How to use metview - 10 common examples
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 
def plot_geo(geo, coastline, symbol, legend, title):
with NamedTemporaryFile(suffix=".png") as png, NamedTemporaryFile(
delete=False, suffix=".pdf"
) as pdf:
png_obj = mv.png_output(output_name=png.name.replace(".png", ""))
mv.setoutput(png_obj)
mv.plot(coastline, symbol, legend, title, geo)
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
os.chmod(pdf.name, 0o444)
with open(png.name.replace(".png", ".1.png"), "rb") as img:
return {"preview": b64encode(img.read()).decode(), "pdf": pdf.name}def plot_geo(geo, coastline, symbol, legend, title):
with NamedTemporaryFile(suffix=".png") as png, NamedTemporaryFile(
delete=False, suffix=".pdf"
) as pdf:
png_obj = mv.png_output(output_name=png.name.replace(".png", ""))
mv.setoutput(png_obj)
mv.plot(coastline, symbol, legend, title, geo)
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
os.chmod(pdf.name, 0o444)
with open(png.name.replace(".png", ".1.png"), "rb") as img:
return {"preview": b64encode(img.read()).decode(), "pdf": pdf.name}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))
return plot_geo(geo, coastline, symbol, legend, title)"""
classmethod to compute the vector of sequence of Fieldset instances.
:param args: (Tuple[Fieldset]) Sequence of Fieldset instances.
:return: New `Fieldset` instance containing the vector value
:rtype: Fieldset
"""
if len(args) == 0:
raise Exception
term_1 = args[0]
sum_squared_values = sum(abs(term.values) ** 2 for term in args)
values = np.sqrt(sum_squared_values)
mv_fieldset = metview.set_values(term_1, values)
mv_fieldset.__class__ = cls
return mv_fieldsettitle = 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))
return plot_geo(geo, coastline, symbol, legend, title)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 min_of(cls, *args):
if len(args) == 0:
raise Exception
term_1 = args[0]
values = reduce(np.minimum, (arg.values for arg in args))
mv_fieldset = metview.set_values(term_1, values)
mv_fieldset.__class__ = cls
return mv_fieldsetdef max_of(cls, *args):
if len(args) == 0:
raise Exception
term_1 = args[0]
values = reduce(np.maximum, (arg.values for arg in args))
mv_fieldset = metview.set_values(term_1, values)
mv_fieldset.__class__ = cls
return mv_fieldsetdef plot_geo(geo, coastline, symbol, legend, title):
with NamedTemporaryFile(suffix=".png") as png, NamedTemporaryFile(
delete=False, suffix=".pdf"
) as pdf:
png_obj = mv.png_output(output_name=png.name.replace(".png", ""))
mv.setoutput(png_obj)
mv.plot(coastline, symbol, legend, title, geo)
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
os.chmod(pdf.name, 0o444)
with open(png.name.replace(".png", ".1.png"), "rb") as img:
return {"preview": b64encode(img.read()).decode(), "pdf": pdf.name}def plot_geo(geo, coastline, symbol, legend, title):
with NamedTemporaryFile(suffix=".png") as png, NamedTemporaryFile(
delete=False, suffix=".pdf"
) as pdf:
png_obj = mv.png_output(output_name=png.name.replace(".png", ""))
mv.setoutput(png_obj)
mv.plot(coastline, symbol, legend, title, geo)
pdf_obj = mv.pdf_output(output_name=pdf.name.replace(".pdf", ""))
mv.setoutput(pdf_obj)
mv.plot(coastline, symbol, legend, title, geo)
os.chmod(pdf.name, 0o444)
with open(png.name.replace(".png", ".1.png"), "rb") as img:
return {"preview": b64encode(img.read()).decode(), "pdf": pdf.name}
