From 46745c1fd3298b036b5cbdde797995b95a525f64 Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Wed, 22 Jan 2025 10:41:46 +0000 Subject: [PATCH 1/7] Added script to read the metrics file --- read_metric.py | 67 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 read_metric.py diff --git a/read_metric.py b/read_metric.py new file mode 100644 index 0000000..b6e60d5 --- /dev/null +++ b/read_metric.py @@ -0,0 +1,67 @@ +### Clone metrics.py file on CSD3 and read it here +### Read density.py file + +import importlib +import xarray as xr +import sys +from pathlib import Path + +# Define paths to the .nc file and metric file +nc_file_path = "../nemo_4.2.1/tests/DINO/EXP00/DINO_1y_grid_T.nc" # Replace with your .nc file +metric_file_path = "../Metrics-Ocean/metrics.py" # Path to your metrics file +density_file_path = 'lib/density.py' + +# Import the metrics module dynamically +metric_module = Path(metric_file_path).stem # Extract module name from file +spec = importlib.util.spec_from_file_location(metric_module, metric_file_path) +metrics = importlib.util.module_from_spec(spec) +sys.modules[metric_module] = metrics +spec.loader.exec_module(metrics) + +# Open the .nc file using xarray +try: + dataset = xr.open_dataset(nc_file_path) + print(f"Successfully loaded dataset from {nc_file_path}") +except FileNotFoundError: + print(f"Error: File {nc_file_path} not found.") + sys.exit(1) + + +# List all variables in the dataset +print("\nAvailable variables in the dataset:") +print(dataset.data_vars) + +# Define variable mapping (dataset name -> metric file name) +variable_mapping = { + "rhop": "density", + "deptht": "depth", +} + +# Define variables and metrics to apply +variables_to_check = ["rhop"] # Replace with your variable names +metrics_to_apply = ["check_density"] # Replace with your metric functions + +results = {} +for var in variables_to_check: + if var in dataset: + print(f"\nApplying metrics to variable: {var}") + variable_data = dataset[var] + metric_var_name = variable_mapping.get(var, var) + results[var] = {} + + # Apply metrics from metrics.py + for metric in metrics_to_apply: + if hasattr(metrics, metric): + metric_function = getattr(metrics, metric) + try: + print(variable_data) + print(metric_var_name) + print(metric_function) + result = metric_function(variable_data, metric_var_name) + print(result) + results[var][metric] = result + print(f"Metric '{metric}' applied to '{metric_var_name}'. Result: {result}") + except Exception as e: + print(f"Error applying metric '{metric}' to '{metric_var_name}': {e}") + else: + print(f"Metric '{metric}' not found in {metric_file_path}") \ No newline at end of file From f4b78e852cb4ff95c6c008cfd43df9f1d74fac14 Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Wed, 22 Jan 2025 14:47:35 +0000 Subject: [PATCH 2/7] Added script to read the metric file --- read_metric.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/read_metric.py b/read_metric.py index b6e60d5..8a738e7 100644 --- a/read_metric.py +++ b/read_metric.py @@ -54,9 +54,6 @@ if hasattr(metrics, metric): metric_function = getattr(metrics, metric) try: - print(variable_data) - print(metric_var_name) - print(metric_function) result = metric_function(variable_data, metric_var_name) print(result) results[var][metric] = result @@ -64,4 +61,6 @@ except Exception as e: print(f"Error applying metric '{metric}' to '{metric_var_name}': {e}") else: - print(f"Metric '{metric}' not found in {metric_file_path}") \ No newline at end of file + print(f"Metric '{metric}' not found in {metric_file_path}") + + From 628f53ea3b494c15bcbda314d81de8febe3f85aa Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Tue, 4 Feb 2025 14:26:21 +0000 Subject: [PATCH 3/7] Adding a notebook to read data files and run metric functions --- metric_check.ipynb | 2154 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 2154 insertions(+) create mode 100644 metric_check.ipynb diff --git a/metric_check.ipynb b/metric_check.ipynb new file mode 100644 index 0000000..e15d6bf --- /dev/null +++ b/metric_check.ipynb @@ -0,0 +1,2154 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Libraries\n", + "\n", + "import torch\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import einops\n", + "from torch.nn.functional import interpolate\n", + "from glob import glob\n", + "import xarray as xr\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_deptht(restart, mask):\n", + " \"\"\"\n", + " Calculate the depth of each vertical level on grid T in the 3D grid.\n", + "\n", + " Parameters:\n", + " restart (xarray.Dataset) : The dataset containing ocean model variables.\n", + " mask (xarray.Dataset) : The dataset containing mask variables.\n", + "\n", + " Returns:\n", + " deptht (numpy.array) : The depth of each vertical level.\n", + " \"\"\"\n", + " ssh = restart.sshn.squeeze()\n", + " e3w_0 = mask.e3w_0.squeeze() # initial z axis cell's thickness on grid W - (t,z,y,x)\n", + " e3t_0 = mask.e3t_0.squeeze() # initial z axis cell's thickness on grid T - (t,z,y,x)\n", + " tmask = mask.tmask.squeeze() # grid T continent mask - (t,z,y,x)\n", + " ssmask = tmask[:, 0] # bathymetry - (t,y,x)\n", + " bathy = e3t_0.sum(\n", + " dim=\"depth\"\n", + " ) # initial condition depth 0 - (t,z,y,x)\n", + " depth_0 = e3w_0.copy().squeeze()\n", + " depth_0[:, 0] = 0.5 * e3w_0[:, 0]\n", + " depth_0[:, 1:] = depth_0[:, 0:1].data + e3w_0[:, 1:].cumsum(dim=\"depth\")\n", + " deptht = depth_0 * (1 + ssh / (bathy + 1 - ssmask)) * tmask\n", + " return deptht\n", + "\n", + "\n", + "def get_density(thetao, so, depth, tmask):\n", + " \"\"\"\n", + " Compute potential density referenced at the surface and density anomaly.\n", + "\n", + " Parameters:\n", + " thetao (numpy.array) : Temperature array - (t,z,y,x).\n", + " so (numpy.array) : Salinity array - (t,z,y,x).\n", + " depth (numpy.array) : Depth array - (t,z,y,x).\n", + " tmask (numpy.array) : Mask array - (t,z,y,x).\n", + "\n", + " Returns:\n", + " tuple: A tuple containing:\n", + " array: Potential density referenced at the surface.\n", + " array: Density anomaly.\n", + " \"\"\"\n", + " rdeltaS = 32.0\n", + " r1_S0 = 0.875 / 35.16504\n", + " r1_T0 = 1.0 / 40.0\n", + " r1_Z0 = 1.0e-4\n", + "\n", + " EOS000 = 8.0189615746e02\n", + " EOS100 = 8.6672408165e02\n", + " EOS200 = -1.7864682637e03\n", + " EOS300 = 2.0375295546e03\n", + " EOS400 = -1.2849161071e03\n", + " EOS500 = 4.3227585684e02\n", + " EOS600 = -6.0579916612e01\n", + " EOS010 = 2.6010145068e01\n", + " EOS110 = -6.5281885265e01\n", + " EOS210 = 8.1770425108e01\n", + " EOS310 = -5.6888046321e01\n", + " EOS410 = 1.7681814114e01\n", + " EOS510 = -1.9193502195\n", + " EOS020 = -3.7074170417e01\n", + " EOS120 = 6.1548258127e01\n", + " EOS220 = -6.0362551501e01\n", + " EOS320 = 2.9130021253e01\n", + " EOS420 = -5.4723692739\n", + " EOS030 = 2.1661789529e01\n", + " EOS130 = -3.3449108469e01\n", + " EOS230 = 1.9717078466e01\n", + " EOS330 = -3.1742946532\n", + " EOS040 = -8.3627885467\n", + " EOS140 = 1.1311538584e01\n", + " EOS240 = -5.3563304045\n", + " EOS050 = 5.4048723791e-01\n", + " EOS150 = 4.8169980163e-01\n", + " EOS060 = -1.9083568888e-01\n", + " EOS001 = 1.9681925209e01\n", + " EOS101 = -4.2549998214e01\n", + " EOS201 = 5.0774768218e01\n", + " EOS301 = -3.0938076334e01\n", + " EOS401 = 6.6051753097\n", + " EOS011 = -1.3336301113e01\n", + " EOS111 = -4.4870114575\n", + " EOS211 = 5.0042598061\n", + " EOS311 = -6.5399043664e-01\n", + " EOS021 = 6.7080479603\n", + " EOS121 = 3.5063081279\n", + " EOS221 = -1.8795372996\n", + " EOS031 = -2.4649669534\n", + " EOS131 = -5.5077101279e-01\n", + " EOS041 = 5.5927935970e-01\n", + " EOS002 = 2.0660924175\n", + " EOS102 = -4.9527603989\n", + " EOS202 = 2.5019633244\n", + " EOS012 = 2.0564311499\n", + " EOS112 = -2.1311365518e-01\n", + " EOS022 = -1.2419983026\n", + " EOS003 = -2.3342758797e-02\n", + " EOS103 = -1.8507636718e-02\n", + " EOS013 = 3.7969820455e-01\n", + "\n", + " zh = depth * r1_Z0 # depth\n", + " zt = thetao * r1_T0 # temperature\n", + " zs = np.sqrt(np.abs(so + rdeltaS) * r1_S0) # square root salinity\n", + " ztm = tmask.squeeze()\n", + "\n", + " zn3 = EOS013 * zt + EOS103 * zs + EOS003\n", + " zn2 = (\n", + " (EOS022 * zt + EOS112 * zs + EOS012) * zt + (EOS202 * zs + EOS102) * zs + EOS002\n", + " )\n", + " zn1 = (\n", + " (\n", + " (\n", + " (EOS041 * zt + EOS131 * zs + EOS031) * zt\n", + " + (EOS221 * zs + EOS121) * zs\n", + " + EOS021\n", + " )\n", + " * zt\n", + " + ((EOS311 * zs + EOS211) * zs + EOS111) * zs\n", + " + EOS011\n", + " )\n", + " * zt\n", + " + (((EOS401 * zs + EOS301) * zs + EOS201) * zs + EOS101) * zs\n", + " + EOS001\n", + " )\n", + " zn0 = (\n", + " (\n", + " (\n", + " (\n", + " (\n", + " (EOS060 * zt + EOS150 * zs + EOS050) * zt\n", + " + (EOS240 * zs + EOS140) * zs\n", + " + EOS040\n", + " )\n", + " * zt\n", + " + ((EOS330 * zs + EOS230) * zs + EOS130) * zs\n", + " + EOS030\n", + " )\n", + " * zt\n", + " + (((EOS420 * zs + EOS320) * zs + EOS220) * zs + EOS120) * zs\n", + " + EOS020\n", + " )\n", + " * zt\n", + " + ((((EOS510 * zs + EOS410) * zs + EOS310) * zs + EOS210) * zs + EOS110)\n", + " * zs\n", + " + EOS010\n", + " )\n", + " * zt\n", + " + (\n", + " ((((EOS600 * zs + EOS500) * zs + EOS400) * zs + EOS300) * zs + EOS200) * zs\n", + " + EOS100\n", + " )\n", + " * zs\n", + " + EOS000\n", + " )\n", + "\n", + " zn = ((zn3 * zh + zn2) * zh + zn1) * zh + zn0\n", + "\n", + " rhop = zn0 * ztm # potential density referenced at the surface\n", + " rho_insitu = zn * ztm # density anomaly (masked)\n", + " return rhop, rho_insitu\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def check_density(density, epsilon=1e-5) :\n", + " \"\"\"\n", + " args :\n", + " density (xarray) : DataArray (t, depth, lat, lon) with density value for each point of the grid.\n", + " return :\n", + " (float) proportion of points not respecting density decreasing constraint\n", + " \"\"\"\n", + " density=density.where(density!=0)\n", + " diff = density - density.shift(depth=-1)\n", + " return (diff > epsilon).mean().data # Proportion of points not respecting decreasing density\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "def temperature_500m_30NS_metric(temperature, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Average Temperature at 500m depth between 30N and 30S.\n", + " Unit : °C \n", + " \n", + " \n", + " Input : \n", + " - thetao : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files \n", + " \n", + " \"\"\"\n", + " \n", + " # Taking Temperature At 500m depth and between 30N and 30S.\n", + "\n", + " t500_30NS=temperature.sel(depth=500,method='nearest').where(abs(temperature.nav_lat)<30,drop=False)\n", + "\n", + " # Computing Area Weights from Mask over 30N-30S latitude zone and @500m depth\n", + " e1t=file_mask.e1t.squeeze()\n", + " e2t=file_mask.e2t.squeeze()\n", + " tmask=file_mask.tmask.squeeze()\n", + " area_500m_30NS=e1t*e2t*tmask.sel(depth=500,method='nearest').where(abs(temperature.nav_lat)<30,drop=False)\n", + "\n", + " #Returning Average Temperature at 500m depth as a numpy scalar\n", + " return ((t500_30NS*area_500m_30NS).sum(dim=[\"nav_lat\",\"nav_lon\"])/area_500m_30NS.sum(dim=[\"nav_lat\",\"nav_lon\"]))\n", + "\n", + "\n", + "\n", + "def temperature_BWbox_metric(thetao, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Average Temperature in a U-shaped \"Bottom Water\" box corresponding to waters below 3000m or beyond 30 degrees of latitude North and South.\n", + " \n", + " ________________________________________________ _Surface\n", + " | . . . . |__________________________| . . . . |_500m\n", + " | . . . . | | . . . . |\n", + " | . . . . | Deep Water | . . . . |\n", + " | . . . . |__________________________| . . . . |_3000m\n", + " | . . . . . . . . Bottom Water . . . . . . . . |\n", + " |______________________________________________|_Bottom\n", + " S 30S Eq. 30N N\n", + " \n", + " Figure : Schematic Representation of the Bottom Water box used in this metric.\n", + "\n", + " Unit : °C \n", + " \n", + " Input : \n", + " - thetao : xarray.DataArray\n", + " - file_mask : xarray.Dataset \n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files \n", + " \n", + " \"\"\"\n", + "\n", + " t_BW=thetao.where(1-(thetao.depth<3000)*(abs(thetao.nav_lat)<30))\n", + "\n", + " # Computing Area Weights from Mask over Box\n", + " e1t=file_mask.e1t.squeeze()\n", + " e2t=file_mask.e2t.squeeze()\n", + " tmask=file_mask.tmask.squeeze()\n", + " area_BW=e1t*e2t*tmask.where(1-(thetao.depth<3000)*(abs(thetao.nav_lat)<30))\n", + "\n", + " #Returning Average Temperature on Box\n", + " return ((t_BW*area_BW).sum(dim=[\"nav_lat\",\"nav_lon\",\"depth\"])/area_BW.sum(dim=[\"nav_lat\",\"nav_lon\",\"depth\"]))\n", + "\n", + "\n", + "\n", + "def temperature_DWbox_metric(thetao, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Average Temperature in a \"Deep Water\" box corresponding to waters between 500m and 3000m depth and 30°N and 30°S.\n", + " \n", + " ________________________________________________ _Surface\n", + " | |__________________________| |_500m\n", + " | | . . . . . . . . . . . . .| |\n", + " | | . . . .Deep Water . . . .| |\n", + " | |__________________________| |_3000m\n", + " | Bottom Water |\n", + " |______________________________________________|_Bottom\n", + " S 30S Eq. 30N N\n", + " \n", + " Figure : Schematic Representation of the Deep Water box used in this metric.\n", + "\n", + " Unit : °C \n", + " \n", + " Input : \n", + " - thetao : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files \n", + " \n", + " \"\"\"\n", + " e1t=file_mask.e1t.squeeze()\n", + " e2t=file_mask.e2t.squeeze()\n", + " tmask=file_mask.tmask.squeeze()\n", + " t_DW=thetao.where(abs((thetao.depth-1750)<1250)*(abs(thetao.nav_lat)<30))\n", + "\n", + " # Computing Area Weights from Mask over Box\n", + " area_DW=e1t*e2t*tmask.where(abs((thetao.depth-1750)<1250)*(abs(thetao.nav_lat)<30))\n", + "\n", + "\n", + " #Returning Average Temperature on Box\n", + " return ((t_DW*area_DW).sum(dim=[\"nav_lat\",\"nav_lon\",\"depth\"])/area_DW.sum(dim=[\"nav_lat\",\"nav_lon\",\"depth\"]))\n", + "\n", + "\n", + "\n", + "\n", + "## Version 1 of ACC metric : Computes the flux assuming rigid lid (as if ssh didn’t change)\n", + "\n", + "def ACC_Drake_metric(uo, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Antarctic Circumpolar Current Transport at the DINO equivalent of the Drake Passage (x=0).\n", + " Unit : Sv\n", + "\n", + "\n", + " Input :\n", + " - uo : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files\n", + " \n", + " \n", + " \"\"\"\n", + " \n", + " umask_Drake=file_mask.umask.isel(nav_lon=0).squeeze()\n", + " e3u=file_mask.e3u_0.squeeze()\n", + " e2u=file_mask.e2u.squeeze()\n", + "\n", + " # Masking the variables onto the Drake Passage\n", + " \n", + " u_masked=uo.isel(nav_lon=0)*umask_Drake\n", + " e3u_masked=e3u.isel(nav_lon=0)*umask_Drake\n", + " e2u_masked=e2u.isel(nav_lon=0)*umask_Drake\n", + "\n", + " # Multiplying zonal velocity by the sectional areas (e2u*e3u)\n", + " \n", + " ubar=(u_masked*e3u_masked)\n", + " flux=(e2u_masked*ubar).sum(dim=[\"nav_lat\",'depth'])\n", + "\n", + " #Returning Total Transport across Drake passage as a numpy scalar (unit : Sv)\n", + " return flux/1e6\n", + "\n", + "\n", + "\n", + "### Version 2 of ACC metric : Computes the flux assuming varying ssh, thus needing to recompute e3u variable from e3u_0\n", + "\n", + "\n", + "def ACC_Drake_metric_2(uo, ssh, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Antarctic Circumpolar Current Transport at the DINO equivalent of the Drake Passage (x=0).\n", + " Unit : Sv\n", + "\n", + "\n", + " Input :\n", + " - uo : xarray.DataArray\n", + " - ssh : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files\n", + " \n", + " \n", + " \"\"\"\n", + "\n", + "\n", + " e3u_0=file_mask.e3u_0\n", + " e2u=file_mask.e2u\n", + " umask_Drake=file_mask.umask.isel(nav_lon=0)\n", + "\n", + " # Recomputing e3u, using ssh to refactor the original e3u_0 cell heights)\n", + " \n", + " ssh_u = (ssh + ssh.roll(_nav_lon=-1))/2\n", + " bathy_u = e3u_0.sum(dim=\"depth\")\n", + " ssumask = umask_Drake[:,0]\n", + " e3u = e3u_0*(1+ssh_u*ssumask/(bathy_u+1-ssumask))\n", + " \n", + " # Masking the variables onto the Drake Passage\n", + " \n", + " u_masked=uo.isel(nav_lon=0)*umask_Drake\n", + " e3u_masked=e3u.isel(nav_lon=0)*umask_Drake\n", + " e2u_masked=e2u.isel(nav_lon=0)*umask_Drake\n", + "\n", + " # Multiplying zonal velocity by the sectional areas (e2u*e3u)\n", + " \n", + " ubar=(u_masked*e3u_masked).sum(dim='depth')\n", + " flux=(e2u_masked*ubar).sum()\n", + "\n", + " #Returning Total Transport across Drake passage as a numpy scalar (unit : Sv)\n", + " return flux.data/1e6\n", + "\n", + "\n", + "\n", + "### Intensity of the North-Atlantic SubTropical Gyre (NASTG) computed from the local maximum of the Barotropic Stream Function (BSF)\n", + "\n", + "def NASTG_BSF_max(vo,ssh,file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Intensity of the North-Atlantic SubTropical Gyre (NASTG) which contains the Gulf-Stream Current.\n", + " Computed using the Barotropic Stream Function (BSF).\n", + " Unit : Sv\n", + " \n", + " \n", + " Input :\n", + " - vo : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files \n", + " \"\"\"\n", + "\n", + " e3v_0=file_mask.e3v_0.squeeze()\n", + " e1v=file_mask.e1v.squeeze()\n", + " vmask=file_mask.vmask.squeeze()\n", + " # Updating e3v from e3v_0 and SSH\n", + " ssh_v = (ssh + ssh.roll(nav_lat=-1))/2\n", + " bathy_v = e3v_0.sum(dim=\"depth\")\n", + " ssvmask = vmask.isel(depth=0)\n", + " e3v = (e3v_0*(1+ssh_v*ssvmask/(bathy_v+1-ssvmask)))\n", + "\n", + " # Integrating Meridional Transport (e3v*e1v*vo) along depth and X from Western boundary eastward \n", + " # to get Barotropic Stream Function with the \"American continent\" as reference point (BSF=0)\n", + " V = (vo * e3v).sum(dim='depth') # == \"Barotropic Velocity\" * Bathymetry\n", + " BSF=(V * e1v * ssvmask).cumsum(dim='nav_lon') / 1e6 # Integrating from the West, and converting from m³/s to Sv\n", + " # Selecting 0N-40N window where to search for the maximum, which will correspond to the center of rotation for the gyre\n", + " BSF_NASPG=BSF.where(abs(BSF.nav_lat-20)<20)\n", + "\n", + " return BSF_NASPG.max(dim=[\"nav_lat\",\"nav_lon\"])\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded dataset from ../nemo_4.2.1/tests/DINO/EXP00/DINO_1y_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/mesh_mask.nc\n", + "\n" + ] + } + ], + "source": [ + "# Read restart file\n", + "# restart_file_path = \"../nemo_4.2.1/tests/DINO/EXP00/DINO_00011520_restart_0000.nc\"\n", + "# restart_file_path = \"../nc_files/nc_files/NEW_DINO_00576000_restart.nc\"\n", + "restart_file_path = \"../nemo_4.2.1/tests/DINO/EXP00/DINO_1y_grid_T.nc\"\n", + "restart_file=xr.open_dataset(restart_file_path)\n", + "restart_file=restart_file.rename({\"deptht\":\"depth\"})\n", + "# restart_file=restart_file.rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {restart_file_path}\")\n", + "\n", + "# Read the corresponding mesh mask file\n", + "mask_path = \"../nc_files/nc_files/mesh_mask.nc\"\n", + "file_mask=xr.open_dataset(mask_path)\n", + "file_mask=file_mask.rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {mask_path}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def check_density(density, epsilon=1e-5) :\n", + " \"\"\"\n", + " args :\n", + " density (xarray) : DataArray (t, depth, lat, lon) with density value for each point of the grid.\n", + " return :\n", + " (float) proportion of points not respecting density decreasing constraint\n", + " \"\"\"\n", + " density=density.where(density!=0)\n", + " diff = density - density.shift(depth=-1)\n", + " return (diff > epsilon).mean().data # Proportion of points not respecting decreasing density\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(0.00071595)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "density = restart_file.rhop\n", + "check_density(density, epsilon=1e-5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 199, 62)\n", + "(199, 62)\n", + "(199, 62)\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "Coordinates:\n",
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+       "    nav_lon        (y, x) float32 -0.5 0.5 1.5 2.5 3.5 ... 57.5 58.5 59.5 60.5\n",
+       "    depth          float32 513.9\n",
+       "    time_centered  (time_counter) object 0001-07-01 00:00:00\n",
+       "  * time_counter   (time_counter) object 0001-07-01 00:00:00\n",
+       "Dimensions without coordinates: y, x
" + ], + "text/plain": [ + "\n", + "array([[[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]]])\n", + "Coordinates:\n", + " nav_lat (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85 69.85\n", + " nav_lon (y, x) float32 -0.5 0.5 1.5 2.5 3.5 ... 57.5 58.5 59.5 60.5\n", + " depth float32 513.9\n", + " time_centered (time_counter) object ...\n", + " * time_counter (time_counter) object 0001-07-01 00:00:00\n", + "Dimensions without coordinates: y, x" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def temperature_500m_30NS_metric(temperature, file_mask):\n", + " \"\"\"\n", + " Metric Extraction Function :\n", + " Average Temperature at 500m depth between 30N and 30S.\n", + " Unit : °C \n", + " \n", + " \n", + " Input : \n", + " - thetao : xarray.DataArray\n", + " - file_mask : xarray.Dataset\n", + " Output : \n", + " - np.float32 or np.float64 depending on recording precision of simulation files \n", + " \n", + " \"\"\"\n", + " \n", + " # Taking Temperature At 500m depth and between 30N and 30S.\n", + "\n", + " t500_30NS=temperature.sel(depth=500,method='nearest').where(abs(temperature.nav_lat)<30,drop=False)\n", + "\n", + " # Computing Area Weights from Mask over 30N-30S latitude zone and @500m depth\n", + " print(file_mask.e1t.shape)\n", + " e1t=file_mask.e1t.squeeze()\n", + " print(e1t.shape)\n", + " e2t=file_mask.e2t.squeeze()\n", + " tmask=file_mask.tmask.squeeze()\n", + " print(temperature.nav_lat.shape)\n", + " area_500m_30NS=e1t*e2t*tmask.sel(depth=500,method='nearest')#.where(abs(temperature.nav_lat)<30,drop=False)\n", + "\n", + " #Returning Average Temperature at 500m depth as a numpy scalar\n", + " return ((t500_30NS*area_500m_30NS).sum(dim=[\"nav_lat\",\"nav_lon\"])/area_500m_30NS.sum(dim=[\"nav_lat\",\"nav_lon\"]))\n", + "\n", + "\n", + "temperature = restart_file.toce\n", + "temperature_500m_30NS_metric(temperature, file_mask)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Problem is that the shape of temperature variable is 2D (199,62) and teh condition used is that it compared to a scaler value (1D) 30 in abs(temperature.nav_lat)<30" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "  * time_counter          (time_counter) object 0001-07-01 00:00:00\n",
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+       "    toce                  (time_counter, depth, y, x) float32 ...\n",
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+       "    rhop                  (time_counter, depth, y, x) float32 0.0 0.0 ... 0.0\n",
+       "Attributes:\n",
+       "    name:         DINO_1y_grid_T\n",
+       "    description:  ocean T grid variables\n",
+       "    title:        ocean T grid variables\n",
+       "    Conventions:  CF-1.6\n",
+       "    timeStamp:    2024-Oct-24 16:22:04 GMT\n",
+       "    uuid:         10c96598-8e8a-4fb3-a5e3-2ba8e62c97e8
" + ], + "text/plain": [ + "\n", + "Dimensions: (axis_nbounds: 2, depth: 36, time_counter: 1, x: 62, y: 199)\n", + "Coordinates:\n", + " nav_lat (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85\n", + " nav_lon (y, x) float32 ...\n", + " * depth (depth) float32 5.034 15.32 ... 3.757e+03 4.253e+03\n", + " time_centered (time_counter) object ...\n", + " * time_counter (time_counter) object 0001-07-01 00:00:00\n", + "Dimensions without coordinates: axis_nbounds, x, y\n", + "Data variables:\n", + " deptht_bounds (depth, axis_nbounds) float32 ...\n", + " time_centered_bounds (time_counter, axis_nbounds) object ...\n", + " time_counter_bounds (time_counter, axis_nbounds) object ...\n", + " toce (time_counter, depth, y, x) float32 ...\n", + " soce (time_counter, depth, y, x) float32 ...\n", + " e3t (time_counter, depth, y, x) float32 ...\n", + " rhop (time_counter, depth, y, x) float32 0.0 0.0 ... 0.0\n", + "Attributes:\n", + " name: DINO_1y_grid_T\n", + " description: ocean T grid variables\n", + " title: ocean T grid variables\n", + " Conventions: CF-1.6\n", + " timeStamp: 2024-Oct-24 16:22:04 GMT\n", + " uuid: 10c96598-8e8a-4fb3-a5e3-2ba8e62c97e8" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "restart_file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "Dimensions:       (depth: 36, nav_lat: 199, nav_lon: 62, time_counter: 1)\n",
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+       "  * depth         (depth) float32 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n",
+       "  * time_counter  (time_counter) float32 0.0\n",
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+       "    nav_lat       (nav_lat, nav_lon) float32 -69.85 -69.85 ... 69.85 69.85\n",
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+       "    glamu         (time_counter, nav_lat, nav_lon) float64 0.0 1.0 ... 60.0 61.0\n",
+       "    glamv         (time_counter, nav_lat, nav_lon) float64 -0.5 0.5 ... 60.5\n",
+       "    glamf         (time_counter, nav_lat, nav_lon) float64 0.0 1.0 ... 60.0 61.0\n",
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+       "    e1v           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
+       "    e1f           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
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+       "    ff_f          (time_counter, nav_lat, nav_lon) float64 -0.0001368 ... 0.0...\n",
+       "    ff_t          (time_counter, nav_lat, nav_lon) float64 -0.0001369 ... 0.0...\n",
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+       "    e3t_1d        (time_counter, depth) float64 10.14 10.45 ... 474.8 506.4\n",
+       "    e3w_1d        (time_counter, depth) float64 10.07 10.29 ... 453.7 495.9\n",
+       "    e3t_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3u_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3v_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3f_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3w_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3uw_0        (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    e3vw_0        (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    gdept_1d      (time_counter, depth) float64 5.034 15.32 ... 4.253e+03\n",
+       "    gdepw_1d      (time_counter, depth) float64 0.0 10.14 ... 3.525e+03 4e+03\n",
+       "    gdept_0       (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "    gdepw_0       (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
+       "Attributes:\n",
+       "    CfgName:    DINO\n",
+       "    CfgIndex:   1\n",
+       "    Iperio:     1\n",
+       "    Jperio:     0\n",
+       "    NFold:      0\n",
+       "    NFtype:     -\n",
+       "    VertCoord:  zco\n",
+       "    IsfCav:     0\n",
+       "    file_name:  mesh_mask.nc\n",
+       "    TimeStamp:  13/11/2024 17:12:02 -0000
" + ], + "text/plain": [ + "\n", + "Dimensions: (depth: 36, nav_lat: 199, nav_lon: 62, time_counter: 1)\n", + "Coordinates:\n", + " * depth (depth) float32 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", + " * time_counter (time_counter) float32 0.0\n", + "Dimensions without coordinates: nav_lat, nav_lon\n", + "Data variables:\n", + " nav_lon (nav_lat, nav_lon) float32 ...\n", + " nav_lat (nav_lat, nav_lon) float32 ...\n", + " tmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", + " umask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", + " vmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", + " fmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", + " tmaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", + " umaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", + " vmaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", + " glamt (time_counter, nav_lat, nav_lon) float64 ...\n", + " glamu (time_counter, nav_lat, nav_lon) float64 ...\n", + " glamv (time_counter, nav_lat, nav_lon) float64 ...\n", + " glamf (time_counter, nav_lat, nav_lon) float64 ...\n", + " gphit (time_counter, nav_lat, nav_lon) float64 ...\n", + " gphiu (time_counter, nav_lat, nav_lon) float64 ...\n", + " gphiv (time_counter, nav_lat, nav_lon) float64 ...\n", + " gphif (time_counter, nav_lat, nav_lon) float64 ...\n", + " e1t (time_counter, nav_lat, nav_lon) float64 ...\n", + " e1u (time_counter, nav_lat, nav_lon) float64 ...\n", + " e1v (time_counter, nav_lat, nav_lon) float64 ...\n", + " e1f (time_counter, nav_lat, nav_lon) float64 ...\n", + " e2t (time_counter, nav_lat, nav_lon) float64 ...\n", + " e2u (time_counter, nav_lat, nav_lon) float64 ...\n", + " e2v (time_counter, nav_lat, nav_lon) float64 ...\n", + " e2f (time_counter, nav_lat, nav_lon) float64 ...\n", + " ff_f (time_counter, nav_lat, nav_lon) float64 ...\n", + " ff_t (time_counter, nav_lat, nav_lon) float64 ...\n", + " mbathy (time_counter, nav_lat, nav_lon) int32 ...\n", + " misf (time_counter, nav_lat, nav_lon) int32 ...\n", + " e3t_1d (time_counter, depth) float64 ...\n", + " e3w_1d (time_counter, depth) float64 ...\n", + " e3t_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3u_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3v_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3f_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3w_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3uw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " e3vw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " gdept_1d (time_counter, depth) float64 ...\n", + " gdepw_1d (time_counter, depth) float64 ...\n", + " gdept_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + " gdepw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", + "Attributes:\n", + " CfgName: DINO\n", + " CfgIndex: 1\n", + " Iperio: 1\n", + " Jperio: 0\n", + " NFold: 0\n", + " NFtype: -\n", + " VertCoord: zco\n", + " IsfCav: 0\n", + " file_name: mesh_mask.nc\n", + " TimeStamp: 13/11/2024 17:12:02 -0000" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_mask" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b5d9fb64c2b94dcf745f4068307b31bbe21d668a Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Thu, 6 Feb 2025 14:49:27 +0000 Subject: [PATCH 4/7] Upated the file after running the metric notebook --- requirements.txt | 130 +++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 126 insertions(+), 4 deletions(-) diff --git a/requirements.txt b/requirements.txt index 8aea04a..009271b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,131 @@ +asttokens==3.0.0 +attrs==25.1.0 +bokeh==3.6.3 +Bottleneck==1.4.2 +certifi==2025.1.31 +cfgv==3.4.0 +cftime==1.6.4.post1 +charset-normalizer==3.4.1 +click==8.1.8 +cloudpickle==3.1.1 +comm==0.2.2 +contourpy==1.3.1 +coverage==7.6.10 +crc32c==2.7.1 +cycler==0.12.1 +dask==2025.1.0 +debugpy==1.8.12 +decorator==5.1.1 +Deprecated==1.2.18 +distlib==0.3.9 +distributed==2025.1.0 +donfig==0.8.1.post1 +einops==0.8.0 +execnet==2.1.1 +executing==2.2.0 +filelock==3.17.0 +flox==0.10.0 +fonttools==4.55.8 +fsspec==2025.2.0 +h5netcdf==1.5.0 +h5py==3.12.1 +hypothesis==6.125.2 +identify==2.6.6 +idna==3.10 +importlib_metadata==8.6.1 +iniconfig==2.0.0 +ipykernel==6.29.5 +ipython==8.32.0 +jedi==0.19.2 +Jinja2==3.1.5 +joblib==1.4.2 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +kiwisolver==1.4.8 +llvmlite==0.44.0 +locket==1.0.0 +lz4==4.4.3 +MarkupSafe==3.0.2 +matplotlib==3.9.2 +matplotlib-inline==0.1.7 +mpmath==1.3.0 +msgpack==1.1.0 +mypy==1.15.0 +mypy-extensions==1.0.0 +nc-time-axis==1.4.1 +nest-asyncio==1.6.0 +netCDF4==1.7.2 +networkx==3.4.2 +nodeenv==1.9.1 +numba==0.61.0 +numbagg==0.8.2 +numcodecs==0.15.0 numpy==2.0.2 +numpy-groupies==0.11.2 +nvidia-cublas-cu12==12.4.5.8 +nvidia-cuda-cupti-cu12==12.4.127 +nvidia-cuda-nvrtc-cu12==12.4.127 +nvidia-cuda-runtime-cu12==12.4.127 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.2.1.3 +nvidia-curand-cu12==10.3.5.147 +nvidia-cusolver-cu12==11.6.1.9 +nvidia-cusparse-cu12==12.3.1.170 +nvidia-cusparselt-cu12==0.6.2 +nvidia-nccl-cu12==2.21.5 +nvidia-nvjitlink-cu12==12.4.127 +nvidia-nvtx-cu12==12.4.127 +opt_einsum==3.4.0 +packaging==24.2 pandas==2.2.2 +parso==0.8.4 +partd==1.4.2 +pexpect==4.9.0 +pillow==11.1.0 +platformdirs==4.3.6 +pluggy==1.5.0 +pooch==1.8.2 +pre_commit==4.1.0 +prompt_toolkit==3.0.50 +psutil==6.1.1 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pyarrow==19.0.0 +Pygments==2.19.1 +pyparsing==3.2.1 +pytest==8.3.4 +pytest-cov==6.0.0 +pytest-env==1.1.5 +pytest-timeout==2.3.1 +pytest-xdist==3.6.1 python-dateutil==2.9.0.post0 pytz==2024.1 -six==1.16.0 -matplotlib==3.9.2 -scipy==1.13.1 -xarray[complete]==2024.7.0 +PyYAML==6.0.2 +pyzmq==26.2.1 +requests==2.32.3 +ruff==0.9.4 scikit-learn==1.5.1 +scipy==1.13.1 +seaborn==0.13.2 +six==1.16.0 +sortedcontainers==2.4.0 +stack-data==0.6.3 +sympy==1.13.1 +tblib==3.0.0 +threadpoolctl==3.5.0 +toolz==1.0.0 +torch==2.6.0 +tornado==6.4.2 +traitlets==5.14.3 +triton==3.2.0 +typing_extensions==4.12.2 +tzdata==2025.1 +urllib3==2.3.0 +virtualenv==20.29.1 +wcwidth==0.2.13 +wrapt==1.17.2 +xarray==2024.7.0 +xyzservices==2025.1.0 +zarr==3.0.2 +zict==3.0.0 +zipp==3.21.0 From f5f4536b7b0da775da8befb66af3f9811520bd52 Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Thu, 6 Feb 2025 15:07:23 +0000 Subject: [PATCH 5/7] Corrected formatting errors using ruff --- metric_check.ipynb | 1220 +++++++++++--------------------------------- 1 file changed, 306 insertions(+), 914 deletions(-) diff --git a/metric_check.ipynb b/metric_check.ipynb index e15d6bf..4146f15 100644 --- a/metric_check.ipynb +++ b/metric_check.ipynb @@ -2,7 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 3.11.9\n" + ] + } + ], + "source": [ + "!python --version\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -15,12 +32,13 @@ "from torch.nn.functional import interpolate\n", "from glob import glob\n", "import xarray as xr\n", - "import numpy as np" + "import numpy as np\n", + "import netCDF4" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -187,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -442,14 +460,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Successfully loaded dataset from ../nemo_4.2.1/tests/DINO/EXP00/DINO_1y_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_00576000_restart.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_U.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_V.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_W.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\n", "Successfully loaded dataset from ../nc_files/nc_files/mesh_mask.nc\n", "\n" ] @@ -457,74 +481,126 @@ ], "source": [ "# Read restart file\n", - "# restart_file_path = \"../nemo_4.2.1/tests/DINO/EXP00/DINO_00011520_restart_0000.nc\"\n", - "# restart_file_path = \"../nc_files/nc_files/NEW_DINO_00576000_restart.nc\"\n", - "restart_file_path = \"../nemo_4.2.1/tests/DINO/EXP00/DINO_1y_grid_T.nc\"\n", - "restart_file=xr.open_dataset(restart_file_path)\n", - "restart_file=restart_file.rename({\"deptht\":\"depth\"})\n", - "# restart_file=restart_file.rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {restart_file_path}\")\n", + "DINO_restart_path = \"../nc_files/nc_files/DINO_00576000_restart.nc\"\n", + "DINO_restart_file=xr.open_dataset(DINO_restart_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_restart_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1y_grid_T_path = \"../nc_files/nc_files/DINO_1y_grid_T.nc\"\n", + "DINO_1y_grid_T=xr.open_dataset(DINO_1y_grid_T_path).rename({\"deptht\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_T_path}\")\n", + "\n", + "#Read grid_U file\n", + "DINO_1y_grid_U_path = \"../nc_files/nc_files/DINO_1y_grid_U.nc\"\n", + "DINO_1y_grid_U=xr.open_dataset(DINO_1y_grid_U_path).rename({\"depthu\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_U_path}\")\n", + "\n", + "#Read grid_V file\n", + "DINO_1y_grid_V_path = \"../nc_files/nc_files/DINO_1y_grid_V.nc\"\n", + "DINO_1y_grid_V=xr.open_dataset(DINO_1y_grid_V_path).rename({\"depthv\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_V_path}\")\n", + "\n", + "#Read grid_W file\n", + "DINO_1y_grid_W_path = \"../nc_files/nc_files/DINO_1y_grid_W.nc\"\n", + "DINO_1y_grid_W=xr.open_dataset(DINO_1y_grid_W_path).rename({\"depthw\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_W_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1m_grid_T_path = \"../nc_files/nc_files/DINO_1m_grid_T.nc\"\n", + "DINO_1m_grid_T=xr.open_dataset(DINO_1m_grid_T_path).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1m_grid_T_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1m_To_1y_grid_T_path = \"../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\"\n", + "DINO_1m_To_1y_grid_T=xr.open_dataset(DINO_1m_To_1y_grid_T_path,decode_cf=False).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1m_To_1y_grid_T_path}\")\n", + "\n", + "\n", + "# DINO_1y_grid_T[\"time_counter\"] = DINO_1y_grid_T[\"time_counter\"]\n", + "\n", + "\n", "\n", "# Read the corresponding mesh mask file\n", - "mask_path = \"../nc_files/nc_files/mesh_mask.nc\"\n", - "file_mask=xr.open_dataset(mask_path)\n", - "file_mask=file_mask.rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {mask_path}\\n\")" + "DINO_mask_path = \"../nc_files/nc_files/mesh_mask.nc\"\n", + "DINO_mask_file=xr.open_dataset(DINO_mask_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_mask_path}\\n\")" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "def check_density(density, epsilon=1e-5) :\n", - " \"\"\"\n", - " args :\n", - " density (xarray) : DataArray (t, depth, lat, lon) with density value for each point of the grid.\n", - " return :\n", - " (float) proportion of points not respecting density decreasing constraint\n", - " \"\"\"\n", - " density=density.where(density!=0)\n", - " diff = density - density.shift(depth=-1)\n", - " return (diff > epsilon).mean().data # Proportion of points not respecting decreasing density\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "array(0.00071595)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded dataset from ../nc_files/nc_files/DINO_00576000_restart.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_U.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_V.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_W.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\n", + "Successfully loaded dataset from ../nc_files/nc_files/mesh_mask.nc\n", + "\n" + ] } ], "source": [ - "density = restart_file.rhop\n", - "check_density(density, epsilon=1e-5)" + "# Read restart file\n", + "DINO_restart_path = \"../nc_files/nc_files/DINO_00576000_restart.nc\"\n", + "DINO_restart_file=xr.open_dataset(DINO_restart_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_restart_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1y_grid_T_path = \"../nc_files/nc_files/DINO_1y_grid_T.nc\"\n", + "DINO_1y_grid_T=xr.open_dataset(DINO_1y_grid_T_path,decode_cf=False).rename({\"deptht\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_T_path}\")\n", + "\n", + "#Read grid_U file\n", + "DINO_1y_grid_U_path = \"../nc_files/nc_files/DINO_1y_grid_U.nc\"\n", + "DINO_1y_grid_U=xr.open_dataset(DINO_1y_grid_U_path,decode_cf=False).rename({\"depthu\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_U_path}\")\n", + "\n", + "#Read grid_V file\n", + "DINO_1y_grid_V_path = \"../nc_files/nc_files/DINO_1y_grid_V.nc\"\n", + "DINO_1y_grid_V=xr.open_dataset(DINO_1y_grid_V_path,decode_cf=False).rename({\"depthv\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_V_path}\")\n", + "\n", + "#Read grid_W file\n", + "DINO_1y_grid_W_path = \"../nc_files/nc_files/DINO_1y_grid_W.nc\"\n", + "DINO_1y_grid_W=xr.open_dataset(DINO_1y_grid_W_path,decode_cf=False).rename({\"depthw\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1y_grid_W_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1m_grid_T_path = \"../nc_files/nc_files/DINO_1m_grid_T.nc\"\n", + "DINO_1m_grid_T=xr.open_dataset(DINO_1m_grid_T_path,decode_cf=False).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1m_grid_T_path}\")\n", + "\n", + "#Read grid_T file\n", + "DINO_1m_To_1y_grid_T_path = \"../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\"\n", + "DINO_1m_To_1y_grid_T=xr.open_dataset(DINO_1m_To_1y_grid_T_path).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_1m_To_1y_grid_T_path}\")\n", + "\n", + "DINO_1m_To_1y_grid_T[\"time_counter\"] = DINO_1y_grid_T[\"time_counter\"]\n", + "\n", + "# Read the corresponding mesh mask file\n", + "DINO_mask_path = \"../nc_files/nc_files/mesh_mask.nc\"\n", + "DINO_mask_file=xr.open_dataset(DINO_mask_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", + "print(f\"Successfully loaded dataset from {DINO_mask_path}\\n\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 199, 62)\n", - "(199, 62)\n", - "(199, 62)\n" - ] - }, { "data": { "text/html": [ @@ -559,6 +635,8 @@ "}\n", "\n", "html[theme=dark],\n", + "html[data-theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -571,7 +649,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -788,6 +866,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -809,14 +892,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -826,13 +911,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -870,7 +958,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -879,118 +968,91 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.DataArray (time_counter: 1, y: 199, x: 62)>\n",
-       "array([[[0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        ...,\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.],\n",
-       "        [0., 0., 0., ..., 0., 0., 0.]]])\n",
+       "
<xarray.DataArray (time_counter: 50)> Size: 400B\n",
+       "array([37.37823257, 50.26876607, 53.99026863, 53.63378683, 52.25463586,\n",
+       "       50.67156167, 49.14597273, 47.70837255, 46.46927904, 45.37983725,\n",
+       "       44.3223467 , 43.52822327, 42.72478865, 41.94973811, 41.33527703,\n",
+       "       40.7860674 , 40.32864996, 39.95237118, 39.63370376, 39.34591195,\n",
+       "       39.07324367, 38.8113178 , 38.5579836 , 38.31391372, 38.08504955,\n",
+       "       37.87343851, 37.67977213, 37.50365954, 37.34350209, 37.19826026,\n",
+       "       37.0667913 , 36.94998701, 36.84391707, 36.74824769, 36.65981263,\n",
+       "       36.57487045, 36.49228322, 36.4110251 , 36.33137004, 36.25212382,\n",
+       "       36.17376929, 36.09568445, 36.01735414, 35.93781489, 35.8564326 ,\n",
+       "       35.77270767, 35.68743818, 35.60379858, 35.52292313, 35.44562478])\n",
        "Coordinates:\n",
-       "    nav_lat        (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85 69.85\n",
-       "    nav_lon        (y, x) float32 -0.5 0.5 1.5 2.5 3.5 ... 57.5 58.5 59.5 60.5\n",
-       "    depth          float32 513.9\n",
-       "    time_centered  (time_counter) object 0001-07-01 00:00:00\n",
-       "  * time_counter   (time_counter) object 0001-07-01 00:00:00\n",
-       "Dimensions without coordinates: y, x
"
+       "  * time_counter  (time_counter) float64 400B -5.905e+10 ... -5.753e+10\n",
+       "    depth         float32 4B 5.034
" ], "text/plain": [ - "\n", - "array([[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]])\n", + " Size: 400B\n", + "array([37.37823257, 50.26876607, 53.99026863, 53.63378683, 52.25463586,\n", + " 50.67156167, 49.14597273, 47.70837255, 46.46927904, 45.37983725,\n", + " 44.3223467 , 43.52822327, 42.72478865, 41.94973811, 41.33527703,\n", + " 40.7860674 , 40.32864996, 39.95237118, 39.63370376, 39.34591195,\n", + " 39.07324367, 38.8113178 , 38.5579836 , 38.31391372, 38.08504955,\n", + " 37.87343851, 37.67977213, 37.50365954, 37.34350209, 37.19826026,\n", + " 37.0667913 , 36.94998701, 36.84391707, 36.74824769, 36.65981263,\n", + " 36.57487045, 36.49228322, 36.4110251 , 36.33137004, 36.25212382,\n", + " 36.17376929, 36.09568445, 36.01735414, 35.93781489, 35.8564326 ,\n", + " 35.77270767, 35.68743818, 35.60379858, 35.52292313, 35.44562478])\n", "Coordinates:\n", - " nav_lat (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85 69.85\n", - " nav_lon (y, x) float32 -0.5 0.5 1.5 2.5 3.5 ... 57.5 58.5 59.5 60.5\n", - " depth float32 513.9\n", - " time_centered (time_counter) object ...\n", - " * time_counter (time_counter) object 0001-07-01 00:00:00\n", - "Dimensions without coordinates: y, x" + " * time_counter (time_counter) float64 400B -5.905e+10 ... -5.753e+10\n", + " depth float32 4B 5.034" ] }, - "execution_count": 8, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def temperature_500m_30NS_metric(temperature, file_mask):\n", - " \"\"\"\n", - " Metric Extraction Function :\n", - " Average Temperature at 500m depth between 30N and 30S.\n", - " Unit : °C \n", - " \n", - " \n", - " Input : \n", - " - thetao : xarray.DataArray\n", - " - file_mask : xarray.Dataset\n", - " Output : \n", - " - np.float32 or np.float64 depending on recording precision of simulation files \n", - " \n", - " \"\"\"\n", - " \n", - " # Taking Temperature At 500m depth and between 30N and 30S.\n", - "\n", - " t500_30NS=temperature.sel(depth=500,method='nearest').where(abs(temperature.nav_lat)<30,drop=False)\n", - "\n", - " # Computing Area Weights from Mask over 30N-30S latitude zone and @500m depth\n", - " print(file_mask.e1t.shape)\n", - " e1t=file_mask.e1t.squeeze()\n", - " print(e1t.shape)\n", - " e2t=file_mask.e2t.squeeze()\n", - " tmask=file_mask.tmask.squeeze()\n", - " print(temperature.nav_lat.shape)\n", - " area_500m_30NS=e1t*e2t*tmask.sel(depth=500,method='nearest')#.where(abs(temperature.nav_lat)<30,drop=False)\n", - "\n", - " #Returning Average Temperature at 500m depth as a numpy scalar\n", - " return ((t500_30NS*area_500m_30NS).sum(dim=[\"nav_lat\",\"nav_lon\"])/area_500m_30NS.sum(dim=[\"nav_lat\",\"nav_lon\"]))\n", - "\n", - "\n", - "temperature = restart_file.toce\n", - "temperature_500m_30NS_metric(temperature, file_mask)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Problem is that the shape of temperature variable is 2D (199,62) and teh condition used is that it compared to a scaler value (1D) 30 in abs(temperature.nav_lat)<30" + "check_density(DINO_1y_grid_T.rhop)\n", + "check_density(get_density(DINO_1y_grid_T.toce,DINO_1y_grid_T.soce,get_deptht(DINO_restart_file,DINO_mask_file),DINO_mask_file.tmask)[0])\n", + "\n", + "temperature_500m_30NS_metric(DINO_1y_grid_T.toce,DINO_mask_file)\n", + "temperature_BWbox_metric(DINO_1y_grid_T.toce,DINO_mask_file)\n", + "temperature_DWbox_metric(DINO_1y_grid_U.uoce,DINO_mask_file)\n", + "ACC_Drake_metric(DINO_1y_grid_U.uoce,DINO_mask_file)\n", + "NASTG_BSF_max(DINO_1y_grid_V.voce,DINO_1m_To_1y_grid_T.ssh,DINO_mask_file)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1027,6 +1089,8 @@ "}\n", "\n", "html[theme=dark],\n", + "html[data-theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -1039,7 +1103,7 @@ "}\n", "\n", ".xr-wrap {\n", - " display: block;\n", + " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", @@ -1256,6 +1320,11 @@ " grid-column: 4;\n", "}\n", "\n", + ".xr-index-preview {\n", + " grid-column: 2 / 5;\n", + " color: var(--xr-font-color2);\n", + "}\n", + "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", @@ -1277,14 +1346,16 @@ "}\n", "\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", - ".xr-var-data-in:checked ~ .xr-var-data {\n", + ".xr-var-data-in:checked ~ .xr-var-data,\n", + ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", @@ -1294,13 +1365,16 @@ "\n", ".xr-var-name span,\n", ".xr-var-data,\n", + ".xr-index-name div,\n", + ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", - ".xr-var-data {\n", + ".xr-var-data,\n", + ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", @@ -1338,7 +1412,8 @@ "}\n", "\n", ".xr-icon-database,\n", - ".xr-icon-file-text2 {\n", + ".xr-icon-file-text2,\n", + ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", @@ -1347,792 +1422,109 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
<xarray.Dataset>\n",
-       "Dimensions:               (axis_nbounds: 2, depth: 36, time_counter: 1, x: 62, y: 199)\n",
+       "
<xarray.Dataset> Size: 51MB\n",
+       "Dimensions:       (nav_lat: 199, nav_lon: 62, depth: 36, time_counter: 1)\n",
        "Coordinates:\n",
-       "    nav_lat               (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85\n",
-       "    nav_lon               (y, x) float32 -0.5 0.5 1.5 2.5 ... 58.5 59.5 60.5\n",
-       "  * depth                 (depth) float32 5.034 15.32 ... 3.757e+03 4.253e+03\n",
-       "    time_centered         (time_counter) object 0001-07-01 00:00:00\n",
-       "  * time_counter          (time_counter) object 0001-07-01 00:00:00\n",
-       "Dimensions without coordinates: axis_nbounds, x, y\n",
-       "Data variables:\n",
-       "    deptht_bounds         (depth, axis_nbounds) float32 0.0 10.14 ... 4.506e+03\n",
-       "    time_centered_bounds  (time_counter, axis_nbounds) object 0001-01-01 00:0...\n",
-       "    time_counter_bounds   (time_counter, axis_nbounds) object 0001-01-01 00:0...\n",
-       "    toce                  (time_counter, depth, y, x) float32 ...\n",
-       "    soce                  (time_counter, depth, y, x) float32 ...\n",
-       "    e3t                   (time_counter, depth, y, x) float32 ...\n",
-       "    rhop                  (time_counter, depth, y, x) float32 0.0 0.0 ... 0.0\n",
+       "  * depth         (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n",
+       "  * time_counter  (time_counter) float32 4B 5.76e+05\n",
+       "Dimensions without coordinates: nav_lat, nav_lon\n",
+       "Data variables: (12/31)\n",
+       "    nav_lon       (nav_lat, nav_lon) float32 49kB ...\n",
+       "    nav_lat       (nav_lat, nav_lon) float32 49kB ...\n",
+       "    kt            float64 8B ...\n",
+       "    ndastp        float64 8B ...\n",
+       "    adatrj        float64 8B ...\n",
+       "    ntime         float64 8B ...\n",
+       "    ...            ...\n",
+       "    sshn          (time_counter, nav_lat, nav_lon) float64 99kB ...\n",
+       "    un            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
+       "    vn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
+       "    tn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
+       "    sn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
+       "    rhop          (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
        "Attributes:\n",
-       "    name:         DINO_1y_grid_T\n",
-       "    description:  ocean T grid variables\n",
-       "    title:        ocean T grid variables\n",
-       "    Conventions:  CF-1.6\n",
-       "    timeStamp:    2024-Oct-24 16:22:04 GMT\n",
-       "    uuid:         10c96598-8e8a-4fb3-a5e3-2ba8e62c97e8
  • time_counter
    (time_counter)
    float32
    5.76e+05
    array([576000.], dtype=float32)
    • nav_lon
      (nav_lat, nav_lon)
      float32
      ...
      [12338 values with dtype=float32]
    • nav_lat
      (nav_lat, nav_lon)
      float32
      ...
      [12338 values with dtype=float32]
    • kt
      ()
      float64
      ...
      [1 values with dtype=float64]
    • ndastp
      ()
      float64
      ...
      [1 values with dtype=float64]
    • adatrj
      ()
      float64
      ...
      [1 values with dtype=float64]
    • ntime
      ()
      float64
      ...
      [1 values with dtype=float64]
    • utau_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • vtau_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • qns_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • emp_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • sfx_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • en
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • avt_k
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • avm_k
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • dissl
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • sbc_hc_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • sbc_sc_b
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • qsr_hc_b
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • fraqsr_1lev
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • rdt
      ()
      float64
      ...
      [1 values with dtype=float64]
    • sshb
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • ub
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • vb
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • tb
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • sb
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • sshn
      (time_counter, nav_lat, nav_lon)
      float64
      ...
      [12338 values with dtype=float64]
    • un
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • vn
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • tn
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • sn
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • rhop
      (time_counter, depth, nav_lat, nav_lon)
      float64
      ...
      [444168 values with dtype=float64]
    • depth
      PandasIndex
      PandasIndex(Index([ 5.033581733703613, 15.322633743286133, 25.958648681640625,\n",
+       "       37.014060974121094,  48.57634735107422,  60.75108337402344,\n",
+       "        73.66564178466797,  87.47360229492188, 102.36001586914062,\n",
+       "       118.54764556884766, 136.30438232421875,  155.9520263671875,\n",
+       "          177.87646484375, 202.53965759277344, 230.49343872070312,\n",
+       "        262.3951721191406,  299.0254821777344, 341.30792236328125,\n",
+       "        390.3298034667969,  447.3644104003906,   513.892822265625,\n",
+       "         591.623779296875,    682.51025390625,    788.75830078125,\n",
+       "        912.8260498046875, 1057.4073486328125,  1225.396240234375,\n",
+       "        1419.828369140625, 1643.7969970703125,    1900.3427734375,\n",
+       "           2192.322265625,   2522.26416015625,  2892.223876953125,\n",
+       "        3303.654541015625,   3757.30908203125,          4253.1875],\n",
+       "      dtype='float32', name='depth'))
    • time_counter
      PandasIndex
      PandasIndex(Index([576000.0], dtype='float32', name='time_counter'))
  • file_name :
    DINO_00576000_restart.nc
    TimeStamp :
    13/11/2024 17:53:42 -0000
    • " ], "text/plain": [ - "\n", - "Dimensions: (axis_nbounds: 2, depth: 36, time_counter: 1, x: 62, y: 199)\n", + " Size: 51MB\n", + "Dimensions: (nav_lat: 199, nav_lon: 62, depth: 36, time_counter: 1)\n", "Coordinates:\n", - " nav_lat (y, x) float32 -69.85 -69.85 -69.85 ... 69.85 69.85\n", - " nav_lon (y, x) float32 ...\n", - " * depth (depth) float32 5.034 15.32 ... 3.757e+03 4.253e+03\n", - " time_centered (time_counter) object ...\n", - " * time_counter (time_counter) object 0001-07-01 00:00:00\n", - "Dimensions without coordinates: axis_nbounds, x, y\n", - "Data variables:\n", - " deptht_bounds (depth, axis_nbounds) float32 ...\n", - " time_centered_bounds (time_counter, axis_nbounds) object ...\n", - " time_counter_bounds (time_counter, axis_nbounds) object ...\n", - " toce (time_counter, depth, y, x) float32 ...\n", - " soce (time_counter, depth, y, x) float32 ...\n", - " e3t (time_counter, depth, y, x) float32 ...\n", - " rhop (time_counter, depth, y, x) float32 0.0 0.0 ... 0.0\n", + " * depth (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", + " * time_counter (time_counter) float32 4B 5.76e+05\n", + "Dimensions without coordinates: nav_lat, nav_lon\n", + "Data variables: (12/31)\n", + " nav_lon (nav_lat, nav_lon) float32 49kB ...\n", + " nav_lat (nav_lat, nav_lon) float32 49kB ...\n", + " kt float64 8B ...\n", + " ndastp float64 8B ...\n", + " adatrj float64 8B ...\n", + " ntime float64 8B ...\n", + " ... ...\n", + " sshn (time_counter, nav_lat, nav_lon) float64 99kB ...\n", + " un (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", + " vn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", + " tn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", + " sn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", + " rhop (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", "Attributes:\n", - " name: DINO_1y_grid_T\n", - " description: ocean T grid variables\n", - " title: ocean T grid variables\n", - " Conventions: CF-1.6\n", - " timeStamp: 2024-Oct-24 16:22:04 GMT\n", - " uuid: 10c96598-8e8a-4fb3-a5e3-2ba8e62c97e8" + " file_name: DINO_00576000_restart.nc\n", + " TimeStamp: 13/11/2024 17:53:42 -0000" ] }, - "execution_count": 9, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "restart_file" + "DINO_restart_file" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
    <xarray.Dataset>\n",
-       "Dimensions:       (depth: 36, nav_lat: 199, nav_lon: 62, time_counter: 1)\n",
-       "Coordinates:\n",
-       "  * depth         (depth) float32 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n",
-       "  * time_counter  (time_counter) float32 0.0\n",
-       "Dimensions without coordinates: nav_lat, nav_lon\n",
-       "Data variables:\n",
-       "    nav_lon       (nav_lat, nav_lon) float32 -0.5 0.5 1.5 2.5 ... 58.5 59.5 60.5\n",
-       "    nav_lat       (nav_lat, nav_lon) float32 -69.85 -69.85 ... 69.85 69.85\n",
-       "    tmask         (time_counter, depth, nav_lat, nav_lon) int8 ...\n",
-       "    umask         (time_counter, depth, nav_lat, nav_lon) int8 ...\n",
-       "    vmask         (time_counter, depth, nav_lat, nav_lon) int8 ...\n",
-       "    fmask         (time_counter, depth, nav_lat, nav_lon) int8 ...\n",
-       "    tmaskutil     (time_counter, nav_lat, nav_lon) int8 0 0 0 0 0 ... 0 0 0 0 0\n",
-       "    umaskutil     (time_counter, nav_lat, nav_lon) int8 0 0 0 0 0 ... 0 0 0 0 0\n",
-       "    vmaskutil     (time_counter, nav_lat, nav_lon) int8 0 0 0 0 0 ... 0 0 0 0 0\n",
-       "    glamt         (time_counter, nav_lat, nav_lon) float64 -0.5 0.5 ... 60.5\n",
-       "    glamu         (time_counter, nav_lat, nav_lon) float64 0.0 1.0 ... 60.0 61.0\n",
-       "    glamv         (time_counter, nav_lat, nav_lon) float64 -0.5 0.5 ... 60.5\n",
-       "    glamf         (time_counter, nav_lat, nav_lon) float64 0.0 1.0 ... 60.0 61.0\n",
-       "    gphit         (time_counter, nav_lat, nav_lon) float64 -69.85 ... 69.85\n",
-       "    gphiu         (time_counter, nav_lat, nav_lon) float64 -69.85 ... 69.85\n",
-       "    gphiv         (time_counter, nav_lat, nav_lon) float64 -69.68 ... 70.02\n",
-       "    gphif         (time_counter, nav_lat, nav_lon) float64 -69.68 ... 70.02\n",
-       "    e1t           (time_counter, nav_lat, nav_lon) float64 3.83e+04 ... 3.83e+04\n",
-       "    e1u           (time_counter, nav_lat, nav_lon) float64 3.83e+04 ... 3.83e+04\n",
-       "    e1v           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
-       "    e1f           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
-       "    e2t           (time_counter, nav_lat, nav_lon) float64 3.83e+04 ... 3.83e+04\n",
-       "    e2u           (time_counter, nav_lat, nav_lon) float64 3.83e+04 ... 3.83e+04\n",
-       "    e2v           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
-       "    e2f           (time_counter, nav_lat, nav_lon) float64 3.862e+04 ... 3.79...\n",
-       "    ff_f          (time_counter, nav_lat, nav_lon) float64 -0.0001368 ... 0.0...\n",
-       "    ff_t          (time_counter, nav_lat, nav_lon) float64 -0.0001369 ... 0.0...\n",
-       "    mbathy        (time_counter, nav_lat, nav_lon) int32 1 30 30 30 ... 30 30 1\n",
-       "    misf          (time_counter, nav_lat, nav_lon) int32 1 1 1 1 1 ... 1 1 1 1 1\n",
-       "    e3t_1d        (time_counter, depth) float64 10.14 10.45 ... 474.8 506.4\n",
-       "    e3w_1d        (time_counter, depth) float64 10.07 10.29 ... 453.7 495.9\n",
-       "    e3t_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3u_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3v_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3f_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3w_0         (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3uw_0        (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    e3vw_0        (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    gdept_1d      (time_counter, depth) float64 5.034 15.32 ... 4.253e+03\n",
-       "    gdepw_1d      (time_counter, depth) float64 0.0 10.14 ... 3.525e+03 4e+03\n",
-       "    gdept_0       (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "    gdepw_0       (time_counter, depth, nav_lat, nav_lon) float64 ...\n",
-       "Attributes:\n",
-       "    CfgName:    DINO\n",
-       "    CfgIndex:   1\n",
-       "    Iperio:     1\n",
-       "    Jperio:     0\n",
-       "    NFold:      0\n",
-       "    NFtype:     -\n",
-       "    VertCoord:  zco\n",
-       "    IsfCav:     0\n",
-       "    file_name:  mesh_mask.nc\n",
-       "    TimeStamp:  13/11/2024 17:12:02 -0000
    " - ], - "text/plain": [ - "\n", - "Dimensions: (depth: 36, nav_lat: 199, nav_lon: 62, time_counter: 1)\n", - "Coordinates:\n", - " * depth (depth) float32 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", - " * time_counter (time_counter) float32 0.0\n", - "Dimensions without coordinates: nav_lat, nav_lon\n", - "Data variables:\n", - " nav_lon (nav_lat, nav_lon) float32 ...\n", - " nav_lat (nav_lat, nav_lon) float32 ...\n", - " tmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", - " umask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", - " vmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", - " fmask (time_counter, depth, nav_lat, nav_lon) int8 ...\n", - " tmaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", - " umaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", - " vmaskutil (time_counter, nav_lat, nav_lon) int8 ...\n", - " glamt (time_counter, nav_lat, nav_lon) float64 ...\n", - " glamu (time_counter, nav_lat, nav_lon) float64 ...\n", - " glamv (time_counter, nav_lat, nav_lon) float64 ...\n", - " glamf (time_counter, nav_lat, nav_lon) float64 ...\n", - " gphit (time_counter, nav_lat, nav_lon) float64 ...\n", - " gphiu (time_counter, nav_lat, nav_lon) float64 ...\n", - " gphiv (time_counter, nav_lat, nav_lon) float64 ...\n", - " gphif (time_counter, nav_lat, nav_lon) float64 ...\n", - " e1t (time_counter, nav_lat, nav_lon) float64 ...\n", - " e1u (time_counter, nav_lat, nav_lon) float64 ...\n", - " e1v (time_counter, nav_lat, nav_lon) float64 ...\n", - " e1f (time_counter, nav_lat, nav_lon) float64 ...\n", - " e2t (time_counter, nav_lat, nav_lon) float64 ...\n", - " e2u (time_counter, nav_lat, nav_lon) float64 ...\n", - " e2v (time_counter, nav_lat, nav_lon) float64 ...\n", - " e2f (time_counter, nav_lat, nav_lon) float64 ...\n", - " ff_f (time_counter, nav_lat, nav_lon) float64 ...\n", - " ff_t (time_counter, nav_lat, nav_lon) float64 ...\n", - " mbathy (time_counter, nav_lat, nav_lon) int32 ...\n", - " misf (time_counter, nav_lat, nav_lon) int32 ...\n", - " e3t_1d (time_counter, depth) float64 ...\n", - " e3w_1d (time_counter, depth) float64 ...\n", - " e3t_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3u_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3v_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3f_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3w_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3uw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " e3vw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " gdept_1d (time_counter, depth) float64 ...\n", - " gdepw_1d (time_counter, depth) float64 ...\n", - " gdept_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - " gdepw_0 (time_counter, depth, nav_lat, nav_lon) float64 ...\n", - "Attributes:\n", - " CfgName: DINO\n", - " CfgIndex: 1\n", - " Iperio: 1\n", - " Jperio: 0\n", - " NFold: 0\n", - " NFtype: -\n", - " VertCoord: zco\n", - " IsfCav: 0\n", - " file_name: mesh_mask.nc\n", - " TimeStamp: 13/11/2024 17:12:02 -0000" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "file_mask" - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "myenv", "language": "python", "name": "python3" }, @@ -2146,7 +1538,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.11.9" } }, "nbformat": 4, From 35022153ee879d75398d5ab07f468f2999f2aea9 Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Thu, 20 Feb 2025 12:32:46 +0000 Subject: [PATCH 6/7] Updated restart file with predictions and ran metrics functions --- metric_check.ipynb | 313 ++++++++++++++++++++++++++++++--------------- 1 file changed, 212 insertions(+), 101 deletions(-) diff --git a/metric_check.ipynb b/metric_check.ipynb index 4146f15..b3170f8 100644 --- a/metric_check.ipynb +++ b/metric_check.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -460,75 +460,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully loaded dataset from ../nc_files/nc_files/DINO_00576000_restart.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_T.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_U.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_V.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1y_grid_W.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_grid_T.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\n", - "Successfully loaded dataset from ../nc_files/nc_files/mesh_mask.nc\n", - "\n" - ] - } - ], - "source": [ - "# Read restart file\n", - "DINO_restart_path = \"../nc_files/nc_files/DINO_00576000_restart.nc\"\n", - "DINO_restart_file=xr.open_dataset(DINO_restart_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_restart_path}\")\n", - "\n", - "#Read grid_T file\n", - "DINO_1y_grid_T_path = \"../nc_files/nc_files/DINO_1y_grid_T.nc\"\n", - "DINO_1y_grid_T=xr.open_dataset(DINO_1y_grid_T_path).rename({\"deptht\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1y_grid_T_path}\")\n", - "\n", - "#Read grid_U file\n", - "DINO_1y_grid_U_path = \"../nc_files/nc_files/DINO_1y_grid_U.nc\"\n", - "DINO_1y_grid_U=xr.open_dataset(DINO_1y_grid_U_path).rename({\"depthu\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1y_grid_U_path}\")\n", - "\n", - "#Read grid_V file\n", - "DINO_1y_grid_V_path = \"../nc_files/nc_files/DINO_1y_grid_V.nc\"\n", - "DINO_1y_grid_V=xr.open_dataset(DINO_1y_grid_V_path).rename({\"depthv\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1y_grid_V_path}\")\n", - "\n", - "#Read grid_W file\n", - "DINO_1y_grid_W_path = \"../nc_files/nc_files/DINO_1y_grid_W.nc\"\n", - "DINO_1y_grid_W=xr.open_dataset(DINO_1y_grid_W_path).rename({\"depthw\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1y_grid_W_path}\")\n", - "\n", - "#Read grid_T file\n", - "DINO_1m_grid_T_path = \"../nc_files/nc_files/DINO_1m_grid_T.nc\"\n", - "DINO_1m_grid_T=xr.open_dataset(DINO_1m_grid_T_path).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1m_grid_T_path}\")\n", - "\n", - "#Read grid_T file\n", - "DINO_1m_To_1y_grid_T_path = \"../nc_files/nc_files/DINO_1m_To_1y_grid_T.nc\"\n", - "DINO_1m_To_1y_grid_T=xr.open_dataset(DINO_1m_To_1y_grid_T_path,decode_cf=False).rename({\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_1m_To_1y_grid_T_path}\")\n", - "\n", - "\n", - "# DINO_1y_grid_T[\"time_counter\"] = DINO_1y_grid_T[\"time_counter\"]\n", - "\n", - "\n", - "\n", - "# Read the corresponding mesh mask file\n", - "DINO_mask_path = \"../nc_files/nc_files/mesh_mask.nc\"\n", - "DINO_mask_file=xr.open_dataset(DINO_mask_path).rename({\"nav_lev\":\"depth\",\"y\":\"nav_lat\",\"x\":\"nav_lon\"})\n", - "print(f\"Successfully loaded dataset from {DINO_mask_path}\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -598,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -981,7 +913,7 @@ " 35.77270767, 35.68743818, 35.60379858, 35.52292313, 35.44562478])\n", "Coordinates:\n", " * time_counter (time_counter) float64 400B -5.905e+10 ... -5.753e+10\n", - " depth float32 4B 5.034
    • " ], "text/plain": [ " Size: 400B\n", @@ -1034,16 +966,26 @@ " depth float32 4B 5.034" ] }, - "execution_count": 22, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "check_density(DINO_1y_grid_T.rhop)\n", "check_density(get_density(DINO_1y_grid_T.toce,DINO_1y_grid_T.soce,get_deptht(DINO_restart_file,DINO_mask_file),DINO_mask_file.tmask)[0])\n", "\n", - "temperature_500m_30NS_metric(DINO_1y_grid_T.toce,DINO_mask_file)\n", + "temperature_500m_30NS_metric(DINO_1y_grid_T.toce,DINO_mask_file).plot()\n", "temperature_BWbox_metric(DINO_1y_grid_T.toce,DINO_mask_file)\n", "temperature_DWbox_metric(DINO_1y_grid_U.uoce,DINO_mask_file)\n", "ACC_Drake_metric(DINO_1y_grid_U.uoce,DINO_mask_file)\n", @@ -1052,7 +994,123 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(0.00724717)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "check_density(get_density(DINO_1y_grid_T.toce,DINO_1y_grid_T.soce,get_deptht(DINO_restart_file,DINO_mask_file),DINO_mask_file.tmask)[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# DINO_restart_file" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded dataset from so_pred\n", + "Successfully loaded dataset from thatao_pred\n", + "Successfully loaded dataset from zos_pred\n" + ] + } + ], + "source": [ + "# Read prediction file\n", + "so_pred_path = \"../nc_files/nc_files/pred_so.npy\"\n", + "so_pred = np.load(so_pred_path)\n", + "print(f\"Successfully loaded dataset from so_pred\")\n", + "\n", + "thetao_pred_path = \"../nc_files/nc_files/pred_thetao.npy\"\n", + "thetao_pred = np.load(thetao_pred_path)\n", + "print(f\"Successfully loaded dataset from thatao_pred\")\n", + "\n", + "zos_pred_path = \"../nc_files/nc_files/pred_zos.npy\"\n", + "zos_pred = np.load(zos_pred_path)\n", + "print(f\"Successfully loaded dataset from zos_pred\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "soce_array = so_pred\n", + "soce_array = soce_array[-1,:,:,:][np.newaxis]\n", + "\n", + "toce_array = thetao_pred\n", + "toce_array = toce_array[-1,:,:,:][np.newaxis] \n", + "\n", + "\n", + "DINO_restart_file.sn[:]=soce_array #salinity(new)\n", + "DINO_restart_file.tn[:]=toce_array\n", + "\n", + "DINO_restart_file.sb[:]=soce_array #salinity(begining)\n", + "DINO_restart_file.tb[:]=toce_array" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "DINO_restart_file.to_netcdf(\"../nc_files/nc_files/restart_updated.nc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "soce_array shape: (1, 36, 199, 62)\n", + "DINO_restart_file.sn shape: (1, 36, 199, 62)\n" + ] + } + ], + "source": [ + "print(\"soce_array shape:\", soce_array.shape)\n", + "print(\"DINO_restart_file.sn shape:\", DINO_restart_file.sn.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "restart_updated = xr.open_dataset(\"../nc_files/nc_files/restart_updated.nc\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1425,33 +1483,42 @@ "
    <xarray.Dataset> Size: 51MB\n",
        "Dimensions:       (nav_lat: 199, nav_lon: 62, depth: 36, time_counter: 1)\n",
        "Coordinates:\n",
-       "  * depth         (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n",
-       "  * time_counter  (time_counter) float32 4B 5.76e+05\n",
-       "Dimensions without coordinates: nav_lat, nav_lon\n",
-       "Data variables: (12/31)\n",
        "    nav_lon       (nav_lat, nav_lon) float32 49kB ...\n",
        "    nav_lat       (nav_lat, nav_lon) float32 49kB ...\n",
+       "  * depth         (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n",
+       "  * time_counter  (time_counter) float32 4B 5.76e+05\n",
+       "Data variables: (12/29)\n",
        "    kt            float64 8B ...\n",
        "    ndastp        float64 8B ...\n",
        "    adatrj        float64 8B ...\n",
        "    ntime         float64 8B ...\n",
+       "    utau_b        (time_counter, nav_lat, nav_lon) float64 99kB ...\n",
+       "    vtau_b        (time_counter, nav_lat, nav_lon) float64 99kB ...\n",
        "    ...            ...\n",
        "    sshn          (time_counter, nav_lat, nav_lon) float64 99kB ...\n",
        "    un            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
        "    vn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
        "    tn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
        "    sn            (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
-       "    rhop          (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n",
+       "    rhop          (time_counter, depth, nav_lat, nav_lon) float64 4MB 0.0 ......\n",
        "Attributes:\n",
        "    file_name:  DINO_00576000_restart.nc\n",
-       "    TimeStamp:  13/11/2024 17:53:42 -0000
  • file_name :
    DINO_00576000_restart.nc
    TimeStamp :
    13/11/2024 17:53:42 -0000
    • " ], "text/plain": [ " Size: 51MB\n", "Dimensions: (nav_lat: 199, nav_lon: 62, depth: 36, time_counter: 1)\n", "Coordinates:\n", - " * depth (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", - " * time_counter (time_counter) float32 4B 5.76e+05\n", - "Dimensions without coordinates: nav_lat, nav_lon\n", - "Data variables: (12/31)\n", " nav_lon (nav_lat, nav_lon) float32 49kB ...\n", " nav_lat (nav_lat, nav_lon) float32 49kB ...\n", + " * depth (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", + " * time_counter (time_counter) float32 4B 5.76e+05\n", + "Data variables: (12/29)\n", " kt float64 8B ...\n", " ndastp float64 8B ...\n", " adatrj float64 8B ...\n", " ntime float64 8B ...\n", + " utau_b (time_counter, nav_lat, nav_lon) float64 99kB ...\n", + " vtau_b (time_counter, nav_lat, nav_lon) float64 99kB ...\n", " ... ...\n", " sshn (time_counter, nav_lat, nav_lon) float64 99kB ...\n", " un (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", " vn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", " tn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", " sn (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", - " rhop (time_counter, depth, nav_lat, nav_lon) float64 4MB ...\n", + " rhop (time_counter, depth, nav_lat, nav_lon) float64 4MB 0.0 ......\n", "Attributes:\n", " file_name: DINO_00576000_restart.nc\n", " TimeStamp: 13/11/2024 17:53:42 -0000" ] }, - "execution_count": 23, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "DINO_restart_file" + "restart_updated" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "TypeError", + "evalue": "No numeric data to plot.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[50], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m check_density(restart_updated\u001b[38;5;241m.\u001b[39mrhop)\n\u001b[1;32m 2\u001b[0m check_density(get_density(restart_updated\u001b[38;5;241m.\u001b[39mtn,restart_updated\u001b[38;5;241m.\u001b[39msn,get_deptht(restart_updated,DINO_mask_file),DINO_mask_file\u001b[38;5;241m.\u001b[39mtmask)[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[43mtemperature_500m_30NS_metric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrestart_updated\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mDINO_mask_file\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m temperature_BWbox_metric(restart_updated\u001b[38;5;241m.\u001b[39mtn,DINO_mask_file)\n\u001b[1;32m 5\u001b[0m temperature_DWbox_metric(restart_updated\u001b[38;5;241m.\u001b[39mun,DINO_mask_file)\n", + "File \u001b[0;32m~/Spinup-NEMO/myenv/lib/python3.11/site-packages/xarray/plot/accessor.py:48\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\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", + "File \u001b[0;32m~/Spinup-NEMO/myenv/lib/python3.11/site-packages/xarray/plot/dataarray_plot.py:282\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m plotfunc: Callable\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m darray\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo numeric data to plot.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m):\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m row \u001b[38;5;129;01mor\u001b[39;00m col:\n", + "\u001b[0;31mTypeError\u001b[0m: No numeric data to plot." + ] + } + ], + "source": [ + "check_density(restart_updated.rhop)\n", + "check_density(get_density(restart_updated.tn,restart_updated.sn,get_deptht(restart_updated,DINO_mask_file),DINO_mask_file.tmask)[0])\n", + "temperature_500m_30NS_metric(restart_updated.tn,DINO_mask_file)\n", + "temperature_BWbox_metric(restart_updated.tn,DINO_mask_file)\n", + "temperature_DWbox_metric(restart_updated.un,DINO_mask_file)\n", + "ACC_Drake_metric(restart_updated.un,DINO_mask_file)\n", + "\n", + "# NASTG_BSF_max(restart_updated.vn,DINO_1m_To_1y_grid_T.ssh,DINO_mask_file)\n", + "\n" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 4MB\n", + "[444168 values with dtype=float64]\n", + "Coordinates:\n", + " nav_lon (nav_lat, nav_lon) float32 49kB ...\n", + " nav_lat (nav_lat, nav_lon) float32 49kB -69.85 -69.85 ... 69.85 69.85\n", + " * depth (depth) float32 144B 5.034 15.32 25.96 ... 3.757e+03 4.253e+03\n", + " * time_counter (time_counter) float32 4B 5.76e+05\n" + ] + } + ], + "source": [ + "print(restart_updated[\"sn\"].isel(time_counter=slice(5))) # Replace \"your_variable\" with the actual variable name\n", + "\n" + ] } ], "metadata": { From fd1c2a5b5ce2e376829644223f54170ec5519dff Mon Sep 17 00:00:00 2001 From: Surbhi Goel Date: Thu, 20 Feb 2025 12:37:20 +0000 Subject: [PATCH 7/7] Ran NASTG_BSF_max metrics function after updating DINO_1m_to_1y file to updated restart file --- metric_check.ipynb | 423 +++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 408 insertions(+), 15 deletions(-) diff --git a/metric_check.ipynb b/metric_check.ipynb index b3170f8..3554ced 100644 --- a/metric_check.ipynb +++ b/metric_check.ipynb @@ -1574,17 +1574,389 @@ "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "No numeric data to plot.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[50], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m check_density(restart_updated\u001b[38;5;241m.\u001b[39mrhop)\n\u001b[1;32m 2\u001b[0m check_density(get_density(restart_updated\u001b[38;5;241m.\u001b[39mtn,restart_updated\u001b[38;5;241m.\u001b[39msn,get_deptht(restart_updated,DINO_mask_file),DINO_mask_file\u001b[38;5;241m.\u001b[39mtmask)[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[43mtemperature_500m_30NS_metric\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrestart_updated\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtn\u001b[49m\u001b[43m,\u001b[49m\u001b[43mDINO_mask_file\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m temperature_BWbox_metric(restart_updated\u001b[38;5;241m.\u001b[39mtn,DINO_mask_file)\n\u001b[1;32m 5\u001b[0m temperature_DWbox_metric(restart_updated\u001b[38;5;241m.\u001b[39mun,DINO_mask_file)\n", - "File \u001b[0;32m~/Spinup-NEMO/myenv/lib/python3.11/site-packages/xarray/plot/accessor.py:48\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\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", - "File \u001b[0;32m~/Spinup-NEMO/myenv/lib/python3.11/site-packages/xarray/plot/dataarray_plot.py:282\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m plotfunc: Callable\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m darray\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo numeric data to plot.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m):\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m row \u001b[38;5;129;01mor\u001b[39;00m col:\n", - "\u001b[0;31mTypeError\u001b[0m: No numeric data to plot." - ] + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray (time_counter: 1)> Size: 8B\n",
+       "array([35.51578141])\n",
+       "Coordinates:\n",
+       "  * time_counter  (time_counter) float32 4B 5.76e+05\n",
+       "    depth         float32 4B 5.034
    " + ], + "text/plain": [ + " Size: 8B\n", + "array([35.51578141])\n", + "Coordinates:\n", + " * time_counter (time_counter) float32 4B 5.76e+05\n", + " depth float32 4B 5.034" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1594,17 +1966,38 @@ "temperature_BWbox_metric(restart_updated.tn,DINO_mask_file)\n", "temperature_DWbox_metric(restart_updated.un,DINO_mask_file)\n", "ACC_Drake_metric(restart_updated.un,DINO_mask_file)\n", - "\n", - "# NASTG_BSF_max(restart_updated.vn,DINO_1m_To_1y_grid_T.ssh,DINO_mask_file)\n", + "NASTG_BSF_max(restart_updated.vn,restart_updated.sshn,DINO_mask_file)\n", "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 4B\n", + "array([576000.], dtype=float32)\n", + "Coordinates:\n", + " * time_counter (time_counter) float32 4B 5.76e+05\n", + "Only one time step available in the restart file.\n" + ] + } + ], "source": [ + "# Check time variable (usually named \"time_counter\" or similar)\n", + "print(restart_updated[\"time_counter\"]) # Replace with actual time variable if different\n", + "\n", + "# Get time resolution\n", + "time_values = restart_updated[\"time_counter\"].values\n", + "if len(time_values) > 1:\n", + " time_resolution = time_values[1] - time_values[0]\n", + " print(f\"Time resolution: {time_resolution} (time units)\")\n", + "else:\n", + " print(\"Only one time step available in the restart file.\")\n", "\n" ] },