| 1 | ############################################################## |
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| 2 | ### Python script to visualize a variable in a NetCDF file ### |
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| 3 | ############################################################## |
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| 4 | |
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| 5 | ### This script can display any variable of a NetCDF file. |
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| 6 | ### The file name, the variable to display and eventually the |
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| 7 | ### dimension are asked to the user in the terminal. |
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| 8 | |
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| 9 | import os |
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| 10 | import readline |
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| 11 | import glob |
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| 12 | from netCDF4 import Dataset |
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| 13 | import matplotlib.pyplot as plt |
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| 14 | import numpy as np |
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| 15 | |
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| 16 | ############################################################## |
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| 17 | ### Setup readline for file name autocompletion |
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| 18 | def complete(text,state): |
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| 19 | line = readline.get_line_buffer().split() |
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| 20 | # Use glob to find all matching files/directories for the current text |
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| 21 | if '*' not in text: |
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| 22 | text += '*' |
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| 23 | matches = glob.glob(os.path.expanduser(text)) |
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| 24 | # Add '/' if the match is a directory |
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| 25 | matches = [match + '/' if os.path.isdir(match) else match for match in matches] |
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| 26 | try: |
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| 27 | return matches[state] |
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| 28 | except IndexError: |
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| 29 | return None |
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| 30 | |
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| 31 | ### Function to handle autocomplete for variable names |
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| 32 | def complete_variable_names(variable_names): |
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| 33 | def completer(text, state): |
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| 34 | options = [name for name in variable_names if name.startswith(text)] |
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| 35 | if state < len(options): |
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| 36 | return options[state] |
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| 37 | else: |
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| 38 | return None |
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| 39 | return completer |
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| 40 | |
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| 41 | ### Function to visualize a variable from a NetCDF file |
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| 42 | def visualize_variable(): |
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| 43 | # Ask for the NetCDF file name |
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| 44 | readline.set_completer(complete) |
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| 45 | readline.parse_and_bind('tab: complete') |
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| 46 | file = input("Enter the name of the NetCDF file: ") |
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| 47 | |
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| 48 | # Open the NetCDF file |
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| 49 | try: |
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| 50 | dataset = Dataset(file,mode='r') |
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| 51 | except FileNotFoundError: |
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| 52 | print(f"File '{file}' not found.") |
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| 53 | return |
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| 54 | |
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| 55 | # Display available variables |
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| 56 | variable_names = list(dataset.variables.keys()) |
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| 57 | print("Available variables:\n",variable_names) |
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| 58 | |
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| 59 | # Ask for the variable to display |
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| 60 | readline.set_completer(complete_variable_names(variable_names)) |
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| 61 | variable_name = input("\nEnter the name of the variable you want to visualize: ") |
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| 62 | |
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| 63 | # Check if the variable exists |
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| 64 | if variable_name not in dataset.variables: |
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| 65 | print(f"Variable '{variable_name}' not found in the dataset.") |
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| 66 | dataset.close() |
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| 67 | return |
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| 68 | |
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| 69 | # Extract the selected variable |
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| 70 | variable = dataset.variables[variable_name][:] |
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| 71 | |
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| 72 | # Extract latitude, longitude and altitude |
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| 73 | latitude = dataset.variables['latitude'][:] |
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| 74 | longitude = dataset.variables['longitude'][:] |
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| 75 | |
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| 76 | # Check if the variable has altitude and time dimensions |
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| 77 | dimensions = dataset.variables[variable_name].dimensions |
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| 78 | print(f"\nDimensions of '{variable_name}': {dimensions}") |
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| 79 | |
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| 80 | # If the variable has a time dimension, ask for the time index |
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| 81 | if 'Time' in dimensions: |
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| 82 | if variable.shape[0] == 1: |
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| 83 | time_index = 0 |
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| 84 | else: |
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| 85 | time_index = int(input(f"Enter the time index (0 to {variable.shape[0] - 1}): ")) |
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| 86 | else: |
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| 87 | time_index = None |
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| 88 | |
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| 89 | # If the variable has an altitude dimension, ask for the altitude index |
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| 90 | if 'altitude' in dimensions: |
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| 91 | altitude = dataset.variables['altitude'][:] |
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| 92 | altitude_index = int(input(f"Enter the altitude index (0 to {altitude.shape[0] - 1}): ")) |
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| 93 | else: |
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| 94 | altitude_index = None |
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| 95 | |
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| 96 | # Prepare the 2D slice for plotting |
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| 97 | if time_index is not None and altitude_index is not None: |
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| 98 | data_slice = variable[time_index,altitude_index,:,:] |
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| 99 | elif time_index is not None: |
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| 100 | data_slice = variable[time_index,:,:] |
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| 101 | elif altitude_index is not None: |
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| 102 | data_slice = variable[altitude_index,:,:] |
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| 103 | else: |
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| 104 | data_slice = variable[:,:] |
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| 105 | |
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| 106 | # Plot the selected variable |
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| 107 | plt.figure(figsize = (10,6)) |
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| 108 | plt.contourf(longitude,latitude,data_slice,cmap = 'jet') |
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| 109 | plt.colorbar(label=f"{variable_name.capitalize()} (units)") # Adjust units based on your data |
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| 110 | plt.xlabel('Longitude (degrees)') |
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| 111 | plt.ylabel('Latitude (degrees)') |
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| 112 | plt.title(f"{variable_name.capitalize()} visualization") |
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| 113 | |
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| 114 | # Show the plot |
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| 115 | plt.show() |
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| 116 | |
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| 117 | # Close the NetCDF file |
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| 118 | dataset.close() |
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| 119 | |
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| 120 | ### Call the main function |
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| 121 | visualize_variable() |
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