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.
  • Draw the position of New-York City
  • Draw a line to materialise the Xsection we are going to visualise in a next tutorial
  • Add a text.

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, 110oW] and its upper-right corner [70oN, 30oW].

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

Parameters to check

 

 

Useful subpage parameters

subpage_lower_left_longitude

subpage_lower_left_latitude
subpage_upper_right_longitude
subpage_upper_right_latitude
Python - Setting a projection 
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:

  • The land coloured in cream.
  • The coastlines in grey.
  • The grid as a grey dash line.

Parameters to check

 

 

Useful Coastlines parameters

map_coastline_land_shade

map_coastline_land_shade_colour
map_coastline_colour
map_grid_colour
map_grid_line_style
Python - Coastlines 
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 mcont, using black for the lines. We want an interval of 5 hPa between isolines. We also want to add an automatic legend, with our own text "Mean Sea Level Pressure". Follow the link to access the Contouring Documentation.

Parameters to check

 

 

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
Python - Msl Visualisation 
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 a 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 following 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"]

Parameters to check

 

 

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
Python - Use of Shading 
#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)

 

 

Overlaying the 2 layers and playing with legend

Here, we want to demonstrate the plot command.

The actions added to the plot command will be executed sequentially, and their result will be displayed from the background to the foreground .

Then, try to put the shaded layer (precipitation) behind the contoured layer (msl)

and try to improve the legend to make it look continuous, by checking the Legend Documentation.

Parameters to check

 

 

Useful legend parameters

legend

legend_display_type
legend_text_colour
legend_text_font_size
Python - Overlay 
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step5",
                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.)
#definition of shading
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")
#definition of msl
msl = mgrib(grib_input_file_name="msl.grib")
#Definition of the black contouring
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_legend_text= "Mean Sea Level Pressure",
                contour_level_selection_type= "interval",
                contour_line_colour= "black",
                contour_line_thickness= 2,
                legend='on'
            )
#Definition of the legend
legend = mlegend(legend='on', 
            legend_display_type='continuous', 
            legend_text_colour='charcoal', 
            legend_text_font_size=0.4,
            )    
plot(output, area, coast, precip, shading, msl, contour, legend)

 

 

Adding the position of New York City

To demonstrate the use of symbols plotting, we are going to add a big red dot where New York City is.We will the add the text NYC in black on top of the dot.

The position of New York is [41oN, 74oW], we can give this position to Magics using the minput object, documented in Input Data Page.

The symbol is performed using the msymb object. You can find the full options in the Symbol Documentation.

 

Parameters to check

 

 

Useful input parameters

input_x_values

input_y_values
Useful Symbol parameters
symbol_colour
symbol_marker_index
symbol_height

symbol_type

symbol_text_list
symbol_text_font_size
symbol_text_font_colour
symbol_text_font_style
symbol_text_position
Python - Adding New-York City 
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step6",
                output_name_first_page_number = "off"
        )

             
#---------------------------------------
# definition of the previsous layers 
# ...
# ...
#---------------------------------------
#definition of New-York city
new_york = minput(
            input_x_values    =    [-74.],
            input_y_values    =    [41.]
            )
#definition of the symbol
point = msymb(
            symbol_type    =    "both",
            symbol_text_list    =    ["NYC"],
            symbol_marker_index    =    28,
            symbol_colour    =    "red",
            symbol_height    =    0.5,
            symbol_text_font_size = 0.40,
            symbol_text_font_colour = "black",
            symbol_text_position = "top",
            symbol_text_font_style = "bold ", 
            )
plot(output, area, coast, precip, shading, msl, contour, new_york, point, legend)

 

 

Adding a line to show our next Cross-section

In the next exercise, we will display a Cross-section of the Vorticity across the Storm.

We want to show this line of our plot as a black thick line.

The stating point is [50oN, 90oW], the end point is  [30oN, 60oW] : it can be passed to Magics using the minput object, documented in Input Data Page.

To draw the line we will use the mgraph object. All the parameters are available in the Graph Plotting Documentation.

 

Parameters to check

 

 

Useful input parameters

input_x_values

input_y_values
Useful Graph parameters
graph_line
graph_line_colour
Python - Adding a line 
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step7",
                output_name_first_page_number = "off"
        )

             
#---------------------------------------
# definition of the previous layers 
# ...
# ...
#---------------------------------------
#definition of the Xsection Line
xsection = minput(
            input_x_values    =    [-90., -60.],
            input_y_values    =    [50., 30.]
            )
#definition of the graph
line = mgraph( 
            graph_line_colour    =    "black",
            graph_line_thickness    =    4
            )
plot(output, area, coast, precip, shading, msl, contour, new_york, point, xsection, line, legend)

 

 

 

 

Adding a title

We have now a very nice plot... let's add a title at the top.

Text box can be embedded everywhere on a plot, but the default position will be a title at the top of the plot, above an potential legend.

The text facility is documented in the Text Plotting Page.

Parameters to check

 

 

Useful Text parameters

text_lines

text_font_size
text_colour
text_font_style

 

 

Python - Adding a text 
from Magics.macro import *
#setting the output 
output = output(    
                output_formats = ['png'],
                output_name = "map_step8",
                output_name_first_page_number = "off"
        )

             
#---------------------------------------
# definition of the previous layers 
# ...
# ...
#---------------------------------------
#definition of the text
title = mtext(text_lines=['Sandy', '30th of October 2012'],
            text_font_size = 0.8,
            text_colour= "charcoal",
            text_font_style='bold',
        )
plot(output, area, coast, precip, shading, msl, contour, new_york, point, xsection, line, legend, title)

 

 

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