# Visualization Gallery

This notebook shows common visualization issues encountered in xarray.

```ipython3
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import xarray as xr

%matplotlib inline
```

Load example dataset:

```ipython3
ds = xr.tutorial.load_dataset("air_temperature")
```

## Multiple plots and map projections

Control the map projection parameters on multiple axes

This example illustrates how to plot multiple maps and control their extent
and aspect ratio.

For more details see [this discussion](https://github.com/pydata/xarray/issues/1397#issuecomment-299190567) on github.

```ipython3
air = ds.air.isel(time=[0, 724]) - 273.15

# This is the map projection we want to plot *onto*
map_proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=45)

p = air.plot(
    transform=ccrs.PlateCarree(),  # the data's projection
    col="time",
    col_wrap=1,  # multiplot settings
    aspect=ds.sizes["lon"] / ds.sizes["lat"],  # for a sensible figsize
    subplot_kws={"projection": map_proj},
)  # the plot's projection

# We have to set the map's options on all axes
for ax in p.axs.flat:
    ax.coastlines()
    ax.set_extent([-160, -30, 5, 75])
```

## Centered colormaps

Xarray’s automatic colormaps choice

```ipython3
air = ds.air.isel(time=0)

f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))

# The first plot (in kelvins) chooses "viridis" and uses the data's min/max
air.plot(ax=ax1, cbar_kwargs={"label": "K"})
ax1.set_title("Kelvins: default")
ax2.set_xlabel("")

# The second plot (in celsius) now chooses "BuRd" and centers min/max around 0
airc = air - 273.15
airc.plot(ax=ax2, cbar_kwargs={"label": "°C"})
ax2.set_title("Celsius: default")
ax2.set_xlabel("")
ax2.set_ylabel("")

# The center doesn't have to be 0
air.plot(ax=ax3, center=273.15, cbar_kwargs={"label": "K"})
ax3.set_title("Kelvins: center=273.15")

# Or it can be ignored
airc.plot(ax=ax4, center=False, cbar_kwargs={"label": "°C"})
ax4.set_title("Celsius: center=False")
ax4.set_ylabel("")

# Make it nice
plt.tight_layout()
```

## Control the plot’s colorbar

Use `cbar_kwargs` keyword to specify the number of ticks.
The `spacing` kwarg can be used to draw proportional ticks.

```ipython3
air2d = ds.air.isel(time=500)

# Prepare the figure
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))

# Irregular levels to illustrate the use of a proportional colorbar
levels = [245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 310, 340]

# Plot data
air2d.plot(ax=ax1, levels=levels)
air2d.plot(ax=ax2, levels=levels, cbar_kwargs={"ticks": levels})
air2d.plot(
    ax=ax3, levels=levels, cbar_kwargs={"ticks": levels, "spacing": "proportional"}
)

# Show plots
plt.tight_layout()
```

## Multiple lines from a 2d DataArray

Use `xarray.plot.line` on a 2d DataArray to plot selections as
multiple lines.

See `plotting.multiplelines` for more details.

```ipython3
air = ds.air - 273.15  # to celsius

# Prepare the figure
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharey=True)

# Selected latitude indices
isel_lats = [10, 15, 20]

# Temperature vs longitude plot - illustrates the "hue" kwarg
air.isel(time=0, lat=isel_lats).plot.line(ax=ax1, hue="lat")
ax1.set_ylabel("°C")

# Temperature vs time plot - illustrates the "x" and "add_legend" kwargs
air.isel(lon=30, lat=isel_lats).plot.line(ax=ax2, x="time", add_legend=False)
ax2.set_ylabel("")

# Show
plt.tight_layout()
```