Contents:
.. currentmodule:: eomaps.eomaps
To visualize a dataset, first assign the dataset to the :py:class:`Maps` object, then select how you want to visualize the data and finally call :py:meth:`Maps.plot_map`.
- :ref:`Assign the data <assign_the_data>` to a :py:class:`Maps` object via :py:meth:`Maps.set_data`
- (optional) :ref:`Set the shape <set_the_shape>` used to represent the data via :py:class:`Maps.set_shape`
- (optional) :ref:`Classify the data <classify_the_data>` via :py:class:`Maps.set_classify`
- :ref:`Plot the data <plot_the_data>` by calling :py:meth:`Maps.plot_map`
from eomaps import Maps
m = Maps()
m.add_feature.preset("ocean", "land")
m_data = m.new_layer() # Create a new layer for the data
m_data.set_data( # Assign the data
data=[1, 2, 3, 4, 5], # Data values
x=[-10, 20, 30, 40, 50], # x coordinate values
y=[-50, -20, 0, 20, 40], # y coordinate values
crs=4326 # Coordinate system of (x, y)
)
m_data.set_shape.geod_circles(radius=1e6) # Draw geodesic circles
m_data.set_classify.EqualInterval(k=3) # Classify into 3 equal intervals
m_data.plot_map(vmin=0, ec="k", cmap="magma") # Plot the data
m_data.add_colorbar(hist_bins="bins") # Add a colorbarNote
A :py:class:`Maps` object can only manage a single dataset!
To plot multiple datasets on the same map, use :py:meth:`Maps.new_layer` to get a unique :py:class:`Maps` object for each dataset! To quickly create a new layer that uses the same dataset, cassification and shape as the parent, use:
m_1 = m.new_layer(inherit_data=True, # m_1 inherits the data from m
inherit_classification=True, # m_1 inherits the classification and colormap from m
inherit_shape=True # m_1 inherits the plot-shape from m
)To assign a dataset to a :py:class:`Maps` object, use :py:meth:`Maps.set_data`.
.. autosummary::
:nosignatures:
Maps.set_data
A dataset is fully specified by setting the following properties:
data: The data-valuesx,y: The coordinates of the provided datacrs: The coordinate-reference-system of the provided coordinatesparameter(optional): The parameter nameencoding(optional): The encoding of the datacpos,cpos_radius(optional): the pixel offset
The following data-types are currently accepted as input:
.. dropdown:: **pandas.DataFrames**
:icon: square-fill
:color: info
.. tab-set::
.. tab-item:: Assignments
- ``data``: pandas.DataFrame
- ``x``, ``y``: The column-names to use as coordinates (``string``)
- ``parameter``: The column-name to use as data-values (``string``)
.. tab-item:: basic DataFrame
.. code-block:: python
:name: test_assign_pandas_01
from eomaps import Maps
import pandas as pd
df = pd.DataFrame(dict(lon=[1,2,3], lat=[2,5,4], data=[12, 43, 2]))
m = Maps()
m.set_data(df, x="lon", y="lat", crs=4326, parameter="data")
m.plot_map()
.. tab-item:: multiindex DataFrame
.. code-block:: python
:name: test_assign_pandas_02
from eomaps import Maps
import pandas as pd
data = dict(param=[10, 29, 39])
index = pd.MultiIndex.from_arrays([[10,20,30], [10,20,30]], names=("lon", "lat"))
df = pd.DataFrame(data=data, index=index)
m = Maps()
m.set_data(df, x="lon", y="lat", crs=4326, parameter="param")
m.plot_map()
.. dropdown:: **numpy.Array** | **pandas.Series** | **list**
:icon: square-fill
:color: info
.. tab-set::
.. tab-item:: Assignments
- ``data``, ``x``, ``y``: ``numpy.array``, ``pandas.Series`` or ``list``
- either data and coordinates have the same 1D/2D shape or
- 2D ``data=(m, n)`` and 1D coordinates ``x=(m,)``, ``y=(n,)``
- ``parameter``: (optional) parameter name (``string``)
.. tab-item:: 1D list
.. code-block:: python
:name: test_assign_list
from eomaps import Maps
x, y, data = [1,2,3], [2, 5, 4], [12, 43, 2]
m = Maps()
m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()
.. tab-item:: 1D numpy
.. code-block:: python
:name: test_assign_numpy_1d
from eomaps import Maps
import numpy as np
x, y, data = np.array([1,2,3]), np.array([5, 7, 9]), np.array([1, 2, 3])
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()
.. tab-item:: 2D numpy
.. code-block:: python
:name: test_assign_numpy_2d
from eomaps import Maps
import numpy as np
x, y = np.meshgrid(np.array([1,2,3]), np.array([5, 7, 9]))
data = np.random.randint(0, 10, x.shape)
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()
.. tab-item:: 2D + 1D numpy
.. code-block:: python
:name: test_assign_numpy_1d2d
from eomaps import Maps
import numpy as np
x, y = np.linspace(-20, 20, 100), np.linspace(15, 34, 50)
data = np.random.randint(0, 100, size=(100, 50)
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()
.. tab-item:: pandas Series
.. code-block:: python
:name: test_assign_series
from eomaps import Maps
import pandas as pd
x, y, data = pd.Series([1,2,3]), pd.Series([2, 5, 4]), pd.Series([12, 43, 2])
m = Maps()
m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()
.. dropdown:: **xarray.Dataset**
:icon: square-fill
:color: info
.. tab-set::
.. tab-item:: Assignments
- ``data``: xarray.Dataset
- ``x``, ``y``: The variables to use as coordinates (``string``)
- ``parameter``: The variable to use as data-values (``string``)
.. tab-item:: 2D data and coords
.. code-block:: python
:name: test_assign_xarray_2d
from eomaps import Maps
import xarray as xar
import numpy as np
param = np.random.randint(0, 10, (2,2,3))
lon = [[-20, 20], [23, 54]]
lat = [[-10, 20], [-10, 20]]
time = [1,2,3]
ds = xar.Dataset(
data_vars=dict(my_param=(["x", "y", "time"], param)),
coords=dict(lon=(["x", "y"], lon), lat=(["x", "y"], lat), time=time),
)
m = Maps()
m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
m.plot_map()
.. tab-item:: 1D coords 2D data
.. code-block:: python
:name: test_assign_xarray_1d2d
from eomaps import Maps
import xarray as xar
import numpy as np
param = np.random.randint(0, 10, (2,2,3))
lon = [-20, 20]
lat = [30, 60]
time = [1,2,3]
ds = xar.Dataset(
data_vars=dict(my_param=(["lon", "lat", "time"], param)),
coords=dict(lon=lon, lat=lat, time=time),
)
m = Maps()
m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
m.plot_map()
Note
Make sure to use a individual :py:class:`Maps` object (e.g. with m2 = m.new_layer()) for each dataset!
Calling :py:meth:`Maps.plot_map` multiple times on the same :py:class:`Maps` object will remove
and override the previously plotted dataset!
A note on data-reprojection...
EOmaps handles the reprojection of the data from the input-crs to the plot-crs.
Plotting data in its native crs will omit the reprojection step and is therefore a lot faster!
If your dataset is 2D (e.g. a raster), it is best (for speed and memory) to provide the coordinates as 1D vectors!
- 1D coordinate vectors will be broadcasted using matrix-indexing! (e.g.
x[nx], y[ny] -> data[nx, ny]) - Note that reprojecting 1D coordinate vectors to a different crs will result in (possibly very large) 2D coordinate arrays!
- 1D coordinate vectors will be broadcasted using matrix-indexing! (e.g.
To specify how a dataset is visualized on the map, you have to set the "plot-shape" via :py:meth:`Maps.set_shape`.
.. currentmodule:: eomaps.eomaps
.. autosummary::
:nosignatures:
Maps.set_shape
Available shapes (see bleow for details on each plot-shape!):
.. currentmodule:: eomaps.shapes.Shapes
.. autosummary::
:nosignatures:
ellipses
rectangles
geod_circles
voronoi_diagram
delaunay_triangulation
contour
scatter_points
raster
shade_raster
shade_points
A note on speed and performance
Some ways to visualize the data require more computational effort than others! Make sure to select an appropriate shape based on the size of the dataset you want to plot!
.. currentmodule:: eomaps.eomaps
EOmaps dynamically pre-selects the data with respect to the current plot-extent before the actual plot is created! If you do not need to see the whole extent of the data, make sure to set the desired plot-extent via :py:meth:`Maps.set_extent` or :py:meth:`Maps.set_extent_to_location` BEFORE calling :py:meth:`Maps.plot_map` to get a (possibly huge) speedup!
The suggested "suitable datasizes" mentioned below always refer to the number of datapoints that are visible in the desired plot-extent.
.. currentmodule:: eomaps.eomaps
For very large datasets, make sure to have a look at the :py:class:`raster <Maps.set_shape.raster>`, :py:class:`shade_raster <Maps.set_shape.shade_raster>`, and :py:class:`shade_points <Maps.set_shape.shade_points>` shapes which use fast aggregation techniques to resample the data prior to plotting. This way datasets with billions of datapoints can be visualized fast.
Optional dependencies
:py:class:`shade_raster <Maps.set_shape.shade_raster>`, and :py:class:`shade_points <Maps.set_shape.shade_points>` require the datashader package! You can install it via:
mamba install -c conda-forge datashaderWhat's used by default?
By default, the plot-shape is assigned based on the associated dataset.
For datasets with less than 500 000 pixels, :py:class:`ellipses <Maps.set_shape.ellipses>` is used.
- For larger 2D datasets :py:class:`raster <m.set_shape.raster>` is used... and :py:class:`shade_points <Maps.set_shape.shade_points` is attempted to be used for the rest.
.. autosummary::
:nosignatures:
Maps.set_shape.ellipses
| Suitable data size | Supported data structures |
|---|---|
| up to ~500k datapoints | 1D, 2D or mixed |
m.set_shape.ellipses(radius=(2, 5), # ellipse dimensions [rx , ry]
radius_crs=4326, # projection in which the ellipse is defined
n=50 # number of calculated points on the ellipse
).. autosummary::
:nosignatures:
Maps.set_shape.rectangles
| Suitable data size | Supported data structures |
|---|---|
| up to ~500k datapoints | 1D, 2D or mixed |
m.set_shape.rectangles(radius=(2, 5), # rectangle dimensions [rx , ry]
radius_crs=4326, # projection in which the rectangle is defined
n=50 # number of calculated points on the ellipse
).. autosummary::
:nosignatures:
Maps.set_shape.geod_circles
| Suitable data size | Supported data structures |
|---|---|
| up to ~500k | 1D, 2D or mixed |
m.set_shape.geod_circles(radius=(2, 5), # radius in meters
n=50 # number of calculated points on the circle
).. autosummary::
:nosignatures:
Maps.set_shape.voronoi_diagram
| Suitable data size | Supported data structures |
|---|---|
| up to ~500k datapoints | 1D, 2D or mixed |
m.set_shape.voronoi_diagram(masked=True, # mask too large polygons
mask_radius=10, # min. size for masked polygons
).. autosummary::
:nosignatures:
Maps.set_shape.delaunay_triangulation
| Suitable data size | Supported data structures |
|---|---|
| up to ~500k datapoints | 1D, 2D or mixed |
m.set_shape.delaunay_triangulation(flat=False, # color=mean of triplet (True) or interpolated values (False)
masked=True, # mask too large polygons
mask_radius=10, # min. size for masked polygons
mask_crs="in", # projection of the mask dimension
).. autosummary::
:nosignatures:
Maps.set_shape.contour
| Suitable data size | Supported data structures |
|---|---|
| up to a few million datapoints | 1D, 2D or mixed |
m.set_shape.contour(filled=True) # filled contour polygons (True) or contour lines (False).. autosummary::
:nosignatures:
Maps.set_shape.scatter_points
| Suitable data size | Supported data structures |
|---|---|
| ~500k datapoints | 1D, 2D or mixed |
m.set_shape.scatter_points(size=[1, 2, 3], # the marker size in points**2
marker="*", # the marker shape to use
).. autosummary::
:nosignatures:
Maps.set_shape.raster
| Suitable data size | Supported data structures |
|---|---|
| billions of datapoints (large datasets are pre-aggregated) | 2D or 1D coordinates + 2D data |
m.set_shape.raster(maxsize=5e5, # data size at which aggregation kicks in
aggregator='mean', # aggregation method to use
valid_fraction=0.5, # % of masked values in aggregation bin for masked result
interp_order=0, # spline interpolation order for "spline" aggregator.. autosummary::
:nosignatures:
Maps.set_shape.shade_raster
| Suitable data size | Supported data structures | Optional dependencies |
|---|---|---|
| billions of datapoints (large datasets are pre-aggregated) | 2D or 1D coordinates + 2D data | datashader |
m.set_shape.shade_raster(aggregator='mean', # aggregation method
shade_hook=None, # datashader shade hook callback
agg_hook=None, # datashader aggregation hook callback
).. autosummary::
:nosignatures:
Maps.set_shape.shade_raster
| Suitable data size | Supported data structures | Optional dependencies |
|---|---|---|
| no limit (large datasets are pre-aggregated) | 1D, 2D or mixed | datashader |
m.set_shape.shade_raster(aggregator='mean', # aggregation method
shade_hook=None, # datashader shade hook callback
agg_hook=None, # datashader aggregation hook callback
).. currentmodule:: eomaps.eomaps
EOmaps provides an interface for mapclassify to classify datasets prior to plotting.
To assign a classification scheme to a :py:class:`Maps` object, use m.set_classify.< SCHEME >(...).
- Available classifier names are accessible via
Maps.CLASSIFIERS.
.. autosummary::
:nosignatures:
Maps.set_classify
.. grid:: 1 1 1 2
.. grid-item::
.. code-block:: python
:name: test_classify_data
from eomaps import Maps
import numpy as np
data = np.random.normal(0, 1, (50, 50))
x = np.linspace(-45, 45, 50)
y = np.linspace(-45, 45, 50)
m = Maps(figsize=(4, 5))
m.add_feature.preset.coastline(lw=2)
m.add_feature.preset.ocean(zorder=99, alpha=0.5)
m.set_data(data, x, y)
m.set_shape.ellipses()
m.set_classify.StdMean(multiples=[-1.5, -.5, .5, 1.5])
m.plot_map(vmin=-3, vmax=3)
cb = m.add_colorbar(pos=0.2, label="StdMean classification")
cb.tick_params(labelsize=7)
.. grid-item::
.. image:: _static/minigifs/classify_data_01.png
:width: 75%
Currently available classification-schemes are (see mapclassify for details):
- BoxPlot(hinge)
- EqualInterval(k)
- FisherJenks(k)
- FisherJenksSampled(k, pct, truncate)
- HeadTailBreaks()
- JenksCaspall(k)
- JenksCaspallForced(k)
- JenksCaspallSampled(k, pct)
- MaxP(k, initial)
- MaximumBreaks(k, mindiff)
- NaturalBreaks(k, initial)
- Quantiles(k)
- Percentiles(pct)
- StdMean(multiples)
- UserDefined(bins)
Now that the data is assigned to a :py:class:`Maps` object, you can trigger plotting the data via :py:meth:`Maps.plot_map`.
Any additional keyword-arguments passed to :py:meth:`Maps.plot_map` are forwarded to the actual
matplotlib plot-command for the selected shape.
Useful arguments that are supported by all shapes are:
"cmap": the colormap to use"vmin", "vmax" : the range of values used when assigning the colors"alpha": the color transparency"zorder": the "stacking-order" of the feature
- Arguments that are supported by all shapes except
shadeshapes are: "fc"or"facecolor": set the face color for the whole dataset"ec"or"edgecolor": set the edge color for the whole dataset"lw"or"linewidth": the line width of the shapes
By default, the plot-extent of the axis is adjusted to the extent of the data if the extent has not been set explicitly before.
To always keep the extent as-is, use m.plot_map(set_extent=False).
from eomaps import Maps
m = Maps()
m.add_feature.preset.coastline(lw=0.5)
m.set_data([1,2,3,4,5], [10,20,40,60,70], [10,20,50,70,30], crs=4326)
m.set_shape.geod_circles(radius=7e5)
m.plot_map(cmap="viridis", ec="b", lw=1.5, alpha=0.85, set_extent=False)You can then continue to add a :ref:`colorbar` or create :ref:`zoomed_in_views_on_datasets`.
.. currentmodule:: eomaps.eomaps
.. autosummary::
:nosignatures:
Maps.plot_map
Maps.savefig
.. currentmodule:: eomaps.eomaps
All arguments to customize the appearance of a dataset are passed to :py:meth:`Maps.plot_map`.
In general, the colors assigned to the shapes are specified by
- selecting a colormap (
cmap)- either a name of a pre-defined
matplotlibcolormap (e.g."viridis","RdYlBu"etc.) - or a general
matplotlibcolormap object (see matplotlib-docs for more details)
- either a name of a pre-defined
- (optionally) setting appropriate data-limits via
vminandvmax.vminandvmaxset the range of data-values that are mapped to the colorbar-colors- Any values outside this range will get the colormaps
overandundercolors assigned.
from eomaps import Maps
m = Maps()
m.set_data([1,2,3,4,5], [10,20,40,60,70], [10,20,50,70,30])
m.set_shape.ellipses(radius=5)
m.plot_map(cmap="viridis", vmin=2, vmax=4, ec="b", lw=0.5)Colors can also be set manually by providing one of the following arguments to :py:meth:`Maps.plot_map`:
- to set both facecolor AND edgecolor use
color=... - to set the facecolor use
fc=...orfacecolor=... - to set the edgecolor use
ec=...oredgecolor=...
Note
- Manual color specifications do not work with the
shade_rasterandshade_pointsshapes! - Providing manual colors will override the colors assigned by the
cmap! - The
colorbardoes not represent manually defined colors!
To apply a uniform color to all datapoints, you can use matpltolib's named colors or pass an RGB or RGBA tuple.
m.plot_map(fc="orange")m.plot_map(fc=(0.4, 0.3, 0.2))m.plot_map(fc=(1, 0, 0.2, 0.5))
from eomaps import Maps
m = Maps()
m.set_data(data=None, x=[10,20,30], y=[10,20,30])
# Use any of matplotlibs "named colors"
m1 = m.new_layer(inherit_data=True)
m1.set_shape.ellipses(radius=10)
m1.plot_map(fc="r", zorder=0)
m2 = m.new_layer(inherit_data=True)
m2.set_shape.ellipses(radius=8)
m2.plot_map(fc="orange", zorder=1)
# Use RGB or RGBA tuples
m3 = m.new_layer(inherit_data=True)
m3.set_shape.ellipses(radius=6)
m3.plot_map(fc=(1, 0, 0.5), zorder=2)
m4 = m.new_layer(inherit_data=True)
m4.set_shape.ellipses(radius=4)
m4.plot_map(fc=(1, 1, 1, .75), zorder=3)
# For grayscale use a string of a number between 0 and 1
m5 = m.new_layer(inherit_data=True)
m5.set_shape.ellipses(radius=2)
m5.plot_map(fc="0.3", zorder=4)To explicitly color each datapoint with a pre-defined color, simply provide a list or array of the aforementioned types.
from eomaps import Maps
m = Maps()
m.set_data(data=None, x=[10, 20, 30], y=[10, 20, 30])
# Use any of matplotlibs "named colors"
# (https://matplotlib.org/stable/gallery/color/named_colors.html)
m1 = m.new_layer(inherit_data=True)
m1.set_shape.ellipses(radius=10)
m1.plot_map(fc=["indigo", "g", "orange"], zorder=1)
# Use RGB tuples
m2 = m.new_layer(inherit_data=True)
m2.set_shape.ellipses(radius=6)
m2.plot_map(fc=[(1, 0, 0.5),
(0.3, 0.4, 0.5),
(1, 1, 0)], zorder=2)
# Use RGBA tuples
m3 = m.new_layer(inherit_data=True)
m3.set_shape.ellipses(radius=8)
m3.plot_map(fc=[(1, 0, 0.5, 0.25),
(1, 0, 0.5, 0.75),
(0.1, 0.2, 0.5, 0.5)], zorder=3)
# For grayscale use a string of a number between 0 and 1
m4 = m.new_layer(inherit_data=True)
m4.set_shape.ellipses(radius=4)
m4.plot_map(fc=[".1", ".2", "0.3"], zorder=4)To create an RGB or RGBA composite from 3 (or 4) datasets, pass the datasets as tuple:
- the datasets must have the same size as the coordinate arrays!
- the datasets must be scaled between 0 and 1
If you pass a tuple of 3 or 4 arrays, they will be used to set the RGB (or RGBA) colors of the shapes, e.g.:
m.plot_map(fc=(<R-array>, <G-array>, <B-array>))m.plot_map(fc=(<R-array>, <G-array>, <B-array>, <A-array>))
You can fix individual color channels by passing a list with 1 element, e.g.:
m.plot_map(fc=(<R-array>, [0.12345], <B-array>, <A-array>))
from eomaps import Maps
import numpy as np
x, y = np.meshgrid(np.linspace(-20, 40, 100),
np.linspace(50, 70, 50))
# values must be between 0 and 1
r = np.random.randint(0, 100, x.shape) / 100
g = np.random.randint(0, 100, x.shape) / 100
b = [0.4]
a = np.random.randint(0, 100, x.shape) / 100
m = Maps()
m.add_feature.preset.ocean()
m.set_data(data=None, x=x, y=y)
m.plot_map(fc=(r, g, b, a)).. currentmodule:: eomaps.eomaps
Before adding a colorbar, you must plot the data using m.plot_map(vmin=..., vmax=...).
vminandvmaxhereby specify the value-range used for assigning colors (e.g. the limits of the colorbar).- If no explicit limits are provided, the min/max values of the data are used.
- For more details, see :ref:`visualize_data`.
Once a dataset has been plotted, a colorbar with a colored histogram on top can be added to the map by calling :py:meth:`Maps.add_colorbar`.
Note
m2 = m.new_layer())Note
Colorbars are only visible if the layer at which the data was plotted is visible!
.. grid:: 1 1 1 2
.. grid-item::
.. code-block:: python
:name: test_colorbars
from eomaps import Maps
import numpy as np
data = np.random.normal(0, 1, (50, 50))
x = np.linspace(-45, 45, 50)
y = np.linspace(-45, 45, 50)
m = Maps(layer="all")
m.add_feature.preset.coastline()
m.add_feature.preset.ocean(zorder=99, alpha=0.5)
m.util.layer_selector(loc="upper left")
mA = m.new_layer("A")
mA.set_data(data, x, y)
mA.set_classify.Quantiles(k=5)
mA.plot_map(vmin=-3, vmax=3)
cbA = mA.add_colorbar(label="Quantile classification")
cbA.tick_params(rotation=45)
mB = m.new_layer("B")
mB.set_data(data, x, y)
mB.set_classify.EqualInterval(k=5)
mB.plot_map(vmin=-3, vmax=3)
cbB = mB.add_colorbar(label="EqualInterval classification")
cbB.tick_params(labelcolor="darkblue", labelsize=9)
m.subplots_adjust(bottom=0.1)
m.show_layer(mA.layer)
.. grid-item::
.. image:: _static/minigifs/colorbar.gif
.. autosummary::
:nosignatures:
Maps.add_colorbar
The returned ColorBar-object has the following useful methods defined:
.. currentmodule:: eomaps.colorbar
.. autosummary::
:nosignatures:
ColorBar.set_position
ColorBar.set_labels
ColorBar.set_hist_size
ColorBar.tick_params
ColorBar.set_visible
.. currentmodule:: eomaps.colorbar
To label the colorbar with custom names for a given set of bins, use :py:meth:`ColorBar.set_bin_labels`:
.. grid:: 1 1 1 2
.. grid-item::
.. code-block:: python
:name: test_colorbar_bin_labels
import numpy as np
from eomaps import Maps
# specify some random data
lon, lat = np.mgrid[-45:45, -45:45]
data = np.random.normal(0, 50, lon.shape)
# use a custom set of bins to classify the data
bins = np.array([-50, -30, -20, 20, 30, 40, 50])
names = np.array(["below -50", "A", "B", "C", "D", "E", "F", "above 50"])
m = Maps()
m.add_feature.preset.coastline()
m.set_data(data, lon, lat)
m.set_classify.UserDefined(bins=bins)
m.plot_map(cmap="tab10")
m.add_colorbar()
# set custom colorbar-ticks based on the bins
m.colorbar.set_bin_labels(bins, names)
.. grid-item::
.. image:: _static/minigifs/colorbar_ticks.png
.. autosummary::
:nosignatures:
ColorBar.set_bin_labels
Note
This will only work if you use m.set_shape.shade_raster() or m.set_shape.shade_points() as plot-shape!
For shade shapes, the colorbar can be used to indicate the distribution of the shaded
pixels within the current field of view by setting dynamic_shade_indicator=True.
.. grid:: 1 1 1 2
.. grid-item::
.. code-block:: python
:name: test_colorbar_dynamic_shade_indicator
from eomaps import Maps
import numpy as np
x, y = np.mgrid[-45:45, 20:60]
m = Maps()
m.add_feature.preset.coastline()
m.set_data(data=x+y, x=x, y=y, crs=4326)
m.set_shape.shade_raster()
m.plot_map()
m.add_colorbar(dynamic_shade_indicator=True, hist_bins=20)
.. grid-item::
.. image:: _static/minigifs/dynamic_colorbar.gif