Plotting wind flags coloured by temperature

Wind flags coloured by temperature

This is one cell notebook example that will help you plot wind flags that are coloured by temperature using Magics.

When plotting grib data we need to give magics indexes of x and y wind component, as well as index of parameter used for colouring the arrows (or flags).

One way of showing temperature advection is by colouring wind flags by temperature. If wind_advanced_colour_parameter is set to "parameter", then Magics will use parameter that was given by mnetcdf or mgrib.

List of all mwind parameters you can find in Magics documentation.

Installing Magics

If you don't have Magics installed, run the next cell to install Magics using conda.

# Install Magics in the current Jupyter kernel
import sys
!conda install --yes --prefix {sys.prefix} Magics
import Magics.macro as magics

# Setting the projection
projection = magics.mmap(
    subpage_clipping               = "on",
    subpage_map_projection         = 'cylindrical',
    subpage_lower_left_latitude    = 35.,
    subpage_lower_left_longitude   = -20.,
    subpage_upper_right_latitude   = 70.,
    subpage_upper_right_longitude  = 40.,
    page_id_line                   = "off"
)

# Defining the coastlines
black_background = magics.mcoast(
    map_coastline_sea_shade         = "on",
    map_coastline_land_shade_colour = "black",
    map_grid                        = "on",
    map_grid_line_style             = "dash",
    map_grid_colour                 = "white",
    map_coastline_land_shade        = "on",
    map_coastline_sea_shade_colour  = "rgb(0.15,0.15,0.15)",
    map_label                       = "on",
    map_coastline_colour            = "tan")

# Load the grib data
wind_with_t = magics.mgrib(
    grib_input_file_name = '../../data/tuv.grib', 
    grib_wind_position_1 = 1,
    grib_wind_position_2 = 2,
    grib_wind_position_colour=3)

# Defining Wind plotting
my_wind = magics.mwind(
    wind_field_type                          = "flags",
    wind_arrow_unit_velocity                 = 15,
    legend                                   = "off",
    wind_advanced_method                     = "on",
    wind_advanced_colour_parameter           = "parameter",
    wind_advanced_colour_selection_type      = "list",
    wind_advanced_colour_table_colour_method = "list",
    wind_flag_min_speed                      = 2.0, 
    wind_advanced_colour_list                = ['#ffffff','#ceffff','#91ffff','#00ffff','#ffefd5',
                                                '#ffccab','#ffa981','#ff835a','#ff5832','#ff0000'],
    wind_advanced_colour_level_list          = [253.,258.,263.,268.,273.,278.,283.,288.,293.,298.,303.],
    wind_arrow_thickness                     = 1,
    wind_arrow_legend_text                   = "  m/s"
    )

magics.plot(projection, black_background, wind_with_t, my_wind)

Note that these examples are only skeletons to illustrate the functionality of Magics.


You can download this Jupyter notebook and wind and temperature grib data data from ECMWF github space.












More about projections in projection examples.







More about coastline plotting in  coastline examples.




















Plotting in directly in Jupyter notebooks doesn't need output to be specified. Most of the time you will want an output.

To run this outside of Jupyter notebooks add this piece of code:
png = magics.output(
    output_formats = ['png'],
    output_name_first_page_number = "off",
    output_name = "wind_temperature")

magics.plot(png, projection, black_background, wind_from_nc, my_wind, legend)