zxIeoD8JkmSikKSWouTrJpEP9I1VLmrsNvs1NedwqEzrqeh4cySa9DcsLxdHF8LX44ULRwZdodZ++YFvPXWRcUTKLScsKiIkng9whnzKAb8LvI5UI8fUVtlaCHoAVFzmd0pEcUI2RW9+vajjCXCPq74aqjP5bEhkZWzxuY+KAos/AF86nv0OEXi5VCGd6NWUYbUuZ2GJebnMpBPkwGyaq+oIfUJk7Ow/Z/4ukRzRcPyr466iOTFjwPDFDzHXIrn4n8Yu6ZMwQlzD6dZq5zHrboeSYPEqpzLkNZCpEPsOSfLWRJyM4d7xGfKKGGYyYm5d8NxwqAgug0ThVqhwDC6D/mdftJqvk3+icsNWY8QYeLfdskDEXU48yA9yEKeKt0Jyxk0tkjLTR2XWcemTT8lmRrYVSoSYer8yGyvcO9w0M6z2r6DwZlMmHBhv/lpmghLoX5OhpuTleyGjU9A8zskc0n98x9y+unufp3Orpew2XqdexpmwfTFupMa8IkJJgEMOL9o7YuAmDrntCwRpkZItOT8sOWEWXyk8dXidHipU5OuDQ1JShJ86ko4ZSk9iIteWcO7NVQnLJwXX1DDnDA1fyDeqzR+SH3CwhPgS3fjm38JYGyLiqSajD8sJ8zhxqOexIwLRyYL4cjhCEM5j+f1uwFIZ40JJ6V7jUzxDCHXCYSz8+Zb5/ON+jR2FMPep3Hp1/nlhCSNjdLwRJg2h3TrC4asJ6O2zMhLDpCH5m77nD4yqhOWU9sojJRYJoZby+NLxUDJg90FgQFyfxDnC00QKkrGkEapWgjOjtjXYBMXtArJjmSH3tg1Z7AIGwwtdBpXRCFKergirGU9PHwh/Oli3QVzSA7scg8gY7O5cbl69duafioseQSOE2K8Oiiak1fbcyMP5WuVt+4w0WycsU6ZBnkXsdgHA7/OZofF/4572r8AamK+5YRZfGQ549dwbQtjq6cB5iXnD2l4t4aWE5YVtrNhOWGqmIv1Ejx2m32gVxTjDsLsL+KbIKYfGBaOpOBgDbf7s+6oGBWOHH8Uqbmi7cWQm8cCOFx41Ka2aYPep3SqB5ekIKHgHkLrBaAo18RrM1CsymI/tvzwnDBt3FRq3QOGLCejVospqQTcUA3b/ln2a/1OP2k1BJhf9END1hPLRHUnzBHrFv8IT+i3R5hGyBUi1asbQ86AvDAhwhRs6u/MOYgI0xL4u9Jd+oijbG5krvxQRZi2XSQvzlPp1DDP05IE44+GsXP0+Y9hd5h0SjiTHs+E/mc2AvXhI3mw08WDna6R3xSr7SnwVRPNRJnjzVHb8xd277l/8NcedzmuqaK1SMh2cCTmH7Qd8y0GQe1C3ehv5O22t411wlI90LEF/HVDG96toVVHZsXJ0jgRpjphHnGxHE5SPhRaSBjmhK17kNS2FWLfwxFhbZvwrPx3QIgLRVEGPKGWhc1OSs3XGHazVrWfmlEh0lSqi0tq08zwyLS3PUljY/nJ7JJkx273k8/H8dgU0h2bQb0BGQmSnAIbOHJDF2HpdBuX1O5GUXIktw7vPd6fXF6IFT3F011+Y1qvw0tGS4b3GtOiIpGJ6LM17THVAQmNH/R1QVcQBQklryDZJfL5GFCecOmP7lS3Omxd/JCDOmFaODLZiTMsXCIjw5H5fIIdO3+Py1nL+PEXlPzOajevHbk84xlBOHLSp+DrzwHQvfcVfd9aSNbrnVD6dekoOH24XVVszArR1JPpKau4oV+03mveGqKZKA3qfXCREzcALnWQedAKR1ocDIxVh+Ua6oTtfh1+twj++K9Dr4yEQmK+KppimRh52YBRM1pSvnqxHFJSvsb7f8e3TfQ4MswJ2/I8ic3PAMMUPMluPBvFjL28kidn0GxEfXbkcHLCAK9dDaEY5Dql0xF8drBJDFxF1Q96w1ZJIWVQnqHW7d6BE4bYysNmc+O35QjYjROq7dIY/mufm321X4CrP4Qxh5f9Wp/Tx+sJBysT1QQDMw1ZTyoXISmDgg17XHVAgoMXIWj5o3kD88K609141dCoJDmKc6BKUJwTVgWMXIRpLlSNp4Z0eh/btv2GzVtu6femSVvD3rTCUfMeZP5RD43o+FCclJ9KikIJj6eECPvVDLh5gj61QDuHj3h0kRaO9FYTyUQIqsO7NXE1IO2bCXX0YK//Dne2ua1wpMVHmJZ34cHzaNzyovhv3EARpo0s8gxzeLfqhIVT4sSrYNAdj5YT5hIn32E5YX/9N7wrbwGMnB2Z0OdGDrc60isXYjfJvAEX9G3/JN28bvhroiDe0gaFSFOZCF610m6g4cv9oY8uskEqbczJ2yYJwes85jKRszIE7PaCaEsZkfAMRPMy2zN28E2CwBjR/bxM/A4/axMOnutSCLz6EMRG3hOqM5Pmx3t82Kf9D1Jc3d8g+WDQS4S5qoCRi7CcnCOSieDV22WEBnWLe4sw7fM2UhGm9SqrkZykNz0KgMvVv6ukraEjE6W6+lh8vkOGd+BenfZ73xgn1XCkt5QI00a6xUQVadgl3oMRizDNCfMJJyykCmN3OU7Yc9fj+N/zCXd005O3WU6YxUeYXAo+WEFj21bAYBGmVb94hzm8W80Jc6ajugAwJDlf3UdcdZuG5YS5A/jUWI9xlYix4Y8sAnD6cAIOrU2FEc5T6wZSaoh6uE6YW+/ib0wlYjodxaeesLWWAUPBoTZ49doUUgbdQWsjh1yDhLZKYbM5yCviFJw2aHSR9pn0DWPKgd7/Lp9GWf3rwpibEaBdJIOuYEHUBcYM8AoK2wPbnPOYdfit+PxTRrQOLaVBF/EDDO/WKFUdaVhi/rM3knnpPwBwZZR+t9fOmyMen3TX8fCbOdD8TpEIS+vNYkv03fvKE/DjPXDI8QDMcGc5O5wh1v3yyNbiHwMTj4MxM4lmor2csDJEWM1kGHM4AfWm3soJs/joUjMFzrqNRrLw7m9ojhuYE1bCCSt7eDeIHkKLrgV/LaFtD5LMJY3JC3MFYfx8YsE6iHcMzwlzBfCprlMyl9QbL46IbIKkbQQiTA2vehSFmCQZIw4nHE1q63jIdAw7J8xr9wAJ0rIxA6pT6V5OhhoeGgpFTphBJ28nQjy5h+HMgahitJMha5dFC5UhOFel0ELk3vWPwu4PYPFNZb9WE255SSIjgVtLoB4BmggLuAK6o4J/cBGmOWHb8zVDyv3rD+1msMblBdKD5oNBr+rIVAce9zgmTPjKwB3dy6AzIbrl16RjZKrE99a1bwu0fwh10/tsr00Z0c6jw0JRRI5uPg2eUHE4Us0xc7tLhIjDxbl7TY44k0M5ovF3h78WgDnnwZzzyMk5ElvvITQUEXbqDXDqDQTjLfDB74lmo8bkwI4ilhP2ccVXA/O/QsPhnwNEGbZRuUS9nTDtC1/2yCIAfy2c+AM4+hJCamjSkF5hMz4DX3+e+GGni8MMV4T1svYNcZ0ycZIjdMKgV6WdEWsaP5+02qF6WNWRFMYdGSXCkplOPcl7sMq2Uth75YQZ1TbDKYlcRdf6/xvW62VJiK6MzaaHy0dCQ34r51anCfS8IYbVDwGvw8sYh8xRvhz7wq5CFdsIiKvvc8AZgJja9mIITlh0z+tw39kjDo1q7k+tS3y/yvn81LgLTpjLVcehM37GpEO+OaJ1dCaEEK0JTyJzgmj/4Mrk4IXSYllLgO9Od7Nn7194Z/3ltLQ8PrSDxtuFAEOC4LiCE+YK6w1rS4qw/ZCc4v1QsiOYX9mLWCaGV0KfdVpWTphKd/MDfL0uRZMjbWgz6NHAEmEfc2o8NdglOwqK3s15xJTKCRtiXycNfY6cgaOLtLvz4YkwPx5FQbvvMiQvLBMnKY0kJ0xzwowNk2rtLtyO4YUjPaqzkjJI3GdqxZ15VrFhsw3dMSpywowSYWqlnbdz77Ber6giLGeX9MKRkVCjtHN8II/baxvS8G4Au83OHL+Ni2oz7Gt0j1iEZeUsUxxRlo5N0rbtZpgwX4ShBuiWr6F/7xPtsG0VdG0b0Vo04RF0epEkV1lOmBYKzMk5QwZFJyN7SShCtNcs+imZvFiTK6vAhsehZ0+f12gRBFmR6Yysp61tBZHo+qEdOKI2pVVzBAvhyCATJ15CY+PncLtLuFA718DjV4hZn4BDHZ0k5Y0paolmoroL5nCEsA8h7SEZe49ZXpl650e/QtISYR9ntr+E7b3HqFNzH9qSBg1nTalf0t45YUNxwgD2bYDtLxFW87aMHF2k3Z0PS4S5A0iATxUBhlRIZuL6sOxhiTCbHexuY0cXdW7TQ3bDdsJcQoRllbwh1a0ZMurfQ5ytqaJXR9oUQ4Z4y3IOl3oG9c7/+rD2oUjiwpN1YIgTJqnVms68ohe4DG09LnU9thGLsEQ2gd8OVQ4FRU7A2bfDJSugfsagr9WcsLBfYluTl459T49oLZrwaHPO4NOLNjJr1n8N+hqPw6OfI0ackwV0rRN9sJwKBA49k0xGOEquwCTRxPbdvs6l0+bUBWnOJs6FQ27Yqg3hVluDaOfksKeWaVN/wKzD/xObrYQA6t4pxs99qFZuq3ljTnmEn9PffRp+dSiRXS8PLR8MYOtKWLYA996NAARtH/2GrZYI+zjztyvhz5cwRu0a35owpku2Ho70VA2vRQXA/Z+Hez9bmB9phBO24sdw62ziatWfNih4SKiv8UkindI4J2wE3ekBXH69QtKQcOSqX5BSy+mH3Sesl8g1ImSgze3LScPLm5rYdAlvdhzCih6nIW0zZEXmDx0uHu504ZuyeFj7kNSLn2yT9D52I8GG6GbuyslDdsIAvW1Dzi6NWIRFM1F9bqRziC1FNBHW6M2zdbKfjugbI1rL/uchW5nTO/TqxFQHPT1vsa/1HwPOnByIzk1PiH06A0g2G1l1GLjrsHPgXx+CBaVDndoaktrootQQ83d1ESZElPZeDFosFVCFkVrV6vMIEedWUsgjSTGINEOshaicIWAbQnsKADkP7ZtwxcWNdND+0W/YaomwjzPqQNR6u/hytyUMcsLUcGTOExz68G6NmslQM5WgVh1phAiLtkDPLuLqBXg4FWSaCPOq4cMRO2FyHnLJkeWEgR4mBePy1NKaMBymE+ZxFirQjHDn8l2bAcjlh3facrvHoBAkpUikDGjjkVXyvJlw8Ercga+c2XclkGzi9y3bJTDgjl6r1nTnFb3KeDjryRsgwuLZeGFupM03pDFKWhguot5Y5NIjc6KG1TSaglDpSnXxwYc38e67VxCJvD30BaRjdKpzEmsCojdjRhNhTQvh0NPBWfp7pomwqKx2zR+qE9ajhiPDog1F78T8AdEKKNRcvpB3HDlFNN9Pp0dww/61p+Ebq4j6qlmXdPC/6eM4Yvbt5b1WvWa5UuKmLmhTDAkVjyaWCPs4o4kwNbTWatC8OM0J67GLsNGQhndrXLICvvMmwZqpgEGlyKfeAJc+T9xXBQzTCXOrTpj61RmxE6aKuBH1CQNw+nQRZkROWDYTJzdCd87m8uFSL6JpA9pUxDo7+d8OF3viw+/mrgnKlAHr6S123e2bh7UPOXAUf+9x0po2xglzqLNaPbn8sMKRDrtwL/M2g5wwbW5krAN+Xi8c7jLQnLCE+pnOZ0d2EzaoI7/nTXjrf8WNWi+K21RovcKGkRPlDtD56R+LfarixmZ3qzMbB3aBNCHYkxffx0ymDUUpX9D2dsJ657e5ss20d6zs31nzq05YshPyWao81XSra0ilR1BNX9UE4+YSVd00rys8YK+0ItRiIVdCnDctJ8zio40mwhB3WMY5YeIk1a3eBQ9pePd+aOLNkLh/VRNMmE9cdQuGNLxbQ02C96mp+SMWPBlVhI04HGmsCEv3EpfDXtOJP9JnPBrhhMV8E3g94SDuGv64IY/qNhgRHtVEmEtWsK9/ZFj7cISO45mIk5acE0ZYwCArMk5JLRTIKcMLR6o3AYqdQmfzYdLbCXPk8uLnK7Odi8/hwy7Z9SHeOXlkhRRagdD4+N95+ZVFtLfvNzB9z1r467fhtqNgS+E5TYR1pbp6dc0fnjjVe4SpVZefOHYFJ534Hl7vIdC+GZ5dCqt+2ed1+uiiTA6woSg53UUri4ia8B8aX9Rotaf1r7z99tdobnm09Ot8NYXfV7ydkCtEd04VgsMMyfZGO6cHh+IiqxEVV1J89w6G0UUj7hOWTCZ57bXX2L17N+3t7fh8Purr6zniiCOYOnWqEWu0MAu1D84Y1a0wJDFfzoP6Re9W74KH1Kh1P7SGqkYmX2qJ+b4hjpkBRK8x0POvRhyOVO/iknYhhIfvhPnxqoP/DBEYmTh4hIvpGkYlIiCEocNDNBs1JESarp0KLS/irh/eSJ1EYhvjPB+wpCZNd8/I22ZE45s5NZQlkaHQXXyI6M7c+PlwxJdGtJ5ULqXORgRfXoah9OZTcdoDoCBmQ43UCcv2csLGHg3f+3nZQlOSJIKuIClFfJbzcnpEfdQ08eGSIyTTO1HYz0ma86/w+u+h7X3401fgspegqkkvKOpMdeJUXZghD/HOZ0GR9eT+3sO79f5WPbtg9a0ieX7h1SLmp6LnpaW7cLnqyGRaSadbSlc0lqKECAu6gmQyIvLRb+8zmx18dRBvhXgb4eoJRFQnLDncQeLdu+C1/4GqiUScqpByDkWEic+0Sz3XBW0K3R/xxPxhibBkMskf//hH7rnnHl577TVyOfHF2r9p2tixY/n85z/PN77xDY444ghjVmxhHJoTpv7+DHHCUoUTVLfqOA05KR/gpdvgnYcJTv8UgDFx/5eXgSITV/PLhtUxX3PCVNdpxOFImwOmnERS2QVkh5enBjDpU3j2JCDfbojgSalumtvmGFEjxMLoIgOEodYyY5gd/PP5FF57K4d6YJU0ccTricU/4Ixwll0pachzIzU0l9GIAo9ELqGLHt8wnTCXMwgZRIxkpDlhmV45Yc5Qn+afgxF0BUmpjk/OIQmhUju8G3stHGlX0siow7sVpSB23AH45j/hntNhzxvw3PXwxd8X5YQ5nKK1xpBHF21+Dv50MZ1TZwOFJrBFNB0LR30Fxs8XN7L2wqW5txvn8TeqImwfUMY1VZYL4cjweLrToiKzyl2l55YN2CMsMEYVYa2EGg6nNSexNyMxURnmOaFrG7x8G9QdSvSYz/H1uhST0/8gEv0XQsHZg7/eZgdPGJcang7YYbdBc2BHiyGJsEwmw6233sovfvELuru78fv9HHfcccyfP5+GhgZqampIJpN0dnayadMm1qxZwx133MGdd97Jpz/9aX75y18yd+5ck34UiyGj3tnVZ8QF0hAnzFMFV22CVA9dXaKfzbBEWLwN9r1LcIL4YhrihP3zV5DsIj5DOCnDbVGBzVHICRupE1Z9CFz0V1KPLIJk+/CdsEU/xvO6Azb8wZDZkaJ6UMIzTMEDwO438KghLSPCkeHUuywKZgkyvPCD3idMgpRn+HllGhn1QpCX0cX5UHHlezjKl8PNyFsgJLKJQvhvmIn5LocQYTa7hJLsQuotVIZINFuojnTYhy5SQ64QEVW75+ySuIAPV4RpFduy+G44HSHhfH34NCz8ATQtEC7bmb+G3y6E9X+CE64q5ISlO3E6hegZsgjb9SrkkoXEfE9N3y7vLh+cfVvJl/fumu+uaYAo5VdoJjognwEkCDTSHRG5i/+fvfMOk+Mqs/6vqnOeKM0oW8mSHCXnnHDAxuAlGAMGYzDBYANLWPAasPEC9rKkxV7CegGbJRkDBj4MjjjbOOKsLCuNNJrY07m6u6q+P+6t6h5N6lA1Euyc59Ejqae6+06HqnPPe97zChL2KgDByUiY5QvL9ONVvTySb+WuVJY/Hv+62p5/zHpGz41c6TcI670o9Tijgi34kiOYgAJomjPhsfsKdZGw5cuX09PTwwUXXMDFF1/Mueeei883eWbPli1b+N///V9+8pOfcOSRR3LzzTdz6aWXNrXoGTgEqYTN0nKgCMm9pJfweRrLYQJAVSHWBbEukr2PAA2SMLmLj0mC2DQJM03QMphAzuqObES9WHUBrLqA8FPXw7pfODbE2/JxNey/qrqvc0pYuGHVCYCRnQTzwxAIOGLMn519lhUtZQaHGxubYpEwvwqlYvNEtVgWJKxs0HA5kvxa3tNeZF2uVyi1x1/R8HpypQw7iiohxRQkrAFjfsBXuY8+bzXecgF8jW0MsqUsYXlt9ay/D158Tqg9NeSEgVDC+qUnTPcoMLytoXUYpiEnbpiYuvi+ej1ReOpmGFgPy84SJAyg+zBY8QZY90d46r9pWy1KxMOFYXtead3zI8+4Bg65kKHHr4LSEG3BNnp2/YJNm/6drtnns2LFlye9e/X8yETi9ZimTiAw9dQBYMKg1vZAlHJZnFMnHcVkTTfIitJlwp8gW8o2PsTbylsLtZIpponKz0fNOWEAwQQKkGMOG3J7yPn+vj1hdRnzTz31VNatW8ftt9/Om970pikJGMDixYu55ppr2LBhAzfffDOqOtMLsN9AkrCW/IhtnB/IO7eraGh4twXpZ4mVxMWy6Q6YsgZGiaICZZla3VA5UlFAUWzFyomwVtM0bRLWsBIGhFQHSZguO5cajKcAoOsQAomFAI6oc15V+EC8nsbiILxVWVVGrvmB9WVpCDZ0GlbCLNLjVQzY09xMvoJe5Nt9QX6yOySVsPpJWNiX4OqeEA/7L8LzvvsaJmAgNk5PZ70MBo4gvOGv8MRNlfmRNSDuj1OQ1q2yV8FsMDU/XUzLpgUwpRrl9cbhrT+Ckz4Fh7599B2OkXldL9xGqyKucaI7sgWo5NXVDEWB2asYLIvPS3uwnWJxAF3PjPam6SXRpfnK70bd3S5HasMsXPghDjvsZjo7z6ztuVsWwttugbME0bNI2Gy/8NZ5PNHJh5nbSpgkYfK83DAJs5SwUBvF4iCqAiYKfv84JdqJICs4OfM4bh0MMFSuo1N0P0RdStgtt9zS8BN5PJ4ZBWx/Q1h88JXcMJ2d89md3U1fvo9umWPTEHqehRdvh65DGhvebUGWUmKaMNEX9AJFvYjf06BJXJK4TFV3VkNKmHVf6d1quhPxxdsp3fkpjDni922YhN1/HcEX/hvaW5snYYaBJpPXA00oc7QvIdi2BHb1OqKEeaXfyedvaej+iuIBxQ9mETO9o+n1lPUMHsA0TFFOagBBGXvgVcE84n006LQBKp/FsC8CS06H6NTzAPdGyBcmayhkda3pocjZUpYnsl5OaDmP6OAXxI3R2gdgC2O+gmGqBIsljNQW2cddH0bPjcwDKh5vFLoOFn/2xqKToGM5DGygbYcIiU0Wkni9LUAD5UjERmtYhh+3BdsYGZRp+dXxFMPb4ObTwBuEFeeBrEhYJCypJdENHY9ax6sQboODKrEg1mvR5lWhPIUKBhUSJgNb4/44Ciap/NgRSzXB8hmGWzGLWwBQPHHx3awVskMyKqdwzERUzODvF1IJIz9MZ1h82Zo25+9+EZ78Hqz7Y2PDuy3IXXykKjupqZKkHHuUk4pFyBtCrbFdfhQy/fCLdxJ+8XbxeM2WI7UR8lVNBw2XIz3+yuzIZlWnqvDYYBNEFZwz5pumid8mYY1326oyjJQaVPypoOtig2A2UY4M+mW3l2JSmnt4U+uxPouhtiXw7jugo/4oD2tzkXVgtqZ1cYyofrtjupbh3Rbi/jhFU+Fvyhs5SX8znhVvbGgdFvnpDAjFx+uNTU4wFQUOvRCAtnV3AVA2y2iSAtaVE/bnz8Gv309m++OUDKHCtQZbKyOLqklY22Kx+SwXYM8r9s1W0LVhGk2HVlvn5Bav+C5N6geDqnKkuC60+sN8fV6e4I6rKZcb+IxUKWHo4nfx+upQwaCyQZcNZTNhrePgiSee4Oqrr+bLX/4yu3Y1Nth2BtMAi4SV88wKirC8pkcXdR0CJ/4zrHhDc8O7pZ/Fo6WdiamQZC4rw1YbMuUDYML6OwnvEbPL8qUmCc+hbyd/6Z8B8KpefGqD5ODYywm+/huAA+XIYq6Slt9EeZSyRlCWa5tdk2EU8MizVcBXh39kL6jS16OGmiOXALr0F5lNlCNDklAG1OZV1XwpQ0AxiTTxnlnfi1zvi/DNVWKwdIOw8puiupwb6gnU1SwQl+eAdCAsDPOHXdTQOizi0REQ77lXDcPvPwqv3DHxnQ5+CwD+rY8QkcQ0YwY44fhHOPmkp2p7YtOEl38DL/+aoawof4e9IrZlXBKmqtB1qPh3VWnap/rsLK2h/CCFwi5Gak3t33Q/rP1/YlQQFYtIVJGTFaZSwtqXwYHnwoLjxf0CbZQFf7MjLupCvmLM98jst5pHFlmQ5chIOcssr4Gn3HxTy75EUyTs05/+NMFgkKGhyovw61//mpNOOonrr7+eL37xi6xZs4aengalyxm4i0AcFA/4InRKb0rTnrB5R8LrroXV72p8eDdU/CyFEfsE1FQonyYIXCYgLlAN+cFASOFv+Bbhoy4DHFDCAjFycbHbbMYPRjBBSCZxN03CSjkKkoQ1VY5M9xLceB/QvBJmlYB0EwLBGtO1x4FH+sIUs/nyqCk77VTdaNwT5hWf84BiUtjQ3JDqYnYt/z4vz+s9Tzf8GGFfmLe3ary+o58ke2xDdiPIltKsDpUJ5V8Vzqfo7Lo6La38qJTWnPpjbQZbZVCvTwf+9lN4fJJROW2LRVyEadAmfWFJLUUwOAdPrZ2ee14Rr58vzFCLGBlklRZtEubbi4B0ydiJ3tH+QLtLM7eLxx4/iWeeeXNtStQj34DbLoZtj8nfIQlAUBGf/ylJ2Pyj4B2/gFM+A0A8kLCT+zWtgaqJVMLKwQQBU5ynglOtYW8sO0tcY2YF+NfuAkf5m/d37ks0RcIeeOABTjvtNNraKnLiF77wBRKJBD/5yU/42te+xuDgIN/4xjeaXugMXICiwNW74epdzGpZBDg4xJvKya+h7kirs6uQsklYU1K8LI3k/ILoNOwH8/rhyPcRXny6eDwHjPkWaWqKhFHVHdlsHER8LoXT/1U8ZjPGfH+EgAy1bVYxLBeTAOQMCDUQvWDBMud7zObDWk3dImE0XI70yDFBfgXy91/b1HpKstvNLKSFitUAwt4ws3wmnX6d4rn/Bge9ueH1FEspLukoUk7fJrIEovUpmJYSliqmxCaq96WGssssJUz1d7Jo4eV0pSVhXnza5HeUXqrWkvg+WWXNmrFFJu8vPJ4huYG0MsLsuZF7q0CzDxJ/79WkYZ1Dk2XN/szUFFMx+yCYe6SYxUvltYjNuYyTTnySBQveX9evlAgk7MBWrdhAar58/7K+EHGPODdEgnV6kA84GU78Z8KzjwAgQPPf5X2JpkjY9u3bWbZsmf3/jRs3sn79ej72sY9x8cUX8+lPf5pzzz2XP/3pT00vtF48/fTTnHvuubS2thKJRDj66KP5+c9/XtdjGIbBTTfdxKGHHkooFKKzs5MLL7yQjRs3uvq80wqv8Ox0hMTJoGkSNvQaDG2hrKXt8mFD5UhLCdM1onK8UHPlSHHfrPx9Gy9HClgkrmkl7JU7yD/5faBJErbnFYJP/xBwQAnzeClIo3kzkRn4QvYoJa3J0SKlgjjh5wyFYCOkXiIWO5yX8h6GywZ6uon5d0DZNCkYoOhmw2GtXlnq8quQb8RjU4WS7L4zdUQprAFEfBFkGDl6YrbdvNPMekBFManLlA+VcTbHqy/w0MNr6P/V6bDlwbrXYak/gdBCliz+FAvWbhY/WDIFCVtxHiw9k7aYULGG6h0ibo0/WnwagwVButoCbeh6AV0Xr81YEiYbBfa8POo9rA6NtdSrmkjQuf8BH7hfqHpUXovWYBt+fwc+Xw1VCtOEQgr0Mgl/hYQVGxniLcuRKZ+fmCRhwWCdSphELCTIW1TVHQmD3ldoioRlMhmi0coO8NFHH0VRFF7/+tfbt61atYqdO3c28zR148EHH+TEE0/kkUce4a1vfSuXX345AwMDvOtd7+KrX/1qzY/z4Q9/mCuvvBJd17nyyis599xz+cMf/sBRRx3Fq6++6trz7gvMColSVtOBrX/8Z/jOakZevA1ocHg3jMo4iksi0FQXjFWOdIKEbXuCkOyaapqErb+L/EvitWqKhKV2EZT+HSeCUa1uxqaUMG+IgE3CmiQYBXHCzxmKPY+yERxwwJX8cCDAuoIHrclE+PWek/hcTxgtZTYc5VBd2sqbhYbJE0BZNgoQnw/vu6uhxwh7w2gyDb2sN/7ZNk2TsiQaHtMruj7rMOVDZW6sYhYpewzK4Rg0oKhaClYikIDBzcJk7gnAvKMmv2PbYrj417R1HQaInK6NG7/K08+8hcHBRya/b6kA2x4X/15yWqUzUkYzACiKf2w8ROcKYRHJD0PVJqE6Nd8mYXXObzRNs2LMr2cj881VcMN8GNhAIpBgxLCUsDpJmGHYSlja42Vt3sNzhRixWpLyq1HMQs9zxFJWar7p6Fi76UZTJKy7u5v169fb/7/rrruIRqMcccQR9m2pVIpAoInAxzpRLpe57LLLUBSFhx9+mJtvvpmvf/3rvPDCCxx00EFcc801kypZFh544AFuvvlmTjrpJJ577jm+9rWvceutt3LnnXeSSqW4/PLLXXneaccT/wU/u5COvnWAA54wObYo6RXpJw0P71Y99pzGmCo+P04oYTm5rqZI2B+uIHzP58XjNVuOLGXtTsSmSJgvTEhewJtWwva8Sn6T8CcFvE18d1WVoGw9LzT5OpnlPBkdMoZCsInsqurwWc1KUW8QBVW+b6de3XCqvKL40SXvyquIzrgGocsNgeKJiEkMDSDsC1OU6ylvvhee/0VDj1M0ingQ5m+PKS8zdSphFgnLSmO/fs51cPg7617LqC7tnmfFjd2H2VWAqVCdWJ/LvUYq9TwFbYqGs51PQTkvfudZqyq+tEArxZJlym8f26XpC0KHrC5VdUiOSs2vlYQZuiA+EplShnIjo+RkVyk5McTbLkfWq4QV07KVGNKKwtM5L4+XDqC1ZQoyvDf61sLNpxG66zoAoiqkm/QN7ks0RcJOOeUU7rzzTv7rv/6LH/7wh/zud7/jrLPOwuOpZH5s2rSJefPmNb3QWvGXv/yFzZs38853vpPVq1fbt8diMb7whS9QLpf58Y9/POXj3HzzzQB8+ctfHkUizzjjDM4++2wefvhhNmzY4PjzTjt6X4KNd9OZEl+opJakpJcafzx5YUvKUN6G/GAWZEkyqgji5IQnLCszdiLeJkiYP0LY8jo1mxNWzFYu5s2QMH/YVp2aJmED69F2/Q1oUgkDgrLbU2tSMewMH8Hnd4X54UCgqRR/j+rBJ0lGodHASQl7ykETpFBRFG4rHMFndoYo6NhdvI1AN8RrrHoaX0/YF0azlI7X7oVnb2nocdLFNEFrhJJ8vLqVsMBoElYuN/ba2ATIHKa/94/kA6pdnqsFrfIyOZQbwCtz3abMCtv8F/H34lNBURgqiDJca7CVYKCL5cu+yKKFHx7/vpYvrPelyhqs1PzCkD3r0Zr9OCHW/xm+Mht++S6gUoqc5fPz/LMX8MILH5j8/hYu+QP86y444GShhNkkrE5PWLkouiy7DyNtCKXdKjnXhVArxObgk+VIjwKZQgP+tP0ETZGwq6++mlAoxMc+9jE+8IEP4PP5uOaaa+yf9/f38+CDD3LCCSc0vdBa8eCDDwJw1llnjfmZddtDDz1U0+NEIpFx13722WePeRynnnfacdg74I030nLg+c6k5ueTACTlzLiG0vItHPMhOO3zxGSGWVPdkYE4tC8j4xekItJgNxsA/ighuaMrGaXmSGsxR17mlTVFeHwRgpIYFvQChtlEinTbEjQZYtmUJwwIyHDdZpUwrWqn28yadD1Pt9egzWNQKDRHwqySbVOjnQDFG6dkKqIjtQm115RlaE9yF7z824YeI+gJUpKRsZpHrUQK1IlsKYvfmhtpJZpH6iNh1gW6YI0u0hv7/ltKWCDzKC/6Hqe/3V87CTNN2p74HgBDyS22h2rK0UUWCVsiGniqg1oDgdnMn38J8+ZdPP59bXN+RQlrqByZ2iXmRkq1zXod5oVCZLMbyWTXT3bvCmJddvdvtTG/WKzTuhLthPf9GT70sF3VaIiEtS+BT61Fvew+CqbYVKcLf78JDE2RsKVLl/Lqq6/yn//5n3znO9/h5Zdf5uCDK/Xdbdu28ZGPfGRak/Ktkl91w4CF1tZWOjo6piwLZrNZdu/ezQEHHDBK1bNgPXb14zjxvJqmkUqlRv1xHYtPgTXvQek6iM6QDGxt1BdmmnY5cliOB2konsLCCR+HUz5DPD4XaLIcefwVcOUz5GbJ4d1NKWFRWwmDJn1hxapyZBOKCv5KORKajIToPpR8l7gQNKuEBVRJwppU5zS5i4fmSE9f35/55JwCb2stojXxedJ1jXPMP3NNd57Quv/X8ONARQEtKEpTSpgpW/49wz3w2sMNPYaiKBiI96zoURrqRgTh3wxYcyNLcpNSZznSp/oIeUNo1uiiV34FNx0t50TVDksB8spYEp9uwtw1td1ZUWibK+ZKDpey9vzISZWwTD/sljlesgOzWgmbEp3iHMVAhSRZFYX6SJgkJvL8WRlZJL4/gamCWsfB6HJk4+pTutDHoaEyc73NdTYWTPFZzf0dK2ENmHVGo7u7myuuGH/o7JFHHsmRRx7Z7FPUhZERQQISifHb2OPx+JSNArU8RvVxTj3v9ddfz5e+9KVJj3ETnaFOdmd3N56aX8zI1ixIGuLL1dDIor3gSFirhJUE3pQnzB/BB/gUlZJpkC/nG/89S1nyTpQjfZVyJAjS08zjWSSuWSUs6AkAJbQmmwUKbYsB8CqexjyGElZERUiFQlMkLINf0fF7wRjc2vDjQFW0iKrYZfNGoBgaeCTJaGBupAVT8QNZSl5Jwkyzbs9bppQhKJUwT7AdFi6D+Jy61xL3xymYYjOqZ3fDQEqQi5YFNd2/2oyuypgTrxIWpvsa0XrSv8CdFzFslmwlbNL5kVYH5+xDICYIk6WE1UbCDhR/D2wUhFP17KWE1ViOnICEdfrF92fKjDALrz0ML94GXYcSOvqDpAwfvxgy+Oxx12GaZkPjrYqF7byvo0jBqDH4dgKUlCCQJ98oIfzdR2H383DGF2H52U2tpVHMjC3aj3DVVVcxMjJi/9mxo/n5dlMiNyRSlbc8ZMdUNKyEyVIkHj/Dst2+qXJkdgB6Xyamy/EU+wsJk6n7YelVa2rES5US1mwmlwfwGw74wrKDFORFo9lSW1Dev9BkC/lr267jujk5Dos0d8ryyG60oGo2ScLEe64ZEFh1QVNrOohX+OTsAsQ9TSlhilSffbo5qru47seRg+BLHgWMckPEMFPM4JdvlXfeCXDpn6Blft2PEw9UDfEOyk7S4a013z9fztvjgpDjhryty+silW0hEQ48XBiuzRO2+X7x91JRijRN0/altQXa6Ou7m56eX5LPbx///q2LhO+p80D7nGpHVGjDBPyirFss9mOak6iCKdk8IMmv3Rkpizs1K2FDW0S47ea/oCgKEX8LT2a9GKGV9RGwZ34EX18Od11FSYbVGmqDodm/fBfcdDRlU3hOS7LjtG5EZ4nXusnNZjNoWgnLZDL88Ic/5IUXXqCnp4dSaaw/RlEU7r///mafqiZYSlS1SlWNVCo1oVpVz2NUH+fU8wYCgWntJAVg13Pw07fA7EPoXH0O0AQJs7rNgi32rqspY/4DX4FnfkTsmPcCTc4I+9V7YHAz2S5Rcm1OCZNqiuJhhCbN+cUcebkzbUoJ8/hB8RA0DYp4mpsf+di3KWx7HELB5pUwXwgM0PTmyg6l4iBxD3gbHeskYUUCBBXQio2TZyutXDOhc9HJTa0pSo5Wv8Euf3OesGcLcZ4sZvngSLYpEpZT4zyV7ePYjNj8kB+udMjViFFKmLfxEVExXwxNk5EZAflZHN4GB9R2f4t4+FSf8JSp4O2sLxLBIkBls0xpQBCnUmmCMq1pVkrB0g+WLqUpG2X7sV7ZcCvJ5JMcdNC3CYXGUfRUD/zLa6OIorWGklGipIRZffj/ShI1CQkakZWXhGiMs87JMY8Oeg1zIy1YXj5riHcgzmBhsP5GqUw/ZPZAKYdhvX6eBj+nQ6/BwHp262/gl/0jnN+ypLHHed01Ux/jMpoiYc8++yznnHMOQ0NDmJPk2zQiVzaKar9WdVQGwPDwMAMDAxx//PGTPkYkEqG7u5vXXnsNXdfH+MLG83858bz7BDLBmfyw7Qlr2JhvGZ2DiapQwCaUsHAHRDqJSSLQlBI2sBH6XiXbeRzgDAkLSyG54ZgK04RihrwqXqOGU/xBnLD9EYKmSYomlbBS3u7YbNoT5g1CEQpGcyRMlztd3WhOCfN6qpSwJhTMihKmNP0aoQZBB93TnCdsk6bQl/PyyVy5qXJkSp3NX/p3sDrlAZJCLa+x/GchU8pwX8qHv+V1XLvoEw2vJR6I0y+v9bpPXq7qUMKqN4NlpBI2u/bOSBBqcMQTJKsXyD38bThwEhKmKPChR2D9n2CBONdYpchJ50aO9zhVCHlDhLwh8uU8SS3F/LYpriWGUckZk0qYNUYurIjvYs3lyIicdJARJCwhh86P1NtdfPQH4MDXi0rC3y4HBTy1hMWOBzk/0mPG2Fb0MCKrJX+PaOqMduWVVzI8PMwNN9zA9u3bKZVKGIYx5o+u12ekbAannHIKAPfcM3YOm3WbdcxUj5PNZnnsscfG/Ozuu+8e8zhOPe+0wxrinR+iU3YhNpyab5UjQy32F74pJez0q+Ezm4gd8xGgSRL2T9+Hd99BFjkqo0lPGEBYhlo2bMwvFwDTntPY7Ngix7LCStUDvJslYeK1KhjNnSQNOZLHKDZH5ixPWFCFQjNKmOzUK5gQzDQXcKzK8p/RpCfMUmRDplm3clUNexpEQH5HGjDnZ4oZyij4cll8NyyDX9Sf7wXCE7anrDIcPoG5AekvboCEtQViWPzdO/e4utfRKkuSmZx4vFIpObHwEGmHNe+2c8j29oNV5kbWMAO1OjVfNjnVlNyfGxCdkSgQE1EO9uQAU5yvai5HWuOmsv1gmiQCCVYEdbS+XzA4WEfHf7gNug+FtsWohvic+2p5DcaDHF0Wla9Pw0HeTYQjO4WmSNjf/vY3LrroIj7zmc8wb968cTsJpxtnnHEGixcv5uc//znPP/+8fXs6nebf/u3f8Hq9vPe977VvHxgYYN26dQwMjFZ/PvjBDwLw+c9/nmLVif/+++/n7rvv5uSTT2b58uUNP+9+A4uElXJ0SL9D40pYUvwdbGlubuResNqYs6Usep2dUTa6D4Mlp5OVFypHPGHyvw2XIyUJyKsORFQA+MOVmIomSZg9wLtZT9hJnwZAUxo/1RhGCRRB4sxGPSQS1QnlRaOJTK5ylRK27o9NrUlVxSfJ9CgNJcJbsD6HYaM5T5jVOZyzunUbIWEyTiZmGKJZx9NY0SXujzNQVtnhW82cjnPFjcltNd9/pCjUmg6LUKLgTSyqex2WLyxpqMwqz2XOnAsxzdqiaazOyLZgG4ZRpFwWa5pUCdvxNHz3eLjlPPsma/xbTTMsR6SfODobPNYA8iRg4tHFhqZuJaych2KGuD/OgUGdYPpBhobGihS1wGsTwfpiS2zI1yIqw2gbii8yDPjyLPj3AyDboKfMATRFwtrb2+nsrG8oq9vwer38z//8D4ZhcNJJJ/HBD36QT3/60xx22GG88sorXHvttaPI00033cTKlSu56aabRj3OaaedxmWXXcYjjzzC6tWr+Zd/+RcuueQSzjvvPOLxON/73veaet79BoE4yAtkp7zgNtwdWV2OtJSwRuZG7oWYr3LhbCorDKe6IyUJkyeAhsuRNgkTm5fmlbCIPauxKU9YMUdBfiaaHioud+HNRGaUyxXviRKvryy2N1TVjy4VzGIT0SJWcKhmQrCRsVzVa7LCVTsOgJM/3dBjlPQSJ4bznBYr4fU01x0Z8QaY5zPQI1aUf+MkLDLvWPjkOjj7+obWMmqId+sicWMdStiIPCe1+cVr7PFEUBrYELQFhG1jSFU55LmtrDjgKlQZv2Jj57Pwo3Pg6f8ZdXO1ElYsCUKmKB58vpaJnzAQhb5XYPeLtlpTPT+yr+9uXnr5Snp6fjn+/ZOShFWVkUe0ESIqKHKSQc0EyB8B63yZ7ReBreUGRhf99fvwyDcguZ0g4nwQDtbfMQvY5ci4nuXCVo2V5afrf4xiWqiF+SG7urEv0BQJe/Ob38xf/vIXjKrRCPsDTjvtNB599FFOPPFEfvWrX/Hd736X9vZ2fvrTn3L11VfX/Dg/+MEP+M53voOiKHznO9/hzjvv5Pzzz+epp55i1apVrj3vtEJV7V1FJ4IMDBWGbCNpXZDlyFIwbpvom8oJ63kOfnwevt99xFaJGipJlovw+E2Unv4RRelNcsSYb5Gwhi/mJsxZQ0Hu0psmYfOPIijfS6fKkU0rYbKcWTbLlS61epcju9ryBgSaSIO3oCM7qjoaJ3RFSQw1QyHYhOoE4LUy68zGiWqunOOsRIk3tZTweWiqHBn3mHy6q8CcdhmB0GA58i0tRRZkf8uwvgMScxtbiyS4KS0FLXIUU7a/Zu+cXY6U5Sqft7H3yiZAkXax2XxlnDDc526F7U/AjtGxC6NGFslSpM/XPjkZbFsC77wdLq8oTRYRFOOTttDX9ydGRp4d//6WElbVkZrUkphAqOMC5sx5O6pax3c7IlW7TD/xQLwqNb8OEvbUD+D+62Ckh5AizgWxUGOfC+uaFdYLHB/VWaD0TupLHxcFublTfTWPsHIDTRnzv/rVr3L66afzrne9i69//evMndvgC+oCjj76aP785z9Pedy1117LtddeO+7PVFXlyiuv5Morr3T8efcrhNsgP0SbruNRPOimzmB+kNmROqfbH/dROOStjOga3H1f48O7LZQLsO1RaFtCrKuFQr7QmBJWSMI9V4uRRQvFZ9QRT5ihg9qEEta6CD74ALnf/xMkNzUX1grwhm8Ruv9K2Plg0yQs73PIE7azcpHQyho+f/3djWWZyZQ1lOZmWUoYahDMIuVI4xl2BZk5VTAhGGwuC8/rEaReMRv3u+VKOQLSyx3UgSby+YLSlqAqJiagNKiErQoYBIqvNZx0DxYJM4mXNrFr+D5mh1rw5JOQ3A6zx26E94ZVjlS3vszJA4PoF32roXXYY4O6VkHvdnjyB3D4u0Yb6M/4InQsh4WjPWfV5ciaTPkAXj8sHz19pVoJC7SL9tAJA1OTMv4iMZqE5Q2FeYs+zoJ6FeXoLFEGzvaR8FcN8a4nnysnXodyME5MFRvYRLj+2BLAVsKimk4a8CoGup61PZ81oapy0+jsVyfQlBIWjUb5wQ9+wN13382CBQtob29n8eLFY/4sWdJg++gMpgfSF6bmk7QHhfehIV9YqAU6DyQZEB6XRCCBR23CJ2gpDFrK9oU1pITJ4d3ZoHiMgCfQVOAni06Ef91N+JCLgCYT86maQdisJ4yq4M8mwlHLpRxlJ7LLgEBfJfW70TVZSljOUAiO7G5qPQA7w2dwY1+AERpXiwKxQ7hrxMu6vAdfk+VIv7xwqMUUPPjvDT1GvpTCI68jHr05Y37AXyFweudSW3WoB5lihoAVUfHcL0SkQAOwNnHHq6+wdt1nKbXLi3aNJUmrgy8R6sDnayE4//SG1mGFpQ7Fu8lHoyRzr1J4ca9ZwJEOMZljr5FI1Wn5FRJWvyF99PxIsUEuTEjCRithRb1on2caCpaOVMz58cDo1PyaFChDt0nPiFLCKz+rrZHGBs1bxvy4VrQnKhTrzQqzRqE1uYlqFk2RsPvvv58TTjiBZDKJ1+slHA5jmuaYP/tbuXIGe8HukBymI9xkYCs4Z8q3fC2FFFFZAmxoiLdFwvyCHDalgoEwuvrDlS6yJuci2l1tzZYjqZCwZrLLqvOzmlWelHlHElQEEW/UF1YqWyQMAp7mcsIAdM9cNmsecqkGu4ABb3gVd6X8bM6pTftJ/D7xOfeYZdj6SEOPkStW1CpVCQglpUFEvHF0eV3VL/sTnPKZuh8jU8rYypznxd9UmnbqhPCEKWjSx6e3yo6+OklYy3EfE9lbgcYaO+zE+nKWTWtW8ezhLfS9cL2IbRjZKSwPE6B6bqRFFKZUwkDMjnzgenj21tFrqCU1/4wvwttuhSVnAJWyrEfxNDavsSqmIuGvDPE2jHxtSmdhBGRnej7Qwr/sDPH1vhYCvgYbbeTGIKJlSEtVrliqUziwlbDmNlHNoqly5Gc/+1lM0+SXv/wlb33rW1HVmQD+v0tUkbBZoVm8yquNkbC/fg/ySZKdIhywaRJmKWG6RkyqBQ21IlskLBAGis2TMAmLNDWshL36B7j7avJt4nsTbiLUEoB7vkBw020QCTRVjhT3FWSuWU8Y848i4I9S0Ebsgdf1IhZdSV8yytN6kaURB9TCrDhZFxokPFClXhom+Jt734LB+Tyc9ZDXQ3DsRxpbjyy7lU1QF57Q1HrC/ghFE0IK6Hpjn+1MKUNAGvu9uln38G4LVlNOwVQIAeW4fJwaOyRHhUY3UXKqLgX6lh0Fu7dQ0jPw/RNF12BiAbztFuhYOua+1cZ8o7gVRfHVTsIeugEWHA9HXDLu/Ehdz1AuZyu+QguzV40q11prOCgSZHjoMaKxlQRqWYOFKiUsEUiIgfOGSlA10LQ9o7qOx4VV0vbHSJfzFE2FotpS+/PvDVmOjBVSZHSFDq9JvtBHSz2iVuEfQAl79dVXufjii7nwwgtnCNjfM8ZTwhrpkHz2FnjoBpIjwo/QdGek3AUDxCUZaKocKT1XTZOwYg7uuJzwi7cDTahOuUHMke0UEEpx054wo0yoJIhOMyTMmvMYUP2oTURLWAjYo4saW1M0eiC7hwM8m/MSaJaoAgFJ7jVf44TOnq1pmtCIslCFWGwVPxsK8EAhASvObegx8nKMTgkPvHsc03gdCHvDaFJdaJSEZYtVSphO5SJeJ6zuyLyU5vS5h8HrroUaR0VZSlgw+zjPPnsRPT2/aGgddjmyMIQvIH6XUiQOmV6hqKhq5Ty6F6o9YYsXf5zTTl3LksWfnPpJrRmS/evANCtKmDaM1xvFI72EtfiyrNfh9Gie5194L4MDf5n6+asRtVLz++wS8Ug9g7ylH4xwq30Ot6obDcFSwvIjthKWzvfU9xiWEtZkY02zaEoJ6+zsJBRqvoQyg32M6tT8DhGj0ZASdujbYWQHSa8oGTXVGQnixBaIgZYiKtvBGyJhUj3L+gQZaJqEqV544eeEoxHobG+8HLnyjWidKzAfFJl0TXvCTvg4wXgENt7WuCfMNO37Bp3oGCoVCMqu26bUOdlZGXCgZBsNFXlraxF/pMH2eCCT/CuHhcoYut60EmYpqs34+LTSCCqgNz+JjrAvzB5Zjiz/5r1QCMKHa1cNDdNAK2dQLRLma2m4PGpd8AvWejoWwaraBy1b5MP76k9JtpVpaT2moXVUEyB7iPeBp8NBZ4m5g0tfZ+dxVcM0zTFhrYqioCg1vB7tywBFRChk+0epcSByvnK5DJrWSyRSNZB8pAde/b1oElj2OnvdAHGPYd+3LnQeCCveAPOOsj1lSd1ktq/GDklLCQtVSFhTTVtSvfIVRsib3YBOrjDFQPO9YSX+72MlrKlv7Lve9S5uv/128vn8DBn7e4a1gytm7CHeA7kGjPknid3d8NNfA5zJCCMQF8Z86StqaH6kNGDmvH4oO0DCvH448zrC+V3Q86fGlbBIO3nPCvu/TXvCYl0E5YiShglPWaMgvRsBBxoF2P0CgeQ28PubIxmmiEwJOlBKDpZ3c2K0zEajcU9Yoe82Lu0ocnexeU9YtY/P2Hgv6rIz634MrZQmhHMkrGh5wkZegxFEVlWN5bx8OY9fqZi1PeEGAzkRr41f9aMZ4rNTT6elYRo2CfMYBcDbdERF2SijK3LAuZGGg/5p0vvlyjk7FqfuTak/LDqoh1+DvrW0zlttP6ama5KEbR6rRO1+Ae6+CroPt0mY9TpEVLmZqTUt38LiU8UfICbji/pKKoe1LkRRami+ykslLNRGYeD3fGxWgSFfA/5eC+E2OPGfIdhCMXkroJGvJy4DRndH7kM0VWu49tprOfjggzn77LN59NFHyWSaC9KcwT7CmvfA5/vgLf/DLHnCbMaY78jIIgvSNBmTakoz5ciM3KlG9vZPNIITPk545RuB5rojLQLnV/3NdZJKWGpaw4TH46PwTyKI2IlGAXwhQjLFv1FPWH//fbRENBIeg6ADoYqWf0VtIhLCMOR7bpiVIMsGEfIEmOszWOzXKfz8woZGqZSssU6FHPzhY02tZ1Q58rR/gcvurev+6WK60hmpmyjROlWXvRDzxyhIY365nIHBzcJPmZn8opsupjFkqV8Ni/fc2yAJC3gC9uYtL+cflWQJeDJYpcigJ9jYbNhZK8Xf/euI+WJ2V3e1L2wMCQu3iXLtktPsm4YLw3gxCWCRsMbfE6/qJeqL8pukn7mr/oeurjdOfacqJUzXdrA4YBD31kbqx19EQJSlT/wEZUW8rlbnac3YTzxhTW2bLPXLNM1J5yIqikK5/Pc7YPMfHlXeGGuId92esHIRUjtHjSxquhwJdr3ect00RsJkOVKOTok4lI7cdHfkhnso9DwBOOAHA+h9ieBGccFsWAlTPRRksGagyYwwAPwRApJUNNoduXXb9zm0W+epfg/BRrupquBVw5QBjzYsxgQ18NqbegEFUMtm00qYT4HPdIn3K7cZwsVs3V18ml4gZ4ChG6A3FoprIeKLoFlKWOdi6F5d1/2zpSwBub336GbFT9Qg4oE4BWMXIGd2/vYD0POs6P476IIJ75eSCnjIMDCCESDfMAkDcT7LlrJkJSGccIh3FUaZ8o0yjz52LD5fG0cecTs+Xw0X/84VYhh431oURaE10Ep/vp+hwhDx+KGUSkME906dX3Cs+FOFpJYk4RFvqqoG8XobIB6mKaoK/hiJQIJMKWPnsE0J2xPWhlneKtbhdeD6ABTVBFu0Pha31tFoAP8YnrCTTjoJZR+GnM3AeVjlyMHCILqh167ODG+F/zpKjCw6WHRnOaqESTWlGSUsJ5tHHFHC+tYS3rNOPG7D3ZG/I//q7TC3y5GMMPrXE1x/F8zqcKA7svmgVgB8IZuENarOlaXikDMUAk3kX1nw+1spIwIeKWYbImGY4nfx6DSdtu3zRjFMUBXI+VThYayThA15FvCVnjAXLjgbVn+8qfVEfBF+n/Rz5wj87oTTpr7DXkgX02gGvJoL8LqRJHQ0p4TF/XG0klTmylmRw2Uawps5CazOyIRhUPZ5QWfqLr5J0BZsY2dmJ6myUNfqJWGl0rD8k8TjqfEcVKWEgbB49Of7GS4Ms2ree5g/7z01PUxSS5LwSptBYHb9123ThOvnic/mP79K3B+nhx67zDklqpQwVc6u9Pra6lvD3kjugNwASaWL7+zZwVeWv66+++8nOWFNkbAHH3zQoWXMYJ8iNwR3XQXFDO0X3oqCgm7qDGvDNimbElXDu62Tn+WjaAqWEiaz5poqR8rduSMRFb+/gvCe52H+nCZmR2bISfeyI6U/vwOzI1O70F7+NeBMeCy+sL2mQiPxIlTKPjlTIdBkJyKA399CDvCpCBIWqW8HbZqGnW7vWXJ202nbiqJQNBWCikneqwrlts5f086ai3aNGlXTCMK+MH1l8WUpbriPUKpXmLLbawvdzpayDOoqDw+HuXJzDyxqUgnzxylIEbWsZ+Dc/6jpfiNSzW/RDcqqLkiYrwklzApLLRVpAwxDQ9fzeCYZpTVeWr7P14paa1h0p/SM9q0VHZJVo4smRHqP6EatSixIaklaPBYJq9MPBuIz7o8KEiYDWwFGCklKpeTkczBhlCfMmxfny2Cjw7st/Pp9sPMpokeIjuK6rw0nfQoOfissaKxZwyk07+Kcwd8/FAVeFINgvYZOW7CNwcIgA/mBOkhY1fDu6myeZmEpYWVRYmmsO1JGVMj/OkLC/BF7dmS+nMcwjfrjHIpZ8oqDJMwXErlVNFGOHN5G4dXfQWd78xlhAL5wpRzZAAkzTaMS1qo3PywbICAvGH7VhAYItK7nsGiX14mSO1BCJYhO3qPYn9d6YJMwB8raPtWHX/VTNIpkn/4fEtufEoOgayRhVvNM1ArpdsATdl/axyFLP8Xpyy+r+X7JPS8BkFA8lKWHr1FjPlR3SOZoV3yYZolSabhuElZTRpiFjuWgqGKTm+kb0yE5BqYJ31kNRhk++iS0ifFGyUKSTk9FCWsIH3pIqEa+EIkNCRIeg9D2T/PIDpXTTn118lmYsW6YtQojNht/XjDqUCNksBrxbojNISpnYNadIbnoRPFnH6Ouq8Yb3vAGnn12goGhUyCfz/P1r3+d733vew3dfwYuIpCAM6+DN94EQGdY+ML6cnV0m1jDu0MJe76jIyRs8alw3BXE5qwBGuyO9Ech0klOmoUdIWGBGGFJLkzMxkhPMUfBURIWIWSKC1/DJCzcTkH6SRwpR3r9BKW/qBElTNezIM3VeVMh4ADJCMoySEBFZL41tCbhyffXM6tuEpRNGePh9dQ8nLoaNgnb+jj0vtz0eqzvSMYq/9YxPzIrJy5E5capaU+YP07RVEiWCqMv9HoJJpnGMjKwFhAlPCvvrJlypE2AtCRHHnE7xx17P37/5L+bXY4MtDZGwnxBaBVEiv61o0iYaZpo2h5SqRcxTV0ck+mDUhaMEsQrXrGklqRNkrBgsMEZz7Euu3SfCCTI6AoKuk1GJ8XZX4GPPEFxmbCq6CbEQk2SsAt/Ap9aS3TWQQQUk6LWg2E05jvdl6iLhO3YsYOjjz6aM844g1tuuYVUauoW02eeeYZPfOITLFy4kC9+8Yt0dNRpnpuB+1BVOOHjsObd4A1UYirqmR8py5FJ6WVRFbWx8Rh7Y9Wb4OyvEFsqhtlmipnaZpVV45++D5/ZREaqak4pYcGqdTTkCytmyMuSgTPlyKrSX6MkrHM5hVXnAw6VI4GgIgR3rZSd4sixsEqRJQNKpuLI6xQOVEiYodVP6stl8XtoJgR3v9j0egDKsvu36FHsXLt6sLD4DF+ak6M99QgMrJ/6DlPg4JDJxW0ag3GpcNdBwjKlDHHVoEvNo/mVhtPyLVilr1Eq+I9eD1/pBql2jYeR4S0AtEcrQbEeT3OeMECa4g8hHF40ZVnRblIKttpjdQL+OoNrLV9Y37pR8yPB4LHHT+LpZ/6p0hk4JH5nEvNGeRWry5FjjPwNIBFIoKNQUsT3saasMKAoj0vrCrEmhsxXI+qPclVXgVWZn5HJbqz9ji//BjbeCw12bTuFusqRzz//PD/+8Y+57rrreN/73sdll13GihUrWLNmDbNnz6a1tZV8Ps/Q0BAbN27kmWeeYWRkBFVVufDCC/nKV77CokWLXPpVZuAUGuqQlCRs2B8EDRL+Jod37wWL0OmmTr6cb6jdOytJgFMkTAVCipe8WSZfykO9/KCUc7gcGW7eEwZVYa3OkLCAvFAVmiBheVOQVSdKpKGAGJ7sUUArDNb9tllZVQVDITC8ven1ABiKD8ijeZWGlDDVzJHwgKYglO0m0eVXODKgU7Ay8KzuthqQKWU4OqJzbEuZze2LWRXrbmotVqjnqLmxXr9Qe3qehe7Dxt7JNEmmd0LIQzyxgO7uFRh6oXYv1jioJmG1YlQ5UhPKXF1KGIig1HV/hP61tC07HhBKmKJ48Ps70bReNG2PKDNaJKytUjouGSUypQw/GvRz38l30BpqsBy54W4RAjv/GJsYF8wAPvJoxT3EWDnlQ1hkMWUorHBik44YbZUxoIU6YirKmvCUAXx2W9PNNc2grk+koii8733v473vfS933nknt9xyCw899BA//elPxxyrqiqHHnooF1xwAZdddhlz5jTPvmfgIvo3wMh26FxplyPrygqT5cikTKVPOLTLoVyEbB9BvYxX8VI2y6SKqYZImKVWOUPChOIXVjzkzXKDSliWvMPG/FCznrBijoL09zniCcNSwkwKDfivKsO7JQlz4GQZ9rWwpySGQq/W6idh5bIkYSaE5jtj6hUkDEoNesIUowge8JUNMWWiSSiqeFV0K3RVfr9rQaaYIaDKuZEr/gki7U2tJe6P0+UzOKJ0L8+/MMDhh/0Q5h4JWx4UJOzI94290+AmknoBiBDtOJxVK9/f1BqAqf1Y42AwLwZ2t4faKY7UMby7GvOPgQPPgzmr7QBsy3cbCMyWJKwXOBSGNov7tFUS9K0ORkXx0B5d3PjmuG8tPP8zMMokDjkHgKzpI6ZUFK5xUS7Ctw6CcDvaGz4MQFqHeBNNEgCs/SM89p9EO7rZ6VMAsz4Stugk4WV24PvSDBraFqiqyvnnn8/554uyxdq1a9m5cyeDg4OEQiE6Ozs56KCDSCT2bevnDOrAPZ+HjXfDG2+skLAGlLAhmcXVHmruxGtjywPw8wtRug8nlogxrA0LA2Y9POpH5wAKmbCTSpgkYagM0uD8yGKWQsgdJaxklCgbZTvcsWY8+2O0p78PiZgzawICHj+goTXwGlnxFFlp/XGiROr3+Pn33hAG8IZWve77t7Yey13KuTzS9wCfOOroptcDYMgxNmWP2qASJro1fbppN7M0A49HbHJ0Vb7wdZYj7bmRnuZnfcb9cVSgXRkhnZZ+t7lHiL93TuBR3voIKVnqbwk7Y4GpJmG7d99Bf//ddHaeSXf3Wya8zygS1i/Op/56y5HLzxZ/gLbep4GKwmaZ7AtWYKuthFVImBWeHffHm6tOWN6+TB8Jv7i2j+gKXd4p5kfmBiHbB7kB/JElPJ7xsLuk2mpawygkYedTxALHkmmR2W3FwdruG4zDe//Y3PM7BEe6I1euXMnKlVNLkTPYjxGWpCk3RGf7IUC9njCx2xqUyo4l3TeNQBxUH6geYv4Qw9pwfeZ804TtfwVMckvFXExnjPkWCRO/b90xFaYplDCZ5O00CQMRjlo3CSvmKMj30DElTJKwQiMkTKpOGd0apdT8mhRFIYBKHqOhZgFFUcnoZbKG4ljJFiUAJpQb9ISpVhK6bjqys/d6I2CAoUiSWg8JK2bolEqYx4HGBRHWKv5tfR5sEta/TkTQ7P07975E0iNImFOqfHtQnCOHtCGy2Y30D9xLIDhnQhJmmAaDBUEKOoIdbLWM+YHGhplDJQDb8pqNSc0fHKuEWcc23ShlDWHPDtgEalg3YSoSFumADz8GhSTe6EH8atihGb5SFYwW86TlMPFirSRsP0JTY4tm8A+EsCRNuUE65M6xkXLkkCLOlo6RsAXHwhf64QN/ISrVp7piKkwTLv41+lt/RF56nZzyhAGEJeepuxxZ1sDU7ZwwRy7mqjpq3mND6lyp0rHpFMEISuLUyNiiuXMv4tjE5/n5kN/ZNckuu0IjkSeAJtWFoNb4yKpq7PIdzn/0BhkcNhpSwjwIshQoG44kgPs8FnmSk07qIGHpUrqihL3426bXEvfH0WRKvWEUMIwyxGZDYgFgwo4nx97pvG8y0roQgJiqk0q9SEGrc8DzXqieH2moQuErTzK6KKkl0WXXYluojXnz3sPCBR8iEl484X0mhGnCSA+tuji/jmgjlI0yAb/oMNS0XtEpOrhJHN+xzL7riDbCgQGdS2Jb2bDh3+p/bgs2CeuzfXoDRUH+teIk5UiPD7oOhkUn2ufuiC9S/wZxb4RaAIgWMqQNi4Q1MPN4H2OGhM1AwBrinR9iVqgyP7LmTkSrHCkHLVu7xqahKHYYpmXOH2XQnQqqCktfR275WfZNjpYjZYt83SRMtvE7aswHFH/Yzi9ryBdWyqFZJMyh7sjA6neL9QQbU2hKwYR9EXbOp+bBp5gUah27shcKIzvF42x50JH1KP5Z9JRUckZjnjCfJGFB3bQ/m83AZ4+HspSw2s3o2WLWnh3pVZv/DMX8MVsJg0pECItPFn9vfmDsnRSFEdkIomaf4eln/onNm2oLeZ0IAU+AsFeQLw3h4SuWJn5drFJkS6AFn+pj7tyLWLr0XxrrTvx/H4dvraLlld+hSPU9qSVHK2HJbSL3zhOoxFoglLBOn0GrmqNQ6Kn/uS1UKWEJ6efao2mV568B1rnbkc55qYTFtDSZepWwtX+Efz8Abr+0+XU0iRkSNgOBqnKkFVFRNsq2AXRKWOVIqTY55gmrgrX7aiSw1Qry86k+/B5/84vZm4TVW46UF4i89NA5RcJGJdQ3SMLyTithCaFIFMzG5scWFomOMK/ibX73LPGWtiz/MS9Pms1133fHjls4r72PE6IlR2ZZQuW1zh/5Pjjvm3Xf3ycV6JASGJWU3vB65FxDpVoJq3FDlillKrMjD35H02uJ++MiDkE+vV2SXHKG+HvTfaPvYJqUjbJtW/DJ38Hra/7Cb6lhlSHeEyuElp2j5sDrydC+FBQPnvywXV4dM8S7T3Rf0rEcPJXvyYg2QqsV1NpMPIVFwkyduCHI+XBZBjFPZszf9jg8/HXY/ADDgw8y36+TcGCjYI0biuRTthKmFWus3uSHxJ9Gp504iJnE/BkI2OXIIXweHy0BMX6oP99f2/ihvCBhQ1IRcqwcaZpw28VQGCF2wEFAnSQsvQc23kNGGowd2YGBXY4MGWXwNKCEGWVoW0LBbwC6cySs6xCC2quA0disxmJFCXOiE7H6cRod4G3dzzH/FWCoAaBIMVa/PyeX30qXv0yi4CXo0DB46/0vKIjyTR0oGSX8UnkKq86sxyJhHouE6UVxwarh900X0wRC0hMWm9f0WiK+CB7Fg2aAz1OJCGHxqSJNvn+d8EK1LwFDh+8eR6qjUvLzyKaFZoZ3W2gPttOT6SEtlbnJlBfLD+ZIVeDIS+HoD4AvRMsd55PUkiS1JHNiVjlyD/RLEjZrtD97uDBMq5wbGWo0qBVELEgwAYURQoU0PtVHf7lI96J/YVbiwInvt/kBePhr6Ee+h2L4T3xqNvyy6MDnVJYjY6WC7QnTai1H7ifDu2FGCZuBhZAkTbLsYAe25mr8UF92H7zvboZkkKVjJExR4LVHYOsjxFVxcUppdZQj+9fCH64g84SYBhB1SLmwSZguLlJ1K2Fti+Fjz5Gfu1o8jlMk7B2/INgilKfGPGF525gfmmQcSz0I7hEXh0IdJa1qWIqeU6VIAMPq/ovUfxLWrbBWQyHoEKmP631c3KbRVax/IkmulMMvPViODKcHgrL7TcXAsEhhjb6wbClrK2FeB7ojFUURJUlZki5bJCzcBovlgPHnfy7+3v4EDKwnufMpQGRIGVI5c4KEWRvSEakAFYuDE1o2qjsj8/nt7Nr1a5LJZxp74kDMTquvziuzlDBdz1Duk8G1s1aMumtSS9LebFq+BRm8q+T6SQSETUBtOYWO9lMnvk9OvA5aWG40DAj6W5pbB4hqhOLBD2im+IyWS8OY5sRTFGwU9o/h3TBDwmZgwS5Hii/MrHDFF1YTOpfDgmMZlK3TjpYjrfmRimivrqs7Uiaip/3iBOCIHwxg9sHwqfWEDxd+p4ZywqgaN+MUCaPi5WqsHJm1jflOKWFWqrzWQDo9gPb4d8TjlIuOrAcsJQzK5QYS86UnqWBC0KHOu5CicWREp7WwGe77Ul33zZey3JXycf+Il9BUg5RrRMTfxlZNZbfRgtmxVJS4amisKBklCnrB9oR58vUH9I6Hal+YXq5qXFgjvn88e4tQNxadCB96mJETPw6Izkgra66ZuZEWKkO8xebLNIsTfoascmR7qJ1k8mnWrvssr229ybE1DBeG8XojrD78JxxzzF14+qUpf9aqUccntSQdXlmuDi9q7sltX1i/bQ+xcsgmhNzIF4Ki8DaiK8T9DnxvFMVWw0w1wo8G/HQu/XeghrK5Pet4Rgmbwf4CqxyZHwbDsJWwejok8+W8TUYcU8LAlozjcrRLXUqY7DTL+IQPzLFypNcPsS7C0hxatxIm4QYJs0tbjZQjS/lKd6RTxvzuw8V6VGXyAydAQTZ9BOodkD4JTMTnQdfqb2m3SEDBQSXMK6McVEODV+6o674Fvci9KR8PDnvxOHFxAyL+BN/uC/L/CkvxXP5XuOLpmgZ4W97L7/cHOeyFJCHVmQHncX8czbCUsCpit+IN0L5MXOgf+pq4rfswhueImJ3WYCvlsjhfOKGEWee1QS2NR3aQTtSRZylhHaEOu0zm9zexOf3bT+Hm02lNCnO9FRrb1nYC0cgylPfdDZfeJTrKq5DN9xGR8WDh0ILGnx/AGgGV6be9aVM2SmWlEiYF1aSuOHcelkpW1BPkxbyXUmAhilJDFpq8hlw98jwn//Jk7thY33fOScyQsBkIWOVI04BCsr7RRUOvwQPXM/zcrYAwvztW9gN7txKXO+G6uiOl+pLxiouuo+sCu1uq7tLf+j/D906gkN4NOEjC7vpXgjLAstGh4prTxnw597NQyw51HFj5YgHVgYYKC0WpZAzXMWtOoiRJmGZA0KFyRkAOlvZ4vWKOax2wNj4h1Qsdk3hz6oD1ua53c5EpiddmSIOOkTIe2ZTRLOL+OK8WPJSixxIMVJnLPT54443gDcL8SnCuPTh7FAlr/sJfXQr0+8W/JyJh1cb8yvDuxjPCKKSg51la0332GkbBH4GFx1U63SXMkjhe9bY1H55rzQHN9tuBrdnU39i0+ev0998z/n2kEqZ5RJyFUMIcUqCsrDB5bsjUmrEnlbAhs8SwNoyiNLZBdAIzJGwGAl4/WLuT/HB9o4v618FDNzD0ghhf1R5qd/ZDLZWwmKz112XMlzuejOwWijrRlQOiYeDPnyP8yu+ABsqRmT2w52XyhiixOWY6LyQJlgRpaUwJy1FQnJvTCKON+XUPXwc0SSaDTnS1WpB+N7OBAHHLk6SZCkGHFN+AZYRXFWHCrgO2mpqYD+d81ZH1WGV7i1TVCusiGDMM8EUcGwnTEmzh/rSPvsjZJBKHj/7hwuPgQ4/AsrPtm+zB2YFWu1zopCdsuDBMW+sJdHacicc7PrGpNuYXi1ZafhOdkotOAKBteIdYgza1R880TXyGTNdvVgWD0Vlh8rxczq5l27bvMTAwTlQI2BaXgiI+p44qYVZWmCLO7zVbVaQnLCNz3GIOdM42ihkSNoMKwnIHlRu0lbCaUvPjc+DI9zE4X6RYO1qKhIoSVhZfmLqUMHlRyMi2fceUMEWBZ28hvO0JADHAux4sOxvefQd5OUbEMSXspE8RWHw60HhERcHJeZZYsyNFgnjZqD+moiCHkQccJGFqRKopkZa672td1AsGBJxSwmTuks9KqK8DuUIva8JllvpLjqwFxPfEr5goehr9kf+A7x4PT9085f0s0hYxzMqYGwdgJcVPGJnTuRx8lY3M+EqYc+XIocIQK1Z8mUMP/T7x2MHjHjueEhZoRgmbfQgEW2gpSjIjy/TpzDpe/stZrLvnTNjzyqi75Mo5WlTxuYg1EhK7N7oOgZXnQ/fhlcggU3wv84WdY483DHv4u2aI92Gk7GQ5sgWAGCqHhcrQ97OJyWA1pBKWlptgxzbnDWCGhM2gArtDMmkrYX25SfJfLHQfBm/4FkNLTgVcIGGWEqaLk0lD5UiLhDn5ZTv5U4QPeTvQgBIW78ZYfCoFeRJwjIS1LyEUFwSjERKml3KULGO+U8Gom/5i/7sRdc5K2nfKowagSj+PajbwGunivTZ1EyXgUE6YLO34MDC3PymGHteIQm4z72kvcmJglyNrAQj7wlwxS+Pq2cMMFtZC3yuVuYSTIF1M0+IxOK+zzMYFziUgWSN3alF/oIqEBeL2++VrdmA0tQ/x1g3dJoztofaqcmQTSpiqwsITaJPZhEOaIDdGOcceNjOgb7QJj4XB/CBbNJWHM0Fmd76u8ee2sOJcePtP4aj3256wpC6nT4wXBFtIglSbCmWhBg7qqnMk7MBz4cR/JhrtZknAIJR7iuRIDR3G2gwJm8H+iPf8Dj7fD8vPqkRU5AdqLiE5motTDUsJK4mLcaaYwailDRkqxnzZreWo7HzyZwgf9k6gMWN+NUlyozuykYgK7W23VB7HoRKpzx9DaSJAVtMdLtkCHhnloBr1ZZeZpokhL+qKgSPp9ABhv7i4qwoYPz5LlKtrRFEqc3qxAC/e7sh6Ir4IRWt297zVcPFv4ZgPT3m/bClL3GOyNG7SF3UuCLMl2EJUNVHyW8hkNkx5vEVQWgNtHHPMXRxxxK+cUcICbfbjT3ZeHCoMYZgGqqLSEmhB0xwoRwIsPoVWXQalSiIYDooyoxbwoM8ZrcoNFYbYqHl4sjSfWbPOxklYJGxApugWCrvHxkNkBfk0A3HykqQNlR30hB36NnjdtURbD7ADW2sa4m2VI+U5cqYcOYP9A6FW4Q0Duxyp6drUdfbsAOSGGJS1/7aQO0pYXMrwJmbtvjArogJxcog4FK5pIeyTBuZ6lbDXHib/3C32fx0jGLtfJNj7MtCY6pSfc5j9b6eUMCUQqaT4N7AmSy0MOEhUPUqMXw352NhfXzCqaZYJJU7huZwHUzfB13wOFkDYVzFT63UO8S7J76epG2A4U5IMeAIUTXF5yAVDsPQMaJ3aZJ8uVs2NVJzLdWsNtLImXOYEHmXr1v+a8ni7czDUQTSyjJbEEbV1zU0BK3qnbJQZzveTz28nmx2rEFoVhI5gB4pZplxOApWB2w1jxRvs+ZHJwrDwfAU78MnPT644ej6mvTF2MjLINCE/bBOpvmIRRfFgmsWxTQrSlF+OtdnjphwlYRJRf7QqsHUKH7NpgpZCB3LyfDSjhM1gv0PQG7Ql4ykDW+/8FHztAIZ6xCBdt5Qwv5a2VZ7aSZiLBsyRHkLD24EGSNjzvyB/7+cBoVypTsUv7Hya4GsPA42qTkIZCngCzq3JFyIgSVgjQ7w1SSyCExigG4Hfm+DxrI+dA6m6Sn+q6iPQeiE/GQzgNQCHstTCvqitPBU9Sl1DvEvy4maGOmHpmY6sR1EUdOnlK5Rqn6+ZKWUqGWEOzI200BJsseeH2mGtk8DyS7VIz5BT8Hv8tt1ie+8fefyJ01i79l/GHNebE2RodmS2PVdRVQN4vU16CBNzaZtzJABlU7etGWHp98pmR6uEVkyGYxYRLQP/1gn/voiEzNpLFtME/IJcjilJyo25L9jJySc9z9f3xCjjoCeslIeh14iWioxYJGyqOZZaCkyDTFVkzowSNoP9A5vug998AJ78AVBRw/ryU/jCZJL2oCkuls57wuSJS0vZO6iafWFSUchKQ7ijO54/XEn4lxcDjc2OzEuS42QpEn+EkNFg6U9LU3juJ4Cz6fT4KkpYI6OLrJmTAb9zJMzK99IUxZ7jWfN6ZPBvsONAe7h80+vxBtlSVNlQUCkq9SlhVoI/vnglx8kBmIpQCbXCADz3v/DE1ApU9dxIK/vMCbQGWqvCWqd+vyzvmFU+dBKzw4JwJPVKav7e2JMVRGBWeBal0hCK4iUQmO1I17h/zSVEpC8s+eIv4f99nKh/PgDpzKujjs1k1nFcpMx8v0OXen8EZDNRQq5hRBuxk/jz+R2jj5flSMLtlBUvO4tiM5xwKOSYDXfDdw4n9vJvSdokbPfk9/H44U3fJXPq5wBxrvPVOSrMSfxDkrDe3l4uu+wyuru7CQaDLF++nOuuu45isf7E7bvvvptTTz2VeDxOLBbj1FNP5e677x732Pe+970oijLunxUrVox7n/0KQ6/BS7+CrY8C1J4VJsfR9EmvjHWScgxWB1ohZbdF112OlN4fR3PCAlHCklyUjBKlekpBxSx5h7sQgdEDvOst/WX6KDz6dcBZ/xW+EEGjwTWZJgVJoANOTTsAArIsXVAUKNZHoAuWR83BkkrAE+AH/SG+2x/EKJn1kTDLo+ag8gRgKsKaoBWT8Icr4O6rQZ+8uzVTzBC0lDAHjPAWEoEEBaM2JSxfztt+SI/2Gi+9fCXbtk/d2VkrZkfE+W2gKM4pxdJYEmaVI2eHZ5NIrOa0U9dy1JG/c2YBh15IiywVD9/3eXj2FmJ7BPHIpNeOOlTNvcTb24osMTc589yKAh/7G1y9h7jMpBvRRgiF5ai0/LbRx/vCMOsgaF9qJ+t7Va+dQ9c0ggnwhYmqPpJl6QkrDaNPdp7xhWD1u8gc/E+AgwHeDeIfboB3b28vxxxzDDt27OCCCy5g+fLlPProo1xzzTU88cQT3HnnnahqbdzzZz/7GRdffDEdHR1ccsklKIrCr371K8455xx++tOf8q53vWvc+3384x+npaVl1G0dHU0aMqcDC46FM//NHnvREa6Y8ydFPglAvyRGVmelY2hdBKsvhrbFxDIvAHUoYR4/ePxkrNq/kyTMHyVsVIyo+XIen7/GHVUxZyfTO6uEVUhY3cZ8bwDtgFOguMHRTkT8EbscWahXMdRLaFI8CDr43gW9QY6OlFkWgkJ2G8FEbTP1SqUkWvYVOr2GY2OdQJT/gt6gIBBKfeVIwxCvqZpNCsOxU6NYJKkrUxKDsk1DlJdiE2+yMsWKEuZxYj6gREugBU364Eulyb/7VinSp/owij309f0Jw9BYuOADjqzF2mTu0fK0ArqeRdfzeKpmre7JCSXMImyKouLzOaT+eHx0tC2lZ2QTAx4PzD+W2OpPwAvvIJ1Zi2matuLmKwlypgYdyAizILuvE7r4fXLlHMGQIGG53Gujjz3s7eIPkBxaJ+7nTziXI7n4VLh6N9Fdj5O/94OUTBWfYqBpuwmHD5j0rtZG3ukA73rxD0fCPvvZz7J9+3a++93vcvnllwOio+nSSy/l1ltv5dZbb+XSS6cOQxweHuaKK66go6OD5557jvnzhdx71VVXsWbNGq644grOPfdcWlvHjuX4xCc+waJFixz9vaYFXYeIPxKzQjXOj8wNkVMUMjLPyZo76Rg6l8ObRCkkfv8VQB2jiz7yBKZpkvnpGsDhcqQ/gg/wolLGIFfK1W44LWbExRanVacqE3y95cjEPPKnfAru/ZDjSphNwuoJ2oXRsywdfO+CniCnx0p0+Uxy2c0EOb6m+yVHniXQ+59c3Kbyt1TtHYy1IOQNkS/nRVhuHUqYaUjVZ2hbTfMda4WlrJX0nJgtm+2HbN8UJCxNVCph3oBz3tCgN4ipBgFtSiWs0hnZim4HtTqndnRFugDYnR1ilRrAMDSKxQFCofn2MTYJc7oqIDGr5QAY2UTfKZ+Go/6ZiFlGUTyUSkNoWi/BYDemaZIwB0GBcPQgx9cwSkHydRMKLcDnH7/8+8orn6IvtY7Ffh1Fxo04AnluEJ4uhZThod1jUNB6JyZhyR3Qv450oXfs77EP8A9Vjkyn09x2220sXryYD3+40k6tKArXX389qqpy8821ydK33347yWSSK6+80iZgAN3d3XziE58gmUxy++3OtIPvr7DnR05WjixrUMrS7xE+gbA37NyQ7HFgfWHqSc3XdM0OCXX0CydJQVh2XdVlzi/lyKsueMJ8IUJSnWvImO9CJhfeUMUTVs/cTwBvEK1bdGyGHCRhAW/ALm/ppdqyp6DiRyqYEEw7S8KiHj9R1STnVewyem2LEu+zt2w6llAP2MqOrmcrSemZyf2hmWKqooQ53KATlMPJdX1yT5hbQa0WbCUs34ffLzace5vBrXKk4xtSCduv6/WC6sHjCRCJiPJgMvk0IEzyIaWIbkJL/HDnnnzt/4M7Pozn5d9UDO3Rwzj+uAdYvuzz495lOPkkRn4dBg76wapgba6tkqRW6J344E33ws/eSuaFn4v77mMl7B+KhD3xxBNomsaZZ545Ru7s7u7mkEMO4cknn6RQmPri9OCDDwJw1llnjfnZ2WeLvJWHHnpo3Pveeeed3HDDDXzrW9/i/vvvR9frT8HeJ9DL0PMsbLwPTLO20UXSlN/nFaKqKycd0xRllpGdxOWXvp7AVivFW0Fx3AQPEJZfo7pS84tZWwlzek0Ne8IM3S4XOqqEqSpBrPbxOpUwX4hCXFz0nGwWCHqC5GUluVysvfvPHllkKATbph5oXQ/eFBvgy3PzpDv8dSlhSL+j11RHpcY3C49HfL4NI181rmZyVTxdztndkU6qTwAhmaWGWcaYJN+tmoSVHBxZZMEmYbk9BIPdAGha5aJvmqZtzO8Kd/H8C+/nr0++nmFJjpyAdZ6tPjd3db2JuXPfRTi8CIBk8ikAdhZV2sNdjj03vS/BC7+AbY/bHt0JKxM/OJnyd4+2zfJ9ZdVZEmaa8LMLid3+fgC2awbx+GGjSsNjEIhD16GkI0K125fxFPAPVo7cuFEM4122bNm4P1+2bBkvvPACW7ZsYdWqVQ0/lnWbdczeuOKKK0b9f/ny5fziF79gzZo1kz6npmloWuXkkkrVqRo0C70IN4uRN3xux6jA1gkhE5r7Q+LL6LgfDMQX7YYFgEn89f8K1EjCktvhjstJR8SXPuqLOhe7ALbqEJYEoy4lrJgl73fXmF+3J+yVOyjc/THobHe2OxIISLWwUA+5kLDUOSc9WEFvkIIVeVBHBIMuh3cXTAjMdrbEY8huxJK3Pk/YA7l5rE3luC7t3NgiAM2/iB/3/o2TF62BqDwHTKGEZUtZkh4Vb2gRgeCcSY+tF+FAO4YpAm3L5TR+//ifh/GUMJ+TJEz6vHqzvQQCYp5joYqEpYopewM0KzKL7dmNFAo9KA6eeywSVj3RZOGCy0Yd0zdwHwDrCypvcfK8LD1hpHeTiCfoyfSMfz42TehfTy5YAlopK2FyRmX6gSNQFNj+VyLFFCyaz+9H/Fx13v9OXo055K1wyFvJvPjf8LcXZ8qRTmJkRJxME4nxmXY8Hh91XKOPFYlE8Hg8Yx7nlFNO4Te/+Q07duwgn8+zdu1aPvGJT7B582bOOussdu2afKzI9ddfTyKRsP9Ul0GnBf4wWApIfmjUF33CdGiphPUHxYfekskdhaoKs7HqI6aKjq2aSFi2H7Y9Snb384ALOx5LCZMvTc0xFaY5qjvSUdXJH650ItZbjixVmgUcXRMQbJSE5YbQpPriZIk04AnYkQfWhboWVCthjpJnwJDdiCWPWlc5MlsukDUUQh5nbQBefxcv5L0MmHGISIV7CiUsU0xzd8rHwlX/TXfXBY6upyXQWpM5346nCLZVlSOdU1+s82K+nEf1CjWlWgnrzfbK9bbgV31omiBKAb9zatRUY+XK5TSDgyIvcEMx5Gw4akySsFSP/bhW52OxODCKkPLBB8mdJXLU8qp4DxwvR4YSBE0TrzzH1GpVsYbNz5Qjx0FHR8eEUQ/j/bFKh/sal156KW9+85uZN28ewWCQFStW8K1vfYvPfvazDA4O8q1vfWvS+1911VWMjIzYf3bs2DHp8a4gLH0cuSHbgJov5+0v2RhY8RQ+sSt1ywPBp9bDF/qJtwqzZU0krPUAeOuPSR/9PsANEiYeLyQJas1KWFkDU3cnJ8wXsddTqFcJK+VFbhYOe8KAgB38WWd35O7nyQ8Jxdn5cqRUwsr1Z3JpJrbi6Bhk+KXuwQ4ZrgV5Sbad9MwBtpqQLWUr+WOTkDDDNOzSf/SvP3B0LSCUrZv6gqyPXkwoNG/C4ywlrCXQUiFhDoZxhrwhm0gUFPHdLRQq2VQ7M2KQ9dzoXEqlIUyZnxgIOLdBtcuRE/h1t23/H0wjT29JoeDtcq4bESpKWGqX/Tqkiim2vHYjjzx6DFu3flf8XFFg1kqyEaHwpkwRS+E4CQsmUICoPGdlatzoWdeQfa2E7ZflyHe84x2k07XvBLu6BFmwVKuJlC6rvDeRUlaN6sdqbx9tMM1ms+i6XtPjALz//e/nq1/9Ko899tikxwUCAQIBZ8tAdSPUBqkeyA8R8AToDHXSn++nJ9Mzfvq0pYR5vWC6pISByHahTmN+uA0OfjOZbffCBhd2PJYx39DBU4cSJo+zlDDHMnMAvH6CktwV9MKodvUpUaXOOVn6AwguOR123IsWbqnvjp4AmjcAmI6qc9XlyFINCewWytIUXjAUAjuegaMcW5KIhDDl2KJc7SXSvG6RMGcvJtb3JVvKQou0b0xSjsyVcli0NOrkmByJlkALPSWVvrKCKhXx8WCN6mkLtlFOOu8JA+iOdDOijZD2LWHNml8SClZIYU9apMbPi82jUBDVj4B/9qRrrhdW53q6lCZXytnj00DkZPX0CNP53SM+Zk3SzdoQLBKWGyQhA3lHtBHCbYvEmtKvjDo8nRHZZYO6OKc4T8JaAIiqfpJkSWtD5PPbCYUmiOX45bug71VSi4WdwOkRSvVivyRhN954Y0P3m8qrtXHjRlRVZfHixTU91jPPPMPGjRvHkLCpvGd7w8oIy+WcG2jrGiLyd82KE9mc6BybhB3UMY4HRnrC+lRAd1EJk7AT8+tQClyTnQOShOmShNWqhMn1FFTx9XO6rBW0utpMg7JRrj0NupRzTQkLtiyEHVB3gMKiE9AinZDvc1QJqy5HFo3aE/MtT5hmQsjBBH8AVQ2CDkbXKjjqpzXf7+0tgxgY+HLOEuewCmfHSyzQX4LoeeLGSUiYpYLFPSreNe+sbwNQA1oDwpif1JKTHmeNWesMdVLud94TBjA/Np91Q+vYkc9yygGjmbilhM2LziNfEP8OhmrLoasVEV/EjjTpz/ez0FeZ61ko7KK19TjW5RX+tuMvvH62wyQs1CpsK+UCCVlMG9FGiMdeD0AmsxbDKKP2voS54V7SPAvAjpI43znqCQMIiceLKV4CisnQq+/gceCUk1/E6x2nRD+8DYa2kFogqipWc8G+wn5ZjmwUxx57LIFAgHvvvXeMh2n37t289NJLHHPMMQSDU19gTjnlFADuueeeMT+zEvOtY6bCk0+KmYp/F9lhdheUONnOjYqTx67MBH42ecLbg4iAcI2E/fV78Mt3Ed8tdlk1lSP3vAqv/p7MkEiLds0TJlPEazbC6yWIdJK3xt847L8KtS6y/53X6yhJlmRGlQtrsghUQwO85X2cJIZBb9COqChOYPAeD5YnrGAoBB0a3m1BVcXnwVQM+8JSC+YHyiwNGgT9zioMYa+X1ydKrFS2YUalnyk9sa/V2ux8tivHQ0+dRCa73tH1WEq8VW6cCAMFcU7qCHUQTxxGPL7aHnDtFObHhF93R3qsZWRnWpKw2DwKcoxPKOisv1dRFLtLc29fWCx2EIcc/B22ItQ5x8/JimKrYXFDdP6PFEcIhRbi8UQxDI1cbjNsewLt8espGikUxcNrBVGWTTj8ObWVMEVFMxVMVZyX84UJ7DwyzDctJ5zsayXsH4qExeNx3v72t7Nlyxa+//3v27ebpslVV12FYRh84AOjU5NzuRzr1q1j+/bto26/8MILSSQS3HjjjaO8Wbt37+bb3/42LS0tvO1tb7Nv7+3tZfPmzWPW1NPTw8c+9jFAlFn3e1gG3MxoEmbt7sbguCvQL72LXtkyPifqbEeUjZ7nYN0fiaeE9yJdTE/cLGDh1d/Br95DZpsYw+RWOTKkiy9zzeXI9iXwmU3kl4lhy04rYd4PPoRHmlTrGphdrASjOq6EJcV3qDDJRXwiWPMmnSSGqqJSwkvBALOOzrGyjDzIGc6OUQJQPYLUmUbtRFUr5/BJsSnicC5XVAZvqoqJGZfnhdwglMZfn6WE+a2ICocbBdqD7RwdKXOc+gK9vX8Y9xjTNO1u7s5wJ4ce8j2OOvLXBB3u1LRI2Pb09jE/s5Ww2DzycqC1NVvRSUxlzrdud6VjXZrzE2WxAU1pKRRFJR47GBChxmR6GYkL9SsSWc6QbDZx3pjfAkDU6nb2CMKdz20b/3hpoUnJDeoMCXMYN9xwA/Pnz+ejH/0ob3nLW7jqqqs46aSTuPXWWzn77LO55JJLRh3/1FNPsXLlSt7znveMur21tZWbbrqJgYEB1qxZw5VXXsnHP/5xVq9eTW9vLzfeeOOotPx169axbNkyTj75ZD74wQ/yuc99josuuogDDzyQdevWcckll3DhhRdOy2vQFPYy4FqkakIlLNZFf8cBlE0dr+J1zxMmR7HES+KCXDJKU6sqBeGrSctQVMeVsMQ8+OhThNeICQx1RVRQUc6cJmGKolSUp3o6JEt5Cm50bAKB3S8BoKWnGK67F8wn/9smYU7HZmwqx/hcT5hZS66t+T4HH/Sf/L/eGFs0j+NGeJu0FAbhj58UXbRTIFusqELRoLOKR6Rq+LXu80N8Lsw+2FYS9kYmsxsVE581O9JhEtYR6mCOz2CZL00ms3bcY1LFlB3M3BZ0fni3hQUx4Tfamd7Jltdu5MWXLiedWYdhGhVPWLRKCQs53+k+1Wzf6vmVjsNSwuTc1RGZtdfaJiZPDA09AqldxDNllniOY86ct9vNXY6XI+Vs4ZgMqS7KLsx8fuvYY/WSbQdJyU3zDAlzGN3d3Tz55JNceumlPPbYY3zzm99kz549fOlLX+L3v/99zXMjAS6++GL+/Oc/s2rVKm655RZ+9KMfceCBB3LXXXdx8cUXjzp2yZIlvP/972dkZIRf//rXfOMb3+C+++7j+OOP55e//CW33HKLsx0qbiFSJwkDdmfFhXV2ZDYe1ePOumTdPlzM2SrPlL6wgvh5Rq0ebeEgPD7oPJBwVJzkalbCJCzS5saEAYtE1VeOzLrnCWtbCkChTsO/Ji9o4EJshkVU6yCGodA8ejSFvKkQdNgIb/lXFD0Hz/zQbuCYDLmC8GSWTQg4rHjEAi2UJA/UjTx88lW4/DGIjR+1kEluI1B1ihvXj9MEOsOd5GQJOV8cGvcYi5AkAgn8HueM8HvDUsJ2ZnYyNPQo/f33kM1soC/XR9Eo4lW8dEW6XFXC7Lyy3Pjp8K6m9lskrCDULetc3N52EgBDQ49jjOwkVDBY1HYBLbMuQDdF6dItY35ckrAMQlHO5ceqlNacYwNIWx7GfewJ2y+N+c2iu7ubH/7whzUde+qpp05a1jrnnHM455xzpnyc+fPn1zwSab/GXuXIeVHhK9iV3TW+0fbRb9MjZV/XSpFgK2GKlibmj5HUkqSLaftENC7kiSGF+HK6teOxOpNqVsI23guPfJNcUJwEHO2OBPjz5wjlhkGtXwmzUvwd745cdibsvhetzuHS1bliTithQenl0x74Crz7ztrXhLiYOE3CPIH53DrgZ4Wngzed+sGa7pOTI5eKBhB1VvGI+WNoBvg8oJVSU5b00pndBFRxLlUUH6rqcKOAN0wZH1Aip40fIG37wYId6HqOUimJz9c6eYJ6A5gdmY1f9VM0ipg+QX5z+W3s0oX6tjC+EK/qZf7895LLvUYkstTR54fKubmnaqNiwTCNaSFhiVwSqIp7iB1EwD8brbiHPnUbXQCJebYKFvQEHd9MERIVqUS5BAqMGH5mA/n8OOVIqeJmgglM2cu7ryMq/uGUsBk0iYjo5LSUsK5IFwoK+XKeocJeu0/ThAevZ/crvwZE27ZrsHYrWqrSITmVOV+WI1NyB+bKjueRbxJe+yegDhKW3A7bH7eVs7DDBm9GdhCUXrC6SFixqjtyPzHmWwO/vSh4VWf3jAEZGVAw6kuaL8hNW9DhHX0g0Mnf8l52tRwGp37ObvyYDDlZjizhgRXnOrqemC9GUfpsstr4ylM10pleWwlzuhQJoszukV2OhQmUMMsP1hHuYGj4CR57/CSee+6djq9FVVQWxEVJ0lJe8vmtbE4KX/CSFjHSat7cd7J82dUEAs6XBCfz6w7kBygaRVRFnXyj2igsEpYR14kRbURu0j3MmSu8zzlTxqxUkTBXzsFSCUtIq8qAnB85nids3dav88KqGH3twkoQ8AQc39zVixkSNoPRiFYlYxsGfo/fNnb2ZPbacRllWH0xuzrFLs86KbgCWfenMFJ7VphFwowi4JIS9tTNhDbeC9QxO3LJ6fC2W8jKgFunPWGc/Gl7rmFdpMfNiAr5ePWGtWry+IDDBAwgHGnni915sgt3TZrAbqFUSvHiS5dzdocGmI6TMOs1qmfcVF6SsLILRQ2fx0fJFJeIjDYAz/8c/usYuOcL4x6fyvXZSpjTpUgLQTk/slQeP0fNiqfoCHVQLiXFWnzOD4wGWNG2AoBdmlBUc7ltbEqKTuylLc4rX3vDOt/2ZHrGVHOsc3VXuAufWmNETT3oWA4r3kB8iZitrJu6yJMDFsy/lHBwITvmBMiGvRDtsqcYOO4HA9uYnyiKc92eogyr1naj73X+8xigBVSyMl5mX/vBYIaEzWBvWJ4wo2xLt5YJdVtqr52FxwfnfYNds5cDLithVSSsZiXMKkfKL6IrX7gjLiG88k1AHUpY2wFw0D+RN8XJ23FP2JzVBCOV0So1o5RzZ5QSENggYl20kfqmQFikLaA4fyHxeyPEPSYYefQaAltLpSH6++9hZVgHFIIhZ2MPLDJeKKahb53tX5kMmiSPukvOkjLCf5ktDkO5AP3rYHDTuMem80OuKmEAIb/oANXL42/AbCUs2EFJkjCn4yksHNh6IAAbM4IQ5vPb2DQsXhtLCXMTc6JzJqxSWDEZc2MubYw7D4SLfkbwtH/Fv9coOa83ylHzruPgdWn8gVng8drra3e4gxcQDSNrLiGx5AwA9hRz8j03yWZHZ4YuC57B0X8boYD4TMyQsBnsf/AG5IigAZE4DxyQEKF2r428Nu5drF2Xq0pYdTkyUGs5UpIwSURckcJP/Rzhoz8E1GfM1w3dJkiOlyOpkKi6ypGv/xqaTNd2XAmTv2NBloZrhSZfo6ALu/mAN0Denh859QQGawSONe4o4DAJC3j8nB0vcbj+Evr3j4FN9015H62YBMAoFmoibfXCGiqe04Zh2Vnw7t/BOdePe2yqmCJoKWEukbCINfrHGP+7ZnnCOsOdVSTMHSXswDZBwl5IiqalUmmIbcl1AKxsW0l//z3s2v1r8vmxni0nMFmVYlrOyYgSsWW0rx5t500P0D5cwhcRvrUhOd6uLeRCx2q8G974HRJHXibXkSIaEe9NJrNXVl1WfD5SAfH53NemfJghYTMYD7EuoXJJTEjCtAyldK+961oYX4hrsAzdVeXISbsjTRO0FCUg63IeTN3G/G1PUHjpV5X7O23M732JUGoPUCcJW3KaneLveCei7EzVZJNErbBJmAudbkETO7C1NhImjinIyk8o1OHoesK+CGfGS6yJFyl51ZrmR+bLKk9nPQxldHCBzBeVEMNlhYKuiUiWJadBVRiwDdMkVc65roQlQkJt95jauA1VVndke6jdLln6vC2urMUqR25K7cTnE5+FNo9GW7CNebF57NhxK2vXfpZk8q+uPD9UmfMnIGHWz12BaUKmj4TMXxw1ySAl15MQJNBSwtyMDbFCYEe0ERYv+SRHHnE7s2fv5ZPMiUkwKZ84v+1rUz7MkLAZ1IDFCTHmaQwJe+W37PjOQeimTtgbdndkkaU6FEZI+GpQwko5MMp2Rhi49IXLJwlnxQmm5tLfk98n94ePAMLg67gxdOM9BHe/ADRghJfHO72mQECSMLM+EmYNIXfDPBtUfTahqo2EiZKlrYTVYJyvByFvCE2+PLpHsZXcyZD2z+NnQwF2Rk8Er/NE9TllNV/aHSIdOHDyA7P9pBSTLZqKMuv9LFj4gcmPbxCtYXFRVzDR9bHjpnqzIq6hK9xFSXaO+nwt7qwl2Gp7v4pece6b6zM4tONQADLZDQBEIrWNt2sE82KTkzDXypEAd3wIvr6MdmmIt2Z2AmI0EEDLglE/c6UcCcKmIs/DuXKOSPRQEok1eDyVjUkq9RK5/DZMIC2/KzPlyBnsn3j+5/Cby2DtH4GKErYtvc0OQgQgO8BWn1DMFsYXupuDZpEw06BVemcss+e4sEqRUtGL+CKOd9cBcOcnCd8i5urlSrmpU/wBihlycjxQ2Bt2/nXzRQjKzJzaRymV4YVf2h4sx8uRTsqbIwAAYbVJREFUkoTlFWp7jSQ0vSjX4wIJ80VsQlWuwZhfKUeCV/E4/nkKeoN2N6IgYVMP8c5K0hxum3oebiOIyw2P3QSz9v/BA1+Fwb2mgwxvJa2qDOkqLZ3n0N52oivraQ93s6mgsq2cwDTLo35mmmaFhEW6KJfE6+d1iYQBHNt9LAA7iuJ9m+c3OXbOsRQKPZRKQyiKl0hkCgLbBCy/7t4b5G0jggRZeWauID4XUGiXvsHBfBUJG5KfD9kg5LoS9uPziP3gZBTE+2CFx1Zj3frP80T4AfrOuIRUQuaczZCwGeyX6HkOXroddv0NECe0kDdE2SjbpUdAkjBxIVqUWOTumrwBkAb2hCKeMzlBcjdQMeUHhVTu2pfNHyVsCFJhYtamPGkZctIA73gpEsAfJiiJTs3lyGIa/Y4PUZKeLceN+VXei6LsVq0FeUuZc7qDFDF2yC5HTpA7VQ27HGkohFx430LeEJqlzHmUmsqRVgnclc8RjO1EfuK78NC/2+cGG0NbSEnV2c0Sz6zwLG7qD/KT4fgYhWuoMETRKKIg5iranjCvO54wgFPnnwrAn3s38XJeZXtR5fT5p5NKvwhANLoCj4sRCJYSt3G4YkBPFVP05UVG2JKEiw0CJ30SPr+HjgNOAypNEQC0HgCzVokuSiokrD3kkhIWaUf1RYjLzePeVpVyuTJlIXHU5yrXhf3AE/YPGdY6gyax4jxoXQjzxS5PVVQWxRexdmgtm0c2VwhXtt9Wwg6IH+D+ug55C5gmrXI3NcqDsDeKWVB9FQOmiyQsWKXs5Eq5qSMnihmy8oLlhikfX5iQRcJqLUeaJtriU8HcArjgCatqTS+UCzWXF4US5iXoAgkL+qKVcmRxElVVwiJheVNxPnASqxypACaal5qUMH1kA3N9BvHUVsfXA+OQsLbFsP1xGNoy6jizb61NwtxUF6wO7MHCIJqujfocWSpYZ6hTxGuUk4B73ZEAR3cdzdKWpaxNbmJtwcNp80+jO9rNht0/AiAeP9S15wZY2ipI2Gsjr6EbOh7Vw5akeG9mh2c7P6qtGlLd7pDeyFEk7LyvjzrUdSXsXb8Gj4/4b89lJL2DkeIIe/bcyc6en9HZeSah4HxMUycUWkgwOMcmaTNK2Az2Tyw5DY6/EuYfZd9kdQK9Ovhq5bhML+v9goRNR0s2b7wR3nQTiVZRepmUhM1dA1/oJ3XOlwEXdzyBKCoQkpJ8TeZ8LU1OliBdIWH+CEGjTiUs3EbubZUpE06XI32BOB5JDK1ZkFPCNO1yZMClDlKrHKnLLsPJUNYtJayStu8kqpWwnMdTkyesq/Qcn+kq0JFyx/w9R1/P9XNzzMnJTs12WfbcqxyZ73sVXVE4LFRmpPcXpFIvurKeRCBhb3J2Z0aPm6ouRQKUZDnSre5IEN2BXzv5a6xqX8VRXUdx9TFXAzA4+BAAra3HufbcIIz3AU+Agl6wfWDTmVUGFRLWnx9/hqVpmpXuSLdImLSdVJvzi8UBkskn2dP7B/r7RUROm2cR7HyGlLx2zBjzZ/B3g4PaDwLglcFX7NtK6d1s9AuD46r2VdO2ltag2NlOSsIAFMWOp7C+nI5DmrPD0uNVU0xFMUNOrXjCHIevUo6sK/izaqC44z41f5iARcLKtZOwwvIzAQi4sKMPeAMUpBG+VIsSVqpEVASKY03hzcKreu1croK3tnKkYhblfZ1X5kB48UIqmIb8HFnes6HRJCx1wAkArAkb7Nh2IyMje5UrHYKiKCyKdtHmMehJbRj1M2uGrUXCDj3kuxx88I0EAi7mFwLLWpdx2xtu40dn/4jZkdlkMuvJ5TajKB7aWt3xxlnwqB67cWrDsHg91g+JWIZp2Rjf+0U6HvwaUOUJKxXAqDTgZEtZ24JgnbvdQnVcxuzZ56EoPlLpF9nd+1sAuh79A/zPGaTltWNGCZvB/olyUfjCZBI8wMEdBwPwysArtrF6c36AkqIQ80Zcz6MBZOxEhhaZXZQtZSnqk/uLrA5K15QwScJC0hBaE+nRMvaMRteUsHo9YVXHOp7gD+ALVYhhDeQCAFVFm3ekWJMLJCzoCbJR87Cx36QzeNCUx4fDB4B3HgNlhZBLFxMrdLXgUWsqRyqIkUt+1Z1IiIA05mNI4ty5Uvy959VRF9qRFa8HIOIVJNLjUmI+wLnRQb44p8BQ3+h5n3uTsLa2E5g961w8Dqu6kyGT2cDLr3wCgI6O1+HzuX+RtzbAL/S/MOrvQzoOcf256XmOjl7htbLLkU9+D66fB/f/G1DpjAx7w+6cWwBeewR+9jbiQ1sBQcL8/g7mzHmbfUhLfA2JyCpIzCclN8szJGwG+ycKSbj5NPjZ20AXJ/nlrcvxql6SWlLMKitmeVkVBGhl24HudkZauPOTcP1cYs/8BFUqTxOqYc//HH7xTlI7nwTc9IQJOTtslZGmUsLKRdA1d435vjAhGQVRsydswz3kfyhUJ3dIWKSihGlTkwsLbkVmgChHbtI8vDyk0u6Zurtw0aLLKc2/ihfyXgIJd7rODEUoy5q3togKVQ4T93vdKasE/S1ARXGjYxl4Q1DKjlLDrM1OWCq8Xo97XiSv9HhlC3tG3b4rI0JTXZ3cMQW2vPZtstkNqGqIxQd8fFqe84jZRwDwTO8z5Eo5WxE7rPMw95981io6dPEZTGpJSnoJ+taKz4ccDTQdGWEUkrDxHhIypsLqjly29GoWLvwwc+e8g4MP/R7Khx6Gf36ZYWk/cFuZqwUzJGwGYxHuANUHmJARJzq/x8/B7UIN++vuv0K6l6eCYoe5puuoiR7JWcjRRao2Ys8gm5CE7X4R1t9JKiu6hNwjYbIcKT1YU3rCiiJryo6ocEUJC9fvCdNS5MvuxFMA4PFV1jTVzE97TRl7zJEbJMweKq4o9vsyFapLtm6gpARJlhVK1FaO9Cqym9XvzsUk7BcXTq9FwlQPdInzADKLjv4NpAdFd15IXlG8LpFCgFBAeJC00uCo27elRSyDq6HRU+Dgg77DYYf9kGOPuYto1L1oimocJc+/a4fWcu+2e9FNne5It60IuorZq0gYht3hN1gYhDd9Fz76FBwmBqdbCplrnZFgj9tLyPm9Vnq/xxNk6ZLPsGLFlwn4xeembJRtY74rsyzrxAwJm8FYqCrE5G4ytcu++cS5wt/w6M5HMVK7eDIkLtbHyKwc13HSp+Ffd8HZX7Vr/xPGVBzyNnjDt0lFxEXENRIWEDv+cK25XJogIDkZFuiOEhap3xNWzAoygksEQ1EIypKtVms5sm8thVd/59qa7KHiimK/L1MhJ0l9yKXYgVfMpVy7O0RpT1mQMGPyMU8+RbzPoYA7F7iIvHD5lKp1dEuFZffz4u8Hryf1508DEFCsAd7ukbBYUJyb9FJFUTVMg+2p7YAgYan0y2zY8G/s2nW7a+sYD6rqpaP9VEIhF5Pq90JXpIsDWw9EN3U+/9jnATh9wenTU52YswYVaC+L899AfgA8XjFbMi7epz1ZsZGfHZ7t3jrC4nOaKIiN5GTTVFLFFCbic2pdR/YlZkjYDMZHXITZVZOwk+adBMDjux7nvp0PMOTxEDOV6ZG9QRAefwQUhdaA2PlPGNg67wg48lJSHuFRca87UpYjZbdctjSFYdtSwiwS5pYSZpvgaydheVlKCvncUXkC7aJbq1Dz45vkLc+dGyTMG8SnmMyJmuzKPTnl8fn8TrR1d6BgErISwR2GRcqtxo2p1DCfnNUYDrmjesSCIgnej45hTTuYd7T4e4voAMQbsEORfYr4HrhJwtoii8Q/9Apx7sv1oekaXsXLnOgcMum17Nh5C339f3ZtHfsT3rzszaP+f8HSC6bniWcfBIEEHdK2MiqmQqIvJzYurk5UiYhNiK2EjRPWyvM/h/88nOT91wFiY+5KgHedmCFhMxgf45CwlW0rObD1QAp6gU9tETvMc3wd+KrmTE4XxhsaOx6sn7unhIl1RGSDwJQkTBMkLCtfM1eUMG+oqhxZBwmTO2dXypFAICp2woVazzrzjya/VARBupHLFfAE8CtwdrfB2vK9GMbEsROmafLEX09n4ayNxD2mK2GtUCHl2ZP+GT7+QmVw/bhrMvBLsSMacUd5iYfkQHcVslZH6JLTxd+9L0J6D/zT90md+DEUTLyI19DjIgmbmxABoH6lxHBBbMK2ypy0ebF5eFUvxaIoVfp9zs733F/xtgPfxinzTsGreLn8sMvtuZauQ/XAgmPp0AVB73/pNjFtZeuj9iF7ctOghAVbQPWSkP60ca8LIz0w/BrDcsj7/uAHgxkSNoOJYJOwykwyRVG4/PDL7f8HDYP3tbgbRjgKQ6/B7z4Kf/yk/QWyTsJjsPE+2PIQSWkKbQm2uLMmOVg8WqqRhJUL4AmQk11krihhqkpQvj61J+ZXOjbd8jtZ5K7miAoq63eDhImcsMr/y+WJCb2uZzDlNIGcoRBy432jSgkLt4lB2apnwmMLxRFkfweRmDs+qLBfKAweBdLWVIFoJ8wVZnCevQWAtFHEr4BVAPN63CNhUTnEO6qadiaWNbbH8oMVpV/M73fRh7Qfwaf6uOmMm3ju3c/xkcM/Mr1PvuxMZpcF+e7d/qiYtpLutX88LUqYokC4g4S0hYzrFc6INSWlhWR/8IPBDAmbwUSwSFh6dCDiGQvO4KsnfpU3Ljyb/z7yauYde+X0ramUh+d/Cq/+bmpj/h0fwvzJGxmWnrG2gEudOVKpiMgvf3oq0/niU+ALfeTmi5KOW+Nmgh+4H4B8rSOCSjnyqsskTHb7FdK7pjiyAtsI73HHE2ag2KOLSqWJSZg1Akc3oGQqhHzudP91K4NcPzdH59CtUx6byYnXUTch4pIZ3euNIEVVRgqVCyvHWJsx8cNUMUVIlkYVxYeqOj9M3IJfdkeGVdgyLEjY2kERk2CFSpekEub7P0LCLEyLD2xvHPwWuuVmZnc5K86JB55r/9giYbMjLiphALHZtEtFzurIHAVJDId9ws9pWVr2NWZI2AzGxzjlSAvnLzmfr5z6dVYf8k5on4ZAQAvWEO/8MC0yfHVcEmaakB8mqyiU5JBf15QwXxDedzfRY8Xuc0olTCIvFR5XlDAqRKpQLtQ4VDxLXnZsuqE6AQQGROu8ttfImwnx/M8p7HkZcIcYBrziZJy1SFh54sBWi4SV5LFu5JYBBL0hQioopRG49xrY+czEayoU+FpvkO/v8eOPuFN2UxSFx7VZ3DHsI1MqVX5w8FvgqMvgxV9BqcCINoJXAUONEwjMcpUMWDMjPQpsSYoJHuuG1gGwqk1kZlXKkS7GIsxAINzGnCVnA7DL64FTPmvHU5imaZcjXVXCAGJzaJPlyHw5PzYuSHb6J73CBzZTjpzB/o2YJGEjPZMfN50ItYi/TYMWWdoa15ivpcHUGfZIo7k35F5IIMCCY4nIUUqZUm1RB24PXrZKfyZmbQOzixkKbmaXAUG5Ey54a/QQJrfbOWGulCPla5SVu/jyZEqYLFUWZZNgyKVGD59XPK5iFOCxb0PPsxMem88Nsauk0q+pk5Ytm8UGFvNQxkeqXEXCVBXO+wZ87DnwBUlqSQbKKr4l3+KE4x92bS3iqQOYinjvNg48R6FcYGNSRGSsaBdeqP9r5ch9jTlHfRiA3S3dcNxH7dtTxZQ9psx9EtZF2DQJKOK7MEYNSwsSNiz3B65tzOvEDAmbwfhoWSD+TvXYga02yhrc83l48r/BhRl6E8IXEkGRQIv8oo2MlyouS5DDPnGino7af1SWp6ZUwl64DX5+ETnpT4j4XEo6v+86+981+cKqjPluEdbAQjHaphCvsZOvyqfmlicMICeJVak0sRJWlkpYwSZh7rS2Wwn1qgoc+xHRfTYBsrL0HVbc7fCasvRPxZs5XT6bxQdez419AV4Y3s4DOx6gbJSZHZ7NnIjYPBb/j5Yj9xW6o+J131NMUTYrcSbWPM/WQKsrWX+jEJ+DArTJ1LJRJMw0K54wGXA8U46cwf6NWJcgPKYOye2jfzayEx6/Ee67xtUd+LgIi/JCq1QvxlXC8uK2ZEhc0Fy/MLz0a6LrRSv8lEpY3yuw4c/kXA799PW9ireerLCqnDC3ypF1G/OrSqRuvE7WY2Zr8oSJn+WlQSrk0i7aImEexYBzrodFE88ezLWIC1+4deq0/2ZQCwmzfjZdJZ7Fc94IweUUTfjSE18C4Lg5x6EoCqZpUCqJC7DfN0PCpgOd4U68qhfd1OnPVQZ570iLsOX5MXcmTIxCTI6rku6LUSQsPwyyg31IF+fDGWP+DPZvKAq0HSD+PfTa6J95A3DsR2H1xeK46YT0hbXKi+G4BkxJwoYCQmVydVwGwHO3En3+NqAGJWzVBXD+f5KVL5tbnjBO/vQoX9iUKGYqOWFuGfO9FgmrtWMza5dI3ViTV/XiU33kbE9YcsJjrZ/ldLkel0hYyEqoVwwMozTpsdmRZ7i4TeOIYI3htw1irifPsZEyhdzmcX9e0kv25mM6L2znLT4PqHznXr9IzK8sl1OY0gvq9894wqYDqqLSFRYkaFe24iO2SNjc2DTMFpYWGssXNuraIP1ghFpJyuy9GU/YDPZ/tB4Aqhey/aNvT8yDc74K5/7H9K9JkrAO2RI9rgHTUsL8snTpdu1/2dlEVpwPQGaq8Tdz12CseQ956ZNwy3/F4lMJyrmWNc2PLGbJuV2O3P6UWM/2x2s6vqxlKLmcXRbyhmxPmGW+H3ctUgnLmhYJc2lMUFXyfXl4E0wSClvMb+XIiM5czxSjsprEXGMjF7UV8RY2jvtzSwU7LVbm+SdPZcPGr7i6HgtvW/42ezTP0V1Hc+wcMblD1/PEogcRDi9BVV0ugc3AxhxZkuzJVHzE06qEdSyFI95LW4tQhkeRMCsyI9plV0/2FyVs38fFzmD/xQXfBX9UjKHYXyC7wMKFFCFviHw5z2B+cLSilBNfviGvD4xpqP0ffwXRTA/85pyauiOrlSnXlDAqylNNStjrrqXwyvcg1+NeOVIVhvxCjbEZ1TMm3UrxD/vCthI2qTFfErSU1R3p0vsW8cVIGSIctXzz8fjbDoMPPjjuseW+F/D7wCxNrpg1C683BhqUyuPHr1gXtTZfQKpQ7q4HYHDwEQYG7uf7x1xKn+cAVs9ajWp19wa7OfroP7i+hhmMxgGJA3iq9yk7sw2mmYS1LoLz/5O2Z78JL29gMF81W1QqYWZ0FoN5Ebvk6izLOjCjhM1gYoRaxidgfesgO3Y8xbRADmpVcgO0B8WXaKCw11py4ss3na3IljG/oBcoTVZGeu0RspvuBsCjeFxTeBjYSLAsyE5NnrBVbyIfEmZz15Qw2YRQ0GskYZLQKij4XcqdCnmC/DXrJdZ/CAetmFjZXbDwAxw693O8lBMeSLdeo4gvYueWlb2KreqOh3JJliENd0/jAZ/4XBj6+CqvNb817hMk282gVguZ7Dp29vwvhdQzHDfnONc2DjOoHYsTQoHanKyUraeVhElY14VRStiIWEc23mVXBjpC+8c0hRkSNoP68at3w38sgc0PTP9zR2Sbc7bf/hKNmVcmCeKw9BO5LjsbOuFyhViMKY9W467Pkf7tZYC44LqWp/Tq7wgNi4aKWlPz8y43CwRltpZm1tZRm5evY1D1ufY6hbwhiqZCYesTKJOomNHIMjoDK9lhuEvCwt4weWksLntVyE1MwvSYuNioLrf+B2U4qmmMT+YtJSwq57S6OTfSQsAv4k40bY/rzzWD2rBYlgG3jIgcwGwpa5cmLYLmOkp52orifDeKhMnmsv6ouGZEfVF3Y4vqwAwJm8HEKBXgjsvh+ydBURILvQRW2GbHsulfkxVKmZmMhAkP2zDC7OO6Mf+v38P3H0sJyq/TpB2ShRRZWTaJupS6DoA/Zg/xntITppdg/Z8pSEXDNRImh51rk8xorEZekqKQW2ohlbJiTlVFvtwkKM0+mLLLvrmQL8RDaR93DPsI5nXQRiaMgTEUYUBWXTafWz411Rj/c2QpYRGZyzctJCwgiKdW7Bvzs3I5jVHrpIgZOIYlCRHcvSO9A03X2DgsPISzQrOmzwT/s7fRfs8Xgb1I2OHvgrO/ykCXCPPdX1QwmCFhM5gM3gBsvEcM6u0XY0EY2gJGWXjF4tPQ8bI3ZDmSbL9d05+IhA3KE7HrJwA5PzIiJ+dNas4vjJCRCl3E705GGACBqE3CpixHFkbgFxeRz4nX0bVypByiXqjKEZp0WVLBC3ndM1dbv2teUezh6hMh76mcLt0sR/416+WhjA+KUhKbqCQplSmfz13SEw2K75yHIrox9r2zlLCgFCs9Xhc3FxKBgKWEjSVh69dfwwMPrmTHjqlHP83AOXSEOkgEEhimwcbhjWwYFhMylrVN42a97QDaVPHdHOUJW3AsHPdRBqLt9lr3F8yQsBlMDEWBs78CF/0c2peK23a/KP7uXDH98RQAbYthxRtg8al0hsTFYdSXDUBLYwIDZXFRnRVyOalZKjxRec2cUAkzDNBSZNXpUMKihOU8yylJmKHD3CPIq+6W2iwlrIAxxZEClbR898oGYW8YDybB5QEe3/ABdH1sKdk0Tdatv4atW76FXzHxKB58ao2p/3Ui6AnaBvOc9OhNSMIk+fGr7jV3AMQl4Qkq489GtbojA6ogaNOhhPn94ruv6xnK5dFl5IImOuF8vv0jguD/ChRF4bDOwwB4ds+zvND/AgAr21ZO3yLO/QYdH30aEErY3v5ca8M+Q8Jm8PeDwy6CFedBUF4QrDEqc4/YN+uZvQou+hmc8YWJy5Efeoj0p9dTkF/AjrDLXzg5wiYqs8sm7JAsZgCT9HSQsECMsFTCJvWoAcRmY1x2H5rk1K51R0pFskANsywN3R4+HnIrxgNBOHXAG1bIl3opFsc2nJTLaXp6fkr/rp9gmBBy0aOmKIodW5K1xnTlBsceWMqjSsIY8LirPAX8Yh1B1Rw3sNVKy/chyqbTQcK83ige+XsX9ypJWj4xSy2bwfRhzaw1gCBhT/cKMnTU7KOmbwFeP22hdryqFxOTgdwAFFLw6u9h1/P050WVZIaEuYze3l4uu+wyuru7CQaDLF++nOuuu45isT6fwI033sill17KoYceitfrRVEUHnzwwWl57v0WPXKg8Lwj9+06qHyRrC9WNQbkbMZpMWBKghqVpZoJy5FyxFJWdpy6TsKkEmbNqZwM1eZ91/xO0ptXUBBjRCaDoZNf9SYAgi7GeIjfVUHGzo1LwqzbFNNLGYVQLQPRm8DCgJdjImWGW+X7kB1bciPbj1UdDYW7XV2PV5YXg+r4qfmW98ZjinPcdHRHQpUvrKokaZommlTCZkjY9MPKantgxwPszu4m4Alw+KzDp3UNqqIyOyze+95cL+x5GX71Hrjt3YKUIRL+9xfsRwFQzqC3t5djjjmGHTt2cMEFF7B8+XIeffRRrrnmGp544gnuvPNOVLU27vmxj30MgO7ubjo7O+nt7Z22596vsOUh2PwXOPyd0POcuG3+MftuPaYJWooO2fU4RgmjQsym5csmlbBIuQw+38TlSJnUnJEhsq56wvxRmyxMqYQxmqi5FowaFn6MnKJglvIo/knIlddPYeW58NgT7pIwmT9WKoPfPwEJk8OgFTUKFAm5NO/TwspQmRODRVI5WUrJjE/C9hQVkiisCLhbbreULb9i2qpXNcT3z0SRyuV0KGEgSpK53JZRHZLlcgpDNhAEAjXOKJ2BY1jVtorlrcttP9iZC890NQtxDMpF+NW7mZ3fTY8X9mT3gBKDeUdDy/wZJWw68NnPfpbt27fzX//1X/z2t7/lhhtu4JFHHuGSSy7hrrvu4tZbazdr/vGPf2T37t3s2rWLN73pTdP63PsV7v0iPPZtuOPDYpZk5wpoXbjv1nPjEXDDAtpT4uQ7lB/CMK1hklvh52+n/683Adi+MVchjflRGVMxYTnSUsJ8wmjuvhImSdhUSti6P1H471MBoQy5FgcREkqYriiUZEfdZHA7MqP6sQu2Eja29FeSt5nSoB5ymdibqiDBZa88PY9HwjL93N7v45t7gsRj7hqfg8F5/N58HVf1hBgsjH19+vP9qEDL3MtYtOgKfL7pGRVkm/OLFRJmqWBebwKPi121MxgfiqLwxeO+SEeog6UtS/n4mo9P7wK8ftj5DF0F4V3szfbCohPgsnvhrT+yB4pbStn+gH8oEpZOp7nttttYvHgxH/7wh+3bFUXh+uuvR1VVbr755pof77zzzqOrq7bdlNPPvV/h8HeKv3dJFeyQt+67tYBd/usol1AVlbJZrpjzR3bChrvo73tJHDMdOx5LCZPlvwmVsIJQwtIeETzqLgmrGPOz45ipRyE3SD4jTk6uEp4q5c/qxJwQxRyFkZ2ur8nyX+Vl099k5UhDjbi+HgBFGu11j7whM04WVrZveho8AEVRaQt1A8qY0n9RLzKijWCgsHjhB1iy+J/xeKZnVFA0soxY7BB83hb7tnxehHKGQvOmZQ0zGIvDOg/jL2/7C3e86Q57rNS0outgZpfFF7o3V6lemabJ7qxIy7dGLO0P+IciYU888QSapnHmmWeO2c13d3dzyCGH8OSTT1Io1DhAeJqfW9M0UqnUqD/7BVZfDO1yt52YD0ddtm/X867b4epevCvOY5YMqrSHxrYthvP/k/55wiA6LUqYLwSql6hU4yZUwmQ5MiuT/KN+d3PCbGP+VCRMS5N3cVC2BZ/qw2fFZuTHGbxejV1/o/DEdwD3yqNQ+X2twdzjKWHWbWXTP+o+bkH1CLJnyG7DMbNbAT2zxyZhEZfLo1Dlv8yNXou1+fGqXhKBhOvrqMaiRR/h6KN+x5w5b7Nvy+dFKGcotA+V+hm4F0JdC7oOpUuaPPdke+0KxGBhEE3XUFDsYeP7A/6hSNjGjSIcbtmy8eX5ZcuWYRgGW7Zs2S+f+/rrryeRSNh/5s+fvlEPk8Ifgcvug7f/TMyxC+3j1u9IhyA+wJyI2NHszogdDvE5cMR7GZDJyNPiCVMUCMQrStgUxvzMdFw8PV4iiiB7+amGimtpCi4PyrZgdTrmwi2TH2iUyMv32M2RNBahSstq9mSesNJOEc8SKk49H7QZ2DlbMox1PCUsk+4hrpr4FJOY330PlrXZ2VsJs/yY7cF2O1pjXyJfkCQsuJ+cO2cw/eg6lC5dKmGpHXDDQvj+iexOC2V9VngWPo87ETONYN9/axzEyIi4yCUS4+/I4vH4qOP2t+e+6qqrGBkZsf/s2LHD8XU2jFALrHxDJbF+P0F3VHSG2UqYhPX/7oi7nWM2gvGpIyosT5gkPG6XkcKSvOSmGiqupURYKe6rPCFZus1PdRJcfCr5Iy5xfU1WOTJtK2ETlyO1othdB11WngJWeU0pi03PRT8fc8yAtpnr5ub5zOyCa3M1q9Ge+gP/2pXHVxi9ibRI2ZJwlF27bmc4+bTra5kMFSVswT5dxwz2IeYdSbdUwnrSOwATVC89sjS5P5UiYT/tjuzo6GBwcJxsnAnwwAMPcOqpp7q3oGlCIBAgEJgeP8XfNXb9DZ76H4h3M6dFfKF2ZSQJ2/YE6Jr88sHc6DSl+gdbiKaEgXpCT5hUpNKKIGuuliOBsCcEGOSmCmvV0uSlOmd1C7oFu/xXS2yGHdbqvhKW9ESADMXSeCRMnIsKpTLgJ+Ty+xbwt4AGqlnE7D583NJORhPkp2h6pqX04zMyzPKZlPcy5ltK2LKgydp1n6Oj4wxaW6YvF8owShQKPQQCs/F4QixZ/Glmz34jLYk107aGGexnaDuAhbEFQIlhPc+IqpI44GT7GjFtG/MasV+SsHe84x2k01P4WKpgmectFWoitcnyWE2kVjWDffnc/+eQHYDnfwqzD6H7DNF9YxkueeArFLY9ysAiUY6Ytl1PqJVocopy5OuuhVM+R/Z350M57b4SdtHP4c6LyE2VUK+lbSXM9XKkNYlnaAt0TX6xnpbuSEk6+wKzmNN9LqHQ2DJWKDgXTeslW9wKfgjJ8UuurckvZzViYBjauF1+hZI4z5Sm6RQe8rdTyoJeTmOYhl16tEhYi88PZfB5p/f89tTT55PNbmT14T+hre0EYrFVxGKrpnUNM9j/EF75RmZvu409Xi+v+bwcvuoCtm79HQAL4/uXX3C/JGE33nhjQ/ez/FiWP2tvbNy4EVVVWbzY+Ynu+/K5/88hLolVqsf2hNlKWGoXu72irSzsDdMis8Rcx+uuJTLwEvzta5MP8PYFycgxK24bqsOSLEyZE6alyUljvtuZPuGsuGjne56CVW+f+MBnb6Gw9RFgeiIq+kplVq68ftxjDjrom5Af5qFnVgsS5rIBPeJvwzBBVaD89PfwpAbh2I9UPveGQd7IAH50ZXqU83BwFikgqOoMF4btua1Wy3/C64EyeH0t07IeC5HwUrLZjaTTr9DWdsK0PvcM9mMc+xEWbfkVe7ywdf6RHD53DVte+CYAi1v2r2vwP5Qn7NhjjyUQCHDvvfdi7pVqvXv3bl566SWOOeYYgkHnd/v78rn/z8EaHJ4fYk5AZBL1ZHowDQPSu9kluw/nROdMX5fOnMOJzTsagFRx4q5W0zRtz5jbSpil8uTL+UqO2njQ0uSV6em0s1Sk/FRlz751FGQHpZvqnOUJy5VyIuhxIhRSdgdpOODywOxAjIIJhgn6i/8Lj98I/esrB2T7Kcrts6G6PA1CIuAX37OIao4KR7a8lzG58ZluJSwWOxiAVPrlaX3eGezniM7igJVvBmDrga/DNE22JIWfcXFihoS5hng8ztvf/na2bNnC97//fft20zS56qqrMAyDD3zgA6Puk8vlWLduHdu3b5/2555BgwgmQJKFeaaKV/WSL+fpHd4IpRw7JAmbNj+YhNWinyqmxhBxAO6+mvwdH7QJkeuesOdvA8DEHDWWaAyKGbIWwXBxTiNAaPZBAORmT1Eyqi6RToMnLF/KYl4/SbbUNDYvxP1xrt0V4of5IwmveAccfyXEqnwsWppSSH52XB7ebcEiV2G1krUEFQU6JH2OXt90kzDxeUqnX2LLlv9k/fpryWTWT3GvGfxfwOJO8dlYP7KJPbk9pEtpVEWdKUe6jRtuuIEHHniAj370o9x3330sX76cRx55hMcee4yzzz6bSy65ZNTxTz31FKeddhqnnHLKmLmQN9xwA+vWrQNEDph12y233ALAZZddxoknntjwc8+gQSgKJObCwAZ8mT0sii9iU3ITm3ufoxvYHBIEbVp3PP3riW+8G4CyUSZfzo8t7a3/E5mRbbBgLh7F47r/Kti/HsU0MRWFXDk3calRS5GTxny3y5E26ZmyWSA1Ldll1mObikLSX6bc+ydC0aVEo8sB0PUcpmngLYxUmhdcJmFRX5SiqZApZeHkz4w9oGMpevcKKLyI4nE/IwzAJ8uMYdVkp2z1N0zDJmR+SpSZfiUsHj8MRfGSz2/nta0iV66j43Si0QOndR0z2P9waOehALzY/yLP9z8PwPLW5QSmKUy4VvxDKWEgglGffPJJLr30Uh577DG++c1vsmfPHr70pS/x+9//vq7ZjdaooVtvvZUNG8QsrLvvvtu+bdOmTa499wymgOWPGemxydbmfpHjtDkoiMSSliXTt56e5wjd8wV7VzNuSfLUq8icIOaRRnwR10ul6qFvJyTT+Sf1hWnpaVPC7IT6Gjo2C9OgPFU/9vbXv4cXX72SPX1/tG/b3fs7Hnr4MF7a9S1yyvS8RpZCmp4kZFfXhe9QnaY5jZbCFVZhh+w87s/1UzbKeBQPiiE+X75p9oT5fHFaW4+z/+/xRGmZxu7MGey/WN66nKAnSKqY4tcbfg3A4Z2H79tFjYN/OCUMBBn64Q9/WNOxp5566vilIxijjDn93DNoAgnZxZbcxtKWpdyz7R42DQtSvNkjLpZLW5ZO33o6lqEc9E/EtVcY0guMaCNjR3YceiGpvudhx2+mJWCTRScQeaqFXL5/4kgI0xTG/FgLMA2esGFR9s+v/QOsvmLiA6tiM9wsR3pUD37VT9EoogRFyU8rVMpt1ixCn+m3E+rdVgtjPvHZSBfTYBiQ7YP8MMxaWTlIl6TH626npgVrNFBYNdmZEUqY5QfrinRRLotuzekuRwIsmP8+hoZEE8e8eRfj8UyPT24G+zd8qo8juo7gsZ7HeHL3kwAc233sPl7VWMxIMzP4+0S7JFgDG1neKkpHL2d76PV4GELszg9IHDB965l3JLztFuKSeE1kzrduj7scc2DBIgwTKmGmCefcQK5j6ajj3UJIlSn+k3WQglDC1OmJzbB+Z9PTAkChUAn+tf4dNAJ2B6nbRDXqj3JKtMSnZ6XY/OLV8I0D4WeV0Tzc9m7UvMikmy7lyXqeSFU58rWR1wCYH5tPuSw+19NFCqvR3n4yqw//Xw5a9S2WLP7ktD//DPZfvGnJm+x/twRaOHHeiZMcvW8wQ8Jm8PeJDjkeanATq2evBmCTnub+iNgFr2hb4TqhGA9xmQg/hoQVUrDpfkb6XgGYnjl7mT7CukiOnlAJU1U4+gNko2IsTcTrcmyG7AjN65N0IgKmlq6U/6bJp1Z+RXj68oXKpIp8fps4phyYtnJkxBchrMJsn0kaWZJM9VS6N3tf4oVeg1sH/PiC07PRCARmkeg8n8czXnamd6IbOpuTmwFYljgAw9AA8Pnap2U9e6Ot7Xi6ut6IonimPngG/2dw1sKzeOvyt7IgtoDrjr9uv/ODwT9oOXIG/wdgKWGDm2gLtLIksYTNI5v5RpuYa7l61urpX5Ohk/CIC3RK24uE9a+Hn76Z1OwFEJ4mJWzbY4QGN0MwOGVWmEXSXE/MlyQ1Z5QmPU7T0hiKeC9d79iUJKy04TFYE6VQ6EHXc3g8YXK5rWINRWXaBmarikpZCQIlCmYWAnEx/H1wE8xeBRf/hq0Pf4q/pTZzUWh6woh9vlZWH/QNPvLi4xSNPFtTW20SdkDrMk47eh3lcgrvNHnUZjCDWuBRPVxz3DX7ehmTYkYJm8HfJ1oXgaKKUUCZPbxu/mkAlKRacebCM6d3PYYO/9ZJfNP9wDhKmMy8SvlEac1SzFxFMEFYzrOcUAnLJ2Hro2QLSWAaPGHBFvG0Znnigwxj1Hpdn2cpH1/TwaeIf2dzWyiVRiiVxPsWOOLj09ZBCpX8r2KxygvW96r4u30JKY/YP0+LoirhUT2saFsBwKuDr7JuSHSOL2tZhqIo+HyJ6cvlm8EM/kEwQ8Jm8PcJb0AQMYC+V7mwfTUxXeRvrZm1evqVMNUDgShxuYYRba/xVflhAFI+IYdPixIWTBA2xHomHCre+xLcch75rPAYud4dGRTqVt7UJz6olCUnz0whTxCP6m6JyY7NUFUitACQy24mlxOep4B/NqVEZSC020QVABk9US6PwCyZqbbnFfvHI0Xx+Zoub6GFVe1iLXdtvYvBwiA+1cfK9pVT3GsGM5jBRJgpR87g7xeHXCjKNLFuZqV2cdtQnufa53PaGTftmx15qJWEIcnW3kpYTiphXi+Up0nBCLYQlp2/E0ZCKCq0LyOrFgHTfSVMjrvJYwqPk9c/9iAtTU6xBoq7rzrZJExRiOgRkipkshsp63K8VGSZTWJVRXW9UQBA9QhyZZTTFRLW+yL87afkR9Zzor+HXsWYViUsk9nAmoiHP3kMHt75MAAHdxy8X/psZjCDvxfMkLAZ/P3itKsq/561kvmf2sz8YhZcTqKfEKFW4plBYJJypCxpTY8S1lIpR040VHzRCZQ/+le0/xXKoev+K4uEqYog0N6OsQdVz7J0eT1Q1UGqKkRLIQhAauRvlIrivYzHDyH73K0ARLyhaSH4HivqwUjDAtlWv+0JGNlJurSZEw9NsKuoTKsStnnzf+AZ/AsHh8M8IvsFzlhwBjt3/pQdO39CV9ebOGDRR6dtPTOYwT8CZsqRM/jHgaJAYB8RMIBQG3FZ/htjzLfKkQhSND0kLE5YjkjKSc/XeKj2X7muhMn3J6eoUBgZ/yB/lNzK84Hp8V9Zv3NOUWnNC2WuWBokOfIMAPH4oeSeFKPIQur0qD5+ryjbKmaZ/9/enYdHUWUNHP5VZ+nse0IIhD2AIouCMCAKKCKoKIKKiIhssjgiIo7LKIszDjij6KjjhijM4MKM6OgHKoKIDiggioIIKpuEnezdSWfrvt8f1dWk6YTNUEU6532ePIRb1d33BJKcPvfWKXdKS4hKgYpiOLqdIru+POv0aOb0mzPmFK4nzJdndAGgcUxjBmcNxlWaTUnJTioravj3FELUSCphom5zV8DW9yC5JTTqbO1colOOJWE1LEcWou+FMmVjfkgY0VqY/vJlBTWeZlw5GWoLJSwk7KxOydcx36bVnITFN8J14XBY/bU5lTDvazhtGlEOF5dc/T8iIjIoK88hN+czEhO7s6dZd3B+T7RJVdbI8ETcJRCiQYW7kJD2N8H6FwFwNGgJHKaMsLO+X66qcHsqAO0Tm7J8yOMkRyZjD7Gz11sxDA+3pj2FEHWZVMJE3fbls/DuOHjzFtj/rbVziUkj3l1TEpajj3tbM8SHm7OXJ9Lbbb6kplvgfP5XSv6pNzQ0Y8N51f1XqqYkDCiu1PdgmVEJM24TVGyzoZXkExGht32wh6eQkXEToaGxFHcdC0C0Sct/cfZ4ivX/SlRUFOj3kGzRGxpcgCuzoz7O2d+bVpVRCSsrP0pGTIZvL5ixbBsmSZgQp00qYaJu6/57vRFqRYleDbNSdFrNy5HOo/q4t6mlKZUwICo0EiireU9Y/h5K8ndBVLopVScjCfNoGuWuXKpd3CstpMSh3y7IjDnFhB1Lwoxk+XjGxnxTroxEv3BjXXEoreJbcklYAkQkw+3vA1D2jZ4QqhBzmxGHh+uVsPLyo37j5UYlzKJGrULUZZKEibot1A5XzrJ6FrqYBsR79OXGovIiPMqDzXuVH87DlGoaZd5KmGm3LQqNBspqblHhKvDdvNvMShiAq6SGJOybBZRs+BskJZpSCTNew6lpUJKr38qp6ub7ynKKS/P9zj3bEuwJfFgYziXRTRgX4d+QtbwiT//BbTO3MapRCSsv909US73317TbG5g6HyGCgSxHClFbYlJJ8C5HupVbvwEz6PvWXHm+KyNtms20ioqxh6m4pmatpQXHmpCaUHUKsYVg1/T3fiXpF1R/UkWpr0WF6ZUwdzmUHbd0u/tzSj5+EDCvEpZgTwAgvyw/4JjHe7NsW6i5N8u2V5OEeTxlVFTolTC7Pb3axwkhaiZJmBC1JTqNMCDO2xYi16X/cqJYX77J93Y5T7AnHKuQnWUx3j5Szpr6hJUWUmzSPRoNkd7E0BVXwy/t3g9Q0uMu0+ZkJFbFId5N7iW5/ie48n23LDIjKQRI8N5ZIKDpL4Bxs+ywRFPmYjCWI93uYtxuPakvK9Ob/NpsdtPnI0QwkCRMiNoSoy/HJFXqt+TJLTWSML1ykBudpB+PSDJtSrHeX+ZOTw03zHYdq4SZVeXxbc6vKTEESrzHzEh6fEmYPRpaD/BfigQoyfX1LTOzEhZjU6Sqw+Tlf+V3LMSjV+rCTV7+CwmJJiRET6BLSw/of/qWItPllkVCnAFJwoSoLVFJoNlI8u4LyyvV21LQsAM8coT8K/UbySZHmLeBOebSPwDgVG6Ut3u+n9ICSjTzGqMCRNr0Nhiug9/XeI7Ru8yUqyO9y5FOeyzc+vax22EZnIcpNpZHTdwT1jrCzR1JReza9XffuMdTRjHRFLkhyt7QlLkYNE0jMrIxcCwJKys9CMhSpBBnSpIwIWqLLQRu/TfJmT2BKkkYQKidPPQKWWKEecs2sdFpgL5HLaDy5K6AcuexpTaTEozoSr0qV7z5repP+PAPuH79Up+TmZWwmi5ecB41vRIWGx6Lw6P/u7jKDvvGbTY7y90XM/1AFInR5iZhAB3av8xll24iOfkyAKKiW9C06QQapF1j+lyECAZydaQQtSnrSpJy18PRjf5JGMeSMjOXIyNDIwnRQnArN84Kp3+i5e3T5fQmGEZF6GyLDo8F1yGKI2vYWL5jJcXhDoiMMHVPWKm7lEp3BaGaTU+oDcVHfImqWUmYTbNBSDxQSsVxLSGs+H9kMCphhrjYC4iLreECCyHESUklTIhaZvxyzPPeL5J1L8K7d5J/ZKvfcTNoR34k2rsK6Ty+V5g3CXOE6Y0iYkzqBh8d3wSA4nbXV39CaYGp946smlgVz8mElTP9T3AeNv3iBQCb99ZFyuPybYSHY3sNrUjChBC1S5IwIWrToS0kHfwBqLIcuWs1bF5Mbol+JZmZy5G48omt0BvEOiqOa73gKgDAGaonYWb1LjOSHmdFNQ1kldIvFjBxD1Z4SDjhNv2ekcWqPPDqSOdRU9t4GKLtyZR6u+aXluob4CsriymQJEyIoCFJmBC1aecqkra8C1RJwi4eB31nkheif7uZuTGf5FZEe/eFBVbC9B5UDm9rBrOWI30b4avr4l/uBOU2tRIGVfaF3fYuXDP32AGPB4qP4vDOx6xEFSDenkBepf66rtK9AGz75XHmZDi4Mq7CkiSsoqKATd/dwdove1FZWcy+fW+Ql7e2+os+hBAnJUmYELWpYSeSmlwCVEnCsvpCz3vJd5cCkBRp4i/P2HRiEpoB1VTCvDcV9yVhZi1Heq+OLFn/IlQe1zrDW50zsxIGVZKwqAQIq3JPxtIC8FTg8FbCzEpUQa+Y5lR6N+e79CTMUbwLm6bfNzI8JNy0uRhCQ2MpLPyG0tJ9HDnyIT/9PJ2tP06V9hRCnCFJwoSoTS16kTTgbwAcdR3bUK2UIsel9wszu4IRG67f3qa4/Lir/xpcAJc/ijM82u+8sy3G6F3mLgXXcR3hSwtRgMvkSpiRgAYskTqPoACnNwkz62sEesU01+2thLmyASgr3QdAZYg1jVE1LYSY6DYA7D+gX90aF9vBkrkIEQwkCROilqVH6T2TiiuKceTthG1LKTq02dciokGUuU02Y9x6awynd0+aT4Pz4bJpOL1FjNgwcxKMaO/r6DfMPm7/VWkBFUClyRvhffvUvl0I700EI2Et2odL03B752NmEpYWlUaOsRzp2otSbtzl3n/DsFTT5nG8uPhOABQV6X3e4uI6WjYXIeo6ScKEqGVRoZHEexONQzuWw+LhHP7wXkBvwhkRGnGih9e6mF/XAeDI3x1wTCnlW6Y0bTnSW3lz2rTAJKwk11d1Av8bfp/VOXmTsJKdK+H7N6FIb0JK4T7fUmSIFmLafABSo1LJ9S5HVlTkU1q6Hw03lQrCLWyOmprS1+/vKal9azhTCHEykoQJUdv+eT0NnXpycfCo3pbiUIL+SzM92vxfnrHeJT3ncX3LOPQDpQe+pdKjV8pMW440bpitVVMJK87xJWGRoZGE2sxpZeirhEUm6ANF+/U/z7sOx+CXAP3rY+bep7TINHaV2XihoAmdL1qMw/EjAAcrNBpY8P/IkJDQlfj4LgAkJ/fyLU8KIU6fNGsVorYlNiN931a228M5VKhXnw5HJYLT/KVI8Fa4Kh04yor8Dyy7D+eBDdCkMTbNZv6ViNUtR5bk+prHmrU8ClUSwwjvazq8lbCoJJyprfX5mLgUCXolrFxp7HIWoFA4HHpCv6/cRoaFSZim2biw00KcxT8RG9NWNuUL8RtIJUyI2tbgAtLd+v0jDxXpV7UdtusJjiWVMG9bBWf5cVdHRsTjiE4B9MTIrF+mvhYVNs13haZP8VFfJcys5VGoUgkL9yaiBdm+Y0XlevJq5pWRAMmRyWhoVKpK8krzyC9YD8De8hBLkvmqQkIiiI/riM1mt3QeQtR1koQJUdsad6Zhpb7Ed9BdAloI+73faVYkYTHe5rDOyuOujhz+b5wjlgDm9r86YSWsOOdYOwgTkzCjyuXwJsvk/qL/ufoJHD9/DJj7NQIIs4X5rqQ97MymrEzflL+91GbJ/yMhRO2T5UghalvDC8mw6b/M94aGQuMu/Fqs7zFqFtfM9OnEROrNYR2VpQHHHN7qmJlVnqpJmKc4x/+dYLOeFHsKoHSHqXOKD9fvY1kYqvcwI+dnKC2C1X/BGRsDKUmmJoWGtKg0cktzOZT9KpRm86MrlHy3JGFCBAuphAlR22w2WmZdDcCu8DDURSPZU7QHgKZxTU2fTqzRMd9TEXDM7Csjj3+tkhL/m1Nz8RgcHW7UzzMzCbPrSViRsSSb8wu4y6HbBBzp+g2qzd4TBtAkTr/P5sHQNjRu+SgLc8OwaTbSItNMn4sQovYFZRJ26NAhxo4dS8OGDYmIiKB169Y89thjlJeXn/zBVTz33HOMGjWKDh06EBoaiqZprF69usbz77jjDjRNq/ajbdu2vzEqUZc0u+LPhKLhtNnY1rgTjnIHGhqZsZmmzyUuthEARary2ODBzfD8xTi/+gdg7ib4cFs4oZrepT/gik2O3c7IzKTHWGos9JSBLVS/fVJFCQx4Akfb/qbPx9AkVk/CdhUXUGRvR5nSyIjOICwkzPS5CCFqX9AtRx46dIhu3bqRnZ3NoEGDaN26NWvWrGHGjBl89dVXLFu2DJvt1HLPyZMnA9CwYUNSU1M5dOjQKT3unnvuISEhwW8sJSXltOIQdVuYPYYm8c3ZVbiL/9u9FIDM2EzTe4QBxCe1AqBIU7jdlYSEhEJhNuT8TFGkDUIhzm7efidN04gJjaSgwkmJq8qeMKXAcQhnWSFgUSWs3AHp7eHAJti7DhKa+JZszUxUDc3imwGwt2gvu71X2jaPb276PIQQZ0fQJWEPPPAAe/fu5YUXXmDixImA3pBy1KhRLFy4kIULFzJq1KhTeq6lS5fSuXNn0tPTmTBhAi+//PIpPW7KlCk0a9bsTEMQQeK85PPYVbiLRdsWAdA+tb0l84hP1lssKE2jqGgviYktoOgAAAX2aHCX+JIQs0SHRVNQ4cRZkgseN9hC9FsYzW2LMzkJ4mJ8TV3NYFTCisqLoOmVehL230nQ9lpLKnMGY/l6d9FuXxJmJGZCiLovqJYjHQ4HixcvpkWLFkyYMME3rmkas2fPxmazMW/evFN+vmuuuYb0dNkAK85M94bd/f5+UdpFlswjzB5LrEcBUJC3Qx/0JmGFYXqLgQR7gqlzivYmfc5Lfg/Kow8WHwVbGM5wvVpoZuXJqAS6Kl2Ut9GXH/FUwD+6HWtRYcHG/JbxLbFpNo6UHOHzfZ8DkJWQZfo8hBBnR1AlYV999RVlZWVceeWVAT2PGjZsSPv27Vm/fj2lpYFXidWmZcuWMWfOHJ5++mk+/fRT3N6eUaJ+6dmoJ+G2cABsmo3Lm1xu2Vzi0fdgFRZ4b13kbUaa712aNzsJ8zVHbfo7MPY3pbaBR47gaNZDP8fkFhUa+s+MorS20PkOCIuGy/9IoXd51OyvEehfg9aJeiUz26H3LuuU1sn0eQghzo6gWo785Re9t09WVvXvFLOysvj+++/ZtWsX559//lmbx+9//3u/v7du3Zq33nqLiy46cSWkrKyMsrIy39+LiopOcLY41yVHJvNo90eZt3keI9uNJCXSun2BCSF29ikX+UYneKMSpukVMiuWI+HYJnwfm41it34BjZmVMJtmIzY8lqLyIgrLi0gZ+He49hnQNPJ3LACsScIALk6/mO152wG9z5wVbU6EEGdHUFXCCgv1d6zx8dX/QomLi/M7r7b16tWLJUuWkJ2djcvlYtu2bUyZMoWdO3fSr18/Dhw4cMLHz549m/j4eN9HZqb5V9KJ2jWo1SCWDV7GzW1utnQeCQ06AFDQqKM+4L2Zd4H3ikmzEwxj+a/owDdw9Ge/Y84KPTEzc08YVN2c733z462mF5QVAJDobXprtptb3+yrqN7S5ha5TZAQQeScTMJSUlJqbPVQ3ceJ2kaYadSoUQwePJjGjRsTERFB27Ztefrpp3nggQfIzc3l6aefPuHjH3roIQoLC30f2dnZJzxfiFOVEJUKoC+tVZZB4T4ACtwu/bjJSZivOep3/4TNb+uD/3sK/n07jpIcwPyrEX1tKsqOvUmrcFdQXKHfacCqSliz+Ga8cc0bPNPnGUZfMNqSOQghzo5zcjly2LBhOByOk5/oZWyeNypgNVW6jOW9miplZ8uYMWP4y1/+wtq1a094nt1ux26Xe7GJ2mckEPll+ZC/B5QHFR5LkXc50OzlSF8lLDoZovUEkd1fwK7VOFvqLTXM3ggfUAnjWBXMWK60StuktrRNkl6DQgSbczIJe+65587occZeMGNv2PF++eUXbDYbLVq0OOO5nQmjR1hJSYmpryuEIT4kEoDCzW9DpP7/35ncnEpVAFhYCcvqC7/TW8mQt4tKoNjj3RNmctJTXSUsvywf0L8+Nu2cXDgQQtRhQfVT5Xe/+x12u50VK1aglPI7dvDgQbZs2UK3bt2IiDC3Yeb69esBpHeYsExilH6bmwJXDvzwrv55ql5xigiJML2JbEDVqaIUCrIpqtJI2ewbZhtzMqpfAAWl+udWLUUKIYJbUCVhcXFxDB06lF27dvHSSy/5xpVSPPTQQ3g8HsaNG+f3mJKSErZv387evXt/02sfOnSInTt3Bozv37/f13l/2LBhv+k1hDhT8ZEJABSktwPnEQAKk1vqx0xeiqz6moVlhXqn/LydgKIwQh+PDYsl1GZuoT45Qr/ReV6VWykZCZkkYUKIs+GcXI78LebMmcNnn33GXXfdxcqVK2ndujX/+9//WLt2LVdddRUjR470O3/Dhg306dOHXr16BWzwnzNnDtu365eGf/XVV76xBQsWADB27Fh69uwJwPbt27n88svp2bMnbdu2JSkpiT179rB06VKKi4sZOXIkN99s7RVyov4yEoxczQMHvwfgaFIm7NFbaZjNt/SX+zPMbgwd9TcohUmZQIEliaHxdchx5fjGJAkTQpxNQZeENWzYkPXr1/PII4+wbNkyli5dSpMmTZg1axYPPPDAKd83EuDjjz/m888/9xtbvny57/PevXv7krCWLVsyZswYNmzYwDvvvIPD4SA+Pp4ePXowZswYhg4dWjsBCnEGUiP1ze9HXTkw+mPY8DI54VEApEWmmT4fY2N+IW79Ztlf63eyKExuDkWbLEl6jCQsz3WsEmZUxRIizJ+PECL4BV0SBnoiNn/+/FM6t3fv3gH7xwyn0/oiMzPztG6JJISZUr0tKooriilOaUn09f8g57sXAUiJMr+JrLEx34kHN3j7+UNBQmMo2mRJJcxoplu1EmZ8bmWjXSFE8AqqPWFCiOpFh0X7utQfLTmq/+nS/zSqZGYyKmEKcBrVaS2EgkS9QbHVSZjxxsz4WllRLRRCBD9JwoSoJ44tSfonYVZUecJsYb6ksPCiEYAGfR6i0FMBWJOEGfvmyj3lvq79vkQ1yvxEVQgR/CQJE6KeSPO2qThSol8dmePtTG9FJQyqbM7vNg4eOQyX3W/pzbIjQiN8NxY3liGNr5XxtRNCiNokSZgQ9YRRzTGW2I64jviNm83XpqK8EELtxz7HmkoY+F8h6VEecl25gHWJqhAiuEkSJkQ9YVRzDpccpsxd5kvGGsU0smQ+SRFJQPV9uaxKwhpENQDgUPEh8kvzqVSVaGiWtPEQQgQ/ScKEqCcaxzQGYK9jL/ud+1EoosOiLeuBVd3ViEblydifZbbGsfrXaJ9zHweLDwJ6FczsxrFCiPpBkjAh6onm8c0B2FO4h32OfYCemGmaZsl8qmuOaiRhVrWEMBLVfY597C3S76KRGZdpyVyEEMFPkjAh6ommcU0B2O/cz+7C3cCxyo8VUiK8lTDvBQIV7grfDbMtS8JiqyRhDj0JaxLbxJK5CCGCnyRhQtQTqZGpRIVG4VZuPvn1EwBaJbSybD6+5chSPQnLLdWrYKFaqGV7woxKWLYjm2xHNgCZsVIJE0KcHZKECVFPaJpGm6Q2AGw+uhmA85LOs2w+x+8JM5YikyKTsGnW/GhqHt8cDY2jrqN8eeBLAFrEt7BkLkKI4CdJmBD1SJcGXfz+3j61vUUzCUzCzoVbBMWEx/iSLmM+7VLaWTYfIURwkyRMiHrk8iaX+z7vmNrR0iakxsZ8R7mD0spSX98yq+/T2DGto+/zhtENfW0rhBCitkkSJkQ9ckHKBUzqNIkOKR14uNvDls4lLjyOyNBIAA4WH+SA8wAAGdEZVk6LgS0G+j6/psU1ll09KoQIftL8Roh6ZmLHiUzsONHqaaBpGpmxmfyc/zPZjmz2O/YD1l6xCdAlvQuzeswi25HNnR3utHQuQojgJkmYEMIyVZOwfU69d5lVHfyrGpw12OopCCHqAVmOFEJYxmj/sM+xz9dA9lxIwoQQwgyShAkhLGMkYd8c/ob8snw0NF9TWSGECHaShAkhLHN+8vkAbMvbBkCz+GZEhUVZOSUhhDCNJGFCCMu0SWyDPcTu+/sFyRdYOBshhDCXJGFCCMuEhYTRPaO77++9M3tbNxkhhDCZXB0phLDUPRfew+7C3bRObO3XTFYIIYKdJGFCCEu1SmzF0huWWj0NIYQwnSxHCiGEEEJYQJIwIYQQQggLSBImhBBCCGEBScKEEEIIISwgSZgQQgghhAUkCRNCCCGEsIAkYUIIIYQQFpAkTAghhBDCApKECSGEEEJYQJIwIYQQQggLSBImhBBCCGEBScKEEEIIISwgSZgQQgghhAUkCRNCCCGEsECo1RMQNVNKAVBUVGTxTIQQQghxqozf28bv8ZpIEnYOczgcAGRmZlo8EyGEEEKcLofDQXx8fI3HNXWyNE1YxuPxcODAAWJjY9E0zdK5FBUVkZmZSXZ2NnFxcZbOxSwSs8QcrCTm+hEz1M+4z4WYlVI4HA4yMjKw2Wre+SWVsHOYzWajcePGVk/DT1xcXL35RjZIzPWDxFw/1MeYoX7GbXXMJ6qAGWRjvhBCCCGEBSQJE0IIIYSwgCRh4pTY7XZmzJiB3W63eiqmkZjrB4m5fqiPMUP9jLsuxSwb84UQQgghLCCVMCGEEEIIC0gSJoQQQghhAUnChBBCCCEsIEmYEEIIIYQFJAkLMosWLWL8+PF06dIFu92OpmksWLCgxvPXr1/P9ddfT0pKCna7ndatWzN9+nRcLlfAuXv27EHTtBo/3n777Wpf45dffuHmm28mNTWVyMhIOnTowPPPP4/H4znnYzaUl5czd+5cunTpQmxsLLGxsVxwwQXcdddd1Z5fl2O+4447TvjvrGkaf/rTn4IqZgCXy8XcuXO56KKLSExMJCEhgY4dO/L4449TWFhY7WPOdsxw9uPOz89n2rRptGrVCrvdTmpqKjfeeCNbt26t8TXOZtz79+/nmWeeoV+/fjRp0oTw8HDS09MZMmQI69evr/YxRUVFTJ06laZNm2K322natClTp0494X1333zzTbp27Up0dDSJiYlcffXVbNy4MShjLikp4amnnuLWW2+lbdu22Gw2NE1jz549J5xXXY75u+++49FHH+V3v/sdaWlp2O12WrRowaRJk9i/f78lMVdLiaDStGlTBaiUlBTf56+//nq15y5ZskSFhoYqu92ubr31VjV16lTVrVs3BahLLrlElZaW+p2/e/duBaiOHTuqGTNmBHxs2bIl4DW2bt2q4uPjVVhYmBo+fLj6wx/+oNq3b68ANW7cuHM+ZqWUysvLU127dlWA6tGjh7rvvvvUfffdpwYPHqySk5ODLub33nuv2n/fGTNmqOjoaAWo9evXB1XM5eXlvuOdOnVS99xzj5oyZYrq2LGjAlS7du1UcXGx6TGf7bhzcnJUVlaWAlT37t3V1KlT1bBhw1R4eLiKiopS69atC3iNsx33Aw88oADVsmVLNXr0aPXggw+qIUOGqJCQEGWz2dTixYv9znc6napTp04KUFdeeaV64IEHVP/+/X3/lk6nM+A1Hn/8cQWoJk2aqKlTp6o777xTxcXFqfDwcPXZZ58FXczGz25ANW3aVCUlJSlA7d69u8Y51fWYu3XrpjRNU127dlV33323mjZtmrr00kt930vbtm0zPebqSBIWZFasWKH27NmjlFJq9uzZNf7ALikpUSkpKSosLExt3LjRN+7xeNRdd92lADV79my/xxjfyCNHjjzl+Vx22WUKUMuWLfONlZeXqyuuuEIBatWqVacXYDXOZsxKKXXDDTcoTdPUG2+8EXCsoqIiYCwYYq7Oxo0bFaDat28fcKyux7x48WIFqMGDBwc836BBgxSgFi5c6DduRsxKnd24jfGpU6f6jX/55ZcqJCREnX/++crtdvsdO9txL1myRH3xxRcB41988YUKCwtTSUlJfsnk9OnTFaD+8Ic/+J1vjE+fPt1v/Oeff1ahoaGqdevWqqCgwDf+ww8/qKioKNWyZcuA7+u6HrPD4VCffPKJys3NVUopddVVV500CavrMT/33HNqx44dAc8/Z84cBairr7464JhZ39NVSRIWxE70A3vFihUKUDfddFPAsfz8fN87Jo/H4xs/3STsp59+UoDq06dPwLF169YpQA0bNuyU4zkVtR2zMc8RI0ac0usHQ8w1mTBhggLUM8884zceDDEbzzdv3ryAx7zyyisKUH/72998Y1bEXHWetRV3o0aNlM1mUw6HI+AxRvJZ9RePVXEb+vXrpwD19ddfK6X0BDMjI0PFxMQEVEJcLpdKTExUjRo18ov5oYceqjapVurY//Hly5f7xoIh5uOdLAkLxpgNlZWVKioqSkVHR/uNWxWz7Amrpw4fPgxA8+bNA44lJCSQmJjIr7/+yq5duwKOHzhwgBdffJHZs2ezcOFC9u3bV+1rrF69GoB+/foFHOvatSsJCQl8/vnnvyGK03MmMS9evBiAm266iZycHF577TVmz57NokWLyM3NDXieYIi5Oi6Xi7feegu73c6IESP8jgVDzO3atQPg448/DnjMRx99hKZp9O7d2zd2rsUMZxb34cOHSUlJISYmJuAxxvOsWrXKN2Z13GFhYQCEhoYC+v6dAwcOcMkllxAdHe13bkREBJdddhn79+9nx44dvvETxXDVVVcB+MUQDDGfrmCOWdM0QkJCfM9tsCpmScLqqdTUVAB2794dcKywsJD8/HwAfv7554DjK1asYNKkSTz88MPccccdNG/enPvuuy9g4+Ivv/wCQFZWVsBzaJpGq1atOHDgACUlJb85nlNxJjEbG3V37NhBq1atGDNmDA8//DAjRoygWbNmviTNEAwxV+edd96hsLCQG264gaSkJL9jwRDztddey8CBA1myZAmdO3dm6tSpTJ06lYsuuoiVK1fywgsv0KVLF9/551rMcGZxp6amkpOTg9PpDHiM8TxVz7cy7r1797Jy5UrS09Np3779SedTddw4z/g8JiaG9PT0Uz6/pteoKzGfrmCO+Z133sHhcAQkW1bFLElYPdWjRw/i4uL473//y6ZNm/yOPfroo77PCwoKfJ9HRUUxY8YMvvvuO4qKijhy5AgffPABWVlZzJ07lz/+8Y9+z2NcURYfH1/tHOLi4vzOO9vOJOYjR44AcP/993P99dezc+dO8vPzWbRoETabjREjRrB582bf+cEQc3Xmz58PwNixYwOOBUPMmqbx3nvvMW3aNDZt2sTTTz/N008/zaZNmxg0aBD9+/f3e55zLWY4s7gHDBiAx+Nh1qxZfudv2LCBpUuXBpxvVdwVFRWMGDGCsrIy/vrXvxISEnLG8yksLDzt80/3NWpDbcZ8uoI15uzsbCZPnkxkZGTAFd5WxSxJWD0VExPD3LlzqaiooHv37tx2221MmzaNHj168PLLL9O2bVsA3zcBQFpaGjNnzqRjx47ExsaSmprKwIEDWbVqFcnJycydO9f3bvtcdCYxG9W9Dh06sGDBAlq0aEFCQgLDhw/niSeeoKKigmeffdaSeE7FmcR8vB07dvDFF1/QvHlzLr/8crOmfsbOJGaXy8XgwYP517/+xZtvvklOTg65ubn8+9//ZsWKFVx88cXs3LnTqpBOyZnEPWvWLBo2bMiTTz5Jz549mTZtGsOHD+fSSy/l/PPPDzjfCh6Ph9GjR/PFF18wbty4gOXwYCQx137MeXl5XH311Rw5coRXXnmFNm3a1OrznylJwuqxMWPG8OGHH9K9e3fef/99XnjhBUJDQ/n0009p1aoVcGyJ40TS09O5+uqrKS8v5+uvv/aNG+8oanrnYPR2Md5hmOF0YzZiuPbaa9E0ze+5Bg4cCODXWygYYj7e/PnzUUoxevTogK8BBEfMs2fP5oMPPuCVV17hlltuITk5maSkJG666SZef/11cnJyeOyxx3znn4sxw+nH3bhxY77++mvGjBnD7t27efbZZ1m3bh2PPfYYDz/8cMD5ZsetlGLcuHEsWrSI2267jZdeesnv+KnOp2p1Iz4+/rTPP5XXOJdjPl3BFnN+fj59+/Zl69atvPjii9x2220B51j1PR168lNEMBswYAADBgwIGB8xYgQ2m42LLrrolJ4nJSUFwG+9/ETr9EopduzYQUZGRsBGy7PtdGJu06YNGzduJCEhIeB8Y6xqE8xgiLkqt9vNwoULCQkJYdSoUdWeEwwxL1u2DIA+ffoEnN+nTx80TeObb77xjZ2rMcPp/1s3atSIV199NeD8mTNnAvjthTMzbo/Hw9ixY3n99dcZNmwYCxYswGbzrxucbC9Qdft8srKy+Oqrrzh06FDAvrCazq/pNepKzKcrmGLOy8ujb9++bNq0iX/84x+MHz++2uew6ntaKmEiwNq1a9mzZw/9+/c/5XdTGzZsAKBZs2a+MeNqsk8++aTa8wsKCujVq9dvnm9tqClmY/ntxx9/DHiMMRZsMVf14YcfcvDgQfr370+jRo2qPScYYi4vLwfg6NGjAY/JyclBKYXdbveN1aWY4fS/p91uN2+//TahoaEMGTLEN25W3FV/MQ8dOpR//etf1S6LZmVlkZGRwdq1aykuLvY7VlpayhdffEFGRoavCgj45lddDMuXL/c7B4Ij5tMVLDFXTcCee+45Jk2aVONcLPuervWmF+KccaKeQkopVVhYGDC2f/9+1bZtWxUaGqq++eYbv2Pr169X5eXlAY956qmnFKDOP//8gD4tNTW/69u371lpflfbMRcWFqqUlBQVERGhNm/e7BsvKytTAwYMUIB69dVX/R5T12Ou6vrrr1eAevfdd084h7oe8/jx4xWgbr/9dlVZWekbd7vdavTo0QpQ9913n99jzI5ZqdqPu7y8XJWUlPiNud1uNWXKFAWoe++9N+D5znbcbrdb3XHHHb6eZ9U1RK7qdJt4/vTTT7XWrLWuxHy839Ksta7EnJub6+uw//e///2U5mTF97SmlFK1n9oJq7z66qusWbMGgC1btvDtt99yySWX+N4hDBo0iEGDBgHw5z//mUWLFtGzZ0/S0tLIzs7m/fffp6SkhPnz5zNy5Ei/5+7duzfbt2+nV69eZGZm4nK5+Oqrr9i0aROJiYmsXLkyYKnjxx9/pEePHrhcLm6++WYyMjL4+OOP2bx5M2PHjmXevHnndMwA//3vf7nxxhux2+3ceOONvli3bt3K1VdfzQcffOD37i0YYga9h1Tjxo1JTk5m3759AX11qqrrMWdnZ9OtWzcOHjxIu3btuPzyy9E0jc8++4wtW7bQrFkzNmzY4Lc/yoyYz3bc+/bto127dvTr14/mzZtTXl7O8uXL2b59O9dccw1LlizxqwCaEffMmTOZNWsWMTEx3HPPPdX+vxs0aBCdOnUCoLi4mJ49e/Ldd99x5ZVX0rlzZ77//ns++ugjOnXqxJo1awKWkB5//HEeeeQRmjRpwo033khxcTFvvfUWLpeL5cuXByxLB0PM06ZNIycnB9DbDB04cIAhQ4b4esQ9+OCDvos3giHm3r178/nnn9O2bVuGDh1a7RymTJnit9XErO9pP7We1glLjRw5UgE1fsyYMcN37qeffqr69u2r0tLSVFhYmEpPT1dDhw5V3377bbXPPW/ePNW/f3/VuHFjFRERoSIiIlSbNm3UPffco7Kzs2uc008//aRuvPFGlZycrOx2u2rXrp169tlnA26Hci7GbFizZo3q37+/SkhIUOHh4apdu3bqiSeeqPHdWzDE/MQTT1T7zrMmdT3mgwcPqrvvvlu1atVKhYeHK7vdrlq3bq2mTp2qcnJyLIn5bMddVFSkRowYoVq0aKEiIiJUbGys6t69u5o3b94JYzibcZ8sXqqpBBYUFKh7771XZWZmqrCwMJWZmanuvfdev0rX8RYtWqS6dOmiIiMjVXx8vOrfv7/asGFD0MZs3He0po/q7plZl2M+WbzUUAk043u6KqmECSGEEEJYQDbmCyGEEEJYQJIwIYQQQggLSBImhBBCCGEBScKEEEIIISwgSZgQQgghhAUkCRNCCCGEsIAkYUIIIYQQFpAkTAghhBDCApKECSGEEEJYQJIwIURQ6d27N5qmWT2NU+Z0OmnYsCGTJk2yeipn7LPPPkPTND788EOrpyJEnSJJmBDinKVp2ml91EV//etfycvL46GHHrJ6KmesT58+9OrVi/vvvx+32231dISoMwJvWy6EEOeIGTNmBIzNmjWL+Ph4pkyZUu1j/vnPf1JSUnKWZ1Y7CgoKmDt3LsOGDSMzM9Pq6fwm06ZNY+DAgbz11lvcdtttVk9HiDpBbuAthKhTNE2jadOm7Nmzx+qp/GbPPfcckydPZuXKlVxxxRVWT+c3qaysJCMjg9atW7NmzRqrpyNEnSDLkUKIoFLdnrAFCxagaRoLFizg//7v/+jWrRtRUVE0atSIRx99FI/HA8Abb7zBhRdeSGRkJE2aNOHJJ5+s9jWUUrz22mtccsklxMXFERUVRZcuXXjttddOa64LFiwgOTmZPn36+MY8Hg/NmzcnOTmZsrKyah/XtWtXwsPDOXLkiN/4+++/zxVXXEFiYiIRERFccMEFPPnkkwFLhIWFhTzxxBP06tWLjIwMwsPDycjI4Pbbb2fnzp0Brzdz5kw0TWP16tUsXLiQzp07ExUVRe/evX3nhIaGMmjQINauXcsvv/xyWl8HIeorScKEEPXGe++9x80330yLFi2YMGECMTEx/PnPf2b69Ok89dRTTJo0ifbt23PnnXfi8Xi4//77eeONN/yeQynFbbfdxpgxY8jJyeHWW29l7NixFBcXM2bMGKZNm3ZKc8nPz2fTpk107doVm+3Yj2Kbzca4cePIy8tjyZIlAY/bsmULX3/9Nddddx1paWm+8YcffphBgwbx888/M2TIECZNmkRERAT3338/t9xyi99zbNu2jenTpxMZGckNN9zAlClT6NKlC2+++SZdu3bl119/rXbOf/vb35g4cSJZWVlMnjyZnj17+h3v3r07AKtWrTqlr4EQ9Z4SQog6BFBNmzat8XivXr3U8T/aXn/9dQWosLAwtWHDBt94UVGRSktLU1FRUSo9PV3t3LnTd2zv3r0qPDxcdejQwe+5XnnlFQWoMWPGqIqKCt94WVmZGjhwoALUxo0bTxrHsmXLFKD++Mc/Bhw7ePCgCg0NVX369Ak4NnnyZAWojz76yDf2ySefKEANGDBAFRcX+8Y9Ho+aMGGCAtQ777zjGy8oKFC5ubkBz71q1Spls9nU2LFj/cZnzJihABUdHa02b95cY0zff/+9AtTtt99+4uCFEEoppaQSJoSoN4YPH87FF1/s+3tsbCzXXnstJSUlTJw4kRYtWviOZWZm0rNnT7Zu3UplZaVv/Pnnnyc6Oprnn3+e0NBj1zaFh4fz+OOPA/DWW2+ddC779u0DoEGDBgHH0tPTue6661i9erXf8mBZWRmLFi2iSZMm9OvXz29OAC+//DJRUVG+cU3TmDNnDpqm+c0pPj6epKSkgNft06cP7dq1Y+XKldXO+c4776R9+/Y1xmTEYsQmhDgxuTpSCFFvXHjhhQFjDRs2BKBTp07VHnO73Rw+fJhGjRpRUlLCli1byMjIYM6cOQHnV1RUALB9+/aTziU3NxeAxMTEao+PHz+ed999l/nz5/OXv/wF0JdT8/LymDx5st8S5rp164iOjmb+/PnVPldkZGTAnFavXs0zzzzD+vXrycnJ8Us0w8PDq32erl27njAmI7HLyck54XlCCJ0kYUKIeiMuLi5gzKhmneiYkVzl5+ejlGL//v3MmjWrxtcpLi4+6VwiIyMBcLlc1R6/8sorad68OQsWLOBPf/oTISEhvPrqq9hsNkaPHu13bl5eHpWVlac8p//85z8MHTqUmJgYrrrqKpo1a0ZUVJTv4oWa9oRVV7WryoilajVOCFEzScKEEOIUGYla586d2bhx4296rtTUVEBPoKqjaRrjxo3j4YcfZtmyZbRv355Vq1YxYMCAgJ5icXFxaJp2yhWomTNnEhERwTfffENWVpbfsbfffrvGx52sIa4RixGbEOLEZE+YEEKcotjYWM477zy2bdtGQUHBb3ouY2/Vido5jB49mrCwMF599VVee+01lFKMHTs24Lxu3bqRm5t7yq0hdu7cyXnnnReQgB04cKDaFhWn6qeffgI44b4xIcQxkoQJIcRpmDx5MiUlJYwbN67aZcfdu3efUiPZ9u3bk5SUxIYNG2o8p0GDBlx33XV8+OGHvPLKK6SnpzNw4MBq5wR60mbsNavq0KFDbNu2zff3pk2bsmPHDg4fPuwbKy0tZeLEiX57w07X+vXrAejVq9cZP4cQ9YkkYUIIcRrGjx/PyJEjeeedd8jKyuL222/nwQcfZNSoUXTv3p2WLVuybt26kz6Ppmlcd911bN26lYMHD57w9dxuN0eOHGHkyJF+V2Qa+vfvz6OPPsqaNWto1aoVw4YN48EHH2TcuHH06dOHxo0b8/777/vOv/vuuykqKuLCCy9k8uTJvv5oW7dupWPHjmf2hQFWrFhBYmIil1122Rk/hxD1iSRhQghxGozN64sXL6Zdu3YsXbqUuXPnsmLFCiIiInjyySfp27fvKT3X+PHj8Xg8J2xp0bdvXxo1aoSmadUuRRoee+wxVqxYwaWXXsqnn37K3LlzWbp0KWVlZcycOZPhw4f7zr3rrrt46aWXSEpKYt68ebz33nv06tWLL7/8koSEhFP+WlT166+/snbtWkaOHElERMQZPYcQ9Y3cO1IIISzUo0cPCgsL+eGHH6rd+H7gwAGaNm3KpZdeek53op8+fTpz5sxh27ZttGzZ0urpCFEnSCVMCCEs9OSTT/Ljjz/yn//8p9rjzzzzDJWVlUyYMMHkmZ26goICnn32WSZOnCgJmBCnQVpUCCGEhXr06MFLL73k60UG+g22X3zxRX799VfmzZtHu3btGDJkiIWzPLE9e/YwZcoU7r77bqunIkSdIsuRQghxjtmzZw/NmzcnMjKSbt268dJLL9GmTRurpyWEqGWShAkhhBBCWED2hAkhhBBCWECSMCGEEEIIC0gSJoQQQghhAUnChBBCCCEsIEmYEEIIIYQFJAkTQgghhLCAJGFCCCGEEBaQJEwIIYQQwgL/Dy0A4QkHc2h+AAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1b and S2b\n", + "# Fig S1a. IERS EOP C01 LOD time-series with (green) and without (orange) removal of the Atmopheric Angular Momentum (AAM) and C04 LOD time-series\n", + "# Fig S2b. α estimated from C01 LOD - AAM time-series (orange and lime) and C04 LOD- AAM time-series (blue and light blue) for the range values of Γ\n", + "# The band-pass filters are between 5.5 & 6.5 years\n", + "\n", + "p = 6 #looking for a signal with a period of 6 years\n", + "l = lod[-1,0] - lod[0,0] #length of the LOD time series for C04\n", + "\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 6 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "print(np.max(filt_lod[:,6]) - np.min(filt_lod[:,6]))\n", + "print(np.std(filt_lod[:,6]))\n", + "\n", + "l = lod2[-1, 0] - lod2[0, 0] #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 6 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + 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diff --git a/readthedocs.yml b/readthedocs.yml old mode 100644 new mode 100755 diff --git a/requirements.txt b/requirements.txt old mode 100644 new mode 100755 index 86591e4e..c32c620c --- a/requirements.txt +++ b/requirements.txt @@ -1,8 +1,13 @@ boto3 future lxml -matplotlib -numpy python-dateutil pyyaml +matplotlib +cartopy --no-binary=cartopy +matplotlib scipy +numpy +datetime +ipython +setuptools \ No newline at end of file diff --git a/scripts/aod1b_geocenter.py b/scripts/aod1b_geocenter.py old mode 100644 new mode 100755 diff --git a/scripts/aod1b_oblateness.py b/scripts/aod1b_oblateness.py old mode 100644 new mode 100755 diff --git a/scripts/calc_mascon.py b/scripts/calc_mascon.py old mode 100644 new mode 100755 diff --git a/scripts/calc_sensitivity_kernel.py b/scripts/calc_sensitivity_kernel.py old mode 100644 new mode 100755 diff --git a/scripts/combine_harmonics.py b/scripts/combine_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/convert_harmonics.py b/scripts/convert_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/dealiasing_global_uplift.py b/scripts/dealiasing_global_uplift.py old mode 100644 new mode 100755 diff --git a/scripts/esa_costg_swarm_sync.py b/scripts/esa_costg_swarm_sync.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_compare_tellus.py b/scripts/geocenter_compare_tellus.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_monte_carlo.py b/scripts/geocenter_monte_carlo.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_ocean_models.py b/scripts/geocenter_ocean_models.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_processing_centers.py b/scripts/geocenter_processing_centers.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_icgem_costg_ftp.py b/scripts/gfz_icgem_costg_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_isdc_dealiasing_ftp.py b/scripts/gfz_isdc_dealiasing_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_isdc_grace_ftp.py b/scripts/gfz_isdc_grace_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/grace_mean_harmonics.py b/scripts/grace_mean_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/make_grace_index.py b/scripts/make_grace_index.py old mode 100644 new mode 100755 diff --git a/scripts/mascon_reconstruct.py b/scripts/mascon_reconstruct.py old mode 100644 new mode 100755 diff --git a/scripts/monte_carlo_degree_one.py b/scripts/monte_carlo_degree_one.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_GrIS_maps.py b/scripts/plot_AIS_GrIS_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_3maps.py b/scripts/plot_AIS_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_4maps.py b/scripts/plot_AIS_grid_4maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_maps.py b/scripts/plot_AIS_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_movie.py b/scripts/plot_AIS_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_regional_maps.py b/scripts/plot_AIS_regional_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_regional_movie.py b/scripts/plot_AIS_regional_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_3maps.py b/scripts/plot_GrIS_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_maps.py b/scripts/plot_GrIS_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_movie.py b/scripts/plot_GrIS_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_QML_grid_3maps.py b/scripts/plot_QML_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_3maps.py b/scripts/plot_global_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_4maps.py b/scripts/plot_global_grid_4maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_5maps.py b/scripts/plot_global_grid_5maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_9maps.py b/scripts/plot_global_grid_9maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_maps.py b/scripts/plot_global_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_movie.py b/scripts/plot_global_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/podaac_cumulus.py b/scripts/podaac_cumulus.py old mode 100644 new mode 100755 diff --git a/scripts/quick_mascon_plot.py b/scripts/quick_mascon_plot.py old mode 100644 new mode 100755 diff --git a/scripts/run_grace_date.py b/scripts/run_grace_date.py index 0a0fb12f..151e7fad 100755 --- a/scripts/run_grace_date.py +++ b/scripts/run_grace_date.py @@ -99,6 +99,22 @@ def run_grace_date(base_dir, PROC, DREL, VERBOSE=0, MODE=0o775): 'RL05':['GAA', 'GAB', 'GAC', 'GAD', 'GSM'], 'RL06':['GAA', 'GAB', 'GAC', 'GAD', 'GSM']} VALID['JPL'] = ['RL04','RL05','RL06'] + # -- CNES RL04/5 at LMAX 90 + DSET['CNES'] = {'RL04': ['GSM'], + 'RL05': ['GSM'],} + VALID['CNES'] = ['RL04', 'RL05'] + # -- GRAZ/ITSG RL14/16/18 at LMAX 120 + DSET['GRAZ'] = {'RL14': ['GSM'], + 'RL16': ['GSM'], + 'RL18': ['GSM']} + VALID['GRAZ'] = ['RL14', 'RL16', 'RL18'] + # -- Swarm RL01 at LMAX 40 + DSET['SWARM'] = {'RL01': ['GSM'],} + VALID['SWARM'] = ['RL01'] + # -- COSTG RL01 at LMAX 90 + DSET['COSTG'] = {'RL01': ['GSM'], + 'RL06': ['GSM']} + VALID['COSTG'] = ['RL01', 'RL06'] # for each processing center for p in PROC: @@ -132,7 +148,7 @@ def arguments(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], - choices=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES','GRAZ','SWARM', 'COSTG'], help='GRACE/GRACE-FO Processing Center') # GRACE/GRACE-FO data release parser.add_argument('--release','-r', diff --git a/scripts/run_sea_level_equation.py b/scripts/run_sea_level_equation.py old mode 100644 new mode 100755 diff --git a/scripts/scale_grace_maps.py b/scripts/scale_grace_maps.py old mode 100644 new mode 100755 diff --git a/setup.cfg b/setup.cfg old mode 100644 new mode 100755 diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 diff --git a/test/__init__.py b/test/__init__.py old mode 100644 new mode 100755 diff --git a/test/conftest.py b/test/conftest.py old mode 100644 new mode 100755 diff --git a/test/out.combine.green_ice.0.5.2008.60.gz b/test/out.combine.green_ice.0.5.2008.60.gz old mode 100644 new mode 100755 diff --git a/test/out.geoid.green_ice.0.5.2008.60.gz b/test/out.geoid.green_ice.0.5.2008.60.gz old mode 100644 new mode 100755 diff --git a/test/out.green_ice.grid.0.5.2008.cmh20.gz b/test/out.green_ice.grid.0.5.2008.cmh20.gz old mode 100644 new mode 100755 diff --git a/test/requirements.txt b/test/requirements.txt old mode 100644 new mode 100755 diff --git a/test/test_download_and_read.py b/test/test_download_and_read.py old mode 100644 new mode 100755 diff --git a/test/test_gia.py b/test/test_gia.py old mode 100644 new mode 100755 diff --git a/test/test_legendre.py b/test/test_legendre.py old mode 100644 new mode 100755 diff --git a/test/test_love_numbers.py b/test/test_love_numbers.py old mode 100644 new mode 100755 diff --git a/test/test_point_masses.py b/test/test_point_masses.py old mode 100644 new mode 100755 diff --git a/test/test_time.py b/test/test_time.py old mode 100644 new mode 100755 diff --git a/test/test_units.py b/test/test_units.py old mode 100644 new mode 100755 diff --git a/version.txt b/version.txt old mode 100644 new mode 100755