prettymaps.plot(your_query)

%reload_ext autoreload %autoreload 2 import prettymaps plot = prettymaps.plot('Stad van de Zon, Heerhugowaard, Netherlands')

import prettymaps plot = prettymaps.plot( 'Stad van de Zon, Heerhugowaard, Netherlands', preset = 'minimal' )

prettymaps.presets()

import prettymaps prettymaps.presets()

import prettymaps prettymaps.preset('default')

plot = prettymaps.plot( # Your query. Example: "Porto Alegre" or (-30.0324999, -51.2303767) (GPS coords) your_query, # Dict of OpenStreetMap Layers to plot. Example: # {'building': {'tags': {'building': True}}, 'water': {'tags': {'natural': 'water'}}} # Check the /presets folder for more examples layers, # Dict of style parameters for matplotlib. Example: # {'building': {'palette': ['#f00','#0f0','#00f'], 'edge_color': '#333'}} style, # Preset to load. Options include: # ['default', 'minimal', 'macao', 'tijuca'] preset, # Save current parameters to a preset file. # Example: "my-preset" will save to "presets/my-preset.json" save_preset, # Whether to update loaded preset with additional provided parameters. Boolean update_preset, # Plot with circular boundary. Boolean circle, # Plot area radius. Float radius, # Dilate the boundary by this amount. Float dilate )

@dataclass class Plot: # A dictionary of GeoDataFrames (one for each plot layer) geodataframes: Dict[str, gp.GeoDataFrame] # A matplotlib figure fig: matplotlib.figure.Figure # A matplotlib axis object ax: matplotlib.axes.Axes

import prettymaps plot = prettymaps.plot( 'Praça Ferreira do Amaral, Macau', circle = True, radius = 1100, layers = { "green": { "tags": { "landuse": "grass", "natural": ["island", "wood"], "leisure": "park" } }, "forest": { "tags": { "landuse": "forest" } }, "water": { "tags": { "natural": ["water", "bay"] } }, "parking": { "tags": { "amenity": "parking", "highway": "pedestrian", "man_made": "pier" } }, "streets": { "width": { "motorway": 5, "trunk": 5, "primary": 4.5, "secondary": 4, "tertiary": 3.5, "residential": 3, } }, "building": { "tags": {"building": True}, }, }, style = { "background": { "fc": "#F2F4CB", "ec": "#dadbc1", "hatch": "ooo...", }, "perimeter": { "fc": "#F2F4CB", "ec": "#dadbc1", "lw": 0, "hatch": "ooo...", }, "green": { "fc": "#D0F1BF", "ec": "#2F3737", "lw": 1, }, "forest": { "fc": "#64B96A", "ec": "#2F3737", "lw": 1, }, "water": { "fc": "#a1e3ff", "ec": "#2F3737", "hatch": "ooo...", "hatch_c": "#85c9e6", "lw": 1, }, "parking": { "fc": "#F2F4CB", "ec": "#2F3737", "lw": 1, }, "streets": { "fc": "#2F3737", "ec": "#475657", "alpha": 1, "lw": 0, }, "building": { "palette": [ "#FFC857", "#E9724C", "#C5283D" ], "ec": "#2F3737", "lw": 0.5, } } )

radius = False

import prettymaps plot = prettymaps.plot( 'Bom Fim, Porto Alegre, Brasil', radius = False, )

import prettymaps # Run prettymaps in show = False mode (we're only interested in obtaining the GeoDataFrames) plot = prettymaps.plot('Centro Histórico, Porto Alegre', show = False) plot.geodataframes['building']

plot.geodataframes['building'][ plot.geodataframes['building'].name == 'Catedral Metropolitana Nossa Senhora Mãe de Deus' ].geometry[0]

import prettymaps import numpy as np import osmnx as ox from matplotlib import pyplot as plt # Run prettymaps in show = False mode (we're only interested in obtaining the GeoDataFrames) plot = prettymaps.plot('Porto Alegre', show = False) # Get list of buildings from plot's geodataframes dict buildings = plot.geodataframes['building'] # Project from lat / long buildings = ox.projection.project_gdf(buildings) buildings = [b for b in buildings.geometry if b.area > 0] # Draw Matplotlib mosaic of n x n building footprints n = 6 fig,axes = plt.subplots(n,n, figsize = (7,6)) # Set background color fig.patch.set_facecolor('#5cc0eb') # Figure title fig.suptitle( 'Buildings of Porto Alegre', size = 25, color = '#fff' ) # Draw each building footprint on a separate axis for ax,building in zip(np.concatenate(axes),buildings): ax.plot(*building.exterior.xy, c = '#ffffff') ax.autoscale(); ax.axis('off'); ax.axis('equal')

import prettymaps plot = prettymaps.plot( (41.39491,2.17557), preset = 'barcelona', show = False # We don't want to render the map yet ) # Change background color plot.fig.patch.set_facecolor('#F2F4CB') # Add title _ = plot.ax.set_title( 'Barcelona', font = 'serif', size = 50 )

import prettymaps plot = prettymaps.plot( (41.39491,2.17557), mode = 'plotter', layers = dict(perimeter = {}), preset = 'barcelona-plotter', scale_x = .6, scale_y = -.6, )

import prettymaps plot = prettymaps.plot( 'Barra da Tijuca', dilate = 0, figsize = (22,10), preset = 'tijuca', adjust_aspect_ratio = False )

import prettymaps prettymaps.create_preset( "my-preset", layers = { "building": { "tags": { "building": True, "leisure": [ "track", "pitch" ] } }, "streets": { "width": { "trunk": 6, "primary": 6, "secondary": 5, "tertiary": 4, "residential": 3.5, "pedestrian": 3, "footway": 3, "path": 3 } }, }, style = { "perimeter": { "fill": False, "lw": 0, "zorder": 0 }, "streets": { "fc": "#F1E6D0", "ec": "#2F3737", "lw": 1.5, "zorder": 3 }, "building": { "palette": [ "#fff" ], "ec": "#2F3737", "lw": 1, "zorder": 4 } } ) prettymaps.preset('my-preset')

import prettymaps # Draw several regions on the same canvas plot = prettymaps.multiplot( prettymaps.Subplot( 'Cidade Baixa, Porto Alegre', style={'building': {'palette': ['#49392C', '#E1F2FE', '#98D2EB']}} ), prettymaps.Subplot( 'Bom Fim, Porto Alegre', style={'building': {'palette': ['#BA2D0B', '#D5F2E3', '#73BA9B', '#F79D5C']}} ), prettymaps.Subplot( 'Farroupilha, Porto Alegre', layers = {'building': {'tags': {'building': True}}}, style={'building': {'palette': ['#EEE4E1', '#E7D8C9', '#E6BEAE']}} ), # Load a global preset preset='cb-bf-f', # Figure size figsize=(12, 12) )

plot = prettymaps.plot( 'Honolulu', radius = 5500, figsize = 'a4', layers = {'hillshade': { 'azdeg': 315, 'altdeg': 45, 'vert_exag': 1, 'dx': 1, 'dy': 1, 'alpha': 0.75, }}, )

plot = prettymaps.plot( 'Garopaba', radius = 5000, figsize = 'a4', layers = {'building': False}, keypoints = { # Search for general keypoints specified by OSM tags 'tags': {'natural': ['beach']}, # Or, search by specific name or free-text search # pretymaps will use a fuzzy string matching to search for the specified name 'specific': { 'pedra branca': {'tags': {'natural': ['peak']}}, } }, )