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#Metview# Metview MacroExample

#  **************************** LICENSE START ***********************************
# 
#  Copyright 2019 ECMWF. This software is distributed under the terms
#  of the Apache License version 2.0. In applying this license, ECMWF does not
#  waive the privileges and immunities granted to it by virtue of its status as
#  an Intergovernmental Organization or submit itself to any jurisdiction.
# 
#  ***************************** LICENSE END ************************************
# 

import metview as mv

# read CSV file with the track positions and dates
tblfilename = read_table(
	table_delimiter	:	" ",
	table_combine_delimiters	:	"sandy_track.txt"
if not mv.exist(filename):
    mv.gallery.load_dataset(filename)

tbl = mv.read_table(
    table_delimiter=" ",
    table_combine_delimiters="on",
	    table_header_row	:	=0,
	    table_filename	:	="sandy_track.txt",
	)
	
# read track details into a set of vectors
val_date = mv.values(tbl,1 0)
val_time = mv.values(tbl,2 1)
val_lon  = mv.values(tbl,3 2)
val_lat  = mv.values(tbl,4 3)

# defineto dateplot and timetext labels forat track points
val_label=nil
for i=1 to count(val_date) do
	val_label = val_label & ["   " & substring(string(val_date[i]),7,8) & "/" & val_time[i] ]
end for

# define line and symbol properties
track_graph = mgraph(
	graph_line_colour	:	"red",
	graph_line_thickness	:	4,
	graph_symbol	:	"on",
	graph_symbol_colour	:	"white",
	graph_symbol_height	:	0.5,
	graph_symbol_marker_index	:	15,
	graph_symbol_outline	:	"on",
	graph_symbol_outline_colour	:	"red"
	)

# define label properties
track_text = msymb(
	symbol_type	:	"text",
	symbol_text_font_colour : "black",
	symbol_text_font_size: "0.3",
	symbol_text_font_style: "bold",
	symbol_text_list	:	val_label	
	)

# create a visualiser for the track
track_vis = input_visualiser(
	input_plot_type	:	"geo_points",
	input_longitude_values	:	tolist(val_lon),
	input_latitude_values	:	tolist(val_lat)
	)

# read mslp forecast from grib file
g_mslp=read(source: "sandy_mslp.grib")
	
# define mslp contouring		
cont_mslp = mcont(
	contour_line_thickness	:	2,
	contour_line_colour	:	"black",
	contour_highlight	:	"off",
	contour_level_selection_type	:	"interval",
	contour_interval	:	5,
	grib_scaling_of_derived_fields	:	"on"
	)

# define coastline
coast = mcoast(
    map_coastline_colour            : "RGB(0.4449,0.4414,0.4414)",
    map_coastline_resolution        : "low",
    map_coastline_land_shade        : "on",
    map_coastline_land_shade_colour : "RGB(0.5333,0.5333,0.5333)",
    map_coastline_sea_shade         : "on",
    map_coastline_sea_shade_colour  : "RGB(0.7765,0.8177,0.8941)",
    map_boundaries                  : "on",
    map_boundaries_colour           : "mustard",
    map_boundaries_thickness        : 2,
    map_grid_colour                 : "RGB(0.2627,0.2627,0.2627)"
    )

# define geographical view
view = geoview(
    map_projection         : "polar_stereographic",
    map_area_definition    : "corners",
    area                   : [19.72,-98.59,42.61,-47.28],
    map_vertical_longitude : -85,
    coastlines	:	coast
    )

# define the output plot file
setoutput(pdf_output(output_name : 'storm_track')) 

#Ploteach point, we will need to use the 'text' mode
# of msymb(). This requires associating each point with its text label, so we will
# generate values of 0,1,2,3,...,N-1 for the points and create an msymb() that
# maps each value to a generated date/time label.
val_idx = list(range(len(val_lat)+1)) # indexes: 0->N

# define date and time labels for track points
val_label = []
for i in range(len(val_date)):
    val_label.append(
        "   " + str(val_date[i])[6:8] + "/" + "{:02d}".format(int(val_time[i]))
    )

# define line and symbol properties
track_graph = mv.mgraph(
    graph_line_colour="red",
    graph_line_thickness=4,
    graph_symbol="on",
    graph_symbol_colour="white",
    graph_symbol_height=0.5,
    graph_symbol_marker_index=15,
    graph_symbol_outline="on",
    graph_symbol_outline_colour="red",
)

# define label properties
track_text = mv.msymb(
    legend="off",
    symbol_type="text",
    symbol_table_mode="advanced",
    symbol_advanced_table_selection_type="list",
    symbol_advanced_table_level_list=val_idx,
    symbol_advanced_table_text_list=val_label,
    symbol_advanced_table_text_font_size=0.5,
    symbol_advanced_table_text_font_style="bold",
    symbol_advanced_table_text_font_colour="black",
    symbol_advanced_table_text_display_type="right",
    )

# create a visualiser for the track
track_vis = mv.input_visualiser(
    input_plot_type="geo_points",
    input_longitude_values=val_lon,
    input_latitude_values=val_lat,
    input_values=val_idx
)

# read mslp forecast from grib file
filename = "sandy_mslp.grib"
if mv.exist(filename):
    g_mslp = mv.read(filename)
else:
    g_mslp = mv.gallery.load_dataset(filename)

# define mslp contouring
cont_mslp = mv.mcont(
    contour_line_thickness=2,
    contour_line_colour="black",
    contour_highlight="off",
    contour_level_selection_type="interval",
    contour_interval=5,
    grib_scaling_of_derived_fields="on",
)

# define coastline
coast = mv.mcoast(
    map_coastline_colour="RGB(0.4449,0.4414,0.4414)",
    map_coastline_resolution="low",
    map_coastline_land_shade="on",
    map_coastline_land_shade_colour="RGB(0.5333,0.5333,0.5333)",
    map_coastline_sea_shade="on",
    map_coastline_sea_shade_colour="RGB(0.7765,0.8177,0.8941)",
    map_boundaries="on",
    map_boundaries_colour="mustard",
    map_boundaries_thickness=2,
    map_grid_colour="RGB(0.2627,0.2627,0.2627)",
)

# define geographical view
view = mv.geoview(
    map_projection="polar_stereographic",
    map_area_definition="corners",
    area=[19.72, -98.59, 42.61, -47.28],
    map_vertical_longitude=-85,
    coastlines=coast,
)

# define the output plot file
mv.setoutput(mv.pdf_output(output_name="storm_track"))

# Plot the track and the mslp 
mv.plot(view, trackg_vismslp, trackcont_graphmslp, track_textvis, gtrack_mslptext, conttrack_mslpgraph)

#Metview Macro

#  **************************** LICENSE START ***********************************
# 
#  Copyright 2019 ECMWF. This software is distributed under the terms
#  of the Apache License version 2.0. In applying this license, ECMWF does not
#  waive the privileges and immunities granted to it by virtue of its status as
#  an Intergovernmental Organization or submit itself to any jurisdiction.
# 
#  ***************************** LICENSE END *************************************
# 

import metview as mv

# read CSV file with the track positions and dates
tbl = mv.read_table(
	table_delimiter	=	" ",
	table_combine_delimiters	=	"on",
	table_header_row	=	0,
	table_filename	=	"sandy_track.txt"
	)
	
# read track details into a set of vectors
val_date = mv.values(tbl, 0)
val_time = mv.values(tbl, 1)
val_lon  = mv.values(tbl, 2)
val_lat  = mv.values(tbl, 3)

# define date and time labels for track points
val_label = []
for i in range(len(val_date)):
	val_label.append("   " + str(val_date[i])[6:8] + "/"  + "{:02d}".format(int(val_time[i])))
	
# define line and symbol properties
track_graph = mv.mgraph(
	graph_line_colour	     =	"red",
	graph_line_thickness	 =	4,
	graph_symbol	         =	"on",
	graph_symbol_colour	     =	"white",
	graph_symbol_height	     =	0.5,
	graph_symbol_marker_index	=	15,
	graph_symbol_outline	    =	"on",
	graph_symbol_outline_colour	=	"red"
	)

# define label properties
track_text = mv.msymb(
	symbol_type	            = "text",
	symbol_text_font_colour = "black",
	symbol_text_font_size   = "0.3",
	symbol_text_font_style  = "bold",
	symbol_text_list	    = val_label	
	)
# 

# read CSV file with the track positions and dates
tbl = read_table(
    table_delimiter : " ",
    table_combine_delimiters : "on",
    table_header_row : 0,
    table_filename : "sandy_track.txt"
 )
 
# read track details into a set of vectors
val_date = values(tbl,1)
val_time = values(tbl,2)
val_lon  = values(tbl,3)
val_lat  = values(tbl,4)

# define date and time labels for track points
val_label=nil
for i=1 to count(val_date) do
    val_label = val_label & ["   " & substring(string(val_date[i]),7,8) & "/" & val_time[i] ]
end for


# to plot text labels at each point, we will need to use the 'text' mode
# of msymb(). This requires associating each point with its text label, so we will
# generate values of 0,1,2,3,...,N-1 for the points and create an msymb() that
# maps each value to a generated date/time label.
val_idx = []
num_vals = count(val_lat)
for i = 0 to num_vals do
    val_idx = val_idx & [i]
end for


# define line and symbol properties
track_graph = mgraph(
    graph_line_colour : "red",
    graph_line_thickness : 4,
    graph_symbol : "on",
    graph_symbol_colour : "white",
    graph_symbol_height : 0.5,
    graph_symbol_marker_index : 15,
    graph_symbol_outline : "on",
    graph_symbol_outline_colour : "red"
 )

# define label properties
track_text = msymb(
    legend : "off",
    symbol_type : "text",
    symbol_table_mode : "advanced",
    symbol_advanced_table_selection_type : "list",
    symbol_advanced_table_level_list : val_idx,
    symbol_advanced_table_text_list : val_label,
    symbol_advanced_table_text_font_size : 0.5,
    symbol_advanced_table_text_font_style : "bold",
    symbol_advanced_table_text_font_colour : "black",
    symbol_advanced_table_text_display_type : "right",
    )


# create a visualiser for the track
track_vis = mv.input_visualiser(
	    input_plot_type	        =	 : "geo_points",
	    input_longitude_values	=	list : tolist(val_lon),
	    input_latitude_values	=	list : tolist(val_lat)
	 )

# read mslp forecast from grib file
g_mslp = mv.read(source: "sandy_mslp.grib")
	 
# define mslp contouring		  
cont_mslp = mv.mcont(
	contour_line_thickness	    =	2,
	contour_line_colour	thickness : 2,
    contour_line_colour : =	"black",
	contour_highlight	    contour_highlight  :  =	"off",
	    contour_level_selection_type : =	"interval",
	    contour_interval	 : 5,
       =	5,
	grib_scaling_of_derived_fields	=	 : "on"
	 )

# define coastline
coast = mv.mcoast(
    map_coastline_colour            =: "RGB(0.4449,0.4414,0.4414)",
    map_coastline_resolution        =: "low",
    map_coastline_land_shade        =: "on",
    map_coastline_land_shade_colour =: "RGB(0.5333,0.5333,0.5333)",
    map_coastline_sea_shade         =: "on",
    map_coastline_sea_shade_colour  =: "RGB(0.7765,0.8177,0.8941)",
    map_boundaries                  =: "on",
    map_boundaries_colour           =: "mustard",
    map_boundaries_thickness        =: 2,
    map_grid_colour                 =: "RGB(0.2627,0.2627,0.2627)"
    )

# define geographical view
view = mv.geoview(
    map_projection         =: "polar_stereographic",
    map_area_definition    =: "corners",
    area                   =: [19.72,-98.59,42.61,-47.28],
    map_vertical_longitude =: -85,
    coastlines	           = : coast
    )

# define the output plot file
mv.setoutput(mv.pdf_output(output_name =: 'storm_track')) 

#Plot the track and the mslp 
mv.plot(view, track_vis, track_graph, track_text, g_mslp, cont_mslp)