diff --git a/GOSC-25_SHIVAM_CODES/NetCDF_DataReader.py b/GOSC-25_SHIVAM_CODES/NetCDF_DataReader.py
new file mode 100644
index 0000000..6afe185
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/NetCDF_DataReader.py
@@ -0,0 +1,79 @@
+"""
+NetCDF_DataReader.py
+Purpose: Load and manipulate netCDF data (e.g., CBOFS) using xarray for ocean model analysis. 
+Explore data attributes, variables, and perform basic data manipulation.
+
+"""
+#%%%%
+
+# Import necessary libraries
+import xarray as xr
+import numpy as np
+import matplotlib.pyplot as plt
+
+#%%%
+
+class NetCDFDataReader:
+    """
+     Create class to load netcdf file and explore its functionalities
+    """
+    def __init__(self, file_path): # constructor for initializing the class
+        
+        
+        self.file_path = file_path
+        self.dataset = None
+
+    def load_data(self):      # fucntion for reading a netcdf file from specified file path
+
+        try:
+            self.dataset = xr.open_dataset(self.file_path, decode_times=True)  #load the dataset 
+            print("Dataset loaded successfully.")
+            return self.dataset
+        except Exception as e:
+            print(f"Error loading dataset: {e}")
+            return None
+
+    def get_subset(self, **kwargs): # function for extracting a subset of the dataset based on time, latitude, and longitude ranges
+        
+        # the dictionary passed will have key value pairs of {features:columns} for data sclicing
+        if self.dataset is None:
+            print("Error: Dataset not loaded.")
+            return None
+        try:
+            keys=list(kwargs.get('features'))
+            values=list(kwargs.get('columns'))
+            subset = self.dataset.sel(**{keys[0]:slice(values[0], values[1])})
+
+            
+            print("Subset extracted successfully.")
+            return subset
+        except Exception as e:
+            print(f"Error extracting subset: {e}")
+            return None
+
+
+# %%
+
+# for demonstration purpose using a netcdf file hosted on opendap server
+file_loc = 'https://opendap1.nodc.no/opendap/physics/point/cruise/nansen_legacy-single_profile/NMDC_Nansen-Legacy_PR_CT_58US_2021708/CTD_station_P1_NLEG01-1_-_Nansen_Legacy_Cruise_-_2021_Joint_Cruise_2-1.nc'
+# create an instance of the NetCDFDataReader class
+ncd = NetCDFDataReader(file_loc)
+data =ncd.load_data() # load the data
+print(data)
+# %%
+# exploring the dimenisons, variales and attrivutes of data
+
+""" Dimensions"""
+print(data.dims) # print the dimensions of the dataset 
+# output is ({'PRES': 320}) so it has data across 320 values of pressure 
+
+
+"""Exploring the Temp vars"""
+print(data['TEMP']) # print the data of the dataset for TEMP variable
+print(data['TEMP'].to_dataframe()) # print the data of the dataset for TEMP variable in pandas dataframe format
+
+
+"""Attributes"""
+print(data.attrs) # print the attributes of the dataset
+# output is a dictionary of attributes of the dataset
+# %%
diff --git a/GOSC-25_SHIVAM_CODES/Plotting_data.py b/GOSC-25_SHIVAM_CODES/Plotting_data.py
new file mode 100644
index 0000000..d5d5ed9
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/Plotting_data.py
@@ -0,0 +1,129 @@
+"""
+Plotting_data.py
+Purpose: Plot the combined data created in TimeSeries_Data_Exploration.py 
+Explore multiple plotting functions for gridded data
+
+"""
+
+#%%# Import necessary libraries
+import xarray as xr
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import scipy
+import cartopy 
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
+import cmocean
+
+
+# %%
+
+
+# Loading the combined data
+data = xr.load_dataset(r'Example Data/combined_data.nc') # load the combined data
+
+
+# %%
+
+combined_data = data.copy() #create a copy of the data
+# Set up the map projection (Mercator is good for ocean data)
+projection = ccrs.PlateCarree()
+
+# Create a figure with subplots for each time step
+fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 15), 
+                         subplot_kw={'projection': projection})
+
+# Plot surface temperature for each time step
+for t in range(len(combined_data.ocean_time)):
+    ax = axes[t]
+    
+    # Select surface temperature at this time step
+    surface_temp = combined_data.temp.isel(ocean_time=t,s_rho=10)
+
+    print(surface_temp.shape)
+    print(surface_temp.to_dataframe())
+    # Create the map
+    ax.coastlines(resolution='110m')
+    ax.add_feature(cfeature.LAND)
+    ax.add_feature(cfeature.OCEAN)
+    ax.gridlines(draw_labels=True)
+    
+    # Plot the temperature data
+    p = ax.pcolormesh(combined_data.lon_rho, combined_data.lat_rho, surface_temp,
+                      transform=ccrs.PlateCarree(),  # Data is in lat/lon coordinates
+                      cmap=cmocean.cm.thermal,  # Use cmocean's thermal colormap
+                      vmin=surface_temp.min(), vmax=surface_temp.max())
+    
+    # Add a colorbar
+    plt.colorbar(p, ax=ax, label='Temperature (°C)')
+    
+    # Set title
+    ax.set_title(f'Surface Temperature at {combined_data.ocean_time[t].values}')
+
+plt.tight_layout()
+plt.show()
+# %%
+
+import numpy as np
+from scipy.ndimage import gaussian_filter1d
+
+# Compute the spatially averaged surface temperature
+surface_temp = combined_data.temp.isel(s_rho=19).mean(dim=['eta_rho', 'xi_rho'])
+
+# Convert to numpy for smoothing
+temp_values = surface_temp.values
+time_values = combined_data.ocean_time.values
+
+# Smooth the temperature data using a Gaussian filter
+smoothed_temp = gaussian_filter1d(temp_values, sigma=1)  # Adjust sigma for more/less smoothing
+
+# Plot the original and smoothed time series
+plt.figure(figsize=(8, 4))
+plt.plot(time_values, temp_values, marker='o', label='Original', color='blue')
+plt.plot(time_values, smoothed_temp, label='Smoothed (Gaussian)', color='red')
+plt.xlabel('Time')
+plt.ylabel('Temperature (°C)')
+plt.title('Spatially Averaged Surface Temperature (Smoothed)')
+plt.grid(True)
+plt.legend()
+plt.show()
+# %%
+
+# Some data preprocessing to plot on curve
+df = combined_data.temp.isel(ocean_time=0,s_rho=0).to_dataframe().reset_index()
+df
+print(df['lat_rho'].min(),df['lat_rho'].max())
+print(df['lon_rho'].min(),df['lon_rho'].max())
+# %%
+# %%
+# %%
+
+# plot to highlight regions of data in the US MAP
+
+# intializing figure
+plt.figure(figsize=(10, 6))
+m1= plt.axes(projection=ccrs.PlateCarree())
+
+# adding features to the map
+m1.add_feature(cfeature.LAND)
+m1.add_feature(cfeature.OCEAN)
+m1.gridlines(draw_labels=True)
+m1.add_feature(cfeature.COASTLINE)
+m1.add_feature(cfeature.BORDERS, linestyle=':')
+m1.add_feature(cfeature.LAKES, color='lightblue')
+m1.add_feature(cfeature.RIVERS, color='blue')
+m1.add_feature(cfeature.STATES, linestyle=':')
+
+# adding subset to see in the map 
+m1.set_extent([-130,-60,20,35])
+
+# plotting the data as a scatter plot to see the regions covered by the data
+for i in df.itertuples():
+    m1.plot(i.lon_rho,i.lat_rho,color='red',marker='o')
+m1.stock_img()
+
+
+
+# %%
diff --git a/GOSC-25_SHIVAM_CODES/TimeSeries_Data_Exploration.py b/GOSC-25_SHIVAM_CODES/TimeSeries_Data_Exploration.py
new file mode 100644
index 0000000..517171d
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/TimeSeries_Data_Exploration.py
@@ -0,0 +1,70 @@
+"""
+TimeSeries_Data_Exploration.py
+Purpose: Load and explore the data of different time steps to get a flavour of timeseries data 
+Handling multiple time periods data ,plotting and saving it
+
+"""
+
+#%%%%
+
+# Import necessary libraries
+import xarray as xr
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+#%%%
+# LOADING DATA 
+
+loc = "../Example Data"  # specify folder where data will be loaded
+files=  os.listdir(loc)  # list all files in the folder
+print(files) # print the list of files
+"""
+we have 3 files for the same day but executed at gap of 6 hrs
+these files are :
+1.  nos.cbofs.fields.f001.20231127.t00z.nc
+2.  nos.cbofs.fields.f001.20231127.t06z.nc
+3 . nos.cbofs.fields.f001.20231127.t12z.nc
+"""
+
+data1  = xr.open_dataset(f"{files[0]}",engine='netcdf4')
+data2  = xr.open_dataset(f"{files[1]}",engine='netcdf4')
+data3  = xr.open_dataset(f"{files[2]}",engine='netcdf4')
+
+
+# %%
+
+"""
+We would append temp data for all time-steps and create a combined data
+
+Then we would slice data to create timeseries data for a specific location
+
+Then we would try to plot the same
+
+"""
+
+datasets = [data1, data2, data3]
+
+# Combine along the ocean_time dimension
+combined_data = xr.concat(datasets, dim='ocean_time')
+
+# Check the combined dataset
+print(combined_data)
+
+
+""" PLOTTING TEMP AT A SPECIFIC LOCATION """
+specific_point_temp = combined_data.temp.isel(eta_rho=100, xi_rho=100, s_rho=19)
+
+# Plot
+plt.figure(figsize=(8, 4))
+plt.plot(combined_data.ocean_time, specific_point_temp, marker='o', label='Temp at (eta_rho=100, xi_rho=100, s_rho=19)')
+plt.xlabel('Time')
+plt.ylabel('Temperature (°C)')
+plt.title('Temperature Time Series at (eta_rho=100, xi_rho=100, s_rho=19 Surface)')
+plt.grid(True)
+plt.legend()
+plt.show()
+
+combined_data.to_netcdf('combined_data.nc') # save the combined data/
+
+#%%
diff --git a/GOSC-25_SHIVAM_CODES/Validation_module.py b/GOSC-25_SHIVAM_CODES/Validation_module.py
new file mode 100644
index 0000000..a793076
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/Validation_module.py
@@ -0,0 +1,68 @@
+"""
+Validation_module.py 
+Purpose: Create RMSE error between 2 different NETCDF Files  
+Assuming 2 model prediction files(at 6 hrs assuming one is predicted and one is observed) create a module for creating RMSE
+
+"""
+
+#%%%%
+
+# Import necessary libraries
+import xarray as xr
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+# %%
+
+# creating a class to preprocess data and evaluation code using RMSE after slicing entered by user
+class Eval():
+    def __init__(self, data1, data2):
+        self.pred = data1
+        self.actual = data2
+
+
+# create a function taking preproceess data and calculate RMSE 
+    def rmse(self,filter):
+        
+        # the dictionary passed will have key value pairs of {features:columns} for data sclicing
+        if self.pred is None:
+            print("Error: Dataset not loaded.")
+            return None
+        
+        keys=list(filter.keys())
+        values=list(filter.values())
+
+        for i in range(len(keys)):
+            pred_subset = self.pred['temp'].sel(**{keys[i]:slice(values[i][0], values[i][1])})
+            actual_subset = self.actual['temp'].sel(**{keys[i]:slice(values[i][0], values[i][1])})
+        # convert temp column to numpy array 
+        
+        pred_subset_df= pred_subset.to_dataframe().reset_index()
+        actual_subset_df =actual_subset.to_dataframe().reset_index()
+
+        pred_subset_df= pred_subset_df[~(pred_subset_df['temp'].isna())]['temp']
+        actual_subset_df= actual_subset_df[~(actual_subset_df['temp'].isna())]['temp']
+        
+        # Calculate RMSE
+        return np.sqrt(((pred_subset_df - actual_subset_df) ** 2).mean())
+
+    # abstract class for computing any metric provided by user
+     def Metric(Self,filter) : 
+         pass
+
+        
+
+
+# %%
+
+pred = xr.open_dataset("../Example Data/nos.cbofs.fields.f001.20231127.t00z.nc") # load predicted data for now
+actual = xr.open_dataset("../Example Data/nos.cbofs.fields.f001.20231127.t06z.nc") # load actual data for now
+
+#%%
+eval = Eval(pred, actual)
+filter= {"s_rho":[-0.975,-0.875]}
+rmse = eval.rmse(filter)
+print("RMSE extracted successfully")
+print(f"RMSE is :{rmse}")
+# %%
diff --git a/GOSC-25_SHIVAM_CODES/in-situ_analysis.ipynb b/GOSC-25_SHIVAM_CODES/in-situ_analysis.ipynb
new file mode 100644
index 0000000..2835439
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/in-situ_analysis.ipynb
@@ -0,0 +1,796 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9ec52310-2ac8-44c2-be9a-1b825dc3f3cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' \\nIn this demo we would achieve the following things\\n\\n* stream data using virtualizarr (not loading directly in memory thus creating streaming in-situ processing)\\n* Add batch processing functionality (compairing 2 datasets representing timeseries data) as in provide more flexibility while comparision\\n* add visualizing plots\\n\\nall this using data stored at s3 bucket \\n'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\" \n",
+    "In this demo we would achieve the following things\n",
+    "\n",
+    "* stream data using virtualizarr (not loading directly in memory thus creating streaming in-situ processing)\n",
+    "* Calculate rmse of a variable across 2 datsets\n",
+    "* add visualizing plots\n",
+    "\n",
+    "all this using data stored at s3 bucket \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "91f198ea-ab59-44f4-8186-9a47dcbe32c8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# loading dependencies\n",
+    "from virtualizarr import open_virtual_dataset\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.animation import FuncAnimation\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6504063d-4552-4f8a-9576-05c52b096caa",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\" creating class data_streamer\n",
+    "input  - file_name : the file you want to load from s3\n",
+    "       - storage_options : parameters for storage options defined in above cell\n",
+    "       \n",
+    "functions -\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "class data_streamer():\n",
+    "    def __init__(self,bucket_name,file_name,storage_options):\n",
+    "        self.bucket_name= bucket_name\n",
+    "        self.file_name= file_name\n",
+    "        self.storage_options= storage_options\n",
+    "        \n",
+    "    def virtual_referance(self):\n",
+    "        url =f\"s3://{self.bucket_name}/{self.file_name}\"\n",
+    "        virtual_ds = open_virtual_dataset(\n",
+    "        url, indexes={}, reader_options={\"storage_options\": storage_options})\n",
+    "        # Write the virtual dataset to disk as a Kerchunk JSON. We could alternative write to a Kerchunk JSON or Icechunk Store.\n",
+    "        virtual_ds.virtualize.to_kerchunk(f\"{self.file_name}_kerchunk_reference.json\", format=\"json\")\n",
+    "        return f\"{self.file_name}_kerchunk_reference.json\"\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "0f1288bd-215a-47c4-869a-0662cf08dcc5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    " # adding storage options\n",
+    "storage_options = dict(anon=True, default_fill_cache=False, default_cache_type=\"none\") # enables not loading anything in memory\n",
+    "s3_bucket_name= \"ioostestbucket\"  # s3 bucket where file is stored - currently hardcoded for demo can be taken as input\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7ce364d2-b60d-4947-b330-a0737c83899a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nos.cbofs.fields.f001.20231127.t12z.nc_kerchunk_reference.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# creating referance jsons for 2 netcdf files in s3 buckets\n",
+    "\n",
+    "file1 = \"nos.cbofs.fields.f001.20231127.t06z.nc\"\n",
+    "data1= data_streamer(s3_bucket_name,file1,storage_options)\n",
+    "json1 = data1.virtual_referance()\n",
+    "print(json1)\n",
+    "\n",
+    "\n",
+    "file2=\"nos.cbofs.fields.f001.20231127.t12z.nc\"\n",
+    "data2 = data_streamer(s3_bucket_name,file2,storage_options)\n",
+    "json2= data2.virtual_referance()\n",
+    "print(json2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "3a90af9a-472b-408f-b0d2-2c2663f90b5f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#json1 = \"nos.cbofs.fields.f001.20231127.t06z.nc_kerchunk_reference.json\"\n",
+    "#json2= \"nos.cbofs.fields.f001.20231127.t12z.nc_kerchunk_reference.json\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "13575ccf-8de0-4314-8f0b-1d2b20370b1e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# streaming data using xarray from refrance jsons using kerchunk engine\n",
+    "\n",
+    "ds1 = xr.open_dataset(\n",
+    "    f\"{json1}\",\n",
+    "    engine=\"kerchunk\",\n",
+    "    backend_kwargs={\"storage_options\": storage_options},\n",
+    ")\n",
+    "ds2 = xr.open_dataset(\n",
+    "    f\"{json2}\",\n",
+    "    engine=\"kerchunk\",\n",
+    "    backend_kwargs={\"storage_options\": storage_options},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "808083b4-2452-4dbe-813c-5694effbf66b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\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",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1f1f1f;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  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",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\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-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\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-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (ocean_time: 1, s_rho: 20, eta_rho: 291, xi_rho: 332)&gt; Size: 8MB\n",
+       "array([[[[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]]]], shape=(1, 20, 291, 332), dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat_rho     (eta_rho, xi_rho) float64 773kB ...\n",
+       "    lon_rho     (eta_rho, xi_rho) float64 773kB ...\n",
+       "  * ocean_time  (ocean_time) datetime64[ns] 8B 2023-11-27T07:00:00\n",
+       "  * s_rho       (s_rho) float64 160B -0.975 -0.925 -0.875 ... -0.075 -0.025\n",
+       "Dimensions without coordinates: eta_rho, xi_rho\n",
+       "Attributes:\n",
+       "    cell_methods:   ocean_time: point\n",
+       "    field:          temperature\n",
+       "    grid:           grid\n",
+       "    location:       face\n",
+       "    long_name:      potential temperature\n",
+       "    standard_name:  sea_water_potential_temperature\n",
+       "    time:           ocean_time\n",
+       "    units:          Celsius</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span class='xr-has-index'>s_rho</span>: 20</li><li><span>eta_rho</span>: 291</li><li><span>xi_rho</span>: 332</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-426b5259-9f4f-4e9d-83f7-d752a3e22d8f' class='xr-array-in' type='checkbox' checked><label for='section-426b5259-9f4f-4e9d-83f7-d752a3e22d8f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]]]], shape=(1, 20, 291, 332), dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e4dff275-d1d6-4a30-a6aa-8a26cee1952e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4dff275-d1d6-4a30-a6aa-8a26cee1952e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4737c80c-f761-47f3-83b7-912a2bb7b307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4737c80c-f761-47f3-83b7-912a2bb7b307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-072ee083-d611-4eb0-906c-8c8474979d0e' class='xr-var-data-in' type='checkbox'><label for='data-072ee083-d611-4eb0-906c-8c8474979d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>field :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>grid_latitude_at_cell_center</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>[96612 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2b5b161d-0df8-477d-b8e4-036b94173649' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b5b161d-0df8-477d-b8e4-036b94173649' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef4c1642-a931-4544-a2e8-df7b1d24c5ed' class='xr-var-data-in' type='checkbox'><label for='data-ef4c1642-a931-4544-a2e8-df7b1d24c5ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>field :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>grid_longitude_at_cell_center</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>[96612 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-11-27T07:00:00</div><input id='attrs-d0f6bda8-42cc-4bb9-92d1-183980a10025' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0f6bda8-42cc-4bb9-92d1-183980a10025' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-679e43fc-1713-4f7d-b60d-3b1ad6c3fa95' class='xr-var-data-in' type='checkbox'><label for='data-679e43fc-1713-4f7d-b60d-3b1ad6c3fa95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>field :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time since initialization</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-11-27T07:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>s_rho</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.975 -0.925 ... -0.075 -0.025</div><input id='attrs-ea48ca02-966b-46ac-b794-8227025ad82a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea48ca02-966b-46ac-b794-8227025ad82a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f8a28a3-0ae9-4e70-9e73-713ccb67871e' class='xr-var-data-in' type='checkbox'><label for='data-6f8a28a3-0ae9-4e70-9e73-713ccb67871e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g1</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd></dl></div><div class='xr-var-data'><pre>array([-0.975, -0.925, -0.875, -0.825, -0.775, -0.725, -0.675, -0.625, -0.575,\n",
+       "       -0.525, -0.475, -0.425, -0.375, -0.325, -0.275, -0.225, -0.175, -0.125,\n",
+       "       -0.075, -0.025])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab6e5bef-d770-4afd-8089-64b7ae7ba2ce' class='xr-section-summary-in' type='checkbox'  ><label for='section-ab6e5bef-d770-4afd-8089-64b7ae7ba2ce' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9aa15770-4631-4e8f-bf10-f9e4e1ee7546' class='xr-index-data-in' type='checkbox'/><label for='index-9aa15770-4631-4e8f-bf10-f9e4e1ee7546' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-11-27 07:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;ocean_time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>s_rho</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-04d87176-3c5f-4a4e-8024-d800d29be2fc' class='xr-index-data-in' type='checkbox'/><label for='index-04d87176-3c5f-4a4e-8024-d800d29be2fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -0.9750000000000001,               -0.925,               -0.875,\n",
+       "        -0.8250000000000001,               -0.775,  -0.7250000000000001,\n",
+       "                     -0.675,               -0.625,  -0.5750000000000001,\n",
+       "                     -0.525, -0.47500000000000003, -0.42500000000000004,\n",
+       "                     -0.375,               -0.325,               -0.275,\n",
+       "                     -0.225, -0.17500000000000002,               -0.125,\n",
+       "       -0.07500000000000001,               -0.025],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;s_rho&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48018930-115e-4d88-8af0-2194f8265c3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48018930-115e-4d88-8af0-2194f8265c3b' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>ocean_time: point</dd><dt><span>field :</span></dt><dd>temperature</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'temp' (ocean_time: 1, s_rho: 20, eta_rho: 291, xi_rho: 332)> Size: 8MB\n",
+       "array([[[[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[nan, ..., nan],\n",
+       "         ...,\n",
+       "         [nan, ..., nan]]]], shape=(1, 20, 291, 332), dtype=float32)\n",
+       "Coordinates:\n",
+       "    lat_rho     (eta_rho, xi_rho) float64 773kB ...\n",
+       "    lon_rho     (eta_rho, xi_rho) float64 773kB ...\n",
+       "  * ocean_time  (ocean_time) datetime64[ns] 8B 2023-11-27T07:00:00\n",
+       "  * s_rho       (s_rho) float64 160B -0.975 -0.925 -0.875 ... -0.075 -0.025\n",
+       "Dimensions without coordinates: eta_rho, xi_rho\n",
+       "Attributes:\n",
+       "    cell_methods:   ocean_time: point\n",
+       "    field:          temperature\n",
+       "    grid:           grid\n",
+       "    location:       face\n",
+       "    long_name:      potential temperature\n",
+       "    standard_name:  sea_water_potential_temperature\n",
+       "    time:           ocean_time\n",
+       "    units:          Celsius"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1['temp']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "609efd24-c964-4f17-8520-759f3ac3ec8f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def RMSE(ds1,ds2, var_name='temp'):\n",
+    "    time_dim='ocean_time'\n",
+    "    \n",
+    "    # Calculate means over time and vertical layers to get 2D spatial data\n",
+    "    mean1 = ds1[var_name].mean(dim=[time_dim, 's_rho'], skipna=True)\n",
+    "    mean2 = ds2[var_name].mean(dim=[time_dim, 's_rho'], skipna=True)\n",
+    "    diff = mean1 - mean2\n",
+    "    squared_diff = diff ** 2\n",
+    "    rmse = np.sqrt(squared_diff.mean())\n",
+    "    print(f\"RMSE between datasets for {var_name}: {rmse.values} °C\")\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "bcca68ed-a5a1-4c9f-82a7-feda33a3c149",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RMSE between datasets for temp: 0.2916180491447449 °C\n"
+     ]
+    }
+   ],
+   "source": [
+    "RMSE(ds1,ds2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "6199f105-6abf-44b1-958e-68d01c4cf053",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def comparison_plots(ds1, ds2, var_name='temp'):\n",
+    "\n",
+    "    time_dim='ocean_time'\n",
+    "    # Calculate means over time and vertical layers to get 2D spatial data\n",
+    "    mean1 = ds1[var_name].mean(dim=[time_dim, 's_rho'], skipna=True)\n",
+    "    mean2 = ds2[var_name].mean(dim=[time_dim, 's_rho'], skipna=True)\n",
+    "    diff = mean1 - mean2\n",
+    "\n",
+    "    \n",
+    "    # Create a heatmap \n",
+    "    fig, ax = plt.subplots(figsize=(14, 10), dpi=100)\n",
+    "\n",
+    "    im1 = ax.contourf(ds1['lon_rho'], ds1['lat_rho'], mean1.values, cmap='Blues', levels=20, alpha=0.8,\n",
+    "                      vmin=mean1.min(), vmax=mean1.max(), label=f\"{ds1.attrs.get('title', 'Dataset 1')}\")\n",
+    "\n",
+    "    im2 = ax.contourf(ds2['lon_rho'], ds2['lat_rho'], mean2.values, cmap='viridis', levels=20, alpha=0.6,\n",
+    "                      vmin=mean2.min(), vmax=mean2.max(), label=f\"{ds2.attrs.get('title', 'Dataset 2')}\")\n",
+    "\n",
+    "    cf = ax.contour(ds1['lon_rho'], ds1['lat_rho'], diff.values, colors='red', levels=15, linewidths=1.5,\n",
+    "                    linestyles='--', label=\"Difference\")\n",
+    "    ax.clabel(cf, fmt='%1.2f', colors='black', fontsize=8)\n",
+    "    \n",
+    "    # Add colorbars\n",
+    "    cbar1 = plt.colorbar(im1, ax=ax, orientation='vertical', pad=0.05)\n",
+    "    cbar1.set_label(f\"{var_name} Dataset 1 (°C)\", rotation=270, labelpad=15)\n",
+    "    \n",
+    "    cbar2 = plt.colorbar(im2, ax=ax, orientation='vertical', pad=0.12)\n",
+    "    cbar2.set_label(f\"{var_name} Dataset 2 (°C)\", rotation=270, labelpad=15)\n",
+    "    \n",
+    "    cbar_diff = plt.colorbar(cf, ax=ax, orientation='vertical', pad=0.19)\n",
+    "    cbar_diff.set_label(f\"{var_name} Difference (°C)\", rotation=270, labelpad=15)\n",
+    "    \n",
+    "    # Customize the plot\n",
+    "    ax.set_title(f\" Comparison of {var_name} Across Datasets\", fontsize=16, pad=15)\n",
+    "    ax.set_xlabel(\"Longitude\", fontsize=12)\n",
+    "    ax.set_ylabel(\"Latitude\", fontsize=12)\n",
+    "    ax.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9), fontsize=10)\n",
+    "    ax.grid(True, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Save the plot\n",
+    "    plt.savefig(f\"dataset_comparison_{var_name}_enhanced.png\", bbox_inches='tight', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "b8284971-baa3-4eb8-99d2-fadf3ed5b156",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_654/3057794091.py:14: UserWarning: The following kwargs were not used by contour: 'label'\n",
+      "  im1 = ax.contourf(ds1['lon_rho'], ds1['lat_rho'], mean1.values, cmap='Blues', levels=20, alpha=0.8,\n",
+      "/tmp/ipykernel_654/3057794091.py:18: UserWarning: The following kwargs were not used by contour: 'label'\n",
+      "  im2 = ax.contourf(ds2['lon_rho'], ds2['lat_rho'], mean2.values, cmap='viridis', levels=20, alpha=0.6,\n",
+      "/tmp/ipykernel_654/3057794091.py:22: UserWarning: The following kwargs were not used by contour: 'label'\n",
+      "  cf = ax.contour(ds1['lon_rho'], ds1['lat_rho'], diff.values, colors='red', levels=15, linewidths=1.5,\n",
+      "/tmp/ipykernel_654/3057794091.py:40: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  ax.legend(loc='upper left', bbox_to_anchor=(0.1, 0.9), fontsize=10)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enhanced comparison plot saved as: dataset_comparison_temp_enhanced.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "comparison_plots(ds1,ds2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cda230ba-f9f9-4660-bc0d-50f4fd51be98",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_braket",
+   "language": "python",
+   "name": "conda_braket"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/GOSC-25_SHIVAM_CODES/s3bucket_boto_interaction.py b/GOSC-25_SHIVAM_CODES/s3bucket_boto_interaction.py
new file mode 100644
index 0000000..583728a
--- /dev/null
+++ b/GOSC-25_SHIVAM_CODES/s3bucket_boto_interaction.py
@@ -0,0 +1,108 @@
+"""
+s3bucket_boto_interaction.py 
+
+PURPOSE : interact with s3 bucket using boto3
+Will read and push data to s3 bucket
+
+"""
+
+#%%
+class CloudDataHandler:
+    def __init__(self, s3_bucket, s3_key, aws_access_key=None, aws_secret_key=None):
+        """
+        Initialize with S3 bucket details for in-situ data access.
+        Args:
+            s3_bucket (str): S3 bucket name
+            s3_key (str): S3 object key (e.g., path to NetCDF file)
+            aws_access_key (str, optional): AWS access key
+            aws_secret_key (str, optional): AWS secret key
+        """
+        self.s3_bucket = s3_bucket
+        self.s3_key = s3_key
+        self.s3_path = f"s3://{s3_bucket}/{s3_key}"
+        self.fs = fsspec.filesystem('s3', anon=False, 
+                                   key=aws_access_key, secret=aws_secret_key)
+
+    def inSituLoad(self, variables=None, time_slice=None, lat_slice=None, lon_slice=None):
+        """
+        Load specific data subsets in-situ using xarray and kerchunk.
+        Args:
+            variables (list, optional): Variables to load (e.g., ['temperature'])
+            time_slice (slice, optional): Time range to subset
+            lat_slice (slice, optional): Latitude range
+            lon_slice (slice, optional): Longitude range
+        Returns:
+            xarray.Dataset: Subsetted dataset
+        """
+        # Create kerchunk reference for in-situ access
+        with self.fs.open(self.s3_path) as infile:
+            h5chunks = SingleHdf5ToZarr(infile, 'reference.json')
+        ds = xr.open_dataset('reference.json', engine='kerchunk')
+        
+        # Apply filters
+        if variables:
+            ds = ds[variables]
+        if time_slice:
+            ds = ds.isel(time=time_slice)
+        if lat_slice:
+            ds = ds.isel(lat=lat_slice)
+        if lon_slice:
+            ds = ds.isel(lon=lon_slice)
+        
+        return ds
+
+    def integrityCheck(self, ds):
+        """
+        Basic integrity check for dataset dimensions and completeness.
+        Args:
+            ds (xarray.Dataset): Dataset to check
+        Returns:
+            bool: True if intact, False otherwise
+        """
+        return all(dim > 0 for dim in ds.dims.values()) and not ds.isnull().all().all()
+
+    def filterData(self, ds, time_range=None, lat_range=None, lon_range=None):
+        """
+        Apply user-defined filters to the dataset.
+        Args:
+            ds (xarray.Dataset): Dataset to filter
+            time_range (tuple, optional): (start, end) time indices
+            lat_range (tuple, optional): (start, end) latitude indices
+            lon_range (tuple, optional): (start, end) longitude indices
+        Returns:
+            xarray.Dataset: Filtered dataset
+        """
+        if time_range:
+            ds = ds.isel(time=slice(*time_range))
+        if lat_range:
+            ds = ds.isel(lat=slice(*lat_range))
+        if lon_range:
+            ds = ds.isel(lon=slice(*lon_range))
+        return ds
+
+
+# %%
+ print("Starting In-Situ Processing...")
+    start_time = time.time()
+    
+    # Initialize CloudDataHandler
+    handler = CloudDataHandler(s3_bucket, s3_key, aws_access_key, aws_secret_key)
+    ds_in_situ = handler.inSituLoad(variables=['temperature'], 
+                                   time_slice=slice(0, 10),  # First 10 time steps
+                                   lat_slice=slice(0, 5),    # First 5 latitude points
+                                   lon_slice=slice(0, 5))    # First 5 longitude points
+    if handler.integrityCheck(ds_in_situ):
+        mean_temp_in_situ = ds_in_situ['temperature'].mean().values
+    
+    in_situ_time = time.time() - start_time
+    print(f"In-Situ Processing Time: {in_situ_time:.2f} seconds")
+    
+    # Plot result
+    plt.figure(figsize=(6, 4))
+    plt.plot(mean_temp_in_situ, label="In-Situ Mean Temp")
+    plt.title("In-Situ Processing Result")
+    plt.legend()
+    plt.show()
+
+    # Compare
+    print(f"Time Difference: {trad_time - in_situ_time:.2f} seconds saved")