Objectives

  • Set-up a cylindrical projection over the United States.
  • Apply land-shading on the coastlines.
  • Load a grib file containing Mean-Sea level Presssure and visualise it using black isolines.
  • Load a grib file containing Precipitation  and visualise it using shading technique.
  • Add a legend.
  • Add a text.
  • Draw the position of New-York City
  • Draw a line to materialise the Xsection we are going to visualise in a next tutorial

You will need to download

 

 

Setting of the geographical area

The Geographical area we want to work with today is defined by its lower-left corner [20oN, 110oE] and its upper-right corner [70oN, 30oE].

Have a look at the  subpage documentation to learn how to setup a projection .

subpage_lower_left_longitude

subpage_lower_left_latitude
subpage_upper_right_longitude
subpage_upper_right_latitude
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step1",
                output_name_first_page_number = "off"
        )
#settings of the geographical area 
area = mmap(subpage_map_projection="cylindrical",
        subpage_lower_left_longitude=-110.,
        subpage_lower_left_latitude=20.,
        subpage_upper_right_longitude=-30.,
        subpage_upper_right_latitude=70.,
    )    
#Using a default coastlines to see the result
plot(output, area, mcoast())

Setting the coastlines

Until now you have used the mcoast to add coastlines to your plot. The mcoast object comes with a lot of parameters to allow you to style your coastlines layer.

The full list of parameters can be found in the Coastlines documentation.

In this first exercise, we will like to see:

map_coastline_land_shade

map_coastline_land_shade_colour
map_coastline_colour
map_grid_colour
map_grid_line_style
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step2",
                output_name_first_page_number = "off"
        )
#settings of the geographical area 
area = mmap(subpage_map_projection="cylindrical",
        subpage_lower_left_longitude=-110.,
        subpage_lower_left_latitude=20.,
        subpage_upper_right_longitude=-30.,
        subpage_upper_right_latitude=70.,
    )     
 #settings of the caostlines
coast = mcoast(map_coastline_land_shade = "on",
      map_coastline_land_shade_colour = "cream",
      map_grid_line_style = "dash",
      map_grid_colour = "grey",
      map_label = "on",
      map_coastline_colour = "grey")
plot(output, area, coast)

 

 

 

Visualising the Mean Sea Level Pressure field

The visualisation of any data in Magics is done by combining 2 kind of objects. One, the Data Action,  is used to define the data and explain to Magics how to interpret it, the other one is called Visual Action and will define the type of visualisation and its attributes.

In this example our data are in a grib file msl.grib. The Data Action to be used is mgrib in is documented in Grib Input Documentation.

The Visualisation we want to apply is a basic contouring, using black for the lines and an interval of 5 hPa, between isolines. We also want to add a automatic legend, with our own text "Mean Sea Level Pressure". Follow the link to access the Contouring Documentation.

mgrib action to load the data

grib_input_file_name
mcont action to define a contouring
contour_line_colour
contour_line_thickness
contour_highlight_colour
contour_highlight_thickness
contour_hilo
contour_level_selection_type
contour_interval
legend
contour_legend_text
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step3",
                output_name_first_page_number = "off"
        )
#settings of the geographical area 
area = mmap(subpage_map_projection="cylindrical",
        subpage_lower_left_longitude=-110.,
        subpage_lower_left_latitude=20.,
        subpage_upper_right_longitude=-30.,
        subpage_upper_right_latitude=70.,
    )     
 #settings of the caostlines
coast = mcoast(map_coastline_land_shade = "on",
      map_coastline_land_shade_colour = "cream",
      map_grid_line_style = "dash",
      map_grid_colour = "grey",
      map_label = "on",
      map_coastline_colour = "grey")
#Loading the msl Grib data
msl = mgrib(grib_input_file_name="msl.grib")
#Defining the controur
contour = mcont(contour_highlight_colour= "black",
                contour_highlight_thickness= 4,
                contour_hilo= "off",
                contour_interval= 5.,
                contour_label= "on",
                contour_label_frequency= 2,
                contour_label_height= 0.4,
                contour_level_selection_type= "interval",
                contour_line_colour= "black",
                contour_line_thickness= 2, 
                legend='on',
                contour_legend_text= "Mean Sea Level Pressure",
            )
plot(output, area, coast, msl, contour)

 

 

 

Visualising the precipitation field

The goal of this exercise is to discover a bit more the diverse styles of visualisation offered by the mcont object.

We are pre-processed grib field containing the precipitation accumulated in the last 6 hours of the valid time. We want to disable the automatic scaling appled by Magics and use our own scaling factor, in this case 1000.

Here we will work with shading, and we will use a different technique to setup the levels we want to contour.

We want to use the follwing list of levels for contouring [0.5, 2., 4., 10., 25., 50., 100., 250.]

and the following list of colours ["cyan", "greenish_blue", "blue", "bluish_purple", "magenta", "orange", "red", "charcoal"]

mgrib action to load the data

grib_input_file_name
grib_automatic_scaling
grib_scaling_factor
mcont action to define a contouring
contour_level_selection_type
contour_level_list
contour_shade
contour_shade_method
contour_shade_colour_method
contour_level_selection_type
contour_shade_colour_list
legend

 

 

#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step4",
                output_name_first_page_number = "off"
        )
#settings of the geographical area 
area = mmap(subpage_map_projection="cylindrical",
        subpage_lower_left_longitude=-110.,
        subpage_lower_left_latitude=20.,
        subpage_upper_right_longitude=-30.,
        subpage_upper_right_latitude=70.,
    )     
 #settings of the caostlines
coast = mcoast(map_coastline_land_shade = "on",
      map_coastline_land_shade_colour = "cream",
      map_grid_line_style = "dash",
      map_grid_colour = "grey",
      map_label = "on",
      map_coastline_colour = "grey")
#definition of the input data
precip = mgrib(grib_input_file_name="precip.grib", 
    grib_automatic_scaling='off', 
    grib_scaling_factor=1000.)
shading = mcont( contour_highlight= "off",
    contour_hilo= "off",
    contour_label="off",
    contour_level_list=[0.5, 2., 4., 10., 25., 50., 100., 250.],
    contour_level_selection_type= "level_list",
    contour_shade= "on",
    contour_shade_method= "area_fill",
    contour_shade_colour_method= "list",
    contour_shade_colour_list= ["cyan", "greenish_blue", "blue", "bluish_purple", "magenta", "orange", "red", "charcoal"],
    legend="on")
    
plot(output, area, coast, precip, shading)