def test_visdef_object():
vd = Visdef(
"msymb",
{
"symbol_type": "text",
"symbol_table_mode": "advanced",
"symbol_advanced_table_text_font_colour": "black",
},
)
# clone
v = vd.clone()
assert v.verb == vd.verb
assert v.params == vd.params
assert id(v) != id(vd)
assert id(v.params) != id(vd.params)
# change
vd.change("msymb", "symbol_advanced_table_text_font_colour", "red")
assert vd.params["symbol_advanced_table_text_font_colour"] == "red"
vd.change("mcont", "symbol_advanced_table_text_font_colour", "blue")
assert vd.params["symbol_advanced_table_text_font_colour"] == "red"
# from request
r = mv.msymb({
"symbol_type": "text",
"symbol_table_mode": "advanced",
"symbol_advanced_table_text_font_colour": "black",
})
v = Visdef.from_request(r)
assert v.verb == vd.verb
for k, v in v.params.items():
assert r[k.upper()].lower() == v.lower()
def plot_obs_freq(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[1, 2, 5, 10, 15, 20, 25, 30],
symbol_max_table=[2, 5, 10, 15, 20, 25, 30, 100000],
symbol_colour_table=[
"RGB(0.7020,0.7020,0.7020)",
"RGB(0.4039,0.4039,0.4039)",
"blue",
"RGB(0.4980,1.0000,0.0000)",
"RGB(1.0000,0.8549,0.0000)",
"orange",
"red",
"magenta",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
title = mv.mtext(
text_line_count=4,
text_line_1=
"OBS Frequency", # To sostitute with "FE" values when relevant.
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", "OBS"]]
grouped_df = df.groupby(["LatOBS", "LonOBS"], as_index=False).count()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df["OBS"].to_numpy(dtype=np.float))
with NamedTemporaryFile(delete=False, suffix=".pdf") as pdf:
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
return pdf.name
def plot_std(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[0, 0.0001, 0.5, 1, 2, 5],
symbol_max_table=[0.0001, 0.5, 1, 2, 5, 1000],
symbol_colour_table=[
"RGB(0.7020,0.7020,0.7020)",
"RGB(0.2973,0.2973,0.9498)",
"RGB(0.1521,0.6558,0.5970)",
"RGB(1.0000,0.6902,0.0000)",
"red",
"RGB(1.0000,0.0000,1.0000)",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
error = "FER" if "FER" in predictor_matrix.columns else "FE"
title = mv.mtext(
text_line_count=4,
text_line_1=f"{error} Standard Deviation",
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", error]]
grouped_df = df.groupby(["LatOBS", "LonOBS"])[error].mean().reset_index()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df[error].to_numpy(dtype=np.float))
with NamedTemporaryFile(delete=False, suffix=".pdf") as pdf:
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
return pdf.name
def plot_avg(predictor_matrix, code):
coastline = mv.mcoast(map_coastline_thickness=2,
map_boundaries="on",
map_coastline_colour="chestnut")
symbol = mv.msymb(
legend="on",
symbol_type="marker",
symbol_table_mode="on",
symbol_outline="on",
symbol_min_table=[-1, -0.25, 0.25, 2],
symbol_max_table=[-0.025, 0.25, 2, 1000],
symbol_colour_table=[
"RGB(0.0000,0.5490,0.1882)",
"black",
"RGB(1.0000,0.6902,0.0000)",
"red",
],
symbol_marker_table=15,
symbol_height_table=0.3,
)
legend = mv.mlegend(
legend_text_font="arial",
legend_text_font_size=0.35,
legend_entry_plot_direction="row",
legend_box_blanking="on",
legend_entry_text_width=50,
)
error = "FER" if "FER" in predictor_matrix.columns else "FE"
title = mv.mtext(
text_line_count=4,
text_line_1=f"{error} Mean",
text_line_2=f"WT Code = {code}",
text_line_4=" ",
text_font="arial",
text_font_size=0.4,
)
df = predictor_matrix[["LonOBS", "LatOBS", error]]
grouped_df = df.groupby(["LatOBS", "LonOBS"])[error].mean().reset_index()
geo = mv.create_geo(len(grouped_df), "xyv")
geo = mv.set_latitudes(geo, grouped_df["LatOBS"].to_numpy(dtype=np.float))
geo = mv.set_longitudes(geo, grouped_df["LonOBS"].to_numpy(dtype=np.float))
geo = mv.set_values(geo, grouped_df[error].to_numpy(dtype=np.float))
return plot_geo(geo, coastline, symbol, legend, title)
def test_definitions():
mcont_def = mv.mcont({'legend': True})
msymb_def = mv.msymb({'symbol_type': 'marker'})
mcoast_def = mv.mcoast({'map_coastline_land_shade': True})
mobs_def = mv.mobs({'obs_temperature': False})
mtext_def = mv.mtext({'text_font_size': '0.80'})
geoview_def = mv.geoview({'map_projection': 'polar_stereographic'})
ps_output_def = mv.ps_output({'output_name': 'test'})
assert mcont_def['LEGEND'] == 'ON'
assert msymb_def['SYMBOL_TYPE'] == 'MARKER'
assert mcoast_def['MAP_COASTLINE_LAND_SHADE'] == 'ON'
assert mobs_def['OBS_TEMPERATURE'] == 'OFF'
assert mtext_def['TEXT_FONT_SIZE'] == 0.8
assert geoview_def['MAP_PROJECTION'] == 'POLAR_STEREOGRAPHIC'
assert ps_output_def['OUTPUT_NAME'] == 'test'
obs_plotting = mv.mobs(
obs_temperature=False,
obs_cloud=False,
obs_low_cloud=False,
obs_dewpoint_colour="purple",
)
mv.setoutput(mv.png_output(output_width=1200, output_name="./obsplot2"))
mv.plot(area_view, obs, obs_plotting)
# ALTERNATIVELY, if we don't want to plot the whole observations, but instead want to
# extract a specific parameter from the BUFR messages, then we use the Observation Filter
# as shown here:
# dewpoint_t is a 'geopoints' variable
dewpoint_t = mv.obsfilter(output="geopoints", parameter="012006", data=obs)
# add an optional Symbol Plotting definition to get nice coloured circles
# at each point
symb_visdef = mv.msymb(
legend=True,
symbol_type="marker",
symbol_table_mode="advanced",
symbol_advanced_table_max_level_colour="red",
symbol_advanced_table_min_level_colour="blue",
symbol_advanced_table_colour_direction="clockwise",
)
mv.setoutput(mv.png_output(output_width=1200, output_name="./obsplot3"))
mv.plot(area_view, dewpoint_t, symb_visdef)
# define a view over the area of interest
area_view = mv.geoview(map_area_definition="corners",
area=[45.83, -13.87, 62.03, 8.92])
t2m_grib = mv.read("./t2m_grib.grib")
obs_3day = mv.read("./obs_3day.bufr")
t2m_gpt = mv.obsfilter(parameter="012004", output="geopoints", data=obs_3day)
diff = t2m_grib - t2m_gpt
diff_symb = mv.msymb(
legend=True,
symbol_type="marker",
symbol_table_mode="advanced",
)
mv.setoutput(mv.png_output(output_width=1000, output_name="./obsdiff1"))
mv.plot(area_view, diff, diff_symb)
# Extract geopoints that are hotter by 1 deg or more
# hotter = mv.filter(diff, diff >= 1)
hotter = diff.filter(diff >= 1)
# Extract geopoints that are colder by 1 deg or more
# colder = mv.filter(diff, diff <= -1)
colder = diff.filter(diff <= -1)
# Get geopoints that are within +/-1
# define a view over the area of interest
area_view = mv.geoview(map_area_definition='corners',
area=[45.83, -13.87, 62.03, 8.92])
t2m_grib = mv.read('./t2m_grib.grib')
obs_3day = mv.read('./obs_3day.bufr')
t2m_gpt = mv.obsfilter(parameter='012004', output='geopoints', data=obs_3day)
diff = t2m_grib - t2m_gpt
diff_symb = mv.msymb(
legend=True,
symbol_type='marker',
symbol_table_mode='advanced',
)
mv.setoutput(mv.png_output(output_width=1000, output_name='./obsdiff1'))
mv.plot(area_view, diff, diff_symb)
# Extract geopoints that are hotter by 1 deg or more
#hotter = mv.filter(diff, diff >= 1)
hotter = diff.filter(diff >= 1)
# Extract geopoints that are colder by 1 deg or more
#colder = mv.filter(diff, diff <= -1)
colder = diff.filter(diff <= -1)
# Get geopoints that are within +/-1
mv.plot(area_view, obs)
# ALTERNATIVELY, add an Observations Plotting visual definition
obs_plotting = mv.mobs(obs_temperature=False,
obs_cloud=False,
obs_low_cloud=False,
obs_dewpoint_colour='purple')
mv.setoutput(mv.png_output(output_width=1200, output_name='./obsplot2'))
mv.plot(area_view, obs, obs_plotting)
# ALTERNATIVELY, if we don't want to plot the whole observations, but instead want to
# extract a specific parameter from the BUFR messages, then we use the Observation Filter
# as shown here:
# dewpoint_t is a 'geopoints' variable
dewpoint_t = mv.obsfilter(output="geopoints", parameter='012006', data=obs)
# add an optional Symbol Plotting definition to get nice coloured circles
# at each point
symb_visdef = mv.msymb(legend=True,
symbol_type='marker',
symbol_table_mode='advanced',
symbol_advanced_table_max_level_colour='red',
symbol_advanced_table_min_level_colour='blue',
symbol_advanced_table_colour_direction='clockwise')
mv.setoutput(mv.png_output(output_width=1200, output_name='./obsplot3'))
mv.plot(area_view, dewpoint_t, symb_visdef)