# -*- coding: utf-8 -*-
"""
Formatting Coordinates
===================================

Before proceeding with all the steps, first import some necessary libraries and packages
"""
import easyclimate as ecl
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import cartopy.crs as ccrs

# %%
# The data we use here is monthly data from Jan 2022 to Feb 2022 and contains 17 vertical levels.
#
# .. tip::
#
#   You can download following datasets here: :download:`Download uwnd_202201_mon_mean.nc <https://raw.githubusercontent.com/shenyulu/easyclimate-data/refs/heads/main/uwnd_202201_mon_mean.nc>`
#
u_data = ecl.tutorial.open_tutorial_dataset("uwnd_202201_mon_mean").sortby("lat").uwnd
u_data

# %%
# Formatting of the Latitude and Lontitude Tickes
# ------------------------------------------------------------------------
# `draw_data1` is extracted from time level 0 and 500hPa vertical level.
draw_data1 = u_data.isel(time=0).sel(level=500)
draw_data1

# %%
# Now we call :py:func:`xarray.plot.pcolormesh <xarray.plot.pcolormesh>` to plot the latitudinal wind field on the 500hPa isobaric surface.
# Noting that the x-axis and y-axis are not in standard geographic coordinate format, our next step is to format these coordinates.
fig, ax = plt.subplots(1, 1)

draw_data1.plot.pcolormesh(
    ax=ax,
)

# %%
# :py:func:`easyclimate.plot.set_lon_format_axis <easyclimate.plot.set_lon_format_axis>`,
# :py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` can help us quickly
# format the coordinates on the x-axis and y-axis, respectively, into a geographic coordinate format.
fig, ax = plt.subplots(1, 1)

draw_data1.plot.pcolormesh(
    ax=ax,
)

ecl.plot.set_lon_format_axis(ax, axis="x")
ecl.plot.set_lat_format_axis(ax, axis="y")

# %%
# It is worth mentioning that the :py:func:`easyclimate.plot.set_lon_format_axis <easyclimate.plot.set_lon_format_axis>`,
# :py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` methods contain a parameter `dmi`
# which helps us to convert DD (Decimal Degrees) format to DMS (Degrees Minutes Seconds) format.
#
# Now let's start by selecting a smaller area
draw_data1_1 = (
    u_data.isel(time=0).sel(level=500).sel(lon=slice(100, 110), lat=slice(20, 23))
)
draw_data1_1

# %%
# Note the difference in geo-labeling on the x-axis.
fig, ax = plt.subplots(1, 2, figsize=(15, 5))

for axi in ax.flat:
    draw_data1_1.plot(ax=axi, cmap="Reds")
    axi.xaxis.set_major_locator(ticker.LinearLocator(5))

ecl.plot.set_lon_format_axis(ax[0], axis="x")
ecl.plot.set_lat_format_axis(ax[0], axis="y")
ax[0].set_title("dms = False")

ecl.plot.set_lon_format_axis(ax[1], axis="x", dms=True)
ecl.plot.set_lat_format_axis(ax[1], axis="y", dms=True)
ax[1].set_title("dms = True")

# %%
# Barometric Profile Label Formatting
# ------------------------------------------------------------------------
# Here we choose the longitudinally averaged latitudinal direction at time level 0 to plot the profile
draw_data2 = u_data.isel(time=0).mean(dim="lon")

# %%
# Notice that the x-axis and y-axis labels are unformatted, so we'll take care of that next.
fig, ax = plt.subplots(1, 1)

draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)

# %%
# :py:func:`easyclimate.plot.set_p_format_axis <easyclimate.plot.set_p_format_axis>` can help us format barometric vertical labels
# and similarly :py:func:`easyclimate.plot.set_lat_format_axis <easyclimate.plot.set_lat_format_axis>` can help us format latitude labels.
fig, ax = plt.subplots(1, 1)

draw_data2.plot.contourf(ax=ax, levels=21, yincrease=False)

ecl.plot.set_lat_format_axis(ax, axis="x")
ecl.plot.set_p_format_axis(ax, axis="y")

# %%
# Polar Stereo of the Circle Boundary
# ------------------------------------------------------------------------
# For the sake of illustration, we use here the sea ice concentration (SIC) data from the Barents-Kara Seas (30°−90°E, 65°−85°N).
# The results of the data under the 10th time level are described below.
sic_data = ecl.tutorial.open_tutorial_dataset("mini_HadISST_ice").sic.isel(time=10)
sic_data.plot.contourf(cmap="Blues", levels=11)

# %%
# :py:func:`easyclimate.plot.draw_Circlemap_PolarStereo <easyclimate.plot.draw_Circlemap_PolarStereo>` helps us to easily
# establish the boundary of the circle under the projection of the polar stereo.
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.NorthPolarStereo()})

ax.coastlines(edgecolor="black", linewidths=0.5)
ax.stock_img()

gl, meta = ecl.plot.draw_polar_basemap(
    ax=ax,
    lon_step=30,
    lat_step=10,
    lat_range=[50, 90],
    draw_labels=True,
    lat_label_lon=180
)

sic_data.plot.contourf(cmap="Blues", levels=11, transform=ccrs.PlateCarree())

ax.set_title("")
ecl.plot.set_polar_title("Mytest 1", meta, ax, size = 15)

# %%
# Adjusting `north_pad` and `south_pad` appropriately can help us compensate for not completing the circle boundaries.

# sphinx_gallery_thumbnail_number = -1
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.NorthPolarStereo()})

ax.coastlines(edgecolor="black", linewidths=0.5)
ax.stock_img()

gl, meta = ecl.plot.draw_polar_basemap(
    ax=ax,
    lon_step=30,
    lat_step=10,
    lat_range=[50, 90],
    draw_labels=True,
    lat_label_lon=180,
    set_map_boundary_kwargs = {"south_pad": 0.9}
)

sic_data.plot(cmap="Blues", levels=11, transform=ccrs.PlateCarree())

ax.set_title("")
ecl.plot.set_polar_title("Mytest 2", meta, ax, size = 15)