๐ŸŒ EOmaps examples๏ƒ

โ€ฆ a collection of examples that show how to create beautiful interactive maps.

๐Ÿฃ Quickly visualize your data๏ƒ

There are 3 basic steps required to visualize your data:

  1. Initialize a Maps-object with m = Maps()

  2. Set the data and its specifications via m.set_data (or m.set_data_specs)

  3. Call m.plot_map() to generate the map!

๐Ÿ source-code ๐Ÿ

# EOmaps example 1:
from eomaps import Maps
import pandas as pd
import numpy as np

# ----------- create some example-data
lon, lat = np.meshgrid(np.arange(-20, 40, 0.25), np.arange(30, 60, 0.25))
data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data_variable=np.sqrt(lon**2 + lat**2).flat)
)
data = data.sample(15000)  # take 15000 random datapoints from the dataset
# ------------------------------------

m = Maps(crs=4326)
m.add_feature.preset.ocean()
m.add_feature.preset.coastline()
m.set_data(
    data=data,  # a pandas-DataFrame holding the data & coordinates
    parameter="data_variable",  # the DataFrame-column you want to plot
    x="lon",  # the name of the DataFrame-column representing the x-coordinates
    y="lat",  # the name of the DataFrame-column representing the y-coordinates
    crs=4326,
)  # the coordinate-system of the x- and y- coordinates
m.plot_map()
m.add_colorbar()

c = m.add_compass((0.05, 0.86), scale=7, patch=None)

m.cb.pick.attach.annotate()  # attach a basic pick-annotation (on left-click)
m.add_logo()  # add a logo


_images/fig1.gif

๐ŸŒ Data-classification and multiple Maps in one figure๏ƒ

  • Create grids of maps via MapsGrid

  • Classify your data via m.set_classify_specs(scheme, **kwargs)
    (using classifiers provided by the mapclassify module)
  • Add individual callback functions to each subplot via
    m.cb.click.attach, m.cb.pick.attach
  • Share events between Maps-objects of the MapsGrid via
    mg.share_click_events() and mg.share_pick_events()

๐Ÿ source-code ๐Ÿ

# EOmaps example 2: Data-classification and multiple Maps in one figure

from eomaps import MapsGrid, Maps
import pandas as pd
import numpy as np

# ----------- create some example-data
lon, lat = np.meshgrid(np.arange(-20, 40, 0.5), np.arange(30, 60, 0.5))
data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data_variable=np.sqrt(lon**2 + lat**2).flat)
)
data = data.sample(4000)  # take 4000 random datapoints from the dataset
# ------------------------------------

# initialize a grid of Maps objects
mg = MapsGrid(
    1,
    3,
    crs=[4326, Maps.CRS.Stereographic(), 3035],
    figsize=(11, 5),
    bottom=0.15,
    layer="layer 1",
)
# set the data on ALL maps-objects of the grid
mg.set_data(data=data, x="lon", y="lat", in_crs=4326)

# --------- set specs for the first axis
mg.m_0_0.ax.set_title("epsg=4326")
mg.m_0_0.set_classify_specs(scheme="EqualInterval", k=10)

# --------- set specs for the second axis
mg.m_0_1.ax.set_title("Stereographic")
mg.m_0_1.set_shape.rectangles()
mg.m_0_1.set_classify_specs(scheme="Quantiles", k=8)
mg.m_0_1.plot_map()

# --------- set specs for the third axis
mg.m_0_2.ax.set_extent(mg.m_0_2.crs_plot.area_of_use.bounds)

mg.m_0_2.ax.set_title("epsg=3035")
mg.m_0_2.set_classify_specs(
    scheme="StdMean",
    multiples=[-1, -0.75, -0.5, -0.25, 0.25, 0.5, 0.75, 1],
)

# --------- plot all maps and add colorbars to all maps
mg.plot_map()
mg.add_colorbar()

# clip the ocean shape by the plot-extent to avoid re-projection issues
mg.add_feature.preset.ocean()
mg.add_feature.preset.land()
mg.add_feature.preset.coastline(layer="all")  # add the coastline to all layers

# --------- add a new layer for the second axis
# (simply re-plot the data with a different classification and plot-shape)

# NOTE: this layer is not visible by default but it can be shown by clicking
# on the layer-switcher utility buttons (bottom center of the figure)
# or by using `m2.show()`   or via  `m.show_layer("layer 2")`
m2 = mg.m_0_1.new_layer(layer="layer 2", copy_data_specs=True)
m2.set_shape.delaunay_triangulation(mask_radius=max(m2.shape.radius) * 2)
m2.set_classify_specs(scheme="Quantiles", k=4)
m2.plot_map(cmap="RdYlBu")
m2.add_colorbar()
# add an annotation that is only executed if "layer 2" is active
m2.cb.click.attach.annotate(text="callbacks are layer-sensitive!")

# --------- add some callbacks to indicate the clicked data-point to all maps
for m in mg:
    m.cb.pick.attach.mark(fc="r", ec="none", buffer=1, permanent=True)
    m.cb.pick.attach.mark(fc="none", ec="r", lw=1, buffer=5, permanent=True)
    m.cb.click.attach.mark(fc="none", ec="k", lw=2, buffer=10, permanent=False)

# add an annotation-callback to the second map
mg.m_0_1.cb.pick.attach.annotate(text="the closest point is here!", zorder=99)

# share click & pick-events between all Maps-objects of the MapsGrid
mg.share_click_events()
mg.share_pick_events()

# --------- add a layer-selector widget
mg.util.layer_selector(ncol=2, loc="lower center", draggable=False)

# --------- rotate the ticks of the colorbars
for m in mg:
    m.figure.ax_cb.tick_params(rotation=90, labelsize=8)
m2.figure.ax_cb.tick_params(rotation=90, labelsize=8)

# add logos to all maps
mg.add_logo(size=0.05)

# trigger a final re-draw of all layers to make sure the manual
# changes to the ticks are properly reflected in the cached layers.
mg.redraw()


_images/fig2.gif

๐Ÿ—บ Customize the appearance of the plot๏ƒ

  • use m.set_plot_specs() to set the general appearance of the plot

  • after creating the plot, you can access individual objects via m.figure.<...> โ€ฆ most importantly:

    • f : the matplotlib figure

    • ax, ax_cb, ax_cb_plot : the axes used for plotting the map, colorbar and histogram

    • gridspec, cb_gridspec : the matplotlib GridSpec instances for the plot and the colorbar

    • coll : the collection representing the data on the map

๐Ÿ source-code ๐Ÿ

# EOmaps example 3: Customize the appearance of the plot

from eomaps import Maps
import pandas as pd
import numpy as np

# ----------- create some example-data
lon, lat = np.meshgrid(np.arange(-30, 60, 0.25), np.arange(30, 60, 0.3))
data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data_variable=np.sqrt(lon**2 + lat**2).flat)
)
data = data.sample(3000)  # take 3000 random datapoints from the dataset
# ------------------------------------

m = Maps(
    crs=3857, figsize=(9, 5)
)  # create a map in a pseudo-mercator (epsg 3857) projection
m.add_feature.preset.ocean(fc="lightsteelblue")
m.add_feature.preset.coastline(lw=0.25)
m.set_data(
    data=data,
    x="lon",
    y="lat",
    in_crs=4326,
    cpos="c",  # pixel-coordinates represent "center-position" (default)
    cpos_radius=None,  # radius to shift the center-position if "cpos" is not "c"
)

m.ax.set_title("What a nice figure")
m.set_shape.geod_circles(radius=30000)  # plot geodesic-circles with 30 km radius

# set the classification scheme that should be applied to the data
m.set_classify_specs(
    scheme="UserDefined", bins=[35, 36, 37, 38, 45, 46, 47, 48, 55, 56, 57, 58]
)

m.plot_map(
    edgecolor="k",  # give shapes a black edgecolor
    linewidth=0.5,  # ... with a linewidth of 0.5
    cmap="RdYlBu",  # use a red-yellow-blue colormap
    vmin=35,  # map colors to values above 35
    vmax=60,  # map colors to values below 60
    alpha=0.75,  # add some transparency
)  # pass some additional arguments to the plotted collection

# ------------------ add a colorbar and change it's appearance
m.add_colorbar(histbins="bins", label="some parameter", density=True)
_ = m.figure.ax_cb_plot.set_ylabel("The Y label")  # add a y-label to the histogram

m.subplots_adjust(
    bottom=0.1, top=0.95, left=0.075, right=0.95, hspace=0.2
)  # adjust the padding
m.figure.set_colorbar_position(
    pos=[0.125, 0.1, 0.83, 0.15], ratio=999
)  # manually re-position the colorbar

m.add_logo(position="lr", pad=(-1.1, 0), size=0.1)


_images/fig3.png

๐Ÿ›ธ Turn your maps into powerful widgets๏ƒ

  • Callback functions can easily be attached to the plot to turn it into an interactive plot-widget!

    • thereโ€™s a nice list of (customizeable) pre-defined callbacks accessible via:
      m.cb.click, m.cb.pick, m.cb.keypress and m.cb.dynamic
      • use annotate (and clear_annotations) to create text-annotations

      • use mark (and clear_markers) to add markers

      • use peek_layer (and switch_layer) to compare multiple layers of data

      • โ€ฆ and many more: plot, print_to_console, get_values, load โ€ฆ

    • โ€ฆ but you can also define a custom one and connect it via
      m.cb.click.attach(<my custom function>) (works also with pick and keypress)!

๐Ÿ source-code ๐Ÿ

# EOmaps example 4: Turn your maps into a powerful widgets
# %matplotlib widget
from eomaps import Maps
import pandas as pd
import numpy as np

# create some data
# lon, lat = np.mgrid[-20:40, 30:60]
lon, lat = np.meshgrid(np.linspace(-20, 40, 50), np.linspace(30, 60, 50))

data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data=np.sqrt(lon**2 + lat**2).flat)
)

# --------- initialize a Maps object and plot a basic map
m = Maps(crs=3035, figsize=(10, 8))
m.set_data(data=data, x="lon", y="lat", in_crs=4326)
m.ax.set_title("A clickable widget!")
m.set_shape.rectangles()
# double the estimated radius in x-direction to make the plot dense
m.shape.radius = (m.shape.radius[0] * 2, m.shape.radius[1])

m.set_classify_specs(scheme="EqualInterval", k=5)
m.add_feature.preset.ocean()  # add ocean-coloring in the background
m.add_feature.preset.coastline()  # add coastlines on top
m.plot_map()

# --------- attach pre-defined CALLBACK funcitons ---------

### add a temporary annotation and a marker if you left-click on a pixel
m.cb.pick.attach.mark(
    button=1,
    permanent=False,
    fc=[0, 0, 0, 0.5],
    ec="w",
    ls="--",
    buffer=2.5,
    shape="ellipses",
    zorder=1,
)
m.cb.pick.attach.annotate(
    button=1,
    permanent=False,
    bbox=dict(boxstyle="round", fc="w", alpha=0.75),
    zorder=10,
)
### save all picked values to a dict accessible via m.cb.get.picked_vals
cid = m.cb.pick.attach.get_values(button=1)

### add a permanent marker if you right-click on a pixel
m.cb.pick.attach.mark(
    button=3,
    permanent=True,
    facecolor=[1, 0, 0, 0.5],
    edgecolor="k",
    buffer=1,
    shape="rectangles",
    zorder=1,
)

### add a customized permanent annotation if you right-click on a pixel
def text(m, ID, val, pos, ind):
    return f"ID={ID}"


cid = m.cb.pick.attach.annotate(
    button=3,
    permanent=True,
    bbox=dict(boxstyle="round", fc="r"),
    text=text,
    xytext=(10, 10),
    zorder=2,  # use zorder=2 to put the annotations on top of the markers
)

### remove all permanent markers and annotations if you middle-click anywhere on the map
cid = m.cb.pick.attach.clear_annotations(button=2)
cid = m.cb.pick.attach.clear_markers(button=2)

# --------- define a custom callback to update some text to the map
# (use a high zorder to draw the texts above all other things)
txt = m.figure.f.text(
    0.5,
    0.35,
    "You clicked on 0 pixels so far",
    fontsize=15,
    horizontalalignment="center",
    verticalalignment="top",
    color="w",
    fontweight="bold",
    animated=True,
    zorder=99,
)
txt2 = m.figure.f.text(
    0.18,
    0.9,
    "   lon    /    lat " + "\n",
    fontsize=12,
    horizontalalignment="right",
    verticalalignment="top",
    fontweight="bold",
    animated=True,
    zorder=99,
)

# add the custom text objects to the blit-manager (m.BM) to avoid re-drawing the whole
# image if the text changes.
m.BM.add_artist(txt)
m.BM.add_artist(txt2)


def cb1(m, pos, ID, val, **kwargs):
    # update the text that indicates how many pixels we've clicked
    nvals = len(m.cb.pick.get.picked_vals["ID"])
    txt.set_text(
        f"You clicked on {nvals} pixel"
        + ("s" if nvals > 1 else "")
        + "!\n... and the "
        + ("average " if nvals > 1 else "")
        + f"value is {np.mean(m.cb.pick.get.picked_vals['val']):.3f}"
    )

    # update the list of lon/lat coordinates on the top left of the figure
    d = m.data.loc[ID]
    lonlat_list = txt2.get_text().splitlines()
    if len(lonlat_list) > 10:
        lonlat_txt = lonlat_list[0] + "\n" + "\n".join(lonlat_list[-10:]) + "\n"
    else:
        lonlat_txt = txt2.get_text()
    txt2.set_text(lonlat_txt + f"{d['lon']:.2f}  /  {d['lat']:.2f}" + "\n")


cid = m.cb.pick.attach(cb1, button=1, m=m)


def cb2(m, pos, ID, val, **kwargs):
    # plot a marker at the pixel-position
    (l,) = m.figure.ax.plot(*pos, marker="*", animated=True)
    # print the value at the pixel-position
    # use a low zorder so the text will be drawn below the temporary annotations
    t = m.figure.ax.text(
        pos[0],
        pos[1] - 150000,
        f"{val:.2f}",
        horizontalalignment="center",
        verticalalignment="bottom",
        color=l.get_color(),
        animated=True,
        zorder=1,
    )
    # add the artists to the Blit-Manager (m.BM) to avoid triggering a re-draw of the
    # whole figure each time the callback triggers

    m.BM.add_artist(l)
    m.BM.add_artist(t)


cid = m.cb.pick.attach(cb2, button=3, m=m)

# add some static text
infotext = (
    "Left-click: temporary annotations\n"
    + "Right-click: permanent annotations\n"
    + "Middle-click: clear permanent annotations"
)

_ = m.figure.f.text(
    0.66,
    0.92,
    infotext,
    fontsize=10,
    horizontalalignment="left",
    verticalalignment="top",
    color="k",
    fontweight="bold",
    bbox=dict(facecolor="w", alpha=0.75),
)

m.cb.click.attach.mark(
    fc="r", ec="none", radius=10000, shape="geod_circles", permanent=False
)
m.cb.click.attach.mark(
    fc="none", ec="r", radius=50000, shape="geod_circles", permanent=False
)


m.add_colorbar(histbins="bins", bottom=0.05)
m.add_logo()


_images/fig4.gif

๐ŸŒฒ ๐Ÿก๐ŸŒณ Add overlays and indicators๏ƒ

(โ€ฆ plot-generation might take a bit longer since overlays need to be downloaded first!)

  • add basic overlays with m.add_overlay

  • add static annotations / markers with m.add_annotation and m.add_marker

  • use โ€œconnectedโ€ Maps-objects to get multiple interactive data-layers!

๐Ÿ source-code ๐Ÿ

# EOmaps example 5: Add overlays and indicators

from eomaps import Maps
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Patch

# create some data
lon, lat = np.meshgrid(np.linspace(-20, 40, 100), np.linspace(30, 60, 100))
data = pd.DataFrame(
    dict(
        lon=lon.flat,
        lat=lat.flat,
        param=(((lon - lon.mean()) ** 2 - (lat - lat.mean()) ** 2)).flat,
    )
)
data_OK = data[data.param >= 0]
data_OK.var = np.sqrt(data_OK.param)
data_mask = data[data.param < 0]

# --------- initialize a Maps object and plot a basic map
m = Maps(Maps.CRS.Orthographic(), figsize=(10, 7))
m.ax.set_title("Wooohoo, a flashy map-widget with static indicators!")
m.set_data(data=data_OK, x="lon", y="lat", in_crs=4326)
m.set_shape.rectangles(mesh=True)
m.set_classify_specs(scheme="Quantiles", k=10)
# double the estimated radius in x-direction to make the plot dense
m.shape.radius = (m.shape.radius[0] * 2, m.shape.radius[1])

m.plot_map(cmap="Spectral_r")

# ... add a basic "annotate" callback
cid = m.cb.click.attach.annotate(bbox=dict(alpha=0.75), color="w")

# --------- add another layer of data to indicate the values in the masked area
#           (copy all defined specs but the classification)
m2 = m.new_layer(copy_classify_specs=False)
m2.data_specs.data = data_mask
m2.set_shape.rectangles(mesh=False)
# double the estimated radius in x-direction to make the plot dense
m2.shape.radius = (m2.shape.radius[0] * 2, m2.shape.radius[1])
m2.plot_map(cmap="magma")
# --------- add another layer with data that is dynamically updated if we click on the masked area
m3 = m.new_layer(copy_classify_specs=False)
m3.data_specs.data = data_OK.sample(1000)
m3.set_shape.ellipses(radius=25000, radius_crs=3857)
# plot the map and assign a "dynamic_layer_idx" to allow dynamic updates of the collection

m3.plot_map(cmap="gist_ncar", edgecolor="w", linewidth=0.25, layer=10, dynamic=True)

# --------- define a callback that will change the position and data-values of the additional layer
#           NOTE: this is not possible for the shapes:  "shade_points" and "shade_raster" !
def callback(m, **kwargs):
    selection = np.random.randint(0, len(m3.data), 1000)
    m.figure.coll.set_array(data_OK.param.iloc[selection])


# attach the callback to the second Maps object such that it triggers when we click on the masked-area
m2.cb.click.attach(callback, m=m3)

# --------- add some basic overlays from NaturalEarth

m.add_feature.preset.coastline("10m", zorder=101)
m.add_feature.physical_10m.lakes(ec="none", fc="b", zorder=100)
m.add_feature.physical_10m.rivers_lake_centerlines(
    ec="b", fc="none", lw=0.5, zorder=100
)
m.add_feature.cultural_10m.admin_0_countries(ec=".75", fc="none", lw=0.5, zorder=100)
m.add_feature.cultural_10m.urban_areas(ec="none", fc="r")

# add a customized legend
leg = m.figure.ax.legend(
    [
        Patch(fc="b"),
        plt.Line2D([], [], c="b"),
        Patch(fc="r"),
        plt.Line2D([], [], c=".75"),
    ],
    ["lakes", "rivers", "urban areas", "countries"],
    ncol=2,
    loc="lower center",
    facecolor="w",
    framealpha=1,
)
# add the legend to the blit-manager to keep it on top of dynamically updated artists
leg.zorder = 999
m.BM.add_artist(leg)

# --------- add some fancy (static) indicators for selected pixels
mark_id = 6060
for buffer in np.linspace(1, 5, 10):
    m.add_marker(
        ID=mark_id,
        shape="ellipses",
        radius="pixel",
        fc=(1, 0, 0, 0.1),
        ec="r",
        buffer=buffer * 5,
        n=100,  # use 100 points to represet the ellipses
    )
m.add_marker(
    ID=mark_id, shape="rectangles", radius="pixel", fc="g", ec="y", buffer=3, alpha=0.5
)
m.add_marker(
    ID=mark_id, shape="ellipses", radius="pixel", fc="k", ec="none", buffer=0.2
)
m.add_annotation(
    ID=mark_id,
    text=f"Here's Vienna!\n... the data-value is={m.data.param.loc[mark_id]:.2f}",
    xytext=(80, 85),
    textcoords="offset points",
    bbox=dict(boxstyle="round", fc="w", ec="r"),
    horizontalalignment="center",
    arrowprops=dict(arrowstyle="fancy", facecolor="r", connectionstyle="arc3,rad=0.35"),
)

mark_id = 3324
m.add_marker(ID=mark_id, shape="ellipses", radius=3, fc="none", ec="g", ls="--", lw=2)
m.add_annotation(
    ID=mark_id,
    text="",
    xytext=(0, 98),
    textcoords="offset points",
    arrowprops=dict(
        arrowstyle="fancy", facecolor="g", connectionstyle="arc3,rad=-0.25"
    ),
)

m.add_marker(
    ID=mark_id,
    shape="geod_circles",
    radius=500000,
    radius_crs=3857,
    fc="none",
    ec="b",
    ls="--",
    lw=2,
)

m.add_annotation(
    ID=mark_id,
    text=(
        "Here's the center of:\n"
        + "    $\\bullet$ a blue 'circle' with 50km radius\n"
        + "    $\\bullet$ a green 'circle' with 3deg radius"
    ),
    xytext=(-80, 100),
    textcoords="offset points",
    bbox=dict(boxstyle="round", fc="w", ec="k"),
    horizontalalignment="left",
    arrowprops=dict(arrowstyle="fancy", facecolor="w", connectionstyle="arc3,rad=0.35"),
)

cb = m.add_colorbar(label="The Data", tick_precision=1)
m.add_logo()


_images/fig5.gif
The data displayed in the above gif is taken from:

๐Ÿ›ฐ WebMap services and layer-switching๏ƒ

  • add WebMap services using m.add_wms and m.add_wmts

  • compare different data-layers and WebMap services using m.cb.click.peek_layer and m.cb.keypress.switch_layer

๐Ÿ source-code ๐Ÿ

# EOmaps example 6: WebMap services and layer-switching

# %matplotlib widget
from eomaps import Maps
import numpy as np
import pandas as pd

# create some data
lon, lat = np.meshgrid(np.linspace(-50, 50, 150), np.linspace(30, 60, 150))
data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data=np.sqrt(lon**2 + lat**2).flat)
)
# --------------------------------

m = Maps(Maps.CRS.GOOGLE_MERCATOR, layer="S1GBM_vv")
# set the crs to GOOGLE_MERCATOR to avoid reprojecting the WebMap data
# (makes it a lot faster and it will also look much nicer!)
# ------------- LAYER 0
# add a layer showing S1GBM
m.add_wms.S1GBM.add_layer.vv()

# ------------- LAYER 1
# if you just want to add features, you can also do it within the same Maps-object!
# add OpenStreetMap on the currently invisible layer (OSM)
m.add_wms.OpenStreetMap.add_layer.default(layer="OSM")

# ------------- LAYER 2
# create a new layer and plot some data
m2 = m.new_layer(layer="data")
m2.set_data(data=data.sample(5000), x="lon", y="lat", crs=4326)
m2.set_shape.geod_circles(radius=20000)
m2.plot_map()

# add a callback that is only executed if the "data" layer is visible
m2.cb.pick.attach.annotate(zorder=100)  # use a high zorder to put it on top

# ------------ CALLBACKS
# since m.layer == "all", the callbacks assigned to "m" will be executed on all layers!

# on a left-click, show layers ("data", "OSM") in a rectangle
# (with a size of 20% of the axis)
m.all.cb.click.attach.peek_layer(layer=["data", "OSM"], how=0.2)

# on a right-click, "swipe" the layers ("data", "S1GBM_vv") from the left
m.all.cb.click.attach.peek_layer(
    layer=["data", "S1GBM_vv"], how="left", button=3, overlay=True
)

# switch between the layers with the keys 0, 1 and 2
m.all.cb.keypress.attach.switch_layer(layer="S1GBM_vv", key="0")
m.all.cb.keypress.attach.switch_layer(layer="OSM", key="1")
m.all.cb.keypress.attach.switch_layer(layer="data", key="2")

# ------------------------------
m.figure.f.set_size_inches(9, 4)
m.figure.gridspec.update(left=0.01, right=0.99, bottom=0.01, top=0.99)

m.add_logo()

# add a utility-widget for switching the layers
m.util.layer_selector(
    loc="upper left",
    ncol=3,
    bbox_to_anchor=(0.01, 0.99),
    layers=["OSM", "S1GBM_vv", "data"],
)

m.util.layer_slider(
    pos=(0.5, 0.93, 0.38, 0.025),
    color="r",
    handle_style=dict(facecolor="r"),
    txt_patch_props=dict(fc="w", ec="none", alpha=0.75, boxstyle="round, pad=.25"),
    layers=["OSM", "S1GBM_vv", "data"],
)

# show the S1GBM layer on start
m.show_layer("S1GBM_vv")


_images/fig6.gif
The data displayed in the above gif is taken from:

๐Ÿš€ Using geopandas - interactive shapes!๏ƒ

geopandas.GeoDataFrames can be used to assign callbacks with EOmaps.
  • to make a GeoDataFrame pickable, first use m.add_gdf(picker_name="MyPicker")
    • now you can assign callbacks via m.cb.MyPicker.attach... just as you would do with the ordinary m.cb.click or m.cb.pick callbacks

Note

For large datasets that are visualized as simple rectangles, ellipses etc. it is recommended to use EOmaps to visualize the data with m.plot_map() since the generation of the plot and the identification of the picked pixels will be much faster!

If the GeoDataFrame contains multiple different geometry types (e.g. Lines, Patches, etc.) a unique pick-collection will be assigned for each of the geometry types!

๐Ÿ source-code ๐Ÿ

# EOmaps example 7: Using geopandas - interactive shapes!

from eomaps import Maps, MapsGrid
import pandas as pd
import numpy as np
import geopandas as gpd

# geopandas is used internally... the import is just here to show that!

# While this example focusses on "m.add_feature", it works completely similar
# with a custom GeoDataFrame by replacing "m.add_feature..." with "m.add_gdf()"
# (the functionalities and arguments are completely similar)


# ----------- create some example-data
lon, lat = np.meshgrid(np.linspace(-180, 180, 25), np.linspace(-90, 90, 25))
data = pd.DataFrame(
    dict(lon=lon.flat, lat=lat.flat, data=np.sqrt(lon**2 + lat**2).flat)
)

# ----------- setup some maps objects and assign datasets and the plot-crs
mg = MapsGrid(1, 2, crs=[4326, Maps.CRS.Orthographic(45, 45)], figsize=(10, 6))
mg.m_0_0.set_data(data=data.sample(100), x="lon", y="lat", crs=4326)

mg.m_0_1.set_data(data=data, x="lon", y="lat", crs=4326)

mg.add_feature.preset.ocean(reproject="cartopy")

mg.add_feature.cultural_50m.admin_0_countries(
    picker_name="countries",
    pick_method="contains",
    val_key="NAME",
    fc="none",
    ec="k",
    lw=0.5,
)

mg.set_shape.rectangles(radius=3, radius_crs=4326)
mg.plot_map(alpha=0.75, ec=(1, 1, 1, 0.5), pick_distance=25)

for m in mg:
    # attach a callback to highlite the rectangles
    m.cb.pick.attach.mark(
        permanent=False, shape="rectangles", fc="none", ec="b", lw=2, zorder=5
    )

    # attach a callback to highlite the countries and indicate the names
    m.cb.pick["countries"].attach.highlight_geometry(fc="r", ec="k", lw=0.5)
    m.cb.pick["countries"].attach.annotate(text=lambda val, **kwargs: str(val))


mg.share_pick_events()  # share default pick events
mg.share_pick_events("countries")  # share the events of the "countries" picker
mg.m_0_1.add_logo(size=0.25, pad=0)


_images/fig7.gif
The data displayed in the above gif is taken from:

๐Ÿ“ Adding scalebars - what about distances?๏ƒ

EOmaps has a nice customizable scalebar feature!
  • use s = m.add_scalebar(lon, lat, azim) to attach a scalebar to the plot

  • once the scalebar is there, you can drag it around and change its properties via s.set_position, s.set_scale_props(), s.set_label_props() and s.set_patch_props()

Note

You can also simply drag the scalebar with the mouse!

  • LEFT-click on it to make it interactive!

  • RIGHT-click anywhere on the map to make it fixed again

There are also some useful keyboard shortcuts you can use while the scalebar is interactive

  • use +/- to rotate the scalebar

  • use alt + +/- to set the text-offset

  • use the arrow-keys to increase the frame-widths

  • use alt + arrow-keys to decrease the frame-widths

  • use delete to remove the scalebar from the plot

๐Ÿ source-code ๐Ÿ

# EOmaps example 8: Adding scalebars - what about distances?

from eomaps import Maps
import matplotlib.pyplot as plt

plt.get_backend()

m = Maps(figsize=(9, 5))
m.add_feature.preset.ocean(ec="k", scale="110m")

s1 = m.add_scalebar(
    -11,
    -50,
    -45,
    scale=500000,
    scale_props=dict(n=10, width=5, colors=("k", ".25", ".5", ".75", ".95")),
    patch_props=dict(offsets=(1, 1.4, 1, 1), fc=(0.7, 0.8, 0.3, 1)),
    label_props=dict(offset=0.5, scale=1.4, every=5, weight="bold", family="Calibri"),
)

s2 = m.add_scalebar(
    50,
    -20,
    45,
    scale_props=dict(n=6, width=3, colors=("k", "r")),
    patch_props=dict(fc="none", ec="r", lw=0.5, offsets=(1, 1, 1, 2)),
    label_props=dict(rotation=45, weight="bold", family="Impact"),
)

s3 = m.add_scalebar(
    -73,
    8,
    0,
    scale=500000,
    scale_props=dict(n=6, width=3, colors=("w", "r")),
    patch_props=dict(fc=".25", ec="k", lw=0.5, offsets=(1, 1, 1, 2)),
    label_props=dict(color="w", rotation=45, weight="bold", family="Impact"),
)

# it's also possible to update the properties of an existing scalebar
# via the setter-functions!
s4 = m.add_scalebar()
s4.set_position(-140, -55, 0)
s4.set_scale_props(scale=750000, n=10, width=4, colors=("k", "w"))
s4.set_patch_props(fc="none", ec="none", offsets=(1, 1.6, 1, 1))
s4.set_label_props(scale=1.5, offset=0.5, every=2, weight="bold", family="Courier New")

# NOTE that the black-and-white scalebar is automatically re-scaled and re-positioned
#      on zoom events (the default if you don't provide an explicit scale & position)!
#      ... to manually override this behaviour, uncomment the following lines

# s4._auto_position = None
# s4._autoscale = None


m.add_logo()


_images/fig8.gif
The data displayed in the above gif is taken from:

๐ŸŒŒ Data analysis widgets - Interacting with a database๏ƒ

Callback-functions can be used to trigger updates on other plots. This example shows how to use EOmaps to analyze a database that is associated with a map.

  • create a grid of Maps objects and ordinary matplotlib axes via MapsGrid

  • define a custom callback to update the plots if you click on the map

๐Ÿ source-code ๐Ÿ

# EOmaps example 9:  Data analysis widgets - Interacting with a database

from eomaps import MapsGrid, Maps
import pandas as pd
import numpy as np


# just a helper-function to calculate axis-limits with a margin
def get_limits(data, margin=0.05):
    mi, ma = np.nanmin(data), np.nanmax(data)
    dm = margin * (ma - mi)
    return mi - dm, ma + dm


# ============== create a random database =============
length, Nlon, Nlat = 1000, 100, 50
lonmin, lonmax, latmin, latmax = -70, 175, 0, 75

database = np.full((Nlon * Nlat, length), np.nan)
for i in range(Nlon * Nlat):
    size = np.random.randint(1, length)
    x = np.random.normal(loc=np.random.rand(), scale=np.random.rand(), size=size)
    np.put(database, range(i * length, i * length + size), x)
lon, lat = np.meshgrid(
    np.linspace(lonmin, lonmax, Nlon), np.linspace(latmin, latmax, Nlat)
)

IDs = [f"point_{i}" for i in range(Nlon * Nlat)]
database = pd.DataFrame(database, index=IDs)
coords = pd.DataFrame(dict(lon=lon.flat, lat=lat.flat), index=IDs)

# -------- calculate the number of values in each dataset
#          (e.g. the data actually shown on the map)
data = pd.DataFrame(dict(count=database.count(axis=1), **coords))
# =====================================================


# -------- initialize a MapsGrid with a map on top and 2 ordinary axes below
mg = MapsGrid(
    2,
    2,
    m_inits=dict(top=(0, slice(0, 2))),
    ax_inits=dict(left=(1, 0), right=(1, 1)),
    height_ratios=(3, 2),
)

mg.add_feature.preset.ocean()
mg.add_feature.preset.coastline()

# -------- set the specs for the Maps-object of the grid and plot the map
mg.m_top.set_data(data=data, x="lon", y="lat", crs=4326)
mg.m_top.set_classify_specs(
    scheme=Maps.CLASSIFIERS.UserDefined, bins=[50, 100, 200, 400, 800]
)
mg.m_top.set_shape.ellipses(radius=0.5)
mg.m_top.plot_map()

# -------- set some axis labels
mg.ax_left.set_ylabel("data-values")
mg.ax_left.set_xlabel("data-index")
mg.ax_right.set_ylabel("data-values")
mg.ax_right.set_xlabel("histogram count")

# -------- add the axes to the blit-manager so that their artists
#          as well as axis limits etc. are dynamically updated
mg.parent.BM.add_artist(mg.ax_left)
mg.parent.BM.add_artist(mg.ax_right)

# -------- define a custom callback function to update the plots
def update_plots(ID, **kwargs):
    # get the data
    x = database.loc[ID].dropna()

    # plot the lines and histograms
    (l,) = mg.ax_left.plot(x, lw=0.5, marker=".", c="C0")
    cnt, val, art = mg.ax_right.hist(
        x.values, bins=50, orientation="horizontal", fc="C0"
    )
    # add all artists as "temporary artists" so that they are removed
    # when the next datapoint is selected
    for a in [l, *art]:
        mg.m_top.cb.pick.add_temporary_artist(a)

    # manually set the axis limits (autoscaling not always works as expected)
    mg.ax_left.set_ylim(*get_limits(x))
    mg.ax_left.set_xlim(*get_limits(x.index))
    mg.ax_right.set_ylim(*get_limits(x))
    mg.ax_right.set_xlim(*get_limits(cnt))


# attach the custom callback (and some pre-defined)
mg.m_top.cb.pick.attach(update_plots)
mg.m_top.cb.pick.attach.annotate()
mg.m_top.cb.pick.attach.mark(permanent=False, buffer=1, fc="none", ec="r")
mg.m_top.cb.pick.attach.mark(permanent=False, buffer=2, fc="none", ec="r", ls=":")


# add a colorbar
mg.m_top.add_colorbar(0.25, left=0, right=0, label="Number of observations")
mg.m_top.figure.ax_cb_plot.tick_params(labelsize=6)

# update the padding for the axes
mg.gridspec.update(bottom=0.1, left=0.12, right=0.94, wspace=0.3, hspace=0.2)

mg.add_logo()


_images/fig9.gif

๐Ÿงฎ Select 1D slices of a 2D dataset๏ƒ

Use custom callback functions to perform arbitrary tasks on the data when clicking on the map.

  • Identify clicked row/col in a 2D dataset

  • Highlight the found row and column using a new layer

(requires EOmaps >= v3.1.4)

๐Ÿ source-code ๐Ÿ

# %matplotlib widget
from eomaps import Maps, MapsGrid
import numpy as np
import itertools

# setup some random 2D data
lon, lat = np.meshgrid(np.linspace(-180, 180, 200), np.linspace(-90, 90, 100))
data = np.sqrt(lon**2 + lat**2) + np.random.normal(size=lat.shape) ** 2 * 20

name = "some parameter"
# -------------------------

# initialize a map and 2 ordinary plots that will be used to visualize the data
mg = MapsGrid(
    2,
    2,
    m_inits={"map": (slice(0, 2), 0)},
    ax_inits={"row": (0, 1), "col": (1, 1)},
    crs=Maps.CRS.InterruptedGoodeHomolosine(),
    figsize=(8, 5),
)

mg.gridspec.update(top=0.95, bottom=0.1, left=0.01, right=0.99, hspace=0.3, wspace=0.15)

# set the limits and labels for the axes
mg.ax_row.set_xlabel("Longitude")
mg.ax_row.set_ylabel(name)
mg.ax_row.set_xlim(-185, 185)
mg.ax_row.set_ylim(data.min(), data.max())

mg.ax_col.set_xlabel("Latitude")
mg.ax_col.set_ylabel(name)
mg.ax_col.set_xlim(-92.5, 92.5)
mg.ax_col.set_ylim(data.min(), data.max())

# ---- plot the map
m = mg.m_map  # get the Maps-object

m.set_data(data, lon, lat, parameter=name)
m.set_classify_specs(Maps.CLASSIFIERS.NaturalBreaks, k=5)
m.plot_map()
m.add_colorbar(bottom=0.1, top=0)
m.figure.ax_cb.tick_params(rotation=90)
m.add_feature.preset.coastline()

# add some new layers that will be used to indicate rows and columns
m2 = m.new_layer()
m3 = m.new_layer()

# ---- define a custom callback to indicate the clicked row/column
def cb(m, ind, ID, coords, *args, **kwargs):

    # get row and column from the data
    # NOTE: "ind" always represents the index of the flattened array!
    r, c = next(itertools.islice(np.ndindex(m.data.shape), ind, None))
    # update the coordinates in our dictionary
    coords.update(dict(r=r, c=c))

    # ---- highlight the picked column
    m2.set_data(m.data_specs.data[:, c], m.data_specs.x[:, c], m.data_specs.y[:, c])
    m2.set_shape.ellipses(m.shape.radius)
    # use "dynamic=True" to avoid re-drawing the background all the time
    # use "set_extent=False" to avoid resetting the plot extent on each draw
    m2.plot_map(fc="none", ec="b", set_extent=False, dynamic=True)
    m.cb.pick.add_temporary_artist(m2.figure.coll)  # remove the highlight on next pick

    # ---- highlight the picked row
    m3.set_data(m.data_specs.data[r, :], m.data_specs.x[r, :], m.data_specs.y[r, :])
    m3.set_shape.ellipses(m.shape.radius)
    m3.plot_map(fc="none", ec="r", set_extent=False, dynamic=True)
    m.cb.pick.add_temporary_artist(m3.figure.coll)  # remove the highlight on next pick

    # ---- plot the data for the selected column
    (art0,) = mg.ax_col.plot(m.data_specs.y[:, c], m.data_specs.data[:, c], c="b")
    (art01,) = mg.ax_col.plot(
        m.data_specs.y[r, c],
        m.data_specs.data[r, c],
        c="k",
        marker="o",
        markerfacecolor="none",
        ms=10,
    )

    m.cb.pick.add_temporary_artist(art0)
    m.cb.pick.add_temporary_artist(art01)

    # ---- plot the data for the selected row
    (art1,) = mg.ax_row.plot(m.data_specs.x[r, :], m.data_specs.data[r, :], c="r")
    (art11,) = mg.ax_row.plot(
        m.data_specs.x[r, c],
        m.data_specs.data[r, c],
        c="k",
        marker="o",
        markerfacecolor="none",
        ms=10,
    )
    m.cb.pick.add_temporary_artist(art1)
    m.cb.pick.add_temporary_artist(art11)

    # ---- add a temporary pick-annotation
    # NOTE: *args, **kwargs must be forwarded to the additional callback!
    m.cb.pick._cb.annotate(
        ID=ID,
        text=(
            f"row/col={r}/{c}\n"
            + f"lon/lat={m.data_specs.x[r,c]:.2f}/{m.data_specs.y[r,c]:.2f}"
            + f"\nval={m.data[r,c]:.2f}"
        ),
        permanent=False,
        *args,
        **kwargs,
    )


# initialize a dict that can be used to access the last clicked (row, col)
coords = dict(r=None, c=None)
# attach the custom callback
m.cb.pick.attach(cb, coords=coords, m=m)


_images/example_row_col_selector.gif

๐Ÿ”ฌ Inset-maps - get a zoomed-in view on selected areas๏ƒ

Quickly create nice inset-maps to show details for specific regions.

  • the location and extent of the inset can be defined in any given crs - (or as a geodesic circle with a radius defined in meters)

  • the inset-map can have a different crs than the โ€œparentโ€ map

(requires EOmaps >= v4.1)

๐Ÿ source-code ๐Ÿ

from eomaps import Maps

m = Maps(Maps.CRS.Orthographic())
m.add_feature.preset.coastline(lw=0.25)  # add some coastlines

# ---------- create a new inset-map
#            showing a 15 degree rectangle around the xy-point
m2 = m.new_inset_map(
    xy=(5, 45),
    xy_crs=4326,
    shape="rectangles",
    radius=15,
    plot_position=(0.75, 0.4),
    plot_size=0.5,
    inset_crs=4326,
    edgecolor="r",
    indicate_extent=dict(fc=(1, 0, 0, 0.25)),
)

# populate the inset with some features
m2.add_feature.preset.coastline()
m2.add_feature.preset.ocean()
m2.add_feature.preset.land()

# ---------- create another inset-map
#            showing a 400km circle around the xy-point
m3 = m.new_inset_map(
    xy=(5, 45),
    xy_crs=4326,
    shape="geod_circles",
    radius=400000,
    plot_position=(0.25, 0.4),
    plot_size=0.5,
    inset_crs=3035,
    edgecolor="g",
    indicate_extent=dict(fc=(0, 1, 0, 0.25)),
)

# populate the inset with some more detailed features
m3.add_feature.preset.coastline()
m3.add_feature.preset.ocean()
m3.add_feature.preset.land()
m3.add_feature.cultural_10m.admin_0_countries(fc="none", ec=".5")
m3.add_feature.cultural_10m.urban_areas(fc="r", ec="none")

# print some data on all of the maps
m3.set_shape.ellipses(n=100)  # use a higher ellipse-resolution on the inset-map
for m_i in [m, m2, m3]:
    m_i.set_data([1, 2, 3, 1], [5, 6, 7, 6.6], [45, 46, 47, 48.5], crs=4326)
    m_i.plot_map(alpha=0.75, ec="k", lw=0.5, set_extent=False)

# add an annotation for the second datapoint to the inset-map
m3.add_annotation(ID=1, xytext=(-120, 80))

# indicate the extent of the second inset on the first inset
m3.indicate_inset_extent(m2)

# add some additional text to the inset-maps
for m_i, txt, color in zip([m2, m3], ["epsg: 4326", "epsg: 3035"], ["r", "g"]):
    txt = m_i.ax.text(
        0.5,
        0,
        txt,
        transform=m_i.ax.transAxes,
        horizontalalignment="center",
        bbox=dict(facecolor=color),
    )
    # add the text-objects as artists to the blit-manager
    m_i.BM.add_artist(txt)

m3.add_colorbar(histbins=20, top=0.15)
# move the inset map (and the colorbar) to a different location
m3.set_inset_position(x=0.3)
# set the y-ticks of the colorbar histogram
m3.figure.ax_cb_plot.set_yticks([0, 1, 2])
m3.redraw()


_images/example_inset_maps.png