From d424bbc99873a2bf6c3450dc84141128fcf3e21c Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 8 Sep 2022 16:52:16 +0100 Subject: [PATCH 001/150] tidy variable names. Improve docstr --- coast/__init__.py | 2 +- ...idded_monthly_hydrographic_climatology.py} | 37 ++++++++++--------- 2 files changed, 20 insertions(+), 19 deletions(-) rename coast/diagnostics/{annual_hydrographic_climatology.py => gridded_monthly_hydrographic_climatology.py} (61%) diff --git a/coast/__init__.py b/coast/__init__.py index 0e9ad3a9..d06e4f65 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -6,7 +6,7 @@ from .diagnostics.eof import compute_eofs, compute_hilbert_eofs from .diagnostics.internal_tide import InternalTide from .diagnostics.climatology import Climatology -from .diagnostics.annual_hydrographic_climatology import Annual_Climatology +from .diagnostics.gridded_monthly_hydrographic_climatology import GriddedMonthlyHydrographicClimatology from ._utils import logging_util, general_utils, plot_util, crps_util from .data.index import Indexed from .data.profile import Profile diff --git a/coast/diagnostics/annual_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py similarity index 61% rename from coast/diagnostics/annual_hydrographic_climatology.py rename to coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 36f7d69e..590bed06 100644 --- a/coast/diagnostics/annual_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -4,19 +4,20 @@ import xarray as xr -class Annual_Climatology(Gridded): +class GriddedMonthlyHydrographicClimatology(Gridded): """ - Calculates a mean annual cycle from multi-annual monthly data - Because it calculates dervied properties (e.g PEA), data must be loaded. - Currently hardwired to calculate SST, SSS and PEA, placing these in the Gridded Objected + Calculates the monthly climatology for SSS, SST and PEA from multi-annual monthly Gridded data. + Derived fields (SSS, SST, PEA) are placed into supplied coast.Gridded object. """ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): """ + Assumes monthly values in gridded_t, starting from Jan and multiyear + Args: - gridded_t: Input gridded object. - gridded_t: - Zmax: Max z. + gridded_t: Input Gridded object. + gridded_t: Target Gridded object + Zmax: Max z for PEA integral calculation """ # calculate a depth mask @@ -27,12 +28,12 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): nt = gridded_t.dataset.dims["t_dim"] - SSTy = np.zeros((12, ny, nx)) - SSSy = np.zeros((12, ny, nx)) - PEAy = np.zeros((12, ny, nx)) + SST_monthy_clim = np.zeros((12, ny, nx)) + SSS_monthy_clim = np.zeros((12, ny, nx)) + PEA_monthy_clim = np.zeros((12, ny, nx)) # NBTy=np.zeros((12,ny,nx)) #will add near bed temperature later - PEAy = np.zeros((12, ny, nx)) + PEA_monthy_clim = np.zeros((12, ny, nx)) nyear = int(nt / 12) # hard wired for monthly data starting in Jan for iy in range(nyear): @@ -45,8 +46,8 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): print("copied", im) PEA = InternalTide(gridded_t2, gridded_t2) PEA.calc_pea(gridded_t2, Zd_mask) - PEAy[im, :, :] = PEAy[im, :, :] + PEA.dataset["PEA"].values - PEAy = PEAy / nyear + PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values + PEA_monthy_clim = PEA_monthy_clim / nyear # need to find efficient method for bottom temperature # NBT=np.zeros((nt,ny,nx)) @@ -58,8 +59,8 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): for im in range(12): print("Month", im) it = np.arange(im, nt, 12).astype(int) - SSTy[im, :, :] = np.mean(SST[it, :, :], axis=0) - SSSy[im, :, :] = np.mean(SSS[it, :, :], axis=0) + SST_monthy_clim[im, :, :] = np.mean(SST[it, :, :], axis=0) + SSS_monthy_clim[im, :, :] = np.mean(SSS[it, :, :], axis=0) # NBTy[im,:,:]=np.mean(NBT[it,:,:],axis=0) # save hard work in netcdf file coords = { @@ -71,6 +72,6 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): attributes_SST = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_SSS = {"units": "", "standard name": "Absolute Sea Surface Salinity"} attributes_PEA = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(Zmax) + "m"} - gridded_t_out.dataset["SSTy"] = xr.DataArray(np.squeeze(SSTy), coords=coords, dims=dims, attrs=attributes_SST) - gridded_t_out.dataset["SSSy"] = xr.DataArray(np.squeeze(SSSy), coords=coords, dims=dims, attrs=attributes_SSS) - gridded_t_out.dataset["PEAy"] = xr.DataArray(np.squeeze(PEAy), coords=coords, dims=dims, attrs=attributes_PEA) + gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray(np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST) + gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray(np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS) + gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray(np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA) From 619192c0ccb8ec7dc3f086442182ed8244bb4107 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Sep 2022 15:55:06 +0000 Subject: [PATCH 002/150] Apply Black formatting to Python code. --- .../gridded_monthly_hydrographic_climatology.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 590bed06..b3c321ee 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -72,6 +72,12 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): attributes_SST = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_SSS = {"units": "", "standard name": "Absolute Sea Surface Salinity"} attributes_PEA = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(Zmax) + "m"} - gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray(np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST) - gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray(np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS) - gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray(np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA) + gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray( + np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST + ) + gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray( + np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS + ) + gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray( + np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA + ) From 7d71fc70ed27f4bf1c29a6bc95ac53bb6bcf87d3 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 8 Sep 2022 19:07:23 +0100 Subject: [PATCH 003/150] rename class and camel-case methods. tidy variable names. Improve docstr --- coast/__init__.py | 2 +- ...es.py => profile_hydrographic_analysis.py} | 116 +++++++++++------- 2 files changed, 76 insertions(+), 42 deletions(-) rename coast/diagnostics/{hydrographic_profiles.py => profile_hydrographic_analysis.py} (82%) diff --git a/coast/__init__.py b/coast/__init__.py index d06e4f65..5c70561d 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -27,7 +27,7 @@ from .data.copernicus import Copernicus, Product from ._utils.experiments_file_handling import experiments from ._utils.experiments_file_handling import nemo_filename_maker -from .diagnostics.hydrographic_profiles import Hydrographic_Profiles +from .diagnostics.profile_hydrographic_analysis import ProfileHydrography # Set default for logging level when coast is imported import logging diff --git a/coast/diagnostics/hydrographic_profiles.py b/coast/diagnostics/profile_hydrographic_analysis.py similarity index 82% rename from coast/diagnostics/hydrographic_profiles.py rename to coast/diagnostics/profile_hydrographic_analysis.py index 9f456be7..01c499bc 100644 --- a/coast/diagnostics/hydrographic_profiles.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -8,55 +8,73 @@ from ..data.profile import Profile from ..data.index import Indexed from dask.diagnostics import ProgressBar +from .._utils.logging_util import get_slug, debug, info, warn, warning + # -Re = 6367456 * np.pi / 180 +earth_radius = 6367456 * np.pi / 180 -class Hydrographic_Profiles(Indexed): +class ProfileHydrography(Indexed): ############################################################################### - def __init__(self, filename="none", datasetnames="none", config="", regionbounds=[]): + def __init__(self, filename="none", dataset_names="none", config="", region_bounds=[]): """Reads and manipulates lists of hydrographic profiles. - Reads and manipulates lists of hydrographic profiles if called with datasetnames and regionbounds, + Reads and manipulates lists of hydrographic profiles if called with dataset_names and region_bounds, extract profiles in these bounds, and if a filenames is provided, saves them there. """ - if datasetnames != "none" and len(regionbounds) == 4: - self.extractprofiles(datasetnames, regionbounds, config) + if dataset_names != "none" and len(region_bounds) == 4: + self.extract_profiles(dataset_names, region_bounds, config) if filename != "none": - self.saveprofiles(filename) + self.save_profiles(filename) - def extractprofiles(self, datasetnames, regionbounds, config): + def extract_profiles(self, dataset_names, region_bounds, config): """ + Helper method to load EN4 data file, subset by region and process. + Args: - datasetnames: list of file names. - regionbounds: [lon min, lon max, lat min lat max] + dataset_names: list of file names. + region_bounds: [lon min, lon max, lat min lat max] config : a configuration file (optional) """ - x_min = regionbounds[0] - x_max = regionbounds[1] - y_min = regionbounds[2] - y_max = regionbounds[3] + x_min = region_bounds[0] + x_max = region_bounds[1] + y_min = region_bounds[2] + y_max = region_bounds[3] self.profile = Profile(config=config) - self.profile.read_en4(datasetnames, multiple=True) + self.profile.read_en4(dataset_names, multiple=True) self.profile = self.profile.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) self.profile = self.profile.process_en4() ######################################################################################## - def saveprofiles(self, filename): - """Saves profile and gridded objects to netcdf.""" - filename_profile = filename[:-3] + "_profile.nc" - filename_gridded = filename[:-3] + "_gridded.nc" + def save_profiles(self, filename): + """ + Helper method to saves profile and gridded datasets (in self) to netcdf. + """ + if filename[:-3] is ".nc": + filename_profile = filename[:-3] + "_profile.nc" + filename_gridded = filename[:-3] + "_gridded.nc" + else: + warn( + "filename: \n" + "{0} \n" + "was expected to end with .nc".format( + filename + ), + UserWarning, + ) print("saving Profile data") with ProgressBar(): self.profile.dataset.to_netcdf(filename_profile) print("saving gridded data") with ProgressBar(): - self.gridded.dataset.to_netcdf(filename_gridded) + self.gridded.dataset.to_netcdf(filename_gridded) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? - def loadprofiles(self, filename): + def load_profiles(self, filename): + """ Helper method to load Profile and Gridded data from netcdf files """ + ### COMMENT: WHY IS THIS CLASS, WHICH INHERITS FROM INDEXED< LOADING profile AND gridded DATA filename_profile = filename[:-3] + "_profile.nc" filename_gridded = filename[:-3] + "_gridded.nc" self.profile = Profile() @@ -72,7 +90,11 @@ def match_to_grid(self, gridded: Gridded, limits: List = [0, 0, 0, 0], rmax: int Args: gridded (Gridded): Gridded object. limits (List): [jmin,jmax,imin,imax] - Subset to this region. - rmax (int): 7000 m maxmimum search distance. + rmax (int): 7000 m - maxmimum search distance (metres). + + ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + + ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO """ self.gridded = gridded if sum(limits) != 0: @@ -103,7 +125,7 @@ def match_to_grid(self, gridded: Gridded, limits: List = [0, 0, 0, 0], rmax: int lon_grd = gridded.dataset.longitude.values lat_grd = gridded.dataset.latitude.values - rr = Hydrographic_Profiles.distance_on_grid( + rr = ProfileHydrography.distance_on_grid( lat_grd, lon_grd, j_prf[ip, :].ravel(), i_prf[ip, :].ravel(), lat_prf4, lon_prf4 ) r[ip, :] = np.reshape(rr, (ip.size, 4)) @@ -124,14 +146,18 @@ def match_to_grid(self, gridded: Gridded, limits: List = [0, 0, 0, 0], rmax: int self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) ############################################################################### - def stratificationmetrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: - """Calculates various stratification metrics for observed profiles. + def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: + """Calculates various stratification metrics for observed profiles. Currently: PEA, PEAT, SST, SSS, NBT. Args: - Zmax = 200 m maximum depth of integration. + Zmax = 200 m (int) maximum depth of integration. DZMAX = 30 m depth of surface layer. + + COMMENT: IMPROVE DOC STRING + COMMENT: DEFINE OUTPUTS ESPECIALLY NON-STANDARD: PEAT, NBT, DT. + COMMENT: WHAT IS INPUT DZMAX USED FOR. """ i_prf = self.profile.dataset.i_prf - self.profile.dataset.i_min j_prf = self.profile.dataset.j_prf - self.profile.dataset.j_min @@ -154,7 +180,7 @@ def stratificationmetrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: else: npr = int(nprof / 10) - for ichnk in Hydrographic_Profiles.chunks(range(0, nprof), npr): + for ichnk in ProfileHydrography.chunks(range(0, nprof), npr): Ichnk = list(ichnk) print(min(Ichnk), max(Ichnk)) tmp = self.profile.dataset.potential_temperature[Ichnk, :].values @@ -255,14 +281,14 @@ def stratificationmetrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: good_profile[ip] = 0 ### - T = Hydrographic_Profiles.fillholes(T) - S = Hydrographic_Profiles.fillholes(S) + T = ProfileHydrography.fillholes(T) + S = ProfileHydrography.fillholes(S) tmp[ip, :] = T sal[ip, :] = S ############################################################################### print("Calculate metrics") - metrics = Hydrographic_Profiles.profile_metrics(tmp, sal, ZZ, DZ, Zd_mask, lon, lat) + metrics = ProfileHydrography.profile_metrics(tmp, sal, ZZ, DZ, Zd_mask, lon, lat) PEAc = metrics["PEA"] PEATc = metrics["PEAT"] @@ -290,7 +316,7 @@ def grid_hydro_mnth(self): for varname in varnames: print("Gridding", varname) mnth = self.profile.dataset.time.values.astype("datetime64[M]").astype(int) % 12 + 1 - var, nvar = Hydrographic_Profiles.grid_vars_mnth(self, varname, i_prf, j_prf, mnth) + var, nvar = ProfileHydrography.grid_vars_mnth(self, varname, i_prf, j_prf, mnth) self.gridded.dataset[varname] = xr.DataArray(var, dims=["12", "y_dim", "x_dim"]) self.gridded.dataset["n" + varname] = xr.DataArray(nvar, dims=["12", "y_dim", "x_dim"]) @@ -298,14 +324,14 @@ def grid_hydro_mnth(self): @staticmethod def makefilenames(path, dataset, yr_start, yr_stop): if dataset == "EN4": - datasetnames = [] + dataset_names = [] january = 1 december = 13 # range is non-inclusive so we need 12 + 1 for yr in range(yr_start, yr_stop + 1): for im in range(january, december): name = os.path.join(path, f"EN.4.2.1.f.profiles.l09.{yr}{im:02}.nc") - datasetnames.append(name) - return datasetnames + dataset_names.append(name) + return dataset_names print("Data set not coded") # Functions @@ -328,8 +354,8 @@ def subsetgrid(var_dom, limits): ############################################################################### ########################################### def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): - DX = (Xpts - X[jpts, ipts]) * Re * np.cos(Ypts * np.pi / 180.0) - DY = (Ypts - Y[jpts, ipts]) * Re + DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) + DY = (Ypts - Y[jpts, ipts]) * earth_radius r = np.sqrt(DX**2 + DY**2) return r @@ -368,16 +394,21 @@ def fillholes(Y): ########################################### def chunks(lst, n): - """Yield successive n-sized chunks from lst.""" + """ + Helper function that yields successive n-sized chunks from lst. + COMMENT: CHANGE NAME TO SOMETHING UNIQUE, PERHAPS: chunk_lst() + """ for i in range(0, len(lst), n): yield lst[i : i + n] ########################################### @staticmethod def profile_metrics(tmp, sal, Z, DZ, Zd_mask, lon, lat): - + """ + ADD: DOC STRING + """ metrics = {} - g = 9.81 + gravity = 9.81 DD = np.sum(DZ * Zd_mask, axis=1) nz = Z.shape[1] lat = np.repeat(lat[:, np.newaxis], nz, axis=1) @@ -395,8 +426,8 @@ def profile_metrics(tmp, sal, Z, DZ, Zd_mask, lon, lat): Sbar_2d = np.repeat(Sbar[:, np.newaxis], nz, axis=1) rhoT = np.ma.masked_invalid(gsw.rho(Sbar_2d, temp_conservative, 0.0)) # density with constant salinity - PEA = -np.sum(Z * (rho - rhobar_2d) * DZ * Zd_mask, axis=1) * g / DD - PEAT = -np.sum(Z * (rhoT - rhobar_2d) * DZ * Zd_mask, axis=1) * g / DD + PEA = -np.sum(Z * (rho - rhobar_2d) * DZ * Zd_mask, axis=1) * gravity / DD + PEAT = -np.sum(Z * (rhoT - rhobar_2d) * DZ * Zd_mask, axis=1) * gravity / DD metrics["PEA"] = PEA metrics["PEAT"] = PEAT @@ -406,6 +437,9 @@ def profile_metrics(tmp, sal, Z, DZ, Zd_mask, lon, lat): ########################################### def grid_vars_mnth(self, var, i_var, j_var, mnth_var): + """ + ADD: DOC STRING + """ VAR = self.profile.dataset[var].values nx = self.gridded.dataset.dims["x_dim"] ny = self.gridded.dataset.dims["y_dim"] From 2ce1fe9f02daa6b1dd872a2e077f83de53043d06 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Sep 2022 18:17:16 +0000 Subject: [PATCH 004/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_hydrographic_analysis.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 01c499bc..92aa8be7 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -57,11 +57,7 @@ def save_profiles(self, filename): filename_gridded = filename[:-3] + "_gridded.nc" else: warn( - "filename: \n" - "{0} \n" - "was expected to end with .nc".format( - filename - ), + "filename: \n" "{0} \n" "was expected to end with .nc".format(filename), UserWarning, ) @@ -70,10 +66,12 @@ def save_profiles(self, filename): self.profile.dataset.to_netcdf(filename_profile) print("saving gridded data") with ProgressBar(): - self.gridded.dataset.to_netcdf(filename_gridded) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? + self.gridded.dataset.to_netcdf( + filename_gridded + ) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? def load_profiles(self, filename): - """ Helper method to load Profile and Gridded data from netcdf files """ + """Helper method to load Profile and Gridded data from netcdf files""" ### COMMENT: WHY IS THIS CLASS, WHICH INHERITS FROM INDEXED< LOADING profile AND gridded DATA filename_profile = filename[:-3] + "_profile.nc" filename_gridded = filename[:-3] + "_gridded.nc" From ac2986e54f4e99fb4b1a2dd4c80863c09d10578b Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 8 Sep 2022 20:49:54 +0100 Subject: [PATCH 005/150] boolean test error --- coast/diagnostics/profile_hydrographic_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 92aa8be7..156929d1 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -52,7 +52,7 @@ def save_profiles(self, filename): """ Helper method to saves profile and gridded datasets (in self) to netcdf. """ - if filename[:-3] is ".nc": + if filename[:-3] == ".nc": filename_profile = filename[:-3] + "_profile.nc" filename_gridded = filename[:-3] + "_gridded.nc" else: From d51567247881402299319a4f0a2d889fb8911216 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 9 Sep 2022 09:09:34 +0000 Subject: [PATCH 006/150] Apply Black formatting to Python code. --- coast/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/coast/__init__.py b/coast/__init__.py index b75fad06..a19e1706 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -6,6 +6,7 @@ from .diagnostics.eof import compute_eofs, compute_hilbert_eofs from .diagnostics.internal_tide import InternalTide from .diagnostics.climatology import Climatology + # from .diagnostics.gridded_monthly_hydrographic_climatology import GriddedMonthlyHydrographicClimatology # from .diagnostics.profile_hydrographic_analysis import ProfileHydrography from ._utils import logging_util, general_utils, plot_util, crps_util From 9cac65498d47ecefcc6ef0362d74d2657cc7601c Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 16 Sep 2022 13:53:43 +0100 Subject: [PATCH 007/150] InternalTide() --> GriddedStratification() --- coast/diagnostics/gridded_monthly_hydrographic_climatology.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index b3c321ee..122da60c 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -1,5 +1,5 @@ from ..data.gridded import Gridded -from ..diagnostics.internal_tide import InternalTide +from ..diagnostics.gridded_stratification import GriddedStratification import numpy as np import xarray as xr @@ -44,7 +44,7 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): print(itt) gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) print("copied", im) - PEA = InternalTide(gridded_t2, gridded_t2) + PEA = GriddedStratification(gridded_t2, gridded_t2) PEA.calc_pea(gridded_t2, Zd_mask) PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values PEA_monthy_clim = PEA_monthy_clim / nyear From c8e7dd6fa3aff5ee5e8013c2477e3f34b52f9f0c Mon Sep 17 00:00:00 2001 From: jpolton Date: Sat, 12 Nov 2022 20:34:09 +0000 Subject: [PATCH 008/150] Typo: S and T, not S and S --- coast/diagnostics/profile_hydrographic_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 156929d1..3f65df5b 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -275,7 +275,7 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: if np.size(I) == 0: good_profile[ip] = 0 - elif ~(np.any((np.isfinite(S[I]))) and np.any((np.isfinite(S[I])))): + elif ~(np.any((np.isfinite(S[I]))) and np.any((np.isfinite(T[I])))): good_profile[ip] = 0 ### From ecbebe185107980fe4ac2234ffffb7f8e05a8f1e Mon Sep 17 00:00:00 2001 From: jpolton Date: Sat, 12 Nov 2022 20:35:04 +0000 Subject: [PATCH 009/150] new feature: fills_holes_1d() --- coast/_utils/general_utils.py | 17 ++++++++++ .../profile_hydrographic_analysis.py | 33 +++---------------- unit_testing/test_general_utils.py | 11 +++++++ 3 files changed, 33 insertions(+), 28 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 9ba438e9..79861155 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -368,3 +368,20 @@ def nan_helper(y): return np.isnan(y), lambda z: z.nonzero()[0] else: return np.isnan(y).values, lambda z: z.nonzero()[0] + +def fill_holes_1d(y): + """ + extrapolate and linearly interpolate over nans in 1d vectors + Input: + - y, 1d numpy array, or xr.DataArray, with possible NaNs + Output: + - 1d array with nans filled in + Examples: + pp = xr.DataArray(np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64')) + fill_holes_new(pp).values + Returns: + array([2., 2., 2., 3., 4., 5., 6.]) + """ + nans, x = general_utils.nan_helper(y) # location interior nans + y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate + return y \ No newline at end of file diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 3f65df5b..0cdbe7c4 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -9,6 +9,7 @@ from ..data.index import Indexed from dask.diagnostics import ProgressBar from .._utils.logging_util import get_slug, debug, info, warn, warning +from .._utils import general_utils # @@ -361,34 +362,10 @@ def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): # Functions for stratification metrics @staticmethod def fillholes(Y): - YY = np.ones(np.shape(Y)) - YY[:] = Y - I = np.nonzero(np.isfinite(YY)) - N = len(YY) - - if np.size(I) > 0: - if not np.isfinite(YY[0]): - YY[0 : np.min(I) + 1] = YY[np.min(I)] - - if ~np.isfinite(YY[N - 1]): - YY[np.max(I) : N] = YY[np.max(I)] - I = np.array(np.nonzero(~np.isfinite(YY))) - YY[I] = 0.5 * (YY[I - 1] + YY[I + 1]) - YYp = YY[0] - ip = 0 - for i in range(N): - if np.isfinite(YY[i]): - YYp = YY[i] - ip = i - else: - j = i - while ~np.isfinite(YY[j]): - j = j + 1 - Jp = np.arange(ip + 1, j - 1 + 1) - - pT = np.arange(1.0, (j - ip - 1.0) + 1.0) / (j - ip) - YY[Jp] = YYp + (YY[j] - YYp) * pT - return YY + """ + extrapolate and linearly interpolate 1d vectors + """ + return general_utils.fill_holes_1d(Y) ########################################### def chunks(lst, n): diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index 99851c26..ebc98929 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -59,3 +59,14 @@ def test_nan_helper(self): self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") + + def test_fill_holes_1d(self): + input = np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64') + input_xr = xr.DataArray(input) + target = np.array([2., 2., 2., 3., 4., 5., 6.]) + + check1 = all(fill_holes_1d(input) == target) + check2 = all(fill_holes_1d(input_xr).values == target) + + self.assertTrue(check1, msg="check1") + self.assertTrue(check2, msg="check2") From 2519e802afc8b7767a05fc0852416edc0acd2c02 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Sat, 12 Nov 2022 20:35:47 +0000 Subject: [PATCH 010/150] Apply Black formatting to Python code. --- coast/_utils/general_utils.py | 3 ++- unit_testing/test_general_utils.py | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 79861155..62bc9411 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -369,6 +369,7 @@ def nan_helper(y): else: return np.isnan(y).values, lambda z: z.nonzero()[0] + def fill_holes_1d(y): """ extrapolate and linearly interpolate over nans in 1d vectors @@ -384,4 +385,4 @@ def fill_holes_1d(y): """ nans, x = general_utils.nan_helper(y) # location interior nans y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate - return y \ No newline at end of file + return y diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index ebc98929..6364ba8a 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -61,9 +61,9 @@ def test_nan_helper(self): self.assertTrue(check2, msg="check2") def test_fill_holes_1d(self): - input = np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64') + input = np.array([np.nan, np.nan, 2.0, np.nan, 4, 5, 6], dtype="float64") input_xr = xr.DataArray(input) - target = np.array([2., 2., 2., 3., 4., 5., 6.]) + target = np.array([2.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) check1 = all(fill_holes_1d(input) == target) check2 = all(fill_holes_1d(input_xr).values == target) From 19d17f5a5456aeaf4e41187be78f9b884aa806aa Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Sat, 12 Nov 2022 20:36:32 +0000 Subject: [PATCH 011/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index 95d36df1..d330a7e9 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -17,8 +17,9 @@ b. coast_variable_renaming c. copy_coast_object d. day_of_week - e. getitem - f. nan_helper + e. fill_holes_1d + f. getitem + g. nan_helper 3. test_gridded_harmonics a. combine_and_convert_harmonics From 86d6cdd5c3830c029203e38414e964f3637371ca Mon Sep 17 00:00:00 2001 From: jpolton Date: Wed, 16 Nov 2022 15:05:27 +0000 Subject: [PATCH 012/150] Remove duplicate code --- coast/data/profile.py | 159 ------------------------------------------ 1 file changed, 159 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9b94e74b..5df79db4 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -509,165 +509,6 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def process_en4(self, sort_time=True): - """ - VERSION 1.4 (05/07/2021) - - PREPROCESSES EN4 data ready for comparison with model data. - This routine will cut out a desired geographical box of EN4 data and - then apply quality control according to the available flags in the - netCDF files. Quality control happens in two steps: - 1. Where a whole data profile is flagged, it is completely removed - from the dataset - 2. Where a single datapoint is rejected in either temperature or - salinity, it is set to NaN. - This routine attempts to use xarray/dask chunking magic to keep - memory useage low however some memory is still needed for loading - flags etc. May be slow if using large EN4 datasets. - - Routine will return a processed profile object dataset and can write - the new dataset to file if fn_out is defined. If saving to the - PROFILE object, be aware that DASK computations will not have happened - and will need to be done using .load(), .compute() or similar before - accessing the values. IF using multiple EN4 files or large dataset, - make sure you have chunked the data over N_PROF dimension. - - INPUTS - fn_out (str) : Full path to a desired output file. If unspecified - then nothing is written. - - EXAMPLE USEAGE: - profile = coast.PROFILE() - profile.read_EN4(fn_en4, chunks={'N_PROF':10000}) - fn_out = '~/output_file.nc' - new_profile = profile.preprocess_en4(fn_out = fn_out, - lonbounds = [-10, 10], - latbounds = [45, 65]) - """ - - ds = self.dataset - - # REJECT profiles that are QC flagged. - debug(f" Applying QUALITY CONTROL to EN4 data...") - ds.qc_flags_profiles.load() - - # This line reads converts the QC integer to a binary string. - # Each bit of this string is a different QC flag. Which flag is which can - # be found on the EN4 website: - # https://www.metoffice.gov.uk/hadobs/en4/en4-0-2-profile-file-format.html - qc_str = [np.binary_repr(ds.qc_flags_profiles.values[pp]).zfill(30)[::-1] for pp in range(ds.dims["id_dim"])] - - # Determine indices of kept profiles - reject_tem_prof = np.array([int(qq[0]) for qq in qc_str], dtype=bool) - reject_sal_prof = np.array([int(qq[1]) for qq in qc_str], dtype=bool) - reject_both_prof = np.logical_and(reject_tem_prof, reject_sal_prof) - ds["reject_tem_prof"] = (["id_dim"], reject_tem_prof) - ds["reject_sal_prof"] = (["id_dim"], reject_sal_prof) - debug( - " >>> QC: Completely rejecting {0} / {1} profiles".format(np.sum(reject_both_prof), ds.dims["id_dim"]) - ) - - ds = ds.isel(id_dim=~reject_both_prof) - reject_tem_prof = reject_tem_prof[~reject_both_prof] - reject_sal_prof = reject_sal_prof[~reject_both_prof] - qc_lev = ds.qc_flags_levels.values - - debug(f" QC: Additional profiles converted to NaNs: ") - debug(f" >>> {0} temperature profiles ".format(np.sum(reject_tem_prof))) - debug(f" >>> {0} salinity profiles ".format(np.sum(reject_sal_prof))) - - reject_tem_lev = np.zeros((ds.dims["id_dim"], ds.dims["z_dim"]), dtype=bool) - reject_sal_lev = np.zeros((ds.dims["id_dim"], ds.dims["z_dim"]), dtype=bool) - - int_tem, int_sal, int_both = self.calculate_all_en4_qc_flags() - for ii in range(len(int_tem)): - reject_tem_lev[qc_lev == int_tem[ii]] = 1 - for ii in range(len(int_sal)): - reject_sal_lev[qc_lev == int_sal[ii]] = 1 - for ii in range(len(int_both)): - reject_tem_lev[qc_lev == int_both[ii]] = 1 - reject_sal_lev[qc_lev == int_both[ii]] = 1 - - ds["reject_tem_datapoint"] = (["id_dim", "z_dim"], reject_tem_lev) - ds["reject_sal_datapoint"] = (["id_dim", "z_dim"], reject_sal_lev) - - debug(f"MASKING rejected datapoints, replacing with NaNs...") - ds["temperature"] = xr.where(~reject_tem_lev, ds["temperature"], np.nan) - ds["potential_temperature"] = xr.where(~reject_tem_lev, ds["temperature"], np.nan) - ds["practical_salinity"] = xr.where(~reject_tem_lev, ds["practical_salinity"], np.nan) - - if sort_time: - debug(f"Sorting Time Dimension...") - ds = ds.sortby("time") - - debug(f"Finished processing data. Returning new Profile object.") - - return_prof = Profile() - return_prof.dataset = ds - return return_prof - - def calculate_all_en4_qc_flags(self): - """ - Brute force method for identifying all rejected points according to - EN4 binary integers. It can be slow to convert large numbers of integers - to a sequence of bits and is actually quicker to just generate every - combination of possible QC integers. That's what this routine does. - Used in PROFILE.preprocess_en4(). - - INPUTS - NO INPUTS - - OUTPUTS - qc_integers_tem : Array of integers signifying the rejection of ONLY - temperature datapoints - qc_integers_sal : Array of integers signifying the rejection of ONLY - salinity datapoints - qc_integers_both : Array of integers signifying the rejection of BOTH - temperature and salinity datapoints. - """ - - reject_tem_ind = 0 - reject_sal_ind = 1 - reject_tem_reasons = [2, 3, 8, 9, 10, 11, 12, 13, 14, 15, 16] - reject_sal_reasons = [2, 3, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29] - - qc_integers_tem = [] - qc_integers_sal = [] - qc_integers_both = [] - n_tem_reasons = len(reject_tem_reasons) - n_sal_reasons = len(reject_sal_reasons) - bin_len = 30 - - # IF reject_tem = 1, reject_sal = 0 - for ii in range(n_tem_reasons): - bin_tmp = np.zeros(bin_len, dtype=int) - bin_tmp[reject_tem_ind] = 1 - bin_tmp[reject_tem_reasons[ii]] = 1 - qc_integers_tem.append(int("".join(str(jj) for jj in bin_tmp)[::-1], 2)) - - # IF reject_tem = 0, reject_sal = 1 - for ii in range(n_sal_reasons): - bin_tmp = np.zeros(bin_len, dtype=int) - bin_tmp[reject_sal_ind] = 1 - bin_tmp[reject_sal_reasons[ii]] = 1 - qc_integers_sal.append(int("".join(str(jj) for jj in bin_tmp)[::-1], 2)) - - # IF reject_tem = 1, reject_sal = 1 - for tt in range(n_tem_reasons): - for ss in range(n_sal_reasons): - bin_tmp = np.zeros(bin_len, dtype=int) - bin_tmp[reject_tem_ind] = 1 - bin_tmp[reject_sal_ind] = 1 - bin_tmp[reject_tem_reasons[tt]] = 1 - bin_tmp[reject_sal_reasons[ss]] = 1 - qc_integers_both.append(int("".join(str(jj) for jj in bin_tmp)[::-1], 2)) - - qc_integers_tem = list(set(qc_integers_tem)) - qc_integers_sal = list(set(qc_integers_sal)) - qc_integers_both = list(set(qc_integers_both)) - - return qc_integers_tem, qc_integers_sal, qc_integers_both - """================Reshape to 2D================""" def reshape_2d(self, var_user_want): From 6cdc7a728d69e4c0ba09205b822ac9251f42612f Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:07:13 +0000 Subject: [PATCH 013/150] add profile.calculate_vertical_spacing() --- coast/data/profile.py | 41 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 5df79db4..e24877f1 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -2,11 +2,13 @@ from .index import Indexed import numpy as np import xarray as xr +import gsw from .._utils import general_utils, plot_util import matplotlib.pyplot as plt import glob import datetime -from .._utils.logging_util import get_slug, debug, info, warn, warning +from .._utils.logging_util import get_slug, debug, info, warn, warning, error + from typing import Union from pathlib import Path import pandas as pd @@ -659,3 +661,40 @@ def time_slice(self, date0, date1): t_ind = pd.to_datetime(dataset.time.values) < date1 dataset = dataset.isel(id_dim=t_ind) return Profile(dataset=dataset) + + def calculate_vertical_spacing(self): + """ + Profile data is given at depths, z, however for some calculations a thickness measure, dz, is required + Define the upper thickness: dz[0] = 0.5*(z[0] + z[1]) and thereafter the centred difference: + dz[k] = 0.5*(z[k-1] - z[k+1]) + + Notionally, dz is the separation between w-points, when w-points are estimated from depths + at t-points. + """ + + if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + pass + else: + # Compute dz on w-pts + depth_t = self.dataset.depth + self.dataset['dz'] = xr.where(depth_t == depth_t.min(dim="z_dim"), + 0.5 * (depth_t + depth_t.shift(z_dim=-1)), + 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)) # .fillna(0.) + ) + + attributes = {"units": "m", "standard name": "centre difference thickness"} + if hasattr(self.dataset.dz, 'coords'): # xarray object. Just add title and units + self.dataset.dz.attrs = attributes + + else: # not an xarray object + coords = { + "time": (("id_dim"), self.dataset.time.values), + "latitude": (("id_dim"), self.dataset.latitude.values), + "longitude": (("id_dim"), self.dataset.longitude.values), + } + dims = ["z_dim", "id_dim"] + + dz = np.squeeze(dz) + self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + + From 97e901e24f5431fb7b6e80cecbf2c18664e8862a Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:08:30 +0000 Subject: [PATCH 014/150] add profile.construct_density() --- coast/data/profile.py | 173 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) diff --git a/coast/data/profile.py b/coast/data/profile.py index e24877f1..9db92fe7 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -698,3 +698,176 @@ def calculate_vertical_spacing(self): self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + def construct_density( + self, eos="EOS10", rhobar=False, Zd_mask:xr.DataArray=None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True + ): + + """ + Constructs the in-situ density using the salinity, temperature and + depth fields. Adds a density attribute to the profile dataset + + Requirements: The supplied Profile dataset must contain the + Practical Salinity and the Potential Temperature variables. The depth + field must also be supplied. The GSW package is used to calculate + The Absolute Pressure, Absolute Salinity and Conservative Temperature. + + Note that currently density can only be constructed using the EOS10 + equation of state. + + Parameters + ---------- + eos : equation of state, optional + DESCRIPTION. The default is 'EOS10'. + + rhobar : Calculate density with depth mean T and S + DESCRIPTION. The default is 'False'. + Zd_mask : (xr.DataArray) Provide a (id_dim, z_dim) mask for rhobar calculation + Calculate using calculate_vertical_mask + DESCRIPTION. The default is empty. + + CT_AS : Conservative Temperature and Absolute Salinity already provided + DESCRIPTION. The default is 'False'. + pot_dens :Calculation at zero pressure + DESCRIPTION. The default is 'False'. + Tbar and Sbar : If rhobar is True then these can be switch to False to allow one component to + remain depth varying. So Tbar=Flase gives temperature component, Sbar=False gives Salinity component + DESCRIPTION. The default is 'True'. + + Returns + ------- + None. + adds attribute profile.dataset.density + + """ + debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') + try: + if eos != "EOS10": + raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") + + try: + shape_ds = ( + self.dataset.z_dim.size, + self.dataset.id_dim.size, + ) + sal = self.dataset.practical_salinity.to_masked_array() + temp = self.dataset.potential_temperature.to_masked_array() + + if np.shape(sal) != shape_ds: + sal = sal.T + temp = temp.T + except AttributeError: + error(f"We have a problem with {self.dataset.dims}") + + density = np.ma.zeros(shape_ds) + + print(f"shape sal:{np.shape(sal)}") + print(f"shape rho:{np.shape(density)}") + + s_levels = self.dataset.depth.to_masked_array() + if np.shape(s_levels) != shape_ds: + s_levels = s_levels.T + + lat = self.dataset.latitude.values + lon = self.dataset.longitude.values + # Absolute Pressure + if pot_dens: + pressure_absolute = 0.0 # calculate potential density + else: + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative + if not rhobar: # calculate full depth + # Absolute Salinity + if not CT_AS: # abs salinity not provided + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + else: # abs salinity provided + sal_absolute = np.ma.masked_invalid(sal) + sal_absolute = np.ma.masked_less(sal_absolute, 0) + # Conservative Temperature + if not CT_AS: # conservative temp not provided + temp_conservative = np.ma.masked_invalid(gsw.CT_from_pt(sal_absolute, temp)) + else: # conservative temp provided + temp_conservative = np.ma.masked_invalid(temp) + # In-situ density + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + new_var_name = "density" + else: # calculate density with depth integrated T S + + if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + pass + else: # Create it + self.calculate_vertical_spacing() + + # prepare coordinate variables + if Zd_mask is None: + DZ = self.dataset.dz + else: + DZ = (self.dataset.dz * Zd_mask) + DP = DZ.sum(dim="z_dim").to_masked_array() + DZ = DZ.to_masked_array() + if np.shape(DZ) != shape_ds: + DZ = DZ.T + # DP=np.repeat(DP[np.newaxis,:,:],shape_ds[1],axis=0) + + #DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) + #DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) + + # Absolute Salinity + if not CT_AS: # abs salinity not provided + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + else: # abs salinity provided + sal_absolute = np.ma.masked_invalid(sal) + + # Conservative Temperature + if not CT_AS: # Conservative temperature not provided + temp_conservative = np.ma.masked_invalid(gsw.CT_from_pt(sal_absolute, temp)) + else: # conservative temp provided + temp_conservative = np.ma.masked_invalid(temp) + + if pot_dens and (Sbar and Tbar): # usual case pot_dens and depth averaged everything + sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP + temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + density = np.repeat(density[np.newaxis, :], shape_ds[0], axis=0) + + else: # Either insitu density or one of Tbar or Sbar False + if Sbar: + sal_absolute = np.repeat( + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP)[np.newaxis, :], + shape_ds[0], + axis=0, + ) + if Tbar: + temp_conservative = np.repeat( + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP)[np.newaxis, :], + shape_ds[0], + axis=0, + ) + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + + if Tbar and Sbar: + new_var_name = "density_bar" + + else: + if not Tbar: + new_var_name = "density_T" + else: + new_var_name = "density_S" + + # rho and rhobar + coords = { + "time": (("id_dim"), self.dataset.time.values), + "latitude": (("id_dim"), self.dataset.latitude.values), + "longitude": (("id_dim"), self.dataset.longitude.values), + } + dims = ["z_dim", "id_dim"] + + if pot_dens: + attributes = {"units": "kg / m^3", "standard name": "Potential density "} + else: + attributes = {"units": "kg / m^3", "standard name": "In-situ density "} + + density = np.squeeze(density) + self.dataset[new_var_name] = xr.DataArray(density, coords=coords, dims=dims, attrs=attributes) + + except AttributeError as err: + error(err) + From c0dcd7aae238ee9b21e8be23f96ffb7b20545eeb Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:09:20 +0000 Subject: [PATCH 015/150] add profile.calculate_vertical_mask() --- coast/data/profile.py | 68 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9db92fe7..5c9b6b4d 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -871,3 +871,71 @@ def construct_density( except AttributeError as err: error(err) + def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): + """ + Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed + and linearly ramped for last level + + Inputs: + depth (id_dim, z_dim) postitive values - passing as a variable facilitates testing + Zmax float - max depth (m( + Returns + Zd_mask (id_dim, z_dim) xr.DataArray, float mask. + #kmax (id_dim) deepest index above Zmax + """ + + ## Contruct a mask array that is: + # zeros below Zmax + # ones above Zmax, except the closest shallower depth which has a value [0,1] that is the weighted distance to Zmax + + ## prepare depth profiles + depth_t = depth + # remove deep nans + # depth_t = depth_t.fillna(1E6) + # depth_t = depth_t.interpolate_na(dim="z_dim", method="nearest", fill_value="extrapolate") + # print(depth_t) + + ## construct a mask to identify location of and separation from Zmax + + # mask_arr = np.zeros((depth_t.shape))*np.nan + # print(np.shape(mask_arr)) + # mask_arr[depth_t <= Zmax] = 1 + # mask_arr[depth_t > Zmax] = 0 + # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) + mask = depth * np.nan + + mask = xr.where(depth_t <= Zmax, 1, mask) + mask = xr.where(depth_t > Zmax, 0, mask) + + # print(mask) + # print('\n') + + max_shallower_depth = (depth_t * mask).max(dim="z_dim") + min_deeper_depth = (depth_t.roll(z_dim=-1) * mask).max(dim="z_dim") + # NB if max_shallower_depth was already deepest value in profile, then this produces the same value + # I.e. + # max_shallower_depth <= Zmax + # min_deeper_depth > Zmax or min_deeper_depth = max_shallower_depth + + # print(f"max_shallower_depth:{max_shallower_depth}") + # print(f"min_deeper_depth:{min_deeper_depth}") + # print('\n') + + # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) + fraction = xr.where(min_deeper_depth != max_shallower_depth, + (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + 1) + + max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + + # locate the depth index for the deepest level above Zmax + kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") + #print(kmax) + + # replace mask values with fraction_2d at depth above Zmax) + mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) + + return mask, kmax + + From 357fb1f388435a8df4fd7a932316ad40eae38eb6 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:10:46 +0000 Subject: [PATCH 016/150] WIP: profile_stratification.py --- coast/diagnostics/profile_stratification.py | 355 ++++++++++++++++++++ 1 file changed, 355 insertions(+) create mode 100644 coast/diagnostics/profile_stratification.py diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py new file mode 100644 index 00000000..8a50ecad --- /dev/null +++ b/coast/diagnostics/profile_stratification.py @@ -0,0 +1,355 @@ +from ..data.profile import Profile +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr +import copy +from .._utils.logging_util import get_slug, debug + + +class ProfileStratification(Profile): # TODO All abstract methods should be implemented + """ + Object for handling and storing necessary information, methods and outputs + for calculation of stratification diagnostics. + + + UPDATE THE FOLLOWING + + Parameters + ---------- + gridded_t : xr.Dataset + Gridded object on t-points. + gridded_w : xr.Dataset, optional + Gridded object on w-points. + + Example basic usage: + ------------------- + # Create Internal tide diagnostics object + strat_obj = GriddedStratification(gridded_t, gridded_w) # For Gridded objects on t and w-pts + strat_obj.construct_pycnocline_vars( gridded_t, gridded_w ) + # Make maps of pycnocline thickness and depth + strat_obj.quick_plot() + """ + + def __init__(self, profile: xr.Dataset): + # TODO Super __init__ should be called at some point + debug(f"Creating new {get_slug(self)}") + self.dataset = xr.Dataset() + + # Define the dimensional sizes as constants + self.nid = profile.dataset.dims["id_dim"] + self.nz = gridded_t.dataset.dims["z_dim"] + debug(f"Initialised {get_slug(self)}") + + def construct_pycnocline_vars(self, gridded_t: Gridded, gridded_w: Gridded, strat_thres=-0.01): + """ + Computes depth moments of stratification. Under the assumption that the + stratification approximately represents a two-layer fluid, these can be + interpreted as pycnocline depths and thicknesses. They are computed on + w-points. + + 1st moment of stratification: \int z.strat dz / \int strat dz + In the limit of a two layer fluid this is equivalent to the + pycnocline depth, or z_d (units: metres) + + 2nd moment of stratification: \sqrt{\int (z-z_d)^2 strat dz / \int strat dz} + where strat = d(density)/dz + In the limit of a two layer fluid this is equivatlent to the + pycnocline thickness, or z_t (units: metres) + + Parameters + ---------- + gridded_t : Gridded + Gridded object on t-points. + gridded_w : Gridded, optional + Gridded object on w-points. + strat_thres: float - Optional + limiting stratification (rho_dz < 0) to trigger masking of mixed waters + + Output + ------ + self.dataset.strat_1st_mom - (t,y,x) pycnocline depth + self.dataset.strat_2nd_mom - (t,y,x) pycnocline thickness + self.dataset.strat_1st_mom_masked - (t,y,x) pycnocline depth, masked + in weakly stratified water beyond strat_thres + self.dataset.strat_2nd_mom_masked - (t,y,x) pycnocline thickness, masked + in weakly stratified water beyond strat_thres + self.dataset.mask - (t,y,x) [1/0] stratified/unstrafied + water column according to strat_thres not being met anywhere + in the column + + Returns + ------- + None. + + Example Usage + ------------- + # load some example data + dn_files = "./example_files/" + dn_fig = 'unit_testing/figures/' + fn_nemo_grid_t_dat = 'nemo_data_T_grid_Aug2015.nc' + fn_nemo_dom = 'coast_example_nemo_domain.nc' + gridded_t = coast.Gridded(dn_files + fn_nemo_grid_t_dat, + dn_files + fn_nemo_dom, grid_ref='t-grid') + # create an empty w-grid object, to store stratification + gridded_w = coast.Gridded( fn_domain = dn_files + fn_nemo_dom, + grid_ref='w-grid') + + # initialise GriddedStratification object + strat = coast.GriddedStratification(gridded_t, gridded_w) + # Construct pycnocline variables: depth and thickness + strat.construct_pycnocline_vars( gridded_t, gridded_w ) + # Plot pycnocline depth and thickness + strat.quickplot() + + """ + + debug(f"Constructing pycnocline variables for {get_slug(self)}") + # Construct in-situ density if not already done + if not hasattr(gridded_t.dataset, "density"): + gridded_t.construct_density(eos="EOS10") + + # Construct stratification if not already done. t-pts --> w-pts + if not hasattr(gridded_w.dataset, "rho_dz"): + gridded_w = gridded_t.differentiate("density", dim="z_dim", out_var_str="rho_dz", out_obj=gridded_w) + + # Define the spatial dimensional size and check the dataset and domain arrays are the same size in + # z_dim, ydim, xdim + nt = gridded_t.dataset.dims["t_dim"] + # nz = gridded_t.dataset.dims['z_dim'] + ny = gridded_t.dataset.dims["y_dim"] + nx = gridded_t.dataset.dims["x_dim"] + + # Create a mask for weakly stratified waters + # Preprocess stratification + strat = copy.copy(gridded_w.dataset.rho_dz) # (t_dim, z_dim, ydim, xdim). w-pts. + # Ensure surface value is 0 + strat[:, 0, :, :] = 0 + # Ensure bed value is 0 + strat[:, -1, :, :] = 0 + # mask out the Nan values + strat = strat.where(~np.isnan(gridded_w.dataset.rho_dz), drop=False) + # create mask with a stratification threshold + strat_m = gridded_w.dataset.latitude * 0 + 1 # create a stratification mask: [1/0] = strat/un-strat + strat_m = strat_m.where(strat.min(dim="z_dim").squeeze() < strat_thres, 0, drop=False) + strat_m = strat_m.transpose("t_dim", "y_dim", "x_dim", transpose_coords=True) + + # Compute statification variables + # initialise pycnocline variables + pycnocline_depth = np.zeros((nt, ny, nx)) # pycnocline depth + zt = np.zeros((nt, ny, nx)) # pycnocline thickness + + # Construct intermediate variables + # Broadcast to fill out missing (time) dimensions in grid data + _, depth_0_4d = xr.broadcast(strat, gridded_w.dataset.depth_0) + _, e3_0_4d = xr.broadcast(strat, gridded_w.dataset.e3_0.squeeze()) + + # integrate strat over depth + intN2 = (strat * e3_0_4d).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + # integrate (depth * strat) over depth + intzN2 = (strat * e3_0_4d * depth_0_4d).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + + # compute pycnocline depth + pycnocline_depth = intzN2 / intN2 # pycnocline depth + + # compute pycnocline thickness + intz2N2 = (np.square(depth_0_4d - pycnocline_depth) * e3_0_4d * strat).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + zt = np.sqrt(intz2N2 / intN2) # pycnocline thickness + + # Define xarray attributes + coords = { + "time": ("t_dim", gridded_t.dataset.time.values), + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + } + dims = ["t_dim", "y_dim", "x_dim"] + + # Save a xarray objects + self.dataset["strat_2nd_mom"] = xr.DataArray(zt, coords=coords, dims=dims) + self.dataset.strat_2nd_mom.attrs["units"] = "m" + self.dataset.strat_2nd_mom.attrs["standard_name"] = "pycnocline thickness" + self.dataset.strat_2nd_mom.attrs["long_name"] = "Second depth moment of stratification" + + self.dataset["strat_1st_mom"] = xr.DataArray(pycnocline_depth, coords=coords, dims=dims) + self.dataset.strat_1st_mom.attrs["units"] = "m" + self.dataset.strat_1st_mom.attrs["standard_name"] = "pycnocline depth" + self.dataset.strat_1st_mom.attrs["long_name"] = "First depth moment of stratification" + + # Mask pycnocline variables in weak stratification + zd_m = pycnocline_depth.where(strat_m > 0) + zt_m = zt.where(strat_m > 0) + + self.dataset["mask"] = xr.DataArray(strat_m, coords=coords, dims=dims) + + self.dataset["strat_2nd_mom_masked"] = xr.DataArray(zt_m, coords=coords, dims=dims) + self.dataset.strat_2nd_mom_masked.attrs["units"] = "m" + self.dataset.strat_2nd_mom_masked.attrs["standard_name"] = "masked pycnocline thickness" + self.dataset.strat_2nd_mom_masked.attrs[ + "long_name" + ] = "Second depth moment of stratification, masked in weak stratification" + + self.dataset["strat_1st_mom_masked"] = xr.DataArray(zd_m, coords=coords, dims=dims) + self.dataset.strat_1st_mom_masked.attrs["units"] = "m" + self.dataset.strat_1st_mom_masked.attrs["standard_name"] = "masked pycnocline depth" + self.dataset.strat_1st_mom_masked.attrs[ + "long_name" + ] = "First depth moment of stratification, masked in weak stratification" + + # Inherit horizontal grid information from gridded_w + self.dataset["e1"] = xr.DataArray( + gridded_w.dataset.e1, + coords={ + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + }, + dims=["y_dim", "x_dim"], + ) + self.dataset["e2"] = xr.DataArray( + gridded_w.dataset.e2, + coords={ + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + }, + dims=["y_dim", "x_dim"], + ) + + def calc_pea(self, profile: xr.Dataset, Zmax): + """ + Calculates Potential Energy Anomaly + + UPDATE THE DOCSTR + + The density and depth averaged density can be supplied within gridded_t as "density" and + "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. + "density_bar" is calculated using depth averages of temperature and salinity. + + Example Usage: PEA in upper 200m + -------------------------------- + # load some example data. E.g. + root = "~/work/coast/" + dn_files = root + "./example_files/" + fn_nemo_grid_t_dat = dn_files + "nemo_data_T_grid_Aug2015.nc" + fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" + config_t = root + "./config/example_nemo_grid_t.json" + dn_fig = 'unit_testing/figures/' + gridded_t = coast.Gridded(fn_nemo_grid_t_dat, fn_nemo_dom, config=config_t) + Zd_mask,kmax,Ikmax=gridded_t.calculate_vertical_mask(200.) + strat=coast.GriddedStratification(gridded_t) + strat.calc_pea(gridded_t,Zd_mask) + strat.quick_plot('PEA') + """ + # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach + gravity = 9.81 + + # Define grid spacing, dz. Required for depth integral + """ + The thickness, dz, for integrals on t-points, should be the separation + between w-point depths. + DZ[:, I] = Zw[:, I] - Zw[:, I + 1] + = 0.5 * ( Zt[:, I-1] - Zt[:, I+1] ) + where Zw[:, I + 1] = 0.5 * (Zt[:, I] + Zt[:, I + 1]) + for I = 2:end-1 + DZ[:, 0] = 0.5 * ( Zt[:, 0] + Zt[:, 1] ) + """ + + # Compute dz on w-pts + profile.calculate_vertical_spacing() + dz = profile.dataset.dz + + # Z=gridded_t.dataset.variables['depth_0'].values + # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask + + #_, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) + #height = profile.dataset.depth * Zd_mask # water depth or Zmax , + #height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , + # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] + # nt=gridded_t.dataset.dims['t_dim'] + + # Construct a mask of zeros below threshold, floats above depth of Zmax threshold. + # Floats are in the range (0,1] and represent the fractional proximity to Zmax. + # Used for scaling layer thickness, which would then sum to Zmax. + Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) + + + # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. + height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + + if not "density" in profile.dataset: + profile.construct_density(CT_AS=True, pot_dens=True) + if not "density_bar" in profile.dataset: + profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) + rho = profile.dataset.variables["density"].values # density + rho[np.isnan(rho)] = 0 + rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S + + + + PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height + #%% + # return PEA + coords = { + "time": ("t_dim", gridded_t.dataset.time.values), + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + } + dims = ["t_dim", "y_dim", "x_dim"] + attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} + self.dataset["PEA"] = xr.DataArray(PEA, coords=coords, dims=dims, attrs=attributes) + + def quick_plot(self, var: xr.DataArray = None): + """ + + Map plot for pycnocline depth and thickness variables. + + Parameters + ---------- + var : xr.DataArray, optional + Pass variable to plot. The default is None. In which case both + strat_1st_mom and strat_2nd_mom are plotted. + + Returns + ------- + None. + + Example Usage + ------------- + strat.quick_plot( 'strat_1st_mom_masked' ) + + """ + + debug(f"Generating quick plot for {get_slug(self)}") + + if var is None: + var_lst = [self.dataset.strat_1st_mom_masked, self.dataset.strat_2nd_mom_masked] + else: + var_lst = [self.dataset[var]] + + fig = None + ax = None + for var in var_lst: + fig = plt.figure(figsize=(10, 10)) + ax = fig.gca() + plt.pcolormesh(self.dataset.longitude.squeeze(), self.dataset.latitude.squeeze(), var.isel(t_dim=0)) + # var.mean(dim = 't_dim') ) + # plt.contourf( self.dataset.longitude.squeeze(), + # self.dataset.latitude.squeeze(), + # var.mean(dim = 't_dim'), levels=(0,10,20,30,40) ) + title_str = ( + self.dataset.time[0].dt.strftime("%d %b %Y: ").values + + var.attrs["standard_name"] + + " (" + + var.attrs["units"] + + ")" + ) + plt.title(title_str) + plt.xlabel("longitude") + plt.ylabel("latitude") + plt.clim([0, 50]) + plt.colorbar() + plt.show() + return fig, ax From eb379370a6b96f7a88f8f095f9638e4f2fc51bf3 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:11:39 +0000 Subject: [PATCH 017/150] Add tests for profile.calculate_Vertical_spacing() --- unit_testing/test_profile_methods.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 6931e036..d64256c1 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -6,6 +6,7 @@ import coast import unittest import numpy as np +import xarray as xr import matplotlib.pyplot as plt import unit_test_files as files import datetime @@ -37,6 +38,13 @@ def test_load_process_and_compare_profile_data(self): self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + with self.subTest("Compute vertical spacing"): + profile.calculate_vertical_spacing() + check1 = np.allclose(profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5,10,15]).values, + np.array([1949.1846, 1972.8088, 21.5])) + self.assertTrue(check1, "check1") + + def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) From 952a68d82458dbdc0d9e0a9996d2291e520f22d2 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:11:59 +0000 Subject: [PATCH 018/150] Add tests for profile.calculate_vertical_mask() --- unit_testing/test_profile_methods.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index d64256c1..35354c00 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -141,3 +141,19 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + + def test_calculate_vertical_mask(self): + + profile = coast.Profile() + + arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) + depth = xr.DataArray(arr, dims=["i_dim", "z_dim"]) + + mask, kmax = profile.calculate_vertical_mask(depth, 21) + mask = mask.fillna(-999) + + check1 = (kmax == np.array([2,1,2])).all() + check2 = (mask.values == np.array([[1., 1., 1., -999], [1., 0.8, 0., 0.], [1., 1., 1., -999]])).all() + + self.assertTrue(check1, "check1") + self.assertTrue(check2, "check2") From 764d2432ad278738fc9d488ab52baec2f95b1d88 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 17 Nov 2022 21:12:57 +0000 Subject: [PATCH 019/150] Apply Black formatting to Python code. --- coast/data/profile.py | 40 ++++++++++----------- coast/diagnostics/profile_stratification.py | 9 ++--- unit_testing/test_profile_methods.py | 11 +++--- 3 files changed, 29 insertions(+), 31 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 5c9b6b4d..c0607d64 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -672,18 +672,19 @@ def calculate_vertical_spacing(self): at t-points. """ - if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Compute dz on w-pts depth_t = self.dataset.depth - self.dataset['dz'] = xr.where(depth_t == depth_t.min(dim="z_dim"), - 0.5 * (depth_t + depth_t.shift(z_dim=-1)), - 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)) # .fillna(0.) - ) + self.dataset["dz"] = xr.where( + depth_t == depth_t.min(dim="z_dim"), + 0.5 * (depth_t + depth_t.shift(z_dim=-1)), + 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)), # .fillna(0.) + ) attributes = {"units": "m", "standard name": "centre difference thickness"} - if hasattr(self.dataset.dz, 'coords'): # xarray object. Just add title and units + if hasattr(self.dataset.dz, "coords"): # xarray object. Just add title and units self.dataset.dz.attrs = attributes else: # not an xarray object @@ -695,11 +696,10 @@ def calculate_vertical_spacing(self): dims = ["z_dim", "id_dim"] dz = np.squeeze(dz) - self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) - + self.dataset["dz"] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) def construct_density( - self, eos="EOS10", rhobar=False, Zd_mask:xr.DataArray=None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True + self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True ): """ @@ -791,7 +791,7 @@ def construct_density( new_var_name = "density" else: # calculate density with depth integrated T S - if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Create it self.calculate_vertical_spacing() @@ -800,15 +800,15 @@ def construct_density( if Zd_mask is None: DZ = self.dataset.dz else: - DZ = (self.dataset.dz * Zd_mask) + DZ = self.dataset.dz * Zd_mask DP = DZ.sum(dim="z_dim").to_masked_array() DZ = DZ.to_masked_array() if np.shape(DZ) != shape_ds: DZ = DZ.T # DP=np.repeat(DP[np.newaxis,:,:],shape_ds[1],axis=0) - #DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) - #DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) + # DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) + # DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) # Absolute Salinity if not CT_AS: # abs salinity not provided @@ -871,7 +871,7 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): + def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level @@ -922,20 +922,20 @@ def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): # print('\n') # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) - fraction = xr.where(min_deeper_depth != max_shallower_depth, - (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), - 1) + fraction = xr.where( + min_deeper_depth != max_shallower_depth, + (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + 1, + ) max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") - #print(kmax) + # print(kmax) # replace mask values with fraction_2d at depth above Zmax) mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) return mask, kmax - - diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 8a50ecad..7689ea88 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -264,9 +264,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # Z=gridded_t.dataset.variables['depth_0'].values # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask - #_, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) - #height = profile.dataset.depth * Zd_mask # water depth or Zmax , - #height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , + # _, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) + # height = profile.dataset.depth * Zd_mask # water depth or Zmax , + # height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] # nt=gridded_t.dataset.dims['t_dim'] @@ -275,7 +275,6 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # Used for scaling layer thickness, which would then sum to Zmax. Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) - # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax @@ -287,8 +286,6 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho[np.isnan(rho)] = 0 rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - - PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height #%% # return PEA diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 35354c00..a1471018 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -40,11 +40,12 @@ def test_load_process_and_compare_profile_data(self): with self.subTest("Compute vertical spacing"): profile.calculate_vertical_spacing() - check1 = np.allclose(profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5,10,15]).values, - np.array([1949.1846, 1972.8088, 21.5])) + check1 = np.allclose( + profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5, 10, 15]).values, + np.array([1949.1846, 1972.8088, 21.5]), + ) self.assertTrue(check1, "check1") - def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) @@ -152,8 +153,8 @@ def test_calculate_vertical_mask(self): mask, kmax = profile.calculate_vertical_mask(depth, 21) mask = mask.fillna(-999) - check1 = (kmax == np.array([2,1,2])).all() - check2 = (mask.values == np.array([[1., 1., 1., -999], [1., 0.8, 0., 0.], [1., 1., 1., -999]])).all() + check1 = (kmax == np.array([2, 1, 2])).all() + check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.8, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") From b666d542de7b820f990901adee12be9a0c8f01a2 Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Thu, 17 Nov 2022 21:13:32 +0000 Subject: [PATCH 020/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index d330a7e9..d75a3c07 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -73,8 +73,9 @@ c. calculate_pressure_along_contour 13. test_profile_methods - a. compare_processed_profile_with_model - b. load_process_and_compare_profile_data + a. calculate_vertical_mask + b. compare_processed_profile_with_model + c. load_process_and_compare_profile_data 14. test_plot_utilities a. determine_clim_by_stdev From 2a4ca405309cecaaa84657e9c95413b539dfc8bd Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 12:09:23 +0000 Subject: [PATCH 021/150] calculate_vertical_mask(): made inputs same between Gridded and Profile versions --- coast/data/profile.py | 15 ++++++++------- unit_testing/test_profile_methods.py | 12 ++++++++---- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index c0607d64..feca75ef 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -871,25 +871,26 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): + def calculate_vertical_mask(self, Zmax = 200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level Inputs: - depth (id_dim, z_dim) postitive values - passing as a variable facilitates testing - Zmax float - max depth (m( + Zmax float - max depth (m) Returns Zd_mask (id_dim, z_dim) xr.DataArray, float mask. - #kmax (id_dim) deepest index above Zmax + kmax (id_dim) deepest index above Zmax """ + depth_t = self.dataset.depth + ## Contruct a mask array that is: # zeros below Zmax # ones above Zmax, except the closest shallower depth which has a value [0,1] that is the weighted distance to Zmax ## prepare depth profiles - depth_t = depth + # remove deep nans # depth_t = depth_t.fillna(1E6) # depth_t = depth_t.interpolate_na(dim="z_dim", method="nearest", fill_value="extrapolate") @@ -902,7 +903,7 @@ def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): # mask_arr[depth_t <= Zmax] = 1 # mask_arr[depth_t > Zmax] = 0 # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) - mask = depth * np.nan + mask = depth_t * np.nan mask = xr.where(depth_t <= Zmax, 1, mask) mask = xr.where(depth_t > Zmax, 0, mask) @@ -932,7 +933,7 @@ def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax - kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") + kmax = xr.where(depth_t == max_shallower_depth, 1, 0).argmax(dim="z_dim") # print(kmax) # replace mask values with fraction_2d at depth above Zmax) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index a1471018..7287ece0 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -144,13 +144,17 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check3, "check3") def test_calculate_vertical_mask(self): + # load example profile data + profile = coast.Profile(config=fn_profile_config) + profile.read_en4(fn_profile) + profile.dataset = profile.dataset.isel(id_dim=slice(0, 3)).isel(z_dim=slice(0, 4)) - profile = coast.Profile() - + # Reassign values to depth, within a full profile object, to make it transparent arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) - depth = xr.DataArray(arr, dims=["i_dim", "z_dim"]) + depth = xr.DataArray(arr, dims=["id_dim", "z_dim"]) + profile.dataset['depth'] = depth - mask, kmax = profile.calculate_vertical_mask(depth, 21) + mask, kmax = profile.calculate_vertical_mask( 21) mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() From 6bb372445397eb516a16f3434d93b6d46ec60432 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 12:10:39 +0000 Subject: [PATCH 022/150] add test for pPofile.contruct.density() --- unit_testing/test_profile_methods.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 7287ece0..d44abe88 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -46,6 +46,34 @@ def test_load_process_and_compare_profile_data(self): ) self.assertTrue(check1, "check1") + def test_compute_density(self): + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) + profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() + + profile.construct_density() + + check1 = np.allclose(profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), + 4248551.199925806, + ) + # Density depth mean T and S limited to 200m + Zmax = 200 # m + Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) + profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Zd_mask=Zd_mask) + check2 = np.allclose( + profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021 + ) + # Temperature component of density (ie from depth mean Sal). full depth + profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Tbar=False) + check3 = np.allclose( + profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557 + ) + self.assertTrue(check1, msg="check1") + self.assertTrue(check2, msg="check2") + self.assertTrue(check3, msg="check3") + + + def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) From ea6f553eb8cf60844b87e5040717452df44cf591 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 18 Nov 2022 12:11:27 +0000 Subject: [PATCH 023/150] Apply Black formatting to Python code. --- coast/data/profile.py | 2 +- unit_testing/test_profile_methods.py | 21 ++++++++------------- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index feca75ef..981699de 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -871,7 +871,7 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, Zmax = 200): + def calculate_vertical_mask(self, Zmax=200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index d44abe88..74d8dc33 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -53,27 +53,22 @@ def test_compute_density(self): profile.construct_density() - check1 = np.allclose(profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), - 4248551.199925806, - ) + check1 = np.allclose( + profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), + 4248551.199925806, + ) # Density depth mean T and S limited to 200m Zmax = 200 # m Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Zd_mask=Zd_mask) - check2 = np.allclose( - profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021 - ) + check2 = np.allclose(profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021) # Temperature component of density (ie from depth mean Sal). full depth profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Tbar=False) - check3 = np.allclose( - profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557 - ) + check3 = np.allclose(profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557) self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") self.assertTrue(check3, msg="check3") - - def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) @@ -180,9 +175,9 @@ def test_calculate_vertical_mask(self): # Reassign values to depth, within a full profile object, to make it transparent arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) depth = xr.DataArray(arr, dims=["id_dim", "z_dim"]) - profile.dataset['depth'] = depth + profile.dataset["depth"] = depth - mask, kmax = profile.calculate_vertical_mask( 21) + mask, kmax = profile.calculate_vertical_mask(21) mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() From 90f3ed400a384e2294e1b1e5597da9516a96f2de Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Fri, 18 Nov 2022 12:12:09 +0000 Subject: [PATCH 024/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index d75a3c07..ca167927 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -75,7 +75,8 @@ 13. test_profile_methods a. calculate_vertical_mask b. compare_processed_profile_with_model - c. load_process_and_compare_profile_data + c. compute_density + d. load_process_and_compare_profile_data 14. test_plot_utilities a. determine_clim_by_stdev From 2e4b67b061c8c9f7ad2687758f83676d9b37f2a2 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:11:00 +0000 Subject: [PATCH 025/150] add ProfileStratification.calc_pea() and .quick_plot() --- coast/diagnostics/profile_stratification.py | 283 +++----------------- 1 file changed, 34 insertions(+), 249 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 7689ea88..e62f47f2 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -3,6 +3,7 @@ import numpy as np import xarray as xr import copy +from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug @@ -37,277 +38,64 @@ def __init__(self, profile: xr.Dataset): # Define the dimensional sizes as constants self.nid = profile.dataset.dims["id_dim"] - self.nz = gridded_t.dataset.dims["z_dim"] + self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def construct_pycnocline_vars(self, gridded_t: Gridded, gridded_w: Gridded, strat_thres=-0.01): - """ - Computes depth moments of stratification. Under the assumption that the - stratification approximately represents a two-layer fluid, these can be - interpreted as pycnocline depths and thicknesses. They are computed on - w-points. - - 1st moment of stratification: \int z.strat dz / \int strat dz - In the limit of a two layer fluid this is equivalent to the - pycnocline depth, or z_d (units: metres) - - 2nd moment of stratification: \sqrt{\int (z-z_d)^2 strat dz / \int strat dz} - where strat = d(density)/dz - In the limit of a two layer fluid this is equivatlent to the - pycnocline thickness, or z_t (units: metres) - - Parameters - ---------- - gridded_t : Gridded - Gridded object on t-points. - gridded_w : Gridded, optional - Gridded object on w-points. - strat_thres: float - Optional - limiting stratification (rho_dz < 0) to trigger masking of mixed waters - - Output - ------ - self.dataset.strat_1st_mom - (t,y,x) pycnocline depth - self.dataset.strat_2nd_mom - (t,y,x) pycnocline thickness - self.dataset.strat_1st_mom_masked - (t,y,x) pycnocline depth, masked - in weakly stratified water beyond strat_thres - self.dataset.strat_2nd_mom_masked - (t,y,x) pycnocline thickness, masked - in weakly stratified water beyond strat_thres - self.dataset.mask - (t,y,x) [1/0] stratified/unstrafied - water column according to strat_thres not being met anywhere - in the column - - Returns - ------- - None. - - Example Usage - ------------- - # load some example data - dn_files = "./example_files/" - dn_fig = 'unit_testing/figures/' - fn_nemo_grid_t_dat = 'nemo_data_T_grid_Aug2015.nc' - fn_nemo_dom = 'coast_example_nemo_domain.nc' - gridded_t = coast.Gridded(dn_files + fn_nemo_grid_t_dat, - dn_files + fn_nemo_dom, grid_ref='t-grid') - # create an empty w-grid object, to store stratification - gridded_w = coast.Gridded( fn_domain = dn_files + fn_nemo_dom, - grid_ref='w-grid') - - # initialise GriddedStratification object - strat = coast.GriddedStratification(gridded_t, gridded_w) - # Construct pycnocline variables: depth and thickness - strat.construct_pycnocline_vars( gridded_t, gridded_w ) - # Plot pycnocline depth and thickness - strat.quickplot() - - """ - - debug(f"Constructing pycnocline variables for {get_slug(self)}") - # Construct in-situ density if not already done - if not hasattr(gridded_t.dataset, "density"): - gridded_t.construct_density(eos="EOS10") - - # Construct stratification if not already done. t-pts --> w-pts - if not hasattr(gridded_w.dataset, "rho_dz"): - gridded_w = gridded_t.differentiate("density", dim="z_dim", out_var_str="rho_dz", out_obj=gridded_w) - - # Define the spatial dimensional size and check the dataset and domain arrays are the same size in - # z_dim, ydim, xdim - nt = gridded_t.dataset.dims["t_dim"] - # nz = gridded_t.dataset.dims['z_dim'] - ny = gridded_t.dataset.dims["y_dim"] - nx = gridded_t.dataset.dims["x_dim"] - - # Create a mask for weakly stratified waters - # Preprocess stratification - strat = copy.copy(gridded_w.dataset.rho_dz) # (t_dim, z_dim, ydim, xdim). w-pts. - # Ensure surface value is 0 - strat[:, 0, :, :] = 0 - # Ensure bed value is 0 - strat[:, -1, :, :] = 0 - # mask out the Nan values - strat = strat.where(~np.isnan(gridded_w.dataset.rho_dz), drop=False) - # create mask with a stratification threshold - strat_m = gridded_w.dataset.latitude * 0 + 1 # create a stratification mask: [1/0] = strat/un-strat - strat_m = strat_m.where(strat.min(dim="z_dim").squeeze() < strat_thres, 0, drop=False) - strat_m = strat_m.transpose("t_dim", "y_dim", "x_dim", transpose_coords=True) - - # Compute statification variables - # initialise pycnocline variables - pycnocline_depth = np.zeros((nt, ny, nx)) # pycnocline depth - zt = np.zeros((nt, ny, nx)) # pycnocline thickness - - # Construct intermediate variables - # Broadcast to fill out missing (time) dimensions in grid data - _, depth_0_4d = xr.broadcast(strat, gridded_w.dataset.depth_0) - _, e3_0_4d = xr.broadcast(strat, gridded_w.dataset.e3_0.squeeze()) - - # integrate strat over depth - intN2 = (strat * e3_0_4d).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - # integrate (depth * strat) over depth - intzN2 = (strat * e3_0_4d * depth_0_4d).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - - # compute pycnocline depth - pycnocline_depth = intzN2 / intN2 # pycnocline depth - - # compute pycnocline thickness - intz2N2 = (np.square(depth_0_4d - pycnocline_depth) * e3_0_4d * strat).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - zt = np.sqrt(intz2N2 / intN2) # pycnocline thickness - - # Define xarray attributes - coords = { - "time": ("t_dim", gridded_t.dataset.time.values), - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - } - dims = ["t_dim", "y_dim", "x_dim"] - - # Save a xarray objects - self.dataset["strat_2nd_mom"] = xr.DataArray(zt, coords=coords, dims=dims) - self.dataset.strat_2nd_mom.attrs["units"] = "m" - self.dataset.strat_2nd_mom.attrs["standard_name"] = "pycnocline thickness" - self.dataset.strat_2nd_mom.attrs["long_name"] = "Second depth moment of stratification" - - self.dataset["strat_1st_mom"] = xr.DataArray(pycnocline_depth, coords=coords, dims=dims) - self.dataset.strat_1st_mom.attrs["units"] = "m" - self.dataset.strat_1st_mom.attrs["standard_name"] = "pycnocline depth" - self.dataset.strat_1st_mom.attrs["long_name"] = "First depth moment of stratification" - - # Mask pycnocline variables in weak stratification - zd_m = pycnocline_depth.where(strat_m > 0) - zt_m = zt.where(strat_m > 0) - - self.dataset["mask"] = xr.DataArray(strat_m, coords=coords, dims=dims) - - self.dataset["strat_2nd_mom_masked"] = xr.DataArray(zt_m, coords=coords, dims=dims) - self.dataset.strat_2nd_mom_masked.attrs["units"] = "m" - self.dataset.strat_2nd_mom_masked.attrs["standard_name"] = "masked pycnocline thickness" - self.dataset.strat_2nd_mom_masked.attrs[ - "long_name" - ] = "Second depth moment of stratification, masked in weak stratification" - - self.dataset["strat_1st_mom_masked"] = xr.DataArray(zd_m, coords=coords, dims=dims) - self.dataset.strat_1st_mom_masked.attrs["units"] = "m" - self.dataset.strat_1st_mom_masked.attrs["standard_name"] = "masked pycnocline depth" - self.dataset.strat_1st_mom_masked.attrs[ - "long_name" - ] = "First depth moment of stratification, masked in weak stratification" - - # Inherit horizontal grid information from gridded_w - self.dataset["e1"] = xr.DataArray( - gridded_w.dataset.e1, - coords={ - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - }, - dims=["y_dim", "x_dim"], - ) - self.dataset["e2"] = xr.DataArray( - gridded_w.dataset.e2, - coords={ - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - }, - dims=["y_dim", "x_dim"], - ) - def calc_pea(self, profile: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly - UPDATE THE DOCSTR - - The density and depth averaged density can be supplied within gridded_t as "density" and + The density and depth averaged density can be supplied within profile as "density" and "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. "density_bar" is calculated using depth averages of temperature and salinity. - Example Usage: PEA in upper 200m - -------------------------------- - # load some example data. E.g. - root = "~/work/coast/" - dn_files = root + "./example_files/" - fn_nemo_grid_t_dat = dn_files + "nemo_data_T_grid_Aug2015.nc" - fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" - config_t = root + "./config/example_nemo_grid_t.json" - dn_fig = 'unit_testing/figures/' - gridded_t = coast.Gridded(fn_nemo_grid_t_dat, fn_nemo_dom, config=config_t) - Zd_mask,kmax,Ikmax=gridded_t.calculate_vertical_mask(200.) - strat=coast.GriddedStratification(gridded_t) - strat.calc_pea(gridded_t,Zd_mask) - strat.quick_plot('PEA') + Writes self.dataset.PEA """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach gravity = 9.81 # Define grid spacing, dz. Required for depth integral - """ - The thickness, dz, for integrals on t-points, should be the separation - between w-point depths. - DZ[:, I] = Zw[:, I] - Zw[:, I + 1] - = 0.5 * ( Zt[:, I-1] - Zt[:, I+1] ) - where Zw[:, I + 1] = 0.5 * (Zt[:, I] + Zt[:, I + 1]) - for I = 2:end-1 - DZ[:, 0] = 0.5 * ( Zt[:, 0] + Zt[:, 1] ) - """ - - # Compute dz on w-pts profile.calculate_vertical_spacing() dz = profile.dataset.dz - # Z=gridded_t.dataset.variables['depth_0'].values - # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask - - # _, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) - # height = profile.dataset.depth * Zd_mask # water depth or Zmax , - # height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , - # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] - # nt=gridded_t.dataset.dims['t_dim'] + # Depth, relabel for convenience + depth_t = profile.dataset.depth # Construct a mask of zeros below threshold, floats above depth of Zmax threshold. # Floats are in the range (0,1] and represent the fractional proximity to Zmax. # Used for scaling layer thickness, which would then sum to Zmax. - Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) + Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) - # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. + # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) - rho = profile.dataset.variables["density"].values # density - rho[np.isnan(rho)] = 0 + rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height - #%% - # return PEA + pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / Zmax + coords = { - "time": ("t_dim", gridded_t.dataset.time.values), - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + "time": ("id_dim", profile.dataset.time.values), + "latitude": (("id_dim"), profile.dataset.latitude.values), + "longitude": (("id_dim"), profile.dataset.longitude.values), } - dims = ["t_dim", "y_dim", "x_dim"] + dims = ["id_dim"] attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} - self.dataset["PEA"] = xr.DataArray(PEA, coords=coords, dims=dims, attrs=attributes) + self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes) def quick_plot(self, var: xr.DataArray = None): """ - - Map plot for pycnocline depth and thickness variables. + Map plot for potential energy anomaly. Parameters ---------- var : xr.DataArray, optional - Pass variable to plot. The default is None. In which case both - strat_1st_mom and strat_2nd_mom are plotted. + Pass variable to plot. The default is None. In which case + potential energy anomaly is plotted. Returns ------- @@ -315,38 +103,35 @@ def quick_plot(self, var: xr.DataArray = None): Example Usage ------------- - strat.quick_plot( 'strat_1st_mom_masked' ) - + For a Profile object, profile + pa = coast.ProfileStratification(profile) + pa.calc_pea(profile, 200) + pa.quick_plot( 'pea' ) """ debug(f"Generating quick plot for {get_slug(self)}") if var is None: - var_lst = [self.dataset.strat_1st_mom_masked, self.dataset.strat_2nd_mom_masked] + var_lst = [self.dataset.pea] else: var_lst = [self.dataset[var]] fig = None ax = None for var in var_lst: - fig = plt.figure(figsize=(10, 10)) - ax = fig.gca() - plt.pcolormesh(self.dataset.longitude.squeeze(), self.dataset.latitude.squeeze(), var.isel(t_dim=0)) - # var.mean(dim = 't_dim') ) - # plt.contourf( self.dataset.longitude.squeeze(), - # self.dataset.latitude.squeeze(), - # var.mean(dim = 't_dim'), levels=(0,10,20,30,40) ) + title_str = ( - self.dataset.time[0].dt.strftime("%d %b %Y: ").values - + var.attrs["standard_name"] + var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" ) - plt.title(title_str) - plt.xlabel("longitude") - plt.ylabel("latitude") - plt.clim([0, 50]) - plt.colorbar() - plt.show() + + fig,ax = geo_scatter( + self.dataset.longitude, + self.dataset.latitude, + self.dataset[var], + title=title_str, + ) + return fig, ax From 2e2e3497789cd23ad55f1f5749553116c1b248d0 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:12:05 +0000 Subject: [PATCH 026/150] add test for GriddedStratification.calc_pea() --- ...py => test_gridded_diagnostics_methods.py} | 26 +++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) rename unit_testing/{test_diagnostic_methods.py => test_gridded_diagnostics_methods.py} (87%) diff --git a/unit_testing/test_diagnostic_methods.py b/unit_testing/test_gridded_diagnostics_methods.py similarity index 87% rename from unit_testing/test_diagnostic_methods.py rename to unit_testing/test_gridded_diagnostics_methods.py index f054d90f..2eb21f0d 100644 --- a/unit_testing/test_diagnostic_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -11,7 +11,7 @@ import unit_test_files as files -class test_diagnostic_methods(unittest.TestCase): +class test_gridded_diagnostics_methods(unittest.TestCase): def test_compute_vertical_spatial_derivative(self): nemo_t = coast.Gridded( fn_data=files.fn_nemo_grid_t_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid @@ -125,8 +125,30 @@ def test_construct_pycnocline_depth_and_thickness(self): self.assertTrue(check4, msg=log_str) self.assertTrue(check5, msg=log_str) - with self.subTest("Plot pycnocline depth"): + with self.subTest("Test quick_plot pycnocline depth"): fig, ax = strat.quick_plot("strat_1st_mom_masked") fig.tight_layout() fig.savefig(files.dn_fig + "strat_1st_mom.png") plt.close("all") + + def test_calc_pea(self): + + nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) + + # Compute a vertical max to exclude depths below 200m + Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.) + + # Initiate a stratification diagnostics object + strat = coast.GriddedStratification(nemo_t) + + # calculate PEA for unmasked depths + strat.calc_pea(nemo_t, Zd_mask) + # Check the calculations are as expected + check1 = np.isclose(strat.dataset.PEA.mean().item(), 124.5029568214227) + self.assertTrue(check1, msg="check1") + + with self.subTest("Test quick_plot()"): + fig, ax = strat.quick_plot('PEA') + fig.tight_layout() + fig.savefig(files.dn_fig + "gridded_pea.png") + plt.close("all") \ No newline at end of file From 221856417e216e06012a4f5ec02d910f9560d522 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:13:02 +0000 Subject: [PATCH 027/150] add test for ProfileStratification.calc_pea() and .quick_plot() --- .../test_profile_stratification_methods.py | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 unit_testing/test_profile_stratification_methods.py diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py new file mode 100644 index 00000000..615dda1d --- /dev/null +++ b/unit_testing/test_profile_stratification_methods.py @@ -0,0 +1,33 @@ +""" + +""" + +# IMPORT modules. Must have unittest, and probably coast. +import coast +import unittest +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt +import unit_test_files as files +import datetime + + +class test_profile_stratification_methods(unittest.TestCase): + + def test_calculate_pea(self): + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) + profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() + + pa = coast.ProfileStratification(profile) + Zmax = 200 # metres + pa.calc_pea(profile, Zmax) + + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 153.0590043361475) + self.assertTrue(check1, "check1") + + with self.subTest("Test quick_plot()"): + fig, ax = pa.quick_plot('PEA') + fig.tight_layout() + fig.savefig(files.dn_fig + "profile_pea.png") + plt.close("all") \ No newline at end of file From 1976961401f4d6f77dde8c15ba7993ce6f98f20f Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:14:14 +0000 Subject: [PATCH 028/150] update unit_test framework with new, aligned, profile and gridded tests --- unit_testing/unit_test.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test.py b/unit_testing/unit_test.py index 023edc43..08383bdc 100644 --- a/unit_testing/unit_test.py +++ b/unit_testing/unit_test.py @@ -17,7 +17,7 @@ from test_gridded_harmonics import test_gridded_harmonics from test_general_utils import test_general_utils from test_xesmf_convert import test_xesmf_convert -from test_diagnostic_methods import test_diagnostic_methods +from test_gridded_diagnostics_methods import test_gridded_diagnostics_methods from test_transect_methods import test_transect_methods from test_object_manipulation import test_object_manipulation from test_altimetry_methods import test_altimetry_methods @@ -25,6 +25,7 @@ from test_isobath_contour_methods import test_contour_t_methods, test_contour_f_methods from test_eof_methods import test_eof_methods from test_profile_methods import test_profile_methods +from test_profile_stratification_methods import test_profile_stratification_methods from test_plot_utilities import test_plot_utilities from test_stats_utilities import test_stats_utilities from test_maskmaker_methods import test_maskmaker_methods @@ -41,7 +42,7 @@ test_general_utils, test_xesmf_convert, test_gridded_harmonics, - test_diagnostic_methods, + test_gridded_diagnostics_methods, test_transect_methods, test_object_manipulation, test_altimetry_methods, @@ -51,6 +52,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, + test_profile_stratification_methods, test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From 78c5f88d40a9a0b845e4d18a5ae9033cd561ce7c Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:15:14 +0000 Subject: [PATCH 029/150] update imports with new ProfileStratification class --- coast/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/coast/__init__.py b/coast/__init__.py index 4f52489f..1bbb0e8d 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -9,6 +9,7 @@ from ._utils import logging_util, general_utils, plot_util, crps_util, seasons from .diagnostics.gridded_monthly_hydrographic_climatology import GriddedMonthlyHydrographicClimatology from .diagnostics.profile_hydrographic_analysis import ProfileHydrography +from .diagnostics.profile_stratification import ProfileStratification from .data.index import Indexed from .data.profile import Profile from .diagnostics.profile_analysis import ProfileAnalysis From 8502d4f0e4965e43f16b972516ea54402bd19cac Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:22:55 +0000 Subject: [PATCH 030/150] update unit_test contents with new Gridded, GriddedStratification and ProfileStratification tests --- unit_testing/generate_unit_test_contents.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/unit_testing/generate_unit_test_contents.py b/unit_testing/generate_unit_test_contents.py index b9d835ce..9504fbcc 100644 --- a/unit_testing/generate_unit_test_contents.py +++ b/unit_testing/generate_unit_test_contents.py @@ -22,7 +22,8 @@ from test_gridded_harmonics import test_gridded_harmonics from test_general_utils import test_general_utils from test_xesmf_convert import test_xesmf_convert -from test_diagnostic_methods import test_diagnostic_methods +from test_gridded_diagnostics_methods import test_gridded_diagnostics_methods +from test_profile_stratification_methods import test_profile_stratification_methods from test_transect_methods import test_transect_methods from test_object_manipulation import test_object_manipulation from test_altimetry_methods import test_altimetry_methods @@ -49,7 +50,7 @@ test_general_utils, test_gridded_harmonics, test_xesmf_convert, - test_diagnostic_methods, + test_gridded_diagnostics_methods, test_transect_methods, test_object_manipulation, test_altimetry_methods, @@ -58,6 +59,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, + test_profile_stratification_methods test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From 7b542aa8f010139aa80b300bf04aae665d8ca9ae Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:16:17 +0000 Subject: [PATCH 031/150] fix nan_helper tests --- coast/_utils/general_utils.py | 2 +- unit_testing/test_general_utils.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 62bc9411..98efbe0f 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -383,6 +383,6 @@ def fill_holes_1d(y): Returns: array([2., 2., 2., 3., 4., 5., 6.]) """ - nans, x = general_utils.nan_helper(y) # location interior nans + nans, x = nan_helper(y) # location interior nans y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate return y diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index 6364ba8a..b273099b 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -65,8 +65,8 @@ def test_fill_holes_1d(self): input_xr = xr.DataArray(input) target = np.array([2.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) - check1 = all(fill_holes_1d(input) == target) - check2 = all(fill_holes_1d(input_xr).values == target) + check1 = all(general_utils.fill_holes_1d(input) == target) + check2 = all(general_utils.fill_holes_1d(input_xr).values == target) self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") From 4cfc6d4cc0472a3573fa3aa219850e8fb22f4438 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:17:51 +0000 Subject: [PATCH 032/150] fix test for calculate vertical mask --- unit_testing/test_profile_methods.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 74d8dc33..60084b92 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -168,8 +168,8 @@ def test_compare_processed_profile_with_model(self): def test_calculate_vertical_mask(self): # load example profile data - profile = coast.Profile(config=fn_profile_config) - profile.read_en4(fn_profile) + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) profile.dataset = profile.dataset.isel(id_dim=slice(0, 3)).isel(z_dim=slice(0, 4)) # Reassign values to depth, within a full profile object, to make it transparent From 0331717d7d588e8cbff70b912b8afc6a78833250 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:36:04 +0000 Subject: [PATCH 033/150] fix quick_plot --- coast/diagnostics/profile_stratification.py | 4 ++-- unit_testing/test_profile_stratification_methods.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e62f47f2..78880498 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -49,7 +49,7 @@ def calc_pea(self, profile: xr.Dataset, Zmax): "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. "density_bar" is calculated using depth averages of temperature and salinity. - Writes self.dataset.PEA + Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach gravity = 9.81 @@ -130,7 +130,7 @@ def quick_plot(self, var: xr.DataArray = None): fig,ax = geo_scatter( self.dataset.longitude, self.dataset.latitude, - self.dataset[var], + var, title=title_str, ) diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 615dda1d..8f0f20b3 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -23,11 +23,11 @@ def test_calculate_pea(self): Zmax = 200 # metres pa.calc_pea(profile, Zmax) - check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 153.0590043361475) + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): - fig, ax = pa.quick_plot('PEA') + fig, ax = pa.quick_plot('pea') fig.tight_layout() fig.savefig(files.dn_fig + "profile_pea.png") plt.close("all") \ No newline at end of file From 655029d3d0992af3de648c6fe225e28257c596b6 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:38:59 +0000 Subject: [PATCH 034/150] missing comma --- unit_testing/generate_unit_test_contents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/unit_testing/generate_unit_test_contents.py b/unit_testing/generate_unit_test_contents.py index 9504fbcc..44e18a39 100644 --- a/unit_testing/generate_unit_test_contents.py +++ b/unit_testing/generate_unit_test_contents.py @@ -59,7 +59,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, - test_profile_stratification_methods + test_profile_stratification_methods, test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From af1c37786654c1d67e6325d084a163b523d443dc Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 18 Nov 2022 22:39:28 +0000 Subject: [PATCH 035/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 17 ++++++----------- .../test_gridded_diagnostics_methods.py | 6 +++--- .../test_profile_stratification_methods.py | 7 +++---- 3 files changed, 12 insertions(+), 18 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 78880498..07876e9f 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -120,18 +120,13 @@ def quick_plot(self, var: xr.DataArray = None): ax = None for var in var_lst: - title_str = ( - var.attrs["standard_name"] - + " (" - + var.attrs["units"] - + ")" - ) + title_str = var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" - fig,ax = geo_scatter( - self.dataset.longitude, - self.dataset.latitude, - var, - title=title_str, + fig, ax = geo_scatter( + self.dataset.longitude, + self.dataset.latitude, + var, + title=title_str, ) return fig, ax diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index 2eb21f0d..7e726556 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -136,7 +136,7 @@ def test_calc_pea(self): nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m - Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.) + Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.0) # Initiate a stratification diagnostics object strat = coast.GriddedStratification(nemo_t) @@ -148,7 +148,7 @@ def test_calc_pea(self): self.assertTrue(check1, msg="check1") with self.subTest("Test quick_plot()"): - fig, ax = strat.quick_plot('PEA') + fig, ax = strat.quick_plot("PEA") fig.tight_layout() fig.savefig(files.dn_fig + "gridded_pea.png") - plt.close("all") \ No newline at end of file + plt.close("all") diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 8f0f20b3..2fd14fcb 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -13,21 +13,20 @@ class test_profile_stratification_methods(unittest.TestCase): - def test_calculate_pea(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() pa = coast.ProfileStratification(profile) - Zmax = 200 # metres + Zmax = 200 # metres pa.calc_pea(profile, Zmax) check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): - fig, ax = pa.quick_plot('pea') + fig, ax = pa.quick_plot("pea") fig.tight_layout() fig.savefig(files.dn_fig + "profile_pea.png") - plt.close("all") \ No newline at end of file + plt.close("all") From 3e378761f94d99dd6bd4eb29ae7a525fe7788a6a Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Fri, 18 Nov 2022 22:40:04 +0000 Subject: [PATCH 036/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index ca167927..8addde26 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -27,10 +27,11 @@ 4. test_xesmf_convert a. basic_conversion_to_xesmf -5. test_diagnostic_methods - a. compute_vertical_spatial_derivative - b. construct_density - c. construct_pycnocline_depth_and_thickness +5. test_gridded_diagnostics_methods + a. calc_pea + b. compute_vertical_spatial_derivative + c. construct_density + d. construct_pycnocline_depth_and_thickness 6. test_transect_methods a. calculate_transport_velocity_and_depth @@ -78,28 +79,31 @@ c. compute_density d. load_process_and_compare_profile_data -14. test_plot_utilities +14. test_profile_stratification_methods + a. calculate_pea + +15. test_plot_utilities a. determine_clim_by_stdev b. determine_colorbar_extension c. geo_axes d. scatter_with_fit -15. test_stats_utilities +16. test_stats_utilities a. find_maxima -16. test_maskmaker_methods +17. test_maskmaker_methods a. fill_polygon_by_index b. fill_polygon_by_lonlat c. make_region_from_vertices -17. test_climatology +18. test_climatology a. monthly_and_seasonal_climatology -18. test_wod_read_data +19. test_wod_read_data a. load_wod b. reshape_wod -19. test_bgc_gridded_initialisation +20. test_bgc_gridded_initialisation a. gridded_load_bgc_data b. gridded_load_bgc_data_and_domain c. gridded_load_bgc_dimensions_correctly_renamed From 2bac1771fe1bb2b724c607570c368bae9383d5d4 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:57:24 +0000 Subject: [PATCH 037/150] improve docstr --- coast/diagnostics/profile_stratification.py | 17 +++-------------- 1 file changed, 3 insertions(+), 14 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 07876e9f..f558e589 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -12,23 +12,12 @@ class ProfileStratification(Profile): # TODO All abstract methods should be imp Object for handling and storing necessary information, methods and outputs for calculation of stratification diagnostics. - - UPDATE THE FOLLOWING + Related to GriddedStratification class Parameters ---------- - gridded_t : xr.Dataset - Gridded object on t-points. - gridded_w : xr.Dataset, optional - Gridded object on w-points. - - Example basic usage: - ------------------- - # Create Internal tide diagnostics object - strat_obj = GriddedStratification(gridded_t, gridded_w) # For Gridded objects on t and w-pts - strat_obj.construct_pycnocline_vars( gridded_t, gridded_w ) - # Make maps of pycnocline thickness and depth - strat_obj.quick_plot() + profile : xr.Dataset + Profile object on assumed on t-points. """ def __init__(self, profile: xr.Dataset): From 388be057d44f0995a228c4177ae7d3cc56b1e459 Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 11:22:04 +0000 Subject: [PATCH 038/150] calculate_vertical_spacing(). Remove redundant if-not xarray condition --- coast/data/profile.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 981699de..0901d173 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -682,21 +682,8 @@ def calculate_vertical_spacing(self): 0.5 * (depth_t + depth_t.shift(z_dim=-1)), 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)), # .fillna(0.) ) - attributes = {"units": "m", "standard name": "centre difference thickness"} - if hasattr(self.dataset.dz, "coords"): # xarray object. Just add title and units - self.dataset.dz.attrs = attributes - - else: # not an xarray object - coords = { - "time": (("id_dim"), self.dataset.time.values), - "latitude": (("id_dim"), self.dataset.latitude.values), - "longitude": (("id_dim"), self.dataset.longitude.values), - } - dims = ["z_dim", "id_dim"] - - dz = np.squeeze(dz) - self.dataset["dz"] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + self.dataset.dz.attrs = attributes def construct_density( self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True From fb653c82b6ee0929bc63020619a05d98db1468f9 Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 12:10:51 +0000 Subject: [PATCH 039/150] remove debugging print statements --- coast/data/profile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 0901d173..fefd7cec 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -747,8 +747,8 @@ def construct_density( density = np.ma.zeros(shape_ds) - print(f"shape sal:{np.shape(sal)}") - print(f"shape rho:{np.shape(density)}") + #print(f"shape sal:{np.shape(sal)}") + #print(f"shape rho:{np.shape(density)}") s_levels = self.dataset.depth.to_masked_array() if np.shape(s_levels) != shape_ds: From e3823119d11a6bb412c6bfbdb4650af268b05085 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Mon, 21 Nov 2022 12:11:17 +0000 Subject: [PATCH 040/150] Apply Black formatting to Python code. --- coast/data/profile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index fefd7cec..73a66b04 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -747,8 +747,8 @@ def construct_density( density = np.ma.zeros(shape_ds) - #print(f"shape sal:{np.shape(sal)}") - #print(f"shape rho:{np.shape(density)}") + # print(f"shape sal:{np.shape(sal)}") + # print(f"shape rho:{np.shape(density)}") s_levels = self.dataset.depth.to_masked_array() if np.shape(s_levels) != shape_ds: From 101a9f5f7dbdf60b61b7b9e04de908c40646c0e2 Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 12:13:33 +0000 Subject: [PATCH 041/150] Add profile PEA notebook --- .../profile/potential_energy_tutorial.ipynb | 331 ++++++++++++++++++ 1 file changed, 331 insertions(+) create mode 100644 example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb diff --git a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb new file mode 100644 index 00000000..0cdb9a3a --- /dev/null +++ b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5eca7994-6fa1-44e1-b95c-fc8a0fecf7bd", + "metadata": {}, + "source": [ + "A demonstration to calculate the Potential Energy Anomaly for Profile data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "14277e0d-4dbc-4e0f-b3a2-6853dca66d46", + "metadata": {}, + "source": [ + "### Relevant imports and filepath configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "c4773751-3544-4ebd-a795-cfe128b70743", + "metadata": {}, + "outputs": [], + "source": [ + "import coast\n", + "import numpy as np\n", + "from os import path\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors # colormap fiddling" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", + "metadata": {}, + "outputs": [], + "source": [ + "# set some paths\n", + "root = \"./\"\n", + "dn_files = root + \"./example_files/\"\n", + "fn_prof = path.join(dn_files, \"coast_example_en4_201008.nc\")\n", + "fn_cfg_prof = path.join(\"config\",\"example_en4_profiles.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "5d3f6987-f05d-4a54-a932-e4bbf84becb1", + "metadata": {}, + "source": [ + "### Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "7677050c-775d-4172-9561-61c3c89aa77b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "config/example_en4_profiles.json\n" + ] + } + ], + "source": [ + "# Create a Profile object and load in the data:\n", + "profile = coast.Profile(config=fn_cfg_prof)\n", + "profile.read_en4( fn_prof )" + ] + }, + { + "cell_type": "markdown", + "id": "d566249d", + "metadata": {}, + "source": [ + "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "29e0256b", + "metadata": {}, + "outputs": [], + "source": [ + "processed_profile = profile.process_en4()\n", + "profile = processed_profile" + ] + }, + { + "cell_type": "markdown", + "id": "d9093ecd", + "metadata": {}, + "source": [ + "### Inspect profile locations\n", + "Have a look inside the `profile.py` class to see what it can do. But first have a look at the spatial distribution of profiles." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "3561dd1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "profile.plot_map()" + ] + }, + { + "cell_type": "markdown", + "id": "d3e75a6d", + "metadata": {}, + "source": [ + "### Calculates Potential Energy Anomaly\n", + "\n", + "Similar to the Gridded object, potential energy anomaly can be calculated for Profile objects. This method exists within a `ProfileStratifiction` object, which must be initialised\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "2dd5cc2f", + "metadata": {}, + "outputs": [], + "source": [ + "pa = coast.ProfileStratification(profile)" + ] + }, + { + "cell_type": "markdown", + "id": "6faf9a84", + "metadata": {}, + "source": [ + "Potential energy anomaly is calculated to a prescribed depth, Zmax:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "23d49bb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape sal:(400, 1100)\n", + "shape rho:(400, 1100)\n", + "shape sal:(400, 1100)\n", + "shape rho:(400, 1100)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + } + ], + "source": [ + "Zmax = 200 # metres\n", + "pa.calc_pea(profile, Zmax)" + ] + }, + { + "cell_type": "markdown", + "id": "6ecf2b7b", + "metadata": {}, + "source": [ + "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", + "\n", + "``profile.calculate_vertical_spacing()``\n", + "\n", + "Then a depth mask is calculated to exclude depth below the Zmax threshold.\n", + "(The last depth level is a float between 0,1 denoting how much of the next spacing below is deeper than Zmax - To facilitate the integral to Zmax)\n", + "\n", + "``Zd_mask, kmax = profile.calculate_vertical_mask(Zmax)``\n", + "\n", + "Then densities (depth varying and depth averaged) are computed from the temperature and salinity fields\n", + "``profile.construct_density()``\n", + "\n", + "Finally the depth integrals are calculated.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f897042-3697-4ddd-a812-04572500f0ec", + "metadata": {}, + "source": [ + "## Make a plot\n", + "\n", + "\n", + "THERE IS OBVIOUSLY AN ISSUE HERE WITH NEGATIVE PEA VALUES AND SMALL POSITIVE VALUES..." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b8383443", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pa.quick_plot(\"pea\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "9c56bf79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter( pa.dataset.longitude,\n", + " pa.dataset.latitude,\n", + " s=4, c=pa.dataset.pea)\n", + "plt.clim([0,10])\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fbeb641", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70696b95", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 6c91b75e6dafabd19877b68ca48bb66e1259bcff Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 12:14:42 +0000 Subject: [PATCH 042/150] Add clean profile PEA notebook --- .../profile/potential_energy_tutorial.ipynb | 144 ++++-------------- 1 file changed, 26 insertions(+), 118 deletions(-) diff --git a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb index 0cdb9a3a..cd3bb93b 100644 --- a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -54,18 +54,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "config/example_en4_profiles.json\n" - ] - } - ], + "outputs": [], "source": [ "# Create a Profile object and load in the data:\n", "profile = coast.Profile(config=fn_cfg_prof)\n", @@ -74,7 +66,7 @@ }, { "cell_type": "markdown", - "id": "d566249d", + "id": "798994a1", "metadata": {}, "source": [ "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" @@ -82,8 +74,8 @@ }, { "cell_type": "code", - "execution_count": 74, - "id": "29e0256b", + "execution_count": null, + "id": "58406dca", "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "d9093ecd", + "id": "84a15c7b", "metadata": {}, "source": [ "### Inspect profile locations\n", @@ -102,31 +94,10 @@ }, { "cell_type": "code", - "execution_count": 75, - "id": "3561dd1e", + "execution_count": null, + "id": "f5b2d233", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "profile.plot_map()" ] @@ -144,8 +115,8 @@ }, { "cell_type": "code", - "execution_count": 76, - "id": "2dd5cc2f", + "execution_count": null, + "id": "e70f5db2", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "6faf9a84", + "id": "3e056769", "metadata": {}, "source": [ "Potential energy anomaly is calculated to a prescribed depth, Zmax:" @@ -162,31 +133,10 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "23d49bb0", + "execution_count": null, + "id": "c49b40d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape sal:(400, 1100)\n", - "shape rho:(400, 1100)\n", - "shape sal:(400, 1100)\n", - "shape rho:(400, 1100)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n", - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - } - ], + "outputs": [], "source": [ "Zmax = 200 # metres\n", "pa.calc_pea(profile, Zmax)" @@ -194,7 +144,7 @@ }, { "cell_type": "markdown", - "id": "6ecf2b7b", + "id": "74603291", "metadata": {}, "source": [ "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", @@ -225,31 +175,10 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "b8383443", + "execution_count": null, + "id": "a696835b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n", - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = pa.quick_plot(\"pea\")\n", "fig.tight_layout()" @@ -257,31 +186,10 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "9c56bf79", + "execution_count": null, + "id": "bb540223", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.scatter( pa.dataset.longitude,\n", " pa.dataset.latitude,\n", @@ -293,7 +201,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8fbeb641", + "id": "85229256", "metadata": {}, "outputs": [], "source": [] @@ -301,7 +209,7 @@ { "cell_type": "code", "execution_count": null, - "id": "70696b95", + "id": "a37a8291", "metadata": {}, "outputs": [], "source": [] From 467a172dc93a82d3caa9efe75139e2bd61dd6d83 Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Fri, 25 Nov 2022 16:57:27 +0000 Subject: [PATCH 043/150] profile.py Zd_max changed to use w-levels, order of variables in construct density chnaged to i_dim, z_dim profile_stratification.py added function to clean profile data, starting with filling holes. More to come. --- coast/data/profile.py | 53 +++++++++++------- coast/diagnostics/profile_stratification.py | 60 +++++++++++++++++++-- 2 files changed, 90 insertions(+), 23 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 73a66b04..9762e896 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -727,14 +727,18 @@ def construct_density( """ debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') + try: + if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") try: shape_ds = ( - self.dataset.z_dim.size, self.dataset.id_dim.size, + self.dataset.z_dim.size, +#jth self.dataset.z_dim.size, +# self.dataset.id_dim.size, ) sal = self.dataset.practical_salinity.to_masked_array() temp = self.dataset.potential_temperature.to_masked_array() @@ -810,23 +814,23 @@ def construct_density( temp_conservative = np.ma.masked_invalid(temp) if pot_dens and (Sbar and Tbar): # usual case pot_dens and depth averaged everything - sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP - temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP + sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP + temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) - density = np.repeat(density[np.newaxis, :], shape_ds[0], axis=0) + density = np.repeat(density[:,np.newaxis], shape_ds[1], axis=1) else: # Either insitu density or one of Tbar or Sbar False if Sbar: sal_absolute = np.repeat( - (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP)[np.newaxis, :], - shape_ds[0], - axis=0, + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:,np.newaxis], + shape_ds[1], + axis=1, ) if Tbar: temp_conservative = np.repeat( - (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP)[np.newaxis, :], - shape_ds[0], - axis=0, + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:,np.newaxis], + shape_ds[1], + axis=1, ) density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) @@ -845,7 +849,9 @@ def construct_density( "latitude": (("id_dim"), self.dataset.latitude.values), "longitude": (("id_dim"), self.dataset.longitude.values), } - dims = ["z_dim", "id_dim"] +# dims = ["z_dim", "id_dim"] + dims = ["id_dim", "z_dim"] + if pot_dens: attributes = {"units": "kg / m^3", "standard name": "Potential density "} @@ -859,6 +865,7 @@ def construct_density( error(err) def calculate_vertical_mask(self, Zmax=200): + """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level @@ -871,6 +878,12 @@ def calculate_vertical_mask(self, Zmax=200): """ depth_t = self.dataset.depth + ##construct a W array, zero at surface 1/2 way between T-points + + depth_w=xr.zeros_like(depth_t) + I = np.arange(depth_w.shape[1] - 1) + depth_w[:,0]=0.0 + depth_w[:, I + 1] = 0.5 * (depth_t[:, I] + depth_t[:, I + 1]) ## Contruct a mask array that is: # zeros below Zmax @@ -890,16 +903,16 @@ def calculate_vertical_mask(self, Zmax=200): # mask_arr[depth_t <= Zmax] = 1 # mask_arr[depth_t > Zmax] = 0 # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) - mask = depth_t * np.nan + mask = depth_w * np.nan - mask = xr.where(depth_t <= Zmax, 1, mask) - mask = xr.where(depth_t > Zmax, 0, mask) + mask = xr.where(depth_w <= Zmax, 1, mask) + mask = xr.where(depth_w > Zmax, 0, mask) # print(mask) # print('\n') - max_shallower_depth = (depth_t * mask).max(dim="z_dim") - min_deeper_depth = (depth_t.roll(z_dim=-1) * mask).max(dim="z_dim") + max_shallower_depth = (depth_w * mask).max(dim="z_dim") + min_deeper_depth = (depth_w.roll(z_dim=-1) * mask).max(dim="z_dim") # NB if max_shallower_depth was already deepest value in profile, then this produces the same value # I.e. # max_shallower_depth <= Zmax @@ -912,18 +925,18 @@ def calculate_vertical_mask(self, Zmax=200): # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) fraction = xr.where( min_deeper_depth != max_shallower_depth, - (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + (Zmax - max_shallower_depth) / (min_deeper_depth - max_shallower_depth), 1, ) - max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_w.sizes["z_dim"]}) fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax - kmax = xr.where(depth_t == max_shallower_depth, 1, 0).argmax(dim="z_dim") + kmax = xr.where(depth_w == max_shallower_depth, 1, 0).argmax(dim="z_dim") # print(kmax) # replace mask values with fraction_2d at depth above Zmax) - mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) + mask = xr.where(depth_w == max_shallower_depth_2d, fraction_2d, mask) return mask, kmax diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index f558e589..e7cd47d9 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -3,6 +3,7 @@ import numpy as np import xarray as xr import copy +import coast from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug @@ -30,6 +31,56 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") + def clean_data (profile: xr.Dataset): + """ + Cleaning data for stratification metric calculations + Stage 1:... + + stage 2... + + Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value + + """ + print('Cleaning the data') + #fill holes in data + #jth is slow, there may bea more 'vector' way of doing it + n_prf = profile.dataset.id_dim.shape[0] + + tmp_clean = profile.dataset.potential_temperature.values[:,:] + sal_clean = profile.dataset.practical_salinity.values[:,:] + + + any_tmp=np.sum(~ np.isnan(tmp_clean),axis=1) != 0 + + any_sal=np.sum(~ np.isnan(sal_clean),axis=1) != 0 + + for i_prf in range(n_prf): + tmp=profile.dataset.potential_temperature.values[i_prf,:] + sal=profile.dataset.practical_salinity.values[i_prf,:] + z=profile.dataset.depth.values[i_prf,:] + if any_tmp[i_prf]: + tmp=coast.general_utils.fill_holes_1d(tmp) + tmp[np.isnan(z)] = np.nan + tmp_clean[i_prf,:]=tmp + if any_sal[i_prf]: + sal = coast.general_utils.fill_holes_1d(sal) + sal[np.isnan(z)] = np.nan + sal_clean[i_prf,:]=sal + + + coords = { + "time": ("id_dim", profile.dataset.time.values), + "latitude": (("id_dim"), profile.dataset.latitude.values), + "longitude": (("id_dim"), profile.dataset.longitude.values), + } + dims = ["id_dim","z_dim"] + profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) + profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) + + print('All nice and clean') + + return profile + def calc_pea(self, profile: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly @@ -41,7 +92,10 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach +#%% gravity = 9.81 +#Clean data This is quit slow and over writes potneital temperature and practical salinity valirables + profile = ProfileStratification.clean_data (profile) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -56,7 +110,7 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax #jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) @@ -65,8 +119,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / Zmax - + pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / (height.sum(dim="z_dim", skipna=True)) +#%% coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), From 1e8123dc2f1f8d137f3f51007b3cb8927d9b5292 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 25 Nov 2022 16:59:44 +0000 Subject: [PATCH 044/150] Apply Black formatting to Python code. --- coast/data/profile.py | 21 ++++--- coast/diagnostics/profile_stratification.py | 64 +++++++++++---------- 2 files changed, 44 insertions(+), 41 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9762e896..88d8bdf3 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -729,7 +729,7 @@ def construct_density( debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') try: - + if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") @@ -737,8 +737,8 @@ def construct_density( shape_ds = ( self.dataset.id_dim.size, self.dataset.z_dim.size, -#jth self.dataset.z_dim.size, -# self.dataset.id_dim.size, + # jth self.dataset.z_dim.size, + # self.dataset.id_dim.size, ) sal = self.dataset.practical_salinity.to_masked_array() temp = self.dataset.potential_temperature.to_masked_array() @@ -817,18 +817,18 @@ def construct_density( sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) - density = np.repeat(density[:,np.newaxis], shape_ds[1], axis=1) + density = np.repeat(density[:, np.newaxis], shape_ds[1], axis=1) else: # Either insitu density or one of Tbar or Sbar False if Sbar: sal_absolute = np.repeat( - (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:,np.newaxis], + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:, np.newaxis], shape_ds[1], axis=1, ) if Tbar: temp_conservative = np.repeat( - (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:,np.newaxis], + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:, np.newaxis], shape_ds[1], axis=1, ) @@ -849,10 +849,9 @@ def construct_density( "latitude": (("id_dim"), self.dataset.latitude.values), "longitude": (("id_dim"), self.dataset.longitude.values), } -# dims = ["z_dim", "id_dim"] + # dims = ["z_dim", "id_dim"] dims = ["id_dim", "z_dim"] - if pot_dens: attributes = {"units": "kg / m^3", "standard name": "Potential density "} else: @@ -879,10 +878,10 @@ def calculate_vertical_mask(self, Zmax=200): depth_t = self.dataset.depth ##construct a W array, zero at surface 1/2 way between T-points - - depth_w=xr.zeros_like(depth_t) + + depth_w = xr.zeros_like(depth_t) I = np.arange(depth_w.shape[1] - 1) - depth_w[:,0]=0.0 + depth_w[:, 0] = 0.0 depth_w[:, I + 1] = 0.5 * (depth_t[:, I] + depth_t[:, I + 1]) ## Contruct a mask array that is: diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e7cd47d9..a7fa3955 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -31,53 +31,51 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data (profile: xr.Dataset): + def clean_data(profile: xr.Dataset): """ Cleaning data for stratification metric calculations Stage 1:... - + stage 2... - + Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value - + """ - print('Cleaning the data') - #fill holes in data - #jth is slow, there may bea more 'vector' way of doing it + print("Cleaning the data") + # fill holes in data + # jth is slow, there may bea more 'vector' way of doing it n_prf = profile.dataset.id_dim.shape[0] - tmp_clean = profile.dataset.potential_temperature.values[:,:] - sal_clean = profile.dataset.practical_salinity.values[:,:] - + tmp_clean = profile.dataset.potential_temperature.values[:, :] + sal_clean = profile.dataset.practical_salinity.values[:, :] + + any_tmp = np.sum(~np.isnan(tmp_clean), axis=1) != 0 + + any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 - any_tmp=np.sum(~ np.isnan(tmp_clean),axis=1) != 0 - - any_sal=np.sum(~ np.isnan(sal_clean),axis=1) != 0 - for i_prf in range(n_prf): - tmp=profile.dataset.potential_temperature.values[i_prf,:] - sal=profile.dataset.practical_salinity.values[i_prf,:] - z=profile.dataset.depth.values[i_prf,:] + tmp = profile.dataset.potential_temperature.values[i_prf, :] + sal = profile.dataset.practical_salinity.values[i_prf, :] + z = profile.dataset.depth.values[i_prf, :] if any_tmp[i_prf]: - tmp=coast.general_utils.fill_holes_1d(tmp) + tmp = coast.general_utils.fill_holes_1d(tmp) tmp[np.isnan(z)] = np.nan - tmp_clean[i_prf,:]=tmp + tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) sal[np.isnan(z)] = np.nan - sal_clean[i_prf,:]=sal - + sal_clean[i_prf, :] = sal coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), "longitude": (("id_dim"), profile.dataset.longitude.values), } - dims = ["id_dim","z_dim"] + dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - - print('All nice and clean') + + print("All nice and clean") return profile @@ -92,10 +90,10 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach -#%% + #%% gravity = 9.81 -#Clean data This is quit slow and over writes potneital temperature and practical salinity valirables - profile = ProfileStratification.clean_data (profile) + # Clean data This is quit slow and over writes potneital temperature and practical salinity valirables + profile = ProfileStratification.clean_data(profile) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -110,7 +108,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax #jth why not just use depth here? + height = ( + np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + ) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) @@ -119,8 +119,12 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / (height.sum(dim="z_dim", skipna=True)) -#%% + pot_energy_anom = ( + (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) + * gravity + / (height.sum(dim="z_dim", skipna=True)) + ) + #%% coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), From b45fa6e9d6a9ddb0f4a63f2b4c9bb4463685ef3e Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Wed, 30 Nov 2022 18:22:33 +0000 Subject: [PATCH 045/150] Adding match to grid method --- coast/_utils/general_utils.py | 4 +- coast/data/profile.py | 79 ++++++++ .../profile/potential_energy_tutorial.ipynb | 168 ++++++++++++++++-- example_scripts/profile_test.py | 27 +++ requirements.txt | 4 + 5 files changed, 262 insertions(+), 20 deletions(-) create mode 100644 example_scripts/profile_test.py diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 98efbe0f..5a5c5f91 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -235,7 +235,7 @@ def reinstate_indices_by_mask(array_removed, mask, fill_value=np.nan): return array -def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None): +def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors = 1): """ Obtains the 2 dimensional indices of the nearest model points to specified lists of longitudes and latitudes. Makes use of sklearn.neighbours @@ -294,7 +294,7 @@ def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None): # Do nearest neighbour interpolation using BallTree (gets indices) tree = nb.BallTree(mod_loc, leaf_size=5, metric="haversine") - _, ind_1d = tree.query(new_loc, k=1) + _, ind_1d = tree.query(new_loc, k=number_of_neighbors) if mask is None: # Get 2D indices from 1D index output from BallTree diff --git a/coast/data/profile.py b/coast/data/profile.py index 88d8bdf3..f7a66fc8 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -510,6 +510,85 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_y"] = (["id_dim"], ind_y.values) mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) + def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: + """Match profiles locations to grid, finding 4 nearest neighbours for each profile. + + Args: + gridded (Gridded): Gridded object. + limits (List): [jmin,jmax,imin,imax] - Subset to this region. + rmax (int): 7000 m - maxmimum search distance (metres). + + ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + + ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO + """ + + if sum(limits) != 0: + gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) + # keep the grid or subset on the hydrographic profiles object + gridded.dataset["limits"] = limits + prf = self.dataset + grd = gridded.dataset + grd['landmask']=grd.bottom_level == 0 + lon_prf = prf["longitude"] + lat_prf = prf["latitude"] + lon_grd = grd["latitude"] + lat_grd = grd["latitude"] + # SPATIAL indices - 4 nearest neighbour + ind_x, ind_y = general_utils.nearest_indices_2d( + lon_grd,lat_grd, + lon_prf,lat_prf, + mask = grd.landmask, + number_of_neighbors = 4 + ) + + #Exclude out of bound points + I_exc =np.concatenate(( + np.where(lon_prf < lon_grd.values.ravel().min())[0], + np.where(lon_prf > lon_grd.values.ravel().max())[0], + np.where(lat_prf < lat_grd.values.ravel().min())[0], + np.where(lat_prf > lat_grd.values.ravel().max())[0], + )) + ind_x[I_exc] = np.nan + ind_y[I_exc] = np.nan + prf["ind_x_min"] = limits[0] # reference back to original grid + prf["ind_y_min"] = limits[2] + + ind_x_min = limits[0] + ind_y_min = limits[2] + + + # Sort 4 NN by distance on grid + + ip = np.where(np.logical_or(ind_x[:, 0] != 0, ind_y[:, 0] != 0))[0] + lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() + r = np.ones(ind_x.shape) * np.nan + + rr = general_utils.calculate_haversine_distance( + lon_prf4, lat_prf4, + lon_grd[ind_y.values.ravel(),ind_x.values.ravel()], + lat_grd[ind_y.values.ravel(),ind_x.values.ravel()] + ) + + r[ip, :] = np.reshape(rr, (ip.size, 4)) + # sort by distance + ii = np.argsort(r, axis=1) + rmin_prf = np.take_along_axis(r, ii, axis=1) + ind_x.values = np.take_along_axis(ind_x.values, ii, axis=1) + ind_y.values = np.take_along_axis(ind_y.values, ii, axis=1) + + ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) + ind_x.values = ind_x.values + i_min + ind_y.values = ind_y.values+ j_min + ind_x.values[ii, :] = 0 # should the be nan? + ind_y.values[ii, :] = 0 + + self.profile.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "4"]) + self.profile.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "4"]) + self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + + """================Reshape to 2D================""" diff --git a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb index cd3bb93b..09020937 100644 --- a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -54,10 +54,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "config\\example_en4_profiles.json\n" + ] + } + ], "source": [ "# Create a Profile object and load in the data:\n", "profile = coast.Profile(config=fn_cfg_prof)\n", @@ -74,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "58406dca", "metadata": {}, "outputs": [], @@ -94,10 +102,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "f5b2d233", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "profile.plot_map()" ] @@ -115,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e70f5db2", "metadata": {}, "outputs": [], @@ -133,10 +162,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c49b40d3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cleaning the data\n", + "All nice and clean\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + } + ], "source": [ "Zmax = 200 # metres\n", "pa.calc_pea(profile, Zmax)" @@ -175,10 +221,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a696835b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = pa.quick_plot(\"pea\")\n", "fig.tight_layout()" @@ -186,10 +251,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "bb540223", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.scatter( pa.dataset.longitude,\n", " pa.dataset.latitude,\n", @@ -208,18 +294,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a37a8291", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\\example_scripts\\notebooks\n" + ] + } + ], + "source": [ + "cd ../../" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca0c825f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\n" + ] + } + ], + "source": [ + "cd ../../" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25670a0f", + "metadata": {}, + "outputs": [], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd695e91", + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "coast_dev", "language": "python", - "name": "python3" + "name": "coast_dev" }, "language_info": { "codemirror_mode": { @@ -231,7 +363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py new file mode 100644 index 00000000..1c707e2d --- /dev/null +++ b/example_scripts/profile_test.py @@ -0,0 +1,27 @@ +import coast +import numpy as np +from os import path +import matplotlib.pyplot as plt +import matplotlib.colors as colors # colormap fiddling +# set some paths +root = "./" +dn_files = root + "./example_files/" +fn_prof = path.join(dn_files, "coast_example_en4_201008.nc") +fn_cfg_prof = path.join("config","example_en4_profiles.json") + +# Create a Profile object and load in the data: +profile = coast.Profile(config=fn_cfg_prof) +profile.read_en4( fn_prof ) + +processed_profile = profile.process_en4() +profile = processed_profile + +pa = coast.ProfileStratification(profile) + +Zmax = 200 # metres +pa.calc_pea(profile, Zmax) + +fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' +fn_grd_cfg = 'config/example_nemo_grid_t.json' +nemo = coast.Gridded(fn_domain=fn_grd_dom,config = fn_grd_cfg) +profile.match_to_grid(nemo) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 2028effc..155024db 100644 --- a/requirements.txt +++ b/requirements.txt @@ -17,5 +17,9 @@ statsmodels>=0.13.2 pydap>=3.2.2 lxml>=4.9.0 # Required for pydap CAS parsing requests>=2.27.1 +spyder>=5.1.5 +cartopy>=0.21.0 +ipykernel +jupyterlab #xesmf>=0.3.0 # Optional. Not part of main package #esmpy>=8.0.0 # Optional. Not part of main package \ No newline at end of file From 09673dfa7d07bfe28a154a86eeb9ccd07eb7b0ad Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Wed, 30 Nov 2022 20:06:59 +0000 Subject: [PATCH 046/150] Adding match to grid method --- coast/data/profile.py | 59 +++++++++++++++++++-------------- example_scripts/profile_test.py | 2 +- 2 files changed, 35 insertions(+), 26 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index f7a66fc8..fa9fa10d 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -527,12 +527,13 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) # keep the grid or subset on the hydrographic profiles object gridded.dataset["limits"] = limits + prf = self.dataset grd = gridded.dataset grd['landmask']=grd.bottom_level == 0 lon_prf = prf["longitude"] lat_prf = prf["latitude"] - lon_grd = grd["latitude"] + lon_grd = grd["longitude"] lat_grd = grd["latitude"] # SPATIAL indices - 4 nearest neighbour ind_x, ind_y = general_utils.nearest_indices_2d( @@ -541,52 +542,60 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: mask = grd.landmask, number_of_neighbors = 4 ) + ind_x=ind_x.values + ind_y=ind_y.values #Exclude out of bound points - I_exc =np.concatenate(( + i_exc =np.concatenate(( np.where(lon_prf < lon_grd.values.ravel().min())[0], np.where(lon_prf > lon_grd.values.ravel().max())[0], np.where(lat_prf < lat_grd.values.ravel().min())[0], np.where(lat_prf > lat_grd.values.ravel().max())[0], )) - ind_x[I_exc] = np.nan - ind_y[I_exc] = np.nan - prf["ind_x_min"] = limits[0] # reference back to original grid - prf["ind_y_min"] = limits[2] + ind_x[i_exc,:] = -1 + ind_y[i_exc,:] = -1 + prf["ind_x_min"] = limits[2] # reference back to original grid + prf["ind_y_min"] = limits[0] - ind_x_min = limits[0] - ind_y_min = limits[2] + ind_x_min = limits[2] + ind_y_min = limits[0] # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] != 0, ind_y[:, 0] != 0))[0] + ip = np.where(np.logical_or(ind_x[:, 0] >=0 , + ind_y[:, 0] >=0 ))[0] + lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan - +#distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( lon_prf4, lat_prf4, - lon_grd[ind_y.values.ravel(),ind_x.values.ravel()], - lat_grd[ind_y.values.ravel(),ind_x.values.ravel()] + lon_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()], + lat_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()] ) r[ip, :] = np.reshape(rr, (ip.size, 4)) - # sort by distance + # sort by distance and re-order the indices with closest first ii = np.argsort(r, axis=1) rmin_prf = np.take_along_axis(r, ii, axis=1) - ind_x.values = np.take_along_axis(ind_x.values, ii, axis=1) - ind_y.values = np.take_along_axis(ind_y.values, ii, axis=1) - - ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) - ind_x.values = ind_x.values + i_min - ind_y.values = ind_y.values+ j_min - ind_x.values[ii, :] = 0 # should the be nan? - ind_y.values[ii, :] = 0 - - self.profile.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "4"]) - self.profile.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "4"]) - self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + ind_x = np.take_along_axis(ind_x, ii, axis=1) + ind_y = np.take_along_axis(ind_y, ii, axis=1) + + ii = np.nonzero(np.min(r, axis=1) > rmax) + #Reference to original grid + ind_x = ind_x + ind_x_min + ind_y = ind_y + ind_y_min + #mask bad values with -1 + ind_x[ii, :] = -1 + ind_y[ii, :] = -1 + ind_x[i_exc, :] = -1 + ind_y[i_exc, :] = -1 + #Add to profile object + self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) + self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) + self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 1c707e2d..fbc2e6c7 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -19,7 +19,7 @@ pa = coast.ProfileStratification(profile) Zmax = 200 # metres -pa.calc_pea(profile, Zmax) +#pa.calc_pea(profile, Zmax) fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' fn_grd_cfg = 'config/example_nemo_grid_t.json' From 75bf6800df83ed1e7dd224c4376e808961511eda Mon Sep 17 00:00:00 2001 From: BlackBot Date: Wed, 30 Nov 2022 20:10:07 +0000 Subject: [PATCH 047/150] Apply Black formatting to Python code. --- coast/_utils/general_utils.py | 2 +- coast/data/profile.py | 57 ++++++++++++++++----------------- example_scripts/profile_test.py | 15 +++++---- 3 files changed, 36 insertions(+), 38 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 5a5c5f91..aeee1294 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -235,7 +235,7 @@ def reinstate_indices_by_mask(array_removed, mask, fill_value=np.nan): return array -def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors = 1): +def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors=1): """ Obtains the 2 dimensional indices of the nearest model points to specified lists of longitudes and latitudes. Makes use of sklearn.neighbours diff --git a/coast/data/profile.py b/coast/data/profile.py index fa9fa10d..7dc10a27 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -510,7 +510,8 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_y"] = (["id_dim"], ind_y.values) mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: + + def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: @@ -530,50 +531,48 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: prf = self.dataset grd = gridded.dataset - grd['landmask']=grd.bottom_level == 0 + grd["landmask"] = grd.bottom_level == 0 lon_prf = prf["longitude"] lat_prf = prf["latitude"] lon_grd = grd["longitude"] lat_grd = grd["latitude"] # SPATIAL indices - 4 nearest neighbour ind_x, ind_y = general_utils.nearest_indices_2d( - lon_grd,lat_grd, - lon_prf,lat_prf, - mask = grd.landmask, - number_of_neighbors = 4 + lon_grd, lat_grd, lon_prf, lat_prf, mask=grd.landmask, number_of_neighbors=4 + ) + ind_x = ind_x.values + ind_y = ind_y.values + + # Exclude out of bound points + i_exc = np.concatenate( + ( + np.where(lon_prf < lon_grd.values.ravel().min())[0], + np.where(lon_prf > lon_grd.values.ravel().max())[0], + np.where(lat_prf < lat_grd.values.ravel().min())[0], + np.where(lat_prf > lat_grd.values.ravel().max())[0], + ) ) - ind_x=ind_x.values - ind_y=ind_y.values - - #Exclude out of bound points - i_exc =np.concatenate(( - np.where(lon_prf < lon_grd.values.ravel().min())[0], - np.where(lon_prf > lon_grd.values.ravel().max())[0], - np.where(lat_prf < lat_grd.values.ravel().min())[0], - np.where(lat_prf > lat_grd.values.ravel().max())[0], - )) - ind_x[i_exc,:] = -1 - ind_y[i_exc,:] = -1 + ind_x[i_exc, :] = -1 + ind_y[i_exc, :] = -1 prf["ind_x_min"] = limits[2] # reference back to original grid prf["ind_y_min"] = limits[0] ind_x_min = limits[2] ind_y_min = limits[0] - # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] >=0 , - ind_y[:, 0] >=0 ))[0] + ip = np.where(np.logical_or(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan -#distance between nearest neighbors and grid + # distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( - lon_prf4, lat_prf4, - lon_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()], - lat_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()] + lon_prf4, + lat_prf4, + lon_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], + lat_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], ) r[ip, :] = np.reshape(rr, (ip.size, 4)) @@ -584,21 +583,19 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: ind_y = np.take_along_axis(ind_y, ii, axis=1) ii = np.nonzero(np.min(r, axis=1) > rmax) - #Reference to original grid + # Reference to original grid ind_x = ind_x + ind_x_min ind_y = ind_y + ind_y_min - #mask bad values with -1 + # mask bad values with -1 ind_x[ii, :] = -1 ind_y[ii, :] = -1 ind_x[i_exc, :] = -1 ind_y[i_exc, :] = -1 - #Add to profile object + # Add to profile object self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) - - """================Reshape to 2D================""" def reshape_2d(self, var_user_want): diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index fbc2e6c7..7b236126 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -3,15 +3,16 @@ from os import path import matplotlib.pyplot as plt import matplotlib.colors as colors # colormap fiddling + # set some paths root = "./" dn_files = root + "./example_files/" fn_prof = path.join(dn_files, "coast_example_en4_201008.nc") -fn_cfg_prof = path.join("config","example_en4_profiles.json") +fn_cfg_prof = path.join("config", "example_en4_profiles.json") # Create a Profile object and load in the data: profile = coast.Profile(config=fn_cfg_prof) -profile.read_en4( fn_prof ) +profile.read_en4(fn_prof) processed_profile = profile.process_en4() profile = processed_profile @@ -19,9 +20,9 @@ pa = coast.ProfileStratification(profile) Zmax = 200 # metres -#pa.calc_pea(profile, Zmax) +# pa.calc_pea(profile, Zmax) -fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' -fn_grd_cfg = 'config/example_nemo_grid_t.json' -nemo = coast.Gridded(fn_domain=fn_grd_dom,config = fn_grd_cfg) -profile.match_to_grid(nemo) \ No newline at end of file +fn_grd_dom = "example_files/coast_example_nemo_domain.nc" +fn_grd_cfg = "config/example_nemo_grid_t.json" +nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) +profile.match_to_grid(nemo) From 6893ca1a025d32782e6900c319c5f57f713fc318 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 2 Dec 2022 14:03:18 +0000 Subject: [PATCH 048/150] Added gridded_to_profile_2d method to profile.py --- coast/data/profile.py | 75 +++++++++++++++++++++++++-------- example_scripts/profile_test.py | 1 + 2 files changed, 58 insertions(+), 18 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 7dc10a27..9f64c68f 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -511,17 +511,21 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: + def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: gridded (Gridded): Gridded object. limits (List): [jmin,jmax,imin,imax] - Subset to this region. - rmax (int): 7000 m - maxmimum search distance (metres). + rmax (int): 7 km - maxmimum search distance (metres). - ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + Adds to the profile object: + ind_x, ind_y (int array ) (id_dim,4) + Index of the 4 closest grid cells to each profile, in distance order. + Profiles outside the gridded region are set to -9999 + rmin_prf float array (id_dim,4) + Distance (km) of the losest grid cells to each profile, in distance order - ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO """ if sum(limits) != 0: @@ -552,8 +556,8 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: np.where(lat_prf > lat_grd.values.ravel().max())[0], ) ) - ind_x[i_exc, :] = -1 - ind_y[i_exc, :] = -1 + ind_x[i_exc, :] = -9999 + ind_y[i_exc, :] = -9999 prf["ind_x_min"] = limits[2] # reference back to original grid prf["ind_y_min"] = limits[0] @@ -562,20 +566,20 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] #good points - lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() - lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() + lon_prf4 = np.repeat(lon_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan # distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( lon_prf4, lat_prf4, - lon_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], - lat_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], + lon_grd.values[ind_y[ind_good, :].ravel(), ind_x[ind_good, :].ravel()], + lat_grd.values[ind_y[ind_good, :].ravel(), ind_x[ind_good, :].ravel()], ) - r[ip, :] = np.reshape(rr, (ip.size, 4)) + r[ind_good, :] = np.reshape(rr, (ind_good.size, 4)) # sort by distance and re-order the indices with closest first ii = np.argsort(r, axis=1) rmin_prf = np.take_along_axis(r, ii, axis=1) @@ -586,15 +590,50 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: # Reference to original grid ind_x = ind_x + ind_x_min ind_y = ind_y + ind_y_min - # mask bad values with -1 - ind_x[ii, :] = -1 - ind_y[ii, :] = -1 - ind_x[i_exc, :] = -1 - ind_y[i_exc, :] = -1 + # mask bad values with -9999 + ind_x[ii, :] = -9999 + ind_y[ii, :] = -9999 + ind_x[i_exc, :] = -9999 + ind_y[i_exc, :] = -9999 + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] + # Add to profile object self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) - self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "NNs"]) + self.dataset["ind_good"] = xr.DataArray(ind_good, dims=["Ngood"]) + + def gridded_to_profile_2d(self, gridded, variable) -> None: + """ + Evaluated a gridded data variable on each profile. Here just 2D, but could be extended to 3 or 4D + + Args: + gridded (Gridded): Gridded object + variable string : Name of variable in gridded object to interpolate + + Output variable is distance weighted mean and is added to profile object with + same name as in the gridded object + + + """ + #ensure there are indices in profile + if not 'ind_x' in profile.dataset: + self.match_to_grid(gridded) + # + prf = self.dataset + grd = gridded.dataset + grd["landmask"] = grd.bottom_level == 0 + nprof = self.dataset.id_dim.shape[0] + var=np.ma.masked_where(grd["landmask"],grd[variable]) + ig=prf.ind_good + #Distance weighted mean + v = var[prf.ind_y[ig, :], prf.ind_x[ig, :]] / prf.rmin_prf[ig, :] + norm = 1.0 / prf.rmin_prf[ig, :] + norm = np.ma.masked_where(v.mask,norm) + var_int=np.nansum(v,axis=1)/np.nansum(norm,axis=1) + var_prf=np.ones(nprof)*np.nan + var_prf[ig]=var_int + self.dataset[variable]=xr.DataArray(var_prf, dims=["id_dim"]) """================Reshape to 2D================""" diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 7b236126..03b8dc9c 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -26,3 +26,4 @@ fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) +profile.gridded_to_profile_2d(nemo,'bathymetry') From 7ef003073373eaf9aa5898e22a5beddba7abd9cb Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 2 Dec 2022 14:05:11 +0000 Subject: [PATCH 049/150] Apply Black formatting to Python code. --- coast/data/profile.py | 22 +++++++++++----------- example_scripts/profile_test.py | 2 +- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9f64c68f..d39c7d94 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -566,7 +566,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: # Sort 4 NN by distance on grid - ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] #good points + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] # good points lon_prf4 = np.repeat(lon_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() @@ -616,24 +616,24 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ - #ensure there are indices in profile - if not 'ind_x' in profile.dataset: + # ensure there are indices in profile + if not "ind_x" in profile.dataset: self.match_to_grid(gridded) # prf = self.dataset grd = gridded.dataset grd["landmask"] = grd.bottom_level == 0 nprof = self.dataset.id_dim.shape[0] - var=np.ma.masked_where(grd["landmask"],grd[variable]) - ig=prf.ind_good - #Distance weighted mean + var = np.ma.masked_where(grd["landmask"], grd[variable]) + ig = prf.ind_good + # Distance weighted mean v = var[prf.ind_y[ig, :], prf.ind_x[ig, :]] / prf.rmin_prf[ig, :] norm = 1.0 / prf.rmin_prf[ig, :] - norm = np.ma.masked_where(v.mask,norm) - var_int=np.nansum(v,axis=1)/np.nansum(norm,axis=1) - var_prf=np.ones(nprof)*np.nan - var_prf[ig]=var_int - self.dataset[variable]=xr.DataArray(var_prf, dims=["id_dim"]) + norm = np.ma.masked_where(v.mask, norm) + var_int = np.nansum(v, axis=1) / np.nansum(norm, axis=1) + var_prf = np.ones(nprof) * np.nan + var_prf[ig] = var_int + self.dataset[variable] = xr.DataArray(var_prf, dims=["id_dim"]) """================Reshape to 2D================""" diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 03b8dc9c..6c4fbcbe 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -26,4 +26,4 @@ fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) -profile.gridded_to_profile_2d(nemo,'bathymetry') +profile.gridded_to_profile_2d(nemo, "bathymetry") From 5214fa994c917a303cf5edaed278feb213f2e2e2 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 2 Dec 2022 17:02:32 +0000 Subject: [PATCH 050/150] Added added unit test for gridded_to_profile_2d method to profile.py Fixed unit tests for profiles and stratification Fixed construct density for cases that need 2D lon,lat field --- coast/data/profile.py | 9 ++++++--- unit_testing/test_profile_methods.py | 15 ++++++++++++++- .../test_profile_stratification_methods.py | 2 +- unit_testing/unit_test.py | 1 - 4 files changed, 21 insertions(+), 6 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9f64c68f..80dc3e27 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -617,7 +617,7 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ #ensure there are indices in profile - if not 'ind_x' in profile.dataset: + if not 'ind_x' in self.dataset: self.match_to_grid(gridded) # prf = self.dataset @@ -884,15 +884,18 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values + if not pot_dens or not CT_AS: + lat2d = np.repeat(lat[:,np.newaxis],shape_ds[1],axis=1) + lon2d = np.repeat(lon[:,np.newaxis],shape_ds[1],axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels,lat2d)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided - sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon2d, lat2d)) else: # abs salinity provided sal_absolute = np.ma.masked_invalid(sal) sal_absolute = np.ma.masked_less(sal_absolute, 0) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index cddaa1a7..a0a76b97 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -168,6 +168,18 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + with self.subTest("Gridded match_to_grid & profile_to_gridded"): + nemo_t = coast.Gridded( + fn_domain=files.fn_nemo_dom, + config=files.fn_config_t_grid + ) + processed.match_to_grid(nemo_t) + processed.gridded_to_profile_2d(nemo_t, 'bathymetry') + + check1 = np.isclose(processed.dataset.bathymetry[4],29.06689187) + + self.assertTrue(check1, "check1") + def test_calculate_vertical_mask(self): # load example profile data @@ -184,7 +196,8 @@ def test_calculate_vertical_mask(self): mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() - check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.8, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() + check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.7, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") + diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 2fd14fcb..de96333c 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -22,7 +22,7 @@ def test_calculate_pea(self): Zmax = 200 # metres pa.calc_pea(profile, Zmax) - check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 5.8750878507) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): diff --git a/unit_testing/unit_test.py b/unit_testing/unit_test.py index 08383bdc..cbb72df1 100644 --- a/unit_testing/unit_test.py +++ b/unit_testing/unit_test.py @@ -62,7 +62,6 @@ test_process_data_methods, ] - # UNIT TESTING CONTROL SCRIPT # Import modules, including unittest From e27c3c6e4385fc91e21e0e418bff1dec8b7be2f8 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 2 Dec 2022 17:09:57 +0000 Subject: [PATCH 051/150] Apply Black formatting to Python code. --- coast/data/profile.py | 28 ++++++++++++++-------------- unit_testing/test_profile_methods.py | 11 +++-------- 2 files changed, 17 insertions(+), 22 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 80dc3e27..217c3c1c 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -566,7 +566,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: # Sort 4 NN by distance on grid - ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] #good points + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] # good points lon_prf4 = np.repeat(lon_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() @@ -616,24 +616,24 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ - #ensure there are indices in profile - if not 'ind_x' in self.dataset: + # ensure there are indices in profile + if not "ind_x" in self.dataset: self.match_to_grid(gridded) # prf = self.dataset grd = gridded.dataset grd["landmask"] = grd.bottom_level == 0 nprof = self.dataset.id_dim.shape[0] - var=np.ma.masked_where(grd["landmask"],grd[variable]) - ig=prf.ind_good - #Distance weighted mean + var = np.ma.masked_where(grd["landmask"], grd[variable]) + ig = prf.ind_good + # Distance weighted mean v = var[prf.ind_y[ig, :], prf.ind_x[ig, :]] / prf.rmin_prf[ig, :] norm = 1.0 / prf.rmin_prf[ig, :] - norm = np.ma.masked_where(v.mask,norm) - var_int=np.nansum(v,axis=1)/np.nansum(norm,axis=1) - var_prf=np.ones(nprof)*np.nan - var_prf[ig]=var_int - self.dataset[variable]=xr.DataArray(var_prf, dims=["id_dim"]) + norm = np.ma.masked_where(v.mask, norm) + var_int = np.nansum(v, axis=1) / np.nansum(norm, axis=1) + var_prf = np.ones(nprof) * np.nan + var_prf[ig] = var_int + self.dataset[variable] = xr.DataArray(var_prf, dims=["id_dim"]) """================Reshape to 2D================""" @@ -885,13 +885,13 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values if not pot_dens or not CT_AS: - lat2d = np.repeat(lat[:,np.newaxis],shape_ds[1],axis=1) - lon2d = np.repeat(lon[:,np.newaxis],shape_ds[1],axis=1) + lat2d = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) + lon2d = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels,lat2d)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat2d)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index a0a76b97..d3adca43 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -169,18 +169,14 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") with self.subTest("Gridded match_to_grid & profile_to_gridded"): - nemo_t = coast.Gridded( - fn_domain=files.fn_nemo_dom, - config=files.fn_config_t_grid - ) + nemo_t = coast.Gridded(fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid) processed.match_to_grid(nemo_t) - processed.gridded_to_profile_2d(nemo_t, 'bathymetry') + processed.gridded_to_profile_2d(nemo_t, "bathymetry") - check1 = np.isclose(processed.dataset.bathymetry[4],29.06689187) + check1 = np.isclose(processed.dataset.bathymetry[4], 29.06689187) self.assertTrue(check1, "check1") - def test_calculate_vertical_mask(self): # load example profile data profile = coast.Profile(config=files.fn_profile_config) @@ -200,4 +196,3 @@ def test_calculate_vertical_mask(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") - From 6a83f7b27109573b11faf03d0c57d261ed6036c2 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Tue, 6 Dec 2022 21:12:51 +0000 Subject: [PATCH 052/150] Find good SST and SSS depths in profiles --- coast/diagnostics/profile_stratification.py | 31 ++++++++++++++++++--- example_scripts/profile_test.py | 6 ++-- 2 files changed, 31 insertions(+), 6 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index a7fa3955..2f825e08 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -42,17 +42,40 @@ def clean_data(profile: xr.Dataset): """ print("Cleaning the data") - # fill holes in data - # jth is slow, there may bea more 'vector' way of doing it + # find profiles good for SST and NBT + dz_max=25.0 n_prf = profile.dataset.id_dim.shape[0] - + n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] sal_clean = profile.dataset.practical_salinity.values[:, :] any_tmp = np.sum(~np.isnan(tmp_clean), axis=1) != 0 - any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 + # Find good SST and SSS depths + if "bathymetry" in profile.dataset: + D_prf=profile.dataset.bathymetry.values + profile.gridded_to_profile_2d(nemo, "bathymetry") + z = profile.dataset.depth + test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) + test_tmp=np.logical_and(test_surface, + ~np.isnan(tmp_clean)) + test_sal=np.logical_and(test_surface, + ~np.isnan(sal_clean)) + good_sst=np.zeros(n_prf)*np.nan + good_sss=np.zeros(n_prf)*np.nan + I_tmp=np.nonzero(np.any(test_tmp.values,axis=1))[0] + I_sal=np.nonzero(np.any(test_sal.values,axis=1))[0] + # + for ip in I_tmp: + good_sst[ip]=np.min(np.nonzero(test_tmp.values[ip,:])) + for ip in I_sal: + good_sss[ip]=np.min(np.nonzero(test_sal.values[ip,:])) + I = np.where(np.isfinite(good_sss))[0] + SSS=sal_clean[I, good_sss[I].astype(int)] + # fill holes in data + # jth is slow, there may bea more 'vector' way of doing it + for i_prf in range(n_prf): tmp = profile.dataset.potential_temperature.values[i_prf, :] sal = profile.dataset.practical_salinity.values[i_prf, :] diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 6c4fbcbe..29785c95 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -19,11 +19,13 @@ pa = coast.ProfileStratification(profile) -Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) + fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) profile.gridded_to_profile_2d(nemo, "bathymetry") + +Zmax = 200 # metres +# pa.calc_pea(profile, Zmax) \ No newline at end of file From fd33e8312810f2b7b7ed52e66bf400c6dfd0a0e7 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Wed, 7 Dec 2022 15:22:05 +0000 Subject: [PATCH 053/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 26 ++++++++++----------- example_scripts/profile_test.py | 3 +-- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 2f825e08..e629052b 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -43,7 +43,7 @@ def clean_data(profile: xr.Dataset): """ print("Cleaning the data") # find profiles good for SST and NBT - dz_max=25.0 + dz_max = 25.0 n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] @@ -54,25 +54,23 @@ def clean_data(profile: xr.Dataset): # Find good SST and SSS depths if "bathymetry" in profile.dataset: - D_prf=profile.dataset.bathymetry.values + D_prf = profile.dataset.bathymetry.values profile.gridded_to_profile_2d(nemo, "bathymetry") z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) - test_tmp=np.logical_and(test_surface, - ~np.isnan(tmp_clean)) - test_sal=np.logical_and(test_surface, - ~np.isnan(sal_clean)) - good_sst=np.zeros(n_prf)*np.nan - good_sss=np.zeros(n_prf)*np.nan - I_tmp=np.nonzero(np.any(test_tmp.values,axis=1))[0] - I_sal=np.nonzero(np.any(test_sal.values,axis=1))[0] - # + test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) + test_sal = np.logical_and(test_surface, ~np.isnan(sal_clean)) + good_sst = np.zeros(n_prf) * np.nan + good_sss = np.zeros(n_prf) * np.nan + I_tmp = np.nonzero(np.any(test_tmp.values, axis=1))[0] + I_sal = np.nonzero(np.any(test_sal.values, axis=1))[0] + # for ip in I_tmp: - good_sst[ip]=np.min(np.nonzero(test_tmp.values[ip,:])) + good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) for ip in I_sal: - good_sss[ip]=np.min(np.nonzero(test_sal.values[ip,:])) + good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) I = np.where(np.isfinite(good_sss))[0] - SSS=sal_clean[I, good_sss[I].astype(int)] + SSS = sal_clean[I, good_sss[I].astype(int)] # fill holes in data # jth is slow, there may bea more 'vector' way of doing it diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 29785c95..08ed5345 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -20,7 +20,6 @@ pa = coast.ProfileStratification(profile) - fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) @@ -28,4 +27,4 @@ profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) \ No newline at end of file +# pa.calc_pea(profile, Zmax) From 7db197eef584f8a947df46a2dc9abcadaba5e9cd Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Thu, 8 Dec 2022 17:25:28 +0000 Subject: [PATCH 054/150] Profile analysis construct density to correctly see 2d lon,lat PEA, SSS, SST data cleaning finished --- coast/data/profile.py | 8 ++-- coast/diagnostics/profile_stratification.py | 46 +++++++++++++++------ example_scripts/profile_test.py | 2 +- 3 files changed, 39 insertions(+), 17 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 217c3c1c..43ef40ea 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -885,17 +885,17 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values if not pot_dens or not CT_AS: - lat2d = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) - lon2d = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) + lat = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) + lon = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat2d)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided - sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon2d, lat2d)) + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) else: # abs salinity provided sal_absolute = np.ma.masked_invalid(sal) sal_absolute = np.ma.masked_less(sal_absolute, 0) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e629052b..7e68b68d 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -31,7 +31,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -55,7 +55,7 @@ def clean_data(profile: xr.Dataset): # Find good SST and SSS depths if "bathymetry" in profile.dataset: D_prf = profile.dataset.bathymetry.values - profile.gridded_to_profile_2d(nemo, "bathymetry") + profile.gridded_to_profile_2d(gridded, "bathymetry") z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) @@ -69,10 +69,29 @@ def clean_data(profile: xr.Dataset): good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) for ip in I_sal: good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) - I = np.where(np.isfinite(good_sss))[0] - SSS = sal_clean[I, good_sss[I].astype(int)] + I_tmp = np.where(np.isfinite(good_sst))[0] + I_sal = np.where(np.isfinite(good_sss))[0] + + + # + # find good profiles + DD = np.minimum(Zmax, np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) + good_profile = np.array(np.ones(n_prf),dtype=bool) + quart = [0, 0.25, 0.5, 0.75, 1] + for iq in range(4): + test = ~np.any(np.logical_and(z >= DD*quart[iq] ,z <= DD*quart[iq+1]),axis=1) + good_profile[test]=0 + + ### + SST = np.zeros(n_prf)*np.nan + SSS = np.zeros(n_prf) * np.nan + + SSS[I_sal] = sal_clean[I_sal, good_sss[I_sal].astype(int)] + SST[I_tmp] = tmp_clean[I_tmp, good_sst[I_tmp].astype(int)] + + # fill holes in data - # jth is slow, there may bea more 'vector' way of doing it + # jth This is slow, there may be a more 'vector' way of doing it for i_prf in range(n_prf): tmp = profile.dataset.potential_temperature.values[i_prf, :] @@ -95,12 +114,14 @@ def clean_data(profile: xr.Dataset): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - + profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") return profile - def calc_pea(self, profile: xr.Dataset, Zmax): + def calc_pea(self, profile: xr.Dataset, gridded, Zmax): """ Calculates Potential Energy Anomaly @@ -113,8 +134,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach #%% gravity = 9.81 - # Clean data This is quit slow and over writes potneital temperature and practical salinity valirables - profile = ProfileStratification.clean_data(profile) + # Clean data This is quit slow and over writes potential temperature and practical salinity variables + profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -134,9 +155,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): ) # jth why not just use depth here? if not "density" in profile.dataset: - profile.construct_density(CT_AS=True, pot_dens=True) + profile.construct_density(CT_AS=False, pot_dens=True) if not "density_bar" in profile.dataset: - profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) + profile.construct_density(CT_AS=False, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S @@ -145,7 +166,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): * gravity / (height.sum(dim="z_dim", skipna=True)) ) - #%% + # mask bad profiles + pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 08ed5345..e81723af 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -27,4 +27,4 @@ profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) +pa.calc_pea(profile, nemo, Zmax) From 2f8521cbf576909477479a978988811de54c19fc Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Dec 2022 17:26:01 +0000 Subject: [PATCH 055/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 7e68b68d..c0b6bae7 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -72,24 +72,22 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): I_tmp = np.where(np.isfinite(good_sst))[0] I_sal = np.where(np.isfinite(good_sss))[0] - - # + # # find good profiles DD = np.minimum(Zmax, np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) - good_profile = np.array(np.ones(n_prf),dtype=bool) + good_profile = np.array(np.ones(n_prf), dtype=bool) quart = [0, 0.25, 0.5, 0.75, 1] for iq in range(4): - test = ~np.any(np.logical_and(z >= DD*quart[iq] ,z <= DD*quart[iq+1]),axis=1) - good_profile[test]=0 + test = ~np.any(np.logical_and(z >= DD * quart[iq], z <= DD * quart[iq + 1]), axis=1) + good_profile[test] = 0 ### - SST = np.zeros(n_prf)*np.nan + SST = np.zeros(n_prf) * np.nan SSS = np.zeros(n_prf) * np.nan SSS[I_sal] = sal_clean[I_sal, good_sss[I_sal].astype(int)] SST[I_tmp] = tmp_clean[I_tmp, good_sst[I_tmp].astype(int)] - # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it From 5721b1925b095a81991c254717c17a8f12419916 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Thu, 15 Dec 2022 09:12:35 +0000 Subject: [PATCH 056/150] Update profile_stratification.py --- coast/diagnostics/profile_stratification.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index c0b6bae7..504b9600 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -148,9 +148,9 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = ( - np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax - ) # jth why not just use depth here? + #height = ( + # np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + #) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=False, pot_dens=True) @@ -159,11 +159,11 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S + pot_energy_anom = ( - (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) + (depth_t * (rho - rhobar) * dz * Zd_mask).sum(dim="z_dim", skipna=True) * gravity - / (height.sum(dim="z_dim", skipna=True)) - ) + / (dz * Zd_mask).sum(dim="z_dim", skipna=True)) # mask bad profiles pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { From b188dc86c1950f3575ddae21ae366e56e6ce9cd6 Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Tue, 20 Dec 2022 18:36:57 +0000 Subject: [PATCH 057/150] Added option to subset dataset and domain on loading. This reduces the big overhead of calculating depth witth big models. --- coast/data/gridded.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 0d8ba322..32d3b73b 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -69,10 +69,14 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): self.set_grid_vars() self.set_dimension_mapping() self.set_variable_mapping() - + lims=kwargs.get('lims',[]) if self.fn_data is not None: self.load(self.fn_data, chunks, multiple) - +#jth subset + if len(lims) == 4: + self.dataset = self.dataset.isel(y=range(lims[2],lims[3]),x=range(lims[0],lims[1])) +# + self.set_dimension_names(self.config.dataset.dimension_map) self.set_variable_names(self.config.dataset.variable_map) @@ -82,7 +86,10 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): else: self.filename_domain = self.fn_domain # store domain fileanme dataset_domain = self.load_domain(self.fn_domain, chunks) - +#jth subset + if len(lims) == 4: + dataset_domain=dataset_domain.isel(y_dim=range(lims[2],lims[3]),x_dim=range(lims[0],lims[1])) +# # Define extra domain attributes using kwargs dictionary # This is a bit of a placeholder. Some domain/nemo files will have missing variables for key, value in kwargs.items(): @@ -189,7 +196,7 @@ def get_contour_complex(self, var, points_x, points_y, points_z, tolerance: int smaller = self.dataset[var].sel(z=points_z, x=points_x, y=points_y, method="nearest", tolerance=tolerance) return smaller - def set_timezero_depths(self, dataset_domain, calculate_bathymetry=False): + def set_timezero_depths(self, dataset_domain, **kwargs): """ Calculates the depths at time zero (from the domain_cfg input file) for the appropriate grid. @@ -204,6 +211,7 @@ def set_timezero_depths(self, dataset_domain, calculate_bathymetry=False): # keyword to allow calcution of bathymetry from scale factors # All bathymetry should now be mapped to bathy_metry + calculate_bathymetry = kwargs.get('calculate_bathymetry',False) try: if calculate_bathymetry: # calculate bathymetry from scale factors bathymetry, mask, time_mask = self.calc_bathymetry(dataset_domain) From e59118803bd54d567d8f1ac0f6294b2e25954e1d Mon Sep 17 00:00:00 2001 From: BlackBot Date: Tue, 20 Dec 2022 18:38:57 +0000 Subject: [PATCH 058/150] Apply Black formatting to Python code. --- coast/data/gridded.py | 18 +++++++++--------- coast/diagnostics/profile_stratification.py | 8 ++++---- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 32d3b73b..842215e7 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -69,14 +69,14 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): self.set_grid_vars() self.set_dimension_mapping() self.set_variable_mapping() - lims=kwargs.get('lims',[]) + lims = kwargs.get("lims", []) if self.fn_data is not None: self.load(self.fn_data, chunks, multiple) -#jth subset + # jth subset if len(lims) == 4: - self.dataset = self.dataset.isel(y=range(lims[2],lims[3]),x=range(lims[0],lims[1])) -# - + self.dataset = self.dataset.isel(y=range(lims[2], lims[3]), x=range(lims[0], lims[1])) + # + self.set_dimension_names(self.config.dataset.dimension_map) self.set_variable_names(self.config.dataset.variable_map) @@ -86,10 +86,10 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): else: self.filename_domain = self.fn_domain # store domain fileanme dataset_domain = self.load_domain(self.fn_domain, chunks) -#jth subset + # jth subset if len(lims) == 4: - dataset_domain=dataset_domain.isel(y_dim=range(lims[2],lims[3]),x_dim=range(lims[0],lims[1])) -# + dataset_domain = dataset_domain.isel(y_dim=range(lims[2], lims[3]), x_dim=range(lims[0], lims[1])) + # # Define extra domain attributes using kwargs dictionary # This is a bit of a placeholder. Some domain/nemo files will have missing variables for key, value in kwargs.items(): @@ -211,7 +211,7 @@ def set_timezero_depths(self, dataset_domain, **kwargs): # keyword to allow calcution of bathymetry from scale factors # All bathymetry should now be mapped to bathy_metry - calculate_bathymetry = kwargs.get('calculate_bathymetry',False) + calculate_bathymetry = kwargs.get("calculate_bathymetry", False) try: if calculate_bathymetry: # calculate bathymetry from scale factors bathymetry, mask, time_mask = self.calc_bathymetry(dataset_domain) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 504b9600..3bacd6c2 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -148,9 +148,9 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - #height = ( + # height = ( # np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax - #) # jth why not just use depth here? + # ) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=False, pot_dens=True) @@ -159,11 +159,11 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = ( (depth_t * (rho - rhobar) * dz * Zd_mask).sum(dim="z_dim", skipna=True) * gravity - / (dz * Zd_mask).sum(dim="z_dim", skipna=True)) + / (dz * Zd_mask).sum(dim="z_dim", skipna=True) + ) # mask bad profiles pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { From ecf907535b940e20f83b2dbf6ae50c39a8e71a0f Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Wed, 8 Feb 2023 17:11:15 +0000 Subject: [PATCH 059/150] update gridded_monthly_hydrographic_climatology.py --- coast/diagnostics/gridded_monthly_hydrographic_climatology.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 122da60c..29ef91d1 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -44,7 +44,7 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): print(itt) gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) print("copied", im) - PEA = GriddedStratification(gridded_t2, gridded_t2) + PEA = GriddedStratification(gridded_t2) PEA.calc_pea(gridded_t2, Zd_mask) PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values PEA_monthy_clim = PEA_monthy_clim / nyear From 19534c4239bb513e6c08777dd1511f3a64776178 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Wed, 8 Feb 2023 17:12:08 +0000 Subject: [PATCH 060/150] Apply Black formatting to Python code. --- coast/_utils/crps_util.py | 1 - coast/_utils/docsy_tools.py | 1 - coast/_utils/mask_maker.py | 1 - coast/_utils/plot_util.py | 1 - coast/data/altimetry.py | 1 - coast/data/gridded.py | 1 - coast/data/profile.py | 7 ----- coast/diagnostics/gridded_stratification.py | 2 +- .../profile_hydrographic_analysis.py | 3 --- coast/diagnostics/profile_stratification.py | 3 +-- coast/diagnostics/tidegauge_analysis.py | 6 ----- .../amm15_example_plot.py | 6 ++--- .../anchor_plots_of_nsea_wvel.py | 16 ++++++------ .../configuration_gallery/blz_example_plot.py | 6 ++--- .../seasia_dic_example_plot.py | 6 ++--- .../seasia_r12_example_plot.py | 6 ++--- .../wcssp_india_example_plot.py | 6 ++--- .../plot_validation_gridded_data.py | 6 ++--- .../plot_validation_mask_means.py | 7 +++-- .../plot_validation_surface_errors.py | 6 ++--- .../stratification_pycnocline_diagnostics.py | 26 +++++++++---------- tests/test_tidetable.py | 2 +- unit_testing/generate_unit_test_contents.py | 1 - unit_testing/test_TEMPLATE.py | 1 + unit_testing/test_altimetry_methods.py | 1 - .../test_bgc_gridded_initialisation.py | 1 + .../test_gridded_diagnostics_methods.py | 1 - unit_testing/test_gridded_harmonics.py | 2 -- unit_testing/test_isobath_contour_methods.py | 1 - unit_testing/test_object_manipulation.py | 1 - unit_testing/test_profile_methods.py | 2 -- unit_testing/test_tidegauge_methods.py | 6 ----- unit_testing/test_wod_read_data.py | 3 +-- unit_testing/test_xesmf_convert.py | 3 +-- unit_testing/unit_test.py | 1 - 35 files changed, 52 insertions(+), 92 deletions(-) diff --git a/coast/_utils/crps_util.py b/coast/_utils/crps_util.py index aca5e500..ca5eec00 100644 --- a/coast/_utils/crps_util.py +++ b/coast/_utils/crps_util.py @@ -188,7 +188,6 @@ def crps_sonf_fixed( else: # Subset model data in time and space: model -> obs for ii in neighbourhood_indices: - mod_subset_time = mod_subset.interp( time=obs_time[ii], method=time_interp, kwargs={"fill_value": "extrapolate"} ) diff --git a/coast/_utils/docsy_tools.py b/coast/_utils/docsy_tools.py index 131ff5f2..9898d520 100644 --- a/coast/_utils/docsy_tools.py +++ b/coast/_utils/docsy_tools.py @@ -12,7 +12,6 @@ def __init__(self): def write_class_to_markdown( cls, class_to_write, fn_out, method_to_omit=[], omit_private_methods=True, omit_parent_methods=True ): - methods_to_write = cls._get_list_of_methods(class_to_write) for method in methods_to_write: diff --git a/coast/_utils/mask_maker.py b/coast/_utils/mask_maker.py index 0ac0f74a..72b5bb63 100644 --- a/coast/_utils/mask_maker.py +++ b/coast/_utils/mask_maker.py @@ -36,7 +36,6 @@ class MaskMaker: """ def __init__(self): - return @staticmethod diff --git a/coast/_utils/plot_util.py b/coast/_utils/plot_util.py index a281781e..65d9adef 100644 --- a/coast/_utils/plot_util.py +++ b/coast/_utils/plot_util.py @@ -222,7 +222,6 @@ def create_geo_axes(lonbounds, latbounds): def ts_diagram(temperature, salinity, depth): - fig = plt.figure(figsize=(10, 7)) ax = plt.scatter(salinity, temperature, c=depth) cbar = plt.colorbar() diff --git a/coast/data/altimetry.py b/coast/data/altimetry.py index 2f02f767..17a7f49f 100644 --- a/coast/data/altimetry.py +++ b/coast/data/altimetry.py @@ -234,7 +234,6 @@ def crps( time_interp: str = "linear", create_new_object=True, ): - """ Comparison of observed variable to modelled using the Continuous Ranked Probability Score. This is done using this ALTIMETRY object. diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 842215e7..e48127e6 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -518,7 +518,6 @@ def interpolate_in_time(model_array, new_times, interp_method="nearest", extrapo def construct_density( self, eos="EOS10", rhobar=False, Zd_mask=[], CT_AS=False, pot_dens=False, Tbar=True, Sbar=True ): - """ Constructs the in-situ density using the salinity, temperture and depth_0 fields and adds a density attribute to the t-grid dataset diff --git a/coast/data/profile.py b/coast/data/profile.py index 43ef40ea..30e992ba 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -158,7 +158,6 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): """======================= Plotting =======================""" def plot_profile(self, var: str, profile_indices=None): - fig = plt.figure(figsize=(7, 10)) if profile_indices is None: @@ -177,7 +176,6 @@ def plot_profile(self, var: str, profile_indices=None): return fig, ax def plot_map(self, var_str=None): - profiles = self.dataset if var_str is None: @@ -188,7 +186,6 @@ def plot_map(self, var_str=None): return fig, ax def plot_ts_diagram(self, profile_index, var_t="potential_temperature", var_s="practical_salinity"): - profile = self.dataset.isel(id_dim=profile_index) temperature = profile[var_t].values salinity = profile[var_s].values @@ -812,7 +809,6 @@ def calculate_vertical_spacing(self): def construct_density( self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True ): - """ Constructs the in-situ density using the salinity, temperature and depth fields. Adds a density attribute to the profile dataset @@ -853,7 +849,6 @@ def construct_density( debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') try: - if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") @@ -908,7 +903,6 @@ def construct_density( density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) new_var_name = "density" else: # calculate density with depth integrated T S - if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Create it @@ -991,7 +985,6 @@ def construct_density( error(err) def calculate_vertical_mask(self, Zmax=200): - """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level diff --git a/coast/diagnostics/gridded_stratification.py b/coast/diagnostics/gridded_stratification.py index 55130b65..5c670505 100644 --- a/coast/diagnostics/gridded_stratification.py +++ b/coast/diagnostics/gridded_stratification.py @@ -277,7 +277,7 @@ def calc_pea(self, gridded_t: xr.Dataset, Zd_mask): # z_axis=1 PEA = (z_4d * (rho - rhobar) * dz_4d).sum(dim="z_dim", skipna=True) * gravity / height - #%% + # %% # return PEA coords = { "time": ("t_dim", gridded_t.dataset.time.values), diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 0cdbe7c4..980216c8 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -17,7 +17,6 @@ class ProfileHydrography(Indexed): - ############################################################################### def __init__(self, filename="none", dataset_names="none", config="", region_bounds=[]): """Reads and manipulates lists of hydrographic profiles. @@ -205,7 +204,6 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: # depth from model print("Depth from model") for ip in range(nprof): - DP[ip] = 0.0 rr = 0.0 for iS in range(0, 4): @@ -227,7 +225,6 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: DP[DP == 0] = np.nan for ip in range(nprof): - Dp = DP[ip] T[:] = tmp[ip, :] S[:] = sal[ip, :] diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 3bacd6c2..3ca4ff44 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -130,7 +130,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach - #%% + # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables profile = ProfileStratification.clean_data(profile, gridded, Zmax) @@ -207,7 +207,6 @@ def quick_plot(self, var: xr.DataArray = None): fig = None ax = None for var in var_lst: - title_str = var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" fig, ax = geo_scatter( diff --git a/coast/diagnostics/tidegauge_analysis.py b/coast/diagnostics/tidegauge_analysis.py index 2fdb30ed..cd87d78d 100644 --- a/coast/diagnostics/tidegauge_analysis.py +++ b/coast/diagnostics/tidegauge_analysis.py @@ -99,7 +99,6 @@ def harmonic_analysis_utide( # Loop over ports for pp in range(0, n_port): - # Temporary in-loop datasets ds_port = ds.isel(id_dim=pp).load() number_of_nan = np.sum(np.isnan(ds_port.values)) @@ -149,7 +148,6 @@ def reconstruct_tide_utide(cls, data_array, utide_solution_list, constit=None, o # Loop over ports for pp in np.arange(n_port): - # Reconstruct full tidal signal using utide pp_solution = utide_solution_list[pp] if len(pp_solution) == 0: @@ -236,7 +234,6 @@ def threshold_statistics(cls, dataset, thresholds=np.arange(-0.4, 2, 0.1), peak_ # Loop over vars in the input dataset for vv in var_list: - empty_thresh = np.zeros((n_port, n_thresholds)) * np.nan ds_thresh["peak_count_" + vv] = (["id_dim", "threshold"], np.array(empty_thresh)) ds_thresh["time_over_threshold_" + vv] = (["id_dim", "threshold"], np.array(empty_thresh)) @@ -244,7 +241,6 @@ def threshold_statistics(cls, dataset, thresholds=np.arange(-0.4, 2, 0.1), peak_ ds_thresh["monthlymax_count_" + vv] = (["id_dim", "threshold"], np.array(empty_thresh)) for pp in range(n_port): - # Identify NTR peaks for threshold analysis data_pp = ds[vv].isel(id_dim=pp) if np.sum(np.isnan(data_pp.values)) == ds.sizes["t_dim"]: @@ -255,7 +251,6 @@ def threshold_statistics(cls, dataset, thresholds=np.arange(-0.4, 2, 0.1), peak_ # Threshold Analysis for nn in range(0, n_thresholds): - # Calculate daily and monthly maxima for threshold analysis ds_daily = data_pp.groupby("time.day") ds_daily_max = ds_daily.max(skipna=True) @@ -451,7 +446,6 @@ def crps( # Loop over location indices for ii in range(n_id): - obs_ii = obs_var.isel(id_dim=ii) # Calculate CRPS diff --git a/example_scripts/configuration_gallery/amm15_example_plot.py b/example_scripts/configuration_gallery/amm15_example_plot.py index a87f1759..b3151f89 100755 --- a/example_scripts/configuration_gallery/amm15_example_plot.py +++ b/example_scripts/configuration_gallery/amm15_example_plot.py @@ -36,7 +36,7 @@ print("* Loaded ", config, " data") ################################################# -#%% subset of data and domain ## +# %% subset of data and domain ## ################################################# # Pick out a North Sea subdomain print("* Extract North Sea subdomain") @@ -45,7 +45,7 @@ ind_sci = sci_w.subset_indices(start=[51, -4], end=[62, 15]) sci_nwes_w = sci_w.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nwes = northwest europe shelf -#%% Apply masks to temperature and salinity +# %% Apply masks to temperature and salinity if config == "AMM15": sci_nwes_t.dataset["temperature_m"] = sci_nwes_t.dataset.temperature.where( sci_nwes_t.dataset.mask.expand_dims(dim=sci_nwes_t.dataset["t_dim"].sizes) > 0 @@ -60,7 +60,7 @@ sci_nwes_t.dataset["salinity_m"] = sci_nwes_t.dataset.salinity -#%% Plots +# %% Plots fig = plt.figure() plt.pcolormesh(sci_t.dataset.longitude, sci_t.dataset.latitude, sci_t.dataset.temperature.isel(z_dim=0).squeeze()) diff --git a/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py b/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py index f412fc0f..a3e705e3 100755 --- a/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py +++ b/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py @@ -5,14 +5,14 @@ This needs to move to the above """ -#%% +# %% import coast import matplotlib.pyplot as plt # import matplotlib.colors as colors # colormap fiddling ################################################# -#%% Loading and initialising methods ## +# %% Loading and initialising methods ## ################################################# dir_nam = "/projectsa/anchor/NEMO/AMM60/" @@ -32,18 +32,18 @@ sci_w = coast.Gridded(dir_nam + fil_nam, dom_nam, config=config) sci_w.dataset.chunk(chunks) -#% NEMO output is not standard with u,v fields included with w-pts. Tidy to avoid confusion +# % NEMO output is not standard with u,v fields included with w-pts. Tidy to avoid confusion sci_w.dataset = sci_w.dataset.drop_vars(["uo", "vo", "depthv"]) sci_w.dataset = sci_w.dataset.swap_dims({"depthw": "z_dim"}) ################################################# -#%% subset of data and domain ## +# %% subset of data and domain ## ################################################# # Pick out a North Sea subdomain ind_sci = sci_w.subset_indices(start=[51, -4], end=[60, 15]) sci_nwes_w = sci_w.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nswes = northwest europe shelf -#%% Compute a diffusion from w-vel +# %% Compute a diffusion from w-vel Kz = (sci_nwes_w.dataset.wo * sci_nwes_w.dataset.e3_0).sum(dim="z_dim").mean(dim="t_dim") # plot map @@ -61,7 +61,7 @@ plt.colorbar() # fig.savefig("") -#%% Transect Method +# %% Transect Method tran_w = coast.TransectT(sci_nwes_w, (51, 2.5), (61, 2.5)) lat_sec = tran_w.data.latitude.expand_dims(dim={"z_dim": 51}) @@ -70,7 +70,7 @@ # wo_sec = tran.data_F.wo.mean(dim='t_dim') -#%% Make map and profile plots +# %% Make map and profile plots ################################################# for i in range(2): for lat0 in [54, 57]: @@ -132,7 +132,7 @@ plt.close("all") -#%% Plot sections +# %% Plot sections fig = plt.figure() plt.rcParams["figure.figsize"] = 8, 8 diff --git a/example_scripts/configuration_gallery/blz_example_plot.py b/example_scripts/configuration_gallery/blz_example_plot.py index 6aa0b642..07ca6186 100755 --- a/example_scripts/configuration_gallery/blz_example_plot.py +++ b/example_scripts/configuration_gallery/blz_example_plot.py @@ -5,12 +5,12 @@ """ -#%% +# %% import coast import matplotlib.pyplot as plt ################################################# -#%% Loading data +# %% Loading data ################################################# @@ -34,6 +34,6 @@ sci_w = coast.Gridded(fn_domain=dom_nam, config=config_w) -#%% Plot +# %% Plot plt.pcolormesh(sci_t.dataset.ssh.isel(t_dim=0)) plt.show() diff --git a/example_scripts/configuration_gallery/seasia_dic_example_plot.py b/example_scripts/configuration_gallery/seasia_dic_example_plot.py index 469290b6..a2d72f37 100644 --- a/example_scripts/configuration_gallery/seasia_dic_example_plot.py +++ b/example_scripts/configuration_gallery/seasia_dic_example_plot.py @@ -4,13 +4,13 @@ Make simple SEAsia 1/12 deg DIC plot. """ -#%% +# %% import coast import matplotlib.pyplot as plt ################################################# -#%% Loading data +# %% Loading data ################################################# path_examples = "./example_files/" ## data local in livljobs : /projectsa/COAsT/NEMO_example_data/SEAsia_R12/ @@ -22,7 +22,7 @@ seasia_bgc = coast.Gridded(fn_data=fn_seasia_var, fn_domain=fn_seasia_domain, config=fn_seasia_config_bgc) -#%% Plot DIC +# %% Plot DIC fig = plt.figure() plt.pcolormesh( seasia_bgc.dataset.longitude, diff --git a/example_scripts/configuration_gallery/seasia_r12_example_plot.py b/example_scripts/configuration_gallery/seasia_r12_example_plot.py index d68495d5..2792f34c 100644 --- a/example_scripts/configuration_gallery/seasia_r12_example_plot.py +++ b/example_scripts/configuration_gallery/seasia_r12_example_plot.py @@ -5,13 +5,13 @@ """ -#%% +# %% import coast import matplotlib.pyplot as plt ################################################# -#%% Loading data +# %% Loading data ################################################# dir_nam = "/projectsa/COAsT/NEMO_example_data/SEAsia_R12/" @@ -21,7 +21,7 @@ sci_t = coast.Gridded(dir_nam + fil_nam, dir_nam + dom_nam, config=config_t) -#%% Plot +# %% Plot fig = plt.figure() plt.pcolormesh(sci_t.dataset.longitude, sci_t.dataset.latitude, sci_t.dataset.salinity.isel(t_dim=0).isel(z_dim=0)) diff --git a/example_scripts/configuration_gallery/wcssp_india_example_plot.py b/example_scripts/configuration_gallery/wcssp_india_example_plot.py index bbc712a6..63b3fcb8 100644 --- a/example_scripts/configuration_gallery/wcssp_india_example_plot.py +++ b/example_scripts/configuration_gallery/wcssp_india_example_plot.py @@ -7,12 +7,12 @@ Simple plot of sea surface temperature """ -#%% +# %% import coast import matplotlib.pyplot as plt ################################################# -#%% Loading data +# %% Loading data ################################################# dir_nam = "/projectsa/COAsT/NEMO_example_data/MO_INDIA/" @@ -22,7 +22,7 @@ sci_t = coast.Gridded(dir_nam + fil_nam, dir_nam + dom_nam, config=config_t) -#%% Plot +# %% Plot fig = plt.figure() plt.pcolormesh(sci_t.dataset.longitude, sci_t.dataset.latitude, sci_t.dataset.temperature.isel(t_dim=0).isel(z_dim=0)) diff --git a/example_scripts/profile_validation/plot_validation_gridded_data.py b/example_scripts/profile_validation/plot_validation_gridded_data.py index 9c18aa82..4bcc2176 100644 --- a/example_scripts/profile_validation/plot_validation_gridded_data.py +++ b/example_scripts/profile_validation/plot_validation_gridded_data.py @@ -18,7 +18,7 @@ import coast import pandas as pd -#%% File settings +# %% File settings run_name = "test" # List of analysis output files. Profiles from each will be plotted @@ -31,7 +31,7 @@ # Filename for the output fn_out = "/Users/dbyrne/transfer/surface_gridded_errors_{0}.png".format(run_name) -#%% General Plot Settings +# %% General Plot Settings var_name = "abs_diff_temperature" # Variable name in analysis file to plot # If you used var modified to make gridded data # then this is where to select season etc. @@ -72,7 +72,7 @@ plot_seasons = True season_suffixes = ["DJF", "MAM", "JJA", "SON"] -#%% Read and plotdata +# %% Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] diff --git a/example_scripts/profile_validation/plot_validation_mask_means.py b/example_scripts/profile_validation/plot_validation_mask_means.py index 09eaf9d6..18b26098 100644 --- a/example_scripts/profile_validation/plot_validation_mask_means.py +++ b/example_scripts/profile_validation/plot_validation_mask_means.py @@ -15,7 +15,7 @@ import matplotlib.pyplot as plt import numpy as np -#%% File settings +# %% File settings run_name = "test" # List of analysis output files. Profiles from each will be plotted @@ -25,7 +25,7 @@ # Filename for the output fn_out = "/Users/dbyrne/transfer/regional_means_{0}.png".format(run_name) -#%% General Plot Settings +# %% General Plot Settings region_ind = [0, 1, 2, 3, 4, 5, 6, 7, 8] # Region indices (in analysis) to plot region_names = ["A", "B", "C", "D", "E", "F", "G", "H", "I"] # Region names, will be used for titles in plot var_name = "profile_average_abs_diff_temperature" # Variable name in analysis file to plot @@ -64,7 +64,7 @@ title_fontweight = "bold" # Fontweight to use for title -#%% SCRIPT: READ AND PLOT DATA +# %% SCRIPT: READ AND PLOT DATA # Read all datasets into list ds_list = [xr.open_dataset(dd) for dd in fn_list] @@ -78,7 +78,6 @@ # Loop over regions for ii in range(n_ax): - if ii >= n_reg: a_flat[ii].axis("off") continue diff --git a/example_scripts/profile_validation/plot_validation_surface_errors.py b/example_scripts/profile_validation/plot_validation_surface_errors.py index ea16f9c7..fc4f45e7 100644 --- a/example_scripts/profile_validation/plot_validation_surface_errors.py +++ b/example_scripts/profile_validation/plot_validation_surface_errors.py @@ -18,7 +18,7 @@ import coast import pandas as pd -#%% File settings +# %% File settings run_name = "test" # List of analysis output files. Profiles from each will be plotted @@ -31,7 +31,7 @@ # Filename for the output fn_out = "/Users/dbyrne/transfer/surface_errors_{0}.png".format(run_name) -#%% General Plot Settings +# %% General Plot Settings var_name = "diff_temperature" # Variable name in analysis file to plot save_plot = False @@ -64,7 +64,7 @@ season_str = "DJF" # DJF, MAM, JJA or SON -#%% Read and plotdata +# %% Read and plotdata # Read all datasets into list ds_list = [xr.open_dataset(dd)[var_name] for dd in fn_list] diff --git a/example_scripts/stratification_pycnocline_diagnostics.py b/example_scripts/stratification_pycnocline_diagnostics.py index e30cdd16..b69fcae9 100755 --- a/example_scripts/stratification_pycnocline_diagnostics.py +++ b/example_scripts/stratification_pycnocline_diagnostics.py @@ -8,7 +8,7 @@ """ -#%% +# %% import coast import numpy as np import os @@ -16,7 +16,7 @@ import matplotlib.colors as colors # colormap fiddling ################################################# -#%% Loading data +# %% Loading data ################################################# # Loading AMM60 data if it is available @@ -71,7 +71,7 @@ print("* Loaded ", config, " data") ################################################# -#%% subset of data and domain ## +# %% subset of data and domain ## ################################################# # Pick out a North Sea subdomain print("* Extract North Sea subdomain") @@ -80,7 +80,7 @@ ind_sci = sci_w.subset_indices(start=[51, -4], end=[62, 15]) sci_nwes_w = sci_w.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nwes = northwest europe shelf -#%% Apply masks to temperature and salinity +# %% Apply masks to temperature and salinity if config == "AMM60": sci_nwes_t.dataset["temperature_m"] = sci_nwes_t.dataset.temperature.where( sci_nwes_t.dataset.mask.expand_dims(dim=sci_nwes_t.dataset["t_dim"].sizes) > 0 @@ -95,32 +95,32 @@ sci_nwes_t.dataset["salinity_m"] = sci_nwes_t.dataset.salinity -#%% Construct in-situ density and stratification +# %% Construct in-situ density and stratification print("* Construct in-situ density and stratification") sci_nwes_t.construct_density(eos="EOS10") -#%% Construct stratification. t-pts --> w-pts +# %% Construct stratification. t-pts --> w-pts print("* Construct stratification. t-pts --> w-pts") sci_nwes_w = sci_nwes_t.differentiate( "density", dim="z_dim", out_var_str="rho_dz", out_obj=sci_nwes_w ) # --> sci_nwes_w.rho_dz ################################################# -#%% Create internal tide diagnostics object +# %% Create internal tide diagnostics object print("* Create stratification diagnostics object") strat = coast.GriddedStratification(sci_nwes_t, sci_nwes_w) -#%% Construct pycnocline variables: depth and thickness +# %% Construct pycnocline variables: depth and thickness print("* Compute density and rho_dz if they didn" "t exist") print("* Compute 1st and 2nd moments of stratification as pycnocline vars") strat.construct_pycnocline_vars(sci_nwes_t, sci_nwes_w) -#%% Plot pycnocline variables: depth and thickness +# %% Plot pycnocline variables: depth and thickness print("* Sample quick plot") strat.quick_plot() -#%% Make transects +# %% Make transects print("* Construct transects to inspect stratification. This is an abuse of the transect code...") # Example usage: tran = coast.Transect( (54,-15), (56,-12), nemo_f, nemo_t, nemo_u, nemo_v ) tran_it = coast.TransectT(strat, (51, 2.5), (61, 2.5)) @@ -143,7 +143,7 @@ zt_m_sec = tran_it.data.strat_2nd_mom_masked.mean(dim="t_dim", skipna=False) -#%% Plot sections +# %% Plot sections ################# print("* Plot sections with pycnocline depth and thickness overlayed") plt.pcolormesh(lat_sec, dep_sec, rho_sec) @@ -174,7 +174,7 @@ plt.show() -#%% Plot profile of density and stratification with strat_1st_mom in deep water +# %% Plot profile of density and stratification with strat_1st_mom in deep water ############################################################################# print("* Plot profile of density and stratification with strat_1st_mom in deep water") print( @@ -213,7 +213,7 @@ plt.show() -#%% Map pretty plots of North Sea pycnocline depth +# %% Map pretty plots of North Sea pycnocline depth print("* Map pretty plots of North Sea pycnocline depth") print(" - we expect a RunTimeError here") diff --git a/tests/test_tidetable.py b/tests/test_tidetable.py index ce81a623..eb643b74 100644 --- a/tests/test_tidetable.py +++ b/tests/test_tidetable.py @@ -9,7 +9,7 @@ # -----------------------------------------------------------------------------# -#%% day of the week function # +# %% day of the week function # # # def test_dayoweek(): check1 = general_utils.day_of_week(np.datetime64("2020-10-16")) == "Fri" diff --git a/unit_testing/generate_unit_test_contents.py b/unit_testing/generate_unit_test_contents.py index 44e18a39..70dc570c 100644 --- a/unit_testing/generate_unit_test_contents.py +++ b/unit_testing/generate_unit_test_contents.py @@ -76,7 +76,6 @@ # Open output file with open(fn_contents, "w") as file: - # Write title things file.write(" UNIT TEST CONTENTS FILE TEST \n") file.write("\n") diff --git a/unit_testing/test_TEMPLATE.py b/unit_testing/test_TEMPLATE.py index 93f417f5..74a8b823 100644 --- a/unit_testing/test_TEMPLATE.py +++ b/unit_testing/test_TEMPLATE.py @@ -18,6 +18,7 @@ # IMPORT THIS TO HAVE ACCESS TO EXAMPLE FILE PATHS: import unit_test_files as files + # Define a testing class. Absolutely fine to have one or multiple per file. # Each class must inherit unittest.TestCase class test_TEMPLATE(unittest.TestCase): diff --git a/unit_testing/test_altimetry_methods.py b/unit_testing/test_altimetry_methods.py index f82b4cca..b8eff883 100644 --- a/unit_testing/test_altimetry_methods.py +++ b/unit_testing/test_altimetry_methods.py @@ -15,7 +15,6 @@ class test_altimetry_methods(unittest.TestCase): def test_altimetry_load_subset_and_comparison(self): - sci = coast.Gridded(files.fn_nemo_dat, files.fn_nemo_dom, config=files.fn_config_t_grid) with self.subTest("Load example altimetry file"): diff --git a/unit_testing/test_bgc_gridded_initialisation.py b/unit_testing/test_bgc_gridded_initialisation.py index 8aab94e8..37fde941 100644 --- a/unit_testing/test_bgc_gridded_initialisation.py +++ b/unit_testing/test_bgc_gridded_initialisation.py @@ -12,6 +12,7 @@ # IMPORT THIS TO HAVE ACCESS TO EXAMPLE FILE PATHS: import unit_test_files as files + # Define a testing class. Absolutely fine to have one or multiple per file. # Each class must inherit unittest.TestCase class test_bgc_gridded_initialisation(unittest.TestCase): diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index 7e726556..6c794e4c 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -132,7 +132,6 @@ def test_construct_pycnocline_depth_and_thickness(self): plt.close("all") def test_calc_pea(self): - nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m diff --git a/unit_testing/test_gridded_harmonics.py b/unit_testing/test_gridded_harmonics.py index b1390072..92acc0da 100644 --- a/unit_testing/test_gridded_harmonics.py +++ b/unit_testing/test_gridded_harmonics.py @@ -11,7 +11,6 @@ class test_gridded_harmonics(unittest.TestCase): def test_combine_and_convert_harmonics(self): - harmonics = coast.Gridded(files.fn_nemo_harmonics, files.fn_nemo_harmonics_dom, config=files.fn_config_t_grid) with self.subTest("test_combine_harmonics"): @@ -26,7 +25,6 @@ def test_combine_and_convert_harmonics(self): self.assertTrue(check2, msg="check2") with self.subTest("test_convert_harmonics"): - harmonics_combined.harmonics_convert(direction="cart2polar") harmonics_combined.harmonics_convert(direction="polar2cart", x_var="x_test", y_var="y_test") diff --git a/unit_testing/test_isobath_contour_methods.py b/unit_testing/test_isobath_contour_methods.py index df5f6523..a4f4290d 100644 --- a/unit_testing/test_isobath_contour_methods.py +++ b/unit_testing/test_isobath_contour_methods.py @@ -13,7 +13,6 @@ class test_contour_f_methods(unittest.TestCase): def test_extract_isobath_contour_between_two_points(self): - with self.subTest("Extract contour"): nemo_f = coast.Gridded( fn_domain=files.fn_nemo_dom, config=files.fn_config_f_grid, calculate_bathymetry=False diff --git a/unit_testing/test_object_manipulation.py b/unit_testing/test_object_manipulation.py index 6b9d0f54..3ceac248 100644 --- a/unit_testing/test_object_manipulation.py +++ b/unit_testing/test_object_manipulation.py @@ -113,7 +113,6 @@ def test_indices_by_distance(self): self.assertTrue(check1, "check1") def test_interpolation_to_altimetry(self): - sci = coast.Gridded(files.fn_nemo_dat, files.fn_nemo_dom, config=files.fn_config_t_grid) with self.subTest("Find nearest xy indices"): diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index d3adca43..a4331be8 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -14,7 +14,6 @@ class test_profile_methods(unittest.TestCase): def test_load_process_and_compare_profile_data(self): - with self.subTest("Load profile data from EN4"): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) @@ -70,7 +69,6 @@ def test_compute_density(self): self.assertTrue(check3, msg="check3") def test_compare_processed_profile_with_model(self): - profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() diff --git a/unit_testing/test_tidegauge_methods.py b/unit_testing/test_tidegauge_methods.py index 39e39bae..0a5466a6 100644 --- a/unit_testing/test_tidegauge_methods.py +++ b/unit_testing/test_tidegauge_methods.py @@ -51,7 +51,6 @@ def test_demean_timeseries(self): self.assertTrue(np.array_equal(tg1.dataset.ssh[0, :].values, demeaned), "check1") def test_harmonic_analysis_utide(self): - tganalysis = coast.TidegaugeAnalysis() date0 = datetime.datetime(2007, 1, 10) date1 = datetime.datetime(2007, 1, 12) @@ -134,7 +133,6 @@ def test_read_gesla_formats(self): self.assertTrue(check2, "check2") def test_read_gesla_and_compare_to_model(self): - sci = coast.Gridded(files.fn_nemo_dat, files.fn_nemo_dom, config=files.fn_config_t_grid) sci.dataset["landmask"] = sci.dataset.bottom_level == 0 tganalysis = coast.TidegaugeAnalysis() @@ -210,7 +208,6 @@ def test_time_slice(self): self.assertTrue(check2, "check2") def test_tidegauge_resample_and_apply_doodsonx0(self): - with self.subTest("Resample to 1H"): tganalysis = coast.TidegaugeAnalysis() date0 = datetime.datetime(2007, 1, 10) @@ -235,7 +232,6 @@ def test_tidegauge_resample_and_apply_doodsonx0(self): self.assertTrue(check2, "check2") def test_load_multiple_tidegauge(self): - with self.subTest("Load multiple gauge"): date0 = datetime.datetime(2007, 1, 10) date1 = datetime.datetime(2007, 1, 12) @@ -286,7 +282,6 @@ def test_tidegauge_for_tabulated_data(self): self.assertTrue(check5, "check5") def test_tidegauge_finding_extrema(self): - with self.subTest("Find extrema"): date0 = datetime.datetime(2007, 1, 10) date1 = datetime.datetime(2007, 1, 20) @@ -321,7 +316,6 @@ def test_tidegauge_finding_extrema(self): plt.close("all") def test_tidegauge_cubic_spline_extrema(self): - with self.subTest("Fit cubic spline"): date_start = np.datetime64("2020-10-12 23:59") date_end = np.datetime64("2020-10-14 00:01") diff --git a/unit_testing/test_wod_read_data.py b/unit_testing/test_wod_read_data.py index 5f0a434a..5d009782 100644 --- a/unit_testing/test_wod_read_data.py +++ b/unit_testing/test_wod_read_data.py @@ -11,13 +11,12 @@ # IMPORT THIS TO HAVE ACCESS TO EXAMPLE FILE PATHS: import unit_test_files as files + # Define a testing class. Absolutely fine to have one or multiple per file. # Each class must inherit unittest.TestCase class test_wod_read_data(unittest.TestCase): def test_load_wod(self): - with self.subTest("Load profile data from WOD"): - wod_profile_1D = coast.Profile(config=files.fn_wod_config) wod_profile_1D.read_wod(files.fn_wod) diff --git a/unit_testing/test_xesmf_convert.py b/unit_testing/test_xesmf_convert.py index 7ec9dd5a..a5b8eab7 100644 --- a/unit_testing/test_xesmf_convert.py +++ b/unit_testing/test_xesmf_convert.py @@ -4,15 +4,14 @@ import os.path as path import unit_test_files as files + # Single unit test. Can contain multiple test methods and subTests. class test_xesmf_convert(unittest.TestCase): - # Test for conversion from gridded to xesmf. # Here I've used one test and then subtests for each smaller test. # This could also be split into multiple methods but the file would need # to be loaded multiple times. Using subtests allows a sequential testing. def test_basic_conversion_to_xesmf(self): - # Read data files sci = coast.Gridded(files.fn_nemo_dat, files.fn_nemo_dom, config=files.fn_config_t_grid) diff --git a/unit_testing/unit_test.py b/unit_testing/unit_test.py index cbb72df1..632eba55 100644 --- a/unit_testing/unit_test.py +++ b/unit_testing/unit_test.py @@ -110,7 +110,6 @@ # Open output file with open(fn_contents, "w") as file: - # Write title things file.write(" UNIT TEST CONTENTS FILE TEST \n") file.write("\n") From 71e5f1113549ee3fe8b3c6831dae39d6db5a7b0c Mon Sep 17 00:00:00 2001 From: Jason T Holt Date: Wed, 19 Jul 2023 16:09:11 +0100 Subject: [PATCH 061/150] (old?) updates to profile_stratification.py --- coast/diagnostics/profile_stratification.py | 44 +++++++++++++++------ 1 file changed, 31 insertions(+), 13 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 3ca4ff44..a0d7ab01 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -7,6 +7,10 @@ from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug +#### + + +#### class ProfileStratification(Profile): # TODO All abstract methods should be implemented """ @@ -31,7 +35,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def clean_data(self,profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -41,9 +45,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value """ +#%% print("Cleaning the data") # find profiles good for SST and NBT dz_max = 25.0 + n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] @@ -53,9 +59,12 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 # Find good SST and SSS depths - if "bathymetry" in profile.dataset: - D_prf = profile.dataset.bathymetry.values + def first_nonzero(arr, axis=0, invalid_val=np.nan): + mask = arr!=0 + return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) + if "bathymetry" in gridded.dataset: profile.gridded_to_profile_2d(gridded, "bathymetry") + D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) @@ -65,10 +74,15 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): I_tmp = np.nonzero(np.any(test_tmp.values, axis=1))[0] I_sal = np.nonzero(np.any(test_sal.values, axis=1))[0] # - for ip in I_tmp: - good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) - for ip in I_sal: - good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) + #for ip in I_tmp: + # good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) + #for ip in I_sal: + # good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) + + good_sst=first_nonzero(test_tmp.values,axis=1) + good_sss=first_nonzero(test_sal.values,axis=1) + + I_tmp = np.where(np.isfinite(good_sst))[0] I_sal = np.where(np.isfinite(good_sss))[0] @@ -90,17 +104,20 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it - +#%% for i_prf in range(n_prf): + tmp = profile.dataset.potential_temperature.values[i_prf, :] sal = profile.dataset.practical_salinity.values[i_prf, :] z = profile.dataset.depth.values[i_prf, :] if any_tmp[i_prf]: tmp = coast.general_utils.fill_holes_1d(tmp) + tmp[np.isnan(z)] = np.nan tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) + sal[np.isnan(z)] = np.nan sal_clean[i_prf, :] = sal @@ -112,11 +129,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) - profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) - profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) + self.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + self.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + self.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") - +#%% return profile def calc_pea(self, profile: xr.Dataset, gridded, Zmax): @@ -133,7 +150,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables - profile = ProfileStratification.clean_data(profile, gridded, Zmax) + #profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -217,3 +234,4 @@ def quick_plot(self, var: xr.DataArray = None): ) return fig, ax + ############################################################################## From 6463a6bbac0a8ce5ba505de3a4b8a3d35220bcc7 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Wed, 30 Aug 2023 17:35:26 +0100 Subject: [PATCH 062/150] Update potential_energy_tutorial.ipynb changes to get notebook path right when running from examples folder --- .../gridded/potential_energy_tutorial.ipynb | 1480 ++++++++++++++++- 1 file changed, 1451 insertions(+), 29 deletions(-) diff --git a/example_scripts/notebooks/runnable_notebooks/gridded/potential_energy_tutorial.ipynb b/example_scripts/notebooks/runnable_notebooks/gridded/potential_energy_tutorial.ipynb index f5dc1d40..770356c0 100644 --- a/example_scripts/notebooks/runnable_notebooks/gridded/potential_energy_tutorial.ipynb +++ b/example_scripts/notebooks/runnable_notebooks/gridded/potential_energy_tutorial.ipynb @@ -18,14 +18,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], "source": [ + "import os\n", + "os.chdir('../../../../')\n", "import coast\n", "import numpy as np\n", - "import os\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors # colormap fiddling\n", "import xarray as xr" @@ -33,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -56,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, "outputs": [], @@ -80,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "269a51fc", "metadata": {}, "outputs": [], @@ -102,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8f55363d", "metadata": {}, "outputs": [], @@ -121,24 +122,506 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6d0f5239-6f1d-4f7d-aa22-e51a9736fff6", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+       "    mask          (dim_mask, y_dim, x_dim) float64 1.0 1.0 1.0 ... 0.0 0.0 0.0
" + ], + "text/plain": [ + "\n", + "Dimensions: (y_dim: 375, x_dim: 297, dim_mask: 9)\n", + "Coordinates:\n", + " longitude (y_dim, x_dim) float32 -19.89 -19.78 -19.67 ... 12.89 13.0\n", + " latitude (y_dim, x_dim) float32 40.07 40.07 40.07 ... 65.0 65.0 65.0\n", + " region_names (dim_mask) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mm.quick_plot(mask_list)\n" ] @@ -244,10 +1197,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c1217563", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Show overlap\n", "mask_list.mask.sum(dim='dim_mask').plot( levels=(1,2,3,4))\n", @@ -287,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4b72009d", "metadata": {}, "outputs": [], @@ -297,20 +1282,457 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "6ac2a67a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:       (t_dim: 7, dim_mask: 9)\n",
+       "Coordinates:\n",
+       "    time          (t_dim) datetime64[ns] 2015-08-01T12:00:00 ... 2015-08-07T1...\n",
+       "    region_names  (dim_mask) <U18 'whole domain' 'north sea' ... 'kattegat'\n",
+       "Dimensions without coordinates: t_dim, dim_mask\n",
+       "Data variables:\n",
+       "    PEA           (t_dim, dim_mask) float64 130.9 4.603 7.291 ... 0.2 1.515
" + ], + "text/plain": [ + "\n", + "Dimensions: (t_dim: 7, dim_mask: 9)\n", + "Coordinates:\n", + " time (t_dim) datetime64[ns] 2015-08-01T12:00:00 ... 2015-08-07T1...\n", + " region_names (dim_mask) " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot timeseries per region\n", "\n", @@ -328,9 +1750,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "coast_dev2", "language": "python", - "name": "python3" + "name": "coast_dev2" }, "language_info": { "codemirror_mode": { @@ -342,9 +1764,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From ebe69519f819892546b394cb82e6824a189a03f9 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 20 Oct 2023 16:12:48 +0100 Subject: [PATCH 063/150] Bug fixes to cleaning data - need to check this works ok. Update to notebook to test this --- coast/diagnostics/profile_stratification.py | 15 ++- .../profile/potential_energy_tutorial.ipynb | 125 +++++++----------- example_scripts/profile_test.py | 4 +- 3 files changed, 59 insertions(+), 85 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index a0d7ab01..d3af87fa 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -35,7 +35,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(self,profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -96,6 +96,9 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): good_profile[test] = 0 ### + else: + print('error no bathy provided, cant clean the data') + return profile SST = np.zeros(n_prf) * np.nan SSS = np.zeros(n_prf) * np.nan @@ -129,14 +132,14 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - self.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) - self.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) - self.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") #%% return profile - def calc_pea(self, profile: xr.Dataset, gridded, Zmax): + def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly @@ -150,7 +153,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables - #profile = ProfileStratification.clean_data(profile, gridded, Zmax) + profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() diff --git a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb index 09020937..f8817ce2 100644 --- a/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebooks/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "5eca7994-6fa1-44e1-b95c-fc8a0fecf7bd", "metadata": {}, "source": [ "A demonstration to calculate the Potential Energy Anomaly for Profile data.\n" @@ -10,7 +9,6 @@ }, { "cell_type": "markdown", - "id": "14277e0d-4dbc-4e0f-b3a2-6853dca66d46", "metadata": {}, "source": [ "### Relevant imports and filepath configuration" @@ -18,11 +16,12 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "c4773751-3544-4ebd-a795-cfe128b70743", + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "import os\n", + "os.chdir('../../../../')\n", "import coast\n", "import numpy as np\n", "from os import path\n", @@ -32,8 +31,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +44,6 @@ }, { "cell_type": "markdown", - "id": "5d3f6987-f05d-4a54-a932-e4bbf84becb1", "metadata": {}, "source": [ "### Loading data" @@ -54,8 +51,7 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "7677050c-775d-4172-9561-61c3c89aa77b", + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -74,7 +70,6 @@ }, { "cell_type": "markdown", - "id": "798994a1", "metadata": {}, "source": [ "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" @@ -82,8 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "58406dca", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +87,6 @@ }, { "cell_type": "markdown", - "id": "84a15c7b", "metadata": {}, "source": [ "### Inspect profile locations\n", @@ -102,9 +95,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "f5b2d233", - "metadata": {}, + "execution_count": 5, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -122,7 +116,7 @@ "(
, )" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -133,7 +127,6 @@ }, { "cell_type": "markdown", - "id": "d3e75a6d", "metadata": {}, "source": [ "### Calculates Potential Energy Anomaly\n", @@ -144,8 +137,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "e70f5db2", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,24 @@ }, { "cell_type": "markdown", - "id": "3e056769", + "metadata": {}, + "source": [ + "Define a gridded object to supply the bathymetry" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fn_nemo_dom = dn_files + \"coast_example_nemo_domain.nc\"\n", + "config_t = root + \"./config/example_nemo_grid_t.json\"\n", + "nemo = coast.Gridded(fn_domain=fn_nemo_dom, config=config_t)" + ] + }, + { + "cell_type": "markdown", "metadata": {}, "source": [ "Potential energy anomaly is calculated to a prescribed depth, Zmax:" @@ -162,8 +171,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "c49b40d3", + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -178,19 +186,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + "C:\\Users\\home\\anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "C:\\Users\\home\\anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] } ], "source": [ "Zmax = 200 # metres\n", - "pa.calc_pea(profile, Zmax)" + "pa.calc_pea(profile, nemo, Zmax)" ] }, { "cell_type": "markdown", - "id": "74603291", "metadata": {}, "source": [ "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", @@ -210,7 +219,6 @@ }, { "cell_type": "markdown", - "id": "8f897042-3697-4ddd-a812-04572500f0ec", "metadata": {}, "source": [ "## Make a plot\n", @@ -221,21 +229,12 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "a696835b", + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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AVVEri9lsJi4ursHS0hVuLhSLxk3AiBEjGDVqFK+//jq5ublERERw8uRJ3n77bXr27MkjjzxiG9u1a1cWLVrE77//TuvWrXF0dKRr165Mnz6dP//8kzvvvJOXXnqJbt26IcsysbGxbNy4kVdeeYV+/frVSK6uXbsC8OWXXzJ16lS0Wi3t27e3iwMoZezYsXz++ec8+OCDPPXUU2RkZPDvf/+71oW1ioqK2L9/f4XbalpfIzQ0lHfffZc333yT6OhoRo8ejaenJykpKRw8eBBnZ2feeeedKvcxevRoIiIieOWVV8jNzeW2225j3759/PLLL0B59wtYH5il137u3LnXlfP06dMcPHiQv/71rxXWPYiIiOCzzz5jzpw5141ZuJYPP/yQESNGMGTIEF599VV0Oh3ffvstp0+fZuHChbWqO1GWurgfRowYQadOndi2bRsPP/xwteMuBg8ezLp16yrdXtU5Hj9+nEuXLjV5RePtt98mJSWFO++8k+DgYLKzs1m/fj0//vgjkyZN4rbbbrMbf/LkSQoLCxkyZEgjSazQnFEUjZsASZJYvnw5M2fOZO7cuXzwwQf4+PjwyCOPMGvWLLsf53feeYekpCSefPJJ8vLyaNWqFVeuXMHZ2Zldu3bx0Ucf8cMPP3D58mWcnJxo2bIlw4cPvyGLxuDBg5kxYwbz5s3jxx9/RJZltm3bVmG56KFDh/LTTz/x8ccfM378eIKDg3nyySfx8/PjiSeeuOFrEx0dzYABAyrcZjKZ0Ghq9icwY8YMOnXqxJdffsnChQsxGAwEBATQp0+fark0VCoVq1at4pVXXuGjjz7CaDQSERHB/Pnz6d+/f7nCSmA1Z4eGhuLk5GRnnaqM0iDQp59+usLtWq2Wxx57jI8++oijR4/Sq1ev6+6zlEGDBrF161befvttHnvsMWRZpnv37qxcubJc4GdtqKv7YfLkycycObNGCtVDDz3ETz/9xKFDh+zcjoWFhUDVVWX//PNPWrVqVe5B3dTo3bs3//nPf1i+fDkZGRk4OjrSqVMnZs+ezV//+tdy45cvX46Pjw8jR45sBGkVmjuSEPVcDUlBQeG6/Pbbbzz00EPs2bOHgQMH2m07efIk3bt355tvvuHZZ59tJAmbJ7179652cbOydOvWjYiICL777jvbur/85S/s27evyoyXTp06cdddd/HZZ5/dsMxNDYvFQtu2bXnwwQf54IMPGlschWaIYtFQUGhgFi5cSEJCAl27dkWlUrF//34+/fRT7rzzTjsl49KlS8TExPCPf/yDwMDABulPczOQm5vL6dOnWb16NUeOHGHZsmU13scnn3zCPffcw5tvvsmVK1fYu3cva9euva5l5OzZszcqdpNl/vz55Ofn2yr9KijUFEXRUFBoYFxdXVm0aBHvv/8+BQUFNiXi/ffftxv33nvv2UqCL168uMH7pDRXjh49ypAhQ/D29ubtt9++oc6jo0eP5tNPP+Xy5csMGjQIX19fnnrqKWbNmlX3AjdxZFlmwYIFFbr1FBSqg+I6UVBQUFBQUKg3lPRWBQUFBQUFhXpDUTQUFBQUFBQU6o2bLkajuLgYo9HY2GIoKCgoKDQDdDpdrUrOV4e6ei41hKz1wU2laBQXFxMWFlZpOWcFBQUFBYWyBAQEcPny5Xp7gBcXFxPWyoXkVEut91XfstYXN5WiYTQaSU5OJi4urlwzq6bOhg0bOHr0qN26gIAAevXqVWHDrarYvHlzuYZZTYXCwkJiY2NtXTWvXLmC0Whk3LhxtG3bts4qS9YXixcvZtKkSY0txk2Hcl3rj9JrazQa+emnn9BoNIwZM6bCKrS3Gnl5efTu3Ruj0VhvD2+j0UhyqoWYI6G4ud74Nc/Nk2l125V6lbW+uKkUjVLc3NyanaLRu3dvnJ2dKSoq4vTp09x99922roo1xcnJqcIy342FEILExESOHz9OQkICkiTh5uaGEIJWrVoxcuTIem/aVldU1JFVofYo17X+KL22a9aswWQycffdd+Pu7t7YYt1yuLhKuLje+IuUTNN+CauKm1LRaI6EhYUREhLCzz//jL+//w0rGU2N+Ph4Dh8+TGpqKgEBAdx999106NABJyenxhZNQeGW4cKFCxw+fJiIiAhFyWgkLELGUotiEhYh150wDYyiaDQRhBAsWbKEpKQkxowZ09ji1JqcnBz27dtHbGwswcHBPPjgg83CNaKgcLORm5vLH3/8QcuWLZXuqwqNgqJoNDApKSmsX7+egQMHEh4eDlh9eLt37yYyMpIRI0YQFBTUyFLWjri4OLZs2YKTkxOTJk2iY8eOioKhoNAIHDt2jOTkZNq1a8cdd9xR5d+hLMvk5+fj6uqq/L3WAzICmRs3adRmbmOjKBoNzMGDB7ly5QoJCQkMGjSI+Ph4oqOjMZvN3HbbbYSFhTW2iLXi0qVLbN26lTZt2nDffffVus27goLCjbNv3z6cnJwYNGhQpcqDEIKoqCgOHjxIQUEBHh4e3H333c0u4LCpIyNTG+dH7WY3Loqi0cCYzWY8PDywWCxs3rwZf39/unfvTtu2bSsN4CwqKiIxMRE3Nzd8fX0bWOLqc+XKFbZt20aXLl2YMGGCEtWuoNCIxMXFkZaWhq+vb6VKhtlsZufOnURFRREcHExBQQHZ2dlkZ2ffNHFiCo2Pomg0MF5eXkRGRvLQQw9hNpuv+9aQnp7OunXrKCoqQqPR8Pjjjzc5s6bFYuH06dMcOnSI9u3bK0qGgkIjk5KSwooVK/D29q4y8Hr//v1ERUUxcOBA9u7dC8C4ceMUJaMesAiBpRatxWozt7FRFI0GJigoCIPBwObNm9Hr9ZhMJiwWi91iNptt/87PzycgIABPT08SExM5c+YMnTp1atQHuRCC+Ph4Ll++TH5+PhkZGRQXF9O3b19GjBihKBkKCo1IfHw8P//8M+7u7gwbNowDBw5UOjY8PJyzZ88SFRVlW6e4TOoHJUZDocFo3bo1vr6+5OTkYDKZcHBwQKPR4OTkhEajQa1Wo9FobIubmxs9evRAkiQ2bNjA3r17OXLkCN7e3gwZMoQ9e/aQlZXFvffei0ZTP1+n2WwmPT2d5ORkkpOTSU1Npbi4GG9vb3x9fQkNDaVr1674+fnVy/EVFBSsFBcXs3XrVqKjo3n88cdxdna22y6EsFkyxo8fj1qtrnJ/Op0OrVZLSEgIAwYMYMWKFVy8eJF+/frV52ko3GIoikYDo1arefbZZ29o7tixY+nduzcHDx7k6NGjLFiwwLYtPT29zs2diYmJnDp1iri4OGRZRqfTERwcTN++fWnbti0tWrRocm4cBYWbkaSkJHbs2EFUVBQWi7WU9blz5+jdu7fduJSUFNLT0xk8eDCiClO7EIJz585x4MABTCYTkiSxYsUK/P39adeuXb2ey62KjMCiWDQUmgP+/v6MHz+eTp06sXXrVhITE2+oTPn1yM3NZe3atXh4eDB8+HBCQ0Px9/dX3CIKzQohBMe3n2X/mqOYDCba9ghlyP0DcXJpPu6Bc+fO8ccff5RbX1HsRXp6OgDbt28nKiqqwpo8+fn57Ny5k/j4eNu6Y8eO0bt3b3r27Km8PNQTiutEodnRpk0bwsLCmD9/PqdOncLHx4fQ0NA62//u3bsBePLJJxWfrUKzJCMpi7cmfErUsSuoNWqQwGK28N+/z+cf81+g35iejS1itQgMDMTDwwOdTodaraagoIDc3FyWLl3KiRMn6N69u60QV1n3ZXx8PNnZ2bbPQggiIyPZt28fJpMJAG9vb3r37k1wcHC9uV4VrCjBoArNEpVKxQMPPMCyZcvYuHEjrVu3xtHRkezsbKKiomjRokWNlASj0YjZbMbJyckWg6EoGQrNEYtFZsaYj4g9n2D9bL7aObMov5i37/03n235F50Htm8sEauNh4cHTz75JMuWLSMqKorQ0FBuu+02jEYjkZGRLFmyhPDwcGJjYzEYDOh0OltL8r1796JSqYiKiuLMmTOkpKTQo0cP8vPzSUpK4p577lGslAr1jqJoNHN0Oh2TJ09m7969nDt3jsLCQgoKCti6dStqtZqwsDA6dOhAQEDAdX9Q1q9fT3JyMj4+PqSnp/OXv/ylgc5CQaFuObDmKFfOxFW63WKWeWXYu7zyw1OMeGRQA0pWfWRZxmAw4OTkxJo1a4iLi2P06NG0bNnSNsZisZCens7FixcJDw8nLCyMwMBA4uPj2bJlC/Hx8UiSRGxsLIGBgUyaNIlOnTqxevVqYmNjG/Hsbj3kkqU285sriqJxEyBJEhEREURERACwcOFCxo8fz4kTJzh69CirV6/G0dGRoKAg3N3dcXFxwcXFBVdXV7RaLcnJycTHx5OcnAyAs7MzI0aMUILCFJotu5cfQqVRIZsr/nmWJAmLWeaTqV8DNCllo7i4mN27d3P06FGMRiPTpk0jOzsbNzc3DAYDxcXFNktjZmYmYHWvDBo0yPYy0aZNG5KSkjh79ixCCCRJIikpicWLF+Pu7k5OTg4AaWlpdR7fpVAxlloGg9ZmbmOjKBo3KS4uLkRERDBw4EASEhKIjIzkypUrREVFkZ+fXy4i3cfHh/79+9O7d2+8vb0bSWoFhbqhuMCAuE6rTEmSEMAnj3/D5XOJPPHe/ajVje9GWLduHSdPnqRr165ER0dz4sQJIiIiWL16Ndu2bbN1QQbo3bs3AQEBhIaGlrNYDhw4kLNnzwLW7tCllpDMzEwsFgu+vr7K37pCg6AoGjc5kiTRokULWrRoYVtnsVjIy8sjJyeHwsJCgoODcXNza0QpFRTqllYdg9m78nClyoadoi0LlsxeQ25GHq/+96kGkrBy2rRpw8mTJ4mKiqKoqAitVkvbtm2ZMmUKW7duJSUlxTbWxcWFDh06VLgflUrFiBEjOHToEMOGDVOySRoZi6CWbeLrTpaGRlE0bkHUajUeHh54eHg0tigKCvXCXdOG8NtHy6seJJdxqwjBpl93kZGQyYxfnsfNy6Ve5auKbt264enpycWLF/H19eXixYt8+OGHgPXFoSbFtMLCwoiMjFSUjCbArRyjUWM7YUJCAg8//DDe3t7o9Xp69OjBkSNHbNtnzpxJhw4dcHZ2xtPTk+HDh5crgRsZGUlERAQtWrTg3XfftdsWGhqKJEns37/fbv306dMZPHhwTcVVUFC4BfFr6cMz/34EoJybUAgBQoAo89Nd4kY5uvkULw58k6KC4gaUtjwhISEMHToUIQSnTp2ib9++3HPPPTz++ON069atUWVTUKgpNVI0srKyiIiIQKvVsm7dOs6ePctnn31m92bcrl07vv76a06dOsXu3bsJDQ1l5MiRpKWl2cY899xzPPLII6xYsYJVq1axZ88eu+M4Ojry+uuv1+7MFBQUbmnueX40b/3+N9Saqz9zQgirJUO+mu6KSoVUEt8ggMRLKUzr/DJn9l1oYInLk5CQgLu7Oz169MDX11epddGMkZGw1GKRab5WqRrdtR9//DEhISHMnTvXtu7aIlEPPvig3efPP/+cOXPmcPLkSYYNGwZAdnY2PXv2pFu3bgQFBdkioEt5+umn+e6771i7dm2Fle0UFBQUqsMd9/TjzQUv8t6kzyotyS1ptVc/lIzJSMjk7yPe57Mtb9GxX3i9yymEYM+ePZw+fRqAwsJCvLy8SEhIwGw2k5ubq8RRNXNkYV1qM7+5UiOLxsqVK+nduzeTJk3Cz8+Pnj178uOPP1Y63mg08sMPP+Du7k737t1t6999911GjBiBXq9HpVIxatQou3mhoaE888wzzJgxA1luzp4pBQWFxuaOif34YO0/cLk27kKlQnJwsFkzALu4DbPJzN9HvM+uPyvvfloX5ObmsnjxYrZs2YKrqyteXl60adMGlUqF2WwGYNWqVWRlZdWrHLcipRVSFeqXGika0dHRfPfdd4SHh7NhwwaeeeYZXnzxRX755Re7catXr8bFxQVHR0dmz57Npk2b8PHxsW0fM2YMaWlpJCYmsmzZsgo7DP7zn//k8uXLdo3DFBQUFG6EPqN68M3Bj3Dx87QqF46OqEqUDJulw2KxmyNJEiajmXcnf86W33bXqTzFxcUcPnyYuXPnMnv2bKKiohg5ciRDhw7ljjvuoG/fvowYMYK+ffsCUFBQwPr16ykubtzYkZuNixcvNtixauM2KV2aK5KoqsXfNeh0Onr37s3evXtt61588UUOHTrEvn37bOsKCgpISkoiPT2dH3/8ka1bt3LgwIFqtREPDQ1l+vTpTJ8+nXfffZe5c+cSGRnJa6+9xvHjx9m+fXulc3Nzc3F3d+d///sfer2+uqd105GQkEBwcHBji3FTolzb+qGhrmtOcgHbvjtKdkK+bZ0QwqpkyPbBoaWWDtlsBkli4LSedBjc8tpd1hiLxUJMTAxmsxlHR0ecnZ3R6/WVZoZkZGRQUFAAgFarJTAwsEbHS0tLw9fXt9Zy34zk5eXxt7/9jZycnHpzTZU+l/aeCcTF9cbrtOTnyQzsnFSvstYXNYrRCAwMtDXvKaVjx478+eefduucnZ1p27Ytbdu2pX///oSHhzNnzhxmzJhRI+Fefvllvv32W7799tsazZs0aVKz+yLqkoULFzJlypTGFuOmRLm29UNDXtf/e97MixFvcelkDEIWtrgMO8o+9AUgBPt/PU27Nh156LW7a3X8HTt2EB0dzaRJk/D09KzWnCtXrrBx40ZMJhMqlYqRI0dW+3jr169n9OjRNyruTUV+fj6rV69mwIABBAcH2wqaNQSykJDFjVslajO3samRehUREUFkZKTdugsXLtCqVasq5wkhMBgMNRbOxcWFt956iw8++IDc3Nwaz1dQUFC4Fo1Ww7+3/Is77+1XsZKhUlmrhgphVURKEELw6/vL+OGjVZhNlvLzqkFaWhq7du2ie/fu1VYywGrpfeCBBwCrRUThxigsLCQ3N5cNGzbw008/VWkhb+6Uloq4dnnuuecA6/08c+ZMgoKCcHJyYvDgwZw5c8ZuHwaDgRdeeAEfHx+cnZ25++67iY+Pr7EsNVI0XnrpJfbv38+sWbOIiorit99+44cffrAJXlBQwD/+8Q/2799PTEwMR48e5f/+7/+Ij49n0qRJNRYO4KmnnsLd3Z2FCxfe0HwFBQWFa9G7OvHmgr/x0Fv3gUplWyS12qZkAIgyabClro2l323inekLbihQfefOnTg7O9OxY0eOHz/Oli1bOHTokO0BePnyZQ4cOMDvv//ODz/8wJIlS2yyuLm58eSTT9bImqFgT9lYQbAWNGsoGjpG49ChQyQlJdmWTZs2AdiexZ988gmff/45X3/9NYcOHSIgIIARI0aQl5dn28f06dNZtmwZixYtYvfu3eTn5zNu3LgaK7s1cp306dOHZcuWMWPGDN59913CwsL44osveOihhwBrxcnz588zb9480tPT8fb2pk+fPuzatYvOnTvXSLBStFot7733Xrm0WQUFBYXa8uhb9+Lu7cr3f//VznoBICwydn2sSoPWTRYO7Yzk5b/O483378PX17Xax4uOjqawsJCFCxeiVqsJDg7mzJkzHDt2rMLxmZmZFBUV2WLOJEmqMHheoXqoVCratWtHRkYGjz/+OGazmaeffrpBjm1BhaXmNTLLzK8Z18blfPTRR7Rp04ZBgwYhhOCLL77gzTffZOLEiQDMmzcPf39/fvvtN55++mlycnKYM2cOv/76K8OHDwdg/vz5hISEsHnz5nLZolVR4+ov48aNY9y4cRVuc3R0ZOnSpTXdpR1Xrlwpt27KlCmKX1xBQaHOkSSJCc+NwifIk3cmf16iWAiubZQp6bRXgzXVKgRw7vBlnn12Lt9/Pw1v74pLlgshSE9Pp7i4mMzMTJuSUFoeoFWrVjg7O3Py5Em2bNkCQM+ePQkODsbR0RG9Xm/r1KpQe8xmM5cuXUIIgaOjoy3ItjlxbRiBg4MDDg4OVc4xGo3Mnz+fl19+GUmSiI6OJjk52c465uDgwKBBg9i7dy9PP/00R44cwWQy2Y0JCgqiS5cu7N27t34VDQUFBYWbjdvv6cvbf7zMrIf/g6nYvraCpNOiKlvUy9kJCavFIyOjgGfemM+/37+fVv72MRdCCH7//Xe7uDYPDw+8vb1tcQJgfcvu2LGjbYy7uztBQUF1f5IKaDQaHBwcKCws5Ny5c7aOtg2BqGUwqCiZGxISYrf+7bffZubMmVXOXb58OdnZ2Tz22GMAJCcnA+Dv7283zt/fn5iYGNsYnU5XLpbI39/fNr+6KIqGgoKCAlZl44td7zF9yLuYjWZr4klJzIYNZyfQaq2JKJKEBGREZ/HIR7/xyxtTCPX3sg1NSUkhMjKSAQMGEBQUhKurKzqdzrbdYDCQk5NDfHw8J06csK1XihTWHSaTicTEREJCQjAYDMTGxlJUVESXLl1o27YtRqOxwWSpbS2M0rlxcXF2WZXXs2YAzJkzh7vuuqucAnttSrUQ4roN+Koz5loURUNBQUGhhHa9wvhk3QzenvIfCrKuMau76MHL3fZROFqtHJKAokIjny3ZwVfP3QNYTdWLFy/Gzc2NFi1akJyczMGDB3F1dSUiIgJJknBwcMDPzw8/Pz86dOhAUlISvr6+t3Rqfl1z8uRJjhw5gru7OwaDgeLiYkJDQ7nnnntQqVQNqmjUFW5ubjW6R2JiYti8ebNdWENAQABgtVqUrcuSmppqs3IEBARgNBrJysqys2qkpqYycODAGsmsKBoKCgoKZegysB0/Hf+Y/xvxMXlpuSABekdbMKgAUEnIjhrbZ7Mk2H3mCrsOHObKhXNIkkRmZiYAixcvtu1bpVIRERFR7ph6vZ42bdrU85ndemRkZODg4IAkSeh0OqZOnYqfnx8q1Y0HZd4oFqHCImoRDHqDvU7mzp2Ln58fY8eOta0LCwsjICCATZs20bNnT8CqHO/YsYOPP/4YgNtuuw2tVsumTZuYPHkyAElJSZw+fZpPPvmkRjIoioaCQhOjrGmyoKCATZs2MXLkyFu62m1D4+7pzH+WTeet534h/nKaLTZUAoRGhcXN0dZa3uSiApX1+1qycj2hPs4V1v0JCQnhzjvvrLHZWeHG8fLyIikpieeff77Rs3VkJORaZJ3I10YoV2eOLDN37lymTp1q1/lXkiSmT5/OrFmzCA8PJzw8nFmzZqHX620Znu7u7jzxxBO88soreHt74+XlxauvvkrXrl1tWSjVRVE0FBSaCImJiezatYvo6Gi8vLy4++67KSws5MSJE2RkZDBt2jTlIdWABIZ48eOKv/H6Sws4tv8SAEKrRmjLWDYkKPK5+jM67q6RdG7dglOnThEXF4eLiwshISG0aNHCLj5DoWFo3bo1R48eZc+ePdx5552NLU6Ds3nzZmJjY5k2bVq5ba+99hpFRUU8++yzZGVl0a9fPzZu3Iir69V07dmzZ6PRaJg8eTJFRUUMGzaMn3/+ucZKm6JoKNQpWak5rPnvJrYt2kNRXhGtOocw/q8jGTC+t/KQvA4XLlzg/PnzdO/enfj4eH744Qdb8FZ8fDxRUVGEh9d/y3KFq0iSxPufPMCn/17Dls1Xy1VbLRuQH6jF4mh9S23p60bn1i2QJIlu3brRrVu3RpJaoRQvLy969uzJtm3bsFgsDBkypNFkqatg0JowcuRIKmtnJkkSM2fOrDJjxdHRka+++oqvvvqqxscui6JoKNQZ0SdjeHXoTAqyC5BLih9lJGVxeMNxhj54O6/Na3zzZVNFlmVbKX8PDw/69OlDVFSUXYlkd3f3SmYr1Cc6nYY3//EXCNKzasNxJBksDhImZ1VJTxQBSDw2pLuiTDdBWrVqxbFjx8jIyGhUOWofo3GDQRpNAEXRUKgTLGYL/xz/IQU5hTYlA0C2WFP1ti7cTfvebZk4fWxlu7hlWblypa0ypF6v5+zZs+Tn59v6DvTr14/+/fsrikYj88ajw3By1fHbtmNYZIGEQCDQqVW8OK4f/dsrXX2bIpGRkXh4eNgqYDYW1hiNWjRVa8Zt4hVFQ6FO2L/6CGlxVbwxCPjzi9VMePGuRon4bsqU+u59fHxIT0+nsLCQtLQ02/YuXbrg4eHRSNIplKJWqXj53kE8NLQnr3/8Lf7BIbRtEcCwXuE46bTX34FCo5CamkpoaKjyu9OIKIqGQp1watc51Fo1liq6WqbGppOekImzu5648wlodBrCurRErbm13SmjRo2ys2CEhYVx+fJlAO644w6Cg5U35aaERjYR5mRgdN+2DVpZUqHmGI1GMjMzGTBgQGOLglzLXic3knXSVFAUDYU6obq+6TkzFrBr6QFbmWfPAA/u//tfmDh97C3r35Ykifvuu49x48YhSRJarZaFCxcSFRVFbGzsLXtdmiqenp64ublx5MgR0tLSKCoqwtXVlcDAQHx8fJQ35yZEQkICQogmUaNEidFQUKgl3Qd3ZsnnqyofIIFGo2bboj12XTKzkrP5/pV5JF9J5bkvy6dg3UqUNs8ym80UFxfj7u5O69atG1kqhbIkJSWxceNGhBCkpaXZubgAnJycCAkJoVWrVrRo0QKtVosQgqysLBITEykqKqJXr15KUHQDkZaWhqurq+J6bGQURUOhTuhzVw8CW/uTEpNmCwC1Q4DZbCnXFbOU5V+tY9ui3fQe1YN7XhhD+z5t61fgJk58fDwA27Zto1WrVraMFIXGZefOncTHx+Pl5YVer0en06FSqYiLi0OWZYqKirhw4QIXLlyodB8tW7Ys18xKoX7Izc3Fy8vr+gMbABlVgxfsaiooioZCnaBWq3l/9QxeHfI22am5ttxttUaFxSzj6OxAcYGhyn3kpOWxbeEetizYxYtf/x/j/1r9NsQ3ExqNhgkTJrBq1SosFgvR0dGKotFEyM3NpXXr1gwePNhufXFxMZGRkZw7d67CqqBg7XrZu3dvRcloQJydnW1Ke2NjERKWWnRvrc3cxkZRNBTqjJYdgplz9gvW/7SN7b/voTC3iNAuIYx/ZiSvj3qvWvsotYb85/n/0aFfOOG9bk3XQffu3XFzc2Pjxo1Kka4mhJOTU4WNuBwdHenevTvdunUjNTWV+Ph4tFoter0eJycnXFxclPTkBsRoNLJz506Ki4vJysoiPz8fFxeXxhbrlkVRNBTqFFdPFya9Mp5Jr4y3W+/i7kx+dkElsypAwNIv1/D6vBfqWMKmj8VioaioiLCwMJ5++unGFkehDH5+fhw9epTCwsIKe89IkoS/v79itWhkCgoKiI6Otn2+ePGirXlYY2GpZdaJpRm7TpTwaIUGYfjDd6LS1Ox2O7DmaD1J07TZtWsXn332GQcPHsRiqTxdWKHhGTBgACqVit9//52srKzGFkehEkqDdCdNmsTDDz9M586dG1kikIWq1ktzpflKrtCsuPflcTg5O6JSV/+WK8ovrkeJmi6l2Sfr1q3j0KFDjSyNQllcXV15/vnncXR05MiRI8hyBYHPCo1OZGQkrVu3plOnTrRp00ZpaNfIKIqGQoMQEOrHv7fNJCDUt9pzbtX6EZ07d7a1dE5OTm5kaRSuRa/XM2TIEKKjo1myZEmFMRsKjYtWq21yKcSlrpPaLM2V5iu5QrOjbY8w5kb+h483/Qv/UL/rjvcO9m4AqZoerq6ujBw5EoATJ040sjQKZRFCsGLFCjZs2ABAdnZ2rRUNIQSHDx8mMTGxLkRUAFxcXJqca0vmaubJjSzN2XamBIMqNCgqlYpew7ry5EcP8/4Ds6m0sIYkcfdfR9b6ePnZBWSlZOPm7Yq7j1ut99dQ9O7dm7Nnz5KVlYUQ4pa17jQ1iouLOX78OO3btyc0NBRfX98Kg0JrwoEDBzh58iT5+fkEBQXVkaS3NoWFhTarYFOh9nU0mq9doGl9Ewq3DBH39KFDv3AiD19CXBPwqNKo8W/ly5j/G3bD+0+ISmLuPxey688D1pRZCXqP6M5j7z3QLIqBSZLE1KlTG1sMhWsoVfiCg4NrXNuksLAQJycnO6XRbDZz8uRJAHr06FFnct7q+Pr6cvjwYYqKinBycmpscW55mq+KpNCs0Wg1fLT+Te68tz+SSg2Syrb0GNKFz3e8g7N79d8U0xMziTp+mayUbGLPJ/Bc3zeuKhkAAo5uOcX0O97ixPYz9XRWCjc7jo6OODo6kp+fX6N5cXFxzJ8/39Ysr5SyJcyVOht1h0ajsRUNbCqU9jqpzdJcUSwaCo2Gs7uefy6aTmpsOie2n8Fikek8sD0h7atvPj5/8CJzZvzG8W2nrSskay2PorzicqXQZYsMQvDp49/wy6WvleZXCjWisLCQU6dO4enpyfnz5+nYsSMODg4VjrVYLEiSZLvHjh8/DsCRI0cwGAy0adMGrVZrl778448/0r59ewYNGlTv53Kzo9VqAZpUtomMhMyNu0BrM7exURQNhUbHr6UPIx6t+Y/ryZ1neX3ke/YKhYC8zMrfNmVZkBKTxvFtZ+g1rOuNiKtwi7J792727dtnUy6WLl3KsGHD8PT05Ny5c8TExFBUVER2djZgLcvv6+uLp6cnSUlJAGRlZbFr1y527dpV4TGUdNm6oTQ+w2KxNLnsk1sRRdFQaJYIIfj8ye+xmC123WCrgyRBfGSiomgo1Ij09HQADAYDw4YN49ChQyxfvrzS8RaLheTkZLsUZR8fH9t+ytK+fXvatGlDcHBwnct9K1JqySguLm4yVo3at4lvvhZYRdFQaJac2XOehItJNzRXCNC7KQFiCjUjKCiI2NhYtFotW7ZsKbddr9cTGBhIcHAwer2elJQUkpOTMZvN+Pv706tXLxwdHUlNTbUpKEOHDiUsLEx5665jSoveFRYW4ubWNLLNal+CXFE0FBQalISoGy9kpXXQ0H/cbXUojcKtwODBgxk8eDCffvopXbp0ISAgAKPRiKOjI+7u7nh4eNhllLRs2bLC/fj5Xa0h4+rqqigZ9UCpolFQUIP+Sgr1hqJo3EIIIbh04gpJl1Jw8XSm6x0d0Wib5y3g4uF8YxMluPel8Tc+X+GWpzRFtXXrG+8sfMcdd7B3714lILmeaUq9gmQhIdei1Xtt5jY2zfMpo1Bjzh24yJfP/JdLJ2Js6zz83Hn8vQcY8+TwRpTsxrhtZHecXByv2w9FUkmo1Spki4wQMOHFu3jsvfsbSEqFUsxmM/n5+Xh4eDS2KLXGYDDYshpulI4dO9KhQwelEFs94erqil6vJyYmhnbt2jW2OIC14FZt3B9KwS6FJs2FI5d4ZcjbWIxmu/XZqTnMfvq/GIqM3PPimEaS7sZw1Dvw0D/v439vzK90zFOfPopWpyE1Ng0PP3cGPxCBX4hPA0qpAJCQkMD//vc/AJ5//nm8vZt3aXmVSlUn2SGKklF/SJJEUFBQubolCo2DomjcAvz42nwsJgtyJdkZ/3tjASMfG4yzW+1KKTc0k/9+N8YiIws++BPZIqPSqJDNMhqdmsfff5BJr4xvbBFveYxGI0eOHGlsMeoUDw+PGhfsUmh4hBC1tjzVJbVt9d6c28QrisZNTlp8xtViVpVgNBjZ9ecBRj8+pIGkqhskSeKRtycx/tmR7Fy8n8zkLHyCvRk0eQCuni6NLd4tj8Vi4euvvyYvLw+wWgKauzUDwMvLi5iYGFsPGqPRyJo1a/Dz8yMiIqKxxVMoobi4GEmSmkyvIAsSlloU3arN3MZGUTRucjISM687Rq1Wk5Fw/XFNFQ9fd+5+dlRji6FQgsFg4NKlS1gsFpuS0aJFC+67775Glqxu6NKlC8ePH+fy5cv4+fmxbNkyioqKlOyRJkanTp3YtGkTn3zyCWq1mrFjx9KxY8dGk0exaCjctHj6e1x3jMViwTPg+uMUFK6HEIIFCxYQFxdHQECAbf20adOaxFtlXdC6dWs6duzI1q1b7WI1Ro6sfbdhhbojLCyMu+66i/T0dGJiYli9ejUZGRns27eP0NBQJk2a1Ngi3jI0XxVJoVr4t/Kly+0dUKkr/6q1Og133NuvAaVSuFlZtWoVcXFxALi4XHVfNbUGV7VBkiTGjRtnUzJat27NtGnTbLUbKkMIQUpKCkeOHGH9+vVcuXKlAaS9tQkJCaFnz5707t2bwsJCtmzZgtls5uzZs+Tm5jaoLBauuk9ubGm+KBaNW4D/++hhXh3yNkJIFZbrfvTtyUpMg0KdcPHiRcBaMCkqKoqQkBB0Ot1NUy8iPz+fPXv2kJ+fj1ar5Y477qBt27ZVzsnOziYqKoqoqCi7h1tZi49C/RIcHMzYsWNxcXHB0dGRP/74g5UrVzJ+fMMFjCuuE4Wbms4D2/PRhrf4/MnvSLyUYluvd9Pz6NuTmDh9bCNKp3AzMW3aNBYsWIBarcZkMtmsGzcLe/bsYf/+/bi4uBAaGlqlkmE2m9m/fz9nz561Wx8QEEDHjh1p06ZNfYurUIIkSXZ9ZAYPHsy6devYs2dPI0p166AoGrcI3Qd35ucLX3Fmz3mSolNx8XSm1/CuODhV3OZaQeFG8PT0JDw8nBMnTvDoo48yd+5cWrVq1dhi1RmlcSYGg4GcnJxKx1ksFlatWkVaWprdeldXV8aMGWPrLqrQOISEhNC7d+9Ku+jWB0pTNYVbAkmS6HJ7R7rc3niR1wo3L3v37mXfvn22CqBqtRpnZ2dCQkIaW7Q6o7RFvMlkIjMzE4PBYFtXlsjISJuSERoail6v5+zZs4SHhytKRhOhZ8+e6HQ6PvroowY5nkBCrkWKqmjG6a01VpESEhJ4+OGH8fb2Rq/X06NHD7uCPEIIZs6cSVBQEE5OTgwePJgzZ87Y7SMyMpKIiAhatGjBu+++a7ctNDQUSZLYv3+/3frp06czePDgmoqroKDQQJw/f95WyGr8+PEUFxdTUFBAYGBgI0tWdzg5Xe36K4Tg9OnyNWrS09PZvXs3AP7+/gwdOpQLFy4A1t83haaBJEk3lbWtKVMjRSMrK4uIiAi0Wi3r1q3j7NmzfPbZZ3b9Cz755BM+//xzvv76aw4dOkRAQAAjRoyw5dMDPPfcczzyyCOsWLGCVatWlfOTOTo68vrrr9fuzBQUFBqUkSNH4uNjLfH+66+/snnzZsDaXv1moWxMRo8ePTh69Ch79+7l8OHDXLp0CVmW2bFjh23M0KFD0Wg0mM3W8v83Q8EyhRuj1HVSm6W5UiMb3scff0xISAhz5861rSuroQsh+OKLL3jzzTeZOHEiAPPmzcPf35/ffvuNp59+GrBGYffs2ZNu3boRFBRUztf59NNP891337F27VrGjGlePTiaI0IITu06x+r/bibmbBx6dz2DJw1gxKOD0Ls6XX8H9YTZZGbfysNEHopCrVHTZ3QPOkcojaiaKi1atOCJJ55g9+7dREdHk5SUhF6vb1aN1E5fTOTPjcc5eSERtVrF7T1bM3FkD1qU1KPx8vLihRdeIDs721YV9OLFixQXW5v7DRgwgIyMDNq2bUt0dDSurq6AtWW8Xq9X7t1bmFu5e2uNVKSVK1fSu3dvJk2ahJ+fHz179uTHH3+0bb98+TLJycl2hWscHBwYNGgQe/futa179913GTFiBHq9HpVKxahR9lUdQ0NDeeaZZ5gxY0adNC9SqBwhBP95fg6vDnuXXUv3c/l0HGf2RvLNSz/zf11fIfFScqPIdXb/BR5q9VfenfQZS2av5vdPlvPSnf/ihf4zyEjKahSZFK6Po6Mjw4cPp0WLFkDzSuGcv/IgT/5rIZv2nicxNYe4pCz+WH+UB1/9mX3Hrzbn8vLywmAw8Msvv3Dq1CmbtSIsLMz20pScnGwXmzJ69GjuvPPOhj0hBYUmQo0UjejoaL777jvCw8PZsGEDzzzzDC+++CK//PILYP3jAqtfsiz+/v62bQBjxowhLS2NxMREli1bVmHp3n/+859cvnyZBQsW1PikFKrPqu83seYHq4nbYi5R6oR1yUzO5q2/fNLgyl5SdApvjHyP7DRrzQGLyWKTLerYZV4b/g4mo6lBZVKoGSkp1jRqNze3Rpakehw6HcM3C60ZCJYytWYsssBstvDG5yvIzC6wrS+NRbn99tvp06cPY8eOJScnx5bKajAYGDBgQAOegUJTx1LSJr42S02pi5hKg8HACy+8gI+PD87Oztx9993Ex8fXSI4auU5kWaZ3797MmjULsEbtnjlzhu+++45HH33UNu5a82BFTW0cHBzw9fWt9Fi+vr68+uqr/Otf/+L++++viZgsXrwYvb55dSKtSxISEli4cCGFWUVc2B5DZmwOap2alr0CaXVbICqN9YYVsmDJu5sq3Y9skYmLTOTzf35FcFe/hhKfffOOU1xoqLC4mMUsE3sugU9enU3rAQ2fzVB6bRUqRwhBbGwsAGlpadW6Xo19XVfuz0CSoKICpgIwmSy8/+UC+rSzukKKiooAiIqKQqfTcfLkSQoLC23xGJ6ennZW3MYkLS2N9evXN7YYTZLS77EhaGjXSWlM5ZAhQ1i3bh1+fn5cunSpwpjKn3/+mXbt2vH+++8zYsQIIiMjbW6/6dOns2rVKhYtWoS3tzevvPIK48aN48iRI9Xu71MjRSMwMJBOnTrZrevYsSN//vkncNVMmpycbBdpnpqaWs7KUR1efvllvv32W7799tsazZs0aVKzeZOqDxYuXIiXIYB5079HyAIhQFJJXNodS3B4IB9vfAv/Vr6kxqbzc/rKKvel1qjxxJcpU6Y0kPSw5MWNFSoZpahUEuYEqUFlKmXhwoWNctzmREFBAf/+978BawGv6lQFbezr+uOG/1SoZJQiAJPGkylTrP0xdu/eTUJCAmPGjCE9PZ2VK1fSpk0bLl26xJAhQwgPD28YwavB+vXrGT16dGOL0SQpm6RQ38iokGvR9aN07rWl0x0cHCpMsa6LmMqcnBzmzJnDr7/+yvDhwwGYP38+ISEhbN68uVzYQ2XU6KwjIiKIjIy0W3fhwgVbilBYWBgBAQFs2nT1LdloNLJjxw4GDhxYk0MB1l4Jb731Fh988EGD16VvziSdTePfT3yLxSwjywIhBLLF6npIvpzCG6Pew2K2VNslIlfx0K8PigoMVW6XZUF+TmEDSaNQU5ydnRk9ejSjRo1qNqXHBde/x0uNskIIDh48SNu2bVGr1Zw6dQp3d3dcXFxwcHDAYrFw4MABFi9ebEtzVVCoK0JCQnB3d7ctH374YYXj6iKm8siRI5hMJrsxQUFBdOnSpUYWuxr9Crz00kvs37+fWbNmERUVxW+//cYPP/zAc889B1hdJtOnT2fWrFksW7aM06dP89hjj6HX63nwwQdrcigbTz31FO7u7oq5ugacXHWh0h94i1km/kIS+1cfwTfEBydXxyobXlnMFjoPbF9folZIcNsAqgrOV2tUhLQPrnyAQqPTr18/+vfv39hiVIu45CyM5qpbVkmSRK9OLQFrsa68vDyCg4PZuXMnly9fZujQoXTq1AmDwcDOnTs5ceIEWVlZDfrGXF3S0tI4depUY4txy2ERUq0XgLi4OHJycmzLjBkzKjxeXcRUJicno9Pp8PT0rHRMdaiRotGnTx+WLVvGwoUL6dKlC++99x5ffPEFDz30kG3Ma6+9xvTp03n22Wfp3bs3CQkJbNy40ebvqSlarZb33nvPlj6mUDVmk5mEkyk2C0ZFqDUq9q86DEIgqhgnhNUa4unfsG6ou/9atTnOYpYZ+9TwBpJG4Wbn5xUHqrRnCECrUXH3kK4Att+iEydOEBkZiYODA5cuXcLPz48OHToAoNPpuOOOO5qky2LZsmXs27evscW45SiN0ajNAtYA67JLRW4TsMZU9urVi1mzZtGzZ0+efvppnnzySb777ju7cdWJqbyW6owpS41r4Y4bN45x48ZVul2SJGbOnMnMmTNrumuAClsnT5kyRfGLVxPLdd7MwBrwZjSYiD0XT1FeEZRYP8rePDYrh5DZvfQgHfu1qzeZr2X0E0PZsXgfJ3eetY/VkAABD7w+gTbdQxtMHoWbF5PZwsZ955EBJJCEVbEo/Qkt/fftvdvi5W4NMC8tIZ6ZmQlYo/JPnjzJyZMnAejbty+dO3dGq9U25KlUi7LWS1mWm41rS6Hm1EVMZUBAAEajkaysLDurRmpqao3CIZS77CZD56jD1U9PVWXxhRC06R6KpdSaIcsIi8W2TQhh1UZkGQQs/2otsecTGkB6K1qdlllr/8GDMybi6ulsWx/UJoCXf3yGabNuzA2noHAt0XHpmErTulUSpcUXSzK8rcqHRkKrVZOdV4RFltHr9XTtarVuaDQaBg4cyNSpUxk+fDgDBw6ke/fudkqG2WxuMvWAyr6FZmdnN54gtyCipE38jS6ihpVB6yKm8rbbbkOr1dqNSUpK4vTp0zVSNJTuPjcZkiTRaVRbDs4/WbE5WLJmkox6fAiOzg44uTpSlFfilqrkx9BkNPPZE9/y5Z4P6k3ua9E56njsvQd46K17SY1NR6PV4NfSR6msqFBnFBQZeeOjZValuvS+kiS4JmNPFrD2wDlWHzmHi5MDEwd1Zero0Vy8eJEOHTrQpUsXAFq3bm03z2g0snPnTqKjo+nVqxe9e/duiNOqFFmWbVYXsMqn0HBYkLDUojFaTee+9NJLDBw4kFmzZjF58mQOHjzIDz/8wA8//ADYx1SGh4cTHh7OrFmz7GIq3d3deeKJJ3jllVfw9vbGy8uLV199la5du9qyUKqDomjchHQc3hqRrubQ+uPWaPoSjUOlkhAC/vbtk7j7WOMuxj01giWzV1eZTipkwdl9F7hyJo7Qzg1bu0Kr0xLc9uZpyqXQdFi77TSpGflIGgmhLqNsVIDL5QJMrhoKvGXmbzzClsORdNEV07JlywrHFxYWsnz5clthr9JKqY1JdnY2Bw8eJCAggOTkZMVtQvlU0ZuJ0pjKGTNm8O677xIWFlZhTGVRURHPPvssWVlZ9OvXr1xM5ezZs9FoNEyePJmioiKGDRvGzz//XO0aGqAoGjclKo2Kd5a/xur/buLP2atJvpwKXE1Tnf3MDxzacJzX5j3P1HfvZ//qI8RFJl53vzGNoGgoKNQHQgh+/mMvCIFkBqGW7C0bVweiSytGm2VAXSzjkGUmt7UTSRl56Fwc8fMrX8iuoKCANWvW2JSM++67Dy8vr4Y4rSopLWLo7Ox8nZE3P/n5+ezbt4+LFy822DFlUbt+JTdSZaAuYiodHR356quv+Oqrr2ouQAmKSnuTotFqGPHoIDRaNagkrM5mFSAhm2V2LjnA5IAnSb6SxmPvPVCtfTroK45uVrg5KSws5Ny5c00mvqAuOXA4muzcIpAkJEBlFHDtaQqByijjlFCIZBG2xSWuGNkiiMnTYjRfnWQymTh//jxLliyxxT/cddddTULJAOsDIyAgwPZ9ZmRkNLJEjUNiYiKLFy8mISHB1qemIahNfEbp0lxRLBo3Mau+20j8xWTsgjXKvLEV5hbxt4i3+ObgLBycdBiKKvfZOjo70H1wp0q3K9x8/PHHH8TExNCtWzcmTJjA+fPnbXn1AwcObNbxMivWHrdmmJRYMSQBapNAmMXVQGqLQFNoLk12QjJbQKNCUyyjKZQx69X8tO4Y0+7qwcXI8xw/ftyupPWYMWOahMukLCaTieDgYBwcHDh8+Cjh4eG2LJpbhZSUFEwma68kxX3UMNxad9gtxrr/bSkxB1fyxyRJFOQWsuHnHdz38ngWzPqTygoKTHrlbpxcGq9lvELDExYWRkxMDCdPnkSr1do1Y+rfv3+NfLRNjdiETCSzQDjY/21ItnQTQJLQZpWpUiuu/kdTbMGiV7PhUBTnLifRySmZXj26M2jQIBYtWoSHh0ejKBkpRQUsib7ApoQrFJnNhLq6E24xMELIWExmUtNySMnM4ciJNAoLZTbv/p3Bd4Qz/q7O+HjfGi6VLl264OLiQl5eHl5eXrbeXfWNjIRci2DQ2sxtbBRF4yYmNS4DqMT3XIqAjfO2s+DKN+Rk5LH6+42oS5uuCWtjtbufHcXD/7qv4QRXaBLceeeduLu7s2LFCpuS4eLiwsSJE5u1kgHg5uKIJGNVNkpPpezfiBBIZoEmr0yX4DLbBaX1ZiAmLZ/Q9i2YMGECYM3uaAxrz9msDF7cu4Uiixm5pF7G6cx0TiLIOLSbMWZnDhzXYDIn2uK1jCaZzdsj2bP/Mu/8YzTBQe4NLndDo9Vqbb1oGjIYtGx1zxud31xRFI2blPMHozCbSop3XedHLz+rALVazd++fZJ7XhzD5l93kJmUjXeQJyMeHUSLdkENILFCU8BisbBq1SpSUlLo2rUrPXv2xMnJasmaNm0a3t7ezdplUsrwwZ04dyEJTNamg7LGXslQmQTatCLbO6QEyNqr1g+zSxlFS0icSTKVTBXo9foGr2Rski28dmA7RWYzcplMMxmrK2hHUhzRlwVqk1Su5YAsQ2GhgQ8/W8v0v/YhLCys2SuS1SUuLq7BjlXbOAslRkOhyfHnF2uQVBKiGnF8fi19bP9u2SGYaR8oBbFuVWJiYjhx4gTBwcFs2rSJ3NxcnnvuObRaLTqdrrHFqzNGD+/C70sPkZ6Rh8VstV5Y/SaAbA361OaXKA8AWhWoVdYurq5qZF2ZH30J0nMLSUrNwViUTWxsLEOGDGnQ89mRFE9WbjGOaRLaXBWSDGYnMHjLmFxBqCAhAFqcrLiWn0AiI8vMilXb6NUjrsHlb2gKCwvZv38/Z86caWxRbgmar4qkUCUH1hytsjZGKZIkMe6ZEQ0gUcUUFRSz6dcdzH9vCSu/3UBWSnajyaJgrfoHMHDgQHQ6HQcOHECW5ZtKyQBw1jvw5cdTCG1lVbLVKgl1SVCoyiTjkFyAJFt7ugqdGtnB+k5mdlJREORYfodC8NWctTZrQU5ODrIsc/bsWY4ePVpl48K6YNnxSNwjVThkSKhNEiqLhDYfXGPU6JMkkMHiKGGpQPSy5BeoiI2NrVdZG5vc3FxWrlxJUlJSjYpO1RaZWvY6UWI0FJoaVTVVK0ubHqGM+b9h9SxNecwmM7Of/J5N83di+w0Wgm/+Nof7Xr6bJz58UIkIbwRKTeaLFy+2KRezZ8+mX79+jBpVdbO75obeQUuXIB8SziRR6KLCrNdgUUuojWB20KMyCyRZoDILhFrC6KHF5KKusNaGygR7j8XjrvodjUbD0aNHOXnypC19slOnTjg6Xucpf4Mk5+Zz+WgGVi/JVdlK/+2YocKslzF6iEqDvUuJiBhAz27+VQ9qpqSlpXHkyBESExNxcXHh8ccfb9DfGFHLYFChKBoKTY3QLiFcOBxd5ZtUmx6h/Hvrv3Bs4PoYhmIjj7R+nqzUHKz1PayWFSEEsiz449MVqFQST3z40HX3pVC3ZGRkoFarsVgsthLVQghOnTp1UykaeblFTH9qLkmJWRS5qSnysypVKpNACDUWrRoLYNRb70+VSVjtvxUoGQBqg8AsS7Rp04azZ88C2JSMCRMm1JuSAfDf7cfKKRl2IiJwTJcwuletaEgS9LktDHe3myu7TJZlzp07x6FDhxBC0KtXL4YOHYqDg8NNXRm0KaEoGjcZhXlFbJm9n5jDiZWntQKSSuLdZX/H2U3fgNJZ+cdds8hOy60wqFBSqRAWC4s/W8l9r4y3lUpXaBiio6OxWCw4OjoyduxYzp07R2xsLLfffntji1an/P7rHpISsrDIgiIfbaWZWZIAIYHKBEJjXbhGedcWClQyeLg5EhERQZs2bdi6dSv5+fkMHz68wuqhdcnFhMwqt0tIaErKe1icQFNBuRxJgkA/M7KlGLh5FA2j0cjKlSvJzMyke/fujB49ul6Vvqoo2+r9Ruc3VxTb9E2EEIK3J3xC7NGk0hX2/wVUahWSJPH3n561CwJtKLJTczi1+3yF20qtGqhUWMwye5YfamDpFHr27AlAcXExV65c4ezZs7i6utKvX79GlqzusFhk1iw/iiwLLI4qhFZlUzKEyj5YUmW2/u1YHCS0BQJtvozaCGojaIoEulyBymw1FGRiZN7+U3j5+nLfffcxceLEco3W6oPqZAGJElPGsIEdAFCX/A6o1da5/fuEEt7K1ODZMvWJ2Wxm586d5Ofn89RTT9W7Zel6KJVBFW4KTmw/w/Ftp8usKWn3XqaWhn8rH/6x4G906Nu2UWRc99O2KrfbfjQliYLsggaQSKEsAwcOJCMjgwsXLuDp6QmAh4dH4wpVxxTkGyjItxbiuvYlUahK1pUUCFWZwKIFoQaLrkTBsNhbNEo/WQwyv687zYaoK3w1cTg+PvWryCcX53MhLwNPHy0ZqZWPEwjMztDOy5v3HhhP9Pj+rN90mrT0PDw9nBk1rDNBgc7Mnn3OVjGzuXPp0iUOHDhAYWEhEyZMIDBQaczYmCiKxk3E1t92o9aosZgt12y5atnITMykfZ82DS5bKYW5hQghrvsWJoQgoPXNGZTWlFGpVAwaNIg+ffrg6emJVqulVatWjS1WneLopEWlkpBlgdool2sTb3YQaIpLyo4D2iKB2UHC5KxCqAQag7BlwgqsCoiQQG2WUJkhM76At7ft4eux9ZPRkGks4uPIPezJiLP+ZXuCm8YRzKLCOA0JiWIfCy/3jUCSJNqE+fHcU0PtxpSWTm/I3h+1wWKxcPHiRQwGA46Ojjg4WOPMhBAkJSVx+vRp2rdvz8iRI5tMr5lb2XWiKBo3EXlZ+dfNNjEUGbGYLWi0jfPVd+gXXi1Tr6OLE/3H9WoAiW5OhBBkZWXh7u5eZfElWZaJj4/n8uXL5OTkkJKSQmLi1U6+48aNw9//5lL4dDoNEYM6sGvbOVQW0OVYMLqXySZRSZgdrS4RlRkQoDEITM4SBncJA5Jtm6wG1BLafBkBqI0CUSxxOjGdyPRM2vvU7UPuQkYGL55eT55svOrjUUNBVwPOpxygxNUjISGwKh5F/jLv3T2S0W3aVbpfR0dHVCqVXa+WpsyuXbu4cOFChdskSWLkyJEMGDCggaWqGqUEucJNQWCYHyq1hMVceWi5Z4BHoykZAP3G9MTR2YGi/OIKFY7SLJnXfn4WrU7b0OLdFAghWLBgAZcuXcLBwYExY8aQnJxMSkoKw4YN49y5cyQmJpKTk0NOTg5msxkHBwfc3NxwdXVl2LBhODs7s3LlSgoLCxv7dOqFKVMj2L3jPLIs0KcYMOudkLXYKRuyViBrwPVKEWqTIKuDE0JnVdosOpCNoCrR6yWL9bkvlXxWybAnNqFOFY1lpy8w+9wBLD7mclW3LK6CvD7F6JI1uKQ5IFsETm46BvcJp21OGg906mY33mg28976LWw6F0W2XAwuAv8ALY45KXSmc53JXF/06NEDjUZDTEwMGo2GnJwcAIKCgpg6depNV/eluaMoGjcRo58YxuLPVlW6XaVWMe6pxivOBdb29X+f+yzvTZ5dzoVS+vmxdydzx8T+jShl00MIweHDh8nPz79u1caoqCguXbpEREQEsbGxLFu2zLYtOjoaBwcHAgMDCQoKol27dvj5+eHr62tXUyAmJgaAjh071s8JNTJt2wdy/5N38NuPO1FZwDW6CIOPFoOHFqGxxjRJFnCOL0ZbICNU4BFZTHpPPaikq36VknEqi3VVabyeLMG8o2c4k5zO1F6d6RZQu8yTg7FJ/GfPUSxh17pFryK0YG4pc8cdYXw+4G7b+oULF1JgNLL4zGkWnT5FTFY2BpMFyQLqQnDMlhAWiRQPHd9b4lm2eQHt1B483OE2OgcG1Eru+sLDw4Pbb7/dlg0lhCA2NpYNGzZw+PBhBg4c2MgSlkdxnSjcFLTsEMzkV+/mj3+vLLdNpVYRHB7IvS+NbQTJ7Lnjnn7MWjODr174iaToFNv6oNb+vP7L83TqX7mJ91bEZDKxYsUKW7nknj17VhmgmZ6ejkajoXPnzrRp04bTp0/Ttm1bsrOzAQgJCbluL4vSoEBXV9c6OYemSNa5ZMxOYHHWoTLKqE2gKZARWgkkCcekAnT5JW3kZVCbBY6ZZop9tNbaLyWVd7UFpe4KMOklLA7Wh75kgGOJKRxLSuXd4RHc3urGu7kuPH4OlSSBRpSzZpTFImSSCu1rQ+TLFiYs/I3orMyrZTRUVqXI7A75rqDNAadUQGhJCTWS7RXPkbg4upwN5N9Dxjf54nmRkZFERkYC2O7zpoaiaCjcNPzfxw8TmxbDxU2xZCRmAaDRaRj20B089ckjOLs3jVbQvUd2Z17kl8RFJpKTlotfS59GSbdt6mRkZLB06VJSU1Pp06cPhw4dIjs7u0pFw2g0otFY/7QdHR3p3bs3ULPskeJiq2urqT9gbpSCvCK2rz6Bo8mCyduRYn8nZL3VVacpMOGQXIg2y4CsdwCd9VoKIdDmWSj2ttbdUBmsgaKl1gyLg7VOhdEVJDMggzCB0Ar+tXE3IYVOtPb3ZGzvcG5rE1Dt5nQmi4XjSSVpJRaqLEqgliT8nVzs1i3LyeGKyWhfq6v00CWWGbMbGI2gywFLlgaTmxq1xsxptyTe2bWRdwaNrpasjcXRo0dxcHDg3nvvpXPnpun6URQNhZsGSZLoNLINM/83g8unYjEZzIS0D8LFo2koGNcS0j6IkPZKd9iK2Lt3L1u3bkWv1zN+/Hhbpc7r1QKQZbmkyqp8Q4pCQUEBx44do2PHjmi1N2ecTFJcJmaTBQnQZRSjyyi2uj3K9FYTgCTLFRTTFDhkClvhKwGY9WDwkGwxHrosMLuUZJcLqyslyVhE5sViDl5IZFSv1rwwtk+VykahwURcSg4my9XMGHWOBot3+RiNUixCMKFlF3ZvPsPG5UdITMwitzgbp3AH8ltprW6fskhXz8HoCQ5ZoMpTYTZJUHKb7dfEkG8w4OLQsBWEa4Kfnx8Gg4EuXbo0tigKFaAoGjcparWatj3CGlsMhRskOTmZTZs20bFjRwYMGIBGo2HHjh14eHhUmgViNpu5fPkyKpUKg8FAdHQ0bdvWrF5KWloaGzduRKPRMHp0036LrQ2OjuWDBaUKE7Yku38Z3DQIScLgAeaSkhNmB0B9dZxDDsiaq3NsdfM0IJd82HA0mvBAL+66rfz3U2Qw8cv6E2w+cAmzwWoucddAkZfAIGmwuJutv9wV6AwD9SGseGML507EolKrkGWBswQu8SYK/TQkjHK1uoaunSiVyKcFlUWyK5IqdIL1F85zX9fuFV2gJkFQUBB79+6luLi4UYtyVYVi0VBQUGgSxMbGsnLlSluxJ29vbzQaDcXFxVy8eJHbb7+90rfgpUuXcu7cOdvn6prmS0lKSmLdunX4+fnxwAMP3NTxGcFhPgSF+pAYk15p/w8JkLXWWBYByDqJYi81kgRCK2GuxNgjydaslHJcc5xl+yIZ3auN3fdkNFl468etXI7OQDIKNCXTJCNoikCbB/kqB8yBJoRziWYkBM6RZlrvV5OWcJ40CZBBli2gkmy1NZzSzPjtLSBlkL1rpZyYWhm1xl7rKjBVULe8CdGiRQtkWSY6OppOnTo1tjgVIqhdimr99v+tXxRFQ0GhCbF27VoyMjLIyMgAICsrC1mWWb9+PQDdu1f+VpmRkUGbNm2IiIhArVbXyO2RkpLC+vXradGiBQ8++OBN6zIpRZIkHn5hOJ+8sqjC7QIQWjWoVbZsksxOTqCSbAknFU4qUTKE9uqqUouB5prq3gmZeeQWGXEv09Rw0+FLRF9KR12mQKftWAIccwSWVIliiwNCK+MSX4znoSI0RQJDWTlUWHsdWWRbMzhJgFu0kfQ+MhZ9GZdayRNMKqkZYvY146Czz27p5te0K2umpFiDyl1cqlaiFBoHRdFQUGhC5OTk0KNHD9q2bYujoyNqtZr//e9/ANxzzz2VVjk0GAxkZ2fTsmXLGpuO09PTWbduHQEBAUyZMuWmVzJKGTK+J1lpecz5ZC1gdWuI0qQOjRpZ74AEWBwlMjvosTirSjQQyisbpe4RtTVeg5IM2NLATXVRSfGva7hWYVm35wKqSqqAl1RGxyUdzD4S3nsLcLpSor1Ikv0rrwyoBKhVYJZBYz2SJMAp2UR+a4ercpfEpeiywegrow0swhbaI4NbriM9+9x4xkxDkJ6ejpeXFy1btmxsUSpFcZ0oKCg0CVxdXTl+/Dht27ZFr9dTUFCARqPBbDbbgkErIjY2FqPRSFBQzQJrSy0Zvr6+PPTQQ7dcoaOJ0+5k8LgebF52hMTYDPLzDSSk5HA+OROLTsLgo8PkorZm4BhAtl0eYW/KVklYtAKhlRBSSSlwyap4qIvBIdv+uAKBxkHFzq2b0Ov1+Pj4YDabSUrOrdK4LgEUW7hth5GU6Cxbiq1Qq0CnBa3mqsJRqg2prvY6gpJgV1sdEOuiKQDhbkLVodCmZEhCIJlU/KNj/ZRSr0uEEE0+Q0pRNBQUFJoELVu2JC0tzdZzQq/X07lzZ06cOIG3t3el80JCQnB1dWX//v2MHz/+unUyAC5fvszWrVsJCgpiypQptn4Rtxpefm5MfvpqEbSCvGLumfBvclo6lbg9rG4Htam0XbxASKDNteCUJVCZrOXJi31UWHQSsgZUZhmzgwpNMagtFfcfkT0tuLi4UFhYyOHDh1GpVGhVDpir8sYLgf58OikF1yidFhmpyICQZXDQ2RQIwKpoyFfrbzi1dMWskREyuGjyae8dR7fOFzmUHUZkbgAyKtSShY7uSYxxbkuv4KZtzYDmoWjcyiiKhoJCE6Jv376cOXOG5cuXM3LkSC5fvszFixcZNWoUYWGVZxE5OjoyceJE5s2bx+7duxk0aFCVxykqKmLLli20b9+eiRMn2upuKICzqyP3ju7FH8sPkR/siMldd7UKKKBLM+GYbkRycCjpdQIOeQKHPPu4BotOpiBIg1CVBkFc7T9i9BQM6hPOlDHWCp6ybA2+zP58Jbv3RZUXSpYBCVWBEaGxPlDLqi+l/5YMJoRWY43PuAaVWkWf28N55++PAJBYcIClUW+Qne8CRg3jgk4xOvAMBosGR7UZtSQjmWOQxSOopOsrro1FUVERFy9etHUbbqooFg0FBYUmga+vLy1atCAqKort27cjSRJ33303PXv2rHC8wWBg3rx5FBYW2vo9REZG0qNHD9zd3Ss9TkpKCrIs079/f0XJqIBp00eSkpjN3i1nwUGNWRKoLSCZZDCZMfg5l/Tlkcq1li9FbRQ4x5owuakoDFCDkFHnG9DGZOGQX0Q7zxCK7izGycXR9jb+t0cHs2ffRSSDQGWS0RSaUBWaUcklsaZqGZO7A4YW7jjE56DJsY8wFQAGEziWxmBYZZRUEBTixUsz7wHgUnomr6/ez8n48ZRK7eWSx5BuJ+kUEn91f9p8ikUqeqnpBoOeOXMGo9HY5LsMK4qGgoJCk2DDhg1ERUWhUqkwGo3cd999VVY6PHz4MCkpKXTr1g1ZlvHx8cFgMODsXHWBtlLXyty5c5k8efJN29PkRtFqNbz1+RROHr7MpuVHSU3OwdvPjeHje/DrR6uIPBWH0ccV4aVHOKqRHQQqg7AFikJJeIQAh8R88jxkAhfGIFmsAyQJ1p5eye4vtzJr5WuE97Jaq+Ki0vDKh6I8I1KxyU55kbC2olfnyRCbhLGFB8gCTZ4t38RqeJHlMgKo0Gph2t/GMGribeidHbiSmcXkuYsoMMqUVY0y8134c28Eqj4H6N/mMoUyGGuYIt0YpKWl0bZtW+66667GFkWhEhRFQ0GhCVEa8CnLMgMGDLhuOeWzZ88SGhpK3759a3SckJAQ+vbty8GDBxXfdiVIkkT3Pq3p3qe13XqPDyfz8oQvEMk5aFNyKG7tjezmiOxgzfyQSjwosgpA4HgphaCTxWAWdqmqALmZ+Uwf8i7e3dpgMMrkFBqQnXRXFZLyQln/6+qC46U0DCFedoqGLZ225L/BLb0Ycr8f9zxsbTImhODtlespMBqRy4WCWM0yq4/14vWOp3DWmjhb7E6S4RxtnJquRUOtVmMwGK4/sJERQkLUwipRm7mNjaJoKCg0AcxmM0VFRdx5550UFRXRokULIiIibNstFgtpaWn4+fnZFAOj0UhSUpLduOoSFxfHoUOH6NOnD+3atcNgMHDixAmOHz9OXl4eLVq0oGPHjmRnZ5Obm2ury6HVatFoNGg0GlJTU8nPz6dv3760adOmzq5FU6dN5xbMXvkSf3voW0xp+ThGZ2AI8cTirQcJZK1UEtIhcDgRi6rAAJIKQQWdVwWYjWZSz8UhebqDJKEymhFODlCZNUGSQKdF1mrQJucgO+hQmcoU2NJocHDWcd9jd/DgU4P4Y/EfgFV5XbJ6NfvikirfNxJFZg0bLrfh3vbn6eCYQ4E8i4MFJro6j6rVdasv3NzciI+Pv/7ACkhOTq5jaSpHRqpVwa7azG1sFEVDodakJ2ZyZONJzEYz4be1pt1tra8/SQGAxMREFi5cSH5+PmAN6nzmmWfKxVds376d3bt30759e+6//34kSSItLQ0hhK2KaE3Yt28fYWFhDB06lF27drF3716bnzsgIID4+HiWLVuGVqvF3d0dWZYxm812i7u7O2q1mvnz5zN16lRCQ0Pr4pI0C1p3DOaN7x9nxjtLkIxmhFaNKPPwFoD2QjKarELr5+u4IITBiGQ0ITnoECpVFYpA6QQBKhUqgxGLg7AdU6VW8emiZ+nUs5VdxdHs7GzWrFnDr0cSwKvqOikaSSYh31oVVi2Bs8qMWv6ZYnkwjqqml5kkhKhxFVywKupr166tB4kqRonRUFC4AYoLDXz1/Bw2/boTUcYOG94rjBnzX1SapVWD06dPk5+fT0hICP7+/hw+fJiYmBi6desGWN+4Dh8+zOnTpwFroOf58+fp2LEjHh4eSJLElStX8PHxqbYLJDc3l+zsbMxmM1988QUmk4lOnTrRvXt3W2xHnz59KCwsRKfTVRksKssya9asYfXq1Tz33HM39IPfXIno25YZfxvDh7PXIHRlrpHJgu5SCtrEbOtnlepqw5PKEAIsFut/q1NrWpIQJmtlL8lkbenq6qHngwV/pV3XEMD63WzYsIHo6Gi+/PJL9qY5k6i7/k++WajYkRDCxPbnaeGSh1oS3O6SxKq8w0S41dx6Vt84ODhQWFhY43nr168nMzOzHiRSuBbFOatwQwgheOe+z9h8jZIBcOlEDNPv+Bdp8RmNJF3zoV+/fgQHBxMfH8+ZM2dwdna29Wowm80sW7aMI0eO0KFDBx566CE8PT25fPkyAE5OTgghOH78OKdOnar2MTUaDcHBwfj6+tK5c2ceeOABBg4cWC6AVK/XXzcjRaVS0bNnTzIyMkhISKjh2Td/7hrZjQe6t8LheAy684k4nIxFv+fCVSVDsnZ0leQKO7ZdRVtynS0ywmIpSWetAiGQ8guRLRYGDOvCm98/zsIj79mUDIDdu3dz6NAhnJycaNm5H+kGNUYPEJK4ttyY/a6BM9m+3LvmXmJy3aziqQSZ5ktVy9RIeHl5UVRUZCvbXx2Kioo4duxYpdlc9UFpjEZtluaKYtFQuCFObD/D4Q0nKtwmW2Tyswv484s1PPPvRxtYsuaFu7s706ZN4/Dhw2RlZREeHm57uC9dupTU1FR8fX3p168fAM7OzuTl5QHY0l/Dw8Np165dtY+p1+sZO3ZsnZ1DUFAQOp2OyMhIWrRo+sWd6pr/mzmJk7siuXI23tpbpBSVCpVGTXBbf2JPXKlyH5KtWJpAJUnI2Xng6VaxC0UIyCtAmM04uzrw9k9PlrMkJScns337dnr06EFGRgabzqQiIzA7AzKoDNhqetjtGoFQg4d7Pg46E+8dG8D/Bm3AIsBiaZp9RIKDgwGIiYmpsqhdWUqDRz08POpLrHLcyq4TxaKhcENsWbAbtaby20e2yGz8eXvDCdSMMZvNBAcHM3LkSFq3tsa3yLLMuXPncHd3Z8SIEbaxBoPB1svk/PnztG3blsGDB+Pk5NQosoM1zuRGyp/fLOgctXy27nWG3T8QlYMONBrQaFDrNAyfMpAvN/2DYQ/dXul8Se+EpCkpiFXq/srLh7wC67+FuLoAFBZDRjbCZMJJp6rQXbV582bc3d257bbbAEjPLeNaUJctpV6iXJT+TwOufvm4ts1B07KYKz5anoi+g2VZreBy0yxPX6p4V1U3RqFxUSwaCjVGCMHl07FYzFWbd/OyCm44UOtWYu3atZw4cYIWLVrwxBNPAFaXROfOnbl48SInT56kTZs2+Pv7I0mSrYqkj48Ply9fJiYmptGKFRkMBrZu2UaLoBDat2/fKDI0BZzd9Pz9+yf4v3cncf5INJIk0aF3azx8rEGVf//xKa5EpXLpyCUwl2SfaDVIjo5IupLgTJXqqqLh4gypGZCTB24uoFFbrSX5hVBUjDCbQZbJSCwfY3DlyhUuXbrE8OHDbXE76pJMGHURWJwADdaqphZh7X1S0pcFSaByNiOXvIPKqIgu9OTbfE86ZScxob4uYC04deoULi4utr+BqKgocnNz6dGjR6VxS6VuwqKiogaTU0lvVVCoJmaTmZn3fkbkoev7a70CPRQloxqYSoL6ro1xGDt2LNu2beP8+fOcPn2aiRMn4uHhwZkzZwgPD6dv375kZ2ezadMmHn300QZviHZyZyR/zF5Lwrl0ALZ+foaQ8ABatg+iQ5/W3H53b3SOt0Yn2FI8/dwYcFePcuslSeK9RS/y8oTZpCVkIsvXKOAS1qZopR81GiyFhahkGcl0te2rEAJhMiFKTP+yWSYtPgPfFt627Zs3b8bX19euZH3bjt6k7i7EIVNQWOrdkoAyPdgQAkkS6D3tH76SZK18ety9iIXHzjClZ9W1XRqSgoICLl68yLBhw9BoNFgsFhYsWABY62t07969wnnFxcUVrq9PRC1dJ7eMojFz5kzeeecdu3X+/v62XOTKHiqffPIJf//73wFr1Py0adOIiYnhqaee4l//+pdtXGhoKDExMezbt4/+/fvb1k+fPp3jx4+zffv2moirUMdsW7SHL/76A0X5hqudHytBpVYx9smm3/WxKVDqLxZCYLFYbFU7nZycGDNmDKNHj+Y///kPx44do1+/fqSnp/Pnn3/a5kuSxMmTJ+ndu3eDybzpl738NmuNXUWpjMQsMhKzOL7zHCt/2ML3ry/kXwuep8vA6sePNDZCCI5uPsn6n7aSfCUNFw89zu7OJF9Jw2KWCe3cAiFkoo5eRghBpwHtcXLTc2jDSXIy8nF2c0Lv6oBaBcYiE6nxGRiLTTjqHTAWG5EtFhy8PTCrtcglXdxFcTFyQSEOnq6079+euJNXSItPBiGQi4pApUJSqax/bubyveavnI61KRpnz54lISGBMWPG2P0e9+0cxI4TMWjyJHQZYPTGvte9sFYH8w7NQq0p/4ctqUDnamLOsaN08PehZ5B/nV/7G+HSpUuoVCqbiygmJsa2rSoLW0pKCnBzu1uu97wWQvDOO+/www8/kJWVRb9+/fjmm2/sigQaDAZeffVVFi5cSFFREcOGDePbb7+tcSxWjS0anTt3ZvPmzbbPZbtEJiUl2Y1dt24dTzzxBPfee69t3XPPPccjjzxCnz59eOaZZxg2bJhdwSFHR0def/11duzYUVPRFOqRbYv2MOvBL2ymXUmSSno9lEdSSfi38mHCC02/JLAQgtO7zxN9MgYHJx19x/TEK6BhmzOZTCbb9TSZTOU6r6pUKu68805WrVpFYWEh+fn5tG3bltatW7Nx40acnJxIT09vMHmTr6Tz24drrB8qugVK1uVnF/DmxM/5bs87BLVpGg+mqjCbzLz/wGz2LDuISq1CtlzrGpSIOmbN+EFYt8Wes1qhJI0WSa0mP/tqXIUwXe2wWmC8qiAUJl/NjiirNshGE0eX7imvxMvy1bLiFZCTn0NGRgbR0dFs2LCB0NDQcg+C23wDKegko49U4ZQqoSkAo2eJG0WAq0sh+rB8NDoLRSYNxUYtspBQqWT0WhM6jQVJAq2zkdk7DzN73FC8XRovLqgUWZZRq9U2a15p9pVer7fFMlVEWFgYrq6uREVV0MCunhBcP8v5evNrSlXP608++YTPP/+cn3/+mXbt2vH+++8zYsQIIiMjcXW1uvymT5/OqlWrWLRoEd7e3rzyyiuMGzeOI0eOVKtDdCk1VjQ0Gg0BAQEVbrt2/YoVKxgyZIgtwA2shWN69uxJt27dCAoKsjWCKuXpp5/mu+++Y+3atYwZM6am4inUAxlJmXz2f98ClDf3VnD3+4Z48+Xu93DzappR6qVcPBrNhw9/Sdz5RKt5WFgtMaOnDeW5/0xD59AwZv/AwEBiY2MBaxGhin4ge/bsicFgIDExEYPBgMFg4NSpU+j1epycnK7b26Qu2fHHIVQqCdlS9U+fLAtMJjPLvtvEc/9+uIGku3F+fmsRe5cfAqhAyQB7E4D9zS/MJmsaa4kiLrC6P0RZC0TpTVbJH47tmDV4oqh0ErtObGPPGeuLWXh4OHfeeWe5cZ46R0a3CGWtKhpVoYQ2S4VKgKwSWLQSPoE5GHSQWaDHLKuvnquswmjWolOb8dAXIWQVsbl5PPz9Sl4c2Zu7ujVuRVhPT08MBgNZWVl4enpy4cIFgOv27lGr1XTs2JEjR440hJiAtbLntVk+NZ1fUyp7Xgsh+OKLL3jzzTeZOHEiAPPmzcPf35/ffvuNp59+mpycHObMmcOvv/7K8OFW6/T8+fMJCQlh8+bNjBpV/UqxNc46uXjxIkFBQYSFhfHAAw8QHR1d4biUlBTWrFljC24r5d1332XEiBHo9XpUKlU5YUNDQ3nmmWeYMWOGLehNofHITsvh2d6vYyg0ltsmSRKSSrL+bpYsaq2KXkO74unv0dCi1oj4i0m8MvhtEi6WmhGt62WLzLo5W/j40a8aTJa+ffsSEBBAUFBQpdkjxcXFuLi4MGHCBFq1akVcXBxJSUmMGzeO7OzsBk3Tiz2XdF0loxTZLLPjzwPVGmsoMrBt0R5+/2QF6+duu2odaACKCopZ8c36Sq10Nuy2X5MaarlaYlySJFCVfeMryRqp45ilEU/cwZ2D72Ts2LFMmTKFIUOGVPqmeY+TLwHFMhZnMIUIikJkZB+Q3WTcdW3JLXLELJc+EiS7/xotavKKHTBkW9NwLUIwe8Mhjl5puBLeFVGazpqenk5mZqatcFd16mP06dOHgoKGu8fqitzcXLulqj4vlT2vL1++THJyMiNHjrSNdXBwYNCgQezduxeAI0eOYDKZ7MYEBQXRpUsX25jqUiOLRr9+/fjll19o164dKSkpvP/++wwcOJAzZ86Uy1+eN28erq6uNm2plDFjxpCWlkZubi6+vr4VHuef//wnc+fOZcGCBTzyyCM1OiGFuuWXt/8gK/mq1an0h7isZaPsv2WLoPuQphMsVhmLPlxa4jMvr8wKWbBz8T4uvHaJdrfV7xtbdnY2f/75J7fffnuVDdTmzp1LWloaaWlptGjRghMnTjBkyBCuXLmCRqNpsIwPi9lCYXYBwmi0vbVL16lIWnyNkpoSk8a2hbvJTsvFL8SHoQ/dzsVdMdz/7FMU5BRa3RayzH+e+5GH3ryXB/8xscZBxWnxGWyYu43Y8/E4OTty+739uW1EtwqzEFLj0jmw5ijFBTVozGWzTpRBtgBlAjolyVp6/NpxFa2rAZJKQsiCfvf2xBCYy+7du3F3d2fcuHGVzhFCcOl8JI9pvQkfPozFF0+RVJhHoN6VSeFd8dbDAzvnV3VUioxahAxSiZFGJUksOnCWXqEVW7gbgtKgTgcHB5vbxMPDo1qp1g3pboS6yzoJCQmxW//2228zc+bMcuOrel6Xxmn4+9u7NP39/W1xLsnJyeh0Ojw9PcuNqWmPmBopGmXb8Hbt2pUBAwbQpk0b5s2bx8svv2w39qeffuKhhx6q0Azs4OBQqZIB4Ovry6uvvsq//vUv7r///pqICMDixYvR6/U1nnezkJCQwMKFC2u9H7PBzNqftti/5QlR6YNFksDRzYFkSwwLF95Yk6OGQLbIbJq/E7mK9FxJJfHtv/5Hv4e72a2vq2tbSmxsLMXFxaxZs4aTJ09WOEYIQVpaGgDHjx8nMDCQ8PBwLly4QEJCAu7u7mzdurXOZKqMlMgM9v5wnKLsMt1CDQbQaFCV/p2XvLXbFAMJnH0cWbhwIbIsOPDrSc5tumS1hklW98p3r/4MZb6KUuXPVGzi57cWcerUKbr/pfqK1NmNlzjwy4mrD3RJYu3/tuAd5sHI1yJwcrO+ladezODQojOknG/YB05tCOzqg1uAC60jWmDUWbNDvH28SUlJYcuWLRVmHqWlpbF27VqSk5Px9/cnZfdBrM4VDeQVkZJykN+dr2DvGqoASQK9jDqvJPVVCI7HprJq7Tq0qsbJiMjLy0OSJPbs2WOrmKvRaFi0aNF155a6KxsKWUhIdVCwKy4uDjc3N9t6B4eK+89U9bwuTba4VoGvTjmCGylZUKv0VmdnZ7p27crFixft1u/atYvIyEh+//33G973yy+/zLfffsu3335b47mTJk2y+yJuNRYuXMiUKVNqvZ/ES8n8Ylxpv1IIhCxbo+BLbrjS/7p4uvDJhn/SpkdorY9dnxTkFPCzeXmVYyRJIsA7sNx1rKtrW0pkZCRRUVH06dMHPz+/SsfFxcVx7NgxIiIi8Pb2JjMzk6+++gq1Ws3EiRPRaus3niTmdDx//PVzLOYKOpCazcglTeFKkbRaJJ0OSaVi6uv3MnrKncyZsYBzmy6VeBHKFMG+zsv96VUX+dePr6N3vX7w4d4Vh/hp3tKS/Qq7/2bF5nJ83kW+3PM+J3ec5ZfH3qskFqMaVGSRUKmvGSLKj6uFJcM9yJWP1r2FJEmkpqayfPlyJk+ejEqlYtGiRQwfPrzCF6z169czcOBAFi1aZFcUrixXTi1iZ9T568qg9S7i9l6RHN3akaICa1zQ0GHDcHZonGJeGzduxM3NjeDgYNtzaPTo0dXqJvzhhx/Wt3h2VHQ71HQ+WLvV3sjzrezzesKECYDVahEYGGgbk5qaarNyBAQEYDQabfEvZccMHDiwRseuVWVQg8HAuXPn7AQFmDNnDrfddlulOczVwcXFhbfeeosPPviA3Nzc2oipcIM4VfbDLoTVHy2Ezdox4pE7mXt2dpNXMgAcXRxxcq08Ir0Uv5Cad0WtKe3bt2fs2LFVKhlgNZfefffdNheli4sLAQEBWCyWBvEzL/9iPbJFLtfXpjKEyYRcWEi329sz4sEI8rLyWTJ79Q2FzhuKjCyZvZqvX/yJWQ/9hzlvLiQhqmLT7YIP/kRVydu1bJE5t/8Cp3ef5/Mnv8NikZGreT42bJaa8gGdUpnYCFHaJO3auRXMqy7jnxthe5O8ePEirq6utG/fntzcXFQqVZXVYUvj3SrqXWMxW7jDpxNVWjMAEFhcZC4bfbjn/j24e+Tg4+KEXtd4tVLS0tJwcXFh+/bttvOvbjfjF154genTp9ejdE2Lss/rsLAwAgIC2LRpk2270Whkx44dNiXitttuQ6vV2o1JSkri9OnTNVY0amTRePXVVxk/fjwtW7YkNTWV999/n9zcXKZOnWobk5uby+LFi/nss89qJEhFPPXUU8yePZuFCxfaej0oNByefu50GtCO8wcuVvyDXKKiP//1E/zl2dENL+ANolaruWvaMJZ/va7SN1rZIjPysSENLFn1iImJ4cyZM2RkZBAcHGxLRQOrzzo3N/e6iktNMBQZObrhVLWVjFIkCby8nFBr1BxYcxSzsXwNiOru6Nd3lqDWqG2K7aKPlvPA639h2gdTbA/fnPRcLhyuupCcWqNm5XcbSLyUciOCYHMvCPv7RtJqrS7F0oBPWUZYKjjfKl5pbSm1ZXQRtUaFxSzTdnAII6YOKtmF4MqVK3h4ePDjjz+SnJyMs7Nzlebs0tgUS4nyI4Rg57pTLP15FxdOxYMELcMk0iLUFHWs6LEgcNMWk250JdfoiAEto0cc4MgTHdjidoBBk3qjrUZn2LokLy+PgoICCgoKcHd3RwiBn59ftd/2XVxcGjThoKErg1b1vJYkienTpzNr1izCw8MJDw9n1qxZ6PV6HnzwQcBaY+SJJ57glVdewdvbGy8vL1599VW6du1qy0KpLjW6M+Lj45kyZQrp6en4+vrSv39/9u/fb1f+eNGiRQgh6sS8rNVqee+992wnrtDwPDpzMm+Mfr/CbZJKYsDdvZuVklHKA29MYMeSfWQkZlX44z/4gQhahAdWMLNxuXDhAgsXLkSSJDp27Ej//v1tWQaFhYXMn28N6Hvqqafq7JhFecU1VjLAGlS744+9PP/VExTlFV23yFvFSLZn+7Vum0Ufr8A7yJMJz1t90Yai8plR5fYmQU5a9SykOkcdbt4ueAV6khafiRCC1l1boXPSEnMmFiGg++DOhHRowam9F0iLy8BUbCQrKRNDkYxnoA9uPq7oPfRo1Bpk2UJhTiF52QVkp+agklR4BnogZBkHJwe6DerEoMkDObH9DDv+2EtxgYHW3Vrh3d2VQsccsrOzycjI4MqVK7YHbKmF4npWrbKKhhCCHz9ey7J5u61ZYwACVJcF/tFmsoYLcoZqKPuF6TUmsoucQAh0KjMgYXHVgD6LBR+v48CG07z630dxcGo4F0psbCwqlYqEhATCwsK4cOFCuUJlTYmGVjSu97x+7bXXKCoq4tlnn7UV7Nq4caPdi8vs2bPRaDRMnjzZVrDr559/rlENDaiholGdAJunnnrqhn/krly5Um7dlClT6tQnrlAzbhvRnRm/vsjnT/0XQ5EBTclbpcUsM/AvfXjj1xcbW8Qbws3bFVdPFzISsqxlD+GqwiEJdi/dT9Sxy7TtGVb5ThqA7OxsVq1aRXh4OP369ePcuXOA9Y20Q4cOtgeNwWBg1apVAHXeQdXFQ4/OSYexGg/yazGbLCRfSSW4XdCNeQwk2/9VyG8fLmf8MyNRa9R4BXjg6ulMXlblD12zyUJI+yCObTl13UPPWvsPug+uXgZVzUPWK6dLRAceevNqkcOCggLmzZvHkiVLEELYUplbtGjB6NGjWbdu3XVjdMwl9Tx0Oh3H9kaxbN5uAHsFsuSfnpstWDrIFLfQoFVZ0Egy2YVOWIQaJPBUF5cMVxEwtoDL3+i4dCKeFd9tZ/LLI2kIZFnmzJkz+Pn52Sw6UD4j41bmes9rSZKYOXNmhRkrpTg6OvLVV1/x1Ve1S/dXurcqXJehD97BH0k/8tL3T/OX5+/ioTfv48dTnzPzz7/jqK844rkpI8sy/7z7Y66cjrOva1D6byFhMQt+fXdx4whYgslk4ssvvyQ6OpqCggLmz5/P8ePHbdtLK/GaTCbWr19vK35XttJuXaDRabjzgf6o1Df2NrZ/1RF6DOmMf6jv1Tfo6lCNN9Os5GxbtU6NVsO4Z0ZWGqMhSRJOrk488vak68boePq70+X2DtWXtZ6IjIzkp59+Qq1WExgYyAMPPEC7du1wdHRk8ODBqFQqxowZY1froCJKXSYGg4FVC/ZV+T0IFWh3qyksdiCnUE9GgYtVyRCASaIg+2rAqWyy7kfIgu2LD2MymGp/0tXg/PnzZGdn4+joiI+PD0VFRfj6+jbpbMPSNvG1WZoriqKhUC30rk6MeXI4z3w2lUfenkRo5+b75nB4wwkObzhR8YOsJGBPyDL7Vh2moGx77QamVHFwc3OjQ4cOREdH061bN3r06IEkSRw8eJA9e/awdu1asrKy8PLywtvbu176N4x+ZhA6V631AaVSgVqDpNGCWlOSbVH5j+C8t39n9pPf8/xXT6DWqFGp7X92VBoVGkcNHfq1tVvvHeSJJF3/J8pYfPXh9uCb99Kud5tyD1K1RoVKreLN3/6Gh687j7w1qcp9PvbeFNSampmH64Pz58+TlZWF2WwmMTGR9evXc+HCBQIDA20P1eq4Cjw9PfHz82P16tWcPXGlSleYJINjfJkqpaVDjSpI02E0Wa0nKiwkLLla/bco30BqfNYNnWdNEEJw8uRJOnXqRFxcHG3btiUpKanROhhXl9Ksk9oszRWle6vCLcfa/22pumZSSZloIQvyMvNxdmuctyQfHx9effVV9Ho9FouFsLAwTp48yYABA/D09CQzM5MLFy7g6urKXXfdxYoVK+yaEdYWIQQ5OTkkJiZy8uRJej7RjhM/xVjjLUoUi9KHnFCrrQWrKrmo6+duY8tvuxg1dTCp8RkcWn8MhFUBuPO+AXj3debp6U+SEpNGWlw67r5upMVn8vrIiuODSlFpVIR0CLZ9dtQ78OnWmSz7ci0rvl1PRkImKrWKiAl9mfzaBNr3tqY93vfKeIwGE/PfW4LFZEGlUWExW9A5aHniw4cY83/D6uAK1p7i4mKbiyQ2NpYdO3ZQXFxMu3Y1a1SnUqkYPnw4mzZtotCQVuVYq24hQZoOdCUKR7EaTCokScbFqRgQWJLBnGn/CGkI5SwlJYXc3Fw6dOjA2bNncXJyIicnp8krGrcyiqJxC2AxWyguMODo4oharRixEqKSq347KNFCNDoNHn6N292x1Pes0Wh4+OGH+e9//0tKSgr33nsvR44c4dixYzz//PMcPXrUFiBaF6SmprJ9+3ays7MBaxMqKdoFQ+FFylkvSrIsrhftaTKYWfPjZh5+6z7e+PUFcjPy8fR3x9lNbyuC5t/KF/9W1mJ+weGBBLb2IyUmvcLsIJVaxZ339sfD1z7LwFHvwJQZ9/DAGxMwFBnR6jTlHoCSJPHQm/cy/q8j2bVkP5nJ2fgEe3Hnff1xdm+4vjHXo7i4GJ1OhyRJtGrVigkTJnDhwgU6depU4325uLjwl7/8hYsnV3FuR0zlNigJ8ls5gEFtXcoghIq2wUlois3smWofLO0d5I5fSP03JIyNjcXJycmW6p2ZmQnQ5BUNq1WiNsGgdShMA6MoGjcx8RcSWfjRcrYu3IPZaEbv5sRd04Zy/2t3N/leJPWJu4+rrYxzhZSkKA578PYmFYNiNBrJzc2lZcuWSJKERqPBwcEBSZKIjIzE19e31oW7hBBs3rzZVmURrA/lju078sFL31dcsl2IkvLb1WPRx8u558Ux183qUamsro5Xh72LyWDCUqaSq0qtwq+lD8/OnlrpfEmSrvv9uXm5MvapEdWWvaEpLi62K5bk5uZG7969b3h/KpWKsQ/04eyuGJDLO7wEIGskctpXXJOjY3Asxq0Wds0OQrrG8z72iTsqLPFe1yQkJNCmTRtbIHRCQgKenp522RJNkYbOOmlKKK+3NykXjkTzbJ8ZbFmwy1a/oDC3iGVfrePZvjNIT8hsZAkbj2EP3lF1uqYk4aDX8sjbkxtOqOuQl5fHxx9/THFxMVlZWaxbt46jR4+i1+sxGAxERUXRtm3b6+/oOsiybFMyOnXqxJ133knbtm1ZumgFhXlF5cZbC1PVrD6G2WRm97KD1Rrbvk9bvj30EcMfvhNNSZ0GFw9n7nt5HN8cmHXTK8wGg6HCsuK1oXP7ILxHhyHUki0Ew7ZoJLJ7eeDhqEdTVmlQyWiciyj4yMTl2R62TK3S8JBRUwcyeNKNK0DVpaioiLS0NNq0aWNLsUxPT2/y1gyg/LW+gaW5olg0bkKEEHz48H8wFJVvGiZbZDKTs/n6bz8xc8mrjSRh4zL0wdtZ/NkqEi8l270lWxFoHbR8vu0dmwm/KWA0Xk0tvXLlCiEhIQwePJiOHTsSFxeHEMIurdVoNCJJUo0tHGq1ulx6eocOHTDmmzlCRZ2aa/7zp1aryE2vfrXfkPZBvDrnr7z0w9MYi4w4Ojs02VoJdY3JZKqwmueNkJuby8aNG+ncuTOvTBvEC4YCpLhCtNkmkMDgqcMY5IRe78Dchyfj7eHMpYxMdBo13i6O7Ei9SN6dRRTuTuHgnF1YDAK9l5aWvTy5/+WRDfKdJCQkANhZNMDa9Vuh6aIoGjchKZEZxF9IqnS7bJbZu+IwGUlZeAfWv0+1qeHgpOPfW9/mvftnc3r3eVSqkjc7WdCqUwjvrXiNwNb+191PQ+Lt7c0rr7xCfn4+Xl5edm+5kZGRaDQaW7ZJTEwMGzZsoGPHjtxxxx03fEyDwcCpU6fIz88nIS0Ov7ZepEVnXVN7oeaKhsUs4x9a88qlarUKJ5frl46/mbBYLHXmjti+fTuZmZns2rULFxcXBnkXYurUnj1RqRQZTeg0aib068S0EX0I9rHeS71bXA20fdClDwCio+DzvFjyS/rbtGxZ+7otQghMJpPNguPg4IAsyyQnJ5Ofn4+vry8eHh5cvHgRPz8/m5skJCSEnJycG4pZaWhuZdeJomjchGTG5tqanVWGkAUxZ+NvSUUDwCvAg9k73iHq2GWObz+DkAWdB7anY//wJvu27OLigovL1XRCs9lMWloa+fn56PV6JEkiPT2dDRs2ANaCTr/++it33XVXtfs/lGKxWNi0aZOtqmC3bt0Y3G0k79w7u9bn4eyuZ+Bf6t/MfjMgy3KNqzBWRHFxMcnJyYwbN46EhASKi4t58I47CAwMxCLLFBpM6B20qKtQaiwWCwkJCWg0Gm6//XY2btzI5MmTcXV1rdXfTExMDNu2bbOz2un1eoQQFBVdddcFBgaSlJTEpElXU5Mff/xxhBANEhtSa2rr/2jGvhNF0bgJ0ehUVSoZpTRkueCmStueYY1e/fNGyM/P5/vvv6egoICAgADc3NzIz89n6VJr59Lhw4dz5MgRux/qiigsLGTFihX06tWL8PBwVCoVsiyzY8cOUlJSePTRR2nZsqVt/EvfP8lXL/yExSKjUqkQsoTFWL2KoaUpxc9/9QQOTk0nyLYp4+joaLMc1Ia0NGtKa2hoKLfddpvdNrVKhet1vo+srCwWL15sKxJXalGIjo6mR48etZLt/PnzaDQaxo8fj5OTE4WFhWRkZGA2m+nYsSO+vr4cOXKEbdu2MWzYMDvrhSRJTfbFQOEqiqJxE9Kiu//VBk2V4O7rRvs+12+lrND0EELw559/2vpbJCcnExERwbZt2wCrm8XHx8eW9leV0qnRaMjLy2PHjh3s2LEDb29vZFkmJyeHiRMn4u/vz5YtW2wPu/YD27Pg8tdsWbCbuMhEHF0cOLT2KAlRScjl4l3sCQ4P5IkPH+L2e5QGidUlNDSU+Pj4Wu8nJSUFJycnvLy8EELU6OGckJDA/Pnz0Wq1jBs3DoCTJ0+Sl5fHqVOn6NKlyw3FkURHR7N7926Ki4txcXGhS5culY4dMGAA/fr1ax6Wi8qopesExXWi0JTQezox6rHBrJ+7rdLsiilvTECjVb7+5kh6erqtL1BoaChXrlwhIyPD9raZn5/PokWL0Gg0SJLEsmXLGDlyZIUBczqdjvvuu48lS5YAkJGRAYCXlxfZ2dn8+OOP5Obm4uX1/+ydd3wU5daAn5ndbHpCeiMNSGghEIr0GnpTEVBEFAvgFQsq6mfH3q4d8YoiICgoKNJ7Cb2GFiCBQBJCOqT3ZGe+P5YshLTd9IR5fneu7Mw775zZncycOdWe4uJiTp06haenJ8MnDWdiyzEAPPDCaF4e9C6J0cm6ShryrU6k94wO4oE5Y7F1sqFVoLfy9mkkPj4+nD59moKCAkxNq28FSklJwcPDA0mSWLRoEZ07d66wI3ZOTg7Xrl3D3NwcU1NTFi1ahLOzMyNHjtTLkJuby9WrV8nLy6tWXY/o6Gh27tyJl5cXeXl5+Pn5VblPk1YyqHl1T6WOhkKj49nvHic7PYd9fx+51V5b0AWC+nVrxaZFO9m8aBdBwZ0Y/5/heLZ1b2iRFaogJyeHv//+W98gC6Bz585ER0dTVHSrDHdBQQHe3t4MGjSIpUuXArBt2zYefvjhUjEeJdjb2zNz5kxyc3NJSkoiKSmJxMREdu3ahZ2dHffff7++kVdsbCxHjhxh0aJFdOjQgTFjxuDs6cjC0/9lx7K97Px9H1mp2bRs687oGUO5Z1SQolzUgBJLQU2/w4yMDAICAsjJySEhIYGEhAQ6duxY7vWwaNEi0tLSUKlUBAcHI8synTp1KqXoREVF4eHhga2tLWfOnKFdu3YGKwLp6ens3r2bdu3aMWnSJOX6uAtQFI1misZMwzt/vcTFE1fY9cd+0lMyyc/J59D641w+Ha03c8dejGfdgq28tmQ2Qx7u18BSG8+VMzGs+2ELp/eeR6US6T6iC/fOHtnoskZqgiRJ/P3335w/f77UekdHR31Kq4ODA5cvX9bHWEyePJldu3ZhZmbGwIEDUalU+iqjFWFhYYGvry++vr76497pA/f09MTDw4NLly5x+PBh1q5dy0MPPYS5lTnj/jOCcf8ZUctnf3eTkZGBRqOpVi2N3Nxcdu/erU8JdXBwwMzsVtZOREREmXiNvLw80tLS6NKlC6dOncLExAQzMzN9hdgSZFnG1NSUfv36sXDhQi5cuEDHjoZ1ut23bx82Njbce++9d5WSoWSdKDRb/Lu1wr9bK2Ij4nmq08tI2tKhzyUKx2fTf6B1Fx+8O9Rui/G6ZMNP2/n2mYWoVKK+HkZsRDxr52/m7VUv02d8jwaWsHYICwvj/PnziKJIp06dOH36NKDzW5fEX5w5cwaAkSNHEhgYiKmpKTdu3MDd3b3axYwqekMVRZG2bdtiamrKtm3bOHXqFEFBQdU6hkLl5ObmYm5efpXOqoiPjycuLg6NRkOrVq1o27atPq4HwNXVtcw++/btw8TEBH9/f06dOkVmZib5+flluqLa2dkRGRmJm5sb3bp148iRI3h7e5drIbmdvLw8EhISuO+++2rkCmqSyELN4iyasKLRtJ1eCgaz7sdtlTXYRBBg7YKt9SdQDQk/eolvn1kIMqWKbklaieJiLR9M/orkq5U3j2oqlGQMSNKt8+zYsSOdO3fG3t4e0KUvgq7gVom5PTc3t9QbbG3j4+ODv78/W7ZsKfPGq1A71OQ3bNWqlf7B/+CDD2JpaYmVlZXesuXm5oYsy5w6dYr169ezfft2jhw5QmBgoD6r5OjRo1hYWJSJobC2tiYrKwtJkhg+fDiyLOvjhiqj5Dq9vay6QvNHUTTuEk5sP11pVoC2WOLEttP1KFHNWPPdpoobxMk6hWPDT9vrV6g6IjY2Vv/vknoZLVu2RKVS6TNLnJ11BbDWr1/P4sWL0Wq1yLJMQUFBncrWp08fNBoNa9euLaUIKdQOOTk51VI0ZFlm7969ZGdnl3KZmZiY8PTTT/Pss88iiiKZmZmsXbuW0NBQzpw5g6+vL507d0YURezs7CgoKNBfa3fOD7qGZhqNhhYtWpCVlVWlXCVKcGam4ZVhmwt3c5t4RdG4SzDkIm1KF/LJnWfLKR9+C0krEbrzbIXbtVote1cf4tVh7zPFcxYzOr3Eyk/XkHmj6ptlfTNs2DB9kaJDhw4hyzLu7rrg3VOnTmFubq6vCurl5UVcXByRkZF07dqVy5cvc/HixTqTTaPRMHDgQGJiYtiwYYNB9VsUDKe6ikZMTAwXL17k3nvvxcnJicLCQiIiIsjJycHU1JTk5GS0Wi3W1tZoNBqcnZ2ZPHkywcHB+myle++9l379+tG1a1f9vLIsc+XKFY4fPw7Ajh07AF3Je0PK3VtbW+Pq6kpoaKjR59TkuYubnSgxGncJQYM7klBubw8dKrVI0JCK89gbGwY9zyoYVFxUzAeTv+Lg2mP6Lq7X41L59a0V/PPdJr7a8x4t/RtPFo6HhweOjo6YmJggCAIPPPCAvohWTEwMHh4eXL9+HdD1JUlLSyMmJoZhw4aRlJTEvn37aNmyZRk/e23KN2DAAEJCQvDw8CgTYKhQM5KTk8nJyakymLeE69evs3fvXnx8fAgICODgwYN899135OTkYGFhgaWlJSkpKQwfPpzevXszYcIEVq5cWSZNVaPRlElbPXr0KKdPn8bGxoa8vDxycnLQarX6uQ2hTZs2HDhwgLy8vGrHnzRF7uZgUMWicZcw/pkRSJV0LJW0MvfObjoZA50Hd0SlrvjyFVUiXQaXrzj9Nu8vDq49BlCqzogsyWSkZPLOfZ81ujdzU1NTXnnlFV577TX8/f0BXcxGUlISLVq0IC0tTf+mamdnR0JCAoIgMHz4cLRaba0UfaqMtm3b4uvry7Fjx+r0OHcb48aNQ6vV8u+//5KYmFjleEmS2L17Ny1atMDFxYX//ve/+hoa48ePx8bGhsLCQiwsLLhw4QKg++2srKzIyMiodO7U1FROnz7N0KFDGTx4MDY2NgwfPpyMjAwkSdJb1aqiJDbk6tWrBo1XaPooisZdgk9HT+b+8h8EUSj1gFapRQRB4MWfZtIqsPG3Wi5hwvOjK3adCCCIAmOfHl5mU15OHn99sbbCeSWtRGx4PCcrcbs0FCYmJqUyQa5fv05xcbFeKfLw8CAiIoKrV6/qa22UxE3UR7EjX19fkpKSyM3NrfNj3S24uroyY8YM7O3tWbduHYcPHy5VR+VOMjMzSUtLY+DAgYSHh+Pk5IS7uzuDBg3C1dWV8ePH8+CDD9K5c2fi4+P1cwUEBHDx4sVK42xKgpK7detGly5dePHFF/H09NT/3hW5TvLz80uVUT979iympqZ6999dxV3oNgHFddJsKSwoIvNGFpa2Fphb6ny8wx8bSJsgH9Yt2MqJ7WeQZeg6tBPjnxlBmy4+DSuwkXTo3ZbZ3z3BD8//ikp9K71Vp0QJvPnHHFzL6RC6dv7WSmM7QKeknN13ga5DA+tC9HIpKChArVYb1UArKSkJ0DVX02g0jB07lk2bNpGenk779u0BXR8JoF5u6iWN25KSkvS1OBRqjpWVFdOnT+fQoUPs3r2b3NxchgwZUu5YGxsb1Go16enpyLKMk5OTvtrr7bi7u6PVaomJiaF169Y4OTlVGTjs5OSEIAj8888/3HfffXpXSUREBKArKS7LMs7OzhQUFBAdHc3ly5eJj49HlmVsbW1xdHTk8uXLjBkzRp/ZcrdwN7tOFEWjmXE9PpWDi0/y+1MbKMgrRBQF+tzbg0femUTrzj60CvRmzv9mNrSYtcJ9z46iQ29/XcGuPecRVSI9Rnbh3mdH4tnWo9x9Dqw5WuW8smRcL4iakpKSwoIFCwB4+umncXExrNhYScO0yMhIfHx8sLOzY+rUqfrtkiSxb98+fH196yw+43ZsbGxQqVSKolEHiKJI3759yc/PrzSQUhRFrK2tSUtLAyruc2Nvb4+1tTXnz5+ndevW5OXlYWpqWqnly97enuDgYHbv3s3Jkyfp27cvoLOGZGRkcPHiRc6cOYOZmRmFhYVIkoSPjw+jR4/G0tKSK1eukJCQQI8ePZQ4nrsMRdFoRiTHXue5Xm+QlpSujz2QJJkD/x7j4PrjvLH8BQZO7tPAUtYu/t1aM/fX2QaPz043rBNm58GGVTmsLlqtltTUVCwtLQkJCdGvX7p0KRMmTKBNmzZVztGxY0fCwsLIzMxk0KBBZbZHRESQkZFBcHBwbYpeIaIoYm9vb1AsgUL1cHJyIjc3l6KiogpdFTY2NqSkpODq6kpycnK5Dc8EQcDBwYHk5GRAF/hZVFREdnZ2pUW3fH19OX78uD6tGsDFxYUJEyYgSRLXrl0jMjISS0tLOnToUMpqUWJlu5OkpCROnz5N+/bt8fT0NOh7aJIobeIVmgMLXlhMenJGmUZqsiwjF8t8+NDXhB+9xIzPpzX5BkXVxdnLkbhLiZUGe2rMNQQOMK5JlKHk5+eTl5fHihUr9D5vExMTOnXqRKtWrTh+/Di///47o0ePJigoiEuXLnH06FHi4uKws7OjVatW+Pj40LZtWywtLXniiScqPNaRI0dwdXXVuzTqAwcHB0XRqENKUl0rUzQsLCy4fv063bt3Z+vWrRW6zbKzs/Ul7AMDA9m5cyd79uyhZcuWODg44ObmpldSZFkmKSmJs2fPkp6eXm61WVEU8fLy0mdEGcru3buJiIjg6NGjjB8/nsDA+nNZ1i8ClVZNNGj/pomiaDQTUhPTOLjuWIXdWktY/dUGrO2tefiNCfUkWeNi1JPBhO6oPNDzkbceqHXXyZUrV7h48SInTpwoE8xnZmZG7969KSoq0qf7bdq0ic2bNyPLMq6urnTt2pXr169z9uxZjhw5wsyZM8stIV1CUlISMTEx9WbNKMHJyYmIiIhKH4QK1afkuizJHLmTrKwsoqKi6NixIx06dGDnzp36eI07r+kShSQsLIyAgAB69epFSEgIycnJFBcXY2lpyaRJk9BoNFy4cIH9+/djb2/PvffeW6vKQEpKCh07dqSoqIg1a9aQnJxMcHDwXdUHpbmjKBrNhGsXE6pUMkr48/N/mTBnDGYWd1mvAaDfhJ4E9GvHuYMRZb4vQRDw7eTFhDljavWYV65cYdmyZZiamtKxY0c8PDwQRRGNRsM///xDVlYWsiyzZ88eoqKiAF3zMi8vL9zc3PRlxkHncvn777/Zt2+fvohXeZw+fRpzc/N6j5VwdnZGlmXi4+Or3WNFoWL++ecfgHKbrMmyzM6dOzE1NSU4OBhzc3N69erF3r17WbNmDT179sTd3R1BEMjMzNSnl167do2AgAAGDhxIUFAQNjY2xMTEsHTpUhITE/Hy8iI5ORk3NzdmzJhRqwqALMtkZmbSpk0bOnfujK2tLQcOHKBdu3Z6a0uzQXGdKDR1zCwNVxpyM/M4veccPUd3rXpwM0NtoubjzW+y4IVf2f7bXrTFWkBXd2Pg5D48/8NTmJrXrgJ27tw5LC0tefjhh0vdpEsyQkCXdVLichBFkYEDB5b7xqpSqfDy8qqyr0RkZCSenp717iKzs7NDrVYTFxenKBq1yIULF7h06ZK+ymd518aNGzdITk6mXbt2REdHc+zYMQYPHkxUVBRarZaNGzdib2+Pg4MD8fHx2NralqqdIQiCvhaGt7c3pqampKam4uXlRW5uLra2tqxatQq1Ws2gQYNKKcA1wcnJidDQUFQqld5KV1kKb5NFUTQUmjqtu/jg2NKB69fKprKVR352fh1L1HgxtzTj5V+e4clPpnL+0EWQoV3PNti71n6jp+TkZEJDQ+nevXuZN8ESRUOtVmNiYkK/fv24cOECHTp0qDRLxNXVlTNnzhAfH1/G/15YWMjChQu5ceOG0eZtWZbJzc3Vx6+o1Wqjy1+LooiTk1Op/iwKNeevv/4CYOjQoezatQutVlsmFbrk4RweHk5MTAx5eXlcvXoVFxcXpk+fTkxMDKGhoWRnZ+Pt7U1hYSEZGRl06dKlzPEEQcDGxkbfk0QQBHJycvS/67lz55g8eTJt27at0XkJgsD06dPZsWMHhw4dAnQunWYdFHoXoigazQSVSsW0tyfy9ayfDBrv1b789M+7iRZOtnXeSr4k4LO8iPuSB/oDDzyASqXC19fXIFeHl5cXtra27Nmzh4cffrjUtri4OG7cuIG1tbVRN+uUlBR27txZptnVPffcU+6DqDLc3Nw4f/58uXEBCsZze2dcNzc3JEkiJycHGxubUuNcXV2ZOXMmCQkJrF+/HlEUcXd3JyEhgR9//JG0tDSef/557OzsuHr1KosXL9bvt3jxYjIzM2nbti0jRozQN+4rca/IslyquqwkSRw8eLDGigbo3ECjR4+md+/eXLx4EU9PT6PqyTQZ7uI28Yqi0YwY9VQw6SmZLH5rRYVjRJWIf7dW+HZSzNr1QcnDID09vUxfh3HjxiGKotHuDVEU6d69Ozt37tQH8pVQ4rsPDg5Go9EgSVKZ+W9fV1hYyJkzZwgLC8PR0ZHg4GDWrFkDgLm5ebXeLN3d3QkNDSUpKanSgFUFw7i9Kd6yZcsA3e9WESVWKEmS9BYIGxsb0tLS+O677xg6dCiXL18GdNdLSkoKV69epV27dhw5cgStVsvw4cOxsrLS9zK5/Xh9+/bFysqKVq1a1ep52tnZ0bNnz1qdszFR0w6sjawrglEoikYzQhAEHn5jAvE5sez88jDFRcWl/HoqtYiZpRkvL3qm4YS8i8jPz2fLli1YWFhgZ1fWLVNefQNDadWqFdHR0axbtw53d3e9v9zV1RVzc3MuXrxIYWEhW7dupUOHDvToobPc7Nq1i5iYGAYMGEDr1q3ZunUrKSkpBAQEMGLECLZt24Zarebee+/FwcGhWrK5uLigUqmIiopSFI1aoLyW6kVFRRWOt7GxQRAEAgICyMrK4saNG0yaNIlly5aRlJTErl279KXGW7duTWFhIW5ubgwYMABHR0cOHTpEeHg4rq6u5OfnEx4eTn5+Pm3atKFPnz5NthhbWloaFy9eJC4ujpSUFLKyssjJyak/AZQYDYXmhEeAMz+f+ZJl768i5K9DaIu1qNQqBj3Uh2nvTMKjjVtDi9jsiY+P5++//yYtLY377ruvWq2+K0MQBAYMGMDixYuJjIzknnvuAXQutD59+rB7926uXbuGVqvl/PnzXLlyBbVaTW5uLo6OjoSEhBASEoIoijz66KN4e3sTERHByZMnGTBgQLWVjBIZXF1duXz5Mr17966tU75riY+PB6BHjx7Y2dmxbds20tLScHMr/+9YpVLh4eHB2bNnad26Na6urlhaWvLkk09y7tw5bG1t+e233wDw9/fn0qVL5OfnU1hYSIcOHXB1dSU8PJwLFy6gVqs5cOAAKpWKHj16NCklQ5Ik4uLiiIiI4OLFi6SkpOhjiOzs7GjZsiX5+XdvrFp9oigazZSW/u68vvwFXlz4NFmp2VjbWzV4OmthQRF5WXlYtbBEpW6GPtib5ObmsmTJEmxsbBg5ciROTk61fgytVsuRI0cAylhLevbsycmTJ0lNTaVPnz507dqV/fv3k5+fz6BBg2jRogXnz59HkiTc3d31D6xTp07h5ORUK353X19fDhw4QFpaWrnWHAXDGTx4MKampvrOuG5ubpw8eRJ/f/8KrWIjR44kKiqKXbt26Qu2mZiY6ONtAgICiIqKwt/fHysrKyIjI1m9ejUDBw7Ew8ODPn36YG9vz969exkyZAhBQUGVVgy9natXr3LlyhW6du1aJo6kvoiJiWHjxo2kpKRgZmaGl5cXnTt3pmXLlqVSg7OysupPKCVGQ6G5YmZh2uAKRsz5WP74+B9CVh1CW6TFzNKUkY8PYcob99dJpkdDEx0dTVFREaIoYmJiQl5eHtevXyc9PZ3WrVvXSt+RqKgozp8/D8Dq1asZMmSI3r9tYmLClClT+OGHH/D29sbBwYF777231P5BQUGlPhcXF3P58mW6dOlSKwGcfn5+nDp1ir/++ov7778fZ+eyDe4UDMPT05M+ffoQFRWFJEn069eP1atXExERQceO5ZfKF0WR1q1bc/XqVX1tltuZMGECsiwjiiJt2rThP//5D2vXrmXjxo20bduWjIwMsrOzMTExoU+fPgYHZyYkJLBs2TKKi4spLCxk+PCyHZTrEq1Wy6ZNmwgNDcXFxYVx48bh4uLSKCohC7Juqcn+TRVF0VCoUy4cucQrwe9RXFik75qan1PAvz9s5d8ftmLZwoLhjw5k4otjcfaqv1LZdUm7du24//772b9/P+vWrSu1zdbW1ugSzeVhY2ODi4sLbdq0ISkpie3btxMYGKgPOHV0dOSdd94xWGmIioqiqKio1mpfmJiYMHz4cHbs2MH//vc/unfvzqBBg+qluVtzxNPTk1dffRVZllGpVLRv356zZ8/Srl27SpUABwcHIiMjy6wXBKHUtdGiRQseffRRjhw5wq5du3Bzc8Pf35/AwECjMkBOnjyptyAcOXIEURQZNGhQjeKRqkKWZVJSUoiKiuLs2bMkJCTQr18/2rdvr2Q9NRIURUOhzpAkiU8f+ZaigkIkbYk6fvMPX5ZBEMhJz2Xtgq1sWxrCV3vm0Sqw6WfDiKJIYGAgnTp14vLly+Tn53P27Fni4uJqrdqhs7Mz9957LxcvXiQ6OhqtVktUVBQdOtzq0WLMTfbq1asVBq1WF0dHRyZNmkRYWBgnT57k7NmzjBs3rpSMzQVZlikqKMLE1KTGDzdJkjix/Qz7/z5Mfm4BXu1aMuKJwTi63yqQ1b9/f37++WfOnDlTxjp1u0yxsbFVKgrp6ekkJiZiZmZGz5499ZYxY8+juLiYc+fO4e7uTq9evdBoNBw4cABra+s6yybRarUsX76c6OhoRFHExcWF0aNHV9jfpUFRgkEVFGqf03vOEX856bY1N29cd9zApGKJvOx83p/8FYsvfNNs3kIEQcDd3Z2jR48SFRVF69ata92Em5mZqS/UlJycXO2HuLm5eaWZDNVFpVLRuXNn/P392b9/P6tWreKRRx6hdevWtX6shiArLZvVX21g48IdZFzPQmNmQqcBujfp1MQ0bOytGTtrKAMm9q7wuk5PyWTdjzplOyMlE6lYS2F+ISq1qC+T/9t7f/Hs908y7mmdK8LFxQUPDw9u3Ki4QF9oaChxcXEA/P7772Uq05awZcsWIiIiAF3KddeuVVcMzsvL49y5c9jb2+Ph4YGpqSn5+fnk5ubi6+uLWq3WZxxZWlpWOV912bp1K1evXmXo0KF4eXnVqeWkxjRwjMYnn3zCG2+8wQsvvMA333yjm1KWee+991i4cCFpaWn07NmTH374oZRLrqCggLlz57JixQry8vIIDg5mwYIFRr00NeJfRaGpEx0WiyAKpXuKVHCzlbQScZcSObkrjK7BnepJwrrl+vXr/Pbbb+Tn59O6des6eau73RVhalp5LE5MTAyiKJZbG8PFxYWioiISEhLq5G3Q3NycoUOHsmnTJv7991/+85//NHk3SnpKJnP6v03ClWQkrc4tWJhfxIltZ3QDbl7qp0POs+DFpXx34ANcvEvHquz+cy8fT/3h1oqbaaeA3tWoQ+a7Z37G0cMec081Z86cITY2ttJrqiQGQ61WExkZyUcffYSDgwOjRo3Cx8fntkPeOs6JEyeqVDS0Wi1//vknMTExgC5Nu02bNri4uAC3FIuEhASAUseqTcLDwzl27Bj9+vWr9ZoezY1jx46xcOHCMtWCP//8c7766iuWLFmCv78/H374IcOGDSMiIgJra2sA5syZw/r161m5ciUODg68/PLLjB07lhMnThjsVmv4CBmFZouphalBSkYJokrk4okrdSxV/SBJkr4vxOTJkxkwYECVikB1jwO6yqMltTLK4/jx4yxZsoRff/21XMtFq1atcHFxITQ0tNZlLEEQBAYNGkRRURGbN2+us+NUB1mWdXVngML8QlKu3SAvO6/ccXtXH+LFAW8z2X0GcZcS9UpG2cG3/pmWlM6soFfJvHEry2GYapJeyTDEiieKAj+/uYy1a9eSl5fH0KFD6dy5c4Xju3fvzlNPPYWrqyutWrXCzc0NURRZunQpmzZtIj09nW3btukb4Lm7uxtkadq6dSuxsbGMHTuWSZMm0bVrV9LS0ggJCQF0Cq9Wq9U3cFu+fHmVcxpLYWEh27Ztw9PTs9yqu40SuRaWapCdnc3UqVP5+eefS7lGZVnmm2++4c0332TChAkEBASwdOlScnNz+eOPPwDIyMhg0aJFfPnllwwdOpSgoCCWL1/O2bNn2bFjh8EyKBYNhTqj55iuiCqx4hvxHciyjImm8V6SOTk5yLLMyZMniYqKIj8/H3Nzc2xtbenbty/5+fkkJCTg4uKCqakpycnJdO/evU5Nx23atOHgwYOoVKoKzcZFRUXs2bMHa2trfQGnOwtpCYLAwIED+euvv0hMTKyzQluWlpb07t2bPXv20LlzZ9q0aVMnxzGU5NjrrPpiHVuX7iYvKx+1Ro2klZC0EqIo0Ht8D6a9O4nWnX2QZZnvn/2F9T9uQxBvujUMdvMJ5Gbl8ffXG3j8wymMNp8CMgjibfuXKv0olJ5blpEkmdiweNxH26DRaPQl7A1h6NChN6eROXfuHEePHuXYsWOYmJhQVFSEk5MTM2bMqHKeixcv6q0IJZYvOzs7AgMDWbVqFRkZGYSFhaHRaDh58iQASUlJ5Ofn11otmby8PDZs2EB2djZDhw5tOq7WWorRuLOAm6mpaaUvMbNnz2bMmDEMHTqUDz/8UL8+KiqKxMTEUplBpqamDBw4kIMHDzJr1ixOnDhBUVFRqTHu7u4EBARw8OBBRowYYZDoRlk05s2bp49WLlkquiHNmjULQRD0vqASIiIi6Nu3Ly1btuT9998vtc3HxwdBEDh8+HCp9XPmzGHQoEHGiKrQCHBws2PkE0Nu3UyruDHKkkyPkV3qXrBqcOjQIf773/9y6dIl1q1bR3JyMoWFhSQnJ3P48GGWLl3KwoUL+fPPP/nuu++YP38+QKn+ELVNTEyMvhFVZabj3bt3k5ubS58+fYDSvTNup127dri6unL48GGjHmLG4ufnh4eHB+vWravfyox3EHPhGk8HvcK6/20lL0tXuKm4sFivGEuSzKH1x3mu9xuE7b/Avr8Ps/7HbQDIkmSEklGCwMafdW+BRQXFIAjlf8/lzSsIIOhu1+YaC5KTk9m9e3e5VUMrleBmxdCJEyfSt29fHnroIbp06UJycnKV+0ZHR/PPP/+Ua0UQRVFvXTl37hwnT56kc+fOeHp6Ym1tXW5be2NITExky5Yt/PDDD3zxxRecP3+efv363ZU1Wjw9PbG1tdUvn3zySYVjV65cSWhoaLljSrpFl7i8SnBxcdFvS0xMRKPRlPmebx9jCEa/Pnbs2LGUyaQ8H82///7LkSNHyvX1zp49m2nTptGjRw+efvppgoOD6du3r367mZkZr732mt4Mp9C0mf3dE+Rm5rLnz4PoVPLyb86iSqTHyC54tWuczd527twJ6NJKhw4dqi+CBLo/xpI01vHjx1NYWEh8fDwRERF1VrAoJyeHrVu3YmVlRXBwcIUm9MTERA4dOkTPnj25fPky5ubmFfrMBUFg5MiRLFmyhNOnT9OhQwdiY2ORJIk2bdrU2ptjiQvln3/+YfXq1UydOrVW5jWWTx/5jpyM3EotbpJWQpZlPp32PY4t7Y2y0JVHZmo238/59dYKGWRuNsGQAYSbfyYVfNeCyHMvPEuhXMAPP/xQbnM1Q7CxsdEH/CUlJVUa2FdUVERqaip//fUXDg4OBAcHl3stlNTfOH36NI6OjgwcOJAWLVroa8oYS05ODmfPnuXUqVMkJSXpr90OHTrg5eXV9GJ8asmiERsbW+o3r8iaERsbywsvvMC2bdsqtSbd+Vsa0gzR2IaJRisat0cTl0dcXBzPPvssW7duZcyYMWW2p6enExQURGBgIO7u7mRkZJTaPmvWLH788Uc2bdrE6NGjjRVPoZGhMTVh0tzxhKw+hKyV9Wmt+v/exK+bL//327MNKGnlaLVa/X9vVzJA119k4sSJiKJIixYtAF2H1V69etWZPCWBeFUFVcbGxiKKIs7Ozhw9epShQ4dWetPx9vamX79+7N+/nxMnTujP287Orsx51wRLS0uCg4PZtGkTK1asqPdunRHHLxN5smwhq/KQJZmkmBSSY6/fijkSBAS16qaVQdZZOKpQQOSbysTGH7ffXIFO75ZuewIJVT+QD649TvfxnW6KUTPl79SpUyQkJOitXbdz+fJlNm/erM9s0Wg0DBo0qELrhCAI3HPPPWg0Go4dO8aSJUv0pc2HDRtGr169qlQ4ZFkmOjqao0eP6pvJeXl5MWLECDw9PRtF4a1qU0tZJzY2NgYplydOnCA5OZlu3brp12m1Wvbu3cv8+fP1mUaJiYmlytknJyfrrRyurq4UFhaWqfCbnJxc7jVTEUb/apcuXcLd3R1fX18eeughrly5FbwnSRLTpk3jlVdeqbBi3fvvv8+wYcOwsLBAFMUyPh4fHx+efvppXn/99VLR0ApNl1VfrkPU3xBlkG/+riXtDGWJ+2aPwKpF3cUy1JTZs2fj5+dHTk5OqW6aJdjb2+uVjLoiNTWVX375hZMnT3L69Gnatm1b5VvdhQsXkCSJ9evX4+7uru+JUhmDBw+mS5cueHt7660NlXULrS7u7u7Y2dlx5coVfYpufXH5VLTR+5QoGYJajWhmBioVgkoEUURQqxE0JmX2KVEEbneR6LJJqvfAUalFUpPSa8W1deXKFY4ePUr//v1LdQAGnSL7xx9/YGZmxsCBAxk/fjxTpkypsgy5IAgEBQUxZMgQfHx89Ja27du3s2rVqgpTqAsLCzl+/Dg//vgjv/32G0lJSfTq1YtHHnmE4cOH4+3t3bSVDG5VBq3JYgzBwcF6i1DJ0r17d6ZOncqpU6do1aoVrq6ubN++Xb9PYWEhISEheiWiW7dumJiYlBqTkJBAWFiYUYqGURaNnj178ttvv+Hv709SUhIffvghffr04dy5czg4OPDZZ5+hVqt5/vnnK5xj9OjRpKSkkJmZWWEPiLfeeovFixfz+++/M23aNGNEVGiEHFx7/I5UPbjdhiiKAofWn2DoIwPrVzADOXHiBHFxcfoOqampqfUug1arJTIyEkmSOHbsGNbW1gaVd3ZyciI1NRVBEJg8ebJBdQZEUdSXLI+OjgYo0+K+NigoKCAtLY3Bgwfr6z3UFyam1Qw6VqkQ1GqQZZ2qcJsJWQYEjQly4c2HqUBZhUCgHNeI4U8QrVbC0d1eP29NLBppaWkIgkCLFi3Yt28fbdq0wc3NjczMTP766y9cXFwYNWpUtR7wbdq00Qf6lvRoCQ8PZ9myZUydOlVv7i8oKGD//v0cP36cgoICvL29ueeee/QZKwrVx9rauowCaWlpiYODg379nDlz+Pjjj/Hz88PPz4+PP/4YCwsLHn74YUBXyfjJJ5/k5ZdfxsHBAXt7e+bOnUunTp30AcaGYNRf26hRo/T/7tSpE71796Z169YsXbqUgQMH8u233xIaGlrlBWJqalppoyknJyfmzp3LO++8w4MPPmiMiACsWrWq6fnvapG4uDhWrFjR0GIAuhttYX7lb8OSJBMVGdVoZL6dnJycMg/B2NjYCgMq64rs7GxSU1NRqVQ4OTlhbW3N1q1bDdq3xAy6ceNGo497/fp1BEHQN3CrTSRJQpIkLly4QFZWVr3+/nk5+bce+kYgqNU6r9/tK2+uEARdpogsCjfdIbdtv32Pyo5bophUcA9VmahI1MawZYvOdXbgwIEq73UpKSls2bKlzPri4mIEQWD9+vUIgsCuXbswMzNDq9UiSRIqlYpt27ZVOrchtGjRgvT0dERR5Nq1a3zzzTe4urrqewBJkoSVlRUODg5IksTZs2c5e/ZsjY9rCHl5ZVOY64xGWBn01VdfJS8vj2eeeUZfsGvbtm36GhoAX3/9tT5Nv6Rg15IlS4xyd9Yol9DS0pJOnTpx6dIlRFEkOTm5VB8HrVbLyy+/zDfffKN/MzKUl156iQULFrBgwQKj5Zo0aVKDdQ1sDKxYsYIpU6Y0tBh69nx2guiw2ArNvaJKpP+oPo1K5hIKCgo4cOAAERER+sh8Ly8v7OzscHFxwdbWtl7kCAsL48iRI7zxxhv1ZkIODQ3l4sWLBAQEGGUmNYZVq1bh5OSEjY1Nvf/+5/+K4fSecwaPF0w0CBW5PGTd/+mMFQKyrC39YChRHKp8Sb+plNwRw1TCjE+nMuqhwZibm7N48WK9P93Z2bnCgN0tW7YwcuTIco+m1WrRarWo1Wqio6MJCwujoKCAwYMH11pMTlRUFNu3b8fFxYVu3bqxYcMG/fPAz8+PHj16GNwZtrap1+6tjYA9e/aU+iwIAvPmzWPevHkV7mNmZsb333/P999/X+3j1uiOVVBQwIULF3Bzc2PatGmcOXOmlD/I3d2dV155xeA3r9uxsrLi7bff5qOPPjI6hUuhcXHfs6Nu3oIrQJYZPcNwM1x9YmpqypAhQ5g+fbo+iPLMmTPs2bOnWtd1dXFxcUGr1XLhwoV6O+bZs2fx9PSkd+/edXYMPz8/Lly4oA86rU+GPtLf8MGiiFATBc+omIqyY82tzHjxp5nk2N3giy++YOHChbRo0YKCggLOnTvH7t27Wb9+vdGxGyqVCo1GgyiKtGrVivHjxzNp0qRaDfwteTtOSEhgw4YNpdZ7eXnVaZ0ZhcaBUX85c+fOJSQkhKioKI4cOcLEiRPJzMzkscce0/t9bl9MTExwdXWlbdu21RJu5syZ2NraNkqTenNGlmUiT0ZxctdZEqKSqt6hCkY8Ppje43rcNC3fWi+qdJffs/OfwsW7YldaY8Dc3JwXXngBNzc3/dt9fQYrOzk54ebmxpkzZ+rtmEVFRZibm9epr7xdu3YIgkBaWlqdHaMiugzpZHBMpiCqqn6IyzetGZVdF+VOUZ4QN4OmZQlLGzOWX/mewJFt9T1zrKysuHr1Kq6urvj5+QG67IHyOrU2NPb29vr4pl69evHwww8THByMtbU1O3fu1Jcqb+4I1DAYtKFPoAYY5Tq5du0aU6ZM4fr16zg5OdGrVy8OHz5ca62l78TExIQPPvhAH5iiUPfsXX2IRW/8QXzkrWIsnQd1ZPa3j+PbqXq/s0qt4t3VL7P+x22s+X4T8ZGJCIJu3gdfvZduwyouo9yYkCSJ1NRUkpKSaN26Nf7+/vV6fFdXVyIiIozOYa8uJSXJfXx8aqVfhSzL7Nu3j6SkJLy8vEhPT8fDw4P27dvrTfZ1Uaa9Ilx9nOk9rgdHNp6oujbGHW3Vy0PnPZFL9SspOw9llQ1B0Lte7sTJ05FPt76FjYMNZ/afwcTEhMGDB5dxn3Xr1o2VK1eye/dufHx8MDEpmwHTUIiiyKhRo9i4cSOHDx/m4sWLBAUFMXHiRL777rs6yWhqlDRwU7WGxChFY+XKlUZNbmxcRnnjp0yZ0ih9982RbUv38MXjP5RRnc/uu8Dzfd7ku0Mf4xvgVf7OVaBSq7jvuVHc++xI8nMLUJuoMCknHbAxExkZSUFBAffddx/Ozs5V71DLODg4kJubS25ubr2Ym++55x5CQ0PJzs6ulfkkSSI8PBxAb8GIiYmhbdu2SJJEWFhYqZz/+mDuov/wSvB7XDkTczO2QkYUBSRJxqtDS65FxIMsI99cKlI2SlQEuaCg0uOVHEP34bYdbyobghosnEyx9rDAwd+GR1+coi9il5WVhZWVVbkxOjY2NrRt25aIiAh27NhRKnC/MWBpackDDzxAXFwcV65cYffu3foieArNn8bbWEKhXsnLyWf+c4t0H+54sZK0EoX5RSx8ZRmfbH6zRscRBAFzy9rpeVDflAQY//vvv0yYMKFW/diGUGJ+jouLqxdrSmhoKObm5rRr165W5lOpVMyYMYMbN26QmprKjRs3OHv2rL5w0IYNG+pd0bBxsOb7wx+z64/9bFm8m9SENJy9HRn1RDADJvUi/Egki99awdkDEYiVRdnLMrJWW0EshoClnSV+3VtxIzOFlm1d6dKlC7v/OkDMpavYuFjRrl9rWnf3xr21CyoTEa1Wy5UrV9ixcwftO7TnwoUL5OfnV+q+6devHxEREcTGxpKUlFSmtHRDo1Kp8PLywsvLi+7du3PixAkiIiLunhi8Rph1Ul8oioYCAAf/PUZedn6F2yWtxPFtp7gedwNHD4d6lKzhkWWZzZs3c+nSJf26a9eu1buiYWtri729PWfPnq0XRSMsLAw/Pz+Dam8YiiAIODo66r87tVqtb75VUZG/ukZjpmHkE0MY+cSQMts69W/PVyHvk3Q1hU+n/8iFw5FlHvaiSsTG3pLMlHSKJRAFnUVEpRZp19Ofae9MIig4gL/++ouICAlPT1cmPDEay/YqQkNDmTJlSrkuI3d3d/78809+/vnnUjEskiSVa9VQqVSMHj2aTZs2sXbtWgIDA+u0Om1NsLKyYuDAgQQEBJRKpWzWKIqGwt1OUkwKKrUKbXEl0f8ypFxLvesUjezsbI4dO6av/zJu3LhaffgaSslDur5qeBQUFNS5i6Z79+4UFhZy7ty5WrOc1ITMG1lsW7qHK2di0Jhp6HtfD7oN74yLlxOfbfo/Fr35JxsX7aaoQFeUS1SJDHigJ7O/fpTcnBz2rDpAXmY+vu29EZy17Nm7m5isSLqJgQwbNoygoCB9K/bw8HD8/PwqjEuxsrLC2dnZqGDJli1b6pWN+i7rXh0cHO6ue8ndiqJoKABg62htULMoG4eGyXdvSEpy/EtM0Q2hZJRQ3dLTsiyTmJiILMvlNjssb3yJ+b5Tp07VOqYhCIJAnz59iI6OZs2aNbi4uFRazK8u2fXHPr54YgHaYi2CKCAgsHHhdloFevPx5jdxcLPjP19O49F3HuD8kUi0RcVYuppy5VokS5b/esvqIEC/rj30xa7MzMw4ceIEaWlpFBQUcPXqVfr06UN6ejpdunSpUB5JkkhK0mV9tWrVSt/uoao6Ki1btmTmzJk1/0IUapXqlBG/c/+miqJoKADQb0JP5j+3iOKi8i0agijQJsgXjzZu5W5vzgiCgIWFBVevXgUqL4BUH7JUp+bEmjVr9NUWe/ToUWXDwn///RfQdfbMzMys0wJ4giDg4OBAWloau3btqlY14JpyZu95Pp32vV6Rk2+r7BlzPpY3Rn/Ejyc+RxRFLG0taNXNg/nz55eaw8vLi4yMDDQaDX///TcFNwNDjx07hiAIWFlZodFoSE9P11ebraxQlSiK9O/fnzNnzhAQEFCqr5RCE0RxnSjc7dg62vDga/fx+4d/l9lWEmj/1CcN0867MfDYY49x4cIF9uzZw9WrV1m3bh2dOnXC19e3XuXIzc01ury+LMucPXuWzp07Y2pqytGjR7G1tSUgIABLS0uuXr2KmZkZ8fHxHDp0iLy8vFKWk5UrV9K5c2d69uxZ26dTim7durFnzx7i4+MNsrrUJis+WYMgCroOw3egLZa4cjqGE9tO02NkEIWFhcTExKBSqRBFkdGjR+Pv78+hQ4c4ePAglpaWtG/fHicnJzQaDaamprRo0UJvCTt//jz79+8H4OTJk7i5uVWYzdK2bVt9HaKxY8c2+cZidzWKoqGgAI/Om4xKpWLFZ2soyi/S3XglGVsnW178aRZdhwY2tIgNhrOzM87Ozpw+fRpZlsnKyuLEiRO4uLiQn59Pbm4upqamODo61mmNCwsLC6PLJguCgKmpKWZmZgQGBpKSksKOHTsIDQ2luLi4VNS/r68vdnZ2XL58GbVajUqloqCggNOnT9OtW7c6dRu1adOGQ4cOcfHixXpVNAoLijix7VSlxTtVahUH/j2GvZ8Ny5cvB2DWrFm4urqSmJjIzz//TGZmJkFBQXTp0qXS+Ij27dtz7do1oqOjiYuLIzY2tlTrhoqob+VLQaG2UBQNBT2iKDLt3Unc/8JoDq07TlZqNm6tXegxsgtqE+VSAV2FzilTphAREcHKlSv1D50SrK2tGTFihD4VtbZxdnYmMjKSwsJCNBqNwfvZ2dlx7do1OnXqpH8Ipqam4u3tTXBwMLIsY21tre/S6uHhwYULFxg1ahQpKSk4OTnVeWyKKIq4uroSExNTp8e5k6KCoiorhJc0Byxplz1q1ChcXV0pKChg5cqVqNVqJk6cSIsWLao8niAIDBs2jJSUFOLj45WAyLsEJUZDoVmRcyOXrUt2oy2WaHdPG1oFGlfR06qFJcMebZwt2xsLbdu2ZebMmWRkZGBhYUFeXh47d+4kJSWF1NTUOlM0XF1dkSSJuLg4o9w2vXv3Zs2aNfzyyy+ArofLsGHDKnxLbteunT4LxNPTs+aCG4ibmxvHjx9Hq9XWW9aEhbU5jh72XI9LrXCMLMvYe9oSl3SFUaNGcc899wBw7tw5MjMzefDBB42KYxEEQW8lU7hLUCqDKjQH8rLz+HrWT+xeeaCUP69Db3/+b/nzuPk2rgI+TR03NzecnZ1ZtmwZMTExmJiY0LlzZ336Yl1Q4utPTEw0StHo1KkTcXFxHD16FFNTUyZMmNAo6xdkZ2djampaLyXWSxAEgRFPDOaPD/+pMKtHpRJJMYkHGb0CpNVqOXHiBB4eHnd1t2gFhapQIouaCVqtlrfGfUrIX4fKBA2FH4vkxf5vk56S0TDCNWNCQ0OJiYlhyJAhPPzww/Ts2bNOH5KiKGJtbW10EzJBEAgM1MXYDB48uFEqGYmJiVy4cIEuXbrUW9BjYUER859bxIpP1lSoZAiCwP1vjKRQ1hW0MzMzo6ioiD///JPExET996qgUClyLSxNFMWi0Uw4tvkUZ0LOl7tNKpZIS8pg7fwtPPZe/acONlcKCwsJCQnBz8+PNm3a1NtxNRqN0Y2oMjMzWbNmDS1atKBly5Z1JFn1kSSJnTt34uHhwaBBg8jKyuLcuXNkZWVhbW1Ny5Yt9TEctaWEyLLMhw9+xaH1xyu9iZtZmdK2T2sSDsbg5OSEt7c3y5YtIyEhgREjRjTK71Oh8aHEaCg0eXYsC0FUiRUW3ZK0ElsX71YUjVokLCyMnJwcffphfWFiYqKv0WAI+fn5/PbbbxQUFNCrVy/Cw8MpKirC399fH/zZ0KSnp1NcXMwDDzyAKIosX76c5ORkbGxsyMnJ0dcOMTMzo0OHDgwYMABbW9sy8+Rk5HLg32NkXM/EqaUDvcd3x9S8bNBsYnQy25bu4dC641XKVpBTQNT+OOwc7BBFkR9//BGtVsvYsWOVGAsFBQNQFI1mQlpSRpWVPTOu3yXNi+qJkliMGzdu1FvqYUmremOOt3nzZm7cuIGdnR07duzQr7ewsMDPz68uxDSavLw8bG1tkWWZ06dPk5yczP3334+TkxNarZYbN24gSRJXr17lwoULhIWFMW3aNL01QZZl/vxiHcveW0VhfpFe6ba0NeeZrx9n+GO64OYbCWl8Pesnjmw8YbApWpJktv22m7bTdd95q1at6N27d7100FVoRih1NBSaOs7ejqjUItriipUNR4+6yYS4Wzl//jyCIODtbVxWT02IjIwkNzeXoKAgg8ZLksSZM2cAnWVjwoQJeHh48P333xuVHlvXODs7k5WVxY8//ogkSbRu3VpfilylUuktB66uus6nW7ZsYfny5Twy9RGunkzg7282lXIdlijdORl5fPHEAkwtNHQd2okX+79N8tUUo2/a2Rk5uLm5MXbs2HoNVFVoRtTQdaIoGgoNzsjHh7Bj2d4KtwuiwOgZw+pRouZNcnIye/bsoV27dvqMg9TUVC5evEiPHj3qLDUzMjISX19fXF1dDRovCAIdOnQAYOjQodjZ2SFJElZWVsTGxtarklQZJiYm3H///Rw6dAgTE5NKq5BqNBpGjhzJip//4j+dXyM/vRCEyuM2fvm/Pxjx+ECSopORJOPu2KJKxNzBFFdXV0XJUFCoBoqi0UwIHNiBARN7se/vI+W2sW7p78bYpxVFozbIycnhjz/+wNraWv9AlCSJHTt2kJ6ezpUrVxg6dGit++8lSSIxMZHBgwcbvI8gCEyaNKnUOlEU6dChA+Hh4bUqX00xNzdnyJCyrdrLIzc9n4PfnyE/u6hKJQN0MRnrf9xqtJIBOuuIc1db7OzsjN5XQUHPXew6UdJbmwmCIPD67y8w+ZXxqE1vvU2LKpEBk3rx9d4PsLQxrkeGQlm0Wi1//PEHGRkZZGdnEx4ezrlz59i4caO+fXt2drbeXVGbZGZmUlxcjJtbzRvbZWdnY2ZmVgtSNQwbf9xBXnZB5TdfWQZZ0i9pidVI7xag2+hAHNrbGFT1U0GhQpT0VoXmgNpEzVOfPoKmrUyAZ2e0xVpad/HB3rXu38Sy0rI5vP4E2ek5uLdxpfvwzqjU9VPZsb7Izs7m8uXL+s8FBQUcPny41BhXV1cCAgLw8fGp9ePn5uYC1EpxqOzs7EaTcVId9q48qrvxVuTKkMvemSuqk1ERLZxteeDFsfgFe7Fh43pF0VCoEUp6q0KzwsRMXW8N0CRJYsnbK1n15XqKC4v1jdjs3eyYu+g/9BhpWNBiU+D69etl1pmYmODo6Ii3tzc+Pj51WiEyOzsbwOjureXh5eXF/v37iY6OrhOlqC6RJIm8rHyjlAxDefC1++jQxx9REPFs545HGze2b9+OtbV1nfd6UVBorih/OQo14vtnfmHDwu36z/JNH3haYjpvjfuU/+6aR6f+7RtKvFrFx8cHX19fcnJyyM/PZ9y4cfWa4njhwgW8vLxqxRJhZWUF0CTf0gtyqypWZrySIYgCnQa0J/lqCqu/XI+2WFe3o3UXH3yD3XBq38J4QRUUFAAlRkOhmhzZFMqsLnNLKRm3I8syyDK/vvlHPUtWd2RnZxMTE0N2djbDhw+vVyUjMTGRpKQk+vTpUyvznT9/Hi8vryapaJiaa1CZlOOWK4nJMBKVWsWgh/pyLTyevasO6ZUMgCtnYtjx5SGSThtX8l1BoQx3cYyGomgoGM3230J4a9wnRJ2tvJ23JMmE7Q8n5dqNepKsbsnLy0OSJPz8/OqsO2t5pKens337dlxcXPD396/xfFlZWcTGxjY5l0kJokqk/6QecLvnpBrukud/eIq3/3qJlXE/oVaryLieWaYOTYmFbt/Px8nLzq+h5AoKdyeKoqFgFDmZuXz7n4Ug37y3G0BTr0ha0vW0uLgYjUZjVPnv2iAzM5O8vDwCAwNrpY7Dvn37UKlURnV/bWyMe24YZhamt60xQskQwMPPjbFPD2fAxN6YmJqwe8X+SovdFRUUc2RdaPUFVrjrKQkGrcnSVFFiNBSMYs/KAxTkG97QSxCEJl2R9PDhw2zdurXUuvDwcO655546Sw/du3cvDg4OuLi4IAgCnp6euLm5sW/fPnr06IGJiUm15w4JCeHYsWP07dsXU1PTqndopGTdyKa4oFCn7RqrfMkwee54vdJ2PS6V4iJtpbuo1CqSossGAysoGEUTVhZqgqJoKBhF3KUE1GpVlTdm0Jm4e43tRgunss2vmgLR0dFs3bqVwMBAevToQUREBKGhoQQEBNRJ+e7i4mK0Wm2ZQlol3Vo7dOhQ7cyHlJQUdu3aRXh4ON27d6djx461IXKDseT//kSrlTDKtCboht87eySjngrWr7a0rTqLR5YkLGyabjqwgkJDoigaCkZhaWtpcHVFjbmGpz6dWscS1R1Xr14FICkpiX/++Yfc3FxUKhVdunSptWNkZ2dz4sQJEhMTycgoXVBq7Nix2Nracu3aNdzd3fHz8zPadSJJEvv37yckJARLS0uGDBlSry3t64Kr5+OIPnvN6P3cW7vy0sKnCRzYodT36OhuT4fe/oQfuVThtS1JMveM7VJdkRUU7urKoIqioWAUAyb1Ysk7Kw0a27GPP55tPepYorojICCAgoICcnJyKCgoIDw8vFZrKciyzLZt28jJyaFTp076/iVeXl7Y2elakgPVVgxSUlJYu3Yt8fHxdOnSha5du9ZZD5b6JDmmei6MN/6YQ9vurcvd9tj7D/F/wz/QWz1uRxAE+k3qgYuPU7WOq6AASsEuBQWD8WzrwaCH+hLy54EqLdYnd4aRlZaNtZ1V/QhXy9jb2zNs2DBCQ0PZuHEjFhYWWFtb19r88fHxXL9+nSlTptRKNkkJycnJ7Nixg0uXLmFjY8P48eNxcXGptfkbElmW0VgYGaMiQPDD/StUMgC6BnfirT9f5MunfiQ3M+9m+xQBWZbp/2BPpn8yuUZyKyjczSiKhoLRvPLrM1w5Hc3VC3GVjpO0EqkJaU1W0QCIiopi/fr1tGvXjl69erFr164az5mcnMyhQ4dISkrC3d2d1q0rfgAaS0pKCosXL8bMzIyBAwfSunXrZlXRMiQkhIjwCEws1RTlFBu0j9pEzek95/j5teXc99wonFo6lDtuwMTetOnpzUdzvsBG3QK/dm3oPqYzTp7lj1dQMIq72HWipLcqGI3GTMPIx4eUrmNQATYOtWcBqG/y8/P5999/cXd3p3///qUCQOPj48nMrDptNzc3l/Xr17N7927y8/MpKipi+3ZdkbMHHniAxx9/vFbdGZs2bUKSJO677z7atm3brJQMALVajSAKTJs30aDxgiBQXFjM9bhUVn+1nhmdXuJS6JUKx58LP4fXPa7M/vJJRj09RFEyFGoNJb1VQcFIBj3Ul5//b3mFjapElUjnQR2xc2lRv4LVIhcvXiQzM5Nu3bohCAJarZbc3FwWLlwIQKtWrRg6dGilcxQVFZGQkADAjRs3MDExoaCggCeffLLW247LskxqaiqFhYU1SoFtjJQ0gStxXbXv3lbfV6cybr8+Ja1EXnY+79z3Ocuv/FCm6Z8sy4SHh+Pl5dUsYlkUGhl3sUVDUTQUqoVTSwfuf340/3y7sWzwnCggCALTP3ioYYSrJUrase/evZuLFy+Slpam76Bqa2vLoEGDqpzD1tYWd3d3MjMzycnJwcrKikcffbTWlQzQvb2PHTuWP/74g8zMTGxtb6UVFxQUUFhYiIWFRY0forIs10rhMEPQarUcOXKEsLAw1Go1xcXFdO7cmZ9fXV6lklEeklbi+rUbHNkYSp97e5Q6zo0bN0hNTaVly5a1eQoKCnc9iqKhUG1mfjENjakJq7/eoOvcKuiC5xw97Jn762w69Kq9AMeGwMnJiccff5xz586RmJioVzLatm1L3759KS4urtI1cfnyZa5fv05gYCCjRo1CEIQ6fUiXNFy7XdEICwvj8OHDSJKElZUV9913X7U6wBYXF/Prr7/i6OjIhAkTalXuiti9ezdXrlyhT58+mJmZ0aJFC8yKLPnh2O/VnlNlouLC4Yt6RSM5OZlff/2VgoICNBoN58+fp0OHDnXaiVfhLuQutmgoMRoK1UalUvHkJ1P5M34hry59lme+fZxPt77F8qgFdA3u1NDi1QpeXl6MGjWKwMBA/brs7Gx+/fVXfvvtN5KSksrdLyUlhYMHD7Jz504cHBzo06cPoijWuSXAzc0Nd3d3tm3bxtmzZ9m9ezcHDx6kW7duPPzww8iyzO+//87vv/9OWprhjcIkSeLvv/8GqFe3gqenJxqNhpiYGPr370+nTp34ae7Smk0qU8ptUlRUpC8rX1hYiFarZd++fTU7hoLCHdR3jMaPP/5IYGAgNjY22NjY0Lt3bzZv3qzfLssy8+bNw93dHXNzcwYNGsS5c+dKzVFQUMBzzz2Ho6MjlpaWjB8/nmvXjK9hoygaCjXGxt6aYdMGct+zo+g2rLO+/kNzobCwkA0bNgA690RcnC7bxtbWtkz3U1mWOXfuHGvWrCE8PJzg4GBmzpxZJ66S8lCpVEyfPp0uXbpw6NAhYmNjuffeexk1ahR+fn7MmjULWZbJycnhwoULnD59msLCykvKa7Va9u/fry8oNmbMmPo4FUBnPerSpQuJiYm6GJTENE7vOV+jObXFWoKG6hThhIQEYmNj6dixo95lIssycXFxZGdn11h+BYWGomXLlnz66accP36c48ePM2TIEO699169MvH555/z1VdfMX/+fI4dO4arqyvDhg0jKytLP8ecOXNYs2YNK1euZP/+/WRnZzN27Fi02qorQ9+O4jpRqFVkWebkzrNs+Gkb0WGxWLawZPCDfRk+fRBWLeqvrXpt4+fnh0ajIScnh6SkJJydnenUqZM+RTUzM5NLly4RGRlJTk4OQUFBjB07tkGULhMTE8aOHUuXLl2ws7Mr1c7+9v4sYWFhgC7oddSoUVhZlU5Dzs/P5/jx41y+fFn/xj9y5Mh6yWQpCWxNT08nPDycli1bIggC5w5erNG8KrWIT4AXgQM6EB8fz88//4xKpcLU1FTvGgOdwtbcAmoVGphacp3cme1mampabt+icePGlfr80Ucf8eOPP3L48GE6dOjAN998w5tvvql3gy5duhQXFxf++OMPZs2aRUZGBosWLWLZsmX6oPfly5fj6enJjh07GDFihMGiK4qGQq0hSRJfz/wfW37djUot6rthRhyN5M8v1vLlnvdo6efWwFIaj0aj4eGHHwbghx9+IC8vj+TkZL2VQ6VSodVqMTc3p2PHjgQGBuofjA1JeUGNarWaRx99lKysLH2w6/Lly9m+fTvjxo3TKxHZ2dls2LCBoqIi3N3diYqKol27dnh5edW53CkpKaxZs0b/2cPDQ3/TlLQVd1g1BBcfZ95f+xqCIJCeng7AoEGDaNWqFWlpaWzevJmcnBy9+6SqrCIFBYOpJUXD09Oz1Op3332XefPmVbqrVqtl1apV5OTk0Lt3b6KiokhMTGT48OH6MaampgwcOJCDBw8ya9YsTpw4QVFRUakx7u7uBAQEcPDgQUXRUGgY1s7fwpZfdwOUarktyzLpyRm8Pe4TFp3/pkm7VkqUB2dnZ/r374+FhQVhYWF4eXnRpk2bRp8WKQhCmfbwDz74IL/++it79+6lS5cumJmZsXnzZgRB4PHHH2fFihU4OzvTp0+fepHx/PlbrpH777+/VHyMqUXNmtl9s/9D7Jx1QbLt27enVatW7Ny5k4MHD9KxY0fMzMzIyckBdLVS6jPDRkHBEGJjY0sFKlfWhfns2bP07t2b/Px8rKysWLNmDR06dODgwYMAZSoGu7i4EBMTA0BiYiIajaaM29fFxYXExESjZFYUDYVaQZIkVn25ruLtWolrFxM4sf0MPUZ0qT/Bahk7OzueeeaZUutKLANNFXd3d8aNG8fatWuJjIwEdNkrTz75JGFhYWRkZDBx4sR6K/7Vv39/+vbty5o1a9i+fTtxcXE4OjqSn5/P/q1Hqz2vIAhYtbCgsLCQkydPcuzYMW7cuAFAXl4ex48fLzW+Q4cOipKhUGvUVq+TkuBOQ2jbti2nTp0iPT2dv//+m8cee4yQkJBbc95xfRuiWFdH+VYUDYVa4drFeFJib1Q6RmWi4vTusCataDRXOnfujL+/P8nJydy4cQMfHx/Mzc05dOgQHTp0KBP0WpeIoogoigwbNowzZ85w4cIFcnJyMDEx4dTmyGrPa+tkQ+y1WNavX09mZiY+Pj4EBgZSWFhIREQE169fx87ODhMTE5KTk+vFTaRwF9EA6a0ajUbflLF79+4cO3aMb7/9ltdeew3QWS1uf1FKTk7WWzlcXV0pLCwkLS2tlFUjOTnZaOumUYrGvHnzeO+990qtu92M8s8///DTTz9x4sQJbty4wcmTJ8u01I6IiOCJJ54gJiaGmTNn8s477+i3+fj4EBMTw6FDh+jVq5d+/Zw5czh16hR79uwx6uQU6p7UxDR+m7eKbUt3GzS+qkZsCvWHLMtkZ2eTn5+PKIpYW1vj7e2Nt7c3ADt37kSr1Zb5G64vWrRowYABA0qt2//h3GrPl5OVx7Jly3Bzc2PEiBGlCpp17NgRSZLqvM6JgkJDIssyBQUF+Pr64urqyvbt2wkKCgJ02XUhISF89tlnAHTr1g0TExO2b9/O5Mm6poIJCQmEhYXx+eefG3Vcoy0aHTt2ZMeOHfrPt/ukc3Jy6Nu3L5MmTWLGjBnl7j979mymTZtGjx49ePrppwkODqZv37767WZmZrz22mulzDsKjZPr8ak81+sNUhPTkIqrDtLTFmnp1L99PUimUBnR0dHs27evVBEy0FkSPD09cXV1JScnh7CwMLp27Vqt4l51hVRhNdBy+rvfuV0l07dv3wpdIk05dkih8VPfbeLfeOMNRo0ahaenJ1lZWaxcuZI9e/awZcsWBEFgzpw5fPzxx/j5+eHn58fHH3+MhYWFPvDd1taWJ598kpdffhkHBwfs7e2ZO3cunTp1MjpI2mhFQ61W4+rqWu62adOmAbobWUWkp6cTFBREYGAg7u7u+tz8EmbNmsWPP/7Ipk2bGD16tLHiKdQjv7y2nDQDlQxRJeLk6UCPUV3qXjCFCpFlmfXr16PVamnXrh0ODg6Ym5sjSRKpqanExcVx8eJF1Go199xzD507d25okUvh7O1AXER5gWgypbv8ldyVdesElUDQsAA6duxYxxIqKFRAPbtOkpKSmDZtGgkJCdja2hIYGMiWLVsYNmwYAK+++ip5eXk888wzpKWl0bNnT7Zt26bvJwTw9ddfo1armTx5Mnl5eQQHB7NkyRKjg96NVjQuXbqEu7s7pqam9OzZk48//phWrVoZvP/777/PsGHDyMvLY+zYsWVSZHx8fHj66ad5/fXXGTlypPKW0UjJTM1iz58HS2WXVIQoCli1sOSDdf/X6LMymjtJSUmkpqbq33RupyR1rbFw/sBFti/ex+WTMahNVFg5WhIflVz1joLAna2FBQTGP2N4Op6CQq1Tz4rGokWLKt0uCALz5s2rNDXWzMyM77//nu+//964g9+BUYpGz549+e233/D39ycpKYkPP/yQPn36cO7cORwcDGunPHr0aFJSUsjMzMTJyancMW+99RaLFy/m999/11tJFBoXiVHJaIurrg5n62jD/c+PZvTMofq0QoWGIS8vj4MHD2JqaoqHh0dDi1Mpqz7byPrvtyOqRH3tjOvX0kCuTLG9adUoCQQSBARBZ037z/xH8ergXudyKygolMUoRWPUqFH6f3fq1InevXvTunVrli5dyksvvWTwPKamphUqGaBrZjV37lzeeecdHnzwQWNEVKgnzK3MqhwjiAJTXr+fB14cWw8SKVTG2bNn9S6Tnj17NrilsLiwmOObz7B/9TEyr2eh1RThbupNwMC2nNx+jvXfbwfuKNBlUCTxLZeJIAqM+c8Qgh/rh4N7/ZSAV1CoiLJ2NuP3b6rUKL3V0tKSTp06cenSpdqSR89LL73EggULWLBggdH7rlq1qlEFsNU3cXFxrFixok6PIcsytm5WZCRU3A9ClmRuqBLrXJb6pD6+27rg2rVrCIKAm5sbcXFx+n4tdYFWq0WSpApLeBdkF7L76+OkxmTeiuEU4b/TfsLe14aMuGydUlEmYNMY27FMx9G+2ASZcOzMEThTzZNpBqSkpLBly5aGFqNRkpeXV38Hu4u7t9ZI0SgoKODChQv079+/tuTRY2Vlxdtvv828efPK1GyvikmTJt3VLZ5XrFjBlClT6vw4HmofPnzo63K3CaLA0EcGMPP5p+pcjvqkvr7b2ubbb7+lTZs2pdLG64rt27cTFRWFj48Pbm5utG7dupTi/+WjC0mPvdm4qeTmKQGyTOqVdEAoR8kwjr4P9GDG11MRVUqM15YtWxg5cmRDi9Eoub2BmELdYdRf4dy5cwkJCSEqKoojR44wceJEMjMzeeyxxwBITU3l1KlT+hLCERERnDp1yuhypSXMnDkTW1vbJvkGeTcwcHIfZn/7BKJY9qEQ2L89c/43swGkUrgTSZLIycmpNyufu7suFiI6Oppjx47xxx9/cOLECRJiE/lyxv84vet82TRV2ZDXPcOUjxcXz2Dmt48oSoZCo6K+28Q3JoyyaFy7do0pU6Zw/fp1nJyc6NWrF4cPH9YX+Fm3bh2PP/64fvxDDz0EGNb0pTxMTEz44IMP9Hm9Co0LrVbLqT1hyHf4zkWVwOmQ82z5dbc+0l+WZS4cucS5/eEgCAQNCaBNkG950yrUMmfOnKGoqKhMX4OqKC4uJjk5GUEQcHZ2LjdjKD4+Hq1Wi1ar5fr16+Tk5BAREYFGo6GwsJBu3bqhVqvZu2sfpxb9Sd71ylvS6ynXdQIIYqUBoaYWGoKGdzL0FBUU6g/FdWIYK1eurHT79OnTmT59erWFKa/+xpQpU5qkqfpuYOviPRxYU7b3hKTV/UXMf34R94wOQpZlPpj0JZdCo/TWD0mS6di3HW/9+SKO7vb1KvfdRGpqKps3b8bPz89gRSMzM5Pz588TERGhbw9vbm5O79699eWMSyjpYAu6mK3i4mJAV/q4VatWHDlyhH79+uGQ1ZL8G+GVHFUu/W+hHGuEKCKIok7PkMpXNkS1kj6toNDYUHqdKFSbf+dvQhAF5AqqNQqCwJrvNrF39WFSE9OA0pUdw49c5JUh8/gx9AvMLCruQKhQPbRaLf/88w9mZmalqu9WhCzL7N69m8jISMzMzOjatSudO3dGkiT279/Prl27iI+PJy0tDbVajbOzM6CLp3rqqaewsbHhu+++05c4LinxbW1lw65lB6pIGrmjsqcs3bRelKyTdf+TtNxyoQi3/iOIIAjYOt+9sVkKTYAmbJWoCYqioVBtYs5dq1DJAF1q4tYlu8lJzy13u7ZY4tqlBHb9vo/RM4wraatQNYcPHyY+Pp7x48ej0VTdXv3atWtERkYyfPhwunfvXiprZOLEiezZs4e9e/ei0Wjw8fEhIiIC0DVfKlEqnnvuOY4cOcL27duRZRk/Pz8cLZzJzapGdH+FLpLbrrkS94osI4gizt6Oxh9HQaEeqO8S5I0JRdFQqDYmpiYU5BZUOqYiJaMEQRDYsXyvomjUMhcuXGDHjh1lXCb5OQXEhF1DlsE7wKNUPZSIiAhcXV3p1atXmV4ggiAwePBgevfuTXFxMVZWVsiyXCbIVBRFevfujY+PDykpKZhLVswdUroRY7kItxXaMhj5ZiyHzs0iyzJtuilxPwoKjQ1F0VCoNv3uv4c9fx4wqAx5RciSTEZKZi1KpVBYWMj69evx9PSkd+/eABQVFLP6843s/G0/hXlFAGjMTBj0cG8mvz4WjbmGtLQ02rRpU2n3UjOzW4qJIAhYWVmVO87NzQ1nJ2cebfMs2WnZt9XRqixzpKrGaBVQEjgqy3To06bq8QoKDcFdHAyq5H8pVJuJL41DV/Og+nOIKhF3v/Kb9ClUjz179lBYWEi/fv0wMzND0kp8/cQvbPl5j17JACjML2Lb4r28f9+3nNp9jhtJqfq4i9pg+7IQkq9ev+leM/QuWZ2LSTe3qBLw72F43yUFhfpESW9VUKgGbYJ8eWf1y3w05RuK8osQRF1vCWMsHJJWYsyMYXUo5d1FVFQUhw8fpnv37voujEc2nCQspIKMDxmunovjq2k/I6gELJOc6PhtAOaWVZeYv5OM65lsWbybk7vCyM8pIPzIxbIHk7nlJrndunF70Gd1kGVatnWr3r4KCvXBXWzRUBQNhRrRZ3wPVl77ie2/hXDxxGVMTNSc3R9O3KWEKvcVBIE+9/bgntFB9SBp8ycjI4PVq1fj7u6ub+8eHh7On1//a5BXQtbK7Fiyl8NrQ3H1ccLZy5ER0wdzz+ggVFUUvzq95xxvjf+M/JyCqhufyXe0dK+pknGT8c8pCquCQmNEUTQUaoy1nRUTXhij/7zk7ZWs+HRN6YZYd6AyUfHQq/fxyDsTG7zBV1MnLy+P48ePc/ToUURRJDg4GFEUOX78OKGhoWhzMPgZLsuQlZpNVmo2l0/HcODfYwQFd+KDta9ial5+5sr1+FTeHPcZhfmFRgR0yneMrVnJcQtbc+4Z26VGcygo1CV3c9aJcodXqHXGzBqGSi1WGvf3353vMv2Dh1CbKLpuTSguLmbx4sWEhITg4eHB2LFjMTMzIzQ0lNDQUIYMGUJLX/dKAzwrQtJKIMuc2nWWucHv8e/3m0m4klRm3OZfdlKUX4islaieVaKkt0k1lQ0B3tv0cvX2VVCoL+RaWJooiqKhUOs4tXTg3dVzUWvUpfpNiGoRQRB4ceHTBPRr34ASNh/OnDlDSkoK7du3x9bWFpVKxfXr1zl+/DiDBg2if//+9B7fvUyZeIO42X9EliTCD19kwZzFPOr3LB88+CV52bfqYhzaEFq2d4lxB7rN3WK8svHwO/fj4uNUg+MrKCjUJcrrpEKd0HNMN3698C0b/reNY1tOodVKBA7owPhnRuDT0bOhxWs2FBbqeoeEhYWhVqs5e/Ysrq6uWFpa0r9/f1IT0/n3+83GT1xOk7MSZWX/P0fJSs3hs21vIwgC2qLimp5GyRGMGi0IAhNfG8OIGYNq6fgKCnWIEgyqoFD7uPo489Snj/DUp480tCjNloCAAOLj4+nTpw9WVlYsXLiQqKgofZzGknf/JDUxvRozV17x9eTOs5zdd4FWgd6YlclQud0qUXd3xxEzBzPuueF1Nr+CQm1yN8doKIqGgkITxsrKigkTJug/z5gxg+LiYuzs7MjLyWfH8n2VBuWWiwFuFpVaxboFWzkTco605MzSaavckVVSJwjVcwcpKCjUO4qioaDQjCipnQFwIz6NovyiSkZXH22xlsMbTlBUUHSbYnKbclGtkuKGI4gCLZxt62x+BYVa5y52nSjBoAoKzRRLG/MqxwiiQM8xXXn8o4dw9nRAEA2zRIiiQEFuQWlriSyhVzZqrGTcnoVyZ0aKLkulz/3dangMBYX6Q5DlGi9NFUXRUFBopti5tKBVoHelY2RJZtYX03j4/+5n4en/MuPTqXi2b4mJmUml+0mSjEpd3u3jzuJbRrpQBFHf8v3W/mVvsGP+MwQ7V8WioaDQFFAUDQWFZsqVMzFcvXCt4gECDJrcG8+27gBY2low6eVx/HruK/6K/5mW/m6l0pNvx6+rbxUN0sA4W7Gg78J6a9XtSobuvybmGib+31gm/d9YA+dVUGgk3MV1NJQYDQWFZsrit/+svL6FDI9/+FC5m6xaWPLN/g/56ZXf2P3HfoqLtADYu9kx5f/up4WzDR9N+ab2hL1TabnNTKwyUdO+Txt6jA2iz31dMbUwrb3jKijUE0rWiYKCQrMiMzWbIxtDK83MEEWBA/8eY9LL48rdbutow6uLn+XpLx8jNjwejZkJrQK9UalVFBUWYe9mR3pyhvFZLSXoA0bvCBzVKx0y984ZyYS5o6pV2VRBoVGhBIMqKCg0JzKvZ1aZ/imqRINqbNjYW9OxT1tad/EhNy8XABONCR+u/z/Mrc1KBZCKqjsUAlG8tajUCGoTUKlBVHGr9HjpO7BOp5CZ+OoYHnhltKJkKCg0cRRFQ0GhGdLC2bbKDBKtVsLRw96g+WRZ5qeffuKrr74iMzMTAL+urfgl7GvGzA7GzF6DtaMV7fv434rrEASEOxbdagFBFBFUKgSV+qbiIVKicHh2ceU/ix9h/Asjqnv6CgqNjhLXSU2WporiOlFQaIbIsoy5lRm5mXkVjhFFgcEP9TVovhs3bpCcnAyAmdmtSqCO7vY8+9VTFHhk4O/vT8+ePZnZ/jXybx5XluUqLRKCIICg0lk5ZAm1K+SImQbJpaDQZFBcJwoKCs2Jb5/5hbzs/ErHTH3rAexdWxg0n6OjIw8++CD3338/Gk3pdvEqlYoOHTpw+fJlZFmm+8jOOgvFnUqGfGdr+Ns3yXpXj387fwYMGGCQXAoKCo0fRdFQUGhmXI9PZe/qw8hVdFQNntrfqHnbtWtHYGBgudsCAgLIzs4mMTGRkTMG3XSfCKUUCP0r2R3Khn67LCGIAiMfHlJGmVFQaOrcza4TRdFQUGhmRBy9XKWSARB++FK1jxEbG8t7773HypUryc3NxdvbGwcHB86cOYNXB3de+PkJRBMV3JTjTmWjTKCqJCGqRPpO6I69W4tqy6Wg0Gi5i+toKIqGgkIzw9Ay4oaOK49NmzYBEBERwfHjxxEEgf79+xMTE0N6ejpBwwL47sR7tO7mrVc2SpBlCWQJubgYWavVlS4XoPOQDjz28cRqy6SgoNA4UYJBFRSaGV7tPaocI4gCnfq3r/YxPDw8SEtLo6CgABsbGwC8vXXlzrOzs2nRogU2Dta8u+4lYi/Es++vIyRFX6e4qBiViZq8zHyy03KwtrfEo50bKvdipv7nQSWVVaFZ05TdHzVBUTQUFJoZy95fXeWYQQ/2MTi1tTzGjh3L2LFj0Wq1qFQqAP1/Jal0AS/P9u48/O79lc63ZcsWRclQaN5UEgxt8P5NFMV1oqDQjEhLzmDPXwerHHffsyNr5XglysXt/75T0VBQULi7URQNBYVmROTJKKTiqh/00Wdja/3YJYqGVqut9bkVFJo6StaJgoJCs0BVQbfVOxHLbfFe02MrFg0FhQqp56yTTz75hB49emBtbY2zszP33XcfERERpUWSZebNm4e7uzvm5uYMGjSIc+fOlRpTUFDAc889h6OjI5aWlowfP55r1yrpCl0OiqKhoNCMaNfTD1OLymtQCIJA1+BOZdbLskxCQgJHjhwhJCSE9PR0o44tirrbiaJoKCiURZBqvhhDSEgIs2fP5vDhw2zfvp3i4mKGDx9OTk6Ofsznn3/OV199xfz58zl27Biurq4MGzaMrKws/Zg5c+awZs0aVq5cyf79+8nOztbHZxmKEgyqoNCM0JiZ0NLPjcuno4GywZWiSqTf/ffg7OVYar0sy6xbt45Tp04hiiIqlYqQkBCCgoIYO3asQYGaoigiCAKFhYW1dDYKCgp3UtJrqARTU1NMTU3LjNuyZUupz4sXL8bZ2ZkTJ04wYMAAZFnmm2++4c0332TChAkALF26FBcXF/744w9mzZpFRkYGixYtYtmyZQwdOhSA5cuX4+npyY4dOxgxwrB+RIpFQ0GhGTH/+V91Soa+PlbpQlkt/d146edZZGVlce3aNf1/ly1bxqlTp+jXrx/Tp0/nkUceoUePHoSGhhIeHl7pMW+3YPj4+BAVFVX7J6ag0NSpJdeJp6cntra2+uWTTz4x6PAZGRkA2Nvrss2ioqJITExk+PDh+jGmpqYMHDiQgwd1AeUnTpygqKio1Bh3d3cCAgL0YwxBsWgoKDQTkq+msGnhjlvKhSwDwq3Pgowsavln7d9ERUWVqs5pZWXF6NGjadmypX5dly5diI+PZ/v27fj7+6NSqSgoKCA5ORlRFBFFETMzM5YsWYKfnx9jxoyhU6dOrFu3jrCwMAICAurx7BUUGjc1Degs2Tc2NlZfuwYo15pxJ7Is89JLL9GvXz/932ViYiIALi4upca6uLgQExOjH6PRaLCzsyszpmR/Q1AUDQWFZkLIqsMIt+kVOuRS/4wNi+f6tVT69++Po6Mj2dnZmJub4+joWCpVtYRevXrx999/c/z4cfz8/Fi2bFm5sRsnTpzgxIkT+s+nTp2iY8eOSm0MBYVaxsbGppSiYQjPPvssZ86cYf/+/WW23fk3akjHZUPG3I6iaCgoNBOy07IRVCJIlQdp9bmnL17tdNVDHR0dKx1rb29P27Zt2blzJ3v37kWj0XDvvfeiVqspLi5m7dq1ODg40LZtW0RRRKPRoNFocHR0VJQMBYXbaaCCXc899xzr1q1j7969pSyWrq6ugM5q4ebmpl+fnJyst3K4urpSWFhIWlpaKatGcnIyffr0MVgGRdFQUGgmuLdxRVtcuZIhqkQc3O0qHXMnvXv3JiUlBZVKxciRIzEzM9Nve+ihh9BoNKXWKSgolKW2XCeGIssyzz33HGvWrGHPnj34+vqW2u7r64urqyvbt28nKCgIgMLCQkJCQvjss88A6NatGyYmJmzfvp3JkycDkJCQQFhYGJ9//rnBsiiKhoJCM2HApN7Mf24R+TkF5W4XVSL3jO2CZQsLo+Y1MTHh/vvvRxCEMlYKY024CgoK9cPs2bP5448/WLt2LdbW1vqYCltbW8zNzREEgTlz5vDxxx/j5+eHn58fH3/8MRYWFjz88MP6sU8++SQvv/wyDg4O2NvbM3fuXDp16qTPQjEERdFQUGgmmFua8fyCGXz+2HwEQSgV7CmqBEytNAx6qpfR/lW4VSNDQUGhmtS01buR+/74448ADBo0qNT6xYsXM336dABeffVV8vLyeOaZZ0hLS6Nnz55s27YNa2tr/fivv/4atVrN5MmTycvLIzg4mCVLlpQb01URiqKhoNCMGDZtIFYtLFny9kqunNFFjiOAXVtrfIe5sv/4XqITrzBq1CglhkJBoR5pCNdJlXMKAvPmzWPevHkVjjEzM+P777/n+++/N06A21AUDQWFZkbvcd3pNbYb8ZcTycnIRWUpsvzP3/Tbr127Rn5+Pubm5g0opYKCwt1Cjeyhn3zyid7PU0J2djbPPvssLVu2xNzcnPbt2+tNOCVERETQt29fWrZsyfvvv19qm4+PD4IgcPjw4VLr58yZU8YEpKDQEMiyTE5GDgX5jbcCpiAIeLRxw79ba1q19dFHjLdu3ZopU6YoSoaCQn1TknVSk6WJUm1F49ixYyxcuJDAwMBS61988UW2bNnC8uXLuXDhAi+++CLPPfcca9eu1Y+ZPXs206ZNY+3ataxfv54DBw6UmsPMzIzXXnutuqIpKNQJBXkFHP7tNGOtHuE+u+mMtZjKGIuH+f65XygqLGpo8SpEEAQeeeQR/Pz8uHz5stE9TBQUFGqO0r3VSLKzs5k6dSo///xzmYphhw4d4rHHHmPQoEH4+Pgwc+ZMOnfuzPHjx/Vj0tPTCQoKIjAwEHd3d31p1BJmzZrF4cOH2bRpU3XEU1CodfJzC3im+2uc33qZwrxblozC/CLW/bCVKZ6zyMvOa0AJKyY9PZ3Q0FBSUlIAJVNEQaFBqOfurY2Jaikas2fPZsyYMeWmt/Tr149169YRFxeHLMvs3r2bixcvlmq+8v777zNs2DAsLCwQRbFMYxYfHx+efvppXn/9daUTpEKjYMUn/3D1QlyF2zNSsph9z/8ZFIBV3/z9998cOHAAR0dHHnjgAWxtbRtaJAUFhbsIoxWNlStXEhoaWmEjl++++44OHTrQsmVLNBoNI0eOZMGCBfTr108/ZvTo0aSkpBAfH8+aNWvKTZN56623iIqK4vfffzdWRAWFWkWr1fLvd5urHBcbHs/hjaH1IJFxWFlZAZCXl4eJiUkDS6OgcHdyN7tOjMo6iY2N5YUXXmDbtm0VVgL87rvvOHz4MOvWrcPb25u9e/fyzDPP4ObmVsoCYmpqipOTU4XHcnJyYu7cubzzzjs8+OCDxojJqlWrsLAwrihRcyIuLo4VK1Y0tBjNhrzMAnKzDHOL/PTWYqKzLtaxRMbj4eFBYmIi69atq7LseEOQkpJSpq21Qu2gfLcVk5dXj+5OSdYtNdm/iWKUonHixAmSk5Pp1q2bfp1Wq2Xv3r3Mnz+fjIwM3njjDdasWcOYMWMACAwM5NSpU/z3v/81qpIYwEsvvcSCBQtYsGCBUftNmjTprvZDr1ixgilTpjS0GM2G3Kw8Vvxno0FjxSKTRvvdh4SEcPDgQYKDg8nNzSUnJwcbGxu9xaMh2bJlCyNHjmxoMZolyndbMVlZWQ0twl2BUYpGcHAwZ8+eLbXu8ccfp127drz22mtotVqKiorKVBFUqVTVirWwsrLi7bffZt68eYwbN87o/RUUagMLa3NaBXrfKoBVCfauLepeoGoSGBjIvn37WLNmTanME3d3dwICAvDy8lIqgCoo1BX1XBm0MWGUomFtba3vZV+CpaUlDg4O+vUDBw7klVdewdzcHG9vb0JCQvjtt9/46quvqiXgzJkz+frrr1mxYgU9e/as1hwKCjVlxufTeH3kh1WOGzp1QD1IUz3s7OyYOHEip0+fpm/fvvj4+BAfH8+xY8f0ZYd79+6tKBwKCnWAQA0rg9aaJPVPrVcGXblyJa+//jpTp04lNTUVb29vPvroI55++ulqzWdiYsIHH3ygb/KioNAQdB/emenvP8iSd/4sf4Ag4NnWg8FT+tavYEbSrl072rVrp//s6OhIYGAgcXFx7Ny5k23btmFiYoK7uzudOnXC3d29AaVVUFBoDtRY0dizZ0+pz66urixevLja80VHR5dZN2XKlEbr91a4e5j61kSStYns/e442Wk5pbZ16t+Bt1bOwdRc00DS1QwPDw8eeughzpw5Q2RkJJcvXyYpKYmxY8eSk5ODiYkJrq6uDS2mgkLTpabVPRth6ryhKL1OFBSMwNXfgX+uL+bC4UucOxiBIAp0GRxAmy4+DS1ajdFoNHTv3p3u3buTk5PD//73P1avXq3fPnHiROzt7RtQQgWFpkt9N1VrTCiKhoKCkQiCQIfe/nTo7d/QotQZlpaWPPvss0RFRXHy5EkuXrzIgQMHsLGxoXv37lhaWja0iAoKCk0ERdFQUFAoF1NTU9q1a4csy6SmpmJjY8PVq1fJzc1l1KhRDS2egkLTQsk6UVBQUCif9u3b0759eyRJ4tdff0Wr1Ta0SAoKTQ5BlhFqEGdRk30bGiWHTUGhESNJEpdPRxO2/wJpSekNKktUVBRxcXFlUtwVFBQMQKqFpYmiWDSaOblZeeRm5mLjaIPGVOlz0ZTY/lsIS+f9SVK0ruuqIAr0ubcHz3w9HWevisv31xUl1XYjIyNJTk7m+vXrXLt2DTMzM+677767uhqvgoJCxSgWjWbKxROXefveT7nP7jGmeD7NBPvpfPfMz6QmpjW0aAoGsPqr9Xw+fb5eyQCQJZnD64/zbK83uB53o95lcnJyYujQoaSkpHD58mWuXbsGQH5+PitXriQyMrLeZVJQaCqUuE5qsjRVFEWjGZJwLoUX+r7F0U0nkW824inIK2TjzzuY3eP/GuQhpWA4ackZ/PJ/5Xct1hZLZF7P5Ld5f9WzVDr69u3LSy+9pK9r07dvX33F3nPnzjWITAoKTQK5FpYmiqJoNDO0xVr2LDiGtliLpC3t1JO0EmlJ6fxv7m8NJJ1CecRFJnB6zzmiz8UiyzI7lu2ttDeQtlhix+/7KMgrqEcpS5Oamqr/d+fOnZkxY4bSj0hBQaFclBiNZsbRzSfJS8+vcLu2WGLf6sOkf5dBCyfbepRM4U7Cj17ixxeXcP7Qrbbyvp28cPV1RqUSKZYqzu4oyi8iPTkTF+/6j9UAXSZKhw4dOHjwIF5eXlhbWyMITbkbg4JCHXMXVwZVLBrNjJjz1xDEym/4klYiPjKxniRSKI/zhy/y0sB3CD9yqdT66HOxHFp3HK22ihBzAaxaWNShhJUjiiLjxo1DlmViY2MbTA4FhaZCSWXQmixNFcWi0cwwszRFNkDzNbcyqwdpFCpi/rOL0BZpkaTSv1VJTI0sVfwbiiqR7iO7YGnbsNU5ExISAF2QqIKCgkJFKBaNZkaf8d2rHOPq64x3R896kEahPKLCrnIp9EoZJeNOynNFCKKAKAo8+s6kuhLPYKKjozE3N8fR0bGhRVFQaPyUuE5qsjRRFEWjmeHs5YTfAO9K3SePvjsZUVR++oYiOSal6kFAQL92iCoRQRAQVbrfy86lBR9tepO2PdrUpYgGodVqlbgMBQUDEaSaL00VxXXSDOnzeBdaurVk98oDqNQiCAKSVkIQBJ769BGGPTqwoUU0GG2xlv3/HGHTLztIuJJMC2dbhj06kKHTBmBu2TTdP9YO1gaNu//50byxYg6H1h0nLysPr/Yt6TGyCyq1qo4lNAxfX18OHDhAfHw8Hh4eDS2OgoJCI0VRNJohKhMVb/wxhylvTGD3iv1kpWbj6uvCsEcHYO9q19DiGUxhfiFvj/+U0B1nEVUiklYiMSqZC0cusubbjfx397x6OZ+czFz2rwvlRmI6kTHxZI/Oxcq2+oGY7e5pg7O3U6WWDXNrM3qMCsLMwpRxTw+v9rHqklatWuHu7s7Ro0e57777FOuGgkJl3MVZJ4qi0YzxDfDC96OHG1qMarPo9T84uSsMQF8TpCTQNe5yIp9M/Y4vdr5bpzL8/cM2ln70L4X5RajUItpiiWOrX+axN+7jgWeHV+vhKooiMz97hA8f+rrCMY/NexAzC9OaiF7nCILA8OHDWbJkCdHR0fj6+ja0SAoKjZe7uHur4qhXaJTkZuWxceH2CrMvpGKJU7vDiD5Xd6mVGxfv4ee3V1GYXwToapAAFBUU88u7q1n/y+5qzz1wch9eXfIsljctIyUxGKYWpsz84lEmzBlTQ+nrB29vbzw8PLhw4UJDi6Kg0Ki5m0uQKxYNhUZJ5MkoCvIKKx0jCHAm5Dw+HT2RJIldf+xn7Q9biD4Xi6mZhgETezFhzhha+rsbffyiwmKWfvRvpWN++2QtIx/tX+1mdcMeHcjAyb05uO4YyVdvYO/agj739sDC2rxa8zUU3bp1Y926dWRlZWFtbVj8iYKCwt2DomgoNEoMqQVSMk6r1fLxlG/Yu/owoiggSTL52fls+mUHW5fs5qONb9BlsHGtzc/sjyAzNafSMdnpuZzeG06PYZ2MmruEcwcj+OvLDRzdcgpJK+HZ1p283EJGPzG4yoDPwvxCLoVGoS3W4hPgiY19wz3gO3bsyNatW9m7dy9Dhw7F1LRxu3wUFBqEuzhGQ3GdKDRK2gT5ojGr3FIgyxA4oD3rftjK3r8PA5SqTaEtligqLGbehC/Iy6m4LHt5ZKfnGjYuw7Bxd7Lzj/28HPwBRzaHUlxQiFRczNXzV/n+uUV8+PB3aIvLLz+u1WpZ9v4qHnSfyZx+b/HyoHd50G0G/31yAdnplStGdYVGo+HBBx/kxo0brFu3jszMzAaRQ0GhUSMDUg2WpqtnKIqGQuPE0saC0TOGVlgPRKUWCRzYAZ8AL/75dmOF88iSTE5GLrtXHDDq+K4+ZYtQycVapLx8pOwc3ZKTS8SRixTkV+7iuZPUxHT+O+MntIWFaPMLQZJAkpC1EnJRMfv/OcSCV5Zxcs958nNuNU6TZZkvn/yR3977q5RSUVykZftvIbw08B3ysvOMkqW28PX15cknn0QQBP7991/i4+MbRA4FBYXGh6JoKDRanvp0Kp0HdgRALFE4BN3i4uPMG3/MITs9h8So5Eq1fZVaLNNTpCr8g3zwauumV3TkoiLk/HzQ3rI0yJLEP99u5j77x/nl/5Yb3E11y5I9aAuLKjaFSjLrF2zj9fFf8FDrF/h13iqKi4o5f+gi238LKfdcJa1E9LlYNvy0w6jzrE0cHR156qmncHV1ZcOGDfzzzz8cP37cYDeYgkJz5m4OBlUUDYVGi6m5KZ9seZPXf3+BTgPa4+zliH+31jz73ZP8L/RzHNzs9NkalSPoCpcZgSAIvPD1o4iCjJyfr1MyZOnmIt+cFZBltEUSf/13Pa+P/IjCgqIq57544jKytuLOrHBTsZFl8nMLWPX1Zj6f8TObF+2s9DxkSWbjT9uMOc1ax9zcnGnTpjF27Fjs7OwIDQ0lL69hrCwKCo0KmRqWIG/oE6g+SjBoM0SSZI5sPEHkqWg0pib0HNsNr3ZNs3Kj2kTNkCn9GDKlX7nbLW0s8O/emksnLldoINAWa+k+oovRx87LzEHOyUUuulMpKBvUJSMQdiCcDf/bxoQXKk9NzU4zMJZCqwW1GlmW2fvPUXz9nPQpthWRHHvdsLnrEFEU6datG46Ojly6dInCwkIsLBqu06yCgkLDolg0mhnnD19k1ZwtvDXuU5a99xe//N9ynuwwh3fu/YyczOoFLt5JwpUkFr66nKe7vcbT3V7jp1eWEX+5YdrO30hIIy87r0IlQ1SLuLVyodfYbkbNm5aUzryJX1b5YC9BV7dLYPHbf1JUWFzp2NaB3gbNebvLQaUSycrIrdKCY21nZdDc9UFJMTPFdaKgQL03Vdu7dy/jxo3D3d1dHztVWhyZefPm4e7ujrm5OYMGDeLcuXOlxhQUFPDcc8/h6OiIpaUl48eP59q1a0afuqJoNCOuXYzntWHvk5uqM1VriyV9FsaRTaG8Pf7TGt/0Q1Yd4vH2c/j7m41cPhXN5VPR/P3NRh5v9wJ/frG2Xh8q+bkFzB38LvGRFSs5LZxt+Xjzm0b3B9myeDdFBUXGnY8gUJBXyOZFuyodNvKJwYZNd1vjO61WwqKFlb5CanmIKpHhjw0yaO76oCTNtbDQuGBZBYVmSU0yTkoWI8jJyaFz587Mnz+/3O2ff/45X331FfPnz+fYsWO4uroybNgwsrKy9GPmzJnDmjVrWLlyJfv37yc7O5uxY8eircL1eyeKotGM+PPztTcfjmW3SVqJs3svcGp3WLXnv3Yxnk8e+Q6tVtI98GQZZAlZq0XSSvzy2nKmtZrN/jVHanAWhrPr931cu5hQodVBEGDc08Np6edm9NynQ85XWJW0KpZ/uJqUazcq3O4b4EW7e/wqn0QUEVQ65Uin7MiYaNS4t3Et16ohqkSs7a247/nR1ZK5Nrl+/Trz58/Xp7kqioaCQv0zatQoPvzwQyZMmFBmmyzLfPPNN7z55ptMmDCBgIAAli5dSm5uLn/88QcAGRkZLFq0iC+//JKhQ4cSFBTE8uXLOXv2LDt2GBd0rigazQRZltm1Yn+lpn6VWjQ6zfN21i7YpotHKglqKic6KSkmhfce+C87lu+t9nEMZcfveytMfwWdiHtWVvN8a2CYSU/O5JHWz7Lw1WVIUvm/x+u/P4+tk02F8otmus60siQh5eejzcnj4pFLxEcm6a0agoB+f58AT77e+z4Obg3fNO/y5cvcuHGD1atXA1BUVHWArIJCc6e2sk4yMzNLLQUFhmW73U5UVBSJiYkMH36rYaOpqSkDBw7k4MGDAJw4cYKioqJSY9zd3QkICNCPMRRF0WgmaIu1FFZRslvSymSnZxs8Z8KVJNZ8t4kVn/3L4Y0nOLblJJJekan8STz/uUUUGllfwlgyrmdVaXXITDX8fG+n86AOlSoxVSFLMqu/3sjyD/4ud7t7a1d+PPE54/8zAjPLm5U0BQFRY4JoYYEgijolIy8fbj9HnXYBooh3gDdPf/UY3x78iP+FfoFn28YR8Ovnp7PWlFgyqnMjVFBodtRSjIanpye2trb65ZNPPjFalMREnbvZxcWl1HoXFxf9tsTERDQaDXZ2dhWOMRQl66SZoDZRY+/WgtSE9ArHiCoBt1auVc6Vn1vAVzN/Ys9fBxEEAVEU0BZLt0z2BsQt5GTkcnDtMQY92NfQUzAajzauXIuIrzBuQRQF3FtXfb7lMfLxwfz+0Rrj4zTuYNWX63ngxTFY2pTNunBq6cCz3z/JM98+Tn5OAekpmXw+YyHhx64AIBdVElQqQ3RYLG27t6FDL/9qy1cX2Nvb07t3bw4dOoRarS7l81VQuGuppRLksbGx2NjY6FfXpOT/nd2nZVmusiO1IWPuRLFoNCPGzhpe6Vu4tlhi1JNDKp1DlmU+evhb9q46pMvilGS9O0bSSga/5YsqkeSrdZtqOWbG0EqDIyVJZuysYdWa286lBe+ufgm1RlU6JqKkbpiBf2gFeYUc33q60jGiKGJhbY57Kxe+2fk2n298jSfem4iqiu9apVaxY/k+g+Sob3r16gXozi0tLa2BpVGoLwoKCigu1inIRUVF5OQ0XrzW+wAAKg9JREFUTFn85oyNjU2ppTqKhqur7gXsTstEcnKy3srh6upKYWFhmb/f28cYimLRaEZoi7WVuhIe+r/7q+xkGn40kiMbQyseIKN72FahmEuShI1D3Tb66jEqiH4TenJgzdEyVgdBFOg8qCODHuxT/flHdOGXs1+x4aftHN5wguKiYizcNDz2ysP8Nu8vIk9FGzRPbmbFBauKCos5tu0MN+LTSLl2g1O7zxFx/KZFQwZBpaqwuJeklUhPzjD6vOoDGxsb7OzsSEtLIyYmplpvQQpNi8zMTNasWYMoinTs2JHw8HCys7MZNWoUnp6eDS1ew9OImqr5+vri6urK9u3bCQoKAnSuzpCQED777DNA15XZxMSE7du3M3nyZAASEhIICwvj888/N+p4iqLRTNj3zxF+/7D8eAAAqxaWTHt3UpXz7PnzICq1qsKmXoYoGQAmGjV977+n6oE1QBRF3lwxh98//Js1320i52aDM3MrM8bOGsb0Dx5CbVKzS9zN15kZn05lxqdTAVixYgW9xnSl5+ggdv2xj8+mL6hyDg//8rNedq48wI+v/E5WWo7uJnKHkigIArIo6v5bXNaNIqoEnDwdqnFW9UPJm1bbtm0bWBKF+uDMmTMUFBQQEBDAyZMn0Wg0WFpacvToUVq2bKkomhJ6i2i19zeC7OxsIiMj9Z+joqI4deoU9vb2eHl5MWfOHD7++GP8/Pzw8/Pj448/xsLCgocffhgAW1tbnnzySV5++WUcHBywt7dn7ty5dOrUiaFDhxoli6JoNBP++vxffYv08shOz+Hgv0erjJnQP/QqQRBFOvVrx5mQcxWOmfL6hHopHqU2UfPYew8y5fX7iTp7FVmW8e7oibmlWZ0eVxAEgqcOYMPPO7lw+FK5LhxRFHBr5UKnfu3KbNuz+gifP7VQ96EcJeP248i6yXTN125DWywx8nHDanI0BAMHDuTPP/9EFBUPbXMmNzeXU6dOcf78eYYPH07v3r3JyspCq9WSlZXFr7/+yuXLl2nTpk1Di3pXcfz4cQYPvnV/eOmllwB47LHHWLJkCa+++ip5eXk888wzpKWl0bNnT7Zt24a19S1L9Ndff41arWby5Mnk5eURHBzMkiVLUKmMq0uk3AGaAfm5BYQfjaxQyQCdPz90x9kq53L1darSYGFpY87nO97hndVzsXXUXZQlsRsaMxOmv/8Qj7w90WD5awONmYa2PdrQ7h6/OlcybueFH57CzNK0TG0LUSWi0qiZ+8t/yrzJabUSP7+54taKKr5wAfQ1NW5n7KyhtDKwymhD0K5dO8aPH8+FCxc4ePCgUiG0GZGamkphYSGZmZmsXbuWyMhIBg4cqI/Nsba2pkWLFvoYAGMfTM2R+m6qNmjQIGRZLrMsWbJEJ48gMG/ePBISEsjPzyckJISAgIBSc5iZmfH9999z48YNcnNzWb9+fbXcYIpFoxlQWUBkCTKyQdXcRjw2iN8//KfC7aJKZNRTwahUIv0n9KT3uG4c3XySxCvJWDtY0Wd8dyxtLUsf++YF3hzfbH06ejL/0Ecsefcv9q85qguYFQS6j+jM9HmTaRPkW2af84cvcT3u9gCrqjQNoZTHytLWgkkvj2XK6/fX1mnUGVFRUQCcO3cOW1vbMjcyhaaFJEkcPnyYsDBd4T+NRoOVlRVPPvkktra2pcYePXqULVu20LFjR3x9y/4d3HU0ohiN+kZRNJoB5lZmtPR3I+5SQsWdx7WSQWmQLt5OPDpvEkvf/avMNlEl4uLtyEOv3qtfpzZR02d8j3LnOr7tNKv+u5ZTu8KQZRm/bq15YM4YBk/p16z8tS393XlrxRxyMnJJS0rHxsG60kDYjOtZpW8aBtw/RJXIe+tew9zSlPa9/NCYaWpB8rpFlmUuXryo/3zs2DHatGmDmVn9WZwUapcjR47olQyAgIAAhg4dirm5ealxubm5bN68mXbt2tG7d+/6FlOhkaEoGs0AQRB44MVxfPufheVvFwXMrcwY8nD5HVDvZOobE7B3bcHvH/2jT1FVm6gY9GAfZnz2iEHZJH9/vYH/vbwUUSXqXTqRoVf45JHvCDsQznPzn2pWygboLA2WtpV3KU26ep0/v1xfemUVAbaiSqTPuK70GtO15kLWIyVVC1u1asWVK1coKiriypUrdOjQoaFFU6gGkiRx7tw5OnXqhLm5OR07dsTLy6vcsaampoiiiKOjY7O0ZFYLSQahBlaJarZEaAwoikYzYfSMYML2X2Dn7/sQhFsvzCq1iEqt4r01r2JuZV75JLcx6okhjJg+iJhz1yjIK8S9jSs29oYFd0afi+V/Ly8FSrt1ShSO9T9uo8fIIHqP626wPM2BtKQMXhzyPmnJmQbvU6KLTX55XB1JVXckJSUBupoaKpWKS5cuER0drSgaTZTMzEwkSSIoKKhKV4hKpcLBwYHU1NQy2woKCjhy5AgajYbOnTuXsYY0W+5i14miajYTRFHk1aXP8tafL+Hc1hHLFhbYu7Zg3NMjWHjmS7oMNt43Looivp28aHdPG4OVDIAN/9uGSl3xpSWqRP79fpPR8jR1/v5uM2nJmWVjagShTNqbqBJBAFMLU95Z+QJtu7eqP0HLQauVuHLmKuHHIslON6wIU2pqKiqVCktLS7p31ymV1WkxrdA4yM3VpY/fXpWyMpydncst1nb+/HnCw8MJDw/nzz//VAq63QXUSNH45JNPEASBOXPm6NdNnz4dQRBKLSWRyCVERETQt29fWrZsyfvvv19qm4+PD4IgcPjw4VLr58yZw6BBg2oibrNHFEUGTurNmLcH8G/qUv6M/5nZ3z2BRxvju5fWhIjjlytt7iZpJS6euFKPEjUONi8NqThw96ayYW5tTt/7ujPmqSG88P3jrIyeT++xDecykWWZDT/v5FG/F3i6+//xXJ+3meQ+i08enU/mjcpLi2dmZmJlZYUgCKVS5ipqNKfQuCnpWVNcTk2X8nB2di7XopGUlESbNm14/vnnEUWRw4cP3yUZSTXtc9J0v6NqKxrHjh1j4cKFBAYGltk2cuRIEhIS9MumTaXfXmfPns20adNYu3Yt69ev58CB0h02zczMeO2116ormkIDozEzqXKMiebu8tppi7Vkp1VhCRAFgoZ05J0VL/DsN48x+skhmFs1bODk0nmr+G72IpJjr+sqlGqLKc4vYNfyEB72eYbj2your56VlYWl5a0MpJKYHKX3SdPE3d0da2tr9u/fb9B4W1vbUiXJS8jKyiIrK4vIyEjy8/OJjY0tFWDabKmlpmpNkWopGtnZ2UydOpWff/65TGc30AUCubq66hd7e/tS29PT0wkKCiIwMBB3d3cyMkqXUZ41axaHDx8uo6AoNA16j+teaaCnKAp1XjW0MZFxI4vXx39R5TiVSsTe1bbKcfVFXGQif3zyr+5tU1sMcmlLREFOPm+M+oijm0+Wu392dnapDJMSv356enqdyaxQd5iamuLv78+lS5cMSpUvsWLd3u8kIyODtLQ0kpKS2LVrl3794cOHSUhIqH2hGxOSXPOliVItRWP27NmMGTOmwjKke/bswdnZGX9/f2bMmEFycnKp7e+//z7Dhg3DwsICURQZMWJEqe0+Pj48/fTTvP7664qZtQkyfPogrFpYVhhtLkkysRHxFBUW1bNk9Y+2WMsb931J2KFLtyI7KxwrMeyR/vrP1+NTWf7h37w38Us+nvodu1YcoLCg/r6zrUv26GJFpIofKrIs8/HUb8r9LQsKCtBobqXhligae/bsqXVZFeoHHx8fCgoKuHKlatenq6srarW6VBnss2fP6kvTZ2Zm4unpiVqtxtzc3GBLiULTw2hFY+XKlYSGhvLJJ5+Uu33UqFH8/vvv7Nq1iy+//JJjx44xZMgQvX8PYPTo0aSkpBAfH8+aNWvKrRr31ltvERUVxe+//26siAoNjI29NZ9tf7vSTq9n9pzn9w/+RpZlYs5f48y+CyREJVc4vq6RJKlOHuJHtpzm8ukYXWxGJYqGIAgMmHAPbbvpgj63LQ3hEd/ZLHt/NQf+PUrIqkN88sh3PNHhReIvJ1Y4T22SEJWsU/SrMNnmpOdycO3xMuvz8/NLKRqtWunOraCggOzs7NoVVqFesLe3x8HBgU2bNlX5G1pYWNCtWzfCwsIoLCykoKCAixcv6gODQdfyvLi4mNzcXNLS0vQBp80SWar50kQxylEeGxvLCy+8wLZt2yosuvPggw/q/x0QEED37t3x9vZm48aNTJgwQb/N1NQUJyenCo/l5OTE3Llzeeedd0rNaQirVq3CwqLyegbNmbi4OFasWFH1wDok5XJqxY3Z0L0J//nff9m0bBfp8bduWC5t7ek5NQAH7/pxIdyITufkmnBiTyYhSzIWdma0H9aKjiNaoTYt++dh7Hcb8ssZBFF3jyhpknZnzxKAdkO88B5ux8qVK0kMv86mD0u/3ck3g0iTr15ndp/XeeC/QyvN7KkNEq8bbspe/fO/xBdHl1qXmZlJTExMKVdJy5YtyczMZN++faXcaykpKWzZsqWmIiuUQ21/t2ZmZsTHx/Prr79Weg8HXeBoQUEB69at03+OiYnRb7e2ti4Vs1PZs6UuyMuruLNyrXMXp7capWicOHGC5ORkunXrpl+n1WrZu3cv8+fPp6CgoIx1ws3NDW9vby5dumS0cC+99BILFixgwYKqO2TezqRJkwxOwWqOrFixgilTpjSoDKv+u05XrKuS8ujFBVrS47O4PbczJTKdLR8f4qs97+LftW5TOrct3cPit9eWinjPTcsndNUFsqMK+GLnO2X6phj73Yb9FU+0dMsCIQgCqFS6Y8oyCAIqtcg36z7Qj3lr/GcVfneyJJN9PZeWGl8GPdjHmNM1mo4tu/Dyng8NGuvh5lXme/nwww/p2LGjQWXHt2zZwsiRI6slp0Ll1PZ3q9VqWbp0KT169DCo6ufvv/9OZqaudkyrVq3o3bu33lI9YMAALCwskGWZ1atX06dPH5ydnWtN1qpQApPrB6NeiYKDgzl79iynTp3SL927d2fq1KmcOnWqXBfIjRs3iI2Nxc3N+BRLKysr3n77bT766CP9harQNDA0Xe3OcZJWorhIyw9zltaFWHquhsfxxZM/liunLMtcPH6FPz5eU+PjeLRxLdfyIAgCgigiiAKuPrdurJIkcWzzyUoVNFElcnhjaI1lq4pO/drRpouPrnNsFQQO7Fjqc3FxMVqttpTrRKHps2fPHhYtWkRxcbHeFVYVQUFBJCcnk5ycTI8ePfDw8NBvEwSBdevWsWHDBoDmfb0owaCGYW1tTUBAQKnF0tISBwcHAgICyM7OZu7cuRw6dIjo6Gj27NnDuHHjcHR05P77q9cAaubMmdja2ja4K0DBOAL6tzeo2VuZSlXolI0Lhy9x7WLdRaF/+dT/Kk1Ll2WZ9T9uo6jQsJoBFTFq+oBKa4oICIx98lYr5ytnYyvtwlsiW1E9BIUKgsAnG15DbVF55UaVmSlDHiz9ZlsSk3X+/Pm7pEZC80eSJH3vmh49euDi4mLQfu3atSM4OJjhw4fj7++Pubk57u7uAKSlpVFQUEB+fj7QzLu8KumttYNKpeLs2bPce++9+Pv789hjj+Hv78+hQ4dKFewxBhMTEz744AP9hajQNGjf0w+/bq0qjyMQxErTYBNjUupAMsjJyOXCkapdeTkZudyIL1twyBh8O3oy8YVR5W4TRQH/br6MeXKwrn3zu38xu+cb5eleZfDrWj/dMFs42fDYu5MQKvKbq9VMeeMBzCxNS60uUTSSk5O5evVqXYupUA+UBGpOnDiRUaPKv6bLQxRF+vXrR+/evfWZaN7e3vrtHTvesoYVFTX/TLS7kRpXTbo9Vc3c3JytW7fWaL7o6Ogy66ZMmdLgMQcKxiEIAu+unsuLA97m+rVUZGSQuS32QKjSJG/jYHjZc2O4HpeKbKAZsja6pD75/iTcfJ3488uNJMfeAMDM0pTRjw/i0Tfvx9Rcw9Yle1jx2VoABEFEriTCXKUSGfH44Aq31zaTXxxFdkYuq7/bglxcdDOYVQATNROeHcm0N+4ts09hYaH+30lJSaUeLApNkxIlQa1W17ghYok15Nq1azg6OgK6e0ZcXFyZukvNBpkaBoPWmiT1zt1VnlGhXnHxduLnM1+y5dfdbF8WQuaNLNzbuDJs2kC+f34JhfkVvL0I4Obrgl9Q3by1W9lZVtkx9f/bu/Oopu60D+DfG5KQhCUSthBAQAEVKChEwA1EKi0oYOu0zrSv9p3axVPrHNsZp57T9m1f+471tKdOz0zVcdrRM+OZqb7jMmpbx6HDInspssmiVJBFsWwiIawhv/cP3qSmoKLkhgDP55z8kZube3/3csl97m97AMBZ5QSFctaE98dxHNY8H4fE/4zF9avfQzeog4efGySykVoAxhg+/+D0D2UScP//o2S6HYGNAIwx7Di8FU5ulpvYSyAQ4IVdTyH5hVVI/998dNy8DYVSjlVPR0M522XM7xg69Pn4+JgMZyRTl0QiAcdxZulAOX/+fACAnZ0dli1bhuLiYjDGUFlZiaCgoOnZhEKjTgjhh53cDutfW4v1r601WX6rVYNDbx0d+0sMeGH3z3hLI+/s4YSQ5fNxKbvmnus9+9aT9/z8QQkEAngHjO4UfbO+DS11P8whYhiZAsaMw1oBwCvQA7/8bAuCogPNWq7xcp/tjJ/9au39VwTQ3t4OYKRanNKETw8CgQBSqdQsc6DY2tpix44dEAqFEIvFCA0NRXl5Obq7u1FSUkLB6TRDvwBkUmzYkYyfv7cBItuRvCgCm5FL0U4uw68Pv4IVT/I7RfnPd20AZ3P3QGZBdACSX07gtQwGQ0OjO5waRqUIREJwQhsIpWIseyJq0oKMB2WYO2OsFAVk6pLJZGabbE0mkxlHmSxfvty4vKSkBB0dHWbZh1XR6yf+mqKoRoNMCo7j8LM3UpGyZTVyT3+L2+3dcPN2wZLkcLP0i7if0Jgg/PeJX+HDzQeg6ewZmUzr/6sm459dgV99toX3MhgofVwgc5CiVzP25EEcx0Gv0yMwYnJTxT8Iw81IKr33iBXy4Jqbm3HlyhU4OjoiMDDQonMGSaVSXuaecHFxQXBwMCorKwEAWVlZWLdu3fSqDaOmE0Imh51choRNMWbd5tCgDnlni1GRexkcxyEsZgGWrFkEG6Fpu++SZDWONR9E/tlvcb22BTIHKZati4SLp2U7o4klYqx5cRVOfPzVmENbBQIOcjdHRK9ZZNFyTYQhodq0ulFMMr1ej8LCQlRUVMDFxQXNzc0oKytDREQEQkNDLXKunZycxuywP1Ecx+Hxxx9HZWUlGGNob29HQUEBlixZwlsTqsVRoEHI9PBdWQPeXr8XnTe7YCOyARhw5uDXcPVS4H9O/BK+wV4m64vEQsSsj56k0v7gP95aj0u5l1HzzVXjCB0AsBEKIBQL8V9HX4NQNHX+XXt6emZ0GgA+VFRU4NKlS0hISEB0dDSGhoaQmZmJgoIClJSUICgoCJGRkbzemN3c3FBeXg6NRvPQUxbcjb29PZ588kmcPHkSAHDp0iUMDQ0hNjbWrPshlkePG2Ta6Py+C2+s2YOutpFZZIeHho35VjpauvDrNXvQ3WGdybwkMlt8cP5NvPTBs1DNcYdAwEHmKEXi86tw4Jv3ERQdMNlFfCBarZaaTcyop6cHxcXFiIyMND7li8ViJCQk4KWXXoJarUZZWRny8vJ4nSDtzmGpfJgzZ45JrpPLly9bNh8Jn2bwzKBT5xGJkPv48rMM9Hb3jdn8oB/Wo7uzB+ePXMBT25MmoXT3J5aI8eS2RDy5bfyTIVmrnp4eCjTMKDc3F1KpFHFxo+dPUSqVUCqVcHZ2xhdffAHGGJYtW8ZLzYadnR0cHBxQU1ODBQsWmG27Op0OGo0GYrEY/f398Pb2Rnx8PLq6uqbNdcSY/p7z44zn+1MV1WiQaSPrZOE9p+9meoask99YsEQzFzWdmE9DQwMaGhrw+OOPw9bW9q7rRUREIDk5GVVVVcjNzeWtZuORRx5BRUUF2trMN3NvcXExfve73+Hvf/87gJGpyQUCgUUTrBH+UKBBpo2+noH7r3OXkR3EvKjpxDwYYyguLoavr++4ahDCw8ORkpKCqqoqnD59mpcmjvnz58PR0REnT56ETjexXEAGc+fOBQBjCnmtVovS0lKzbNtqsAk2m0zhzqAUaJBpwy/Eyzgfx1gENgL4hXhbsEQz09DQEAYGBqhGwwyamprQ3t6O2NjYcTeFLFq0CM899xxEIhG++uorfP/998Y8JeYgFAoRHx+P1tZWfP3112bZpouLC/z9/Y3vGWMoKyubXlm7KakaIVNf8ovx98wYqx/WY+0L8RYs0cxkmKzL3p6fXDUzBWMMFy9ehJeX1wPnivH19cXmzZvxzDPPQKfT4csvvzRrYkoXFxdERUWhsLAQJ06cMEuHzXXr1pm81+v1yMvLm/B2yeSjQINMG5GPhSFh44qRN3c8/BkeBJNfikdYzHzLF2yGMUw/LpdbLh/LdHT16lW0trYiJibmoTp2chyHgIAAeHt7Q6PRoKbm3lPuP6iQkBDExcXhypUrOHjw4IRnDLWzszMOZZXJZGCMobGxkZd5OybFDJ4ZlAINMm1wHIfX9j2PV/dugofvD53IPP2V2P7Jz7H1o43TZ/IfK9bR0QGxWEx9NB6SRqNBeno60tPTERISYtKk8DDEYjHmzp2LxsZGM5VwhCGQWb9+PYaGhnDs2LEJ99lYvnw5goODsXDhQsTExEChUCAzMxO3b982U6kn0QxuOqHhrWRaEQgESH4pHmtfXIXbbRqAA+QuDlYXYDDG0NbWBldXV6sr20R1dHRALpdPu+Pi0+DgIOrq6tDQ0IDm5mZIJBKsWbMG4eHhZjmPXl5evNUMODg4ICEhAWfPnsUXX3yB1NTUhy6zUCjET37yE+P7JUuW4LPPPsO5c+eQlJRk0enWiflQoEGmJY7jMMvN+n6U9Ho9Ojs7kZ6ejurqaiiVSigUCiQlJcHOzm6yi2cW7e3t1GwyTowxXLt2DXl5eejt7YWXlxdWrVoFtVptTDhmDo6OjhgYGMDQ0BBEIpHZtmvg5uaGmJgYZGRkwN3dHUuWLDHLdiUSCZ599lkcOXIEZ86cQUpKypQNNpheD8bNzHk0KNAgxEJu376NI0eOGJsW1Go1WltbUV9fj7/85S948cUXIRSO/1+yt7cXUqnU6moOOjo6EBwcPNnFsHqMMeTk5KC6uhqBgYFITEzErFmzeNmX4eas1Wp520dAQAA6OzuRlpYGgUBgtunQnZyc8Pzzz+NPf/oTvv76a6SmpsLGxub+X7Q2jMGYW+Chvz81UaBBiAW0tbXhr3/9K/R6PZKSkuDq6mqcfKmjowOnTp3Cv//9byQkjKSm7+rqQn9/PwYHB2FnZweRSIQbN26gu7sbNjY2KCsrQ3NzM+bNm4e1a9dazQiP3t5e9PX18XYz49vw8DBu3boFZ2dn3gO4yspKVFdXIzk5GeHh4bzuyxKBBgAsXrwYw8PD+Oc//4na2lo8/fTTZqmZMeRBOXToEFpaWuDl5XX/L1kbPQM4CjQIITzo7+/HoUOHIJPJkJSUNCoZlbOzMxYvXoyCggLU1NRAJBLdddZFgUAAvV4PlUqF6OholJaW4qOPPoJcLoebmxu8vb2hVqsnrSNma2srgKk54uT69etIT09HX18foqOjERoaytu+Ojo6UFhYiKioKN6DDADGa06r1fK6H4FAgKVLl8Lb2xtpaWnIzMw0Bs8TpVCMZFUeGBiAXq9HW1sbtFotfHx8pmYNxwxCgQYhPGtvb0d/fz/i4+PvmvEyNDQU7u7uqKmpgU6nw6JFi2Bvbw+hUAitVgudTgcXFxfIZDLodDpjO3tAQACamprQ2dmJW7du4cKFC8jJyUFYWBjmzZsHHx+fuzbHtLW14eLFi5DL5SgrK4NGo0FiYuJDN3swxpCdnQ1HR0c4OTk91DYmi06nQ3Z2NpydnSESiVBSUgJPT08oFAqz12zodDqkp6fD2dkZjz76qFm3fTcikQhSqdRiE2B5e3sjKCgIpaWliI+PN0sgIJVKIZfLkZ+fj/z8fOMkZK6urnj00UfNnk3W7BgDMIF+FlSjQQi5G3d3d3Ach66uLnh6eo65DsdxxuRYP/bjqu47O/NJpVIEBgYa3/f19aG8vBzV1dUoKiqCWCzGnDlzEBgYCA8PD3Ach6amJlRVVeHatWsQi8UYGBiASqWCk5MTTp48idbWVqjVatjb2yM9PR1Xr16Fn58f5s+fDy8vrzFvvAMDA/j2229RV1eHhIQECARTZ+S8Xq9HVlYWent7sXHjRojFYhw6dAgnTpyAQqGAWq2Gj48POI5DW1sbmpqaIBaLERIS8sD7YowhLy8PGo3mgfvkTJS/vz9qa2sRHh5ukb+Pv78/ysvLUVdXh4CAiWcfFggE2LBhA4qKiiCRSLBgwQJwHIfjx4/j+PHjCA4ORkhIiNXOSMv0DGwCTSd8ZuXlGwUahPCorq4O586dA8dxcHFx4X1/UqkUUVFRiIyMxK1bt9DQ0IDGxkacOXPGuA7HcVCpVFi+fDkCAgLAGINIJIJer0dBQQHy8/ORk5MDT09PNDU1wdvbGyUlJcjLy4Ovry/Wrl0LZ2dnACM/foWFhcjIyMDg4KCxFmWqYIzhwoULqKurw/r1641/o61bt6Kurg6FhYX417/+BaVSidbWVujvmDQpICAAYrF43DUejDFkZmaitrYWycnJFk8YFh0djYqKCly7dg1z5szhfX/Ozs5wcnJCRUWFWQINAPDw8EBKSorxfVlZGZRKJbq7u3Hp0iVUVFRg6dKlZs0sO9Xt378fH374IVpaWhAcHIyPP/4YK1assGgZKNAgxIyampqQnZ2NWbNmYWhoCGVlZXB3d0dqaipcXV0tVg6O46BQKKBQKLBo0SL09fVBo9GAMQa5XA6JRDLqO4b2dbVajZqaGtTU1CAsLAxRUVHQ6/VoampCXl4eDhw4YGxeaWhowO3btxEUFISFCxdaTafU8TCM+rhy5QqeeOIJkyYjkUiEefPmITAwEFevXkV2djaEQiF8fX0RGhqK48eP489//jMcHByQmpo6rqfo5uZm1NbWYt26dQgLC+Pz0MakUqng6emJ7777ziKBBsdx8PPzQ1VVFRhjvHSuvXDhAjo7O2Fvbw+dTgeBQIC8vDx4enpa3zBYpsfEmk4e/LvHjh3D9u3bsX//fixbtgwHDx5EYmIiqqqqMHv27IcvywOiQIMQM+nu7sbRo0chEomM03Cr1WqEhYVNelOCVCoddwdRsViM0NBQk86QAoEAPj4+8PT0RElJCW7cuAHGGLy9vREbGztmk481MzRhVFdXIzU19a4dPzmOg7+//6iEX5s2bYJWq8Xp06dx5coVLFy48L77vHHjBhwcHHjtZHo/vr6+Fs2K6unpiYsXL+LmzZvw8PAw+/YlEgmEQiEcHR0hFovR2dkJqVSKnJwcJCYmWtXQ78loOtm7dy82b96MF154AQDw8ccf4/z58zhw4ADef//9hy7Lg6JAgxAzaG1txeeffw6BQICUlJRpO/22UCjE4sWLJ7sYE2Jo7qmsrMTatWvHFSTcyfCkDgA1NTUoLy8HYwyBgYF3nXStp6cHNTU1xn4Fk8XLywu5ubno6emxSO2Tm5sbhEIh6urqzB5oaDQaDA8PQ6/Xo6urC3K5HPHx8XBxccGxY8dQW1tr0n9puvhxh15bW1vjUPk7DQ4Oori4GDt37jRZnpCQYPFkddMy0JhWqYUfQm9v74w/B3wZ69wODw/j008/hUgkQnx8PHQ6HTQazSSVcGoyNO1YQktLC7755husXr0aAQEBE/pfiYqKwsDAAPLz85Gbm4v4+HiTOR46Oztx5coV1NfXQyQSISoqyuL/m3des1KpFP39/WhqarLYXBSzZs1CVVUVHnnkEbNu19AHCRg5xs7OTvT09EClUkGhUOD8+fPo6+u7ZzORJf9PdWzgoZo/jN/HEICRET13euedd/Duu++OWr+9vR3Dw8Nwd3c3We7u7o6bN28+dDkeBsemclfWHxkYGBiz7ZkQQggZi1KpRH19PW/3jv7+fvj5+Znl5q5UKlFWVmZS1rvVaNy4cQOenp7Iy8szmRL+N7/5DY4cOWL2bL73Mq1qNGxtbdHf34+BgYHJLgohhJApQCwW8/qAKpFIUF9fj8HBwQlv60HK6uLiAhsbm1EBTmtr66haDr5Nq0ADuHt0RwghhEwGiURi8dp2sViMiIgIpKWl4YknnjAuT0tLQ2pqqkXLMu0CDUIIIYQAr7/+OjZu3Ai1Wo0lS5bgj3/8IxobG7FlyxaLloMCDUIIIWQa2rBhAzo6OrBr1y60tLQgJCQEX331lcUn1ZtWnUEJIYQQYl2mTkICQgghhEw5FGgQQgghhDcUaBBCCCGENxRoWJH3338fixcvhoODA9zc3LBu3TpcvnzZZB3GGN59912oVCpIpVKsXLkSlZWVJutcvnwZy5Ytg5eXF3bt2mVc/tOf/hSJiYkm6xoyi7799tsmy9977z2oVCozH+HkuXDhApKTk6FSqcBxHP7xj3+YfE7n1XL2798PPz8/SCQSREREIDs72/jZzZs3kZiYCJVKhVdeecUkW+pMxuf1C4zkQOE4btRrz549fB8amQEo0LAiWVlZ2Lp1KwoKCpCWlgadToeEhARotVrjOh988AH27t2LTz75BEVFRVAqlVi9erXJVLpbt27Fxo0bcfr0aZw9exa5ubkAgLi4OOTk5ECn0xnXzczMhLe3NzIyMkzKkpmZibi4OJ6P2HK0Wi3CwsLwySefjPk5nVfLMGSTfPPNN1FSUoIVK1YgMTERjY2NAIC33noLixcvxrlz53Dt2jV8/vnnk1xi68Dn9WtgGJlw52vbtm28HheZIRixWq2trQwAy8rKYowxptfrmVKpZHv27DGu09/fz+RyOfvDH/5gXBYREcEKCgrY4OAgS0lJYV9++SVjjLHLly8zACw/P9+4bmRkJNu3bx8Ti8VMq9UyxhgbGBhgUqmUffrpp5Y4TIsDwE6dOmV8T+fVciIjI9mWLVtMls2fP5/t3LmTMcbY+vXr2dGjR9nw8DB75ZVX2L59+yajmFbN3NcvY4z5+Piw3/72t5YoPpmBqEbDit2+fRsAoFAoAAD19fW4efMmEhISjOvY2toiNjbWJBvfrl27sHr1ashkMggEAjz22GMAgMDAQKhUKuNTtkajwcWLF/HUU09h7ty5xiecgoIC9PX1zZgnbzqvlmHIJnnneQZMs0nu3LkTv/jFL2Bra4uSkhJs2rRpMoo6pUz0+iWEbxRoWCnGGF5//XUsX74cISEhAGCcs/5+2fiSkpLQ1taGGzdu4NSpU7CxsTF+tnLlSmRmZgIAsrOzERgYCFdXV8TGxhqXG6r9586dy+MRWg86r5YxnmySarUa169fR1NTE/Ly8iySynyqM8f1CwBvvPEG7O3tTV6Ga5eQiaBAw0q9+uqrKC8vH7ONmuM4k/eMsVHLbG1t4erqOuq7cXFxyM3NxdDQEDIzM7Fy5UoAGHVDXLVqlXkOZAqh82oZ9zvPQqEQSqXS0sWa8iZy/QLAjh07UFpaavKKiorirbxk5qBAwwpt27YNZ86cQUZGBry8vIzLDT++E8nGFxcXB61Wi6KiImRkZCA2NhbAyA2xqKgInZ2dyM/Pn1HV+3ReLcOasklOJ+a4foGRv4+/v7/JSyqVmrWsZGaiQMOKMMbw6quv4uTJk0hPT4efn5/J535+flAqlUhLSzMuGxwcRFZWFpYuXTqufcydOxfe3t44c+YMSktLjTdEDw8P+Pr64qOPPkJ/f/+MuiHSebWMO7NJ3iktLW3c55mMZo7rlxA+UVI1K7J161b87W9/w+nTp+Hg4GB8QpHL5ZBKpeA4Dtu3b8fu3bsREBCAgIAA7N69GzKZDM8888y49xMXF4f9+/fD39/f5IknNjYWv//97zFnzhzMnj3b7Mc3mXp6evDdd98Z39fX16O0tBQKhQKzZ8+m82oh1pJNcqqxxPWr0WhG1YrIZDI4Ojqa7TjIDDWZQ16IKQBjvg4fPmxcR6/Xs3feeYcplUpma2vLYmJiWEVFxQPt5/DhwwzAqGGGR44cYQDY5s2bzXE4ViUjI2PMc/vcc88xxui8WtK+ffuYj48PE4vFLDw83Dh8m9wd39evj4/PmNt/+eWXeToiMpNQ9lZCCCGE8Ib6aBBCCCGENxRoEEIIIYQ3FGgQQgghhDcUaBBCCCGENxRoEEIIIYQ3FGgQQgghhDcUaBBCCCGENxRoEEIIIYQ3FGgQQgghhDcUaBBCCCGENxRoEEIIIYQ3/wfsh1TUrNBPDQAAAABJRU5ErkJggg==\n", 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" ] @@ -251,23 +250,22 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "bb540223", + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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Me3Ygw54d6OlQhBBCCI+SBEIIIYQoJtKEIYQQQgiXuWMxLG9dTMs7oxJCCCHKAJ2/nHdRNu1iH4q8vDyefPJJYmNj8ff3p1atWjz33HMYhuHWZ5MaCCGEEKIMmTx5Mu+99x4zZ86kUaNGrFu3jttvv53Q0FCGDx/utvdxuQbi4MGD3HLLLVSsWJGAgACaN2/O+vXrnee11owfP57o6Gj8/f3p1q0bW7ZsuWC533zzDQ0bNsRqtdKwYUPmzp3ramhCCCGEVznZhFHUzRWrV6/muuuuo3fv3tSsWZMbb7yRq666inXr1rn12VyKKikpiU6dOuHj48OCBQvYunUrr776KhUqVHBe89JLLzFlyhTeeust1q5dS2RkJD169CAtLa3QclevXs3AgQO59dZb+euvv7j11lsZMGAAf/zxxyU/mBBCCOFpJ1fjLOoGkJqaWmCz2WznfM/LLruMX3/9lZ07dwLw119/sXLlSq655hq3PpvSWuuLvXjs2LH8/vvvrFix4pzntdZER0czYsQIHnvsMQBsNhsRERFMnjyZe+4590qNAwcOJDU1lQULFjiPXX311YSFhfHFF19cVGypqamEhoaSkpJCSEjIxT6SEEKIcqi4PzNOlv/I732wBvkUqSxbei6vdvrhrOPPPPMM48ePP+u41prHH3+cyZMnYzabsdvtvPDCC4wbN65IcZzJpRqI+fPn07p1a2666SaqVKlCixYtmD59uvP8nj17SEhI4KqrrnIes1qtdO3alVWrVhVa7urVqwvcA9CzZ8/z3mOz2c7KxoQQQghvUtSlvE9uAPv37yclJcW5FZYQzJkzh1mzZvH555+zYcMGZs6cySuvvMLMmTPd+mwuJRC7d+/m3XffJS4ujl9++YV7772Xhx9+mE8++QSAhIQEACIiCq5SGRER4Tx3LgkJCS7fM2nSJEJDQ51bTEyMK49SrD7+awO9Z3/K6EULSMs5dxWTN1vzw3o+GDuLDb9u9nQoQnglbaRgJD2IcfRKdPrbuFCRK8oZdzZhhISEFNisVus53/PRRx9l7NixDBo0iCZNmnDrrbcycuRIJk2a5NZnc2kUhmEYtG7dmokTJwLQokULtmzZwrvvvsvQoUOd1ylVcMiJ1vqsY2dy9Z5x48YxatQo535qaqpXJBGr9sfz3PIlAGw/dpQAH1+e63aFh6O6eCu+/YPnbnwFk9nEnJfn8dKip2lxeRNPhyWEV9FpU8C2GDDQ6a+jfBqDtaunwxICgMzMTEymgvUDZrPZ7cM4XaqBiIqKomHDhgWONWjQgPj4eAAiIyMBzqo5SExMPKuG4XSRkZEu32O1Ws/KxrzB/tQU52utNfGn7ZcG6xf+hclswrAbmEwmNkothBBnsycAp9U62I94LBTh3QxMbtlc0bdvX1544QV+/PFH9u7dy9y5c5kyZQr9+vVz67O5FFWnTp3YsWNHgWM7d+6kRo0aAMTGxhIZGcmiRYuc53Nycli2bBkdO3YstNwOHToUuAdg4cKF572nJKz49g9euPk1Pp/4LXm5eRd1z+WxtagUEODcH9yodH17b9qlgSN5yE8iGl/WwNMhCeF1VOCtgNmxY4oGvys9Go/wXnat3LK54s033+TGG2/k/vvvp0GDBowePZp77rmH559/3q3P5lITxsiRI+nYsSMTJ05kwIAB/Pnnn0ybNo1p06YBjmaIESNGMHHiROLi4oiLi2PixIkEBAQwePBgZzlDhw6latWqzvaY4cOH06VLFyZPnsx1113HvHnzWLx4MStXrnTjo7pm6+odPHfjKyiTYtmXq7Dn2rn1mZsueF8l/wAeSI7ip7V/0Tw2hu4xNYs/WDe6fHBnAP5ZuZ3WPZvTtlcLD0ckhHfROhuMVAh+FsyRKN8WKFOQp8MSwik4OJipU6cyderUYn0flxKINm3aMHfuXMaNG8dzzz1HbGwsU6dOZciQIc5rxowZQ1ZWFvfffz9JSUm0a9eOhQsXEhwc7LwmPj6+QPtMx44dmT17Nk8++SRPPfUUtWvXZs6cObRr184Nj3hpdm3YA4A2HNWUO9f/d1H3rZz7J589+hkAS01bqBEYypAn+xdPkMXk8sGdnYmEEOIUrQ30iWGQu9FxwH8wyk/+rYjCnd4JsihleCOXp7Lu06cPffr0KfS8Uorx48efc2zqSUuXLj3r2I033siNN97oajjFptVVzfD19yU3OxetNZ36XVwyc3DnIZRJoQ1HJ9ADuw4Vc6RCiBJjjz+VPABkfQ2h4z0WjvB+2g2rcWovXUxL1sIoRLW4KN5d/xJ//LCe2KY1aH1Vs4u677Ib2vH5xG/JSs8GoMtNHcjJzsHXz7c4wxVClARTJVABoB3/vrHU9Gg4wvvZUdhdXAzrXGV4I0kgzqN6/apUr1/VpXuq1Y3mg3+m8Pfybez+ay/jr38JZTYx8v176Hlb92KKVAhR3HTuNnT2Qgi4A3I3gApCBT/q6bCE8BhJIIpBleqVad8ngJdue8vRh8Kw88b907ny1i6YzWZPhyeEcJHO24M+fhOQBxio4LGowP+d+1ptc1yj/EsyROGlDF30PgyGl85T5p0NK2XAWXNgKXXBybSEEN5J234HcgAjf38Z2rYcnTELbT81h43O/BZ9pIVjy/jIM8EKr2Lk94Eo6uaNvDMqLxS//SCr5q8lLSn9oq4PDA3k3leHYTKb8PG1MPL9e86aGUwI4f107mZIe/mMo2Z00p3otOfQx67FSHsd40grdOo4TtZS6LTJaEPW6BFllzRhXITV36/jmX4vYVhM+Darzp3PD+K6K5tjvkBCcMPw3vS5pwfKpPDxLdpqbEIIz9AZHwCnrWlj7QV58addkAwZb5d0WKKUMFAYRewEWdT7i4t8Jb4I37+3EENB+tWNOVY/ghc/X8qLMxZf1L2+fr6SPAhRmqkgOO0XuPK7Cnwb4/j1qQqcO8WMCn4cVDDafsQx+ZQolzwxE2VJkQTiIkTWqAwVAjAqBDg7Nyz6c8cF7hJClAUqaAT4NHUM3/S/CfyuRgWPg4DbwXoFVHjb8X8AzBD6MipiEwTc7GjmONoZndgRnbPJcw/hIVprtM71dBiimEgTxgUciD/O8ZBQKrasT7rdQJtNKJOibvUqng5NCFEClLkyquKcMw4GoEIec+5q6xVg3w2qAspc0XEsezHkrMi/IBOdPhUVPqOEoi4eWueiU54G22/g2woV+lKh03jrnHXopAdAp6AD78QUPLqEo/UO7ugE6a2dKCWBOA+tNeMemU3iEceKmlVSTTTuWZfgAD/uucGzC30JIbyHUgostQsc08ax0/fASELrXJQqxU2aWd9A9jeO17bf0BnTUcEjz3mpTn3G0T8EDRnT0H59UD71SyxUb2HghqmsvbQPhCQQ55GXZ5BwONm5n3U8i0cHdSMsXBbOEUKcojPnoLN/Q/k2h8C7IesrSH369Csgbxs6eRQq7E1PhVl0RhKOPh/6tP1C6DNXML64FY1F6eGd9SJewsfHTJfup5azbtGqJhXCAj0YkRDC2+js39CpT0HOEnT6VMicgU5//9wX235B65wSjc9djKyf0Olv40weMMCnTaHXq5DHgfwp/P1vBEuj4g7RK+n8URhF2bTUQJROjz9zPV26N8BuN7isa71SPRlUbk4ui2YuIzMtix5DuxJaKcTTIQlRKmj7YdC5KEt1tO13dNZclCUWAu+CvB04vosZgELn7gBzFTAOc3LiKed3NXM0UEqbMFIeBc7oEGn7AQL6nvNyZe0KEWtBZ6JM4cUfn5eS1TjLMbPFRNfLG1z4Qi/3yYe/MG3mL5j2pWDdn8oP7y9i+uZXZYipEBegMz5Ap73keO13LWT/4HiNBiMV5X8DpL+NY6ZKjfLrBZZYdMo4MI6CXz/I2w5YUMHDS/GXkDObIBSo89fIKuUHyq/4QioFpBNlObNz/X9s+u0fGnaoS+PLSn/y8Mfmf3nx33+gczXoEkP419s5uPMwh/47Qo0G1TwdnhBeS2sDnfbaqQPZ8wtekPsXKmQcVJoHttXg0wTlm79yb4VX0Jlfo0zBUOFVlCrlK/Jae4Lt51P75jhU0CjPxSM8ThKIM2xds5ORnZ9CGwYamPD9ONpd09LTYRXJku17HPNXKAWGJqteOFWO5VCleiVPhyaEl1P5y3ennH0cjfLr4diz1C4wCkMb6ejjNzpGXqAhdxP4X4/WZkeVvrnKqUSjlDCFvYFhWwXGEbD2xWSSj4+LIU0Y5cgfP6wHQGswmU2snre21CcQHZvW5pO//nEs6aagmjWA15Y/h39g+a5aFOJClFJQYSo6+SHQJ9fBUWCphwp6EKw9nNdqIx0yP3WsxunTHIzjpwrK/hmd3/QBjm6I2lQVFTAAAu8tNc0aJqsMX3dVWZ7KWhKIM9RtXRvDbqBMCsNuENeqlqdDKrIu9WoxqX9PflizmYaRlRn+fHdMJu/8CymEt1HWThA+y1GjgAEYqMA7HVNan0Yn3Qe5ax07piggBMjIv+ccQxiNg+j011DmKPC/vjgfwSU6bzfk7Qbf1ihTBU+HI7yYJBBn6HR9W0Z/dD/rftlEo471ueauKz0dkltc16Ih17Vo6OkwhCiVlE9DqPgl2FaAT0PHCIPTaK0h909OzY9wEMJmgG2JoxNhxkc4kgh9RskmdN5er/l+qbOXoJPvAwwwVYaK36HMlT0dVqkmTRjlTM/butPztu6eDkMI4U0sdcFcw9EpEtA6C53yJOSsBWs3sLSEvI2Oa81RKN82qPwqf+3bFp3+OhjpYN/PqeGQPo5RGx6ms75HZ80H+x5OJUFHHVNWBwz0aGylnSQQQghRjmnbSnTyg441LfwHgzkG0qfiXOY7azYEj0PpjmhtQwXc4pyyWtsPgSkSAu/Pn53ytLkUgh9F+dRzXKc1GIlgCivRERs6ZwM65ZHTjpz2YWWuWuTyjbz9kPaKo/aGbPAfhAp5qtT0+xCFkwRCCCEuQKc+DzrLsZP1+TmuMIGRhgp+uEBzhM78Ap06nrObLvJlfYOR9hKYwkCFgX274//hnzgTi2KXt6vgvqoM5jCUfz+U9TKXitI61/G8tiVgjgX7UTD2Frwoaxb4dcUwbJDxpqOppMIUTGW0v4XUQAghRLl2gV/gKhR822OkvQ7GCUf1vzkGsr6l0OQBIG+b4/9GInDE8VqnoNPfRoW94Y7AL8zaGVQw6DQAVMgjKP9+l1ZW1leODaDAYmIF6bw9kDYxf28HHOsPVX69tPf0cpJACCFEOaZCxqOTH3AM5fS/DezxkPMrYILAe8F/ABzvd2r1SXCcU/6cmub6zEIjQOcnDWcmGflNGI5v9C84lgX3bY8KecbtzRvKHA2VvgfbcrDURvkWvr7FBRnHKfR5T7I0AMN2xn0HL/09hcdIAiGEEBegrO2hyp9AHkpZHf0V7LtBhaLMldA569H6zJUpDTBV5GTzBoF3Oialsh9wfNu39oXsL/KnxraAJc5RI2Gujgp62FFE5uxTTSZZB8AS6yjnEui8vY5mGEv9s/ofKHM0BAxyoawD6LQJYCShgu5DWbs5TvjfAJmfOWphMAP2/Dt8wbcVBNyJsnZC2/dDxhSciZM59pKeqTTQFH0eh/PUYXmUJBBCCHERlDLj+FDMn2Dq9JknzTXPfY//AFTQ3aeuy/wKnTkdMEHWlxD+DQQ9Amkvg+0nxz2Bd6Is1R3X2xM4tXy2CW1PvKSPotPX88CnA4TPcLkTo9Y20BkoUzg6ZSTk/gMY6KT7ofIylLkyylwVKi2CvB1oc3VU3jZH0uTTusD7KUsNjLCZkP46mKpA6KRLeKrSQZowhBBCFEqZgvOXXD7tu2Lo6+B3dYHrtG1p/itHFb/KXYP27exMHgB02hTHDJUAeftOK9MAv2svKT6ddlp/itzV6Iz3UUH3Xvg+Ixnsh9C2dZD+IpCHtvaC3H2cql3Ic0xvnT9fhDIFOyahAseqpIUwWduDtf0lPU9pUpYTCO9c4ksIIUoRpXxRQSNOHQi8D5N/r7O/5eftLrCrzY0g5YnTSwJT0Knd3JWnX33p/R+UteC+bdkFb9E5G9CJXdDHr4f0CThn07QtAP/LT11oaQKWEhoxIryK1EAIIYQbqKD78qekNhxV+WfQ2g72/woezP0d8v467YAZFfryqV1LQ8jdCGjH0tnm6EsLLngspI47GSn4NCn0UsN+DFLGQc6fQPa5L/IbgPLvD0YyWDs757y4GNpIckzAlbcTFTAQdYl9OkqLslwDIQmEEEK4iTJHFX5OmdGWRpC3HWezRMb7p11hAp/mKN8Wp+6p8CY6/U3QaajAO1Cn1064wBTQH20KQmd95xhpEfTQOa/Tefvg2LVAVuGFmRugfFtc8kRQOvUlsP0KGI5+GZbGjk6qZZQkEEIIIYpMhU1HZ7wPuVsgd93pZ0CFQPDjBa83V0SFjnfPe/v1RPn1PO81Ov0tzk4e/MGvJ5iqgaUKyv+mos0iaRymQF8RI+HSyxIeJQmEEEKUEGWuhAp5Ap39Gzr59ARCg06BjLfB991ij0Nr7agJyVkLvi1QziYNDad3BlXhqMqLnOt/XPr7GZC3FZQ/KmAoOmeN4z3MVcFattcd0lqhi1iDUNT7i4skEEIIUdKs3VFBI9CZnzlmrQRAg+1XdMYsVOAt57xN5/2HTn8XlAUV+CDKUu2Cb6VztziGXPp2cMxymXRf/iyRp032FDYTZe2ACnoQnbPWUUtgaQBhH7shedDolFGQnT9MNegRVKWFYN8HPi0uuVmmtDBQRZ4Hoqj3FxeXRmGMHz8epVSBLTIy0nn+zHMnt5dffrnQMmfMmHHOe7KzC+m8I4QQpZxSChV0P6rS945psE+js+ac8x6tc9Enhjomnsqah076n6Mm4Ty0bRn6+A3o1KfQx3qjkx87O3kAdIaj1kNZaqIqL0FVWY+q+B0mc/ilP+RJ9gPO5AFwTNNtqY6ydkZjwkifgZH5Y9HfR5Q4l2sgGjVqxOLFi537ZrPZ+frw4cMFrl2wYAF33HEH/fv3P2+ZISEh7Nixo8AxPz8/V0MTQohSRZnC0WHvw4mTs0CaCkxQVYCRclptBWDfi056GEJGoCy10RkfojM+BUsNVOhklDkSnfUDp5okcvLvP5U8aA1KAfZT/RCUMjnWxnAXUzCnZqU0gakCxtGe+UuHn/Z4ac86khdToPve2wtIJ8rTb7BYCtQ6nO7M4/PmzaN79+7UqlXrvGWeWZMhhBDlhcm3JbrC6+iMWWCOQYWMLeTCiuDTEnI3nDqWswh9YhM69DVIm5x/7Ag6dQIq7C2w1OFUwuDoZ3EyaTAM2LSmAs3apmL27+j25zKMHLAthZw/cCQQGkxVwadu/iiMM+hkx0Jcgbe5PRZPKst9IFyeSGrXrl1ER0cTGxvLoEGD2L179zmvO3LkCD/++CN33HHHBctMT0+nRo0aVKtWjT59+rBx48YL3mOz2UhNTS2wlTVaazas/pdfvttASlKGp8MRQhQT5dcLU8XPMFV4EVXIstZKKVTYRxD81GlHDccskAUWo7Lnr+6JY22Ks8o59fqf9eGsWtEPFTymyM9wOsNIhcS2kPIgZH0K5OTHeoyT04EXcqdb4xDFy6UEol27dnzyySf88ssvTJ8+nYSEBDp27Mjx48fPunbmzJkEBwdzww1n/wU+Xf369ZkxYwbz58/niy++wM/Pj06dOrFr167z3jdp0iRCQ0OdW0xMjCuPUip8NWMlj9/3Ca+N/44Hb36PjDTpFyJEeaZMAZgCbwXfLqcO+nZBWXuApW7+AZNzciZlCgdTpQJlLJ4Xw3/bg/hxTjW+mVmDudOsKFOAewNNnwZknuNENgQMPvc95ljHqqZlzMkmjKJu3kjpC/XCOY+MjAxq167NmDFjGDVqVIFz9evXp0ePHrz55psulWkYBi1btqRLly688cYbhV5ns9mw2U4tCZuamkpMTAwpKSmEhIS49iBe6s7rX+fA3lPJ2fNv3UKby+qe5w4hRHmgdQ5k/wJo8LsapXwdi13l/g2mqAKjM4zcfZB8h2MJcqws+mkEr435A0wmdLYNU66NHzNmYTK5b2UDI/1jSD/HAlk+bcCnIWR+iqO2QUHQcFTAYMfKpkWZX8JFqamphIaGFttnxsnyW30zEkug9cI3nEdeho31/V/zus+3Iv2NCQwMpEmTJmfVFqxYsYIdO3Zw552uT1FqMplo06bNBWsgrFYrISEhBbayJjYuEpNJoRSYTIqq1St6OiQhhBdQyhfl3xflf61zfQylrCjfNmcN7TT51MBUeTGq8mpUxFo63zQYIykZ+7HjGOnp5Nly2bDob/cGGDAMOGPdjqCRmCp+Bmes56GUHzrvKNq2xJEYlTHaDbUP3toHokjzQNhsNrZt20bnzp0LHP/www9p1aoVzZo1c7lMrTWbNm2iSZPC52ovLx5+6lqCQ/1JPJxC34FtiZYEQghxiZTZ8fsjIBjCIsM4cfjEyTP8PGMJrXs2d9t7mUwmDBUIpyUEyq+H4/+Bdzrmmsj9C3w7ovOOQ1pvADQmdMXFmHwuPL+F8DyXEojRo0fTt29fqlevTmJiIhMmTCA1NZVhw4Y5r0lNTeWrr77i1VdfPWcZQ4cOpWrVqkya5KjeevbZZ2nfvj1xcXGkpqbyxhtvsGnTJt5+++0iPFbpte9oEk/M/oWjqenceUVbHn7y0pbvFcLdHK2dNpSSIdalXd97ezDzmS8BUCaF2ez+hZlV8Ah06nhAg/VqMNfOf78wVMUv0VqjlMJIaHjaXQYc74URcDMqeAxKlf65DjWO4bJFLcMbufTTOXDgADfffDPHjh2jcuXKtG/fnjVr1lCjRg3nNbNnz0Zrzc0333zOMuLj4wu0tSUnJ3P33XeTkJBAaGgoLVq0YPny5bRt2/YSH6l0+uf37Rzdf5zpCfvYcjARQ2ue+/pXWteqRmwVN0zmIkQRaOME+sQdkLcFbWkKYdNRtl/Qef+h/HqhfFt6OkThgv6j+vDnzxvZtnonoZVCGPz4+Tu7XwoVcDNYu4KRDpa4s/o3nNr3wblUOAA2yJwB5upQyIycpYmBQpXRmSiL1InSmxR3h5ji9N2bC3h7+EcAnLi7DTlBp5bGnfnAAFrGnr00sBAlyUibChnv4ej4ZgLf9pCzCseQPIWqOA/lE+fRGEta8tEU3ntkJkf3H6f/iD50vK4NP67eypxfN1KtcgXGDL6cCsH+LPpkGV+++j2Vq1VkxHt3USWmEtpIR6eMhpwNYL2clOzRfDBuDicSkrlxVB9sAQG88spPGIbB8OE9Ofj7Vua89B3hUWGMmfEgiz9dRvy2g1z9v8tp1r0Rk4e+ScKeRPo9fA03jurrjHHDkq188PRX+Pj58ODLQ4hrfurLntaa44eTCK0UjI/vxS/H7W5G1s+Q8vAZR80QeDsmNw8vPV1JdaJs9vUjmAOK1onSnmnjrxtf9brPN0kgPOxEQhKjLx/P/u2HAMhsVIXUnnUAaFEzmg/vuxEf8/nGTZc9//21l62rdtK4c31iG1f3dDgCMNJehYzpOBMIUzQYB5znVcgEVEDZG4J3Pk9d+yJ/LtiIYTcwmU08s+QZHp7xM+Do9Hx5yzge6N6S/zUa6ZhDyWyiWbdGvLTwSYy0KZAxjZPzHnw3ox3vP5WDNjRmiwnVsSm2/G+dZrMJY+l6lKExmRQhlUJIPZaGYTjubdSpPtvW7MSwO/bfXDOR+m3jyM6wMbDuKHKyc0ApwiNC+WxL4csKeIphPwrHB4MRz6lFvPwh/EtU7kZ0zh8o3w5Q1FVAz1BSCUTTr0a7JYH4+6ZXvO7zrfQ3MJViHz3xOV9Mmgs4qvO01gRuPcorHzyEf0QIjWIiyl3ysHnFNkZf8RyG3cBsMfPa8mdp0K58fbP1RipgKDp7Edh3O6ZatvaAjHc4WQNBOWzCiN9+0PmhbdgNdvwT7zxnGJr9iUkcO3jC+Zlo2A0S4/OnojaSwVktbcKWkYhhd3ww5OXaITUTQhxTOtvtRv4Hp8bQmqz0LGfyAHD80AlnHABJR1IAyEjNwpaV34lRa5ISUzEMw63DNd0i6S4w9hU8FngfKm8bOvVpwITO/hGlAsG/t0dCLApDK1QZncray/4mlR/JR1OcyQMAChp3rs+Tc0bSoV19mteMLnfJA8Dyb/5wvtZas/LbP85ztSgpylwZVeknVJU1qIrfo4IeRoVMgoBhqPAvUJY6bnmfHWv/5esp37P9z/MP4/YGfe7u4XwdXSeSvjd2ILrSqW+HN3VrTqOOdand7FSzwY0j+wCgAm4BlT95k6ky5uAbnddUqxvF0Ieucu7fPLgDHXq1AMBiMXPNXVc6z1WoHMLN4/phMjk+YGo3r0mLKxwj2MIjQ+l8XSvntf3uu9L7kgcA+5FzHLSgc//BkaAagDl/X3gTqYHwEIuPBZNJYRgaFPgH+TFl6XMlOpGKN4ptFFPgW11NacLwCLvdYNlXa0g5nkb3mzpQoUpI/iJLp3XoDejvcteuf37fzur564hrGUvXAR0L/H3/e/lWRl8+Hm1olEnx0qKnad69sVue56S0pHQ+m/AN6UkZ9Bt+DbWb1bzgPbk5ecRvP0TlauGEhJ9aevqm0ddSr20djh08QbtrWhAYGsjnT9/Kn9viiaoYQoOaEQC8/vsE/l6+lYpRYdRq6kgmlE9dqLwE7HvBXIebHvUntlkbkhNT6HhtawJDA7m6dwu01lSpEoK+sysH/00gpGIQIeHBXH7zZRzYeZhWVzUjrEooLa9syrEDx6nbpg6+Vkd/BqUU4z68m+vu/heLj4V6rWLd+mfpNv79IXNawWM5yyDgrvwJpxRgoPy6eyK6ItPaDaMwvLSjgfSB8KCfPviVd0d8jMVqYcyMB+nQt7WnQ/I4wzD46tXv+XvZVlpe2ZQbhl9T7pMqT3hr1Ey+f/9XlILKMZWYvm4SfqfNppeTnUPy0VQqVQ2/6G+1O9f/x0PtxoFSGHaDEe/dTe/TvsW/98hM5r7xk7NPwfUP9uK+125z63ONvfp5Nv7q+CbrH+THZ3vfITC08NUfM9OyGHn58+zdegCrvy+Tvh9Dow4yG6y7OTpSPgmcWtNIhc8CLJCzDnzbonybu/U9S6oPRMPZY9zSB2LroJe87vPNC+uzyo9r7ryC+Wmf8u2xjyV5yGcymRj46HW88MM4+o/oLcmDhyz/5k/A8c0nMf4Ye7ee6jD53197GVTtHobUuI+RnZ8iO9NWWDEF/LV0K4ahMfLb9NcvLjj7YZ0Wsc5zht2gTgv3f2Pe/se/GHYDw26QkZLJ4d2J573+93nrnM+eY8vlyyk/uj0mASb/q8GnDqd/JOkTt6BPDEKnv4Y2yUg0byQJhIcppeRDUniduq1qYTKbMJkUfoFWomKrOM99NuEbMpIdq8NuXb2TFV+vuagyG3Wqh1IKk9mE1pqmnRsWOH/FkM48/PadXNa/HQ+9dSdX3trlrDJycvL47J1feWXcV/z957lXAj6fTv0c88sopYioWZmY+tHnvT6wwqlFppRSBFVw86JT5YjWGq3zCr8gZHz+wl9WCrau2+F4/+INrhidXM67qJs3kj4QwuvY7XZWfvsnWWlZdL6xPYEh8ku7pD324b18Nuk7Uo+ncd39VxFaKdh5ztfPJ39NaEfrp8X34n6NNGxflxd/eZJV89ZSp2Utet7WrcB5pRR97+tJ3/t6FlrGR6/8zPzPVqMULP3hL96bP5xqsZUv+rlGTbuXJpc1IC0pgytv7YLV//xVy+2vacH191/FolkrqNmoGv97rnwNVXUXnfcfOukusB9E+12HCp2EUmd0Es/84tQy5GcVkICRsx7S34GcP8HaARU61f2riBaDsjwKQ/pACK+SmWnjxfs/YNXnK1B5dmo1rcE76yZjtpS/ESne6vCeIzzZZxL7dxyi64COjP3koRL7+Tx809vs2nLQuT/u1UF06dW0RN5bXDoj6T6wLeHkvBcET8AUWDAZM460Bp169s0nWRpC3nZOruKpgkajgu665JhKqg9E3c/GuqUPxM4hL3rd55s0YQivkXAkhSF3TGf5kQzyOjdFB/qx++99HNh5yNOhidNExUbw4Zap/Jwzm7GfPsTyr9fw47RFZKRkFPt7d+rRCHDUVgQEWWnYssYF7hBeQefgTB4A0l9Fa3vBaywNcQzbNDmGuPoPO+2kCVT+nCMAKLQu/r9v4vykCUN4jZ9++ZuU1CzHjsWMEVOFoINHqVRNViH1FLs2SM/NIsQn4Ky+OiaTiSl3vcuCD38DYO4bP/Hexpex+BTfr5UBd3UlunpFDsUfp3PPJlSKCC229xJuFHQ/nFhxal8nOSbTMp/6t63CpqLT3wAjFRV4G1gaosmG7IXg2wyCHoGk/4FxFExVUAGDSvwxLoVjGGfRmiC8tZ1AEgjhNUJC/NFG/r8Uk6JGnUgem3Gv9IHwkIOZx3l4w/scyU6mSWgNprS8C3+zb4FrlsxZ5Xy9b+sB9m09wP7tB0lPzqTrgA4EhwWdWWyRKKXofHUTt5Ypip/JtxWGpQ3krXUcsDQEU1iBa5QpHBUyvuCx0Och9Hnnvq78K9gPgDkGpYrWLFBS3NEJUjpRCnEB117TnB27Evhz3R6aN4lh7Ohr8PfzvfCNoljM2ruEo9mOaZE3p+zj58Pr6VetQ4FrajetwbY/dqG1xi/Qj+/eWsDP+TUS3079gfc2veKc2EiUbyp8OmR9DeTlr2vhegu6Un7gpllPRdFJAnEBWmuycvLw97XIcMti5utr4YlH+3g6DHGSOnP31AGtNTOfnsORfUepVDWcuJaxDH6iP2OufM55zf4dh4jfdoA6zb10BkRRopQpAAKHejqMEqdxLodSpDK8kXSiPI8jyelc/8JMOjz6Fv97/SsybTmeDkmIEjOs5hVE+zumrm4VVodeUafWVfjjxw189sI3HDt4gmMHTxAWUYG6rWpTq1nNAvNHRNS4+CGWQpRFMg9EOfXx4rXsP5YMwMbdB5m7+h+GdCt/qw6K8inSP4wvOo7BZuTid0bfhxOHk5yvtaE5nr//1JejmPn0HNKT07lxVF+394EQQngPSSDOo0C1kQLDW+uRhCgmSqmzkgeAy25ox+eTvuXI3qOYfcz0e/gaAMKqhDLivbtLOkwhvFcZbsOQJozzuO3yVkSFOSbtaFQ9kn7tG3k4IiG8Q0jFYKb//Sqv/DaeT/59ixaXy8gIIc7JHc0Xl9CEcfDgQW655RYqVqxIQEAAzZs3Z/369W59NKmBOI+o8BC+f+p20rNtBPtbpROlEKfxD/KnWTdJqoU4H08s552UlESnTp3o3r07CxYsoEqVKvz3339UqFChaIGcQRKICzCZFCEBfp4OQwghhLgokydPJiYmho8//th5rGbNmm5/H2nCEEIIIYqJO0dhpKamFthsNts533P+/Pm0bt2am266iSpVqtCiRQumT5/u9meTBEIIIYQoLif7MBR1A2JiYggNDXVukyZNOudb7t69m3fffZe4uDh++eUX7r33Xh5++GE++eQTtz6aNGFcgrT0bF57eyG79x6j11VNGNivjadDEkIIUcbt37+/wGqcVuu5p/M2DIPWrVszceJEAFq0aMGWLVt49913GTrUfZN5SQ3EJZg2YxlLVu5gT/wx3vlgCRv+2ufpkDxm39b9jOr6NPe2fJQ1P7i3h68QQpR2JztRFnUDCAkJKbAVlkBERUXRsGHDAscaNGhAfHy8W59NEohLcPhICsZpk0IkJJ5nDfsy7rmbXmXLqh3899denu3/MqnH0zwdkhBCeA/tps0FnTp1YseOHQWO7dy5kxo1alz6c5yDJBCXoF/vFpjyh3RWrhRMx7a1PRyR5xzdfxzDboCGvFw7KcfKbzIlhBDeYOTIkaxZs4aJEyfy77//8vnnnzNt2jQeeOABt76P9IG4BJ3ax/HJ+3ew/+AJmjaKISiwdCwrWxz6j+zDrOe/BqBlj6ZUjYvycERCCOE9PLGcd5s2bZg7dy7jxo3jueeeIzY2lqlTpzJkyJAixXEmSSAuUUzVcGKqhns6DI8b9uxA2vVuSWZaNs26NsRkuvRKra1rdvLG/dPJy8njnleH0aZnc/cFKoQQnuKBqaj79OlDnz7Fu7qxJBCiyOq3jStyGVprnug9kYzkDDQw/oaX+eboR/gFlN/aHSGE8GaSQAiv8NUr80lPynDu52TlkJWWJQmEEKJU80QTRkmRBEJ4hQUf/lpgv/kVTQiLqOCZYIQQwl1kNc7yJS0pg5zsXE+HUa7E1K+KMjmybKXg4bfv8HBEQgjhDspNm/dxKYEYP348SqkCW2RkpPP8bbfddtb59u3bX7Dcb775hoYNG2K1WmnYsCFz5851/UncQGvNayM+ZUDdRxhQ7xHWL9nqkTjKo1HT7+XywZfRpEsDnv56NDF1q3o6JCGEEOfhchNGo0aNWLx4sXPfbDYXOH/11VcXWAHM19f3vOWtXr2agQMH8vzzz9OvXz/mzp3LgAEDWLlyJe3atXM1vCLZ9Vc8Cz/7HQBbVg4v3/cRXa5uRM/bu1O7Wc0SjaW8qVA5lLGfPOzpMIQQwr3KcBOGywmExWIpUOtwJqvVet7zZ5o6dSo9evRg3LhxAIwbN45ly5YxdepUvvjiC1fDKxKT6bRqIg3JR5KZ9/bP/PTBr8zY8TqVqlYs0XiEEEKUcmU4gXC5D8SuXbuIjo4mNjaWQYMGsXv37gLnly5dSpUqVahbty533XUXiYmJ5y1v9erVXHXVVQWO9ezZk1WrVp33PpvNdtbSpkVVu0kM/e+/Mr/5BewZGRh2A1umjX837i1y+UIIIURZ4VIC0a5dOz755BN++eUXpk+fTkJCAh07duT48eMA9OrVi88++4zffvuNV199lbVr13L55ZcXumY5QEJCAhEREQWORUREkJCQcN5YJk2aVGBZ05iYGFce5ZyUUtz57I3MO/AmfYa0g7w8TGYT/kF+xLWqVeTyhRBClDNuXM7b27jUhNGrVy/n6yZNmtChQwdq167NzJkzGTVqFAMHDnSeb9y4Ma1bt6ZGjRr8+OOP3HDDDYWWq1TBPxyt9VnHzjRu3DhGjRrl3E9NTXVLEgHg42vhnpeHElM3miP7jtJjaFcqRoW5pWwhhBDlx+mraRalDG9UpHkgAgMDadKkCbt27Trn+aioKGrUqFHoeYDIyMizahsSExPPqpU4k9VqLXQpU3cwW8z0va9nsZUvhBBClGZFmgfCZrOxbds2oqLOvYDS8ePH2b9/f6HnATp06MCiRYsKHFu4cCEdO3YsSmhCCCGE53lgOe+S4lICMXr0aJYtW8aePXv4448/uPHGG0lNTWXYsGGkp6czevRoVq9ezd69e1m6dCl9+/alUqVK9OvXz1nG0KFDnSMuAIYPH87ChQuZPHky27dvZ/LkySxevJgRI0a47SGFEEIIj5A+EA4HDhzg5ptv5tixY1SuXJn27duzZs0aatSoQVZWFps3b+aTTz4hOTmZqKgounfvzpw5cwgODnaWER8fX2DFxo4dOzJ79myefPJJnnrqKWrXrs2cOXNKfA4IIYQQQlw8pbW3ds9wTWpqKqGhoaSkpBASEuLpcIQQQnix4v7MOFl+zOvPYfL3K1JZRlY2+4c/7XWfb7KYVhmTm5NLXk4e/kH+ng5FCCGETCQlSoM1P6ynX/htXBsylOmPferpcIQQQpThPhCSQJQhbz7wAbasHAC+fHk+a5ds4c7rptK39TNMe/knykhrlRBCCC8gCURZogou+jr7o5Uc2nec3Bw73366ir/X7fFYaEKIs+Xl2os9sbdl2Zj5zBxeuv0tNq/YVqzvJc5BhnGK0mD4u3djDbCiFAwa2w+tVIEZzLKzcj0XnBDCyW43ePWRz+gbN5phnZ5n3y7HZHpfb/qHWz+czYh35rD/v8Mul3swLZV+X35Os2lv8eLvy9Fa886IGXz2wjf8OmsFY658lsO7j7j7ccT5lOEEQjpRliFte7Xgu6SZ5OXmYfW3sumP/3jmoVnYsnNp1rYWLTvU9nSIQghg/bJtLP5mHQBHDyUxpt8UbnjlBp5euzp/7mNYc/+rzHrxAeq0iL1gef9u3sd9zUZz+LY4MpuFo02K9zespWNMdbau3oE2NBqNYYc9m+OJqnX+mX6FuBiSQJQxZosZs8UMQPN2tfn8t8dIScogqlr4BdcXEUIUzjCM/JV6Hf+O0pMzUCZFYEjABe/96YNfmTbmE/wD/Rj76cPk5RrOc1prUo6l8fbLX0PXqqAUoLFV9GPxrOUXlUDc12y0I8YAc4Evq6nZ2XS+oT17tx1EBQfhbzXToH2cS88tikhGYYjSKjDIj+iYipI8CFEEc9/4id4BQ7i+wjBWf7+Ozyd+S7+Kt3FDxdv5cdqi896blJjC1HvfJyM5k+OHkph0y+u0vbwhrbrWd1xgaOzJKZg2JeBz8t+pUgT8ddTlmoKwRYdQdsenTaPKVbg8tjbXj+xDhXZNMMVEY6sSwZqV/7r8/KIIZBSGKE0S44/yxgMf8MYDH5C4/5inwxGiVEs+msI7Iz8mLyePzLQsXhr2Fh8/9QVoMOwGbw//GMMwCr0/JysHbTg+1LXWZKVnY/Ex8/yMu+nYoTr2g4cgJ5d2revxw31DucoWTOvlx7m9b2f63NvDpVgDdqVS85kNtN2eQ6VQC4//Mo8VK7eRlmZzXvPTN2sv7Q9CiDNIE0YZYxgGoy9/liP7jgKwYfHffLz9damBEOIS2fOMAlXIebl5mJTCyO+hbLac/3tYRI3K9L77Sn6cthgU3PniLQAopXh81sNcObgzeTl5tO/bCh9fH96ccOc5y8nKysFq9cFkOvvfcofrWrN68RYIC8FkymNLlWyq3DSPg5l2drauCioYNJhMiuqxlS/xT0JcCqUdW1HL8EaSQJQxmalZBXpZH9x1mOyMbJmZUohLVDEqjJvH9eOLSXMB8A/yo+N1bVjx9RpMFhOjP7zfub6PPc/OLzOWknI0lStv7ULlahUBGPHePQwZW58Qvw/x8f0anVcLZamD2WymQ9/WZ73nl3//wyfrNxJTIZTxV3Tn/am/snTZdsLCAnlp8gBq147gwL7jTHl+HsknMuhxbUf+PO4YEmoYmvAZ/2HKsgOQu+4gNz03hB3/HiW6ekXuHt2rhP7kBFCm+0BIAlHGBIYG0KBDXbav2YVG07hTffwCizYPuxDl3f9eGMzW1TvZvGIbSUdS+O3zlby/6RVqNo4psDjg28M/4vt3F6JMiu/e/ImPtr9OYEgAWudQ0X806DTIAX1iB1Re6qwZ3Ld1P/Y8g6Wb9/PZN3+SbbJzoqGZXcHHST6RyZFlewFISclk2vSlTH5xIJOf/pZ/tx/GMDQz3vkNw8cESqEU+OaaC8TfvFUN7n7y+pL64xLlhCQQZYxSihd/fpJfPl6CUoqet3eT5gsh3CDpSAqG3ThtP5laTWsUuOb37/4EQBuaEwnJ7Pl7H406VkdnzQOdcupC4zCQC/jy8ZNf8PnEbwGwV6+MvUksZqDCLjtHWygSszMKBpL/bfT40TQMQxc4bjIrDENz3YN9+PWN78lISKXHsG607tnMTX8KQpwiCUQZFBDsT7+Hr/F0GEKUKQMevZZX7ngHNNRuXpMmnRucdU2jjvVZOfcPlFJY/X2pVrcK+sTNkLe94IV+16KUL3m5ecx+ca7zsDn+KPZ61VC+PihHCwT3dW7H5sSdrFi5k+BgP+68sysAA2+7jHdeXgBAkxY1uOHWDqxeuYs6dSPp268VDz52HfY8OxYf+TXvSQo39IFwSyTuJ3+zhBDiIvS8rTsN2tfl+KETNOpYD18/37OuefTj+4mpF01yYgp97+9JaNgR9PHTkgcVgAqdAlZHEmAymwgIDSAjORONRplNYDZjsZgYNLgdl3eqT73KlbixaWPS07Px9/fFbHY0mVw3oC3NWtUkLSWLBk2rYbGY6di5XoF4JHnwAu4Yhumlwzjlb5cQQlyk6vWrUr1+1ULP+wf5c/uEm5372jgBWIEcwASWeii/y53nTSYT4795lNfufR97rp37XruN2LZxBAX5ERxUsO9SUNDZfZlq1q5S1EcS4pJJAlEC9mzeR3ZmDvXa1C7Q4UoIUbYpUziEfYDOmAamUFTwmLOuadatETO2v+GB6ESJkFEY4lLNev5rZj4zB4AuN7XnydmjpFOjEOWIsrZDWdt5OgzhKWU4gZCvw8Xsi9M6SC3/ao1zgichhBCiNJMEophVqByCMilQYPExE1Qh0NMhCSGEKCEnZ6Is6uaNJIEoZk9/PZpaTWtQtU4UT8weKQmEEEKUJ9pNmxeSPhDFrF7r2ry34WVPhyGEcKPsTBtfvjSPYwdP0PueHtRrXdvTIQlR4iSBEEIIF71x/3QWz1qOUopfP1/BzF1vUik63NNhCW8knSiFEEKctHnFNrShMewGOVk57Nuy39MhCS8lfSCEEEI4dbq+LeBYHjs4PIi4lrU8HJEQJU+aMIQQwkV3vXQLsU2qc+zgCa4Y0pmQisGeDkl4K5nKWgghxElms5met3X3dBiiNCjDfSAkgTiN1po/F2wk5Wgqna5vQ2CoDLkUQghx6dzRh8Fb+0BIAnGaGU/N5vOJ3wLwxYvRvL/x5XOuuCeEKH+y0rNYv+hvwiJC2f13PInxx7jyls7UaBjj6dCE8AhJIE6z4KPfnK8P7DjErg17aNSx3nnuEEKUB9mZNh5sN474bQedx0xmE/PeWsBH21+XIZyicGW4CUNGYZymZqMYTGYTSiksvhYiY2WpXCEEbF21o0DyAGDYDbLSs9m1freHohKlgjuGcHppAiE1EKcZN+thPhj7GUlHkrnxkWupGBXm6ZCEEEVkGJrFf+3ieFomV7WoS8XggAves2/rfuK3H6JZ14aEVAymSo3KKJNCG6d+k5vMJnx8LdRuXrMYoxfCe0kCcZqwiAo8+vEDng5DCOFGr/+wkhm/rgNgxq/r+HbcUALP07dp1by1jO//MtrQhEWE8v6mV6gWF8UTn4/g6ynfU7FqGLWa1CQtKZ2et3WnSkylknoUURpJE4bD+PHjUUoV2CIjIwHIzc3lscceo0mTJgQGBhIdHc3QoUM5dOjQecucMWPGWWUqpcjOzr70pxJClFu7txzgo2e/YcHM5djtBgvWb3eeS0hOY/uBxPPe/+P0RWjt+I2ddCSF1d+vB6DrgI68uWYS478Zw9DxA3jg9f9Rp0Vs8T2IKBtkMa1TGjVqxOLFi537ZrMZgMzMTDZs2MBTTz1Fs2bNSEpKYsSIEVx77bWsW7fuvGWGhISwY8eOAsf8/PxcDU0IUc4lxB/jwWsnkxXii+VoJokHTtAwJoKjKRkAWMwmYipXOG8ZUbERmEwmDLsBIH2hhCiEywmExWJx1jqcLjQ0lEWLFhU49uabb9K2bVvi4+OpXr16oWWeXpMhhBCXasnyfzh4TU20jwmVa7B01RZe/+oR3vt5DcdSMxjUuTlVQoPOW8btL9xMVno2//21l563daflFU1KKHpRFsk8EKfZtWsX0dHRWK1W2rVrx8SJE6lV69zzwKekpKCUokKFCuctMz09nRo1amC322nevDnPP/88LVq0OO89NpsNm83m3E9NTXX1UYQQZcy/2NBmx7S/2qzQTSIICfBjzA3dCl63aQ+v3vkumalZ/O+FwXS9qYPzXGBIgPSFEuIiuNQHol27dnzyySf88ssvTJ8+nYSEBDp27Mjx48fPujY7O5uxY8cyePBgQkJCCi2zfv36zJgxg/nz5/PFF1/g5+dHp06d2LVr13ljmTRpEqGhoc4tJqbsTuZyIiGJ799byB8/bXC2zQohzlanRgSY8tcNMCmuuPrcX0QmDJzC7k17OfRvApOGTCX5aEoJRilE2aB0ET6RMjIyqF27NmPGjGHUqFHO47m5udx0003Ex8ezdOnS8yYQZzIMg5YtW9KlSxfeeOONQq87Vw1ETEwMKSkpLr2ft0tLSufORiM5kZAMwB0TBzNobD/PBiWEl8qzG0z5fjmrduyjQ93qjLq2Cz75/bROd12FoWSmZjn3P9o2lZh6VV16r6MHjpOTnUN07UiU8s7FjkThUlNTCQ0NLbbPjJPl1x43EXMR+/TZs7P5b9LjXvf5VqRhnIGBgTRp0qRAbUFubi4DBgxgz549/Pbbby4/rMlkok2bNhesgbBarVit1kuK29tprUnYk4h/sB/b//jXmTwALJ61XBIIIQphMZsYc323s46nHEtl46+bia4TSd1WtRn8eH8+GDsLgA7XtqFa3WiX3mfuGz/xzoiPAehzbw+Gv3N3kWMXZZP0gSiEzWZj27ZtdO7cGTiVPOzatYslS5ZQsWJFl8vUWrNp0yaaNCmfHZe01kwe9ia/zlqByaS448UhmC0mDLtGmZQMGxPCBVprUo6lck/zRzlxOAmAxz55iIFjrqNd75ZkpmVRr01tl2sQPnz8c+frH95bxODH+1O5muu/70Q54aUJQFG5lECMHj2avn37Ur16dRITE5kwYQKpqakMGzaMvLw8brzxRjZs2MAPP/yA3W4nISEBgPDwcHx9HRO3DB06lKpVqzJp0iQAnn32Wdq3b09cXBypqam88cYbbNq0ibffftvNj1o67N2yn19nrQAcM+h989oPPD9/LD+8v4gq1Stx2/ODPByhEN7t2OFk0pIyOH7gKJOGvEFGSib2PLvz/E/TF3PlLV2o2ejS+00FBPuRk5WD1hqT2YTVXxbdE+WPSwnEgQMHuPnmmzl27BiVK1emffv2rFmzhho1arB3717mz58PQPPmzQvct2TJErp16wZAfHw8JtOpvpvJycncfffdJCQkEBoaSosWLVi+fDlt27Yt2pOVUn6Bp5pllEnhH+xPm6tb0KaQzmBCiFMWzVnNa8M/dXywp6eSm51boOOxyWyiWt2oIr/P45+P4KXb3sKWmcM9rwwlpGJwkcsUZVQZnomySJ0ovUlxd4gpSd9O/ZEZz8wmOCyIh966A2uAlTotYgkOO//4dSHKu6EtH+foQUdTRd6x43Dar7c6LWKp3bwm900ZRmBooKdCFF6ipDpRxo2ZiNlaxE6Utmx2vVTGOlGK4nHDiN7cMKI3uzbsZmSXp7Fl2gitFMLba18kokZlT4cnhNcKCg3g+OFkDENjCgqEzEwMu8H1D/Xigdf/5+nwhChTZDlvL/bj+4vIteUCkHYijcWfLvdwREJ4t0feHEa1OhGEVgzigSnD+OboR8w+OE2SB+E5shaG8ITwqDBn+61haCpGy/LiQpxP7cYxvL/imQLHgipIc4XwHBnGKTxiwJjrOPRfAv+s3E6Ha1vTY1hXT4ckhBBCANKE4dX8AqyM/fRhZu15hwde/59z5VMhROF2rv+P5V+vJj05gx3r/mPdwr/Izcl1nk8+msKjVzzLDRVv562HPsQwDA9GK8o8DzdhTJo0CaUUI0aMuPRCCiE1EEKIMuPnj37j1TvfBRxNF+nJjmW8m3RuwMu/PYPZbGbGU3P4e/lWDLvBvLd/pknnBnQd0NGTYYuyzIPDONeuXcu0adNo2rRpEQM4N6mBEEKUGfPe/tn5+mTyALB5xTb2bI7PP55eYHhn6on0kgtQiCJITU0tsJ2+HtSZ0tPTGTJkCNOnTycsrHj6z0kCIYQoE5Z/vZrdm/cVPKgcm9liIjyyAgD9R/bFN3/myKpxUXQd0AEhisvJTpRF3QBiYmIKrEJ9ckbnc3nggQfo3bs3V155ZbE9mzRhCCFKvWMHjzNh0Gtoo2Bdb+NO9clKy+aWp28kKCyIjLQsGrSL47N973Jk71FqNKyGr59MQy2KkRubMPbv319gIqnCFpScPXs2GzZsYO3atUV84/OTBEIIUeolHUk5K3kIj6zAK7+Nx2wxs37pVgY2GEN2po0+t3Xh/kkDCQmX6adFCXBjAhESEnLBmSj379/P8OHDWbhwIX5FXEb8QqQJQwhR6tVoVA0fq49z3+rvy0u/PoPZ4hi59O4TX5Kd5Wgv/mHGcnZvOeCROIUobuvXrycxMZFWrVphsViwWCwsW7aMN954A4vFgt1uv3AhF0lqIEqhvVv289ZDH5CVbuN/L9xMqx7NPB2SEB6VcjTNOWsrgC0rp8CiWUopFAqd/1XO1eW7hbhUJT2R1BVXXMHmzZsLHLv99tupX78+jz32mFunA5AEohR6pt9LJOw5gjbg6esm8+Xh6bI4kCjXDMPAx2oh15YHQLNujQr8ouzyv858/vS3kKdpc31LYhtW9VSoorwp4WGcwcHBNG7cuMCxwMBAKlaseNbxopIEohQ6uv8Yht3xNyonO5fU4+mSQIhy7dHLn3UmDwCd+7dzvs7Ls/PBis3YutQEQ7PMlkGe3cDHIhOzCVEUkkCUQv0e7s2XL88DoHXPZkTGVvFwREJ41rGDJwrsx2891cchz26Qk5sHSoFZkZOTR16eJBCiZHjDWhhLly4tWgGFkE6UpUDSkWQyUjOd+3e+OITXVjzPi788yYTvx0l7rij32l7TosD+ht/+oXfAEN566EN8fcz8r9+puR5u79cefz+fM4sQonjIapzCE7TWvPXwh8x/+xfMPmbGffowXQd0RClF4071PR2eEF7j6a8e4bs3F7D3n3iOxB9j02//OKeqbtOrBXf170jvLo0AiK4c6uFohSgbpAbCix3YeYj5b/8CgD3XzjsjP/ZwREJ4J3ueQVzLWgwa2w+TSaFPm6o6Ky0LcCQOkjyIEic1EMITLL6nfjxKqQL7QpR3WRk2jhxKonJECGN6PM/OdbtRSjH48X78s2I72Zk26retQ4drW3s6VFGO5c+mXuQyvJF8InmxqNgI7pg4mJnPzME/xJ9HPrjf0yEJUeLW/XuAiV//it3QPHZDdzrWr8G+XUcYPXQa6SlZhAVaOLphN+Bo9vt93lpmH5rGicNJRNeJdOu4dyHEKZJAeLlBY/sxYMx1jolwpLOkKGcMQzPiw/mkZ9tAw6iP5rN84n3M/eR3MtOzAThx/NRqmiaziYrRYQSGBBAYEuCpsIU4xYPLeRc3SSBKAZNJuqqIsiUhOY3Jc5dyIi2TO65oQ5dGtQqct9vt7N9+iKBKwaRn2Zy/P7Ny8sjOySMgyOr4pWpoVE4eKjgInZFJRGwEI969q8SfR4jCeMMwzuIin0xCiBI3btYClv7zHxv3HGLER9+TkJzmPJdjy2X05c9yV5NR3FrjPnrUrOY819QngBmPzqJDlzgataqJxk5SmyocG9iIlH5NoXIYkTVlXhThRcpwJ0pJIEoBu91Ozmnz/AtRGmmtOZ6YSo4tl31Hk7EbGktaHmErj3FPzynM+WAZABsX/80/K7YBjtFHWXM2MvuRwbTalkrClF/5cdoinrrmBe54pCdZMcHkVAkApcirYOV4NWm2EKKkSBOGl1v7yyYmDJhCVkY2g8fdwG3PD/J0SEK4LDc3j/EPzWL977vwDfGjZutqpGTYCd2Wjk+mgY0cPn59IW271CMwtGASkLg3kTljv2L/r1tBawy7JiMlk5+/Xov2UTRt8h8hIZls2VKD8KBqhUQghAd5aQ1CUUkNhJd7/d5pZKVnoQ3NZy98w4Fdh897fcqxVOa+8ROLPl3m1mVbhSiKdSt2sv73XRg+ZjJ8Lez7O4EK/+VSwc+/wHVZmTYadarPzeP6YfYxo7UmKTGF379YBr6+zutCK4fQoGVNOl/5N72u/pMO7bcw7NZfuL5rTEk/mhDndbIPRFE3byQ1EF7OMHSB5FUbRqHX2rJsPNT+ccdKnRr++X07I9+7p/iDFOICLD6OoZTa1wxag1KYTIqGrWPZ+Ms/2O0GTdrGUrNeFD9+upL5X64HkxmwO7+9mQP90f5WTEDrXi1ZMn0hoYPT2b00gqhmSfiH5VCjnSTNQpQUqYHwcg+9dQe+fo5vXv1H9iGmXuHLEO/dcoDDux3JA8CKr9aURIhCXFCrTnH07NcKlWc4kwfD0FzdpzlvzX2IwFbRrE5KYuDd7/POE19jy8pFW63O+62BVtpe3YLYxjUJCglg6bdr2bDqP5ZNaMryV5vw3QPtST3qT52w5p57SCHOpQx3opQaCC/XoW9r5h7/mFxb7jmX7P7vr71Mues90pMzGDT2evyD/LBl5QAQ17rWWdcLUVLsdjsfjvucPxdspHn3Rjz4ylAefuZ6lizZylczVrBv416mP/o5zYd25GhGNihFakomAfkZsAoMxGQxY5xIxpZhg8wM9v69FwBtMoHZjLYbKLMZW5ov/HUvFTpEe/CJhThbWR7GKQlEKeDr5+ushTjTC4Ne4+CuwxiG5vV7pzHhh3EsnrWc4LAgbn36phKOVHhCVnYOO3cnUjWyApXCgzwdjtOCD37jq1fmA7Bv6342r9xB88ub0P7aNsQv3YYCjqZls3bRP2iz4zek3WJCB5hRmXZMJhN52TZneZuWbKVApanNhn33Pswx0Sg/P6pF1C3BpxNCSAJRyp1ISMYw8n/55hlUjqnE2E8e9nBUoqSkpGVxx6OfkpCYio+PmVef6k/LxtU9HRbZGTZ+m/07SuUvbKVhz+Z44ncc5t9/Dpy6UGsqZNmp2rk2q9ftxnQ8lbx9h1F2TYP2cexMPIJhcszCWq9VTf7dnkhWejZKKYycHDAM7EnJxFzRgquubem5BxaiMGV4JkrpA1HKDX78Bufrdr1bUr1+4X0kRNmzdPVOEhJTAcjLs/P1jxucr//+5wD7D5zwSFyv3vchW9ftKbAqJkph2A0Sdx+mXY/GAPj4WrhjXF+uiA7Hf96fWFduR+XawTDYvmYn2tAEhgbQ8fq2XHZDe4a/MQytFPb0dHRuLiho3bU+0+c8iNksv86E95FRGPnGjx/Ps88+W+BYREQECQkJgGOimGeffZZp06aRlJREu3btePvtt2nUqNF5y/3mm2946qmn+O+//6hduzYvvPAC/fr1c/FRyqcBj15H22takpGSSf12dWS9jHImvMKpfjFKKcIrBJKXZ2fEmC/4Z+tBAMaM7MU1PZu6/b211nz74TLWLd1Bk3a1aNWhFsvm/E5U7Ug2/74DlAlTQADabiekgj9pSZkARNaO4snpd3A8IYXgCgHs+Xsfc9/48ezyDY1Gk56Uwe/fb2TVD5uoUr0SD7xyC58+O4eUg8eo2SiGkW/dgdkiyYMQJc3lJoxGjRqxePFi5/7pK9299NJLTJkyhRkzZlC3bl0mTJhAjx492LFjB8HBwecsb/Xq1QwcOJDnn3+efv36MXfuXAYMGMDKlStp167dJTxS+VOzkYx9L68ua1ObW29ox8/LthAXW4W7Bl/Glm2HnMkDwKzZq7mmZ1MMw2D2O7+ydul2mrarzdCRPTFbLn2lyiXzNvDBxB8A2LhsK58kHkEbGsNuENsqjuSjqZgtZkIiKnDrUzfw5qhZYDKx+e+DjOz1EvbsHDr2bs4Xz80hNyfPWa6Pnw8R1Ss55jzR4BfkT06u4ytYYvwxoqqH8dW+dy45biFKVBluwnA5gbBYLERGRp51XGvN1KlTeeKJJ7jhBke1+syZM4mIiODzzz/nnnvOPR/B1KlT6dGjB+PGjQNg3LhxLFu2jKlTp/LFF1+4Gp4Q5YpSintu6cw9t3R2HgurcGomR5NJUTG/Y+Wv323g06kLAdi+KZ4KlYLpd3tnLlX8riOYzArDriEvF3uuYw4GVSmc+GwF4aE0bRPLAy8MYOGXazAF+qMNDQr+3bwfbcth9z/7sedpyO/HE1ophPc2v8q6w4fYMn8j9aMimPfVSvau/M/xS9QEK1OP0uaSoxaihJXhBMLler9du3YRHR1NbGwsgwYNYvfu3QDs2bOHhIQErrrqKue1VquVrl27smrVqkLLW716dYF7AHr27HneewBsNhupqakFNiHKm4SkNI6mphc4Vj2mIqOH9yQqMpSGDaJ57JFrADi09xgmk6OJy2Q2cWjvUQDseXYWf/4786f9SlpSxkW/92W9mjpXijVZfbH4mDFZzKiISqAUhASxaUM897d6lNnPfYlzghIN5J2qcQgOD4b8lrfLb+nME6uW8cDin3nHL4E5K9aSeiIVW6Q/tuohJPWqzfdb/nN2HBbC20kfiHzt2rXjk08+oW7duhw5coQJEybQsWNHtmzZ4uwHERERUeCeiIgI9u3bV2iZCQkJ57znZHmFmTRp0ln9MYQoT6Z8v5wZS9YD8Oh1Xbm166lRCH16NadPr+YFru/SuxnffriMHFseJqXofp3j+ikPfMSvX6wCBT9M/5V3Vj2HxefCvxrqNK7GuwseYcvaPdRtVp3cjEwWf7qcH5ftxm7PHxl0JBEjOwcMTe6Ro5hCgulwTXP++GkTAGFVQtC965Hxx79oqw87aofy1+wtVD9mJ8/I5tCynSjAF0juXQ9b9VBsSRnsP55MjcphRf4zFEJcOpcSiF69ejlfN2nShA4dOlC7dm1mzpxJ+/btAc7qxKe1vmDHvku5Z9y4cYwaNcq5n5qaSkyM9AUQ5UNSepYzeQCY+sMKBndujtlUeKVibL0opv3yKNs27qNO42pUi60MwMrv1jku0BC/4zCHdidSvd7FTchUrVYVqtWqgj3PztvDP2Ll3D+pWqca8Sfy52/IzTs1EsNuh9w8/vjpL/JMkHZFFQ60DCfgnzSCLRVQefD33K0EpdsxAT5YMSqHwdEkUGA5nklOXEV8LGbCgvwLjUkIr1KGmzCKNA9EYGAgTZo0YdeuXVx//fWAo0YhKirKeU1iYuJZNQyni4yMPKu24UL3gKN5xHraVLdClCc+FhNmk8JuaBRg9bFguogROBHVwomoFl7gWO2m1dm+ztEU6R/kR+Wq4ee6FYB/Vm5j+mOzsPhauG/KbdRpEQvAok+X8/27jv4VKcfTwccx8ZkyWzCbIM+W51gMy2xGGwZJXSpyrHM4oKh0xJbfvKEw54Apf60MAJOPDxqw+Fiod3kjcisH8ODVHQnx93PtD0wID1Fao3TRMoCi3l9cipRA2Gw2tm3bRufOnYmNjSUyMpJFixbRokULAHJycli2bBmTJ08utIwOHTqwaNEiRo4c6Ty2cOFCOnbsWJTQhCjTgvysPDfoKl6cuxQfs4nnB/W85CG8T3/+ELMmzSMrPZv+D/XEP+jcH8452Tk80WeSYyIn4Ik+k5h94H2UUqQcTUWZlKOTpKEdfS2UwgCGPDGQSjEVmf7UV2hDk5mahd3f7PhWZQZt1o423vzwVVomOiSQ4BB/Rn90D8f3HKFZt4bnXQdGCFHyXEogRo8eTd++falevTqJiYlMmDCB1NRUhg0bhlKKESNGMHHiROLi4oiLi2PixIkEBAQwePBgZxlDhw6latWqTJo0CYDhw4fTpUsXJk+ezHXXXce8efNYvHgxK1eudO+TClHG9G3dkL6tGxa5nAqVQ3hwyq0XvC4jNYvM1CzA8dmflJCEPc+OxcfCFbd05tvXf+TE4SR8/SyM+uBu9mw5QNXakVw+qAMWHwtX39KZnOxcnh0yhdV/7CW9ZQVy/c3oWAv8ZQOzCZWSSd0aFRk2vj9160cREhpw/qCE8HbShOFw4MABbr75Zo4dO0blypVp3749a9asoUaNGgCMGTOGrKws7r//fudEUgsXLiwwB0R8fLyz5zZAx44dmT17Nk8++SRPPfUUtWvXZs6cOTIHhBDncTw1gzU746kVEU6DmPM397lLWJVQOvVry+9z/wSg111XOjtbVooO5+Ptr7P7730EhPjz66fLMZlN7PlrL33ueIeA0ACe+WoUzbrG8Py788nJOkauycwBHiR66DDG9XuD3dsOYTGbGPLKzbRuV7tEnkmI4laWF9NSWntp44qLUlNTCQ0NJSUlhZCQEE+HI0SxSUxJ56bJn5Kc4WhKmHzbNfRsUa9E3ttut7Ppt38wW8w069borGYTu93OHQ1Hcnj3EdDaMdxSmVBKEV0nklf+vJJVh6dQwZxDx8BETJbamCovIDcnj3//3k+l6ApUjpbRFaL4FfdnxsnyWwx5AbNv0frs2HOy2fjZE173+SaLaQlRyqzcuofkjGzAUbM5b82WEksgzGYzrXo0K/T8od1HObjrsHM/N8yPzCaVsKTkkJeXy/h/fyE9ryYaxR5bCLdWbQA41sRo0Dq22OMXosRJE4YQwlucPv+BSSlqRhQ+aqKkLf7xb7D6gi0He6APR+5uivY1g0kRFmDDnLeLk70lFxytjv1Yf/7X1bMxC1GcynIThqxAI0Qp06pONZ4eeCXNY6Pp37EJD/bu5OmQnAzDwFKrOqpKRXIaRaP9LJA/++WBdIWRq9AGaAPSjwfw6s9/nDWTphCidJAaCCFKof4dm9C/YxNPh3GW62/pyO+/buGQyYQ1V4PdcMzpoMB03MLhT6oT3DIZm93C/kOOzp8yK7Uo08pwE4bUQAhRiuw6dIxBkz+j1zMf8MOfWz0dzlkqVglh+vyRfDBvOA0rBBD59T4q7c1iSKNG1MsLIeewP9nLanJoTzSG3cQ93dsRERrk6bCFKDayFoYQwiuMm/ETuxNOYGjNU7N+oU1cDBFhwRe+sQSZzSaq1arCG4ufIOVYGsFhgZgtZvL62UlITKVK5WDytCbPbpcZJUXZV4ZrICSBEKIUOZGWiZE/8lprSM7I9roE4iSlFBUqnxpyZrGYqZY/RNMx0bWPR+ISQriHNGEIUYrcfXV75+tODWsSF13Jg9EIIS5GWWy+AKmBEKJUGdS1OR0a1CA100bD6lUca04IIbyX1vmLxRWxDC8kCYQQpcTPy7by9qxl+Ft9ePLBXuddulsIIYqb/AYSohRITs3khXd+5kRyJocSU3h66g+eDkkIcRFkFIYQwqOysnMd60rgqM1Mz7B5OCIhxEUpw6MwpAZCiFIgsnIIvbo1cu7fNch7Zp8UQpRPUgMhRCmglOKJ+3tyy3VtsPpaiKoS6umQhBAXQRmOrahleCNJIIQoJZRS1KxW0dNhCCFcUYabMCSBEMLLaa1Z/cMG9m0/RMc+LanRoKqnQxJCCEkghPB2899fzDujZ6FMis8nz+O9P16gau0IT4clhLgIspy3EMJj1vy0EQBtaHKyc/l7xTYPRySEuGgnJ5Iq6uaFJIEQwsvVb10bpcBkUiilqNO8pqdDEkJcJJkHQgjhMbc8fj1+QX7Ebz9It5vaEycJhBDCC0gCIYSXM1vMDBzV29NhCCEuhYzCEEIIIYSrpBOlEEIIIcRppAZCCCGEKC6ynLcQQgghXCVNGEIIIYQQp5EaCFHm2LJsmC1mLD7y11sI4WFleBSG1ECIMuXDxz+jT+At9LLezC11H+RoQpKnQxJClGNleSIpSSBEmZGwN5HZL353av/fI9xyw4scS8nwXFBCCFFGSQIhygx9jp7KmelZLPhjuweiEUIIwNDu2byQJBCizIiKjaDfw9c497WvhexGUYQG+XkwKiFEuabdtHkh6WUmypT7p95Ov0f78uy7P7IrNYPr29TlmnYNPB2WEKKcUrhhGKdbInE/SSBEmRNVtRLvTRjm6TCEEKJMK1ITxqRJk1BKMWLECOcxpdQ5t5dffrnQcmbMmHHOe7Kzs4sSnhBCCOFZJ2eiLOrmhS65BmLt2rVMmzaNpk2bFjh++PDhAvsLFizgjjvuoH///uctLyQkhB07dhQ45ucnbddCCCFKL5mJ8gzp6ekMGTKE6dOnExYWVuBcZGRkgW3evHl0796dWrVqnbdMpdRZ9wohhBDCNZMmTaJNmzYEBwdTpUoVrr/++rO+oLvDJSUQDzzwAL179+bKK68873VHjhzhxx9/5I477rhgmenp6dSoUYNq1arRp08fNm7ceN7rbTYbqampBTYhhBDCq3hgFMayZct44IEHWLNmDYsWLSIvL4+rrrqKjAz3zonjchPG7Nmz2bBhA2vXrr3gtTNnziQ4OJgbbrjhvNfVr1+fGTNm0KRJE1JTU3n99dfp1KkTf/31F3Fxcee8Z9KkSTz77LOuhi+EEEKUGKU1qoh9GE7ef+YXZavVitVqPev6n3/+ucD+xx9/TJUqVVi/fj1dunQpUiync6kGYv/+/QwfPpxZs2ZdVP+Ejz76iCFDhlzw2vbt23PLLbfQrFkzOnfuzJdffkndunV58803C71n3LhxpKSkOLf9+/e78ihCCCFEqRITE0NoaKhzmzRp0kXdl5KSAkB4eLhb43GpBmL9+vUkJibSqlUr5zG73c7y5ct56623sNlsmM1mAFasWMGOHTuYM2eOy0GZTCbatGnDrl27Cr2msMxLCCGE8BpG/lbUMnB8iQ8JCXEevpjPQK01o0aN4rLLLqNx48ZFDKQglxKIK664gs2bNxc4dvvtt1O/fn0ee+wxZ/IA8OGHH9KqVSuaNWvmclBaazZt2kSTJk1cvlcIIYTwFu5swggJCSmQQFyMBx98kL///puVK1cWKYZzcSmBCA4OPiuDCQwMpGLFigWOp6am8tVXX/Hqq6+es5yhQ4dStWpVZ/XLs88+S/v27YmLiyM1NZU33niDTZs28fbbb7v6PEJ4lcT9xzh+KIm4lrGyvLgQokQ99NBDzJ8/n+XLl1OtWjW3l18sv9Fmz56N1pqbb775nOfj4+MxmU51v0hOTubuu+8mISGB0NBQWrRowfLly2nbtm1xhHfRstKzyMu1ExwW5NE4ROm0cu4fPD9gCobdoG7r2ry2/Dl8/Xw9HZYQoiS5Yy0LF+/XWvPQQw8xd+5cli5dSmxsbBEDODelz7WEYSmUmppKaGgoKSkpLlfxnMuvn63g5f+9jT3XzuDHb+D2CedOhoQozP1tHmPXht3Of/wTvh9Lu96tzn+TEKJEuPszo7Dyu3R6CoulaJMi5uVls/z35y861vvvv5/PP/+cefPmUa9ePefx0NBQ/P39ixTL6WQ1zkK89dCH2HPtAHw+8VuOHjju4YhEaRNWJbRATVtoZff/khJCeLeTM1EWdXPFu+++S0pKCt26dSMqKsq5XcqghvORRtlCmC0mxxJo+T84k1lyLXHKsYPHSdiTSJ2WtfALOHdP6IfevpNJt7xBwp5E+j18DfXbnntOEyGEcKeSaliQBKIQj3x4P5OGvE5Odg53TBxCxaiwC98kyoX1i/7iyT6TyMu1UzUuirf+mERQhcCzrousWYXXV07wQIRCCK/hjsWwvLSngSQQhejQtzXfJc9EGxqzxXzhG0S58fWUH7DnOQZmH9x1mNXz19FjaFcPRyWE8EbKcGxFLcMbSQJxHiaTSXqJiLOER1ZAmRTa7vhWUCEi1MMRCSFEyZMEQggX3Tn5Fo4fPsG+LQfoeXt3Wl/l+mRpQohyQpowhBAnhVUJ5cWfn/J0GEKI0sAD80CUFKmgF+ekteaLSXO5vcFwJgycQnqye5eBFUIIUbpJDYQ4p7U/b+KjJz4H4NC/CYRUCuHht+/0cFRn03l7IXshWGLBeiVKKU+HJIQQTu5cC8PbSAIhzunYaRNnacPg6P5jHozm3LT9EPr49aCzAI0KHgeBt3s6LCGEOKUM94GQJgxxTh2vb0Olqo61401mE9fe39PDEZ1Dzp+gMznZQKizF3o2HiGEKEekBkKcU4XKoXzwzxS2rNpJtbpRRNeOvOA9Wms+Gf8l3725gCo1KvHUl49QLS6q+IK0NORUDqzBp+RGQ2htB0zSZCKEOD8NFHUeB++sgJAaCFG4wNBA2vZqcVHJA8DW1TuZ9fzXpCdnsPef/bz14AfFGp/yqYsK+wD8+qCChqOCRxXr+52k099EH2mMTmyPzvnzksvJzcnln9+3c3jPETdGJ4TwJif7QBR180ZSAyHcJiMl0/laG0aJjNxQ1stQ1suK/X1O0nm7sR1/i6/ejyYh3kqPgc/SvO+PLpeTk53DyC5Ps3PdfyiTYuynD3P5zSX3HEKIEqJxQx8It0TidlIDIdym+eWNady5AQBmi5lbn76pRN8/M3M+Bw7V48Ch+qSnz2P/jkNkZWS79010Lh9Oqs5nU6vy29xKPD44jIP/JjhOaTs6Zy06958LFvPXsq3sXPef4z5DM/vFue6NUwghipnUQAi38bX68MpvzxC/9QBhkRWoULnkpnjW2sbxpIeAHAASE+/n/mYd8Q8M5pXfnqZ2s5rueSNLXbZsqI7WOY4vFQbs/jue6NoR6OT7wbbEEU/gA5iChxdaTNhp01+bzCbCZbE2IcomGYUhxMUxm83ENqlRIsnD/hPJ/O/Dr+k7dSY/bNoO5J6Kw2JgMmlSyWXW1Plue0+lFJf1v9bx2qQICPanYfs4sO93Jg8AZHx43nLqNI/l/qm3U6V6JRp2qMuI9+52W4xCCC9iuGnzQlIDcYbcnFwWzlhKVno2Vw3rRkjFYE+HVCps/3MXfy/bSuPL6tOwQ70Sec+xX/7CX/sPY2jN2K9/45t7HyXQ9BIA375Sk0N9q5HcO5LdOpP6u/5iYJx7RmncPOZaomtFkLA3kc43tKNidBjaMAE+QB6gwFzlguX0e/ga+j18jVtiEkKIkiYJxBkm3/omy75ajTIpfpy+mOl/v4rFp2T+mBIPJvHOU19zIjGVAfdfyWXXlI5Fmv5evpXRl4/Pr2ZTvPjLk7S8smmxv29CShpGftWe1pBlDCQuejD2PIOkrK9I7u3o1KkVPLt2sdsSCKUU3W5qX/CYKRQqvIVOfxVUACrkObe8lxCidCvLM1FKE8YZVs1fCzg6th3YcYjDu0tuiN3LI2axdslWdv29n0kPzGD3P/v5a9kWko+mlFgMl2LN9+tQSqG1o1p/9ffrSuR9b+/cyvm6aUwkjatFYDZH4GuNYsSbd3JyhgYF+JrMxR6P8uuOqdIPmCp+ifKpX+zvJ4QoBU72gSjq5oWkBuIMdVvXZtuaXQAEVQikckylEnvvI/uPY9gdf1HstlxGdX6SjJRM/IP9mLpiArWa1iixWFwR16o2ht1AmRSG3SCuZa0Sed9bOragTWw1jmdk0rpmVXzMp5KEcL8Anm3bgxfW/4bVZGHKZX0wtGbuns38m3qc3tUb0Dj84ua3cIeN6/Zw5HAy7S+rS4WwwBJ7XyGEKC6SQJzh2blj+PyFb8lKz6b/qD74BVhL7L2vvb0LH77g6PBXIdBM0lHHEERbZg4/TlvEQ29532JWAN0GdiQrLYuNv22maZeG9BjatcTeu15U5ULPDa3filvrtQQczQ7vbFnFK38vxawUH+/4kwXX3EVscHixx/jNF2t4/3XHNNvhlYKY/vl9BIf4F/v7CiG8QBkehSEJxBlCK4Vw32u3FUvZ63cfYNPew7SPi6FRzNnffm+853KatKtN0tE0Dm+L550RHwOOKaLDI713mJ9SimvuupJr7rrS06Gc5fSpplcc3g2AXWvs2s6GowdKJIFY+MMm5+sTx9LZvCmejl1KpqOpEMLDJIEQRbV82x4e+OA7FPCGUsx8cADNa0afdV295o5mirxu9dm37QB//LieZl0b0X9UnxKOuOxpF1GdP47GY1YKhaJZxbP//ItDrToR7NtzFMPQKJOiWvWKJfK+QghRnCSBOM2yr1Yz760FRNSszL2vDiO0Uojbyl7yz3+YTMrxIYIjoThXAnGSxcfCiHdlbgB3eqjRZYRbA9idepw+NRpSJ9S9/Vu0kQaAMhUc+vvgo73wD7Ry5HAyffq1onrNkutXI4TwMAMo6pp7Mg+Ed9u37QAvDHoNrTVbVu3AlpXD018+4rbyG8VE8PWazZiUwtCaRtUi3Fa2uDhmk4mhdVu7pSxt+wPse8HaDWWOQGd8ik57AdAQPAYVeIfz2sAgPx4eI/M9CFEeleVhnJJA5EvYfQSd/0My7Abx2w66tfz+7RqTm2dn455DdKpfgyua1HFr+aLk6MzZ6NSnHTsqDF3xO0ibyMmvCTrtZfC/GWUK8FiMQggvIX0gyr7GnRsQUbMyR/YeBaDPPT3cWr5Sipsva87NlzV3a7mi5Oms0xa+0kmQ8wdgBuz5B02gZIoVIUTZJglEvsCQAN7b8DJrf95E5ZiKNO4kEwGJQljqQ+5fnGzcVD51IfQFdMqTgEaFjEcpPw8HKYTwCoYGVcQaBENqILxeUIVAug/q5OkwXKa1RmuNySTfekuCCn4MrayQtxsVcCPKpyH4NAQ/x0gZpYp/1kshRClRhpsw5BOnlNv422b6V/kfvf2HMOeleZ4Op8zSOhud/Rs65y+UKQBTyOOYwj9A+V3tvEYpsyQPQohyQxKIUu6VO94h/UQGebl5fDB2Fgl7Ez0dUpmjdQ76+M3o5HvRJ25Cp0/3dEhCiFLDHetglMEaiEmTJqGUYsSIEc5jt912G0qpAlv79u0LLyTfN998Q8OGDbFarTRs2JC5c+de8B4BubY85+gRgLycPA9GU0blboW8Lc5dnTnTg8EIIUqVMryY1iUnEGvXrmXatGk0bXr2ss1XX301hw8fdm4//fTTectavXo1AwcO5NZbb+Wvv/7i1ltvZcCAAfzxxx+XGl65cd+U2zD7OKrNr32gJ1XjojwcURlkjuDUPxUTmGM8GU2x0VqTnbOV3Lz9ng5FCFEKXFInyvT0dIYMGcL06dOZMGHCWeetViuRkRe/0uHUqVPp0aMH48aNA2DcuHEsW7aMqVOn8sUXX1xKiOVG90GdaHtNC3KycgiLqODpcMokZY6CCm+g098Dc2VUyNOeDsnttNYcOn436Vk/orXCN+dpqla9Dat/yS0mJ0SZZLihCcJLR2FcUg3EAw88QO/evbnyynMvnrR06VKqVKlC3bp1ueuuu0hMPH+7/OrVq7nqqqsKHOvZsyerVq0q9B6bzUZqamqBrbwKDAmQ5MHNtNbo7F/Q6W+ic7ei/K7CVOlbTGHvo8xVPR2e2+Xk7SQ960fseTDtznrcU3cxN0Xcyd/Lt3o6NCFKN224Z/NCLicQs2fPZsOGDUyaNOmc53v16sVnn33Gb7/9xquvvsratWu5/PLLsdlshZaZkJBARETBqZ0jIiJISEgo9J5JkyYRGhrq3GJiyma18sX69bMV3NV0FGOvnsCRfUc9HU7pl/UFOvkhdPrb6OM3ofP+9XRExcqkggHFrlUV2LzYsdhXdkYWH4792LOBCSG8lksJxP79+xk+fDizZs3Cz+/cE+UMHDiQ3r1707hxY/r27cuCBQvYuXMnP/7443nLPn3ZZXB8Azzz2OnGjRtHSkqKc9u/v/y22x789zCTh77J3n/2s/HXzbx021ueDqnU09nL8l8ZQG7+bJOuWbH2X5589Xumzf6dnFzv7tzqY4kmMuwVKlgDnceUAotFklEhiqQMd6J0qQ/E+vXrSUxMpFWrVs5jdrud5cuX89Zbb2Gz2TCbC46Dj4qKokaNGuzatavQciMjI8+qbUhMTDyrVuJ0VqsVq1XaZwGOH0oqsI5HYTUQhjY4kLkXP3MAVfwuvo+KJxiGRhsas8UzI42Vb3N0zhIcObYGnyYu3b/9vyOMe2le/ip8itzcPB64tWsxROo+oUE30/lqG32GzeTHTyoSHpHLfS+W75o9IYqsDPeBcCmBuOKKK9i8eXOBY7fffjv169fnscceOyt5ADh+/Dj79+8nKqrw0QEdOnRg0aJFjBw50nls4cKFdOzY0ZXwyq367eKIa1mLXRt2A3DTI9eedY3Wmg92T2VzynoA+le7lW5Vrj7rOm/w27JtTJn0A7k5dob9rwuDb/HA34PAu1EqAJ23A+XXC+Vz9mij8/kv/qjjV0b+f3bsPlIMQbqfChjAQ68u5f4JqzD5NsAUPtzTIQlRupXhmShdSiCCg4Np3LhxgWOBgYFUrFiRxo0bk56ezvjx4+nfvz9RUVHs3buXxx9/nEqVKtGvXz/nPUOHDqVq1arOfhTDhw+nS5cuTJ48meuuu4558+axePFiVq5c6YZHLPt8rT5MXfk8fy/fRlhEKLWb1TzrmoTsg87kAWDB4W+9MoHYsO8gz7/wHSabRgEfTV/KFVc2IiIytETjUMoMgcMovBHt/Fo1rk6Any+Z2TkAdO9Qz33BFSNlCkCFz0DpPJSSme6FEIVz628Is9nM5s2b+eSTT0hOTiYqKoru3bszZ84cgoODndfFx8cXWLehY8eOzJ49myeffJKnnnqK2rVrM2fOHNq1a+fO8Mo0Xz9fWl/VrNDzAeZAFAqNRqEItoSUYHQX76OV68+qrrMb3tkD+XwiK4cw45Vb+X3dbmpUDadd85qeDsklkjwI4SYaN9RAuCUStyvyb4mlS5c6X/v7+/PLL7+4dM9JN954IzfeeGNRwxGFCPUN49Ya9/L94S8JMAcypMY9ng7pnEID/EiN86XCthzQ0OaKukRHh3k6rIuis+ajs38CSz1U0ANUjajAgN4tPR2WEMKTpAlDlAVtKl5Gm4qXeTqM8xrZoxMHTqSwIyqRaxrV5cl+555rxNvonLXolNGOHdsSNKCCR573HiGEKM0kgRBepVJQIDPvuMnTYbgub+dpOxrydngsFCGEFzEMHMPBi1qG95EEQgh38O0MKgB0JgDK7xoPBySE8ArShCGEOB9lqQ4V50POCrDURfm28XRIQghRrCSBEMJNlKU6WIZ4OgwhhDeRGgghhBBCuExmohTCddq2Am37DWVpAv79zru2iRBCiNJFEghRLHTOOnTSnYAJzWcociBgkKfDEkKIEqW1gS7ictxFvb+4eGalIlH25ZycNtsOmNA5az0ZjRBCeIbWjiaIomzSB0KUK77t81+YATvKVxZGE0KUQ9oNfSAkgRDlifJtBuGz0LalKJ8mYO3p6ZCEEEK4kSQQotgo3zYyH4IQonwzDFBF7MPgpX0gJIEQQgghiksZbsKQTpRCCCGEcJnUQAghhBDFRBsGuohNGN46jFMSCCGEEKK4SBOGEEIIIcQpUgMhhBBCFBdDgyqbNRCSQAghhBDFRWugqMM4vTOBkCYMIYQQQrhMaiCEEEKIYqINjS5iE4b20hoISSCEEEKI4qINit6E4Z3DOKUJQwghhCgm2tBu2Vz1zjvvEBsbi5+fH61atWLFihVufzZJIIQQQogyZM6cOYwYMYInnniCjRs30rlzZ3r16kV8fLxb30dpb21ccVFKSgoVKlRg//79hISEeDocIYQQXiw1NZWYmBiSk5MJDQ0tlvJDQ0O5jGuw4FOksvLIZSU/nfX5ZrVasVqtZ13frl07WrZsybvvvus81qBBA66//nomTZpUpFhOV2b6QKSlpQEQExPj4UiEEEKUFmlpacWSQPj6+hIZGcnKhJ/cUl5QUNBZn2/PPPMM48ePL3AsJyeH9evXM3bs2ALHr7rqKlatWuWWWE4qMwlEdHQ0+/fvJzg4GKWUS/eezETLau2FPF/pVpafryw/G8jzeTOtNWlpaURHRxdL+X5+fuzZs4ecnBy3lKe1Puuz7Vy1D8eOHcNutxMREVHgeEREBAkJCW6J5aQyk0CYTCaqVatWpDJCQkJK3T8CV8jzlW5l+fnK8rOBPJ+3Ko6ah9P5+fnh5+dXrO9RmDOTjXMlIEUlnSiFEEKIMqJSpUqYzeazahsSExPPqpUoKkkghBBCiDLC19eXVq1asWjRogLHFy1aRMeOHd36XmWmCaMorFYrzzzzzDnbk8oCeb7SrSw/X1l+NpDnE54xatQobr31Vlq3bk2HDh2YNm0a8fHx3HvvvW59nzIzjFMIIYQQDu+88w4vvfQShw8fpnHjxrz22mt06dLFre8hCYQQQgghXCZ9IIQQQgjhMkkghBBCCOEySSCEEEII4TJJIIQQQgjhsnKdQOzdu5c77riD2NhY/P39qV27Ns8888xZU4/Gx8fTt29fAgMDqVSpEg8//LDbpictbi+88AIdO3YkICCAChUqnPMapdRZ23vvvVeygV6ii3m+0vzzO1PNmjXP+lmdOed9aVISSw57wvjx48/6OUVGRno6rEu2fPly+vbtS3R0NEopvvvuuwLntdaMHz+e6Oho/P396datG1u2bPFMsKLElOt5ILZv345hGLz//vvUqVOHf/75h7vuuouMjAxeeeUVAOx2O71796Zy5cqsXLmS48ePM2zYMLTWvPnmmx5+ggvLycnhpptuokOHDnz44YeFXvfxxx9z9dVXO/eLe4pXd7nQ85X2n9+5PPfcc9x1113O/aCgIA9Gc+lOLjn8zjvv0KlTJ95//3169erF1q1bqV69uqfDK7JGjRqxePFi577ZbPZgNEWTkZFBs2bNuP322+nfv/9Z51966SWmTJnCjBkzqFu3LhMmTKBHjx7s2LGD4OBgD0QsSoQWBbz00ks6NjbWuf/TTz9pk8mkDx486Dz2xRdfaKvVqlNSUjwR4iX5+OOPdWho6DnPAXru3LklGo+7FfZ8ZeXnd1KNGjX0a6+95ukw3KJt27b63nvvLXCsfv36euzYsR6KyH2eeeYZ3axZM0+HUSzO/H1hGIaOjIzUL774ovNYdna2Dg0N1e+9954HIhQlpVw3YZxLSkoK4eHhzv3Vq1fTuHHjAiu29ezZE5vNxvr16z0RYrF48MEHqVSpEm3atOG9997DMAxPh+QWZfHnN3nyZCpWrEjz5s154YUXSmVzzMklh6+66qoCx4tjyWFP2bVrF9HR0cTGxjJo0CB2797t6ZCKxZ49e0hISCjws7RarXTt2rXM/CzFuZXrJowz/ffff7z55pu8+uqrzmMJCQlnLUASFhaGr6+v25dG9ZTnn3+eK664An9/f3799VceeeQRjh07xpNPPunp0IqsrP38hg8fTsuWLQkLC+PPP/9k3Lhx7Nmzhw8++MDTobmkJJcc9oR27drxySefULduXY4cOcKECRPo2LEjW7ZsoWLFip4Oz61O/rzO9bPct2+fJ0ISJaRM1kCcqwPTmdu6desK3HPo0CGuvvpqbrrpJu68884C5861BKouhqVRL9alPN/5PPnkk3To0IHmzZvzyCOP8Nxzz/Hyyy8X4xOcn7ufz9t+fmdy5XlHjhxJ165dadq0KXfeeSfvvfceH374IcePH/fwU1yaklhy2BN69epF//79adKkCVdeeSU//vgjADNnzvRwZMWnrP4sReHKZA3Egw8+yKBBg857Tc2aNZ2vDx06RPfu3Z2LjpwuMjKSP/74o8CxpKQkcnNz3b406sVy9flc1b59e1JTUzly5IhHntGdz+eNP78zFeV527dvD8C///5bqr7ZluSSw94gMDCQJk2asGvXLk+H4nYnR5ckJCQQFRXlPF5Wf5bilDKZQFSqVIlKlSpd1LUHDx6ke/futGrVio8//hiTqWClTIcOHXjhhRc4fPiw8x/HwoULsVqttGrVyu2xXwxXnu9SbNy4ET8/v0KHRRY3dz6fN/78zlSU5924cSNAgV/cpcHpSw7369fPeXzRokVcd911HoyseNhsNrZt20bnzp09HYrbxcbGEhkZyaJFi2jRogXg6OOybNkyJk+e7OHoRHEqkwnExTp06BDdunWjevXqvPLKKxw9etR57mRWfdVVV9GwYUNuvfVWXn75ZU6cOMHo0aO56667CAkJ8VToFy0+Pp4TJ04QHx+P3W5n06ZNANSpU4egoCC+//57EhIS6NChA/7+/ixZsoQnnniCu+++u1Qs0Xuh5yvtP7/TrV69mjVr1tC9e3dCQ0NZu3YtI0eO5Nprry2Vwx5LaslhTxg9ejR9+/alevXqJCYmMmHCBFJTUxk2bJinQ7sk6enp/Pvvv879PXv2sGnTJsLDw6levTojRoxg4sSJxMXFERcXx8SJEwkICGDw4MEejFoUO88OAvGsjz/+WAPn3E63b98+3bt3b+3v76/Dw8P1gw8+qLOzsz0UtWuGDRt2zudbsmSJ1lrrBQsW6ObNm+ugoCAdEBCgGzdurKdOnapzc3M9G/hFutDzaV26f36nW79+vW7Xrp0ODQ3Vfn5+ul69evqZZ57RGRkZng7tkr399tu6Ro0a2tfXV7ds2VIvW7bM0yG5xcCBA3VUVJT28fHR0dHR+oYbbtBbtmzxdFiXbMmSJef8dzZs2DCttWMo5zPPPKMjIyO11WrVXbp00Zs3b/Zs0KLYyXLeQgghhHBZmRyFIYQQQojiJQmEEEIIIVwmCYQQQgghXCYJhBBCCCFcJgmEEEIIIVwmCYQQQgghXCYJhBBCCCFcJgmEEEIIIVwmCYQQQgghXCYJhBBCCCFcJgmEEEIIIVz2f3H57h974HWhAAAAAElFTkSuQmCC\n", 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" ] @@ -287,61 +285,34 @@ { "cell_type": "code", "execution_count": null, - "id": "85229256", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 1, - "id": "a37a8291", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\\example_scripts\\notebooks\n" - ] - } - ], - "source": [ - "cd ../../" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 2, - "id": "ca0c825f", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\n" - ] - } - ], - "source": [ - "cd ../../" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "25670a0f", "metadata": {}, "outputs": [], - "source": [ - "pwd" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "fd695e91", "metadata": {}, "outputs": [], "source": [] diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index e81723af..61c6a6f8 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -23,8 +23,8 @@ fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) -profile.match_to_grid(nemo) -profile.gridded_to_profile_2d(nemo, "bathymetry") +#profile.match_to_grid(nemo) +#profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres pa.calc_pea(profile, nemo, Zmax) From eba39b90c02279290323071814317eb1e07e0d63 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:34:47 +0000 Subject: [PATCH 064/150] merge develop --- coast/__init__.py | 3 +- ...ridded_monthly_hydrographic_climatology.py | 77 +++++++++++++++++++ 2 files changed, 78 insertions(+), 2 deletions(-) create mode 100644 coast/diagnostics/gridded_monthly_hydrographic_climatology.py diff --git a/coast/__init__.py b/coast/__init__.py index 46a20af8..c2af0384 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -7,8 +7,7 @@ from .diagnostics.gridded_stratification import GriddedStratification from .diagnostics.climatology import Climatology from ._utils import logging_util, general_utils, plot_util, crps_util, seasons - -# from .diagnostics.annual_hydrographic_climatology import Annual_Climatology +from .diagnostics.gridded_monthly_hydrographic_climatology import GriddedMonthlyHydrographicClimatology from .data.index import Indexed from .data.profile import Profile from .diagnostics.profile_analysis import ProfileAnalysis diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py new file mode 100644 index 00000000..590bed06 --- /dev/null +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -0,0 +1,77 @@ +from ..data.gridded import Gridded +from ..diagnostics.internal_tide import InternalTide +import numpy as np +import xarray as xr + + +class GriddedMonthlyHydrographicClimatology(Gridded): + """ + Calculates the monthly climatology for SSS, SST and PEA from multi-annual monthly Gridded data. + Derived fields (SSS, SST, PEA) are placed into supplied coast.Gridded object. + """ + + def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): + """ + Assumes monthly values in gridded_t, starting from Jan and multiyear + + Args: + gridded_t: Input Gridded object. + gridded_t: Target Gridded object + Zmax: Max z for PEA integral calculation + """ + + # calculate a depth mask + Zd_mask, _, _ = gridded_t.calculate_vertical_mask(Zmax) + + ny = gridded_t.dataset.dims["y_dim"] + nx = gridded_t.dataset.dims["x_dim"] + + nt = gridded_t.dataset.dims["t_dim"] + + SST_monthy_clim = np.zeros((12, ny, nx)) + SSS_monthy_clim = np.zeros((12, ny, nx)) + PEA_monthy_clim = np.zeros((12, ny, nx)) + # NBTy=np.zeros((12,ny,nx)) #will add near bed temperature later + + PEA_monthy_clim = np.zeros((12, ny, nx)) + + nyear = int(nt / 12) # hard wired for monthly data starting in Jan + for iy in range(nyear): + print("Calc PEA", iy) + it = np.arange((iy) * 12, (iy) * 12 + 12).astype(int) + for im in range(12): + itt = [it[im]] + print(itt) + gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) + print("copied", im) + PEA = InternalTide(gridded_t2, gridded_t2) + PEA.calc_pea(gridded_t2, Zd_mask) + PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values + PEA_monthy_clim = PEA_monthy_clim / nyear + + # need to find efficient method for bottom temperature + # NBT=np.zeros((nt,ny,nx)) + # for it in range(nt): + # NBT[it,:,:]=np.reshape(tmp[it,:,:,:].values.ravel()[Ikmax],(ny,nx)) + SST = gridded_t.dataset.variables["temperature"][:, 0, :, :] + SSS = gridded_t.dataset.variables["salinity"][:, 0, :, :] + + for im in range(12): + print("Month", im) + it = np.arange(im, nt, 12).astype(int) + SST_monthy_clim[im, :, :] = np.mean(SST[it, :, :], axis=0) + SSS_monthy_clim[im, :, :] = np.mean(SSS[it, :, :], axis=0) + # NBTy[im,:,:]=np.mean(NBT[it,:,:],axis=0) + # save hard work in netcdf file + coords = { + "Months": (("mon_dim"), np.arange(12).astype(int)), + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + } + dims = ["mon_dim", "y_dim", "x_dim"] + attributes_SST = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} + attributes_SSS = {"units": "", "standard name": "Absolute Sea Surface Salinity"} + attributes_PEA = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(Zmax) + "m"} + gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray(np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST) + gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray(np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS) + gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray(np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA) From 2d39c9443549b9a5d32a36cd7929804abcd2d376 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Sep 2022 15:55:06 +0000 Subject: [PATCH 065/150] Apply Black formatting to Python code. --- .../gridded_monthly_hydrographic_climatology.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 590bed06..b3c321ee 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -72,6 +72,12 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): attributes_SST = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_SSS = {"units": "", "standard name": "Absolute Sea Surface Salinity"} attributes_PEA = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(Zmax) + "m"} - gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray(np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST) - gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray(np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS) - gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray(np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA) + gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray( + np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST + ) + gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray( + np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS + ) + gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray( + np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA + ) From d72cf73ce3e1117582ca4a00756eaa979e0e4326 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:37:02 +0000 Subject: [PATCH 066/150] correct init --- coast/__init__.py | 2 +- .../profile_hydrographic_analysis.py | 461 ++++++++++++++++++ 2 files changed, 462 insertions(+), 1 deletion(-) create mode 100644 coast/diagnostics/profile_hydrographic_analysis.py diff --git a/coast/__init__.py b/coast/__init__.py index c2af0384..942b49ca 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -28,7 +28,7 @@ from ._utils.experiments_file_handling import experiments from ._utils.experiments_file_handling import nemo_filename_maker from .diagnostics.circulation import CurrentsOnT - +from .diagnostics.profile_hydrographic_analysis import ProfileHydrography # Set default for logging level when coast is imported import logging diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py new file mode 100644 index 00000000..01c499bc --- /dev/null +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -0,0 +1,461 @@ +import os +import numpy as np +import xarray as xr +import gsw +from typing import List + +from ..data.gridded import Gridded +from ..data.profile import Profile +from ..data.index import Indexed +from dask.diagnostics import ProgressBar +from .._utils.logging_util import get_slug, debug, info, warn, warning + + +# +earth_radius = 6367456 * np.pi / 180 + + +class ProfileHydrography(Indexed): + + ############################################################################### + def __init__(self, filename="none", dataset_names="none", config="", region_bounds=[]): + """Reads and manipulates lists of hydrographic profiles. + + Reads and manipulates lists of hydrographic profiles if called with dataset_names and region_bounds, + extract profiles in these bounds, and if a filenames is provided, saves them there. + """ + if dataset_names != "none" and len(region_bounds) == 4: + self.extract_profiles(dataset_names, region_bounds, config) + if filename != "none": + self.save_profiles(filename) + + def extract_profiles(self, dataset_names, region_bounds, config): + """ + Helper method to load EN4 data file, subset by region and process. + + Args: + dataset_names: list of file names. + region_bounds: [lon min, lon max, lat min lat max] + config : a configuration file (optional) + """ + x_min = region_bounds[0] + x_max = region_bounds[1] + y_min = region_bounds[2] + y_max = region_bounds[3] + self.profile = Profile(config=config) + self.profile.read_en4(dataset_names, multiple=True) + self.profile = self.profile.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) + self.profile = self.profile.process_en4() + + ######################################################################################## + def save_profiles(self, filename): + """ + Helper method to saves profile and gridded datasets (in self) to netcdf. + """ + if filename[:-3] is ".nc": + filename_profile = filename[:-3] + "_profile.nc" + filename_gridded = filename[:-3] + "_gridded.nc" + else: + warn( + "filename: \n" + "{0} \n" + "was expected to end with .nc".format( + filename + ), + UserWarning, + ) + + print("saving Profile data") + with ProgressBar(): + self.profile.dataset.to_netcdf(filename_profile) + print("saving gridded data") + with ProgressBar(): + self.gridded.dataset.to_netcdf(filename_gridded) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? + + def load_profiles(self, filename): + """ Helper method to load Profile and Gridded data from netcdf files """ + ### COMMENT: WHY IS THIS CLASS, WHICH INHERITS FROM INDEXED< LOADING profile AND gridded DATA + filename_profile = filename[:-3] + "_profile.nc" + filename_gridded = filename[:-3] + "_gridded.nc" + self.profile = Profile() + dataset = xr.load_dataset(filename_profile) + self.profile.insert_dataset(dataset) + dataset = xr.load_dataset(filename_gridded) + self.gridded.dataset = dataset + + ############################################################################## + def match_to_grid(self, gridded: Gridded, limits: List = [0, 0, 0, 0], rmax: int = 7000) -> None: + """Match profiles locations to grid, finding 4 nearest neighbours for each profile. + + Args: + gridded (Gridded): Gridded object. + limits (List): [jmin,jmax,imin,imax] - Subset to this region. + rmax (int): 7000 m - maxmimum search distance (metres). + + ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + + ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO + """ + self.gridded = gridded + if sum(limits) != 0: + gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) + # keep the grid or subset on the hydrographic profiles object + gridded.dataset["limits"] = limits + self.gridded = gridded + lon_prf = self.profile.dataset.longitude.values + lat_prf = self.profile.dataset.latitude.values + + # Find 4 nearest neighbours on grid + j_prf, i_prf, rmin_prf = gridded.find_j_i_list(lat=lat_prf, lon=lon_prf, n_nn=4) + + self.profile.dataset["i_min"] = limits[0] # reference back to origianl grid + self.profile.dataset["j_min"] = limits[2] + + i_min = self.profile.dataset.i_min.values + j_min = self.profile.dataset.j_min.values + + # Sort 4 NN by distance on grid + ii = np.nonzero(np.isnan(lon_prf)) + i_prf[ii, :] = 0 + j_prf[ii, :] = 0 + ip = np.where(np.logical_or(i_prf[:, 0] != 0, j_prf[:, 0] != 0))[0] + lon_prf4 = np.repeat(lon_prf[ip, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf[ip, np.newaxis], 4, axis=1).ravel() + r = np.ones(i_prf.shape) * np.nan + lon_grd = gridded.dataset.longitude.values + lat_grd = gridded.dataset.latitude.values + + rr = ProfileHydrography.distance_on_grid( + lat_grd, lon_grd, j_prf[ip, :].ravel(), i_prf[ip, :].ravel(), lat_prf4, lon_prf4 + ) + r[ip, :] = np.reshape(rr, (ip.size, 4)) + # sort by distance + ii = np.argsort(r, axis=1) + rmin_prf = np.take_along_axis(r, ii, axis=1) + i_prf = np.take_along_axis(i_prf, ii, axis=1) + j_prf = np.take_along_axis(j_prf, ii, axis=1) + + ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) + i_prf = i_prf + i_min + j_prf = j_prf + j_min + i_prf[ii, :] = 0 # should the be nan? + j_prf[ii, :] = 0 + + self.profile.dataset["i_prf"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) + self.profile.dataset["j_prf"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) + self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + + ############################################################################### + def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: + """Calculates various stratification metrics for observed profiles. + + Currently: PEA, PEAT, SST, SSS, NBT. + + Args: + Zmax = 200 m (int) maximum depth of integration. + DZMAX = 30 m depth of surface layer. + + COMMENT: IMPROVE DOC STRING + COMMENT: DEFINE OUTPUTS ESPECIALLY NON-STANDARD: PEAT, NBT, DT. + COMMENT: WHAT IS INPUT DZMAX USED FOR. + """ + i_prf = self.profile.dataset.i_prf - self.profile.dataset.i_min + j_prf = self.profile.dataset.j_prf - self.profile.dataset.j_min + D = self.gridded.dataset.bathymetry # uses bathymetry from gridded object + i_prf = np.ma.masked_less(i_prf, 0) + j_prf = np.ma.masked_less(j_prf, 0) + + nprof = self.profile.dataset.dims["id_dim"] + nz = self.profile.dataset.dims["z_dim"] + sst = np.ones((nprof)) * np.nan + sss = np.ones((nprof)) * np.nan + nbt = np.ones((nprof)) * np.nan + kbot = np.ones((nprof), dtype=int) * np.nan + PEA = np.ones((nprof)) * np.nan + PEAT = np.ones((nprof)) * np.nan + quart = [0, 0.25, 0.5, 0.75, 1] + # fix memory issues for very large data sets, if this still needed with xarray? + if nprof < 1000000: + npr = nprof + else: + npr = int(nprof / 10) + + for ichnk in ProfileHydrography.chunks(range(0, nprof), npr): + Ichnk = list(ichnk) + print(min(Ichnk), max(Ichnk)) + tmp = self.profile.dataset.potential_temperature[Ichnk, :].values + sal = self.profile.dataset.practical_salinity[Ichnk, :].values + ZZ = -self.profile.dataset.depth[Ichnk, :].values + lat = self.profile.dataset.latitude[Ichnk].values + lon = self.profile.dataset.longitude[Ichnk].values + rmin = self.profile.dataset.rmin_prf[Ichnk, :].values + nprof = len(Ichnk) + Zd_mask = np.zeros((nprof, nz)) + + ################################################################################ + # define interface layers and DZ associated with Z + Zw = np.empty_like(ZZ) + DZ = np.empty_like(ZZ) + Zw[:, 0] = 0.0 + I = np.arange(0, nz - 1) + Zw[:, I + 1] = 0.5 * (ZZ[:, I] + ZZ[:, I + 1]) + DZ[:, I] = Zw[:, I] - Zw[:, I + 1] + DZ[~np.isfinite(DZ)] = 0.0 + ZZ[~np.isfinite(ZZ)] = 0.0 + DP = np.ones((nprof)) * np.nan + # depth from model + print("Depth from model") + for ip in range(nprof): + + DP[ip] = 0.0 + rr = 0.0 + for iS in range(0, 4): + if D[j_prf[ip, iS], i_prf[ip, iS]] != 0: + DP[ip] = DP[ip] + D[j_prf[ip, iS], i_prf[ip, iS]] / rmin[ip, iS] + rr = rr + 1 / rmin[ip, iS] + if rr != 0.0: + DP[ip] = DP[ip] / rr + print("define good profiles") + good_profile = np.zeros((nprof)) + sstc = np.ones((nprof)) * np.nan + sssc = np.ones((nprof)) * np.nan + nbtc = np.ones((nprof)) * np.nan + kbot = np.ones((nprof), dtype=int) * np.nan + T = np.zeros(nz) * np.nan + S = np.zeros(nz) * np.nan + Z = np.zeros(nz) * np.nan + ZW = np.zeros(nz) * np.nan + DP[DP == 0] = np.nan + + for ip in range(nprof): + + Dp = DP[ip] + T[:] = tmp[ip, :] + S[:] = sal[ip, :] + # Z always -ve downwards + Z[:] = -np.abs(ZZ[ip, :]) + ZW[:] = -np.abs(Zw[ip, :]) + I = np.nonzero(np.isfinite(T))[0] + + if np.size(I) > 0 and np.isfinite(Dp): + kbot[ip] = np.max(I) + + # SST + if -Z[np.min(I)] < np.min([DZMAX, 0.25 * Dp]): + sstc[ip] = T[np.min(I)] + # SSS + if -Z[np.min(I)] < np.min([DZMAX, 0.25 * Dp]): + sssc[ip] = S[np.min(I)] + # Near bototm or ~Zmax temp. + if Dp < Zmax: + if Dp + Z[int(kbot[ip])] < np.min([DZMAX, 0.25 * Dp]): + nbtc[ip] = T[np.max(I)] + elif kbot[ip] == nz - 1: + nbtc[ip] = T[int(kbot[ip])] + elif Z[int(kbot[ip])] < -Zmax and np.size(np.nonzero(Z[I] > -Zmax)) != 0: + k = np.max(np.nonzero(Z[I] > -Zmax)[0]) + k = int(I[k]) + r = (-Zmax - Z[k]) / (Z[k + 1] - Z[k]) + nbt[ip] = T[k] * r + T[k + 1] * (1.0 - r) + + # Depth mask + Zd_mask[ip, 0 : int(kbot[ip])] = 1 + Imax = np.max(np.nonzero(ZW > -Zmax)[0]) # note ZW index + if Imax < kbot[ip]: + Zd_mask[ip, Imax:nz] = 0 + Zd_mask[ip, Imax] = (ZW[Imax] - (-Zmax)) / (ZW[Imax] - ZW[Imax + 1]) + if Zd_mask[ip, Imax - 1] < 0 or Zd_mask[ip, Imax - 1] > 1: + print("error", ip, Zd_mask[ip, Imax - 1]), Imax, kbot[ip] + # find good profiles + + DD = np.min([Dp, Zmax]) + good_profile[ip] = 1 + for iq in range(len(quart) - 1): + I = np.nonzero( + np.all(np.concatenate(([Z <= -DD * quart[iq]], [Z >= -DD * quart[iq + 1]]), axis=0), axis=0) + ) + + if np.size(I) == 0: + good_profile[ip] = 0 + elif ~(np.any((np.isfinite(S[I]))) and np.any((np.isfinite(S[I])))): + good_profile[ip] = 0 + ### + + T = ProfileHydrography.fillholes(T) + S = ProfileHydrography.fillholes(S) + tmp[ip, :] = T + sal[ip, :] = S + + ############################################################################### + print("Calculate metrics") + metrics = ProfileHydrography.profile_metrics(tmp, sal, ZZ, DZ, Zd_mask, lon, lat) + + PEAc = metrics["PEA"] + PEATc = metrics["PEAT"] + PEAc[good_profile == 0] = np.nan + PEATc[good_profile == 0] = np.nan + sst[Ichnk] = sstc + sss[Ichnk] = sssc + nbt[Ichnk] = nbtc + PEA[Ichnk] = PEAc + PEAT[Ichnk] = PEATc + # Next chunk + + DT = sst - nbt + self.profile.dataset["PEA"] = xr.DataArray(PEA, dims=["id_dim"]) + self.profile.dataset["PEAT"] = xr.DataArray(PEAT, dims=["id_dim"]) + self.profile.dataset["SST"] = xr.DataArray(sst, dims=["id_dim"]) + self.profile.dataset["SSS"] = xr.DataArray(sss, dims=["id_dim"]) + self.profile.dataset["NBT"] = xr.DataArray(nbt, dims=["id_dim"]) + self.profile.dataset["DT"] = xr.DataArray(DT, dims=["id_dim"]) + + def grid_hydro_mnth(self): + i_prf = self.profile.dataset.i_prf.values[:, 0] + j_prf = self.profile.dataset.j_prf.values[:, 0] + varnames = ["SST", "SSS", "PEA", "PEAT", "DT", "NBT"] + for varname in varnames: + print("Gridding", varname) + mnth = self.profile.dataset.time.values.astype("datetime64[M]").astype(int) % 12 + 1 + var, nvar = ProfileHydrography.grid_vars_mnth(self, varname, i_prf, j_prf, mnth) + self.gridded.dataset[varname] = xr.DataArray(var, dims=["12", "y_dim", "x_dim"]) + self.gridded.dataset["n" + varname] = xr.DataArray(nvar, dims=["12", "y_dim", "x_dim"]) + + ############################################################################### + @staticmethod + def makefilenames(path, dataset, yr_start, yr_stop): + if dataset == "EN4": + dataset_names = [] + january = 1 + december = 13 # range is non-inclusive so we need 12 + 1 + for yr in range(yr_start, yr_stop + 1): + for im in range(january, december): + name = os.path.join(path, f"EN.4.2.1.f.profiles.l09.{yr}{im:02}.nc") + dataset_names.append(name) + return dataset_names + print("Data set not coded") + + # Functions + ############################################################################### + # Functions for match to grid + @staticmethod + def subsetgrid(var_dom, limits): + i_min = limits[0] + i_max = limits[1] + j_min = limits[2] + j_max = limits[3] + if i_max > i_min: + return var_dom[i_min : i_max + 1, j_min : j_max + 1] + # special case for wrap-around + gvar1 = var_dom[i_min:, j_min : j_max + 1] + gvar2 = var_dom[:i_max, j_min : j_max + 1] + var_dom = np.concatenate((gvar1, gvar2), axis=0) + return var_dom + + ############################################################################### + ########################################### + def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): + DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) + DY = (Ypts - Y[jpts, ipts]) * earth_radius + r = np.sqrt(DX**2 + DY**2) + return r + + ############################################################################### + # Functions for stratification metrics + @staticmethod + def fillholes(Y): + YY = np.ones(np.shape(Y)) + YY[:] = Y + I = np.nonzero(np.isfinite(YY)) + N = len(YY) + + if np.size(I) > 0: + if not np.isfinite(YY[0]): + YY[0 : np.min(I) + 1] = YY[np.min(I)] + + if ~np.isfinite(YY[N - 1]): + YY[np.max(I) : N] = YY[np.max(I)] + I = np.array(np.nonzero(~np.isfinite(YY))) + YY[I] = 0.5 * (YY[I - 1] + YY[I + 1]) + YYp = YY[0] + ip = 0 + for i in range(N): + if np.isfinite(YY[i]): + YYp = YY[i] + ip = i + else: + j = i + while ~np.isfinite(YY[j]): + j = j + 1 + Jp = np.arange(ip + 1, j - 1 + 1) + + pT = np.arange(1.0, (j - ip - 1.0) + 1.0) / (j - ip) + YY[Jp] = YYp + (YY[j] - YYp) * pT + return YY + + ########################################### + def chunks(lst, n): + """ + Helper function that yields successive n-sized chunks from lst. + COMMENT: CHANGE NAME TO SOMETHING UNIQUE, PERHAPS: chunk_lst() + """ + for i in range(0, len(lst), n): + yield lst[i : i + n] + + ########################################### + @staticmethod + def profile_metrics(tmp, sal, Z, DZ, Zd_mask, lon, lat): + """ + ADD: DOC STRING + """ + metrics = {} + gravity = 9.81 + DD = np.sum(DZ * Zd_mask, axis=1) + nz = Z.shape[1] + lat = np.repeat(lat[:, np.newaxis], nz, axis=1) + lon = np.repeat(lon[:, np.newaxis], nz, axis=1) + pressure_absolute = gsw.p_from_z(Z, lat) + salinity_absolute = gsw.SA_from_SP(sal, pressure_absolute, lon, lat) + temp_conservative = gsw.CT_from_pt(salinity_absolute, tmp) + rho = np.ma.masked_invalid(gsw.rho(salinity_absolute, temp_conservative, 0.0)) + + Tbar = np.sum(temp_conservative * DZ * Zd_mask, axis=1) / DD + Sbar = np.sum(salinity_absolute * DZ * Zd_mask, axis=1) / DD + + rhobar = np.ma.masked_invalid(gsw.rho(Sbar, Tbar, 0.0)) + rhobar_2d = np.repeat(rhobar[:, np.newaxis], nz, axis=1) + Sbar_2d = np.repeat(Sbar[:, np.newaxis], nz, axis=1) + rhoT = np.ma.masked_invalid(gsw.rho(Sbar_2d, temp_conservative, 0.0)) # density with constant salinity + + PEA = -np.sum(Z * (rho - rhobar_2d) * DZ * Zd_mask, axis=1) * gravity / DD + PEAT = -np.sum(Z * (rhoT - rhobar_2d) * DZ * Zd_mask, axis=1) * gravity / DD + + metrics["PEA"] = PEA + metrics["PEAT"] = PEAT + + return metrics + + ########################################### + + def grid_vars_mnth(self, var, i_var, j_var, mnth_var): + """ + ADD: DOC STRING + """ + VAR = self.profile.dataset[var].values + nx = self.gridded.dataset.dims["x_dim"] + ny = self.gridded.dataset.dims["y_dim"] + + Ig = np.nonzero(np.isfinite(VAR))[0] + + var = VAR[Ig] + VAR_g = np.zeros((12, ny, nx)) + nVAR_g = np.zeros((12, ny, nx)) + for ip in range(0, np.size(Ig)): + i = i_var[Ig[ip]] + j = j_var[Ig[ip]] + im = int(mnth_var[Ig[ip]]) - 1 + + VAR_g[im, j, i] = VAR_g[im, j, i] + var[ip] + nVAR_g[im, j, i] = nVAR_g[im, j, i] + 1 + + VAR_g = VAR_g / nVAR_g + return VAR_g, nVAR_g From ed588398a6dbd9be088630df2c99a4827f17c87d Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Sep 2022 18:17:16 +0000 Subject: [PATCH 067/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_hydrographic_analysis.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 01c499bc..92aa8be7 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -57,11 +57,7 @@ def save_profiles(self, filename): filename_gridded = filename[:-3] + "_gridded.nc" else: warn( - "filename: \n" - "{0} \n" - "was expected to end with .nc".format( - filename - ), + "filename: \n" "{0} \n" "was expected to end with .nc".format(filename), UserWarning, ) @@ -70,10 +66,12 @@ def save_profiles(self, filename): self.profile.dataset.to_netcdf(filename_profile) print("saving gridded data") with ProgressBar(): - self.gridded.dataset.to_netcdf(filename_gridded) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? + self.gridded.dataset.to_netcdf( + filename_gridded + ) ## THIS IS A BIT ODD. WHY IS THERE gridded DATA IN AN INDEX OBJ? def load_profiles(self, filename): - """ Helper method to load Profile and Gridded data from netcdf files """ + """Helper method to load Profile and Gridded data from netcdf files""" ### COMMENT: WHY IS THIS CLASS, WHICH INHERITS FROM INDEXED< LOADING profile AND gridded DATA filename_profile = filename[:-3] + "_profile.nc" filename_gridded = filename[:-3] + "_gridded.nc" From fde09e075ae215b7f3c97f29ec45b848bd81c7c0 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 8 Sep 2022 20:49:54 +0100 Subject: [PATCH 068/150] boolean test error --- coast/diagnostics/profile_hydrographic_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 92aa8be7..156929d1 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -52,7 +52,7 @@ def save_profiles(self, filename): """ Helper method to saves profile and gridded datasets (in self) to netcdf. """ - if filename[:-3] is ".nc": + if filename[:-3] == ".nc": filename_profile = filename[:-3] + "_profile.nc" filename_gridded = filename[:-3] + "_gridded.nc" else: From 07012012fcca60d100fe7ccc5c060f8430e8491f Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 16 Sep 2022 13:53:43 +0100 Subject: [PATCH 069/150] InternalTide() --> GriddedStratification() --- coast/diagnostics/gridded_monthly_hydrographic_climatology.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index b3c321ee..122da60c 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -1,5 +1,5 @@ from ..data.gridded import Gridded -from ..diagnostics.internal_tide import InternalTide +from ..diagnostics.gridded_stratification import GriddedStratification import numpy as np import xarray as xr @@ -44,7 +44,7 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): print(itt) gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) print("copied", im) - PEA = InternalTide(gridded_t2, gridded_t2) + PEA = GriddedStratification(gridded_t2, gridded_t2) PEA.calc_pea(gridded_t2, Zd_mask) PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values PEA_monthy_clim = PEA_monthy_clim / nyear From cc9d43ebe46160fea14aeeca3edda2c7f186ba60 Mon Sep 17 00:00:00 2001 From: jpolton Date: Sat, 12 Nov 2022 20:34:09 +0000 Subject: [PATCH 070/150] Typo: S and T, not S and S --- coast/diagnostics/profile_hydrographic_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 156929d1..3f65df5b 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -275,7 +275,7 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: if np.size(I) == 0: good_profile[ip] = 0 - elif ~(np.any((np.isfinite(S[I]))) and np.any((np.isfinite(S[I])))): + elif ~(np.any((np.isfinite(S[I]))) and np.any((np.isfinite(T[I])))): good_profile[ip] = 0 ### From b20068cf61642f56973cdd7eda435e8bfdf9a01e Mon Sep 17 00:00:00 2001 From: jpolton Date: Sat, 12 Nov 2022 20:35:04 +0000 Subject: [PATCH 071/150] new feature: fills_holes_1d() --- coast/_utils/general_utils.py | 17 ++++++++++ .../profile_hydrographic_analysis.py | 33 +++---------------- unit_testing/test_general_utils.py | 11 +++++++ 3 files changed, 33 insertions(+), 28 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 9ba438e9..79861155 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -368,3 +368,20 @@ def nan_helper(y): return np.isnan(y), lambda z: z.nonzero()[0] else: return np.isnan(y).values, lambda z: z.nonzero()[0] + +def fill_holes_1d(y): + """ + extrapolate and linearly interpolate over nans in 1d vectors + Input: + - y, 1d numpy array, or xr.DataArray, with possible NaNs + Output: + - 1d array with nans filled in + Examples: + pp = xr.DataArray(np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64')) + fill_holes_new(pp).values + Returns: + array([2., 2., 2., 3., 4., 5., 6.]) + """ + nans, x = general_utils.nan_helper(y) # location interior nans + y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate + return y \ No newline at end of file diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 3f65df5b..0cdbe7c4 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -9,6 +9,7 @@ from ..data.index import Indexed from dask.diagnostics import ProgressBar from .._utils.logging_util import get_slug, debug, info, warn, warning +from .._utils import general_utils # @@ -361,34 +362,10 @@ def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): # Functions for stratification metrics @staticmethod def fillholes(Y): - YY = np.ones(np.shape(Y)) - YY[:] = Y - I = np.nonzero(np.isfinite(YY)) - N = len(YY) - - if np.size(I) > 0: - if not np.isfinite(YY[0]): - YY[0 : np.min(I) + 1] = YY[np.min(I)] - - if ~np.isfinite(YY[N - 1]): - YY[np.max(I) : N] = YY[np.max(I)] - I = np.array(np.nonzero(~np.isfinite(YY))) - YY[I] = 0.5 * (YY[I - 1] + YY[I + 1]) - YYp = YY[0] - ip = 0 - for i in range(N): - if np.isfinite(YY[i]): - YYp = YY[i] - ip = i - else: - j = i - while ~np.isfinite(YY[j]): - j = j + 1 - Jp = np.arange(ip + 1, j - 1 + 1) - - pT = np.arange(1.0, (j - ip - 1.0) + 1.0) / (j - ip) - YY[Jp] = YYp + (YY[j] - YYp) * pT - return YY + """ + extrapolate and linearly interpolate 1d vectors + """ + return general_utils.fill_holes_1d(Y) ########################################### def chunks(lst, n): diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index c235df75..7c27d412 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -61,3 +61,14 @@ def test_nan_helper(self): self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") + + def test_fill_holes_1d(self): + input = np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64') + input_xr = xr.DataArray(input) + target = np.array([2., 2., 2., 3., 4., 5., 6.]) + + check1 = all(fill_holes_1d(input) == target) + check2 = all(fill_holes_1d(input_xr).values == target) + + self.assertTrue(check1, msg="check1") + self.assertTrue(check2, msg="check2") From f0dfda4138aebf1c19d1ab54247fd04ae2f8ca94 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Sat, 12 Nov 2022 20:35:47 +0000 Subject: [PATCH 072/150] Apply Black formatting to Python code. --- coast/_utils/general_utils.py | 3 ++- unit_testing/test_general_utils.py | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 79861155..62bc9411 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -369,6 +369,7 @@ def nan_helper(y): else: return np.isnan(y).values, lambda z: z.nonzero()[0] + def fill_holes_1d(y): """ extrapolate and linearly interpolate over nans in 1d vectors @@ -384,4 +385,4 @@ def fill_holes_1d(y): """ nans, x = general_utils.nan_helper(y) # location interior nans y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate - return y \ No newline at end of file + return y diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index 7c27d412..2a607059 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -63,9 +63,9 @@ def test_nan_helper(self): self.assertTrue(check2, msg="check2") def test_fill_holes_1d(self): - input = np.array([np.nan, np.nan, 2., np.nan, 4,5,6], dtype='float64') + input = np.array([np.nan, np.nan, 2.0, np.nan, 4, 5, 6], dtype="float64") input_xr = xr.DataArray(input) - target = np.array([2., 2., 2., 3., 4., 5., 6.]) + target = np.array([2.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) check1 = all(fill_holes_1d(input) == target) check2 = all(fill_holes_1d(input_xr).values == target) From c1c124cbdce4bcd9b5902e00dd97c75f51b54171 Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Sat, 12 Nov 2022 20:36:32 +0000 Subject: [PATCH 073/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index 4038fa3b..0ca8dc06 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -18,8 +18,9 @@ b. coast_variable_renaming c. copy_coast_object d. day_of_week - e. getitem - f. nan_helper + e. fill_holes_1d + f. getitem + g. nan_helper 3. test_gridded_harmonics a. combine_and_convert_harmonics From 1804338aa8b47a8449680b645cfd2d3c6e6d18f2 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:07:13 +0000 Subject: [PATCH 074/150] add profile.calculate_vertical_spacing() --- coast/data/profile.py | 41 ++++++++++++++++++++++++++++++++++++++++- 1 file changed, 40 insertions(+), 1 deletion(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 779ac677..072dec4b 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -2,11 +2,13 @@ from .index import Indexed import numpy as np import xarray as xr +import gsw from .._utils import general_utils, plot_util import matplotlib.pyplot as plt import glob import datetime -from .._utils.logging_util import get_slug, debug, info, warn, warning +from .._utils.logging_util import get_slug, debug, info, warn, warning, error + from typing import Union from pathlib import Path import pandas as pd @@ -685,3 +687,40 @@ def time_slice(self, date0, date1): t_ind = pd.to_datetime(dataset.time.values) < date1 dataset = dataset.isel(id_dim=t_ind) return Profile(dataset=dataset) + + def calculate_vertical_spacing(self): + """ + Profile data is given at depths, z, however for some calculations a thickness measure, dz, is required + Define the upper thickness: dz[0] = 0.5*(z[0] + z[1]) and thereafter the centred difference: + dz[k] = 0.5*(z[k-1] - z[k+1]) + + Notionally, dz is the separation between w-points, when w-points are estimated from depths + at t-points. + """ + + if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + pass + else: + # Compute dz on w-pts + depth_t = self.dataset.depth + self.dataset['dz'] = xr.where(depth_t == depth_t.min(dim="z_dim"), + 0.5 * (depth_t + depth_t.shift(z_dim=-1)), + 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)) # .fillna(0.) + ) + + attributes = {"units": "m", "standard name": "centre difference thickness"} + if hasattr(self.dataset.dz, 'coords'): # xarray object. Just add title and units + self.dataset.dz.attrs = attributes + + else: # not an xarray object + coords = { + "time": (("id_dim"), self.dataset.time.values), + "latitude": (("id_dim"), self.dataset.latitude.values), + "longitude": (("id_dim"), self.dataset.longitude.values), + } + dims = ["z_dim", "id_dim"] + + dz = np.squeeze(dz) + self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + + From 7b0b085a10a8263a5253c077d1f08ad78435104d Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:08:30 +0000 Subject: [PATCH 075/150] add profile.construct_density() --- coast/data/profile.py | 173 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 173 insertions(+) diff --git a/coast/data/profile.py b/coast/data/profile.py index 072dec4b..b9479466 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -724,3 +724,176 @@ def calculate_vertical_spacing(self): self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + def construct_density( + self, eos="EOS10", rhobar=False, Zd_mask:xr.DataArray=None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True + ): + + """ + Constructs the in-situ density using the salinity, temperature and + depth fields. Adds a density attribute to the profile dataset + + Requirements: The supplied Profile dataset must contain the + Practical Salinity and the Potential Temperature variables. The depth + field must also be supplied. The GSW package is used to calculate + The Absolute Pressure, Absolute Salinity and Conservative Temperature. + + Note that currently density can only be constructed using the EOS10 + equation of state. + + Parameters + ---------- + eos : equation of state, optional + DESCRIPTION. The default is 'EOS10'. + + rhobar : Calculate density with depth mean T and S + DESCRIPTION. The default is 'False'. + Zd_mask : (xr.DataArray) Provide a (id_dim, z_dim) mask for rhobar calculation + Calculate using calculate_vertical_mask + DESCRIPTION. The default is empty. + + CT_AS : Conservative Temperature and Absolute Salinity already provided + DESCRIPTION. The default is 'False'. + pot_dens :Calculation at zero pressure + DESCRIPTION. The default is 'False'. + Tbar and Sbar : If rhobar is True then these can be switch to False to allow one component to + remain depth varying. So Tbar=Flase gives temperature component, Sbar=False gives Salinity component + DESCRIPTION. The default is 'True'. + + Returns + ------- + None. + adds attribute profile.dataset.density + + """ + debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') + try: + if eos != "EOS10": + raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") + + try: + shape_ds = ( + self.dataset.z_dim.size, + self.dataset.id_dim.size, + ) + sal = self.dataset.practical_salinity.to_masked_array() + temp = self.dataset.potential_temperature.to_masked_array() + + if np.shape(sal) != shape_ds: + sal = sal.T + temp = temp.T + except AttributeError: + error(f"We have a problem with {self.dataset.dims}") + + density = np.ma.zeros(shape_ds) + + print(f"shape sal:{np.shape(sal)}") + print(f"shape rho:{np.shape(density)}") + + s_levels = self.dataset.depth.to_masked_array() + if np.shape(s_levels) != shape_ds: + s_levels = s_levels.T + + lat = self.dataset.latitude.values + lon = self.dataset.longitude.values + # Absolute Pressure + if pot_dens: + pressure_absolute = 0.0 # calculate potential density + else: + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative + if not rhobar: # calculate full depth + # Absolute Salinity + if not CT_AS: # abs salinity not provided + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + else: # abs salinity provided + sal_absolute = np.ma.masked_invalid(sal) + sal_absolute = np.ma.masked_less(sal_absolute, 0) + # Conservative Temperature + if not CT_AS: # conservative temp not provided + temp_conservative = np.ma.masked_invalid(gsw.CT_from_pt(sal_absolute, temp)) + else: # conservative temp provided + temp_conservative = np.ma.masked_invalid(temp) + # In-situ density + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + new_var_name = "density" + else: # calculate density with depth integrated T S + + if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + pass + else: # Create it + self.calculate_vertical_spacing() + + # prepare coordinate variables + if Zd_mask is None: + DZ = self.dataset.dz + else: + DZ = (self.dataset.dz * Zd_mask) + DP = DZ.sum(dim="z_dim").to_masked_array() + DZ = DZ.to_masked_array() + if np.shape(DZ) != shape_ds: + DZ = DZ.T + # DP=np.repeat(DP[np.newaxis,:,:],shape_ds[1],axis=0) + + #DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) + #DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) + + # Absolute Salinity + if not CT_AS: # abs salinity not provided + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + else: # abs salinity provided + sal_absolute = np.ma.masked_invalid(sal) + + # Conservative Temperature + if not CT_AS: # Conservative temperature not provided + temp_conservative = np.ma.masked_invalid(gsw.CT_from_pt(sal_absolute, temp)) + else: # conservative temp provided + temp_conservative = np.ma.masked_invalid(temp) + + if pot_dens and (Sbar and Tbar): # usual case pot_dens and depth averaged everything + sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP + temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + density = np.repeat(density[np.newaxis, :], shape_ds[0], axis=0) + + else: # Either insitu density or one of Tbar or Sbar False + if Sbar: + sal_absolute = np.repeat( + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP)[np.newaxis, :], + shape_ds[0], + axis=0, + ) + if Tbar: + temp_conservative = np.repeat( + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP)[np.newaxis, :], + shape_ds[0], + axis=0, + ) + density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) + + if Tbar and Sbar: + new_var_name = "density_bar" + + else: + if not Tbar: + new_var_name = "density_T" + else: + new_var_name = "density_S" + + # rho and rhobar + coords = { + "time": (("id_dim"), self.dataset.time.values), + "latitude": (("id_dim"), self.dataset.latitude.values), + "longitude": (("id_dim"), self.dataset.longitude.values), + } + dims = ["z_dim", "id_dim"] + + if pot_dens: + attributes = {"units": "kg / m^3", "standard name": "Potential density "} + else: + attributes = {"units": "kg / m^3", "standard name": "In-situ density "} + + density = np.squeeze(density) + self.dataset[new_var_name] = xr.DataArray(density, coords=coords, dims=dims, attrs=attributes) + + except AttributeError as err: + error(err) + From 8f33b370b431900f719bf9e990f8668184316e85 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:09:20 +0000 Subject: [PATCH 076/150] add profile.calculate_vertical_mask() --- coast/data/profile.py | 68 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) diff --git a/coast/data/profile.py b/coast/data/profile.py index b9479466..5ba05145 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -897,3 +897,71 @@ def construct_density( except AttributeError as err: error(err) + def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): + """ + Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed + and linearly ramped for last level + + Inputs: + depth (id_dim, z_dim) postitive values - passing as a variable facilitates testing + Zmax float - max depth (m( + Returns + Zd_mask (id_dim, z_dim) xr.DataArray, float mask. + #kmax (id_dim) deepest index above Zmax + """ + + ## Contruct a mask array that is: + # zeros below Zmax + # ones above Zmax, except the closest shallower depth which has a value [0,1] that is the weighted distance to Zmax + + ## prepare depth profiles + depth_t = depth + # remove deep nans + # depth_t = depth_t.fillna(1E6) + # depth_t = depth_t.interpolate_na(dim="z_dim", method="nearest", fill_value="extrapolate") + # print(depth_t) + + ## construct a mask to identify location of and separation from Zmax + + # mask_arr = np.zeros((depth_t.shape))*np.nan + # print(np.shape(mask_arr)) + # mask_arr[depth_t <= Zmax] = 1 + # mask_arr[depth_t > Zmax] = 0 + # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) + mask = depth * np.nan + + mask = xr.where(depth_t <= Zmax, 1, mask) + mask = xr.where(depth_t > Zmax, 0, mask) + + # print(mask) + # print('\n') + + max_shallower_depth = (depth_t * mask).max(dim="z_dim") + min_deeper_depth = (depth_t.roll(z_dim=-1) * mask).max(dim="z_dim") + # NB if max_shallower_depth was already deepest value in profile, then this produces the same value + # I.e. + # max_shallower_depth <= Zmax + # min_deeper_depth > Zmax or min_deeper_depth = max_shallower_depth + + # print(f"max_shallower_depth:{max_shallower_depth}") + # print(f"min_deeper_depth:{min_deeper_depth}") + # print('\n') + + # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) + fraction = xr.where(min_deeper_depth != max_shallower_depth, + (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + 1) + + max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + + # locate the depth index for the deepest level above Zmax + kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") + #print(kmax) + + # replace mask values with fraction_2d at depth above Zmax) + mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) + + return mask, kmax + + From 5ca23f875052a7755628b3c5c74120ff3e2b1407 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:10:46 +0000 Subject: [PATCH 077/150] WIP: profile_stratification.py --- coast/diagnostics/profile_stratification.py | 355 ++++++++++++++++++++ 1 file changed, 355 insertions(+) create mode 100644 coast/diagnostics/profile_stratification.py diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py new file mode 100644 index 00000000..8a50ecad --- /dev/null +++ b/coast/diagnostics/profile_stratification.py @@ -0,0 +1,355 @@ +from ..data.profile import Profile +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr +import copy +from .._utils.logging_util import get_slug, debug + + +class ProfileStratification(Profile): # TODO All abstract methods should be implemented + """ + Object for handling and storing necessary information, methods and outputs + for calculation of stratification diagnostics. + + + UPDATE THE FOLLOWING + + Parameters + ---------- + gridded_t : xr.Dataset + Gridded object on t-points. + gridded_w : xr.Dataset, optional + Gridded object on w-points. + + Example basic usage: + ------------------- + # Create Internal tide diagnostics object + strat_obj = GriddedStratification(gridded_t, gridded_w) # For Gridded objects on t and w-pts + strat_obj.construct_pycnocline_vars( gridded_t, gridded_w ) + # Make maps of pycnocline thickness and depth + strat_obj.quick_plot() + """ + + def __init__(self, profile: xr.Dataset): + # TODO Super __init__ should be called at some point + debug(f"Creating new {get_slug(self)}") + self.dataset = xr.Dataset() + + # Define the dimensional sizes as constants + self.nid = profile.dataset.dims["id_dim"] + self.nz = gridded_t.dataset.dims["z_dim"] + debug(f"Initialised {get_slug(self)}") + + def construct_pycnocline_vars(self, gridded_t: Gridded, gridded_w: Gridded, strat_thres=-0.01): + """ + Computes depth moments of stratification. Under the assumption that the + stratification approximately represents a two-layer fluid, these can be + interpreted as pycnocline depths and thicknesses. They are computed on + w-points. + + 1st moment of stratification: \int z.strat dz / \int strat dz + In the limit of a two layer fluid this is equivalent to the + pycnocline depth, or z_d (units: metres) + + 2nd moment of stratification: \sqrt{\int (z-z_d)^2 strat dz / \int strat dz} + where strat = d(density)/dz + In the limit of a two layer fluid this is equivatlent to the + pycnocline thickness, or z_t (units: metres) + + Parameters + ---------- + gridded_t : Gridded + Gridded object on t-points. + gridded_w : Gridded, optional + Gridded object on w-points. + strat_thres: float - Optional + limiting stratification (rho_dz < 0) to trigger masking of mixed waters + + Output + ------ + self.dataset.strat_1st_mom - (t,y,x) pycnocline depth + self.dataset.strat_2nd_mom - (t,y,x) pycnocline thickness + self.dataset.strat_1st_mom_masked - (t,y,x) pycnocline depth, masked + in weakly stratified water beyond strat_thres + self.dataset.strat_2nd_mom_masked - (t,y,x) pycnocline thickness, masked + in weakly stratified water beyond strat_thres + self.dataset.mask - (t,y,x) [1/0] stratified/unstrafied + water column according to strat_thres not being met anywhere + in the column + + Returns + ------- + None. + + Example Usage + ------------- + # load some example data + dn_files = "./example_files/" + dn_fig = 'unit_testing/figures/' + fn_nemo_grid_t_dat = 'nemo_data_T_grid_Aug2015.nc' + fn_nemo_dom = 'coast_example_nemo_domain.nc' + gridded_t = coast.Gridded(dn_files + fn_nemo_grid_t_dat, + dn_files + fn_nemo_dom, grid_ref='t-grid') + # create an empty w-grid object, to store stratification + gridded_w = coast.Gridded( fn_domain = dn_files + fn_nemo_dom, + grid_ref='w-grid') + + # initialise GriddedStratification object + strat = coast.GriddedStratification(gridded_t, gridded_w) + # Construct pycnocline variables: depth and thickness + strat.construct_pycnocline_vars( gridded_t, gridded_w ) + # Plot pycnocline depth and thickness + strat.quickplot() + + """ + + debug(f"Constructing pycnocline variables for {get_slug(self)}") + # Construct in-situ density if not already done + if not hasattr(gridded_t.dataset, "density"): + gridded_t.construct_density(eos="EOS10") + + # Construct stratification if not already done. t-pts --> w-pts + if not hasattr(gridded_w.dataset, "rho_dz"): + gridded_w = gridded_t.differentiate("density", dim="z_dim", out_var_str="rho_dz", out_obj=gridded_w) + + # Define the spatial dimensional size and check the dataset and domain arrays are the same size in + # z_dim, ydim, xdim + nt = gridded_t.dataset.dims["t_dim"] + # nz = gridded_t.dataset.dims['z_dim'] + ny = gridded_t.dataset.dims["y_dim"] + nx = gridded_t.dataset.dims["x_dim"] + + # Create a mask for weakly stratified waters + # Preprocess stratification + strat = copy.copy(gridded_w.dataset.rho_dz) # (t_dim, z_dim, ydim, xdim). w-pts. + # Ensure surface value is 0 + strat[:, 0, :, :] = 0 + # Ensure bed value is 0 + strat[:, -1, :, :] = 0 + # mask out the Nan values + strat = strat.where(~np.isnan(gridded_w.dataset.rho_dz), drop=False) + # create mask with a stratification threshold + strat_m = gridded_w.dataset.latitude * 0 + 1 # create a stratification mask: [1/0] = strat/un-strat + strat_m = strat_m.where(strat.min(dim="z_dim").squeeze() < strat_thres, 0, drop=False) + strat_m = strat_m.transpose("t_dim", "y_dim", "x_dim", transpose_coords=True) + + # Compute statification variables + # initialise pycnocline variables + pycnocline_depth = np.zeros((nt, ny, nx)) # pycnocline depth + zt = np.zeros((nt, ny, nx)) # pycnocline thickness + + # Construct intermediate variables + # Broadcast to fill out missing (time) dimensions in grid data + _, depth_0_4d = xr.broadcast(strat, gridded_w.dataset.depth_0) + _, e3_0_4d = xr.broadcast(strat, gridded_w.dataset.e3_0.squeeze()) + + # integrate strat over depth + intN2 = (strat * e3_0_4d).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + # integrate (depth * strat) over depth + intzN2 = (strat * e3_0_4d * depth_0_4d).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + + # compute pycnocline depth + pycnocline_depth = intzN2 / intN2 # pycnocline depth + + # compute pycnocline thickness + intz2N2 = (np.square(depth_0_4d - pycnocline_depth) * e3_0_4d * strat).sum( + dim="z_dim", skipna=True + ) # TODO Can someone sciencey give me the proper name for this? + zt = np.sqrt(intz2N2 / intN2) # pycnocline thickness + + # Define xarray attributes + coords = { + "time": ("t_dim", gridded_t.dataset.time.values), + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + } + dims = ["t_dim", "y_dim", "x_dim"] + + # Save a xarray objects + self.dataset["strat_2nd_mom"] = xr.DataArray(zt, coords=coords, dims=dims) + self.dataset.strat_2nd_mom.attrs["units"] = "m" + self.dataset.strat_2nd_mom.attrs["standard_name"] = "pycnocline thickness" + self.dataset.strat_2nd_mom.attrs["long_name"] = "Second depth moment of stratification" + + self.dataset["strat_1st_mom"] = xr.DataArray(pycnocline_depth, coords=coords, dims=dims) + self.dataset.strat_1st_mom.attrs["units"] = "m" + self.dataset.strat_1st_mom.attrs["standard_name"] = "pycnocline depth" + self.dataset.strat_1st_mom.attrs["long_name"] = "First depth moment of stratification" + + # Mask pycnocline variables in weak stratification + zd_m = pycnocline_depth.where(strat_m > 0) + zt_m = zt.where(strat_m > 0) + + self.dataset["mask"] = xr.DataArray(strat_m, coords=coords, dims=dims) + + self.dataset["strat_2nd_mom_masked"] = xr.DataArray(zt_m, coords=coords, dims=dims) + self.dataset.strat_2nd_mom_masked.attrs["units"] = "m" + self.dataset.strat_2nd_mom_masked.attrs["standard_name"] = "masked pycnocline thickness" + self.dataset.strat_2nd_mom_masked.attrs[ + "long_name" + ] = "Second depth moment of stratification, masked in weak stratification" + + self.dataset["strat_1st_mom_masked"] = xr.DataArray(zd_m, coords=coords, dims=dims) + self.dataset.strat_1st_mom_masked.attrs["units"] = "m" + self.dataset.strat_1st_mom_masked.attrs["standard_name"] = "masked pycnocline depth" + self.dataset.strat_1st_mom_masked.attrs[ + "long_name" + ] = "First depth moment of stratification, masked in weak stratification" + + # Inherit horizontal grid information from gridded_w + self.dataset["e1"] = xr.DataArray( + gridded_w.dataset.e1, + coords={ + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + }, + dims=["y_dim", "x_dim"], + ) + self.dataset["e2"] = xr.DataArray( + gridded_w.dataset.e2, + coords={ + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + }, + dims=["y_dim", "x_dim"], + ) + + def calc_pea(self, profile: xr.Dataset, Zmax): + """ + Calculates Potential Energy Anomaly + + UPDATE THE DOCSTR + + The density and depth averaged density can be supplied within gridded_t as "density" and + "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. + "density_bar" is calculated using depth averages of temperature and salinity. + + Example Usage: PEA in upper 200m + -------------------------------- + # load some example data. E.g. + root = "~/work/coast/" + dn_files = root + "./example_files/" + fn_nemo_grid_t_dat = dn_files + "nemo_data_T_grid_Aug2015.nc" + fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" + config_t = root + "./config/example_nemo_grid_t.json" + dn_fig = 'unit_testing/figures/' + gridded_t = coast.Gridded(fn_nemo_grid_t_dat, fn_nemo_dom, config=config_t) + Zd_mask,kmax,Ikmax=gridded_t.calculate_vertical_mask(200.) + strat=coast.GriddedStratification(gridded_t) + strat.calc_pea(gridded_t,Zd_mask) + strat.quick_plot('PEA') + """ + # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach + gravity = 9.81 + + # Define grid spacing, dz. Required for depth integral + """ + The thickness, dz, for integrals on t-points, should be the separation + between w-point depths. + DZ[:, I] = Zw[:, I] - Zw[:, I + 1] + = 0.5 * ( Zt[:, I-1] - Zt[:, I+1] ) + where Zw[:, I + 1] = 0.5 * (Zt[:, I] + Zt[:, I + 1]) + for I = 2:end-1 + DZ[:, 0] = 0.5 * ( Zt[:, 0] + Zt[:, 1] ) + """ + + # Compute dz on w-pts + profile.calculate_vertical_spacing() + dz = profile.dataset.dz + + # Z=gridded_t.dataset.variables['depth_0'].values + # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask + + #_, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) + #height = profile.dataset.depth * Zd_mask # water depth or Zmax , + #height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , + # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] + # nt=gridded_t.dataset.dims['t_dim'] + + # Construct a mask of zeros below threshold, floats above depth of Zmax threshold. + # Floats are in the range (0,1] and represent the fractional proximity to Zmax. + # Used for scaling layer thickness, which would then sum to Zmax. + Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) + + + # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. + height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + + if not "density" in profile.dataset: + profile.construct_density(CT_AS=True, pot_dens=True) + if not "density_bar" in profile.dataset: + profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) + rho = profile.dataset.variables["density"].values # density + rho[np.isnan(rho)] = 0 + rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S + + + + PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height + #%% + # return PEA + coords = { + "time": ("t_dim", gridded_t.dataset.time.values), + "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + } + dims = ["t_dim", "y_dim", "x_dim"] + attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} + self.dataset["PEA"] = xr.DataArray(PEA, coords=coords, dims=dims, attrs=attributes) + + def quick_plot(self, var: xr.DataArray = None): + """ + + Map plot for pycnocline depth and thickness variables. + + Parameters + ---------- + var : xr.DataArray, optional + Pass variable to plot. The default is None. In which case both + strat_1st_mom and strat_2nd_mom are plotted. + + Returns + ------- + None. + + Example Usage + ------------- + strat.quick_plot( 'strat_1st_mom_masked' ) + + """ + + debug(f"Generating quick plot for {get_slug(self)}") + + if var is None: + var_lst = [self.dataset.strat_1st_mom_masked, self.dataset.strat_2nd_mom_masked] + else: + var_lst = [self.dataset[var]] + + fig = None + ax = None + for var in var_lst: + fig = plt.figure(figsize=(10, 10)) + ax = fig.gca() + plt.pcolormesh(self.dataset.longitude.squeeze(), self.dataset.latitude.squeeze(), var.isel(t_dim=0)) + # var.mean(dim = 't_dim') ) + # plt.contourf( self.dataset.longitude.squeeze(), + # self.dataset.latitude.squeeze(), + # var.mean(dim = 't_dim'), levels=(0,10,20,30,40) ) + title_str = ( + self.dataset.time[0].dt.strftime("%d %b %Y: ").values + + var.attrs["standard_name"] + + " (" + + var.attrs["units"] + + ")" + ) + plt.title(title_str) + plt.xlabel("longitude") + plt.ylabel("latitude") + plt.clim([0, 50]) + plt.colorbar() + plt.show() + return fig, ax From ccf00e4b9384e9836235c0ecbd6e3c666d5f4444 Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:11:39 +0000 Subject: [PATCH 078/150] Add tests for profile.calculate_Vertical_spacing() --- unit_testing/test_profile_methods.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 10e33224..dde37283 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -6,6 +6,7 @@ import coast import unittest import numpy as np +import xarray as xr import matplotlib.pyplot as plt plt.switch_backend("agg") @@ -38,6 +39,13 @@ def test_load_process_and_compare_profile_data(self): self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + with self.subTest("Compute vertical spacing"): + profile.calculate_vertical_spacing() + check1 = np.allclose(profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5,10,15]).values, + np.array([1949.1846, 1972.8088, 21.5])) + self.assertTrue(check1, "check1") + + def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) From cb710eeaba7cf768fd8ac9e27960c3df557300be Mon Sep 17 00:00:00 2001 From: jpolton Date: Thu, 17 Nov 2022 21:11:59 +0000 Subject: [PATCH 079/150] Add tests for profile.calculate_vertical_mask() --- unit_testing/test_profile_methods.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index dde37283..728aa46e 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -158,3 +158,19 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + + def test_calculate_vertical_mask(self): + + profile = coast.Profile() + + arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) + depth = xr.DataArray(arr, dims=["i_dim", "z_dim"]) + + mask, kmax = profile.calculate_vertical_mask(depth, 21) + mask = mask.fillna(-999) + + check1 = (kmax == np.array([2,1,2])).all() + check2 = (mask.values == np.array([[1., 1., 1., -999], [1., 0.8, 0., 0.], [1., 1., 1., -999]])).all() + + self.assertTrue(check1, "check1") + self.assertTrue(check2, "check2") From e69f79783c556db0f1dfb5c08098831b6c798eb5 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 17 Nov 2022 21:12:57 +0000 Subject: [PATCH 080/150] Apply Black formatting to Python code. --- coast/data/profile.py | 40 ++++++++++----------- coast/diagnostics/profile_stratification.py | 9 ++--- unit_testing/test_profile_methods.py | 11 +++--- 3 files changed, 29 insertions(+), 31 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 5ba05145..5e0fd216 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -698,18 +698,19 @@ def calculate_vertical_spacing(self): at t-points. """ - if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Compute dz on w-pts depth_t = self.dataset.depth - self.dataset['dz'] = xr.where(depth_t == depth_t.min(dim="z_dim"), - 0.5 * (depth_t + depth_t.shift(z_dim=-1)), - 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)) # .fillna(0.) - ) + self.dataset["dz"] = xr.where( + depth_t == depth_t.min(dim="z_dim"), + 0.5 * (depth_t + depth_t.shift(z_dim=-1)), + 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)), # .fillna(0.) + ) attributes = {"units": "m", "standard name": "centre difference thickness"} - if hasattr(self.dataset.dz, 'coords'): # xarray object. Just add title and units + if hasattr(self.dataset.dz, "coords"): # xarray object. Just add title and units self.dataset.dz.attrs = attributes else: # not an xarray object @@ -721,11 +722,10 @@ def calculate_vertical_spacing(self): dims = ["z_dim", "id_dim"] dz = np.squeeze(dz) - self.dataset['dz'] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) - + self.dataset["dz"] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) def construct_density( - self, eos="EOS10", rhobar=False, Zd_mask:xr.DataArray=None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True + self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True ): """ @@ -817,7 +817,7 @@ def construct_density( new_var_name = "density" else: # calculate density with depth integrated T S - if hasattr(self.dataset, 'dz'): # Requires spacing variable. Test to see if variable exists + if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Create it self.calculate_vertical_spacing() @@ -826,15 +826,15 @@ def construct_density( if Zd_mask is None: DZ = self.dataset.dz else: - DZ = (self.dataset.dz * Zd_mask) + DZ = self.dataset.dz * Zd_mask DP = DZ.sum(dim="z_dim").to_masked_array() DZ = DZ.to_masked_array() if np.shape(DZ) != shape_ds: DZ = DZ.T # DP=np.repeat(DP[np.newaxis,:,:],shape_ds[1],axis=0) - #DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) - #DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) + # DZ = np.repeat(DZ[np.newaxis, :, :, :], shape_ds[0], axis=0) + # DP = np.repeat(DP[np.newaxis, :, :], shape_ds[0], axis=0) # Absolute Salinity if not CT_AS: # abs salinity not provided @@ -897,7 +897,7 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): + def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level @@ -948,20 +948,20 @@ def calculate_vertical_mask(self, depth:xr.DataArray, Zmax=200): # print('\n') # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) - fraction = xr.where(min_deeper_depth != max_shallower_depth, - (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), - 1) + fraction = xr.where( + min_deeper_depth != max_shallower_depth, + (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + 1, + ) max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") - #print(kmax) + # print(kmax) # replace mask values with fraction_2d at depth above Zmax) mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) return mask, kmax - - diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 8a50ecad..7689ea88 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -264,9 +264,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # Z=gridded_t.dataset.variables['depth_0'].values # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask - #_, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) - #height = profile.dataset.depth * Zd_mask # water depth or Zmax , - #height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , + # _, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) + # height = profile.dataset.depth * Zd_mask # water depth or Zmax , + # height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] # nt=gridded_t.dataset.dims['t_dim'] @@ -275,7 +275,6 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # Used for scaling layer thickness, which would then sum to Zmax. Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) - # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax @@ -287,8 +286,6 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho[np.isnan(rho)] = 0 rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - - PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height #%% # return PEA diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 728aa46e..1fdc2960 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -41,11 +41,12 @@ def test_load_process_and_compare_profile_data(self): with self.subTest("Compute vertical spacing"): profile.calculate_vertical_spacing() - check1 = np.allclose(profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5,10,15]).values, - np.array([1949.1846, 1972.8088, 21.5])) + check1 = np.allclose( + profile.dataset.dz.sum(dim="z_dim").isel(id_dim=[5, 10, 15]).values, + np.array([1949.1846, 1972.8088, 21.5]), + ) self.assertTrue(check1, "check1") - def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) @@ -169,8 +170,8 @@ def test_calculate_vertical_mask(self): mask, kmax = profile.calculate_vertical_mask(depth, 21) mask = mask.fillna(-999) - check1 = (kmax == np.array([2,1,2])).all() - check2 = (mask.values == np.array([[1., 1., 1., -999], [1., 0.8, 0., 0.], [1., 1., 1., -999]])).all() + check1 = (kmax == np.array([2, 1, 2])).all() + check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.8, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") From b14e3d84120fe6c54cc274635b4a0116bb381c16 Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Thu, 17 Nov 2022 21:13:32 +0000 Subject: [PATCH 081/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index 0ca8dc06..283a4217 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -75,8 +75,9 @@ c. calculate_pressure_along_contour 13. test_profile_methods - a. compare_processed_profile_with_model - b. load_process_and_compare_profile_data + a. calculate_vertical_mask + b. compare_processed_profile_with_model + c. load_process_and_compare_profile_data 14. test_plot_utilities a. determine_clim_by_stdev From 1887762a9db1b472fa4d363fbd12c513db5efc60 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 12:09:23 +0000 Subject: [PATCH 082/150] calculate_vertical_mask(): made inputs same between Gridded and Profile versions --- coast/data/profile.py | 15 ++++++++------- unit_testing/test_profile_methods.py | 12 ++++++++---- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 5e0fd216..662506e8 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -897,25 +897,26 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): + def calculate_vertical_mask(self, Zmax = 200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level Inputs: - depth (id_dim, z_dim) postitive values - passing as a variable facilitates testing - Zmax float - max depth (m( + Zmax float - max depth (m) Returns Zd_mask (id_dim, z_dim) xr.DataArray, float mask. - #kmax (id_dim) deepest index above Zmax + kmax (id_dim) deepest index above Zmax """ + depth_t = self.dataset.depth + ## Contruct a mask array that is: # zeros below Zmax # ones above Zmax, except the closest shallower depth which has a value [0,1] that is the weighted distance to Zmax ## prepare depth profiles - depth_t = depth + # remove deep nans # depth_t = depth_t.fillna(1E6) # depth_t = depth_t.interpolate_na(dim="z_dim", method="nearest", fill_value="extrapolate") @@ -928,7 +929,7 @@ def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): # mask_arr[depth_t <= Zmax] = 1 # mask_arr[depth_t > Zmax] = 0 # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) - mask = depth * np.nan + mask = depth_t * np.nan mask = xr.where(depth_t <= Zmax, 1, mask) mask = xr.where(depth_t > Zmax, 0, mask) @@ -958,7 +959,7 @@ def calculate_vertical_mask(self, depth: xr.DataArray, Zmax=200): fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax - kmax = xr.where(depth == max_shallower_depth, 1, 0).argmax(dim="z_dim") + kmax = xr.where(depth_t == max_shallower_depth, 1, 0).argmax(dim="z_dim") # print(kmax) # replace mask values with fraction_2d at depth above Zmax) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 1fdc2960..71ce8064 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -161,13 +161,17 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check3, "check3") def test_calculate_vertical_mask(self): + # load example profile data + profile = coast.Profile(config=fn_profile_config) + profile.read_en4(fn_profile) + profile.dataset = profile.dataset.isel(id_dim=slice(0, 3)).isel(z_dim=slice(0, 4)) - profile = coast.Profile() - + # Reassign values to depth, within a full profile object, to make it transparent arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) - depth = xr.DataArray(arr, dims=["i_dim", "z_dim"]) + depth = xr.DataArray(arr, dims=["id_dim", "z_dim"]) + profile.dataset['depth'] = depth - mask, kmax = profile.calculate_vertical_mask(depth, 21) + mask, kmax = profile.calculate_vertical_mask( 21) mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() From fa4adcb80f7575c69a8ea824ec5aa61544b36818 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 12:10:39 +0000 Subject: [PATCH 083/150] add test for pPofile.contruct.density() --- unit_testing/test_profile_methods.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 71ce8064..5c03c096 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -47,6 +47,34 @@ def test_load_process_and_compare_profile_data(self): ) self.assertTrue(check1, "check1") + def test_compute_density(self): + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) + profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() + + profile.construct_density() + + check1 = np.allclose(profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), + 4248551.199925806, + ) + # Density depth mean T and S limited to 200m + Zmax = 200 # m + Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) + profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Zd_mask=Zd_mask) + check2 = np.allclose( + profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021 + ) + # Temperature component of density (ie from depth mean Sal). full depth + profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Tbar=False) + check3 = np.allclose( + profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557 + ) + self.assertTrue(check1, msg="check1") + self.assertTrue(check2, msg="check2") + self.assertTrue(check3, msg="check3") + + + def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) From e7ef7858026e8c579c3d2705e95be4cfe9f6a3f6 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:11:00 +0000 Subject: [PATCH 084/150] add ProfileStratification.calc_pea() and .quick_plot() --- coast/diagnostics/profile_stratification.py | 283 +++----------------- 1 file changed, 34 insertions(+), 249 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 7689ea88..e62f47f2 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -3,6 +3,7 @@ import numpy as np import xarray as xr import copy +from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug @@ -37,277 +38,64 @@ def __init__(self, profile: xr.Dataset): # Define the dimensional sizes as constants self.nid = profile.dataset.dims["id_dim"] - self.nz = gridded_t.dataset.dims["z_dim"] + self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def construct_pycnocline_vars(self, gridded_t: Gridded, gridded_w: Gridded, strat_thres=-0.01): - """ - Computes depth moments of stratification. Under the assumption that the - stratification approximately represents a two-layer fluid, these can be - interpreted as pycnocline depths and thicknesses. They are computed on - w-points. - - 1st moment of stratification: \int z.strat dz / \int strat dz - In the limit of a two layer fluid this is equivalent to the - pycnocline depth, or z_d (units: metres) - - 2nd moment of stratification: \sqrt{\int (z-z_d)^2 strat dz / \int strat dz} - where strat = d(density)/dz - In the limit of a two layer fluid this is equivatlent to the - pycnocline thickness, or z_t (units: metres) - - Parameters - ---------- - gridded_t : Gridded - Gridded object on t-points. - gridded_w : Gridded, optional - Gridded object on w-points. - strat_thres: float - Optional - limiting stratification (rho_dz < 0) to trigger masking of mixed waters - - Output - ------ - self.dataset.strat_1st_mom - (t,y,x) pycnocline depth - self.dataset.strat_2nd_mom - (t,y,x) pycnocline thickness - self.dataset.strat_1st_mom_masked - (t,y,x) pycnocline depth, masked - in weakly stratified water beyond strat_thres - self.dataset.strat_2nd_mom_masked - (t,y,x) pycnocline thickness, masked - in weakly stratified water beyond strat_thres - self.dataset.mask - (t,y,x) [1/0] stratified/unstrafied - water column according to strat_thres not being met anywhere - in the column - - Returns - ------- - None. - - Example Usage - ------------- - # load some example data - dn_files = "./example_files/" - dn_fig = 'unit_testing/figures/' - fn_nemo_grid_t_dat = 'nemo_data_T_grid_Aug2015.nc' - fn_nemo_dom = 'coast_example_nemo_domain.nc' - gridded_t = coast.Gridded(dn_files + fn_nemo_grid_t_dat, - dn_files + fn_nemo_dom, grid_ref='t-grid') - # create an empty w-grid object, to store stratification - gridded_w = coast.Gridded( fn_domain = dn_files + fn_nemo_dom, - grid_ref='w-grid') - - # initialise GriddedStratification object - strat = coast.GriddedStratification(gridded_t, gridded_w) - # Construct pycnocline variables: depth and thickness - strat.construct_pycnocline_vars( gridded_t, gridded_w ) - # Plot pycnocline depth and thickness - strat.quickplot() - - """ - - debug(f"Constructing pycnocline variables for {get_slug(self)}") - # Construct in-situ density if not already done - if not hasattr(gridded_t.dataset, "density"): - gridded_t.construct_density(eos="EOS10") - - # Construct stratification if not already done. t-pts --> w-pts - if not hasattr(gridded_w.dataset, "rho_dz"): - gridded_w = gridded_t.differentiate("density", dim="z_dim", out_var_str="rho_dz", out_obj=gridded_w) - - # Define the spatial dimensional size and check the dataset and domain arrays are the same size in - # z_dim, ydim, xdim - nt = gridded_t.dataset.dims["t_dim"] - # nz = gridded_t.dataset.dims['z_dim'] - ny = gridded_t.dataset.dims["y_dim"] - nx = gridded_t.dataset.dims["x_dim"] - - # Create a mask for weakly stratified waters - # Preprocess stratification - strat = copy.copy(gridded_w.dataset.rho_dz) # (t_dim, z_dim, ydim, xdim). w-pts. - # Ensure surface value is 0 - strat[:, 0, :, :] = 0 - # Ensure bed value is 0 - strat[:, -1, :, :] = 0 - # mask out the Nan values - strat = strat.where(~np.isnan(gridded_w.dataset.rho_dz), drop=False) - # create mask with a stratification threshold - strat_m = gridded_w.dataset.latitude * 0 + 1 # create a stratification mask: [1/0] = strat/un-strat - strat_m = strat_m.where(strat.min(dim="z_dim").squeeze() < strat_thres, 0, drop=False) - strat_m = strat_m.transpose("t_dim", "y_dim", "x_dim", transpose_coords=True) - - # Compute statification variables - # initialise pycnocline variables - pycnocline_depth = np.zeros((nt, ny, nx)) # pycnocline depth - zt = np.zeros((nt, ny, nx)) # pycnocline thickness - - # Construct intermediate variables - # Broadcast to fill out missing (time) dimensions in grid data - _, depth_0_4d = xr.broadcast(strat, gridded_w.dataset.depth_0) - _, e3_0_4d = xr.broadcast(strat, gridded_w.dataset.e3_0.squeeze()) - - # integrate strat over depth - intN2 = (strat * e3_0_4d).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - # integrate (depth * strat) over depth - intzN2 = (strat * e3_0_4d * depth_0_4d).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - - # compute pycnocline depth - pycnocline_depth = intzN2 / intN2 # pycnocline depth - - # compute pycnocline thickness - intz2N2 = (np.square(depth_0_4d - pycnocline_depth) * e3_0_4d * strat).sum( - dim="z_dim", skipna=True - ) # TODO Can someone sciencey give me the proper name for this? - zt = np.sqrt(intz2N2 / intN2) # pycnocline thickness - - # Define xarray attributes - coords = { - "time": ("t_dim", gridded_t.dataset.time.values), - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - } - dims = ["t_dim", "y_dim", "x_dim"] - - # Save a xarray objects - self.dataset["strat_2nd_mom"] = xr.DataArray(zt, coords=coords, dims=dims) - self.dataset.strat_2nd_mom.attrs["units"] = "m" - self.dataset.strat_2nd_mom.attrs["standard_name"] = "pycnocline thickness" - self.dataset.strat_2nd_mom.attrs["long_name"] = "Second depth moment of stratification" - - self.dataset["strat_1st_mom"] = xr.DataArray(pycnocline_depth, coords=coords, dims=dims) - self.dataset.strat_1st_mom.attrs["units"] = "m" - self.dataset.strat_1st_mom.attrs["standard_name"] = "pycnocline depth" - self.dataset.strat_1st_mom.attrs["long_name"] = "First depth moment of stratification" - - # Mask pycnocline variables in weak stratification - zd_m = pycnocline_depth.where(strat_m > 0) - zt_m = zt.where(strat_m > 0) - - self.dataset["mask"] = xr.DataArray(strat_m, coords=coords, dims=dims) - - self.dataset["strat_2nd_mom_masked"] = xr.DataArray(zt_m, coords=coords, dims=dims) - self.dataset.strat_2nd_mom_masked.attrs["units"] = "m" - self.dataset.strat_2nd_mom_masked.attrs["standard_name"] = "masked pycnocline thickness" - self.dataset.strat_2nd_mom_masked.attrs[ - "long_name" - ] = "Second depth moment of stratification, masked in weak stratification" - - self.dataset["strat_1st_mom_masked"] = xr.DataArray(zd_m, coords=coords, dims=dims) - self.dataset.strat_1st_mom_masked.attrs["units"] = "m" - self.dataset.strat_1st_mom_masked.attrs["standard_name"] = "masked pycnocline depth" - self.dataset.strat_1st_mom_masked.attrs[ - "long_name" - ] = "First depth moment of stratification, masked in weak stratification" - - # Inherit horizontal grid information from gridded_w - self.dataset["e1"] = xr.DataArray( - gridded_w.dataset.e1, - coords={ - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - }, - dims=["y_dim", "x_dim"], - ) - self.dataset["e2"] = xr.DataArray( - gridded_w.dataset.e2, - coords={ - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), - }, - dims=["y_dim", "x_dim"], - ) - def calc_pea(self, profile: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly - UPDATE THE DOCSTR - - The density and depth averaged density can be supplied within gridded_t as "density" and + The density and depth averaged density can be supplied within profile as "density" and "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. "density_bar" is calculated using depth averages of temperature and salinity. - Example Usage: PEA in upper 200m - -------------------------------- - # load some example data. E.g. - root = "~/work/coast/" - dn_files = root + "./example_files/" - fn_nemo_grid_t_dat = dn_files + "nemo_data_T_grid_Aug2015.nc" - fn_nemo_dom = dn_files + "coast_example_nemo_domain.nc" - config_t = root + "./config/example_nemo_grid_t.json" - dn_fig = 'unit_testing/figures/' - gridded_t = coast.Gridded(fn_nemo_grid_t_dat, fn_nemo_dom, config=config_t) - Zd_mask,kmax,Ikmax=gridded_t.calculate_vertical_mask(200.) - strat=coast.GriddedStratification(gridded_t) - strat.calc_pea(gridded_t,Zd_mask) - strat.quick_plot('PEA') + Writes self.dataset.PEA """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach gravity = 9.81 # Define grid spacing, dz. Required for depth integral - """ - The thickness, dz, for integrals on t-points, should be the separation - between w-point depths. - DZ[:, I] = Zw[:, I] - Zw[:, I + 1] - = 0.5 * ( Zt[:, I-1] - Zt[:, I+1] ) - where Zw[:, I + 1] = 0.5 * (Zt[:, I] + Zt[:, I + 1]) - for I = 2:end-1 - DZ[:, 0] = 0.5 * ( Zt[:, 0] + Zt[:, 1] ) - """ - - # Compute dz on w-pts profile.calculate_vertical_spacing() dz = profile.dataset.dz - # Z=gridded_t.dataset.variables['depth_0'].values - # DZ=gridded_t.dataset.variables['e3_0'].values*Zd_mask - - # _, dz_4d = xr.broadcast(profile.dataset.salinity, profile.dataset.e3_0.squeeze() * Zd_mask) - # height = profile.dataset.depth * Zd_mask # water depth or Zmax , - # height = dz_4d.sum(dim="z_dim", skipna=True) # water depth or Zmax , - # H=xr.broadcast(gridded_t.dataset.salinity,H)[0] - # nt=gridded_t.dataset.dims['t_dim'] + # Depth, relabel for convenience + depth_t = profile.dataset.depth # Construct a mask of zeros below threshold, floats above depth of Zmax threshold. # Floats are in the range (0,1] and represent the fractional proximity to Zmax. # Used for scaling layer thickness, which would then sum to Zmax. - Zd_mask, kmax = profile.calculate_vertical_mask(profile.dataset.depth, Zmax) + Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) - # Height is depth_t above Zmax. Height is Zmax for the last level above Zmax. + # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) - rho = profile.dataset.variables["density"].values # density - rho[np.isnan(rho)] = 0 + rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - PEA = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / height - #%% - # return PEA + pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / Zmax + coords = { - "time": ("t_dim", gridded_t.dataset.time.values), - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + "time": ("id_dim", profile.dataset.time.values), + "latitude": (("id_dim"), profile.dataset.latitude.values), + "longitude": (("id_dim"), profile.dataset.longitude.values), } - dims = ["t_dim", "y_dim", "x_dim"] + dims = ["id_dim"] attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} - self.dataset["PEA"] = xr.DataArray(PEA, coords=coords, dims=dims, attrs=attributes) + self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes) def quick_plot(self, var: xr.DataArray = None): """ - - Map plot for pycnocline depth and thickness variables. + Map plot for potential energy anomaly. Parameters ---------- var : xr.DataArray, optional - Pass variable to plot. The default is None. In which case both - strat_1st_mom and strat_2nd_mom are plotted. + Pass variable to plot. The default is None. In which case + potential energy anomaly is plotted. Returns ------- @@ -315,38 +103,35 @@ def quick_plot(self, var: xr.DataArray = None): Example Usage ------------- - strat.quick_plot( 'strat_1st_mom_masked' ) - + For a Profile object, profile + pa = coast.ProfileStratification(profile) + pa.calc_pea(profile, 200) + pa.quick_plot( 'pea' ) """ debug(f"Generating quick plot for {get_slug(self)}") if var is None: - var_lst = [self.dataset.strat_1st_mom_masked, self.dataset.strat_2nd_mom_masked] + var_lst = [self.dataset.pea] else: var_lst = [self.dataset[var]] fig = None ax = None for var in var_lst: - fig = plt.figure(figsize=(10, 10)) - ax = fig.gca() - plt.pcolormesh(self.dataset.longitude.squeeze(), self.dataset.latitude.squeeze(), var.isel(t_dim=0)) - # var.mean(dim = 't_dim') ) - # plt.contourf( self.dataset.longitude.squeeze(), - # self.dataset.latitude.squeeze(), - # var.mean(dim = 't_dim'), levels=(0,10,20,30,40) ) + title_str = ( - self.dataset.time[0].dt.strftime("%d %b %Y: ").values - + var.attrs["standard_name"] + var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" ) - plt.title(title_str) - plt.xlabel("longitude") - plt.ylabel("latitude") - plt.clim([0, 50]) - plt.colorbar() - plt.show() + + fig,ax = geo_scatter( + self.dataset.longitude, + self.dataset.latitude, + self.dataset[var], + title=title_str, + ) + return fig, ax From 565dff8e1e54215ff604a49eb57dd45ea3da917e Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:41:09 +0000 Subject: [PATCH 085/150] correct conflict --- ...py => test_gridded_diagnostics_methods.py} | 26 +++++++++++++++++-- 1 file changed, 24 insertions(+), 2 deletions(-) rename unit_testing/{test_diagnostic_methods.py => test_gridded_diagnostics_methods.py} (89%) diff --git a/unit_testing/test_diagnostic_methods.py b/unit_testing/test_gridded_diagnostics_methods.py similarity index 89% rename from unit_testing/test_diagnostic_methods.py rename to unit_testing/test_gridded_diagnostics_methods.py index bb7fc9f4..b6a25e36 100644 --- a/unit_testing/test_diagnostic_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -13,7 +13,7 @@ import unit_test_files as files -class test_diagnostic_methods(unittest.TestCase): +class test_gridded_diagnostics_methods(unittest.TestCase): def test_compute_vertical_spatial_derivative(self): nemo_t = coast.Gridded( fn_data=files.fn_nemo_grid_t_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid @@ -127,7 +127,7 @@ def test_construct_pycnocline_depth_and_thickness(self): self.assertTrue(check4, msg=log_str) self.assertTrue(check5, msg=log_str) - with self.subTest("Plot pycnocline depth"): + with self.subTest("Test quick_plot pycnocline depth"): fig, ax = strat.quick_plot("strat_1st_mom_masked") fig.tight_layout() fig.savefig(files.dn_fig + "strat_1st_mom.png") @@ -172,3 +172,25 @@ def test_circulation(self): fig.tight_layout() fig.savefig(files.dn_fig + "surface_circulation.png") plt.close("all") + + def test_calc_pea(self): + + nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) + + # Compute a vertical max to exclude depths below 200m + Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.) + + # Initiate a stratification diagnostics object + strat = coast.GriddedStratification(nemo_t) + + # calculate PEA for unmasked depths + strat.calc_pea(nemo_t, Zd_mask) + # Check the calculations are as expected + check1 = np.isclose(strat.dataset.PEA.mean().item(), 124.5029568214227) + self.assertTrue(check1, msg="check1") + + with self.subTest("Test quick_plot()"): + fig, ax = strat.quick_plot('PEA') + fig.tight_layout() + fig.savefig(files.dn_fig + "gridded_pea.png") + plt.close("all") From 47c3d61374d7040680d9127859a27867641ad475 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:13:02 +0000 Subject: [PATCH 086/150] add test for ProfileStratification.calc_pea() and .quick_plot() --- .../test_profile_stratification_methods.py | 33 +++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 unit_testing/test_profile_stratification_methods.py diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py new file mode 100644 index 00000000..615dda1d --- /dev/null +++ b/unit_testing/test_profile_stratification_methods.py @@ -0,0 +1,33 @@ +""" + +""" + +# IMPORT modules. Must have unittest, and probably coast. +import coast +import unittest +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt +import unit_test_files as files +import datetime + + +class test_profile_stratification_methods(unittest.TestCase): + + def test_calculate_pea(self): + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) + profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() + + pa = coast.ProfileStratification(profile) + Zmax = 200 # metres + pa.calc_pea(profile, Zmax) + + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 153.0590043361475) + self.assertTrue(check1, "check1") + + with self.subTest("Test quick_plot()"): + fig, ax = pa.quick_plot('PEA') + fig.tight_layout() + fig.savefig(files.dn_fig + "profile_pea.png") + plt.close("all") \ No newline at end of file From 2837669e8909ca30e6e33fa2fcd047e0ebeeda62 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:14:14 +0000 Subject: [PATCH 087/150] update unit_test framework with new, aligned, profile and gridded tests --- unit_testing/unit_test.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/unit_testing/unit_test.py b/unit_testing/unit_test.py index 21d51272..4544c62b 100644 --- a/unit_testing/unit_test.py +++ b/unit_testing/unit_test.py @@ -18,7 +18,7 @@ from test_general_utils import test_general_utils from test_crps_util import test_crps_util from test_xesmf_convert import test_xesmf_convert -from test_diagnostic_methods import test_diagnostic_methods +from test_gridded_diagnostics_methods import test_gridded_diagnostics_methods from test_transect_methods import test_transect_methods from test_object_manipulation import test_object_manipulation from test_altimetry_methods import test_altimetry_methods @@ -26,6 +26,7 @@ from test_isobath_contour_methods import test_contour_t_methods, test_contour_f_methods from test_eof_methods import test_eof_methods from test_profile_methods import test_profile_methods +from test_profile_stratification_methods import test_profile_stratification_methods from test_plot_utilities import test_plot_utilities from test_stats_utilities import test_stats_utilities from test_maskmaker_methods import test_maskmaker_methods @@ -43,7 +44,7 @@ test_crps_util, test_xesmf_convert, test_gridded_harmonics, - test_diagnostic_methods, + test_gridded_diagnostics_methods, test_transect_methods, test_object_manipulation, test_altimetry_methods, @@ -53,6 +54,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, + test_profile_stratification_methods, test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From 39e9240f87f3b57cb08c98761082db4999a4da8c Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:41:55 +0000 Subject: [PATCH 088/150] correct conflict --- coast/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/coast/__init__.py b/coast/__init__.py index 942b49ca..09403549 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -8,6 +8,8 @@ from .diagnostics.climatology import Climatology from ._utils import logging_util, general_utils, plot_util, crps_util, seasons from .diagnostics.gridded_monthly_hydrographic_climatology import GriddedMonthlyHydrographicClimatology +from .diagnostics.profile_hydrographic_analysis import ProfileHydrography +from .diagnostics.profile_stratification import ProfileStratification from .data.index import Indexed from .data.profile import Profile from .diagnostics.profile_analysis import ProfileAnalysis From bdcebc3ab358b5e5679cfd298a1360b2325570ed Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 17:22:55 +0000 Subject: [PATCH 089/150] update unit_test contents with new Gridded, GriddedStratification and ProfileStratification tests --- unit_testing/generate_unit_test_contents.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/unit_testing/generate_unit_test_contents.py b/unit_testing/generate_unit_test_contents.py index 77868aaa..a019e9cc 100644 --- a/unit_testing/generate_unit_test_contents.py +++ b/unit_testing/generate_unit_test_contents.py @@ -22,7 +22,8 @@ from test_gridded_harmonics import test_gridded_harmonics from test_general_utils import test_general_utils from test_xesmf_convert import test_xesmf_convert -from test_diagnostic_methods import test_diagnostic_methods +from test_gridded_diagnostics_methods import test_gridded_diagnostics_methods +from test_profile_stratification_methods import test_profile_stratification_methods from test_transect_methods import test_transect_methods from test_object_manipulation import test_object_manipulation from test_altimetry_methods import test_altimetry_methods @@ -49,7 +50,7 @@ test_general_utils, test_gridded_harmonics, test_xesmf_convert, - test_diagnostic_methods, + test_gridded_diagnostics_methods, test_transect_methods, test_object_manipulation, test_altimetry_methods, @@ -58,6 +59,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, + test_profile_stratification_methods test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From 58b6a454e377dfc092fb931ccd3cf4c92b5ebfce Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 18 Nov 2022 12:11:27 +0000 Subject: [PATCH 090/150] Apply Black formatting to Python code. --- coast/data/profile.py | 2 +- unit_testing/test_profile_methods.py | 21 ++++++++------------- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 662506e8..d9dfd0b9 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -897,7 +897,7 @@ def construct_density( except AttributeError as err: error(err) - def calculate_vertical_mask(self, Zmax = 200): + def calculate_vertical_mask(self, Zmax=200): """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 5c03c096..fa427a59 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -54,27 +54,22 @@ def test_compute_density(self): profile.construct_density() - check1 = np.allclose(profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), - 4248551.199925806, - ) + check1 = np.allclose( + profile.dataset.density.sum(dim=["id_dim", "z_dim"]).item(), + 4248551.199925806, + ) # Density depth mean T and S limited to 200m Zmax = 200 # m Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Zd_mask=Zd_mask) - check2 = np.allclose( - profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021 - ) + check2 = np.allclose(profile.dataset.density_bar.mean(dim=["id_dim", "z_dim"]).item(), 1023.211151279021) # Temperature component of density (ie from depth mean Sal). full depth profile.construct_density(rhobar=True, pot_dens=True, CT_AS=True, Tbar=False) - check3 = np.allclose( - profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557 - ) + check3 = np.allclose(profile.dataset.density_T.mean(dim=["id_dim", "z_dim"]).item(), 1026.749192955557) self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") self.assertTrue(check3, msg="check3") - - def test_compare_processed_profile_with_model(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) @@ -197,9 +192,9 @@ def test_calculate_vertical_mask(self): # Reassign values to depth, within a full profile object, to make it transparent arr = np.array([[1, 2, 3, np.nan], [15, 20, 25, 30], [4, 5, 15, np.nan]]) depth = xr.DataArray(arr, dims=["id_dim", "z_dim"]) - profile.dataset['depth'] = depth + profile.dataset["depth"] = depth - mask, kmax = profile.calculate_vertical_mask( 21) + mask, kmax = profile.calculate_vertical_mask(21) mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() From 84e186e7f85014c87f1020086b0fe2eae08f9def Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Fri, 18 Nov 2022 12:12:09 +0000 Subject: [PATCH 091/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index 283a4217..c80b1019 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -77,7 +77,8 @@ 13. test_profile_methods a. calculate_vertical_mask b. compare_processed_profile_with_model - c. load_process_and_compare_profile_data + c. compute_density + d. load_process_and_compare_profile_data 14. test_plot_utilities a. determine_clim_by_stdev From 83bba4ea753b7a49db946fb65b70a4008e758029 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:16:17 +0000 Subject: [PATCH 092/150] fix nan_helper tests --- coast/_utils/general_utils.py | 2 +- unit_testing/test_general_utils.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 62bc9411..98efbe0f 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -383,6 +383,6 @@ def fill_holes_1d(y): Returns: array([2., 2., 2., 3., 4., 5., 6.]) """ - nans, x = general_utils.nan_helper(y) # location interior nans + nans, x = nan_helper(y) # location interior nans y[nans] = np.interp(x(nans), x(~nans), y[~nans]) # interpolate and extrapolate return y diff --git a/unit_testing/test_general_utils.py b/unit_testing/test_general_utils.py index 2a607059..26d32c71 100644 --- a/unit_testing/test_general_utils.py +++ b/unit_testing/test_general_utils.py @@ -67,8 +67,8 @@ def test_fill_holes_1d(self): input_xr = xr.DataArray(input) target = np.array([2.0, 2.0, 2.0, 3.0, 4.0, 5.0, 6.0]) - check1 = all(fill_holes_1d(input) == target) - check2 = all(fill_holes_1d(input_xr).values == target) + check1 = all(general_utils.fill_holes_1d(input) == target) + check2 = all(general_utils.fill_holes_1d(input_xr).values == target) self.assertTrue(check1, msg="check1") self.assertTrue(check2, msg="check2") From a141a684c5517d2552d1604ee6dfb24a8a000b03 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:17:51 +0000 Subject: [PATCH 093/150] fix test for calculate vertical mask --- unit_testing/test_profile_methods.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index fa427a59..b2d8985c 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -185,8 +185,8 @@ def test_compare_processed_profile_with_model(self): def test_calculate_vertical_mask(self): # load example profile data - profile = coast.Profile(config=fn_profile_config) - profile.read_en4(fn_profile) + profile = coast.Profile(config=files.fn_profile_config) + profile.read_en4(files.fn_profile) profile.dataset = profile.dataset.isel(id_dim=slice(0, 3)).isel(z_dim=slice(0, 4)) # Reassign values to depth, within a full profile object, to make it transparent From 932d4e4371750ca25b60d4e81a0c0c8e27b380f6 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:36:04 +0000 Subject: [PATCH 094/150] fix quick_plot --- coast/diagnostics/profile_stratification.py | 4 ++-- unit_testing/test_profile_stratification_methods.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e62f47f2..78880498 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -49,7 +49,7 @@ def calc_pea(self, profile: xr.Dataset, Zmax): "density_bar" DataArrays, respectively. If they are not supplied they will be calculated. "density_bar" is calculated using depth averages of temperature and salinity. - Writes self.dataset.PEA + Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach gravity = 9.81 @@ -130,7 +130,7 @@ def quick_plot(self, var: xr.DataArray = None): fig,ax = geo_scatter( self.dataset.longitude, self.dataset.latitude, - self.dataset[var], + var, title=title_str, ) diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 615dda1d..8f0f20b3 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -23,11 +23,11 @@ def test_calculate_pea(self): Zmax = 200 # metres pa.calc_pea(profile, Zmax) - check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 153.0590043361475) + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): - fig, ax = pa.quick_plot('PEA') + fig, ax = pa.quick_plot('pea') fig.tight_layout() fig.savefig(files.dn_fig + "profile_pea.png") plt.close("all") \ No newline at end of file From de847e9402b598f6a040f3e83febfff2eb878965 Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:38:59 +0000 Subject: [PATCH 095/150] missing comma --- unit_testing/generate_unit_test_contents.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/unit_testing/generate_unit_test_contents.py b/unit_testing/generate_unit_test_contents.py index a019e9cc..70dc570c 100644 --- a/unit_testing/generate_unit_test_contents.py +++ b/unit_testing/generate_unit_test_contents.py @@ -59,7 +59,7 @@ test_contour_f_methods, test_contour_t_methods, test_profile_methods, - test_profile_stratification_methods + test_profile_stratification_methods, test_plot_utilities, test_stats_utilities, test_maskmaker_methods, From 7f01c243192015669436ccc8254999628187811b Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 18 Nov 2022 22:39:28 +0000 Subject: [PATCH 096/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 17 ++++++----------- .../test_gridded_diagnostics_methods.py | 4 ++-- .../test_profile_stratification_methods.py | 7 +++---- 3 files changed, 11 insertions(+), 17 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 78880498..07876e9f 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -120,18 +120,13 @@ def quick_plot(self, var: xr.DataArray = None): ax = None for var in var_lst: - title_str = ( - var.attrs["standard_name"] - + " (" - + var.attrs["units"] - + ")" - ) + title_str = var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" - fig,ax = geo_scatter( - self.dataset.longitude, - self.dataset.latitude, - var, - title=title_str, + fig, ax = geo_scatter( + self.dataset.longitude, + self.dataset.latitude, + var, + title=title_str, ) return fig, ax diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index b6a25e36..53ea159d 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -178,7 +178,7 @@ def test_calc_pea(self): nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m - Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.) + Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.0) # Initiate a stratification diagnostics object strat = coast.GriddedStratification(nemo_t) @@ -190,7 +190,7 @@ def test_calc_pea(self): self.assertTrue(check1, msg="check1") with self.subTest("Test quick_plot()"): - fig, ax = strat.quick_plot('PEA') + fig, ax = strat.quick_plot("PEA") fig.tight_layout() fig.savefig(files.dn_fig + "gridded_pea.png") plt.close("all") diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 8f0f20b3..2fd14fcb 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -13,21 +13,20 @@ class test_profile_stratification_methods(unittest.TestCase): - def test_calculate_pea(self): profile = coast.Profile(config=files.fn_profile_config) profile.read_en4(files.fn_profile) profile.dataset = profile.dataset.isel(id_dim=np.arange(0, profile.dataset.dims["id_dim"], 10)).load() pa = coast.ProfileStratification(profile) - Zmax = 200 # metres + Zmax = 200 # metres pa.calc_pea(profile, Zmax) check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): - fig, ax = pa.quick_plot('pea') + fig, ax = pa.quick_plot("pea") fig.tight_layout() fig.savefig(files.dn_fig + "profile_pea.png") - plt.close("all") \ No newline at end of file + plt.close("all") From 1140dd2d1bdb6bc69d781db14e0f45eb1869df8d Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:42:48 +0000 Subject: [PATCH 097/150] correct conflict --- unit_testing/unit_test_contents.txt | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index c80b1019..0d756338 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -30,9 +30,10 @@ 5. test_diagnostic_methods a. circulation - b. compute_vertical_spatial_derivative - c. construct_density - d. construct_pycnocline_depth_and_thickness + b. calc_pea + c. compute_vertical_spatial_derivative + d. construct_density + e. construct_pycnocline_depth_and_thickness 6. test_transect_methods a. calculate_transport_velocity_and_depth @@ -80,29 +81,32 @@ c. compute_density d. load_process_and_compare_profile_data -14. test_plot_utilities +14. test_profile_stratification_methods + a. calculate_pea + +15. test_plot_utilities a. determine_clim_by_stdev b. determine_colorbar_extension c. geo_axes d. scatter_with_fit -15. test_stats_utilities +16. test_stats_utilities a. find_maxima -16. test_maskmaker_methods +17. test_maskmaker_methods a. fill_polygon_by_index b. fill_polygon_by_lonlat c. make_mask_dataset_and_quick_plot d. make_region_from_vertices -17. test_climatology +18. test_climatology a. monthly_and_seasonal_climatology -18. test_wod_read_data +19. test_wod_read_data a. load_wod b. reshape_wod -19. test_bgc_gridded_initialisation +20. test_bgc_gridded_initialisation a. gridded_load_bgc_data b. gridded_load_bgc_data_and_domain c. gridded_load_bgc_dimensions_correctly_renamed From b08f1a25d0f348b7795355550ec665a6dbb5df8e Mon Sep 17 00:00:00 2001 From: jpolton Date: Fri, 18 Nov 2022 22:57:24 +0000 Subject: [PATCH 098/150] improve docstr --- coast/diagnostics/profile_stratification.py | 17 +++-------------- 1 file changed, 3 insertions(+), 14 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 07876e9f..f558e589 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -12,23 +12,12 @@ class ProfileStratification(Profile): # TODO All abstract methods should be imp Object for handling and storing necessary information, methods and outputs for calculation of stratification diagnostics. - - UPDATE THE FOLLOWING + Related to GriddedStratification class Parameters ---------- - gridded_t : xr.Dataset - Gridded object on t-points. - gridded_w : xr.Dataset, optional - Gridded object on w-points. - - Example basic usage: - ------------------- - # Create Internal tide diagnostics object - strat_obj = GriddedStratification(gridded_t, gridded_w) # For Gridded objects on t and w-pts - strat_obj.construct_pycnocline_vars( gridded_t, gridded_w ) - # Make maps of pycnocline thickness and depth - strat_obj.quick_plot() + profile : xr.Dataset + Profile object on assumed on t-points. """ def __init__(self, profile: xr.Dataset): From 4035b9fb35a01165470ba65423216b543f26841e Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 11:22:04 +0000 Subject: [PATCH 099/150] calculate_vertical_spacing(). Remove redundant if-not xarray condition --- coast/data/profile.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index d9dfd0b9..73bcb8d4 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -708,21 +708,8 @@ def calculate_vertical_spacing(self): 0.5 * (depth_t + depth_t.shift(z_dim=-1)), 0.5 * (depth_t.shift(z_dim=-1) - depth_t.shift(z_dim=+1)), # .fillna(0.) ) - attributes = {"units": "m", "standard name": "centre difference thickness"} - if hasattr(self.dataset.dz, "coords"): # xarray object. Just add title and units - self.dataset.dz.attrs = attributes - - else: # not an xarray object - coords = { - "time": (("id_dim"), self.dataset.time.values), - "latitude": (("id_dim"), self.dataset.latitude.values), - "longitude": (("id_dim"), self.dataset.longitude.values), - } - dims = ["z_dim", "id_dim"] - - dz = np.squeeze(dz) - self.dataset["dz"] = xr.DataArray(dz, coords=coords, dims=dims, attrs=attributes) + self.dataset.dz.attrs = attributes def construct_density( self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True From f5150fdf83a6e62932925d25d345dae157e542ba Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 12:10:51 +0000 Subject: [PATCH 100/150] remove debugging print statements --- coast/data/profile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 73bcb8d4..5f305a31 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -773,8 +773,8 @@ def construct_density( density = np.ma.zeros(shape_ds) - print(f"shape sal:{np.shape(sal)}") - print(f"shape rho:{np.shape(density)}") + #print(f"shape sal:{np.shape(sal)}") + #print(f"shape rho:{np.shape(density)}") s_levels = self.dataset.depth.to_masked_array() if np.shape(s_levels) != shape_ds: From 17602f5c13fc4cc53f2c74a3a0f42761c5279c5e Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:46:20 +0000 Subject: [PATCH 101/150] correct conflict --- .../profile/potential_energy_tutorial.ipynb | 331 ++++++++++++++++++ 1 file changed, 331 insertions(+) create mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb new file mode 100644 index 00000000..0cdb9a3a --- /dev/null +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "5eca7994-6fa1-44e1-b95c-fc8a0fecf7bd", + "metadata": {}, + "source": [ + "A demonstration to calculate the Potential Energy Anomaly for Profile data.\n" + ] + }, + { + "cell_type": "markdown", + "id": "14277e0d-4dbc-4e0f-b3a2-6853dca66d46", + "metadata": {}, + "source": [ + "### Relevant imports and filepath configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "c4773751-3544-4ebd-a795-cfe128b70743", + "metadata": {}, + "outputs": [], + "source": [ + "import coast\n", + "import numpy as np\n", + "from os import path\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.colors as colors # colormap fiddling" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", + "metadata": {}, + "outputs": [], + "source": [ + "# set some paths\n", + "root = \"./\"\n", + "dn_files = root + \"./example_files/\"\n", + "fn_prof = path.join(dn_files, \"coast_example_en4_201008.nc\")\n", + "fn_cfg_prof = path.join(\"config\",\"example_en4_profiles.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "5d3f6987-f05d-4a54-a932-e4bbf84becb1", + "metadata": {}, + "source": [ + "### Loading data" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "7677050c-775d-4172-9561-61c3c89aa77b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "config/example_en4_profiles.json\n" + ] + } + ], + "source": [ + "# Create a Profile object and load in the data:\n", + "profile = coast.Profile(config=fn_cfg_prof)\n", + "profile.read_en4( fn_prof )" + ] + }, + { + "cell_type": "markdown", + "id": "d566249d", + "metadata": {}, + "source": [ + "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "29e0256b", + "metadata": {}, + "outputs": [], + "source": [ + "processed_profile = profile.process_en4()\n", + "profile = processed_profile" + ] + }, + { + "cell_type": "markdown", + "id": "d9093ecd", + "metadata": {}, + "source": [ + "### Inspect profile locations\n", + "Have a look inside the `profile.py` class to see what it can do. But first have a look at the spatial distribution of profiles." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "3561dd1e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "profile.plot_map()" + ] + }, + { + "cell_type": "markdown", + "id": "d3e75a6d", + "metadata": {}, + "source": [ + "### Calculates Potential Energy Anomaly\n", + "\n", + "Similar to the Gridded object, potential energy anomaly can be calculated for Profile objects. This method exists within a `ProfileStratifiction` object, which must be initialised\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "2dd5cc2f", + "metadata": {}, + "outputs": [], + "source": [ + "pa = coast.ProfileStratification(profile)" + ] + }, + { + "cell_type": "markdown", + "id": "6faf9a84", + "metadata": {}, + "source": [ + "Potential energy anomaly is calculated to a prescribed depth, Zmax:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "23d49bb0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape sal:(400, 1100)\n", + "shape rho:(400, 1100)\n", + "shape sal:(400, 1100)\n", + "shape rho:(400, 1100)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + } + ], + "source": [ + "Zmax = 200 # metres\n", + "pa.calc_pea(profile, Zmax)" + ] + }, + { + "cell_type": "markdown", + "id": "6ecf2b7b", + "metadata": {}, + "source": [ + "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", + "\n", + "``profile.calculate_vertical_spacing()``\n", + "\n", + "Then a depth mask is calculated to exclude depth below the Zmax threshold.\n", + "(The last depth level is a float between 0,1 denoting how much of the next spacing below is deeper than Zmax - To facilitate the integral to Zmax)\n", + "\n", + "``Zd_mask, kmax = profile.calculate_vertical_mask(Zmax)``\n", + "\n", + "Then densities (depth varying and depth averaged) are computed from the temperature and salinity fields\n", + "``profile.construct_density()``\n", + "\n", + "Finally the depth integrals are calculated.\n" + ] + }, + { + "cell_type": "markdown", + "id": "8f897042-3697-4ddd-a812-04572500f0ec", + "metadata": {}, + "source": [ + "## Make a plot\n", + "\n", + "\n", + "THERE IS OBVIOUSLY AN ISSUE HERE WITH NEGATIVE PEA VALUES AND SMALL POSITIVE VALUES..." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b8383443", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = pa.quick_plot(\"pea\")\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "9c56bf79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter( pa.dataset.longitude,\n", + " pa.dataset.latitude,\n", + " s=4, c=pa.dataset.pea)\n", + "plt.clim([0,10])\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8fbeb641", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70696b95", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From f98f679c8b1c979ae49f83224cf13e7cf8a75cfc Mon Sep 17 00:00:00 2001 From: BlackBot Date: Mon, 21 Nov 2022 12:11:17 +0000 Subject: [PATCH 102/150] Apply Black formatting to Python code. --- coast/data/profile.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 5f305a31..736017f2 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -773,8 +773,8 @@ def construct_density( density = np.ma.zeros(shape_ds) - #print(f"shape sal:{np.shape(sal)}") - #print(f"shape rho:{np.shape(density)}") + # print(f"shape sal:{np.shape(sal)}") + # print(f"shape rho:{np.shape(density)}") s_levels = self.dataset.depth.to_masked_array() if np.shape(s_levels) != shape_ds: From e49ff7222df7aa6eaffb29322587691dcefac4f5 Mon Sep 17 00:00:00 2001 From: jpolton Date: Mon, 21 Nov 2022 12:14:42 +0000 Subject: [PATCH 103/150] Add clean profile PEA notebook --- .../profile/potential_energy_tutorial.ipynb | 144 ++++-------------- 1 file changed, 26 insertions(+), 118 deletions(-) diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb index 0cdb9a3a..cd3bb93b 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -54,18 +54,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "config/example_en4_profiles.json\n" - ] - } - ], + "outputs": [], "source": [ "# Create a Profile object and load in the data:\n", "profile = coast.Profile(config=fn_cfg_prof)\n", @@ -74,7 +66,7 @@ }, { "cell_type": "markdown", - "id": "d566249d", + "id": "798994a1", "metadata": {}, "source": [ "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" @@ -82,8 +74,8 @@ }, { "cell_type": "code", - "execution_count": 74, - "id": "29e0256b", + "execution_count": null, + "id": "58406dca", "metadata": {}, "outputs": [], "source": [ @@ -93,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "d9093ecd", + "id": "84a15c7b", "metadata": {}, "source": [ "### Inspect profile locations\n", @@ -102,31 +94,10 @@ }, { "cell_type": "code", - "execution_count": 75, - "id": "3561dd1e", + "execution_count": null, + "id": "f5b2d233", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(
, )" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "profile.plot_map()" ] @@ -144,8 +115,8 @@ }, { "cell_type": "code", - "execution_count": 76, - "id": "2dd5cc2f", + "execution_count": null, + "id": "e70f5db2", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "6faf9a84", + "id": "3e056769", "metadata": {}, "source": [ "Potential energy anomaly is calculated to a prescribed depth, Zmax:" @@ -162,31 +133,10 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "23d49bb0", + "execution_count": null, + "id": "c49b40d3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "shape sal:(400, 1100)\n", - "shape rho:(400, 1100)\n", - "shape sal:(400, 1100)\n", - "shape rho:(400, 1100)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n", - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - } - ], + "outputs": [], "source": [ "Zmax = 200 # metres\n", "pa.calc_pea(profile, Zmax)" @@ -194,7 +144,7 @@ }, { "cell_type": "markdown", - "id": "6ecf2b7b", + "id": "74603291", "metadata": {}, "source": [ "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", @@ -225,31 +175,10 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "b8383443", + "execution_count": null, + "id": "a696835b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: divide by zero encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n", - "/Users/jeff/opt/anaconda3/envs/coast/lib/python3.8/site-packages/dask/core.py:119: RuntimeWarning: invalid value encountered in true_divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = pa.quick_plot(\"pea\")\n", "fig.tight_layout()" @@ -257,31 +186,10 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "9c56bf79", + "execution_count": null, + "id": "bb540223", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.scatter( pa.dataset.longitude,\n", " pa.dataset.latitude,\n", @@ -293,7 +201,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8fbeb641", + "id": "85229256", "metadata": {}, "outputs": [], "source": [] @@ -301,7 +209,7 @@ { "cell_type": "code", "execution_count": null, - "id": "70696b95", + "id": "a37a8291", "metadata": {}, "outputs": [], "source": [] From bde5537e539421cd116400a52253dfc725877e97 Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Fri, 25 Nov 2022 16:57:27 +0000 Subject: [PATCH 104/150] profile.py Zd_max changed to use w-levels, order of variables in construct density chnaged to i_dim, z_dim profile_stratification.py added function to clean profile data, starting with filling holes. More to come. --- coast/data/profile.py | 53 +++++++++++------- coast/diagnostics/profile_stratification.py | 60 +++++++++++++++++++-- 2 files changed, 90 insertions(+), 23 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 736017f2..e121e235 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -753,14 +753,18 @@ def construct_density( """ debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') + try: + if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") try: shape_ds = ( - self.dataset.z_dim.size, self.dataset.id_dim.size, + self.dataset.z_dim.size, +#jth self.dataset.z_dim.size, +# self.dataset.id_dim.size, ) sal = self.dataset.practical_salinity.to_masked_array() temp = self.dataset.potential_temperature.to_masked_array() @@ -836,23 +840,23 @@ def construct_density( temp_conservative = np.ma.masked_invalid(temp) if pot_dens and (Sbar and Tbar): # usual case pot_dens and depth averaged everything - sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP - temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP + sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP + temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) - density = np.repeat(density[np.newaxis, :], shape_ds[0], axis=0) + density = np.repeat(density[:,np.newaxis], shape_ds[1], axis=1) else: # Either insitu density or one of Tbar or Sbar False if Sbar: sal_absolute = np.repeat( - (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=0) / DP)[np.newaxis, :], - shape_ds[0], - axis=0, + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:,np.newaxis], + shape_ds[1], + axis=1, ) if Tbar: temp_conservative = np.repeat( - (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=0) / DP)[np.newaxis, :], - shape_ds[0], - axis=0, + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:,np.newaxis], + shape_ds[1], + axis=1, ) density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) @@ -871,7 +875,9 @@ def construct_density( "latitude": (("id_dim"), self.dataset.latitude.values), "longitude": (("id_dim"), self.dataset.longitude.values), } - dims = ["z_dim", "id_dim"] +# dims = ["z_dim", "id_dim"] + dims = ["id_dim", "z_dim"] + if pot_dens: attributes = {"units": "kg / m^3", "standard name": "Potential density "} @@ -885,6 +891,7 @@ def construct_density( error(err) def calculate_vertical_mask(self, Zmax=200): + """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level @@ -897,6 +904,12 @@ def calculate_vertical_mask(self, Zmax=200): """ depth_t = self.dataset.depth + ##construct a W array, zero at surface 1/2 way between T-points + + depth_w=xr.zeros_like(depth_t) + I = np.arange(depth_w.shape[1] - 1) + depth_w[:,0]=0.0 + depth_w[:, I + 1] = 0.5 * (depth_t[:, I] + depth_t[:, I + 1]) ## Contruct a mask array that is: # zeros below Zmax @@ -916,16 +929,16 @@ def calculate_vertical_mask(self, Zmax=200): # mask_arr[depth_t <= Zmax] = 1 # mask_arr[depth_t > Zmax] = 0 # mask = xr.DataArray( mask_arr, dims=["id_dim", "z_dim"]) - mask = depth_t * np.nan + mask = depth_w * np.nan - mask = xr.where(depth_t <= Zmax, 1, mask) - mask = xr.where(depth_t > Zmax, 0, mask) + mask = xr.where(depth_w <= Zmax, 1, mask) + mask = xr.where(depth_w > Zmax, 0, mask) # print(mask) # print('\n') - max_shallower_depth = (depth_t * mask).max(dim="z_dim") - min_deeper_depth = (depth_t.roll(z_dim=-1) * mask).max(dim="z_dim") + max_shallower_depth = (depth_w * mask).max(dim="z_dim") + min_deeper_depth = (depth_w.roll(z_dim=-1) * mask).max(dim="z_dim") # NB if max_shallower_depth was already deepest value in profile, then this produces the same value # I.e. # max_shallower_depth <= Zmax @@ -938,18 +951,18 @@ def calculate_vertical_mask(self, Zmax=200): # Compute fraction, the relative closeness of Zmax to max_shallower_depth from 1 to 0 (as Zmax -> min_deeper_depth) fraction = xr.where( min_deeper_depth != max_shallower_depth, - (min_deeper_depth - Zmax) / (min_deeper_depth - max_shallower_depth), + (Zmax - max_shallower_depth) / (min_deeper_depth - max_shallower_depth), 1, ) - max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) + max_shallower_depth_2d = max_shallower_depth.expand_dims(dim={"z_dim": depth_w.sizes["z_dim"]}) fraction_2d = fraction.expand_dims(dim={"z_dim": depth_t.sizes["z_dim"]}) # locate the depth index for the deepest level above Zmax - kmax = xr.where(depth_t == max_shallower_depth, 1, 0).argmax(dim="z_dim") + kmax = xr.where(depth_w == max_shallower_depth, 1, 0).argmax(dim="z_dim") # print(kmax) # replace mask values with fraction_2d at depth above Zmax) - mask = xr.where(depth_t == max_shallower_depth_2d, fraction_2d, mask) + mask = xr.where(depth_w == max_shallower_depth_2d, fraction_2d, mask) return mask, kmax diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index f558e589..e7cd47d9 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -3,6 +3,7 @@ import numpy as np import xarray as xr import copy +import coast from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug @@ -30,6 +31,56 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") + def clean_data (profile: xr.Dataset): + """ + Cleaning data for stratification metric calculations + Stage 1:... + + stage 2... + + Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value + + """ + print('Cleaning the data') + #fill holes in data + #jth is slow, there may bea more 'vector' way of doing it + n_prf = profile.dataset.id_dim.shape[0] + + tmp_clean = profile.dataset.potential_temperature.values[:,:] + sal_clean = profile.dataset.practical_salinity.values[:,:] + + + any_tmp=np.sum(~ np.isnan(tmp_clean),axis=1) != 0 + + any_sal=np.sum(~ np.isnan(sal_clean),axis=1) != 0 + + for i_prf in range(n_prf): + tmp=profile.dataset.potential_temperature.values[i_prf,:] + sal=profile.dataset.practical_salinity.values[i_prf,:] + z=profile.dataset.depth.values[i_prf,:] + if any_tmp[i_prf]: + tmp=coast.general_utils.fill_holes_1d(tmp) + tmp[np.isnan(z)] = np.nan + tmp_clean[i_prf,:]=tmp + if any_sal[i_prf]: + sal = coast.general_utils.fill_holes_1d(sal) + sal[np.isnan(z)] = np.nan + sal_clean[i_prf,:]=sal + + + coords = { + "time": ("id_dim", profile.dataset.time.values), + "latitude": (("id_dim"), profile.dataset.latitude.values), + "longitude": (("id_dim"), profile.dataset.longitude.values), + } + dims = ["id_dim","z_dim"] + profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) + profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) + + print('All nice and clean') + + return profile + def calc_pea(self, profile: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly @@ -41,7 +92,10 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach +#%% gravity = 9.81 +#Clean data This is quit slow and over writes potneital temperature and practical salinity valirables + profile = ProfileStratification.clean_data (profile) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -56,7 +110,7 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax #jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) @@ -65,8 +119,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / Zmax - + pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / (height.sum(dim="z_dim", skipna=True)) +#%% coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), From 732a4156cdff95ff24ba66e081b326a34967a2fb Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 25 Nov 2022 16:59:44 +0000 Subject: [PATCH 105/150] Apply Black formatting to Python code. --- coast/data/profile.py | 21 ++++--- coast/diagnostics/profile_stratification.py | 64 +++++++++++---------- 2 files changed, 44 insertions(+), 41 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index e121e235..7c8c6339 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -755,7 +755,7 @@ def construct_density( debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') try: - + if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") @@ -763,8 +763,8 @@ def construct_density( shape_ds = ( self.dataset.id_dim.size, self.dataset.z_dim.size, -#jth self.dataset.z_dim.size, -# self.dataset.id_dim.size, + # jth self.dataset.z_dim.size, + # self.dataset.id_dim.size, ) sal = self.dataset.practical_salinity.to_masked_array() temp = self.dataset.potential_temperature.to_masked_array() @@ -843,18 +843,18 @@ def construct_density( sal_absolute = np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP temp_conservative = np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) - density = np.repeat(density[:,np.newaxis], shape_ds[1], axis=1) + density = np.repeat(density[:, np.newaxis], shape_ds[1], axis=1) else: # Either insitu density or one of Tbar or Sbar False if Sbar: sal_absolute = np.repeat( - (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:,np.newaxis], + (np.sum(np.ma.masked_less(sal_absolute, 0) * DZ, axis=1) / DP)[:, np.newaxis], shape_ds[1], axis=1, ) if Tbar: temp_conservative = np.repeat( - (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:,np.newaxis], + (np.sum(np.ma.masked_less(temp_conservative, 0) * DZ, axis=1) / DP)[:, np.newaxis], shape_ds[1], axis=1, ) @@ -875,10 +875,9 @@ def construct_density( "latitude": (("id_dim"), self.dataset.latitude.values), "longitude": (("id_dim"), self.dataset.longitude.values), } -# dims = ["z_dim", "id_dim"] + # dims = ["z_dim", "id_dim"] dims = ["id_dim", "z_dim"] - if pot_dens: attributes = {"units": "kg / m^3", "standard name": "Potential density "} else: @@ -905,10 +904,10 @@ def calculate_vertical_mask(self, Zmax=200): depth_t = self.dataset.depth ##construct a W array, zero at surface 1/2 way between T-points - - depth_w=xr.zeros_like(depth_t) + + depth_w = xr.zeros_like(depth_t) I = np.arange(depth_w.shape[1] - 1) - depth_w[:,0]=0.0 + depth_w[:, 0] = 0.0 depth_w[:, I + 1] = 0.5 * (depth_t[:, I] + depth_t[:, I + 1]) ## Contruct a mask array that is: diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e7cd47d9..a7fa3955 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -31,53 +31,51 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data (profile: xr.Dataset): + def clean_data(profile: xr.Dataset): """ Cleaning data for stratification metric calculations Stage 1:... - + stage 2... - + Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value - + """ - print('Cleaning the data') - #fill holes in data - #jth is slow, there may bea more 'vector' way of doing it + print("Cleaning the data") + # fill holes in data + # jth is slow, there may bea more 'vector' way of doing it n_prf = profile.dataset.id_dim.shape[0] - tmp_clean = profile.dataset.potential_temperature.values[:,:] - sal_clean = profile.dataset.practical_salinity.values[:,:] - + tmp_clean = profile.dataset.potential_temperature.values[:, :] + sal_clean = profile.dataset.practical_salinity.values[:, :] + + any_tmp = np.sum(~np.isnan(tmp_clean), axis=1) != 0 + + any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 - any_tmp=np.sum(~ np.isnan(tmp_clean),axis=1) != 0 - - any_sal=np.sum(~ np.isnan(sal_clean),axis=1) != 0 - for i_prf in range(n_prf): - tmp=profile.dataset.potential_temperature.values[i_prf,:] - sal=profile.dataset.practical_salinity.values[i_prf,:] - z=profile.dataset.depth.values[i_prf,:] + tmp = profile.dataset.potential_temperature.values[i_prf, :] + sal = profile.dataset.practical_salinity.values[i_prf, :] + z = profile.dataset.depth.values[i_prf, :] if any_tmp[i_prf]: - tmp=coast.general_utils.fill_holes_1d(tmp) + tmp = coast.general_utils.fill_holes_1d(tmp) tmp[np.isnan(z)] = np.nan - tmp_clean[i_prf,:]=tmp + tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) sal[np.isnan(z)] = np.nan - sal_clean[i_prf,:]=sal - + sal_clean[i_prf, :] = sal coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), "longitude": (("id_dim"), profile.dataset.longitude.values), } - dims = ["id_dim","z_dim"] + dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - - print('All nice and clean') + + print("All nice and clean") return profile @@ -92,10 +90,10 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach -#%% + #%% gravity = 9.81 -#Clean data This is quit slow and over writes potneital temperature and practical salinity valirables - profile = ProfileStratification.clean_data (profile) + # Clean data This is quit slow and over writes potneital temperature and practical salinity valirables + profile = ProfileStratification.clean_data(profile) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -110,7 +108,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax #jth why not just use depth here? + height = ( + np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + ) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=True, pot_dens=True) @@ -119,8 +119,12 @@ def calc_pea(self, profile: xr.Dataset, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) * gravity / (height.sum(dim="z_dim", skipna=True)) -#%% + pot_energy_anom = ( + (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) + * gravity + / (height.sum(dim="z_dim", skipna=True)) + ) + #%% coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), From 3425ef0cf01c42e34ef6f9a552c54c56eebbc3ae Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:47:14 +0000 Subject: [PATCH 106/150] correct conflict --- coast/_utils/general_utils.py | 4 +- coast/data/profile.py | 79 ++++++++ .../profile/potential_energy_tutorial.ipynb | 168 ++++++++++++++++-- example_scripts/profile_test.py | 27 +++ requirements.txt | 4 + 5 files changed, 262 insertions(+), 20 deletions(-) create mode 100644 example_scripts/profile_test.py diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 98efbe0f..5a5c5f91 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -235,7 +235,7 @@ def reinstate_indices_by_mask(array_removed, mask, fill_value=np.nan): return array -def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None): +def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors = 1): """ Obtains the 2 dimensional indices of the nearest model points to specified lists of longitudes and latitudes. Makes use of sklearn.neighbours @@ -294,7 +294,7 @@ def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None): # Do nearest neighbour interpolation using BallTree (gets indices) tree = nb.BallTree(mod_loc, leaf_size=5, metric="haversine") - _, ind_1d = tree.query(new_loc, k=1) + _, ind_1d = tree.query(new_loc, k=number_of_neighbors) if mask is None: # Get 2D indices from 1D index output from BallTree diff --git a/coast/data/profile.py b/coast/data/profile.py index 7c8c6339..d8cbc636 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -462,6 +462,85 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_y"] = (["id_dim"], ind_y.values) mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) + def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: + """Match profiles locations to grid, finding 4 nearest neighbours for each profile. + + Args: + gridded (Gridded): Gridded object. + limits (List): [jmin,jmax,imin,imax] - Subset to this region. + rmax (int): 7000 m - maxmimum search distance (metres). + + ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + + ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO + """ + + if sum(limits) != 0: + gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) + # keep the grid or subset on the hydrographic profiles object + gridded.dataset["limits"] = limits + prf = self.dataset + grd = gridded.dataset + grd['landmask']=grd.bottom_level == 0 + lon_prf = prf["longitude"] + lat_prf = prf["latitude"] + lon_grd = grd["latitude"] + lat_grd = grd["latitude"] + # SPATIAL indices - 4 nearest neighbour + ind_x, ind_y = general_utils.nearest_indices_2d( + lon_grd,lat_grd, + lon_prf,lat_prf, + mask = grd.landmask, + number_of_neighbors = 4 + ) + + #Exclude out of bound points + I_exc =np.concatenate(( + np.where(lon_prf < lon_grd.values.ravel().min())[0], + np.where(lon_prf > lon_grd.values.ravel().max())[0], + np.where(lat_prf < lat_grd.values.ravel().min())[0], + np.where(lat_prf > lat_grd.values.ravel().max())[0], + )) + ind_x[I_exc] = np.nan + ind_y[I_exc] = np.nan + prf["ind_x_min"] = limits[0] # reference back to original grid + prf["ind_y_min"] = limits[2] + + ind_x_min = limits[0] + ind_y_min = limits[2] + + + # Sort 4 NN by distance on grid + + ip = np.where(np.logical_or(ind_x[:, 0] != 0, ind_y[:, 0] != 0))[0] + lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() + r = np.ones(ind_x.shape) * np.nan + + rr = general_utils.calculate_haversine_distance( + lon_prf4, lat_prf4, + lon_grd[ind_y.values.ravel(),ind_x.values.ravel()], + lat_grd[ind_y.values.ravel(),ind_x.values.ravel()] + ) + + r[ip, :] = np.reshape(rr, (ip.size, 4)) + # sort by distance + ii = np.argsort(r, axis=1) + rmin_prf = np.take_along_axis(r, ii, axis=1) + ind_x.values = np.take_along_axis(ind_x.values, ii, axis=1) + ind_y.values = np.take_along_axis(ind_y.values, ii, axis=1) + + ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) + ind_x.values = ind_x.values + i_min + ind_y.values = ind_y.values+ j_min + ind_x.values[ii, :] = 0 # should the be nan? + ind_y.values[ii, :] = 0 + + self.profile.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "4"]) + self.profile.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "4"]) + self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + + def calculate_en4_qc_flags_levels(self): """ diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb index cd3bb93b..09020937 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -54,10 +54,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "config\\example_en4_profiles.json\n" + ] + } + ], "source": [ "# Create a Profile object and load in the data:\n", "profile = coast.Profile(config=fn_cfg_prof)\n", @@ -74,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "58406dca", "metadata": {}, "outputs": [], @@ -94,10 +102,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "f5b2d233", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(
, )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "profile.plot_map()" ] @@ -115,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e70f5db2", "metadata": {}, "outputs": [], @@ -133,10 +162,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c49b40d3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cleaning the data\n", + "All nice and clean\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + } + ], "source": [ "Zmax = 200 # metres\n", "pa.calc_pea(profile, Zmax)" @@ -175,10 +221,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a696835b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = pa.quick_plot(\"pea\")\n", "fig.tight_layout()" @@ -186,10 +251,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "bb540223", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.scatter( pa.dataset.longitude,\n", " pa.dataset.latitude,\n", @@ -208,18 +294,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a37a8291", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\\example_scripts\\notebooks\n" + ] + } + ], + "source": [ + "cd ../../" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca0c825f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\n" + ] + } + ], + "source": [ + "cd ../../" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25670a0f", + "metadata": {}, + "outputs": [], + "source": [ + "pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd695e91", + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "coast_dev", "language": "python", - "name": "python3" + "name": "coast_dev" }, "language_info": { "codemirror_mode": { @@ -231,7 +363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" } }, "nbformat": 4, diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py new file mode 100644 index 00000000..1c707e2d --- /dev/null +++ b/example_scripts/profile_test.py @@ -0,0 +1,27 @@ +import coast +import numpy as np +from os import path +import matplotlib.pyplot as plt +import matplotlib.colors as colors # colormap fiddling +# set some paths +root = "./" +dn_files = root + "./example_files/" +fn_prof = path.join(dn_files, "coast_example_en4_201008.nc") +fn_cfg_prof = path.join("config","example_en4_profiles.json") + +# Create a Profile object and load in the data: +profile = coast.Profile(config=fn_cfg_prof) +profile.read_en4( fn_prof ) + +processed_profile = profile.process_en4() +profile = processed_profile + +pa = coast.ProfileStratification(profile) + +Zmax = 200 # metres +pa.calc_pea(profile, Zmax) + +fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' +fn_grd_cfg = 'config/example_nemo_grid_t.json' +nemo = coast.Gridded(fn_domain=fn_grd_dom,config = fn_grd_cfg) +profile.match_to_grid(nemo) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index ebbf324a..201f5768 100644 --- a/requirements.txt +++ b/requirements.txt @@ -18,5 +18,9 @@ pydap>=3.2.2 lxml>=4.9.0 # Required for pydap CAS parsing requests>=2.27.1 pyproj>=3.5.0 +# spyder>=5.1.5 +# cartopy>=0.21.0 +# ipykernel +# jupyterlab #xesmf>=0.3.0 # Optional. Not part of main package #esmpy>=8.0.0 # Optional. Not part of main package \ No newline at end of file From 5a9c4fdb04ba73c57fc6e6eaef99159edae52da9 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Wed, 30 Nov 2022 20:06:59 +0000 Subject: [PATCH 107/150] Adding match to grid method --- coast/data/profile.py | 59 +++++++++++++++++++-------------- example_scripts/profile_test.py | 2 +- 2 files changed, 35 insertions(+), 26 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index d8cbc636..a32a2f77 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -479,12 +479,13 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) # keep the grid or subset on the hydrographic profiles object gridded.dataset["limits"] = limits + prf = self.dataset grd = gridded.dataset grd['landmask']=grd.bottom_level == 0 lon_prf = prf["longitude"] lat_prf = prf["latitude"] - lon_grd = grd["latitude"] + lon_grd = grd["longitude"] lat_grd = grd["latitude"] # SPATIAL indices - 4 nearest neighbour ind_x, ind_y = general_utils.nearest_indices_2d( @@ -493,52 +494,60 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: mask = grd.landmask, number_of_neighbors = 4 ) + ind_x=ind_x.values + ind_y=ind_y.values #Exclude out of bound points - I_exc =np.concatenate(( + i_exc =np.concatenate(( np.where(lon_prf < lon_grd.values.ravel().min())[0], np.where(lon_prf > lon_grd.values.ravel().max())[0], np.where(lat_prf < lat_grd.values.ravel().min())[0], np.where(lat_prf > lat_grd.values.ravel().max())[0], )) - ind_x[I_exc] = np.nan - ind_y[I_exc] = np.nan - prf["ind_x_min"] = limits[0] # reference back to original grid - prf["ind_y_min"] = limits[2] + ind_x[i_exc,:] = -1 + ind_y[i_exc,:] = -1 + prf["ind_x_min"] = limits[2] # reference back to original grid + prf["ind_y_min"] = limits[0] - ind_x_min = limits[0] - ind_y_min = limits[2] + ind_x_min = limits[2] + ind_y_min = limits[0] # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] != 0, ind_y[:, 0] != 0))[0] + ip = np.where(np.logical_or(ind_x[:, 0] >=0 , + ind_y[:, 0] >=0 ))[0] + lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan - +#distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( lon_prf4, lat_prf4, - lon_grd[ind_y.values.ravel(),ind_x.values.ravel()], - lat_grd[ind_y.values.ravel(),ind_x.values.ravel()] + lon_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()], + lat_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()] ) r[ip, :] = np.reshape(rr, (ip.size, 4)) - # sort by distance + # sort by distance and re-order the indices with closest first ii = np.argsort(r, axis=1) rmin_prf = np.take_along_axis(r, ii, axis=1) - ind_x.values = np.take_along_axis(ind_x.values, ii, axis=1) - ind_y.values = np.take_along_axis(ind_y.values, ii, axis=1) - - ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) - ind_x.values = ind_x.values + i_min - ind_y.values = ind_y.values+ j_min - ind_x.values[ii, :] = 0 # should the be nan? - ind_y.values[ii, :] = 0 - - self.profile.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "4"]) - self.profile.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "4"]) - self.profile.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + ind_x = np.take_along_axis(ind_x, ii, axis=1) + ind_y = np.take_along_axis(ind_y, ii, axis=1) + + ii = np.nonzero(np.min(r, axis=1) > rmax) + #Reference to original grid + ind_x = ind_x + ind_x_min + ind_y = ind_y + ind_y_min + #mask bad values with -1 + ind_x[ii, :] = -1 + ind_y[ii, :] = -1 + ind_x[i_exc, :] = -1 + ind_y[i_exc, :] = -1 + #Add to profile object + self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) + self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) + self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 1c707e2d..fbc2e6c7 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -19,7 +19,7 @@ pa = coast.ProfileStratification(profile) Zmax = 200 # metres -pa.calc_pea(profile, Zmax) +#pa.calc_pea(profile, Zmax) fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' fn_grd_cfg = 'config/example_nemo_grid_t.json' From 5f99bc9d1c9558fb469a7ed88def25406f65e454 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:48:28 +0000 Subject: [PATCH 108/150] correct conflict --- coast/_utils/general_utils.py | 2 +- coast/data/profile.py | 57 ++++++++++++++++----------------- example_scripts/profile_test.py | 15 +++++---- 3 files changed, 36 insertions(+), 38 deletions(-) diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index 5a5c5f91..aeee1294 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -235,7 +235,7 @@ def reinstate_indices_by_mask(array_removed, mask, fill_value=np.nan): return array -def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors = 1): +def nearest_indices_2d(mod_lon, mod_lat, new_lon, new_lat, mask=None, number_of_neighbors=1): """ Obtains the 2 dimensional indices of the nearest model points to specified lists of longitudes and latitudes. Makes use of sklearn.neighbours diff --git a/coast/data/profile.py b/coast/data/profile.py index a32a2f77..0c1e4b3b 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -462,7 +462,8 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_y"] = (["id_dim"], ind_y.values) mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: + + def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: @@ -482,50 +483,48 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: prf = self.dataset grd = gridded.dataset - grd['landmask']=grd.bottom_level == 0 + grd["landmask"] = grd.bottom_level == 0 lon_prf = prf["longitude"] lat_prf = prf["latitude"] lon_grd = grd["longitude"] lat_grd = grd["latitude"] # SPATIAL indices - 4 nearest neighbour ind_x, ind_y = general_utils.nearest_indices_2d( - lon_grd,lat_grd, - lon_prf,lat_prf, - mask = grd.landmask, - number_of_neighbors = 4 + lon_grd, lat_grd, lon_prf, lat_prf, mask=grd.landmask, number_of_neighbors=4 + ) + ind_x = ind_x.values + ind_y = ind_y.values + + # Exclude out of bound points + i_exc = np.concatenate( + ( + np.where(lon_prf < lon_grd.values.ravel().min())[0], + np.where(lon_prf > lon_grd.values.ravel().max())[0], + np.where(lat_prf < lat_grd.values.ravel().min())[0], + np.where(lat_prf > lat_grd.values.ravel().max())[0], + ) ) - ind_x=ind_x.values - ind_y=ind_y.values - - #Exclude out of bound points - i_exc =np.concatenate(( - np.where(lon_prf < lon_grd.values.ravel().min())[0], - np.where(lon_prf > lon_grd.values.ravel().max())[0], - np.where(lat_prf < lat_grd.values.ravel().min())[0], - np.where(lat_prf > lat_grd.values.ravel().max())[0], - )) - ind_x[i_exc,:] = -1 - ind_y[i_exc,:] = -1 + ind_x[i_exc, :] = -1 + ind_y[i_exc, :] = -1 prf["ind_x_min"] = limits[2] # reference back to original grid prf["ind_y_min"] = limits[0] ind_x_min = limits[2] ind_y_min = limits[0] - # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] >=0 , - ind_y[:, 0] >=0 ))[0] + ip = np.where(np.logical_or(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan -#distance between nearest neighbors and grid + # distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( - lon_prf4, lat_prf4, - lon_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()], - lat_grd.values[ind_y[ip,:].ravel(),ind_x[ip,:].ravel()] + lon_prf4, + lat_prf4, + lon_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], + lat_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], ) r[ip, :] = np.reshape(rr, (ip.size, 4)) @@ -536,21 +535,19 @@ def match_to_grid(self, gridded, limits = [0, 0, 0, 0], rmax = 7000.) -> None: ind_y = np.take_along_axis(ind_y, ii, axis=1) ii = np.nonzero(np.min(r, axis=1) > rmax) - #Reference to original grid + # Reference to original grid ind_x = ind_x + ind_x_min ind_y = ind_y + ind_y_min - #mask bad values with -1 + # mask bad values with -1 ind_x[ii, :] = -1 ind_y[ii, :] = -1 ind_x[i_exc, :] = -1 ind_y[i_exc, :] = -1 - #Add to profile object + # Add to profile object self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) - - def calculate_en4_qc_flags_levels(self): """ Brute force method for identifying all rejected points according to diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index fbc2e6c7..7b236126 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -3,15 +3,16 @@ from os import path import matplotlib.pyplot as plt import matplotlib.colors as colors # colormap fiddling + # set some paths root = "./" dn_files = root + "./example_files/" fn_prof = path.join(dn_files, "coast_example_en4_201008.nc") -fn_cfg_prof = path.join("config","example_en4_profiles.json") +fn_cfg_prof = path.join("config", "example_en4_profiles.json") # Create a Profile object and load in the data: profile = coast.Profile(config=fn_cfg_prof) -profile.read_en4( fn_prof ) +profile.read_en4(fn_prof) processed_profile = profile.process_en4() profile = processed_profile @@ -19,9 +20,9 @@ pa = coast.ProfileStratification(profile) Zmax = 200 # metres -#pa.calc_pea(profile, Zmax) +# pa.calc_pea(profile, Zmax) -fn_grd_dom = 'example_files/coast_example_nemo_domain.nc' -fn_grd_cfg = 'config/example_nemo_grid_t.json' -nemo = coast.Gridded(fn_domain=fn_grd_dom,config = fn_grd_cfg) -profile.match_to_grid(nemo) \ No newline at end of file +fn_grd_dom = "example_files/coast_example_nemo_domain.nc" +fn_grd_cfg = "config/example_nemo_grid_t.json" +nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) +profile.match_to_grid(nemo) From e25d238573e089feb43bbf0017268d13657b9a71 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 2 Dec 2022 14:03:18 +0000 Subject: [PATCH 109/150] Added gridded_to_profile_2d method to profile.py --- coast/data/profile.py | 75 +++++++++++++++++++++++++-------- example_scripts/profile_test.py | 1 + 2 files changed, 58 insertions(+), 18 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 0c1e4b3b..6ab09feb 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -463,17 +463,21 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: + def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: gridded (Gridded): Gridded object. limits (List): [jmin,jmax,imin,imax] - Subset to this region. - rmax (int): 7000 m - maxmimum search distance (metres). + rmax (int): 7 km - maxmimum search distance (metres). - ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + Adds to the profile object: + ind_x, ind_y (int array ) (id_dim,4) + Index of the 4 closest grid cells to each profile, in distance order. + Profiles outside the gridded region are set to -9999 + rmin_prf float array (id_dim,4) + Distance (km) of the losest grid cells to each profile, in distance order - ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO """ if sum(limits) != 0: @@ -504,8 +508,8 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: np.where(lat_prf > lat_grd.values.ravel().max())[0], ) ) - ind_x[i_exc, :] = -1 - ind_y[i_exc, :] = -1 + ind_x[i_exc, :] = -9999 + ind_y[i_exc, :] = -9999 prf["ind_x_min"] = limits[2] # reference back to original grid prf["ind_y_min"] = limits[0] @@ -514,20 +518,20 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: # Sort 4 NN by distance on grid - ip = np.where(np.logical_or(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] #good points - lon_prf4 = np.repeat(lon_prf.values[ip, np.newaxis], 4, axis=1).ravel() - lat_prf4 = np.repeat(lat_prf.values[ip, np.newaxis], 4, axis=1).ravel() + lon_prf4 = np.repeat(lon_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() r = np.ones(ind_x.shape) * np.nan # distance between nearest neighbors and grid rr = general_utils.calculate_haversine_distance( lon_prf4, lat_prf4, - lon_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], - lat_grd.values[ind_y[ip, :].ravel(), ind_x[ip, :].ravel()], + lon_grd.values[ind_y[ind_good, :].ravel(), ind_x[ind_good, :].ravel()], + lat_grd.values[ind_y[ind_good, :].ravel(), ind_x[ind_good, :].ravel()], ) - r[ip, :] = np.reshape(rr, (ip.size, 4)) + r[ind_good, :] = np.reshape(rr, (ind_good.size, 4)) # sort by distance and re-order the indices with closest first ii = np.argsort(r, axis=1) rmin_prf = np.take_along_axis(r, ii, axis=1) @@ -538,15 +542,50 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7000.0) -> None: # Reference to original grid ind_x = ind_x + ind_x_min ind_y = ind_y + ind_y_min - # mask bad values with -1 - ind_x[ii, :] = -1 - ind_y[ii, :] = -1 - ind_x[i_exc, :] = -1 - ind_y[i_exc, :] = -1 + # mask bad values with -9999 + ind_x[ii, :] = -9999 + ind_y[ii, :] = -9999 + ind_x[i_exc, :] = -9999 + ind_y[i_exc, :] = -9999 + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] + # Add to profile object self.dataset["ind_x"] = xr.DataArray(ind_x, dims=["id_dim", "NNs"]) self.dataset["ind_y"] = xr.DataArray(ind_y, dims=["id_dim", "NNs"]) - self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "NNs"]) + self.dataset["ind_good"] = xr.DataArray(ind_good, dims=["Ngood"]) + + def gridded_to_profile_2d(self, gridded, variable) -> None: + """ + Evaluated a gridded data variable on each profile. Here just 2D, but could be extended to 3 or 4D + + Args: + gridded (Gridded): Gridded object + variable string : Name of variable in gridded object to interpolate + + Output variable is distance weighted mean and is added to profile object with + same name as in the gridded object + + + """ + #ensure there are indices in profile + if not 'ind_x' in profile.dataset: + self.match_to_grid(gridded) + # + prf = self.dataset + grd = gridded.dataset + grd["landmask"] = grd.bottom_level == 0 + nprof = self.dataset.id_dim.shape[0] + var=np.ma.masked_where(grd["landmask"],grd[variable]) + ig=prf.ind_good + #Distance weighted mean + v = var[prf.ind_y[ig, :], prf.ind_x[ig, :]] / prf.rmin_prf[ig, :] + norm = 1.0 / prf.rmin_prf[ig, :] + norm = np.ma.masked_where(v.mask,norm) + var_int=np.nansum(v,axis=1)/np.nansum(norm,axis=1) + var_prf=np.ones(nprof)*np.nan + var_prf[ig]=var_int + self.dataset[variable]=xr.DataArray(var_prf, dims=["id_dim"]) def calculate_en4_qc_flags_levels(self): """ diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 7b236126..03b8dc9c 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -26,3 +26,4 @@ fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) +profile.gridded_to_profile_2d(nemo,'bathymetry') From 1c09a9819dfc9e39ed23f56a55970fd9487858a7 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 2 Dec 2022 17:02:32 +0000 Subject: [PATCH 110/150] Added added unit test for gridded_to_profile_2d method to profile.py Fixed unit tests for profiles and stratification Fixed construct density for cases that need 2D lon,lat field --- coast/data/profile.py | 9 ++++++--- unit_testing/test_profile_methods.py | 15 ++++++++++++++- .../test_profile_stratification_methods.py | 2 +- unit_testing/unit_test.py | 1 - 4 files changed, 21 insertions(+), 6 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 6ab09feb..e84b2bc3 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -569,7 +569,7 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ #ensure there are indices in profile - if not 'ind_x' in profile.dataset: + if not 'ind_x' in self.dataset: self.match_to_grid(gridded) # prf = self.dataset @@ -910,15 +910,18 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values + if not pot_dens or not CT_AS: + lat2d = np.repeat(lat[:,np.newaxis],shape_ds[1],axis=1) + lon2d = np.repeat(lon[:,np.newaxis],shape_ds[1],axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels,lat2d)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided - sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon2d, lat2d)) else: # abs salinity provided sal_absolute = np.ma.masked_invalid(sal) sal_absolute = np.ma.masked_less(sal_absolute, 0) diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index b2d8985c..1a0a5d67 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -182,6 +182,18 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") + with self.subTest("Gridded match_to_grid & profile_to_gridded"): + nemo_t = coast.Gridded( + fn_domain=files.fn_nemo_dom, + config=files.fn_config_t_grid + ) + processed.match_to_grid(nemo_t) + processed.gridded_to_profile_2d(nemo_t, 'bathymetry') + + check1 = np.isclose(processed.dataset.bathymetry[4],29.06689187) + + self.assertTrue(check1, "check1") + def test_calculate_vertical_mask(self): # load example profile data @@ -198,7 +210,8 @@ def test_calculate_vertical_mask(self): mask = mask.fillna(-999) check1 = (kmax == np.array([2, 1, 2])).all() - check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.8, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() + check2 = (mask.values == np.array([[1.0, 1.0, 1.0, -999], [1.0, 0.7, 0.0, 0.0], [1.0, 1.0, 1.0, -999]])).all() self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") + diff --git a/unit_testing/test_profile_stratification_methods.py b/unit_testing/test_profile_stratification_methods.py index 2fd14fcb..de96333c 100644 --- a/unit_testing/test_profile_stratification_methods.py +++ b/unit_testing/test_profile_stratification_methods.py @@ -22,7 +22,7 @@ def test_calculate_pea(self): Zmax = 200 # metres pa.calc_pea(profile, Zmax) - check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 17.139333147742676) + check1 = np.isclose(pa.dataset.pea.mean(dim="id_dim").item(), 5.8750878507) self.assertTrue(check1, "check1") with self.subTest("Test quick_plot()"): diff --git a/unit_testing/unit_test.py b/unit_testing/unit_test.py index 4544c62b..0b64c006 100644 --- a/unit_testing/unit_test.py +++ b/unit_testing/unit_test.py @@ -64,7 +64,6 @@ test_process_data_methods, ] - # UNIT TESTING CONTROL SCRIPT # Import modules, including unittest From 1de170ea0f3a72273e4334b16345b813556097f2 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:49:29 +0000 Subject: [PATCH 111/150] correct conflict --- coast/data/profile.py | 18 +++++++++--------- example_scripts/profile_test.py | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index e84b2bc3..fbfb5205 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -518,7 +518,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: # Sort 4 NN by distance on grid - ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] #good points + ind_good = np.where(np.logical_and(ind_x[:, 0] >= 0, ind_y[:, 0] >= 0))[0] # good points lon_prf4 = np.repeat(lon_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() lat_prf4 = np.repeat(lat_prf.values[ind_good, np.newaxis], 4, axis=1).ravel() @@ -576,16 +576,16 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: grd = gridded.dataset grd["landmask"] = grd.bottom_level == 0 nprof = self.dataset.id_dim.shape[0] - var=np.ma.masked_where(grd["landmask"],grd[variable]) - ig=prf.ind_good - #Distance weighted mean + var = np.ma.masked_where(grd["landmask"], grd[variable]) + ig = prf.ind_good + # Distance weighted mean v = var[prf.ind_y[ig, :], prf.ind_x[ig, :]] / prf.rmin_prf[ig, :] norm = 1.0 / prf.rmin_prf[ig, :] - norm = np.ma.masked_where(v.mask,norm) - var_int=np.nansum(v,axis=1)/np.nansum(norm,axis=1) - var_prf=np.ones(nprof)*np.nan - var_prf[ig]=var_int - self.dataset[variable]=xr.DataArray(var_prf, dims=["id_dim"]) + norm = np.ma.masked_where(v.mask, norm) + var_int = np.nansum(v, axis=1) / np.nansum(norm, axis=1) + var_prf = np.ones(nprof) * np.nan + var_prf[ig] = var_int + self.dataset[variable] = xr.DataArray(var_prf, dims=["id_dim"]) def calculate_en4_qc_flags_levels(self): """ diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 03b8dc9c..6c4fbcbe 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -26,4 +26,4 @@ fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) -profile.gridded_to_profile_2d(nemo,'bathymetry') +profile.gridded_to_profile_2d(nemo, "bathymetry") From b787da52f528a869e7fab381ea1ac34ccc3fc2ad Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Tue, 6 Dec 2022 21:12:51 +0000 Subject: [PATCH 112/150] Find good SST and SSS depths in profiles --- coast/diagnostics/profile_stratification.py | 31 ++++++++++++++++++--- example_scripts/profile_test.py | 6 ++-- 2 files changed, 31 insertions(+), 6 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index a7fa3955..2f825e08 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -42,17 +42,40 @@ def clean_data(profile: xr.Dataset): """ print("Cleaning the data") - # fill holes in data - # jth is slow, there may bea more 'vector' way of doing it + # find profiles good for SST and NBT + dz_max=25.0 n_prf = profile.dataset.id_dim.shape[0] - + n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] sal_clean = profile.dataset.practical_salinity.values[:, :] any_tmp = np.sum(~np.isnan(tmp_clean), axis=1) != 0 - any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 + # Find good SST and SSS depths + if "bathymetry" in profile.dataset: + D_prf=profile.dataset.bathymetry.values + profile.gridded_to_profile_2d(nemo, "bathymetry") + z = profile.dataset.depth + test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) + test_tmp=np.logical_and(test_surface, + ~np.isnan(tmp_clean)) + test_sal=np.logical_and(test_surface, + ~np.isnan(sal_clean)) + good_sst=np.zeros(n_prf)*np.nan + good_sss=np.zeros(n_prf)*np.nan + I_tmp=np.nonzero(np.any(test_tmp.values,axis=1))[0] + I_sal=np.nonzero(np.any(test_sal.values,axis=1))[0] + # + for ip in I_tmp: + good_sst[ip]=np.min(np.nonzero(test_tmp.values[ip,:])) + for ip in I_sal: + good_sss[ip]=np.min(np.nonzero(test_sal.values[ip,:])) + I = np.where(np.isfinite(good_sss))[0] + SSS=sal_clean[I, good_sss[I].astype(int)] + # fill holes in data + # jth is slow, there may bea more 'vector' way of doing it + for i_prf in range(n_prf): tmp = profile.dataset.potential_temperature.values[i_prf, :] sal = profile.dataset.practical_salinity.values[i_prf, :] diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 6c4fbcbe..29785c95 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -19,11 +19,13 @@ pa = coast.ProfileStratification(profile) -Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) + fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) profile.match_to_grid(nemo) profile.gridded_to_profile_2d(nemo, "bathymetry") + +Zmax = 200 # metres +# pa.calc_pea(profile, Zmax) \ No newline at end of file From e63d4bb0609f4b87098cad54b4bfcf0198028547 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:50:30 +0000 Subject: [PATCH 113/150] correct conflict --- coast/data/profile.py | 10 +++++----- unit_testing/test_profile_methods.py | 11 +++-------- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index fbfb5205..0a049228 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -568,8 +568,8 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ - #ensure there are indices in profile - if not 'ind_x' in self.dataset: + # ensure there are indices in profile + if not "ind_x" in self.dataset: self.match_to_grid(gridded) # prf = self.dataset @@ -911,13 +911,13 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values if not pot_dens or not CT_AS: - lat2d = np.repeat(lat[:,np.newaxis],shape_ds[1],axis=1) - lon2d = np.repeat(lon[:,np.newaxis],shape_ds[1],axis=1) + lat2d = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) + lon2d = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels,lat2d)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat2d)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided diff --git a/unit_testing/test_profile_methods.py b/unit_testing/test_profile_methods.py index 1a0a5d67..dad08840 100644 --- a/unit_testing/test_profile_methods.py +++ b/unit_testing/test_profile_methods.py @@ -183,18 +183,14 @@ def test_compare_processed_profile_with_model(self): self.assertTrue(check2, "check2") self.assertTrue(check3, "check3") with self.subTest("Gridded match_to_grid & profile_to_gridded"): - nemo_t = coast.Gridded( - fn_domain=files.fn_nemo_dom, - config=files.fn_config_t_grid - ) + nemo_t = coast.Gridded(fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid) processed.match_to_grid(nemo_t) - processed.gridded_to_profile_2d(nemo_t, 'bathymetry') + processed.gridded_to_profile_2d(nemo_t, "bathymetry") - check1 = np.isclose(processed.dataset.bathymetry[4],29.06689187) + check1 = np.isclose(processed.dataset.bathymetry[4], 29.06689187) self.assertTrue(check1, "check1") - def test_calculate_vertical_mask(self): # load example profile data profile = coast.Profile(config=files.fn_profile_config) @@ -214,4 +210,3 @@ def test_calculate_vertical_mask(self): self.assertTrue(check1, "check1") self.assertTrue(check2, "check2") - From 73eab1ce2324d7250b3f7f5432017242c03224b6 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Wed, 7 Dec 2022 15:22:05 +0000 Subject: [PATCH 114/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 26 ++++++++++----------- example_scripts/profile_test.py | 3 +-- 2 files changed, 13 insertions(+), 16 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 2f825e08..e629052b 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -43,7 +43,7 @@ def clean_data(profile: xr.Dataset): """ print("Cleaning the data") # find profiles good for SST and NBT - dz_max=25.0 + dz_max = 25.0 n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] @@ -54,25 +54,23 @@ def clean_data(profile: xr.Dataset): # Find good SST and SSS depths if "bathymetry" in profile.dataset: - D_prf=profile.dataset.bathymetry.values + D_prf = profile.dataset.bathymetry.values profile.gridded_to_profile_2d(nemo, "bathymetry") z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) - test_tmp=np.logical_and(test_surface, - ~np.isnan(tmp_clean)) - test_sal=np.logical_and(test_surface, - ~np.isnan(sal_clean)) - good_sst=np.zeros(n_prf)*np.nan - good_sss=np.zeros(n_prf)*np.nan - I_tmp=np.nonzero(np.any(test_tmp.values,axis=1))[0] - I_sal=np.nonzero(np.any(test_sal.values,axis=1))[0] - # + test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) + test_sal = np.logical_and(test_surface, ~np.isnan(sal_clean)) + good_sst = np.zeros(n_prf) * np.nan + good_sss = np.zeros(n_prf) * np.nan + I_tmp = np.nonzero(np.any(test_tmp.values, axis=1))[0] + I_sal = np.nonzero(np.any(test_sal.values, axis=1))[0] + # for ip in I_tmp: - good_sst[ip]=np.min(np.nonzero(test_tmp.values[ip,:])) + good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) for ip in I_sal: - good_sss[ip]=np.min(np.nonzero(test_sal.values[ip,:])) + good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) I = np.where(np.isfinite(good_sss))[0] - SSS=sal_clean[I, good_sss[I].astype(int)] + SSS = sal_clean[I, good_sss[I].astype(int)] # fill holes in data # jth is slow, there may bea more 'vector' way of doing it diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 29785c95..08ed5345 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -20,7 +20,6 @@ pa = coast.ProfileStratification(profile) - fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) @@ -28,4 +27,4 @@ profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) \ No newline at end of file +# pa.calc_pea(profile, Zmax) From 7704a8e022b91ac89ec8b8b24eb8ba8fbd7813c7 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Thu, 8 Dec 2022 17:25:28 +0000 Subject: [PATCH 115/150] Profile analysis construct density to correctly see 2d lon,lat PEA, SSS, SST data cleaning finished --- coast/data/profile.py | 8 ++-- coast/diagnostics/profile_stratification.py | 46 +++++++++++++++------ example_scripts/profile_test.py | 2 +- 3 files changed, 39 insertions(+), 17 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 0a049228..f213b91f 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -911,17 +911,17 @@ def construct_density( lat = self.dataset.latitude.values lon = self.dataset.longitude.values if not pot_dens or not CT_AS: - lat2d = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) - lon2d = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) + lat = np.repeat(lat[:, np.newaxis], shape_ds[1], axis=1) + lon = np.repeat(lon[:, np.newaxis], shape_ds[1], axis=1) # Absolute Pressure if pot_dens: pressure_absolute = 0.0 # calculate potential density else: - pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat2d)) # depth must be negative + pressure_absolute = np.ma.masked_invalid(gsw.p_from_z(-s_levels, lat)) # depth must be negative if not rhobar: # calculate full depth # Absolute Salinity if not CT_AS: # abs salinity not provided - sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon2d, lat2d)) + sal_absolute = np.ma.masked_invalid(gsw.SA_from_SP(sal, pressure_absolute, lon, lat)) else: # abs salinity provided sal_absolute = np.ma.masked_invalid(sal) sal_absolute = np.ma.masked_less(sal_absolute, 0) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index e629052b..7e68b68d 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -31,7 +31,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -55,7 +55,7 @@ def clean_data(profile: xr.Dataset): # Find good SST and SSS depths if "bathymetry" in profile.dataset: D_prf = profile.dataset.bathymetry.values - profile.gridded_to_profile_2d(nemo, "bathymetry") + profile.gridded_to_profile_2d(gridded, "bathymetry") z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) @@ -69,10 +69,29 @@ def clean_data(profile: xr.Dataset): good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) for ip in I_sal: good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) - I = np.where(np.isfinite(good_sss))[0] - SSS = sal_clean[I, good_sss[I].astype(int)] + I_tmp = np.where(np.isfinite(good_sst))[0] + I_sal = np.where(np.isfinite(good_sss))[0] + + + # + # find good profiles + DD = np.minimum(Zmax, np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) + good_profile = np.array(np.ones(n_prf),dtype=bool) + quart = [0, 0.25, 0.5, 0.75, 1] + for iq in range(4): + test = ~np.any(np.logical_and(z >= DD*quart[iq] ,z <= DD*quart[iq+1]),axis=1) + good_profile[test]=0 + + ### + SST = np.zeros(n_prf)*np.nan + SSS = np.zeros(n_prf) * np.nan + + SSS[I_sal] = sal_clean[I_sal, good_sss[I_sal].astype(int)] + SST[I_tmp] = tmp_clean[I_tmp, good_sst[I_tmp].astype(int)] + + # fill holes in data - # jth is slow, there may bea more 'vector' way of doing it + # jth This is slow, there may be a more 'vector' way of doing it for i_prf in range(n_prf): tmp = profile.dataset.potential_temperature.values[i_prf, :] @@ -95,12 +114,14 @@ def clean_data(profile: xr.Dataset): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - + profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") return profile - def calc_pea(self, profile: xr.Dataset, Zmax): + def calc_pea(self, profile: xr.Dataset, gridded, Zmax): """ Calculates Potential Energy Anomaly @@ -113,8 +134,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach #%% gravity = 9.81 - # Clean data This is quit slow and over writes potneital temperature and practical salinity valirables - profile = ProfileStratification.clean_data(profile) + # Clean data This is quit slow and over writes potential temperature and practical salinity variables + profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -134,9 +155,9 @@ def calc_pea(self, profile: xr.Dataset, Zmax): ) # jth why not just use depth here? if not "density" in profile.dataset: - profile.construct_density(CT_AS=True, pot_dens=True) + profile.construct_density(CT_AS=False, pot_dens=True) if not "density_bar" in profile.dataset: - profile.construct_density(CT_AS=True, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) + profile.construct_density(CT_AS=False, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S @@ -145,7 +166,8 @@ def calc_pea(self, profile: xr.Dataset, Zmax): * gravity / (height.sum(dim="z_dim", skipna=True)) ) - #%% + # mask bad profiles + pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { "time": ("id_dim", profile.dataset.time.values), "latitude": (("id_dim"), profile.dataset.latitude.values), diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 08ed5345..e81723af 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -27,4 +27,4 @@ profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres -# pa.calc_pea(profile, Zmax) +pa.calc_pea(profile, nemo, Zmax) From d03f279ecae98e6795f0c604e8f0864e7fea8810 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 8 Dec 2022 17:26:01 +0000 Subject: [PATCH 116/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 7e68b68d..c0b6bae7 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -72,24 +72,22 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): I_tmp = np.where(np.isfinite(good_sst))[0] I_sal = np.where(np.isfinite(good_sss))[0] - - # + # # find good profiles DD = np.minimum(Zmax, np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) - good_profile = np.array(np.ones(n_prf),dtype=bool) + good_profile = np.array(np.ones(n_prf), dtype=bool) quart = [0, 0.25, 0.5, 0.75, 1] for iq in range(4): - test = ~np.any(np.logical_and(z >= DD*quart[iq] ,z <= DD*quart[iq+1]),axis=1) - good_profile[test]=0 + test = ~np.any(np.logical_and(z >= DD * quart[iq], z <= DD * quart[iq + 1]), axis=1) + good_profile[test] = 0 ### - SST = np.zeros(n_prf)*np.nan + SST = np.zeros(n_prf) * np.nan SSS = np.zeros(n_prf) * np.nan SSS[I_sal] = sal_clean[I_sal, good_sss[I_sal].astype(int)] SST[I_tmp] = tmp_clean[I_tmp, good_sst[I_tmp].astype(int)] - # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it From e5f951a303af5e1acf6fe495da0bebe17afddd97 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Thu, 15 Dec 2022 09:12:35 +0000 Subject: [PATCH 117/150] Update profile_stratification.py --- coast/diagnostics/profile_stratification.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index c0b6bae7..504b9600 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -148,9 +148,9 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - height = ( - np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax - ) # jth why not just use depth here? + #height = ( + # np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax + #) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=False, pot_dens=True) @@ -159,11 +159,11 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S + pot_energy_anom = ( - (height * (rho - rhobar) * dz).sum(dim="z_dim", skipna=True) + (depth_t * (rho - rhobar) * dz * Zd_mask).sum(dim="z_dim", skipna=True) * gravity - / (height.sum(dim="z_dim", skipna=True)) - ) + / (dz * Zd_mask).sum(dim="z_dim", skipna=True)) # mask bad profiles pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { From 3e30fbea1614d89a94d56387e33db9eab7ae19e6 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:54:47 +0000 Subject: [PATCH 118/150] correct conflict --- coast/data/gridded.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 786d74ec..e800e87b 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -86,7 +86,10 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): else: self.filename_domain = self.fn_domain # store domain fileanme dataset_domain = self.load_domain(self.fn_domain, chunks) - +#jth subset + if len(lims) == 4: + dataset_domain=dataset_domain.isel(y_dim=range(lims[2],lims[3]),x_dim=range(lims[0],lims[1])) +# # Define extra domain attributes using kwargs dictionary # This is a bit of a placeholder. Some domain/nemo files will have missing variables for key, value in kwargs.items(): @@ -209,7 +212,11 @@ def set_timezero_depths(self, dataset_domain, **kwargs): # keyword to allow calcution of bathymetry from scale factors # All bathymetry should now be mapped to bathy_metry +<<<<<<< HEAD calculate_bathymetry = kwargs.get("calculate_bathymetry", False) +======= + calculate_bathymetry = kwargs.get('calculate_bathymetry',False) +>>>>>>> b188dc8 (Added option to subset dataset and domain on loading. This reduces the big overhead of calculating depth witth big models.) try: if calculate_bathymetry: # calculate bathymetry from scale factors bathymetry, mask, time_mask = self.calc_bathymetry(dataset_domain) From cbdb6e549d2e74d2651bfed4b7f7541b45d58717 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:55:39 +0000 Subject: [PATCH 119/150] correct conflict --- coast/data/gridded.py | 11 +++++++---- coast/diagnostics/profile_stratification.py | 8 ++++---- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/coast/data/gridded.py b/coast/data/gridded.py index e800e87b..113eaf43 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -75,7 +75,6 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): lims = kwargs.get("lims", []) if self.fn_data is not None: self.load(self.fn_data, chunks, multiple) - self.set_dimension_names(self.config.dataset.dimension_map) self.set_variable_names(self.config.dataset.variable_map) self.dataset = self.spatial_subset(self.dataset, lims) # Trim data size if indices specified @@ -86,10 +85,10 @@ def _setup_grid_obj(self, chunks, multiple, **kwargs): else: self.filename_domain = self.fn_domain # store domain fileanme dataset_domain = self.load_domain(self.fn_domain, chunks) -#jth subset + # jth subset if len(lims) == 4: - dataset_domain=dataset_domain.isel(y_dim=range(lims[2],lims[3]),x_dim=range(lims[0],lims[1])) -# + dataset_domain = dataset_domain.isel(y_dim=range(lims[2], lims[3]), x_dim=range(lims[0], lims[1])) + # # Define extra domain attributes using kwargs dictionary # This is a bit of a placeholder. Some domain/nemo files will have missing variables for key, value in kwargs.items(): @@ -212,11 +211,15 @@ def set_timezero_depths(self, dataset_domain, **kwargs): # keyword to allow calcution of bathymetry from scale factors # All bathymetry should now be mapped to bathy_metry +<<<<<<< HEAD <<<<<<< HEAD calculate_bathymetry = kwargs.get("calculate_bathymetry", False) ======= calculate_bathymetry = kwargs.get('calculate_bathymetry',False) >>>>>>> b188dc8 (Added option to subset dataset and domain on loading. This reduces the big overhead of calculating depth witth big models.) +======= + calculate_bathymetry = kwargs.get("calculate_bathymetry", False) +>>>>>>> e591188 (Apply Black formatting to Python code.) try: if calculate_bathymetry: # calculate bathymetry from scale factors bathymetry, mask, time_mask = self.calc_bathymetry(dataset_domain) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 504b9600..3bacd6c2 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -148,9 +148,9 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Zd_mask, kmax = profile.calculate_vertical_mask(Zmax) # Height is depth_t above Zmax. Except height is Zmax for the last level above Zmax. - #height = ( + # height = ( # np.floor(Zd_mask) * depth_t + (np.ceil(Zd_mask) - np.floor(Zd_mask)) * Zmax - #) # jth why not just use depth here? + # ) # jth why not just use depth here? if not "density" in profile.dataset: profile.construct_density(CT_AS=False, pot_dens=True) @@ -159,11 +159,11 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S - pot_energy_anom = ( (depth_t * (rho - rhobar) * dz * Zd_mask).sum(dim="z_dim", skipna=True) * gravity - / (dz * Zd_mask).sum(dim="z_dim", skipna=True)) + / (dz * Zd_mask).sum(dim="z_dim", skipna=True) + ) # mask bad profiles pot_energy_anom = np.ma.masked_where(~profile.dataset.good_profile.values, pot_energy_anom.values) coords = { From 78be971f9674ea1cad1235691835d85c4558b160 Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Wed, 8 Feb 2023 17:11:15 +0000 Subject: [PATCH 120/150] update gridded_monthly_hydrographic_climatology.py --- coast/diagnostics/gridded_monthly_hydrographic_climatology.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 122da60c..29ef91d1 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -44,7 +44,7 @@ def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): print(itt) gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) print("copied", im) - PEA = GriddedStratification(gridded_t2, gridded_t2) + PEA = GriddedStratification(gridded_t2) PEA.calc_pea(gridded_t2, Zd_mask) PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values PEA_monthy_clim = PEA_monthy_clim / nyear From 7e08f797b443fb81a06db4c0c3b154ed16ca7103 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 09:58:15 +0000 Subject: [PATCH 121/150] correct conflict --- coast/data/gridded.py | 8 - coast/data/profile.py | 4 - .../profile_hydrographic_analysis.py | 3 - coast/diagnostics/profile_stratification.py | 3 +- .../anchor_plots_of_nsea_wvel.py | 150 ++++++++++ .../configuration_gallery/blz_example_plot.py | 39 +++ .../seasia_dic_example_plot.py | 40 +++ .../plot_validation_gridded_data.py | 150 ++++++++++ .../plot_validation_mask_means.py | 131 ++++++++ .../plot_validation_surface_errors.py | 132 +++++++++ .../stratification_pycnocline_diagnostics.py | 279 ++++++++++++++++++ .../test_gridded_diagnostics_methods.py | 1 - 12 files changed, 922 insertions(+), 18 deletions(-) create mode 100755 example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py create mode 100755 example_scripts/configuration_gallery/blz_example_plot.py create mode 100644 example_scripts/configuration_gallery/seasia_dic_example_plot.py create mode 100644 example_scripts/profile_validation/plot_validation_gridded_data.py create mode 100644 example_scripts/profile_validation/plot_validation_mask_means.py create mode 100644 example_scripts/profile_validation/plot_validation_surface_errors.py create mode 100755 example_scripts/stratification_pycnocline_diagnostics.py diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 113eaf43..db1085b9 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -211,15 +211,7 @@ def set_timezero_depths(self, dataset_domain, **kwargs): # keyword to allow calcution of bathymetry from scale factors # All bathymetry should now be mapped to bathy_metry -<<<<<<< HEAD -<<<<<<< HEAD calculate_bathymetry = kwargs.get("calculate_bathymetry", False) -======= - calculate_bathymetry = kwargs.get('calculate_bathymetry',False) ->>>>>>> b188dc8 (Added option to subset dataset and domain on loading. This reduces the big overhead of calculating depth witth big models.) -======= - calculate_bathymetry = kwargs.get("calculate_bathymetry", False) ->>>>>>> e591188 (Apply Black formatting to Python code.) try: if calculate_bathymetry: # calculate bathymetry from scale factors bathymetry, mask, time_mask = self.calc_bathymetry(dataset_domain) diff --git a/coast/data/profile.py b/coast/data/profile.py index f213b91f..050edd9c 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -838,7 +838,6 @@ def calculate_vertical_spacing(self): def construct_density( self, eos="EOS10", rhobar=False, Zd_mask: xr.DataArray = None, CT_AS=False, pot_dens=False, Tbar=True, Sbar=True ): - """ Constructs the in-situ density using the salinity, temperature and depth fields. Adds a density attribute to the profile dataset @@ -879,7 +878,6 @@ def construct_density( debug(f'Constructing in-situ density for {get_slug(self)} with EOS "{eos}"') try: - if eos != "EOS10": raise ValueError(str(self) + ": Density calculation for " + eos + " not implemented.") @@ -934,7 +932,6 @@ def construct_density( density = np.ma.masked_invalid(gsw.rho(sal_absolute, temp_conservative, pressure_absolute)) new_var_name = "density" else: # calculate density with depth integrated T S - if hasattr(self.dataset, "dz"): # Requires spacing variable. Test to see if variable exists pass else: # Create it @@ -1017,7 +1014,6 @@ def construct_density( error(err) def calculate_vertical_mask(self, Zmax=200): - """ Calculates a mask to a specified level Zmax. 1 for sea; 0 for below sea bed and linearly ramped for last level diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 0cdbe7c4..980216c8 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -17,7 +17,6 @@ class ProfileHydrography(Indexed): - ############################################################################### def __init__(self, filename="none", dataset_names="none", config="", region_bounds=[]): """Reads and manipulates lists of hydrographic profiles. @@ -205,7 +204,6 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: # depth from model print("Depth from model") for ip in range(nprof): - DP[ip] = 0.0 rr = 0.0 for iS in range(0, 4): @@ -227,7 +225,6 @@ def stratification_metrics(self, Zmax: int = 200, DZMAX: int = 30) -> None: DP[DP == 0] = np.nan for ip in range(nprof): - Dp = DP[ip] T[:] = tmp[ip, :] S[:] = sal[ip, :] diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 3bacd6c2..3ca4ff44 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -130,7 +130,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): Writes self.dataset.pea """ # may be duplicated in other branches. Uses the integral of T&S rather than integral of rho approach - #%% + # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables profile = ProfileStratification.clean_data(profile, gridded, Zmax) @@ -207,7 +207,6 @@ def quick_plot(self, var: xr.DataArray = None): fig = None ax = None for var in var_lst: - title_str = var.attrs["standard_name"] + " (" + var.attrs["units"] + ")" fig, ax = geo_scatter( diff --git a/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py b/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py new file mode 100755 index 00000000..a3e705e3 --- /dev/null +++ b/example_scripts/configuration_gallery/anchor_plots_of_nsea_wvel.py @@ -0,0 +1,150 @@ +## ANChor_plots_of_NSea_pycnocline.py +""" + +DEV_jelt/NEMO_diag/ANChor +This needs to move to the above +""" + +# %% +import coast +import matplotlib.pyplot as plt + +# import matplotlib.colors as colors # colormap fiddling + +################################################# +# %% Loading and initialising methods ## +################################################# + +dir_nam = "/projectsa/anchor/NEMO/AMM60/" +fil_nam = "AMM60_1h_20100818_20100822_NorthSea.nc" +dom_nam = "/projectsa/FASTNEt/jelt/AMM60/mesh_mask.nc" + +dir_nam = "/projectsa/NEMO/jelt/AMM60_ARCHER_DUMP/AMM60smago/EXP_NSea/OUTPUT/" +fil_nam = "AMM60_1h_20120204_20120208_NorthSea.nc" + +config = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_w.json" + +chunks = { + "x_dim": 10, + "y_dim": 10, + "t_dim": 10, +} # Chunks are prescribed in the config json file, but can be adjusted while the data is lazy loaded. +sci_w = coast.Gridded(dir_nam + fil_nam, dom_nam, config=config) +sci_w.dataset.chunk(chunks) + +# % NEMO output is not standard with u,v fields included with w-pts. Tidy to avoid confusion +sci_w.dataset = sci_w.dataset.drop_vars(["uo", "vo", "depthv"]) +sci_w.dataset = sci_w.dataset.swap_dims({"depthw": "z_dim"}) + +################################################# +# %% subset of data and domain ## +################################################# +# Pick out a North Sea subdomain +ind_sci = sci_w.subset_indices(start=[51, -4], end=[60, 15]) +sci_nwes_w = sci_w.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nswes = northwest europe shelf + +# %% Compute a diffusion from w-vel +Kz = (sci_nwes_w.dataset.wo * sci_nwes_w.dataset.e3_0).sum(dim="z_dim").mean(dim="t_dim") + +# plot map +lon = sci_nwes_w.dataset.longitude.squeeze() +lat = sci_nwes_w.dataset.latitude.squeeze() + +fig = plt.figure() +plt.rcParams["figure.figsize"] = 8, 8 + +fig = plt.figure() +plt.rcParams["figure.figsize"] = 8, 8 +plt.pcolormesh(lon, lat, Kz.squeeze(), shading="auto", cmap="seismic") +plt.title("Kz(w)") +plt.clim([-50e-3, 50e-3]) +plt.colorbar() +# fig.savefig("") + +# %% Transect Method +tran_w = coast.TransectT(sci_nwes_w, (51, 2.5), (61, 2.5)) + +lat_sec = tran_w.data.latitude.expand_dims(dim={"z_dim": 51}) +dep_sec = tran_w.data.depthw +wo_sec = tran_w.data.wo +# wo_sec = tran.data_F.wo.mean(dim='t_dim') + + +# %% Make map and profile plots +################################################# +for i in range(2): + for lat0 in [54, 57]: + if lat0 == 54: + sig0 = 10 + lon0 = 5 # depth level for maps + if lat0 == 57: + sig0 = 40 + lon0 = 2 + [JJ, II] = sci_nwes_w.find_j_i(lat=lat0, lon=lon0) + # short cuts for variable names + lon = sci_nwes_w.dataset.longitude.squeeze() + lat = sci_nwes_w.dataset.latitude.squeeze() + dep = sci_nwes_w.dataset.depth_0[:, :, :] + + fig = plt.figure() + plt.rcParams["figure.figsize"] = 8, 8 + + fig = plt.figure() + plt.rcParams["figure.figsize"] = 8, 8 + plt.pcolormesh(lon, lat, sci_nwes_w.dataset.wo[i, sig0, :, :] * 3600 * 24, shading="auto", cmap="seismic") + plt.plot(lon[JJ, II], lat[JJ, II], "r+") + plt.title(f"t={str(i)}: w-vel (m/day) at level {sig0}") + plt.clim([-5, 5]) + plt.colorbar() + fig.savefig(f"w_map_sig{sig0}_{str(i).zfill(3)}.png") + + fig = plt.figure() + plt.rcParams["figure.figsize"] = 8, 8 + + plt.subplot(1, 2, 1) + plt.plot(sci_nwes_w.dataset.wo[i, :, JJ, II] * 3600 * 24, dep[:, JJ, II], "+") + plt.title(f"w-vel (m/day) at ({lat0}N,{lon0}E)") + plt.xlim([-15, 15]) + plt.ylabel("depth (m)") + plt.gca().invert_yaxis() + + plt.subplot(1, 2, 2) + plt.plot(sci_nwes_w.dataset.avm[i, :, JJ, II] * 1e3, dep[:, JJ, II], "+") + plt.title(f"t={str(i)}: avm*1E3") + plt.ylabel("depth (m)") + plt.xlim([0, 40]) + plt.gca().invert_yaxis() + fig.savefig(f"w_prof_{lat0}N_{str(i).zfill(3)}.png") + + fig = plt.figure() + plt.rcParams["figure.figsize"] = 8, 8 + plt.pcolormesh(lat_sec, dep_sec, wo_sec.isel(t_dim=i) * 3600 * 24, shading="auto", cmap="seismic") + plt.colorbar() + plt.title(f"t={str(i)}: w-vel section (m/day)") + plt.xlim([51, 60]) + plt.ylim([0, 150]) + plt.xlabel("latitude") + plt.ylabel("depth (m)") + plt.clim([-20, 20]) + plt.gca().invert_yaxis() + fig.savefig(f"w_section_t_{str(i).zfill(3)}.png") + + plt.close("all") + + +# %% Plot sections +fig = plt.figure() +plt.rcParams["figure.figsize"] = 8, 8 + +plt.subplot(1, 1, 1) + +plt.pcolormesh(lat_sec, dep_sec, wo_sec.mean(dim="t_dim") * 3600 * 24, shading="auto", cmap="seismic") +plt.title("w-vel t-mean section") +plt.xlim([51, 60]) +plt.ylim([0, 150]) +plt.clim([-20, 20]) +plt.xlabel("latitude") +plt.ylabel("depth (m)") +plt.gca().invert_yaxis() +plt.colorbar() +fig.savefig("w_section_tmean.png") diff --git a/example_scripts/configuration_gallery/blz_example_plot.py b/example_scripts/configuration_gallery/blz_example_plot.py new file mode 100755 index 00000000..07ca6186 --- /dev/null +++ b/example_scripts/configuration_gallery/blz_example_plot.py @@ -0,0 +1,39 @@ +""" +blz_example_plot.py + +Make simple Belize SSH plot. + +""" + +# %% +import coast +import matplotlib.pyplot as plt + +################################################# +# %% Loading data +################################################# + + +dir_nam = "/projectsa/accord/GCOMS1k/OUTPUTS/BLZE12_02/2015/" +fil_nam = "BLZE12_1h_20151101_20151130_grid_T.nc" +dom_nam = "/projectsa/accord/GCOMS1k/INPUTS/BLZE12_C1/domain_cfg.nc" +config_t = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_t.json" +config_u = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_u.json" +config_v = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_v.json" +config_w = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_w.json" + +sci_t = coast.Gridded(dir_nam + fil_nam, dom_nam, config=config_t) + +sci_u = coast.Gridded(dir_nam + fil_nam.replace("grid_T", "grid_U"), dom_nam, config=config_u) + +sci_v = coast.Gridded(dir_nam + fil_nam.replace("grid_T", "grid_V"), dom_nam, config=config_v) + +# sci_v = coast.Nemo(dir_nam + fil_nam.replace("grid_T", "grid_V"), dom_nam, grid_ref="v-grid", multiple=False) + +# create an empty w-grid object, to store stratification +sci_w = coast.Gridded(fn_domain=dom_nam, config=config_w) + + +# %% Plot +plt.pcolormesh(sci_t.dataset.ssh.isel(t_dim=0)) +plt.show() diff --git a/example_scripts/configuration_gallery/seasia_dic_example_plot.py b/example_scripts/configuration_gallery/seasia_dic_example_plot.py new file mode 100644 index 00000000..a2d72f37 --- /dev/null +++ b/example_scripts/configuration_gallery/seasia_dic_example_plot.py @@ -0,0 +1,40 @@ +""" +seasia_r12_example_plot_bgc.py + +Make simple SEAsia 1/12 deg DIC plot. + +""" +# %% +import coast +import matplotlib.pyplot as plt + + +################################################# +# %% Loading data +################################################# +path_examples = "./example_files/" +## data local in livljobs : /projectsa/COAsT/NEMO_example_data/SEAsia_R12/ +path_config = "./config/" + +fn_seasia_domain = path_examples + "coast_example_domain_SEAsia.nc" +fn_seasia_config_bgc = path_config + "example_nemo_bgc.json" +fn_seasia_var = path_examples + "coast_example_SEAsia_BGC_1990.nc" + +seasia_bgc = coast.Gridded(fn_data=fn_seasia_var, fn_domain=fn_seasia_domain, config=fn_seasia_config_bgc) + +# %% Plot DIC +fig = plt.figure() +plt.pcolormesh( + seasia_bgc.dataset.longitude, + seasia_bgc.dataset.latitude, + seasia_bgc.dataset.dic.isel(t_dim=0).isel(z_dim=0), + cmap="RdYlBu_r", + vmin=1600, + vmax=2080, +) +plt.colorbar() +plt.title("DIC, mmol/m^3") +plt.xlabel("longitude") +plt.ylabel("latitude") +plt.show() +# fig.savefig("seasia_DIC_surface.png") diff --git a/example_scripts/profile_validation/plot_validation_gridded_data.py b/example_scripts/profile_validation/plot_validation_gridded_data.py new file mode 100644 index 00000000..4bcc2176 --- /dev/null +++ b/example_scripts/profile_validation/plot_validation_gridded_data.py @@ -0,0 +1,150 @@ +""" +Plot up surface or bottom (or any fixed level) errors from a profile object +with no z_dim (vertical dimension). Provide an array of netcdf files and +mess with the options to get a figure you like. + +You can define how many rows and columns the plot will have. This script will +plot the provided list of netcdf datasets from left to right and top to bottom. + +A colorbar will be placed right of the figure. +""" + +import xarray as xr +import matplotlib.pyplot as plt +import numpy as np +import sys + +sys.path.append("/Users/dbyrne/code/COAsT") +import coast +import pandas as pd + +# %% File settings +run_name = "test" + +# List of analysis output files. Profiles from each will be plotted +# on each axis of the plot +fn_list = [ + "~/transfer/test_grid.nc", + "~/transfer/test_grid.nc", +] + +# Filename for the output +fn_out = "/Users/dbyrne/transfer/surface_gridded_errors_{0}.png".format(run_name) + +# %% General Plot Settings +var_name = "abs_diff_temperature" # Variable name in analysis file to plot +# If you used var modified to make gridded data +# then this is where to select season etc. +save_plot = False + +# Masking out grid cells that don't contain many points +min_points_in_average = 5 +name_of_count_variable = "grid_N" + +# Subplot axes settings +n_r = 2 # Number of subplot rows +n_c = 2 # Number of subplot columns +figsize = (10, 5) # Figure size +lonbounds = [-15, 9.5] # Longitude bounds +latbounds = [45, 64] # Latitude bounds +subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) +# Leave some space on right for colorbar +# Scatter opts +marker_size = 3 # Marker size +cmap = "bwr" # Colormap for normal points +clim = (-1, 1) # Color limits for normal points +discrete_cmap = True # Discretize colormap +cmap_levels = 14 + +# Labels and Titles +fig_title = "SST Errors" # Whole figure title +title_fontsize = 13 # Fontsize of title +title_fontweight = "bold" # Fontweight to use for title +dataset_names = ["CO9p0", "CO9p0", "CO9p0"] # Names to use for labelling plots +subtitle_fontsize = 11 # Fontsize for dataset subtitles +subtitle_fontweight = "normal" # Fontweight for dataset subtitles + +# PLOT SEASONS. Make sure n_r = 2 and n_c = 2 +# If this option is true, only the first dataset will be plotted, with seasonal +# variables on each subplot. The season_suffixes will be added to var_name +# for each subplot panel. +plot_seasons = True +season_suffixes = ["DJF", "MAM", "JJA", "SON"] + +# %% Read and plotdata + +# Read all datasets into list +ds_list = [xr.open_dataset(dd) for dd in fn_list] +n_ds = len(ds_list) +n_ax = n_r * n_c + +# Create plot and flatten axis array +f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) +a_flat = a.flatten() + +# Dicretize colormap maybe +if discrete_cmap: + cmap = plt.cm.get_cmap(cmap, cmap_levels) + +# Determine if we will extend the colormap or not +extend_cbar = [] + +# Loop over dataset +for ii in range(n_ax): + ur_index = np.unravel_index(ii, (n_r, n_c)) + + # Select season if required + if plot_seasons: + ds = ds_list[0] + var_ii = var_name + "_{0}".format(season_suffixes[ii]) + N_var = "{0}_{1}".format(name_of_count_variable, season_suffixes[ii]) + a_flat[ii].text(0.05, 1.02, season_suffixes[ii], transform=a_flat[ii].transAxes, fontweight="bold") + else: + ds = ds_list[ii] + var_ii = var_name + a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) + N_var = name_of_count_variable + + data = ds[var_ii].values + count_var = ds[N_var] + data[count_var < min_points_in_average] = np.nan + + # Scatter and set title + pc = a_flat[ii].pcolormesh( + ds.longitude, + ds.latitude, + data, + cmap=cmap, + vmin=clim[0], + vmax=clim[1], + ) + + # Will we extend the colorbar for this dataset? + extend_cbar.append(coast.plot_util.determine_colorbar_extension(data, clim[0], clim[1])) + +# Set Figure title +f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) + + +# Set tight figure layout +f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) + +# Handle colorbar -- will we extend it? +if "both" in extend_cbar: + extend = "both" +elif "max" in extend_cbar and "min" in extend_cbar: + extend = "both" +elif "max" in extend_cbar: + extend = "max" +elif "min" in extend_cbar: + extend = "min" +else: + extend = "neither" +cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) +f.colorbar(pc, cax=cbar_ax, extend=extend) + +# Save plot maybe +if save_plot: + f.savefig(fn_out) diff --git a/example_scripts/profile_validation/plot_validation_mask_means.py b/example_scripts/profile_validation/plot_validation_mask_means.py new file mode 100644 index 00000000..18b26098 --- /dev/null +++ b/example_scripts/profile_validation/plot_validation_mask_means.py @@ -0,0 +1,131 @@ +""" +For plotting analysis data from a netcdf file created using COAsT.Profile.mask_means(). +This will plot multiple datasets onto a set of subplots. Each subplot is for +a different averaging region. + +At the top of this script, you can set the paths to the netcdf files to plot +and where to save. If you have multiple model runs to plot, provide a list +of file paths (strings). + +Below this section are a bunch of parameters you can set, with explanations in +comments. Edit this as much as you like or even go into the plotting code below. +""" + +import xarray as xr +import matplotlib.pyplot as plt +import numpy as np + +# %% File settings +run_name = "test" + +# List of analysis output files. Profiles from each will be plotted +# on each axis of the plot +fn_list = ["/Users/dbyrne/transfer/mask_means_daily_test.nc", "/Users/dbyrne/transfer/mask_means_daily_test.nc"] + +# Filename for the output +fn_out = "/Users/dbyrne/transfer/regional_means_{0}.png".format(run_name) + +# %% General Plot Settings +region_ind = [0, 1, 2, 3, 4, 5, 6, 7, 8] # Region indices (in analysis) to plot +region_names = ["A", "B", "C", "D", "E", "F", "G", "H", "I"] # Region names, will be used for titles in plot +var_name = "profile_average_abs_diff_temperature" # Variable name in analysis file to plot +plot_zero_line = True # Plot a black vertical line at x = 0 +plot_mean_depth = False # Plot the mean bathymetric depth. Make sure 'bathymetry' is in the analysis dataset +save_plot = False # Boolean to save plot or not + +ref_depth = np.concatenate((np.arange(1, 100, 2), np.arange(100, 300, 5), np.arange(300, 1000, 50))) # Data depths + +# Subplot axes settings +n_r = 2 # Number of subplot rows +n_c = 5 # Number of subplot columns +figsize = (7, 7) # Figure size +sharey = True # Align y axes +sharex = False # Align x axes +subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +fig_pad = (0.075, 0.075, 0.075, 0.1) # Whole figure padding as % (left, top, right, bottom) +max_depth = 200 # Maximum plot depth + +# Legend settings +legend_str = ["CO9p0", "CO9p0_2"] # List of strings to use in legend (match with fn_list ordering) +legend_index = 9 # Axis index to put legend (flattened index, start from 0). +# Good to place in an empty subplot +legend_pos = "upper right" # Position for legend, using matplitlib legend string +legend_fontsize = 9 + +# Labels and Titles +xlabel = "Absolute Error (degC)" # Xlabel string +xlabelpos = (figsize[0] / 2, 0) # (x,y) position of xlabel +ylabel = "Depth (m)" # Ylabel string +ylabelpos = (figsize[1] / 2, 0) # (x,y) position of ylabel +fig_title = "Regional MAE || All Seasons" # Whole figure title +label_fontsize = 11 # Fontsize of all labels +label_fontweight = "normal" # Fontweight to use for labels and subtitles +title_fontsize = 13 # Fontsize of title +title_fontweight = "bold" # Fontweight to use for title + + +# %% SCRIPT: READ AND PLOT DATA + +# Read all datasets into list +ds_list = [xr.open_dataset(dd) for dd in fn_list] +n_ds = len(ds_list) +n_reg = len(region_ind) +n_ax = n_r * n_c + +# Create plot and flatten axis array +f, a = plt.subplots(n_r, n_c, figsize=figsize, sharex=sharex, sharey=sharey) +a_flat = a.flatten() + +# Loop over regions +for ii in range(n_ax): + if ii >= n_reg: + a_flat[ii].axis("off") + continue + + # Get the index of this region + index = region_ind[ii] + + # Loop over datasets and plot their variable + p = [] + for pp in range(n_ds): + ds = ds_list[pp] + p.append(a_flat[ii].plot(ds[var_name][index], ref_depth)[0]) + + # Do some plot things + a_flat[ii].set_title(region_names[ii]) + a_flat[ii].grid() + a_flat[ii].set_ylim(0, max_depth) + + # Plot fixed lines at 0 and mean depth + if plot_zero_line: + a_flat[ii].plot([0, 0], [0, max_depth], c="k", linewidth=1, linestyle="-") + if plot_mean_depth: + a_flat[ii].plot() + + # Invert y axis + a_flat[ii].invert_yaxis() + +# Make legend +a_flat[legend_index].legend(p, legend_str, fontsize=legend_fontsize) + +# Set Figure title +f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) + +# Set x and y labels +f.text( + xlabelpos[0], + xlabelpos[1], + xlabel, + va="center", + rotation="horizontal", + fontweight=label_fontweight, + fontsize=label_fontsize, +) + +# Set tight figure layout +f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) + +# Save plot maybe +if save_plot: + f.savefig(fn_out) diff --git a/example_scripts/profile_validation/plot_validation_surface_errors.py b/example_scripts/profile_validation/plot_validation_surface_errors.py new file mode 100644 index 00000000..fc4f45e7 --- /dev/null +++ b/example_scripts/profile_validation/plot_validation_surface_errors.py @@ -0,0 +1,132 @@ +""" +Plot up surface or bottom (or any fixed level) errors from a profile object +with no z_dim (vertical dimension). Provide an array of netcdf files and +mess with the options to get a figure you like. + +You can define how many rows and columns the plot will have. This script will +plot the provided list of netcdf datasets from left to right and top to bottom. + +A colorbar will be placed right of the figure. +""" + +import xarray as xr +import matplotlib.pyplot as plt +import numpy as np +import sys + +sys.path.append("/Users/dbyrne/code/COAsT") +import coast +import pandas as pd + +# %% File settings +run_name = "test" + +# List of analysis output files. Profiles from each will be plotted +# on each axis of the plot +fn_list = [ + "/Users/dbyrne/transfer/surface_data_test.nc", + "/Users/dbyrne/transfer/surface_data_test.nc", +] + +# Filename for the output +fn_out = "/Users/dbyrne/transfer/surface_errors_{0}.png".format(run_name) + +# %% General Plot Settings +var_name = "diff_temperature" # Variable name in analysis file to plot +save_plot = False + +# Subplot axes settings +n_r = 1 # Number of subplot rows +n_c = 2 # Number of subplot columns +figsize = (10, 5) # Figure size +lonbounds = [-18, 9.5] # Longitude bounds +latbounds = [45, 64] # Latitude bounds +subplot_padding = 0.5 # Amount of vertical and horizontal padding between plots +fig_pad = (0.075, 0.075, 0.1, 0.1) # Figure padding (left, top, right, bottom) +# Leave some space on right for colorbar +# Scatter opts +marker_size = 3 # Marker size +cmap = "bwr" # Colormap for normal points +clim = (-0.35, 0.35) # Color limits for normal points +discrete_cmap = True # Discretize colormap +cmap_levels = 13 + +# Labels and Titles +fig_title = "SST Errors" # Whole figure title +title_fontsize = 13 # Fontsize of title +title_fontweight = "bold" # Fontweight to use for title +dataset_names = ["CO9p0", "CO9p0", "CO9p0"] # Names to use for labelling plots +subtitle_fontsize = 11 # Fontsize for dataset subtitles +subtitle_fontweight = "normal" # Fontweight for dataset subtitles + +# Season opts +select_season = True # Only plot data from specified season +season_str = "DJF" # DJF, MAM, JJA or SON + + +# %% Read and plotdata + +# Read all datasets into list +ds_list = [xr.open_dataset(dd)[var_name] for dd in fn_list] +n_ds = len(ds_list) +n_ax = n_r * n_c + +# Create plot and flatten axis array +f, a = coast.plot_util.create_geo_subplots(lonbounds, latbounds, n_r, n_c, figsize=figsize) +a_flat = a.flatten() + +# Dicretize colormap maybe +if discrete_cmap: + cmap = plt.cm.get_cmap(cmap, cmap_levels) + +# Determine if we will extend the colormap or not +extend_cbar = [] + +# Loop over dataset +for ii in range(n_ax): + ur_index = np.unravel_index(ii, (n_r, n_c)) + + # If we are not differencing datasets + ds = ds_list[ii] + + # Select season if required + if select_season: + seasons = coast.general_utils.determine_season(ds.time) + s_ind = seasons == season_str + ds = ds.isel(profile=s_ind) + + # Scatter and set title + sc = a_flat[ii].scatter( + ds.longitude, ds.latitude, c=ds, s=marker_size, cmap=cmap, vmin=clim[0], vmax=clim[1], linewidths=0 + ) + a_flat[ii].set_title(dataset_names[ii], fontsize=subtitle_fontsize, fontweight=subtitle_fontweight) + + # Will we extend the colorbar for this dataset? + extend_cbar.append(coast.plot_util.determine_colorbar_extension(ds, clim[0], clim[1])) + + +# Set Figure title +f.suptitle(fig_title, fontsize=title_fontsize, fontweight=title_fontweight) + + +# Set tight figure layout +f.tight_layout(w_pad=subplot_padding, h_pad=subplot_padding) +f.subplots_adjust(left=(fig_pad[0]), bottom=(fig_pad[1]), right=(1 - fig_pad[2]), top=(1 - fig_pad[3])) + +# Handle colorbar -- will we extend it? +if "both" in extend_cbar: + extend = "both" +elif "max" in extend_cbar and "min" in extend_cbar: + extend = "both" +elif "max" in extend_cbar: + extend = "max" +elif "min" in extend_cbar: + extend = "min" +else: + extend = "neither" +cbar_ax = f.add_axes([(1 - fig_pad[2] + fig_pad[2] * 0.15), 0.15, 0.025, 0.7]) +f.colorbar(sc, cax=cbar_ax, extend=extend) + +# Save plot maybe +if save_plot: + f.savefig(fn_out) diff --git a/example_scripts/stratification_pycnocline_diagnostics.py b/example_scripts/stratification_pycnocline_diagnostics.py new file mode 100755 index 00000000..b69fcae9 --- /dev/null +++ b/example_scripts/stratification_pycnocline_diagnostics.py @@ -0,0 +1,279 @@ +""" +stratification_pycnocline_diagnostics.py + +Demonstration of pycnocline depth and thickness diagnostics. +The first and second depth moments of stratification are computed as proxies +for pycnocline depth and thickness, suitable for a nearly two-layer fluid. + + +""" + +# %% +import coast +import numpy as np +import os +import matplotlib.pyplot as plt +import matplotlib.colors as colors # colormap fiddling + +################################################# +# %% Loading data +################################################# + +# Loading AMM60 data if it is available +try: + config = "AMM60" + dir_AMM60 = "/projectsa/COAsT/NEMO_example_data/AMM60/" + fil_nam_AMM60 = "AMM60_1d_20100704_20100708_grid_T.nc" + config_t = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_t.json" + config_w = "/work/jelt/GitHub/COAsT/config/example_nemo_grid_w.json" + mon = "July" + # mon = 'Feb' + + if mon == "July": + fil_names_AMM60 = "AMM60_1d_201007*_grid_T.nc" + elif mon == "Feb": + fil_names_AMM60 = "AMM60_1d_201002*_grid_T.nc" + + chunks = { + "x_dim": 10, + "y_dim": 10, + "t_dim": 10, + } # Chunks are prescribed in the config json file, but can be adjusted while the data is lazy loaded. + sci_t = coast.Gridded( + fn_data=dir_AMM60 + fil_names_AMM60, fn_domain=dir_AMM60 + "mesh_mask.nc", config=config_t, multiple=True + ) + sci_t.dataset = sci_t.dataset.chunk(chunks) + + # create an empty w-grid object, to store stratification + sci_w = coast.Gridded(fn_domain=dir_AMM60 + "mesh_mask.nc", config=config_w) + +# OR load in AMM7 example data +except: + config = "AMM7" + dn_files = "./example_files/" + + if not os.path.isdir(dn_files): + print("please go download the examples file from https://linkedsystems.uk/erddap/files/COAsT_example_files/") + dn_files = input("what is the path to the example files:\n") + if not os.path.isdir(dn_files): + print(f"location f{dn_files} cannot be found") + + dn_fig = "unit_testing/figures/" + fn_nemo_grid_t_dat = "nemo_data_T_grid_Aug2015.nc" + fn_nemo_dom = "coast_example_nemo_domain.nc" + config_t = "config/example_nemo_grid_t.json" + config_w = "config/example_nemo_grid_w.json" + + sci_t = coast.Gridded(dn_files + fn_nemo_grid_t_dat, dn_files + fn_nemo_dom, config=config_t, multiple=True) + + # create an empty w-grid object, to store stratification + sci_w = coast.Gridded(fn_domain=dn_files + fn_nemo_dom, config=config_w) +print("* Loaded ", config, " data") + +################################################# +# %% subset of data and domain ## +################################################# +# Pick out a North Sea subdomain +print("* Extract North Sea subdomain") +ind_sci = sci_t.subset_indices(start=[51, -4], end=[62, 15]) +sci_nwes_t = sci_t.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nwes = northwest europe shelf +ind_sci = sci_w.subset_indices(start=[51, -4], end=[62, 15]) +sci_nwes_w = sci_w.isel(y_dim=ind_sci[0], x_dim=ind_sci[1]) # nwes = northwest europe shelf + +# %% Apply masks to temperature and salinity +if config == "AMM60": + sci_nwes_t.dataset["temperature_m"] = sci_nwes_t.dataset.temperature.where( + sci_nwes_t.dataset.mask.expand_dims(dim=sci_nwes_t.dataset["t_dim"].sizes) > 0 + ) + sci_nwes_t.dataset["salinity_m"] = sci_nwes_t.dataset.salinity.where( + sci_nwes_t.dataset.mask.expand_dims(dim=sci_nwes_t.dataset["t_dim"].sizes) > 0 + ) + +else: + # Apply fake masks to temperature and salinity + sci_nwes_t.dataset["temperature_m"] = sci_nwes_t.dataset.temperature + sci_nwes_t.dataset["salinity_m"] = sci_nwes_t.dataset.salinity + + +# %% Construct in-situ density and stratification +print("* Construct in-situ density and stratification") +sci_nwes_t.construct_density(eos="EOS10") + +# %% Construct stratification. t-pts --> w-pts +print("* Construct stratification. t-pts --> w-pts") +sci_nwes_w = sci_nwes_t.differentiate( + "density", dim="z_dim", out_var_str="rho_dz", out_obj=sci_nwes_w +) # --> sci_nwes_w.rho_dz + +################################################# +# %% Create internal tide diagnostics object +print("* Create stratification diagnostics object") +strat = coast.GriddedStratification(sci_nwes_t, sci_nwes_w) + +# %% Construct pycnocline variables: depth and thickness +print("* Compute density and rho_dz if they didn" "t exist") +print("* Compute 1st and 2nd moments of stratification as pycnocline vars") +strat.construct_pycnocline_vars(sci_nwes_t, sci_nwes_w) + +# %% Plot pycnocline variables: depth and thickness +print("* Sample quick plot") +strat.quick_plot() + + +# %% Make transects +print("* Construct transects to inspect stratification. This is an abuse of the transect code...") +# Example usage: tran = coast.Transect( (54,-15), (56,-12), nemo_f, nemo_t, nemo_u, nemo_v ) +tran_it = coast.TransectT(strat, (51, 2.5), (61, 2.5)) +tran_w = coast.TransectT(sci_nwes_w, (51, 2.5), (61, 2.5)) +tran_t = coast.TransectT(sci_nwes_t, (51, 2.5), (61, 2.5)) +print(" - I have forced the w-pt nemo data and w-pt strat data into the t-point Transect objects\n") + +lat_sec = tran_t.data.latitude.expand_dims(dim={"z_dim": strat.nz}) +dep_sec = tran_t.data.depth_0 +tem_sec = tran_t.data.temperature_m.mean(dim="t_dim") + +sal_sec = tran_t.data.salinity_m.mean(dim="t_dim") +rho_sec = tran_t.data.density.mean(dim="t_dim") +strat_sec = tran_w.data.rho_dz.mean(dim="t_dim") + +zd_sec = tran_it.data.strat_1st_mom.mean(dim="t_dim", skipna=False) +zd_m_sec = tran_it.data.strat_1st_mom_masked.mean(dim="t_dim", skipna=False) + +zt_sec = tran_it.data.strat_2nd_mom.mean(dim="t_dim", skipna=False) +zt_m_sec = tran_it.data.strat_2nd_mom_masked.mean(dim="t_dim", skipna=False) + + +# %% Plot sections +################# +print("* Plot sections with pycnocline depth and thickness overlayed") +plt.pcolormesh(lat_sec, dep_sec, rho_sec) +plt.title("density section") +plt.xlim([51, 62]) +plt.ylim([0, 150]) +plt.clim([1025, 1028]) +plt.gca().invert_yaxis() +plt.colorbar() +plt.show() + +plt.pcolormesh(lat_sec, dep_sec, strat_sec) +plt.plot(tran_it.data.latitude, zd_sec, "g", label="unmasked") +plt.plot(tran_it.data.latitude, zd_sec + zt_sec, "g--") +plt.plot(tran_it.data.latitude, zd_sec - zt_sec, "g--") + +plt.plot(tran_it.data.latitude, zd_m_sec, "r.", label="masked") +plt.plot(tran_it.data.latitude, zd_m_sec + zt_m_sec, "r.") +plt.plot(tran_it.data.latitude, zd_m_sec - zt_m_sec, "r.") + +plt.title("stratification section with pycno vars") +plt.xlim([51, 62]) +plt.ylim([0, 150]) +plt.clim([-0.2, 0]) +plt.gca().invert_yaxis() +plt.colorbar() +plt.legend() +plt.show() + + +# %% Plot profile of density and stratification with strat_1st_mom in deep water +############################################################################# +print("* Plot profile of density and stratification with strat_1st_mom in deep water") +print( + " - When the stratification is not nearly two-layer, then then there is no sharp pycnocline for the 1st and 2nd moments to pick out. You end up with a thick \ +pycnocline and reduced precision on the depth\n" +) + +[JJ, II] = sci_nwes_t.find_j_i(lat=60, lon=2.5) +zd_plus = strat.dataset.strat_1st_mom[0, JJ, II] + strat.dataset.strat_2nd_mom[0, JJ, II] +zd_minus = strat.dataset.strat_1st_mom[0, JJ, II] - strat.dataset.strat_2nd_mom[0, JJ, II] +plt.plot(sci_nwes_w.dataset.rho_dz[0, :, JJ, II], sci_nwes_w.dataset.depth_0[:, JJ, II], "+") +plt.plot(strat.dataset.strat_1st_mom[0, JJ, II], "o", label="strat_1st_mom") +plt.plot( + [ + 0, + 0, + ], + [zd_plus, zd_minus], + "-", + label="strat_2nd_mom", +) +plt.title("stratification") +plt.ylabel("depth (m)") +plt.gca().invert_yaxis() +plt.legend() +plt.show() + +plt.plot(sci_nwes_t.dataset.density[0, :, JJ, II], sci_nwes_t.dataset.depth_0[:, JJ, II], "+") +plt.plot(1027, strat.dataset.strat_1st_mom[0, JJ, II], "o", label="strat_1st_mom") +plt.plot([1027, 1027], [zd_plus, zd_minus], "-", label="strat_2nd_mom") +plt.xlim([1026, 1028]) +plt.title("density") +plt.ylabel("depth (m)") +plt.gca().invert_yaxis() +plt.legend() +plt.show() + + +# %% Map pretty plots of North Sea pycnocline depth +print("* Map pretty plots of North Sea pycnocline depth") +print(" - we expect a RunTimeError here") + + +def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100): + new_cmap = colors.LinearSegmentedColormap.from_list( + "trunc({n},{a:.2f},{b:.2f})".format(n=cmap.name, a=minval, b=maxval), cmap(np.linspace(minval, maxval, n)) + ) + return new_cmap + + +cmap = plt.get_cmap("BrBG_r") +new_cmap = truncate_colormap(cmap, 0.2, 0.8) +# new_cmap.set_bad(color = '#bbbbbb') # light grey +new_cmap.set_under(color="w") # white. +# It would be nice to plot the unstratified regions different to the land. + + +H = sci_nwes_t.dataset.depth_0[-1, :, :].squeeze() +lat = sci_nwes_t.dataset.latitude.squeeze() +lon = sci_nwes_t.dataset.longitude.squeeze() + +zd = strat.dataset.strat_1st_mom_masked.where(H > 11).mean(dim="t_dim", skipna=True) # make nan the land +# skipna = True --> ignore masked events when averaging +# skipna = False --> if once masked then mean is masked. + +fig = plt.figure(figsize=(8, 9)) +# plt.rcParams["figure.figsize"] = (8.0, 12.0) + +ax = fig.add_subplot(111) +cz = plt.contour(lon, lat, H, levels=[11, 50, 100, 200], colors=["k", "k", "k", "k"], linewidths=[1, 1, 1, 1]) + +plt.contourf(lon, lat, zd, levels=np.arange(0, 40.0 + 10.0, 10.0), extend="both", cmap=new_cmap) +ax.set_facecolor("#bbbbbb") # Set 'underneath' to grey. contourf plots nothing for bad values + +plt.xlim([-3, 11]) +plt.ylim([51, 62]) +plt.colorbar() + + +lines = [ + cz.collections[i] for i in range(1, len(cz.collections)) +] # [ cz.collections[1], cz.collections[2], cz.collections[-1] ] +labels = [str(int(cz.levels[i])) + "m" for i in range(1, len(cz.levels))] +# labels = ['80m','200m','800m'] + +# Supress legend +# plt.legend(lines, labels, loc="lower right") + +# I expect to see RuntimeWarnings in this block +title_str = ( + strat.dataset["time"].mean(dim="t_dim").dt.strftime("%b %Y: ").values + + strat.dataset.strat_1st_mom.standard_name + + " (" + + strat.dataset.strat_1st_mom.units + + ")" +) +plt.title(title_str) +plt.xlabel("longitude") +plt.ylabel("latitude") +# plt.show() + +fig.savefig("strat_1st_mom.png", dpi=300) diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index 53ea159d..6760ad62 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -174,7 +174,6 @@ def test_circulation(self): plt.close("all") def test_calc_pea(self): - nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m From aeb140880bde57dd57ff51ccd449fe03fbe8e093 Mon Sep 17 00:00:00 2001 From: Jason T Holt Date: Wed, 19 Jul 2023 16:09:11 +0100 Subject: [PATCH 122/150] (old?) updates to profile_stratification.py --- coast/diagnostics/profile_stratification.py | 44 +++++++++++++++------ 1 file changed, 31 insertions(+), 13 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 3ca4ff44..a0d7ab01 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -7,6 +7,10 @@ from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug +#### + + +#### class ProfileStratification(Profile): # TODO All abstract methods should be implemented """ @@ -31,7 +35,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def clean_data(self,profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -41,9 +45,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value """ +#%% print("Cleaning the data") # find profiles good for SST and NBT dz_max = 25.0 + n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] @@ -53,9 +59,12 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 # Find good SST and SSS depths - if "bathymetry" in profile.dataset: - D_prf = profile.dataset.bathymetry.values + def first_nonzero(arr, axis=0, invalid_val=np.nan): + mask = arr!=0 + return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) + if "bathymetry" in gridded.dataset: profile.gridded_to_profile_2d(gridded, "bathymetry") + D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) @@ -65,10 +74,15 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): I_tmp = np.nonzero(np.any(test_tmp.values, axis=1))[0] I_sal = np.nonzero(np.any(test_sal.values, axis=1))[0] # - for ip in I_tmp: - good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) - for ip in I_sal: - good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) + #for ip in I_tmp: + # good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) + #for ip in I_sal: + # good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) + + good_sst=first_nonzero(test_tmp.values,axis=1) + good_sss=first_nonzero(test_sal.values,axis=1) + + I_tmp = np.where(np.isfinite(good_sst))[0] I_sal = np.where(np.isfinite(good_sss))[0] @@ -90,17 +104,20 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it - +#%% for i_prf in range(n_prf): + tmp = profile.dataset.potential_temperature.values[i_prf, :] sal = profile.dataset.practical_salinity.values[i_prf, :] z = profile.dataset.depth.values[i_prf, :] if any_tmp[i_prf]: tmp = coast.general_utils.fill_holes_1d(tmp) + tmp[np.isnan(z)] = np.nan tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) + sal[np.isnan(z)] = np.nan sal_clean[i_prf, :] = sal @@ -112,11 +129,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) - profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) - profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) + self.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + self.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + self.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") - +#%% return profile def calc_pea(self, profile: xr.Dataset, gridded, Zmax): @@ -133,7 +150,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables - profile = ProfileStratification.clean_data(profile, gridded, Zmax) + #profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -217,3 +234,4 @@ def quick_plot(self, var: xr.DataArray = None): ) return fig, ax + ############################################################################## From c41c59e1b23de457513fa65c311c4b6ad375142e Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Wed, 30 Aug 2023 17:35:26 +0100 Subject: [PATCH 123/150] Update potential_energy_tutorial.ipynb changes to get notebook path right when running from examples folder --- .../gridded/potential_energy_tutorial.ipynb | 1480 ++++++++++++++++- 1 file changed, 1451 insertions(+), 29 deletions(-) diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb index f5dc1d40..770356c0 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb @@ -18,14 +18,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "c4773751-3544-4ebd-a795-cfe128b70743", "metadata": {}, "outputs": [], "source": [ + "import os\n", + "os.chdir('../../../../')\n", "import coast\n", "import numpy as np\n", - "import os\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors # colormap fiddling\n", "import xarray as xr" @@ -33,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", "metadata": {}, "outputs": [], @@ -56,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "7677050c-775d-4172-9561-61c3c89aa77b", "metadata": {}, "outputs": [], @@ -80,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "269a51fc", "metadata": {}, "outputs": [], @@ -102,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8f55363d", "metadata": {}, "outputs": [], @@ -121,24 +122,506 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "6d0f5239-6f1d-4f7d-aa22-e51a9736fff6", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+       "    mask          (dim_mask, y_dim, x_dim) float64 1.0 1.0 1.0 ... 0.0 0.0 0.0
" + ], + "text/plain": [ + "\n", + "Dimensions: (y_dim: 375, x_dim: 297, dim_mask: 9)\n", + "Coordinates:\n", + " longitude (y_dim, x_dim) float32 -19.89 -19.78 -19.67 ... 12.89 13.0\n", + " latitude (y_dim, x_dim) float32 40.07 40.07 40.07 ... 65.0 65.0 65.0\n", + " region_names (dim_mask) " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "mm.quick_plot(mask_list)\n" ] @@ -244,10 +1197,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c1217563", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Show overlap\n", "mask_list.mask.sum(dim='dim_mask').plot( levels=(1,2,3,4))\n", @@ -287,7 +1272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4b72009d", "metadata": {}, "outputs": [], @@ -297,20 +1282,457 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "6ac2a67a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:       (t_dim: 7, dim_mask: 9)\n",
+       "Coordinates:\n",
+       "    time          (t_dim) datetime64[ns] 2015-08-01T12:00:00 ... 2015-08-07T1...\n",
+       "    region_names  (dim_mask) <U18 'whole domain' 'north sea' ... 'kattegat'\n",
+       "Dimensions without coordinates: t_dim, dim_mask\n",
+       "Data variables:\n",
+       "    PEA           (t_dim, dim_mask) float64 130.9 4.603 7.291 ... 0.2 1.515
" + ], + "text/plain": [ + "\n", + "Dimensions: (t_dim: 7, dim_mask: 9)\n", + "Coordinates:\n", + " time (t_dim) datetime64[ns] 2015-08-01T12:00:00 ... 2015-08-07T1...\n", + " region_names (dim_mask) " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Plot timeseries per region\n", "\n", @@ -328,9 +1750,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "coast_dev2", "language": "python", - "name": "python3" + "name": "coast_dev2" }, "language_info": { "codemirror_mode": { @@ -342,9 +1764,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From a6574ef54323e194020328aa5c8006d8422d3602 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Fri, 20 Oct 2023 16:12:48 +0100 Subject: [PATCH 124/150] Bug fixes to cleaning data - need to check this works ok. Update to notebook to test this --- coast/diagnostics/profile_stratification.py | 15 ++- .../profile/potential_energy_tutorial.ipynb | 125 +++++++----------- example_scripts/profile_test.py | 4 +- 3 files changed, 59 insertions(+), 85 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index a0d7ab01..d3af87fa 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -35,7 +35,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(self,profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Cleaning data for stratification metric calculations Stage 1:... @@ -96,6 +96,9 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): good_profile[test] = 0 ### + else: + print('error no bathy provided, cant clean the data') + return profile SST = np.zeros(n_prf) * np.nan SSS = np.zeros(n_prf) * np.nan @@ -129,14 +132,14 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): dims = ["id_dim", "z_dim"] profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) - self.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) - self.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) - self.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) + profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") #%% return profile - def calc_pea(self, profile: xr.Dataset, gridded, Zmax): + def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): """ Calculates Potential Energy Anomaly @@ -150,7 +153,7 @@ def calc_pea(self, profile: xr.Dataset, gridded, Zmax): # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables - #profile = ProfileStratification.clean_data(profile, gridded, Zmax) + profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb index 09020937..f8817ce2 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/potential_energy_tutorial.ipynb @@ -2,7 +2,6 @@ "cells": [ { "cell_type": "markdown", - "id": "5eca7994-6fa1-44e1-b95c-fc8a0fecf7bd", "metadata": {}, "source": [ "A demonstration to calculate the Potential Energy Anomaly for Profile data.\n" @@ -10,7 +9,6 @@ }, { "cell_type": "markdown", - "id": "14277e0d-4dbc-4e0f-b3a2-6853dca66d46", "metadata": {}, "source": [ "### Relevant imports and filepath configuration" @@ -18,11 +16,12 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "c4773751-3544-4ebd-a795-cfe128b70743", + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "import os\n", + "os.chdir('../../../../')\n", "import coast\n", "import numpy as np\n", "from os import path\n", @@ -32,8 +31,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "780605fd-ae53-4ec5-b7fd-80b2a2ee07ea", + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -46,7 +44,6 @@ }, { "cell_type": "markdown", - "id": "5d3f6987-f05d-4a54-a932-e4bbf84becb1", "metadata": {}, "source": [ "### Loading data" @@ -54,8 +51,7 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "7677050c-775d-4172-9561-61c3c89aa77b", + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -74,7 +70,6 @@ }, { "cell_type": "markdown", - "id": "798994a1", "metadata": {}, "source": [ "If you are using EN4 data, you can use the process_en4() routine to apply quality control flags to the data (replacing with NaNs):" @@ -82,8 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "58406dca", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +87,6 @@ }, { "cell_type": "markdown", - "id": "84a15c7b", "metadata": {}, "source": [ "### Inspect profile locations\n", @@ -102,9 +95,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "f5b2d233", - "metadata": {}, + "execution_count": 5, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -122,7 +116,7 @@ "(
, )" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -133,7 +127,6 @@ }, { "cell_type": "markdown", - "id": "d3e75a6d", "metadata": {}, "source": [ "### Calculates Potential Energy Anomaly\n", @@ -144,8 +137,7 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "e70f5db2", + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +146,24 @@ }, { "cell_type": "markdown", - "id": "3e056769", + "metadata": {}, + "source": [ + "Define a gridded object to supply the bathymetry" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "fn_nemo_dom = dn_files + \"coast_example_nemo_domain.nc\"\n", + "config_t = root + \"./config/example_nemo_grid_t.json\"\n", + "nemo = coast.Gridded(fn_domain=fn_nemo_dom, config=config_t)" + ] + }, + { + "cell_type": "markdown", "metadata": {}, "source": [ "Potential energy anomaly is calculated to a prescribed depth, Zmax:" @@ -162,8 +171,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "c49b40d3", + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -178,19 +186,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + "C:\\Users\\home\\anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", + " return func(*(_execute_task(a, cache) for a in args))\n", + "C:\\Users\\home\\anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", " return func(*(_execute_task(a, cache) for a in args))\n" ] } ], "source": [ "Zmax = 200 # metres\n", - "pa.calc_pea(profile, Zmax)" + "pa.calc_pea(profile, nemo, Zmax)" ] }, { "cell_type": "markdown", - "id": "74603291", "metadata": {}, "source": [ "In this calculation a number of steps happen within ProfileStratification: for a supplied Profile, first the vertical spacing is calculated\n", @@ -210,7 +219,6 @@ }, { "cell_type": "markdown", - "id": "8f897042-3697-4ddd-a812-04572500f0ec", "metadata": {}, "source": [ "## Make a plot\n", @@ -221,21 +229,12 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "a696835b", + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Anaconda3\\envs\\coast_dev\\lib\\site-packages\\dask\\core.py:119: RuntimeWarning: divide by zero encountered in divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -251,23 +250,22 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "bb540223", + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -287,61 +285,34 @@ { "cell_type": "code", "execution_count": null, - "id": "85229256", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 1, - "id": "a37a8291", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\\example_scripts\\notebooks\n" - ] - } - ], - "source": [ - "cd ../../" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 2, - "id": "ca0c825f", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\jholt\\Documents\\GitHub\\COAsT\n" - ] - } - ], - "source": [ - "cd ../../" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "25670a0f", "metadata": {}, "outputs": [], - "source": [ - "pwd" - ] + "source": [] }, { "cell_type": "code", "execution_count": null, - "id": "fd695e91", "metadata": {}, "outputs": [], "source": [] diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index e81723af..61c6a6f8 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -23,8 +23,8 @@ fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) -profile.match_to_grid(nemo) -profile.gridded_to_profile_2d(nemo, "bathymetry") +#profile.match_to_grid(nemo) +#profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres pa.calc_pea(profile, nemo, Zmax) From 2f81b380046ed06f1b61f751a104e4936d762e62 Mon Sep 17 00:00:00 2001 From: tobfer Date: Wed, 15 Nov 2023 11:32:29 +0000 Subject: [PATCH 125/150] add --- coast/data/config_parser.py | 7 +- coast/data/config_structure.py | 2 + coast/data/gridded.py | 10 +- .../gridded/contour_tutorial.ipynb | 2 +- .../introduction_to_gridded_class.ipynb | 4 +- .../gridded/potential_energy_tutorial.ipynb | 6 +- .../gridded/transect_tutorial.ipynb | 4 +- .../profile/experiments_ZPS.json | 11 ++ .../profile/senemo_grid_t.json | 95 ++++++++++ .../profile/stratification_tests.ipynb | 177 ++++++++++++++++++ 10 files changed, 306 insertions(+), 12 deletions(-) create mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/experiments_ZPS.json create mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json create mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb diff --git a/coast/data/config_parser.py b/coast/data/config_parser.py index d6520b83..9d92a8ed 100644 --- a/coast/data/config_parser.py +++ b/coast/data/config_parser.py @@ -15,7 +15,7 @@ def __init__(self, json_path: Union[Path, str]): Args: json_path (Union[Path, str]): path to json config file. """ - with open(json_path, "r") as j: + with open(json_path, "r", encoding='utf-8') as j: json_content = json.loads(j.read()) conf_type = ConfigTypes(json_content[ConfigKeys.TYPE]) if conf_type == ConfigTypes.GRIDDED: @@ -34,6 +34,7 @@ def _parse_gridded(json_content: dict) -> GriddedConfig: grid_ref = json_content[ConfigKeys.GRID_REF] proc_flags = json_content[ConfigKeys.PROC_FLAGS] chunks = json_content[ConfigKeys.CHUNKS] + zarr_file = json_content.get(ConfigKeys.ZARR, False) dataset = ConfigParser._get_datafile_object(json_content, ConfigKeys.DATASET) static_variables = ConfigParser._get_code_processing_object(json_content) try: @@ -48,6 +49,7 @@ def _parse_gridded(json_content: dict) -> GriddedConfig: domain=domain, processing_flags=proc_flags, code_processing=static_variables, + zarr_file=zarr_file, ) @staticmethod @@ -60,8 +62,9 @@ def _parse_indexed(json_content: dict) -> IndexedConfig: dimensionality = json_content[ConfigKeys.DIMENSIONALITY] proc_flags = json_content[ConfigKeys.PROC_FLAGS] chunks = json_content[ConfigKeys.CHUNKS] + zarr_file = json_content.get(ConfigKeys.ZARR, False) dataset = ConfigParser._get_datafile_object(json_content, ConfigKeys.DATASET) - return IndexedConfig(dimensionality=dimensionality, chunks=chunks, dataset=dataset, processing_flags=proc_flags) + return IndexedConfig(dimensionality=dimensionality, zarr_file=zarr_file, chunks=chunks, dataset=dataset, processing_flags=proc_flags) @staticmethod def _get_code_processing_object(json_content: dict) -> CodeProcessing: diff --git a/coast/data/config_structure.py b/coast/data/config_structure.py index d30f97be..229fb973 100644 --- a/coast/data/config_structure.py +++ b/coast/data/config_structure.py @@ -19,6 +19,7 @@ class ConfigKeys: GRID_REF = "grid_ref" PROC_FLAGS = "processing_flags" DATASET = "dataset" + ZARR = "zarr" DOMAIN = "domain" CODE_PROCESSING = "static_variables" DIM_MAP = "dimension_map" @@ -93,6 +94,7 @@ class Config: dataset: Dataset processing_flags: list chunks: dict + zarr_file: bool type: ConfigTypes diff --git a/coast/data/gridded.py b/coast/data/gridded.py index db1085b9..df92c1ec 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -129,7 +129,11 @@ def load_domain(self, fn_domain, chunks): # TODO Do something with this unused """Loads domain file and renames dimensions with dim_mapping_domain""" # Load xarray dataset info(f'Loading domain: "{fn_domain}"') - dataset_domain = xr.open_dataset(fn_domain) + if self.config.zarr_file: + dataset_domain = xr.open_zarr(fn_domain) + else: + dataset_domain = xr.open_zarr(fn_domain) + # dataset_domain = xr.open_dataset(fn_domain) self.domain_loaded = True # Rename dimensions for key, value in self.config.domain.dimension_map.items(): @@ -219,7 +223,9 @@ def set_timezero_depths(self, dataset_domain, **kwargs): bathymetry = dataset_domain.bathy_metry.squeeze() except AttributeError as err: - bathymetry = xr.zeros_like(dataset_domain.e1.squeeze()) + bathymetry = xr.zeros_like(dataset_domain.vmaskutil.squeeze()) + + # bathymetry = xr.zeros_like(dataset_domain.e1.squeeze()) ( warnings.warn( f"The model domain loaded, '{self.filename_domain}', does not contain the " diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/contour_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/contour_tutorial.ipynb index 57e83b69..f92d07f4 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/contour_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/contour_tutorial.ipynb @@ -302,7 +302,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/introduction_to_gridded_class.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/introduction_to_gridded_class.ipynb index 6a17898b..fece6015 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/introduction_to_gridded_class.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/introduction_to_gridded_class.ipynb @@ -291,9 +291,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb index 770356c0..acd42d53 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/potential_energy_tutorial.ipynb @@ -1750,9 +1750,9 @@ ], "metadata": { "kernelspec": { - "display_name": "coast_dev2", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "coast_dev2" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -1764,7 +1764,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.8" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/transect_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/transect_tutorial.ipynb index 0b9e0625..b18922af 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/gridded/transect_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/gridded/transect_tutorial.ipynb @@ -272,9 +272,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/experiments_ZPS.json b/example_scripts/notebook_tutorials/runnable_notebooks/profile/experiments_ZPS.json new file mode 100644 index 00000000..2f2868f7 --- /dev/null +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/experiments_ZPS.json @@ -0,0 +1,11 @@ +{ + "exp_names": [ + "ZPS_TIDE" + ], + "dirs":[ + "/gws/nopw/j04/class_vol2/senemo/cwilso01/ZPS_REF_TIDE/OUTPUTS/" + ], + "domains":[ + "/gws/nopw/j04/class_vol2/senemo/cwilso01/EXP_REF_NOTIDE/domcfg_eORCA025_v2.nc" + ] +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json b/example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json new file mode 100644 index 00000000..99ba2fc2 --- /dev/null +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json @@ -0,0 +1,95 @@ +{ + "type": "gridded", + "dimensionality": 3, + "chunks": {"time_counter":2}, + "zarr": true, + "grid_ref": { + "t-grid": [ + "glamt", + "gphit", + "e1t", + "e2t", + "e3t_0", + "deptht_0", + "tmask", + "bottom_level", + "mbathy", + "hbatt" + ] + }, + "dataset": { + "dimension_map": { + "time_counter": "t_dim", + "deptht": "z_dim", + "nav_lev": "z_dim", + "y": "y_dim", + "x": "x_dim", + "x_grid_T": "x_dim", + "y_grid_T": "y_dim" + }, + "variable_map": { + "time_counter": "time", + "votemper": "temperature", + "thetao": "temperature", + "temp": "temperature", + "toce": "temperature", + "thetao_con": "temperature", + "so": "salinity", + "vosaline": "salinity", + "soce": "salinity", + "so_abs": "salinity", + "sossheig": "ssh", + "zos": "ssh" + }, + "coord_vars": [ + "longitude", + "latitude", + "time", + "depth_0" + ] + }, + "domain": { + "dimension_map": { + "t": "t_dim0", + "x": "x_dim", + "y": "y_dim", + "z": "z_dim", + "nav_lev": "z_dim" + }, + "variable_map": { + "time_counter": "time0", + "glamt": "longitude", + "gphit": "latitude", + "e1t": "e1", + "e2t": "e2", + "e3t_0": "e3_0", + "e3w_0": "e3w_0", + "tmask":"mask", + "deptht_0": "depth_0", + "bottom_level": "bottom_level", + "mbathy":"bottom_level", + "hbatt":"bathymetry" + } + }, + "static_variables": { + "not_grid_vars": [ + "jpiglo", + "jpjglo", + "jpkglo", + "jperio", + "ln_zco", + "ln_zps", + "ln_sco", + "ln_isfcav" + ], + "delete_vars": [ + "nav_lat", + "nav_lon", + "deptht" + ] + }, + "processing_flags": [ + "example_flag1", + "example_flag2" + ] +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb new file mode 100644 index 00000000..1984b377 --- /dev/null +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 47, + "id": "29d6a04f-f18b-4231-845e-a205fe21b26a", + "metadata": {}, + "outputs": [], + "source": [ + "import coast\n", + "import xarray as xr\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38fb1589-6ed7-4744-8208-f2891e0dcb7d", + "metadata": {}, + "outputs": [], + "source": [ + "# ystart=1990\n", + "# ystop=2019\n", + "\n", + "# EXPNAM = \"ZPS_TIDE\"\n", + "# domain_datapath='/gws/nopw/j04/class_vol2/senemo/cwilso01/ZPS_REF_TIDE/OUTPUTS/'\n", + "# fn_nemo_dom = '/gws/nopw/j04/class_vol2/senemo/cwilso01/EXP_REF_NOTIDE/domcfg_eORCA025_v2.nc'\n", + "# fn_nemo_dat= coast.nemo_filename_maker(domain_datapath,ystart,ystop) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58364032-a05a-4944-9d53-2cc6e8cc1d38", + "metadata": {}, + "outputs": [], + "source": [ + "# domain_outpath='/home/users/jholt/work/SENEMO/ASSESSMENT/'\n", + "# DOMNAM='ORCA025-SE-NEMO'\n", + "# fn_out='{0}/{1}/{1}_{2}_{3}_{4}_SST_SSS_PEA_MonClimate.nc'.format(domain_outpath,DOMNAM,ystart,ystop,EXPNAM)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "ede39607-63d4-460f-9a59-701e9e236732", + "metadata": {}, + "outputs": [], + "source": [ + "fn_nemo_dom = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr\"\n", + "fn_nemo_dat = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr\"\n", + "fn_config_t_grid='senemo_grid_t.json'" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "27c30450-83a5-45fb-ba8f-2af1e3cf4fe4", + "metadata": {}, + "outputs": [], + "source": [ + "dom = xr.open_zarr(fn_nemo_dom)\n", + "t_grid = xr.open_zarr(fn_nemo_dat)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "67a8a9f0-07da-49bf-b7e2-d8e51d352ab1", + "metadata": {}, + "outputs": [], + "source": [ + "# x = coast.data.config_parser.ConfigParser(fn_config_t_grid).config" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "9e06d05d-ef45-4509-a3b5-0ffcd101bf16", + "metadata": {}, + "outputs": [], + "source": [ + "# ds = xr.open_dataset('../../../../example_files/coast_example_nemo_domain.nc')\n", + "# ds.variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063062c4-137e-4161-a643-51742ee87764", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "5c767512-7805-4921-95eb-7c2814493c65", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:230: UserWarning: The model domain loaded, 'https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr', does not contain the bathy_metry' variable. This will result in the NEMO.dataset.bathymetry variable being set to zero, which may result in unexpected behaviour from routines that require this variable.\n", + " warnings.warn(\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'Dataset' object has no attribute 'e3w_0'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nemo_dom\u001b[38;5;241m=\u001b[39m\u001b[43mcoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGridded\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn_domain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfn_nemo_dom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn_config_t_grid\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \u001b[39;00m\n", + "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:49\u001b[0m, in \u001b[0;36mGridded.__init__\u001b[0;34m(self, fn_data, fn_domain, multiple, config, workers, threads, memory_limit_per_worker, **kwargs)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m ConfigParser(config)\u001b[38;5;241m.\u001b[39mconfig\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mchunks:\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setup_grid_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmultiple\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_grid_obj(\u001b[38;5;28;01mNone\u001b[39;00m, multiple, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:100\u001b[0m, in \u001b[0;36mGridded._setup_grid_obj\u001b[0;34m(self, chunks, multiple, **kwargs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 99\u001b[0m dataset_domain \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrim_domain_size(dataset_domain) \u001b[38;5;66;03m# Trim domain size if self.data is smaller\u001b[39;00m\n\u001b[0;32m--> 100\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_timezero_depths\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataset_domain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# THIS ADDS TO dataset_domain. Should it be 'return'ed (as in trim_domain_size) or is implicit OK?\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmerge_domain_into_dataset(dataset_domain)\n\u001b[1;32m 104\u001b[0m debug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialised \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mget_slug(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:246\u001b[0m, in \u001b[0;36mGridded.set_timezero_depths\u001b[0;34m(self, dataset_domain, **kwargs)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrid_ref \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt-grid\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 246\u001b[0m e3w_0 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(\u001b[43mdataset_domain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43me3w_0\u001b[49m\u001b[38;5;241m.\u001b[39mvalues)\n\u001b[1;32m 247\u001b[0m depth_0 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(e3w_0)\n\u001b[1;32m 248\u001b[0m depth_0[\u001b[38;5;241m0\u001b[39m, :, :] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;241m*\u001b[39m e3w_0[\u001b[38;5;241m0\u001b[39m, :, :]\n", + "File \u001b[0;32m/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m )\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'e3w_0'" + ] + } + ], + "source": [ + "nemo_dom=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "18459a44-ed0d-4710-ac95-a2d4eaeb162a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "278272c5-9c3e-412f-a6b4-f16cba5b26b8", + "metadata": {}, + "outputs": [], + "source": [ + "nemo = coast.Gridded(fn_data= fn_nemo_dat, fn_domain = fn_nemo_dom, config=fn_config_t_grid,multiple=True);#nemo = nemo. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180))\n", + "nemo_dom=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \n", + "nemo.dataset['e3_0']=nemo_dom.dataset['e3_0']\n", + " \n", + "nemo_out=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #nemo_out = nemo_out. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \n", + " \n", + "coast.GriddedMonthlyHydrographicClimatology(nemo,nemo_out,Zmax=200) \n", + "nemo_out.dataset.to_netcdf(fn_out)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 06f3085ea2c62a05495dc57df8a9193d29b9978e Mon Sep 17 00:00:00 2001 From: tobfer Date: Fri, 17 Nov 2023 11:04:42 +0000 Subject: [PATCH 126/150] create notebook and part of the tests for monthly climatology and zarr --- coast/_utils/logging_util.py | 3 +- coast/data/coast.py | 7 +- coast/data/config_parser.py | 5 +- coast/data/config_structure.py | 2 - coast/data/gridded.py | 9 +- ...ridded_monthly_hydrographic_climatology.py | 117 +- .../general/climatology_tutorial.ipynb | 38 +- .../general/seasonal_decomp_example.ipynb | 2 +- ...json => example_nemo_monthly_climate.json} | 7 +- .../introduction_to_profile_class.ipynb | 945 +- .../profile/monthly_climatology.ipynb | 21648 ++++++++++++++++ .../profile/stratification_tests.ipynb | 177 - .../test_gridded_diagnostics_methods.py | 75 +- unit_testing/unit_test_files.py | 10 + 14 files changed, 22766 insertions(+), 279 deletions(-) rename example_scripts/notebook_tutorials/runnable_notebooks/profile/{senemo_grid_t.json => example_nemo_monthly_climate.json} (91%) create mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/monthly_climatology.ipynb delete mode 100644 example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb diff --git a/coast/_utils/logging_util.py b/coast/_utils/logging_util.py index fe4549c4..30cd4a43 100644 --- a/coast/_utils/logging_util.py +++ b/coast/_utils/logging_util.py @@ -58,7 +58,8 @@ def get_source(level=1): def add_info(msg, level=3): source = get_source(level=level) if isinstance(msg, Exception): - msg = f"{msg.__class__.__name__}: {str(msg)}\n" + "".join(traceback.format_tb(msg.__traceback__)) + msg = f"{msg.__class__.__name__}: {str(msg)}\n \ + " + "".join(traceback.format_tb(msg.__traceback__)) msg = f"{source[0]}.{source[2]}.{source[1]}: {msg}" return msg diff --git a/coast/data/coast.py b/coast/data/coast.py index a9df20a3..62a7cd87 100644 --- a/coast/data/coast.py +++ b/coast/data/coast.py @@ -85,8 +85,11 @@ def load_single(self, file: str, chunks: Dict = None): chunks (Dict): Chunks to use in Dask [default None]. """ info(f"Loading a single file ({file} for {get_slug(self)}") - with xr.open_dataset(file, chunks=chunks) as xrfile: - self.dataset = xrfile + if isinstance(file, xr.core.dataset.Dataset): + self.dataset = file + else: + with xr.open_dataset(file, chunks=chunks) as xrfile: + self.dataset = xrfile def load_multiple(self, directory_to_files: str, chunks: Dict = None): """Loads multiple files from directory into dataset variable. diff --git a/coast/data/config_parser.py b/coast/data/config_parser.py index 9d92a8ed..2ad7e771 100644 --- a/coast/data/config_parser.py +++ b/coast/data/config_parser.py @@ -34,7 +34,6 @@ def _parse_gridded(json_content: dict) -> GriddedConfig: grid_ref = json_content[ConfigKeys.GRID_REF] proc_flags = json_content[ConfigKeys.PROC_FLAGS] chunks = json_content[ConfigKeys.CHUNKS] - zarr_file = json_content.get(ConfigKeys.ZARR, False) dataset = ConfigParser._get_datafile_object(json_content, ConfigKeys.DATASET) static_variables = ConfigParser._get_code_processing_object(json_content) try: @@ -49,7 +48,6 @@ def _parse_gridded(json_content: dict) -> GriddedConfig: domain=domain, processing_flags=proc_flags, code_processing=static_variables, - zarr_file=zarr_file, ) @staticmethod @@ -62,9 +60,8 @@ def _parse_indexed(json_content: dict) -> IndexedConfig: dimensionality = json_content[ConfigKeys.DIMENSIONALITY] proc_flags = json_content[ConfigKeys.PROC_FLAGS] chunks = json_content[ConfigKeys.CHUNKS] - zarr_file = json_content.get(ConfigKeys.ZARR, False) dataset = ConfigParser._get_datafile_object(json_content, ConfigKeys.DATASET) - return IndexedConfig(dimensionality=dimensionality, zarr_file=zarr_file, chunks=chunks, dataset=dataset, processing_flags=proc_flags) + return IndexedConfig(dimensionality=dimensionality, chunks=chunks, dataset=dataset, processing_flags=proc_flags) @staticmethod def _get_code_processing_object(json_content: dict) -> CodeProcessing: diff --git a/coast/data/config_structure.py b/coast/data/config_structure.py index 229fb973..d30f97be 100644 --- a/coast/data/config_structure.py +++ b/coast/data/config_structure.py @@ -19,7 +19,6 @@ class ConfigKeys: GRID_REF = "grid_ref" PROC_FLAGS = "processing_flags" DATASET = "dataset" - ZARR = "zarr" DOMAIN = "domain" CODE_PROCESSING = "static_variables" DIM_MAP = "dimension_map" @@ -94,7 +93,6 @@ class Config: dataset: Dataset processing_flags: list chunks: dict - zarr_file: bool type: ConfigTypes diff --git a/coast/data/gridded.py b/coast/data/gridded.py index df92c1ec..a65ef57e 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -129,11 +129,10 @@ def load_domain(self, fn_domain, chunks): # TODO Do something with this unused """Loads domain file and renames dimensions with dim_mapping_domain""" # Load xarray dataset info(f'Loading domain: "{fn_domain}"') - if self.config.zarr_file: - dataset_domain = xr.open_zarr(fn_domain) + if isinstance(fn_domain, xr.core.dataset.Dataset): + dataset_domain = fn_domain else: - dataset_domain = xr.open_zarr(fn_domain) - # dataset_domain = xr.open_dataset(fn_domain) + dataset_domain = xr.open_dataset(fn_domain) self.domain_loaded = True # Rename dimensions for key, value in self.config.domain.dimension_map.items(): @@ -223,7 +222,7 @@ def set_timezero_depths(self, dataset_domain, **kwargs): bathymetry = dataset_domain.bathy_metry.squeeze() except AttributeError as err: - bathymetry = xr.zeros_like(dataset_domain.vmaskutil.squeeze()) + bathymetry = xr.zeros_like(dataset_domain.e1.squeeze()) # bathymetry = xr.zeros_like(dataset_domain.e1.squeeze()) ( diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 29ef91d1..246fe88c 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -1,83 +1,104 @@ -from ..data.gridded import Gridded -from ..diagnostics.gridded_stratification import GriddedStratification import numpy as np import xarray as xr +from .._utils.logging_util import debug, warn +from ..data.gridded import Gridded +from ..diagnostics.gridded_stratification import GriddedStratification class GriddedMonthlyHydrographicClimatology(Gridded): """ - Calculates the monthly climatology for SSS, SST and PEA from multi-annual monthly Gridded data. - Derived fields (SSS, SST, PEA) are placed into supplied coast.Gridded object. + Calculates the monthly climatology for sss, sst and pea from multi-annual monthly Gridded data. + Derived fields (sss, sst, pea) are placed into supplied coast.Gridded object. """ - def __init__(self, gridded_t, gridded_t_out, Zmax=200.0): + def __init__(self, gridded_t, z_max=200.0): """ Assumes monthly values in gridded_t, starting from Jan and multiyear Args: gridded_t: Input Gridded object. - gridded_t: Target Gridded object - Zmax: Max z for PEA integral calculation + z_max: max z for pea integral calculation + """ + self.gridded_t = gridded_t + self.dataset = xr.Dataset() + self.z_max = z_max + + def calc_climatologies(self): + """ + Calculate the climatologies for SSH, sss and pea. + + Returns: + gridded_t: Gridded dataset object. """ # calculate a depth mask - Zd_mask, _, _ = gridded_t.calculate_vertical_mask(Zmax) + zd_mask, _, _ = self.gridded_t.calculate_vertical_mask(self.z_max) - ny = gridded_t.dataset.dims["y_dim"] - nx = gridded_t.dataset.dims["x_dim"] + ny = self.gridded_t.dataset.dims["y_dim"] + nx = self.gridded_t.dataset.dims["x_dim"] - nt = gridded_t.dataset.dims["t_dim"] + nt = self.gridded_t.dataset.dims["t_dim"] - SST_monthy_clim = np.zeros((12, ny, nx)) - SSS_monthy_clim = np.zeros((12, ny, nx)) - PEA_monthy_clim = np.zeros((12, ny, nx)) - # NBTy=np.zeros((12,ny,nx)) #will add near bed temperature later + sst_monthy_clim = np.zeros((12, ny, nx)) + sss_monthy_clim = np.zeros((12, ny, nx)) + pea_monthy_clim = np.zeros((12, ny, nx)) - PEA_monthy_clim = np.zeros((12, ny, nx)) + try: + nyear = int(nt / 12) # hard wired for monthly data starting in Jan + for iy in range(nyear): + print("Calc pea", iy) + it = np.arange((iy) * 12, (iy) * 12 + 12).astype(int) + for im in range(12): + itt = [it[im]] + print(itt) + gridded_t2 = self.gridded_t.subset_as_copy(t_dim=itt) + print("copied", im) + pea = GriddedStratification(gridded_t2) + pea.calc_pea(gridded_t2, zd_mask) + pea_monthy_clim[im, :, :] = pea_monthy_clim[ + im, :, :] + pea.dataset["pea"].values + pea_monthy_clim = pea_monthy_clim / nyear + except Exception as error: + ( + warn( + f"Unable to perform pea calculation. Please check the error {error}" + ) + ) + debug( + f"Unable to perform pea calculation. Please check the error {error}" + ) - nyear = int(nt / 12) # hard wired for monthly data starting in Jan - for iy in range(nyear): - print("Calc PEA", iy) - it = np.arange((iy) * 12, (iy) * 12 + 12).astype(int) - for im in range(12): - itt = [it[im]] - print(itt) - gridded_t2 = gridded_t.subset_as_copy(t_dim=itt) - print("copied", im) - PEA = GriddedStratification(gridded_t2) - PEA.calc_pea(gridded_t2, Zd_mask) - PEA_monthy_clim[im, :, :] = PEA_monthy_clim[im, :, :] + PEA.dataset["PEA"].values - PEA_monthy_clim = PEA_monthy_clim / nyear + print('not possible to calculate pea') - # need to find efficient method for bottom temperature - # NBT=np.zeros((nt,ny,nx)) - # for it in range(nt): - # NBT[it,:,:]=np.reshape(tmp[it,:,:,:].values.ravel()[Ikmax],(ny,nx)) - SST = gridded_t.dataset.variables["temperature"][:, 0, :, :] - SSS = gridded_t.dataset.variables["salinity"][:, 0, :, :] + sst = self.gridded_t.dataset.variables["sst"] + sss = self.gridded_t.dataset.variables["sss"] for im in range(12): print("Month", im) it = np.arange(im, nt, 12).astype(int) - SST_monthy_clim[im, :, :] = np.mean(SST[it, :, :], axis=0) - SSS_monthy_clim[im, :, :] = np.mean(SSS[it, :, :], axis=0) + print('it', it) + sst_monthy_clim[im, :, :] = np.mean(sst[it, :, :], axis=0) + sss_monthy_clim[im, :, :] = np.mean(sss[it, :, :], axis=0) # NBTy[im,:,:]=np.mean(NBT[it,:,:],axis=0) # save hard work in netcdf file coords = { "Months": (("mon_dim"), np.arange(12).astype(int)), - "latitude": (("y_dim", "x_dim"), gridded_t.dataset.latitude.values), - "longitude": (("y_dim", "x_dim"), gridded_t.dataset.longitude.values), + "latitude": (("y_dim", "x_dim"), self.gridded_t.dataset.latitude.values), + "longitude": (("y_dim", "x_dim"), self.gridded_t.dataset.longitude.values), } dims = ["mon_dim", "y_dim", "x_dim"] - attributes_SST = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} - attributes_SSS = {"units": "", "standard name": "Absolute Sea Surface Salinity"} - attributes_PEA = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(Zmax) + "m"} - gridded_t_out.dataset["SST_monthy_clim"] = xr.DataArray( - np.squeeze(SST_monthy_clim), coords=coords, dims=dims, attrs=attributes_SST + attributes_sst = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} + attributes_sss = {"units": "", "standard name": "Absolute Sea Surface Salinity"} + attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + + str(self.z_max) + "m"} + + self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( + np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst ) - gridded_t_out.dataset["SSS_monthy_clim"] = xr.DataArray( - np.squeeze(SSS_monthy_clim), coords=coords, dims=dims, attrs=attributes_SSS + self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray( + np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss ) - gridded_t_out.dataset["PEA_monthy_clim"] = xr.DataArray( - np.squeeze(PEA_monthy_clim), coords=coords, dims=dims, attrs=attributes_PEA + self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray( + np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea ) + self.dataset = self.gridded_t.dataset diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/general/climatology_tutorial.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/general/climatology_tutorial.ipynb index ad2b8a66..f7b66692 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/general/climatology_tutorial.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/general/climatology_tutorial.ipynb @@ -23,10 +23,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1217a907-103b-43b5-b673-dbd4171c766e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:16: RuntimeWarning: invalid value encountered in cast\n", + " nshallow = np.ma.masked_invalid(const.nshallow).astype(int)\n", + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:17: RuntimeWarning: invalid value encountered in cast\n", + " ishallow = np.ma.masked_invalid(const.ishallow).astype(int) - 1\n" + ] + } + ], "source": [ "import coast" ] @@ -44,12 +55,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "505111f9-6168-4cca-ae06-c6ba02cec218", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension \"time_counter\" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.\n", + " warnings.warn(\n" + ] + } + ], "source": [ - "root = \"./\"\n", + "root = \"../../../../\"\n", "# Paths to a single or multiple data files.\n", "dn_files = root + \"./example_files/\"\n", "fn_nemo_dat = dn_files + \"coast_example_nemo_data.nc\"\n", @@ -76,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "b5e3a382", "metadata": {}, "outputs": [], @@ -102,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5497105f", "metadata": {}, "outputs": [], @@ -199,9 +219,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/general/seasonal_decomp_example.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/general/seasonal_decomp_example.ipynb index ceb6cf2b..b8c9c1a9 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/general/seasonal_decomp_example.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/general/seasonal_decomp_example.ipynb @@ -253,7 +253,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json b/example_scripts/notebook_tutorials/runnable_notebooks/profile/example_nemo_monthly_climate.json similarity index 91% rename from example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json rename to example_scripts/notebook_tutorials/runnable_notebooks/profile/example_nemo_monthly_climate.json index 99ba2fc2..f44bcf1a 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/senemo_grid_t.json +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/example_nemo_monthly_climate.json @@ -2,7 +2,6 @@ "type": "gridded", "dimensionality": 3, "chunks": {"time_counter":2}, - "zarr": true, "grid_ref": { "t-grid": [ "glamt", @@ -33,11 +32,11 @@ "thetao": "temperature", "temp": "temperature", "toce": "temperature", - "thetao_con": "temperature", + "thetao_con": "temperature", "so": "salinity", "vosaline": "salinity", "soce": "salinity", - "so_abs": "salinity", + "so_abs": "salinity", "sossheig": "ssh", "zos": "ssh" }, @@ -63,7 +62,7 @@ "e1t": "e1", "e2t": "e2", "e3t_0": "e3_0", - "e3w_0": "e3w_0", + "e3w_0": "e3w_0", "tmask":"mask", "deptht_0": "depth_0", "bottom_level": "bottom_level", diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/introduction_to_profile_class.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/introduction_to_profile_class.ipynb index 6d740619..e9d3cf55 100644 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/introduction_to_profile_class.ipynb +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/introduction_to_profile_class.ipynb @@ -71,14 +71,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "773ad868", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:16: RuntimeWarning: invalid value encountered in cast\n", + " nshallow = np.ma.masked_invalid(const.nshallow).astype(int)\n", + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:17: RuntimeWarning: invalid value encountered in cast\n", + " ishallow = np.ma.masked_invalid(const.ishallow).astype(int) - 1\n" + ] + } + ], "source": [ "import coast\n", "from os import path\n", @@ -100,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "30eaeb5c", "metadata": { "pycharm": { @@ -128,22 +139,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e5abc701", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "../../../../config/example_en4_profiles.json\n" + ] + } + ], "source": [ "# Read WOD data into profile object\n", - "fn_prof = path.join(\"example_files\",\"WOD_example_ragged_standard_level.nc\")\n", + "fn_prof = path.join(\"../../../../example_files\",\"WOD_example_ragged_standard_level.nc\")\n", "profile.read_wod( fn_prof )\n", "\n", "# Read EN4 data into profile object (OVERWRITES DATASET)\n", - "fn_prof = path.join(\"example_files\", \"coast_example_en4_201008.nc\")\n", - "fn_cfg_prof = path.join(\"config\",\"example_en4_profiles.json\")\n", + "fn_prof = path.join(\"../../../../example_files\", \"coast_example_en4_201008.nc\")\n", + "fn_cfg_prof = path.join(\"../../../../config\",\"example_en4_profiles.json\")\n", "profile = coast.Profile(config=fn_cfg_prof)\n", "profile.read_en4( fn_prof )" ] @@ -178,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8b5c8d53", "metadata": { "pycharm": { @@ -201,7 +220,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "8f2cdc8c", "metadata": { "pycharm": { @@ -233,14 +252,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "9ca87048", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
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, )" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "profile.plot_map()" ] @@ -267,26 +307,895 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "bba61dbf", "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/xarray/core/dataset.py:278: UserWarning: The specified chunks separate the stored chunks along dimension \"time_counter\" starting at index 2. This could degrade performance. Instead, consider rechunking after loading.\n", + " warnings.warn(\n" + ] + } + ], "source": [ "root = \"./\"\n", "# And by defining some file paths\n", - "dn_files = root + \"./example_files/\"\n", + "dn_files = root + \"../../../../example_files/\"\n", "fn_nemo_dat = path.join(dn_files, \"coast_example_nemo_data.nc\")\n", "fn_nemo_dom = path.join(dn_files, \"coast_example_nemo_domain.nc\")\n", - "fn_nemo_config = path.join(root, \"./config/example_nemo_grid_t.json\")\n", + "fn_nemo_config = path.join(root, \"../../../../config/example_nemo_grid_t.json\")\n", "\n", "# Create gridded object:\n", "nemo = coast.Gridded(fn_nemo_dat, fn_nemo_dom, multiple=True, config=fn_nemo_config)" ] }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1f4765f1-a650-47a6-93d3-79f9f4073be9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset>\n",
+       "Dimensions:              (z_dim: 51, axis_nbounds: 2, t_dim: 7, y_dim: 375,\n",
+       "                          x_dim: 297)\n",
+       "Coordinates:\n",
+       "  * time                 (t_dim) datetime64[ns] 2007-01-01T11:58:56 ... 2007-...\n",
+       "    longitude            (y_dim, x_dim) float32 ...\n",
+       "    latitude             (y_dim, x_dim) float32 ...\n",
+       "    depth_0              (z_dim, y_dim, x_dim) float32 0.5 0.5 0.5 ... 50.5 50.5\n",
+       "Dimensions without coordinates: z_dim, axis_nbounds, t_dim, y_dim, x_dim\n",
+       "Data variables:\n",
+       "    deptht_bounds        (z_dim, axis_nbounds) float32 dask.array<chunksize=(51, 2), meta=np.ndarray>\n",
+       "    ssh                  (t_dim, y_dim, x_dim) float32 dask.array<chunksize=(2, 375, 297), meta=np.ndarray>\n",
+       "    time_counter_bounds  (t_dim, axis_nbounds) datetime64[ns] dask.array<chunksize=(2, 2), meta=np.ndarray>\n",
+       "    time_instant         (t_dim) datetime64[ns] dask.array<chunksize=(2,), meta=np.ndarray>\n",
+       "    temperature          (t_dim, z_dim, y_dim, x_dim) float32 dask.array<chunksize=(2, 26, 188, 149), meta=np.ndarray>\n",
+       "    bathymetry           (y_dim, x_dim) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
+       "    e1                   (y_dim, x_dim) float32 ...\n",
+       "    e2                   (y_dim, x_dim) float32 ...\n",
+       "    e3_0                 (z_dim, y_dim, x_dim) float32 ...\n",
+       "    bottom_level         (y_dim, x_dim) float32 ...\n",
+       "Attributes:\n",
+       "    name:         AMM7_1d_20070101_20070131_25hourm_grid_T\n",
+       "    description:  ocean T grid variables, 25h meaned\n",
+       "    title:        ocean T grid variables, 25h meaned\n",
+       "    Conventions:  CF-1.6\n",
+       "    timeStamp:    2019-Dec-26 04:35:28 GMT\n",
+       "    uuid:         96cae459-d3a1-4f4f-b82b-9259179f95f7\n",
+       "    history:      Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time...\n",
+       "    NCO:          4.4.7
" + ], + "text/plain": [ + "\n", + "Dimensions: (z_dim: 51, axis_nbounds: 2, t_dim: 7, y_dim: 375,\n", + " x_dim: 297)\n", + "Coordinates:\n", + " * time (t_dim) datetime64[ns] 2007-01-01T11:58:56 ... 2007-...\n", + " longitude (y_dim, x_dim) float32 ...\n", + " latitude (y_dim, x_dim) float32 ...\n", + " depth_0 (z_dim, y_dim, x_dim) float32 0.5 0.5 0.5 ... 50.5 50.5\n", + "Dimensions without coordinates: z_dim, axis_nbounds, t_dim, y_dim, x_dim\n", + "Data variables:\n", + " deptht_bounds (z_dim, axis_nbounds) float32 dask.array\n", + " ssh (t_dim, y_dim, x_dim) float32 dask.array\n", + " time_counter_bounds (t_dim, axis_nbounds) datetime64[ns] dask.array\n", + " time_instant (t_dim) datetime64[ns] dask.array\n", + " temperature (t_dim, z_dim, y_dim, x_dim) float32 dask.array\n", + " bathymetry (y_dim, x_dim) float32 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0\n", + " e1 (y_dim, x_dim) float32 ...\n", + " e2 (y_dim, x_dim) float32 ...\n", + " e3_0 (z_dim, y_dim, x_dim) float32 ...\n", + " bottom_level (y_dim, x_dim) float32 ...\n", + "Attributes:\n", + " name: AMM7_1d_20070101_20070131_25hourm_grid_T\n", + " description: ocean T grid variables, 25h meaned\n", + " title: ocean T grid variables, 25h meaned\n", + " Conventions: CF-1.6\n", + " timeStamp: 2019-Dec-26 04:35:28 GMT\n", + " uuid: 96cae459-d3a1-4f4f-b82b-9259179f95f7\n", + " history: Tue May 19 12:07:51 2020: ncks -v votemper,sossheig -d time...\n", + " NCO: 4.4.7" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nemo.dataset" + ] + }, { "cell_type": "markdown", "id": "6438363a", @@ -1098,9 +2007,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/monthly_climatology.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/monthly_climatology.ipynb new file mode 100644 index 00000000..e6e0faae --- /dev/null +++ b/example_scripts/notebook_tutorials/runnable_notebooks/profile/monthly_climatology.ipynb @@ -0,0 +1,21648 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b55c9541-7a73-45b1-98a2-289433367117", + "metadata": {}, + "source": [ + "# Gridded Monthly Hydrographic Climatology" + ] + }, + { + "cell_type": "markdown", + "id": "6f3c16ee-de2d-47a3-91ef-c774b99586e4", + "metadata": {}, + "source": [ + "This tutorial will show you how to perform some monthly climatology calculation using nemo outputs.\n", + "\n", + "The idea is to calculate the climatology of SST, SSS and PEA though a series of NC NEMO output files\n", + "\n", + "This tutorial will also show will how to open ZARR files using NEMO and how to process this data though the pipelines\n" + ] + }, + { + "cell_type": "markdown", + "id": "5ece5ad0-2a66-4dc6-8a21-c81896d9f63c", + "metadata": {}, + "source": [ + "## Open a list of NC files" + ] + }, + { + "cell_type": "markdown", + "id": "4cc36860-f2d3-4e2e-83c3-062b99bcdadd", + "metadata": {}, + "source": [ + "### Import functions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29d6a04f-f18b-4231-845e-a205fe21b26a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:16: RuntimeWarning: invalid value encountered in cast\n", + " nshallow = np.ma.masked_invalid(const.nshallow).astype(int)\n", + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/utide/harmonics.py:17: RuntimeWarning: invalid value encountered in cast\n", + " ishallow = np.ma.masked_invalid(const.ishallow).astype(int) - 1\n" + ] + } + ], + "source": [ + "import coast\n", + "import glob\n", + "import xarray as xr\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "23d1f4f6-00f1-40ca-ab9f-d55fd8aede27", + "metadata": {}, + "source": [ + "### Set the files path that you will use" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6c4d223b-312b-411c-9d2d-596a1d1e80e5", + "metadata": {}, + "outputs": [], + "source": [ + "# Path to a data file\n", + "root = \"./\"\n", + "dn_files = root + \"./example_files/\"\n", + "fn_nemo_dom = dn_files + \"coast_domain_monthly_grid.nc\"\n", + "fn_nemo_dat_path = dn_files + \"coast_monthly/grid_files/\"\n", + "fn_config_t_grid = root + \"./example_nemo_monthly_climate.json\"" + ] + }, + { + "cell_type": "markdown", + "id": "4108930a-f52c-459d-98c4-c88f02503133", + "metadata": {}, + "source": [ + "## Specify years to average" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "13260b3f-3296-4d77-91e4-13114584b212", + "metadata": {}, + "outputs": [], + "source": [ + "#Specify years to average\n", + "ystart=1990\n", + "ystop=2019\n" + ] + }, + { + "cell_type": "markdown", + "id": "30fd5660-1630-4197-8b6a-f334a0fa8cd3", + "metadata": {}, + "source": [ + "### Make a list of files" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a4958daf-d03c-4c3d-9305-0866578eec29", + "metadata": {}, + "outputs": [], + "source": [ + "#make list of filenames\n", + "fn_nemo_dat = glob.glob(fn_nemo_dat_path + \"*.nc\")" + ] + }, + { + "cell_type": "markdown", + "id": "50186218-b293-4b0a-95a1-6200a7419bef", + "metadata": {}, + "source": [ + "It will create a list of nc files. We will use this list as an input of the Griddded method" + ] + }, + { + "cell_type": "markdown", + "id": "7e18e4f7-45a2-4277-8671-c995d8ac50fd", + "metadata": {}, + "source": [ + "### Instantiate the Griddded Classes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "98db8e1e-09d5-4921-b97d-3a0ee3662bdb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nemo = coast.Gridded(fn_data=fn_nemo_dat, fn_domain =fn_nemo_dom, config=fn_config_t_grid,multiple=True)\n", + "nemo_dom=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid)" + ] + }, + { + "cell_type": "markdown", + "id": "d8b0febb-93dd-4e18-9c27-aad4be78d280", + "metadata": {}, + "source": [ + "### Add a variable that is important for the output" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "bca397dc-c077-4f99-986e-29fff387a5ac", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'nemo_dom' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nemo\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124me3_0\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[43mnemo_dom\u001b[49m\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124me3_0\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mNameError\u001b[0m: name 'nemo_dom' is not defined" + ] + } + ], + "source": [ + "nemo.dataset['e3_0']=nemo_dom.dataset['e3_0']" + ] + }, + { + "cell_type": "markdown", + "id": "f726e17d-e640-4c97-b8ae-fcba859dbd04", + "metadata": {}, + "source": [ + "### Calculate the output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "defa18eb-19fa-491b-b738-fcfa65963deb", + "metadata": {}, + "outputs": [], + "source": [ + "gridded_month = coast.GriddedMonthlyHydrographicClimatology(nemo,z_max=200)\n", + "gridded_month.calc_climatologies()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4004fffe-021a-4724-8eb5-3ab9cd6f8370", + "metadata": {}, + "outputs": [], + "source": [ + "gridded_month.dataset" + ] + }, + { + "cell_type": "markdown", + "id": "7bdc56b9-074e-4a52-ba38-9ce1900bb004", + "metadata": {}, + "source": [ + "## Open ZARR files" + ] + }, + { + "cell_type": "markdown", + "id": "0ef3f7f7-d58d-4bd5-ab37-fd1de92bf6a5", + "metadata": {}, + "source": [ + "### Requirements" + ] + }, + { + "cell_type": "markdown", + "id": "e486c745-9d1f-4396-8510-ac273a544202", + "metadata": {}, + "source": [ + "Coast also has the capability to allow you to open zarr files\n", + "In order to do that, you need to install first the library zarr:\n", + "```bash\n", + "pip install zarr\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6b4db3f9-beea-44e2-8877-cb0260ce47d7", + "metadata": {}, + "source": [ + "After that, you can open the datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73edaa87-e69f-4277-b140-8de7291b0296", + "metadata": {}, + "outputs": [], + "source": [ + "fn_nemo_dom_mask = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr\"\n", + "fn_nemo_dom_mesh_zgr = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_zgr.zarr\"\n", + "fn_nemo_dom_mesh_hgr = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_hgr.zarr\"\n", + "fn_nemo_dat = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr\"" + ] + }, + { + "cell_type": "markdown", + "id": "2e4df08d-aab8-4646-80ee-6be4fbf0c6fc", + "metadata": {}, + "source": [ + "In this case, we will use the same configuration files as we used above: `fn_config_t_grid`" + ] + }, + { + "cell_type": "markdown", + "id": "b2423df0-4b24-42ce-9c39-6e0e4ba4aa2f", + "metadata": {}, + "source": [ + "### Open the zarr files as a XARRAY" + ] + }, + { + "cell_type": "markdown", + "id": "51b8619b-64a7-42fc-a826-95f4fee890ed", + "metadata": {}, + "source": [ + "The zarr files that we are using in this example do not have all the variables on the same file. Because of that, we need to open each file separately and then add the variables to a central file" + ] + }, + { + "cell_type": "markdown", + "id": "317b368f-486d-4b85-b64a-c6be46a92973", + "metadata": {}, + "source": [ + "We will do the same steps for the dom and for the data files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "63ac8681-df28-42e1-aaa1-45641a626348", + "metadata": {}, + "outputs": [], + "source": [ + "dom = xr.open_zarr(fn_nemo_dom_mask)\n", + "mesh_zgr = xr.open_zarr(fn_nemo_dom_mesh_zgr)\n", + "mesh_hgr = xr.open_zarr(fn_nemo_dom_mesh_hgr)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "640b7849-5f24-4286-9078-156b0ad29582", + "metadata": {}, + "outputs": [], + "source": [ + "for var_name in mesh_zgr.data_vars:\n", + " dom[var_name] = mesh_zgr[var_name]\n", + "for var_name in mesh_hgr.data_vars:\n", + " dom[var_name] = mesh_hgr[var_name]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "88e1dc77-29c5-46cc-b6dc-9ce231f3a6de", + "metadata": {}, + "outputs": [], + "source": [ + "u_grid = xr.open_zarr(\"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_U.zarr\")\n", + "u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'})\n", + "v_grid = xr.open_zarr(\"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_V.zarr\")\n", + "v_grid = v_grid.isel(time_counter=slice(0,119)).rename({'depthv': 'depth'})\n", + "t_grid = xr.open_zarr(\"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr\")\n", + "t_grid = t_grid.rename({'deptht': 'depth'})" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7e2b3e4d-82df-4546-ac58-7770d1109ced", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/dask/array/core.py:4836: PerformanceWarning: Increasing number of chunks by factor of 15\n", + " result = blockwise(\n", + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/dask/array/core.py:4836: PerformanceWarning: Increasing number of chunks by factor of 15\n", + " result = blockwise(\n" + ] + } + ], + "source": [ + "for var_name in u_grid.data_vars:\n", + " t_grid[var_name] = u_grid[var_name]\n", + "for var_name in v_grid.data_vars:\n", + " t_grid[var_name] = v_grid[var_name]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9c129d4e-3dfd-46e3-b74f-32aa05e2bd90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    description:          ocean T grid variables\n",
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+       "    production:           An IPSL model\n",
+       "    timeStamp:            2014-Dec-03 05:19:35 GMT
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This will result in the NEMO.dataset.bathymetry variable being set to zero, which may result in unexpected behaviour from routines that require this variable.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "nemo_dom=coast.Gridded(fn_domain = dom, config=fn_config_t_grid) #;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \n", + "nemo = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=fn_config_t_grid)" + ] + }, + { + "cell_type": "markdown", + "id": "1107bed7-6bf3-4b89-9670-45e43b793119", + "metadata": {}, + "source": [ + "- Add a variable that is important for the output" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e1c2932f-a628-4de2-8a31-d0e734c0fefd", + "metadata": {}, + "outputs": [], + "source": [ + "nemo.dataset['e3_0']=nemo_dom.dataset['e3_0']\n", + "# nemo_out=coast.Gridded(fn_domain = dom, config=fn_config_t_grid) #nemo_out = nemo_out. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) " + ] + }, + { + "cell_type": "markdown", + "id": "14a49a90-5b94-4529-89bc-d8f3179b9537", + "metadata": {}, + "source": [ + "- Calculate the output" + ] + }, + { + "cell_type": "markdown", + "id": "93b380e6-6c84-43e7-8a69-3a707dfcaaa9", + "metadata": {}, + "source": [ + "In this step, it is important to mention that our zarr data does not have salinity and temperature data, only sst and ssh. Because of that, it is not possible to calculate PEA." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4c0984d8-f1d4-456f-9490-6feec40898ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calc pea 0\n", + "[0]\n", + "copied 0\n", + "not possible to calculate pea\n", + "Month 0\n", + "it [ 0 12]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/code/noc/coast/COAsT/coast/_utils/logging_util.py:80: UserWarning: /mnt/code/code/noc/coast/COAsT/coast/diagnostics/gridded_monthly_hydrographic_climatology.py.calc_climatologies.63: Unable to perform pea calculation. Please check the error 'Dataset' object has no attribute 'salinity'\n", + " return warnings.warn(add_info(msg), *args, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Month 1\n", + "it [ 1 13]\n", + "Month 2\n", + "it [ 2 14]\n", + "Month 3\n", + "it [ 3 15]\n", + "Month 4\n", + "it [ 4 16]\n", + "Month 5\n", + "it [ 5 17]\n", + "Month 6\n", + "it [ 6 18]\n", + "Month 7\n", + "it [ 7 19]\n", + "Month 8\n", + "it [ 8 20]\n", + "Month 9\n", + "it [ 9 21]\n", + "Month 10\n", + "it [10 22]\n", + "Month 11\n", + "it [11 23]\n" + ] + } + ], + "source": [ + "gridded_month = coast.GriddedMonthlyHydrographicClimatology(nemo,z_max=200)\n", + "gridded_month.calc_climatologies()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "6de5fec9-97fc-429d-9fe3-8f3681bbc5c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "Dimensions: (depth: 75, t_dim: 24, y_dim: 200, x_dim: 200, z_dim: 75,\n", + " mon_dim: 12)\n", + "Coordinates:\n", + " * depth (depth) float32 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n", + " * time (t_dim) datetime64[ns] 1960-01-06T12:00:00 ... 1961-12-1...\n", + " longitude (y_dim, x_dim) float32 dask.array\n", + " latitude (y_dim, x_dim) float32 dask.array\n", + " depth_0 (z_dim, y_dim, x_dim) float64 0.5 0.5 ... 5.86e+03\n", + " Months (mon_dim) int64 0 1 2 3 4 5 6 7 8 9 10 11\n", + "Dimensions without coordinates: t_dim, y_dim, x_dim, z_dim, mon_dim\n", + "Data variables: (12/32)\n", + " e3t (t_dim, depth, y_dim, x_dim) float32 dask.array\n", + " mldkz5 (t_dim, y_dim, x_dim) float32 dask.array\n", + " mldr10_1 (t_dim, y_dim, x_dim) float32 dask.array\n", + " potemp (t_dim, depth, y_dim, x_dim) float32 dask.array\n", + " rsntds (t_dim, y_dim, x_dim) float32 dask.array\n", + " salin (t_dim, depth, y_dim, x_dim) float32 dask.array\n", + " ... ...\n", + " e3_0 (z_dim, y_dim, x_dim) float64 dask.array\n", + " mask (z_dim, y_dim, x_dim) int8 dask.array\n", + " bottom_level (y_dim, x_dim) int16 dask.array\n", + " sst_monthy_clim (mon_dim, y_dim, x_dim) float64 0.3749 0.3554 ... 7.246\n", + " sss_monthy_clim (mon_dim, y_dim, x_dim) float64 33.72 33.72 ... 34.22 34.22\n", + " pea_monthy_clim (mon_dim, y_dim, x_dim) float64 0.0 0.0 0.0 ... 0.0 0.0 0.0\n", + "Attributes:\n", + " DOMAIN_number_total: 80\n", + " DOMAIN_size_global: [4322, 3059]\n", + " conventions: CF-1.1\n", + " description: ocean T grid variables\n", + " ibegin: 1\n", + " jbegin: 1\n", + " name: ORCA0083-N06_1m_19591222_19601231\n", + " ni: 4322\n", + " nj: 39\n", + " production: An IPSL model\n", + " timeStamp: 2014-Dec-03 05:19:35 GMT" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gridded_month.dataset" + ] + }, + { + "cell_type": "markdown", + "id": "6eb81ef2-b88c-403e-97f4-d4a22787aaa1", + "metadata": {}, + "source": [ + "## Output your results" + ] + }, + { + "cell_type": "markdown", + "id": "2cedae6b-341b-4403-8697-953ea6c51e89", + "metadata": {}, + "source": [ + "### NETCDF" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3b57ad88-c55e-4bd2-bd49-d16b1d7593ef", + "metadata": {}, + "outputs": [], + "source": [ + "# gridded_month.dataset.to_netcdf('name_of_output_file.nc')" + ] + }, + { + "cell_type": "markdown", + "id": "ddf7e9bf-647d-4a20-9870-f5611b871d44", + "metadata": {}, + "source": [ + "### Plot the outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "301b1033-d6b0-4060-9b9c-ef11e011fb55", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeaturea\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "c2f2cb33-4a97-4c60-a073-9dbff21c48cb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/10m_physical/ne_10m_coastline.zip\n", + " warnings.warn(f'Downloading: {url}', DownloadWarning)\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sst_clim = gridded_month.dataset['sst_monthy_clim']\n", + "projection = ccrs.PlateCarree()\n", + "\n", + "for month, sst in enumerate(sst_clim):\n", + " fig, ax = plt.subplots(subplot_kw={'projection': projection}, figsize=(10, 10))\n", + " plt.pcolormesh(gridded_month.dataset.longitude.squeeze(), gridded_month.dataset.latitude.squeeze(), sst,transform=projection)\n", + " ax.coastlines()\n", + " ax.gridlines(draw_labels=True, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')\n", + " title_str = f\"Month {month + 1} {sst_clim.attrs['standard name']} {sst_clim.attrs['units']}\"\n", + " plt.title(title_str)\n", + " plt.xlabel(\"longitude\")\n", + " plt.ylabel(\"latitude\")\n", + " plt.colorbar()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b40d03f2-d1ec-48fa-8459-bf0a106c6796", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb b/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb deleted file mode 100644 index 1984b377..00000000 --- a/example_scripts/notebook_tutorials/runnable_notebooks/profile/stratification_tests.ipynb +++ /dev/null @@ -1,177 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 47, - "id": "29d6a04f-f18b-4231-845e-a205fe21b26a", - "metadata": {}, - "outputs": [], - "source": [ - "import coast\n", - "import xarray as xr\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "38fb1589-6ed7-4744-8208-f2891e0dcb7d", - "metadata": {}, - "outputs": [], - "source": [ - "# ystart=1990\n", - "# ystop=2019\n", - "\n", - "# EXPNAM = \"ZPS_TIDE\"\n", - "# domain_datapath='/gws/nopw/j04/class_vol2/senemo/cwilso01/ZPS_REF_TIDE/OUTPUTS/'\n", - "# fn_nemo_dom = '/gws/nopw/j04/class_vol2/senemo/cwilso01/EXP_REF_NOTIDE/domcfg_eORCA025_v2.nc'\n", - "# fn_nemo_dat= coast.nemo_filename_maker(domain_datapath,ystart,ystop) " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58364032-a05a-4944-9d53-2cc6e8cc1d38", - "metadata": {}, - "outputs": [], - "source": [ - "# domain_outpath='/home/users/jholt/work/SENEMO/ASSESSMENT/'\n", - "# DOMNAM='ORCA025-SE-NEMO'\n", - "# fn_out='{0}/{1}/{1}_{2}_{3}_{4}_SST_SSS_PEA_MonClimate.nc'.format(domain_outpath,DOMNAM,ystart,ystop,EXPNAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "ede39607-63d4-460f-9a59-701e9e236732", - "metadata": {}, - "outputs": [], - "source": [ - "fn_nemo_dom = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr\"\n", - "fn_nemo_dat = \"https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr\"\n", - "fn_config_t_grid='senemo_grid_t.json'" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "27c30450-83a5-45fb-ba8f-2af1e3cf4fe4", - "metadata": {}, - "outputs": [], - "source": [ - "dom = xr.open_zarr(fn_nemo_dom)\n", - "t_grid = xr.open_zarr(fn_nemo_dat)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "67a8a9f0-07da-49bf-b7e2-d8e51d352ab1", - "metadata": {}, - "outputs": [], - "source": [ - "# x = coast.data.config_parser.ConfigParser(fn_config_t_grid).config" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "id": "9e06d05d-ef45-4509-a3b5-0ffcd101bf16", - "metadata": {}, - "outputs": [], - "source": [ - "# ds = xr.open_dataset('../../../../example_files/coast_example_nemo_domain.nc')\n", - "# ds.variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "063062c4-137e-4161-a643-51742ee87764", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 78, - "id": "5c767512-7805-4921-95eb-7c2814493c65", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:230: UserWarning: The model domain loaded, 'https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr', does not contain the bathy_metry' variable. This will result in the NEMO.dataset.bathymetry variable being set to zero, which may result in unexpected behaviour from routines that require this variable.\n", - " warnings.warn(\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'Dataset' object has no attribute 'e3w_0'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[78], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m nemo_dom\u001b[38;5;241m=\u001b[39m\u001b[43mcoast\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mGridded\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn_domain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mfn_nemo_dom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfn_config_t_grid\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \u001b[39;00m\n", - "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:49\u001b[0m, in \u001b[0;36mGridded.__init__\u001b[0;34m(self, fn_data, fn_domain, multiple, config, workers, threads, memory_limit_per_worker, **kwargs)\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig \u001b[38;5;241m=\u001b[39m ConfigParser(config)\u001b[38;5;241m.\u001b[39mconfig\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mchunks:\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_setup_grid_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmultiple\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setup_grid_obj(\u001b[38;5;28;01mNone\u001b[39;00m, multiple, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:100\u001b[0m, in \u001b[0;36mGridded._setup_grid_obj\u001b[0;34m(self, chunks, multiple, **kwargs)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfn_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 99\u001b[0m dataset_domain \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrim_domain_size(dataset_domain) \u001b[38;5;66;03m# Trim domain size if self.data is smaller\u001b[39;00m\n\u001b[0;32m--> 100\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_timezero_depths\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 101\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataset_domain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 102\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# THIS ADDS TO dataset_domain. Should it be 'return'ed (as in trim_domain_size) or is implicit OK?\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmerge_domain_into_dataset(dataset_domain)\n\u001b[1;32m 104\u001b[0m debug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInitialised \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mget_slug(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m/mnt/code/code/noc/coast/COAsT/coast/data/gridded.py:246\u001b[0m, in \u001b[0;36mGridded.set_timezero_depths\u001b[0;34m(self, dataset_domain, **kwargs)\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgrid_ref \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mt-grid\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 246\u001b[0m e3w_0 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msqueeze(\u001b[43mdataset_domain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43me3w_0\u001b[49m\u001b[38;5;241m.\u001b[39mvalues)\n\u001b[1;32m 247\u001b[0m depth_0 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(e3w_0)\n\u001b[1;32m 248\u001b[0m depth_0[\u001b[38;5;241m0\u001b[39m, :, :] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m \u001b[38;5;241m*\u001b[39m e3w_0[\u001b[38;5;241m0\u001b[39m, :, :]\n", - "File \u001b[0;32m/mnt/code/.pyenv/versions/3.10.12/envs/coast-10/lib/python3.10/site-packages/xarray/core/common.py:278\u001b[0m, in \u001b[0;36mAttrAccessMixin.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mKeyError\u001b[39;00m):\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m source[name]\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[1;32m 279\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m object has no attribute \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 280\u001b[0m )\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Dataset' object has no attribute 'e3w_0'" - ] - } - ], - "source": [ - "nemo_dom=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) " - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "18459a44-ed0d-4710-ac95-a2d4eaeb162a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "278272c5-9c3e-412f-a6b4-f16cba5b26b8", - "metadata": {}, - "outputs": [], - "source": [ - "nemo = coast.Gridded(fn_data= fn_nemo_dat, fn_domain = fn_nemo_dom, config=fn_config_t_grid,multiple=True);#nemo = nemo. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180))\n", - "nemo_dom=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #;nemo_dom = nemo_dom. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \n", - "nemo.dataset['e3_0']=nemo_dom.dataset['e3_0']\n", - " \n", - "nemo_out=coast.Gridded(fn_domain = fn_nemo_dom, config=fn_config_t_grid) #nemo_out = nemo_out. subset_as_copy(y_dim=range(86,1000),x_dim=range(1080,1180)) \n", - " \n", - "coast.GriddedMonthlyHydrographicClimatology(nemo,nemo_out,Zmax=200) \n", - "nemo_out.dataset.to_netcdf(fn_out)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index 6760ad62..7bf7dea3 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -137,25 +137,36 @@ def test_circulation(self): # %% lims = [150, 250, 100, 350] nemo_u = coast.Gridded( - fn_data=files.fn_nemo_grid_u_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_u_grid, lims=lims + fn_data=files.fn_nemo_grid_u_dat, + fn_domain=files.fn_nemo_dom, + config=files.fn_config_u_grid, lims=lims ) nemo_v = coast.Gridded( - fn_data=files.fn_nemo_grid_v_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_v_grid, lims=lims + fn_data=files.fn_nemo_grid_v_dat, + fn_domain=files.fn_nemo_dom, + config=files.fn_config_v_grid, lims=lims ) with self.subTest("Map velocities to t-points"): - nemo_t = coast.CurrentsOnT(fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid, lims=lims) + nemo_t = coast.CurrentsOnT(fn_domain=files.fn_nemo_dom, + config=files.fn_config_t_grid, + lims=lims) nemo_t.currents_on_t(nemo_u, nemo_v) nemo_t.subset(z_dim=[0], t_dim=[0]) u1 = 0.5 * ( - nemo_u.dataset.u_velocity[0, 0, 150, 60].values + nemo_u.dataset.u_velocity[0, 0, 150, 59].values + nemo_u.dataset.u_velocity[ + 0, 0, 150, 60].values + nemo_u.dataset.u_velocity[0, 0, 150, 59].values ) u2 = nemo_t.dataset.ut_velocity[0, 0, 150, 60].values - # print(f"u vel on u-pts: {nemo_u.dataset.u_velocity.isel(x_dim=slice(59,61), y_dim=slice(149,151), t_dim=0, z_dim=0).values}") - # print(f"u vel on t-pts: {nemo_t.dataset.ut_velocity.isel(x_dim=slice(59,61), y_dim=slice(149,151), t_dim=0, z_dim=0).values}") + # print(f"u vel on u-pts: {nemo_u.dataset.u_velocity.isel(x_dim=slice(59,61), \ + # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") + # print(f"u vel on t-pts: \ + # {nemo_t.dataset.ut_velocity.isel(x_dim=slice(59,61), + # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") v1 = 0.5 * ( - nemo_v.dataset.v_velocity[0, 0, 150, 60].values + nemo_v.dataset.v_velocity[0, 0, 149, 60].values + nemo_v.dataset.v_velocity[ + 0,0, 150, 60].values + nemo_v.dataset.v_velocity[0, 0, 149, 60].values ) v2 = nemo_t.dataset.vt_velocity[0, 0, 150, 60].values speed = nemo_t.dataset.speed_t[0, 0, 150, 60].values @@ -174,7 +185,9 @@ def test_circulation(self): plt.close("all") def test_calc_pea(self): - nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) + nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, + files.fn_nemo_dom, + config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.0) @@ -193,3 +206,49 @@ def test_calc_pea(self): fig.tight_layout() fig.savefig(files.dn_fig + "gridded_pea.png") plt.close("all") + + + def test_calc_monthly_grided(self): + """ + This test was created in order to verify if the calculation off monthly + grid are performed in a correct way + """ + dom = xr.open_zarr(files.fn_nemo_zarr_dom_mask) + mesh_zgr = xr.open_zarr(files.fn_nemo_zarr_dom_mesh_zgr) + mesh_hgr = xr.open_zarr(files.fn_nemo_zarr_dom_mesh_hgr) + + for var_name in mesh_zgr.data_vars: + dom[var_name] = mesh_zgr[var_name] + for var_name in mesh_hgr.data_vars: + dom[var_name] = mesh_hgr[var_name] + + + dom = dom.isel(y=slice(500, 700), x=slice(1000,1100)) + + u_grid = xr.open_zarr(files.fn_nemo_zarr_u_grid) + u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) + v_grid = xr.open_zarr(files.fn_nemo_zarr_v_grid) + v_grid = v_grid.isel(time_counter=slice(0,119)).rename({'depthv': 'depth'}) + t_grid = xr.open_zarr(files.fn_nemo_zarr_t_grid) + t_grid = t_grid.rename({'deptht': 'depth'}) + + for var_name in u_grid.data_vars: + t_grid[var_name] = u_grid[var_name] + for var_name in v_grid.data_vars: + t_grid[var_name] = v_grid[var_name] + + t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1100), time_counter=slice(0,15)) + + nemo_dom=coast.Gridded(fn_domain = dom, config=files.fn_config_t_grid) + nemo = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=files.fn_config_t_grid) + + nemo.dataset['e3_0']=nemo_dom.dataset['e3_0'] + + gridded_month = coast.GriddedMonthlyHydrographicClimatology(nemo,z_max=200) + gridded_month.calc_climatologies() + + check1 = np.isclose(gridded_month.dataset['SST_monthy_clim'].mean(), 124.5029568214227) + self.assertTrue(check1, msg="check1") + + check2 = np.isclose(gridded_month.dataset['SSS_monthy_clim'].mean(), 124.5029568214227) + self.assertTrue(check2, msg="check2") diff --git a/unit_testing/unit_test_files.py b/unit_testing/unit_test_files.py index afb3f9fc..42b799ba 100644 --- a/unit_testing/unit_test_files.py +++ b/unit_testing/unit_test_files.py @@ -46,6 +46,15 @@ fn_wod = path.join(dn_files, "WOD_example_ragged_standard_level.nc") fn_nemo_bgc = path.join(dn_files, "coast_example_SEAsia_BGC_1990.nc") +fn_nemo_zarr_dom_mask = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mask.zarr" +fn_nemo_zarr_dom_mesh_zgr = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_zgr.zarr" +fn_nemo_zarr_dom_mesh_hgr = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/mesh_hgr.zarr" +fn_nemo_zarr_dat = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr" + +fn_nemo_zarr_u_grid = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_U.zarr" +fn_nemo_zarr_v_grid = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_V.zarr" +fn_nemo_zarr_t_grid = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/n06-coast-testing/n06_T.zarr" + # Domain files fn_nemo_dom = path.join(dn_files, "coast_example_nemo_domain.nc") fn_nemo_dom_bgc = path.join(dn_files, "coast_example_domain_SEAsia.nc") @@ -60,3 +69,4 @@ fn_config_v_grid = path.join(dn_config, "example_nemo_grid_v.json") fn_config_w_grid = path.join(dn_config, "example_nemo_grid_w.json") fn_nemo_config_bgc = path.join(dn_config, "example_nemo_bgc.json") +fn_nemo_config_monthly_climate = path.join(dn_config, "example_nemo_monthly_climate.json") From b7e3686b1511e2b7940c3d4828c29beecbd9c7b9 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 17 Nov 2023 11:15:34 +0000 Subject: [PATCH 127/150] Apply Black formatting to Python code. --- coast/__init__.py | 1 + coast/_utils/logging_util.py | 7 ++- coast/data/config_parser.py | 2 +- coast/data/profile.py | 1 - ...ridded_monthly_hydrographic_climatology.py | 21 +++---- coast/diagnostics/profile_stratification.py | 29 +++++----- example_scripts/profile_test.py | 4 +- .../test_gridded_diagnostics_methods.py | 55 ++++++++----------- 8 files changed, 53 insertions(+), 67 deletions(-) diff --git a/coast/__init__.py b/coast/__init__.py index 09403549..fb84d4d4 100644 --- a/coast/__init__.py +++ b/coast/__init__.py @@ -31,6 +31,7 @@ from ._utils.experiments_file_handling import nemo_filename_maker from .diagnostics.circulation import CurrentsOnT from .diagnostics.profile_hydrographic_analysis import ProfileHydrography + # Set default for logging level when coast is imported import logging diff --git a/coast/_utils/logging_util.py b/coast/_utils/logging_util.py index 30cd4a43..616ae54c 100644 --- a/coast/_utils/logging_util.py +++ b/coast/_utils/logging_util.py @@ -58,8 +58,11 @@ def get_source(level=1): def add_info(msg, level=3): source = get_source(level=level) if isinstance(msg, Exception): - msg = f"{msg.__class__.__name__}: {str(msg)}\n \ - " + "".join(traceback.format_tb(msg.__traceback__)) + msg = ( + f"{msg.__class__.__name__}: {str(msg)}\n \ + " + + "".join(traceback.format_tb(msg.__traceback__)) + ) msg = f"{source[0]}.{source[2]}.{source[1]}: {msg}" return msg diff --git a/coast/data/config_parser.py b/coast/data/config_parser.py index 2ad7e771..e17b20dc 100644 --- a/coast/data/config_parser.py +++ b/coast/data/config_parser.py @@ -15,7 +15,7 @@ def __init__(self, json_path: Union[Path, str]): Args: json_path (Union[Path, str]): path to json config file. """ - with open(json_path, "r", encoding='utf-8') as j: + with open(json_path, "r", encoding="utf-8") as j: json_content = json.loads(j.read()) conf_type = ConfigTypes(json_content[ConfigKeys.TYPE]) if conf_type == ConfigTypes.GRIDDED: diff --git a/coast/data/profile.py b/coast/data/profile.py index 4b89b9cc..81d2bfbf 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -650,7 +650,6 @@ def calculate_en4_qc_flags_levels(self): return qc_integers_tem, qc_integers_sal, qc_integers_both - """================Reshape to 2D================""" def reshape_2d(self, var_user_want): diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index ce22ef94..c7fee6a1 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -8,6 +8,7 @@ from ..data.gridded import Gridded from ..diagnostics.gridded_stratification import GriddedStratification + class GriddedMonthlyHydrographicClimatology(Gridded): """ Calculates the monthly climatology for sss, sst and pea from multi-annual monthly Gridded data. @@ -29,7 +30,7 @@ def __init__(self, gridded_t, z_max=200.0): def calc_climatologies(self): """ Calculate the climatologies for SSH, sss and pea. - + Returns: gridded_t: Gridded dataset object. """ @@ -58,20 +59,13 @@ def calc_climatologies(self): print("copied", im) pea = GriddedStratification(gridded_t2) pea.calc_pea(gridded_t2, zd_mask) - pea_monthy_clim[im, :, :] = pea_monthy_clim[ - im, :, :] + pea.dataset["pea"].values + pea_monthy_clim[im, :, :] = pea_monthy_clim[im, :, :] + pea.dataset["pea"].values pea_monthy_clim = pea_monthy_clim / nyear except Exception as error: - ( - warn( - f"Unable to perform pea calculation. Please check the error {error}" - ) - ) - debug( - f"Unable to perform pea calculation. Please check the error {error}" - ) + (warn(f"Unable to perform pea calculation. Please check the error {error}")) + debug(f"Unable to perform pea calculation. Please check the error {error}") - print('not possible to calculate pea') + print("not possible to calculate pea") sst = self.gridded_t.dataset.variables["sst"] sss = self.gridded_t.dataset.variables["sss"] @@ -91,8 +85,7 @@ def calc_climatologies(self): dims = ["mon_dim", "y_dim", "x_dim"] attributes_sst = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_sss = {"units": "", "standard name": "Absolute Sea Surface Salinity"} - attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " - + str(self.z_max) + "m"} + attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(self.z_max) + "m"} self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index d3af87fa..3e4bcdc2 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -12,6 +12,7 @@ #### + class ProfileStratification(Profile): # TODO All abstract methods should be implemented """ Object for handling and storing necessary information, methods and outputs @@ -45,11 +46,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): Stage 3. Fill gaps in data and extrapolate so there are T and S values where ever there is a depth value """ -#%% + # %% print("Cleaning the data") # find profiles good for SST and NBT dz_max = 25.0 - + n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] tmp_clean = profile.dataset.potential_temperature.values[:, :] @@ -60,8 +61,9 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): # Find good SST and SSS depths def first_nonzero(arr, axis=0, invalid_val=np.nan): - mask = arr!=0 + mask = arr != 0 return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) + if "bathymetry" in gridded.dataset: profile.gridded_to_profile_2d(gridded, "bathymetry") D_prf = profile.dataset.bathymetry.values @@ -74,15 +76,14 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): I_tmp = np.nonzero(np.any(test_tmp.values, axis=1))[0] I_sal = np.nonzero(np.any(test_sal.values, axis=1))[0] # - #for ip in I_tmp: + # for ip in I_tmp: # good_sst[ip] = np.min(np.nonzero(test_tmp.values[ip, :])) - #for ip in I_sal: + # for ip in I_sal: # good_sss[ip] = np.min(np.nonzero(test_sal.values[ip, :])) - good_sst=first_nonzero(test_tmp.values,axis=1) - good_sss=first_nonzero(test_sal.values,axis=1) - - + good_sst = first_nonzero(test_tmp.values, axis=1) + good_sss = first_nonzero(test_sal.values, axis=1) + I_tmp = np.where(np.isfinite(good_sst))[0] I_sal = np.where(np.isfinite(good_sss))[0] @@ -97,7 +98,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): ### else: - print('error no bathy provided, cant clean the data') + print("error no bathy provided, cant clean the data") return profile SST = np.zeros(n_prf) * np.nan SSS = np.zeros(n_prf) * np.nan @@ -107,9 +108,8 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it -#%% + # %% for i_prf in range(n_prf): - tmp = profile.dataset.potential_temperature.values[i_prf, :] sal = profile.dataset.practical_salinity.values[i_prf, :] z = profile.dataset.depth.values[i_prf, :] @@ -120,7 +120,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) - + sal[np.isnan(z)] = np.nan sal_clean[i_prf, :] = sal @@ -136,7 +136,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) print("All nice and clean") -#%% + # %% return profile def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): @@ -237,4 +237,5 @@ def quick_plot(self, var: xr.DataArray = None): ) return fig, ax + ############################################################################## diff --git a/example_scripts/profile_test.py b/example_scripts/profile_test.py index 61c6a6f8..9e034530 100644 --- a/example_scripts/profile_test.py +++ b/example_scripts/profile_test.py @@ -23,8 +23,8 @@ fn_grd_dom = "example_files/coast_example_nemo_domain.nc" fn_grd_cfg = "config/example_nemo_grid_t.json" nemo = coast.Gridded(fn_domain=fn_grd_dom, config=fn_grd_cfg) -#profile.match_to_grid(nemo) -#profile.gridded_to_profile_2d(nemo, "bathymetry") +# profile.match_to_grid(nemo) +# profile.gridded_to_profile_2d(nemo, "bathymetry") Zmax = 200 # metres pa.calc_pea(profile, nemo, Zmax) diff --git a/unit_testing/test_gridded_diagnostics_methods.py b/unit_testing/test_gridded_diagnostics_methods.py index 5079334e..767e4463 100644 --- a/unit_testing/test_gridded_diagnostics_methods.py +++ b/unit_testing/test_gridded_diagnostics_methods.py @@ -137,36 +137,28 @@ def test_circulation(self): # %% lims = [150, 250, 100, 350] nemo_u = coast.Gridded( - fn_data=files.fn_nemo_grid_u_dat, - fn_domain=files.fn_nemo_dom, - config=files.fn_config_u_grid, lims=lims + fn_data=files.fn_nemo_grid_u_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_u_grid, lims=lims ) nemo_v = coast.Gridded( - fn_data=files.fn_nemo_grid_v_dat, - fn_domain=files.fn_nemo_dom, - config=files.fn_config_v_grid, lims=lims + fn_data=files.fn_nemo_grid_v_dat, fn_domain=files.fn_nemo_dom, config=files.fn_config_v_grid, lims=lims ) with self.subTest("Map velocities to t-points"): - nemo_t = coast.CurrentsOnT(fn_domain=files.fn_nemo_dom, - config=files.fn_config_t_grid, - lims=lims) + nemo_t = coast.CurrentsOnT(fn_domain=files.fn_nemo_dom, config=files.fn_config_t_grid, lims=lims) nemo_t.currents_on_t(nemo_u, nemo_v) nemo_t.subset(z_dim=[0], t_dim=[0]) u1 = 0.5 * ( - nemo_u.dataset.u_velocity[ - 0, 0, 150, 60].values + nemo_u.dataset.u_velocity[0, 0, 150, 59].values + nemo_u.dataset.u_velocity[0, 0, 150, 60].values + nemo_u.dataset.u_velocity[0, 0, 150, 59].values ) u2 = nemo_t.dataset.ut_velocity[0, 0, 150, 60].values # print(f"u vel on u-pts: {nemo_u.dataset.u_velocity.isel(x_dim=slice(59,61), \ - # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") + # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") # print(f"u vel on t-pts: \ - # {nemo_t.dataset.ut_velocity.isel(x_dim=slice(59,61), - # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") + # {nemo_t.dataset.ut_velocity.isel(x_dim=slice(59,61), + # y_dim=slice(149,151), t_dim=0, z_dim=0).values}") v1 = 0.5 * ( - nemo_v.dataset.v_velocity[ - 0,0, 150, 60].values + nemo_v.dataset.v_velocity[0, 0, 149, 60].values + nemo_v.dataset.v_velocity[0, 0, 150, 60].values + nemo_v.dataset.v_velocity[0, 0, 149, 60].values ) v2 = nemo_t.dataset.vt_velocity[0, 0, 150, 60].values speed = nemo_t.dataset.speed_t[0, 0, 150, 60].values @@ -185,9 +177,7 @@ def test_circulation(self): plt.close("all") def test_calc_pea(self): - nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, - files.fn_nemo_dom, - config=files.fn_config_t_grid) + nemo_t = coast.Gridded(files.fn_nemo_grid_t_dat_summer, files.fn_nemo_dom, config=files.fn_config_t_grid) # Compute a vertical max to exclude depths below 200m Zd_mask, kmax, Ikmax = nemo_t.calculate_vertical_mask(200.0) @@ -220,34 +210,33 @@ def test_calc_monthly_grided(self): dom[var_name] = mesh_zgr[var_name] for var_name in mesh_hgr.data_vars: dom[var_name] = mesh_hgr[var_name] - - - dom = dom.isel(y=slice(500, 700), x=slice(1000,1100)) - + + dom = dom.isel(y=slice(500, 700), x=slice(1000, 1100)) + u_grid = xr.open_zarr(files.fn_nemo_zarr_u_grid) - u_grid = u_grid.isel(time_counter=slice(0,119)).rename({'depthu': 'depth'}) + u_grid = u_grid.isel(time_counter=slice(0, 119)).rename({"depthu": "depth"}) v_grid = xr.open_zarr(files.fn_nemo_zarr_v_grid) - v_grid = v_grid.isel(time_counter=slice(0,119)).rename({'depthv': 'depth'}) + v_grid = v_grid.isel(time_counter=slice(0, 119)).rename({"depthv": "depth"}) t_grid = xr.open_zarr(files.fn_nemo_zarr_t_grid) - t_grid = t_grid.rename({'deptht': 'depth'}) + t_grid = t_grid.rename({"deptht": "depth"}) for var_name in u_grid.data_vars: t_grid[var_name] = u_grid[var_name] for var_name in v_grid.data_vars: t_grid[var_name] = v_grid[var_name] - t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000,1100), time_counter=slice(0,15)) + t_grid = t_grid.isel(y=slice(500, 700), x=slice(1000, 1100), time_counter=slice(0, 15)) - nemo_dom=coast.Gridded(fn_domain = dom, config=files.fn_config_t_grid) - nemo = coast.Gridded(fn_data= t_grid, fn_domain = dom, config=files.fn_config_t_grid) + nemo_dom = coast.Gridded(fn_domain=dom, config=files.fn_config_t_grid) + nemo = coast.Gridded(fn_data=t_grid, fn_domain=dom, config=files.fn_config_t_grid) - nemo.dataset['e3_0']=nemo_dom.dataset['e3_0'] + nemo.dataset["e3_0"] = nemo_dom.dataset["e3_0"] - gridded_month = coast.GriddedMonthlyHydrographicClimatology(nemo,z_max=200) + gridded_month = coast.GriddedMonthlyHydrographicClimatology(nemo, z_max=200) gridded_month.calc_climatologies() - check1 = np.isclose(gridded_month.dataset['SST_monthy_clim'].mean(), 124.5029568214227) + check1 = np.isclose(gridded_month.dataset["SST_monthy_clim"].mean(), 124.5029568214227) self.assertTrue(check1, msg="check1") - check2 = np.isclose(gridded_month.dataset['SSS_monthy_clim'].mean(), 124.5029568214227) + check2 = np.isclose(gridded_month.dataset["SSS_monthy_clim"].mean(), 124.5029568214227) self.assertTrue(check2, msg="check2") From 736514a014f87bdbcd3d9f47792d1fc7d192fef3 Mon Sep 17 00:00:00 2001 From: ContentsBot Date: Fri, 17 Nov 2023 11:16:30 +0000 Subject: [PATCH 128/150] Commit generated unit test contents. --- unit_testing/unit_test_contents.txt | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/unit_testing/unit_test_contents.txt b/unit_testing/unit_test_contents.txt index 0d756338..22f25429 100755 --- a/unit_testing/unit_test_contents.txt +++ b/unit_testing/unit_test_contents.txt @@ -28,12 +28,13 @@ 4. test_xesmf_convert a. basic_conversion_to_xesmf -5. test_diagnostic_methods - a. circulation +5. test_gridded_diagnostics_methods + a. calc_monthly_grided b. calc_pea - c. compute_vertical_spatial_derivative - d. construct_density - e. construct_pycnocline_depth_and_thickness + c. circulation + d. compute_vertical_spatial_derivative + e. construct_density + f. construct_pycnocline_depth_and_thickness 6. test_transect_methods a. calculate_transport_velocity_and_depth From 8296722ebed57addda43835101ed5214672f6f51 Mon Sep 17 00:00:00 2001 From: Jason Holt Date: Tue, 19 Dec 2023 17:26:39 +0000 Subject: [PATCH 129/150] Resorting to previous climateology behaviour --- ...ridded_monthly_hydrographic_climatology.py | 26 ++++++++++++++----- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index c7fee6a1..6d62c998 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -32,7 +32,8 @@ def calc_climatologies(self): Calculate the climatologies for SSH, sss and pea. Returns: - gridded_t: Gridded dataset object. +# gridded_t: Gridded dataset object. + dataset: Gridded dataset object containing monthly climatologies """ # calculate a depth mask @@ -48,6 +49,7 @@ def calc_climatologies(self): pea_monthy_clim = np.zeros((12, ny, nx)) try: + nyear = int(nt / 12) # hard wired for monthly data starting in Jan for iy in range(nyear): print("Calc pea", iy) @@ -59,8 +61,9 @@ def calc_climatologies(self): print("copied", im) pea = GriddedStratification(gridded_t2) pea.calc_pea(gridded_t2, zd_mask) - pea_monthy_clim[im, :, :] = pea_monthy_clim[im, :, :] + pea.dataset["pea"].values + pea_monthy_clim[im, :, :] = pea_monthy_clim[im, :, :] + pea.dataset["PEA"].values pea_monthy_clim = pea_monthy_clim / nyear + except Exception as error: (warn(f"Unable to perform pea calculation. Please check the error {error}")) debug(f"Unable to perform pea calculation. Please check the error {error}") @@ -86,14 +89,23 @@ def calc_climatologies(self): attributes_sst = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_sss = {"units": "", "standard name": "Absolute Sea Surface Salinity"} attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(self.z_max) + "m"} - - self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( +#jth this adds the new variables to the full data set, which makes saving difficult, easier just to keep the new variables in seperate object +# self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( +# np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst +# ) +# self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray( +# np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss +# ) +# self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray( +# np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea +# ) +# self.dataset = self.gridded_t.dataset + self.dataset["sst_monthy_clim"] = xr.DataArray( np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst ) - self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray( + self.dataset["sss_monthy_clim"] = xr.DataArray( np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss ) - self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray( + self.dataset["pea_monthy_clim"] = xr.DataArray( np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea ) - self.dataset = self.gridded_t.dataset From 08331ffc9fbfba18c847f3e7ff2907e4185a1a82 Mon Sep 17 00:00:00 2001 From: Jason T Holt Date: Tue, 16 Jan 2024 13:48:33 +0000 Subject: [PATCH 130/150] update strat analysis --- coast/data/profile.py | 31 +++++++++++++++++++ .../profile_hydrographic_analysis.py | 2 +- coast/diagnostics/profile_stratification.py | 2 ++ 3 files changed, 34 insertions(+), 1 deletion(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 81d2bfbf..4b587a7e 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -13,6 +13,8 @@ from pathlib import Path import pandas as pd +import os + class Profile(Indexed): """ @@ -154,7 +156,36 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] ) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) + def extract_en4_profiles(self, dataset_names, region_bounds): + """ + Helper method to load EN4 data file, subset by region and process. + Args: + dataset_names: list of file names. + region_bounds: [lon min, lon max, lat min lat max] + config : a configuration file (optional) + """ + x_min = region_bounds[0] + x_max = region_bounds[1] + y_min = region_bounds[2] + y_max = region_bounds[3] + #self.profile = Profile(config=config) + self.read_en4(dataset_names, multiple=True) + pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) + pr= pr.process_en4() + return pr + @staticmethod + def make_filenames(path, dataset, yr_start, yr_stop): + if dataset == "EN4": + dataset_names = [] + january = 1 + december = 13 # range is non-inclusive so we need 12 + 1 + for yr in range(yr_start, yr_stop + 1): + for im in range(january, december): + name = os.path.join(path, f"EN.4.2.1.f.profiles.l09.{yr}{im:02}.nc") + dataset_names.append(name) + return dataset_names + print("Data set not coded") """======================= Plotting =======================""" def plot_profile(self, var: str, profile_indices=None): diff --git a/coast/diagnostics/profile_hydrographic_analysis.py b/coast/diagnostics/profile_hydrographic_analysis.py index 980216c8..1489956d 100644 --- a/coast/diagnostics/profile_hydrographic_analysis.py +++ b/coast/diagnostics/profile_hydrographic_analysis.py @@ -318,7 +318,7 @@ def grid_hydro_mnth(self): ############################################################################### @staticmethod - def makefilenames(path, dataset, yr_start, yr_stop): + def make_filenames(path, dataset, yr_start, yr_stop): if dataset == "EN4": dataset_names = [] january = 1 diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 3e4bcdc2..02507906 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -194,6 +194,8 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): dims = ["id_dim"] attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes) + self.dataset["sst"] = xr.DataArray(profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes) + self.dataset["sss"] = xr.DataArray(profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes) def quick_plot(self, var: xr.DataArray = None): """ From e4153bf0e8507f781bf55ebffae4f9466a2c03c9 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Tue, 16 Jan 2024 14:10:15 +0000 Subject: [PATCH 131/150] Apply Black formatting to Python code. --- coast/data/profile.py | 7 ++-- ...ridded_monthly_hydrographic_climatology.py | 33 +++++++++---------- coast/diagnostics/profile_stratification.py | 8 +++-- 3 files changed, 27 insertions(+), 21 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 4b587a7e..e3b0f796 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -156,6 +156,7 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] ) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) + def extract_en4_profiles(self, dataset_names, region_bounds): """ Helper method to load EN4 data file, subset by region and process. @@ -169,11 +170,12 @@ def extract_en4_profiles(self, dataset_names, region_bounds): x_max = region_bounds[1] y_min = region_bounds[2] y_max = region_bounds[3] - #self.profile = Profile(config=config) + # self.profile = Profile(config=config) self.read_en4(dataset_names, multiple=True) pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) - pr= pr.process_en4() + pr = pr.process_en4() return pr + @staticmethod def make_filenames(path, dataset, yr_start, yr_stop): if dataset == "EN4": @@ -186,6 +188,7 @@ def make_filenames(path, dataset, yr_start, yr_stop): dataset_names.append(name) return dataset_names print("Data set not coded") + """======================= Plotting =======================""" def plot_profile(self, var: str, profile_indices=None): diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index 6d62c998..fb25f03c 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -29,11 +29,11 @@ def __init__(self, gridded_t, z_max=200.0): def calc_climatologies(self): """ - Calculate the climatologies for SSH, sss and pea. + Calculate the climatologies for SSH, sss and pea. - Returns: -# gridded_t: Gridded dataset object. - dataset: Gridded dataset object containing monthly climatologies + Returns: + # gridded_t: Gridded dataset object. + dataset: Gridded dataset object containing monthly climatologies """ # calculate a depth mask @@ -49,7 +49,6 @@ def calc_climatologies(self): pea_monthy_clim = np.zeros((12, ny, nx)) try: - nyear = int(nt / 12) # hard wired for monthly data starting in Jan for iy in range(nyear): print("Calc pea", iy) @@ -63,7 +62,7 @@ def calc_climatologies(self): pea.calc_pea(gridded_t2, zd_mask) pea_monthy_clim[im, :, :] = pea_monthy_clim[im, :, :] + pea.dataset["PEA"].values pea_monthy_clim = pea_monthy_clim / nyear - + except Exception as error: (warn(f"Unable to perform pea calculation. Please check the error {error}")) debug(f"Unable to perform pea calculation. Please check the error {error}") @@ -89,17 +88,17 @@ def calc_climatologies(self): attributes_sst = {"units": "o^C", "standard name": "Conservative Sea Surface Temperature"} attributes_sss = {"units": "", "standard name": "Absolute Sea Surface Salinity"} attributes_pea = {"units": "Jm^-3", "standard name": "Potential Energy Anomaly to " + str(self.z_max) + "m"} -#jth this adds the new variables to the full data set, which makes saving difficult, easier just to keep the new variables in seperate object -# self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( -# np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst -# ) -# self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray( -# np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss -# ) -# self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray( -# np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea -# ) -# self.dataset = self.gridded_t.dataset + # jth this adds the new variables to the full data set, which makes saving difficult, easier just to keep the new variables in seperate object + # self.dataset = self.gridded_t.dataset["sst_monthy_clim"] = xr.DataArray( + # np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst + # ) + # self.gridded_t.dataset["sss_monthy_clim"] = xr.DataArray( + # np.squeeze(sss_monthy_clim), coords=coords, dims=dims, attrs=attributes_sss + # ) + # self.gridded_t.dataset["pea_monthy_clim"] = xr.DataArray( + # np.squeeze(pea_monthy_clim), coords=coords, dims=dims, attrs=attributes_pea + # ) + # self.dataset = self.gridded_t.dataset self.dataset["sst_monthy_clim"] = xr.DataArray( np.squeeze(sst_monthy_clim), coords=coords, dims=dims, attrs=attributes_sst ) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 02507906..3c8ee5a3 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -194,8 +194,12 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): dims = ["id_dim"] attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes) - self.dataset["sst"] = xr.DataArray(profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes) - self.dataset["sss"] = xr.DataArray(profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes) + self.dataset["sst"] = xr.DataArray( + profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes + ) + self.dataset["sss"] = xr.DataArray( + profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes + ) def quick_plot(self, var: xr.DataArray = None): """ From 4d3d54c680be31942db3b884b56d9143dfd497ad Mon Sep 17 00:00:00 2001 From: jasontempestholt Date: Fri, 19 Jan 2024 13:27:41 +0000 Subject: [PATCH 132/150] Adding match-to-grid capability to profile_stratification.py and correcting highly inefficient bit removing code that breaks env` --- coast/_utils/plot_util.py | 4 +- coast/data/profile.py | 4 +- coast/diagnostics/profile_stratification.py | 87 +++++++++++++++++++-- 3 files changed, 84 insertions(+), 11 deletions(-) diff --git a/coast/_utils/plot_util.py b/coast/_utils/plot_util.py index 8f7e5861..71492725 100644 --- a/coast/_utils/plot_util.py +++ b/coast/_utils/plot_util.py @@ -13,7 +13,7 @@ import numpy as np import pyproj import scipy.interpolate as si -from tqdm import tqdm +#jth from tqdm import tqdm from .logging_util import warn @@ -456,7 +456,7 @@ def grid_angle(lon, lat): """ crs_wgs84 = pyproj.CRS("epsg:4326") angle = np.zeros(lon.shape) - for j in tqdm(range(lon.shape[0] - 1)): + for j in (range(lon.shape[0] - 1)): # breaks env tqdm(range(lon.shape[0] - 1)): for i in range(lon.shape[1] - 1): crs_aeqd = make_projection(lon[j, i], lat[j, i]) transformer = pyproj.Transformer.from_crs(crs_wgs84, crs_aeqd) diff --git a/coast/data/profile.py b/coast/data/profile.py index 4b587a7e..99f4cf68 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -156,7 +156,7 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] ) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) - def extract_en4_profiles(self, dataset_names, region_bounds): + def extract_en4_profiles(self, dataset_names, region_bounds,chunks: dict = {}): """ Helper method to load EN4 data file, subset by region and process. @@ -170,7 +170,7 @@ def extract_en4_profiles(self, dataset_names, region_bounds): y_min = region_bounds[2] y_max = region_bounds[3] #self.profile = Profile(config=config) - self.read_en4(dataset_names, multiple=True) + self.read_en4(dataset_names, multiple=True, chunks = chunks) pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) pr= pr.process_en4() return pr diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 02507906..5ddad65a 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -6,9 +6,11 @@ import coast from .._utils.plot_util import geo_scatter from .._utils.logging_util import get_slug, debug - +from typing import List +from dask.diagnostics import ProgressBar #### - +# +earth_radius = 6367456 * np.pi / 180 #### @@ -109,18 +111,20 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it # %% + tmp1 = profile.dataset.potential_temperature.values[:, :] + sal1 = profile.dataset.practical_salinity.values[:, :] + z1 = profile.dataset.depth.values[:, :] for i_prf in range(n_prf): - tmp = profile.dataset.potential_temperature.values[i_prf, :] - sal = profile.dataset.practical_salinity.values[i_prf, :] - z = profile.dataset.depth.values[i_prf, :] + + tmp = tmp1[i_prf, :] + sal = sal1[i_prf, :] + z = z1[i_prf, :] if any_tmp[i_prf]: tmp = coast.general_utils.fill_holes_1d(tmp) - tmp[np.isnan(z)] = np.nan tmp_clean[i_prf, :] = tmp if any_sal[i_prf]: sal = coast.general_utils.fill_holes_1d(sal) - sal[np.isnan(z)] = np.nan sal_clean[i_prf, :] = sal @@ -153,6 +157,7 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): # %% gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables + profile = ProfileStratification.clean_data(profile, gridded, Zmax) # Define grid spacing, dz. Required for depth integral @@ -241,3 +246,71 @@ def quick_plot(self, var: xr.DataArray = None): return fig, ax ############################################################################## + def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000) -> None: + """Match profiles locations to grid, finding 4 nearest neighbours for each profile. + + Args: + gridded (Gridded): Gridded object. + limits (List): [jmin,jmax,imin,imax] - Subset to this region. + rmax (int): 7000 m - maxmimum search distance (metres). + + ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? + + ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO + """ + self.gridded = gridded + if sum(limits) != 0: + gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) + # keep the grid or subset on the hydrographic profiles object + gridded.dataset["limits"] = limits + self.gridded = gridded + lon_prf = self.dataset.longitude.values + lat_prf = self.dataset.latitude.values + + # Find 4 nearest neighbours on grid + j_prf, i_prf, rmin_prf = gridded.find_j_i_list(lat=lat_prf, lon=lon_prf, n_nn=4) + + self.dataset["i_min"] = limits[0] # reference back to origianl grid + self.dataset["j_min"] = limits[2] + + i_min = self.dataset.i_min.values + j_min = self.dataset.j_min.values + + # Sort 4 NN by distance on grid + ii = np.nonzero(np.isnan(lon_prf)) + i_prf[ii, :] = 0 + j_prf[ii, :] = 0 + ip = np.where(np.logical_or(i_prf[:, 0] != 0, j_prf[:, 0] != 0))[0] + lon_prf4 = np.repeat(lon_prf[ip, np.newaxis], 4, axis=1).ravel() + lat_prf4 = np.repeat(lat_prf[ip, np.newaxis], 4, axis=1).ravel() + r = np.ones(i_prf.shape) * np.nan + lon_grd = gridded.dataset.longitude.values + lat_grd = gridded.dataset.latitude.values + + rr = ProfileStratification.distance_on_grid( + lat_grd, lon_grd, j_prf[ip, :].ravel(), i_prf[ip, :].ravel(), lat_prf4, lon_prf4 + ) + r[ip, :] = np.reshape(rr, (ip.size, 4)) + # sort by distance + ii = np.argsort(r, axis=1) + rmin_prf = np.take_along_axis(r, ii, axis=1) + i_prf = np.take_along_axis(i_prf, ii, axis=1) + j_prf = np.take_along_axis(j_prf, ii, axis=1) + + ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) + i_prf = i_prf + i_min + j_prf = j_prf + j_min + i_prf[ii, :] = 0 # should the be nan? + j_prf[ii, :] = 0 + + self.dataset["i_prf"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) + self.dataset["j_prf"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) + self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + + + + def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): + DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) + DY = (Ypts - Y[jpts, ipts]) * earth_radius + r = np.sqrt(DX**2 + DY**2) + return r \ No newline at end of file From 22f45d882f3fab48a2886f85e583edf420462fef Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 19 Jan 2024 13:34:06 +0000 Subject: [PATCH 133/150] Apply Black formatting to Python code. --- coast/_utils/plot_util.py | 5 +++-- coast/data/profile.py | 7 ++++--- coast/diagnostics/profile_stratification.py | 6 ++---- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/coast/_utils/plot_util.py b/coast/_utils/plot_util.py index 71492725..100ed954 100644 --- a/coast/_utils/plot_util.py +++ b/coast/_utils/plot_util.py @@ -13,7 +13,8 @@ import numpy as np import pyproj import scipy.interpolate as si -#jth from tqdm import tqdm + +# jth from tqdm import tqdm from .logging_util import warn @@ -456,7 +457,7 @@ def grid_angle(lon, lat): """ crs_wgs84 = pyproj.CRS("epsg:4326") angle = np.zeros(lon.shape) - for j in (range(lon.shape[0] - 1)): # breaks env tqdm(range(lon.shape[0] - 1)): + for j in range(lon.shape[0] - 1): # breaks env tqdm(range(lon.shape[0] - 1)): for i in range(lon.shape[1] - 1): crs_aeqd = make_projection(lon[j, i], lat[j, i]) transformer = pyproj.Transformer.from_crs(crs_wgs84, crs_aeqd) diff --git a/coast/data/profile.py b/coast/data/profile.py index 00b83e58..604a8026 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -156,7 +156,8 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] ) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) - def extract_en4_profiles(self, dataset_names, region_bounds,chunks: dict = {}): + + def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): """ Helper method to load EN4 data file, subset by region and process. @@ -169,8 +170,8 @@ def extract_en4_profiles(self, dataset_names, region_bounds,chunks: dict = {}): x_max = region_bounds[1] y_min = region_bounds[2] y_max = region_bounds[3] - #self.profile = Profile(config=config) - self.read_en4(dataset_names, multiple=True, chunks = chunks) + # self.profile = Profile(config=config) + self.read_en4(dataset_names, multiple=True, chunks=chunks) pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) pr = pr.process_en4() return pr diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index b6907b0d..cc010abf 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -8,6 +8,7 @@ from .._utils.logging_util import get_slug, debug from typing import List from dask.diagnostics import ProgressBar + #### # earth_radius = 6367456 * np.pi / 180 @@ -113,9 +114,8 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # %% tmp1 = profile.dataset.potential_temperature.values[:, :] sal1 = profile.dataset.practical_salinity.values[:, :] - z1 = profile.dataset.depth.values[:, :] + z1 = profile.dataset.depth.values[:, :] for i_prf in range(n_prf): - tmp = tmp1[i_prf, :] sal = sal1[i_prf, :] z = z1[i_prf, :] @@ -311,8 +311,6 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: self.dataset["j_prf"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) - - def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) DY = (Ypts - Y[jpts, ipts]) * earth_radius From 75b5fa41b85e1780005f1f515976d12e7ea74930 Mon Sep 17 00:00:00 2001 From: jasontempestholt Date: Fri, 19 Jan 2024 16:42:22 +0000 Subject: [PATCH 134/150] lablel indices with grid name incase want to store indices from multiple grids in one file --- coast/diagnostics/profile_stratification.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index cc010abf..aed1d738 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -250,7 +250,7 @@ def quick_plot(self, var: xr.DataArray = None): return fig, ax ############################################################################## - def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000) -> None: + def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000, grid_name = 'prf') -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: @@ -307,9 +307,9 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: i_prf[ii, :] = 0 # should the be nan? j_prf[ii, :] = 0 - self.dataset["i_prf"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) - self.dataset["j_prf"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) - self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + self.dataset[f"i_{grid_name}"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) + self.dataset[f"j_{grid_name}"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) + self.dataset[f"rmin_{grid_name}"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) From 6a075fbcfcc742008680d3e00540f9d0024411b3 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 19 Jan 2024 16:43:58 +0000 Subject: [PATCH 135/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index aed1d738..c31dde84 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -250,7 +250,9 @@ def quick_plot(self, var: xr.DataArray = None): return fig, ax ############################################################################## - def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000, grid_name = 'prf') -> None: + def match_to_grid( + self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000, grid_name="prf" + ) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: From d4af9119df7ea3e4d9f0f4e4d7092bc71164582a Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Thu, 25 Jan 2024 20:30:05 +0000 Subject: [PATCH 136/150] Update profile_stratification.py --- coast/diagnostics/profile_stratification.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index c31dde84..6cd8df3d 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -251,7 +251,7 @@ def quick_plot(self, var: xr.DataArray = None): ############################################################################## def match_to_grid( - self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax: int = 7000, grid_name="prf" + self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax = 7000., grid_name="prf" ) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. @@ -264,12 +264,14 @@ def match_to_grid( ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO """ - self.gridded = gridded + if sum(limits) != 0: - gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) - # keep the grid or subset on the hydrographic profiles object + gridded.subset(y_dim=range(limits[0], limits[1] + 1), x_dim=range(limits[2], limits[3] + 1)) + # keep the bathymetry and mask or subset on the hydrographic profiles object gridded.dataset["limits"] = limits - self.gridded = gridded + self.gridded_bathymetry = gridded.dataset.bathymetry + self.gridded_mask = gridded.dataset.bottom_level != 0 + lon_prf = self.dataset.longitude.values lat_prf = self.dataset.latitude.values From e3ada952ccfd9761bbbeeee744e1ca1c608386ee Mon Sep 17 00:00:00 2001 From: BlackBot Date: Thu, 25 Jan 2024 20:30:47 +0000 Subject: [PATCH 137/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 6cd8df3d..5305fa72 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -250,9 +250,7 @@ def quick_plot(self, var: xr.DataArray = None): return fig, ax ############################################################################## - def match_to_grid( - self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax = 7000., grid_name="prf" - ) -> None: + def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7000.0, grid_name="prf") -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: From 31ebee559cec35ed82669587d84e52a8df6afc02 Mon Sep 17 00:00:00 2001 From: jasontempestholt Date: Fri, 26 Jan 2024 17:27:21 +0000 Subject: [PATCH 138/150] Update with develop --- coast/diagnostics/profile_stratification.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 5305fa72..7fc04666 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -265,10 +265,10 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7 if sum(limits) != 0: gridded.subset(y_dim=range(limits[0], limits[1] + 1), x_dim=range(limits[2], limits[3] + 1)) + #gridded.spatial _subset(limits) might need this one for wrapping # keep the bathymetry and mask or subset on the hydrographic profiles object gridded.dataset["limits"] = limits - self.gridded_bathymetry = gridded.dataset.bathymetry - self.gridded_mask = gridded.dataset.bottom_level != 0 + lon_prf = self.dataset.longitude.values lat_prf = self.dataset.latitude.values @@ -312,6 +312,8 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7 self.dataset[f"i_{grid_name}"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) self.dataset[f"j_{grid_name}"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) self.dataset[f"rmin_{grid_name}"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) + #self.dataset[f"bathy_{grid_name}"] = gridded.dataset.bathymetry + #self.dataset[f"mask_{grid_name}"] = gridded.dataset.bottom_level != 0 def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) From 8d1a275538301947e4dd147860de31f99a36de8a Mon Sep 17 00:00:00 2001 From: BlackBot Date: Fri, 26 Jan 2024 17:28:02 +0000 Subject: [PATCH 139/150] Apply Black formatting to Python code. --- coast/_utils/docsy_tools.py | 1 + coast/_utils/general_utils.py | 1 + coast/_utils/logging_util.py | 1 + coast/_utils/mask_maker.py | 1 + coast/_utils/xesmf_convert.py | 1 + coast/data/altimetry.py | 1 + coast/data/argos.py | 1 + coast/data/coast.py | 1 + coast/data/config_parser.py | 1 + coast/data/config_structure.py | 1 + coast/data/glider.py | 1 + coast/data/gridded.py | 1 + coast/data/index.py | 1 + coast/data/lagrangian.py | 1 + coast/data/oceanparcels.py | 1 + coast/data/profile.py | 1 + coast/data/tidegauge.py | 1 + coast/data/timeseries.py | 1 + coast/data/track.py | 1 + coast/diagnostics/climatology.py | 1 + coast/diagnostics/contour.py | 1 + coast/diagnostics/eof.py | 1 + ...ridded_monthly_hydrographic_climatology.py | 1 + coast/diagnostics/gridded_stratification.py | 12 ++++---- coast/diagnostics/profile_analysis.py | 1 + coast/diagnostics/profile_stratification.py | 7 ++--- coast/diagnostics/tidegauge_analysis.py | 1 + coast/diagnostics/transect.py | 30 +++++++++---------- .../seasia_dic_example_plot.py | 1 + markdown_doc_builder.py | 1 + setup.py | 2 +- tests/test_velocity_plot_util.py | 1 + .../test_index_classes_load_datasets.py | 1 + 33 files changed, 54 insertions(+), 26 deletions(-) diff --git a/coast/_utils/docsy_tools.py b/coast/_utils/docsy_tools.py index 9898d520..4be19a38 100644 --- a/coast/_utils/docsy_tools.py +++ b/coast/_utils/docsy_tools.py @@ -1,4 +1,5 @@ """A class to help with writting markdown.""" + from typing import List, Type diff --git a/coast/_utils/general_utils.py b/coast/_utils/general_utils.py index aeee1294..a31bcfa9 100644 --- a/coast/_utils/general_utils.py +++ b/coast/_utils/general_utils.py @@ -1,4 +1,5 @@ """A general utility file.""" + import xarray as xr import numpy as np import sklearn.neighbors as nb diff --git a/coast/_utils/logging_util.py b/coast/_utils/logging_util.py index cc1fb30c..e34f547e 100644 --- a/coast/_utils/logging_util.py +++ b/coast/_utils/logging_util.py @@ -1,4 +1,5 @@ """A logging unilty file""" + import logging import sys import io diff --git a/coast/_utils/mask_maker.py b/coast/_utils/mask_maker.py index 139b84bc..23b48c17 100644 --- a/coast/_utils/mask_maker.py +++ b/coast/_utils/mask_maker.py @@ -1,4 +1,5 @@ """Mask maker""" + import xarray as xr import numpy as np import skimage.draw as draw diff --git a/coast/_utils/xesmf_convert.py b/coast/_utils/xesmf_convert.py index fa1fa04a..e10ce9e8 100644 --- a/coast/_utils/xesmf_convert.py +++ b/coast/_utils/xesmf_convert.py @@ -1,4 +1,5 @@ """A class to convert from coast gridded to xesmf.""" + import os.path as path_lib import warnings from ..data.gridded import Gridded diff --git a/coast/data/altimetry.py b/coast/data/altimetry.py index 17a7f49f..870e0982 100644 --- a/coast/data/altimetry.py +++ b/coast/data/altimetry.py @@ -1,4 +1,5 @@ """Altimetry class""" + from .track import Track import numpy as np import xarray as xr diff --git a/coast/data/argos.py b/coast/data/argos.py index f29c1fb1..aa97cd99 100644 --- a/coast/data/argos.py +++ b/coast/data/argos.py @@ -1,4 +1,5 @@ """Argos class""" + from .index import Indexed import numpy as np import xarray as xr diff --git a/coast/data/coast.py b/coast/data/coast.py index 739c9c81..332deac4 100644 --- a/coast/data/coast.py +++ b/coast/data/coast.py @@ -1,4 +1,5 @@ """The coast class is the main access point into this package.""" + import copy from typing import Any, Dict, List import math diff --git a/coast/data/config_parser.py b/coast/data/config_parser.py index e17b20dc..158a62d7 100644 --- a/coast/data/config_parser.py +++ b/coast/data/config_parser.py @@ -1,4 +1,5 @@ """Config parser.""" + import json from pathlib import Path from typing import Union diff --git a/coast/data/config_structure.py b/coast/data/config_structure.py index d30f97be..76acf431 100644 --- a/coast/data/config_structure.py +++ b/coast/data/config_structure.py @@ -1,4 +1,5 @@ """Classes defining config structure.""" + from dataclasses import dataclass from enum import Enum, unique diff --git a/coast/data/glider.py b/coast/data/glider.py index 2c20ff40..b865a8e7 100644 --- a/coast/data/glider.py +++ b/coast/data/glider.py @@ -1,4 +1,5 @@ """Glider class""" + from .index import Indexed import xarray as xr from .._utils.logging_util import get_slug, debug, info, warn, warning diff --git a/coast/data/gridded.py b/coast/data/gridded.py index 019f3ba7..dedbfc85 100644 --- a/coast/data/gridded.py +++ b/coast/data/gridded.py @@ -1,4 +1,5 @@ """Gridded class""" + import os.path as path_lib import re import warnings diff --git a/coast/data/index.py b/coast/data/index.py index 8d71e40d..61d7d569 100644 --- a/coast/data/index.py +++ b/coast/data/index.py @@ -1,4 +1,5 @@ """Index class.""" + from dask import array from dask.distributed import Client from .._utils.logging_util import get_slug, debug, info, warn, warning diff --git a/coast/data/lagrangian.py b/coast/data/lagrangian.py index 0a98550d..48479654 100644 --- a/coast/data/lagrangian.py +++ b/coast/data/lagrangian.py @@ -1,4 +1,5 @@ """Lagrangian class""" + from .index import Indexed diff --git a/coast/data/oceanparcels.py b/coast/data/oceanparcels.py index 258794f7..7e51ef33 100644 --- a/coast/data/oceanparcels.py +++ b/coast/data/oceanparcels.py @@ -1,4 +1,5 @@ """Oceanparcels class for reading ocean parcels data.""" + from .lagrangian import Lagrangian import xarray as xr from .._utils.logging_util import get_slug, debug, info, warn, warning diff --git a/coast/data/profile.py b/coast/data/profile.py index 604a8026..8dfe7353 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -1,4 +1,5 @@ """Profile Class""" + from .index import Indexed import numpy as np import xarray as xr diff --git a/coast/data/tidegauge.py b/coast/data/tidegauge.py index 52fa4266..65c5377f 100644 --- a/coast/data/tidegauge.py +++ b/coast/data/tidegauge.py @@ -1,4 +1,5 @@ """Tide Gauge class""" + import glob import re from pathlib import Path diff --git a/coast/data/timeseries.py b/coast/data/timeseries.py index 2e069fd4..811a8582 100644 --- a/coast/data/timeseries.py +++ b/coast/data/timeseries.py @@ -1,4 +1,5 @@ """Timeseries Class""" + from .index import Indexed diff --git a/coast/data/track.py b/coast/data/track.py index b13c5ccc..af979e29 100644 --- a/coast/data/track.py +++ b/coast/data/track.py @@ -1,4 +1,5 @@ """Track class""" + from .index import Indexed diff --git a/coast/diagnostics/climatology.py b/coast/diagnostics/climatology.py index 94da23aa..8bb28921 100644 --- a/coast/diagnostics/climatology.py +++ b/coast/diagnostics/climatology.py @@ -1,4 +1,5 @@ """Climatology class""" + import calendar from datetime import date import traceback diff --git a/coast/diagnostics/contour.py b/coast/diagnostics/contour.py index ecd49303..345cfa60 100644 --- a/coast/diagnostics/contour.py +++ b/coast/diagnostics/contour.py @@ -1,4 +1,5 @@ """Contour classes""" + import matplotlib.pyplot as plt import xarray as xr import numpy as np diff --git a/coast/diagnostics/eof.py b/coast/diagnostics/eof.py index c3a9befa..2b454454 100644 --- a/coast/diagnostics/eof.py +++ b/coast/diagnostics/eof.py @@ -1,4 +1,5 @@ """This is file deals with empirical orthogonal functions.""" + import xarray as xr import numpy as np from scipy import linalg diff --git a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py index fb25f03c..53c5bbde 100644 --- a/coast/diagnostics/gridded_monthly_hydrographic_climatology.py +++ b/coast/diagnostics/gridded_monthly_hydrographic_climatology.py @@ -1,6 +1,7 @@ """" This class calculates the monthly hydrographic climatology """ + import numpy as np import xarray as xr diff --git a/coast/diagnostics/gridded_stratification.py b/coast/diagnostics/gridded_stratification.py index d41855da..62284e75 100644 --- a/coast/diagnostics/gridded_stratification.py +++ b/coast/diagnostics/gridded_stratification.py @@ -199,16 +199,16 @@ def construct_pycnocline_vars(self, gridded_t: Gridded, gridded_w: Gridded, stra self.dataset["strat_2nd_mom_masked"] = xr.DataArray(zt_m, coords=coords, dims=dims) self.dataset.strat_2nd_mom_masked.attrs["units"] = "m" self.dataset.strat_2nd_mom_masked.attrs["standard_name"] = "masked pycnocline thickness" - self.dataset.strat_2nd_mom_masked.attrs[ - "long_name" - ] = "Second depth moment of stratification, masked in weak stratification" + self.dataset.strat_2nd_mom_masked.attrs["long_name"] = ( + "Second depth moment of stratification, masked in weak stratification" + ) self.dataset["strat_1st_mom_masked"] = xr.DataArray(zd_m, coords=coords, dims=dims) self.dataset.strat_1st_mom_masked.attrs["units"] = "m" self.dataset.strat_1st_mom_masked.attrs["standard_name"] = "masked pycnocline depth" - self.dataset.strat_1st_mom_masked.attrs[ - "long_name" - ] = "First depth moment of stratification, masked in weak stratification" + self.dataset.strat_1st_mom_masked.attrs["long_name"] = ( + "First depth moment of stratification, masked in weak stratification" + ) # Inherit horizontal grid information from gridded_w self.dataset["e1"] = xr.DataArray( diff --git a/coast/diagnostics/profile_analysis.py b/coast/diagnostics/profile_analysis.py index b138d7e6..00ac605d 100644 --- a/coast/diagnostics/profile_analysis.py +++ b/coast/diagnostics/profile_analysis.py @@ -1,4 +1,5 @@ """Profile Class""" + from ..data.index import Indexed import numpy as np import xarray as xr diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 7fc04666..64047f15 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -265,11 +265,10 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7 if sum(limits) != 0: gridded.subset(y_dim=range(limits[0], limits[1] + 1), x_dim=range(limits[2], limits[3] + 1)) - #gridded.spatial _subset(limits) might need this one for wrapping + # gridded.spatial _subset(limits) might need this one for wrapping # keep the bathymetry and mask or subset on the hydrographic profiles object gridded.dataset["limits"] = limits - lon_prf = self.dataset.longitude.values lat_prf = self.dataset.latitude.values @@ -312,8 +311,8 @@ def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7 self.dataset[f"i_{grid_name}"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) self.dataset[f"j_{grid_name}"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) self.dataset[f"rmin_{grid_name}"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) - #self.dataset[f"bathy_{grid_name}"] = gridded.dataset.bathymetry - #self.dataset[f"mask_{grid_name}"] = gridded.dataset.bottom_level != 0 + # self.dataset[f"bathy_{grid_name}"] = gridded.dataset.bathymetry + # self.dataset[f"mask_{grid_name}"] = gridded.dataset.bottom_level != 0 def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) diff --git a/coast/diagnostics/tidegauge_analysis.py b/coast/diagnostics/tidegauge_analysis.py index a51ffef3..78f753c9 100644 --- a/coast/diagnostics/tidegauge_analysis.py +++ b/coast/diagnostics/tidegauge_analysis.py @@ -1,4 +1,5 @@ """An analysis class for tide gauge.""" + import numpy as np import xarray as xr from ..data.tidegauge import Tidegauge diff --git a/coast/diagnostics/transect.py b/coast/diagnostics/transect.py index 37ba9de9..34888c6a 100644 --- a/coast/diagnostics/transect.py +++ b/coast/diagnostics/transect.py @@ -456,22 +456,22 @@ def calc_flow_across_transect(self, gridded_u: Coast, gridded_v: Coast): # DataArray attributes self.data_cross_tran_flow.normal_velocities.attrs["units"] = "m/s" self.data_cross_tran_flow.normal_velocities.attrs["standard_name"] = "velocity across the transect" - self.data_cross_tran_flow.normal_velocities.attrs[ - "long_name" - ] = "velocity across the transect defined on the normal velocity grid points" + self.data_cross_tran_flow.normal_velocities.attrs["long_name"] = ( + "velocity across the transect defined on the normal velocity grid points" + ) if compute_transports: self.data_cross_tran_flow.normal_transports.attrs["units"] = "Sv" - self.data_cross_tran_flow.normal_transports.attrs[ - "standard_name" - ] = "depth integrated volume transport across transect" - self.data_cross_tran_flow.normal_transports.attrs[ - "long_name" - ] = "depth integrated volume transport across the transect defined on the normal velocity grid points" + self.data_cross_tran_flow.normal_transports.attrs["standard_name"] = ( + "depth integrated volume transport across transect" + ) + self.data_cross_tran_flow.normal_transports.attrs["long_name"] = ( + "depth integrated volume transport across the transect defined on the normal velocity grid points" + ) self.data_cross_tran_flow.depth_0.attrs["units"] = "m" self.data_cross_tran_flow.depth_0.attrs["standard_name"] = "depth" - self.data_cross_tran_flow.depth_0.attrs[ - "long_name" - ] = "Initial depth at time zero defined at the normal velocity grid points" + self.data_cross_tran_flow.depth_0.attrs["long_name"] = ( + "Initial depth at time zero defined at the normal velocity grid points" + ) self.data_cross_tran_flow = self.data_cross_tran_flow.squeeze() @staticmethod @@ -794,9 +794,9 @@ def calc_geostrophic_flow( self.data_cross_tran_flow["depth_0_original"] = xr.DataArray(depth_0, dims=["z_dim", "r_dim"]) self.data_cross_tran_flow.depth_0_original.attrs["units"] = "m" self.data_cross_tran_flow.depth_0_original.attrs["standard_name"] = "original depth coordinate" - self.data_cross_tran_flow.e12.attrs[ - "standard_name" - ] = "horizontal scale factor along the transect at the normal velocity point" + self.data_cross_tran_flow.e12.attrs["standard_name"] = ( + "horizontal scale factor along the transect at the normal velocity point" + ) def plot_normal_velocity(self, time, plot_info: dict, cmap, smoothing_window=0): """ diff --git a/example_scripts/configuration_gallery/seasia_dic_example_plot.py b/example_scripts/configuration_gallery/seasia_dic_example_plot.py index a2d72f37..b984510d 100644 --- a/example_scripts/configuration_gallery/seasia_dic_example_plot.py +++ b/example_scripts/configuration_gallery/seasia_dic_example_plot.py @@ -4,6 +4,7 @@ Make simple SEAsia 1/12 deg DIC plot. """ + # %% import coast import matplotlib.pyplot as plt diff --git a/markdown_doc_builder.py b/markdown_doc_builder.py index 445f6d13..cda9dda0 100644 --- a/markdown_doc_builder.py +++ b/markdown_doc_builder.py @@ -1,4 +1,5 @@ """Script to turn google docstrings into markdown""" + from pathlib import Path from datetime import date from typing import Generator, List, Optional diff --git a/setup.py b/setup.py index c9caa61e..d4763f5c 100644 --- a/setup.py +++ b/setup.py @@ -51,7 +51,7 @@ "lxml>=4.9.0", # Required for pydap CAS parsing, "requests>=2.27.1", "tqdm>=4.66.1", - "pyproj>=3.5.0" + "pyproj>=3.5.0", # "xesmf>=0.3.0", # Optional. Not part of main package # "esmpy>=8.0.0", # Optional. Not part of main package ], diff --git a/tests/test_velocity_plot_util.py b/tests/test_velocity_plot_util.py index 952e7006..e13e5054 100644 --- a/tests/test_velocity_plot_util.py +++ b/tests/test_velocity_plot_util.py @@ -1,5 +1,6 @@ """ tests for plotting preparation methods. At the time of writing specifically targeting cartopy vector plots of polar domains """ + # IMPORT modules. Must have pytest. # import os.path as path diff --git a/unit_testing/test_index_classes_load_datasets.py b/unit_testing/test_index_classes_load_datasets.py index 1ebf5f97..e4a20b55 100644 --- a/unit_testing/test_index_classes_load_datasets.py +++ b/unit_testing/test_index_classes_load_datasets.py @@ -17,6 +17,7 @@ As opposed to having one file per object, with all its methods too. Better filenaming could help point out the parent object. """ + from coast import Altimetry, Profile, Glider, Argos, Oceanparcels, Tidegauge import datetime From ccddc03ee55d8abd00daae14be28a25e79837a98 Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Mon, 29 Jan 2024 12:58:44 +0000 Subject: [PATCH 140/150] Updates to profile and stratification Passing variables needed for match to grid Remove this version of match to grid as provided in profile Profile sub setting across latitude wrap --- coast/data/profile.py | 35 +++++++--- coast/diagnostics/profile_stratification.py | 75 +-------------------- 2 files changed, 30 insertions(+), 80 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 8dfe7353..eed71661 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -153,9 +153,19 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): return: A new profile object containing subsetted data """ - ind = general_utils.subset_indices_lonlat_box( - self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] + if lonbounds[0] < lonbounds[1]: + ind = general_utils.subset_indices_lonlat_box( + self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] ) + else: + ind1 = general_utils.subset_indices_lonlat_box( + self.dataset.longitude, self.dataset.latitude, lonbounds[0], 180.0 , latbounds[0], latbounds[1] + ) + ind2 = general_utils.subset_indices_lonlat_box( + self.dataset.longitude, self.dataset.latitude, -180.0, lonbounds[1], latbounds[0], latbounds[1] + ) + ind={} + ind[0] = np.concatenate((ind1[0],ind2[0])) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): @@ -173,6 +183,7 @@ def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): y_max = region_bounds[3] # self.profile = Profile(config=config) self.read_en4(dataset_names, multiple=True, chunks=chunks) + pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) pr = pr.process_en4() return pr @@ -498,7 +509,7 @@ def obs_operator(self, gridded, mask_bottom_level=True): mod_profiles["nearest_index_t"] = (["id_dim"], ind_t.values) return Profile(dataset=mod_profiles) - def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: + def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=25.0) -> None: """Match profiles locations to grid, finding 4 nearest neighbours for each profile. Args: @@ -516,13 +527,17 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: """ if sum(limits) != 0: - gridded.subset(ydim=range(limits[0], limits[1] + 0), xdim=range(limits[2], limits[3] + 1)) + gridded.subset(y_dim=range(limits[0], limits[1] + 1), x_dim=range(limits[2], limits[3] + 1)) # keep the grid or subset on the hydrographic profiles object gridded.dataset["limits"] = limits prf = self.dataset grd = gridded.dataset - grd["landmask"] = grd.bottom_level == 0 + if "bottom_level" in grd: + grd["landmask"] = grd.bottom_level == 0 + else: #resort to using bathymetry + grd["landmask"] = grd.bathymetry == 0 + lon_prf = prf["longitude"] lat_prf = prf["latitude"] lon_grd = grd["longitude"] @@ -590,7 +605,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=7.0) -> None: self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "NNs"]) self.dataset["ind_good"] = xr.DataArray(ind_good, dims=["Ngood"]) - def gridded_to_profile_2d(self, gridded, variable) -> None: + def gridded_to_profile_2d(self, gridded, variable,limits=[0,0,0,0],rmax=25.0) -> None: """ Evaluated a gridded data variable on each profile. Here just 2D, but could be extended to 3 or 4D @@ -605,11 +620,15 @@ def gridded_to_profile_2d(self, gridded, variable) -> None: """ # ensure there are indices in profile if not "ind_x" in self.dataset: - self.match_to_grid(gridded) + self.match_to_grid(gridded,limits=limits,rmax=rmax) # prf = self.dataset grd = gridded.dataset - grd["landmask"] = grd.bottom_level == 0 + if "botton_level" in grd: + grd["landmask"] = grd.bottom_level == 0 + else: # resort to bathymetry for mask + grd["landmask"] = grd.bathymetry == 0 + nprof = self.dataset.id_dim.shape[0] var = np.ma.masked_where(grd["landmask"], grd[variable]) ig = prf.ind_good diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 64047f15..6fe0921a 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -39,7 +39,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax,limits=[0,0,0,0],rmax=25.): """ Cleaning data for stratification metric calculations Stage 1:... @@ -68,7 +68,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) if "bathymetry" in gridded.dataset: - profile.gridded_to_profile_2d(gridded, "bathymetry") + profile.gridded_to_profile_2d(gridded, "bathymetry",limits=limits,rmax=rmax) D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) @@ -143,7 +143,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # %% return profile - def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax): + def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, limits=[0,0,0,0]): """ Calculates Potential Energy Anomaly @@ -250,72 +250,3 @@ def quick_plot(self, var: xr.DataArray = None): return fig, ax ############################################################################## - def match_to_grid(self, gridded: xr.Dataset, limits: List = [0, 0, 0, 0], rmax=7000.0, grid_name="prf") -> None: - """Match profiles locations to grid, finding 4 nearest neighbours for each profile. - - Args: - gridded (Gridded): Gridded object. - limits (List): [jmin,jmax,imin,imax] - Subset to this region. - rmax (int): 7000 m - maxmimum search distance (metres). - - ### NEED TO DESCRIBE THE OUTPUT. WHAT DO i_prf, j_prf, rmin_prf REPRESENT? - - ### THIS LOOKS LIKE SOMETHING THE profile.obs_operator WOULD DO - """ - - if sum(limits) != 0: - gridded.subset(y_dim=range(limits[0], limits[1] + 1), x_dim=range(limits[2], limits[3] + 1)) - # gridded.spatial _subset(limits) might need this one for wrapping - # keep the bathymetry and mask or subset on the hydrographic profiles object - gridded.dataset["limits"] = limits - - lon_prf = self.dataset.longitude.values - lat_prf = self.dataset.latitude.values - - # Find 4 nearest neighbours on grid - j_prf, i_prf, rmin_prf = gridded.find_j_i_list(lat=lat_prf, lon=lon_prf, n_nn=4) - - self.dataset["i_min"] = limits[0] # reference back to origianl grid - self.dataset["j_min"] = limits[2] - - i_min = self.dataset.i_min.values - j_min = self.dataset.j_min.values - - # Sort 4 NN by distance on grid - ii = np.nonzero(np.isnan(lon_prf)) - i_prf[ii, :] = 0 - j_prf[ii, :] = 0 - ip = np.where(np.logical_or(i_prf[:, 0] != 0, j_prf[:, 0] != 0))[0] - lon_prf4 = np.repeat(lon_prf[ip, np.newaxis], 4, axis=1).ravel() - lat_prf4 = np.repeat(lat_prf[ip, np.newaxis], 4, axis=1).ravel() - r = np.ones(i_prf.shape) * np.nan - lon_grd = gridded.dataset.longitude.values - lat_grd = gridded.dataset.latitude.values - - rr = ProfileStratification.distance_on_grid( - lat_grd, lon_grd, j_prf[ip, :].ravel(), i_prf[ip, :].ravel(), lat_prf4, lon_prf4 - ) - r[ip, :] = np.reshape(rr, (ip.size, 4)) - # sort by distance - ii = np.argsort(r, axis=1) - rmin_prf = np.take_along_axis(r, ii, axis=1) - i_prf = np.take_along_axis(i_prf, ii, axis=1) - j_prf = np.take_along_axis(j_prf, ii, axis=1) - - ii = np.nonzero(np.logical_or(np.min(r, axis=1) > rmax, np.isnan(lon_prf))) - i_prf = i_prf + i_min - j_prf = j_prf + j_min - i_prf[ii, :] = 0 # should the be nan? - j_prf[ii, :] = 0 - - self.dataset[f"i_{grid_name}"] = xr.DataArray(i_prf, dims=["id_dim", "4"]) - self.dataset[f"j_{grid_name}"] = xr.DataArray(j_prf, dims=["id_dim", "4"]) - self.dataset[f"rmin_{grid_name}"] = xr.DataArray(rmin_prf, dims=["id_dim", "4"]) - # self.dataset[f"bathy_{grid_name}"] = gridded.dataset.bathymetry - # self.dataset[f"mask_{grid_name}"] = gridded.dataset.bottom_level != 0 - - def distance_on_grid(Y, X, jpts, ipts, Ypts, Xpts): - DX = (Xpts - X[jpts, ipts]) * earth_radius * np.cos(Ypts * np.pi / 180.0) - DY = (Ypts - Y[jpts, ipts]) * earth_radius - r = np.sqrt(DX**2 + DY**2) - return r From dab27e0b089945f7e9a008adb00eb3e76fa2e47e Mon Sep 17 00:00:00 2001 From: BlackBot Date: Mon, 29 Jan 2024 12:59:17 +0000 Subject: [PATCH 141/150] Apply Black formatting to Python code. --- coast/data/profile.py | 18 +++++++++--------- coast/diagnostics/profile_stratification.py | 6 +++--- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index eed71661..38d0defc 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -156,16 +156,16 @@ def subset_indices_lonlat_box(self, lonbounds, latbounds): if lonbounds[0] < lonbounds[1]: ind = general_utils.subset_indices_lonlat_box( self.dataset.longitude, self.dataset.latitude, lonbounds[0], lonbounds[1], latbounds[0], latbounds[1] - ) + ) else: ind1 = general_utils.subset_indices_lonlat_box( - self.dataset.longitude, self.dataset.latitude, lonbounds[0], 180.0 , latbounds[0], latbounds[1] + self.dataset.longitude, self.dataset.latitude, lonbounds[0], 180.0, latbounds[0], latbounds[1] ) ind2 = general_utils.subset_indices_lonlat_box( self.dataset.longitude, self.dataset.latitude, -180.0, lonbounds[1], latbounds[0], latbounds[1] - ) - ind={} - ind[0] = np.concatenate((ind1[0],ind2[0])) + ) + ind = {} + ind[0] = np.concatenate((ind1[0], ind2[0])) return Profile(dataset=self.dataset.isel(id_dim=ind[0])) def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): @@ -535,7 +535,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=25.0) -> None: grd = gridded.dataset if "bottom_level" in grd: grd["landmask"] = grd.bottom_level == 0 - else: #resort to using bathymetry + else: # resort to using bathymetry grd["landmask"] = grd.bathymetry == 0 lon_prf = prf["longitude"] @@ -605,7 +605,7 @@ def match_to_grid(self, gridded, limits=[0, 0, 0, 0], rmax=25.0) -> None: self.dataset["rmin_prf"] = xr.DataArray(rmin_prf, dims=["id_dim", "NNs"]) self.dataset["ind_good"] = xr.DataArray(ind_good, dims=["Ngood"]) - def gridded_to_profile_2d(self, gridded, variable,limits=[0,0,0,0],rmax=25.0) -> None: + def gridded_to_profile_2d(self, gridded, variable, limits=[0, 0, 0, 0], rmax=25.0) -> None: """ Evaluated a gridded data variable on each profile. Here just 2D, but could be extended to 3 or 4D @@ -620,13 +620,13 @@ def gridded_to_profile_2d(self, gridded, variable,limits=[0,0,0,0],rmax=25.0) -> """ # ensure there are indices in profile if not "ind_x" in self.dataset: - self.match_to_grid(gridded,limits=limits,rmax=rmax) + self.match_to_grid(gridded, limits=limits, rmax=rmax) # prf = self.dataset grd = gridded.dataset if "botton_level" in grd: grd["landmask"] = grd.bottom_level == 0 - else: # resort to bathymetry for mask + else: # resort to bathymetry for mask grd["landmask"] = grd.bathymetry == 0 nprof = self.dataset.id_dim.shape[0] diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 6fe0921a..d9bcbc00 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -39,7 +39,7 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax,limits=[0,0,0,0],rmax=25.): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, limits=[0, 0, 0, 0], rmax=25.0): """ Cleaning data for stratification metric calculations Stage 1:... @@ -68,7 +68,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val) if "bathymetry" in gridded.dataset: - profile.gridded_to_profile_2d(gridded, "bathymetry",limits=limits,rmax=rmax) + profile.gridded_to_profile_2d(gridded, "bathymetry", limits=limits, rmax=rmax) D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) @@ -143,7 +143,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # %% return profile - def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, limits=[0,0,0,0]): + def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, limits=[0, 0, 0, 0]): """ Calculates Potential Energy Anomaly From 60fafc4d9eb4be10b6fb16a93ee9af8971f7933f Mon Sep 17 00:00:00 2001 From: jasontempestholt <42639421+jasontempestholt@users.noreply.github.com> Date: Mon, 29 Jan 2024 13:38:35 +0000 Subject: [PATCH 142/150] Update profile.py added case of no data --- coast/data/profile.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index eed71661..a12ad35f 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -185,7 +185,10 @@ def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): self.read_en4(dataset_names, multiple=True, chunks=chunks) pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) - pr = pr.process_en4() + if pr.dataset.id_dim.shape[0] >0: + pr = pr.process_en4() + else: + print("No data can't process") return pr @staticmethod From 70879df4c005f76db81aaaa0ea7e009fa2678c65 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Mon, 29 Jan 2024 13:39:37 +0000 Subject: [PATCH 143/150] Apply Black formatting to Python code. --- coast/data/profile.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 6bb1fea7..9e67e98a 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -185,7 +185,7 @@ def extract_en4_profiles(self, dataset_names, region_bounds, chunks: dict = {}): self.read_en4(dataset_names, multiple=True, chunks=chunks) pr = self.subset_indices_lonlat_box(lonbounds=[x_min, x_max], latbounds=[y_min, y_max]) - if pr.dataset.id_dim.shape[0] >0: + if pr.dataset.id_dim.shape[0] > 0: pr = pr.process_en4() else: print("No data can't process") From 634225f8ee0a4ccf8dc7e268ef3e26a7c1b90b28 Mon Sep 17 00:00:00 2001 From: jeff polton Date: Mon, 13 May 2024 10:21:44 +0100 Subject: [PATCH 144/150] permit TEOS10 conserv temp and abs sal --- coast/data/profile.py | 11 +++- coast/diagnostics/profile_stratification.py | 70 ++++++++++++++++----- 2 files changed, 63 insertions(+), 18 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 9e67e98a..897312bd 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -934,8 +934,13 @@ def construct_density( # jth self.dataset.z_dim.size, # self.dataset.id_dim.size, ) - sal = self.dataset.practical_salinity.to_masked_array() - temp = self.dataset.potential_temperature.to_masked_array() + + if CT_AS: + temp = self.dataset.conservative_temperature.to_masked_array() + sal = self.dataset.absolute_salinity.to_masked_array() + else: + temp = self.dataset.potential_temperature.to_masked_array() + sal = self.dataset.practical_salinity.to_masked_array() if np.shape(sal) != shape_ds: sal = sal.T @@ -1053,7 +1058,7 @@ def construct_density( else: attributes = {"units": "kg / m^3", "standard name": "In-situ density "} - density = np.squeeze(density) + #density = np.squeeze(density) # squeezing out id_dim, if size=1 is bad. self.dataset[new_var_name] = xr.DataArray(density, coords=coords, dims=dims, attrs=attributes) except AttributeError as err: diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index d9bcbc00..11ec61c9 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -39,8 +39,13 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, limits=[0, 0, 0, 0], rmax=25.0): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=False, limits=[0, 0, 0, 0], rmax=25.0): """ + + parameters: + CT_AS: bool - determines whether conservative_temperature and absolute salinity are expected (if True). + if False: potential_temperature and practical_salinity + Cleaning data for stratification metric calculations Stage 1:... @@ -54,10 +59,17 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, limits=[0, 0, 0, # find profiles good for SST and NBT dz_max = 25.0 + if not CT_AS: + temperature_var = "potential_temperature" + salinity_var = "practical_salinity" + else: + temperature_var = "conservative_temperature" + salinity_var = "absolute_salinity" + n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] - tmp_clean = profile.dataset.potential_temperature.values[:, :] - sal_clean = profile.dataset.practical_salinity.values[:, :] + tmp_clean = profile.dataset[temperature_var].values[:, :] + sal_clean = profile.dataset[salinity_var].values[:, :] any_tmp = np.sum(~np.isnan(tmp_clean), axis=1) != 0 any_sal = np.sum(~np.isnan(sal_clean), axis=1) != 0 @@ -71,6 +83,11 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): profile.gridded_to_profile_2d(gridded, "bathymetry", limits=limits, rmax=rmax) D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth + if np.shape(z.values) != (n_prf, n_depth): z = z.transpose() + if np.shape(z.values) != (n_prf, n_depth): print(f"Problem with the shape of profile.dataset.depth") + + print(f"shape pot temp:{np.shape(profile.dataset[temperature_var].values[:,:])}") + print(f"shape z:{np.shape(z)}. shape D_prf:{np.shape(np.repeat(D_prf[:, np.newaxis], n_depth, axis=1))}") test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) test_tmp = np.logical_and(test_surface, ~np.isnan(tmp_clean)) test_sal = np.logical_and(test_surface, ~np.isnan(sal_clean)) @@ -112,9 +129,12 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # fill holes in data # jth This is slow, there may be a more 'vector' way of doing it # %% - tmp1 = profile.dataset.potential_temperature.values[:, :] - sal1 = profile.dataset.practical_salinity.values[:, :] + tmp1 = profile.dataset[temperature_var].values[:, :] + sal1 = profile.dataset[salinity_var].values[:, :] z1 = profile.dataset.depth.values[:, :] + if np.shape(z1) != (n_prf, n_depth): z1 = z1.transpose() + if np.shape(z1) != (n_prf, n_depth): print(f"Problem with the shape of profile.dataset.depth") + for i_prf in range(n_prf): tmp = tmp1[i_prf, :] sal = sal1[i_prf, :] @@ -134,8 +154,8 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): "longitude": (("id_dim"), profile.dataset.longitude.values), } dims = ["id_dim", "z_dim"] - profile.dataset["potential_temperature"] = xr.DataArray(tmp_clean, coords=coords, dims=dims) - profile.dataset["practical_salinity"] = xr.DataArray(sal_clean, coords=coords, dims=dims) + profile.dataset[temperature_var] = xr.DataArray(tmp_clean, coords=coords, dims=dims) + profile.dataset[salinity_var] = xr.DataArray(sal_clean, coords=coords, dims=dims) profile.dataset["sea_surface_temperature"] = xr.DataArray(SST, coords=coords, dims=["id_dim"]) profile.dataset["sea_surface_salinity"] = xr.DataArray(SSS, coords=coords, dims=["id_dim"]) profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) @@ -143,7 +163,7 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # %% return profile - def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, limits=[0, 0, 0, 0]): + def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=False, rmax=25.0, limits=[0, 0, 0, 0]): """ Calculates Potential Energy Anomaly @@ -158,7 +178,25 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, li gravity = 9.81 # Clean data This is quit slow and over writes potential temperature and practical salinity variables - profile = ProfileStratification.clean_data(profile, gridded, Zmax) + if not CT_AS: + temperature_var = "potential_temperature" + salinity_var = "practical_salinity" + else: + temperature_var = "conservative_temperature" + salinity_var = "absolute_salinity" + + ## JP ## profile = ProfileStratification.clean_data(profile, gridded, Zmax, CT_AS) + n_prf = profile.dataset.id_dim.shape[0] + coords = { + "time": ("id_dim", profile.dataset.time.values), + "latitude": (("id_dim"), profile.dataset.latitude.values), + "longitude": (("id_dim"), profile.dataset.longitude.values), + } + good_profile = np.array(np.ones(n_prf), dtype=bool) + profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) + profile.dataset["sea_surface_temperature"] = profile.dataset[temperature_var].isel(z_dim=0) + profile.dataset["sea_surface_salinity"] = profile.dataset[salinity_var].isel(z_dim=0) + # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() @@ -178,9 +216,9 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, li # ) # jth why not just use depth here? if not "density" in profile.dataset: - profile.construct_density(CT_AS=False, pot_dens=True) + profile.construct_density(CT_AS=CT_AS, pot_dens=True) if not "density_bar" in profile.dataset: - profile.construct_density(CT_AS=False, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) + profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S @@ -197,13 +235,15 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, rmax=25.0, li "longitude": (("id_dim"), profile.dataset.longitude.values), } dims = ["id_dim"] - attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} - self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=attributes) + pea_attributes = {"units": "J / m^3", "standard_name": "Potential Energy Anomaly"} + sst_attributes = {"units": "deg C", "standard_name": "Sea Surface Temperature"} + sss_attributes = {"units": "psu", "standard_name": "Sea Surface Salinity"} + self.dataset["pea"] = xr.DataArray(pot_energy_anom, coords=coords, dims=dims, attrs=pea_attributes) self.dataset["sst"] = xr.DataArray( - profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=attributes + profile.dataset.variables["sea_surface_temperature"], coords=coords, dims=dims, attrs=sst_attributes ) self.dataset["sss"] = xr.DataArray( - profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=attributes + profile.dataset.variables["sea_surface_salinity"], coords=coords, dims=dims, attrs=sss_attributes ) def quick_plot(self, var: xr.DataArray = None): From b9e9d15356fd8109053531680d7afae9c2109fa6 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Mon, 13 May 2024 09:23:01 +0000 Subject: [PATCH 145/150] Apply Black formatting to Python code. --- coast/data/profile.py | 4 +- coast/diagnostics/profile_stratification.py | 49 ++++++++++++--------- 2 files changed, 29 insertions(+), 24 deletions(-) diff --git a/coast/data/profile.py b/coast/data/profile.py index 897312bd..f538be82 100644 --- a/coast/data/profile.py +++ b/coast/data/profile.py @@ -934,7 +934,7 @@ def construct_density( # jth self.dataset.z_dim.size, # self.dataset.id_dim.size, ) - + if CT_AS: temp = self.dataset.conservative_temperature.to_masked_array() sal = self.dataset.absolute_salinity.to_masked_array() @@ -1058,7 +1058,7 @@ def construct_density( else: attributes = {"units": "kg / m^3", "standard name": "In-situ density "} - #density = np.squeeze(density) # squeezing out id_dim, if size=1 is bad. + # density = np.squeeze(density) # squeezing out id_dim, if size=1 is bad. self.dataset[new_var_name] = xr.DataArray(density, coords=coords, dims=dims, attrs=attributes) except AttributeError as err: diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 11ec61c9..c5bc890e 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -39,11 +39,11 @@ def __init__(self, profile: xr.Dataset): self.nz = profile.dataset.dims["z_dim"] debug(f"Initialised {get_slug(self)}") - def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=False, limits=[0, 0, 0, 0], rmax=25.0): + def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool = False, limits=[0, 0, 0, 0], rmax=25.0): """ - - parameters: - CT_AS: bool - determines whether conservative_temperature and absolute salinity are expected (if True). + + parameters: + CT_AS: bool - determines whether conservative_temperature and absolute salinity are expected (if True). if False: potential_temperature and practical_salinity Cleaning data for stratification metric calculations @@ -60,11 +60,11 @@ def clean_data(profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=False dz_max = 25.0 if not CT_AS: - temperature_var = "potential_temperature" - salinity_var = "practical_salinity" + temperature_var = "potential_temperature" + salinity_var = "practical_salinity" else: - temperature_var = "conservative_temperature" - salinity_var = "absolute_salinity" + temperature_var = "conservative_temperature" + salinity_var = "absolute_salinity" n_prf = profile.dataset.id_dim.shape[0] n_depth = profile.dataset.z_dim.shape[0] @@ -83,9 +83,11 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): profile.gridded_to_profile_2d(gridded, "bathymetry", limits=limits, rmax=rmax) D_prf = profile.dataset.bathymetry.values z = profile.dataset.depth - if np.shape(z.values) != (n_prf, n_depth): z = z.transpose() - if np.shape(z.values) != (n_prf, n_depth): print(f"Problem with the shape of profile.dataset.depth") - + if np.shape(z.values) != (n_prf, n_depth): + z = z.transpose() + if np.shape(z.values) != (n_prf, n_depth): + print(f"Problem with the shape of profile.dataset.depth") + print(f"shape pot temp:{np.shape(profile.dataset[temperature_var].values[:,:])}") print(f"shape z:{np.shape(z)}. shape D_prf:{np.shape(np.repeat(D_prf[:, np.newaxis], n_depth, axis=1))}") test_surface = z < np.minimum(dz_max, 0.25 * np.repeat(D_prf[:, np.newaxis], n_depth, axis=1)) @@ -132,9 +134,11 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): tmp1 = profile.dataset[temperature_var].values[:, :] sal1 = profile.dataset[salinity_var].values[:, :] z1 = profile.dataset.depth.values[:, :] - if np.shape(z1) != (n_prf, n_depth): z1 = z1.transpose() - if np.shape(z1) != (n_prf, n_depth): print(f"Problem with the shape of profile.dataset.depth") - + if np.shape(z1) != (n_prf, n_depth): + z1 = z1.transpose() + if np.shape(z1) != (n_prf, n_depth): + print(f"Problem with the shape of profile.dataset.depth") + for i_prf in range(n_prf): tmp = tmp1[i_prf, :] sal = sal1[i_prf, :] @@ -163,7 +167,9 @@ def first_nonzero(arr, axis=0, invalid_val=np.nan): # %% return profile - def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=False, rmax=25.0, limits=[0, 0, 0, 0]): + def calc_pea( + self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool = False, rmax=25.0, limits=[0, 0, 0, 0] + ): """ Calculates Potential Energy Anomaly @@ -179,11 +185,11 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=F # Clean data This is quit slow and over writes potential temperature and practical salinity variables if not CT_AS: - temperature_var = "potential_temperature" - salinity_var = "practical_salinity" + temperature_var = "potential_temperature" + salinity_var = "practical_salinity" else: - temperature_var = "conservative_temperature" - salinity_var = "absolute_salinity" + temperature_var = "conservative_temperature" + salinity_var = "absolute_salinity" ## JP ## profile = ProfileStratification.clean_data(profile, gridded, Zmax, CT_AS) n_prf = profile.dataset.id_dim.shape[0] @@ -194,9 +200,8 @@ def calc_pea(self, profile: xr.Dataset, gridded: xr.Dataset, Zmax, CT_AS: bool=F } good_profile = np.array(np.ones(n_prf), dtype=bool) profile.dataset["good_profile"] = xr.DataArray(good_profile, coords=coords, dims=["id_dim"]) - profile.dataset["sea_surface_temperature"] = profile.dataset[temperature_var].isel(z_dim=0) - profile.dataset["sea_surface_salinity"] = profile.dataset[salinity_var].isel(z_dim=0) - + profile.dataset["sea_surface_temperature"] = profile.dataset[temperature_var].isel(z_dim=0) + profile.dataset["sea_surface_salinity"] = profile.dataset[salinity_var].isel(z_dim=0) # Define grid spacing, dz. Required for depth integral profile.calculate_vertical_spacing() From 21782c44efd1e3b6db8c6ee6e0bc0ac222251117 Mon Sep 17 00:00:00 2001 From: jpolton Date: Tue, 14 May 2024 14:42:22 +0100 Subject: [PATCH 146/150] Update profile_stratification.py update Zd_mask to exclude density levels with NaN --- coast/diagnostics/profile_stratification.py | 1 + 1 file changed, 1 insertion(+) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index c5bc890e..b723a645 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -225,6 +225,7 @@ def calc_pea( if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density + Zd_mask = Zd_mask.where(np.isfinite(profile.dataset.variables["density"]), -1) # update Zd_mask to exclude nan pts rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S pot_energy_anom = ( From 15850ee258a86802ce51bd93ad3db50bfb27f4bd Mon Sep 17 00:00:00 2001 From: BlackBot Date: Tue, 14 May 2024 13:43:10 +0000 Subject: [PATCH 147/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index b723a645..cd8b6df3 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -225,7 +225,9 @@ def calc_pea( if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density - Zd_mask = Zd_mask.where(np.isfinite(profile.dataset.variables["density"]), -1) # update Zd_mask to exclude nan pts + Zd_mask = Zd_mask.where( + np.isfinite(profile.dataset.variables["density"]), -1 + ) # update Zd_mask to exclude nan pts rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S pot_energy_anom = ( From 0d46d1a0a2297066f8c1f739249afd653d96c2fb Mon Sep 17 00:00:00 2001 From: jpolton Date: Tue, 14 May 2024 15:01:08 +0100 Subject: [PATCH 148/150] Update profile_stratification.py fix typo --- coast/diagnostics/profile_stratification.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index cd8b6df3..2ed33446 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -226,7 +226,7 @@ def calc_pea( profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density Zd_mask = Zd_mask.where( - np.isfinite(profile.dataset.variables["density"]), -1 + np.isfinite(profile.dataset.variables["density"]), 0 ) # update Zd_mask to exclude nan pts rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S From 5227549c6e77a887f477cebfb7f3203a3c8e9a6d Mon Sep 17 00:00:00 2001 From: jpolton Date: Tue, 14 May 2024 15:18:08 +0100 Subject: [PATCH 149/150] Update profile_stratification.py --- coast/diagnostics/profile_stratification.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 2ed33446..2b185d03 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -222,12 +222,15 @@ def calc_pea( if not "density" in profile.dataset: profile.construct_density(CT_AS=CT_AS, pot_dens=True) + + # Update Zd_mask to exlude nan points + Zd_mask = Zd_mask.where( + np.isfinite(profile.dataset.variables["density"]), 0 + ) + if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density - Zd_mask = Zd_mask.where( - np.isfinite(profile.dataset.variables["density"]), 0 - ) # update Zd_mask to exclude nan pts rhobar = profile.dataset.variables["density_bar"] # density with depth-mean T and S pot_energy_anom = ( From a3e716a023e3a1c4535834cdf369af9a859faf00 Mon Sep 17 00:00:00 2001 From: BlackBot Date: Tue, 14 May 2024 14:18:31 +0000 Subject: [PATCH 150/150] Apply Black formatting to Python code. --- coast/diagnostics/profile_stratification.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/coast/diagnostics/profile_stratification.py b/coast/diagnostics/profile_stratification.py index 2b185d03..4d8f4759 100644 --- a/coast/diagnostics/profile_stratification.py +++ b/coast/diagnostics/profile_stratification.py @@ -224,10 +224,8 @@ def calc_pea( profile.construct_density(CT_AS=CT_AS, pot_dens=True) # Update Zd_mask to exlude nan points - Zd_mask = Zd_mask.where( - np.isfinite(profile.dataset.variables["density"]), 0 - ) - + Zd_mask = Zd_mask.where(np.isfinite(profile.dataset.variables["density"]), 0) + if not "density_bar" in profile.dataset: profile.construct_density(CT_AS=CT_AS, rhobar=True, Zd_mask=Zd_mask, pot_dens=True) rho = profile.dataset.variables["density"].fillna(0) # density