How to use the metview.mtext function in metview
To help you get started, we’ve selected a few metview examples, based on popular ways it is used in public projects.
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esowc / ecPoint-Calibrate / core / postprocessors / conditional_verification.py View on Github 
],
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))"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))],
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))
