def __init__(self, fields: Volume_T, max_dist: Number_T = 0.1):
# Process data type
self.dtype = get_dtype(fields[0])
# Process time
t_arr = np.array(
[time.mktime(i.scan_time.timetuple()) for i in fields])
if (t_arr.max() - t_arr.min()) / 60 > 10:
raise RadarCalculationError(
"Time difference of input data should not exceed 10 minutes")
mean_time = t_arr.mean()
mean_dtime = datetime.datetime(*time.localtime(int(mean_time))[:6])
time_increment = 10
time_rest = mean_dtime.minute % time_increment
if time_rest > time_increment / 2:
mean_dtime += datetime.timedelta(minutes=(time_increment -
time_rest))
else:
mean_dtime -= datetime.timedelta(minutes=time_rest)
self.scan_time = mean_dtime
self.lon_ravel = np.hstack(
[i["longitude"].values.ravel() for i in fields])
self.lat_ravel = np.hstack(
[i["latitude"].values.ravel() for i in fields])
self.data_ravel = np.ma.hstack(
[i[self.dtype].values.ravel() for i in fields])
self.dist_ravel = np.hstack([
np.broadcast_to(i["distance"], i["longitude"].shape).ravel()
for i in fields
])
self.tree = KDTree(np.dstack((self.lon_ravel, self.lat_ravel))[0])
self.md = max_dist
self.attr = fields[0].attrs.copy()
def __init__(self, r_list: Volume_T):
el = [i.elevation for i in r_list]
if len(el) != len(set(el)):
self.rl = list()
el_list = list()
for data in r_list:
if data.elevation not in el_list:
self.rl.append(data)
el_list.append(data.elevation)
else:
self.rl = r_list
self.dtype = get_dtype(r_list[0])
self.x, self.y, self.h, self.r = self._geocoor()
self.attrs = r_list[0].attrs
def __init__(self,
data: Dataset,
fig: Optional[Any] = None,
norm: Optional[Any] = None,
cmap: Optional[Any] = None,
nlabel: Optional[int] = None,
label: Optional[List[str]] = None,
dpi: Number_T = 350,
highlight: Optional[Union[str, List[str]]] = None,
coastline: bool = False,
extent: Optional[List[Number_T]] = None,
section: Optional[Dataset] = None,
style: str = "black",
add_city_names: bool = False,
plot_labels: bool = True,
**kwargs):
self.data = data
self.dtype = get_dtype(data)
self.settings = {
"cmap": cmap,
"norm": norm,
"nlabel": nlabel,
"label": label,
"highlight": highlight,
"coastline": coastline,
"path_customize": False,
"extent": extent,
"slice": section,
"style": style,
"add_city_names": add_city_names,
"plot_labels": plot_labels,
"is_inline": is_inline(),
}
if fig is None:
self.fig = setup_plot(dpi, style=style)
else:
self.fig = fig
# avoid in-place modification
self.text_pos = TEXT_AXES_POS.copy()
self.cbar_pos = CBAR_POS.copy()
self._plot_ctx = dict()
self.rf_flag = "RF" in data
self._fig_init = False
self._plot(**kwargs)
if is_inline():
# In inline mode, figure will not be dynamically changed
# call this action at initialization
self._text_before_save()
def __init__(
self,
data: Dataset,
hlim: int = 15,
interpolate: bool = True,
figsize: tuple = (10, 5),
):
# TODO: Use context manager to control style
plt.style.use("dark_background")
self.data = data
self.dtype = get_dtype(data)
self.settings = {
"hlim": hlim,
"interp": interpolate,
"figsize": figsize,
}
self.rhi_flag = "azimuth" in data.attrs
self._plot()
def plot_cross_section(
self,
data: Dataset,
ymax: Optional[int] = None,
linecolor: Optional[str] = None,
interpolate: bool = True,
):
# May add check to ensure the data is slice data
r"""Plot cross section data below the PPI plot."""
if self.settings["is_inline"] and self._fig_init:
raise RuntimeError(
"Adding cross section dynamically is not supported in"
"inline backend, add section keyword when initializing PPI instead."
)
if not linecolor:
if self.settings["style"] == "black":
linecolor = "white"
elif self.settings["style"] == "white":
linecolor = "black"
self.settings["slice"] = data
# The axes to plot c-section is below the main axes
# the height of it is a quarter of the height of main axes
# so the positions of the figure, the main axes, the colorbar axes
# should be adjusted accordingly.
# TODO: remove hardcode and calculate positions automatically
self.fig.set_size_inches(10, 10)
self.geoax.set_position([0, 0.2, 0.8, 0.8])
ax2 = self.fig.add_axes([0, 0.01, 0.8, 0.17])
# transform coordinates
self.text_pos[1] = self.text_pos[1] * 0.8 + 0.2
self.text_pos[3] = self.text_pos[3] * 0.8
self.cbar_pos[1] = self.cbar_pos[1] * 0.8 + 0.2
self.cbar_pos[3] = self.cbar_pos[3] * 0.8
ax2.yaxis.set_ticks_position("right")
ax2.set_xticks([])
dtype = get_dtype(data)
sl = data[dtype].values
if dtype == "REF":
# visualization improvement for reflectivity
sl[np.isnan(sl)] = -0.1
xcor = data["x_cor"]
ycor = data["y_cor"]
cmap = sec_plot[dtype]
norm = norm_plot[dtype]
if interpolate:
ax2.contourf(xcor, ycor, sl, 256, cmap=cmap, norm=norm)
else:
ax2.pcolormesh(xcor, ycor, sl, cmap=cmap, norm=norm)
if ymax:
ax2.set_ylim(0, ymax)
else:
ax2.set_ylim(0, 15)
ax2.set_title("Start: {}N {}E".format(data.start_lat, data.start_lon) +
" End: {}N {}E".format(data.end_lat, data.end_lon))
self.geoax.plot(
[data.start_lon, data.end_lon],
[data.start_lat, data.end_lat],
marker="x",
color=linecolor,
transform=self.data_crs,
zorder=5,
)